State politics researchers commonly employ ordinary least squares (OLS) regression or one of its variants to test linear hypotheses. However, OLS is easily influenced by outliers and thus can produce misleading results when the error term distribution has heavy tails. Here we demonstrate that median regression (MR), an alternative to OLS that conditions the median of the dependent variable (rather than the mean) on the independent variables, can be a solution to this problem. Then we propose and validate a hypothesis test that applied researchers can use to select between OLS and MR in a given sample of data. Finally, we present two examples from state politics research in which (1) the test selects MR over OLS and (2) differences in results between the two methods could lead to different substantive inferences. We conclude that MR and the test we propose can improve linear models in state politics research.

The passage of a restrictive immigration law in Arizona in 2010 rekindled an old debate in the United States on immigration policy and the role of federalism. Despite periodic constitutional controversies, scholars of federalism and U.S. state politics have not adequately explained variation in state-level policy making on immigration. The authors explore pressures leading to state immigration policy innovation and adoption in the United States. The article evaluates factors leading to the introduction and adoption of two types of policies: those dictating the cultural and economic incorporation of immigrants and those attempting to control their flow and settlement. Factors such as fiscal federalism, ethnic contact, and ethnic threat generate incentives for states to pass such laws. The authors compiled a comprehensive data set of state immigration laws from the past decade to explain how factors commonly associated with national immigration policy development—economic conditions, rates of immigration, demographics, party control, and political institutions—influence state-level immigration policy activity.

The author improves on the measurement of U.S. state public opinion by (1) applying previous methods used only on cross-sectional data to create dynamic measures of state public opinion and (2) providing a systematic comparison of the performance of the various methodological approaches on these dynamic measures. The author shows that scholars can use multilevel regression, imputation, and poststratification (MRP) coupled with a simple moving average to measure state public opinion over time. Compared to aggregation, the MRP approach has less error and is more reliable, particularly for the less populated states. The author shows the applicability of the MRP approach by measuring and validating state partisanship and state ideology over time. The validated measures are available for public use. Armed with a method to measure state public opinion over time, scholars can begin to more fully understand the dynamic relationship between public opinion and policy in the U.S. states.

How does the demand for lobbying reflected by government policy activity influence the use of lobbying strategies and tactics? The authors examine this question by assessing how the complexity of the policy space affects the political action committee (PAC) system. They hypothesize that the complexity of the policy space indirectly affects the size and activity of the PAC system through its direct effect on interest organization density. The authors test this hypothesis within the health sector using a unique data set that connects individual interest organizations registered to lobby U.S. state legislatures with active PACs in the state. It appears that social, economic, and political measures of policy space complexity influence the size of the lobbying community, which in turn influences the size and activity of the PAC community.

This article examines the role of society and culture in shaping the opportunity structure and ambition formation of female gubernatorial candidates in all 50 states over a 40-year period. Using a new data set consisting of every woman who entered a gubernatorial primary from 1978 to 2008, the author analyzes how cultural factors and historical legacies—including the percentage of women in the workforce, higher education, and statewide elective offices—influence the opportunity structure and ambition formation of female candidates. The author argues that the female sociopolitical subculture within individual states heavily influences whether or not female candidates will enter and win their respective primaries and general elections. Rather than assuming that individual characteristics are the primary determinants of ambition formation, this research implies that it is necessary to analyze the political behavior within cultural contexts.

This article analyzes the relationship between U.S. state governance and policies designed to enhance educational expectations. It examines three policy areas: state participation in voluntary National Assessment of Educational Progress testing, state testing requirements for new teachers, and state high school graduation requirements in math and science. In general, it identifies associations between state institutional and political characteristics and the state policies under study. In particular, state policies that impose higher demands on local school districts are more likely to be present in states with more centralized control of their K–12 systems. Furthermore, state partisanship appears to suggest that Republicans favor policies that push power to lower levels of the U.S. intergovernmental system.