Household behavioural response to health policy recommendations: evidence from the COVID-19 pandemic in Bosnia and Herzegovina, Croatia, Serbia, and Slovenia

Robert Sonora*

Robert Sonora

Affiliation: Accounting and Finance Department, University of Montana, USA

0000-0003-4713-7082

Correspondence
University of Montana, 32 Campus Drivrobert.sonora@mso.umt.edu

   

Josip Tica*

Josip Tica

Affiliation: Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia

0000-0001-7937-1573

Article   |   Year:  2026   |   Pages:  373 - 404   |   Volume:  50   |   Issue:  3
Received:  March 31, 2025   |   Accepted:  June 15, 2026   |   Published online:  September 5, 2026

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https://doi.org/10.3326/pse.50.3.3

Abstract

1 Introduction

2020

Potter and Harries (2006) suggest that the “determinants of policy effectiveness” for health policy models require that public administration systems include diverse social, cultural, and economic motivators to encourage the desired behaviour – or “adherence” – to policy measures. The COVID-19 pandemic represents a rare opportunity to empirically analyse adherence behaviour during a rapidly changing crisis and corresponding responses to policies intended to minimise infection rates and protect public health. The literature on the COVID-19 pandemic spans three primary analytical domains: policy efficacy, policy determinants, and public adherence to policy measures. The first group of papers evaluates how non-pharmaceutical interventions (stringency measures), such as stay-at-home policies, lockdowns, and school closures, directly impacted infection rates and mortality (Hsiang et al., 2020; Walker et al., 2020; Fuller et al., 2021; Chen, Li and Wu, 2022).

In parallel, a second group of researchers investigates the political and economic motivations that led governments to adopt specific rules. These studies often find that a country’s response was less a reflection of viral load and more a result of economic constraints, human capital, and/or political preferences (Capano et al., 2020; Desson et al., 2020; Chen et al., 2021; Jalloh et al., 2022; Toshkov, Carroll and Yesilkagit, 2022; Kuo and Wen, 2023; Sonora, 2023).

The third category focuses on public adherence and behavioural moderation, acknowledging that government mandates only work if people follow them. By analysing how factors like communication strategies, risk perception, institutional trust and socio-economic status influence compliance, this research explains how human agency serves as a critical moderator between a written policy and its actual success in the real world (Bargain and Aminjonov, 2020; Cairney and Wellstead, 2020; Berry et al., 2021; Pagliaro et al., 2021; Berg-Beckhoff et al., 2022; Tu, Chen and Mesler, 2023; Luciano, Crova and Canella, 2023 and Brunekreef et al., 2025).

A related strand of the literature integrates multiple dimensions of pandemic policy analysis within a single empirical framework, including the interaction between policy determinants and behavioural compliance (Waterschoot et al., 2023; Agyapon-Ntra and McSharry, 2023), policy formation and policy effectiveness(Cross, Ng and Scuffham, 2020; Herreros and Svendsen, 2024), and policy effectiveness and public adherence (Dave et al., 2021).

Following the work of Agyapon-Ntra and McSharry (2023), Luciano, Crova and Canella (2023) and Waterschoot et al. (2023), who utilised a wide range of control variables (such as GDP, literacy, and corruption), we employ structural break analysis to capture the mediating effects of diverse cultural and religious settings on adherence levels across the analysed countries.

Using standard, structural break, and rolling unit root tests, we reject a unit root process if individual behaviour eventually converges as prescribed by the policy. Our estimates cannot determine whether individuals are risk-taking or risk-averse, as we only observe changes in risk attitudes relative to policy recommendations. Behavioural adjustment may be interpreted through the lens of a wide range of behavioural economics of attention and inertia theories (Thaler et al., 1997; Berg, Dickhaut and Rietz, 2010; Costa, 2018).

2 Timeline of the COVID 19-pandemic

2020

2021

2020

2021

2023

Shortly after the introduction of restrictions, the COVID-19 case curve flattened, leading to the gradual reopening of economies by late spring 2020 (Mathieu et al., 2020). For instance, Slovenia declared victory over the pandemic on 15 May 2020 (Vladisavljević, 2020). Croatia and Serbia soon followed, even holding elections in June and July 2020 (Karlović, 2021; Uzelac, 2023). As a tourism-dependent economy, Croatia relaxed restrictions to salvage the summer tourist season, reopening its borders to tourists, particularly from the EU (Vlada RH, 2020).

The timeline of the second wave of COVID-19 varied among countries, as did the responses of policymakers. Serbia and Bosnia and Herzegovina (hereafter B&H) were the first to experience the second wave, which began in July 2020 (Mathieu et al., 2020; Hale, 2021). In Serbia, this wave prompted the government to reintroduce restrictions, including curfews. These measures triggered large protests in cities such as Belgrade, where thousands demonstrated against the government’s handling of the crisis, the reintroduction of curfews, and the perceived politicization of the pandemic. The protests turned violent, leading to clashes with the police (BBC, 2020).

Conversely, political fragmentation between the two federal units within B&H (the Federation (FBiH) and Republika Srpska (RS)) resulted in inconsistent measures across B&H – the RS imposed stricter curfews, while FBiH was slower to respond. The lack of a coordinated national strategy made it more difficult to control the spread of the virus (Milinković, 2021; Sajić, 2021).

In Slovenia and Croatia, the second resurgent wave occurred in the autumn of 2020, prompting the respective governments to reintroduce restrictions that remained elevated until the spring of 2021. A similar situation unfolded in B&H and Serbia in November and December 2021. Compared to the spring lockdown, the responses of all governments, except for Slovenia, were more moderate (Mathieu et al., 2020; Hale, 2021). In Slovenia, the government reinstated restrictions, including curfews, school closures, and limitations on social gatherings (Ružić Gorenjec et al., 2021).

The vaccine roll-out began in January 2021 in Croatia, Serbia, and Slovenia, while Bosnia and Herzegovina initially struggled to secure the vaccine (Mathieu et al., 2020). Unlike many EU countries, such as Croatia and Slovenia, B&H did not have direct access to the EU’s vaccine procurement program and relied on the global COVAX initiative, which was slow in delivering doses (Fojnica et al., 2021). In contrast, Serbia’s ability to secure a diverse range of vaccination supplies enabled it to inoculate its population rapidly and to offer vaccines to neighbouring countries (Dartford, 2021).

In the spring of 2021, all four countries experienced a surge in the number of cases attributed to the Alpha variant, and all but Bosnia and Herzegovina saw another surge driven by the Delta variant in the autumn of the same year. A significant surge occurred at the beginning of 2022 with the Omicron variant. The final surge took place in the summer of 2022, and was much milder compared to previous waves, except in Serbia, which recorded major increases in the number of cases (Mathieu et al., 2020).

Anti-COVID stringency policies declined throughout 2021 even as cases slowly increased during the spring and autumn waves. Slovenia maintained relatively high stringency levels throughout the year, while B&H showed the smallest variation in stringency. Croatia, due to its reliance on tourism, adopted a more laissez-faire approach starting in the summer months (Hale, 2021). In the summer of 2021, both Croatia and Slovenia introduced EU Digital Green Certificates, which were required for attending events, gatherings, and entering certain workplaces (European Commission, 2021; Healthcare IT News, 2021).

All countries experienced protests and significant political polarisation regarding policy responses. Serbia was the first to face protests in July 2020 due to the extreme level of stringency (BBC, 2020). In Slovenia, protests erupted in the autumn of 2020, with citizens expressing dissatisfaction with the government’s handling of the crisis and restrictions on personal freedoms (Vladisavljević, 2021; Kriesi and Oana, 2023). In B&H, the slow vaccine roll-out and perceived mishandling of the pandemic led to public discontent in spring 2021, prompting protests in major cities where citizens demanded a more effective government response, including faster vaccination and more transparent decision-making (Reuters, 2021). In Croatia, large protests occurred in cities such as Zagreb and Split against the mandatory use of EU Digital Green Certificates for workplaces and events in autumn 2021, reflecting significant public opposition to vaccine mandates and restrictions (Grgurinović, 2021).

3 Policy effectiveness: theoretical background

1967

Bt*=β̃Pt*

That is, the preferences of both the residents r and policymaker p are equal:

Ur(B*) = Up(P*|β̃)

(2)

Policy “confusion”, or uncertainty, is determined by the variability in either policy saliency, contradictory policies, and/or frequent policy revisions. Angelini et al. (2023) define their policy function evolving as:

Pt = φPt-1 + (1-φ) P*, |φ| ∈ (0,1)

(3)

where P^* is the policymaker’s optimal response to minimising an economy-health loss function, as estimated in Sonora (2023). In this formulation, actual policy responses are staggered over time as the weighted average of previous policy choices, with policy inertia given by φ, and the current optimal policy. Note that as φ → 0, policy converges to the optimal response, reducing uncertainty. Solving this recursively, we get

where λ ≡ (1 − φ) is an adaptive expectations adjustment parameter. If we assume that policy is constant P^* = P and substitute this into equation (1), after passing through the expectation operator, we have

Bt = βPt

(4)

Now suppose that residents’ adjusted behaviour is dictated by current policy and their own lagged behaviour, or

Bt = βPt + ρBt-1 + ηt,|ρ| ∈ (0,1)

(5)

The AR portion of this equation represents habit persistence and/or adjustment dynamics as residents update their responses to previous policy decisions. The behavioural adjustment in equation (4) becomes

B = βLRP

(6)

where  . Note that the relationship between β and β_LR is dependent on the sign of ρ; if ρ ∈ (–1,0) then β_LR < β. In this case, residents reduce their policy compliance over time, in support of myopic loss aversion. If, however, ρ ∈ (0,1) then residents’ preferences converge to their short-term responses, which is the result of “present bias” (see O’Donoghue and Rabin, 1999), information constraints, and/or rational inattention as a form of “behavioural over-shooting”, e.g. Maćkowiak and Wiederholt (2009).

Hypothesis 1: Policy effectiveness in terms of alignment between mobility behaviour and policy recommendations is complex, dynamically inconsistent and incomplete in the short to medium run.

This analysis suggests that because E(η)=0 policy makers implicitly disregard idiosyncratic individual behaviour, irrespective of whether they know residents’ true policy and behavioural response parameters. Moreover, these parameters are likely to create behaviour which deviates from policymakers’ intent.

If we relax the assumption that policy is constant and substitute equation (3) into equation (1) and assume E(P, η) ≠ 0 and P_t and P_t^* are time variants, and recognise that η is non-constant, the variance of equation (1) is given as, after rearranging to behavioural variance, or policy confusion, as

where σ_i² is the variance of variable i and σ_ij is the covariance of variables i and j. We assume Cov(P_t−1,η_t) = 0, which, given the “backward” nature of policy is reasonable, as previous policy is uncorrelated with unforeseen current behavioural shocks. In this formulation, as φ → 0 there is a reduction in policy confusion.As a special case, suppose policy is always optimal, i.e. if P_t^* = P_t-1 then Cov(P_t, P_t^*) = Cov(P_t, η_t) = 0, and (7) collapses, after rearranging, to find the source of unobservable behavioural variance, to

σB2 = ση2

implying behavioural volatility is purely a function of animal spirits. Put another way, even without policy confusion, there will still be policy ineffectiveness due to idiosyncratic behaviour.

Following the same logic, if the behavioural function now includes the AR term, behavioural variance becomes

As before, the policy uncertainty analogue, σ_B², to policy effectiveness, β, is dependent on the sign of |ρ| ∈ (0,1). For ρ<0 behavioural uncertainty falls while the opposite is true if ρ>0.

Due to cross-national variation in religious and national settings, we expect that differences in national and religious calendars will have a moderating role in the mean-reverting properties of residuals η_t as suggested by Hypothesis 2.

Hypothesis 2: There is baseline cross-national variance in the rate and extent of compliance and convergence to policy, which is further compounded by localized structural breaks.

Furthermore, if agents’ behaviour gradually adjusts to policy targets, the discrepancy between policy-prescribed outcomes and observed behavioural responses, η_t, should – at least in the long run – exhibit mean-reverting properties. That is, deviations from policymakers’ intended path should be temporary and tend to converge back over time, as suggested by Hypothesis 3.

Hypothesis 3: Behavioural inertia and shocks in individual preferences prevent both immediate and persistent behaviour in an evolving crisis leading to uncertainty about policy effectiveness.

4 Methodology

There might be concern that policy prescriptions and behaviour are endogenous. That is, policymakers respond to individual voter preferences when determining pandemic restrictions. However, this would require very frequent changes in policymaking behaviour. Moreover, Sonora (2023) demonstrates that the level of stringency is determined by minimising an “economy-health” loss function and can, therefore, be considered exogenous. X is a vector of other country-specific and national controls, discussed in the data section below. Similarly, Jalloh et al. (2022) showed that policy was driven by information on deaths, health expenditures as a percent of GDP while Sonora and Tica (2024) find no evidence for policy endogeneity in their study of four US states during the pandemic.

Of primary interest are the contemporaneous estimates behavioural elasticity with respect to policy, β̌₀ and the longer-term adjustment elasticity estimates derived from ρ̌ and β̌(L). These estimated coefficients represent the policy response parameters introduced in equations (1) and (5) and are an empirical test of Hypothesis 1 with the null given as no policy alignment. Given that people require some time to fully incorporate policy into their decisions, we anticipate the short-run estimate to be more inelastic than the longer-term elasticity. This reflects the benefits of using very high frequency data in empirical modelling; it allows us to better understand individual behaviour in the immediate short run and compare it to individual choices as the time horizon lengthens. In an urgent situation, such as a pandemic, the initial response to new restrictive policy is not likely to be as effective as policymakers may believe.

The second aspect of interest for understanding unobservable human behaviour with respect to policy intended to inhibit social interaction is the time-series properties of η which are the estimated residuals from equation (7). In this context, ή represents country-level unobserved idiosyncratic behaviour and is the manifestation of individual preferences with respect to risk, politics, behavioural norms, information, etc. (see, for example, Allcott et al., 2020; Barrios and Hochberg, 2020; and Poletti, Ajelli and Merler, 2011).

(8)

where M_τ is the target level of mobility, the behavioural empirical analogue to equation (2). Empirically, we can represent this as an AR(1) model

(9)

where, from equation (3), φ = (1 – λ) is the policy “inertia” parameter. ² If λ ∈ (0,1), policy confusion is less prevalent and φ → 0, which means that the deviation from the stated policy objective is short-lived. However, as λ → 0 individuals are less policy compliant and ή converges to a random walk, implying a greater degree of policy uncertainty or confusion.

There are two observations about the equation above. First, the preferences of both individuals and policymakers differ across countries. Second, the above equation does not imply that the policymaker is making optimal health policy to reduce the spread of the disease, but rather the decision is made based on their idiosyncratic set of preferences, as in Sonora (2023). We also interpret this preference matching within the context of the Tiebout (1956) hypothesis.

We employ three models that test for the presence of unit roots. The first is the standard augmented Dickey-Fuller test. Secondly, we employ the one- and two break tests proposed by Clemente Lopez, Montañés and Reyes (1998) (CMR). Structural break unit root tests are useful when analysing time series that may be subject to deterministic breaks which could be misinterpreted as permanent stochastic processes that bias standard unit root tests towards nonstationarity, leading to size distortions. Structural breaks are employed to examine the role of cultural dimensions in explaining cross-national variance, as proposed in Hypothesis 2.

This model can handle both “long-run” innovative and “short-run” additive, or level, shocks. For our purposes, breaks in the data represent structural changes in individual behaviour as it relates to health policy and could therefore be either of these types of inflection points. Alternatively, innovations represent changes in behavioural trends. Note that both breaks could manifest in either less or more mobility as the social environment changes.

It is important to note that the unit root tests do not detect whether behaviour is overly cautious or heedless of policy, just whether or not ή ∼  I(0) and reverts to desired policy outcomes. For each test, we allowed for up to 14 days of lagged differenced terms to account for serial correlation. The suggested rule of thumb for choosing the maximum number of lagged data, suggested by Schwert (1989), is about 18 days, but we chose to use 14, as it corresponds to the number of days required for quarantine.

The third unit root model employed is the rolling-window ADF test, which allows for the examination of time-varying stationarity properties in the data. By applying the test over successive sub-samples, we can detect periods when the series becomes non-stationary, thereby capturing the potential effects of policy uncertainty and other structural changes on the persistence of the variable, as suggested by Hypothesis 3.

5 Data

2021

2021

2020

COVID-19 restriction policies are from the Oxford Coronavirus Government Response Tracker Stringency Index (denoted OxSI), see Hale (2021). This is one of several indices constructed by OxSI but was chosen as it quantifies the degree to which countries restrict individual movements, via lockdown policies, rather than economic support or health containment policies. The index is between 0 and 100 with a value of 100 being the most restrictive. The index represents all containment and closure policy indicators, such as school and workplace closing, public event cancellations, restrictions on gatherings, access to public transport, and travel, as well as stay-at-home recommendations. OxSI daily data captures the intensity and modality of policy interventions for COVID-19.

The control variables are national-level indicators. First, we include the number of new weekly cases or deaths per capita, available from Johns Hopkins (2021). This is used as an information variable; spikes in new recent cases indicate the spread of COVID-19 which leads to less mobility. Second, we include the current and one-week lagged percentage of eligible population that has been vaccinated, Vax rate, at least once. Finally, seasonal fixed effects are included for the summer period 1 June to 1 September for both 2020 and 2021.

6 Results and discussion

(10)

where the numeric subscripts refer to the number of lags. We also present the results for vaccination rate, weekly cases, and deaths. Finally, we include seasonal fixed effects to account for tourism impacts. 

 

Table 2

Behaviour regression results – GMI dependent variable

DISPLAY Table

We begin our discussion by focusing on the estimated response coefficient rather than the immediate estimated elasticities. It is important to recall that the policy implemented by each country may not be optimal for reducing the spread of the virus; rather, it reflects the individual preferences of the policymaker’s and trade-off between the economy and the health of the country’s residents.

Interpreted through our behaviour-policy alignment premise (Hypothesis 1), the pattern that longer-run elasticities are typically smaller in absolute value than the instantaneous ones suggests that residents overreact on impact, but relax their response over the subsequent two-week period, which confirms Hypothesis 1 in terms of an incomplete and dynamically inconsistent response to policy – effectively mitigating the idiosyncratic component of the policy response, ή. Over time, that component regains influence, uncertainty rises, and the eventual response falls short of the initial adjustment. Both sets of estimates remain below the one-for-one benchmark, so the picture is one of incomplete compliance, with the key feature being decay from impact to the medium run rather than sustained alignment.

In Costa’s (2018) study of the equity premium puzzle, he finds that higher inflation is associated with a lower equity premium, consistent with myopic loss aversion operating through short-horizon evaluation of outcomes. The results emphasise that when individuals focus on short-run fluctuations, loss-averse responses are amplified relative to a longer-run perspective. In theCOVID context, this suggests that frequent, short-run updates on cases, deaths, or policy changes may have intensified behavioural responses compared to a setting where risks are evaluated over longer horizons.

The cross-country exceptions are informative, confirming Hypothesis 2. In Serbia, longer-run elasticities exceed the impact estimates (in absolute value), consistent with gradual adjustment, rational inattention, and/or present bias. Slovenia is the closest to the one-for-one benchmark on impact, yet the positive, significant coefficient on OxSI(−14) points to a behavioural reversal over a two-week horizon – plausibly tied to quarantine dynamics or policy salience – again indicating that initial alignment is not fully maintained. Overall, these patterns reconcile negative, statistically significant elasticities with sub-unity magnitudes: compliance is immediate, incomplete, and not durably sustained.

A plausible behavioural mechanism for this pattern is myopic loss aversion (MLA). In this framework, after a policy is announced, individuals evaluate outcomes over short horizons and overweigh the perceived losses from falling short relative to the gains from exact compliance. This induces a precautionary “buffer”; behaviour adjusts sharply on impact but at a level that is difficult to sustain. As the period immediately after the announcement passes and expectations are better understood, the buffer attenuates, leaving a smaller longer-run elasticity. This result suggests that immediate responses are amplified, but over time, they lengthen the subsequent response time, potentially reducing policy effectiveness as the policy matures.

The positive OxSI(−14) estimate for Slovenia is consistent with this overshoot-and-unwind dynamic, whereas Serbia’s larger longer-run elasticities suggest gradual adjustment driven more by adaptation costs than by loss-averse buffering. In Serbia’s case, MLA is “flipped”. Initial behavioural responses are relatively small and then become more aligned to policy restrictions as more information is collected. Therefore, in Serbia’s case, policy becomes more effective over longer time horizons.

Figure 2

Smoothed results

DISPLAY Figure

Figure 2 shows the smoothed relationship between estimated behaviour, that is behaviour explained by policy and actual behaviour in each of the countries – it is the difference between these two lines which represents the unobserved portion of behaviour. This illustrates policy response behaviour; the smaller the distance between these two lines, the greater its effectiveness. This figure illustrates that residents “overcompensated” their behaviour in the early stages of the pandemic, demonstrating a large degree of risk aversion as suggested by Hypothesis 1 and Hypothesis 3 in terms of uncertainty. During the middle stages, there appears to be a convergence of policy and behaviour. However, beginning in the first quarter of 2021, animal spirits took over and mobility expanded well beyond policymaker’s prescriptions, except for Slovenian residents, whose behaviour remained relatively aligned with regulations – discussed below.

Figure 3

270 days rolling response elasticities using cases and deaths

DISPLAY Figure

An alternative explanation could be that Serbians and Slovenians trust their government more than the other countries. According to the Wellcome Global Monitor (Welcome Trust, 2021), in 2020 57% of Serbians trusted their government while in B&H it was only 25%, for Croatia and Slovenia it was 42% and 48% respectively. These patterns may suggest that Serbian residents exhibited stronger medium-run behavioural alignment with policy measures after an initial adjustment period. Alternatively, in the aftermath of the demonstrations in Belgrade in July of 2020, Serbian policy makers reduced restrictions (see figure 1), and Serbs’ behaviour loosened relative to the new more relaxed policies. Third, Serb and Slovenian policy measures may have been more closely aligned with prevailing resident preferences.

To answer the question about how individual preferences changed over the course of the pandemic, we conducted 270-day rolling regressions of equation (3) for each country, presented in figure 3 using both cases and deaths as the COVID-19 information proxy. Changes in policy elasticities reflect residents’ changing attitudes as they become weary of restrictions and as they update their responses to the dynamics of the disease. For example, an overtightening of restrictions could lead to greater mobility as residents weigh the cost-benefit of the policy and decide the cost is too high vis-à-vis the benefit as suggested by Hypothesis 1 and 3.

We might expect the elasticities to be close to one during the initial stages of the pandemic. We do find that there is an immediate reaction to the policy, but, interestingly, within a matter of weeks residents increase their mobility beyond policymakers’ preferences. The only exception is in Bosnia in late 2020 and early 2021, which roughly coincides with the Delta variant of the disease. It was not due to a policy adjustment, as shown in figure 1, but rather a change in Bosnian behavioural responses to the disease, both when identified using cases and deaths.

Next, we estimate whether individuals’ preferences align with the desired behaviour dictated by policy stringency, that is, is ή l(0)? Results of the unit root tests can be found in table 3. We present the Studentized t-statistics for each of the tests. For the break tests, we provide the estimated break dates. If no break dates are tabulated, no statistically significant break date was estimated. Beginning with ADF results, we see that, across the board, ή ∼ l(0) over the full sample suggesting that policy and individual behaviour do converge over the entire sample period, which is roughly 700 days.

Table 3

ADF and CMR one and two – break unit root tests

DISPLAY Table

Because we find evidence of convergence across the sample, the CMR tests are more helpful in identifying specific dates where individual behaviour changed. Interestingly, the single-break test identifies significant structural breaks only in Croatia, Bosnia, and Serbia, while the two-break test reveals that two significant breaks occur only in B&H and Croatia. Importantly, the estimated break dates correspond more closely to national and religious holidays than to major identifiable policy changes – as in Hypothesis 2. This suggests that culturally salient collective events may play an important role in moderating behavioural adherence dynamics. In Bosnia the breaks correspond to Labour Day (1 May), Julian calendar Easter (2 May 2021) and the end of Ramadan (12 May 2021), in Croatia the breaks fall on the Gregorian Easter Holiday (4 April 2021) and the first week of school (6-10 September 2021), while in Serbia, the break corresponds with Julian Easter on 19 April 2020.

This interpretation is broadly consistent with evidence from the COVID-19 literature suggesting that behavioural responses during the early stages of the pandemic were strongly shaped by uncertainty, fear, and voluntary behavioural adjustment prior to formal policy intervention (Alfaro et al., 2020; Czeisler et al., 2021; Goolsbee and Syverson, 2021). Such dynamics may also suggest that the gradual convergence towards policy prescriptions may reflect evolving risk perceptions and behavioural adaptation over time.

Alternatively, residents may undershoot their behavioural adjustment. This can come from either high short-term information costs or rational inattention (Sims, 2003; Maćkowiak and Wiederholt, 2009). In the rational inattention framework, too much information and/or its frequency leads individuals to selectively “ignore” new policies because they face an information processing constraint. Consequently, individuals allocate attention selectively towards the subset of policy information perceived as most relevant. In either case – over- or undershooting – residents are making “rational mistakes” in response to frequent policy updates.

Differencesin population density, infrastructure, and cultural “norms”, as discussed in Best (2020), may contribute to cross-country differences in behavioural responses and policy outcomes in the region. Government responses also differed in both timing and levels of stringency, reflecting varying trade-offs between public health objectives and broader social, economic, and political considerations. Political incentives, voter preferences, and public opinion may also have influenced the formulation and implementation of pandemic policy across countries. Within our empirical framework, policy is treated as exogenously determined and reflects a trade-off between public health outcomes and broader economic costs.

6.1 Dynamic alignment analysis

6.2 Compliance period

2023

2024

The observed slow convergence of behaviour towards benchmark responses suggests a persistent deviation from policy recommendations. While we have focused on an interpretation grounded in myopic loss aversion, whereby agents overweight recent policy signals and underweight accumulated information, alternative explanations may also account for relatively slow policy adherence dynamics. In particular, the patterns closely resemble those associated with pandemic fatigue and behavioural habituation. For example, using large-scale cross-country evidence, Petherick et al. (2021) document a gradual and widespread decline in adherence to high-cost protective behaviours during COVID-19, consistent with a form of fatigue in behavioural responses, alongside evidence of stabilisation and partial rebounds over time.

Complementary evidence using mobility data shows that the behavioural response to non-pharmaceutical interventions weakened over successive waves of the pandemic, suggesting a declining marginal responsiveness to repeated policy exposure (see, for example, Yilmazkuday, 2021). Taken together, this combination of attenuation and non-monotonic adjustment is consistent with a setting in which repeated exposure reduces behavioural responsiveness, even as new information continues to trigger short-run reactions. In this sense, the empirical patterns are consistent with both attention-based distortions, as emphasised by MLA, and a declining responsiveness arising from fatigue or habituation, implying that policy adherence may erode over time not only because agents misweigh information, but also because their responsiveness to that information diminishes with sustained exposure.

More broadly, the findings of this study align with several strands of the emerging literature on behavioural responses to pandemic policy. Consistent with Goolsbee and Syverson (2021) and Gupta et al. (2021), behavioural responses frequently diverged from the timing and intensity of formal policy interventions, suggesting that individual responses to uncertainty may evolve independently of official restrictions. Similarly, the observed attenuation in mobility responses over time is broadly consistent with evidence on pandemic fatigue and a laxening of adherence to high-cost protective behaviours documented by Petherick et al. (2021) and Yilmazkuday (2021). At the same time, the presence of country-specific structural breaks associated with national and religious holidays suggests that socially and culturally salient events may play an important moderating role in shaping behavioural adherence dynamics across institutional settings.

7 Summary

While this paper focuses on a specific event, the COVID-19 pandemic, the results of this research can be easily applied to other similar negative shocks and may provide useful insights for future policy design during periods of crisis. Researchers are already looking for the next pandemic to hit, as seen in Yong (2022), and health institutes, such as the CDC, have begun reconsidering their approach to promulgating clearer, more concise, and more definitive policy to avoid miscommunication and improve the signal-to-noise ratio.

Notes

2021

* We would like to thank the participants of the weekly Brown Bag seminar at the Faculty of Economics at the University of Zagreb, as well as attendees of the seminar at the Institute of Public Finance, for their thoughtful comments and helpful feedback. We also appreciate comments from the editor and two anonymous referees which undoubtedly improved the quality of the paper. Finally, we are grateful to Katarina Ott for her valuable comments and encouragement in completing this paper.

¹ For example, Hale (2021) identify four different types of COVID policies: economic support, containment and health, government stringency, and overall stringency.

² Clearly this could be an AR(k), k ≥ 1, model, but we use an AR(1) representation to simplify the discussion.

³ We also tried a principle components approach. Because the correlation between the mean and principle component index was over 0.94 for all countries we chose to use the mean as the calculation is more transparent.

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12. Brainard, W. C., 1967. Uncertainty and the effectiveness of policy. The American Economic Review, 57(2), pp. 411-425.


13. Brunekreef, J. [et al.], 2025. Impact of adherence and stringency on the effectiveness of lockdown measures: A modelling study. PLOS One, 20(12), e0338818 [CrossRef]


14. Cairney, P. and Wellstead, A., 2020. COVID-19: Effective policymaking depends on trust in experts, politicians, and the public. Policy Design and Practice, 4(1), pp. 1-14 [CrossRef]


15. Capano, G. [et al.], 2020. Mobilizing Policy (In)Capacity to Fight COVID-19: Understanding Variations in State Responses. Policy and Society, 39(3), pp. 285-308 [CrossRef]


16. Chen, D., Li, Y. and Wu, J., 2022. Policy Stringency, Political Conditions, and Public Performances of Pandemic Control: An International Comparison. Public Performance & Management Review, 45(4), pp. 916-939 [CrossRef]


17. Chen, S. [et al.], 2021. Effective COVID-19 Control: A Comparative Analysis of the Stringency and Timeliness of Government Responses in Asia. International Journal of Environmental Research and Public Health, 18(16), 8686 [CrossRef]


18. Clemente Lopez, J., Montañés, A. and Reyes, M., 1998. Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), pp. 175-182 [CrossRef]


19. Costa, R. B., 2018. A Systematic Test for Myopic Loss Aversion Theory. Review of Behavioral Finance, 10(4), pp. 320-335 [CrossRef]


20. Cross, M., Ng, S.-K. and Scuffham, P., 2020. Trading Health for Wealth: The Effect of COVID-19 Response Stringency. International Journal of Environmental Research and Public Health, 17(23), 8725 [CrossRef]


21. Czeisler, M. E. [et al.], 2021. Early public adherence with and support for stay-at-home COVID-19 mitigation strategies despite adverse life impact: A transnational cross-sectional survey study in the United States and Australia. BMC Public Health, 21, 503 [CrossRef]


22. Dartford, K., 2021. Serbia inoculates neighbours as other Balkan countries receive doses of China's vaccine. Euronews, 28 March.


23. Dave, D. [et al.], 2021. When Do Shelter‐in‐Place Orders Fight Covid‐19 Best? Policy Heterogeneity Across States and Adoption Time. Economic Inquiry, 59(1), pp. 29-52 [CrossRef]


24. Desson, Z. [et al.], 2020. An analysis of the policy responses to the COVID-19 pandemic in France, Belgium, and Canada. Health Policy and Technology, 9(4), pp. 430-446 [CrossRef]


25. European Commission, 2021. EU Digital COVID Certificate: EU Gateway goes live with seven countries one month ahead of deadline. European Commission Press Release.


26. Fojnica, A. [et al.], 2021. Lack of Lockdown, Open Borders, and No Vaccination in Sight: Is Bosnia and Herzegovina a Control Group? Preprint, Public and Global Health [CrossRef]


27. Fuller, J. A. [et al.], 2021. Mitigation Policies and COVID-19–Associated Mortality—37 European Countries, January 23–June 30, 2020. MMWR. Morbidity and Mortality Weekly Report, 70(2), pp. 58-62 [CrossRef]


28. Google, 2021. Community mobility reports.  


29. Goolsbee, A. and Syverson, C., 2021. Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. Journal of Public Economics, 193, 104311 [CrossRef]


30. Grgurinović, M., 2021. Mandatory COVID-19 passes cause discontent in Croatia. Balkan Insight, 23 November. 


31. Gupta, S. [et al.], 2021. Tracking public and private responses to the COVID-19 epidemic: Evidence from state and local government actions. American Journal of Health Economics, 7(4) [CrossRef]


32. Hale, T., 2021. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behaviour, 5(4), pp. 529-538 [CrossRef]


33. Healthcare IT News, 2021. Slovenia launches EU Digital COVID Certificate for its two million citizens.


34. Herreros, F. and Svendsen, G. T., 2024. Social trust and the stringency of public policies against the COVID-19 pandemic. European Politics and Society, 25(5), pp. 866-885 [CrossRef]


35. Hsiang, S. [et al.], 2020. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature, 584(7820), pp. 262-267 [CrossRef]


36. Jalloh, M. F. [et al.], 2022. Drivers of COVID-19 policy stringency in 175 countries and territories: COVID-19 cases and deaths, gross domestic products per capita, and health expenditures. Journal of Global Health, 12, 05049 [CrossRef]


37. Johns Hopkins University, 2021. Coronavirus Resource Center.


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40. Kuo, F.-Y. and Wen, T.-H., 2023. Assessing the impact of local context and priorities regarding domestic disease outbreaks and imported risk on early pandemic response: Cross-continental comparisons. Frontiers in Public Health, 11, 1147768 [CrossRef]


41. Lazić, N., Lazić, V. and Kolarić, B., 2020. Prva tri mjeseca COVID-19 u Hrvatskoj, Sloveniji, Srbiji i Federaciji Bosne i Hercegovine – komparativna analiza mjera za suzbijanje bolesti. Infektološki glasnik, 40(2), pp. 43-49 [CrossRef]


42. Luciano, F., Crova, F. and Canella, F., 2023. COVID-19 Containment Measures—a Step Back for Walking Mobility? A 2-Year, 60-Country Analysis of the Apple Mobility Data. Journal of Physical Activity and Health, 20(5), pp. 394-401 [CrossRef]


43. Maćkowiak, B. and Wiederholt, M., 2009. Optimal Sticky Prices under Rational Inattention. American Economic Review, 99(3), pp. 769-803 [CrossRef]


44. Mathieu, E. [et al.], 2020. COVID-19 Pandemic. Our World in Data. 


45. Milinković, I., 2021. Extraordinary measures in extraordinary times: Legal response to the COVID-19 crisis in Bosnia and Herzegovina. Medicine, Law & Society, 14(2), pp. 439-456 [CrossRef]


46. O’Donoghue, T. and Rabin, M., 1999. Doing it now or later. The American Economic Review, 89(1), pp. 103-124 [CrossRef]


47. Pagliaro, S. [et al.], 2021. Trust predicts COVID-19 prescribed and discretionary behavioral intentions in 23 countries. PLOS ONE, 16(3), e0248334 [CrossRef]


48. Petherick, A. [et al.], 2021. A worldwide assessment of changes in adherence to COVID-19 protective behaviours and hypothesized pandemic fatigue. Nature Human Behaviour, 5(9), pp. 1145-1160 [CrossRef]


49. Poletti, P., Ajelli, M. and Merler, S., 2011. The effect of risk perception on the 2009 H1N1 pandemic influenza dynamics. PLOS ONE, 6(2) [CrossRef]


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54. Schwert, G. W., 1989. Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 7(2), pp. 147-160 [CrossRef]


55. Sims, C. A., 2003. Implications of rational inattention. Journal of Monetary Economics, 50(3), pp. 665-690 [CrossRef]


56. Sonora, R. J. and Gottwald-Belinić, M., 2023. Does individual behavior converge to policy recommendations in times of pandemic? Evidence from COVID-19 in US states. EFZG Working Paper Series, No. 23-04. 


57. Sonora, R. J. and Tica, J., 2024. Individual behavior and policy response in times of a pandemic. EFZG Working Paper Series, 24-02. 


58. Sonora, R. J., 2023. Estimates of a ‘Pandemic Taylor rule’ loss function. Applied Economics Letters, 30(15), pp. 2040-2045 [CrossRef]


59. Sonora, R. J., 2024. Household behavioral response and clubs to lockdown policy in Europe: Evidence from COVID. Seminar presentation. Zagreb: Institute of Public Finance.


60. Sulyok, M. and Walker, M., 2020. Community movement and COVID-19: A global study using Google’s Community Mobility Reports. Epidemiology and Infection, 148, e284 [CrossRef]


61. Thaler, R. H. [et al.], 1997. The effect of myopia and loss aversion on risk taking: An experimental test. The Quarterly Journal of Economics, 112(2), pp. 647-661 [CrossRef]


62. Tiebout, C., 1956. A pure theory of local expenditures. Journal of Political Economy, 64(5), pp. 416-424 [CrossRef]


63. Toshkov, D., Carroll, B. and Yesilkagit, K., 2022. Government capacity, societal trust or party preferences: What accounts for the variety of national policy responses to the COVID-19 pandemic in Europe? Journal of European Public Policy, 29(7), pp. 1009-1028 [CrossRef]


64. Tu, K., Chen, S. and Mesler, R. M., 2023. Policy stringency and the spread of COVID-19: The moderating role of culture and its implications on first responses. Health Policy, 137, 104896104896 [CrossRef]


65. Uzelac, M., 2023. Using the pandemic for their own gain: The experiences of COVID in Serbia. Anthropologica, 65(1), pp. 1-23 [CrossRef]


66. Vlada RH, 2020. Božinović: Croatia is safe and one of few countries that will have tourism season. Vlada RH, June 17, 2020. 


67. Vladisavljević, A., 2020. Slovenia declares victory over coronavirus pandemic. Balkan Insight, 15 May 2020.


68. Vladisavljević, A., 2021. Slovenians mobilise for another anti-Jansa push, demanding elections. Balkan Insight, 28 May 2021. 


69. Walker, P. G. T. [et al.], 2020. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science, 369(6502), pp. 413-422 [CrossRef]


70. Waterschoot, J. [et al.], 2023. How the Stringency of the COVID-19 Restrictions Influences Motivation for Adherence and Well-Being: The Critical Role of Proportionality. International Journal of Health Policy and Management, 12(1), pp. 1-13 [CrossRef]


71. Wellcome Trust, 2021. Wellcome global monitor 2020: COVID-19.


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Berry, C. R. [et al.], 2021. Evaluating the effects of shelter-in-place policies during the COVID-19 pandemic. Proceedings of the National Academy of Sciences, 118(15), e2019706118 [CrossRef]

Best, J., 2020. The social problems of COVID-19. In: G. W. Muschert [et al.], eds. Social problems in the age of COVID-19: US perspectives. Bristol: Bristol University Press, pp. 134-142 [CrossRef]

Brainard, W. C., 1967. Uncertainty and the effectiveness of policy. The American Economic Review, 57(2), pp. 411-425.

Brunekreef, J. [et al.], 2025. Impact of adherence and stringency on the effectiveness of lockdown measures: A modelling study. PLOS One, 20(12), e0338818 [CrossRef]

Cairney, P. and Wellstead, A., 2020. COVID-19: Effective policymaking depends on trust in experts, politicians, and the public. Policy Design and Practice, 4(1), pp. 1-14 [CrossRef]

Capano, G. [et al.], 2020. Mobilizing Policy (In)Capacity to Fight COVID-19: Understanding Variations in State Responses. Policy and Society, 39(3), pp. 285-308 [CrossRef]

Chen, D., Li, Y. and Wu, J., 2022. Policy Stringency, Political Conditions, and Public Performances of Pandemic Control: An International Comparison. Public Performance & Management Review, 45(4), pp. 916-939 [CrossRef]

Chen, S. [et al.], 2021. Effective COVID-19 Control: A Comparative Analysis of the Stringency and Timeliness of Government Responses in Asia. International Journal of Environmental Research and Public Health, 18(16), 8686 [CrossRef]

Clemente Lopez, J., Montañés, A. and Reyes, M., 1998. Testing for a unit root in variables with a double change in the mean. Economics Letters, 59(2), pp. 175-182 [CrossRef]

Costa, R. B., 2018. A Systematic Test for Myopic Loss Aversion Theory. Review of Behavioral Finance, 10(4), pp. 320-335 [CrossRef]

Cross, M., Ng, S.-K. and Scuffham, P., 2020. Trading Health for Wealth: The Effect of COVID-19 Response Stringency. International Journal of Environmental Research and Public Health, 17(23), 8725 [CrossRef]

Czeisler, M. E. [et al.], 2021. Early public adherence with and support for stay-at-home COVID-19 mitigation strategies despite adverse life impact: A transnational cross-sectional survey study in the United States and Australia. BMC Public Health, 21, 503 [CrossRef]

Dartford, K., 2021. Serbia inoculates neighbours as other Balkan countries receive doses of China's vaccine. Euronews, 28 March.

Dave, D. [et al.], 2021. When Do Shelter‐in‐Place Orders Fight Covid‐19 Best? Policy Heterogeneity Across States and Adoption Time. Economic Inquiry, 59(1), pp. 29-52 [CrossRef]

Desson, Z. [et al.], 2020. An analysis of the policy responses to the COVID-19 pandemic in France, Belgium, and Canada. Health Policy and Technology, 9(4), pp. 430-446 [CrossRef]

European Commission, 2021. EU Digital COVID Certificate: EU Gateway goes live with seven countries one month ahead of deadline. European Commission Press Release.

Fojnica, A. [et al.], 2021. Lack of Lockdown, Open Borders, and No Vaccination in Sight: Is Bosnia and Herzegovina a Control Group? Preprint, Public and Global Health [CrossRef]

Fuller, J. A. [et al.], 2021. Mitigation Policies and COVID-19–Associated Mortality—37 European Countries, January 23–June 30, 2020. MMWR. Morbidity and Mortality Weekly Report, 70(2), pp. 58-62 [CrossRef]

Google, 2021. Community mobility reports.  

Goolsbee, A. and Syverson, C., 2021. Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. Journal of Public Economics, 193, 104311 [CrossRef]

Grgurinović, M., 2021. Mandatory COVID-19 passes cause discontent in Croatia. Balkan Insight, 23 November. 

Gupta, S. [et al.], 2021. Tracking public and private responses to the COVID-19 epidemic: Evidence from state and local government actions. American Journal of Health Economics, 7(4) [CrossRef]

Hale, T., 2021. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behaviour, 5(4), pp. 529-538 [CrossRef]

Healthcare IT News, 2021. Slovenia launches EU Digital COVID Certificate for its two million citizens.

Herreros, F. and Svendsen, G. T., 2024. Social trust and the stringency of public policies against the COVID-19 pandemic. European Politics and Society, 25(5), pp. 866-885 [CrossRef]

Hsiang, S. [et al.], 2020. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature, 584(7820), pp. 262-267 [CrossRef]

Jalloh, M. F. [et al.], 2022. Drivers of COVID-19 policy stringency in 175 countries and territories: COVID-19 cases and deaths, gross domestic products per capita, and health expenditures. Journal of Global Health, 12, 05049 [CrossRef]

Johns Hopkins University, 2021. Coronavirus Resource Center.

Karlović, A., 2020. Croatia 2020: An unexpectedly smooth victory for the incumbent. Who Governs Europe? 6 July, 2020. 

Kriesi, H. and Oana, I.-E., 2023. Protest in unlikely times: Dynamics of collective mobilization in Europe during the COVID-19 crisis. Journal of European Public Policy, 30(4), pp. 740-765 [CrossRef]

Kuo, F.-Y. and Wen, T.-H., 2023. Assessing the impact of local context and priorities regarding domestic disease outbreaks and imported risk on early pandemic response: Cross-continental comparisons. Frontiers in Public Health, 11, 1147768 [CrossRef]

Lazić, N., Lazić, V. and Kolarić, B., 2020. Prva tri mjeseca COVID-19 u Hrvatskoj, Sloveniji, Srbiji i Federaciji Bosne i Hercegovine – komparativna analiza mjera za suzbijanje bolesti. Infektološki glasnik, 40(2), pp. 43-49 [CrossRef]

Luciano, F., Crova, F. and Canella, F., 2023. COVID-19 Containment Measures—a Step Back for Walking Mobility? A 2-Year, 60-Country Analysis of the Apple Mobility Data. Journal of Physical Activity and Health, 20(5), pp. 394-401 [CrossRef]

Maćkowiak, B. and Wiederholt, M., 2009. Optimal Sticky Prices under Rational Inattention. American Economic Review, 99(3), pp. 769-803 [CrossRef]

Mathieu, E. [et al.], 2020. COVID-19 Pandemic. Our World in Data. 

Milinković, I., 2021. Extraordinary measures in extraordinary times: Legal response to the COVID-19 crisis in Bosnia and Herzegovina. Medicine, Law & Society, 14(2), pp. 439-456 [CrossRef]

O’Donoghue, T. and Rabin, M., 1999. Doing it now or later. The American Economic Review, 89(1), pp. 103-124 [CrossRef]

Pagliaro, S. [et al.], 2021. Trust predicts COVID-19 prescribed and discretionary behavioral intentions in 23 countries. PLOS ONE, 16(3), e0248334 [CrossRef]

Petherick, A. [et al.], 2021. A worldwide assessment of changes in adherence to COVID-19 protective behaviours and hypothesized pandemic fatigue. Nature Human Behaviour, 5(9), pp. 1145-1160 [CrossRef]

Poletti, P., Ajelli, M. and Merler, S., 2011. The effect of risk perception on the 2009 H1N1 pandemic influenza dynamics. PLOS ONE, 6(2) [CrossRef]

Potter, C. C. and Harries, J., 2006. The determinants of policy effectiveness. Bulletin of the World Health Organization, 84(11), p. 843.

Reuters, 2021. Bosnians protest, telling government to resign over lack of COVID vaccines. Reuters, 6 April, 2021.

Ružić Gorenjec, N. [et al.], 2021. COVID-19 in Slovenia, from a success story to disaster: What lessons can be learned? Life, 11(10), 1045 [CrossRef]

Sajić, N., 2021. Federal institutional design and the COVID-19 crisis management in Bosnia and Herzegovina. In: I. Miljenović and E. Hrelja, eds. Federalism and the response to COVID-19: A comparative analysis. Routledge, pp. 49-58. 

Schwert, G. W., 1989. Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 7(2), pp. 147-160 [CrossRef]

Sims, C. A., 2003. Implications of rational inattention. Journal of Monetary Economics, 50(3), pp. 665-690 [CrossRef]

Sonora, R. J. and Gottwald-Belinić, M., 2023. Does individual behavior converge to policy recommendations in times of pandemic? Evidence from COVID-19 in US states. EFZG Working Paper Series, No. 23-04. 

Sonora, R. J. and Tica, J., 2024. Individual behavior and policy response in times of a pandemic. EFZG Working Paper Series, 24-02. 

Sonora, R. J., 2023. Estimates of a ‘Pandemic Taylor rule’ loss function. Applied Economics Letters, 30(15), pp. 2040-2045 [CrossRef]

Sonora, R. J., 2024. Household behavioral response and clubs to lockdown policy in Europe: Evidence from COVID. Seminar presentation. Zagreb: Institute of Public Finance.

Sulyok, M. and Walker, M., 2020. Community movement and COVID-19: A global study using Google’s Community Mobility Reports. Epidemiology and Infection, 148, e284 [CrossRef]

Thaler, R. H. [et al.], 1997. The effect of myopia and loss aversion on risk taking: An experimental test. The Quarterly Journal of Economics, 112(2), pp. 647-661 [CrossRef]

Tiebout, C., 1956. A pure theory of local expenditures. Journal of Political Economy, 64(5), pp. 416-424 [CrossRef]

Toshkov, D., Carroll, B. and Yesilkagit, K., 2022. Government capacity, societal trust or party preferences: What accounts for the variety of national policy responses to the COVID-19 pandemic in Europe? Journal of European Public Policy, 29(7), pp. 1009-1028 [CrossRef]

Tu, K., Chen, S. and Mesler, R. M., 2023. Policy stringency and the spread of COVID-19: The moderating role of culture and its implications on first responses. Health Policy, 137, 104896104896 [CrossRef]

Uzelac, M., 2023. Using the pandemic for their own gain: The experiences of COVID in Serbia. Anthropologica, 65(1), pp. 1-23 [CrossRef]

Vlada RH, 2020. Božinović: Croatia is safe and one of few countries that will have tourism season. Vlada RH, June 17, 2020. 

Vladisavljević, A., 2020. Slovenia declares victory over coronavirus pandemic. Balkan Insight, 15 May 2020.

Vladisavljević, A., 2021. Slovenians mobilise for another anti-Jansa push, demanding elections. Balkan Insight, 28 May 2021. 

Walker, P. G. T. [et al.], 2020. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science, 369(6502), pp. 413-422 [CrossRef]

Waterschoot, J. [et al.], 2023. How the Stringency of the COVID-19 Restrictions Influences Motivation for Adherence and Well-Being: The Critical Role of Proportionality. International Journal of Health Policy and Management, 12(1), pp. 1-13 [CrossRef]

Wellcome Trust, 2021. Wellcome global monitor 2020: COVID-19.

Yilmazkuday, H., 2021. Stay-at-home works to fight against COVID-19: International evidence from Google mobility data. Journal of Human Behavior in the Social Environment, 31(1-4), pp. 210-220 [CrossRef]

Yong, E., 2022. The pandemic’s legacy is already clear: All of this will happen again. The Atlantic, September 30, 2022.