First Order Dominance Techniques and Multidimensional Poverty Indices: An Empirical Comparison of Different Approaches
- 83 Downloads
In this empirically driven paper we compare the performance of two techniques in the literature of poverty measurement with ordinal data: multidimensional poverty indices and first order dominance techniques (FOD). Combining multiple scenario simulated data with observed data from 48 Demographic and Health Surveys around the developing world, our empirical findings suggest that the FOD approach can be implemented as a useful robustness check for ordinal poverty indices like the multidimensional poverty index (MPI; the United Nations Development Program’s flagship poverty indicator) to distinguish between those country comparisons that are sensitive to alternative specifications of basic measurement assumptions and those which are not. To the extent that the FOD approach is able to uncover the socio-economic gradient that exists between countries, it can be proposed as a viable complement to the MPI with the advantage of not having to rely on many of the normatively binding assumptions that underpin the construction of the index.
KeywordsMultidimensional poverty measurement Poverty index First order dominance Robustness Simulations
Financial support from the Spanish Ministry of Economy and Competitiveness “Ramón y Cajal” Research Grant Program and research projects ERC-2014-StG-637768 and ECO2013-46516-C4-1-R is gratefully acknowledged.
- Annoni, P., Fattore, M., & Bruggermann, R. (2011). A multi-criteria fuzzy approach for analyzing poverty structure (pp. 7–30). Special Issue: Statistica & Applicazioni.Google Scholar
- Arcagni, A., & Fattore, M. (2014). PARSEC: An R package for poset-based evaluation of multidimensional poverty. In R. Bruggermann, L. Carlsen, & J. Wittmann (Eds.), Multi-indicator systems and modelling in partial order. Berlin: Springer.Google Scholar
- Arndt, C., Siersbæk, N., Østerdal, L. P. (2015). Multidimensional first-order dominance comparisons of population wellbeing. In WIDER working paper 2015/122.Google Scholar
- Bourguignon, F., & Chakravarty, S. (2008). Multidimensional poverty orderings. In K. Basu & R. Kanbur (Eds.), Arguments for a better world: Essays in Honor of Amartya Sen, volume I—ethics, welfare and measurement. Oxford: Oxford University Press.Google Scholar
- Duclos, J.-Y., Sahn, D., & Younger, S. (2007). Robust multidimensional poverty comparisons with discrete indicators of well-being. In Inequality and poverty re-examined. Oxford: Oxford University Press, pp. 185–208.Google Scholar
- Fattore, M., Bruggermann, R., & Owsinski, J. (2011). Using poset theory to compare fuzzy multidimensional material deprivation across regions. In S. Ingrassia, R. Rocci, & M. Vichi (Eds.), New perspectives in statistical modeling and data analysis. Berlin: Springer.Google Scholar