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Abstract Details
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Activity Number:
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162
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #304125 |
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Title:
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Bayesian Methods in Poverty Mapping
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Author(s):
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Partha Lahiri and Jiraphan Suntornchost*+
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Companies:
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University of Maryland and University of Maryland
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Address:
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4804 Niagara Road, College Park, MD, 20740,
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Keywords:
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Poverty mapping ;
Bayesian method ;
Small areas ;
Monte Carlo ;
Uncertainty ;
Simulation
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Abstract:
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Poverty mapping that displays spatial distribution of various poverty indices such as head count ratio, poverty gap, poverty severity are most useful to policymakers and researchers when they are disaggregated into small geographic units, such as cities, municipalities, regions 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. In this paper, we develop a Bayesian methodology for producing poverty maps and the associated uncertainties. We compare our Bayesian method with other existing methods using Monte Carlo simulations.
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