LongBench v2 / 66ecfe1e821e116aacb1e41c / Based on the challenges and nuances discussed in "Understanding China’s Economic…

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Understanding China’s Economic Statistics – 
Third Edition 
Updated Sept 6
th, 2024 
Directory of Links 
Section I. Introduction 
Overview 
How to Use the Ranking for Each Data Series 
The Ten Most Frequently Cited Chinese Economic Statistics 
A Typical Cycle of China’s Statistical Releases 
Basics of Interpreting the Numbers 
Evaluating Data Surprises  
List of Acronyms 
Section II. Overall Activity and Production 
Gross Domestic Product 
Industrial Production (Value-added of Industry) 
Services Industry Output Index 
Electricity Production and Consumption 
Rail Freight Traffic 
Total Profits of Industrial Enterprises 
Purchasing Managers’ Indices 
GS Proprietary Activity Measures 
Section III. Investment 
Fixed Asset Investment 
Projects Started and Under Construction 
Other Investment-related Data 
Section IV. Real Estate 
Real Estate Investment 
Land Transactions 
Housing Starts, Under Construction and Completions 
Home Sales 
Home Inventory 
Property Price Measures 
Land Price Indices 
GS Proprietary Indicators Related to the Real Estate Sector 
Section V. Consumption 


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Retail Sales of Consumer Goods 
Household Income and Expenditure Survey 
Retail Sales of Major Offline Retailers Reported by China National Commercial Information Center 
(CNCIC) 
Auto Sales 
Consumer Confidence Index 
Section VI. External Sector 
Merchandise Trade 
Services Trade 
Balance of Payments 
Foreign Direct Investment 
External Debt 
Foreign Exchange Reserves 
Exchange Rate Terminology and Offshore RMB Development 
CNY Trade-Weighted Indices 
GS China “Outside-In” Trade Measures 
GS China FX Flow Metric 
Section VII. Money, Credit, and Banking 
Money Supply 
Bank Loans and Deposits 
Total Social Financing 
Central Bank Policy Tools 
Interbank Interest Rates 
Flow of Funds Accounts 
Section VIII. Prices 
Consumer Price Index 
Producer Price Index (ex-Factory Price Index of Industrial Products) 
Agriculture and Raw Material Prices 
Merchandise Trade Price Index 
GDP Deflator 
Section IX. Population and Labor Market 
Total Population, Urban Population, Working Age Population, Migrant Population 
Birth Rate, Death Rate, Natural Growth Rate 
Employment Data 
Unemployment Data 
Wages 
GS China Wage Tracker 
Section X. Government Finance 
Government Revenue, Expenditure and Balance 
Local Government Debt 
GS China Augmented Fiscal Deficit (AFD) 
GS China Augmented Government Debt (AGD) 


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Our updated “Understanding China’s Economic Statistics” manual includes a broader array of data 
series, more explanatory charts and tables, and many GS proprietary indicators that we have 
developed over the years. 
Section I. Introduction 
Overview 
As China’s impact on the global economy has increased, so has the importance of its economic data. 
For some markets such as commodities, monitoring Chinese data has become as crucial as 
monitoring US data. However, many market participants view China’s economic statistics with a high 
degree of skepticism. 
The Goldman Sachs Economics Research Team has invested considerable effort in reviewing Chinese 
statistics, analyzing their relationships with the business cycle and identifying their limitations. We 
have also developed a series of proprietary indices for monitoring the Chinese economy — both at 
the macroeconomic level, such as the Goldman Sachs Current Activity Indicator (CAI) and the 
Goldman Sachs China Financial Conditions Index (FCI), and at the sector level via, for example, our 
trackers of wage growth, inventory changes, and housing policy. 
This “little red book” is a comprehensive update of Understanding China Economic Statistics, which 
we published in 2006 and updated in 2017. It is similar in format to our long-established statistics 
handbooks for the US, UK and Europe, but contains several distinct features owing to the challenges 
of interpreting China’s data and policy settings. It has been expanded further in this edition, 
reflecting the increased importance of China’s economy and economic data for the rest of the world 
and for a diverse set of markets. Since the second edition of the book was published in 2017, some 
data series have been suspended (e.g., land transaction area and value, real retail sales, FAI price 
index, and urban registered unemployment rate) while other data series have been added (e.g., retail 
sales of services and services industry output index). In addition, the definitions of various indicators 
have been revised over the past few years (e.g., total social financing). We hope it will serve as a 
useful reference both for clients investing in China directly and for those who need to track the 
Chinese economy due to its influence on other markets. 
Notable changes to this updated edition include: 
• 
A revised, and longer, list of indicators. In particular, we have expanded the sections on 
real estate and government finance, given these sectors’ importance to China’s 
macroeconomic outlook. Unfortunately, however, not all the changes are additions -- the 
authorities have ceased publication of some series that we found useful in the past. 
• 
Numerous additional charts and tables to summarize key data and display time series. 
• 
Further detail on the growing collection of proprietary indicators we have developed 
over the years. While our colleagues around the world have also developed proprietary 
indicators, and in many cases (e.g., the CAI and FCI) we apply those techniques to China, we 
have also developed many China-specific indicators. 


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In general, with respect to official data provided by the government, we find that: 
1. The production side of the statistics is better at capturing growth momentum than the 
expenditure side, mainly because the basic infrastructure for data compiling in China 
remains geared toward the production-based approach. This assessment may change 
gradually because China’s statistical authorities plan to improve data collection for 
expenditure items. 
2. The monthly growth indicators, especially in the industrial/manufacturing sector, such as 
industrial production and manufacturing PMI, are of better quality than the quarterly and 
annual GDP figures, partly because monthly data are timelier and subject to less non-
economic interference, but also because service sector measurement is generally more 
difficult. 
3. The reported growth rates for data series such as value-added industrial output, fixed asset 
investment and retail sales do not always correspond with the reported levels over time. In 
most cases, this is because of changes in the survey sample. For example, more companies 
have grown above the minimum size threshold required to be included in the sample each 
year, leading to an upward bias to the level of the series over time. The National Bureau of 
Statistics (NBS) does attempt to correct for this bias by requesting companies report year-
over-year (yoy) growth rates. 
4. For some high-profile data series such as GDP, revisions can alter the overall growth pace, 
particularly the seasonal patterns. In November 2019, the NBS revised up its 2018 GDP by 
2.1% which made the government's goal of “doubling income between 2010 and 2020” 
easier to reach. Due to the large swings in activity data driven by Covid-related lockdowns, 
seasonal adjustments have become more difficult over the past two years, with sequential 
growth heavily influenced by how seasonal factors are estimated. 
In terms of where the data are most inadequate but are of great importance, we still believe the 
higher-frequency expenditure side of the data reporting ranks at the top, in particular for 
government investment and consumption, as well as for inventory changes. 
Second on the list are data such as house prices, total housing stock, and the property vacancy ratio 
(referring to properties built but not inhabited, whether sold or not). In the context of China’s major 
housing downturn, reliable and timely figures on house prices and residential vacancy rates would 
be helpful to investors and policymakers. 
Third are data related to the labor market and wage development. Some information (such as the 
surveyed unemployment rate covering both registered and unregistered urban workers) has promise 
but is not released on a consistent and timely basis (e.g., youth unemployment rate was suspended 
after June 2023 and resumed in December 2023 under a different unemployment definition). 
Though we have developed some proprietary measures, such as our wage growth tracker, the lack 
of frequent and reliable data series on labor market slack constitutes a major macroeconomic data 
gap. 
Fourth, on the issue of prices, greater transparency on the Consumer Price Index — particularly in 
terms of the detailed components and weights — would help avoid confusion in the market. 


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Lastly, the breakdowns of many categories are outdated. For example there is limited information on 
RMB loan breakdowns by the type of borrower and industrial sector, on private vs. public investment 
in different industries, and on employment and wage data by sector. 
As with our other research products, we have tried to make this handbook as user-friendly as 
possible and accessible for readers with different levels of understanding of China’s macro data. As 
always, we would greatly appreciate your comments and suggestions. 
How to Use the Ranking for Each Data Series 
To make it easier for readers to put the data in perspective, we have assigned ratings of one to five 
stars for the signal-to-noise ratio and macro importance of each indicator. The ratings are on a 
relative scale within the China space. Therefore, a five-star rating means an indicator is among the 
top series in China’s data space, but does not mean it is free of deficiencies, or that it necessarily 
ranks highest among its international peers. 
The rating for the signal-to-noise ratio is fairly self-explanatory: In our judgment, how well does 
the series measure what it is designed to measure? Where possible, we have tried to corroborate 
data series with other related indicators, including aggregated corporate data or foreign data. 
The rating for macro importance is based on how essential the series is: (1) in helping to read the 
overall state of the economic cycle; and (2) in assessing the likely direction of macroeconomic policy. 
Although these factors are related, there can be significant differences between them. To illustrate, 
GDP has a lower frequency (quarterly) and tends to be smoother than other cyclical indicators. As a 
result, it does not have the highest signal-to-noise ratio in gauging the cyclical state of the economy. 
However, policymakers pay a good deal of attention to this data series, and the tolerance for 
missing the growth target is low. Therefore, the GDP growth data are useful in judging policy risks. 
As a result, we have assigned GDP a higher score for macro importance than for the signal-to-noise 
ratio. By contrast, electricity production and consumption data tend to be reliable, but their macro 
significance has declined over time as the energy intensity of the economy shifts. Therefore, we have 
assigned electricity production and consumption a higher score for signal-to-noise ratio than for 
macro importance. 
Exhibit 1: Overview of official Chinese economic indicators 
 


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Note: Rankings are GS subjective assessments; \"✓\" indicates Bloomberg/Wind consensus forecast 
is available for this indicator. 
Source: Goldman Sachs Global Investment Research 
The Ten Most Frequently Cited Chinese Economic Statistics 


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The indicators that we find most useful are not necessarily the ones discussed most frequently by 
market participants. Here is our take on the ten indicators that, from our subjective point of view, are 
most often cited by government officials, investors, and the media (listed in order of appearance in 
this publication). 
GDP. Despite all its flaws, this is the most comprehensive indicator of economic growth and also the 
growth indicator most watched by the government and the market. 
Industrial Production. Industrial production is perhaps the best gauge of short-term economic 
activity at a higher (monthly) frequency. 
Purchasing Managers’ Indices. Because PMIs are typically the earliest indicators released each 
month, they tend to attract significant market attention. 
Fixed Asset Investment. This is an important indicator for gauging short-term investment 
momentum. However, data quality and reliability are a concern. 
Home Sales. Among major property activity indicators, new property sales are more reliable than 
new property starts and property completions. New property sales are also important for real estate 
developer financing due to China’s pre-sales system. 
Retail Sales. Growth rates appear over-smoothed in some years and the data do not cover service 
consumption except catering. It is still the most frequently used indicator for consumption growth. 
Merchandise Trade. Trade data provide information on both domestic (imports) and foreign 
(exports) demand. 
Total Social Financing. This provides information on broad credit growth, including indirect 
financing, such as bank loans, and direct financing, such as bond/stock issuance, but coverage is still 
not wide enough to capture all credit extended to the real economy. 
Consumer Price Index. This is the most watched indicator of inflation. We believe it does a fair job 
of capturing inflationary pressures on household consumption in China. 
Producer Price Index. This is often assumed to lead downstream inflation and influences industrial 
profitability, although these relationships are not as simple as commonly perceived. 
A Typical Cycle of China’s Statistical Releases 
 


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* The NBS PMIs are reported at the end of the reference month; all other data are reported in the 
following month. \"Two Sessions\" refer to National People's Congress & Chinese People's Political 
Consultative Conference (CPPCC) Annual Sessions. The Statistics Bureau may adjust or suspend 
release times. 
Source: News Media, Goldman Sachs Global Investment Research 
Basics of Interpreting the Numbers 
Economic data are of considerable importance to financial markets – because of their information 


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about the state of the economy and their implications for economic policy. Important considerations 
to be aware of include: 
• 
Year-over-year versus “sequential” growth. We use the term “sequential” to describe 
period-on-period changes within a year (e.g., month-over-month or quarter-over-quarter, 
depending on the series). The Chinese government typically reports year-over-year series as 
a way to minimize seasonal influences. But year-over-year data can mask significant 
changes in sequential momentum, so we often calculate and refer to sequential figures. 
(Note that the term “base effect” refers to a particularly high or low sequential change from 
one year ago that affects the year-over-year calculation. For example, if GDP normally 
grows at a 4% annual rate but temporarily stalls at 0% quarter-over-quarter growth for one 
quarter, growth will be reported at 3% year-over-year in that quarter and the following three 
quarters, then one year later will jump back to 4% yoy due to the “base effect” as the weak 
quarter drops out of the calculation.) 
• 
Seasonal adjustment. This is critical when working with sequential data (more on this topic 
below). 
• 
Revisions. As in other countries, initial data reports may be revised as more comprehensive 
information becomes available or methodological revisions are made. The corollary is that 
the current data series do not necessarily reflect how the historical data looked at the time 
of release. 
• 
Survey versus “hard” data. Government agencies typically report samples or censuses of 
actual economic activity, which we sometimes refer to as “hard” data. In addition, a variety of 
government and private sector surveys (sometimes referred to as “soft” indicators) can give 
a useful qualitative sense of the direction of the economy. Often, these take the form of 
“diffusion indices” where respondents answer questions with either favorable, neutral or 
unfavorable responses; the percentage answering favorably plus half the percentage 
answering neutral are added to yield a score from 0 to 100. The widely quoted Purchasing 
Managers’ Indices (PMIs) take this form. While less precise than hard data and potentially 
subject to other biases (such as inflation), these reports can provide a timelier read on 
changes in direction and therefore are a useful reference for forecasting and policymaking. 
A Note on Seasonal Adjustment 
Seasonal adjustment is a mainstay of macroeconomic analysis, allowing comparisons of growth over 
periods of less than a year. The biggest value of seasonal adjustment is to separate cyclical signals 
from seasonal patterns so as to gauge trends in activity, inflation, or other indicators within the 
period. Though often taken for granted, the choice of seasonal adjustment method inherently 
involves judgment calls about whether incremental changes are seasonal or cyclical, and can at 
times have a major impact on the economic data. 
Conceptually, seasonal adjustment techniques penalize the months that tend to have high values 
and compensate those months that tend to have low values relative to an average month. Many of 
the statistical agencies in China use Census X-12 or its predecessor, X-11.
[1] In addition, some 
reliable data vendors, such as Haver Analytics, will adjust series using GENHOL to parameterize 
holiday factors. Specifically, the program takes a list of dates for holidays and a “window” (days 


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before, days during, and days after) around each holiday, and then generates a set of holiday 
regressors/dummy variables for each. 
Exhibit 2: The choice of seasonal adjustment can have a substantial impact on sequential 
growth 
Real GDP growth, seasonally adjusted 
 
 
Source: NBS, Haver Analytics, Goldman Sachs Global Investment Research 
Seasonal adjustments are a useful tool. Without them, one has to rely on yoy growth rates, which 
can be heavily influenced by last year’s base and are slower to reflect the latest changes in growth 
momentum. However, there are several important challenges to seasonal adjustment: 
Limited data history. It takes three or more years to obtain a result from standard seasonal 
adjustment algorithms, and longer time series are preferable. Short time series are prone to one-off 
shocks such as those during the Global Financial Crisis (GFC). Ensuring that such shocks do not 
throw off the seasonal factors requires manual intervention. 
Rapid structural change. Even when sufficient data are available, economies experiencing rapid 
structural change are more likely to see changing seasonal patterns as well. For example, as China 
grows in economic importance, fluctuations around the Chinese New Year are more likely to affect 
trading partners. Distortions around the large shocks from the Covid pandemic created difficulties 
for seasonal adjustment in many economies, although these are now fading. 
Floating holidays. Patterns of economic activity change around holidays: production tends to slow 
during holidays (factories and ports tend to shut or operate at less than the usual pace), while 
consumption tends to rise before holidays (retail activity is often boosted by holiday gift-giving, 
eating out, etc.). China has several holidays based on the lunar calendar that can fall in one of two 
calendar months each year. These holidays affect each month’s data to a different degree each year 


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and can thus create distortions in both month-over-month and year-over-year changes. Of these 
floating holidays, Chinese New Year (which falls in either January or February) is most important. The 
simplest solution to Chinese New Year seasonal distortions is to average data for the first two 
months of the year. Adjusting monthly growth rates for the number of working days does not help 
much, especially for the production data, because many companies still operate with varying 
capacity during holidays, and some may not resume operation at full capacity until days after the 
holiday. As a result, especially if the Chinese New Year falls in late February, the March data may also 
be affected (and therefore even yoy growth rates for March can be distorted by the Chinese New 
Year effect). According to the NBS, the official seasonal adjustment method adjusts for working day 
difference and floating holidays in China. For most seasonally adjusted series, there is not an obvious 
residual seasonal distortion, though floating holiday effects (especially Chinese New Year) are 
difficult to fully eliminate as their magnitude varies over time with structural changes in the economy 
and behavior. 
Evaluating Data Surprises 
Goldman Sachs China Macro-data Assessment Platform (GS China MAP) 
Source: Goldman Sachs Economics Research 
Availability: Daily since January 2006 
Timing: Real-time 
Release: GS China Proprietary Indicators update 
Overview 
The Goldman Sachs China Macro-data Assessment Platform (GS China MAP) measures economic 
growth surprises in China. It is constructed in the same manner as our surprise indices for other 
economies. 
Compilation 
In the MAP system, the importance of a particular release is calculated in two dimensions. 
• 
First is the relevance score, which is based on the historical correlation with real GDP growth 
(quarter-over-quarter). This score can range from 0 (irrelevant) to 5 (most relevant), as 
illustrated in Exhibit 3. Given the central role of the GDP series in the MAP framework, the 
fact that China’s GDP statistics have tended to be relatively smooth historically (at least pre-
pandemic) may reduce the number of significant indicators. 
• 
Second is the surprise score, measured as the difference between a particular release of 
that indicator and the Bloomberg “consensus” forecast for that indicator, measured in 
standard deviations. We assign a score from -5 to +5 depending on whether the actual 
figure is above or below expectations, and by how much. If the actual release is less than 
half a standard deviation from the consensus expectation, we will assign a score of 0. A 
difference of between 0.5 and 1 standard deviation will generate a surprise score of +1 or -1. 
Surprise scores rise with ½ standard deviation increments, with any surprise of greater than 
2.5 standard deviations generating a score of 5. 


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• 
Multiplying the relevance score by the surprise score gives a range of -25 to +25 for a given 
indicator; the aggregate of time series of MAP scores for those indicators included for China 
creates the China MAP score. 
Exhibit 3: MAP relevance and surprise scales 
 
 
Source: Goldman Sachs Global Investment Research 
• 
Bloomberg consensus forecasts are generally made using data from sell-side economists, 
most of whom work at global financial institutions. As a result, they may not precisely reflect 
the expectations of the broader investor community. In addition, these forecasts are often 
released in publications many days in advance of the official data, and subsequent changes 
in forecasters’ views may not always be updated in the published consensus. 
• 
There was a large shock during the sample period due to the GFC, and an even larger one 
during the Covid pandemic. Relative to the magnitude of the surprises during that period of 
time, any surprises today tend to appear small but are nevertheless significant for the market. 
Therefore, in setting our surprise score thresholds we have used the standard deviation of 
surprises based on data releases since 2010 (Exhibit 4). 
Exhibit 4: Indicators in the MAP for China 
 


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Source: Goldman Sachs Global Investment Research 
Exhibit 5: China activity data surprised modestly to the upside in late 2023 and early 2024 
China MAP 
 
 
Source: Goldman Sachs Global Investment Research 
List of Acronyms 
 


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Related GS Economics Publications 
• 
“Trade Balance Bounces into the Year of the Rabbit”, US Daily, 6 April 2011 
• 
“A redesigned MAP of emerging Asia data”, Asia Economics Analyst, 10 May 2013 
• 
“A Better Global Economic MAP”, Global Economics Weekly 13/39, 5 December 2013 
• 
“Separating cyclical signal from seasonal noise”, Asia Economics Analyst, 27 June 2014 
• 
“Chinese New Year Seasonal Distortions Coming Home to Roost”, US Daily, 15 February 


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2016 
• 
“Revisiting post-reopening seasonal adjustment”, China Data Insight, 23 June 2023 
Section II. Overall Activity and Production 
There are five major sets of macro indicators related to overall activity and production: 
1. Gross Domestic Product (GDP), including its breakdown by production (industry), expenditure, 
income, and region. 
2. Industrial Production and Services Industry Output Index, which measure real value added in the 
industrial sector and services sector, respectively. 
3. Other industrial activity indicators that can serve as alternative growth measures, including 
Electricity Production/Consumption and Rail Freight Traffic. 
4. Total Profits and Operating Income of Industrial Enterprises. 
5. Purchasing Managers’ Indices (PMIs) by the official and private data sources, capturing near-term 
sequential growth momentum in different sectors (e.g., manufacturing, services). 
In addition, we have our own GS proprietary activity measures, including the Current Activity 
Indicator (CAI), and inventory tracker. 
Gross Domestic Product 
Signal to noise ratio: **** 
Macro importance: ***** 
Source: National Bureau of Statistics (NBS) 
Frequency: Quarterly & Annual 
Availability: GDP by industry: Annual from 1952, quarterly from 1992 (both nominal levels and real 
growth rates); GDP by expenditure: Annual from 1952 (only nominal levels), quarterly from Q1 2009 
(for estimated contribution to year-to-date yoy GDP growth; no level data) and from Q1 2015 (for 
estimated contribution to single-quarter yoy GDP growth; no level data). 
Timing: GDP by industry: 2-3 weeks after the reference quarter; GDP by expenditure: Middle of the 
following year for level data, and 2-3 weeks after the reference quarter for quarterly estimated 
contributions to yoy GDP growth; GDP by income: Together with release of yearbook. 
Hour: 10:00am for GDP by three major sectors (i.e., primary, secondary and tertiary sectors; all times 
in this book are China Standard Time); 9:30am on the following day for GDP by all industries. 
Publication: NBS press release; China Statistical Yearbook 
Overview 
Gross Domestic Product (GDP) measures the overall economic activity of an economy on a value-


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added basis (the value of output minus purchased inputs). It is the most comprehensive measure of 
domestic economic activity. 
Signal to Noise Ratio 
• 
China’s GDP data are mostly compiled in accordance with the System of National Accounts 
(SNA) 2008 standard by treating R&D expenditure as part of capital formation. The data are 
historically and internationally comparable. 
• 
Historically, the real GDP growth data were exceptionally smooth relative to other countries 
and to other indicators of activity, especially during economic downturns, contributing to 
skepticism among market participants over their accuracy. It was common for China to 
announce quarterly GDP growth with a variation of less than 0.5 pp before the Covid 
pandemic, especially during 2015-16, even as some high-frequency indicators occasionally 
experienced double-digit swings in growth. To get a better sense of cyclical momentum, we 
cross-check real GDP growth with other indicators including our proprietary Current Activity 
Indicator. During the Covid pandemic in 2020-22, the volatility in the real GDP growth data 
increased considerably due to periodic shifts in Covid-related restrictions. 
• 
GDP revisions are supposed to capture the newly available data in the whole economy, 
though in practice they are most relevant for tertiary industry (services). Measuring services-
sector activity is inherently more difficult, and the Chinese statistical system – which grew out 
of the Soviet system that did not recognize services as value added – is particularly ill-
equipped to do so. The shift from SNA 1993 to SNA 2008 in 2016 increased 2015 total GDP 
by 1.3% to US$11 trillion, and the real growth rate was also revised up slightly. The upward 
revision was due to the fact that China’s R&D expenditure growth has been consistently 
faster than that of overall GDP. In November 2019, the NBS revised up its 2018 GDP by 2.1% 
which made the goal of “doubling real GDP between 2010 and 2020” easier to reach. 
Macro Importance 
GDP measures the value of final goods and services produced by whole economic entities in China. 
Although the GDP data suffer from various quality issues, they are still probably the most widely 
cited macro indicator because: 
1. The government pays considerable attention to GDP growth, and the official real GDP 
growth target is one of the most binding targets in terms of policy making (e.g., compared 
to inflation and job market targets). Therefore, it is useful in judging policy risks. 
2. It is compiled largely in accordance with international standards, and is often used for 
comparison with other countries. It also covers a broad sample for the overall economy, and 
therefore enables analysis to be carried out on many ratios, such as the national savings rate, 
which would be difficult to do using monthly indicators. 
Compilation and Reporting 
There are three approaches to calculating GDP data series in China: 
• 
GDP by industry 


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• 
GDP by expenditure 
• 
GDP by income 
Availability 
Exhibit 6: China's GDP by industry data have more detail than expenditure or income-side data 
 
 
*Constant price (inflation adjusted): Real GDP is generated by using prices that pertained in a rolling 
base year. 2011-2015 data are based on prices in 2010, and 2016-20 data are based on prices in 
2015. **Seasonal adjustment (NBS-SA): Real GDP seasonally adjusted by the China’s NBS is meant to 
adjust for working days and floating holidays such as the Chinese New Year Golden Week, the 
Dragon Boat Festival and the Mid-Autumn Festival. 
Source: NBS 
GDP by Industry 
Within this framework, the whole economy is divided into three broad industries: 
• 
Primary industry: Farming, forestry, animal husbandry and fishing (excluding related services). 
• 
Secondary industry: “ Industry ”  (including mining, manufacturing, and utilities) and 
construction. 
• 
Tertiary industry: Any industry other than the primary and secondary industries. Activities in 
the services sector (such as wholesale and retail trade, finance, catering, and transportation) 
are captured in this category. As in many other countries, this sector has been growing as a 


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share of overall economic activity in China. 
Exhibit 7: Gradually rising share of services in the Chinese economy 
Share of nominal GDP by industry 
 
 
Source: NBS, Goldman Sachs Global Investment Research 
GDP by Expenditure 
The expenditure approach is a method for calculating GDP by totaling household consumption, 
investment, government consumption and net exports. 
• 
All goods and services that consumers have purchased (with the exception of houses and 
housing construction materials) are included in household consumption (houses and 
housing construction-related items are mostly treated as gross fixed capital formation). 
Although retail sales data are often used as an indicator for household consumption, there 
are crucial differences between the two (see Section V. Consumption). 
• 
Government consumption measures the non-investment goods and services purchased by 
government. 
• 
The sum of household and government consumption equals final/total consumption. 
• 
Gross Fixed Capital Formation (GFCF) is a key component in the expenditure approach of 
national accounts reporting. It is different from fixed asset investment (FAI) because the 
latter includes spending on assets that do not directly contribute to GDP (e.g., purchases of 
land and used equipment), while GFCF does not.
[2] Other differences include a minimum 
project size cutoff for FAI, and property developer profits and IT investment being covered 
in GFCF but not FAI. Moreover, FAI data collection is susceptible to a lot of statistical noise 
(see Section III. Investment). 


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• 
Changes in inventories refer to net changes during the observed period. China’s changes in 
inventories data are only available at annual frequency, and appear to be useful in assessing 
the direction, but not necessarily the degree, of inventory adjustments. Despite their 
importance, changes in inventories are generally the least reliable component of GDP by 
expenditure in China, as well as in many other countries, due to difficulties in data collection. 
Some countries estimate this as a residual item by balancing GDP by expenditure data and 
GDP by industry data. The NBS is supposed to make independent estimates for changes in 
inventories based on a wide range of data sources, though in practice the level of volatility 
and the gap between GDP by expenditure and GDP by industry suggest it may have an 
element of residual as well. To better track the contribution of inventory changes to GDP 
growth at a quarterly frequency, we have built a proprietary inventory tracker based on 
commodity inventory measures, PMI inventory sub-indices, and industrial final goods 
inventory and auto inventory data. 
• 
The sum of GFCF and the change in inventories is defined as Gross Capital Formation (GCF, 
or just “investment”, which remains a very large share of economic activity in China). 
• 
Net exports are the balance of trade in goods and services, which is equivalent to the 
balance of payments (BOP) definition for the trade balance. It differs from Customs trade 
data in three main aspects: (1) it includes trade in services, whereas the Customs data only 
cover trade in goods; (2) conceptually, it is based on the principle of exchanges between 
residents and non-residents, instead of goods moving across national frontiers; and (3) 
imports reported by Customs are based on CIF, while BOP/trade under GDP is based on FOB 
(see Section VI. External Sector). 
• 
GDP by expenditure data are only available annually in nominal levels. One needs to deflate 
official data to obtain real levels and growth rates. For example, the Consumer Price Index 
(CPI) can be used to deflate household and government consumption, the Producer Price 
Index (PPI) can be used to deflate GFCF, and goods trade price indices can be used to 
deflate goods trade. Since 2015, the NBS also publishes percentage point contributions of 
consumption, investment, and net exports to headline year-over-year real GDP growth 
which can be used to estimate the year-over-year growth in real consumption, real 
investment, and real net exports. 
Exhibit 8: Chinese “rebalancing” from investment to consumption was limited through 2023 
Share of nominal GDP by expenditure 
 


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Source: NBS, Goldman Sachs Global Investment Research 
GDP by Income 
• 
The income approach accounts for the income generated during the process of production 
activities for various industries. The value-added GDP data are composed of four parts: 
employees’ compensation, net taxes on production, depreciation of fixed assets and 
operating surplus. 
• 
The NBS does not release national aggregates for GDP by income, only for individual 
provinces. Users need to aggregate the provincial GDP-by-income data to get a national 
figure. However, releases of such provincial-level data are uneven and lagged. For example, 
for many provinces, 2017 data are the latest available as of this writing in 2024. 
Conceptually, the level and growth rate of GDP measured by the industry, expenditure, and income 
approaches should be the same. However, in practice there are some discrepancies among them 
due to measurement issues. When calculating the consumption/investment/net export share of GDP, 
GDP by expenditure should be used; in other cases, GDP by industry should be used as the 
benchmark. In addition, we often use the GDP by income approach as a reference of how the 
national income is split. 
Revision 
In early 2017, the NBS simplified GDP data accounting into preliminary estimation and final revision, 
compared to the previous three-step accounting regime that included preliminary estimation, 
preliminary revision and final revision. 
Preliminary estimation: The timeliness requirement is high for the preliminary release of quarterly 
GDP data (approximately two to three weeks after the end of the reference quarter). Annual 
preliminary GDP is calculated by aggregating quarterly results. The first release of annual data comes 
out early in the following year. The preliminary estimate is calculated using the industry approach. 
Final revision: The final revision of annual GDP is released towards the end of the following year, 


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based on audit, fiscal budget outturns and other survey data. 
Economic Census: An economic census is conducted every five years and will revise historical GDP 
series based on the census results. The 4th economic census was conducted on 2018 annual data 
and results were released in late 2019. The latest census was conducted in 2023 with results to be 
released in late 2024. 
There are several windows for NBS GDP revisions if needed based on past practice, including regular 
revisions to sequential GDP growth estimates for historical data series when releasing new GDP data, 
annual GDP final revisions, and some occasional, ad hoc revisions to GDP estimates due to statistical 
investigations, scrutiny, or methodology changes. 
Other Issues 
Regional GDP and per capita GDP 
• 
Historically the discrepancies between national and regional GDP data used to be significant, 
but narrowed in recent years. The sum of provincial GDP tends to be higher than the 
national GDP, and the aggregate of municipal GDP tends to exhibit the same pattern vs. the 
provincial GDP. A number of factors lead to these discrepancies. Among the most important 
are: (1) double-counting of the value added for enterprises that operate across different 
regions; (2) differences in data sources; and (3) human factors in compilation. The revised 
national GDP after the census is much closer to the aggregate of provincial GDP data before 
the revision. Although it has long been assumed that regional GDP data have overstated the 
growth and size of the economy, the revisions made by the NBS subsequently indicate early 
readings of national data may be too conservative as they tend to under report service-
sector activities relative to the census, especially for emerging industries. 
• 
Another problem is the estimation of regional GDP per capita in China, especially at the city 
level, due to China’s large cross-region migrant population. As there are two regional 
population data series in China, those of actual residents and those with household 
registration (Hukou) in the reporting location, GDP per capita calculated using the two series 
can differ substantially, especially for cities with more migrant workers. In 2004, the NBS 
noted that for cities with large migrant populations, such as Shenzhen where 
the registered population is significantly smaller than the residential population, using the 
registered population may overstate GDP per capita. Therefore, it is important to use the 
actual resident population series to avoid serious over-estimation of GDP per capita, though 
even that series suffers from the problem of under-reporting. For example, someone who 
stays less than six months in a city is technically not a resident according to the statistical 
standards, but nevertheless contributes to local activity. Moreover, city-level GDP per capita 
data are not always reported on a timely basis. 
• 
Given population estimates reported are year-end data, to capture the "flow" feature of GDP 
for the whole year, the NBS will average the year-end residential population when 
calculating GDP per capita. For example, average 2020 population= (2020 year-end + 2019 
year-end)/2. 
GDP and GNI/GNP 


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• 
Another concept closely related to GDP is gross national income (GNI), also called gross 
national product (GNP). The difference between the GDP and GNI/GNP is net factor income, 
which is the income of investment and labor by domestic residents earned abroad minus 
those of foreign residents earned in the country. Despite their apparent similarities, these 
two series measure two different aspects of the economy: GDP measures production and 
GNI measures income. Note China's primary income deficit has widened over the past few 
years, mainly driven by a wider investment income deficit, although net international 
investment positions have shown steady growth. 
Industrial Production (Value-added of Industry) 
Signal to noise ratio: **** 
Macro importance: ***** 
Source: National Bureau of Statistics 
Availability: Monthly growth from 1990, monthly growth seasonally adjusted from 2011, annual 
absolute level (value added of industry) from 1993 to 2007. 
To adjust for Chinese New Year related distortions, since 2012 the NBS no longer releases industrial 
production data for January alone in mid-February; instead, it releases January-February combined 
data in mid-March. 
Timing: Typically around the 2
nd /3
rd week of the following month. In January, April, July and October, 
it is released with quarterly GDP data during a press conference around the 2
nd /3
rd week of the 
month. 
Hour: 10:00 am 
Publication: NBS monthly release 
Overview 
This data series measures the real value added in the industrial sector (the deflator for headline IP is 
PPI). This indicator is an important reference for macroeconomic management and is widely used to 
estimate short-term growth momentum in the industrial sector. 
Signal to Noise Ratio 
• 
We have long viewed industrial production (IP) as among the more reliable monthly activity 
indicators China publishes because: (1) related to the structure of the Chinese economy, 
China’s statistical system has focused on tracking growth in industrial production since it was 
founded; and (2) historically there seemed to be less “smoothing” in this series than in some 
other politically more sensitive data series, such as GDP. However, IP became unusually 
smooth during the 2015-16 downturn. The reliability of the IP series appears to have 
improved in recent years with its volatility increasing dramatically during the Covid pandemic 
and with sequential moves largely consistent with high-frequency data such as coal 
consumption and steel production. 


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• 
The IP data series generally tends to be more important than fixed asset investment and 
retail sales data in tracking GDP growth because it is in real terms and because, by being in 
value-added terms, it is more in line with the GDP concept. The only difference between IP 
and manufacturing output is that IP includes the mining and utilities industries. 
Macro Importance 
Historically we have found the IP data quite useful given: (1) their high frequency (monthly), and (2) 
they are a reasonably good proxy for overall economic activity and especially GDP data, since IP is a 
direct and important GDP component. 
Compilation 
• 
The sectoral coverage of IP is selective in the following respects: 
1. It covers only the industrial sector, which includes “ mining and quarrying, 
manufacturing, and utilities” — otherwise known as “secondary industry” by GDP 
classification, excluding construction. There are 41 industrial divisions in total, in 
which manufacturing accounts for the vast majority of the components. Value added 
in a particular industrial division is the sum of value added from companies whose 
primary activities are in that division (in practice, this may include some ancillary 
activities which should technically be categorized in other areas). This issue is 
especially tricky when it comes to conglomerates, and as the level of sector detail 
increases. 
2. By business type, the value added of industries covers state-owned & holding 
enterprises, share-holding enterprises, private enterprises, and foreign, Hong Kong, 
Macau and Taiwan funded enterprises. The classification is based on the controlling 
stake. If the company is controlled by a minority shareholder, it will be classified as a 
company under that shareholder, as is often the case with state-controlled firms. 
3. Minimum threshold: enterprises with annual sales of RMB20 million or above are 
included. Therefore, this series covers only a portion of the total value added by 
industry in the economy, though the portion is large (more than 80% in recent years). 
Given regular adjustments of the minimum threshold, the NBS releases comparable 
growth rates to eliminate statistical discrepancies in levels. 
• 
Export delivery value: Refers to the nominal value of industrial products for exports 
(including to Hong Kong, Macau and Taiwan). Prices are generally denominated in FX, and 
converted back to RMB based on the respective exchange rates. This category includes 
products from processing and assembling trades, etc. The export delivery data are different 
from customs trade mainly in two aspects. Firstly, Customs data includes all the merchandise 
goods that exit customs in a particular month while export delivery value only includes 
industrial goods produced for exports in a particular month. For example, Customs data 
includes exports from all kinds of enterprises and covers both industrial and non-industrial 
products (e.g., agricultural products, roughly 3% of total exports), while export delivery value 
only covers industrial products from industrial enterprises. Besides products sold to other 
countries, export delivery value also includes domestic sales of products which were initially 


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planned for exports. Secondly, these two sets of data are different on treatment of 
processing trade. For processing trade with supplied materials, export delivery value only 
includes processing fees while Customs record the full value of the final product as export 
on a free-on-board (FOB) basis. 
• 
IP data are announced in terms of real single-month growth and real year-to-date growth. 
The reporting of the nominal level of value added by industry was discontinued in 2007 and 
official real level data have never been published. When nominal value-added series are 
needed one can “inflate” official real growth data by the PPI to generate a rough estimate, 
though this approach can yield results quite different from the official nominal series over 
the period when they were still published. This is due to technical issues such as whether the 
deflation is done first at the sector level and then aggregated, and/or whether value added 
is deflated with a single price index or output and intermediate input are deflated separately 
(“double deflation”, technically the preferable approach). 
• 
The most important sub-sectors of manufacturing are electronic equipment (including 
communications equipment, computers and other electronic equipment), transportation 
equipment (including auto manufacturing), smelting and pressing of ferrous/nonferrous 
metals, chemicals, electric machinery and equipment, and textiles. 
Revision 
Seasonal adjustment is carried out every time a new data point comes in, and therefore historical 
seasonally adjusted growth rates will be revised accordingly. More recent growth rates are usually 
the most sensitive to this process. (As with many other macro indicators, estimates of month-over-
month growth are very sensitive to the exact seasonal adjustment method used.) 
Other Issues 
When the Chinese New Year falls into two different months between two consecutive years, yoy 
growth rates for January and February will be seriously distorted. It is not possible to correct the 
distortions by adjusting for the number of working days, mainly because not all businesses are 
closed during holidays, and not all resume operations immediately after the holidays. Since 2012, the 
NBS no longer releases industrial production data for January alone in mid-February; instead, it 
releases January-February combined data—which is often assumed to be free from Chinese New 
Year distortions—in mid-March. However, this assumption is not entirely correct since activity in 
March can also be affected when the Chinese New Year occurs late, as was the case in 2015. The 
NBS does report month-over-month seasonally adjusted growth for January and February and all 
other months of the year back to 2011. These data can be used to derive a year-over-year time 
series for January and February, as they are supposed to be adjusted for Chinese New Year effects 
already. However, the year-over-year growth derived from the NBS month-over-month growth 
series tends to deviate significantly from the NBS headline year-over-year growth. 
Services Industry Output Index 
Signal to noise ratio: *** 
Macro importance: **** 


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Source: National Bureau of Statistics 
Availability: Monthly growth from December 2016 
Timing: Typically around the 2
nd /3
rd week of the following month. In January, April, July and October, 
it is released with quarterly GDP data during a press conference around the 2
nd /3
rd week of the 
month. 
Hour: 10:00 am 
Publication: NBS monthly release 
Overview 
The Services Industry Output Index (SIOI) data series measures the real value added in the services 
sector. This indicator is an important reference for macroeconomic management and is frequently 
used to estimate short-term growth momentum in the services sector. SIOI is on a real basis and 
tracks tertiary GDP growth closely (55% of China’s economy as of 2023). 
Signal to Noise Ratio 
• 
The SIOI has a much shorter history than industrial production (IP), fixed asset investment 
(FAI) and retail sales. The NBS only releases the yoy growth data series for this index, while 
historical level data are not available (unlike IP, FAI and retail sales). As such, one needs to 
assume the monthly index levels for a specific base year, and back out level data for other 
years, to estimate the seasonally adjusted sequential growth. 
• 
Since the data series was introduced in late 2016, NBS has shown January-February SIOI 
growth combined, to smooth out distortions related to the shifting time of the Chinese New 
Year holiday. 
• 
The SIOI growth shares similar trends with retail sales growth in previous years. Their 
occasional divergences for some months may reflect things like growth differences between 
goods consumption and services consumption, growth differences between business 
services and consumer services, and major changes in price deflators. 
• 
The NBS also provides some sectoral breakdown for the SIOI growth, although historical 
level data are not available. These sub-sectors include wholesale & retail sales, transport, 
storage & post, hotel & restaurant, financial services, real estate services, IT and related 
services, leasing & commercial services, etc, with varying start month availability (between 
2018 and 2020). 
Macro Importance 
Despite the short history, we have found the SIOI data useful given: (1) their high frequency 
(monthly), and (2) the lack of many other broad proxies for economic activity in the services sector. 
By NBS definition and historical patterns, the relationship of SIOI growth to the services sector is 
analogous to that of IP growth to the industrial sector. 
Exhibit 9: Growth in the SIOI shares broadly similar trends with retail sales growth 
SIOI vs. nominal retail sales growth 


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Source: NBS, Wind 
Electricity Production and Consumption 
Signal to noise ratio: **** 
Macro importance: *** 
Source: National Bureau of Statistics, National Energy Administration 
Availability: Electricity Production: monthly from January 1995; Electricity Consumption: monthly 
from December 2012 (year-to-date); annual from 2002 
Publication: NBS monthly release, China Energy Statistical Yearbook 
Overview 
• 
Electricity production: Refers to the power generated by industrial enterprises with annual 
revenue from principal business above RMB20 million. 
• 
Electricity consumption: Refers to the electricity consumption of the whole of society 
including the primary sector, industrial sector, tertiary sector and residents in urban and rural 
areas. 
Signal to Noise Ratio 
• 
The signal to noise ratio of electricity production and consumption is relatively high — as 
the information collection is largely automated, there is relatively less room for local 
governments to distort the numbers. In fact, industrial electricity consumption per unit of 
GDP has been closely watched by local governments, with rising concerns about eliminating 
outdated capacity in heavy industries. 


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• 
Periodic divergence between industrial electricity consumption and industrial production 
growth may in part reflect a shift towards less energy-intensive sectors, or a transition in the 
automobile sector towards electric vehicles. Other factors also affect electricity usage. For 
example, weather-related factors (e.g., hot summers which lead to more air conditioning 
demand) lead to strong seasonality in residential electricity consumption. Abnormal weather 
can therefore distort even seasonally adjusted data. 
Macro Importance 
Electricity production and consumption data used to be important as these data series are perceived 
as relatively free from manipulation and can provide a cross-check on the strength of the economy. 
However, their macro importance has declined in recent years as electrification (e.g., electric vehicles) 
gathered steam in China, leading to higher electricity consumption growth relative to real GDP 
growth. 
Rail Freight Traffic 
Signal to noise ratio: **** 
Macro importance: ** 
Source: Ministry of Transport 
Availability: Freight volume: monthly from January 1995; annual from 1949 
Freight turnover: monthly from August 1998 (year-to-date from January 1990); annual from 1952 
Transport distance: annual from 1949 
Publication: NBS monthly release; China Statistical Yearbook 
Overview 
By category of cargo, national rail freight traffic data have information on freight volume, freight 
turnover and transport distance. 
Signal to Noise Ratio 
Rail freight traffic data are generally reliable. However, they have strong seasonality. For example, rail 
shipments for coal tend to increase in winter given rising demand for the fuel from the northern part 
of China. 
Macro Importance 
Similar to electricity data, the macro importance to GDP is relatively low—rail traffic primarily reflects 
the supply and demand in heavy industry. Freight rail was subject to severe under-capacity issues at 
times in the past and hence its historical data may not reflect demand changes well (for example, a 
change from 50% excess demand relative to the capacity to 10% will not show up in the actual 
amount of freight carried). This has generally not been a major issue in recent years as railway 
investment increased further. 


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Total Profits of Industrial Enterprises 
Signal to noise ratio: *** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Monthly from 1999, year-to-date 
Timing: Around 27 days after the end of each month 
Publication: NBS monthly releases 
Overview 
• 
Total profit of industrial enterprises: Total profits are the sum of "operating profit" and net 
"non-operating profit", on a before-income-tax basis. The sample is based on industrial 
enterprises above the designated size threshold.
[3] 
• 
Operating revenue: Operating revenue refers to the total amount of income generated by 
business activities, including revenue from main business and other activities
[4]. 
• 
The NBS also releases financial data stating the operational condition of industrial 
enterprises such as total assets, total liabilities, total owners’ equity, and main business 
income, etc. 
Exhibit 10: How the NBS industrial profits data fit into a simplified income statement 
 
Source: NBS 


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Signal to Noise Ratio 
• 
We believe the data series are generally reliable, at least in terms of the overall growth rates, 
though probably less so at the industry level. SOEs are often perceived as over-reporting 
their profits and under-reporting losses because of performance assessment systems for 
their senior management, while private enterprises tend to under-report them to avoid 
taxes. But to the extent that there is no major change in the degree of over/under-
statement (at least in the short run), the growth rates should be useful in terms of gauging 
profitability trends of large industrial firms. The trend in profit growth is also broadly 
consistent with that of enterprise income tax receipts reported by the Ministry of Finance, 
except for periods with major tax policy adjustments (e.g., tax waivers, cuts and 
postponement). 
• 
Profit/revenue data contain a lot of seasonal noise, e.g., profits and revenue levels usually 
show an uptick in December. Although seasonal adjustment should deal with this, given the 
pronounced seasonal effects an alternative approach to minimize this noise is to compare 
the data point with the same period in the previous year. 
Macro Importance 
• 
Profitability is useful in gauging the strength of the corporate sector. Profitable firms are able 
to, and often do, reinvest their retained profits. Other industrial financial data such as 
interest payments can also help assess the debt sustainability of the industrial sector. 
Compilation and Reporting 
• 
The coverage is the same as that of the industrial production data, i.e., all industrial 
enterprises with annual sales of RMB20 million or more. Similar to industrial production, the 
minimum threshold for profit data sampling increased in 2011. (Previously the minimum 
threshold was annual sales of RMB5 million or above.) Official year-over-year growth rates 
are based on comparable samples, according to the NBS. 
• 
In 2012, the NBS released the new Industrial Classification for National Economic Activities 
which expanded the industrial coverage from 39 to 41 in total. Therefore, many sub-sectors’ 
data are not precisely comparable over longer periods of time even though the sectors may 
have very similar names; analysts should watch out for jumps in series around times of 
revised classifications. 
• 
Profits are on an accrual basis, and China onshore stock market listed companies follow the 
same standard, per enterprise accounting rules from the Ministry of Finance. However, NBS 
profits are pre-income tax, and for listed companies, investors usually pay attention to post-
tax earnings. 
Other Issues 
• 
The NBS typically releases the amount of losses (year-to-date) made by loss-making firms 
at the same time as it publishes the total profits of industrial enterprises. (As one would 
expect, the reported total profits number for all industrial enterprises already nets out the 
losses from loss-making companies.) 


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• 
The main cost categories in the monthly profit report are cost of goods sold, finance costs 
(interest payments and fees paid to financial companies, which can often be significant 
especially when interest rates are high), and management/operation costs. 
Purchasing Managers’ Indices 
Signal to noise ratio: NBS manufacturing: ****; Caixin manufacturing: **** 
NBS non-manufacturing: ***; Caixin services: *** 
Macro importance: National Bureau of Statistics: ****; Caixin: *** 
Source: National Bureau of Statistics - China Federation of Logistics and Purchasing (NBS-CFLP), 
Caixin-Markit Economics/S&P Global
[5] 
Availability: NBS manufacturing PMI: since January 2005 
NBS non-manufacturing PMI: since January 2007 
Caixin manufacturing PMI: since April 2004 
Caixin services PMI: since November 2005 
Timing: NBS PMIs: Last day of each month (starting from March 2017); 9:30am 
Caixin manufacturing PMI: Usually the 1st workday of the following month; 9:45am 
Caixin services PMI: Usually the 3rd workday of the following month; 9:45am 
Publication: NBS monthly release, Caixin PMI Reports 
Overview 
There are two sets of Purchasing Managers’ Indices (PMIs) in China, one compiled jointly by the NBS 
and the China Federation of Logistics and Purchasing (CFLP) (hereafter referred to as the “NBS PMI” 
for convenience), and the other by Caixin/Markit Economics (later published by Caixin/S&P Global). 
Currently, each organization releases separate indicators for the manufacturing and non-
manufacturing sectors. 
Signal to Noise Ratio 
• 
Of the two manufacturing surveys, China’s NBS manufacturing PMI -- also known as the 
official manufacturing PMI -- has historically performed slightly better, as its production and 
new orders indices appear to be the best coincident indicators of sequential industrial 
activity growth among all PMI related data. 
• 
This advantage could be because the NBS manufacturing PMI is more representative, due 
mainly to the fact that it is compiled by the official statistical authority: 
1. The NBS manufacturing PMI is based on a sample size of 3000 firms while the Caixin 
manufacturing PMI is based on over 500 firms. 
2. The NBS survey is likely to have a higher response rate. The response rate of the 


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NBS survey is said to be as high as 99.6%, as the NBS has the legal right to demand 
that firms respond. While this can be a double-edged sword as some firms reporting 
unwillingly might be reporting with less care, the net effect of having a large 
effective sample size is probably a good thing. While the rate for the Caixin survey is 
unknown, it is unlikely to attain such a consistently high response rate. 
• 
China’s two manufacturing PMIs sometimes send different signals (Exhibit 11). Sample size 
difference and discrepancies in data-collecting periods may explain divergences. The Caixin 
survey is conducted in the middle of each month, while the NBS survey is conducted later, at 
around the 20-25
th of each month. When the economy is changing rapidly, this timing 
difference can be significant. 
• 
It is often said that the official PMI is a better reflection of large manufacturers and the 
Caixin PMI is a better reflection of smaller and often export-oriented producers. However, 
the index provider (Markit previously) disagreed with this characterization, and at least in 
terms of design -- both PMIs are designed to capture large manufacturers as well as SMEs. 
Empirically, the Caixin PMI outperformed the NBS PMI significantly in 2020H2 and 2024H1 
when Chinese exports were very strong, suggesting the Caixin PMI may cover more export-
oriented companies. 
• 
The NBS non-manufacturing PMI samples 4000 enterprises of different sizes in the 
construction and services sectors. The surveys ask 10 questions for 10 individual indices 
about production, new orders, input price, sales price, employment, business activity 
expectation, new export order, work backlog, inventory, and suppliers’ delivery time. The 
Caixin Services PMI sample covers more than 400 enterprises in the services sector. 
• 
Unlike the manufacturing PMIs which are conceptually identical, the NBS non-manufacturing 
PMI and the Caixin Services PMI are different—the official one is not just a services PMI but 
includes construction as well. It is therefore natural that these two indicators diverge more 
often than the manufacturing PMIs (Exhibit 12). But even if we look at the service sub-index 
of the official non-manufacturing PMI, it is still quite different from the Caixin Services PMI. 
The construction sub-index of the non-manufacturing PMI has a relatively weak relationship 
with the construction component of GDP. 
• 
The China Federation of Logistics and Purchasing (CFLP), which compiles the official PMIs, 
also publishes the emerging industries PMI (EPMI) each month. The EPMI is released around 
10 days earlier than the NBS and Caixin manufacturing PMIs, and could serve as a leading 
indicator for these PMI prints. The data series was first introduced in 2014, mostly covers the 
country’s “strategic emerging industries” (mostly high-tech manufacturing), and is released 
on the 20th day of each month if it’s not a public holiday. We caution that there are some 
caveats when using EPMI in forecasting, due to its differences with NBS and Caixin 
manufacturing PMIs on samples, survey dates and seasonal adjustment methodologies. 
Exhibit 11: NBS and Caixin manufacturing indices show similar trends, but occasionally send 
different signals 
China manufacturing PMIs 


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Source: NBS, Haver Analytics 
Exhibit 12: Only modest correlation between NBS and Caixin services sector surveys outside of 
the Covid period 
China services PMIs 
 
 
Source: NBS, Haver Analytics 
Exhibit 13: A comparison of NBS manufacturing PMI, Caixin manufacturing PMI and EPMI 
 


 33 / 158 
 
 
Source: NBS, Caixin, China Federation of Logistics & Purchasing, Goldman Sachs Global Investment 
Research 
Macro Importance 
• 
PMIs can be useful leading indicators to gauge sequential growth momentum. They are also 
useful in gauging upstream inflation, via their input price indices, and in tracking inventory 
cycles, via their inventory sub-indices. 
Compilation and Reporting 
• 
Both the NBS and the Caixin PMIs are compiled and summarized through the results of a 
monthly survey of enterprise purchasing managers. In reality, it is not necessarily the 
purchasing managers who are filling out the forms but other employees, especially those in 
the finance department. Statisticians send out questionnaires to firms every month to 
ascertain whether the situation in certain aspects of the business has improved, or if there is 
no change, or if it has deteriorated. Responses in each category are given the weights of 1.0, 
0.5 and 0, respectively. An index between 0 and 100 is then compiled for sub-indices such as 
production, new orders, employment, prices (separately for input and output prices), 
inventory (separately for raw materials and finished goods inventories), and suppliers’ 
delivery times. The overall manufacturing PMI is a weighted average of the new orders (30%), 
production (25%), employment (20%), suppliers’ delivery time (inverted, 15%), and raw 
materials inventory (10%) components in accordance to the weights used internationally. 
Data are then reported as a reading between 0-100 for the overall indicator and different 
categories, by adding the share of respondents indicating “improved” plus half the share 
indicating “no change”. 
• 
The Caixin services PMI and NBS non-manufacturing PMI do not have aggregate readings 
and only report readings for different components. The “Business Activity Index” is often 


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used as the headline reading. The Services Business Activity Index is to the services sector 
what the Manufacturing Output Index is to the manufacturing sector. 
• 
The PMI Composite index (also known as Composite Output index) is a weighted average of 
the Manufacturing PMI Output sub-index and the Services Business Activity Index (i.e., the 
services PMI). 
Interpretation of the Readings 
A reading of 100 means all respondents reported improvement. A reading of 0 means all 
respondents reported deterioration. In theory, the 50 level threshold is important because above this 
level more respondents are reporting expansion than contraction. However, diffusion indices only 
measure breadth of expansion or decline, not intensity, and in China’s case the 50 threshold has not 
consistently separated sequential expansion from contraction in IP or GDP, or necessarily marked a 
change in their second derivatives (accelerating/decelerating growth rates). 
GS Proprietary Activity Measures 
GS China Current Activity Indicator (GS China CAI) 
Source: Goldman Sachs Economics Research 
Availability: Monthly since January 2006 
Timing: The series is updated daily to incorporate new data releases 
Release: GS China Proprietary Indicators update 
Overview 
• 
The GS China Current Activity Indicator (China CAI) was created to provide an alternative 
measure with higher frequency and quality to identify shifts in the economic cycle. It 
attempts to encompass indicators from the main producing sectors of the economy – 
manufacturing, housing, and consumer – as well as the labor market. The China CAI is 
shown on a month-over-month annualized basis, after several rounds of adjustments by the 
GS global economics team to harmonize CAIs around the world and to deal with dramatic 
activity changes in response to the Covid pandemic (it is available on the GS research portal 
or on Bloomberg at ticker: GSCNCAI). The components of CAI include: industrial production, 
employment in composite PMI, Cheung Kong Graduate School of Business (CKGSB) Business 
Conditions Survey sales sub-index, the Caixin Services PMI, electricity consumption, 
automobile sales, the Caixin Manufacturing PMI output sub-index, real imports, real retail 
sales, floor space sold, floor space started, cement production, real exports, freight volume, 
passenger volumes, and floor space completed. 
• 
The CAI is calculated on a sequential basis. Statistically, our CAI is constructed as the first 
principal component of 16 standardized monthly economic indicators after seasonal 
adjustment, converted to GDP-equivalent terms through a regression of historical real GDP 
growth on this principal component. 
• 
We extend the CAI back to 2006, by backcasting a few series (mainly survey indicators) for 
which a complete history is not available. Other indicators on activity growth such as trade 


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flows and sector-level data all showed a higher amplitude of fluctuation than the official 
GDP data, so we adjusted for volatility when constructing the CAI. 
• 
Because the CAI methodology is designed to be a high-frequency proxy for GDP, distortions 
in GDP data (e.g. data that are consistently “too smooth”) can in theory affect the CAI. In 
particular, the mean of growth as measured by the CAI over the sample is effectively equal 
to that of GDP growth over the sample, by construction. 
Exhibit 14: Our China CAI components and weights 
 
 
Source: Goldman Sachs Global Investment Research 
Exhibit 15: Our China CAI has a close correlation with sequential GDP growth 
China Current Activity Indicator and GDP 
 


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Source: NBS, Goldman Sachs Global Investment Research 
GS China Inventory Tracker 
Source: Goldman Sachs Economics Research 
Availability: Quarterly since Q1 2007 
Timing: Preliminary reading available in the last month of the quarter; final reading available one 
month after quarter-end 
Release: GS China Proprietary Indicators update 
Overview 
• 
The GS China inventory tracker is based on six underlying inventory indicators, including 
commodities (i.e., iron ore, aluminum), PMI sub-indices (i.e., raw materials, finished goods), 
industrial enterprises finished goods inventory, and auto inventory. After data cleaning, we 
derive the first principal component, which explains 25% of the total variation of the six series, 
and then map it into percentage of GDP terms as our tracker for inventory changes. Our 
inventory tracker can be used to gauge the contribution of inventory changes to China’s 
GDP growth. 
• 
Our inventory tracker also indicates slowdowns in inventory build during the Global Financial 
Crisis (GFC), around 2015-16 (despite implausibly stable reported real GDP growth) when 
the government implemented “supply-side reforms” in upstream sectors like steel and coal, 
in early 2019 at the height of US-China trade war, and in late 2022 when the Covid “exit 
wave” caused significant supply chain disruptions in China. 
• 
We acknowledge our principal component analysis (PCA) approach to tracking inventory is 
only a proxy and the mapping into the inventory component of real GDP is not perfect. 
However, we think it provides a useful way to track an important part of the economy that is 
opaque and can generate large swings in quarterly growth. 


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Exhibit 16: Our inventory tracker can be used to gauge the contribution of inventory changes 
to China’s GDP growth 
Inventory change contribution to real GDP growth (qoq, non-ann) 
 
 
Source: NBS, Bloomberg, CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“China’s manufacturing PMIs: Which one should we look at and what are they telling 
us?” EM Macro Daily, 13 August 2013 
• 
“Taking stock of China activity: Updating our Current Activity Indicator”, 18 November 2015 
• 
“Tracking all over the world - Our New Global CAI”, Global Economics Analyst, 25 February 
2017 
• 
“China Manufacturing PMI - Still an important signal”, China Data Insights, 27 September 
2019 
• 
“China EPMI, a good leading indicator for manufacturing PMIs”, China Data Insights, 21 July 
2022 
• 
“Tracking China’s Inventory Cycle”, China Data Insights, 7 June 2023 
• 
“Why have post-reopening industrial profits been so weak?”, China Data Insights, 15 June 
2023 
• 
“Peeking into NBS' GDP revision practice”, China Data Insights, 31 August 2023 
• 
“Gauging China’s growth (again)”, Asia Economics Analyst, 7 September 2023 
• 
“A Closer Look at NBS 2022 GDP Revisions”, China Data Insights, 13 March 2024 


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Section III. Investment 
There are three sets of macro indicators related to investment: 
1. Fixed asset investment (FAI) and its breakdown by industry, nature of enterprises, work type 
and region, as well as funds designated for FAI and its breakdown by source; 
2. FAI project starts and under construction; 
3. Other investment-related data series, including infrastructure-related bond issuance, the 
investment values of large-scale projects approved by the National Development and 
Reform Commission (NDRC), excavator operating hours, major raw materials production, 
consumption and prices. 
Fixed Asset Investment 
Signal to noise ratio: ** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly from 1992, annual from 1980 
Type: Monthly: Year to date 
Timing: Typically around the 2nd /3rd week of the following month. In January, April, July and 
October, it is released with quarterly GDP data during a press conference around the 2nd /3rd week 
of the month. 
Publication: NBS monthly releases 
Overview 
Fixed asset investment (FAI) excluding rural households measures spending on durable assets that 
are used repeatedly in the production process. 
Compilation and Reporting 
According to the NBS, FAI data include investment expenses on equipment purchases, construction 
and installation, among others (e.g., land acquisition, old buildings), for projects on fixed assets (real 
estate development also included) with total planned investment of RMB5 million and over. 
The data are broken down into a number of categories. The following breakdowns are provided 
after 2004: 
• 
FAI undertaken by nature of enterprise 
1. State owned enterprises (SOEs): These are enterprises solely owned by the 
government. This category is also known as “State-Owned and State Holding 
Enterprises”, and includes all SOEs and enterprises over which the government has 
effective control, but excludes state sole proprietors that are part of “limited liability 


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companies (LLCs)”, based on the NBS definition. New PPP (Public Private Partnership) 
projects complicate the picture, as projects with 50% state share are accounted for as 
SOE investments, which overstates the true share of the state. PPP projects 
experienced rapid expansion in 2017, stagnated in 2018-22, and declined thereafter. 
2. Collectively owned enterprises (COEs): Another kind of publicly-owned enterprise 
which has become less common (as of 2023, FAI by COEs accounted for less than 1% 
of total FAI). 
3. Foreign-funded enterprises. 
4. Hong Kong, Macau and Taiwan-funded enterprises (note that foreign-funded 
enterprises and Hong Kong, Macau and Taiwan Funded Enterprises are two separate 
categories that do not overlap). 
5. Share-holding enterprises: Enterprises that have a share-holding structure. 
6. Private enterprises and individual businesses: Both are private businesses. Businesses 
run by one person and small private businesses that employ fewer than eight people 
are called “individual businesses”; larger businesses are called private enterprises. 
Private-sector FAI has remained weak in recent years despite fiscal stimulus 
implemented by the government. The private share of FAI increased from 31% in 
2004 to 64% in 2015, but has declined since then. 
7. Joint ownership enterprises: Typically enterprises set up by two or more 
independent firms. This was a popular form of enterprise in the earlier years of 
reform but is now much less common. 
8. Limited liability companies (LLCs): Including state sole proprietors and other limited 
liability companies. 
9. Others (e.g., miscellaneous projects which are not easy to be classified into the 
categories above). 
Exhibit 17: SOE and non-SOE investment showed significant divergence during 2022-23 
FAI growth by business ownership in China 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
• 
FAI by work type 
This set of data is released with a monthly frequency, and the main categories include construction 
and installation, equipment purchase and others. In 2023, construction and installation was the 
major work type under FAI, accounting for 71% of overall FAI. 
1. “Construction and installation” includes construction of buildings (materials and labor), and 
installation of machinery and equipment. Note that land purchase costs are not included 
here. 
2. “Equipment purchase” includes spending not only on new equipment, but also on old 
equipment. 
3. Other FAI primarily includes spending on land acquisition, old buildings, and management 
fees. 
• 
FAI by industry is available in terms of the primary, secondary and tertiary industries. 
Detailed breakdowns for specific industries are available from 2004. As is the case for IP by 
industry, FAI by a given industry is not investment in a particular type of project, but just 
FAI by companies in that industry. For analysis we typically aggregate the sectors into four 
major categories: manufacturing, infrastructure, property and others ( “others” mainly 
includes services and agriculture-related sectors). In 2023, FAI in these four sectoral 
categories accounted for around 33%, 36%, 15% and 26% of total FAI, respectively, compared 
to 30%, 31%, 22% and 19% in 2007. 
1. The share of manufacturing FAI has been close to infrastructure FAI over the past 
decade. Manufacturing investment is mostly carried out by private enterprises in 
China. Over the past few years, the solid growth of manufacturing investment was 
underpinned by POEs. 
2. Our estimates for infrastructure FAI are based on our GS definition, which includes 


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not only the three sectors under the NBS classification (i.e., transport, storage & 
postal service; water conservancy & environmental protection; and electricity, gas & 
water production and supply), but also four more industries that provide public 
goods mainly by the government sector (e.g., scientific research & polytechnic 
service; education; healthcare, social security & welfare; and culture, sport & 
entertainment). Most of the infrastructure investment in China was done by local 
governments (around 70% in 2023). Over the past two decades, the share of 
transportation and utilities in infrastructure investment has fallen from around 60% to 
around 40%. Recent policy communications suggest infrastructure spending will likely 
focus on both traditional and new infrastructure projects in coming years: the former 
may include water conservancy, emergency management and energy, while the 
latter could include 5G telecommunications, supercomputing, EV charging stations, 
ultra-high voltage power transmission, data centers, green capex and industrial 
parks. 
3. Property FAI, often regarded as very important due to its interconnection with local 
government finances, now has a much smaller share than manufacturing and 
infrastructure FAI. Since the unprecedented property downturn that started in 2021, 
the share of property FAI has declined significantly. Furthermore, the value-added 
share of property FAI is likely to be significantly lower than in other components of 
FAI, since a significant share of property FAI is in land transfers (which are non-
value-added and do not enter GDP accounting). Hence, the share of property 
investment in total gross fixed capital formation (GFCF) is notably lower than its 
share in total FAI (for more detail on property FAI see Section IV. Real Estate). On the 
back of the current property downturn, regulatory tightening in sectors such as 
education and internet that are dominated by private enterprises, and the ongoing 
fiscal stimulus, FAI growth has shown a notable divergence between infrastructure 
and property, and between SOEs and non-SOEs since 2022. Our research suggests 
that the weakness of private investment during 2022-23 was mostly due to the large 
contraction of property investment. 
Exhibit 18: Infrastructure and property investment diverged significantly in 2022-23 
FAI growth and breakdown by major component 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
• 
FAI by region: This set of data is available for the 31 provinces and many cities, on a 
monthly basis. The FAI share across provinces is largely in line with their GDP share. FAI 
breakdown by province can help estimate the difference in investment momentum across 
regions, especially when macro policies have an asymmetric impact on different regions.
[6] 
Exhibit 19: FAI in more-indebted regions underperformed that in coastal regions in 2022-24 
FAI in provinces with high debt pressure vs. others (seasonally adjusted) 
 
 
Source: Wind, Goldman Sachs Global Investment Research 
• 
Funds designated for FAI (this is sometimes referred to as FAI by source of funding, but we 


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view this as a misleading term because these data refer to the amount of funds that became 
available for FAI, which is not the same as the amount of FAI completed): 
1. State budget. 
2. Domestic loans from banks. 
3. Bonds: Bonds issued by corporates and financial institutions, including those by local 
government financing vehicles (LGFVs) and policy banks for FAI. 
4. Foreign capital. 
▪ 
Among which: Foreign direct investment (FDI). In practice this can include 
significant capital from “round-tripping” (see more on FDI in Section VI. 
External Sector). 
5. Self-raised funds. 
▪ 
Self-owned: This is intended to be mainly from retained earnings. In practice 
this likely includes a variety of funding channels including non-standard 
loans borrowed from financial institutions. This might be driven by 
regulatory requirements, such as those requiring a project to have a certain 
share of self-owned capital before it is qualified to borrow from banks. 
▪ 
Share issuance: Capital raised through public offerings. 
6. Others (e.g., crowd-funding and donations). 
Signal to Noise Ratio 
• 
There is considerable confusion and controversy over China’s FAI statistics, with different 
definitions and scope of coverage. 
• 
There are major differences between the definitions of FAI and of the investment concept 
(GFCF) in national accounts. 
1. The monthly FAI data are not reported on a value-added basis. That is, they do not 
reflect the incremental new capacity added to the capital stock, but just total 
nominal investment spending reported by companies and governments. FAI 
includes spending on assets that do not directly contribute to GDP (e.g., land 
acquisition and old equipment purchase), while GFCF does not. 
2. FAI includes a minimum project size cutoff (RMB5 million as of 2023) for non-
property investment (and all property investment), while GFCF does not. 
3. FAI does not include spending on intangible fixed assets such as computer software 
and IT investment, while GFCF does. 
• 
FAI data have a few measurement issues. 
1. Both the monthly and annual investment series are in nominal terms. The NBS did 
release an official FAI deflator on a quarterly basis previously, but suspended the 
data series in 2020. Besides, capturing price changes in this sector is difficult – 


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especially land prices. 
2. The monthly yoy FAI growth rate, which is monitored closely by investors, has 
significant measurement issues. For example, the classification for monthly reported 
FAI data changed materially a number of times in the past. This change has made 
the yoy growth rates not completely comparable over time. In addition, the official 
year-over-year growth rate series are not compatible with the official level series, 
mainly due to sample changes over the years. 
3. Over-reporting may be the major factor behind the high level of FAI growth, which 
has also been acknowledged by the government in the past. Problems include 
potential double-counting (e.g., of the same project by different regions) and 
misreporting (e.g., FAI data are based largely on questionnaire responses which do 
not always have to be backed by hard evidence). Given the statistical issues 
mentioned above, we suggest investors supplement FAI data with other indicators, 
such as capital goods imports, raw materials (e.g., steel and cement) production and 
apparent demand, and infrastructure-intensive bond issuance (e.g., local 
government special bond), to help form a better assessment of true investment 
growth momentum on a monthly basis. 
Macro Importance 
Despite all these caveats, the monthly FAI series remains very important in assessing policy risks, 
because policymakers do pay very close attention to it. The NDRC previously announced annual 
targets for FAI growth, and some local governments also set their local FAI growth targets, although 
these targets are not as binding as GDP growth targets. A significant rise in FAI growth rates, if 
accompanied by higher inflation, tends to lead to policy tightening and vice versa. 
Projects Started and Under Construction 
Source: National Bureau of Statistics 
Availability: Monthly from 2004 
Timing and publication: Same as FAI data 
Overview 
• 
The value of total planned investment in new projects and under construction reflects 
changes in the pipeline of investment projects. 
• 
Data are compiled together with FAI data and therefore many of the problems associated 
with FAI data also apply to these series. 
• 
“Projects started” data are often used as a leading indicator of investment activities. 
Policymakers and investors also monitor this series to assess likely future trends in fixed asset 
investment on a 1-2 year horizon. In reality, however, there is no stable lead-lag relationship 
between the two series. 
Compilation and Reporting 


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• 
FAI projects started and under construction are released in terms of value and number of 
projects. 
• 
Project started, planned (value): This refers to all projects planned to start during the 
reporting period (they may or may not be under construction at this stage but will be started 
before the reference period end). It excludes ongoing investment projects (including those 
suspended and restarted in this period). Previously, the NBS also released data on projects 
started and planned in unit terms, but this data series was suspended in early 2018. 
• 
Projects under construction (value): This contains ongoing investment projects and new 
starts. Previously, the NBS also released data on projects under construction in unit terms, 
but this data series was suspended in early 2018. 
• 
Total planned investment: Refers to FAI projects’ planned investment amount. Note these 
plans are not binding and therefore this is not a reliable indicator for future FAI. Availability 
of funding and subsequent changes in costs can have large impacts on actual investment. 
• 
The relationship between different series in this set of data is complicated because of 
regulatory approval issues. For example, sometimes local projects find it difficult or costly to 
obtain approval for a new project and therefore just report the new project as an extension 
of an existing project. 
Other Investment-related Data 
Other indicators related to investment include infrastructure-related bond issuance (especially local 
government special bonds and local government financing vehicle bonds; to be elaborated 
in Section X. Government Finance), large-scale projects approval by the National Development and 
Reform Commission (NDRC), excavator machinery operating hours, cement inventory, and steel 
demand, although each indicator has its own advantages and disadvantages. 
• 
The investment value of projects approved by the NDRC may reflect the central government’
s stronger determination to boost investment if it increases quickly in a short period of time, 
but its total amount in 2022 (RMB1.5 trillion; latest year-end data available) only accounted 
for 2.6% of total FAI. 
• 
Komatsu, a leading global manufacturer of construction machinery, provides data on the 
monthly average operating hours of excavators sold in China. It is updated on a monthly 
basis with a lag of around two weeks. This could help gauge the momentum of construction 
activity, but it is subject to distortions from changes in Komatsu’s market share in China 
(which has declined to 1% in 2023 from 15% in 2008) and not able to distinguish 
infrastructure construction from property construction. This data series is subject to 
significant seasonality. Some investors may also focus on sales of excavators and other 
construction machinery, but these durable capital goods have significant replacement cycles, 
which may distort their yoy sales growth in some years. 
• 
Steel and cement are crucial raw material inputs for construction activity, and there are 
several related indicators available from either official or private data sources, including steel 
and cement output, demand for construction-related steel (measured by production net of 
net exports and inventory changes), the cement inventory to storage capacity ratio, cement 


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production capacity utilization ratio, and steel and cement prices. 
Related GS Economics Publications 
• 
“China: Is credit losing its cyclical growth impact?”, Emerging Markets Macro Daily, 20 May 
2013 
• 
“China infrastructure investment: Is the high growth sustainable?”, Asia Economics Analyst, 
13 April 2017 
• 
“Fast or slow, old or new: A macro view on China's infrastructure investment”, Asia in Focus, 
15 April 2020 
• 
“China Green Capex: Renewable power investment and its impact on the economy”, Asia 
Economics Analyst, 16 January 2022 
• 
“China: Manufacturing sector to drive investment in 2022”, Asia in Focus, 22 February 2022 
• 
“China: Gauging the upside for infrastructure investment in 2022”, Asia in Focus, 28 March 
2022 
• 
“China: Tracking the strength and pace of infrastructure stimulus”, Asia in Focus, 12 May 
2022 
• 
“China: Assessing the implications of local governments’ 2023 targets”, Asia in Focus, 17 
February 2023 
• 
“China: Beijing’s Balancing Act between Infrastructure Stimulus and LGFV Deleveraging”, Asia 
in Focus, 6 March 2024 
• 
“The shifting role of private investment in China”, China Data Insights, 25 April 2024 
Section IV. Real Estate 
There are five major sets of macro indicators related to the real estate sector: 
1. Real estate investment and its breakdown. 
2. Land transaction (sales/purchases) by different data source. 
3. Construction data -- housing starts, property under construction, and completions. 
4. Home sales and inventory. 
5. Property and land sales prices. 
We have also constructed GS proprietary measures related to the real estate sector, including 
estimates of the property sector impact on GDP growth and measures of the regulatory stance (a 
property policy relative tightness index as well as a 24-city housing policy stance indicator). 
Real Estate Investment 
Signal to noise ratio: ** 


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Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Monthly from 2004, annual from 2003 
Overview 
• 
There are two sets of real estate investment data. The most widely tracked one is the total 
FAI conducted by real estate developers (also known as “property investment” or “property 
FAI”). The data are surveyed and released together with other real estate indicators (e.g., 
new home starts, completions, and sales), as well as other major economic activity data (e.g., 
industrial production, headline FAI and retail sales), by the NBS on a monthly basis. They 
cover land purchases, equipment purchases and construction activities for residential, 
commercial and office buildings. In 2023, around 90% of real estate investment was related 
to land purchases and construction. The breakdown details of property investment by 
ownership of enterprises (annual), work type (monthly), and sources of funding (monthly) are 
available. Like headline FAI data, the NBS only releases the year-to-date level and growth 
data for real estate investment at a monthly frequency, while single-month real estate 
investment growth rates require additional estimates given a specific base year. 
• 
The other data on real estate investment is the FAI by industry – real estate (as a composite 
industry in the tertiary sector). The definition of this dataset is different because it 
supposedly includes real estate related investment by all types of enterprises, not just 
property developers. However, the general trends of these two indicators are similar. 
Exhibit 20: Around 90% of property FAI was related to land purchases and construction in 
recent years 
Property FAI breakdown by key procedure (based on 2023 data) 
 
Source: NBS, Wind, Goldman Sachs Global Investment Research 
Exhibit 21: Growth in property investment and FAI growth for the real estate industry share 
similar trends 


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Property investment vs. FAI in the real estate industry 
 
Source: NBS, Wind, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
Both series of real estate investment have similar drawbacks as other FAI data series. For 
example, the official year-over-year growth rate series is not entirely compatible with the 
official level series, mainly due to sample changes over the years (for details on other 
statistical issues with FAI data, see Section III. Investment). Furthermore, these two indicators 
do not capture rural properties without property rights. Rural properties without property 
rights are only for self-use, and transactions with buyers outside the community are deemed 
illegal. As such, there is a lack of data for rural properties in China. 
• 
The NBS made occasional revisions to the definition of private investment, which may also 
affect the data quality. For example, in March 2024 the NBS revised the definition by 
excluding land redevelopment related spending from real estate investment. Although the 
NBS flagged that the reported yoy real estate investment growth estimates are on a 
comparable basis, it did not release the revised historical level data series under the new 
definition. 
Macro Importance 
Despite the drawbacks, real estate investment is still widely tracked by investors, because: (1) real 
estate FAI is informative in terms of gauging the strength of the real estate sector; (2) real estate FAI 
(reported by all property developers) accounted for 15% of the overall FAI in China in 2023 (vs. its 
previous peak of 23% in 2004), and thus is still an important component of the overall FAI. 
Land Transactions 
Signal to noise ratio: ** 


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Macro importance: *** 
Source: National Bureau of Statistics (NBS), Ministry of Finance (MOF), China Real Estate Index 
System (CREIS), Soufun, Wind 
Availability: NBS and MOF: Monthly and annual; CREIS/Soufun and Wind: Weekly and monthly 
Timing: NBS and MOF: Collected monthly with complete enumeration typically around the 3rd week 
of the following month (January-February data are combined for release in mid-March by official 
sources including NBS and MOF). 
Overview 
There are several useful sources of data on land transactions and development. 
• 
Land transaction area (NBS): This is defined as land area (squared meters) purchased by 
real estate developers during the reporting time period. As all land in China is owned by the 
state, buyers can only purchase the right to use land instead of ownership. These rights 
range from 20-70 years, hinging on how the land will be used (e.g., for the construction of 
industrial, commercial, or residential buildings). In the latest version of Civil Code of the 
People's Republic of China, effective in January 2021, the land-use right will be automatically 
renewed when the right leases are up.
[7] 
• 
Land transaction value (NBS): This refers to the final amount (value) of the land-use right 
transacted in both the primary and secondary markets by real estate developers, based on 
the flows of their actual payment. Land purchase value is on a comparable basis with land 
purchase area in terms of statistical coverage, so we can derive the average price of land 
purchases from these two indicators. However, this is not precisely the same as a land price 
index because the quality of land transacted may not be comparable. Land purchase area 
and value data used to be available at the city level for 40 major cities, released by the NBS, 
but these city-level data series were suspended in January 2019. The NBS also suspended 
the release of land transaction area and value data series in January 2023. 
• 
Developer land purchase value (NBS): These refer to the total amount of land transactions, 
in value terms, based on the contracts signed by property developers in the primary and 
secondary markets. "Developer land purchase value” is much larger than “land transaction 
value” above because it includes various taxes, fees and spending associated with land 
compensation, land preparation, and land management. This series is recorded at the time 
of disbursement by property developers and may lag the transaction time somewhat. 
• 
Government revenue from land sales (Ministry of Finance): This is reported by the 
Ministry of Finance (MOF) on a monthly basis – along with other government income and 
expenditure data – and is a major component of government-managed fund revenue. The 
MOF series is slightly different in scope from the land-use right transfer value reported by 
the NBS and third-party data vendors. For instance, the former series also includes income 
from renting land by land administrations. Land sales remain a very important source of 
income/expenditure for local governments. Refer to Section X. Government Finance for 
more details. 


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• 
Land transaction area and value by city (CREIS/Soufun/Wind): CREIS/Soufun
[8] collect and 
summarize the data (data access requires a subscription) on government revenue from land 
transactions in more than 300 cities in China (based on 302 cities and is often referred to as 
“CREIS 300-city revenue from land sales”, compiled and tracked by our GS China property 
research team). The definition of the data is broader than the NBS land purchase 
value/volume data mentioned above, given that these data cover land purchases by 
different types of enterprises, not just developers. Wind also compiles a sample of 100 cities 
to track land transaction value and area, on both a weekly and monthly basis. The Wind data 
have breakdowns by city tier (i.e., Tier-1, Tier-2 and Tier-3). The benefit of 
CREIS/Soufun/Wind data is that they are more timely (e.g., daily for the original 
CREIS/Soufun data and weekly for the Wind data) and tend to be a leading indicator (e.g., 
CREIS series on land transactions are recorded when transactions occur, NBS land purchases 
are recorded when investment is completed, and MOF government revenue from land sales 
are recorded when the payment is settled). The drawback is that CREIS/Soufun/Wind data 
aggregate a large number of cities but do not include all transactions nationwide. 
• 
The discrepancies between these land transaction measures mainly come from different 
timings to register land sales (e.g., registered when contracts are signed or when funds are 
paid), different groups of land buyers captured by the sample (property developers only or 
all types of buyers), and some additions and/or deductions before the purchase fees are 
transferred to the MOF's account for land sales revenue. 
Exhibit 22: A summary of various land sales measures in China 
 


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Source: NBS, CREIS, Wind, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
Among all the land sales indicators mentioned above, we tend to rely more on the land 
transaction volume and value series from CREIS. The data coverage of land purchase value 
and volume reported by property developers is much narrower, and the NBS land purchase 
data lag the CREIS data series slightly as discussed above. 
• 
The introduction of a centralized land auction mechanism for major cities in 2021 increased 
the transparency of land supply, reduced panic land purchases among property developers, 
and significantly lowered land auction premia. However, as land supply needs to be 
concentrated in several batches in a year under the new regime, occasional shocks (e.g., 
Covid lockdowns during 2020-22, natural disasters) would affect the timing of land actions 
and thus cause near-term distortions to yoy growth in land sales revenue. 
Macro Importance 
The NBS developer land purchase value typically contributed to around 30% of real estate investment 
in recent years. However, the share of land sales revenue in total (gross) government revenue 


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declined to below 20% in 2023 from its recent peak of 30% in 2021 (the peak share was even higher 
for some local governments with high reliance on land finance). 
Land sales tend to lead property construction activity and thus it is important to track land sales. As 
mentioned above, land sales are also important sources of income/expenditure to local 
governments, and therefore can impact the financing need/spending capability of local 
governments. In addition, land prices are important for future property price trends and the financial 
health of property developers. 
Housing Starts, Under Construction and Completions 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly and annual 
Timing: Typically around the 3
rd week of the following month (together with the release of the NBS 
monthly activity data) 
Overview 
Floor space started: This measure refers to the total floor space of buildings that are newly started 
within the reporting period. Only housing newly started by real estate developers and enterprises 
within the reference timeframe is counted. Continued building activities carried over from the 
preceding reporting period are excluded. Construction areas suspended in previous periods and 
restarted in the reporting period are included, but construction areas suspended and restarted 
within the reporting period are not. 
Floor space under construction: This refers to the total floor space of buildings in different 
construction phases in the reference period, including floor space of newly started buildings during 
the reference period, floor space of construction extended from the previous period to the reference 
period, floor space of construction suspended or postponed in the previous period and resumed in 
the reference period, floor space of construction completed in the reference period, and floor space 
of constructions started and then suspended or postponed both in the reference period. Floor space 
under construction has a strong correlation with property FAI. 
Floor space completed: This refers to the total floor space that fulfills all the completion 
requirements of property developers and is ready for occupancy. 
Other housing related construction indicators: 
• 
The three indicators above are all reported by property developers, and released monthly 
together with the real estate FAI indicator. There are other indicators related to broader 
property activity: “housing under construction” and “housing completion”, for example, 
cover not just commercial housing but also other types of housing constructed by all types 
of enterprises. 


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• 
The government also publishes data on social housing. There are annual data on the total 
number of flats planned to be started, as well as the total number of flats completed. In 
recent years, these data points were mostly unveiled by the Ministry of Housing and Urban-
Rural Development on ad hoc occasions. Although social housing related data are not quite 
as important as commercial housing related data, and the former's data quality could be 
lower than the latter, construction of social housing still drives upstream/downstream 
industries (such as construction materials) and thus is useful to track. Social housing units 
could also be converted from commercial housing — during the property down-cycle in 
2014-2015 and also in recent years, this was one inventory destocking measure adopted by 
many local governments. More recently, the PBOC announced in May 2024 a relending 
program encouraging local SOEs to purchase completed but unsold properties and to turn 
them into social housing to destock existing property inventories. 
Signal to Noise Ratio 
The survey target of the first three indicators above includes only property developers, so these data 
cover only commercial floor spaces constructed by developers. Properties built by other corporates 
and institutions (often as a form of welfare, e.g. employee housing by SOEs) which are not for sale 
on the market are not included. Rural properties without property rights are also not captured. 
Moreover, the series for floor space under construction, completed, and started are not consistent 
with each other, and researchers have different views on which indicator is more reliable. In theory, 
new home starts should lead completions by 2-3 years, but completions ran persistently and 
significantly below new starts from 2000 to 2022, and the two series even went in opposite 
directions in 2018 and 2021. On the back of the prolonged property downturn since mid-2021, 
increased developer funding stress and higher policy priority to secure the delivery of pre-sold new 
homes boosted completions relative to new starts dramatically in 2022-23. 
Macro Importance 
The floor space started, under construction and completion series are all widely tracked by investors 
and researchers. Historically, floor space started has lagged property sales (given the majority of 
property sales are pre-sales) and used to lead demand in other sectors such as metals/cement, 
though this may change in the future as government policy discourages pre-sales. Floor space 
completed, on the other hand, can be informative on the future demand of moving-in related items 
such as furniture, home appliances, and interior design materials. 
Exhibit 23: Property starts and completions trends can diverge from each other 
Property-related activity (seasonally adjusted) 
 


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Source: NBS, Goldman Sachs Global Investment Research 
Home Sales 
Signal to noise ratio: **** 
Macro importance: **** 
Source: National Bureau of Statistics, Wind, CREIS, Goldman Sachs Economics Research 
Availability: Daily, monthly and annual 
Timing: NBS monthly home sales: Typically around the 3rd week of the following month (together 
with the release of the NBS monthly activity data) 
Overview 
Floor area sales and total value sales of commercial buildings: These two indicators measure the 
sales volume and value of residential buildings, offices, and commercial buildings in the primary 
property market. They are released by the NBS together with new starts, under construction, and 
completion data mentioned in Section III. Investment. 
Due to China's presales system, most sales are presales of units that are still under construction vs. 
sales of completed units. In 2023, 82%/77% of the sales value/volume for all commercial property 
transactions was presales, lower than their peaks in mid-2021 (91%/89%) but well above their pre-
GFC levels (73%/67% in 2007). Revenue from presales is therefore a major source of funding for 
Chinese property developers. 
Wind 30-city daily property transaction volume in the primary market: According to Wind 
definition, its 30-city sample of property sales in the primary (new) market is actually composed of 
32 cities, including 4 Tier-1 cities, 14 Tier-2 cities and 14 Tier-3/4 cities. However, based on a 
bottom-up estimation using city-level data in recent years, we find that the series from Wind include 
21 cities only (4 Tier-1 cities, 8 Tier-2 cities and 9 Tier-3/4 cities) currently, as property sales data for 


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several cities (e.g., Tianjin and Nanchang) became unavailable in recent years.
[9] Our bottom-up 
estimates using property sales data from 21 cities match closely the headline number of Wind 30-
city property transaction volume in recent years. The original source is various local housing bureaus. 
Other third-party data vendors, such as CREIS/Soufun, also have their own home transaction 
tracking for different city samples. 
Wind 19-city daily property transaction volume in the secondary market (compiled by Goldman 
Sachs Economics Research): Our tracker is built on city-level daily data for the secondary (existing) 
property market transaction volume which is compiled by Wind and originally released by local 
housing authorities. Our sample covers 19 cities, including 2 Tier-1 cities, 7 Tier-2 cities, and 10 Tier-
3/4 cities. 
As China's housing market evolves, the secondary market becomes more important, especially in 
top-tier cities where land supply and new residential buildings are more limited. Secondary market 
transactions are important in tracking price trends and property market sentiment, but are less 
important in terms of GDP contributions. 
Real estate investment, new starts, under construction, completions, and sales data are also available 
at the province level and for 40 major cities in China, reported by local housing bureaus and 
collected by the NBS on a monthly basis. However, there are many missing data points in city level 
indicators, and the NBS suspended the release of 40-city property activity indicators in January 2019. 
According to the 2020 data (latest data available), lower-tier (Tier-3/4) cities accounted for 66% of 
nationwide new home sales volume, followed by Tier-2 (31%) and Tier-1 cities (3%). In value terms, 
the share of Tier-1, Tier-2, and lower-tier cities was 11%, 40% and 49%, respectively. Combining data 
from the NBS and private sources and based on some reasonable assumptions, we estimate a 
breakdown of new home sales by city tier. 
Exhibit 24: Property activity declined significantly beginning in 2021 
Property-related activity (seasonally adjusted) 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
Property transactions data are generally reliable, especially when compared with new starts 
and completions data. But people sometimes under-report the transaction value to reduce 
the transaction tax, though this mostly occurs in the secondary market. 
• 
The year-over-year growth rates and the levels reported in the NBS property data used to 
be consistent with each other until 2023. In May 2023, the NBS revised down the 
comparison base when reporting yoy property activity growth data, especially for new home 
sales, to adjust for false sales data.
[10] The practice continued through the remainder of 2023 
and resulted in a meaningful divergence between the officially-reported level data and 
growth rate data. However, the NBS has not released the revised level data for previous 
years. We adjust the property-related activity data series from 2023 in our analysis to take 
into account the revisions. We estimate new home sales volume in 2022 has been revised 
down by around 10%, especially for April-May 2022 (amid the Covid-related Shanghai 
lockdown). 
Macro Importance 
Property transactions are important to track for several reasons. Most directly, they serve as an 
indicator of housing demand. The fluctuations of property sales impact housing construction 
activities, and also the profitability of property developers. In addition, property sales are correlated 
with a number of other industries such as furniture, interior design and real estate agencies (which is 
linked to real estate related services in GDP). 
Home Inventory 
Signal to noise ratio: ** 
Macro importance: *** 
Source: National Bureau of Statistics, local housing bureaus, CREIS 
Availability: Weekly, monthly and annual 
Overview 
Housing vacant area: This data refer to completed but unsold floor area. It is published monthly by 
the NBS along with other property indicators and has a breakdown by different types of properties 
(residential, office building, and other commercial real estate). By province data are also available, 
but only at an annual frequency. Note that due to China's presales system, this series only covers a 
fraction of total new home inventory (i.e., all floor areas that are “saleable” but unsold) because the 
vast majority of the new home inventory is uncompleted. 
Inventory months: Other common (and more widely used) measures of housing inventory include 
the ratio of total saleable gross floor area divided by monthly floor area sold, which measures the 
number of months needed to digest inventory. There is no official data on this, but total saleable 
gross floor areas are reported in major cities in China by the local housing bureaus, compiled by 


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third-party data vendors including CREIS. The CREIS inventory months data series, regularly tracked 
by our China property research team, is available on both a weekly and monthly basis. 
There are no vacancy statistics on properties sold but not inhabited, which are sometimes referred to 
as the “shadow inventory” in the Chinese property market. There was a report by the Southwestern 
University of Finance and Economics in 2018 based on 2011-17 Chinese household financial surveys 
suggesting around 21.4% of urban housing apartments were left vacant in 2017 (vs. 18.4% in 2011).
[11] 
Signal to Noise Ratio 
Inventory data tend to be noisy. Total saleable gross floor area data can be patchy, with missing 
values within the series. Also, there is no nationwide data on inventory months, because only major 
cities report total saleable gross floor area data. 
Macro Importance 
Despite challenges in interpretation, inventory data are very important to gauge the cycle of the 
property market, and thus can be indicative of future housing price trends. After significant inventory 
destocking, housing prices may face upward pressure, and vice versa. 
Exhibit 25: Housing inventory months trended higher in 2022-24 despite increased easing 
efforts 
Inventory months across city tiers 
 
Source: CREIS, Goldman Sachs Global Investment Research 
Property Price Measures 
Signal to noise ratio: ** 
Macro importance: **** 
Source: National Bureau of Statistics, CREIS/Soufun, Centaline, Beike, Zhuge 


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Availability: NBS: Monthly from 2005, quarterly from 1998 
CREIS/Soufun: Monthly from June 2010 
Other third-party data vendors: Monthly 
Overview 
There are two main sources of property indices for major cities, from the official (NBS) and private 
sources (e.g., CREIS, Centaline, Beike). “Properties” refer to commercial buildings built to be sold in 
the market, including both residential and non-residential properties, newly constructed properties, 
and second-hand properties. 
Exhibit 26: A comparison of home price measures for the primary and secondary markets 
 


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Source: NBS, Beike, CREIS, Wind, Zhuge, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
These price indices generally follow a well-designed methodology, and most indices control 
to varying degrees for the impact of quality, though their original data sources differ 
somewhat. For some of the indices such as the NBS 70-city property price index, given that 
there is a higher degree of estimation involved in compiling the index and pressures on local 


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governments to control property prices (especially new home prices), there are market 
concerns that the indices are over-smoothed, showing smaller fluctuations in prices than 
actual property price changes. For example, the rise in property prices may be hidden when 
developers are forced to sell properties at prices below the market equilibrium because of 
local government pressures, and the excess demand leads to alternative expenses such as 
requirements for property purchasers to deposit funds well in advance to be able to 
purchase the property. During downturns, the decline in property prices may be disguised 
by free parking spots and extensive internal renovations offered by developers in lieu of 
price cuts. These distortions are typically more relevant for new properties. 
• 
Price indices on second-hand properties are therefore often a useful reference when they 
diverge from primary market property prices. On secondary-market home prices, besides 
the NBS 70-city secondary home price index, some third-party data vendors such as 
Centaline, Zhuge, and Beike (mostly real estate agents, information platforms and consulting 
firms) also have their own measures. Home price data from private sources usually have a 
much shorter time series than the NBS series, but they tend to be released earlier than the 
official indices. In previous years, secondary home prices have also been affected by many 
non-market-based policy interventions such as government guided prices and reference 
prices (related to tax payments and mortgage borrowing for secondary home transactions). 
We also note some third-party data vendors such as Beike suspended their release of 
secondary home prices in late 2023 when secondary home prices showed notable declines. 
Exhibit 27: “Tier 1 cities” have seen more pronounced price appreciation in boom periods, 
while home price declines were broadly based across city tiers in 2022-24 
Average house price in the primary market 
 
Source: NBS, Goldman Sachs Global Investment Research 
Exhibit 28: Different home price measures for the secondary market shared similar trends in 
2022-24 


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Property price measures for the secondary market 
 
Source: NBS, Wind, Centaline, Beike, Zhuge, Goldman Sachs Global Investment Research 
Macro Importance 
Home prices could drive economic growth, matter for the risk spillover to the banking system 
directly (through mortgage loans and property developer loans) and indirectly (through other types 
of loans collateralized with real estate properties), and affect household consumption (through 
wealth effects, moving-in related purchases, and consumer confidence channels). Moreover, NBS 
new home prices have been set by policymakers as one determinant for city-level mortgage rate 
adjustments. 
Compilation and Reporting 
When compiling the Property Price Index, the statistical authorities in China try to control for 
differences in the quality of properties. They consider the features of the property and attempt to 
compare prices for comparable properties. Factors such as location, structure, and type of property 
are all taken into consideration to make price comparisons. All data sources face significant 
challenges for compiling a portfolio that is relatively stable for pricing tracking, implying no perfect 
home price measure. For example, the CREIS/Soufun property index is compiled by taking the 
weighted (by floor area) average of indices of 100 underlying cities in the reporting period. It 
includes the price changes for both commercial residential buildings and social housing. Data inputs 
include information collected through field visits and corporate surveys, from real estate 
intermediaries, and based on information provided by local governments. 
Other Issues 
• 
The NBS changed the methodology for the Property Price Index in 2011. Property sales 
prices were split into primary and secondary housing price indices. The index initially 
covered 35 major cities and was expanded to 70 cities in 2005. Rural areas are not covered. 
Data are collected via a mixture of reporting forms from real estate companies and site visits 


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by NBS staff. In January 2018, the NBS suspended the release of the “new residential 
property (including social housing) price index” series. Since then, it has only released the 
“new residential commodity property (excluding social housing) price index” series. The 
patterns of these two data series are similar given the small share of social housing in total 
housing. 
• 
The divergence of property price trends among different city tiers in China reflects 
differences in housing market fundamentals. Top-tier cities face more supply restrictions and 
resilient demand, and thus upward pressures on housing prices are the strongest. Lower-tier 
cities tend to have diverse circumstances but on average face less restrictive supply and 
weaker demand, and thus housing price growth tends to be slower and/or prices tend to fall 
more in property market down-cycles. 
• 
The table below shows the NBS classification for the 70 large and medium-sized cities by 
tier. Although there is widespread agreement on the definition of Tier-1 cities (Beijing, 
Shanghai, Guangzhou, and Shenzhen), categorization beyond that point is not always 
consistent among different sources, particularly for lower-tier cities. 
Exhibit 29: Most cities in the NBS 70-city property price dataset are in Tiers 2 and 3 
 
Source: NBS, Goldman Sachs Global Investment Research 
Land Price Indices 
Signal to noise ratio: ** 
Macro importance: ** 
Source: NBS, Ministry of Land and Resources; CREIS, Wind, academic research 
Availability: NBS average land transaction price: monthly from January-February 2004 to December 
2022; Ministry of Natural Resources (MNR) land data: quarterly from Q1 2008 to Q3 2021 
Wharton/NUS/Tsinghua Chinese Residential Land Price Indices (CRLPI): quarterly from Q1 2004 to 


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Q1 2017 
Wind average land sales price: weekly and monthly from January 2008 
GS 300+ city residential land price tracker: monthly from January 2008 
Overview 
There are multiple sources of land price data. Unfortunately many of them have been discontinued. 
NBS average land transaction prices: These are estimated based on the NBS real estate developers 
land transaction value and area data series (as elaborated in the “Land Transactions” sub-section). 
The implied land price data series can be distorted by the mix-shift in land transactions as the land 
purchased in different periods may not be identical in terms of location and quality. This data series 
has been no longer available since January 2023 when the NBS suspended the release of land 
transaction area and value data. 
Ministry of Natural Resources (MNR) land price data: This indicator monitors the average market 
land price in 105 major Chinese cities on a quarterly basis. The data are categorized by land prices 
for residential, commercial and service, and industrial purposes. In May 2002, the Ministry of Land 
and Resources (the predecessor of the MNR) permitted the transfer of state-owned land-use right 
mainly by bidding, auction and quotation. In January 2022 when Q4 2021 data were supposed to be 
released, the MNR suspended this data series. 
Wharton/NUS/Tsinghua Chinese Residential Land Price Indices (CRLPI): This indicator tracks 
national land price growth in real (CPI-deflated) constant quality terms based on data from 35 cities 
in China on a quarterly basis.
[12] The provider also reports region-/city-level land price indices on a 
semi-annual/annual basis. While technically this series may be preferable, this series has not been 
updated since Q1 2017. 
Wind 100-city land transaction price data: These are estimated based on the Wind land 
transaction value and area data series. The breakdown of Wind 100-city land transaction by city tier 
is also available. 
GS 300+ city Residential Land Price Tracker: The GS property sector equity research team has 
aggregated land base prices, transacted prices, and land price premiums in 302 major cities based 
on data from the China Real Estate Index System (CREIS). The average headline series is then 
grouped into 3 city tiers. 
Signal to Noise Ratio 
As discussed above, there are no nationwide data on overall land prices. We tend to rely more on 
our GS 300-city residential land price tracker (based on the same sample that we use to estimate the 
“300-city land transactions” measure, as previously elaborated in the "Land Transactions" subsection) 
given it is more timely and has a relatively wide coverage compared with other land price indices. 
Similar to property price indices, land price indices ideally should adjust for quality differences. The 
Wharton/NUS/Tsinghua land price index is adjusted for quality differences, but not the 300-city 
residential land price series. The average price of land sold is subject to policy distortions similar to 
property-related policy distortions. Facing pressures to control land prices, governments often 
restrict or suspend the supply of premium land (or properties) relative to non-premium land in order 


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to lower the average selling price (total land value divided by total land area). As a result, it is 
conceptually better to look at the quality-adjusted data, though adjusting for quality differences is a 
difficult job that cannot be done without significant effort by specialists (and it is not always clear 
how much effort has been made). 
Macro Importance 
Land price inflation is important because it is a main factor behind the input cost of property 
developers, and thus also affects housing price trends. Given land sales revenue is an important 
financing source for local governments, land price fluctuations will also impact the financing needs 
of local governments. 
Exhibit 30: Land price increase over the past decades was mainly led by Tier 1 and 2 cities 
100-city average land transaction price by city tier (12mma) 
 
Source: Wind, Goldman Sachs Global Investment Research 
GS Proprietary Indicators Related to the Real Estate Sector 
Signal to noise ratio: *** 
Macro importance: **** 
Source: Goldman Sachs Economics Research 
Availability: GS China property impact on GDP growth: annual; 
GS 24-city housing policy stance indicator: monthly; 
GS property policy relative tightness index: daily. 
Overview 
GS China property impact on GDP growth: We decompose the property sector's contribution to 


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China's yoy GDP growth into five major channels, i.e., construction, real estate services, upstream 
effects (mainly through commodities demand), consumption, and fiscal channels. These estimates 
are based on a series of property activity data – including new home starts, sales, completions, 
under construction, property FAI, land sales, and average new home sales price – and their 
correlations with GDP growth in specific areas. By aggregating these channels, we estimate China's 
property sector impact on GDP growth on an annual basis, and project its future path based on in-
house forecasts for major property activity indicators. 
Exhibit 31: The property sector has turned to a growth drag since 2022 
Housing contribution to yoy GDP growth 
 
Source: Haver Analytics, Goldman Sachs Global Investment Research 
GS 24-city housing policy stance indicator: We use quantitative measures of housing policy in 24 
large cities across five policy dimensions (i.e., home purchase restrictions, down-payment ratios, 
mortgage rate fluctuations around benchmark interest rates, mortgage restrictions, and sales 
restrictions), and then average them to create our housing policy stance indicator. This is also an 
input of our proprietary China domestic macro policy proxy. 
Exhibit 32: Our 24-city housing policy stance indicator suggests the 2022-24 housing easing in 
large cities has exceeded previous cycles 
Housing component of GS China domestic macro policy proxy 


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Source: CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research 
GS property policy relative tightness index: This proprietary indicator measures the relative 
tightness of property policies in over 100 cities from the following aspects: 1) demand: purchase 
restrictions (household registration, social welfare contribution, etc.), credit restrictions (mortgage 
rate, down payment), sales restrictions; 2) supply: caps on selling prices, presales restrictions, land 
transaction tax, etc.; 3) others: property speculation, land supply. Original data sources include 
government announcements, media reports, and industry association data. 
Exhibit 33: Our city-level property relative tightness index has shown almost no major local 
housing tightening policies in cities that we track since December 2021 
Property policy relative tightness index: relative tightening share 
 
Source: Local governments, Songfang.com, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 


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As our tracking for China property impact on GDP growth is built mainly on NBS property activity 
indicators, concerns around the quality of underlying data may apply to this proprietary indicator. 
The GS property policy relative tightness index and 24-city housing policy stance indicator are based 
on a limited size of city sample, which may not always be representative. Besides, during the period 
of outright housing easing, the GS property policy relative tightness index (which is a diffusion index) 
may remain at or close to zero, failing to capture the sequential change in the magnitude of housing 
easing. 
Macro Importance 
The property sector has been the largest single sector in the Chinese economy for many years, and 
its ups and downs have significant implications for China's headline GDP growth. Our proprietary 
indicators track the sequential change in housing policy stance and gauge the growth impact of the 
property sector in a timely and comprehensive manner. 
Related GS Economics Publications 
• 
“How China's property policy tightening lowered sales and prices”, Asia Economics Analyst, 
26 September 2017 
• 
“Tracking residential housing’s impulse to Chinese growth”, Asia Economics Analyst, 27 
March 2018 
• 
“China property policy: capturing the big picture from localized measures”, Asia Economics 
Analyst, 29 November 2020 
• 
“Q&A on Evergrande’s macro implications”, Asia in Focus, 24 September 2021 
• 
“Credit supply holds the key to China housing outlook in 2022”, Asia Economics Analyst, 11 
October 2021 
• 
“How big is China's property sector?”, China Data Insights, 11 October 2021 
• 
“Lessons from Japan: Credit Tightening in the Property Market”, Asia in Focus, 29 November 
2021 
• 
“Demystifying the discrepancy in different land sales measures”, China Data Insights, 14 April 
2022 
• 
“Understanding the recent rise and fall of high-frequency property sales data”, China Data 
Insights, 14 July 2022 
• 
“China: 'L-shaped' Property Sector Recovery Ahead without a Quick Fix”, Asia Economics 
Analyst, 11 June 2023 
• 
“Understanding differences in China’s home price measures”, China Data Insights, 9 July 
2023 
• 
“Q&A on China's property downturn and its implications”, Asia Economics Analyst, 23 
August 2023 
• 
“China: Tracking the impact of ongoing housing easing”, Asia in Focus, 8 October 2023 


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• 
“Comparing China and US Housing Downturns: Different Fiscal Backdrop, Same Need to 
Prevent Spillovers”, Asia Economics Analyst, 12 February 2024 
• 
“A Closer Look at NBS 2022 GDP Revisions”, China Data Insights, 13 March 2024 
• 
“China: Housing easing underway, but no signs yet of game-changing measures”, 14 April 
2024 
Section V. Consumption 
There are four main sources of consumption-related data: 
1. Retail sales reported by the NBS: Compiled using a combination of administrative reporting 
and sampling. 
2. Household Income and Expenditure Survey: Compiled using sample surveys. 
3. Household consumption (in GDP by expenditure): Compiled using mainly the two series 
mentioned above. 
4. Retail sales of 100 (more recently 50) major offline retailers reported by the China National 
Commercial Information Center (CNCIC). 
There are alternative micro data that can be collated to give a partial picture as well, such as auto 
sales, tourism revenue during long holidays and parcel volumes. 
Exhibit 34: A comparison of three different sources of consumption data 


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Source: MOFCOM, NBS, Goldman Sachs Global Investment Research 
Retail Sales of Consumer Goods 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly from January 1990, annual from 1952 
Timing: Typically around the middle of the following month. In January, April, July, and October, it is 
released with GDP data around 3 weeks after the end of the quarter. 
Overview 
• 
Total Retail Sales of Consumer Goods measure goods and restaurant services sold at the 
retail level (as opposed to wholesale), including both online and offline sales. These goods 
and restaurant services may be purchased by households, firms or the government. 
• 
A separate indicator named “Online Retail Sales of Consumer Goods” refers to goods 
transacted over online platforms. 
Signal to Noise Ratio 


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• 
Before the Covid pandemic, the main issue with retail sales data was that growth appeared 
overly smooth. We suspect the problem is caused by the fact that sales by companies below 
the minimum threshold are compiled using sample surveys, which involve a significant 
degree of discretion, whereas sales by companies above the minimum threshold are 
compiled using census surveys. Reported retail sales growth was also higher relative to the 
growth rate of the economy for many years, even after adjusting for inflation. During 2005-
2015 for example, real retail sales (nominal retail sales growth adjusted for price factors) 
grew 15% per year, significantly above the 10% per year real GDP growth. In addition, sample 
changes over the years caused discrepancies between the reported retail sales level and 
reported retail sales yoy growth rate. Volatility in the series increased dramatically with the 
onset of the Covid pandemic along with more volatility in the underlying economy induced 
by Covid-related lockdowns, and the signal to noise ratio has therefore improved in recent 
years. 
Macro Importance 
• 
Although monthly retail sales data are often used as the main indicator for private 
consumption, users should be aware of a few data issues: 
1. The most serious problem is that services consumption (apart from restaurant 
services) is not included in the retail sales data. Experiences of other countries show 
that the share of services in total consumption typically rises as the economy 
develops. However, retail sales data are unable to reflect this increasingly important 
component of consumption.
[13] 
2. Retail sales also include non-household (i.e., government and corporate) purchases, 
but it is currently impossible to obtain a breakdown between household and non-
household purchases. While household consumption probably constitutes the 
largest part of the reported retail sales, some of the retail sales expenditure is 
classified as government consumption vs. private consumption under the GDP by 
expenditure framework. 
• 
That said, the monthly retail sales data have the advantage of being timely. Policymakers 
also pay close attention to these figures, roughly on par with IP, FAI and trade data. 
• 
Sporadic waves of Covid in 2020-2022 and stay-at-home policies disrupted offline activities 
in China, drawing increased engagement to online platforms. As consumption behavior 
shifted from offline to online purchases in China, the share of online goods sales in total 
retail goods sales rose to 31% in 2023 from 23% in 2019. 
Compilation Methodology 
• 
All large firms above the designated size report sales data monthly. Data from small firms 
below the designated size are compiled through sampling. In practice, it is not clear how 
often, how much, and how well the sampling of small firms is actually carried out. Data from 
major online platforms are compiled for online goods sales, but the list of surveyed 
platforms may change over time. 
• 
Retailers with annual revenue from primary business of RMB5 million and above, hotels and 


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restaurants with annual revenue from primary business of RMB2 million and above, and 
wholesalers with annual revenue from primary business of RMB20 million and above are 
jointly defined as enterprises above designated size. 
Reporting 
• 
Retail sales data are available in nominal terms only. The NBS discontinued the release of the 
Retail Price Index at end-2022. To deflate the series, the Consumer Price Index for goods 
could be used, though the breakdown and weights of CPI goods baskets and retail sales 
categories differ somewhat. 
• 
There are three kinds of breakdown available for retail sales data: 
1. By location, i.e., consumer goods sold in urban and rural areas. However, urban retail 
sales may be goods purchased by rural households who live in urban areas and do 
not necessarily represent urban household consumption, and vice-versa. 
2. By commodities. There are two layers of breakdown data available - the first layer is 
total retail sales by goods vs. catering, and online retail sales by goods and services. 
The second layer of breakdown is retail sales by specific commodities (e.g., food, 
clothes, home appliances, autos, etc.). The second set of data are only available for 
sales made by firms at or above the designated size and may not always give an 
accurate picture of overall consumption growth. Goods sales by above-designated-
size retailers accounted for 40% of total retail goods sales in 2023. 
Exhibit 35: Growth of online sales have outpaced that of offline sales in recent years 
Retail sales 
 
Source: NBS, Goldman Sachs Global Investment Research 
Household Income and Expenditure Survey 


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Household Income Survey 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Frequency: Quarterly, annual 
Timing: Around 2-3 weeks after the end of each quarter, along with the release of GDP data 
Availability: See the table below 
Exhibit 36: Availability of China NBS income and expenditure data 
 
Source: Goldman Sachs Global Investment Research 
Overview 
• 
Urban and rural income is classified in terms of total and disposable income. 
• 
Total income is composed of pre-tax wages, business profits, return on assets (e.g., interest, 
dividends, rents) and other “transfer income” (gifts, insurance claims, retirement pensions, 
transfer payments from members in other households etc.). Prior to 2012, samples were 
collected from 66,000 households which were presumably rotated every three years. Starting 
from Q4 2012, the NBS issued a new survey format to reflect the reform of urban-rural 


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integration, which adjusted the sample to 2 million households nationwide for general 
investigation and selected 160,000 households for direct survey.
[14] One-third of the surveyed 
households are rotated each year. 
• 
Disposable income is total income for final consumption expenditure and households’ 
savings excluding income tax and social security contributions. It includes both cash income 
and in-kind income. 
Disposable Income per Capita 
Starting from year-end 2012, disposable income per capita is disclosed in the new survey (“Urban-
Rural Unified Household Survey”) at both a quarterly and annual frequency. Urban and rural 
households are still surveyed separately but the statistical standards are the same. NBS then 
calculates the nationwide per capita disposable income as the weighted average income of urban 
and rural households. Weights are based on urban/rural population levels. 
By source: Per capita disposable income is the sum of wage and salary, net business income, net 
income from property and net income from transfers at the urban and rural level separately. 
By income level (nationwide): Per capita disposable income is divided into five income levels: low 
(bottom 20% of income distribution), low middle (20-40
th percentile), middle (40-60
th percentile), 
upper middle (60-80
th percentile) and high (top 20% of income distribution). In our view, data for the 
high-income group are by far the least reliable, for reasons discussed under "Signal to Noise Ratio" 
below. 
There are some differences between the concepts of rural net income and urban disposable income. 
Rural households are treated as production as well as consumption units. As a result, their net 
income excludes “household operation costs”, such as costs of fertilizers and pesticides. 
Disposable Income by Sources 
1. Income from wages and salaries (56%) 
2. Net business income (17%) 
3. Net income from property (9%) 
4. Net income from transfers (18%) 
Signal to Noise Ratio 
• 
Two factors seriously affect the reliability of household surveys: 
1. Households participating in the survey are required to provide very detailed notes 
on their expenditure, which is time-consuming. Households are rewarded financially 
for taking part in the survey, but the financial payments are small. For example, 
financial payment was RMB70 per month for households in selected cities in 
Zhejiang Province in 2020. Therefore, the incentive for households to respond 
accurately may not be high. This problem is especially serious for urban high-
income households. 
2. Lack of confidentiality may lead people to under-report their income/expenditure or 


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simply refuse to participate in surveys. Again, this is likely to be especially serious for 
high-income groups and households with gray or illegal income. As a result, there is 
likely a downward bias in reported income levels. However, the direction of any bias 
on growth rates is less clear as under the aggressive anti-corruption campaign of 
recent years, under-reported gray and illegal income growth likely fell dramatically. 
Since these sources of income were never reported in the first place, the impact may 
not show up in official statistics. 
Macro Importance 
This series is useful in gauging growth rates in purchasing power, living standards and labor market 
performance. 
Exhibit 37: Wage growth has historically been the largest driver of household disposable 
income growth 
China household disposable income per capita 
 
 
Source: NBS, Goldman Sachs Global Investment Research 
Household Expenditure Survey 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Timing: Around 2-3 weeks after the end of each quarter 
Availability: See table in the "Household Income Survey" sub-section 


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Overview 
• 
Household consumption expenditure survey measures households ’  expenditure on 
consumption (including both money and non-money expenditure) in eight broad categories: 
1. Food (including tobacco, liquor, and catering) 
2. Clothing 
3. Housing
[15] 
4. Household appliances, articles, and services 
5. Healthcare, medicines, and medical equipment and services 
6. Transportation and communication 
7. Recreation, education, and cultural goods and services 
8. Others 
• 
Key changes since the implementation of the Urban-Rural Unified Household Survey: 1) 
income and spending sub-categories have been standardized; 2) urban population covers 
migrant workers living in urban areas, but migrant workers are excluded from the rural 
population; 3) college students supported by the surveyed households but living in separate 
residence areas are counted as members of the households. 
Signal to Noise Ratio 
• 
The compilation methodology for expenditure data is the same as for disposable Income 
data (described above), and therefore entails the same problems. 
Macro Importance 
• 
Household survey data provide useful information that is not available from other data 
sources. The household survey consumption per capita data is the only data set that 
captures all consumption categories, including both goods and services, and therefore 
should provide the most comprehensive gauge of the state of household consumption at a 
quarterly frequency. Household consumption in GDP data incorporates data from retail sales 
as well as household surveys. As the Chinese economy rebalances towards more 
consumption, this data set has become more important. 
• 
In addition, one can calculate the household saving rate (the difference between disposable 
income and consumption, divided by disposable income) based on the income and 
consumption data. This is the most timely read of households’ savings/consumption 
behaviors. In general, the household saving rate has been trending up over the past decade 
on the back of continued urbanization (urban households have higher income and higher 
savings rates than rural households). The household saving rate rose during 2020-2022 
amid the Covid pandemic, and declined in 2023 along with China’s reopening as the Covid-
related restrictions were lifted. 
Exhibit 38: Household consumption growth saw large swings during the Covid pandemic 


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China household nominal consumption expenditure per capita 
 
Source: NBS, Goldman Sachs Global Investment Research 
Exhibit 39: Household savings rate has trended higher and increased sharply during the Covid 
pandemic 
China household savings rate (seasonally adjusted) 
 
Source: NBS, Goldman Sachs Global Investment Research 
Retail Sales of Major Offline Retailers Reported by China National Commercial Information Center 
(CNCIC) 
Source: China National Commercial Information Center 
Availability: Top 100 retailers data are available on a monthly basis from July 2007-March 2024; top 


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50 since July 2011 
Timing: Typically around the third week of the following month, but can be irregular at times 
Overview 
• 
This series reports the year-over-year growth rate of the total retail sales of the top 50/100 
retailers in China. The top retailers include both offline retailers such as chain stores and 
department stores and online retailers such as Tmall and JD.com. These goods may be 
purchased by households, firms, or the government. Retail sales data by product are 
available, though there are missing values in this data. Similar to the official retail sales data, 
this series is reported in nominal terms. 
Signal to Noise Ratio 
• 
Total retail sales of the top 50/100 retailers data are subject to sample changes, as the list of 
retailers included is updated regularly based on the latest ranking. In addition, releases of 
this indicator are occasionally delayed for a few weeks. CNCIC suspended the release of the 
full top 100 retailers data in April 2024, although the top 50 are still available. 
Macro Importance 
• 
Total retail sales of the top 50/100 retailers are reported separately by non-official sources 
(CNCIC) and therefore could provide cross-checks for the official retail sales/household 
consumption data
[16]. For example, year-over-year growth in total retail sales of the top 
50/100 retailers dropped from around 20% in 2011 to 0% in 2014-15 amid a housing 
downturn and overall economic slowdown. Growth in the official retail sales series over this 
period was more stable by contrast, decelerating gradually from 17% yoy in 2011 to 11% yoy 
in 2015. 
Auto Sales 
Signal to noise ratio: **** 
Macro importance: *** 
Source: China Passenger Car Association (CPCA); China Association of Automobile Manufacturers 
(CAAM), National Bureau of Statistics 
Availability: CPCA auto sales volume: monthly from Apr 2007; weekly from 2015 
CAAM auto sales volume: monthly from 2000, annual from 1998 
NBS auto sales value: monthly from January 1997 
Timing: Around 10th day of the following month 
Overview 
• 
CAAM reports car sales in units on a monthly basis, capturing different types of cars, such as 
passenger and commercial cars, through wholesale channels. The CAAM is regulated by 
SASAC and authorized by the government to collect auto production and sales data from 


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auto manufacturers. CPCA is a data exchange platform among automakers and not 
accredited by the government. It reports auto sales in units on a weekly basis, capturing 
passenger car sales through both retail and wholesale channels. For the value of sales of 
automobiles, the NBS monthly retail sales data report sales by above-designated-sized 
enterprises. 
• 
Autos-related data tend to be highly cyclical, though sales are sensitive to policy measures 
such as auto purchase tax changes and changes in purchase restrictions in large cities. 
Policymakers may also apply differentiated policies (e.g., on new energy vehicles vs. 
traditional vehicles) to provide targeted support for certain types of vehicles. New energy 
vehicle sales have expanded at a very fast pace in recent years on the back of policy support. 
For example, license plates for new energy vehicles are not restricted in top-tier cities, unlike 
traditional vehicles. New energy vehicle sales rose from 0.04% of total auto sales in 2011 to 
32% of total auto sales in 2023, based on CAAM data. 
Signal to Noise Ratio 
• 
Auto sales data are generally reliable over time. CAAM and CPCA data are directly reported 
by automobile companies, though subject to potential temporary distortions due to a lack of 
cross-check mechanism. Companies that already reached their annual sales targets, for 
example, may delay booking some sales to make it easier to reach the target in the 
following year. 
Macro Importance 
• 
Auto sales data have gained importance in recent years. From the demand side, automobile 
consumption accounts for around 10% of total household consumption and plays an 
important role in overall household consumption growth. On the production side, 
automobiles accounts for around 6% in overall industrial value-added, and is a key sub-
industry in the industrial sector. According to the National Development and Reform 
Commission (NDRC), as of 2020, auto and related sectors accounted for roughly 10% of 
GDP.
[17] As policymakers push for energy transition and emissions reduction, new energy 
vehicle production and sales data could shed light on the progress of China’s economic 
transformation. 
Exhibit 40: New energy car sales rose very rapidly in recent years and took around 32% of total 
automobile sales in 2023 
China automobile sales 


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Source: CEIC 
Consumer Confidence Index 
Signal to noise ratio: ** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Monthly from 1990 
Timing: Usually lagged by one month 
Overview 
• 
The NBS publishes the consumer confidence index (CCI) on a monthly basis. It is a survey-
based diffusion index that ranges from 0 to 200, with 0 implying extreme pessimism and 200 
implying extreme optimism. The index is imputed based on a monthly telephone survey of 
more than 6,000 urban and rural consumers in 15 provinces. Survey questions include 
respondents’ assessment of current employment situation, income level, employment 
prospects, income expectations and willingness to spend. 
Signal to Noise Ratio 
• 
The NBS CCI is based on a telephone survey. It is less precise than hard data (e.g., retail sales) 
and potentially subject to biases such as sampling and response rates, but it can be a useful 
gauge of consumer sentiment, especially at times when there is a major shift in consumer 
confidence. 
Macro Importance 
• 
Consumer confidence is an important concept as in theory it drives household savings, 
spending, and investment decisions. Our research shows that consumer confidence does 


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matter to household consumption after controlling for other variables like household 
disposable income. Since the Shanghai Covid lockdown in Q1 2022, the NBS consumer 
confidence index has remained depressed despite China’s lifting of the zero-Covid policy at 
the end of 2022. Muted domestic demand, a weak labor market, and the continued fall in 
house prices hurt consumer confidence and hindered willingness to spend. 
Exhibit 41: Consumer confidence plunged in Q1 2022 
China Consumer Confidence 
 
Source: NBS 
Other Consumption-related Data 
Other indicators on consumption include per-head spending during long holidays released by the 
Ministry of Culture and Tourism (MCT), which shows post-Covid recovery has been bumpy due to 
muted confidence and continued consumption downgrading. Some consumption-related indicators 
released by third-party sources are also worth monitoring. Movie box office revenue data released 
by Dengta App during long holidays partially reflect people’s willingness to spend, though the box 
office statistics are significantly affected by the release of blockbusters. As online sales account for an 
increasingly larger share in total retail sales, parcel delivery volume data published by the State Post 
Bureau each month is useful for tracking the momentum of online shopping (as 80%+ of parcels in 
China are e-commerce parcels). Parcel volume growth has significantly exceeded that of online 
goods since early 2023, which could partially be explained by lower value of single packages (e.g., e-
commerce platforms/merchants continue to lower the minimum purchase amount eligible for free 
shipping), and higher return rates on the back of more favorable return policies across platforms. 
Exhibit 42: Per-head holiday spending remained at low levels compared with pre-pandemic 
period 
Nationwide domestic visitors and tourism revenue vs. pre-pandemic levels 


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Source: Ministry of Culture and Tourism, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“China consumption worries: Goods spending decelerates as credit impulse fades”, Asia 
Economics Analyst, 8 September 2018 
• 
“How has online shopping affected CPI inflation in China?”, Asia Economics Analyst, 3 
October 2019 
• 
“A bit more confident about the Chinese consumer confidence data”, China Data Insights, 2 
December 2019 
• 
“Household cash flow: Still the key driver for consumption in China”, Asia Economics Analyst, 
24 May 2020 
• 
“China: Households’ excess savings around the pandemic”, Asia in Focus, 15 March 2021 
• 
“A Pulse Check on Chinese Household Consumption Growth: Still Sluggish”, Asia Economics 
Analyst, 4 July 2022 
• 
“ China post-reopening consumption recovery: Large potential, lingering scars ” , Asia 
Economics Analyst, 18 January 2023 
• 
“FAQs on “excess savings” in China”, China Data Insights, 7 February 2023 
• 
“Lower housing cost burdens can lower the savings rate in China, but confidence remains 
key”, Asia Economics Analyst, 21 January 2024 


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Section VI. External Sector 
Merchandise Trade 
Signal to noise ratio: ***** 
Macro importance: **** 
Source: General Administration of Customs; State Administration of Foreign Exchange (SAFE) 
Availability: China Customs: monthly from 1992, annual from 1950; SAFE: monthly from January 2015, 
quarterly from Q1 1998 
Timing: China Customs: 7-14 days after the end of the month; SAFE: end of next month 
Publication: China Customs Statistics; Semi-Annual Report on Balance of Payments 
Overview 
• 
Merchandise trade by China Customs measures the value of goods transactions (both in 
RMB and USD terms) across national borders. Exports are valued on an FOB (free on board) 
basis, which includes costs to deliver goods onto the vessels but not further costs, such as 
insurance or freight. Imports are calculated on a CIF (cost, insurance and freight) basis, which 
includes insurance and freight charges. 
• 
Trade data in the Balance of Payments standard, as published by SAFE, place greater 
emphasis on transactions between residents and non-residents, rather than physical 
movements across borders as shown in the China Customs data. (Though making this 
distinction can be difficult in practice.) The credit and debit sides of trade under SAFE data 
are both accounted on an FOB basis. 
Signal to Noise Ratio 
• 
Customs trade data are among the most reliable macro-economic data in China. Their high 
volatility is a clear indication of absence of the smoothing prevalent in other economic data. 
However, in 2012-13 and 2015-16, there were distortions to trade reporting due to 
importer/exporter incentives to move capital either onshore or offshore. For example, when 
there were strong capital inflows into China, exporters tended to over-report exports in 
order to facilitate higher payments from offshore. When there are outflow pressures, 
importers tend to over-report imports to disguise the movement of capital offshore. 
Exporters sometimes also create “false exports” to gain government subsidies such as export 
rebates. Therefore, at the end of this section, we have estimated an alternative series that 
relies on information from China’s trading partners. 
• 
Besides over-/under-reporting, there are significant discrepancies between bilateral trade 
data compiled by China and the corresponding data from its counterparts due to re-exports 
via Hong Kong. There are various ways to adjust for these data problems. For example, the 
statistical authorities in Hong Kong provide detailed data on the region’s re-export trade by 
country and surveys of re-export margins. The difficulty with this analysis is that much of the 


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false reporting is done via companies set up in Hong Kong, often solely for this purpose. 
These companies tend to use high-value-added goods that are easy to report large values 
for, but are often hard to judge in terms of underlying value. 
Macro Importance 
Trade data are very useful in judging economic cycles. Imports can give a useful indication of the 
strength of domestic demand. Exports can shed light on the strength of global demand. Net exports 
may provide clues about the potential misalignment of the foreign exchange rate. The government 
also often quotes the total amount of trade (exports plus imports) to gauge the level of openness of 
the economy. 
Compilation and Reporting 
• 
Commodity breakdowns are available by the Standard International Trade Classification 
(SITC), the Harmonized System (HS), the Broad Economic Categories (BEC) (e.g., 
consumption goods, capital goods and intermediate goods, etc.), and customs regime (e.g., 
ordinary trade, processing trade, etc.). 
• 
China Customs releases monthly trade indices on export value, volume, and unit value with a 
slightly longer lag (usually 20-25 days). Prior to 2014, the indices were denominated in USD. 
From 2014 onwards, the indices have been denominated in RMB. 
Other Issues 
• 
Some of China’s imports are for eventual export (e.g., raw materials or intermediate inputs 
into manufacturing goods exports). Total imports therefore can be divided into imports for 
processing trade and those for domestic use. We derive our estimates of total imports for 
processing trade by aggregating the following import categories: 
1. Imports for processing and assembling 
2. Equipment imported for processing and assembling 
3. Customs warehousing trade 
4. Entrepot trade by bonded area 
5. Imports for outward processing 
• 
The share of “imports for processing” in total imports has declined in recent years. “Imports 
for processing” declined to around 34% of total imports in 2023, from around 51% in 2005 
when the series started. 
• 
Note that there are a few differences between the Customs trade data and the net exports 
data in GDP. Net exports in GDP capture the net trade balance in goods as well as in 
services, whereas Customs trade data only cover trade in goods. Furthermore, in GDP data, 
both exports and imports are valued on an FOB basis, whereas Customs imports data are 
valued on a CIF basis. GDP standard is consistent with BOP standard. The NBS does not 
separately release data on exports of goods and services and imports of goods and services. 


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Services Trade 
Signal to noise ratio: *** 
Macro importance: *** 
Source: SAFE 
Availability: Monthly from January 2014, quarterly from Q1 1998 
Timing: At the end of the following month 
Overview 
• 
Exports/Imports of services refer to income/payment from/to foreigners on intangible 
products such as transport, tourism, entertainment, telecommunication and financial services. 
• 
After expanding through 2018, China’s service trade deficit narrowed sharply in 2020 after 
the onset of the Covid pandemic when strict travel restrictions were imposed. In 2019, travel 
services imports totaled $250bn. During the Covid pandemic, they averaged only $120bn 
per year, most of which were tuition and medical services payments made by Chinese 
residents overseas. China’s service trade deficit widened in 2023 after the end of its zero-
Covid policy. 
• 
Service trade data are probably subject to a significantly higher level of misreporting, 
because it is usually harder to verify the underlying fair value of services provided than 
goods traded. Although less subject to reporting distortions that are aimed at benefiting 
from government subsidies (as local governments are much more focused on goods trade) 
on balance services trade data appear to mask substantial net capital outflows. Spending per 
traveler roughly doubled in 2013-16, for example, suggesting the possibility that a 
significant part of “travel expenditure” is really hidden capital outflow. According to some 
academic research, correcting for this would suggest a current account surplus 1-1.5% of 
GDP larger (and commensurately higher capital outflows).
[18] When faced with capital outflow 
pressures, the authority tends to crack down on outflows through services trade as well. For 
example, in 2016/2017, bank card overseas withdrawal and large-amount purchases (which 
would show up in services imports) were under tighter restrictions. 
Exhibit 43: Services trade deficit widened notably post China’s reopening 
Services trade balance 
 


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Source: SAFE, Haver Analytics 
Balance of Payments 
Signal to noise ratio: **** 
Macro importance: **** 
Source: SAFE 
Availability: Quarterly from Q1 2010, semi-annual from 2000, annual from 1982 
Timing: Preliminary readings after 30 days; final readings after 3 months. 
Publication: Semi-Annual Report on Balance of Payments 
Overview 
The Balance of Payments (BOP) records the external transactions of an economy with the rest of the 
world over a certain period of time. It records transactions between residents and non-residents. 
Transactions are flows of goods, services, capital and financial claims. 
Signal to Noise Ratio 
• 
SAFE began to compile BOP data in 1985 in accordance with the 4
th edition of the IMF BOP 
Manual, and subsequently adopted the 5
th edition in 1996 and the 6
th edition (BPM6) in 
2015.
[19] In this edition, the IMF changed “Income” to “Primary Income”, and “Current Transfer” 
to “Secondary Income”. 
• 
SAFE must rely on other government agencies for data on various sub-components, which 
are often not compiled in accordance with IMF standards and are difficult to adjust (for 
example, the FDI data). We discuss the difference in FDI data from SAFE and Ministry of 
Commerce later in this section. 


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• 
Misreporting of data to evade capital controls are common and likely worsened after 2015 
given significant capital outflow pressures. The misreporting of goods trade data is well 
known, but the problem is likely to be more serious in terms of services trade because it is 
more difficult for the government to prove any wrongdoing. 
Macro Importance 
The BOP data have attracted rising interest in recent years because of the increased attention on the 
CNY. 
Compilation and Reporting 
• 
Balance of payments data (China’s are summarized in Exhibit 44) consist of two main 
components. 
1. Current Account (CA): This records the flow of international trade (both goods and 
services), primary income (i.e., income that accrues to foreign-owned inputs to 
production and other assets, e.g., profits generated by foreign-owned enterprises 
and investment income paid to foreign-owned property and financial securities), 
and secondary income (i.e., income transferred to or received from foreigners 
without “quid pro quo”, e.g., remittances from workers working overseas). 
2. Capital and Financial Accounts: This essentially records the flow of investment. 
Main 
components 
are: direct 
investment, 
portfolio 
investment, 
other 
investment, reserve assets, net errors and omissions. 
The current account captures current transfers and transactions in goods, services, and 
income… 
• 
The merchandise trade by BOP accounting is conceptually different from the monthly trade 
data from the Customs Administration as it is supposed to capture transactions between 
residents and non-residents instead of across national borders. Besides, its valuation 
standards are different: BOP measures both exports and imports on an FOB basis, whereas 
Customs values exports on an FOB basis and imports on a CIF basis. As FOB excludes costs 
of insurance and freight, imports are smaller in BOP accounting than in Customs accounting 
(by around 5%). SAFE also makes other minor technical adjustments to the customs trade 
data in accordance with IMF standards, such as subtracting goods exported but then 
returned. SAFE counts only the cargo with ownership change. Goods trade with no 
ownership change is accounted for as service trade in BOP. 
• 
The services component in the current account consists of items such as transportation, 
travel, and insurance. Data are compiled by SAFE directly, as well as by various other 
government agencies, such as the Ministry of Transportation and the Ministry of Culture and 
Tourism. Most of the service trade in China is through the “travel” channel, and “travel” 
under services trade includes spending while traveling, and education tuition abroad. 
• 
The income account records compensation for employees working abroad and returns on 
investments. The numbers of Chinese residents working abroad and foreign residents 
working in China are both relatively small, and most primary income is investment income. 


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In addition to primary income, secondary income through current transfer is also part of the 
current account. 
…and the capital account captures investment flows 
• 
The capital account includes capital transfers and reduction or cancellation of debts. 
Financial accounts include international reserve assets and non-reserve financial accounts. 
Non-reserve financial accounts in turn include direct investment, portfolio investment, 
financial derivatives and other investment. 
• 
Reserve assets are external financial assets held by the monetary authorities, including gold, 
foreign exchange, special drawing rights (SDRs) with the IMF, and the use of the Fund’s 
credits. Reserve assets are transaction-based and therefore not affected by valuation effects 
from asset price and exchange rate fluctuations. 
• 
Net Errors and Omissions (NEO) (also called “statistical discrepancy”) balances the credit and 
debit items in the BOP. It captures the errors and inconsistencies in data recording and 
processing in all sections of the BOP. 
Exhibit 44: China’s current account surplus remained solid but the financial account has seen 
significant outflows 
 
Source: SAFE, Goldman Sachs Global Investment Research 
International Investment Position (IIP) 
The IIP is closely related to the BOP. Whereas the BOP measures the flow of transactions between an 
economy and the outside world, the IIP measures the stock of assets and liabilities an economy has 
with the rest of the world at a given point in time. Although most BOP transactions are reflected in 
IIP changes, there are other factors that impact IIP that do not appear in the BOP, including market 
price changes, exchange rate changes and other volume changes (such as write-offs and re-
classifications). In other words, the IIP is the result of cumulative current account surpluses/deficits 


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plus valuation changes. China began to publish IIP data in 2006 on an annual basis and in Q4 2010 
on a quarterly basis. 
Foreign Direct Investment 
Signal to noise ratio: * 
Macro importance: *** 
Source: Ministry of Commerce (MOFCOM) 
State Administration of Foreign Exchange (SAFE) 
Availability: MOFCOM series: monthly from 1997; SAFE series: quarterly/annual with BOP data from 
1998 
Overview 
• 
Foreign Direct Investment (FDI) measures investments made by foreign residents who seek 
to have significant long-term interest in and have direct influence over a domestic enterprise. 
FDI also reflects the confidence of international investors in the Chinese economy. In 
addition, FDI data are useful in estimating total capital flows. 
• 
There are multiple sources reporting China’s FDI data. Investors usually pay most attention 
to the FDI numbers from the BOP releases, given that it is compatible with GDP accounting, 
can be compared with other countries’ FDI data, and is published quarterly. The MOFCOM 
provides another official dataset on FDI with breakdowns by sector and by 
country/jurisdiction of origination. But it follows a different accounting principle, with 
reinvested earnings excluded. The IMF Coordinated Direct Investment Survey (CDIS) data 
cover over 100 countries and regions and offer mirror data from other economies, which 
can be used to formulate an “outside-in” measure to cross-check China’s official data. From 
a bottom-up perspective, Bloomberg reports investment value on its Mergers & Acquisitions 
(M&A) page, which can be aggregated into a timely series of China’s inward FDI. In addition, 
OECD and United Nations Conference on Trade and Development (UNCTAD) provide cross-
country panel data on FDI as well. 
Compilation and Reporting 
Different FDI measures vary in three main aspects: compilation methods, data coverage and 
availability (Exhibit 45). 
• 
Compilation methodology: Most FDI data are compiled based on one of two principles: 
asset/liability principle or directional principle, except for Bloomberg data. FDIs under the 
directional principle account for net investment on the basis of ultimate parent companies, 
while those under the asset/liability principle will simply add up net assets/liabilities for 
outward/inward FDI. For example, if a Chinese parent company receives investment from its 
own foreign subsidiary, this flow will be captured under the asset/liability principle but will 
not be captured under the directional principle. The data prepared by SAFE and OECD 
follows the asset/liability principle. In contrast, MOFCOM and UNCTAD compile FDI data 


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based on the directional principle. IMF CDIS shares the same inputs as SAFE, but the data is 
presented under the directional principle. Lastly, Bloomberg’s M&A investment amount is 
based on project-level accounting, effectively adding up announced deals. 
• 
Data coverage: We summarize data coverage by availability of flow/stock data, detailed 
breakdowns and mirror data. SAFE provides FDI flows under BOP and FDI stock under 
International Investment Position (IIP) without any breakdowns. MOFCOM provides 
breakdowns of FDI flows by sector and by country/jurisdiction of origination in their annual 
statistical bulletin. The monthly FDI flows do not include direct investment in financial 
services (e.g., banks), although the size would be relatively small (around 4-6% of inward FDI 
in 2018-2022). IMF CDIS offers mirror data of FDI stock, which can be compared with the 
MOFCOM's FDI data by-country breakdowns.
[20] Bloomberg data are FDI flows with industry 
classifications. Yet, its numbers reflect planned cross-border M&A investment flows only, 
missing other FDI flows such as greenfield projects. Both UNCTAD and OECD provide FDI 
flow and stock data without further breakdowns. Among these data sources, only SAFE, IMF 
CDIS and OECD incorporate reinvested earnings in their FDI measures, as required by the 
Balance of Payments and International Investment Position Manual, 6th edition (BPM6) 
standard. They also effectively assume all non-repatriated earnings are reinvested. 
• 
Data availability: Reporting frequency ranges from monthly to annual. MOFCOM provides 
monthly data from 1984 with a one-month lag. Bloomberg also publishes monthly data 
from 1998 with daily updates of the latest released FDI projects. SAFE’s FDI data come on a 
quarterly basis from 1998 with a one-quarter lag. The IMF CDIS, UNCTAD and OECD offer 
annual data. UNCTAD has the longest data history, tracking the FDI series from 1990 
onwards, followed by OECD, which provides FDI numbers from 2005 onwards. Both have a 
publication lag of around one year. IMF CDIS is the least timely data source, with the earliest 
statistics dated back to 2009 and the most recent data for 2022 (a 2-year publication lag). 
Exhibit 45: Summary of different FDI measures 
 


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\"-\" indicates \"not applicable\" 
Source: NBS, Wind, Goldman Sachs Global Investment Research 
Exhibit 46: The 2022-23 drop in China’s inward FDI flows was partly driven by lower reinvested 
earnings 
Decomposition of FDI flows 
 
Source: CEIC, Haver Analytics, Goldman Sachs Global Investment Research 
Other Issues 
• 
As with GDP data, there are considerable issues in FDI data compilation at the regional level. 
The authorities have strengthened the requirements for the verification of data reporting. 


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For example, data on cash FDI are double-checked against capital flow records at the SAFE, 
and data on goods FDI are double-checked against the records of China Customs. Data that 
cannot be verified are, in principle, discarded. Moreover, FDI’s importance as a part of the 
assessment metric for local government officials has declined, and hence the incentives to 
over-report have dropped relative to earlier years. 
• 
Assessment of the true strength of FDI is also complicated by the issue of round-tripping, 
which refers to funds originated in China but reinvested back as FDI. Money laundering and 
preferential policies for foreign enterprises are the most important reasons for round-
tripping. 
Outward Direct Investment (ODI) 
Although FDI attracts most attention, ODI has gained momentum in recent years, especially against 
the backdrop of increasing capital outflow pressures and Chinese manufacturers building factories 
overseas in response to rising tariffs. Corporate outward direct investments face tight regulation by 
the authorities. For investment value above a certain threshold, in 2015 and 2016, ODI from China 
soared to a record high, likely in part reflecting capital flight motivations, but declined afterwards on 
a tightening in outbound capital controls. According to SAFE data, ODI surpassed FDI for the first 
time in late 2015. Direct investment has shifted to a net outflow again since 2H 2022. 
External Debt 
Signal to noise ratio: *** 
Macro importance: * 
Source: State Administration of Foreign Exchange 
Availability: Quarterly from Q2 2003, semi-annual from 2001, annual from 1985 
Timing: Together with the final release of BOP 
Overview 
• 
Data prior to 2015 only included FX-denominated external debts. The SAFE started to 
publish data with both CNY and FX denominated external debt since 2015. At the end of 
2023, the outstanding external debt of China (excluding that of Hong Kong and Macao, but 
including debt both in FX and RMB) was US$2.45 trillion. 
• 
In regard to currency type, the total external debt is composed of foreign debt in RMB and 
foreign debt in other currencies. By the end of 2023, 47% of external debt was denominated 
in CNY. Of FX-denominated external debt, 84% was denominated in USD, 7% in EUR, 4% in 
HKD, 3% in JPY, respectively. 
• 
By original maturity, short-term refers to external debt with a term of one year or less. 
Medium- and long-term refer to external debt with a contract term of more than one year. 
As of 2023, short-term external debt took around 56% of China’s total external debt, and 
long-term external debt 44%. 


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• 
The external debt is also classified by institutional sector, with general government 
accounting for 18%, central bank 4%, deposit-taking corporations except central bank 41%, 
other sectors 25% (including other financial corporations and non-financial corporations), 
and intercompany lending under direct investment 12%, as of the end of 2023. 
• 
As we think official data do not capture some of the FX debt raised by offshore Chinese 
companies, we compile our own estimate of Chinese corporates and households’ FX debt by 
aggregating onshore FX loans, claims on the Chinese non-bank sector by BIS reporting 
banks (offshore, non-Chinese), FX bonds, and trade liabilities. Our approach indicates that 
Chinese corporates’ and households’ total FX-denominated debt stood at around US$2.1 
trillion as of 2023 year-end. 
Foreign Exchange Reserves 
Signal to noise ratio: *** 
Macro importance: *** 
Source: State Administration of Foreign Exchange, PBOC 
Availability: SAFE: Quarterly from 1993, annual from 1982 
PBOC: Monthly from 1989 
Timing: PBOC data are usually released on the 7th day of the following month. 
SAFE data are released with other BOP data. 
Overview 
Foreign exchange reserves are liquid external foreign currency assets readily available to and 
controlled by central banks, which can be used to finance current account deficits and influence the 
foreign exchange rate. They include securities, bank deposits, derivatives and other assets, as long as 
they meet the above criteria. Apart from foreign exchange reserves, the more broadly defined 
“international reserves” can take other forms, such as gold and Special Drawing Rights (SDRs); 
however, these holdings are typically small relative to foreign exchange reserves. 
Signal to Noise Ratio 
• 
PBOC FX reserves data can be opaque and influenced by factors other than capital flow 
fundamentals. Because reserves data are based on market prices and denominated in USD, 
exchange rate and asset price fluctuations affect reserve values. When calculating changes in 
reserves, we usually adjust for the estimated effect of exchange rate fluctuations, but the 
impacts of asset price changes are difficult to estimate because of the lack of detailed 
information on portfolio holdings. 
• 
China started to disclose its historical dollar share of FX reserves from 2019 in SAFE’s annual 
reports with a five-year lag. The share remained in the range of 57% - 59% in 2014-2018, 
down from 79% in 1995. However, SAFE does not provide further details on shares of other 
currencies, or USD shares for the most recent few years. USD holdings remain prominent in 


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the official FX reserves, despite continued selling of US Treasury securities over the past few 
years. We estimate USD assets account for roughly 60% of China’s official FX reserves via 
both China’s data and US Treasury International Capital (TIC) data. The TIC data can also be 
used to gauge the composition of the USD portfolio in China’s FX reserves. 
• 
In order to estimate valuation effects on reserves, we assume the currency composition of 
China’s FX reserves is similar to that of the global average and follows the Currency 
Composition of Official Foreign Exchange Reserves (COFER) published by the IMF. 
• 
Given possible PBOC balance sheet management and shifts in banks’ net open position, we 
prefer the SAFE data on banks’ FX settlements on behalf of their onshore clients as a gauge 
of the FX-RMB conversion trend among onshore non-banks. 
• 
PBOC’s reserve data and IIP data on reserve assets are based on market price, and thus will 
be impacted by valuation effects. Reserve changes in BOP data on the other hand are free 
from valuation effects as they are supposed to measure flows. 
• 
China’s FX reserves have been surprisingly stable at just over USD 3 trillion since 2017 
despite the large inflow pressures in 2020-2021 and outflow pressures in recent years. Our 
analysis suggests that the FX holdings of China’s commercial banks may serve as a buffer for 
capital inflows/outflows, with official FX reserves changing relatively little as a result. China’s 
commercial banks accumulated a large amount of FX from 2H 2020 to 2021 on the back of 
China's elevated goods trade surplus. The authorities can guide banks to sell FX first before 
selling the government’s own FX reserves to defend the currency, if necessary. 
Macro Importance 
Foreign exchange reserves are often used to measure a country’s external vulnerability. China’s 
foreign exchange reserves are still the largest in the world, though standard adequacy ratios have 
eroded somewhat since the 2015-16 episode. Exhibit 47 compares Chinese reserves to IMF metrics 
for reserve adequacy, although it should be noted that experts hold different views on the adequate 
level of reserves.
[21] 
Compilation and Reporting 
Foreign exchange reserve data are compiled by the PBOC and the SAFE, and are reported in USD 
levels. They do not include holdings in gold and SDRs, which are reported separately. 
Exhibit 47: Reserve adequacy consistent with IMF floating-rate, but not fixed-rate, guidelines 
PBOC FX Reserves 
 


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Source: IMF, Haver Analytics, Goldman Sachs Global Investment Research 
FX Purchases/PBOC FX Position 
Released by the PBOC, these data measure the net amount the PBOC pays to financial institutions 
each month for the foreign currency they receive from trade surpluses, foreign investments, and 
other sources. PBOC’s FX purchase for RMB is one channel for creating reserve money. This indicator 
is based on cumulative flows and thus is free from valuation effects. 
Exchange Rate Terminology and Offshore RMB Development 
Renminbi (RMB) 
The official currency of the People’s Republic of China, translated as “the people’s currency”. The 
currency is issued by the People’s Bank of China, the monetary authority of China and is the official 
legal tender in mainland China. 
Yuan 
The Yuan is the basic unit of the renminbi, but is often used synonymously with renminbi in referring 
to the Chinese currency more generally. 
CNY 
• 
CNY is the code determined by the International Organization for Standardization (ISO code) 
for the renminbi/yuan. In practice, it refers to the Chinese currency traded onshore in 
mainland China. 
• 
Despite some gradual steps toward liberalization, the CNY market remains heavily managed 
by Chinese officials, who maintain strict capital controls and set a daily “CNY fix” against a 
basket of world currencies and a pre-determined trading band around that fix, which the 
“CNY spot” must settle within. The trading band was initially established at 0.3% but was 
expanded to 0.5% in May 2007, to 1.0% in April 2012, and to 2.0% in March 2014. 
• 
The PBOC adjusted the CNY fixing regime in August 2015 so that CNY fixing reflected the 


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previous day’s closing price of USDCNY and overnight USD moves. In May 2017, the PBOC 
further introduced a countercyclical factor in the CNY fixing to lean against herding 
behaviors in the market. The PBOC has utilized countercyclical factors since then to guide 
market expectations on the exchange rate direction. The PBOC doesn’t release any data on 
the countercyclical factors. We measure the countercyclical factor by calculating the 
difference between the official daily CNY fixing and our estimation of the fixing based on 
official documents of CNY fixing mechanism. That is, the CNY fixing should be determined 
by the previous close of USDCNY spot and the overnight move of USD against a basket of 
currencies. Our estimates suggest that the countercyclical factors have risen since 2023 and 
reached record levels in mid-2024, suggesting the authorities’ preference to slow CNY 
depreciation against USD. 
CNH 
• 
CNH refers to RMB that is traded outside of the Chinese mainland. Establishment of the 
offshore CNH market by Chinese policy makers reflected their desire to pursue greater use 
of RMB for international trade and financial transactions (i.e., the “internationalization” of the 
RMB) post the 2008 financial crisis. The CNH market was established in July 2010 when the 
PBOC and HKMA jointly announced that RMB would be deliverable in Hong Kong. 
• 
Although the CNH market remains concentrated in Hong Kong, RMB has since become 
deliverable in Singapore, Taiwan, Paris, Luxembourg, London, etc. Any offshore corporate 
entity or individual investor can participate in CNH by establishing non-resident accounts in 
a country where CNH is delivered, typically through local banks that have a relationship with 
banks in those countries/regions. 
• 
Unlike the CNY market, the CNH market is not directly managed by the Chinese authorities 
and instead is determined by the supply of and demand for CNH. It is therefore a “floating” 
currency much like the US dollar (with the important caveat that the PBOC and SAFE 
regulate RMB flows between onshore and offshore accounts, and official entities may 
participate in the market to influence the exchange rate). Having said that, CNH and CNY are 
essentially the same currency, and the active international trade between mainland China 
and the rest of the world, as well as financial investment channels such as Stock Connect, 
Bond Connect, and Wealth Management Connect Program, help keep the two exchange 
rates closely aligned. 
CNY Trade-Weighted Indices 
Source: PBOC, China Foreign Exchange Trade System (CFETS) 
Availability: Weekly data quoted by CFETS 
Overview 
• 
The Chinese government has for some time been revamping its foreign exchange 
mechanism in an effort to make the RMB more market-oriented and relatively stable against 
a basket of currencies. Between mid-2023 and mid-2024, policymakers prioritized slowing 
CNY depreciation against USD amid elevated capital outflow pressures, leading to CNY 
appreciation against the CFETS basket. Chinese exports still remain competitive in the global 


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markets, thanks to low domestic inflation in China and falling export prices. 
• 
On August 11, 2015, the PBOC announced a major reform to the formation of the RMB’s 
central parity rate against the US dollar, by referring to the closing price on the inter-bank 
foreign exchange market of the previous day. The PBOC considered this a “one-time 
correction” to remedy previously accumulated differences between the central parity rate 
and the spot market rate. However, an abrupt weakening in the RMB occurred during the 
days following the announcement of this reform, triggering considerable market volatility 
and additional capital outflows. Starting from December 2015, the PBOC released a new 
index called the CFETS RMB index based upon international trade weights after adjusting for 
re-export factors. The basket weight is updated annually to reflect the latest trade flows of 
China vs. the rest of the world. Compared with CFETS, the BIS and IMF also compile RMB 
trade-weighted indices. Exhibit 48 shows the currency share in each basket and Exhibit 49 
shows the movement of indices. 
Exhibit 48: Significant differences between alternative currency reference baskets 
 
Note: These weights were reported as of end 2023. 
Source: BIS, CFETS, IMF 
Exhibit 49: RMB strength reached peak levels in early 2022 


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Source: CEIC, Wind, Goldman Sachs Global Investment Research 
GS China “Outside-In” Trade Measures 
Source: Goldman Sachs Economics Research 
Data since: January 2009 
Timing: Around 1-2 months after the end of each month 
Publication: GS China Proprietary Indicators update 
Overview 
• 
In 2012-13, the prevalence of export/import over-invoicing to bring in/move out funds to 
invest in the “carry trade”/transfer assets offshore distorted officially reported trade data. In 
an attempt to identify the underlying trends in export and import flows, we compiled an 
“outside-in” trade measure based on trading partners’ reported data on trade with China. 
• 
We made two improvements to our previous measures over time. First, we allow for lags to 
reflect shipping time in matching China’s data with trading partners’ data. Secondly, we 
collect data from more countries and now include 21 of China’s major trading partners that 
together make up 79% (67%) of the value of total Chinese exports (imports) in 2022. 
• 
Because exports are usually reported on an FOB basis and imports on a CIF basis (see the 
discussion above on goods trade indicators and re-exports), there will be a gap between 
China’s reported exports and trading partners’ reported imports from China. In addition, re-
exports and delayed data availability may also create discrepancies in level terms between 
the China’s official trade data and our “outside-in” trade measure. However, these shouldn’t 
affect year-over-year changes on a persistent basis. 
Methodology 
• 
We collect trade data from 21 major Chinese trading partners (including the Euro area as a 


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single “partner”). Where necessary, we convert import data into US dollars. Note that all 
figures discussed here are nominal dollars (not adjusted for inflation). 
• 
We use 2010-2019 monthly data to estimate the lead-lag relationship between China’s 
official import/export data vs. trading partner reported export/import data due to different 
shipping time needed for different trading partners. 
• 
Adding up the import/export data for the trading partners (after adjusting for the lead-lag 
relationship described above) gives us “adjusted exports/imports to/from China’s major 
trading partners”. Comparisons of the outside-in estimates to China’s official data are shown 
in Exhibit 50 and Exhibit 51. 
Exhibit 50: Our outside-in export tracker tracks China’s official export growth relatively well 
except 2012-2014 
China exports with major trading partners 
 
Source: CEIC, Haver Analytics, Goldman Sachs Global Investment Research 
Exhibit 51: Our outside-in import tracker was broadly in line with China’s official import 
growth 
China imports with major trading partners 


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Source: CEIC, Haver Analytics, Goldman Sachs Global Investment Research 
GS China FX Flow Metric 
We focus on two separate sets of SAFE data to gauge the underlying FX flow situation: One for 
onshore FX settlement, and the other for the cross-border movement of RMB. These are combined 
in our preferred FX flow metric (Exhibit 52). 
• 
SAFE dataset on onshore FX settlement: FX settlement and sales on behalf of clients by 
banks refer to the transaction of FX settlement, sales and other business conducted by banks 
for their clients. Deals on their own behalf and interbank market transactions are not taken 
into account. 
• 
Cross-border RMB flows: In late 2015 through 2016, while there was a large amount of net 
cross-border RMB flow from onshore to offshore, there was no corresponding observed 
increase in foreigners’ holdings of RMB assets. It is possible that some Chinese financial 
institutions buy RMB in the offshore market and either sell it back in the onshore FX market 
or invest it in onshore RMB assets. In addition, foreign equity and bond investments in China 
through the “Stock Connect” and “Bond Connect” channels are conducted in the offshore 
RMB market (i.e., in CNH instead of CNY). These RMB/FX transactions conducted do not 
necessarily show up on the PBOC’s balance sheet. Therefore, we believe that tracking the 
data on cross-border RMB flow (foreign-related receipts and payments reported by SAFE) is 
also important to developing a comprehensive view of the underlying flow picture. 
Exhibit 52: Currency outflows picked up sharply in August 2015, reflecting concerns over CNY 
depreciation 


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Source: SAFE, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“How fast are Chinese exports really growing?”, Emerging Markets Macro Daily, 17 March 
2014 
• 
“How does a weaker RMB impact China credit”, Asia Credit Line, 14 August 2015 
• 
“Sources and sizes of China’s capital outflows”, Asia Economics Analyst, 26 January 2016 
• 
“ China capital flows update — how cross-border RMB flow might mask outflow 
pressures”, Asia Economics Analyst, 4 July 2016 
• 
“Index inflows and rate differentials support our constructive view on CNY”, Asia in Focus, 20 
July 2020 
• 
“Gauging Downside Risks to Chinese Exports”, Asia Economics Analyst, 6 April 2023 
• 
“Analyzing recent puzzles on China trade data”, China Data Insights, 24 May 2023 
• 
“Q&A on the recent RMB depreciation and policy reaction”, Asia in Focus, 23 August 2023 
• 
“Deciphering China’s Inward Foreign Direct Investment Data”, China Data Insights, 24 
October 2023 
• 
“The Enigma of China’s FX Reserves – Size, Adequacy, and Composition”, Asia Economics 
Analyst, 12 January 2024 
Section VII. Money, Credit, and Banking 
There are three major sets of money, credit and banking data: 
1. Quantity-based data feature money supply, loans and deposits, total social financing, usage 
of central bank policy tools, and balance sheets of the PBOC and financial institutions – all 


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compiled by the PBOC. 
2. Price-based data feature various interest rates, including policy rates (such as 7-day reverse 
repo rate and interest rates of other monetary policy tools) and market interest rates (such 
as interbank repo rates and bond yields), published by the PBOC and the National Interbank 
Funding Center. 
3. Flow of funds accounts record financial flows amongst five key economic sectors, which are 
jointly compiled by the PBOC and the NBS, and provide a bird ’ s-eye view of the 
interdependence between the real economy and the financial economy. 
Money Supply 
Signal to noise ratio: *** 
Macro importance: **** 
Source: The People’s Bank of China 
Availability: Monthly from 1997, quarterly from 1990 
Timing: Typically 9 to 15 days after the end of each month 
Overview 
• 
The People’s Bank of China (PBOC) reports three series on China’s money supply: 
1. M0: currency in circulation (bills and coins) 
2. M1: M0 + demand deposits (excluding household demand deposit) 
3. M2: M1 + quasi-money (time, savings, and other deposits, excluding fiscal deposits) 
Exhibit 53: A breakdown of M2 by major component 
 


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Note: Numbers in the bracket refer to the share of each major component in M2, based on 2023 
data and definitions. NBFI refers to non-bank financial institution. 
Source: PBOC, Wind, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
China’s money supply data are subject to significant distortions, in large part arising from 
the attempts of financial institutions to evade regulatory controls. For example, commercial 
banks used to depress month-end money and credit data and minimize regulatory costs 
such as required reserves; the PBOC responded by changing the required data from month-
end to month-average in September 2014. This alleviated the old problem but the time 
series is no longer directly comparable. Since 2016, the PBOC has started to assess banks’ 
performance under the new macroprudential assessment framework (MPA), including 
indicators for capital and leverage, asset and debt, liquidity, pricing, asset quality, risk of 
cross-border financing and the implementation of credit policy. The quarterly MPA tends to 
distort quarter-end data. 
Macro Importance 
• 
Changes in broad money supply provide useful leading information for short-term 
economic activity because banks remain the dominant financial intermediaries in China. M2 
remains one of the intermediate policy targets for the central bank, but its importance has 
been declining due to the ongoing reform of the monetary policy framework. 
Compilation and Reporting 
• 
Breakdowns are available from M0 to M2. M2 is the broadest measure currently available 
but is becoming inadequate as direct financing (such as government/corporate bond 
issuance) has increased in importance over the past decade. 


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• 
The PBOC has expanded the definition of money supply five times due to China’s evolving 
financial markets. Specifically, the PBOC incorporated clients’ margin deposits into M2 on 
the development of the stock market in 2001. Domestic RMB deposits in foreign financial 
institutions have been included into money supply since 2002. From October 2011, M2 has 
included both non-bank financial institutions deposits and housing provident fund deposits. 
In January 2018, the PBOC employed money market funds held by non-bank sectors to 
measure money market related deposits (previously measured by deposits of money market 
funds) and incorporated it into M2. Since December 2022, e-CNY in circulation has been 
included in M0. In June 2024, PBOC governor Pan Gongsheng acknowledged that the M1 
definition was outdated, and the central bank may expand the scope of M1 to include 
household demand deposits. 
Other Issues 
• 
Before 2018, the government used to set a specific target for M2 growth at the annual “Two 
Sessions” in early March, which would usually be consistent with its desired real GDP growth 
and inflation targets. In recent years, the government did not set a specific target for M2 
growth and only stated an objective “to keep money and credit growth broadly consistent 
with nominal GDP growth”. At the 2023 Central Economic Work Conference, the phrase was 
changed to “keeping total social financing and M2 growth broadly in line with economic 
growth and inflation target” amid deflationary pressures and weak nominal GDP growth. 
Most recently, the PBOC has started to downplay the importance of quantity-based 
indicators, emphasize the composition and structure of money and credit, and push forward 
the transition towards a more price-based monetary policy framework. 
Exhibit 54: M1 growth turned negative in 2024 
 
Source: PBOC, Goldman Sachs Global Investment Research 


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Bank Loans and Deposits 
Signal to noise ratio: **** 
Macro importance: *** 
Source: The People’s Bank of China 
Availability: Monthly from 1997 
Timing: Around 9 to 15 days after the end of each month 
Overview 
• 
Loan data published by the PBOC include loans made by depository institutions and non-
depository financial institutions (e.g., trust and insurance corporations). Recipients of loans 
are non-financial institutions and individuals, including non-residents. Loans to non-bank 
financial institutions have been added since the start of 2015. Loans denominated in CNY 
and foreign currencies are compiled separately and aggregated to provide total loans data. 
• 
Similarly, deposit data include deposits in CNY and other currencies. There is further 
information on deposits broken down by different sectors of the economy (e.g., households, 
government, and non-financial corporates) and by type of deposits (demand deposits, time 
deposits and other deposits). 
• 
Balance sheet of Monetary Authority: The main liabilities are currency issued and commercial 
bank reserves, and main assets come from foreign assets (funds outstanding for foreign 
exchange) and claims on other depository corporations (liquidity injection via monetary 
policy tools, such as OMO, MLF, PSL, relending, etc.). 
• 
Balance sheet of Other Depository Corporations: Other depository corporations include 
policy banks, commercial banks, credit unions and finance companies. Main liabilities come 
from deposits of households and non-financial institutions (around 60%), interbank 
borrowings (around 10%), and bond issuance (around 10%). 
• 
Source and uses of funds of financial institutions: This describes major sources and uses of 
commercial banks and other financial institutions. Sources can be deposits and financial 
bonds. Uses include loans and portfolio investments. 
Signal to Noise Ratio 
• 
The new loans and changes in loan stock announced by the PBOC are not always consistent 
with each other. The main reason for this discrepancy is non-performing loan (NPL) write-
offs, which have been subtracted from new loans data, while changes in loan stock are 
calculated after adjusting for these write-offs. It is difficult to reconcile fully the two series 
using announced data. 
Macro Importance 
• 
Like M2 data, loan growth data are useful for gauging the current monetary policy stance, as 
well as possible macro policy changes, when combined with other government-driven 


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activity indicators such as infrastructure FAI. Given that China’s capital markets are still 
under-developed, bank loans are the major source of external funding for the non-financial 
corporate sector. However, their importance has been falling as more alternative financing 
methods have become available and as the PBOC gradually shifts its focus from quantity-
based metrics to a price-based framework. 
Compilation and Reporting 
• 
Loans are broken down by borrower type – households, nonfinancial enterprises and 
government agencies/organizations, and nonbank financial institutions. To some degree, 
loans are broken down by tenor as well: short-term vs. medium to long-term loans for 
households, and short-term loans, bill financing, vs. medium to long-term loans for 
nonfinancial enterprises and government agencies/organizations. In addition, loans to some 
specific sectors (e.g., property, green and inclusive financing) are available, and the PBOC 
also publishes loans by sector on an annual basis with a lag of about one year and a half. 
But there is insufficient detail from the PBOC’s data to allow one to identify finer categories 
such as LGFV borrowings. The National Financial Regulatory Administration (NFRA) releases 
quarterly data of NPL ratios for banks.
[22] 
• 
The PBOC has expanded its definition of non-depository financial institutions for data 
releases of loans and deposits over the past decades, which also contributed to the 
inconsistency between the level and growth rate of loans and deposits. The latest two 
revisions happened in 2015 and early 2023. In 2015, the PBOC incorporated loan companies 
into non-depository financial institutions. In early 2023, the definition was further expanded 
to include consumer finance companies, wealth management companies, as well as financial 
asset investment companies. 
• 
The PBOC publishes the initial release of new loans and deposits flows first, with detailed 
breakdown data (such as balance sheets of the PBOC and other depository corporations) 
published a few days later. 
• 
Foreign Exchange Loans include foreign-currency-dominated loans extended to domestic 
and foreign residents/institutions by domestic and foreign financial institutions based in 
mainland China. 
Exhibit 55: The PBOC’s liquidity injection has been the major driver of balance sheet expansion 
since 2015 
PBOC balance sheet: assets 


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Source: PBOC, Goldman Sachs Global Investment Research 
Exhibit 56: Required reserves remain the lion’s share of the PBOC's liabilities 
PBOC balance sheet: liabilities 
 
Source: PBOC, Goldman Sachs Global Investment Research 
Exhibit 57: Loans make up more than half of Chinese banking system assets 
Banks’ assets by component (June 2024) 


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Source: PBOC, Goldman Sachs Global Investment Research 
Exhibit 58: Deposits remain the dominant funding source of China’s banking system 
Banks’ liabilities & equities by component (June 2024) 
 
Source: PBOC, Goldman Sachs Global Investment Research 
Total Social Financing 
Signal to noise ratio: **** 
Macro importance: **** 
Source: The People’s Bank of China 


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Availability: Monthly from 2002 
Timing: Around 9 to 15 days after the end of each month 
Overview 
Total social financing (TSF) includes both direct and indirect financing from the financial industry to 
the “real economy”. TSF consists of RMB bank loans, FX loans, trust loans (higher-yielding loans 
intermediated by trust companies), entrusted loans (lending from enterprises with favorable access 
to credit or excess cash, intermediated by banks), undiscounted bankers’ acceptance bills, net 
corporate bond issuance, equity financing, net government bond issuance, asset-backed securities 
of depository financial institutions and loan write-offs. The concept captures total financing of the 
real economy, mostly through different debt instruments. 
Signal to Noise Ratio 
• 
TSF statistics are intended to be a comprehensive measure, but historically there have been 
multiple rounds of revision on TSF data. In 2018, asset-backed securities and loan write-offs 
were included in TSF, and in 2018-2019, net government bond issuance and net corporate 
bond issuance statistical standards were also adjusted. In April 2024, the PBOC adjusted 
2024 year-to-date corporate bond net financing according to the latest industry 
classification results. These revisions also created occasional distortions to TSF growth 
measures. 
• 
The PBOC publishes the stock and flow data of TSF separately each month. However, the 
new TSF flows and changes of TSF stock can diverge from time to time. The divergence 
mainly comes from FX loans and corporate bond financing, for which the PBOC uses 
different statistical methodology for flow and stock data. 
• 
M2 and TSF growth can diverge from time to time, as M2/TSF refer to liabilities/assets of the 
financial sector’s balance sheet, respectively. By definition, TSF includes overall financing by 
the real economy (non-financial sectors), while M2 includes borrowings between financial 
institutions as well. FX purchases by the PBOC and fiscal deposit changes can affect M2 
growth but not TSF growth, while net equity financing and shadow banking credit extension 
(undiscounted bankers’ acceptance bills, trust loans, entrusted loans) can add to TSF but not 
M2. 
Exhibit 59: New loans and government bond net issuance have been the main drivers of TSF 
growth 


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*Other financing primarily include equity financing, loan write-offs and depository financial 
institutions’ ABS 
Source: PBOC, Goldman Sachs Global Investment Research 
Central Bank Policy Tools 
The PBOC uses the following tools to conduct monetary policy: 
1. Open Market Operations (OMO) 
2. Changes in the Reserve Requirement Ratio (RRR) 
3. Adjustments in interest rates (e.g., policy rate) 
4. Lending facilities (such as PSL, SLF, MLF) and relending programs 
5. Policy communications and window guidance (e.g., quarterly monetary policy reports and 
monetary policy committee meeting minutes, and administrative / regulatory instruments 
that affect both prices and quantity of credit supply from financial institutions) 
A full summary of the PBOC’s toolkit appears in Exhibit 60 toward the end of this section. 
Open Market Operation (OMO) 
Open market operations by the PBOC currently include: 
1. Repurchase (REPO) agreements, reverse repo 
2. Issuance of PBOC bills 
The PBOC started to conduct repo operations twice a week in 2004, using repo (reverse repo) to 
withdraw (inject) liquidity. It shifted to daily operations in January 2016, with brief notices explaining 
the rationale of open market operations. The central bank stopped using repo to withdraw liquidity 
from the banking system in late 2016. Repo / reverse repo maturities range from one week to one 
year. The most common tenors are one week for daily operations and two weeks for month-


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end/quarter-end operations. In July 2024, the PBOC introduced temporary overnight repo / reverse 
repo operations to gain better control over short-term market rates. 
The central bank used to issue PBOC bills domestically to offset passive liquidity injection from fast 
growing FX reserves in the 2000s. The tenors of domestically issued PBOC bills range from 3 months 
to 3 years. The central bank largely phased out the usage of PBOC bills onshore in late 2016, as FX 
reserves stabilized at slightly above USD 3 trillion. Since November 2018, the PBOC has been issuing 
bills in Hong Kong to manage CNH liquidity, with maturities ranging from 3 months to one year. 
Reserve Requirement Ratio 
The Reserve Requirement Ratio (RRR) is the ratio of deposits that financial institutions are required 
to keep at the central bank. It is effectively a tax levied on the banking system, and central banks can 
use it to manage commercial banks’ capacity to lend. The PBOC set up a system of “Discretionary 
Reserve Requirements” in April 2004, which allows for differential reserve requirements for different 
types of banks according to a number of criteria, including the capital adequacy ratio, the NPL ratio 
and the soundness of the internal control system. In 2016, this was upgraded to become the macro 
prudential assessment (MPA) system. According to the PBOC, the weighted average effective RRR 
for all financial institutions was 7.0% as of mid-2024 – 8.5% for large banks, 6.5% for medium-sized 
banks, and 5.0% for small banks.
[23] 
Adjustments in Interest Rates 
China has made significant progress in interest rate liberalization during the years before the Covid 
pandemic. Currently, the system is a hybrid of market and regulated interest rates. De jure, the 
interest rate system has been liberalized, though de facto this is not the case, as banks still rely 
heavily on LPR in their pricing model and the ceiling of deposits rates is still regulated by the 
“deposit rate self-regulatory mechanism” (存款利率自律机制) described below. 
Policy Interest Rates 
Policy interest rates are those that are directly controlled by the central bank, and changes to them 
will impact other interest rates. There are two policy interest rates in China: 
1. OMO interest rates: 7-day reverse repo is the most frequently used open market operation 
by the PBOC, so 7-day OMO rate is usually viewed as a policy interest rate in China. 
2. Medium-term Lending Facility (MLF) interest rate: Interest rate of medium-term PBOC 
liquidity injections in the interbank market. MLF is medium-term base money from the PBOC 
to commercial banks and policy banks backed by high-quality collateral. This tool is 
designed to provide guidance to the medium-term interest rate and to adjust the funding 
cost of the real economy. Banks’ Loan Prime Rate (LPR, more discussions below) is 
benchmarked against the 1-year MLF interest rate, and therefore changes to the MLF rate 
will impact funding cost of the real economy through the LPR. However, communications 
from the central bank in mid-2024 suggested that the PBOC may de-emphasize the MLF 
rate and potentially re-anchor LPR to the 7-day OMO rate. 
Other Interest Rates 
• 
Loan prime rate (LPR): This is the interest rate banks offer to their prime clients. LPR serves 


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as the pricing reference for bank lending, especially after the LPR reform in mid-2019, 
including lending to corporates and households. Currently, the LPR consists of rates with two 
maturities: 1-year LPR and 5-year LPR. Mortgage rates are benchmarked against the 5-year 
LPR. By definition, LPR is based on the quotes made by quoting banks by adding a few basis 
points to the interest rate of MLF. The LPR is calculated by the National Interbank Funding 
Center (NIFC), serving as the pricing reference for bank lending. At present, the LPR quoting 
banks are comprised of 20 banks. The quoting banks submit their quotes to the NIFC on the 
20th day of every month (postponed in case of holidays), in increments of 0.05 percentage 
points. The NIFC will calculate the arithmetic average of rates after excluding the highest and 
lowest submissions, and find its nearest integral multiple of 0.05% to be the LPR, which will 
then be published at 9 a.m. on the same day. In theory, the LPR is the average interest rate 
submitted by large banks; in reality, the PBOC can guide banks on their quotes and 
therefore impact the LPR. 
• 
The rediscount rate is the rate at which central banks discount commercial banks' unexpired 
paper. Since this rate affects borrowing costs for commercial banks, it can be used as a 
policy tool. However, this rate is infrequently adjusted in China. 
• 
The re-lending rate is the rate the PBOC uses to lend to financial institutions. Loans from 
the PBOC are a regular source of funding for Chinese commercial banks to support areas of 
policy priorities (e.g., SMEs, agriculture and rural development). 
• 
The PBOC pays interest on required reserves and excess reserves that financial institutions 
hold at the PBOC. The interest rate for excess reserves creates the floor for China's short-
term interest rate. 
• 
Deposit rate: In late 2015, the PBOC removed the official ceiling for the deposit rates, but 
an implicit ceiling set by “the self-regulatory pricing mechanism for market interest rates” (市
场利率定价自律机制) remains in place. The implicit ceiling for the deposit rates was 
anchored to the benchmark deposit rates, multiplied by a designated factor. But the 
benchmark deposit rates have remained unchanged since 2015. In June 2021, the PBOC 
improved the formation of the implicit ceiling for deposit rates by shifting towards 
benchmark deposit rates plus a few basis points. In April 2022, under the PBOC’s instruction, 
the “deposit rate self-regulatory mechanism” was established. This mechanism guides banks 
to set deposit rates based on the 10-year central government bond yields and the 1-year 
LPR. This implies that when corporates and households’ funding cost is guided lower by the 
PBOC through lowering MLF rate and LPR, banks can also reduce their deposit rates to 
protect their net interest margins to some degree. 
Other Liquidity Management Tools 
We summarize the major instruments for the PBOC to implement monetary policy in the table below. 
Exhibit 60: The PBOC utilizes a wide range of tools to affect the price and quantity of money 
and credit 
 


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Source: PBOC, Goldman Sachs Global Investment Research 


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Exhibit 61: A summary of the PBOC’s relending tools announced since 2020 
 
Source: PBOC, Goldman Sachs Global Investment Research 
Interbank Interest Rates 
Source: National Interbank Funding Center 
Availability: Daily data since 3 January, 1996 


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• 
China's interbank market was established in 1996, and market participants include all 
financial institutions. The interbank rate is a good indicator of liquidity in the market. 
Interbank Offered Rates and Interbank Repo Rates are the two main rates quoted in the 
market. 
• 
Interbank markets have been fully liberalized, with the interest rate paid on excess reserves 
the lower bound and the 7-day SLF rate the upper bound for interbank rates, creating an 
effective, albeit wide, interest rate corridor. Repo rates are typically lower than the offered 
rate, as they have higher liquidity and are collateralized. The 7-day repo rate for all financial 
institutions (R007) is the best proxy for the overall interbank market rate. 
• 
Large and medium banks are net suppliers of liquidity in the interbank market, while small 
banks and non-bank financial institutions are net borrowers (Exhibit 62). Around 90% of 
interbank funding trades are based on repo rates. Within repo trades, around 60% of them 
are based on repo rates for all financial institutions (R rates), while the rest are based on 
repo rates for depository institutions (DR rates). Interbank term funding is confined to the 
front end – more than 80% of funding trades are overnight borrowings. 
Exhibit 62: Large and medium-sized banks are net suppliers of liquidity, while small banks and 
non-bank financial institutions are net borrowers 
Net lending / borrowing in the interbank market 
 
Source: PBOC, Goldman Sachs Global Investment Research 
7-day repo rates (R007 & DR007): These are broadly considered to be monetary policy operating 
targets. The PBOC emphasized that the 7-day depository institution repo rate (DR007) would be a 
good proxy to monitor front-end interbank liquidity, and they monitor DR007 closely in their 
liquidity management and operations. R007 is typically more volatile and higher than DR007. DR007 
only accepts rates bonds as collateral, whereas R007 does not impose such restrictions on collateral. 


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Shanghai Interbank Offered Rate (SHIBOR): This consists of eight maturities: overnight, 1-week, 2-
week, 1-month, 3-month, 6-month, 9-month and 1-year. This is a simple short-term wholesale 
interest rate with no collateral required. SHIBOR is calculated as the arithmetical averages of 
interbank lending rates offered by 18 commercial banks. 
Negotiable Certificates of Deposits (NCDs): In December 2013, the PBOC launched the NCD 
program. NCDs allow banks to broaden their funding beyond deposits to manage liquidity. As 
designed by the PBOC, NCDs are priced on SHIBOR, which could improve the credibility of SHIBOR 
with actual transactions. 1y NCD serves as a benchmark market rate for short-term rates, and 1y 
MLF rate serves as an anchor for 1y NCD yields. The major issuers of NCDs are national banks, joint 
stock banks, and city banks. More than half of outstanding NCDs have maturity of 1 year, and more 
than 90% of outstanding NCDs are rated “AAA” by credit rating agencies. 
Policy Financial Bond (PFB) yields: Issued by the three policy banks in China (China Development 
Bank, the Export-Import Bank of China, and the Agricultural Development Bank of China), these are 
the non-government bonds with the highest credit rating because they are typically viewed as 
quasi-government bonds. Major tenors of the bonds range from 3 months to 10 years. 
Interest Rate Swap Rate (IRS rate): This refers to the fixed payment rate for interest rate swaps that 
exchange a fixed payment for a floating payment. Around 90% of IRS are referenced against the 
fixing of interbank 7-day repo rate (FR007), and the rest are mostly referenced against 3m SHIBOR. 
Therefore, FR007 swap rates are often used as the gauge for China interbank interest rates. 
Exhibit 63: The PBOC’s interest rate corridor has been established since 2015 
PBOC’s interest rate corridor 
 
Source: Wind, Goldman Sachs Global Investment Research 
Flow of Funds Accounts 
Source: National Bureau of Statistics 
Availability: Annual data since 1992 


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Timing: Long release lag, roughly two years after the period end 
Overview 
• 
These accounts record financial flows amongst five key economic sectors: non-financial 
enterprises, the financial corporate sector, the general government sector, households, and 
the rest of the world (ROW). The main financial instruments covered include deposits, loans, 
securities, insurance technical reserves, foreign direct investment, etc. Their main value is to 
provide a bird’s-eye view of the interdependence between the real economy and the 
financial economy from a macroeconomic perspective, describing inflows, outflows, and 
stock of funds in the economic system. 
• 
For China, the main features shown by the flow of funds data (Exhibit 64) are that: (1) 
Households have been a constant supplier of funds, initially almost all to corporates, then for 
a time to foreigners and lately more to the Chinese government; (2) The financial deficit (i.e., 
when investment>savings) in the non-financial corporate sector in China gradually narrowed 
from -12.3% in 1992 to -2.5% in 2020 before widening to -8.2% in 2022; (3) The financial 
deficit of the government sector in China was relatively stable at about -1% from 1992 to 
2004. However, from 2005 to 2015, the government sector changed from running a financial 
deficit to a surplus (except 2007), meaning that the Chinese government’s asset increases 
exceeded its liability increases. The government’s financial deficit reemerged after 2015 and 
has widened significantly since 2018, peaking at -8.2% in 2020 and remaining elevated at -
6.9% in 2022; (4) The rest of the world sector has absorbed funds except for 1993, that is, 
there has been a net outflow of domestic funds from China, with the financial balance 
widening from -1.3% of GDP in 1992 to a peak of -9.8% in 2008 before narrowing to roughly 
-2.3% in 2022. 
Compilation and Reporting 
• 
China’s Flows of Funds Accounts (FFA) is similar to the Japanese model, which is divided into 
physical transactions and financial transactions. The NBS is responsible for compiling data on 
physical transactions, while financial transactions are prepared by the PBOC. Due to limited 
original data on various sectors and industries (e.g., inventories), the classification of sectors 
and transaction items in the financial transaction section is usually more detailed than in the 
physical transaction section of FFA. 
• 
Compared with FFA in the US and Japan, FFA in China is still at the developing stage in 
terms of sector classification and transaction items, does not yet have quarterly or stock data, 
and has no reconciliation account. Moreover, the time lag of FFA releases in China is roughly 
two years for physical transaction tables, quite long compared with typical release lags of 
less than one year in other countries. Finally, China’s FFA lacks more granular decomposition 
of sectors. 
Exhibit 64: Households have been a supplier of funds to the Chinese government in recent 
years 
Financial surplus/deficit by sector 


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Source: NBS, PBOC 
Related GS Economics Publications 
“China’s monetary policy transmission efficiency weakened after Covid”, Asia Economics Analyst, 18 
February 2022 
“Navigating China rates: A shifting market to open further”, Asia Economics Analyst, 5 September 
2022 
“Demystifying the widening M2-TSF growth differential”, China Data Insights, 22 March 2023 
“China H2 monetary policy outlook: Expecting further easing to facilitate demand stimulus”, Asia in 
Focus, 2 August 2023 
“Understanding China’s Flow-of-Funds Accounts”, China Data Insights, 12 October 2023 
“Why China’s monetary easing has become less effective post-Covid”, Asia Economics Analyst, 18 
December 2023 
“Demystifying China’s falling M1-M2 growth differential: Drivers and implications”, Asia in Focus, 20 
January 2024 
“Navigating banking system liquidity in China”, Asia Economics Analyst, 3 March 2024 
“Why is the PBOC reluctant to cut policy rates?”, Asia in Focus, 2 June 2024 
Section VIII. Prices 
This section features the consumer price index, producer price index, agriculture and raw material 
prices, merchandise trade price index, and GDP deflator. (Refer to Section IV. Real Estate for land 
price and property price indices.) 


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Consumer Price Index 
Signal to noise ratio: **** 
Macro importance: ***** 
Source: National Bureau of Statistics (NBS) 
Availability: Monthly and annual from 1985 
Timing: Usually the 2nd week of the following month 
Overview 
• 
The Consumer Price Index (CPI) measures the price of a basket of goods and services that a 
typical household purchases. The basket is updated every five years. The last major update 
was in 2021, when the NBS updated the goods and services covered to reflect several new 
consumption patterns (e.g., food takeaways, new energy vehicles, ridesharing services), and 
adjusted the weights of each category according to an updated mix of household purchases. 
The latest index weights therefore may reflect some pandemic-related changes in spending 
patterns. Unlike most DM economies, the NBS does not publish the weighting scheme of the 
CPI basket. Weights mentioned in this section are our estimates (see Compilation and 
reporting below for details of our methodology and our most recent estimate of the basket’s 
composition). Category weights vary over time as prices change. 
Signal to Noise Ratio 
• 
Despite some data quality issues, we believe the CPI inflation rate is largely reliable, at least 
in terms of the direction of change. Some common criticisms of China’s CPI—not all 
necessarily warranted—include: 
1. Some prices, such as gasoline prices, are regulated despite being benchmarked to 
the broad trend of global oil prices. 
2. Its components and weights are revised infrequently, so the basket composition 
might diverge somewhat from actual consumption behavior. 
3. Compared with other countries, the NBS does not publish data on weights of 
different items in the CPI baskets. The NBS also does not publish “core goods” CPI 
inflation. 
4. The NBS revised down the weight of pork prices significantly in January 2021 to 
reflect the fading effect of the African Swine Fever (ASF) outbreak in 2019-2020, but 
the magnitude of the downgrade appears too large, mitigating the impact of hog 
cycles on China’s reported CPI inflation. 
• 
It is standard international practice to exclude property prices from the CPI basket. The CPI 
basket does have a housing component, which captures price changes in rent, implied rent 
for self-owned houses, utilities, and real estate management fees. Inflation for private 
housing (implied rent for self-owned houses) has correlated closely with rent inflation 


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(market rent) since 2011. However, home purchases are usually considered an investment 
following the international standard, and thus are not counted in the CPI basket. 
• 
In terms of other components, such as medical services and education, it is possible that the 
CPI data may fail to capture all the price changes. For example, CPI captures standardized 
education costs, such as school tuition fees; however, most of the “grey” charges levied by 
schools (tuition for supplementary classes, other administrative charges, etc.) are not 
included. This is likely to bias reported inflation downward at least slightly, and possibly 
significantly. 
• 
CPI inflation is also distorted by regulatory measures in various administered categories, 
including gasoline, electricity, education services, medical goods/services and telecom 
services. Even if global oil prices were to jump, regulatory measures would initially cap the 
extent of price increases faced by consumers. That said, the pass-through of international oil 
prices to domestic refined product prices is fairly timely and meaningful, occurring every 10 
working days if changes are large enough to warrant an adjustment in the period. The NDRC 
requires refineries to start to reduce profit margin towards zero once global crude oil prices 
reach $80/bbl and caps petroleum product prices via fiscal subsidies once crude oil prices 
reach and/or exceed 130/bbl.Thereisalsoapricefloorforrefinedproductswhencrudeoilpricesarebelow40/bbl. 
Macro Importance 
Policymakers in China pay close attention to CPI data. Each year, the government sets a target for 
headline CPI inflation in the Government Work Report during the “Two Sessions” (e.g., 3% for 2021-
2023). The target is effectively a ceiling, and any rapid change in inflation tends to lead to swift 
policy communications and reactions (e.g., monetary policy by the PBOC and administrative policies 
by the NDRC). Monetary policy, proxied by the 7-day repo rate among depository institutions 
(DR007), tends to react to core inflation rather than food/PPI/headline CPI inflation over the past 
decade. 
Compilation and Reporting 
• 
The CPI basket has eight broad categories. Under each category, there are a few more 
detailed subcomponents (e.g., the “Transportation and communication” category is further 
broken down into “transportation equipment”, “vehicle fuel”, “vehicle use and maintenance”, 
“communication equipment”, “communication service” and “postal service”). The 
components of the CPI and their weights are determined by the regular household 
expenditure surveys and other non-regular surveys. The latest round of quinquennial 
updating was carried out at the beginning of 2021. 
• 
As in many other developing countries, food accounts for a relatively large share of 
consumer expenditure in China. However, the food share in the CPI basket has fallen as 
living standards in the country have improved. Currently, food (not including dining out) 
accounts for about 19% of the total CPI basket in China. 
• 
Pork accounts for around 2-3% of China’s CPI basket on average historically, higher than 
other major economies. As pork prices declined notably in 2021, the weight of pork prices 


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fell to and has remained below 2% since mid-2022. Hog cycles can be important in driving 
CPI inflation. In late 2019 and early 2020, for example, an outbreak of African Swine Fever 
(ASF) caused pork prices to more than double, sending CPI inflation above 4%. 
Exhibit 65: China’s CPI basket has a higher weight of food and lower weight of services than 
DM economies 
 
* China CPI weights are estimated by GS based on historical data. ** Prices of motor fuel are highly 
regulated in China. This table is based on CPI weights as of 2023. 
Source: BLS, Eurostat, ONS, SBJ, NBS, Goldman Sachs Global Investment Research 
• 
Data are collected at different retail outlets by statisticians 2-3 times a month. Goods that 
have large weights and are subject to frequent price changes, such as vegetables, are 
sampled every five days. On the other hand, goods/services whose prices are regulated by 
the government or are relatively stable, are only sampled once a month. 
• 
The NBS does not officially publish weights of the items in the CPI basket. It does mention 
weight changes in a few major categories following each quinquennial basket update. The 
NBS also comments on the contribution to year-over-year headline CPI for a few categories 
(mostly in food) in its monthly release, which allows us to track weights of select items more 
frequently. For subcomponents of each category, we follow NBS documentation and 
academic papers to construct a detailed weight scheme in a two-step procedure. Firstly, we 
use regression-based estimates and NBS releases to gauge the weights of major categories. 
Second, we estimate the relative importance of subcomponents in each category. China’s 
Yearbook of Household Survey provides shares of subcomponents in household 
consumption as a benchmark of CPI basket weights. That said, the weights of each category 
will vary over time as prices change. The coefficients only represent averaged weights over 
certain periods, and the actual weights may swing significantly for specific categories, such 
as pork prices. Exhibit 66 below summarizes our estimates of subcomponent weights in the 
2016 and 2021 baskets with major categories highlighted in blue. 
Exhibit 66: China’s CPI basket in detail 
 


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Source: NBS, Goldman Sachs Global Investment Research 
Other Issues 
• 
Core CPI is often a useful indicator of the underlying inflationary pressures because it 
excludes high volatility of food and energy prices. Even though China’s energy prices are 
regulated, they still show higher volatility than many other components. There is no separate 
energy component in the CPI, but energy inflation and its weight can be inferred from other 
data. If we estimate energy’s weight by regressing the headline CPI on food CPI, core CPI, 
and China domestic gasoline prices, the implied weight for energy is around 2%; if we 
estimate energy’s weight by summing up the estimated weights for “utilities” and 
“transportation fuel and parts” (which would suggest at least some over-estimation as these 
categories contain elements other than energy), the implied energy weight is around 7%. 
Producer Price Index (ex-Factory Price Index of Industrial Products) 
Signal to noise ratio: ***** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly from 1996, annual from 1980 
Overview 
• 
The Producer Price Index (PPI) measures the price of industrial products when they are sold 
for the first time after production. 
Signal to Noise Ratio 
• 
We believe the PPI index is generally reliable. It is based on a large sample of 20,000+ 
products at factory gate prices. Both large and small enterprises, i.e., those above or below 
the standard minimum threshold, are sampled. 
Macro Importance 
• 
PPI is more sensitive to changes in investment demand, and to some extent export demand. 
Therefore, it contains more information on the state of the industrial cycle than the CPI. It 
can also be affected by controls on production in relevant sectors, such as the “supply-side 
reform” in upstream sectors (e.g., coal and steel) during 2015-2017. 
Compilation and Reporting 
• 
Currently, more than 50,000 industrial enterprises report prices twice a month (the 5th and 
20th day of the month). Weights of components are determined by the sales value. The 
basket is adjusted every five years, though adjustments can also be made during the interim 
if there are rapid changes in the production pattern. Similar to CPI, weights of the PPI are 
not disclosed and the NBS does not publish industrial sales value by sector either. We 
estimate the weighting scheme based on industrial revenues data, which serve as a proxy of 
sales value by sector.
[24] Our estimates suggest that computer, communication & other 


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electronic equipment takes up the largest share (around 11%), followed by the automobile 
sector (about 8%). 
• 
There are sub-indices of the PPI for consumer goods and producer goods. Consumer goods 
can be further broken down into food, clothing, daily articles, and durables. Producer goods 
can be further broken down into mining, raw materials, and manufacturing goods. Producer 
goods account for around 75% of China’s PPI basket. The other breakdown available is by 
industry, i.e., metallurgical, power, coal, petroleum, chemicals, machinery, building materials, 
timber, food, textiles, sewing, leather, paper, cultural/education articles, and others. 
Other Issues 
• 
There has been extensive commentary by media and analysts on the relationship between 
the PPI and the CPI. It is commonly assumed that the gap between the two represents the 
producer margin. While broadly true at a very high level, the linkage is more complicated 
when it comes to specific industries and companies. For instance, the transmission from PPI 
to CPI seems to vary for different components of the CPI. PPI inflation (particularly consumer 
goods) seems to matter for the CPI’s goods component excluding food, but not for services. 
• 
There is another price indicator named “input price for industrial enterprises” (also known as 
the “raw materials purchasing price index”), which measures the change in raw material 
input costs. The difference between this indicator and PPI can be informative for the margins 
of industrial enterprises, although other factors also affect margins. 
Exhibit 67: PPI inflation exhibits much larger swings than CPI inflation 
China inflation measures 
 
 
Source: NBS, CEIC, Goldman Sachs Global Investment Research 
Agriculture and Raw Material Prices 


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Source: Ministry of Agriculture and Rural Affairs, National Bureau of Statistics, National Development 
and Reform Commission 
Availability: Wholesale prices of agriculture products: daily from 18 November 2013; Retail prices of 
agriculture products: daily from 25 June 2015; Raw material prices: every 10 days from end-2013 
Overview 
• 
Wholesale prices of agricultural products cover major food items, including pork, vegetables, 
eggs, fruits, etc. Retail prices of agricultural products measure the average retail prices of 
major food items in 36 cities. Both are reliable and informative for high-frequency food CPI 
tracking. 
• 
Raw material prices measure wholesale and retail prices, which include ex-factory prices, 
transportation fees, profits, and taxes. Although these prices could deviate from ex-factory 
prices, they are still informative for PPI tracking. 
Compilation and Reporting 
• 
Wholesale prices are compiled by the Ministry of Agriculture and Rural Affairs based on a 
sample of 200+ markets. The prices of fresh vegetables and fruits are weighted average 
indices (Passche index) based on 28 major vegetables and 9 major fruits. The NDRC collects 
retail prices data from supermarkets and farmers’ markets on a daily basis. 
• 
The NBS collects raw material prices from around 2,000 distributors located across the 
country, covering 9 major categories, i.e., ferrous metals, non-ferrous metals, chemicals, 
petrol/gas, coal, non-metal minerals, agricultural products, fertilizer/pesticide, and forest 
products. The data are published on the 4th, 14th, and 24th of each month. 
Merchandise Trade Price Index 
Signal to noise ratio: *** 
Macro importance: *** 
Source: China Customs 
Availability: Monthly from 1993 (previous year = 100) 
Timing: Around 25th day of the following month 
Overview 
• 
The Merchandise Trade Price Index measures changes in goods export/import prices, 
compiled by China Customs. The price indices are based on unit value indices from detailed 
goods trade data. 
Signal to Noise Ratio 
• 
We believe the Merchandise Trade Price Index is generally reliable, as it is based on a 
bottom-up aggregation of detailed goods trade data. However, as the price index is not 
based on a fixed basket of products, the index may change notably due to changes in the 


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product mix of goods trade. 
Macro Importance 
• 
Given exports remain an important growth engine for China, the Merchandise Trade Price 
Index is helpful to gauge trade growth in real terms and the price competitiveness of 
Chinese exports in the global market. 
Compilation and Reporting 
• 
Export prices are calculated on an FOB (free on board) basis, which includes costs of 
delivering goods onto the vessels but not further costs, such as insurance or freight. Import 
prices are on a CIF (cost, insurance and freight) basis. 
• 
Price indices before 2014 were reported in USD terms, but they were subsequently changed 
to CNY terms. It is important to convert price indices into the same currency before applying 
them as deflators. 
• 
Four kinds of breakdown are available based on various sector classifications: the 
Harmonized System (HS), the Standard International Trade Classification (SITC), the Broad 
Economic Categories (BEC), and China’s industrial classification for national economic 
activities. 
GDP Deflator 
Signal to noise ratio: *** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Quarterly from 2000, annual from 1978 (previous year = 100) 
Overview 
The GDP deflator covers the prices of all final goods and services in consumption expenditure, 
investment, and trade. 
Signal to Noise Ratio 
The GDP deflator is derived indirectly from official nominal and real GDP data. An ex-official of the 
NBS has briefly discussed the methodology of GDP accounting and related price/volume 
indices.
[25] However, it is difficult to replicate the calculation as many indices are not publicly available. 
Our estimates suggest that a weighted average with 60% PPI inflation and 40% CPI inflation could 
serve as a workable proxy of the GDP deflator in recent years. Academic literature suggests that the 
GDP deflator is generally overestimated during boom years but underestimated during downturn 
years to smooth the series of real GDP growth.
[26] 
Macro Importance 
The GDP deflator reflects inflationary pressures in the broad economy, whereas PPI only covers 
secondary industry. With the development of the service sector, the weight of PPI in the GDP 


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deflator has been falling over the past decade. 
Related GS Economics Publications 
• 
“Rebound in PPI poses limited risk to China's CPI inflation”, Asia Economics Analyst, 15 
January, 2017 
• 
“China: CPI inflation likely higher, but not a big constraint for monetary policy”, Asia in Focus, 
20 May 2019 
• 
“China: Gauging the impact of imported inflation and supply chain disruptions on CPI and 
PPI inflation”, Asia in Focus, 8 April 2022 
• 
“China Post-Reopening Inflation Outlook: A Bottom-Up Approach”, Asia Economics Analyst, 
3 February, 2023 
Section IX. Population and Labor Market 
This section features a detailed breakdown of total population and related indicators. We also take 
stock of China’s labor market, including employment, unemployment, wage indicators and our 
proprietary wage tracker. 
Total Population, Urban Population, Working Age Population, Migrant Population 
Source: National Bureau of Statistics 
Availability: Annual from 1949 
Timing: January of the following year 
Publication: Annual Statistical Communiqué on National Economic and Social Development 
Overview 
Total population refers to the total number of inhabitants of a particular area at a certain point of 
time. It can be broken down by urban vs. rural, female vs. male, and age structure. Total population 
is also available by province/city. 
Under the population data set, urban population is based on the total number of usual residents 
who have lived in an urban area for more than six months within a year, and “rural population” is the 
remainder after subtracting urban population from total population. There is also a set of data on 
registered population, split into agricultural and non-agricultural, available at the total national level 
and provincial level. 
Working-age population: There is no explicitly defined series from the NBS on this, but under 
population by age group, one sub-group captures the population aged 15-64, which follows the 
usual international definition of working-age population. China’s working-age population peaked in 
2013 and population aging is expected to accelerate in the coming decades. We note that while 
China’s official retirement age is 60 for men, 55 for female civil servants and 50 for female workers, a 
significant portion of elderly may find part-time or other employment. 
Migrant population: This refers to people who reside in a particular area but do not have 


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household registration (Hukou) in that area. It excludes the group of people who have household 
registration in another district of the same county in which they reside. China had nearly 300 million 
migrant workers in 2023 and around half of them work in manufacturing and construction sectors. 
This set of data is based on surveys conducted by the NBS annually. During years ending with “0,” 
such as 2020, the NBS conducts a comprehensive population census. During years ending with “5,” 
such as 2015, a survey based on 1% of the total population is conducted. In other years, a survey 
based on 0.1% of the total population is conducted. Population censuses and 1% population surveys 
are used as benchmarks to adjust annual surveys. 
Exhibit 68: China’s population structure in 2000 and 2020 
China’s population by age group 
 
 
Source: NBS, United Nations World Population Prospects 
Birth Rate, Death Rate, Natural Growth Rate 
Source: National Bureau of Statistics 
Availability: Annual from 1949 for birth rate and death rate, annual from 1981 for life expectancy 
Timing: January of the following year 
Publication: Annual Statistical Communiqué on national economic and social development 
Overview 


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Birth rate: Also called “crude birth rate”. It refers to the ratio of total number of new births to 
average population in a particular area within a particular time range. 
Death rate: Also called “crude death rate”. It refers to the ratio of total number of deaths to average 
population in a particular area within a particular time range. 
Natural growth rate: The crude birth rate minus the crude death rate. 
These rates are typically expressed in permillage (per-thousand). There are also other relevant 
population data in this release such as the dependency ratio. NBS data show that China’s population 
declined for two consecutive years – 2022 and 2023 – for the first time in six decades, with a record 
low birth rate of 0.639% in 2023. While China’s birth rate has trended lower over the longer term, it is 
possible that some of the recent decline could relate to temporary effects of the Covid pandemic. 
Signal to Noise Ratio 
Due to sampling difficulty, population-related data in China are in general exposed to uncertainties 
such as relatively big revisions (based on population census) and sampling errors. 
Macro Importance 
Population data are very important to economic analysis given their close link to labor supply, which 
is a key component of potential growth. 
• 
Working-age population is related to the potential labor supply to the whole economy, and 
a shrinking working-age population (as in China in the coming years) will be a headwind to 
potential economic growth. The NBS also releases another set of population data called 
economically active population, which refers to the population aged 16 or over who are able 
and willing to participate in the labor market, including both employed and unemployed 
people in both urban and rural areas. Its historical readings are revised based on new 
information in population surveys. It differs from the commonly used working-age 
population because it does not have an upper limit on age, while the working-age 
population usually excludes the population aged above 64. 
• 
The size of migrant population can serve as a barometer of cyclical employment conditions 
given they are in general more sensitive to changes in urban labor market conditions. 
Migration to urban areas is more likely when urban labor market conditions are strong. New 
migration should slow and existing migrants may return to agricultural work in rural areas 
when urban employment conditions are weak. 
Exhibit 69: China’s population and especially labor force growth slowed to record low levels 
 


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Source: NBS, UN, Goldman Sachs Global Investment Research 
Employment Data 
1. New Urban Jobs 
Signal to noise ratio: * 
Macro importance: *** 
Source: Ministry of Human Resources and Social Security 
Availability: Monthly from December 2009 
Overview 
This indicator measures the number of new jobs created in urban areas and is published monthly. 
Signal to Noise Ratio 
The new increases in urban jobs indicator reflects the gross increase (rather than net new jobs) in 
urban employment minus natural reduction in urban employment (which includes retirement and 
quits due to severe illness and death) and thus include those who were unemployed and re-
employed during a reporting period. For example, the new increases in urban jobs in 2022 were 
12.1mn while the net change in total urban employment was -8.4mn. The potential double-counting 
makes this indicator less informative in gauging the underlying employment trend. 
Macro Importance 
This indicator is one of the government’s employment targets (the other being surveyed urban 
unemployment rate). Therefore, it is important to track the progress of this indicator to gauge 
whether policymakers are likely to achieve their employment target; if not, policy support is likely to 
be forthcoming. 


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Compilation and Reporting 
Enterprises will report new increases in urban jobs to the Ministry of Human Resources and Social 
Security. Flexible employment is included in urban new jobs as well. Data coverage and reporting 
method appear sound, but as discussed above, the key issue with this indicator is its definition which 
does not capture layoffs and job changes — and therefore could involve significant double counting. 
2. Total Employment 
Source: National Bureau of Statistics 
Availability: Headline and by sector series are available at an annual frequency from 1952. Detailed 
breakdown has a shorter history. 
Publication: China Statistical Yearbook, China Population and Employment Statistical Yearbook 
Overview 
• 
Employment is defined as people who have worked for more than 1 hour during the 
surveyed week, as well as people who have positions but are on vacation/temporary leave. 
This series measures the overall employment of laborers 16 years of age or older in the 
economy based on sample surveys of the Chinese population. The NBS publishes the 
population and employment statistical yearbook since 1988. This yearbook provides data on 
employment/unemployment in urban areas with details about gender, age, industry, and 
education background. 
• 
There were 769mn people in China’s labor force in 2022 (including both employed and 
unemployed persons), accounting for 54.5% of China’s total population. Within the labor 
force, 60% is urban employment, 35% is rural employment, and the unemployment rate is 
around 5%. In the urban area, private enterprise (私营单位) workers constitute approximately 
two-thirds of total employment whereas non-private enterprise workers constitute the 
remaining one-third. 
• 
Flexible employment has seen strong expansion in China amid the development of the 
digital and platform economy. Flexible workers are not bound by formal employment 
contracts and enjoy fewer non-wage benefits compared to other workers. For example, 
many flexible workers are not included in public insurance schemes such as work-related 
injury insurance and unemployment insurance, and do not have paid maternity/paternity 
leaves. By the end of 2021, the number of flexible workers had reached 200 million in China, 
mainly in manufacturing, construction, delivery, platform livestreaming and ride-sharing 
companies. 
Exhibit 70: Labor force participants accounted for 54.5% of China’s total population in 2022 
 


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Note: The non-private enterprises include state-owned and controlled enterprises, foreign-funded 
enterprises and other enterprises. 
Source: NBS, Goldman Sachs Global Investment Research 
Exhibit 71: Tertiary industry employment has become more important in recent years 
Employment by industry 
 
 
Source: CEIC, Goldman Sachs Global Investment Research 


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Signal to Noise Ratio 
Although the total employment data provide useful information, growing flexibility in the labor 
market—such as self-employed online store owners, retired employees who are re-hired on a part 
time basis, as well as greater geographic mobility of labor—are making it difficult to measure the 
true picture. Historical readings are revised as new population surveys become available. 
Macro Importance 
The total employment data series is useful, as we see it as the only official/most reliable measure 
available for overall employment in the economy. 
3. Employed Persons in Urban Non-private Units 
Source: Ministry of Human Resources and Social Security 
Availability: Annual from 1952. Detailed breakdowns have shorter history. 
Timing: Annual data are released with a 10-month lag 
Overview 
• 
This data series covers the number of persons employed in government agencies and non-
private enterprises. It does not include private enterprises or self-employed individuals. 
• 
Details behind “employed persons in urban non-private units” include industry breakdown 
such as primary industry, manufacturing, construction, financial industry, property, wholesale 
and retail industry etc. 
• 
There is another set of data based on a subset of “employed persons in urban non-private 
units”, named “on the spot” staff (“在岗职工”). This “on the spot” dataset refers to persons 
who hold certain positions in the enterprise, and who still receive payments even though 
they are temporarily absent for reasons such as vacation, study or sickness. Compared with 
total employed persons in urban non-private units, this dataset excludes the retired but re-
employed population, soldiers, and religious workers. There are also industry breakdowns 
available for “on the spot” staff, but the series is released with a significant lag (e.g., 2020 is 
the latest data available). 
Signal to Noise Ratio 
Potential inaccuracies arise from intentional under-reporting by employers who pay social security 
contributions on a per-head basis. Frequent re-organization of SOEs and other previously state-
owned organizations has also complicated data collection. Because of local government pressures 
not to lay off employees, some companies nominally still employ workers but do not require them to 
go to work and only pay the minimum wage, potentially overstating employment during economic 
downturns. 
Macro Importance 
The usefulness of this series is limited because it only covers employment in non-private enterprises 
which can be distorted by major SOE reforms. For example, the level of this series fell in the late 
1990s and early 2000s from nearly 150mn to less than 110mn before rising to the peak of over 


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180mn in 2015. It has been declining gradually since 2015 and fell to 167mn in 2022. 
Compilation and Reporting 
Labor bureaus in different regions collect raw data, which are then reported to the Ministry of 
Human Resources and Social Security (MOHRSS) for compilation. 
4. Employed Persons in Private Enterprises and Self-employed Individuals 
Source: Ministry of Human Resources and Social Security 
Availability: Headline series available in annual frequency from 1990; detailed breakdowns have a 
shorter history 
Timing: Previously available one and a half years after the reporting period, but the latest data 
available is for 2019 
Overview 
• 
This indicator covers people who work in private enterprises/individual business, which have 
been registered at the local departments of industrial and commercial administration, 
including self-employed persons as well as helpers and hired laborers who work in individual 
households. There is industry breakdown information available, such as “employed persons 
in private enterprises and self-employed individuals” in manufacturing, construction, 
financial industry, wholesale and retail etc. 
• 
A subset of the “employed persons in private enterprises and self-employed individuals” 
dataset is “urban employed persons in private enterprises and self-employed individuals”. It 
covers only the portion of the employed persons in urban areas. Similar to all employed 
persons in private enterprises and self-employed individuals, this “urban employed persons 
in private enterprises and self-employed individuals” also has industry details available. In 
2019, China had 405mn employed persons in private enterprises and self-employed 
individuals, of which 263mn were in urban areas. 
5. Employment in Industrial Enterprises 
Source: National Bureau of Statistics 
Availability: Monthly from December 1998 
Timing: Around 27 days after the end of each month 
Overview 
This indicator is available monthly, and has a breakdown of employment for sub-industries including 
mining, various manufacturing sectors, and utilities. Note the construction sector does not belong to 
the “industrial sector” and instead is one of the two components of the secondary industry, together 
with the “industrial sector” (manufacturing, mining, and utilities). The by-industry employment is 
based on the same survey that the NBS conducts with industrial enterprises, and thus captures only 
the above-designated-size enterprises. Similar to the industrial profits data, continued monthly 
reports of this data are only available from 2011. 


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6. Employment Sub-indices Under Business Surveys 
Source: NBS for the employment sub-indices under the NBS manufacturing/nonmanufacturing PMI 
surveys; Caixin for the employment sub-indices under the Caixin manufacturing/services PMI surveys; 
Cheung Kong Graduate School of Business (CKGSB) for the employment sub-index under the 
CKGSB Business Conditions Index survey. 
Availability: Monthly frequency. NBS manufacturing PMI employment sub-index from January 2005; 
NBS nonmanufacturing PMI employment sub-index from January 2007; the Caixin manufacturing 
PMI employment sub-index from April 2004; the Caixin services PMI employment sub-index from 
November 2005. CKGSB BCI employment sub-index available from September 2011. 
Timing: NBS data available on the last day of the reporting month; Caixin data available within the 
first week after the reporting month ends; CKGSB index available around one week before the 
reporting month ends. 
Overview 
The sub-indices on employment under various PMIs and business surveys are in nature diffusion 
indices, based on questions like “whether the total employment in your company increased or 
decreased over the reporting month”. This set of data is not a direct report on the total number of 
employment, but provides a sense of the trend (particularly the breadth) of employment changes. 
Unemployment Data 
1. Urban Registered Unemployment Rate 
Source: Ministry of Human Resources and Social Security 
Availability: Quarterly during 1999Q4-2021Q4, annual during 1949-2021 
Overview 
• 
This series reports the share of urban registered unemployed persons in the urban labor 
force. Urban registered unemployment measures the number of urban residents who are 
capable of working, but are out of work, want to work, and register themselves as such. 
• 
The urban registered unemployment rate series has not been updated since December 2021, 
although the number of urban registered unemployed individuals is still being updated 
annually. As of 2023, there were 10.74 million urban registered unemployed individuals. 
Signal to Noise Ratio and Macro Importance 
• 
The usefulness of the “Urban Registered Unemployment Rate” is very limited because: (1) it 
covers only people who have an urban registration (Hukou) and excludes a large number of 
migrant workers who normally live in urban areas but do not have urban registrations, and 
(2) many unemployed people may not have registered with government agencies. As a 
result, the official urban unemployment rate has been very stable at around 4%. As more 
people seek jobs only through online platforms rather than through registration with local 
job market centers, this registered unemployment rate can be more biased. Having said that, 
the direction of changes may still be indicative, as small deviations from the stable trend 


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level are typically counter-cyclical and lag activity growth as one would expect. 
Other Issues 
• 
One useful source of information on the status of the labor market is the quarterly Labor 
Supply and Demand in Major Cities published by the MOHRSS (Ministry of Human 
Resources and Social Security). The report provides data on the number of jobs offered and 
the number of job seekers, as well as other information on the labor market. This report was 
collected together with the registered unemployment rate data through affiliated regional 
job centers. Although the indicator does not cover the whole labor market, as many 
employment activities do not take place at these employment centers, it nevertheless covers 
a significant portion of it, and can provide some useful color on changes in the labor market. 
2. Urban Surveyed Unemployment Rate 
Signal to noise ratio: ** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: 31 major city irregular reports from June 2013, regular reports from January 2017; 
nationwide series regular reports from January 2017 
Timing: Typically around the 2nd/3rd week of the following month, released together with monthly 
activity indicators such as industrial production 
Overview 
• 
The surveyed unemployment rate is the ratio of urban surveyed unemployment to the sum 
of surveyed unemployment and employment. One would be defined as “unemployed” if 
he/she is 16 years or older, does not have a job currently, seeks for job actively within the 
three months prior to the survey, and can start working within two weeks if provided a job. 
• 
Prior to the Covid pandemic, large cities tended to have stronger labor markets and lower 
urban unemployment rates. However, Covid controls took a heavier toll on large cities where 
population is denser and mobility is higher, leading to sharper increases in the 
unemployment rate in large cities relative to smaller cities especially in 2022. By late 2023, 
the pattern had finally reversed, and urban unemployment rates in large cities dipped below 
the national average. 
• 
The NBS used to release urban surveyed unemployment rates by two broad age groups: 16-
24 year-old, and 25-59 year-old. These by age decomposition data were suspended after 
June 2023. The NBS resumed the release of unemployment rate data for the 16-24 age 
group in December 2023, but the new series excludes students from the survey and resulted 
in a much lower youth unemployment rate (e.g., 14.9% in December 2023 under the new 
definition vs. 21.3% in June 2023 under the old definition). According to the NBS, over 60% of 
the 16-24 year-olds in China are students. Under the new definition, the NBS releases 
unemployment rates for 16-24, 25-29 and 30-59 year-olds separately, around three days 


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later than general labor market statistics. 
• 
Since January 2021, the NBS releases survey-based urban unemployment rates for people 
with local Hukou and those without (i.e., migrant workers) separately each month. Although 
the history of these series is relatively short, they show migrant workers’ unemployment rate 
is more cyclical. The unemployment rate of people with local Hukou increased from 5.1% in 
2021 to 5.4% in 2022 before falling back to 5.2% in 2023 after the end of China’s zero-Covid 
policy. In contrast, the unemployment rate of migrant workers jumped from 4.9% in 2021 to 
5.7% in 2022 before dropping to 4.7% in 2023. 
Signal to Noise Ratio 
• 
Urban surveyed unemployment rate data shed light on the labor market conditions. When 
activity growth is weak, surveyed unemployment rates tend to increase. Surveyed 
unemployment rates show bigger swings than the registered unemployment rate and this 
set of data is therefore the best official data gauging unemployment pressures. Having said 
that, the NBS releases surveyed unemployment rates without seasonal adjustments. Due to 
the short history of the survey (regular publication started in 2017) and Covid distortions 
over the past few years, seasonal adjustments can be challenging. 
• 
One main drawback of the urban surveyed unemployment rate is the lack of coverage of the 
migrant worker population if they move back to rural areas. By design, the urban 
unemployment rate is based on urban household surveys and thus only captures the 
unemployed population in the urban area. When the economy is weak and unemployment 
pressure increases, migrant workers might return to rural areas. The urban surveyed 
unemployment rate therefore tends to underestimate the unemployment pressure during 
economic downturns. 
Macro Importance 
The urban surveyed unemployment rate data series is quite important as it is one of the two 
employment targets policymakers set each year in their government work reports. Policymakers 
have reiterated multiple times in recent years that stability of employment is their priority and 
bottom line. 
Compilation and Reporting 
This survey started some time ago (the first data that we have seen mentioned by officials was for 
June 2013), but results were not regularly released to the public until 2017. Surveyed unemployment 
rates are based on surveys of 340,000 households. It differs from the registered unemployment rate 
in the following ways: 
• 
Compilation method: The registered unemployment rate is derived from official registration, 
while surveyed unemployment is based on labor surveys; 
• 
Definition of unemployment: Surveyed unemployment follows ILO standards, while the 
registered unemployment rate is based on administrative records; 
• 
Data coverage: Registered unemployment is based on population with local Hukou, while 
surveyed unemployment also covers migrants in theory, though the number of migrant 


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workers in urban areas shifts with economic cycles as discussed above. 
Exhibit 72: Surveyed unemployment rates spiked in the initial stage of the Covid outbreak and 
during lockdowns 
Urban surveyed unemployment rate (after seasonal adjustment) 
 
 
Source: CEIC, Goldman Sachs Global Investment Research 
Exhibit 73: Surveyed youth unemployment rate dropped after the NBS changed the definition 
to exclude students 
Surveyed unemployment rates (CNY-distortion adjusted) 
 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
Wages 
1. Per Capita Wage Income from Household Income Survey 
Source: National Bureau of Statistics 
Frequency: Quarterly, annual 
Timing: Around 20 days after the end of each quarter, along with the release of GDP data 
Availability: Available in quarterly frequency from 2013 Q1, annual from 1998 
Overview 
Since Q4 2012, the NBS selects 160,000 households from urban and rural areas for direct survey on 
their income and expenditure (see Section V. Consumption for more details). The wage share of 
household disposable income has been relatively stable over the past decade, according to this 
survey, and wages accounted for 56% of household disposable income in 2023. 
2. Average/Total Wage of Employees in Urban Non-private Units 
Source: National Bureau of Statistics 
Availability: Headline series is available at a quarterly frequency from 2000 to 2014, annual from 
2015. Detailed breakdowns have a shorter history. 
Timing: Quarterly data are released at around 25 days after the end of the quarter; annual data are 
released around May of the following year. 
Overview 
• 
Average/total wages cover labor compensation to all employees, including wages, bonus, 
subsidies, etc. in urban non-private units. They are on a pre-tax basis and include rents, 
utility bills, etc. paid by employers to employees. In practice, when employees receive 
salaries, they are after-tax and social contribution deductions. The average wage is total 
wages divided by the average number of employees within the reporting period. 
• 
Similar to the employment data, quarterly data were suspended after 2014 and only annual 
data are available since then. Note that foreign companies and large domestic companies 
listed overseas are included in “urban non-private” enterprises, raising the average wage of 
urban non-private units. In 2023, the average wage of urban non-private enterprises was 
RMB121,000. 
• 
As this set of data only captures wage growth for non-private units, it may fail to capture the 
genuine situation of the labor market. Wages in non-private units are generally viewed as 
more sticky and less sensitive to economic growth/labor market developments. 
3. Average/Total Wage of Employees in Urban Private Units 
Source: National Bureau of Statistics 
Availability: Headline series is available in annual frequency from 2008. Detailed breakdowns have a 


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shorter history. 
Timing: Around May of the following year 
Overview 
Average/total wages cover labor compensation to all employees, including wages, bonus, subsidies, 
etc. in urban private enterprises. They are on a pre-tax basis and include rents, utility bills, etc. paid 
by employers to employees. In practice, when employees receive salaries, they are after-tax and 
social contribution deductions. In 2023, the average wage of urban private enterprises was 
RMB68,000
[27]. 
4. Average Income of Migrant Workers 
Signal to noise ratio: ** 
Macro importance: ** 
Source: National Bureau of Statistics 
Availability: Quarterly from Q4 2008 
Timing: One month after the end of the quarter 
Overview 
This measure is based on quarterly surveys of migrant workers conducted by the NBS since 2008. 
The survey sample covers 85,000 households in rural areas of all 31 provinces. Income of migrant 
workers are inherently difficult to capture as by definition migrant workers move around and often 
are employed in informal sectors. This set of data shows greater volatility than other wage indicators 
because migrant workers are often the most vulnerable in the labor market (e.g., they sometimes do 
not have proper labor contracts and are therefore not protected by the Labor Law) and face more 
significant income fluctuations with economic cycles. As a result, migrant worker income can be a 
more useful tracker on labor market turning points in China. 
5. Compensation to Laborers 
Source: National Bureau of Statistics 
Availability: Annually from 1992 
Timing: Lags can be as long as three years 
Publication: China Statistical Yearbook 
Overview 
This series is recorded under Flow of Funds data, and it covers all employed persons. 
Signal to Noise Ratio and Macro Importance 
• 
“Compensation to Laborers” in input-output data does not suffer from the problem of 
narrow coverage like the few series mentioned above, as it covers all employed persons. 


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Furthermore, it has the advantage of including non-monetary income, such as social security 
contributions. However, this measure has two limitations: (1) the data are reported with a 
significant delay (the last reported data were for 2020), and (2) many forms of non-
monetary compensations have to be estimated roughly, which affects the signal to noise 
ratio of the data. 
• 
The issue of under-reporting of income data also likely affects wage data, especially in terms 
of state sectors. In these sectors, official wages can be restricted by regulations, so there is a 
tendency to compensate employees via other types of payments. Furthermore, some private 
company owners can choose to forgo cash wages to help with the development of their 
own companies. This may also bias wage data to the downside. 
6. The CKGSB Business Conditions Survey of Labor Cost Expectations 
Source: Cheung Kong Graduate School of Business 
Availability: Monthly from September 2011 
Timing: One week before the end of the reporting month 
Publication: Cheung Kong Graduate School of Business (CKGSB) Business Conditions Index (BCI) 
Overview 
The CKGSB BCI survey asks companies in mainland China (mostly private companies) about their 
expectations for future labor costs over the next six months. The responses are then converted into a 
diffusion index on labor cost expectations, similar to PMIs (e.g., >50 means a majority of surveyed 
companies expect labor costs to increase in the next 6 months). This index appears to lead migrant 
worker income changes historically based on our analysis, although the two appear to have diverged 
recently. 
Exhibit 74: Wage growth diverged among different indicators 
Wage related indicators 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
7. Other Wage Indicators 
Other indicators on wages include the minimum wage threshold, available by province, and 
corporate wage growth guidance, also available by province. These two indicators are released 
annually and can be informative for overall wage growth trends. Typically, the minimum wage 
threshold is not a binding constraint, since the informal sectors usually pay below minimum wages 
and legal enforcement is rare. In January 2004, China promulgated new minimum wage regulations 
that required local governments to raise minimum wages at least once every two years, and 
extended coverage to self-employed and part-time workers. By the mid-2010s, Guangdong 
province changed the minimum wage adjustment period to once every three years to slow the 
outflow of manufacturing industries to lower-cost inland provinces, and other provinces soon 
followed suit. At the end of 2015, the Ministry of Human Resources and Social Security (MHRSS) 
announced the minimum wage would be adjusted once every 2-3 years. 
GS China Wage Tracker 
Source: NBS, Goldman Sachs Economics Research 
Availability: Quarterly from Q1 2004 
Timing: Two weeks after the end of the reporting quarter 
Publication: GS China Proprietary Indicators update 
Overview 
• 
GS China wage tracker gauges nominal wage growth based on the average reading across 
different wage indicators. Currently, components of this wage tracker include NBS data on 
urban disposable income – wage income, migrant worker income, income sentiment index 
from the quarterly urban depositor survey conducted by the PBOC, and hiring wage from 
the online recruitment company Zhaopin.com. 


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• 
We first transform all input series into year-over-year growth rates and then calculate the 
average growth rate of these indicators. As we incorporate survey-based information as well 
as third-party data from the online recruitment company, our wage tracker is arguably a 
more comprehensive gauge of wage growth trend in China than official indicators such as 
the urban wage income reported by the NBS quarterly household income survey alone. Our 
wage tracker shows that wage growth has been decelerating from around 10% during the 
pre-pandemic period to around 4% in recent years, more consistent with listed companies’ 
reported wage growth than the official series. 
Exhibit 75: GS wage tracker suggests wage growth slowed to around 4% in recent years 
 
 
Note: The urban wage data from the NBS household survey started at 2014Q1, so we use the urban 
disposable income growth as proxy for urban wage growth from 2003Q1 to 2013Q4. 
Source: NBS, CEIC, PBOC, Zhaopin.com, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“China: Labor market consequences of the virus outbreak”, Asia Economics Analyst, 6 March 
2020. 
• 
“Tracking the labor market post the coronavirus”, China Data Insights, 14 June 2020. 
• 
“The implications of China's shrinking working-age population”, Asia Economics Analyst, 25 
May 2021. 
• 
“Population Aging, Pension System, and Individual Retirement Savings in China”, Asia 
Economics Analyst, 10 February 2023. 
• 
“Why has youth unemployment risen so much in China?”, China Data Insights, 22 May 2023. 
• 
“Tracking wage growth in China”, China Data Insights, 6 June 2024. 


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Section X. Government Finance 
This section reviews the following data and indicators on government finance: 
1. Government revenue, expenditure and balance data by the Ministry of Finance (MOF), on both 
monthly and annual basis. The government budget reports released during the “Two Sessions” every 
year set clear targets for these indicators. 
2. Central and local government debt by the MOF and China Central Depository & Clearing (CCDC), 
including outstanding amount and net issuance. These are among the main financing sources for 
the government’s balance. 
3. GS proprietary indicators related to government finance, including the augmented fiscal deficit 
(AFD) and augmented government debt (AGD). 
Government Revenue, Expenditure and Balance 
Signal to noise ratio: **** 
Macro importance: **** 
Source: Ministry of Finance 
Availability: Monthly from 1995, annual from 1950 
Timing: Around 3 weeks after the end of the month 
Publication: Ministry of Finance monthly releases, China Fiscal Statistical Yearbook 
Overview 
• 
In China, the national fiscal budget is composed of four accounts: the General Public Budget 
Account, the Government Managed Fund Account (GMF), the State Capital Operation 
Account, and the Social Insurance Fund Account. Fiscal data for the former two accounts are 
released monthly by the Ministry of Finance (MOF),
[28] while those for the remaining two 
accounts are released on an annual basis. 
Exhibit 76: Fiscal balance only involves a part of China’s fiscal budget system 


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Source: Goldman Sachs Global Investment Research 
• 
The fiscal balance that the government targets each year is the difference between revenue 
and expenditure in the General Public Budget Account. 
1. On-budget fiscal revenue comes from various taxes and charges the government imposes, 
categorized as tax and non-tax revenue, in the MOF budget. Government borrowings (via bond 
issuance) were included in the revenue statistics before 1994 but have been excluded since then. 
2. On-budget fiscal expenditure includes funds the government spends on goods and services that 
it provides, as well as on interest payments. These funds come from on-budget fiscal revenue and 
the proceeds raised through central government general bonds and local government general 
bonds.
[29] Conceptually, the fiscal balance is the difference between on-budget fiscal revenue and 
expenditure, although in China’s case there are some intricacies related to the fiscal deficit, especially 
the fiscal stabilization fund at the central government level and carryover/surplus funds at the local 
government level. 
• 
Top policymakers set the target for China’s official fiscal deficit at the “Two Sessions” every 
year, and this is directly linked to the quota for government bond net issuance under the 
General Public Budget Account. In other words, the official fiscal deficit (target) in value 
terms refers to the combined quota for the net issuance of central government general 
bonds (CGGB) and local government general bonds (LGGB). Unlike the official fiscal deficit, 
the effective fiscal deficit refers to the gap between on-budget fiscal revenue and 
expenditure, which equals the official fiscal deficit plus net drawdown of fiscal deposits and 
transfer from other fiscal accounts (i.e., the adjustment by central fiscal stabilization fund and 
carryover/surplus funds). 
• 
The Government Managed Fund (GMF) Account is comprised of around 20 sub-funds or 
sub-accounts (e.g., the railway construction fund, the national major water conservancy 
project construction fund), each of which is designed for a specific area. Land sales proceeds 
account for 82% of national GMF revenue in 2023 (vs. 86% in 2019). Moreover, land sales-
related spending, of which more than 70% is for land acquisition and re-development, 
accounts for around 55% of national GMF expenditure, based on 2023 data (vs. 90% in 2019). 
Land sales revenue and expenditure are collected and spent by local governments, subject 


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to approval by the central government. 
• 
In addition to the General Public Budget Account and GMF Account, there are two other 
accounts within the National Fiscal Budget – the State Capital Operation Account, and the 
Social Insurance Fund Account, although the operation of the latter two is less connected 
with the former two, and less relevant to the government’s fiscal stance. 
• 
Ranking these four Budget Accounts by revenue size based on 2023 data, the General Public 
Budget Account remains the largest (RMB21.7 trillion; before the adjustment by central fiscal 
stabilization fund and carryover/surplus funds), followed by the Social Insurance Fund 
Account (RMB11.1 trillion), the GMF Account (RMB7.1 trillion), and the State Capital 
Operation Account (RMB0.7 trillion). By expenditure size based on 2023 data, the General 
Public Budget Account (RMB27.5 trillion) is larger than the GMF Account (RMB10.1 trillion), 
followed by the Social Insurance Fund Account (RMB9.9 trillion) and the State Capital 
Operation Account (RMB0.3 trillion). 
Signal to Noise Ratio 
• 
Fiscal data are usually considered to be among the most reliable data since mis-reporting 
can be financially and politically costly. However, this may not always be the case. For 
example, the government disclosed cases of over-reporting of fiscal revenue by Liaoning 
Province in 2011-2014, and by Inner Mongolia in 2016. The MOF listed four methods used: 
(1) fake tax collection from corporates that is refunded back; (2) over-reporting of tax 
revenue from state asset sales or the right to use state assets, offset by over-reporting of 
government expenditure, often related to these transactions; (3) over-reporting of non-tax 
revenue such as revenue from the rights to use state resources; and (4) outright number 
cooking.
[30] In recent years, the government has strengthened its scrutiny over central and 
local statistics agencies to protect against data fraud and falsification. 
• 
An important definition-related issue is that on-budget fiscal balance does not cover the 
GMF, most of which is based on land sales-related revenue and expenditure, and is 
important to local governments. 
• 
As China’s economic growth trended down over the past decade on multiple headwinds, the 
government turned to more proactive fiscal policy, through both on-budget fiscal policy 
and quasi-fiscal spending in areas such as infrastructure. This increased the importance of 
tracking off-budget spending to assess the fiscal deficit and fiscal impulse. We have tried to 
“augment” the official fiscal policy measures by incorporating off-budget quasi-fiscal policy 
to obtain a comprehensive picture of the stance of China’s fiscal authority, as described in 
the “GS China Augmented Fiscal Deficit (AFD)” sub-section. 
• 
Fiscal revenue growth is also affected by the degree of effort in collecting taxes, which is 
often counter-cyclical (e.g., local governments may strengthen tax collection efforts amid 
economic downturns), and includes scheduled tax cuts, rebates and deferrals, as well as 
profit transfer from the operation of state-owned capital. 
• 
The government has some flexibility in controlling the pace of scheduled fiscal measures 
within a year, especially on government bond issuance and fiscal spending, resulting in 


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“frontloaded” and “backloaded” fiscal patterns. 
Macro Importance 
Government revenue and expenditure data are fundamental to understanding the stance of fiscal 
policy. In addition, growth rates in tax revenue also provide useful information on the strength of the 
economy. 
Compilation and Reporting 
• 
Reported fiscal revenues and expenditures cover both central and local governments. 
• 
Besides government agencies, a portion of state-controlled non-governmental institutions 
(事业单位) are also included. These institutions are set up, owned, and fully or partly funded 
by the government. However, as economic reforms progressed, many of these institutions 
changed in nature and became self-funded and profit-making. 
• 
China’s fiscal year is the same as the calendar year. Revenue and expenditure data are 
currently released by the MOF monthly. Complete annual data including additional 
adjustments, such as those related to the fiscal stabilization fund, only become available 
when the Ministry of Finance reports to the National People’s Congress in March of the 
following year and are then released in the Statistical Yearbook and Fiscal Statistical 
Yearbook. 
• 
State-owned enterprises (SOEs) were an integral part of the public sector and were 
previously included in government finance statistics. With progress in economic reforms, 
SOEs have increasingly become responsible for their own financing and are now mostly 
excluded from government financial accounts (e.g., the operation of state sole proprietors 
(国有独资企业/公司) is under the management of the State Capital Operation Account). 
• 
Major tax reforms in recent decades also had a meaningful impact on on-budget fiscal 
revenue, including several rounds of VAT reforms that have replaced the former business tax 
with VAT, and have lowered the effective VAT rate for the real economy. Individual income 
tax (IIT) reforms have relieved tax burdens on households, by raising the threshold for IIT 
exemption, and introducing IIT reduction items. 
Other Issues 
• 
Government expenditure is closely related to government consumption expenditure in GDP 
accounting. However, not all government expenditures are government consumption. Most 
notably, expenditures on capital construction, mine exploration, and new product R&D costs 
are counted as gross fixed capital formation (GFCF), as part of investment. 
• 
Gross local government revenue through General Public Budget and GMF accounts include 
three major sources, i.e., the on-budget fiscal revenue, GMF revenue (mostly land sales 
revenue), and transfer from the central government. Based on our estimates, the share of 
land sales revenue in gross local government revenue fell sharply in 2022-23 amid the 
prolonged property downturn and may trend lower in coming years. 
Exhibit 77: Gross local government revenue by major source 


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Gross local govt revenue breakdown vs. share of land sales revenue 
 
 
Source: MOF, Wind, Goldman Sachs Global Investment Research 
Local Government Debt 
Source: Ministry of Finance (MOF), National Audit Office, China Central Depository & Clearing 
(CCDC), Wind 
Availability: Monthly from November 2017, Annual from 2014 (also available for 2010, 2012 and first 
half of 2013 from audit reports) for local government debt data series; CCDC and Wind also provide 
high-frequency data for local government bond issuance and maturity 
Timing: Released monthly by MOF around 1 month after the end of month 
Overview 
• 
In China, local governments had been prohibited to borrow directly according to the budget 
law until 2009, when pilot programs were initiated to allow some local governments to issue 
bonds. At the beginning, the central government continued to help with issuance and 
repayment on behalf of local governments, and the quota of issuance was limited. In late 
2014, the budget law was revised to allow all local government to issue government bonds, 
subject to quota restrictions. 
• 
Given their limited on-budget revenue space and prohibition by law to borrow from banks, 
local governments turned to off-balance sheet entities (e.g., local government financing 
vehicles or LGFVs) as a financing and spending platform, but they had repayment 
obligations for a large part of those off-budget borrowings, which was technically not in line 
with the budget law. 
• 
In late 2014, the central government started to implement local government debt reform, 
and a series of documents were issued, including Document #43, which required local 


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governments to classify proper local government debt from existing LGFV debt and also 
reinforced the policy of no new borrowing through LGFVs. From 2015, local governments, in 
theory, could only borrow by issuing bonds, and local government debts accrued before 
2015 (excluding outstanding local government bonds) started to be swapped into bonds 
through the debt swap program. Since 2017, the government had launched several rounds 
of regulation on local government borrowing. These measures put additional caps on how 
and how much local governments could borrow apart from bonds, but local government 
debt continued to grow. Furthermore, the rapidly expanding Public Private Partnership (PPP) 
projects in 2016-17 became the substitute for financing functions that LGFVs had played, 
although the government has made continued efforts in containing the debt risks and 
improving the project implementation. There is so far no public information on the size of 
potential local government liabilities related to PPPs. 
• 
As of the end of 2023, outstanding (official) local government debt stood at around RMB41 
trillion (32% of GDP), including RMB16 trillion in local government general bonds (LGGB), 
RMB25 trillion in local government special bonds (LGSB), and RMB0.2 trillion in non-bond 
debt which local governments are obligated to repay
[31]. Compared to LGGB, LGSB are under 
the management of the GMF account, and thus their issuance quota is not subject to the 
restrictions from the official fiscal deficit. For LGSB-funded projects, there are prerequisites 
for the return on capital and thus require the approval of the National Development and 
Reform Commission and MOF, while LGGB-funded projects do not have such restrictions 
and thus cover more projects related to people’s livelihood. LGSB usually has had longer 
tenors than LGGB in recent years. 
• 
In recent years, the central government has become more cautious on local government 
hidden debt and strengthened related regulations, but local government implicit debt is 
unlikely to be eliminated anytime soon. 
Local Government Special Bond Uses 
Compared to on-budget fiscal expenditure (only 23% of which was spent on infrastructure-related 
areas in 2023), LGSB are mostly focused on infrastructure, and thus more commodity-intensive. MOF 
data suggest around two-thirds of LGSB proceeds have been spent on infrastructure-related 
projects in recent years, with municipal construction, industrial parks and transportation-related 
projects taking the lion’s share. 
Exhibit 78: Around two thirds of LGSB proceeds have been spent on infrastructure-related 
projects in recent years 
Investment target of local government special bonds (LGSB) issued 


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Source: MOF, Goldman Sachs Global Investment Research 
Unspent Local Government Bond Quota 
• 
Unspent local government bond (LGB) quota refers to the difference between the official 
limit for local government debt and the outstanding amount of LGBs, if any. In theory, there 
are two sources of unspent LGB quota available for use, including the quota never used 
before, and that used previously but renewed after debt repayment (without rolling over). 
• 
Based on our estimates, as of end-2023, the unspent LGB quota was RMB1.4 trillion (vs. 
RMB2.6 trillion at end-2022), including RMB680 billion for local government general bonds 
(LGGB) and RMB750 billion for local government special bonds (LGSB). Policymakers can 
utilize this buffer to raise extra-budget funding if necessary and upon approval. For example, 
in August 2022, the then-Premier Li Keqiang approved the use of RMB500 billion unspent 
LGSB quota and required local governments to fulfill the quota by end-October 2022, in an 
effort to support fiscal spending and offset strong growth headwinds. 
• 
However, unspent LGSB quota has been unevenly distributed across provinces. Some 
provinces with significant funding challenges do not have sufficient LGSB quota (e.g., 
Qinghai, Gansu, Heilongjiang and Jilin), while those with more quotas may not have an 
immediate need to raise funds (e.g., Beijing and Shanghai). As such, in 2022, policymakers 
assigned 30% of the RMB500 billion additional quota for regional redistribution, in favor of 
provinces with more ready-to-go projects. 
Local Government Debt Swap Program 
• 
To reduce the servicing costs for outstanding debt and restructure some non-performing 
debt (especially for local government implicit debt), debt swap programs could be one 
option. There was a large-scale local government debt swap program during 2015-18. The 
then-Finance Minister Lou Jiwei in August 2015 indicated that the outstanding amount of 
non-bond liabilities that local governments had obligations to repay was around RMB14.2 
trillion. According to the National Audit Office, local governments issued a total of RMB12.2 


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trillion bonds (with low interest rates) to replace these non-bond debts (with high interest 
rates). 
• 
There was another, albeit smaller, round of local government debt swap program more 
recently. At the July 2023 Politburo meeting, President Xi pledged to launch “a basket of 
local government debt resolution plans”. Subsequently, media reports suggested that 
provincial governments were allowed to raise RMB1.5 trillion via refinancing bond sales, 
utilizing previously unspent LGGB and LGSB quota, to repay local government implicit debt. 
Additionally, banks were also asked to roll over part of local government implicit debt to 
manage potential risks. 
GS China Augmented Fiscal Deficit (AFD) 
Source: Goldman Sachs Economics Research 
Availability: Monthly from January 2004 
Timing: Around 25 days after the end of month 
Release: GS China Proprietary Indicators update 
Overview 
• 
The stance of fiscal policy is typically measured by the fiscal balance to GDP ratio, after 
seasonal adjustments. An increase in this ratio (i.e., a narrowing in the deficit) means a 
contractionary fiscal policy, and a decrease in the ratio (i.e., a widening in the deficit) 
indicates an expansionary fiscal policy. 
• 
In addition to on-budget fiscal tools, the government can affect the economy through off-
budget activities (which are also referred to as “quasi-fiscal policy”). The most important and 
frequently used measure is the infrastructure investment funded by LGSB, policy banks, 
LGFVs, other state-owned enterprises (e.g., China Railway Corporation) and Public-Private 
Partnership (PPP) programs. 
Compilation 
• 
We “augment” the official fiscal policy measures by incorporating off-budget quasi-fiscal 
policy to obtain a comprehensive picture of the stance of China’s fiscal authority, and to 
examine its implications for the growth of the Chinese economy. Specifically, our measure of 
AFD is a sum of effective on-budget and off-budget fiscal deficits. We estimate the off-
budget spending by major channels that finance quasi-fiscal activities. This further includes 
central government special bonds (CGSB), LGSB, net land sales revenue (excluding the costs 
of land acquisition and redevelopment), LGFV bonds, railway construction bonds, policy 
banks support (mostly via policy bank bonds and PBOC’s PSL), shadow banking loans, etc. 
As in some years there could be a significant time lag between LGSB issuance and proceeds 
spending (e.g., in 2021), we use a projected pace of LGSB proceeds spending as the input 
for LGSB in AFD estimates.
[32] 
• 
Our AFD metric is constructed with monthly frequency, in order to monitor the 
government’s overall fiscal stance in a timely manner and accordingly serve as a relatively 


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reliable policy parameter input for our forecasting of the real economy. By interpolating GDP 
data, we are able to derive a monthly AFD-to-GDP ratio. To mitigate potential distortions 
from residual seasonality, we also focus on 3-month and 12-month moving averages of 
AFD-to-GDP ratio. 
• 
We note several caveats to this approach: First, some off-budget government financing 
channels may not be fully captured due to the availability of monthly data, such as LGFV 
loans and PPP projects. Second, the government’s off-budget activities might not be 
confined to infrastructure spending and the sectors we choose, and the share of 
government spending on debt repayment/servicing has been trending up over the past 
decade. 
Exhibit 79: Augmented fiscal deficit peaked in 2020 amid the initial Covid outbreak, and 
remained relatively wide in recent years 
 
Source: MOF, CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research 
Exhibit 80: The augmented fiscal deficit has shown several rounds of expansion since 2015 


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Source: MOF, Wind, Goldman Sachs Global Investment Research 
Other Issues 
• 
In the Government Work Report and budget report released during the “Two Sessions” 
every year, the government usually unveils the annual target for some key fiscal statistics, 
including official fiscal deficits (in both value terms and percentage of GDP terms) and LGSB 
net issuance quota. Official fiscal deficits are equivalent to the sum of government target for 
the net issuance of central government general bonds (CGGB) and LGGB. 
• 
On very rare occasions, the government may consider approving a central government 
special bond (CGSB) quota. This happened for the first time in 1998 to replenish the equity 
capital of big four banks, the second time in 2007 to establish China Investment Corp, the 
third time in 2020 to counteract the initial Covid outbreak, and the fourth time in 2024 for 
key projects and strategically important initiatives (e.g., high-tech manufacturing, 
urbanization, food and energy supply chains, green industries, equipment upgrade). CGSB 
are part of the central government official debt, but different from CGGB. Similar to local 
government special bonds, CGSB are managed under the Government Managed Fund (GMF) 
Account, outside the General Public Budget Account, and therefore their issuance does not 
lead to a higher official fiscal deficit. 
• 
Our AFD is similar in spirit to the IMF’s “augmented government deficit” metric.
[33] However, 
in recent years IMF’s measure has been larger than our estimate. Our AFD differs from the 
IMF measure on two major dimensions: 1) on the local government implicit debt financing 
(mostly through LGFVs), our measure captures LGFV bonds, net land sales revenue, and part 
of trust loans, to make sure our data series can be updated on a timely (monthly) manner. 
By comparison, the IMF measure leverages their own channel checks and projections to 
gauge LGFV loan financing and shadow banking financing (including trust loans, entrusted 
loans and PPP related debt), but this is only reported on an annual basis. 2) On the central 
govt implicit debt financing, our measure covers debt financing by policy banks and China 
Railway Corp., mainly via bond financing channels, e.g., policy bank bond net issuance, China 


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Railway construction bond net issuance, and PBOC's pledged supplementary lending (PSL). 
By comparison, the IMF measure does not fully include all these funding channels. 
Exhibit 81: A summary of central government special bond (CGSB) issuance in history 
 
Note: We exclude CGSB issuance for refinancing purposes (e.g., in 2017 and 2022). Tenors are 
ranked by the amount of bond issuance in each batch. 
Source: MOF, NBS, Goldman Sachs Global Investment Research 
GS China Augmented Government Debt (AGD) 
Source: Goldman Sachs Economics Research 
Availability: Annual from 2004 
Timing: Around the middle of the following year 
• 
Our proprietary Augmented Government Debt (AGD) measure aims to capture all debt 
raised by the broad government sector (including its agents). 
• 
We divide the AGD into two categories based on issuers: official debt and implicit debt. 
Official government debt includes debt directly borrowed by central and local governments, 
such as CGGB, CGSB, LGGB and LGSB. Implicit government debt is off the government 
budget, including debt of government agents such as policy banks, China Railway and LGFVs, 
which has implicit guarantees from either central or local governments, or at least perceived 
by markets as having such guarantees. Of course, some of these implicit debts are backed 
by high-quality assets such as railway and land, while others might not. Within the AGD, 
there is a very small proportion raised through offshore dollar bond markets, mainly by 
policy banks and LGFVs, but it has been counted into the balance sheet of these entities. 
• 
There could be some double counting issues between the above-mentioned sub-categories, 
as policy banks may hold some liabilities of the government, LGFVs and China Railway. To 
adjust for the distortion, we exclude PSL and a fraction of remaining liabilities from policy 
bank debts. 
• 
Based on our estimates, China’s AGD reached RMB166 trillion in 2023 (or 131% of GDP), 
marking a more than ten-fold increase from its 2008 level in RMB terms (RMB14 trillion, or 


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43% of GDP). The ratio of central to local government debts, and that of official to implicit 
government debts, were both close to 40% vs. 60% in 2022-23. Offshore government debt 
(mostly raised through offshore dollar bonds) accounted for only 1% of AGD. China’s AGD 
has been rising rapidly over the past decades, led mainly by implicit and local government 
debts. 
• 
Our estimates for implicit local government debt (or LGFV debt) are based on a bottom-up 
approach using firm-level balance sheet data of LGFV companies, plus some conservative 
assumptions. We estimate total LGFV debt reached ~RMB62 trillion at end-2023, and our 
estimates for historical data are broadly in line with academic and policy studies. For 
example, according to a team led by Mr. Zhang Xiaojing at the China Academy of Social 
Science, total LGFV debt ranged from RMB30 trillion to RMB50 trillion at end-2016, hinging 
on different definitions used. Professor Bai Chong’en at Tsinghua University estimated total 
LGFV debt at RMB47 trillion as of mid-2017. In its 2018 China Financial Stability Report, the 
PBOC cited an unnamed province as an example in highlighting local governments’ surging 
implicit debt: the unnamed province’s implicit debt was 80% higher than its official debt.
[34] 
• 
In general, government financing through official debt usually has lower costs and longer 
tenors than that through implicit debt, for both central and local governments. The financing 
cost for central government debt by nature is also lower than that for local government debt. 
Among major financing channels of local governments, LGSB has become an increasingly 
important funding source. The financing cost of LGSB is higher than central government 
bonds (CGB), policy bank bonds and LGGB, but much lower than implicit financing channels 
such as LGFV bonds and railway construction bonds. Moreover, CGSB and LGSB usually have 
a longer tenor due to their focus to fund major infrastructure projects. 
Exhibit 82: A breakdown of China’s augmented government debt based on 2023 data 
 
* We adjust for double counting issues by excluding some debt from policy banks (deducted from 
“other liabilities”). Numbers outside and inside the parentheses refer to the outstanding amount of 


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debt (in RMB value terms) and its proportion in AGD, respectively for each category at end-2023. 
CGGB, CGSB and CR refer to central government general bond, central government special bond 
and China Railway, respectively. 
Source: MOF, Bloomberg, CEIC, Wind, Goldman Sachs Global Investment Research 
Exhibit 83: A comparison of major government financing channels 
 
*Interest rates for LGGB and LGSB refer to 3y yield to maturity of LGB due to data availability. Usually, 
LGSB yield is slightly higher than LGGB yield. 
Source: MOF, Bloomberg, Wind, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“Tracking China’s fiscal stance: Beyond the official fiscal balance”, Asia Economics Analyst, 18 
January 2016 
• 
“China: Fiscal stimulus a potent policy lever early this year, but moderate headwinds building 
in H2”, Asia in Focus, 20 June 2018 
• 
“Can Chinese fiscal policy be “more proactive” in H2?”, Asia in Focus, 29 July 2018 


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• 
“China: Navigating regions with high reliance on the property sector”, Asia Economics 
Analyst, 16 June 2022 
• 
“China fiscal update: Borrowing more from the future to offset increased headwinds”, Asia in 
Focus, 19 September 2022 
• 
“China: Local government special bond, an increasingly important source of government 
financing”, Asia in Focus, 9 February 2023 
• 
“Population Aging, Pension System, and Individual Retirement Savings in China”, Asia 
Economics Analyst, 10 February 2023 
• 
“The Size, Form and Implications of China’s Growing Government Debt”, Asia Economics 
Analyst, 2 April 2023 
• 
“China: A likely return of PSL-backed property easing, in a different way”, Asia in Focus, 3 
January 2024 
• 
“China: Beijing’s Balancing Act between Infrastructure Stimulus and LGFV Deleveraging”, Asia 
in Focus, 6 March 2024 
• 
“Potential Reform Remedy for China’s Fiscal Challenges”, Asia Economics Analyst, 9 June 
2024 
The authors would like to thank Maggie Wei, a former member of the Asia Economics team, for her 
contribution to this book. Bernadette Chan and Christopher Dixon provided extensive editorial and 
formatting assistance. 
1 ^ “Census X-12” is a program originally developed by the US Census Bureau in the 1960s (“x” is 
for experimental, and 12 is for the twelfth in the series). 
2 ^ See the NBS definitions for these two indicators: 
https://www.stats.gov.cn/hd/cjwtjd/202302/t20230207_1902275.html. 
3 ^ In 1998, the NBS divided the scope of industrial statistics into two parts: above- and below- 
designated size. NBS has defined the above-designated size as industrial legal entities with annual 
principal business revenue of RMB20 million or more since 2011. 
4 ^ In order to comprehensively capture the revenue of industrial enterprises, NBS started to 
disclose “operating revenue” instead of “prime operating revenue” in 2019. 
5 ^ Caixin took over sponsorship from HSBC of Markit’s China PMI and officially added Financial 
Data Services in July 2015. 
6 ^ In late 2023, Chinese policymakers reportedly ordered 12 heavily indebted local governments 
(i.e., Guizhou, Tianjin, Yunnan, Inner Mongolia, Liaoning, Jilin, Chongqing, Guangxi, Heilongjiang, 
Gansu, Ningxia, and Qinghai) to curtail fiscal spending and halt some infrastructure projects, in an 
effort to manage debt payments and reduce LGFV default risks (i.e., the "Document #47"; 
unconfirmed by official source yet). Based on our estimates, the 12 indebted provinces accounted 
for around 26% of China’s infrastructure investment, 22% of FAI and 18% of GDP in 2022-23. 
7 ^ See https://www.gov.cn/xinwen/2020-06/01/content_5516649.htm. 


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8 ^ For reference, Soufun provides the underlying data and CREIS is the index compiler. 
9 ^ Wind uses residential housing data for some cities (e.g., Beijing) but total housing data for others 
(e.g., Hangzhou) in its 30-city sample, but it is mainly due to data availability. Hence, we did not 
count this as one of the statistical discrepancies. 
10 ^ See http://www.stats.gov.cn/sj/zxfb/202305/t20230516_1939489.html. 
11 ^ See for example “Housing survey probes sensitive vacancy statistic”, Global Times, 6 September, 
2010 (http://www.globaltimes.cn/content/570440.shtml); and “China property firm apologizes for 
vacancy rate report after public debate”, Reuters, 11 August 2022 
(https://www.reuters.com/markets/asia/china-property-think-tank-apologises-high-vacancy-rate-
report-2022-08-11/). 
12 ^ See the official website for more information: 
http://real.wharton.upenn.edu/~gyourko/chineselandpriceindex.html 
13 ^ In mid-2023, the NBS started to release a new series called “retail sales of services”, but it only 
shows the year-to-date year-over-year growth and has a very short history. 
14 ^ See the official release at: https://www.stats.gov.cn/sj/ndsj/2021/html/sm06.htm. 
15 ^ Note that the housing component contains rent, cost of decoration and utilities, and property 
management fees. Purchases of property are not included. For self-owned properties, owners’ 
equivalent rents are included in the “housing” category of consumption expenditures. 
16 ^ The Ministry of Commerce used to compile a monthly series of year-over-year sales growth 
based on data from 5000 large retailers, but this was discontinued after July 2016. 
17 ^ See the NDRC website: 
https://www.ndrc.gov.cn/fggz/jyysr/jysrsbxf/202003/t20200311_1222869.html. 
18 ^ As described in Anna Wong, “China’s Current Account: External Rebalancing or Capital 
Exodus?”, presentation at the 8th annual International Conference on the Chinese Economy, Hong 
Kong, January 13, 2017. 
19 ^ For the document, see https://www.imf.org/en/Publications/Manuals-
Guides/Issues/2016/12/31/Balance-of-Payments-Manual-Sixth-Edition-22588. 
20 ^ Mirror data refers to data reported by counterpart economies. For example, the mirror data for 
China’s inward FDI from the US is the outward FDI to China reported by the US. 
21 ^ “Assessing Reserve Adequacy—Specific Proposals”, International Monetary Fund, April 2015 
(see http://www.imf.org/external/np/spr/ara/). 
22 ^ See the latest rules on NPL classification effective 1 July 2023 at: 
https://www.gov.cn/zhengce/2023-02/11/content_5750184.htm. 
23 ^ See the changes of RRR since 2018 at: http://www.pbc.gov.cn/rmyh/4027845/index.html. 
24 ^ Industrial revenues cover income from product sales and other sources, such as labor provision 
and transfer of asset usage rights. However, industrial sales value only covers the income from 


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product sales. 
25 ^ See Xianchun Xu, "Accurately Understanding China’s Current Gross Domestic Product 
Accounting", Statistical Research (in Chinese), May 2019. 
26 ^ See Pingyao Lai and Tian Zhu, "Deflating China's nominal GDP: 2004–2018", China Economic 
Review, 2022. 
27 ^ According to NBS' definition, the non-private units include state-owned and controlled 
enterprises, foreign-funded enterprises and other enterprises, and the private units here mainly refer 
to SMEs. 
28 ^ Since 2019, MOF has released January-February combined fiscal data only, rather than for 
January and February separately, to avoid Chinese New Year related distortions. 
29 ^ Proceeds raised through central government special bonds (CGSB) and local government 
special bonds (LGSB) are managed under the GMF account. 
30 ^ See http://www.mof.gov.cn/zhengwuxinxi/caizhengxinwen/201701/t20170120_2524620.htm fo
r example. 
31 ^ The actual amount of LGFV debt which local governments have obligations to repay or have 
provided implicit guarantee on could be larger than the MOF estimates in recent years, based on 
our estimates. 
32 ^ For normal years, we assume local governments to spend 50% of LGSB proceeds in the month 
when the bond was issued, and 30% and 20%, for the following two months, respectively. However, 
for 2021 as an exception, we assume local governments to spend 20%, 30% and 50% of LGSB 
proceeds in the bond issuance month and the following two months, respectively. 
33 ^ The IMF pioneered research to estimate the augmented fiscal balance for China, though with 
annual frequency and a different approach compared to our AFD metric. See Yuanyan Zhang, 
Steven Barnett, “Fiscal Vulnerabilities and Risks from Local Government Finance in China", IMF 
Working Paper, January 2014; Rui Mano and Phil Stokoe, “Reassessing the Perimeter of Government 
Accounts in China,” IMF Working Paper, December 2017. 
34 ^ See 
http://www.pbc.gov.cn/jinrongwendingju/146766/146772/3656006/2018110716123679821.pdf. 
Investors should consider this report as only a single factor in making their investment decision. For 
Reg AC certification and other important disclosures, see the Disclosure Appendix, or go 
to www.gs.com/research/hedge.html.

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Understanding China’s Economic Statistics – 
Third Edition 
Updated Sept 6
th, 2024 
Directory of Links 
Section I. Introduction 
Overview 
How to Use the Ranking for Each Data Series 
The Ten Most Frequently Cited Chinese Economic Statistics 
A Typical Cycle of China’s Statistical Releases 
Basics of Interpreting the Numbers 
Evaluating Data Surprises  
List of Acronyms 
Section II. Overall Activity and Production 
Gross Domestic Product 
Industrial Production (Value-added of Industry) 
Services Industry Output Index 
Electricity Production and Consumption 
Rail Freight Traffic 
Total Profits of Industrial Enterprises 
Purchasing Managers’ Indices 
GS Proprietary Activity Measures 
Section III. Investment 
Fixed Asset Investment 
Projects Started and Under Construction 
Other Investment-related Data 
Section IV. Real Estate 
Real Estate Investment 
Land Transactions 
Housing Starts, Under Construction and Completions 
Home Sales 
Home Inventory 
Property Price Measures 
Land Price Indices 
GS Proprietary Indicators Related to the Real Estate Sector 
Section V. Consumption 


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Retail Sales of Consumer Goods 
Household Income and Expenditure Survey 
Retail Sales of Major Offline Retailers Reported by China National Commercial Information Center 
(CNCIC) 
Auto Sales 
Consumer Confidence Index 
Section VI. External Sector 
Merchandise Trade 
Services Trade 
Balance of Payments 
Foreign Direct Investment 
External Debt 
Foreign Exchange Reserves 
Exchange Rate Terminology and Offshore RMB Development 
CNY Trade-Weighted Indices 
GS China “Outside-In” Trade Measures 
GS China FX Flow Metric 
Section VII. Money, Credit, and Banking 
Money Supply 
Bank Loans and Deposits 
Total Social Financing 
Central Bank Policy Tools 
Interbank Interest Rates 
Flow of Funds Accounts 
Section VIII. Prices 
Consumer Price Index 
Producer Price Index (ex-Factory Price Index of Industrial Products) 
Agriculture and Raw Material Prices 
Merchandise Trade Price Index 
GDP Deflator 
Section IX. Population and Labor Market 
Total Population, Urban Population, Working Age Population, Migrant Population 
Birth Rate, Death Rate, Natural Growth Rate 
Employment Data 
Unemployment Data 
Wages 
GS China Wage Tracker 
Section X. Government Finance 
Government Revenue, Expenditure and Balance 
Local Government Debt 
GS China Augmented Fiscal Deficit (AFD) 
GS China Augmented Government Debt (AGD) 


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Our updated “Understanding China’s Economic Statistics” manual includes a broader array of data 
series, more explanatory charts and tables, and many GS proprietary indicators that we have 
developed over the years. 
Section I. Introduction 
Overview 
As China’s impact on the global economy has increased, so has the importance of its economic data. 
For some markets such as commodities, monitoring Chinese data has become as crucial as 
monitoring US data. However, many market participants view China’s economic statistics with a high 
degree of skepticism. 
The Goldman Sachs Economics Research Team has invested considerable effort in reviewing Chinese 
statistics, analyzing their relationships with the business cycle and identifying their limitations. We 
have also developed a series of proprietary indices for monitoring the Chinese economy — both at 
the macroeconomic level, such as the Goldman Sachs Current Activity Indicator (CAI) and the 
Goldman Sachs China Financial Conditions Index (FCI), and at the sector level via, for example, our 
trackers of wage growth, inventory changes, and housing policy. 
This “little red book” is a comprehensive update of Understanding China Economic Statistics, which 
we published in 2006 and updated in 2017. It is similar in format to our long-established statistics 
handbooks for the US, UK and Europe, but contains several distinct features owing to the challenges 
of interpreting China’s data and policy settings. It has been expanded further in this edition, 
reflecting the increased importance of China’s economy and economic data for the rest of the world 
and for a diverse set of markets. Since the second edition of the book was published in 2017, some 
data series have been suspended (e.g., land transaction area and value, real retail sales, FAI price 
index, and urban registered unemployment rate) while other data series have been added (e.g., retail 
sales of services and services industry output index). In addition, the definitions of various indicators 
have been revised over the past few years (e.g., total social financing). We hope it will serve as a 
useful reference both for clients investing in China directly and for those who need to track the 
Chinese economy due to its influence on other markets. 
Notable changes to this updated edition include: 
• 
A revised, and longer, list of indicators. In particular, we have expanded the sections on 
real estate and government finance, given these sectors’ importance to China’s 
macroeconomic outlook. Unfortunately, however, not all the changes are additions -- the 
authorities have ceased publication of some series that we found useful in the past. 
• 
Numerous additional charts and tables to summarize key data and display time series. 
• 
Further detail on the growing collection of proprietary indicators we have developed 
over the years. While our colleagues around the world have also developed proprietary 
indicators, and in many cases (e.g., the CAI and FCI) we apply those techniques to China, we 
have also developed many China-specific indicators. 


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In general, with respect to official data provided by the government, we find that: 
1. The production side of the statistics is better at capturing growth momentum than the 
expenditure side, mainly because the basic infrastructure for data compiling in China 
remains geared toward the production-based approach. This assessment may change 
gradually because China’s statistical authorities plan to improve data collection for 
expenditure items. 
2. The monthly growth indicators, especially in the industrial/manufacturing sector, such as 
industrial production and manufacturing PMI, are of better quality than the quarterly and 
annual GDP figures, partly because monthly data are timelier and subject to less non-
economic interference, but also because service sector measurement is generally more 
difficult. 
3. The reported growth rates for data series such as value-added industrial output, fixed asset 
investment and retail sales do not always correspond with the reported levels over time. In 
most cases, this is because of changes in the survey sample. For example, more companies 
have grown above the minimum size threshold required to be included in the sample each 
year, leading to an upward bias to the level of the series over time. The National Bureau of 
Statistics (NBS) does attempt to correct for this bias by requesting companies report year-
over-year (yoy) growth rates. 
4. For some high-profile data series such as GDP, revisions can alter the overall growth pace, 
particularly the seasonal patterns. In November 2019, the NBS revised up its 2018 GDP by 
2.1% which made the government's goal of “doubling income between 2010 and 2020” 
easier to reach. Due to the large swings in activity data driven by Covid-related lockdowns, 
seasonal adjustments have become more difficult over the past two years, with sequential 
growth heavily influenced by how seasonal factors are estimated. 
In terms of where the data are most inadequate but are of great importance, we still believe the 
higher-frequency expenditure side of the data reporting ranks at the top, in particular for 
government investment and consumption, as well as for inventory changes. 
Second on the list are data such as house prices, total housing stock, and the property vacancy ratio 
(referring to properties built but not inhabited, whether sold or not). In the context of China’s major 
housing downturn, reliable and timely figures on house prices and residential vacancy rates would 
be helpful to investors and policymakers. 
Third are data related to the labor market and wage development. Some information (such as the 
surveyed unemployment rate covering both registered and unregistered urban workers) has promise 
but is not released on a consistent and timely basis (e.g., youth unemployment rate was suspended 
after June 2023 and resumed in December 2023 under a different unemployment definition). 
Though we have developed some proprietary measures, such as our wage growth tracker, the lack 
of frequent and reliable data series on labor market slack constitutes a major macroeconomic data 
gap. 
Fourth, on the issue of prices, greater transparency on the Consumer Price Index — particularly in 
terms of the detailed components and weights — would help avoid confusion in the market. 


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Lastly, the breakdowns of many categories are outdated. For example there is limited information on 
RMB loan breakdowns by the type of borrower and industrial sector, on private vs. public investment 
in different industries, and on employment and wage data by sector. 
As with our other research products, we have tried to make this handbook as user-friendly as 
possible and accessible for readers with different levels of understanding of China’s macro data. As 
always, we would greatly appreciate your comments and suggestions. 
How to Use the Ranking for Each Data Series 
To make it easier for readers to put the data in perspective, we have assigned ratings of one to five 
stars for the signal-to-noise ratio and macro importance of each indicator. The ratings are on a 
relative scale within the China space. Therefore, a five-star rating means an indicator is among the 
top series in China’s data space, but does not mean it is free of deficiencies, or that it necessarily 
ranks highest among its international peers. 
The rating for the signal-to-noise ratio is fairly self-explanatory: In our judgment, how well does 
the series measure what it is designed to measure? Where possible, we have tried to corroborate 
data series with other related indicators, including aggregated corporate data or foreign data. 
The rating for macro importance is based on how essential the series is: (1) in helping to read the 
overall state of the economic cycle; and (2) in assessing the likely direction of macroeconomic policy. 
Although these factors are related, there can be significant differences between them. To illustrate, 
GDP has a lower frequency (quarterly) and tends to be smoother than other cyclical indicators. As a 
result, it does not have the highest signal-to-noise ratio in gauging the cyclical state of the economy. 
However, policymakers pay a good deal of attention to this data series, and the tolerance for 
missing the growth target is low. Therefore, the GDP growth data are useful in judging policy risks. 
As a result, we have assigned GDP a higher score for macro importance than for the signal-to-noise 
ratio. By contrast, electricity production and consumption data tend to be reliable, but their macro 
significance has declined over time as the energy intensity of the economy shifts. Therefore, we have 
assigned electricity production and consumption a higher score for signal-to-noise ratio than for 
macro importance. 
Exhibit 1: Overview of official Chinese economic indicators 
 


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Note: Rankings are GS subjective assessments; \"✓\" indicates Bloomberg/Wind consensus forecast 
is available for this indicator. 
Source: Goldman Sachs Global Investment Research 
The Ten Most Frequently Cited Chinese Economic Statistics 


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The indicators that we find most useful are not necessarily the ones discussed most frequently by 
market participants. Here is our take on the ten indicators that, from our subjective point of view, are 
most often cited by government officials, investors, and the media (listed in order of appearance in 
this publication). 
GDP. Despite all its flaws, this is the most comprehensive indicator of economic growth and also the 
growth indicator most watched by the government and the market. 
Industrial Production. Industrial production is perhaps the best gauge of short-term economic 
activity at a higher (monthly) frequency. 
Purchasing Managers’ Indices. Because PMIs are typically the earliest indicators released each 
month, they tend to attract significant market attention. 
Fixed Asset Investment. This is an important indicator for gauging short-term investment 
momentum. However, data quality and reliability are a concern. 
Home Sales. Among major property activity indicators, new property sales are more reliable than 
new property starts and property completions. New property sales are also important for real estate 
developer financing due to China’s pre-sales system. 
Retail Sales. Growth rates appear over-smoothed in some years and the data do not cover service 
consumption except catering. It is still the most frequently used indicator for consumption growth. 
Merchandise Trade. Trade data provide information on both domestic (imports) and foreign 
(exports) demand. 
Total Social Financing. This provides information on broad credit growth, including indirect 
financing, such as bank loans, and direct financing, such as bond/stock issuance, but coverage is still 
not wide enough to capture all credit extended to the real economy. 
Consumer Price Index. This is the most watched indicator of inflation. We believe it does a fair job 
of capturing inflationary pressures on household consumption in China. 
Producer Price Index. This is often assumed to lead downstream inflation and influences industrial 
profitability, although these relationships are not as simple as commonly perceived. 
A Typical Cycle of China’s Statistical Releases 
 


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* The NBS PMIs are reported at the end of the reference month; all other data are reported in the 
following month. \"Two Sessions\" refer to National People's Congress & Chinese People's Political 
Consultative Conference (CPPCC) Annual Sessions. The Statistics Bureau may adjust or suspend 
release times. 
Source: News Media, Goldman Sachs Global Investment Research 
Basics of Interpreting the Numbers 
Economic data are of considerable importance to financial markets – because of their information 


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about the state of the economy and their implications for economic policy. Important considerations 
to be aware of include: 
• 
Year-over-year versus “sequential” growth. We use the term “sequential” to describe 
period-on-period changes within a year (e.g., month-over-month or quarter-over-quarter, 
depending on the series). The Chinese government typically reports year-over-year series as 
a way to minimize seasonal influences. But year-over-year data can mask significant 
changes in sequential momentum, so we often calculate and refer to sequential figures. 
(Note that the term “base effect” refers to a particularly high or low sequential change from 
one year ago that affects the year-over-year calculation. For example, if GDP normally 
grows at a 4% annual rate but temporarily stalls at 0% quarter-over-quarter growth for one 
quarter, growth will be reported at 3% year-over-year in that quarter and the following three 
quarters, then one year later will jump back to 4% yoy due to the “base effect” as the weak 
quarter drops out of the calculation.) 
• 
Seasonal adjustment. This is critical when working with sequential data (more on this topic 
below). 
• 
Revisions. As in other countries, initial data reports may be revised as more comprehensive 
information becomes available or methodological revisions are made. The corollary is that 
the current data series do not necessarily reflect how the historical data looked at the time 
of release. 
• 
Survey versus “hard” data. Government agencies typically report samples or censuses of 
actual economic activity, which we sometimes refer to as “hard” data. In addition, a variety of 
government and private sector surveys (sometimes referred to as “soft” indicators) can give 
a useful qualitative sense of the direction of the economy. Often, these take the form of 
“diffusion indices” where respondents answer questions with either favorable, neutral or 
unfavorable responses; the percentage answering favorably plus half the percentage 
answering neutral are added to yield a score from 0 to 100. The widely quoted Purchasing 
Managers’ Indices (PMIs) take this form. While less precise than hard data and potentially 
subject to other biases (such as inflation), these reports can provide a timelier read on 
changes in direction and therefore are a useful reference for forecasting and policymaking. 
A Note on Seasonal Adjustment 
Seasonal adjustment is a mainstay of macroeconomic analysis, allowing comparisons of growth over 
periods of less than a year. The biggest value of seasonal adjustment is to separate cyclical signals 
from seasonal patterns so as to gauge trends in activity, inflation, or other indicators within the 
period. Though often taken for granted, the choice of seasonal adjustment method inherently 
involves judgment calls about whether incremental changes are seasonal or cyclical, and can at 
times have a major impact on the economic data. 
Conceptually, seasonal adjustment techniques penalize the months that tend to have high values 
and compensate those months that tend to have low values relative to an average month. Many of 
the statistical agencies in China use Census X-12 or its predecessor, X-11.
[1] In addition, some 
reliable data vendors, such as Haver Analytics, will adjust series using GENHOL to parameterize 
holiday factors. Specifically, the program takes a list of dates for holidays and a “window” (days 


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before, days during, and days after) around each holiday, and then generates a set of holiday 
regressors/dummy variables for each. 
Exhibit 2: The choice of seasonal adjustment can have a substantial impact on sequential 
growth 
Real GDP growth, seasonally adjusted 
 
 
Source: NBS, Haver Analytics, Goldman Sachs Global Investment Research 
Seasonal adjustments are a useful tool. Without them, one has to rely on yoy growth rates, which 
can be heavily influenced by last year’s base and are slower to reflect the latest changes in growth 
momentum. However, there are several important challenges to seasonal adjustment: 
Limited data history. It takes three or more years to obtain a result from standard seasonal 
adjustment algorithms, and longer time series are preferable. Short time series are prone to one-off 
shocks such as those during the Global Financial Crisis (GFC). Ensuring that such shocks do not 
throw off the seasonal factors requires manual intervention. 
Rapid structural change. Even when sufficient data are available, economies experiencing rapid 
structural change are more likely to see changing seasonal patterns as well. For example, as China 
grows in economic importance, fluctuations around the Chinese New Year are more likely to affect 
trading partners. Distortions around the large shocks from the Covid pandemic created difficulties 
for seasonal adjustment in many economies, although these are now fading. 
Floating holidays. Patterns of economic activity change around holidays: production tends to slow 
during holidays (factories and ports tend to shut or operate at less than the usual pace), while 
consumption tends to rise before holidays (retail activity is often boosted by holiday gift-giving, 
eating out, etc.). China has several holidays based on the lunar calendar that can fall in one of two 
calendar months each year. These holidays affect each month’s data to a different degree each year 


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and can thus create distortions in both month-over-month and year-over-year changes. Of these 
floating holidays, Chinese New Year (which falls in either January or February) is most important. The 
simplest solution to Chinese New Year seasonal distortions is to average data for the first two 
months of the year. Adjusting monthly growth rates for the number of working days does not help 
much, especially for the production data, because many companies still operate with varying 
capacity during holidays, and some may not resume operation at full capacity until days after the 
holiday. As a result, especially if the Chinese New Year falls in late February, the March data may also 
be affected (and therefore even yoy growth rates for March can be distorted by the Chinese New 
Year effect). According to the NBS, the official seasonal adjustment method adjusts for working day 
difference and floating holidays in China. For most seasonally adjusted series, there is not an obvious 
residual seasonal distortion, though floating holiday effects (especially Chinese New Year) are 
difficult to fully eliminate as their magnitude varies over time with structural changes in the economy 
and behavior. 
Evaluating Data Surprises 
Goldman Sachs China Macro-data Assessment Platform (GS China MAP) 
Source: Goldman Sachs Economics Research 
Availability: Daily since January 2006 
Timing: Real-time 
Release: GS China Proprietary Indicators update 
Overview 
The Goldman Sachs China Macro-data Assessment Platform (GS China MAP) measures economic 
growth surprises in China. It is constructed in the same manner as our surprise indices for other 
economies. 
Compilation 
In the MAP system, the importance of a particular release is calculated in two dimensions. 
• 
First is the relevance score, which is based on the historical correlation with real GDP growth 
(quarter-over-quarter). This score can range from 0 (irrelevant) to 5 (most relevant), as 
illustrated in Exhibit 3. Given the central role of the GDP series in the MAP framework, the 
fact that China’s GDP statistics have tended to be relatively smooth historically (at least pre-
pandemic) may reduce the number of significant indicators. 
• 
Second is the surprise score, measured as the difference between a particular release of 
that indicator and the Bloomberg “consensus” forecast for that indicator, measured in 
standard deviations. We assign a score from -5 to +5 depending on whether the actual 
figure is above or below expectations, and by how much. If the actual release is less than 
half a standard deviation from the consensus expectation, we will assign a score of 0. A 
difference of between 0.5 and 1 standard deviation will generate a surprise score of +1 or -1. 
Surprise scores rise with ½ standard deviation increments, with any surprise of greater than 
2.5 standard deviations generating a score of 5. 


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• 
Multiplying the relevance score by the surprise score gives a range of -25 to +25 for a given 
indicator; the aggregate of time series of MAP scores for those indicators included for China 
creates the China MAP score. 
Exhibit 3: MAP relevance and surprise scales 
 
 
Source: Goldman Sachs Global Investment Research 
• 
Bloomberg consensus forecasts are generally made using data from sell-side economists, 
most of whom work at global financial institutions. As a result, they may not precisely reflect 
the expectations of the broader investor community. In addition, these forecasts are often 
released in publications many days in advance of the official data, and subsequent changes 
in forecasters’ views may not always be updated in the published consensus. 
• 
There was a large shock during the sample period due to the GFC, and an even larger one 
during the Covid pandemic. Relative to the magnitude of the surprises during that period of 
time, any surprises today tend to appear small but are nevertheless significant for the market. 
Therefore, in setting our surprise score thresholds we have used the standard deviation of 
surprises based on data releases since 2010 (Exhibit 4). 
Exhibit 4: Indicators in the MAP for China 
 


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Source: Goldman Sachs Global Investment Research 
Exhibit 5: China activity data surprised modestly to the upside in late 2023 and early 2024 
China MAP 
 
 
Source: Goldman Sachs Global Investment Research 
List of Acronyms 
 


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Related GS Economics Publications 
• 
“Trade Balance Bounces into the Year of the Rabbit”, US Daily, 6 April 2011 
• 
“A redesigned MAP of emerging Asia data”, Asia Economics Analyst, 10 May 2013 
• 
“A Better Global Economic MAP”, Global Economics Weekly 13/39, 5 December 2013 
• 
“Separating cyclical signal from seasonal noise”, Asia Economics Analyst, 27 June 2014 
• 
“Chinese New Year Seasonal Distortions Coming Home to Roost”, US Daily, 15 February 


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2016 
• 
“Revisiting post-reopening seasonal adjustment”, China Data Insight, 23 June 2023 
Section II. Overall Activity and Production 
There are five major sets of macro indicators related to overall activity and production: 
1. Gross Domestic Product (GDP), including its breakdown by production (industry), expenditure, 
income, and region. 
2. Industrial Production and Services Industry Output Index, which measure real value added in the 
industrial sector and services sector, respectively. 
3. Other industrial activity indicators that can serve as alternative growth measures, including 
Electricity Production/Consumption and Rail Freight Traffic. 
4. Total Profits and Operating Income of Industrial Enterprises. 
5. Purchasing Managers’ Indices (PMIs) by the official and private data sources, capturing near-term 
sequential growth momentum in different sectors (e.g., manufacturing, services). 
In addition, we have our own GS proprietary activity measures, including the Current Activity 
Indicator (CAI), and inventory tracker. 
Gross Domestic Product 
Signal to noise ratio: **** 
Macro importance: ***** 
Source: National Bureau of Statistics (NBS) 
Frequency: Quarterly & Annual 
Availability: GDP by industry: Annual from 1952, quarterly from 1992 (both nominal levels and real 
growth rates); GDP by expenditure: Annual from 1952 (only nominal levels), quarterly from Q1 2009 
(for estimated contribution to year-to-date yoy GDP growth; no level data) and from Q1 2015 (for 
estimated contribution to single-quarter yoy GDP growth; no level data). 
Timing: GDP by industry: 2-3 weeks after the reference quarter; GDP by expenditure: Middle of the 
following year for level data, and 2-3 weeks after the reference quarter for quarterly estimated 
contributions to yoy GDP growth; GDP by income: Together with release of yearbook. 
Hour: 10:00am for GDP by three major sectors (i.e., primary, secondary and tertiary sectors; all times 
in this book are China Standard Time); 9:30am on the following day for GDP by all industries. 
Publication: NBS press release; China Statistical Yearbook 
Overview 
Gross Domestic Product (GDP) measures the overall economic activity of an economy on a value-


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added basis (the value of output minus purchased inputs). It is the most comprehensive measure of 
domestic economic activity. 
Signal to Noise Ratio 
• 
China’s GDP data are mostly compiled in accordance with the System of National Accounts 
(SNA) 2008 standard by treating R&D expenditure as part of capital formation. The data are 
historically and internationally comparable. 
• 
Historically, the real GDP growth data were exceptionally smooth relative to other countries 
and to other indicators of activity, especially during economic downturns, contributing to 
skepticism among market participants over their accuracy. It was common for China to 
announce quarterly GDP growth with a variation of less than 0.5 pp before the Covid 
pandemic, especially during 2015-16, even as some high-frequency indicators occasionally 
experienced double-digit swings in growth. To get a better sense of cyclical momentum, we 
cross-check real GDP growth with other indicators including our proprietary Current Activity 
Indicator. During the Covid pandemic in 2020-22, the volatility in the real GDP growth data 
increased considerably due to periodic shifts in Covid-related restrictions. 
• 
GDP revisions are supposed to capture the newly available data in the whole economy, 
though in practice they are most relevant for tertiary industry (services). Measuring services-
sector activity is inherently more difficult, and the Chinese statistical system – which grew out 
of the Soviet system that did not recognize services as value added – is particularly ill-
equipped to do so. The shift from SNA 1993 to SNA 2008 in 2016 increased 2015 total GDP 
by 1.3% to US$11 trillion, and the real growth rate was also revised up slightly. The upward 
revision was due to the fact that China’s R&D expenditure growth has been consistently 
faster than that of overall GDP. In November 2019, the NBS revised up its 2018 GDP by 2.1% 
which made the goal of “doubling real GDP between 2010 and 2020” easier to reach. 
Macro Importance 
GDP measures the value of final goods and services produced by whole economic entities in China. 
Although the GDP data suffer from various quality issues, they are still probably the most widely 
cited macro indicator because: 
1. The government pays considerable attention to GDP growth, and the official real GDP 
growth target is one of the most binding targets in terms of policy making (e.g., compared 
to inflation and job market targets). Therefore, it is useful in judging policy risks. 
2. It is compiled largely in accordance with international standards, and is often used for 
comparison with other countries. It also covers a broad sample for the overall economy, and 
therefore enables analysis to be carried out on many ratios, such as the national savings rate, 
which would be difficult to do using monthly indicators. 
Compilation and Reporting 
There are three approaches to calculating GDP data series in China: 
• 
GDP by industry 


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• 
GDP by expenditure 
• 
GDP by income 
Availability 
Exhibit 6: China's GDP by industry data have more detail than expenditure or income-side data 
 
 
*Constant price (inflation adjusted): Real GDP is generated by using prices that pertained in a rolling 
base year. 2011-2015 data are based on prices in 2010, and 2016-20 data are based on prices in 
2015. **Seasonal adjustment (NBS-SA): Real GDP seasonally adjusted by the China’s NBS is meant to 
adjust for working days and floating holidays such as the Chinese New Year Golden Week, the 
Dragon Boat Festival and the Mid-Autumn Festival. 
Source: NBS 
GDP by Industry 
Within this framework, the whole economy is divided into three broad industries: 
• 
Primary industry: Farming, forestry, animal husbandry and fishing (excluding related services). 
• 
Secondary industry: “ Industry ”  (including mining, manufacturing, and utilities) and 
construction. 
• 
Tertiary industry: Any industry other than the primary and secondary industries. Activities in 
the services sector (such as wholesale and retail trade, finance, catering, and transportation) 
are captured in this category. As in many other countries, this sector has been growing as a 


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share of overall economic activity in China. 
Exhibit 7: Gradually rising share of services in the Chinese economy 
Share of nominal GDP by industry 
 
 
Source: NBS, Goldman Sachs Global Investment Research 
GDP by Expenditure 
The expenditure approach is a method for calculating GDP by totaling household consumption, 
investment, government consumption and net exports. 
• 
All goods and services that consumers have purchased (with the exception of houses and 
housing construction materials) are included in household consumption (houses and 
housing construction-related items are mostly treated as gross fixed capital formation). 
Although retail sales data are often used as an indicator for household consumption, there 
are crucial differences between the two (see Section V. Consumption). 
• 
Government consumption measures the non-investment goods and services purchased by 
government. 
• 
The sum of household and government consumption equals final/total consumption. 
• 
Gross Fixed Capital Formation (GFCF) is a key component in the expenditure approach of 
national accounts reporting. It is different from fixed asset investment (FAI) because the 
latter includes spending on assets that do not directly contribute to GDP (e.g., purchases of 
land and used equipment), while GFCF does not.
[2] Other differences include a minimum 
project size cutoff for FAI, and property developer profits and IT investment being covered 
in GFCF but not FAI. Moreover, FAI data collection is susceptible to a lot of statistical noise 
(see Section III. Investment). 


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• 
Changes in inventories refer to net changes during the observed period. China’s changes in 
inventories data are only available at annual frequency, and appear to be useful in assessing 
the direction, but not necessarily the degree, of inventory adjustments. Despite their 
importance, changes in inventories are generally the least reliable component of GDP by 
expenditure in China, as well as in many other countries, due to difficulties in data collection. 
Some countries estimate this as a residual item by balancing GDP by expenditure data and 
GDP by industry data. The NBS is supposed to make independent estimates for changes in 
inventories based on a wide range of data sources, though in practice the level of volatility 
and the gap between GDP by expenditure and GDP by industry suggest it may have an 
element of residual as well. To better track the contribution of inventory changes to GDP 
growth at a quarterly frequency, we have built a proprietary inventory tracker based on 
commodity inventory measures, PMI inventory sub-indices, and industrial final goods 
inventory and auto inventory data. 
• 
The sum of GFCF and the change in inventories is defined as Gross Capital Formation (GCF, 
or just “investment”, which remains a very large share of economic activity in China). 
• 
Net exports are the balance of trade in goods and services, which is equivalent to the 
balance of payments (BOP) definition for the trade balance. It differs from Customs trade 
data in three main aspects: (1) it includes trade in services, whereas the Customs data only 
cover trade in goods; (2) conceptually, it is based on the principle of exchanges between 
residents and non-residents, instead of goods moving across national frontiers; and (3) 
imports reported by Customs are based on CIF, while BOP/trade under GDP is based on FOB 
(see Section VI. External Sector). 
• 
GDP by expenditure data are only available annually in nominal levels. One needs to deflate 
official data to obtain real levels and growth rates. For example, the Consumer Price Index 
(CPI) can be used to deflate household and government consumption, the Producer Price 
Index (PPI) can be used to deflate GFCF, and goods trade price indices can be used to 
deflate goods trade. Since 2015, the NBS also publishes percentage point contributions of 
consumption, investment, and net exports to headline year-over-year real GDP growth 
which can be used to estimate the year-over-year growth in real consumption, real 
investment, and real net exports. 
Exhibit 8: Chinese “rebalancing” from investment to consumption was limited through 2023 
Share of nominal GDP by expenditure 
 


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Source: NBS, Goldman Sachs Global Investment Research 
GDP by Income 
• 
The income approach accounts for the income generated during the process of production 
activities for various industries. The value-added GDP data are composed of four parts: 
employees’ compensation, net taxes on production, depreciation of fixed assets and 
operating surplus. 
• 
The NBS does not release national aggregates for GDP by income, only for individual 
provinces. Users need to aggregate the provincial GDP-by-income data to get a national 
figure. However, releases of such provincial-level data are uneven and lagged. For example, 
for many provinces, 2017 data are the latest available as of this writing in 2024. 
Conceptually, the level and growth rate of GDP measured by the industry, expenditure, and income 
approaches should be the same. However, in practice there are some discrepancies among them 
due to measurement issues. When calculating the consumption/investment/net export share of GDP, 
GDP by expenditure should be used; in other cases, GDP by industry should be used as the 
benchmark. In addition, we often use the GDP by income approach as a reference of how the 
national income is split. 
Revision 
In early 2017, the NBS simplified GDP data accounting into preliminary estimation and final revision, 
compared to the previous three-step accounting regime that included preliminary estimation, 
preliminary revision and final revision. 
Preliminary estimation: The timeliness requirement is high for the preliminary release of quarterly 
GDP data (approximately two to three weeks after the end of the reference quarter). Annual 
preliminary GDP is calculated by aggregating quarterly results. The first release of annual data comes 
out early in the following year. The preliminary estimate is calculated using the industry approach. 
Final revision: The final revision of annual GDP is released towards the end of the following year, 


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based on audit, fiscal budget outturns and other survey data. 
Economic Census: An economic census is conducted every five years and will revise historical GDP 
series based on the census results. The 4th economic census was conducted on 2018 annual data 
and results were released in late 2019. The latest census was conducted in 2023 with results to be 
released in late 2024. 
There are several windows for NBS GDP revisions if needed based on past practice, including regular 
revisions to sequential GDP growth estimates for historical data series when releasing new GDP data, 
annual GDP final revisions, and some occasional, ad hoc revisions to GDP estimates due to statistical 
investigations, scrutiny, or methodology changes. 
Other Issues 
Regional GDP and per capita GDP 
• 
Historically the discrepancies between national and regional GDP data used to be significant, 
but narrowed in recent years. The sum of provincial GDP tends to be higher than the 
national GDP, and the aggregate of municipal GDP tends to exhibit the same pattern vs. the 
provincial GDP. A number of factors lead to these discrepancies. Among the most important 
are: (1) double-counting of the value added for enterprises that operate across different 
regions; (2) differences in data sources; and (3) human factors in compilation. The revised 
national GDP after the census is much closer to the aggregate of provincial GDP data before 
the revision. Although it has long been assumed that regional GDP data have overstated the 
growth and size of the economy, the revisions made by the NBS subsequently indicate early 
readings of national data may be too conservative as they tend to under report service-
sector activities relative to the census, especially for emerging industries. 
• 
Another problem is the estimation of regional GDP per capita in China, especially at the city 
level, due to China’s large cross-region migrant population. As there are two regional 
population data series in China, those of actual residents and those with household 
registration (Hukou) in the reporting location, GDP per capita calculated using the two series 
can differ substantially, especially for cities with more migrant workers. In 2004, the NBS 
noted that for cities with large migrant populations, such as Shenzhen where 
the registered population is significantly smaller than the residential population, using the 
registered population may overstate GDP per capita. Therefore, it is important to use the 
actual resident population series to avoid serious over-estimation of GDP per capita, though 
even that series suffers from the problem of under-reporting. For example, someone who 
stays less than six months in a city is technically not a resident according to the statistical 
standards, but nevertheless contributes to local activity. Moreover, city-level GDP per capita 
data are not always reported on a timely basis. 
• 
Given population estimates reported are year-end data, to capture the "flow" feature of GDP 
for the whole year, the NBS will average the year-end residential population when 
calculating GDP per capita. For example, average 2020 population= (2020 year-end + 2019 
year-end)/2. 
GDP and GNI/GNP 


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• 
Another concept closely related to GDP is gross national income (GNI), also called gross 
national product (GNP). The difference between the GDP and GNI/GNP is net factor income, 
which is the income of investment and labor by domestic residents earned abroad minus 
those of foreign residents earned in the country. Despite their apparent similarities, these 
two series measure two different aspects of the economy: GDP measures production and 
GNI measures income. Note China's primary income deficit has widened over the past few 
years, mainly driven by a wider investment income deficit, although net international 
investment positions have shown steady growth. 
Industrial Production (Value-added of Industry) 
Signal to noise ratio: **** 
Macro importance: ***** 
Source: National Bureau of Statistics 
Availability: Monthly growth from 1990, monthly growth seasonally adjusted from 2011, annual 
absolute level (value added of industry) from 1993 to 2007. 
To adjust for Chinese New Year related distortions, since 2012 the NBS no longer releases industrial 
production data for January alone in mid-February; instead, it releases January-February combined 
data in mid-March. 
Timing: Typically around the 2
nd /3
rd week of the following month. In January, April, July and October, 
it is released with quarterly GDP data during a press conference around the 2
nd /3
rd week of the 
month. 
Hour: 10:00 am 
Publication: NBS monthly release 
Overview 
This data series measures the real value added in the industrial sector (the deflator for headline IP is 
PPI). This indicator is an important reference for macroeconomic management and is widely used to 
estimate short-term growth momentum in the industrial sector. 
Signal to Noise Ratio 
• 
We have long viewed industrial production (IP) as among the more reliable monthly activity 
indicators China publishes because: (1) related to the structure of the Chinese economy, 
China’s statistical system has focused on tracking growth in industrial production since it was 
founded; and (2) historically there seemed to be less “smoothing” in this series than in some 
other politically more sensitive data series, such as GDP. However, IP became unusually 
smooth during the 2015-16 downturn. The reliability of the IP series appears to have 
improved in recent years with its volatility increasing dramatically during the Covid pandemic 
and with sequential moves largely consistent with high-frequency data such as coal 
consumption and steel production. 


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• 
The IP data series generally tends to be more important than fixed asset investment and 
retail sales data in tracking GDP growth because it is in real terms and because, by being in 
value-added terms, it is more in line with the GDP concept. The only difference between IP 
and manufacturing output is that IP includes the mining and utilities industries. 
Macro Importance 
Historically we have found the IP data quite useful given: (1) their high frequency (monthly), and (2) 
they are a reasonably good proxy for overall economic activity and especially GDP data, since IP is a 
direct and important GDP component. 
Compilation 
• 
The sectoral coverage of IP is selective in the following respects: 
1. It covers only the industrial sector, which includes “ mining and quarrying, 
manufacturing, and utilities” — otherwise known as “secondary industry” by GDP 
classification, excluding construction. There are 41 industrial divisions in total, in 
which manufacturing accounts for the vast majority of the components. Value added 
in a particular industrial division is the sum of value added from companies whose 
primary activities are in that division (in practice, this may include some ancillary 
activities which should technically be categorized in other areas). This issue is 
especially tricky when it comes to conglomerates, and as the level of sector detail 
increases. 
2. By business type, the value added of industries covers state-owned & holding 
enterprises, share-holding enterprises, private enterprises, and foreign, Hong Kong, 
Macau and Taiwan funded enterprises. The classification is based on the controlling 
stake. If the company is controlled by a minority shareholder, it will be classified as a 
company under that shareholder, as is often the case with state-controlled firms. 
3. Minimum threshold: enterprises with annual sales of RMB20 million or above are 
included. Therefore, this series covers only a portion of the total value added by 
industry in the economy, though the portion is large (more than 80% in recent years). 
Given regular adjustments of the minimum threshold, the NBS releases comparable 
growth rates to eliminate statistical discrepancies in levels. 
• 
Export delivery value: Refers to the nominal value of industrial products for exports 
(including to Hong Kong, Macau and Taiwan). Prices are generally denominated in FX, and 
converted back to RMB based on the respective exchange rates. This category includes 
products from processing and assembling trades, etc. The export delivery data are different 
from customs trade mainly in two aspects. Firstly, Customs data includes all the merchandise 
goods that exit customs in a particular month while export delivery value only includes 
industrial goods produced for exports in a particular month. For example, Customs data 
includes exports from all kinds of enterprises and covers both industrial and non-industrial 
products (e.g., agricultural products, roughly 3% of total exports), while export delivery value 
only covers industrial products from industrial enterprises. Besides products sold to other 
countries, export delivery value also includes domestic sales of products which were initially 


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planned for exports. Secondly, these two sets of data are different on treatment of 
processing trade. For processing trade with supplied materials, export delivery value only 
includes processing fees while Customs record the full value of the final product as export 
on a free-on-board (FOB) basis. 
• 
IP data are announced in terms of real single-month growth and real year-to-date growth. 
The reporting of the nominal level of value added by industry was discontinued in 2007 and 
official real level data have never been published. When nominal value-added series are 
needed one can “inflate” official real growth data by the PPI to generate a rough estimate, 
though this approach can yield results quite different from the official nominal series over 
the period when they were still published. This is due to technical issues such as whether the 
deflation is done first at the sector level and then aggregated, and/or whether value added 
is deflated with a single price index or output and intermediate input are deflated separately 
(“double deflation”, technically the preferable approach). 
• 
The most important sub-sectors of manufacturing are electronic equipment (including 
communications equipment, computers and other electronic equipment), transportation 
equipment (including auto manufacturing), smelting and pressing of ferrous/nonferrous 
metals, chemicals, electric machinery and equipment, and textiles. 
Revision 
Seasonal adjustment is carried out every time a new data point comes in, and therefore historical 
seasonally adjusted growth rates will be revised accordingly. More recent growth rates are usually 
the most sensitive to this process. (As with many other macro indicators, estimates of month-over-
month growth are very sensitive to the exact seasonal adjustment method used.) 
Other Issues 
When the Chinese New Year falls into two different months between two consecutive years, yoy 
growth rates for January and February will be seriously distorted. It is not possible to correct the 
distortions by adjusting for the number of working days, mainly because not all businesses are 
closed during holidays, and not all resume operations immediately after the holidays. Since 2012, the 
NBS no longer releases industrial production data for January alone in mid-February; instead, it 
releases January-February combined data—which is often assumed to be free from Chinese New 
Year distortions—in mid-March. However, this assumption is not entirely correct since activity in 
March can also be affected when the Chinese New Year occurs late, as was the case in 2015. The 
NBS does report month-over-month seasonally adjusted growth for January and February and all 
other months of the year back to 2011. These data can be used to derive a year-over-year time 
series for January and February, as they are supposed to be adjusted for Chinese New Year effects 
already. However, the year-over-year growth derived from the NBS month-over-month growth 
series tends to deviate significantly from the NBS headline year-over-year growth. 
Services Industry Output Index 
Signal to noise ratio: *** 
Macro importance: **** 


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Source: National Bureau of Statistics 
Availability: Monthly growth from December 2016 
Timing: Typically around the 2
nd /3
rd week of the following month. In January, April, July and October, 
it is released with quarterly GDP data during a press conference around the 2
nd /3
rd week of the 
month. 
Hour: 10:00 am 
Publication: NBS monthly release 
Overview 
The Services Industry Output Index (SIOI) data series measures the real value added in the services 
sector. This indicator is an important reference for macroeconomic management and is frequently 
used to estimate short-term growth momentum in the services sector. SIOI is on a real basis and 
tracks tertiary GDP growth closely (55% of China’s economy as of 2023). 
Signal to Noise Ratio 
• 
The SIOI has a much shorter history than industrial production (IP), fixed asset investment 
(FAI) and retail sales. The NBS only releases the yoy growth data series for this index, while 
historical level data are not available (unlike IP, FAI and retail sales). As such, one needs to 
assume the monthly index levels for a specific base year, and back out level data for other 
years, to estimate the seasonally adjusted sequential growth. 
• 
Since the data series was introduced in late 2016, NBS has shown January-February SIOI 
growth combined, to smooth out distortions related to the shifting time of the Chinese New 
Year holiday. 
• 
The SIOI growth shares similar trends with retail sales growth in previous years. Their 
occasional divergences for some months may reflect things like growth differences between 
goods consumption and services consumption, growth differences between business 
services and consumer services, and major changes in price deflators. 
• 
The NBS also provides some sectoral breakdown for the SIOI growth, although historical 
level data are not available. These sub-sectors include wholesale & retail sales, transport, 
storage & post, hotel & restaurant, financial services, real estate services, IT and related 
services, leasing & commercial services, etc, with varying start month availability (between 
2018 and 2020). 
Macro Importance 
Despite the short history, we have found the SIOI data useful given: (1) their high frequency 
(monthly), and (2) the lack of many other broad proxies for economic activity in the services sector. 
By NBS definition and historical patterns, the relationship of SIOI growth to the services sector is 
analogous to that of IP growth to the industrial sector. 
Exhibit 9: Growth in the SIOI shares broadly similar trends with retail sales growth 
SIOI vs. nominal retail sales growth 


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Source: NBS, Wind 
Electricity Production and Consumption 
Signal to noise ratio: **** 
Macro importance: *** 
Source: National Bureau of Statistics, National Energy Administration 
Availability: Electricity Production: monthly from January 1995; Electricity Consumption: monthly 
from December 2012 (year-to-date); annual from 2002 
Publication: NBS monthly release, China Energy Statistical Yearbook 
Overview 
• 
Electricity production: Refers to the power generated by industrial enterprises with annual 
revenue from principal business above RMB20 million. 
• 
Electricity consumption: Refers to the electricity consumption of the whole of society 
including the primary sector, industrial sector, tertiary sector and residents in urban and rural 
areas. 
Signal to Noise Ratio 
• 
The signal to noise ratio of electricity production and consumption is relatively high — as 
the information collection is largely automated, there is relatively less room for local 
governments to distort the numbers. In fact, industrial electricity consumption per unit of 
GDP has been closely watched by local governments, with rising concerns about eliminating 
outdated capacity in heavy industries. 


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• 
Periodic divergence between industrial electricity consumption and industrial production 
growth may in part reflect a shift towards less energy-intensive sectors, or a transition in the 
automobile sector towards electric vehicles. Other factors also affect electricity usage. For 
example, weather-related factors (e.g., hot summers which lead to more air conditioning 
demand) lead to strong seasonality in residential electricity consumption. Abnormal weather 
can therefore distort even seasonally adjusted data. 
Macro Importance 
Electricity production and consumption data used to be important as these data series are perceived 
as relatively free from manipulation and can provide a cross-check on the strength of the economy. 
However, their macro importance has declined in recent years as electrification (e.g., electric vehicles) 
gathered steam in China, leading to higher electricity consumption growth relative to real GDP 
growth. 
Rail Freight Traffic 
Signal to noise ratio: **** 
Macro importance: ** 
Source: Ministry of Transport 
Availability: Freight volume: monthly from January 1995; annual from 1949 
Freight turnover: monthly from August 1998 (year-to-date from January 1990); annual from 1952 
Transport distance: annual from 1949 
Publication: NBS monthly release; China Statistical Yearbook 
Overview 
By category of cargo, national rail freight traffic data have information on freight volume, freight 
turnover and transport distance. 
Signal to Noise Ratio 
Rail freight traffic data are generally reliable. However, they have strong seasonality. For example, rail 
shipments for coal tend to increase in winter given rising demand for the fuel from the northern part 
of China. 
Macro Importance 
Similar to electricity data, the macro importance to GDP is relatively low—rail traffic primarily reflects 
the supply and demand in heavy industry. Freight rail was subject to severe under-capacity issues at 
times in the past and hence its historical data may not reflect demand changes well (for example, a 
change from 50% excess demand relative to the capacity to 10% will not show up in the actual 
amount of freight carried). This has generally not been a major issue in recent years as railway 
investment increased further. 


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Total Profits of Industrial Enterprises 
Signal to noise ratio: *** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Monthly from 1999, year-to-date 
Timing: Around 27 days after the end of each month 
Publication: NBS monthly releases 
Overview 
• 
Total profit of industrial enterprises: Total profits are the sum of "operating profit" and net 
"non-operating profit", on a before-income-tax basis. The sample is based on industrial 
enterprises above the designated size threshold.
[3] 
• 
Operating revenue: Operating revenue refers to the total amount of income generated by 
business activities, including revenue from main business and other activities
[4]. 
• 
The NBS also releases financial data stating the operational condition of industrial 
enterprises such as total assets, total liabilities, total owners’ equity, and main business 
income, etc. 
Exhibit 10: How the NBS industrial profits data fit into a simplified income statement 
 
Source: NBS 


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Signal to Noise Ratio 
• 
We believe the data series are generally reliable, at least in terms of the overall growth rates, 
though probably less so at the industry level. SOEs are often perceived as over-reporting 
their profits and under-reporting losses because of performance assessment systems for 
their senior management, while private enterprises tend to under-report them to avoid 
taxes. But to the extent that there is no major change in the degree of over/under-
statement (at least in the short run), the growth rates should be useful in terms of gauging 
profitability trends of large industrial firms. The trend in profit growth is also broadly 
consistent with that of enterprise income tax receipts reported by the Ministry of Finance, 
except for periods with major tax policy adjustments (e.g., tax waivers, cuts and 
postponement). 
• 
Profit/revenue data contain a lot of seasonal noise, e.g., profits and revenue levels usually 
show an uptick in December. Although seasonal adjustment should deal with this, given the 
pronounced seasonal effects an alternative approach to minimize this noise is to compare 
the data point with the same period in the previous year. 
Macro Importance 
• 
Profitability is useful in gauging the strength of the corporate sector. Profitable firms are able 
to, and often do, reinvest their retained profits. Other industrial financial data such as 
interest payments can also help assess the debt sustainability of the industrial sector. 
Compilation and Reporting 
• 
The coverage is the same as that of the industrial production data, i.e., all industrial 
enterprises with annual sales of RMB20 million or more. Similar to industrial production, the 
minimum threshold for profit data sampling increased in 2011. (Previously the minimum 
threshold was annual sales of RMB5 million or above.) Official year-over-year growth rates 
are based on comparable samples, according to the NBS. 
• 
In 2012, the NBS released the new Industrial Classification for National Economic Activities 
which expanded the industrial coverage from 39 to 41 in total. Therefore, many sub-sectors’ 
data are not precisely comparable over longer periods of time even though the sectors may 
have very similar names; analysts should watch out for jumps in series around times of 
revised classifications. 
• 
Profits are on an accrual basis, and China onshore stock market listed companies follow the 
same standard, per enterprise accounting rules from the Ministry of Finance. However, NBS 
profits are pre-income tax, and for listed companies, investors usually pay attention to post-
tax earnings. 
Other Issues 
• 
The NBS typically releases the amount of losses (year-to-date) made by loss-making firms 
at the same time as it publishes the total profits of industrial enterprises. (As one would 
expect, the reported total profits number for all industrial enterprises already nets out the 
losses from loss-making companies.) 


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• 
The main cost categories in the monthly profit report are cost of goods sold, finance costs 
(interest payments and fees paid to financial companies, which can often be significant 
especially when interest rates are high), and management/operation costs. 
Purchasing Managers’ Indices 
Signal to noise ratio: NBS manufacturing: ****; Caixin manufacturing: **** 
NBS non-manufacturing: ***; Caixin services: *** 
Macro importance: National Bureau of Statistics: ****; Caixin: *** 
Source: National Bureau of Statistics - China Federation of Logistics and Purchasing (NBS-CFLP), 
Caixin-Markit Economics/S&P Global
[5] 
Availability: NBS manufacturing PMI: since January 2005 
NBS non-manufacturing PMI: since January 2007 
Caixin manufacturing PMI: since April 2004 
Caixin services PMI: since November 2005 
Timing: NBS PMIs: Last day of each month (starting from March 2017); 9:30am 
Caixin manufacturing PMI: Usually the 1st workday of the following month; 9:45am 
Caixin services PMI: Usually the 3rd workday of the following month; 9:45am 
Publication: NBS monthly release, Caixin PMI Reports 
Overview 
There are two sets of Purchasing Managers’ Indices (PMIs) in China, one compiled jointly by the NBS 
and the China Federation of Logistics and Purchasing (CFLP) (hereafter referred to as the “NBS PMI” 
for convenience), and the other by Caixin/Markit Economics (later published by Caixin/S&P Global). 
Currently, each organization releases separate indicators for the manufacturing and non-
manufacturing sectors. 
Signal to Noise Ratio 
• 
Of the two manufacturing surveys, China’s NBS manufacturing PMI -- also known as the 
official manufacturing PMI -- has historically performed slightly better, as its production and 
new orders indices appear to be the best coincident indicators of sequential industrial 
activity growth among all PMI related data. 
• 
This advantage could be because the NBS manufacturing PMI is more representative, due 
mainly to the fact that it is compiled by the official statistical authority: 
1. The NBS manufacturing PMI is based on a sample size of 3000 firms while the Caixin 
manufacturing PMI is based on over 500 firms. 
2. The NBS survey is likely to have a higher response rate. The response rate of the 


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NBS survey is said to be as high as 99.6%, as the NBS has the legal right to demand 
that firms respond. While this can be a double-edged sword as some firms reporting 
unwillingly might be reporting with less care, the net effect of having a large 
effective sample size is probably a good thing. While the rate for the Caixin survey is 
unknown, it is unlikely to attain such a consistently high response rate. 
• 
China’s two manufacturing PMIs sometimes send different signals (Exhibit 11). Sample size 
difference and discrepancies in data-collecting periods may explain divergences. The Caixin 
survey is conducted in the middle of each month, while the NBS survey is conducted later, at 
around the 20-25
th of each month. When the economy is changing rapidly, this timing 
difference can be significant. 
• 
It is often said that the official PMI is a better reflection of large manufacturers and the 
Caixin PMI is a better reflection of smaller and often export-oriented producers. However, 
the index provider (Markit previously) disagreed with this characterization, and at least in 
terms of design -- both PMIs are designed to capture large manufacturers as well as SMEs. 
Empirically, the Caixin PMI outperformed the NBS PMI significantly in 2020H2 and 2024H1 
when Chinese exports were very strong, suggesting the Caixin PMI may cover more export-
oriented companies. 
• 
The NBS non-manufacturing PMI samples 4000 enterprises of different sizes in the 
construction and services sectors. The surveys ask 10 questions for 10 individual indices 
about production, new orders, input price, sales price, employment, business activity 
expectation, new export order, work backlog, inventory, and suppliers’ delivery time. The 
Caixin Services PMI sample covers more than 400 enterprises in the services sector. 
• 
Unlike the manufacturing PMIs which are conceptually identical, the NBS non-manufacturing 
PMI and the Caixin Services PMI are different—the official one is not just a services PMI but 
includes construction as well. It is therefore natural that these two indicators diverge more 
often than the manufacturing PMIs (Exhibit 12). But even if we look at the service sub-index 
of the official non-manufacturing PMI, it is still quite different from the Caixin Services PMI. 
The construction sub-index of the non-manufacturing PMI has a relatively weak relationship 
with the construction component of GDP. 
• 
The China Federation of Logistics and Purchasing (CFLP), which compiles the official PMIs, 
also publishes the emerging industries PMI (EPMI) each month. The EPMI is released around 
10 days earlier than the NBS and Caixin manufacturing PMIs, and could serve as a leading 
indicator for these PMI prints. The data series was first introduced in 2014, mostly covers the 
country’s “strategic emerging industries” (mostly high-tech manufacturing), and is released 
on the 20th day of each month if it’s not a public holiday. We caution that there are some 
caveats when using EPMI in forecasting, due to its differences with NBS and Caixin 
manufacturing PMIs on samples, survey dates and seasonal adjustment methodologies. 
Exhibit 11: NBS and Caixin manufacturing indices show similar trends, but occasionally send 
different signals 
China manufacturing PMIs 


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Source: NBS, Haver Analytics 
Exhibit 12: Only modest correlation between NBS and Caixin services sector surveys outside of 
the Covid period 
China services PMIs 
 
 
Source: NBS, Haver Analytics 
Exhibit 13: A comparison of NBS manufacturing PMI, Caixin manufacturing PMI and EPMI 
 


 33 / 158 
 
 
Source: NBS, Caixin, China Federation of Logistics & Purchasing, Goldman Sachs Global Investment 
Research 
Macro Importance 
• 
PMIs can be useful leading indicators to gauge sequential growth momentum. They are also 
useful in gauging upstream inflation, via their input price indices, and in tracking inventory 
cycles, via their inventory sub-indices. 
Compilation and Reporting 
• 
Both the NBS and the Caixin PMIs are compiled and summarized through the results of a 
monthly survey of enterprise purchasing managers. In reality, it is not necessarily the 
purchasing managers who are filling out the forms but other employees, especially those in 
the finance department. Statisticians send out questionnaires to firms every month to 
ascertain whether the situation in certain aspects of the business has improved, or if there is 
no change, or if it has deteriorated. Responses in each category are given the weights of 1.0, 
0.5 and 0, respectively. An index between 0 and 100 is then compiled for sub-indices such as 
production, new orders, employment, prices (separately for input and output prices), 
inventory (separately for raw materials and finished goods inventories), and suppliers’ 
delivery times. The overall manufacturing PMI is a weighted average of the new orders (30%), 
production (25%), employment (20%), suppliers’ delivery time (inverted, 15%), and raw 
materials inventory (10%) components in accordance to the weights used internationally. 
Data are then reported as a reading between 0-100 for the overall indicator and different 
categories, by adding the share of respondents indicating “improved” plus half the share 
indicating “no change”. 
• 
The Caixin services PMI and NBS non-manufacturing PMI do not have aggregate readings 
and only report readings for different components. The “Business Activity Index” is often 


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used as the headline reading. The Services Business Activity Index is to the services sector 
what the Manufacturing Output Index is to the manufacturing sector. 
• 
The PMI Composite index (also known as Composite Output index) is a weighted average of 
the Manufacturing PMI Output sub-index and the Services Business Activity Index (i.e., the 
services PMI). 
Interpretation of the Readings 
A reading of 100 means all respondents reported improvement. A reading of 0 means all 
respondents reported deterioration. In theory, the 50 level threshold is important because above this 
level more respondents are reporting expansion than contraction. However, diffusion indices only 
measure breadth of expansion or decline, not intensity, and in China’s case the 50 threshold has not 
consistently separated sequential expansion from contraction in IP or GDP, or necessarily marked a 
change in their second derivatives (accelerating/decelerating growth rates). 
GS Proprietary Activity Measures 
GS China Current Activity Indicator (GS China CAI) 
Source: Goldman Sachs Economics Research 
Availability: Monthly since January 2006 
Timing: The series is updated daily to incorporate new data releases 
Release: GS China Proprietary Indicators update 
Overview 
• 
The GS China Current Activity Indicator (China CAI) was created to provide an alternative 
measure with higher frequency and quality to identify shifts in the economic cycle. It 
attempts to encompass indicators from the main producing sectors of the economy – 
manufacturing, housing, and consumer – as well as the labor market. The China CAI is 
shown on a month-over-month annualized basis, after several rounds of adjustments by the 
GS global economics team to harmonize CAIs around the world and to deal with dramatic 
activity changes in response to the Covid pandemic (it is available on the GS research portal 
or on Bloomberg at ticker: GSCNCAI). The components of CAI include: industrial production, 
employment in composite PMI, Cheung Kong Graduate School of Business (CKGSB) Business 
Conditions Survey sales sub-index, the Caixin Services PMI, electricity consumption, 
automobile sales, the Caixin Manufacturing PMI output sub-index, real imports, real retail 
sales, floor space sold, floor space started, cement production, real exports, freight volume, 
passenger volumes, and floor space completed. 
• 
The CAI is calculated on a sequential basis. Statistically, our CAI is constructed as the first 
principal component of 16 standardized monthly economic indicators after seasonal 
adjustment, converted to GDP-equivalent terms through a regression of historical real GDP 
growth on this principal component. 
• 
We extend the CAI back to 2006, by backcasting a few series (mainly survey indicators) for 
which a complete history is not available. Other indicators on activity growth such as trade 


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flows and sector-level data all showed a higher amplitude of fluctuation than the official 
GDP data, so we adjusted for volatility when constructing the CAI. 
• 
Because the CAI methodology is designed to be a high-frequency proxy for GDP, distortions 
in GDP data (e.g. data that are consistently “too smooth”) can in theory affect the CAI. In 
particular, the mean of growth as measured by the CAI over the sample is effectively equal 
to that of GDP growth over the sample, by construction. 
Exhibit 14: Our China CAI components and weights 
 
 
Source: Goldman Sachs Global Investment Research 
Exhibit 15: Our China CAI has a close correlation with sequential GDP growth 
China Current Activity Indicator and GDP 
 


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Source: NBS, Goldman Sachs Global Investment Research 
GS China Inventory Tracker 
Source: Goldman Sachs Economics Research 
Availability: Quarterly since Q1 2007 
Timing: Preliminary reading available in the last month of the quarter; final reading available one 
month after quarter-end 
Release: GS China Proprietary Indicators update 
Overview 
• 
The GS China inventory tracker is based on six underlying inventory indicators, including 
commodities (i.e., iron ore, aluminum), PMI sub-indices (i.e., raw materials, finished goods), 
industrial enterprises finished goods inventory, and auto inventory. After data cleaning, we 
derive the first principal component, which explains 25% of the total variation of the six series, 
and then map it into percentage of GDP terms as our tracker for inventory changes. Our 
inventory tracker can be used to gauge the contribution of inventory changes to China’s 
GDP growth. 
• 
Our inventory tracker also indicates slowdowns in inventory build during the Global Financial 
Crisis (GFC), around 2015-16 (despite implausibly stable reported real GDP growth) when 
the government implemented “supply-side reforms” in upstream sectors like steel and coal, 
in early 2019 at the height of US-China trade war, and in late 2022 when the Covid “exit 
wave” caused significant supply chain disruptions in China. 
• 
We acknowledge our principal component analysis (PCA) approach to tracking inventory is 
only a proxy and the mapping into the inventory component of real GDP is not perfect. 
However, we think it provides a useful way to track an important part of the economy that is 
opaque and can generate large swings in quarterly growth. 


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Exhibit 16: Our inventory tracker can be used to gauge the contribution of inventory changes 
to China’s GDP growth 
Inventory change contribution to real GDP growth (qoq, non-ann) 
 
 
Source: NBS, Bloomberg, CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“China’s manufacturing PMIs: Which one should we look at and what are they telling 
us?” EM Macro Daily, 13 August 2013 
• 
“Taking stock of China activity: Updating our Current Activity Indicator”, 18 November 2015 
• 
“Tracking all over the world - Our New Global CAI”, Global Economics Analyst, 25 February 
2017 
• 
“China Manufacturing PMI - Still an important signal”, China Data Insights, 27 September 
2019 
• 
“China EPMI, a good leading indicator for manufacturing PMIs”, China Data Insights, 21 July 
2022 
• 
“Tracking China’s Inventory Cycle”, China Data Insights, 7 June 2023 
• 
“Why have post-reopening industrial profits been so weak?”, China Data Insights, 15 June 
2023 
• 
“Peeking into NBS' GDP revision practice”, China Data Insights, 31 August 2023 
• 
“Gauging China’s growth (again)”, Asia Economics Analyst, 7 September 2023 
• 
“A Closer Look at NBS 2022 GDP Revisions”, China Data Insights, 13 March 2024 


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Section III. Investment 
There are three sets of macro indicators related to investment: 
1. Fixed asset investment (FAI) and its breakdown by industry, nature of enterprises, work type 
and region, as well as funds designated for FAI and its breakdown by source; 
2. FAI project starts and under construction; 
3. Other investment-related data series, including infrastructure-related bond issuance, the 
investment values of large-scale projects approved by the National Development and 
Reform Commission (NDRC), excavator operating hours, major raw materials production, 
consumption and prices. 
Fixed Asset Investment 
Signal to noise ratio: ** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly from 1992, annual from 1980 
Type: Monthly: Year to date 
Timing: Typically around the 2nd /3rd week of the following month. In January, April, July and 
October, it is released with quarterly GDP data during a press conference around the 2nd /3rd week 
of the month. 
Publication: NBS monthly releases 
Overview 
Fixed asset investment (FAI) excluding rural households measures spending on durable assets that 
are used repeatedly in the production process. 
Compilation and Reporting 
According to the NBS, FAI data include investment expenses on equipment purchases, construction 
and installation, among others (e.g., land acquisition, old buildings), for projects on fixed assets (real 
estate development also included) with total planned investment of RMB5 million and over. 
The data are broken down into a number of categories. The following breakdowns are provided 
after 2004: 
• 
FAI undertaken by nature of enterprise 
1. State owned enterprises (SOEs): These are enterprises solely owned by the 
government. This category is also known as “State-Owned and State Holding 
Enterprises”, and includes all SOEs and enterprises over which the government has 
effective control, but excludes state sole proprietors that are part of “limited liability 


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companies (LLCs)”, based on the NBS definition. New PPP (Public Private Partnership) 
projects complicate the picture, as projects with 50% state share are accounted for as 
SOE investments, which overstates the true share of the state. PPP projects 
experienced rapid expansion in 2017, stagnated in 2018-22, and declined thereafter. 
2. Collectively owned enterprises (COEs): Another kind of publicly-owned enterprise 
which has become less common (as of 2023, FAI by COEs accounted for less than 1% 
of total FAI). 
3. Foreign-funded enterprises. 
4. Hong Kong, Macau and Taiwan-funded enterprises (note that foreign-funded 
enterprises and Hong Kong, Macau and Taiwan Funded Enterprises are two separate 
categories that do not overlap). 
5. Share-holding enterprises: Enterprises that have a share-holding structure. 
6. Private enterprises and individual businesses: Both are private businesses. Businesses 
run by one person and small private businesses that employ fewer than eight people 
are called “individual businesses”; larger businesses are called private enterprises. 
Private-sector FAI has remained weak in recent years despite fiscal stimulus 
implemented by the government. The private share of FAI increased from 31% in 
2004 to 64% in 2015, but has declined since then. 
7. Joint ownership enterprises: Typically enterprises set up by two or more 
independent firms. This was a popular form of enterprise in the earlier years of 
reform but is now much less common. 
8. Limited liability companies (LLCs): Including state sole proprietors and other limited 
liability companies. 
9. Others (e.g., miscellaneous projects which are not easy to be classified into the 
categories above). 
Exhibit 17: SOE and non-SOE investment showed significant divergence during 2022-23 
FAI growth by business ownership in China 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
• 
FAI by work type 
This set of data is released with a monthly frequency, and the main categories include construction 
and installation, equipment purchase and others. In 2023, construction and installation was the 
major work type under FAI, accounting for 71% of overall FAI. 
1. “Construction and installation” includes construction of buildings (materials and labor), and 
installation of machinery and equipment. Note that land purchase costs are not included 
here. 
2. “Equipment purchase” includes spending not only on new equipment, but also on old 
equipment. 
3. Other FAI primarily includes spending on land acquisition, old buildings, and management 
fees. 
• 
FAI by industry is available in terms of the primary, secondary and tertiary industries. 
Detailed breakdowns for specific industries are available from 2004. As is the case for IP by 
industry, FAI by a given industry is not investment in a particular type of project, but just 
FAI by companies in that industry. For analysis we typically aggregate the sectors into four 
major categories: manufacturing, infrastructure, property and others ( “others” mainly 
includes services and agriculture-related sectors). In 2023, FAI in these four sectoral 
categories accounted for around 33%, 36%, 15% and 26% of total FAI, respectively, compared 
to 30%, 31%, 22% and 19% in 2007. 
1. The share of manufacturing FAI has been close to infrastructure FAI over the past 
decade. Manufacturing investment is mostly carried out by private enterprises in 
China. Over the past few years, the solid growth of manufacturing investment was 
underpinned by POEs. 
2. Our estimates for infrastructure FAI are based on our GS definition, which includes 


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not only the three sectors under the NBS classification (i.e., transport, storage & 
postal service; water conservancy & environmental protection; and electricity, gas & 
water production and supply), but also four more industries that provide public 
goods mainly by the government sector (e.g., scientific research & polytechnic 
service; education; healthcare, social security & welfare; and culture, sport & 
entertainment). Most of the infrastructure investment in China was done by local 
governments (around 70% in 2023). Over the past two decades, the share of 
transportation and utilities in infrastructure investment has fallen from around 60% to 
around 40%. Recent policy communications suggest infrastructure spending will likely 
focus on both traditional and new infrastructure projects in coming years: the former 
may include water conservancy, emergency management and energy, while the 
latter could include 5G telecommunications, supercomputing, EV charging stations, 
ultra-high voltage power transmission, data centers, green capex and industrial 
parks. 
3. Property FAI, often regarded as very important due to its interconnection with local 
government finances, now has a much smaller share than manufacturing and 
infrastructure FAI. Since the unprecedented property downturn that started in 2021, 
the share of property FAI has declined significantly. Furthermore, the value-added 
share of property FAI is likely to be significantly lower than in other components of 
FAI, since a significant share of property FAI is in land transfers (which are non-
value-added and do not enter GDP accounting). Hence, the share of property 
investment in total gross fixed capital formation (GFCF) is notably lower than its 
share in total FAI (for more detail on property FAI see Section IV. Real Estate). On the 
back of the current property downturn, regulatory tightening in sectors such as 
education and internet that are dominated by private enterprises, and the ongoing 
fiscal stimulus, FAI growth has shown a notable divergence between infrastructure 
and property, and between SOEs and non-SOEs since 2022. Our research suggests 
that the weakness of private investment during 2022-23 was mostly due to the large 
contraction of property investment. 
Exhibit 18: Infrastructure and property investment diverged significantly in 2022-23 
FAI growth and breakdown by major component 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
• 
FAI by region: This set of data is available for the 31 provinces and many cities, on a 
monthly basis. The FAI share across provinces is largely in line with their GDP share. FAI 
breakdown by province can help estimate the difference in investment momentum across 
regions, especially when macro policies have an asymmetric impact on different regions.
[6] 
Exhibit 19: FAI in more-indebted regions underperformed that in coastal regions in 2022-24 
FAI in provinces with high debt pressure vs. others (seasonally adjusted) 
 
 
Source: Wind, Goldman Sachs Global Investment Research 
• 
Funds designated for FAI (this is sometimes referred to as FAI by source of funding, but we 


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view this as a misleading term because these data refer to the amount of funds that became 
available for FAI, which is not the same as the amount of FAI completed): 
1. State budget. 
2. Domestic loans from banks. 
3. Bonds: Bonds issued by corporates and financial institutions, including those by local 
government financing vehicles (LGFVs) and policy banks for FAI. 
4. Foreign capital. 
▪ 
Among which: Foreign direct investment (FDI). In practice this can include 
significant capital from “round-tripping” (see more on FDI in Section VI. 
External Sector). 
5. Self-raised funds. 
▪ 
Self-owned: This is intended to be mainly from retained earnings. In practice 
this likely includes a variety of funding channels including non-standard 
loans borrowed from financial institutions. This might be driven by 
regulatory requirements, such as those requiring a project to have a certain 
share of self-owned capital before it is qualified to borrow from banks. 
▪ 
Share issuance: Capital raised through public offerings. 
6. Others (e.g., crowd-funding and donations). 
Signal to Noise Ratio 
• 
There is considerable confusion and controversy over China’s FAI statistics, with different 
definitions and scope of coverage. 
• 
There are major differences between the definitions of FAI and of the investment concept 
(GFCF) in national accounts. 
1. The monthly FAI data are not reported on a value-added basis. That is, they do not 
reflect the incremental new capacity added to the capital stock, but just total 
nominal investment spending reported by companies and governments. FAI 
includes spending on assets that do not directly contribute to GDP (e.g., land 
acquisition and old equipment purchase), while GFCF does not. 
2. FAI includes a minimum project size cutoff (RMB5 million as of 2023) for non-
property investment (and all property investment), while GFCF does not. 
3. FAI does not include spending on intangible fixed assets such as computer software 
and IT investment, while GFCF does. 
• 
FAI data have a few measurement issues. 
1. Both the monthly and annual investment series are in nominal terms. The NBS did 
release an official FAI deflator on a quarterly basis previously, but suspended the 
data series in 2020. Besides, capturing price changes in this sector is difficult – 


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especially land prices. 
2. The monthly yoy FAI growth rate, which is monitored closely by investors, has 
significant measurement issues. For example, the classification for monthly reported 
FAI data changed materially a number of times in the past. This change has made 
the yoy growth rates not completely comparable over time. In addition, the official 
year-over-year growth rate series are not compatible with the official level series, 
mainly due to sample changes over the years. 
3. Over-reporting may be the major factor behind the high level of FAI growth, which 
has also been acknowledged by the government in the past. Problems include 
potential double-counting (e.g., of the same project by different regions) and 
misreporting (e.g., FAI data are based largely on questionnaire responses which do 
not always have to be backed by hard evidence). Given the statistical issues 
mentioned above, we suggest investors supplement FAI data with other indicators, 
such as capital goods imports, raw materials (e.g., steel and cement) production and 
apparent demand, and infrastructure-intensive bond issuance (e.g., local 
government special bond), to help form a better assessment of true investment 
growth momentum on a monthly basis. 
Macro Importance 
Despite all these caveats, the monthly FAI series remains very important in assessing policy risks, 
because policymakers do pay very close attention to it. The NDRC previously announced annual 
targets for FAI growth, and some local governments also set their local FAI growth targets, although 
these targets are not as binding as GDP growth targets. A significant rise in FAI growth rates, if 
accompanied by higher inflation, tends to lead to policy tightening and vice versa. 
Projects Started and Under Construction 
Source: National Bureau of Statistics 
Availability: Monthly from 2004 
Timing and publication: Same as FAI data 
Overview 
• 
The value of total planned investment in new projects and under construction reflects 
changes in the pipeline of investment projects. 
• 
Data are compiled together with FAI data and therefore many of the problems associated 
with FAI data also apply to these series. 
• 
“Projects started” data are often used as a leading indicator of investment activities. 
Policymakers and investors also monitor this series to assess likely future trends in fixed asset 
investment on a 1-2 year horizon. In reality, however, there is no stable lead-lag relationship 
between the two series. 
Compilation and Reporting 


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• 
FAI projects started and under construction are released in terms of value and number of 
projects. 
• 
Project started, planned (value): This refers to all projects planned to start during the 
reporting period (they may or may not be under construction at this stage but will be started 
before the reference period end). It excludes ongoing investment projects (including those 
suspended and restarted in this period). Previously, the NBS also released data on projects 
started and planned in unit terms, but this data series was suspended in early 2018. 
• 
Projects under construction (value): This contains ongoing investment projects and new 
starts. Previously, the NBS also released data on projects under construction in unit terms, 
but this data series was suspended in early 2018. 
• 
Total planned investment: Refers to FAI projects’ planned investment amount. Note these 
plans are not binding and therefore this is not a reliable indicator for future FAI. Availability 
of funding and subsequent changes in costs can have large impacts on actual investment. 
• 
The relationship between different series in this set of data is complicated because of 
regulatory approval issues. For example, sometimes local projects find it difficult or costly to 
obtain approval for a new project and therefore just report the new project as an extension 
of an existing project. 
Other Investment-related Data 
Other indicators related to investment include infrastructure-related bond issuance (especially local 
government special bonds and local government financing vehicle bonds; to be elaborated 
in Section X. Government Finance), large-scale projects approval by the National Development and 
Reform Commission (NDRC), excavator machinery operating hours, cement inventory, and steel 
demand, although each indicator has its own advantages and disadvantages. 
• 
The investment value of projects approved by the NDRC may reflect the central government’
s stronger determination to boost investment if it increases quickly in a short period of time, 
but its total amount in 2022 (RMB1.5 trillion; latest year-end data available) only accounted 
for 2.6% of total FAI. 
• 
Komatsu, a leading global manufacturer of construction machinery, provides data on the 
monthly average operating hours of excavators sold in China. It is updated on a monthly 
basis with a lag of around two weeks. This could help gauge the momentum of construction 
activity, but it is subject to distortions from changes in Komatsu’s market share in China 
(which has declined to 1% in 2023 from 15% in 2008) and not able to distinguish 
infrastructure construction from property construction. This data series is subject to 
significant seasonality. Some investors may also focus on sales of excavators and other 
construction machinery, but these durable capital goods have significant replacement cycles, 
which may distort their yoy sales growth in some years. 
• 
Steel and cement are crucial raw material inputs for construction activity, and there are 
several related indicators available from either official or private data sources, including steel 
and cement output, demand for construction-related steel (measured by production net of 
net exports and inventory changes), the cement inventory to storage capacity ratio, cement 


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production capacity utilization ratio, and steel and cement prices. 
Related GS Economics Publications 
• 
“China: Is credit losing its cyclical growth impact?”, Emerging Markets Macro Daily, 20 May 
2013 
• 
“China infrastructure investment: Is the high growth sustainable?”, Asia Economics Analyst, 
13 April 2017 
• 
“Fast or slow, old or new: A macro view on China's infrastructure investment”, Asia in Focus, 
15 April 2020 
• 
“China Green Capex: Renewable power investment and its impact on the economy”, Asia 
Economics Analyst, 16 January 2022 
• 
“China: Manufacturing sector to drive investment in 2022”, Asia in Focus, 22 February 2022 
• 
“China: Gauging the upside for infrastructure investment in 2022”, Asia in Focus, 28 March 
2022 
• 
“China: Tracking the strength and pace of infrastructure stimulus”, Asia in Focus, 12 May 
2022 
• 
“China: Assessing the implications of local governments’ 2023 targets”, Asia in Focus, 17 
February 2023 
• 
“China: Beijing’s Balancing Act between Infrastructure Stimulus and LGFV Deleveraging”, Asia 
in Focus, 6 March 2024 
• 
“The shifting role of private investment in China”, China Data Insights, 25 April 2024 
Section IV. Real Estate 
There are five major sets of macro indicators related to the real estate sector: 
1. Real estate investment and its breakdown. 
2. Land transaction (sales/purchases) by different data source. 
3. Construction data -- housing starts, property under construction, and completions. 
4. Home sales and inventory. 
5. Property and land sales prices. 
We have also constructed GS proprietary measures related to the real estate sector, including 
estimates of the property sector impact on GDP growth and measures of the regulatory stance (a 
property policy relative tightness index as well as a 24-city housing policy stance indicator). 
Real Estate Investment 
Signal to noise ratio: ** 


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Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Monthly from 2004, annual from 2003 
Overview 
• 
There are two sets of real estate investment data. The most widely tracked one is the total 
FAI conducted by real estate developers (also known as “property investment” or “property 
FAI”). The data are surveyed and released together with other real estate indicators (e.g., 
new home starts, completions, and sales), as well as other major economic activity data (e.g., 
industrial production, headline FAI and retail sales), by the NBS on a monthly basis. They 
cover land purchases, equipment purchases and construction activities for residential, 
commercial and office buildings. In 2023, around 90% of real estate investment was related 
to land purchases and construction. The breakdown details of property investment by 
ownership of enterprises (annual), work type (monthly), and sources of funding (monthly) are 
available. Like headline FAI data, the NBS only releases the year-to-date level and growth 
data for real estate investment at a monthly frequency, while single-month real estate 
investment growth rates require additional estimates given a specific base year. 
• 
The other data on real estate investment is the FAI by industry – real estate (as a composite 
industry in the tertiary sector). The definition of this dataset is different because it 
supposedly includes real estate related investment by all types of enterprises, not just 
property developers. However, the general trends of these two indicators are similar. 
Exhibit 20: Around 90% of property FAI was related to land purchases and construction in 
recent years 
Property FAI breakdown by key procedure (based on 2023 data) 
 
Source: NBS, Wind, Goldman Sachs Global Investment Research 
Exhibit 21: Growth in property investment and FAI growth for the real estate industry share 
similar trends 


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Property investment vs. FAI in the real estate industry 
 
Source: NBS, Wind, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
Both series of real estate investment have similar drawbacks as other FAI data series. For 
example, the official year-over-year growth rate series is not entirely compatible with the 
official level series, mainly due to sample changes over the years (for details on other 
statistical issues with FAI data, see Section III. Investment). Furthermore, these two indicators 
do not capture rural properties without property rights. Rural properties without property 
rights are only for self-use, and transactions with buyers outside the community are deemed 
illegal. As such, there is a lack of data for rural properties in China. 
• 
The NBS made occasional revisions to the definition of private investment, which may also 
affect the data quality. For example, in March 2024 the NBS revised the definition by 
excluding land redevelopment related spending from real estate investment. Although the 
NBS flagged that the reported yoy real estate investment growth estimates are on a 
comparable basis, it did not release the revised historical level data series under the new 
definition. 
Macro Importance 
Despite the drawbacks, real estate investment is still widely tracked by investors, because: (1) real 
estate FAI is informative in terms of gauging the strength of the real estate sector; (2) real estate FAI 
(reported by all property developers) accounted for 15% of the overall FAI in China in 2023 (vs. its 
previous peak of 23% in 2004), and thus is still an important component of the overall FAI. 
Land Transactions 
Signal to noise ratio: ** 


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Macro importance: *** 
Source: National Bureau of Statistics (NBS), Ministry of Finance (MOF), China Real Estate Index 
System (CREIS), Soufun, Wind 
Availability: NBS and MOF: Monthly and annual; CREIS/Soufun and Wind: Weekly and monthly 
Timing: NBS and MOF: Collected monthly with complete enumeration typically around the 3rd week 
of the following month (January-February data are combined for release in mid-March by official 
sources including NBS and MOF). 
Overview 
There are several useful sources of data on land transactions and development. 
• 
Land transaction area (NBS): This is defined as land area (squared meters) purchased by 
real estate developers during the reporting time period. As all land in China is owned by the 
state, buyers can only purchase the right to use land instead of ownership. These rights 
range from 20-70 years, hinging on how the land will be used (e.g., for the construction of 
industrial, commercial, or residential buildings). In the latest version of Civil Code of the 
People's Republic of China, effective in January 2021, the land-use right will be automatically 
renewed when the right leases are up.
[7] 
• 
Land transaction value (NBS): This refers to the final amount (value) of the land-use right 
transacted in both the primary and secondary markets by real estate developers, based on 
the flows of their actual payment. Land purchase value is on a comparable basis with land 
purchase area in terms of statistical coverage, so we can derive the average price of land 
purchases from these two indicators. However, this is not precisely the same as a land price 
index because the quality of land transacted may not be comparable. Land purchase area 
and value data used to be available at the city level for 40 major cities, released by the NBS, 
but these city-level data series were suspended in January 2019. The NBS also suspended 
the release of land transaction area and value data series in January 2023. 
• 
Developer land purchase value (NBS): These refer to the total amount of land transactions, 
in value terms, based on the contracts signed by property developers in the primary and 
secondary markets. "Developer land purchase value” is much larger than “land transaction 
value” above because it includes various taxes, fees and spending associated with land 
compensation, land preparation, and land management. This series is recorded at the time 
of disbursement by property developers and may lag the transaction time somewhat. 
• 
Government revenue from land sales (Ministry of Finance): This is reported by the 
Ministry of Finance (MOF) on a monthly basis – along with other government income and 
expenditure data – and is a major component of government-managed fund revenue. The 
MOF series is slightly different in scope from the land-use right transfer value reported by 
the NBS and third-party data vendors. For instance, the former series also includes income 
from renting land by land administrations. Land sales remain a very important source of 
income/expenditure for local governments. Refer to Section X. Government Finance for 
more details. 


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• 
Land transaction area and value by city (CREIS/Soufun/Wind): CREIS/Soufun
[8] collect and 
summarize the data (data access requires a subscription) on government revenue from land 
transactions in more than 300 cities in China (based on 302 cities and is often referred to as 
“CREIS 300-city revenue from land sales”, compiled and tracked by our GS China property 
research team). The definition of the data is broader than the NBS land purchase 
value/volume data mentioned above, given that these data cover land purchases by 
different types of enterprises, not just developers. Wind also compiles a sample of 100 cities 
to track land transaction value and area, on both a weekly and monthly basis. The Wind data 
have breakdowns by city tier (i.e., Tier-1, Tier-2 and Tier-3). The benefit of 
CREIS/Soufun/Wind data is that they are more timely (e.g., daily for the original 
CREIS/Soufun data and weekly for the Wind data) and tend to be a leading indicator (e.g., 
CREIS series on land transactions are recorded when transactions occur, NBS land purchases 
are recorded when investment is completed, and MOF government revenue from land sales 
are recorded when the payment is settled). The drawback is that CREIS/Soufun/Wind data 
aggregate a large number of cities but do not include all transactions nationwide. 
• 
The discrepancies between these land transaction measures mainly come from different 
timings to register land sales (e.g., registered when contracts are signed or when funds are 
paid), different groups of land buyers captured by the sample (property developers only or 
all types of buyers), and some additions and/or deductions before the purchase fees are 
transferred to the MOF's account for land sales revenue. 
Exhibit 22: A summary of various land sales measures in China 
 


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Source: NBS, CREIS, Wind, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
Among all the land sales indicators mentioned above, we tend to rely more on the land 
transaction volume and value series from CREIS. The data coverage of land purchase value 
and volume reported by property developers is much narrower, and the NBS land purchase 
data lag the CREIS data series slightly as discussed above. 
• 
The introduction of a centralized land auction mechanism for major cities in 2021 increased 
the transparency of land supply, reduced panic land purchases among property developers, 
and significantly lowered land auction premia. However, as land supply needs to be 
concentrated in several batches in a year under the new regime, occasional shocks (e.g., 
Covid lockdowns during 2020-22, natural disasters) would affect the timing of land actions 
and thus cause near-term distortions to yoy growth in land sales revenue. 
Macro Importance 
The NBS developer land purchase value typically contributed to around 30% of real estate investment 
in recent years. However, the share of land sales revenue in total (gross) government revenue 


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declined to below 20% in 2023 from its recent peak of 30% in 2021 (the peak share was even higher 
for some local governments with high reliance on land finance). 
Land sales tend to lead property construction activity and thus it is important to track land sales. As 
mentioned above, land sales are also important sources of income/expenditure to local 
governments, and therefore can impact the financing need/spending capability of local 
governments. In addition, land prices are important for future property price trends and the financial 
health of property developers. 
Housing Starts, Under Construction and Completions 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly and annual 
Timing: Typically around the 3
rd week of the following month (together with the release of the NBS 
monthly activity data) 
Overview 
Floor space started: This measure refers to the total floor space of buildings that are newly started 
within the reporting period. Only housing newly started by real estate developers and enterprises 
within the reference timeframe is counted. Continued building activities carried over from the 
preceding reporting period are excluded. Construction areas suspended in previous periods and 
restarted in the reporting period are included, but construction areas suspended and restarted 
within the reporting period are not. 
Floor space under construction: This refers to the total floor space of buildings in different 
construction phases in the reference period, including floor space of newly started buildings during 
the reference period, floor space of construction extended from the previous period to the reference 
period, floor space of construction suspended or postponed in the previous period and resumed in 
the reference period, floor space of construction completed in the reference period, and floor space 
of constructions started and then suspended or postponed both in the reference period. Floor space 
under construction has a strong correlation with property FAI. 
Floor space completed: This refers to the total floor space that fulfills all the completion 
requirements of property developers and is ready for occupancy. 
Other housing related construction indicators: 
• 
The three indicators above are all reported by property developers, and released monthly 
together with the real estate FAI indicator. There are other indicators related to broader 
property activity: “housing under construction” and “housing completion”, for example, 
cover not just commercial housing but also other types of housing constructed by all types 
of enterprises. 


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• 
The government also publishes data on social housing. There are annual data on the total 
number of flats planned to be started, as well as the total number of flats completed. In 
recent years, these data points were mostly unveiled by the Ministry of Housing and Urban-
Rural Development on ad hoc occasions. Although social housing related data are not quite 
as important as commercial housing related data, and the former's data quality could be 
lower than the latter, construction of social housing still drives upstream/downstream 
industries (such as construction materials) and thus is useful to track. Social housing units 
could also be converted from commercial housing — during the property down-cycle in 
2014-2015 and also in recent years, this was one inventory destocking measure adopted by 
many local governments. More recently, the PBOC announced in May 2024 a relending 
program encouraging local SOEs to purchase completed but unsold properties and to turn 
them into social housing to destock existing property inventories. 
Signal to Noise Ratio 
The survey target of the first three indicators above includes only property developers, so these data 
cover only commercial floor spaces constructed by developers. Properties built by other corporates 
and institutions (often as a form of welfare, e.g. employee housing by SOEs) which are not for sale 
on the market are not included. Rural properties without property rights are also not captured. 
Moreover, the series for floor space under construction, completed, and started are not consistent 
with each other, and researchers have different views on which indicator is more reliable. In theory, 
new home starts should lead completions by 2-3 years, but completions ran persistently and 
significantly below new starts from 2000 to 2022, and the two series even went in opposite 
directions in 2018 and 2021. On the back of the prolonged property downturn since mid-2021, 
increased developer funding stress and higher policy priority to secure the delivery of pre-sold new 
homes boosted completions relative to new starts dramatically in 2022-23. 
Macro Importance 
The floor space started, under construction and completion series are all widely tracked by investors 
and researchers. Historically, floor space started has lagged property sales (given the majority of 
property sales are pre-sales) and used to lead demand in other sectors such as metals/cement, 
though this may change in the future as government policy discourages pre-sales. Floor space 
completed, on the other hand, can be informative on the future demand of moving-in related items 
such as furniture, home appliances, and interior design materials. 
Exhibit 23: Property starts and completions trends can diverge from each other 
Property-related activity (seasonally adjusted) 
 


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Source: NBS, Goldman Sachs Global Investment Research 
Home Sales 
Signal to noise ratio: **** 
Macro importance: **** 
Source: National Bureau of Statistics, Wind, CREIS, Goldman Sachs Economics Research 
Availability: Daily, monthly and annual 
Timing: NBS monthly home sales: Typically around the 3rd week of the following month (together 
with the release of the NBS monthly activity data) 
Overview 
Floor area sales and total value sales of commercial buildings: These two indicators measure the 
sales volume and value of residential buildings, offices, and commercial buildings in the primary 
property market. They are released by the NBS together with new starts, under construction, and 
completion data mentioned in Section III. Investment. 
Due to China's presales system, most sales are presales of units that are still under construction vs. 
sales of completed units. In 2023, 82%/77% of the sales value/volume for all commercial property 
transactions was presales, lower than their peaks in mid-2021 (91%/89%) but well above their pre-
GFC levels (73%/67% in 2007). Revenue from presales is therefore a major source of funding for 
Chinese property developers. 
Wind 30-city daily property transaction volume in the primary market: According to Wind 
definition, its 30-city sample of property sales in the primary (new) market is actually composed of 
32 cities, including 4 Tier-1 cities, 14 Tier-2 cities and 14 Tier-3/4 cities. However, based on a 
bottom-up estimation using city-level data in recent years, we find that the series from Wind include 
21 cities only (4 Tier-1 cities, 8 Tier-2 cities and 9 Tier-3/4 cities) currently, as property sales data for 


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several cities (e.g., Tianjin and Nanchang) became unavailable in recent years.
[9] Our bottom-up 
estimates using property sales data from 21 cities match closely the headline number of Wind 30-
city property transaction volume in recent years. The original source is various local housing bureaus. 
Other third-party data vendors, such as CREIS/Soufun, also have their own home transaction 
tracking for different city samples. 
Wind 19-city daily property transaction volume in the secondary market (compiled by Goldman 
Sachs Economics Research): Our tracker is built on city-level daily data for the secondary (existing) 
property market transaction volume which is compiled by Wind and originally released by local 
housing authorities. Our sample covers 19 cities, including 2 Tier-1 cities, 7 Tier-2 cities, and 10 Tier-
3/4 cities. 
As China's housing market evolves, the secondary market becomes more important, especially in 
top-tier cities where land supply and new residential buildings are more limited. Secondary market 
transactions are important in tracking price trends and property market sentiment, but are less 
important in terms of GDP contributions. 
Real estate investment, new starts, under construction, completions, and sales data are also available 
at the province level and for 40 major cities in China, reported by local housing bureaus and 
collected by the NBS on a monthly basis. However, there are many missing data points in city level 
indicators, and the NBS suspended the release of 40-city property activity indicators in January 2019. 
According to the 2020 data (latest data available), lower-tier (Tier-3/4) cities accounted for 66% of 
nationwide new home sales volume, followed by Tier-2 (31%) and Tier-1 cities (3%). In value terms, 
the share of Tier-1, Tier-2, and lower-tier cities was 11%, 40% and 49%, respectively. Combining data 
from the NBS and private sources and based on some reasonable assumptions, we estimate a 
breakdown of new home sales by city tier. 
Exhibit 24: Property activity declined significantly beginning in 2021 
Property-related activity (seasonally adjusted) 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
Property transactions data are generally reliable, especially when compared with new starts 
and completions data. But people sometimes under-report the transaction value to reduce 
the transaction tax, though this mostly occurs in the secondary market. 
• 
The year-over-year growth rates and the levels reported in the NBS property data used to 
be consistent with each other until 2023. In May 2023, the NBS revised down the 
comparison base when reporting yoy property activity growth data, especially for new home 
sales, to adjust for false sales data.
[10] The practice continued through the remainder of 2023 
and resulted in a meaningful divergence between the officially-reported level data and 
growth rate data. However, the NBS has not released the revised level data for previous 
years. We adjust the property-related activity data series from 2023 in our analysis to take 
into account the revisions. We estimate new home sales volume in 2022 has been revised 
down by around 10%, especially for April-May 2022 (amid the Covid-related Shanghai 
lockdown). 
Macro Importance 
Property transactions are important to track for several reasons. Most directly, they serve as an 
indicator of housing demand. The fluctuations of property sales impact housing construction 
activities, and also the profitability of property developers. In addition, property sales are correlated 
with a number of other industries such as furniture, interior design and real estate agencies (which is 
linked to real estate related services in GDP). 
Home Inventory 
Signal to noise ratio: ** 
Macro importance: *** 
Source: National Bureau of Statistics, local housing bureaus, CREIS 
Availability: Weekly, monthly and annual 
Overview 
Housing vacant area: This data refer to completed but unsold floor area. It is published monthly by 
the NBS along with other property indicators and has a breakdown by different types of properties 
(residential, office building, and other commercial real estate). By province data are also available, 
but only at an annual frequency. Note that due to China's presales system, this series only covers a 
fraction of total new home inventory (i.e., all floor areas that are “saleable” but unsold) because the 
vast majority of the new home inventory is uncompleted. 
Inventory months: Other common (and more widely used) measures of housing inventory include 
the ratio of total saleable gross floor area divided by monthly floor area sold, which measures the 
number of months needed to digest inventory. There is no official data on this, but total saleable 
gross floor areas are reported in major cities in China by the local housing bureaus, compiled by 


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third-party data vendors including CREIS. The CREIS inventory months data series, regularly tracked 
by our China property research team, is available on both a weekly and monthly basis. 
There are no vacancy statistics on properties sold but not inhabited, which are sometimes referred to 
as the “shadow inventory” in the Chinese property market. There was a report by the Southwestern 
University of Finance and Economics in 2018 based on 2011-17 Chinese household financial surveys 
suggesting around 21.4% of urban housing apartments were left vacant in 2017 (vs. 18.4% in 2011).
[11] 
Signal to Noise Ratio 
Inventory data tend to be noisy. Total saleable gross floor area data can be patchy, with missing 
values within the series. Also, there is no nationwide data on inventory months, because only major 
cities report total saleable gross floor area data. 
Macro Importance 
Despite challenges in interpretation, inventory data are very important to gauge the cycle of the 
property market, and thus can be indicative of future housing price trends. After significant inventory 
destocking, housing prices may face upward pressure, and vice versa. 
Exhibit 25: Housing inventory months trended higher in 2022-24 despite increased easing 
efforts 
Inventory months across city tiers 
 
Source: CREIS, Goldman Sachs Global Investment Research 
Property Price Measures 
Signal to noise ratio: ** 
Macro importance: **** 
Source: National Bureau of Statistics, CREIS/Soufun, Centaline, Beike, Zhuge 


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Availability: NBS: Monthly from 2005, quarterly from 1998 
CREIS/Soufun: Monthly from June 2010 
Other third-party data vendors: Monthly 
Overview 
There are two main sources of property indices for major cities, from the official (NBS) and private 
sources (e.g., CREIS, Centaline, Beike). “Properties” refer to commercial buildings built to be sold in 
the market, including both residential and non-residential properties, newly constructed properties, 
and second-hand properties. 
Exhibit 26: A comparison of home price measures for the primary and secondary markets 
 


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Source: NBS, Beike, CREIS, Wind, Zhuge, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
These price indices generally follow a well-designed methodology, and most indices control 
to varying degrees for the impact of quality, though their original data sources differ 
somewhat. For some of the indices such as the NBS 70-city property price index, given that 
there is a higher degree of estimation involved in compiling the index and pressures on local 


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governments to control property prices (especially new home prices), there are market 
concerns that the indices are over-smoothed, showing smaller fluctuations in prices than 
actual property price changes. For example, the rise in property prices may be hidden when 
developers are forced to sell properties at prices below the market equilibrium because of 
local government pressures, and the excess demand leads to alternative expenses such as 
requirements for property purchasers to deposit funds well in advance to be able to 
purchase the property. During downturns, the decline in property prices may be disguised 
by free parking spots and extensive internal renovations offered by developers in lieu of 
price cuts. These distortions are typically more relevant for new properties. 
• 
Price indices on second-hand properties are therefore often a useful reference when they 
diverge from primary market property prices. On secondary-market home prices, besides 
the NBS 70-city secondary home price index, some third-party data vendors such as 
Centaline, Zhuge, and Beike (mostly real estate agents, information platforms and consulting 
firms) also have their own measures. Home price data from private sources usually have a 
much shorter time series than the NBS series, but they tend to be released earlier than the 
official indices. In previous years, secondary home prices have also been affected by many 
non-market-based policy interventions such as government guided prices and reference 
prices (related to tax payments and mortgage borrowing for secondary home transactions). 
We also note some third-party data vendors such as Beike suspended their release of 
secondary home prices in late 2023 when secondary home prices showed notable declines. 
Exhibit 27: “Tier 1 cities” have seen more pronounced price appreciation in boom periods, 
while home price declines were broadly based across city tiers in 2022-24 
Average house price in the primary market 
 
Source: NBS, Goldman Sachs Global Investment Research 
Exhibit 28: Different home price measures for the secondary market shared similar trends in 
2022-24 


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Property price measures for the secondary market 
 
Source: NBS, Wind, Centaline, Beike, Zhuge, Goldman Sachs Global Investment Research 
Macro Importance 
Home prices could drive economic growth, matter for the risk spillover to the banking system 
directly (through mortgage loans and property developer loans) and indirectly (through other types 
of loans collateralized with real estate properties), and affect household consumption (through 
wealth effects, moving-in related purchases, and consumer confidence channels). Moreover, NBS 
new home prices have been set by policymakers as one determinant for city-level mortgage rate 
adjustments. 
Compilation and Reporting 
When compiling the Property Price Index, the statistical authorities in China try to control for 
differences in the quality of properties. They consider the features of the property and attempt to 
compare prices for comparable properties. Factors such as location, structure, and type of property 
are all taken into consideration to make price comparisons. All data sources face significant 
challenges for compiling a portfolio that is relatively stable for pricing tracking, implying no perfect 
home price measure. For example, the CREIS/Soufun property index is compiled by taking the 
weighted (by floor area) average of indices of 100 underlying cities in the reporting period. It 
includes the price changes for both commercial residential buildings and social housing. Data inputs 
include information collected through field visits and corporate surveys, from real estate 
intermediaries, and based on information provided by local governments. 
Other Issues 
• 
The NBS changed the methodology for the Property Price Index in 2011. Property sales 
prices were split into primary and secondary housing price indices. The index initially 
covered 35 major cities and was expanded to 70 cities in 2005. Rural areas are not covered. 
Data are collected via a mixture of reporting forms from real estate companies and site visits 


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by NBS staff. In January 2018, the NBS suspended the release of the “new residential 
property (including social housing) price index” series. Since then, it has only released the 
“new residential commodity property (excluding social housing) price index” series. The 
patterns of these two data series are similar given the small share of social housing in total 
housing. 
• 
The divergence of property price trends among different city tiers in China reflects 
differences in housing market fundamentals. Top-tier cities face more supply restrictions and 
resilient demand, and thus upward pressures on housing prices are the strongest. Lower-tier 
cities tend to have diverse circumstances but on average face less restrictive supply and 
weaker demand, and thus housing price growth tends to be slower and/or prices tend to fall 
more in property market down-cycles. 
• 
The table below shows the NBS classification for the 70 large and medium-sized cities by 
tier. Although there is widespread agreement on the definition of Tier-1 cities (Beijing, 
Shanghai, Guangzhou, and Shenzhen), categorization beyond that point is not always 
consistent among different sources, particularly for lower-tier cities. 
Exhibit 29: Most cities in the NBS 70-city property price dataset are in Tiers 2 and 3 
 
Source: NBS, Goldman Sachs Global Investment Research 
Land Price Indices 
Signal to noise ratio: ** 
Macro importance: ** 
Source: NBS, Ministry of Land and Resources; CREIS, Wind, academic research 
Availability: NBS average land transaction price: monthly from January-February 2004 to December 
2022; Ministry of Natural Resources (MNR) land data: quarterly from Q1 2008 to Q3 2021 
Wharton/NUS/Tsinghua Chinese Residential Land Price Indices (CRLPI): quarterly from Q1 2004 to 


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Q1 2017 
Wind average land sales price: weekly and monthly from January 2008 
GS 300+ city residential land price tracker: monthly from January 2008 
Overview 
There are multiple sources of land price data. Unfortunately many of them have been discontinued. 
NBS average land transaction prices: These are estimated based on the NBS real estate developers 
land transaction value and area data series (as elaborated in the “Land Transactions” sub-section). 
The implied land price data series can be distorted by the mix-shift in land transactions as the land 
purchased in different periods may not be identical in terms of location and quality. This data series 
has been no longer available since January 2023 when the NBS suspended the release of land 
transaction area and value data. 
Ministry of Natural Resources (MNR) land price data: This indicator monitors the average market 
land price in 105 major Chinese cities on a quarterly basis. The data are categorized by land prices 
for residential, commercial and service, and industrial purposes. In May 2002, the Ministry of Land 
and Resources (the predecessor of the MNR) permitted the transfer of state-owned land-use right 
mainly by bidding, auction and quotation. In January 2022 when Q4 2021 data were supposed to be 
released, the MNR suspended this data series. 
Wharton/NUS/Tsinghua Chinese Residential Land Price Indices (CRLPI): This indicator tracks 
national land price growth in real (CPI-deflated) constant quality terms based on data from 35 cities 
in China on a quarterly basis.
[12] The provider also reports region-/city-level land price indices on a 
semi-annual/annual basis. While technically this series may be preferable, this series has not been 
updated since Q1 2017. 
Wind 100-city land transaction price data: These are estimated based on the Wind land 
transaction value and area data series. The breakdown of Wind 100-city land transaction by city tier 
is also available. 
GS 300+ city Residential Land Price Tracker: The GS property sector equity research team has 
aggregated land base prices, transacted prices, and land price premiums in 302 major cities based 
on data from the China Real Estate Index System (CREIS). The average headline series is then 
grouped into 3 city tiers. 
Signal to Noise Ratio 
As discussed above, there are no nationwide data on overall land prices. We tend to rely more on 
our GS 300-city residential land price tracker (based on the same sample that we use to estimate the 
“300-city land transactions” measure, as previously elaborated in the "Land Transactions" subsection) 
given it is more timely and has a relatively wide coverage compared with other land price indices. 
Similar to property price indices, land price indices ideally should adjust for quality differences. The 
Wharton/NUS/Tsinghua land price index is adjusted for quality differences, but not the 300-city 
residential land price series. The average price of land sold is subject to policy distortions similar to 
property-related policy distortions. Facing pressures to control land prices, governments often 
restrict or suspend the supply of premium land (or properties) relative to non-premium land in order 


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to lower the average selling price (total land value divided by total land area). As a result, it is 
conceptually better to look at the quality-adjusted data, though adjusting for quality differences is a 
difficult job that cannot be done without significant effort by specialists (and it is not always clear 
how much effort has been made). 
Macro Importance 
Land price inflation is important because it is a main factor behind the input cost of property 
developers, and thus also affects housing price trends. Given land sales revenue is an important 
financing source for local governments, land price fluctuations will also impact the financing needs 
of local governments. 
Exhibit 30: Land price increase over the past decades was mainly led by Tier 1 and 2 cities 
100-city average land transaction price by city tier (12mma) 
 
Source: Wind, Goldman Sachs Global Investment Research 
GS Proprietary Indicators Related to the Real Estate Sector 
Signal to noise ratio: *** 
Macro importance: **** 
Source: Goldman Sachs Economics Research 
Availability: GS China property impact on GDP growth: annual; 
GS 24-city housing policy stance indicator: monthly; 
GS property policy relative tightness index: daily. 
Overview 
GS China property impact on GDP growth: We decompose the property sector's contribution to 


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China's yoy GDP growth into five major channels, i.e., construction, real estate services, upstream 
effects (mainly through commodities demand), consumption, and fiscal channels. These estimates 
are based on a series of property activity data – including new home starts, sales, completions, 
under construction, property FAI, land sales, and average new home sales price – and their 
correlations with GDP growth in specific areas. By aggregating these channels, we estimate China's 
property sector impact on GDP growth on an annual basis, and project its future path based on in-
house forecasts for major property activity indicators. 
Exhibit 31: The property sector has turned to a growth drag since 2022 
Housing contribution to yoy GDP growth 
 
Source: Haver Analytics, Goldman Sachs Global Investment Research 
GS 24-city housing policy stance indicator: We use quantitative measures of housing policy in 24 
large cities across five policy dimensions (i.e., home purchase restrictions, down-payment ratios, 
mortgage rate fluctuations around benchmark interest rates, mortgage restrictions, and sales 
restrictions), and then average them to create our housing policy stance indicator. This is also an 
input of our proprietary China domestic macro policy proxy. 
Exhibit 32: Our 24-city housing policy stance indicator suggests the 2022-24 housing easing in 
large cities has exceeded previous cycles 
Housing component of GS China domestic macro policy proxy 


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Source: CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research 
GS property policy relative tightness index: This proprietary indicator measures the relative 
tightness of property policies in over 100 cities from the following aspects: 1) demand: purchase 
restrictions (household registration, social welfare contribution, etc.), credit restrictions (mortgage 
rate, down payment), sales restrictions; 2) supply: caps on selling prices, presales restrictions, land 
transaction tax, etc.; 3) others: property speculation, land supply. Original data sources include 
government announcements, media reports, and industry association data. 
Exhibit 33: Our city-level property relative tightness index has shown almost no major local 
housing tightening policies in cities that we track since December 2021 
Property policy relative tightness index: relative tightening share 
 
Source: Local governments, Songfang.com, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 


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As our tracking for China property impact on GDP growth is built mainly on NBS property activity 
indicators, concerns around the quality of underlying data may apply to this proprietary indicator. 
The GS property policy relative tightness index and 24-city housing policy stance indicator are based 
on a limited size of city sample, which may not always be representative. Besides, during the period 
of outright housing easing, the GS property policy relative tightness index (which is a diffusion index) 
may remain at or close to zero, failing to capture the sequential change in the magnitude of housing 
easing. 
Macro Importance 
The property sector has been the largest single sector in the Chinese economy for many years, and 
its ups and downs have significant implications for China's headline GDP growth. Our proprietary 
indicators track the sequential change in housing policy stance and gauge the growth impact of the 
property sector in a timely and comprehensive manner. 
Related GS Economics Publications 
• 
“How China's property policy tightening lowered sales and prices”, Asia Economics Analyst, 
26 September 2017 
• 
“Tracking residential housing’s impulse to Chinese growth”, Asia Economics Analyst, 27 
March 2018 
• 
“China property policy: capturing the big picture from localized measures”, Asia Economics 
Analyst, 29 November 2020 
• 
“Q&A on Evergrande’s macro implications”, Asia in Focus, 24 September 2021 
• 
“Credit supply holds the key to China housing outlook in 2022”, Asia Economics Analyst, 11 
October 2021 
• 
“How big is China's property sector?”, China Data Insights, 11 October 2021 
• 
“Lessons from Japan: Credit Tightening in the Property Market”, Asia in Focus, 29 November 
2021 
• 
“Demystifying the discrepancy in different land sales measures”, China Data Insights, 14 April 
2022 
• 
“Understanding the recent rise and fall of high-frequency property sales data”, China Data 
Insights, 14 July 2022 
• 
“China: 'L-shaped' Property Sector Recovery Ahead without a Quick Fix”, Asia Economics 
Analyst, 11 June 2023 
• 
“Understanding differences in China’s home price measures”, China Data Insights, 9 July 
2023 
• 
“Q&A on China's property downturn and its implications”, Asia Economics Analyst, 23 
August 2023 
• 
“China: Tracking the impact of ongoing housing easing”, Asia in Focus, 8 October 2023 


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• 
“Comparing China and US Housing Downturns: Different Fiscal Backdrop, Same Need to 
Prevent Spillovers”, Asia Economics Analyst, 12 February 2024 
• 
“A Closer Look at NBS 2022 GDP Revisions”, China Data Insights, 13 March 2024 
• 
“China: Housing easing underway, but no signs yet of game-changing measures”, 14 April 
2024 
Section V. Consumption 
There are four main sources of consumption-related data: 
1. Retail sales reported by the NBS: Compiled using a combination of administrative reporting 
and sampling. 
2. Household Income and Expenditure Survey: Compiled using sample surveys. 
3. Household consumption (in GDP by expenditure): Compiled using mainly the two series 
mentioned above. 
4. Retail sales of 100 (more recently 50) major offline retailers reported by the China National 
Commercial Information Center (CNCIC). 
There are alternative micro data that can be collated to give a partial picture as well, such as auto 
sales, tourism revenue during long holidays and parcel volumes. 
Exhibit 34: A comparison of three different sources of consumption data 


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Source: MOFCOM, NBS, Goldman Sachs Global Investment Research 
Retail Sales of Consumer Goods 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly from January 1990, annual from 1952 
Timing: Typically around the middle of the following month. In January, April, July, and October, it is 
released with GDP data around 3 weeks after the end of the quarter. 
Overview 
• 
Total Retail Sales of Consumer Goods measure goods and restaurant services sold at the 
retail level (as opposed to wholesale), including both online and offline sales. These goods 
and restaurant services may be purchased by households, firms or the government. 
• 
A separate indicator named “Online Retail Sales of Consumer Goods” refers to goods 
transacted over online platforms. 
Signal to Noise Ratio 


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• 
Before the Covid pandemic, the main issue with retail sales data was that growth appeared 
overly smooth. We suspect the problem is caused by the fact that sales by companies below 
the minimum threshold are compiled using sample surveys, which involve a significant 
degree of discretion, whereas sales by companies above the minimum threshold are 
compiled using census surveys. Reported retail sales growth was also higher relative to the 
growth rate of the economy for many years, even after adjusting for inflation. During 2005-
2015 for example, real retail sales (nominal retail sales growth adjusted for price factors) 
grew 15% per year, significantly above the 10% per year real GDP growth. In addition, sample 
changes over the years caused discrepancies between the reported retail sales level and 
reported retail sales yoy growth rate. Volatility in the series increased dramatically with the 
onset of the Covid pandemic along with more volatility in the underlying economy induced 
by Covid-related lockdowns, and the signal to noise ratio has therefore improved in recent 
years. 
Macro Importance 
• 
Although monthly retail sales data are often used as the main indicator for private 
consumption, users should be aware of a few data issues: 
1. The most serious problem is that services consumption (apart from restaurant 
services) is not included in the retail sales data. Experiences of other countries show 
that the share of services in total consumption typically rises as the economy 
develops. However, retail sales data are unable to reflect this increasingly important 
component of consumption.
[13] 
2. Retail sales also include non-household (i.e., government and corporate) purchases, 
but it is currently impossible to obtain a breakdown between household and non-
household purchases. While household consumption probably constitutes the 
largest part of the reported retail sales, some of the retail sales expenditure is 
classified as government consumption vs. private consumption under the GDP by 
expenditure framework. 
• 
That said, the monthly retail sales data have the advantage of being timely. Policymakers 
also pay close attention to these figures, roughly on par with IP, FAI and trade data. 
• 
Sporadic waves of Covid in 2020-2022 and stay-at-home policies disrupted offline activities 
in China, drawing increased engagement to online platforms. As consumption behavior 
shifted from offline to online purchases in China, the share of online goods sales in total 
retail goods sales rose to 31% in 2023 from 23% in 2019. 
Compilation Methodology 
• 
All large firms above the designated size report sales data monthly. Data from small firms 
below the designated size are compiled through sampling. In practice, it is not clear how 
often, how much, and how well the sampling of small firms is actually carried out. Data from 
major online platforms are compiled for online goods sales, but the list of surveyed 
platforms may change over time. 
• 
Retailers with annual revenue from primary business of RMB5 million and above, hotels and 


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restaurants with annual revenue from primary business of RMB2 million and above, and 
wholesalers with annual revenue from primary business of RMB20 million and above are 
jointly defined as enterprises above designated size. 
Reporting 
• 
Retail sales data are available in nominal terms only. The NBS discontinued the release of the 
Retail Price Index at end-2022. To deflate the series, the Consumer Price Index for goods 
could be used, though the breakdown and weights of CPI goods baskets and retail sales 
categories differ somewhat. 
• 
There are three kinds of breakdown available for retail sales data: 
1. By location, i.e., consumer goods sold in urban and rural areas. However, urban retail 
sales may be goods purchased by rural households who live in urban areas and do 
not necessarily represent urban household consumption, and vice-versa. 
2. By commodities. There are two layers of breakdown data available - the first layer is 
total retail sales by goods vs. catering, and online retail sales by goods and services. 
The second layer of breakdown is retail sales by specific commodities (e.g., food, 
clothes, home appliances, autos, etc.). The second set of data are only available for 
sales made by firms at or above the designated size and may not always give an 
accurate picture of overall consumption growth. Goods sales by above-designated-
size retailers accounted for 40% of total retail goods sales in 2023. 
Exhibit 35: Growth of online sales have outpaced that of offline sales in recent years 
Retail sales 
 
Source: NBS, Goldman Sachs Global Investment Research 
Household Income and Expenditure Survey 


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Household Income Survey 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Frequency: Quarterly, annual 
Timing: Around 2-3 weeks after the end of each quarter, along with the release of GDP data 
Availability: See the table below 
Exhibit 36: Availability of China NBS income and expenditure data 
 
Source: Goldman Sachs Global Investment Research 
Overview 
• 
Urban and rural income is classified in terms of total and disposable income. 
• 
Total income is composed of pre-tax wages, business profits, return on assets (e.g., interest, 
dividends, rents) and other “transfer income” (gifts, insurance claims, retirement pensions, 
transfer payments from members in other households etc.). Prior to 2012, samples were 
collected from 66,000 households which were presumably rotated every three years. Starting 
from Q4 2012, the NBS issued a new survey format to reflect the reform of urban-rural 


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integration, which adjusted the sample to 2 million households nationwide for general 
investigation and selected 160,000 households for direct survey.
[14] One-third of the surveyed 
households are rotated each year. 
• 
Disposable income is total income for final consumption expenditure and households’ 
savings excluding income tax and social security contributions. It includes both cash income 
and in-kind income. 
Disposable Income per Capita 
Starting from year-end 2012, disposable income per capita is disclosed in the new survey (“Urban-
Rural Unified Household Survey”) at both a quarterly and annual frequency. Urban and rural 
households are still surveyed separately but the statistical standards are the same. NBS then 
calculates the nationwide per capita disposable income as the weighted average income of urban 
and rural households. Weights are based on urban/rural population levels. 
By source: Per capita disposable income is the sum of wage and salary, net business income, net 
income from property and net income from transfers at the urban and rural level separately. 
By income level (nationwide): Per capita disposable income is divided into five income levels: low 
(bottom 20% of income distribution), low middle (20-40
th percentile), middle (40-60
th percentile), 
upper middle (60-80
th percentile) and high (top 20% of income distribution). In our view, data for the 
high-income group are by far the least reliable, for reasons discussed under "Signal to Noise Ratio" 
below. 
There are some differences between the concepts of rural net income and urban disposable income. 
Rural households are treated as production as well as consumption units. As a result, their net 
income excludes “household operation costs”, such as costs of fertilizers and pesticides. 
Disposable Income by Sources 
1. Income from wages and salaries (56%) 
2. Net business income (17%) 
3. Net income from property (9%) 
4. Net income from transfers (18%) 
Signal to Noise Ratio 
• 
Two factors seriously affect the reliability of household surveys: 
1. Households participating in the survey are required to provide very detailed notes 
on their expenditure, which is time-consuming. Households are rewarded financially 
for taking part in the survey, but the financial payments are small. For example, 
financial payment was RMB70 per month for households in selected cities in 
Zhejiang Province in 2020. Therefore, the incentive for households to respond 
accurately may not be high. This problem is especially serious for urban high-
income households. 
2. Lack of confidentiality may lead people to under-report their income/expenditure or 


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simply refuse to participate in surveys. Again, this is likely to be especially serious for 
high-income groups and households with gray or illegal income. As a result, there is 
likely a downward bias in reported income levels. However, the direction of any bias 
on growth rates is less clear as under the aggressive anti-corruption campaign of 
recent years, under-reported gray and illegal income growth likely fell dramatically. 
Since these sources of income were never reported in the first place, the impact may 
not show up in official statistics. 
Macro Importance 
This series is useful in gauging growth rates in purchasing power, living standards and labor market 
performance. 
Exhibit 37: Wage growth has historically been the largest driver of household disposable 
income growth 
China household disposable income per capita 
 
 
Source: NBS, Goldman Sachs Global Investment Research 
Household Expenditure Survey 
Signal to noise ratio: *** 
Macro importance: **** 
Source: National Bureau of Statistics 
Timing: Around 2-3 weeks after the end of each quarter 
Availability: See table in the "Household Income Survey" sub-section 


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Overview 
• 
Household consumption expenditure survey measures households ’  expenditure on 
consumption (including both money and non-money expenditure) in eight broad categories: 
1. Food (including tobacco, liquor, and catering) 
2. Clothing 
3. Housing
[15] 
4. Household appliances, articles, and services 
5. Healthcare, medicines, and medical equipment and services 
6. Transportation and communication 
7. Recreation, education, and cultural goods and services 
8. Others 
• 
Key changes since the implementation of the Urban-Rural Unified Household Survey: 1) 
income and spending sub-categories have been standardized; 2) urban population covers 
migrant workers living in urban areas, but migrant workers are excluded from the rural 
population; 3) college students supported by the surveyed households but living in separate 
residence areas are counted as members of the households. 
Signal to Noise Ratio 
• 
The compilation methodology for expenditure data is the same as for disposable Income 
data (described above), and therefore entails the same problems. 
Macro Importance 
• 
Household survey data provide useful information that is not available from other data 
sources. The household survey consumption per capita data is the only data set that 
captures all consumption categories, including both goods and services, and therefore 
should provide the most comprehensive gauge of the state of household consumption at a 
quarterly frequency. Household consumption in GDP data incorporates data from retail sales 
as well as household surveys. As the Chinese economy rebalances towards more 
consumption, this data set has become more important. 
• 
In addition, one can calculate the household saving rate (the difference between disposable 
income and consumption, divided by disposable income) based on the income and 
consumption data. This is the most timely read of households’ savings/consumption 
behaviors. In general, the household saving rate has been trending up over the past decade 
on the back of continued urbanization (urban households have higher income and higher 
savings rates than rural households). The household saving rate rose during 2020-2022 
amid the Covid pandemic, and declined in 2023 along with China’s reopening as the Covid-
related restrictions were lifted. 
Exhibit 38: Household consumption growth saw large swings during the Covid pandemic 


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China household nominal consumption expenditure per capita 
 
Source: NBS, Goldman Sachs Global Investment Research 
Exhibit 39: Household savings rate has trended higher and increased sharply during the Covid 
pandemic 
China household savings rate (seasonally adjusted) 
 
Source: NBS, Goldman Sachs Global Investment Research 
Retail Sales of Major Offline Retailers Reported by China National Commercial Information Center 
(CNCIC) 
Source: China National Commercial Information Center 
Availability: Top 100 retailers data are available on a monthly basis from July 2007-March 2024; top 


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50 since July 2011 
Timing: Typically around the third week of the following month, but can be irregular at times 
Overview 
• 
This series reports the year-over-year growth rate of the total retail sales of the top 50/100 
retailers in China. The top retailers include both offline retailers such as chain stores and 
department stores and online retailers such as Tmall and JD.com. These goods may be 
purchased by households, firms, or the government. Retail sales data by product are 
available, though there are missing values in this data. Similar to the official retail sales data, 
this series is reported in nominal terms. 
Signal to Noise Ratio 
• 
Total retail sales of the top 50/100 retailers data are subject to sample changes, as the list of 
retailers included is updated regularly based on the latest ranking. In addition, releases of 
this indicator are occasionally delayed for a few weeks. CNCIC suspended the release of the 
full top 100 retailers data in April 2024, although the top 50 are still available. 
Macro Importance 
• 
Total retail sales of the top 50/100 retailers are reported separately by non-official sources 
(CNCIC) and therefore could provide cross-checks for the official retail sales/household 
consumption data
[16]. For example, year-over-year growth in total retail sales of the top 
50/100 retailers dropped from around 20% in 2011 to 0% in 2014-15 amid a housing 
downturn and overall economic slowdown. Growth in the official retail sales series over this 
period was more stable by contrast, decelerating gradually from 17% yoy in 2011 to 11% yoy 
in 2015. 
Auto Sales 
Signal to noise ratio: **** 
Macro importance: *** 
Source: China Passenger Car Association (CPCA); China Association of Automobile Manufacturers 
(CAAM), National Bureau of Statistics 
Availability: CPCA auto sales volume: monthly from Apr 2007; weekly from 2015 
CAAM auto sales volume: monthly from 2000, annual from 1998 
NBS auto sales value: monthly from January 1997 
Timing: Around 10th day of the following month 
Overview 
• 
CAAM reports car sales in units on a monthly basis, capturing different types of cars, such as 
passenger and commercial cars, through wholesale channels. The CAAM is regulated by 
SASAC and authorized by the government to collect auto production and sales data from 


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auto manufacturers. CPCA is a data exchange platform among automakers and not 
accredited by the government. It reports auto sales in units on a weekly basis, capturing 
passenger car sales through both retail and wholesale channels. For the value of sales of 
automobiles, the NBS monthly retail sales data report sales by above-designated-sized 
enterprises. 
• 
Autos-related data tend to be highly cyclical, though sales are sensitive to policy measures 
such as auto purchase tax changes and changes in purchase restrictions in large cities. 
Policymakers may also apply differentiated policies (e.g., on new energy vehicles vs. 
traditional vehicles) to provide targeted support for certain types of vehicles. New energy 
vehicle sales have expanded at a very fast pace in recent years on the back of policy support. 
For example, license plates for new energy vehicles are not restricted in top-tier cities, unlike 
traditional vehicles. New energy vehicle sales rose from 0.04% of total auto sales in 2011 to 
32% of total auto sales in 2023, based on CAAM data. 
Signal to Noise Ratio 
• 
Auto sales data are generally reliable over time. CAAM and CPCA data are directly reported 
by automobile companies, though subject to potential temporary distortions due to a lack of 
cross-check mechanism. Companies that already reached their annual sales targets, for 
example, may delay booking some sales to make it easier to reach the target in the 
following year. 
Macro Importance 
• 
Auto sales data have gained importance in recent years. From the demand side, automobile 
consumption accounts for around 10% of total household consumption and plays an 
important role in overall household consumption growth. On the production side, 
automobiles accounts for around 6% in overall industrial value-added, and is a key sub-
industry in the industrial sector. According to the National Development and Reform 
Commission (NDRC), as of 2020, auto and related sectors accounted for roughly 10% of 
GDP.
[17] As policymakers push for energy transition and emissions reduction, new energy 
vehicle production and sales data could shed light on the progress of China’s economic 
transformation. 
Exhibit 40: New energy car sales rose very rapidly in recent years and took around 32% of total 
automobile sales in 2023 
China automobile sales 


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Source: CEIC 
Consumer Confidence Index 
Signal to noise ratio: ** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Monthly from 1990 
Timing: Usually lagged by one month 
Overview 
• 
The NBS publishes the consumer confidence index (CCI) on a monthly basis. It is a survey-
based diffusion index that ranges from 0 to 200, with 0 implying extreme pessimism and 200 
implying extreme optimism. The index is imputed based on a monthly telephone survey of 
more than 6,000 urban and rural consumers in 15 provinces. Survey questions include 
respondents’ assessment of current employment situation, income level, employment 
prospects, income expectations and willingness to spend. 
Signal to Noise Ratio 
• 
The NBS CCI is based on a telephone survey. It is less precise than hard data (e.g., retail sales) 
and potentially subject to biases such as sampling and response rates, but it can be a useful 
gauge of consumer sentiment, especially at times when there is a major shift in consumer 
confidence. 
Macro Importance 
• 
Consumer confidence is an important concept as in theory it drives household savings, 
spending, and investment decisions. Our research shows that consumer confidence does 


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matter to household consumption after controlling for other variables like household 
disposable income. Since the Shanghai Covid lockdown in Q1 2022, the NBS consumer 
confidence index has remained depressed despite China’s lifting of the zero-Covid policy at 
the end of 2022. Muted domestic demand, a weak labor market, and the continued fall in 
house prices hurt consumer confidence and hindered willingness to spend. 
Exhibit 41: Consumer confidence plunged in Q1 2022 
China Consumer Confidence 
 
Source: NBS 
Other Consumption-related Data 
Other indicators on consumption include per-head spending during long holidays released by the 
Ministry of Culture and Tourism (MCT), which shows post-Covid recovery has been bumpy due to 
muted confidence and continued consumption downgrading. Some consumption-related indicators 
released by third-party sources are also worth monitoring. Movie box office revenue data released 
by Dengta App during long holidays partially reflect people’s willingness to spend, though the box 
office statistics are significantly affected by the release of blockbusters. As online sales account for an 
increasingly larger share in total retail sales, parcel delivery volume data published by the State Post 
Bureau each month is useful for tracking the momentum of online shopping (as 80%+ of parcels in 
China are e-commerce parcels). Parcel volume growth has significantly exceeded that of online 
goods since early 2023, which could partially be explained by lower value of single packages (e.g., e-
commerce platforms/merchants continue to lower the minimum purchase amount eligible for free 
shipping), and higher return rates on the back of more favorable return policies across platforms. 
Exhibit 42: Per-head holiday spending remained at low levels compared with pre-pandemic 
period 
Nationwide domestic visitors and tourism revenue vs. pre-pandemic levels 


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Source: Ministry of Culture and Tourism, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“China consumption worries: Goods spending decelerates as credit impulse fades”, Asia 
Economics Analyst, 8 September 2018 
• 
“How has online shopping affected CPI inflation in China?”, Asia Economics Analyst, 3 
October 2019 
• 
“A bit more confident about the Chinese consumer confidence data”, China Data Insights, 2 
December 2019 
• 
“Household cash flow: Still the key driver for consumption in China”, Asia Economics Analyst, 
24 May 2020 
• 
“China: Households’ excess savings around the pandemic”, Asia in Focus, 15 March 2021 
• 
“A Pulse Check on Chinese Household Consumption Growth: Still Sluggish”, Asia Economics 
Analyst, 4 July 2022 
• 
“ China post-reopening consumption recovery: Large potential, lingering scars ” , Asia 
Economics Analyst, 18 January 2023 
• 
“FAQs on “excess savings” in China”, China Data Insights, 7 February 2023 
• 
“Lower housing cost burdens can lower the savings rate in China, but confidence remains 
key”, Asia Economics Analyst, 21 January 2024 


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Section VI. External Sector 
Merchandise Trade 
Signal to noise ratio: ***** 
Macro importance: **** 
Source: General Administration of Customs; State Administration of Foreign Exchange (SAFE) 
Availability: China Customs: monthly from 1992, annual from 1950; SAFE: monthly from January 2015, 
quarterly from Q1 1998 
Timing: China Customs: 7-14 days after the end of the month; SAFE: end of next month 
Publication: China Customs Statistics; Semi-Annual Report on Balance of Payments 
Overview 
• 
Merchandise trade by China Customs measures the value of goods transactions (both in 
RMB and USD terms) across national borders. Exports are valued on an FOB (free on board) 
basis, which includes costs to deliver goods onto the vessels but not further costs, such as 
insurance or freight. Imports are calculated on a CIF (cost, insurance and freight) basis, which 
includes insurance and freight charges. 
• 
Trade data in the Balance of Payments standard, as published by SAFE, place greater 
emphasis on transactions between residents and non-residents, rather than physical 
movements across borders as shown in the China Customs data. (Though making this 
distinction can be difficult in practice.) The credit and debit sides of trade under SAFE data 
are both accounted on an FOB basis. 
Signal to Noise Ratio 
• 
Customs trade data are among the most reliable macro-economic data in China. Their high 
volatility is a clear indication of absence of the smoothing prevalent in other economic data. 
However, in 2012-13 and 2015-16, there were distortions to trade reporting due to 
importer/exporter incentives to move capital either onshore or offshore. For example, when 
there were strong capital inflows into China, exporters tended to over-report exports in 
order to facilitate higher payments from offshore. When there are outflow pressures, 
importers tend to over-report imports to disguise the movement of capital offshore. 
Exporters sometimes also create “false exports” to gain government subsidies such as export 
rebates. Therefore, at the end of this section, we have estimated an alternative series that 
relies on information from China’s trading partners. 
• 
Besides over-/under-reporting, there are significant discrepancies between bilateral trade 
data compiled by China and the corresponding data from its counterparts due to re-exports 
via Hong Kong. There are various ways to adjust for these data problems. For example, the 
statistical authorities in Hong Kong provide detailed data on the region’s re-export trade by 
country and surveys of re-export margins. The difficulty with this analysis is that much of the 


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false reporting is done via companies set up in Hong Kong, often solely for this purpose. 
These companies tend to use high-value-added goods that are easy to report large values 
for, but are often hard to judge in terms of underlying value. 
Macro Importance 
Trade data are very useful in judging economic cycles. Imports can give a useful indication of the 
strength of domestic demand. Exports can shed light on the strength of global demand. Net exports 
may provide clues about the potential misalignment of the foreign exchange rate. The government 
also often quotes the total amount of trade (exports plus imports) to gauge the level of openness of 
the economy. 
Compilation and Reporting 
• 
Commodity breakdowns are available by the Standard International Trade Classification 
(SITC), the Harmonized System (HS), the Broad Economic Categories (BEC) (e.g., 
consumption goods, capital goods and intermediate goods, etc.), and customs regime (e.g., 
ordinary trade, processing trade, etc.). 
• 
China Customs releases monthly trade indices on export value, volume, and unit value with a 
slightly longer lag (usually 20-25 days). Prior to 2014, the indices were denominated in USD. 
From 2014 onwards, the indices have been denominated in RMB. 
Other Issues 
• 
Some of China’s imports are for eventual export (e.g., raw materials or intermediate inputs 
into manufacturing goods exports). Total imports therefore can be divided into imports for 
processing trade and those for domestic use. We derive our estimates of total imports for 
processing trade by aggregating the following import categories: 
1. Imports for processing and assembling 
2. Equipment imported for processing and assembling 
3. Customs warehousing trade 
4. Entrepot trade by bonded area 
5. Imports for outward processing 
• 
The share of “imports for processing” in total imports has declined in recent years. “Imports 
for processing” declined to around 34% of total imports in 2023, from around 51% in 2005 
when the series started. 
• 
Note that there are a few differences between the Customs trade data and the net exports 
data in GDP. Net exports in GDP capture the net trade balance in goods as well as in 
services, whereas Customs trade data only cover trade in goods. Furthermore, in GDP data, 
both exports and imports are valued on an FOB basis, whereas Customs imports data are 
valued on a CIF basis. GDP standard is consistent with BOP standard. The NBS does not 
separately release data on exports of goods and services and imports of goods and services. 


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Services Trade 
Signal to noise ratio: *** 
Macro importance: *** 
Source: SAFE 
Availability: Monthly from January 2014, quarterly from Q1 1998 
Timing: At the end of the following month 
Overview 
• 
Exports/Imports of services refer to income/payment from/to foreigners on intangible 
products such as transport, tourism, entertainment, telecommunication and financial services. 
• 
After expanding through 2018, China’s service trade deficit narrowed sharply in 2020 after 
the onset of the Covid pandemic when strict travel restrictions were imposed. In 2019, travel 
services imports totaled $250bn. During the Covid pandemic, they averaged only $120bn 
per year, most of which were tuition and medical services payments made by Chinese 
residents overseas. China’s service trade deficit widened in 2023 after the end of its zero-
Covid policy. 
• 
Service trade data are probably subject to a significantly higher level of misreporting, 
because it is usually harder to verify the underlying fair value of services provided than 
goods traded. Although less subject to reporting distortions that are aimed at benefiting 
from government subsidies (as local governments are much more focused on goods trade) 
on balance services trade data appear to mask substantial net capital outflows. Spending per 
traveler roughly doubled in 2013-16, for example, suggesting the possibility that a 
significant part of “travel expenditure” is really hidden capital outflow. According to some 
academic research, correcting for this would suggest a current account surplus 1-1.5% of 
GDP larger (and commensurately higher capital outflows).
[18] When faced with capital outflow 
pressures, the authority tends to crack down on outflows through services trade as well. For 
example, in 2016/2017, bank card overseas withdrawal and large-amount purchases (which 
would show up in services imports) were under tighter restrictions. 
Exhibit 43: Services trade deficit widened notably post China’s reopening 
Services trade balance 
 


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Source: SAFE, Haver Analytics 
Balance of Payments 
Signal to noise ratio: **** 
Macro importance: **** 
Source: SAFE 
Availability: Quarterly from Q1 2010, semi-annual from 2000, annual from 1982 
Timing: Preliminary readings after 30 days; final readings after 3 months. 
Publication: Semi-Annual Report on Balance of Payments 
Overview 
The Balance of Payments (BOP) records the external transactions of an economy with the rest of the 
world over a certain period of time. It records transactions between residents and non-residents. 
Transactions are flows of goods, services, capital and financial claims. 
Signal to Noise Ratio 
• 
SAFE began to compile BOP data in 1985 in accordance with the 4
th edition of the IMF BOP 
Manual, and subsequently adopted the 5
th edition in 1996 and the 6
th edition (BPM6) in 
2015.
[19] In this edition, the IMF changed “Income” to “Primary Income”, and “Current Transfer” 
to “Secondary Income”. 
• 
SAFE must rely on other government agencies for data on various sub-components, which 
are often not compiled in accordance with IMF standards and are difficult to adjust (for 
example, the FDI data). We discuss the difference in FDI data from SAFE and Ministry of 
Commerce later in this section. 


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• 
Misreporting of data to evade capital controls are common and likely worsened after 2015 
given significant capital outflow pressures. The misreporting of goods trade data is well 
known, but the problem is likely to be more serious in terms of services trade because it is 
more difficult for the government to prove any wrongdoing. 
Macro Importance 
The BOP data have attracted rising interest in recent years because of the increased attention on the 
CNY. 
Compilation and Reporting 
• 
Balance of payments data (China’s are summarized in Exhibit 44) consist of two main 
components. 
1. Current Account (CA): This records the flow of international trade (both goods and 
services), primary income (i.e., income that accrues to foreign-owned inputs to 
production and other assets, e.g., profits generated by foreign-owned enterprises 
and investment income paid to foreign-owned property and financial securities), 
and secondary income (i.e., income transferred to or received from foreigners 
without “quid pro quo”, e.g., remittances from workers working overseas). 
2. Capital and Financial Accounts: This essentially records the flow of investment. 
Main 
components 
are: direct 
investment, 
portfolio 
investment, 
other 
investment, reserve assets, net errors and omissions. 
The current account captures current transfers and transactions in goods, services, and 
income… 
• 
The merchandise trade by BOP accounting is conceptually different from the monthly trade 
data from the Customs Administration as it is supposed to capture transactions between 
residents and non-residents instead of across national borders. Besides, its valuation 
standards are different: BOP measures both exports and imports on an FOB basis, whereas 
Customs values exports on an FOB basis and imports on a CIF basis. As FOB excludes costs 
of insurance and freight, imports are smaller in BOP accounting than in Customs accounting 
(by around 5%). SAFE also makes other minor technical adjustments to the customs trade 
data in accordance with IMF standards, such as subtracting goods exported but then 
returned. SAFE counts only the cargo with ownership change. Goods trade with no 
ownership change is accounted for as service trade in BOP. 
• 
The services component in the current account consists of items such as transportation, 
travel, and insurance. Data are compiled by SAFE directly, as well as by various other 
government agencies, such as the Ministry of Transportation and the Ministry of Culture and 
Tourism. Most of the service trade in China is through the “travel” channel, and “travel” 
under services trade includes spending while traveling, and education tuition abroad. 
• 
The income account records compensation for employees working abroad and returns on 
investments. The numbers of Chinese residents working abroad and foreign residents 
working in China are both relatively small, and most primary income is investment income. 


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In addition to primary income, secondary income through current transfer is also part of the 
current account. 
…and the capital account captures investment flows 
• 
The capital account includes capital transfers and reduction or cancellation of debts. 
Financial accounts include international reserve assets and non-reserve financial accounts. 
Non-reserve financial accounts in turn include direct investment, portfolio investment, 
financial derivatives and other investment. 
• 
Reserve assets are external financial assets held by the monetary authorities, including gold, 
foreign exchange, special drawing rights (SDRs) with the IMF, and the use of the Fund’s 
credits. Reserve assets are transaction-based and therefore not affected by valuation effects 
from asset price and exchange rate fluctuations. 
• 
Net Errors and Omissions (NEO) (also called “statistical discrepancy”) balances the credit and 
debit items in the BOP. It captures the errors and inconsistencies in data recording and 
processing in all sections of the BOP. 
Exhibit 44: China’s current account surplus remained solid but the financial account has seen 
significant outflows 
 
Source: SAFE, Goldman Sachs Global Investment Research 
International Investment Position (IIP) 
The IIP is closely related to the BOP. Whereas the BOP measures the flow of transactions between an 
economy and the outside world, the IIP measures the stock of assets and liabilities an economy has 
with the rest of the world at a given point in time. Although most BOP transactions are reflected in 
IIP changes, there are other factors that impact IIP that do not appear in the BOP, including market 
price changes, exchange rate changes and other volume changes (such as write-offs and re-
classifications). In other words, the IIP is the result of cumulative current account surpluses/deficits 


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plus valuation changes. China began to publish IIP data in 2006 on an annual basis and in Q4 2010 
on a quarterly basis. 
Foreign Direct Investment 
Signal to noise ratio: * 
Macro importance: *** 
Source: Ministry of Commerce (MOFCOM) 
State Administration of Foreign Exchange (SAFE) 
Availability: MOFCOM series: monthly from 1997; SAFE series: quarterly/annual with BOP data from 
1998 
Overview 
• 
Foreign Direct Investment (FDI) measures investments made by foreign residents who seek 
to have significant long-term interest in and have direct influence over a domestic enterprise. 
FDI also reflects the confidence of international investors in the Chinese economy. In 
addition, FDI data are useful in estimating total capital flows. 
• 
There are multiple sources reporting China’s FDI data. Investors usually pay most attention 
to the FDI numbers from the BOP releases, given that it is compatible with GDP accounting, 
can be compared with other countries’ FDI data, and is published quarterly. The MOFCOM 
provides another official dataset on FDI with breakdowns by sector and by 
country/jurisdiction of origination. But it follows a different accounting principle, with 
reinvested earnings excluded. The IMF Coordinated Direct Investment Survey (CDIS) data 
cover over 100 countries and regions and offer mirror data from other economies, which 
can be used to formulate an “outside-in” measure to cross-check China’s official data. From 
a bottom-up perspective, Bloomberg reports investment value on its Mergers & Acquisitions 
(M&A) page, which can be aggregated into a timely series of China’s inward FDI. In addition, 
OECD and United Nations Conference on Trade and Development (UNCTAD) provide cross-
country panel data on FDI as well. 
Compilation and Reporting 
Different FDI measures vary in three main aspects: compilation methods, data coverage and 
availability (Exhibit 45). 
• 
Compilation methodology: Most FDI data are compiled based on one of two principles: 
asset/liability principle or directional principle, except for Bloomberg data. FDIs under the 
directional principle account for net investment on the basis of ultimate parent companies, 
while those under the asset/liability principle will simply add up net assets/liabilities for 
outward/inward FDI. For example, if a Chinese parent company receives investment from its 
own foreign subsidiary, this flow will be captured under the asset/liability principle but will 
not be captured under the directional principle. The data prepared by SAFE and OECD 
follows the asset/liability principle. In contrast, MOFCOM and UNCTAD compile FDI data 


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based on the directional principle. IMF CDIS shares the same inputs as SAFE, but the data is 
presented under the directional principle. Lastly, Bloomberg’s M&A investment amount is 
based on project-level accounting, effectively adding up announced deals. 
• 
Data coverage: We summarize data coverage by availability of flow/stock data, detailed 
breakdowns and mirror data. SAFE provides FDI flows under BOP and FDI stock under 
International Investment Position (IIP) without any breakdowns. MOFCOM provides 
breakdowns of FDI flows by sector and by country/jurisdiction of origination in their annual 
statistical bulletin. The monthly FDI flows do not include direct investment in financial 
services (e.g., banks), although the size would be relatively small (around 4-6% of inward FDI 
in 2018-2022). IMF CDIS offers mirror data of FDI stock, which can be compared with the 
MOFCOM's FDI data by-country breakdowns.
[20] Bloomberg data are FDI flows with industry 
classifications. Yet, its numbers reflect planned cross-border M&A investment flows only, 
missing other FDI flows such as greenfield projects. Both UNCTAD and OECD provide FDI 
flow and stock data without further breakdowns. Among these data sources, only SAFE, IMF 
CDIS and OECD incorporate reinvested earnings in their FDI measures, as required by the 
Balance of Payments and International Investment Position Manual, 6th edition (BPM6) 
standard. They also effectively assume all non-repatriated earnings are reinvested. 
• 
Data availability: Reporting frequency ranges from monthly to annual. MOFCOM provides 
monthly data from 1984 with a one-month lag. Bloomberg also publishes monthly data 
from 1998 with daily updates of the latest released FDI projects. SAFE’s FDI data come on a 
quarterly basis from 1998 with a one-quarter lag. The IMF CDIS, UNCTAD and OECD offer 
annual data. UNCTAD has the longest data history, tracking the FDI series from 1990 
onwards, followed by OECD, which provides FDI numbers from 2005 onwards. Both have a 
publication lag of around one year. IMF CDIS is the least timely data source, with the earliest 
statistics dated back to 2009 and the most recent data for 2022 (a 2-year publication lag). 
Exhibit 45: Summary of different FDI measures 
 


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\"-\" indicates \"not applicable\" 
Source: NBS, Wind, Goldman Sachs Global Investment Research 
Exhibit 46: The 2022-23 drop in China’s inward FDI flows was partly driven by lower reinvested 
earnings 
Decomposition of FDI flows 
 
Source: CEIC, Haver Analytics, Goldman Sachs Global Investment Research 
Other Issues 
• 
As with GDP data, there are considerable issues in FDI data compilation at the regional level. 
The authorities have strengthened the requirements for the verification of data reporting. 


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For example, data on cash FDI are double-checked against capital flow records at the SAFE, 
and data on goods FDI are double-checked against the records of China Customs. Data that 
cannot be verified are, in principle, discarded. Moreover, FDI’s importance as a part of the 
assessment metric for local government officials has declined, and hence the incentives to 
over-report have dropped relative to earlier years. 
• 
Assessment of the true strength of FDI is also complicated by the issue of round-tripping, 
which refers to funds originated in China but reinvested back as FDI. Money laundering and 
preferential policies for foreign enterprises are the most important reasons for round-
tripping. 
Outward Direct Investment (ODI) 
Although FDI attracts most attention, ODI has gained momentum in recent years, especially against 
the backdrop of increasing capital outflow pressures and Chinese manufacturers building factories 
overseas in response to rising tariffs. Corporate outward direct investments face tight regulation by 
the authorities. For investment value above a certain threshold, in 2015 and 2016, ODI from China 
soared to a record high, likely in part reflecting capital flight motivations, but declined afterwards on 
a tightening in outbound capital controls. According to SAFE data, ODI surpassed FDI for the first 
time in late 2015. Direct investment has shifted to a net outflow again since 2H 2022. 
External Debt 
Signal to noise ratio: *** 
Macro importance: * 
Source: State Administration of Foreign Exchange 
Availability: Quarterly from Q2 2003, semi-annual from 2001, annual from 1985 
Timing: Together with the final release of BOP 
Overview 
• 
Data prior to 2015 only included FX-denominated external debts. The SAFE started to 
publish data with both CNY and FX denominated external debt since 2015. At the end of 
2023, the outstanding external debt of China (excluding that of Hong Kong and Macao, but 
including debt both in FX and RMB) was US$2.45 trillion. 
• 
In regard to currency type, the total external debt is composed of foreign debt in RMB and 
foreign debt in other currencies. By the end of 2023, 47% of external debt was denominated 
in CNY. Of FX-denominated external debt, 84% was denominated in USD, 7% in EUR, 4% in 
HKD, 3% in JPY, respectively. 
• 
By original maturity, short-term refers to external debt with a term of one year or less. 
Medium- and long-term refer to external debt with a contract term of more than one year. 
As of 2023, short-term external debt took around 56% of China’s total external debt, and 
long-term external debt 44%. 


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• 
The external debt is also classified by institutional sector, with general government 
accounting for 18%, central bank 4%, deposit-taking corporations except central bank 41%, 
other sectors 25% (including other financial corporations and non-financial corporations), 
and intercompany lending under direct investment 12%, as of the end of 2023. 
• 
As we think official data do not capture some of the FX debt raised by offshore Chinese 
companies, we compile our own estimate of Chinese corporates and households’ FX debt by 
aggregating onshore FX loans, claims on the Chinese non-bank sector by BIS reporting 
banks (offshore, non-Chinese), FX bonds, and trade liabilities. Our approach indicates that 
Chinese corporates’ and households’ total FX-denominated debt stood at around US$2.1 
trillion as of 2023 year-end. 
Foreign Exchange Reserves 
Signal to noise ratio: *** 
Macro importance: *** 
Source: State Administration of Foreign Exchange, PBOC 
Availability: SAFE: Quarterly from 1993, annual from 1982 
PBOC: Monthly from 1989 
Timing: PBOC data are usually released on the 7th day of the following month. 
SAFE data are released with other BOP data. 
Overview 
Foreign exchange reserves are liquid external foreign currency assets readily available to and 
controlled by central banks, which can be used to finance current account deficits and influence the 
foreign exchange rate. They include securities, bank deposits, derivatives and other assets, as long as 
they meet the above criteria. Apart from foreign exchange reserves, the more broadly defined 
“international reserves” can take other forms, such as gold and Special Drawing Rights (SDRs); 
however, these holdings are typically small relative to foreign exchange reserves. 
Signal to Noise Ratio 
• 
PBOC FX reserves data can be opaque and influenced by factors other than capital flow 
fundamentals. Because reserves data are based on market prices and denominated in USD, 
exchange rate and asset price fluctuations affect reserve values. When calculating changes in 
reserves, we usually adjust for the estimated effect of exchange rate fluctuations, but the 
impacts of asset price changes are difficult to estimate because of the lack of detailed 
information on portfolio holdings. 
• 
China started to disclose its historical dollar share of FX reserves from 2019 in SAFE’s annual 
reports with a five-year lag. The share remained in the range of 57% - 59% in 2014-2018, 
down from 79% in 1995. However, SAFE does not provide further details on shares of other 
currencies, or USD shares for the most recent few years. USD holdings remain prominent in 


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the official FX reserves, despite continued selling of US Treasury securities over the past few 
years. We estimate USD assets account for roughly 60% of China’s official FX reserves via 
both China’s data and US Treasury International Capital (TIC) data. The TIC data can also be 
used to gauge the composition of the USD portfolio in China’s FX reserves. 
• 
In order to estimate valuation effects on reserves, we assume the currency composition of 
China’s FX reserves is similar to that of the global average and follows the Currency 
Composition of Official Foreign Exchange Reserves (COFER) published by the IMF. 
• 
Given possible PBOC balance sheet management and shifts in banks’ net open position, we 
prefer the SAFE data on banks’ FX settlements on behalf of their onshore clients as a gauge 
of the FX-RMB conversion trend among onshore non-banks. 
• 
PBOC’s reserve data and IIP data on reserve assets are based on market price, and thus will 
be impacted by valuation effects. Reserve changes in BOP data on the other hand are free 
from valuation effects as they are supposed to measure flows. 
• 
China’s FX reserves have been surprisingly stable at just over USD 3 trillion since 2017 
despite the large inflow pressures in 2020-2021 and outflow pressures in recent years. Our 
analysis suggests that the FX holdings of China’s commercial banks may serve as a buffer for 
capital inflows/outflows, with official FX reserves changing relatively little as a result. China’s 
commercial banks accumulated a large amount of FX from 2H 2020 to 2021 on the back of 
China's elevated goods trade surplus. The authorities can guide banks to sell FX first before 
selling the government’s own FX reserves to defend the currency, if necessary. 
Macro Importance 
Foreign exchange reserves are often used to measure a country’s external vulnerability. China’s 
foreign exchange reserves are still the largest in the world, though standard adequacy ratios have 
eroded somewhat since the 2015-16 episode. Exhibit 47 compares Chinese reserves to IMF metrics 
for reserve adequacy, although it should be noted that experts hold different views on the adequate 
level of reserves.
[21] 
Compilation and Reporting 
Foreign exchange reserve data are compiled by the PBOC and the SAFE, and are reported in USD 
levels. They do not include holdings in gold and SDRs, which are reported separately. 
Exhibit 47: Reserve adequacy consistent with IMF floating-rate, but not fixed-rate, guidelines 
PBOC FX Reserves 
 


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Source: IMF, Haver Analytics, Goldman Sachs Global Investment Research 
FX Purchases/PBOC FX Position 
Released by the PBOC, these data measure the net amount the PBOC pays to financial institutions 
each month for the foreign currency they receive from trade surpluses, foreign investments, and 
other sources. PBOC’s FX purchase for RMB is one channel for creating reserve money. This indicator 
is based on cumulative flows and thus is free from valuation effects. 
Exchange Rate Terminology and Offshore RMB Development 
Renminbi (RMB) 
The official currency of the People’s Republic of China, translated as “the people’s currency”. The 
currency is issued by the People’s Bank of China, the monetary authority of China and is the official 
legal tender in mainland China. 
Yuan 
The Yuan is the basic unit of the renminbi, but is often used synonymously with renminbi in referring 
to the Chinese currency more generally. 
CNY 
• 
CNY is the code determined by the International Organization for Standardization (ISO code) 
for the renminbi/yuan. In practice, it refers to the Chinese currency traded onshore in 
mainland China. 
• 
Despite some gradual steps toward liberalization, the CNY market remains heavily managed 
by Chinese officials, who maintain strict capital controls and set a daily “CNY fix” against a 
basket of world currencies and a pre-determined trading band around that fix, which the 
“CNY spot” must settle within. The trading band was initially established at 0.3% but was 
expanded to 0.5% in May 2007, to 1.0% in April 2012, and to 2.0% in March 2014. 
• 
The PBOC adjusted the CNY fixing regime in August 2015 so that CNY fixing reflected the 


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previous day’s closing price of USDCNY and overnight USD moves. In May 2017, the PBOC 
further introduced a countercyclical factor in the CNY fixing to lean against herding 
behaviors in the market. The PBOC has utilized countercyclical factors since then to guide 
market expectations on the exchange rate direction. The PBOC doesn’t release any data on 
the countercyclical factors. We measure the countercyclical factor by calculating the 
difference between the official daily CNY fixing and our estimation of the fixing based on 
official documents of CNY fixing mechanism. That is, the CNY fixing should be determined 
by the previous close of USDCNY spot and the overnight move of USD against a basket of 
currencies. Our estimates suggest that the countercyclical factors have risen since 2023 and 
reached record levels in mid-2024, suggesting the authorities’ preference to slow CNY 
depreciation against USD. 
CNH 
• 
CNH refers to RMB that is traded outside of the Chinese mainland. Establishment of the 
offshore CNH market by Chinese policy makers reflected their desire to pursue greater use 
of RMB for international trade and financial transactions (i.e., the “internationalization” of the 
RMB) post the 2008 financial crisis. The CNH market was established in July 2010 when the 
PBOC and HKMA jointly announced that RMB would be deliverable in Hong Kong. 
• 
Although the CNH market remains concentrated in Hong Kong, RMB has since become 
deliverable in Singapore, Taiwan, Paris, Luxembourg, London, etc. Any offshore corporate 
entity or individual investor can participate in CNH by establishing non-resident accounts in 
a country where CNH is delivered, typically through local banks that have a relationship with 
banks in those countries/regions. 
• 
Unlike the CNY market, the CNH market is not directly managed by the Chinese authorities 
and instead is determined by the supply of and demand for CNH. It is therefore a “floating” 
currency much like the US dollar (with the important caveat that the PBOC and SAFE 
regulate RMB flows between onshore and offshore accounts, and official entities may 
participate in the market to influence the exchange rate). Having said that, CNH and CNY are 
essentially the same currency, and the active international trade between mainland China 
and the rest of the world, as well as financial investment channels such as Stock Connect, 
Bond Connect, and Wealth Management Connect Program, help keep the two exchange 
rates closely aligned. 
CNY Trade-Weighted Indices 
Source: PBOC, China Foreign Exchange Trade System (CFETS) 
Availability: Weekly data quoted by CFETS 
Overview 
• 
The Chinese government has for some time been revamping its foreign exchange 
mechanism in an effort to make the RMB more market-oriented and relatively stable against 
a basket of currencies. Between mid-2023 and mid-2024, policymakers prioritized slowing 
CNY depreciation against USD amid elevated capital outflow pressures, leading to CNY 
appreciation against the CFETS basket. Chinese exports still remain competitive in the global 


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markets, thanks to low domestic inflation in China and falling export prices. 
• 
On August 11, 2015, the PBOC announced a major reform to the formation of the RMB’s 
central parity rate against the US dollar, by referring to the closing price on the inter-bank 
foreign exchange market of the previous day. The PBOC considered this a “one-time 
correction” to remedy previously accumulated differences between the central parity rate 
and the spot market rate. However, an abrupt weakening in the RMB occurred during the 
days following the announcement of this reform, triggering considerable market volatility 
and additional capital outflows. Starting from December 2015, the PBOC released a new 
index called the CFETS RMB index based upon international trade weights after adjusting for 
re-export factors. The basket weight is updated annually to reflect the latest trade flows of 
China vs. the rest of the world. Compared with CFETS, the BIS and IMF also compile RMB 
trade-weighted indices. Exhibit 48 shows the currency share in each basket and Exhibit 49 
shows the movement of indices. 
Exhibit 48: Significant differences between alternative currency reference baskets 
 
Note: These weights were reported as of end 2023. 
Source: BIS, CFETS, IMF 
Exhibit 49: RMB strength reached peak levels in early 2022 


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Source: CEIC, Wind, Goldman Sachs Global Investment Research 
GS China “Outside-In” Trade Measures 
Source: Goldman Sachs Economics Research 
Data since: January 2009 
Timing: Around 1-2 months after the end of each month 
Publication: GS China Proprietary Indicators update 
Overview 
• 
In 2012-13, the prevalence of export/import over-invoicing to bring in/move out funds to 
invest in the “carry trade”/transfer assets offshore distorted officially reported trade data. In 
an attempt to identify the underlying trends in export and import flows, we compiled an 
“outside-in” trade measure based on trading partners’ reported data on trade with China. 
• 
We made two improvements to our previous measures over time. First, we allow for lags to 
reflect shipping time in matching China’s data with trading partners’ data. Secondly, we 
collect data from more countries and now include 21 of China’s major trading partners that 
together make up 79% (67%) of the value of total Chinese exports (imports) in 2022. 
• 
Because exports are usually reported on an FOB basis and imports on a CIF basis (see the 
discussion above on goods trade indicators and re-exports), there will be a gap between 
China’s reported exports and trading partners’ reported imports from China. In addition, re-
exports and delayed data availability may also create discrepancies in level terms between 
the China’s official trade data and our “outside-in” trade measure. However, these shouldn’t 
affect year-over-year changes on a persistent basis. 
Methodology 
• 
We collect trade data from 21 major Chinese trading partners (including the Euro area as a 


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single “partner”). Where necessary, we convert import data into US dollars. Note that all 
figures discussed here are nominal dollars (not adjusted for inflation). 
• 
We use 2010-2019 monthly data to estimate the lead-lag relationship between China’s 
official import/export data vs. trading partner reported export/import data due to different 
shipping time needed for different trading partners. 
• 
Adding up the import/export data for the trading partners (after adjusting for the lead-lag 
relationship described above) gives us “adjusted exports/imports to/from China’s major 
trading partners”. Comparisons of the outside-in estimates to China’s official data are shown 
in Exhibit 50 and Exhibit 51. 
Exhibit 50: Our outside-in export tracker tracks China’s official export growth relatively well 
except 2012-2014 
China exports with major trading partners 
 
Source: CEIC, Haver Analytics, Goldman Sachs Global Investment Research 
Exhibit 51: Our outside-in import tracker was broadly in line with China’s official import 
growth 
China imports with major trading partners 


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Source: CEIC, Haver Analytics, Goldman Sachs Global Investment Research 
GS China FX Flow Metric 
We focus on two separate sets of SAFE data to gauge the underlying FX flow situation: One for 
onshore FX settlement, and the other for the cross-border movement of RMB. These are combined 
in our preferred FX flow metric (Exhibit 52). 
• 
SAFE dataset on onshore FX settlement: FX settlement and sales on behalf of clients by 
banks refer to the transaction of FX settlement, sales and other business conducted by banks 
for their clients. Deals on their own behalf and interbank market transactions are not taken 
into account. 
• 
Cross-border RMB flows: In late 2015 through 2016, while there was a large amount of net 
cross-border RMB flow from onshore to offshore, there was no corresponding observed 
increase in foreigners’ holdings of RMB assets. It is possible that some Chinese financial 
institutions buy RMB in the offshore market and either sell it back in the onshore FX market 
or invest it in onshore RMB assets. In addition, foreign equity and bond investments in China 
through the “Stock Connect” and “Bond Connect” channels are conducted in the offshore 
RMB market (i.e., in CNH instead of CNY). These RMB/FX transactions conducted do not 
necessarily show up on the PBOC’s balance sheet. Therefore, we believe that tracking the 
data on cross-border RMB flow (foreign-related receipts and payments reported by SAFE) is 
also important to developing a comprehensive view of the underlying flow picture. 
Exhibit 52: Currency outflows picked up sharply in August 2015, reflecting concerns over CNY 
depreciation 


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Source: SAFE, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“How fast are Chinese exports really growing?”, Emerging Markets Macro Daily, 17 March 
2014 
• 
“How does a weaker RMB impact China credit”, Asia Credit Line, 14 August 2015 
• 
“Sources and sizes of China’s capital outflows”, Asia Economics Analyst, 26 January 2016 
• 
“ China capital flows update — how cross-border RMB flow might mask outflow 
pressures”, Asia Economics Analyst, 4 July 2016 
• 
“Index inflows and rate differentials support our constructive view on CNY”, Asia in Focus, 20 
July 2020 
• 
“Gauging Downside Risks to Chinese Exports”, Asia Economics Analyst, 6 April 2023 
• 
“Analyzing recent puzzles on China trade data”, China Data Insights, 24 May 2023 
• 
“Q&A on the recent RMB depreciation and policy reaction”, Asia in Focus, 23 August 2023 
• 
“Deciphering China’s Inward Foreign Direct Investment Data”, China Data Insights, 24 
October 2023 
• 
“The Enigma of China’s FX Reserves – Size, Adequacy, and Composition”, Asia Economics 
Analyst, 12 January 2024 
Section VII. Money, Credit, and Banking 
There are three major sets of money, credit and banking data: 
1. Quantity-based data feature money supply, loans and deposits, total social financing, usage 
of central bank policy tools, and balance sheets of the PBOC and financial institutions – all 


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compiled by the PBOC. 
2. Price-based data feature various interest rates, including policy rates (such as 7-day reverse 
repo rate and interest rates of other monetary policy tools) and market interest rates (such 
as interbank repo rates and bond yields), published by the PBOC and the National Interbank 
Funding Center. 
3. Flow of funds accounts record financial flows amongst five key economic sectors, which are 
jointly compiled by the PBOC and the NBS, and provide a bird ’ s-eye view of the 
interdependence between the real economy and the financial economy. 
Money Supply 
Signal to noise ratio: *** 
Macro importance: **** 
Source: The People’s Bank of China 
Availability: Monthly from 1997, quarterly from 1990 
Timing: Typically 9 to 15 days after the end of each month 
Overview 
• 
The People’s Bank of China (PBOC) reports three series on China’s money supply: 
1. M0: currency in circulation (bills and coins) 
2. M1: M0 + demand deposits (excluding household demand deposit) 
3. M2: M1 + quasi-money (time, savings, and other deposits, excluding fiscal deposits) 
Exhibit 53: A breakdown of M2 by major component 
 


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Note: Numbers in the bracket refer to the share of each major component in M2, based on 2023 
data and definitions. NBFI refers to non-bank financial institution. 
Source: PBOC, Wind, Goldman Sachs Global Investment Research 
Signal to Noise Ratio 
• 
China’s money supply data are subject to significant distortions, in large part arising from 
the attempts of financial institutions to evade regulatory controls. For example, commercial 
banks used to depress month-end money and credit data and minimize regulatory costs 
such as required reserves; the PBOC responded by changing the required data from month-
end to month-average in September 2014. This alleviated the old problem but the time 
series is no longer directly comparable. Since 2016, the PBOC has started to assess banks’ 
performance under the new macroprudential assessment framework (MPA), including 
indicators for capital and leverage, asset and debt, liquidity, pricing, asset quality, risk of 
cross-border financing and the implementation of credit policy. The quarterly MPA tends to 
distort quarter-end data. 
Macro Importance 
• 
Changes in broad money supply provide useful leading information for short-term 
economic activity because banks remain the dominant financial intermediaries in China. M2 
remains one of the intermediate policy targets for the central bank, but its importance has 
been declining due to the ongoing reform of the monetary policy framework. 
Compilation and Reporting 
• 
Breakdowns are available from M0 to M2. M2 is the broadest measure currently available 
but is becoming inadequate as direct financing (such as government/corporate bond 
issuance) has increased in importance over the past decade. 


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• 
The PBOC has expanded the definition of money supply five times due to China’s evolving 
financial markets. Specifically, the PBOC incorporated clients’ margin deposits into M2 on 
the development of the stock market in 2001. Domestic RMB deposits in foreign financial 
institutions have been included into money supply since 2002. From October 2011, M2 has 
included both non-bank financial institutions deposits and housing provident fund deposits. 
In January 2018, the PBOC employed money market funds held by non-bank sectors to 
measure money market related deposits (previously measured by deposits of money market 
funds) and incorporated it into M2. Since December 2022, e-CNY in circulation has been 
included in M0. In June 2024, PBOC governor Pan Gongsheng acknowledged that the M1 
definition was outdated, and the central bank may expand the scope of M1 to include 
household demand deposits. 
Other Issues 
• 
Before 2018, the government used to set a specific target for M2 growth at the annual “Two 
Sessions” in early March, which would usually be consistent with its desired real GDP growth 
and inflation targets. In recent years, the government did not set a specific target for M2 
growth and only stated an objective “to keep money and credit growth broadly consistent 
with nominal GDP growth”. At the 2023 Central Economic Work Conference, the phrase was 
changed to “keeping total social financing and M2 growth broadly in line with economic 
growth and inflation target” amid deflationary pressures and weak nominal GDP growth. 
Most recently, the PBOC has started to downplay the importance of quantity-based 
indicators, emphasize the composition and structure of money and credit, and push forward 
the transition towards a more price-based monetary policy framework. 
Exhibit 54: M1 growth turned negative in 2024 
 
Source: PBOC, Goldman Sachs Global Investment Research 


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Bank Loans and Deposits 
Signal to noise ratio: **** 
Macro importance: *** 
Source: The People’s Bank of China 
Availability: Monthly from 1997 
Timing: Around 9 to 15 days after the end of each month 
Overview 
• 
Loan data published by the PBOC include loans made by depository institutions and non-
depository financial institutions (e.g., trust and insurance corporations). Recipients of loans 
are non-financial institutions and individuals, including non-residents. Loans to non-bank 
financial institutions have been added since the start of 2015. Loans denominated in CNY 
and foreign currencies are compiled separately and aggregated to provide total loans data. 
• 
Similarly, deposit data include deposits in CNY and other currencies. There is further 
information on deposits broken down by different sectors of the economy (e.g., households, 
government, and non-financial corporates) and by type of deposits (demand deposits, time 
deposits and other deposits). 
• 
Balance sheet of Monetary Authority: The main liabilities are currency issued and commercial 
bank reserves, and main assets come from foreign assets (funds outstanding for foreign 
exchange) and claims on other depository corporations (liquidity injection via monetary 
policy tools, such as OMO, MLF, PSL, relending, etc.). 
• 
Balance sheet of Other Depository Corporations: Other depository corporations include 
policy banks, commercial banks, credit unions and finance companies. Main liabilities come 
from deposits of households and non-financial institutions (around 60%), interbank 
borrowings (around 10%), and bond issuance (around 10%). 
• 
Source and uses of funds of financial institutions: This describes major sources and uses of 
commercial banks and other financial institutions. Sources can be deposits and financial 
bonds. Uses include loans and portfolio investments. 
Signal to Noise Ratio 
• 
The new loans and changes in loan stock announced by the PBOC are not always consistent 
with each other. The main reason for this discrepancy is non-performing loan (NPL) write-
offs, which have been subtracted from new loans data, while changes in loan stock are 
calculated after adjusting for these write-offs. It is difficult to reconcile fully the two series 
using announced data. 
Macro Importance 
• 
Like M2 data, loan growth data are useful for gauging the current monetary policy stance, as 
well as possible macro policy changes, when combined with other government-driven 


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activity indicators such as infrastructure FAI. Given that China’s capital markets are still 
under-developed, bank loans are the major source of external funding for the non-financial 
corporate sector. However, their importance has been falling as more alternative financing 
methods have become available and as the PBOC gradually shifts its focus from quantity-
based metrics to a price-based framework. 
Compilation and Reporting 
• 
Loans are broken down by borrower type – households, nonfinancial enterprises and 
government agencies/organizations, and nonbank financial institutions. To some degree, 
loans are broken down by tenor as well: short-term vs. medium to long-term loans for 
households, and short-term loans, bill financing, vs. medium to long-term loans for 
nonfinancial enterprises and government agencies/organizations. In addition, loans to some 
specific sectors (e.g., property, green and inclusive financing) are available, and the PBOC 
also publishes loans by sector on an annual basis with a lag of about one year and a half. 
But there is insufficient detail from the PBOC’s data to allow one to identify finer categories 
such as LGFV borrowings. The National Financial Regulatory Administration (NFRA) releases 
quarterly data of NPL ratios for banks.
[22] 
• 
The PBOC has expanded its definition of non-depository financial institutions for data 
releases of loans and deposits over the past decades, which also contributed to the 
inconsistency between the level and growth rate of loans and deposits. The latest two 
revisions happened in 2015 and early 2023. In 2015, the PBOC incorporated loan companies 
into non-depository financial institutions. In early 2023, the definition was further expanded 
to include consumer finance companies, wealth management companies, as well as financial 
asset investment companies. 
• 
The PBOC publishes the initial release of new loans and deposits flows first, with detailed 
breakdown data (such as balance sheets of the PBOC and other depository corporations) 
published a few days later. 
• 
Foreign Exchange Loans include foreign-currency-dominated loans extended to domestic 
and foreign residents/institutions by domestic and foreign financial institutions based in 
mainland China. 
Exhibit 55: The PBOC’s liquidity injection has been the major driver of balance sheet expansion 
since 2015 
PBOC balance sheet: assets 


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Source: PBOC, Goldman Sachs Global Investment Research 
Exhibit 56: Required reserves remain the lion’s share of the PBOC's liabilities 
PBOC balance sheet: liabilities 
 
Source: PBOC, Goldman Sachs Global Investment Research 
Exhibit 57: Loans make up more than half of Chinese banking system assets 
Banks’ assets by component (June 2024) 


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Source: PBOC, Goldman Sachs Global Investment Research 
Exhibit 58: Deposits remain the dominant funding source of China’s banking system 
Banks’ liabilities & equities by component (June 2024) 
 
Source: PBOC, Goldman Sachs Global Investment Research 
Total Social Financing 
Signal to noise ratio: **** 
Macro importance: **** 
Source: The People’s Bank of China 


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Availability: Monthly from 2002 
Timing: Around 9 to 15 days after the end of each month 
Overview 
Total social financing (TSF) includes both direct and indirect financing from the financial industry to 
the “real economy”. TSF consists of RMB bank loans, FX loans, trust loans (higher-yielding loans 
intermediated by trust companies), entrusted loans (lending from enterprises with favorable access 
to credit or excess cash, intermediated by banks), undiscounted bankers’ acceptance bills, net 
corporate bond issuance, equity financing, net government bond issuance, asset-backed securities 
of depository financial institutions and loan write-offs. The concept captures total financing of the 
real economy, mostly through different debt instruments. 
Signal to Noise Ratio 
• 
TSF statistics are intended to be a comprehensive measure, but historically there have been 
multiple rounds of revision on TSF data. In 2018, asset-backed securities and loan write-offs 
were included in TSF, and in 2018-2019, net government bond issuance and net corporate 
bond issuance statistical standards were also adjusted. In April 2024, the PBOC adjusted 
2024 year-to-date corporate bond net financing according to the latest industry 
classification results. These revisions also created occasional distortions to TSF growth 
measures. 
• 
The PBOC publishes the stock and flow data of TSF separately each month. However, the 
new TSF flows and changes of TSF stock can diverge from time to time. The divergence 
mainly comes from FX loans and corporate bond financing, for which the PBOC uses 
different statistical methodology for flow and stock data. 
• 
M2 and TSF growth can diverge from time to time, as M2/TSF refer to liabilities/assets of the 
financial sector’s balance sheet, respectively. By definition, TSF includes overall financing by 
the real economy (non-financial sectors), while M2 includes borrowings between financial 
institutions as well. FX purchases by the PBOC and fiscal deposit changes can affect M2 
growth but not TSF growth, while net equity financing and shadow banking credit extension 
(undiscounted bankers’ acceptance bills, trust loans, entrusted loans) can add to TSF but not 
M2. 
Exhibit 59: New loans and government bond net issuance have been the main drivers of TSF 
growth 


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*Other financing primarily include equity financing, loan write-offs and depository financial 
institutions’ ABS 
Source: PBOC, Goldman Sachs Global Investment Research 
Central Bank Policy Tools 
The PBOC uses the following tools to conduct monetary policy: 
1. Open Market Operations (OMO) 
2. Changes in the Reserve Requirement Ratio (RRR) 
3. Adjustments in interest rates (e.g., policy rate) 
4. Lending facilities (such as PSL, SLF, MLF) and relending programs 
5. Policy communications and window guidance (e.g., quarterly monetary policy reports and 
monetary policy committee meeting minutes, and administrative / regulatory instruments 
that affect both prices and quantity of credit supply from financial institutions) 
A full summary of the PBOC’s toolkit appears in Exhibit 60 toward the end of this section. 
Open Market Operation (OMO) 
Open market operations by the PBOC currently include: 
1. Repurchase (REPO) agreements, reverse repo 
2. Issuance of PBOC bills 
The PBOC started to conduct repo operations twice a week in 2004, using repo (reverse repo) to 
withdraw (inject) liquidity. It shifted to daily operations in January 2016, with brief notices explaining 
the rationale of open market operations. The central bank stopped using repo to withdraw liquidity 
from the banking system in late 2016. Repo / reverse repo maturities range from one week to one 
year. The most common tenors are one week for daily operations and two weeks for month-


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end/quarter-end operations. In July 2024, the PBOC introduced temporary overnight repo / reverse 
repo operations to gain better control over short-term market rates. 
The central bank used to issue PBOC bills domestically to offset passive liquidity injection from fast 
growing FX reserves in the 2000s. The tenors of domestically issued PBOC bills range from 3 months 
to 3 years. The central bank largely phased out the usage of PBOC bills onshore in late 2016, as FX 
reserves stabilized at slightly above USD 3 trillion. Since November 2018, the PBOC has been issuing 
bills in Hong Kong to manage CNH liquidity, with maturities ranging from 3 months to one year. 
Reserve Requirement Ratio 
The Reserve Requirement Ratio (RRR) is the ratio of deposits that financial institutions are required 
to keep at the central bank. It is effectively a tax levied on the banking system, and central banks can 
use it to manage commercial banks’ capacity to lend. The PBOC set up a system of “Discretionary 
Reserve Requirements” in April 2004, which allows for differential reserve requirements for different 
types of banks according to a number of criteria, including the capital adequacy ratio, the NPL ratio 
and the soundness of the internal control system. In 2016, this was upgraded to become the macro 
prudential assessment (MPA) system. According to the PBOC, the weighted average effective RRR 
for all financial institutions was 7.0% as of mid-2024 – 8.5% for large banks, 6.5% for medium-sized 
banks, and 5.0% for small banks.
[23] 
Adjustments in Interest Rates 
China has made significant progress in interest rate liberalization during the years before the Covid 
pandemic. Currently, the system is a hybrid of market and regulated interest rates. De jure, the 
interest rate system has been liberalized, though de facto this is not the case, as banks still rely 
heavily on LPR in their pricing model and the ceiling of deposits rates is still regulated by the 
“deposit rate self-regulatory mechanism” (存款利率自律机制) described below. 
Policy Interest Rates 
Policy interest rates are those that are directly controlled by the central bank, and changes to them 
will impact other interest rates. There are two policy interest rates in China: 
1. OMO interest rates: 7-day reverse repo is the most frequently used open market operation 
by the PBOC, so 7-day OMO rate is usually viewed as a policy interest rate in China. 
2. Medium-term Lending Facility (MLF) interest rate: Interest rate of medium-term PBOC 
liquidity injections in the interbank market. MLF is medium-term base money from the PBOC 
to commercial banks and policy banks backed by high-quality collateral. This tool is 
designed to provide guidance to the medium-term interest rate and to adjust the funding 
cost of the real economy. Banks’ Loan Prime Rate (LPR, more discussions below) is 
benchmarked against the 1-year MLF interest rate, and therefore changes to the MLF rate 
will impact funding cost of the real economy through the LPR. However, communications 
from the central bank in mid-2024 suggested that the PBOC may de-emphasize the MLF 
rate and potentially re-anchor LPR to the 7-day OMO rate. 
Other Interest Rates 
• 
Loan prime rate (LPR): This is the interest rate banks offer to their prime clients. LPR serves 


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as the pricing reference for bank lending, especially after the LPR reform in mid-2019, 
including lending to corporates and households. Currently, the LPR consists of rates with two 
maturities: 1-year LPR and 5-year LPR. Mortgage rates are benchmarked against the 5-year 
LPR. By definition, LPR is based on the quotes made by quoting banks by adding a few basis 
points to the interest rate of MLF. The LPR is calculated by the National Interbank Funding 
Center (NIFC), serving as the pricing reference for bank lending. At present, the LPR quoting 
banks are comprised of 20 banks. The quoting banks submit their quotes to the NIFC on the 
20th day of every month (postponed in case of holidays), in increments of 0.05 percentage 
points. The NIFC will calculate the arithmetic average of rates after excluding the highest and 
lowest submissions, and find its nearest integral multiple of 0.05% to be the LPR, which will 
then be published at 9 a.m. on the same day. In theory, the LPR is the average interest rate 
submitted by large banks; in reality, the PBOC can guide banks on their quotes and 
therefore impact the LPR. 
• 
The rediscount rate is the rate at which central banks discount commercial banks' unexpired 
paper. Since this rate affects borrowing costs for commercial banks, it can be used as a 
policy tool. However, this rate is infrequently adjusted in China. 
• 
The re-lending rate is the rate the PBOC uses to lend to financial institutions. Loans from 
the PBOC are a regular source of funding for Chinese commercial banks to support areas of 
policy priorities (e.g., SMEs, agriculture and rural development). 
• 
The PBOC pays interest on required reserves and excess reserves that financial institutions 
hold at the PBOC. The interest rate for excess reserves creates the floor for China's short-
term interest rate. 
• 
Deposit rate: In late 2015, the PBOC removed the official ceiling for the deposit rates, but 
an implicit ceiling set by “the self-regulatory pricing mechanism for market interest rates” (市
场利率定价自律机制) remains in place. The implicit ceiling for the deposit rates was 
anchored to the benchmark deposit rates, multiplied by a designated factor. But the 
benchmark deposit rates have remained unchanged since 2015. In June 2021, the PBOC 
improved the formation of the implicit ceiling for deposit rates by shifting towards 
benchmark deposit rates plus a few basis points. In April 2022, under the PBOC’s instruction, 
the “deposit rate self-regulatory mechanism” was established. This mechanism guides banks 
to set deposit rates based on the 10-year central government bond yields and the 1-year 
LPR. This implies that when corporates and households’ funding cost is guided lower by the 
PBOC through lowering MLF rate and LPR, banks can also reduce their deposit rates to 
protect their net interest margins to some degree. 
Other Liquidity Management Tools 
We summarize the major instruments for the PBOC to implement monetary policy in the table below. 
Exhibit 60: The PBOC utilizes a wide range of tools to affect the price and quantity of money 
and credit 
 


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Source: PBOC, Goldman Sachs Global Investment Research 


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Exhibit 61: A summary of the PBOC’s relending tools announced since 2020 
 
Source: PBOC, Goldman Sachs Global Investment Research 
Interbank Interest Rates 
Source: National Interbank Funding Center 
Availability: Daily data since 3 January, 1996 


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• 
China's interbank market was established in 1996, and market participants include all 
financial institutions. The interbank rate is a good indicator of liquidity in the market. 
Interbank Offered Rates and Interbank Repo Rates are the two main rates quoted in the 
market. 
• 
Interbank markets have been fully liberalized, with the interest rate paid on excess reserves 
the lower bound and the 7-day SLF rate the upper bound for interbank rates, creating an 
effective, albeit wide, interest rate corridor. Repo rates are typically lower than the offered 
rate, as they have higher liquidity and are collateralized. The 7-day repo rate for all financial 
institutions (R007) is the best proxy for the overall interbank market rate. 
• 
Large and medium banks are net suppliers of liquidity in the interbank market, while small 
banks and non-bank financial institutions are net borrowers (Exhibit 62). Around 90% of 
interbank funding trades are based on repo rates. Within repo trades, around 60% of them 
are based on repo rates for all financial institutions (R rates), while the rest are based on 
repo rates for depository institutions (DR rates). Interbank term funding is confined to the 
front end – more than 80% of funding trades are overnight borrowings. 
Exhibit 62: Large and medium-sized banks are net suppliers of liquidity, while small banks and 
non-bank financial institutions are net borrowers 
Net lending / borrowing in the interbank market 
 
Source: PBOC, Goldman Sachs Global Investment Research 
7-day repo rates (R007 & DR007): These are broadly considered to be monetary policy operating 
targets. The PBOC emphasized that the 7-day depository institution repo rate (DR007) would be a 
good proxy to monitor front-end interbank liquidity, and they monitor DR007 closely in their 
liquidity management and operations. R007 is typically more volatile and higher than DR007. DR007 
only accepts rates bonds as collateral, whereas R007 does not impose such restrictions on collateral. 


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Shanghai Interbank Offered Rate (SHIBOR): This consists of eight maturities: overnight, 1-week, 2-
week, 1-month, 3-month, 6-month, 9-month and 1-year. This is a simple short-term wholesale 
interest rate with no collateral required. SHIBOR is calculated as the arithmetical averages of 
interbank lending rates offered by 18 commercial banks. 
Negotiable Certificates of Deposits (NCDs): In December 2013, the PBOC launched the NCD 
program. NCDs allow banks to broaden their funding beyond deposits to manage liquidity. As 
designed by the PBOC, NCDs are priced on SHIBOR, which could improve the credibility of SHIBOR 
with actual transactions. 1y NCD serves as a benchmark market rate for short-term rates, and 1y 
MLF rate serves as an anchor for 1y NCD yields. The major issuers of NCDs are national banks, joint 
stock banks, and city banks. More than half of outstanding NCDs have maturity of 1 year, and more 
than 90% of outstanding NCDs are rated “AAA” by credit rating agencies. 
Policy Financial Bond (PFB) yields: Issued by the three policy banks in China (China Development 
Bank, the Export-Import Bank of China, and the Agricultural Development Bank of China), these are 
the non-government bonds with the highest credit rating because they are typically viewed as 
quasi-government bonds. Major tenors of the bonds range from 3 months to 10 years. 
Interest Rate Swap Rate (IRS rate): This refers to the fixed payment rate for interest rate swaps that 
exchange a fixed payment for a floating payment. Around 90% of IRS are referenced against the 
fixing of interbank 7-day repo rate (FR007), and the rest are mostly referenced against 3m SHIBOR. 
Therefore, FR007 swap rates are often used as the gauge for China interbank interest rates. 
Exhibit 63: The PBOC’s interest rate corridor has been established since 2015 
PBOC’s interest rate corridor 
 
Source: Wind, Goldman Sachs Global Investment Research 
Flow of Funds Accounts 
Source: National Bureau of Statistics 
Availability: Annual data since 1992 


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Timing: Long release lag, roughly two years after the period end 
Overview 
• 
These accounts record financial flows amongst five key economic sectors: non-financial 
enterprises, the financial corporate sector, the general government sector, households, and 
the rest of the world (ROW). The main financial instruments covered include deposits, loans, 
securities, insurance technical reserves, foreign direct investment, etc. Their main value is to 
provide a bird’s-eye view of the interdependence between the real economy and the 
financial economy from a macroeconomic perspective, describing inflows, outflows, and 
stock of funds in the economic system. 
• 
For China, the main features shown by the flow of funds data (Exhibit 64) are that: (1) 
Households have been a constant supplier of funds, initially almost all to corporates, then for 
a time to foreigners and lately more to the Chinese government; (2) The financial deficit (i.e., 
when investment>savings) in the non-financial corporate sector in China gradually narrowed 
from -12.3% in 1992 to -2.5% in 2020 before widening to -8.2% in 2022; (3) The financial 
deficit of the government sector in China was relatively stable at about -1% from 1992 to 
2004. However, from 2005 to 2015, the government sector changed from running a financial 
deficit to a surplus (except 2007), meaning that the Chinese government’s asset increases 
exceeded its liability increases. The government’s financial deficit reemerged after 2015 and 
has widened significantly since 2018, peaking at -8.2% in 2020 and remaining elevated at -
6.9% in 2022; (4) The rest of the world sector has absorbed funds except for 1993, that is, 
there has been a net outflow of domestic funds from China, with the financial balance 
widening from -1.3% of GDP in 1992 to a peak of -9.8% in 2008 before narrowing to roughly 
-2.3% in 2022. 
Compilation and Reporting 
• 
China’s Flows of Funds Accounts (FFA) is similar to the Japanese model, which is divided into 
physical transactions and financial transactions. The NBS is responsible for compiling data on 
physical transactions, while financial transactions are prepared by the PBOC. Due to limited 
original data on various sectors and industries (e.g., inventories), the classification of sectors 
and transaction items in the financial transaction section is usually more detailed than in the 
physical transaction section of FFA. 
• 
Compared with FFA in the US and Japan, FFA in China is still at the developing stage in 
terms of sector classification and transaction items, does not yet have quarterly or stock data, 
and has no reconciliation account. Moreover, the time lag of FFA releases in China is roughly 
two years for physical transaction tables, quite long compared with typical release lags of 
less than one year in other countries. Finally, China’s FFA lacks more granular decomposition 
of sectors. 
Exhibit 64: Households have been a supplier of funds to the Chinese government in recent 
years 
Financial surplus/deficit by sector 


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Source: NBS, PBOC 
Related GS Economics Publications 
“China’s monetary policy transmission efficiency weakened after Covid”, Asia Economics Analyst, 18 
February 2022 
“Navigating China rates: A shifting market to open further”, Asia Economics Analyst, 5 September 
2022 
“Demystifying the widening M2-TSF growth differential”, China Data Insights, 22 March 2023 
“China H2 monetary policy outlook: Expecting further easing to facilitate demand stimulus”, Asia in 
Focus, 2 August 2023 
“Understanding China’s Flow-of-Funds Accounts”, China Data Insights, 12 October 2023 
“Why China’s monetary easing has become less effective post-Covid”, Asia Economics Analyst, 18 
December 2023 
“Demystifying China’s falling M1-M2 growth differential: Drivers and implications”, Asia in Focus, 20 
January 2024 
“Navigating banking system liquidity in China”, Asia Economics Analyst, 3 March 2024 
“Why is the PBOC reluctant to cut policy rates?”, Asia in Focus, 2 June 2024 
Section VIII. Prices 
This section features the consumer price index, producer price index, agriculture and raw material 
prices, merchandise trade price index, and GDP deflator. (Refer to Section IV. Real Estate for land 
price and property price indices.) 


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Consumer Price Index 
Signal to noise ratio: **** 
Macro importance: ***** 
Source: National Bureau of Statistics (NBS) 
Availability: Monthly and annual from 1985 
Timing: Usually the 2nd week of the following month 
Overview 
• 
The Consumer Price Index (CPI) measures the price of a basket of goods and services that a 
typical household purchases. The basket is updated every five years. The last major update 
was in 2021, when the NBS updated the goods and services covered to reflect several new 
consumption patterns (e.g., food takeaways, new energy vehicles, ridesharing services), and 
adjusted the weights of each category according to an updated mix of household purchases. 
The latest index weights therefore may reflect some pandemic-related changes in spending 
patterns. Unlike most DM economies, the NBS does not publish the weighting scheme of the 
CPI basket. Weights mentioned in this section are our estimates (see Compilation and 
reporting below for details of our methodology and our most recent estimate of the basket’s 
composition). Category weights vary over time as prices change. 
Signal to Noise Ratio 
• 
Despite some data quality issues, we believe the CPI inflation rate is largely reliable, at least 
in terms of the direction of change. Some common criticisms of China’s CPI—not all 
necessarily warranted—include: 
1. Some prices, such as gasoline prices, are regulated despite being benchmarked to 
the broad trend of global oil prices. 
2. Its components and weights are revised infrequently, so the basket composition 
might diverge somewhat from actual consumption behavior. 
3. Compared with other countries, the NBS does not publish data on weights of 
different items in the CPI baskets. The NBS also does not publish “core goods” CPI 
inflation. 
4. The NBS revised down the weight of pork prices significantly in January 2021 to 
reflect the fading effect of the African Swine Fever (ASF) outbreak in 2019-2020, but 
the magnitude of the downgrade appears too large, mitigating the impact of hog 
cycles on China’s reported CPI inflation. 
• 
It is standard international practice to exclude property prices from the CPI basket. The CPI 
basket does have a housing component, which captures price changes in rent, implied rent 
for self-owned houses, utilities, and real estate management fees. Inflation for private 
housing (implied rent for self-owned houses) has correlated closely with rent inflation 


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(market rent) since 2011. However, home purchases are usually considered an investment 
following the international standard, and thus are not counted in the CPI basket. 
• 
In terms of other components, such as medical services and education, it is possible that the 
CPI data may fail to capture all the price changes. For example, CPI captures standardized 
education costs, such as school tuition fees; however, most of the “grey” charges levied by 
schools (tuition for supplementary classes, other administrative charges, etc.) are not 
included. This is likely to bias reported inflation downward at least slightly, and possibly 
significantly. 
• 
CPI inflation is also distorted by regulatory measures in various administered categories, 
including gasoline, electricity, education services, medical goods/services and telecom 
services. Even if global oil prices were to jump, regulatory measures would initially cap the 
extent of price increases faced by consumers. That said, the pass-through of international oil 
prices to domestic refined product prices is fairly timely and meaningful, occurring every 10 
working days if changes are large enough to warrant an adjustment in the period. The NDRC 
requires refineries to start to reduce profit margin towards zero once global crude oil prices 
reach $80/bbl and caps petroleum product prices via fiscal subsidies once crude oil prices 
reach and/or exceed $130/bbl. There is also a price floor for refined products when crude oil 
prices are below $40/bbl. 
Macro Importance 
Policymakers in China pay close attention to CPI data. Each year, the government sets a target for 
headline CPI inflation in the Government Work Report during the “Two Sessions” (e.g., 3% for 2021-
2023). The target is effectively a ceiling, and any rapid change in inflation tends to lead to swift 
policy communications and reactions (e.g., monetary policy by the PBOC and administrative policies 
by the NDRC). Monetary policy, proxied by the 7-day repo rate among depository institutions 
(DR007), tends to react to core inflation rather than food/PPI/headline CPI inflation over the past 
decade. 
Compilation and Reporting 
• 
The CPI basket has eight broad categories. Under each category, there are a few more 
detailed subcomponents (e.g., the “Transportation and communication” category is further 
broken down into “transportation equipment”, “vehicle fuel”, “vehicle use and maintenance”, 
“communication equipment”, “communication service” and “postal service”). The 
components of the CPI and their weights are determined by the regular household 
expenditure surveys and other non-regular surveys. The latest round of quinquennial 
updating was carried out at the beginning of 2021. 
• 
As in many other developing countries, food accounts for a relatively large share of 
consumer expenditure in China. However, the food share in the CPI basket has fallen as 
living standards in the country have improved. Currently, food (not including dining out) 
accounts for about 19% of the total CPI basket in China. 
• 
Pork accounts for around 2-3% of China’s CPI basket on average historically, higher than 
other major economies. As pork prices declined notably in 2021, the weight of pork prices 


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fell to and has remained below 2% since mid-2022. Hog cycles can be important in driving 
CPI inflation. In late 2019 and early 2020, for example, an outbreak of African Swine Fever 
(ASF) caused pork prices to more than double, sending CPI inflation above 4%. 
Exhibit 65: China’s CPI basket has a higher weight of food and lower weight of services than 
DM economies 
 
* China CPI weights are estimated by GS based on historical data. ** Prices of motor fuel are highly 
regulated in China. This table is based on CPI weights as of 2023. 
Source: BLS, Eurostat, ONS, SBJ, NBS, Goldman Sachs Global Investment Research 
• 
Data are collected at different retail outlets by statisticians 2-3 times a month. Goods that 
have large weights and are subject to frequent price changes, such as vegetables, are 
sampled every five days. On the other hand, goods/services whose prices are regulated by 
the government or are relatively stable, are only sampled once a month. 
• 
The NBS does not officially publish weights of the items in the CPI basket. It does mention 
weight changes in a few major categories following each quinquennial basket update. The 
NBS also comments on the contribution to year-over-year headline CPI for a few categories 
(mostly in food) in its monthly release, which allows us to track weights of select items more 
frequently. For subcomponents of each category, we follow NBS documentation and 
academic papers to construct a detailed weight scheme in a two-step procedure. Firstly, we 
use regression-based estimates and NBS releases to gauge the weights of major categories. 
Second, we estimate the relative importance of subcomponents in each category. China’s 
Yearbook of Household Survey provides shares of subcomponents in household 
consumption as a benchmark of CPI basket weights. That said, the weights of each category 
will vary over time as prices change. The coefficients only represent averaged weights over 
certain periods, and the actual weights may swing significantly for specific categories, such 
as pork prices. Exhibit 66 below summarizes our estimates of subcomponent weights in the 
2016 and 2021 baskets with major categories highlighted in blue. 
Exhibit 66: China’s CPI basket in detail 
 


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Source: NBS, Goldman Sachs Global Investment Research 
Other Issues 
• 
Core CPI is often a useful indicator of the underlying inflationary pressures because it 
excludes high volatility of food and energy prices. Even though China’s energy prices are 
regulated, they still show higher volatility than many other components. There is no separate 
energy component in the CPI, but energy inflation and its weight can be inferred from other 
data. If we estimate energy’s weight by regressing the headline CPI on food CPI, core CPI, 
and China domestic gasoline prices, the implied weight for energy is around 2%; if we 
estimate energy’s weight by summing up the estimated weights for “utilities” and 
“transportation fuel and parts” (which would suggest at least some over-estimation as these 
categories contain elements other than energy), the implied energy weight is around 7%. 
Producer Price Index (ex-Factory Price Index of Industrial Products) 
Signal to noise ratio: ***** 
Macro importance: **** 
Source: National Bureau of Statistics 
Availability: Monthly from 1996, annual from 1980 
Overview 
• 
The Producer Price Index (PPI) measures the price of industrial products when they are sold 
for the first time after production. 
Signal to Noise Ratio 
• 
We believe the PPI index is generally reliable. It is based on a large sample of 20,000+ 
products at factory gate prices. Both large and small enterprises, i.e., those above or below 
the standard minimum threshold, are sampled. 
Macro Importance 
• 
PPI is more sensitive to changes in investment demand, and to some extent export demand. 
Therefore, it contains more information on the state of the industrial cycle than the CPI. It 
can also be affected by controls on production in relevant sectors, such as the “supply-side 
reform” in upstream sectors (e.g., coal and steel) during 2015-2017. 
Compilation and Reporting 
• 
Currently, more than 50,000 industrial enterprises report prices twice a month (the 5th and 
20th day of the month). Weights of components are determined by the sales value. The 
basket is adjusted every five years, though adjustments can also be made during the interim 
if there are rapid changes in the production pattern. Similar to CPI, weights of the PPI are 
not disclosed and the NBS does not publish industrial sales value by sector either. We 
estimate the weighting scheme based on industrial revenues data, which serve as a proxy of 
sales value by sector.
[24] Our estimates suggest that computer, communication & other 


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electronic equipment takes up the largest share (around 11%), followed by the automobile 
sector (about 8%). 
• 
There are sub-indices of the PPI for consumer goods and producer goods. Consumer goods 
can be further broken down into food, clothing, daily articles, and durables. Producer goods 
can be further broken down into mining, raw materials, and manufacturing goods. Producer 
goods account for around 75% of China’s PPI basket. The other breakdown available is by 
industry, i.e., metallurgical, power, coal, petroleum, chemicals, machinery, building materials, 
timber, food, textiles, sewing, leather, paper, cultural/education articles, and others. 
Other Issues 
• 
There has been extensive commentary by media and analysts on the relationship between 
the PPI and the CPI. It is commonly assumed that the gap between the two represents the 
producer margin. While broadly true at a very high level, the linkage is more complicated 
when it comes to specific industries and companies. For instance, the transmission from PPI 
to CPI seems to vary for different components of the CPI. PPI inflation (particularly consumer 
goods) seems to matter for the CPI’s goods component excluding food, but not for services. 
• 
There is another price indicator named “input price for industrial enterprises” (also known as 
the “raw materials purchasing price index”), which measures the change in raw material 
input costs. The difference between this indicator and PPI can be informative for the margins 
of industrial enterprises, although other factors also affect margins. 
Exhibit 67: PPI inflation exhibits much larger swings than CPI inflation 
China inflation measures 
 
 
Source: NBS, CEIC, Goldman Sachs Global Investment Research 
Agriculture and Raw Material Prices 


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Source: Ministry of Agriculture and Rural Affairs, National Bureau of Statistics, National Development 
and Reform Commission 
Availability: Wholesale prices of agriculture products: daily from 18 November 2013; Retail prices of 
agriculture products: daily from 25 June 2015; Raw material prices: every 10 days from end-2013 
Overview 
• 
Wholesale prices of agricultural products cover major food items, including pork, vegetables, 
eggs, fruits, etc. Retail prices of agricultural products measure the average retail prices of 
major food items in 36 cities. Both are reliable and informative for high-frequency food CPI 
tracking. 
• 
Raw material prices measure wholesale and retail prices, which include ex-factory prices, 
transportation fees, profits, and taxes. Although these prices could deviate from ex-factory 
prices, they are still informative for PPI tracking. 
Compilation and Reporting 
• 
Wholesale prices are compiled by the Ministry of Agriculture and Rural Affairs based on a 
sample of 200+ markets. The prices of fresh vegetables and fruits are weighted average 
indices (Passche index) based on 28 major vegetables and 9 major fruits. The NDRC collects 
retail prices data from supermarkets and farmers’ markets on a daily basis. 
• 
The NBS collects raw material prices from around 2,000 distributors located across the 
country, covering 9 major categories, i.e., ferrous metals, non-ferrous metals, chemicals, 
petrol/gas, coal, non-metal minerals, agricultural products, fertilizer/pesticide, and forest 
products. The data are published on the 4th, 14th, and 24th of each month. 
Merchandise Trade Price Index 
Signal to noise ratio: *** 
Macro importance: *** 
Source: China Customs 
Availability: Monthly from 1993 (previous year = 100) 
Timing: Around 25th day of the following month 
Overview 
• 
The Merchandise Trade Price Index measures changes in goods export/import prices, 
compiled by China Customs. The price indices are based on unit value indices from detailed 
goods trade data. 
Signal to Noise Ratio 
• 
We believe the Merchandise Trade Price Index is generally reliable, as it is based on a 
bottom-up aggregation of detailed goods trade data. However, as the price index is not 
based on a fixed basket of products, the index may change notably due to changes in the 


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product mix of goods trade. 
Macro Importance 
• 
Given exports remain an important growth engine for China, the Merchandise Trade Price 
Index is helpful to gauge trade growth in real terms and the price competitiveness of 
Chinese exports in the global market. 
Compilation and Reporting 
• 
Export prices are calculated on an FOB (free on board) basis, which includes costs of 
delivering goods onto the vessels but not further costs, such as insurance or freight. Import 
prices are on a CIF (cost, insurance and freight) basis. 
• 
Price indices before 2014 were reported in USD terms, but they were subsequently changed 
to CNY terms. It is important to convert price indices into the same currency before applying 
them as deflators. 
• 
Four kinds of breakdown are available based on various sector classifications: the 
Harmonized System (HS), the Standard International Trade Classification (SITC), the Broad 
Economic Categories (BEC), and China’s industrial classification for national economic 
activities. 
GDP Deflator 
Signal to noise ratio: *** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: Quarterly from 2000, annual from 1978 (previous year = 100) 
Overview 
The GDP deflator covers the prices of all final goods and services in consumption expenditure, 
investment, and trade. 
Signal to Noise Ratio 
The GDP deflator is derived indirectly from official nominal and real GDP data. An ex-official of the 
NBS has briefly discussed the methodology of GDP accounting and related price/volume 
indices.
[25] However, it is difficult to replicate the calculation as many indices are not publicly available. 
Our estimates suggest that a weighted average with 60% PPI inflation and 40% CPI inflation could 
serve as a workable proxy of the GDP deflator in recent years. Academic literature suggests that the 
GDP deflator is generally overestimated during boom years but underestimated during downturn 
years to smooth the series of real GDP growth.
[26] 
Macro Importance 
The GDP deflator reflects inflationary pressures in the broad economy, whereas PPI only covers 
secondary industry. With the development of the service sector, the weight of PPI in the GDP 


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deflator has been falling over the past decade. 
Related GS Economics Publications 
• 
“Rebound in PPI poses limited risk to China's CPI inflation”, Asia Economics Analyst, 15 
January, 2017 
• 
“China: CPI inflation likely higher, but not a big constraint for monetary policy”, Asia in Focus, 
20 May 2019 
• 
“China: Gauging the impact of imported inflation and supply chain disruptions on CPI and 
PPI inflation”, Asia in Focus, 8 April 2022 
• 
“China Post-Reopening Inflation Outlook: A Bottom-Up Approach”, Asia Economics Analyst, 
3 February, 2023 
Section IX. Population and Labor Market 
This section features a detailed breakdown of total population and related indicators. We also take 
stock of China’s labor market, including employment, unemployment, wage indicators and our 
proprietary wage tracker. 
Total Population, Urban Population, Working Age Population, Migrant Population 
Source: National Bureau of Statistics 
Availability: Annual from 1949 
Timing: January of the following year 
Publication: Annual Statistical Communiqué on National Economic and Social Development 
Overview 
Total population refers to the total number of inhabitants of a particular area at a certain point of 
time. It can be broken down by urban vs. rural, female vs. male, and age structure. Total population 
is also available by province/city. 
Under the population data set, urban population is based on the total number of usual residents 
who have lived in an urban area for more than six months within a year, and “rural population” is the 
remainder after subtracting urban population from total population. There is also a set of data on 
registered population, split into agricultural and non-agricultural, available at the total national level 
and provincial level. 
Working-age population: There is no explicitly defined series from the NBS on this, but under 
population by age group, one sub-group captures the population aged 15-64, which follows the 
usual international definition of working-age population. China’s working-age population peaked in 
2013 and population aging is expected to accelerate in the coming decades. We note that while 
China’s official retirement age is 60 for men, 55 for female civil servants and 50 for female workers, a 
significant portion of elderly may find part-time or other employment. 
Migrant population: This refers to people who reside in a particular area but do not have 


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household registration (Hukou) in that area. It excludes the group of people who have household 
registration in another district of the same county in which they reside. China had nearly 300 million 
migrant workers in 2023 and around half of them work in manufacturing and construction sectors. 
This set of data is based on surveys conducted by the NBS annually. During years ending with “0,” 
such as 2020, the NBS conducts a comprehensive population census. During years ending with “5,” 
such as 2015, a survey based on 1% of the total population is conducted. In other years, a survey 
based on 0.1% of the total population is conducted. Population censuses and 1% population surveys 
are used as benchmarks to adjust annual surveys. 
Exhibit 68: China’s population structure in 2000 and 2020 
China’s population by age group 
 
 
Source: NBS, United Nations World Population Prospects 
Birth Rate, Death Rate, Natural Growth Rate 
Source: National Bureau of Statistics 
Availability: Annual from 1949 for birth rate and death rate, annual from 1981 for life expectancy 
Timing: January of the following year 
Publication: Annual Statistical Communiqué on national economic and social development 
Overview 


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Birth rate: Also called “crude birth rate”. It refers to the ratio of total number of new births to 
average population in a particular area within a particular time range. 
Death rate: Also called “crude death rate”. It refers to the ratio of total number of deaths to average 
population in a particular area within a particular time range. 
Natural growth rate: The crude birth rate minus the crude death rate. 
These rates are typically expressed in permillage (per-thousand). There are also other relevant 
population data in this release such as the dependency ratio. NBS data show that China’s population 
declined for two consecutive years – 2022 and 2023 – for the first time in six decades, with a record 
low birth rate of 0.639% in 2023. While China’s birth rate has trended lower over the longer term, it is 
possible that some of the recent decline could relate to temporary effects of the Covid pandemic. 
Signal to Noise Ratio 
Due to sampling difficulty, population-related data in China are in general exposed to uncertainties 
such as relatively big revisions (based on population census) and sampling errors. 
Macro Importance 
Population data are very important to economic analysis given their close link to labor supply, which 
is a key component of potential growth. 
• 
Working-age population is related to the potential labor supply to the whole economy, and 
a shrinking working-age population (as in China in the coming years) will be a headwind to 
potential economic growth. The NBS also releases another set of population data called 
economically active population, which refers to the population aged 16 or over who are able 
and willing to participate in the labor market, including both employed and unemployed 
people in both urban and rural areas. Its historical readings are revised based on new 
information in population surveys. It differs from the commonly used working-age 
population because it does not have an upper limit on age, while the working-age 
population usually excludes the population aged above 64. 
• 
The size of migrant population can serve as a barometer of cyclical employment conditions 
given they are in general more sensitive to changes in urban labor market conditions. 
Migration to urban areas is more likely when urban labor market conditions are strong. New 
migration should slow and existing migrants may return to agricultural work in rural areas 
when urban employment conditions are weak. 
Exhibit 69: China’s population and especially labor force growth slowed to record low levels 
 


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Source: NBS, UN, Goldman Sachs Global Investment Research 
Employment Data 
1. New Urban Jobs 
Signal to noise ratio: * 
Macro importance: *** 
Source: Ministry of Human Resources and Social Security 
Availability: Monthly from December 2009 
Overview 
This indicator measures the number of new jobs created in urban areas and is published monthly. 
Signal to Noise Ratio 
The new increases in urban jobs indicator reflects the gross increase (rather than net new jobs) in 
urban employment minus natural reduction in urban employment (which includes retirement and 
quits due to severe illness and death) and thus include those who were unemployed and re-
employed during a reporting period. For example, the new increases in urban jobs in 2022 were 
12.1mn while the net change in total urban employment was -8.4mn. The potential double-counting 
makes this indicator less informative in gauging the underlying employment trend. 
Macro Importance 
This indicator is one of the government’s employment targets (the other being surveyed urban 
unemployment rate). Therefore, it is important to track the progress of this indicator to gauge 
whether policymakers are likely to achieve their employment target; if not, policy support is likely to 
be forthcoming. 


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Compilation and Reporting 
Enterprises will report new increases in urban jobs to the Ministry of Human Resources and Social 
Security. Flexible employment is included in urban new jobs as well. Data coverage and reporting 
method appear sound, but as discussed above, the key issue with this indicator is its definition which 
does not capture layoffs and job changes — and therefore could involve significant double counting. 
2. Total Employment 
Source: National Bureau of Statistics 
Availability: Headline and by sector series are available at an annual frequency from 1952. Detailed 
breakdown has a shorter history. 
Publication: China Statistical Yearbook, China Population and Employment Statistical Yearbook 
Overview 
• 
Employment is defined as people who have worked for more than 1 hour during the 
surveyed week, as well as people who have positions but are on vacation/temporary leave. 
This series measures the overall employment of laborers 16 years of age or older in the 
economy based on sample surveys of the Chinese population. The NBS publishes the 
population and employment statistical yearbook since 1988. This yearbook provides data on 
employment/unemployment in urban areas with details about gender, age, industry, and 
education background. 
• 
There were 769mn people in China’s labor force in 2022 (including both employed and 
unemployed persons), accounting for 54.5% of China’s total population. Within the labor 
force, 60% is urban employment, 35% is rural employment, and the unemployment rate is 
around 5%. In the urban area, private enterprise (私营单位) workers constitute approximately 
two-thirds of total employment whereas non-private enterprise workers constitute the 
remaining one-third. 
• 
Flexible employment has seen strong expansion in China amid the development of the 
digital and platform economy. Flexible workers are not bound by formal employment 
contracts and enjoy fewer non-wage benefits compared to other workers. For example, 
many flexible workers are not included in public insurance schemes such as work-related 
injury insurance and unemployment insurance, and do not have paid maternity/paternity 
leaves. By the end of 2021, the number of flexible workers had reached 200 million in China, 
mainly in manufacturing, construction, delivery, platform livestreaming and ride-sharing 
companies. 
Exhibit 70: Labor force participants accounted for 54.5% of China’s total population in 2022 
 


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Note: The non-private enterprises include state-owned and controlled enterprises, foreign-funded 
enterprises and other enterprises. 
Source: NBS, Goldman Sachs Global Investment Research 
Exhibit 71: Tertiary industry employment has become more important in recent years 
Employment by industry 
 
 
Source: CEIC, Goldman Sachs Global Investment Research 


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Signal to Noise Ratio 
Although the total employment data provide useful information, growing flexibility in the labor 
market—such as self-employed online store owners, retired employees who are re-hired on a part 
time basis, as well as greater geographic mobility of labor—are making it difficult to measure the 
true picture. Historical readings are revised as new population surveys become available. 
Macro Importance 
The total employment data series is useful, as we see it as the only official/most reliable measure 
available for overall employment in the economy. 
3. Employed Persons in Urban Non-private Units 
Source: Ministry of Human Resources and Social Security 
Availability: Annual from 1952. Detailed breakdowns have shorter history. 
Timing: Annual data are released with a 10-month lag 
Overview 
• 
This data series covers the number of persons employed in government agencies and non-
private enterprises. It does not include private enterprises or self-employed individuals. 
• 
Details behind “employed persons in urban non-private units” include industry breakdown 
such as primary industry, manufacturing, construction, financial industry, property, wholesale 
and retail industry etc. 
• 
There is another set of data based on a subset of “employed persons in urban non-private 
units”, named “on the spot” staff (“在岗职工”). This “on the spot” dataset refers to persons 
who hold certain positions in the enterprise, and who still receive payments even though 
they are temporarily absent for reasons such as vacation, study or sickness. Compared with 
total employed persons in urban non-private units, this dataset excludes the retired but re-
employed population, soldiers, and religious workers. There are also industry breakdowns 
available for “on the spot” staff, but the series is released with a significant lag (e.g., 2020 is 
the latest data available). 
Signal to Noise Ratio 
Potential inaccuracies arise from intentional under-reporting by employers who pay social security 
contributions on a per-head basis. Frequent re-organization of SOEs and other previously state-
owned organizations has also complicated data collection. Because of local government pressures 
not to lay off employees, some companies nominally still employ workers but do not require them to 
go to work and only pay the minimum wage, potentially overstating employment during economic 
downturns. 
Macro Importance 
The usefulness of this series is limited because it only covers employment in non-private enterprises 
which can be distorted by major SOE reforms. For example, the level of this series fell in the late 
1990s and early 2000s from nearly 150mn to less than 110mn before rising to the peak of over 


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180mn in 2015. It has been declining gradually since 2015 and fell to 167mn in 2022. 
Compilation and Reporting 
Labor bureaus in different regions collect raw data, which are then reported to the Ministry of 
Human Resources and Social Security (MOHRSS) for compilation. 
4. Employed Persons in Private Enterprises and Self-employed Individuals 
Source: Ministry of Human Resources and Social Security 
Availability: Headline series available in annual frequency from 1990; detailed breakdowns have a 
shorter history 
Timing: Previously available one and a half years after the reporting period, but the latest data 
available is for 2019 
Overview 
• 
This indicator covers people who work in private enterprises/individual business, which have 
been registered at the local departments of industrial and commercial administration, 
including self-employed persons as well as helpers and hired laborers who work in individual 
households. There is industry breakdown information available, such as “employed persons 
in private enterprises and self-employed individuals” in manufacturing, construction, 
financial industry, wholesale and retail etc. 
• 
A subset of the “employed persons in private enterprises and self-employed individuals” 
dataset is “urban employed persons in private enterprises and self-employed individuals”. It 
covers only the portion of the employed persons in urban areas. Similar to all employed 
persons in private enterprises and self-employed individuals, this “urban employed persons 
in private enterprises and self-employed individuals” also has industry details available. In 
2019, China had 405mn employed persons in private enterprises and self-employed 
individuals, of which 263mn were in urban areas. 
5. Employment in Industrial Enterprises 
Source: National Bureau of Statistics 
Availability: Monthly from December 1998 
Timing: Around 27 days after the end of each month 
Overview 
This indicator is available monthly, and has a breakdown of employment for sub-industries including 
mining, various manufacturing sectors, and utilities. Note the construction sector does not belong to 
the “industrial sector” and instead is one of the two components of the secondary industry, together 
with the “industrial sector” (manufacturing, mining, and utilities). The by-industry employment is 
based on the same survey that the NBS conducts with industrial enterprises, and thus captures only 
the above-designated-size enterprises. Similar to the industrial profits data, continued monthly 
reports of this data are only available from 2011. 


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6. Employment Sub-indices Under Business Surveys 
Source: NBS for the employment sub-indices under the NBS manufacturing/nonmanufacturing PMI 
surveys; Caixin for the employment sub-indices under the Caixin manufacturing/services PMI surveys; 
Cheung Kong Graduate School of Business (CKGSB) for the employment sub-index under the 
CKGSB Business Conditions Index survey. 
Availability: Monthly frequency. NBS manufacturing PMI employment sub-index from January 2005; 
NBS nonmanufacturing PMI employment sub-index from January 2007; the Caixin manufacturing 
PMI employment sub-index from April 2004; the Caixin services PMI employment sub-index from 
November 2005. CKGSB BCI employment sub-index available from September 2011. 
Timing: NBS data available on the last day of the reporting month; Caixin data available within the 
first week after the reporting month ends; CKGSB index available around one week before the 
reporting month ends. 
Overview 
The sub-indices on employment under various PMIs and business surveys are in nature diffusion 
indices, based on questions like “whether the total employment in your company increased or 
decreased over the reporting month”. This set of data is not a direct report on the total number of 
employment, but provides a sense of the trend (particularly the breadth) of employment changes. 
Unemployment Data 
1. Urban Registered Unemployment Rate 
Source: Ministry of Human Resources and Social Security 
Availability: Quarterly during 1999Q4-2021Q4, annual during 1949-2021 
Overview 
• 
This series reports the share of urban registered unemployed persons in the urban labor 
force. Urban registered unemployment measures the number of urban residents who are 
capable of working, but are out of work, want to work, and register themselves as such. 
• 
The urban registered unemployment rate series has not been updated since December 2021, 
although the number of urban registered unemployed individuals is still being updated 
annually. As of 2023, there were 10.74 million urban registered unemployed individuals. 
Signal to Noise Ratio and Macro Importance 
• 
The usefulness of the “Urban Registered Unemployment Rate” is very limited because: (1) it 
covers only people who have an urban registration (Hukou) and excludes a large number of 
migrant workers who normally live in urban areas but do not have urban registrations, and 
(2) many unemployed people may not have registered with government agencies. As a 
result, the official urban unemployment rate has been very stable at around 4%. As more 
people seek jobs only through online platforms rather than through registration with local 
job market centers, this registered unemployment rate can be more biased. Having said that, 
the direction of changes may still be indicative, as small deviations from the stable trend 


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level are typically counter-cyclical and lag activity growth as one would expect. 
Other Issues 
• 
One useful source of information on the status of the labor market is the quarterly Labor 
Supply and Demand in Major Cities published by the MOHRSS (Ministry of Human 
Resources and Social Security). The report provides data on the number of jobs offered and 
the number of job seekers, as well as other information on the labor market. This report was 
collected together with the registered unemployment rate data through affiliated regional 
job centers. Although the indicator does not cover the whole labor market, as many 
employment activities do not take place at these employment centers, it nevertheless covers 
a significant portion of it, and can provide some useful color on changes in the labor market. 
2. Urban Surveyed Unemployment Rate 
Signal to noise ratio: ** 
Macro importance: *** 
Source: National Bureau of Statistics 
Availability: 31 major city irregular reports from June 2013, regular reports from January 2017; 
nationwide series regular reports from January 2017 
Timing: Typically around the 2nd/3rd week of the following month, released together with monthly 
activity indicators such as industrial production 
Overview 
• 
The surveyed unemployment rate is the ratio of urban surveyed unemployment to the sum 
of surveyed unemployment and employment. One would be defined as “unemployed” if 
he/she is 16 years or older, does not have a job currently, seeks for job actively within the 
three months prior to the survey, and can start working within two weeks if provided a job. 
• 
Prior to the Covid pandemic, large cities tended to have stronger labor markets and lower 
urban unemployment rates. However, Covid controls took a heavier toll on large cities where 
population is denser and mobility is higher, leading to sharper increases in the 
unemployment rate in large cities relative to smaller cities especially in 2022. By late 2023, 
the pattern had finally reversed, and urban unemployment rates in large cities dipped below 
the national average. 
• 
The NBS used to release urban surveyed unemployment rates by two broad age groups: 16-
24 year-old, and 25-59 year-old. These by age decomposition data were suspended after 
June 2023. The NBS resumed the release of unemployment rate data for the 16-24 age 
group in December 2023, but the new series excludes students from the survey and resulted 
in a much lower youth unemployment rate (e.g., 14.9% in December 2023 under the new 
definition vs. 21.3% in June 2023 under the old definition). According to the NBS, over 60% of 
the 16-24 year-olds in China are students. Under the new definition, the NBS releases 
unemployment rates for 16-24, 25-29 and 30-59 year-olds separately, around three days 


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later than general labor market statistics. 
• 
Since January 2021, the NBS releases survey-based urban unemployment rates for people 
with local Hukou and those without (i.e., migrant workers) separately each month. Although 
the history of these series is relatively short, they show migrant workers’ unemployment rate 
is more cyclical. The unemployment rate of people with local Hukou increased from 5.1% in 
2021 to 5.4% in 2022 before falling back to 5.2% in 2023 after the end of China’s zero-Covid 
policy. In contrast, the unemployment rate of migrant workers jumped from 4.9% in 2021 to 
5.7% in 2022 before dropping to 4.7% in 2023. 
Signal to Noise Ratio 
• 
Urban surveyed unemployment rate data shed light on the labor market conditions. When 
activity growth is weak, surveyed unemployment rates tend to increase. Surveyed 
unemployment rates show bigger swings than the registered unemployment rate and this 
set of data is therefore the best official data gauging unemployment pressures. Having said 
that, the NBS releases surveyed unemployment rates without seasonal adjustments. Due to 
the short history of the survey (regular publication started in 2017) and Covid distortions 
over the past few years, seasonal adjustments can be challenging. 
• 
One main drawback of the urban surveyed unemployment rate is the lack of coverage of the 
migrant worker population if they move back to rural areas. By design, the urban 
unemployment rate is based on urban household surveys and thus only captures the 
unemployed population in the urban area. When the economy is weak and unemployment 
pressure increases, migrant workers might return to rural areas. The urban surveyed 
unemployment rate therefore tends to underestimate the unemployment pressure during 
economic downturns. 
Macro Importance 
The urban surveyed unemployment rate data series is quite important as it is one of the two 
employment targets policymakers set each year in their government work reports. Policymakers 
have reiterated multiple times in recent years that stability of employment is their priority and 
bottom line. 
Compilation and Reporting 
This survey started some time ago (the first data that we have seen mentioned by officials was for 
June 2013), but results were not regularly released to the public until 2017. Surveyed unemployment 
rates are based on surveys of 340,000 households. It differs from the registered unemployment rate 
in the following ways: 
• 
Compilation method: The registered unemployment rate is derived from official registration, 
while surveyed unemployment is based on labor surveys; 
• 
Definition of unemployment: Surveyed unemployment follows ILO standards, while the 
registered unemployment rate is based on administrative records; 
• 
Data coverage: Registered unemployment is based on population with local Hukou, while 
surveyed unemployment also covers migrants in theory, though the number of migrant 


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workers in urban areas shifts with economic cycles as discussed above. 
Exhibit 72: Surveyed unemployment rates spiked in the initial stage of the Covid outbreak and 
during lockdowns 
Urban surveyed unemployment rate (after seasonal adjustment) 
 
 
Source: CEIC, Goldman Sachs Global Investment Research 
Exhibit 73: Surveyed youth unemployment rate dropped after the NBS changed the definition 
to exclude students 
Surveyed unemployment rates (CNY-distortion adjusted) 
 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
Wages 
1. Per Capita Wage Income from Household Income Survey 
Source: National Bureau of Statistics 
Frequency: Quarterly, annual 
Timing: Around 20 days after the end of each quarter, along with the release of GDP data 
Availability: Available in quarterly frequency from 2013 Q1, annual from 1998 
Overview 
Since Q4 2012, the NBS selects 160,000 households from urban and rural areas for direct survey on 
their income and expenditure (see Section V. Consumption for more details). The wage share of 
household disposable income has been relatively stable over the past decade, according to this 
survey, and wages accounted for 56% of household disposable income in 2023. 
2. Average/Total Wage of Employees in Urban Non-private Units 
Source: National Bureau of Statistics 
Availability: Headline series is available at a quarterly frequency from 2000 to 2014, annual from 
2015. Detailed breakdowns have a shorter history. 
Timing: Quarterly data are released at around 25 days after the end of the quarter; annual data are 
released around May of the following year. 
Overview 
• 
Average/total wages cover labor compensation to all employees, including wages, bonus, 
subsidies, etc. in urban non-private units. They are on a pre-tax basis and include rents, 
utility bills, etc. paid by employers to employees. In practice, when employees receive 
salaries, they are after-tax and social contribution deductions. The average wage is total 
wages divided by the average number of employees within the reporting period. 
• 
Similar to the employment data, quarterly data were suspended after 2014 and only annual 
data are available since then. Note that foreign companies and large domestic companies 
listed overseas are included in “urban non-private” enterprises, raising the average wage of 
urban non-private units. In 2023, the average wage of urban non-private enterprises was 
RMB121,000. 
• 
As this set of data only captures wage growth for non-private units, it may fail to capture the 
genuine situation of the labor market. Wages in non-private units are generally viewed as 
more sticky and less sensitive to economic growth/labor market developments. 
3. Average/Total Wage of Employees in Urban Private Units 
Source: National Bureau of Statistics 
Availability: Headline series is available in annual frequency from 2008. Detailed breakdowns have a 


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shorter history. 
Timing: Around May of the following year 
Overview 
Average/total wages cover labor compensation to all employees, including wages, bonus, subsidies, 
etc. in urban private enterprises. They are on a pre-tax basis and include rents, utility bills, etc. paid 
by employers to employees. In practice, when employees receive salaries, they are after-tax and 
social contribution deductions. In 2023, the average wage of urban private enterprises was 
RMB68,000
[27]. 
4. Average Income of Migrant Workers 
Signal to noise ratio: ** 
Macro importance: ** 
Source: National Bureau of Statistics 
Availability: Quarterly from Q4 2008 
Timing: One month after the end of the quarter 
Overview 
This measure is based on quarterly surveys of migrant workers conducted by the NBS since 2008. 
The survey sample covers 85,000 households in rural areas of all 31 provinces. Income of migrant 
workers are inherently difficult to capture as by definition migrant workers move around and often 
are employed in informal sectors. This set of data shows greater volatility than other wage indicators 
because migrant workers are often the most vulnerable in the labor market (e.g., they sometimes do 
not have proper labor contracts and are therefore not protected by the Labor Law) and face more 
significant income fluctuations with economic cycles. As a result, migrant worker income can be a 
more useful tracker on labor market turning points in China. 
5. Compensation to Laborers 
Source: National Bureau of Statistics 
Availability: Annually from 1992 
Timing: Lags can be as long as three years 
Publication: China Statistical Yearbook 
Overview 
This series is recorded under Flow of Funds data, and it covers all employed persons. 
Signal to Noise Ratio and Macro Importance 
• 
“Compensation to Laborers” in input-output data does not suffer from the problem of 
narrow coverage like the few series mentioned above, as it covers all employed persons. 


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Furthermore, it has the advantage of including non-monetary income, such as social security 
contributions. However, this measure has two limitations: (1) the data are reported with a 
significant delay (the last reported data were for 2020), and (2) many forms of non-
monetary compensations have to be estimated roughly, which affects the signal to noise 
ratio of the data. 
• 
The issue of under-reporting of income data also likely affects wage data, especially in terms 
of state sectors. In these sectors, official wages can be restricted by regulations, so there is a 
tendency to compensate employees via other types of payments. Furthermore, some private 
company owners can choose to forgo cash wages to help with the development of their 
own companies. This may also bias wage data to the downside. 
6. The CKGSB Business Conditions Survey of Labor Cost Expectations 
Source: Cheung Kong Graduate School of Business 
Availability: Monthly from September 2011 
Timing: One week before the end of the reporting month 
Publication: Cheung Kong Graduate School of Business (CKGSB) Business Conditions Index (BCI) 
Overview 
The CKGSB BCI survey asks companies in mainland China (mostly private companies) about their 
expectations for future labor costs over the next six months. The responses are then converted into a 
diffusion index on labor cost expectations, similar to PMIs (e.g., >50 means a majority of surveyed 
companies expect labor costs to increase in the next 6 months). This index appears to lead migrant 
worker income changes historically based on our analysis, although the two appear to have diverged 
recently. 
Exhibit 74: Wage growth diverged among different indicators 
Wage related indicators 
 


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Source: CEIC, Goldman Sachs Global Investment Research 
7. Other Wage Indicators 
Other indicators on wages include the minimum wage threshold, available by province, and 
corporate wage growth guidance, also available by province. These two indicators are released 
annually and can be informative for overall wage growth trends. Typically, the minimum wage 
threshold is not a binding constraint, since the informal sectors usually pay below minimum wages 
and legal enforcement is rare. In January 2004, China promulgated new minimum wage regulations 
that required local governments to raise minimum wages at least once every two years, and 
extended coverage to self-employed and part-time workers. By the mid-2010s, Guangdong 
province changed the minimum wage adjustment period to once every three years to slow the 
outflow of manufacturing industries to lower-cost inland provinces, and other provinces soon 
followed suit. At the end of 2015, the Ministry of Human Resources and Social Security (MHRSS) 
announced the minimum wage would be adjusted once every 2-3 years. 
GS China Wage Tracker 
Source: NBS, Goldman Sachs Economics Research 
Availability: Quarterly from Q1 2004 
Timing: Two weeks after the end of the reporting quarter 
Publication: GS China Proprietary Indicators update 
Overview 
• 
GS China wage tracker gauges nominal wage growth based on the average reading across 
different wage indicators. Currently, components of this wage tracker include NBS data on 
urban disposable income – wage income, migrant worker income, income sentiment index 
from the quarterly urban depositor survey conducted by the PBOC, and hiring wage from 
the online recruitment company Zhaopin.com. 


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• 
We first transform all input series into year-over-year growth rates and then calculate the 
average growth rate of these indicators. As we incorporate survey-based information as well 
as third-party data from the online recruitment company, our wage tracker is arguably a 
more comprehensive gauge of wage growth trend in China than official indicators such as 
the urban wage income reported by the NBS quarterly household income survey alone. Our 
wage tracker shows that wage growth has been decelerating from around 10% during the 
pre-pandemic period to around 4% in recent years, more consistent with listed companies’ 
reported wage growth than the official series. 
Exhibit 75: GS wage tracker suggests wage growth slowed to around 4% in recent years 
 
 
Note: The urban wage data from the NBS household survey started at 2014Q1, so we use the urban 
disposable income growth as proxy for urban wage growth from 2003Q1 to 2013Q4. 
Source: NBS, CEIC, PBOC, Zhaopin.com, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“China: Labor market consequences of the virus outbreak”, Asia Economics Analyst, 6 March 
2020. 
• 
“Tracking the labor market post the coronavirus”, China Data Insights, 14 June 2020. 
• 
“The implications of China's shrinking working-age population”, Asia Economics Analyst, 25 
May 2021. 
• 
“Population Aging, Pension System, and Individual Retirement Savings in China”, Asia 
Economics Analyst, 10 February 2023. 
• 
“Why has youth unemployment risen so much in China?”, China Data Insights, 22 May 2023. 
• 
“Tracking wage growth in China”, China Data Insights, 6 June 2024. 


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Section X. Government Finance 
This section reviews the following data and indicators on government finance: 
1. Government revenue, expenditure and balance data by the Ministry of Finance (MOF), on both 
monthly and annual basis. The government budget reports released during the “Two Sessions” every 
year set clear targets for these indicators. 
2. Central and local government debt by the MOF and China Central Depository & Clearing (CCDC), 
including outstanding amount and net issuance. These are among the main financing sources for 
the government’s balance. 
3. GS proprietary indicators related to government finance, including the augmented fiscal deficit 
(AFD) and augmented government debt (AGD). 
Government Revenue, Expenditure and Balance 
Signal to noise ratio: **** 
Macro importance: **** 
Source: Ministry of Finance 
Availability: Monthly from 1995, annual from 1950 
Timing: Around 3 weeks after the end of the month 
Publication: Ministry of Finance monthly releases, China Fiscal Statistical Yearbook 
Overview 
• 
In China, the national fiscal budget is composed of four accounts: the General Public Budget 
Account, the Government Managed Fund Account (GMF), the State Capital Operation 
Account, and the Social Insurance Fund Account. Fiscal data for the former two accounts are 
released monthly by the Ministry of Finance (MOF),
[28] while those for the remaining two 
accounts are released on an annual basis. 
Exhibit 76: Fiscal balance only involves a part of China’s fiscal budget system 


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Source: Goldman Sachs Global Investment Research 
• 
The fiscal balance that the government targets each year is the difference between revenue 
and expenditure in the General Public Budget Account. 
1. On-budget fiscal revenue comes from various taxes and charges the government imposes, 
categorized as tax and non-tax revenue, in the MOF budget. Government borrowings (via bond 
issuance) were included in the revenue statistics before 1994 but have been excluded since then. 
2. On-budget fiscal expenditure includes funds the government spends on goods and services that 
it provides, as well as on interest payments. These funds come from on-budget fiscal revenue and 
the proceeds raised through central government general bonds and local government general 
bonds.
[29] Conceptually, the fiscal balance is the difference between on-budget fiscal revenue and 
expenditure, although in China’s case there are some intricacies related to the fiscal deficit, especially 
the fiscal stabilization fund at the central government level and carryover/surplus funds at the local 
government level. 
• 
Top policymakers set the target for China’s official fiscal deficit at the “Two Sessions” every 
year, and this is directly linked to the quota for government bond net issuance under the 
General Public Budget Account. In other words, the official fiscal deficit (target) in value 
terms refers to the combined quota for the net issuance of central government general 
bonds (CGGB) and local government general bonds (LGGB). Unlike the official fiscal deficit, 
the effective fiscal deficit refers to the gap between on-budget fiscal revenue and 
expenditure, which equals the official fiscal deficit plus net drawdown of fiscal deposits and 
transfer from other fiscal accounts (i.e., the adjustment by central fiscal stabilization fund and 
carryover/surplus funds). 
• 
The Government Managed Fund (GMF) Account is comprised of around 20 sub-funds or 
sub-accounts (e.g., the railway construction fund, the national major water conservancy 
project construction fund), each of which is designed for a specific area. Land sales proceeds 
account for 82% of national GMF revenue in 2023 (vs. 86% in 2019). Moreover, land sales-
related spending, of which more than 70% is for land acquisition and re-development, 
accounts for around 55% of national GMF expenditure, based on 2023 data (vs. 90% in 2019). 
Land sales revenue and expenditure are collected and spent by local governments, subject 


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to approval by the central government. 
• 
In addition to the General Public Budget Account and GMF Account, there are two other 
accounts within the National Fiscal Budget – the State Capital Operation Account, and the 
Social Insurance Fund Account, although the operation of the latter two is less connected 
with the former two, and less relevant to the government’s fiscal stance. 
• 
Ranking these four Budget Accounts by revenue size based on 2023 data, the General Public 
Budget Account remains the largest (RMB21.7 trillion; before the adjustment by central fiscal 
stabilization fund and carryover/surplus funds), followed by the Social Insurance Fund 
Account (RMB11.1 trillion), the GMF Account (RMB7.1 trillion), and the State Capital 
Operation Account (RMB0.7 trillion). By expenditure size based on 2023 data, the General 
Public Budget Account (RMB27.5 trillion) is larger than the GMF Account (RMB10.1 trillion), 
followed by the Social Insurance Fund Account (RMB9.9 trillion) and the State Capital 
Operation Account (RMB0.3 trillion). 
Signal to Noise Ratio 
• 
Fiscal data are usually considered to be among the most reliable data since mis-reporting 
can be financially and politically costly. However, this may not always be the case. For 
example, the government disclosed cases of over-reporting of fiscal revenue by Liaoning 
Province in 2011-2014, and by Inner Mongolia in 2016. The MOF listed four methods used: 
(1) fake tax collection from corporates that is refunded back; (2) over-reporting of tax 
revenue from state asset sales or the right to use state assets, offset by over-reporting of 
government expenditure, often related to these transactions; (3) over-reporting of non-tax 
revenue such as revenue from the rights to use state resources; and (4) outright number 
cooking.
[30] In recent years, the government has strengthened its scrutiny over central and 
local statistics agencies to protect against data fraud and falsification. 
• 
An important definition-related issue is that on-budget fiscal balance does not cover the 
GMF, most of which is based on land sales-related revenue and expenditure, and is 
important to local governments. 
• 
As China’s economic growth trended down over the past decade on multiple headwinds, the 
government turned to more proactive fiscal policy, through both on-budget fiscal policy 
and quasi-fiscal spending in areas such as infrastructure. This increased the importance of 
tracking off-budget spending to assess the fiscal deficit and fiscal impulse. We have tried to 
“augment” the official fiscal policy measures by incorporating off-budget quasi-fiscal policy 
to obtain a comprehensive picture of the stance of China’s fiscal authority, as described in 
the “GS China Augmented Fiscal Deficit (AFD)” sub-section. 
• 
Fiscal revenue growth is also affected by the degree of effort in collecting taxes, which is 
often counter-cyclical (e.g., local governments may strengthen tax collection efforts amid 
economic downturns), and includes scheduled tax cuts, rebates and deferrals, as well as 
profit transfer from the operation of state-owned capital. 
• 
The government has some flexibility in controlling the pace of scheduled fiscal measures 
within a year, especially on government bond issuance and fiscal spending, resulting in 


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“frontloaded” and “backloaded” fiscal patterns. 
Macro Importance 
Government revenue and expenditure data are fundamental to understanding the stance of fiscal 
policy. In addition, growth rates in tax revenue also provide useful information on the strength of the 
economy. 
Compilation and Reporting 
• 
Reported fiscal revenues and expenditures cover both central and local governments. 
• 
Besides government agencies, a portion of state-controlled non-governmental institutions 
(事业单位) are also included. These institutions are set up, owned, and fully or partly funded 
by the government. However, as economic reforms progressed, many of these institutions 
changed in nature and became self-funded and profit-making. 
• 
China’s fiscal year is the same as the calendar year. Revenue and expenditure data are 
currently released by the MOF monthly. Complete annual data including additional 
adjustments, such as those related to the fiscal stabilization fund, only become available 
when the Ministry of Finance reports to the National People’s Congress in March of the 
following year and are then released in the Statistical Yearbook and Fiscal Statistical 
Yearbook. 
• 
State-owned enterprises (SOEs) were an integral part of the public sector and were 
previously included in government finance statistics. With progress in economic reforms, 
SOEs have increasingly become responsible for their own financing and are now mostly 
excluded from government financial accounts (e.g., the operation of state sole proprietors 
(国有独资企业/公司) is under the management of the State Capital Operation Account). 
• 
Major tax reforms in recent decades also had a meaningful impact on on-budget fiscal 
revenue, including several rounds of VAT reforms that have replaced the former business tax 
with VAT, and have lowered the effective VAT rate for the real economy. Individual income 
tax (IIT) reforms have relieved tax burdens on households, by raising the threshold for IIT 
exemption, and introducing IIT reduction items. 
Other Issues 
• 
Government expenditure is closely related to government consumption expenditure in GDP 
accounting. However, not all government expenditures are government consumption. Most 
notably, expenditures on capital construction, mine exploration, and new product R&D costs 
are counted as gross fixed capital formation (GFCF), as part of investment. 
• 
Gross local government revenue through General Public Budget and GMF accounts include 
three major sources, i.e., the on-budget fiscal revenue, GMF revenue (mostly land sales 
revenue), and transfer from the central government. Based on our estimates, the share of 
land sales revenue in gross local government revenue fell sharply in 2022-23 amid the 
prolonged property downturn and may trend lower in coming years. 
Exhibit 77: Gross local government revenue by major source 


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Gross local govt revenue breakdown vs. share of land sales revenue 
 
 
Source: MOF, Wind, Goldman Sachs Global Investment Research 
Local Government Debt 
Source: Ministry of Finance (MOF), National Audit Office, China Central Depository & Clearing 
(CCDC), Wind 
Availability: Monthly from November 2017, Annual from 2014 (also available for 2010, 2012 and first 
half of 2013 from audit reports) for local government debt data series; CCDC and Wind also provide 
high-frequency data for local government bond issuance and maturity 
Timing: Released monthly by MOF around 1 month after the end of month 
Overview 
• 
In China, local governments had been prohibited to borrow directly according to the budget 
law until 2009, when pilot programs were initiated to allow some local governments to issue 
bonds. At the beginning, the central government continued to help with issuance and 
repayment on behalf of local governments, and the quota of issuance was limited. In late 
2014, the budget law was revised to allow all local government to issue government bonds, 
subject to quota restrictions. 
• 
Given their limited on-budget revenue space and prohibition by law to borrow from banks, 
local governments turned to off-balance sheet entities (e.g., local government financing 
vehicles or LGFVs) as a financing and spending platform, but they had repayment 
obligations for a large part of those off-budget borrowings, which was technically not in line 
with the budget law. 
• 
In late 2014, the central government started to implement local government debt reform, 
and a series of documents were issued, including Document #43, which required local 


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governments to classify proper local government debt from existing LGFV debt and also 
reinforced the policy of no new borrowing through LGFVs. From 2015, local governments, in 
theory, could only borrow by issuing bonds, and local government debts accrued before 
2015 (excluding outstanding local government bonds) started to be swapped into bonds 
through the debt swap program. Since 2017, the government had launched several rounds 
of regulation on local government borrowing. These measures put additional caps on how 
and how much local governments could borrow apart from bonds, but local government 
debt continued to grow. Furthermore, the rapidly expanding Public Private Partnership (PPP) 
projects in 2016-17 became the substitute for financing functions that LGFVs had played, 
although the government has made continued efforts in containing the debt risks and 
improving the project implementation. There is so far no public information on the size of 
potential local government liabilities related to PPPs. 
• 
As of the end of 2023, outstanding (official) local government debt stood at around RMB41 
trillion (32% of GDP), including RMB16 trillion in local government general bonds (LGGB), 
RMB25 trillion in local government special bonds (LGSB), and RMB0.2 trillion in non-bond 
debt which local governments are obligated to repay
[31]. Compared to LGGB, LGSB are under 
the management of the GMF account, and thus their issuance quota is not subject to the 
restrictions from the official fiscal deficit. For LGSB-funded projects, there are prerequisites 
for the return on capital and thus require the approval of the National Development and 
Reform Commission and MOF, while LGGB-funded projects do not have such restrictions 
and thus cover more projects related to people’s livelihood. LGSB usually has had longer 
tenors than LGGB in recent years. 
• 
In recent years, the central government has become more cautious on local government 
hidden debt and strengthened related regulations, but local government implicit debt is 
unlikely to be eliminated anytime soon. 
Local Government Special Bond Uses 
Compared to on-budget fiscal expenditure (only 23% of which was spent on infrastructure-related 
areas in 2023), LGSB are mostly focused on infrastructure, and thus more commodity-intensive. MOF 
data suggest around two-thirds of LGSB proceeds have been spent on infrastructure-related 
projects in recent years, with municipal construction, industrial parks and transportation-related 
projects taking the lion’s share. 
Exhibit 78: Around two thirds of LGSB proceeds have been spent on infrastructure-related 
projects in recent years 
Investment target of local government special bonds (LGSB) issued 


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Source: MOF, Goldman Sachs Global Investment Research 
Unspent Local Government Bond Quota 
• 
Unspent local government bond (LGB) quota refers to the difference between the official 
limit for local government debt and the outstanding amount of LGBs, if any. In theory, there 
are two sources of unspent LGB quota available for use, including the quota never used 
before, and that used previously but renewed after debt repayment (without rolling over). 
• 
Based on our estimates, as of end-2023, the unspent LGB quota was RMB1.4 trillion (vs. 
RMB2.6 trillion at end-2022), including RMB680 billion for local government general bonds 
(LGGB) and RMB750 billion for local government special bonds (LGSB). Policymakers can 
utilize this buffer to raise extra-budget funding if necessary and upon approval. For example, 
in August 2022, the then-Premier Li Keqiang approved the use of RMB500 billion unspent 
LGSB quota and required local governments to fulfill the quota by end-October 2022, in an 
effort to support fiscal spending and offset strong growth headwinds. 
• 
However, unspent LGSB quota has been unevenly distributed across provinces. Some 
provinces with significant funding challenges do not have sufficient LGSB quota (e.g., 
Qinghai, Gansu, Heilongjiang and Jilin), while those with more quotas may not have an 
immediate need to raise funds (e.g., Beijing and Shanghai). As such, in 2022, policymakers 
assigned 30% of the RMB500 billion additional quota for regional redistribution, in favor of 
provinces with more ready-to-go projects. 
Local Government Debt Swap Program 
• 
To reduce the servicing costs for outstanding debt and restructure some non-performing 
debt (especially for local government implicit debt), debt swap programs could be one 
option. There was a large-scale local government debt swap program during 2015-18. The 
then-Finance Minister Lou Jiwei in August 2015 indicated that the outstanding amount of 
non-bond liabilities that local governments had obligations to repay was around RMB14.2 
trillion. According to the National Audit Office, local governments issued a total of RMB12.2 


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trillion bonds (with low interest rates) to replace these non-bond debts (with high interest 
rates). 
• 
There was another, albeit smaller, round of local government debt swap program more 
recently. At the July 2023 Politburo meeting, President Xi pledged to launch “a basket of 
local government debt resolution plans”. Subsequently, media reports suggested that 
provincial governments were allowed to raise RMB1.5 trillion via refinancing bond sales, 
utilizing previously unspent LGGB and LGSB quota, to repay local government implicit debt. 
Additionally, banks were also asked to roll over part of local government implicit debt to 
manage potential risks. 
GS China Augmented Fiscal Deficit (AFD) 
Source: Goldman Sachs Economics Research 
Availability: Monthly from January 2004 
Timing: Around 25 days after the end of month 
Release: GS China Proprietary Indicators update 
Overview 
• 
The stance of fiscal policy is typically measured by the fiscal balance to GDP ratio, after 
seasonal adjustments. An increase in this ratio (i.e., a narrowing in the deficit) means a 
contractionary fiscal policy, and a decrease in the ratio (i.e., a widening in the deficit) 
indicates an expansionary fiscal policy. 
• 
In addition to on-budget fiscal tools, the government can affect the economy through off-
budget activities (which are also referred to as “quasi-fiscal policy”). The most important and 
frequently used measure is the infrastructure investment funded by LGSB, policy banks, 
LGFVs, other state-owned enterprises (e.g., China Railway Corporation) and Public-Private 
Partnership (PPP) programs. 
Compilation 
• 
We “augment” the official fiscal policy measures by incorporating off-budget quasi-fiscal 
policy to obtain a comprehensive picture of the stance of China’s fiscal authority, and to 
examine its implications for the growth of the Chinese economy. Specifically, our measure of 
AFD is a sum of effective on-budget and off-budget fiscal deficits. We estimate the off-
budget spending by major channels that finance quasi-fiscal activities. This further includes 
central government special bonds (CGSB), LGSB, net land sales revenue (excluding the costs 
of land acquisition and redevelopment), LGFV bonds, railway construction bonds, policy 
banks support (mostly via policy bank bonds and PBOC’s PSL), shadow banking loans, etc. 
As in some years there could be a significant time lag between LGSB issuance and proceeds 
spending (e.g., in 2021), we use a projected pace of LGSB proceeds spending as the input 
for LGSB in AFD estimates.
[32] 
• 
Our AFD metric is constructed with monthly frequency, in order to monitor the 
government’s overall fiscal stance in a timely manner and accordingly serve as a relatively 


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reliable policy parameter input for our forecasting of the real economy. By interpolating GDP 
data, we are able to derive a monthly AFD-to-GDP ratio. To mitigate potential distortions 
from residual seasonality, we also focus on 3-month and 12-month moving averages of 
AFD-to-GDP ratio. 
• 
We note several caveats to this approach: First, some off-budget government financing 
channels may not be fully captured due to the availability of monthly data, such as LGFV 
loans and PPP projects. Second, the government’s off-budget activities might not be 
confined to infrastructure spending and the sectors we choose, and the share of 
government spending on debt repayment/servicing has been trending up over the past 
decade. 
Exhibit 79: Augmented fiscal deficit peaked in 2020 amid the initial Covid outbreak, and 
remained relatively wide in recent years 
 
Source: MOF, CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research 
Exhibit 80: The augmented fiscal deficit has shown several rounds of expansion since 2015 


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Source: MOF, Wind, Goldman Sachs Global Investment Research 
Other Issues 
• 
In the Government Work Report and budget report released during the “Two Sessions” 
every year, the government usually unveils the annual target for some key fiscal statistics, 
including official fiscal deficits (in both value terms and percentage of GDP terms) and LGSB 
net issuance quota. Official fiscal deficits are equivalent to the sum of government target for 
the net issuance of central government general bonds (CGGB) and LGGB. 
• 
On very rare occasions, the government may consider approving a central government 
special bond (CGSB) quota. This happened for the first time in 1998 to replenish the equity 
capital of big four banks, the second time in 2007 to establish China Investment Corp, the 
third time in 2020 to counteract the initial Covid outbreak, and the fourth time in 2024 for 
key projects and strategically important initiatives (e.g., high-tech manufacturing, 
urbanization, food and energy supply chains, green industries, equipment upgrade). CGSB 
are part of the central government official debt, but different from CGGB. Similar to local 
government special bonds, CGSB are managed under the Government Managed Fund (GMF) 
Account, outside the General Public Budget Account, and therefore their issuance does not 
lead to a higher official fiscal deficit. 
• 
Our AFD is similar in spirit to the IMF’s “augmented government deficit” metric.
[33] However, 
in recent years IMF’s measure has been larger than our estimate. Our AFD differs from the 
IMF measure on two major dimensions: 1) on the local government implicit debt financing 
(mostly through LGFVs), our measure captures LGFV bonds, net land sales revenue, and part 
of trust loans, to make sure our data series can be updated on a timely (monthly) manner. 
By comparison, the IMF measure leverages their own channel checks and projections to 
gauge LGFV loan financing and shadow banking financing (including trust loans, entrusted 
loans and PPP related debt), but this is only reported on an annual basis. 2) On the central 
govt implicit debt financing, our measure covers debt financing by policy banks and China 
Railway Corp., mainly via bond financing channels, e.g., policy bank bond net issuance, China 


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Railway construction bond net issuance, and PBOC's pledged supplementary lending (PSL). 
By comparison, the IMF measure does not fully include all these funding channels. 
Exhibit 81: A summary of central government special bond (CGSB) issuance in history 
 
Note: We exclude CGSB issuance for refinancing purposes (e.g., in 2017 and 2022). Tenors are 
ranked by the amount of bond issuance in each batch. 
Source: MOF, NBS, Goldman Sachs Global Investment Research 
GS China Augmented Government Debt (AGD) 
Source: Goldman Sachs Economics Research 
Availability: Annual from 2004 
Timing: Around the middle of the following year 
• 
Our proprietary Augmented Government Debt (AGD) measure aims to capture all debt 
raised by the broad government sector (including its agents). 
• 
We divide the AGD into two categories based on issuers: official debt and implicit debt. 
Official government debt includes debt directly borrowed by central and local governments, 
such as CGGB, CGSB, LGGB and LGSB. Implicit government debt is off the government 
budget, including debt of government agents such as policy banks, China Railway and LGFVs, 
which has implicit guarantees from either central or local governments, or at least perceived 
by markets as having such guarantees. Of course, some of these implicit debts are backed 
by high-quality assets such as railway and land, while others might not. Within the AGD, 
there is a very small proportion raised through offshore dollar bond markets, mainly by 
policy banks and LGFVs, but it has been counted into the balance sheet of these entities. 
• 
There could be some double counting issues between the above-mentioned sub-categories, 
as policy banks may hold some liabilities of the government, LGFVs and China Railway. To 
adjust for the distortion, we exclude PSL and a fraction of remaining liabilities from policy 
bank debts. 
• 
Based on our estimates, China’s AGD reached RMB166 trillion in 2023 (or 131% of GDP), 
marking a more than ten-fold increase from its 2008 level in RMB terms (RMB14 trillion, or 


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43% of GDP). The ratio of central to local government debts, and that of official to implicit 
government debts, were both close to 40% vs. 60% in 2022-23. Offshore government debt 
(mostly raised through offshore dollar bonds) accounted for only 1% of AGD. China’s AGD 
has been rising rapidly over the past decades, led mainly by implicit and local government 
debts. 
• 
Our estimates for implicit local government debt (or LGFV debt) are based on a bottom-up 
approach using firm-level balance sheet data of LGFV companies, plus some conservative 
assumptions. We estimate total LGFV debt reached ~RMB62 trillion at end-2023, and our 
estimates for historical data are broadly in line with academic and policy studies. For 
example, according to a team led by Mr. Zhang Xiaojing at the China Academy of Social 
Science, total LGFV debt ranged from RMB30 trillion to RMB50 trillion at end-2016, hinging 
on different definitions used. Professor Bai Chong’en at Tsinghua University estimated total 
LGFV debt at RMB47 trillion as of mid-2017. In its 2018 China Financial Stability Report, the 
PBOC cited an unnamed province as an example in highlighting local governments’ surging 
implicit debt: the unnamed province’s implicit debt was 80% higher than its official debt.
[34] 
• 
In general, government financing through official debt usually has lower costs and longer 
tenors than that through implicit debt, for both central and local governments. The financing 
cost for central government debt by nature is also lower than that for local government debt. 
Among major financing channels of local governments, LGSB has become an increasingly 
important funding source. The financing cost of LGSB is higher than central government 
bonds (CGB), policy bank bonds and LGGB, but much lower than implicit financing channels 
such as LGFV bonds and railway construction bonds. Moreover, CGSB and LGSB usually have 
a longer tenor due to their focus to fund major infrastructure projects. 
Exhibit 82: A breakdown of China’s augmented government debt based on 2023 data 
 
* We adjust for double counting issues by excluding some debt from policy banks (deducted from 
“other liabilities”). Numbers outside and inside the parentheses refer to the outstanding amount of 


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debt (in RMB value terms) and its proportion in AGD, respectively for each category at end-2023. 
CGGB, CGSB and CR refer to central government general bond, central government special bond 
and China Railway, respectively. 
Source: MOF, Bloomberg, CEIC, Wind, Goldman Sachs Global Investment Research 
Exhibit 83: A comparison of major government financing channels 
 
*Interest rates for LGGB and LGSB refer to 3y yield to maturity of LGB due to data availability. Usually, 
LGSB yield is slightly higher than LGGB yield. 
Source: MOF, Bloomberg, Wind, Goldman Sachs Global Investment Research 
Related GS Economics Publications 
• 
“Tracking China’s fiscal stance: Beyond the official fiscal balance”, Asia Economics Analyst, 18 
January 2016 
• 
“China: Fiscal stimulus a potent policy lever early this year, but moderate headwinds building 
in H2”, Asia in Focus, 20 June 2018 
• 
“Can Chinese fiscal policy be “more proactive” in H2?”, Asia in Focus, 29 July 2018 


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• 
“China: Navigating regions with high reliance on the property sector”, Asia Economics 
Analyst, 16 June 2022 
• 
“China fiscal update: Borrowing more from the future to offset increased headwinds”, Asia in 
Focus, 19 September 2022 
• 
“China: Local government special bond, an increasingly important source of government 
financing”, Asia in Focus, 9 February 2023 
• 
“Population Aging, Pension System, and Individual Retirement Savings in China”, Asia 
Economics Analyst, 10 February 2023 
• 
“The Size, Form and Implications of China’s Growing Government Debt”, Asia Economics 
Analyst, 2 April 2023 
• 
“China: A likely return of PSL-backed property easing, in a different way”, Asia in Focus, 3 
January 2024 
• 
“China: Beijing’s Balancing Act between Infrastructure Stimulus and LGFV Deleveraging”, Asia 
in Focus, 6 March 2024 
• 
“Potential Reform Remedy for China’s Fiscal Challenges”, Asia Economics Analyst, 9 June 
2024 
The authors would like to thank Maggie Wei, a former member of the Asia Economics team, for her 
contribution to this book. Bernadette Chan and Christopher Dixon provided extensive editorial and 
formatting assistance. 
1 ^ “Census X-12” is a program originally developed by the US Census Bureau in the 1960s (“x” is 
for experimental, and 12 is for the twelfth in the series). 
2 ^ See the NBS definitions for these two indicators: 
https://www.stats.gov.cn/hd/cjwtjd/202302/t20230207_1902275.html. 
3 ^ In 1998, the NBS divided the scope of industrial statistics into two parts: above- and below- 
designated size. NBS has defined the above-designated size as industrial legal entities with annual 
principal business revenue of RMB20 million or more since 2011. 
4 ^ In order to comprehensively capture the revenue of industrial enterprises, NBS started to 
disclose “operating revenue” instead of “prime operating revenue” in 2019. 
5 ^ Caixin took over sponsorship from HSBC of Markit’s China PMI and officially added Financial 
Data Services in July 2015. 
6 ^ In late 2023, Chinese policymakers reportedly ordered 12 heavily indebted local governments 
(i.e., Guizhou, Tianjin, Yunnan, Inner Mongolia, Liaoning, Jilin, Chongqing, Guangxi, Heilongjiang, 
Gansu, Ningxia, and Qinghai) to curtail fiscal spending and halt some infrastructure projects, in an 
effort to manage debt payments and reduce LGFV default risks (i.e., the "Document #47"; 
unconfirmed by official source yet). Based on our estimates, the 12 indebted provinces accounted 
for around 26% of China’s infrastructure investment, 22% of FAI and 18% of GDP in 2022-23. 
7 ^ See https://www.gov.cn/xinwen/2020-06/01/content_5516649.htm. 


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8 ^ For reference, Soufun provides the underlying data and CREIS is the index compiler. 
9 ^ Wind uses residential housing data for some cities (e.g., Beijing) but total housing data for others 
(e.g., Hangzhou) in its 30-city sample, but it is mainly due to data availability. Hence, we did not 
count this as one of the statistical discrepancies. 
10 ^ See http://www.stats.gov.cn/sj/zxfb/202305/t20230516_1939489.html. 
11 ^ See for example “Housing survey probes sensitive vacancy statistic”, Global Times, 6 September, 
2010 (http://www.globaltimes.cn/content/570440.shtml); and “China property firm apologizes for 
vacancy rate report after public debate”, Reuters, 11 August 2022 
(https://www.reuters.com/markets/asia/china-property-think-tank-apologises-high-vacancy-rate-
report-2022-08-11/). 
12 ^ See the official website for more information: 
http://real.wharton.upenn.edu/~gyourko/chineselandpriceindex.html 
13 ^ In mid-2023, the NBS started to release a new series called “retail sales of services”, but it only 
shows the year-to-date year-over-year growth and has a very short history. 
14 ^ See the official release at: https://www.stats.gov.cn/sj/ndsj/2021/html/sm06.htm. 
15 ^ Note that the housing component contains rent, cost of decoration and utilities, and property 
management fees. Purchases of property are not included. For self-owned properties, owners’ 
equivalent rents are included in the “housing” category of consumption expenditures. 
16 ^ The Ministry of Commerce used to compile a monthly series of year-over-year sales growth 
based on data from 5000 large retailers, but this was discontinued after July 2016. 
17 ^ See the NDRC website: 
https://www.ndrc.gov.cn/fggz/jyysr/jysrsbxf/202003/t20200311_1222869.html. 
18 ^ As described in Anna Wong, “China’s Current Account: External Rebalancing or Capital 
Exodus?”, presentation at the 8th annual International Conference on the Chinese Economy, Hong 
Kong, January 13, 2017. 
19 ^ For the document, see https://www.imf.org/en/Publications/Manuals-
Guides/Issues/2016/12/31/Balance-of-Payments-Manual-Sixth-Edition-22588. 
20 ^ Mirror data refers to data reported by counterpart economies. For example, the mirror data for 
China’s inward FDI from the US is the outward FDI to China reported by the US. 
21 ^ “Assessing Reserve Adequacy—Specific Proposals”, International Monetary Fund, April 2015 
(see http://www.imf.org/external/np/spr/ara/). 
22 ^ See the latest rules on NPL classification effective 1 July 2023 at: 
https://www.gov.cn/zhengce/2023-02/11/content_5750184.htm. 
23 ^ See the changes of RRR since 2018 at: http://www.pbc.gov.cn/rmyh/4027845/index.html. 
24 ^ Industrial revenues cover income from product sales and other sources, such as labor provision 
and transfer of asset usage rights. However, industrial sales value only covers the income from 


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product sales. 
25 ^ See Xianchun Xu, "Accurately Understanding China’s Current Gross Domestic Product 
Accounting", Statistical Research (in Chinese), May 2019. 
26 ^ See Pingyao Lai and Tian Zhu, "Deflating China's nominal GDP: 2004–2018", China Economic 
Review, 2022. 
27 ^ According to NBS' definition, the non-private units include state-owned and controlled 
enterprises, foreign-funded enterprises and other enterprises, and the private units here mainly refer 
to SMEs. 
28 ^ Since 2019, MOF has released January-February combined fiscal data only, rather than for 
January and February separately, to avoid Chinese New Year related distortions. 
29 ^ Proceeds raised through central government special bonds (CGSB) and local government 
special bonds (LGSB) are managed under the GMF account. 
30 ^ See http://www.mof.gov.cn/zhengwuxinxi/caizhengxinwen/201701/t20170120_2524620.htm fo
r example. 
31 ^ The actual amount of LGFV debt which local governments have obligations to repay or have 
provided implicit guarantee on could be larger than the MOF estimates in recent years, based on 
our estimates. 
32 ^ For normal years, we assume local governments to spend 50% of LGSB proceeds in the month 
when the bond was issued, and 30% and 20%, for the following two months, respectively. However, 
for 2021 as an exception, we assume local governments to spend 20%, 30% and 50% of LGSB 
proceeds in the bond issuance month and the following two months, respectively. 
33 ^ The IMF pioneered research to estimate the augmented fiscal balance for China, though with 
annual frequency and a different approach compared to our AFD metric. See Yuanyan Zhang, 
Steven Barnett, “Fiscal Vulnerabilities and Risks from Local Government Finance in China", IMF 
Working Paper, January 2014; Rui Mano and Phil Stokoe, “Reassessing the Perimeter of Government 
Accounts in China,” IMF Working Paper, December 2017. 
34 ^ See 
http://www.pbc.gov.cn/jinrongwendingju/146766/146772/3656006/2018110716123679821.pdf. 
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