Chapter
Poverty Indexes: Checking for Robustness
Summary There are four main reasons why measures of poverty may not be robust. Sampling error occurs because measures of poverty are based on sample data, which gives the true poverty rate only with some degree of uncertainty. It is good practice to report standard deviations and confidence intervals for poverty measures; this can be done by bootstrapping. Because household surveys tend to be relatively small, it is not possible to disaggregate the results to units smaller than relatively broad regions. Measurement error occurs in all survey data; we know, for instance, that households underreport income and expenditure, which tends to overstate the degree of poverty. The effect can be large: in some cases a 5 percent understatement of consumption can translate into a 10 percent overstatement of the headcount poverty rate. Poverty rates vary depending on the equivalence scale used, although the variation is typically fairly modest. Equivalence scales are not widely used because of the difficulty of agreeing on an appropriate set of weights. The choice of a poverty line and associated poverty rate (for example, headcount index, poverty gap index) is arbitrary. Sometimes, although not always, these choices matter. By comparing the cumulative distribution function of expenditure (or income) per capita—sometimes called the poverty incidence curve—between two situations, one may judge whether the choice of poverty line affects the conclusion about the change in poverty. If there is first order stochastic dominance, the choice of poverty line is not crucial; otherwise it is often possible to use higher-order tests 83
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