outdetect: Outlier detection for inequality and poverty analysis

Extreme values are common in survey data and represent a recurring threat to the reliability of both poverty and inequality estimates. The adoption of a consistent criterion for outlier detection is useful in many practical applications, particularly when international and intertemporal comparisons are involved. In this talk, I discuss a simple univariate detection procedure to flag outliers. I present outdetect, a command that implements the procedure and provides useful diagnostic tools. The output of outdetect compares statistics obtained before and after the exclusion of outliers, with a focus on inequality and poverty measures. Finally, I carry out an extensive sensitivity exercise where the same outlier detection method is applied consistently to per capita expenditure across more than 30 household budget surveys. The results are clear and provide a sense of the influence of extreme values on poverty and inequality estimates.