Band 6, Heft 1-2

The sales tax has long been hailed a workhouse of state and local public finance, but its future looks grim due in no small part to the continued proliferation of exemptions. While scholars have noted the role politics plays in eroding the sales tax base, no study has empirically investigated the matter. Using a panel of 44 states from 1980 to 2010, this study addresses this gap by exploring if states’ political institutions predict variations in the level and rate of sales tax base erosion. The models show that political institutions do not systematically determine base erosion in general; though, some factors such as gubernatorial election years and political competition are found to exert some influence. The implications of the study’s abundance of null findings are also discussed.

Every 10 years, states set about redrawing the lines of their Congressional districts. Scholars in political science have long been interested in the strategic behavior and representational outcomes of this process. While majority-minority districts are intended to provide a constraint on strategic party behavior in order to ensure substantive representation of minority interests, researchers have noted a perverse effect that results in potentially less-representative political outcomes. In 2003, Kenneth Shotts, David Lublin, and D. Stephen Voss debated the veracity of the perverse effects claim, but Shotts’ critique was missing a key interaction between partisanship and the liberalizing effect of majority-minority districts. In the course of performing this necessary extension on Shotts’ work, we found that our results have an unexpected, and important, methodological implication for Congress scholars. Specifically, we were unable to replicate his results precisely due to sublte changes in DW-NOMINATE estimates that result from periodic updating of the database. Further-more, after substantially expanding the dataset, we continue to find the same null results and the evidence supporting the interaction is statistically ambiguous. Though these null results do not prove or disprove the perverse-effects hypothesis, they do undermine Shotts’ evidence of a liberalizing effect of majority-minority districting. While we lack sufficient precision to estimate whether majority-minority districting has a positive, negative, or truly no effect on minority representation (and the conditional effect of party control), it is more concerning that small changes to DW-NOMINATE would prevent the replication of these past results, given the abundance of studies that use it to measure legislator ideology.

Many estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple, unbiased, location-invariant, and covariate-adjusted estimators of the average treatment effect in experiments with random allocation of clusters, along with associated variance estimators. We then analyze a cluster-randomized field experiment on voter mobilization in the US, demonstrating that the proposed estimators have precision that is comparable, if not superior, to that of existing, biased estimators of the average treatment effect.

Political science is gradually moving away from an exclusive focus on statistical significance and toward an emphasis on the magnitude and importance of effects. While we welcome this change, we argue that the current practice of “magnitude-and-significance,” in which researchers only interpret the magnitude of a statistically significant point estimate, barely improves the much-maligned “sign-and-significance” approach, in which researchers focus only on the statistical significance of an estimate. This exclusive focus on the point estimate hides the uncertainty behind a veil of statistical significance. Instead, we encourage researchers to explicitly account for uncertainty by interpreting the range of values contained in the confidence interval. Especially when making judgments about the importance of estimated effects, we advise researchers to make empirical claims if and only if those claims hold for the entire confidence interval.

We assess whether racial redistricting increases the number of Southern representatives to the left of the US House median. Our results, which are based on Monte Carlo simulations and an alternative measure of representatives’ preferences, are generally null findings. The data do not support the claim that racial redistricting promotes liberal policy outcomes; nor do they support the claim that it promotes conservative policy outcomes. At a methodological level, we suggest techniques that researchers can use to assess how robust their findings are to noise in variables based on estimated values like DW-Nominate scores.