COVID-19 pandemic and lockdown fine optimality

Niavis, Spyros; Kallioras, Dimitris; Vlontzos, George; Duquenne, Marie-Noëlle Article COVID-19 pandemic and lockdown fine optimality Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Niavis, Spyros; Kallioras, Dimitris; Vlontzos, George; Duquenne, Marie-Noëlle (2021) : COVID-19 pandemic and lockdown fine optimality, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 9, Iss. 1, pp. 1-26, https://doi.org/10.3390/economies9010036 This Version is available at: https://hdl.handle.net/10419/257194 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ economies Article COVID-19 Pandemic and Lockdown Fine Optimality Spyros Niavis 1,* , Dimitris Kallioras 1, George Vlontzos 2and Marie-Noelle Duquenne 1   Citation: Niavis, Spyros, Dimitris Kallioras, George Vlontzos, and Marie-Noelle Duquenne. 2021. COVID-19 Pandemic and Lockdown Fine Optimality. Economies 9: 36. https://doi.org/10.3390/ economies9010036 Received: 14 January 2021 Accepted: 5 March 2021 Published: 11 March 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Planning and Regional Development, University of Thessaly, 382 21 Volos, Greece; [email protected] (D.K.); [email protected] (M.-N.D.) 2Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 382 21 Volos, Greece; [email protected] *Correspondence: [email protected]; Tel.: +30-2-421-074-398 Abstract: The first stream of economic studies on public policy responses during the COVID-19 pandemic focused on the stringency, the effectiveness, and the impact of the countries’ interventions and paid rather little attention to the corresponding means used to support them. The present paper scrutinizes the lockdown measures and, particularly, examines the optimality of the lockdown fines imposed by countries worldwide towards ensuring citizens’ compliance. Initially, a triad of fine stringency indicators are compiled, and the stringency of fines is evaluated in a comparative context, among the countries considered. Consequently, the fine stringency is incorporated into a regression analysis with various epidemiological, socioeconomic, and policy factors to reveal any drivers of fine variability. Finally, theoretical approaches behind fine optimality are capitalized and real data are used towards estimating the optimal fine for each country considered. The objectives of the paper are, first, to check for any drivers of fine stringency around the world and, second, to develop and test a formula that could be used in order to assist policy makers to formulate evidence-based fines for confronting the pandemic. The findings of the paper highlight that fines do not seem to have been imposed with any sound economic reasoning and the majority of countries considered imposed larger real fines, compared to the optimal ones, to support the lockdowns. The paper stresses the need for the imposition of science-based fines that reflect the social cost of non-compliance with the lockdown measures. Keywords: COVID-19 pandemic; lockdown measures; fines; optimality; stringency 1. Introduction The coronavirus disease (henceforth: COVID-19) was characterized as a pandemic by the World Health Organization (WHO) as, after its initial outbreak in Wuhan, China, it spread very fast to the rest of the world (WHO 2020a). Countries worldwide adopted a different set of social distancing measures to contain the virus and help decrease the rate of transmission among their citizens. The initial response of countries was to control their borders to decrease the incoming transmission from other countries (McBryde et al. 2020). When the local transmission was increased, domestic social distancing became the core concept underlying all the containment efforts around the world (Brodeur et al. 2020), and several measures were implemented to this end (Gabutti et al. 2020). The Oxford COVID-19 Government Response Tracker recognizes the following categories of restrictions: school closing, workplace closing, cancellation of public events, restrictions on gathering size, closure of public transport, stay at home requirements, restrictions on internal movement, and restrictions on international travel (Hale et al. 2020). The optimal mix of policies and their effectiveness have been, extensively, studied even from the first stream of papers on the COVID-19 pandemic policy responses (Kong and Prinz 2020;Nussbaumer-Streit et al. 2020;Tian et al. 2020; inter alia). Many authors show that strict preventive policy measures seem to have worked in reducing peoples’ mobility and thus adequately promoted the targets of social distancing (Lapatinas 2020; Economies 2021,9, 36. https://doi.org/10.3390/economies9010036 https://www.mdpi.com/journal/economies Economies 2021,9, 36 2 of 26 Wielechowski et al. 2020). Nevertheless, this is not always the case, as state capacity also plays a role in rendering policy responses more effective (Frey et al. 2020). The general consensus is that there is not a “one-size-fits-all” policy mix and the policies should be adapted to the specific epidemiological, socioeconomic, and policy context of each country (European Centre for Disease Prevention and Control 2020). In particular, a lockdown is a policy option that may damage economic activity, with negative potential impacts on a range of economic sectors (Yu et al. 2020). It is noteworthy that even the WHO (2020b) stresses that any preventive measures that slow down social and economic activities should be taken with caution and only after careful examination. A critical parameter for the success of the social distancing measures is citizens’ compliance. Governments worldwide mostly relied on fines to deter non-compliance (Beaumont 2020). It is widely known that fines have a major role in securing the compliance of citizens in a range of instances, such as traffic laws and smoking prohibition. For the COVID-19 pandemic, a relevant case study in Germany has shown that the imposition of fines had a decisive role in reducing the speed of transmission of the disease (Chae and Park 2020). In general, fines, when supporting the enforcement of laws, play multiple roles, including incapacitation, deterrence, rehabilitation, and retribution (Piehl and Williams 2011). Bentham (1780 in O’Malley 2009) considered fines as a license paid arrear and, much later, Becker (1968) as a price which is a function of the violators’ benefit and the cost to the society that the violation incurs. When attention is drawn to the optimality of fines, the very old principle that the punishment should fit the severity of the crime runs through the relevant research through the years (Montag and Tremewan 2018). Concerning traffic fines, for example, effective tickets should encompass three basic principles (Sun 2011; Hummel 2015): first, they should make the drivers aware of the externalities and social cost of disrespecting the traffic law; second, they should act as a constant reminder to drivers that they should respect the speed limits; third, they should be set by acknowledging that people tend to underestimate the danger of driving carelessly. On the same rationale, and regarding competition laws, Kobayashi et al. (2016) claim that fines should internalize the social cost to the violator considering also the probability of compliance together with the probability of getting caught. Incorporating the aforementioned rationale into the case of pandemics, the social cost of non-compliance could be approximated by the cost of treatment. Besides, an effective fine should, inter alia, incorporate factors such as the probability that someone is infected, the risk of a violator transmitting the virus, and the probability that an infected person would need hospitalization. Moreover, from an operational point of view, the probability of people violating the rules and the capacity of the authorities to spot the violators should, also, be considered. In general, several epidemiological, socioeconomic, and policy factors should be considered when fines are being set. The imposition of well-documented fines is critical for conveying the right message to citizens in the sense that citizens will be able to understand the risk of not obeying the laws. This is extremely critical since people are aware of the measures taken in other countries and, thus, they can easily estimate if the stringency of fines in their country is analogous to the corresponding fines of other countries, ceteris paribus (Evans 2020;Richings 2020). When huge differences occur in the stringency of fines, which cannot be attributed to epidemiological, socioeconomic, and policy factors, reverse effects may turn up (Bull 2020; Verseck 2020). Particularly, people in a country where fines seem to be higher than normal may consider the policies as a means for restricting their freedom or increasing public revenues. Correspondingly, people in a country where fines seem to be lower than normal may think that the risk is lower than the government claims. It is true that, despite the early warnings of scientists, governments were not highly prepared for confronting a disease on such a large scale (McKay and Dvorak 2020). The lack of preparedness may be due to structural factors, having to do with the countries’ overall adequacy in providing reliable health services to citizens (Eissa 2020), but also because the impact of the disease was so asymmetric compared to many past simulations of various Economies 2021,9, 36 3 of 26 task forces (Maxmen and Tollefson 2020). In many cases, countries imposed a state of emergency based on disease laws over a century old (Desval 2020;Ling 2020). Therefore, many governments had to issue special laws, acts, and decrees in order to adjust their policy responses to the particular challenges of the COVID-19 pandemic, just like Canada did when it adjusted the outdated Quarantine Act for confronting the SARS disease in 2003 (Ling 2020). These laws, acts, and decrees covered issues such as self-isolation and quarantines. Yet, a great number of countries, and especially the Western ones, did not have the experience to impose measures such as lockdowns and curfews as these measures had rarely been used in the past due to their incompatibility with the democratic political systems (Bull 2020). Moreover, policy responses were driven by the contextual economic, institutional, and political factors prevailing in each country (Ferraresi et al. 2020). Therefore, factors other than the epidemiological conditions might have driven the stringency of policy responses as well. As far as fines are concerned, in particular, governments faced a situation where they needed to define a value of a fine without the existence of a law or adequate formulas. The lack of previous experience may have rendered the selection of proper fines a rather difficult task. Notable is the example of France, where on the first day of the lockdown, the fine was set at EUR 35, just to be increased to EUR 135 on the day after (France 24 2020). This shows that even the most developed countries were not ready to justify the setting of the fines. The present paper analyzes the way(s) different governments worldwide dealt with the imposition of fines to support lockdown measures and to what degree those measures were based on reasonable economic evidence considering the corresponding epidemiological, socioeconomic, and policy factors. It does so by examining the fines imposed by many governments for supporting compliance with “stay-at-home” measures in the first wave of the pandemic. In total, 44 countries, from different parts of the world, are considered. The analysis covers the first wave of lockdowns, starting with the Italian lockdown (10 March 2020) and ending with the Singaporean lockdown (7 April 2020), and is divided into two parts. The first part, more descriptive, answers the first research question of the paper which seeks to reveal whether the stringency of fines is driven by epidemiological, socioeconomic, and policy factors. To do so, initially, a triad of fine stringency indices is computed and then a composite index of fine stringency is compiled. Finally, the relationship among the values of the composite index with various epidemiological, socioeconomic, and policy variables is systematically tested in order to shed light on the factors that may have driven fine imposition for the considered countries. The second part answers the second research question which seeks to reveal what the optimal height of the fines would be, considering the epidemiological, socioeconomic, and policy factors of each country. The contribution of the paper is twofold. First, it incorporates the critical issue of sound economic reasoning policy measures for the confrontation of a pandemic and the COVID-19 pandemic in particular. Second, it proposes a set of factors that could be considered for countries to set effective fines. The need for evidence-based fines is critical, especially when many citizens stand skeptical about the very existence of the virus. If the fines are estimated based on a specific rationale, they could be adjusted when major conditions change and so they can convey messages about the severity of the disease not only within a country but also in an international context. The paper could be used as a starting point for building global-driven standards for fine setting. This could lead to a more harmonized public policy response to pandemics, which could be extremely helpful for coping with the subsequent waves of COVID-19 or any future pandemics. The paper proceeds as follows: the next section elaborates on data and methodology. The third section performs the empirical analysis and discusses the findings. The last section offers the conclusions and some policy recommendations. Economies 2021,9, 36 4 of 26 2. Materials and Methods 2.1. Data Lockdown is considered as a general “stay-at-home” governmental directive with restrictions on public gatherings and the use of public transport means. Particularly, two main types of “stay-at-home” directives are considered, namely, total and partial lockdowns. Total lockdown regards directives addressed to the whole population with a mandate not to leave home for the whole day without permission. In this type are, also, included cases where, in addition to the general mandate to leave home only with permission, curfews for specific hours or on specific parts of the population are imposed. Partial lockdown includes those cases where “stay-at-home” directives are enforced voluntarily, accompanied by sanctions only for special violations. These violations mainly concern the failure to keep a distance from others, public and private gatherings of members of different households, and excessive use of public means of transport. In this category are, also, included cases where partial lockdowns are complemented with curfews. Table A1 (Appendix A) presents in detail the type of lockdown and the date of imposition in each country. In the first stage, data were collected for 62 countries that enforced lockdown measures and imposed fines in order to achieve the maximum compliance of their citizens. Yet, in a second stage, some outliers, as well as countries with unverifiable data, were identified and removed from the sample. Thus, the final sample consists of 44 countries. Data for the date and the type of lockdowns as well as for the size of the fines were retrieved from various online sources, comprising mostly governmental and news sites. Especially for the collection of the fines, official sources were not always easy to find and when this was possible, translations on the official documents would possibly lead to confusing results considering the large range of measures taken by the countries. Therefore, English language news sites and translations with the assistance of Google were used for collecting the information for the considered countries. Particular attention was given to the reliability of sources and a great effort was made in the cross-checking of the information for each country. The same holds for the collection of traffic fines that are, also, utilized in the paper. Tables A2 and A3 (Appendix A) provide the sources used for collecting lockdown fines and traffic fines, respectively. Fines are converted to the euro currency, considering the exchange rate at the time of the study (i.e., the year 2020). Note that countries were included in the sample only when it had become evident that the fine imposed was a standard fine and not the maximum feasible fine (that rarely was imposed). Therefore, in order to avoid any biased estimations, the fines presented in the paper are the standard minimum fines imposed on citizens for a single violation of the lockdown rules. This distinction is important because the fines could be increased for repeated violations. Note, also, that the fines incorporated into the analysis are those that were announced by the governments to support a particular lockdown measure whose date of imposition is considered as the benchmark for the empirical analysis. Fines such as those associated with self-isolation of infected persons or the operation of an enterprise when this was forbidden are not considered. 2.2. Methodology 2.2.1. Is the Stringency of Fines Driven by Epidemiological, Socioeconomic, and Policy Factors? To extract a comprehensive comparison among the countries considered, a triad of fine stringency indices is calculated by taking three different variables as a benchmark for the fines’ stringency. The first Fine Stringency Index (FSI 1 ) is the ratio of the fine value to the minimum monthly wage. The second Fine Stringency Index (FSI 2 ) is the ratio of the fine value to the gross domestic product (GDP) per capita. The third Fine Stringency Index (FSI 3 ) is the ratio of the fine value to the maximum traffic fine among the traffic fines considered. FSI1=LF/MW (1) FSI2=LF/GDPPC (2) Economies 2021,9, 36 5 of 26 FSI3=LF/MAX(TF1,TF2,TF3)(3) where FSI is the fine stringency index, LF is the lockdown fine, MW is the minimum monthly wage, GDP PC is the GDP per capita, TF 1 is the traffic fine for red light crossing, TF 2 is the traffic fine for driving without wearing a seatbelt, and TF 3 is the traffic fine for using a mobile phone while driving. Having estimated the three indices, the rank for each country, according to its score, in each index is extracted. Then the total fine stringency index is computed as the average ranking of each country. The lower the score of each country in the index, the larger the stringency of the corresponding fine. TFSI =(RFSI1+RFSI2+RFSI3)/3(4) where TFSI is the total fine stringency index, and RFSI 1 ,RFSI 2 , and RFSI 3 are the rankings in the FSI1,FSI2, and FSI3, respectively. Is the Stringency of Fines Driven by Epidemiological Factors? The scores of the TFSI are associated with key epidemiological variables, namely, the Total Active Cases per Million People (TAC PMP ) at the time of the imposition of the lockdown, the Transmission Rate (R) in the period just before the imposition of the fines and the Risk of Hospitalization for the general public (RoH) in each country. The TAC PMP variable is measured on the day that the lockdown was imposed. The active cases are collected by the crosstabulation of data provided by the websites Worldometer.info (2020) and Ourworldindata.org (2020). The Rvariable is the effective reproductive number of COVID-19. This variable is critical for capturing the dynamics of the pandemic since it shows how many secondary infections are generated by one infected person (Kohlberg, Elon, and Abraham Neyman 2020) and is heavily considered by governments worldwide in order to shape and evaluate any policy measures related to the containment of the pandemic (Fisher 2020). The estimation of the variable is based on the new daily infections, and therefore it puts weight on the active cases rather than on the total cases at the time of estimation. The Rvariable is estimated under an assumption regarding the generation interval. The generation interval is estimated as the difference between the time that the symptoms appear in a person and the symptom onset time of the people infected by him/her (Kenah et al. 2008). The paper considers the value of 4 as a realistic approximation of the generation interval considering that Du et al. (2020), in an early study in Wuhan, China, found that the average interval was 3.96 days. R=New inf ections at the current week New in f ections be f ore 4days (5) The index is estimated on a weekly—and not on a daily—basis to eliminate any randomness of reported cases (Kohlberg, Elon, and Abraham Neyman 2020). The final R incorporated into the study is the average value of Rfor the five-day interval before the announcement of the lockdown under consideration. The variable of RoH quantifies the risk of a patient being hospitalized due to infection, considering the general health and demographic characteristics of each country. The variable draws from Clark et al. (2020) that provide an estimation of the proportion of the population at high risk per county considering the age structure as well as the fraction of population with various underlying conditions. Is the Stringency of Fines Driven by Socioeconomic Factors? The scores of the total fine stringency index are associated with key socioeconomic variables, namely, the GDP PC , the wider trust of citizens in the political system (TP), and the Police Reliability (PR). The GDP PC variable is used in order to see whether the stringency of fines is affected by the economic development level of the countries. Economies 2021,9, 36 6 of 26 The TP variable is used in order to see whether countries with higher trust levels imposed lower fines. Bargain and Aminjonov (2020) shown that European regions whose citizens show a higher trust to their governments presented a higher level of compliance with the policy measures against COVID-19. The same stands for countries with higher civic capital (Barrios et al. 2020). Tullis (2020), under a different perspective, showed that governments which are aware of the high levels of citizens’ trust and public support may enforce looser measures. The formula for constructing the variable builds on the 7-scale trust measurement provided by the World Economic Forum (WEF 2018) and the record for each country is extracted by taking the ratio of its score to the maximum score of 7. The PR variable is used since several containment measures require the involvement of police forces in order to ensure citizens’ compliance. To successfully deal with the pandemic, police forces need to intensify their tasks, often with the same number of resources. Overall, the adaptation of police forces to the new reality depends on their overall effectiveness and preparedness (Laufs and Waseem 2020). Therefore, regarding the fines, countries in which the police forces are reliable are expected to detect any violators more easily and secure the collection of fines and therefore impose lower fines. The formula for constructing the variable builds on the 7-scale trust measurement provided by the WEF (2019) and the record for each country is extracted by the ratio of its score to the maximum score of 7. Is the Stringency of Fines Driven by Policy Factors? The scores of the total fine stringency index are associated with key policy variables, namely, the Government Response Stringency Index (GRSI), the Type of the Lockdown (LT), and the Date of the Lockdown (DoL). The GRSI variable refers to the overall stringency of measures implemented at the time of the fine imposition in each country. The variable refers to the Oxford COVID-19 Government Response Tracker, and, particularly, to the homonymous index that presents the stringency of responses according to how many of the 11 considered policy responses were implemented in each country at the time of the fine imposition (see https://www.bsg. ox.ac.uk/research/research-projects/coronavirus-government-response-tracker (accessed on 5 September 2020) for details). The index uses a 0–100 scale, with higher values denoting tougher measures. The Type of Lockdown (LT) variable is a dummy variable that takes a value of 1 for total lockdowns and 0 otherwise (i.e., partial lockdowns). The test here is conducted to examine whether total lockdowns were associated with higher fines than those imposed in partial lockdowns. The DoL variable is computed as the difference of the date of fine imposition in each country with the date of the Italian lockdown enforcement, which was the first to be realized among the countries considered. Multivariate Analysis on TFSI Determinants To detect whether, and to what extent, the epidemiological, socioeconomic, and policy factors considered are determinants of the lockdown fine stringency, the paper performs a series of regression analyses. The basic assumption, as stems from the preceding discussion, is that governments had imposed lockdown measures considering key epidemiolocal factors, such as the transmission rate (R) and the dispersion of the disease in the population, as measured by the total active cases per million people (TAC PMP ). Furthermore, the setting of fine size (fine stringency) is affected by considering the particularities of the countries, which is a joint elaboration of the three types of factors considered by the present study. An ordinary least squares model is composed where the TFSI scores of the countries are incorporated as the dependent variable and the different factors as independent ones. Before presenting the model, there are some issues to be discussed regarding the applicability of such a process. First, many independent variables may be correlated with other ones and therefore their estimated effect could be biased. Therefore, Economies 2021,9, 36 7 of 26 a multicollinearity test is conducted and the collinearity diagnostics of the condition index and variance inflation factors (VIFs) are reported (Gujarati 2009). Second, endogeneity and selection issues should also be considered. There are two main sources of endogeneity in regression analyses similar to the one conducted in this paper. The first occurs when the dependent variable is not only a response of the independent variable but also a predictor of it. This is called simultaneity bias. For the current setting, this type of endogeneity should not be a problem. This is because simultaneity bias arises mainly in panel data analysis which is not the case with the present research design (Lynch and Brown 2011). As was stated in the preceding sections, the present analysis seeks to reveal if the setting of the fines was driven by the consideration of the prevailing conditions in the countries under consideration. Therefore, the analysis ends at the time that the size of the fines is announced in public without seeking to test any potential effects of fines on those conditions after the date of the announcement. The latter question would require a different and more time-dynamic model setting, as the outcome (TFSI) would surely affect the predictors. The second source of endogeneity stems from the omission of important variables, resulting in the presence of the omitted variable bias (Lynch and Brown 2011). This type of endogeneity may arise in the present analysis. Therefore, to limit the extent of this bias, we have included a number of variables able to quantify the prevailing epidemiological, socioeconomic, and policy conditions of each country. Moreover, to detect any endogeneity, we run multiple forms of regression analyses either by using instrumental variables or interaction terms to detect any meditation effects of the variables. The basic rationale behind the formulation of the various models is that the governments considered the Rand TAC pmp as the factors affecting their decision to take measures but the fines were imposed by taking into account other factors, too. Therefore, we sought to check if the effect of the other variables was mediated by these two variables. All regression analyses are then compared with the following baseline model (Equation (5)) which uses OLS to test the main effects of the considered variables. In the model, all epidemiological, socioeconomic, and policy factors are incorporated together with the D CSA ,D CEA D ME ,D NA , and D AFR variables which are some regional dummies for Central–South America, Central–East Asia, Middle East, Northern Asia, and Africa. The dummies were formulated with reference to Europe. TFSI =α+βTACpmp ×TACpmp +βR×R+βRoH ×RoH +βGDPpc ×GDPpc +βTP ×TP +βPR ×PR +βGRSI ×GRSI +βDoL ×DoL +βDCSA ×DCSA +βDCEA ×DCEA +βDME ×DME +βDNA ×DNA +βDAFR ×DAFR (6) After the introduction of different forms, we also present the results of a model with two additional interaction terms. This model comes up with substantially higher predictive power than the baseline model considering the improved value of the R 2 coefficient. The first interaction term ( βDOL∗R∗DDoLDR ) shows how the transmission rate mediates the effect of the time lag between the Italian announcement of fines and the announcement in other countries. The second examines the same mediation effect but by considering the TAC pmp ( βDOL∗TACpmp ∗DDoLDTACpmp ) variable. All in all, these interaction terms sought to reveal if the epidemiological factors had a stable effect on governments’ choices for setting fines regardless of the time they had available to elaborate the fines for supporting lockdowns. In the Results section, the pairwise relationships among fine stringency and all of the considered variables are first graphically presented and briefly commented upon. Then, the results of the multivariate models are presented and discussed with regard to the initial results. Economies 2021,9, 36 8 of 26 2.2.2. What the Optimal Height of the Fines Should Be, Considering the Epidemiological, Policy, and Socioeconomic Factors of Each Country The empirical analysis proceeds to a theory-driven evaluation of the imposed fines’ optimality. Particularly, the empirical analysis capitalizes on the discussion on the fines’ optimality under economic reasoning (Becker 1968;Polinsky and Shavell 1979,2000; Mookherjee and Png 1994 inter alia) and a couple of formulas that may assist governments in formulating adequate fines to support the lockdown (or other) measures. The optimal fines for each country are estimated under both formulas and are compared to the actual ones. Under the first formula, the fine is viewed as a function of the cost of treatment and of some pure epidemiological factors. The specification of the fine under epidemiological considerations is as follows: Fineep =CoT ∗R∗RoH ∗P(I)(7) where Fine ep is the Optimal Fine based on Epidemiological data, CoT is the Cost of Treatment, Ris the transmission rate of COVID-19, RoH is the risk of hospitalization, and P(I) is the probability of being infected. Towards incorporating the CoT variable into the formula, the US tally is taken as a baseline and the cost for each country is adjusted accordingly by considering the differences among the standards of living (O’Brian (2020) estimated the average cost of treatment for a COVID-19 patient in the USA to be about USD 30,000). The adjustment is made according to the purchasing power parity conversion provided by The World The World Bank (2020). Towards incorporating the P(I) variable, the proportion of COVID-19 active cases to the total population is estimated. The COVID-19 cases may be either detected or undetected and therefore the real detected cases are multiplied by a relevant factor (10) in order to estimate the total real cases. To date, there is no general consensus about the underestimation of COVID-19 active cases. Bohk-Ewald et al. (2020), based on a sample of 10 countries, estimated that the real numbers could have surpassed the reported ones by 2 to 11 times, depending to the country under consideration. Wu et al. (2020) , examining the USA, for the period between February and April 2020, found that the total cases could have exceeded the reported ones by 2 to 20 times. As the paper refers to the initial stage of the pandemic, when testing capacity was rather minimal, a factor of 10 is used in order to approximate the relationship between detected and undetected cases. The index could be further elaborated to incorporate only the susceptible portion of the population. Nevertheless, although the study refers to the first wave of the pandemic when immunity levels in the population were extremely low, it still provides an adequate representation of the probability that someone gets infected. It is noted that the index should incorporate such a metric to be used for estimating fines in the succeeding waves of pandemic. P(I)=detected active COVID −19 cases ∗10 population ∗100 (8) Under the second formula, the fine is viewed as a function of the first formula and some pure socioeconomic factors. The formula incorporates the rationale of Becker (1968) on fine optimality who, among other factors, highlighted the need for accounting for conditions that may make people more prone to incompliance as well the probability of getting caught when setting fines for law violations. The specification of the fine under epidemiological and socioeconomic considerations is as follows: Fineep+s=Fineep ∗[1−P(C)] P(D)(9) where P(C) is the probability of compliance and P(D) is the probability of being detected. The P(C) variable is proxied by the TP variable. The P(D) variable is proxied by the PR variable. Economies 2021,9, 36 15 of 26 coefficients are non-significant. Finally, between the two interaction terms, only the one representing the interaction of DoL and Ris found to be statistically significant. Considering the negative sign of the main effects coefficient and the positive one for the interaction term, it is concluded that as we moved far from the first day of the imposition of the Italian fine, the effect of Rin the setting of the fines was lowered. This may be because countries had the necessary time to also consider other factors that governments that had to take measures right after receiving the shocking pictures coming from the pandemic in Italy could not consider. In any case, this interaction should be treated with caution due to the lack of any statistical significance of the main effect coefficients of the two variables. 3.4. What the Optimal Size of the Fines Should Be, Considering the Epidemiological, Policy, and Socioeconomic Factors of Each Country. Empirical Findings Figure 6a,b visualize the relation between the actual fines and the Fine ep and the Fine ep+s formulas. Considering the y = x identity line (i.e., the dotted line), for the Fine ep ,33 out of 43 countries lie above the line and thus had imposed fines that were stricter than what the epidemiological data of these countries would imply. For the Fine ep+s , the number of countries that overestimated the risks of the pandemic and thus imposed higher than optimal fines is 35. Six countries overestimated the real risks of the pandemic, imposing fines that exceed the EUR 500 level of those that would occur if epidemiological and social factors under both formulas were taken into account. Additionally, considering the first formula, it seems that the countries which underestimated the most the risk of the pandemic were all European, namely, the UK, France, and Hungary. Nevertheless, when the social factors are incorporated into the analysis (Figure 6b), the level of underestimation is reduced and the UK is surpassed by Panama. Economies 2021, 9, x FOR PEER REVIEW 15 of 26 (a) (b) Figure 6. The relation between the actual and estimated fines. (a) The relationship between actual fines and those obtained by the epidemiological fines formula (Fineep). (b) The relationship between actual fines and those obtained by the epidemiological and socioeconomic formula (Fineep+s). The current figures should be considered as only indicative of countries’ real deviations. This is because the social cost, as expressed by the cost of COVID-19 patients’ treatment, is a rough estimation, taking as a proxy the cost of treatment in the USA. Nevertheless, the formulas could be extremely useful for researchers and policymakers as they can reveal what the actual cost of treatment should be for the imposed fines to be justified by the prevailing conditions. For example, in the case of France, the cost of treatment was assumed to be EUR 21,700. With this record, it was found that France imposed fines that lay far below the optimal ones. For the French fines to be justified, the cost of treatment should not exceed EUR 3087 for the Fine ep formula and EUR 4776 for the Fine ep+s formula. Similar, for Costa Rica, which imposed higher than optimal fines under both fine specifications, the cost of treatment should be around EUR 47,000 under the first formula and EUR 57,000 under the second to justify such harsh fines. These tallies would even exceed the respective cost of treatment in the USA. Table 4 presents some more detailed results regarding the optimality of fines in the various (macro-)regions worldwide, based on the FODI ep and the FODI ep+s . The imposed fines deviated, in absolute terms, more than 43 times from their optimal fine when only epidemiological factors were considered and about 50 times when both epidemiological and socioeconomic factors were considered. These tallies show that when social data are incorporated into the estimation of an optimal fine, the deviation of countries seems to be amplified. For the first formula, the largest average deviation is found in Europe, as the fines deviate more than 51 times from their optimum value and the lowest deviation is found for the three countries of Northern Asia, where the deviation is only 123%. When the second formula is considered, the deviation of countries in the Middle East skyrockets, while the Northern Asian countries still present the largest optimality. Apparently, the majority of countries imposed stricter than optimum fines. In addition, underestimation was a major issue in the Middle East, where half of the considered countries imposed lower than optimal fines, and in Europe, since the proportion of countries with rather low fines reached 32%. In contrast, Africa, Central–East Asia, and Northern Asia did not have any country with these characteristics. Additionally, when social factors are considered, underestimation weakens. This is because half of the European countries that fell into this category under the first formula shifted to the category of overestimation and the Middle East remains the region with the largest proportion of countries that underestimated risk. BH BG DK FR HU IE IT NL SA TH UA UAE UK 0 500 1000 1500 2000 2500 3000 0 200 400 600 800 1000 1200 Actual fine (€) Fineep (€) BHBG FR HU IE IT PA SA TH UA 0 500 1000 1500 2000 2500 3000 0 100 200 300 400 500 600 Actual fine (€) Fineep+s (€) Figure 6. The relation between the actual and estimated fines. ( a ) The relationship between actual fines and those obtained by the epidemiological fines formula (Fine ep) . ( b ) The relationship between actual fines and those obtained by the epidemiological and socioeconomic formula (Fineep+s). The current figures should be considered as only indicative of countries’ real deviations. This is because the social cost, as expressed by the cost of COVID-19 patients’ treatment, is a rough estimation, taking as a proxy the cost of treatment in the USA. Nevertheless, the formulas could be extremely useful for researchers and policymakers as they can reveal what the actual cost of treatment should be for the imposed fines to be justified by the prevailing conditions. For example, in the case of France, the cost of treatment was assumed to be EUR 21,700. With this record, it was found that France imposed fines that lay far below the optimal ones. For the French fines to be justified, the cost of treatment should not exceed EUR 3087 for the Fine ep formula and EUR 4776 for the Fine ep+s formula. Similar, for Costa Rica, which imposed higher than optimal fines under both fine specifications, the Economies 2021,9, 36 16 of 26 cost of treatment should be around EUR 47,000 under the first formula and EUR 57,000 under the second to justify such harsh fines. These tallies would even exceed the respective cost of treatment in the USA. Table 4presents some more detailed results regarding the optimality of fines in the various (macro-)regions worldwide, based on the FODI ep and the FODI ep+s . The imposed fines deviated, in absolute terms, more than 43 times from their optimal fine when only epidemiological factors were considered and about 50 times when both epidemiological and socioeconomic factors were considered. These tallies show that when social data are incorporated into the estimation of an optimal fine, the deviation of countries seems to be amplified. For the first formula, the largest average deviation is found in Europe, as the fines deviate more than 51 times from their optimum value and the lowest deviation is found for the three countries of Northern Asia, where the deviation is only 123%. When the second formula is considered, the deviation of countries in the Middle East skyrockets, while the Northern Asian countries still present the largest optimality. Apparently, the majority of countries imposed stricter than optimum fines. In addition, underestimation was a major issue in the Middle East, where half of the considered countries imposed lower than optimal fines, and in Europe, since the proportion of countries with rather low fines reached 32%. In contrast, Africa, Central–East Asia, and Northern Asia did not have any country with these characteristics. Additionally, when social factors are considered, underestimation weakens. This is because half of the European countries that fell into this category under the first formula shifted to the category of overestimation and the Middle East remains the region with the largest proportion of countries that underestimated risk. Table 4. Lockdown fine optimality using fine optimality deviation index (FODI). Regions Fineep Fineep+S FODI Type of Deviation FODI Type of Deviation Over Under Over Under Africa 5120% 5 0 4618% 5 0 Central–South America 2319% 5 1 830% 4 1 Central–East Asia 3574% 4 0 3846% 4 0 Europe 5866% 13 6 5329% 16 3 Middle East 3359% 3 3 10,594% 3 3 Northern Asia 123% 3 0 252% 3 0 Global 4321% 33 10 4957% 35 7 Table A4 (Appendix A) provides the full records of the countries for the FODIep and the FODIep+s. Concerning the FODIep, the top five countries with the highest deflection were Ukraine, Kenya, Saudi Arabia, Thailand, and Bolivia, whereas the top five countries with the lowest deflection were Singapore, Cyprus, Spain, Belgium, and Slovenia. Concerning the FODIep+s, the top five countries with the highest deflection were Ukraine, Saudi Arabia, Kenya, Bahrain, and Thailand, whereas the top five countries with the lowest deflection were South Africa, Israel, Denmark, Cyprus, and Turkey. 4. Discussion The present paper, by examining the fine policy for backing lockdown measures in an international context, has revealed that the imposition of fines has been rather asymmetric and without clear economic reasoning. This is because the variations of the fines across countries do not seem to be driven by epidemiological, policy, or socioeconomic factors. As the regression analyses revealed, the only clear driver of fines found by the present study seems to be the level of the economic development of each country. Therefore, epidemiological factors played a minimal role and the policy-making differences that are usually found between developed and less-developed countries may have also driven the response of governments in terms of the imposition of fines. This was evident in the Economies 2021,9, 36 17 of 26 study of Frey et al. (2020), who found that the level of democracy was inversely related to the stringency of the overall government response to COVID-19. The worrying fact of the study of Frey et al. (2020) is that stronger measures were not found to be more effective than less intensive responses. This may be also the case with fine stringency for two reasons. First, because the less-developed countries imposed heftier fines, it is possible that COVID-19 may increase the inequalities among developed and less-developed countries at the policy level. Moreover, the analysis of the optimality of fines has revealed that the imposition of fines may also increase the inequalities within countries. Many citizens of countries, especially those with existing high levels of inequality, may see their economic position deteriorate further, as in many instances they cannot stay at home as they need to work to earn the essentials for living. This necessity is higher for informal workers, as they hardly receive state support (International Labour Organisation—ILO 2020). Nevertheless, the risk of violating lockdown rules results in a loss that exceeds their monthly salary. So, in situations where the state cannot apply adequate welfare policies, many people may take the risk to leave home and work, despite still risking more than their monthly income. The pressure exerted on citizens by the improper imposition of fines is added to other types of disparities amplified by the pandemic, in domains such as food security (Power et al. 2020; Wolfson and Leung 2020), migrants’ living conditions (Sengupta and Jha 2020), capacity for protecting against diseases (Clouston et al. 2021), racial and ethnic disparities (Raine et al. 2020), and many other aspects of economic and social life. Moreover, the very effectiveness of the fines as policy instruments may be jeopardized when they are not driven by the prevailing conditions. Therefore, citizens of many countries may turn out to be very reluctant towards the real intentions of their governments and may defy lockdowns. For example, Ukrainian citizens may find it very difficult to understand why they were faced with a fine that was five times higher than their minimum monthly salary and 39 times higher than the maximum fine for traffic penalties when, by the time of the imposition of lockdown, the country recorded the lowest number of active cases per million people of the countries of the sample. At the same time, the Netherlands, with 400 cases per million people, had imposed fines that accounted for 25% of the minimum salary and only 1.6 times the highest traffic penalty. The analysis of fine optimality has shown that the deviation of the real fine from an optimal fine did not exceed 100%, while the results for Ukraine showed that the fine imposed was over 774 to over 900 times higher than the optimal one, according to the formula considered for estimating the optimal fines. 5. Conclusions In the time of an unprecedented pandemic outbreak, policy responses for the containment of the disease acquire great importance. Even though the need for adjusting preventive measures, such as lockdowns and border closures, in order to alleviate the economic shocks, is evident, there is, still, an absence of recommendations to guide countries in achieving a state of optimality when setting fines to support various types of responses. Since fines, when properly imposed, have been long recognized as effective means for enhancing citizens’ compliance, it is evident that an effective fine policy may help countries to render their policy responses more successful and thus become more effective in confronting the COVID-19 pandemic. During a pandemic, and according to the standard economic theory, fines, to be effective, must incorporate the risks associated with the pandemic and thus consider all the factors that may affect the risk levels. For the citizens to perceive the role of the state as an institution that does its best to protect them, the latter should make explicit the factors considered to estimate the fines’ size. Then, any differences will be well understood and thus people are expected to comply with the measures more easily. In contrast, when such great differences remain unexplained, people may lose their confidence in their governments and thus not follow the official guidelines. Without question, if fines are disproportionate to peoples’ ability to pay them, then negative social effects may amplify inequalities. Economies 2021,9, 36 18 of 26 Responding to this necessity, the paper proposes a couple of formulas for assisting governments in estimating evidence-based fines to support their measures. Even though these formulas should by no means be considered as a panacea, the paper fulfills its role in introducing and highlighting the need for open and evidence-based fine formulation. Given that the majority of countries considered overestimated the risk of the pandemic, by imposing fines that did not correspond to the epidemiological, policy, or socioeconomic conditions at the time of imposition, the need for a more thorough examination of fines and their overall role in confronting the pandemic still exists. To this end, the paper paves the way. As with all particular issues related to the COVID-19 literature, there are many additional steps to be taken in order for more light to be thrown on the issue of fine optimality. As countries adjust their responses according to the severity of new waves of the pandemic, new data will become available and the samples for performing multivariate analyses will grow, thus enhancing the validity of the findings. Moreover, data not only on fines but also on some of their possible drivers will also become available as the literature on COVID-19 expands. Therefore, panel analyses on the drivers of fines and new variables, not considered by the present paper, could complement the present analysis. Moreover, questionnaires and relevant surveys could be also used to directly investigate the logic behind policy makers’ decisions on fine settings. Those developments may lead to models of higher explanatory power which will more effectively treat any potential issues of omitted variables and potential sources of endogeneity. Finally, what remains as a future challenge is to expand the studies on how fines helped the confrontation of the pandemic. To this end, panel data will be extremely valuable. Author Contributions: Conceptualization, S.N. and G.V.; methodology, D.K. and M.-N.D.; software, S.N. and D.K.; validation, G.V. and M.-N.D.; data curation, S.N. and D.K.; writing—original draft preparation, D.K. and S.N.; writing—review and editing, M.-N.D. and G.V.; visualization, M.-N.D. and S.N. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors would like to thank the editor and the four reviewers for their constructive comments that improved the quality of the paper. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Type of lockdown and date of imposition per country. No. Country Region Type of Lockdown Date of Lockdown 1 Albania Europe 2 16/3/2020 2 Algeria Africa 2 24/3/2020 3 Armenia North Asia 1 24/3/2020 4 Azerbaijan North Asia 1 31/3/2020 5 Bahrain Middle East 3 22/3/2020 6 Belgium Europe 1 18/3/2020 7 Bolivia South America 2 22/3/2020 8 Bosnia Europe 4 21/3/2020 9 Bulgaria Europe 3 2/4/2020 10 Chile South America 4 19/3/2020 Economies 2021,9, 36 19 of 26 Table A1. Cont. No. Country Region Type of Lockdown Date of Lockdown 11 Colombia South America 1 24/3/2020 12 Costa Rica Central America 4 1/4/2020 13 Cyprus Europe 1 24/3/2020 14 Denmark Europe 3 18/3/2020 15 Ecuador South America 4 16/3/2020 16 France Europe 1 17/3/2020 17 Greece Europe 1 22/3/2020 18 Hungary Europe 3 28/3/2020 19 India Central Asia 1 24/3/2020 20 Iraq Middle East 4 18/3/2020 21 Ireland Europe 1 27/3/2020 22 Israel Middle East 1 20/3/2020 23 Italy Europe 1 10/3/2020 24 Jordan Middle East 1 21/3/2020 25 Kenya Africa 4 27/3/2020 26 Lithuania Europe 3 16/3/2020 27 Malaysia East Asia 3 18/3/2020 28 Morocco Africa 1 20/3/2020 29 Netherlands Europe 3 23/3/2020 30 Panama Central America 4 25/3/2020 31 Romania Europe 2 24/3/2020 32 Russia North Asia 1 30/3/2020 33 Saudi Arabia Middle East 2 23/3/2020 34 Serbia Europe 2 18/3/2020 35 Singapore East Asia 3 7/4/2020 36 Slovenia Europe 1 20/3/2020 37 South Africa Africa 1 26/3/2020 38 Spain Europe 1 14/3/2020 39 Thailand East Asia 4 3/4/2020 40 Turkey Middle East 3 22/3/2020 41 UAE Middle East 4 26/3/2020 42 UK Europe 1 23/3/2020 43 Ukraine Europe 3 17/3/2020 44 Zimbabwe Africa 1 30/3/2020 Notes: Type of lockdown: 1. Lockdown, 2. Lockdown with curfew, 3. Partial lockdown, 4. Partial lockdown and curfew. Table A2. Sources used for collecting data on lockdown fines. No. Country Links 1 Albania https://www.albaniandailynews.com/index.php?idm=40441&mod=2 2 Algeria https://www.ispionline.it/it/pubblicazione/algeria-coronavirus-effects-nation-turmoil-25715 3 Armenia https://www.evnreport.com/raw-unfiltered/armenia-state-of-emergency-guidelines 4 Azerbaijan https://emerging-europe.com/news/azerbaijan-cracks-down-on-critics-amid-pandemic/ 5 Bahrain https://www.arabnews.com/node/1647041/middle-east 6 Belgium https://www.aa.com.tr/en/europe/belgium-extends-coronavirus-lockdown/1806440 7 Bolivia https://www.batimes.com.ar/news/latin-america/bolivia-begins-total-quarantine-postponespresidential-election.phtml 8 Bosnia http://hr.n1info.com/English/NEWS/a493883/Coronavirus-46-new-cases-confirmed-inCroatia-tally-rises-to-361.html 9 Bulgaria https: //www.euractiv.com/section/health-consumers/short_news/bulgaria-update-covid-19/ 10 Chile https://www.senado.cl/hasta-540-dias-de-carcel-o-multa-de-un-millon-de-pesos-las/ senado/2020-03-27/193738.html 11 Colombia https: //www.nytimes.com/2020/04/15/world/americas/virus-colombia-bogota-men-women.html Economies 2021,9, 36 20 of 26 Table A2. Cont. No. Country Links 12 Costa Rica https://www.elmundo.cr/costa-rica/aprueban-leyes-en-primer-debate-para-aumentarmultas-por-incumplir-restriccion-vehicular-y-orden-sanitaria/ 13 Cyprus https: //cyprus-mail.com/2020/03/30/coronavirus-harsher-penalties-measures-on-their-way/ 14 Denmark https://politi.dk/coronavirus-i-danmark/boedetakster-for-at-overtraede-restriktioner-somfoelge-af-covid-19 https: //nyheder.tv2.dk/samfund/2020-03-11-danmark-lukker-ned-her-er-regeringens-nye-tiltag 15 Ecuador https://www.garda.com/crisis24/news-alerts/329201/ecuador-additional-domesticrestrictions-announced-april-13-update-5 16 France https://www.theguardian.com/world/2020/mar/24/how-strict-are-the-uks-distancingrules-compared-with-other-countries 17 Greece https://www.reuters.com/article/us-health-coronavirus-greece-curfew/greece-imposeslockdown-after-coronavirus-infections-jump-idUSKBN2190Z1 18 Hungary https://welovebudapest.com/en/article/2020/3/27/new-curfew-restrictions-in-hungaryfrom-tomorrow-saturday-28-march-1 19 India https://www.straitstimes.com/world/coronavirus-how-some-countries-are-keeping-or-notkeeping-people-indoors 20 Iraq https://aawsat.com/home/article/2187921/%D8%A7%D9%84%D9%85%D8%B7%D8%B1-% D9%88%D8%AA%D8%AC%D9%86%D8%A8-%D8%A7%D9%84%D8%BA%D8%B1%D8%A7 %D9%85%D8%A9-%D9%8A%D9%8F%D9%86%D8%AC%D8%AD%D8%A7%D9%86-%C2 %AB%D8%AD%D8%B8%D8%B1-%D8%A7%D9%84%D8%AA%D8%AC%D9%88%D9%84% C2%BB-%D8%A7%D9%84%D8%B9%D8%B1%D8%A7%D9%82%D9%8A 21 Ireland https: //www.theguardian.com/world/2020/mar/29/ireland-adapts-to-coronavirus-lockdown 22 Israel https://www.al-monitor.com/pulse/originals/2020/03/armed-troops-enforce-israelcoronavirus.html 23 Italy https://www.aljazeera.com/news/2020/03/italy-punishment-time-coronavirus-2003121711 28553.html 24 Jordan https://en.royanews.tv/news/20448/Razzaz--Government-will-take-more-measures-basedon-the-evolving-circumstances 25 Kenya https://www.aa.com.tr/en/africa/kenyan-under-curfew-amid-covid-19-pandemic-/1782727 26 Lithuania https://www.lrt.lt/en/news-in-english/19/1160397/lithuanian-police-fine-over-150-peoplefor-flouting-quarantine-rules 27 Malaysia https://www.bangkokpost.com/world/1897425/malaysia-extends-lockdown-by-2-weeks 28 Morocco https://english.aawsat.com//home/article/2205271/morocco-arrests-450-individualsviolating-emergency-state-measures 29 Netherlands https://www.bloomberg.com/news/features/2020-06-05/netherlands-coronaviruslockdown-dutch-followed-the-rules 30 Panama https://www.prensa.com/politica/en-la-noche-detenciones-por-incumplir-el-toque-dequeda-en-el-dia-la-calle-no-para-se-desafia-al-coronavirus/ 31 Romania https://www.bbc.com/news/world-europe-52370421 32 Russia https://www.themoscowtimes.com/2020/04/13/how-do-moscows-coronavirus-lockdownpasses-work-a69960 33 Saudi Arabia https://www.khaleejtimes.com/coronavirus-outbreak/covid-19-saudi-implements-curfew-10 000-riyal-fine-jail-time-for-violators 34 Serbia http://rs.n1info.com/English/NEWS/a586026/Minister-Up-to-700-people-violate-curfew-inSerbia-all-face-severe-fines.html 35 Singapore https://www.straitstimes.com/singapore/300-fine-for-flouting-measureshttps: //www.straitstimes.com/politics/parliament-private-social-gatherings-of-any-size-no-longerallowed-under-proposed-law 36 Slovenia https: //www.euractiv.com/section/coronavirus/short_news/slovenia-update-covid-19/1441892/ 37 South Africa https://www.iol.co.za/news/politics/you-could-be-fined-r1-000-r5-000-if-you-commit-thefollowing-lockdown-offences-47669568 38 Spain https://www.thelocal.es/20200417/how-much-can-you-be-fined-for-breaching-spainslockdown-and-how-to-appeal 39 Thailand https://www.bloomberg.com/news/articles/2020-04-02/thailand-set-to-step-up-fightagainst-coronavirus-with-a-curfew Economies 2021,9, 36 21 of 26 Table A2. Cont. No. Country Links 40 Turkey https://en.rayhaber.com/2020/04/koronavirus-onlemleri-kapsaminda-20-yas-altina-sokagacikma-yasagi-geldi/ 41 UAE https://gulfnews.com/uae/crime/coronavirus-fines-of-dh2000-in-uae-if-you-leave-homefor-recreation-1.70691483 42 UK https://www.reuters.com/article/us-health-coronavirus-britain-fines/britons-who-breaklockdown-face-30-pound-fine-pm-johnsons-spokesman-idUSKBN21B276 43 Ukraine https://www.ukrinform.net/rubric-society/2899162-ten-facts-about-coronavirus-lockdownin-ukraine.html 44 Zimbabwe http://www.rfi.fr/en/international/20200404-booze-ban-in-zimbabwe-as-covid-19 -lockdown-is-enforced Table A3. Sources used for collecting the data on fines for traffic law violations. No. Country Links 1 Albania https://autotraveler.ru/en/albania/#.XrtH_MBS_IU 2 Algeria http://autoalgerie.com/attachez-votre-ceinture,2040 3 Armenia https://swift.rent/blog/driving-car-in-Armenia https://travelcar.am/rent-a-car-in-yerevan/travel-news/guide-to-driving-car-in-armenia http://www.orangesmile.com/carrental/armenia/traffic_rules_habits.htm 4 Azerbaijan http://www.turan.az/ext/news/2018/6/free/Social/en/72710.htm https://karavan.az/main-traffic-rules-of-roads-in-azerbaijan/ 5 Bahrain https://bahrainofw.com/guide-traffic-laws-driving-bahrain/ https://services.bahrain.bh/wps/PA_GDTDhBoardServices/faces/javax.faces.resource/en/ GDTLaw.pdf?rel=v1 6 Belgium https://rijbewijs-online.be/en/belgium/traffic-rules/violations https://www.rac.co.uk/drive/travel/country/belgium/ 7 Bolivia https://bolivia.infoleyes.com/articulo/57049 8 Bosnia https://www.osac.gov/Content/Report/6084aa33-fa35-4398-9caa-1846c1cdaf2d https://www.sarajevotimes.com/new-law-fines-for-all-passengers-in-the-car-who-did-notfasten-their-seatbelt/+&cd=1&hl=en&ct=clnk&gl=gr&client=firefox-b-d https://autotraveler.ru/en/spravka/penalties-for-using-mobile-phone-while-driving-ineurope.html#.Xrwy3MBS_IU 9 Bulgaria https://myrentacar.com/en/journal/bulgaria/articles/pdd-shtrafy/ 10 Chile https://www.chiletraveltips.com/chile-traffic-violations-fine-fees-2019/ 11 Colombia https://www.colombia.com/actualidad/codigos-leyes/codigo-de-transito/Tit4Cp2 -sanciones-por-incumplimiento.aspx 12 Costa Rica https://costa-rica-guide.com/travel/transportation/car/traffic-violations-citations-ticketsfines-in-costa-rica/ 13 Cyprus https://www.offsite.com.cy/eidiseis/topika/sas-tsakose-i-trohaia-deite-posa-tha-pliroseteprostimo https://www.kathimerini.com.cy/gr/apopseis/arthrografia/iasonas-senekkis/tha-einaiapotreptikes-oi-nees-poines-gia-tis-troxaies-parabaseis https: //www.kathimerini.com.cy/gr/kypros/ayxisi-sta-prostima-300-eyrw-me-kinito-sto-xeri 14 Denmark https://www.fyidenmark.com/traffic-fines.html 15 Ecuador https://ecuador.seguros123.com/aumento-de-multas-de-transito-en-el-2020/ 16 France https://www.connexionfrance.com/French-news/Drivers-and-passengers-warned-over-noseat-belt-fines https: //www.french-property.com/guides/france/driving-in-france/driving-offences-penalties 17 Greece https://www.gocar.gr/news/feed/28768,Poia_einai_ta_prostima_gia_ypervolikh_ta.html https://www.newsauto.gr/news/neos-kok-pos-diamorfononte-i-vasikes-paravasis/ 18 Hungary https://www.orangesmile.com/carrental/hungary/traffic_rules_habits.htm https://zsebremegy.hu/rovid-hirek/mennyi-egy-gyorshajtas-es-egy-buszsavhasznalat/ 19 India https://www.business-standard.com/article/economy-policy/new-motor-vehicles-bill-2019 -penalties-full-list-fines-for-traffic-rules-violation-from-children-driving-drunken-driving-rs10000-fine-jail-all-you-need-to-know-119080100254_1.html Economies 2021,9, 36 22 of 26 Table A3. Cont. No. Country Links 20 Iraq https://magazine.imn.iq/%D8%AA%D8%AD%D9%82%D9%8A%D9%82%D8%A7%D8%AA/ %D8%AD%D8%B2%D8%A7%D9%85%D9%8F-%D8%A7%D9%84%D8%A3%D9%85%D9%80% D9%80%D9%80%D8%A7%D9%86-%D9%81%D9%8A-%D8%A7%D9%84%D8%B9%D8%B1 %D8%A7%D9%82-%D9%82%D8%A7%D9%86%D9%88%D9%86%D9%8C-%D8%B4%D8%A8 %D9%80%D9%80/ 21 Ireland https://www.theaa.ie/aa/motoring-advice/penalty-points.aspx 22 Israel https://anglo-list.com/general-3/transport-18/traffic-fines-violations-israel https://en.globes.co.il/en/article-israel-raises-fines-for-traffic-violations-1001182358 http://archive.jewishagency.org/aliyah/content/42546 23 Italy https://www.thetimes.co.uk/article/running-red-light-costs-italian-4-600-in-fines-plus-hiscar-79rsv0xb8 http://www.aci.it/fileadmin/documenti/viaggia_con_noi/pdf/ing/Fines_for_foreign_ motorists.pdf https://www.poliziadistato.it/articolo/cinture-di-sicurezza-e-casco 24 Jordan https://www.rhinocarhire.com/Drive-Smart-Blog/Drive-Smart-Jordan.aspx#/searchcars https://www.hala.jo/2016/12/05/%D9%85%D8%B4%D8%B1%D9%88%D8%B9-%D9%82% D8%A7%D9%86%D9%88%D9%86-%D9%8A%D8%B1%D9%81%D8%B9-%D9%85%D8%AF% D8%A9-%D8%A7%D9%84%D8%AD%D8%A8%D8%B3-%D9%88%D8%A7%D9%84%D8 %BA%D8%B1%D8%A7%D9%85%D8%A7%D8%AA-%D8%B9/ 25 Kenya https: //www.capitalfm.co.ke/business/2018/11/ntsa-fines-and-penalties-for-traffic-offences/https: //nairobinews.nation.co.ke/news/how-motorists-are-losing-millions-in-illegal-traffic-fines 26 Lithuania https://www.alfa.lt/straipsnis/192119/lithuanian-roads-most-dangerous-in-europe 27 Malaysia https://mypay.com.my/blog/post/Malaysias-Common-Traffic-Offences: -Are-You-Aware-Of-The-Rates https://en.wikipedia.org/wiki/National_Speed_Limits_(Malaysia) 28 Morocco http://www.riad-rihani.com/Uk/visit-marrakesh/Morocco%20in%20practice/To%20move% 20around 29 Netherlands https://trans.info/en/netherlands-here-are-the-fines-for-road-traffic-offences-in-2020-163679 https://trans.info/en/netherlands-here-are-the-fines-for-road-traffic-offences-in-2020-163679 30 Panama http://livinginpanama.com/living-in-panama/how-to-pay-traffic-ticket-panama/ https://cerquitita.com/estitlo-de-vida/multas-de-transito-costo-de-las-infracciones-masfrecuentes-y-como-evitarlas/ 31 Romania https://en.wikipedia.org/wiki/Speed_limits_in_Romania https://www.promotor.ro/utile/ce-amenda-risti-daca-nu-porti-centura-de-siguranta-candstai-in-spate-18155861 32 Russia https://www.angloinfo.com/how-to/russia/transport/driving/on-the-road https://www.rbth.com/lifestyle/328307-security-service-putin-seatbelt https://www.rbth.com/lifestyle/330604-paying-fines 33 Saudi Arabia https://www.moi.gov.sa/wps/portal/Home/sectors/publicsecurity/traffic/trafficriyadh/ contents/!ut/p/z0/04_Sj9CPykssy0xPLMnMz0vMAfIjo8ziDTxNTDwMTYy83UOcTQwcA5 0DAr09Q4wMLMz0vfSj8CsAmpCZVVgY5agflZyfV5 JaUaIfUVKUmJaWmaxqAGUoFGVWJqZkqBrkJmbmqRqUZebngB1 QrBCvUJAKdEdJZmqxfkF2VCQAec3urA!!/ 34 Serbia https://www.telegraf.rs/english/2904685-new-brutal-traffic-penalties-seat-belt-10000alcohol-prison-new-punishment-for-violent-driving 35 Singapore https://www.budgetdirect.com.sg/blog/car-insurance/speeding-offences-and-theirpenalties-in-singapore https://www.straitstimes.com/singapore/viral-text-saying-anyone-on-phone-while-engineis-running-will-lose-licence-on-the-spot-is https://www.directasia.com/blog/seatbelt-rules-in-singapore/ 36 Slovenia http://pnevmatike.amzs.si/en/758/49/Slovenia.aspx 37 South Africa https://www.arrivealive.mobi/south-african-law-on-wearing-of-seatbelts http://www.autoworld.co.za/TrafficLaw.aspx 38 Spain https://www.thelocal.es/20181031/why-now-more-than-ever-you-shouldnt-use-your-phonewhile-driving-in-spain https://www.euroweeklynews.com/2017/11/13/belt-up-in-spain-and-dont-get-fined/ https://www.aph.com/community/holidays/speeding-fine-spain-cost-points-licence/ Economies 2021,9, 36 23 of 26 Table A3. Cont. No. Country Links 39 Thailand https://nashaplaneta.net/en/asia/thai/art-thailand-shtrafi-za-narusheniya-pdd https://www.bangkokpost.com/thailand/general/1220662/strict-seat-belt-rule-to-applyfrom-april https://www.thaizer.com/travel-in-thailand/thailands-drivers-face-mobile-phone-ban/ 40 Turkey https://www.dailysabah.com/turkey/2018/10/27/fines-tighter-controls-target-trafficviolations-celebratory-gunfire https://www.hurriyetdailynews.com/parliament-passes-law-to-introduce-severe-traffic-fines138011 https://www.sabah.com.tr/otomobil/2020/04/07/emniyet-kemeri-cezasi-2020-emniyetkemeri-takmama-cezasi-ne-kadar-k1 41 UAE https://gulfnews.com/how-to/your-money/all-139-uae-traffic-violations-fines-and-blackpoints-1.1546486 42 UK https://www.thisismoney.co.uk/money/cars/article-6974171/Drivers-penalty-points-notwearing-seat-belt-survey-suggests.html 43 Ukraine https://destinations.com.ua/news/big-cities-life/214-updated-rules-for-drivers-in-ukraine2018https://whatson-kyiv.com/human-traffic/ https://www.ukrinform.net/rubric-society/2579430-only-15-of-drivers-in-ukraine-use-seatbelts-survey.html 44 Zimbabwe http://newsofthesouth.com/zimbabwe-police-reveal-road-traffic-fines-rates/ Table A4. Countries’ records in the FSIs, TFSI, and FODIs. No. Country FSI1FSI2FSI3TFSI FODIep FODIep+s 1 Albania 0.364 0.020 8.119 16.000 62% 91% 2 Algeria 0.100 0.006 0.596 35.000 378% 447% 3 Armenia 1.209 0.051 4.751 9.000 139% 215% 4 Azerbaijan 0.767 0.014 1.658 20.333 100% 383% 5 Bahrain 3.291 0.112 3.289 8.333 5433% 14,000% 6 Belgium 0.152 0.006 1.437 30.333 25% 49% 7 Bolivia 0.235 0.021 9.404 17.000 8509% N/A 8 Bosnia 1.141 0.054 2.567 11.667 330% 212% 9 Bulgaria 7.755 0.336 50.000 1.333 4157% 3631% 10 Chile 0.850 0.025 1.084 18.333 425% 644% 11 Colombia 0.940 0.037 0.372 19.333 2985% 1994% 12 Costa Rica 0.388 0.016 0.370 27.000 195% 258% 13 Cyprus 0.156 0.009 1.765 27.667 12% 29% 14 Denmark 0.084 0.004 0.749 36.000 58% 28% 15 Ecuador 0.313 0.019 0.826 24.000 1731% 1184% 16 France 0.024 0.001 0.281 42.000 86% 78% 17 Greece 0.191 0.009 0.214 33.667 111% 79% 18 Hungary 0.027 0.001 0.101 42.667 76% 77% 19 India 0.247 0.007 0.203 33.333 3598% 5449% 20 Iraq 0.400 0.017 6.650 16.000 N/A N/A 21 Ireland 1.460 0.039 20.833 7.000 192% 590% 22 Israel 0.101 0.003 0.332 38.667 44% 21% 23 Italy 0.170 0.007 0.510 32.000 43% 46% 24 Jordan 0.372 0.035 3.321 16.333 786% 1453% 25 Kenya 0.597 0.061 4.987 11.000 22,817% 20,766% 26 Lithuania 0.399 0.016 2.174 20.333 542% 690% 27 Malaysia 0.860 0.023 3.349 13.333 497% 1101% 28 Morocco 0.110 0.010 0.428 32.333 399% 711% 29 Netherlands 0.237 0.009 1.667 25.667 49% 119% 30 Panama 0.137 0.003 0.667 35.667 67% 71% 31 Romania 0.856 0.042 5.886 10.333 764% 757% 32 Russia 0.309 0.005 3.690 24.667 130% 157% 33 Saudi Arabia 3.472 0.136 14.879 4.333 13,757% 47,981% Economies 2021,9, 36 24 of 26 Table A4. Cont. No. Country FSI1FSI2FSI3TFSI FODIep FODIep+s 34 Serbia 1.198 0.100 3.273 10.000 1347% 1239% 35 Singapore 0.231 0.004 0.301 35.667 1% 1000% 36 Slovenia 0.411 0.019 1.333 21.333 42% 72% 37 South Africa 0.187 0.009 1.340 27.667 85% 18% 38 Spain 0.525 0.023 3.005 16.333 17% 54% 39 Thailand 4.859 0.191 56.716 2.000 10,201% 7833% 40 Turkey 0.111 0.005 1.332 32.667 44% 41% 41 UAE 0.668 0.014 2.002 20.000 1193% 5717% 42 UK 0.020 0.001 0.151 43.667 87% 69% 43 Ukraine 4.668 0.250 39.098 2.667 102,357% 87,697% 44 Zimbabwe 0.036 0.005 0.380 37.333 1921% 1151% Notes: N/A: Indices are not provided due to the lack of data. FODI ep , FODI ep+s scores in bold denote underestimation. References Bargain, Olivier, and Ulugbek Aminjonov. 2020. Trust and compliance to public health policies in times of COVID-19. Journal of Public Economics 192: 104316. [CrossRef] [PubMed] Barrios, John M., Efraim Benmelech, Yael V. Hochberg, Paola Sapienza, and Luigi Zingales. 2020. 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Link. 2021. Socioeconomic inequalities in the spread of coronavirus-19 in the United States: A examination of the emergence of social inequalities. Social Science & Medicine 268: 113554. [CrossRef] countryeconomy.com. 2020. NMW—National Minimum Wage. Countryeconomy. Available online: https://countryeconomy.com/ national-minimum-wage (accessed on 18 November 2020). Desval, Vageshwari. 2020. Covid-19: Laws Related to Quarantine in India. The Times of India Blogs, March 26. Available online: https://timesofindia.indiatimes.com/blogs/legally-speaking/covid-19-law-related-to-quarantine-in-india/ (accessed on 25 April 2020). Dormann, Carsten F., Jane Elith, Sven Bacher, Carsten Buchmann, Gudrun Carl, Gabriel Carré, Jaime R. García Marquéz, Bernd Gruber, Bruno Lafourcade, Pedro J. Leitão, and et al. 2013. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36: 27–46. [CrossRef] Du, Zhanwei, Xiaoke Xu, Ye Wu, Lin Wang, Benjamin J. Cowling, and Lauren Ancel Meyers. 2020. Serial interval of COVID-19 among publicly reported confirmed cases. Emerging Infectious Diseases 26: 1341. [CrossRef] [PubMed] Eissa, Noura. 2020. Pandemic preparedness and public health expenditure. Economies 8: 60. [CrossRef] European Centre for Disease Prevention and Control. 2020. Considerations Relating to Social Distancing Measures in Response to COVID-19—Second Update. European Centre for Disease Prevention and Control. Available online: https://www. ecdc.europa.eu/sites/default/files/documents/covid-19-social-distancing-measuresg-guide-second-update.pdf (accessed on 25 November 2020).