Abstract
An important topic of how the burgeoning green digital finance development affects the resident migration decisions is explored in this research. Green digital finance has considerably elevated the quality of life for residents, which largely promotes migration. To conduct the empirical analysis, a unique dataset was compiled, combining the China Family Panel Studies in micro-level and the Digital Inclusive Finance Index in macro-level. This micro-level survey contains 156,133 observations covering 25 provinces in mainland China from 2012 to 2020 biannually. The logit model is employed as a robust tool for examining the importance of green digital finance in predicting migration decisions, which is more efficient than the traditional ordinary least squares method. Due to the relative minority proportion of migrants within the overall sample, SMOTE is utilized to address the sample imbalance issue. Our primary findings indicate that green digital finance significantly and positively affects migration decisions. This means that for areas with higher green digital finance, residents prefer to immigrate. The relationship is distinguished in terms of several subdimensions. The results of the sub-dimensional analysis show that coverage breadth, use depth, payment, insurance, and credit strongly promote migration decisions. Further analysis of the moderator effects on several personal characteristics reveals that marriage and education strengthen this positive effect on attracting migrants. Heterogeneity analysis suggested that green digital finance is more attractive to non-coastal residents than to coastal residents.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets used during the current study are available from the corresponding author on reasonable request.
References
Agresti, A. (2012). Categorical data analysis (3 ed.). Wiley.
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. SAGE Publications, Inc.
Bastian, J. E., & Black, D. A. (2024). Relaxing financial constraints with tax credits and migrating out of rural and distressed America. Journal of Public Economics, 234, 105111. https://doi.org/10.1016/j.jpubeco.2024.105111.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artifical Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953.
Chen, R., Xu, P., Li, F., & Song, P. (2018). Internal migration and regional differences of population aging: An empirical study of 287 cities in China. Bioscience Trends, 12(2), 132–141. https://doi.org/10.5582/bst.2017.01246.
Da, H., **aotao, Z., Shuang, M., Gang, W., Junhang, L., & Ruoqi, D. (2022). Green finance and energy efficiency: Dynamic study of the spatial externality of institutional support in a digital economy by using hidden Markov chain. Energy Economics, 116, 106431. https://doi.org/10.1016/j.eneco.2022.106431.
Deng, L., & Qian, P. (2024). Mitigating effect of digital payments on the micro-enterprises’ financing constraints. Finance Research Letters, 62, 105209. https://doi.org/10.1016/j.frl.2024.105209.
Domenech, T., Bleischwitz, R., Doranova, A., Panayotopoulos, D., & Roman, L. (2019). Map** Industrial Symbiosis Development in Europe_ typologies of networks, characteristics, performance and contribution to the Circular Economy. Resources Conservation and Recycling, 141, 76–98. https://doi.org/10.1016/j.resconrec.2018.09.016.
Du, Y., Wang, Q., & Zhou, J. (2023). How does digital inclusive finance affect economic resilience: Evidence from 285 cities in China. International Review of Financial Analysis, 88, 102709. https://doi.org/10.1016/j.irfa.2023.102709.
Feng, S., Liu, J., & Xu, D. (2023). Digital financial development and indirect household carbon emissions: Empirical evidence from China. Environment Development and Sustainability. https://doi.org/10.1007/s10668-023-03603-4.
Fonseca, J., & Bacao, F. (2023). Geometric SMOTE for imbalanced datasets with nominal and continuous features. Expert Systems with Applications, 234, 121053. https://doi.org/10.1016/j.eswa.2023.121053.
Gu, H., Wang, J., & Ling, Y. (2024). Economic geography of talent migration and agglomeration in China: A dual-driver framework. China Economic Review, 86, 102180. https://doi.org/10.1016/j.chieco.2024.102180.
Haddad, C., & Hornuf, L. (2018). The emergence of the global fintech market: Economic and technological determinants. Small Business Economics, 53(1), 81–105. https://doi.org/10.1007/s11187-018-9991-x.
Han, L., **ao, J. J., & Su, Z. (2019). Financing knowledge, risk attitude and P2P borrowing in China. International Journal of Consumer Studies, 43(2), 166–177. https://doi.org/10.1111/ijcs.12494.
Hu, D., Guo, F., & Zhai, C. (2023). Digital finance, entrepreneurship and the household income gap: Evidence from China. Information Processing & Management, 60(5), 103478. https://doi.org/10.1016/j.ipm.2023.103478.
Iqbal, M., Rehman, H., Arshed, N., & Sardar, M. S. (2021). The macroeconomic and demographic determinants of saving behavior in selected countries of Asia. Journal Global Policy and Governance, 10(1), 49–65. https://doi.org/10.14666/2194-7759-10-1-004.
Iqbal, M., Kalim, R., Ul-Durar, S., & Varma, A. (2024). Environmental sustainability through aggregate demand behavior – does knowledge economy have global responsibility? Journal of Global Responsibility, 15(2), 158–178. https://doi.org/10.1108/JGR-02-2023-0018.
Kwan, F., Wu, Y., & Zhuo, S. (2018a). Surplus agricultural labour and China’s Lewis turning point. China Economic Review, 48, 244–257. https://doi.org/10.1016/j.chieco.2017.01.009.
Kwan, F., Zhang, Y., & Zhuo, S. (2018b). Labour reallocation, productivity growth and dualism: The case of China. International Review of Economics & Finance, 57, 198–210. https://doi.org/10.1016/j.iref.2018.01.004.
Li, J., Wu, Y., & **ao, J. J. (2020). The impact of digital finance on household consumption: Evidence from China. Economic Modelling, 86, 317–326. https://doi.org/10.1016/j.econmod.2019.09.027.
Li, X., Ma, L., & Tang, Y. (2024). Migration and resource misallocation in China. Journal of Development Economics, 167, 103218. https://doi.org/10.1016/j.jdeveco.2023.103218.
Lin, H., & Zhang, Z. (2023). The impacts of digital finance development on household income, consumption, and financial asset holding: An extreme value analysis of China’s microdata. Personal and Ubiquitous Computing, 27(4), 1607–1627. https://doi.org/10.1007/s00779-022-01667-z.
Long, F., Liu, J., & Zheng, L. (2022). The effects of public environmental concern on urban-rural environmental inequality: Evidence from Chinese industrial enterprises. Sustainable Cities and Society, 80, 103787. https://doi.org/10.1016/j.scs.2022.103787.
Luo, H., & Hu, Q. (2023). A re-examination of the influence of human capital on urban-rural income gap in China: College enrollment expansion, digital economy and spatial spillover. Economic Analysis and Policy, 81, 494–519. https://doi.org/10.1016/j.eap.2023.12.018.
Luo, S., Sun, Y., & Zhou, R. (2022). Can fintech innovation promote household consumption? Evidence from China family panel studies. International Review of Financial Analysis, 82, 1–12. https://doi.org/10.1016/j.irfa.2022.102137.
Meng, X., & Zhang, J. S. (2001). The two-tier labor market in urban China - Occupational segregation and wage differentials between urban residents and rural migrants in Shanghai. Journal of Comparative Economics, 29(3), 485–504. https://doi.org/10.1006/jcec.2001.1730.
Muse, E. D., Godino, J. G., Netting, J. F., Alexander, J. F., Moran, H. J., & Topol, E. J. (2018). From second to hundredth opinion in medicine: A global consultation platform for physicians. NPJ Digital Medicine, 1, 55. https://doi.org/10.1038/s41746-018-0064-y.
Song, Y., Gong, Y., Song, Y., & Chen, X. (2024). Exploring the impact of digital inclusive finance on consumption volatility: Insights from household entrepreneurship and income volatility. Technological Forecasting and Social Change, 200, 123179. https://doi.org/10.1016/j.techfore.2023.123179.
Tan, F., Hou, X., Zhang, J., Wei, Z., & Yan, Z. (2019). A deep learning approach to competing risks representation in peer-to-peer lending. IEEE Transactions on Neural Networks and Learning Systems, 30(5), 1565–1574. https://doi.org/10.1109/tnnls.2018.2870573.
Tao, Y., Yang, R., Zhuo, X., Wang, F., & Yang, X. (2023). Financing the capital-constrained online retailer with risk aversion: Coordinating strategy analysis. Annals of Operations Research, 321–349. https://doi.org/10.1007/s10479-022-04632-4.
Taylor, M. (2011). Measuring financial capability and its determinants using survey data. Social Indicators Research, 102(2), 297–314. https://doi.org/10.1007/s11205-010-9681-9.
Thabtah, F., Kamalov, F., & Rajab, K. (2018). A new computational intelligence approach to detect autistic features for autism screening. International Journal of Medical Informatics, 117, 112–124. https://doi.org/10.1016/j.ijmedinf.2018.06.009.
Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429–441. https://doi.org/10.1016/j.ins.2019.11.004.
Todaro, M. P. (1969). A model of labor migration and urban unemployment in less developed countries. The American Economic Review, 59, 138–148. https://doi.org/stable/1811100
Wen, X., Cheng, Z., & Tani, M. (2024). Rural-urban migration, financial literacy, and entrepreneurship. Journal of Business Research, 175, 114302. https://doi.org/10.1016/j.jbusres.2023.114302.
Wu, J., Zhuo, S., & Wu, Z. (2017). National innovation system, social entrepreneurship, and rural economic growth in China. Technological Forecasting and Social Change, 121, 238–250. https://doi.org/10.1016/j.techfore.2016.10.014.
**e, Y., & Hu, J. W. (2014). An introduction to the China Family Panel Studies (CFPS). Chinese Sociological Review, 47(1), 3–29. https://doi.org/10.2753/csa2162-0555470101.
**n, C., Fan, S., & Guo, Z. (2023). Can digital finance promote inclusive growth to meet sustainable development in China? A machine learning approach. Environment Development and Sustainability. https://doi.org/10.1007/s10668-023-03748-2.
Yin, Z., Gong, X., Guo, P., & Wu, T. (2019). What drives entrepreneurship in digital economy? Evidence from China. Economic Modelling, 82, 66–73. https://doi.org/10.1016/j.econmod.2019.09.026.
Yue, P., Korkmaz, A. G., Yin, Z., & Zhou, H. (2022). The rise of digital finance: Financial inclusion or debt trap? Finance Research Letters, 47, 102604–102611. https://doi.org/10.1016/j.frl.2021.102604.
Zhang, S., Li, X., & Shi, D. (2023). How does digital finance affect energy consumption in China? Empirical evidence from China. Environment Development and Sustainability. https://doi.org/10.1007/s10668-023-04329-z.
Zhao, H., Peng, H., & Li, W. (2022). Analysis of factors affecting individuals’ online consumer credit behavior: Evidence from China. Frontiers in Psychology, 13, 922571–922571. https://doi.org/10.3389/fpsyg.2022.922571.
Zhong, W., & Jiang, T. (2021). Can internet finance alleviate the exclusiveness of traditional finance? Evidence from Chinese P2P lending markets. Finance Research Letters, 40, 101731–101738. https://doi.org/10.1016/j.frl.2020.101731.
Zhuo, S., & Jia, L. (2023). Energy poverty, internal immigration, and sustainable development: Empirical evidence from China. Energies, 16(21), 7241. https://doi.org/10.3390/en16217241.
Zou, J., & Deng, X. (2022). To inhibit or to promote: How does the digital economy affect urban migrant integration in China? Technological Forecasting and Social Change, 179, 121647–121657. https://doi.org/10.1016/j.techfore.2022.121647.
Funding
The authors would like to acknowledge the financial support provided by Macau University of Science and Technology Faculty Research Grants for this research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No potential conflict of interest was reported by the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhuo, S., Jia, L. The impact of green digital finance on migration decisions: Evidence from China. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05141-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10668-024-05141-z