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A Behavior‐Based Approach to the Estimation of Poverty in India

Author

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  • Ingvild Almås
  • Anders Kjelsrud
  • Rohini Somanathan

Abstract

Estimates of poverty in India are crucial inputs for the understanding of world poverty, yet there is much disagreement about the numbers and the legitimacy of methods used to derive them. In this paper, we propose and justify an alternative approach to identify the poor, which uses the proportion of income spent on food. Our estimates have weaker data requirements than official methods, and they compare favorably with several validation tests. Most notably, households around our state poverty lines obtain their calories from similar sources, whereas this is not true of official poverty lines. We also find that rates of self‐reported hunger are higher in states that we classify as poor.

Suggested Citation

  • Ingvild Almås & Anders Kjelsrud & Rohini Somanathan, 2019. "A Behavior‐Based Approach to the Estimation of Poverty in India," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(1), pages 182-224, January.
    • Handle: RePEc:bla:scandj:v:121:y:2019:i:1:p:182-224
    DOI: 10.1111/sjoe.12282
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    1. Bazillier, Remi & Girard, Victoire, 2020. "The gold digger and the machine. Evidence on the distributive effect of the artisanal and industrial gold rushes in Burkina Faso," Journal of Development Economics, Elsevier, vol. 143(C).
    2. Amita Majumder & Ranjan Ray, 2020. "National and subnational purchasing power parity: a review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(2), pages 103-124, June.
    3. Amita Majumder & Ranjan Ray & Sattwik Santra, 2016. "Global and Country Poverty Rates, Welfare Rankings of the Regions and Purchasing Power Parities: How Robust Are the Results?," Monash Economics Working Papers 11-16, Monash University, Department of Economics.
    4. Chen,Xiaomeng & Mungai,Rose & Nakamura,Shohei & Pearson,Thomas Patrick & Wambile,Ayago Esmubancha & Yoshida,Nobuo, 2020. "How Useful is CPI Price Data for Spatial Price Adjustment in Poverty Measurement? : A Case from Ghana," Policy Research Working Paper Series 9388, The World Bank.
    5. Andersson, Magnus & Hayakawa, Kazunobu & Keola, Souknilanh & Yamanouchi, Kenta, 2025. "Impacts of international transport infrastructure: Evidence from Laotian households," Journal of Asian Economics, Elsevier, vol. 97(C).
    6. Andrew Dabalen & Isis Gaddis & Nga Thi Viet Nguyen, 2020. "CPI Bias and its Implications for Poverty Reduction in Africa," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(1), pages 13-44, March.
    7. Gibson, John & Le, Trinh, 2019. "Using local expert knowledge to measure prices: Evidence from a survey experiment in Vietnam," MPRA Paper 92533, University Library of Munich, Germany.
    8. Majumder,Amita & Ray,Ranjan & Santra,Sattwik, 2015. "Preferences, purchasing power parity, and inequality : analytical framework, propositions, and empirical evidence," Policy Research Working Paper Series 7395, The World Bank.
    9. Ingvild Almas & Ashild Johnsen, 2018. "The cost of a growth miracle - reassessing price and poverty trends in China," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 30, pages 239-264, October.

    More about this item

    JEL classification:

    • D10 - Microeconomics - - Household Behavior - - - General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • F01 - International Economics - - General - - - Global Outlook
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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