Assessing the Impact of Transportation Infrastructure on Rural Residents' Income: Using the Quantile Regression Approach

Authors

  • Bin Xu School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian, 361005, PR China

DOI:

https://doi.org/10.6000/1929-7092.2022.11.02

Keywords:

Highway transportation infrastructure, Railway transportation infrastructure, Wage income, Operating income, Quantile regression model

Abstract

The impact of transportation infrastructure on farmers’ income has been the focus of attention by government managers and related scholars in recent years. Based on the panel data from 2000 to 2018, this paper uses the quantile regression model to explore the effect of highway and railway transportation infrastructures on wage income and operating income. The findings show that the highway transportation infrastructure makes a minimal contribution to the wage income in Shanghai, Beijing and Zhejiang provinces, because the highway mileage and highway passenger turnover in these provinces are small. However, the operating income in the upper 90th quantile provinces such as Jilin, Heilongjiang, and Zhejiang, receives the biggest impact from the highway transportation infrastructure, because the construction of rural roads in these provinces is growing faster. The impact of railway transportation infrastructure on the wage income in the 10th-25th, 25th-50th and 50th-75th quantile provinces is small, since their railway passenger turnover is less. The railway transportation infrastructure has not played a role in boosting the operating income in these provinces such as Guizhou, Shaanxi, Gansu, Shanxi, Qinghai, and Ningxia. Therefore, each quantile province should formulate specific policies to promote the construction of transportation infrastructure.

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Published

2022-08-23

How to Cite

Xu, B. (2022). Assessing the Impact of Transportation Infrastructure on Rural Residents’ Income: Using the Quantile Regression Approach. Journal of Reviews on Global Economics, 11, 7–21. https://doi.org/10.6000/1929-7092.2022.11.02

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