Using Discrepancies to Correct for False Matches in Historical Linked Data

Yuya Sasaki and Ariell Zimran

No 33881, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We propose a method to correct estimates from historical linked data for bias arising from type-I error—"false matches." We estimate the rate of false matching from the disagreement rate in characteristics that should agree across the two linked datasets. Combined with an understanding of the empirical patterns arising from false matches, knowledge of this rate enables us to correct for bias from false matches. Our method enables correction of estimates of both population moments and regression coefficients with valid inference. We illustrate the properties of our method via simulation and demonstrate them using linked US census data.

JEL-codes: C10 C23 C49 C55 C81 C83 J61 J62 N30 N31 N32 (search for similar items in EconPapers)
Date: 2025-05
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