Regional Perspectives on the Multidimensional Poverty Index

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe Multidimensional Poverty Index (MPI) is not the first attempt to examine poverty along multiple definitions. Vis-a-vis the existing work and other presently available measures, this method has greater advantage in terms of international comparability and reporting. However, the methodology of the Multidimensional Poverty Index (MPI) has been under strong scrutiny since its inception. One of the reasons for these critiques lies in the variation in the MPI country ranks and scores based on different indicators and a different weighing scheme. This paper analyzes the consequences of a different weighting scheme within the MPI, using a more data driven approach rather than a normative or equal weighting scheme. It attempts to assess this alternative weighting via its impact on the scores and relative ranking of various countries. Moreover, it attempts to resolve the differences in the definition of poverty that might emerge upon changing indicators, and thereby evaluate how this affects the construction of the MPI. An analysis covering 22 countries, using the Demographic and Health Survey data, is carried out to quantitatively evaluate the weights assigned to each of the indicators, using the technique of Principal Component Analysis (PCA) and Multiple Correspondence Analysis (MCA). A more detailed country-level analysis is carried out for India, wherein additional indicators based on the data are made available and therefore an alternative multidimensional construct is possible. The analysis shows that equal weighting of the three dimensions cannot be statistically justified and that in trying to capture a more multidimensional view of poverty and well-being, there might actually not be so much multidimensionality in the MPI.

An Asian Poverty Line? Issues and Options

“…She uses Principal Components Analysis to derive statistical weights for the indicators included in the MPI. Pasha (2014) indeed finds substantial differences across countries in these weights. In India, the weights for child mortality and nutrition are higher than in all the 22 countries included in her sample.…”

“…Here the results from the illustrative exercise of Pasha (2014) are interesting. She uses Principal Components Analysis to derive statistical weights for the indicators included in the MPI.…”

Readiness of Russian regions to digitize the economy

“…Pasha offers not to exclude factors when calculating multidimensional indicators, but to assign them a different value by establishing the appropriate coefficients using the example of MPI. In her study, she uses the multiple correlation analysis (MCA) to weigh indicators and explores its impact on estimates and relative poverty rates of 28 countries [21].…”

“…., Melnikova, N., & Vishnevskaya, N. / Economic Annals-XXI (2018), 174(11-12),[16][17][18][19][20][21] reasons,%), x 13 (the share of electronic document circulation between state and local authorities in the total volume of interdepartmental document circulation,%) and x16 (the cost of information and consulting technology per 1 organisation, thousand rubles) should be excluded from the calculation of the multidimensional average.…”

Eliciting, Applying And Exploring Multidimensional Welfare Weights: Evidence From The Field

“…() and Hsieh (). The results of Pasha () challenge the statistical desirability of equal weighting. The economics literature has taking a slightly different angle, focusing in particular on the robustness or sensitivity of distribution rankings to the use of alternative weighting schemes—Saisana et al .…”