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.

中文翻译：

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使用数字化报纸解决历史数据中的测量误差

本文展示了如何通过使用可以从数字化报纸轻松生成的辅助测量来消除由于给定感兴趣变量的历史数据中的测量误差而导致的回归分析中的衰减偏差。我们提供了三种使用此辅助变量来处理二元处理中的非经典测量误差的方法：集合识别、通过样本限制减少偏差以及参数偏差校正。我们通过复制最近的四篇经济史论文来证明我们的方法的有用性。相对于最初的分析，我们的结果产生了明显更大的系数估计。