Issue |
E3S Web Conf.
Volume 409, 2023
International Conference on Management Science and Engineering Management (ICMSEM 2023)
|
|
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Article Number | 03002 | |
Number of page(s) | 16 | |
Section | Data Science | |
DOI | https://doi.org/10.1051/e3sconf/202340903002 | |
Published online | 01 August 2023 |
The Effect of Non-agriculture Employment on The Rural Household Poverty Alleviation: Evidence from a Deeply Impoverished County in Southwest China
Business School, Sichuan University, Chengdu, 610064, People’s Republic of China
* e-mail: huzn@scu.edu.cn
Encouraging rural households from deeply impoverished areas to participate in non-agricultural employment has been regarded as an effective way to alleviate rural poverty. China’s targeted poverty alleviation (TPA) project has made significant achievements, with its policy to encourage rural households to participate in non-agricultural employments playing an important TPA role. Taking a deeply impoverished county in Southwest China as an example, this paper used an endogenous switching regression (ESR) model under a counterfactual inference framework to evaluate the effects of nonagricultural employment on alleviating household poverty, with the simplified “Organization for Economic Co-operation and Development (OECD) equivalent scale” formula used to adjust the income to measure household welfare. It was found that non-agricultural employment had reduced participant poverty and greatly improved the welfare of the participating households. However, for the non-participants, the non-agricultural employment income would be lower than the agricultural income, and the transfer of the household labor force to non-agricultural employment would deepen household poverty. This paper concluded with a discussion of the policy options to consolidate the achievements of poverty alleviation in deeply impoverished areas.
Key words: Non-agricultural employment / Poverty alleviation / Endogenous switching regression / Poverty-stricken areas
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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