1

Democracy and Trust: A Quantitative and Qualitative Comparative Analysis of

European Countries1

Marta Kołczyńska2

Paper prepared for the 4th ECPR Graduate Conference, Bremen, July 4th-6th, 2012.

Trust is essential to sustain and strengthen political institutions (Putnam 1993, 2000, Brehm and

Rahn 1997, Norris 2002). Since in democratic regimes citizens’ decisions in the process of

delegating power are made in conditions of uncertainty about motivations and future actions

of political leaders, trust in state institutions is one of the key principles providing

sustainability and legitimacy of political systems (Gibson 1997, Klingemann 1999, Seligson

2002).

The objective of this paper is to explore a puzzling, well confirmed empirical result: In some

European countries average trust in public institutions clearly diverges from the relationship

typical for democratic countries with market economies. According to the theory-stipulated

pattern, trust should be positively related to the level of a country’s democratization.

However, in some European countries trust in institutions and the quality of democracy seem

to be inversely proportional, which has caused, and still causes interpretational difficulties.

These difficulties are due to theoretical and methodological issues. To date, comparative

empirical studies dealing with trust in institutions usually focused on Western countries with

stable democracies and high levels of economic development, limiting the scope of

theoretically-relevant variables.

The hereby proposed hypothesis is that in some countries there exist specific configurations

(interactions) of cultural and structural factors, which distort the relation “democracy-trust”.

The effects of these factors will be established with a mixed methodology, combining

quantitative methods (multi-level modeling) and qualitative (contextual document analysis),

overcoming the deficiencies of simplified linear regression models applied to the two-level

data.

In this paper analyses are based on the data provided by the European Values Study, in

addition to political, economic and cultural variables as contextual data. Studying relations

between trust and the influence of social, economic and cultural factors provides better

1 Panel ID 19: Characteristics of Democracy and its Quality; Section ID 5: Democracy and Democratisation;

Paper ID 599. 2 PhD student at the Graduate School for Social Research, Polish Academy of Sciences. For correspondence

please contact Marta Kołczyńska, Institute of Philosophy and Sociology, Polish Academy of Sciences, Room

211, 72 Nowy Swiat, 00-330 Warsaw, Poland; mkolczyn@sns.waw.pl, mkolczynska@ifispan.waw.pl.

2

understanding of mechanisms that lead to the generation of trust, and of favorable

circumstances. Results of this analysis will contribute to the knowledge of the concept of

trust, as well as improve the understanding of trust in specific contexts.

Theories and hypotheses

Political trust may be defined as confidence that one’s “own interests would be attended to

even if the authorities were exposed to little supervision or scrutiny” (Easton 1975: 447). There

are three major theoretical approaches to explaining origins of trust. The psychological

approach see trust as a matter of personality types – there are trusters and there are cynics, and

these traits are enduring and general (Gamson 1986, Gabriel 1995, Newton and Norris 1999).

Along the same lines, Cvetovich and Earle (1997) argue that in situations in people find

complex risk issues too difficult to analyze, they resort to a general sense of sympathy with

the institution rather than cognition.

The second group of theories emphasizes the role of the social and cultural environment,

arguing that individuals who share the same values build the community’s or society’s social

capital and cooperation leading to stronger social and formal institutions, which – through

their robustness – in turn contribute to higher trust (Almond and Verba 1963, Inglehart 1990,

1997, Ostrom 1990, Rose 1994, Mishler and Rose 1997, Newton 1997, Rose, Mishler and Haerpfer

1998). According to the third, institutional performance approach, political trust is a

consequence of the institutions meeting the public’s expectations, both in terms of perceived

(DeHoog, Lowery and Lyons 1990, Glaser and Hildreth 1999, Huseby 2000) and actual

performance (Miller 1974, Mishler and Rose 2001, for a review see Vigoda-Gadot and Yuval 2003).

The variety of theoretical approaches to institutional trust leads to the general hypothesis that

different aspects of people’s experiences play a role, and it is the combination of specific

factors that causes variation in the level of trust in institutions, which is the starting point of

my analysis. A review of literature allowed for the selection of the following determinants of

trust in institutions, each of which corresponds to a respective hypothesis.

Gender and Age. Most researchers in their analyses of determinants of trust in institutions

include these basic sociodemographic variables as standard control variables, and devote little

room to the interpretation of outcomes. These usually show that the relation between gender

and trust is insignificant, or that men are slightly more trusting than women (Wang 2005; Smith

2009, Slomczynski and Janicka 2009). The relation between age and trust in institutions is also

ambiguous. According to Dalton (1996) and Inglehart (1997), younger voters should be less

trusting than the old, as a result of more postmaterialist values shared by the younger

generation. Meanwhile empirical research shows that in most Western European countries

this relation is adverse (Bäck and Kestilä 2009, Smith 2009, Slomczynski and Janicka 2009), while

it is in some South American countries and in Eastern Europe that younger people were found

to trust less (Mishler and Rose 1997). I expect gender and age to have some impact on trust in

institutions, although its direction remains unclear (Hypothesis 1).

3

Education. Result of research on the relation of trust in institutions and education are inconsistent. In

the analysis of countries covered by the European Social Survey Round 2 carried out in 2004, Berg

and Hjerm (2010) found that education had a positive effect on political trust, which was confirmed in

Bäck’s and Kestilä’s study of Finland (2009). Meanwhile a comparative analysis of individual

determinants of political trust by Catterberg and Moreno (2005) showed that in six former

Soviet republics and four Latin American nations people with higher education tend to

express lower trust, while in established democracies and new Eastern European democracies

no impact can be observed. Having all the above in mind, I expect higher education to have a

positive effect on institutional trust, but this impact will likely be rather weak (Hypothesis 2).

Household income. Prior research shows that in countries covered by the 4th

wave of ESS

richer people tend to express more trust in institutions (Bjerg and Hjerm 2010), and I don’t expect

extending the set of countries to change this result. Hypothesis 3 is hence that declared trust in

institutions is higher among people with high household income than among those with low

household income.

Size of town. The size of town describes the kind of socialization environment an individual

operates in. Mishler and Rose (Mishler and Rose 2001) have shown that people tend to declare

higher institutional trust in smaller settlements. The same has been observed in Bulgaria,

Macedonia and Serbia and Montenegro in country Balkan British Social Surveys Gallup

(Ganev, Papazova and Dorosiev 2004).

Hypothesis 4: People from smaller towns tend to declare more trust in institutions than

inhabitants of large cities.

Religious affiliation. Most studies on relations between social and political trust and religion

have been conducted in the US and focus on the religiosity aspect (Leege and Welch 1989,

Williams 1996, 1999, Smidt 2001, Weithman 2002). Kristin M. Smith in a cross-national

study of 21 European countries included in the European Social Survey, mostly those from

Western and Central Europe, found that “the larger the size of the largest religious group, the

lower the level of trust in democratic institutions among religious minorities”, which in her

opinion supports the hypothesis that “the larger the size of the largest religious group, the

more likely minorities are to fear that the government is dominated by the largest religious

group and their interests are not represented” (Smith 2009: 30-31). The nature and meaning of

religions and religiousness in Europe is not universal. Some countries are traditionally not

homogenous in this respect (e.g. Switzerland or Germany), in others belonging to a religious

minority is usually related to immigrant status, while e.g. in the Balkans religious affiliation is

often related to cultural, national, and/or regional identity. Hence the potential relation of

religious affiliation to institutional trust needs to be interpreted in a broader context. I expect

religious affiliation to have some effect on institutional trust, although the direction is yet to

be determined (Hypothesis 5).

Satisfaction with democracy. Building directly on institutional performance theories, it may

be further expected that individuals with more positive assessment of state performance are

more likely to trust institutions (Hypothesis 6).

4

Support of the government parties. The mechanism linking partisan support and institutional

confidence may be explained using the concept of policy distance, i.e. the gap between the

individual’s position on the spectrum of preferred policies, and the policies implemented by

the government, which should be negatively related to trust in institutions (Kaase and Newton

1995). The link between partisan support for the government and trust in state institutions

may, beside the rational component, be also rooted in the emotional attachment to politicians

and parties in power. Evidence from numerous studies conducted mainly in the United States

and Western Europe show that institutional trust is strongly influenced by partisan support for

the party (parties) currently in power (e.g. Citrin, Green, Muste and Wong 1997, Listhaug 1995,

Kaase and Newton 1995), and I expect support for the current government to be a valid

predictor for confidence in institutions also for all Europe.

Hypothesis 7: Individuals who declare support for one of the government coalition parties

tend to express higher trust in state institutions.

Hypotheses regarding contextual characteristics pertain to the country’s economic situation

(the level of economic welfare measured by Gross Domestic Product per capita and the Gini

Index of inequality) and cultural features (Ethnic fractionalization and Political Culture).

Economic performance. State policies and practices have a large impact on the economic

realm, and – as literature shows – citizens hold state institutions responsible for economic

performance (Lewis-Beck 1988, Mishler and Rose 1997). Research shows also that large

differences in income levels adversely affect political trust in two ways: first, it may impede

institutional performance and economic growth, which in turns leads to lower confidence in

institutions, and secondly, by raising concerns about fairness of institutions’ policies (Ritzen,

Easterly and Woolcock 2000, Tyler, Rasinski and McGraw 1985, Rahn and Rudolph 2005). To avoid

the risks of using a single measure for describing economies, in this paper I describe the

economy of each country by its average standard of living (measured by GDP per capita),

income inequality (Gini Index) and the scope of the welfare state (government revenue as

percentage of GDP), combined in a single factor.

According to Hypothesis 8, countries with higher levels of economic development, low

inequality and more developed welfare states should translate to higher trust.

Culture. The role of specific cultural factors in shaping individual opinions and attitudes is

often revoked to account for the otherwise hard to explain cross-country variation in levels of

trust. It has been shown that ethnic fractionalization is negatively related to economic

performance (Easterly and Levine 1997, Brock and Durlauf 2001, Doppelhofer, Miller and

Sala-i-Martin 2000) and has also an adverse impact on the efficiency of state institutions or

social participation (Alesina, Baqir and Easterly 1999, Alesina and La Ferrara 2000, Alesina

and La Ferrara 2002, Luttmer 2001, Goldin and Katz 1999, Costa and Khan 2002).

Meanwhile democraticness of political culture, including the preference of democracy over

any other regime type, should – almost by definition - lead to high trust in state institutions

(Anderson and Guillory 1997; Newton and Norris 2000; Booth and Richard 2001: 55).

5

As a result I expect trust in institutions to be higher in countries with lower ethnic

heterogeneity and better quality of democratic political culture (Hypothesis 9)

Data and measurement

Data for this analysis come from the 4th

wave of the European Values Study (EVS,

www.europeanvaluesstudy.eu), which was conducted in 2008 in 47 countries or regions3.

EVS is a cross-national representative survey research program conducted since 1981 in 9

year intervals, and is administered in face-to-face interviews to nationally representative

samples with questionnaire covering a wide array of questions related to quality of life,

family, work, religion, politics and society. Sample sizes in countries included in this analysis

measured around 1500 respondents.

Of the 47 countries or regions covered by the 4th

wave of EVS in the current analysis I chose

to include only the 45 countries, i.e. I excluded Northern Ireland and Northern Cyprus, to

avoid methodological issues pertaining to comparisons between territories of unequal status.

In subsequent analyses I eliminated also Kosovo and Iceland because of data gaps: in case of

Kosovo I was not able to obtain the country’s Political Culture score (a component of one of

the country-level independent variables), while the EVS 2008 survey in Iceland didn’t include

information on the size of town, which is one of my individual-level independent variables.

The final model hence covers 43 countries listed in Appendix 1.

Measuring Trust in institutions

The measure of trust in democratic institutions, which itself is a latent concept, has been

constructed using factor analysis of three variables reflecting confidence in the parliament,

political parties, and justice system4. The parliament, political parties and justice system

represent fundamental institutions of democratic regimes. In order to make the measure more

intuitive, coding of components has been inversed into 4 as “a great deal” and 1 as “none at

all.”

Results of factor analysis of all three measures in countries without gaps are presented in

Table 1. Factor loadings for the country-wide sample are very high: 0.864, 0.802 and 0.885

for trust in the parliament, political parties and justice system respectively, while the factor

explains over 65% of variance.

On the country level factor loadings remain high and always above 0.65, up to almost 0.9 in

case of Montenegro. In Malta the country-level factor explains almost 70%% of the variance,

3The dataset used in this study was obtained respectively from the GESIS Data Archive for the Social Sciences

in Cologne through the online download facility ZACAT at zacat.gesis.org, which also hosts additional

information on the data and the download of European Values Study. EVS (2011). 4

Question wording in EVS: Please look at this card and tell me, for each item listed, how much confidence you

have in them, is it a great deal, quite a lot, not very much or none at all? … Parliament … The justice system …

Political parties.

6

while the lowest result is 55.52% in Turkey. Correlations between the country measure and

the total sample measure were almost ideal, and in some cases actually equal to 1.

Table 1. Factor Analysis of Trust in Institutions.

Items Mean Std.

deviation

Factor loadings

All Europe Country with highest

value

Country with lowest

value

Trust in parliament 2.25 0.864 0.847 0.875 (Montenegro) 0.768 (Azerbaijan)

Trust in political parties 1.95 0.802 0.806 0.854 (Ireland) 0.694 (Switzerland)

Trust in justice system 2.43 0.885 0.771 0.836 (Macedonia) 0.651 (Denmark)

Eigenvalue 1.961 2.096 (Malta) 1.666 (Turkey)

% of explained variance 65.38 69.87 (Malta) 55.52 (Turkey)

Data source: EVS.

Measuring individual independent variables

As independent variables I used individual-level variables provided by EVS: age, sex,

education, size of town, religious affiliation, partisan support, satisfaction with democracy,

interpersonal trust and interest in politics.

Gender was coded 0 for women and 1 for men, while age at the time of survey was calculated

using the respondent’s year of birth. Education groups respondents in three categories,

“lower”, “middle” and “upper”, which is a recode provided in the original dataset, and ensures

inter-country comparability. Size of town was coded into four categories, for settlements with

population up to 5 thousand, between 5 and 20 thousand, 20 - 100 thousand, and above 100

thousand.

EVS questionnaires include several questions aimed at estimating the level of respondent’s

household income. In this analysis I was using the question about monthly household income

in euro5, in which respondents were asked to choose one of 12 income categories from a

showcard. Individual values represent middle values of the indicated intervals, and missing

values were replaced with country means.

Religious affiliation is measured in two aspects: adherents to a particular religion, and being a

member of the country’s majority religious group. For individual denominations I included

five dummy variables, for Muslims, Orthodox, Catholics, Protestants, adherents to other,

smaller religious groups (Protestants, Jews, Buddhist, Evangelical, Free church/Non

denominational, Hindu, or “other”), and the fifth for those who didn’t declare any religious

affiliation – in the statistical analysis they will constitute the reference group.

5 Question wording in EVS: Here is a list of incomes and we would like to know in what group your household

is, counting all wages, salaries, pensions and other incomes that come in. Just give the letter of the group your

household falls into, after taxes and other deductions. Options: less than €150, €150 to under €300, €300 to

under €500, €500 to under €1000, €1000 to under €1500, €1500 to under €2000, €2000 to under €2500, €2500 to

under €3000, €3000 to under €5000, €5000 to under €7500, €7500 to under €10000, €10000 or more.

7

After a set of questions on confidence towards different national and international institutions,

EVS also asks respondents about their level of satisfaction with political development and

performance. In the 4th

wave these questions were asked to express their level of satisfaction

with the way democracy develops in their country6 by choosing one of the four options: very

satisfied (1), rather satisfied (2), not very satisfied (3) and not at all satisfied (4). I inversed the

values to make the measure “satisfaction with democracy” more intuitive.

Support for the government coalition is based on answers to the show-card question on the

party the respondent would vote for if there were a national, general or country’s election

tomorrow – variants depend on the study and wave. Knowing historical government setups7,

the variable was coded with “1” for “would vote for current government” and “0” otherwise.

Measuring country-level independent variables

Contextual data for countries include two synthetic indices to characterize each country’s

economic situation and cultural specifics. Components of the indices were chosen in course of

contextual analyses of relevant documents and reports produced by national, regional and international

organizations. A table summarizing scores for all countries is presented in Appendix 2.

Economic performance is measured by a three-dimensional index and includes the country’s

Gross Domestic Product per capita, Gini Index and the percentage share of government

revenue in a country’s GDP.

Gross Domestic Product is probably the most popular measure of the level of economic

development. GDP per capita based on purchasing power parity is a comparable measure of

the relative value of gross value added by all resident producers in the economy converted

into USD dollars using purchasing power parity rates8. In the current analysis I am using GDP

per capita data provided by the EconStats database, in which GDP pc PPP is calculated using

country GDP in current prices and national currency9 provided, with some exceptions

10, by

country statistical offices, the IMF Implied Conversion rates11

and the country’s population12

.

From among the studied countries, Moldova had the lowest GDP pc PPP in 2008, USD 3 003,

followed by Georgia (USD 4 911), while the leader was Luxembourg with USD 82,926.52

and way ahead of Norway, ranked second with USD 52 840.

6 Question wording in EVS: On the whole are you very satisfied, rather satisfied, not very satisfied or not at all

satisfied with the way democracy is developing in our country? 7 Detailed information on fieldwork timing, can be found in Appendix 1. Exact fieldwork dates were used to

determine the government setup of the time. 8

GDP per capita, PPP (current international $), The World Bank Database Online, The World Bank, at

http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD. 9 GDP current prices. EconStats. http://www.econstats.com/weo/V003.htm.

10 For Albania IMF data was used, and for Cyprus, Malta and Romania – statistics provided by EUROSTAT.

11 Implied PPP Conversion Rate. EconStats. http://www.econstats.com/weo/V013.htm.

12 Due to the unavailability of population information on Kosovo in the EconStats database, and since estimates

vary from 1.9 mln (OSCE) to 2.1 mln (Statistical Office of Kosovo), I divided global GDP PPP by the mean

value, i.e. 2 mln. Taking into account any of the values from the available range would not significantly change

the results of the analysis.

8

The Gini Index is one of the most commonly used measures of income inequality. It ranges

from 0, which indicates an ideally equal wealth distribution, to 1 which reflects complete

inequality. Values of the Gini Index were taken from the CIA Factbook13

. In the Western

Balkans the Index varies from 27.71 in Croatia of 1999 to 44.2 in Macedonia in 2008.

The percentage share of government revenue in the country’s GDP was used as an indicator

of welfare state scope. Government revenue consists of taxes, grants receivable and other

revenue sources14

. Data available in the EconStats database comes from official country

statistics (with the exception of Albania in which case it is the IMF).

The structure of the Economy index is presented in Table 2. The index is highest in countries

characterized by high GDP per capita, high government revenue as percentage of GDP, and

low Gini Index.

Table 2. Factor Analysis of the Economy index.

Items Mean Standard deviation Factor loadings

GDP per capita PPP1

24 454 15 698 0.761

Government revenue % GDP1

40.929 8.196 0.793

Gini Index2

31.553 5.524 -0.813

Eigenvalue 1.870

% of explained variance 62.331

Data source: 1Econstats,

2 The World Factbook (CIA).

Country culture is described by the level of ethnic heterogeneity and democraticness of

political culture. Ethnic Fractionalization is a measure of ethnic diversity, and at the same

time an implicit measure of a country’s proneness to ethnic conflict. The Ethnic

Fractionalization measure was calculated by Alesina et al. (2003) and reflects the probability

that two randomly selected individuals from a population belonged to different ethnic groups,

hence it ranges from 0 (when all people from a country are of the same ethnicity) to 1 (when

each person represents a different ethnic group).

Political culture is a component of the Democracy Index developed by the Economist

Intelligence Unit15

and measures to what extent the political culture in a given country is

conducive to sustaining and developing democracy. The measure incorporates information on

public support for democracy and it’s preference over any other regime type, based on expert

assessments and country results from the World Values Survey, and takes values ranging

from 0 (non-existent democratic political culture) to 10 (perfect democratic political culture).

13

Distribution of Family Income - Gini Index. CIA, The World Factbook, at

https://www.cia.gov/library/publications/the-world-factbook/fields/2172.html. 14

Government Revenue. Percent of GDP. EconStats database. http://www.econstats.com/weo/V031.htm. 15

The Economist Intelligence Unit’s Index of Democracy 2008. The Economist Intelligence Unit.

http://graphics.eiu.com/PDF/Democracy%20Index%202008.pdf.

9

The resulting Culture index, whose structure is presented in Table 3, is highest among

countries with low ethnic fractionalization and high level of democraticness of political

culture.

Table 3. Factor Analysis of the Culture index.

Items Mean Std. deviation Factor loadings

Ethnic Fractionalization1

0.291 0.195 -0.837

EIU DI: Political culture2

6.849 1.920 0.837

Eigenvalue 1.400

% of explained variance 69.986

Data source: 1Alesina et al. (2003),

2Economist Intelligence Unit.

Individual level variation

The regression model in Table 4, M0, estimates the impact of individual characteristics on the

respondent’s trust in public institutions for the whole Europe-wide sample. It shows that most

trusting are individuals who are older, from richer families, living in small towns or villages,

Muslim either Protestant, and those who would vote or otherwise support one of the political

parties in power.

According to this model, the impact of gender and education is minimal with women being

slightly more trustful than men, and those higher educated more than people with a low level

of education.

Table 4. Regression of individual level variables on trust in institutions (unstandardized coefficients).

M0 Estimate Std. Error t Sig.

(Intercept) -1.446 0.027 -54.403 ***

Age 0.002 0.000 6.756 ***

Sex (1M) -0.035 0.009 -3.923 ***

Education 0.005 0.007 0.812

Household income / 1000 0.065 0.004 16.398 ***

Size of town -0.019 0.004 -4.809 ***

Catholic -0.027 0.012 -2.176 *

Protestant 0.228 0.016 14.369 ***

Orthodox 0.008 0.013 0.637

Muslim 0.441 0.018 24.171 ***

Other 0.043 0.031 1.386

Satisfaction with democracy 0.525 0.006 88.802 ***

Support for government 0.113 0.009 12.125 ***

R2 = 0.241, Adjusted R

2 = 0.241, Residual std. error = 0.864. *** 0, ** 0.001, * 0.01.

Data source: EVS 2008.

10

Country level variation

Mean values of trust in institutions by country are presented in Figure 1 (see Appendix 3 for

details). Trust is relatively very high in Denmark, Norway and Luxembourg, all of the three

ranking among Europe’s richest countries will long-established democracies, but trust is

actually highest in Azerbaijan whose economy and politics leave a lot to be desired. Also in

Belarus and Turkey average institutional trust is also above European average. Lowest on

trust are Ukraine, Serbia, Croatia and Bulgaria, while similar levels are shared e.g. by

Germany, Armenia, Moldova and Portugal.

Figure 1. Average Trust in institutions by country.

11

An attempt to link average levels of trust in institutions with country measures of economic

and political performance presented in Table 5 leads to the conclusion that these relations

usually – where significant – do confirm the theory and resulting hypotheses.

Table 5. Correlations between average trust in public institutions with economic and political country indicators.

Pearson correlation coefficient

GDP per capita PPP1

.378**

Government revenue % GDP1

-.066

Gini Index2 -.130

Ethnic Fractionalization3

-.309*

EIU DI: Political culture4

.298*

*. Correlation is significant at the 0.05 level (1-tailed).

**. Correlation is significant at the 0.01 level (1-tailed).

Data source: EVS and: 1Econstats,

2CIA Factbook,

3Alesina et al. (2003),

4Economist Intelligence Unit.

Average trust in institutions is adversely affected by the country’s level of ethnic

fractionalization, and positively related to the respective country’s level of GDP per capita

and the democraticness of political culture. In case of the size of welfare state, as measured by

government revenue as percentage of GDP the correlation is very weakly negative, and not

significant.

The multi-level model

To estimate the impact of the above individual and contextual factors on trust in institutions I

am using hierarchical linear models, whose advantage over regression analysis is that it

allows to assess relationships at both the individual and the district level simultaneously

accounting for cross-level interactions. The number of second-level cases (countries) is not

very big (43), but nevertheless it is large enough to justify applying a random intercept model

(Snijders and Boskers 1999: 44). All multilevel models were estimated using R’s lmer

function, part of lme4 package16

.

The division of Europe into countries explains 15.3% of the variation in levels of trust in

institutions, as indicated by the intraclass correlation coefficient ρ = τ00/(τ00+ σ2) of an empty

model, i.e. a model without any country or individual level indicators (M1, see Table 6).

Models 2-4 include individual and country level indicators. The set of individual independent

variables is the same as in the OLS regression model presented above: age, sex, education,

household income, size of town, religion dummies (Catholic, Protestant, Orthodox, Muslim

and Other; not religious is the reference category), satisfaction with democracy and support

for the ruling coalition.

16

Download available at http://lme4.r-forge.r-project.org/; more information about functions available in lme4

may be found here: http://cran.r-project.org/web/packages/lme4/lme4.pdf.

12

Models 2 and 3 include one country level indicator each, the measure Economy and Culture

respectively, and Model 4 includes both Economy and Culture.

Table 6. Multi-level models. Dependent variable: Trust in institutions.

M0 M1 M2 M3 M4

Individual level indicators

(Intercept) -1.446 -1.193 -1.188 -1.190

(0.027) (0.045) (0.045) (0.045)

Age 0.002 0.002 0.002 0.002

(0.000) (0.000) (0.000) (0.000)

Gender (1M) -0.035 -0.036 -0.036 -0.036

(0.009) (0.009) (0.009) (0.009)

Education 0.005 0.013 0.013 0.013

(0.007) (0.007) (0.007) (0.007)

Household income / 1000 0.065 0.018 0.018 0.018

(0.004) (0.004) (0.004) (0.004)

Size of town -0.019 -0.023 -0.023 -0.023

(0.004) (0.004) (0.004) (0.004)

Catholic -0.027 0.026 0.025 0.025

(0.012) (0.014) (0.014) (0.014)

Protestant 0.228 0.024 0.024 0.024

(0.016) (0.019) (0.019) (0.019)

Orthodox 0.008 0.017 0.016 0.018

(0.013) (0.018) (0.018) (0.018)

Muslim 0.441 0.228 0.227 0.229

(0.018) (0.026) (0.026) (0.026)

Other 0.043 0.049 0.049 0.049

(0.031) (0.031) (0.031) (0.031)

Satisfaction with democracy 0.525 0.450 0.450 0.450

(0.006) (0.006) (0.006) (0.006)

Support for government 0.113 0.136 0.137 0.137

(0.009) (0.009) (0.009) (0.009)

Country level indicators

Economy 0.106 0.072

(0.037) (0.046)

Culture 0.101 0.058

(0.038) (0.047)

Variance component

Individual level (σ2) 0.816 0.696 0.696 0.696

(0.384) (0.834) (0.834) (0.834)

Country level (τ00) 0.147 0.055 0.056 0.054

(0.903) (0.235) (0.237) (0.233)

Intraclass correlation (ρ) 0.153 0.073 0.074 0.072

BIC 169870 93190 93191 93203

LogLikelihood -84918 -46511 -46511 -46512

Deviance 169833 92917 92918 92915

13

The comparisons of the individual level model (M0) and multilevel models with explanatory

variables (M2, M3 and M4) shows that enhancing the model with country level indicators

does not change the impact of age or gender.

This situation changes with reference to other independent individual level variables: when

accounting for the division of Europe into countries, the impact of respondent’s education

becomes statistically significant, the impact of household income decreases (as it is to some

extent derivate of the country’s GDP per capita), and the impact of the size of village, town or

city remains more or less the same.

Pertaining to religious affiliation, the most striking difference between the individual model

and multilevel models occurs in case of the Protestants. The impact of being Protestant on the

respondent’s trust in institutions from strongly positive becomes insignificant, while the

impact of being Muslim decreases, but remains strong.

The two individual level variables of political character, Satisfaction with democracy and

Support for government also undergo some changes in the strength of their impact.

On the country level, in models with single country level measures both of them, Economy in

M2 and Culture in M3, are positively and significantly related to trust in institutions of

individuals in respective countries. This means that institutional trust is generally higher in

countries with higher values on the Economy scale (translating into higher GDP per capita,

higher share of government revenue in GDP, and lower Gini Index) and higher values on the

Culture scale (i.e. low ethnic fractionalization and highly assessed democraticness of political

culture).

The last model (M4) includes both country level indicators, Economy and Culture, and

suddenly the explanatory power observed in M2 and M3 decreases to hardly significant in

case of the variable Economy, and insignificant in case of Culture. The matrix of correlations

of fixed effects shows that the correlation between Economy and Culture is -0.6, so it is clear

that the effects are to a large extent overlapping, and in the final model Economy prevails and

is attributed greater impact than Culture.

The close relationship between Economy and culture are also confirmed by fit statistics.

Adding independent variables to the empty model (M1) significantly improves the model fit,

as indicated by the major drops in BIC (Bayesian information criterion) and Deviance.

Meanwhile among models M2, M3 and M4 it is difficult to choose the one that fits best, and

according to Raftery’s (1995) rule of thumb, M2 and M3 are even better than M4 (since the

difference in BIC values is larger than 6).

The intraclass correlation coefficient ρ shows the percentage of observed variation in the level

of trust in institutions attributable to country-level characteristics. In all three models with

independent variables the coefficient equal slightly above 7%, regardless of whether one

country level variable is included or both.

14

Conclusion

Results of the analyses confirm the multi-level structure of determinants of trust in

institutions, which is a product of both individual characteristics, and the environment the

individual operates in. Inter-country differences in the level of economic condition and the

cultural setting explain a significant portion of variation in individual trust in institutions.

Although both measures, despite their seemingly different nature, are to a large extent

correlated, the share of each in explaining trust in institutions is not clear, it seems that the

economic conditions prevail.

As assumed, trust in institutions is generally higher in countries with higher GDP per capita

and the scope of welfare state, and lower income inequality, which constitute the synthetic

measure Economy. At the same time low ethnic fractionalization and high level of

democraticness of political culture, combined in the Culture measure, promote higher trust in

institutions.

On the individual level the above analyses confirm hypotheses listed at the beginning of the

paper. The impact of age and gender on trust in institutions is statistically significant, but very

weak. Among individual-level determinants, trust is positively correlated with age, which

makes older people more trusting than the young generation, and – as assumed – gender has

no significant influence of declared trust in institutions. The analysis also confirmed that, as

expected, trust is strongly related to political support and assessment of democratic

development. Even controlling for those however, the impact of socioeconomic status is

significant, although rather weak.

Trust is also higher among people with higher education, those living in richer households,

and those in smaller settlements rather than big cities. In all these three cases the impact

changes when country level variables are added to the model: the role of education and size of

town increases, while the role of household income diminishes.

Given Europe’s religious heterogeneity both with regard to denominations, and the role of

religious affiliation in different contexts, estimates pertaining to impact of religious affiliation

are of particular interest. Understandably, the impact of religion decreases when country level

variation is accounted for, but it still remains significant and high especially in case of the

Muslim community.

Finally, political variables, coefficients for Satisfaction with democracy and Support for

government confirm assumptions and prior study results, and both positively affect trust in

institutions, with the impact changing when country contexts are accounted for.

Cross-national comparative analyses of trust in institutions to date usually failed to cover all

European countries and often focus on Western European countries which share high levels of

economic growth and stable political regimes. Analyses in this paper show the need to extend

the scope of cross-national analyses of trust in institutions to account for the economic,

political and cultural diversity among countries, and improve the understanding of

determinants and consequences of institutional trust.

15

Appendices

Appendix 1: Fieldwork start/end dates, EVS 2008.

Fieldwork

Albania 10-07-2008 to 09-09-2008

Armenia 16-06-2008 to 19-09-2008

Austria 21-07-2008 to 22-10-2008

Azerbaijan 11-07-2008 to 10-08-2008

Belarus 11-06-2008 to 31-07-2008

Belgium 30-04-2009 to 02-08-2009

BiH 12-07-2008 to 31-07-2008

Bulgaria 21-04-2008 to 15-06-2008

Croatia 31-04-2008 to 31-10-2008

Cyprus 25-10-2008 to 28-11-2008

Czech 05-05-2008 to 02-11-2008

Denmark 01-04-2008 to 15-09-2008

Estonia 01-07-2008 to 31-08-2008

Finland 09-07-2009 to 15-07-2009

France 07-05-2008 to 04-09-2008

Georgia 21-08-2008 to 30-09-2008

Germany 17-09-2008 to 10-02-2009

Greece 12-09-2008 to 26-10-2008

Hungary 26-11-2008 to 28-01-2009

Ireland 07-06-2008 to 31-08-2008

Italy 02-10-2009 to 30-12-2009

Latvia 01-06-2008 to 31-10-2008

Lithuania 21-07-2008 to 14-09-2008

Luxembourg 03-05-2008 to 15-12-2008

Macedonia 03-07-2008 to 13-10-2008

Malta 16-06-2008 to 23-09-2008

Moldova 02-07-2008 to 04-10-2008

Montenergo 12-11-2008 to 08-12-2008

Netherlands 21-05-2008 to 31-10-2008

Norway 07-04-2008 to 02-09-2008

Poland 27-06-2008 to 28-09-2008

Portugal 26-05-2008 to 31-08-2008

Romania 24-04-2008 to 30-06-2008

Russia 28-06-2008 to 26-07-2008

Serbia 14-07-2008 to 31-07-2008

Slovakia 14-07-2008 to 29-08-2008

Slovenia 27-03-2008 to 18-06-2008

Spain 28-05-2008 to 15-07-2008

Sweden 25-09-2009 to 10-01-2010

Switzerland 08-05-2008 to 06-10-2008

Turkey 26-11-2008 to 01-03-2009

UK 01-08-2009 to 10-03-2010

Ukraine 12-07-2008 to 09-10-2008

Source: EVS 2008.

16

Appendix 2: Summary of country-level indices.

Country

name

Average

trust in

institutions

GDP pc

PPP1

Gini

Index2

Gov revenue

% GDP1

Economy

Ethnic

Fractiona-

lization3

EIU DI

Political

Culture4

Culture

Albania -0.318 6 901 34.5 26.71 -1.423 0.220 5.63 -0.180

Armenia 0.927 5 807 30.9 20.48 -1.491 0.127 3.13 -0.668

Austria 0.034 39 876 26 48.29 1.218 0.107 8.13 0.952

Azerbaijan -0.109 8 725 33.7 51.11 -0.050 0.205 3.75 -0.716

Belarus 0.037 12 555 27.2 50.59 0.534 0.322 4.38 -0.886

Belgium -0.254 36 249 28 48.81 0.994 0.555 7.5 -0.640

BiH -0.806 7 796 36.2 46.01 -0.535 0.630 5 -1.650

Bulgaria 0.307 13 187 45.3 38.04 -1.524 0.402 5.63 -0.745

Croatia -0.671 18 686 27 39.5 0.135 0.369 5.63 -0.642

Cyprus 0.406 29 014 29 42.6 0.406 0.094 7.5 0.796

Czech -0.378 25 182 31 40.22 0.026 0.322 8.13 0.282

Denmark 0.798 37 364 24.8 55.16 1.603 0.082 9.38 1.418

Estonia -0.137 20 320 31.3 39.22 -0.176 0.506 7.5 -0.487

Finland 0.176 36 245 26.8 53.23 1.317 0.132 8.75 1.068

France 0.091 33 959 32.7 49.94 0.623 0.103 7.5 0.767

Georgia -0.120 4 911 40.8 30.69 -1.765 0.492 4.38 -1.415

Germany -0.077 35 728 27 44 0.810 0.168 8.75 0.954

Greece -0.122 29 968 33 39.84 -0.027 0.158 7.5 0.598

Hungary -0.380 19 460 24.7 45.17 0.629 0.152 6.88 0.421

Ireland 0.174 42 178 33.9 34.33 -0.067 0.121 8.75 1.102

Italy -0.220 30 402 32 46.14 0.389 0.115 8.13 0.928

Latvia -0.339 17 181 35.2 35.38 -0.763 0.573 5.63 -1.275

Lithuania -0.344 19 138 35.5 34.2 -0.797 0.322 6.25 -0.304

Luxembourg 0.551 82 927 26 39.82 1.896 0.530 8.75 -0.172

Macedonia -0.003 9 602 44.2 32.48 -1.818 0.502 3.75 -1.642

Malta 0.322 24 769 26 38.85 0.338 0.041 8.75 1.348

Moldova -0.085 3 003 38 40.55 -1.083 0.554 5 -1.412

Montenegro -0.123 11 059 24.3 49.9 0.688 0.684 5.63 -1.621

Netherlands 0.239 41 303 30.9 46.28 0.765 0.105 10 1.538

Norway 0.590 52 840 25 59.93 2.236 0.059 8.75 1.295

Poland -0.363 17 592 34.2 39.52 -0.459 0.118 5.63 0.138

Portugal -0.116 23 080 38.5 41.11 -0.573 0.047 7.5 0.942

Romania -0.262 12 640 33.3 32.16 -0.898 0.307 5 -0.645

Russia -0.040 16 040 42 39.17 -1.132 0.245 3.75 -0.842

Serbia -0.633 10 316 28.2 42.82 -0.005 0.574 5.63 -1.278

Slovakia 0.019 21 995 26 32.88 -0.043 0.254 5 -0.480

Slovenia 0.135 29 606 28.4 41.15 0.393 0.222 6.88 0.206

Spain 0.016 30 848 32 37.14 -0.065 0.417 8.75 0.182

Sweden 0.370 37 787 23 51.86 1.585 0.060 9.38 1.487

Switzerland 0.431 41 471 33.7 34.5 -0.060 0.531 9.38 0.020

Turkey 0.494 13 108 40.2 31.45 -1.466 0.320 5 -0.686

UK -0.137 36 067 34 37.77 -0.055 0.121 8.75 1.100

Ukraine -0.655 7 287 27.5 44.27 0.047 0.474 6.25 -0.775

Data source: EVS and: 1Econstats,

2The World Factbook (CIA),

3Alesina et al. (2003),

4Economist Intelligence

Unit.

17

Appendix 3: Average trust in institutions by country.

Country Mean N Std. Deviation

Albania -.31765 1534 .953

Azerbaijan .92745 1505 1.110

Austria .03431 1510 .866

Armenia -.10921 1500 1.080

Belgium .03744 1509 .871

Bosnia Herzegovina -.25444 1512 .986

Bulgaria -.80610 1500 .886

Belarus .30735 1500 .865

Croatia -.67105 1525 .801

Cyprus .40633 1000 .914

Czech Republic -.37831 1821 .926

Denmark .79783 1507 .721

Estonia -.13658 1518 .827

Finland .17588 1134 .816

France .09110 1501 .810

Georgia -.11986 1500 .946

Germany -.07686 2075 .842

Greece -.12247 1500 .969

Hungary -.38034 1513 .866

Ireland .17360 1013 .929

Italy -.21976 1519 .844

Latvia -.33931 1506 .883

Lithuania -.34395 1500 .687

Luxembourg .55052 1610 .867

Malta .32175 1500 1.130

Moldova -.08506 1551 .919

Montenegro -.12268 1516 .988

Netherlands .23881 1554 .814

Norway .58975 1090 .747

Poland -.36258 1510 .859

Portugal -.11567 1553 1.012

Romania -.26246 1489 1.015

Russian Federation -.03981 1504 .936

Serbia -.63324 1512 .822

Slovak Republic .01929 1509 .902

Slovenia .13456 1366 .720

Spain .01570 1500 .847

Sweden .36966 1187 .762

Switzerland .43118 1272 .718

Turkey .49384 2384 1.037

Ukraine -.65465 1507 .917

Macedonia -.00345 1500 1.094

Great Britain -.13720 1561 .886

Total .00000 66786 .993

Data source: EVS 2008.

18

Bibliography

Alesina A., R. Baqir and W. Easterly. (1999), “Public Goods and Ethnic Divisions,” Quarterly

Journal of Economics 114 (4): 1243-1284.

Alesina A. and E. La Ferrara. (2000). “Participation in Heterogeneous Communities”, Quarterly

Journal of Economics 115 (3): 847-904.

Alesina, A., A. Devleeschauwer, W. Easterly, S. Kurlat and R. Wacziarg. (2003).

“Fractionalization”. Journal of Economic Growth 8: 155-194.

Almond, G. and S. Verba. (1963). The Civil Culture. Princeton, NJ: Princeton University Press.

Anderson, Ch. and Ch. A. Guillory. (1997). Political Institutions and Satisfaction With Democracy.

American Political Science Review 91(1): 66–81.

Bäck, M., and E. Kestilä. (2009). Social Capital and Political Trust in Finland: An Individual-level

Assessment. Scandinavian Political Studies 32(2): 171-194.

Berg, L. and M. Hjerm. (2010). “National identity and political trust”, Perspectives on European

Politics and Society 11(4): 390-407

Brehm, J. and W. Rahn. (1997). Individual-level evidence for the causes and consequences of social

capital. American Journal of Political Science 41(3): 999–1023.

Brock, S. and S. Durlauf. (2001). “Growth Empirics and Reality”, World Bank Economic Review

15(2): 229-272.

Catterberg, G. and A. Moreno. (2005). “The Individual Bases of Political Trust: Trends in New and

Established Democracies.” International Journal of Public Opinion Research 18 (1): 31-48.

Citrin, J., D. P. Green, C. Muste, and C. Wong. (1997). “Public opinion toward immigration reform,”

Journal of politics 59(3): 858–881.

Costa, D. and M. Kahn. (2002). “Understanding the Decline in Social Capital, 1952-1998”, Kyklos.

Cvetovich, G. and T. Earle. (1997). “Culture, cosmopolitanism and risk management.” Risk Analysis

17(1): 55-66.

Dalton, R. J. (1996). Citizen Politics: Public Opinion and Political Parties in Advanced Industrial

Democracies. Chatham, NJ: Chatham House.

DeHoog, R., D.L. and W. E. Lyons. (1990). “Citizen Satisfaction with Local Government.” Journal of

Politics 52 (3): 807 – 837.

Doppelhofer, G., R. Miller and X. Sala-i-Martin. (2000). “Determinants of Long-Term Growth: A

Bayesian Averaging of Classical Estimates (BACE) Approach”, NBER Working Paper No. 7750,

June.

Easterly W. and R. Levine. (1997). “Africa’s Growth Tragedy: Policies and Ethnic Divisions”,

Quarterly Journal of Economics 111(4): 1203-1250.

EVS (2011): European Values Study 1981-2008, Longitudinal Data File. GESIS Data Archive,

Cologne, Germany, ZA4804 Data File Version 2.0.0 (2011-12-30), doi: 10.4232/1.11005.

Gabriel, O. W. (1995). Political efficacy and trust. In J. van Deth and E. Scarbrough (Eds.). The

Impact of Values. Oxford: Oxford University Press.

Gamson, W. A. (1968). Power and Discontent . Homewood, Ill.: Dorsey.

Ganev, G., Y. Papazova and R. Dorosiev. (2004). “Social Capital in the Balkans: Are Socio-

Demographic Factors Important? Can Policy Assumptions Be Supported?” Working paper 2.3, WP

2: Civil Society and Social Capital, IBEU project.

Gibson, J. L. (1997). “Mass Opposition to the Soviet Putsch of August 1991: Collective Action,

Rational Choice, and Democratic Values.” American Political Science Review 91: 671–84.

19

Glaser, M and W. B. Hildreth. (1999). Service Delivery Satisfaction and Willingness to Pay Taxes.

Public Productivity and Management Review 23 (1): 48 – 67.

Goldin, C. and L. F. Katz. (1999). “Human Capital and Social Capital: The Rise of Secondary

Schooling in America, 1910-1940.” Journal of Interdisciplinary History 29(4): 683-723.

Huseby, B.M. (2000). “Government Performance and Political Support. A Study of How Evaluations

of Economic Performance, Social Policy and Environmental Protection Influence the Popular

Assessments of the Political System.” Trondheim, NO: Institutt for Sosiologi og statsvitenskap.

Norges teknisknaturvitenskapelige universitet, NTNU.

Inglehart, R. (1990). Culture Shift in Advanced Industrial Societies. Princeton: Princeton University

Press.

Inglehart, R. (1997). Modernization and Postmodernization. Princeton: Princeton University Press.

Kaase, M., and K. Newton. (1995). Beliefs in Government. New York: Oxford University Press.

Klingemann, H-D. (1999). Mapping Political Support in the 1990s: A Global Analysis. In P. Norris

(Ed.), Critical Citizens: Global Support for Democratic Governance, (pp. 31–56), New York:

Oxford University Press.

Leege, D. C., and M. R. Welch. (1989). “Religious Roots of Political Orientations: Variations Among

American Catholic Parishioners.” The Journal of Politics 51(1): 137-162.

Lewis-Beck, M. S. (1988). Economics and Elections: The Major Western Democracies. Ann Arbor:

University of Michigan Press.

Listhaug, O. (1995). The Dynamics of Trust in Politicians. In H-D., and D. Fuchs, Citizens and the

State. (pp. 267-291). Oxford: Oxford University Press.

Luttmer, E.F.P. (2001). “Group Loyalty and the Taste for Redistribution”. Journal of Political

Economy 109(3): 500-528.

Miller, A. H. (1974). Political issues and trust in government: 1964–1970. The American Political

Science Review 68(3): 951–972.

Mishler, W. and R. Rose. (1997). Trust, distrust, and skepticism: Popular evaluations of civil and

political institutions in post-communist societies. Journal of Politics 59(2): 418-451.

Mishler, W. and R. Rose. (2001). What are the origins of political trust? Testing institutional and

cultural theories in post-communist societies. Comparative Political Studies 34(1): 30–62.

Newton, K. (1997). Social capital and democracy. American Behavioral Scientist 40(5): 575-586.

Newton, K. and P. Norris. (1999). Confidence in public institutions: Faith, culture, or performance?

Paper for presentation at the Annual Meeting of the American Political Science Association,

Atlanta, 1-5th September. Available at

http://www.hks.harvard.edu/fs/pnorris/Acrobat/NEWTON.PDF.

Norris, P. (2002). Democratic Phoenix: Political Activism Worldwide. Cambridge: Cambridge

University Press.

Ostrom, E. (1990). Governing the Commons: The Evolution of Institutions for Collective Action. New

York: Cambridge University Press.

Putnam, R. D. (1993). Making Democracy Work. Princeton, NJ: Princeton.

Putnam, R. D. (2000). Bowling Alone: The Collapse and Revival of American Community. NJ: Simon

and Schuster.

Raftery, A.E. (1995). “Bayesian model selection in social research.” Sociological Methodology 25:

111-163.

Rahn, W. M., and Rudolph, T. J. (2005). “A Tale of Political Trust in American Cities.” The Public

Opinion Quarterly 69 (4): 530-560.

20

Ritzen, J., W. Easterly, and M. Woolcock. (2000). “On 'Good' Politicians and 'Bad' Policies: Social

Cohesion, Institutions, and Growth.” World Bank Working Paper no. 2448.

Rose, R. (1994). “Postcommunism and the problem of trust.” Journal of Democracy 5(3): 18-30.

Rose, R., W. Mishler and Ch. Haerpfer. (1998). “Social capital in civic and stressful societies.” Studies

in International Comparative Development, 32, 85-111.

Seligman, A. B. (1997). The Problem of Trust. Princeton, NJ: Princeton University Press.

Seligson, M. (2002). “The Renaissance of Political Culture or the Renaissance of the Ecological

Fallacy.” Comparative Politics, 34, 273–92.

Slomczynski, K. M. and K. Janicka. (2009). “Structural Determinants of Trust in Public Institutions.

Cross-national Differentiation.” International Journal of Sociology 39(1): 8–29.

Smidt, C. E. (Ed.). (2001). In God We Trust: Religion and American Political Life. Grand Rapids,

Michigan: Baker Academic.

Smith, K. M. (2009). “Differentiated Trust in Democratic Institutions Among Religious Minorities:

Does the Size of the Largest Religious Group Matter?” International Journal of Sociology 39 (1):

30–48.

Snijders, T. A. B. and R. J. Bosker. (1999). Multilevel Analysis. An introduction to basic and

advanced multilevel modeling. London, UK: Sage.

Sztompka, P. (1999). Trust. A Sociological Theory. Cambridge: Cambridge University Press.

Tyler, T. R., K.A. Rasinski and K.M. McGraw. (1985). “The Influence of Perceived Injustice on the

Endorsement of Political Leaders.” Journal of Applied Social Psychology 15(8): 700-725.

Vigoda-Gadot, E. and F. Yuval. (2003). “Managerial Quality, Administrative Performance and Trust

in Governance Revisited.” International Journal of Public Sector Management 16 (7): 502 – 522.

Weithman, P. J. (2002). Religion and the Obligations of Citizenship. Cambridge University Press.

Wang, Z. (2005). “Before the Emergence of Critical Citizens: Economic Development and Political

Trust in China.” International Review of Sociology 15 (1): 155-/171.

Williams, R. H. (1996). Religion as Political Resource: Culture or Ideology? Journal for the Scientific

Study of Religion 35(4): 368-378.

Williams, R. H. (1999). Visions of the Good Society and the Religious Roots of American Political

Culture. Sociology of Religion 60(1): 1-34.