







































 Humanities and Social Science Research; Vol. 6, No. 3; 2023 

ISSN 2576-3024   E-ISSN 2576-3032 

https://doi.org/10.30560/hssr.v6n3p32 

 32 Published by IDEAS SPREAD 

 

Institutional Trust in Times of Corona 

Erik Snel1,3,5, Btissame El Farisi1,3,5, Godfried Engbersen1,3,6, André Krouwel2,4,6 

1 Department of Public Administration and Sociology (DPAS), Erasmus University Rotterdam, The Netherlands 

2 Faculty of Social Sciences, Free University (VU), Amsterdam, The Netherlands 

3 Current address: Department of Public Administration and Sociology, Erasmus University Rotterdam, PO Box 

1733, 3000DR Rotterdam, The Netherlands 

4 Current address: Faculty of Social Sciences, Free University (VU), De Boelelaan 1105, 1081 HV Amsterdam, 

The Netherlands 

5 These authors contributed equally to this work 

6 These authors supervised this work 

Correspondence: Erik Snel, Department of Public Administration and Sociology (DPAS), Erasmus University 

Rotterdam, The Netherlands. E-mail: snel@essb.eur.nl 

 

Received: October 18, 2023; Accepted: November 4, 2023; Published: November 8, 2023 

 

Abstract 

During the corona pandemic, governments of all countries appealed strongly to the trust of their populations by 

implementing drastic social and economic measures to prevent the spread of the virus. This study seeks to 

understand mechanisms that influence the level of institutional trust at the time of the corona pandemic. We are 

specifically interested in how three explanatory factors (socioeconomic status, experienced economic insecurity 

and dissatisfaction with the implemented corona policies) can, in mutual association, explain differences in 

institutional trust. This study is based on data from a large-scale panel survey on the social impact of COVID-19 

in the Netherlands, carried out by Kieskompas research agency (N=22,696). Using a serial mediation analysis, we 

show that SES has both a direct and indirect effect on the level of institutional trust. People with higher SES 

experience less economic insecurity and have less dissatisfaction with the corona policies and, partly as a result of 

this, stronger institutional trust. It is also true that economic insecurity increases dissatisfaction with the corona 

policies and, partly as a result of this, weakens the level of trust.  

Keywords: socioeconomic status (SES), institutional trust, economic insecurity, discontent with corona policies, 

serial mediation 

1. Introduction 

During the corona pandemic of 2020, governments of all countries appealed strongly to the trust of their 

populations. Across the world, drastic measures were taken to reduce the number of COVID-19 infections and to 

prevent hospitals and other care organisations from becoming overburdened. Measures ranged from cancelling 

festivals and large sports events, to shutting down the hospitality sector, shops, schools and universities, up to 

imposing a night-time curfew. For the Dutch population it was the first time since the Second World War that they 

were forbidden from leaving their house at night. Although public disorder incidents occurred in several countries, 

including the Netherlands (the ‘curfew riots’ of late January 2020 stand out), overall the populations were 

compliant. One important reason pertains to the surge in trust in political institutions during the crisis, both in the 

Netherlands and in other affected countries [1-4]. 

This sudden and sometimes substantial increase in political trust in times of crisis is known as the ‘rally around 

the flag’ effect: in the event of external threats such as war, terrorist attacks or natural disasters, people collectively 

throw their weight behind their political leaders and institutions [5-8]. Rally effects are only temporary, however. 

Also with respect to the ‘corona-rally’, political trust initially increased strongly after the virus outbreak in the 

spring of 2020, to subsequently reduce again [9]. This also emerges from our research. In April 2020, 69 per cent 

of our respondents had (strong) trust in the national government and 60 per cent in their local government. These 

numbers had dropped to 55 and 50 per cent, respectively, in November 2020, and dropped further to a meagre 29 

and 37 per cent, respectively, in September 2021 [10, 11].  



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Although this paper is not about fluctuations in political trust, the decreasing trust in the government and in 

important public health bodies such as the RIVM (Dutch National Institute for Public Health and the Environment) 

and GGD (municipal health service) can become problematic, especially in a long-term crisis such as the corona 

pandemic. One reason for this steadily decreasing political trust is thought to be the increasing doubts about and 

criticism of the corona policies pursued by the Dutch government [1]. After all, trust is situational and often 

strongly related to perceived procedural justice. Other factors also contribute to decreased institutional trust in 

times of corona. Research showed, for instance, that the consequences of the pandemic are unequally divided in 

society [10, 11]. That is why factors such as socioeconomic status and economic insecurity could also affect the 

level of institutional trust. 

This paper seeks to understand the factors that jointly can explain the level of trust: The socioeconomic status of 

people, the extent to which the pandemic creates economic insecurity, and the subjective evaluation of, or 

discontent with, the government’s corona policies. This brings us to the following two-pronged problem definition 

of this study: 

1) To what extent does institutional trust during the pandemic correlate with the respondents’ socioeconomic 

status? 

2) To what extent is institutional trust during the pandemic influenced by specific experiences relating to the 

pandemic (fear of income loss due to COVID-19) and to discontent with the government’s measures? 

The data used in this article derive from a large-scale survey (N= 22,696) on the societal impact of COVID-19, 

conducted in the Netherlands in November 2020. The survey contained many questions about the pandemic’s 

possible social consequences, including the level of trust in public authorities and in important public health bodies 

such as the RIVM and GGD.  

2. Socioeconomic Status and Institutional Trust in Times of Corona 

2.1 Political Versus Institutional Trust 

Institutional trust is the main dependent variable in this study. The literature often distinguishes between political 

and institutional trust. Political trust pertains to people’s trust in political parties, in parliament or at the local level: 

the mayor and alderpersons and the municipal council. Institutional trust, on the other hand, pertains to trust in 

government institutions [cf. 12, 13]. In the survey, we asked about the trust that citizens have in the national and 

local government and in important public health bodies (RIVM and GGD). Another dimension of trust is 

interpersonal trust or trust in people in general (general trust). This will not be discussed in this article. 

2.2 Social Status and Institutional Trust 

It is a classic sociological premise that political or institutional trust correlates with social characteristics, in 

particular with education level, income and social class, labour market position (employed or unemployed), but 

also with health and the family situation (cohabiting or separated, with or without children). Research has shown 

that persons with a higher education level and higher income tend to have more political and institutional trust, 

with the education level being a particularly important determinant. As Uslaner [14: 108] observed: “virtually 

every study of generalized trust, in every setting, has found that education is a powerful predictor of trust” [cf. 15-

21]. A general satisfaction regarding one’s own economic situation also correlates positively with a stronger level 

of trust in political institutions and authorities [22,23]. Institutional distrust occurs more often among people in 

structurally vulnerable positions with little opportunities for upward social mobility. Negative experiences such as 

unemployment, perceived discrimination or poor health also have a negative impact on social and political trust 

and tend to consolidate the distrust [24-26]. More generally, Zmerli and Newton [26: 70] find that especially the 

so-called ‘winners’ stand out for a strong level of social trust (in other people) and in political and institutional 

trust: “...those in dominant majority groups, people of high class, status, income and education, the happy and 

satisfied, and individuals who benefit from better health and post-materialist security.” 

The literature reports various reasons and mechanisms to explain the connection between social status and trust. 

People with a higher socioeconomic position generally tend to be more optimistic about their life and also more 

positive about others. People in a more vulnerable socioeconomic position, on the other hand, are more inclined 

to be cynical, pessimistic and distrustful, both towards others and the government [28]. The latter are additionally 

more prone to a poorer health status, less economic security and greater economic risks, have less means to protect 

themselves against these risks and feel less protected by public bodies [20]. People in higher socioeconomic 

positions have more reasons to trust the government and other institutions. They are better equipped to utilise 

political and social institutions based on their understanding of how they operate, and if necessary, they have more 

political self-confidence and opportunities to positively influence those institutions [27: 71]. The social resilience 



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displayed by people in higher status groups results in greater interpersonal and institutional trust, compared to 

people in lower status groups. Or, as Putnam [18: 188] says: “In virtually all societies, ‘have-nots’ are less trusting 

than ‘haves’, probably because haves are treated with more respect and honesty”.  

This positive relationship between education level and institutional trust is not found worldwide. While we do see 

such a relation in developed western democracies, it is less obvious in post-communist countries in Central and 

Eastern Europe and also in ‘new democracies’ in Latin America. We even find inverse relationships, where more 

highly educated persons have less trust than those with lower education levels [29, 30]. One possible explanation, 

offered by Van der Meer and Hakhverdian [31: 97], is that more highly educated persons are more critical of 

corruption than those with low education levels, so that the positive effect of education on trust is reduced or even 

inverted in countries where corruption is widespread. Also in the west, the relationship should not be taken as an 

absolute law, as shown by a cross-country comparative study that did not find a relationship between level of 

education and political trust in Germany, Switzerland and Spain. The researchers’ explanation of this finding is 

that ‘well-being’ was also included in the analysis as an explanatory factor, which may have blotted out the effect 

of education on trust [32: 112]. Despite these deviating outcomes, we assume here that respondents with a higher 

socioeconomic status have more institutional trust than those with a lower status. 

Hypothesis 1: Socioeconomic status (SES) has a direct positive effect on institutional trust, whereby people with 

higher SES have more institutional trust. 

2.3 Perceived Economic Insecurity and Institutional Trust 

Education, income, health and job market position are individual determinants of political and institutional trust. 

Social and economic factors also play a role, including especially the economic climate. It is generally thought 

that citizens trust the government while the economy is strong, and distrust the government when the economy 

deteriorates. If this is so, then we would expect that the COVID pandemic, with its massive economic impact on 

particular economic sectors, would also lead to less political and institutional trust.  

The question is however whether people’s perception of the overall economic situation is more important than 

how they assess their own financial-economic situation. Wroe [33: 135] challenges the “…conventional wisdom 

(...) that citizens’ perceptions of the performance of the wider economy matter more than citizens’ perceptions of 

their own or their families’ financial situation”. Various studies show that it is not so much the assessment of the 

economic situation that determines the level of political trust, but much more the extent to which people feel 

vulnerable in case of an economic downturn [34]. Wroe [33] argues that a growing prosperity often goes hand in 

hand with growing economic insecurity for certain groups. An increasing economic insecurity would lead to a 

decline in political trust because citizens feel that the welfare state today offers too little protection against 

insecurities [33, 35]. Wroe [33] measures this economic insecurity by means of questions as to whether people are 

concerned about loss of job, reduced pension payments, inaccessibility of health care, and potential financial 

misery for their family. All these aspects of perceived economic insecurity correlate significantly with the level of 

political trust.  

Further, positive assessments of the economy and the education level of respondents have a positive effect on 

political trust: the less concern about job loss and the higher the educational level, the higher the level of trust [33: 

148]. Inspired by this research, we not only assume that economic insecurity – that is, the concern for loss of 

income due to the pandemic – has a negative effect on institutional trust, but also that this concern for loss of 

income occurs more strongly among lower status groups. These vulnerable groups more often work in sectors and 

occupations where homeworking is problematic, and where job and income security are lower [36]. People with 

lower incomes also have less financial reserves to rely on and the lower quality of housing is often a source of 

stress. That is why we expect that the economic insecurity as a result of the pandemic and the anti-corona measures 

fulfil a mediating role in the relationship between SES and trust. SES could therefore strengthen or weaken 

institutional trust through economic insecurity. 

Hypothesis 2: Socioeconomic status (SES) has an indirect positive effect on trust through economic security, 

whereby people with a higher SES experience less economic insecurity and therefore have more trust. 

2.4 Discontent with Government Measures and Institutional Trust 

Besides the effect of respondents’ socioeconomic status and their perceived economic insecurity due to the 

pandemic on institutional trust, their perceptions and assessments of government policies also – or perhaps 

particularly – affect their trust in the government. The premise here is that trust always pertains to a relationship 

between someone who trusts and someone who is trusted [37]. If the government pursues effective policies, is just 

and not corrupt, then political trust will be strong. Conversely, ineffective policies, injustice and especially 



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corruption undermine political trust [38, 39]. Van der Meer and Dekker [39] add that it is not so much a matter of 

the factual government policies (as can be determined using objective criteria of effectiveness, justice or corruption) 

as of citizens’ perceptions regarding these aspects [40]. According to Van der Meer and Hakverdian [31: 82], this 

evaluative nature of political trust is ‘surprisingly understudied’ in the existing literature. 

During 2020 and 2021, governments have taken unprecedented measures to counter and control COVID-19: from 

social distancing, avoiding contact, working at home as much as possible, avoiding public transport, shutting shops 

and hospitality outlets, to imposing a curfew (in many countries). Unsurprisingly, such drastic measures provoke 

discontent and resistance among citizens, particularly among those who are affected economically and/or feel 

overly constrained in their personal freedom. It is not without reason that riots occurred in several Dutch cities 

following the imposition of the curfew in January 2021. Following Van der Meer and Hakverdian [31], we presume 

that positive or negative assessments of the corona policies pursued by public authorities correlate with people’s 

social status, particularly their level of education. According to both authors, education fulfils both a ‘norm-

inducing’ and an ‘accuracy-inducing’ function [31: 86]. Higher educated persons are thought to be more critical 

of rule violations such as corruption on the one hand, and are better equipped to effectively process relevant 

information and to form well-considered judgements regarding corona policies on the other [41]. The first aspect 

would explain why corruption undermines political trust especially among higher educated citizens (and why 

higher educated people in corrupt countries in East Europe or Latin America have less trust). The second aspect 

could explain why shortcomings in government policies do not necessarily provoke frustration and discontent with 

the policies among the higher educated, and hence to less trust in the government and its implementing bodies. 

Instead, they might be more understanding of the difficult circumstances under which the policies are determined 

and implemented. Higher educated people furthermore tend to enjoy better resources (larger dwellings, more 

opportunity to work at home), so that they are better able to cope with the restrictions imposed by the corona 

policies. 

In this study we not only assume that discontent with the corona policies has a negative effect on institutional trust, 

but also that discontent occurs more strongly among lower status groups. We therefore expect that discontent with 

the corona policies functions as mediator in the relationship between socioeconomic status and institutional trust. 

SES therefore strengthens or weakens institutional trust through discontent with the corona policies. 

Hypothesis 3: Socioeconomic status (SES ) has an indirect positive effect on trust via discontent with the corona 

policies, whereby people with a higher SES experience less discontent about the policies and hence have more 

trust. 

2.5 Social Status, Economic Insecurity, Discontent with Government Measures and Reduced Trust 

Finally, in this study we presume that all factors discussed so far are interrelated and reinforce each other. 

Respondents with a lower social status experience more economic insecurity, meaning that they are more 

concerned about possible income loss or have already suffered income loss. Respondents that experience more 

economic insecurity are also more negative about the corona policies pursued by the government [see 10]. The 

reason is, on the one hand, that they are more strongly affected by the economic consequences of government 

measures, for instance through loss of income, job or one’s own business, or on the other hand, because the corona 

measures hinder their efforts to improve their economic position, for instance by finding a job. We therefore 

presume that both economic insecurity and discontent with the corona policies act as mediators in the relationship 

between social status and institutional trust, simultaneously. Social status could hence both strengthen and weaken 

institutional trust through economic insecurity and through discontent with the corona policies. 

Hypothesis 4: Socioeconomic status (SES) has an indirect positive effect on trust via economic insecurity and 

dissatisfaction with government policies, whereby people with a higher SES experience less economic insecurity, 

and therefore less dissatisfaction with the policies, resulting in stronger trust. 

Based on the research findings and considerations described above, we present the conceptual research model 

below, where every letter represents a regression weight that indicates the relationship between the variables. More 

specifically, c represents the total effect of SES on trust and c’ represents the direct effect of SES on trust 

(controlled for economic insecurity and discontent with the corona policies). Accordingly, both weights are based 

on different linear regression analyses. 



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Figure 1. Conceptual research model 

 

3. Method and Data 

3.1 Data, and Sample and Weighting 

The data used in this study were obtained through a large-scale survey on the societal impact of COVID-19, 

performed by Kieskompas research agency. The data used here were collected between 28 October and 13 

November 2020. This was the third wave in a long-running study, with earlier measurements performed in April 

and July 2020, and a fourth wave performed in March 2021 [see 10, 11]. The data were collected using 

Kieskompas’s permanent nationwide VIP panel. This panel is composed of a stratified random sampling of Dutch 

persons of voting age (18+). The survey was fielded among 48,329 members of the Kieskompas panel, and it was 

completed by 19,581 respondents (response rate of 40.5%). Additionally, three of the cities participating in the 

study (Amsterdam, The Hague and Rotterdam) performed additional activities to reach underrepresented groups 

by posting advertisements on Facebook and inviting specific groups with a weaker social status to participate. In 

Amsterdam, the questionnaire was also presented to the Amsterdam city panel. At the end of the field work period, 

the survey could also be completed using an anonymous participation link. In the end, the survey was completed 

by a sample of 22,696 respondents. To make the survey data representative for the (voting age) Dutch population, 

a weighting was performed retroactively. The goal of the weighting is to count under-represented groups in the 

database more often in order to more ‘accurately’ model the population and increase the representativeness of the 

sample. For example, the unweighted base contains more males (59%) than females (41%); in the weighted base, 

these sample frequencies have been updated to 50.2% males and 49.8% females. The weighting factor in our study 

varied from .06 to 16.35.  

By weighing the results in terms of gender, age, education region, ethnicity and voting behaviour, the data 

regarding these variables are (within the categories used) representative for the Dutch population aged 18 and over.  

3.2 Operationalisation of the Variables 

Institutional trust is the central dependent variable in this study. In the survey, respondents were asked to what 

level they have trust in national and local governments, and in important public health bodies such as the RIVM 

and GGD. Respondents could indicate whether they have (very) strong trust or (very) weak trust (1-5) in these 

four entities. The average of these items was taken as a measure of institutional trust. A Cronbach’s Alpha analysis 

shows a reliable scale (Cronbach’s Alpha .88).  

Socioeconomic Status (SES) is operationalised as a combination score based on income and education level. The 

survey asked respondents both about their level of education (ranging from none to university level, Bachelor or 

Master), and about their net monthly income (ranging from less than €1150 for a single-person and €1600 for 

multiple-person households, to more than three times modal income (€5000 or more)). Using Principal Component 



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Analysis (PCA), a factor score was created based on a least squares regression approach in order to reduce the 

variables to a single component. Note that according to the literature, education level plays a stronger role in 

explaining trust. Our data confirms this finding (we tested both education and income separately). However, 

income still explains a part of the variance in trust. Therefore we argue it is useful to use these variables in a 

combined measure. Further, in order to construct the SES variable, we have used the average income to impute 

the missing values for income. 

Economic insecurity. Respondents were asked to what degree they felt anxious about losing their income due to 

the corona pandemic, using a 4-point scale where 1 represents ‘not anxious’, 2 ‘slightly anxious’, 3 ‘very anxious’, 

and 4 to indicate that the respondent had already lost (part of) his/her income due to the pandemic (and had 

therefore reached maximum economic insecurity). The rough scores of this variable were used to represent 

economic insecurity. 

Discontent with government policies. Respondents were also asked for their opinion on the corona policies pursued 

by the Dutch government. They could indicate whether they (completely) agree or (completely) disagree (1 – 5) 

with respect to the following statements: “the Dutch government and media exaggerate the danger”, “the measures 

cause more damage than they prevent”, and “the government takes too little account of the economic and social 

consequences”. The answers to these three statements together form a reliable scale (Cronbach’s Alpha .83). The 

average of these items was taken as a measure of dissatisfaction with government policy. To be certain, we verified 

whether these items about dissatisfaction are not overly consistent with the items about the dependent variable of 

institutional trust. The four trust items and three dissatisfaction items cluster separately in a PCA with varimax 

rotation, and hence appear to be two different constructs.  

Control variables. In all analyses reported below, controls are performed simultaneously for respondents’ age (in 

years) and gender. The descriptive information for all variables is presented in Table 1 below (see appendix). 

3.2 Statistical Analysis 

The model shown in Figure 1 serves as the theoretical framework to test the associations between the different 

variables. In order to test this framework, we performed a serial mediation analysis using the PROCESS tool, a 

macro for SPSS [42], which incorporates the age and gender of the respondents as co-variates in the model. This 

procedure uses various ordinary-least-squares regression analyses to estimate the coefficients in the models and to 

determine the direct effects. Additional procedures were performed to estimate the indirect effects, specifically the 

multiplication of several regression weights. This approach uses bootstrapping (with replacement), with repeated 

sampling from the dataset (in our case, 5000 bootstrap-samples). In this way the tool generates an estimate for the 

sampling distribution of the indirect effects, in order to determine the reliability intervals of the estimated effects. 

The indirect effect is deemed to be statistically significant if the reliability interval does not contain a zero [cf. 42]. 

The analyses described below were performed on the unweighted survey data because the PROCESS tool 

precludes the use of weighted data. Although we make use of unweighted data in this article, the differences 

between the weighted and unweighted data are negligible. (Several regression analyses were performed manually 

on the weighted data to simulate the PROCESS results). Partly for this reason, and also given the size of the sample, 

we have confidence in the conclusions of our study. 

4. Results 

4.1 SES and Trust 

Our first research question is whether having a higher socioeconomic status (SES) in terms of education and 

income goes hand in hand with stronger trust in institutions (H1). The results indicate that, indeed, when SES 

increases, so does institutional trust (c = .22, p <.001). This result supports H1. Although this simple model 

explains 5.21% of the variance in trust, the explained variance increases to 30.97% when economic insecurity and 

discontent with the corona policies are included in the analysis. In this more elaborate model, SES still has a 

significant direct effect on trust, albeit slightly weaker (c’ = .135, p <.000). The reason is that part of the effect of 

SES now runs via the mediators (economic insecurity and discontent with the corona policies). See figure 2 for all 

standardized effects. For an overview of all un-standardized effects, see Table 2 in the appendix. 



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Figure2. Standardised path coefficients (rounded off to two decimals) 

 

4.2 SES, Trust and (Perceived) Economic Insecurity 

According to Hypothesis 2, the relationship between SES and trust is partly mediated by perceived economic 

insecurity. The results suggest that, as expected, respondents with higher SES experience less economic insecurity 

(𝑎1 = -.14, p <.000). Additionally, we see that respondents who experience more economic insecurity have less 

institutional trust (𝑏1= -.07, p <.000). The results also show that the respondents’ socioeconomic status can 

strengthen (or weaken) institutional trust through economic insecurity, whereby a higher status strengthens trust 

via lesser economic insecurity (𝑎1𝑏1 = .0097, SE = .001, 95% CI = 0.008, .0117; For all direct and indirect effects, 

see Table 3 in the appendix). The results support H2; economic insecurity operates as a mediator in the relationship 

between SES and trust. 

4.3 SES, Trust and Discontent 

Hypothesis 3 holds that the association between socioeconomic status and trust is also mediated partly via 

respondents’ subjective evaluation of the corona policies. The results suggest that respondents with a lower SES 

are also less satisfied regarding the corona policies (𝑎2 = -.13, p <.000). Additionally, we see that as discontent 

with the corona policies increases, institutional trust decreases (𝑏2= -.51, p <.000). The results also show that SES 

can strengthen (or weaken) trust indirectly via discontent with the corona policies: a higher SES increases trust via 

less discontent with the corona policies (𝑎2𝑏2 = .0662, SE = .0035, 95% CI = .0591, .0733). These results support 

H3; discontent with the corona policies operates as a mediator in the relationship between SES and trust. 

4.4 Economic Insecurity and Discontent 

Finally, our fourth hypothesis holds that perceived economic insecurity and discontent with the corona policies 

also influence the relationship between SES and trust in conjunction with each other (that is, simultaneously). The 

results show that economic insecurity has a positive association with discontent with the corona policies (𝑑12 = .11, 

p <.001); respondents who experience more economic insecurity also experience more discontent with the corona 

policies. The indirect effect of SES on trust via economic insecurity and discontent with the corona policies is also 

significant (𝑎1𝑑12𝑏2 = .0078, SE = .0006, 95% CI = .0765, .0912). These results support our fourth hypothesis 

that there is an indirect and positive association between socioeconomic status and institutional trust via economic 

insecurity and discontent with the corona policies, whereby a higher SES is associated with less economic 

insecurity, and hence with less dissatisfaction, resulting in stronger institutional trust.  

Although all our hypotheses are supported by the results, some effects are stronger than others, as shown in Figure 

2. For instance, discontent with the corona policies shows the strongest association with institutional trust. Table 

2 (in the appendix) furthermore shows that the explained variance of economic uncertainty (3.81%) and discontent 

with the corona policies (5.06%) is lower compared to institutional trust (30.97%). This implies that SES and 

economic uncertainty – despite statistical significance – do not explain the largest share of the variance in the 



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associated variables. It does seem clear nevertheless that (a) SES has an effect on economic insecurity, (b) 

discontent with the corona policies is still affected by both SES and perceived economic insecurity, and (c) the 

effect of these variables plays a non-negligible role in the explanation of the level of institutional trust, both directly 

and indirectly (through dissatisfaction). 

4.5 The Role of Age and Gender 

All analyses were controlled for age and gender simultaneously. As Table 2 shows (see appendix), in some cases 

there is a significant association between these control variables and the other variables in this study. For instance, 

elderly respondents experience less economic insecurity (β= -.01, p <.001) and also feel less discontent with the 

corona policies (β= -.01, p <.001). Nevertheless, there is a negative association between age and institutional trust. 

Although elderly respondents experience less economic insecurity and are less dissatisfied with the corona policies, 

they still have less institutional trust than younger respondents (in both the simplified and the elaborate model). 

The (negative) effect of age on trust becomes stronger as we keep the degree of economic insecurity and of 

dissatisfaction constant for the respondents in the elaborate model (β= -.007, p <.001). Hence, higher age in alle 

cases corresponds with less trust, and this effect becomes stronger when we keep the degree of economic insecurity 

and dissatisfaction constant for the respondents. 

We do not find any statistically significant differences between males and females regarding the degree of 

perceived economic insecurity (β= .01, p = .29). Males do show more discontent with the corona policies than 

females (β= .11, p <.001), but notwithstanding this difference, males and females show the same level of 

institutional trust. It is only when we keep the degree of dissatisfaction and the degree of economic insecurity 

constant for both sexes, that males appear to have more trust than females (β= .04, p <.001, Table 2). We can 

therefore conclude that the differences between the sexes are minimal in this study. 

5. Conclusion and Discussion 

The main research question of this study was whether the usual research outcomes regarding political and 

institutional trust, namely that the higher educated and higher income groups (the societal ‘winners’) show more 

trust, also applies during the corona pandemic. We focused on institutional trust, that is trust in the government 

(both national and local) and in public health organizations (GGD and RIVM). However, we not only investigated 

the relationship between respondents’ social status and the level of institutional trust, but also the role of perceived 

economic insecurity and the respondents’ subjective evaluation of the corona policies pursued by the Dutch 

government. For our study we used survey data collected in November 2020, when institutional trust in the 

Netherlands had weakened considerably. 

The main outcome of our study is that SES has both a direct and indirect effect on the level of institutional trust. 

In line with earlier research, SES was found to have a direct effect on institutional trust. Also when respondents 

experience the same degree of economic insecurity and discontent with the policies, those with a higher SES show 

stronger institutional trust. One possible explanation is that people in higher social positions tend to be more 

optimistic and positive about public authorities, while people in more vulnerable social positions are more cynical, 

pessimistic and distrustful. It has also been noted that people in higher social positions generally experience less 

insecurity and problems in their life and therefore feel better protected by public authorities than less successful 

people. 

This direct effect is strengthened by the indirect relationship between SES and both other factors. We concluded 

that SES is also associated with both perceived economic insecurity and discontent with the corona policies, which 

in turn predicted the level of institutional trust. People with higher SES experience less economic insecurity and 

are less dissatisfied regarding the corona policies, and accordingly show more institutional trust. The corona 

pandemic clearly reveals that people with lower education levels and lower incomes are hit hardest by the 

economic consequences of corona, for instance through loss of job or income or because they fear such loss. People 

with low education levels and flexible jobs, or self-employed people with comparable positions, often lost their 

jobs (unless they worked in the parcels or meals delivery sector). Higher educated people, on the other hand, are 

generally much better positioned to work at home and could hence continue to live and work relatively ‘normally’. 

Their permanent jobs furthermore provided greater income security. We also found that the perceived economic 

insecurity is related to discontent with the corona policies. People who experience a stronger degree of economic 

insecurity tend to be more negative about the policies pursued by the government. These are all factors that 

contribute to a weaker level of institutional trust among lower SES groups. 

Additionally, we found that a more advanced age is also associated with less trust, even though older respondents 

on average experience less economic insecurity and less discontent with the corona policies. Younger people (in 

our study, respondents aged 18 to 34) on average show more trust in institutions, although they are hit harder by 



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both the economic and mental effects of the pandemic than older respondents [43]. Apparently, younger Dutch 

nationals have more trust in the ability of government and public health bodies such as the RIVM and GGD to 

adequately manage the pandemic than elderly compatriots. Finally, we found little difference in institutional trust 

between males and females, although the latter do show more discontent with the corona policies. Only if the 

degree of economic insecurity and dissatisfaction with the policies were the same for both sexes, then males would 

appear to have more institutional trust than females. This finding is striking, since available research shows that 

females generally have slightly stronger political or institutional trust than males [44]. Perhaps the “triple burden” 

experienced by many women during the pandemic (looking after work, the household, and having the children at 

home) can explain this deviating outcome.  

The relevance of this study and our findings is two-fold. The scientific relevance is that we present a more complete 

theoretical model that allows us to better explain differences in institutional trust, compared to only considering 

respondents’ social status (their level of education and/or income) as a predictor of trust. The two theoretically 

substantiated additions to this classic relationship - perceived economic insecurity [33], and especially the 

subjective evaluation of governmental policies [31] - considerably increase the model’s explanatory power. The 

model using only SES (and both control variables) as predictor explained 5.21% of the variance in trust, while this 

increased to 30.97% after including perceived economic insecurity and discontent with the corona policies in the 

analysis. Particularly the latter factor is strongly associated with institutional trust. SES continues to explain part 

of the variance in trust, but this relationship is reinforced through perceived economic insecurity and especially 

discontent with the corona policies. Both SES and economic insecurity have both a direct and indirect role in 

explaining the level of institutional trust, and more strongly so than if we were to only consider the individual 

(direct) effects on trust of the two factors. In sum: because all the relevant variables are interconnected, we may 

conclude that the complete picture as presented in Figure 1 offers a better and more comprehensive picture of the 

hypothetical relationships. 

The societal relevance of our study is that, particularly in this time of the corona pandemic, institutional trust is 

crucially important. Governments are imposing drastic social and economic measures to combat the virus. The 

extent to which people are willing to obey the rules, and to which they are prepared to be vaccinated, is closely 

associated with their trust in government and in important public health bodies such as the RIVM and GGD [cf. 

10, 45]. It is therefore very important that the government also gains (or regains) the trust of lower social status 

groups, of people who have personally suffered the economic consequences of the pandemic, and of those who 

are critical of the government’s corona policies. This implies on the one hand, a policy aimed at strengthening the 

economic security of vulnerable groups, and on the other an adequate public health service and clear 

communication policy to explain to groups why certain measures are vital. As regards communication policy, the 

fundamental and practical guidelines formulated by Tiemeijer [46] are relevant, including fundamental principles 

such as legitimacy (’be truthful, just and reasonable’) and effectiveness (’be clear and unambiguous’) and practical 

matters such as ‘know your target group’ and ‘offer hope, small intermediate steps, celebrate achievements, 

acknowledge emotions’. 

Limitations of the Study 

This study, like every study, has certain limitations. First, the cross-sectional research design limits the extent to 

which cause-effect relationships can be deduced from the findings. For example, we describe the relationship 

between discontent with the policies and institutional trust, but cannot pinpoint the exact causal relationships. 

Satisfaction regarding the policies can lead to more institutional trust because people take a positive view of the 

measures. Yet one could also argue that people who have more institutional trust will naturally be inclined to take 

a positive view of governmental policies. In other words, the causal direction of the observed relationships is not 

immediately apparent. Still, our research model as presented in Figure 1 is theoretically grounded in the literature, 

and we discuss the outcomes of the model under the assumption that these are the correct causal directions. 

Another limitation pertains to the operationalisation of some of the variables. Thus, the variable ‘income’ lacked 

several values (12.7%). To construct the SES variable, we used averages to impute these missing values, and such 

estimates could of course be wide of the mark. However, we did not want to drop respondents from the analysis 

because it would also rule out using other information about these respondents (regarding economic insecurity, 

discontent with the corona policies and institutional trust) in our analysis.  

Finally, we limited the scope of this study to three factors to explain the differences in trust. There are however 

other factors that could also contribute to explaining differences in trust, such as political orientation and 

psychological factors (e.g. stress) [10]. Our recommendation for further research is accordingly to include such 

factors as covariates in future analyses. Further, the outcome measurement in our study pertains to the level of 



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‘trust’. It would be interesting for future studies to also include the level of ‘distrust’ as an outcome measurement, 

in order to test to what extent the results do or do not overlap.  

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Appendix 

 

Table 1. Descriptives of all variables in the analysis 

 Share/ 

Mean 
SD Range 

Missing 

% 

Gender     

  Male 49%   0 

  Female 51%   0 

Age 50,2 17.54 19 - 96 0 

SES 0 1 -2.5 - 1.97 0 

Institutional trust 3.36 1.01 1-5 .7 

Economic insecurity 1.55 .82 1-4 1.0 

Discontent government policies 2.64 1.10 1-5 .5 

Note: To construct the SES variable, we used the mean income to impute the missing values for income. 

 

Table 2. Unstandardised regression-coefficients and standard errors in various linear regression-analyses 

 Dependent variable           

 
Institutional 

trust 
  

Economic 

insecurity 
 

Discontent 

corona policies 
 Institutional trust 

Predictor  Coeff. (SE)  Coeff. (SE)  Coeff. (SE)  Coeff. (SE) 

Constant  3.598(.02) ***  1.822(.02) ***  2.507(.02) ***  5.017(.02) *** 



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Age  -.003(.0004) *** -.007(.0003) ***  -.008(.0004) ***  -.007(.0003) *** 

Gender  -.009(.01)  .011(.01)  .108(.01) ***  .042(.01) *** 

SES c .215(.01) *** a1 -.118(.01) *** a2 -.141(.01) *** c' .133(.01) *** 

Economic 

insecurity 
 ——  —— d12 .142(.01) *** b1 -.081(.01) *** 

Discontent 

corona policies 
 ——  ——  —— b2 -.459(.01) *** 

𝑅2   .0521   .0381  .0506  .3097 

  
F(3, 22396) = 

410.31, p<.001 
 

F(3, 22396) = 

295.67, p<.001 
 

F(4,22395) = 

298.68, p<.001 
 

F(5, 22394) = 

2009.55, p<.001 

Note: All regression-coefficients rounded off to 3 decimals. p<.05*, p<.01**, p<.001***. 

 

Table 3. Complete standardised estimates of the direct and indirect effects of SES on trust, with the associated 

reliability intervals. 

Total & Direct effect Coeff. Boot. SE 
95%  

reliability interval 
Figure-path 

Total effect of SES on Vert .2190 — — c 

Direct effect of SES on Vert .1352 — — c′ 

Indirecte effecten     

SES—>Ec_onz—>Vert  .0097 .0010 [.0079, .0117] Ind1= (a1b1) 

SES—>Onvr—>Vert .0662 .0035 [.0591, .0733] Ind2= (a2b2) 

SES—>Ec_onz—>Onvr—>Vert .0078 .0006 [.0066, .0091] Ind3= (a1d12b2) 

Total indirect effects .0838 .0037 [.0765, .0912] a1b1 + a2b2 + a1d12b2 

Note: The interval estimates are based on 5000 bootstrap samples. 

 

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