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.
This paper investigates the degree of association in the identification of the poor between the standard monetary FGT measure and the Alkire-Foster Multidimensional Poverty Index. For this purpose, we use a measure of redundancy between the two poverty measures (R0). In Chile, over the past 25 years, R0 has declined at a rate of 1.5% per year. The decline is unimportant during the 1990s, a decade of rapid economic growth, while it is notable thereafter, in a period characterized by modest economic growth and the progressive introduction and deepening of social policies. The conditional correlation between socioeconomic and demographic characteristics with R0 is examined at the province and household levels. After controlling for household non-eligibility across some of the indicators of the Multidimensional Poverty Index, we find that the divergence in the identification of the poor can be explained by improvements in education, increasing urbanization, and a reduction in the household size. Consequently, the divergent identification of the poor seems to be a real process, which is not randomly distributed across the population. On the basis of our results, we hypothesize that this divergence is a general phenomenon that tends to occur in countries undergoing demographic transition, urbanization, and progress in education. If so, and given the fact that poverty alleviation strategies are adopted partly on the basis of poverty statistics, the diverging identification of the poor might have distributive consequences for the poor themselves.
Citation: Klasen, S. and Villalobos, C. (2019). ‘Diverging identification of the poor: A non-random process. Chile 1992–2017’, OPHI Working Paper 128, University of Oxford.
Detailed MPI data is available to download from the tables below. Tables 1.1 – 5.3 were updated in Winter 2014/2015 and are appendices to the Methodological Note – Winter 2014/2015. They include:
Table 6.1-6.6 was updated in June 2014 and covers changes to multidimensional poverty over time for 34 countries and their sub-national regions where possible. It is an appendix to the Methodological Note – Winter 2014/2015 andMultidimensional Poverty Dynamics: Methodology and Results for 34 countries.
The tables are divided into sheets to help in navigating through the data. The chart below provides detailed information on what is included in each data table and sheet. You can download the tables by clicking on the icons in the right-hand column.
| Table | Contents | Download |
| Tables 1.1-2.3 | Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (110 countries) |  |
| Tables 3.1-4.3 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (108 countries) |  |
| Tables 5.1-5.3 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level (803 regions of 71 countries) | |
| Tables 6.1-6.6 | Changes to MPI poverty over time, including annualised changes in headcount ratio and intensity, changes in each indicator at the national level and changes in destitution where available (34 countries) | |
Citations: Please cite MPI data from tables 1.1 – 5.3 as: Alkire, S., Conconi, A., Robles, G. and Seth, S. (2015). “Multidimensional Poverty Index, Winter 2014/2015: Brief Methodological Note and Results.” OPHI Briefing 27, University of Oxford, January.
Please cite data from tables 6.1-6.6 as: Alkire, S., J. M. Roche and A. Vaz (2014): “Multidimensional Poverty Dynamics: Methodology and Results for 34 countries”, Oxford Poverty and Human Development Initiative, Oxford University. ophi.qeh.ox.ac.uk
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Afghanistan (AFG)
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Guatemala (GTM) | Nigeria (NGA) |
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Albania (ALB)
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Guinea (GIN)
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Pakistan (PAK)
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Algeria (DZA)
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Guinea-Bissau (GNB)
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Palestine, State of (PSE)
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Angola (AGO)
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Guyana (GUY)
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Paraguay (PRY)
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Armenia (ARM)
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Haiti (HTI)
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Peru (PER)
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Azerbaijan (AZE)
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Honduras (HND)
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Philippines (PHL)
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Bangladesh (BGD)
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India (IND)
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Rwanda (RWA)
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Barbados (BRB)
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Indonesia (IDN)
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Saint Lucia (LCA)
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Benin (BEN)
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Iraq (IRQ)
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Sao Tome & Principe (STP)
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Belize (BLZ)
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Jamaica (JAM)
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Senegal (SEN)
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Bhutan (BTN)
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Jordan (JOR)
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Serbia (SRB)
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Bolivia (BOL)
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Kazakhstan (KAZ)
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Sierra Leone (SLE)
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Bosnia & Herzegovina (BIH)
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Kenya (KEN)
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Somalia (SOM)
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Brazil (BRA)
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Kyrgyzstan (KGZ)
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South Africa (ZAF)
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Burkina Faso (BFA)
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Lao PDR (LAO)
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South Sudan (SSD)
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Burundi (BDI)
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Liberia (LBR)
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Sudan (SDN)
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Cambodia (KHM)
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Libya (LBY)
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Suriname (SUR)
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Cameroon (CMR)
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Lesotho (LSO)
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Swaziland (SWZ)
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Central African Rep. (CAF)
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Macedonia (MKD)
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Syrian Arab Republic (SYR)
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Chad (TCD)
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Madagascar (MDG)
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Tajikistan (TJK)
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China (CHN)
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Malawi (MWI)
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Tanzania (TZA)
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Colombia (COL)
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Maldives (MDV)
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Thailand (THA)
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Comoros (COM)
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Mali (MLI)
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Timor-Leste (TLS)
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Congo (COG)
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Mauritania (MRT)
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Togo (TGO)
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Congo DR (COD)
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Mexico (MEX)
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Trinidad and Tobago (TTO)
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Cote d’Ivoire (CIV)
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Moldova (MDA)
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Tunisia (TUN)
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Djibouti (DJI)
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Mongolia (MNG)
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Turkmenistan (TKM)
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Dominican Republic (DOM)
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Montenegro (MNE)
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Uganda (UGA)
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Ecuador (ECU)
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Morocco (MAR)
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Ukraine (UKR)
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Egypt (EGY)
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Mozambique (MOZ)
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Uzbekistan (UZB)
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El Salvador (SLV)
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Myanmar (MMR)
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Vanuatu (VUT)
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Ethiopia (ETH)
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Namibia (NAM)
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Viet Nam (VNM)
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Gabon (GAB)
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Nepal (NPL)
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Yemen (YEM)
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Gambia (GMB)
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Nicaragua (NIC)
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Zambia (ZMB)
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Ghana (GHA)
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Niger (NER)
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Zimbabwe (ZWE)
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Many poverty measures identify a household as poor or non-poor based on the achievements of all its members. Using the household as the unit of identification has the benefit of enabling a poverty measure to draw on information about persons of different ages and genders, and in different life situations. However, it also loses individual information because this is summarized at the level of the household. For example, the underlying microdata contain additional information on individual children. As a consequence, gendered and intrahousehold inequalities, for instance, are not evident even when data for them exist. This paper proposes methods to augment a household multidimensional poverty index (MPI) by applying individual-level analyses to the same dataset, and analysing these alongside the matrix of deprivations underlying an MPI. In particular we scrutinise (i) what proportion of deprived children live in multidimensionally poor households; (ii) what proportion of deprived children are girls or boys; and (iii) what proportion of deprived children live in households in which other children are not deprived in that same indicator. We also observe (iv) what other deprivations deprived and poor children experience in addition to the focal deprivation. Finally, we study what proportion of people live in households where children of different ages experience two different child deprivations concurrently. More complex analyses can also be undertaken that combine information on the deprivation status of more than one eligible member, and we illustrate this to identify pioneer children, who completed six years of schooling although adults in their household have not. Overall, this study provides a prototype methodology that can be mainstreamed into subsequent national and global MPI analyses in order to shine a light on child poverty multidimensionally. We illustrate the methodology with analyses of the global MPI for seven countries in South Asia.
Citation: Alkire, S., Ul Haq, R. and Alim, A. (2019). ‘The state of multidimensional child poverty in South Asia: a contextual and gendered view’, OPHI Working Paper 127, University of Oxford.
This joint handbook by United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford, provides practical guidance for policymakers, statisticians, practitioners and others seeking or engaged in developing a national Multidimensional Poverty Index (MPI). It is also intended as a resource for academics, journalists and others interested in broadening their country’s poverty metrics, in line with the 2030 Agenda and Sustainable Development Goals (SDGs). With the use of the national MPI, UNDP and OPHI hope to strengthen national capacities of policymakers, technical experts and other stakeholders to:
Download the Handbook here.
| Tables 1.1-1.7 | Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (105 countries). Download |
| Tables 2.1-2.12 | Multidimensional poverty, headcount ratio by dimension and contribution by different poverty cut-offs (105 countries). Download |
| Tables 3.1-3.6 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations by age groups. Download |
| Table 4.1-4.6 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (105 countries). Download |
| Table 5.1-5.6
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Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level. Download
5a. India disaggregation by district. Download |
| Table 6a | 6a. India: Changes over time. Download |
| Table 7 | The table presents an archive of all MPI estimations published since 2010. These are not harmonised for comparisons over time. Table 7 covers 361 estimations for 120 countries in 2018, of which 256 estimates are based on the original MPI and 105 estimates are based on the 2018 revised MPI. Download |
| Data tables 1.1-2.3 | Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (104 countries). Download |
| Data tables 3.1-4.3 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (104 countries). Download |
| Data tables 5.1-5.4 | Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level. Download |
| Table 7 | The table presents an archive of all MPI estimations published since 2010. These are not harmonized for comparisons over time. Table 7 covers 256 estimations for 120 countries in 2016. Download |
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Children’s Multidimensional Poverty: disaggregating the global MPI Download |
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Global Multidimensional Poverty Index 2017 Download |
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‘Multidimensional Poverty Index – Summer 2017: |
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Press release: Half the world’s poor are children Download |
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Data tables 1.1-2.3 Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (103 countries) Download |
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Data tables 3.1-4.3 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (102 countries) Download |
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Data tables 5.1-5.4 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level (988 regions of 78 countries) Download |
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Table 7 The table presents an archive of all MPI estimations published since 2010. These are not harmonized for comparisons over time. Table 7 covers 256 estimations for 120 countries in 2016. Download |
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Table 8 Multidimensional poverty, headcount ratio by dimensions and contribution of deprivations for different age groups at the national level (103 countries). Download |
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Multidimensional Poverty Index – Winter 2016: Brief methodological note and results.
OPHI MPI Methodological Note 43, 2016. |
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Data tables 1.1-2.3 Main MPI results, headcount ratio by dimensions, contribution of deprivations and other measures of poverty and wellbeing at the national level (103 countries) Download |
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Data tables 3.1-4.3 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations in rural and urban areas (101 countries) Download |
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Data tables 5.1-5.4 Multidimensional poverty, headcount ratio by dimension and contribution of deprivations at the sub-national level (962 regions of 77 countries) Download |
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Table 7 The table presents an archive of all MPI estimations published since 2010. These are not harmonized for comparisons over time (for harmonized estimations see Table 6 from summer 2016 update). Table 7 covers 241 estimations for 119 countries in Winter 2016. Download |
14th February 2011
In an interview with The Economic Times on poverty in India, the renowned economist Angus S. Deaton commented on the MPI stating ‘Income is only one dimension of poverty , though clearly an important one. So I very much support the development of multidimensional indexes, and think the OPHI indexes are important. Clearly, they are to some extent still experimental, but this is clearly the right direction in which to go.’ Read more in The Economic Times
