Abstract

Context: During the COVID-19 pandemic, governments varied in their implementation of social distancing rules. Some governments were able to target their social distancing requirements toward specific segments of the population, whereas others had to resort to more indiscriminate applications. This article will argue that state capacity crucially affected the manner in which social distancing rules were applied.

Methods: Using data from the Oxford COVID-19 Government Response Tracker, the author performed a series of ordered logistic regressions to examine whether state capacity increased the likelihood of more targeted applications of each social distancing rule.

Findings: Given the same level of infectivity, more capable states were indeed more likely to resort to targeted applications of each social distancing restriction. Interestingly, the size of state capacity's effect varied by the type of restriction. State capacity had a stronger influence on face-covering requirements and private-gathering restrictions than it had on school closures, workplace closures, and stay-at-home orders.

Conclusions: The way in which social distancing rules are applied is endogenous to state capacity. Effective governance is a precursor to more targeted and nuanced applications of social distancing rules.

During the COVID-19 pandemic, governments around the world introduced significant restrictions on normal life. Often referred to as “nonpharmaceutical interventions,” these rules were designed to combat the disease in the absence of clear pharmaceutical solutions by inducing greater social distancing among citizens. Governments varied, however, in their implementation of the social distancing requirements. In Singapore, for instance, the TraceTogether smartphone application could identify via Bluetooth signals whether a person was within 2 meters of a COVID-19 patient for more than 30 minutes (Tham 2020). With such technology at hand, the Singaporean government managed to keep shops open, even as it surpassed 1,000 daily infections on April 21, 2020 (Lee 2020). Singapore's response contrasts with that of its neighbour, Malaysia. As that country experienced a little more than 100 daily infections, the Malaysian government introduced a nationwide lockdown, closing all but essential businesses (New Straits Times2020).

The above comparison highlights an interesting difference in governments' approaches to social distancing. In general, restrictions on freedom were implemented either on a defined subgroup of the national population or indiscriminately on the entire nation. Following Rose (2001), restrictions that were selectively implemented on relevant sections of the population will be referred to as targeted responses to the pandemic. Examples of targeted responses include measures such as contact tracing, vaccine passports, partial school closures, and conditional requirements of face coverings, where different policies are applied to individuals depending on whether they were at high risk or low risk of infection. If targeted responses seek to restrict the activities of only high-risk individuals, at the other end of the spectrum are population-wide measures, which apply indiscriminately to the entire population (Rose 2001). These will be referred to as population-wide responses. Nationwide lockdowns or mandatory face coverings in all public areas at all times serve as good examples of population-wide measures.

Is it possible that there is a systemic difference between countries that opted for targeted rather than population-wide restrictions? State capacity, this article will argue, is key to understanding why some governments adopted for more targeted restrictions than others. Governments understand the limitations of their own state institutions. When determining what type of social distancing rules to implement, therefore, governments must consider what policies will yield the best outcome within the limits of the state's capability. The governments of Malaysia and Singapore, for instance, do not command the same resources. Postponing a full lockdown through fine-grained contact-tracing techniques may have been a viable strategy for Singapore's capable state apparatus, but perhaps not for Malaysia's. It is important to understand that governments would have approached the pandemic in such nuanced ways, because those backroom discussions may constitute a crucial part of the picture. Without considering state capacity's role in these deliberations, we may reach incorrect conclusions about the effectiveness and determinants of each public health restriction.

This article finds that, during the pandemic,1 more capable states were much more inclined to respond to infections with strategies targeting a particular segment of the population. State capacity was essential for the government to understand where it should target its restrictions. Importantly, state capacity also increased the amount of social distancing practiced by citizens by adding credibility to both promises of rewards and threats of punishment. Citizens of more capable states, believing that the government will follow through with what it says, generally comply better with social distancing rules. This already higher level of social distancing in turn provides high-capacity states with some room to breathe, since infections will fall faster when citizens comply better with public health restrictions. Given the same reproduction number, then, high-capacity states are able to pursue more targeted restrictions not only because (1) the state has the administrative capacity to devise and implement complex restrictions but also because (2) citizens comply better with the rules imposed, and (3) the government is confident that infections will drop quickly as a result of this high level of compliance.

Using a variable constructed from Oxford COVID-19 Government Response Tracker data and daily estimates of the reproduction number (Arroyo-Marioli et al. 2021) from January 30, 2020, to September 20, 2020,2 this article will empirically demonstrate that states with higher capacity were indeed more likely to resort to targeted responses in the face of rising infections. Specifically, the empirical models will use interaction terms to assess how state capacity affected governments' sensitivity to the reproduction number, which was one of the most important frames of reference for policy makers during the pandemic.

Before continuing, it is imperative to conceptually clarify what “state capacity” refers to in this article. Broadly speaking, state capacity refers to the state's ability to implement political decisions from above, or to “get things done” (Mann 1984: 189). It is hard to pin down, however, what that ability essentially boils down to in practical terms. For one, state capacity should not be confounded with notions of democratic governance (Fukuyama 2013; Rothstein and Teorell 2008), insofar as even autocracies could be highly capable without exhibiting features of good governance such as transparency or accountability. Furthermore, the state comprises multiple branches that are each designed to facilitate the implementation of distinct policies. Some of these policies are targeted at the society that the state seeks to control. For instance, studies have variously focused on extractive (Besley and Persson 2009; Geddes 1996; Levi 1988; Tilly 1975), coercive (Fearon and Laitin 2003; Geddes 1996; Skocpol 1985; Tilly 1975), and informational (Brambor et al. 2020; Lee and Zhang 2017) capacity. Meanwhile, other policies are concerned more with principal-agent issues within the state, rather than the state's control over society. For these types of policies, capable states suffer less from agency loss than states without capacity. Englehart (2009, 2017) and Chae (2021), for instance, argue that human rights violations are often perpetrated by low-level state employees, who abuse human rights in pursuit of their own private interests.

Therefore, this article will not attempt to present any single overarching operationalization of state capacity that can be applied across diverse fields of study. Instead, it will accept Lindvall and Teorell's (2016) suggestion to measure state capacity indirectly, by estimating the amount of “key” resources that are necessary for implementing a particular policy area of interest. In the context of a pandemic, a meritocratic, professional, and competent Weberian bureaucracy is essential to policy making, since it requires a highly skilled and knowledgeable workforce to design and execute a set of coordinated schemes in real time. Indeed, bureaucratic competence has been frequently associated with public health outcomes in the past (e.g., Burkle 2006; Campbell, DiGiuseppe, and Murdie 2019; Rull, Kickbusch, and Lauer 2015; WHO 2018). As a result, this article will use the World Bank's government effectiveness index (Kaufmann and Kraay n.d.), which is also one of the most widely used measures of state capacity in the COVID-19 literature.3 The index uses unobserved components models to gauge the bureaucrats' ability to craft and implement high-quality policies while retaining the public's confidence. The index, however, has its critics. It would be particularly problematic for our analysis if the index does not permit government effectiveness to be compared across countries (Knack 2006; Oman and Arndt 2006), the index is overly affected by short-term economic performance (Kurtz and Schrank 2007), or the variable relies too much on a small set of similar sources (Knack 2006; Oman and Arndt 2006). While Kauffman, Kraay, and Mastruzzi (2007) present reasonable rebuttals to these claims, the article will conduct a set of robustness checks using Hanson and Sigman's (2021) latent capacity variable as an alternative measure. Using Bayesian MCMC (Markov-Chain Monte Carlo) techniques, Hanson and Sigman claim that there is a single latent dimension of capacity that underlies different dimensions of state capacity.

The article's contribution to the literature is twofold. First, borrowing Rose's (2001) juxtaposition of individual versus population approaches to illness, the article conceptualizes the observed variation in the implementation of social distancing rules on a spectrum from “targeted” to “population-wide” restrictions. By doing so, the article seeks to present a common theoretical foundation through which we may compare the implementation of different social distancing rules. Second, to the author's knowledge, this is the first empirical analysis that compares the implementation of each individual social distancing rule across different countries. The article by Cepaluni, Dorsch, and Kovarek (2022), for instance, does not examine each social distancing rule in detail and only considers an aggregate “stringency” measure. And while Cronert (2020) has a distinct focus on school closures, the study reduces the phenomenon to a binary variable (open and closed). By comparing the degree to which each of the five rules (school closure, workplace closure, stay-at-home requirements, face-covering requirements, and gathering restrictions) are targeted, this article aims to present a more comprehensive analysis of the variation.

Literature Review

What determined government responses to the COVID-19 pandemic? Scholars have considered numerous factors in their efforts to explain the huge variation in government responses to the pandemic.

Did democracy affect the choice of COVID-19 responses? According to Ruger (2005), a democratic government enhances the health of its population to increase its chances of electoral success, if not for the intrinsic moral value of health. Before the pandemic, studies had generally found support for Ruger's argument (Bellinger 2018; Burroway 2016; Gerring et al. 2015; Przeworski 2004). During the pandemic, however, the relationship between democracy and health has been ambiguous at best. On the one hand, research has found that COVID-19 mortality was lower in countries with higher levels of democracy and political trust (Bosancianu et al. 2020; Bollyky et al. 2019). At the same time, democratic principles have been found to be inconsistent with some of the stronger interventions designed to tackle the virus (Engler et al. 2021), and autocracies seemed to perform better in terms of the number of infections (Cepaluni, Dorsch, and Branyiczki 2020; Karabulut et al. 2021). The reason for this ambiguity could be that the mechanisms designed to secure democratic accountability in normal times may not be as functional for preparing countries against crises, which far exceeds the short time horizons of the terms of elected officials (Dionne 2011; Healy and Malhotra 2009).

Studies have also assessed whether other political elements have affected the efficacy of health interventions. For instance, researchers have found that partisanship affects citizens' perceptions of the seriousness of COVID-19 (Allcott et al. 2020b; Barrios and Hochberg 2020), while structural racism (Bailey and Moon 2020), female political leadership (Aldrich and Lotito 2020), populist attitudes (Ansell, Cansunar, and Elkjaer 2021), and federalism (Huberfeld, Gordon, and Jones 2020) have arguably affected each government's response to the pandemic. For scholars of the United States, there has been particular attention to the effects of federalism. Critics argue that the United States suffered from greater losses during the pandemic owing to its decentralized form of governance. Because individual states were treated as just some of the many interest groups trying to lobby Congress, federal funds became “crowded out”(Rocco, Béland, and Waddan 2020), and existing inequities were exacerbated during the crisis (Huberfeld, Gordon, and Jones 2020). With the federal government refusing to release its stockpile of protective equipment, individual states had to compete against one another as well as with the federal government to gain access to medical supplies (Goelzhauser and Konisky 2021). “The reliance on state and local actors in this pandemic,” Knauer (2020: 5–6) sums up, was “ill-suited to stop a novel virus in search of its next host.” To aggravate matters further, partisan divide in the United States even politicized COVID-19 responses, with conservative states taking significantly longer to adopt social distancing measures (Adolph et al. 2021).

In addition to these variables, however, there has been a growing interest in state capacity. The capacity of the state has been associated with the attainment of a number of desirable policy outcomes, including economic growth (Bockstette, Chanda, and Putterman 2002; Dincecco and Katz 2016; Dincecco and Prado 2012; Evans 1995; Evans and Rauch 1999; Hamm, King, and Stuckler 2012; Rauch and Evans 2000; Weiss 1998) and peace (Braithwaite 2010; de Rouen and Sobek 2004; Fearon 2005; Fearon and Laitin 2003; McBride, Milante, and Skaperdas 2011; Sobek 2010). More recently, the literature on human rights has found that state capacity critically affects the protection of rights (Acemoglu, Ticchi, and Vindigni 2010; Butler, Gluch, and Mitchell 2007; Chae 2021; Englehart 2009, 2017; Fearon and Laitin 2003; Sullivan 2012). Importantly, scholars have found strong associations between capacity and health in general. State capacity is linked to lower infant and child mortality (Dawson 2010; Hanson 2015) as well as higher life expectancy (Burkle 2006).

Outside normal times, capacity has been central to the control of epidemics (Burkle 2006; Rull, Kickbusch, and Lauer 2015; WHO 2018), including the Ebola virus (Anderson and Beresford 2016) and the African swine flu (Brown et al. 2018). During the recent COVID-19 pandemic, a number of studies have also found state capacity to affect the effectiveness of pandemic responses. Qualitative studies have identified how capacity (Hartley and Jarvis 2020; Woo 2020; Wu, Wang, and Zhang 2019) or the lack thereof (Capano 2020; McCormack and Jones 2020; Woo 2020) conditioned the successfulness of states' responses to the pandemic. Quantitative studies add further empirical support to the qualitative literature: higher government effectiveness (Cepaluni, Dorsch, and Branyiczki 2020; Serikbayeva, Abdulla, and Oskenbayev 2021; Winkelmann et al. 2021) and better quality of government (Cepaluni, Dorsch, and Branyiczki 2020) led to fewer deaths from COVID-19. Yet there has also been a substantial variation among states that had been regarded as highly capable and well prepared against a global pandemic (Kavanagh and Singh 2020; Weiss and Thurbon 2021), underscoring the complexity of the issue.

If the majority of studies exploring the relationship between state capacity and COVID-19 focused on the effectiveness of government responses, a small number of studies have also explored how state capacity may shape the way in which these responses are implemented during a pandemic. According to Rose (2001), there are largely two approaches to prevention. The “individual based” strategy's goal is to identify the individuals most likely to be affected by a disease and offer them targeted protection. By contrast, the “population strategy” aims to control the determinants of the infection directly and lower the mean level of risks (Rose 2001: 431). During the COVID-19 pandemic, more capable states, it has been argued, have been able to postpone the closure of schools, as they “are in a better position to suspend drastic precautionary measures like school closures longer in favor of a more proportional strategy with less disruptive initial responses” (Cronert 2020: 5). At the same time, González-Bustamante (2021) and Capano et al. (2020) find that state capacity accelerated the implementation of more stringent policies. Meanwhile, Weiss and Thurbon (2021) argue that the lack of relevant capacities led the British and Australian governments to seek creative solutions by repurposing other strengths of the state or by uncovering capacities that lay dormant.

Scholars of poverty have made similar comparisons between targeted versus “universalist” social policies. In general, the literature finds that state capacity is crucial for implementing targeted poverty-reducing measures because of the administrative challenges of identifying relevant individuals in the population (Banerjee, Niehaus, and Suri 2019; Coady and Le 2020; Sen 1992). An important insight from this scholarship is that the efficacy of targeted policies depends not only on the state's ability to deliver its intended goals but also on the citizens' behavioral reactions to the interventions. Using income thresholds to target subsidies could, for instance, distort individual incentives to engage in economic activities (Sen 1992: 13). Furthermore, the literature considers whether targeted and universalist approaches could beget contrasting political outcomes (Brady and Bostic 2015; Jacques and Noël 2018; Korpi and Palme 1998; Marx, Salanauskaite, and Verbist 2013). The key perception is that redistributive preferences are negatively associated with policies that target low-income groups. There are limitations, however, in applying these lessons directly to the context of the pandemic. First of all, because of the extraordinary nature of the phenomenon, the time horizon is much shorter during a pandemic. Except for a select few countries that successfully held elections in the midst of the pandemic, the public's political preference had little opportunity to impact social distancing restrictions. Second, devising and enforcing social distancing measures puts the state under exceptional pressure. Even states that had been considered to be well-prepared for the pandemic failed to respond as expected (Abbey et al. 2020; Aitken et al. 2020; Kachali et al. 2022). Thus, uncovering a relationship between state capacity and targeted restrictions would be interesting, because the states' latent capabilities seemed almost irrelevant amid the chaos of a global pandemic. Finally, scholars of the welfare state do not explicitly model how state capacity causes national governments to select themselves into targeted or universalist approaches. Despite subnational self-selection into targeted programs (e.g., poor localities are less likely to apply to targeted schemes) and evidence of corruption, weak state capacity does not prevent national governments from pursuing targeted welfare programs (e.g., Baird, McIntosh, and Özler 2013; Muralidharan, Niehaus, and Sukhtankar 2016; Niehaus et al. 2013).

In short, there is much more to explore in terms of how state capacity could have affected COVID-19 responses. While there is ample reason to believe that the capacity of the state would have affected the government's responses to the pandemic, this relationship is not fully explored in the field. The comparative social policy literature does explore relationships between state capacity and targeted social welfare programs, but their mechanisms do not map onto the pandemic context. Therefore, this article will build on some of the existing insights to propose a theoretical framework through which we may understand the manner in which state capacity affects each government's implementation of social distancing rules.

State Capacity and Public Health Restrictions

From the perspective of both the government and its citizens, state capacity affects the effectiveness of targeted (as opposed to population-wide) restrictions. Importantly, national governments self-select into targeted or population-wide social distancing rules in anticipation of how state agents and citizens may react to their policies.

Governments are less inclined to pursue population-wide restrictions if the state has high capacity. As discussed in the literature review, high capacity here is akin to the Weberian ideal of a meritocratic, professional, and competent bureaucracy. Specifically, the aspect of state capacity discussed in this article is the bureaucrats' ability to craft and implement high-quality policies. As Cronert (2020) notes, states that have such resources could resort to less disruptive initial measures before considering drastic preventive strategies. Without a capable bureaucracy, the state may not know how to best target restrictions or possess the appropriate resources to implement the rules it crafts. In Italy, for example, contact tracing measures that were introduced in April 2020 did not effectively start until mid-June, because the state could not hire anyone to perform the contact tracing, and the app that was designed to contact-trace affected individuals was not functional (Capano 2020). A more capable state, on the other hand, has a well-functioning public sector that can efficiently administer restrictions. Singapore's early introduction of contact tracing serves as a case in point. By mobilizing personnel from other departments, such as investigative officers from the police and the military, Singapore was able to swiftly and effectively carry out contact tracing operations (Woo 2020). When the state can devise high-quality restrictions and implement them efficiently, it will find little need for disruptive, population-wide measures.

Meanwhile, citizens are more inclined to socially distance when the state is capable of detecting (non)compliance. Higher state capacity increases the attractiveness of social distancing. During the pandemic, governments introduced various measures to incentivize social distancing and impose penalties on those who do not practice distancing. Without enough capacity, however, states cannot devise functioning (dis)incentive programs or efficiently deliver the promised (dis)incentives. For example, in Italy subsidies did not reach the people for months, and other socioeconomic measures designed to reward and alleviate the costs of social distancing were delayed as a result of numerous bureaucratic hurdles and plentiful red tape (Capano 2020). The United Kingdom, by contrast, quickly repurposed its extractive capacity for distributive functions to successfully administer furlough schemes (Weiss and Thurbon 2021). When the state's ability to devise effective (dis)incentives and deliver them as promised vary in this way, citizens facing a low-capacity state have less reason to socially distance. The costs are higher and the gains are lower because of the government's limited ability to devise and administer policies. Since state capacity increases the citizens' practice of social distancing, it allows the government to accomplish its epidemiological goals without resorting to population-wide restrictions. This, in turn, further motivates highly capable states to opt for targeted measures.

Empirical Analysis

The author conducted 10 logistic regressions to empirically assess whether stronger states were more inclined to pursue targeted restrictions as opposed to population-wide restrictions. The author used logistic regressions because the dependent variables are ordered categorical variables.4 All main empirical models use Huber-White robust standard errors. The study period ranges from January 30, 2020, to September 20, 2020, but the start dates vary depending on when the pandemic started to affect each country. More recent periods are not included in the study, since it is still unclear how the introduction of vaccines may have altered the strategic considerations of governments and citizens. The unit of analysis is country-day. The main statistical models consider only the days in which restrictions were in place.5 All models interact state capacity with COVID-19 infectivity to examine how state capacity influences governments' responsiveness to rising infections. The author also performed a series of robustness checks, the results of which are summarized in the online appendix.

Dependent Variable

The Oxford COVID-19 Government Response Tracker's indicators for school closure, workplace closure, stay-at-home orders, mandatory face coverings, and restrictions on gatherings are used to code the extent to which a health restriction is targeted or population-wide (Hale et al. 2021). A restriction is understood to be more targeted if it applies to a more specific situational context (situational scope). The main models will only consider restrictions applied nationwide (table 1). In the appendix, some of the models will consider an alternative operationalization of the variable, where a situationally specific restriction is considered to be even more targeted if applied to certain geographic regions only (geographic scope) (table 2).6 In either case, recommendations (such as government-recommended work from home) are excluded from the analysis, because recommendations are not enforced by the state, and the theoretical considerations in the previous section do not apply.

Independent Variable

The main independent variable is an interaction between state capacity and the reproduction number. The interaction term allows us to assess whether the same change in infectivity resulted in different types of social distancing measures, depending on the capacity of the state.

State capacity is measured using the government effectiveness index from the World Bank's World Governance Indicators (World Bank 2021). This variable is frequently used as a measure of the state's capacity among studies of COVID-19 responses. To mitigate concerns of reverse causality, the index is lagged by one year. There are, however, a few potential drawbacks to using government effectiveness to capture bureaucratic competence. First, Knack (2006) and Oman and Arndt (2006) argue that because the index uses different underlying sources for different countries, comparing the World Bank indicator cross-sectionally might be problematic. Second, Kurtz and Shrank (2007) claim that governance is highly correlated with short-term growth rates. Third, both Knack (2006) and Oman and Arndt (2006) assert that the index unjustifiably weights a cluster of related sources more heavily than others. In response, Kauffman, Kraay and Mastruzzi (2007) argue that cross-sectional comparison is possible precisely because their method allows us to identify latent relationships between different measurements of governance. Furthermore, the authors demonstrate that governance is only correlated with short-term growth under particular model specifications. However, the authors tend to agree with the final point about the potential problems with the weights they use. As a result, the article will use Hanson and Sigman's (2021) latent state capacity variable as an alternative independent variable. Hanson and Sigman use Bayesian MCMC analysis to generate a new general-purpose measure of capacity. The empirical analysis will use values for 2015, which is the final year in Hanson and Sigman's data. Both measures of capacity are min-max normalized to range between 0 and 100.

To measure infectivity, the article will use estimates of the daily reproduction number (Arroyo-Marioli et al. 2021), lagged by one day. The reproduction number is widely used in the public health field because it outperforms other potential indicators of infectivity. For example, surveys and interviews are the least accurate measure of infections, since citizens' perceptions of the seriousness of a given epidemic situation have been found to be heavily influenced by their political predispositions (Allcott et al. 2020a; Barrios and Hochberg 2020; Chigudu 2019). As another example, major media outlets such as The New York Times have used the growth rate in confirmed cases of infection to illustrate whether a nation was in control of COVID-19 or not (Katz and Sanger-Katz 2020). However, the growth rate in cases is also not a satisfactory indicator of disease control, because the number of new infected cases is affected by the infections in previous days. And once we consider the incubation period (the time between the moment of infection and the onset of symptoms), the effect of a policy is even harder to estimate solely on the basis of the growth rate in cases. By contrast, the reproduction number is unaffected by citizens' political predispositions, and it takes into consideration the path-dependent and dynamic nature of the pandemic.

Control Variables

The models include a number of control variables. First, in light of recent studies arguing that COVID-19 restrictions were not compatible with liberal democratic practices (Engler et al. 2021), the models include a liberal democracy index from the Varieties of Democracy project (Coppedge et al. 2021), lagged by one year. Finally, a set of variables is included to account for the different circumstances under which each nation entered the pandemic. GDP per capita in 2019 (logged) and population size in 2019 (logged) are included to control for the diverse socioeconomic contexts in which countries fought against the pandemic. And to consider the different levels of proximity to China, the first country to be affected by the disease, the models control for the distance of each state's capital (in kilometers) from Beijing (Mayer and Zignago 2012). This serves as a proxy of both geographic and cultural closeness to China, which affected not only the timing of the first COVID-19 cases in each country but also the initial levels of preparedness for cross-border transmissions. In the appendix, some of the models consider using a categorical region variable as an alternative proxy.

Models 1 to 5 (table 3) demonstrate how state capacity shapes the reproduction number's effect on the type of public health restrictions applied. In all models, the interaction term is significant in the expected direction: while a higher reproduction number leads to less targeted (and more population-wide) restrictions, the effect of infectivity is smaller in more capable states. In other words, a higher reproduction number generally leads to more universalist restrictions on freedom, but more so among weaker, less capable states. Even in the face of the same change in the reproduction number, stronger states are found to be less inclined to pursue population-wide measures. Figure 1 demonstrates how the reproduction number affects the marginal probabilities of population-wide restrictions differently, depending on a state's capacity. For instance, if the reproduction number rises from 1 to 1.5, a country at the mean level of government effectiveness experiences a 0.065 increase in the predicted probability of a complete nationwide closure of all schools. Meanwhile, the same change in the reproduction number results in a 0.016 rise in the probability of complete school closure if effectiveness is a standard deviation higher than the mean. As another example, when the reproduction number rises from 1 to 1.5, the probability of mandatory face coverings in all public areas rises by 0.034 for countries with the mean level of capacity, but the probability falls by 0.01 among countries whose effectiveness is a standard deviation higher than the mean.

Table 4 conducts the same set of analyses using Hanson and Signman's (2021) latent state capacity variable instead of the government effectiveness index. Again, the models demonstrate how state capacity could affect the type of restrictions imposed. With the exception of model 8, states with higher latent capacity are less likely to implement population-wide measures in response to a higher reproduction number. Meanwhile, countries with low latent capacity are much more inclined to impose universal restrictions on the entire population.

Tables 3 and 4 both demonstrate substantial variations in the effects of state capacity, depending on the type of restriction. For each policy area, state capacity has a different effect on the probability of population-wide restrictions. For stay-at-home orders and the closure of schools and workplaces, high-capacity, medium-capacity, and low-capacity states are all more likely to introduce population-wide restrictions as the reproduction number rises, although low-capacity states are even more likely than states with relatively higher capacity. The pattern is not the same for restrictions on face coverings and private gatherings. While a rising reproduction number renders a low-capacity state more and more likely to introduce population-wide restrictions on face coverings and gatherings, this is not the case among high-capacity states, where the line is flat. Among more capable states, the likelihood of population-wide restrictions on face coverings and gatherings remains low even as infectivity grows.

Indeed, some of the most capable governments in the study were hesitant to impose the broadest population-wide face-covering requirement, “mandatory face coverings at all times outside your home” (category 0 in table 1), despite rising infections. Instead, these countries opted for relatively narrower population-wide requirements, such as “face coverings required nationwide in all shared places or when social distancing is not possible” (category 2 in table 1). Of the 2,623 country-days for governments with high effectiveness (index above 70), only 62 imposed the strongest population-wide restriction. Among these highly capable governments, the most popular measure was a slightly more lenient population-wide measure (category 2). Even as the daily reproduction number exceeded 1.5, countries such as Ireland and the United Arab Emirates maintained this relatively lenient requirement for face coverings.

Similarly, states with high government effectiveness were reluctant to impose the most indiscriminate population-wide restriction on gatherings, even as the reproduction number exceeded 1. Of the 3,593 country-days in which highly capable governments (index above 70) faced an exponential growth in infections, only 874 country-days imposed “gathering restrictions for fewer than 10 people” (category 0 in table 1). An overwhelmingly large number of datapoints (1,249 country-days) had the most lenient population-wide restriction: “restrictions on very large gatherings in only some regions of the country” (category 4 in table 1). For instance, in the early days of the pandemic, Switzerland introduced category 4 restrictions on gatherings, even though the reproduction number soared above 2. Meanwhile, Austria maintained category 4 gathering restrictions from June through mid-September 2020, even though it experienced an exponential growth in infections during that same period.

These contrasting patterns highlight the complexity of state capacity's influence on the strategic considerations of citizens and governments. From the government's point of view, different levels and types of effort are required according to the type of restriction being imposed on the whole population; moreover, the extent to which a government can benefit from enhanced capacity also varies by the type of restriction. From the citizen's perspective, too, state capacity's effect on the costs and benefits of abiding by a restriction differs according to the type of restriction. Based on our empirical observations, it seems that state capacity has a greater effect on the strategic behaviors of the government and its citizens when it comes to restrictions on personal choice. Whereas the state can easily observe adherences to school closures, workplace closures, and stay-at-home orders, the state's ability to monitor the wearing of masks and personal gatherings is more limited. Consequently, governments with low capacity resort to population-wide face-covering requirements and gathering restrictions to increase the visibility of violators, while more capable governments take on more targeted restrictions. For inherently more visible restrictions such as school closures, workplace closures, and stay-at-home orders, by contrast, even a weak state does not have too much trouble enforcing targeted restrictions; consequently, state capacity has a smaller effect on the targeted nature of these restrictions.

Conclusion

This article has found that the way in which governments implement social distancing rules is dependent on their state capacity. Each social distancing rule can be implemented either indiscriminately on the entire population or in a more targeted manner. And state capacity increases the government's proclivity to resort to targeted (as opposed to population-wide) restrictions.

A series of empirical models was employed to evaluate the merit of the theoretical expectations. A set of ordered logistic regressions demonstrated that more effective governments and greater latent capacity do increase the likelihood of states opting for more targeted restrictions. Interestingly, the size of state capacity's effect varied by the type of restriction. For school closures, workplace closures, and stay-at-home orders, state capacity had a relatively small influence on the likelihood of the measures being targeted. For these types of restrictions, higher reproduction numbers still increased the likelihood of population-wide restrictions among more capable states. By contrast, for restrictions on personal choice, such as face coverings and private gatherings, state capacity had a much stronger effect. For these restrictions, rising reproduction numbers had little or no effect on the probability of population-wide restrictions among high-capacity states.

What lessons can be learned from these results? First of all, the analysis reveals that not all countries are at liberty to pursue highly targeted social distancing measures. Without the required capacity, some states do not know where to target their restrictions or have the staffing to monitor and enforce restrictions on a defined segment of the population. For citizens, too, targeted restrictions are only effective when the state is capable. Incapable states cannot maintain social distancing under targeted rules because neither the threat of punishment nor the promise of reward are credible.

Second of all, the models identify potentially fruitful areas for further research. In the empirical analysis, the main effects vary depending on the type of restriction under consideration. It is possible, therefore, that different forces are at play for each respective field of restriction. If that is the case, each field of restriction may require theoretical and empirical models that are specific to that type of restriction.

Acknowledgments

I would like to thank my DPhil examiners, Andrea Ruggeri and Steffen Hertog, for their valuable feedback. I am also grateful to Ben Ansell, Nancy Bermeo, Thomas Hale, and numerous friends and colleagues for their input. Finally, I would like to thank the Leverhulme Trust (ECF-2022-270) for funding this research.

Notes

1.

The study will examine the first year of the pandemic. Without any vaccine or treatment, the virus was both infectious and deadly during this time (January 30, 2020, to September 20, 2020).

2.

The start dates vary depending on when the pandemic started to affect each country. More recent periods are not included in the study, since it is still unclear how the introduction of vaccines may have altered the strategic considerations of governments and citizens.

3.

Table A1 of the online appendix lists each country's effectiveness from highest to lowest.

4.

Ordinary least squares models would be inappropriate for analyzing ordered categorical dependent variables, since we cannot assume the distance between categories to be uniform. Ordered logistic regressions are better equipped for the analysis of such variables, as they do not assume the distances between each ordered category to be the same.

5.

One of the robustness checks recodes the restriction variables to have “no restrictions” as the most targeted category of intervention.

6.

The Oxford COVID-19 Government Response Tracker records whether a restriction was applied nationally, or to a particular geographic region only (for example, just one state in a federal system). For the purposes of this study, situational scope is the primary indicator of how targeted a restriction is. The assumption here is that applying restrictions by specific contexts requires more targeted attention from the state than applying restrictions by specific geographic locations, as geographic locations are more visible than situational contexts.

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