Please wait a minute...
Frontiers of Economics in China

ISSN 1673-3444

ISSN 1673-3568(Online)

CN 11-5744/F

邮发代号 80-978

Frontiers of Economics in China  2016, Vol. 11 Issue (2): 302-320   https://doi.org/10.3868/s060-005-016-0017-4
  本期目录
How County-Level Agricultural Loans and Fiscal Expenditure Impact Rural Residents’ Income in China——An Empirical Study of the Hierarchical Effect by Quantile Regression
Xiaohua Wang1,Li Liu2()
1. College of Economics and Management & Postdoctoral Station of Statistics, Southwest University, Chongqing 400715, China
2. Center for Research in Management, The University of Toulouse 1, Capitole, Toulouse 31042, France
 全文: PDF(718 KB)  
Abstract

Using cross-sectional data from 853 counties in 11 western China provinces, we employ quantile regression (QR) and instrumental variable quantile regression (IVQR) to investigate the hierarchical effect of fiscal expenditure and agricultural loan on rural residents’ income. We find: (1) the relationship between agricultural loan and income is consistent with the inverted U-shape (Kuznets curve); (2) the coefficient of quantile regression for rural residents’ loan gradually decreases; particularly, the impact on the high-income group is insignificant (at 0.90 quantile); (3) for 0.10 and 0.50 quantile, the increase of fiscal expenditure would hinder rather than promote income growth; (4) the restraining effect becomes more pronounced for the lower groups; in contrast, there is a significant positive relationship between income and fiscal expenditure for 0.90 quantile’s income group. Implications for government policy formulation are propounded accordingly.

Key wordsfiscal expenditure    agricultural loan    loan for rural residents    rural residents’ income    quantile regression
出版日期: 2016-06-08
 引用本文:   
. [J]. Frontiers of Economics in China, 2016, 11(2): 302-320.
Xiaohua Wang,Li Liu. How County-Level Agricultural Loans and Fiscal Expenditure Impact Rural Residents’ Income in China——An Empirical Study of the Hierarchical Effect by Quantile Regression. Front. Econ. China, 2016, 11(2): 302-320.
 链接本文:  
https://academic.hep.com.cn/fec/CN/10.3868/s060-005-016-0017-4
https://academic.hep.com.cn/fec/CN/Y2016/V11/I2/302
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed