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Measuring Multidimensional Poverty: Insights from Around the World May 2015

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Measuring Multidimensional Poverty: Insights from Around the World May 2015 OPHIOXFORD POVERTY & HUMAN DEVELOPMENT INITIATIVE OXFORD DEPARTMENT OF INTERNATIONAL DEVELOPMENT Measuring Multidimensional Poverty: Insights from Around the World May 2015 www.ophi.org.uk Why Multidimensional Poverty? How we measure poverty can importantly infuence how we nicate, it does not provide an incentive to reduce the depriva- come to understand it, how we analyse it, and how we create tions of the poorest of the poor. Nor can it be broken down by policies to tackle it. For this reason, measurement methodolo- dimension to show how people are poor. gies can be of tremendous practical relevance. In 2007, OPHI Director Sabina Alkire and Professor James Most countries of the world defne poverty in a unidimensional Foster created a new method for measuring multidimensional way, using income or consumption levels. poverty (referred to as AF for Alkire Foster). It uses a count- But poor people go beyond income in defning their experi- ing approach to identifying ‘who is poor’ by considering the ence of poverty. They often include a lack of education, health, range of deprivations they suffer, and combines this with the housing, empowerment, humiliation, employment, personal se- Foster-Greer-Thorbecke (FGT) methodology that is the most curity and more. No one indicator, such as income or consump- widely used class of income poverty measures. The resulting tion, is uniquely able to capture the multiple disadvantages that measure aggregates information to refect societal poverty in a contribute to poverty. Furthermore, levels and trends of income way that is robust, can be broken down by regions and groups poverty are not highly correlated with trends in other basic to show who is poor and where they live, and decomposed by variables such as child mortality, primary school completion dimension and indicator to show how people are poor. rates, or undernourishment (Bourguignon et al 2010: 24, 27). A person or household can be income poor but not multidimen- sionally poor, or income rich yet in multidimensional poverty. It is intuitive and easy to calculate At the same time, the number of countries conducting To identify the poor, the AF method counts the overlapping or multi-topic household surveys that provide the required inputs simultaneous deprivations that a person or household experi- for the construction of multidimensional measures have ences in different indicators of poverty. The indicators may be increased dramatically from the mid-1980s, to around 130 devel- equally weighted or may take different weights. People are iden- oping countries at present. This phenomenon, together with ad- tifed as multidimensionally poor if the weighted sum of their vances in techniques and the increasing demand to understand deprivations is greater than or equal to a poverty cutoff – such poverty and social policies, has generated a unique framework as 20%, 30%, or 50% of all possible (weighted) deprivations. for the implementation of these kinds of measures. Having identifed who is poor, the AF method then summarises information to show the deprivations experienced by the poor Counting approaches to multidimensional as a proportion of all possible deprivations in society. The sim- poverty measurement: the AF method plest measure in the class of AF measures – which is the most Multidimensional poverty measures that are based on people’s widely applied – can be computed by simple multiplication. It own deprivation profles can, at a glance, prvide an integrated is the product of H × A: the headcount ratio or percentage of view of the situation. The most widely used multidimensional people who are identifed as poor (H) multiplied by the average poverty measures since the 1970s have been what are called share of weighted deprivations that poor people experience (A), ‘counting approaches.’1 which is termed the intensity of poverty. This product is called the adjusted headcount or M in the AF method; in the con- Most applications of counting measures tend to report a head- 0 struction of a Multidimensional Poverty Index it is termed the count ratio. While this is very easy to understand and commu- MPI value. This measure has been found to be rigorous, easy to ‘unpack’ and to use for policy, and fexible, which makes it adaptable to different contexts. It is unique One unique aspect of the AF method is that it reveals the intensity of poverty. Thus it can distinguish between, for example, a group of poor people who suffer only two depriva- tions on average, and a group of poor people who suffer four deprivations on average at the same time. This approach can be employed fexibly in a variety of situa- tions by using different dimensions (e.g. education), indicators (e.g. how many years of education a person has), deprivation 1. These are widely applied because most poverty data use categorical or ordinal variables, and counting measures can be created that use these data in a rigorous and appropriate manner. See Alkire, Sabina & Foster, James (2011). 2 Why Multidimensional Poverty? cutoffs (e.g. a person with fewer than fve years of education is Using the Alkire Foster method An AF M measure can be intuitively constructed considered deprived), weights (e.g. education and health dimen- 0 in 12 steps. The frst 6 steps are common to many sions are equally weighted), and poverty cutoffs (e.g. a person multidimensional poverty measures; the remainder who is deprived in one-third or more of the weighted indicators are specifc to the AF counting method. is poor). Step 1 Choose the purpose of the measure, and identify the It reveals diferences within and between institutional framework groups and regions The measure can be decomposed by geographic area, ethnic- Step 2 ity, gender or other social groups, to show the composition Choose a unit of analysis of poverty within and between them. (e.g. a person, household, or community) The measure can be broken down after identifcation to show which deprivations (i.e. which dimensions and indicators) are Step 3 driving poverty within groups. Choose dimensions (e.g. education, health, living standards) It gives information across time Step 4 The measure can be used to monitor changes in poverty and Choose indicators for each dimension (e.g. years of the composition of poverty over time using time series or panel schooling, body mass index) data. The AF method refects deprivations directly and changes immediately as these change. This time sensitivity makes it an Step 5 effective monitoring tool because improvements in the dimen- Set deprivation cutofs for each indicator sions measured, such as health and education, are refected more quickly than with traditional approaches. Step 6 Set and apply weights for each indicator Common uses Poverty measures: The AF method can be used to create national, regional or international measures of poverty, using dimensions Step 7 Sum the share of weighted deprivations for each person and indicators that are tailored to the specifc context. (or other unit of analysis) Geographic: The AF method can be used to identify which re- gions are the poorest, for example for geographic targeting, or Step 8 to inform allocation decisions. Set and apply the poverty cutof (i.e. the percentage of weighted indicators a person must be deprived in to be Monitoring and Evaluation: The AF method can be used to moni- considered poor) tor the effectiveness of programmes over time. Targeting the poorest groups and benefciaries: A person’s ‘deprivation Step 9 score’ can be used to target the poorest benefciaries and can Calculate the percentage of people identifed as poor be broken down to show the indicators in which they are most (the headcount ratio) (i.e. divide the number of poor deprived, to further inform interventions. people by the total number of people) Complement other metrics: The AF poverty method can be used to Step 10 complement other measures, such as income poverty, GDP, and Calculate the intensity of poverty (i.e. add up all poor inequality measures. people’s share of weighted deprivations and divide by These are widely applied because most poverty data use categor- the number of poor people) ical or ordinal variables, and counting measures can be created that use these data in a rigorous and appropriate manner. See Step 11 Alkire, Sabina & Foster, James (2011). Calculate the adjusted headcount ratio (M0 or the MPI = H x A) Step 12 Calculate the consistent indices: censored headcount ratios for each indicator, percentage contributions of each indicator to overall poverty, standard errors, etc. 3 Colombia: Pioneering an MPI-based National Development Plan In 2011, Colombian President Juan Manuel Santos announced a education outcomes of the members of the household improve. new National Development Plan, with poverty reduction as the Since 2012, the MPI-Colombia has been used to geographically focus. Devised by Ministry of Planning, it features a Colombian target the programme, tailoring coverage, intervention, and total Multidimensional Poverty Index (MPI-Colombia), based on the monetary value to the particular needs of each region. AF method, which is used both to set specifc targets and track UNIDOS progress. The most recent National Development Plan of 2015 The national strategy (safety net) UNIDOS, currently targeting again places a focus on the MPI. 1.5 million families (about 5 million people), is the main policy The plan includes targets for multidimensional poverty reduc- initiative to reduce extreme poverty in Colombia. The strategy tion, as well as income poverty and inequality. It also has specifc aims to enhance the income-generating abilities and the quality targets for each of the dimensions and indicators included in of life conditions of the families involved. the MPI. Benefciaries are categorised according to the type of poverty The government’s strategy, which combines targets and outputs prevailing in the household (income or multidimensional) and based on budget priorities, is on track to reduce overall multidi- a set of social programmes based on each household’s needs is mensional poverty from 21.9% in 2014 to 17.8% by the end of then selected for the families.
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