Welfare Rankings From Multivariate Data, A Non-Parametric Approach
AbstractEconomic and Social Welfare is inherently multidimensional. However choosing a measure which combines several indicators is difficult and may have unintended and undesireable effects on the incentives for policymakers. We develope a nonparametric empirical method for deriving welfare rankings based on data envelopment which avoids the need to specify a weighting scheme. The results are valid for all possible social welfare functions which share certain cannonical properties. We apply this method to data on human development.
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Bibliographic InfoPaper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-386.
Length: 17 pages
Date of creation: 14 Jan 2010
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Welfare Rankings; Data Envelopment; Human development;
Other versions of this item:
- Anderson, Gordon & Crawford, Ian & Leicester, Andrew, 2011. "Welfare rankings from multivariate data, a nonparametric approach," Journal of Public Economics, Elsevier, Elsevier, vol. 95(3-4), pages 247-252, April.
- Anderson, Gordon & Crawford, Ian & Leicester, Andrew, 2011. "Welfare rankings from multivariate data, a nonparametric approach," Journal of Public Economics, Elsevier, Elsevier, vol. 95(3), pages 247-252.
- I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-01-23 (All new papers)
- NEP-ECM-2010-01-23 (Econometrics)
- NEP-LTV-2010-01-23 (Unemployment, Inequality & Poverty)
- NEP-MIC-2010-01-23 (Microeconomics)
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