Patterns in the politics of drugs and tobacco: The Supreme Court and issue attention by policymakers and the press

Past research has demonstrated lasting effects of important Supreme Court decisions on issue attention in the national media. In this light, the Court has served as an important agenda setter. We significantly expand on these findings by arguing that these salient Court decisions can raise the perceived importance of political issues and induce heightened, short-term policy attention in the broader political system. Using measures of media attention, congressional policy actions, and presidential policy actions, we utilize dynamic vector autoregressive modelling to examine the Court’s impact on issue attention in the macro policy system regarding tobacco and drug policy. Overall, this study suggests that the Supreme Court’s most important decisions might significantly affect broader issue attention in the American political system

How states make their own air pollution somebody else’s problem

For now, one of the unfortunate byproducts of an industrial economy is air pollution, but states can often reap the benefits of industry and production while forcing other states to bear the costs. In a new study of tens of thousands of air polluters in the US, James E. Monogan III, David M. Konisky, and Neal D. Woods find that air polluters are more likely to be located near a downwind border compared to solid waste polluters; in effect, making air pollution another state’s problem

If the next president wants to put an ideologue on the Supreme Court, they will have to sacrifice their initial domestic policy goals.

One of the first tasks for the new president this January – whoever they may be – could be to nominate a new justice to the Supreme Court. But how should the next president go about this? In new research, Anthony J. Madonna, James E. Monogan III, and Richard L. Vining, Jr. find that the more a president supports a particular Supreme Court nominee, the lower the chance that they can get a major new policy initiative through the Senate. If the new president wishes to focus on achieving their policy goals in their first 150 days, they argue, they should compromise by appointing a moderate to the Supreme Court, rather than an ideologue

Political analysis using R

Political Analysis Using R can serve as a textbook for undergraduate or graduate students as well as a manual for independent researchers. It is unique among competitor books in its usage of 21 example datasets that are all drawn from political research. All of the data and example code is available from the Springer website, as well as from Dataverse (http://dx.doi.org/10.7910/DVN/ARKOTI). The book provides a narrative of how R can be useful for addressing problems common to the analysis of public administration, public policy, and political science data specifically, in addition to the social sciences more broadly. While the book uses data drawn from political science, public administration, and policy analyses, it is written so that students and researchers in other fields should find it accessible and useful as well. Political Analysis Using R is perfect for the first-time R user who has no prior knowledge about the program. By working through the first seven chapters of this book, an entry-level user should be well acquainted with how to use R as a traditional econometric software program. These chapters explain how to install R, open and clean data, draw graphs, compute descriptive statistics, conduct bivariate inferences, and estimate common models such as linear and logistic regression. This portion of the book is ideal for undergraduate students, graduate students, or professionals trying to learn R in their spare time. This book also can be useful for an intermediate R user wishing to develop additional skills within the program. The last four chapters of the book introduce the user to advanced techniques that R offers but many other programs do not make available. Topics in these l ast chapters include: using user-contributed packages, conducting time series analysis, conducting matrix algebra, and writing programs in R

Replication data for: Modeling Policy Adoption and Impact with Multilevel Methods

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The Consequences of Religious Strictness for Political Participation

Subset of data from the 2006 Portraits of American Life Study. Corresponding R code estimates measurement models for religious strictness, church participation, and civic participation. Also estimates a recursive model of political participation, using multiple imputation to handle missing data

Replication data for: Measuring State and District Ideology with Spatial Realignment

Data and R code for measuring state and district public opinion in 2008 with a kriging model. Survey data are from the 2008 Cooperative Congressional Election Study. Geographic data include shapefiles, ZIP code centroids and attributes, and USDA urban-rural classifications. Demographic data are drawn from Census data and the Association of Statisticians of American Religious Bodies' 2000 Religious Congregations and Membership Study

Immigration Politics and Partisan Realignment: California, Texas, and the 1994 Election

Public opinion data on United States and California macropartisanship, or mass party identification, from 1969-2010. Based on Gallup and Field Poll results, respectively. Also includes US-level measures of consumer sentiment, presidential approval, presidential party, and indicator for presidential administration. Additional data are from the Texas Poll from 1990-1998 to capture macropartisanship in Texas

Modeling Time-Series Count Data: The Unique Challenges Facing Political Communication Studies

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The Fifty American States in Space and Time: Applying Conditionally Autoregressive Models

Spatial conditionally autoregressive (CAR) models in a hierarchical Bayesian framework can be informative for understanding state politics, or any similar population of border-defined observations. This article explains how a hierarchical CAR model is specified and estimated and then uses Monte Carlo analyses to show when the CAR model offers efficiency gains. We apply this model to data structures common to state politics: A cross-sectional example replicates Erikson, Wright & McIver's (1993) Statehouse Democracy model, and a multilevel panel model example replicates Margalit's (2013) study of social welfare policy preferences. The CAR model fits better in each case and some inferences differ from models that ignore geographic correlation