Distributional Effects with Two-Sided Measurement Error: An Application to Intergenerational Income Mobility∗ (joint with Brantly Callaway, Irina Murtazashvili, Emmanuel S. Tsyawo)

Abstract: This paper considers 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 (particu-
larly, Hausman, Liu, Luo, and Palmer (2021)), we show 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 treat-
ment. Besides these conditions, our 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, we find that accounting for measurement error notably reduces several
estimates of intergenerational mobility parameters.

Abstract: This talk will survey recent advances in understanding Chatterjee’s graph correlation coefficient. I will introduce, for the first time, a comprehensive theoretical framework for statistical inference based on this coefficient. The framework involves results on asymptotic normality, bias correction, and the (in)consistency of bootstrap methods.

Another title: Necessary and sufficient conditions for convergence in distribution of quantile and P-P processes in L1(0,1), (joint with Tetsuya Kaji, Chicago Booth)
https://arxiv.org/abs/2502.01254

Abstract: We present a dynamic model of the labor market, where workers may commit crimes and employers can gather information about workers’ criminal history from a publicly available record and set wages accordingly. We characterize the socially optimal duration of the record, which balances two conflicting objectives: deter inefficient crimes for workers without a record and keep the share of the population with a record low to reduce recidivism. We also show that, when the social harm from crime is neither too high nor too low, it is optimal to impose finite nonmonetary sanctions followed by a finite criminal-record period.