OPHI Working Paper available online in Journal of Public Economics

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Anti-poverty programs increasingly target disadvantages in multiple outcomes to address cur- rent and future poverty. Conventional evaluation exercises, however, mostly estimate pro- grams’ impacts separately. We present a framework, drawing from the counting approach, that captures the joint distribution of disadvantages and allows the evaluation of programs’ impacts on multiple disadvantages. We apply the framework to scrutinise the Philippine conditional cash transfer program using an embedded randomised control trial survey. Examining the program’s impact on the distribution of multiple disadvantages, we observe that the program successfully reduced multiple disadvantages overall, but did not necessarily benefit the families experiencing a higher number of disadvantages simultaneously. Our results exemplify the valuable contribution of considering the joint distribution of disadvantages in evaluating anti-poverty programs’ impacts.

Citation: Seth, S. and Tutor, M. J. (2019): ‘Evaluation of anti-poverty programs’ impact on joint disadvantages: Insights from the Philippine experience’ OPHI Working Paper 132, University of Oxford.

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:

Global MPI 2019 : Illuminating Inequalities. Link.
Global MPI 2018: The Most Detailed Picture to Date of the World’s Poorest People. Link.
Global MPI 2017 (2 pages). OPHI Policy Briefing 48. Link.
Global MPI 2017 (16 pages). OPHI Policy Briefing 47. Link.
Global MPI 2016 (2 pages). OPHI Policy Briefing 41. Link.
Global MPI 2015 (8 pages). OPHI Policy Briefing 33. Link.
Global MPI 2015 (2 pages). OPHI Policy Briefing 32. Link.
Global MPI 2014 (8 pages). OPHI Policy Briefing 21. Link.
Global MPI 2014 (2 pages). OPHI Policy Briefing 20. Link.
Global MPI 2013 (2 pages). OPHI Policy Briefing 17. Link.
Global MPI 2013 (8 pages). OPHI Policy Briefing 13. Link.
Global MPI 2012: Least Developed Countries and the MPI (2 pages). OPHI Briefing 10. Link.
Global MPI 2011 (8 pages). OPHI Policy Briefing 7. Link.
Global MPI 2011 (2 pages). OPHI Policy Briefing 6. Link.
Global MPI 2010 (2 pages). OPHI Policy Briefing 2. Link.
Global MPI 2010 (8 pages). OPHI Policy Briefing 1. Link.

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, R⁰has 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 R⁰is 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.

Global MPI Data Tables for 2014/2015

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:

Detailed MPI results at the country level (110 countries)
Breakdown of MPI results by rural and urban areas (108 countries)
MPI at the sub-national level for 803 regions of 71 countries

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

Afghanistan (AFG)	Guatemala (GTM)	Nigeria (NGA)
Albania (ALB)	Guinea (GIN)	Pakistan (PAK)
Algeria (DZA)	Guinea-Bissau (GNB)	Palestine, State of (PSE)
Angola (AGO)	Guyana (GUY)	Paraguay (PRY)
Armenia (ARM)	Haiti (HTI)	Peru (PER)
Azerbaijan (AZE)	Honduras (HND)	Philippines (PHL)
Bangladesh (BGD)	India (IND)	Rwanda (RWA)
Barbados (BRB)	Indonesia (IDN)	Saint Lucia (LCA)
Benin (BEN)	Iraq (IRQ)	Sao Tome & Principe (STP)
Belize (BLZ)	Jamaica (JAM)	Senegal (SEN)
Bhutan (BTN)	Jordan (JOR)	Serbia (SRB)
Bolivia (BOL)	Kazakhstan (KAZ)	Sierra Leone (SLE)
Bosnia & Herzegovina (BIH)	Kenya (KEN)	Somalia (SOM)
Brazil (BRA)	Kyrgyzstan (KGZ)	South Africa (ZAF)
Burkina Faso (BFA)	Lao PDR (LAO)	South Sudan (SSD)
Burundi (BDI)	Liberia (LBR)	Sudan (SDN)
Cambodia (KHM)	Libya (LBY)	Suriname (SUR)
Cameroon (CMR)	Lesotho (LSO)	Swaziland (SWZ)
Central African Rep. (CAF)	Macedonia (MKD)	Syrian Arab Republic (SYR)
Chad (TCD)	Madagascar (MDG)	Tajikistan (TJK)
China (CHN)	Malawi (MWI)	Tanzania (TZA)
Colombia (COL)	Maldives (MDV)	Thailand (THA)
Comoros (COM)	Mali (MLI)	Timor-Leste (TLS)
Congo (COG)	Mauritania (MRT)	Togo (TGO)
Congo DR (COD)	Mexico (MEX)	Trinidad and Tobago (TTO)
Cote d’Ivoire (CIV)	Moldova (MDA)	Tunisia (TUN)
Djibouti (DJI)	Mongolia (MNG)	Turkmenistan (TKM)
Dominican Republic (DOM)	Montenegro (MNE)	Uganda (UGA)
Ecuador (ECU)	Morocco (MAR)	Ukraine (UKR)
Egypt (EGY)	Mozambique (MOZ)	Uzbekistan (UZB)
El Salvador (SLV)	Myanmar (MMR)	Vanuatu (VUT)
Ethiopia (ETH)	Namibia (NAM)	Viet Nam (VNM)
Gabon (GAB)	Nepal (NPL)	Yemen (YEM)
Gambia (GMB)	Nicaragua (NIC)	Zambia (ZMB)
Ghana (GHA)	Niger (NER)	Zimbabwe (ZWE)

OPHI Working Paper available online in Journal of Public Economics

4th May 2011

OPHI Working Paper 32, ‘Counting and Multidimensional Poverty Measurement’ by Sabina Alkire and James Foster has been published online in the Journal of Public Economics.