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

By S. Alkire, James E. Foster, Suman Seth, Maria Emma Santos, J. Roche, P. Ballon. 2015

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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.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c60a0fb37dccf1f4c48d061213692be6927e9054

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Citations in the corpus

Ending Extreme Poverty and Sharing Prosperity: Progress and Policies (2015, background): “In this way, the adjusted headcount ratio and its dimensional indicators form a coordinated dashboard for policy analysis with a headline number for monitoring and communication purposes and dimensional indicators for deeper analysis (Alkire et al. 2015a).”
Dynamics of Multidimensional Poverty and Uni-dimensional Income Poverty: An Evidence of Stability Analysis from China (2019, methodology): “See Alkire et al (2015) chapter 1. poor if they do not have enough money to meet their food requirements or their basic needs.”
A scoping review of the development resilience literature: Theory, methods and evidence (2021, background): “…conceptualizations and associated measures – each rooted in an explicit aspiration to figure out what works – has fostered steady advances in both the production and use of poverty research (Foster, Greer & Thorbecke 1984, Alkire et al. 2015, Ravallion 2015, Barrett, Carter & Chavas 2019).”
The Quality of Employment (QoE) in nine Latin American countries: A multidimensional perspective (2020, unknown): “…correspondence analysis or factor analysis) or fuzzy set approaches are also valuable in this context but not as useful to the objective of measuring the QoE as the AF method, as they often have limitations as a result of the specific axiomatic properties they hold (Alkire et al., 2015: 99-100).”
Using publicly available satellite imagery and deep learning to understand economic well-being in Africa (2020, background): “Because asset indices constitute a relative measure of wealth and it is not obvious how to set an absolute cut-off to de ﬁ ne who is poor, standard practice is instead to divide the population into percentiles in the asset distribution and then designate bottom percentiles as poor 15 .”
Changes Over Time in Multidimensional Poverty: Methodology and Results for 34 Countries (2017, background): “The percentage contribution of the jth dimension is (wj hj(k))/M0 (Alkire et al., 2015, chap.”