In recent months, the majority of the US population has been subject to stay at home and social distancing orders to help prevent the spread of COVID-19. In new research which analyses cellphone data, Daniel A. N. Goldstein and Johannes Wiedemann find that people’s sense of trust in government and others is related to their compliance with going along with preventative measures like stay at home orders. They write that this link is fueled by partisanship and social capital: people are more likely to trust preventative policies if they have high levels of social capital or if the measures are implemented by officials who are from the party they support.
Going along with preventative measures, e.g., social distancing and stay-at-home orders, is the most critical behavioral change citizens can make to prevent an exponential spread of the novel coronavirus. Yet, even as such public health policies have become increasingly standardized across the globe, compliance with these policies differs across the United States. At the outbreak of the virus, news reports were full of American university-aged students woefully ignoring mitigation orders. Moreover, recent easing of measures in the United States has been accompanied by a lack of social distancing in re-opened establishments. This underscores the potentially dire fallout for public health and effective government when uneven compliance with public policy becomes normalized. In new research, we have found evidence that trust in government and trust in others plays a significant role in generating inconsistent policy compliance by citizens.
We analyze cellphone data provided by the technology firm Cuebiq Inc, which tracks how much people move within a US county from January 1, to April 19, 2020. Leveraging staggered roll-out of mitigation orders by US states, we measure the effect of stay-at-home orders on compliance. We define policy compliance as decreased movement in response to a stay-at-home order. The stay-at-home orders prove extremely effective at diminishing movement, decreasing traveled distance by 24-45 percent.
Shifting to the factors that impact uneven compliance, we find that partisanship is a key determinant of compliance with stay-at-home orders. Within the same states, counties that are generally more Democratic-leaning comply at higher rates with stay-at-home orders than Republican-leaning counties: a 10 percent increase in Democratic vote-share within a county in the 2016 Presidential election (our measure of partisanship) is associated with increased compliance, as measured by a 12-18 percent decrease in traveled distance.
Figure 1 shows the weekly trends of movement broken down by partisanship. The vertical line indicates a week before a given US state implemented a stay-at-home order, and 0 indicates when an order was put in place. The two lines are approximately parallel until shortly before the orders. After an order is in place, mobility greatly differs by partisan affiliation. Mobility is on a logarithmic scale so the differences are of sizeable magnitude.
Figure 1 – Mobility by partisan affiliation
We argue that this effect is driven by Democrats having greater trust in those who craft COVID-19 policy, namely scientists and professional bureaucrats, e.g., the Director of the National Institute of Allergy and Infectious Diseases, Dr. Anthony Fauci (a career bureaucrat). To support this claim, note that a 2018 Pew Survey on trust in government found that respondents who identified as Democrats, versus those who identified as Republicans, were much more likely to trust scientists (89 percent vs. 75 percent), academic professors (84 percent vs. 48 percent), and non-appointed career bureaucrats (71 percent vs. 48 percent). Additionally, as a secondary measure of trust in expertise and science, flu vaccine rates are examined, and we find that higher-uptake is associated with increased compliance.
Photo by Brittani Burns on Unsplash
Conversely, compliance depends on trusting the politicians who implement policy. One strong indicator for trusting a politician is partisanship. Citizens are generally more trusting of and more likely to approve of policies from politicians from the party that they support. In the case of the COVID-19 crisis, implementation has largely fallen to state governors. We find that when stay-at-home orders were given by a Republican governor rather than a Democratic governor, the compliance gap between Republican-leaning and Democratic-leaning counties shrinks by 5-7 percent when comparing two counties whose Republican vote-share differs by 10 percent. This suggests that even when public policy is essentially the same, individuals are more willing to comply with policies issued by a politician from the party which they support.
Consider the example of the Republican Governor of Ohio, Mike DeWine. Despite the initial skepticism of much of the Republican Party and President Donald Trump, DeWine proved to be one of the more forceful administers of mitigation policies. DeWine’s early and decisive actions potentially convinced supporters from the Republican Party who may have been more mistrustful of the policy had it come from a Democratic politician.
We also find that social trust, i.e., trust in other people, is impactful on compliance. Higher social trust is thought to orient citizens toward preferring policies that benefit the public good. Moreover, social trust is conducive to holding government accountable, which can improve trust in government.
While social trust is difficult to directly measure on a county-level, the closely related concept of social capital, i.e., the networks that forge reciprocity, can be quantified by considering factors such as voter turnout, response rate to the census, violent crimes, non-profits per capita, etc. Using an index of social capital created by the United States Congress Joint Economic Committee, we find that an increase in social capital of 10 percent is associated with an additional reduction in traveled distance by 4-6 percent.
Figure 2 shows the weekly time trends of mobility by social capital. Once more, the vertical line indicates a week before stay-at-home orders were implemented and 0 indicates when an order was put in place. Again, the two lines are approximately parallel before orders are put in place, but the gap widens after the orders are implemented.
Figure 2 – Mobility by level of social capital
However, a complication arises in that trust in others interacts with one’s trust in government. Consider the protests against stay-at-home orders and how such groups of people might amplify one another’s resistance. These instances demonstrate how citizens gather information about policy and politicians from their social networks, which reinforces beliefs and actions. If many people in one’s community, e.g., family, friends, and neighbors, are skeptical of a policy or hold low trust in government, higher social trust can make one even less likely to comply with a policy. Supporting this claim, we find that the compliance gap between Republican-leaning counties and Democratic-leaning counties is further widened by the effect of social capital. To illustrate this effect, consider two counties that differ by 10 percent with regard to their respective Republican vote-shares; the compliance gap between two such counties will increase by an additional 2-3 percent for high social capital counties compared to low social capital counties. Hence, an individual in a Republican-leaning county is more likely to not comply – due to a mistrust in government – and increased social trust will reinforce this behavior.
Trust has long been a focus of inquiry in the social sciences and has regained importance in the context of the COVID-19 pandemic. We find that political trust and social trust play significant roles in public responsiveness to mitigation orders, emphasizing their importance in the successful implementation of public policy. Lastly, it is worth noting that how political institutions operate under a crisis holds implications for future political trust. Adept reactions to COVID-19 by political leaders on the federal and state-levels may enhance political trust and, in the end, improve policy compliance during the next crisis.
- Note: Aggregated mobility data is provided by Cuebiq, a location intelligence and measurement platform. Through its Data for Good program, Cuebiq provides access to aggregated mobility data for academic research and humanitarian initiatives. This first-party data is collected from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR-compliant framework. It is then aggregated to the county level to provide insights on changes in human mobility over time.
Note: This article gives the views of the author, and not the position of USAPP– American Politics and Policy, nor of the London School of Economics.
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About the author
Johannes Wiedemann – Yale University
Johannes Wiedemann is a PhD candidate in political science and economics at Yale University. His research revolves around firms’ political activities, their allocative effects, and the role of democratic accountability, particularly in the context of regulatory enforcement. He is moreover interested in the role of trust in policy-making, with applications to public procurement and public health policies. He holds an undergraduate degree from the University of Konstanz (Germany), and an MSc from the London School of Economics.
Daniel A. N. Goldstein – Yale University
Daniel A. N. Goldstein is a PhD candidate in Political Science at Yale University with a focus on comparative political economy and formal theory. His primary research interest is in studying norms and their impact on the effectiveness of state capacity. He also studies democratic institutional change, with a special interest in the impact of political executives. He holds a BA from Washington University in St. Louis and an MSc from the London School of Economics, both in political economy, and an MA in Economics as well as an MPhil in Political Science from Yale University.