Spatial Disaggregation of Social Indicators: An Info-Metrics Approach
In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale....
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Veröffentlicht in: | Social indicators research 2020-11, Vol.152 (2), p.809-821 |
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description | In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale. The main novelties of the technique presented are that (i) the small-scale mapping produced is perfectly consistent with the aggregates -regional or national- observed in the sample, and (ii) it does not require imposing strong distributional assumptions. The methodology suggested here follows the basics presented on Golan (2018) by adapting a cross-moment constrained Generalized Maximum Entropy (GME) estimator to the spatial disaggregation problem. This procedure is compared with the equivalent methodology of Tarozzi and Deaton (2009) by means of numerical experiments, providing a comparatively better performance. Additionally, the practical implementation of the methodology proposed is illustrated by estimating poverty rates for small areas for the region of Andalusia (Spain). |
doi_str_mv | 10.1007/s11205-020-02455-z |
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subjects | Census Census of Population Censuses Economics Entropy Family income Households Human Geography Informetrics Mapping Maximum entropy Microeconomics Original Research Polls & surveys Poverty Public Health Quality of Life Research Research methodology Scientific Concepts Small areas Social indicators Social Sciences Sociology |
title | Spatial Disaggregation of Social Indicators: An Info-Metrics Approach |
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