100+ datasets found
  1. d

    Data from: DID FDI REALLY CAUSE CHINESE ECONOMIC GROWTH? A META-ANALYSIS

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 21, 2023
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    Jin, Yinghua; Gunby, Philip; Reed, W. Robert (2023). DID FDI REALLY CAUSE CHINESE ECONOMIC GROWTH? A META-ANALYSIS [Dataset]. http://doi.org/10.7910/DVN/Y09KOV
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    Dataset updated
    Nov 21, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Jin, Yinghua; Gunby, Philip; Reed, W. Robert
    Description

    This study performs a meta-analysis of research that estimates the relationship between FDI and Chinese economic growth. Our sample includes 37 studies and a total of 280 estimates. We include both English- and Chinese-language studies. Our initial “raw” finding is that FDI has had a substantial, positive impact on Chinese economic growth. Furthermore, our results suggest that the effect is not inflated by endogeneity, nor impacted by publication bias. However, the positive effect is found to be smaller for more recent and better designed studies. When we adjust for preferred study and sample characteristics, we find that the estimated economic effect of FDI on Chinese economic growth is much smaller than indicated by the overall literature, and statistically insignificant. This suggests that the cause(s) of the Chinese “economic miracle” likely lie elsewhere.

  2. Manufacturing PLI related indices in China 2023-2025

    • statista.com
    Updated Apr 9, 2020
    + more versions
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    Statista Research Department (2020). Manufacturing PLI related indices in China 2023-2025 [Dataset]. https://www.statista.com/study/72133/coronavirus-economic-impact-in-china/
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    Dataset updated
    Apr 9, 2020
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    China
    Description

    In April 2025, the Manufacturing Purchasing Leader Index (PLI) in China ranged at 49 points. That month, the index for future expectations ranged at 52.1 points.

  3. Replication dataset and calculations for PIIE Working Paper 25-9 Stopping...

    • piie.com
    Updated May 8, 2025
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    Marcus Noland; Julieta Contreras; Lucas Rengifo-Keller (2025). Replication dataset and calculations for PIIE Working Paper 25-9 Stopping the flow: The effects of US-China cooperation on fentanyl markets and overdose deaths by Marcus Noland, Julieta Contreras, and Lucas Rengifo-Keller (2025). [Dataset]. https://www.piie.com/publications/working-papers/2025/stopping-flow-effects-us-china-cooperation-fentanyl-markets-and
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    Dataset updated
    May 8, 2025
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Marcus Noland; Julieta Contreras; Lucas Rengifo-Keller
    Area covered
    China, United States
    Description

    This data package includes the underlying data to replicate the charts, tables, and calculations presented in Stopping the flow: The effects of US-China cooperation on fentanyl markets and overdose deaths, PIIE Working Paper 25-9.

    If you use the data, please cite as:

    Noland, Marcus, Julieta Contreras, and Lucas Rengifo-Keller. 2025. Stopping the flow: The effects of US-China cooperation on fentanyl markets and overdose deaths. PIIE Working Paper 25-9. Washington: Peterson Institute for International Economics.

  4. China CN: GDP Nowcast: China: Impact: Next Quarter: External Trade: Exports

    • ceicdata.com
    Updated Feb 15, 2025
    + more versions
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    CEICdata.com (2025). China CN: GDP Nowcast: China: Impact: Next Quarter: External Trade: Exports [Dataset]. https://www.ceicdata.com/en/china/gross-domestic-product-nowcast-next-quarter/cn-gdp-nowcast-china-impact-next-quarter-external-trade-exports
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jul 1, 2024 - Dec 1, 2024
    Area covered
    China
    Description

    CN:(GDP) Gross Domestic ProductNowcast: China: Impact: Next Quarter: External Trade: Exports data was reported at 0.112 % Point in Mar 2025. This records an increase from the previous number of -0.114 % Point for Feb 2025. CN:(GDP) Gross Domestic ProductNowcast: China: Impact: Next Quarter: External Trade: Exports data is updated monthly, averaging 0.019 % Point from Oct 2024 (Median) to Mar 2025, with 6 observations. The data reached an all-time high of 0.112 % Point in Mar 2025 and a record low of -0.114 % Point in Feb 2025. CN:(GDP) Gross Domestic ProductNowcast: China: Impact: Next Quarter: External Trade: Exports data remains active status in CEIC and is reported by CEIC Data. The data is categorized under China Premium Database’s National Accounts – Table CN.AJ: Gross Domestic Product: Nowcast: Next Quarter. China GDP releases in January for Q4, April for Q1, July for Q2 and October for Q3. 'Next Quarter' during periods between - October release to January release refers to Q1 GDP data estimate; January release to April release refers to Q2 GDP data estimate; April release to July release refers Q3 GDP data estimate; July release to October release refers to Q4 GDP data estimate.

  5. Impact of U.S.-China trade tensions on business strategy in China 2024

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Impact of U.S.-China trade tensions on business strategy in China 2024 [Dataset]. https://www.statista.com/statistics/1406750/impact-of-us-china-trade-tensions-on-business-strategy-in-china/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 21, 2024 - Nov 15, 2024
    Area covered
    United States, China
    Description

    According to a survey conducted among U.S. enterprises in China in November 2024, around ** percent of respondents reported that the U.S.-China trade tensions made them delay or cancel investment decisions in China. Around ** percent of companies stated that the trade tensions did not impact their business strategy.

  6. Non-manufacturing PLI related indices in China 2023-2025

    • statista.com
    Updated Apr 9, 2020
    + more versions
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    Statista Research Department (2020). Non-manufacturing PLI related indices in China 2023-2025 [Dataset]. https://www.statista.com/study/72133/coronavirus-economic-impact-in-china/
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    Dataset updated
    Apr 9, 2020
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    China
    Description

    In April 2025, the non-manufacturing Purchasing Leader Index (PLI) in China ranged at 50.4 points, down from 50.8 points in the previous month. The index for the expectation of business activities ranged at 56 points that month.

  7. J

    Do words hurt more than actions? The impact of trade tensions on financial...

    • jda-test.zbw.eu
    • journaldata.zbw.eu
    csv, txt
    Updated Jul 22, 2024
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    Massimo Ferrari Minesso; Frederik Kurcz; Maria Sole Pagliari; Massimo Ferrari Minesso; Frederik Kurcz; Maria Sole Pagliari (2024). Do words hurt more than actions? The impact of trade tensions on financial markets (replication data) [Dataset]. https://jda-test.zbw.eu/dataset/do-words-hurt-more-than-actions-the-impact-of-trade-tensions-on-financial-markets
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    csv(54580), csv(390855), csv(13887), txt(1543)Available download formats
    Dataset updated
    Jul 22, 2024
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Massimo Ferrari Minesso; Frederik Kurcz; Maria Sole Pagliari; Massimo Ferrari Minesso; Frederik Kurcz; Maria Sole Pagliari
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We use machine learning techniques to quantify trade tensions between the United States and China. Our measure matches well-known events in the US-China trade dispute and is exogenous to the developments on global financial markets. Local projections show that rising trade tensions leave US markets largely unaffected, except for firms that are more exposed to China, while negatively impacting stock market indices and exchange rates in China and emerging markets. We complement these findings with additional evidence suggesting that the US-China trade tensions have been interpreted as a negative demand shock for the Chinese economy rather than as a global risk shock.

  8. Entrepreneurial activity hinders economic growth: evidence from China

    • figshare.com
    xlsx
    Updated Dec 19, 2023
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    Leap (2023). Entrepreneurial activity hinders economic growth: evidence from China [Dataset]. http://doi.org/10.6084/m9.figshare.24864372.v1
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    xlsxAvailable download formats
    Dataset updated
    Dec 19, 2023
    Dataset provided by
    figshare
    Authors
    Leap
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    China
    Description

    Using the annual numbers of newly registered firms and new high-tech firms across 278 Chinese cities between 2011 and 2019, we employ latent class models to cluster cities exhibiting heterogeneous effects of entrepreneurial activity on economic growth. We find that entrepreneurial activity hinders economic growth, with more pronounced effects over a two-year time lag. Chinese cities form two distinct clusters: one large group covering predominantly southern cities and a smaller group comprising mostly northeastern and northwestern cities. Entrepreneurial activity in the former group negatively impacts economic growth significantly, while this effect is insignificant for the latter group.

  9. China Consumer Price Index: Carryover Effect of Last Year

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). China Consumer Price Index: Carryover Effect of Last Year [Dataset]. https://www.ceicdata.com/en/china/consumer-price-index-price-factor-effect-of-last-and-current-year/consumer-price-index-carryover-effect-of-last-year
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 1, 2024 - Feb 1, 2025
    Area covered
    China
    Variables measured
    Consumer Prices
    Description

    China Consumer Price Index (CPI): Carryover Effect of Last Year data was reported at -0.200 % Point in Mar 2025. This records an increase from the previous number of -1.200 % Point for Feb 2025. China Consumer Price Index (CPI): Carryover Effect of Last Year data is updated monthly, averaging 0.700 % Point from May 2011 (Median) to Mar 2025, with 146 observations. The data reached an all-time high of 4.000 % Point in Jan 2020 and a record low of -1.800 % Point in Feb 2021. China Consumer Price Index (CPI): Carryover Effect of Last Year data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IA: Consumer Price Index: Price Factor Effect of Last and Current Year.

  10. China Producer Price Index: Carryover Effect of Last Year

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). China Producer Price Index: Carryover Effect of Last Year [Dataset]. https://www.ceicdata.com/en/china/producer-price-index-price-factor-effect-of-last-and-current-year/producer-price-index-carryover-effect-of-last-year
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 1, 2024 - Feb 1, 2025
    Area covered
    China
    Variables measured
    Producer Prices
    Description

    China Producer Price Index: Carryover Effect of Last Year data was reported at -1.800 % Point in Mar 2025. This records an increase from the previous number of -1.900 % Point for Feb 2025. China Producer Price Index: Carryover Effect of Last Year data is updated monthly, averaging -0.100 % Point from Jan 2013 (Median) to Mar 2025, with 135 observations. The data reached an all-time high of 9.300 % Point in Jan 2022 and a record low of -4.800 % Point in Jan 2016. China Producer Price Index: Carryover Effect of Last Year data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IE: Producer Price Index: Price Factor Effect of Last and Current Year.

  11. o

    Replication data for: The Distortionary Effects of Incentives in Government:...

    • openicpsr.org
    Updated Apr 1, 2017
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    Raymond Fisman; Yongxiang Wang (2017). Replication data for: The Distortionary Effects of Incentives in Government: Evidence from China's "Death Ceiling" Program [Dataset]. http://doi.org/10.3886/E113677V1
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    Dataset updated
    Apr 1, 2017
    Dataset provided by
    American Economic Association
    Authors
    Raymond Fisman; Yongxiang Wang
    Area covered
    China
    Description

    We study a 2004 program designed to motivate Chinese bureaucrats to reduce accidental deaths. Each province received a set of "death ceilings" that, if exceeded, would impede government officials' promotions. For each category of accidental deaths, we observe a sharp discontinuity in reported deaths at the ceiling, suggestive of manipulation. Provinces with safety incentives for municipal officials experienced larger declines in accidental deaths, suggesting complementarities between incentives at different levels of government. While realized accidental deaths predict the following year's ceiling, we observe no evidence that provinces manipulate deaths upward to avoid ratchet effects in the setting of death ceilings.

  12. f

    Variable definitions and descriptive statistics.

    • figshare.com
    xls
    Updated Aug 8, 2024
    + more versions
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    Wenjing Sun; Zheng Wang; Yongxin Huang; Yifan Li (2024). Variable definitions and descriptive statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0304589.t002
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    xlsAvailable download formats
    Dataset updated
    Aug 8, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Wenjing Sun; Zheng Wang; Yongxin Huang; Yifan Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Small and medium-sized enterprises (SMEs) were an important part of China’s economy, but they faced challenges to growth due to financing difficulties. Government subsidies are considered as a potential way to address this problem. This study aims to assess the effectiveness of the Chinese government’s subsidy program aimed at improving the accessibility of financing for SMEs. We analyze a comprehensive dataset of Chinese firms’ subsidy programs from 2011 to 2020. We classify subsidies into unconditional and conditional categories and use fixed-effects regression models to control for the effects of time and between-group variation to more accurately assess the effectiveness of government subsidies. In addition, we use a PSM-DID model to reduce the effect of selectivity bias to more accurately estimate the causal effect of subsidies on financing strategies. We also use a mediated effects model to help understand the mechanisms by which different types of subsidies affect financing strategies. The results show that government subsidies can significantly improve SMEs’ financing ability, but different types of subsidies produce subtle differences. Conditional subsidies support debt financing mainly through incentives, while unconditional subsidies help SMEs improve their equity financing ability through information effects. Furthermore, we find that over-reliance on a single subsidy type may reduce its effectiveness, suggesting a complex relationship between government intervention and SME financing. Thus, well-designed policies are crucial for promoting SMEs and fostering economic growth.

  13. Disruption Downstream: The Ripple Effects of Trump’s China Tariffs on...

    • ibisworld.com
    Updated Apr 17, 2025
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    IBISWorld (2025). Disruption Downstream: The Ripple Effects of Trump’s China Tariffs on Australia [Dataset]. https://www.ibisworld.com/blog/disruption-downstream/61/1126/
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    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    IBISWorld
    Time period covered
    Apr 17, 2025
    Area covered
    Australia, China
    Description

    Trump’s renewed tariffs on China are shaking up global trade. Here’s what they could mean for Australian industries facing shifting demand, prices and supply chains.

  14. Replication dataset for PIIE PB 24-9, Economic implications of revoking...

    • piie.com
    Updated Sep 17, 2024
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    Megan Hogan; Warwick J. McKibbin; Marcus Noland (2024). Replication dataset for PIIE PB 24-9, Economic implications of revoking China's permanent normal trade relations (PNTR) status by Megan Hogan, Warwick J. McKibbin, and Marcus Noland (2024). [Dataset]. https://www.piie.com/publications/policy-briefs/2024/economic-implications-revoking-chinas-permanent-normal-trade
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    Dataset updated
    Sep 17, 2024
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Megan Hogan; Warwick J. McKibbin; Marcus Noland
    Area covered
    China
    Description

    This data package includes the underlying data files to replicate the data and charts presented in Economic implications of revoking China's permanent normal trade relations (PNTR) status by Megan Hogan, Warwick J. McKibbin, and Marcus Noland, PIIE Policy Brief 24-9.

    If you use the data, please cite as: Hogan, Megan, Warwick J. McKibbin, and Marcus Noland. 2024. Economic implications of revoking China's permanent normal trade relations (PNTR) status, PIIE Policy Brief 24-9. Washington, DC: Peterson Institute for International Economics.

  15. Estimated COVID-19 outbreak impact on economy in China 2020, by industry

    • statista.com
    Updated May 22, 2024
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    Statista (2024). Estimated COVID-19 outbreak impact on economy in China 2020, by industry [Dataset]. https://www.statista.com/statistics/1103062/china-estimated-coronavirus-covid-19-impact-on-economic-growth-by-sector/
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    Dataset updated
    May 22, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    China
    Description

    The impact of coronavirus COVID-19 outbreak with a prolonged shutdown of business operation could be devastating on China's economy. Recreation industry was estimated to suffer the most with a drop by 5.8 percentage points form the baseline of no virus outbreak. Transportation, trade and communication services were other hard-hit industries.

  16. H

    Replication data for Competing with the dragon: Employment effects of...

    • dataverse.harvard.edu
    Updated Sep 3, 2017
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    Stefan Thewissen; Olaf Van Vliet (2017). Replication data for Competing with the dragon: Employment effects of Chinese trade competition in 17 sectors across 18 OECD countries [Dataset]. http://doi.org/10.7910/DVN/ZJDLGE
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 3, 2017
    Dataset provided by
    Harvard Dataverse
    Authors
    Stefan Thewissen; Olaf Van Vliet
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    China’s rapid rise on the global economic stage has substantial and unequal employment effects in advanced industrialised democracies given China’s large volume of low-wage labour. Thus far, these effects have not been analysed in the comparative political economy literature. Building on pooled time-series data, we analyse the effects of Chinese trade competition across 17 sectors in 18 countries. We devote attention to a new channel, increased competition from China in foreign export markets. Our empirical findings reveal overall employment declines in sectors more exposed to Chinese imports. Furthermore, our results suggest that employment effects are not equally shared across skill levels, as the share of hours worked worsen for low-skilled workers.

  17. China Producer Price Index: YoY

    • ceicdata.com
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    CEICdata.com, China Producer Price Index: YoY [Dataset]. https://www.ceicdata.com/en/china/producer-price-index-price-factor-effect-of-last-and-current-year/producer-price-index-yoy
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    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 1, 2024 - Feb 1, 2025
    Area covered
    China
    Variables measured
    Producer Prices
    Description

    China Producer Price Index: YoY data was reported at -2.200 % in Feb 2025. This records an increase from the previous number of -2.300 % for Jan 2025. China Producer Price Index: YoY data is updated monthly, averaging 1.350 % from Jan 1993 (Median) to Feb 2025, with 386 observations. The data reached an all-time high of 26.000 % in May 1993 and a record low of -8.220 % in Jul 2009. China Producer Price Index: YoY data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IE: Producer Price Index: Price Factor Effect of Last and Current Year.

  18. J

    Economic impact of the most drastic lockdown during COVID‐19 pandemic—The...

    • jda-test.zbw.eu
    txt
    Updated Nov 8, 2022
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    Xiao Ke; Cheng Hsiao; Xiao Ke; Cheng Hsiao (2022). Economic impact of the most drastic lockdown during COVID‐19 pandemic—The experience of Hubei, China (replication data) [Dataset]. https://jda-test.zbw.eu/dataset/economic-impact-of-the-most-drastic-lockdown-during-covid19-pandemicthe-experience-of-hubei-china
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    txt(2283), txt(13558)Available download formats
    Dataset updated
    Nov 8, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Xiao Ke; Cheng Hsiao; Xiao Ke; Cheng Hsiao
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Hubei, China
    Description

    This paper uses a panel data approach to assess the evolution of economic consequences of the drastic lockdown policy in the epicenter of COVID-19-the Hubei Province of China during worldwide curbs on economic activity. We find that the drastic 76-day COVID-19 lockdown policy brought huge negative impacts on Hubei's economy. In 2020:q1, the lockdown quarter, the treatment effect on GDP was about 37% of the counterfactual. However, the drastic lockdown also brought the spread of COVID-19 under control in little more than two months. After the government lifted the lockdown in early April, the economy quickly recovered with the exception of passenger transportation sector which rebounded not as quickly as the rest of the general economy.

  19. D

    Data from: Is the China-effect real? Ideational change and the increasing...

    • dataverse.nl
    Updated Mar 11, 2024
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    Milan Babic; Milan Babic (2024). Is the China-effect real? Ideational change and the increasing political contestation of Chinese state-led investment in Europe. [Dataset]. http://doi.org/10.34894/VUESC3
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    Dataset updated
    Mar 11, 2024
    Dataset provided by
    DataverseNL
    Authors
    Milan Babic; Milan Babic
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Europe, China
    Description

    The 10 expert interviews have been conducted online due to the ongoing Covid-restrictions. 9 interviews have been conducted in-person, 1 has been conducted in written form. 5 interviewees were from the UK and 5 from Germany. We conducted 7 interviews in English together, while 3 interviews were conducted by Milan Babić in German. 6 interviewees consented to being recorded and we saved the recording for further analysis. All interviewees that were recorded gave us their explicit oral consent to do so.

  20. f

    S1 File -

    • plos.figshare.com
    xlsx
    Updated Oct 12, 2023
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    Qin Wang; Chao Zhou (2023). S1 File - [Dataset]. http://doi.org/10.1371/journal.pone.0292223.s001
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    xlsxAvailable download formats
    Dataset updated
    Oct 12, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Qin Wang; Chao Zhou
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The 14th Five-Year Plan stated that China should accelerate green development and promote a comprehensive green transition in economic and social development. As China’s economic growth slows, green development becomes greatly significant for the high-quality development of the economy. Based on China’s provincial panel data from 2005 to 2021, this study applies fixed effects model and mediating effect model to explore the influence of the government environmental investment on green development. The results indicate that (1) the government environmental investment was conducive to green development, but such effect weakened with time. (2) The government environmental investment indirectly promoted green development through the application of green patents and economic agglomeration. (3) The promotional effect of the government environmental investment varied according to region and time. Specifically, investment exerted the most significant effect on the green development of eastern China, which became more evident after 2015. The government should promote green development by implementing long-term assessment and accountability mechanisms, expanding the scale of economic agglomeration, improving the efficiency of the application of green patents, and improving the accuracy of environmental investment.

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Jin, Yinghua; Gunby, Philip; Reed, W. Robert (2023). DID FDI REALLY CAUSE CHINESE ECONOMIC GROWTH? A META-ANALYSIS [Dataset]. http://doi.org/10.7910/DVN/Y09KOV

Data from: DID FDI REALLY CAUSE CHINESE ECONOMIC GROWTH? A META-ANALYSIS

Related Article
Explore at:
Dataset updated
Nov 21, 2023
Dataset provided by
Harvard Dataverse
Authors
Jin, Yinghua; Gunby, Philip; Reed, W. Robert
Description

This study performs a meta-analysis of research that estimates the relationship between FDI and Chinese economic growth. Our sample includes 37 studies and a total of 280 estimates. We include both English- and Chinese-language studies. Our initial “raw” finding is that FDI has had a substantial, positive impact on Chinese economic growth. Furthermore, our results suggest that the effect is not inflated by endogeneity, nor impacted by publication bias. However, the positive effect is found to be smaller for more recent and better designed studies. When we adjust for preferred study and sample characteristics, we find that the estimated economic effect of FDI on Chinese economic growth is much smaller than indicated by the overall literature, and statistically insignificant. This suggests that the cause(s) of the Chinese “economic miracle” likely lie elsewhere.

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