Do Local Government Talent Introduction Policies Promote Regional Innovation？ Evidence from a Quasi-Natural Experiment
ZHONG Teng, LUO Jigang, WANG Changyun
School of Banking and Finance, University of International Business and Economics; School of Economics, Fudan University; China Financial Policy Research Center, Renmin University of China
Summary:
The importance of highly skilled human capital in China's economic transformation is self-evident. Since 2008, China's central government and local governments have implemented a large number of talent introduction policies, making competition for talent increasingly fierce across regions. However, whether talent introduction policies, especially at the local level, promote innovation is still a controversial issue. On the one hand, such policies can introduce advanced concepts and technologies to a region and accelerate local innovative upgrades and industrial transformations. Furthermore, the policy subsidies can increase firm profits and productivity, thereby promoting regional innovation. On the other hand, some local governments may enact talent policies for the purpose of maintaining demographic dividends and performance projects. The introduction and implementation of policies are often accompanied by problems such as inefficient investment and fiscal waste, which may have a negative effect on regional innovation. Therefore, this study uses data on local governments' talent policies to conduct a systematic empirical study of whether the policies significantly improve regional innovation and whether there are incentive distortions. It also explores the underlying mechanisms and provides guidelines for optimizing local talent introduction policies so that they better achieve innovative development. We take the local talent introduction policies enacted in 39 cities between 2009 and 2012 as a quasi-natural experiment and use the multi-period difference-in-differences (DID) method to explore the impact of local government talent introduction policies on regional innovation. First, using the “Guidelines for the Introduction of Overseas Technological and Innovative Talents in China's Provinces and Cities” compiled by the Department of International Cooperation of the Ministry of Science and Technology in 2013, we determine the time at which each city implemented its talent introduction policy. We consider this an appropriate setting for a quasi-natural experiment. To simplify our inter-city comparisons, we adopt the propensity score matching method (PSM). Applying one-to-one nearest neighbor matching to the cities' characteristic variables, we construct a control group of cities that are similar to those in the experimental group. Through the above process, we obtain a sample with 39 cities in the experimental group and 39 cities in the control group for the period 2006-2015. Further, we use two indicators to reflect a city's innovation level. The first is the number of valid patent applications in a city over the sample period, collected from the Soopat database. This indicator measures regional innovation capabilities from the perspective of patent quantity. The second indicator is a city's score on the China City Innovation Index, issued by the Industrial Development Research Center of Fudan University, which measures regional innovation capabilities on the basis of patent value. Based on the above specifications, we implement a multiple-period DID regression to investigate whether there are significant differences in the innovation capabilities of the experimental and control cities before and after the enactment of talent introduction policies. We draw the following conclusions. Local government talent introduction policies increase both the quantity and value of patents in the region. Specifically, the policies increase innovation by expanding the scale of R&D investments rather than by enhancing innovation efficiency. In regions with poorer business environments and less protection of intellectual property rights the main effect of the policies is an increase in the quantity of patents, but in regions with better business environments and more protections the policies improve the value of patents. Furthermore, we find that the policies are more effective in regions with lower fiscal investment in science and education than in regions with higher investment. This study makes three contributions. First, previous research focuses on the role of talent policies at the national level and concentrates on qualitative analysis. In contrast, this study focuses on city-level talent polices. Through rigorous empirical analysis, we explore the impact of talent introduction policies on innovation at the local level. Second, we not only examine the direct effects of the policies but also discuss the underlying mechanisms, thus providing a more comprehensive and in-depth description of how the policies function. Third, China is currently facing a reduction in labor force and a slowdown in economic growth. Talent has become a new driving force that is urgently needed for local economic development. Hence, our conclusions have important policy implications for local governments seeking to optimize talent incentive policies and implement innovation-driven development strategies.
钟腾, 罗吉罡, 汪昌云. 地方政府人才引进政策促进了区域创新吗？——来自准自然实验的证据[J]. 金融研究, 2021, 491(5): 135-152.
ZHONG Teng, LUO Jigang, WANG Changyun. Do Local Government Talent Introduction Policies Promote Regional Innovation？ Evidence from a Quasi-Natural Experiment. Journal of Financial Research, 2021, 491(5): 135-152.
Acemoglu, D., P. Antràs, and E. Helpman. 2007. “Contracts and Technology Adoption”, American Economic Review, 97(3): 916 ～ 943.
[23]
Beck, T., R. Levine, and A. Levkov. 2010. “Big Bad Banks？ The Winners and Losers from Bank Deregulation in the United States”, Journal of Finance, 65(5): 1637～1667.
[24]
Boeing, P. 2016. “The Allocation and Effectiveness of China's R&D Subsidies: Evidence from Listed Firms”, Research Policy, 45(9): 1774～1789.
[25]
Borjas, G. J., and K. B. Doran. 2012. “The Collapse of the Soviet Union and the Productivity of American Mathematicians”, Quarterly Journal of Economics, 127(3): 1143～1203.
[26]
Branstetter, L., and M. Sakakibara. 1998. “Japanese Research Consortia: A Microeconometric Analysis of Industrial Policy”, Journal of Industrial Economics, 46(2): 207～233.
[27]
Burrus, R. T., J. E. Graham, and A. T. Jones. 2018. “Regional Innovation and Firm Performance”, Journal of Business Research, 88: 357～362.
[28]
Cai, J., Y. Chen, and X. Wang. 2018. “The Impact of Corporate Taxes on Firm Innovation: Evidence from the Corporate Tax Collection Reform in China”, NBER Working Paper No. 25146.
[29]
Evans, N., and R. Bosua. 2017. “Exploring Innovation in Regional Manufacturing SMEs”, Small Enterprise Research, 24(2): 149～166.
[30]
Fang, L., J. Lerner, C. Wu, and Q. Zhang. 2018. “Corruption, Government Subsidies, and Innovation: Evidence from China”, NBER Working Paper No. 25098.
[31]
Ganguli, I. 2015. “Immigration and Ideas: What Did Russian Scientists ‘Bring’ to the United States？”, Journal of Labor Economics, 33(3):257～288.
[32]
Görg, H., and E. Strobl. 2007. “The Effect of R&D Subsidies on Private R&D”, Economica, 74(294): 215～234.
[33]
Guo, D., Y. Guo, and K. Jiang. 2016. “Government-Subsidized R&D and Firm Innovation: Evidence from China”, Research Policy, 45(1): 1129～1144.
[34]
Howell, A. 2017. “Picking ‘Winners’ in China: Do Subsidies Matter for Indigenous Innovation and Firm Productivity？”, China Economic Review, 44: 154～165.
[35]
Hunt, J., and M. Gauthier-Loisell. 2010. “How Much Does Immigration Boost Innovation？”, American Economic Journal: Macroeconomics, 2(2): 31～56.
[36]
Kerr, W. R., and W. F. Lincoln. 2010. “The Supply Side of Innovation: H-1B Visa Reforms and US Ethnic Invention”, Journal of Labor Economics, 28(3): 473～508.
[37]
Moser, P., A. Voena, and F. Waldinger. 2014. “German-Jewish Emigres and US Invention”, American Economic Review, 104(10): 32～55.
[38]
Wang, N., and J. Hagedoorn. 2014. “The Lag Structure of the Relationship between Patenting and Internal R&D Revisited”, Research Policy, 43(8): 1275～1285.
