Measurement Muddle: China’s GDP Growth Data and Potential Proxies

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How Do We Know if China is Growing? 

How fast is China’s economy growing? Or given the recent trends, how much is it slowing down? Obtaining a reliable answer to these seemingly straightforward questions has proved amazingly elusive. Despite the production of mountains of official data and a plethora of unofficial estimates, these are questions for which there are not yet consensus answers.

There has long been a concern that China’s statistical officials have inflated the country’s data for its gross domestic product (GDP), which is ascribed to political incentives to present a rosy economic picture to its populace and the rest of the world (see Figure 1). Another recurring concern has been the undercounting of the contribution of the private sector to the country’s growth and employment, which may be due to its second-class citizen status, which may make it harder for statistical authorities to effectively monitor, and the large share of economic activity that comes from the service sector, which is inherently harder to count than produced goods.

As a result, a cottage industry has emerged to offer unofficial estimates and unconventional proxy measures of China’s GDP. Moreover, low confidence in quantitative measures of all sorts has also driven some analysts to use anecdotal evidence, including from visits to individual localities and interviews with businesspeople and consumers to draw broader judgments about the economy’s trajectory.

Policy communities in capitals worldwide are highly attuned to these debates, and, in fact, may be driving them, either out of fear that China is becoming an economic superpower or is heading toward a financial crisis caused by mounting debt. Regardless, skepticism about Chinese data is endemic, leaving these debates unresolved.

The goal of this Big Data China feature is to carefully, yet briskly, introduce these debates and the most relevant data to help unpack them. This report builds on an earlier CSIS report, Broken Abacus? A More Accurate Gauge of China’s Economy, by Rosen and Bao, that is one of the most exhaustive examinations of Chinese official statistics ever conducted, as well as a recent CSIS online roundtable discussion. Our new analysis does not reach a simple conclusion about which single data source to trust, but it does evaluate the various approaches and offers some tips for how observers can most productively draw their own judgments.

This feature draws on three sources of information. The first is the vast scholarly literature and public policy analyses about GDP and potential proxies, both in China and elsewhere. The second is quantitative data from a wide range of sources assembled particularly for examination and presentation here.

Finally, we provide insights from our interviews with 15 leading economists from around the United States, Europe, and Asia who offered their individual perspectives on these puzzles (see Figure 2). Their views vary widely, and it is important to note that these differences do not align with the nationality, gender, or age of those with whom we spoke. However, on one point there is consensus: all agree that observers of China need to regularly consider multiple data points and then draw their own conclusions. In short, they suggest analyzing China’s economic growth trajectory is one part science and one part art.

Concerns about Chinese Data are Not Unique

Those who focus on China might assume the data problems around GDP are unique to China, but that is far from the case. The overall size of an economy and its growth rate are inherently difficult to calculate. Concerns about American economic growth data center around whether analysts fully account for all economic activity and if price inflation – which determines the difference between the nominal and real growth rates – is properly measured. Partly as a result, observers look to other kinds of metrics to get a more textured sense of the U.S. economy’s performance. These include a variety of composite metrics, such as the Census Bureau’s Index of Economic Activity and the Conference Board’s three key indices – the Leading Economic Index, the Coincident Economic Index, and the Lagging Economic Index.

Developing countries typically have a larger set of challenges, both technical and political, that leads observers to be less confident in their official economic numbers. For example, doubt about Russia’s official data has led some experts to instead depend on trade data from Russia’s trading partners (who it is assumed are more likely to provide accurate figures) as well as satellite and ship-location data to follow its crude oil trade. Because India’s official data is highly suspect, economists interviewed by Reuters said they were moving to use alternative data sources, among them high-frequency indicators such as car sales, air and rail cargo levels, purchasing managers’ index data, and proprietary indices. The Asian Development Bank (ADB) has contributed to the analysis by offering a table of “coincident economic indicators” to see how various kinds of data align with India’s official growth data.

Skepticism about China’s GDP Statistics

Chinese official economic data is commonly mistrusted. This skepticism persists even though, as Figure 3 shows, the official figures show a long secular decline in growth over the past 15 years and the absolute levels of the official numbers are not radically higher than most unofficial estimates. One reason for the continued discomfort with the government’s data is how much less volatile they are compared to private-sector estimates.

The core explanation for this exaggerated growth and smoother figures is political. The Chinese Communist Party (CCP) has staked its legitimacy in part on economic performance and stability, and as Cornell University’s Jeremy Wallace has recently documented, the CCP has explicitly rooted the achievement of continued economic growth in quantitative metrics. Doing so provides clarity not only for the top leadership but also for officials in localities across this large country. The danger, of course, is that officials will modify their reporting to ensure compliance, or often, overcompliance with the center’s performance goals.

Empirical observation by economists tends to show that although there is a consistent political logic to engage in hyperbole, the actual degree of exaggeration about China’s growth level has varied over time. Drawing on data about energy usage and consumption, Rawski concluded that data manipulation was particularly problematic during the Asian financial crisis at the turn of the century, with actual growth likely no more than one-third of official claims.

Analyzing the global financial crisis a decade later, Chen and his colleagues estimated that official data was overstated by two percentage points per year during 2010-2016. Two important consequences from their analysis are that the official debt/GDP ratio likely understates debt as a percentage of the economy’s size and that the contribution of total factor productivity (TFP) to economic growth is likely even smaller than the anemic numbers usually reported.

Concerns about Chinese data have intensified during the pandemic and post-pandemic period. Although official data can be somewhat higher than unofficial estimates, the bigger problem, according to some, is the smoothing of official numbers.

One sign of progress in the extent of overly high growth estimates is that the large differences that used to exist between provincial reports of growth and national estimates of growth in each province have narrowed and essentially been eliminated (see Figure 4).

There are several potential explanations for the elimination of this gap. One would be that the political incentive for local officials to fib about their numbers has fallen. Another would be that even if those incentives are still there, central authorities at least have been able to impose more rigorous requirements on how localities calculate and report their growth figures, which has been followed by increased inspections by Beijing of localities’ statistical offices. A more cynical reading is that this does not show that Beijing is more committed to publishing accurate data, but that it wants the distortions, whether locally or nationally generated, to be aligned with each other.

No Consensus

When we interviewed our group of experts on China’s GDP data, we heard a range of opinions. A small minority were absolutist in their positions. Two economists entirely dismissed the official data. Calling official data “garbage,” one claimed, “No one thinks it’s reliable. Both the Chinese GDP number and GDP growth number are unreliable.” Another said that GDP data is “wildly unreliable” and “a political figure,” with the government financing “useless constructions to boost the number.” This expert noted that even when the initial pandemic brought China’s economy to a halt, no domestic economists issued a forecast calling for a drop in GDP “because basically everyone assumes the GDP data is fake.”

At the other extreme, two experts we interviewed took the opposite view, endorsing the veracity of official data. One said the skepticism encountered in policy circles in Washington is “because they don’t know statistics well enough.” This expert said that the largest long-time weakness in official data, the undercounting of the service sector, has been resolved. Another expert was equally unequivocal: “The official Chinese GDP statistics have improved over the years and are now clearly more reliable than any proxy measure I am familiar with.”

Most of those we surveyed were in the messier middle, pointing to signs of substantial progress or identifying continuing challenges.

Those who see progress admit that the specific figures may not be precisely accurate, but nevertheless seem broadly close to reality. Said one economist: “China’s GDP number is not highly precise but not inaccurate.” Another agreed, arguing that, “If you look at the GDP numbers of just one or two years, I don’t think GDP numbers are correct, but if you look at the longer term like – more than five or ten years – then the numbers become technically real…The longer-term trend of GDP number is roughly correct.” And another agreed, saying that official data is “useful over the long term even if there are oddities in individual years or segments.” One economist said that although relative to wealthy countries that are members of the Organisation for Economic Co-operation and Development (OECD), Chinese data “is not reliable, but compared to other countries with a similar development level, China’s GDP figures are not so bad.”

Some economists credit improvements in either the technical capacity of statistical officials or a greater willingness of political leaders to permit lower figures and oppose inflating the data. 

One noted that although the official numbers are usually higher than non-official estimates, they were impressed that the official figures are, in fact, going down. “There’s improvement in China’s GDP numbers. China did publish a significantly negative number during Covid, and there has been volatility in retail sales and other Chinese official data,” which led him to conclude, “I think there’re some tentative signs that things are getting closer to reality.”

Another noted that the narrowing of the gap between provincial and central data is the result of the National Bureau of Statistics (NBS) gaining more authority and not allowing provincial governments to lobby for reports of stronger output. And, in general, Beijing depends less on local governments for data than ever before.

Finally, despite their reputation for presenting false narratives, one economist said that Chinese statistical authorities, do not actually have the skills to “massage everything in the data.” Hence, by looking broadly at a wide range of official data like a detective would, one can identify “contradictions” and get a better sense of what is really happening.

Despite signs of progress, many of those with whom we spoke identified continuing problems with China’s GDP statistics.

Contrary to those who compare China favorably to other developing countries, one economist asserted that “even Russia and Pakistan have better data.” He stressed that despite the narrowing of the provincial-central discrepancies, “local and provincial governments [still] have incentives to overreport their numbers to the central government.” Others agreed that specific growth rate is unreliable and blamed “political incentives to make the numbers look good.” One observer highlighted the conflicting interests of different levels of government and bureaucracies, generating conflicting numbers throughout the system. Another said: “It seemed like with COVID the government was moving away from manipulating the GDP data, but the numbers from 2022 showed that we’re back to measuring Xi Jinping’s performance through GDP growth.”

Several criticized the declining transparency, specifically the drop in the amount of data being issued. One said, “The most valuable thing that’s gone is the value-added data at the firm level.” Another recent example is suspending the release of data on youth unemployment.

Others criticized the reliability of specific publicly available datasets. Of these, the housing sector and investment sector data were most criticized.

As noted above, aside from inflating growth, several believe that smoothing GDP growth is equally problematic. Said one source, “When the economy is bad, the NBS tends to inflate the GDP numbers, and when the economy is good, the NBS tends to suppress (the number).” And another: “We discovered that China is smoothing the numbers. In periods where it was much weaker, they were showing a higher number, and in periods when it was much stronger, they were showing a lower number. It makes sense since there’s political incentives to present this stability or smoothness so it’s neither too crazy nor too weak.”

The Search for Proxies: The Li Keqiang Index

The skepticism about China’s official GDP data has sent experts – in and out of China – searching for alternative metrics to get a better handle on where China’s economy is going.

The most infamous proxy of all time is the “Li Keqiang Index” (LKQ), named after the former premier, who according to revelations in WikiLeaks, speculated in 2007 about the problems of China’s official growth measure and suggested that instead, observers should focus their attention on three data points: industrial electricity usage, railway freight volume, and bank loans.

As Figure 5 shows, the LKQ Index reveals a great deal more volatility than China’s real GDP data or even the nominal GDP figures, volatility that comports more with the vicissitudes that many observers seem to believe is actually occurring in China’s economy. Another notable observation is that since early 2020 the LKQ Index has not deviated radically from either the real or nominal GDP growth figures, which is surprising given the criticism of the GDP data and the fact that the LKQ Index is composed of data from the real economy and credit, and does not include anything from the service sector, which is a growing share of the economy.

Within China, the LKQ Index has generally, though not universally, drawn praise. The Chinese media has been most positive, as in 2013 when a Xinhua report praised it as a “reflection of leadership’s attention to detail and pragmaticism when they were conducting grassroots investigations.” Some economists, such as Lu Feng (Peking University) and Qiao Yongyuan (Shougang Fund and Wanwu Research Institute), have said that the index is useful in anticipating major shifts in the economy before they show up in the more traditional macroeconomic data. That said, some believe the three components are not equally valuable all of the time. One study by Zhang Jin and others found that power generation forecasted GDP best during the financial crisis period, while bank loan data is more useful during non-crisis periods.

Beyond China, reviews of the LKQ Index are more mixed. Some, such as Clark, Pinkovskiy and Sala-i-Martin, see the index as a better approximation of actual economic activity than GDP, but suggest that the weighting of the three components be shifted to give bank loans a dominant weighting in the overall measure. (In the standard LKQ Index, bank loans account for 35%, rail traffic for 25%, and energy usage for 40%.) Some have praised the use of credit data, while others have highlighted the value of the rail transport figures. Bloomberg’s Tom Orlik wrote in 2011, “Transport data, statistics on the brute tons of freight hauled and the raw number of passengers carried, provides another way of thinking about the state of the real economy.” 

But international criticism is more prominent. Some, like Arthur Kroeber from Gavekal Dragonomics, have critiqued the index outright, saying it was “quite useless,” since it focuses on heavy industry and cannot be applied to the service sector,  while Peter Cai has suggested that the effort to find proxies is well intended but suggested that other metrics, such as unemployment, income, and retail sales, would be more valuable. These statistics can better reflect the changing reality in China, where the service sector’s share of GDP now exceeds that of industry.  As one expert said, “The Li Keqiang Index only covers the old economy but not the new economy. Given how fast China can change, you need to know how well those proxies cover the new economy.”

Among those we surveyed, only one endorsed the LKQ Index, saying it “is still useful.” Others were more critical. The most strident said that “all proxies based on government data are unreliable, [and so] you have to develop your own dataset.” More common is the kind of suspicion (but not derision) of official GDP data. Typical of the responses was: “The Li Keqiang index can be manipulated too; people create too much hype about the Li Keqiang index.” Another similarly said, “As soon as an index becomes popular, it gets manipulated, such as the Li Keqiang Index.”

Other Potential Proxies

The mixed reviews of the LKQ Index have not led economists to give up on proxies, but instead to search for and develop other alternatives. There have been a myriad of proposals of one type or another. At one extreme are those who criticize any and all data derived from official Chinese sources. They suggest using entirely independent, high-frequency metrics. Although potentially valuable, the downside of such sources is that they are usually proprietary, and hence, expensive for users to procure. Also, it is still difficult for outside observers to fully review their quality and robustness.

One of the more distinctive proposals of late has been to examine the changing intensity of night lights as a proxy for economic activity. As one interviewee noted, “Proxies like nightlight are popular because they are straightforward and easier to translate without too much of a time lag.” Dr. Chor and his colleagues found through analyzing changes in night lights that U.S. tariffs on Chinese goods were slowing Chinese production and therefore growth. Through their separate analysis of lights over an extended period, Clark, Dawson and Pinkovskiy (2020) confirmed the view discussed above that official data smooths out actual growth volatility.

To provide a visual aid and better understand this proxy, CSIS procured photos of an area in the Jianghan District (江汉区) of Wuhan from January 19, 2020, and February 4, 2020, just a little over two weeks apart (see Figure 6). The official start of the Lunar New Year was January 25, 2020, so commercial activity should have resumed by the time of the second photo. But as one can see, the level of activity in the second photo is substantially less, which is best explained by the lockdown in Wuhan that was implemented during this period. Although it is difficult to use night lights as a precise proxy, they are suggestive nevertheless.

Most proposals for proxies, though, draw on more traditional sources, utilizing one or more specific economic metrics, as a window into broader trends. The suggested measures run the gamut and include fiscal revenues and expenditures, industrial value added, wholesale and retail sales, commercial construction activity, freight and passenger traffic, international trade, as well as producer- and consumer-confidence indices. Some have even suggested using data on sales of underwear or pickled vegetables. Another potentially useful tool is trends of economic performance. For example, an increase in queries for job postings may indicate a rise in unemployment and a slowdown in the economy. In any case, the strategy is to identify measures that are either inherently difficult to fudge (such as trade data, because one can compare Chinese data against other sources) or are seemingly innocuous enough that authorities have not paid them much attention.

The economists we interviewed saw some utility in more narrowly gauged measures to obtain a differentiated view of various aspects of China’s economy, but not as outright proxies for the economy as a whole. One analyst probably spoke for most when they said: “The GDP proxies people have produced are basically the same as GDP, just with more volatility. Anybody can use a Bloomberg terminal to make up their own monthly index of whatever indicators they like and call it a GDP proxy; I don’t think this stuff adds a lot of value, to be honest.”

Recognizing the potential benefits of alternative metrics, but also their inherent limitations, CSIS has assembled 20 different measures of economic activity here for users themselves to examine. (See Figure 7) They fall into four categories: Finance, Industry, Services, and Sentiment. The first three are specific measures of actual activity, while the fourth reflects the opinions of various economic actors (producers and consumers). Users can pick any of these measures, individually and together, and compare them against both the nominal and real GDP year-over-year growth rates in the interactive chart.

These measures provide a variety of different perspectives on China’s economy and potentially offer a sharper sense of actual trends than GDP data. If you have little confidence in official GDP statistics, then these other metrics could potentially provide a sharper and more nuanced sense of actual trends. They also help identify variations across different parts of the economy. On the other hand, if you have some confidence in the official GDP data, then you would likely look to those measures that are closest to the GDP data as the most accurate and worth continuing to use as complementary proxies.

How to Analyze Chinese Economic Data

The challenge with analyzing China’s economy is not that there is no data, but that we are awash in data, official and unofficial. Observers are not blind; instead, they need sunglasses to see the true picture.

Moreover, it is clear that there is no panacea to the challenges of evaluating Chinese official growth data. There is no one series of data or source that can estimate the economy as a whole. GDP is inherently a net metric that covers a wide range of activities. It is, in fact, meant to be THE proxy for everything.

The economists we interviewed provided some valuable additional advice to China watchers.

Several stressed the need to compare multiple sources of data from a variety of sources. They particularly emphasized the value of high-frequency data. As one noted, “People in the market don’t use the GDP data a lot because of the question of accounting and composition and the low frequency of data so people go to higher frequency data.” Put another way, “You want accurate, timely, granular data.”

Some also said that in addition to quantitative data, it is important to consider qualitative assessments of the economy, examining different localities, sectors, companies, and consumers. Hence, one source stressed, “It’s still important to be on the ground in China and the inability to travel, even for my colleagues within China, is a major impediment to understanding what’s going on.”

Failing all of that, one economist suggested making friends with China’s statisticians – “It’s best to find a contact in the NBS.” That may be unrealistic for most, but the underlying premise is that observers need to better understand how the official data are collected in order to evaluate their value. There certainly are experts within the Chinese bureaucracy who are also striving to better wrap their heads around these questions.

Guidance for the Policy Community

The lessons for the policy community are not radically different than for economists, but they deserve emphasis. These are conclusions that emerge from the interviews, a review of the existing research, and our assembled data.

The first point, which may be the hardest to accept, is to not obsess so much about the exact overall size of the Chinese economy and the country’s overall growth rate. One can become lost in a sea of numbers with conflicting arguments about their relative validity.

  • The first point, which may be the hardest to accept, is to not obsess so much about the exact overall size of the Chinese economy and the country’s overall growth rate. One can become lost in a sea of numbers with conflicting arguments about their relative validity.
  • Second, given the inherent potential weaknesses in any individual data series or source, one should focus on relative trends and changes from one period to another and between one kind of data and another. Using multiple metrics will provide the chance to be both specific and comparative at the same time. In addition, physical and sentiment-based measures have both advantages and disadvantages. Opinion surveys may reveal important underlying trends not reflected in physical data, but they are also subject to “group think” and may lead to conclusions not justified by actual activity.
  • Third, given that China’s economy is evolving over time, the quality of various data is likely also changing. So be open to switching which metrics you rely on to evaluate any specific aspect of the economy or its overall trajectory.

Following these tips will likely not yield a simple, straightforward picture of the direction of China’s economy, but they will help provide greater confidence and yield a more complex, nuanced understanding. This will then allow policymakers to better evaluate the effect China’s economy is having on other countries and, in turn, how these countries ought to respond.



Our Interviews

For this Big Data China feature, we interviewed 15 leading economists about China’s economy to obtain their views on the reliability and quality of China’s official GDP data and alternative metrics. We attempted to survey a group with diverse backgrounds and experiences. They come from various professions, including academia, international organizations, the private sector, and government. Some are based in China, while others have traveled to China for many years. Interviews started in early January 2023 and ended in April 2023. Each interview lasted from 30-60 minutes and was conducted in English. The semi-structured discussions focused on three topics: (1) Their views on the reliability of the official Chinese GDP measures; (2) Their recommendations of reliable proxies to measure China’s economy and the shortcomings of using proxies; and (3) Their suggestions for the best ways to measure economic growth in China going forward.

GDP Proxies Sources

For Figure 8, we collected data from multiple sources: the National Bureau of Statistics of China, WIND, the People’s Bank of China, the China Electricity Council, the National Energy Administration of China, the Ministry of Finance, and Caixin Media. To be specific, we calculated the year-over-year growth rate based on these data sources:

  • Auto Sales: WIND
  • Caixin Manufacturing PMI: Caixin
  • Caixin Service PMI: Caixin
  • CCI: National Bureau of Statistics of China
  • CPI: National Bureau of Statistics of China
  • Electricity Consumption: National Energy Administration of China & China Electricity Council
  • Entrepreneurs’ Macroeconomic Heat Index: People’s Bank of China
  • Fixed Assets Investment: National Bureau of Statistics of China
  • Foreign Currency Reserves: People’s Bank of China
  • Industrial Electricity Consumption: National Energy Administration of China & China Electricity Council
  • Industrial IVA: National Bureau of Statistics of China
  • Li Keqiang Index: Calculated by the authors using the monthly growth rate of industrial electricity consumption, rail freight, and mid- and long-term loans.
  • Mid- and Long-term Loans: WIND
  • Official Manufacturing PMI: National Bureau of Statistics of China
  • Official Nominal GDP: National Bureau of Statistics of China
  • Official Real GDP: National Bureau of Statistics of China
  • Official Service PMI: National Bureau of Statistics of China
  • PPI: National Bureau of Statistics of China
  • Rail Freight: National Bureau of Statistics of China
  • Real Estate Investment: National Bureau of Statistics of China
  • Sold Floorspace of Commercial Building: National Bureau of Statistics of China
  • Tax Revenue: Ministry of Finance of China
  • Total Passenger Turnover: National Bureau of Statistics of China

Additional Resources


Chinese Language Sources 

  1. Qisheng Cai 蔡琦晟, Yuwei Guo 郭于玮, and Zhengwei Lu 鲁政委, “Gaopin jingji jingqi zhishu goujian” 高频经济景气指数构建 [Construction of high-frequency economic prosperity index]. CIB Research (March 16, 2023).
  2. Hui Liu 刘慧. “‘Keqiang Zhishu’ yu jingji zengzhang de dongtai guanxi yanjiu—jiyu VAR he VEC moxing de shizheng fenxi” “克强指数”与经济增长的动态关系研究—基于VAR和VEC模型的实证分析 [Empirical Analysis of the Dynamic Relationship between ‘Keqiang Index’ and Economic Growth Based on VAR and VEC Models]. Shangye shidai 商业时代 1 (2014): 11-13. 
  3. Feng Lu 卢峰. “Lu Feng: ‘Keqiang Zhishu’ zheshe le shenme” 卢锋:‘克强指数’ 折射了什么[Lu Feng: Implications of Keqiang Index]. EFN China (April 14, 2013).
  4. Ru Ma 马茹, Hui Luo 罗晖, Hongwei Wang 王宏伟, and Tiecheng Wang 王铁成. “Zhongguo quyu jingji gaozhiliang fazhan pingjia zhibiao tixi ji cedu yanjiu” 中国区域经济高质量发展评价指标体系及测度研究 [Study of Evaluating High-quality Economic Development in Chinese Regions]. Zhongguo ruan kexue 中国软科学343, no. 7 (2019): 60-67. 
  5. Shaoye Ma 马少晔, and Lianghua Chen 陈良华. “Jiyu xin fazhan linian de xin jingji fazhan zhishu goujian ji cedu” 基于新发展理念的新经济发展指数构建及测度 [Construction and measurement of new economic development index based on new development concept]. Caihui yuekan 财会月刊 18 (2020): 8. 
  6. “Qian Jun Jiaoshou: GDP shuju buzai shi zhengfu hengliang jingji fazhan de weiyi zhibiao” 钱军教授:GDP数据不再是政府衡量经济发展的唯一指标 [Professor Qian Jun: GDP data is no longer the only indicator for the government to measure economic development]. Shanghai Advanced Institute of Finance (March 6, 2015).
  7. Yongyuan Qiao 乔永远. “Chongjian hongguan zhouqi de diwei kuangjia (Shang)” 重建宏观周期的思维框架(上) [Reconstruct the Framework of the Macroeconomic Cycle (Part I)]. Caixin 财新网 (March 7, 2022).
  8. Qiang Xu 徐强, and Kan Tao 陶侃. “Zhongguo xin Keqiang Zhishu de goujian yu shizheng fenxi” 中国新克强指数的构建与实证分析 [Construction and empirical analysis of China’s new Keqiang index]. Zhongguo renkou ziyuan yu huanjing 中国人口 ∙ 资源与环境 27, no. 3 (2017): 107-115. 
  9. Zhang, Xiaofang 张潇方, and Yingying Zhang 张应应. “Keqiang Zhishu fanying Zhongguo jingji xianshi zhuangkuang de youyuexing yanjiu” 克强指数反映中国经济现实状况的优越性研究 [Research on the superiority of Keqiang Index in reflecting the reality of China’s economy]. Tongji yu juece 统计与决策 22 (2014): 30-32.

English Language Sources 

  1. Catherine Boyle, “Li Keqiang Index: Is It Defunct and Can We Replace It?” CNBC (September 8, 2015).
  2. Peter Cai, “The Li Keqiang Index is dead, long live the new Li Keqiang Index,” Lowy Institute, (Mar 11, 2016).
  3. Cascaldi-Garcia, W. L. Barcelona, J. J. Hoek, and E. Van Leemput, “What happens in China does not stay in China,” International Finance Discussion Paper no.1360 (November 2022): 1–59.
  4. Wei Chen, Xilu Chen, Chang-Tai Hsieh, and Zheng (Michael) Song, “A forensic examination of China’s national accounts,” Brookings Institution (March 7, 2019). 
  5. Davin Chor and Bingjing Li, “Illuminating the Effects of the US-China Tariff War on China’s Economy,” NBER Working Paper W29349 (October 2021).
  6. Hunter L. Clark, Jeffrey Dawson, and Maxim Pinkovskiy, “How Stable Is China’s Growth? Shedding Light on Sparse Data,” Economic Policy Review 26, no. 4 (2020): 1-38.
  7. Hunter L. Clark, Maxim L. Pinkovskiy, and Xavier Sala-i-Martin, “Is Chinese Growth Overstated?” Liberty Steet Economics (March 11, 2016).     
  8. “Economic Indicators.” S. Census Bureau Economic Indicators (April 27, 2023).
  9. John G. Fernald, Eric Hsu, and Mark M. Spiegel, “Is China Fudging Its GDP Figures? Evidence from Trading Partner Data,” Federal Reserve Bank of San Francisco (August 2019).
  10. Anna Fifield, “Underwear or pickles? These are some of the alternative ways for measuring China’s economy,” The Washington Post (April 16, 2019).
  11. Jan J. Groen and Michael Nattinger, “Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression,” Economic Policy Review 26, no. 4 (2020): 39-68.
  12. Tom Orlik. Understanding China’s Economic Indicators: Translating the Data into Investment Opportunities. Upper Saddle River: Financial Times/Prentice Hall, (2011). 
  13. Michael T. Owyang and Hannah Shell, “China’s Economic Data: An Accurate Reflection, or Just Smoke and Mirrors?” Federal Reserve Bank of St. Louis (July 25, 2017).
  14. Dmitriy Plekhanov, “In Search of ‘Real’ Data on China’s Economy.” The Diplomat (March 19, 2019).
  15. “Prototype Measures of Economic Well-Being and Growth.” Bureau of Economic Analysis (May 26, 2022).   
  16. Thomas G Rawski, “What is happening to China’s GDP statistics?” China Economic Review 12, issue 4 (2001): 347-354.
  17. Daniel Rosen and Beibei Bao, “Broken Abacus? A More Accurate Gauge of China’s Economy,” Center for Strategic and International Studies (2015).
  18. Jin Zhang, Pujiang Li, and Guochang Zhao, “Is Power Generation Really the Gold Measure of the Chinese Economy? A Conceptual and Empirical Assessment,” Energy Policy 121 (2018): 211–216.

Header Image: This aerial photo taken on March 10, 2023 shows a night view of the Yellow Crane Tower in Wuhan, in China’s central Hubei province. (Photo by STR/AFP via Getty Images)


  • Scott Kennedy
    Scott Kennedy is senior adviser and Trustee Chair in Chinese Business and Economics at the Center for Strategic and International Studies (CSIS). A leading authority on Chinese economic policy, Kennedy has been traveling to China for over 30 years. His specific areas of expertise include industrial policy, technology innovation, business lobbying, U.S.-China commercial relations, and global governance. He is the editor of China’s Uneven High-Tech Drive: Implications for the United States (CSIS, February 2020) and (with Jude Blanchette) Chinese State Capitalism: Diagnosis and Prognosis (CSIS, October 2021) and the author of The State and the State of the Art on Philanthropy in China (Voluntas, August 2019), China’s Risky Drive into New-Energy Vehicles (CSIS, November 2018), The Fat Tech Dragon: Benchmarking China’s Innovation Drive (CSIS, August 2017), and The Business of Lobbying in China (Harvard University Press, 2005). He has edited three books, including Global Governance and China: The Dragon’s Learning Curve (Routledge, 2018). His articles have appeared in a wide array of policy, popular, and academic venues, including the New York Times, Wall Street Journal, Foreign Affairs, Foreign Policy, and China Quarterly. He is currently finishing a report, Beyond Decoupling: Maintaining America’s Hi-Tech Advantages over China (CSIS, forthcoming spring 2023). From 2000 to 2014, Kennedy was a professor at Indiana University (IU), where he established the Research Center for Chinese Politics & Business and was the founding academic director of IU’s China Office. Kennedy received his PhD in political science from George Washington University, his MA in China studies from the Johns Hopkins School of Advanced International Studies, and his BA from the University of Virginia.
  • Qin (Maya) Mei
    Qin (Maya) Mei is a research associate for the Trustee Chair in Chinese Business and Economics at the Center for Strategic and International Studies. Prior to joining the CSIS Trustee Chair, she conducted research on Chinese foreign policy and political economy for the Wilson Center’s Kissinger Institute on China and the United States, the CSIS China Power Project, and the Eurasia Group. Ms. Mei earned her MA in Asian studies from the Edmund A. Walsh School of Foreign Service (SFS) at Georgetown University. She also holds a BA cum laude in diplomacy and world affairs with a minor in sociology from Occidental College.

This feature was made possible through the generous support of the Stanford Center on China’s Economy and Institutions (SCCEI). Special thanks go to the SCCEI team: Scott Rozelle, Matthew Boswell, and Jennifer Choo for their dedication to this collaboration. The authors are also grateful to Ilaria Mazzocco for her help with the interviews, as well as the hard work and professionalism of our CSIS Trustee Chair colleagues Matt Barocas, Nic Rogers, and research intern Jie Gao. All opinions and errors are solely the authors’.

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This feature was made possible through the generous support of the Stanford Center on China’s Economy and Institutions (SCCEI). Special thanks goes to the SCCEI team: Scott Rozelle, Matthew Boswell, and Jennifer Choo for their dedication to this collaboration. The authors are also grateful to Ilaria Mazzocco for her help with the interviews, as well as the hard work and professionalism of our CSIS Trustee Chair colleagues Matt Barocas, Nic Rogers, and research intern Jie Gao. All opinions and errors are solely the authors'.

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Scott Kennedy and Qin (Maya) Mei, "Measurement Muddle: China’s GDP Growth Data and Potential Proxies," Big Data China, Center for Strategic and International Studies, September 13, 2023, last modified September 13, 2023,