Celia Reyes and Jeremy de Jesus of the Community Based Monitoring System in the Philippines visited OPHI as part of their collaboration on the missing dimensions of poverty data. During their visit they participated in a one day workshop on empowerment and presented the results of their work, which will be published in two papers on the missing dimensions and implications for local poverty measurement and monitoring in the Philippines later this year.
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This report presents the findings of the Multidimensional Poverty Index of Angola (A-MPI) to guide more informed decisions on issues related to poverty eradication. The report is the product of a long and strategic partnership between the National Statistics Institute (INE), the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI). The Angolan MPI responds to one of the priority actions included in Angola’s National Development Plan (PDN) 2018–2022. Resulting from in-depth technical consultations, the Angolan MPI is made up four essential dimensions: health, education, quality of life and employment.
The report is based on the 2015–2016 Multiple Health Indicators Survey (IIMS) and considers people living with at least 30% of the deprivations analyzed to be multidimensionally poor. These deprivations are divided into four dimensions: health, education, quality of life and employment .
Key findings based on the 2015–2016 data include:
- 54% of Angolans are multidimensionally poor.
- Poverty is more pronounced among children under the age of 10.
- In urban areas, about 1 in 3 people (35% of the population) is multidimensionally poor, while in rural areas this figure rises to 9 in 10 people (88% of the population).
- In Luanda, 23.7% of the population is multidimensionally poor, but in Bié, Cunene, Lunda Norte, Moxico, Cuando Cubango, Uíge, Huíla, Cuanza Sul and Huambo, multidimensional poverty affects at least 70% of the province’s population.
- MD Poverty in Angola 2020
- Ghana MPI 2020
- MD Poverty Profile in Palestine 2017 (2020)
- National MD Poverty in Maldives 2020
- MD Poverty Index 2019: Seychelles (2020)
- Sierra Leone MPI 2019
- Afghanistan MPI 2016/17
- Nepal MPI 2018
- Bhutan MPI 2017
- Arab MD Poverty Report 2017
- Andhra Pradesh MPI 2017
- MD Poverty in Pakistan (2016)
This report (in Spanish) presents simulations of the possible impacts of COVID-19 on multidimensional poverty in the Dominican Republic. The report uses the official Multidimensional Poverty Index for the Dominican Republic (IPM-RD), which was launched in 2017 and updated in 2020. The report examines six possible scenarios of how a change in indicator deprivations could affect the MPI. The deprivations examined are: 1) access to health services in the event of illness, 2) health insurance, 3) access to food, 4) school attendance or dropout, 5) family support and 6) informality. For each scenario, the analysis considers three possible magnitudes: mild (25%), moderate (50%) and severe (75%). In all scenarios and magnitudes, an increase in the incidence of multidimensional poverty is observed and the estimated effect is statistically significant. The three major effects on multidimensional poverty are related to the increase in deprivation of access to medical services due to illness, followed by family support and school attendance.
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.
OPHI Briefing 55 (PDF, 12 pages)
It is vital to document inequalities across ethnic groups to inform policies that can enable inclusive poverty reduction measures and prioritise the poorest. The Sustainable Development Goals (SDGs) demand greater disaggregation of indicators in order to make visible the inequalities that exist across social groups. This briefing presents disaggregations of the global Multidimensional Poverty Index (MPI)2 by ethnicity for the 24 countries and 650 million people whose current surveys present figures by ethnic group.
Download OPHI Briefing 55 here.
Authors: Sabina Alkire and Fanni Kovesdi.
Citation for the briefing: Alkire, S. and Kovesdi, F. (2020). ‘Multidimensional poverty across ethnic groups: Disaggregating the global MPI’, OPHI Briefing 55. Oxford Poverty and Human Development Initiative, University of Oxford.
Citation for the Ethnicity Table: Kovesdi, F. and Mitchell, C. (2020). ‘Ethnicity disaggregation of the 2019 global Multidimensional Poverty Index’, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.
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.