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Weighty Evidence? Poverty Estimation with Missing Data

Author

Listed:
  • Jean Drèze
  • Anmol Somanchi

Abstract

Attempts have been made to estimate poverty in India using a biased dataset, by adjusting household weights to remove or reduce the bias. The effectiveness of this method, however, is uncertain. Simulation exercises suggest that its ability to correct poverty estimates varies wildly depending on the nature of the underlying bias, which may be hard to guess—there lies the rub. When the bias changes over time, estimating poverty trends becomes truly problematic. There are wider lessons for poverty estimation with biased or missing data. JEL Classifications: C83, I32

Suggested Citation

  • Jean Drèze & Anmol Somanchi, 2024. "Weighty Evidence? Poverty Estimation with Missing Data," Studies in Microeconomics, , vol. 12(1), pages 93-106, April.
    • Handle: RePEc:sae:miceco:v:12:y:2024:i:1:p:93-106
    DOI: 10.1177/23210222241238846
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    References listed on IDEAS

    as
    1. Ashok Kotwal & Bharat Ramaswami & Wilima Wadhwa, 2011. "Economic Liberalization and Indian Economic Growth: What's the Evidence?," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 1152-1199, December.
    2. Martin Wittenberg, 2010. "An introduction to maximum entropy and minimum cross-entropy estimation using Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 315-330, September.
    3. Somanchi, Anmol, 2021. "Missing the Poor, Big Time: A Critical Assessment of the Consumer Pyramids Household Survey," SocArXiv qmce9, Center for Open Science.
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    More about this item

    Keywords

    India; poverty estimation; missing data; max-entropy;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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