Multidimensional Poverty Measurement and Analysis: Chapter 8 – Robustness Analysis and Statistical Inference

OPHI Working Papers

The design of a poverty measure involves the selection of a set of parameters and poverty figures. In most cases the measures are estimated from sample surveys. This raises the question of how conclusive particular poverty comparisons are subject to both the set of selected parameters (or variations within a plausible range) and the sample datasets. This chapter shows how to apply dominance and rank robustness tests to assess comparisons as poverty cutoffs and other parameters changes. It presents ingredients of statistical inference, including standard errors, confidence intervals, and hypothesis tests. And it discusses how robustness and statistical inference tools can be used together to assert concrete policy conclusions. An appendix presents methods for computing standard errors, including the bootstrapped standard errors.

Citation: Alkire, S., Foster, J. E., Seth, S., Santos, M. E., Roche, J. M., and Ballon, P. (2015). Multidimensional Poverty Measurement and Analysis, Oxford: Oxford University Press, ch. 8.

Also published in Multidimensional Poverty Measurement and Analysis, Oxford University Press, 2015.

robustness analysis, statistical inference, dominance analysis, rank robustness, standard errors, bootstrap


Sabina Alkire, James E. Foster, Suman Seth, Maria Emma Santos, Jose M. Roche and Paola Ballon

Series Name
OPHI Working Papers
Publication date
JEL Codes
C10, C12, I32
Publication Number
WP 89