arbiter
JournalsPricingAccount

Weighty Evidence? Poverty Estimation with Missing Data

Jean Drèze & Anmol Somanchi

Studies in Microeconomics2024https://doi.org/10.1177/23210222241238846article
AJG 1ABDC C
Weight
0.67

What the paper says

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

8 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1177/23210222241238846

Or copy a formatted citation

@article{jean2024,
  title        = {{Weighty Evidence? Poverty Estimation with Missing Data}},
  author       = {Jean Drèze & Anmol Somanchi},
  journal      = {Studies in Microeconomics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1177/23210222241238846},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Weighty Evidence? Poverty Estimation with Missing Data

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.67

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.87 × 0.4 = 0.35
M · momentum0.65 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.