Introduction

In March 2020, governments were faced with the need to implement restrictive measures to reduce social coming closer and promote public behaviour change in social distance (Pedersen & Favero, 2020). Such non-pharmaceutical interventions included several containment measures whose most typical examples were lockdowns. In this chapter, lockdowns are conceptualised as a cluster of heterogeneous interventions according to the categories used in the EXCEPTIUS dataset (Egger et al., 2022).

As a result of the restrictive measures, European Member States experienced different rates of compliance, conceptualised in this case as the change in the rate of mobility for retail and recreation, which differs across countries.

At the onset of the pandemic, academic attention focused on investigating possible triggers for compliance with social distancing measures. However, there is no comprehensive narrative describing the variability of mobility rates due to restrictive measures and providing a comparative description of possible compliance triggers across Europe. Therefore, this chapter aims to contribute to the literature on mobility rate variability in COVID-19 by elucidating the mobility rate variability during the first strict national lockdown in 15 EU Member States, and to identify possible research clues focused on improving crisis-management strategies for future possible crises and enhancing public safety.

Indeed, although the effectiveness of lockdowns in containing the spread of COVID-19 is still controversial (Dainton & Hay, 2021), this chapter follows the empirical findings of Mégarbane et al. (2021) and Alfano and Ercolano (2020), which positively correlate restrictive social mobility policies with rule compliance and public safety.

In addition, the importance of contextual factors in shaping citizens’ rule compliance (lower mobility rates) is considered, such as the level of stringency of containment measures (Caselli et al., 2022), modalities of public enforcement (Mills et al., 2021), government trust (Bargain & Aminjonov, 2020), risk perception (Xie et al., 2020) and the influence of time (Six et al., 2021).

Mobility Rate and Compliance

The extant literature highlights how societal and institutional macro-level factors, such as the stringency of restrictions, the modalities of public enforcement and government trust, are important triggers of rule compliance towards lockdowns. As lockdowns aim to reduce the mobility rate, authors such as Dainton and Hay (2021) and Caselli et al. (2022) analysed the variation in mobility rate to estimate the level of compliance with COVID-19 containment measures.

Caselli et al. (2022) and Sun et al. (2021) demonstrate how a higher level of lockdown stringency leads to a greater decrease in mobility rate. However, despite the robustness of the authors’ results, the former focuses only on three EU Member States while the latter focuses only on the US. Similar to the deterrent logic of higher stringency, another macro element such as rule enforcement has been shown to be influential in reducing the mobility rate during lockdowns. Indeed, Huntley et al. (2020) and Jennings and Perez (2020) provide empirical evidence on how stronger enforcement, in this case higher fines and a higher number of police officers on the street, leads to a greater decrease in the mobility rate. Nevertheless, there is no analysis focusing on the whole European continent that differentiates between the stringency of legislation and the depth of control of enforcement modalities nor is there a comparative analysis of the enforcement rate during the COVID-19 per EU Member State.

In addition, not only macro factors directly related to the regulations implemented, but also public perceptions of government reliability, such as higher public trust, lead people to comply more (Bargain & Aminjonov, 2020; Sarracino et al., 2022). In addition, previous literature on preventive behaviour and COVID-19 has highlighted how higher levels of risk perception leads to higher compliance (Xie et al., 2020).

This chapter aims to contribute to current research on compliance with COVID-19 regulations by providing a descriptive analysis of the above factors. A novelty is the use of risk perception at the macro level. It also aims to identify the most influential dimension of compliance, be it policy or public opinion.

Measuring Compliance and Its Drivers

This chapter uses the Google Mobility Rate dataset (2022) to operationalise the dependent variable (compliance with social distancing measures). The dataset collects aggregated and anonymised data from Google Maps, which reports mobility trends per country and region on a daily basis from February 2020 to October 2022. In addition, these trends are divided into six main categories (1) retail and recreation, (2) food and pharmacy, (3) parks, (4) transit stations, (5) workplaces, (6) and residential. Finally, the variability of mobility rates is calculated based on a baseline, which is the median of a five-week period between 3 January 2020 and 6 February 2020. Nevertheless, in the current chapter, only the mobility category retail and recreation is considered, as it was the one that was first targeted by various lockdown policies. Therefore, when using the term ‘mobility rate’, the current chapter only refers to the mobility rate towards retail and recreational places such as bars and clubs.

Second, this chapter uses EXCEPTIUS data (Egger et al., 2022) as an independent variable of the stringency of social distancing measures adopted by governments. However, the EXCEPTIUS dataset does not include a dedicated category on lockdowns as this terminology was used by most European members only in official government press conferences and mass media, and not in the relevant legislative decrees (see Chazel in this volume). Specifically, the current chapter uses seven measures falling under the lockdowns umbrella as presented by Chazel in this chapter and aggregates them into a single stringency score.

In order to operationalise ‘regulatory enforcement’, this chapter uses Eurostat’s Government Expenditure (2020) dataset. The dataset collects the annual expenditure as a percentage of GDP of each government by function, some of which are health, public services, education, defence, public order and safety from 2012 to 2021 in all European Member States. In this case, the chapter only analyses annual government expenditure on ‘public order and safety’, which includes expenditure on ‘police, fire brigade, courts, prisons, R&D related to public order and safety’ as well as expenditure classified elsewhere (Eurostat, 2021).

Furthermore, in the absence of a longitudinal dataset on risk perception, this chapter draws upon a proxy for risk perception by using the number of COVID-19 cases and deaths per day at the national level. Indeed, a linear statistical correlation has already been observed between a higher rate of recorded COVID-19 cases and an increase in individual risk perception (Vulcano, 2025).

For this purpose, the current chapter uses the “Coronavirus (COVID-19) cases” of Our World in Data (data collected in 2023), which collects the number of COVID-19 deaths, cases, tests, hospitalisations and vaccinations worldwide on a daily basis until today (Edouard et al., 2020).

Finally, to measure public trust in government, this chapter uses the European Parliament COVID-19 Survey—Round 1 (GESIS, 2021). The survey aims to collect data on the general satisfaction of the public with the COVID-19 responses. The survey was carried out in April and May 2020 in 24 EU Member States. The current paper uses the mean of the answers given by people per nation using an ordinal scale (1 = I do not trust the government at all to 4 = I really trust the government).

General Analysis of Lockdown and Mobility Rates

In general, across all three waves of COVID-19, the Czech Republic and Austria have implemented the highest number of national lockdowns, followed by Belgium, France, Hungary, Ireland, Italy and Portugal (see Table 16.1). Croatia, Malta and Finland are the countries with the lowest number of implemented lockdowns. France, Germany, Italy and Portugal have implemented the most restrictive measures, followed by Austria, the Czech Republic and Croatia. On the other hand, Estonia, Denmark, Belgium and Luxembourg have the least restrictive measures. By simply subtracting the mean mobility rate before the ban from the mean mobility rate after the ban, it is possible to grasp the variation of mobility rates before and after the ban. France, Austria, Croatia and Italy are the countries where the mobility rate decreased the most, while Hungary and Malta experienced the smallest decrease in the mobility rate. Finally, Belgium is the only country where the mobility rate increased after the introduction of the lockdown.

Table 16.1 Comparison between the level of stringency of the first implemented lockdown and mobility rates
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Analysis of Compliance

The following analysis considers the previously mentioned macro factors: (1) stringency, (2) enforcement (security expenditure), (3) trust in government, (4) risk perception by considering only the first national lockdown per country and 15 days pre- and post-lockdown. As the dataset includes 15 EU Member StatesFootnote 1 with 30 observations each, the total number of observations is 450.Footnote 2 Finally, as the European Parliament’s COVID-19 survey is based on a large sample of individuals per country, the mean of the answers per country is used in this chapter.

The following graphs show the multivariate OLS regression with mobility rate as the dependent variable and security expenditure, government trust, stringency and risk perception as independent variables. These data are collected 15 days before and 15 days after the first lockdown to observe its impact. Some data, such as stringency or risk perception, are collected every day and do not require special processing. Other measures, such as security expenditure and government trust, are collected annually. Their impact on mobility is, therefore, expected to be conditional on the implementation of the lockdown. This is why a dummy variable (pre-lockdown implementation = 0 and post-lockdown implementation = 1) was introduced to create an interaction term for the variables that have identical observations.

The scatterplot (Fig. 16.1) shows the relationship between mobility rate and security expenditure before and after the implementation of the lockdown, while the fitted line values highlight the coefficient relationship between the two variables. The linear regression shows a negative relationship between enforcement and mobility rate with a coefficient of −732. However, the relationship is not significant (p = 0.280). Looking at the relationship between mobility rate and security expenditure with the interaction term (after the introduction of the lockdown), there is a significant (p < 0.01) and stronger negative correlation with a coefficient of −1466.

Fig. 16.1
2 strip plots between mobility and rules enforcement. Both fit lines have a negative correlation. Mobility before lockdown fit line has a mild slope, while mobility after lockdown has a larger slope.

Relationship between security expenditure and mobility rate before and after the first lockdown. Source: EXCEPTIUS, Google Mobility Report

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Figure 16.2 shows the scatterplot with line-fitted values of mobility rate and trust in government. The OLS shows a positive but not significant correlation between trust in government and mobility rate before the lockdown, with a coefficient of 2.2 and a p-value of 0.08. After the lockdown, however, there is a statistically significant negative correlation with a coefficient of −14.6948.

Fig. 16.2
2 strip plots between mobility and government trust. Both fit lines have a negative correlation. Mobility before lockdown fit line has a mild slope, while mobility after lockdown has a larger slope.

Relationship between trust in government and mobility rate before and after the first lockdown. Source: EXCEPTIUS, Google Mobility Report

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Figure 16.3 shows the correlation between mobility rate and stringency. The lockdown dummy was not used as an interaction term because the stringency observations are daily collected and most of them start with the first lockdown. OLS shows a significant and negative correlation between the two variables (p = 0.07) with a correlation coefficient of −51.

Fig. 16.3
A strip plot of stringency and mobility rate over fitted values. The fit line has a negative correlation. The dots are high at 0 between 5000 and negative 15000, then scatter toward the right.

Relationship between social distancing stringency and mobility rate. Source: EXCEPTIUS, Google Mobility Report

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Figure 16.4 shows the relationship between mobility rate and risk perception. When analysed alone, risk perception has a negative and significant correlation with a decrease in the mobility rate. However, OLS shows a positive but not significant relationship (p = 0.166) between risk perception and decline in mobility rate, with a coefficient close to 0, when analysed in a multivariable linear regression with policy-related factors. Finally, the lockdown dummy was not used due to the quality of the data, which is heterogeneous throughout the year.

Fig. 16.4
A strip plot of risk perception and mobility rate over fitted values. The fit line has a negative correlation. The dots are high at 0 between 5000 and negative 15000, then scatter toward the right.

Relationship between risk perception and mobility rate

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Conclusion

Empirical evidence collected by the Google Mobility Rate showed how the decline in mobility rate during the COVID-19 lockdowns varied significantly across European countries. Such a phenomenon boded well for a comparative and macro-level analysis across EU member states. Indeed, previous research identified how a loyal citizen fearing to be infected by the COVID-19 virus in an emergency context of high regulation and strong rule enforcement is the scenario with higher public rule compliance. However, previous studies focused on risk perception as a micro-level factor, did not compare the EU Member States or distinguished between how citizens’ attitudes and perception and rule stringency shape compliance. This left open the question of why people in different countries have responded differently to the implementation of lockdown and what are the most important factors for the institution to consider.

According to the empirical results, rule enforcement is not statistically significant before the introduction of lockdown, but after its introduction it is the most influential variable in reducing mobility rates and promoting compliance. Similarly, the stringency of the lockdown and trust in the government lead to lower mobility rates. These initial results are consistent with past studies. The results regarding the influence of COVID-19 risk perception on mobility rates are, however, new. Indeed, according to the empirical findings of Xie et al. (2020), risk perception has a positive influence on compliance that we don’t observe in our macro analyses. The limitation of the current chapter may be the use of COVID-19 cases as a proxy for risk perception, while Xie et al. (2020) used an ad hoc 22-question online survey with 317 Chinese participants. Therefore, it is promising to investigate other variables as a proxy for risk perception, such as COVID-19 death rate, COVID-19 case percentage, COVID-19 relative growth.

In conclusion, this research engaged with a comparative analysis of the different rates of compliance with the lockdown measures with different levels of stringency across the EU. Consequently, the research investigated two influential dimensions in the development of compliance, namely policy-related factors such as stringency versus enforcement, and public opinion-related factors such as trust in government and risk perception. Enforcement is found to be the most influential factor in reducing the mobility rate after the introduction of lockdowns, followed by regulatory stringency. Trust in government is important but less influential. Highlighting the importance of policy-related factors across the EU may be important to provide insights for policy makers in formulating effective regulations in terms of compliance in possible future crises, and to better understand the social dynamics of compliance.