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Key Text Measuring Inequality with Asset Indicators

Author: David J. McKenzie
Date: 2005
Size: 33 pages (350 KB)

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Summary

Are household infrastructure, building materials and ownership of certain durable assets significant in measuring inequality in living standards? This paper from the Journal of Population Economics focuses on poverty in Mexico and shows that where there is no information on household income and consumption, asset indicators can be used to provide a reasonable measure of inequalities in living standards. When used in practice, the study found that, after controlling for household income and demographics, school attendance of boys in Mexico is negatively related to state-level inequality.

Data on household infrastructure, building materials and ownership of certain durable assets are the predominant source of comparable and nationally representative survey information on fertility, health, mortality and other demographic factors in developing countries. Most surveys do not contain information on income and consumption. Therefore, it is useful to be able to use asset indicators. However, there are a number of challenges connected with measuring inequality levels using such data. The first principal component asset index has mean zero and takes negative values for some households. As a result, many standard measures of inequality are not well-defined.

The asset index may suffer from clumping and truncation issues, which can affect inequality measures much more than measures of levels of well-being. The relationship between the asset index and non-durable consumption is likely to be monotone but non-linear. Therefore, inequality in asset holdings will differ from inequality in consumption. If the interest lies in consumption inequality, auxiliary surveys that have data on consumption and assets can be used to predict consumption inequality. Three approaches can be used to predict non-durable consumption inequality in these circumstances. The bootstrap prediction method results in the most accurate predictions.

When the inequality measures are applied to Mexican data to examine the relationship between school attendance and state-level inequality, the following issues emerge:

There is a significant effect of inequality on the probability that a boy aged 14–18 attends school, with more inequality being associated with lower attendance.
The effect of the difference in inequality between the Federal District and Chiapas is estimated to be a 0.12–0.14 difference in school attendance rates for boys.
Inequality is not found to have a significant effect on the school attendance of girls, or on state spending per capita on education.
This suggests that a simple political economy story of inequality affecting educational provision is not the cause of the relationship.
Although the relationship between the asset index and non-durable consumption is stronger in levels than in inequality terms, the results for inequality are still strong enough to conclude that asset indicators provide a viable method of inequality measurement.
Care needs to be taken to ensure that sufficient indicators are used to prevent clumping of households at just a few levels of the asset index and to avoid truncation and thereby allow inequality among the poor to be measured.
If inequality in non-durable consumption is truly the object of interest, and an auxiliary survey is available, then the bootstrap prediction method can be used to predict inequality in the main survey of interest.
Using all indicators separately in the prediction regression, and groups in the bootstrapping stage, give the best results.

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Source: McKenzie D. J., 2005, 'Measuring Inequality with Asset Indicators', Journal of Population Economics, vol. 18, no. 2, pp. 229-260