Can Centralized Intervention Solve Rural Credit Exclusion? Evidence from China
- DOI
- 10.2991/978-94-6463-198-2_142How to use a DOI?
- Keywords
- Credit Exclusion; Centralized Intervention; Financial Capability; Blockchain
- Abstract
The purpose of this study is to analyze whether the centralized intervention represented by government intervention can effectively solve the problem of rural credit exclusion, and provide evidence for the development of global rural inclusive credit. In the process of research, this study uses the random effect model (REM) as the analysis method to analyze the data of China’s Household Financial Survey (CHFS) from 2013 to 2019. The results show that centralized intervention can significantly improve the rural credit exclusion under moderate conditions, but with the strengthening of the intervention, this effect continues to decline and become a constraint, showing an inverted “U” type. Therefore, this study suggests that to solve the problem of rural credit exclusion, in addition to moderate centralized intervention, we also need to rely more on the role of decentralized subjects.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Bingwu Liao AU - Murong Shen PY - 2023 DA - 2023/08/10 TI - Can Centralized Intervention Solve Rural Credit Exclusion? Evidence from China BT - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023) PB - Atlantis Press SP - 1362 EP - 1369 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-198-2_142 DO - 10.2991/978-94-6463-198-2_142 ID - Liao2023 ER -