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.

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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 apolicy diffusion networkconnecting 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.

This article analyzes the consequences of nonrandom sample selection for continuous-time duration analyses and develops a new estimator to correct for it when necessary. We conduct a series of Monte Carlo analyses that estimate common duration models as well as our proposed duration model with selection. These simulations show that ignoring sample selection issues can lead to biased parameter estimates, including the appearance of (nonexistent) duration dependence. In addition, our proposed estimator is found to be superior in root mean-square error terms when nontrivial amounts of selection are present. Finally, we provide an empirical application of our method by studying whether self-selectivity is a problem for studies of leaders' survival during and following militarized conflicts.

I demonstrate a source of bias in the common implementation of the dyadic event history model as applied to policy diffusion. This bias tends to severely overstate the extent to which policy changes depend on explicit emulation of other states rather than on a state's internal characteristics. This happens because the standard implementation conflates policy emulation and policy adoption: since early adopters are policy leaders, later adopters will appear to emulate them, even if they are acting independently. I demonstrate this ambiguity analytically and through Monte Carlo simulation. I then propose a simple modification of the dyadic emulation model that conditions on the opportunity to emulate and show that it produces much more accurate findings.An examination of state pain management policy illustrates the inferential differences that arise from the appropriately modified dyadic event history model.