``Validating Self-reported Turnout by Linking Public Opinion Surveys with Administrative Records.''



Although it is widely known that the self-reported turnout rates obtained from public opinion surveys tend to substantially over-estimate the actual turnout rates, scholars sharply disagree on what causes this bias. While some blame misreporting due to social desirability, others attribute it to non-response bias and the accuracy of turnout validation. While we can validate self-reported turnout by directly linking surveys with administrative records, most existing studies rely on proprietary merging algorithms with limited scientific transparency and yield conflicting results. To shed a light on this debate, we apply a canonical probabilistic record linkage model, implemented via the open-source software package fastLink, to merge two major election studies - the American National Election Studies and the Cooperative Congressional Election Survey - with a national voter file of over 180 million records. For both studies, fastLink successfully produces validated turnout rates close to the actual turnout rates, leading to public-use validated turnout data for the two studies. Using these merged data sets, we find that the bias of self-reported turnout originates primarily from misreporting rather than non-response. Our findings suggest that those who are educated and interested in politics are more likely to over-report turnout. Finally, we show that fastLink performs as well as a proprietary algorithm. (Last Revised, June 2018)
Our method is used to validate the self-reported turnout in the 2016 American National Election Study. Our turnout validation data are available at the ANES website.

  Related Paper and Software

Enamorado, Ted, Benjamin Fifield, and Kosuke Imai. ``Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records.''American Political Science Review, Forthcoming.
Enamorado, Ted, Benjamin Fifield, and Kosuke Imai. ``fastLink: Fast Probabilistic Record Linkage.'' available through The Comprehensive R Archive Network.

© Kosuke Imai
 Last modified: Wed Oct 17 14:45:28 EDT 2018