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The influence of contextual and household factors on multidimensional poverty in rural Vietnam: A multilevel regression analysis

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  • Tran, Tuyen Quang
  • Thi Nguyen, Hoai Thu
  • Hoang, Quang Ngoc
  • Van Nguyen, Dinh

Abstract

Using a multilevel regression modeling technique, this paper examines the influence of contextual and household factors on multidimensional poverty in rural Vietnam. We find that unobservable characteristics at the province, district and commune levels account for about 28% and 25%, respectively, of the variation in multidimensional and income poverty risk. In addition, the study shows that several micro-factors, such as ethnicity, better education, social capital, nonfarm activities and public employment, play a key role in reducing both the likelihood of falling into poverty and the number of dimensions of deprivation. The risk of poverty is higher for households in remote communes and lower for those in communes with access to transportation and nonfarm jobs. At the macro-level, it was found that living in provinces with a higher level of economic development, greater population density, and more international integration increases the chances of households to escape both multidimensional and income poverty.

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  • Tran, Tuyen Quang & Thi Nguyen, Hoai Thu & Hoang, Quang Ngoc & Van Nguyen, Dinh, 2022. "The influence of contextual and household factors on multidimensional poverty in rural Vietnam: A multilevel regression analysis," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 390-403.
  • Handle: RePEc:eee:reveco:v:78:y:2022:i:c:p:390-403
    DOI: 10.1016/j.iref.2021.12.012
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    2. Wenguang Zhang & Ting Lei & Yu Gong & Jun Zhang & Yirong Wu, 2022. "Using Explainable Artificial Intelligence to Identify Key Characteristics of Deep Poverty for Each Household," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
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    4. Runqiao Zhang & Yawen He & Wenkai Cui & Ziwen Yang & Jingyu Ma & Haonan Xu & Duxian Feng, 2022. "Poverty-Returning Risk Monitoring and Analysis of the Registered Poor Households Based on BP Neural Network and Natural Breaks: A Case Study of Yunyang District, Hubei Province," Sustainability, MDPI, vol. 14(9), pages 1-18, April.

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    More about this item

    Keywords

    Contextual factors; Multidimensional poverty; Multilevel modeling; International integration; Rural Vietnam;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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