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Assessing Alternative Poverty Proxy Methods in Rural Vietnam

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  • Linh Vu
  • Bob Baulch

Abstract

This paper compares and contrasts the use of four “short-cut” methods for identifying poor households: the poverty probability method; ordinary least squares regressions; principal components analysis; and quantile regressions. After evaluating these four methods using two alternative criteria (total and balanced poverty accuracy) and representative household survey data from rural Vietnam, it is concluded that the poverty probability method—which can correctly identify around four-fifths of poor and non-poor households—is the most accurate “short-cut” method for measuring poverty for specific subpopulations, or in years when household surveys are not available. The performance of the poverty probability method was then tested with different poverty lines and using an alternative household survey, and found to be robust.

Suggested Citation

  • Linh Vu & Bob Baulch, 2011. "Assessing Alternative Poverty Proxy Methods in Rural Vietnam," Oxford Development Studies, Taylor & Francis Journals, vol. 39(3), pages 339-367, September.
    • Handle: RePEc:taf:oxdevs:v:39:y:2011:i:3:p:339-367
    DOI: 10.1080/13600818.2011.599207
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    References listed on IDEAS

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    1. Sabina Alkire, Maria Emma Santos, 2010. "Acute Multidimensional Poverty: A New Index for Developing Countries," OPHI Working Papers 38, Queen Elizabeth House, University of Oxford.
    2. Shaohua Chen & Martin Ravallion, 2010. "The Developing World is Poorer than We Thought, But No Less Successful in the Fight Against Poverty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(4), pages 1577-1625.
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    Cited by:

    1. Talip Kilic & Thomas Pave Sohnesen, 2019. "Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(1), pages 144-165, March.
    2. Aradhna Aggarwal & Ari Kokko, 2021. "SEZs and poverty reduction: evidence from Andhra Pradesh, India," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 17(8), pages 1793-1814, January.
    3. Nguyen, Cuong & Lo, Duc, 2016. "Testing Proxy Means Tests in the Field: Evidence from Vietnam," MPRA Paper 80002, University Library of Munich, Germany.
    4. Pham, Huong Dien & Liebenehm,Sabine & Waibel, Hermann, 2017. "Experimentally validated general risk attitude among different ethnic groups in Vietnam," TVSEP Working Papers wp-004, Leibniz Universitaet Hannover, Institute of Development and Agricultural Economics, Project TVSEP.
    5. M. Akram, Mian & Afzal, Muhammad, 2014. "Dynamic Role of Zakat in Alleviating Poverty: A Case Study of Pakistan," MPRA Paper 56013, University Library of Munich, Germany.
    6. Abdul Rehman & Laura Mariana Cismas & Ioana Anda Milin, 2022. "“The Three Evils”: Inflation, Poverty and Unemployment’s Shadow on Economic Progress—A Novel Exploration from the Asymmetric Technique," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    7. Nguyen, Thao Phuong, 2020. "The determinants impact on poverty reduction in Vietnam," OSF Preprints 3f9xc, Center for Open Science.
    8. Cuong Viet Nguyen & Anh Tran, 2014. "Poverty identification: practice and policy implications in Vietnam," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 28(1), pages 116-136, May.
    9. Astrid Mathiassen & Bjørn K. Wold, 2019. "Challenges in predicting poverty trends using survey to survey imputation. Experiences from Malawi," Discussion Papers 900, Statistics Norway, Research Department.

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