Abstract

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this article, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession.

摘要

个人和家庭的经济流动至关重要。尽管存在许多衡量经济流动性的措施，但由于简单易懂，对过渡矩阵的依赖仍然普遍存在。然而，转换矩阵的估计由于自报告的甚至管理数据中的测量误差而广为人知的问题变得复杂。解决测量误差的现有方法很复杂，依赖于众多强有力的假设，并且通常需要两个以上周期的数据。在本文中，我们研究了可以通过过渡矩阵测得的经济流动性，同时又以合理透明的方式正式考虑了测算误差。为此，我们针对测量误差和迁移率过程的各种假设，针对边界转移概率开发了一种非参数的部分识别方法。该方法适用于来自美国的面板数据，以探讨大萧条前后的短期流动性。