A Robust Multi-Dimensional Poverty Profile for Uganda

In this paper we compute a multi-dimensional poverty index (MPI) for Uganda following the approach proposed by Alkire and Forster (2007). Using household survey data we show how the incidence of multi-dimensional poverty has fallen in recent years and we use the decomposability features of the index to explain the drivers of reduction in multi-dimensional poverty. We also compare the results from Uganda with other countries for which the MPI has been computed and we note some caveats in such a comparison. The robustness of our estimates is tested in a stochastic dominance framework and using statistical inference. Notably, we extend the one-dimensional analysis of stochastic dominance to take into account household size in a second dimension, which is particularly important as some of the MPI indicators are sensitive to the number of household members. By exploiting a unique subsample of the integrated household survey programme in Uganda, which has not previously been analysed, we are also able to match the data-set used for the MPI with data used to compute the conventional estimates of monetary poverty. This enables a more robust assessment of the complementarities of the two types of poverty measures than has been previously possible.multidimensional poverty, counting approach, Uganda, household size, robustness analysis, international comparisons.

A robust multidimensional poverty profile for Uganda: OPHI working paper no. 55

We compute a national multidimensional poverty index (MPI) for Uganda following the approach proposed by Alkire and Foster (2007). Using household survey data, we show how the incidence of multidimensional poverty has fallen in recent years, and we use the decomposability features of the index to explain the drivers of the reduction in multidimensional poverty. We extend the standard application of the MPI to distinguish between domains and dimensions, which is particularly important given the high degree of multiple deprivations within the standard-of-living domain. We also compare the results from Uganda with other countries for which the MPI has been computed, and we note some caveats in such a comparison. The robustness of our estimates is tested in a stochastic dominance framework as well as through statistical inference. Notably, we extend the one-dimensional analysis of stochastic dominance to take into account household size in a second dimension, which is particularly important as some of the MPI indicators are sensitive to the number of household members. By exploiting a unique sub-sample of the integrated household survey programme in Uganda, which has not previously been analysed, we are also able to match the data set used for the MPI with data used to compute the conventional estimates of monetary poverty. This enables a more robust assessment of the complementarities of the two types of poverty measures than has been previously possible.Copyright © Oxford Poverty & Human Development Initiative 2012. This publication is copyright, however it may be reproduced without fee for teaching or non-profit purposes, but not for resale. Formal permission is required for all such uses, and will normally be granted immediately. For copying in any other circumstances, or for re-use in other publications, or for translation or adaptation, prior written permission must be obtained from OPHI and may be subject to a fee

A robust multidimensional poverty profile for Uganda

We compute a national multidimensional poverty index (MPI) for Uganda following the approach proposed by Alkire and Foster (2007). Using household survey data, we show how the incidence of multidimensional poverty has fallen in recent years, and we use the decomposability features of the index to explain the drivers of the reduction in multidimensional poverty. We extend the standard application of the MPI to distinguish between domains and dimensions, which is particularly important given the high degree of multiple deprivations within the standard-of-living domain. We also compare the results from Uganda with other countries for which the MPI has been computed, and we note some caveats in such a comparison. The robustness of our estimates is tested in a stochastic dominance framework as well as through statistical inference. Notably, we extend the one-dimensional analysis of stochastic dominance to take into account household size in a second dimension, which is particularly important as some of the MPI indicators are sensitive to the number of household members. By exploiting a unique sub-sample of the integrated household survey programme in Uganda, which has not previously been analysed, we are also able to match the data set used for the MPI with data used to compute the conventional estimates of monetary poverty. This enables a more robust assessment of the complementarities of the two types of poverty measures than has been previously possible.</p