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.

How do politicians distribute government resources in regimes with no electoral considerations? We propose that new politicians minimize political risks by favoring politically important actors: state-owned enterprises (SOEs), but they adjust their behavior as they establish personal ties with private businesses. Using firm-level subsidies data after the 2008 stimulus in China, we find that new provincial governors, immediately after taking office, distribute a significantly larger proportion of subsidies to SOEs relative to private firms. The effect attenuates as new governors learn about local conditions and establish connections with private firms. We find suggestive evidence that governors who adopt such a strategy are more likely to be promoted. Contrary to conventional wisdom that the state always favors state-owned firms, we show that SOEs benefit from the stimulus package only in the short-run.

Oil prices surge due to global economic changes and China's stimulus efforts, impacting demand and market dynamics significantly.

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Explore the impact of China's underwhelming economic stimulus on iron ore prices, as they slip below $100, affecting global mining and trading operations.

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.

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.

In 2023, the construction industry accounted for about 6.8 percent of China's gross domestic product (GDP), representing a slight increase of 0.1 percent from the previous year.

A vital industry for the economy Since the 1998 housing reform, China's real estate industry has expanded dramatically and has become one of the country's pillar industries. Similarly, China's infrastructure construction has also boomed since the early 2000s. To mitigate the impact of the 2008 global financial crisis and maintain the country's economic output, the Chinese government launched a four trillion yuan stimulus plan and invested substantial resources in infrastructure development across the country, such as high-speed railway and highway projects. These developments have all made the construction industry one of the most important segments of the Chinese economy.

An important employer nationwide The construction industry also plays a key role in China's labor market, with more than 50 million people employed in the sector in 2023. It is also one of the top sectors for China's migrant workers, with more than 15 percent working in construction in 2023. However, due to the challenging working environment, more and more young migrant workers are choosing to work in other professions, such as couriers and food delivery. With China's real estate sector facing significant headwinds, infrastructure construction stagnating, and local governments now under substantial fiscal pressure, the future of China's construction industry is becoming increasingly uncertain.

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 study explores the relationship between language and thought in similarity judgments by testing how monolingual children who speak languages with partial typological differences in motion description (English and Chinese) respond to visual motion stimuli. Participants were, either Chinese- or English-speaking, 3-year-olds, 8-year-olds and adults (32 in each group) who judged the similarity between caused motion scenes in a match-to-sample task. The results suggest, first of all, that the two younger groups of 3-year-olds are predominantly path-oriented, irrespective of language, as evidenced by their significantly longer fixation on path-match videos rather than manner-match videos in a preferential looking scheme. Using categorical measurement of overt choices, older children and adults also showed a shared tendency of being more pathoriented. However, the analysis using continuous measurement of reaction time revealed significant variations in spatial cognition that can be related to linguistic differences: English speakers tended to be more manneroriented while Chinese speakers were equally manner- and path-oriented. Our findings indicate a likelihood that children’s non-linguistic thought is similar prior to internalising the lexicalisation pattern of motion events in their native languages, but shows divergences after such habitual use, thus suggesting that the pattern of non-linguistic thought may be linked to linguistic structure.

Nickel prices fell to a four-year low as 2025 began, influenced by cautious views on China's stimulus measures and a rising US dollar, impacting the global metals market.

In 2023, the construction industry in China generated an output of over 31 trillion yuan, representing an increase of almost 100 percent from a decade ago.

Stimulus from the real estate sector and government-funded projects With the liberalization of the housing market in the late 1990s, China's real estate industry enjoyed a 20-year boom since the early 2000s. Amidst the surge in housing prices, local governments across China received considerable non-tax revenue from land leases and in turn, invested the funds in infrastructure development projects. This led to the continuous prosperity of the country’s construction industry.

A vital segment of China’s economy Thanks to the stimulus from housing developments and infrastructure projects, the construction industry is now one of the pillar industries of China's economy, accounting for around seven percent of the country's GDP. However, with the recent difficulties experienced by many major real estate enterprises, and the stagnation in infrastructure construction owing to local governments' debt situation, China's construction sector faces a somewhat uncertain future.

Central Bank Balance Sheet in China decreased to 455203.33 CNY Hundred Million in April from 455311.81 CNY Hundred Million in March of 2025. This dataset provides - China Central Bank Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

China A50 index poised for moderate gains amid stimulus hopes.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

China’s stimulus policies have caused overleveraging and overcapacity for the sustainable development of most industries (particularly high-pollution and energy-intensive industries). Thus, deleveraging and decapacity have become the two best options for the above industries to achieve long-term sustainable development. Based on China’s A-share listed companies from 2009 to 2019, this study investigated the effect of deleveraging and decapacity on corporate capital allocation using fixed effects, propensity score matching (PSM) and difference-in-differences (DID). A homogeneity analysis of geographical and firm characteristics was also conducted. The results show that: (1) Deleveraging and decapacity can significantly increase financial capital allocation by 3.67%, and decapacity can increase investment-related capital allocation by 0.63%. This indicates asset allocation optimization for sustainable development. (2) High asset reversibility can weaken the effect of deleveraging on financial capital allocation while strengthening the effect of decapacity on capital investment. (3) The impact of deleveraging and decapacity may vary among companies due to heterogeneous asset reversibility resulting from geographical locations and technological intensities. Given the current global energy crisis, optimizing capital allocation has become essential in addressing resource shortages and achieving long-term sustainable development. This study may provide a reference for alleviating corporate capital misallocation.

Discover how oil prices are reacting to the upcoming OPEC+ meeting and potential stimulus measures from China as markets prepare for possible global oil supply changes.

European auto stocks gain momentum as investors eye potential gains from new Chinese stimulus measures, despite sector challenges including regulations and operational costs.

Mean accuracies for experiment 1, with standard deviation in parentheses.

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.

In 2019, the market size of digital upgrade services for micro-, small-, and medium-sized enterprises (MSMEs) in China was estimated at around 133.9 billion yuan. The overall digital level of MSMEs in China is still low and the market is forecast to grow at high pace in the future. The coronavirus pandemic is a further stimulus to this development.