Tracking Poverty with Coarse Data
Evidence from South Africa
Household surveys often contain coarse data, which consist of a mixture of missing values, interval-censored values and point (fully-observed) values, making it difficult to construct a continuous money-metric measure of wellbeing. This paper assesses the sensitivity of poverty and inequality estimates to the multiple imputation of coarse earnings data and reported zero values using the 2001–2006 South African Labour Force Surveys. Estimates of poverty amongst the employed are shown not to be sensitive to multiple imputation of missing and interval-censored data, but are sensitive to the treatment of workers reporting zero earnings. Poverty trends are generally robust to the choice of method, and a significant decline in poverty is evident. Inequality estimates, on the other hand, appear more sensitive to the treatment of zero values and the choice of imputation methods, and, overall, no particular trends in inequality could be discerned.