What can the IMMP tell us we don’t already know?

The Individual Measure of Multidimensional Poverty uncovers nuanced information that other widely-used surveys can miss.

A new multidimensional poverty measure, initially known as the Individual Deprivation Measure, is contributing to a better understanding of the patterns of poverty in data-poor Districts in Indonesia. But what can this measure, now renamed the Individual Measure of Multidimensional Poverty (IMMP), add to our understanding of poverty in already data-rich environments?

There are already two major measures of poverty globally – the International Poverty Line (IPL) and the Multidimensional Poverty Index (MPI); and surveys such as the Demographic and Health Survey (DHS), the Household Income and Expenditure Survey (HIES), and Multiple Indicator Cluster Survey (MICS)1 provide rich data at the country level. So what can the IMMP add compared to such widely-used surveys?

Firstly, the IMMP is not a measure of income poverty or consumption like the IPL and the HIES respectively –  rather it is a  multidimensional measure  of  an individual’s  actual experience of poverty across 14 dimensions of life which were ranked as important by men and women living in poverty from 18 sites in six countries. And importantly, unlike household surveys, it examines this experience of poverty for each adult member of the household, which reveals intra-household inequalities with gender and age differences such surveys can never reveal.  It was designed to be sensitive to gender, hence it measures aspects of dimensions which that have been ignored to date. For example it asks women about access to sanitary products and it asks men about their contribution to family planning.

Secondly, surveys such as the DHS, which though they ask individual men and women about their lives, do not ask all age groups and genders questions across all 14 of the IMMP’s important dimensions (food, water, shelter, health, education, energy, sanitation, relationships, clothing and footwear, family planning, environment, voice, time use, and paid and unpaid work). While the DHS survey includes children, which the IMMP does not, its questions for individuals tend to focus on issues of health, childbirth (for women), fertility and family planning.  It also covers education but only paid employment for individual men (15-59) and women (15-49).  Older aged groups are ignored altogether, which may miss important pockets of poverty.

The DHS household survey covers drinking water sources, sanitation, housing materials and household ownership of various durable goods.  But while it covers water sources, it says nothing about reliability of supply, treatment of the water for drinking, or who collects water; and sanitation questions ignore whether women have a private place to change when menstruating.  Nor can we see which member in the household owns the various assets, which may mask considerable gender inequality.

Comparing the IDM study conducted in data-rich South Africa to the South African MPI, UK-based Development Initiatives stated,

 ‘The largest effort to measure multidimensional poverty, the MPI, is constructed primarily using household-level indicators, limiting disaggregation. The international MPI does not specifically consider gender inequality and could mask significant inequalities within a household2 (p4) 

The South African MPI study, like earlier studies of poverty in South Africa, used South African Census data; it therefore reports largely at the household level, with the exception of education and paid employment which are recorded for individuals. The poverty ‘headcount’ reported simply reflects the number of people living in poor households. The much earlier Living Conditions Survey (2008/9) was also predominantly a household survey and diary process, although it collected some information from individuals (e.g. health, water and fuel collection, education, employment, income and some others).

Across the 14 dimensions of the IMMP, Development Initiatives (DI) found only limited overlap of the individual questions with the DHS or other surveys it reviewed. In other words the IMMP is measuring a number of aspects of poverty not frequently surveyed. These include access to basic clothing and footwear as well as personal care items including sanitary products, experience of chronic health conditions and mental health problems, unpaid work and the risks associated with it, ability to call on others for support, as well as one’s perceived ability to exercise ‘voice’ in the community. Further, using the Washington Group’s Short set questions on disability, all the dimensions can be disaggregated by disability status.

Comparing the South African DHS 2016, (which is extremely comprehensive), with the IDM, DI concluded:

“ The South Africa DHS provides several points that are more comprehensive then the IDM, particularly through its tests for a range of health issues. However, IDM provides a much more nuanced information about gender inequalities in the dimensions of poverty measured by the IDM, the relationship between disability and multidimensional poverty, and provides data on many indicators beyond what is currently offered  in other multi-topic household surveys” (p8)

Furthermore, the IMMP enables us to explore patterns of multidimensional poverty for different age groups and/or genders, as well as by a range of other characteristics (disability, geography, household type etc.). It enables us to see the way different individuals or social groups experience poverty in different dimensions across the life course, and can calculate an overall index of multidimensional poverty to identify those who experience poverty across many dimensions and are hence extremely vulnerable.


1 There has been no MICS survey carried out in South Africa.

2 This unpublished report was commissioned towards the end of the IDM Program in order to gain an independent assessment of the IDM’s contribution to poverty measurement compared to other widely used surveys.

 

Originally published in The United Nations World Data Forum Blog

Publication Date: 
14 October 2020

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