1
John Hudson
This paper analyzes the impact of institutions upon happiness through their intermediary impact upon individual trust. The empirical work is based on Eurobarometer data covering the 15 countries of the EU prior to its expansion in 2004. With respect to trust, we present evidence that, although it is endogenous with respect to the performance of the institution, changes in the individual’s personal circumstances can also have an impact, indicating that trust is not simply learned at an early age. Hence unemployed people tend to have lower levels of trust not only in the main economic institutions – government and the Central Bank – but in other state institutions too such as the police and the law. Trust also differs in a systematic manner with respect to education and household income, increases (decreases) in either increase (decrease) trust in most institutions. If we assume that more educated people make better judgments, this suggests that on average people tend to have too little trust in institutions. However, it is also possible that both of these variables impact on the interaction between institutions such as the police and other government agencies and the citizen, with prosperous, well educated people being at an advantage and possibly able to command more respect. Age too impacts on institutional trust.
For the UN, the unions, big business, voluntary organizations and the EU, trust first declines and then increases with the estimated turning points ranging between 44 and 56 years. For most other organizations trust significantly increases with age. Turning to subjective well-being, we find the standard set of socio-economic variables to be significant. But the focus here is on the impact of institutional trust. We find that trust (mistrust) in the European Central Bank, the EU, national government, the law and the UN all impact positively
(negatively) on well-being. Hence overall our results support the conclusion that happiness does not solely lie within the realm of the individual, but that institutional performance also has a direct impact upon subjective well-being.

2
III. THEORY
We assume the i’th person’s trust in the j’th institution (T ij
) to be a function of his/her knowledge of the specific institution. This will be both direct and indirect. Indirect knowledge comes primarily through the media and word of mouth. People read about the institution in the newspaper and hear about it on television. This is likely to be the case with an institution such as the ECB or the UN. It is also likely to be the case with respect to institutions such as the police and the justice system. But in these cases personal, direct experience may also be relevant. Both direct and indirect knowledge may be expected to vary with socio-economic variables. Crucially, we assume in accordance with institutional theories that beliefs about institutions are linked to the ‘quality’ of the institution (Q j
). Hence:
(2) T ij
= f( X i
Q j
)
To summarize this part of the analysis, we would expect variables such as urban-rural location, income, education and marital status to affect attitudes to specific institutions with which individuals may come into direct contact such as the law and the police. Specifically we would expect people who live in large cities, the young, the poorly educated and those who are divorced to be less trusting of these institutions. Unemployed people may also blame the institution most readily identifiable as being to blame for their misfortune – arguably the government and the central bank. More generally misfortune may lead to a generalized loss of trust. We would also anticipate that attitudes differ more generally with respect to age.
In terms of the impact of institutional trust upon well-being (H i
) we assume that, in common with the findings of others, this is a function of socio-economic variables such as income, employment status, education and age, for reasons which are well documented in the literature reviewed earlier. Institutional quality, particularly with respect to governmental

3 institutions, may be expected to impact on socio-economic variables such as income, employment status, and even education and marital status and thus it will also impact upon subjective well-being. In addition we also assume a more direct effect:
H j
= g( X i
, Q ) (3)
Where Q is a vector of institutional quality in all institutions. The exact weight given to the j’th institution will depend upon the impact it has on the individual’s life. State institutions impact upon the quality of public services and public goods as may NGOs and voluntary organizations. We have no direct measure of institutional quality, but given (2) it follows that in general there exists a functional relationship linking quality to trust:
Q ij
= h(T ij
, X i
) (4)
If we assume seperability, e.g. f( X i
Q j
) = m( X i
) + q(Q j
) and that q is a bijection with respect to T ij
- m( X i
), then h(T ij
, X i
) = q
-1
(T ij
- m( X i
)). Inserting (4) into (3) gives:
H j
= g( X i
, Σh(T ij
, X i
)) (5) where the summation is across institutions. The important point is that institutional trust, when conditioned by socio-economic variables, is a proxy for institutional quality. Thus happiness can be modeled as a function of socio-economic variables and institutional trust.
The hypothesis we are testing is not that happiness is a function of trust, but of institutional quality and that this is reflected in differing levels of trust.
IV. THE DATA AND MODEL SPECIFICATION

4
The data are derived from the Eurobarometer survey carried out in April/May 2001 of the
EU member countries. The surveys cover the population of the respective nationalities of the
European Union member states aged fifteen years and over in each of the member states.
The dependent variable relating to well-being is based on the responses to a question which asked “On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with the life you lead? Would you say you are……?”
Possible responses were: (i) very satisfied, (ii) fairly satisfied, (iii) not very satisfied and (iv) not at all satisfied. A fifth possibility was “don’t know”. The regression analysis excludes the latter and, of course, those who otherwise did not answer the question. The potential problems with this measure including the econometrics’ problems posed by measurement errors are discussed in Frey and Stutzer (2002a). They conclude that the problems are reduced when they are used to estimate the relative determinants of happiness. The responses are ordinal and ordered probit will be used to estimate the data. This type of question has been used before in empirical work on well-being and happiness and in particular the Eurobarometer series has been used by for example Di Tella, MacCulloch and Oswald (2001) The analysis involves the assumption that we can imply a cardinal interpretation to the subjective, qualitative responses to the question proxying well-being or satisfaction.
The data relating to satisfaction is summarized in Table 1. It can be seen that there are wide variations between countries with, in general, the richer countries being more satisfied than the poorer ones.
Table 2 reports the proportion trusting the institution where the alternative includes both don’t knows and those who do not trust. There is considerable consistency in that both the radio and the police were the most trusted institutions in all but one country, Germany and Belgium respectively. There are however significant differences between countries with
Denmark, Luxembourg, the Netherlands and Finland emerging as the most trusting countries and Italy and the UK as the least trusting.
A potential problem with much of this research is that of simultaneity. For example are the unemployed unhappy because they are unemployed, or are unhappy people more likely to perform less satisfactory in the workforce and hence more likely to become unemployed?
Frey and Stutzer conclude that the main direction of causality appears to run from unemployment to unhappiness. This conclusion has not, of course, been made with respect to trust in the institutions where relatively little work has been done. Hence for these variables we will tackle the problem of causality in a specific way. The basic problem is that dissatisfied people are likely to be dissatisfied on a range of issues including those of trust and it may be this underlying mistrust, a general mistrust of everything and everyone, which interacts on happiness rather than trust in any one institution per se. Hence trust in the j'th institution by the i'th person (T ij
) may be defined as:

5
T ij
=
 i
+
 ij
(6) where it is possible that underlying trust,
 i
, is simultaneously linked to well-being.
 ij
will however be exogenous with respect to well-being and represents the additional trust, which may be negative, the individual has in the j'th institution over and above the average. This has resonance too for the difference theories of trust formation outlined above. If holistic trust is learned at an early age or largely determined by dyadic trust then
 i
should be large relative to
 ij
, where
 ij.
more clearly relates to endogenous trust, i.e. trust which responds to the institution’s perceived performance
.
An approximate measure of
 i
can be obtained by taking the average level of mistrust across the m institutions. A measure of the impact of
 ij
can then be found by dividing T ij
by this estimate and utilizing the estimate of
 i
= 1/m

T ij
:
Relative Trust = T ij
/(1/m

T ij
)
= (
 i
+
 ij
)/
 i
= 1 +
 ij
/
 i
(7)
A value of 1 means that the individual has, for him/her, an average level of trust in this institution.
SATLIFE = 2.367 + 0.0727SEX - 0.0276AGE + 0.000280AGE
2 - 0.0769SINGLE
(20.01) (3.16) (6.62) (6.65) (2.00)
- 0.181SINGLE2 - 0.314DIVORCED - 0.214WIDOWED + 0.134Ln(EDUCATION)
(3.55) (7.32) (4.63) (4.17)
+ 0.121VILLAGE + 0.0584TOWN - 0.291FARMER - 0.437UNEMPLOYED
(4.03) (2.05) (2.82) (8.94)
- 0.128HDUNEMPLOYED + 0.259Ln(INCOME) + 0.104TRGOV + 0.0297TREU + 0.140TRECB
(0.78) (11.05) (3.87) (1.00) (5.62)
+ 0.0661TRLAW + 0.127TRPOL + 0.0063TRUNION + 0.0738TRBUS + 0.0171TRRADIO

6
(2.45) (4.24) (0.26) (2.90) (0.66)
- 0.0084TRVOL + 0.0156TRNGOS + 0.0774TRUN
(0.32) (0.58) (2.75)
Log Like = -9907.7, Log Likelihood ratio: X 2 =2632.9, Number of observations =10,505 (8)
The trust variables are included as binary variables taking a value of 1 if the individual trusts the institution, with responses related to don't knows and people who do not trust the institution being coded 0. Several are significant, including trust in the national government, the ECB, the police, big business and the UN at the 1% level and in the law at the 5% level.
The impacts are such that trust tends to increase happiness. This therefore provides strong evidence that institutions do affect well-being and that the impact varies from person to person. In this regression we use absolute trust, which as we mentioned earlier may suffer from problems of simultaneity. In the following regression we report the results from using relative trust as defined in equation (7)
SATLIFE = 2.538 + 0.0736SEX - 0.0290AGE + 0.000295AGE
2 - 0.0822SINGLE
(21.32) (3.21) (7.01) (7.05) (2.14)
- 0.188SINGLE2 - 0.328DIVORCED - 0.201WIDOWED + 0.159Ln(EDUCATION)
(3.69) (7.69) (4.35) (5.01)
+ 0.128VILLAGE + 0.0715TOWN - 0.286FARMER - 0.466UNEMP LOYED
(4.26) (2.52) (2.78) (9.57)
- 0.122HDUNEMPLOYED + 0.282Ln(INCOME) + 0.0608TRGOV + 0.0340TREU + 0.0389TRECB
(0.75) (12.11) (5.23) (2.65) (4.32)
+ 0.0317TRLAW + 0.0124TRPOL + 0.0179TRBUS + 0.0293TRUN
(3.17) (1.58) (1.60) (2.71)
Log Like = -10017.8, Log Likelihood ratio: X 2 =2412.7, Number of observations =10,505 (9)
5.2 The Determinants of Trust

7
APPENDIX: DATA DEFINITIONS
SATLIFE : Responses to a question which asked: “Now lets talk about the quality of life.
Please tell me whether you are very satisfied, fairly satisfied, not very satisfied or not at all satisfied with ….. your life in general?" A fifth possibility “don’t know” was excluded from the regression analysis.
Trust Questions : Based primarily on a question which asked: "I would like to ask you a question about how much trust you have in certain institutions. For each of the following institutions would you please tell me if you tend to trust it or not to trust it.?" The options included: The Radio ( TRRADIO ), The UN ( TRUN ), Non-Governmental Organizations
( TRNGOS ), Charitable or voluntary organizations,( TRVOL ), the police ( TRPOL ), the law
( TRLAW ) trade unions ( TRUNIONS ), big companies ( TRBIGCOS ), the (national) government ( TRGOV ) and the European Union ( TREU ). There was also a question which asked: " Do you tend to trust or distrust the European Central Bank?” ( TRECB ). In Table 3 these were coded as 0 (trust), 1 (do not know) or 3 (do not trust).
LIFE IMPROVED: Those who felt that their current position compared with that five years ago had improved (1), stayed the same (2) or got worse (3)
SEX : male=0, female=1.
AGE: The age, in years, of the respondent.
SINGLE1/SINGLE2/DIVORCED/WIDOWED : binary variables operative for those who were either single, single having previous lived with partner, divorced/separated or widowed.
The default case is therefore those who are married or living with partner.
EDUCATION Age at which the individual finished full time education Coded: 1, if <16 years; 2 if 16-19 years; 3 if >19 years.
VILLAGE/TOWN binary variables taking a value of one if the individual lived in a village or small/medium town based on individual perceptions
FARMER binary variables taking a value of one if the individual was a farmer
UNEMPLOYED/HDUNEMPLOYED binary variables taking a value of either the individual or the main household earner were unemployed
INCOME The ratio of household income to average income in the respondent's country. The data on income is based on a fifteen point scale and to proxy income the mid point value of the range corresponding to the point on the scale was taken.

8
Table 1: Country Scores on Happiness
Country
Austria
Belgium
Score
2.09
Country
2.06
Italy
Luxembourg
Score
2.29
1.92
Denmark
Finland
France
Germany
2.58
2.11
1.90
1.91
Netherlands
Portugal 1.63
Spain
Sweden 2.37
2.42
2.02
Greece
Ireland
1.50
2.22
UK 2.20
Source : Data derived from Eurobarometer Survey in 2001. The 'score' is the mean when the happiness question defined in the appendix is coded zero for not at all satisfied, one for not very satisfied, etc. Hence the higher the score the more satisfied are people on average anda score of 2 corresponds to an average response of 'fairly satisfied'.

Table 2: Cross-Country Comparisons of Institutional Trust
9
Belgium
Denmark
Germany
Greece
Spain
France
Ireland
Italy
Luxembourg.
Netherlands
UN ECB NGOs LAW POLICE UNIONS Busin-
ess
Govern- RADIO
ment Voluntary
Org.
EU Average
48.55
70.40
49.33
51.40
47.98
39.51
32.76
75.52
48.36
88.93
42.20
56.06
38.34
53.96
40.46
50.93
72.06
79.49
51.45
59.32
50.67
34.38
47.47
59.99
40.45
42.22
57.54
44.50
48.33
43.93
44.39
44.04
29.55
43.08
64.56
51.22
55.02
63.42
41.75
44.95
71.37
62.05
55.61
58.26
38.28
42.74
37.89
37.31
27.91
36.07
36.32
40.98
39.66
37.26
46.32
36.85
64.02
52.48
72.11
68.20
45.17
65.64
65.09
73.09
32.83
55.90
55.44
43.58
44.78
49.53
52.46
49.36
64.47
49.40
52.51
60.13
62.83
48.80
64.38
69.68
54.93
38.35
57.99
50.71
55.59
39.56
58.45
62.71
72.37
64.86
77.17
71.48
51.97
29.52
51.60
48.13
35.53
35.74
42.92
41.42
40.79
32.73
68.95
63.87
77.30
54.62
73.97
80.39
80.59
53.41
61.19
61.16
56.91
55.22
48.86
44.65
59.39
45.66
59.82
59.48
Portugal
UK
Austria
Sweden
Finland
57.04
54.70
45.20
71.25
67.64
46.65
26.67
46.49
50.06
50.47
47.36
39.15
43.54
41.99
39.02
36.44
50.09
71.40
61.54
65.42
60.21
64.96
79.15
72.38
89.84
45.07
39.83
38.56
45.02
57.83
42.61
28.21
34.69
35.69
46.03
49.47
30.77
42.07
46.03
52.22
72.18
61.20
73.06
77.05
74.30
61.27
69.23
63.84
50.32
56.19
64.26
29.40
34.13
27.62
34.35
52.96
44.93
52.01
52.63
57.57
Average 55.07 49.83 45.93 54.31 69.13 44.13 38.43 45.23 70.16 61.13 44.55
Source : Data derived from Eurobarometer Survey in 2001 The figures denote the proportion (as %’s) trusting the institution in each country where the alternative includes don't knows and those who do not trust the institution.

10
Table 3: The Socio-Economic Determinants of Institutional Trust
Constant
UN ECB NGOs Law Police Unions Big Com- Govern- Radio Voluntary EU panies ment Organizations
-0.0680 1.125
** 0.740
** 0.460
* 0.488
* -0.419
** -0.0393 1.203
** -0.807
** -0.269 0.628
**
SEX
(0.52) (6.46) (4.29) (2.47) (2.42) (3.29) (0.31) (6.54) (4.12) (0.21) (4.92)
-0.0156 0.0511
* -0.0558
* -0.0287 -0.123
** -0.0852
** -0.0004 -0.0083 -0.0506 -0.113
** -0.0534
*
(0.65) (2.19) (2.41) (1.15) (4.62) (3.55) (0.02) (0.34) (1.93) (4.60) (2.23)
AGE or Ln(AGE) 0.0158
** -0.114
** -0.0040 -0.0804
* -0.302
** 0.0149
** 0.0259
** -0.188
** 0.129
** 0.0181
** 0.0206
**
AGE 2 x 100
(3.61) (3.05)
-0.0181
**
(0.11) (2.00) (6.99) (3.45) (5.99) (4.73)
-0.0134
** -0.0244
**
(3.06) (4.08) (4.75)
-0.0190
** -0.0232
**
(4.15) (3.09) (5.58) (4.23) (5.31)
-0.0332 -0.0360 -0.0808
* -0.0519 0.0505 0.0443 0.0064 -0.0495 -0.0143 0.0012 -0.0110 SINGLE1
SINGLE2
(Left partner)
DIVORCED
(0.82) (0.95) (2.15) (1.28) (1.18) (1.10) (0.16) (1.24) (0.33) (0.03) (0.28)
0.0465 0.0540 -0.0715 0.118
* 0.150
* 0.0885 0.0301 0.109
* 0.0623 -0.0249 -0.0124
(0.87) (1.05) (1.39) (2.13) (2.56) (1.66) (0.60) (2.00) (1.07) (0.46) (0.23)
-0.0447 0.0198 0.0247 0.179
** 0.295
** 0.0870 0.0021 0.184
** 0.0122 0.102
* 0.0197
(0.99) (0.46) (0.57) (3.85) (6.05) (1.92) (0.05) (3.98) (0.25) (2.24) (0.44)
-0.0529 0.0155 -0.137
** -0.138
** -0.106
* -0.0253 -0.103
* -0.162
** -0.103
* -0.0449 -0.0357 WIDOW
VILLAGE
(1.10) (0.35) (3.10) (2.88) (1.99) (0.53) (2.14) (3.43) (2.06) (0.91) (0.74)
0.0022 -0.0263 0.0788
** -0.0069 -0.159
** 0.00139 -0.0492 0.0331 -0.0116 -0.0093 0.0758
*
(0.07) (0.86) (2.59) (0.21) (4.57) (0.04) (1.55) (1.03) (0.34) (0.29) (2.41)
-0.0048 -0.0223 -0.0245 -0.0133 -0.177
** -0.0512 -0.0557 0.0050 -0.0272 -0.0496 0.0522 TOWN
(0.16) (0.77) (0.86) (0.43) (5.36) (1.72) (1.86) (0.16) (0.83) (1.63) (1.76)
Ln(EDUCATION) -0.142
** -0.219
** -0.0937
** -0.179
** -0.112
** 0.107
** 0.0394 -0.126
** 0.0266 -0.124
** -0.193
**
FARMER
(4.23) (6.78) (2.91) (5.16) (3.01) (3.21) (1.17)
0.0207 -0.145 -0.0653 -0.252
* -0.237 0.237
* 0.169
(0.19) (1.33) (0.62) (2.13) (1.81) (2.18) (1.51)
UNEMPLOYED 0.0972 0.169
** 0.0402 0.186
** 0.189
** 0.0418 0.090
(3.68)
-0.119
(1.06)
(0.73)
0.0407
(0.35)
(3.65)
0.0213
(0.19)
(5.77)
0.0526
(0.48)
0.231
** 0.148
** -0.231 0.175
**
(1.88)
HDUNEMPLOYED 0.106
(3.42)
-0.223
(0.81)
-0.209
(3.48)
0.176
(3.39)
0.138
(0.80)
-0.0003
(1.71)
-0.0278
(4.28)
0.109
(relates to main earner)(0.62) (1.32) (1.24)
INCOME
(0.97) (0.74) (0.00) (0.16) (0.59)
-0.108
** -0.177
** -0.0835
** -0.0750** -0.0473 0.0332 -0.131
** 0.003
(2.68) (0.44) (3.34)
-0.354 -0.176 0.0518
(1.74) (0.96) (0.30)
-0.0638
* -0.0345 -0.0780
**
(4.05) (7.04) (3.37) (2.81) (1.66) (1.29) (5.05) (0.12) (2.26) (1.29) (2.97)
LIFE IMPROVED -0.181
** -0.141** -0.114
** -0.138** -0.128** -0.0869** -0.126
** -0.212
** -0.120
** -0.114
** -0.175
**
(10.19) (8.23) (6.78) (7.47) (6.56) (4.90) (7.07) (11.65) (6.30) (6.31) (9.83)
Log L

2
10044
719.5
10713
602.4
11100
388.2
8884
858.3
7409
938.6
10208
342.9
10132
440.1
9455
572.9
7893
435.4
9601
452.8
10252
921.3
Notes: when age 2 is not reported age represents the log of age. (.) denotes t statistic, ** and * denote significance at the 1% and 5% levels, regression estimated by ordered probit,

2 represents the likelihood ratio statistic, the coefficient on Age 2 has been multiplied by 100. The number of observations in all regressions are 10505. Higher values for “life improved” indicates the individual’s current position has worsened over apst five years.

11
APPENDIX
Underlying this work is something which has seldom been explored and certainly not in this paper. If you look at your lecture notes on ordered probit you will see that the coefficients relate to
Y i
= β’X i
+ u i
(2 on page 5.1)
Where Y i
is the unobserved response variable relating to the i’th individual and X i
his/her characteristics. We only observe Y i
within ranges characterized, in this case, By (i) very satisfied, (ii) fairly satisfied, (iii) not very satisfied and (iv) not at all satisfied. But implicit in
(2) is that the estimated coefficients can be used to get a predicted value of, in this case happiness. The measure is not unique and is, I think, subject to a linear and multiplicative transformation. Nonetheless, we have a measure of well-being or happiness which at any one time can be used to calculate a predicted level of happiness for any one individual and thus different individuals can be compared. At the point many economists break out into a rash – cant compare different people’s utilities they would say. Well I am not so certain. We could also calculate the actual impact on well being if e.g. we put everyone on the highest level of education. The same of course goes for trust.
What is more because income is generally included in these analyses and hence we can calculate from the regression coefficients:
The value in well-being terms of an extra say 1,000 Euros of income (say an increase of 2 on the well being scale)
The value in well being terms of transforming someone with medium level of education to the highest level (say an increase of 6 on the wellbeing scale)
Then we can see this improvement in education is worth three times as much as an increase of
1000 euros. Indeed we could go further and put an actual value on it. (This of course is in addition to the impact education might have in increasing income).