Multidimensional Poverty Measures

Context

Most countries of the world define poverty as a lack of money. Yet poor people themselves consider their experience of poverty much more broadly. A person who is poor can suffer from multiple disadvantages at the same time – for example they may have poor health or malnutrition, a lack of clean water or electricity, poor quality of work or little schooling. Focusing on one factor alone, such as income, is not enough to capture the true reality of poverty.

Multidimensional poverty measures can be used to create a more comprehensive picture. They reveal who is poor and how they are poor – the range of different disadvantages they experience. As well as providing a headline measure of poverty, multidimensional measures can be broken down to reveal the poverty level in different areas of a country, and among different sub-groups of people.

Content

Most recent MPI data harmonized for comparisons across time.

OPHI researchers apply the AF method and related multidimensional measures to a range of different countries and contexts. Their analyses span a number of different topics, such as changes in multidimensional poverty over time, comparisons in rural and urban poverty, and inequality among the poor. For more information on OPHI’s research, see our working paper series and research briefings.

OPHI also calculates the Global Multidimensional Poverty Index MPI, which has been published since 2010 in the United Nations Development Programme’s Human Development Report. The Global MPI is an internationally-comparable measure of acute poverty covering more than 100 developing countries. It is updated by OPHI twice a year and constructed using the AF method.

The Alkire Foster (AF) method is a way of measuring multidimensional poverty developed by OPHI’s Sabina Alkire and James Foster. Building on the Foster-Greer-Thorbecke poverty measures, it involves counting the different types of deprivation that individuals experience at the same time, such as a lack of education or employment, or poor health or living standards. These deprivation profiles are analysed to identify who is poor, and then used to construct a multidimensional index of poverty (MPI). For free online video guides on how to use the AF method, see OPHI’s online training portal.

To identify the poor, the AF method counts the overlapping or simultaneous deprivations that a person or household experiences in different indicators of poverty. The indicators may be equally weighted or take different weights. People are identified as multidimensionally poor if the weighted sum of their deprivations is greater than or equal to a poverty cut off – such as 20%, 30% or 50% of all deprivations.

It is a flexible approach which can be tailored to a variety of situations by selecting different dimensions (e.g. education), indicators of poverty within each dimension (e.g. how many years schooling a person has) and poverty cut offs (e.g. a person with fewer than five years of education is considered deprived).

The most common way of measuring poverty is to calculate the percentage of the population who are poor, known as the headcount ratio (H). Having identified who is poor, the AF method generates a unique class of poverty measures (Mα) that goes beyond the simple headcount ratio. Three measures in this class are of high importance:

Adjusted headcount ratio (M0), otherwise known as the MPI: This measure reflects both the incidence of poverty (the percentage of the population who are poor) and the intensity of poverty (the percentage of deprivations suffered by each person or household on average). M0 is calculated by multiplying the incidence (H) by the intensity (A). M0 = H x A.

Find out about other ways the AF method is used in research and policy.

Additional data here.

This dataset contains the Summer 2016 Subnational data from Table 6.3 as it is the most recent dataset for MPI comparisons over time.

Data Cleaning Notes

The original format was significantly different in many unusable ways.

I converted all survey years (year1 and year2) from a Period format that looked some like "2005/6 - 2009". Note, "2005/6" meant the survey was conducted from sometime in 2005 through sometime in 2006. Additionally, the year2 aspect could follow a similar format (eg "2009/10"). To keep simplicity, I dropped the /%s portion of both year1 and year2. This still maintains consistency in the case that either year column becomes used for a comparison statistic.

The raw data file from OPHI has their Total Population and Number of Poor in Thousands. I converted the decimals to make it a raw population number. For example, 3.142 becomes 3142.

The original file is in an excel format that needed to be converted into a csv in order to upload into Kaggle. I decided to keep values to the10^-6 decimal place.

The statistical significance columns comes from OPHI's test of significant changes. Directly from the excel file:
Note, *** statistically significant at α=0.01, ** statistically significant at α=0.05, * statistically significant at α=0.10

Acknowledgements

Alkire, S. and Robles, G. (2017). “Multidimensional Poverty Index Summer 2017: Brief methodological note and results.” OPHI Methodological Note 44, University of Oxford.

Alkire, S. and Santos, M. E. (2010). “Acute multidimensional poverty: A new index for developing countries.” OPHI Working Papers 38, University of Oxford.

Alkire, S. Jindra, C. Robles, G. and Vaz, A. (2017). ‘Multidimensional Poverty Index – Summer 2017: brief methodological note and results’. OPHI MPI Methodological Notes No. 44, Oxford Poverty and Human Development Initiative, University of Oxford.

OPHI Kaggle's Page

Inspiration

Further evaluate OPHI's approach to comparing subnational regions for various years.
Then, consider how much Kiva's microcredit impacted the subnational MPI change.