Download pdf
Analysis of the Impact of Talent Introduction Policies on Urban Development Based on a Multi-Period Difference-in-Differences Model: A Case Study of the Yangtze River Delta Urban Agglomeration
- 1 Tianjin University
* Author to whom correspondence should be addressed.
Abstract
Since the beginning of the new century, the process of globalization has deepened, prompting local governments to implement talent introduction policies to maintain sustainable urban development. Focusing on 27 cities in the Yangtze River Delta region, this study collects talent introduction policy texts and panel data from 2000 to 2010. Using Latent Dirichlet Allocation (LDA) for topic mining on text data, five main topics are identified: talent structure, industrial development, scientific and technological expenditures, financial development level, and innovation and entrepreneurship. To investigate the impact of talent introduction policies on these five topics and the overall level of urban economic development, this study uses the “talent introduction policy” as a quasi-natural experiment. Empirical analysis is conducted with a multi-period difference-in-differences (DID) model and double machine learning to explore the intrinsic mechanisms by which these policies empower urban economic development. The results reveal that: (1) Talent introduction policies have significantly promoted urban economic development, and this conclusion remains robust after a series of tests; (2) These policies indirectly boost urban economic development by enhancing financial development efficiency and increasing educational expenditure; (3) The economic growth-promoting effects of talent introduction policies are more evident in provincial capitals or municipalities, eastern cities, and cities with "211 Project" universities; (4) Talent introduction policies positively impact scientific and technological expenditures and innovation and entrepreneurship, negatively impact industrial structure, and have no significant effect on talent structure and financial development level. This study confirms the necessity of talent introduction policies since the beginning of the new century and provides recommendations for their continued implementation.
Keywords
Talent Introduction Policy, multi-period difference-in-differences Model, LDA topic model, economic growth, double machine learning
[1]. Zong, Z. (2010). Research on local government policies for attracting high-level overseas talent [Master's thesis, Shanghai Jiao Tong University].
[2]. Su, J. (2019). Research on high-level talent introduction policies in Tianjin. Shanxi Finance and Economics University Journal.
[3]. Yang, H., & Chen, Y. (2013). Evaluation of the implementation effects of overseas high-level talent introduction policies: A case study of the Central "Thousand Talents Plan". Science and Technology Progress and Policy, 30(16), 107–112.
[4]. Chen, Y., & Li, C. (2024). Research on the impact of talent introduction policies on China's economic growth: An empirical test using the difference-in-differences method. Business Review, 10(15), 47–52, 75.
[5]. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(4–5), 993–1022.
[6]. Xu, C. (2015). The spillover effects of educational investment and its internalization. Educational Academic Monthly, (3), 40–47. https://doi.org/10.16477/j.cnki.issn1674-2311.2015.03.005
[7]. Liu, J., & Zhang, L. (2019). Dynamic effect analysis of China's fiscal policy on the quality of economic growth. Finance and Economics Review, (7), 23–34. https://doi.org/10.13762/j.cnki.cjlc.2019.07.001
[8]. Lin, Y. (2019). Research on the mechanism of human capital driving economic growth. Financial Economics, (2), 131–132. https://doi.org/10.14057/j.cnki.cn43-1156/f.2019.02.053
[9]. Li, L. (2023). Research on the impact of technological innovation on regional economic development: An empirical analysis based on provincial panel data in China. Price Theory and Practice, (2), 184–187. https://doi.org/10.19851/j.cnki.cn11-1010/f.2023.02.161
[10]. Li, G., & Wang, C. (2012). Spatial characteristics and temporal evolution of innovation output in China's provinces: An exploratory spatial data analysis. Geographical Research, 31(1), 95–106.
[11]. Zhang, T., & Li, J. (2023). Network infrastructure, inclusive green growth, and regional disparities: Causal inference based on double machine learning. Quantitative Economic and Technical Economic Research, 40(4), 113–135. https://doi.org/10.13653/j.cnki.jqte.20230310.005
[12]. Jiang, T. (2022). Mediating and moderating effects in causal inference empirical research. China Industrial Economy, (5), 100–120. https://doi.org/10.19581/j.cnki.ciejournal.2022.05.005
[13]. Pan, H., & Wei, H. (2018). Identifying spatial effects characteristics of the relationship among financial development, FDI, and economic growth. Statistics and Decision, 34(22), 154–157. https://doi.org/10.13546/j.cnki.tjyjc.2018.22.036
[14]. Chen, L., & Zhang, Z. (2011). Service industry agglomeration and regional economic disparities: A perspective from labor productivity. Research Management, 32(12), 126–133. https://doi.org/10.19571/j.cnki.1000-2995.2011.12.018
[15]. Yuan, H., & Zhu, C. (2018). Has the national high-tech zone promoted the transformation and upgrading of China's industrial structure? China Industrial Economy, (8), 60–77. https://doi.org/10.19581/j.cnki.ciejournal.2018.08.004
[16]. Gan, C., Zheng, R., & Yu, D. (2011). The impact of industrial structural changes in China on economic growth and fluctuations. Economic Research, 46(5), 4–16, 31.
[17]. Liu, Z., & Hu, S. (2024). What is the geometric impact of talent introduction policies on urban innovation performance? Based on a quasi-natural experiment of talent introduction policies. Journal of Hunan University of Technology, 38(3), 80–87.
[18]. Zhang, B. (2012). A review of new growth theory of human capital. Cooperative Economy and Technology, (18), 22–24. https://doi.org/10.13665/j.cnki.hzjjykj.2012.18.015
Cite this article
Zheng,Y. (2024). Analysis of the Impact of Talent Introduction Policies on Urban Development Based on a Multi-Period Difference-in-Differences Model: A Case Study of the Yangtze River Delta Urban Agglomeration. Journal of Applied Economics and Policy Studies,13,20-32.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Journal of Applied Economics and Policy Studies
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).