企业工资溢价测量中信号与噪声的区别

N. Chanut
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引用次数: 0

摘要

人们对企业方面的工资差异驱动因素越来越感兴趣,因为不同的研究表明,这一因素正在推动许多发达国家的不平等加剧。在本文中,我从三个方面对这一文献做出了贡献。首先,我重新考虑了Abowd, Kramarz和Margolis(1999)广泛使用的模型,该模型用于分解企业和个人异质性的各自贡献。我建议采用一种易于应用的分样程序来揭示该模型中过拟合的程度。利用法国的行政数据,我发现了相当大的过拟合证据:保守估计表明,企业异质性对工资不平等的贡献至少被高估了25%。其次,我提供了一个简单的程序来恢复正确的企业效应的信号方差和个人和企业效应之间的协方差。第三,我展示了如何使用收缩估计器更好地恢复对企业效应的预测。这在数量上很重要:由于收缩,一半的企业效应收缩了38%或更多,40%的企业根据其企业效应排名时最终处于不同的十分位数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distinguishing Between Signal and Noise in the Measurement of the Firm Wage Premium
There is a growing interest about firm-side drivers of wage differentials, as different studies show that this component is driving the increase in inequality in many developed countries. In this paper, I contribute to this literature in three respects. First, I reconsider the widely used model from Abowd, Kramarz and Margolis (1999) used to decompose the respective contributions of firm and individual heterogeneity. I suggest an easily applicable split-sample procedure to uncover the extent of overfitting in this model. Using French administrative data, I find evidence of sizeable overfitting: conservative estimates suggest that the contribution of firm heterogeneity to wage inequality is overestimated by at least 25%. Second, I provide a simple procedure to recover the correct signal variance of firm effects and the covariance between individual and firm effects. Third, I show how to recover better prediction of the firm effects using shrinkage estimators. This matters quantitatively: due to shrinkage, half of the firm effects are shrunk by 38% or more, and 40% of firms end up in different deciles when ranked according to their firm effects.
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