V oter turnout theories based on rational self-interested behavior generally fail to predict significant turnout unless they account for the utility that citizens receive from performing their civic duty. We distinguish between two aspects of this type of utility, intrinsic satisfaction from behaving in accordance with a norm and extrinsic incentives to comply, and test the effects of priming intrinsic motives and applying varying degrees of extrinsic pressure. A large-scale field experiment involving several hundred thousand registered voters used a series of mailings to gauge these effects. Substantially higher turnout was observed among those who received mailings promising to publicize their turnout to their household or their neighbors. These findings demonstrate the profound importance of social pressure as an inducement to political participation.

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Prior experimental research has demonstrated that voter turnout rises substantially when people receive mailings that indicate whether they voted in previous elections. This effect suggests that voters are sensitive to whether their compliance with the norm of voting is being monitored. The present study extends this line of research by investigating whether disclosure of past participation has a stronger effect on turnout when it calls attention to a past abstention or a past vote. A sample of 369,211 registered voters who voted in just one of two recent elections were randomly assigned to receive no mail, mail that encouraged them to vote, and mail that both encouraged them to vote and indicated their turnout in one previous election. The latter type of mailing randomly reported either the election in which they voted or the one in which they abstained. Results suggest that mailings disclosing past voting behavior had strong effects on voter turnout and that these effects were significantly enhanced when it disclosed an abstention in a recent election.

Regression discontinuity (RD) designs enable researchers to estimate causal effects using observational data. These causal effects are identified at the point of discontinuity that distinguishes those observations that do or do not receive the treatment. One challenge in applying RD in practice is that data may be sparse in the immediate vicinity of the discontinuity. Expanding the analysis to observations outside this immediate vicinity may improve the statistical precision with which treatment effects are estimated, but including more distant observations also increases the risk of bias. Model specification is another source of uncertainty; as the bandwidth around the cutoff point expands, linear approximations may break down, requiring more flexible functional forms. Using data from a large randomized experiment conducted by Gerber, Green, and Larimer (2008), this study attempts to recover an experimental benchmark using RD and assesses the uncertainty introduced by various aspects of model and bandwidth selection. More generally, we demonstrate how experimental benchmarks can be used to gauge and improve the reliability of RD analyses.

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