Tài liệu The Effect of Social Trust and Economic Growth pdf

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 unlik

Master of Science in International Economic Consulting

Master Thesis

The Effect of Social Trust and Economic Growth

September 2010

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

Index I Figures and tables II

0 Introduction 1

1 Overview of the literature 3

1.1 Social Trust 3

1.2 Social trust and growth 5

1.2.1 Direct effects of social trust on economic performance 5

1.2.2 Indirect effects of social trust on economic performance 6

1.3 Who trusts others? 8

2 Methodology and Data 10

2.1 Data description 10

2.2 Methodology 16

2.2.1 Basic assumptions 16

2.2.2 Random effects 19

2.2.3 Logit estimation 20

3 Econometric Analysis 23

3.1 Social trust and economic growth 23

3.2 Extreme Bound Analysis 29

3.3 Instrumental variables 31

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

Figure 1: Social Trust and Log GDPper capita 1950 23

Figure 2: Social Trust and Log GDPper capita 2005 24

Table 1: correlation matrix PWT 25

Table 2: PWT regression 1 26

Table 3: PWT regression 2 27

Table 4: EBA 30

Table 5: Sagran Hansen statistic 32

Table 6: Instrumental variables 32

Table 7: Jackknife exercise 34

Table 8: Divided sample 36

Table 9: Human capital 39

Table 10: Human capital, divided sample 39

Table 11: Legal quality 41

Table 12: Legal quality, divided sample 43

Table 13: Overview GSS, WVS 1 44

Table 14: Overview GSS, WVS 2 45

Table 15: Overview GSS, WVS 3 46

Table 16: correlation matrix, GSS 47

Table 17: Social trust, GSS 48

Table 18: correlation matrix, WVS 50

Table 19: Social trust, WVS 51

Table 20: Social trust, WVS, USA/Canada 53

0 Introduction

The notion of social capital started to develop throughout the 20th century It did not, however, have its breakthrough until 1993 when Robert Putnam published “Making 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

The second chapter describes the data used for the analysis Furthermore, the methodology applied is amplified In this context the random effects model, which is applied in the third and fourth chapter and the logit model, which is applied in the fifth chapter are introduced In the third chapter the effect of social trust on economic growth is investigated Additionally, to verify the robustness of the results, an extreme bound analysis and a Jackknife exercise are conducted Moreover, an instrumental variable approach is applied to control for endogeneity The fourth chapter further examines the association between social trust and economic growth To shed more light on how social trust might influence economic growth, the relationship between social trust and human capital and social trust and legal quality as two potential transmission channels are analysed In the fifth chapter individual level data is applied to find out more about the characteristics of the trusting citizen Moreover, it

is investigated if the grandparents’ trust levels still influence their grandchildrens’ trust today This could give more information about the stability of trust The analysis of this chapter aims to get a deeper understanding of social trust, which could be useful for policy makers In the conclusion, which is presented in the final chapter, the results of the analysis are summed up and evaluated

1 Overview of the literature

In 1993 Robert Putnam published the book “Making Democracy Work: Civic 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,

which in turn originates from previous experience or information obtained about others How important this differentiation is shows the paper from Alesina et al (2009) where they confirm Banfield’s (1958) theory of “amoral familiarsm” and show that there is a negative association between trust within the family and generalized trust

In recent years the following question has shown to be a good measure of social trust:

“Generally speaking, would you say that most people can be trusted, or that you can’t

be too careful in dealing with people?“ (trust question).This question originates from the German political scientist Elisabeth Noelle-Neumann who formulated it in 1948 It was adapted by for instance the General Social Survey (GSS) in 1972, the World Value Survey (WVS) in 1981 and the Barometers These are today the main sources for researchers within this area and the number of countries for which the data is available rises every year

Yet, the validity of the trust question was questioned on several accounts Glaeser et

al (2000) conducted an experiment with 189 students of the introductory economics course at Harvard University to investigate the validity of survey questions about hard-to-measure characteristics like trust and trustworthiness First the students had

to answer survey questions about trusting attitudes and trusting behaviour Subsequently, trust and trustworthiness were measured by experimental behaviour

by playing the Berg et al (1995) “trust game” Finally the two results were compared They concluded that “standard attitudinal survey questions about trust predict trustworthy behaviour in our experiments much better than they predict trusting behaviour” (Glaeser et al., 2000) However, there was a severe problem with the setup of the experiment They allowed students who knew each other to play together Their reasoning was that non-random pairing procedure generates more variation in social connections This had however the consequence that particularized 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

extrapolate 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 consequence of it Bjørnskov (2009a) provides an overview of the existing literature about social trust and economic growth in the “Handbook of Social Capital” He gives

an overview of several studies that find a positive association, which differs, however,

in size Bjørnskov calculates the average effect in these studies and concludes that

an increase of trust by 10 percentage points increases the annual GDP growth rate

by approximately half a percentage point

The following sections introduce direct and indirect effects social trust might have on economic activity

1.2.1 Direct effects of social trust on economic performance

As Knack and Keefer (1997) point out, in an economy many commercial transactions are determined by mutual trust between two or more parties This can be a transaction between parties where goods or services are supplied in exchange for future payments As within a company, managers have to trust their employees In general, the principle agent problem arises when there is asymmetric or incomplete

information Thus, need for trust rises with the extent to which the performed task is not monitorable, which is mainly the case for highly educated employees Also, investment and saving decisions are dependent on the assurance of banks and governments that these assets are protected The same is valid for the trust of agents that laws and rights will be abided or otherwise enforced like for example property rights

As outlined before, trust and trustworthiness are highly correlated (Sapienza et al., 2007) This means that the likelihood of dishonest behaviour in commercial transaction is lower in a high trust society since less money for protection is needed 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

The definition of institution varies across the literature Here Acemoglu’s definition is applied, who decides between economic institutions, “which correspond to taxes, the security of property rights, contracting institutions, entry barriers, and other economic arrangements” and political institutions, “which correspond to the rules and regulations affecting political decision making, including checks and balances against presidents, prime ministers, or dictators, as well as methods of aggregating the different opinions of individuals in the society “(Acemoglu, 2009, p 782)

Social trust seems to have influence on both kinds of institutions It has shown to improve the quality of formal institutions, legal quality and bureaucratic efficiency (Knack and Keefer, 1997; Knack, 2002; Bjørnskov, 2006a; Bjørnskov, 2009b), which

in turn influences the security of property rights As explained in the paragraph above, the probability of legal disputes decreases in a high trust society However, if they do occur laws can be enforced more easily This leads as well to a safer investment environment with higher returns and hence to a higher investment rate Trust has also shown to lower the corruption rate This is due to the fact that the necessary bribe increases with higher trust levels (Bjørnskov, in press)

Furthermore, social trust increases voter turnout and political participation Knack (1992) finds that the probability of voting increases by 8.6% if the person responds positively to the trust question This has the consequence that politicians have to account for their actions

Bjørnskov (in press) tries to get to the bottom the relationship between social trust and governance He investigates if the association is due to the political responsiveness to the demands of the voters or due to a higher supply of honest politicians His findings suggest that social trust affects economic-judicial governance but not electoral institutions

Also, social trust has shown to be influential on the accumulation of human capital Coleman (1988) was one of the first to establish this association He finds that social capital increases the accumulation of human capital through the family and the society Coleman argues that a higher endowment of social capital eases the transfer

of human capital from the parents to their children Moreover, he finds that families 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

Knack 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

in general confirmed Secondly, their education enhances them with a higher level of social skills and a higher status, which gives them a better opportunity to reward and punish others

1.3 Who trusts others?

After social trust gained popularity within social science, several relationships between social trust and other variables were investigated After finding out that high trust levels enhance for example economic growth and increase institutional quality, other questions came to light Where does social trust come from? What are people who trust others about whom they posses no information about like? And what, in turn, are those that do not trust others like? Can social trust be created?

Several studies find associations between social trust and individual characteristics like gender, age, race, income and education (Alesina et al., 2002; Demaris et al., 1994)

There are mainly two different ideas about how a person’s trust level is determined One states that if a person trusts or not depends on his surroundings and experiences he makes in his everyday life So this would mean that highly educated people with a higher income are more trusting because they have more positive experiences in life and have it easier, which in turn creates higher trust level (Glaeser

et al., 2000; Alesina et al 2002)

The other concept states that social trust is something that we learn from our parents and that is more or less determined after having obtained it or not if no traumatic experience occurs As a consequence, trust levels in countries are more or less stable over time and thus can not be actively increased by policies This means that 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

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 Project The basic assumption is that social trust is stable over time Bjørnskov (2007) points out that this is based on the idea that a person’s trust is established at a young age and therewith often a reflection of the parents’ trust through socialization Furthermore, it can be expected that an equilibrium like this is self-enforcing since trust and trustworthiness are highly correlated, meaning that an individual’s expectations get a reality check and are likely to alter if others do not behave as expected He repeats the stability exercise introduced by Volken (2002) and finds that social trust appears to fluctuate around stable levels Recently this gets further confirmation by studies that show that second and third generation immigrants’ trust levels correlate with those of their ancestors One of the first to show this was Uslaner (2008) who came to the conclusion that “where your ancestors came from matters more for trust than who your neighbours are now (p.7)”

On average there are about three trust observations per country, whereas there are more observations for richer than for poorer countries In the sample the average of all available observations is used and assumed to be the same for all years The trust scores range from a low of 3.4% in Cape Verde to a high of 64.3% in Sweden with the average trust being 25.5% A full list A1 of all the countries and their trust scores can be found in the appendix Other control variables that are taken from the WVS are education, religiosity and income of the respondent These variables are applied

in the analysis about the determinants of trust Education is measured on a scale from 1 to 8 reaching from “inadequately completed elementary education” to

“university with degree/higher education” Income is measured on a scale from 1 to

10 with 10 being the highest income Religiosity is a dummy variable that takes on the value 1 if the respondent considers religion as an important part of his life and 0 otherwise

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

Another 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 economic performance, the real GDPper capita is used Several variables that have shown to be determinants of growth are used to isolate the effect of social trust on growth The PWT table provides data on the consumption, government and investment share of GDP As an indicator for the openness of a country, the exports plus the imports are divided by GDP This is applied as a measure on how much a country is exposed to international competition and thus how big the incentives are to increase productivity Moreover, the population of the countries measured in 1000s is included in the dataset

Two of the control variables are from the Major Episodes of Politcal Violence (MEPV) data set, which is compiled by Monty G Marshall the director of the Center for Systemic Peace It covers major armed conflicts in the world over the period 1946-

2009, which are characterized in different types, e.g civil or ethnic conflict, inter-state

or intra-state Furthermore, the magnitude of the conflicts on the directly-affected society or societies is evaluated on a scale of 0 (smallest) to 9 (greatest) for the civil conflicts and on a scale from 0 (smallest) to 6 (greatest) for the international conflicts They serve as an indicator if economic activity was limited due to violent conflicts Further control variables are obtained from the World Bank’s World Development Indicators (WDI), which cover more than 900 economic, social and environmental indicators for 210 economies between 1960 and today Fertility rate and population density are used as potential determinants of growth The former is measured in births per woman ranging from 0.93 in Hong Kong in 2005 and 8.25 in Rwanda in

1980 with an average of 3.76 The population density is measured in people per square km with a minimum of 0.66 in Mongolia in 1965, a maximum of 6484.81 in Hong Kong in 2005 and an average of 176.15

As a measure of human capital, data from the Barro Lee dataset is employed It is available for 142 countries from 1950 to 1995 with projections to 2000, disaggregated

by sex and by 5-year age intervals Moreover, it accounts for the distribution of educational attainment in the population in, for most instances, six categories: no formal education, incomplete primary, complete primary, first cycle of secondary,

secondary 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, namely Europe, Asia, Africa, South America and North America to control for effects specific to regional differences For the analysis on the individual level based on the WVS six regional dummies are included, i.e Africa, Asia and the Pacific, Latin America, post-communist countries, the Middle East and North America They are binary variables taking on the value 1 if a country is from the respective region and 0 otherwise Furthermore it includes year dummies to allow for different intercepts across years and to capture the aggregate time effects

Finally, the dataset includes several variables that are candidates for instrumental variables, fulfilling the condition to be correlated with social trust but not with a measure of economic growth Bjørnskov (2007) suggests that social trust is positively

associated with a country being a monarchy, which might be due to the following factors Having a royal family is something that the whole population has in common despite their social background, which provides a national feeling and a strong sign

of unity It also might endow with social and political stability, since it is not as temporary as presidencies Furthermore, Bjørnskov points out that high trust countries like for example the Scandinavian countries and the Netherlands have a rather peaceful political history and that their royal families have been highly accessible to the public throughout history In Denmark, neither a leading politician nor a royal family member has been assassinated since 1284, which has been a different story for many other European countries This moreover indicates that social trust is rather stable over time

A first look at our data provides more evidence for this theory The average trust score of the 18 countries being monarchies in the sample is 40.5% whereas the average for the remaining countries is 22.8% It should be pointed out that the high average for monarchies is especially due to the trust scores of the three 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

reflects 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 grammatical person The idea is that in cultures where the language allows the pronoun-drop, the importance of the individual and his rights are relatively low (Kashima and Kashima, 1998), which in turn leads to a low trust level

Finally, the average temperature of the coldest month of the year is applied There is

a consensus that norms develop the best where their payoff is the highest This means that social trust has most likely developed best in countries were it had the highest payoff In countries where the winters are very hard, farmers were more dependent on each other in form of collective action and mutual insurance Today agriculture is far more advanced and not as dependent on the climate anymore, furthermore, the share of the population being involved in jobs that are related to the weather has shrunken to a minimum However, trust levels seem to be stable over time and to have survived the technological revolution that made trust less important

in the question of survival This would be an explanation of the north-south divide in trust levels in Europe, since the winters become milder with approaching the south

2.2 Methodology

The following paragraph describes the properties of panel data and the possibilities

and problems that arise in that context Furthermore, the estimation method of

random effects is elaborated Finally, the logit estimation model is introduced The

paragraph is based on Wooldridge (2002, 2006) if not stated otherwise

2.2.1 Basic assumptions

The starting point for the analysis is a multiple linear regression model as follows:

Yt=β0 + β1xt1 + β2xt2 + … + βkxtk + ut, t=1, 2, …, n (3.1)

where y is the dependent variable and x1, x2, …, xk are the independent variables

that determine y The error term denoted as u includes the factors that affect y other

than x1, x2, …, xk It can include omitted variables and measurement errors The

independent variables can be observed within the sample, whereas the goal is to

estimate the coefficients β1, β2, …, βk

A panel data set contains a time series for each cross-sectional unit in the data set

This means that the same units, which can be individuals, cities, firms, countries and

so on, are followed over a certain time period It gets obvious from equation 3.1 that

β is the same in each time period whereas x can but does not have to change over

time An i subscript can be added to refer to specific observations within the sample

and a t subscript refers to different time periods

For the remaining methodology part the equations are written in vector form

Equation 3.1 can now be expressed as:

where the vector xt =(1, xt1, …, xtk)is defined as a 1 x (k+1) vector for each t and

β=( β0, β1, …, βk.) is defined as a (k+1) x 1 vector for all parameters

To ensure consistent estimators for pooled OLS the following assumptions are

sufficient:

Assumption 1

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 and ut for s≠t In this case we speak of contemporaneous exogeneity instead of strict exogeneity, where the error term at each time is uncorrelated with the independent variables in each time period The latter condition

is required to obtain unbiased estimators

Assumption 3

) ,(E

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

Based on Equation 3.2 the unobserved effects model for a randomly drawn cross section observation i takes on the following form:

where

Again, it can be seen from the notation that x can change across i and t even though

it does not have to u changes across i and t, whereas c changes only over t

By definition, the conditional expected value of u given x is 0

There are several methods that can be applied to deal with this problem The two most common ones for estimating unobserved effects panel data models are fixed 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

Fixed effects

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

does not only depend on the explanatory variables but also on the unobserved effect Assumption 3.11b holds if ci and xi are orthogonal

Equation 3.6 expressed for all time periods takes on the following form:

Assumption 2

The first two assumptions are sufficient to obtain consistent estimators However, the serial correlation in the error term is not taking care of Therefore, Assumption 3 takes the unobserved effects structure of vit into account

assume that the variance matrix vi conditional on xi is constant

variables 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(β0+β1x1+ … + βkxk)=G(β0+xβ), (3.20) where 0<G(z)<1 for all real numbers z

In the logit model G takes on the logistic function, which ensures that P is strictly between 0 and 1:

The interest in this case does not lie in the effect of xj on y but on the effect of xj on

the response probability P(y=1|x) Since y rarely has a clear unit of measurement,

the maginitude of βj is not very useful To obtain the effect of xj on P(y=1|x) = p(x), its

partial derivative has to be calculated:

To get the results, a maximum likelihood estimation is applied Therefore, the density

For the whole sample it is expressed in the following manner:

L(β)=∑=

n

This log –likelihood is maximised by the maximum likelihood estimation of β, denoted

as βˆ The standard error, which is a k x k matrix is estimated as follows:

3 Econometric Analysis

This chapter presents the econometric analysis In the first paragraph, the relationship between social trust and economic growth is investigated Furthermore, the robustness of the results is tested in an extreme bound analysis, a Jackknife exercise and with instrumental variables Subsequently, the channels through which social trust might influence economic growth are examined, and finally the determinants of trust are further explored

3.1 Social trust and economic growth

To give a first impression of the association between social trust and economic growth, figure 1 plots social trust levels against the log of GDP in 1950 The positive correlation is obvious and it suggests that low economic performance of countries like the Philippines and Peru might be influenced by low trust levels, whereas the positive performance of countries like Finland and Denmark might be influenced by high trust levels Thailand and India appear to be outliers, which may be because of unreliable trust scores from these countries

Figure 1: Social Trust and Log GDPper capita 1950

ARG

AUS

AUT BEL

BOL

BRA

CAN

COL CRI CYP

DNK

EGY SLV

ETH

FIN FRA

GTM HND

LUX

MEX

MAR

NLD NZL

NIC

NGA

NOR

PAK PAN PER

PHL

PRT PRI

ZAF

ESP

SWE CHE

THA

TTO

TUR

GBR USA

UGA

URY VEN

The dataset at hand is an unbalanced panel with more observations in recent years Whereas data for only 51 countries is available in 1950, data for 116 countries is available in 2005 Figure 2 plots the same association as figure 1 but for the year

2005 with more observations:

Figure 2: Social Trust and Log GDPper capita 2005

ALB DZA

ARG

ARM

AUS AUT

AZE

BGD

BLR BEL

BEN

BOL BIH BWA

IRQ

IRL

ISR ITA

JAM JPN

LSO LTU

LUX

MKD

MDG MWI

MNE MAR

MOZ NAM

NLD NZL

NIC NGA

NOR

PAK

PAN

PRY PHL

POL PRT PRI

SGP

SVK SVN

TUR

GBR USA

UGA

UKR URY VEN

VNM

ZMB ZWE

The main model analysed is the following

Log GDPper capita = β0 + β1 Social Trust + control variables + u (4.1)

The dataset comprises data from different sources, whereas the two key variables GDP per capita and social trust are taken from the Penn World Table (PWT) and World Values Survey (WVS)/ Barometers respectively Adding control variables from sources that are not as comprehensive as the above mentioned leads to fewer

between additional information from control variables and less information due to fewer observations

Based on this consideration the strategy for the analysis is as follows The first regression contains only variables from the PWT and social trust in order to have a maximum of available observations Subsequently, further control variables are added in an extreme bound analysis (EBA) Afterwards, additional robustness tests

to control for the consistency and unbiasedness of the estimators are conducted The first regression with data only from the PWT and WVS contains the additional variables of government and investment share of GDP, openness and population

In order to give a first impression, the next table provides the correlation between the single variables:

Table 1: correlation matrix PWT Log GDPcap Social Trust Government Investment Openness Social Trust 0.4624 1.0000

Additionally to the variables above, the analysis includes year dummies for all the years except for 1950, which serves as the base year and regional dummies, where Europe serves as the base group:

Ln GDPper cap = β0 + β1 Social Trust + β2 consumption + β3 government+ β4 investment+β5 open+β6 pop + year dummies + regional dummies + u (4.2) Since the dataset at hand is panel data, the structure of the error term requires special attention as described in section 2.2 Therefore, the table below contains the

results from an OLS and random effects estimation A Breusch-Pagan test is subsequently conducted to determine the preferred estimation method:

Table 2: PWT regression 1 Dependent variable

to a decrease in economic growth Furthermore, it could also be the case that the causation runs both ways and that the government starts consuming relatively more money in times of economic recession The coefficient on the investment share of GDP is positive and highly significant, which is not surprising since investment is an important stimulator of economic growth Also the coefficient on the openness variable has a positive sign and is highly significant Again, the variable is calculated

as exports plus imports divided by GDP of a country Thereby it expresses the total

trade as a percentage of GDP Trade stimulates growth because it encourages a country to specialise in an industry in which it has a relative cost advantage, increases the market for local producers and eases the process of adapting new technology (Marrewijk et al., 2007) Moreover, it can be assumed that a country whose GDP has a high trade share has lowered or removed trade barriers like tariffs and quotas, which leads to cost savings and increases economic growth The last variable in the table is the population variable, where the sign on the coefficient is negative This is not surprising either, since India by far has the biggest population in the sample However, other factors like the fertility rate and human capital are taken into consideration in the EBA

Table 3 shows the coefficients on the year and regional dummies from the same regression:

Table 3: PWT regression 2

(-0.11)

0.067 (1.42)