双侧测量误差下的分布效应：代际收入流动性的应用
Distributional Effects with Two-Sided Measurement Error: An Application to Intergenerational Income Mobility
AI总结：
本文提出在双侧测量误差下识别和估计分布效应参数的方法，通过分位数回归恢复结果与处理的联合分布，并应用于代际收入流动性发现测量误差显著降低流动性估计值。
摘要：
本文考虑了在“双侧”测量误差（即两个变量都可能存在测量误差）的情况下，依赖于结果变量和另一个感兴趣变量（“处理”）的联合分布的分布效应参数的识别和估计。在代际收入流动性的背景下，这些参数的例子包括转移矩阵、秩-秩相关性以及儿童贫困率作为其父母收入的函数等。基于最近关于结果变量存在测量误差的分位数回归（QR）的研究（特别是Hausman, Liu, Luo, and Palmer (2021)），我们证明，给定（i）分别针对结果变量和处理变量在其他观测协变量条件下的两个线性分位数回归模型，以及（ii）关于每个变量测量误差的假设，可以恢复结果变量和处理变量的联合分布。除了这些条件外，我们的方法不需要工具变量、重复测量或关于测量误差的分布假设。使用1997年全国青年纵向调查的最新数据，我们发现考虑测量误差显著降低了几项代际流动性参数的估计值。
英文摘要：
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 (particularly, 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 treatment. 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.
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