Category Archives: In Progress

How poor are People with Disabilities around the Globe? A Multidimensional Perspective

People with disabilities and their families have been recognised as a high risk population and are particularly likely to be poor and deprived (Mitra, Posarac, & Vick, 2013). Although the number of studies analysing the levels of poverty of this group has increased in the last decade, there is still a lack of empirical evidence that establishes whether and how people with disabilities are significantly poorer (Groce, Kembhavi, et al., 2011). This study aims to analyse the levels of multidimensional poverty of people living in households with members with disabilities, in 11 developing countries from different regions of the world. Using the Global Multidimensional Poverty Index (Global MPI), the incidence and intensity of multidimensional poverty of people living in households with and without members with disabilities were calculated and rigorously compared the poverty levels experienced by people living in households in which no member has disabilities. In addition, it studies the levels of destitution and the percentage of individuals living in households with members with disabilities facing severe multidimensional poverty. The results reveal that people living in households with disabled members in four countries face significantly higher levels of multidimensional poverty. These households also contribute more to the national levels of multidimensional poverty than their share in the population. More worryingly, a large percentage of households are not only severely multidimensionally poor but also destitute. It is important to highlight that if disability questions are consistently included in future international multi-topic surveys, these kinds of empirical explorations could become widespread, providing the information required to support households whose members have disabilities and are multidimensionally poor.

Pinilla-Roncancio, M. and Alkire, S. (2017). ‘How poor are people with disabilities around the globe? A multidimensional perspective.’ OPHI Research in Progress 48a, University of Oxford.

Exploring Multidimensional Poverty in China: 2010 to 2014

Most poverty research has explored monetary poverty. This paper presents and analyses the Global Multidimensional Poverty Index (MPI) estimations for China. Using China Family Panel Studies (CFPS), we find China’s global MPI is 0.035 in 2010, and decreases significantly to 0.017 in 2014. The dimensional composition of MPI suggests that nutrition, education, safe drinking water and cooking fuel contribute most to overall non-monetary poverty in China. Such analysis is also applied to sub-groups including geographic areas (rural/urban, east/central/west, provinces), as well as social characteristics such as gender of the household heads, age, education level, marital status, household size, migration status, ethnicity, and religion. We find the level and composition of poverty differs significantly across certain subgroups. We also find high levels of mismatch between monetary and multidimensional poverty at the household level, which highlights the importance of using both complementary measures to track progress in eradicating poverty.

Citation: Alkire, S. and Shen, Y. (2017). “Exploring Multidimensional Poverty in China: 2010 to 2014.” OPHI Research in Progress 47a, University of Oxford.

Measuring Multidimensional Poverty: Dashboards, Union Identification, and the Multidimensional Poverty Index (MPI)

We analyse three approaches to measuring multidimensional poverty, using a consistent set of data for 10 indicators in 101 developing countries. First we implement a simple dashboard of deprivations in ten indicators. While most dashboards stop there, we next describe the simultaneous deprivations experienced by people which conveys information on their joint distribution, yet fails to identify multidimensional poverty. We then implement a ‘union’ approach to measurement, and identify people as multidimensionally poor if they experience any one or more of the ten deprivations. The resulting Union headcount ratio of poverty is very high and may reflect errors of inclusion. We then implement an intermediary identification approach following Alkire and Foster (2011): the global Multidimensional Poverty Index (MPI). Exploring the censoring process of the intermediary identification, we observe that a Union MPI (or intersection) identification approach does not avoid normative choices as often claimed; rather these are made at the stage of indicator selection, and the identification process can be highly sensitive to these choices. The latter approaches often imply equal weights –which is itself a value judgement made out of the public eye. The global MPI clearly states value judgements, and performs robustness tests for them. The paper thus discusses strengths and challenges of different measurement approaches to multidimensional poverty.

Citation: Alkire, S. and Robles, G. (2016). “Measuring multidimensional poverty: Dashboards, Union identification, and the Multidimensional Poverty Index (MPI).” OPHI Research in Progress 46a, University of Oxford.

Towards Frequent and Accurate Poverty Data

It is increasingly acknowledged that data availability plays a crucial role in the fight against poverty. Poverty data has increased in both quantity and frequency over the past 30 years, but still lags behind the data available on most other economic phenomena. Yet there are vibrant experiences that are often overlooked:

  • Data for monetary & multidimensional poverty dramatically increased since 1980.
  • Sixty countries already produce annual updates to key statistics.
  • Some have continuous household surveys with cost-cutting synergies.
  • International agencies have probed short surveys for comparable data.
  • Certain regions have agreed on harmonised variable definitions across countries.
  • New technologies can drastically reduce lags between data collection and analysis.

The post-2015 agenda identified the need for regularly updated data to monitor the Sustainable Development Goals (SDGs). This paper points out existing experiences that shed light on how to break the cycle of outdated poverty data and strengthen statistical systems. Such experiences show that it is possible to generate and analyse frequent and accurate poverty data that energizes and enables poverty eradication.

Citation: Alkire, S. (2016). “Towards frequent and accurate poverty data.” OPHI Research in Progress 43c, University of Oxford.

Download supplementary data (xlsx)

How Many Children Live in Poverty? An Estimation of Global Child Multidimensional Poverty

Forthcoming paper, not yet published

The main purpose of this paper is to estimate child multidimensional poverty in developing countries. In this paper a child is defined as poor if she lives in a multidimensionally poor household according to the Global Multidimensional Poverty Index (MPI). Based on Oxford Poverty and Human Development Initiative’s (OPHI) Global MPI figures and using as reference 2010 population estimates, we estimate the incidence and intensity of child poverty in developing countries and by world regions. We also present these estimates for the different children age sub-groups (0-4, 5-9, 10-14 and 15-17) and compare them with the incidence and intensity of poverty among adults. Finally, we break down the world and regional child multidimensional poverty estimates by indicator. We find that more than one third of children living in developing countries are multidimensionally poor; that children are more afflicted by poverty, both in terms of incidence and intensity, than adults; that South Asia houses close to half of the poor children and Sub-Saharan Africa houses one third; and that in rural areas, over 1 in every 2 children is multidimensionally poor on average, while in urban areas it is 1 in every 6 children.

Citation: Vaz, A. (xxxx). “How Many Children Live in Poverty? An Estimation of Global Child Multidimensional Poverty.” OPHI Research in Progress 45a, University of Oxford.

Measuring Women’s Autonomy in Chad and its Associations with Breastfeeding Practices Using the Relative Autonomy Index

Increasing women’s voice and agency is widely recognized as a key strategy to reduce gender inequalities and improve health outcomes. Although recent studies have found associations between women’s autonomy and a number of health outcomes, fundamental issues regarding adequate measurement of women’s autonomy remain. The Relative Autonomy Index (RAI) provides a direct measure of motivational autonomy. It expresses the extent to which a woman faces coercive or internalized social pressure to undertake domain-specific actions. This addresses a key critique of current measures of autonomy, which focus on decision-making or ignore women’s values. This paper examines the measurement properties and added value of a number of domain-specific RAIs using new nationally representative data from The Republic of Chad. A striking finding is that women on average have less autonomous motivation in all eight domains compared to their male counterparts. The paper also investigates the relationship between domain-specific RAIs and breastfeeding, a contextually relevant behavior that affects children’s health.

Citation: Vaz, A., Pratley, P., and Alkire, S. (2015) “Measuring Women’s Autonomy in Chad and its Associations with Breastfeeding Practices Using the Relative Autonomy Index.” OPHI Research in Progress 44a.

OPHI Research in Progress 41a

This paper analyses changes in multidimensional poverty over time for over thirty countries and 338 sub-national regions, for which we have comparable data across at least two periods of time. The paper first describes the absolute and relative changes in the multidimensional poverty index (MPI) and their significance, as well as changes in the composition of multidimensional poverty. In so doing demonstrates the core statistics of dynamic multidimensional poverty analyses. Second, the paper examines changes in the MPI and its consistent partial indices over time across over 338 sub-national regions, plus a diversity of ethnic groups. In each case it identifies regions or ethnic groups where national poverty reduction is at risk of leaving the poorest subgroups behind. This extensive body of empirical evidence points to some fundamental research questions on the study of multidimensional poverty reduction.

Citation: Alkire, S., Roche, J. M., and Vaz, A. (2014). ‘Multidimensional poverty dynamics: Methodology and results for 34 countries’.<em> OPHI Research in Progress Paper</em> 41a. Oxford Poverty and Human Development Institution, University of Oxford.

OPHI Research in Progress 31a

This paper discusses the construction and analysis of the Multidimensional Poverty Index (MPI). It explains how the international MPI, which compares the situation of countries with respect to acute poverty, is calculated using globally comparable data. And it also explains how the MPI can be adapted to better suit national needs and realities.

OPHI Research in Progress 42a

Overall poverty reduction may leave the poorest behind and thus it is a fair question to ask if the poverty reduction has taken place among the poorest of the poor. A typical approach is to set a more stringent poverty cutoff and assess the situation of those that are the poorest or destitute. In income poverty measurement, they are often referred as ultra poor. This paper instead pursues a multidimensional counting methodology, building on Alkire and Foster (2011), and presuming that most of the variables assessing deprivations are ordinal. A person in this framework is identified as poor if the person’s intensity of deprivation or the joint deprivation score is equal to or larger than a particular poverty cutoff. There are two ways to assess the situations of the poorest in this framework. The first – which has already been implemented – is to use a higher poverty cutoff to identify those with higher intensity of deprivation across the same indicators. The second – developed in this paper – is to apply a second vector of extreme deprivation cutoffs for key indicators, and assess who is poor by these cutoffs. We call those who are poor according to these deeper deprivation cutoffs as ‘destitute’. If the indicators, weights and poverty cutoff remain unchanged, then we can undertake certain rigorous comparisons between the destitute and the poor – identified by less extreme deprivation cutoffs. We apply these two approaches to understand the extent of destitution in 49 developing countries across the world using the same set of dimensions and indicators used for constructing the MPI (Alkire and Santos 2010), which has been reported in the Human Development Reports since 2010. We find surprisingly widespread destitution across these 49 countries housing 1.2 billion poor people – indeed around half of the MPI poor people are destitute by this measure. The paper also reports results sub-nationally for 41 countries, and illustrates how the overall change in poverty may be decomposed into changes affecting those that are destitute and those that are not using strictly harmonized variables.

Citation: Alkire, S., Conconi, A., and Seth, S. (2014). ‘Measuring destitution in developing countries: An ordinal approach for identifying linked subset of multidimensionally poor’. OPHI Research in Progress 42a. Oxford Poverty and Human Development Initiation, University of Oxford.

OPHI Research in Progress 43b

It is increasingly acknowledged that data availability plays a crucial role in the fight against poverty. Poverty data has increased in both quantity and frequency over the past 30 years, but still lags behind the data available on most other economic phenomena. Yet there are vibrant experiences that are often overlooked:

  • Data for monetary & multidimensional poverty dramatically increased since 1980.
  • Sixty countries already produce annual updates to key statistics.
  • Some have continuous household surveys with cost-cutting synergies.
  • International agencies have probed short surveys for comparable data.
  • Certain regions have agreed on harmonised variable definitions across countries.
  • New technologies can drastically reduce lags between data collection and analysis.

The post-2015 agenda identified the need for regularly updated data to monitor the Sustainable Development Goals (SDGs). This paper points out existing experiences that shed light on how to break the cycle of outdated poverty data and strengthen statistical systems. Such experiences show that it is possible to generate and analyse frequent and accurate poverty data that energizes and enables poverty eradication.

Citation: Alkire, S. (2014). “Towards Frequent and Accurate Poverty Data”. OPHI Research in Progress 43b, Oxford University.

Download supplementary data – Questionnaire (pdf)

Download Supplementary Data – Survey Modules (pdf)