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dc.contributor.authorAbramitzky, Ranen_US
dc.contributor.authorBoustan, Leah Platten_US
dc.contributor.authorEriksson, Katherineen_US
dc.contributor.authorFeigenbaum, Jamesen_US
dc.contributor.authorPérez, Santiagoen_US
dc.date.accessioned2020-04-24T15:26:14Z
dc.date.available2020-04-24T15:26:14Z
dc.date.issued2019-05
dc.identifier.citationRan Abramitzky, Leah Platt Boustan, Katherine Eriksson, James Feigenbaum, Santiago Pérez. 2019. "Automated Linking of Historical Data." NBER Working Paper No. w25825. https://doi.org/10.3386/w25825
dc.identifier.urihttps://hdl.handle.net/2144/40337
dc.description.abstractThe recent digitization of complete count census data is an extraordinary opportunity for social scientists to create large longitudinal datasets by linking individuals from one census to another or from other sources to the census. We evaluate different automated methods for record linkage, performing a series of comparisons across methods and against hand linking. We have three main findings that lead us to conclude that automated methods perform well. First, a number of automated methods generate very low (less than 5%) false positive rates. The automated methods trace out a frontier illustrating the tradeoff between the false positive rate and the (true) match rate. Relative to more conservative automated algorithms, humans tend to link more observations but at a cost of higher rates of false positives. Second, when human linkers and algorithms use the same linking variables, there is relatively little disagreement between them. Third, across a number of plausible analyses, coefficient estimates and parameters of interest are very similar when using linked samples based on each of the different automated methods. We provide code and Stata commands to implement the various automated methods.en_US
dc.language.isoen_US
dc.titleAutomated linking of historical dataen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.description.versionFirst author draften_US
dc.identifier.doi10.3386/w25825
pubs.elements-sourcessrnen_US
pubs.notesSource info: NBER Working Paper No. w25825en_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
dc.identifier.orcid0000-0002-1625-2021 (Feigenbaum, James)
dc.identifier.mycv476553


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