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An Absolute Multidimensional Poverty Measure in the Functioning Space (and Relative Measure in the Resource Space): An Illustration Using Indian Data

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Book cover Dimensions of Poverty

Part of the book series: Philosophy and Poverty ((PPOV,volume 2))

Abstract

In this paper we develop a multidimensional poverty measure that attempts to capture absolute poverty in the functioning space. As suggested by Sen, if the measure aims to be absolute in the functioning space, it needs to be relative in the resource space. To generate a relative measure, this measure adapts the poverty cut-off in resource-related indicators in a multidimensional poverty measure to prevailing standards in a region. As illustration, this poverty measure utilizes the Indian Demographic and Health Survey (DHS) and is based on UNDP’s global Multidimensional Poverty Index (MPI). Similar to the global MPI, we apply the Alkire-Foster dual cut-off approach (Alkire and Foster 2011a) and broadly follow the global MPI in the choice of indicators, weights, and overall cut-offs. However, adaptable indicator thresholds are considered when appropriate. We argue that global MPI indicators in the health dimension are not open to a relative assessment, as they reflect specific health functionings (i.e. being free from premature mortality and being well nourished). In the education and standard of living dimensions, we set indicator thresholds at the median of the reference population, while experimenting with different reference populations. Empirically we find that the overall ranking of poverty in India does not change using our relative MPI, but the differentials in poverty are substantially smaller between states and rural and urban areas, also depending on the choice of the reference population.

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Notes

  1. 1.

    We thank seminar participants at two research seminars at the University of Göttingen, the Dimensions of Poverty Conference in Berlin in 2017, and at the IARIW General Conference 2014 for useful comments. We also thank Sabina Alkire, Cecilia Calderon, Nicole Rippin, Sripad Motiram, Christopher Whelan, anonymous referees, and Quang-Van Tran for helpful comments and discussion. All remaining errors remain our own.

  2. 2.

    Capabilities (the ability to achieve functionings) are hard to measure and most empirical applications, including this one, focus on measuring functionings.

  3. 3.

    Sperman rank correlations range from 0.9979 to 0.9982 for 2004.

  4. 4.

    The weight is determined by the percentage of people wanting, but not having an item.

  5. 5.

    She observes poverty rates as low as 0.2% for Cyprus and Poland.

  6. 6.

    For details on these approaches, please refer to D’Ambrosio et al. (2011).

  7. 7.

    They apply factor analysis to identify six dimensions but end up using only four due to missing data and the effect of location on some indicators.

  8. 8.

    Sen (2003) argues that illiteracy and innumeracy are forms of insecurity.

  9. 9.

    In the same speech, Sen (2003) argues that most aspects of education depend on a gap in education within communities (among groups and genders). Illiterate people then have problems to invoke their legal rights or participate in the political arena.

  10. 10.

    Such a relative view of education is even more relevant if education is mainly a signaling device of ability, rather than an absolute measure of human capital (cf. Pritchett 2001; Spence 1973).

  11. 11.

    Derived from UNESCO education statistics.

  12. 12.

    As children with birthdays in the current school year can only enter school in the next school year.

  13. 13.

    This covers primary and lower secondary education.

  14. 14.

    The global MPI does not change the order in this way.

  15. 15.

    In the category floor in the original DHS dataset, cement is above ceramic tiles and below carpet. We reorder the category floor so that cement is below finished and above stone floor.

  16. 16.

    Bottled water seems to be a voluntary choice consumed largely by the richest quintile.

  17. 17.

    Rank correlations at the household level also show a high correlation.

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Authors and Affiliations

  1. University of Göttingen, Göttingen, Germany

    Caroline Dotter & Stephan Klasen

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  1. Caroline Dotter
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  2. Stephan Klasen
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Correspondence to Stephan Klasen .

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Editors and Affiliations

  1. Institute of Philosophy, Freie Universität Berlin, Berlin, Berlin, Germany

    Valentin Beck  & Henning Hahn  & 

  2. Helmholtz Centre for Environmental Research GmbH – UFZ, Leipzig, Sachsen, Germany

    Robert Lepenies

Appendix

Appendix

Table 4 Thresholds in the indicator sanitation across regions
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Table 5 Threshold in the indicator water across regions
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Table 6 Thresholds in the indicator floor across regions
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Table 7 Thresholds in the indicator cooking fuel across regions
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Table 8 Thresholds in the indicator assets across regions
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Table 9 Decomposition of global MPI across states
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Table 10 Decomposition of relative multidimensional poverty (1) across states (reference group whole country)
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Table 11 Decomposition of relative multidimensional poverty (2) across states (reference group state)
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Table 12 Decomposition of relative multidimensional poverty (3) across states (reference group state urban/rural)
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Table 13 Relative contribution of indicators to overall poverty
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Dotter, C., Klasen, S. (2020). An Absolute Multidimensional Poverty Measure in the Functioning Space (and Relative Measure in the Resource Space): An Illustration Using Indian Data. 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_14

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