The Global Financial Crisis (2007-2008), which began due to the collapse of the U.S. housing market, had a negative effect in many regions across the globe. The global recession which followed the crisis in 2008 and 2009 showed how interdependent and synchronized many of the world's economies had become, with the largest advanced economies showing very similar patterns of negative GDP growth during the crisis. Among the largest emerging economies (commonly referred to as the 'E7'), however, a different pattern emerged, with some countries avoiding a recession altogether. Some commentators have particularly pointed to 2008-2009 as the moment in which China emerged on the world stage as an economic superpower and a key driver of global economic growth. The Great Recession in the developing world While some countries, such as Russia, Mexico, and Turkey, experienced severe recessions due to their connections to the United States and Europe, others such as China, India, and Indonesia managed to record significant economic growth during the period. This can be partly explained by the decoupling from western financial systems which these countries undertook following the Asian financial crises of 1997, making many Asian nations more wary of opening their countries to 'hot money' from other countries. Other likely explanations of this trend are that these countries have large domestic economies which are not entirely reliant on the advanced economies, that their export sectors produce goods which are inelastic (meaning they are still bought during recessions), and that the Chinese economic stimulus worth almost 600 billion U.S. dollars in 2008/2009 increased growth in the region.

Graph and download economic data for OECD based Recession Indicators for China from the Period following the Peak through the Trough (CHNRECD) from 1978-01-01 to 2022-09-30 about peak, trough, recession indicators, and China.

According to a median projection in January 2025, China's GDP was expected to grow by 4.9 percent in 2024, largely meeting the annual growth target of five percent set by the Chinese government. In the first quarter of 2020, the second-largest economy recorded the first contraction in decades due to the epidemic.  A root-to-branch shutdown of factories To curb the spread of the virus, the Chinese government imposed a lockdown in Wuhan, the epicenter, and other cities in Hubei province on January 23, 2020. A strict nationwide lockdown soon followed. Many factories remained closed in February, resulting in a plunge in manufacturing Purchasing Managers' Index (PMI). The shutdown of the “world’s factory” had severely disrupted global supply chains, especially automobile production. In March 2020, very few industrial sectors reported positive production growth.  The pharmaceuticals sector recorded a production increase, which was mainly driven by the global demand for vital medical supplies. China had exported over seven billion yuan worth of face masks. Ripple effects on global tourism Apart from the manufacturing industry, the prolonged closures of business had caused significant losses in various sectors in China. The travel and tourism sector was massively affected by a drastic decline in flight ticket sales  and hotel occupancy rates. The domestic tourism market expects a loss of 20 percent in revenues for 2020. Industry experts predicted that the global travel and tourism industry could lose about 2.5 trillion U.S. dollars in that year. 

The graph shows national debt in China related to gross domestic product until 2023, with forecasts to 2029. In 2023, gross national debt ranged at around 84 percent of the national gross domestic product.

The debt-to-GDP ratio

In economics, the ratio between a country's government debt and its gross domestic product (GDP) is generally defined as the debt-to-GDP ratio. It is a useful indicator for investors to measure a country's ability to fulfill future payments on its debts. A low debt-to-GDP ratio also suggests that an economy produces and sells a sufficient amount of goods and services to pay back those debts. Among the important industrial and emerging countries, Japan displayed one of the highest debt-to-GDP ratios. In 2023, the estimated national debt of Japan amounted to about 255 percent of its GDP, up from around 180 percent in 2004. One reason behind Japan's high debt load lies in its low annual GDP growth rate.

Development in China

China's national debt related to GDP grew slowly but steadily from around 23 percent in 2000 to 34 percent in 2012, only disrupted by the global financial crisis in 2008. In recent years, China increased credit financing to spur economic growth, resulting in higher levels of debt. China's real estate crisis and a difficult global economic environment require further stimulating measures by the government and will predictably lead to even higher debt growth in the years ahead.

The main stock market index in China (SHANGHAI) increased 22 points or 0.66% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on March of 2025.

In 2023, the employment rate in China decreased to around 63.09 percent, from 63.57 percent in the previous year. China is the world’s most populous country and its rapid economic development over the past decades has profited greatly from its large labor market. While the overall working conditions for the Chinese people are improving, the actual size of the working-age population in China has been shrinking steadily in recent years. This is mainly due to a low birth rate in the country.

Economic slowdown – impact on labor market

After decades of rapid development, the world’s second largest economy now seems to have difficulties to boost its economy further. The GDP growth rate indicated a declining trend over the last decade and the number of employed people decreased for the first time since decades in 2015. Under the influence of the global economic downturn, the coronavirus pandemic, and the US-China tensions, many Chinese enterprises are having tough times, which leads to a recession in China’s labor market.

Chances for better employment situation

The long-lasting Sino-U.S. trade war has caused China great loss on its international trade sector, which has been driving China’s economic growth for decades. However, there is also a lot China could improve. First, the potential of domestic demands could be further developed and satisfied with high-quality products. Second, it’s a good timing to eliminate backward industries with low value added, and the high-tech and environment-friendly industries should be further promoted. In addition, China’s market could be more open to services, especially in the financial sector and IT services, to attract more foreign investors. Highly skilled talents should be better valued in the labor market. Efficient vocational education and further education could also help change the structure of China’s labor market.

According to a survey conducted by Ipsos on predictions for global issues in 2020, 30 percent of Chinese believed it that major stock markets might crash in 2020. The results of the survey showed that Chinese were among the most optimistic regarding the stock market in 2020.

In January and February 2025, industrial production in China increased by 5.9 percent. On a month-to-month basis, industrial production grew by 0.51 percent in February 2025.

China’s export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China’s macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China’s financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China’s macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China’s and the United States’ economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model’s sampling results are good. China’s pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China’s pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market.

This data package includes the underlying data to replicate the charts presented in Lessons from China's fiscal policy during the COVID-19 pandemic, PIIE Working Paper 24-7.

If you use the data, please cite as: Huang, Tianlei. 2024. Lessons from China's fiscal policy during the COVID-19 pandemic. PIIE Working Paper 24-7. Washington: Peterson Institute for International Economics.

According to preliminary figures, the growth of real gross domestic product (GDP) in China amounted to 5.0 percent in 2024. For 2025, the IMF expects a GDP growth rate of around 4.6 percent. Real GDP growth The current gross domestic product is an important indicator of the economic strength of a country. It refers to the total market value of all goods and services that are produced within a country per year. When analyzing year-on-year changes, the current GDP is adjusted for inflation, thus making it constant. Real GDP growth is regarded as a key indicator for economic growth as it incorporates constant GDP figures. As of 2023, China was among the leading countries with the largest gross domestic product worldwide, second only to the United States which had a GDP volume of almost 27.5 trillion U.S. dollars. The Chinese GDP has shown remarkable growth over the past years. Upon closer examination of the distribution of GDP across economic sectors, a gradual shift from an economy heavily based on industrial production towards an economy focused on services becomes visible, with the service industry outpacing the manufacturing sector in terms of GDP contribution. Key indicator balance of trade Another important indicator for economic assessment is the balance of trade, which measures the relationship between imports and exports of a nation. As an economy heavily reliant on manufacturing and industrial production, China has reached a trade surplus over the last decade, with a total trade balance of around 823 billion U.S. dollars in 2023.

This paper documents a 2007 Social Accounting Matrix (SAM) for China. This SAM was constructed for the China CGE model to assess the impact of the 2008-09 global recession shocks and the Chinese government's stimulus policy on China's economic growth. The SAM is constructed using data from various sources including an existing input-output table, national accounts, government budgets, balance of payments, commodity exports and imports, labor employment and wage statistics, household expenditure surveys and agricultural production statistics. Cross-entropy estimation techniques are used to balance the SAM. This SAM is a detailed representation of China’s economy in 2007. It covers 61 production activities and commodity sectors, 4 types of factors (low skilled labor, skilled labor, capital, and land), and 2 representative household (rural and urban) groups. The structural characteristics of China’s economy presented in the SAM would be helpful to better understand the economic linkages. And the SAM also provides an ideal tool for economy-wide impact assessments, such as a SAM-based multiplier analysis and computable general equilibrium (CGE) modeling.

Inflation Rate in China decreased to -0.70 percent in February from 0.50 percent in January of 2025. This dataset provides - China Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

CWT plots comparison of the COVID-19 and the GFC.

The USDCNY increased 0.0137 or 0.19% to 7.2796 on Wednesday March 26 from 7.2658 in the previous trading session. Chinese Yuan - values, historical data, forecasts and news - updated on March of 2025.

Housing markets are often characterized by price bubbles, and governments have instituted policies to stabilize them. Under this circumstance, this study addresses the following questions. (1) Does policy tightening change expectations in housing prices, revealing a regime change? (2) If so, what determines the housing market’s reaction to policy tightening? To answer these questions, we examine the effects of policy tightening that occurred in 2016 on the Chinese housing market where a price boom persisted in the post-2000 period. Using a log-periodic power law model and employing a modified multi-population genetic algorithm for parameter estimation, we find that tightening policy in China did not cause a market crash; instead, shifting the Chinese housing market from faster-than-exponential growth to a soft landing. We attribute this regime shift to low sensitivity in the Chinese housing market to global perturbations. Our findings suggest that government policies can help stabilize housing prices and improve market conditions when implemented expediently. Moreover, policymakers should consider preparedness for the possibility of an economic crisis and other social needs (e.g., housing affordability) for overall social welfare when managing housing price bubbles.

The benchmark interest rate in China was last recorded at 3.10 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2023, final consumption of the economy in China accounted for about 55.7 percent of the gross domestic product (GDP). The share of final consumption in the total GDP of China is expected to increase gradually in the upcoming years. Level of consumption in China Final consumption refers to the part of the GDP that is consumed, in contrast to what is invested or exported. In matured economies, final consumption often accounts for 70 or more percent of the total GDP. In developing countries, however, a significantly larger share may be spent on investments in infrastructure, real estate, and industrial capacities.Since its economic opening up, China was among the countries with the highest ratio of spending on investment and the lowest on consumption. Especially since 2000, China spent increasing amounts of money on infrastructure and housing, while the share spent on consumption dropped to an all-time low. This was not only related to China’s rapid economic ascendence, but also to a large working-age population and a low dependency ratio. Recent developments and outlook As the rate of returns on investment has dropped gradually since the global financial crisis in 2008, China is trying to shift to a more consumption-driven growth model. Accordingly, the share of final consumption has increased since 2010. Although this trend was interrupted by the coronavirus pandemic, it will most probably continue in the future. Lower demand for new infrastructure and housing, as well as an aging population, are the main drivers of this development.

A survey of bank managers working in the operation and credit risk department at different hierarchical levels of individual commercial banks in China responsible for bank credit analyses and risk evaluations covering the procedure from loan application to final decision. The objective is to understand the internal organization arrangement of Chinese commercial banks in the provision of bank credit to SMEs. The focus is on the incentives and constraints faced by branch managers in the interaction with SMEs. The enquiry reflects the notion that the branch manager who directly interacts with the SME borrower plays a critical role in the information collection and processing in the lending decision. The incentives and constraints faced by branch manager are shaped by the type of organization of the bank: the degree of decision-making centralization, modes of communication between hierarchical levels, and the adoption of statistical techniques for risk evaluation.

The Chinese financial system has served the Chinese economy well in the early stages of development in channeling domestic savings to domestic investment. But, continued financial repression, along with a growing middle class and ageing population has created pressure on savings to 'search for yield'. At the same time, the dominance of lending to state-owned-enterprises, political constraints, inefficiencies and weak risk management practice by financial institutions (FI) have pushed SMEs to alternative sources of funding. The demand for yield from savers and funds from private investment has been met by the rapid growth in shadow banking.

This study encompasses two of the identified themes of the research call. The research theme 'alternative strategies for reform and liberalization' covers the role of the Shadow Bank system in the credit intermediation process. This research is of critical importance because it informs the macroeconomic research necessary for investigating 'the role of the Chinese financial system in sustaining economic growth'. Addressing the first research theme we take a dual track approach to better understand the role of the financial system in sustaining in economic growth. The first track examines the role of bank and non-bank finance in promoting long-term economic growth at the regional level. The second track is aimed at the more short-term issue of identifying the potential frequency of macro-economic crises generated by a banking crisis.

The finance-growth nexus is a well-established area of economic development, however the China experience questions the supposition that financial development is a necessary precondition. The empirical findings are mixed. Part of the reason for this could be the failure to distinguish between the quality of financial institutions across regions, and the openness of the local environment in terms of the balance between private and public enterprises. Our research would build on the existing literature in two ways. First, it would utilise imperfect but available data on informal finance to examine direct and spill-over effects on medium term growth from contiguous provinces. Second, primary data on the geographic dimension in shadow bank lending gleaned from Theme 2 research will be used to design a weighting system to adjust financial flows for the quality of the local financial environment. The second prong will develop a small macroeconomic model of a hybrid DSGE type that incorporates a banking sector including shadow banks.

Such models have been developed for China in recent times but only a few have attempted to incorporate a banking sector. These models are mostly calibrated versions and make no attempt to test the structure against the data. Recent attempts to test a hybrid New Keynesian-RBC DSGE type model for the Chinese economy using the method of indirect inference have been successful and inclusion of a shadow banks have shown some success. The results of the Theme 2 study will inform the development of a fuller shadow banking sector in the macroeconomic model that will be used to estimate the frequency of economic crises generated by bank crises. Theme 2 research will examine the relationship between the banking system and the shadow banking system as complements or substitutes. It will aim to determine the variable interest rate on the P2P online lending platform on the basis of risk-return, the home bias in online investments, and the signaling and screening in the P2P online lending platform. Finally, it will aim to identify the impact of shadow banking on entrepreneurial activity, the industrial growth rate and regional housing investment and price differentials. These results would inform the theme 1 research on the interconnectedness of shadow banking with the mainstream and the fragility of the financial system to shocks and financial crises.

Additional file 1: Spearman correlation matrix.