Bank Geographical Proximity and its Impact on Local Enterprise R&D Investment: A Financial Geography Perspective

Authors

  • Zhihuan Huang Ph.D Candidate, Macau University of Science and Technology, The Institute for Sustainable Development, O707, Avenida Wai Long, Taipa, 999078, Macau SAR, China
  • Kunyuan Liu Post-doctoral Research Fellow, Guangzhou University, Guangzhou Institute of International Finance, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, 510006, Guangzhou, Guangdong Province, P. R. China
  • Dongshu Cheng Ph.D, Assistant Researcher, Guangdong University of Science and Technology, College of Management, No. 99, Xihu Road, Nancheng District, 523000, Guangzhou, Guangdong Province, P. R. China

DOI:

https://doi.org/10.52152/23.1.162-185(2025)

Keywords:

R&D investment, geographical proximity, local governance, financial geography, spatial measurement

Abstract

The purpose of the paper is to investigate the relationship between the geographical proximity of banks to enterprises and the level of R&D investment of enterprises in local governance process by constructing a credit model based on the perspective of financial geography. By using China’s 2012–2019 provincial panel data and spatial measurement method, it is possible to demonstrate the process of argumentation. Based on the findings of our research, we found that a regional enterprise’s proximity to a bank branch and its cognitive proximity to the bank branch’s headquarters positively impact the level of R&D that an enterprise in the region is able to achieve. There is also a spatial spillover effect associated with the geographic and cultural proximity between bank branches and their headquarters. In conclusion, to give full play to the financial support role of banks in the R&D of enterprises in various regions of China, the impact of geographical proximity on financial activities and the optimization of the geographical distribution structure of the banking industry is suggested to be noticed.

References

Alessandrini, P., Croci, M., & Zazzaro, A. (2009). The geography of banking power: The role of functional distance. In D. Silipo (Ed.), The Banks and the Italian Economy (pp. 93-123). Berlin: Springer. doi:10.1007/978-3-7908-2112-3_5

Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77-104. doi:10.1016/0166-0462(95)02111-6

Arbia, G., & Piras, G. (2005). Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects (ISAE Working Paper No. 51). doi:10.2139/ssrn.936327

Bartoli, F., Ferri, G., Murro, P., & Rotondi, Z. (2013). SME financing and the choice of lending technology in Italy: Complementarity or substitutability?. Journal of Banking & Finance, 37(12), 5476-5485. doi:10.1016/j.jbankfin.2013.08.007

Carlstrom, C. T., & Fuerst, T. S. (1997). Agency costs, net worth, and business fluctuations: A computable general equilibrium analysis. The American Economic Review, 87(5), 893-910. doi:10.26509/frbc-wp-199602

Del Bo, C. F., & Florio, M. (2012). Infrastructure and growth in a spatial framework: evidence from the EU regions. European Planning Studies, 20(8), 1393-1414. doi:10.1080/09654313.2012.680587

Ding, L., Wu, Y., & Long, J. (2023). Incentive effect of tax preferences towards the technological innovation of enterprises—Based on China’s GEM listed companies. PLoS ONE, 18(4), e0282692. doi:10.1371/journal.pone.0282692

Elhorst, J. P. (2014). Spatial econometrics from cross-sectional data to spatial panels. Boston, MA: Springer. doi:10.1007/978-3-642-40340-8

Ge, Y., & Qiu, J. (2007). Financial development, bank discrimination and trade credit. Journal of Banking & Finance, 31(2), 513-530. doi:10.1016/j.jbankfin.2006.07.009

Hsiao, C. (2022). Analysis of Panel Data. Cambridge, UK: Cambridge University Press. doi:10.1017/9781009057745

Hughes, A. (2023). Klaviyo to bring first saas ipo since late 2021. Retrieved from https://www.ifre.com/story/4095346/klaviyo-to-bring-first-saas-ipo-since-late-2021-s97plnkv6n

Lee, L. F., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165-185. doi:10.1016/j.jeconom.2009.08.001

LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. London, UK: Chapman and Hall/CRC. doi:10.1201/9781420064254

Li, Z., Huang, G., Chen, F., & Zhang, Z. (2014). The geographic distribution characteristics research of Chinese joint-stock commercial bank. Economic Geography, 34(2), 19-27.

Liu, X., & Zeng, F. (2022). Poverty reduction in China: Does the Agricultural Products Circulation Infrastructure Matter in Rural and Urban Areas?. Agriculture, 12(8), 1208. doi:10.3390/agriculture12081208

Ma, Y. (2015). The risk control of constructing China's "Belt and Road" initiative. China Review of Political Economy, 6(4), 189-203.

Torre, A., & Rallet, A. (2005). Proximity and localization. Regional Studies, 39(1), 47-59. doi:10.1080/0034340052000320842

Wang, F., Wu, J., Wu, M., Zheng, W., & Huang, D. (2021). Has the economic structure optimization in China’s supply-side structural reform improved the inclusive green total factor productivity?. Sustainability, 13(22), 12911. doi:10.3390/su132212911

Xiao, W., Lin, G. B., & He, J. S. (2009, September). FDI, independent innovation and economic growth: Evidences from the interprovincial experience of China. In 2009 International Conference on Management Science and Engineering (pp. 916-924). Piscataway, NJ: IEEE. doi:10.1109/ICMSE.2009.5318212

Yazdanfar, D., & Oehman, P. (2015). Debt financing and firm performance: An empirical study based on swedish data. Journal of Risk Finance, 16(1), 102-118.

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2025-01-31

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