代际收入流动性测度的识别与估计

中文摘要 AI 辅助

研究背景：衡量终身经济地位的代际传递时，研究者常仅观测特定年龄的收入快照，这一情况使研究复杂化；现有标准方法用收入均值估计代际流动性，会引入生命周期偏差，损害不同研究、不同时间、不同地点间结果的可靠性与可比性。提出的方法：构建缺失数据框架，利用可得收入数据与可观测特征消除生命周期偏差；该方法结合非参数识别与奈曼正交矩，在合理的随机缺失假设与可检验独立性假设下，构建代际收入流动性测度的去偏机器学习估计量。实验设置：将该框架应用于美国，使用收入动态面板研究（Panel Study of Income Dynamics），针对1954至1977年的出生队列，采用滚动10年窗口估计代际弹性。实验结果：现有方法估计值在0.41至0.54之间，所提方法得出的估计值显著更高，介于0.6至0.7之间，均值为0.64。结论与意义：这些结果与近期利用职业生涯中期长期均值的证据高度一致，进一步证实美国代际收入流动性的低水平（即代际持续性高）。

英文摘要

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.

发表机构

• Department of Economics, Universidad Carlos III（卡洛斯三世大学经济学系）

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