Aarhus School of Business, Aarhus University

Master of Science in International Economic Consulting

Master Thesis
The Effect of Social Trust and Economic Growth Author: Lena Pfister
Academic Supervisor: Christian Bjørnskov
September 2010
Abstract:

In recent years, social trust has gained in importance within social science, especially
in an economic growth context. The thesis examines social trust as a potential
determinant of economic growth using a panel data set including 116 countries over
a time span from 1950 until 2005. The findings suggest a strong association between
social trust and economic growth and stay robust throughout a Jackknife exercise
and an extreme bound analysis implying that it is unlikely that these results are driven
by outliers or omitted variables. Reverse causation is ruled out by adopting an
instrumental variable approach. Further findings suggest that the effect of social trust
on economic performance depends, however, on the development level of the
country. Moreover, the analysis provides further evidence that human capital and
legal quality are indirect links through which social trust has an economic effect.
Finally, the thesis gives insight on the individual characteristics of the respondents
that answer the trust question in the affirmative.
Index

3.4 Jackknife exercise 33
3.5 Divided sample 34

4 Transmission channels 38
4.1 Human capital 38
4.2 Legal quality 40

5 Determinants of trust 44
5.1 GSS 46
5.3 WVS 50

6 Conclusion 54

Appendix 60

Figures and tables

IIFigures and tables

Figure 1: Social Trust and Log GDP
per capita
1950 23
Figure 2: Social Trust and Log GDP
per capita
2005 24

Table 1: correlation matrix PWT 25

Democracy Work: Civic Traditions in Modern Italy”. In his book, Putnam investigates
different regions in Italy with the same institutions and governmental structure and
tries to explain why there are nevertheless huge disparities in economic performance
between Northern and Southern Italy. His findings suggest that the economic
disparities are due to different endowments of social capital in the two regions.
Putnam’s work appeared to be a starting shot for social scientists to explore the topic,
since it subsequently enjoyed a surge in popularity. Today a wide literature can be
found and known journals like the “American Economic Review” and “Quarterly
Journal of Economics” publish articles on social capital. It was in the latter that Knack
and Keefer published their paper “Does Social Capital have an Economic Payoff?” in
1997. They were the first to examine different features of social capital separately in
a standard empirical growth framework, and they provided proof that social trust in
particular is positively associated with economic performance. Subsequently, more
papers have been written on the issue, however, relatively few compared to the size
of the social capital literature. Moreover, research within this topic has mainly been
performed on the basis of cross sectional data.
The aim of this thesis is to obtain further insight into the relationship between social
trust and economic performance and thus to contribute to a deeper understanding of
economic growth and social trust. To achieve this goal the analysis is based on a
panel dataset and thus more comprehensive than previous studies. The questions
investigated are:
1. Does Social trust influence economic growth? If so, how and to what extend?
2. Who does trust others?
The thesis is structured as follows: The first chapter gives an overview of the existing
literature and the current state of research. The concept of social trust is introduced
in detail and its measurement is discussed. Subsequently, an overview of social trust
within an economic growth context is given and direct and indirect links through
which social trust might have an economic effect are presented. Finally, the literature
on who trusts others is explored.
0 Introduction

Traditions in Modern Italy”. He looks into the question why some democratic
governments succeed and why others fail and aims at to contribute to the
understanding of the performance of democratic institutions. He concludes that their
success is based on their endowment of social capital, which he defines as “features
of social organization, such as trust, norms, and networks that can improve the
efficiency of society by facilitating coordinated actions” (Putnam, 1993: 167). Even
though there is no consistent definition for social capital, Putnam’s is the one most
referred to.
Today social capital is well established as a determinant of growth. So much that the
World Bank started a “Social Capital Initiative” in 1996 with the goal of further
investigating the formation of social capital and its impacts on project effectiveness
and development (World Bank, 2010).
However, there have also been discussions about whether to treat social capital as
an entity since its different features have different effects (Bjørnskov, 2006b). Several
papers have shown that the three pillars of social capital, namely trust, cooperative
norms and associations within groups have different or no effect on economic growth,
whereas social trust appears to be the most promising candidate (Knack and Keefer,
1997; Newton, 1999; Whiteley, 2000).
The first chapter is structured as follows: In section 1.1 the concept of social trust is
introduced in detail and the way to measure it is discussed. Subsequently, an
overview of social trust within an economic growth context is given in section 1.2.

1.1 Social Trust

When the concept of social trust gained popularity within social science there were
several discussions about what it consist of and how it can be measured. It became
apparent that it is very important to distinguish between two kinds of trust
(generalized and particularized trust). Social trust is mainly defined as generalized
trust, i.e. it measures how much people trust others about whom they possess no
information. This is opposing to particularized trust or reputation that is based on trust,

trust rather than social trust was measured.
In a recent study, Ostrom et al. (2009) repeat the experiment but ensure that the
game is played anonymously. They find that “the response to the survey question
regarding trust is highly significant and in the expected direction [positively
correlated]”, which confirms the validity of the trust question. Another study by
Sapienza et al. (2007) gets to the same result under the condition that the stakes of
the game are sufficiently high. By the same token, they show that trust and
trustworthiness are strongly related. In the trust game they find that “players
1 Overview of the literature

5extrapolate their opponent’s behavior from their own”, which means that people who
trust others are also more trustworthy.
In general, an increasing support for the trust question as a measure for generalized
trust, trustworthiness as well as a proxy for economically relevant beliefs can be
observed. The latter is covered in the next section.

1.2 Social trust and growth

Already Putnam associates social capital with economic performance. However,
Knack and Keefer (1997) were the first to examine different features of social capital
separately in a standard empirical growth framework (Bjørnskov, 2009a). In a cross
section of 29 countries they show that social trust and civic norms are positively
associated with economic performance whereas being a member of a network shows
no effect. Zak and Knack (2001) found, in conformance with Knack and Keefer’s
results that social trust is positively associated with economic growth, when they
repeated the study with a larger sample of 41 countries. Additionally, they show that
this relationship is causal, i.e. social trust promotes economic growth and is not just a

Contracts for example do not have to be as specified and cover every contingency
(La Porta et al., 1997). The probability of legal disputes decreases and therewith
reduces the deadweight burdens of enforcing and policing agreements (Whiteley,
2000). Besides, managers can save money on monitoring their employees since they
are more reliable in a high trust society. Moreover, Zak and Knack (2001) show that
social trust and the investment rate are positively correlated. They find that
investment/GDP share rises by nearly one percentage point for each seven
percentage point increase in trust. This might be due to two reasons. Firstly, property
rights are better protected in high trust societies. This increases the return to
investment in innovation and hence incentives to invest in new products. Additionally,
entrepreneurs have to direct less of their resources to protect themselves from
possible dishonest behaviour of their employees and business associates. Secondly,
since trust and trustworthiness create a safer investment climate, agents tend to
invest more and choose investment projects with a longer time horizon, which might
appear too risky in a low trust society. Long term financing is essential for the
infrastructure industry, which in turn is an important driver of economic growth.
In a nutshell, social trust decreases the costs of economic transactions and increases
the investment rate and therewith enhances the economy with a larger capability for
production.

1.2.2 Indirect effects of social trust on economic performance

In addition to the direct influence of social trust on economic growth elaborated
above, social trust might have a positive effect on the quality of institution and thus
indirectly on economic performance (Knack and Keefer, 1997; Whiteley, 2000).
1 Overview of the literature

7
with multiple social relations are more likely to be rewarded and sanctioned for their
behaviour and hence to comply with norms and trustworthiness. This decreases the
chance of their children dropping out of school. Coleman’s study was one of the first
to combine these two kinds of capital, yet, it his study is limited to particularized trust.
1 Overview of the literature

8Knack and Keefer (1997) find in their regressions that social trust influences human
capital accumulation positively. They base this on the idea that the return to human
capital increases in a high trust society, since there will be a higher weight on
educational credentials than on other factors that signal trustworthiness.
Bjørnskov (2009b) formalizes and explores this mechanism further and shows that
the association is causal. He points out that this is especially true for highly educated
employees as their tasks are often hard to monitor. Also, he stresses that hiring costs
in the labour market decrease in a high trust society, since employers rather hire and
trust than closely screen the applicants. Moreover, Bjørnskov points out that a
country needs to have a certain level of technological sophistication before this
mechanism starts to work, since a demand for highly educated workers has to arise
in the first place. In summary, lower costs associated with hiring educated employees
and lower monitoring costs for employers increase the demand for educated worker
in a high trust society and, hence, accelerate the accumulation of human capital.
This association is also detected by others studies but interpreted in a different way.
Alesina et al. (2002) find that trust is positively correlated with education and income.
They suggest that this can be due to the fact that individuals that are successful on a
professional level are more likely to trust others. Glaeser et al. (2000) find in their
sample that trust is higher among well-educated people for which they provide two
possible explanations. Firstly, educated individuals mainly socialize with other
educated that are more trustworthy. Hence, their expectation of trustworthiness gets

trusting people do not become trusting because they experience positive things.
They experience positive things because they are trusting. So people who are more
prone to trusting others have more positive experiences in life and are more likely to
succeed in educational institutions and later on in their professional lives (Uslaner,
2008; Bjørnskov, 2007).
2 Methodology and Data

102 Methodology and Data

2.1 Data description

The dataset that serves as a foundation for this thesis is an unbalanced panel. It
includes data of 116 countries from 1950 until 2005. It is presented for averages of
five years' sub-periods.
One of the variables of main interest is the measure of social trust. The social trust
scores are measured in how many percent of the population of a country answer the
trust question in the affirmative. They are obtained from the five waves of the World
Values Survey (WVS) conducted between 1981 and 2007. The WVS obtains data
from 97 societies covering 88% of the world population. Its goal is to capture
changing values and beliefs and hence provide a base for researchers to study the
impact of these on for example economic or political development. The survey
started out as the European Values Survey. That is why in the older waves data from
European and developed countries in general is dominating. However, the number of
countries investigated increased with the number of waves and most recent waves
cover a variety of countries providing a range from very poor to very rich countries.
To attain a bigger sample further trust scores are taken from the Afro Barometer,
Asian and East Asian Barometers, LatinoBarometer and the Danish Social Capital

Another source for trust scores is the general social survey (GSS), which is
conducted for the United States of America by the National Opinion Research Center
(NORC) of the University of Chicago on an annual base. It conducts basic scientific
research on the structure and development of American society by asking
demographic, behavioural, and attitudinal questions, plus topics of special interest. It
covers the years between 1972 and 2008 with only a few exceptions. The trust
scores are measured the same way as in the WVS. However, the GSS is applied for
individual level analysis, so social trust serves as a dependent variable taking on the
value 1 if the respondent trusts others and 0 otherwise. Moreover, other individual
characteristics are obtained from the WVS, i.e. education, income, religion and skin
colour. Education is measured in the highest year of school completed ranging from 0
to 20 with and average of 13 years. Income is measured in family income and divided
into 12 subgroups where the lowest income is less than 1,000 USD and the highest
more that 25,000 USD. About 5% of the respondents refused to answer the question.
Religiosity is measured in how often the respondent attends religious services. There
are nine different answers to choose from ranging from to “never” to “more than once
a week”. The dummy on skin colour indicates if the person is black or not. About 13%
of the respondents consider themselves as black, which is representative with the
American population. Also, age cohorts are generated dividing the respondents into 7
groups of equal size to control for the time period they were born in.
2 Methodology and Data

12Another important source for some of the key variables is the Penn World Table
(PWT), which is a set of national accounts economic time series. The variables are
provided in a common set of prices and currencies, which allows for a direct
comparison of the different countries. The newest version 6.3 covers 189 countries
over a time period from 1950 until 2007 with 2005 as base year. As a measure of

formal education, incomplete primary, complete primary, first cycle of secondary,
2 Methodology and Data

13secondary cycle of secondary, and tertiary (Barro and Lee, 2001). The values for
complete primary range from 0.4% in Benin in 1970 to 67.1% in the UK in 1960. For
complete secondary from 0% in Zimbabwe in 1970 to 47.5% in Austria in 1980.
Another factor that might determ economic growth is inequality. The gini coefficient is
obtained from the World Income Inequality Database, which is a compilation of
several gini coefficient sources, e.g. World Bank’s Deininger and Square database. It
includes 5313 observations from 159 regions or countries reaching more than 100
years back. It is, however, fragmentary. To make the coefficients comparable, only
those calculated on the basis of gross income and consumption based on a
representative sample covering all of the population are used. Moreover, a score of
6.6 is added to the consumption-based gini coefficient to make them comparable to
the income-based (Deininger and Squire, 1996). The coefficient ranges from 18.7 in
the Czechoslovakia in 1990 to 80.5 in Namibia in 1995.
Also, a measure for economic freedom from the Fraser Institute is applied. The
Fraser Institute publishes an annual with a measure of the degree of economic
freedom in 141 nations. It is constructed by using forty-two data points in five broad
areas: 1 Size of Government: Expenditures, Taxes, and Enterprises; 2 Legal
Structure and Security of Property Rights; 3 Access to Sound Money; 4 Freedom to
Trade Internationally; and 5 Regulation of Credit, Labor, and Business. The rating
takes on values between 0 and 10, with a higher rating indicating a greater degree of
economic freedom. The minimum value in the sample is 2.3 in Nicaragua in 1985, the
maximum value 9.08 in Honk Kong in1995 and a mean of 6.06. Moreover, the
measure for legal structure and security of property rights is applied by itself.
The dataset contains five regional dummies for the analysis on the country level,

Scandinavian countries, which are all above 60%. However, also most of the other
monarchies have a trust score above average compared to other countries in the
same region.
Also, Bjørnskov (2007) introduces the post-communist (postcom) dummy variable,
which takes on the value 1 if the respective country has been a Central or Eastern
European communist state and 0 if otherwise. The idea that the postcom dummy and
social trust are correlated is based on the dictatorship theory of Paldam and
Svendson (2001). Most communist countries had a suppressive government with an
intelligence apparatus with an enormous number of informants among the population.
With that kind of surveillance and potential danger to be discredited it seems only
natural to only trust people, who are close to you and that you have a lot of
information about. Thus, Paldam and Svendson reason that communism lead to
deterioration of social trust in East and Central Europe. In favour of this theory is the
example of Germany. When Germany was divided, Eastern Germany’ had a
repressive government that easily punished people that were accused to be political
dissidents and its secret service, the Ministerium für Staatssicherheit (Stasi),
observed its population in an unprecedented way. During its existence about 624.000
unofficial employers were engaged (Müller-Enbergs, 2008). This part of history
2 Methodology and Data

15reflects in the trust scores today. While Germany as a whole has trust score of 37.7%
Eastern Germany has only a trust score of 25.8%.
Another candidate for an instrumental variable is the pronoun-drop dummy variable
following Tabellini (2008). It takes on the value 1 if the personal pronoun in a
language can be dropped and 0 otherwise. In some languages the personal pronoun
is only used when the speaker intends to emphasize who is doing something but is
generally left out. In these cases the verb is normally conjugated so that it reflects the

t
=β
0
+ β
1
x
t1
+ β
2
x
t2
+ … + β
k
x
tk
+ u
t
, t=1, 2, …, n (3.1)
where y is the dependent variable and x
1
, x
2
, …, x
k
are the independent variables
that determine y. The error term denoted as u includes the factors that affect y other
than x
1
, x
2

, …, x
tk
)

is defined as a 1 x (k+1) vector for each t and

β=( β
0
, β
1
, …, β
k.
) is defined as a (k+1) x 1 vector for all parameters.
To ensure consistent estimators for pooled OLS the following assumptions are
sufficient:
2 Methodology and Data

17Assumption 1
E (x
t
’u
t
) = 0, t=1, 2, … , T. (3.3)
Assumption 1 holds if the error term u is uncorrelated with the independent variables
in the respective time period. It does, however, not imply information about the
association between x
s

|x) cannot depend on x and that Var(u
t
) has to be constant over time. 3.5b
holds if the conditional covariance of the errors is zero across different time periods.
However, for panel data it can not be expected that the observations are
independently distributed across time. Often, there are time-constant unobserved
attributes of the units that cause problems within the estimators, called the
unobserved effect c. These can be individual attributes, company attributes,
geographical location and so on, depending on the kind of observation unit. As
before, the idiosyncratic error u includes unobserved factors that change over time.
2 Methodology and Data

18Based on Equation 3.2 the unobserved effects model for a randomly drawn cross
section observation i takes on the following form:
y
t
= x
it
β + v
it
(3.6)
where
v
it
= c
i
+ u

effects estimation (FE) and random effects estimation (RE).
The main difference between these two methods is the basic assumption about the
relationship between the unobserved effect c and the explanatory variables. The
unobserved effect is called a random effect when there is no correlation and fixed
effect when those two are correlated. Or expressed in a formula:
Random effects
Cov(x
it
,c
i
) = 0, t=1, 2, … , T (3.10)
2 Methodology and Data

19Fixed effects
Cov(x
it
,c
i
) ≠ 0, t=1, 2, … , T (3.11)
However, the FE has one major disadvantage over the RE. It does not allow for
variables that are constant over time since the estimator subtracts the time averages
from the corresponding variable. As one of the key variables in the following analysis
is constant over time, namely social trust, FE can not be applied. Thus, only the RE
method is described further in the following paragraph.
2.2.2 Random effects
Using equation 3.6 as a starting point, RE imposes the following assumptions to
obtain unbiased and consistent estimators

is
has no partial effect on y
it
for s≠t, once x
it
and c
i
are
controlled for. Hence, the difference to pooled OLS is that the expected value of y
it

does not only depend on the explanatory variables but also on the unobserved effect.
Assumption 3.11b holds if c
i
and x
i
are orthogonal.
Equation 3.6 expressed for all time periods takes on the following form:
y
i
= X
i
β + v
i
(3.13)
The unconditional variance matrix of v
i
is defined as
Ω≡E(v
i

|x
i
,c
i
)=σ
2
u
I
T
(3.16a)
E(c
2
i
|x
i
)= σ
2
c
(3.16b)
Assumption 3a implies that the idiosyncratic errors u
it
have a constant unconditional
variance across t and that they are serially uncorrelated. Assumption 3b is a
homoskedasticity assumption on the unobserved effect. Based on this we can
assume that the variance matrix v
i
conditional on x
i
is constant
E(v

−
=
−−
=
(3.19)
which is applied in the analysis in chapter 3 and 4.

2.2.3 Logit estimation

In this section the logit model is introduced. Contrary to the fixed effect model, the
logit model is designed for limited dependent variables. The range of values these
2 Methodology and Data

21variables can take on is restricted in a fundamental way. This section only deals with
the case of binary independent variables, which implies that the independent variable
can only take on two values. In most of the cases these are 0 and 1. The binary
response models are about the response probability that takes on the following form:

P(y=1|x)=G(β
0
+β
1
x
1
+ … + β
k
x

j
, where g(z)
dz
dG
≡
(z) (3.22)
g is a probability density function, since G is the cumulative distribution function of a
continuous random variable. Also, G )(
⋅
being a logistic function has a strictly positive
slope. Therefore it follows that g(z)>0, which has the consequence that the sign of
the partial effect is determined by the sign β
j
, This means that the direction of the
effect of x
j
can be obtained before calculating the partial derivative.
To get the results, a maximum likelihood estimation is applied. Therefore, the density
of y
i
given x
i
is required:

F(y|x
i
;β)=[G(x
i
β)]
y