Bias in Self-reported Voting and How it Distorts Turnout Models: Disentangling Nonresponse Bias and Overreporting Among Danish Voters

Abstract

Most nonexperimental studies of voter turnout rely on survey data. However, surveys overestimate turnout because of (1) nonresponse bias and (2) overreporting. We investigate this possibility using a rich dataset of Danish voters, which includes validated turnout indicators from administrative data for both respondents and nonrespondents, as well as respondents’ self-reported voting from the Danish National Election Studies. We show that both nonresponse bias and overreporting contribute significantly to overestimations of turnout. Further, we use covariates from the administrative data available for both respondents and nonrespondents to demonstrate that both factors also significantly bias the predictors of turnout. In our case, we find that nonresponse bias and overreporting masks a gender gap of two and a half percentage points in women’s favor as well as a gap of 25 percentage points in ethnic Danes’ favor compared with Danes of immigrant heritage.

Core and Peripheral Voters: Predictors of Turnout Across Three Types of Elections

“…This leaves them vulnerable to well-known problems of over-reporting of self-reported turnout (Karp & Brockington 2005;Bernstein et al 2001;Dahlgaard et al 2018). Furthermore, most studies also use self-reported independent variables such as income and educational attainment, which can also be misreported (Hariri & Lassen 2017).…”

“…As we rely on a large administrative, individual-level dataset with validated turnout and reliable covariates in different types of elections, we overcome well-known problems of self-reported voting and small survey samples which have characterized much of the previous literature (Smets & Van Ham 2013;Karp & Brockington 2005;Bernstein et al 2001;Dahlgaard et al 2018).…”

“…This allows us to analyze the predictors of cumulative voting, the total number of votes cast in a number of consecutive elections, in a high-turnout context and the potential differential drop-off of voters across different types of elections. As we rely on a large administrative, individual-level dataset with validated turnout and reliable covariates in different types of elections, we overcome well-known problems of self-reported voting and small survey samples which have characterized much of the previous literature (Bernstein et al, 2001; Dahlgaard et al, 2018; Karp and Brockington, 2005; Smets and Van Ham, 2013).…”

Do Survey Estimates of the Public’s Compliance with COVID-19 Regulations Suffer from Social Desirability Bias?

“…Unfortunately, respondents are not always honest. One classic example is turnout at elections -if you ask people whether they voted, almost everyone will say yes, but if you compare these survey estimates to official statistics, it becomes evident that there is massive over-reporting of voting (Dahlgaard et al, 2019). This dishonesty stems from a desire to deny socially undesirable traits -a social desirability bias (Krumpal 2013).…”

Effects of Nonresponse, Measurement, and Coverage Bias in Survey Estimates of Voting

“…A third approach uses a general population frame linked or matched to voting records (Daahlgaard et al., 2019; Jackman and Spahn, 2019). Nonresponse bias is estimated as the difference between survey respondents’ voting record to that for the full sample including nonrespondents.…”