Measurement Error in Income and Schooling and the Bias of Linear Estimators
提出一个通用框架，用于评估线性模型OLS和IV估计中测量误差导致的偏误，并利用丹麦行政登记数据验证欧洲健康、老龄化和退休调查（SHARE）数据，发现收入测量误差为经典误差，而教育测量误差非经典，导致IV估计的教育回报偏误放大38%。
We propose a general framework for determining the extent of measurement error bias in OLS and IV estimators of linear models, while allowing for measurement error in the validation source. We apply this method by validating Survey of Health, Ageing and Retirement in Europe (SHARE) data with Danish administrative registers.Contrary to most validation studies, we find measurement error in income is classical, once we account for imperfect validation data. We find non-classical measurement error in schooling, causing a 38 percent amplification bias in IV estimators of the returns, with important implications for the program evaluation literature.