Imputation Strategies for Rightcensored Wages in Longitudinal Datasets
纵向数据中右删失工资的插补策略AI 解读 ↓
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纵向数据中右删失工资的插补策略
- 研究问题
- 如何插补纵向工资数据中超过上限的观测值，既改善经典 Tobit 模型的拟合，又降低插补模型与未来分析不相容造成的偏差？
- 方法与思路
- 作者使用留一均值缓解插补与分析不相容问题，并采用分位数回归或左删失改善拟合，在德国工资数据中评估这些调整。
- 材料陈述的贡献
- 摘要称，经典 Tobit 对行政工资数据拟合较差，并以几乎不受删失影响的工资调查作为检验平台，展示改进插补策略的收益。
- 阅读关注点
- 应核对留一均值的计算层级、左删失在方法中的具体作用，以及插补尾部、后续分析偏差和不确定性如何评估。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核验。摘要未报告偏差或拟合改善数值，也未保证插补适合所有未来分析。
原文摘要
Censoring from above is a common problem with wage information as the reported wages are typically top-coded for confidentiality reasons. In administrative databases the information is often collected only up to a pre-specified threshold, for example, the contribution limit for the social security system. While directly accounting for the censoring is possible for some analyses, the most flexible solution is to impute the values above the censoring point. This strategy offers the advantage that future users of the data no longer need to implement possibly complicated censoring estimators. However, standard cross-sectional imputation routines relying on the classical Tobit model to impute right-censored data have a high risk of introducing bias from uncongeniality (Meng, 1994) as future analyses to be conducted on the imputed data are unknown to the imputer. Furthermore, as we show using a large-scale administrative database from the German Federal Employment agency, the classical Tobit model offers a poor fit to the data. In this paper, we present some strategies to address these problems. Specifically, we use leave-one-out means as suggested by Card et al. (2013) to avoid biases from uncongeniality and rely on quantile regression or left censoring to improve the model fit. We illustrate the benefits of these modeling adjustments using the German Structure of Earnings Survey, which is (almost) unaffected by censoring and can thus serve as a testbed to evaluate the imputation procedures.