Small area estimation of non-monetary poverty with geospatial data

Autor:	Takaaki Masaki, David Newhouse, Ani Rudra Silwal, Adane Bedada, Ryan Engstrom
Rok vydání:	2022
Předmět:	Economics and Econometrics Statistics Probability and Uncertainty Management Information Systems
Zdroj:	Statistical Journal of the IAOS. 38:1035-1051
ISSN:	1875-9254 1874-7655
DOI:	10.3233/sji-210902
Popis:	This paper evaluates the benefits of combining household surveys with satellite and other geospatial data to generate small area estimates of non-monetary poverty. Using data from Tanzania and Sri Lanka and applying a household-level empirical best (EB) predictor mixed model, we find that combining survey data with geospatial data significantly improves both the precision and accuracy of our non-monetary poverty estimates. While the EB predictor model moderately underestimates standard errors of those point estimates, coverage rates are similar to standard survey-based standard errors that assume independent outcomes across clusters.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::754d0f9b2e32be20118046f213483bf1 https://doi.org/10.3233/sji-210902 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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