Despite a broad theoretical literature on multidimensional inequality and a widespread belief that welfare is not synonymous to income—not the least in a developing context—empirical inequality examinations rarely includes several welfare attributes. We explore three techniques on how to evaluate multidimensional inequality using Zambian household data on consumption, education, health and land. The examination indicates that level and changes in non-monetary inequality are at odds with consumption inequality. Moreover, assessment of a multidimensional index shows evidence of that dimensions of wellbeing compensate and reinforce each other with respect to inequality. However, a majority of the results using this technique are sensitive to the degree of substitution between attributes. In applying a stochastic dominance method few combinations fulfill the required dominance conditions. Accordingly, less imposed structure come at a cost. Clearly, sensitivity analyzes, explicitness and analyzes involving more than one technique are constructive in portraying multidimensional inequality.
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The multidimensional approach to poverty has also been addressed theoretically by several researchers; see Duclos et al. (2001) and Deutsch and Silber (2005). In contrast to the study of inequality, there are currently more empirical applications in this field, see e.g., Chambaz and Maurin (1998) and Maltzahn and Durrheim (2008).
We drop 265 households in 1998 and 161 households in 2004 from the original samples since these units do not have complete information concerning one or more of the variables used in the analysis. This represents a drop of 1.6 and 0.8%, respectively, of the original samples.
Detailed information of this procedure and estimation results are available from author upon request.
To ensure that empirical results are not driven by how we create the different partitions we will also use discrete partitions, where the dataset is divided into quintiles corresponding to 20% of the population.
These countries are the Central African Republic (0.61), Lesotho (0.63), Panama (0.55), South Africa (0.58) and Zimbabwe (0.57).
The Pearson’s test of correlation generates the same general result as above with the difference that the correlations are overall lower and that correlation between expenditures and land is positive, but insignificant.
For a test of statistical inference, a bootstrap procedure is used to generate estimates of the standard errors of M(0) and M(1). The bootstrapped samples mimic the empirical distributions of the LCMS II and LCMS IV survey samples.
The results of the Maasoumi index for all possible combinations of S2 and S3 are available upon request.
All results in this section are confirmed using a different discrete partition of the variables. In this case the dataset is divided into quintiles corresponding to 20% of the population.
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The author wish to thank Peter Lambert, participants at the 2nd Meeting of the Society for the Study of Economic Inequality (ECINEQ), Berlin, 2007, Carl Hampus Lyttkens, Andreas Bergh, Göte Hansson, Carl-Johan Belfrage, Nils Janlöv and seminal participants in Lund for very useful comments and suggestions. Financial support from Anna Nilssons stipendiefond and Per Westlings stipendiefond is gratefully acknowledged.
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Nilsson, T. Health, Wealth and Wisdom: Exploring Multidimensional Inequality in a Developing Country. Soc Indic Res 95, 299 (2010). https://doi.org/10.1007/s11205-009-9461-6
- Multidimensional inequality
- Inequality indices
- Stochastic dominance
- Land holdings