HIP, RIP, and the robustness of empirical earnings processes
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异质与随机收入剖面及经验收入过程的稳健性
- 研究问题
- 利用收入协方差随年龄的变化估计个人经验回报异质性是否可靠，年龄相关的冲击异方差会如何改变识别来源与估计结果？
- 方法与思路
- 作者分析年龄效应与收入协方差的关系，灵活建模各类冲击的年龄效应，并利用德国长期季度行政收入数据比较模型及开展模拟。
- 材料陈述的贡献
- 摘要称，忽略年龄效应会使经验回报异质性估计向上偏误；纳入年龄效应后估计较小且不显著，识别依赖滞后协方差剖面的尾部形状。
- 阅读关注点
- 应核对年龄异方差如何解释协方差变化、滞后剖面尾部为何提供剩余识别，以及长期观测、样本流失和教育组选择对结果的影响。
- 阅读边界
- 仅依据所给 Crossref 摘要版本；摘要中的德国样本比较不能证明所有群体都不存在经验回报异质性，完整识别条件与估计误差未说明。
原文摘要
The dispersion of individual returns to experience, often referred to as heterogeneity of income profiles (HIP), is a key parameter in empirical human capital models, in studies of life‐cycle income inequality, and in heterogeneous agent models of life‐cycle labor market dynamics. It is commonly estimated from age variation in the covariance structure of earnings. In this study, I show that this approach is invalid and tends to deliver estimates of HIP that are biased upward. The reason is that any age variation in covariance structures can be rationalized by age‐dependent heteroscedasticity in the distribution of earnings shocks. Once one models such age effects flexibly the remaining identifying variation for HIP is the shape of the tails of lag profiles. Credible estimation of HIP thus imposes strong demands on the data since one requires many earnings observations per individual and a low rate of sample attrition. To investigate empirically whether the bias in estimates of HIP from omitting age effects is quantitatively important, I thus rely on administrative data from Germany on quarterly earnings that follow workers from labor market entry until 27 years into their career. To strengthen external validity, I focus my analysis on an education group that displays a covariance structure with qualitatively similar properties like its North American counterpart. I find that a HIP model with age effects in transitory, persistent and permanent shocks fits the covariance structure almost perfectly and delivers small and insignificant estimates for the HIP component. In sharp contrast, once I estimate a standard HIP model without age‐effects the estimated slope heterogeneity increases by a factor of thirteen and becomes highly significant, with a dramatic deterioration of model fit. I reach the same conclusions from estimating the two models on a different covariance structure and from conducting a Monte Carlo analysis, suggesting that my quantitative results are not an artifact of one particular sample.