Category Archives: In Progress

Multidimensional Poverty Reduction in India 2005/6–2015/16: Still a Long Way to Go but the Poorest Are Catching Up

This paper assesses the change in multidimensional poverty in India from 2005/6 to 2015/16 using data from the NFHS-3 and NFHS-4 surveys. Estimates of changes are disaggregated by age cohort, state and by socio-economic group-level, and broken down by indicator; sampling errors are considered throughout. Multidimensional poverty is defined using the global Multidimensional Poverty Index 2018 (Alkire and Jahan 2018).  The paper finds a very strong reduction, indeed a halving of the MPI during that decade. Furthermore, subnational patterns of poverty reduction are strongly pro-poor, whereas from 1998/9 to 2005/6 they had been regressive. The reductions of MPI are hardly correlated with state level growth in GDP, making this a rich terrain for future research. District level analyses in 2015/16 only document extensive ongoing intra and interstate variation. These explorations confirm that at the end of the decade under study, at least 271 million fewer persons were living in multidimensional poverty – a magnitude of change rivalling the numbers exiting monetary poverty in China.

Citation: Alkire, S., Oldiges, C. and Kanagaratnam, U. (2018). ‘Multidimensional poverty reduction in India 2005/6–2015/16: still a long way to go but the poorest are catching up’, OPHI Research in Progress 54a, University of Oxford.

Towards a Global Assets Indicator: Re-assessing the Assets Indicator in the Global Multidimensional Poverty Index

This paper explains the revision of the assets indicator of the updated global Multidimensional Poverty Index (global MPI), which was launched just before the 73rd Session of the United Nations General Assembly in September 2018. The joint decision of the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) to revise the global MPI in 2018 to align it with the Sustainable Development Goals and to best monitor progress towards “leaving no one behind” provided the opportunity to assess the statistical validities of the assets indicator contained in the Original MPI, jointly designed by OPHI and UNDP Human Development Report Office (HDRO) in 2010, and an assets indicator included in an Innovative MPI, which was developed by UNDP HDRO in 2014. Further, considering the improvements in many Demographic and Health Surveys, Multiple Indicators Cluster Surveys and selected national surveys in recent years, from which the global MPI is constructed, the revision also offered an occasion to assess whether the inclusion of additional assets would add value to a revised asset index for the updated global MPI 2018. Taking into account a blend of inputs, including statistical test results, public consultations, normative reasoning and substantive trial measures of possible asset indices as outlined in detail in this paper, the revised assets indicator maintained the structure of the Original MPI, but added computer and animal cart as additional items. Here we explain the reasons and delineate the many decisions that were taken along the way.

Citation: Vollmer, F. and Alkire, S. (2018). ‘Towards a global asset indicator: re-assessing the asset indicator in the global Multidimensional Poverty Index’, OPHI Research in Progress 53a, Oxford Poverty and Human Development Initiative, University of Oxford.

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On Data Availability for Assessing Monetary and Multidimensional Poverty

Data availability plays a crucial role in the fight against poverty. Yet, it lags behind the data available on most other economic phenomena. We catalogue and review existing data availability aiming to break the cycle of outdated poverty data. We identify countries that generate and analyse frequent and accurate poverty data to highlight potential improvements. Results show, data for both monetary and multidimensional poverty dramatically increased since 1980. Sixty countries now produce annual datasets, while internationally comparable short surveys and regional harmonised variable definitions are being implemented. These existing resources and experiences can inform much-needed efforts to expand data availability.

Citation: Alkire, S. and Robson, M. (2018). ‘On data availability for assessing monetary and multidimensional poverty’, OPHI Research in Progress 52a, University of Oxford.

Statistical Note: Disaggregating Bhutan’s MPI 2017 by Disability Status

Since 2010, Bhutan has used a Multidimensional Poverty Index (MPI) alongside consumption poverty to measure and fight poverty in all its forms and dimensions. Bhutan’s National MPI was updated on 2012 and 2017 using the Bhutan Living Standards Survey (BLSS). In 2017, the BLSS questionnaire included questions on disability status. This statistical note shows different ways by which the MPI can be disaggregated using the available information. Each way is implemented, and the results analysed. Thus, by presenting worked out empirical examples, we hope to contribute to the evolving methodological discussions of how best to disaggregate poverty measures including the MPI by disability status. In addition, we hope to contribute to robust and detailed understanding in Bhutan of the relationship between poverty and disability status, hence to inform policies that seek to address both. However, survey data are limited, and so, very importantly, we also advise re-running these results with the 2017 census data for a more precise picture. It is hoped this note will provide some structure for a census-based analysis.

Citation: Pinilla-Roncancio, M. and Alkire, S. (2018). ‘Statistical note: disaggregating Bhutan’s MPI 2017 by disability status’, OPHI Research in Progress 51a, University of Oxford.

Incorporating Environmental and Natural Resources within Analyses of Multidimensional Poverty

How can multidimensional poverty measures – that currently encompass social and economic dimensions – be extended to include environmental deprivations that strike the poor simultaneously? And can such extended measures better inform effective and integrated policy responses? Research on joint Environmental and Natural Resources (ENR) and poverty issues is rich, and has contributed to bringing the poverty-environment nexus to the fore. Yet, no widely used multidimensional poverty measure identifies who and how the socio-economically poor people are affected by ENR-issues, at a large enough scale, and in ways that can respond to and inform public policies over the medium term. This paper sets out how such a measure could be built. In particular, it sets out how to include indicators of ENR deprivations into the profile of the joint deprivations people experience. These deprivation profiles could then be used to compute multidimensional measures using the Alkire Foster (AF) methodology, with the difference that these would now encompass a subset of pertinent ENR deprivations. The paper clarifies the ENR data requirements for developing and analysing such a measure empirically.

Citation: Thiry, G., Alkire, S. and Schleicher, J. (2018). ‘Incorporating environmental and natural resources within analyses of multidimensional poverty’. OPHI Research in Progress 50a, University of Oxford.

Walls of Glass. Measuring Deprivation in Social Participation

This paper proposes a measure for deprivation in social participation, an important but so far neglected dimension of human well-being. Operationalisation and empirical implementation of the measures are conceptually guided by the capability approach. Essentially, the paper argues that deprivation in social participation can often be convincingly established by drawing on extensive non-participation in customary social activities. In doing so, the present paper synthesises philosophical considerations, axiomatic research on poverty and deprivation, previous empirical research on social exclusion and subjective well-being. An empirical application illustrates the measurement approach using high-quality survey data for Germany. To evaluate the validity of the proposed measures, I also explore the empirical relation to adjacent concepts including material deprivation, income poverty, other potential determinants of social participation, and life satisfaction using regression techniques.

Citation: Suppa, N. (2017). ‘Walls of glass. Measuring deprivation in social participation.’ OPHI Research in Progress 49a, University of Oxford.

How poor are People with Disabilities around the Globe? A Multidimensional Perspective

People with disabilities and their families have been recognised as a high risk population and are particularly likely to be poor and deprived (Mitra, Posarac, & Vick, 2013). Although the number of studies analysing the levels of poverty of this group has increased in the last decade, there is still a lack of empirical evidence that establishes whether and how people with disabilities are significantly poorer (Groce, Kembhavi, et al., 2011). This study aims to analyse the levels of multidimensional poverty of people living in households with members with disabilities, in 11 developing countries from different regions of the world. Using the Global Multidimensional Poverty Index (Global MPI), the incidence and intensity of multidimensional poverty of people living in households with and without members with disabilities were calculated and rigorously compared the poverty levels experienced by people living in households in which no member has disabilities. In addition, it studies the levels of destitution and the percentage of individuals living in households with members with disabilities facing severe multidimensional poverty. The results reveal that people living in households with disabled members in four countries face significantly higher levels of multidimensional poverty. These households also contribute more to the national levels of multidimensional poverty than their share in the population. More worryingly, a large percentage of households are not only severely multidimensionally poor but also destitute. It is important to highlight that if disability questions are consistently included in future international multi-topic surveys, these kinds of empirical explorations could become widespread, providing the information required to support households whose members have disabilities and are multidimensionally poor.

Pinilla-Roncancio, M. and Alkire, S. (2017). ‘How poor are people with disabilities around the globe? A multidimensional perspective.’ OPHI Research in Progress 48a, University of Oxford.

Exploring Multidimensional Poverty in China: 2010 to 2014

Most poverty research has explored monetary poverty. This paper presents and analyses the Global Multidimensional Poverty Index (MPI) estimations for China. Using China Family Panel Studies (CFPS), we find China’s global MPI is 0.035 in 2010, and decreases significantly to 0.017 in 2014. The dimensional composition of MPI suggests that nutrition, education, safe drinking water and cooking fuel contribute most to overall non-monetary poverty in China. Such analysis is also applied to sub-groups including geographic areas (rural/urban, east/central/west, provinces), as well as social characteristics such as gender of the household heads, age, education level, marital status, household size, migration status, ethnicity, and religion. We find the level and composition of poverty differs significantly across certain subgroups. We also find high levels of mismatch between monetary and multidimensional poverty at the household level, which highlights the importance of using both complementary measures to track progress in eradicating poverty.

Citation: Alkire, S. and Shen, Y. (2017). “Exploring Multidimensional Poverty in China: 2010 to 2014.” OPHI Research in Progress 47a, University of Oxford.

Updated version of this paper is published in “Research in Economic Inequality: Poverty, Inequality and Welfare” (ed. J. Bishop), 25: 161–228, 2017.

Measuring Multidimensional Poverty: Dashboards, Union Identification, and the Multidimensional Poverty Index (MPI)

We analyse three approaches to measuring multidimensional poverty, using a consistent set of data for 10 indicators in 101 developing countries. First we implement a simple dashboard of deprivations in ten indicators. While most dashboards stop there, we next describe the simultaneous deprivations experienced by people which conveys information on their joint distribution, yet fails to identify multidimensional poverty. We then implement a ‘union’ approach to measurement, and identify people as multidimensionally poor if they experience any one or more of the ten deprivations. The resulting Union headcount ratio of poverty is very high and may reflect errors of inclusion. We then implement an intermediary identification approach following Alkire and Foster (2011): the global Multidimensional Poverty Index (MPI). Exploring the censoring process of the intermediary identification, we observe that a Union MPI (or intersection) identification approach does not avoid normative choices as often claimed; rather these are made at the stage of indicator selection, and the identification process can be highly sensitive to these choices. The latter approaches often imply equal weights –which is itself a value judgement made out of the public eye. The global MPI clearly states value judgements, and performs robustness tests for them. The paper thus discusses strengths and challenges of different measurement approaches to multidimensional poverty.

Citation: Alkire, S. and Robles, G. (2016). “Measuring multidimensional poverty: Dashboards, Union identification, and the Multidimensional Poverty Index (MPI).” OPHI Research in Progress 46a, University of Oxford.

Towards Frequent and Accurate Poverty Data

It is increasingly acknowledged that data availability plays a crucial role in the fight against poverty. Poverty data has increased in both quantity and frequency over the past 30 years, but still lags behind the data available on most other economic phenomena. Yet there are vibrant experiences that are often overlooked:

  • Data for monetary & multidimensional poverty dramatically increased since 1980.
  • Sixty countries already produce annual updates to key statistics.
  • Some have continuous household surveys with cost-cutting synergies.
  • International agencies have probed short surveys for comparable data.
  • Certain regions have agreed on harmonised variable definitions across countries.
  • New technologies can drastically reduce lags between data collection and analysis.

The post-2015 agenda identified the need for regularly updated data to monitor the Sustainable Development Goals (SDGs). This paper points out existing experiences that shed light on how to break the cycle of outdated poverty data and strengthen statistical systems. Such experiences show that it is possible to generate and analyse frequent and accurate poverty data that energizes and enables poverty eradication.

Citation: Alkire, S. (2016). “Towards frequent and accurate poverty data.” OPHI Research in Progress 43c, University of Oxford.

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