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Do minority voters respond to co-racial or co-ethnic candidates? That is does the increased chance of substantive representation translate into increased participation? Here, we focus on this question among African American voters. While much of the empirical literature on this question has produced conflicting answers, recent studies suggest that minority candidates can significantly increase minority turnout. We argue that past work on this topic does not adequately account for the fact that minority voters in places with minority candidates may systematically differ in their level of participation than minority voters in places without minority candidates. In this study we address the weaknesses of previous research designs and offer a new design that exploits the redistricting process to gain additional leverage over this question. We find little evidence that African American voter turnout increases when voters are moved to African American candidates. We find some evidence that white voters, however, tend to vote at lower rates when they are represented by African American candidates.

A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effects in political processes and as a method for ridding the model of autocorrelation. But recent work contends that the lagged dependent variable specification is too problematic for use in most situations. More specifically, if residual autocorrelation is present, the lagged dependent variable causes the coefficients for explanatory variables to be biased downward. We use a Monte Carlo analysis to assess empirically how much bias is present when a lagged dependent variable is used under a wide variety of circumstances. In our analysis, we compare the performance of the lagged dependent variable model to several other time series models. We show that while the lagged dependent variable is inappropriate in some circumstances, it remains an appropriate model for the dynamic theories often tested by applied analysts. From the analysis, we develop several practical suggestions on when and how to use lagged dependent variables on the right-hand side of a model.

Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural experiment due to its relatively weak assumptions. We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas occurs in an as-if random fashion. We show how this design is equivalent to a standard RD with two running variables, but we also clarify several methodological differences that arise in geographical contexts. We also offer a method for estimation of geographically located treatment effects that can also be used to validate the identification assumptions using observable pretreatment characteristics. We illustrate our methodological framework with a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries.

Many different elements make up the strength of a president, but a president with a majority in both houses of Congress works from a position of power. While a congressional majority does not guarantee that a president can pass his agenda (witness Jimmy Carter and the first two years of the Clinton presidency), a president that has majority in Congress will have natural allies instead of natural enemies when he works to enact his legislative agenda.