{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"longbench-v2","formal_name":"LongBench v2","introduction":"LongBench v2 evaluates deep understanding and reasoning over long contexts through multiple-choice questions. 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Introduction \\nOverview \\nHow to Use the Ranking for Each Data Series \\nThe Ten Most Frequently Cited Chinese Economic Statistics \\nA Typical Cycle of China’s Statistical Releases \\nBasics of Interpreting the Numbers \\nEvaluating Data Surprises  \\nList of Acronyms \\nSection II. Overall Activity and Production \\nGross Domestic Product \\nIndustrial Production (Value-added of Industry) \\nServices Industry Output Index \\nElectricity Production and Consumption \\nRail Freight Traffic \\nTotal Profits of Industrial Enterprises \\nPurchasing Managers’ Indices \\nGS Proprietary Activity Measures \\nSection III. Investment \\nFixed Asset Investment \\nProjects Started and Under Construction \\nOther Investment-related Data \\nSection IV. Real Estate \\nReal Estate Investment \\nLand Transactions \\nHousing Starts, Under Construction and Completions \\nHome Sales \\nHome Inventory \\nProperty Price Measures \\nLand Price Indices \\nGS Proprietary Indicators Related to the Real Estate Sector \\nSection V. Consumption \\n\\n\\n 2 / 158 \\n \\nRetail Sales of Consumer Goods \\nHousehold Income and Expenditure Survey \\nRetail Sales of Major Offline Retailers Reported by China National Commercial Information Center \\n(CNCIC) \\nAuto Sales \\nConsumer Confidence Index \\nSection VI. External Sector \\nMerchandise Trade \\nServices Trade \\nBalance of Payments \\nForeign Direct Investment \\nExternal Debt \\nForeign Exchange Reserves \\nExchange Rate Terminology and Offshore RMB Development \\nCNY Trade-Weighted Indices \\nGS China “Outside-In” Trade Measures \\nGS China FX Flow Metric \\nSection VII. Money, Credit, and Banking \\nMoney Supply \\nBank Loans and Deposits \\nTotal Social Financing \\nCentral Bank Policy Tools \\nInterbank Interest Rates \\nFlow of Funds Accounts \\nSection VIII. Prices \\nConsumer Price Index \\nProducer Price Index (ex-Factory Price Index of Industrial Products) \\nAgriculture and Raw Material Prices \\nMerchandise Trade Price Index \\nGDP Deflator \\nSection IX. Population and Labor Market \\nTotal Population, Urban Population, Working Age Population, Migrant Population \\nBirth Rate, Death Rate, Natural Growth Rate \\nEmployment Data \\nUnemployment Data \\nWages \\nGS China Wage Tracker \\nSection X. Government Finance \\nGovernment Revenue, Expenditure and Balance \\nLocal Government Debt \\nGS China Augmented Fiscal Deficit (AFD) \\nGS China Augmented Government Debt (AGD) \\n\\n\\n 3 / 158 \\n \\n  \\nOur updated “Understanding China’s Economic Statistics” manual includes a broader array of data \\nseries, more explanatory charts and tables, and many GS proprietary indicators that we have \\ndeveloped over the years. \\nSection I. Introduction \\nOverview \\nAs China’s impact on the global economy has increased, so has the importance of its economic data. \\nFor some markets such as commodities, monitoring Chinese data has become as crucial as \\nmonitoring US data. However, many market participants view China’s economic statistics with a high \\ndegree of skepticism. \\nThe Goldman Sachs Economics Research Team has invested considerable effort in reviewing Chinese \\nstatistics, analyzing their relationships with the business cycle and identifying their limitations. We \\nhave also developed a series of proprietary indices for monitoring the Chinese economy — both at \\nthe macroeconomic level, such as the Goldman Sachs Current Activity Indicator (CAI) and the \\nGoldman Sachs China Financial Conditions Index (FCI), and at the sector level via, for example, our \\ntrackers of wage growth, inventory changes, and housing policy. \\nThis “little red book” is a comprehensive update of Understanding China Economic Statistics, which \\nwe published in 2006 and updated in 2017. It is similar in format to our long-established statistics \\nhandbooks for the US, UK and Europe, but contains several distinct features owing to the challenges \\nof interpreting China’s data and policy settings. It has been expanded further in this edition, \\nreflecting the increased importance of China’s economy and economic data for the rest of the world \\nand for a diverse set of markets. Since the second edition of the book was published in 2017, some \\ndata series have been suspended (e.g., land transaction area and value, real retail sales, FAI price \\nindex, and urban registered unemployment rate) while other data series have been added (e.g., retail \\nsales of services and services industry output index). In addition, the definitions of various indicators \\nhave been revised over the past few years (e.g., total social financing). We hope it will serve as a \\nuseful reference both for clients investing in China directly and for those who need to track the \\nChinese economy due to its influence on other markets. \\nNotable changes to this updated edition include: \\n• \\nA revised, and longer, list of indicators. In particular, we have expanded the sections on \\nreal estate and government finance, given these sectors’ importance to China’s \\nmacroeconomic outlook. Unfortunately, however, not all the changes are additions -- the \\nauthorities have ceased publication of some series that we found useful in the past. \\n• \\nNumerous additional charts and tables to summarize key data and display time series. \\n• \\nFurther detail on the growing collection of proprietary indicators we have developed \\nover the years. While our colleagues around the world have also developed proprietary \\nindicators, and in many cases (e.g., the CAI and FCI) we apply those techniques to China, we \\nhave also developed many China-specific indicators. \\n\\n\\n 4 / 158 \\n \\nIn general, with respect to official data provided by the government, we find that: \\n1. The production side of the statistics is better at capturing growth momentum than the \\nexpenditure side, mainly because the basic infrastructure for data compiling in China \\nremains geared toward the production-based approach. This assessment may change \\ngradually because China’s statistical authorities plan to improve data collection for \\nexpenditure items. \\n2. The monthly growth indicators, especially in the industrial/manufacturing sector, such as \\nindustrial production and manufacturing PMI, are of better quality than the quarterly and \\nannual GDP figures, partly because monthly data are timelier and subject to less non-\\neconomic interference, but also because service sector measurement is generally more \\ndifficult. \\n3. The reported growth rates for data series such as value-added industrial output, fixed asset \\ninvestment and retail sales do not always correspond with the reported levels over time. In \\nmost cases, this is because of changes in the survey sample. For example, more companies \\nhave grown above the minimum size threshold required to be included in the sample each \\nyear, leading to an upward bias to the level of the series over time. The National Bureau of \\nStatistics (NBS) does attempt to correct for this bias by requesting companies report year-\\nover-year (yoy) growth rates. \\n4. For some high-profile data series such as GDP, revisions can alter the overall growth pace, \\nparticularly the seasonal patterns. In November 2019, the NBS revised up its 2018 GDP by \\n2.1% which made the government's goal of “doubling income between 2010 and 2020” \\neasier to reach. Due to the large swings in activity data driven by Covid-related lockdowns, \\nseasonal adjustments have become more difficult over the past two years, with sequential \\ngrowth heavily influenced by how seasonal factors are estimated. \\nIn terms of where the data are most inadequate but are of great importance, we still believe the \\nhigher-frequency expenditure side of the data reporting ranks at the top, in particular for \\ngovernment investment and consumption, as well as for inventory changes. \\nSecond on the list are data such as house prices, total housing stock, and the property vacancy ratio \\n(referring to properties built but not inhabited, whether sold or not). In the context of China’s major \\nhousing downturn, reliable and timely figures on house prices and residential vacancy rates would \\nbe helpful to investors and policymakers. \\nThird are data related to the labor market and wage development. Some information (such as the \\nsurveyed unemployment rate covering both registered and unregistered urban workers) has promise \\nbut is not released on a consistent and timely basis (e.g., youth unemployment rate was suspended \\nafter June 2023 and resumed in December 2023 under a different unemployment definition). \\nThough we have developed some proprietary measures, such as our wage growth tracker, the lack \\nof frequent and reliable data series on labor market slack constitutes a major macroeconomic data \\ngap. \\nFourth, on the issue of prices, greater transparency on the Consumer Price Index — particularly in \\nterms of the detailed components and weights — would help avoid confusion in the market. \\n\\n\\n 5 / 158 \\n \\nLastly, the breakdowns of many categories are outdated. For example there is limited information on \\nRMB loan breakdowns by the type of borrower and industrial sector, on private vs. public investment \\nin different industries, and on employment and wage data by sector. \\nAs with our other research products, we have tried to make this handbook as user-friendly as \\npossible and accessible for readers with different levels of understanding of China’s macro data. As \\nalways, we would greatly appreciate your comments and suggestions. \\nHow to Use the Ranking for Each Data Series \\nTo make it easier for readers to put the data in perspective, we have assigned ratings of one to five \\nstars for the signal-to-noise ratio and macro importance of each indicator. The ratings are on a \\nrelative scale within the China space. Therefore, a five-star rating means an indicator is among the \\ntop series in China’s data space, but does not mean it is free of deficiencies, or that it necessarily \\nranks highest among its international peers. \\nThe rating for the signal-to-noise ratio is fairly self-explanatory: In our judgment, how well does \\nthe series measure what it is designed to measure? Where possible, we have tried to corroborate \\ndata series with other related indicators, including aggregated corporate data or foreign data. \\nThe rating for macro importance is based on how essential the series is: (1) in helping to read the \\noverall state of the economic cycle; and (2) in assessing the likely direction of macroeconomic policy. \\nAlthough these factors are related, there can be significant differences between them. To illustrate, \\nGDP has a lower frequency (quarterly) and tends to be smoother than other cyclical indicators. As a \\nresult, it does not have the highest signal-to-noise ratio in gauging the cyclical state of the economy. \\nHowever, policymakers pay a good deal of attention to this data series, and the tolerance for \\nmissing the growth target is low. Therefore, the GDP growth data are useful in judging policy risks. \\nAs a result, we have assigned GDP a higher score for macro importance than for the signal-to-noise \\nratio. By contrast, electricity production and consumption data tend to be reliable, but their macro \\nsignificance has declined over time as the energy intensity of the economy shifts. Therefore, we have \\nassigned electricity production and consumption a higher score for signal-to-noise ratio than for \\nmacro importance. \\nExhibit 1: Overview of official Chinese economic indicators \\n \\n\\n\\n 6 / 158 \\n \\n \\nNote: Rankings are GS subjective assessments; \\\\\\\"✓\\\\\\\" indicates Bloomberg/Wind consensus forecast \\nis available for this indicator. \\nSource: Goldman Sachs Global Investment Research \\nThe Ten Most Frequently Cited Chinese Economic Statistics \\n\\n\\n 7 / 158 \\n \\nThe indicators that we find most useful are not necessarily the ones discussed most frequently by \\nmarket participants. Here is our take on the ten indicators that, from our subjective point of view, are \\nmost often cited by government officials, investors, and the media (listed in order of appearance in \\nthis publication). \\nGDP. Despite all its flaws, this is the most comprehensive indicator of economic growth and also the \\ngrowth indicator most watched by the government and the market. \\nIndustrial Production. Industrial production is perhaps the best gauge of short-term economic \\nactivity at a higher (monthly) frequency. \\nPurchasing Managers’ Indices. Because PMIs are typically the earliest indicators released each \\nmonth, they tend to attract significant market attention. \\nFixed Asset Investment. This is an important indicator for gauging short-term investment \\nmomentum. However, data quality and reliability are a concern. \\nHome Sales. Among major property activity indicators, new property sales are more reliable than \\nnew property starts and property completions. New property sales are also important for real estate \\ndeveloper financing due to China’s pre-sales system. \\nRetail Sales. Growth rates appear over-smoothed in some years and the data do not cover service \\nconsumption except catering. It is still the most frequently used indicator for consumption growth. \\nMerchandise Trade. Trade data provide information on both domestic (imports) and foreign \\n(exports) demand. \\nTotal Social Financing. This provides information on broad credit growth, including indirect \\nfinancing, such as bank loans, and direct financing, such as bond/stock issuance, but coverage is still \\nnot wide enough to capture all credit extended to the real economy. \\nConsumer Price Index. This is the most watched indicator of inflation. We believe it does a fair job \\nof capturing inflationary pressures on household consumption in China. \\nProducer Price Index. This is often assumed to lead downstream inflation and influences industrial \\nprofitability, although these relationships are not as simple as commonly perceived. \\nA Typical Cycle of China’s Statistical Releases \\n \\n\\n\\n 8 / 158 \\n \\n \\n* The NBS PMIs are reported at the end of the reference month; all other data are reported in the \\nfollowing month. \\\\\\\"Two Sessions\\\\\\\" refer to National People's Congress & Chinese People's Political \\nConsultative Conference (CPPCC) Annual Sessions. The Statistics Bureau may adjust or suspend \\nrelease times. \\nSource: News Media, Goldman Sachs Global Investment Research \\nBasics of Interpreting the Numbers \\nEconomic data are of considerable importance to financial markets – because of their information \\n\\n\\n 9 / 158 \\n \\nabout the state of the economy and their implications for economic policy. Important considerations \\nto be aware of include: \\n• \\nYear-over-year versus “sequential” growth. We use the term “sequential” to describe \\nperiod-on-period changes within a year (e.g., month-over-month or quarter-over-quarter, \\ndepending on the series). The Chinese government typically reports year-over-year series as \\na way to minimize seasonal influences. But year-over-year data can mask significant \\nchanges in sequential momentum, so we often calculate and refer to sequential figures. \\n(Note that the term “base effect” refers to a particularly high or low sequential change from \\none year ago that affects the year-over-year calculation. For example, if GDP normally \\ngrows at a 4% annual rate but temporarily stalls at 0% quarter-over-quarter growth for one \\nquarter, growth will be reported at 3% year-over-year in that quarter and the following three \\nquarters, then one year later will jump back to 4% yoy due to the “base effect” as the weak \\nquarter drops out of the calculation.) \\n• \\nSeasonal adjustment. This is critical when working with sequential data (more on this topic \\nbelow). \\n• \\nRevisions. As in other countries, initial data reports may be revised as more comprehensive \\ninformation becomes available or methodological revisions are made. The corollary is that \\nthe current data series do not necessarily reflect how the historical data looked at the time \\nof release. \\n• \\nSurvey versus “hard” data. Government agencies typically report samples or censuses of \\nactual economic activity, which we sometimes refer to as “hard” data. In addition, a variety of \\ngovernment and private sector surveys (sometimes referred to as “soft” indicators) can give \\na useful qualitative sense of the direction of the economy. Often, these take the form of \\n“diffusion indices” where respondents answer questions with either favorable, neutral or \\nunfavorable responses; the percentage answering favorably plus half the percentage \\nanswering neutral are added to yield a score from 0 to 100. The widely quoted Purchasing \\nManagers’ Indices (PMIs) take this form. While less precise than hard data and potentially \\nsubject to other biases (such as inflation), these reports can provide a timelier read on \\nchanges in direction and therefore are a useful reference for forecasting and policymaking. \\nA Note on Seasonal Adjustment \\nSeasonal adjustment is a mainstay of macroeconomic analysis, allowing comparisons of growth over \\nperiods of less than a year. The biggest value of seasonal adjustment is to separate cyclical signals \\nfrom seasonal patterns so as to gauge trends in activity, inflation, or other indicators within the \\nperiod. Though often taken for granted, the choice of seasonal adjustment method inherently \\ninvolves judgment calls about whether incremental changes are seasonal or cyclical, and can at \\ntimes have a major impact on the economic data. \\nConceptually, seasonal adjustment techniques penalize the months that tend to have high values \\nand compensate those months that tend to have low values relative to an average month. Many of \\nthe statistical agencies in China use Census X-12 or its predecessor, X-11.\\n[1] In addition, some \\nreliable data vendors, such as Haver Analytics, will adjust series using GENHOL to parameterize \\nholiday factors. Specifically, the program takes a list of dates for holidays and a “window” (days \\n\\n\\n 10 / 158 \\n \\nbefore, days during, and days after) around each holiday, and then generates a set of holiday \\nregressors/dummy variables for each. \\nExhibit 2: The choice of seasonal adjustment can have a substantial impact on sequential \\ngrowth \\nReal GDP growth, seasonally adjusted \\n \\n \\nSource: NBS, Haver Analytics, Goldman Sachs Global Investment Research \\nSeasonal adjustments are a useful tool. Without them, one has to rely on yoy growth rates, which \\ncan be heavily influenced by last year’s base and are slower to reflect the latest changes in growth \\nmomentum. However, there are several important challenges to seasonal adjustment: \\nLimited data history. It takes three or more years to obtain a result from standard seasonal \\nadjustment algorithms, and longer time series are preferable. Short time series are prone to one-off \\nshocks such as those during the Global Financial Crisis (GFC). Ensuring that such shocks do not \\nthrow off the seasonal factors requires manual intervention. \\nRapid structural change. Even when sufficient data are available, economies experiencing rapid \\nstructural change are more likely to see changing seasonal patterns as well. For example, as China \\ngrows in economic importance, fluctuations around the Chinese New Year are more likely to affect \\ntrading partners. Distortions around the large shocks from the Covid pandemic created difficulties \\nfor seasonal adjustment in many economies, although these are now fading. \\nFloating holidays. Patterns of economic activity change around holidays: production tends to slow \\nduring holidays (factories and ports tend to shut or operate at less than the usual pace), while \\nconsumption tends to rise before holidays (retail activity is often boosted by holiday gift-giving, \\neating out, etc.). China has several holidays based on the lunar calendar that can fall in one of two \\ncalendar months each year. These holidays affect each month’s data to a different degree each year \\n\\n\\n 11 / 158 \\n \\nand can thus create distortions in both month-over-month and year-over-year changes. Of these \\nfloating holidays, Chinese New Year (which falls in either January or February) is most important. The \\nsimplest solution to Chinese New Year seasonal distortions is to average data for the first two \\nmonths of the year. Adjusting monthly growth rates for the number of working days does not help \\nmuch, especially for the production data, because many companies still operate with varying \\ncapacity during holidays, and some may not resume operation at full capacity until days after the \\nholiday. As a result, especially if the Chinese New Year falls in late February, the March data may also \\nbe affected (and therefore even yoy growth rates for March can be distorted by the Chinese New \\nYear effect). According to the NBS, the official seasonal adjustment method adjusts for working day \\ndifference and floating holidays in China. For most seasonally adjusted series, there is not an obvious \\nresidual seasonal distortion, though floating holiday effects (especially Chinese New Year) are \\ndifficult to fully eliminate as their magnitude varies over time with structural changes in the economy \\nand behavior. \\nEvaluating Data Surprises \\nGoldman Sachs China Macro-data Assessment Platform (GS China MAP) \\nSource: Goldman Sachs Economics Research \\nAvailability: Daily since January 2006 \\nTiming: Real-time \\nRelease: GS China Proprietary Indicators update \\nOverview \\nThe Goldman Sachs China Macro-data Assessment Platform (GS China MAP) measures economic \\ngrowth surprises in China. It is constructed in the same manner as our surprise indices for other \\neconomies. \\nCompilation \\nIn the MAP system, the importance of a particular release is calculated in two dimensions. \\n• \\nFirst is the relevance score, which is based on the historical correlation with real GDP growth \\n(quarter-over-quarter). This score can range from 0 (irrelevant) to 5 (most relevant), as \\nillustrated in Exhibit 3. Given the central role of the GDP series in the MAP framework, the \\nfact that China’s GDP statistics have tended to be relatively smooth historically (at least pre-\\npandemic) may reduce the number of significant indicators. \\n• \\nSecond is the surprise score, measured as the difference between a particular release of \\nthat indicator and the Bloomberg “consensus” forecast for that indicator, measured in \\nstandard deviations. We assign a score from -5 to +5 depending on whether the actual \\nfigure is above or below expectations, and by how much. If the actual release is less than \\nhalf a standard deviation from the consensus expectation, we will assign a score of 0. A \\ndifference of between 0.5 and 1 standard deviation will generate a surprise score of +1 or -1. \\nSurprise scores rise with ½ standard deviation increments, with any surprise of greater than \\n2.5 standard deviations generating a score of 5. \\n\\n\\n 12 / 158 \\n \\n• \\nMultiplying the relevance score by the surprise score gives a range of -25 to +25 for a given \\nindicator; the aggregate of time series of MAP scores for those indicators included for China \\ncreates the China MAP score. \\nExhibit 3: MAP relevance and surprise scales \\n \\n \\nSource: Goldman Sachs Global Investment Research \\n• \\nBloomberg consensus forecasts are generally made using data from sell-side economists, \\nmost of whom work at global financial institutions. As a result, they may not precisely reflect \\nthe expectations of the broader investor community. In addition, these forecasts are often \\nreleased in publications many days in advance of the official data, and subsequent changes \\nin forecasters’ views may not always be updated in the published consensus. \\n• \\nThere was a large shock during the sample period due to the GFC, and an even larger one \\nduring the Covid pandemic. Relative to the magnitude of the surprises during that period of \\ntime, any surprises today tend to appear small but are nevertheless significant for the market. \\nTherefore, in setting our surprise score thresholds we have used the standard deviation of \\nsurprises based on data releases since 2010 (Exhibit 4). \\nExhibit 4: Indicators in the MAP for China \\n \\n\\n\\n 13 / 158 \\n \\n \\nSource: Goldman Sachs Global Investment Research \\nExhibit 5: China activity data surprised modestly to the upside in late 2023 and early 2024 \\nChina MAP \\n \\n \\nSource: Goldman Sachs Global Investment Research \\nList of Acronyms \\n \\n\\n\\n 14 / 158 \\n \\n \\nRelated GS Economics Publications \\n• \\n“Trade Balance Bounces into the Year of the Rabbit”, US Daily, 6 April 2011 \\n• \\n“A redesigned MAP of emerging Asia data”, Asia Economics Analyst, 10 May 2013 \\n• \\n“A Better Global Economic MAP”, Global Economics Weekly 13/39, 5 December 2013 \\n• \\n“Separating cyclical signal from seasonal noise”, Asia Economics Analyst, 27 June 2014 \\n• \\n“Chinese New Year Seasonal Distortions Coming Home to Roost”, US Daily, 15 February \\n\\n\\n 15 / 158 \\n \\n2016 \\n• \\n“Revisiting post-reopening seasonal adjustment”, China Data Insight, 23 June 2023 \\nSection II. Overall Activity and Production \\nThere are five major sets of macro indicators related to overall activity and production: \\n1. Gross Domestic Product (GDP), including its breakdown by production (industry), expenditure, \\nincome, and region. \\n2. Industrial Production and Services Industry Output Index, which measure real value added in the \\nindustrial sector and services sector, respectively. \\n3. Other industrial activity indicators that can serve as alternative growth measures, including \\nElectricity Production/Consumption and Rail Freight Traffic. \\n4. Total Profits and Operating Income of Industrial Enterprises. \\n5. Purchasing Managers’ Indices (PMIs) by the official and private data sources, capturing near-term \\nsequential growth momentum in different sectors (e.g., manufacturing, services). \\nIn addition, we have our own GS proprietary activity measures, including the Current Activity \\nIndicator (CAI), and inventory tracker. \\nGross Domestic Product \\nSignal to noise ratio: **** \\nMacro importance: ***** \\nSource: National Bureau of Statistics (NBS) \\nFrequency: Quarterly & Annual \\nAvailability: GDP by industry: Annual from 1952, quarterly from 1992 (both nominal levels and real \\ngrowth rates); GDP by expenditure: Annual from 1952 (only nominal levels), quarterly from Q1 2009 \\n(for estimated contribution to year-to-date yoy GDP growth; no level data) and from Q1 2015 (for \\nestimated contribution to single-quarter yoy GDP growth; no level data). \\nTiming: GDP by industry: 2-3 weeks after the reference quarter; GDP by expenditure: Middle of the \\nfollowing year for level data, and 2-3 weeks after the reference quarter for quarterly estimated \\ncontributions to yoy GDP growth; GDP by income: Together with release of yearbook. \\nHour: 10:00am for GDP by three major sectors (i.e., primary, secondary and tertiary sectors; all times \\nin this book are China Standard Time); 9:30am on the following day for GDP by all industries. \\nPublication: NBS press release; China Statistical Yearbook \\nOverview \\nGross Domestic Product (GDP) measures the overall economic activity of an economy on a value-\\n\\n\\n 16 / 158 \\n \\nadded basis (the value of output minus purchased inputs). It is the most comprehensive measure of \\ndomestic economic activity. \\nSignal to Noise Ratio \\n• \\nChina’s GDP data are mostly compiled in accordance with the System of National Accounts \\n(SNA) 2008 standard by treating R&D expenditure as part of capital formation. The data are \\nhistorically and internationally comparable. \\n• \\nHistorically, the real GDP growth data were exceptionally smooth relative to other countries \\nand to other indicators of activity, especially during economic downturns, contributing to \\nskepticism among market participants over their accuracy. It was common for China to \\nannounce quarterly GDP growth with a variation of less than 0.5 pp before the Covid \\npandemic, especially during 2015-16, even as some high-frequency indicators occasionally \\nexperienced double-digit swings in growth. To get a better sense of cyclical momentum, we \\ncross-check real GDP growth with other indicators including our proprietary Current Activity \\nIndicator. During the Covid pandemic in 2020-22, the volatility in the real GDP growth data \\nincreased considerably due to periodic shifts in Covid-related restrictions. \\n• \\nGDP revisions are supposed to capture the newly available data in the whole economy, \\nthough in practice they are most relevant for tertiary industry (services). Measuring services-\\nsector activity is inherently more difficult, and the Chinese statistical system – which grew out \\nof the Soviet system that did not recognize services as value added – is particularly ill-\\nequipped to do so. The shift from SNA 1993 to SNA 2008 in 2016 increased 2015 total GDP \\nby 1.3% to US$11 trillion, and the real growth rate was also revised up slightly. The upward \\nrevision was due to the fact that China’s R&D expenditure growth has been consistently \\nfaster than that of overall GDP. In November 2019, the NBS revised up its 2018 GDP by 2.1% \\nwhich made the goal of “doubling real GDP between 2010 and 2020” easier to reach. \\nMacro Importance \\nGDP measures the value of final goods and services produced by whole economic entities in China. \\nAlthough the GDP data suffer from various quality issues, they are still probably the most widely \\ncited macro indicator because: \\n1. The government pays considerable attention to GDP growth, and the official real GDP \\ngrowth target is one of the most binding targets in terms of policy making (e.g., compared \\nto inflation and job market targets). Therefore, it is useful in judging policy risks. \\n2. It is compiled largely in accordance with international standards, and is often used for \\ncomparison with other countries. It also covers a broad sample for the overall economy, and \\ntherefore enables analysis to be carried out on many ratios, such as the national savings rate, \\nwhich would be difficult to do using monthly indicators. \\nCompilation and Reporting \\nThere are three approaches to calculating GDP data series in China: \\n• \\nGDP by industry \\n\\n\\n 17 / 158 \\n \\n• \\nGDP by expenditure \\n• \\nGDP by income \\nAvailability \\nExhibit 6: China's GDP by industry data have more detail than expenditure or income-side data \\n \\n \\n*Constant price (inflation adjusted): Real GDP is generated by using prices that pertained in a rolling \\nbase year. 2011-2015 data are based on prices in 2010, and 2016-20 data are based on prices in \\n2015. **Seasonal adjustment (NBS-SA): Real GDP seasonally adjusted by the China’s NBS is meant to \\nadjust for working days and floating holidays such as the Chinese New Year Golden Week, the \\nDragon Boat Festival and the Mid-Autumn Festival. \\nSource: NBS \\nGDP by Industry \\nWithin this framework, the whole economy is divided into three broad industries: \\n• \\nPrimary industry: Farming, forestry, animal husbandry and fishing (excluding related services). \\n• \\nSecondary industry: “ Industry ”  (including mining, manufacturing, and utilities) and \\nconstruction. \\n• \\nTertiary industry: Any industry other than the primary and secondary industries. Activities in \\nthe services sector (such as wholesale and retail trade, finance, catering, and transportation) \\nare captured in this category. As in many other countries, this sector has been growing as a \\n\\n\\n 18 / 158 \\n \\nshare of overall economic activity in China. \\nExhibit 7: Gradually rising share of services in the Chinese economy \\nShare of nominal GDP by industry \\n \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nGDP by Expenditure \\nThe expenditure approach is a method for calculating GDP by totaling household consumption, \\ninvestment, government consumption and net exports. \\n• \\nAll goods and services that consumers have purchased (with the exception of houses and \\nhousing construction materials) are included in household consumption (houses and \\nhousing construction-related items are mostly treated as gross fixed capital formation). \\nAlthough retail sales data are often used as an indicator for household consumption, there \\nare crucial differences between the two (see Section V. Consumption). \\n• \\nGovernment consumption measures the non-investment goods and services purchased by \\ngovernment. \\n• \\nThe sum of household and government consumption equals final/total consumption. \\n• \\nGross Fixed Capital Formation (GFCF) is a key component in the expenditure approach of \\nnational accounts reporting. It is different from fixed asset investment (FAI) because the \\nlatter includes spending on assets that do not directly contribute to GDP (e.g., purchases of \\nland and used equipment), while GFCF does not.\\n[2] Other differences include a minimum \\nproject size cutoff for FAI, and property developer profits and IT investment being covered \\nin GFCF but not FAI. Moreover, FAI data collection is susceptible to a lot of statistical noise \\n(see Section III. Investment). \\n\\n\\n 19 / 158 \\n \\n• \\nChanges in inventories refer to net changes during the observed period. China’s changes in \\ninventories data are only available at annual frequency, and appear to be useful in assessing \\nthe direction, but not necessarily the degree, of inventory adjustments. Despite their \\nimportance, changes in inventories are generally the least reliable component of GDP by \\nexpenditure in China, as well as in many other countries, due to difficulties in data collection. \\nSome countries estimate this as a residual item by balancing GDP by expenditure data and \\nGDP by industry data. The NBS is supposed to make independent estimates for changes in \\ninventories based on a wide range of data sources, though in practice the level of volatility \\nand the gap between GDP by expenditure and GDP by industry suggest it may have an \\nelement of residual as well. To better track the contribution of inventory changes to GDP \\ngrowth at a quarterly frequency, we have built a proprietary inventory tracker based on \\ncommodity inventory measures, PMI inventory sub-indices, and industrial final goods \\ninventory and auto inventory data. \\n• \\nThe sum of GFCF and the change in inventories is defined as Gross Capital Formation (GCF, \\nor just “investment”, which remains a very large share of economic activity in China). \\n• \\nNet exports are the balance of trade in goods and services, which is equivalent to the \\nbalance of payments (BOP) definition for the trade balance. It differs from Customs trade \\ndata in three main aspects: (1) it includes trade in services, whereas the Customs data only \\ncover trade in goods; (2) conceptually, it is based on the principle of exchanges between \\nresidents and non-residents, instead of goods moving across national frontiers; and (3) \\nimports reported by Customs are based on CIF, while BOP/trade under GDP is based on FOB \\n(see Section VI. External Sector). \\n• \\nGDP by expenditure data are only available annually in nominal levels. One needs to deflate \\nofficial data to obtain real levels and growth rates. For example, the Consumer Price Index \\n(CPI) can be used to deflate household and government consumption, the Producer Price \\nIndex (PPI) can be used to deflate GFCF, and goods trade price indices can be used to \\ndeflate goods trade. Since 2015, the NBS also publishes percentage point contributions of \\nconsumption, investment, and net exports to headline year-over-year real GDP growth \\nwhich can be used to estimate the year-over-year growth in real consumption, real \\ninvestment, and real net exports. \\nExhibit 8: Chinese “rebalancing” from investment to consumption was limited through 2023 \\nShare of nominal GDP by expenditure \\n \\n\\n\\n 20 / 158 \\n \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nGDP by Income \\n• \\nThe income approach accounts for the income generated during the process of production \\nactivities for various industries. The value-added GDP data are composed of four parts: \\nemployees’ compensation, net taxes on production, depreciation of fixed assets and \\noperating surplus. \\n• \\nThe NBS does not release national aggregates for GDP by income, only for individual \\nprovinces. Users need to aggregate the provincial GDP-by-income data to get a national \\nfigure. However, releases of such provincial-level data are uneven and lagged. For example, \\nfor many provinces, 2017 data are the latest available as of this writing in 2024. \\nConceptually, the level and growth rate of GDP measured by the industry, expenditure, and income \\napproaches should be the same. However, in practice there are some discrepancies among them \\ndue to measurement issues. When calculating the consumption/investment/net export share of GDP, \\nGDP by expenditure should be used; in other cases, GDP by industry should be used as the \\nbenchmark. In addition, we often use the GDP by income approach as a reference of how the \\nnational income is split. \\nRevision \\nIn early 2017, the NBS simplified GDP data accounting into preliminary estimation and final revision, \\ncompared to the previous three-step accounting regime that included preliminary estimation, \\npreliminary revision and final revision. \\nPreliminary estimation: The timeliness requirement is high for the preliminary release of quarterly \\nGDP data (approximately two to three weeks after the end of the reference quarter). Annual \\npreliminary GDP is calculated by aggregating quarterly results. The first release of annual data comes \\nout early in the following year. The preliminary estimate is calculated using the industry approach. \\nFinal revision: The final revision of annual GDP is released towards the end of the following year, \\n\\n\\n 21 / 158 \\n \\nbased on audit, fiscal budget outturns and other survey data. \\nEconomic Census: An economic census is conducted every five years and will revise historical GDP \\nseries based on the census results. The 4th economic census was conducted on 2018 annual data \\nand results were released in late 2019. The latest census was conducted in 2023 with results to be \\nreleased in late 2024. \\nThere are several windows for NBS GDP revisions if needed based on past practice, including regular \\nrevisions to sequential GDP growth estimates for historical data series when releasing new GDP data, \\nannual GDP final revisions, and some occasional, ad hoc revisions to GDP estimates due to statistical \\ninvestigations, scrutiny, or methodology changes. \\nOther Issues \\nRegional GDP and per capita GDP \\n• \\nHistorically the discrepancies between national and regional GDP data used to be significant, \\nbut narrowed in recent years. The sum of provincial GDP tends to be higher than the \\nnational GDP, and the aggregate of municipal GDP tends to exhibit the same pattern vs. the \\nprovincial GDP. A number of factors lead to these discrepancies. Among the most important \\nare: (1) double-counting of the value added for enterprises that operate across different \\nregions; (2) differences in data sources; and (3) human factors in compilation. The revised \\nnational GDP after the census is much closer to the aggregate of provincial GDP data before \\nthe revision. Although it has long been assumed that regional GDP data have overstated the \\ngrowth and size of the economy, the revisions made by the NBS subsequently indicate early \\nreadings of national data may be too conservative as they tend to under report service-\\nsector activities relative to the census, especially for emerging industries. \\n• \\nAnother problem is the estimation of regional GDP per capita in China, especially at the city \\nlevel, due to China’s large cross-region migrant population. As there are two regional \\npopulation data series in China, those of actual residents and those with household \\nregistration (Hukou) in the reporting location, GDP per capita calculated using the two series \\ncan differ substantially, especially for cities with more migrant workers. In 2004, the NBS \\nnoted that for cities with large migrant populations, such as Shenzhen where \\nthe registered population is significantly smaller than the residential population, using the \\nregistered population may overstate GDP per capita. Therefore, it is important to use the \\nactual resident population series to avoid serious over-estimation of GDP per capita, though \\neven that series suffers from the problem of under-reporting. For example, someone who \\nstays less than six months in a city is technically not a resident according to the statistical \\nstandards, but nevertheless contributes to local activity. Moreover, city-level GDP per capita \\ndata are not always reported on a timely basis. \\n• \\nGiven population estimates reported are year-end data, to capture the \\\"flow\\\" feature of GDP \\nfor the whole year, the NBS will average the year-end residential population when \\ncalculating GDP per capita. For example, average 2020 population= (2020 year-end + 2019 \\nyear-end)/2. \\nGDP and GNI/GNP \\n\\n\\n 22 / 158 \\n \\n• \\nAnother concept closely related to GDP is gross national income (GNI), also called gross \\nnational product (GNP). The difference between the GDP and GNI/GNP is net factor income, \\nwhich is the income of investment and labor by domestic residents earned abroad minus \\nthose of foreign residents earned in the country. Despite their apparent similarities, these \\ntwo series measure two different aspects of the economy: GDP measures production and \\nGNI measures income. Note China's primary income deficit has widened over the past few \\nyears, mainly driven by a wider investment income deficit, although net international \\ninvestment positions have shown steady growth. \\nIndustrial Production (Value-added of Industry) \\nSignal to noise ratio: **** \\nMacro importance: ***** \\nSource: National Bureau of Statistics \\nAvailability: Monthly growth from 1990, monthly growth seasonally adjusted from 2011, annual \\nabsolute level (value added of industry) from 1993 to 2007. \\nTo adjust for Chinese New Year related distortions, since 2012 the NBS no longer releases industrial \\nproduction data for January alone in mid-February; instead, it releases January-February combined \\ndata in mid-March. \\nTiming: Typically around the 2\\nnd /3\\nrd week of the following month. In January, April, July and October, \\nit is released with quarterly GDP data during a press conference around the 2\\nnd /3\\nrd week of the \\nmonth. \\nHour: 10:00 am \\nPublication: NBS monthly release \\nOverview \\nThis data series measures the real value added in the industrial sector (the deflator for headline IP is \\nPPI). This indicator is an important reference for macroeconomic management and is widely used to \\nestimate short-term growth momentum in the industrial sector. \\nSignal to Noise Ratio \\n• \\nWe have long viewed industrial production (IP) as among the more reliable monthly activity \\nindicators China publishes because: (1) related to the structure of the Chinese economy, \\nChina’s statistical system has focused on tracking growth in industrial production since it was \\nfounded; and (2) historically there seemed to be less “smoothing” in this series than in some \\nother politically more sensitive data series, such as GDP. However, IP became unusually \\nsmooth during the 2015-16 downturn. The reliability of the IP series appears to have \\nimproved in recent years with its volatility increasing dramatically during the Covid pandemic \\nand with sequential moves largely consistent with high-frequency data such as coal \\nconsumption and steel production. \\n\\n\\n 23 / 158 \\n \\n• \\nThe IP data series generally tends to be more important than fixed asset investment and \\nretail sales data in tracking GDP growth because it is in real terms and because, by being in \\nvalue-added terms, it is more in line with the GDP concept. The only difference between IP \\nand manufacturing output is that IP includes the mining and utilities industries. \\nMacro Importance \\nHistorically we have found the IP data quite useful given: (1) their high frequency (monthly), and (2) \\nthey are a reasonably good proxy for overall economic activity and especially GDP data, since IP is a \\ndirect and important GDP component. \\nCompilation \\n• \\nThe sectoral coverage of IP is selective in the following respects: \\n1. It covers only the industrial sector, which includes “ mining and quarrying, \\nmanufacturing, and utilities” — otherwise known as “secondary industry” by GDP \\nclassification, excluding construction. There are 41 industrial divisions in total, in \\nwhich manufacturing accounts for the vast majority of the components. Value added \\nin a particular industrial division is the sum of value added from companies whose \\nprimary activities are in that division (in practice, this may include some ancillary \\nactivities which should technically be categorized in other areas). This issue is \\nespecially tricky when it comes to conglomerates, and as the level of sector detail \\nincreases. \\n2. By business type, the value added of industries covers state-owned & holding \\nenterprises, share-holding enterprises, private enterprises, and foreign, Hong Kong, \\nMacau and Taiwan funded enterprises. The classification is based on the controlling \\nstake. If the company is controlled by a minority shareholder, it will be classified as a \\ncompany under that shareholder, as is often the case with state-controlled firms. \\n3. Minimum threshold: enterprises with annual sales of RMB20 million or above are \\nincluded. Therefore, this series covers only a portion of the total value added by \\nindustry in the economy, though the portion is large (more than 80% in recent years). \\nGiven regular adjustments of the minimum threshold, the NBS releases comparable \\ngrowth rates to eliminate statistical discrepancies in levels. \\n• \\nExport delivery value: Refers to the nominal value of industrial products for exports \\n(including to Hong Kong, Macau and Taiwan). Prices are generally denominated in FX, and \\nconverted back to RMB based on the respective exchange rates. This category includes \\nproducts from processing and assembling trades, etc. The export delivery data are different \\nfrom customs trade mainly in two aspects. Firstly, Customs data includes all the merchandise \\ngoods that exit customs in a particular month while export delivery value only includes \\nindustrial goods produced for exports in a particular month. For example, Customs data \\nincludes exports from all kinds of enterprises and covers both industrial and non-industrial \\nproducts (e.g., agricultural products, roughly 3% of total exports), while export delivery value \\nonly covers industrial products from industrial enterprises. Besides products sold to other \\ncountries, export delivery value also includes domestic sales of products which were initially \\n\\n\\n 24 / 158 \\n \\nplanned for exports. Secondly, these two sets of data are different on treatment of \\nprocessing trade. For processing trade with supplied materials, export delivery value only \\nincludes processing fees while Customs record the full value of the final product as export \\non a free-on-board (FOB) basis. \\n• \\nIP data are announced in terms of real single-month growth and real year-to-date growth. \\nThe reporting of the nominal level of value added by industry was discontinued in 2007 and \\nofficial real level data have never been published. When nominal value-added series are \\nneeded one can “inflate” official real growth data by the PPI to generate a rough estimate, \\nthough this approach can yield results quite different from the official nominal series over \\nthe period when they were still published. This is due to technical issues such as whether the \\ndeflation is done first at the sector level and then aggregated, and/or whether value added \\nis deflated with a single price index or output and intermediate input are deflated separately \\n(“double deflation”, technically the preferable approach). \\n• \\nThe most important sub-sectors of manufacturing are electronic equipment (including \\ncommunications equipment, computers and other electronic equipment), transportation \\nequipment (including auto manufacturing), smelting and pressing of ferrous/nonferrous \\nmetals, chemicals, electric machinery and equipment, and textiles. \\nRevision \\nSeasonal adjustment is carried out every time a new data point comes in, and therefore historical \\nseasonally adjusted growth rates will be revised accordingly. More recent growth rates are usually \\nthe most sensitive to this process. (As with many other macro indicators, estimates of month-over-\\nmonth growth are very sensitive to the exact seasonal adjustment method used.) \\nOther Issues \\nWhen the Chinese New Year falls into two different months between two consecutive years, yoy \\ngrowth rates for January and February will be seriously distorted. It is not possible to correct the \\ndistortions by adjusting for the number of working days, mainly because not all businesses are \\nclosed during holidays, and not all resume operations immediately after the holidays. Since 2012, the \\nNBS no longer releases industrial production data for January alone in mid-February; instead, it \\nreleases January-February combined data—which is often assumed to be free from Chinese New \\nYear distortions—in mid-March. However, this assumption is not entirely correct since activity in \\nMarch can also be affected when the Chinese New Year occurs late, as was the case in 2015. The \\nNBS does report month-over-month seasonally adjusted growth for January and February and all \\nother months of the year back to 2011. These data can be used to derive a year-over-year time \\nseries for January and February, as they are supposed to be adjusted for Chinese New Year effects \\nalready. However, the year-over-year growth derived from the NBS month-over-month growth \\nseries tends to deviate significantly from the NBS headline year-over-year growth. \\nServices Industry Output Index \\nSignal to noise ratio: *** \\nMacro importance: **** \\n\\n\\n 25 / 158 \\n \\nSource: National Bureau of Statistics \\nAvailability: Monthly growth from December 2016 \\nTiming: Typically around the 2\\nnd /3\\nrd week of the following month. In January, April, July and October, \\nit is released with quarterly GDP data during a press conference around the 2\\nnd /3\\nrd week of the \\nmonth. \\nHour: 10:00 am \\nPublication: NBS monthly release \\nOverview \\nThe Services Industry Output Index (SIOI) data series measures the real value added in the services \\nsector. This indicator is an important reference for macroeconomic management and is frequently \\nused to estimate short-term growth momentum in the services sector. SIOI is on a real basis and \\ntracks tertiary GDP growth closely (55% of China’s economy as of 2023). \\nSignal to Noise Ratio \\n• \\nThe SIOI has a much shorter history than industrial production (IP), fixed asset investment \\n(FAI) and retail sales. The NBS only releases the yoy growth data series for this index, while \\nhistorical level data are not available (unlike IP, FAI and retail sales). As such, one needs to \\nassume the monthly index levels for a specific base year, and back out level data for other \\nyears, to estimate the seasonally adjusted sequential growth. \\n• \\nSince the data series was introduced in late 2016, NBS has shown January-February SIOI \\ngrowth combined, to smooth out distortions related to the shifting time of the Chinese New \\nYear holiday. \\n• \\nThe SIOI growth shares similar trends with retail sales growth in previous years. Their \\noccasional divergences for some months may reflect things like growth differences between \\ngoods consumption and services consumption, growth differences between business \\nservices and consumer services, and major changes in price deflators. \\n• \\nThe NBS also provides some sectoral breakdown for the SIOI growth, although historical \\nlevel data are not available. These sub-sectors include wholesale & retail sales, transport, \\nstorage & post, hotel & restaurant, financial services, real estate services, IT and related \\nservices, leasing & commercial services, etc, with varying start month availability (between \\n2018 and 2020). \\nMacro Importance \\nDespite the short history, we have found the SIOI data useful given: (1) their high frequency \\n(monthly), and (2) the lack of many other broad proxies for economic activity in the services sector. \\nBy NBS definition and historical patterns, the relationship of SIOI growth to the services sector is \\nanalogous to that of IP growth to the industrial sector. \\nExhibit 9: Growth in the SIOI shares broadly similar trends with retail sales growth \\nSIOI vs. nominal retail sales growth \\n\\n\\n 26 / 158 \\n \\n \\n \\nSource: NBS, Wind \\nElectricity Production and Consumption \\nSignal to noise ratio: **** \\nMacro importance: *** \\nSource: National Bureau of Statistics, National Energy Administration \\nAvailability: Electricity Production: monthly from January 1995; Electricity Consumption: monthly \\nfrom December 2012 (year-to-date); annual from 2002 \\nPublication: NBS monthly release, China Energy Statistical Yearbook \\nOverview \\n• \\nElectricity production: Refers to the power generated by industrial enterprises with annual \\nrevenue from principal business above RMB20 million. \\n• \\nElectricity consumption: Refers to the electricity consumption of the whole of society \\nincluding the primary sector, industrial sector, tertiary sector and residents in urban and rural \\nareas. \\nSignal to Noise Ratio \\n• \\nThe signal to noise ratio of electricity production and consumption is relatively high — as \\nthe information collection is largely automated, there is relatively less room for local \\ngovernments to distort the numbers. In fact, industrial electricity consumption per unit of \\nGDP has been closely watched by local governments, with rising concerns about eliminating \\noutdated capacity in heavy industries. \\n\\n\\n 27 / 158 \\n \\n• \\nPeriodic divergence between industrial electricity consumption and industrial production \\ngrowth may in part reflect a shift towards less energy-intensive sectors, or a transition in the \\nautomobile sector towards electric vehicles. Other factors also affect electricity usage. For \\nexample, weather-related factors (e.g., hot summers which lead to more air conditioning \\ndemand) lead to strong seasonality in residential electricity consumption. Abnormal weather \\ncan therefore distort even seasonally adjusted data. \\nMacro Importance \\nElectricity production and consumption data used to be important as these data series are perceived \\nas relatively free from manipulation and can provide a cross-check on the strength of the economy. \\nHowever, their macro importance has declined in recent years as electrification (e.g., electric vehicles) \\ngathered steam in China, leading to higher electricity consumption growth relative to real GDP \\ngrowth. \\nRail Freight Traffic \\nSignal to noise ratio: **** \\nMacro importance: ** \\nSource: Ministry of Transport \\nAvailability: Freight volume: monthly from January 1995; annual from 1949 \\nFreight turnover: monthly from August 1998 (year-to-date from January 1990); annual from 1952 \\nTransport distance: annual from 1949 \\nPublication: NBS monthly release; China Statistical Yearbook \\nOverview \\nBy category of cargo, national rail freight traffic data have information on freight volume, freight \\nturnover and transport distance. \\nSignal to Noise Ratio \\nRail freight traffic data are generally reliable. However, they have strong seasonality. For example, rail \\nshipments for coal tend to increase in winter given rising demand for the fuel from the northern part \\nof China. \\nMacro Importance \\nSimilar to electricity data, the macro importance to GDP is relatively low—rail traffic primarily reflects \\nthe supply and demand in heavy industry. Freight rail was subject to severe under-capacity issues at \\ntimes in the past and hence its historical data may not reflect demand changes well (for example, a \\nchange from 50% excess demand relative to the capacity to 10% will not show up in the actual \\namount of freight carried). This has generally not been a major issue in recent years as railway \\ninvestment increased further. \\n\\n\\n 28 / 158 \\n \\nTotal Profits of Industrial Enterprises \\nSignal to noise ratio: *** \\nMacro importance: *** \\nSource: National Bureau of Statistics \\nAvailability: Monthly from 1999, year-to-date \\nTiming: Around 27 days after the end of each month \\nPublication: NBS monthly releases \\nOverview \\n• \\nTotal profit of industrial enterprises: Total profits are the sum of \\\"operating profit\\\" and net \\n\\\"non-operating profit\\\", on a before-income-tax basis. The sample is based on industrial \\nenterprises above the designated size threshold.\\n[3] \\n• \\nOperating revenue: Operating revenue refers to the total amount of income generated by \\nbusiness activities, including revenue from main business and other activities\\n[4]. \\n• \\nThe NBS also releases financial data stating the operational condition of industrial \\nenterprises such as total assets, total liabilities, total owners’ equity, and main business \\nincome, etc. \\nExhibit 10: How the NBS industrial profits data fit into a simplified income statement \\n \\nSource: NBS \\n\\n\\n 29 / 158 \\n \\nSignal to Noise Ratio \\n• \\nWe believe the data series are generally reliable, at least in terms of the overall growth rates, \\nthough probably less so at the industry level. SOEs are often perceived as over-reporting \\ntheir profits and under-reporting losses because of performance assessment systems for \\ntheir senior management, while private enterprises tend to under-report them to avoid \\ntaxes. But to the extent that there is no major change in the degree of over/under-\\nstatement (at least in the short run), the growth rates should be useful in terms of gauging \\nprofitability trends of large industrial firms. The trend in profit growth is also broadly \\nconsistent with that of enterprise income tax receipts reported by the Ministry of Finance, \\nexcept for periods with major tax policy adjustments (e.g., tax waivers, cuts and \\npostponement). \\n• \\nProfit/revenue data contain a lot of seasonal noise, e.g., profits and revenue levels usually \\nshow an uptick in December. Although seasonal adjustment should deal with this, given the \\npronounced seasonal effects an alternative approach to minimize this noise is to compare \\nthe data point with the same period in the previous year. \\nMacro Importance \\n• \\nProfitability is useful in gauging the strength of the corporate sector. Profitable firms are able \\nto, and often do, reinvest their retained profits. Other industrial financial data such as \\ninterest payments can also help assess the debt sustainability of the industrial sector. \\nCompilation and Reporting \\n• \\nThe coverage is the same as that of the industrial production data, i.e., all industrial \\nenterprises with annual sales of RMB20 million or more. Similar to industrial production, the \\nminimum threshold for profit data sampling increased in 2011. (Previously the minimum \\nthreshold was annual sales of RMB5 million or above.) Official year-over-year growth rates \\nare based on comparable samples, according to the NBS. \\n• \\nIn 2012, the NBS released the new Industrial Classification for National Economic Activities \\nwhich expanded the industrial coverage from 39 to 41 in total. Therefore, many sub-sectors’ \\ndata are not precisely comparable over longer periods of time even though the sectors may \\nhave very similar names; analysts should watch out for jumps in series around times of \\nrevised classifications. \\n• \\nProfits are on an accrual basis, and China onshore stock market listed companies follow the \\nsame standard, per enterprise accounting rules from the Ministry of Finance. However, NBS \\nprofits are pre-income tax, and for listed companies, investors usually pay attention to post-\\ntax earnings. \\nOther Issues \\n• \\nThe NBS typically releases the amount of losses (year-to-date) made by loss-making firms \\nat the same time as it publishes the total profits of industrial enterprises. (As one would \\nexpect, the reported total profits number for all industrial enterprises already nets out the \\nlosses from loss-making companies.) \\n\\n\\n 30 / 158 \\n \\n• \\nThe main cost categories in the monthly profit report are cost of goods sold, finance costs \\n(interest payments and fees paid to financial companies, which can often be significant \\nespecially when interest rates are high), and management/operation costs. \\nPurchasing Managers’ Indices \\nSignal to noise ratio: NBS manufacturing: ****; Caixin manufacturing: **** \\nNBS non-manufacturing: ***; Caixin services: *** \\nMacro importance: National Bureau of Statistics: ****; Caixin: *** \\nSource: National Bureau of Statistics - China Federation of Logistics and Purchasing (NBS-CFLP), \\nCaixin-Markit Economics/S&P Global\\n[5] \\nAvailability: NBS manufacturing PMI: since January 2005 \\nNBS non-manufacturing PMI: since January 2007 \\nCaixin manufacturing PMI: since April 2004 \\nCaixin services PMI: since November 2005 \\nTiming: NBS PMIs: Last day of each month (starting from March 2017); 9:30am \\nCaixin manufacturing PMI: Usually the 1st workday of the following month; 9:45am \\nCaixin services PMI: Usually the 3rd workday of the following month; 9:45am \\nPublication: NBS monthly release, Caixin PMI Reports \\nOverview \\nThere are two sets of Purchasing Managers’ Indices (PMIs) in China, one compiled jointly by the NBS \\nand the China Federation of Logistics and Purchasing (CFLP) (hereafter referred to as the “NBS PMI” \\nfor convenience), and the other by Caixin/Markit Economics (later published by Caixin/S&P Global). \\nCurrently, each organization releases separate indicators for the manufacturing and non-\\nmanufacturing sectors. \\nSignal to Noise Ratio \\n• \\nOf the two manufacturing surveys, China’s NBS manufacturing PMI -- also known as the \\nofficial manufacturing PMI -- has historically performed slightly better, as its production and \\nnew orders indices appear to be the best coincident indicators of sequential industrial \\nactivity growth among all PMI related data. \\n• \\nThis advantage could be because the NBS manufacturing PMI is more representative, due \\nmainly to the fact that it is compiled by the official statistical authority: \\n1. The NBS manufacturing PMI is based on a sample size of 3000 firms while the Caixin \\nmanufacturing PMI is based on over 500 firms. \\n2. The NBS survey is likely to have a higher response rate. The response rate of the \\n\\n\\n 31 / 158 \\n \\nNBS survey is said to be as high as 99.6%, as the NBS has the legal right to demand \\nthat firms respond. While this can be a double-edged sword as some firms reporting \\nunwillingly might be reporting with less care, the net effect of having a large \\neffective sample size is probably a good thing. While the rate for the Caixin survey is \\nunknown, it is unlikely to attain such a consistently high response rate. \\n• \\nChina’s two manufacturing PMIs sometimes send different signals (Exhibit 11). Sample size \\ndifference and discrepancies in data-collecting periods may explain divergences. The Caixin \\nsurvey is conducted in the middle of each month, while the NBS survey is conducted later, at \\naround the 20-25\\nth of each month. When the economy is changing rapidly, this timing \\ndifference can be significant. \\n• \\nIt is often said that the official PMI is a better reflection of large manufacturers and the \\nCaixin PMI is a better reflection of smaller and often export-oriented producers. However, \\nthe index provider (Markit previously) disagreed with this characterization, and at least in \\nterms of design -- both PMIs are designed to capture large manufacturers as well as SMEs. \\nEmpirically, the Caixin PMI outperformed the NBS PMI significantly in 2020H2 and 2024H1 \\nwhen Chinese exports were very strong, suggesting the Caixin PMI may cover more export-\\noriented companies. \\n• \\nThe NBS non-manufacturing PMI samples 4000 enterprises of different sizes in the \\nconstruction and services sectors. The surveys ask 10 questions for 10 individual indices \\nabout production, new orders, input price, sales price, employment, business activity \\nexpectation, new export order, work backlog, inventory, and suppliers’ delivery time. The \\nCaixin Services PMI sample covers more than 400 enterprises in the services sector. \\n• \\nUnlike the manufacturing PMIs which are conceptually identical, the NBS non-manufacturing \\nPMI and the Caixin Services PMI are different—the official one is not just a services PMI but \\nincludes construction as well. It is therefore natural that these two indicators diverge more \\noften than the manufacturing PMIs (Exhibit 12). But even if we look at the service sub-index \\nof the official non-manufacturing PMI, it is still quite different from the Caixin Services PMI. \\nThe construction sub-index of the non-manufacturing PMI has a relatively weak relationship \\nwith the construction component of GDP. \\n• \\nThe China Federation of Logistics and Purchasing (CFLP), which compiles the official PMIs, \\nalso publishes the emerging industries PMI (EPMI) each month. The EPMI is released around \\n10 days earlier than the NBS and Caixin manufacturing PMIs, and could serve as a leading \\nindicator for these PMI prints. The data series was first introduced in 2014, mostly covers the \\ncountry’s “strategic emerging industries” (mostly high-tech manufacturing), and is released \\non the 20th day of each month if it’s not a public holiday. We caution that there are some \\ncaveats when using EPMI in forecasting, due to its differences with NBS and Caixin \\nmanufacturing PMIs on samples, survey dates and seasonal adjustment methodologies. \\nExhibit 11: NBS and Caixin manufacturing indices show similar trends, but occasionally send \\ndifferent signals \\nChina manufacturing PMIs \\n\\n\\n 32 / 158 \\n \\n \\n \\nSource: NBS, Haver Analytics \\nExhibit 12: Only modest correlation between NBS and Caixin services sector surveys outside of \\nthe Covid period \\nChina services PMIs \\n \\n \\nSource: NBS, Haver Analytics \\nExhibit 13: A comparison of NBS manufacturing PMI, Caixin manufacturing PMI and EPMI \\n \\n\\n\\n 33 / 158 \\n \\n \\nSource: NBS, Caixin, China Federation of Logistics & Purchasing, Goldman Sachs Global Investment \\nResearch \\nMacro Importance \\n• \\nPMIs can be useful leading indicators to gauge sequential growth momentum. They are also \\nuseful in gauging upstream inflation, via their input price indices, and in tracking inventory \\ncycles, via their inventory sub-indices. \\nCompilation and Reporting \\n• \\nBoth the NBS and the Caixin PMIs are compiled and summarized through the results of a \\nmonthly survey of enterprise purchasing managers. In reality, it is not necessarily the \\npurchasing managers who are filling out the forms but other employees, especially those in \\nthe finance department. Statisticians send out questionnaires to firms every month to \\nascertain whether the situation in certain aspects of the business has improved, or if there is \\nno change, or if it has deteriorated. Responses in each category are given the weights of 1.0, \\n0.5 and 0, respectively. An index between 0 and 100 is then compiled for sub-indices such as \\nproduction, new orders, employment, prices (separately for input and output prices), \\ninventory (separately for raw materials and finished goods inventories), and suppliers’ \\ndelivery times. The overall manufacturing PMI is a weighted average of the new orders (30%), \\nproduction (25%), employment (20%), suppliers’ delivery time (inverted, 15%), and raw \\nmaterials inventory (10%) components in accordance to the weights used internationally. \\nData are then reported as a reading between 0-100 for the overall indicator and different \\ncategories, by adding the share of respondents indicating “improved” plus half the share \\nindicating “no change”. \\n• \\nThe Caixin services PMI and NBS non-manufacturing PMI do not have aggregate readings \\nand only report readings for different components. The “Business Activity Index” is often \\n\\n\\n 34 / 158 \\n \\nused as the headline reading. The Services Business Activity Index is to the services sector \\nwhat the Manufacturing Output Index is to the manufacturing sector. \\n• \\nThe PMI Composite index (also known as Composite Output index) is a weighted average of \\nthe Manufacturing PMI Output sub-index and the Services Business Activity Index (i.e., the \\nservices PMI). \\nInterpretation of the Readings \\nA reading of 100 means all respondents reported improvement. A reading of 0 means all \\nrespondents reported deterioration. In theory, the 50 level threshold is important because above this \\nlevel more respondents are reporting expansion than contraction. However, diffusion indices only \\nmeasure breadth of expansion or decline, not intensity, and in China’s case the 50 threshold has not \\nconsistently separated sequential expansion from contraction in IP or GDP, or necessarily marked a \\nchange in their second derivatives (accelerating/decelerating growth rates). \\nGS Proprietary Activity Measures \\nGS China Current Activity Indicator (GS China CAI) \\nSource: Goldman Sachs Economics Research \\nAvailability: Monthly since January 2006 \\nTiming: The series is updated daily to incorporate new data releases \\nRelease: GS China Proprietary Indicators update \\nOverview \\n• \\nThe GS China Current Activity Indicator (China CAI) was created to provide an alternative \\nmeasure with higher frequency and quality to identify shifts in the economic cycle. It \\nattempts to encompass indicators from the main producing sectors of the economy – \\nmanufacturing, housing, and consumer – as well as the labor market. The China CAI is \\nshown on a month-over-month annualized basis, after several rounds of adjustments by the \\nGS global economics team to harmonize CAIs around the world and to deal with dramatic \\nactivity changes in response to the Covid pandemic (it is available on the GS research portal \\nor on Bloomberg at ticker: GSCNCAI). The components of CAI include: industrial production, \\nemployment in composite PMI, Cheung Kong Graduate School of Business (CKGSB) Business \\nConditions Survey sales sub-index, the Caixin Services PMI, electricity consumption, \\nautomobile sales, the Caixin Manufacturing PMI output sub-index, real imports, real retail \\nsales, floor space sold, floor space started, cement production, real exports, freight volume, \\npassenger volumes, and floor space completed. \\n• \\nThe CAI is calculated on a sequential basis. Statistically, our CAI is constructed as the first \\nprincipal component of 16 standardized monthly economic indicators after seasonal \\nadjustment, converted to GDP-equivalent terms through a regression of historical real GDP \\ngrowth on this principal component. \\n• \\nWe extend the CAI back to 2006, by backcasting a few series (mainly survey indicators) for \\nwhich a complete history is not available. Other indicators on activity growth such as trade \\n\\n\\n 35 / 158 \\n \\nflows and sector-level data all showed a higher amplitude of fluctuation than the official \\nGDP data, so we adjusted for volatility when constructing the CAI. \\n• \\nBecause the CAI methodology is designed to be a high-frequency proxy for GDP, distortions \\nin GDP data (e.g. data that are consistently “too smooth”) can in theory affect the CAI. In \\nparticular, the mean of growth as measured by the CAI over the sample is effectively equal \\nto that of GDP growth over the sample, by construction. \\nExhibit 14: Our China CAI components and weights \\n \\n \\nSource: Goldman Sachs Global Investment Research \\nExhibit 15: Our China CAI has a close correlation with sequential GDP growth \\nChina Current Activity Indicator and GDP \\n \\n\\n\\n 36 / 158 \\n \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nGS China Inventory Tracker \\nSource: Goldman Sachs Economics Research \\nAvailability: Quarterly since Q1 2007 \\nTiming: Preliminary reading available in the last month of the quarter; final reading available one \\nmonth after quarter-end \\nRelease: GS China Proprietary Indicators update \\nOverview \\n• \\nThe GS China inventory tracker is based on six underlying inventory indicators, including \\ncommodities (i.e., iron ore, aluminum), PMI sub-indices (i.e., raw materials, finished goods), \\nindustrial enterprises finished goods inventory, and auto inventory. After data cleaning, we \\nderive the first principal component, which explains 25% of the total variation of the six series, \\nand then map it into percentage of GDP terms as our tracker for inventory changes. Our \\ninventory tracker can be used to gauge the contribution of inventory changes to China’s \\nGDP growth. \\n• \\nOur inventory tracker also indicates slowdowns in inventory build during the Global Financial \\nCrisis (GFC), around 2015-16 (despite implausibly stable reported real GDP growth) when \\nthe government implemented “supply-side reforms” in upstream sectors like steel and coal, \\nin early 2019 at the height of US-China trade war, and in late 2022 when the Covid “exit \\nwave” caused significant supply chain disruptions in China. \\n• \\nWe acknowledge our principal component analysis (PCA) approach to tracking inventory is \\nonly a proxy and the mapping into the inventory component of real GDP is not perfect. \\nHowever, we think it provides a useful way to track an important part of the economy that is \\nopaque and can generate large swings in quarterly growth. \\n\\n\\n 37 / 158 \\n \\nExhibit 16: Our inventory tracker can be used to gauge the contribution of inventory changes \\nto China’s GDP growth \\nInventory change contribution to real GDP growth (qoq, non-ann) \\n \\n \\nSource: NBS, Bloomberg, CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research \\nRelated GS Economics Publications \\n• \\n“China’s manufacturing PMIs: Which one should we look at and what are they telling \\nus?” EM Macro Daily, 13 August 2013 \\n• \\n“Taking stock of China activity: Updating our Current Activity Indicator”, 18 November 2015 \\n• \\n“Tracking all over the world - Our New Global CAI”, Global Economics Analyst, 25 February \\n2017 \\n• \\n“China Manufacturing PMI - Still an important signal”, China Data Insights, 27 September \\n2019 \\n• \\n“China EPMI, a good leading indicator for manufacturing PMIs”, China Data Insights, 21 July \\n2022 \\n• \\n“Tracking China’s Inventory Cycle”, China Data Insights, 7 June 2023 \\n• \\n“Why have post-reopening industrial profits been so weak?”, China Data Insights, 15 June \\n2023 \\n• \\n“Peeking into NBS' GDP revision practice”, China Data Insights, 31 August 2023 \\n• \\n“Gauging China’s growth (again)”, Asia Economics Analyst, 7 September 2023 \\n• \\n“A Closer Look at NBS 2022 GDP Revisions”, China Data Insights, 13 March 2024 \\n\\n\\n 38 / 158 \\n \\nSection III. Investment \\nThere are three sets of macro indicators related to investment: \\n1. Fixed asset investment (FAI) and its breakdown by industry, nature of enterprises, work type \\nand region, as well as funds designated for FAI and its breakdown by source; \\n2. FAI project starts and under construction; \\n3. Other investment-related data series, including infrastructure-related bond issuance, the \\ninvestment values of large-scale projects approved by the National Development and \\nReform Commission (NDRC), excavator operating hours, major raw materials production, \\nconsumption and prices. \\nFixed Asset Investment \\nSignal to noise ratio: ** \\nMacro importance: **** \\nSource: National Bureau of Statistics \\nAvailability: Monthly from 1992, annual from 1980 \\nType: Monthly: Year to date \\nTiming: Typically around the 2nd /3rd week of the following month. In January, April, July and \\nOctober, it is released with quarterly GDP data during a press conference around the 2nd /3rd week \\nof the month. \\nPublication: NBS monthly releases \\nOverview \\nFixed asset investment (FAI) excluding rural households measures spending on durable assets that \\nare used repeatedly in the production process. \\nCompilation and Reporting \\nAccording to the NBS, FAI data include investment expenses on equipment purchases, construction \\nand installation, among others (e.g., land acquisition, old buildings), for projects on fixed assets (real \\nestate development also included) with total planned investment of RMB5 million and over. \\nThe data are broken down into a number of categories. The following breakdowns are provided \\nafter 2004: \\n• \\nFAI undertaken by nature of enterprise \\n1. State owned enterprises (SOEs): These are enterprises solely owned by the \\ngovernment. This category is also known as “State-Owned and State Holding \\nEnterprises”, and includes all SOEs and enterprises over which the government has \\neffective control, but excludes state sole proprietors that are part of “limited liability \\n\\n\\n 39 / 158 \\n \\ncompanies (LLCs)”, based on the NBS definition. New PPP (Public Private Partnership) \\nprojects complicate the picture, as projects with 50% state share are accounted for as \\nSOE investments, which overstates the true share of the state. PPP projects \\nexperienced rapid expansion in 2017, stagnated in 2018-22, and declined thereafter. \\n2. Collectively owned enterprises (COEs): Another kind of publicly-owned enterprise \\nwhich has become less common (as of 2023, FAI by COEs accounted for less than 1% \\nof total FAI). \\n3. Foreign-funded enterprises. \\n4. Hong Kong, Macau and Taiwan-funded enterprises (note that foreign-funded \\nenterprises and Hong Kong, Macau and Taiwan Funded Enterprises are two separate \\ncategories that do not overlap). \\n5. Share-holding enterprises: Enterprises that have a share-holding structure. \\n6. Private enterprises and individual businesses: Both are private businesses. Businesses \\nrun by one person and small private businesses that employ fewer than eight people \\nare called “individual businesses”; larger businesses are called private enterprises. \\nPrivate-sector FAI has remained weak in recent years despite fiscal stimulus \\nimplemented by the government. The private share of FAI increased from 31% in \\n2004 to 64% in 2015, but has declined since then. \\n7. Joint ownership enterprises: Typically enterprises set up by two or more \\nindependent firms. This was a popular form of enterprise in the earlier years of \\nreform but is now much less common. \\n8. Limited liability companies (LLCs): Including state sole proprietors and other limited \\nliability companies. \\n9. Others (e.g., miscellaneous projects which are not easy to be classified into the \\ncategories above). \\nExhibit 17: SOE and non-SOE investment showed significant divergence during 2022-23 \\nFAI growth by business ownership in China \\n \\n\\n\\n 40 / 158 \\n \\n \\nSource: CEIC, Goldman Sachs Global Investment Research \\n• \\nFAI by work type \\nThis set of data is released with a monthly frequency, and the main categories include construction \\nand installation, equipment purchase and others. In 2023, construction and installation was the \\nmajor work type under FAI, accounting for 71% of overall FAI. \\n1. “Construction and installation” includes construction of buildings (materials and labor), and \\ninstallation of machinery and equipment. Note that land purchase costs are not included \\nhere. \\n2. “Equipment purchase” includes spending not only on new equipment, but also on old \\nequipment. \\n3. Other FAI primarily includes spending on land acquisition, old buildings, and management \\nfees. \\n• \\nFAI by industry is available in terms of the primary, secondary and tertiary industries. \\nDetailed breakdowns for specific industries are available from 2004. As is the case for IP by \\nindustry, FAI by a given industry is not investment in a particular type of project, but just \\nFAI by companies in that industry. For analysis we typically aggregate the sectors into four \\nmajor categories: manufacturing, infrastructure, property and others ( “others” mainly \\nincludes services and agriculture-related sectors). In 2023, FAI in these four sectoral \\ncategories accounted for around 33%, 36%, 15% and 26% of total FAI, respectively, compared \\nto 30%, 31%, 22% and 19% in 2007. \\n1. The share of manufacturing FAI has been close to infrastructure FAI over the past \\ndecade. Manufacturing investment is mostly carried out by private enterprises in \\nChina. Over the past few years, the solid growth of manufacturing investment was \\nunderpinned by POEs. \\n2. Our estimates for infrastructure FAI are based on our GS definition, which includes \\n\\n\\n 41 / 158 \\n \\nnot only the three sectors under the NBS classification (i.e., transport, storage & \\npostal service; water conservancy & environmental protection; and electricity, gas & \\nwater production and supply), but also four more industries that provide public \\ngoods mainly by the government sector (e.g., scientific research & polytechnic \\nservice; education; healthcare, social security & welfare; and culture, sport & \\nentertainment). Most of the infrastructure investment in China was done by local \\ngovernments (around 70% in 2023). Over the past two decades, the share of \\ntransportation and utilities in infrastructure investment has fallen from around 60% to \\naround 40%. Recent policy communications suggest infrastructure spending will likely \\nfocus on both traditional and new infrastructure projects in coming years: the former \\nmay include water conservancy, emergency management and energy, while the \\nlatter could include 5G telecommunications, supercomputing, EV charging stations, \\nultra-high voltage power transmission, data centers, green capex and industrial \\nparks. \\n3. Property FAI, often regarded as very important due to its interconnection with local \\ngovernment finances, now has a much smaller share than manufacturing and \\ninfrastructure FAI. Since the unprecedented property downturn that started in 2021, \\nthe share of property FAI has declined significantly. Furthermore, the value-added \\nshare of property FAI is likely to be significantly lower than in other components of \\nFAI, since a significant share of property FAI is in land transfers (which are non-\\nvalue-added and do not enter GDP accounting). Hence, the share of property \\ninvestment in total gross fixed capital formation (GFCF) is notably lower than its \\nshare in total FAI (for more detail on property FAI see Section IV. Real Estate). On the \\nback of the current property downturn, regulatory tightening in sectors such as \\neducation and internet that are dominated by private enterprises, and the ongoing \\nfiscal stimulus, FAI growth has shown a notable divergence between infrastructure \\nand property, and between SOEs and non-SOEs since 2022. Our research suggests \\nthat the weakness of private investment during 2022-23 was mostly due to the large \\ncontraction of property investment. \\nExhibit 18: Infrastructure and property investment diverged significantly in 2022-23 \\nFAI growth and breakdown by major component \\n \\n\\n\\n 42 / 158 \\n \\n \\nSource: CEIC, Goldman Sachs Global Investment Research \\n• \\nFAI by region: This set of data is available for the 31 provinces and many cities, on a \\nmonthly basis. The FAI share across provinces is largely in line with their GDP share. FAI \\nbreakdown by province can help estimate the difference in investment momentum across \\nregions, especially when macro policies have an asymmetric impact on different regions.\\n[6] \\nExhibit 19: FAI in more-indebted regions underperformed that in coastal regions in 2022-24 \\nFAI in provinces with high debt pressure vs. others (seasonally adjusted) \\n \\n \\nSource: Wind, Goldman Sachs Global Investment Research \\n• \\nFunds designated for FAI (this is sometimes referred to as FAI by source of funding, but we \\n\\n\\n 43 / 158 \\n \\nview this as a misleading term because these data refer to the amount of funds that became \\navailable for FAI, which is not the same as the amount of FAI completed): \\n1. State budget. \\n2. Domestic loans from banks. \\n3. Bonds: Bonds issued by corporates and financial institutions, including those by local \\ngovernment financing vehicles (LGFVs) and policy banks for FAI. \\n4. Foreign capital. \\n▪ \\nAmong which: Foreign direct investment (FDI). In practice this can include \\nsignificant capital from “round-tripping” (see more on FDI in Section VI. \\nExternal Sector). \\n5. Self-raised funds. \\n▪ \\nSelf-owned: This is intended to be mainly from retained earnings. In practice \\nthis likely includes a variety of funding channels including non-standard \\nloans borrowed from financial institutions. This might be driven by \\nregulatory requirements, such as those requiring a project to have a certain \\nshare of self-owned capital before it is qualified to borrow from banks. \\n▪ \\nShare issuance: Capital raised through public offerings. \\n6. Others (e.g., crowd-funding and donations). \\nSignal to Noise Ratio \\n• \\nThere is considerable confusion and controversy over China’s FAI statistics, with different \\ndefinitions and scope of coverage. \\n• \\nThere are major differences between the definitions of FAI and of the investment concept \\n(GFCF) in national accounts. \\n1. The monthly FAI data are not reported on a value-added basis. That is, they do not \\nreflect the incremental new capacity added to the capital stock, but just total \\nnominal investment spending reported by companies and governments. FAI \\nincludes spending on assets that do not directly contribute to GDP (e.g., land \\nacquisition and old equipment purchase), while GFCF does not. \\n2. FAI includes a minimum project size cutoff (RMB5 million as of 2023) for non-\\nproperty investment (and all property investment), while GFCF does not. \\n3. FAI does not include spending on intangible fixed assets such as computer software \\nand IT investment, while GFCF does. \\n• \\nFAI data have a few measurement issues. \\n1. Both the monthly and annual investment series are in nominal terms. The NBS did \\nrelease an official FAI deflator on a quarterly basis previously, but suspended the \\ndata series in 2020. Besides, capturing price changes in this sector is difficult – \\n\\n\\n 44 / 158 \\n \\nespecially land prices. \\n2. The monthly yoy FAI growth rate, which is monitored closely by investors, has \\nsignificant measurement issues. For example, the classification for monthly reported \\nFAI data changed materially a number of times in the past. This change has made \\nthe yoy growth rates not completely comparable over time. In addition, the official \\nyear-over-year growth rate series are not compatible with the official level series, \\nmainly due to sample changes over the years. \\n3. Over-reporting may be the major factor behind the high level of FAI growth, which \\nhas also been acknowledged by the government in the past. Problems include \\npotential double-counting (e.g., of the same project by different regions) and \\nmisreporting (e.g., FAI data are based largely on questionnaire responses which do \\nnot always have to be backed by hard evidence). Given the statistical issues \\nmentioned above, we suggest investors supplement FAI data with other indicators, \\nsuch as capital goods imports, raw materials (e.g., steel and cement) production and \\napparent demand, and infrastructure-intensive bond issuance (e.g., local \\ngovernment special bond), to help form a better assessment of true investment \\ngrowth momentum on a monthly basis. \\nMacro Importance \\nDespite all these caveats, the monthly FAI series remains very important in assessing policy risks, \\nbecause policymakers do pay very close attention to it. The NDRC previously announced annual \\ntargets for FAI growth, and some local governments also set their local FAI growth targets, although \\nthese targets are not as binding as GDP growth targets. A significant rise in FAI growth rates, if \\naccompanied by higher inflation, tends to lead to policy tightening and vice versa. \\nProjects Started and Under Construction \\nSource: National Bureau of Statistics \\nAvailability: Monthly from 2004 \\nTiming and publication: Same as FAI data \\nOverview \\n• \\nThe value of total planned investment in new projects and under construction reflects \\nchanges in the pipeline of investment projects. \\n• \\nData are compiled together with FAI data and therefore many of the problems associated \\nwith FAI data also apply to these series. \\n• \\n“Projects started” data are often used as a leading indicator of investment activities. \\nPolicymakers and investors also monitor this series to assess likely future trends in fixed asset \\ninvestment on a 1-2 year horizon. In reality, however, there is no stable lead-lag relationship \\nbetween the two series. \\nCompilation and Reporting \\n\\n\\n 45 / 158 \\n \\n• \\nFAI projects started and under construction are released in terms of value and number of \\nprojects. \\n• \\nProject started, planned (value): This refers to all projects planned to start during the \\nreporting period (they may or may not be under construction at this stage but will be started \\nbefore the reference period end). It excludes ongoing investment projects (including those \\nsuspended and restarted in this period). Previously, the NBS also released data on projects \\nstarted and planned in unit terms, but this data series was suspended in early 2018. \\n• \\nProjects under construction (value): This contains ongoing investment projects and new \\nstarts. Previously, the NBS also released data on projects under construction in unit terms, \\nbut this data series was suspended in early 2018. \\n• \\nTotal planned investment: Refers to FAI projects’ planned investment amount. Note these \\nplans are not binding and therefore this is not a reliable indicator for future FAI. Availability \\nof funding and subsequent changes in costs can have large impacts on actual investment. \\n• \\nThe relationship between different series in this set of data is complicated because of \\nregulatory approval issues. For example, sometimes local projects find it difficult or costly to \\nobtain approval for a new project and therefore just report the new project as an extension \\nof an existing project. \\nOther Investment-related Data \\nOther indicators related to investment include infrastructure-related bond issuance (especially local \\ngovernment special bonds and local government financing vehicle bonds; to be elaborated \\nin Section X. Government Finance), large-scale projects approval by the National Development and \\nReform Commission (NDRC), excavator machinery operating hours, cement inventory, and steel \\ndemand, although each indicator has its own advantages and disadvantages. \\n• \\nThe investment value of projects approved by the NDRC may reflect the central government’\\ns stronger determination to boost investment if it increases quickly in a short period of time, \\nbut its total amount in 2022 (RMB1.5 trillion; latest year-end data available) only accounted \\nfor 2.6% of total FAI. \\n• \\nKomatsu, a leading global manufacturer of construction machinery, provides data on the \\nmonthly average operating hours of excavators sold in China. It is updated on a monthly \\nbasis with a lag of around two weeks. This could help gauge the momentum of construction \\nactivity, but it is subject to distortions from changes in Komatsu’s market share in China \\n(which has declined to 1% in 2023 from 15% in 2008) and not able to distinguish \\ninfrastructure construction from property construction. This data series is subject to \\nsignificant seasonality. Some investors may also focus on sales of excavators and other \\nconstruction machinery, but these durable capital goods have significant replacement cycles, \\nwhich may distort their yoy sales growth in some years. \\n• \\nSteel and cement are crucial raw material inputs for construction activity, and there are \\nseveral related indicators available from either official or private data sources, including steel \\nand cement output, demand for construction-related steel (measured by production net of \\nnet exports and inventory changes), the cement inventory to storage capacity ratio, cement \\n\\n\\n 46 / 158 \\n \\nproduction capacity utilization ratio, and steel and cement prices. \\nRelated GS Economics Publications \\n• \\n“China: Is credit losing its cyclical growth impact?”, Emerging Markets Macro Daily, 20 May \\n2013 \\n• \\n“China infrastructure investment: Is the high growth sustainable?”, Asia Economics Analyst, \\n13 April 2017 \\n• \\n“Fast or slow, old or new: A macro view on China's infrastructure investment”, Asia in Focus, \\n15 April 2020 \\n• \\n“China Green Capex: Renewable power investment and its impact on the economy”, Asia \\nEconomics Analyst, 16 January 2022 \\n• \\n“China: Manufacturing sector to drive investment in 2022”, Asia in Focus, 22 February 2022 \\n• \\n“China: Gauging the upside for infrastructure investment in 2022”, Asia in Focus, 28 March \\n2022 \\n• \\n“China: Tracking the strength and pace of infrastructure stimulus”, Asia in Focus, 12 May \\n2022 \\n• \\n“China: Assessing the implications of local governments’ 2023 targets”, Asia in Focus, 17 \\nFebruary 2023 \\n• \\n“China: Beijing’s Balancing Act between Infrastructure Stimulus and LGFV Deleveraging”, Asia \\nin Focus, 6 March 2024 \\n• \\n“The shifting role of private investment in China”, China Data Insights, 25 April 2024 \\nSection IV. Real Estate \\nThere are five major sets of macro indicators related to the real estate sector: \\n1. Real estate investment and its breakdown. \\n2. Land transaction (sales/purchases) by different data source. \\n3. Construction data -- housing starts, property under construction, and completions. \\n4. Home sales and inventory. \\n5. Property and land sales prices. \\nWe have also constructed GS proprietary measures related to the real estate sector, including \\nestimates of the property sector impact on GDP growth and measures of the regulatory stance (a \\nproperty policy relative tightness index as well as a 24-city housing policy stance indicator). \\nReal Estate Investment \\nSignal to noise ratio: ** \\n\\n\\n 47 / 158 \\n \\nMacro importance: *** \\nSource: National Bureau of Statistics \\nAvailability: Monthly from 2004, annual from 2003 \\nOverview \\n• \\nThere are two sets of real estate investment data. The most widely tracked one is the total \\nFAI conducted by real estate developers (also known as “property investment” or “property \\nFAI”). The data are surveyed and released together with other real estate indicators (e.g., \\nnew home starts, completions, and sales), as well as other major economic activity data (e.g., \\nindustrial production, headline FAI and retail sales), by the NBS on a monthly basis. They \\ncover land purchases, equipment purchases and construction activities for residential, \\ncommercial and office buildings. In 2023, around 90% of real estate investment was related \\nto land purchases and construction. The breakdown details of property investment by \\nownership of enterprises (annual), work type (monthly), and sources of funding (monthly) are \\navailable. Like headline FAI data, the NBS only releases the year-to-date level and growth \\ndata for real estate investment at a monthly frequency, while single-month real estate \\ninvestment growth rates require additional estimates given a specific base year. \\n• \\nThe other data on real estate investment is the FAI by industry – real estate (as a composite \\nindustry in the tertiary sector). The definition of this dataset is different because it \\nsupposedly includes real estate related investment by all types of enterprises, not just \\nproperty developers. However, the general trends of these two indicators are similar. \\nExhibit 20: Around 90% of property FAI was related to land purchases and construction in \\nrecent years \\nProperty FAI breakdown by key procedure (based on 2023 data) \\n \\nSource: NBS, Wind, Goldman Sachs Global Investment Research \\nExhibit 21: Growth in property investment and FAI growth for the real estate industry share \\nsimilar trends \\n\\n\\n 48 / 158 \\n \\nProperty investment vs. FAI in the real estate industry \\n \\nSource: NBS, Wind, Goldman Sachs Global Investment Research \\nSignal to Noise Ratio \\n• \\nBoth series of real estate investment have similar drawbacks as other FAI data series. For \\nexample, the official year-over-year growth rate series is not entirely compatible with the \\nofficial level series, mainly due to sample changes over the years (for details on other \\nstatistical issues with FAI data, see Section III. Investment). Furthermore, these two indicators \\ndo not capture rural properties without property rights. Rural properties without property \\nrights are only for self-use, and transactions with buyers outside the community are deemed \\nillegal. As such, there is a lack of data for rural properties in China. \\n• \\nThe NBS made occasional revisions to the definition of private investment, which may also \\naffect the data quality. For example, in March 2024 the NBS revised the definition by \\nexcluding land redevelopment related spending from real estate investment. Although the \\nNBS flagged that the reported yoy real estate investment growth estimates are on a \\ncomparable basis, it did not release the revised historical level data series under the new \\ndefinition. \\nMacro Importance \\nDespite the drawbacks, real estate investment is still widely tracked by investors, because: (1) real \\nestate FAI is informative in terms of gauging the strength of the real estate sector; (2) real estate FAI \\n(reported by all property developers) accounted for 15% of the overall FAI in China in 2023 (vs. its \\nprevious peak of 23% in 2004), and thus is still an important component of the overall FAI. \\nLand Transactions \\nSignal to noise ratio: ** \\n\\n\\n 49 / 158 \\n \\nMacro importance: *** \\nSource: National Bureau of Statistics (NBS), Ministry of Finance (MOF), China Real Estate Index \\nSystem (CREIS), Soufun, Wind \\nAvailability: NBS and MOF: Monthly and annual; CREIS/Soufun and Wind: Weekly and monthly \\nTiming: NBS and MOF: Collected monthly with complete enumeration typically around the 3rd week \\nof the following month (January-February data are combined for release in mid-March by official \\nsources including NBS and MOF). \\nOverview \\nThere are several useful sources of data on land transactions and development. \\n• \\nLand transaction area (NBS): This is defined as land area (squared meters) purchased by \\nreal estate developers during the reporting time period. As all land in China is owned by the \\nstate, buyers can only purchase the right to use land instead of ownership. These rights \\nrange from 20-70 years, hinging on how the land will be used (e.g., for the construction of \\nindustrial, commercial, or residential buildings). In the latest version of Civil Code of the \\nPeople's Republic of China, effective in January 2021, the land-use right will be automatically \\nrenewed when the right leases are up.\\n[7] \\n• \\nLand transaction value (NBS): This refers to the final amount (value) of the land-use right \\ntransacted in both the primary and secondary markets by real estate developers, based on \\nthe flows of their actual payment. Land purchase value is on a comparable basis with land \\npurchase area in terms of statistical coverage, so we can derive the average price of land \\npurchases from these two indicators. However, this is not precisely the same as a land price \\nindex because the quality of land transacted may not be comparable. Land purchase area \\nand value data used to be available at the city level for 40 major cities, released by the NBS, \\nbut these city-level data series were suspended in January 2019. The NBS also suspended \\nthe release of land transaction area and value data series in January 2023. \\n• \\nDeveloper land purchase value (NBS): These refer to the total amount of land transactions, \\nin value terms, based on the contracts signed by property developers in the primary and \\nsecondary markets. \\\"Developer land purchase value” is much larger than “land transaction \\nvalue” above because it includes various taxes, fees and spending associated with land \\ncompensation, land preparation, and land management. This series is recorded at the time \\nof disbursement by property developers and may lag the transaction time somewhat. \\n• \\nGovernment revenue from land sales (Ministry of Finance): This is reported by the \\nMinistry of Finance (MOF) on a monthly basis – along with other government income and \\nexpenditure data – and is a major component of government-managed fund revenue. The \\nMOF series is slightly different in scope from the land-use right transfer value reported by \\nthe NBS and third-party data vendors. For instance, the former series also includes income \\nfrom renting land by land administrations. Land sales remain a very important source of \\nincome/expenditure for local governments. Refer to Section X. Government Finance for \\nmore details. \\n\\n\\n 50 / 158 \\n \\n• \\nLand transaction area and value by city (CREIS/Soufun/Wind): CREIS/Soufun\\n[8] collect and \\nsummarize the data (data access requires a subscription) on government revenue from land \\ntransactions in more than 300 cities in China (based on 302 cities and is often referred to as \\n“CREIS 300-city revenue from land sales”, compiled and tracked by our GS China property \\nresearch team). The definition of the data is broader than the NBS land purchase \\nvalue/volume data mentioned above, given that these data cover land purchases by \\ndifferent types of enterprises, not just developers. Wind also compiles a sample of 100 cities \\nto track land transaction value and area, on both a weekly and monthly basis. The Wind data \\nhave breakdowns by city tier (i.e., Tier-1, Tier-2 and Tier-3). The benefit of \\nCREIS/Soufun/Wind data is that they are more timely (e.g., daily for the original \\nCREIS/Soufun data and weekly for the Wind data) and tend to be a leading indicator (e.g., \\nCREIS series on land transactions are recorded when transactions occur, NBS land purchases \\nare recorded when investment is completed, and MOF government revenue from land sales \\nare recorded when the payment is settled). The drawback is that CREIS/Soufun/Wind data \\naggregate a large number of cities but do not include all transactions nationwide. \\n• \\nThe discrepancies between these land transaction measures mainly come from different \\ntimings to register land sales (e.g., registered when contracts are signed or when funds are \\npaid), different groups of land buyers captured by the sample (property developers only or \\nall types of buyers), and some additions and/or deductions before the purchase fees are \\ntransferred to the MOF's account for land sales revenue. \\nExhibit 22: A summary of various land sales measures in China \\n \\n\\n\\n 51 / 158 \\n \\n \\nSource: NBS, CREIS, Wind, Goldman Sachs Global Investment Research \\nSignal to Noise Ratio \\n• \\nAmong all the land sales indicators mentioned above, we tend to rely more on the land \\ntransaction volume and value series from CREIS. The data coverage of land purchase value \\nand volume reported by property developers is much narrower, and the NBS land purchase \\ndata lag the CREIS data series slightly as discussed above. \\n• \\nThe introduction of a centralized land auction mechanism for major cities in 2021 increased \\nthe transparency of land supply, reduced panic land purchases among property developers, \\nand significantly lowered land auction premia. However, as land supply needs to be \\nconcentrated in several batches in a year under the new regime, occasional shocks (e.g., \\nCovid lockdowns during 2020-22, natural disasters) would affect the timing of land actions \\nand thus cause near-term distortions to yoy growth in land sales revenue. \\nMacro Importance \\nThe NBS developer land purchase value typically contributed to around 30% of real estate investment \\nin recent years. However, the share of land sales revenue in total (gross) government revenue \\n\\n\\n 52 / 158 \\n \\ndeclined to below 20% in 2023 from its recent peak of 30% in 2021 (the peak share was even higher \\nfor some local governments with high reliance on land finance). \\nLand sales tend to lead property construction activity and thus it is important to track land sales. As \\nmentioned above, land sales are also important sources of income/expenditure to local \\ngovernments, and therefore can impact the financing need/spending capability of local \\ngovernments. In addition, land prices are important for future property price trends and the financial \\nhealth of property developers. \\nHousing Starts, Under Construction and Completions \\nSignal to noise ratio: *** \\nMacro importance: **** \\nSource: National Bureau of Statistics \\nAvailability: Monthly and annual \\nTiming: Typically around the 3\\nrd week of the following month (together with the release of the NBS \\nmonthly activity data) \\nOverview \\nFloor space started: This measure refers to the total floor space of buildings that are newly started \\nwithin the reporting period. Only housing newly started by real estate developers and enterprises \\nwithin the reference timeframe is counted. Continued building activities carried over from the \\npreceding reporting period are excluded. Construction areas suspended in previous periods and \\nrestarted in the reporting period are included, but construction areas suspended and restarted \\nwithin the reporting period are not. \\nFloor space under construction: This refers to the total floor space of buildings in different \\nconstruction phases in the reference period, including floor space of newly started buildings during \\nthe reference period, floor space of construction extended from the previous period to the reference \\nperiod, floor space of construction suspended or postponed in the previous period and resumed in \\nthe reference period, floor space of construction completed in the reference period, and floor space \\nof constructions started and then suspended or postponed both in the reference period. Floor space \\nunder construction has a strong correlation with property FAI. \\nFloor space completed: This refers to the total floor space that fulfills all the completion \\nrequirements of property developers and is ready for occupancy. \\nOther housing related construction indicators: \\n• \\nThe three indicators above are all reported by property developers, and released monthly \\ntogether with the real estate FAI indicator. There are other indicators related to broader \\nproperty activity: “housing under construction” and “housing completion”, for example, \\ncover not just commercial housing but also other types of housing constructed by all types \\nof enterprises. \\n\\n\\n 53 / 158 \\n \\n• \\nThe government also publishes data on social housing. There are annual data on the total \\nnumber of flats planned to be started, as well as the total number of flats completed. In \\nrecent years, these data points were mostly unveiled by the Ministry of Housing and Urban-\\nRural Development on ad hoc occasions. Although social housing related data are not quite \\nas important as commercial housing related data, and the former's data quality could be \\nlower than the latter, construction of social housing still drives upstream/downstream \\nindustries (such as construction materials) and thus is useful to track. Social housing units \\ncould also be converted from commercial housing — during the property down-cycle in \\n2014-2015 and also in recent years, this was one inventory destocking measure adopted by \\nmany local governments. More recently, the PBOC announced in May 2024 a relending \\nprogram encouraging local SOEs to purchase completed but unsold properties and to turn \\nthem into social housing to destock existing property inventories. \\nSignal to Noise Ratio \\nThe survey target of the first three indicators above includes only property developers, so these data \\ncover only commercial floor spaces constructed by developers. Properties built by other corporates \\nand institutions (often as a form of welfare, e.g. employee housing by SOEs) which are not for sale \\non the market are not included. Rural properties without property rights are also not captured. \\nMoreover, the series for floor space under construction, completed, and started are not consistent \\nwith each other, and researchers have different views on which indicator is more reliable. In theory, \\nnew home starts should lead completions by 2-3 years, but completions ran persistently and \\nsignificantly below new starts from 2000 to 2022, and the two series even went in opposite \\ndirections in 2018 and 2021. On the back of the prolonged property downturn since mid-2021, \\nincreased developer funding stress and higher policy priority to secure the delivery of pre-sold new \\nhomes boosted completions relative to new starts dramatically in 2022-23. \\nMacro Importance \\nThe floor space started, under construction and completion series are all widely tracked by investors \\nand researchers. Historically, floor space started has lagged property sales (given the majority of \\nproperty sales are pre-sales) and used to lead demand in other sectors such as metals/cement, \\nthough this may change in the future as government policy discourages pre-sales. Floor space \\ncompleted, on the other hand, can be informative on the future demand of moving-in related items \\nsuch as furniture, home appliances, and interior design materials. \\nExhibit 23: Property starts and completions trends can diverge from each other \\nProperty-related activity (seasonally adjusted) \\n \\n\\n\\n 54 / 158 \\n \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nHome Sales \\nSignal to noise ratio: **** \\nMacro importance: **** \\nSource: National Bureau of Statistics, Wind, CREIS, Goldman Sachs Economics Research \\nAvailability: Daily, monthly and annual \\nTiming: NBS monthly home sales: Typically around the 3rd week of the following month (together \\nwith the release of the NBS monthly activity data) \\nOverview \\nFloor area sales and total value sales of commercial buildings: These two indicators measure the \\nsales volume and value of residential buildings, offices, and commercial buildings in the primary \\nproperty market. They are released by the NBS together with new starts, under construction, and \\ncompletion data mentioned in Section III. Investment. \\nDue to China's presales system, most sales are presales of units that are still under construction vs. \\nsales of completed units. In 2023, 82%/77% of the sales value/volume for all commercial property \\ntransactions was presales, lower than their peaks in mid-2021 (91%/89%) but well above their pre-\\nGFC levels (73%/67% in 2007). Revenue from presales is therefore a major source of funding for \\nChinese property developers. \\nWind 30-city daily property transaction volume in the primary market: According to Wind \\ndefinition, its 30-city sample of property sales in the primary (new) market is actually composed of \\n32 cities, including 4 Tier-1 cities, 14 Tier-2 cities and 14 Tier-3/4 cities. However, based on a \\nbottom-up estimation using city-level data in recent years, we find that the series from Wind include \\n21 cities only (4 Tier-1 cities, 8 Tier-2 cities and 9 Tier-3/4 cities) currently, as property sales data for \\n\\n\\n 55 / 158 \\n \\nseveral cities (e.g., Tianjin and Nanchang) became unavailable in recent years.\\n[9] Our bottom-up \\nestimates using property sales data from 21 cities match closely the headline number of Wind 30-\\ncity property transaction volume in recent years. The original source is various local housing bureaus. \\nOther third-party data vendors, such as CREIS/Soufun, also have their own home transaction \\ntracking for different city samples. \\nWind 19-city daily property transaction volume in the secondary market (compiled by Goldman \\nSachs Economics Research): Our tracker is built on city-level daily data for the secondary (existing) \\nproperty market transaction volume which is compiled by Wind and originally released by local \\nhousing authorities. Our sample covers 19 cities, including 2 Tier-1 cities, 7 Tier-2 cities, and 10 Tier-\\n3/4 cities. \\nAs China's housing market evolves, the secondary market becomes more important, especially in \\ntop-tier cities where land supply and new residential buildings are more limited. Secondary market \\ntransactions are important in tracking price trends and property market sentiment, but are less \\nimportant in terms of GDP contributions. \\nReal estate investment, new starts, under construction, completions, and sales data are also available \\nat the province level and for 40 major cities in China, reported by local housing bureaus and \\ncollected by the NBS on a monthly basis. However, there are many missing data points in city level \\nindicators, and the NBS suspended the release of 40-city property activity indicators in January 2019. \\nAccording to the 2020 data (latest data available), lower-tier (Tier-3/4) cities accounted for 66% of \\nnationwide new home sales volume, followed by Tier-2 (31%) and Tier-1 cities (3%). In value terms, \\nthe share of Tier-1, Tier-2, and lower-tier cities was 11%, 40% and 49%, respectively. Combining data \\nfrom the NBS and private sources and based on some reasonable assumptions, we estimate a \\nbreakdown of new home sales by city tier. \\nExhibit 24: Property activity declined significantly beginning in 2021 \\nProperty-related activity (seasonally adjusted) \\n \\n\\n\\n 56 / 158 \\n \\nSource: CEIC, Goldman Sachs Global Investment Research \\nSignal to Noise Ratio \\n• \\nProperty transactions data are generally reliable, especially when compared with new starts \\nand completions data. But people sometimes under-report the transaction value to reduce \\nthe transaction tax, though this mostly occurs in the secondary market. \\n• \\nThe year-over-year growth rates and the levels reported in the NBS property data used to \\nbe consistent with each other until 2023. In May 2023, the NBS revised down the \\ncomparison base when reporting yoy property activity growth data, especially for new home \\nsales, to adjust for false sales data.\\n[10] The practice continued through the remainder of 2023 \\nand resulted in a meaningful divergence between the officially-reported level data and \\ngrowth rate data. However, the NBS has not released the revised level data for previous \\nyears. We adjust the property-related activity data series from 2023 in our analysis to take \\ninto account the revisions. We estimate new home sales volume in 2022 has been revised \\ndown by around 10%, especially for April-May 2022 (amid the Covid-related Shanghai \\nlockdown). \\nMacro Importance \\nProperty transactions are important to track for several reasons. Most directly, they serve as an \\nindicator of housing demand. The fluctuations of property sales impact housing construction \\nactivities, and also the profitability of property developers. In addition, property sales are correlated \\nwith a number of other industries such as furniture, interior design and real estate agencies (which is \\nlinked to real estate related services in GDP). \\nHome Inventory \\nSignal to noise ratio: ** \\nMacro importance: *** \\nSource: National Bureau of Statistics, local housing bureaus, CREIS \\nAvailability: Weekly, monthly and annual \\nOverview \\nHousing vacant area: This data refer to completed but unsold floor area. It is published monthly by \\nthe NBS along with other property indicators and has a breakdown by different types of properties \\n(residential, office building, and other commercial real estate). By province data are also available, \\nbut only at an annual frequency. Note that due to China's presales system, this series only covers a \\nfraction of total new home inventory (i.e., all floor areas that are “saleable” but unsold) because the \\nvast majority of the new home inventory is uncompleted. \\nInventory months: Other common (and more widely used) measures of housing inventory include \\nthe ratio of total saleable gross floor area divided by monthly floor area sold, which measures the \\nnumber of months needed to digest inventory. There is no official data on this, but total saleable \\ngross floor areas are reported in major cities in China by the local housing bureaus, compiled by \\n\\n\\n 57 / 158 \\n \\nthird-party data vendors including CREIS. The CREIS inventory months data series, regularly tracked \\nby our China property research team, is available on both a weekly and monthly basis. \\nThere are no vacancy statistics on properties sold but not inhabited, which are sometimes referred to \\nas the “shadow inventory” in the Chinese property market. There was a report by the Southwestern \\nUniversity of Finance and Economics in 2018 based on 2011-17 Chinese household financial surveys \\nsuggesting around 21.4% of urban housing apartments were left vacant in 2017 (vs. 18.4% in 2011).\\n[11] \\nSignal to Noise Ratio \\nInventory data tend to be noisy. Total saleable gross floor area data can be patchy, with missing \\nvalues within the series. Also, there is no nationwide data on inventory months, because only major \\ncities report total saleable gross floor area data. \\nMacro Importance \\nDespite challenges in interpretation, inventory data are very important to gauge the cycle of the \\nproperty market, and thus can be indicative of future housing price trends. After significant inventory \\ndestocking, housing prices may face upward pressure, and vice versa. \\nExhibit 25: Housing inventory months trended higher in 2022-24 despite increased easing \\nefforts \\nInventory months across city tiers \\n \\nSource: CREIS, Goldman Sachs Global Investment Research \\nProperty Price Measures \\nSignal to noise ratio: ** \\nMacro importance: **** \\nSource: National Bureau of Statistics, CREIS/Soufun, Centaline, Beike, Zhuge \\n\\n\\n 58 / 158 \\n \\nAvailability: NBS: Monthly from 2005, quarterly from 1998 \\nCREIS/Soufun: Monthly from June 2010 \\nOther third-party data vendors: Monthly \\nOverview \\nThere are two main sources of property indices for major cities, from the official (NBS) and private \\nsources (e.g., CREIS, Centaline, Beike). “Properties” refer to commercial buildings built to be sold in \\nthe market, including both residential and non-residential properties, newly constructed properties, \\nand second-hand properties. \\nExhibit 26: A comparison of home price measures for the primary and secondary markets \\n \\n\\n\\n 59 / 158 \\n \\n \\nSource: NBS, Beike, CREIS, Wind, Zhuge, Goldman Sachs Global Investment Research \\nSignal to Noise Ratio \\n• \\nThese price indices generally follow a well-designed methodology, and most indices control \\nto varying degrees for the impact of quality, though their original data sources differ \\nsomewhat. For some of the indices such as the NBS 70-city property price index, given that \\nthere is a higher degree of estimation involved in compiling the index and pressures on local \\n\\n\\n 60 / 158 \\n \\ngovernments to control property prices (especially new home prices), there are market \\nconcerns that the indices are over-smoothed, showing smaller fluctuations in prices than \\nactual property price changes. For example, the rise in property prices may be hidden when \\ndevelopers are forced to sell properties at prices below the market equilibrium because of \\nlocal government pressures, and the excess demand leads to alternative expenses such as \\nrequirements for property purchasers to deposit funds well in advance to be able to \\npurchase the property. During downturns, the decline in property prices may be disguised \\nby free parking spots and extensive internal renovations offered by developers in lieu of \\nprice cuts. These distortions are typically more relevant for new properties. \\n• \\nPrice indices on second-hand properties are therefore often a useful reference when they \\ndiverge from primary market property prices. On secondary-market home prices, besides \\nthe NBS 70-city secondary home price index, some third-party data vendors such as \\nCentaline, Zhuge, and Beike (mostly real estate agents, information platforms and consulting \\nfirms) also have their own measures. Home price data from private sources usually have a \\nmuch shorter time series than the NBS series, but they tend to be released earlier than the \\nofficial indices. In previous years, secondary home prices have also been affected by many \\nnon-market-based policy interventions such as government guided prices and reference \\nprices (related to tax payments and mortgage borrowing for secondary home transactions). \\nWe also note some third-party data vendors such as Beike suspended their release of \\nsecondary home prices in late 2023 when secondary home prices showed notable declines. \\nExhibit 27: “Tier 1 cities” have seen more pronounced price appreciation in boom periods, \\nwhile home price declines were broadly based across city tiers in 2022-24 \\nAverage house price in the primary market \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nExhibit 28: Different home price measures for the secondary market shared similar trends in \\n2022-24 \\n\\n\\n 61 / 158 \\n \\nProperty price measures for the secondary market \\n \\nSource: NBS, Wind, Centaline, Beike, Zhuge, Goldman Sachs Global Investment Research \\nMacro Importance \\nHome prices could drive economic growth, matter for the risk spillover to the banking system \\ndirectly (through mortgage loans and property developer loans) and indirectly (through other types \\nof loans collateralized with real estate properties), and affect household consumption (through \\nwealth effects, moving-in related purchases, and consumer confidence channels). Moreover, NBS \\nnew home prices have been set by policymakers as one determinant for city-level mortgage rate \\nadjustments. \\nCompilation and Reporting \\nWhen compiling the Property Price Index, the statistical authorities in China try to control for \\ndifferences in the quality of properties. They consider the features of the property and attempt to \\ncompare prices for comparable properties. Factors such as location, structure, and type of property \\nare all taken into consideration to make price comparisons. All data sources face significant \\nchallenges for compiling a portfolio that is relatively stable for pricing tracking, implying no perfect \\nhome price measure. For example, the CREIS/Soufun property index is compiled by taking the \\nweighted (by floor area) average of indices of 100 underlying cities in the reporting period. It \\nincludes the price changes for both commercial residential buildings and social housing. Data inputs \\ninclude information collected through field visits and corporate surveys, from real estate \\nintermediaries, and based on information provided by local governments. \\nOther Issues \\n• \\nThe NBS changed the methodology for the Property Price Index in 2011. Property sales \\nprices were split into primary and secondary housing price indices. The index initially \\ncovered 35 major cities and was expanded to 70 cities in 2005. Rural areas are not covered. \\nData are collected via a mixture of reporting forms from real estate companies and site visits \\n\\n\\n 62 / 158 \\n \\nby NBS staff. In January 2018, the NBS suspended the release of the “new residential \\nproperty (including social housing) price index” series. Since then, it has only released the \\n“new residential commodity property (excluding social housing) price index” series. The \\npatterns of these two data series are similar given the small share of social housing in total \\nhousing. \\n• \\nThe divergence of property price trends among different city tiers in China reflects \\ndifferences in housing market fundamentals. Top-tier cities face more supply restrictions and \\nresilient demand, and thus upward pressures on housing prices are the strongest. Lower-tier \\ncities tend to have diverse circumstances but on average face less restrictive supply and \\nweaker demand, and thus housing price growth tends to be slower and/or prices tend to fall \\nmore in property market down-cycles. \\n• \\nThe table below shows the NBS classification for the 70 large and medium-sized cities by \\ntier. Although there is widespread agreement on the definition of Tier-1 cities (Beijing, \\nShanghai, Guangzhou, and Shenzhen), categorization beyond that point is not always \\nconsistent among different sources, particularly for lower-tier cities. \\nExhibit 29: Most cities in the NBS 70-city property price dataset are in Tiers 2 and 3 \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nLand Price Indices \\nSignal to noise ratio: ** \\nMacro importance: ** \\nSource: NBS, Ministry of Land and Resources; CREIS, Wind, academic research \\nAvailability: NBS average land transaction price: monthly from January-February 2004 to December \\n2022; Ministry of Natural Resources (MNR) land data: quarterly from Q1 2008 to Q3 2021 \\nWharton/NUS/Tsinghua Chinese Residential Land Price Indices (CRLPI): quarterly from Q1 2004 to \\n\\n\\n 63 / 158 \\n \\nQ1 2017 \\nWind average land sales price: weekly and monthly from January 2008 \\nGS 300+ city residential land price tracker: monthly from January 2008 \\nOverview \\nThere are multiple sources of land price data. Unfortunately many of them have been discontinued. \\nNBS average land transaction prices: These are estimated based on the NBS real estate developers \\nland transaction value and area data series (as elaborated in the “Land Transactions” sub-section). \\nThe implied land price data series can be distorted by the mix-shift in land transactions as the land \\npurchased in different periods may not be identical in terms of location and quality. This data series \\nhas been no longer available since January 2023 when the NBS suspended the release of land \\ntransaction area and value data. \\nMinistry of Natural Resources (MNR) land price data: This indicator monitors the average market \\nland price in 105 major Chinese cities on a quarterly basis. The data are categorized by land prices \\nfor residential, commercial and service, and industrial purposes. In May 2002, the Ministry of Land \\nand Resources (the predecessor of the MNR) permitted the transfer of state-owned land-use right \\nmainly by bidding, auction and quotation. In January 2022 when Q4 2021 data were supposed to be \\nreleased, the MNR suspended this data series. \\nWharton/NUS/Tsinghua Chinese Residential Land Price Indices (CRLPI): This indicator tracks \\nnational land price growth in real (CPI-deflated) constant quality terms based on data from 35 cities \\nin China on a quarterly basis.\\n[12] The provider also reports region-/city-level land price indices on a \\nsemi-annual/annual basis. While technically this series may be preferable, this series has not been \\nupdated since Q1 2017. \\nWind 100-city land transaction price data: These are estimated based on the Wind land \\ntransaction value and area data series. The breakdown of Wind 100-city land transaction by city tier \\nis also available. \\nGS 300+ city Residential Land Price Tracker: The GS property sector equity research team has \\naggregated land base prices, transacted prices, and land price premiums in 302 major cities based \\non data from the China Real Estate Index System (CREIS). The average headline series is then \\ngrouped into 3 city tiers. \\nSignal to Noise Ratio \\nAs discussed above, there are no nationwide data on overall land prices. We tend to rely more on \\nour GS 300-city residential land price tracker (based on the same sample that we use to estimate the \\n“300-city land transactions” measure, as previously elaborated in the \\\"Land Transactions\\\" subsection) \\ngiven it is more timely and has a relatively wide coverage compared with other land price indices. \\nSimilar to property price indices, land price indices ideally should adjust for quality differences. The \\nWharton/NUS/Tsinghua land price index is adjusted for quality differences, but not the 300-city \\nresidential land price series. The average price of land sold is subject to policy distortions similar to \\nproperty-related policy distortions. Facing pressures to control land prices, governments often \\nrestrict or suspend the supply of premium land (or properties) relative to non-premium land in order \\n\\n\\n 64 / 158 \\n \\nto lower the average selling price (total land value divided by total land area). As a result, it is \\nconceptually better to look at the quality-adjusted data, though adjusting for quality differences is a \\ndifficult job that cannot be done without significant effort by specialists (and it is not always clear \\nhow much effort has been made). \\nMacro Importance \\nLand price inflation is important because it is a main factor behind the input cost of property \\ndevelopers, and thus also affects housing price trends. Given land sales revenue is an important \\nfinancing source for local governments, land price fluctuations will also impact the financing needs \\nof local governments. \\nExhibit 30: Land price increase over the past decades was mainly led by Tier 1 and 2 cities \\n100-city average land transaction price by city tier (12mma) \\n \\nSource: Wind, Goldman Sachs Global Investment Research \\nGS Proprietary Indicators Related to the Real Estate Sector \\nSignal to noise ratio: *** \\nMacro importance: **** \\nSource: Goldman Sachs Economics Research \\nAvailability: GS China property impact on GDP growth: annual; \\nGS 24-city housing policy stance indicator: monthly; \\nGS property policy relative tightness index: daily. \\nOverview \\nGS China property impact on GDP growth: We decompose the property sector's contribution to \\n\\n\\n 65 / 158 \\n \\nChina's yoy GDP growth into five major channels, i.e., construction, real estate services, upstream \\neffects (mainly through commodities demand), consumption, and fiscal channels. These estimates \\nare based on a series of property activity data – including new home starts, sales, completions, \\nunder construction, property FAI, land sales, and average new home sales price – and their \\ncorrelations with GDP growth in specific areas. By aggregating these channels, we estimate China's \\nproperty sector impact on GDP growth on an annual basis, and project its future path based on in-\\nhouse forecasts for major property activity indicators. \\nExhibit 31: The property sector has turned to a growth drag since 2022 \\nHousing contribution to yoy GDP growth \\n \\nSource: Haver Analytics, Goldman Sachs Global Investment Research \\nGS 24-city housing policy stance indicator: We use quantitative measures of housing policy in 24 \\nlarge cities across five policy dimensions (i.e., home purchase restrictions, down-payment ratios, \\nmortgage rate fluctuations around benchmark interest rates, mortgage restrictions, and sales \\nrestrictions), and then average them to create our housing policy stance indicator. This is also an \\ninput of our proprietary China domestic macro policy proxy. \\nExhibit 32: Our 24-city housing policy stance indicator suggests the 2022-24 housing easing in \\nlarge cities has exceeded previous cycles \\nHousing component of GS China domestic macro policy proxy \\n\\n\\n 66 / 158 \\n \\n \\nSource: CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research \\nGS property policy relative tightness index: This proprietary indicator measures the relative \\ntightness of property policies in over 100 cities from the following aspects: 1) demand: purchase \\nrestrictions (household registration, social welfare contribution, etc.), credit restrictions (mortgage \\nrate, down payment), sales restrictions; 2) supply: caps on selling prices, presales restrictions, land \\ntransaction tax, etc.; 3) others: property speculation, land supply. Original data sources include \\ngovernment announcements, media reports, and industry association data. \\nExhibit 33: Our city-level property relative tightness index has shown almost no major local \\nhousing tightening policies in cities that we track since December 2021 \\nProperty policy relative tightness index: relative tightening share \\n \\nSource: Local governments, Songfang.com, Goldman Sachs Global Investment Research \\nSignal to Noise Ratio \\n\\n\\n 67 / 158 \\n \\nAs our tracking for China property impact on GDP growth is built mainly on NBS property activity \\nindicators, concerns around the quality of underlying data may apply to this proprietary indicator. \\nThe GS property policy relative tightness index and 24-city housing policy stance indicator are based \\non a limited size of city sample, which may not always be representative. Besides, during the period \\nof outright housing easing, the GS property policy relative tightness index (which is a diffusion index) \\nmay remain at or close to zero, failing to capture the sequential change in the magnitude of housing \\neasing. \\nMacro Importance \\nThe property sector has been the largest single sector in the Chinese economy for many years, and \\nits ups and downs have significant implications for China's headline GDP growth. Our proprietary \\nindicators track the sequential change in housing policy stance and gauge the growth impact of the \\nproperty sector in a timely and comprehensive manner. \\nRelated GS Economics Publications \\n• \\n“How China's property policy tightening lowered sales and prices”, Asia Economics Analyst, \\n26 September 2017 \\n• \\n“Tracking residential housing’s impulse to Chinese growth”, Asia Economics Analyst, 27 \\nMarch 2018 \\n• \\n“China property policy: capturing the big picture from localized measures”, Asia Economics \\nAnalyst, 29 November 2020 \\n• \\n“Q&A on Evergrande’s macro implications”, Asia in Focus, 24 September 2021 \\n• \\n“Credit supply holds the key to China housing outlook in 2022”, Asia Economics Analyst, 11 \\nOctober 2021 \\n• \\n“How big is China's property sector?”, China Data Insights, 11 October 2021 \\n• \\n“Lessons from Japan: Credit Tightening in the Property Market”, Asia in Focus, 29 November \\n2021 \\n• \\n“Demystifying the discrepancy in different land sales measures”, China Data Insights, 14 April \\n2022 \\n• \\n“Understanding the recent rise and fall of high-frequency property sales data”, China Data \\nInsights, 14 July 2022 \\n• \\n“China: 'L-shaped' Property Sector Recovery Ahead without a Quick Fix”, Asia Economics \\nAnalyst, 11 June 2023 \\n• \\n“Understanding differences in China’s home price measures”, China Data Insights, 9 July \\n2023 \\n• \\n“Q&A on China's property downturn and its implications”, Asia Economics Analyst, 23 \\nAugust 2023 \\n• \\n“China: Tracking the impact of ongoing housing easing”, Asia in Focus, 8 October 2023 \\n\\n\\n 68 / 158 \\n \\n• \\n“Comparing China and US Housing Downturns: Different Fiscal Backdrop, Same Need to \\nPrevent Spillovers”, Asia Economics Analyst, 12 February 2024 \\n• \\n“A Closer Look at NBS 2022 GDP Revisions”, China Data Insights, 13 March 2024 \\n• \\n“China: Housing easing underway, but no signs yet of game-changing measures”, 14 April \\n2024 \\nSection V. Consumption \\nThere are four main sources of consumption-related data: \\n1. Retail sales reported by the NBS: Compiled using a combination of administrative reporting \\nand sampling. \\n2. Household Income and Expenditure Survey: Compiled using sample surveys. \\n3. Household consumption (in GDP by expenditure): Compiled using mainly the two series \\nmentioned above. \\n4. Retail sales of 100 (more recently 50) major offline retailers reported by the China National \\nCommercial Information Center (CNCIC). \\nThere are alternative micro data that can be collated to give a partial picture as well, such as auto \\nsales, tourism revenue during long holidays and parcel volumes. \\nExhibit 34: A comparison of three different sources of consumption data \\n\\n\\n 69 / 158 \\n \\n \\nSource: MOFCOM, NBS, Goldman Sachs Global Investment Research \\nRetail Sales of Consumer Goods \\nSignal to noise ratio: *** \\nMacro importance: **** \\nSource: National Bureau of Statistics \\nAvailability: Monthly from January 1990, annual from 1952 \\nTiming: Typically around the middle of the following month. In January, April, July, and October, it is \\nreleased with GDP data around 3 weeks after the end of the quarter. \\nOverview \\n• \\nTotal Retail Sales of Consumer Goods measure goods and restaurant services sold at the \\nretail level (as opposed to wholesale), including both online and offline sales. These goods \\nand restaurant services may be purchased by households, firms or the government. \\n• \\nA separate indicator named “Online Retail Sales of Consumer Goods” refers to goods \\ntransacted over online platforms. \\nSignal to Noise Ratio \\n\\n\\n 70 / 158 \\n \\n• \\nBefore the Covid pandemic, the main issue with retail sales data was that growth appeared \\noverly smooth. We suspect the problem is caused by the fact that sales by companies below \\nthe minimum threshold are compiled using sample surveys, which involve a significant \\ndegree of discretion, whereas sales by companies above the minimum threshold are \\ncompiled using census surveys. Reported retail sales growth was also higher relative to the \\ngrowth rate of the economy for many years, even after adjusting for inflation. During 2005-\\n2015 for example, real retail sales (nominal retail sales growth adjusted for price factors) \\ngrew 15% per year, significantly above the 10% per year real GDP growth. In addition, sample \\nchanges over the years caused discrepancies between the reported retail sales level and \\nreported retail sales yoy growth rate. Volatility in the series increased dramatically with the \\nonset of the Covid pandemic along with more volatility in the underlying economy induced \\nby Covid-related lockdowns, and the signal to noise ratio has therefore improved in recent \\nyears. \\nMacro Importance \\n• \\nAlthough monthly retail sales data are often used as the main indicator for private \\nconsumption, users should be aware of a few data issues: \\n1. The most serious problem is that services consumption (apart from restaurant \\nservices) is not included in the retail sales data. Experiences of other countries show \\nthat the share of services in total consumption typically rises as the economy \\ndevelops. However, retail sales data are unable to reflect this increasingly important \\ncomponent of consumption.\\n[13] \\n2. Retail sales also include non-household (i.e., government and corporate) purchases, \\nbut it is currently impossible to obtain a breakdown between household and non-\\nhousehold purchases. While household consumption probably constitutes the \\nlargest part of the reported retail sales, some of the retail sales expenditure is \\nclassified as government consumption vs. private consumption under the GDP by \\nexpenditure framework. \\n• \\nThat said, the monthly retail sales data have the advantage of being timely. Policymakers \\nalso pay close attention to these figures, roughly on par with IP, FAI and trade data. \\n• \\nSporadic waves of Covid in 2020-2022 and stay-at-home policies disrupted offline activities \\nin China, drawing increased engagement to online platforms. As consumption behavior \\nshifted from offline to online purchases in China, the share of online goods sales in total \\nretail goods sales rose to 31% in 2023 from 23% in 2019. \\nCompilation Methodology \\n• \\nAll large firms above the designated size report sales data monthly. Data from small firms \\nbelow the designated size are compiled through sampling. In practice, it is not clear how \\noften, how much, and how well the sampling of small firms is actually carried out. Data from \\nmajor online platforms are compiled for online goods sales, but the list of surveyed \\nplatforms may change over time. \\n• \\nRetailers with annual revenue from primary business of RMB5 million and above, hotels and \\n\\n\\n 71 / 158 \\n \\nrestaurants with annual revenue from primary business of RMB2 million and above, and \\nwholesalers with annual revenue from primary business of RMB20 million and above are \\njointly defined as enterprises above designated size. \\nReporting \\n• \\nRetail sales data are available in nominal terms only. The NBS discontinued the release of the \\nRetail Price Index at end-2022. To deflate the series, the Consumer Price Index for goods \\ncould be used, though the breakdown and weights of CPI goods baskets and retail sales \\ncategories differ somewhat. \\n• \\nThere are three kinds of breakdown available for retail sales data: \\n1. By location, i.e., consumer goods sold in urban and rural areas. However, urban retail \\nsales may be goods purchased by rural households who live in urban areas and do \\nnot necessarily represent urban household consumption, and vice-versa. \\n2. By commodities. There are two layers of breakdown data available - the first layer is \\ntotal retail sales by goods vs. catering, and online retail sales by goods and services. \\nThe second layer of breakdown is retail sales by specific commodities (e.g., food, \\nclothes, home appliances, autos, etc.). The second set of data are only available for \\nsales made by firms at or above the designated size and may not always give an \\naccurate picture of overall consumption growth. Goods sales by above-designated-\\nsize retailers accounted for 40% of total retail goods sales in 2023. \\nExhibit 35: Growth of online sales have outpaced that of offline sales in recent years \\nRetail sales \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nHousehold Income and Expenditure Survey \\n\\n\\n 72 / 158 \\n \\nHousehold Income Survey \\nSignal to noise ratio: *** \\nMacro importance: **** \\nSource: National Bureau of Statistics \\nFrequency: Quarterly, annual \\nTiming: Around 2-3 weeks after the end of each quarter, along with the release of GDP data \\nAvailability: See the table below \\nExhibit 36: Availability of China NBS income and expenditure data \\n \\nSource: Goldman Sachs Global Investment Research \\nOverview \\n• \\nUrban and rural income is classified in terms of total and disposable income. \\n• \\nTotal income is composed of pre-tax wages, business profits, return on assets (e.g., interest, \\ndividends, rents) and other “transfer income” (gifts, insurance claims, retirement pensions, \\ntransfer payments from members in other households etc.). Prior to 2012, samples were \\ncollected from 66,000 households which were presumably rotated every three years. Starting \\nfrom Q4 2012, the NBS issued a new survey format to reflect the reform of urban-rural \\n\\n\\n 73 / 158 \\n \\nintegration, which adjusted the sample to 2 million households nationwide for general \\ninvestigation and selected 160,000 households for direct survey.\\n[14] One-third of the surveyed \\nhouseholds are rotated each year. \\n• \\nDisposable income is total income for final consumption expenditure and households’ \\nsavings excluding income tax and social security contributions. It includes both cash income \\nand in-kind income. \\nDisposable Income per Capita \\nStarting from year-end 2012, disposable income per capita is disclosed in the new survey (“Urban-\\nRural Unified Household Survey”) at both a quarterly and annual frequency. Urban and rural \\nhouseholds are still surveyed separately but the statistical standards are the same. NBS then \\ncalculates the nationwide per capita disposable income as the weighted average income of urban \\nand rural households. Weights are based on urban/rural population levels. \\nBy source: Per capita disposable income is the sum of wage and salary, net business income, net \\nincome from property and net income from transfers at the urban and rural level separately. \\nBy income level (nationwide): Per capita disposable income is divided into five income levels: low \\n(bottom 20% of income distribution), low middle (20-40\\nth percentile), middle (40-60\\nth percentile), \\nupper middle (60-80\\nth percentile) and high (top 20% of income distribution). In our view, data for the \\nhigh-income group are by far the least reliable, for reasons discussed under \\\"Signal to Noise Ratio\\\" \\nbelow. \\nThere are some differences between the concepts of rural net income and urban disposable income. \\nRural households are treated as production as well as consumption units. As a result, their net \\nincome excludes “household operation costs”, such as costs of fertilizers and pesticides. \\nDisposable Income by Sources \\n1. Income from wages and salaries (56%) \\n2. Net business income (17%) \\n3. Net income from property (9%) \\n4. Net income from transfers (18%) \\nSignal to Noise Ratio \\n• \\nTwo factors seriously affect the reliability of household surveys: \\n1. Households participating in the survey are required to provide very detailed notes \\non their expenditure, which is time-consuming. Households are rewarded financially \\nfor taking part in the survey, but the financial payments are small. For example, \\nfinancial payment was RMB70 per month for households in selected cities in \\nZhejiang Province in 2020. Therefore, the incentive for households to respond \\naccurately may not be high. This problem is especially serious for urban high-\\nincome households. \\n2. Lack of confidentiality may lead people to under-report their income/expenditure or \\n\\n\\n 74 / 158 \\n \\nsimply refuse to participate in surveys. Again, this is likely to be especially serious for \\nhigh-income groups and households with gray or illegal income. As a result, there is \\nlikely a downward bias in reported income levels. However, the direction of any bias \\non growth rates is less clear as under the aggressive anti-corruption campaign of \\nrecent years, under-reported gray and illegal income growth likely fell dramatically. \\nSince these sources of income were never reported in the first place, the impact may \\nnot show up in official statistics. \\nMacro Importance \\nThis series is useful in gauging growth rates in purchasing power, living standards and labor market \\nperformance. \\nExhibit 37: Wage growth has historically been the largest driver of household disposable \\nincome growth \\nChina household disposable income per capita \\n \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nHousehold Expenditure Survey \\nSignal to noise ratio: *** \\nMacro importance: **** \\nSource: National Bureau of Statistics \\nTiming: Around 2-3 weeks after the end of each quarter \\nAvailability: See table in the \\\"Household Income Survey\\\" sub-section \\n\\n\\n 75 / 158 \\n \\nOverview \\n• \\nHousehold consumption expenditure survey measures households ’  expenditure on \\nconsumption (including both money and non-money expenditure) in eight broad categories: \\n1. Food (including tobacco, liquor, and catering) \\n2. Clothing \\n3. Housing\\n[15] \\n4. Household appliances, articles, and services \\n5. Healthcare, medicines, and medical equipment and services \\n6. Transportation and communication \\n7. Recreation, education, and cultural goods and services \\n8. Others \\n• \\nKey changes since the implementation of the Urban-Rural Unified Household Survey: 1) \\nincome and spending sub-categories have been standardized; 2) urban population covers \\nmigrant workers living in urban areas, but migrant workers are excluded from the rural \\npopulation; 3) college students supported by the surveyed households but living in separate \\nresidence areas are counted as members of the households. \\nSignal to Noise Ratio \\n• \\nThe compilation methodology for expenditure data is the same as for disposable Income \\ndata (described above), and therefore entails the same problems. \\nMacro Importance \\n• \\nHousehold survey data provide useful information that is not available from other data \\nsources. The household survey consumption per capita data is the only data set that \\ncaptures all consumption categories, including both goods and services, and therefore \\nshould provide the most comprehensive gauge of the state of household consumption at a \\nquarterly frequency. Household consumption in GDP data incorporates data from retail sales \\nas well as household surveys. As the Chinese economy rebalances towards more \\nconsumption, this data set has become more important. \\n• \\nIn addition, one can calculate the household saving rate (the difference between disposable \\nincome and consumption, divided by disposable income) based on the income and \\nconsumption data. This is the most timely read of households’ savings/consumption \\nbehaviors. In general, the household saving rate has been trending up over the past decade \\non the back of continued urbanization (urban households have higher income and higher \\nsavings rates than rural households). The household saving rate rose during 2020-2022 \\namid the Covid pandemic, and declined in 2023 along with China’s reopening as the Covid-\\nrelated restrictions were lifted. \\nExhibit 38: Household consumption growth saw large swings during the Covid pandemic \\n\\n\\n 76 / 158 \\n \\nChina household nominal consumption expenditure per capita \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nExhibit 39: Household savings rate has trended higher and increased sharply during the Covid \\npandemic \\nChina household savings rate (seasonally adjusted) \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nRetail Sales of Major Offline Retailers Reported by China National Commercial Information Center \\n(CNCIC) \\nSource: China National Commercial Information Center \\nAvailability: Top 100 retailers data are available on a monthly basis from July 2007-March 2024; top \\n\\n\\n 77 / 158 \\n \\n50 since July 2011 \\nTiming: Typically around the third week of the following month, but can be irregular at times \\nOverview \\n• \\nThis series reports the year-over-year growth rate of the total retail sales of the top 50/100 \\nretailers in China. The top retailers include both offline retailers such as chain stores and \\ndepartment stores and online retailers such as Tmall and JD.com. These goods may be \\npurchased by households, firms, or the government. Retail sales data by product are \\navailable, though there are missing values in this data. Similar to the official retail sales data, \\nthis series is reported in nominal terms. \\nSignal to Noise Ratio \\n• \\nTotal retail sales of the top 50/100 retailers data are subject to sample changes, as the list of \\nretailers included is updated regularly based on the latest ranking. In addition, releases of \\nthis indicator are occasionally delayed for a few weeks. CNCIC suspended the release of the \\nfull top 100 retailers data in April 2024, although the top 50 are still available. \\nMacro Importance \\n• \\nTotal retail sales of the top 50/100 retailers are reported separately by non-official sources \\n(CNCIC) and therefore could provide cross-checks for the official retail sales/household \\nconsumption data\\n[16]. For example, year-over-year growth in total retail sales of the top \\n50/100 retailers dropped from around 20% in 2011 to 0% in 2014-15 amid a housing \\ndownturn and overall economic slowdown. Growth in the official retail sales series over this \\nperiod was more stable by contrast, decelerating gradually from 17% yoy in 2011 to 11% yoy \\nin 2015. \\nAuto Sales \\nSignal to noise ratio: **** \\nMacro importance: *** \\nSource: China Passenger Car Association (CPCA); China Association of Automobile Manufacturers \\n(CAAM), National Bureau of Statistics \\nAvailability: CPCA auto sales volume: monthly from Apr 2007; weekly from 2015 \\nCAAM auto sales volume: monthly from 2000, annual from 1998 \\nNBS auto sales value: monthly from January 1997 \\nTiming: Around 10th day of the following month \\nOverview \\n• \\nCAAM reports car sales in units on a monthly basis, capturing different types of cars, such as \\npassenger and commercial cars, through wholesale channels. The CAAM is regulated by \\nSASAC and authorized by the government to collect auto production and sales data from \\n\\n\\n 78 / 158 \\n \\nauto manufacturers. CPCA is a data exchange platform among automakers and not \\naccredited by the government. It reports auto sales in units on a weekly basis, capturing \\npassenger car sales through both retail and wholesale channels. For the value of sales of \\nautomobiles, the NBS monthly retail sales data report sales by above-designated-sized \\nenterprises. \\n• \\nAutos-related data tend to be highly cyclical, though sales are sensitive to policy measures \\nsuch as auto purchase tax changes and changes in purchase restrictions in large cities. \\nPolicymakers may also apply differentiated policies (e.g., on new energy vehicles vs. \\ntraditional vehicles) to provide targeted support for certain types of vehicles. New energy \\nvehicle sales have expanded at a very fast pace in recent years on the back of policy support. \\nFor example, license plates for new energy vehicles are not restricted in top-tier cities, unlike \\ntraditional vehicles. New energy vehicle sales rose from 0.04% of total auto sales in 2011 to \\n32% of total auto sales in 2023, based on CAAM data. \\nSignal to Noise Ratio \\n• \\nAuto sales data are generally reliable over time. CAAM and CPCA data are directly reported \\nby automobile companies, though subject to potential temporary distortions due to a lack of \\ncross-check mechanism. Companies that already reached their annual sales targets, for \\nexample, may delay booking some sales to make it easier to reach the target in the \\nfollowing year. \\nMacro Importance \\n• \\nAuto sales data have gained importance in recent years. From the demand side, automobile \\nconsumption accounts for around 10% of total household consumption and plays an \\nimportant role in overall household consumption growth. On the production side, \\nautomobiles accounts for around 6% in overall industrial value-added, and is a key sub-\\nindustry in the industrial sector. According to the National Development and Reform \\nCommission (NDRC), as of 2020, auto and related sectors accounted for roughly 10% of \\nGDP.\\n[17] As policymakers push for energy transition and emissions reduction, new energy \\nvehicle production and sales data could shed light on the progress of China’s economic \\ntransformation. \\nExhibit 40: New energy car sales rose very rapidly in recent years and took around 32% of total \\nautomobile sales in 2023 \\nChina automobile sales \\n\\n\\n 79 / 158 \\n \\n \\nSource: CEIC \\nConsumer Confidence Index \\nSignal to noise ratio: ** \\nMacro importance: *** \\nSource: National Bureau of Statistics \\nAvailability: Monthly from 1990 \\nTiming: Usually lagged by one month \\nOverview \\n• \\nThe NBS publishes the consumer confidence index (CCI) on a monthly basis. It is a survey-\\nbased diffusion index that ranges from 0 to 200, with 0 implying extreme pessimism and 200 \\nimplying extreme optimism. The index is imputed based on a monthly telephone survey of \\nmore than 6,000 urban and rural consumers in 15 provinces. Survey questions include \\nrespondents’ assessment of current employment situation, income level, employment \\nprospects, income expectations and willingness to spend. \\nSignal to Noise Ratio \\n• \\nThe NBS CCI is based on a telephone survey. It is less precise than hard data (e.g., retail sales) \\nand potentially subject to biases such as sampling and response rates, but it can be a useful \\ngauge of consumer sentiment, especially at times when there is a major shift in consumer \\nconfidence. \\nMacro Importance \\n• \\nConsumer confidence is an important concept as in theory it drives household savings, \\nspending, and investment decisions. Our research shows that consumer confidence does \\n\\n\\n 80 / 158 \\n \\nmatter to household consumption after controlling for other variables like household \\ndisposable income. Since the Shanghai Covid lockdown in Q1 2022, the NBS consumer \\nconfidence index has remained depressed despite China’s lifting of the zero-Covid policy at \\nthe end of 2022. Muted domestic demand, a weak labor market, and the continued fall in \\nhouse prices hurt consumer confidence and hindered willingness to spend. \\nExhibit 41: Consumer confidence plunged in Q1 2022 \\nChina Consumer Confidence \\n \\nSource: NBS \\nOther Consumption-related Data \\nOther indicators on consumption include per-head spending during long holidays released by the \\nMinistry of Culture and Tourism (MCT), which shows post-Covid recovery has been bumpy due to \\nmuted confidence and continued consumption downgrading. Some consumption-related indicators \\nreleased by third-party sources are also worth monitoring. Movie box office revenue data released \\nby Dengta App during long holidays partially reflect people’s willingness to spend, though the box \\noffice statistics are significantly affected by the release of blockbusters. As online sales account for an \\nincreasingly larger share in total retail sales, parcel delivery volume data published by the State Post \\nBureau each month is useful for tracking the momentum of online shopping (as 80%+ of parcels in \\nChina are e-commerce parcels). Parcel volume growth has significantly exceeded that of online \\ngoods since early 2023, which could partially be explained by lower value of single packages (e.g., e-\\ncommerce platforms/merchants continue to lower the minimum purchase amount eligible for free \\nshipping), and higher return rates on the back of more favorable return policies across platforms. \\nExhibit 42: Per-head holiday spending remained at low levels compared with pre-pandemic \\nperiod \\nNationwide domestic visitors and tourism revenue vs. pre-pandemic levels \\n\\n\\n 81 / 158 \\n \\n \\nSource: Ministry of Culture and Tourism, Goldman Sachs Global Investment Research \\nRelated GS Economics Publications \\n• \\n“China consumption worries: Goods spending decelerates as credit impulse fades”, Asia \\nEconomics Analyst, 8 September 2018 \\n• \\n“How has online shopping affected CPI inflation in China?”, Asia Economics Analyst, 3 \\nOctober 2019 \\n• \\n“A bit more confident about the Chinese consumer confidence data”, China Data Insights, 2 \\nDecember 2019 \\n• \\n“Household cash flow: Still the key driver for consumption in China”, Asia Economics Analyst, \\n24 May 2020 \\n• \\n“China: Households’ excess savings around the pandemic”, Asia in Focus, 15 March 2021 \\n• \\n“A Pulse Check on Chinese Household Consumption Growth: Still Sluggish”, Asia Economics \\nAnalyst, 4 July 2022 \\n• \\n“ China post-reopening consumption recovery: Large potential, lingering scars ” , Asia \\nEconomics Analyst, 18 January 2023 \\n• \\n“FAQs on “excess savings” in China”, China Data Insights, 7 February 2023 \\n• \\n“Lower housing cost burdens can lower the savings rate in China, but confidence remains \\nkey”, Asia Economics Analyst, 21 January 2024 \\n\\n\\n 82 / 158 \\n \\nSection VI. External Sector \\nMerchandise Trade \\nSignal to noise ratio: ***** \\nMacro importance: **** \\nSource: General Administration of Customs; State Administration of Foreign Exchange (SAFE) \\nAvailability: China Customs: monthly from 1992, annual from 1950; SAFE: monthly from January 2015, \\nquarterly from Q1 1998 \\nTiming: China Customs: 7-14 days after the end of the month; SAFE: end of next month \\nPublication: China Customs Statistics; Semi-Annual Report on Balance of Payments \\nOverview \\n• \\nMerchandise trade by China Customs measures the value of goods transactions (both in \\nRMB and USD terms) across national borders. Exports are valued on an FOB (free on board) \\nbasis, which includes costs to deliver goods onto the vessels but not further costs, such as \\ninsurance or freight. Imports are calculated on a CIF (cost, insurance and freight) basis, which \\nincludes insurance and freight charges. \\n• \\nTrade data in the Balance of Payments standard, as published by SAFE, place greater \\nemphasis on transactions between residents and non-residents, rather than physical \\nmovements across borders as shown in the China Customs data. (Though making this \\ndistinction can be difficult in practice.) The credit and debit sides of trade under SAFE data \\nare both accounted on an FOB basis. \\nSignal to Noise Ratio \\n• \\nCustoms trade data are among the most reliable macro-economic data in China. Their high \\nvolatility is a clear indication of absence of the smoothing prevalent in other economic data. \\nHowever, in 2012-13 and 2015-16, there were distortions to trade reporting due to \\nimporter/exporter incentives to move capital either onshore or offshore. For example, when \\nthere were strong capital inflows into China, exporters tended to over-report exports in \\norder to facilitate higher payments from offshore. When there are outflow pressures, \\nimporters tend to over-report imports to disguise the movement of capital offshore. \\nExporters sometimes also create “false exports” to gain government subsidies such as export \\nrebates. Therefore, at the end of this section, we have estimated an alternative series that \\nrelies on information from China’s trading partners. \\n• \\nBesides over-/under-reporting, there are significant discrepancies between bilateral trade \\ndata compiled by China and the corresponding data from its counterparts due to re-exports \\nvia Hong Kong. There are various ways to adjust for these data problems. For example, the \\nstatistical authorities in Hong Kong provide detailed data on the region’s re-export trade by \\ncountry and surveys of re-export margins. The difficulty with this analysis is that much of the \\n\\n\\n 83 / 158 \\n \\nfalse reporting is done via companies set up in Hong Kong, often solely for this purpose. \\nThese companies tend to use high-value-added goods that are easy to report large values \\nfor, but are often hard to judge in terms of underlying value. \\nMacro Importance \\nTrade data are very useful in judging economic cycles. Imports can give a useful indication of the \\nstrength of domestic demand. Exports can shed light on the strength of global demand. Net exports \\nmay provide clues about the potential misalignment of the foreign exchange rate. The government \\nalso often quotes the total amount of trade (exports plus imports) to gauge the level of openness of \\nthe economy. \\nCompilation and Reporting \\n• \\nCommodity breakdowns are available by the Standard International Trade Classification \\n(SITC), the Harmonized System (HS), the Broad Economic Categories (BEC) (e.g., \\nconsumption goods, capital goods and intermediate goods, etc.), and customs regime (e.g., \\nordinary trade, processing trade, etc.). \\n• \\nChina Customs releases monthly trade indices on export value, volume, and unit value with a \\nslightly longer lag (usually 20-25 days). Prior to 2014, the indices were denominated in USD. \\nFrom 2014 onwards, the indices have been denominated in RMB. \\nOther Issues \\n• \\nSome of China’s imports are for eventual export (e.g., raw materials or intermediate inputs \\ninto manufacturing goods exports). Total imports therefore can be divided into imports for \\nprocessing trade and those for domestic use. We derive our estimates of total imports for \\nprocessing trade by aggregating the following import categories: \\n1. Imports for processing and assembling \\n2. Equipment imported for processing and assembling \\n3. Customs warehousing trade \\n4. Entrepot trade by bonded area \\n5. Imports for outward processing \\n• \\nThe share of “imports for processing” in total imports has declined in recent years. “Imports \\nfor processing” declined to around 34% of total imports in 2023, from around 51% in 2005 \\nwhen the series started. \\n• \\nNote that there are a few differences between the Customs trade data and the net exports \\ndata in GDP. Net exports in GDP capture the net trade balance in goods as well as in \\nservices, whereas Customs trade data only cover trade in goods. Furthermore, in GDP data, \\nboth exports and imports are valued on an FOB basis, whereas Customs imports data are \\nvalued on a CIF basis. GDP standard is consistent with BOP standard. The NBS does not \\nseparately release data on exports of goods and services and imports of goods and services. \\n\\n\\n 84 / 158 \\n \\nServices Trade \\nSignal to noise ratio: *** \\nMacro importance: *** \\nSource: SAFE \\nAvailability: Monthly from January 2014, quarterly from Q1 1998 \\nTiming: At the end of the following month \\nOverview \\n• \\nExports/Imports of services refer to income/payment from/to foreigners on intangible \\nproducts such as transport, tourism, entertainment, telecommunication and financial services. \\n• \\nAfter expanding through 2018, China’s service trade deficit narrowed sharply in 2020 after \\nthe onset of the Covid pandemic when strict travel restrictions were imposed. In 2019, travel \\nservices imports totaled $250bn. During the Covid pandemic, they averaged only $120bn \\nper year, most of which were tuition and medical services payments made by Chinese \\nresidents overseas. China’s service trade deficit widened in 2023 after the end of its zero-\\nCovid policy. \\n• \\nService trade data are probably subject to a significantly higher level of misreporting, \\nbecause it is usually harder to verify the underlying fair value of services provided than \\ngoods traded. Although less subject to reporting distortions that are aimed at benefiting \\nfrom government subsidies (as local governments are much more focused on goods trade) \\non balance services trade data appear to mask substantial net capital outflows. Spending per \\ntraveler roughly doubled in 2013-16, for example, suggesting the possibility that a \\nsignificant part of “travel expenditure” is really hidden capital outflow. According to some \\nacademic research, correcting for this would suggest a current account surplus 1-1.5% of \\nGDP larger (and commensurately higher capital outflows).\\n[18] When faced with capital outflow \\npressures, the authority tends to crack down on outflows through services trade as well. For \\nexample, in 2016/2017, bank card overseas withdrawal and large-amount purchases (which \\nwould show up in services imports) were under tighter restrictions. \\nExhibit 43: Services trade deficit widened notably post China’s reopening \\nServices trade balance \\n \\n\\n\\n 85 / 158 \\n \\n \\nSource: SAFE, Haver Analytics \\nBalance of Payments \\nSignal to noise ratio: **** \\nMacro importance: **** \\nSource: SAFE \\nAvailability: Quarterly from Q1 2010, semi-annual from 2000, annual from 1982 \\nTiming: Preliminary readings after 30 days; final readings after 3 months. \\nPublication: Semi-Annual Report on Balance of Payments \\nOverview \\nThe Balance of Payments (BOP) records the external transactions of an economy with the rest of the \\nworld over a certain period of time. It records transactions between residents and non-residents. \\nTransactions are flows of goods, services, capital and financial claims. \\nSignal to Noise Ratio \\n• \\nSAFE began to compile BOP data in 1985 in accordance with the 4\\nth edition of the IMF BOP \\nManual, and subsequently adopted the 5\\nth edition in 1996 and the 6\\nth edition (BPM6) in \\n2015.\\n[19] In this edition, the IMF changed “Income” to “Primary Income”, and “Current Transfer” \\nto “Secondary Income”. \\n• \\nSAFE must rely on other government agencies for data on various sub-components, which \\nare often not compiled in accordance with IMF standards and are difficult to adjust (for \\nexample, the FDI data). We discuss the difference in FDI data from SAFE and Ministry of \\nCommerce later in this section. \\n\\n\\n 86 / 158 \\n \\n• \\nMisreporting of data to evade capital controls are common and likely worsened after 2015 \\ngiven significant capital outflow pressures. The misreporting of goods trade data is well \\nknown, but the problem is likely to be more serious in terms of services trade because it is \\nmore difficult for the government to prove any wrongdoing. \\nMacro Importance \\nThe BOP data have attracted rising interest in recent years because of the increased attention on the \\nCNY. \\nCompilation and Reporting \\n• \\nBalance of payments data (China’s are summarized in Exhibit 44) consist of two main \\ncomponents. \\n1. Current Account (CA): This records the flow of international trade (both goods and \\nservices), primary income (i.e., income that accrues to foreign-owned inputs to \\nproduction and other assets, e.g., profits generated by foreign-owned enterprises \\nand investment income paid to foreign-owned property and financial securities), \\nand secondary income (i.e., income transferred to or received from foreigners \\nwithout “quid pro quo”, e.g., remittances from workers working overseas). \\n2. Capital and Financial Accounts: This essentially records the flow of investment. \\nMain \\ncomponents \\nare: direct \\ninvestment, \\nportfolio \\ninvestment, \\nother \\ninvestment, reserve assets, net errors and omissions. \\nThe current account captures current transfers and transactions in goods, services, and \\nincome… \\n• \\nThe merchandise trade by BOP accounting is conceptually different from the monthly trade \\ndata from the Customs Administration as it is supposed to capture transactions between \\nresidents and non-residents instead of across national borders. Besides, its valuation \\nstandards are different: BOP measures both exports and imports on an FOB basis, whereas \\nCustoms values exports on an FOB basis and imports on a CIF basis. As FOB excludes costs \\nof insurance and freight, imports are smaller in BOP accounting than in Customs accounting \\n(by around 5%). SAFE also makes other minor technical adjustments to the customs trade \\ndata in accordance with IMF standards, such as subtracting goods exported but then \\nreturned. SAFE counts only the cargo with ownership change. Goods trade with no \\nownership change is accounted for as service trade in BOP. \\n• \\nThe services component in the current account consists of items such as transportation, \\ntravel, and insurance. Data are compiled by SAFE directly, as well as by various other \\ngovernment agencies, such as the Ministry of Transportation and the Ministry of Culture and \\nTourism. Most of the service trade in China is through the “travel” channel, and “travel” \\nunder services trade includes spending while traveling, and education tuition abroad. \\n• \\nThe income account records compensation for employees working abroad and returns on \\ninvestments. The numbers of Chinese residents working abroad and foreign residents \\nworking in China are both relatively small, and most primary income is investment income. \\n\\n\\n 87 / 158 \\n \\nIn addition to primary income, secondary income through current transfer is also part of the \\ncurrent account. \\n…and the capital account captures investment flows \\n• \\nThe capital account includes capital transfers and reduction or cancellation of debts. \\nFinancial accounts include international reserve assets and non-reserve financial accounts. \\nNon-reserve financial accounts in turn include direct investment, portfolio investment, \\nfinancial derivatives and other investment. \\n• \\nReserve assets are external financial assets held by the monetary authorities, including gold, \\nforeign exchange, special drawing rights (SDRs) with the IMF, and the use of the Fund’s \\ncredits. Reserve assets are transaction-based and therefore not affected by valuation effects \\nfrom asset price and exchange rate fluctuations. \\n• \\nNet Errors and Omissions (NEO) (also called “statistical discrepancy”) balances the credit and \\ndebit items in the BOP. It captures the errors and inconsistencies in data recording and \\nprocessing in all sections of the BOP. \\nExhibit 44: China’s current account surplus remained solid but the financial account has seen \\nsignificant outflows \\n \\nSource: SAFE, Goldman Sachs Global Investment Research \\nInternational Investment Position (IIP) \\nThe IIP is closely related to the BOP. Whereas the BOP measures the flow of transactions between an \\neconomy and the outside world, the IIP measures the stock of assets and liabilities an economy has \\nwith the rest of the world at a given point in time. Although most BOP transactions are reflected in \\nIIP changes, there are other factors that impact IIP that do not appear in the BOP, including market \\nprice changes, exchange rate changes and other volume changes (such as write-offs and re-\\nclassifications). In other words, the IIP is the result of cumulative current account surpluses/deficits \\n\\n\\n 88 / 158 \\n \\nplus valuation changes. China began to publish IIP data in 2006 on an annual basis and in Q4 2010 \\non a quarterly basis. \\nForeign Direct Investment \\nSignal to noise ratio: * \\nMacro importance: *** \\nSource: Ministry of Commerce (MOFCOM) \\nState Administration of Foreign Exchange (SAFE) \\nAvailability: MOFCOM series: monthly from 1997; SAFE series: quarterly/annual with BOP data from \\n1998 \\nOverview \\n• \\nForeign Direct Investment (FDI) measures investments made by foreign residents who seek \\nto have significant long-term interest in and have direct influence over a domestic enterprise. \\nFDI also reflects the confidence of international investors in the Chinese economy. In \\naddition, FDI data are useful in estimating total capital flows. \\n• \\nThere are multiple sources reporting China’s FDI data. Investors usually pay most attention \\nto the FDI numbers from the BOP releases, given that it is compatible with GDP accounting, \\ncan be compared with other countries’ FDI data, and is published quarterly. The MOFCOM \\nprovides another official dataset on FDI with breakdowns by sector and by \\ncountry/jurisdiction of origination. But it follows a different accounting principle, with \\nreinvested earnings excluded. The IMF Coordinated Direct Investment Survey (CDIS) data \\ncover over 100 countries and regions and offer mirror data from other economies, which \\ncan be used to formulate an “outside-in” measure to cross-check China’s official data. From \\na bottom-up perspective, Bloomberg reports investment value on its Mergers & Acquisitions \\n(M&A) page, which can be aggregated into a timely series of China’s inward FDI. In addition, \\nOECD and United Nations Conference on Trade and Development (UNCTAD) provide cross-\\ncountry panel data on FDI as well. \\nCompilation and Reporting \\nDifferent FDI measures vary in three main aspects: compilation methods, data coverage and \\navailability (Exhibit 45). \\n• \\nCompilation methodology: Most FDI data are compiled based on one of two principles: \\nasset/liability principle or directional principle, except for Bloomberg data. FDIs under the \\ndirectional principle account for net investment on the basis of ultimate parent companies, \\nwhile those under the asset/liability principle will simply add up net assets/liabilities for \\noutward/inward FDI. For example, if a Chinese parent company receives investment from its \\nown foreign subsidiary, this flow will be captured under the asset/liability principle but will \\nnot be captured under the directional principle. The data prepared by SAFE and OECD \\nfollows the asset/liability principle. In contrast, MOFCOM and UNCTAD compile FDI data \\n\\n\\n 89 / 158 \\n \\nbased on the directional principle. IMF CDIS shares the same inputs as SAFE, but the data is \\npresented under the directional principle. Lastly, Bloomberg’s M&A investment amount is \\nbased on project-level accounting, effectively adding up announced deals. \\n• \\nData coverage: We summarize data coverage by availability of flow/stock data, detailed \\nbreakdowns and mirror data. SAFE provides FDI flows under BOP and FDI stock under \\nInternational Investment Position (IIP) without any breakdowns. MOFCOM provides \\nbreakdowns of FDI flows by sector and by country/jurisdiction of origination in their annual \\nstatistical bulletin. The monthly FDI flows do not include direct investment in financial \\nservices (e.g., banks), although the size would be relatively small (around 4-6% of inward FDI \\nin 2018-2022). IMF CDIS offers mirror data of FDI stock, which can be compared with the \\nMOFCOM's FDI data by-country breakdowns.\\n[20] Bloomberg data are FDI flows with industry \\nclassifications. Yet, its numbers reflect planned cross-border M&A investment flows only, \\nmissing other FDI flows such as greenfield projects. Both UNCTAD and OECD provide FDI \\nflow and stock data without further breakdowns. Among these data sources, only SAFE, IMF \\nCDIS and OECD incorporate reinvested earnings in their FDI measures, as required by the \\nBalance of Payments and International Investment Position Manual, 6th edition (BPM6) \\nstandard. They also effectively assume all non-repatriated earnings are reinvested. \\n• \\nData availability: Reporting frequency ranges from monthly to annual. MOFCOM provides \\nmonthly data from 1984 with a one-month lag. Bloomberg also publishes monthly data \\nfrom 1998 with daily updates of the latest released FDI projects. SAFE’s FDI data come on a \\nquarterly basis from 1998 with a one-quarter lag. The IMF CDIS, UNCTAD and OECD offer \\nannual data. UNCTAD has the longest data history, tracking the FDI series from 1990 \\nonwards, followed by OECD, which provides FDI numbers from 2005 onwards. Both have a \\npublication lag of around one year. IMF CDIS is the least timely data source, with the earliest \\nstatistics dated back to 2009 and the most recent data for 2022 (a 2-year publication lag). \\nExhibit 45: Summary of different FDI measures \\n \\n\\n\\n 90 / 158 \\n \\n \\n\\\\\\\"-\\\\\\\" indicates \\\\\\\"not applicable\\\\\\\" \\nSource: NBS, Wind, Goldman Sachs Global Investment Research \\nExhibit 46: The 2022-23 drop in China’s inward FDI flows was partly driven by lower reinvested \\nearnings \\nDecomposition of FDI flows \\n \\nSource: CEIC, Haver Analytics, Goldman Sachs Global Investment Research \\nOther Issues \\n• \\nAs with GDP data, there are considerable issues in FDI data compilation at the regional level. \\nThe authorities have strengthened the requirements for the verification of data reporting. \\n\\n\\n 91 / 158 \\n \\nFor example, data on cash FDI are double-checked against capital flow records at the SAFE, \\nand data on goods FDI are double-checked against the records of China Customs. Data that \\ncannot be verified are, in principle, discarded. Moreover, FDI’s importance as a part of the \\nassessment metric for local government officials has declined, and hence the incentives to \\nover-report have dropped relative to earlier years. \\n• \\nAssessment of the true strength of FDI is also complicated by the issue of round-tripping, \\nwhich refers to funds originated in China but reinvested back as FDI. Money laundering and \\npreferential policies for foreign enterprises are the most important reasons for round-\\ntripping. \\nOutward Direct Investment (ODI) \\nAlthough FDI attracts most attention, ODI has gained momentum in recent years, especially against \\nthe backdrop of increasing capital outflow pressures and Chinese manufacturers building factories \\noverseas in response to rising tariffs. Corporate outward direct investments face tight regulation by \\nthe authorities. For investment value above a certain threshold, in 2015 and 2016, ODI from China \\nsoared to a record high, likely in part reflecting capital flight motivations, but declined afterwards on \\na tightening in outbound capital controls. According to SAFE data, ODI surpassed FDI for the first \\ntime in late 2015. Direct investment has shifted to a net outflow again since 2H 2022. \\nExternal Debt \\nSignal to noise ratio: *** \\nMacro importance: * \\nSource: State Administration of Foreign Exchange \\nAvailability: Quarterly from Q2 2003, semi-annual from 2001, annual from 1985 \\nTiming: Together with the final release of BOP \\nOverview \\n• \\nData prior to 2015 only included FX-denominated external debts. The SAFE started to \\npublish data with both CNY and FX denominated external debt since 2015. At the end of \\n2023, the outstanding external debt of China (excluding that of Hong Kong and Macao, but \\nincluding debt both in FX and RMB) was US$2.45 trillion. \\n• \\nIn regard to currency type, the total external debt is composed of foreign debt in RMB and \\nforeign debt in other currencies. By the end of 2023, 47% of external debt was denominated \\nin CNY. Of FX-denominated external debt, 84% was denominated in USD, 7% in EUR, 4% in \\nHKD, 3% in JPY, respectively. \\n• \\nBy original maturity, short-term refers to external debt with a term of one year or less. \\nMedium- and long-term refer to external debt with a contract term of more than one year. \\nAs of 2023, short-term external debt took around 56% of China’s total external debt, and \\nlong-term external debt 44%. \\n\\n\\n 92 / 158 \\n \\n• \\nThe external debt is also classified by institutional sector, with general government \\naccounting for 18%, central bank 4%, deposit-taking corporations except central bank 41%, \\nother sectors 25% (including other financial corporations and non-financial corporations), \\nand intercompany lending under direct investment 12%, as of the end of 2023. \\n• \\nAs we think official data do not capture some of the FX debt raised by offshore Chinese \\ncompanies, we compile our own estimate of Chinese corporates and households’ FX debt by \\naggregating onshore FX loans, claims on the Chinese non-bank sector by BIS reporting \\nbanks (offshore, non-Chinese), FX bonds, and trade liabilities. Our approach indicates that \\nChinese corporates’ and households’ total FX-denominated debt stood at around US$2.1 \\ntrillion as of 2023 year-end. \\nForeign Exchange Reserves \\nSignal to noise ratio: *** \\nMacro importance: *** \\nSource: State Administration of Foreign Exchange, PBOC \\nAvailability: SAFE: Quarterly from 1993, annual from 1982 \\nPBOC: Monthly from 1989 \\nTiming: PBOC data are usually released on the 7th day of the following month. \\nSAFE data are released with other BOP data. \\nOverview \\nForeign exchange reserves are liquid external foreign currency assets readily available to and \\ncontrolled by central banks, which can be used to finance current account deficits and influence the \\nforeign exchange rate. They include securities, bank deposits, derivatives and other assets, as long as \\nthey meet the above criteria. Apart from foreign exchange reserves, the more broadly defined \\n“international reserves” can take other forms, such as gold and Special Drawing Rights (SDRs); \\nhowever, these holdings are typically small relative to foreign exchange reserves. \\nSignal to Noise Ratio \\n• \\nPBOC FX reserves data can be opaque and influenced by factors other than capital flow \\nfundamentals. Because reserves data are based on market prices and denominated in USD, \\nexchange rate and asset price fluctuations affect reserve values. When calculating changes in \\nreserves, we usually adjust for the estimated effect of exchange rate fluctuations, but the \\nimpacts of asset price changes are difficult to estimate because of the lack of detailed \\ninformation on portfolio holdings. \\n• \\nChina started to disclose its historical dollar share of FX reserves from 2019 in SAFE’s annual \\nreports with a five-year lag. The share remained in the range of 57% - 59% in 2014-2018, \\ndown from 79% in 1995. However, SAFE does not provide further details on shares of other \\ncurrencies, or USD shares for the most recent few years. USD holdings remain prominent in \\n\\n\\n 93 / 158 \\n \\nthe official FX reserves, despite continued selling of US Treasury securities over the past few \\nyears. We estimate USD assets account for roughly 60% of China’s official FX reserves via \\nboth China’s data and US Treasury International Capital (TIC) data. The TIC data can also be \\nused to gauge the composition of the USD portfolio in China’s FX reserves. \\n• \\nIn order to estimate valuation effects on reserves, we assume the currency composition of \\nChina’s FX reserves is similar to that of the global average and follows the Currency \\nComposition of Official Foreign Exchange Reserves (COFER) published by the IMF. \\n• \\nGiven possible PBOC balance sheet management and shifts in banks’ net open position, we \\nprefer the SAFE data on banks’ FX settlements on behalf of their onshore clients as a gauge \\nof the FX-RMB conversion trend among onshore non-banks. \\n• \\nPBOC’s reserve data and IIP data on reserve assets are based on market price, and thus will \\nbe impacted by valuation effects. Reserve changes in BOP data on the other hand are free \\nfrom valuation effects as they are supposed to measure flows. \\n• \\nChina’s FX reserves have been surprisingly stable at just over USD 3 trillion since 2017 \\ndespite the large inflow pressures in 2020-2021 and outflow pressures in recent years. Our \\nanalysis suggests that the FX holdings of China’s commercial banks may serve as a buffer for \\ncapital inflows/outflows, with official FX reserves changing relatively little as a result. China’s \\ncommercial banks accumulated a large amount of FX from 2H 2020 to 2021 on the back of \\nChina's elevated goods trade surplus. The authorities can guide banks to sell FX first before \\nselling the government’s own FX reserves to defend the currency, if necessary. \\nMacro Importance \\nForeign exchange reserves are often used to measure a country’s external vulnerability. China’s \\nforeign exchange reserves are still the largest in the world, though standard adequacy ratios have \\neroded somewhat since the 2015-16 episode. Exhibit 47 compares Chinese reserves to IMF metrics \\nfor reserve adequacy, although it should be noted that experts hold different views on the adequate \\nlevel of reserves.\\n[21] \\nCompilation and Reporting \\nForeign exchange reserve data are compiled by the PBOC and the SAFE, and are reported in USD \\nlevels. They do not include holdings in gold and SDRs, which are reported separately. \\nExhibit 47: Reserve adequacy consistent with IMF floating-rate, but not fixed-rate, guidelines \\nPBOC FX Reserves \\n \\n\\n\\n 94 / 158 \\n \\n \\nSource: IMF, Haver Analytics, Goldman Sachs Global Investment Research \\nFX Purchases/PBOC FX Position \\nReleased by the PBOC, these data measure the net amount the PBOC pays to financial institutions \\neach month for the foreign currency they receive from trade surpluses, foreign investments, and \\nother sources. PBOC’s FX purchase for RMB is one channel for creating reserve money. This indicator \\nis based on cumulative flows and thus is free from valuation effects. \\nExchange Rate Terminology and Offshore RMB Development \\nRenminbi (RMB) \\nThe official currency of the People’s Republic of China, translated as “the people’s currency”. The \\ncurrency is issued by the People’s Bank of China, the monetary authority of China and is the official \\nlegal tender in mainland China. \\nYuan \\nThe Yuan is the basic unit of the renminbi, but is often used synonymously with renminbi in referring \\nto the Chinese currency more generally. \\nCNY \\n• \\nCNY is the code determined by the International Organization for Standardization (ISO code) \\nfor the renminbi/yuan. In practice, it refers to the Chinese currency traded onshore in \\nmainland China. \\n• \\nDespite some gradual steps toward liberalization, the CNY market remains heavily managed \\nby Chinese officials, who maintain strict capital controls and set a daily “CNY fix” against a \\nbasket of world currencies and a pre-determined trading band around that fix, which the \\n“CNY spot” must settle within. The trading band was initially established at 0.3% but was \\nexpanded to 0.5% in May 2007, to 1.0% in April 2012, and to 2.0% in March 2014. \\n• \\nThe PBOC adjusted the CNY fixing regime in August 2015 so that CNY fixing reflected the \\n\\n\\n 95 / 158 \\n \\nprevious day’s closing price of USDCNY and overnight USD moves. In May 2017, the PBOC \\nfurther introduced a countercyclical factor in the CNY fixing to lean against herding \\nbehaviors in the market. The PBOC has utilized countercyclical factors since then to guide \\nmarket expectations on the exchange rate direction. The PBOC doesn’t release any data on \\nthe countercyclical factors. We measure the countercyclical factor by calculating the \\ndifference between the official daily CNY fixing and our estimation of the fixing based on \\nofficial documents of CNY fixing mechanism. That is, the CNY fixing should be determined \\nby the previous close of USDCNY spot and the overnight move of USD against a basket of \\ncurrencies. Our estimates suggest that the countercyclical factors have risen since 2023 and \\nreached record levels in mid-2024, suggesting the authorities’ preference to slow CNY \\ndepreciation against USD. \\nCNH \\n• \\nCNH refers to RMB that is traded outside of the Chinese mainland. Establishment of the \\noffshore CNH market by Chinese policy makers reflected their desire to pursue greater use \\nof RMB for international trade and financial transactions (i.e., the “internationalization” of the \\nRMB) post the 2008 financial crisis. The CNH market was established in July 2010 when the \\nPBOC and HKMA jointly announced that RMB would be deliverable in Hong Kong. \\n• \\nAlthough the CNH market remains concentrated in Hong Kong, RMB has since become \\ndeliverable in Singapore, Taiwan, Paris, Luxembourg, London, etc. Any offshore corporate \\nentity or individual investor can participate in CNH by establishing non-resident accounts in \\na country where CNH is delivered, typically through local banks that have a relationship with \\nbanks in those countries/regions. \\n• \\nUnlike the CNY market, the CNH market is not directly managed by the Chinese authorities \\nand instead is determined by the supply of and demand for CNH. It is therefore a “floating” \\ncurrency much like the US dollar (with the important caveat that the PBOC and SAFE \\nregulate RMB flows between onshore and offshore accounts, and official entities may \\nparticipate in the market to influence the exchange rate). Having said that, CNH and CNY are \\nessentially the same currency, and the active international trade between mainland China \\nand the rest of the world, as well as financial investment channels such as Stock Connect, \\nBond Connect, and Wealth Management Connect Program, help keep the two exchange \\nrates closely aligned. \\nCNY Trade-Weighted Indices \\nSource: PBOC, China Foreign Exchange Trade System (CFETS) \\nAvailability: Weekly data quoted by CFETS \\nOverview \\n• \\nThe Chinese government has for some time been revamping its foreign exchange \\nmechanism in an effort to make the RMB more market-oriented and relatively stable against \\na basket of currencies. Between mid-2023 and mid-2024, policymakers prioritized slowing \\nCNY depreciation against USD amid elevated capital outflow pressures, leading to CNY \\nappreciation against the CFETS basket. Chinese exports still remain competitive in the global \\n\\n\\n 96 / 158 \\n \\nmarkets, thanks to low domestic inflation in China and falling export prices. \\n• \\nOn August 11, 2015, the PBOC announced a major reform to the formation of the RMB’s \\ncentral parity rate against the US dollar, by referring to the closing price on the inter-bank \\nforeign exchange market of the previous day. The PBOC considered this a “one-time \\ncorrection” to remedy previously accumulated differences between the central parity rate \\nand the spot market rate. However, an abrupt weakening in the RMB occurred during the \\ndays following the announcement of this reform, triggering considerable market volatility \\nand additional capital outflows. Starting from December 2015, the PBOC released a new \\nindex called the CFETS RMB index based upon international trade weights after adjusting for \\nre-export factors. The basket weight is updated annually to reflect the latest trade flows of \\nChina vs. the rest of the world. Compared with CFETS, the BIS and IMF also compile RMB \\ntrade-weighted indices. Exhibit 48 shows the currency share in each basket and Exhibit 49 \\nshows the movement of indices. \\nExhibit 48: Significant differences between alternative currency reference baskets \\n \\nNote: These weights were reported as of end 2023. \\nSource: BIS, CFETS, IMF \\nExhibit 49: RMB strength reached peak levels in early 2022 \\n\\n\\n 97 / 158 \\n \\n \\nSource: CEIC, Wind, Goldman Sachs Global Investment Research \\nGS China “Outside-In” Trade Measures \\nSource: Goldman Sachs Economics Research \\nData since: January 2009 \\nTiming: Around 1-2 months after the end of each month \\nPublication: GS China Proprietary Indicators update \\nOverview \\n• \\nIn 2012-13, the prevalence of export/import over-invoicing to bring in/move out funds to \\ninvest in the “carry trade”/transfer assets offshore distorted officially reported trade data. In \\nan attempt to identify the underlying trends in export and import flows, we compiled an \\n“outside-in” trade measure based on trading partners’ reported data on trade with China. \\n• \\nWe made two improvements to our previous measures over time. First, we allow for lags to \\nreflect shipping time in matching China’s data with trading partners’ data. Secondly, we \\ncollect data from more countries and now include 21 of China’s major trading partners that \\ntogether make up 79% (67%) of the value of total Chinese exports (imports) in 2022. \\n• \\nBecause exports are usually reported on an FOB basis and imports on a CIF basis (see the \\ndiscussion above on goods trade indicators and re-exports), there will be a gap between \\nChina’s reported exports and trading partners’ reported imports from China. In addition, re-\\nexports and delayed data availability may also create discrepancies in level terms between \\nthe China’s official trade data and our “outside-in” trade measure. However, these shouldn’t \\naffect year-over-year changes on a persistent basis. \\nMethodology \\n• \\nWe collect trade data from 21 major Chinese trading partners (including the Euro area as a \\n\\n\\n 98 / 158 \\n \\nsingle “partner”). Where necessary, we convert import data into US dollars. Note that all \\nfigures discussed here are nominal dollars (not adjusted for inflation). \\n• \\nWe use 2010-2019 monthly data to estimate the lead-lag relationship between China’s \\nofficial import/export data vs. trading partner reported export/import data due to different \\nshipping time needed for different trading partners. \\n• \\nAdding up the import/export data for the trading partners (after adjusting for the lead-lag \\nrelationship described above) gives us “adjusted exports/imports to/from China’s major \\ntrading partners”. Comparisons of the outside-in estimates to China’s official data are shown \\nin Exhibit 50 and Exhibit 51. \\nExhibit 50: Our outside-in export tracker tracks China’s official export growth relatively well \\nexcept 2012-2014 \\nChina exports with major trading partners \\n \\nSource: CEIC, Haver Analytics, Goldman Sachs Global Investment Research \\nExhibit 51: Our outside-in import tracker was broadly in line with China’s official import \\ngrowth \\nChina imports with major trading partners \\n\\n\\n 99 / 158 \\n \\n \\nSource: CEIC, Haver Analytics, Goldman Sachs Global Investment Research \\nGS China FX Flow Metric \\nWe focus on two separate sets of SAFE data to gauge the underlying FX flow situation: One for \\nonshore FX settlement, and the other for the cross-border movement of RMB. These are combined \\nin our preferred FX flow metric (Exhibit 52). \\n• \\nSAFE dataset on onshore FX settlement: FX settlement and sales on behalf of clients by \\nbanks refer to the transaction of FX settlement, sales and other business conducted by banks \\nfor their clients. Deals on their own behalf and interbank market transactions are not taken \\ninto account. \\n• \\nCross-border RMB flows: In late 2015 through 2016, while there was a large amount of net \\ncross-border RMB flow from onshore to offshore, there was no corresponding observed \\nincrease in foreigners’ holdings of RMB assets. It is possible that some Chinese financial \\ninstitutions buy RMB in the offshore market and either sell it back in the onshore FX market \\nor invest it in onshore RMB assets. In addition, foreign equity and bond investments in China \\nthrough the “Stock Connect” and “Bond Connect” channels are conducted in the offshore \\nRMB market (i.e., in CNH instead of CNY). These RMB/FX transactions conducted do not \\nnecessarily show up on the PBOC’s balance sheet. Therefore, we believe that tracking the \\ndata on cross-border RMB flow (foreign-related receipts and payments reported by SAFE) is \\nalso important to developing a comprehensive view of the underlying flow picture. \\nExhibit 52: Currency outflows picked up sharply in August 2015, reflecting concerns over CNY \\ndepreciation \\n\\n\\n 100 / 158 \\n \\n \\nSource: SAFE, Goldman Sachs Global Investment Research \\nRelated GS Economics Publications \\n• \\n“How fast are Chinese exports really growing?”, Emerging Markets Macro Daily, 17 March \\n2014 \\n• \\n“How does a weaker RMB impact China credit”, Asia Credit Line, 14 August 2015 \\n• \\n“Sources and sizes of China’s capital outflows”, Asia Economics Analyst, 26 January 2016 \\n• \\n“ China capital flows update — how cross-border RMB flow might mask outflow \\npressures”, Asia Economics Analyst, 4 July 2016 \\n• \\n“Index inflows and rate differentials support our constructive view on CNY”, Asia in Focus, 20 \\nJuly 2020 \\n• \\n“Gauging Downside Risks to Chinese Exports”, Asia Economics Analyst, 6 April 2023 \\n• \\n“Analyzing recent puzzles on China trade data”, China Data Insights, 24 May 2023 \\n• \\n“Q&A on the recent RMB depreciation and policy reaction”, Asia in Focus, 23 August 2023 \\n• \\n“Deciphering China’s Inward Foreign Direct Investment Data”, China Data Insights, 24 \\nOctober 2023 \\n• \\n“The Enigma of China’s FX Reserves – Size, Adequacy, and Composition”, Asia Economics \\nAnalyst, 12 January 2024 \\nSection VII. Money, Credit, and Banking \\nThere are three major sets of money, credit and banking data: \\n1. Quantity-based data feature money supply, loans and deposits, total social financing, usage \\nof central bank policy tools, and balance sheets of the PBOC and financial institutions – all \\n\\n\\n 101 / 158 \\n \\ncompiled by the PBOC. \\n2. Price-based data feature various interest rates, including policy rates (such as 7-day reverse \\nrepo rate and interest rates of other monetary policy tools) and market interest rates (such \\nas interbank repo rates and bond yields), published by the PBOC and the National Interbank \\nFunding Center. \\n3. Flow of funds accounts record financial flows amongst five key economic sectors, which are \\njointly compiled by the PBOC and the NBS, and provide a bird ’ s-eye view of the \\ninterdependence between the real economy and the financial economy. \\nMoney Supply \\nSignal to noise ratio: *** \\nMacro importance: **** \\nSource: The People’s Bank of China \\nAvailability: Monthly from 1997, quarterly from 1990 \\nTiming: Typically 9 to 15 days after the end of each month \\nOverview \\n• \\nThe People’s Bank of China (PBOC) reports three series on China’s money supply: \\n1. M0: currency in circulation (bills and coins) \\n2. M1: M0 + demand deposits (excluding household demand deposit) \\n3. M2: M1 + quasi-money (time, savings, and other deposits, excluding fiscal deposits) \\nExhibit 53: A breakdown of M2 by major component \\n \\n\\n\\n 102 / 158 \\n \\n \\nNote: Numbers in the bracket refer to the share of each major component in M2, based on 2023 \\ndata and definitions. NBFI refers to non-bank financial institution. \\nSource: PBOC, Wind, Goldman Sachs Global Investment Research \\nSignal to Noise Ratio \\n• \\nChina’s money supply data are subject to significant distortions, in large part arising from \\nthe attempts of financial institutions to evade regulatory controls. For example, commercial \\nbanks used to depress month-end money and credit data and minimize regulatory costs \\nsuch as required reserves; the PBOC responded by changing the required data from month-\\nend to month-average in September 2014. This alleviated the old problem but the time \\nseries is no longer directly comparable. Since 2016, the PBOC has started to assess banks’ \\nperformance under the new macroprudential assessment framework (MPA), including \\nindicators for capital and leverage, asset and debt, liquidity, pricing, asset quality, risk of \\ncross-border financing and the implementation of credit policy. The quarterly MPA tends to \\ndistort quarter-end data. \\nMacro Importance \\n• \\nChanges in broad money supply provide useful leading information for short-term \\neconomic activity because banks remain the dominant financial intermediaries in China. M2 \\nremains one of the intermediate policy targets for the central bank, but its importance has \\nbeen declining due to the ongoing reform of the monetary policy framework. \\nCompilation and Reporting \\n• \\nBreakdowns are available from M0 to M2. M2 is the broadest measure currently available \\nbut is becoming inadequate as direct financing (such as government/corporate bond \\nissuance) has increased in importance over the past decade. \\n\\n\\n 103 / 158 \\n \\n• \\nThe PBOC has expanded the definition of money supply five times due to China’s evolving \\nfinancial markets. Specifically, the PBOC incorporated clients’ margin deposits into M2 on \\nthe development of the stock market in 2001. Domestic RMB deposits in foreign financial \\ninstitutions have been included into money supply since 2002. From October 2011, M2 has \\nincluded both non-bank financial institutions deposits and housing provident fund deposits. \\nIn January 2018, the PBOC employed money market funds held by non-bank sectors to \\nmeasure money market related deposits (previously measured by deposits of money market \\nfunds) and incorporated it into M2. Since December 2022, e-CNY in circulation has been \\nincluded in M0. In June 2024, PBOC governor Pan Gongsheng acknowledged that the M1 \\ndefinition was outdated, and the central bank may expand the scope of M1 to include \\nhousehold demand deposits. \\nOther Issues \\n• \\nBefore 2018, the government used to set a specific target for M2 growth at the annual “Two \\nSessions” in early March, which would usually be consistent with its desired real GDP growth \\nand inflation targets. In recent years, the government did not set a specific target for M2 \\ngrowth and only stated an objective “to keep money and credit growth broadly consistent \\nwith nominal GDP growth”. At the 2023 Central Economic Work Conference, the phrase was \\nchanged to “keeping total social financing and M2 growth broadly in line with economic \\ngrowth and inflation target” amid deflationary pressures and weak nominal GDP growth. \\nMost recently, the PBOC has started to downplay the importance of quantity-based \\nindicators, emphasize the composition and structure of money and credit, and push forward \\nthe transition towards a more price-based monetary policy framework. \\nExhibit 54: M1 growth turned negative in 2024 \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\n\\n\\n 104 / 158 \\n \\nBank Loans and Deposits \\nSignal to noise ratio: **** \\nMacro importance: *** \\nSource: The People’s Bank of China \\nAvailability: Monthly from 1997 \\nTiming: Around 9 to 15 days after the end of each month \\nOverview \\n• \\nLoan data published by the PBOC include loans made by depository institutions and non-\\ndepository financial institutions (e.g., trust and insurance corporations). Recipients of loans \\nare non-financial institutions and individuals, including non-residents. Loans to non-bank \\nfinancial institutions have been added since the start of 2015. Loans denominated in CNY \\nand foreign currencies are compiled separately and aggregated to provide total loans data. \\n• \\nSimilarly, deposit data include deposits in CNY and other currencies. There is further \\ninformation on deposits broken down by different sectors of the economy (e.g., households, \\ngovernment, and non-financial corporates) and by type of deposits (demand deposits, time \\ndeposits and other deposits). \\n• \\nBalance sheet of Monetary Authority: The main liabilities are currency issued and commercial \\nbank reserves, and main assets come from foreign assets (funds outstanding for foreign \\nexchange) and claims on other depository corporations (liquidity injection via monetary \\npolicy tools, such as OMO, MLF, PSL, relending, etc.). \\n• \\nBalance sheet of Other Depository Corporations: Other depository corporations include \\npolicy banks, commercial banks, credit unions and finance companies. Main liabilities come \\nfrom deposits of households and non-financial institutions (around 60%), interbank \\nborrowings (around 10%), and bond issuance (around 10%). \\n• \\nSource and uses of funds of financial institutions: This describes major sources and uses of \\ncommercial banks and other financial institutions. Sources can be deposits and financial \\nbonds. Uses include loans and portfolio investments. \\nSignal to Noise Ratio \\n• \\nThe new loans and changes in loan stock announced by the PBOC are not always consistent \\nwith each other. The main reason for this discrepancy is non-performing loan (NPL) write-\\noffs, which have been subtracted from new loans data, while changes in loan stock are \\ncalculated after adjusting for these write-offs. It is difficult to reconcile fully the two series \\nusing announced data. \\nMacro Importance \\n• \\nLike M2 data, loan growth data are useful for gauging the current monetary policy stance, as \\nwell as possible macro policy changes, when combined with other government-driven \\n\\n\\n 105 / 158 \\n \\nactivity indicators such as infrastructure FAI. Given that China’s capital markets are still \\nunder-developed, bank loans are the major source of external funding for the non-financial \\ncorporate sector. However, their importance has been falling as more alternative financing \\nmethods have become available and as the PBOC gradually shifts its focus from quantity-\\nbased metrics to a price-based framework. \\nCompilation and Reporting \\n• \\nLoans are broken down by borrower type – households, nonfinancial enterprises and \\ngovernment agencies/organizations, and nonbank financial institutions. To some degree, \\nloans are broken down by tenor as well: short-term vs. medium to long-term loans for \\nhouseholds, and short-term loans, bill financing, vs. medium to long-term loans for \\nnonfinancial enterprises and government agencies/organizations. In addition, loans to some \\nspecific sectors (e.g., property, green and inclusive financing) are available, and the PBOC \\nalso publishes loans by sector on an annual basis with a lag of about one year and a half. \\nBut there is insufficient detail from the PBOC’s data to allow one to identify finer categories \\nsuch as LGFV borrowings. The National Financial Regulatory Administration (NFRA) releases \\nquarterly data of NPL ratios for banks.\\n[22] \\n• \\nThe PBOC has expanded its definition of non-depository financial institutions for data \\nreleases of loans and deposits over the past decades, which also contributed to the \\ninconsistency between the level and growth rate of loans and deposits. The latest two \\nrevisions happened in 2015 and early 2023. In 2015, the PBOC incorporated loan companies \\ninto non-depository financial institutions. In early 2023, the definition was further expanded \\nto include consumer finance companies, wealth management companies, as well as financial \\nasset investment companies. \\n• \\nThe PBOC publishes the initial release of new loans and deposits flows first, with detailed \\nbreakdown data (such as balance sheets of the PBOC and other depository corporations) \\npublished a few days later. \\n• \\nForeign Exchange Loans include foreign-currency-dominated loans extended to domestic \\nand foreign residents/institutions by domestic and foreign financial institutions based in \\nmainland China. \\nExhibit 55: The PBOC’s liquidity injection has been the major driver of balance sheet expansion \\nsince 2015 \\nPBOC balance sheet: assets \\n\\n\\n 106 / 158 \\n \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\nExhibit 56: Required reserves remain the lion’s share of the PBOC's liabilities \\nPBOC balance sheet: liabilities \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\nExhibit 57: Loans make up more than half of Chinese banking system assets \\nBanks’ assets by component (June 2024) \\n\\n\\n 107 / 158 \\n \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\nExhibit 58: Deposits remain the dominant funding source of China’s banking system \\nBanks’ liabilities & equities by component (June 2024) \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\nTotal Social Financing \\nSignal to noise ratio: **** \\nMacro importance: **** \\nSource: The People’s Bank of China \\n\\n\\n 108 / 158 \\n \\nAvailability: Monthly from 2002 \\nTiming: Around 9 to 15 days after the end of each month \\nOverview \\nTotal social financing (TSF) includes both direct and indirect financing from the financial industry to \\nthe “real economy”. TSF consists of RMB bank loans, FX loans, trust loans (higher-yielding loans \\nintermediated by trust companies), entrusted loans (lending from enterprises with favorable access \\nto credit or excess cash, intermediated by banks), undiscounted bankers’ acceptance bills, net \\ncorporate bond issuance, equity financing, net government bond issuance, asset-backed securities \\nof depository financial institutions and loan write-offs. The concept captures total financing of the \\nreal economy, mostly through different debt instruments. \\nSignal to Noise Ratio \\n• \\nTSF statistics are intended to be a comprehensive measure, but historically there have been \\nmultiple rounds of revision on TSF data. In 2018, asset-backed securities and loan write-offs \\nwere included in TSF, and in 2018-2019, net government bond issuance and net corporate \\nbond issuance statistical standards were also adjusted. In April 2024, the PBOC adjusted \\n2024 year-to-date corporate bond net financing according to the latest industry \\nclassification results. These revisions also created occasional distortions to TSF growth \\nmeasures. \\n• \\nThe PBOC publishes the stock and flow data of TSF separately each month. However, the \\nnew TSF flows and changes of TSF stock can diverge from time to time. The divergence \\nmainly comes from FX loans and corporate bond financing, for which the PBOC uses \\ndifferent statistical methodology for flow and stock data. \\n• \\nM2 and TSF growth can diverge from time to time, as M2/TSF refer to liabilities/assets of the \\nfinancial sector’s balance sheet, respectively. By definition, TSF includes overall financing by \\nthe real economy (non-financial sectors), while M2 includes borrowings between financial \\ninstitutions as well. FX purchases by the PBOC and fiscal deposit changes can affect M2 \\ngrowth but not TSF growth, while net equity financing and shadow banking credit extension \\n(undiscounted bankers’ acceptance bills, trust loans, entrusted loans) can add to TSF but not \\nM2. \\nExhibit 59: New loans and government bond net issuance have been the main drivers of TSF \\ngrowth \\n\\n\\n 109 / 158 \\n \\n \\n*Other financing primarily include equity financing, loan write-offs and depository financial \\ninstitutions’ ABS \\nSource: PBOC, Goldman Sachs Global Investment Research \\nCentral Bank Policy Tools \\nThe PBOC uses the following tools to conduct monetary policy: \\n1. Open Market Operations (OMO) \\n2. Changes in the Reserve Requirement Ratio (RRR) \\n3. Adjustments in interest rates (e.g., policy rate) \\n4. Lending facilities (such as PSL, SLF, MLF) and relending programs \\n5. Policy communications and window guidance (e.g., quarterly monetary policy reports and \\nmonetary policy committee meeting minutes, and administrative / regulatory instruments \\nthat affect both prices and quantity of credit supply from financial institutions) \\nA full summary of the PBOC’s toolkit appears in Exhibit 60 toward the end of this section. \\nOpen Market Operation (OMO) \\nOpen market operations by the PBOC currently include: \\n1. Repurchase (REPO) agreements, reverse repo \\n2. Issuance of PBOC bills \\nThe PBOC started to conduct repo operations twice a week in 2004, using repo (reverse repo) to \\nwithdraw (inject) liquidity. It shifted to daily operations in January 2016, with brief notices explaining \\nthe rationale of open market operations. The central bank stopped using repo to withdraw liquidity \\nfrom the banking system in late 2016. Repo / reverse repo maturities range from one week to one \\nyear. The most common tenors are one week for daily operations and two weeks for month-\\n\\n\\n 110 / 158 \\n \\nend/quarter-end operations. In July 2024, the PBOC introduced temporary overnight repo / reverse \\nrepo operations to gain better control over short-term market rates. \\nThe central bank used to issue PBOC bills domestically to offset passive liquidity injection from fast \\ngrowing FX reserves in the 2000s. The tenors of domestically issued PBOC bills range from 3 months \\nto 3 years. The central bank largely phased out the usage of PBOC bills onshore in late 2016, as FX \\nreserves stabilized at slightly above USD 3 trillion. Since November 2018, the PBOC has been issuing \\nbills in Hong Kong to manage CNH liquidity, with maturities ranging from 3 months to one year. \\nReserve Requirement Ratio \\nThe Reserve Requirement Ratio (RRR) is the ratio of deposits that financial institutions are required \\nto keep at the central bank. It is effectively a tax levied on the banking system, and central banks can \\nuse it to manage commercial banks’ capacity to lend. The PBOC set up a system of “Discretionary \\nReserve Requirements” in April 2004, which allows for differential reserve requirements for different \\ntypes of banks according to a number of criteria, including the capital adequacy ratio, the NPL ratio \\nand the soundness of the internal control system. In 2016, this was upgraded to become the macro \\nprudential assessment (MPA) system. According to the PBOC, the weighted average effective RRR \\nfor all financial institutions was 7.0% as of mid-2024 – 8.5% for large banks, 6.5% for medium-sized \\nbanks, and 5.0% for small banks.\\n[23] \\nAdjustments in Interest Rates \\nChina has made significant progress in interest rate liberalization during the years before the Covid \\npandemic. Currently, the system is a hybrid of market and regulated interest rates. De jure, the \\ninterest rate system has been liberalized, though de facto this is not the case, as banks still rely \\nheavily on LPR in their pricing model and the ceiling of deposits rates is still regulated by the \\n“deposit rate self-regulatory mechanism” (存款利率自律机制) described below. \\nPolicy Interest Rates \\nPolicy interest rates are those that are directly controlled by the central bank, and changes to them \\nwill impact other interest rates. There are two policy interest rates in China: \\n1. OMO interest rates: 7-day reverse repo is the most frequently used open market operation \\nby the PBOC, so 7-day OMO rate is usually viewed as a policy interest rate in China. \\n2. Medium-term Lending Facility (MLF) interest rate: Interest rate of medium-term PBOC \\nliquidity injections in the interbank market. MLF is medium-term base money from the PBOC \\nto commercial banks and policy banks backed by high-quality collateral. This tool is \\ndesigned to provide guidance to the medium-term interest rate and to adjust the funding \\ncost of the real economy. Banks’ Loan Prime Rate (LPR, more discussions below) is \\nbenchmarked against the 1-year MLF interest rate, and therefore changes to the MLF rate \\nwill impact funding cost of the real economy through the LPR. However, communications \\nfrom the central bank in mid-2024 suggested that the PBOC may de-emphasize the MLF \\nrate and potentially re-anchor LPR to the 7-day OMO rate. \\nOther Interest Rates \\n• \\nLoan prime rate (LPR): This is the interest rate banks offer to their prime clients. LPR serves \\n\\n\\n 111 / 158 \\n \\nas the pricing reference for bank lending, especially after the LPR reform in mid-2019, \\nincluding lending to corporates and households. Currently, the LPR consists of rates with two \\nmaturities: 1-year LPR and 5-year LPR. Mortgage rates are benchmarked against the 5-year \\nLPR. By definition, LPR is based on the quotes made by quoting banks by adding a few basis \\npoints to the interest rate of MLF. The LPR is calculated by the National Interbank Funding \\nCenter (NIFC), serving as the pricing reference for bank lending. At present, the LPR quoting \\nbanks are comprised of 20 banks. The quoting banks submit their quotes to the NIFC on the \\n20th day of every month (postponed in case of holidays), in increments of 0.05 percentage \\npoints. The NIFC will calculate the arithmetic average of rates after excluding the highest and \\nlowest submissions, and find its nearest integral multiple of 0.05% to be the LPR, which will \\nthen be published at 9 a.m. on the same day. In theory, the LPR is the average interest rate \\nsubmitted by large banks; in reality, the PBOC can guide banks on their quotes and \\ntherefore impact the LPR. \\n• \\nThe rediscount rate is the rate at which central banks discount commercial banks' unexpired \\npaper. Since this rate affects borrowing costs for commercial banks, it can be used as a \\npolicy tool. However, this rate is infrequently adjusted in China. \\n• \\nThe re-lending rate is the rate the PBOC uses to lend to financial institutions. Loans from \\nthe PBOC are a regular source of funding for Chinese commercial banks to support areas of \\npolicy priorities (e.g., SMEs, agriculture and rural development). \\n• \\nThe PBOC pays interest on required reserves and excess reserves that financial institutions \\nhold at the PBOC. The interest rate for excess reserves creates the floor for China's short-\\nterm interest rate. \\n• \\nDeposit rate: In late 2015, the PBOC removed the official ceiling for the deposit rates, but \\nan implicit ceiling set by “the self-regulatory pricing mechanism for market interest rates” (市\\n场利率定价自律机制) remains in place. The implicit ceiling for the deposit rates was \\nanchored to the benchmark deposit rates, multiplied by a designated factor. But the \\nbenchmark deposit rates have remained unchanged since 2015. In June 2021, the PBOC \\nimproved the formation of the implicit ceiling for deposit rates by shifting towards \\nbenchmark deposit rates plus a few basis points. In April 2022, under the PBOC’s instruction, \\nthe “deposit rate self-regulatory mechanism” was established. This mechanism guides banks \\nto set deposit rates based on the 10-year central government bond yields and the 1-year \\nLPR. This implies that when corporates and households’ funding cost is guided lower by the \\nPBOC through lowering MLF rate and LPR, banks can also reduce their deposit rates to \\nprotect their net interest margins to some degree. \\nOther Liquidity Management Tools \\nWe summarize the major instruments for the PBOC to implement monetary policy in the table below. \\nExhibit 60: The PBOC utilizes a wide range of tools to affect the price and quantity of money \\nand credit \\n \\n\\n\\n 112 / 158 \\n \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\n\\n\\n 113 / 158 \\n \\nExhibit 61: A summary of the PBOC’s relending tools announced since 2020 \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\nInterbank Interest Rates \\nSource: National Interbank Funding Center \\nAvailability: Daily data since 3 January, 1996 \\n\\n\\n 114 / 158 \\n \\n• \\nChina's interbank market was established in 1996, and market participants include all \\nfinancial institutions. The interbank rate is a good indicator of liquidity in the market. \\nInterbank Offered Rates and Interbank Repo Rates are the two main rates quoted in the \\nmarket. \\n• \\nInterbank markets have been fully liberalized, with the interest rate paid on excess reserves \\nthe lower bound and the 7-day SLF rate the upper bound for interbank rates, creating an \\neffective, albeit wide, interest rate corridor. Repo rates are typically lower than the offered \\nrate, as they have higher liquidity and are collateralized. The 7-day repo rate for all financial \\ninstitutions (R007) is the best proxy for the overall interbank market rate. \\n• \\nLarge and medium banks are net suppliers of liquidity in the interbank market, while small \\nbanks and non-bank financial institutions are net borrowers (Exhibit 62). Around 90% of \\ninterbank funding trades are based on repo rates. Within repo trades, around 60% of them \\nare based on repo rates for all financial institutions (R rates), while the rest are based on \\nrepo rates for depository institutions (DR rates). Interbank term funding is confined to the \\nfront end – more than 80% of funding trades are overnight borrowings. \\nExhibit 62: Large and medium-sized banks are net suppliers of liquidity, while small banks and \\nnon-bank financial institutions are net borrowers \\nNet lending / borrowing in the interbank market \\n \\nSource: PBOC, Goldman Sachs Global Investment Research \\n7-day repo rates (R007 & DR007): These are broadly considered to be monetary policy operating \\ntargets. The PBOC emphasized that the 7-day depository institution repo rate (DR007) would be a \\ngood proxy to monitor front-end interbank liquidity, and they monitor DR007 closely in their \\nliquidity management and operations. R007 is typically more volatile and higher than DR007. DR007 \\nonly accepts rates bonds as collateral, whereas R007 does not impose such restrictions on collateral. \\n\\n\\n 115 / 158 \\n \\nShanghai Interbank Offered Rate (SHIBOR): This consists of eight maturities: overnight, 1-week, 2-\\nweek, 1-month, 3-month, 6-month, 9-month and 1-year. This is a simple short-term wholesale \\ninterest rate with no collateral required. SHIBOR is calculated as the arithmetical averages of \\ninterbank lending rates offered by 18 commercial banks. \\nNegotiable Certificates of Deposits (NCDs): In December 2013, the PBOC launched the NCD \\nprogram. NCDs allow banks to broaden their funding beyond deposits to manage liquidity. As \\ndesigned by the PBOC, NCDs are priced on SHIBOR, which could improve the credibility of SHIBOR \\nwith actual transactions. 1y NCD serves as a benchmark market rate for short-term rates, and 1y \\nMLF rate serves as an anchor for 1y NCD yields. The major issuers of NCDs are national banks, joint \\nstock banks, and city banks. More than half of outstanding NCDs have maturity of 1 year, and more \\nthan 90% of outstanding NCDs are rated “AAA” by credit rating agencies. \\nPolicy Financial Bond (PFB) yields: Issued by the three policy banks in China (China Development \\nBank, the Export-Import Bank of China, and the Agricultural Development Bank of China), these are \\nthe non-government bonds with the highest credit rating because they are typically viewed as \\nquasi-government bonds. Major tenors of the bonds range from 3 months to 10 years. \\nInterest Rate Swap Rate (IRS rate): This refers to the fixed payment rate for interest rate swaps that \\nexchange a fixed payment for a floating payment. Around 90% of IRS are referenced against the \\nfixing of interbank 7-day repo rate (FR007), and the rest are mostly referenced against 3m SHIBOR. \\nTherefore, FR007 swap rates are often used as the gauge for China interbank interest rates. \\nExhibit 63: The PBOC’s interest rate corridor has been established since 2015 \\nPBOC’s interest rate corridor \\n \\nSource: Wind, Goldman Sachs Global Investment Research \\nFlow of Funds Accounts \\nSource: National Bureau of Statistics \\nAvailability: Annual data since 1992 \\n\\n\\n 116 / 158 \\n \\nTiming: Long release lag, roughly two years after the period end \\nOverview \\n• \\nThese accounts record financial flows amongst five key economic sectors: non-financial \\nenterprises, the financial corporate sector, the general government sector, households, and \\nthe rest of the world (ROW). The main financial instruments covered include deposits, loans, \\nsecurities, insurance technical reserves, foreign direct investment, etc. Their main value is to \\nprovide a bird’s-eye view of the interdependence between the real economy and the \\nfinancial economy from a macroeconomic perspective, describing inflows, outflows, and \\nstock of funds in the economic system. \\n• \\nFor China, the main features shown by the flow of funds data (Exhibit 64) are that: (1) \\nHouseholds have been a constant supplier of funds, initially almost all to corporates, then for \\na time to foreigners and lately more to the Chinese government; (2) The financial deficit (i.e., \\nwhen investment>savings) in the non-financial corporate sector in China gradually narrowed \\nfrom -12.3% in 1992 to -2.5% in 2020 before widening to -8.2% in 2022; (3) The financial \\ndeficit of the government sector in China was relatively stable at about -1% from 1992 to \\n2004. However, from 2005 to 2015, the government sector changed from running a financial \\ndeficit to a surplus (except 2007), meaning that the Chinese government’s asset increases \\nexceeded its liability increases. The government’s financial deficit reemerged after 2015 and \\nhas widened significantly since 2018, peaking at -8.2% in 2020 and remaining elevated at -\\n6.9% in 2022; (4) The rest of the world sector has absorbed funds except for 1993, that is, \\nthere has been a net outflow of domestic funds from China, with the financial balance \\nwidening from -1.3% of GDP in 1992 to a peak of -9.8% in 2008 before narrowing to roughly \\n-2.3% in 2022. \\nCompilation and Reporting \\n• \\nChina’s Flows of Funds Accounts (FFA) is similar to the Japanese model, which is divided into \\nphysical transactions and financial transactions. The NBS is responsible for compiling data on \\nphysical transactions, while financial transactions are prepared by the PBOC. Due to limited \\noriginal data on various sectors and industries (e.g., inventories), the classification of sectors \\nand transaction items in the financial transaction section is usually more detailed than in the \\nphysical transaction section of FFA. \\n• \\nCompared with FFA in the US and Japan, FFA in China is still at the developing stage in \\nterms of sector classification and transaction items, does not yet have quarterly or stock data, \\nand has no reconciliation account. Moreover, the time lag of FFA releases in China is roughly \\ntwo years for physical transaction tables, quite long compared with typical release lags of \\nless than one year in other countries. Finally, China’s FFA lacks more granular decomposition \\nof sectors. \\nExhibit 64: Households have been a supplier of funds to the Chinese government in recent \\nyears \\nFinancial surplus/deficit by sector \\n\\n\\n 117 / 158 \\n \\n \\nSource: NBS, PBOC \\nRelated GS Economics Publications \\n“China’s monetary policy transmission efficiency weakened after Covid”, Asia Economics Analyst, 18 \\nFebruary 2022 \\n“Navigating China rates: A shifting market to open further”, Asia Economics Analyst, 5 September \\n2022 \\n“Demystifying the widening M2-TSF growth differential”, China Data Insights, 22 March 2023 \\n“China H2 monetary policy outlook: Expecting further easing to facilitate demand stimulus”, Asia in \\nFocus, 2 August 2023 \\n“Understanding China’s Flow-of-Funds Accounts”, China Data Insights, 12 October 2023 \\n“Why China’s monetary easing has become less effective post-Covid”, Asia Economics Analyst, 18 \\nDecember 2023 \\n“Demystifying China’s falling M1-M2 growth differential: Drivers and implications”, Asia in Focus, 20 \\nJanuary 2024 \\n“Navigating banking system liquidity in China”, Asia Economics Analyst, 3 March 2024 \\n“Why is the PBOC reluctant to cut policy rates?”, Asia in Focus, 2 June 2024 \\nSection VIII. Prices \\nThis section features the consumer price index, producer price index, agriculture and raw material \\nprices, merchandise trade price index, and GDP deflator. (Refer to Section IV. Real Estate for land \\nprice and property price indices.) \\n\\n\\n 118 / 158 \\n \\nConsumer Price Index \\nSignal to noise ratio: **** \\nMacro importance: ***** \\nSource: National Bureau of Statistics (NBS) \\nAvailability: Monthly and annual from 1985 \\nTiming: Usually the 2nd week of the following month \\nOverview \\n• \\nThe Consumer Price Index (CPI) measures the price of a basket of goods and services that a \\ntypical household purchases. The basket is updated every five years. The last major update \\nwas in 2021, when the NBS updated the goods and services covered to reflect several new \\nconsumption patterns (e.g., food takeaways, new energy vehicles, ridesharing services), and \\nadjusted the weights of each category according to an updated mix of household purchases. \\nThe latest index weights therefore may reflect some pandemic-related changes in spending \\npatterns. Unlike most DM economies, the NBS does not publish the weighting scheme of the \\nCPI basket. Weights mentioned in this section are our estimates (see Compilation and \\nreporting below for details of our methodology and our most recent estimate of the basket’s \\ncomposition). Category weights vary over time as prices change. \\nSignal to Noise Ratio \\n• \\nDespite some data quality issues, we believe the CPI inflation rate is largely reliable, at least \\nin terms of the direction of change. Some common criticisms of China’s CPI—not all \\nnecessarily warranted—include: \\n1. Some prices, such as gasoline prices, are regulated despite being benchmarked to \\nthe broad trend of global oil prices. \\n2. Its components and weights are revised infrequently, so the basket composition \\nmight diverge somewhat from actual consumption behavior. \\n3. Compared with other countries, the NBS does not publish data on weights of \\ndifferent items in the CPI baskets. The NBS also does not publish “core goods” CPI \\ninflation. \\n4. The NBS revised down the weight of pork prices significantly in January 2021 to \\nreflect the fading effect of the African Swine Fever (ASF) outbreak in 2019-2020, but \\nthe magnitude of the downgrade appears too large, mitigating the impact of hog \\ncycles on China’s reported CPI inflation. \\n• \\nIt is standard international practice to exclude property prices from the CPI basket. The CPI \\nbasket does have a housing component, which captures price changes in rent, implied rent \\nfor self-owned houses, utilities, and real estate management fees. Inflation for private \\nhousing (implied rent for self-owned houses) has correlated closely with rent inflation \\n\\n\\n 119 / 158 \\n \\n(market rent) since 2011. However, home purchases are usually considered an investment \\nfollowing the international standard, and thus are not counted in the CPI basket. \\n• \\nIn terms of other components, such as medical services and education, it is possible that the \\nCPI data may fail to capture all the price changes. For example, CPI captures standardized \\neducation costs, such as school tuition fees; however, most of the “grey” charges levied by \\nschools (tuition for supplementary classes, other administrative charges, etc.) are not \\nincluded. This is likely to bias reported inflation downward at least slightly, and possibly \\nsignificantly. \\n• \\nCPI inflation is also distorted by regulatory measures in various administered categories, \\nincluding gasoline, electricity, education services, medical goods/services and telecom \\nservices. Even if global oil prices were to jump, regulatory measures would initially cap the \\nextent of price increases faced by consumers. That said, the pass-through of international oil \\nprices to domestic refined product prices is fairly timely and meaningful, occurring every 10 \\nworking days if changes are large enough to warrant an adjustment in the period. The NDRC \\nrequires refineries to start to reduce profit margin towards zero once global crude oil prices \\nreach $80/bbl and caps petroleum product prices via fiscal subsidies once crude oil prices \\nreach and/or exceed $130/bbl. There is also a price floor for refined products when crude oil \\nprices are below $40/bbl. \\nMacro Importance \\nPolicymakers in China pay close attention to CPI data. Each year, the government sets a target for \\nheadline CPI inflation in the Government Work Report during the “Two Sessions” (e.g., 3% for 2021-\\n2023). The target is effectively a ceiling, and any rapid change in inflation tends to lead to swift \\npolicy communications and reactions (e.g., monetary policy by the PBOC and administrative policies \\nby the NDRC). Monetary policy, proxied by the 7-day repo rate among depository institutions \\n(DR007), tends to react to core inflation rather than food/PPI/headline CPI inflation over the past \\ndecade. \\nCompilation and Reporting \\n• \\nThe CPI basket has eight broad categories. Under each category, there are a few more \\ndetailed subcomponents (e.g., the “Transportation and communication” category is further \\nbroken down into “transportation equipment”, “vehicle fuel”, “vehicle use and maintenance”, \\n“communication equipment”, “communication service” and “postal service”). The \\ncomponents of the CPI and their weights are determined by the regular household \\nexpenditure surveys and other non-regular surveys. The latest round of quinquennial \\nupdating was carried out at the beginning of 2021. \\n• \\nAs in many other developing countries, food accounts for a relatively large share of \\nconsumer expenditure in China. However, the food share in the CPI basket has fallen as \\nliving standards in the country have improved. Currently, food (not including dining out) \\naccounts for about 19% of the total CPI basket in China. \\n• \\nPork accounts for around 2-3% of China’s CPI basket on average historically, higher than \\nother major economies. As pork prices declined notably in 2021, the weight of pork prices \\n\\n\\n 120 / 158 \\n \\nfell to and has remained below 2% since mid-2022. Hog cycles can be important in driving \\nCPI inflation. In late 2019 and early 2020, for example, an outbreak of African Swine Fever \\n(ASF) caused pork prices to more than double, sending CPI inflation above 4%. \\nExhibit 65: China’s CPI basket has a higher weight of food and lower weight of services than \\nDM economies \\n \\n* China CPI weights are estimated by GS based on historical data. ** Prices of motor fuel are highly \\nregulated in China. This table is based on CPI weights as of 2023. \\nSource: BLS, Eurostat, ONS, SBJ, NBS, Goldman Sachs Global Investment Research \\n• \\nData are collected at different retail outlets by statisticians 2-3 times a month. Goods that \\nhave large weights and are subject to frequent price changes, such as vegetables, are \\nsampled every five days. On the other hand, goods/services whose prices are regulated by \\nthe government or are relatively stable, are only sampled once a month. \\n• \\nThe NBS does not officially publish weights of the items in the CPI basket. It does mention \\nweight changes in a few major categories following each quinquennial basket update. The \\nNBS also comments on the contribution to year-over-year headline CPI for a few categories \\n(mostly in food) in its monthly release, which allows us to track weights of select items more \\nfrequently. For subcomponents of each category, we follow NBS documentation and \\nacademic papers to construct a detailed weight scheme in a two-step procedure. Firstly, we \\nuse regression-based estimates and NBS releases to gauge the weights of major categories. \\nSecond, we estimate the relative importance of subcomponents in each category. China’s \\nYearbook of Household Survey provides shares of subcomponents in household \\nconsumption as a benchmark of CPI basket weights. That said, the weights of each category \\nwill vary over time as prices change. The coefficients only represent averaged weights over \\ncertain periods, and the actual weights may swing significantly for specific categories, such \\nas pork prices. Exhibit 66 below summarizes our estimates of subcomponent weights in the \\n2016 and 2021 baskets with major categories highlighted in blue. \\nExhibit 66: China’s CPI basket in detail \\n \\n\\n\\n 121 / 158 \\n \\n \\n\\n\\n 122 / 158 \\n \\nSource: NBS, Goldman Sachs Global Investment Research \\nOther Issues \\n• \\nCore CPI is often a useful indicator of the underlying inflationary pressures because it \\nexcludes high volatility of food and energy prices. Even though China’s energy prices are \\nregulated, they still show higher volatility than many other components. There is no separate \\nenergy component in the CPI, but energy inflation and its weight can be inferred from other \\ndata. If we estimate energy’s weight by regressing the headline CPI on food CPI, core CPI, \\nand China domestic gasoline prices, the implied weight for energy is around 2%; if we \\nestimate energy’s weight by summing up the estimated weights for “utilities” and \\n“transportation fuel and parts” (which would suggest at least some over-estimation as these \\ncategories contain elements other than energy), the implied energy weight is around 7%. \\nProducer Price Index (ex-Factory Price Index of Industrial Products) \\nSignal to noise ratio: ***** \\nMacro importance: **** \\nSource: National Bureau of Statistics \\nAvailability: Monthly from 1996, annual from 1980 \\nOverview \\n• \\nThe Producer Price Index (PPI) measures the price of industrial products when they are sold \\nfor the first time after production. \\nSignal to Noise Ratio \\n• \\nWe believe the PPI index is generally reliable. It is based on a large sample of 20,000+ \\nproducts at factory gate prices. Both large and small enterprises, i.e., those above or below \\nthe standard minimum threshold, are sampled. \\nMacro Importance \\n• \\nPPI is more sensitive to changes in investment demand, and to some extent export demand. \\nTherefore, it contains more information on the state of the industrial cycle than the CPI. It \\ncan also be affected by controls on production in relevant sectors, such as the “supply-side \\nreform” in upstream sectors (e.g., coal and steel) during 2015-2017. \\nCompilation and Reporting \\n• \\nCurrently, more than 50,000 industrial enterprises report prices twice a month (the 5th and \\n20th day of the month). Weights of components are determined by the sales value. The \\nbasket is adjusted every five years, though adjustments can also be made during the interim \\nif there are rapid changes in the production pattern. Similar to CPI, weights of the PPI are \\nnot disclosed and the NBS does not publish industrial sales value by sector either. We \\nestimate the weighting scheme based on industrial revenues data, which serve as a proxy of \\nsales value by sector.\\n[24] Our estimates suggest that computer, communication & other \\n\\n\\n 123 / 158 \\n \\nelectronic equipment takes up the largest share (around 11%), followed by the automobile \\nsector (about 8%). \\n• \\nThere are sub-indices of the PPI for consumer goods and producer goods. Consumer goods \\ncan be further broken down into food, clothing, daily articles, and durables. Producer goods \\ncan be further broken down into mining, raw materials, and manufacturing goods. Producer \\ngoods account for around 75% of China’s PPI basket. The other breakdown available is by \\nindustry, i.e., metallurgical, power, coal, petroleum, chemicals, machinery, building materials, \\ntimber, food, textiles, sewing, leather, paper, cultural/education articles, and others. \\nOther Issues \\n• \\nThere has been extensive commentary by media and analysts on the relationship between \\nthe PPI and the CPI. It is commonly assumed that the gap between the two represents the \\nproducer margin. While broadly true at a very high level, the linkage is more complicated \\nwhen it comes to specific industries and companies. For instance, the transmission from PPI \\nto CPI seems to vary for different components of the CPI. PPI inflation (particularly consumer \\ngoods) seems to matter for the CPI’s goods component excluding food, but not for services. \\n• \\nThere is another price indicator named “input price for industrial enterprises” (also known as \\nthe “raw materials purchasing price index”), which measures the change in raw material \\ninput costs. The difference between this indicator and PPI can be informative for the margins \\nof industrial enterprises, although other factors also affect margins. \\nExhibit 67: PPI inflation exhibits much larger swings than CPI inflation \\nChina inflation measures \\n \\n \\nSource: NBS, CEIC, Goldman Sachs Global Investment Research \\nAgriculture and Raw Material Prices \\n\\n\\n 124 / 158 \\n \\nSource: Ministry of Agriculture and Rural Affairs, National Bureau of Statistics, National Development \\nand Reform Commission \\nAvailability: Wholesale prices of agriculture products: daily from 18 November 2013; Retail prices of \\nagriculture products: daily from 25 June 2015; Raw material prices: every 10 days from end-2013 \\nOverview \\n• \\nWholesale prices of agricultural products cover major food items, including pork, vegetables, \\neggs, fruits, etc. Retail prices of agricultural products measure the average retail prices of \\nmajor food items in 36 cities. Both are reliable and informative for high-frequency food CPI \\ntracking. \\n• \\nRaw material prices measure wholesale and retail prices, which include ex-factory prices, \\ntransportation fees, profits, and taxes. Although these prices could deviate from ex-factory \\nprices, they are still informative for PPI tracking. \\nCompilation and Reporting \\n• \\nWholesale prices are compiled by the Ministry of Agriculture and Rural Affairs based on a \\nsample of 200+ markets. The prices of fresh vegetables and fruits are weighted average \\nindices (Passche index) based on 28 major vegetables and 9 major fruits. The NDRC collects \\nretail prices data from supermarkets and farmers’ markets on a daily basis. \\n• \\nThe NBS collects raw material prices from around 2,000 distributors located across the \\ncountry, covering 9 major categories, i.e., ferrous metals, non-ferrous metals, chemicals, \\npetrol/gas, coal, non-metal minerals, agricultural products, fertilizer/pesticide, and forest \\nproducts. The data are published on the 4th, 14th, and 24th of each month. \\nMerchandise Trade Price Index \\nSignal to noise ratio: *** \\nMacro importance: *** \\nSource: China Customs \\nAvailability: Monthly from 1993 (previous year = 100) \\nTiming: Around 25th day of the following month \\nOverview \\n• \\nThe Merchandise Trade Price Index measures changes in goods export/import prices, \\ncompiled by China Customs. The price indices are based on unit value indices from detailed \\ngoods trade data. \\nSignal to Noise Ratio \\n• \\nWe believe the Merchandise Trade Price Index is generally reliable, as it is based on a \\nbottom-up aggregation of detailed goods trade data. However, as the price index is not \\nbased on a fixed basket of products, the index may change notably due to changes in the \\n\\n\\n 125 / 158 \\n \\nproduct mix of goods trade. \\nMacro Importance \\n• \\nGiven exports remain an important growth engine for China, the Merchandise Trade Price \\nIndex is helpful to gauge trade growth in real terms and the price competitiveness of \\nChinese exports in the global market. \\nCompilation and Reporting \\n• \\nExport prices are calculated on an FOB (free on board) basis, which includes costs of \\ndelivering goods onto the vessels but not further costs, such as insurance or freight. Import \\nprices are on a CIF (cost, insurance and freight) basis. \\n• \\nPrice indices before 2014 were reported in USD terms, but they were subsequently changed \\nto CNY terms. It is important to convert price indices into the same currency before applying \\nthem as deflators. \\n• \\nFour kinds of breakdown are available based on various sector classifications: the \\nHarmonized System (HS), the Standard International Trade Classification (SITC), the Broad \\nEconomic Categories (BEC), and China’s industrial classification for national economic \\nactivities. \\nGDP Deflator \\nSignal to noise ratio: *** \\nMacro importance: *** \\nSource: National Bureau of Statistics \\nAvailability: Quarterly from 2000, annual from 1978 (previous year = 100) \\nOverview \\nThe GDP deflator covers the prices of all final goods and services in consumption expenditure, \\ninvestment, and trade. \\nSignal to Noise Ratio \\nThe GDP deflator is derived indirectly from official nominal and real GDP data. An ex-official of the \\nNBS has briefly discussed the methodology of GDP accounting and related price/volume \\nindices.\\n[25] However, it is difficult to replicate the calculation as many indices are not publicly available. \\nOur estimates suggest that a weighted average with 60% PPI inflation and 40% CPI inflation could \\nserve as a workable proxy of the GDP deflator in recent years. Academic literature suggests that the \\nGDP deflator is generally overestimated during boom years but underestimated during downturn \\nyears to smooth the series of real GDP growth.\\n[26] \\nMacro Importance \\nThe GDP deflator reflects inflationary pressures in the broad economy, whereas PPI only covers \\nsecondary industry. With the development of the service sector, the weight of PPI in the GDP \\n\\n\\n 126 / 158 \\n \\ndeflator has been falling over the past decade. \\nRelated GS Economics Publications \\n• \\n“Rebound in PPI poses limited risk to China's CPI inflation”, Asia Economics Analyst, 15 \\nJanuary, 2017 \\n• \\n“China: CPI inflation likely higher, but not a big constraint for monetary policy”, Asia in Focus, \\n20 May 2019 \\n• \\n“China: Gauging the impact of imported inflation and supply chain disruptions on CPI and \\nPPI inflation”, Asia in Focus, 8 April 2022 \\n• \\n“China Post-Reopening Inflation Outlook: A Bottom-Up Approach”, Asia Economics Analyst, \\n3 February, 2023 \\nSection IX. Population and Labor Market \\nThis section features a detailed breakdown of total population and related indicators. We also take \\nstock of China’s labor market, including employment, unemployment, wage indicators and our \\nproprietary wage tracker. \\nTotal Population, Urban Population, Working Age Population, Migrant Population \\nSource: National Bureau of Statistics \\nAvailability: Annual from 1949 \\nTiming: January of the following year \\nPublication: Annual Statistical Communiqué on National Economic and Social Development \\nOverview \\nTotal population refers to the total number of inhabitants of a particular area at a certain point of \\ntime. It can be broken down by urban vs. rural, female vs. male, and age structure. Total population \\nis also available by province/city. \\nUnder the population data set, urban population is based on the total number of usual residents \\nwho have lived in an urban area for more than six months within a year, and “rural population” is the \\nremainder after subtracting urban population from total population. There is also a set of data on \\nregistered population, split into agricultural and non-agricultural, available at the total national level \\nand provincial level. \\nWorking-age population: There is no explicitly defined series from the NBS on this, but under \\npopulation by age group, one sub-group captures the population aged 15-64, which follows the \\nusual international definition of working-age population. China’s working-age population peaked in \\n2013 and population aging is expected to accelerate in the coming decades. We note that while \\nChina’s official retirement age is 60 for men, 55 for female civil servants and 50 for female workers, a \\nsignificant portion of elderly may find part-time or other employment. \\nMigrant population: This refers to people who reside in a particular area but do not have \\n\\n\\n 127 / 158 \\n \\nhousehold registration (Hukou) in that area. It excludes the group of people who have household \\nregistration in another district of the same county in which they reside. China had nearly 300 million \\nmigrant workers in 2023 and around half of them work in manufacturing and construction sectors. \\nThis set of data is based on surveys conducted by the NBS annually. During years ending with “0,” \\nsuch as 2020, the NBS conducts a comprehensive population census. During years ending with “5,” \\nsuch as 2015, a survey based on 1% of the total population is conducted. In other years, a survey \\nbased on 0.1% of the total population is conducted. Population censuses and 1% population surveys \\nare used as benchmarks to adjust annual surveys. \\nExhibit 68: China’s population structure in 2000 and 2020 \\nChina’s population by age group \\n \\n \\nSource: NBS, United Nations World Population Prospects \\nBirth Rate, Death Rate, Natural Growth Rate \\nSource: National Bureau of Statistics \\nAvailability: Annual from 1949 for birth rate and death rate, annual from 1981 for life expectancy \\nTiming: January of the following year \\nPublication: Annual Statistical Communiqué on national economic and social development \\nOverview \\n\\n\\n 128 / 158 \\n \\nBirth rate: Also called “crude birth rate”. It refers to the ratio of total number of new births to \\naverage population in a particular area within a particular time range. \\nDeath rate: Also called “crude death rate”. It refers to the ratio of total number of deaths to average \\npopulation in a particular area within a particular time range. \\nNatural growth rate: The crude birth rate minus the crude death rate. \\nThese rates are typically expressed in permillage (per-thousand). There are also other relevant \\npopulation data in this release such as the dependency ratio. NBS data show that China’s population \\ndeclined for two consecutive years – 2022 and 2023 – for the first time in six decades, with a record \\nlow birth rate of 0.639% in 2023. While China’s birth rate has trended lower over the longer term, it is \\npossible that some of the recent decline could relate to temporary effects of the Covid pandemic. \\nSignal to Noise Ratio \\nDue to sampling difficulty, population-related data in China are in general exposed to uncertainties \\nsuch as relatively big revisions (based on population census) and sampling errors. \\nMacro Importance \\nPopulation data are very important to economic analysis given their close link to labor supply, which \\nis a key component of potential growth. \\n• \\nWorking-age population is related to the potential labor supply to the whole economy, and \\na shrinking working-age population (as in China in the coming years) will be a headwind to \\npotential economic growth. The NBS also releases another set of population data called \\neconomically active population, which refers to the population aged 16 or over who are able \\nand willing to participate in the labor market, including both employed and unemployed \\npeople in both urban and rural areas. Its historical readings are revised based on new \\ninformation in population surveys. It differs from the commonly used working-age \\npopulation because it does not have an upper limit on age, while the working-age \\npopulation usually excludes the population aged above 64. \\n• \\nThe size of migrant population can serve as a barometer of cyclical employment conditions \\ngiven they are in general more sensitive to changes in urban labor market conditions. \\nMigration to urban areas is more likely when urban labor market conditions are strong. New \\nmigration should slow and existing migrants may return to agricultural work in rural areas \\nwhen urban employment conditions are weak. \\nExhibit 69: China’s population and especially labor force growth slowed to record low levels \\n \\n\\n\\n 129 / 158 \\n \\n \\nSource: NBS, UN, Goldman Sachs Global Investment Research \\nEmployment Data \\n1. New Urban Jobs \\nSignal to noise ratio: * \\nMacro importance: *** \\nSource: Ministry of Human Resources and Social Security \\nAvailability: Monthly from December 2009 \\nOverview \\nThis indicator measures the number of new jobs created in urban areas and is published monthly. \\nSignal to Noise Ratio \\nThe new increases in urban jobs indicator reflects the gross increase (rather than net new jobs) in \\nurban employment minus natural reduction in urban employment (which includes retirement and \\nquits due to severe illness and death) and thus include those who were unemployed and re-\\nemployed during a reporting period. For example, the new increases in urban jobs in 2022 were \\n12.1mn while the net change in total urban employment was -8.4mn. The potential double-counting \\nmakes this indicator less informative in gauging the underlying employment trend. \\nMacro Importance \\nThis indicator is one of the government’s employment targets (the other being surveyed urban \\nunemployment rate). Therefore, it is important to track the progress of this indicator to gauge \\nwhether policymakers are likely to achieve their employment target; if not, policy support is likely to \\nbe forthcoming. \\n\\n\\n 130 / 158 \\n \\nCompilation and Reporting \\nEnterprises will report new increases in urban jobs to the Ministry of Human Resources and Social \\nSecurity. Flexible employment is included in urban new jobs as well. Data coverage and reporting \\nmethod appear sound, but as discussed above, the key issue with this indicator is its definition which \\ndoes not capture layoffs and job changes — and therefore could involve significant double counting. \\n2. Total Employment \\nSource: National Bureau of Statistics \\nAvailability: Headline and by sector series are available at an annual frequency from 1952. Detailed \\nbreakdown has a shorter history. \\nPublication: China Statistical Yearbook, China Population and Employment Statistical Yearbook \\nOverview \\n• \\nEmployment is defined as people who have worked for more than 1 hour during the \\nsurveyed week, as well as people who have positions but are on vacation/temporary leave. \\nThis series measures the overall employment of laborers 16 years of age or older in the \\neconomy based on sample surveys of the Chinese population. The NBS publishes the \\npopulation and employment statistical yearbook since 1988. This yearbook provides data on \\nemployment/unemployment in urban areas with details about gender, age, industry, and \\neducation background. \\n• \\nThere were 769mn people in China’s labor force in 2022 (including both employed and \\nunemployed persons), accounting for 54.5% of China’s total population. Within the labor \\nforce, 60% is urban employment, 35% is rural employment, and the unemployment rate is \\naround 5%. In the urban area, private enterprise (私营单位) workers constitute approximately \\ntwo-thirds of total employment whereas non-private enterprise workers constitute the \\nremaining one-third. \\n• \\nFlexible employment has seen strong expansion in China amid the development of the \\ndigital and platform economy. Flexible workers are not bound by formal employment \\ncontracts and enjoy fewer non-wage benefits compared to other workers. For example, \\nmany flexible workers are not included in public insurance schemes such as work-related \\ninjury insurance and unemployment insurance, and do not have paid maternity/paternity \\nleaves. By the end of 2021, the number of flexible workers had reached 200 million in China, \\nmainly in manufacturing, construction, delivery, platform livestreaming and ride-sharing \\ncompanies. \\nExhibit 70: Labor force participants accounted for 54.5% of China’s total population in 2022 \\n \\n\\n\\n 131 / 158 \\n \\n \\nNote: The non-private enterprises include state-owned and controlled enterprises, foreign-funded \\nenterprises and other enterprises. \\nSource: NBS, Goldman Sachs Global Investment Research \\nExhibit 71: Tertiary industry employment has become more important in recent years \\nEmployment by industry \\n \\n \\nSource: CEIC, Goldman Sachs Global Investment Research \\n\\n\\n 132 / 158 \\n \\nSignal to Noise Ratio \\nAlthough the total employment data provide useful information, growing flexibility in the labor \\nmarket—such as self-employed online store owners, retired employees who are re-hired on a part \\ntime basis, as well as greater geographic mobility of labor—are making it difficult to measure the \\ntrue picture. Historical readings are revised as new population surveys become available. \\nMacro Importance \\nThe total employment data series is useful, as we see it as the only official/most reliable measure \\navailable for overall employment in the economy. \\n3. Employed Persons in Urban Non-private Units \\nSource: Ministry of Human Resources and Social Security \\nAvailability: Annual from 1952. Detailed breakdowns have shorter history. \\nTiming: Annual data are released with a 10-month lag \\nOverview \\n• \\nThis data series covers the number of persons employed in government agencies and non-\\nprivate enterprises. It does not include private enterprises or self-employed individuals. \\n• \\nDetails behind “employed persons in urban non-private units” include industry breakdown \\nsuch as primary industry, manufacturing, construction, financial industry, property, wholesale \\nand retail industry etc. \\n• \\nThere is another set of data based on a subset of “employed persons in urban non-private \\nunits”, named “on the spot” staff (“在岗职工”). This “on the spot” dataset refers to persons \\nwho hold certain positions in the enterprise, and who still receive payments even though \\nthey are temporarily absent for reasons such as vacation, study or sickness. Compared with \\ntotal employed persons in urban non-private units, this dataset excludes the retired but re-\\nemployed population, soldiers, and religious workers. There are also industry breakdowns \\navailable for “on the spot” staff, but the series is released with a significant lag (e.g., 2020 is \\nthe latest data available). \\nSignal to Noise Ratio \\nPotential inaccuracies arise from intentional under-reporting by employers who pay social security \\ncontributions on a per-head basis. Frequent re-organization of SOEs and other previously state-\\nowned organizations has also complicated data collection. Because of local government pressures \\nnot to lay off employees, some companies nominally still employ workers but do not require them to \\ngo to work and only pay the minimum wage, potentially overstating employment during economic \\ndownturns. \\nMacro Importance \\nThe usefulness of this series is limited because it only covers employment in non-private enterprises \\nwhich can be distorted by major SOE reforms. For example, the level of this series fell in the late \\n1990s and early 2000s from nearly 150mn to less than 110mn before rising to the peak of over \\n\\n\\n 133 / 158 \\n \\n180mn in 2015. It has been declining gradually since 2015 and fell to 167mn in 2022. \\nCompilation and Reporting \\nLabor bureaus in different regions collect raw data, which are then reported to the Ministry of \\nHuman Resources and Social Security (MOHRSS) for compilation. \\n4. Employed Persons in Private Enterprises and Self-employed Individuals \\nSource: Ministry of Human Resources and Social Security \\nAvailability: Headline series available in annual frequency from 1990; detailed breakdowns have a \\nshorter history \\nTiming: Previously available one and a half years after the reporting period, but the latest data \\navailable is for 2019 \\nOverview \\n• \\nThis indicator covers people who work in private enterprises/individual business, which have \\nbeen registered at the local departments of industrial and commercial administration, \\nincluding self-employed persons as well as helpers and hired laborers who work in individual \\nhouseholds. There is industry breakdown information available, such as “employed persons \\nin private enterprises and self-employed individuals” in manufacturing, construction, \\nfinancial industry, wholesale and retail etc. \\n• \\nA subset of the “employed persons in private enterprises and self-employed individuals” \\ndataset is “urban employed persons in private enterprises and self-employed individuals”. It \\ncovers only the portion of the employed persons in urban areas. Similar to all employed \\npersons in private enterprises and self-employed individuals, this “urban employed persons \\nin private enterprises and self-employed individuals” also has industry details available. In \\n2019, China had 405mn employed persons in private enterprises and self-employed \\nindividuals, of which 263mn were in urban areas. \\n5. Employment in Industrial Enterprises \\nSource: National Bureau of Statistics \\nAvailability: Monthly from December 1998 \\nTiming: Around 27 days after the end of each month \\nOverview \\nThis indicator is available monthly, and has a breakdown of employment for sub-industries including \\nmining, various manufacturing sectors, and utilities. Note the construction sector does not belong to \\nthe “industrial sector” and instead is one of the two components of the secondary industry, together \\nwith the “industrial sector” (manufacturing, mining, and utilities). The by-industry employment is \\nbased on the same survey that the NBS conducts with industrial enterprises, and thus captures only \\nthe above-designated-size enterprises. Similar to the industrial profits data, continued monthly \\nreports of this data are only available from 2011. \\n\\n\\n 134 / 158 \\n \\n6. Employment Sub-indices Under Business Surveys \\nSource: NBS for the employment sub-indices under the NBS manufacturing/nonmanufacturing PMI \\nsurveys; Caixin for the employment sub-indices under the Caixin manufacturing/services PMI surveys; \\nCheung Kong Graduate School of Business (CKGSB) for the employment sub-index under the \\nCKGSB Business Conditions Index survey. \\nAvailability: Monthly frequency. NBS manufacturing PMI employment sub-index from January 2005; \\nNBS nonmanufacturing PMI employment sub-index from January 2007; the Caixin manufacturing \\nPMI employment sub-index from April 2004; the Caixin services PMI employment sub-index from \\nNovember 2005. CKGSB BCI employment sub-index available from September 2011. \\nTiming: NBS data available on the last day of the reporting month; Caixin data available within the \\nfirst week after the reporting month ends; CKGSB index available around one week before the \\nreporting month ends. \\nOverview \\nThe sub-indices on employment under various PMIs and business surveys are in nature diffusion \\nindices, based on questions like “whether the total employment in your company increased or \\ndecreased over the reporting month”. This set of data is not a direct report on the total number of \\nemployment, but provides a sense of the trend (particularly the breadth) of employment changes. \\nUnemployment Data \\n1. Urban Registered Unemployment Rate \\nSource: Ministry of Human Resources and Social Security \\nAvailability: Quarterly during 1999Q4-2021Q4, annual during 1949-2021 \\nOverview \\n• \\nThis series reports the share of urban registered unemployed persons in the urban labor \\nforce. Urban registered unemployment measures the number of urban residents who are \\ncapable of working, but are out of work, want to work, and register themselves as such. \\n• \\nThe urban registered unemployment rate series has not been updated since December 2021, \\nalthough the number of urban registered unemployed individuals is still being updated \\nannually. As of 2023, there were 10.74 million urban registered unemployed individuals. \\nSignal to Noise Ratio and Macro Importance \\n• \\nThe usefulness of the “Urban Registered Unemployment Rate” is very limited because: (1) it \\ncovers only people who have an urban registration (Hukou) and excludes a large number of \\nmigrant workers who normally live in urban areas but do not have urban registrations, and \\n(2) many unemployed people may not have registered with government agencies. As a \\nresult, the official urban unemployment rate has been very stable at around 4%. As more \\npeople seek jobs only through online platforms rather than through registration with local \\njob market centers, this registered unemployment rate can be more biased. Having said that, \\nthe direction of changes may still be indicative, as small deviations from the stable trend \\n\\n\\n 135 / 158 \\n \\nlevel are typically counter-cyclical and lag activity growth as one would expect. \\nOther Issues \\n• \\nOne useful source of information on the status of the labor market is the quarterly Labor \\nSupply and Demand in Major Cities published by the MOHRSS (Ministry of Human \\nResources and Social Security). The report provides data on the number of jobs offered and \\nthe number of job seekers, as well as other information on the labor market. This report was \\ncollected together with the registered unemployment rate data through affiliated regional \\njob centers. Although the indicator does not cover the whole labor market, as many \\nemployment activities do not take place at these employment centers, it nevertheless covers \\na significant portion of it, and can provide some useful color on changes in the labor market. \\n2. Urban Surveyed Unemployment Rate \\nSignal to noise ratio: ** \\nMacro importance: *** \\nSource: National Bureau of Statistics \\nAvailability: 31 major city irregular reports from June 2013, regular reports from January 2017; \\nnationwide series regular reports from January 2017 \\nTiming: Typically around the 2nd/3rd week of the following month, released together with monthly \\nactivity indicators such as industrial production \\nOverview \\n• \\nThe surveyed unemployment rate is the ratio of urban surveyed unemployment to the sum \\nof surveyed unemployment and employment. One would be defined as “unemployed” if \\nhe/she is 16 years or older, does not have a job currently, seeks for job actively within the \\nthree months prior to the survey, and can start working within two weeks if provided a job. \\n• \\nPrior to the Covid pandemic, large cities tended to have stronger labor markets and lower \\nurban unemployment rates. However, Covid controls took a heavier toll on large cities where \\npopulation is denser and mobility is higher, leading to sharper increases in the \\nunemployment rate in large cities relative to smaller cities especially in 2022. By late 2023, \\nthe pattern had finally reversed, and urban unemployment rates in large cities dipped below \\nthe national average. \\n• \\nThe NBS used to release urban surveyed unemployment rates by two broad age groups: 16-\\n24 year-old, and 25-59 year-old. These by age decomposition data were suspended after \\nJune 2023. The NBS resumed the release of unemployment rate data for the 16-24 age \\ngroup in December 2023, but the new series excludes students from the survey and resulted \\nin a much lower youth unemployment rate (e.g., 14.9% in December 2023 under the new \\ndefinition vs. 21.3% in June 2023 under the old definition). According to the NBS, over 60% of \\nthe 16-24 year-olds in China are students. Under the new definition, the NBS releases \\nunemployment rates for 16-24, 25-29 and 30-59 year-olds separately, around three days \\n\\n\\n 136 / 158 \\n \\nlater than general labor market statistics. \\n• \\nSince January 2021, the NBS releases survey-based urban unemployment rates for people \\nwith local Hukou and those without (i.e., migrant workers) separately each month. Although \\nthe history of these series is relatively short, they show migrant workers’ unemployment rate \\nis more cyclical. The unemployment rate of people with local Hukou increased from 5.1% in \\n2021 to 5.4% in 2022 before falling back to 5.2% in 2023 after the end of China’s zero-Covid \\npolicy. In contrast, the unemployment rate of migrant workers jumped from 4.9% in 2021 to \\n5.7% in 2022 before dropping to 4.7% in 2023. \\nSignal to Noise Ratio \\n• \\nUrban surveyed unemployment rate data shed light on the labor market conditions. When \\nactivity growth is weak, surveyed unemployment rates tend to increase. Surveyed \\nunemployment rates show bigger swings than the registered unemployment rate and this \\nset of data is therefore the best official data gauging unemployment pressures. Having said \\nthat, the NBS releases surveyed unemployment rates without seasonal adjustments. Due to \\nthe short history of the survey (regular publication started in 2017) and Covid distortions \\nover the past few years, seasonal adjustments can be challenging. \\n• \\nOne main drawback of the urban surveyed unemployment rate is the lack of coverage of the \\nmigrant worker population if they move back to rural areas. By design, the urban \\nunemployment rate is based on urban household surveys and thus only captures the \\nunemployed population in the urban area. When the economy is weak and unemployment \\npressure increases, migrant workers might return to rural areas. The urban surveyed \\nunemployment rate therefore tends to underestimate the unemployment pressure during \\neconomic downturns. \\nMacro Importance \\nThe urban surveyed unemployment rate data series is quite important as it is one of the two \\nemployment targets policymakers set each year in their government work reports. Policymakers \\nhave reiterated multiple times in recent years that stability of employment is their priority and \\nbottom line. \\nCompilation and Reporting \\nThis survey started some time ago (the first data that we have seen mentioned by officials was for \\nJune 2013), but results were not regularly released to the public until 2017. Surveyed unemployment \\nrates are based on surveys of 340,000 households. It differs from the registered unemployment rate \\nin the following ways: \\n• \\nCompilation method: The registered unemployment rate is derived from official registration, \\nwhile surveyed unemployment is based on labor surveys; \\n• \\nDefinition of unemployment: Surveyed unemployment follows ILO standards, while the \\nregistered unemployment rate is based on administrative records; \\n• \\nData coverage: Registered unemployment is based on population with local Hukou, while \\nsurveyed unemployment also covers migrants in theory, though the number of migrant \\n\\n\\n 137 / 158 \\n \\nworkers in urban areas shifts with economic cycles as discussed above. \\nExhibit 72: Surveyed unemployment rates spiked in the initial stage of the Covid outbreak and \\nduring lockdowns \\nUrban surveyed unemployment rate (after seasonal adjustment) \\n \\n \\nSource: CEIC, Goldman Sachs Global Investment Research \\nExhibit 73: Surveyed youth unemployment rate dropped after the NBS changed the definition \\nto exclude students \\nSurveyed unemployment rates (CNY-distortion adjusted) \\n \\n \\n\\n\\n 138 / 158 \\n \\nSource: CEIC, Goldman Sachs Global Investment Research \\nWages \\n1. Per Capita Wage Income from Household Income Survey \\nSource: National Bureau of Statistics \\nFrequency: Quarterly, annual \\nTiming: Around 20 days after the end of each quarter, along with the release of GDP data \\nAvailability: Available in quarterly frequency from 2013 Q1, annual from 1998 \\nOverview \\nSince Q4 2012, the NBS selects 160,000 households from urban and rural areas for direct survey on \\ntheir income and expenditure (see Section V. Consumption for more details). The wage share of \\nhousehold disposable income has been relatively stable over the past decade, according to this \\nsurvey, and wages accounted for 56% of household disposable income in 2023. \\n2. Average/Total Wage of Employees in Urban Non-private Units \\nSource: National Bureau of Statistics \\nAvailability: Headline series is available at a quarterly frequency from 2000 to 2014, annual from \\n2015. Detailed breakdowns have a shorter history. \\nTiming: Quarterly data are released at around 25 days after the end of the quarter; annual data are \\nreleased around May of the following year. \\nOverview \\n• \\nAverage/total wages cover labor compensation to all employees, including wages, bonus, \\nsubsidies, etc. in urban non-private units. They are on a pre-tax basis and include rents, \\nutility bills, etc. paid by employers to employees. In practice, when employees receive \\nsalaries, they are after-tax and social contribution deductions. The average wage is total \\nwages divided by the average number of employees within the reporting period. \\n• \\nSimilar to the employment data, quarterly data were suspended after 2014 and only annual \\ndata are available since then. Note that foreign companies and large domestic companies \\nlisted overseas are included in “urban non-private” enterprises, raising the average wage of \\nurban non-private units. In 2023, the average wage of urban non-private enterprises was \\nRMB121,000. \\n• \\nAs this set of data only captures wage growth for non-private units, it may fail to capture the \\ngenuine situation of the labor market. Wages in non-private units are generally viewed as \\nmore sticky and less sensitive to economic growth/labor market developments. \\n3. Average/Total Wage of Employees in Urban Private Units \\nSource: National Bureau of Statistics \\nAvailability: Headline series is available in annual frequency from 2008. Detailed breakdowns have a \\n\\n\\n 139 / 158 \\n \\nshorter history. \\nTiming: Around May of the following year \\nOverview \\nAverage/total wages cover labor compensation to all employees, including wages, bonus, subsidies, \\netc. in urban private enterprises. They are on a pre-tax basis and include rents, utility bills, etc. paid \\nby employers to employees. In practice, when employees receive salaries, they are after-tax and \\nsocial contribution deductions. In 2023, the average wage of urban private enterprises was \\nRMB68,000\\n[27]. \\n4. Average Income of Migrant Workers \\nSignal to noise ratio: ** \\nMacro importance: ** \\nSource: National Bureau of Statistics \\nAvailability: Quarterly from Q4 2008 \\nTiming: One month after the end of the quarter \\nOverview \\nThis measure is based on quarterly surveys of migrant workers conducted by the NBS since 2008. \\nThe survey sample covers 85,000 households in rural areas of all 31 provinces. Income of migrant \\nworkers are inherently difficult to capture as by definition migrant workers move around and often \\nare employed in informal sectors. This set of data shows greater volatility than other wage indicators \\nbecause migrant workers are often the most vulnerable in the labor market (e.g., they sometimes do \\nnot have proper labor contracts and are therefore not protected by the Labor Law) and face more \\nsignificant income fluctuations with economic cycles. As a result, migrant worker income can be a \\nmore useful tracker on labor market turning points in China. \\n5. Compensation to Laborers \\nSource: National Bureau of Statistics \\nAvailability: Annually from 1992 \\nTiming: Lags can be as long as three years \\nPublication: China Statistical Yearbook \\nOverview \\nThis series is recorded under Flow of Funds data, and it covers all employed persons. \\nSignal to Noise Ratio and Macro Importance \\n• \\n“Compensation to Laborers” in input-output data does not suffer from the problem of \\nnarrow coverage like the few series mentioned above, as it covers all employed persons. \\n\\n\\n 140 / 158 \\n \\nFurthermore, it has the advantage of including non-monetary income, such as social security \\ncontributions. However, this measure has two limitations: (1) the data are reported with a \\nsignificant delay (the last reported data were for 2020), and (2) many forms of non-\\nmonetary compensations have to be estimated roughly, which affects the signal to noise \\nratio of the data. \\n• \\nThe issue of under-reporting of income data also likely affects wage data, especially in terms \\nof state sectors. In these sectors, official wages can be restricted by regulations, so there is a \\ntendency to compensate employees via other types of payments. Furthermore, some private \\ncompany owners can choose to forgo cash wages to help with the development of their \\nown companies. This may also bias wage data to the downside. \\n6. The CKGSB Business Conditions Survey of Labor Cost Expectations \\nSource: Cheung Kong Graduate School of Business \\nAvailability: Monthly from September 2011 \\nTiming: One week before the end of the reporting month \\nPublication: Cheung Kong Graduate School of Business (CKGSB) Business Conditions Index (BCI) \\nOverview \\nThe CKGSB BCI survey asks companies in mainland China (mostly private companies) about their \\nexpectations for future labor costs over the next six months. The responses are then converted into a \\ndiffusion index on labor cost expectations, similar to PMIs (e.g., >50 means a majority of surveyed \\ncompanies expect labor costs to increase in the next 6 months). This index appears to lead migrant \\nworker income changes historically based on our analysis, although the two appear to have diverged \\nrecently. \\nExhibit 74: Wage growth diverged among different indicators \\nWage related indicators \\n \\n\\n\\n 141 / 158 \\n \\n \\nSource: CEIC, Goldman Sachs Global Investment Research \\n7. Other Wage Indicators \\nOther indicators on wages include the minimum wage threshold, available by province, and \\ncorporate wage growth guidance, also available by province. These two indicators are released \\nannually and can be informative for overall wage growth trends. Typically, the minimum wage \\nthreshold is not a binding constraint, since the informal sectors usually pay below minimum wages \\nand legal enforcement is rare. In January 2004, China promulgated new minimum wage regulations \\nthat required local governments to raise minimum wages at least once every two years, and \\nextended coverage to self-employed and part-time workers. By the mid-2010s, Guangdong \\nprovince changed the minimum wage adjustment period to once every three years to slow the \\noutflow of manufacturing industries to lower-cost inland provinces, and other provinces soon \\nfollowed suit. At the end of 2015, the Ministry of Human Resources and Social Security (MHRSS) \\nannounced the minimum wage would be adjusted once every 2-3 years. \\nGS China Wage Tracker \\nSource: NBS, Goldman Sachs Economics Research \\nAvailability: Quarterly from Q1 2004 \\nTiming: Two weeks after the end of the reporting quarter \\nPublication: GS China Proprietary Indicators update \\nOverview \\n• \\nGS China wage tracker gauges nominal wage growth based on the average reading across \\ndifferent wage indicators. Currently, components of this wage tracker include NBS data on \\nurban disposable income – wage income, migrant worker income, income sentiment index \\nfrom the quarterly urban depositor survey conducted by the PBOC, and hiring wage from \\nthe online recruitment company Zhaopin.com. \\n\\n\\n 142 / 158 \\n \\n• \\nWe first transform all input series into year-over-year growth rates and then calculate the \\naverage growth rate of these indicators. As we incorporate survey-based information as well \\nas third-party data from the online recruitment company, our wage tracker is arguably a \\nmore comprehensive gauge of wage growth trend in China than official indicators such as \\nthe urban wage income reported by the NBS quarterly household income survey alone. Our \\nwage tracker shows that wage growth has been decelerating from around 10% during the \\npre-pandemic period to around 4% in recent years, more consistent with listed companies’ \\nreported wage growth than the official series. \\nExhibit 75: GS wage tracker suggests wage growth slowed to around 4% in recent years \\n \\n \\nNote: The urban wage data from the NBS household survey started at 2014Q1, so we use the urban \\ndisposable income growth as proxy for urban wage growth from 2003Q1 to 2013Q4. \\nSource: NBS, CEIC, PBOC, Zhaopin.com, Goldman Sachs Global Investment Research \\nRelated GS Economics Publications \\n• \\n“China: Labor market consequences of the virus outbreak”, Asia Economics Analyst, 6 March \\n2020. \\n• \\n“Tracking the labor market post the coronavirus”, China Data Insights, 14 June 2020. \\n• \\n“The implications of China's shrinking working-age population”, Asia Economics Analyst, 25 \\nMay 2021. \\n• \\n“Population Aging, Pension System, and Individual Retirement Savings in China”, Asia \\nEconomics Analyst, 10 February 2023. \\n• \\n“Why has youth unemployment risen so much in China?”, China Data Insights, 22 May 2023. \\n• \\n“Tracking wage growth in China”, China Data Insights, 6 June 2024. \\n\\n\\n 143 / 158 \\n \\nSection X. Government Finance \\nThis section reviews the following data and indicators on government finance: \\n1. Government revenue, expenditure and balance data by the Ministry of Finance (MOF), on both \\nmonthly and annual basis. The government budget reports released during the “Two Sessions” every \\nyear set clear targets for these indicators. \\n2. Central and local government debt by the MOF and China Central Depository & Clearing (CCDC), \\nincluding outstanding amount and net issuance. These are among the main financing sources for \\nthe government’s balance. \\n3. GS proprietary indicators related to government finance, including the augmented fiscal deficit \\n(AFD) and augmented government debt (AGD). \\nGovernment Revenue, Expenditure and Balance \\nSignal to noise ratio: **** \\nMacro importance: **** \\nSource: Ministry of Finance \\nAvailability: Monthly from 1995, annual from 1950 \\nTiming: Around 3 weeks after the end of the month \\nPublication: Ministry of Finance monthly releases, China Fiscal Statistical Yearbook \\nOverview \\n• \\nIn China, the national fiscal budget is composed of four accounts: the General Public Budget \\nAccount, the Government Managed Fund Account (GMF), the State Capital Operation \\nAccount, and the Social Insurance Fund Account. Fiscal data for the former two accounts are \\nreleased monthly by the Ministry of Finance (MOF),\\n[28] while those for the remaining two \\naccounts are released on an annual basis. \\nExhibit 76: Fiscal balance only involves a part of China’s fiscal budget system \\n\\n\\n 144 / 158 \\n \\n \\nSource: Goldman Sachs Global Investment Research \\n• \\nThe fiscal balance that the government targets each year is the difference between revenue \\nand expenditure in the General Public Budget Account. \\n1. On-budget fiscal revenue comes from various taxes and charges the government imposes, \\ncategorized as tax and non-tax revenue, in the MOF budget. Government borrowings (via bond \\nissuance) were included in the revenue statistics before 1994 but have been excluded since then. \\n2. On-budget fiscal expenditure includes funds the government spends on goods and services that \\nit provides, as well as on interest payments. These funds come from on-budget fiscal revenue and \\nthe proceeds raised through central government general bonds and local government general \\nbonds.\\n[29] Conceptually, the fiscal balance is the difference between on-budget fiscal revenue and \\nexpenditure, although in China’s case there are some intricacies related to the fiscal deficit, especially \\nthe fiscal stabilization fund at the central government level and carryover/surplus funds at the local \\ngovernment level. \\n• \\nTop policymakers set the target for China’s official fiscal deficit at the “Two Sessions” every \\nyear, and this is directly linked to the quota for government bond net issuance under the \\nGeneral Public Budget Account. In other words, the official fiscal deficit (target) in value \\nterms refers to the combined quota for the net issuance of central government general \\nbonds (CGGB) and local government general bonds (LGGB). Unlike the official fiscal deficit, \\nthe effective fiscal deficit refers to the gap between on-budget fiscal revenue and \\nexpenditure, which equals the official fiscal deficit plus net drawdown of fiscal deposits and \\ntransfer from other fiscal accounts (i.e., the adjustment by central fiscal stabilization fund and \\ncarryover/surplus funds). \\n• \\nThe Government Managed Fund (GMF) Account is comprised of around 20 sub-funds or \\nsub-accounts (e.g., the railway construction fund, the national major water conservancy \\nproject construction fund), each of which is designed for a specific area. Land sales proceeds \\naccount for 82% of national GMF revenue in 2023 (vs. 86% in 2019). Moreover, land sales-\\nrelated spending, of which more than 70% is for land acquisition and re-development, \\naccounts for around 55% of national GMF expenditure, based on 2023 data (vs. 90% in 2019). \\nLand sales revenue and expenditure are collected and spent by local governments, subject \\n\\n\\n 145 / 158 \\n \\nto approval by the central government. \\n• \\nIn addition to the General Public Budget Account and GMF Account, there are two other \\naccounts within the National Fiscal Budget – the State Capital Operation Account, and the \\nSocial Insurance Fund Account, although the operation of the latter two is less connected \\nwith the former two, and less relevant to the government’s fiscal stance. \\n• \\nRanking these four Budget Accounts by revenue size based on 2023 data, the General Public \\nBudget Account remains the largest (RMB21.7 trillion; before the adjustment by central fiscal \\nstabilization fund and carryover/surplus funds), followed by the Social Insurance Fund \\nAccount (RMB11.1 trillion), the GMF Account (RMB7.1 trillion), and the State Capital \\nOperation Account (RMB0.7 trillion). By expenditure size based on 2023 data, the General \\nPublic Budget Account (RMB27.5 trillion) is larger than the GMF Account (RMB10.1 trillion), \\nfollowed by the Social Insurance Fund Account (RMB9.9 trillion) and the State Capital \\nOperation Account (RMB0.3 trillion). \\nSignal to Noise Ratio \\n• \\nFiscal data are usually considered to be among the most reliable data since mis-reporting \\ncan be financially and politically costly. However, this may not always be the case. For \\nexample, the government disclosed cases of over-reporting of fiscal revenue by Liaoning \\nProvince in 2011-2014, and by Inner Mongolia in 2016. The MOF listed four methods used: \\n(1) fake tax collection from corporates that is refunded back; (2) over-reporting of tax \\nrevenue from state asset sales or the right to use state assets, offset by over-reporting of \\ngovernment expenditure, often related to these transactions; (3) over-reporting of non-tax \\nrevenue such as revenue from the rights to use state resources; and (4) outright number \\ncooking.\\n[30] In recent years, the government has strengthened its scrutiny over central and \\nlocal statistics agencies to protect against data fraud and falsification. \\n• \\nAn important definition-related issue is that on-budget fiscal balance does not cover the \\nGMF, most of which is based on land sales-related revenue and expenditure, and is \\nimportant to local governments. \\n• \\nAs China’s economic growth trended down over the past decade on multiple headwinds, the \\ngovernment turned to more proactive fiscal policy, through both on-budget fiscal policy \\nand quasi-fiscal spending in areas such as infrastructure. This increased the importance of \\ntracking off-budget spending to assess the fiscal deficit and fiscal impulse. We have tried to \\n“augment” the official fiscal policy measures by incorporating off-budget quasi-fiscal policy \\nto obtain a comprehensive picture of the stance of China’s fiscal authority, as described in \\nthe “GS China Augmented Fiscal Deficit (AFD)” sub-section. \\n• \\nFiscal revenue growth is also affected by the degree of effort in collecting taxes, which is \\noften counter-cyclical (e.g., local governments may strengthen tax collection efforts amid \\neconomic downturns), and includes scheduled tax cuts, rebates and deferrals, as well as \\nprofit transfer from the operation of state-owned capital. \\n• \\nThe government has some flexibility in controlling the pace of scheduled fiscal measures \\nwithin a year, especially on government bond issuance and fiscal spending, resulting in \\n\\n\\n 146 / 158 \\n \\n“frontloaded” and “backloaded” fiscal patterns. \\nMacro Importance \\nGovernment revenue and expenditure data are fundamental to understanding the stance of fiscal \\npolicy. In addition, growth rates in tax revenue also provide useful information on the strength of the \\neconomy. \\nCompilation and Reporting \\n• \\nReported fiscal revenues and expenditures cover both central and local governments. \\n• \\nBesides government agencies, a portion of state-controlled non-governmental institutions \\n(事业单位) are also included. These institutions are set up, owned, and fully or partly funded \\nby the government. However, as economic reforms progressed, many of these institutions \\nchanged in nature and became self-funded and profit-making. \\n• \\nChina’s fiscal year is the same as the calendar year. Revenue and expenditure data are \\ncurrently released by the MOF monthly. Complete annual data including additional \\nadjustments, such as those related to the fiscal stabilization fund, only become available \\nwhen the Ministry of Finance reports to the National People’s Congress in March of the \\nfollowing year and are then released in the Statistical Yearbook and Fiscal Statistical \\nYearbook. \\n• \\nState-owned enterprises (SOEs) were an integral part of the public sector and were \\npreviously included in government finance statistics. With progress in economic reforms, \\nSOEs have increasingly become responsible for their own financing and are now mostly \\nexcluded from government financial accounts (e.g., the operation of state sole proprietors \\n(国有独资企业/公司) is under the management of the State Capital Operation Account). \\n• \\nMajor tax reforms in recent decades also had a meaningful impact on on-budget fiscal \\nrevenue, including several rounds of VAT reforms that have replaced the former business tax \\nwith VAT, and have lowered the effective VAT rate for the real economy. Individual income \\ntax (IIT) reforms have relieved tax burdens on households, by raising the threshold for IIT \\nexemption, and introducing IIT reduction items. \\nOther Issues \\n• \\nGovernment expenditure is closely related to government consumption expenditure in GDP \\naccounting. However, not all government expenditures are government consumption. Most \\nnotably, expenditures on capital construction, mine exploration, and new product R&D costs \\nare counted as gross fixed capital formation (GFCF), as part of investment. \\n• \\nGross local government revenue through General Public Budget and GMF accounts include \\nthree major sources, i.e., the on-budget fiscal revenue, GMF revenue (mostly land sales \\nrevenue), and transfer from the central government. Based on our estimates, the share of \\nland sales revenue in gross local government revenue fell sharply in 2022-23 amid the \\nprolonged property downturn and may trend lower in coming years. \\nExhibit 77: Gross local government revenue by major source \\n\\n\\n 147 / 158 \\n \\nGross local govt revenue breakdown vs. share of land sales revenue \\n \\n \\nSource: MOF, Wind, Goldman Sachs Global Investment Research \\nLocal Government Debt \\nSource: Ministry of Finance (MOF), National Audit Office, China Central Depository & Clearing \\n(CCDC), Wind \\nAvailability: Monthly from November 2017, Annual from 2014 (also available for 2010, 2012 and first \\nhalf of 2013 from audit reports) for local government debt data series; CCDC and Wind also provide \\nhigh-frequency data for local government bond issuance and maturity \\nTiming: Released monthly by MOF around 1 month after the end of month \\nOverview \\n• \\nIn China, local governments had been prohibited to borrow directly according to the budget \\nlaw until 2009, when pilot programs were initiated to allow some local governments to issue \\nbonds. At the beginning, the central government continued to help with issuance and \\nrepayment on behalf of local governments, and the quota of issuance was limited. In late \\n2014, the budget law was revised to allow all local government to issue government bonds, \\nsubject to quota restrictions. \\n• \\nGiven their limited on-budget revenue space and prohibition by law to borrow from banks, \\nlocal governments turned to off-balance sheet entities (e.g., local government financing \\nvehicles or LGFVs) as a financing and spending platform, but they had repayment \\nobligations for a large part of those off-budget borrowings, which was technically not in line \\nwith the budget law. \\n• \\nIn late 2014, the central government started to implement local government debt reform, \\nand a series of documents were issued, including Document #43, which required local \\n\\n\\n 148 / 158 \\n \\ngovernments to classify proper local government debt from existing LGFV debt and also \\nreinforced the policy of no new borrowing through LGFVs. From 2015, local governments, in \\ntheory, could only borrow by issuing bonds, and local government debts accrued before \\n2015 (excluding outstanding local government bonds) started to be swapped into bonds \\nthrough the debt swap program. Since 2017, the government had launched several rounds \\nof regulation on local government borrowing. These measures put additional caps on how \\nand how much local governments could borrow apart from bonds, but local government \\ndebt continued to grow. Furthermore, the rapidly expanding Public Private Partnership (PPP) \\nprojects in 2016-17 became the substitute for financing functions that LGFVs had played, \\nalthough the government has made continued efforts in containing the debt risks and \\nimproving the project implementation. There is so far no public information on the size of \\npotential local government liabilities related to PPPs. \\n• \\nAs of the end of 2023, outstanding (official) local government debt stood at around RMB41 \\ntrillion (32% of GDP), including RMB16 trillion in local government general bonds (LGGB), \\nRMB25 trillion in local government special bonds (LGSB), and RMB0.2 trillion in non-bond \\ndebt which local governments are obligated to repay\\n[31]. Compared to LGGB, LGSB are under \\nthe management of the GMF account, and thus their issuance quota is not subject to the \\nrestrictions from the official fiscal deficit. For LGSB-funded projects, there are prerequisites \\nfor the return on capital and thus require the approval of the National Development and \\nReform Commission and MOF, while LGGB-funded projects do not have such restrictions \\nand thus cover more projects related to people’s livelihood. LGSB usually has had longer \\ntenors than LGGB in recent years. \\n• \\nIn recent years, the central government has become more cautious on local government \\nhidden debt and strengthened related regulations, but local government implicit debt is \\nunlikely to be eliminated anytime soon. \\nLocal Government Special Bond Uses \\nCompared to on-budget fiscal expenditure (only 23% of which was spent on infrastructure-related \\nareas in 2023), LGSB are mostly focused on infrastructure, and thus more commodity-intensive. MOF \\ndata suggest around two-thirds of LGSB proceeds have been spent on infrastructure-related \\nprojects in recent years, with municipal construction, industrial parks and transportation-related \\nprojects taking the lion’s share. \\nExhibit 78: Around two thirds of LGSB proceeds have been spent on infrastructure-related \\nprojects in recent years \\nInvestment target of local government special bonds (LGSB) issued \\n\\n\\n 149 / 158 \\n \\n \\nSource: MOF, Goldman Sachs Global Investment Research \\nUnspent Local Government Bond Quota \\n• \\nUnspent local government bond (LGB) quota refers to the difference between the official \\nlimit for local government debt and the outstanding amount of LGBs, if any. In theory, there \\nare two sources of unspent LGB quota available for use, including the quota never used \\nbefore, and that used previously but renewed after debt repayment (without rolling over). \\n• \\nBased on our estimates, as of end-2023, the unspent LGB quota was RMB1.4 trillion (vs. \\nRMB2.6 trillion at end-2022), including RMB680 billion for local government general bonds \\n(LGGB) and RMB750 billion for local government special bonds (LGSB). Policymakers can \\nutilize this buffer to raise extra-budget funding if necessary and upon approval. For example, \\nin August 2022, the then-Premier Li Keqiang approved the use of RMB500 billion unspent \\nLGSB quota and required local governments to fulfill the quota by end-October 2022, in an \\neffort to support fiscal spending and offset strong growth headwinds. \\n• \\nHowever, unspent LGSB quota has been unevenly distributed across provinces. Some \\nprovinces with significant funding challenges do not have sufficient LGSB quota (e.g., \\nQinghai, Gansu, Heilongjiang and Jilin), while those with more quotas may not have an \\nimmediate need to raise funds (e.g., Beijing and Shanghai). As such, in 2022, policymakers \\nassigned 30% of the RMB500 billion additional quota for regional redistribution, in favor of \\nprovinces with more ready-to-go projects. \\nLocal Government Debt Swap Program \\n• \\nTo reduce the servicing costs for outstanding debt and restructure some non-performing \\ndebt (especially for local government implicit debt), debt swap programs could be one \\noption. There was a large-scale local government debt swap program during 2015-18. The \\nthen-Finance Minister Lou Jiwei in August 2015 indicated that the outstanding amount of \\nnon-bond liabilities that local governments had obligations to repay was around RMB14.2 \\ntrillion. According to the National Audit Office, local governments issued a total of RMB12.2 \\n\\n\\n 150 / 158 \\n \\ntrillion bonds (with low interest rates) to replace these non-bond debts (with high interest \\nrates). \\n• \\nThere was another, albeit smaller, round of local government debt swap program more \\nrecently. At the July 2023 Politburo meeting, President Xi pledged to launch “a basket of \\nlocal government debt resolution plans”. Subsequently, media reports suggested that \\nprovincial governments were allowed to raise RMB1.5 trillion via refinancing bond sales, \\nutilizing previously unspent LGGB and LGSB quota, to repay local government implicit debt. \\nAdditionally, banks were also asked to roll over part of local government implicit debt to \\nmanage potential risks. \\nGS China Augmented Fiscal Deficit (AFD) \\nSource: Goldman Sachs Economics Research \\nAvailability: Monthly from January 2004 \\nTiming: Around 25 days after the end of month \\nRelease: GS China Proprietary Indicators update \\nOverview \\n• \\nThe stance of fiscal policy is typically measured by the fiscal balance to GDP ratio, after \\nseasonal adjustments. An increase in this ratio (i.e., a narrowing in the deficit) means a \\ncontractionary fiscal policy, and a decrease in the ratio (i.e., a widening in the deficit) \\nindicates an expansionary fiscal policy. \\n• \\nIn addition to on-budget fiscal tools, the government can affect the economy through off-\\nbudget activities (which are also referred to as “quasi-fiscal policy”). The most important and \\nfrequently used measure is the infrastructure investment funded by LGSB, policy banks, \\nLGFVs, other state-owned enterprises (e.g., China Railway Corporation) and Public-Private \\nPartnership (PPP) programs. \\nCompilation \\n• \\nWe “augment” the official fiscal policy measures by incorporating off-budget quasi-fiscal \\npolicy to obtain a comprehensive picture of the stance of China’s fiscal authority, and to \\nexamine its implications for the growth of the Chinese economy. Specifically, our measure of \\nAFD is a sum of effective on-budget and off-budget fiscal deficits. We estimate the off-\\nbudget spending by major channels that finance quasi-fiscal activities. This further includes \\ncentral government special bonds (CGSB), LGSB, net land sales revenue (excluding the costs \\nof land acquisition and redevelopment), LGFV bonds, railway construction bonds, policy \\nbanks support (mostly via policy bank bonds and PBOC’s PSL), shadow banking loans, etc. \\nAs in some years there could be a significant time lag between LGSB issuance and proceeds \\nspending (e.g., in 2021), we use a projected pace of LGSB proceeds spending as the input \\nfor LGSB in AFD estimates.\\n[32] \\n• \\nOur AFD metric is constructed with monthly frequency, in order to monitor the \\ngovernment’s overall fiscal stance in a timely manner and accordingly serve as a relatively \\n\\n\\n 151 / 158 \\n \\nreliable policy parameter input for our forecasting of the real economy. By interpolating GDP \\ndata, we are able to derive a monthly AFD-to-GDP ratio. To mitigate potential distortions \\nfrom residual seasonality, we also focus on 3-month and 12-month moving averages of \\nAFD-to-GDP ratio. \\n• \\nWe note several caveats to this approach: First, some off-budget government financing \\nchannels may not be fully captured due to the availability of monthly data, such as LGFV \\nloans and PPP projects. Second, the government’s off-budget activities might not be \\nconfined to infrastructure spending and the sectors we choose, and the share of \\ngovernment spending on debt repayment/servicing has been trending up over the past \\ndecade. \\nExhibit 79: Augmented fiscal deficit peaked in 2020 amid the initial Covid outbreak, and \\nremained relatively wide in recent years \\n \\nSource: MOF, CEIC, Haver Analytics, Wind, Goldman Sachs Global Investment Research \\nExhibit 80: The augmented fiscal deficit has shown several rounds of expansion since 2015 \\n\\n\\n 152 / 158 \\n \\n \\nSource: MOF, Wind, Goldman Sachs Global Investment Research \\nOther Issues \\n• \\nIn the Government Work Report and budget report released during the “Two Sessions” \\nevery year, the government usually unveils the annual target for some key fiscal statistics, \\nincluding official fiscal deficits (in both value terms and percentage of GDP terms) and LGSB \\nnet issuance quota. Official fiscal deficits are equivalent to the sum of government target for \\nthe net issuance of central government general bonds (CGGB) and LGGB. \\n• \\nOn very rare occasions, the government may consider approving a central government \\nspecial bond (CGSB) quota. This happened for the first time in 1998 to replenish the equity \\ncapital of big four banks, the second time in 2007 to establish China Investment Corp, the \\nthird time in 2020 to counteract the initial Covid outbreak, and the fourth time in 2024 for \\nkey projects and strategically important initiatives (e.g., high-tech manufacturing, \\nurbanization, food and energy supply chains, green industries, equipment upgrade). CGSB \\nare part of the central government official debt, but different from CGGB. Similar to local \\ngovernment special bonds, CGSB are managed under the Government Managed Fund (GMF) \\nAccount, outside the General Public Budget Account, and therefore their issuance does not \\nlead to a higher official fiscal deficit. \\n• \\nOur AFD is similar in spirit to the IMF’s “augmented government deficit” metric.\\n[33] However, \\nin recent years IMF’s measure has been larger than our estimate. Our AFD differs from the \\nIMF measure on two major dimensions: 1) on the local government implicit debt financing \\n(mostly through LGFVs), our measure captures LGFV bonds, net land sales revenue, and part \\nof trust loans, to make sure our data series can be updated on a timely (monthly) manner. \\nBy comparison, the IMF measure leverages their own channel checks and projections to \\ngauge LGFV loan financing and shadow banking financing (including trust loans, entrusted \\nloans and PPP related debt), but this is only reported on an annual basis. 2) On the central \\ngovt implicit debt financing, our measure covers debt financing by policy banks and China \\nRailway Corp., mainly via bond financing channels, e.g., policy bank bond net issuance, China \\n\\n\\n 153 / 158 \\n \\nRailway construction bond net issuance, and PBOC's pledged supplementary lending (PSL). \\nBy comparison, the IMF measure does not fully include all these funding channels. \\nExhibit 81: A summary of central government special bond (CGSB) issuance in history \\n \\nNote: We exclude CGSB issuance for refinancing purposes (e.g., in 2017 and 2022). Tenors are \\nranked by the amount of bond issuance in each batch. \\nSource: MOF, NBS, Goldman Sachs Global Investment Research \\nGS China Augmented Government Debt (AGD) \\nSource: Goldman Sachs Economics Research \\nAvailability: Annual from 2004 \\nTiming: Around the middle of the following year \\n• \\nOur proprietary Augmented Government Debt (AGD) measure aims to capture all debt \\nraised by the broad government sector (including its agents). \\n• \\nWe divide the AGD into two categories based on issuers: official debt and implicit debt. \\nOfficial government debt includes debt directly borrowed by central and local governments, \\nsuch as CGGB, CGSB, LGGB and LGSB. Implicit government debt is off the government \\nbudget, including debt of government agents such as policy banks, China Railway and LGFVs, \\nwhich has implicit guarantees from either central or local governments, or at least perceived \\nby markets as having such guarantees. Of course, some of these implicit debts are backed \\nby high-quality assets such as railway and land, while others might not. Within the AGD, \\nthere is a very small proportion raised through offshore dollar bond markets, mainly by \\npolicy banks and LGFVs, but it has been counted into the balance sheet of these entities. \\n• \\nThere could be some double counting issues between the above-mentioned sub-categories, \\nas policy banks may hold some liabilities of the government, LGFVs and China Railway. To \\nadjust for the distortion, we exclude PSL and a fraction of remaining liabilities from policy \\nbank debts. \\n• \\nBased on our estimates, China’s AGD reached RMB166 trillion in 2023 (or 131% of GDP), \\nmarking a more than ten-fold increase from its 2008 level in RMB terms (RMB14 trillion, or \\n\\n\\n 154 / 158 \\n \\n43% of GDP). The ratio of central to local government debts, and that of official to implicit \\ngovernment debts, were both close to 40% vs. 60% in 2022-23. Offshore government debt \\n(mostly raised through offshore dollar bonds) accounted for only 1% of AGD. China’s AGD \\nhas been rising rapidly over the past decades, led mainly by implicit and local government \\ndebts. \\n• \\nOur estimates for implicit local government debt (or LGFV debt) are based on a bottom-up \\napproach using firm-level balance sheet data of LGFV companies, plus some conservative \\nassumptions. We estimate total LGFV debt reached ~RMB62 trillion at end-2023, and our \\nestimates for historical data are broadly in line with academic and policy studies. For \\nexample, according to a team led by Mr. Zhang Xiaojing at the China Academy of Social \\nScience, total LGFV debt ranged from RMB30 trillion to RMB50 trillion at end-2016, hinging \\non different definitions used. Professor Bai Chong’en at Tsinghua University estimated total \\nLGFV debt at RMB47 trillion as of mid-2017. In its 2018 China Financial Stability Report, the \\nPBOC cited an unnamed province as an example in highlighting local governments’ surging \\nimplicit debt: the unnamed province’s implicit debt was 80% higher than its official debt.\\n[34] \\n• \\nIn general, government financing through official debt usually has lower costs and longer \\ntenors than that through implicit debt, for both central and local governments. The financing \\ncost for central government debt by nature is also lower than that for local government debt. \\nAmong major financing channels of local governments, LGSB has become an increasingly \\nimportant funding source. The financing cost of LGSB is higher than central government \\nbonds (CGB), policy bank bonds and LGGB, but much lower than implicit financing channels \\nsuch as LGFV bonds and railway construction bonds. Moreover, CGSB and LGSB usually have \\na longer tenor due to their focus to fund major infrastructure projects. \\nExhibit 82: A breakdown of China’s augmented government debt based on 2023 data \\n \\n* We adjust for double counting issues by excluding some debt from policy banks (deducted from \\n“other liabilities”). Numbers outside and inside the parentheses refer to the outstanding amount of \\n\\n\\n 155 / 158 \\n \\ndebt (in RMB value terms) and its proportion in AGD, respectively for each category at end-2023. \\nCGGB, CGSB and CR refer to central government general bond, central government special bond \\nand China Railway, respectively. \\nSource: MOF, Bloomberg, CEIC, Wind, Goldman Sachs Global Investment Research \\nExhibit 83: A comparison of major government financing channels \\n \\n*Interest rates for LGGB and LGSB refer to 3y yield to maturity of LGB due to data availability. Usually, \\nLGSB yield is slightly higher than LGGB yield. \\nSource: MOF, Bloomberg, Wind, Goldman Sachs Global Investment Research \\nRelated GS Economics Publications \\n• \\n“Tracking China’s fiscal stance: Beyond the official fiscal balance”, Asia Economics Analyst, 18 \\nJanuary 2016 \\n• \\n“China: Fiscal stimulus a potent policy lever early this year, but moderate headwinds building \\nin H2”, Asia in Focus, 20 June 2018 \\n• \\n“Can Chinese fiscal policy be “more proactive” in H2?”, Asia in Focus, 29 July 2018 \\n\\n\\n 156 / 158 \\n \\n• \\n“China: Navigating regions with high reliance on the property sector”, Asia Economics \\nAnalyst, 16 June 2022 \\n• \\n“China fiscal update: Borrowing more from the future to offset increased headwinds”, Asia in \\nFocus, 19 September 2022 \\n• \\n“China: Local government special bond, an increasingly important source of government \\nfinancing”, Asia in Focus, 9 February 2023 \\n• \\n“Population Aging, Pension System, and Individual Retirement Savings in China”, Asia \\nEconomics Analyst, 10 February 2023 \\n• \\n“The Size, Form and Implications of China’s Growing Government Debt”, Asia Economics \\nAnalyst, 2 April 2023 \\n• \\n“China: A likely return of PSL-backed property easing, in a different way”, Asia in Focus, 3 \\nJanuary 2024 \\n• \\n“China: Beijing’s Balancing Act between Infrastructure Stimulus and LGFV Deleveraging”, Asia \\nin Focus, 6 March 2024 \\n• \\n“Potential Reform Remedy for China’s Fiscal Challenges”, Asia Economics Analyst, 9 June \\n2024 \\nThe authors would like to thank Maggie Wei, a former member of the Asia Economics team, for her \\ncontribution to this book. Bernadette Chan and Christopher Dixon provided extensive editorial and \\nformatting assistance. \\n1 ^ “Census X-12” is a program originally developed by the US Census Bureau in the 1960s (“x” is \\nfor experimental, and 12 is for the twelfth in the series). \\n2 ^ See the NBS definitions for these two indicators: \\nhttps://www.stats.gov.cn/hd/cjwtjd/202302/t20230207_1902275.html. \\n3 ^ In 1998, the NBS divided the scope of industrial statistics into two parts: above- and below- \\ndesignated size. NBS has defined the above-designated size as industrial legal entities with annual \\nprincipal business revenue of RMB20 million or more since 2011. \\n4 ^ In order to comprehensively capture the revenue of industrial enterprises, NBS started to \\ndisclose “operating revenue” instead of “prime operating revenue” in 2019. \\n5 ^ Caixin took over sponsorship from HSBC of Markit’s China PMI and officially added Financial \\nData Services in July 2015. \\n6 ^ In late 2023, Chinese policymakers reportedly ordered 12 heavily indebted local governments \\n(i.e., Guizhou, Tianjin, Yunnan, Inner Mongolia, Liaoning, Jilin, Chongqing, Guangxi, Heilongjiang, \\nGansu, Ningxia, and Qinghai) to curtail fiscal spending and halt some infrastructure projects, in an \\neffort to manage debt payments and reduce LGFV default risks (i.e., the \\\"Document #47\\\"; \\nunconfirmed by official source yet). Based on our estimates, the 12 indebted provinces accounted \\nfor around 26% of China’s infrastructure investment, 22% of FAI and 18% of GDP in 2022-23. \\n7 ^ See https://www.gov.cn/xinwen/2020-06/01/content_5516649.htm. \\n\\n\\n 157 / 158 \\n \\n8 ^ For reference, Soufun provides the underlying data and CREIS is the index compiler. \\n9 ^ Wind uses residential housing data for some cities (e.g., Beijing) but total housing data for others \\n(e.g., Hangzhou) in its 30-city sample, but it is mainly due to data availability. Hence, we did not \\ncount this as one of the statistical discrepancies. \\n10 ^ See http://www.stats.gov.cn/sj/zxfb/202305/t20230516_1939489.html. \\n11 ^ See for example “Housing survey probes sensitive vacancy statistic”, Global Times, 6 September, \\n2010 (http://www.globaltimes.cn/content/570440.shtml); and “China property firm apologizes for \\nvacancy rate report after public debate”, Reuters, 11 August 2022 \\n(https://www.reuters.com/markets/asia/china-property-think-tank-apologises-high-vacancy-rate-\\nreport-2022-08-11/). \\n12 ^ See the official website for more information: \\nhttp://real.wharton.upenn.edu/~gyourko/chineselandpriceindex.html \\n13 ^ In mid-2023, the NBS started to release a new series called “retail sales of services”, but it only \\nshows the year-to-date year-over-year growth and has a very short history. \\n14 ^ See the official release at: https://www.stats.gov.cn/sj/ndsj/2021/html/sm06.htm. \\n15 ^ Note that the housing component contains rent, cost of decoration and utilities, and property \\nmanagement fees. Purchases of property are not included. For self-owned properties, owners’ \\nequivalent rents are included in the “housing” category of consumption expenditures. \\n16 ^ The Ministry of Commerce used to compile a monthly series of year-over-year sales growth \\nbased on data from 5000 large retailers, but this was discontinued after July 2016. \\n17 ^ See the NDRC website: \\nhttps://www.ndrc.gov.cn/fggz/jyysr/jysrsbxf/202003/t20200311_1222869.html. \\n18 ^ As described in Anna Wong, “China’s Current Account: External Rebalancing or Capital \\nExodus?”, presentation at the 8th annual International Conference on the Chinese Economy, Hong \\nKong, January 13, 2017. \\n19 ^ For the document, see https://www.imf.org/en/Publications/Manuals-\\nGuides/Issues/2016/12/31/Balance-of-Payments-Manual-Sixth-Edition-22588. \\n20 ^ Mirror data refers to data reported by counterpart economies. For example, the mirror data for \\nChina’s inward FDI from the US is the outward FDI to China reported by the US. \\n21 ^ “Assessing Reserve Adequacy—Specific Proposals”, International Monetary Fund, April 2015 \\n(see http://www.imf.org/external/np/spr/ara/). \\n22 ^ See the latest rules on NPL classification effective 1 July 2023 at: \\nhttps://www.gov.cn/zhengce/2023-02/11/content_5750184.htm. \\n23 ^ See the changes of RRR since 2018 at: http://www.pbc.gov.cn/rmyh/4027845/index.html. \\n24 ^ Industrial revenues cover income from product sales and other sources, such as labor provision \\nand transfer of asset usage rights. However, industrial sales value only covers the income from \\n\\n\\n 158 / 158 \\n \\nproduct sales. \\n25 ^ See Xianchun Xu, \\\"Accurately Understanding China’s Current Gross Domestic Product \\nAccounting\\\", Statistical Research (in Chinese), May 2019. \\n26 ^ See Pingyao Lai and Tian Zhu, \\\"Deflating China's nominal GDP: 2004–2018\\\", China Economic \\nReview, 2022. \\n27 ^ According to NBS' definition, the non-private units include state-owned and controlled \\nenterprises, foreign-funded enterprises and other enterprises, and the private units here mainly refer \\nto SMEs. \\n28 ^ Since 2019, MOF has released January-February combined fiscal data only, rather than for \\nJanuary and February separately, to avoid Chinese New Year related distortions. \\n29 ^ Proceeds raised through central government special bonds (CGSB) and local government \\nspecial bonds (LGSB) are managed under the GMF account. \\n30 ^ See http://www.mof.gov.cn/zhengwuxinxi/caizhengxinwen/201701/t20170120_2524620.htm fo\\nr example. \\n31 ^ The actual amount of LGFV debt which local governments have obligations to repay or have \\nprovided implicit guarantee on could be larger than the MOF estimates in recent years, based on \\nour estimates. \\n32 ^ For normal years, we assume local governments to spend 50% of LGSB proceeds in the month \\nwhen the bond was issued, and 30% and 20%, for the following two months, respectively. However, \\nfor 2021 as an exception, we assume local governments to spend 20%, 30% and 50% of LGSB \\nproceeds in the bond issuance month and the following two months, respectively. \\n33 ^ The IMF pioneered research to estimate the augmented fiscal balance for China, though with \\nannual frequency and a different approach compared to our AFD metric. See Yuanyan Zhang, \\nSteven Barnett, “Fiscal Vulnerabilities and Risks from Local Government Finance in China\\\", IMF \\nWorking Paper, January 2014; Rui Mano and Phil Stokoe, “Reassessing the Perimeter of Government \\nAccounts in China,” IMF Working Paper, December 2017. \\n34 ^ See \\nhttp://www.pbc.gov.cn/jinrongwendingju/146766/146772/3656006/2018110716123679821.pdf. \\nInvestors should consider this report as only a single factor in making their investment decision. For \\nReg AC certification and other important disclosures, see the Disclosure Appendix, or go \\nto www.gs.com/research/hedge.html.\",\"difficulty\":\"hard\",\"domain\":\"Single-Document QA\",\"length\":\"medium\",\"question\":\"Based on the challenges and nuances discussed in \\\"Understanding China’s Economic Statistics – Third Edition,\\\" which of the following best explains the limitations in accurately assessing China's economic momentum through official data, particularly when comparing industrial production (IP) and GDP growth?\",\"sub_domain\":\"Financial\"}","display_format":"text","language":"","answer_status":"published","assets":[],"source_url":"https://huggingface.co/datasets/zai-org/LongBench-v2","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}