How Satisfied Were European Individuals with Governments during the COVID-19 Period? A Comprehensive Analysis

How Satisfied Were European Individuals with Governments during the COVID-19 Period? 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A Comprehensive Analysis Erdal Eren Kizilirmak, Erdem Kilic This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5972680/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Our study investigates Europeans’ satisfaction with national governments from 2020 to 2022 during the COVID-19 pandemic, using data from Round 10 of the European Social Survey. Using two econometric models, the study explores factors associated with satisfaction, including healthcare services, political ideology, political participation, and demographic factors such as household income, gender, unemployment, generation, and education. Findings reveal a strong correlation between healthcare satisfaction and satisfaction with the national governments. Furthermore, political ideology, unemployment, gender, and generational differences are significantly associated with satisfaction levels with national governments, either positively or negatively. Government satisfaction Healthcare services COVID-19 Political ideology Political participation Figures Figure 1 Figure 2 1 Introduction The COVID-19 pandemic has been a global crisis that has profoundly impacted the lives of individuals and compelled many governments to implement unprecedented measures. These measures have involved significant reductions in economic and social activities. Under such circumstances, while some individuals viewed these measures as necessary and positive, others criticised the government’s approach to pandemic management. Against this backdrop, we aim to understand how satisfied European individuals are with their governments, using the dataset derived from the survey questions based on perceptions of individuals across 18 European nations from 2020 to 2022. This study holds significance in comprehending the dynamics of public satisfaction with governments, particularly amid the unprecedented restrictions posed by the COVID-19 pandemic. Notably, the detailed structure of the dataset presents a valuable opportunity to explore individuals' perceptions across 18 European nations. The emphasis on a comprehensive examination of variables-ranging from healthcare services, political ideology, voting behaviour, and participation in demonstrations to demographic characteristics such as age, education, gender, income, and unemployment-ensures a detailed analysis beyond the conventional scope. The findings of this study promise to provide policymakers, researchers, and the public with essential insights into the factors linking Europeans’ contentment with their governments. Finally, our study is one of the research efforts examining the relationship between the state of healthcare services and government satisfaction during COVID-19, using the most recent available data. 2 Literature Review Research on healthcare services has primarily focused on the relationship between service quality and individual satisfaction. However, our study specifically examines the connection between individuals’ perceptions of healthcare services and their satisfaction with the government. Perceptions of service quality have been shown to significantly influence overall satisfaction, which subsequently strengthens trust in local government officials [ 1 ]. Moreover, government trustworthiness, responsiveness, and the quality of public service delivery have been identified as key factors in improving life satisfaction, particularly in the Chinese context [ 2 ]. Building on this understanding of public satisfaction, the impact of the COVID-19 pandemic on public trust has also been widely studied. Several studies have revealed negative effects on trust in public administration and social interactions [ 3 ]. In particular, research in Asian regions has identified significant declines in trust in government [ 4 , 5 ]. This highlights the broader trend observed globally, with studies suggesting that life satisfaction, influenced by factors beyond GDP growth, plays a crucial role in shaping trust in government, particularly in Asian countries [ 6 ]. In light of the pandemic, several studies have also evaluated government responses to COVID-19. For example, Bahrain received the highest satisfaction scores for its government response, while lower satisfaction was associated with income loss, a history of infection, and higher education [ 7 ]. Conversely, dissatisfaction with local government responses in Indonesia’s Aceh region was linked to lower trust and inadequate healthcare services [ 8 ]. Similarly, research across fifty-seven countries found that media freedom diminishes trust in the government, while higher education tends to increase trust [ 9 ]. Greater trust in the government led to higher adherence to COVID-19 measures [ 10 ]. Moreover, public approval of COVID-19 measures was closely tied to trust in national leaders, particularly among opposition voters [ 11 ]. Other studies showed that lockdowns increased political support for ruling parties, with Schraff's research specifically highlighting the pandemic's boost to political support in the Netherlands [ 12 , 13 ]. Political participation has been another significant factor in studies on government satisfaction. For instance, extra-parliamentary activities, such as signing petitions or demonstrating, are more prevalent in Scandinavian countries, with a stronger relationship between government dissatisfaction and such activities [ 14 ]. Similarly, supportive political participants tend to report the highest government satisfaction, while rights-defending participants report the lowest [ 15 ]. In addition, evidence from Western Europe during the COVID-19 pandemic shows that strict lockdown measures increased support for the Prime Minister’s or President’s party, trust in government, and satisfaction with democracy, although they had no effect on traditional left–right political attitudes [ 20 ]. Finally, country-based studies have provided insights into regional differences in government performance during the pandemic. In the American region, research highlights the importance of health sector expertise in shaping public satisfaction [ 16 ]. In Europe, studies noted the worsening financial conditions for workers due to the pandemic [ 17 , 18 ]. Additionally, there was a decline in trust in government, particularly at federal and state levels, underscoring the broader regional trends in governmental response to the crisis [ 19 ]. In this section, we present the literature, followed by a detailed explanation of the methodology used in our study. Subsequently, the dataset used for our analysis is described. Our findings are then presented in the subsequent section, followed by a comprehensive discussion of the results. Finally, we conclude our research by summarising key findings and implications in the conclusion section. 3 Data and Method 3.1 Data This study aims to analyse the views of individuals living in eighteen selected European countries regarding their satisfaction with the national government. For this purpose, we used survey data from the European Social Survey (ESS), which collected data from thirty-one countries between September 2020 and August 2022 [21,22]. Out of these thirty-one countries, we selected eighteen, as listed in Table 1 in the Appendix. Our dataset comprises 30,128 individuals, including 13,857 men and 16,271 women. Given the hierarchical and categorical structure of the ESS Round 10 survey data, we used the Ordinal Logistic Regression (OLR) method to analyse the data. Two econometric models were established for this purpose. In Model 1, we used the question, "How satisfied are you with the national government?" as the dependent variable and estimated its relationship with the independent variables in general terms, without examining the categories. In Model 2, we conducted a more detailed analysis of European individuals' satisfaction with their government by incorporating all categories detailed in Table 1 in the Appendix, alongside the independent variables. While Model 1 provides a general understanding of the relationships among the variables, Model 2 offers detailed information to facilitate a deeper understanding of these relationships. Regarding the categorical structure of our dependent variables, the original structure consisted of numeric levels ranging from 0-10. To simplify the interpretation of findings, we reclassified responses into three groups: ‘extremely dissatisfied’ (0-3), ‘moderate’ (4-6), and ‘extremely satisfied’ (7-10). This categorisation allows for a more intuitive analysis of satisfaction levels. Similarly, we regrouped the income, generations (age), and education predictors by modifying their original classifications. These adjustments, as illustrated in Table 1 in the appendix, were made to enhance the comparability and interpretability of these predictors across different demographic and socioeconomic groups. This study aimed to establish two econometric models by incorporating variables related to the state of healthcare services, political participation and ideology, and individual demographic factors. The variable ‘State of healthcare services in the country’ includes numeric levels ranging from ‘extremely bad (0)’ to ‘extremely good (10).’ Regarding the political participation predictors, ‘Voted in the last national election’ includes three options: ‘yes’ and ‘no’, and ‘not eligible to vote’. Another predictor, ‘Taken part in a public demonstration in the last 12 months’, has two options: ‘yes’, and ‘no’. Additionally, ‘Placement on the left-right scale’ categorises respondents as ‘left’, ‘moderate’, or ‘right’. The variable ‘Household’s total net income from all sources’ is divided into five scales, and ‘Gender’ identifies respondents as ‘female’ or ‘male’. The unemployment variable is based on the question, ‘Has any of the following happened to you as a result of the COVID-19 pandemic?’ We categorised the age of respondents into five generational groups: The Silent Generation, Baby Boomers, Generation X, Millennials, and Generation Z. Lastly, the variable ‘Highest level of education’ includes seven levels, ranging from ‘Not completed’ to ‘Doctoral Degree.’ To facilitate analysis and ensure clarity, we adjusted the dataset categories. This reclassification enhances the interpretability of the findings while maintaining the integrity of the original data. The analysis focuses on individuals from eighteen European countries: Belgium, Bulgaria, Switzerland, Czechia, Estonia, Finland, the United Kingdom, Croatia, Hungary, Ireland, Iceland, Italy, Lithuania, the Netherlands, Norway, Portugal, Slovenia, and Slovakia. These countries were selected based on geographic representation and the availability of complete data in the ESS dataset. Descriptive statistics for all variables are provided in Table 2 in the Appendix. In designing our models and selecting independent predictors, we address the following research questions: What are the differences or similarities in European individuals’ satisfaction with their national governments? We explore this by examining: The state of healthcare services, Voting behaviour, participation in demonstrations, and ideological positioning on the left–right scale, Demographic factors such as age, gender, education, income, and unemployment. Our analysis focuses on 18 European countries. When selecting independent variables, we prioritised healthcare services, anticipating a link between healthcare quality and government satisfaction. This aligns with existing literature, which frequently investigates correlations between public service satisfaction and government approval. We also incorporate political participation predictors. While prior studies emphasise their role in democratic engagement, our study uniquely examines their relationship with government satisfaction. For instance, voting patterns and participation in demonstrations may reflect broader public trust or discontent. Given the survey was conducted during the COVID-19 pandemic, we hypothesise that changes in individuals’ income status during this period may be associated with their satisfaction with the government and perceptions of its pandemic measures. Therefore, we included income and unemployment variables as control variables in our model. Age, gender, education, and country predictors were also incorporated. We propose that variations in individuals’ ages and education levels are significantly related to government satisfaction. Analysing these demographic differences is critical for interpreting perceptions of the dependent variables. Our findings should be interpreted with caution due to the following limitations: Self-reported data bias: Reliance on survey responses risks social desirability bias, potentially affecting response accuracy. Unmeasured confounders: Cultural differences, media exposure, and regional variations were not accounted for, which may correlate with the observed associations. Cross-sectional design: The data’s non-longitudinal nature precludes causal inference, as results reflect statistical associations rather than causality. 3.2 Econometric Model In this study, we used the Ordinal Logistic Regression (OLR) model due to the ordered categorical nature of the dependent variable, which measures satisfaction with the national government. OLR is a widely accepted method for modelling outcomes with ranked categories—that is, when responses represent a meaningful order but not a continuous scale. It estimates the cumulative probability of the response variable falling at or below a given category, under the proportional odds assumption, which posits that the relationship between predictors and the odds of being in a higher versus lower category is constant across thresholds [23,24]. In our study, we opted for the ordinal logistic model due to the categorical, ordered, and hierarchical nature of our independent variables, which have three categories. The responses elicited by these variables also demonstrate a hierarchical structure. For example, the options ‘extremely dissatisfied’, ‘moderate’, and ‘extremely satisfied’ correspond to a ranking. However, variables such as age, gender, and education do not have a hierarchical structure but are categorical in nature. Given these characteristics, we determined that the ordinal logistic regression model is the most suitable method for analysing our data. The variables we selected were chosen based on both theoretical considerations and previous research [25,26]. Healthcare satisfaction and political participation are commonly linked to broader satisfaction with the government, while demographic factors such as age, gender, and education are well-established determinants of political attitudes. These variables, when combined, allow us to assess how both personal characteristics and broader societal factors affect individuals' satisfaction with the government. We have one dependent variable: ‘How satisfied were you with the national government in the country?’. We developed two econometric models, with ‘How satisfied with the national government’ as the dependent variable in both Model 1 and Model 2. This approach allows us to analyse the two models in terms of the state of healthcare services, political participation, and individual demographic predictors. Furthermore, we estimated Model 2 to explore category-specific relationships and calculate predicted probability values (or marginal effects). In Model 2, J represents the different categories of the dependent variable, while Model 1 does not involve J because we do not estimate the subcategories in Model 1. J−1 indicates that the model will generate predictions for one less than the total number of subcategories of the dependent variable. All estimations are conducted in Stata version 16. The significance level of the study was set at 5%, and the ‘Default Standard Errors’ option is preferred when estimating standard errors. Additionally, the ‘analysis weight’ recommended by the ESS was used as ‘importance weights’. 4 Results In Model 1, we observe statistically significant coefficients, with p-values consistently at 1% or 5%, except for the education predictor. Furthermore, our analysis reveals that the coefficients of the unemployment and generation predictors exhibit a negative relationship with satisfaction with the national government. In contrast, all other predictors demonstrate a positive relationship (All coefficients are given in Table 3 and Table 9). While the coefficients provide insights into the relationships with the dependent variable, interpreting them without considering the categorical structure of the data might lead to misinterpretation. For instance, interpreting the unemployment coefficients without accounting for the ‘yes’ and ‘no’ categories could result in inaccurate conclusions. To address these limitations and gain a more detailed understanding, we re-estimated the OLR for Model 2, explicitly considering the categories within the predictors. This approach allows for a more nuanced interpretation of the relationships and provides category-specific insights that enhance the overall analysis. Initially, we conducted the OLR for Model 2, yielding coefficients, odds ratios, and predicted probabilities about the predictor ‘state of health services’, as presented in Table 4 and Table 5 in the Appendix. Interpreting the results is challenging due to the complexity of the coefficients and odds ratios associated with healthcare services. Therefore, we interpret the results using predicted probability values. To explore the relationship between the state of healthcare services and its impact on the dependent variables, we visualised the predicted probability values in graphical format, as displayed in Figure 1. We find that all the coefficients concerning the relationship between the state of healthcare and the overall performance (or satisfaction) of the government are statistically significant, with p -values at 1% and 5%. The predicted probabilities for the dependent variable within the categories ‘extremely dissatisfied’, ‘moderate’, and ‘extremely satisfied’ are presented in Figure 1, providing a clear depiction of how satisfaction levels vary with changes in the state of healthcare services (see the coefficients in Table 4 in the Appendix). When the results are evaluated as a whole, individuals who rate the state of healthcare services as ‘extremely bad’ have a predicted probability of 0.81 for being ‘extremely dissatisfied’ with the general performance of the government, 0.17 for being ‘moderate’, and only 0.03 for being ‘extremely satisfied.’ In contrast, individuals who describe the state of healthcare services as ‘extremely good’ have a predicted probability of 0.14 for being rated ‘extremely dissatisfied’, 0.45 for being rated ‘moderate’, and 0.41 for being rated ‘extremely satisfied’. These probabilities are detailed in Table 5 in the Appendix. When examining the category ‘extremely dissatisfied’, we observe a clear negative relationship between the state of healthcare services and overall government satisfaction. Specifically, for participants who perceive the state of healthcare services as ‘extremely bad’, the predicted probability of being dissatisfied with the government’s performance is 0.81. Conversely, for those who rate healthcare services as ‘extremely good’, this probability decreases significantly to 0.14. This relationship is further highlighted when considering the category ‘extremely satisfied’ in terms of satisfaction with the government. The dependent variable exhibits a positive relationship with the state of healthcare services. As presented in Table 2 and Table 5, the predicted probabilities across different levels of perceived healthcare quality generally align with the descriptive statistics in the middle categories in Table 1 in the Appendix (e.g., values 4-8), where most respondents are concentrated. In these ranges, the predicted probabilities of government satisfaction reflect the underlying distribution of the data. However, as we move towards the extreme categories such as 0, 1, 9, 10, the predictions appear less consistent with the observed frequencies. For instance, although only 4.51% of respondents rated healthcare services as ‘10 (extremely good)’, the model predicts a 41% probability of being extremely satisfied with the government for this group. A similar imbalance is also observed at the lower end of the scale, where small groups are predicted to have disproportionately high probabilities of extreme dissatisfaction. These discrepancies suggest that while the model performs well in the central range, caution is warranted when interpreting results at the margins due to the limited sample sizes in these categories. In the realm of political participation and ideology predictors, all coefficients regarding satisfaction with the national governments exhibit statistical significance. We also obtained the predicted probability values of political predictors. For instance, those who participated in public demonstrations within the last 12 months may express their satisfaction with the government’s overall performance in the following manner: ‘extremely dissatisfied’ with a probability of 0.4, ‘moderate’ with a probability of 0.46, and ‘extremely satisfied’ with a probability of 0.14. In contrast, non-participants in public protests are likely to rate the national government’s overall performance as ‘extremely dissatisfied’ with a probability of 0.32, ‘moderate’ with a probability of 0.49, and ‘extremely satisfied’ with a probability of 0.19. The probability values and coefficients are presented in Table 4 and Table 5 in the Appendix. The coefficients regarding the relationship between ‘Voting in the last national election’ and ‘Placement on the left-right scale’ predictors are presented in Table 4 in the Appendix. All coefficients are statistically significant except for the category ‘right’ on the left-right scale. The findings indicate that individuals identifying themselves as right-wing tend to evaluate the overall performance of the national government as ‘moderate’ (probability of 0.5) and ‘extremely dissatisfied’ (probability of 0.24). On the other hand, those who consider themselves left-wing are inclined to assess the performance as ‘moderate’ (probability of 0.45) and ‘extremely dissatisfied’ (probability of 0.41). Particularly, left-wing individuals give a probability of 0.14 for ‘extremely satisfied’, while right-wing individuals assign a higher probability of 0.26. This suggests that, compared to left-wing individuals, right-wing individuals express greater satisfaction with the national governments. Analysing voting behaviour patterns reveals a clear result: voters and non-voters tend to perceive the national government’s performance as ‘moderate’ and ‘extremely dissatisfied’, respectively. As for the findings related to individual demographic factors given in Table 6 in the Appendix, the coefficients for household income are statistically significant, except for the category ‘upper-low income’. We also obtained the predicted probability values for the predictor ‘household income’ and presented them in Table 7 in the Appendix. The probability values indicate minimal variation in the categories of satisfaction levels with national governments based on household income. However, it is crucial to emphasise that the probability values derived from household income primarily correspond to the categories ‘moderate’ and ‘extremely dissatisfied’ in terms of satisfaction with national governments, respectively. To analyse the impact of unemployment, we incorporate the predictor ‘unemployment’ into both models, yielding statistically significant coefficients. We found that individuals who lost their jobs express lower satisfaction with the national government, compared to those who have not experienced job loss. However, we must emphasise that individuals, whether they have lost their jobs, are most likely to select the category ‘moderate’ when expressing their satisfaction with national governments. We observed minor variations in satisfaction levels with national governments based on gender. It is important to note that the coefficients obtained in both models are statistically significant at the 1% level. We also present the predicted probability values for gender. Specifically, female respondents have a probability of 0.2 of being ‘extremely satisfied’ with the national governments, compared to 0.18 for male respondents (see all coefficients, odds ratios, and predicted probability values in Table 6 and Table 7 in the Appendix). The coefficients associated with generation are statistically significant at the 1% significance level, except for the Silent Generation in Model 2 and Figure 1. Upon examining the predicted probability values, a distinct trend emerges: we observe a noticeable decrease in satisfaction with the national governments as we move from Baby Boomers to Generation Z, reflecting a relationship with decreasing age. For instance, Baby Boomers are likely to express being ‘extremely satisfied’ with the national government with a probability of 0.21, whereas Generation Z shows a lower probability of 0.15 in the same satisfaction category. Among the coefficients indicating the relationship between education levels and overall satisfaction with national government, only Primary Education, Higher Education, and Master’s Degree are statistically significant in both models (see all the coefficients in Table 6 in the Appendix). Finally, we examine how countries assess their government’ overall performance. Looking at the coefficients in Table 8 in the Appendix, we find that most are statistically significant, except for the United Kingdom and Croatia. Looking at the probability values as visualised in Figure 2, we observe that Iceland assesses its government’s overall performance as ‘extremely satisfied’ with a probability of 0.51. A similar pattern emerges for Norway. The probability that Norway rates the overall performance of the government as ‘extremely satisfied’ is only 0.34. The findings above for Iceland and Norway are also similar for Portugal and Ireland. When examining the category ‘extremely dissatisfied’, which reflects an evaluation of the government’s overall performance, Bulgaria, Belgium, and Slovenia emerge as the nations expressing the highest levels of dissatisfaction with their respective national governments. 5 Discussion In our study, we aimed to assess the level of satisfaction among European individuals with their governments across 18 European countries from 2020 to 2022. We identified a range of findings such as the state of healthcare services, political participation indicators, political ideology, and demographic factors such as household income, unemployment, gender, generational cohorts, and education. As individuals' appreciation of the state of healthcare services increases, there appears to be a correlation with higher levels of satisfaction with national governments. This finding suggests that satisfaction with healthcare services is correlatedwith individuals' perceptions and approval of governmental actions, particularly during crises such as the COVID-19 pandemic. This result, however, differs from prior studies attributableto the variables used, but remains consistent with research on trust in government and healthcare services [1,2]. Whether individuals define themselves as right-wing, left-wing, or moderate reflects their political views. However, this categorization is solely based on individual perceptions. Actively participating in a demonstration can be seen as a tangible indicator, highlightingindividuals’ commitment to their views. The findings suggest that individuals participating in the demonstrations report lower levels of satisfaction with the national governments compared to those who do not attend such protests [14]. This observation is comprehensible, especially when considering their dissatisfaction with certain issues that coincided their participation in the demonstrations. Regarding individuals’ perceptions of their political positions, those who identify as left-leaning are less likely to be satisfied with national governments compared to those identifying as right-leaning. Unemployment, as a factor diminishing income, may have a significant relationship with individuals’ perspectives on governments. Our findings strongly support this observation. The findings reveal a correlation between job loss and lower satisfaction with national governments compared to individuals who have not experienced job loss due to the COVID-19 pandemic. However, this may not necessarily imply causation, but rather an association. Based on our findings, females, albeit to a slight extent, express higher satisfaction levels with national governments compared to males. The findings, obtained by categorising individuals into generations, suggest that as individuals progress from the Baby Boomers generation to Generation X, the Millennial Generation, and Generation Z, the probability of satisfaction with the national government tends to decrease. This may indicate a generational shift in expectations, but further analysis is needed to establish causality. Throughout the COVID-19 pandemic, European individuals have encountered measures restricting their social and economic activities to an unprecedented extent. While some individuals viewed these measures as positive or necessary, others were not pleased or satisfied with them. Based on our findings, we can confidently state that the Swiss, Icelandic, Finnish, Irish, Norwegian, Hungarian, and Portuguese individuals are the most satisfied with their national governments, in this order. Another notable difference between the countries is that Bulgaria, Belgium, Czechia, Slovenia, and Slovakia are the least satisfied with their national governments. 6 Conclusion Our findings suggest that, when European individuals perceive the state of healthcare services as good, they are more likely to evaluate their national governments as moderate or satisfactory. However, this association should be interpreted with caution, as the results do not establish a causal relationship. Moreover, the finding points to the relevance of healthcare service quality or satisfaction in relation to individuals’ perceptions. Our additional focus lies in exploring the political ideology of European individuals. The findings suggest that as the individuals’ political ideology shifts from left to right on the political spectrum, they tend to report higher levels of satisfaction with their national governments. However, we must also note that our findings on political ideology or positions predominantly indicate a moderate level of satisfaction with the national governments, followed by dissatisfaction and then satisfaction, respectively. As expected, individuals who participated in activism in the last 12 months expressed lower satisfaction with the national governments compared to non-participants. Voting behaviour indicates that satisfaction levels are slightly higher among voters compared to non-voters, but lower than those ineligibles to vote. In conclusion, our research aimed to assess the perception of European individuals toward their national governments by utilizing the survey dataset and within the framework of our econometric model. Additionally, future studies can focus on determining country-specific factors, thereby contributing to a more comprehensive understanding of the elements associated with European individuals’ satisfaction with their national governments. Declarations Acknowledgements Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Clinical trial number: not applicable Data Availability Statement The data used in this study are publicly available and can be accessed through the following source: ESS Round 10: European Social Survey Round 10 Data (2020). Data file edition 3.0. Sikt - Norwegian Agency for Shared Services in Education and Research, Norway – Data Archive and distributor of ESS data for ESS ERIC. doi:10.21338/NSD-ESS10-2020. The link : https://www.europeansocialsurvey.org/data - portal Documentation for the dataset can be referenced as follows: ESS Round 10: European Social Survey (2023): ESS-10 2020 Documentation Report. Edition 3.0. Bergen, European Social Survey Data Archive, Sikt - Norwegian Agency for Shared Services in Education and Research, Norway for ESS ERIC. doi:10.21338/NSD-ESS10-2020. The link : https://www.europeansocialsurvey.org/data - portal The data are available for free use, provided that proper attribution is made according to the citation format indicated above. Ethics, Consent to Participate, and Consent to Publish declarations: not applicable. The data used in this study originate from the European Social Survey Round 10 and are publicly available and anonymised. No ethical approval was required as the data were secondary and anonymised; however, the authors confirm their adherence to the ethical standards for secondary data use. Data Transformation Statement The authors declare that data transformation has been performed in this study by narrowing the categorical structure of the data provided by the European Social Survey to enhance interpretability and facilitate analysis, without distorting the true meaning. Conflict of Interest Statement Erdal Eren Kizilirmak and Erdem Kilic declare that they have no conflict of interest regarding the preparation, authorship, and publication of this article. Author Contributions The design and writing of the manuscript were carried out by Erdal Eren Kizilirmak. Erdem Kilic contributed to the manuscript through review, advice, and general guidance. References Adamy, A., & Rani, H. A. (2022). An evaluation of community satisfaction with the government’s COVID-19 pandemic response in Aceh, Indonesia. International Journal of Disaster Risk Reduction, 69, 102723. https://doi.org/10.1016/j.ijdrr.2021.102723 Alamsyah, N., & Zhu, Y. Q. (2022). We shall endure: Exploring the impact of government information quality and partisanship on citizens’ well-being during the COVID-19 pandemic. Government Information Quarterly, 39(1), 101646. https://doi.org/10.1016/j.giq.2021.101646 Altiparmakis, A., Bojar, A., Brouard, S., Foucault, M., Kriesi, H., & Nadeau, R. (2021). 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Cambridge University Press. https://doi.org/10.1017/9781108961400 Schraff, D. (2021). Political trust during the Covid‐19 pandemic: Rally around the flag or lockdown effects?. European Journal of Political Research, 60(4), 1007-1017. https://doi.org/10.1111/1475-6765.12425 Shanka, M. S., & Menebo, M. M. (2022). When and how trust in government leads to compliance with COVID-19 precautionary measures. Journal of Business Research, 139, 1275-1283. https://doi.org/10.1016/j.jbusres.2021.10.036 Suhay, E., Soni, A., Persico, C., & Marcotte, D. E. (2022). Americans’ Trust in Government and health Behaviors During the COVID-19 Pandemic. RSF: The Russell Sage Foundation Journal of the Social Sciences, 8(8), 221-244. https://doi.org/10.7758/RSF.2022.8.8.10 Wilson, J. R., Lorenz, K. A., Wilson, J. R., & Lorenz, K. A. (2015). Hierarchical logistic regression models. Modeling binary correlated responses using SAS, SPSS and R , 201-224. https://doi.org/10.1007/978-3-319-23805-0_10 Wong, G. Y., & Mason, W. M. (1985). The hierarchical logistic regression model for multilevel analysis. Journal of the American Statistical Association , 80 (391), 513-524. https://doi.org/10.2307/2288464 Zhu, M. (2023). The Effect of Political Participation of Chinese Citizens on Government Satisfaction: Based on Modified Causal Forest. Procedia Computer Science, 221, 1044-1051. https://doi.org/10.1016/j.procs.2023.08.086 Table Table 3 Results of Ordinal Logistic Regression for Basic Model 1 Model 1: How satisfied with the national government Variable Coefficient Odds Ratio State of health services in country nowadays 0.36*** (44.62) 1.43*** Taken part in public demonstration last 12 months 0.29*** (4.29) 1.34*** Voted last national election 0.09** (2.83) 1.09** Placement on left-right scale 0.38*** (18.68) 1.47*** Household’s total net income, all sources 0.06*** (4.11) 1.06*** I was made redundant / lost my job -0.34** (-2.88) 0.71** Gender 0.15*** (4.56) 1.17*** Generation -0.11*** (-6.82) 0.90*** Education 0.02 (1.56) 1.02 Country 0.02*** (7.57) 1.02*** Note: AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) aid in model selection, favouring lower values for improved fit. Pseudo R2 gauges the proportion of variance explained by the model. Log Pseudolikelihood quantifies the fit of the model to the data. Number of observations denotes the count of observations used in the models. z statistics in parentheses, and * p<0.05, ** p<0.01, *** p<0.001. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 09 May, 2025 Editor assigned by journal 08 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviews received at journal 03 May, 2025 Reviewers agreed at journal 03 May, 2025 Reviewers invited by journal 28 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 13 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5972680","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448971639,"identity":"d3b6e496-d1b0-47ef-9da8-9e7f072de1b5","order_by":0,"name":"Erdal Eren Kizilirmak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie3RMUvDQBTA8RceXJZnsz4ptF/hRCiCQ75KpJAsF3AqHYoIQrocdRX8FH4CI4FMR+eMBaGTQ3AoLorXBCfT1NHh/nBwHPy4xx2Ay/VvyxmE793aXTRqT8RfCLbknH4I9RO7sNlFV/oYCYZFuQFzEQ4Qs/dqESfPvpFQzwoIh3knOV3FiYSK0Q5296hKlWpS0ntYF0CDqJNIQxOGmsWeoBLzVIOSeJJZcmCy0AS7PaGWfM0TCt4kfvYQSSTYDsYNSTMVEdtbvB7CRkw4MixbsorPdLW9ftHrhMgceDGNW67Lm3C8XL6i2k3H/v30afMxuxz5ups0/XqZHI79pMvlcrl6+wapGEvBfrOkFQAAAABJRU5ErkJggg==","orcid":"","institution":"Türkisch-Deutsche Universität","correspondingAuthor":true,"prefix":"","firstName":"Erdal","middleName":"Eren","lastName":"Kizilirmak","suffix":""},{"id":448971640,"identity":"cd6ca564-3118-482f-b7f7-05ad8c41707c","order_by":1,"name":"Erdem Kilic","email":"","orcid":"","institution":"Türkisch-Deutsche Universität","correspondingAuthor":false,"prefix":"","firstName":"Erdem","middleName":"","lastName":"Kilic","suffix":""}],"badges":[],"createdAt":"2025-02-06 10:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5972680/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5972680/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81678956,"identity":"ff51284a-46f3-44b3-a653-b7d4ff25f564","added_by":"auto","created_at":"2025-04-30 08:27:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136655,"visible":true,"origin":"","legend":"\u003cp\u003eThe Predicted Probability Values of the State of Healthcare Services in Country \u0026nbsp;\u0026nbsp;Nowadays and Generation\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eThe figure represents the results of Model 2. They illustrate the predicted probability values of the relationship between the predictor ‘state of healthcare services in country nowadays’ and the dependent variable. The horizontal axes display the range of values for individuals’ perceptions of the ‘state of healthcare services in country nowadays’, ranging from extremely bad=0 to extremely good=10. The vertical axes depict the probability values corresponding to each category of the predictor ‘state of healthcare services’ within the categories of the dependent variables. The horizontal axes in generation display the range of values for individuals’ age, ranging from Silent Generation=1 to Generation Z=5. The vertical axes depict the probability values corresponding to each category of the predictor ‘Generation’ within the categories of the dependent variables.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5972680/v1/0e07275190997a1771bffce1.png"},{"id":81678954,"identity":"342f684c-907b-46d5-9138-7e13f375df52","added_by":"auto","created_at":"2025-04-30 08:27:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68096,"visible":true,"origin":"","legend":"\u003cp\u003eThe Predicted Probability Values of Country\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eThe figures illustrate the predicted probability values depicting the relationship between the dependent variable and the predictor ‘Country’. The vertical axes depict the probability values corresponding to each category of the predictor ‘Country’ within the categories of the dependent variables. The figure represents the results of Model 2. All the probability values are given in Table 7.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5972680/v1/e8e3c882bb1fa640ce1283e5.png"},{"id":81679729,"identity":"7d1ac0cb-e75e-4d49-82f3-cff24b139b07","added_by":"auto","created_at":"2025-04-30 08:43:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5972680/v1/c5a53195-6687-438a-9db8-2e669add6495.pdf"},{"id":81678957,"identity":"f1815c1b-3f84-42bc-ade9-525f3d19acd3","added_by":"auto","created_at":"2025-04-30 08:27:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65618,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5972680/v1/884d6f95d4a46a15bf775c50.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Satisfied Were European Individuals with Governments during the COVID-19 Period? A Comprehensive Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has been a global crisis that has profoundly impacted the lives of individuals and compelled many governments to implement unprecedented measures. These measures have involved significant reductions in economic and social activities. Under such circumstances, while some individuals viewed these measures as necessary and positive, others criticised the government\u0026rsquo;s approach to pandemic management. Against this backdrop, we aim to understand how satisfied European individuals are with their governments, using the dataset derived from the survey questions based on perceptions of individuals across 18 European nations from 2020 to 2022.\u003c/p\u003e \u003cp\u003eThis study holds significance in comprehending the dynamics of public satisfaction with governments, particularly amid the unprecedented restrictions posed by the COVID-19 pandemic. Notably, the detailed structure of the dataset presents a valuable opportunity to explore individuals' perceptions across 18 European nations. The emphasis on a comprehensive examination of variables-ranging from healthcare services, political ideology, voting behaviour, and participation in demonstrations to demographic characteristics such as age, education, gender, income, and unemployment-ensures a detailed analysis beyond the conventional scope.\u003c/p\u003e \u003cp\u003eThe findings of this study promise to provide policymakers, researchers, and the public with essential insights into the factors linking Europeans\u0026rsquo; contentment with their governments. Finally, our study is one of the research efforts examining the relationship between the state of healthcare services and government satisfaction during COVID-19, using the most recent available data.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cp\u003eResearch on healthcare services has primarily focused on the relationship between service quality and individual satisfaction. However, our study specifically examines the connection between individuals\u0026rsquo; perceptions of healthcare services and their satisfaction with the government. Perceptions of service quality have been shown to significantly influence overall satisfaction, which subsequently strengthens trust in local government officials [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, government trustworthiness, responsiveness, and the quality of public service delivery have been identified as key factors in improving life satisfaction, particularly in the Chinese context [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBuilding on this understanding of public satisfaction, the impact of the COVID-19 pandemic on public trust has also been widely studied. Several studies have revealed negative effects on trust in public administration and social interactions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In particular, research in Asian regions has identified significant declines in trust in government [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This highlights the broader trend observed globally, with studies suggesting that life satisfaction, influenced by factors beyond GDP growth, plays a crucial role in shaping trust in government, particularly in Asian countries [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In light of the pandemic, several studies have also evaluated government responses to COVID-19. For example, Bahrain received the highest satisfaction scores for its government response, while lower satisfaction was associated with income loss, a history of infection, and higher education [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Conversely, dissatisfaction with local government responses in Indonesia\u0026rsquo;s Aceh region was linked to lower trust and inadequate healthcare services [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similarly, research across fifty-seven countries found that media freedom diminishes trust in the government, while higher education tends to increase trust [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Greater trust in the government led to higher adherence to COVID-19 measures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, public approval of COVID-19 measures was closely tied to trust in national leaders, particularly among opposition voters [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Other studies showed that lockdowns increased political support for ruling parties, with Schraff's research specifically highlighting the pandemic's boost to political support in the Netherlands [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePolitical participation has been another significant factor in studies on government satisfaction. For instance, extra-parliamentary activities, such as signing petitions or demonstrating, are more prevalent in Scandinavian countries, with a stronger relationship between government dissatisfaction and such activities [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, supportive political participants tend to report the highest government satisfaction, while rights-defending participants report the lowest [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, evidence from Western Europe during the COVID-19 pandemic shows that strict lockdown measures increased support for the Prime Minister\u0026rsquo;s or President\u0026rsquo;s party, trust in government, and satisfaction with democracy, although they had no effect on traditional left\u0026ndash;right political attitudes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, country-based studies have provided insights into regional differences in government performance during the pandemic. In the American region, research highlights the importance of health sector expertise in shaping public satisfaction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In Europe, studies noted the worsening financial conditions for workers due to the pandemic [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, there was a decline in trust in government, particularly at federal and state levels, underscoring the broader regional trends in governmental response to the crisis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this section, we present the literature, followed by a detailed explanation of the methodology used in our study. Subsequently, the dataset used for our analysis is described. Our findings are then presented in the subsequent section, followed by a comprehensive discussion of the results. Finally, we conclude our research by summarising key findings and implications in the conclusion section.\u003c/p\u003e"},{"header":"3 Data and Method","content":"\u003cp\u003e\u003cstrong\u003e3.1 Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to analyse the views of individuals living in eighteen selected European countries regarding their satisfaction with the national government. For this purpose, we used survey data from the European Social Survey (ESS), which collected data from thirty-one countries between September 2020 and August 2022 [21,22]. Out of these thirty-one countries, we selected eighteen, as listed in Table 1 in the Appendix. Our dataset comprises 30,128 individuals, including 13,857 men and 16,271 women.\u003c/p\u003e\n\u003cp\u003eGiven the hierarchical and categorical structure of the ESS Round 10 survey data, we used the Ordinal Logistic Regression (OLR) method to analyse the data. Two econometric models were established for this purpose. In Model 1, we used the question, \u0026quot;How satisfied are you with the national government?\u0026quot; as the dependent variable and estimated its relationship with the independent variables in general terms, without examining the categories. In Model 2, we conducted a more detailed analysis of European individuals\u0026apos; satisfaction with their government by incorporating all categories detailed in Table 1 in the Appendix, alongside the independent variables.\u003c/p\u003e\n\u003cp\u003eWhile Model 1 provides a general understanding of the relationships among the variables, Model 2 offers detailed information to facilitate a deeper understanding of these relationships. Regarding the categorical structure of our dependent variables, the original structure consisted of numeric levels ranging from 0-10. To simplify the interpretation of findings, we reclassified responses into three groups: \u0026lsquo;extremely dissatisfied\u0026rsquo; (0-3), \u0026lsquo;moderate\u0026rsquo; (4-6), and \u0026lsquo;extremely satisfied\u0026rsquo; (7-10). This categorisation allows for a more intuitive analysis of satisfaction levels. Similarly, we regrouped the income, generations (age), and education predictors by modifying their original classifications. These adjustments, as illustrated in Table 1 in the appendix, were made to enhance the comparability and interpretability of these predictors across different demographic and socioeconomic groups.\u003c/p\u003e\n\u003cp\u003eThis study aimed to establish two econometric models by incorporating variables related to the state of healthcare services, political participation and ideology, and individual demographic factors. The variable \u0026lsquo;State of healthcare services in the country\u0026rsquo; includes numeric levels ranging from \u0026lsquo;extremely bad (0)\u0026rsquo; to \u0026lsquo;extremely good (10).\u0026rsquo; Regarding the political participation predictors, \u0026lsquo;Voted in the last national election\u0026rsquo; includes three options: \u0026lsquo;yes\u0026rsquo; and \u0026lsquo;no\u0026rsquo;, and \u0026lsquo;not eligible to vote\u0026rsquo;. Another predictor, \u0026lsquo;Taken part in a public demonstration in the last 12 months\u0026rsquo;, has two options: \u0026lsquo;yes\u0026rsquo;, and \u0026lsquo;no\u0026rsquo;. Additionally, \u0026lsquo;Placement on the left-right scale\u0026rsquo; categorises respondents as \u0026lsquo;left\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo;, or \u0026lsquo;right\u0026rsquo;. The variable \u0026lsquo;Household\u0026rsquo;s total net income from all sources\u0026rsquo; is divided into five scales, and \u0026lsquo;Gender\u0026rsquo; identifies respondents as \u0026lsquo;female\u0026rsquo; or \u0026lsquo;male\u0026rsquo;. The unemployment variable is based on the question, \u0026lsquo;Has any of the following happened to you as a result of the COVID-19 pandemic?\u0026rsquo; We categorised the age of respondents into five generational groups: The Silent Generation, Baby Boomers, Generation X, Millennials, and Generation Z. Lastly, the variable \u0026lsquo;Highest level of education\u0026rsquo; includes seven levels, ranging from \u0026lsquo;Not completed\u0026rsquo; to \u0026lsquo;Doctoral Degree.\u0026rsquo;\u003c/p\u003e\n\u003cp\u003eTo facilitate analysis and ensure clarity, we adjusted the dataset categories. This reclassification enhances the interpretability of the findings while maintaining the integrity of the original data. The analysis focuses on individuals from eighteen European countries: Belgium, Bulgaria, Switzerland, Czechia, Estonia, Finland, the United Kingdom, Croatia, Hungary, Ireland, Iceland, Italy, Lithuania, the Netherlands, Norway, Portugal, Slovenia, and Slovakia. These countries were selected based on geographic representation and the availability of complete data in the ESS dataset. Descriptive statistics for all variables are provided in Table 2 in the Appendix.\u003c/p\u003e\n\u003cp\u003eIn designing our models and selecting independent predictors, we address the following research questions: What are the differences or similarities in European individuals\u0026rsquo; satisfaction with their national governments? We explore this by examining:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eThe state of healthcare services,\u003c/li\u003e\n \u003cli\u003eVoting behaviour, participation in demonstrations, and ideological positioning on the left\u0026ndash;right scale,\u003c/li\u003e\n \u003cli\u003eDemographic factors such as age, gender, education, income, and unemployment.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOur analysis focuses on 18 European countries. When selecting independent variables, we prioritised healthcare services, anticipating a link between healthcare quality and government satisfaction. This aligns with existing literature, which frequently investigates correlations between public service satisfaction and government approval.\u003c/p\u003e\n\u003cp\u003eWe also incorporate political participation predictors. While prior studies emphasise their role in democratic engagement, our study uniquely examines their relationship with government satisfaction. For instance, voting patterns and participation in demonstrations may reflect broader public trust or discontent.\u003c/p\u003e\n\u003cp\u003eGiven the survey was conducted during the COVID-19 pandemic, we hypothesise that changes in individuals\u0026rsquo; income status during this period may be associated with their satisfaction with the government and perceptions of its pandemic measures. Therefore, we included income and unemployment variables as control variables in our model. Age, gender, education, and country predictors were also incorporated. We propose that variations in individuals\u0026rsquo; ages and education levels are significantly related to government satisfaction. Analysing these demographic differences is critical for interpreting perceptions of the dependent variables.\u003c/p\u003e\n\u003cp\u003eOur findings should be interpreted with caution due to the following limitations:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eSelf-reported data bias: Reliance on survey responses risks social desirability bias, potentially affecting response accuracy.\u003c/li\u003e\n \u003cli\u003eUnmeasured confounders: Cultural differences, media exposure, and regional variations were not accounted for, which may correlate with the observed associations.\u003c/li\u003e\n \u003cli\u003eCross-sectional design: The data\u0026rsquo;s non-longitudinal nature precludes causal inference, as results reflect statistical associations rather than causality.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Econometric Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we used the Ordinal Logistic Regression (OLR) model due to the ordered categorical nature of the dependent variable, which measures satisfaction with the national government. OLR is a widely accepted method for modelling outcomes with ranked categories\u0026mdash;that is, when responses represent a meaningful order but not a continuous scale. It estimates the cumulative probability of the response variable falling at or below a given category, under the proportional odds assumption, which posits that the relationship between predictors and the odds of being in a higher versus lower category is constant across thresholds [23,24].\u003c/p\u003e\n\u003cp\u003eIn our study, we opted for the ordinal logistic model due to the categorical, ordered, and hierarchical nature of our independent variables, which have three categories. The responses elicited by these variables also demonstrate a hierarchical structure. For example, the options \u0026lsquo;extremely dissatisfied\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo;, and \u0026lsquo;extremely satisfied\u0026rsquo; correspond to a ranking. However, variables such as age, gender, and education do not have a hierarchical structure but are categorical in nature. Given these characteristics, we determined that the ordinal logistic regression model is the most suitable method for analysing our data. The variables we selected were chosen based on both theoretical considerations and previous research [25,26]. Healthcare satisfaction and political participation are commonly linked to broader satisfaction with the government, while demographic factors such as age, gender, and education are well-established determinants of political attitudes. These variables, when combined, allow us to assess how both personal characteristics and broader societal factors affect individuals\u0026apos; satisfaction with the government.\u003c/p\u003e\n\u003cp\u003eWe have one dependent variable: \u0026lsquo;How satisfied were you with the national government in the country?\u0026rsquo;. We developed two econometric models, with \u0026lsquo;How satisfied with the national government\u0026rsquo; as the dependent variable in both Model 1 and Model 2. This approach allows us to analyse the two models in terms of the state of healthcare services, political participation, and individual demographic predictors. Furthermore, we estimated Model 2 to explore category-specific relationships and calculate predicted probability values (or marginal effects).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn Model 2, J represents the different categories of the dependent variable, while Model 1 does not involve J because we do not estimate the subcategories in Model 1. J\u0026minus;1 indicates that the model will generate predictions for one less than the total number of subcategories of the dependent variable.\u003c/p\u003e\n\u003cp\u003eAll estimations are conducted in Stata version 16. The significance level of the study was set at 5%, and the \u0026lsquo;Default Standard Errors\u0026rsquo; option is preferred when estimating standard errors. Additionally, the \u0026lsquo;analysis weight\u0026rsquo; recommended by the ESS was used as \u0026lsquo;importance weights\u0026rsquo;.\u003c/p\u003e"},{"header":"4 Results","content":"\u003cp\u003eIn Model 1, we observe statistically significant coefficients, with p-values consistently at 1% or 5%, except for the education predictor. Furthermore, our analysis reveals that the coefficients of the unemployment and generation predictors exhibit a negative relationship with satisfaction with the national government. In contrast, all other predictors demonstrate a positive relationship (All coefficients are given in Table 3 and Table 9). While the coefficients provide insights into the relationships with the dependent variable, interpreting them without considering the categorical structure of the data might lead to misinterpretation. For instance, interpreting the unemployment coefficients without accounting for the \u0026lsquo;yes\u0026rsquo; and \u0026lsquo;no\u0026rsquo; categories could result in inaccurate conclusions. To address these limitations and gain a more detailed understanding, we re-estimated the OLR for Model 2, explicitly considering the categories within the predictors. This approach allows for a more nuanced interpretation of the relationships and provides category-specific insights that enhance the overall analysis.\u003c/p\u003e\n\u003cp\u003eInitially, we conducted the OLR for Model 2, yielding coefficients, odds ratios, and predicted probabilities about the predictor \u0026lsquo;state of health services\u0026rsquo;, as presented in Table 4 and Table 5 in the Appendix. Interpreting the results is challenging due to the complexity of the coefficients and odds ratios associated with healthcare services. Therefore, we interpret the results using predicted probability values. To explore the relationship between the state of healthcare services and its impact on the dependent variables, we visualised the predicted probability values in graphical format, as displayed in Figure 1.\u003c/p\u003e\n\u003cp\u003eWe find that all the coefficients concerning the relationship between the state of healthcare and the overall performance (or satisfaction) of the government are statistically significant, with \u003cem\u003ep\u003c/em\u003e-values at 1% and 5%. The predicted probabilities for the dependent variable within the categories \u0026lsquo;extremely dissatisfied\u0026rsquo;, \u0026lsquo;moderate\u0026rsquo;, and \u0026lsquo;extremely satisfied\u0026rsquo; are presented in Figure 1, providing a clear depiction of how satisfaction levels vary with changes in the state of healthcare services (see the coefficients in Table 4 in the Appendix).\u003c/p\u003e\n\u003cp\u003eWhen the results are evaluated as a whole, individuals who rate the state of healthcare services as \u0026lsquo;extremely bad\u0026rsquo; have a predicted probability of 0.81 for being \u0026lsquo;extremely dissatisfied\u0026rsquo; with the general performance of the government, 0.17 for being \u0026lsquo;moderate\u0026rsquo;, and only 0.03 for being \u0026lsquo;extremely satisfied.\u0026rsquo; In contrast, individuals who describe the state of healthcare services as \u0026lsquo;extremely good\u0026rsquo; have a predicted probability of 0.14 for being rated \u0026lsquo;extremely dissatisfied\u0026rsquo;, 0.45 for being rated \u0026lsquo;moderate\u0026rsquo;, and 0.41 for being rated \u0026lsquo;extremely satisfied\u0026rsquo;. These probabilities are detailed in Table 5 in the Appendix.\u003c/p\u003e\n\u003cp\u003eWhen examining the category \u0026lsquo;extremely dissatisfied\u0026rsquo;, we observe a clear negative relationship between the state of healthcare services and overall government satisfaction. Specifically, for participants who perceive the state of healthcare services as \u0026lsquo;extremely bad\u0026rsquo;, the predicted probability of being dissatisfied with the government\u0026rsquo;s performance is 0.81. Conversely, for those who rate healthcare services as \u0026lsquo;extremely good\u0026rsquo;, this probability decreases significantly to 0.14. This relationship is further highlighted when considering the category \u0026lsquo;extremely satisfied\u0026rsquo; in terms of satisfaction with the government. The dependent variable exhibits a positive relationship with the state of healthcare services. \u003c/p\u003e\n\u003cp\u003eAs presented in Table 2 and Table 5, the predicted probabilities across different levels of perceived healthcare quality generally align with the descriptive statistics in the middle categories in Table 1 in the Appendix (e.g., values 4-8), where most respondents are concentrated. In these ranges, the predicted probabilities of government satisfaction reflect the underlying distribution of the data. However, as we move towards the extreme categories such as 0, 1, 9, 10, the predictions appear less consistent with the observed frequencies. For instance, although only 4.51% of respondents rated healthcare services as \u0026lsquo;10 (extremely good)\u0026rsquo;, the model predicts a 41% probability of being extremely satisfied with the government for this group. A similar imbalance is also observed at the lower end of the scale, where small groups are predicted to have disproportionately high probabilities of extreme dissatisfaction. These discrepancies suggest that while the model performs well in the central range, caution is warranted when interpreting results at the margins due to the limited sample sizes in these categories. \u003c/p\u003e\n\u003cp\u003eIn the realm of political participation and ideology predictors, all coefficients regarding satisfaction with the national governments exhibit statistical significance. We also obtained the predicted probability values of political predictors. For instance, those who participated in public demonstrations within the last 12 months may express their satisfaction with the government\u0026rsquo;s overall performance in the following manner: \u0026lsquo;extremely dissatisfied\u0026rsquo; with a probability of 0.4, \u0026lsquo;moderate\u0026rsquo; with a probability of 0.46, and \u0026lsquo;extremely satisfied\u0026rsquo; with a probability of 0.14. In contrast, non-participants in public protests are likely to rate the national government\u0026rsquo;s overall performance as \u0026lsquo;extremely dissatisfied\u0026rsquo; with a probability of 0.32, \u0026lsquo;moderate\u0026rsquo; with a probability of 0.49, and \u0026lsquo;extremely satisfied\u0026rsquo; with a probability of 0.19. The probability values and coefficients are presented in Table 4 and Table 5 in the Appendix.\u003c/p\u003e\n\u003cp\u003eThe coefficients regarding the relationship between \u0026lsquo;Voting in the last national election\u0026rsquo; and \u0026lsquo;Placement on the left-right scale\u0026rsquo; predictors are presented in Table 4 in the Appendix. All coefficients are statistically significant except for the category \u0026lsquo;right\u0026rsquo; on the left-right scale. The findings indicate that individuals identifying themselves as right-wing tend to evaluate the overall performance of the national government as \u0026lsquo;moderate\u0026rsquo; (probability of 0.5) and \u0026lsquo;extremely dissatisfied\u0026rsquo; (probability of 0.24). On the other hand, those who consider themselves left-wing are inclined to assess the performance as \u0026lsquo;moderate\u0026rsquo; (probability of 0.45) and \u0026lsquo;extremely dissatisfied\u0026rsquo; (probability of 0.41). Particularly, left-wing individuals give a probability of 0.14 for \u0026lsquo;extremely satisfied\u0026rsquo;, while right-wing individuals assign a higher probability of 0.26. This suggests that, compared to left-wing individuals, right-wing individuals express greater satisfaction with the national governments. Analysing voting behaviour patterns reveals a clear result: voters and non-voters tend to perceive the national government\u0026rsquo;s performance as \u0026lsquo;moderate\u0026rsquo; and \u0026lsquo;extremely dissatisfied\u0026rsquo;, respectively. \u003c/p\u003e\n\u003cp\u003eAs for the findings related to individual demographic factors given in Table 6 in the Appendix, the coefficients for household income are statistically significant, except for the category \u0026lsquo;upper-low income\u0026rsquo;. We also obtained the predicted probability values for the predictor \u0026lsquo;household income\u0026rsquo; and presented them in Table 7 in the Appendix. The probability values indicate minimal variation in the categories of satisfaction levels with national governments based on household income. However, it is crucial to emphasise that the probability values derived from household income primarily correspond to the categories \u0026lsquo;moderate\u0026rsquo; and \u0026lsquo;extremely dissatisfied\u0026rsquo; in terms of satisfaction with national governments, respectively. \u003c/p\u003e\n\u003cp\u003eTo analyse the impact of unemployment, we incorporate the predictor \u0026lsquo;unemployment\u0026rsquo; into both models, yielding statistically significant coefficients. We found that individuals who lost their jobs express lower satisfaction with the national government, compared to those who have not experienced job loss. However, we must emphasise that individuals, whether they have lost their jobs, are most likely to select the category \u0026lsquo;moderate\u0026rsquo; when expressing their satisfaction with national governments. We observed minor variations in satisfaction levels with national governments based on gender. It is important to note that the coefficients obtained in both models are statistically significant at the 1% level. We also present the predicted probability values for gender. Specifically, female respondents have a probability of 0.2 of being \u0026lsquo;extremely satisfied\u0026rsquo; with the national governments, compared to 0.18 for male respondents (see all coefficients, odds ratios, and predicted probability values in Table 6 and Table 7 in the Appendix).\u003c/p\u003e\n\u003cp\u003eThe coefficients associated with generation are statistically significant at the 1% significance level, except for the Silent Generation in Model 2 and Figure 1. Upon examining the predicted probability values, a distinct trend emerges: we observe a noticeable decrease in satisfaction with the national governments as we move from Baby Boomers to Generation Z, reflecting a relationship with decreasing age. For instance, Baby Boomers are likely to express being \u0026lsquo;extremely satisfied\u0026rsquo; with the national government with a probability of 0.21, whereas Generation Z shows a lower probability of 0.15 in the same satisfaction category. Among the coefficients indicating the relationship between education levels and overall satisfaction with national government, only Primary Education, Higher Education, and Master\u0026rsquo;s Degree are statistically significant in both models (see all the coefficients in Table 6 in the Appendix).\u003c/p\u003e\n\u003cp\u003eFinally, we examine how countries assess their government\u0026rsquo; overall performance. Looking at the coefficients in Table 8 in the Appendix, we find that most are statistically significant, except for the United Kingdom and Croatia. Looking at the probability values as visualised in Figure 2, we observe that Iceland assesses its government\u0026rsquo;s overall performance as \u0026lsquo;extremely satisfied\u0026rsquo; with a probability of 0.51. A similar pattern emerges for Norway. The probability that Norway rates the overall performance of the government as \u0026lsquo;extremely satisfied\u0026rsquo; is only 0.34. The findings above for Iceland and Norway are also similar for Portugal and Ireland.\u003c/p\u003e\n\u003cp\u003eWhen examining the category \u0026lsquo;extremely dissatisfied\u0026rsquo;, which reflects an evaluation of the government\u0026rsquo;s overall performance, Bulgaria, Belgium, and Slovenia emerge as the nations expressing the highest levels of dissatisfaction with their respective national governments.\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eIn our study, we aimed to assess the level of satisfaction among European individuals with their governments across 18 European countries from 2020 to 2022. We identified a range of findings such as the state of healthcare services, political participation indicators, political ideology, and demographic factors such as household income, unemployment, gender, generational cohorts, and education. As individuals\u0026apos; appreciation of the state of healthcare services increases, there appears to be a correlation with higher levels of satisfaction with national governments. This finding suggests that satisfaction with healthcare services is correlatedwith individuals\u0026apos; perceptions and approval of governmental actions, particularly during crises such as the COVID-19 pandemic. This result, however, differs from prior studies attributableto the variables used, but remains consistent with research on trust in government and healthcare services [1,2].\u003c/p\u003e\n\u003cp\u003eWhether individuals define themselves as right-wing, left-wing, or moderate reflects their political views. However, this categorization is solely based on individual perceptions. Actively participating in a demonstration can be seen as a tangible indicator, highlightingindividuals\u0026rsquo; commitment to their views. The findings suggest that individuals participating in the demonstrations report lower levels of satisfaction with the national governments compared to those who do not attend such protests [14]. This observation is comprehensible, especially when considering their dissatisfaction with certain issues that coincided\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003etheir participation in the demonstrations. Regarding individuals\u0026rsquo; perceptions of their political positions, those who identify as left-leaning are less likely to be satisfied with national governments compared to those identifying as right-leaning.\u003c/p\u003e\n\u003cp\u003eUnemployment, as a factor diminishing income, may have a significant relationship with individuals\u0026rsquo; perspectives on governments. Our findings strongly support this observation. The findings reveal a correlation between job loss and lower satisfaction with national governments compared to individuals who have not experienced job loss due to the COVID-19 pandemic. However, this may not necessarily imply causation, but rather an association. Based on our findings, females, albeit to a slight extent, express higher satisfaction levels with national governments compared to males. The findings, obtained by categorising individuals into generations, suggest that as individuals progress from the Baby Boomers generation to Generation X, the Millennial Generation, and Generation Z, the probability of satisfaction with the national government tends to decrease. This may indicate a generational shift in expectations, but further analysis is needed to establish causality.\u003c/p\u003e\n\u003cp\u003eThroughout the COVID-19 pandemic, European individuals have encountered measures restricting their social and economic activities to an unprecedented extent. While some individuals viewed these measures as positive or necessary, others were not pleased or satisfied with them. Based on our findings, we can confidently state that the Swiss, Icelandic, Finnish, Irish, Norwegian, Hungarian, and Portuguese individuals are the most satisfied with their national governments, in this order. Another notable difference between the countries is that Bulgaria, Belgium, Czechia, Slovenia, and Slovakia are the least satisfied with their national governments.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eOur findings suggest that, when European individuals perceive the state of healthcare services as good, they are more likely to evaluate their national governments as moderate or satisfactory. However, this association should be interpreted with caution, as the results do not establish a causal relationship. Moreover, the finding points to the relevance of healthcare service quality or satisfaction in relation to individuals\u0026rsquo; perceptions.\u003c/p\u003e \u003cp\u003eOur additional focus lies in exploring the political ideology of European individuals. The findings suggest that as the individuals\u0026rsquo; political ideology shifts from left to right on the political spectrum, they tend to report higher levels of satisfaction with their national governments. However, we must also note that our findings on political ideology or positions predominantly indicate a moderate level of satisfaction with the national governments, followed by dissatisfaction and then satisfaction, respectively. As expected, individuals who participated in activism in the last 12 months expressed lower satisfaction with the national governments compared to non-participants. Voting behaviour indicates that satisfaction levels are slightly higher among voters compared to non-voters, but lower than those ineligibles to vote.\u003c/p\u003e \u003cp\u003eIn conclusion, our research aimed to assess the perception of European individuals toward their national governments by utilizing the survey dataset and within the framework of our econometric model. Additionally, future studies can focus on determining country-specific factors, thereby contributing to a more comprehensive understanding of the elements associated with European individuals\u0026rsquo; satisfaction with their national governments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available and can be accessed through the following source:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eESS Round 10: European Social Survey Round 10 Data (2020). Data file edition 3.0. Sikt - Norwegian Agency for Shared Services in Education and Research, Norway \u0026ndash; Data Archive and distributor of ESS data for ESS ERIC. doi:10.21338/NSD-ESS10-2020.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe link : https://www.europeansocialsurvey.org/data\u003ca href=\"https://www.europeansocialsurvey.org/data-portal\"\u003e-\u003c/a\u003eportal\u003ca href=\"https://www.europeansocialsurvey.org/data-portal\"\u003e\u0026nbsp;\u003c/a\u003eDocumentation for the dataset can be referenced as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eESS Round 10: European Social Survey (2023): ESS-10 2020 Documentation Report. Edition 3.0. Bergen, European Social Survey Data Archive, Sikt - Norwegian Agency for Shared Services in Education and Research, Norway for ESS ERIC. doi:10.21338/NSD-ESS10-2020.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe link : https://www.europeansocialsurvey.org/data\u003ca href=\"https://www.europeansocialsurvey.org/data-portal\"\u003e-\u003c/a\u003eportal\u003ca href=\"https://www.europeansocialsurvey.org/data-portal\"\u003e\u0026nbsp;\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available for free use, provided that proper attribution is made according to the citation format indicated above.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations: not applicable.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study originate from the European Social Survey Round 10 and are publicly available and anonymised. No ethical approval was required as the data were secondary and anonymised; however, the authors confirm their adherence to the ethical standards for secondary data use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Transformation Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that data transformation has been performed in this study by narrowing the categorical structure of the data provided by the European Social Survey to enhance interpretability and facilitate analysis, without distorting the true meaning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eErdal Eren Kizilirmak and Erdem Kilic declare that they have no conflict of interest regarding the preparation, authorship, and publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe design and writing of the manuscript were carried out by Erdal Eren Kizilirmak. Erdem Kilic contributed to the manuscript through review, advice, and general guidance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdamy, A., \u0026amp; Rani, H. A. (2022). An evaluation of community satisfaction with the government\u0026rsquo;s COVID-19 pandemic response in Aceh, Indonesia. International Journal of Disaster Risk Reduction, 69, 102723. https://doi.org/10.1016/j.ijdrr.2021.102723\u003c/li\u003e\n\u003cli\u003eAlamsyah, N., \u0026amp; Zhu, Y. Q. (2022). We shall endure: Exploring the impact of government information quality and partisanship on citizens\u0026rsquo; well-being during the COVID-19 pandemic. Government Information Quarterly, 39(1), 101646. https://doi.org/10.1016/j.giq.2021.101646\u003c/li\u003e\n\u003cli\u003eAltiparmakis, A., Bojar, A., Brouard, S., Foucault, M., Kriesi, H., \u0026amp; Nadeau, R. (2021). Pandemic politics: policy evaluations of government responses to COVID-19. 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F., \u0026amp; Ejrn\u0026aelig;s, A. (2015). European Patterns of Participation: How Dissatisfaction Motivates Extra-Parliamentary Activities given the Right Institutional Conditions. Comparative European Politics, 13(2), 151-174. https://doi.org/10.1057/cep.2013.7\u003c/li\u003e\n\u003cli\u003eItani, R., Karout, S., Khojah, H.M.J. \u003cem\u003eet al.\u003c/em\u003e Diverging levels of COVID-19 governmental response satisfaction across middle eastern Arab countries: a multinational study. BMC Public health22, 893 (2022). https://doi.org/10.1186/s12889-022-13292-9\u003c/li\u003e\n\u003cli\u003eLiu, H., Gao, H., \u0026amp; Huang, Q. (2020). Better government, happier residents? Quality of government and life satisfaction in China. Social Indicators Research, 147, 971-990. https://doi.org/10.1007/s11205-019-02172-2\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez, L., \u0026amp; Short, J. R. (2021). The pandemic city: Urban issues in the time of COVID-19. Sustainability, 13(6), 3295. https://doi.org/10.3390/su13063295\u003c/li\u003e\n\u003cli\u003eNielsen, J. H., \u0026amp; Lindvall, J. (2021). Trust in government in Sweden and Denmark during the COVID-19 epidemic. West European Politics, 44(5-6), 1180-1204. https://doi.org/10.1080/01402382.2021.1909964\u003c/li\u003e\n\u003cli\u003eRieger, M. O., \u0026amp; Wang, M. (2022). Trust in government actions during the COVID-19 crisis. Social Indicators Research, 159(3), 967-989. https://doi.org/10.1007/s11205-021-02772-x\u003c/li\u003e\n\u003cli\u003eRobinson, S. E., Gupta, K., Ripberger, J., Ross, J. A., Fox, A., Jenkins-Smith, H., \u0026amp; Silva, C. (2021). Trust in Government Agencies in the Time of COVID-19. Cambridge University Press. https://doi.org/10.1017/9781108961400\u003c/li\u003e\n\u003cli\u003eSchraff, D. (2021). Political trust during the Covid‐19 pandemic: Rally around the flag or lockdown effects?. European Journal of Political Research, 60(4), 1007-1017. https://doi.org/10.1111/1475-6765.12425\u003c/li\u003e\n\u003cli\u003eShanka, M. S., \u0026amp; Menebo, M. M. (2022). When and how trust in government leads to compliance with COVID-19 precautionary measures. Journal of Business Research, 139, 1275-1283. https://doi.org/10.1016/j.jbusres.2021.10.036\u003c/li\u003e\n\u003cli\u003eSuhay, E., Soni, A., Persico, C., \u0026amp; Marcotte, D. E. (2022). Americans\u0026rsquo; Trust in Government and health Behaviors During the COVID-19 Pandemic. RSF: The Russell Sage Foundation Journal of the Social Sciences, 8(8), 221-244. https://doi.org/10.7758/RSF.2022.8.8.10\u003c/li\u003e\n\u003cli\u003eWilson, J. R., Lorenz, K. A., Wilson, J. R., \u0026amp; Lorenz, K. A. (2015). Hierarchical logistic regression models. \u003cem\u003eModeling binary correlated responses using SAS, SPSS and R\u003c/em\u003e, 201-224. https://doi.org/10.1007/978-3-319-23805-0_10\u003c/li\u003e\n\u003cli\u003eWong, G. Y., \u0026amp; Mason, W. M. (1985). The hierarchical logistic regression model for multilevel analysis. \u003cem\u003eJournal of the American Statistical Association\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(391), 513-524. https://doi.org/10.2307/2288464\u003c/li\u003e\n\u003cli\u003eZhu, M. (2023). The Effect of Political Participation of Chinese Citizens on Government Satisfaction: Based on Modified Causal Forest. Procedia Computer Science, 221, 1044-1051. https://doi.org/10.1016/j.procs.2023.08.086\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eResults of Ordinal Logistic Regression for Basic Model 1\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 637px;\"\u003e\n \u003cp\u003eModel 1: How satisfied with the national government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 206px;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eState of health services in country nowadays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.36***\u003c/p\u003e\n \u003cp\u003e(44.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.43***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eTaken part in public demonstration last 12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.29***\u003c/p\u003e\n \u003cp\u003e(4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eVoted last national election\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.09**\u003c/p\u003e\n \u003cp\u003e(2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003ePlacement on left-right scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.38***\u003c/p\u003e\n \u003cp\u003e(18.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.47***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eHousehold\u0026rsquo;s total net income, all sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.06***\u003c/p\u003e\n \u003cp\u003e(4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.06***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eI was made redundant / lost my job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.34**\u003c/p\u003e\n \u003cp\u003e(-2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e0.71**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.15***\u003c/p\u003e\n \u003cp\u003e(4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eGeneration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.11***\u003c/p\u003e\n \u003cp\u003e(-6.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e0.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e(1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.02***\u003c/p\u003e\n \u003cp\u003e(7.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1.02***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 637px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) aid in model selection, favouring lower values for improved fit. Pseudo R2 gauges the proportion of variance explained by the model. Log Pseudolikelihood quantifies the fit of the model to the data. Number of observations denotes the count of observations used in the models. z statistics in parentheses, and * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-global-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Global Society](https://www.springer.com/journal/44282)","snPcode":"44282","submissionUrl":"https://submission.nature.com/new-submission/44282/3","title":"Discover Global Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Government satisfaction, Healthcare services, COVID-19, Political ideology, Political participation","lastPublishedDoi":"10.21203/rs.3.rs-5972680/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5972680/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOur study investigates Europeans\u0026rsquo; satisfaction with national governments from 2020 to 2022 during the COVID-19 pandemic, using data from Round 10 of the European Social Survey. Using two econometric models, the study explores factors associated with satisfaction, including healthcare services, political ideology, political participation, and demographic factors such as household income, gender, unemployment, generation, and education. Findings reveal a strong correlation between healthcare satisfaction and satisfaction with the national governments. Furthermore, political ideology, unemployment, gender, and generational differences are significantly associated with satisfaction levels with national governments, either positively or negatively.\u003c/p\u003e","manuscriptTitle":"How Satisfied Were European Individuals with Governments during the COVID-19 Period? A Comprehensive Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 08:27:26","doi":"10.21203/rs.3.rs-5972680/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T09:29:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-09T12:17:45+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T13:13:59+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"284050910546424505418276488637309228943","date":"2025-05-07T18:08:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-03T17:08:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220816088167420921459182688131580063892","date":"2025-05-03T16:55:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T08:55:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T12:19:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Global Society","date":"2025-04-13T12:08:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-global-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Global Society](https://www.springer.com/journal/44282)","snPcode":"44282","submissionUrl":"https://submission.nature.com/new-submission/44282/3","title":"Discover Global Society","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a16a1ae2-cae5-470a-a994-75ea16f45c5c","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T14:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-30 08:27:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5972680","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5972680","identity":"rs-5972680","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}