Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.