Labour is of utmost importance for human wellbeing. Yet a comprehensive framework that can reflect the empirical diversity of labour activities along with each activities’ manifold effects on human wellbeing is still lacking. An additional challenge for any such framework is to adequately handle fundamental moral ambiguities, which are inherent to many forms of work. This paper argues that a conceptualisation of labour within the capability approach can meet these requirements. Specifically, I argue that labour can be conceived as a characteristic-providing activity, where obtained characteristics are then transformed into functioning achievements, while accounting for both individual and societal heterogeneity. Additionally, paying adequate attention to unfreedoms experienced by agents turns out to be vital for a comprehensive account. Finally, the paper discusses policy handles, offers suggestions for particular applications, and identifies several other benefits for labour economics.
Citation: Suppa, N. (2019). ‘Work and wellbeing: A conceptual proposal.’ OPHI Working Paper 131, University of Oxford.
This paper assesses the impact of the SADA-Northern Ghana Millennium Village Project (MVP) on multidimensional poverty using dashboard and index approaches. Using a unique, large dataset that spans five years and contains data on multiple welfare indicators, we estimate the impact of MVP on the Millennium Development Goals (MDGs) and on the global multidimensional poverty index (global MPI). We find that the project had a limited impact on the MDGs and yet a positive impact on the global MPI. We assess the robustness of the impact of MVP on the global MPI, and we conclude that it was largely driven by the sensitivity of the index to changes in a few MDG indicators. We conclude that the MVP had a limited impact on welfare and that the global MPI should be used with caution in the evaluation of development programmes.
Citation: Masset, E. and García Hombrados, J. (2019): ‘Impact of the SADA-Northern Ghana Millennium Village Project on multidimensional poverty: A comparison of dash-board and index approaches’. OPHI Working Paper 130, University of Oxford.
We examine the welfare effects of India’s workfare program NREGA using a novel, almost sharp regression discontinuity design. We find large seasonal consumption increases in states implementing the program intensely, which are a multiple of the direct income gains. We also find increases in adolescents’ school attendance. Our results imply substantial general equilibrium effects. We conclude that, when properly implemented, the public employment program holds significant potential for reducing poverty and insuring households against various adverse implications of seasonal income shortfalls.
Citation: Klonner, S. and Oldiges, C. (2019). ‘The welfare effects of India’s Rural Employment Guarantee’, OPHI Working Paper 129, University of Oxford.
The following global MPI reports are available:
OPHI MPI Methodological Notes can be found here.
This report presents the findings of the child MPI for Thailand, an official, permanent tool to measure multidimensional poverty among children aged 0-17 years. The child MPI was developed by the National Economic and Social Development Council (NESDC) and the United Nations Children’s Fund (UNICEF) Thailand Country Office, with technical support from the Oxford Poverty and Human Development Initiative (OPHI) and the UNICEF Thailand Country Office. The findings are intended to guide policymakers and other key stakeholders in budget allocation and targeting in order to build human capital, reduce inequality, and eliminate poverty in all its dimensions.
The child MPI is an individual measure of child poverty, with the child as the unit of identification and analysis. While results show that Thailand has reduced monetary poverty since 2005/2006, more than 20% of children in Thailand are still multidimensionally poor. Data from 2015/2016 showed that deprivations in Education contributed the most to child poverty at 41.1% followed by deprivations in Health (15.1%) and Health Prevention (15%). Poverty tended to be higher in rural areas (23% of children identified as poor) compared to urban areas (19%). The Northeast (25.6%) and North (23.2%) regions had the highest incidence of multidimensional poverty. Of the 14 provinces for which data was available, Kalasin had the highest incidence of poverty (40.2%) while Mae Hong Son had the highest intensity (40.6%) and Pattani had the highest overall MPI (0.141).
These child MPI findings will be complemented in due course with a national MPI that assesses the multidimensional poverty of all of Thailand’s population, regardless of age.
Download the Report here.
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.
The video reveals that for those living in poverty in El Salvador, poverty means deprivations in housing, access to work, education, surroundings, health, food, security, and income.
Based on the experiences of poor people themselves, dimensions and deprivations of poverty have been identified to build a multidimensional poverty index to measure poverty in El Salvador.