No abstract

We introduce experimental research design to the study of policy diffusion in order to better understand how political ideology affects policymakers’ willingness to learn from one another's experiences. Our two experiments–embedded in national surveys of U.S. municipal officials–expose local policymakers to vignettes describing the zoning and home foreclosure policies of other cities, offering opportunities to learn more. We find that: (1) policymakers who are ideologically predisposed against the described policy are relatively unwilling to learn from others, but (2) such ideological biases can be overcome with an emphasis on the policy's success or on its adoption by co‐partisans in other communities. We also find a similar partisan‐based bias among traditional ideological supporters, who are less willing to learn from those in the opposing party. The experimental approach offered here provides numerous new opportunities for scholars of policy diffusion.

For decades, the Democrats have been viewed as the party of the poor, with the Republicans representing the rich. Recent presidential elections, however, have shown a reverse pattern, with Democrats performing well in the richer blue states in the northeast and coasts, and Republicans dominating in the red states in the middle of the country and the south. Through multilevel modeling of individuallevel survey data and county-and state-level demographic and electoral data, we reconcile these patterns. Furthermore, we find that income matters more in red America than in blue America. In poor states, rich people are much more likely than poor people to vote for the Republican presidential candidate, but in rich states (such as Connecticut), income has a very low correlation with vote preference.

For decades, the Democrats have been viewed as the party of the poor, with the Republicans representing the rich. Recent presidential elections, however, have shown a reverse pattern, with Democrats performing well in the richer blue states in the northeast and coasts, and Republicans dominating in the red states in the middle of the country and the south. Through multilevel modeling of individuallevel survey data and county-and state-level demographic and electoral data, we reconcile these patterns.Furthermore, we find that income matters more in red America than in blue America. In poor states, rich people are much more likely than poor people to vote for the Republican presidential candidate, but in rich states (such as Connecticut), income has a very low correlation with vote preference.

The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.

Researchers face two major problems when applying ideal point estimation techniques to state legislatures. First, longitudinal roll‐call data are scarce. Second, even when such data exist, scaling ideal points within a single state is an inadequate approach. No comparisons can be made between these estimates and those for other state legislatures or for Congress. Our project provides a solution. We exploit a new comparative dataset of state legislative roll calls to generate ideal points for legislators. Taking advantage of the fact that state legislators sometimes go on to serve in Congress, we create a common ideological scale. Using these bridge actors, we estimate state legislative ideal points in congressional common space for 11 states. We present our results and illustrate how these scores can be used to address important topics in state and legislative politics.

Many theoretical and empirical accounts of representation argue that primary elections are a polarizing influence. Likewise, many reformers advocate opening party nominations to nonmembers as a way of increasing the number of moderate elected officials. Data and measurement constraints, however, have limited the range of empirical tests of this argument. We marry a unique new data set of state legislator ideal points to a detailed accounting of primary systems in the United States to gauge the effect of primary systems on polarization. We find that the openness of a primary election has little, if any, effect on the extremism of the politicians it produces.

T he development and elaboration of the spatial theory of voting has contributed greatly to the study of legislative decision making and elections. Statistical models that estimate the spatial locations of individual decision-makers have made a key contribution to this success. Spatial models have been estimated for the U.S. Congress, the Supreme Court, U.S. presidents, a large number of non-U.S. legislatures, and supranational organizations. Yet one potentially fruitful laboratory for testing spatial theories, the individual U.S. states, has remained relatively unexploited, for two reasons. First, state legislative roll call data have not yet been systematically collected for all states over time. Second, because ideal point models are based on latent scales, comparisons of ideal points across states or even between chambers within a state are difficult. This article reports substantial progress on both fronts. First, we have obtained the roll call voting data for all state legislatures from the mid-1990s onward. Second, we exploit a recurring survey of state legislative candidates to allow comparisons across time, chambers, and states as well as with the U.S. Congress. The resulting mapping of America's state legislatures has great potential to address numerous questions not only about state politics and policymaking, but also about legislative politics in general.