The COVID-19 pandemic and non-pharmaceutical interventions (NPIs) implemented in many countries to suppress contagion have unsettled lives fundamentally and cratered the global economy. Epidemiologists contend that NPIs (i.e., safer-at-home orders, closures of non-essential businesses and schools, or bans on large gatherings) combined with testing, tracing, and isolating are the only options to fight the pandemic until a vaccine is widely available or societies achieve widespread immunity (Ferguson et al. 2006; Karlsson et al. 2014; Tian et al. 2020). Yet, the intensity and vigor of NPI implementation have varied across countries, reflecting skepticism regarding their efficacy and concerns about their social and economic costs.
In the United States, where COVID-19 has taken a high toll in terms of infections and mortality, skepticism toward NPIs reigns high among the public and legislators. Early in the pandemic, President Trump famously criticized NPIs by remarking that “the cure cannot be worse than the problem itself” (Haberman and Sanger 2020). The nation remained divided on the effectiveness of NPIs, even as the pandemic raged from March to early May 2020 in the Northeast, spreading more widely to the rest of the country thereafter. Surveys show that conservative Republicans expressed more skepticism than liberal Democrats about NPIs (Funk and Tyson 2020). State and local implementation and lifting of NPIs were often driven by political ideology. Republican-governed cities were slower to adopt NPIs, whereas cities led by Democrats were more aggressive (Willetts 2020).
Amid the highly partisan response to the pandemic, the question remains: does the timeliness of NPIs save lives in the United States? Have these interventions helped reduce the spread of the virus? Has political ambivalence toward NPIs influenced their effectiveness? We address these questions in this paper using county-level data on mortality, infections, and NPIs.
If NPIs have not been successful in the United States, that would mean the government has needlessly cratered the economy, compromised children’s education, disrupted lives and livelihoods, and reduced the pace at which herd immunity can be achieved—ultimately validating public skepticism about these policies. Arguably, NPIs reduce the pace at which a population can acquire widespread immunity. For this reason, several countries, including the United Kingdom in the initial stages of the pandemic and Sweden, opted against implementing NPIs. Additionally, the implementation of NPIs inevitably brings economies to a halt, resulting in tidal unemployment claims. Many countries and localities delayed their adoption and effective implementation to lessen their economic and social toll. These delays could have adversely affected the spread of the pandemic. Indeed, if NPIs are effective at reducing contagion, the politicization of NPIs can be blamed for the ambivalence and hesitation toward their implementation. This ambivalence and hesitation could explain the United States’ failure to contain the virus, even as other developed countries have successfully reduced infections and mortality.
A rapidly growing body of literature has examined the impact of NPIs on COVID-19 infections and deaths using Chinese data (Pan et al. 2020; Qiu et al. 2020), Spanish data (Amuedo-Dorantes et al. 2020), and cross-national data (e.g., Anderson et al. 2020; Bai et al. 2020; Flaxman et al. 2020; Hsiang et al. 2020; Imai et al. 2020; Viner et al. 2020). Focusing on the United States, several researchers have explored the role of various social distancing measures on the incidence of COVID-19 and COVID-related mortality. For instance, Auger et al. (2020) focus on school closures, Korevaar et al. (2020) use a low/medium/high NPI index, VoPham et al. (2020) use smartphone GPS data to estimate social distancing, and Wright et al. (2020) focus on the role of mask mandates.Footnote 1 Other studies, more closely related to ours, have focused on the role of safer-at-home orders (i.e., Dave et al. 2020; Fowler et al. 2020; Friedson et al. 2020; Harris 2020b).Footnote 2 These studies find that NPIs are associated with lower infection and mortality rates—some focused in California or New York (i.e., Friedson et al. 2020; Harris 2020b) and others countrywide (i.e., Dave et al. 2020; Fowler et al. 2020). We build on this research by assessing the relevance of the timing of two of the most frequently adopted NPIs—safer-at-home orders and non-essential business closures—on mortality. Our paper makes two primary contributions. First, we study the effectiveness NPIs in relation to their timeliness. To that end, we construct a measure that captures the relative speed of NPI adoption based on a county’s rate of contagion when the NPI was adopted.Footnote 3 Using this measure of timeliness, as opposed to just the policy adoption date, is critical from an epidemiological point of view, as what is considered “early” implementation in some localities might be “late” for others depending on their position on the pandemic curve. Second, we investigate the mechanism through which NPIs impacted COVID deaths. Specifically, we investigate whether NPIs saved lives by curtailing the spread of infection or by reducing pressure on the healthcare system.
Further, we investigate whether NPI efficacy differed across counties with different political ideologies and different degrees of demographic, economic, and health-related vulnerabilities. To examine the former, we construct a dummy to identify Republican counties, defined as those where most residents voted for President Trump in the 2016 election, and estimate whether NPI efficacy differed in those areas compared to other counties. For the latter, we use several pre-COVID demographic, economic, and health characteristics to explore the differential efficacy of NPIs across counties with distinct degrees of vulnerability. Ideally, we would use time series data on COVID-19 mortality according to these traits, but such data are currently not available. Instead, we use pre-COVID county-level characteristics to explore differences in the relevance of NPI adoption timing across counties with different characteristics associated with poor COVID-19 health outcomes. Finally, we explore mechanisms through which NPI adoption speed might be critical, focusing on the spread of the infection and the ability to avert an overwhelmed healthcare system.
A challenge in estimating the causal effect of NPIs on mortality is that these interventions are adopted in response to the spread and severity of the virus. Because of the likely presence of reverse causality (i.e., COVID-linked deaths motivating the adoption of NPIs), a simple correlation between NPIs and COVID-linked mortality or infection will likely result in downwardly biased estimates. We address this concern by supplementing our primary analysis with an event study examining how COVID-19 mortality rates respond to NPI adoption.
Because of the ongoing nature of the pandemic, an additional challenge is the chosen temporal frame for our analysis. We focus on the early months of the pandemic, capturing when states and counties first adopted NPIs, through the first re-opening. This means we are comparing counties at various initial stages of the pandemic. To address this limitation, we estimate models that separate specific outliers during that period. Specifically, we experiment with samples that include only Northeastern states—which comprised the epicenter of the pandemic during our study period; exclude Northeastern states; or exclude the state of New York. These sample modifications allow us to compare NPI speed between counties in roughly similar stages of the pandemic.
Any research on the efficacy of NPIs in the United States is affected by the fact that data on reported infections and COVID-linked mortality are highly correlated with COVID-19 testing, which has varied across the country and over time. In counties with inadequate testing, reported infections likely underestimate actual infections and deaths attributable to COVID-19 are likely to be reported as non-COVID mortalities. Furthermore, if testing is correlated with NPIs, it will confound the estimated efficacy of NPIs. To address these concerns, we explicitly control for testing. Similarly, to address the possibility that our NPI estimates reflect endogenous self-distancing occurring before NPI adoption, we include robustness checks that control for the daily median maximum distance traveled by county residents as an estimate of mobility at the county level.
To explore the underlying mechanisms at play, we examine how NPI adoption speed affects infections and conduct state-level analyses of the association between NPIs and non-COVID deaths.Footnote 4 Studies document that non-COVID deaths increased over our study period (Woolf et al. 2020). This could have occurred for various reasons, including the voluntary postponement of procedures or, in some instances, through an overwhelmed healthcare system. If timely adoption of the NPIs helped reduce the burden on the healthcare system, they should also lower non-COVID deaths.
We find that advancing the implementation date of NPIs by 1 day before the doubling of infections would have lowered the COVID-19 death rate by 1.9%. The value of early policy implementation proves robust to the use of alternative measures of NPI adoption speed; to controlling for testing, other NPIs, and mobility; as well as to the removal of outliers (i.e., New York and the Northeast region) from the analysis. We find that NPI adoption speed is associated with fewer infections, suggesting these measures operated by slowing contagion. We also find that NPI adoption speed is not associated with fewer non-COVID deaths, which suggests that the identified effectiveness of NPIs in reducing COVID mortality are not spurious. Finally, our results suggest that the speed of NPI adoption proves less effective in Republican counties, suggesting that the attitudes of residents toward NPIs may influence their efficacy.