Causal Paths that Explain Differential Policy Stringency in 50 states

• In Event: Government Agencies and the COVID Pandemic

Abstract

COVID-19 is such a complex policy problem that more than one complex causal paths can lead states to adopt more or less strict policy measures. We asked what conditions or combinations of conditions (i.e., configuration) may lead the states to respond with different levels of policy stringency. For example, Kettl (2020) suggested that partisan control, public health spending, and policy pattern, were three forces that determine states’ decisions on the March lockdown policy. While extant studies have sporadically discussed the potential influencing factors that may explain why state policy decisions went in divided directions, little is known about the causal mechanism (s) through which those factors influence the policy outcomes. To explore causal paths that explain different policy stringency in 50 states, we performed a fuzzy set Qualitative Comparative Analysis (fs/QCA). The U.S. state response data came from the coronavirus government response tracker (OxCGRT) developed by research teams at Oxford University. The OxCGRT data cover aggregated indices across 50 U.S. states from January 1 to the present. Regarding potential explanatory variables, we included the risk severity, partisan control, public health spending, economic support spending, and the voting rate for former President Trump in the 2020 election. The results of our analysis will uncover necessary and sufficient conditions (or combinations of such conditions) that may explain the high or low level of policy strictness across states in the U.S. This study will contribute to the policy-making literature by exploring the causal diversity underlying the states’ adoption decisions on more or less strict policy measures.

Authors

• Chan Wang, Arizona State University

• Yushim Kim

• Wei Zhong, Tsinghua University