SPRC19: A Database of State Policy Responses to COVID-19 in the United States.

SPRC19 is a new database that seeks to capture a wide range of state policy actions in response to COVID-19 in the United States. Since March 2020 we have monitored state governments' and multi-state associations' websites for executive orders, agency rules, new legislation, and court decisions. We categorize each policy action into one of 206 distinct policies, then document the branch of government, source document, announcement date, implementation date, and expiration date (if applicable). We also record whether the action represents the introduction of a new policy or the expansion or contraction of an existing policy. The current release of SPRC19, v3.0, captures over 13,000 distinct policy actions through April 2020, which constitutes thousands more actions than similar resources over the same time period.

Who benefits from voter identification laws?

In the first two decades of the twenty-first century, many American state governments implemented voter identification (ID) laws for elections held in their states. These laws, which commonly mandate photo ID and/or require significant effort by voters lacking ID, sparked an ongoing national debate over the tension between election security and access in a democratic society. The laws' proponents-primarily politicians in the Republican Party-claim that they prevent voter fraud, while Democratic opponents denounce the disproportionate burden they place on historically disadvantaged groups such as the poor and people of color. While these positions may reflect sincerely held beliefs, they also align with the political parties' rational electoral strategies because the groups most likely to be disenfranchised by the laws tend to support Democratic candidates. Are these partisan views on the impact of voter ID correct? Existing research focuses on how voter ID laws affect voter turnout and fraud. But the extent to which they produce observable electoral benefits for Republican candidates and/or penalize Democrats remains an open question. We examine how voter ID impacts the parties' electoral fortunes in races at the state level (state legislatures and governorships) and federal level (United States Congress and president) during 2003 to 2020. Our results suggest negligible average effects but with some heterogeneity over time. The first laws implemented produced a Democratic advantage, which weakened to near zero after 2012. We conclude that voter ID requirements motivate and mobilize supporters of both parties, ultimately mitigating their anticipated effects on election results.

The Seeds of Policy Change: Leveraging Diffusion to Disseminate Policy Innovations.

We conduct a series of simulations to compare how various strategies for seeding a policy in the American states affect the rate at which that policy spreads. Using empirically derived parameters of the policy diffusion process, we simulate the diffusion of a hypothetical policy after seeding the policy in just a handful of states. We compare these strategies to seeding the ten states the RWJF monitored during the states' implementation of the Affordable Care Act of 2010. We attempt to mimic the choices that policy advocates make when deciding which states to target with their resources. Our results indicate that focusing on innovative states, that is, those that tend to adopt new policies faster, offers a valuable boost in the speed of diffusion. Even better, though, is a strategy that targets policy leaders.

BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models.

Selecting between competing Structural Equation Models (SEMs) is a common problem. Often selection is based on the chi square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with SEMs compared to other fit indices. This article examines several new and old Information Criteria (IC) that approximate Bayes Factors. We compare these IC measures to common fit indices in a simulation that includes the true and false models. In moderate to large samples, the IC measures outperform the fit indices. In a second simulation we only consider the IC measures and do not include the true model. In moderate to large samples the IC measures favor approximate models that only differ from the true model by having extra parameters. Overall, SPBIC, a new IC measure, performs well relative to the other IC measures.