Detailed knowledge of individual income dynamics is crucial for investigating the existence of the American dream: Are we able to improve our income status during our working life? This key question simply boils down to observing individual status and how it moves between two thresholds: the current income and the desired income. Yet, our knowledge of these temporal properties of income remains limited since we rely on estimates coming from transition matrices which simplify income dynamics by aggregating the individual changes into quantiles and thus overlooking significant microscopic variations. Here, we bridge this gap by employing First Passage Time concepts in a baseline stochastic process with resetting used for modeling income dynamics and developing a framework that is able to crucially disaggregate the temporal properties of income to the level of an individual worker. We find analytically and illustrate numerically that our framework is orthogonal to the transition matrix approach and leads to improved and more granular estimates. Moreover, to facilitate empirical applications of the framework, we introduce a publicly available statistical methodology, and showcase the application using the USA income dynamics data. These results help to improve our understanding on the temporal properties of income in real economies and provide a set of tools for designing policy interventions.
翻译:关于个人收入动态的详细知识对于调查美国梦的存在至关重要:我们是否能够在工作生涯中改善我们的收入状况?这个关键问题只是简单地归结为观察个人状况和如何在两个门槛之间移动:当前收入和预期收入。然而,我们对这些时间收入属性的了解仍然有限,因为我们依赖来自过渡矩阵的估计,这些过渡矩阵通过将个人变化综合成四分位而简化了收入动态,从而忽略了显著的微观差异。在这里,我们通过在基线随机进程中采用第一个过关时间概念来弥补这一差距,在基线随机进程中重新确定用于模拟收入动态,并制定一个框架,能够将收入的时间属性关键地分列到个体工人的水平。我们从数字上发现和说明,我们的框架与转型矩阵方法不相符合,导致改进和增加粗略的估计。此外,为了便利框架的经验应用,我们引入了公开的统计方法,并展示了使用美国收入动态数据的应用。这些结果有助于我们更好地了解实际经济中收入的时间属性,并为设计政策干预提供一套工具。