Calculating Comparable Statistics from Incomparable Surveys, with an Application to Poverty in India

Applied economists are often interested in studying changes over time of important economic indicators, such as inequality or poverty, but such comparisons can be made impossible by changes in data collection methodology. We describe an easily implemented procedure to recover comparability that can be adopted whenever the statistic of interest satisfies a moment condition, when the researcher has available a set of auxiliary variables whose reports are not affected by the different survey design, and whose relation with the main variable of interest is stable over time. We analyze the asymptotic properties of the estimator taking into account the presence of clustering, stratification, and sampling weights, which characterize most household surveys. We use the 1999-2000 Round of the Indian National Sample Survey as an empirical illustration. Due to important changes in the adopted questionnaire, the unadjusted figures are likely to understate poverty relative to the previous rounds. We use previous waves of the same survey to provide evidence supporting the plausibility of the identifying assumptions and conclude that most of the very large reduction in poverty implied by the unadjusted figures is real