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The Poverty Reduction Effects of Internet Use Based on the Poverty Vulnerability Model

Published: 21 December 2023 Publication History

Abstract

Internet use is important for poverty alleviation among farm households. Based on the three-period panel data of China Family Tracking Survey (CFPS) data from 2014 to 2018, this paper measures the poverty vulnerability of Chinese farm households using the Vulnerability as Expected Poverty method, and then empirically investigates the effect of Internet use on the poverty vulnerability of farm households as well as the mechanism of mediating effect. The results show that: the poverty vulnerability of Chinese farm households has been significantly reduced in recent years, and the poverty vulnerability of farm households in the central and western regions is higher than that in the eastern region; Internet use significantly reduces the poverty vulnerability of farm households and increases their income; heterogeneity analysis shows that the impact of Internet use on high-vulnerability households is greater than that on low-vulnerability households, and it is more pronounced in the eastern region than the central and western regions, and more pronounced in non-ethnic regions than in the ethnic areas; the empirical estimation results passed the robustness test of instrumental variables method with replacement samples; the analysis of mediating effect mechanism shows that Internet use can reduce the poverty vulnerability of farm households by increasing their social capital.

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    ICIIP '23: Proceedings of the 2023 8th International Conference on Intelligent Information Processing
    November 2023
    341 pages
    ISBN:9798400708091
    DOI:10.1145/3635175
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 December 2023

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    Author Tags

    1. Big Data
    2. Internet Use
    3. Poverty Vulnerability Model
    4. Quantile Regression Model

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