Abstract
Poverty measurement is strewn with imperfection. And yet, even understanding limitations such as data quality and coverage, measures of multidimensional poverty have proven to be relevant policy tools. This paper first situates multidimensional poverty measures in the Sustainable Development Goals, which seek to End Poverty in all its forms and dimensions (italics added). It then explains a key distinguishing feature between multidimensional and monetary poverty measures, namely, that multidimensional poverty measures have an associated ‘information platform’ which provides the deprivations in each indicator, as well as the headcount ratio or poverty rate, and the intensity of poverty overall, and does so both nationally and for all groups by which the dataset can be disaggregated. Furthermore, multiple poverty lines are often set and reported. Bearing this informational richness in mind, the paper then canvasses the main ways that policy actors are using multidimensional poverty indices (MPIs) and their associated informational platform to shape policy. For example, a permanent official MPI complements the national monetary poverty measure, often drawing attention to different groups of poor persons. Also, the MPI design often includes participatory exercises and expert consultations, thus catalysing a national conversation about what is poverty. Like any national statistic, the MPI is used to monitor change and show the trend in a phenomenon of public importance. Further, the MPI, with its disaggregation by group and breakdown by indicator, is often used as part of the budget allocation formulae, for example across subnational regions. The MPI is also used for targeting in two senses: targeting the poorest areas or social groups, and also (using a different dataset) targeting households that are eligible to benefit from certain schemes. One of the most powerful roles of the MPI is to support policy coordination which—in line with the SDG emphasis—facilitates integrated multisectoral policies that can be more cost-effective, and high-impact methods for addressing interconnected deprivations and managing change. Finally, for many countries, the MPI is part of a new emphasis on the transparency and accountability of statistics. The data tables, datasets and computer algorithms that are posted online allow students and researchers to fruitfully join the intellectual task of finding better ways to confront human disadvantage and suffering. The paper closes by referring to some new research areas that might further enrich this unfolding discipline.
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Notes
- 1.
- 2.
Chapter 4 of Alkire et al. (2015) outlines the histories of counting-based measures since the late 1960s in Europe, and since the 1970s in Latin America’s tradition of Unmet Basic Needs. Tony Atkinson’s seminal (2003) paper called for those working on measures from a welfare perspective to take seriously the counting methods and seek to join the two approaches.
- 3.
This could be nicely related to the behavioral economics literature on why headcount ratios are psychologically important, and to the sociology of numbers in public policy (Grusky and Kanbur 2006).
- 4.
The methodological notes explain exactly any adjustments to the core methodology that are required and have been undertaken in each dataset, with sufficient detail to enable the results to be replicated by other analysts. Naturally, these affect the comparability of indicators—for example Afghanistan’s MPI unfortunately lacks nutrition (8 of 104 countries lack nutritional data).
- 5.
For more information on the Roundtable see Zavaleta and Angulo (2017).
- 6.
Alkire (2018) articulates some areas for research more comprehensively than can be presented here.
- 7.
Sophia Obermeyer (FU Berlin) wrote the transcript of the talk of the author in Berlin on June 8, 2017, on which this paper is based.
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Alkire, S. (2020). Multidimensional Poverty Measures as Policy Tools. In: Beck, V., Hahn, H., Lepenies, R. (eds) Dimensions of Poverty. Philosophy and Poverty, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-31711-9_12
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