Weighty Evidence? Poverty Estimation with Missing Data

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...

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Bibliographic Details
Published inStudies in microeconomics Vol. 12; no. 1; pp. 93 - 106
Main Authors Drèze, Jean, Somanchi, Anmol
Format Journal Article
LanguageEnglish
Published New Delhi, India SAGE Publications 01.04.2024
Sage Publications, New Delhi India
Subjects
Bias
Estimation
Missing data
Poverty
India
poverty estimation
missing data
max-entropy
India
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Summary: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
ISSN:2321-0222
2321-8398
DOI:10.1177/23210222241238846