Brantly Callaway, Tong Li, Irina Murtazashvili, Emmanuel Tsyawo
arXiv 20 Jul 2021 · Econometrics · publishedJournal of Econometrics (2026) · 1 citations (OpenAlex)
arXiv:2107.09235 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Haider, Steven, Solon, Gary (2006) Life-cycle variation in the association between current and lifetime earnings | 1.000 | 6 | 3 | 100% |
| 2 | An, Yonghong, Wang, Le, Xiao, Ruli (2020) A nonparametric nonclassical measurement error approach to estimating intergenerational mobility elasticities | 1.000 | 5 | 4 | 100% |
| 3 | Nybom, Martin, Stuhler, Jan (2017) Biases in standard measures of intergenerational income dependence | 1.000 | 5 | 3 | 100% |
| 4 | Hausman, Jerry, Liu, Haoyang, Luo, Ye, Palmer, Christopher (2021) Errors in the dependent variable of quantile regression models | 0.965 | 10 | 5 | 90% |
| 5 | Bhattacharya, Debopam, Mazumder, Bhashkar (2011) A nonparametric analysis of black–white differences in intergenerational income mobility in the United States | 0.928 | 4 | 3 | 100% |
| 6 | Chetty, Raj, Hendren, Nathaniel, Kline, Patrick, Saez, Emmanuel (2014) Where is the land of opportunity? The geography of intergenerational mobility in the United States | 0.928 | 4 | 3 | 100% |
| 7 | Li, Tong, Vuong, Quang (1998) Nonparametric estimation of the measurement error model using multiple indicators self | 0.928 | 4 | 3 | 100% |
| 8 | Black, Sandra E, Devereux, Paul, Ashenfelter, Orley, Card, David (2011) Recent developments in intergenerational mobility | 0.843 | 3 | 3 | 100% |
| 9 | Firpo, Sergio, Galvao, Antonio F, Song, Suyong (2017) Measurement errors in quantile regression models | 0.843 | 3 | 3 | 100% |
| 10 | Mazumder, Bhashkar (2005) Fortunate sons: New estimates of intergenerational mobility in the United States using social security earnings data | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 98 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Endogenous Quantile Regression with Measurement Error in Dependent Variable | 0.511 | 2 | 1 |