Culture and political regimes: How have they influenced the effectiveness of COVID-19 response policy?

Abstract

This study investigates the efficacy of national emergency response policies in addressing the COVID-19 pandemic and their interactions with cultural and political factors. Employing the synthetic control method, we analyze data from countries on five continents that implemented such policies in early 2020. Our results indicate the overall effectiveness of these policies in mitigating the impact of COVID-19 yet reveal significant variability in their outcomes among countries. Notably, we identify a negative association between policy effectiveness and a culture characterized by individualism. Additionally, we observe that the impact of COVID-19 response measures is more prominent in countries with lower levels of democracy. These findings offer valuable insights into the intricate interplay between COVID-19 response policies, cultural dynamics, and political regimes, with potential implications for future policy decisions and research endeavors.

1. Introduction

The COVID-19 pandemic has caused widespread disruption to global business and economic activities, making it one of the most significant global public health crises since the 1918 influenza pandemic. This crisis has affected both public health systems and market economies alike [[1], [2], [3]]. Due to the highly contagious nature of COVID-19, governments worldwide have implemented various measures to curb the spread of virus, such as travel restrictions and social distancing guidelines for the public. By March 25, 2020, 65 countries, including China, had declared national emergency policies like China's lockdown policy in response to the unprecedented threat of the epidemic. However, the effectiveness of these policies has varied significantly across different countries, prompting the important question of why similar national emergency responses had different outcomes across countries. Our paper aims to explore the factors that contribute to the heterogeneity of policy effects on epidemic transmission among countries at the national level.

Culture and social norms are fundamental factors that impact the effectiveness of national emergency policies [[4], [5], [6], [7]]. These policies typically involve measures such as social distancing, bans on public gatherings, and the use of facial masks, which significantly impact people's daily lives and generate externalities related to complying with the policy [8]. In individualistic cultures, personal utility tends to be prioritized, which exacerbates the negative externalities of epidemic policies. On the other hand, in collectivist cultures, individuals may be more inclined to adhere to social norms and suppress personal desires to fulfill group social responsibility. For example, some East Asian countries, such as Vietnam, South Korea, Singapore, and China, where there is a strong emphasis on collective welfare, have witnessed positive responses and compliance from the public in the early stages of the COVID-19 pandemic, yielding favorable outcomes. Recent literature provides empirical evidence that cultures that prioritize individualism are less conducive to collective action in combating epidemics. Specifically, countries or regions characterized by more individualistic cultural traits displayed significantly lower compliance with COVID-19 policies, with less social distancing and mask use [9,10].

Additionally, the relevance of political regimes in determining the effectiveness of policies aimed at responding to epidemics has been underscored [[11], [12], [13], [14]]. The successful management of the COVID-19 pandemic in the early days in a few countries, including China, are seen as evidence of the efficacy of authoritarian rule. Notably, recent studies have found that pandemic policy responses in more democratic countries were slower [15] and less effective in reducing mortality during the initial stages of the crisis [16]. Democratic countries have struggled to balance the need to fight COVID-19 with the defense of civil liberties and the economy. The positive relationship between a country's level of globalization and the spread of COVID-19 may explain why democratic countries are more susceptible to the COVID-19 pandemic [17].

However, most of the studies mentioned above are restricted in scope, concentrating exclusively on policies implemented within a specific national context. Moreover, the previous literature often places a strong emphasis on compulsory policies that impede social activities, such as constraints on mobility or congregating. Given that such policies naturally limit individuals' compliance, it's difficult to isolate the impact of cultural and political factors on adherence to these policies.

To this end, our paper makes two significant contributions. The first contribution pertains to empirical and methodological aspects. In our study, we employ the synthetic control method (hereafter SCM) to construct appropriate comparison groups for each country that implemented a national emergency policy. The key idea underlying the SCM is that a convex combination of control units offers a more reliable comparison for a treated country that has undergone a policy intervention than any single unit alone [18,19]. Therefore, the SCM has been regarded as an important methodology development for policy evaluation [20]. It has been applied across various fields [21], to explore the effects of legalized prostitution [22], immigration policy [23], corporate political connections [24] and taxation [25].

The second contribution involves our context-based investigation, which has not been explored in existing literature and is particularly well-suited for examining interactive effects with culture and political regimes. Unlike previous literature that mainly examines mandatory policies which leave people with limited choices, our study focuses on information-based national emergency policies that provide people with a sense of direction regarding national epidemic prevention policies [26] and allow for more zoom for choosing to comply with the policy. As a result, we are able to investigate variations in citizens’ compliance with epidemic policies in relation to cultural factors and political regimes.

This study has yielded several noteworthy results. We estimate the impact of national emergency policies implemented in response to COVID-19 outbreak across 65 countries. To exemplify the effectiveness of the SCM method in our context, we present a detailed case study using China and compare our results with other studies examining the impact of COVID-19 response policies in China. Our results indicate that national emergency policies effectively mitigated the spread and impact of the COVID-19 pandemic; however, the effects of these policies display notable variations among different countries. Subsequently, we investigate the heterogeneity of the policy effects on epidemic transmission, with a specific focus on their associations with individualistic cultures and political regimes. We find a negative correlation between the impact of national emergency policies on epidemic transmission and the degree of both individualism and democracy within a society. Our study underscores the significant role of cultural and political factors in shaping the implementation of public health policies, thereby contributing to a deeper understanding of the potential adaptability of similar epidemic response strategies in diverse countries.

The remainder of our paper is organized as follows. Section 2 outlines the data, variables and methods used in the analysis. In Section 3, we report the main results of estimating the effect of the national emergency policy, as well as examining the heterogeneity of policy effects based on individualistic culture and political regime factors and discuss our results. Section 4 concludes.

2. Data and methods

2.1. Data and variables

2.1.1. The declaration of the national emergency policy

With the rapid spread of COVID-19 worldwide, many countries have implemented measures to mitigate the impact of the pandemic. China became the first country to implement a level-I emergency response policy, which bears similarities to a national emergency policy, as early as January 27, 2020. As of March 25, 2020, 64 other countries had declared a national emergency policy in response to the COVID-19 pandemic.1

We manually gather data from news archives published on Global Times for the implementation dates of the national emergency policy in the 65 countries under investigation. Table A1 in the online appendix shows the respective dates of policy implementation in all these countries. Notably, a majority of these nations declared the national emergency policy in March 2020. South Korea was the first country to declare a state of national emergency on March 3, whereas Cuba was the last to do so on March 24. We designate the date on which the policy took effect in the treated country as the 14th day after the announcement of the national emergency policy. This choice allows us to measure the policy's impact on transmission reduction, accounting for the 14-day incubation period necessary for latent-period patients to transition into infected individuals. Fig. 1 provides a histogram of the implementation dates of the national emergency policy across these countries.

Fig. 1.

The implementation date of the national emergency policy in 64 countries.

2.1.2. COVID-19 infection and death

For the COVID-19 infection and death data, we utilize daily data at the country-level for COVID-19 cases compiled by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. We collect daily data on the number of COVID-19 infections and associated deaths from 190 countries, spanning from January 15th, 2020, to May 9th, 2020. Within this dataset, 65 countries, including China, situated across five continents, had implemented national emergency policies in early 2020. These countries constitute the treatment group, while the other approximately 130 countries served as control units for our study.

Utilizing daily data on COVID-19 infection cases and population figures, we quantify the policy effect by examining the reduction in infection density, which is calculated as the cumulative number of infection cases divided by the total population. This measure allows us to assess the proportional change in infection reduction within each country.

2.1.3. Culture and democracy indicators

To assess differences in individualism across countries, we employ the Hofstede's individualism index [27], a widely used dataset to explore the impact of individualism in various domains [28,29], such as consumer behavior [30] and entrepreneurship education [31]. This index characterizes societies with high levels of individualism as having less closely-knit interpersonal connections, where individuals prioritize their own interests and those of their immediate families over other relationships. In contrast, in more collectivist societies, individuals expect their extended families or clans to provide care and protection in return for loyalty.

We conduct a preliminary examination of the correlation between individualism index and epidemic outcomes across countries. Fig. 2 illustrates that countries with higher individualism index scores experienced more severe epidemic impact, as evidenced by higher infection cases (Fig. 2a), infection density (Fig. 2b), deaths (Fig. 2c) and death density (Fig. 2d).

Fig. 2.

Heterogenous COVID-19 infection cases and deaths across cultures.

Note: Countries with higher individualism index scores experienced more severe epidemic impact. $\rho$ is the Pearson correlation coefficient and its p-value is shown in the parentheses.

We gather data from two sources to measure the type of political regimes. We utilize the civil and political rights country score from the Freedom House 2020 political rights index [32]. The index captures the extent of free and fair elections, political pluralism and participation, and a functioning government. Lower ratings indicate more rights and a more democratic country. Our second data source is Polity's democracy index, which is constructed through a questionnaire assessing factors such as the competitiveness of political participation, executive recruitment, and constraints on the chief executive. In this context, higher scores on the index correspond to a more democratic country.

2.1.4. Country-level control variables

The spread of COVID-19 depends on various country-level factors. For instance, a more economically advanced country may have greater medical resources available per capita, which can help control the transmission of the virus. Conversely, countries with higher population densities, particularly those in which people are concentrated in urban areas, are likely to face more significant challenges in containing the virus. In our analysis, we control both population density, calculated as total population divided by a country's land area, and ratio of urban population, measured as the ratio of urban population to total population in the model. Additionally, we control for the ratio of agriculture in GDP, as countries with a larger agricultural sector may be less likely to have a highly mobile population, which could impact infection density. Finally, we also include GDP per capita (measured in constant 2010 US dollars) in control variables. Data for these variables are sourced from the World Development Indicators, which are curated by the World Bank. Matching the country-level data results in 182 countries being retained from the initial dataset of 190 countries.

2.2. Data analysis methods

In assessing the impact of a national emergency response on reducing infection density within a country, a fundamental question arises: what would the infection density trajectory be in that country after the policy's implementation point, had the policy not been enacted? However, given the absence of an actual country with this scenario, the central challenge in policy evaluation revolves around the identification or construction of a suitable counterfactual country for comparison with the policy-treated country.

In our study, we use SCM to evaluate the effect of the national emergency policy. The SCM provides a more rigorous, less ad-hoc way of selecting control countries from a large pool of potential controls. More importantly, it leverages the large pool of potential controls to construct permutation-based inference in a manner that is robust to the possibility of country-by-time period specific shocks. Using SCM, we construct a “synthetic” control country for the treated country, using a convex combination of all control countries. The weights on these control countries are nonnegative and chosen to mimic the pre-intervention trajectories of the infection density of the treated country before the policy was implemented.

We proceed to estimate the policy effect by conducting a comparative analysis of infection density trends between the treated country and the synthetic control country over a one-month period following the policy's effective implementation, specifically 14 days after its announcement. This one-month timeframe is chosen as it offers an adequate interval for observing the policy's effects while mitigating potential confounding influences from external factors.

In this context, a key decision involves determining the timing for the before-after comparison of control units that did not implement the policy. The standard approach is to designate the policy implementation date as the demarcation point for before-after comparisons in control units. However, in our study, this approach is not appropriate because the implementation of the national emergency policy in the treated country was prompted by the development of COVID-19 within that nation. Given this endogeneity concern of policy implementation, we use the date when the COVID-19 infection density of the control country was closest to that of the treated country at the time of its state of emergency declaration, instead of the actual implementation date in the treated country, to define “day zero” for control countries. This approach enables us to choose control countries that exhibited comparable levels of epidemic progression to the treated country until the date of policy implementation, ensuring a relatively parallel trajectory of epidemics in both control and treated countries prior to policy implementation.

In our empirical analysis, we first employ China as a detailed case study to exemplify the SCM method for estimating policy effects. The case study also serves the dual purpose of demonstrating the validity and reliability of SCM in our context, as we can compare our estimates with a few other studies investigating the impact of China's COVID-19 lockdown policy.

Following the case study, we extend our SCM analysis to include the national emergency policies of 64 other countries. Similarly, the synthetic control for each treated country is constructed as a convex combination of countries that had not implemented the national emergency policy. With these estimates, we use correlation analysis to investigate the heterogeneity of the effect of the national emergency policy on the reduction of infection density under different cultural and political regimes.

3. Results and discussions

3.1. A case study for China

To address the severe epidemic situation, all thirty-one provinces of mainland China activated the emergency response to major public health emergencies, known as level-I emergency response, in January 2020. The classification of public health emergencies in China is based on the severity of the event, the scope of its impact, and the nature of the emergency, with four levels ranging from particularly significant (level I) to general (level IV). Therefore, the level-I emergency response represents the most critical level of response, indicating the severity of the epidemic and the effectiveness of the prevention and control policies implemented by the Chinese government. In response to the outbreak, local authorities swiftly implemented emergency measures, including restrictions on large public gatherings such as fairs, rallies, and theater performances, as well as suspensions of work, business, and classes.

The nationwide implementation of the level-I emergency response policy was completed on January 27, 2020. Therefore, we evaluate the effectiveness of the policy in reducing the transmission of COVID-19 over a period of one month, starting from February 10, which takes into account the 14-day incubation period of the virus.

We utilize data from all other countries as none of these countries had declared a national emergency policy during the study period for China, to create a synthetic control country for China. Note that the “day zero” for control countries—the placebo effective date for the control countries—was selected as the date when each country's infection density was closest to that of China on February 10. The “synthetic China” is constructed to have country-level variables akin to China, with a particular emphasis on aligning its pre-policy trend in COVID-19 infection density. This alignment pertains to the one-month period leading up to the policy's effectiveness in both “synthetic China” and China itself.

The variables we use to construct the “synthetic China” are listed in Table 1. Our analysis reveals that the epidemic trend in China before the emergency response policy was most accurately replicated through a weighted combination of four countries: Colombia, Guinea, South Africa, and Ukraine, with respective weights of 0.118, 0.113, 0.004, and 0.766. Table 1 also displays summary statistics for the variables for China and the “synthetic China”, alongside the remaining control countries for comparative purposes. Notably, the control variables and infection density for the “synthetic China” closely align with China's values, compared to the averages of the control countries.

Table 1.

Summary statistics for predictors.

Variables	China	Synthetic China	Average of all control countries
Log (infection ${\text{density}}_0$)	−10.3588	−10.3417	−10.3157
Log (infection ${\text{density}}_{-14}$)	−12.3470	−12.5149	−11.9581
Log (infection ${\text{density}}_{-27}$)	−17.3445	−17.3603	−13.5882
Log (GDP per capita)	9.0185	8.1688	9.3062
Population density	148.3488	70.1846	287.8921
Urban population	60.3080	67.1785	59.0873
Agriculture	7.1116	10.0030z	12.5513

Fig. 3 presents the COVID-19 epidemic trends, measured as the log-transformation of infection density, in China and its synthetic counterpart spanning from January 15 to March 11, 2020. The infection density in “synthetic China” closely tracked the trajectory in China during the pre-policy period. Fig. 3 confirms that the constructed “synthetic China” could serve as a plausible counterfactual for China, i.e., what the infection trajectories would look like in China in the absence of the policy. Therefore, using the “synthetic China”, we estimate the effect of the national emergency response policy on epidemic transmission in China by analyzing the difference in infection density between China and its synthetic counterpart following the policy's implementation.

Fig. 3.

Trends in infection density: China vs. synthetic China.

Fig. 3 clearly illustrates a notable divergence in epidemic trends shortly after the policy's implementation on February 10, 2020. While synthetic China saw a rapid escalation in epidemic transmission, actual China witnessed a substantial and pronounced suppression. The widening gap between China and “synthetic China” signifies a substantive and enduring preventive impact of the emergency response policy on epidemic transmission. A back-of-the-envelope calculation indicates that, during the initial 30-day period following the policy's implementation in China, it effectively prevented approximately 300,000 infection cases. This number is in line with results reported in related literature. For instance, a simulation study finds a decline of 500,000–700,000 confirmed COVID-19 cases over a 50-day period attributed to the national emergency response [33]. This alignment lends support to the validity of our method.

Lastly, in addressing statistical concerns, we conduct a placebo test using control countries that did not implement the policy. We apply the same SCM procedure to estimate “post-policy” differences in infection density. Fig. 4 plots the results for all the placebo estimates. The gray dotted lines on the graph correspond to the differences in infection density between each control country and its corresponding synthetic counterpart. The thick solid black line represents the estimated difference for China. Our analysis indicates that the gap between China and its counterpart was notably larger than that observed in most other control countries, resulting in an empirical p-value of 0.033, indicating statistical significance.

Fig. 4.

Infection density gaps in control countries and in China.

3.2. The effect of the national emergency response policy

We expand our analysis to encompass the national emergency policies of an additional 64 countries. This extension follows a similar procedure to our earlier China case study, utilizing the same set of control variables and epidemic infection data. For each treated country, we construct a synthetic control using a convex combination of countries that had not implemented the national emergency policy. Notably, both China and the aforementioned 64 countries are excluded from the pool of potential control countries, resulting in a smaller subset of countries for which we could estimate the policy effect.

Importantly, the application of SCM does not guarantee that we can successfully construct an appropriate control country for each nation that implemented a national emergency policy. Unique and idiosyncratic properties of individual countries, such as distinctive country-level variables and pre-policy COVID-19 trends, may pose challenges in constructing a suitable synthetic control. Additionally, when policies are implemented at distinct stages, it can lead to a scarcity of control countries with a sufficiently long pre-policy period of infection data. As a result, our ability to estimate the policy's effect is limited to 42 out of the 64 countries.

Fig. 5 illustrates the distribution of policy effects, measured by the decrease in log-transformation of infection case density, across countries. Further details on the effects of the national emergency policy on epidemic transmission in each country can be found in Fig. A1 in the online appendix.

Fig. 5.

Distribution of policy effect across countries.

Our analysis reveals a substantial variation in the effectiveness of these implemented emergency response policies among different countries. While the majority of these policies demonstrated some efficacy in reducing infection density, the extent of their impact varied significantly. The mean policy effect stands at 0.5866, representing a considerable effect that corresponds to a roughly 47.3 % decrease in infection density. However, the standard deviation of these effects is notably large at 0.5287, indicating that a few countries, despite implementing emergency response policies, did not experience a significant impact from them.

This variance prompts an intriguing follow-up question: What factors contribute to the considerable disparities in policy effectiveness observed among countries that implemented national emergency responses?

One prominent outlier evident in Fig. 5 is Vietnam, where the implementation of the national emergency response policy resulted in a remarkable reduction of the infection rate by more than 85 %, compared to a counterfactual scenario. Vietnam possesses a cultural heritage deeply influenced by Confucian values, emphasizing societal order and collective interests. Concurrently, Vietnam is characterized as an autocratic regime, with scores of 7 on the Freedom House political rights index and 0 on the Polity's democracy index, denoting the lowest level of democracy in both indices. This striking observation prompts a deeper examination into the potential role of culture and political regimes in elucidating the heterogeneity observed in the policy's effects.

3.3. Individualism, democracy, and the COVID-19 policy effect

We explore the heterogeneity of the impact of the national emergency policy on epidemic transmission under different cultural and political regimes. To investigate this, we analyze the association between individualism and the policy's effects during the initial month following its implementation. Our assessment relies on Hofstede's (2016) dataset to quantify variations in individualism across different cultures and countries. Upon matching our policy effect dataset with data on culture and polity indices, we identify a subset of 35 countries for which values were available in both datasets.

Remarkably, our analysis reveals a statistically significant and negative correlation between individualism and the impact of the national emergency policy on epidemic transmission. This result suggests that the effect of the national emergency policy was less evident in countries with stronger individualistic cultures. To visually represent this relationship, we construct a scatter plot (Fig. 6a) illustrating the correlation between individualism and policy effects. Upon the removal of a few outlier countries, namely Vietnam, Australia, and New Zealand, the negative correlation became even more pronounced (Fig. 6b).

Fig. 6.

The correlation between individualism and the policy effect in 35 countries.

Note: The effect of the national emergency policy was less evident in countries with stronger individualistic cultures. $\rho$ is the Pearson correlation coefficient and the p value is shown in the parentheses.

In the subsequent analysis, we explore the relationship between the impact of the national emergency policy and the level of democracy in various countries. To gauge democratic political regimes, we employ two metrics: the Freedom House political rights index and Polity's democracy index. It's worth noting that a higher ranking on the Freedom House political rights index corresponds to more democratic country, whereas the opposite holds true for Polity's democracy index.

Our findings indicate that the policy effect exhibited greater significance in countries with lower levels of democracy. To illustrate this, we present Fig. 7a and b, which depict the correlations between the policy effect and countries' rankings on the Freedom House political rights index and Polity's democracy index, respectively.2

Fig. 7.

The correlation between democracy and the policy effect in 35 countries.

Note: A more democratic country ranks lower on the Freedom House political rights index but higher on Polity's democracy index. $\rho$ is the Pearson correlation coefficient and the p value is shown in the parentheses.

3.4. Discussions

3.4.1. The effect of the national emergency response policy

Our analysis of cross-national study uncovers the heterogeneous effects of the national emergency response policy across different countries. Our research identifies and estimates the impact of policies on infection cases one month within their implementation in detail. Previous research has largely relied on simulation [34] or reduced-form regression models [[35], [36], [37]] to make estimations, which have been plagued with issues such as statistical inference problems, policy endogeneity, and inadequate counterfactuals. The main limitation of these methods is the inability to find a suitable control group, thereby failing to cleanly identify policy effects. Our study employs the SCM, a quantitative research approach that constructs a weighted combination of control units unaffected by the policy, providing a more appropriate comparison group to estimate the effects of policy interventions. The key advantage of such method lies in its ability to construct a control group with pre-intervention characteristics similar to the treated group (i.e., the country or region where the policy was implemented), thus more accurately estimating the causal effects of policy. This method is particularly applicable in cases where it is challenging to find a perfect natural control group. Additionally, our study innovatively addresses the issue of policy endogeneity. We define the “zero day” for control countries using the COVID-19 infection rate on the day the state of emergency was declared, ensuring that on the policy implementation date, the comparison countries are at a similar stage of epidemic development as the treated country. Other studies on the pandemic have used the actual policy implementation date as the zero point for the control group. Our design helps to reduce endogeneity bias due to policy selection, thereby providing a more accurate assessment of policy effects. Our research offers a new perspective for evaluating pandemic policies and may provide valuable references for policy formulation in other countries.

3.4.2. Individualism, democracy, and the COVID-19 policy effect

Our correlation analysis shows the relationship between policy effects and cultural and political factors. Previous research has primarily focused on exploring the influence of cultural and political attributes on individuals' compliance with policies that limit social distancing and mobility. Such policies are heavily influenced by policy intensity. For instance, individual mobility is significantly constrained by government control over transportation and roadways. Consequently, studies of this nature struggle to isolate the impact of cultural attributes on policy adherence. Our study focuses on information-based national emergency policies that provide people with a sense of direction regarding national epidemic prevention policies, leaving more room for personal choice under these coercive measures. Furthermore, we rule out the possibility that the heterogeneity of policy effect could be attribute to the country's economic development but nothing else. The reality is, if anything, our findings suggest a weak negative correlation between GDP per capita and the policy effect, as shown in Fig. 8.

Fig. 8.

The correlation between economic development and the policy effect.

Note: $\rho$ is the Pearson correlation coefficient and the p value is shown in the parentheses.

3.4.3. Implications

The COVID-19 pandemic has emerged as one of the most severe and globally impactful pandemics in human history. Given the likelihood of respiratory diseases like COVID-19 becoming recurrent in the future, it is imperative to enhance our understanding of how diverse cultural and political contexts influence the efficacy of national emergency responses to such crises. This knowledge can offer valuable insights for informing future pandemic preparedness endeavors.

Unlike the previous studies concentrating exclusively on policies implemented within a specific national context, our study underscores the pivotal role of cultural and political factors in the implementation of public health policies and enriches our understanding of the potential applicability of similar epidemic response policies across different countries. Historical, cultural, and geographical factors give rise to variations in cultural norms and political systems across nations. In times of global health crises, such as the COVID-19 pandemic, coordinated global initiatives, exemplified by organizations like the WHO, become imperative. Given the highly contagious nature of diseases, a failure to respond adequately by one nation can undermine the collective efforts of all others. However, our findings show that it is impractical to consider the adoption of a universal, one-size-fits-all policy to every country. Instead, customized responses, informed by cultural and political considerations, must be taken into account when devising corresponding public health policies.

3.4.4. Limitations

This study has some limitations. First, it should be noted that our findings are derived from the context of national emergency declarations across countries during the COVID-19 era. These conclusions may not be directly extrapolated to scenarios where the epidemic has been effectively controlled or stabilized. Nonetheless, incorporating cultural elements into policy design holds promise for ameliorating the compliance challenges commonly encountered in public health interventions. Furthermore, it is important to recognize that our research did not account for variations in subnational cultures within specific countries, such as China, which exhibits significant regional disparities. Micro-level subcultures within local communities can also shape individual responses to policies [38]. Hence, future investigations should explore the nuanced variations in policy effects attributed to micro-level subcultural differences.

4. Conclusion

This study assesses the impact of national emergency policies implemented by countries to control the spread of the COVID-19 epidemic. We employ the synthetic control method to establish a comparable synthetic country for each nation that enacted such a policy. Our findings demonstrate that the policy was overall effective, reducing the infection density on average by around 47.3 % in each country. However, the effect of national emergency policies on epidemic transmission exhibited large heterogeneity among countries. We delve into the heterogeneity of the policy's impact and its relationship with cultures and types of political regimes. We find that the effect displayed a negative correlation with a country's level of individualism and was more pronounced in nations with lower levels of democracy.

Our study underscores the necessity for a nuanced approach to public health crises that considers the diversity of cultural and political landscapes. Given the recurrent nature of respiratory diseases like COVID-19, enhancing our understanding of these factors is vital for developing more effective global and national response strategies. Our study advocates for customized public health interventions rather than a one-size-fits-all approach, stressing the importance of aligning emergency responses with cultural and political realities to maximize impact.

Ethical statements

Informed consent was not required for this study because this work does not contain any studies with human participants performed by any of the authors and based on publicly available data.

Data availability statement

Data associated with our study have not been deposited into a publicly available repository but will be made available on request.

CRediT authorship contribution statement

Peizhen Wu: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Zhen Sun: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article.