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Crowds in or crowds out? The effect of foreign direct investment on domestic investment in Chinese cities

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Abstract

This study investigates the empirical relationship between foreign direct investment (FDI) and domestic investment (DI) in China using a comprehensive city-level panel over the period from 2003 to 2011. System-generalized method-of-moment estimation reveals mixed results. At the national level, FDI neither crowds in nor crowds out DI, indicating a neutral FDI–DI nexus. However, when the full sample is segmented by geographical topology, a positive and significant FDI–DI nexus can be found in eastern and, to a lesser extent, central cities. A negative, although insignificant, association is reported among western cities. Further, the empirical nexus is conditional on several local absorptive capacities including human capital, financial development, and institutional quality. These findings suggest that a region-based FDI strategy in general and local governments should strengthen their absorptive capacities to fully internalize FDI spillovers.

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Notes

  1. It is unlikely that FDI would increase after the onset of economic crisis. However, it does, owing to “fire-sale,” termed by Krugman (2000), in the scenario that firms in developing countries avoid cash constraints by ceasing their ownership to foreign investors at a discounted price. Using firm-level data from China, Huang et al. (2016) empirically confirm this hypothesis.

  2. In his book-length discussion, Huang (2003) suggests a political pecking order by which firms with different ownerships are offered varying degrees of protection and resources at arbitrary cost. This list in descending order is SOEs, collectively owned firms, foreign firms (including joint ventures), and domestically owned private firms.

  3. Fixed and time effect is controlled in panel regression. These results are available upon request.

  4. The time persistence of the variables has been empirically verified by a panel unit root test. Detailed results are available upon request.

  5. Saving is a stock variable and is unlikely to be influenced by domestic investment (flow variable) over the short term. In a more restricted setting, SAVING and GOV are both treated as endogenous, and the main results remain qualitatively similar. Detailed estimates are available upon request.

  6. The remaining 54 cities (269–215) are dropped to maintain a balanced panel. Using the full set of cities at the expense of having an unbalanced panel produces qualitatively similar results. These estimations are available upon request from authors.

  7. Chinese authority has been very restrictive on FDI in the form of M&A until recently.

  8. Using the dataset from the World Bank, real economic growth in China over the same period is 10.4% annually.

  9. Eastern provinces: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. Central provinces: Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, and Guangxi. Western provinces: Sichuan, Chongqing, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Ningxia, and Qinghai.

  10. The model is said to be explosive or unstable if the estimated coefficient of the lagged dependent variable turns out to be greater than one in magnitude.

  11. FE and RE-effect estimations are also conducted, and their results are in line with the system GMM estimator. The full regression table is available upon request.

  12. The rest three entry modes are contract joint ventures (CJVs), FDI shareholding and joint exploration. More information regarding them can be obtained in Chen et al. (2017a, b) and Wei et al. (2005).

  13. Full table is available from authors upon request.

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Acknowledgements

The authors are grateful to two anonymous referees and Professor Robert Kunst for helpful comments and suggestions, which tremendously improved the quality and presentation of this article. Yao acknowledges the comments from Professor Renato Andrin Villano. However, authors are responsible for any error that remains.

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Authors and Affiliations

  1. School of International Economics and Trade, Shanghai Lixin University of Accounting and Finance, 995 Shangchuan Road, Shanghai, 201209, China

    Yao Yao

  2. School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong

    Yao Yao

  3. School of Economics and Finance, Curtin Business School, Curtin University, Perth, WA, 6845, Australia

    Ruhul Salim

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Appendix A: Construction of local absorptive capacities

Appendix A: Construction of local absorptive capacities

Due to data constraints, conventional HC measures, such as average years of schooling and proportion of workers holding certain academic qualification, are not available at a local level. Instead, one turns to the share of enrolled tertiary students in the total population. This proxy has been widely adopted in the literature to measure HC stock (e.g., Fleisher et al. 2010; Su and Liu 2016; Zhang and Zhuang 2011).

The construction of FD proxy is more complicated. In theory, the private-sector loans-to-GDP ratio provides the best measure for the efficiency of the financial system in China, as it isolates the effects of credit to SOEs and credit from the central bank (Calderón and Liu 2003; Choong 2012). However, calculating such a ratio at a city level is not straightforward, as publicly available statistical sources only provide three of the eight classes of loans in China: industrial, commercial, and agricultural. (The remaining five loan classes are construction loans, loans to township and village enterprises, loans to foreign-invested enterprises, and loans to private enterprises and individuals.) Although imperfect, the volume of private-sector loans is estimated by subtracting the sum of industrial, commercial, and agricultural loans from the total loans. This decision rests on the basis that “industrial loans and commercial loans are designated by the government as loans for SOEs; agricultural loans are also required by the government to promote the development of agriculture, rural areas and farmer” (Yao 2010, 27). To take the size and income level of the city into account, the resulting figure is adjusted by total deposits.

Finally, measuring INST at the local level in China is also challengeable. Lardy (2014) suggests that private investment in China tends to be high in regions with relatively sound INST, for example, less government intervention, red tape, and state expropriation. As such, the share of private investment in total investment could partially reflect local INST. However, as private investment at city level is unavailable, the share of private-sector employees in total employees is used as a proxy for INST.

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Yao, Y., Salim, R. Crowds in or crowds out? The effect of foreign direct investment on domestic investment in Chinese cities. Empir Econ 58, 2129–2154 (2020). https://doi.org/10.1007/s00181-018-1611-8

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