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Author
Lahiri Partha (University of Maryland), Suntornchost Jiraphan (Chulalongkorn University, Thailand)
Title
A General Bayesian Approach to Meet Different Inferential Goals in Poverty Research for Small Areas
Source
Statistics in Transition, 2020, vol. 21, nr 4 Special Issue, s. 237-253, tab., bibliogr. s. 251-253
Keyword
Modele bayesowskie, Symulacja Monte Carlo, Statystyka małych obszarów, Wskaźniki ubóstwa
Bayesian models, Monte Carlo simulation, Small area estimates, Poverty indicators
Note
summ.
Country
Chile
Chile
Abstract
Poverty mapping that displays spatial distribution of various poverty indices is most useful to policymakers and researchers when they are disaggregated into small geographic units, such as cities, municipalities or other administrative partitions of a country. Typically, national household surveys that contain welfare variables such as income and expenditures provide limited or no data for small areas. It is well-known that while direct survey-weighted estimates are quite reliable for national or large geographical areas they are unreliable for small geographic areas. If the objective is to find areas with extreme poverty, these direct estimates will often select small areas due to the high variability in the estimates. Empirical best prediction and Bayesian methods have been proposed to improve on the direct point estimates. These estimates are, however, not appropriate for different inferential purposes. For example, for identifying areas with extreme poverty, these estimates would often select areas with large sample sizes. In this paper, using real life data, we illustrate how appropriate Bayesian methodology can be developed to address different inferential problems. (original abstract)
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The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
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Bibliography
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ISSN
1234-7655
Language
eng
URI / DOI
http://dx.doi.org/10.21307/stattrans-2020-040
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