This article proposes a fully nonparametric model to investigate the dynamics of intergenerational income mobility for discrete outcomes. In our model, an individual’s income class probabilities depend on parental income in a manner that accommodates nonlinearities and interactions among various individual and parental characteristics, including race, education, and parental age at childbearing, and so generalizes Markov chain mobility models. We show how the model may be estimated using kernel techniques from machine learning. Utilizing data from the panel study of income dynamics, we show how race, parental education, and mother’s age at birth interact with family income to determine mobility between generations.

中文翻译：

---

代际收入流动性中个体特异性异质性的解释

本文提出了一个完全非参数的模型来研究离散结果的代际收入流动性的动态。在我们的模型中，个人的收入阶层概率取决于父母的收入，其方式适应了各种个人和父母特征（包括种族、教育和父母生育年龄）之间的非线性和相互作用，因此推广了马尔可夫链移动模型。我们展示了如何使用机器学习的内核技术来估计模型。利用来自收入动态面板研究的数据，我们展示了种族、父母教育程度和母亲的出生年龄如何与家庭收入相互作用，以确定代际之间的流动性。