ISER External Seminar: The Weight of the Rich: Improving Surveys Using Tax Data

Household surveys fail to capture the top tail of income and wealth distributions, as evidenced by studies based on tax data. Yet to date there is no consensus on how to best reconcile both sources of information. This paper presents a novel method, rooted in calibration theory, which helps to solve the problem under reasonable assumptions. It has the advantage of endogenously determining a “merging point” between the datasets before modifying weights along the entire distribution and replacing new observations beyond the survey’s original support. We provide simulations of the method and applications to real data. The former demonstrate that our method improves the accuracy and precision of distributional estimates, even under extreme assumptions, and in comparison to other survey correction methods using external data. The empirical applications provide useful and coherent illustrations in a wide variety of contexts. Results show that not only can income inequality levels change, but also trends. Given that our method preserves the multivariate distributions of survey variables, it provides a more representative framework for researchers to explore the socio-economic dimensions of inequality, as well as to study other related topics, such as fiscal incidence.