Category Archives: In Progress

mpitb: A toolbox for multidimensional poverty indices

This paper presents mpitb a toolbox for multidimensional poverty indices (MPI). The Stata package mpitb comprises several subcommands to facilitate specification, estimation, and analyses of MPIs and supports the popular Alkire-Foster framework to multidimensional poverty measurement. mpitb offers several benefits to researchers, analysts and practitioners working on MPIs, including substantial time savings (e.g., due to lower data management and programming requirements) while allowing for a more comprehensive analysis at the same time. Moreover, the toolbox encourages to report standard errors or confidence intervals.

Citation: Suppa, N. (2022). ‘mpitb: A toolbox for multidimensional poverty indices,’ OPHI Research in Progress 62a, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.

Global multidimensional poverty and COVID-19: A decade of progress at risk?

According to the global Multidimensional Poverty Index (MPI), an internationally comparable measure, poverty in developing countries has fallen substantially over the last 15 years. The COVID-19 pandemic and associated economic contraction are negatively impacting multiple dimensions of poverty and jeopardising this progress. This paper uses quantitative assessments of increases in food insecurity and out of school children made by UN agencies to inform microsimulations of potential impacts of the pandemic under six alternative scenarios. These simulations use the nationally representative datasets underlying the 2020 update of the global MPI. Because these datasets were collected between one and 12 years pre-pandemic, we develop models to translate the simulated impacts to 2020 while accounting for underlying poverty reduction trends and country-specific factors. Aggregating results across 70 countries that account for 89% of the global poor according to the 2020 global MPI, we find that the potential setback to multidimensional poverty reduction is between 3.6 and 9.9 years under the alternative scenarios.

An earlier version of this work was circulated as part of “On track or not? Projecting the global Multidimensional Poverty Index”, OPHI Research in Progress 58a.

Citation: Alkire, S., Nogales, R., Quinn, N. N. and Suppa, N. (2021). ‘Global multidimensional poverty and COVID-19: A decade of progress at risk?’, OPHI Research in Progress 61a, Oxford Poverty and Human Development Initiative, University of Oxford.

A Birdseye View of Well-being: Exploring a Multidimensional Measure for the United Kingdom

This paper explores a new approach to capturing well-being and human development in a single, joint multidimensional index that is at once intuitive, rigorous and policy salient. Based on Amartya Sen’s capability approach and the Alkire-Foster method as adapted in Bhutan’s Gross National Happiness Index, the paper presents a new exploratory Multidimensional Well-being Index (MWI) for the United Kingdom. The aim of the paper is twofold: inform the debate on the measurement of well-being, and of human development more generally, and illustrate the added value of a single rigorous metric in the form of an index, as a complementary headline measure to GDP. The MWI presented here follows a subset of the domains and indicators from the official national well-being dashboard for the UK and is constructed from a single wave of Understanding Society (Wave 9) data. Findings are presented at the national level and decomposed by population subgroups and regions to reveal inequalities in well-being across the population. The indicators are data constrained so we recommend the results be interpreted as illustrating a methodology that could be insightful for policy if appropriate indicators were agreed by due process. Results show that 44% the population enjoys satisfactory levels of well-being, but this varies greatly. For instance, across ethnic groups, 53% of white people enjoy favourable well-being, but only 35% of other ethnic groups, and only 27% of people who self-identify as Black African/Caribbean or Black British. Many people report lacking a balanced diet and minimum physical exercise, as well as feeling unhappy, anxious and not feeling satisfied with income or leisure time, that highlights the need for policy focus on these areas if well-being is to be raised and maintained for all.

Citation: Alkire, S. and Kovesdi, F. (2020). ‘A birdseye view of well-being: Exploring a multidimensional measure for the United Kingdom’, OPHI Research in Progress 60a, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.

Moderate Multidimensional Poverty Index: Paving the Way out of Poverty

This paper introduces a trial Moderate Multidimensional Poverty Index (MMPI) that provides a meaningful superset of existing global multidimensional poverty indices. Eradicating poverty in all its forms everywhere requires indicators that measure sustainable pathways out of poverty, not only the absence of extreme deprivation. The MMPI increases the deprivation cutoff of nine of the ten indicators of the global Multidimensional Poverty Index (gMPI) to reflect moderate rather than acute levels of multidimensional poverty, in line with the ambitions outlined in the Sustainable Development Goals (SDGs). The MMPI is constructed as a superset to the global MPI maintaining the three dimensions of health, education and living standards, but adjusting nine of the indicators to reflect a meaningful change in the level of ambition. The trial MMPI is data-constrained, but provides a methodology and discusses potential indicators for an MMPI that would: i) be globally comparable across countries at all income levels, ii) align the indicators with the higher standards for development as defined in the Agenda 2030, and iii) allow us to study some aspects of intrahousehold deprivation. The trial MMPI is illustrated empirically using nationally representative household surveys from Thailand, Iraq, Tanzania, Serbia, Guatemala, and Bangladesh. The empirical results in the six countries show the added value of having three layered measures of destitution, acute poverty, and moderate poverty. The MMPI aligns reasonably well with the established monetary poverty levels in lower middle-income countries ($3.2 / day) and in upper middle-income countries ($5.5/day), yet with some informative differences. The results demonstrate that the MMPI is feasible, has desirable properties as a global poverty index, and allows to unearth thus far hidden aspects in poverty measurement, such as intrahousehold deprivations in education. Still, challenges remain in terms of data availability for certain indicators and a study across additional countries is required before an MMPI structure can be finalized.

Citation: Alkire, S., Kovesdi, F., Scheja, E. and Vollmer, F. (2020). ‘Moderate Multidimensional Poverty Index: Paving the way out of poverty’, OPHI Research in Progress 59a, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.

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

This paper demonstrates how the revised assets indicator of the updated global Multidimensional Poverty Index (global MPI), launched in September 2018, consolidated and improved the measurement of assets deprivation at the global level. The revision combines normative and statistical methods to assess the validity of the 7-item assets schedule contained in the Original MPI, jointly designed by the Oxford Poverty and Human Development Initiative and the UNDP Human Development Report Office (HDRO) in 2010, and an 11-item schedule of an Experi­mental MPI, which was developed by the UNDP HDRO in 2014. It also analysed whether the inclusion of additional items identified in a review of over 100 Demographic and Health Surveys, Multiple Indicators Cluster Surveys and national surveys from which the global MPI is constructed, would add value to a revised assets indicator. Drawing on the analytical framework developed for the European Union material deprivation indicator, complemented by normative assessment, this paper applies tetrachoric exploratory factor analysis, multiple correspondence analysis, classical test theory, item response theory and alternative measures to identify a set of items that proxy assets deprivation globally. In using a set of 26 purposefully selected countries, test results were used to rule out infeasible assets, and finally to justify the addition of computer and animal cart to the assets schedule of the Original MPI. Based on this statistically validated expansion, and greater reliability of the items in the schedule, we conclude that the consolidated and revised indicator measures assets deprivation more accurately at the global level.

Citation: Vollmer, F. and Alkire, S. (2020). ‘Towards a global asset indicator: Re-assessing the asset indicator in the global Multidimensional Poverty Index’, OPHI Research in Progress 53b, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.

Download supplementary data for RP53b (xlsx)

On Track or Not? Projecting the Global Multidimensional Poverty Index

In this paper we compute projections of global multidimensional poverty. We use recently published estimates of changes over time in multidimensional poverty for 75 countries, which are based on time-consistent indicators. We consider and evaluate different approaches to model the trajectories of countries’ achieved and future poverty reduction. Our preferred model respects theoretical bounds, is supported by empirical evidence, and ensures consistency of our main measure with its sub-indices. We apply this approach to examine whether countries will halve their poverty between 2015 and 2030 if observed trends continue. Our results suggest that if observed trends continue, 47 countries will have halved their poverty by 2030—irrespective of the underlying model. As the current COVID-19 pandemic may severely disrupt progress in poverty reduction, we also assess its potential impact using simulation techniques and evaluate the resulting setback. Our analyses suggest a setback to multidimensional poverty reduction of about 3–10 years.

Citation: Alkire, S., Nogales, R., Quinn, N. N., and Suppa, N. (2020). ‘On Track or Not? Projecting the Global Multidimensional Poverty Index,’ OPHI Research in Progress 58a, University of Oxford.

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

Following Amartya Sen’s pioneering ideas on poverty and inequality measurement, the development economics literature proposes diverse classes of measures as well as poverty orderings. Yet in the Sustainable Development Goals (SDGs), the headcount ratio is the primary statistic for measuring monetary and multidimensional poverty. Rigorously analysing the trends of multidimensional poverty for India between 2005/6 and 2015/16, we illustrate how the headcount ratio is not able to observe certain centrally important requirements of the SDGs – such as whether anyone is being left behind, or how deprivations are interlinked. We propose using the adjusted headcount ratio or Multidimensional Poverty Index (MPI) as the primary poverty measure for policy assessment, supplemented by the headcount ratio, intensity, number of poor, and composition of poverty, to provide more accurate analyses. Exploiting cross-sectional data comprising of more than three million individuals and a panel of 29 states and several socio-economic subgroups, we show empirically how the reduction of multidimensional poverty by 271 million unfolded within a decade. In contrast to earlier periods in time, we find that the poorest of the poor saw the largest reductions in multidimensional poverty due to falling levels of intensity – a feature the headcount ratio alone cannot portray. Despite the importance of the MPI we also recognise the inherent and enduring need to probe the headcount ratio and number of poor statistics. Hence we corroborate these stark findings with an assessment of the dominance of the distribution of attainment scores which establishes the relationship between MPI and H in both periods. To assess the robustness, 19 additional MPIs are constructed, having different indicator definitions and combinations, and it is found that in nearly all of these a greater number of persons left poverty.

Further developed version of this paper has been published in the World Development, Vol. 142, 2021.

Citation: Alkire, S., Oldiges, C. and Kanagaratnam, U. (2020). ‘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 54b, Oxford Poverty and Human Development Initiative, University of Oxford.

Changes over Time in the Global Multidimensional Poverty Index and Other Measures: Towards National Poverty Reports

This paper compares trends in multidimensional and monetary poverty systematically across developing regions. The trends in multidimensional poverty draw on the global Multidimensional Poverty Index (MPI) and related sub- and partial-indices in 80 countries and 647 subnational regions, covering roughly 5 billion people, for which there is a recent MPI estimation and comparable datasets for two time periods. This paper uses two main techniques to assess the pro-poorness of multidimensional poverty reduction and triangulate monetary and nonmonetary poverty measures. First, utilizing the properties of subgroup decomposability and dimensional breakdown, it examines changes in the MPIT and its consistent sub-indices over time across sub-national regions and urban–rural regions. The decomposition analysis identifies relevant national patterns, including those in which the pace of poverty reduction is higher for the poorest subgroups. Next, it assesses overall annualized changes in the incidence of multidimensional poverty, compares this with changes in $1.90 poverty trends, and evaluates the pace and direction of various international poverty lines for monetary poverty, with national monetary and multidimensional measures, and for the family of global MPIT measures. This extensive empirical analysis illustrates how to assess the extent and patterns of reduction of multidimensional poverty, as well as whether it is inclusive or whether some people or groups are left behind, and triangulates various poverty measures to evaluate the reliability and credibility of their purposes. Naturally, some further research questions emerge.

Online Appendix E: Eighty national poverty reports, triangulating monetary measures and the global MPI family of multidimensional poverty measures.

Citation: Alkire, S., F. Kovesdi, M. Pinilla-Roncancio and S. Scharlin-Pettee. (2020). ‘Changes over time in the global Multidimensional Poverty Index and other measures: Towards national poverty reports’, OPHI Research in Progress 57a, Oxford Poverty and Human Development Initiative, University of Oxford.

Revising the Global Multidimensional Poverty Index: Empirical Insights and Robustness

The global Multidimensional Poverty Index, published annually since 2010, captures acute multidimensional poverty in the developing regions of the world. In 2018, five of its ten indicators were revised with the purpose of aligning the index to the SDGs insofar as current data permit. This paper provides comprehensive analyses of the consequences of this revision from three perspectives. First, we offer new empirical insights available from the revised specification. Second, we analyse its robustness to changes in some key parameters, including the poverty cutoff and dimensional weights. Third, we compare the revised and the original specifications by implementing both on the same 105 national datasets. The country orderings in the revised specification are found to be robust to plausible parametric alternatives. Largely, these country orderings are at least as robust as the original one. Additional research on robustness standards is suggested.

Citation: Alkire, S., Kanagaratnam, U., Nogales, R. and Suppa, N. (2020). ‘Revising the global Multidimensional Poverty Index: Empirical insight and robustness’, OPHI Research in Progress 56a, Oxford Poverty and Human Development Initiative, University of Oxford.

A further developed version of this paper is published at the Review of Income and Wealth in 2022, DOI 10.1111/roiw.12573.

Evaluation of Programs with Multiple Objectives: Multidimensional Methods and Empirical Application to Progresa in Mexico

Development programs and policy interventions frequently have multiple simultaneous objectives. Existing quantitative evaluation approaches fail to fully accommodate this multiplicity of objectives. In this paper we adapt the multidimensional poverty measurement approach developed by Alkire and Foster (2011) to the estimation of treatment effects for programs with multiple objectives. We use the potential outcomes framework to show that differences in Alkire-Foster indices between treated and control samples correspond to average treatment effects estimates of outcomes of interest under experimental conditions, and develop further methods of analysis to explore these multidimensional treatment effects. We discuss issues of index design encountered in practice and provide an illustrative example. We apply the methods developed to evaluate the conditional cash transfer program Progresa in Mexico, finding significant multidimensional effects of the program. Further analysis shows that these treatment effects are driven mainly by impacts on school attendance and health visits, objectives that correspond directly to the conditions of the program. There is no evidence for heterogeneity of the treatment effects by the extent to which the beneficiary failed to achieve the objectives at baseline. This study complements the extensive literature on the evaluation of Progresa and other development programs, comprising studies that focus on particular objectives or outcomes of the program. We hope that the methods developed here will find wide application to the evaluation of programs with multiple objectives.

Citation: Vaz, A., Malaeb, B. and Quinn, N.N. (2019). ‘Evaluation of programs with multiple objectives: Multidimensional methods and empirical application to Progresa in Mexico’, OPHI Research in Progress 55a, University of Oxford.