Alejandro Puerta-Cuartas
arXiv 4 Sep 2026 · Econometrics
arXiv:2609.04994 · PDF · Extracted main text
Measuring the intergenerational transmission of lifetime economic status is complicated by researchers often only observing snapshots of income at specific ages. Consequently, standard practice estimates intergenerational mobility using income averages, introducing life-cycle bias that compromises reliability and comparability across studies, time, and place. I develop a missing data framework that exploits available income data and observable characteristics to eliminate life-cycle bias. This method combines nonparametric identification with Neyman-orthogonal moments to construct debiased machine learning estimators for intergenerational income mobility measures under plausible missing-at-random and testable independence assumptions. I apply this framework to estimate the intergenerational elasticity for the U.S. using the Panel Study of Income Dynamics across birth cohorts from 1954 to 1977 with rolling 10-year windows. While existing approaches estimate values between 0.41 and 0.54, the proposed method yields substantially higher estimates ranging from 0.6 to 0.7, averaging 0.64. These results align closely with recent evidence using long time averages over mid-career periods, reinforcing high U.S. intergenerational persistence.
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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 | Mazumder, Bhashkar (2016) Estimating the intergenerational elasticity and rank association in the United States: Overcoming the current limitations of tax… | 1.000 | 5 | 3 | 100% |
| 2 | Mello, Ursula and Nybom, Martin and Stuhler, Jan (2025) A lifecycle estimator of intergenerational income mobility | 0.950 | 14 | 5 | 86% |
| 3 | Jenkins, Stephen (1987) Snapshots versus movies:‘Lifecycle biases’ and the estimation of intergenerational earnings inheritance | 0.874 | 5 | 2 | 100% |
| 4 | Chernozhukov, Victor and Escanciano, Juan Carlos and Ichimura, Hideh… (2022) Locally robust semiparametric estimation | 0.860 | 11 | 3 | 64% |
| 5 | Haider, Steven and Solon, Gary (2006) Life-cycle variation in the association between current and lifetime earnings | 0.830 | 7 | 4 | 57% |
| 6 | Schoeni, Robert F and Wiemers, Emily E (2015) The implications of selective attrition for estimates of intergenerational elasticity of family income | 0.811 | 4 | 2 | 100% |
| 7 | Solon, Gary (1992) Intergenerational income mobility in the United States | 0.776 | 15 | 5 | 47% |
| 8 | Mazumder, Bhashkar (2005) Fortunate sons: New estimates of intergenerational mobility in the United States using social security earnings data | 0.737 | 10 | 3 | 40% |
| 9 | Dahl, Molly W and DeLeire, Thomas (2008) The association between children's earnings and fathers' lifetime earnings: estimates using administrative data | 0.737 | 3 | 3 | 67% |
| 10 | Nybom, Martin and Stuhler, Jan (2017) Biases in standard measures of intergenerational income dependence | 0.737 | 3 | 3 | 67% |
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