Language Fluency and Earnings: Estimation with Misclassified Language Indicators
利用面板数据，在考虑语言能力自评的误分类误差和未观测异质性的情况下，重新估计了语言流利度对移民收入的影响，发现这两点会显著改变标准模型的估计结果。
We use panel data to analyze the determinants of speaking fluency and wages of immigrants. Our model takes account of two problems that may bias OLS estimates of the impact of speaking fluency on earnings. First, subjective variables on an ordinal discrete scale, such as self-reported language ability, can suffer from misclassification errors. The model decomposes misclassification errors into a time-persistent and a time-varying component. Second, the model accounts for correlated unobserved heterogeneity in language and earnings equation. The main finding is that these two generalizations of the standard model both lead to substantial changes in the estimated effect of speaking fluency on earnings.