Economic and social welfare is inherently multidimensional. However, choosing a measure which combines several indicators is difficult and may have unintendend and undesirable effects on the incentives of policy makers. We develop a nonparametric empirical method for deriving welfare rankings for a social planner based on data envelopment, which avoids the need to specify a weighting scheme. We apply this method to data on Human Development.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Ian Crawford
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Andrew Leicester
![Gordon Anderson](/sites/default/files/styles/square_desktop/public/2022-07/Gordon%20Anderson.png?itok=_2gdqYut)
Research Associate University of Toronto
Gordon is a Research Associate of the IFS and a Professor in the Department of Economics at the University of Toronto.
Journal article details
- Publisher
- Elsevier
- ISSN
- 0047-2727
- Issue
- April 2011
Suggested citation
G, Anderson and I, Crawford and A, Leicester. (2011). 'Welfare rankings from multivariate data: a non-parametric approach' (2011)
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