COLLEGE OF EUROPE
BRUGES CAMPUS
EUROPEAN ECONOMICS DEPARTMENT
IN TRADE WE TRUST
The EU home bias puzzle in trade
Supervisor : Thierry Mayer
Thesis presented by
Anya Margaret Ogorkiewicz
for the
Degree of Master of European Studies
Academic Year 2005-2006
Statutory Declaration
“I hereby declare that the thesis has been written by myself without any external
unauthorised help, that it has been neither presented to any institution for evaluation nor
previously published in its entirety or in parts. Any parts, words or ideas, of the thesis,
however limited, and including tables, graphs, maps etc., which are quoted from or
based on other sources have been acknowledged as such without exception.”
11, 547 words.
ii
Abstract
It is this paper’s contention that the lack of trust between EU countries explains in part
the invisible barrier that impedes intra-European trade.
An initial summation of the effects both trust and distance have on international trade
leads to a discussion of the literature surrounding the phenomenon Obstfeld and Rogoff
(2000) dubbed, “the home bias puzzle”. Trust is the oft-ignored underlying variable in
economic science. Paramount to economic transactions, trust reduces transaction costs
and abridges distances in international trade. Before empirically investigating the
effects of the home bias in European bilateral trade flows, we appraise the literature
portending to the effects cultural variables have on international trade flows.
The analytical chapter assesses the gravitational pull of the Eurozone. It opens on the
theoretical trade framework of the gravity model, which we use in our empirical
strategy to analyze the European home bias in trade. Consistent with Nitsch’s (2000)
study, we calibrate the base gravity model with variables indicating bilateral trust,
average origin country trust and average destination trust. After running OLS, GLS,
2SLS regressions on two different databases we conclude that the levels of trust
Europeans have towards each other do influence the seeming deficiency of interMember State trade in the EU.
If one is to consider mistrust as a market imperfection problem, our study indicates a
potential scope for EU-level social policies in the name of consolidating the Single
Market.
iii
Keywords
Home Bias
Trust
Gravity Model
European Union
International Trade
iv
Table of Contents
Statutory Declaration .................................................................................................... ii
Abstract .......................................................................................................................... iii
Keywords ....................................................................................................................... iv
1
Introduction ............................................................................................................ 1
2
Breaching the Distance - Trading in Trust .......................................................... 3
2.1
Trust and Distance ........................................................................................... 3
2.1.1
Trust: the underlying variable in economic science ................................ 3
2.1.2
What is Distance? .................................................................................... 7
2.2
3
4
The home bias puzzle, a descriptive approach ................................................. 9
2.2.1
Definitions and Determinants ................................................................ 10
2.2.2
Is the home bias overstated? .................................................................. 11
2.2.3
The impact of trust, culture and institutions on trade flows .................. 15
The Gravitational Pull of the Euro zone ............................................................ 18
3.1
The Underlining theory of the Gravity Model ............................................... 18
3.2
Empirical Strategy ......................................................................................... 20
3.2.1
Data Analysis ......................................................................................... 20
3.2.2
Estimation .............................................................................................. 25
3.2.3
Interpretation .......................................................................................... 27
Conclusion ............................................................................................................ 34
Bibliography ................................................................................................................. 35
Annex 1: Trust Matrix................................................................................................. 39
Annex 2 : Descriptive Statistics .................................................................................. 43
Annex 3: Estimation Output ....................................................................................... 45
v
« It can be plausibly argued that much of the economic backwardness in the world
can be explained by the lack of mutual confidence »
Kenneth Arrow
“Gifts and Exchanges” Philosophy and Public Affairs, 1972, p. 357
“Borders? I have never seen one but I heard they exist in the minds of some
people.”
Thor Heyerdahl, explorer and archaeologist
vi
1 Introduction
« Nous ne coalisons pas les Etats, nous unissons les hommes »1 declared Jean
Monnet at the dawn of the European Union. Half a century later, Europeans are united
by a single currency, a single market and a judicial system that punishes deviation from
the principle of an “ever closer Union” enshrined in EU law. Scholars still detect
however, trade frictions in the EU in the absence of borders, tariffs, and exchange rate
risk. This issue forms the subject of this paper.
This invisible trade barrier was first termed as the “border effect” to emphasize
the cross-border aspect of the phenomenon that impedes trade over national frontiers.
Empirical studies illustrate that, given both their level of economic activity and the
distance separating trading partners, countries tend to trade less with foreign lands than
expected. Subsequently scholars have enlarged the scope of this puzzle, calling it the
”home bias effect”, which implies that countries may prefer to trade with culturally and
institutionally similar countries. High stakes are involved in resolving the home bias
puzzle: if the cognitive distances between countries can be shrunk, the subsequent
increase in bilateral trade would benefit all trading partners.
This paper investigates the bilateral trade patterns within the EU15 – the 15
European countries that participate in the European Single Market Programme and
share a common currency, eliminating from the outset two “natural” causes of the home
bias effect: exchange rate risk and national tariffs. These and similar rationalizations
fall in line with today’s orthodox theories. It is this paper’s contention however, that
“transaction cost economics” is more adept at assessing the credence of the explanation
we propose: trust or the lack thereof. This paper seeks to determine whether it is the
lack of trust between EU countries that explains at least in part the invisible barrier that
impedes intra-European trade.
The first chapter examines the definitions and determinants of both trust and
distance as they relate to trade, before introducing the home bias puzzle. Beyond the
puzzle’s definition and “common causes”, it is necessary to investigate the literature
assessing its impact as well as the literature arguing that the home bias puzzle is wildly
overstated; before then reviewing the relevant literature concerning the impacts of trust
and culture on international trade flows.
1
Discours Washington, 30 avril 1952
1
The second chapter comprises an analytical investigation of the effects of trust
on bilateral EU15 trade levels, by first specifying the basic structure of the gravity
model, the underlining theory; and the relevant equations. After describing our
empirical strategy, the paper concludes with an interpretation of the results obtained.
2
2 Breaching the Distance - Trading in Trust
Our main inquiry relates to the extent that trust may breach the distances
between trading partners. The first section begins by defining the two most important
variables to our study: trust and distance. The second part introduces the home bias
puzzle through the framework of transaction cost economics, and is concluded by an
overview of previous literature studying the impact of cultural variables, such as trust,
on international trade flows.
2.1 Trust and Distance
To quote Kenneth Arrow (1972), “virtually every commercial transaction has
within itself an element of trust, certainly any transaction conducted over a period of
time.”2 International trade is so rife with unforeseen and insecure contingencies that,
without a certain degree of reciprocal trust, trade between two dissimilar parties would
surely be much lower. Trust is however, rarely taken into account in orthodox trade
theories. Rather, it is often abusively assimilated to cultural priors which cannot be
easily assessed through orthodox theory. Trust must therefore be disentangled from
cultural variables, before it is possible to analyze the underlying notion behind trust or
lack thereof. If individuals trust each other, it is because there is a certain affinity or
connection between them that is conditioned by the subjective distance, agents judge
between themselves. The notion of distance therefore, constitutes a natural bridge
between the concept of trust and that of the home bias puzzle.
2.1.1 Trust: the underlying variable in economic science
What is trust? According to the Merriam-Webster Dictionary, trust is defined as:
(1 a): an assured reliance on the character, ability, strength, or truth of
someone or something. 3
Trust is a bilateral concept that refers to a subjective relationship between two
or more agents. In economics, trust is paramount to economic transactions. According
to Sandelien (2003) economic trust is “one agent’s sentiments of expectations towards
another agent’s positive behavior with respect to oneself, in a situation involving
2
3
Kenneth Arrow, (1972). Gifts and Exchanges. Philosophy and Public Affairs, p. 357
http://www.m-w.com/
3
risk.”4 It is, for example, the determining element of the confidence individuals have in
their money, that “businesses must believe that their banks look after their money.
Individuals must be convinced that the government will protect their property right.
These – and many other types of trust – are prerequisites for a modern society”. 5
Bornhorst et al. (2004) touch the essence of economic trust when they state that
preceding each economic transaction, agents must first select a transaction-worthy
partner. This choice of partners is notably determined by “the agent’s beliefs about the
prospect of building trust and reciprocity with potential partners [and] experience will
play a role as well.” 6
In behavioral economics, trust is depicted in a game with multiple interactions,
where the agents discount their future gains by a certain factor. The rational explanation
for trust is that “individuals are inclined to trust each other due to pay-offs in the long
run.” 7 Both agents use the “trigger strategy,” where they choose to cooperate up to the
point where one of the agents fails to do so, switching both agents’ strategies to noncooperation. The frequent interaction aspect of this game portrays a learning curve
where trust increases with each period. Without an initial level of trust however, the
interaction cannot begin, which suggests that a different source of trust exists.
The sociological model of trust by comparison, strives to explain the
preliminary faith individuals show towards each other. Trust is viewed as a public good
that is reflected in the individual’s identity, whether cultural, moral or based on social
obligations. In behavioral economics, agents that display this type of trust are altruistic,
for an altruistic agent increases his welfare by increasing the welfare of the other
agents. The norms and values that guide the individual’s decision are based on a certain
lack of diversity amongst players. Such trust exists when a player identifies with the
other’s ethic, religious, political, familial, (etc…) insignia and acts accordingly.
Bornhorst et al. (2004) recognize that diversity has a “substantial impact on
agents’ initial beliefs regarding partners as well as the evolution of their interactions
over multiple transactions.” 8 This diversity, reflected through a person’s cultural
affinity, determines the initial level of trust.
4
Sandelien (2003) p. 3
ibid. p.1
6
Bornhorst et al. (2004) p.1
7
Sandelien supra note 4
8
Bornhorst supra note 6
5
4
2.1.1.1
Hofstede’s four cultural dimensions
Linders et al. (2005) note that cultural distance is conventionally assessed
through Hofstede’s (1980) four dimensions of national culture. 9 Hofstede (1980)
analyzed survey data from 116,000 IBM employees in 40 countries and ranked each
country on a scale from 0 to 100 indicating how people from different cultural
backgrounds perceive the following four societal dimensions:
(a) Power distance –How different people accept an unequal distribution of
power and status as a way of organizing social systems.
(b) Uncertainty avoidance – The degree to which people are willing to trade a
high risk/high reward situation for low risk/ low reward. It signifies “the
extent to which people are uncomfortable with uncertain, unknown or
unstructured situations.” 10
(c) Individualism versus Collectivism – A society’s emphasis on the role of
individualism and collectivism: notably experienced through the type of
wage bargaining in place in a given society.
(d) Masculinity versus Femininity – The extent to which a society assesses the
importance of one type of stereotypical value over another.
Hofstede’s (1980) measures of cultural distance rely on the degree of
uncertainty avoidance that characterizes a given population. His uncertainty avoidance
index (UAI) – previously employed in assessing trade flows for perceived cultural
distances – may explain the degree of trust underlying international trade patterns.
2.1.1.2
Sandelien’s five factors that determine trust
Sandelien (2003) identifies five factors that determine trust and regroups the
factors in three categories: predictability, controllability and interdependence. Within
the first category, he cites two factors: (a) reputation/history of interaction/information
and (b) similarity/identity/moral bonds.
(a) Reputation: refers to a history of past interaction that provides information
about the other party’s consistency over a long period of time. Trust can be
Linders et al. (2005) note that Hofstede had subsequently uncovered a fifth cultural dimension, “longterm orientation” but due to the difficulty of empirical applicability and scholars questioning its added
value, it has been left out of our discussion of cultural dimensions.
10
ibid. pp. 3
9
5
institutionalized through a middleman acting as a “bridge of trust”11 where
the two agents trust the middleman more than each other.
(b) Similarity: of ethnicity, religion, social belonging, and gender are
“subjective sentiments of perceived equality”12. This trust -inducing factor is
based on the assumption that someone similar will not act opportunistically.
In essence, a similarity in religion or social belonging may outwardly signal
that certain norms and values are shared, which in turn may create an initial
level of trust. As an example, Sandelien (2003) cites the Japanese business
culture where “a history of trusting business norms [established] a ‘culture
of trust’ in the culture pattern.”13
The controllability category consists of the dyadic aspects of contracts and
sanctions.
(c) Contracts/sanctions:
determine
trust
by
establishing
control
and
transparency, most notably when sanctions are automatic and non-biased
towards either party.
The final category of interdependence presents two final variables:
(d) Altruism: The situation in which one party has interest in the well-being of
the other party: often present in family relationships. According to the
author, emphasizing common goals can also be assimilated to this notion.
(e) Asymmetry: One party has an upper hand or outright power over the other
party. Sandelien (2003) doubts whether asymmetry is a determinant factor
of “trust” despite it leading to cooperation, since asymmetry corresponds
rather to a situation of fear or bondage towards the stronger party.
The notion of trust thus overlaps cultural variables, since it draws from both
rational and sociological sources. Orthodox theory struggles with the concept of trust,
since the neoclassical world operates on the strong assumption that transactions are
costless actions, and market imperfections are at most ephemeral in nature. According
to Sandelien however, mistrust is a market imperfection problem, and one that cannot
be resolved by equilibrating supply and demand.
11
Sandelien, supra note 4, p. 7
ibid.
13
ibid.
12
6
Building on Coases’s (1937) foundations that firms were created in order to
internalize transaction costs, Willliamson (1998) developed the theory of transaction
cost economics: the “science of contracts”. Since contracts are inevitably incomplete,
trust can complete contracts by diminishing transaction costs. How does trust diminish
transaction costs?
Search costs, the costs of matching supply and demand, can be diminished if a
company operates within a trustworthy network of business relations, or if it trusts in
another’s reputation. Trust reduces negotiation costs, the costs of concluding a contract,
because when the two parties to the contract trust each other, their contracts need to be
less exhaustive on prices, qualities and sanctions. Furthermore, it diminishes control
costs, the costs of monitoring an agreement, when a trusting relationship between
buyers and sellers guarantees quality, delivery time, etc. Trust as reflected in
Sandelien’s three categories acts as a safeguard against opportunism, and breaches the
cognitive distance that separates agents involved in an economic transaction. Distance
is indeed an essential concept to our paper; after assessing this notion as a
multidimensional concept we will discuss whether distance has been abridged in a
globalized world.
2.1.2 What is Distance?
Both the impact of trust and the existence of a home bias are conditioned on
distance, whether perceived or real. Distance is however, a broader concept than
Hofstede’s uncertainty avoidance index suggests, with subjective aspects present within
all notions of trust, the home bias puzzle and the gravity equation that will be used to
estimate the effect of trust on the home bias puzzle.
2.1.2.1
Distance - a multidimensional concept
Distance is a fluid concept. For instance, international metropolises are closer to
each other than to secondary cities despite the actual distance covered “as the crow
flies”.14 Beyond actual mileage, Gatrell’s (1983) classification distinguishes between
four different types of distances:
-
Time Distance: the time required to travel between two entities;
14
An example of which is the distance between Paris and London as compared to Paris and Strasbourg.
Respectively 343 and 397 kilometers separate these cities, and yet by train it takes 2 hours 30 minutes to
reach London from Paris as compared to the 4 hours and 30 minutes it takes to reach Strasbourg from the
same point of origin, and this despite London being separated by borders both political and geographical.
Sources: http://www.eurostar.com/ and http://www.voyages-sncf.com/
7
-
Economic Distance: the costs of traveling from one area to another;
-
Cognitive Distance: the subjective perception of distance; and
-
Social Distance: the distance between networks or social classes
could be assimilated to Hofstede’s cultural distance.
These four types of distances, independent of actual longitude and latitude, can
thus be expanded or contracted according to both perceptions and technological
advances, such as transportation improvements. The next paragraph examines distance
reducing factors and whether globalization has had an impact on cognitive distances.
2.1.2.2
Do networks and ICT contract distances?
Travel time and cost influence the cognitive perception of distance - and thus of
trust - by making foreign entities physically more accessible. Two other relevant
variables however, influence the perception of distance: the cultural networks existent
in the foreign land and the penetration of information and communication technology
(ICT) – both consequences of globalization.
Rauch (2001) argues that unlike standardized goods bought on organized
markets, the price of differentiated goods, of variable quality, has to be negotiated. The
risks of moral hazard are substantial, raising the stakes in choosing the appropriate
trading partner. National and regional institutions reduce costs associated with
researching business opportunities and potential partners. In global environments
however, where language and cultural barriers subsist, ICT is said to further diminish
information costs. However, the Internet cannot yet check if your chosen partner is
trustworthy. Leamer and Storper (2001) suggest that the Internet makes long-distance
“conversations” not “handshakes” – in sum, ICT can’t start business relationships but
can only maintain them.
Brun et al (2002) tested the idea that communication only strengthens already
existent relationships, and found that, long distance trade between new partners is
unaffected by ICT. More specifically, they observed that distance is increasing between
poor countries, but elasticity to trade between rich countries is decreasing.
Globalization is therefore marginalizing poor countries, whereas it reduces the
cognitive distance between rich countries. One solution is to induce trade within
cultural networks where risk of opportunism is lessened.
8
According to Rauch (2001), “an economic network can be defined as a group of
agents that pursue repeated and enduring exchange relations with one another.” 15
Trade within co-ethnic networks, such as those spread through globalized migration,
heavily depend on two key factors: information and trust. Information flows concerning
markets, participants and the trustworthiness of external partners to the network, reduce
search costs and allow for international arbitrage. Trust is determined through frequent
interactions, knowledge transfers about past conducts, and various forms of conduct
monitoring. Network-maintenance however, demands frequent interactions and broad
information channels. Consequently, less interaction and poorer information, due to the
breadth of distance separating partners in the same network, reduce trust levels.
Furthermore, studies have discerned that whereas within networks trust is increased,
between co-ethnic networks, the sentiment of trust is lower. This is because co-ethnic
networks are predominately local in character, indicating that instead of increasing
trade over long distances they may actually inhibit it.
If the distance effect were substantially affected by communication and
technological improvements, then empirical studies would show that the distance
elasticity has reduced over time. Yet Rose (1999) demonstrated that despite significant
technological progress, the elasticity of distance to trade has not significantly
diminished over the last twenty years.
Neither ICT nor trade within established networks significantly bridge the
distance between trading partners. From the viewpoint of empirical investigation,
distance is a difficult concept to gauge. The CEPII database makes different measures
available for indicating distance between national borders, distance between capitals,
and the distance between the two largest cities, conditioned or not by the percentage of
urban population. The correct measure of distance is essential when studying the home
bias effect.
2.2 The home bias puzzle, a descriptive approach
Trefler (1995) and McCallum (1995) have demonstrated the breadth of the
home bias phenomenon. Obstfeld and Rogoff (2000) identified it as a major puzzle in
economics today, and Guiso, Sapienza, and Zingales (2004) initiated work on the effect
trust and culture have on the home bias. We will initially define the home bias and
15
Rauch (2001) cited in Sandelien supra note 4
9
provide the most common justifications for it, before reviewing the afore-mentioned
studies.
2.2.1 Definitions and Determinants
2.2.1.1
Definitions
Deardorff's Glossary of International Economics defines a “home bias” as “a
preference, by consumers or other demanders, for products produced in their own
country compared to otherwise identical imports.”
16
Trefler (1995) conceived the
term, proposing it as an explanation to his “mystery of missing trade” where he
observed that the amount of trade is far less than predicted by the Heckscher-Ohlin
model, particularly regarding trade in factors of production. An assimilated notion to
the home bias is the “cultural bias”, which indicates the consumers’ preferences for
products produced in their own cultural sphere. Guiso, Sapienza, and Zingales (2004)
define “cultural biases” as “the role customary beliefs have on trust”17, culture being, to
paraphrase Einstein, “the collection of prejudices acquired by age eighteen18”.
Obstfeld and Rogoff (2000) identified the “home bias puzzle” as one of the six
major puzzles in international finance today, sparking a debate attempting to explain
the phenomenon. Before assessing whether the home bias has been overstated, it is
necessary to review its common explanations.
2.2.1.2
Determinants of home bias
Hillberry (2000) offers four possible explanations for the home bias effect
between the U.S. and Canada that can be extended to account for all instances of home
bias. He first suggests that the home bias effect may have its roots in the hidden costs of
trading internationally, which are individually too minute to be taken into account but,
when aggregated, could amount to a substantial barrier to trade. These additional
expenses may include exchange rate costs, transportation costs and the cost of
additional administrative work. This implies a large scope for aggressive governmental
action in terms of coordinating regulatory, transportation, monetary policy issues, etc.
The plausibility of this explanation is nevertheless quite low since this hypothesis is not
borne out by empirical testing: “ it appears that hindrance costs would have to have an
16
Available at http://www-personal.umich.edu/~alandear/glossary/
Guiso, Sapienza, and Zingales (2004) p. 3
18
ibid. p. 2
17
10
effect equivalent to a tariff of between 60 and 200 percent. These costs seem
implausibly large, given that they appear to have escaped detection (…)”.19
The second justification draws from the theory of comparative advantage that
illustrates trade between two countries. In the case of the U.S. and Canada, these two
similar-good-producing entities don’t trade with each other because there is an
insufficient amount of mutually advantageous trade to be captured. The home bias
therefore, isn’t a harmful aspect of international trade, but a natural one that may arise
between any similar good producing countries.
The third explanation states that home bias exists due to the fact that the
composition of international trade is different from that of domestic trade. This strikes
at the heart of econometric models that study the bias, since the core assumption of the
gravity model is that countries export the same basket of goods. Here we’re considering
the case where the composition of the basket changes. This explanation has received
considerable empirical support. Hillberry relates the main findings, notably that the
composition of trade does depend on the existence of a border, the distance between the
regions and the “output composition” in the destination region.
Finally, Hillberry touches upon the explanation which is of the most interest to
us: The home bias may exist because consumers have national preferences and they
may thus be willing to pay a premium in terms of transport costs - non negligible in
Canada - to buy from distant countrymen than to buy from nearby foreigners (for
reasons which are not taken into account by orthodox economic theory). For
policymakers, this implies a limit to the competition domestic producers face from
imports, since the price differential must be substantial for consumers to substitute
domestic products for imports. Hillberry notes that cultural preferences may hold true
for retail consumption, but for wholesalers or manufacturers, one expects them to be
less sensitive to the existence of cultural preferences that prevail beyond the border.
Beyond these four explanations there exists a fifth explanation, considered
below: what if that the home bias is greatly overstated?
2.2.2 Is the home bias overstated?
After McCallum’s (1995) study on trade levels between Canada and the USA,
many have countered his results by attempting to prove that his bias was overestimated.
19
Hillberry, (2000)
11
As Hillberry notes, if McCallum is correct, such a barrier to trade is equivalent to a
tariff between 60 and 200 percent, a tremendous obstruction to trade. Before assessing
whether the home bias effect is overstated, it’s necessary to review McCallum’s
pioneering work.
2.2.2.1
The home bias effect
Home bias effect is estimated by means of the “gravity model”, which relates
the volume of bilateral trade to the distance between two countries and their economic
size.
At the dawn of the North-American Trade Agreement, which allowed a free
movement of goods between Canada and the US, McCallum, using 1988 data for ten
Canadian provinces and 30 US states, obtained the following regression, standard errors
being in parenthesis:
(1)
log ˆ( xij )  1.21log( y i )  1.06 log( y j )  1.42 log( d ij )  3.09border
(0.03)
(0.03)
(0.06)
(0.13)
Where x ij is the volume of bilateral exports, yi is the GDP of the region of
origin, y j is the GDP of the destination region, d ij is the distance between region, and
border is a dummy variable indicating whether the two trading entities share a common
border.
His results indicate that when the GDP of one of the trading partners increases
by 1%, the trade volume increases more than proportionally. As expected, sharing a
common border increases trade (by 851%). Moreover, two regions distanced by 500
miles, trade 267% more than if 1000 miles separated them, ceteris paribus. Controlling
for trade amongst entities themselves (ie: Canadian province with Canadian province,
or American state with American state) McCallum finds that “trade between two
provinces is more than 20 times larger than trade between a province and a similar
sized state, each pair having the same distance [separating them].”20 While the idea
that national borders impeded international trade is not surprising, the magnitude of the
border effect most certainly is. Obstfeld and Rogoff (2000) have hailed this observation
as “a dramatic suggestion of segmentation.”21
20
21
Sundelien, supra note 4, p. 26
Obstfeld, Rogoff (2000) p. 342
12
Following McCallum (1995), Wei (1996) reconstructed national trade data for
OECD countries (to account for the “border effect”) by defining national trade as the
difference between national production and exports. His estimations found that
countries trade just 2.5 times more with themselves than with others.
Helliwell (1998), using Wei’s definition of home trade, re-estimated
McCallum’s (1995) regressions however, with data from 1988 to 1996, and found that
trade between two provinces remains approximately 12 times larger than trade between
a province and a similar sized state.
Nitsch (2000) estimates the home bias by specifying a dummy variable “Home”
that takes the value of 1 for intranational trade flows and zero for international flows.
He recalibrates McCallum’s (1995) regression with data for bilateral trade in the
European Union and obtains the following regression:
(2)
log (ˆ xij )  1.92 Home  0.67 log( yi )  0.71log( y j )  1.07 log( d ij )
(0.20)
(0.04)
(0.04)
(0.08)
Unlike McCallum, he finds that a 1% increase in GDP of a trading partner
increases the trade volume by less than 1%. He finds however, the European home bias
to be extensive: within the European Union, “a country exports about 6.8 (=exp[1.92])
times as much to itself as it exports to a foreign country of similar economic size and
distance”. 22 Adding dummy variables to measure border adjacency and common
language, his regression becomes:
(3)
log ˆ( xij )  2.59Home  0.74 log( yi )  0.75 log( y j )  1.82 log( d ij )  0.42 Adjacency  0.48language
(0.23)
(0.04)
(0.04)
(0.09)
(0.16)
(0.25)
… indicating that after controlling for a common language and border, the home
bias effect in the EU jumps: a given country exports about 13.3 (=exp[2.59]) times as
much to itself as it exports to a foreign country of similar size and distance that does not
share the home countries language or an adjacent border.
The subsequent sections of this paper build on Nitsch’s findings, with particular
emphasis on his observation that such an important home bias effect exists within such
a highly integrated trading entity as the European Union. However, many scholars
22
Nitsch (2000) p. 1099
13
argue that the home bias has been overstated by either mismeasurements or poor data
analysis. Their analysis contributes to the scholarly debate about the home bias puzzle.
2.2.2.2
How is the home bias overstated?
In addition to contributing to the methodology 23 , Wei (1996) specifies a
“remoteness” variable, taking into account the relative distance of trading partners,
capturing third country effects 24 . Wei (1996) finds that the border effect between
OECD countries does exist, although it is of a magnitude of 2.5 – considerably lower
than McCallum’s results.
Anderson and von Wincoop (2001) include Wei’s remoteness variable in their
study, as well as a “mutual resistance variable”, i.e. the respective price levels that are
dependant on trade barriers such as tariffs. They argue that economically smaller
countries, such as Canada, are subject to bigger border effects than large economies for,
unlike large economies, a small variation in international trade can lead to a large
difference in trade within the small country. They find that a 1% drop in international
trade leads to a 60 % increase in Canadian inter-province trade, but only to a 25%
increase in American inter-state trade. They challenge McCallum’s results, alleging that
they were due to an inherent bias, brought on by the variables he omitted, and the
relative small size of the Canadian economy as compared to the American one.
The measure of distance plays a central role in home bias estimations: Distance
is used in the gravity model as a proxy for freight, time, and other costs. Moreover, the
border effect is sensitive to both internal and external distance specification. Head and
Mayer (2002) recapitulate the measures employed in recent literature and argue that
distance mismeasurements overstate the home bias phenomenon.
To measure internal distances, Wei (1996) uses one fourth of the distance to the
nearest neighbor. This type of measure relies on the distance from one country’s
economic center to another25, and was strongly criticized in Nitsch (2000) who argued
that it assumed that capital cities are of the same distance to their respective borders.
Instead, Nitsch proposes an area-based measure based on the radius of a disk.
Moreover, scholars have also defined distance as sub-unit based weighted averages,
taking natural geographical units, and weighting them by either their population or their
wealth.
By constructing a border effect that doesn’t rely on national data.
ie: taking into account if a bigger trade entity is closer.
25
An economic capital being a capital city or one with a concentrated amount of economic activity.
23
24
14
Head and Mayer (2002) conclude that the closer the nations are to each other the
more these existing measures inflate distances. They estimate a new distance, a
variation on the “sub unit based weighted averages” measure, in an attempt to
neutralize the border effects. Their findings indeed shrink the border effect, notably in
the EU during the years 1993-1995 where they find a border effect of 3.12, but they do
not succeed in eliminating it, proving the point that although all measures of distance
suffer from “distance inflation”, the border effect is not illusory.
While border effects therefore, do exist, their exact scale is subject to scholarly
debate. If the European Single Market Programme, sans tariffs and exchange rate risks,
is nonetheless subject to trade frictions, scholars have begun testing cultural hypothesis
such as the effects of public opinion, of cultural distances and of trust as explanatory
variables for this seeming lack of European trade.
This paper ultimately seeks to analyze the effect of trust on inter-European trade
levels. Guiso, Sapienza, and Zingales (2004) find that trust has a robust impact on
European trade flows. The concluding section overviews the relevant literature,
concerning the effects trust and other cultural variables have on international trade
flows.
2.2.3 The impact of trust, culture and institutions on trade
flows
Culture seems at first a vague notion: difficult to measure and difficult to
empirically test. The causality between culture and economics was also deemed hazy; it
was assumed to fluctuate over a lifetime, or at least alter according to personal
experiences. Becker (1996) noted however: “Because of the difficulties of changing
culture and its low depreciation rate, culture is largely a ‘given’ to individuals
throughout their lifetimes.” 26 An invariant notion of culture allows analysts to
instrument it with other hereditary aspects, like ethnicity or religious denomination – as
Guiso et al. (2006) noted, “[This] prevents cultural explanations from becoming simple
ex post rationalizations”27
Concerning the home bias more specifically, Grossman (1996) argues for “a
model with imperfect information, where familiarity declines rapidly with distance.”28
26
Becker (1996) p. 16
Guiso et al. (2006) p. 3
28
Grossman (1996)
27
15
Huang (2005) specifies such a model by assuming that risk aversion influences the
degree of familiarity populations feels towards each other, given the same amount of
information – “the same distance ‘looks longer’ in the eyes of uncertainty averse
countries.”29 Using Hofstede’s (1980) international survey data and controlling for the
previously documented causes of trade frictions,30 the author confirms that high riskaverse countries trade disproportionately less over long distances than low risk-averse
countries. In determining international trade flows, cultural factors are as important as
geographic ones.
Guiso et al. (2004), using the available the Eurobarometer data, identify three
elements of trust: the average level of a given country’s trust, the average level of other
countries’ trust towards that given country, and the dyad-specific level of trust of one
country towards another. They instrument their trust variable by using the cultural
determinants of trust (genetic distance and religious commonality) and find that a 1%
increase in trust increases inter-European trade levels by 30%.
Disdier and Mayer (2005) follow up Guiso et al.’s (2004) work, again using the
Eurobarometer data, and study the impact of “cultural affinity” on the trade flows
between EU15 and the 10 new member states. They find that the causality goes one
way: although an increase of imports does not significantly influence public opinion, a
rise in public opinion does influence the level of imports.
These studies have ascertained that cultural distances and low levels of trust
have a negative impact on trade flows. Linders et al. (2005) however find the reverse
result. In their empirical investigation, they include measures of cultural effects (such as
common language, common religion) as well as institutional and cultural distances.
They find that although the institutional distances have a negative effect on trade,
cultural distances have a positive effect on trade. The authors explain this seemingly
counter-intuitive result by making a distinction between trade with culturally distant
countries and host production in culturally distant countries. They argue, “firms prefer
trade to host-country production in culturally distance countries.” 31 Although we
expect to find cultural distances to have a negative effect on trade levels, we are now
aware that this result is not necessarily established. Nonetheless, cultural variables have
29
Huang (2005) p. 3
His explanatory variables include: origin and destination dummies, country-pair characteristics, fixed
year effects dummies and UAI: Hofstede’s (1980) uncertainty avoidance indicator.
31
Linders supra note 9, p. 19
30
16
an incontestable impact, be it positive or negative, on trade flows and explain in part the
home bias effect scholars observe in world trade patterns.
The above discussion has illustrated that contrary to classical assumptions, trust
is an important variable to be taken into account when analyzing the home bias puzzle.
Even in a highly integrated area such as the European Union, trade frictions exist,
although its magnitude is subject to scholarly debate. The following section assesses the
gravitational pull of the euro zone, and how far trust goes in explaining the home bias
effect.
17
3 The Gravitational Pull of the Euro zone
Sir Isaac Newton defined in the 17th century the Law of Universal Gravitation,
which contends that the attraction between two given objects i and j is proportional to
the product of their masses, and inversely proportional to the square of the distance
between them. Sandelien (2003) affirms that the first use of the theory of gravity in a
social science context is credited to Carey who in 1858 applied it to the group behavior
phenomenon. In trade economics, Beckerman (1956) was the first to transpose the
gravity model to study trade patterns. Yet no theoretical foundation for the gravity
model was found until Anderson’s 1979 breakthrough. In this second chapter, we will
highlight the impact of the trust variables as we analyze the gravitational pull of several
contiguous countries of the euro zone. In the first part, we will provide a theoretical
basis for the gravity equation, and state the equation we wish to estimate. The second
part will proceed with the regressions and conclude on the credence of our explanatory
variable: trust.
3.1 The Underlining theory of the Gravity Model
The gravity equation is a spatial interaction model that illustrates the two main
factors that influence bilateral trade: the size or scale of the given economies and the
distance between them. The scale impact alludes to the empirical observation that
countries that carry a large economic weight attract more economic activity than
(economically speaking) lightweight countries32. The distance effect conveys the idea
that the further apart the two economic bodies lie; the lower are the observed trade
flows. The interaction between any two countries is the bilateral trade flow, subscripted
by i and j, and expressed as the multiple of the two given GDPs conditioned by the
distance separating the two trading partners.
Anderson (1979) was first to link the gravity equation, empirically validated, to
a theoretical international trade framework. Subsequent scholars have revised and
augmented this initial breakthrough; as for instance did Deardorff (1995).
Deardorff (1995) employs a two-country Heckscher-Ohlin model supplemented
by three primal assumptions. His first assumption is that each good is
32
This economic attraction could be explained by the difference in importance of business opportunities
or size of home markets.
18
exported/produced by only one country 33 , which stands in for the orthodox product
differentiation assumption. His second assumption is that trade incorporates both goods
and factors of production. Given his third assumption, positive transportation costs, the
prices of the production factors are not equalized, as neoclassical theory would have it.
Instead the transportation costs between country i and country j (“ t ij ”) are of the
“iceberg” type, meaning that despite that a fraction (“ t ij  1 ”) of the shipped goods were
lost in transportation, the importer country’s consumers’ pay the full price “ p i t ij ” for
the shipment.
Deardorff (1995) specifies Cobb-Douglas type preferences, meaning that
consumers in both countries spend a fixed ratio  i of their respective incomes on a
given country’s good. Let xi be the output of country i, let Yi be the nominal income of
country i, and let Y W be the world income. Then,
Yi  pi xi    i Y j   i Y W
(4)
j
…which rewritten gives:  i 
Yi
YW
.
If Tij denotes the nominal value of exports from country i to country j, then in
the absence of transportation costs, the nominal value of country i’s exports is thus:
(5)
Tij   i Y j 
Yi Y j
YW
With positive transportation costs, the nominal value of exports becomes:
(5.1)
Tij 
Yi Y j
t ijY W
…which closely mirrors the basic gravity equation:
(6)
Exportsij 
(GDPi GDPj ) 
( DISTij ) 
The two notable differences between Anderson’s and Deardorff’s theoretical
foundation are that the gravity model’s elasticity of exports to GDP, denoted by alpha,
is not present in Deardorff’s (1995) framework due to the Cobb-Douglas preferences he
specifies. This can be remedied by incorporating constant elasticity substitution (CES)
33
This is equivalent to the assumption used by Anderson (1979) that consumers differentiate goods by
country of origin.
19
preferences, which also allow for the fiction variable, the elasticity of bilateral trade to
distance, beta, to immerge.
The natural logarithm of the previous equation, provides a linear relationship:
(7)
ln Exportsij   1 ln GDPi   2 ln GDPj   ln DISTij
Adding an error term, u i allows for the gravity equation to be estimated:
(8)
ln Exportsij   1 ln GDPi   2 ln GDPj   ln DISTij  u i
Equation (8) is the basic framework of the gravity model – it is identical to
mcCallum’s regression in equation (1). To this structure, we add explanatory variables,
the most common being a dummy indicating a shared border. The gravity model this
paper estimates will most notably include variables indicating trust.
3.2 Empirical Strategy
We first describe our data, its sources and run basic descriptive analysis on our
samples; before specifying our model, underlining hypothesis, and our estimation
methods. A discussion of the results our model yields concludes this study.
3.2.1 Data Analysis
3.2.1.1
Sources
Our data comes from five sources. The bilateral trade data, converted to the
relevant units and exchange rates, comes from the United Nations COMTRADE
database.
Our GDP data 34 has been extracted from the Eurostat “National Accounts”
database - it is in millions of ECU (in current prices). The distance indicator, as well as
our dummy variables for common language and common border, has been retreived
from the CEPII data library. Our dummy variable “religion” was complied from the
CIA World Fact Book. Finally, our trust data was made available courtesy of
www.gesis.org, a site that provide Eurobarometer data.
34
As well as the data for every county’s total exports, used to calculate internal trade à la Wei (1996).
20
3.2.1.2
Bilateral Trade
Description of variables and our sample
From the United Nations COMTRADE database, we have extracted the annual
exports of the EU15 countries. We have transformed this data from thousands of US$
(in current prices) to millions of ECU (in current prices)35 to be harmonized with our
GDP data. To construct internal trade flows, we followed Wei’s (1996) approach and
subtracted the total exports of every country from its total production, and this for every
year and country in our series.
GDP and Exports
The GDP data in current prices was retrieved from Eurostat – besides specifying
the relevant years and countries, this data didn’t need any further transformation. Data
for exports were used in constructing internal trade flows.
Distance and Dummies for common languages, adjacent borders and religion
The religion variable indicates a shared (majority or state) religion, as recorded
in the CIA World Fact Book. Instead of the variable indicating a shared official
language, we chose to use the one indicated a shared ethnic language for it shows
whether a given population can understand the foreign language despite the language’s
official status in the home country. Taking into account our previous criticism on
distance mismeasurements, we use the distance indicator that we consider the most
realistic -“distwces”- given that it is weighted by the geographic dispersion of the
population within each country.
Trust
We initially wanted to study the interaction between trust and the home bias in
the 21st century but appropriate trust data for those dates are unavailable either because
they are under embargo or because the Eurobarometer surveys did not include the
relevant questions for those years. Given that the most recent data for trust ends in
1997, we have requested data for 1993, 1994, 1995, 1996 and 1997. Upon delivery, we
noticed several discrepancies, notably that three different questions have been asked
concerning the level of trust Europeans have towards each other.
In 1994, the survey asked, “Which, if any, European Union country or countries
do you think can be politically trusted more than others?” to which respondents list
trustworthy countries. The 1993 and 1996 survey asked, “I would like to ask you a
question about how much trust you have in people from various countries. For each,
35
The past rates have been found on http://www.xe.com/ict/.
21
please tell me whether you have a lot of trust, some trust, not very much trust or no
trust at all?” The 1995 and 1997 survey asked, “I would like to ask you about how
much trust you have in people from various countries. For each, please tell me whether
you tend to trust them or tend not to trust them?” The 1994 data has been dropped since
it explicitly refers to political trust and not trust towards a foreign population.
Following Guiso et al, we have encoded the 1993 and 1996 data as follows: 1 (=no
trust at all), 2 (=not very much trust), 3 (=some trust) and 4 (=a lot of trust). The 1995
and 1997 survies that gave a binary choice to respondents has been coded as 1 (=tend
not to trust them) and 2 (=tend to trust them). Although the questions are similar, they
do not provide for identical answers, so we cannot combine the four years together.
Therefore, we will compare the surveys two by two.
Sample
In 1993, twelve countries were surveyed. To compare this study to the 1996
one, we dropped from the later the countries which were not surveyed in 1993. The
1995 and the 1996 surveys included all EU15 countries. However, since our bilateral
trade data aggregates Belgium and Luxembourg, the latter country has been dropped
from all our samples.
3.2.1.3
Descriptive statistics
Before running the descriptive statistics on all our variables, the trust variable
deserves particular attention. As we mentioned, we aggregated the raw trust response
data following Guiso et al.’s encoding, computing the mean value per country and per
year. For each year, we have built a symmetric trust matrix, included in our annex. Just
as the previous scholars did, we notice substantial variations in our data.
For 1993 and 1996, the maximum values were respectively 3.547 (the level of
Danish trust towards countrymen) and 3.588 (the Swedish trust towards countrymen).
In each year, those countries were the most trusting towards foreigners. The minimum
values for 1993 and 1996 were respectively 2.133 (the level of Greek trust towards
Germans) and 2.00 (the level of Portuguese trust towards Greeks). Once again, each of
these origin countries registered the weakest level of trustiness.
For 1995 and 1997, the maximum values were respectively 1.990 (the level of
Danish trust towards countrymen) and 1.976 (the level of Dutch trust towards Swedes).
Unlike in the previous tables, the Danes in 1995 aren’t the most trusting country
(average of 1.821) for France, the Netherlands and Sweden register larger values. In
22
1997, as expected, Sweden is once again the country that registers the highest levels of
trust towards foreigners. The minimum values for these years are respectively 1.276
(the level of Greek trust towards the Germans) and 1.298 (the level of Greek trust
towards the British), and once again Greece is the country that registers the lowest
average levels of trust. There seems to be a cursory correlation between trusting and
being trusted.
Just as Guiso et al (2004) observe, and as reflecting in the main diagonal of
each trust matrix, citizens trust their own countrymen more, the main exception being
Scandinavian countries, which, as in the case of Sweden in 1997, trust its countrymen
by a value of 1.949, but trust Danes and Austrians more (respectively 1.959, and 1.957).
“This is hardly consistent with differences in trust being driven by differences in
information concerning the other countries citizen (which should decay with distance),
but can be explained by opinions on trustworthiness being highly affected by cultural
sterotypes.”36 Another interesting result is in the levels of bilateral trust between the
UK and France, hereditary enemies. In all the years we surveyed, they still rank each
other’s countrymen as the least trustworthy of all Europeans. Finally, we observe that
the average level of trust seems to be decreasing over the years, although we cannot
definitely conclude on this, due to our small period sample. We have consolidated the
data for 1993 and 1996 in one database, and the data for 1995 and 1997 in another. The
results for our descriptive statistics for all variables are in the annex.
We ran the descriptive statistics on all non-string variables for both databases,
first differentiating by year, then by year and by country. There are 121 observations for
the years 1993 and 1996 (making a total of 242 observations in whole), and 196
observations for the rest of the years (392 observations in total). The results are in the
annex.
For all periods, the minimal registered level of bilateral trade corresponds to
Greek exports towards Ireland, which also coincides with the maximum recorded
measure of distance in our database. Despite the same breadth of distance separating
them, Ireland exports more to Greece than vice versa. For all years, the minimum
distance recorded is the internal Greek distance, and the maximum level of trade
corresponds to internal German trade (with the sole exception of 1993, where internal
French trade is the highest). To check whether there has been a computing error in our
36
Guiso et al (2004) p. 11
23
samples, we verify that the mean GDP of origin countries and of destination countries
are identical for all years. We repeat the test for the average level of bilateral trust for
origin and destination countries, and notice that for 1993, the means of average trust
levels are not identical. Furthermore, 1993 is the only year that records a higher
standard error for average destination trust than for average origin trust. There is a
probable computing error in our 1993 sample, which brings us to reject the 1993 data.
For 1995, 1996 and 1997, we notice that the standard deviation in average
destination trust is lower than the standard deviation in average origin trust, indicating
that although European countries record different levels of national trust, they most
concur in their opinions of other European’s trustworthiness. This falls in line with
Guiso et al’s hypothesis that “opinions on trustworthiness [are] highly affected by
cultural sterotypes.”37
We have rejected the 1993 sample for possible errors, but since we computed
the 1996 data to be compared to the 1993 one (because both use the same base for their
measures of trust), we must reject the 1993-1996 database in its entirety.
Table 7 illustrates that for the EU14 38 countries in our 1995-1997 database,
13.2% of our sample countries share a common border, 44.3% share a common religion
and 6.2% share a common language.
We test the between/within coefficients of our 1995 and 1997 periods – the
results are in the annex. We have 28 observations in each period “T”, 14 different
individuals “n” in each T, which makes for a population “N” of 392. We observe that
for the bilateral trade variable, the dispersion within each period is not as large as the
dispersion between periods. This is expected since the countries surveyed record
bilateral trade ranging from 24 million ECUs to over one million ECUs. Between 1993
and 1997, bilateral trade in our sample countries has accrued. For bilateral trust, we
make the contrary observation: the dispersion within each period is larger than the
dispersion between periods. This confirms that cultural stereotypes, on which trust is
based, do not vary as much over the years as they vary between the countries sampled.
The above descriptive analytics have illustrated our variables and samples.
Below, we will first define the model we wish to estimate and our estimation methods,
before studying the results we find.
37
38
Ibid. p.11
EU14=EU15-Luxembourg
24
3.2.2 Estimation
3.2.2.1
Our Model and Hypothesis
Our results depend on the assumption that although all European populations
may have replenished their ranks over the past decade, the distribution of the population
within each country hasn’t significantly altered from 1995 to 2005. We state this
hypothesis because we use nominal data for all our trade variables, with the sole
exception of the distance measure that is conditioned by the distribution of individuals
in their countries as of 2005. We set out to test whether the home bias effect can be
mitigated by the trust Europeans feel towards each other. We will use a log/log
functional form, consistent with the gravity model, which measures the elasticity of
change in trade in respects to the percentage variation in the regressors. Our base model
is the first one tested by Nitsch (2000):
(9)
log( xij )    1 Home   2 ln( yi )   3 ln( y j )   4 log( d ij )
  5 Border   6 com _ lang  eijt
…where the dependant variable is the log of exports and the explanatory variables are
Home which captures the home bias effect, the log of origin and destination countries’
GDPs, the log of the distance variable, and dummy variables indicating an adjacent
border and a common language, as well as an error term. To dissociate the effects of
trust from the effects of increased information; we follow Guiso et al (2004) in deeming
common borders, distance and commonality between languages, as proxies for the
information Europeans have upon each other.
To equation (9) we introduce the bilateral level of trust:
(10)
log( xij )     1 Home   2 ln( y i )   3 ln( y j )   4 log( d ij )
  5 Border   6 com _ lang   74Trust  eijt
For sensitivity analysis purposes, we modify the estimated equation by
controlling for average level of origin trust and destination trust:
(11)
log( xij )    1 Home   2 ln( y i )   3 ln( y j )   4 log( d ij )
  5 Border   6 com _ lang   7 Trust   8 ave _ o   9 ave _ d  eijt
25
Because of potential measurement errors in our trust variable (as well as the
likelihood that it captures the effect of omitted cultural variables), we will instrument it
with a measure of shared religion. Shared religion makes for an appropriate
instrumental variable for we argue that it is highly relevant given that we determined
trust to be partly based on sociological factors such as religion and, moreover, opinions
on trustworthiness may be based on cultural stereotypes of which religion plays a
prominent role. It is also fulfills the instrument exogeneity condition, for although
religion is related to trust, the former does not influence bilateral trade levels. 39
Nonetheless, to verify that religion is partly correlated to trust, we will regress bilateral
trust on religion:
(12)
bil _ trust     1 Home   2 ln( y i )   3 ln( y j )   4 log( d ij )
  4 Border   4 com _ lang   4 Re ligion  eijt
… before conducting our Instrument Variable estimation of equation (11).
3.2.2.2
Estimation Methods
To carry out our study, we will use four different estimation methods.
Consistent with Nitsch (2000), we first pool our data, and estimate its entirety with the
Ordinary Least Squares (OLS) method, computing heteroskedasticity-robust statistics.
Pooled data however, does not control for period effects. As Wooldridge (2000) notes,
“the errors in such an equation are almost always serially correlated because of an
unobserved effect. Random effects estimation corrects the serial correlation problem
and produces asymptotically efficient estimators”.40
Therefore, by first specifying the periods in our panel data, we carry out
Generalized Least Squares (GLS) estimations, assuming that the unobserved effect is
uncorrelated with all our explanatory variables. Subsequently, we lift this assumption
and test fixed effect estimations. In discussing whether to use fixed or random effects,
Wooldridge indicates that for data that cannot be consider as random draws from a
large population - as for instance, country specific data – it’s judicious to use fixed
effects estimations.
To paraphrase Guiso et al, “Ireland is not Catholic because it traded more with Rome than with
London”
40
Wooldridge (2000), p. 625
39
26
Finally, we instrument our trust variable with a dummy indicating 1 if the
country shares their religion and 0 otherwise, and complete our study with Instrument
Variable (IV) estimations.
3.2.3 Interpretation
The full list of all our regressions is in the annex. Regression (1) details the
standard gravity model augmented by a home dummy variable that captures
intranational trade, a border dummy that indicates whether a common border exists and
a language dummy that denotes a shared language. The estimated equation yields (the
standard errors are in brackets beneath the coefficients):
(13)
ln( xˆ ij )  0.66 ln( y i )  0.65 ln( y j )  1.27 ln( d ij )  0.834 Home  0.103Border  0.049language  eijt
(0.0212)
(0.022)
(0.042)
(0.184)
(0.0922)
(0.108)
With an R2 of over 0.99, the empirical fit of the gravity model is perfect. The
home bias effect is of a similar magnitude to Wei’s (1996) estimation: in our first
model, a country exports about 2.3 (=exp[0.834]) times as much to itself as to a foreign
country with an adjacent border and a similar size, distance and language. Yet this
regression seems erroneous for not all the coefficients of the variables in this standard
gravity have the expected signs. More precisely, we believe our distance variable is
correlated with our GDP variables because the distance variable is weighted by the
population, a measure of economic size that Nitsch (2000) used as an instrumental
variable for GDP. We therefore, choose a different distance that measures the distance
between capitals, distcap. It is unweighted, and isn’t potentially correlated to any other
explanatory variables. Regression (2) reestimates equation (9) with a new measure of
distance:
(14)
ln( xˆ ij )  1.928Home  0.647 ln( y i )  0.642 ln( y j )  1.22 ln( d ij )  0.309 Border  0.024lang  eijt
(0.296)
(0.488)
(0.459)
(0.11)
(0.151)
(0.135)
The empirical fit of the model is once again perfect. The sign on the common
language coefficient is curious since it indicates that a shared language negatively
influences bilateral trade. Otherwise the change in distance measurements significantly
influences the home bias effect. Instead of Wei’s low effects, we obtain results similar
to Nitsch’s magnitude of 6. In our model, a country exports about 6.6 (=exp[1.928])
27
times as much to itself as to a foreign country with an adjacent border and a similar
size, distance and language. The GDP coefficients situate around 0.64, meaning that
when the GDP of one trading partner increases by 10%, the trade volume increases less
than proportionally by 6.4%. A 10% increase in distance decreases bilateral trade more
than proportionally by 122%. The language and border variables are statistically
significant at respectively the 10% and 2% level. For the language variable, we can
argue following Linders et al. (2005) that it may decrease bilateral trade for countries
prefer to implant branches in a country that speaks the same language instead of trade
with it, but we view this explanation as shaky at best. If we add the bilateral trust
variable to our equation, we obtain:
(15)
ln( xˆ ij )  1.99 Home  0.208Trust  0.66 ln( y i )  0.647 ln( y j )  1.208 ln( d ij )  0.308Border  0.0005lang  eijt
(0.186)
(0.183)
(0.26)
(0.24)
(0.052)
(0.106)
(0.125)
Once again the gravity model fit is ideal. The sign on the language coefficient is
now positive, but the coefficient on the trust variable is negative (which could vindicate
Linders et al’s assumption that less bilateral trade due to a greater trust could indicate
that the countries have chosen to implement mutual branches in each other’s countries
instead of partaking in long-distance trade). The home bias coefficient increases: a
country exports about 7.315 (=exp[1.99]) times as much to itself as to a foreign
country, once adjacent borders, similar sizes, distances, languages and trust levels are
taken into account. The GDP coefficients, being similar to the ones found in the
previous regression, carry the same interpretation. We notice that the distance
coefficient in Regression (2) captures some of the trust effects of this regression since it
is now lower. If we control for the average level of origin country trust and the average
level of destination country trust, we obtain:
(16)
ln( xˆij )  2.16 Hm  2.03Trst  .72 ln( yi )  .68 ln( y j )  .93 ln( dij )  .32 Brdr  .21lang  .09ave _ o  3.98ave _ d  eijt
(.155)
(.340)
(.025)
(.025)
(.051)
(.092)
(.128)
(.422)
(.325)
After controlling for average origin and destination trust, regression (4) shows a
positive relationship between trust and trade. A given country exports 7.61 times more
to a country in whose citizens it trusts than to one it distrusts, ceteris paribus. The home
bias effect soars: controlling for all previous variables, Europeans countries export
28
more than 8.5 times to themselves than to foreigners. The coefficients on the GDP
variables haven’t significantly changed since the previous regressions: a 1% increase in
the GDP of a trading partner increases bilateral trade by less than proportionally. Unlike
in regression (3), a 1% increase in the bilateral distance separating trading partners
decreases trade also, less than proportionally. The average trustiness of the origin
country is found to be statistically significant at the 10% level; a common language –
that now has a positive impact on bilateral trade – is significant at the 1% level.
Although these results seem encouraging, Wooldridge (2000) sounds a note of
caution and underlines that pooled data does not control for period-specific effects and
that this unobserved effect is often serially correlated with the errors in our sample. We
have tried correcting this by robustifying our standard deviations, but nonetheless, we
will now include a random-effects specification to correct the potential serial
correlation problem. Assuming that the unobserved effect is uncorrelated with all our
explanatory variables, the GLS estimation yields:
(17)
ln( xˆij )  2.79 Hm  1.35Trst  .70 ln( dij )  .44 Brdr  .76 ln( yi )  .79 ln( y j )  .25lang  .27ave _ o  1.59ave _ d  eijt
(.15)
(.29)
(.05)
(.09)
(.08)
(.02)
(.12)
(.89)
(.42)
This regression (5) yields results that are more in line with the previous
literature. The coefficients on the distance measure, the GDPs, the common border and
common language variables are nearly identical to the previous ones estimated. For the
measure of average origin and destination, we note that their coefficients follow our
previous assumptions. Indeed, a 10% increase of origin country trustiness increases
bilateral trade by 2.7% - this result is significant at the 5% level. The bilateral trust
coefficient has sharply decreased: a given country exports now 3.86 times [and not 7.61
times as in regression (4)] more to a country in whose citizens it trusts than to one it
distrusts, ceteris paribus. The home effect in this non-pooled sample is the largest one
yet recorded: a European country exports about 16.3 (=exp[2.79]) times as much to
itself as to a foreign country, once adjacent borders, similar sizes, distances, language
and trust levels are taken into account. This result is consistent with McCallum’s (1995)
home bias results for the US and Canada.
However, in carrying out this GLS regression, we have assumed that there is a
period-constant effect that is uncorrelated with the explanatory variables. Wooldridge
(2000) indicates that for data such as ours – that can’t be assumed to be random draws
29
from a large population – fixed effect regressions yield more efficient results. In the
following regression (6), we therefore lift the assumption that the unobserved effect is
uncorrelated with the explanatory variables.
(18)
ln( xˆij )  2.8Hm  1.36Trst  .70 ln( dij )  .44 Brdr  .60 ln( yi )  .79 ln( y j )  .25lang  2.65ave _ o  1.59ave _ d  eijt
(.15)
(.29)
(.05)
(.09)
(.29)
(.02)
(.12)
(1.48)
(.42)
Our model now explains about 88% of the variations in bilateral trade. Between
the random-effects estimation and the fixed-effects one, not many variables have
registered any change: the bilateral trust levels, the distance measure, the home bias
measure, the GDP of the destination country, the average level of trust emanating from
the destination country and the language dummy register only minute variations to their
coefficients (and none to their standard errors). On the other hand, the coefficient on the
measure of average origin country trust carries now the wrong sign and the coefficient
of the GDP of the origin country has decreased. We believe that there is a problem of in
our model, where the error term and key explanatory variables are correlated.
Endogeneity will yield biased and inconsistent estimators for the OLS estimations
above. In an attempt to correct this, we introduce an instrumental variable, “religion”,
that we believe is correlated with the explanatory cultural variables, but not with the
bilateral trade flows. When discussing our hypothesis above, we have already argued
that it is a relevant and efficient instrument. The following regression (7) is our IV
estimation that instruments trust with religion:
(19)
ln( xˆij )  2.63Hm  2.96Trst  .63 ln( dij )  .45Brdr  .76 ln( yi )  .80 ln( y j )  .30lang  0.14ave _ o  3.05ave _ d  eijt
(.29)
(1.77)
(.08)
(.10)
(.02)
(.03)
(.13)
(1.71)
(1.69)
For the first time in our study, the average trust variable is statistically
significant at the 10% level. A given country exports about 20 times (=exp[2.96]) more
to a country in whose citizens it trusts than to one in whose citizens it distrusts, ceteris
paribus. This significant result confirms our original hypothesis that trust plays a
significant role in bilateral trade flows. Controlling for trust, distance, GDPS, adjacent
borders, common languages, we find that the home bias effect in the EU15 is
substantial: we find that a European country exports about 14 (=exp[2.63]) times as
much to itself as to a foreign country, ceteris paribus. These results fall in line with the
studies conducted by McCallum (1995) and Nitsch (2000). Furthermore, the common
30
language and the average trust levels for destination countries are significant at
respectively 2% and 1% levels. As far as the EU14 is concerned, the home bias exists
and trust is a significant force in bilateral trade flows.
Yet within the EU14, some countries are outliers in regards to their shared
languages or geographical borders. Since languages in Europe are dispersed we will
ignore the outliers that do not share a common language. But as for borders, we
remarks that about half of our sample countries either do not share any common border
or share a common border with only one other country. (Table 9 and 10 in annex) For
these reasons, we will now discard Denmark, Finland, Greece, Great Britain, Ireland
and Sweden from our sample. A further justification to this choice is that these
countries, being geographically distant from continental Europe, are likely to use
different type of transport costs41 that could carry an inherent bias to our home bias
estimations. To test whether dropping outliers has a significant effect, we will run the
regressions again on the remaining sample – Austria, Belgium, Germany, Spain,
France, Italy, Portugal and the Netherlands. Moreover, this sample eliminates two
natural causes of home bias, for these eight countries share a common currency42 and
are members of the Schengen zone, which eliminates border checks on people and
merchandise. A summery of the descriptive statistics on our new sample shows that all
our countries share at least a border with two other countries. 43 Moreover, Table 12
verifies that 62% share the same religion (Catholicism in this case); 31% share a
contiguous border and 16% share the same language. The descriptive statistics
differentiated per year, Table 13 in the annex, do not raise any red flags.
Although logarithmic functional forms are less sensitive to outlying
observations, we drop the outlier country from our database and see whether the OLS
fixed-effects estimates change by a “large” margin. Our estimation (8) yields the
following:
41
Indeed, these countries are either islands or could proxy as such. Islands use water freight, which
carries a different cost than ground transportation that landlocked or contiguous countries use. For the
purpose of this paper, a discussion regarding which of these modes of transportation is cheaper or more
effective is of no interest to us.
42
Or at the very least, for the years surveyed, the wholesale goods were priced in ECUs although the
retail goods were still in national currencies.
43
Portugal is our sole exception. Taking Portugal out of the sample would have subsequently required us
to take Spain out as well, leaving us with too few countries in our sample.
31
(20)
ln( xˆij )  2.55Hm  1.44Trst  .63 ln( dij )  .43Brdr  1.16 ln( yi )  .66 ln( y j )  .169lang  1.92ave _ o  3.05ave _ d  eijt
(.26)
(0.52)
(.102)
(.11)
(.74)
(.04)
(.14)
(2.0)
(0.71)
We compare equation (20) to equation (18) above and detect that the following
coefficients have only registered minute changes: the distance measure, the dummy
indicating a shared common border and the GDP of the destination country. Our current
model fits the data better (with an R2 of 0.92, as compared with equation (18)’s R2 of
0.88). On the other hand, in this smaller more homogeneous sample we still detect a
substantial home bias. It’s not as large as previously measured in equation (19) where it
was of a magnitude of 16.4; but its current magnitude of 12.8 is noteworthy. The
importance of trust has diminished within this sample, possible due to the long period
of interaction between these Member States. The levels of average trust in the country
of destination and of origin carry a negative sign, which may support both the notion
that average country trustworthiness and average country trustiness move in parallel, as
well as Linders et al’s hypothesis that a greater amount of trust does not necessarily
lead to more bilateral trade, but to more branches implanted in respective countries, and
thus to less bilateral trade. Finally, we notice that the coefficient on the dummy variable
indicating a common language is smaller in this regression. We believe that this is
because these countries can be roughly divided into Germanic countries and Latin
countries – there are no linguistic outliers in our sample – and a latin-language speaking
person, for instance, needs only to learn a Germanic language to comprehend the gist of
all the European languages in this sample population. Once again we instrument our
trust variable with religion, to check whether our trust variable is a “noisy” measure.
We first regress Trust on Religion to confirm whether they are positively correlated.
Regression (9) yields the following results:
(21)
Tˆrust  0.33Home  .07 ln( y i )  .01 ln( y j )  .06 ln( d ij )  .01Border  .20Language  .01 Re ligion  eijt
(0.06)
(0.01)
(0.01)
(0.02)
(0.03)
(0.03)
(0.02)
Religion is significant at the 5% level. We run the last regression of our paper, which
yields:
(22)
ln( xˆij )  26.5Hm  140Trst  1.22 ln( dij )  1.16 Brdr  0.21 ln( yi )  1.1 ln( y j )  8.7lang  103.7ave _ o  110.9ave _ d  eijt
(148.1)
(711.5)
(3.55)
(8.27)
(5.5)
(2.64)
(45.7)
(524.4)
(552.2)
32
As compared to equation (19), this result doesn’t fit with the rest of empirical
analysis despite religion being a relevant instrument for trust. We note that all our
variables are significant at levels between 6% and 10%. Moreover all our variables
carry a negative sign, with the exception of bilateral Trust and the GDP of the country
of destination. The magnitude of trust has increases by over 4000%. Although none of
the previous literature have recorded such results, the magnitude of the trust coefficient
in the study by Guiso et al (2004) has also greatly increased when instrumented by
religion. In view of their results, they concluded, “our measure of trust is a noisy
measure of the true trust between two countries (…) One potential concern with our
instrumental variable regression is that our instruments may be only weakly correlated
with trust. If this is the case then the two stage least squares regressions will be biased
and the standard errors misleading.”44 We accept that the trust variable, derived from
the Eurobarometer surveys, is a noisy measure of “true trust”. Statisticians need to
perfect a measure of “true trust”, perhaps beginning by defining what variables lie
behind the trust countries have towards each other. At the very least, our estimations
have confirmed that if only the GDPs and the distances between countries were the sole
relevant factors in bilateral trade, then the dummy variable representing intranational
trade would register minute coefficients instead of the immense ones it registers in our
models. While, for instance in equation (20), the distance is not significant to bilateral
trade levels, the extent of the home bias and bilateral trust are.
In this chapter we have set out to test whether the home bias effect can be
mitigated by the trust Europeans feel towards each other. On the contrary, we found
that the home bias effect and bilateral mistrust compound each other, which indicates
that the levels of trust Europeans have towards each other influences the seeming
deficiency of inter-Member State trade in the EU. This is not necessarily a pessimistic
observation. If trust or the lack thereof lies behind the EU home bias puzzle in trade,
then we can discard the assumption that it is national preferences that impede European
trade. If one is to consider mistrust as a market imperfection problem, this leaves
considerable scope for aggressive intergovernmental (read EU-level) action in
thwarting this last frontier to the Single Market. The next step in this line of research is
in testing the impact of EU social policies - such as, for instance, the student-exchange
program “ERASMUS” – on breaching the trade distances between European countries.
44
Guiso et al (2004) p. 20
33
4 Conclusion
In this paper we showed that trust influences European bilateral trade flows. We
began by examining the definitions and determinants of both trust and distance as they
relate to trade. After determining that trust is the underlying variable in economic
transactions, we introduced the notion of distance as a multidimensional concept, which
can be expanded and contracted according to certain variables. We considered trust to
be such a variable, capable of contracting long distances when trust is strong, or on the
contrary, making a distance “look longer in the eyes” when trust is absent. The
discussion on distances introduced the “home bias puzzle” which we first analyzed in a
descriptive manner. After defining the concept and the classical determinants of the
home bias puzzle, we gave voice to the literature that estimates the home bias puzzle to
be substantial as well as the literature arguing that the home bias puzzle has been wildly
overstated. We concluded our review of the literature by focusing on the literature
portraying the impact of trust and other cultural variables on international trade flows.
The analytical chapter that followed first introduced the theoretical framework
of the gravity model, which we used in assessing the impact of trust on European
bilateral trade flows. We defined our empirical strategy in three parts: we first
conducted data analysis, then defined the augmented gravity model and estimation
methods, and finally we interpreted our results. We found that the trust Europeans have
towards each other, influence the seeming deficiency of inter-Member State trade in the
EU. This is not a pessimistic observation since it justifies EU-wide action in thwarting
the last hindrance to a Single Market. The founding father of the European Union, Jean
Monnet, already perceived trust as being the last hurdle in uniting Europe when he
declared, « l’Europe [...] est le résultat de la confiance que nous avons en nousmêmes.»45
45
Discours, Strasbourg, 15 juin 1953.
34
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Datasets
The datasets we have used can be accessed at:
http://www.ogorkiewicz.org/1995_1997.dta
http://www.ogorkiewicz.org/1993_1996.dta
http://www.ogorkiewicz.org/1995_1997_sans_outliers.dta
37
ANNEX
Annex 1: Trust Matrix
Table 1: Trust Matrix for 1993 surveyed population.
1993
BEL
DNK
FRA
DEU
GRB
GRC
IRL
ITA
LUX
NLD
PRT
ESP
AVER1
BEL
3.34413
3.243184
3.113169
2.943258
2.893816
2.481436
2.953717
2.785366
2.926882
3.293756
2.890066
2.878229
DNK
3.108796
3.547475
2.883644
2.917199
3.125843
2.24497
2.951872
2.780679
2.972028
3.284129
2.803621
2.860377
2.978917 2.956719
FRA
2.960986
2.895661
3.198582
2.819966
2.280719
2.703108
2.781285
2.87037
2.890558
2.838778
2.98926
2.523243
DEU
2.885598
3.092574
2.788127
2.757166
2.426263
2.133612
2.587514
2.652834
2.690678
2.845287
2.569175
2.709012
GBR
2.89648
3.336401
2.450358
2.750525
3.324663
2.251068
2.765738
2.608741
2.842572
2.980331
2.718866
2.454846
GRC
2.592385
2.58147
2.466149
2.602075
2.611543
3.209137
2.417127
2.460542
2.669136
2.55102
2.589888
2.454765
2.81271 2.678153 2.781716 2.600436
IRL
2.917035
3.057235
2.713508
2.733392
2.670588
2.403743
3.43527
2.515544
2.77381
2.813747
2.608571
2.636912
ITA
2.557394
2.487474
2.488157
2.542625
2.553261
2.201058
2.584464
2.826979
2.677419
2.40836
2.557908
2.565646
LUX
3.361026
3.272931
3.117834
2.997917
2.9437
2.446358
2.945619
2.745989
3.322034
3.372845
2.917553
2.892487
NLD
3.03681
3.355072
2.940067
2.961701
3.159091
2.242673
2.934109
2.856098
3.093478
3.351682
2.865258
3.002375
PRT
2.682703
2.636771
2.577685
2.661324
2.729193
2.575758
2.593974
2.426108
2.713969
2.772619
3.308108
2.491694
AVER2
2.921343
3.012776
2.790734
2.785196
2.772639
2.464294
2.793538
2.68797
2.86461
2.934217
2.789655
2.727865
2.77328 2.537562 3.028024 2.983201 2.680826 2.733293 2.795403
Source: Eurobarometer 39.0 Mar-April 1993. (Gesis code: ZA2346)
Variables: V73-V84.
Following Guiso et al. (2004), the answers have been encoded as “Lot of trust”= 4; “Some trust” = 3; “Not very much” = 2; “No trust”= 1
This matrix was done by MSExcel.
(1) This average indicates how much a given country is trusted by others
(2) This average indicates how much a given country trusts others.
ESP
2.712777
2.647059
2.751527
2.735209
2.552991
2.678611
2.571767
2.726384
2.802752
2.698052
2.657583
3.264798
Table 2: Trust matrix for 1995, surveyed population.
1995
AUT
BEL
DNK
FIN
FRA
DEU
GRB
GRC
IRL
ITA
LUX
NLD
PRT
ESP
SWE
AVER2
AUT
1.950106 1.793151 1.803301 1.781337 1.503038 1.832421 1.665816 1.530283 1.569425 1.447942 1.824675 1.842298
1.533239
BEL
1.788927 1.918864
1.80917 1.784363 1.689546 1.618182 1.697105
1.52451 1.699041 1.513304 1.935966 1.733262
1.609816 1.608496 1.818182 1.716582
DNK
1.921023 1.916175 1.990798 1.926052 1.642452 1.862288 1.933761
1.58264 1.878788 1.497727 1.941383 1.953362
1.681876 1.627771 1.959119 1.821014
FIN
1.873918 1.822193
FRA
1.802706 1.935079 1.868571 1.886574 1.941896 1.786236
1.875 1.958206 1.715159 1.736541 1.878363 1.601242
1.79845 1.484737 1.825333 1.855346
1.61753 1.508685 1.893276 1.762932
1.62199 1.717314 1.853631 1.688161 1.943417 1.874299
1.771798 1.799571 1.886364 1.825174
DEU
1.75931 1.801287 1.813187 1.804619 1.650977 1.677658 1.681322 1.642036 1.734167 1.611627 1.836388 1.811988
GRB
1.827798 1.822134 1.900878 1.873995 1.497309 1.537037 1.911885 1.547124 1.753261 1.577465 1.845188 1.892562
GRC
1.43075
1.4214 1.391645 1.475795 1.440639
1.27694 1.337486 1.846389 1.423127 1.466216 1.412568 1.401028
IRL
1.847021 1.863714 1.869144 1.816926 1.713939
ITA
1.640196 1.761765 1.757843 1.754902 1.645098 1.568071 1.643487 1.526006 1.615686 1.762977 1.748773 1.784314
LUX
1.55984 1.837596 1.698298
1.6267 1.684385 1.564669 1.958071
1.76671 1.806452 1.816643 1.770291 1.693473 1.697538 1.731646 1.622449 1.728933
1.69726 1.853242 1.837209
1.66135 1.955882 1.838951
1.674535 1.692966
1.83027 1.734822
1.741333
1.89525 1.745936
1.48153
1.56582
1.52657 1.517949 1.456669
1.749169 1.613821
1.85873 1.770267
1.604902 1.691176 1.776471 1.685444
1.647713
1.71916 1.806835 1.750935
NLD
1.856371 1.932377 1.978747 1.965269 1.641138 1.793717 1.878852 1.615481 1.874279 1.465247 1.973517 1.957317
1.769231 1.671676 1.967342 1.822704
PRT
1.694379 1.656977 1.694444 1.669725 1.624851
1.55971 1.708726 1.577586 1.655422 1.637951 1.751256 1.757327
1.902273 1.589474 1.739857 1.681331
ESP
1.717647
1.59976 1.508516
1.76875 1.826027
1.649573 1.908034 1.756447 1.693997
1.94269 1.701453 1.807834 1.942189
1.6703 1.902954 1.585366 1.913174 1.910188
1.709924 1.614325 1.962264 1.831769
1.787925 1.807918 1.818111 1.809507 1.641211 1.665375 1.721702
1.60865 1.740673 1.586617 1.835301 1.818365
SWE
AVER1
1.75469 1.742942 1.731861 1.517202
1.94201 1.912519
1.95935
1.56172 1.664858 1.701923
Source: Eurobarometer 44.0 Oct-Nov 1995. (Gesis code: ZA2689)
Variables: V298-V312.
Following Guiso et al. (2004), the answers have been encoded as “Trust them”= 2; “No trust”= 1.
This matrix was done by MSExcel.
(1) This average indicates how much a given country is trusted by others
(2) This average indicates how much a given country trusts others.
1.676296 1.646492
1.83373 1.733192
Table 3: Trust matrix for 1996, surveyed population.
1996
AUT
BEL
DNK
FIN
FRA
DEU
GBR
GRC
IRL
ITA
LUX
NLD
PRT
ESP
SWE
AVER2
AUT
3.564639 2.946179 2.948276 2.938731 2.622449 3.090909 2.588795 2.518947 2.550549 2.430584 3.065887
BEL
2.833713 3.101493 2.965144 2.916877
DNK
3.216331 2.951299 3.591365
FIN
3.290466
FRA
2.703883 2.974332 2.928834 2.911471 3.230303 2.827801 2.396246
DEU
2.771888
GRB
2.888753 2.822904 3.061176 2.981273 2.249732 2.247096 3.330653 2.432184 2.725053 2.508869 2.875949 3.066362 2.792521 2.572707 3.032634 2.772525
GRC
2.322733 2.354651 2.297521 2.419512 2.422581 2.033403 2.117137
IRL
2.928779 2.858543 2.918079 2.915888 2.837264 2.704572 2.682162 2.619658
ITA
2.663344 2.658757 2.782609 2.784748 2.738693 2.794177 2.663265 2.379078 2.520373 2.879056 2.746532 2.844244 2.430095 2.653261 2.885452 2.694912
2.75129 2.632124 2.705263 2.464706 2.674528 2.403415
3.2 2.670514 2.945918 3.100616
2.95 2.495565 2.582627 3.049485 2.822908
3.17176 2.779412 2.590319 2.591723 2.991677 2.771563
2.50655 2.983766 2.501584 3.120551 3.303609
2.68623 2.677838 3.408308 2.990965
3.07387 3.299046 3.691296 2.915327 2.890411 3.180659 2.677313 2.924945 2.507592 3.060502 3.141458 2.673055 2.605234 3.348813 3.018666
2.49214 2.673635 2.514439 3.007955 2.864183 2.678097 2.743199 2.992883
2.79596
2.72816 2.857616 2.812515 2.679948 2.757019 2.538102 2.524649 2.636096 2.513363 2.898245 2.855473 2.681818 2.699708 2.906608 2.724081
3.34413 2.461899 2.441145 2.394541
3.49121 2.804612 2.945559
2.24263 2.524706 2.567749 2.510297 2.430309
2.93992 2.783309 2.731458
2.91954 2.872037
LUX
2.946667 2.713542 2.975104 2.942605 2.865854 2.836806 2.408602
NLD
2.901822
PRT
2.128591 2.219355 2.260695 2.180082 2.530011 2.200696 2.303357 2.008086 2.172185 2.311456 2.380645
ESP
2.652108 2.682961 2.747175 2.708783 2.279446 2.647406 2.058111 2.351459 2.528121 2.541469 2.704648 2.819048 2.639115 3.436249 2.835443 2.642103
SWE
1
AVER
3.527406
3.00969 3.360707 3.250288 2.498522
2.47957
2.592 2.644128 3.493976 2.994575 2.690813 2.770221 2.983471 2.822529
2.88705 2.902344 2.468271 2.856269 2.334004 3.270896 3.381503 2.849945 2.805613 3.339175 2.941073
3.23463 3.569196 3.491545 3.040237 3.130035 3.432432
2.889408 2.822024 2.970836 2.943041 2.688811 2.708361
2.87619 3.260494 2.810714
2.28978 3.228805 2.303974 2.242619 2.317356
3.30914 3.334951 2.966581 2.857314 3.588364 3.228615
2.69385 2.542862 2.736742 2.543095 2.963119 2.920477 2.714065 2.706592 3.002318 2.789707
Source: Eurobarometer 46.0 Mar-April 1996. (Gesis code: ZA2898)
Variables: V81-V95.
Following Guiso et al. (2004), the answers have been encoded as “Lot of trust”= 4; “Some trust” = 3; “Not very much” = 2; “No trust”= 1
This matrix was done by MSExcel.
(1) This average indicates how much a given country is trusted by others
(2) This average indicates how much a given country trusts others.
Table 4: Trust matrix for 1997, surveyed population.
1997
AUT
BEL
DNK
FIN
FRA
DEU
GRB
GRC
IRL
ITA
LUX
NLD
PRT
ESP
SWE
AVER2
AUT
1.974113 1.810127 1.817442 1.822738 1.560043 1.882296 1.584946 1.528409
1.64455 1.513007 1.902045 1.888889
1.543773 1.590703 1.882096 1.712219
BEL
1.779682 1.877633 1.830258 1.823303 1.747078 1.696296 1.682213 1.533993
1.70024 1.536184 1.913502 1.798729
1.617128 1.643447 1.840686 1.731478
DNK
1.900332 1.764505 1.967247 1.907996 1.671964 1.835282 1.889597 1.535211 1.786543 1.503356 1.900932 1.921109
FIN
1.884532 1.797821 1.879581 1.951923 1.768722
1.71888 1.847296 1.587571 1.768879 1.492274 1.837408 1.851276
1.572967 1.491011 1.889872
1.74682
FRA
1.777246 1.881029 1.842604 1.854545 1.943633 1.751848 1.525641 1.614532 1.754345 1.686891 1.905405 1.813018
1.717634 1.767343 1.863208
1.78012
DEU
1.742795 1.731626 1.794149 1.783348 1.686772 1.664441 1.639735 1.626081 1.691084 1.615788 1.812986 1.791917
1.698614
GRB
1.810325
1.77684 1.874251 1.875754
1.80615 1.871767
1.745679 1.564293 1.898678 1.729796
GRC
1.441943
1.43462 1.394207 1.487212 1.471947 1.299046 1.298246 1.899598 1.463804 1.477838 1.436693 1.393868
1.535329 1.593394 1.581645 1.483389
IRL
ITA
1.88685 1.866771 1.884858 1.869416
1.50455
1.466 1.927845 1.564626 1.721608
1.8226
1.66129 1.690608
1.640754 1.712687 1.779932 1.736964 1.717182
1.59188 1.653534
1.625
1.6191
1.95339 1.712401 1.877049 1.889362
1.63191 1.606025 1.925234 1.774779
1.68682 1.826324 1.717835
1.771574 1.671177
1.90169 1.799799
1.55875 1.647443 1.757544 1.781285 1.804721
1.628163 1.751973 1.771018 1.706648
LUX
1.804 1.625688 1.820225 1.746544 1.731047 1.767273 1.643939 1.490698 1.650964 1.620038 1.945899 1.859287
1.614173 1.694949 1.791209 1.714424
NLD
1.803708 1.826763 1.972284 1.951422 1.554705 1.756871 1.800217 1.543616 1.817972 1.443169 1.960827 1.955301
1.752212 1.643026 1.976589 1.782498
PRT
1.616039 1.488189 1.602902 1.641484 1.676364 1.492288 1.662005 1.492734 1.558594 1.601513 1.688378 1.679952
1.931034 1.535585 1.674623
ESP
1.765891 1.772114 1.804044 1.775641 1.568558 1.692209 1.479644 1.637026 1.686502 1.724691 1.811594 1.843023
1.677835 1.933972 1.835596 1.731604
SWE
1.957092 1.838057 1.959607 1.943894 1.737288 1.813793 1.933025 1.652903 1.897856 1.647205 1.917749 1.909425
1.720056
1.785687 1.746965 1.814906 1.811479 1.677497 1.672646 1.683899 1.592717 1.716252 1.596733 1.833194
1.677205 1.654897 1.840539 1.724074
AVER1
Source: Eurobarometer 47.0 Jan-Feb 1997. (Gesis code: ZA2935)
Variables: V64-V78.
Following Guiso et al. (2004), the answers have been encoded as “Tend to trust”= 2; “Tend not to trust”= 1.
This matrix was done by MSExcel.
(1) This average indicates how much a given country is trusted by others
(2) This average indicates how much a given country trusts others.
1.81811
1.62326
1.64974 1.949625 1.826444
Annex 2 : Descriptive Statistics
Table 5: Descriptive Statistics for whole sample, for 1993 and 1996
_______________________________________________________________________________
-> year = 1993
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------bilat_trade |
121
148004.9
1258806
16.043
1.38e+07
gdp_o |
121
534871.6
545257.8
43753.2
1844608
gdp_d |
121
534871.6
545257.8
43753.2
1844608
distwces |
121
1150.405
725.777
13.18305
2838.347
bilat_trust |
121
2.769822
.288337
2.133612
3.547475
-------------+-------------------------------------------------------ave_o |
121
2.789112
.1382025
2.464294
3.012776
aver_d |
121
2.774256
.1440133
2.537562
2.983201
_______________________________________________________________________________
-> year = 1996
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------bilat_trade |
121
43914.77
176784.8
26.68344
1458474
gdp_o |
121
574160.1
574789.2
55562
1948601
gdp_d |
121
574160.1
574789.2
55562
1948601
distwces |
121
1150.405
725.777
13.18305
2838.347
bilat_trust |
121
2.682793
.3169785
2.008086
3.591365
-------------+-------------------------------------------------------ave_o |
121
2.682793
.1626823
2.348036
2.901754
aver_d |
121
2.682793
.1166943
2.508265
2.888266
Table 6: Descriptive Statistics for whole sample, for 1995 and 1997
_______________________________________________________________________________
-> year = 1995
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------bil_trade |
196
29115.55
140643.7
26.97241
1467235
gdp_o |
196
477984.5
531445.6
51324.5
1929422
gdp_d |
196
477984.5
531445.6
51324.5
1929422
distwces |
196
1277.606
760.5966
13.18305
3364.83
bil_trust |
196
1.725155
.1543394
1.27694
1.990798
-------------+-------------------------------------------------------ave_o |
196
1.725155
.0906594
1.459819
1.825955
ave_d |
196
1.725155
.0861746
1.581279
1.835651
_______________________________________________________________________________
-> year = 1997
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------bil_trade |
196
31720.4
145762.5
24.00341
1383286
gdp_o |
196
527749.5
559377.6
71717.6
1907246
gdp_d |
196
527749.5
559377.6
71717.6
1907246
distwces |
196
1277.606
760.5966
13.18305
3364.83
bil_trust |
196
1.721852
.151139
1.298246
1.976589
-------------+-------------------------------------------------------ave_o |
196
1.721852
.0823582
1.483764
1.829255
ave_d |
196
1.721852
.0805003
1.595069
1.844063
Table 7: Summery of our dummy variables
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------religion |
196
.4438776
.4981126
0
1
contig |
196
.1326531
.340068
0
1
comlang_eth |
196
.0612245
.2403556
0
1
Table 8: Between/Within coefficients for 1995, 1997 and all countries in sample.
Variable
|
Mean
Std. Dev.
Min
Max |
Observations
-----------------+--------------------------------------------+---------------bil_trad overall | 30417.97
143048.6
24.00341
1467235 |
N =
392
between |
35160.37
3004.999
119087.2 |
n =
14
within |
138967.7 -86790.85
1378566 |
T =
28
|
|
gdp_o
overall |
502867
545461.5
51324.5
1929422 |
N =
392
between |
562988.1
61521.05
1918334 |
n =
14
within |
49594.71
350822.7
654911.3 |
T =
28
|
|
bil_trus overall | 1.723504
.1525611
1.27694
1.990798 |
N =
392
between |
.0881651
1.471791
1.827605 |
n =
14
within |
.1266435
1.375069
2.15131 |
T =
28
|
|
ave_o
overall | 1.723504
.0865133
1.459819
1.829255 |
N =
392
between |
.0881651
1.471791
1.827605 |
n =
14
within |
.0157551
1.694382
1.752625 |
T =
28
Contingency Tables
Table 9: Country and Border
---------------------|
contig
iso_o |
0
1
----------+----------AUT |
24
4
BLX |
22
6
DEU |
18
10
DNK |
26
2
ESP |
24
4
FIN |
26
2
FRA |
20
8
GBR |
26
2
GRC |
28
IRL |
26
2
ITA |
24
4
NLD |
24
4
PRT |
26
2
SWE |
26
2
----------------------
Table 10: Country and Language
---------------------|comlang_eth
iso_o |
0
1
----------+----------AUT |
12
2
BLX |
10
4
DEU |
12
2
DNK |
14
ESP |
14
FIN |
14
FRA |
13
1
GBR |
13
1
GRC |
14
IRL |
13
1
ITA |
14
NLD |
13
1
PRT |
14
SWE |
14
----------------------
Annex 3: Estimation Output
Regression (1)
Regression with robust standard errors
Number of obs
F( 6,
386)
Prob > F
R-squared
Root MSE
=
392
=16167.39
= 0.0000
= 0.9947
= .59582
-----------------------------------------------------------------------------|
Robust
lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------ly_o |
.6609168
.0212227
31.14
0.000
.6191902
.7026434
ly_d |
.6560164
.0220775
29.71
0.000
.6126091
.6994236
ldis | -1.273898
.0428778
-29.71
0.000
-1.358202
-1.189595
Home |
.83452
.1836042
4.55
0.000
.4735306
1.195509
contig |
.1031118
.092251
1.12
0.264
-.0782655
.2844891
comlang_eth | -.0495423
.108831
-0.46
0.649
-.2635182
.1644335
------------------------------------------------------------------------------
Regression (2)
Regression with robust standard errors
Number of obs
F( 6,
386)
Prob > F
R-squared
Root MSE
=
392
=11659.42
= 0.0000
= 0.9939
= .63631
-----------------------------------------------------------------------------|
Robust
lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------home |
1.928083
.1783013
10.81
0.000
1.57752
2.278646
ldis | -1.221418
.0487213
-25.07
0.000
-1.31721
-1.125626
border |
.3096226
.1076805
2.88
0.004
.0979089
.5213363
lgdp_o |
.6470698
.0242636
26.67
0.000
.5993644
.6947751
lgdp_d |
.6421693
.0243476
26.38
0.000
.5942988
.6900398
language | -.0242921
.1254375
-0.19
0.847
-.2709184
.2223342
------------------------------------------------------------------------------
Regression (3)
Regression with robust standard errors
Number of obs
F( 7,
385)
Prob > F
R-squared
Root MSE
=
392
=10290.45
= 0.0000
= 0.9940
= .63609
-----------------------------------------------------------------------------|
Robust
lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------home |
1.997584
.1862011
10.73
0.000
1.631485
2.363682
trust | -.2088498
.1837325
-1.14
0.256
-.5700944
.1523949
ldis | -1.208975
.0524294
-23.06
0.000
-1.312059
-1.105891
border |
.3083327
.1063322
2.90
0.004
.0992682
.5173972
lgdp_o |
.6625288
.0266275
24.88
0.000
.6101754
.7148822
lgdp_d |
.6478927
.0243743
26.58
0.000
.5999693
.6958161
language |
.0005466
.1255882
0.00
0.997
-.246378
.2474711
------------------------------------------------------------------------------
Regression (4)
Regression with robust standard errors
Number of obs
F( 9,
383)
Prob > F
R-squared
Root MSE
=
392
=12026.81
= 0.0000
= 0.9955
= .55081
-----------------------------------------------------------------------------|
Robust
lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------home |
2.169257
.1553935
13.96
0.000
1.863726
2.474788
trust |
2.032039
.3407697
5.96
0.000
1.362026
2.702053
ldis | -.9300413
.0506991
-18.34
0.000
-1.029725
-.830358
border |
.3256374
.0916328
3.55
0.000
.1454712
.5058037
lgdp_o |
.7206315
.0251697
28.63
0.000
.6711434
.7701195
lgdp_d |
.6830237
.0258302
26.44
0.000
.6322369
.7338105
language |
.2078467
.1280135
1.62
0.105
-.0438505
.4595439
ave_o | -.0904678
.4223943
-0.21
0.831
-.9209699
.7400342
ave_d |
-3.9743
.3254088
-12.21
0.000
-4.614112
-3.334489
------------------------------------------------------------------------------
Regression (5)
Random-effects GLS regression
Group variable (i): ctry_code
Number of obs
Number of groups
=
=
392
14
R-sq:
Obs per group: min =
avg =
max =
28
28.0
28
within = 0.9288
between = 0.9302
overall = 0.9288
Random effects u_i ~ Gaussian
corr(u_i, X)
= 0 (assumed)
Wald chi2(9)
Prob > chi2
=
=
4980.37
0.0000
-----------------------------------------------------------------------------lx |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------home |
2.790289
.1589403
17.56
0.000
2.478772
3.101806
trust |
1.351838
.2964185
4.56
0.000
.7708686
1.932808
ldis |
-.709331
.0578595
-12.26
0.000
-.8227335
-.5959285
border |
.4435777
.0947216
4.68
0.000
.2579268
.6292287
lgdp_o |
.7630332
.0807645
9.45
0.000
.6047378
.9213286
lgdp_d |
.7903213
.0251363
31.44
0.000
.7410552
.8395875
language |
.2597148
.1202689
2.16
0.031
.023992
.4954375
ave_o |
.275094
.8996495
0.31
0.760
-1.488187
2.038375
ave_d | -1.592278
.422366
-3.77
0.000
-2.420101
-.7644562
_cons | -7.044771
2.005167
-3.51
0.000
-10.97483
-3.114717
-------------+---------------------------------------------------------------sigma_u | .31558634
sigma_e | .45097034
rho | .32872915
(fraction of variance due to u_i)
------------------------------------------------------------------------------
Regression (6)
Fixed-effects (within) regression
Group variable (i): ctry_code
Number of obs
Number of groups
=
=
392
14
R-sq:
Obs per group: min =
avg =
max =
28
28.0
28
within = 0.9296
between = 0.8222
overall = 0.8871
corr(u_i, Xb)
= 0.1877
F(9,369)
Prob > F
=
=
541.76
0.0000
-----------------------------------------------------------------------------lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------home |
2.801074
.1591954
17.60
0.000
2.48803
3.114118
trust |
1.360973
.2956274
4.60
0.000
.779647
1.942299
ldis | -.7034577
.0582208
-12.08
0.000
-.8179438
-.5889715
border |
.4460948
.0948203
4.70
0.000
.2596389
.6325507
lgdp_o |
.6085404
.2952991
2.06
0.040
.0278602
1.189221
lgdp_d |
.7915117
.0250989
31.54
0.000
.7421568
.8408666
language |
.2574582
.1202458
2.14
0.033
.0210053
.4939112
ave_o | -2.651216
1.4833
-1.79
0.075
-5.567997
.2655646
ave_d | -1.587346
.4210558
-3.77
0.000
-2.415316
-.7593764
_cons | -.1413576
4.386081
-0.03
0.974
-8.766207
8.483492
-------------+---------------------------------------------------------------sigma_u | .52694944
sigma_e | .45097034
rho | .57722843
(fraction of variance due to u_i)
-----------------------------------------------------------------------------F test that all u_i=0:
F(13, 369) =
10.77
Prob > F = 0.0000
Regression (7)
Instrumental variables (2SLS) regression
Source |
SS
df
MS
-------------+-----------------------------Model |
1418.6915
9 157.632389
Residual | 109.609017
382
.2869346
-------------+-----------------------------Total | 1528.30052
391 3.90869698
Number of obs
F( 9,
382)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
392
550.35
0.0000
0.9283
0.9266
.53566
-----------------------------------------------------------------------------lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------trust |
2.969168
1.775183
1.67
0.095
-.5211857
6.459522
home |
2.632225
.2931127
8.98
0.000
2.055909
3.208542
ldis | -.6363638
.0811599
-7.84
0.000
-.7959398
-.4767878
border |
.4500467
.1069736
4.21
0.000
.239716
.6603775
lgdp_o |
.7633984
.0269485
28.33
0.000
.7104125
.8163844
lgdp_d |
.8021323
.0311932
25.72
0.000
.7408005
.8634641
language |
.3088377
.1377965
2.24
0.026
.0379031
.5797723
ave_o |
.1481123
1.717096
0.09
0.931
-3.22803
3.524255
ave_d | -3.056916
1.690059
-1.81
0.071
-6.379899
.2660676
_cons | -7.741912
2.554551
-3.03
0.003
-12.76465
-2.71917
-----------------------------------------------------------------------------Instrumented: trust
Instruments:
home ldis border lgdp_o lgdp_d language ave_o ave_d religion
------------------------------------------------------------------------------
NEW SAMPLE – outliers removed.
Descriptive Statistics
Table 11: Contingency Table of Countries and Borders
---------------------|
border
iso_o |
0
1
----------+----------AUT |
6
2
BLX |
5
3
DEU |
4
4
ESP |
6
2
FRA |
4
4
ITA |
6
2
NLD |
6
2
PRT |
7
1
----------------------
Table 12: Descriptive statistics of our Dummy Variables
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------religion |
128
.625
.4860252
0
1
border |
128
.3125
.4653337
0
1
language |
128
.15625
.3645189
0
1
Table 13: Descriptive statistics of our non-string variables
_______________________________________________________________________________
-> year = 1995
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------bil_trade |
64
68369.38
228996.6
168.3404
1467235
gdp_o |
64
656645.5
602105.6
87038.4
1929422
gdp_d |
64
656645.5
602105.6
87038.4
1929422
distcap |
64
1046.952
637.6471
68.44467
2314.488
trust |
64
1.716655
.1280362
1.447942
1.957317
_______________________________________________________________________________
-> year = 1997
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------bil_trade |
64
70809.08
228159.7
211.4558
1383286
gdp_o |
64
694925.6
609313.6
98831.6
1907246
gdp_d |
64
694925.6
609313.6
98831.6
1907246
distcap |
64
1046.952
637.6471
68.44467
2314.488
trust |
64
1.714945
.1243087
1.443169
1.974113
Regression (8)
Fixed-effects (within) regression
Group variable (i): ctry_code
Number of obs
Number of groups
=
=
128
8
R-sq:
Obs per group: min =
avg =
max =
16
16.0
16
within = 0.9494
between = 0.9283
overall = 0.9260
corr(u_i, Xb)
= -0.4757
F(9,111)
Prob > F
=
=
231.45
0.0000
-----------------------------------------------------------------------------lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------home |
2.551604
.269733
9.46
0.000
2.01711
3.086098
trust |
1.440578
.5196984
2.77
0.007
.4107606
2.470395
ldis | -.6376644
.1025647
-6.22
0.000
-.8409031
-.4344256
border |
.4339471
.1194321
3.63
0.000
.1972844
.6706099
lgdp_o |
1.164393
.7413277
1.57
0.119
-.3045978
2.633383
lgdp_d |
.6628016
.0438344
15.12
0.000
.5759408
.7496624
language |
.1691973
.1427591
1.19
0.238
-.1136894
.4520839
ave_o |
-1.92129
2.002992
-0.96
0.340
-5.890352
2.047771
ave_d | -3.052562
.7088904
-4.31
0.000
-4.457275
-1.647848
_cons | -4.822907
10.73718
-0.45
0.654
-26.09935
16.45354
-------------+---------------------------------------------------------------sigma_u |
.4253232
sigma_e | .39264014
rho | .53989295
(fraction of variance due to u_i)
-----------------------------------------------------------------------------F test that all u_i=0:
F(7, 111) =
5.84
Prob > F = 0.0000
Regression (9)
Source |
SS
df
MS
-------------+-----------------------------Model | 376.860407
7
53.837201
Residual | 1.97394324
121 .016313581
-------------+-----------------------------Total |
378.83435
128 2.95964336
Number of obs
F( 7,
121)
Prob > F
R-squared
Adj R-squared
Root MSE
=
128
= 3300.15
= 0.0000
= 0.9948
= 0.9945
= .12772
-----------------------------------------------------------------------------trust |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------home |
.333029
.0692476
4.81
0.000
.195935
.470123
religion |
.0162949
.0258372
0.63
0.529
-.0348567
.0674464
ldis |
.0618936
.0236605
2.62
0.010
.0150514
.1087359
border | -.0147735
.0370184
-0.40
0.691
-.0880612
.0585143
lgdp_o |
.0769189
.0104711
7.35
0.000
.0561886
.0976491
lgdp_d |
.0170364
.0104711
1.63
0.106
-.0036938
.0377667
language |
.2067025
.0364114
5.68
0.000
.1346166
.2787885
------------------------------------------------------------------------------
Regression (10)
Instrumental variables (2SLS) regression
Source |
SS
df
MS
-------------+-----------------------------Model | -12233.675
9 -1359.29723
Residual | 12676.7211
118
107.42984
-------------+-----------------------------Total | 443.046098
127 3.48855195
Number of obs
F( 9,
118)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
128
0.44
0.9121
.
.
10.365
-----------------------------------------------------------------------------lx |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------trust |
140.1187
711.538
0.20
0.844
-1268.92
1549.158
home | -26.53683
148.1148
-0.18
0.858
-319.8445
266.7708
ldis | -1.228823
3.551842
-0.35
0.730
-8.262437
5.804792
border |
-1.16677
8.270467
-0.14
0.888
-17.54455
15.21101
lgdp_o |
-.216746
5.506419
-0.04
0.969
-11.12096
10.68746
lgdp_d |
1.125183
2.646265
0.43
0.671
-4.115141
6.365508
language | -8.744569
45.70638
-0.19
0.849
-99.25565
81.76651
ave_o | -103.7906
524.4018
-0.20
0.843
-1142.249
934.6677
ave_d | -110.8918
552.2553
-0.20
0.841
-1204.508
982.7241
_cons |
137.5045
706.8239
0.19
0.846
-1262.199
1537.208
-----------------------------------------------------------------------------Instrumented: trust
Instruments:
home ldis border lgdp_o lgdp_d language ave_o ave_d religion
------------------------------------------------------------------------------