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UNFORTUNATE EVENTS, CONTACT WITH PUBLIC OFFICALS AND BRIBERY: CAN E-GOVERNMENT BREAK THE LINK? 7 June, 2012 ABSTRACT Using data from the European Social Survey, we find that victims of unfortunate events are more likely to be asked for a bribe and offer bribe. These relationships appear to be working partly through more frequent contacts with public officials. Findings are supported when perceived corruption of the police and the judges and contact with the police is considered. Large differences are found across countries in the predictive power of unfortunate event experiences. For instance, unfortunate event victims are 34 percent more likely to be asked for a bribe and 6 percent more likely to offer bribe in Ukraine while these numbers are 12 and 4 percent in Slovakia. Person level regressions find that e-Government availability in a country is negatively correlated with personal bribery incidences. A negative and significant interaction between victimization of unfortunate events and e-Government exists in explaining bribery but the interaction is insignificant for people who does not use internet.

JEL classification: D03; D73 Key words: unfortunate events; bribery; e-Government. Manuscript word count: Table count: Figure count: *Correspondence to: Deakin University, School of Accounting, Economics and Finance, 221 Burwood Highway, Burwood VIC 3125, Australia. E-mail: cahit.guven@deakin.edu.au. R Jowell and the Central Co-ordinating Team, European Social Survey 2004/2005: Technical Report, London: Centre for Comparative Social Surveys, City University (2005) and European Social Survey (2012 - forthcoming). ESS Round 5 (2010/2011) Technical Report. London: Centre for Comparative Social Surveys, City University London.

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1. Introduction

There has been growing amount of evidence in the literature which shows that corruption is related to

unfavorable outcomes, especially in developing countries. For instance, corruption reduces growth (Mauro,

1995) and foreign direct investment (Wei, 2000). On the other hand, recent research has started to use person

level analysis while examining the negative effects of corruption using survey data. Hunt (2007) has shown

that corruption could lead to less equity in a way that people who experience unfortunate events are more

likely to experience bribery in Peru. The author (Hunt, 2007) argues that whichever route leads the victims to

interaction with the corrupt official, the expense or disutility associated with the interaction compounds the

original misfortune. People encounter corruption at the most difficult times of their lives, which is a form of

inequity. The first motivation of this paper is to provide further evidence on the relationship between

victimization of unfortunate events and corruption in 26 European countries previously not analyzed. The

paper also provides evidence on additional detrimental effects of victimization of unfortunate events which

could further increase the bribery incidences and decrease the quality of life eventually.

Another line research has been examining the determinants of corruption. A recent finding in this field is that

press freedom in country reduces corruption (Brunetti and Weder, 2003). Again, with the use of person level

data, Mocan (2008) shows the determinants of likelihood of being asked for a bribe in several countries from

the International Crime and Victimization Survey. In addition, with the increase in the availability of

communication technology around the world, governments have started to use electronic government (e-

Government) with the aim of improving government services, transactions and interactions with citizens,

businesses, and other arms of government. One of the aims of expanding e-Government is to fight with

corruption especially for the candidate countries to European Union and developing countries in the United

Nations. Studies using macro level data has established a significant relationship between widely used

corruption indices and E-government availability/internet usage. (Anderson 2009; Goel et al. 2012; Berthon

et al. 2008; Vinod 1999) Despite its significance as a policy instrument, to the best of our knowledge, there

have been no empirical studies to date on the relationship between availability of e-Government and bribery

using micro level data and examining the potential channels. Therefore, the second motivation of this paper

is to examine the role of e-Government in fighting with corruption, specifically as a breaker of the link

between victimization of unfortunate events and bribery incidences.

The paper uses data from the European Social Survey (ESS), rounds two and five specifically. The second

round of the ESS covers 26 nations and 47537 persons during the period 2004-2006 and the fifth round

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covers 26 countries and 50871 persons during the period 2010-2011. Round two includes information on

bribery incidences (being asked for a bribe and offering bribe) and bribe justification in addition to

experiences of several unfortunate events and contacts with public officials. Round five provides information

on perceptions about bribery in the police and the judges in the country in addition to unfortunate events and

contacts with the public officials and the police. The availability of e-Government in each country is made

available through Eurostat and United Nations.

The paper makes very important contributions to the literature. The previously found positive relationship

between victimization of unfortunate events and bribery is confirmed in 26 European countries in the pooled

data. The results show that victims of unfortunate events are 4.5 percent more likely to be asked for a bribe

by a public official and are 1.1 percent more likely to offer bribe. This relationship appears to be working

through contacts with public officials. Victims are 5.1 percent more likely to contact a politician or a public

officer and are more likely to be involved in bribery conditional on contacting public officials in the second

round of the ESS. Findings using perceptions and contact with the police in round five are also similar.

When unfortunate events are examined separately, the relation to being asked for a bribe and offering bribe is

highest for being sold a second-hand faulty thing. The findings remain even after controlling for respondent’s

own behaviors. However, the association between victimization of unfortunate events and bribery shows

great variation across European countries. The results reveal that the associations between victimization and

bribery are highest in Ukraine, 34 percent for being asked for bribe and 6 percent for offering bribe.

Associations are highest in Italy for the sample of people who contacted public officials suggesting that the

relationship between victimization of unfortunate events and bribery in Italy is mainly working through

contacts with politicians and public officers. This is also confirmed with the findings in the mediation

analysis.

The paper also presents additional dimensions of bribery and unfortunate events which affects the equity in a

country. People who were asked for a bribe in the past have higher tolerance towards bribery than others but

this association is higher for the people who became a victim of unfortunate events. Secondly, when people

are hit by unfortunate events, they become more tolerant towards these unethical activities. For instance,

people who were sold second-hand faulty things have higher tolerance towards this activity. While it is

possible that those prone to unfortunate events have unobservable characteristics making them more likely to

experience bribery, the results are robust to controlling for omitted variables using the Bivariate Probit

estimation with and without exclusion restrictions and controlling for respondent’s own unethical behaviors.

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Victims of unfortunate events exhibit lower trust to public officials and this negative relationship is twice

stronger for the sample of people who were asked for a bribe in the past which also supports the findings.

E-Government availability is negatively correlated with bribery incidences and victims of unfortunate events

are less likely to be involved in bribery in those countries where the e-Government is more available. The

interaction between e-Government and victimization of unfortunate events is insignificant for the people who

do not use internet supporting the idea that e-Government is working through the expected channel.

The results of the paper add to the limited knowledge of the process of bribery by persons and underline the

extent to which corruption lowers the quality of life by compounding other miseries. Victims of unfortunate

events report 2-3 percent lower life satisfaction/happiness and mediation analysis reveal that around 80

percent of the total effects are mediated through bribery incidences. The results also reinforce other studies

emphasizing the importance of e-Government in combating corruption.

The rest of the paper is organized as follows. Section 2 summarizes the data and presents the descriptive

statistics and while Section 3 present the empirical results. Section 4 presents discussions on the robustness

while Section 5 presents further findings and venues for future research and Section 6 concludes.

2. Data

The European Social Survey (ESS) is an academically-driven multi-country survey covering over 20 nations.

In the second round (ESS2 edition 3.2 published 02.02.2011), the survey covers over 20 nations and 47537

persons during the period 2004-2006. It involves strict random probability sampling, a minimum target

response rate of 70% and rigorous translation protocols. The hour-long face-to-face interview includes

(amongst others) questions on family, work and well-being, health and economic morality.

The economic morality module includes questions on bribery. Respondents’ experience about bribery is the

answer to the question: “How often, if ever, has each of these things happened to you in the last five years?

Use this card for your answers. A public official asked you for a favour or a bribe in return for a service?”

The responses are coded as “Never” 1, “Once” 2, “Twice” 3, “Three of four times” 4 and “Five times or

more” 5 for 43723 people who answered this question. Justification of bribery is measured through the

following question: “How wrong, if at all, do you consider the following ways of behaving to be? Use this

card for your answers. How wrong is? ...a public official asking someone for a favour or bribe in return for

their services?” The responses are coded as “Seriously wrong” 1, “Wrong” 2, “A bit wrong” 3 and “Not

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wrong at all” 4 for 46676 people who answered the question. Respondents are also asked about their own

behavior towards bribery with the following question: “How often, if ever, have you done each of these

things in the last five years? Use this card for your answers. How often, if ever, have you? ...offered a favour

or bribe to a public official in return for their services?” The responses are coded as “Never” 1, “Once” 2,

“Twice” 3, “Three of four times” 4 and “Five times or more” 5 for 46728 people who answered the

question.

Respondent’s experiences of unfortunate events are measured through several questions as follows: “How

often, if ever, have each of these things happened to you in the last five years? Use this card for your

answers. (1) A plumber, builder, car mechanic or other repair person overcharged you or did unnecessary

work. (2) You were sold food that was packed to conceal the worse bits. (3) A bank or insurance company

failed to offer you the best deal you were entitled to. (4) You were sold something second-hand that quickly

proved to be faulty.” The responses are coded as “Never” 1, “Once” 2, “Twice” 3, “Three of four times” 4

and “Five times or more” 5 for 42838 people who answered the question. (5) “Have you or a member of

your household been the victim of a burglary or assault in the last 5 years?” This is coded as 1 Yes, 0 No for

47340 persons. Victimization of unfortunate events is defined as 1 if a person has experienced any of these

five events in the last 5 years, 0 otherwise. Contacts with public officials are measured through the following

question: “There are different ways of trying to improve things in [country] or help prevent things from going

wrong. During the last 12 months, have you done any of the following? Have you contacted a politician,

government or local government official?” This is coded as 1 Yes, 0 No for 47298 persons.

In the fifth round (ESS5 edition 2.0 published 28.03.2012), the survey covers 28 countries and 50870 persons

during the period 2010-2011. The hour-long face-to-face interview includes questions on a variety of core

topics repeated from previous rounds of the survey and also two modules developed for Round Five covering

Trust in the Police and Courts and Work, Family and Wellbeing (the latter is a partial repeat of a module

from round 2). Respondents’ perceptions towards bribery among the police and judges in their own countries

are measured through the following questions: “How often would you say that the police in [country] take

bribes? Choose your answer from this card where 0 is never and 10 is always.” and “Using this card please

tell me how often you would say that judges in [country] take bribes?” The responses are coded 0-10 where 0

is never and 10 is always for 33724 and 32960 persons respectively. In addition, contacts with the police are

defined through the following questions: “In the past 2 years, did the police in [country] approach you, stop

you or make contact with you for any reason? This is coded as 1 Yes, 0 No for 50590 persons. In each

question, “Don’t know,” “Refusal” and “No answer” are coded as missing.

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E-Government availability (0-100) is downloaded though Eurostat website and is present for 34 countries

2001-2004, 2006-2007, 2009-2010 depending on availability. United Nations website also provides

information on E-government index (0-1) for 193 countries in the world for 2003-2005, 2008, 2010 and

2012. Internet usage is described in the ESS as follows: “Now, using this card, how often do you use the

internet, the World Wide Web or e-mail - whether at home or at work - for your personal use? This is coded

as 0 No, 1 Never use, 2 Less than once a month, 3 Once a month, 4 Several times a month, 5 Once a week, 6

Several times a week, 7 Every day in each round.

2.1 Descriptive Statistics

Table 1 presents the tabulations of victimization of unfortunate events with respect to some variables of

interest similar to the first table in Hunt (2007). Around 73 percent of the people experienced at least one of

the unfortunate events and 8 percent of these people were asked for a bribe and 3 percent offered bribe in the

last five years while 17 percent contacted a public official in round two. Years of schooling and household

income are higher while life satisfaction is lower for the victims of the unfortunate events. Similar numbers

are provided for each unfortunate event in the table. In round five, 17 percent of the people experienced

burglary/assault and 54 percent of them were contacted by police. 21 percent of people who contacted public

officials were asked for a bribe in round two while 19 percent of them offered a bribe. In the appendix, we

also present the summary statistics of the variables in ESS rounds two and five.

3. Empirical Results

It is possible that people do not tell the truth about bribe offerings or being asked for a bribe which will bias

the estimates. However, Lee and Guven (2012) show that the correlations between offering bribe/being asked

for a bribe and widely used three corruption indices at the country level are very high suggesting that people

indeed tell the truth about their bribery experiences.

Table 2 estimates the associations between victimization of unfortunate events and bribery. Estimations are

carried out using the DProbit model and the marginal effects are presented. Results in round two suggest that

victims of unfortunate events are 4.5 percent more likely to be asked for a bribe and 1.1 percent more likely

to offer bribe while they are 5.2 percent more likely to contact public officials. For the sample of people who

contacted a public official or politician, these numbers are 4.3 percent and 0.5 percent respectively. Each

unfortunate event positively correlates with bribery and contacts with officials. The role of each victimization

event is presented as a binary variable. The associations with bribery incidences are highest for being sold a

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second-hand faulty thing while victims of not receiving best deals are 3.9 percent more likely to contact

public officials.

If extreme perceptions about corruption are formed through bribery incidences, high perceptions might also

provide useful information. High corruption is defined as 1 if perceptions about bribery equal to 9 or 10 and

0 otherwise in the fifth round. Victimization and bribery associations are 1.3 percentage points for the police

corruption and 1 percent for the judge corruption. The associations are strong especially if the respondent

contacted police in the past. Victims are 1 percent more likely to believe high police corruption and 0.3

percent more likely to believe high judge corruption for the sample of people who contacted a police.

It is possible that the findings could be driven by unobserved heterogeneity or omitted variables. For

instance, the respondent’s own behavior has been found to be related to victimization (Deadman and

MacDonald, 2004), therefore Table 3 presents estimates controlling for several events carried out by the

respondents which could potentially bias the estimates. After controlling for these variables, victimization

remains very significant but the coefficients get smaller. Victims are 3.7 percent more likely to be asked for

bribe and 0.4 percent more likely to offer bribe and 4.7 percent more likely to contact public officials in

Table 3.A. Table 3.B presents estimates controlling for own behavior in round five. In these specifications,

victims of burglary are 1.6 percent more likely to believe high corruption in the police and 23 percent more

likely to contact the police.

Next, in order to control for omitted variables, a Bivariate Probit model is estimated with and without

exclusion restrictions in Table 4. The Wald test for the hypothesis that the residuals in two equations are

independent from each other is rejected in all cases as test statistics are higher than the p values. Therefore,

bivariate probit estimations are valid in these cases. The results found above are robust to these

specifications. Bivariate probit model with exclusion restriction is estimated to control for the potential

endogeniety of victimization of unfortunate events in panel B (Wilde, 2000) and in these specifications the

following variables are excluded from the contacts equation: Important to be loyal to friends, sold second-

hand faulty things, misused/altered card/document to pretend eligible, exaggeration or false insurance claim,

falsely claim government benefit. On the other hand, the following variables are excluded from the bribery

equations important to think new ideas and being creative, important to understand different people, age, age-

squared and years of schooling.

In addition, the predicted probabilities and marginal effects provided after these estimations present valuable

information. It is found that average predicted probabilities of being asked for a bribe in the data is around

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6.3 percent and this is around 2.4 percent for offering bribe. Joint probabilities of being asked for a bribe and

contacting public officials is around 1.2 percent and the same number for offering bribe is around 0.4

percent. It is also estimated that the probability to be asked for a bribe conditional on contacting public

officials is around 9 percent while this is around 3 percent for offering bribe. Lastly, victims of unfortunate

events (the treatment effect in this context) are 7.3 percent more likely to be bribe asked for a bribe

conditional on contacting a public official. On the other hand, victims are 3 percent more likely to offer bribe

conditional on contacting a public official. The results show that the Univariate Probit estimates were indeed

biased downwards.

3.1 Country specific analysis

As the estimates in the above regressions are presented using the pooled data, each regression is also

estimated separately for each country in the sample in Table 5 for round two. One caveat here is that the

number of observations, bribery incidences or contacts with public officials can be very low in some

countries however the results reveal an interesting finding which is in line with previous expectations. The

associations between victimization of unfortunate events and bribery are highest for Ukraine followed by

Slovakia. Significant associations are also found for both being asked for a bribe asked and offering bribe in

Turkey, Slovenia, Portugal, Poland, and Czech Republic. However, for the sample who contacted public

officials, the association between victimization and being asked for a bribe is significant and positive in

Czech Republic, Greece and Slovenia. For the same sample, correlations are significant for offering bribe in

Italy and Portugal. Similar country specific analysis is also examined in round five which is presented in the

appendix.

3.2 Mediation analysis

Mediator variables are variables that sit between independent variable and dependent variable and mediate

the effect of the independent variable on the dependent variable. In the current example in Table 6,

victimization of unfortunate events is the independent variable while bribery outcomes are the dependent

variables and contact with public officials is the mediator variable. This analysis is carried out using the user

written command “binary_mediation” which can be used to compute indirect effects using the product of

coefficients approach. The program standardizes all the coefficients for OLS, logit and probit models. The

results using logit or probit, once standardized, are very similar (Kenny, 2009). In the full sample, around 5

percent of the total effect from victimization to bribery is mediated through public official contacts while

around 11 percent of the effect from burglary/assault is mediated through contacts. In Italy, the total indirect

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effect (victimization of unfortunate events to bribe offered through contacts with politicians/public officials)

seems fairly substantial being approximately one and a half times larger than the direct effect (victimization

of unfortunate events to bribe offered). The proportion of the total effect that is mediated is about 0.59 which

is also substantial. The proportion of total effects mediated is low in the full sample. Despite rich information

on bribery incidences and victimization of unfortunate events, admittedly there are data limitations in the

paper. The ESS does not provide information on contacts of respondents with specific public officials and

the contacts with public officials were asked with respect to last year whereas most other variables were

asked with respect to last five years. Despite these concerns, the results are significant but the estimates could

be underestimated in some specifications.

3.3 Victimization and justification of unethical behaviors

Cameron et al. (2009) find that bribe victimization is positively related to later tolerance towards bribery

which depends on the culture in an experimental study. This relationship is also confirmed with the findings

in Table 7 column 1. People who were asked for a bribe have higher tolerance towards bribery but the

relationship appears to be non-linear. The correlation increases with the first bribe victimization and even

with the second one however the correlation starts to decline after three times. It appears that there is an

inverted U-shape relationship between bribe victimization and bribe justification. This relationship is even

stronger for the people who were victims of unfortunate events. This suggests that bribery incidences

occurring through unfortunate events are more harmful for the respondent as it is related to even higher bribe

justification.

It is estimated that victimization of bribery correlates with bribe justification. However, does this depend on

whether or not the respondent offered the bribe to a public official? Columns 5 and 6 estimate the

relationship between bribe victimization frequency and bribe justification using OLS where “never asked for

a bribe in the last five years” is the omitted category. It is shown that the relationship between past bribe

victimization and current bribe justification is significant for the people who did not offer a bribe but is not

significant for those who did offer a bribe. One caveat here is that the number of observations for the sample

of people who did offer a bribe is low. Columns 7-9 estimate the relationship between frequencies of being

sold a second-hang faulty thing and justification of this behavior where “never were sold a second-hand

faulty thing in the last five years” is the omitted category. It is shown that past victimization of unethical

behavior relates to higher justification of this behavior in the full sample.

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3.5 Victimization of unfortunate events, bribery and e-Government

United Nations and European Union have been focusing on the promotion of e-Government in the

member/candidate countries in recent years. One of the advantages argued in several proposals is the

potential benefits of e-Government in decreasing bribery in the community. However, whether or not such a

benefit exists is an empirical question which has never been examined in the previous literature using micro

data. If such benefits exist, it is expected that once a person become a victim of an unfortunate event he/she

will less likely to be involved in bribery using the internet by avoiding the face-to-face relationship with the

public officials. This is exactly what is tested in Table 8.

Including full controls without country dummies, columns 1 and 2 find that availability of e-Government is

negatively and significantly related to being asked for a bribe and offering bribe which is confirmed with

both UN and Eurostat indices. Then, the interactions of these indices with victimization of unfortunate evens

are examined in columns 3 and 4 without country dummies and in columns 5 and 6 including country

dummies. As expected, a negative and significant interaction exists for all outcomes using UN index

however some outcomes are less significant using Eurostat index probably due to the fact that Eurostat index

is not available for all countries in the sample.

If e-Government is working in the expected direction, it should matter more for people who use internet

frequently. One caveat here is that people can still access internet and benefit from e-Government even

though they do not have access at their homes or workplaces or even they do not use internet themselves.

Despite these concerns, it would be interesting to see whether or not the empirical findings give some support

for the expectations. Table 8 replicates the columns 5 and 6 of the Table 7 for subsamples of people who

does not have any internet access, who never use internet and who use internet at different frequencies. The

results using the UN index show that the interaction between e-Government availability and victimization of

unfortunate events is not significant for the non-users of internet but it is still significant for internet users

and for people without access. Some estimations using Eurostat index are not in the expected direction

probably due to lower number of observations.

4. Robustness

The results were presented using binary victimization variables for interpretation purposes. Regressions using

exact coding 1-5 for these variables were also estimated using OLS and a non-linear relationship does not

exist between victimization of unfortunate events and bribery. Further support for the argued relationship

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between victimization of unfortunate events and bribery working through contacts with public officials

comes from the fact that victims of unfortunate events are 9 percent more likely to distrust public officials

but this number is 13 percent for people who contacted public officials and it is around 18 percent for the

people who were asked for a bribe.

5. Discussion and Further Research

The paper presents additional detrimental effects of unfortunate events which make the victims worse-off.

The basic story behind the above findings is that people become victims of some unfortunate events and face

with corrupt public officials and simply offer bribe when asked for it. But this is not the end the story. People

who became a victim of unfortunate events and were asked for a bribe in the past have higher tolerance

towards bribery than others. This could lead to more briber offering in the future regardless of other events.

Secondly, when people are hit by unfortunate events, it could change their behavior towards these

circumstances. In other words, when a person experiences an unethical behavior from someone else, he/she

becomes more tolerant towards this activity which in turn will increase the likelihood of committing the same

crime in the future and facing the public officials again and experiencing more bribery. Overall, findings

suggest a role of unfortunate events in decreasing the quality of life for the victims. This is indeed supported

by the finding that victims report 2-3 percent lower life satisfaction/happiness. Mediation analyses reveal a

very interesting finding which suggests that victims experience lower happiness through bribery. The

proportion of total effects mediated from victimization of unfortunate events to life satisfaction through being

asked for a bribe is around 80 percent. Furthermore, the indirect effect is 5.5 times larger than the direct

effect which is substantial. When estimations are carried out for each country in the unreported regressions,

the negative and significant associations are highest between victimization of unfortunate events and life

satisfaction for the following countries: Austria 3 percent, Hungary 4 percent, Italy 4 percent, Poland 4

percent, Slovakia 6 percent, Turkey 5 percent and Ukraine 4 percent.

6. Conclusion

This paper provides further evidence that victims of unfortunate events are more likely to experience bribery

incidences which appear to be working through more frequent contacts with public officials. Large

differences are found across countries in the predictive power of unfortunate events. E-Government

availability in a country is negatively correlated with personal bribery incidences. A negative and significant

interaction between victimization and E-government exists in explaining bribery but the interaction is

insignificant for people who does not use internet.

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Table 1: Descriptive statistics ESS Round 2 Share

of people

Asked for bribe

Offered Bribe

Contacted public official

Years of schooling

Household income

Life satisfaction

Victim of unfortunate events 0.725 0.0752 0.0292 0.168 12.135 6.378 6.805 Non-victim of unfortunate events 0.275 0.0084 0.0064 0.091 10.161 5.514 7.055 All persons 1 0.055 0.022 0.142 11.505 6.079 6.844 Persons victim of: Burglary/Assault 0.202 0.0791 0.0279 0.193 12.385 6.523 6.748 Plumber/builder/mechanic/repairer overcharged 0.345 0.1067 0.0389 0.186 12.445 6.442 6.697 Were sold food packed to conceal worse bits 0.420 0.0926 0.0362 0.173 12.354 6.348 6.710 Bank/insurance company failed to offer best deal 0.273 0.1002 0.0339 0.221 12.785 6.897 6.897 Were sold things second-hand that proved faulty 0.232 0.1332 0.0486 0.179 12.292 6.022 6.532 Unemployed last 5 years 0.539 0.0851 0.0352 0.132 11.816 5.212 5.880 Asked

for bribe

Offered Bribe

Share of contacted people who were asked for bribe

Share of contacted people who offered bribe

Number of people who were asked for bribe

Number of people who offered bribe

N

Contacted public official 0.209 0.193 0.078 0.030 505 201 6909 ESS Round 5 Share

of people

Perceived Police corruption

Perceived Judges corruption

Contacted public official

Contacted by police

Years of schooling

Household income

Life satisfaction

Burglary/Assault 0.157 4.166 3.766 0.176 0.533 13.003 5.486 6.594 Non-victims of Burglary 0.843 4.144 3.841 0.114 0.294 12.171 5.002 6.694 All persons 1 4.151 3.834 0.124 0.331 12.300 5.076 6.675 Unemployed last 5 years 0.536 4.763 4.385 0.111 0.393 12.312 4.143 5.789 Perceived

Police corruption

Perceived Judges corruption

Share of contacted people with high police corruption

Share of contacted people with high judge corruption

N

Contacted public official 3.604 3.197 0.046 0.040 6277 Contacted police 3.994 3.572 0.062 0.051 16747

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Table 2: Victimization of unfortunate events, contact with public officials, and bribery DProbit: Marginal effects*100

ESS Round 2

Asked for Bribe

Offered Bribe

Asked for bribe if contacted public official

Offered bribe if contacted public official

Contacted public official

Victim of unfortunate events 4.465 1.048 4.223 0.533 5.175 (13.07) (7.32) (4.99) (2.44) (7.28) N 44427 47444 6485 6121 48819 Pseudo R2 0.263 0.229 0.337 0.334 0.091 Persons victim of: Burglary/Assault 0.789 0.221 1.905 0.049 3.028 (4.54) (1.79) (3.66) (0.36) (5.05) Were overcharged 2.047 0.557 2.253 0.201 1.830 (12.58) (6.31) (4.89) (1.04) (4.06) Were sold bad food 1.917 0.544 2.584 0.350 2.375 (6.78) (4.03) (4.04) (3.30) (5.00) Failed to receive best deal 2.726 0.650 1.492 0.645 3.855 (10.27) (3.29) (2.31) (3.45) (3.82) Were sold second-hand faulty things 3.404 0.935 3.141 0.343 1.181 (7.47) (5.58) (4.04) (3.08) (2.38) N 44427 47444 6485 6121 48819 Pseudo R2 0.320 0.248 0.368 0.350 0.097 ESS Round 5

High police corruption

High judge corruption

High police corruption if contacted public official

High judge corruption if contacted public official

Contacted public official

1.323 0.979 0.562 0.221 4.504 (4.89) (7.32) (1.68) (0.82) (8.56) N 39490 38744 4386 4359 46162 Pseudo R2 0.195 0.223 0.247 0.280 0.089 High police

corruption if contacted police

High judge corruption

if contacted police

Contacted police

1.017 0.329 21.713 (3.79) (1.63) (12.88) N 13572 13247 46108 Pseudo R2 0.236 0.254 0.124

Notes: Basic controls are: male, age, age-squared, married, years of schooling, household income, number of household members, house ownership, immigrant, minority, gender role, interpersonal trust, left-right political scale, selfishness, religiosity, city, suburb, town, subjective health, life satisfaction and paid work. The statistics are weighted using the ESS sampling weights. Country, Wave, Industry and Occupation dummies are included in all models. t statistics are presented in parentheses and are in absolute values. Robust standard errors are clustered at the country level.

15

Table 3.A: Controlling for personal unethical behavior DProbit: Marginal effects*100 ESS Round 2

Asked for Bribe ( 0-1)

Asked for Bribe ( 0-1)

Offered Bribe ( 0-1)

Offered Bribe ( 0-1)

Contacted public official

Contacted public official

( 0-1) ( 0-1) Victim of unfortunate events 3.707 0.358 4.668 (13.58) (5.84) (7.27) Burglary/Assault 0.708 0.105 2.652 (4.11) (1.46) (4.61) Were overcharged 1.794 0.201 1.649 (10.73) (3.70) (3.60) Were sold bad food 1.653 0.213 2.183 (6.16) (3.35) (4.87) Failed to receive best deal 2.290 0.145 3.576 (9.55) (1.05) (3.70) Were sold second-hand faulty things 2.721 0.217 0.910 (6.85) (2.21) (1.61) Borrowing constraint 0.152 0.097 -0.067 -0.070 -0.425 -0.475 (1.42) (0.99) (2.27) (2.41) (2.18) (2.45) Ever divorced 0.994 0.865 0.137 0.110 2.002 1.659 (2.83) (2.40) (1.76) (1.59) (3.86) (3.07) Ever worked overseas 0.161 0.073 0.013 -0.008 0.676 0.508 (0.57) (0.24) (0.11) (0.07) (0.76) (0.60) Hampered 0.324 0.111 0.026 0.014 2.432 2.235 (3.16) (1.31) (0.33) (0.18) (5.89) (6.16) Discriminated 0.478 0.144 0.161 0.094 7.395 6.866 (1.98) (0.78) (0.93) (0.65) (7.73) (7.41) Friends to ask for favor 0.674 0.412 0.254 0.239 0.363 0.247 (2.60) (1.68) (3.03) (2.73) (0.55) (0.36) Doctor visits 0.009 0.010 0.013 0.014 0.672 0.653 (0.18) (0.17) (0.51) (0.63) (3.63) (3.36) Kept change when given too much 0.028 -0.183 0.448 0.399 -1.355 -1.526 (0.08) (0.61) (2.23) (2.18) (1.99) (2.32) Paid cash without receipt to avoid tax 1.349 0.864 0.889 0.838 1.802 1.476 (5.76) (4.27) (8.06) (7.69) (6.23) (5.52) Sold second-hand faulty things 2.179 1.125 0.999 0.862 0.255 -0.344 (5.76) (3.44) (4.25) (3.77) (0.17) (0.24) Misused/altered card/document to 1.597 1.101 1.493 1.402 -0.884 -1.311 pretend eligible (4.16) (2.69) (7.21) (6.63) (0.82) (1.19) Exaggeration or false insurance claim 2.065 1.238 2.112 2.003 1.061 0.477 (4.70) (3.38) (10.20) (9.41) (1.12) (0.57) Falsely claim government benefit 3.383 2.721 2.941 2.813 3.178 3.015 (7.80) (5.71) (6.01) (5.94) (2.84) (2.55) Ever unemployed >3 months -1.929 -1.385 -0.296 -0.339 -1.520 -0.751 (1.14) (1.12) (1.14) (1.22) (0.62) (0.29) Ever unemployed >12 months -0.278 -0.175 -0.003 0.020 1.560 1.605 (1.15) (0.84) (0.02) (0.19) (2.07) (2.02) Unemployed last 5 years -0.207 -0.232 0.078 0.056 1.016 1.035 (0.81) (0.78) (0.57) (0.39) (1.77) (1.84) Basic Controls Yes Yes Yes Yes Yes Yes N 44427 44427 47444 47444 48819 48819 Pseudo R2 0.296 0.337 0.363 0.365 0.101 0.105 Notes: Basic controls are: male, age, age-squared, married, years of schooling, household income, number of household members, house ownership, immigrant, minority, gender role, interpersonal trust, left-right political scale, selfishness, religiosity, city, suburb, town, subjective health, life satisfaction and paid work. The statistics are weighted using the ESS sampling weights. Country, Wave, Industry and Occupation dummies are included in all models. t statistics are presented in parentheses and are in absolute values. Robust standard errors are clustered at the country level.

16

Table 3.B: Controlling for personal unethical behavior

DProbit: Marginal effects*100 ESS Round 5

High police corruption

High judges corruption

Contacted public official

Contacted police

Burglary/Assault 1.649 0.457 3.708 22.875 (8.42) (1.73) (6.87) (21.96) Borrowing constraint -0.572 -0.521 0.444 0.669 (6.53) (3.92) (2.02) (2.34) Ever divorced 1.012 0.326 0.148 4.429 (1.32) (1.11) (0.23) (5.05) Ever worked overseas 0.253 0.836 0.870 0.290 (0.21) (1.46) (1.13) (0.11) Hampered 0.863 -0.039 3.329 4.844 (2.06) (0.09) (2.39) (2.70) Discriminated 1.689 0.133 5.411 8.147 (2.20) (0.37) (7.35) (6.49) Made an exaggerated or false insurance claim -1.140 -1.837 0.858 2.902 (1.13) (6.30) (0.48) (1.02) Bought something that might be stolen 0.399 -0.415 1.563 8.729 (0.72) (0.90) (1.06) (4.95) Committed a traffic offence -0.230 -0.407 2.915 13.506 (1.28) (1.02) (6.16) (11.48) Ever unemployed >3 months 6.337 2.473 -0.962 -7.679 (2.70) (0.71) (0.17) (0.69) Ever unemployed >12 months 0.192 0.102 1.166 0.266 (0.43) (0.20) (2.08) (0.17) Unemployed last 5 years 1.132 0.645 -1.491 1.220 (4.03) (2.61) (1.35) (0.73) Basic Controls Yes Yes Yes Yes N 39490 38744 46162 46108 Pseudo R2 0.230 0.254 0.083 0.132 Notes: Basic controls are: male, age, age-squared, married, years of schooling, household income, number of household members, house ownership, immigrant, minority, gender role, interpersonal trust, left-right political scale, selfishness, religiosity, city, suburb, town, subjective health, life satisfaction and paid work. Country, Wave, Industry and Occupation dummies are included in all models. t statistics are presented in parentheses and are in absolute values. Robust standard errors are clustered at the country level.

17

Table 4: Bivariate Probit PANEL A Without exclusion restriction Without exclusion restriction Dependent variables Asked for Contacted Offered Contacted bribe public bribe public officials officials Marginal probabilities Pr(asked=1) 6.28 Pr(offered=1) 2.38 Pr(contact=1) 14.75 Pr(contact=1) 14.63 Joint probabilities Pr(asked=1,contact=1) 1.25 Pr(offered=1,contact=1) 0.45 Conditional probabilities Pr(asked=1|contact=1) 8.67 Pr(offered=1|contact=1) 2.99 Treatment effect on asked |contact==1 7.30 (7.94) on offered |contact==1 0.56 (4.14) Wald test of rho=0 chi2(1) = 15.42 chi2(1) = 6.12 Prob > chi2 0.0001 0.0134 PANEL B With exclusion restriction With exclusion restriction Dependent variables Asked for Contacted Offered Contacted bribe public bribe public officials officials Important to be loyal to friends -0.037 (2.86) Sold second-hand faulty things 0.374 (5.67) 0.043 (4.10) Misused/altered card/document 0.281 (4.54) 0.581 (8.42) to pretend eligible Exaggeration or false insurance 0.322 (4.46) 0.698 (8.23) claim Falsely claim government benefit

0.440 (5.74) 0.806 (5.85)

important to think new ideas and being creative

0.097 (7.13) 0.099 (8.28)

important to understand different people

0.062 (5.83) 0.062 (5.52)

Age 0.037 (11.32) 0.037 (11.42) Age-squared/100 -0.033 (8.41) -0.033 (8.39) Years of schooling 0.029 (6.40) 0.030 (6.36) Marginal probabilities Pr(asked=1) 6.31 Pr(offered=1) 2.37 Pr(contacted=1) 14.42 Pr(contact=1) 14.33 Joint probabilities Pr(asked=1,contacted=1) 1.19 Pr(offered=1,contact=1) 0.42 Conditional probabilities Pr(asked=1|contacted=1) 8.68 Pr(offered=1|contact=1) 2.98 Treatment effect on (asked |contacted=1) 7.33 (8.28) on (offered| contacted=1) 0.51 (3.40) Wald test of rho=0 chi2(1) = 15.77 chi2(1) = 5.052 Prob > chi2 0.0001 0.025 Notes: Models are estimated using the “biprobit” command in STATA and includes the full controls in Table 3.A

18

Table 5: Country specific regressions DProbit: Marginal effects*100

ESS Round 2

Asked for Bribe

Offered Bribe

Contacted public official

Asked for bribe if contacted public official

Offered bribe if contacted public official

Austria 5.378 0.029 6.197 1.333 0.143 (3.98) (0.64) (3.00) (0.34) (0.07) Belgium 6.869 0.668 1.200 2.437 0.631 (2.64) (1.34) (1.05) (0.74) (0.46) Switzerland 0.476 0.221 3.386 5.365 2.238 (2.20) (0.85) (2.36) (1.83) (1.79) Czech Republic 1.226 2.857 9.135 3.160 0.022 (7.37) (3.47) (4.83) (3.11) (0.83) Germany 0.595 0.162 2.864 1.424 0.392 (2.71) (0.46) (2.17) (0.46) (0.46) Denmark 0.544 0.131 11.518 3.053 0.758 (1.41) (0.21) (4.06) (0.72) (0.27) Estonia 6.686 1.056 2.249 5.977 1.685 (3.88) (1.16) (1.50) (0.40) (0.34) Spain 0.964 0.026 2.961 1.709 0.650 (3.05) (0.04) (1.81) (0.37) (0.27) Finland 0.295 0.095 10.455 -0.391 0.160 (1.95) (0.95) (4.96) (0.18) (0.17) France 0.212 0.368 6.636 3.166 1.988 (2.02) (0.78) (3.55) (1.15) (0.78) United Kingdom 0.006 -0.138 8.165 0.134 -1.419 (0.75) (0.46) (4.36) (0.05) (1.29) Greece 13.464 0.221 1.363 23.379 0.317 (9.60) (0.96) (0.98) (4.52) (0.10) Hungary 4.191 0.207 0.726 -0.163 -0.629 (3.06) (1.21) (0.40) (0.02) (0.12) Ireland 0.573 0.144 6.207 0.332 (1.93) (0.62) (3.03) (0.18) Iceland 0.952 -0.495 4.439 5.066 -1.264 (0.69) (1.20) (0.78) (0.89) (0.92) Italy 5.836 0.629 12.183 2.495 2.195 (2.89) (0.63) (3.44) (1.10) (2.28) Luxembourg 1.397 -0.691 1.958 4.497 3.710 (2.17) (0.91) (0.62) (0.85) (1.57) Netherlands 0.018 0.316 3.349 7.276 2.495 (2.11) (0.88) (1.82) (1.69) (0.97) Norway 0.303 -0.093 5.917 2.486 0.128 (1.59) (0.18) (2.11) (1.01) (0.12) Poland 12.544 0.806 1.792 2.481 12.605 (5.35) (1.98) (1.90) (0.16) (0.96) Portugal 3.362 0.733 1.690 2.670 5.812 (6.10) (1.42) (2.36) (0.30) (1.58) Sweden 0.482 0.147 5.387 3.062 -0421 (1.72) (1.21) (2.61) (0.64) (0.21) Slovenia 1.072 0.966 0.745 10.725 1.289 (2.03) (1.97) (0.41) (1.77) (0.49) Slovakia 11.731 3.898 0.892 -10.779 28.493 (4.48) (2.73) (0.60) (0.41) (1.24) Turkey 6.733 1.181 0.957 10.150 4.901 (6.61) (2.42) (1.13) (0.76) (0.97) Ukraine 33.926 6.073 7.054 31.901 4.639 (8.32) (2.74) (3.52) (0.92) (0.22)

Regression of bribery and contacts on victimization of unfortunate events including full controls in Table 3.A for each country

19

Table 6: Mediation analysis

ESS Round 2 Asked for bribe Offered bribe Ratio of Proportion of Ratio of Proportion of Indirect Total Indirect Total to Direct Effects to Direct Effects Effect Mediated Effect Mediated Full sample Victim of unfortunate events 0.044 0.042 0.050 0.047 Persons victims of: Burglary/Assault 0.071 0.067 0.118 0.105 Were overcharged 0.021 0.021 0.029 0.027 Were sold bad food 0.027 0.026 0.025 0.025 Failed to receive best deal 0.039 0.037 0.0055 0.052 Were sold second-hand faulty 0.012 0.012 0.014 0.014 Country samples Austria 0.009 0.009 -0.025 -0.024 Belgium 0.028 0.029 -0.065 -0.069 Switzerland 0.175 0.149 0.196 0.163 Czech Republic 0.084 0.077 0.095 0.086 Germany 0.051 0.054 0.059 0.055 Denmark 0.152 0.132 0.225 0.186 Estonia 0.123 0.109 0.057 0.054 Spain 0.105 0.095 0.013 0.013 Finland 0.258 0.205 0.434 0.303 France 0.051 0.049 0.217 0.178 United Kingdom 0.239 0.193 -1.049 -0.512 Greece 0.043 0.041 0.202 0.168 Hungary 0.029 0.028 0.067 0.063 Ireland 0.034 0.033 -0.0097 -0.107 Iceland 0.315 0.239 0.357 0.263 Italy 0.439 0.305 1.443 0.591 Luxembourg 0.210 0.174 0.010 0.009 Netherlands 0.215 0.177 0.313 0.238 Norway 0.017 0.017 -0.118 -0.133 Poland 0.034 0.033 0.244 0.196 Portugal 0.098 0.089 0.129 0.114 Sweden 0.199 0.166 0.216 0.178 Slovenia 0.038 0.037 -0.026 -0.026 Slovakia -0.001 -0.001 0.018 0.018 Turkey 0.018 0.18 0.061 0.058 Ukraine 0.157 0.136 0.170 0.145 Notes: Mediation analysis is carried out using “binary_mediation” command in STATA.

20

Table 7: Victimization and justification of unethical behavior OLS: Coefficients

ESS Round 2 Bribe justified

Bribe justified if victim of unfortunate events

Bribe justified if contacted public official

Bribe justified if victim of unfortunate events and contacted public official

Bribe justified if offered bribe

Bribe justified if did not offer bribe

Selling second hand faulty things justified

Selling second hand faulty things justified if sold second-hand faulty things

Selling second hand faulty things justified if did not sell second-hand faulty things

Once 0.099 0.120 0.156 0.174 0.005 0.064 0.044 -0.006 0.026 (2.15) (2.38) (2.05) (2.08) (0.12) (1.66) (2.41) (0.14) (1.70) Twice 0.223 0.244 0.315 0.327 0.159 0.204 0.63 -0.014 0.051 (3.81) (4.21) (4.27) (4.05) (0.98) (3.68) (1.86) (0.16) (1.37) Three/Four times 0.102 0.135 -0.103 -0.089 0.068 0.029 0.010 0.129 -0.027 (2.26) (2.76) (0.54) (0.44) (0.59) (0.29) (0.36) (1.02) (0.88) 5 times or more 0.098 0.147 -0.030 0.013 0.024 0.153 -0.019 -0.177 -0.093 (2.10) (3.00) (0.23) (0.10) (0.16) (2.35) (1.18) (1.20) (1.55) Full controls in Table 3.A

Yes Yes Yes Yes Yes Yes Yes Yes Yes

N 47425 32082 6743 5382 1029 45739 47488 1415 41885 Adjusted R2 0.066 0.075 0.127 0.146 0.193 0.063 0.070 0.200 0.067 >=Once 0.135 0.160 0.136 0.154 0.052 0.109 0.043 -0.006 0.024 (3.78) (4.24) (1.66) (1.82) (0.72) (3.10) (1.98) (0.13) (1.09) Full controls in Yes Yes Yes Yes Yes Yes Yes Yes Yes Table 3.A N 47425 32082 6743 5382 1029 45739 47488 1415 41885 Adjusted R2 0.065 0.074 0.123 0.141 0.191 0.063 0.070 0.196 0.067

Notes: Independent variables of interest are “Being asked for a bribe” in columns 1-6 and “Being sold a second-hand faulty thing in columns 7-9. “Never” is the omitted category in all regressions. The statistics are weighted using the ESS sampling weights. Country, Wave, Occupation and Industry dummies are included in all models. t statistics are presented in parentheses and are in absolute values. Robust standard errors are clustered at the country level.

21

Table 8: Victimization of unfortunate events, bribery and e-government DProbit: Marginal effects*100 ESS Round 2

Asked for Bribe

Offered Bribe

Asked for Bribe

Offered Bribe

Asked for Bribe

Offered Bribe

Eurostat E-Government online availability -0.083 -0.023 -0.031 -0.010 (3.44) (3.76) (1.11) (1.71) Victim of unfortunate events 3.436 1.277 3.247 0.444 (5.76) (6.06) (9.83) (8.02) Interaction -0.039 -0.013 -0.047 -0.011 (1.10) (2.95) (2.05) (4.41) Country dummies No No No No Yes Yes N 41437 43737 39784 41061 39784 41061 Countries 24 24 24 24 24 24 Pseudo R2 0.154 0.268 0.187 0.277 0.229 0.325 UN E-Government Index -26.163 -7.177 -10.955 -5.580 (5.90) (7.83) (2.29) (4.83) Victim of unfortunate events 9.373 1.067 9.237 1.039 (7.07) (2.28) (7.57) (2.91) Interaction -11.856 -1.625 -11.791 -1.730 (3.94) (1.96) (4.42) (2.29) Country dummies No No No No Yes Yes N 44427 47444 42591 44251 42591 44251 Countries 26 26 26 26 26 26 Pseudo R2 0.221 0.332 0.268 0.349 0.298 0.371 Notes: Eurostat provides the percentage of online availability of 20 basic public services. The statistics are weighted using the ESS sampling weights. Full controls in Table 3.A, Wave, Industry, and Occupation dummies are included in all models. t statistics are presented in parentheses and are in absolute values. Robust standard errors are clustered at the country level. Table 9: Subsamples for personal use of internet DProbit: Marginal effects*100 Sample No internet access Never use internet Use internet ESS Round 2

Asked for Bribe

Offered Bribe

Asked for Bribe

Offered Bribe

Asked for Bribe

Offered Bribe

Eurostat E-Government online Availability Victim of unfortunate events 3.684 0.372 1.228 0.062 2.871 0.144 (8.17) (5.82) (5.48) (1.91) (7.27) (1.44) Interaction -0.025 -0.008 -0.008 -0.002 -0.063 -0.003 (1.07) (2.80) (0.54) (1.77) (1.97) (0.64) Country dummies Yes Yes Yes Yes Yes Yes N 12452 12613 6454 5827 18460 18978 Countries 23 23 23 23 23 23 Pseudo R2 0.246 0.355 0.341 0.564 0.251 0.377 UN E-Government Index Victim of unfortunate events 13.822 1.526 0.376 0.118 8.986 0.501 (6.85) (2.78) (0.16) (0.58) (8.19) (1.88) Interaction -11.963 -2.262 4.104 -0.111 -19.697 -1.224 (3.21) (1.93) (0.88) (0.29) (6.04) (2.38) Country dummies Yes Yes Yes Yes Yes Yes N 13920 14336 7448 7035 18849 19508 Countries 26 26 26 26 26 26 Pseudo R2 0.326 0.390 0.369 0.506 0.287 0.387 Notes: Eurostat provides the percentage of online availability of 20 basic public services. The statistics are weighted using the ESS sampling weights. Full controls in Table 3.A, Wave, Industry, and Occupation dummies are included in all models. t statistics are presented in parentheses and are in absolute values. Robust standard errors are clustered at the country level.

22

APPENDIX Definition of Variables Used in ESS Household income: Household's total net income from all sources on a scale 1-12. Borrowing constraint: Borrow money to make ends meet, difficult or easy? (1 Very difficult, 2 Quite difficult, 3 Neither easy nor difficult, 4 Quite easy, 5 Very easy.) Subjective health: How is your health in general? Would you say it is ...? (1 Very bad, 2 Bad, 3 Fair, 4 Good, 5 Very good.) Satisfaction with life: All things considered, how satisfied are you with your life as a whole nowadays? (0 Extremely dissatisfied, 10 Extremely satisfied.) Happiness: Taking all things together, how happy would you say you are? Political interest: How interested would you say you are in politics, are you? (1 Not at all interested, 2 Hardly interested, 3 Quite interested, 4 Very interested.) Left-right scale: Placement on left right scale in politics. (0 Extreme left, 10 Extreme right.) Religiosity: How religious are you? (0 Not at all religious, 10 Very religious.) Selfishness: Society would be better off if everyone looked after themselves. (1 Agree strongly, 2 Agree, 3 Neither agree nor disagree, 4 Disagree, 5 Disagree strongly.) Interpersonal Trust: Simple average of the following. (i) Most people can be trusted or you can't be too careful? (0 You can't be too careful, 10 Most people can be trusted) Hampered: Are you hampered in your daily activities in any way by any longstanding illness, or disability, infirmity or mental health problem? Discriminated: Would you describe yourself as being a member of a group that is discriminated against in this country? Friends to ask for favor: how many friends could you ask to get benefits/services not entitled to? (1. 0) Doctor visits: Consulted doctor/specialist/gp, how many times last 12 months? How often, if ever, have you done each of these things in the last five years? Use this card for your answers. Kept change when given too much: How often, if ever, have you? ...the change from a shop assistant or waiter knowing they had given you too much? Paid cash without receipt to avoid tax: paid cash with no receipt so as to avoid paying VAT or other taxes? Sold second-hand faulty things: sold something second-hand and concealed some or all of its faults? Misused/altered card/document to pretend eligible: misused or altered a card or document to pretend you were eligible for something you were not? Exaggeration or false insurance claim: made an exaggerated or false insurance claim? Falsely claim government benefit: over-claimed or falsely claimed government benefits such as social security or other benefits? Bought something that might be stolen: How often have you... ...bought something you thought might be stolen? Committed traffic offence: How often have you... ...committed a traffic offence like speeding or crossing a red light? Justification of selling second-hand faulty things: How wrong, if at all, do you consider the following ways of behaving to be? Use this card for your answers. How wrong is? ...someone selling something second-hand and concealing some or all of its faults? Unemployed last five years: Any period of unemployment and work seeking within last 5 years? Eurostat E-Government availability (2001- 2006 mean) UN E-Government Index (2003-2005 mean)

73.68

63.8658.25

56.8855.55

52.8050.42

47.9647.20

45.5044.33

42.4941.89

38.2132.8132.5032.29

28.4728.33

27.0517.50

15.0013.75

4.02

0 20 40 60 80 100

EstoniaSweden

DenmarkSlovenia

FinlandAustriaNorwayIreland

United KingdomFranceSpain

ItalyPortugal

GermanyIceland

HungaryCzech Republic

BelgiumNetherlands

GreeceSlovakia

PolandLuxembourgSwitzerland

0.880.87

0.860.810.80

0.780.78

0.760.75

0.730.72

0.710.71

0.680.67

0.660.65

0.620.60

0.590.590.59

0.560.56

0.510.50

0 10.2 0.4 0.6 0.8

DenmarkSweden

United Kingdom NorwayFinland

GermanyNetherlandsSwitzerland

IcelandAustria

BelgiumEstoniaIrelandFrance

ItalyLuxembourg

SloveniaPortugal

Czech RepublicSpain

PolandHungaryGreece

SlovakiaUkraineTurkey

Asked for bribe Offered bribe

32.6214.78

13.2212.2311.80

10.016.72

5.475.344.87

3.593.563.462.652.202.031.921.661.651.651.441.441.361.311.230.90

0 20 40 60 80 100

UkraineSlovakia

Czech RepublicPolandGreeceEstoniaTurkeyAustria

ItalyHungary

LuxembourgPortugalSlovenia

SpainDenmarkGermany

NorwayBelgium

IrelandSwedenFranceIceland

NetherlandsSwitzerland

United Kingdom Finland

15.11

7.747.48

4.202.582.011.831.581.341.331.331.161.121.050.790.780.750.680.670.560.430.280.270.220.180.15

0 20 40 60 80 100

UkraineSlovakia

Czech RepublicPolandGreece

ItalyEstoniaAustria

HungarySpain

PortugalTurkey

SloveniaLuxembourg

BelgiumFrance

DenmarkNorwaySweden

GermanyNetherlandsSwitzerland

United Kingdom IrelandIcelandFinland

23

Table A.1: Summary statistics Variable Mean Standard

Deviation Min Max

ESS Round 2 Bribe Justified 0.306 0.461 0 1 Asked for bribe 0.055 0.228 0 1 Offered Bribe 0.022 0.147 0 1 Victim of misfortune 0.725 0.447 0 1 Contact with politician/public official 0.142 0.349 0 1 Burglary/Assault 0.202 0.402 0 1 Plumber/builder/mechanic/repairer overcharged 0.345 0.475 0 1 Were sold food packed to conceal worse bits 0.420 0.494 0 1 Bank/insurance company failed to offer best deal 0.273 0.446 0 1 Were sold things secondhand that proved faulty 0.232 0.422 0 1 Unemployed last 5 years 0.539 0.499 0 1 Male 0.460 0.498 0 1 Age 46.623 18.425 18 102 Years of schooling 11.505 4.047 0 44 Household income 6.079 2.609 1 12 Married 0.511 0.500 0 1 Number of people in household 2.861 1.495 1 18 Home owner 0.733 0.443 0 1 Immigrant 0.081 0.273 0 1 Ethnic minority 0.043 0.202 0 1 Male role index 2.600 0.772 1 5 Interpersonal Trust 4.898 2.486 0 10 Leftright scale 5.136 2.200 0 10 View on Selfishness 3.550 1.134 1 5 Religiosity 4.896 2.980 0 10 City 0.200 0.400 0 1 Suburb 0.116 0.320 0 1 Town 0.316 0.465 0 1 Subjective health 3.758 0.929 1 5 Satisfaction with life 6.844 2.329 0 10 In paid work 0.473 0.499 0 1 ESS Round 5 Perceived police corruption 4.151 2.685 0 10 Perceived judges corruption 3.834 2.809 0 10 Approached stopped or contacted by police 0.124 0.329 0 1 Contact with politician/public official 0.331 0.471 0 1 Burglary/Assault 0.157 0.364 0 1 Unemployed last 5 years 0.536 0.499 0 1 Male 0.457 0.498 0 1 Age 48.508 18.729 14 102 Years of schooling 12.300 4.053 0 55 Household income 5.076 2.785 1 10 Married 0.483 0.500 0 1 Number of people in household 2.722 1.430 1 19 Immigrant 0.095 0.294 0 1 Ethnic minority 0.063 0.243 0 1 Male role index 2.827 1.053 1 5 Interpersonal Trust 4.859 2.461 0 10 Leftright scale 5.166 2.202 0 10 Religiosity 4.693 2.991 0 10 City 0.237 0.425 0 1 Suburb 0.112 0.315 0 1 Town 0.294 0.456 0 1 Subjective health 3.740 0.965 1 5 Satisfaction with life 6.675 2.356 0 10 In paid work 0.461 0.499 0 1

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Table A.2: Victimization frequency of unfortunate events and bribery DProbit: Marginal effects*100

Asked for bribe

Offered bribe

Contacted public official

Asked for bribe if

contacted public official

Offered bribe if

contacted public official

Burglary/Assault 0.639 0.093 2.638 1.241 -0.029 (3.52) (1.53) (4.77) (2.55) (1.23) Were overcharged Once 1.062 0.151 1.702 0.559 -0.043 (4.83) (2.68) (3.23) (1.05) (1.45) Twice 2.953 0.361 0.826 3.341 0.032 (11.05) (3.76) (1.39) (3.72) (0.95) Three/Four times 3.154 0.067 2.335 1.428 -0.007 (5.83) (0.65) (1.65) (1.76) (0.15) 5 times or more 4.426 0.274 3.976 5.449 0.084 (10.23) (0.90) (2.61) (3.69) (1.20) Were sold bad food Once 1.770 0.185 1.736 3.598 0.041 (4.00) (2.17) (3.01) (2.98) (1.11) Twice 2.466 0.111 1.423 2.126 -0.030 (5.96) (1.59) (2.27) (1.97) (0.85) Three/Four 2.119 0.265 3.833 3.505 0.031 (5.55) (3.33) (4.26) (5.92) (1.05) 5 times or more 1.197 0.238 2.325 1.677 0.089 (2.73) (2.24) (2.79) (1.89) (1.93) Failed to receive best deal Once 2.200 0.029 3.065 0.776 0.195 (8.37) (0.19) (2.83) (1.10) (2.49) Twice 3.108 0.169 4.400 0.623 0.026 (7.85) (0.90) (3.51) (0.91) (0.77) Three/Four 1.256 0.272 5.421 1.824 0.379 (2.45) (1.83) (4.07) (1.66) (2.34) 5 times or more 3.433 0.616 1.396 0.563 0.713 (6.59) (2.24) (0.71) (0.60) (3.28) Were sold second-hand faulty Once 2.339 0.089 0.876 2.140 -0.008 (4.61) (1.14) (1.34) (3.06) (0.31) Twice 3.343 0.371 0.817 2.908 0.078 (7.03) (1.91) (0.87) (1.99) (1.43) Three/Four 4.185 0.359 0.057 3.166 0.001 (6.75) (2.09) (0.05) (2.53) (0.00) 5 times or more 6.322 0.197 0.785 4.796 0.037 (5.82) (0.92) (0.32) (2.28) (0.35) N 44427 47444 48819 6485 6121 Pseudo R2 0.325 0.251 0.098 0.374 0.374

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Table A.3: Country specific regressions DProbit: Marginal effects*100 on victim of unfortunate

ESS Round 5 High police corruption

High judges corruption

Contacted politician/public official

Contacted police

Belgium 0.897 0.002 1.131 19.879 (2.22) (0.40) (0.64) (6.11) Bulgaria 2.407 4.489 0.353 27.222 (1.20) (1.76) (0.42) (12.17) Cyprus 3.522 19.766 17.517 (2.55) (0.01) (4.60) (3.32) Spain 23.403 0.411 4.815 29.251 (1.53) (0.61) (2.82) (9.90) Greece 27.957 4.301 0.285 20.993 (2.88) (2.30) (0.24) (9.55) Hungary 17.791 1.597 5.599 27.505 (1.23) (1.57) (2.54) (6.80) Russian Federation 13.332 1.025 5.115 23.714 (1.56) (0.50) (3.49) (9.12) Ukraine 25.422 3.009 2.563 29.823 (2.46) (0.77) (1.30) (9.64)

Notes: Countries where one of the first two columns is significant are shown.

Table A.4: Distrust in public officials and life satisfaction ESS Round 2 Distrust public officials Life Happiness if contacted if asked satisfaction public for bribe official Victim of unfortunate events 8.953 13.076 18.098 -2.563 -2.042 (10.08) (5.77) (3.90) (4.57) (3.47) N 45787 6704 2353 48818 48776 Pseudo R2/Adjusted R2 0.081 0.145 0.131 0.284 0.254

Notes: Marginal effects*100 are presented after DProbit and coefficients*10 are presented after OLS. Distrust to public officials is a dummy 1. Life satisfaction and happiness are on a scale 0-10. The statistics are weighted using the ESS sampling weights. Full controls in Table 3.A, Country, Wave, Occupation, and Industry dummies are included in all models. t statistics are presented in parentheses and are in absolute values. Robust standard errors are clustered at the country level.

Table A.5: Mediation analysis

Dependent variable: Distrust in public officials

Distrust in public officials

Life satisfaction

Life satisfaction

Happiness Happiness

Ratio of Proportion of Ratio of Proportion of Ratio of Proportion of Indirect Total Indirect Total Indirect Total to Direct Effects to Direct Effects to Direct Effects Effect Mediated Effect Mediated Effect Mediated Mediator variable: Asked for bribe 0.605 0.377 5.551 0.847 2.677 0.728 Offered bribe 0.151 0.178 0.773 0.435 0.943 0.485

Notes: Victim of unfortunate events is the independent variable in this analysis and mediation analysis is carried out using “binary_mediation” command in STATA.