Replication data for: Experiments to Reduce the Over-reporting of Voting: A Pipeline to the Truth

Stata do file and data set to replicate results in "Experiments to Reduce the Over-reporting of Voting: A Pipeline to the Truth.

From selection to election and beyond: Understanding the causes and consequences of electoral reform in America.

In the United States, there is wide variation from state to state in the institutional arrangements that structure the environment in which citizens decide whether or not to vote and parties decide whom to mobilize. This has consequences for the types of candidates that get elected, which policies they enact, and the health of democracy in the U.S. I examine how the factors that influence the adoption of a set of voter registration laws, specifically election day registration and motor voter laws, affect political outcomes, focusing on voter turnout. I argue that to understand how these institutional arrangements affect outcomes, one has to understand the interactions between social and political context and these institutions. Previous research fails to account for these interactions and makes assumptions that do not withstand scrutiny. My multi-method design that includes the statistical analysis of survey data, as well as the construction and analysis of legislative histories, and interviews with election officials and party leaders, demonstrates that social and political context shape the ways citizens respond to reform. I find that the two recently implemented registration reforms that do the most to lower the costs of voting and facilitate party mobilization efforts have come up short on the promise of delivering higher rates of turnout and have done little to reduce the inequality of turnout among the most and least well off. Furthermore, I show that the effect of registration laws is not uniform across contexts. In states that adopted reform as a result of federal intervention, the laws have little if any effect. Finally, I demonstrate that variations in implementation and party mobilization do not explain differential effects across contexts. The results of this project have implications for public policy as well as policy evaluation studies. Those seeking to boost turnout will have to turn to longer term strategies that seek to address the motivation to vote. The research design developed in this project serves as a model that can be applied to the analysis of other election reforms, such as those that will result from the Help America Vote Act of 2002 (HAVA).Ph.D.Political scienceSocial SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124683/2/3150211.pd

Replication data for: Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models

Models designed for limited dependent variables are increasingly common in political science. Researchers estimating such models often give little attention to the coefficient estimates and instead focus on marginal effects, predicted probabilities, predicted counts, etc. Since the models are nonlinear, the estimated effects are sensitive to how one generates the predictions. The most common approach involves estimating the effect for the “average case.” But this approach creates a weaker connection between the results and the larger goals of the research enterprise and is thus less preferable than the observed value approach. That is, rather than seeking to understand the effect for the average case, the goal is to obtain an estimate of the average effect in the population. In addition to the theoretical argument in favor of the observed value approach, we illustrate via an empirical example and Monte Carlo simulations that the two approaches can produce substantively different results