7.1 Introduction: Enough to Make You Sick
However controversial its details may be in some quarters, public health is a core function of modern governments. That is true in the sense both of direct public provision of care and facilities and of public responsibility for the overall state of public health and safety. According to a compilation of US Census data by the Urban Institute,Footnote 1 in 2020 states and localities spent $345 billion overall on health and hospitals.Footnote 2 That amounted to the third-largest share of direct general expenditures at the state and local levels. Health and hospitals accounted for 10 percent of all direct expenditures, trailing only public welfare and elementary and secondary education. The states made 46 percent of health and hospital expenditures, while local governments were responsible for 54 percent. In most years, such spending accounts for the largest shares of county and special-district budgets. There are large variations among the states, however: in Vermont, the state accounted for 98 percent of direct health and hospital spending, while Indiana’s corresponding figure was 13 percent. Nationwide, state and local governments spent $1,041 per capita on health and hospitals in 2020, with Wyoming spending the most at $2,961, followed by South Carolina ($1,818) and North Carolina ($1,688). New Hampshire spent the least ($163), edging out Arizona ($281) and North Dakota ($388) at the bottom of the table.
Our concern in this chapter is not primarily with the overall scope of corruption in the public health sector; that would be difficult to estimate with any precision. Rather, we will examine the possible effects of illegal corruption in the states’ government and political systems, as measured by our three indices, upon public health outcomes for whole states and segments of the population. In a field formally dedicated, as much as any other, to maintaining citizens’ wellbeing and quality of life, our data suggest that the extent of corruption in the broader political arena is linked to major variations in public health outcomes including both contrasts in access to, and quality of, health care services, and public cooperation with public policies during the COVID-19 pandemic. Both sets of issues raise profound questions of justice, equity, and the quality of government.
First, we examine the effects of corruption upon compliance with social distancing and stay-at-home orders during the pandemic. Then, we turn to vaccination. Finally, we examine specific issues posed by the COVID-19 pandemic for Black Americans. In that latter connection, we find not only reasons for general concern but also disturbing similarities to our findings regarding police killings of Black Americans as reported in Chapter 5.
7.2 Corruption, Social Distancing, and Vaccination
Social distancing and vaccination were central aspects of the public policy response to COVID-19. Compliance with social distancing and vaccination became a critical concern as the virus spread to ever more vulnerable communities less able to handle large numbers of critically ill patients. Many of these communities lack not only the physical and social infrastructure needed to fight the COVID-19 pandemic but are also often viewed as having endemic corruption.
We find that states with higher corruption had lower levels of social distance compliance and lower levels of vaccination (data available at CDC, 2021). Our findings suggest that communities where corruption is endemic found it difficult to contain and mitigate COVID-19 by relying on public cooperation with social policy-based controls. There is some evidence that public health aid can be effective even in corrupt communities (Dietrich, Reference Dietrich2011). Still, as such communities typically already suffer from poor healthcare infrastructure and outcomes (Azfar and Gurgur, Reference Azfar and Gurgur2008; Ciccone et al., Reference Ciccone, Vian and Maurer2014; Friedman, Reference Friedman2018; Dincer and Teoman, Reference Dincer and Teoman2019), they will face future crises with few effective weapons in their arsenals. Moreover, additional funding directed towards fighting such crises could be diverted from intended beneficiaries by corruption (Suryadarma and Yamauchi, Reference Suryadarma and Yamauchi2013; Briggs, Reference Briggs2014).
7.2.1 Corruption and Compliance with Social Distancing and Vaccination
Starting with California in mid-March of 2020, the majority of American states instituted shelter-in-place/stay-at-home orders as part of their social distancing policies. Schools, restaurants, and bars were closed; nonessential businesses were ordered to keep workers home and let them work remotely. People were asked not to leave their homes unless necessary.
Enforcement and penalties, however, varied significantly across the states. While in some there were no penalties, in most violations were considered a misdemeanor punishable with a small fine, usually not enforced. In Arizona, for example, violators could be charged with a class 1 misdemeanor, which has a fine up to $2,500. In Tennessee, on the other hand, no penalties were specified (see Mazziotta, Reference Mazziotta2020, for state-by-state fines and penalties). In states such as Kentucky, Maryland, Michigan, and Pennsylvania protesters, sometimes armed with assault rifles, packed state capitols and streets in defiance of state orders. In London, by contrast, police arrested people in Hyde Park protesting social distancing orders (Estes, Reference Estes2020; Parveen, Reference Parveen2020). Thus, although social distancing was mandated in theory, in practice states often depended heavily on voluntary compliance. This resulted in significant variations in social distancing across the states.
While policies such as shelter in place/stay at home were somewhat effective, as a response to widespread resistance to complying with social distancing, the federal government introduced Operation Warp Speed (OWS), supporting the development of vaccines. Once vaccines were granted emergency use authorizations by the Food and Drug Administration (Fisk, Reference Fisk2021) in late 2020, OWS worked with pharmacies across the US to make them available at no cost to individuals. To achieve herd immunity, a large majority of the population needed to be vaccinated. Although the herd immunity threshold is not known for COVID-19, according to the World Health Organization, the figure for measles is 95 percent and for polio, 80 percent. As of early 2023, only 65 percent of the US population had been vaccinated against COVID-19, and there was significant variation across states ranging from 50 percent in Alabama and Mississippi to 80 percent in Rhode Island and Vermont.
Corruption might not seem a likely influence upon compliance with social distancing and vaccination, but in many ways, it embodies the ways people sort out their own preferences versus civic responsibility. Our data suggest that it did influence compliance through its effects on trust in government, social capital, and government legitimacy, which in turn are important determinants of compliance with shelter-in-place/stay-at-home orders and vaccination.
7.2.2 Trust in Government and Social Capital
Trust in government is an important determinant of social capital, which is defined as networks and norms shared among people and facilitating cooperation to help solve collective action problems (Fukuyama, Reference Fukuyama1995; Putnam, Reference Putnam2000). Social capital manifests itself in communities as reciprocal relationships between civic participation and interpersonal trust. The more people participate in their communities, the more they trust others, all else being equal; the greater trust people hold for others, the more likely they are to participate (Fukuyama, Reference Fukuyama1995; Brehm and Rahn, Reference Brehm and Rahn1997). Interpersonal trust depends heavily on trust in government (Levi, Reference Levi, Braithwaite and Levi1998; Levi and Stoker, Reference Levi and Stoker2000; Rothstein, Reference Rothstein2000, Reference Rothstein2005). According to Rothstein and Eek (Reference Rothstein and Eek2009), when forming their beliefs about others in a community, people draw inferences from the behavior of government officials: “[I]f it proves that I cannot trust the local policemen and judges, then whom in the society can I trust?” (Rothstein and Eek, Reference Rothstein and Eek2009, 90). Several political science studies find negative effects of corruption on trust in government (Anderson and Tverdova, Reference Anderson and Tverdova2003; Chang and Chu, Reference Chang and Chu2006; Rothstein and Eek, Reference Rothstein and Eek2009; see also Ch. 5 of this book). Because trust in government and interpersonal trust are positively related, corruption affects interpersonal trust negatively. Lower interpersonal trust in turn means lower civic participation, and lower civic participation means lower social capital. Since both social distancing and vaccination are collective action problems, we expect less compliance with shelter-in-place/stay-at-home orders and less vaccination in more corrupt states with low social capital.
Collective action problems arise when short-term interests of individuals conflict with long-term collective interests. According to Olson (Reference Olson1971: 2), if the members of a large group rationally seek to maximize their personal welfare, they will not act to advance their group objectives unless there is coercion to force them to do so, or unless some separate incentive, distinct from the achievement of the group interest, is offered to the members of the group individually on the condition that they help bear the costs or burdens involved in the achievement of the group objectives.
Sønderskov (Reference Sønderskov2009) argues that social capital facilitates collective action in large groups. Individuals are more likely to cooperate in collective action when they expect others to do likewise. Given a large number of individuals, it is costly and difficult, even impossible, to get specific information on other individuals’ trustworthiness. In such situations, social capital serves as a short-cut to information on trustworthiness (Sønderskov, Reference Sønderskov2009: 53–54; see also Ostrom, Reference Ostrom1998).
Gilson (Reference Gilson2003) argues that the production of health and health care requires cooperation within health systems, which in turn requires trust. Trust facilitates the use of the health system and leads to better self-rated outcomes (Radin, Reference Radin2013). Several empirical studies present persuasive evidence regarding the effects of trust in government in particular, and social capital in general, on compliance with public health policies (Buckman et al., Reference Buckman, Liu, Cortright, Tumin and Syed2020). Blair et al. (Reference Blair, Morse and Tsai2017), for example, investigate the behavior of Liberians during the 2014–2015 Ebola outbreak, finding that people with lower trust in government took fewer steps to protect themselves and were less likely to comply with social distancing orders. Vinck et al. (Reference Vinck, Pham and Bindu2019), in the context of a later outbreak in the Democratic Republic of Congo, find similar results: low trust in government explains lower willingness to adopt preventative behavior and accept a vaccine. The relationship among trust, social capital, and compliance with public health policies is not specific to COVID-19, or to Sub-Saharan Africa: Nawa and Fujiwara (Reference Nawa and Fujiwara2019) using data from Japan, Ronnerstrand (Reference Berry, Fording and Ringquist2013) from Sweden, Chuang et al. (Reference Chuang and Ya-Li Huang2015) from Taiwan, and Jung et al. (Reference Berry, Fording and Ringquist2013) and Ronnerstrand (Reference Rönnerstrand2014) from America all find a positive relationship between trust/social capital and vaccination. Yaqub et al. (Reference Yaqub, Castle-Clarke and Sevdalis2014) identify low trust/social capital as a main cause of vaccine hesitancy in Europe. Trust and social capital are also found, in some contexts, to be determinants of mental and physical health (Lochner et al., Reference Lochner, Kawachi and Brennan2003; Yip et al., Reference Yip, Subramanian and Mitchell2007; Kim et al., Reference Kim, Subramanian, Kawachi, Kawachi, Subramanian and Kim2008; Almedom and Glandon, Reference Almedom, Glandon, Kawachi, Subramanian and Kim2008; Ahnquist et al., Reference Ahnquist, Wamala and Lindstrom2012; Rodgers et al., Reference Rodgers, Valuev and Hswen2019). According to Kawachi and Berkman (Reference Kawachi, Berkman, Berkman and Kawachi2000), trust and social capital affect health-related behaviors via diffusion of health information and adoption of healthy norms of behavior (see also Lindstrom, Reference Lindstrom, Kawachi, Subramanian and Kim2008).
7.2.3 Trust in Government and Legitimacy of Government
The second channel through which corruption affects social distancing is the legitimacy of government. Legitimacy is linked to trust in government and is defined as the belief that government does what is appropriate and fair most of the time. As such, we expect legitimacy to affect how people behave toward government in crises (Easton, Reference Easton1965; Tyler, Reference Tyler2006; Christensen and Lægreid, Reference Christensen and Lægreid2020). Legitimacy increases compliance with government policies (Tyler, Reference Tyler2006). Christensen and Lægreid (Reference Christensen and Lægreid2020) argue that the comparative success of the Norwegian government in fighting COVID-19 (Cohen, Reference Cohen2022) was partially due to its legitimacy. It is crucial that policies implemented to fight the pandemic be seen by the people as appropriate and fair (Christensen and Lægreid, Reference Christensen and Lægreid2020). Government legitimacy is important for compliance with laws and regulations. Paternoster et al. (Reference Paternoster, Brame and Bachman1997) find that domestic assault suspects arrested in a procedurally fair manner are less likely to commit further acts than those arrested in procedurally unfair ways. Kuperan and Sutinen (Reference Kuperan and Sutinen1998) find that legitimacy affects compliance by Malaysian fishermen with a regulation banning them from fishing in a zone along the coast. Levi and Sacks (Reference Levi and Sacks2009), using survey data from Sub-Saharan Africa, find that compliance with tax laws is positively related to government legitimacy. Seligson (Reference Seligson2002) and Boly et al. (Reference Boly, Gillanders and Miettinen2019) find negative relationships between corruption and government legitimacy, and Ali et al. (Reference Ali, Fjeldstad and Sjursen2014) find that corruption weakens tax compliance in South Africa and Uganda. In the COVID-19 context, therefore, to the extent that corruption weakens the legitimacy of government, we expect it will also reduce compliance with shelter-in-place/stay-at-home orders.
7.2.4 Empirical Analysis: Data
Investigating the relationship between corruption and social distancing presents several challenges, perhaps the most important being the measurement of compliance with social distancing. We use the Shelter-in-Place Index constructed by SafeGraph,Footnote 3 measuring stay-at-home – the percentage of people in a state staying home on a particular day – compared to population movement data representing 45 million smartphone devices (CDC, 2021). The index ranges from −100 to 100, where 0 indicates no change from a baseline defined as the average percentage of people staying home all day and every day across the seven days ending February 12, 2020. Home is defined as the most common night-time location of the smartphone device in the previous months, identified to a precision of about 100 square meters. The Shelter-in-Place Index for any particular day is constructed as follows:
On May 2, 2020 (the last day of our sample), for example, the actual percentage of people staying home in Illinois was 40.5 while the baseline was 25. Hence, the index for Illinois on May 2 is equal to 15.5 (see data and documentation at CDC, 2021 for details). Our sample covers four consecutive Saturdays starting from April 11. Over the last three weeks of April and the first week of May, shelter-in-place/stay-at-home orders were in place in all but five states. While these data are drawn from the early days of what became a multi-year pandemic, we believe they are appropriate: the pandemic was a new and threatening public concern, people’s attention regarding government countermeasures ran high, and lines of partisan contention over COVID-19 responses had yet to harden into place. We measure corruption using CCI. We also control for several economic and demographic variables to minimize omitted variable bias.
We expect corruption to affect social distancing through two channels: social capital/interpersonal trust and trust in government/legitimacy of government. To control for the mediating effects of social capital, we first control for how generous people are, and how engaged they are civically, in each state in terms of charitable giving and volunteering. These are the most relevant components of social capital in the context of this study. We use the State Generosity Index (SGI) constructed by WalletHub, which ranges from 0 to 100 (see WalletHub, 2022 for details). We construct a dummy variable, SGI, which is equal to 1 if a state falls into the highest quartile of the SGI.
In addition, we control for the percentage of people tested positive for COVID-19 in each state on four consecutive Thursdays starting from April 9, 2020 (COVID-19 Positive). Since higher positivity rates indicate a higher infection risk, they should affect compliance with social distancing orders. The third control variable is Trump – the percentage of votes Donald Trump received in each state in the 2016 presidential election (US Elections Project, 2023). Trump showed his support loudly and repeatedly for people protesting (and violating) shelter-in-place/stay-at-home orders issued by the governors of several states. In addition, he called into question the legitimacy of public institutions and leaders in numerous ways. The variable Trump also controls for political ideology, which affects risk perception: according to a poll conducted by Axios/Ipsos, while 60 percent of Republicans believed that the real number of COVID-19-related deaths was lower than the official count, more than 60 percent of Democrats believed the real number was higher (Axios, 2020). Using state-level data, Hsiehchen, Espinoza, and Slovik (Reference Hsiehchen, Espinoza and Slovic2020) find that Republicans were less likely to comply with social distancing orders.
The next two control variables are per capita personal income (Income) and unionization (Union). Social distancing is costlier for some than others: people living paycheck to paycheck, and those who cannot work from home, may be less likely to comply with stay-at-home orders. Regarding unionization, the pandemic resulted in job losses across the country, but many union workers had various protections due to their collective bargaining agreements. NBC News reported that approximately 150,000 United Auto Workers members at Ford, General Motors, and Fiat Chrysler lost their jobs but continued to receive supplemental unemployment benefits. Their contracts gave members at least six months of extra pay on top of unemployment insurance, adding up to 85 percent of their hourly wages. Unionization also affects social capital via political and civic engagement (Nissen, Reference Nissen2010; Kerrisey and Schofer, Reference Kerrisey and Schofer2013). Our Income data are from the Bureau of Economic Analysis (2023) and Union data are from the Union Membership and Coverage Database (Unionstats, 2023). Finally, we control for population density, as the risk of infection is higher in densely populated urban communities. The data are from the Census Bureau (United States, Department of Commerce, 2023).
In our vaccination regressions, we use the share of population who are fully vaccinated as of January 2023 as our dependent variable (Kaiser Family Foundation, State Health Facts – KFF, 2023). Our control variables are chosen based on several nationally representative surveys conducted during the pandemic (Daly and Robinson, Reference Daly and Robinson2021; Sziglayi et al., Reference Sziglayi, Kyla and Shah2021; Ruiz and Bell, Reference Ruiz and Bell2021) and systematic reviews such as Wang and Liu (Reference Wang and Liu2022). Demographic and economic factors such as education, race, and income play a significant role in people’s decision to get vaccinated. While politically conservative people are also less likely to be vaccinated, vaccination rates are higher in more densely populated states. We measure education as the share of population age 25 and above who have less than a high school degree, race as the population share of Blacks and income as per capita personal income. The data are from the Census Bureau and the Bureau of Economic Analysis as before. Finally, to measure political ideology, we construct a dummy variable, which is equal to 1 if the governor of a state is Republican.
7.2.5 Empirical Analysis: Results
To investigate the effects of corruption on social distancing, we estimate a system of four equations with seemingly unrelated regressions (SUR) in which the dependent variables are the Shelter-in-Place Indices for April 11, April 18, April 25, and May 2, 2020. Each Saturday forms one fourth of the system. Estimating a system with SUR has several advantages: first, SUR is more efficient because it allows errors to be correlated across the equations. Second, because each equation has the same set of independent variables, it allows us to conduct joint tests to investigate whether quarantine fatigue worsens the collective action problem. Quarantine fatigue manifests itself as boredom, anxiety, and stress (Marcus, Reference Marcus2020) and can make it more difficult to comply with stay-at-home/shelter-in-place orders when people are feeling the psychological effects of social distancing. Where trust/social capital is low, this becomes an even greater problem.
The maximum likelihood estimates with robust standard errors clustered at the state level are presented in Table 7.1. To show the mediating effects of social capital, we first estimate our regression without SGI, the generosity index. The estimated coefficient of CCI is negative and statistically significant in all four equations, indicating that in corrupt states people are less likely to comply with shelter-in-place/stay-at-home orders. The absolute value of the estimated coefficient decreases when we estimate the regression with SGI, indicating that one of the channels through which corruption affects social distancing is indeed via deterioration in social capital. The magnitude of the effect is significant as well. Based on the estimated coefficients in Equation 4, a one-standard deviation increase in CCI causes the Shelter-in-place Index to decrease by approximately 10 percent. The standardized effect of COVID-19 Positive is only slightly greater than 10 percent, suggesting that corruption was roughly as strong a determinant of compliance as were trends in COVID-19 itself. Perhaps more interesting, the magnitude of the corruption effect increases each week. The estimated coefficient of CCI in Equation 4 (Shelter-in-Place Index on May 2) is 2.5 times greater than the one in Equation 1 (Shelter-in-Place Index on April 11).Footnote 4 As “quarantine fatigue” developed, people stayed home less and less during the time period that our sample covers. Across all states, on average, the Shelter-in-Place Index decreased by 30 percent from April 11 to May 2. On the other hand, in states such as Mississippi, Montana, Oklahoma, and South Dakota that fall into the highest quartile of CCI, compliance decreased more than 50 percent, even though the risk of infection remained high and the percentage of people testing positive for COVID-19 stayed the same or increased. Our results show that “quarantine fatigue” makes the collective action problem even more difficult where corruption is high.
Table 7.1 Corruption and compliance with stay-at-home orders: maximum likelihood SUR estimation (dependent variable: Log shelter-in-place index)
| Equation 1 April 11, 2020 | Equation 2 April 18, 2020 | Equation 3 April 25, 2020 | Equation 4 May 2, 2020 | |||||
|---|---|---|---|---|---|---|---|---|
| COVID-19 Positive | 0.760 | 0.804 | 0.801 | 0.847 | 1.209 | 1.248 | 1.188 | 1.260 |
| (0.146)Footnote *** | (0.147)Footnote *** | (0.185)Footnote *** | (0.194)Footnote *** | (0.182)Footnote *** | (0.184)Footnote *** | (0.270)Footnote *** | (0.274)Footnote *** | |
| CCI | −0.017 | −0.016 | −0.018 | −0.017 | −0.031 | −0.030 | −0.044 | −0.043 |
| (0.008)Footnote ** | (0.007)Footnote ** | (0.008)Footnote ** | (0.006)Footnote *** | (0.008)Footnote *** | (0.006)Footnote *** | (0.015)Footnote *** | (0.012)Footnote *** | |
| SGI | 0.101 | 0.095 | 0.090 | 0.140 | ||||
| (0.039)Footnote ** | (0.038)Footnote ** | (0.048)Footnote * | (0.053)Footnote *** | |||||
| Trump | −0.010 | −0.009 | −0.008 | −0.007 | −0.013 | −0.012 | −0.019 | −0.018 |
| (0.003)Footnote *** | (0.002)Footnote *** | (0.002)Footnote *** | (0.002)Footnote *** | (0.003)Footnote *** | (0.002)Footnote *** | (0.003)Footnote *** | (0.003)Footnote *** | |
| Log Income | 0.101 | 0.122 | 0.115 | 0.134 | 0.087 | 0.106 | 0.187 | 0.218 |
| (0.079) | (0.065)Footnote * | (0.082) | (0.068)Footnote ** | (0.085) | (0.073) | (0.124)Footnote ** | (0.105)Footnote ** | |
| Union | 0.009 | 0.009 | 0.008 | 0.007 | 0.011 | 0.010 | 0.012 | 0.012 |
| (0.004)Footnote ** | (0.004)Footnote ** | (0.004)Footnote ** | (0.003)Footnote ** | (0.004)Footnote ** | (0.004)Footnote ** | (0.006)Footnote ** | (0.006)Footnote ** | |
| Density | 0.032 | 0.035 | 0.038 | 0.041 | 0.040 | 0.043 | 0.041 | 0.046 |
| (0.009)Footnote *** | (0.008)Footnote *** | (0.009)Footnote *** | (0.008)Footnote *** | (0.012)Footnote *** | (0.011)Footnote *** | (0.013)Footnote *** | (0.012)Footnote *** | |
| Constant | 1.733 | 1.406 | 1.513 | 1.206 | 1.699 | 1.403 | 0.797 | 0.328 |
| (0.712)Footnote ** | (0.712)Footnote ** | (0.940) | (0.779) | (0.969)Footnote * | (0.853)Footnote * | (1.411) | (1.221) | |
| Correlation Matrix of Residuals | ||||||||
|---|---|---|---|---|---|---|---|---|
| April 11 | April 18 | April 25 | May 2 | |||||
| April 11 | 1, 1 | |||||||
| April 18 | 0.829, 0.806 | 1, 1 | ||||||
| April 25 | 0.882, 0.871 | 0.814, 0.794 | 1 | |||||
| May 2 | 0.866, 0.847 | 0.794, 0.763 | 0.859, 0.846 | 1, 1 | ||||
Robust standard errors (clustered at the state level) in parentheses. All models control for region fixed effects.
*** , **, and * represent statistical significance at 1 percent, 5 percent, and 10 percent levels, respectively.
The estimated coefficients of the control variables are also statistically significant and their signs are consistent with our expectations.Footnote 5 People responded to shelter-in-place/stay-at-home orders if more people tested positive for COVID-19. In densely populated and more charitable states, they stayed at home more as well. The estimated coefficient of Trump is not only negative, as expected, but its magnitude is also greater than that of CCI, consistent with the increasing political polarization regarding COVID-19 issues. Based on the estimated coefficients in Equation 4, a one-standard deviation increase in Trump reduces the Shelter-in-Place Index by 20 percent. Finally, in richer states and states where more workers are unionized, more people complied with social distancing policies.Footnote 6
We use the share of population who were fully vaccinated as of January 2023 as our dependent variable in our vaccination regressions and estimate the effect of corruption using ordinary least squares (OLS). The results are given in Table 7.2. The estimated coefficient of CCI is again negative and statistically significant in all estimations, indicating that in more corrupt states people are less likely to be vaccinated. The absolute value of the coefficient decreases when we estimate the regression with SGI, again indicating that one of the channels through which corruption affects vaccination is via deterioration in social capital. The magnitude of the effect is significant as well. Based on the estimated coefficients in Equation 1, a one-standard deviation increase in CCI causes the percentage of people fully vaccinated to decrease by approximately 3.5 percent. Again, the estimated coefficients of the control variables are statistically significant and their signs are consistent with our expectations.Footnote 7
Table 7.2 Corruption and vaccination: OLS estimation (dependent variable: Vaccination)
| Equation 1 | Equation 2 | Equation 3 | ||||
|---|---|---|---|---|---|---|
| CCI | −0.017 | −0.015 | −0.012 | −0.010 | −0.015 | −0.014 |
| (0.005)Footnote ** | (0.006)Footnote ** | (0.005)Footnote ** | (0.005)Footnote ** | (0.006)Footnote ** | (0.005)Footnote ** | |
| SGI | 0.066 | 0.064 | 0.046 | |||
| (0.033)Footnote ** | (0.032)Footnote ** | (0.038) | ||||
| Density | 0.220 | 0.231 | 0.284 | 0.297 | 0.244 | 0.250 |
| (0.055)Footnote *** | (0.051)Footnote *** | (0.058)Footnote *** | (0.056)Footnote *** | (0.054)Footnote *** | (0.052)Footnote *** | |
| Republican | −0.091 | −0.089 | −0.086 | −0.085 | −0.090 | −0.091 |
| (0.030)Footnote *** | (0.029)Footnote *** | (0.027)Footnote *** | (0.025)Footnote *** | (0.029)Footnote *** | (0.029)Footnote *** | |
| Log Income | 0.079 | 0.088 | ||||
| (0.054) | (0.047)Footnote * | |||||
| Black | −0.467 | −0.469 | ||||
| (0.134)Footnote ** | (0.126)Footnote *** | |||||
| Education | −1.109 | −0.834 | ||||
| (0.583)Footnote * | (0.690) | |||||
| Constant | −1.301 | −1.431 | −0.388 | −0.413 | −0.305 | −0.349 |
| (0.609)Footnote ** | (0.530)Footnote *** | (0.033)Footnote *** | (0.035)Footnote *** | (0.062)Footnote *** | (0.082)Footnote *** | |
Robust standard errors (clustered at the state level) in parentheses.
*** , **, and * represent statistical significance at 1%, 5%, and 10% levels, respectively.
Unfortunately, we cannot control for some important variables in our regressions because state-level data are not available. The first two are interpersonal trust and trust in government, both of which are likely to mediate the effects of corruption on social distancing. Low interpersonal trust may make people more likely to self-quarantine because they question others’ social distancing behavior. It may also weaken social networks. Without networks, staying home is less of a sacrifice for an individual and less costly. Trust in government is a crucial determinant of government legitimacy. Unfortunately, two frequently used surveys that ask questions regarding trust, the American National Election Study (ANES) and the General Social Survey (GSS), are both nationally representative surveys, and sampling is not done at the state level.
The second variable is social and traditional media misinformation, which may affect people’s behavior towards social distancing and vaccination by, among other things, undermining government legitimacy. Bursztyn et al. (Reference Bursztyn, Rao, Roth and Yanagizawa-Drott2020) investigate the effects of misinformation by comparing two major cable news shows, Hannity and Tucker Carlson Tonight. These shows aired back-to-back on Fox News and had relatively similar content but differed significantly in their coverage of the COVID-19 pandemic. While Carlson warned his viewers about the dangers of COVID-19, Hannity dismissed the risks as less threatening than the common flu and contended that Democrats were using it against Donald Trump. Using survey data on more than 1,000 Fox News viewers aged 55 or older, Bursztyn et al. (Reference Bursztyn, Rao, Roth and Yanagizawa-Drott2020) found that viewership of Hannity was associated with changing behavior (washing hands, social distancing, etc.) four days later than other Fox News viewers, while viewership of Tucker Carlson Tonight was associated with changing behavior three days earlier. Bridgman et al. (Reference Bridgman, Merkley and Loewen2020) conducted a nationally representative survey of more than 2,000 Canadians 18 or older that included questions about common misperceptions regarding COVID-19, social distancing compliance, and exposure to traditional news and social media. They found that exposure to traditional news media was associated with fewer misperceptions and more social distancing compliance, while conversely, social media exposure was associated with more misperceptions and less compliance. Pierri et al. (Reference Pierri, Perry and DeVerna2022) find similar results regarding vaccination using county and state-level Twitter data from the first quarter of 2021 in the US.
7.2.6 A Matter of Trust?
Studies from across the social sciences have pointed to corruption as corrosive to trust/social capital, which are vital to compliance with public health orders. Using data from American states, we have shown that more corrupt states are likely to have lower compliance with shelter-in-place/stay-at-home orders and lower rates of vaccination.
While it would be facile to expect our results to hold in all other countries, the fact that many of the channels motivating our study are salient in a variety of different contexts does suggest that the links between corruption and compliance with public health orders could be fruitfully investigated in other societies. Even with the external validity caveats that would require, we believe that our results have practical implications. Fang et al. (Reference Fang, Wang and Yang2020) found that lockdown was an effective policy in China, while Dave et al. (Reference Dave, Friedson and Matsuzawa2021) found that stay-at-home/shelter-in-place orders at the outset of the pandemic reduced the number of COVID-19 infections by more than 50 percent over a span of three weeks in America. In that light, our findings suggest corruption has significant implications for the spread of COVID-19. Corruption also reduces the quality of healthcare (Mostert et al., Reference Mostert, Njuguna and Olbara2015; Friedman, Reference Friedman2018), making the problem even worse. Hence, more corrupt states might need additional resources to fight future pandemics, even though such states’ institutions might struggle to deal effectively with both a pandemic and a rapid influx of funds.
Finally, evidence shows that corruption reduces the willingness to contribute to pure public goods (Beekman et al., Reference Beekman, Bulte and Nillesen2014), and to the provision of quasi-public goods such as infrastructure (Gillanders, Reference Gillanders2014) – though perhaps only once it passes a threshold level (Bose et al., Reference Bose, Capasso and Murshid2008). Compliance with public health policies generates a benefit to society that is both non-rival and non-excludable – the two characteristics of a pure public good. Therefore, one interpretation of our results is that corruption reduces individuals’ willingness to contribute to pure public goods in the health domain, a finding with implications reaching well beyond the COVID-19 crisis. Organizations such as the World Bank view public health in general, and pandemic preparedness in particular, as global public goods (Stein and Sridhar, Reference Stein and Sridhar2017). Our results, subject to the external validity caveats noted earlier, identify corruption as a significant barrier to the provision of such global public goods.
7.3 COVID-19, Corruption, and Race: The Pandemic Is Not Colorblind
The COVID-19 pandemic killed over 1.1 million people in America (CDC, 2023). The mortality rate for White Americans, while unprecedented since the 1918 H1N1 pandemic, was nevertheless lower than that for Blacks (Rossen, Reference Rossen2020). In Chicago, for example, seventy of the city’s first 100 recorded deaths were Black (Eldeib et al., Reference Eldeib, Gallardo and Johnson2020). As Egede and Walker (Reference Egede and Walker2020) and Tai et al. (Reference Tai, Shah, Doubeni, Sia and Wieland2020) argue, structural racism is one of the root causes of racial health inequities in America. Bailey et al. (Reference Bailey, Krieger, Agénor, Graves, Linos and Bassett2017) define structural racism as ways in which societies foster racial discrimination through mutually reinforcing inequitable systems (in housing, education, employment, health care, criminal justice, and so on) that in turn reinforce discriminatory beliefs, values, and distribution of resources. A meta-analysis covering more than 300 studies shows that racism leads to poorer physical and mental health outcomes (Paradies et al., Reference Paradies, Ben and Denson2015). The COVID-19 pandemic only reinforced inequities between Blacks and Whites in America. In part because Black Americans are disproportionately employed in businesses featuring frequent face-to-face encounters with the public, and because they often live in inner cities or inner-ring suburbs with high population densities (Egede and Walker, Reference Egede and Walker2020), in 2020 excess mortality for Blacks was approximately three times higher than for Whites (Rossen, Reference Rossen2020).
Corruption affects all races, but Black Americans suffer more severely because of poverty and political vulnerability. Black poverty rates are significantly higher than those for Whites – over twice as high nationally, and five or more times higher in some counties. As poverty increases, reliance on publicly provided services such as health care increases. Corruption lowers both the quantity and the quality of publicly provided health care; furthermore, the poor are less able to afford small bribes for the health care that is supposed to be free, or to pay for private alternatives. Urban Black neighborhoods rely heavily on “safety net hospitals” that provide health care regardless of insurance status or ability to pay. The disproportionate share of unpaid or underpaid care that these hospitals provide often leads them to operate with thin financial margins while assuming responsibility for providing critical services (Chatterjee, Sommers, and Joynt Maddox, Reference Chatterjee, Sommers and Joynt Maddox2020). In Chicago, according to 2022 estimates by the Health Care Council of Chicago, by 2024, a dozen safety net hospitals will have lost $1.8 billion combined (Brown, Reference Brown2022). In urban areas across the country, some safety-net hospitals close their doors entirely while others cut services. In a few cases, such as Houston’s Ben Taub Hospital, aggressive public investment and patient support enable safety-net hospitals to provide essential care to large numbers of poor, or poorly insured, patients (Nuila, Reference Nuila2022). But they are very much the exception and illustrate the essential role of sound, well-resourced, and well-administered institutions and policy in providing quality health care to the less affluent segments of the American people. Corruption, by obstructing the formation and implementation of such policies, and by diverting the resources needed to make them work, is likely to weaken the healthcare safety net for those who need it most.
COVID-19 only compounded the structural racism experienced by Black neighborhoods served by already-struggling safety net hospitals. Anecdotal evidence shows how: when a small safety net hospital in Chicago tried to transfer some patients because its ICU was full, others said they were full despite reporting otherwise to the state of Illinois, and others said they were taking no transfers or only transfer patients needing higher-level care. Some hospitals refused patients covered by publicly funded insurance (Schorsch, Reference Schorsch2020). In Los Angeles, some hospitals declined or delayed transfers of COVID-19 patients due in part to their health insurance or lack thereof (Evans et al., Reference Evans, Berzon and Hernandez2020). In New Orleans, patients, mostly Black, were sent back home to die; in some cases, treatment was discontinued even as relatives begged hospitals to keep trying (Waldman and Kaplan, Reference Waldman and Kaplan2020).
Black Americans have higher rates of heart disease, hypertension, diabetes, lung disease, asthma, and obesity, among other illnesses making them more susceptible to dying from COVID-19, but that has little to do with genetic or cultural factors (Non et al., Reference Non, Gravlee and Mulligan2012; Kaufman et al., Reference Kaufman, Dolman and Rushani2015). Rather, the neighborhoods in which they live, work, play, and age do not allow them to be healthy. If they do not exercise enough, it is likely because they live in neighborhoods in which exercising outdoors is dangerous and there are not any gyms. If they eat foods high in sugar, fat, and sodium, it is likely because those foods are the only affordable options in their “food desert” neighborhoods (Bridges, Reference Bridges2020). If they do not go to the doctor as often as White Americans, it is likely not only because they lack health insurance but also because trustworthy, high-quality health care providers are not available. According to a recent survey by “The Undefeated,” an ESPN television series, and the Kaiser Family Foundation, Black Americans trust hospitals in their neighborhoods less than do Whites, and the majority of Blacks believe people are treated differently based on their race by health care providers (Hamel et al., Reference Hamel, Lopes and Muñana2020).
In several respects, structural corruption parallels structural racism as a phenomenon and affects health outcomes for Black Americans more adversely than for Whites. These effects occur both directly via corruption in health care and indirectly via several economic and social channels. First, corruption affects the composition of government spending. Corrupt governments spend less on health care and more on goods affording large bribes and whose value is difficult to monitor, such as military goods (Gupta et al., Reference Gupta, Reza Davoodi and Tiongson2000). It is much easier to collect large bribes on fighter jets than on doctors’ and nurses’ salaries (Mauro, Reference Mauro1998). As Dincer and Teoman (Reference Dincer and Teoman2019) argue, reduced spending on health care goods is not the only problem. Corruption drains millions of dollars from health care services too; Medicare, Medicaid, and Workers’ Compensation fraud are not uncommon in America. It also increases tax evasion and reduces the progressivity of the tax system, leading to reduced tax revenues. Lower revenues in turn reduce both the quantity and the quality of public services including health care (Gupta and Alonso-Terme, Reference Gupta, Davoodi and Alonso-Terme2002).
Corruption also affects redistributive policies and widens socioeconomic inequities. Corrupt officials divert funds within social programs away from the poor toward wealthier and better-connected individuals. In Washington, DC, in 1996, two city officials were convicted of accepting bribes to enroll unqualified individuals for subsidized housing and move them up the waiting list (Rose-Ackerman, Reference Rose-Ackerman1999). Corruption raises inequities in asset ownership. Better-connected individuals get the most profitable government projects, creating a privileged group who have the resources to bribe officials and increase their own assets. Since they in turn are used as collateral to borrow and invest, high inequities in asset ownership reduce the ability of the poor to borrow and invest, further widening politico-economic inequities (Gupta and Alonso-Terme, Reference Gupta, Davoodi and Alonso-Terme2002). Finally, several studies find positive effects of trust – both institutional and social – on health outcomes (Mohseni and Lindstrom, Reference Mohseni and Lindstrom2007). But corruption lowers institutional trust, such as trust in health care (Radin, Reference Radin2013), and also social trust (Rothstein and Eek, Reference Rothstein and Eek2009). Similarly, Passas (Reference Passas2022) argues that both illegal and legal corruption undermine preparedness for policy crises and implementation of countermeasures, with legal or institutional corruption posing particularly serious challenges in those respects.
7.3.1 Empirical Analysis: Data
Evidence from the 171 large metropolitan counties in thirty-five American statesFootnote 8 for which race-related data are available, as classified by the National Center for Health Statistics (NCHS), shows that corruption increases the gap between Black and White COVID-19-related deaths. We first estimate a linear model using both state and county-level variables with OLS regression, in which the dependent variable is the gap in COVID-19-related deaths between Blacks and Whites. Death Gap is measured as the population share of Blacks died from COVID-19 divided by the population share of Whites died from COVID-19.
The data for COVID-19-related deaths by race are from the Centers for Disease Control and Prevention (CDC) and cover the 15-month period from January 1, 2020 to March 31, 2021. Next, the same model is estimated with quantile regression to analyze whether the effects of corruption on Death Gap differ in counties with high health inequities. Corruption is measured using CCI data as described in Chapter 2.
Several county- and state-level politico-economic and demographic control variables are used in the regressions to minimize omitted-variable bias. If corruption is correlated with any of these control variables, omitting them causes the coefficient of CCI to be estimated with a bias. The first control variable is the Poverty Gap between Blacks and Whites, measured as the population share of Blacks who are poor divided by the population share of Whites who are poor. The data are from the Census Bureau’s American Community Survey (ACS). The second is political support for Donald Trump during the 2020 presidential election (Trump). Several opinion polls show that COVID-19 has strong partisan dimensions: Gollwitzer et al. (Reference Gollwitzer, Martel and Brady2020), for example, find that conservative counties are less likely to follow the CDC’s social distancing/facemask guidelines, leading to higher infections and deaths (see also the results earlier in this chapter). Indeed, two decades’ worth of mortality data from a variety of causes (Denworth, Reference Denworth2022) show that both Blacks and Whites in Democratic-voting counties have lower mortality figures than their counterparts in Republican-voting counties, although it must be noted that mortality rates are higher for Blacks than Whites in both cases. Perhaps a surprise is that differences in vaccine uptake accounted for only 10 percent of the county-level contrasts in COVID-19 deaths; the differences likely had more to do with behavioral contrasts such as the frequency of un-masked social activities. In any event,
…[b]y February 2022 the COVID death rate in all counties Donald Trump won in the 2020 presidential election was substantially higher than in counties that Joe Biden won – 326 deaths per 100,000 people versus 258.
The third control variable is per capita spending (in $1,000s) on public welfare in each state (Public Welfare Spending). It includes cash assistance through Temporary Assistance for Needy Families, Supplemental Security Income, and other payments made directly to individuals as well as payments to health care providers under programs like Medicaid. The data are from the Urban Institute. Demographic control variables are population density (Density) and Age Gap – measured as the population share of Blacks who are older than 64 divided by the population share of Whites who are older than 64.
Several studies, such as Wong and Li (Reference Wong and Li2020), find that densely populated counties with older populations have higher COVID-19-related death rates. The data are from ACS. All control variables are from 2020.
7.3.2 Empirical Analysis: Results
The results of the OLS estimation are presented in Table 7.3. The estimated coefficient of CCI is positive and statistically significant, indicating that corruption widens the gap in COVID-19-related deaths between Blacks and Whites. A one-standard deviation increase in CCI causes Death Gap to increase by approximately 10 percentage points. That result is not trivial: Poverty Gap has the same standardized positive effect on Death Gap. Then, the full model is estimated by adding Public Welfare Spending. The estimated coefficient of CCI stays positive and statistically significant, but its magnitude decreases, indicating that corruption not only affects health inequities directly but also indirectly through spending on public welfare.
Table 7.3 Corruption and the gap in COVID-19-related deaths between Blacks and Whites: OLS estimation
| CCI | 0.07 (0.02)Footnote *** | 0.06 (0.02)Footnote *** |
| Poverty Gap | 0.09 (0.03)Footnote *** | 0.07 (0.03)Footnote ** |
| Trump | 1.09 (0.32)Footnote *** | 0.96 (0.35)Footnote *** |
| Age Gap | 1.37 (0.27)Footnote *** | 1.30 (0.28)Footnote *** |
| Density | 0.04 (0.01)Footnote *** | 0.04 (0.01)Footnote *** |
| Distrust in Health Care | 0.10 (0.05)Footnote * | |
| Public Welfare Spending | −0.03 (0.01)Footnote ** | |
| Constant | −0.69 (0.27)Footnote ** | −0.62 (0.30)Footnote * |
| N | 171 | 171 |
| R2 | 0.25 | 0.27 |
Robust standard errors clustered at the state level given in parentheses.
* , **, and *** represent statistical significance levels at 0.10, 0.05, and 0.01, respectively.
Estimated coefficients of all control variables are statistically significant and have the expected signs: for example, in densely populated counties in which Age Gap is wider, Death Gap is wider as well. While increase in Distrust in Health Care causes Death Gap to widen, increased Public Welfare Spending narrows it. Moreover, in conservative counties supporting Donald Trump, the gap in COVID-19-related deaths between Blacks and Whites is wider.
Finally, the effects of corruption on Death Gap are estimated with quantile regression. OLS regression models the relationship between CCI and the conditional mean of Death Gap across all counties in the data set, thus providing only a partial view of the relationship. Quantile regression, by contrast, allows us to analyze this relationship at different points in the conditional distribution of Death Gap. This is important because the effects of corruption are likely to differ significantly, with a stronger effect on Death Gap at higher quantiles. In other words, corruption widens the Black/White gap in COVID-19-related deaths even more in counties with high health inequities. Figure 7.1 shows how the effects of CCI on Death Gap differ over quantiles and how the magnitude of the effects at various quantiles differs significantly from the overall OLS coefficient estimates, even in terms of the confidence intervals around each estimated coefficient.
Figure 7.1 Differential effects of corruption on the gap in COVID-19-related deaths between Blacks and Whites across quantiles
Black solid line represents the coefficients of CCI estimated by quantile regression while the gray solid line represents the one estimated by ordinary least squares. Dashed black and gray lines represent the respective 95 percent confidence intervals.
These results have important implications regarding vaccine equity; for example, data show that as of mid-2022, Black Americans were still getting vaccinated at a lower rate than Whites (Ndugga et al., Reference Ndugga, Hill and Artiga2022). Some might argue that vaccine hesitancy plays a significant role in that gap, but several nationwide surveys show that Black Americans are not hesitant to get vaccinated (Summers, Reference Summers2021). Political factors are important as well: while federal guidelines regulate the distribution of vaccines both across and within states, state governors decide who receives the first doses of the vaccine and who administers them. The responsibility to distribute and administer vaccines fairly and effectively falls first on governors and only then on health care providers.
Structural corruption affects vaccine equity as well. Corruption in that sense is not merely a matter of misconduct by individuals, but rather a manifestation of bad governance and a lack of impartiality on the part of institutions. There is ample anecdotal evidence of lack of impartiality in vaccination in the form of racial discrimination. In Louisiana and Georgia, two states that score high on the CCI, an NPR study (McMinn et al., Reference McMinn2021) found few vaccine sites in predominantly Black neighborhoods in urban counties. In Florida, another high-scoring state, priority was given to predominantly White gated communities whose developers were major contributors to political action committees supporting the governor (Shaw, Reference Shaw2021). Not surprisingly, the 2021 White-to-Black vaccination ratio was greater than 2 in Florida (Pham and Alam, Reference Pham and Alam2021).
7.4 Conclusion
The COVID-19 pandemic highlighted important health inequities between Blacks and Whites caused by structural racism. The OLS and quantile estimations presented just earlier support the argument that structural corruption exacerbates those health inequities. By impeding or preempting impartiality, corruption causes politico-economic and socio-economic inequities to increase, in turn causing health inequities to increase even more. Berkowitz et al. (Reference Berkowitz, Cené and Chatterjee2020) argue that it is time to take racial health equity seriously, starting with solving the collective action problems evident in our findings regarding social distancing, mask-wearing, and vaccination. Corruption is yet another collective action problem (Persson, Rothstein, and Teorell, Reference Berry, Fording and Ringquist2013) that must be solved if we are to take racial equity seriously.
That said, efforts to isolate statistically separate effects of structural corruption from those of more ordinary varieties did not bear fruit. That, in a way, is not surprising if we understand “structural” to refer to the ways in which corruption, like racism, can be embedded in, perpetuated, and protected by the workings of institutions and inequalities in politics and the economy. The manifest difficulties we have had in reducing corruption in the United States, and around the world, strongly suggest that there is more to the problem than just “deviance,” and that we should also think of corruption as the abuse of imbalances of power (Johnston and Fritzen, Reference Johnston and Fritzen2021).
That leaves us with an aspect of corruption that is difficult to deny, even if it is hard to tease out quantitatively – the ways in which it is made more tenacious – indeed, is protected in important ways – by institutions and political processes, rather than occurring and persisting in defiance of those influences. We believe we have addressed that phenomenon by incorporating long-term, deeply embedded factors in the analysis such as political culture, institutional influences (centralization/decentralization), longer-term aspects of social structure (urbanism and population density), and by our focus on legal as well as illegal corruption.
Still, neither this analysis, nor others in the literature, arrive at “solutions” for racial contrasts in health care and COVID outcomes, any more than for police killings of Black Americans. Indeed, they cannot, for we are neither examining discrete problems nor mere deviance within otherwise fair and just institutions. Further, the evident importance of state-level political cultures and ideologies makes it clear that neither policy reforms, budgetary reallocations, nor health policy changes confined to single jurisdictions can mitigate the tragic trends we have seen. Instead, a fundamental reassessment of institutions and power relationships, of our expectations about the roles of citizens and government, and of social values so familiar that they rarely come up for debate, is required. Health care – like policing – is fundamental not only to individual wellbeing but also to the overall vitality and fairness of our states and communities. Whether we can and will do better is an urgent question that we must all answer.
