Panel Paper: State Health Policy Adoption and Diffusion Among US States, 1980-2010

Saturday, November 9, 2013 : 3:50 PM
3015 Madison (Washington Marriott)

*Names in bold indicate Presenter

James Macinko, Diana Silver, Jin Yung Bae and Geronimo Jimenez, New York University
Are states that have strong regulations on one health area (tobacco) also likely to have strong regulations on other areas (alcohol)? Which internal state level factors predict passage of new health policies in these domains and which factors predict the likelihood of a state being the first to adopt a new health policy innovation? How do external factors such as characteristics and behaviors of neighboring states influence state health policy adoption and diffusion? This paper uses a novel measure of the comprehensiveness of the state public health policy environment across several health domains (tobacco regulation, alcohol control, driving safety, immunization) to examine internal and external state determinants of policy adoption and diffusion 1980-2010.

While scholars have investigated the political and economic determinants of state policies in several areas (environmental hazards, firearms, education, lotteries), relatively few have investigated the adoption of multiple policies within a single domain, or across multiple domains over time. As a result, the generalizability of empirical tests of specific drivers of policy diffusion and adoption may be limited. Examining patterns across domains can provide stronger evidence for understanding the fundamental factors influencing policymaking across states. 

We constructed a dataset of 40 state-level evidence-based public health policies on tobacco, alcohol, driving, and immunizations from 1980-2009 using secondary sources and original legal research. Indices of policies in single domains (intra-comprehensiveness) and across domains (inter-comprehensiveness) were calculated for each state and year. We use latent growth curve and event history analysis approaches to model patterns and predictors of policy adoption in each state. Models include internal factors such as the political environment (unified state legislatures, citizen ideology, voting participation), legislative professionalism (Squire index), government capacity (tax revenue per capita), resources (population, income, unemployment), legislative history (prior adoption of health policies) and policy-specific risk factors (motor vehicle fatality rate, smoking prevalence, excessive drinking). External factors include neighboring states’ political environment, government capacity and resources, and history of prior law adoption. 

We find evidence of patterns of policy adoption across domains and a strong secular trend towards greater regulation over time. Consistent with other studies, we find evidence that the strongest predictor of adoption of new policies is the presence of at least one similar state law within domains.  However, diffusion to additional states does not occur uniformly over time, but clusters within specific years, suggesting evidence of policy emulation or learning from early adopters. While we find that political context and the presence of specific risk factors predict first adoption of new policies, these factors do not explain the total number of policies adopted. Instead, prior state adoption and neighbor-state adoption of previous policies are the strongest predictors of state’s adoption of policy comprehensiveness.  

These results suggest that state policy adoption across multiple domains can be understood as a complex interaction of internal needs and capabilities, and a state’s relationship to both its nearest neighbors and policy innovations in other states. Results have implications for professional and advocacy organizations seeking to stimulate policy change at the state level.