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Government involvement in banking systems and economic growth: a comparison across countries

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  • Wilson X. B. Li
  • Tina T. He
  • Stella P. L. Cho

Abstract

This study investigates 92 countries of different legal origins, including 25 English origin, 44 French origin, 11 Scandinavian and German origin, and 12 socialist origin countries. Compared to other countries, China has the highest government ownership of banks, and lies in the middle in terms of official supervisory power over banks, and government efficiency in governance. As regards economic development measured by per capita GDP growth, in the period from 1995 to 2015, China performed significantly better than all the other countries in the sample – countries varying in legal origin, government ownership of banks, level of economic and financial development, supervisory power over banks, and government efficiency. The findings are robust when we examine the country-years with similar per capita GDP as that of China. The regression results show that in some circumstances, higher government ownership of banks is associated with higher economic growth and the positive association is more significant in socialist origin countries. Further discussions suggest that the high government involvement in commercial banks fits in well with the unique characteristics of China – such as a large population, underdeveloped economy, imbalance in resources and development in different areas, as well as the utmost trust placed on the Chinese government and government owned banks – thus may benefit economic growth.

Suggested Citation

  • Wilson X. B. Li & Tina T. He & Stella P. L. Cho, 2019. "Government involvement in banking systems and economic growth: a comparison across countries," Economic and Political Studies, Taylor & Francis Journals, vol. 7(1), pages 35-65, January.
  • Handle: RePEc:taf:repsxx:v:7:y:2019:i:1:p:35-65
    DOI: 10.1080/20954816.2018.1558981
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    Cited by:

    1. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
    2. Yu, Lean & Huang, Xiaowen & Yin, Hang, 2020. "Can machine learning paradigm improve attribute noise problem in credit risk classification?," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 440-455.

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