http://orcid.org/0000-0001-7741-2271
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ABSTRACT
Will fiscal decentralization policy impact Foreign Direct Investment (FDI) inflows in China? Will cities attract FDI at the expense of the environment? This study aims to answer these questions using China’s city-level data in 2014 and a spatial Durbin modelling approach. We find that: (1) Fiscal decentralization does promote FDI inflows; (2) FDI inflows show significant positive spatial agglomeration and spillover effects; (3) Lower environmental regulation stringency contributes to attracting FDI inflows and a stricter environmental regulation stringency in neighboring cities would impede local FDI inflows; (4) A lower level of environmental regulation stringency would, ceteris paribus, deteriorate fiscal decentralization’s stimulation on FDI inflows.
Acknowledgments
We would like to thank the anonymous reviewers for their very helpful and constructive comments. All the views expressed in this research and any errors are the sole responsibility of the authors.
Funding
This research is supported by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (13XNJ017), the National Social Science Foundation of China (15BJL099), the Fundamental Research Funds for the Central Universities in UIBE (CXTD7-05), the Characteristics Items of International Economy (International Investment) in UIBE (324-8110051101), and the Humanities and Social Sciences Foundation of the Ministry of Education of China (13YJC790157).
Notes
1. The LR test is taken to select the most appropriate model from SDM, SAM, and SEM. LR is defined as LR = －2[LR － LU], where LR is the maximum value of the log-likelihood function for the restricted model (such as SAR model or SEM in this article), and LU is the maximum value of the log-likelihood function for the unrestricted model (such as SDM in this article). The LR test statistic asymptotically follows a chi-squared distribution with degrees of freedom equal to the number of restrictions.
2. China City Statistical Yearbook provides statistics for the entire city and the urban municipal districts under the city. Since most FDI inflows are located in the urban areas, we take the statistics of the urban municipal districts unless specifically stated in the context.
3. Generally, a prefecture-level city in China consists of districts, counties, and county-level cities. The city data in this context refer to the data of the entire prefecture-level city unless specified.
4. The 19 industries include agriculture, forestry, animal husbandry and fishery; mining; manufacturing; production and supply of electricity, heat, gas and water; construction; transportation, storage, and post; information transmission, software and information technology; hotel and catering services; wholesale and retail trades; financial intermediation; real estate; leasing and business services; scientific research, technical services and geological prospecting; management of water conservancy, environment and public facilities; residents service; education; health, social security, and social welfare; culture, sports, and entertainment; public management and social organizations.
5. Tibet is not included in this study due to lack of data availability.
6. It should be noted that some of the selected independent variables may have the endogeneity problem. One usual solution is to implement IV estimation (Arraiz et al. Citation2010; Kelejian and Prucha Citation1998, Citation2010). Unfortunately, like many other studies, we failed to find an appropriate instrument after numerous attempts. It would be an interesting and valuable challenge for future research.