We utilize a new policy adoption database with over 500 policies to test whether the initiative process influences the timing of policy adoption. Prior studies have produced both supportive and null findings of the effect of the initiative, but typically examine policies one policy or a single composite score at a time. Theoretical accounts suggest that the initiative process should have heterogeneous effects on policy outcomes depending on the configuration of public and government preferences. By pooling hundreds of policies we are able to estimate the average effect of the initiative process on state policy adoption more systematically while also evaluating variation in its effect. We find via a pooled event history analysis that the initiative tends to increase innovativeness, but that this effect can be cancelled out by signature and distribution requirements. We find that this effect varies substantially across policies and is more consistently positive on average in states more liberal populations. We also find evidence that the initiative process moderates the effect of ideology on policy adoption, while making the adoption of non-ideological policies more likely on average.

We developed a maximum likelihood estimator corresponding to the predicted hazard rate that emerges from a continuous time game of incomplete information with a fixed time horizon (i.e., Kreps and Wilson, 1982, Journal of Economic Theory27, 253–279). Such games have been widely applied in economics and political science and involve two players engaged in a war of attrition contest over some prize that they both value. Each player can be either a strong or weak competitor. In the equilibrium of interest, strong players do not quit whereas weak players play a mixed strategy characterized by a hazard rate that increases up to an endogenous point in time, after which only strong players remain. The observed length of the contest can therefore be modeled as a mixture between two unobserved underlying durations: one that increases until it abruptly ends at an endogenous point in time and a second involving two strong players that continues indefinitely. We illustrate this estimator by studying the durations of Senate filibusters and international crises.

I study the consequences of interest group campaign contributions for administrative oversight. Unlike the few previous studies in this area, however, I study the influence in state bureaucracies and at the level of individual groups. Specifically, I test whether campaign contributions to state elected officials influence the outcomes of annual inspections of skilled nursing facilities in 16 states, leveraging the context of state politics in two important ways. First, I consider the differing effects of contributions to the legislative and executive branches. Second, I argue that legislative capacity for oversight influences the efficacy of contributions Regression analysis of inspection results with controls for facility characteristics provides evidence that contributing facilities have better overall inspection results, with a large reduction in citations for severe problems. Furthermore, contributions to legislators reduce overall problems, particularly in less professionalized legislatures, while those to the governor reduce severe ones.

Good education requires student experiences that deliver lessons about practice as well as theory and that encourage students to work for the public good—especially in the operation of democratic institutions (Dewey 1923; Dewy 1938). We report on an evaluation of the pedagogical value of a research project involving 23 colleges and universities across the country. Faculty trained and supervised students who observed polling places in the 2016 General Election. Our findings indicate that this was a valuable learning experience in both the short and long terms. Students found their experiences to be valuable and reported learning generally and specifically related to course material. Postelection, they also felt more knowledgeable about election science topics, voting behavior, and research methods. Students reported interest in participating in similar research in the future, would recommend other students to do so, and expressed interest in more learning and research about the topics central to their experience. Our results suggest that participants appreciated the importance of elections and their study. Collectively, the participating students are engaged and efficacious—essential qualities of citizens in a democracy.

Parametric and nonparametric duration models assume proportional hazards: The effect of a covariate on the hazard rate stays constant over time. Researchers have developed techniques to test and correct nonproportional hazards, including interacting the covariates with some function of time. Including this interaction term means that the specification now involves time-varying covariates, and the model specification should reflect this feature. However, in situations with no time-varying covariates initially, researchers often continue to model the duration with only time-invariant covariates. This error results in biased estimates, particularly for the covariates interacted with time. We investigate this issue in over forty political science articles and find that of those studies that begin with time-invariant covariates and correct for nonproportional hazards the majority suffer from incorrect model specification. Proper estimation usually produces substantively or statistically different results.

Pooled event history analysis (PEHA) allows researchers to study the effects of variables across multiple policies by stacking the data and estimating the parameters in a single model. Yet this approach to modeling policy diffusion implies assumptions about homogeneity that are often violated in reality, such that the effect of a given variable is constant across policies. We relax this assumption and use Monte Carlo simulations to compare common strategies for modeling heterogeneity, testing these strategies with increasing levels of variance. We find that multilevel models with random coefficients produce the best estimates and are a significant improvement over other models. In addition, we show how modeling similar policies as multilevel structures allows researchers to more precisely explore the theoretical implications of heterogeneity across policies. We provide an empirical example of these modeling approaches with a unique data set of 29 antiabortion policies.

Once legislators delegate policymaking responsibility to executive agencies, they have the ability to oversee and potentially influence the actions of these agencies. In this article, we examine, first, whether the actions of agencies reflect the preferences of legislators, and second, whether legislative professionalism enhances the ability of legislatures to influence executive agencies and obtain more preferred outcomes. We study these effects in the context of annual nursing home inspections performed by state administrators and make two predictions. First, as Democratic legislators will, on average, prefer a more activist role for government and for government agencies, we should see agencies issue more citations for violations of regulations when state legislatures are Democratic and fewer when they are Republican. Second, as more professionalized legislatures are better able to monitor the agency inspectors' actions and inspection outcomes, this effect should be intensified for legislatures with greater professionalism. We find support for both arguments: agencies faced with more Democrats in the legislature will be more activist, and this effect is strengthened for more professional legislatures.

The study of international relations (IR), and political science more broadly, has derived great benefits from the recent growth of conceptualizing and modeling political phenomena within their broader network contexts. More than just a novel approach to evaluating old puzzles, network analysis provides a whole new way of theoretical thinking. Challenging the traditional dyad-driven approach to the study of IR, networks highlight actor interdependence that goes beyond dyads and emphasizes that many traditional IR variables, such as conflict, trade, alliances, or international organization memberships must be treated and studied as networks. Properties of these networks (e.g., polarization, density), and of actor positions within them (e.g., similarity, centrality), will then reveal important insights about international events. Network analysis, however, is not yet fully adapted to account for important methodological issues common to IR research, specifically the issue of endogeneity or possible nonindependence between actors’ position within international networks and the outcomes of interest: for example, alliance network may be nonindependent from the conflict or trade network. We adopt an instrumental variable approach to explore and address the issue of endogeneity in network context. We illustrate the issue and the advantages of our approach with Monte Carlo analysis, as well as with several empirical examples from IR literature.

Duration data are often subject to various forms of censoring that require adaptations of the likelihood function to properly capture the data generating process, but existing spatial duration models do not yet account for these potential issues. Here, we develop a method to estimate spatial-lag duration models when the outcome suffers from right censoring, the most common form of censoring. We adapt Wei and Tanner's (1991) imputation algorithm for censored (non-spatial) regression data to models of spatially interdependent durations. The algorithm treats the unobserved duration outcomes as censored data and iterates between multiple imputation of the incomplete, that is, right censored, values and estimation of the spatial duration model using these imputed values. We explore the performance of an estimator for log-normal durations in the face of varying degrees of right censoring via Monte Carlo and provide empirical examples of its estimation by analyzing spatial dependence in states' entry dates into World War I.

The transmission of ideas, information, and resources forms the core of many issues studied in political science, including collective action, cooperation, and development. While these processes imply dynamic connections among political actors, researchers often cannot observe such interdependence. One example is public policy diffusion, which has long been a focus of multiple subfields. In the American state politics context, diffusion is commonly conceptualized as a dyadic process whereby states adopt policies (in part) because other states have adopted them. This implies a policy diffusion network connecting the states. Using a dataset of 187 policies, we introduce and apply an algorithm that infers this network from persistent diffusion patterns. The results contribute to knowledge on state policy diffusion in several respects. Additionally, in introducing network inference to political science, we provide scholars across the discipline with a general framework for empirically recovering the latent and dynamic interdependence among political actors.

We provide the first comprehensive study of lobbying across venues by studying interest group registrations in both the legislative and administrative branches. We present four major findings based on Federal and state data. Firstly, groups engage in substantial administrative lobbying relative to legislative lobbying. Secondly, the vast majority of groups lobby the legislature, but a large proportion of groups also lobby the bureaucracy. Thirdly, representational biases in legislative lobbying are replicated across venues: business groups dominate administrative lobbying at least as much as they do legislative lobbying. Finally, the level of interest group activity in one venue for a given policy area is strongly related to its level in the other venue. The findings potentially have important implications for the impact of institutional design on both the form and promotion of broad participation in policy-making as well as the ultimate content of policies chosen by democratic governments, broadly construed.

How do the American states vary in their propensity for innovativeness, or their willingness to adopt new policies sooner or later relative to other states? Most studies today use event history analysis (EHA) to focus almost exclusively on one policy area at a time at the expense of a broader understanding of innovativeness as a characteristic of states. To return to the concept of innovativeness more broadly, our study revisits and updates the original approach taken by Walker by updating his average innovation scores with new data covering more than 180 different policies. We use these data to construct a new, dynamic measure of innovativeness that addresses biases and shortcomings in the original measure and we provide measures of uncertainty for both. These new scores build on the logic of EHA to address issues such as right-censoring and to facilitate measuring changes in innovativeness over time. We then compare the two measures of innovativeness and evaluate differences across states, spatial patterns, and changes in innovativeness over time.

Scholars have begun to move beyond the dichotomous dependent variable—indicating whether a state adopts a policy or not in a given year—usually employed in event history analysis. In particular, they have devoted increasing attention to the components of policies that states adopt. I discuss a variety of estimators that have been employed to analyze the adoption and modification of policies with multiple components, including various forms of event history analysis, OLS, and event count models. With various modifications, the researcher can estimate models that treat each component as distinct, pool these models to leverage commonalities across components, or treat the components as identical parts of the same process. Each of these has its strengths and may be appropriate in certain circumstances. Nonetheless, in the majority of cases, some version of event history analysis for multiple or repeat failures is likely to be preferred. The different approaches are illustrated by studying state adoption of various obesity-related policies.

What effect does the initiative process have on the volatility of interest group populations? Theoretical results suggest that interest group communities in initiative states should be characterized by greater rates of entry and exit since the presence of the initiative process increases mobilizations by potentially less stable groups, particularly broad-based citizen groups. I test this prediction using data on state interest group lobby registrations in 1990 and 1997. Tabular and regression analysis of exit and entry rates for all groups as well as separate analyses for different kinds of groups, including citizen, economic, membership, institutions, and associations, are consistent with the prediction, with the effect strongest and most consistent for citizen and membership groups.

A variety of factors have been shown to influence position timing and the content of positions taken by legislators on important issues. In addition to these observed factors, I argue that unobserved factors such as behind-the-scenes lobbying and party loyalty may also influence position timing and position content. Although hypotheses about observed factors can be tested using traditional methods, hypotheses about unobserved factors cannot. To test for systematic effects of unobserved factors on position timing and content, I develop a seemingly unrelated discrete-choice duration estimator and apply it to data from the vote for the North American Free Trade Agreement. The results indicate that even after controlling for observed factors, there is still evidence that unobserved factors such as Presidential lobbying and/or party loyalty influence both choices.

Event History Modeling: A Guide for Social Scientists. By Janet M. Box-Steffensmeier and Bradford S. Jones. New York: Cambridge University Press, 2004. 232p. $65.00 cloth, $23.99 paper.

The study of durations in political science has been on the rise over the last decade and a half. Their application spans major research questions in virtually every field, including the duration of parliamentary governments, international conflict, policy adoptions in the U.S. states, and issue emergence in campaigns. Testing theoretical arguments regarding these and other questions involving durations has led political scientists to learn about and rely upon statistical models for durations, often referred to as event history models. Perhaps more than models for other classes of data, learning about event history models, particularly those for continuous-time data, presents a formidable task. This is partly due to the unique language of the models (e.g., terms like “spell,” “failure,” “frailty,” and “hazard”) that developed through their application in other disciplines, but also because of the new concerns that they involve. For example, how should one control for duration dependence? Is the proportional hazards assumption met?

Recent work in survey research has made progress in estimating models involving selection bias in a particularly difficult circumstance—all nonrespondents are unit nonresponders, meaning that no data are available for them. These models are reasonably successful in circumstances where the dependent variable of interest is continuous, but they are less practical empirically when it is latent and only discrete outcomes or choices are observed. I develop a method in this article to estimate these models that is much more practical in terms of estimation. The model uses a small amount of auxiliary information to estimate the selection equation parameters, which are then held fixed while estimating the equation of interest parameters in a maximum-likelihood setting. After presenting Monte Carlo analyses to support the model, I apply the technique to a substantive problem: Which interest groups are likely to to be involved in support of potential initiatives to achieve their policy goals?