Using Digitized Newspapers to Refine Historical Measures: The Case of the Boll Weevil
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 which 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 our three procedures by replicating two recent papers that study the economic impact of the spread of the boll weevil across the U.S. South in the late 19th and early 20th century. Relative to the initial analysis, our results yield markedly larger coefficient estimates.
Randall Walsh is professor and director of the Masters of Science in Quantitative Economics (MQE) program in the Department of Economics at the University of Pittsburgh. He is also a research associate with the National Bureau of Economic Research.
Discussant: James Feigenbaum, Assistant Professor Economics at Boston University