First Order Dominance Techniques and Multidimensional Poverty Indices: An Empirical Comparison of Different Approaches

Article

Abstract

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.

Keywords

Multidimensional poverty measurement Poverty index First order dominance Robustness Simulations 

References

  1. Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95(7–8), 476–487.CrossRefGoogle Scholar
  2. 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
  3. 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
  4. Arndt, C., Distante, R., Hussain, M. A., Østerdal, L. P., Huong, P., & Ibraimo, M. (2012). Ordinal welfare comparisons with multiple discrete indicators: A first order dominance approach and application to child poverty. World Development, 40(11), 2290–2301.CrossRefGoogle Scholar
  5. Arndt, C., Siersbæk, N., Østerdal, L. P. (2015). Multidimensional first-order dominance comparisons of population wellbeing. In WIDER working paper 2015/122.Google Scholar
  6. Atkinson, A. (2003). Multidimensional deprivation: Contrasting social welfare and counting approaches. Journal of Economic Inequality, 1, 51–65.CrossRefGoogle Scholar
  7. Bourguignon, F., & Chakravarty, S. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality, 1, 25–49.CrossRefGoogle Scholar
  8. 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
  9. Chakravarty, S. (2009). Inequality, polarization and poverty: Advances in distributional analysis. Ramat Gan: Springer.CrossRefGoogle Scholar
  10. Chakravarty, S., Deutsch, J., & Silber, J. (2008). On the watts multidimensional poverty index and its decomposition. World Development, 36(6), 1067–1077.CrossRefGoogle Scholar
  11. Cherchye, L., Ooghe, E., & Puyenbroeck, T. (2008). Robust human development rankings. Journal of Economic Inequality, 6, 287–321.CrossRefGoogle Scholar
  12. Davey, B., & Priestley, B. (2002). Introduction to lattices and order. Cambridge: CUP.CrossRefGoogle Scholar
  13. Duclos, J.-Y., Sahn, D., & Younger, S. (2006). Robust multidimensional poverty comparisons. The Economic Journal, 116, 943–968.CrossRefGoogle Scholar
  14. 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
  15. Fattore, M. (2016). Partially ordered sets and the measurement of multidimensional ordinal deprivation. Social Indicators Research, 128, 835–858.CrossRefGoogle Scholar
  16. Fattore, M., & Arcagni, A. (2017). A reduced posetic approach to the measurement of multidimensional ordinal deprivation. Social Indicators Research. doi:10.1007/s11205-016-1501-4.Google Scholar
  17. 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
  18. Foster, J., McGillivray, M., & Seth, S. (2013). Composite indices: Rank robustness, statistical association, and redundancy. Econometric Reviews, 32(1), 35–56.CrossRefGoogle Scholar
  19. Hussain, A., Jorgensen, M., & Osterdal, L. (2015). Refining population health comparisons: A multidimensional first order dominance approach. Social Indicators Research. doi:10.1007/s11205-015-1115-2.Google Scholar
  20. Iglesias, K., Suter, C., & Beycan, T. (2016). Exploring multidimensional well-being in Switzerland: Comparing three synthesizing approaches. Social Indicators Research. doi:10.1007/s11205-016-1452-9.Google Scholar
  21. Permanyer, I. (2011). Assessing the robustness of composite indices rankings. Review of Income and Wealth, 57(2), 306–326.CrossRefGoogle Scholar
  22. Permanyer, I. (2012). Uncertainty and robustness in composite indices rankings. Oxford Economic Papers, 64(1), 57–79.CrossRefGoogle Scholar
  23. Permanyer, I. (2014). Assessing individuals’ deprivation in a multidimensional framework. Journal of Development Economics, 109, 1–16.CrossRefGoogle Scholar
  24. Sen, A. (1976). Poverty: An ordinal approach to measurement. Econometrica, 42(2), 219–231.CrossRefGoogle Scholar
  25. Tsui, K. Y. (2002). Multidimensional poverty indices. Social Choice and Welfare, 19, 69–93.CrossRefGoogle Scholar
  26. Yalonetzky, G. (2014). Conditions for the most robust multidimensional poverty comparisons using counting measures and ordinal variables. Social Choice and Welfare, 43, 773–807.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Centre d’Estudis Demogràfics (CED), Edifici E-2Campus de la Universitat Autònoma de BarcelonaBarcelonaSpain
  2. 2.Department of Social Sciences and Business (DSSB)Roskilde UniversityRoskildeDenmark

Personalised recommendations