The Pandemic Policy U-Turn: Partisanship, Public Health, and Race in Decisions to Ease COVID-19 Social Distancing Policies in the United States

Abstract

We explore the US states’ evolving policy responses to the COVID-19 pandemic by examining governors’ decisions to begin easing five types of social distancing policies after the initial case surge in March–April 2020. Applying event history models to original data on state COVID-19 policies, we test the relative influence of health, economic, and political considerations on their decisions. We find no evidence that differences in state economic conditions influenced when governors began easing. Governors of states with larger recent declines in COVID-19 deaths per capita and improving trends in new confirmed cases and test positivity were quicker to ease. However, politics played as powerful a role as epidemiological conditions, driven primarily by governors’ party affiliation. Republican governors made the policy U-turn from imposing social distancing measures toward easing those measures a week earlier than Democratic governors, all else equal. Most troubling of all, we find that states with larger Black populations eased their social distancing policies more quickly, despite Black Americans’ higher exposure to infection from SARS-CoV-2 and subsequent death from COVID-19.

The Influence of Risk, Location, and Relationship on Refusing an Event Invitation During the COVID-19 Pandemic

“…For example, researchers have found differences in steps taken by states to manage the pandemic are influenced by political partisanship (Makridis and Rothwell, 2020), and Hardy (2020) found those who identified as right-wing were less likely to believe it was necessary to engage in recommended hygiene practices. Additionally, Ye (2021) and Adolph et al (2021) found differences by county and state in vaccination rates and the lifting of stay-at-home orders.…”

“…Additionally, some states, like Massachusetts, had limits on the number of people who could gather at one time, while others had no such restrictions (Kaiser Family Foundation, 2020). Some states varied the types of business that could open while others had blanket orders that covered all business types (like restaurants and gyms) (Adolph et al, 2021). One of the explanations for widely different responses by state is political affiliation of the leaders, especially the state's Governor who were the ones making policies.…”

“…One of the explanations for widely different responses by state is political affiliation of the leaders, especially the state's Governor who were the ones making policies. For example, Adolph et al (2021) examined COVID-19 protocols at the state level and found that Governors of Republican states began to ease lockdown restrictions or stayat-home orders 14.5 days earlier than Governors of Democratic states, regardless of actual transmission or hospitalization rates. The Governors of Florida and Texas (both Republican-leaning states with Republican leaders) signed executive orders banning mask mandates by schools in their states.…”

“…Because of these beliefs they would be more likely to understand why someone refused an invitation and would therefore not get upset at receiving a refusal. In the context of state politics, given that blue states had a higher likelihood of instilling strict safety measures compared to red states (Makridis and Rothwell, 2020;Adolph et al, 2021), when declining an invitation to an event during COVID-19, those from red states should expect poorer relationship outcomes as a result of declining an invitation than those from blue states, even if the risk is high. Therefore, the following hypothesis was proposed:…”

Factors consistently associated with utilisation of essential maternal and child health services in Nigeria: analysis of the five Nigerian national household surveys (2003–2018)

“…Rather than adding an arbitrary positive quantity to these count variables, we directly estimate the effect of having zero children (or zero health facilities) by including dummy variables in the model to indicate cases where each is precisely zero. In turn, and without loss of generality, before logging the count of health facilities (or children), we replaced zeros with ones, so that cases in which there are zero health facilities within 20 km (or no children under 5 years of age) affect the outcome only through the dummy variable for that case 31 32…”