A1277
Title: Distributional effects with two-sided measurement error: An application to intergenerational income mobility
Authors: Tong Li - Vanderbilt University (United States) [presenting]
Brantly Callaway - University of Georgia (United States)
Emmanuel Tsyawo - Universite Mohammed VI Polytechnique (Morocco)
Irina Murtazashvili - Drexel University (United States)
Abstract: The aim is to consider the identification and estimation of distributional effect parameters that depend on the joint distribution of an outcome and another variable of interest ("treatment") in a setting with "two-sided" measurement error, that is, where both variables are possibly measured with error. Examples of these parameters in the context of intergenerational income mobility include transition matrices, rank-rank correlations, and the poverty rate of children as a function of their parents' income, among others. Building on recent work on quantile regression (QR) with measurement error in the outcome (particularly, a prior study), it is shown that, given (i) two linear QR models separately for the outcome and treatment conditional on other observed covariates and (ii) assumptions about the measurement error for each variable, one can recover the joint distribution of the outcome and the treatment. Besides these conditions, the approach does not require an instrument, repeated measurements, or distributional assumptions about the measurement error. Using recent data from the 1997 National Longitudinal Study of Youth, it is found that accounting for measurement error notably reduces several estimates of intergenerational mobility parameters.
