How do people explain their behavior in socially unacceptable political situations? Exploring this question will give us insight into how the public responds to and frames collective decisions regarding controversial topics. We analyze accounts of the outcomes of racially sensitive statewide referenda in two states to understand the public responses to such political predicaments. Distinguishing four broad categories of these accounts—denials, justifications, excuses, and confessions—we find some clear-cut differences in their use between proponents and opponents of the ballot measures. These results have implications for political thought and dialogue regarding politically-sensitive issues and other heated policy issues. We also discuss how the different account dynamics in these two cases presaged subsequent political developments in these states, which might provide insights into why some such cases continue to be fiercely contested while others fade from public debate.

In a nationwide survey Carey, Niemi, Powell, and Moncrief (2006) found that term-limited state legislators feel less constrained by their constituencies. I use direct measures of legislative activity to examine how this “Burkean shift” in attitudes is manifested in roll-call behavior. With a new dataset consisting of all competitive state legislative roll calls for the 1999–2000 sessions and a new measure of district constituency preferences, I examine three hypotheses: that term-limited legislators are less representative of their constituents, are more polarized, and participate less in roll-call voting. I find no evidence that term-limited legislators are any less representative, and no differences in levels of party polarization appear associated with the term limits reform. I find that the impact of term limits on roll-call voting is manifested in decreased legislative effort, but this effect only appears in the more demanding legislatures. The results are consistent with the sorting model in which elections are reasonably efficient at selecting leaders whose preferences align with those of their districts, but the prospect of re-election has little role in achieving representation of constituents' ideological preferences.

As state judicial campaigns become progressively more expensive and political, judicial candidates have turned more frequently to lawyers and law firms for campaign contributions. Given that lawyers who contribute to judges' campaigns frequently appear before them in court, the potential for a conflict of interest arises. I ask whether judges are more likely to rule in favor of attorneys who provide financial support to their campaigns. Looking at cases decided in the Supreme Court of Georgia's 2003 term, I show that campaign contributions are indeed correlated with judges' decisions. Furthermore, I use a two-stage probit least squares estimator to show that these campaign contributions directly affect judicial decisionmaking.

This note examines two modeling alterations of Barabas and Jerit's (2004) analysis of the influence of redistricting principles on minority representation in congressional districts. The size of states and the fact that some states cannot have majority-minority or minority-influence districts is taken into account in these new analyses. Overall, even with these two important alterations, Barabas and Jerit's findings are largely replicated. However, two of their most prominently reported findings—that a compactness requirement for redistricting is associated with both fewer majority-minority and minority-influence districts—are not corroborated.

Scholars of state politics and policy have devoted little attention to the public universities where so many of them work. Public higher education is organized at the state level, and its funding and governance have been debated at length in many states in recent years. Moreover, these universities provide opportunities for contributions to a variety of theoretically-grounded research, including the decision to make or buy public services, principal-agent issues and institutional arrangements for governance, the politics of institutional reform, the determinants of government appropriations and budgetary trade-offs, and internal decisionmaking in state-owned enterprises, public bureaucracies, and nonprofit organizations. Research on these issues could not only generate insights relevant to many types of institutions and public services but also contribute to ongoing policy debates over relations between state governments and higher education.

This article compares three approaches to handling missing data at the state level under three distinct conditions. Using Monte Carlo simulation experiments, I compare the results from a linear model using listwise deletion (LD), Markov Chain Monte Carlo with the Gibbs sampler algorithm (MCMC), and multiple imputation by chained equations (MICE) as approaches to dealing with different severity levels of missing data: missing completely at random (MCAR), missing at random (MAR), and nonignorable missingness (NI). I compare the results from each of these approaches under each condition for missing data to the results from the fully observed dataset. I conclude that the MICE algorithm performs best under most missing data conditions, MCMC provides the most stable parameter estimates across the missing data conditions (but often produced estimates that were moderately biased), and LD performs worst under most missing data conditions. I conclude with recommendations for handling missing data in state-level analysis.