Using Digitized Newspapers to Address Measurement Error in Historical Data
提出三种方法，利用数字化报纸生成的辅助变量，消除历史数据中测量误差导致的回归衰减偏差，并通过复制四篇经济史论文验证其有效性。
This paper shows how to remove attenuation bias in regression analyses due to measurement error in historical data for a given variable of interest by using a secondary measure that can be easily generated from digitized newspapers. We provide three methods for using this secondary variable to deal with non-classical measurement error in a binary treatment: set identification, bias reduction via sample restriction, and a parametric bias correction. We demonstrate the usefulness of our methods by replicating four recent economic history papers. Relative to the initial analyses, our results yield markedly larger coefficient estimates.