October e-Update – catch up on all the latest OPHI news

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Multidimensional Poverty Index – Winter 2016: Brief Methodological Note and Results

The Multidimensional Poverty Index (MPI) Winter 2016 updates use the same parameters (dimensions, indicators, cutoffs and weights) and the same functional form (Alkire and Foster Adjusted Headcount Ratio M0) as in previous years.1 This brief methodological note presents the Winter 2016 MPI updates, and releases the tables with the full results: national MPI, destitution and vulnerability results, rural, urban, subnational region, changes over time, and complete estimations, as well as complementary data, dimensional breakdowns, and confidence intervals. Destitution data are now available for 102 countries. It first explains the main updates in the Winter 2016 MPI, following the guidelines for updates presented in the 2014 Methodological Note (Alkire, Conconi and Seth 2014b). It uses the MPI methodology that has been presented in detail in previous methodological notes (Alkire and Santos 2010; Alkire, Roche, Santos and Seth 2011; Alkire, Conconi and Roche 2013; Alkire, Conconi and Seth 2014b; Alkire and Robles 2015; Alkire, Jindra, Robles and Vaz 2016). Then it briefly describes the methodological assumptions considered for the estimation of each dataset. The results of these estimations are presented in the form of 7 main tables, 103 country briefings and the interactive databank, all available on OPHI’s website (www.ophi.org.uk).

Citation: Alkire, S. and Robles, G. (2016). “Multidimensional Poverty Index – 2016: Brief methodological note and results.” MPI Methodological Notes 43, University of Oxford, December.

Multidimensional Poverty Index – Winter 2014/2015: Brief Methodological Note and Results

The Multidimensional Poverty Index (MPI) (released January 2015, henceforth Winter 2014/2015 MPI) uses the same parameters (dimensions, indicators, cutoffs and weights) and the same functional form (Alkire and Foster Adjusted Headcount Ratio M0) as in previous years.1 The main innovations in 2014 consisted in: updating the estimations for a larger series of countries than any previous year, providing further analysis over time, as well as a new measure of destitution, and new measures of inequality among the poor and across subnational regions. This brief methodological note presents the Winter 2014/2015 MPI updates, and the tables with the full results. It first explains the main updates in the 2014/2015 MPI, following the guidelines for updates presented in the 2014 Methodological Note (Alkire, Conconi and Seth 2014b). It summarizes the MPI methodology that has been presented in detail in previous methodological notes (Alkire and Santos 2010; Alkire, Roche, Santos and Seth 2011; Alkire, Conconi and Roche 2013; Alkire, Conconi and Seth 2014b). Then it briefly describes the measures of destitution and the index of inequality among the poor. The methodologies presented in this note were used to generate the tables on the MPI and the 110 country briefings and interactive maps available on OPHI’s website. The tables are presented as appendices and are available for download as Excel files.

Citation: Alkire, S., Conconi, A., Robles, G. and Seth, S. (2015). ‘Multidimensional Poverty Index – Winter 2014/2015: Brief Methodological Note and Results’, OPHI MPI Methodological Note 27 (OPHI Briefing 27), Oxford Poverty and Human Development Initiative, University of Oxford.

This paper is also published as OPHI Briefing 27.

This report presents the findings of the national Multidimensional Poverty Index (MPI) for Ghana. The Ghana MPI was developed by the Ghana Statistical Services with support from the German Agency for International Cooperation (GIZ), the United Nations Development Programme (UNDP), the MPI National Steering Committee, and the University of Cape Coast. Technical support was provided by the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford.

Ghana now joins several countries from across Africa in using an official MPI to track and measure multidimensional poverty. The Ghana MPI tracks twelve indicators relating to three dimensions: Living Standards, Education and Health. The report uses data from the seventh round of the Ghana Living Standards Survey conducted between 2016/2017 survey periods. The report also employed harmonised datasets from the Ghana Multiple Indicator Cluster Surveys conducted in 2011 and 2018 for trend analyses.

The MPI provides a tool to coordinate the efforts of government stakeholders towards the social progress of individuals and households in line with meeting the 2030 Sustainable Development Goals (SDGs) in Ghana. With a decade remaining to achieve the SDGs, this report is timely and will feed into public policy formulation and retooling to address emerging issues. The figures will be updated frequently as new data become available.

Key findings of the report:

The incidence of poverty (H) in Ghana was 45.6%, while the average intensity (A) was 51.7%. The Multidimensional Poverty Index (MPI), which is the product of H and A, was 0.236.
In Ghana, more people are living in multidimensional poverty (45.6%) than monetary poverty (23.4%), but 19.3% of the population were experiencing both monetary and multidimensional poverty.
The report indicates that 64.6% of rural populations in Ghana were experiencing multidimensional poverty, compared with 27.0% of urban populations. The Northern Region of Ghana had the highest proportion of multidimensionally poor people at 80%.
The indicators in which the most people are poor and deprived are sanitation (44.1% of the population) and health insurance (40.1%).
Between the survey years 2011 and 2018, the MPI, incidence, and intensity all saw statistically significant reductions, with particularly large improvements in electricity and cooking fuel.

Download Ghana MPI report.

2019

OPHI Update April 2019
OPHI Update June 2019
OPHI Update September 2019
OPHI Update December 2019

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.

Appendix E

‘Changes over time in the global Multidimensional Poverty Index and other measures: Towards national poverty reports’, OPHI Research in Progress 57a.

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

All eighty national poverty reports (pdf).

Individual country reports (png files):
1. Afghanistan
2. Albania
3. Armenia
4. Bangladesh
5. Belize
6. Benin
7. Bolivia
8. Bosnia and Herzegovina
9. Burkina Faso
10. Burundi
11. Cambodia
12. Cameroon
13. Central African Republic
14. Chad
15. China
16. Colombia
17. Congo, Democratic Republic of
18. Cote D’Ivoire
19. Dominican Republic
20. Egypt
21. eSwatini
22. Ethiopia
23. Gabon
24. Gambia
25. Ghana
26. Guinea
27. Guyana
28. Haiti
29. Honduras
30. India
31. Indonesia
32. Iraq
33. Jamaica
34. Jordan
35. Kazakhstan
36. Kenya
37. Kyrgyzstan
38. Lao PDR
39. Lesotho
40. Liberia
41. Madagascar
42. Malawi
43. Mali
44. Mauritania
45. Mexico
46. Moldova
47. Mongolia
48. Montenegro
49. Mozambique
50. Namibia
51. Nepal
52. Nicaragua
53. Niger
54. Nigeria
55. North Macedonia
56. Pakistan
57. Peru
58. Philippines
59. Republic of the Congo
60. Rwanda
61. Sao Tome and Principe
62. Senegal
63. Serbia
64. Sierra Leone
65. State of Palestine
66. Sudan
67. Suriname
68. Tajikistan
69. Tanzania
70. Thailand
71. Timor-Leste
72. Togo
73. Trinidad and Tobago
74. Turkmenistan
75. Uganda
76. Ukraine
77. Viet Nam
78. Yemen
79. Zambia
80. Zimbabwe

The 2020 global MPI report profiles a global study covering 5 billion people of harmonised trends in multidimensional poverty. It explores if countries, before the pandemic, were on track to halve their multidimensional poverty if observed trends continued. In the context of COVID-19, which unfolded as the report was compiled, the report offers simulations of the possible impacts of the pandemic on the global MPI. A decade away from the targets of the 2030 Agenda for Sustainable Development, the report concludes with an in-depth analysis of multidimensional poverty from the perspective of a selection of SDGs.

Download the Global MPI 2020 report Charting pathways out of multidimensional poverty: Achieving the SDGs

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.

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.

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 MPI_T 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 MPI_T 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.

This methodological note presents an eighty-country study of changes over time in multidimensional poverty, using the global MPI specifications. Accompanying tables show the full results of these disaggregations: national, rural, urban, subnational regions, and age groups, as well as complementary data, indicator breakdowns, and standard errors. This note first explains the choice of the 80 countries for this global study of changes over time. It then describes the principles used to guide the data harmonization process and the estimation procedures. Lastly, it provides the methodological details of harmonization for the estimation of each dataset used. The results of these estimations are presented online in Table 6 of Data Tables 2020.

Citation: Alkire, S., Kovesdi, F., Mitchell, C., Pinilla-Roncancio, M. and Scharlin-Pettee, S. (2020). ‘Changes over Time in the Global Multidimensional Poverty Index’, OPHI MPI Methodological Note 50, Oxford Poverty and Human Development Initiative, University of Oxford.

October e-Update – catch up on all the latest OPHI news

The October issue of the e-Update is now available, featuring all the latest news, publications, and opportunities from OPHI, including:

OPHI’s collaboration with Iraq and Kurdistan statistical offices on Iraq MPI;
State Government in Brazil implements a Multidimensional Poverty Index;
Three new working papers published;
A new video on the Women’s Empowerment in Agriculture Index;
OPHI’s new lunchtime seminar series gets underway;
November workshop on monetary and multidimensional poverty measures; and much more.

Join the OPHI mailing list to receive OPHI’s e-Update. Previous issues are available to download here.