Xu Xianchun explains GDP calculation in China
Former NBS Deputy Commissioner stressed that China's GDP data aligned with actual economic development, supported by continuous verification and adjustments in accounting methods.
In a report last Friday, Bloomberg quoted Gao Shanwen [see his feature on The East is Read], Chief Economist at SDIC Securities Co., Ltd., who suggested that China's economy likely grows slower than official figures suggest:
“We do not know the true number of China’s real growth figure and maybe some other numbers,” Gao said at an event hosted by the Peterson Institute for International Economics in Washington. He noted that many people speculate that “after the pandemic, those numbers may not be so accurate.”
Gao's remarks reinforced international scepticism of the reliability of China's GDP figures—either the National Bureau of Statistics (NBS) of China is guilty of fabrication or overestimation, or local government officials have falsified the economic data that feed into the NBS results, given that some of them have indeed been arrested for this.
Even though the Central Economic Work Conference concluded last week with a positive statement that "the main objectives and tasks of economic and social development are on track for successful completion," the most prominent of which being of course the "around 5%" GDP growth rate target, it seems more efforts are needed to restore global confidence in China's growth.
In the following interview published in August, long before the recent controversy, senior statistician and former NBS official Xu Xianchun provided a comprehensive overview of government GDP accounting in China, including the direct online reporting of key statistics to the NBS to prevent regional authorities from interfering with GDP figures and efforts made to verify the raw data—conducting inquiries and, if necessary, on-site inspections.
"It cannot be ruled out that some localities may interfere with GDP data in pursuit of political achievements," said Xu, so it is necessary to ensure that such interference does not affect the national GDP figures, which are "a matter of national credibility." He explained that just as international standards for GDP accounting are continuously updated, the NBS also works to improve data sources and accounting methods. The NBS also holds quarterly evaluations to ensure coherence between GDP data and the basic data, alignment between GDP calculated using the production and expenditure approaches, and the consistency of GDP data with actual economic development, he stressed.
Xu also suggested that in some sectors of China's economy, where the country has become a global leader, current international accounting standards are insufficient to capture the contribution of data assets and investments to economic growth. He called on China's statistical theorists and practitioners to "be bold in confronting this challenge and willing to take on this responsibility," enabling China to move beyond merely following Western practices and "achieve something that sets a global standard."
Xu Xianchun was Deputy Commissioner of the National Bureau of Statistics (NBS) of China from July 2006 to March 2017. He spent 31 years working at the NBS and served as Director-General of the Department of National Accounts before becoming the Deputy Commissioner. He is currently a Professor at the School of Economics and Management of Tsinghua University.
Xu has kindly authorised us to translate his interview, originally published in August on the official WeChat blog of 网易财经智库 NetEase Finance & Economics Institute. The East is Read, with Xu's blessing, has previously published two pieces featuring his views on Chinese government statistics and discussions on his newly published book, 透视中国政府统计数据：理解与应用 A Probe into China's Official Statistics: Comprehension and Utilization.
—Yuxuan Jia
专访统计学者许宪春：GDP的基本统计准则与现实中的难题
Exclusive Interview with Statistician Xu Xianchun: The Fundamental Principles of GDP Statistics and Challenges in Practice
The Origins and Importance of GDP
In the late 1920s and early 1930s, during the Great Depression in the United States, economist Simon Kuznets tried to understand and quantify the production of goods and services within the country, with the aim of finding ways to pull the nation out of crisis. This led to the invention of Gross Domestic Product (GDP). Over the years, economists and statisticians worked to refine this measure, which eventually became an international standard for economic statistics.
Today, government statistical agencies in most countries use the internationally standardised definition of GDP—the monetary value of final goods and services produced in a country in a given period of time. GDP can be viewed in two ways: as the sum of the value-added across all industries or as the total value of final goods, including consumer goods, investment goods, and exports minus imports.
The advantage of GDP is its ability to reflect economic performance from multiple perspectives:
Economic scale: GDP reflects the overall economic size of a country. While GDP alone does not precisely indicate economic strength, it does provide a broad view of a country's development level. For instance, when China's GDP surpassed Japan's in 2010, it became a significant geopolitical marker, causing concern among some Japanese politicians. As China's GDP continued to rise, it caught the attention of American policymakers as well, who began to worry about the possibility of China surpassing the U.S. in GDP. Thus, GDP is a key metric for assessing economic scale.
Economic growth: Economic growth is measured by GDP growth, with the GDP growth rate serving as the economic growth rate. Rapid GDP growth suggests booming incomes for businesses, households, and the government, and is indicative of the overall health of business activities, living standards, and the capacity of public services.
Economic structure: GDP helps to assess the industrial, demand, and regional structures of an economy. Analysing whether these structures are optimal is crucial for understanding if the economy's development is balanced and sustainable.
Per capita economic development: GDP per capita is often used to gauge a country's per capita standard of living. Although some countries, like Singapore, Switzerland, and Norway, have relatively small economies, their high per capita GDP places them among the developed countries. A economic key target for China is to reach the level of a moderately developed country by 2035, with a significant milestone being the attainment of a middle-level developed country's GDP per capita.
Inflation measurement: While the Consumer Price Index (CPI) is commonly used to measure inflation, it only reflects the price changes of goods and services purchased by households, including both tangible goods and intangible services. However, final products extend beyond consumer goods to include investment goods, imports, and exports. The CPI does not fully capture the price changes of all these final products. In contrast, the GDP deflator, which is the ratio of a country's nominal GDP to its real GDP, can account for these price changes. It also reflects the price changes in the value-added output across all sectors. Therefore, the GDP deflator offers a broader perspective on inflation.
In sum, GDP is a versatile indicator that reflects a nation's economic performance from multiple angles. It is an essential tool for assessing development, shaping policy, and evaluating the effectiveness of economic strategies. This is why economists, policymakers, and officials worldwide place such importance on GDP as a metric.
Limitations of GDP as a Metric
Despite its numerous advantages, GDP, as a metric, also has several limitations, attracting criticism both domestically and internationally from officials and scholars alike. Nevertheless, GDP remains an indispensable economic indicator. Key limitations of GDP include:
Insufficient reflection of growth costs: GDP primarily captures economic growth but fails to account for the costs associated with it. For example, after reform and opening up, China experienced over 30 years of rapid economic growth with an average annual growth rate of nearly 10%. Although GDP sufficiently reflected this growth, it did not account for the high costs in terms of resource consumption and environmental degradation.
No indication of income distribution fairness: A similar GDP level may correspond to very different income distribution patterns across nations, with some having equitable distribution and others experiencing large disparities.
Inability to reflect employment levels: Economies with similar GDP levels may have very different employment rates.
No measurement of the health and well-being of a nation's people.
Overall, the measure of GDP has many limitations. So, how should China address this issue? Over the past 30 years since China's reform and opening up, GDP has served as a primary performance indicator, and its historical significance is undeniable. Without the sound policies of the Central Committee of the Communist Party of China (CPC) and the State Council, the use of GDP as a core indicator, and the efforts of local governments to promote economic growth, China's economic achievements today might not have been possible.
However, GDP also has its limitations. As China enters a new era of high-quality development, relying solely on GDP as the main evaluation criterion has become outdated. Moving forward, a more scientific and comprehensive system of indicators is needed to assess economic and social progress and to evaluate local government performance. Such a system will support economic and social development on both the national and regional levels.
How is GDP Calculated?
There are two types of GDP: nominal GDP, also known as current-price GDP, and real GDP, or constant-price GDP. Nominal GDP is calculated based on current-year prices and can be determined using three methods: the production approach, the income approach, and the expenditure approach.
The production approach calculates the added value of each industry, i.e. gross value of output less the value of intermediate consumption. For instance, in the grain farming industry, total output is the value of grain produced that year; intermediate consumption includes the value of fertilisers, pesticides, seeds, and other inputs used in the production process. The difference between these two amounts is the added value for the grain farming industry using the production approach.
The income approach calculates the returns on factors of production and taxes paid to the government for production activities, such as VAT, sales taxes, and product taxes. Since firms may also receive subsidies, the income approach accounts for the net taxes on products, meaning the difference between production taxes and subsidies. Thus, the income approach subdivides GDP into four components: compensation of employees, net taxes on products, fixed asset depreciation, and gross operating surplus.
The expenditure approach calculates the sum of the three types of demand: consumption demand, investment demand, and net export demand.
In GDP calculations, consumption demand is called final consumption expenditure, consisting of household consumption expenditure and government consumption expenditure. Household consumption includes expenses on food, vegetables, clothing, education, etc. Government consumption expenditure includes public service expenses, such as defence and administrative services.
Investment demand is referred to as gross capital formation, which includes both gross fixed capital formation and changes in existing fixed assets. Gross fixed capital formation covers new infrastructure like roads, bridges, airports, ports, factories, office buildings, and educational facilities, as well as purchased equipment within a specific period in a country. Each industry also maintains inventories, such as grain in agriculture, finished goods and raw materials in industry, and construction materials in the construction sector. Changes in these inventories are also considered part of investment demand.
Net export demand is the difference between exports and imports of goods and services.
These three types of demand reflect the total amount and structure of GDP from the perspective of use or demand.
The above describes current-price GDP accounting. The GDP data published by the National Bureau of Statistics (NBS) of China includes value added of various industries and final demand items, generally based on current prices.
However, actual GDP calculations in China do not follow a uniform approach. For example, the production approach is used to calculate value added in industries such as agriculture, forestry, animal husbandry, and fisheries, while the income approach is primarily used to calculate value added in other industries. This is why in the official language, it is often referred to as GDP production accounting, rather than GDP calculated using the income approach or the production approach.
The total GDP and its component indicators published by the NBS are generally based on current prices, while economic growth rates are calculated based on constant prices. Constant price GDP is calculated using the price index reduction method or extrapolation of volume indexes.
The price index reduction method removes the effects of price changes using corresponding price indexes for nominal value added in GDP using the production approach and nominal final demand value in GDP using the expenditure approach. As a result, economic growth data reflects only changes in volume and quality, excluding price changes, which are captured by other indicators. For example, if a car sold for 100,000 yuan last year and 200,000 yuan this year—with 50,000 yuan attributable to a pure price increase and 50,000 yuan to quality improvements—the volume remains unchanged, but the quality has improved. This quality improvement should be included in economic growth, yet the pure price increase of 50,000 yuan should not be counted toward economic growth and must be excluded.
Of course, GDP also includes volume growth, such as the increased production of new energy vehicles in recent years in China. Thus, GDP growth includes both volume growth and quality improvements but does not include pure price changes.
The NBS applies the price index reduction method to calculate the constant price value added in most industries in China. For example, the constant price value added of the secondary industry is calculated by dividing the current price value added by the producer price index (PPI). In specific calculations, the secondary industry is not treated as a whole; instead, the price index reduction method is applied to 41 major industry categories. Each industry's current price value added is divided by the corresponding industry's PPI to obtain constant price value added, thus excluding price changes.
The extrapolation of volume indexes is used to calculate constant-price value added in industries such as railways and other transportation sectors. For example, in railway transportation calculations, the constant-price passenger and freight turnover rate for 2024 is determined by multiplying the 2020 (base year) current-price value added by the ratio of the 2024 turnover rate to the 2020 turnover rate. In this case, the turnover rate serves as a volume index that excludes price changes. This method of calculating constant-price value added is known as the extrapolation of volume indexes.
[Translator's note: To make it clearer, Value added at constant prices of a certain sector＝Value added at constant prices of the same period in the previous year of the sector ×(1＋Speed of growth of value added at constant prices of the sector). An official guide to GDP accounting in China is accessible on the NBS website.]
How can GDP data from different countries be made comparable?
Countries with varying levels of development have different industrial and demand structures. Therefore, making GDP data comparable between countries worldwide is crucial.
To address this, relevant international organisations have established a unified standard, known as the System of National Accounts (SNA), jointly developed by the United Nations, the World Bank, the International Monetary Fund, the European Commission, and the Organisation for Economic Cooperation and Development. This standard is not fixed; it is revised continuously in response to changes and developments in the global economy.
To date, this standard has been revised four times: in 1953, 1968, 1993, and 2008. As the global economy evolves and new situations and needs arise, the standard must adapt to the changing landscape to meet global economic management demands.
With this international standard, countries, such as United Nations Member States, can calculate their GDP using the same framework. Although different countries may have varying data sources, resulting in differences in specific calculation methods, the fundamental concepts, basic accounting principles, and general methods are consistent. Furthermore, given that countries have widely differing levels of economic development—more advanced in North America and Europe, for instance, and less so in parts of Africa—relevant international organisations like the United Nations, the World Bank, and the International Monetary Fund offer support, such as training and technical guidance, to countries with less developed economies or weaker statistical capacities. This support helps these countries calculate their GDP according to international standards, thereby achieving comparability across nations.
However, this standard serves only as a basic guideline. Each country must also consider its own circumstances, governance needs, and available data when conducting its GDP accounting.
After GDP calculations are completed by national statistical departments, the data is provided to the relevant international organisations, which compile, aggregate, and convert it, including exchange rate adjustments, for publication. For example, China provides its GDP data to the World Bank, which then converts it into U.S. dollar terms. This process involves numerous technical aspects. Converting GDP from the local currency to U.S. dollars can be done using either exchange rate conversion or the purchasing power parity (PPP) method. Currently, the exchange rate method is predominantly used.
What is the biggest challenge in GDP accounting?
GDP accounting involves three basic approaches for current-price calculations and two primary methods for constant-price calculations. These methods require extensive data sources, which are essential. The NBS has established a set of standardised methods for GDP accounting.
The first step in GDP accounting is gathering data from various sources, which can be broadly categorised into three main areas.
Statistical survey information: This includes census, comprehensive survey, and sampling survey information.
Census information includes economic and agricultural census information. Comprehensive survey information covers information directly reported to the NBS by industrial enterprises above designated size, construction companies with designated qualification, wholesale and retail companies above designated size, accommodation and catering above designated size, and real estate development and management companies above designated size, etc. The NBS provides these companies with statistical forms through an online reporting system, which they fill out and submit directly to the NBS.
Sampling survey information spans various fields, including the production and operation of industrial enterprises below designated size and self-employed people, agricultural production volumes, household income and expenditures, prices, and employment. For industrial enterprises below designated size, either random sampling within a sampling frame or cluster sampling methods are used. For example, if a region has a complete directory of all industrial enterprises below designated size, each enterprise is not surveyed individually. Instead, samples are selected based on specific rules from the sampling frame, and only the chosen enterprises are surveyed. If a complete directory is unavailable, cluster sampling is used, where a specific area, such as a street or village, is selected, and all industrial enterprises below designated size within that area are surveyed.
Census, comprehensive survey, and sampling survey are essential methods for gathering data and form an important foundation for GDP accounting.
Administrative record and information: Examples include fiscal records, tax information, and other records retained by finance and tax departments in the course of their duties, which serve as important data sources for GDP accounting.
Accounting records: This includes financial statements from banking, insurance, transportation, and telecommunications systems, which are also key data sources for GDP accounting.
After collecting these basic data, there is no guarantee of their consistency or completeness, nor of the absence of over- or underestimation. Thus, the initial step after collecting these data sources is evaluation to determine if they can be directly used in GDP accounting.
In the process of evaluating basic data, issues may arise. For instance, if a region shows rapid growth in fixed asset investment but slow growth in building materials such as steel, cement, or glass, this may indicate a problem with the investment data. In such cases, the NBS needs to focus on evaluating the investment data for that region, conducting inquiries and, if necessary, on-site inspections. For example, verifying whether the reported investment projects actually exist, whether projects are under construction, and whether they are calculated based on actual progress. Some investment projects may also undergo law enforcement inspections.
Only after the basic data pass evaluation can they proceed to the accounting stage according to GDP accounting methods.
In summary, the steps for GDP accounting are: 1) collecting basic data, 2) evaluating them, 3) using GDP accounting methods for calculations, and 4) verifying that GDP data align with the basic data, ensuring consistency between GDP calculated using the production approach and the expenditure approach. Theoretically, GDP calculated through the production and expenditure approaches should be equal, but discrepancies often arise due to issues with basic data and calculation methods.
Ensuring coherence between GDP data and the basic data, alignment between GDP calculated using the production and expenditure approaches, and the consistency of GDP data with actual economic development are critical aspects of the evaluation process. The NBS holds quarterly evaluations to achieve these goals.
For more than thirty years since the reform and opening up, China has used GDP as the primary performance indicator. It cannot be ruled out that some localities may interfere with GDP data in pursuit of political achievements, so it is necessary to ensure that such interference does not affect the national GDP figures. If affected, GDP data may fail to reflect China's economic condition accurately, limiting its value for economic policy formulation. Thus, maintaining the quality of national GDP data is crucial.
In the 1990s, the World Bank did not recognise China's GDP data. In the late 1980s and early 1990s, the World Bank sent a delegation to review China's statistical practices. After the review, the World Bank concluded that, although China's statistical system had undergone reforms, it was still deeply rooted in the traditional Material Product System (MPS), leading to a significant underestimation of China's economic scale. As a result, the World Bank increased China's 1992 GDP by 34.3% and used this adjusted figure and China's official economic growth rate for projections in subsequent years.
In 1999, a delegation from China's NBS and the Ministry of Finance was formed to consult with the World Bank. As a key participant, I led the discussions with the World Bank. Regarding the World Bank's report on adjustments to China's GDP data, we went through them item by item, explaining to the World Bank which adjustments were reasonable given China's situation in the late 1980s and early 1990s, but no longer reflected the reality of China after the 14th CPC National Congress, which set the goal of establishing a socialist market economy and led to profound, systematic reforms in China's statistical system. We also clarified which World Bank adjustments resulted from misunderstandings of China's statistical system, and which areas in China's government accounting system still needed reform to align with international standards.
For instance, the World Bank's report suggested that the value added of China's service sector was underestimated, given the traditional MPS's disregard for non-material services. Accordingly, the World Bank raised China's 1992 GDP by 6.5%. We clarified that China's first national tertiary industry census, conducted in 1993-1995 and covering 1991 and 1992, raised the 1992 service sector value by 33.1%, increasing GDP by 9.3%—2.8 percentage points higher than the World Bank's adjustment for underestimated services. Thus, we argued that the World Bank should not further increase China's service sector data. [Translator's note: For more information, check out Xu Xianchun's 1999 article, "The World Bank's Adjustment and Reaffirmation of China's Official GDP Data"]
GDP data is a matter of national credibility. The NBS continually improves data sources and accounting methods to ensure China's GDP data reflects economic performance. This ensures a solid foundation for macroeconomic policy and enables the public to assess the country's economic progress.
What challenges does the digital economy bring to government statistics?
The digital economy consists of two basic elements: digital technology—such as the Internet of Things, cloud computing, and artificial intelligence—and data resources.
Digital technology is advancing rapidly, and data resources are growing exponentially, both of which profoundly impact business operations, government governance, and people's lifestyles.
For example, industrial internet companies leverage digital technology and data resources to directly connect product designers with users, enabling designers to understand user needs firsthand. This allows for the creation of more personalised products and the development of flexible production lines capable of manufacturing a variety of customised products on a single line. As a result, companies can significantly enhance their adaptability to market demand, improve productivity, reduce production costs, and increase profitability.
Similarly, many consumer internet companies, such as JD.com and Tmall, possess vast amounts of customer purchasing and search behaviour data, which they use for targeted marketing. This approach has significantly enhanced their adaptability to market demands.
As companies' modes of production and operation are undergoing profound changes, government governance is also evolving due to the rapid advancement of digital technology and the explosive growth of data resources. For example, many government approval procedures that once required long waiting times can now be completed through one-stop services or even entirely online, greatly enhancing the government's capacity to provide public services. People's lifestyles have also changed significantly; through mobile phones, they can access extensive information, make video calls, book train tickets, park entry tickets, and hotel rooms, making life more convenient, higher quality, and cost-effective.
The digital economy, therefore, promotes economic and social development and enhances people's well-being. However, it presents considerable challenges to government statistics. Can government statistics objectively capture the economic and social development changes and improvements in well-being brought about by the rapid progress in digital technology and the exponential growth in data resources? I would argue that while government statistics can reflect these changes to some extent, they remain incomplete and insufficient. At present, there are still many challenges and difficulties. For example, many internet platforms provide free or low-cost services that benefit society as a whole. However, because these services are either free or inexpensive, existing statistical methods struggle to fully capture their contribution to economic growth and public welfare.
In the era of the digital economy, data has become a critical factor of production. I often use the following example: in 2009, the investment in fixed assets in China grew by 25.7%, but by 2023, it had only grown by 2.8%, marking a significant slowdown and a clear decline in the contribution of investment demand to economic growth. However, data expenditures are increasing rapidly. Current international statistical standards do not recognise data as an asset, and data expenditures are not yet classified as fixed capital formation. As a result, the contribution of data to economic growth is not adequately reflected. Relevant international organisations responsible for setting national economic accounting standards are currently researching how to properly account for data assets and data capital formation.
The role of data continues to grow. For instance, Didi Chuxing, China's leading ride-hailing company, matches information between passengers and drivers. The more passenger and driver data it holds, the stronger its matching capability, making data Didi's primary asset.
The Ministry of Finance of China has already issued provisional regulations enabling qualified data to be included in financial statements. If data can be recorded on financial statements, this will provide a solid foundation for government statistics.
If data is treated as an asset and data expenditure as fixed capital formation, GDP would reflect the role of data in economic development, and the balance sheet would reflect the share of data assets in total assets. Data and data asset statistics are not only a challenge for China's government statistics but also a shared challenge for governments worldwide.
In the past, China primarily learned statistical theories and methods from Western developed countries. This was because, due to China's lag in economic and social development, the challenges it faced had already been encountered by these developed nations. Their statisticians and practitioners had the opportunity to summarise and refine advanced statistical theories and methods. As a result, China had to learn from them, and the development of international statistical standards was largely led by Western countries. However, China has now emerged as a leader in certain areas of the digital economy, facing challenges that other countries may not yet have encountered. Therefore, Chinese statistical theorists and practitioners should focus on summarising and refining advanced statistical theories and methods in these fields. This will enable China to play a stronger role in shaping international statistical standards in the future, ensuring that they more accurately reflect the actual circumstances of China's economic and social development.
This presents a significant challenge to Chinese statistical theorists and practitioners: As China leads in certain sectors of the digital economy, can they summarise and refine advanced statistical theories and methods in these areas? If they lack this capability, China will be left to wait for others to develop these theories, only to learn them again later.
China's statistical theorists and practitioners must be bold in confronting this challenge and willing to take on this responsibility. Given the current environment and opportunities, they should strive to achieve something that sets a global standard.
 | A guest post by
|