Discussion Papers In Economics
And Business

Trade policy preference, childhood sporting experience, and informal school
curriculum: Examination from the viewpoint of behavioral economics

Eiji Yamamura
Yoshiro Tsutsui
Discussion Paper 17-25

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPAN

Trade policy preference, childhood sporting experience, and informal school
curriculum: Examination from the viewpoint of behavioral economics

Eiji Yamamura
Yoshiro Tsutsui
Discussion Paper 17-25

August 2017

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPAN

Trade policy preference, childhood sporting experience, and informal school
curriculum: Examination from the viewpoint of behavioral economics

Eiji Yamamura (Seinan Gakuin Univ)
Yoshiro Tsutsui (Konan Univ)
Acknowledgment
We thank Katie Stallard, LLB, from Edanz Group (www.edanzediting.com/ac) for
editing a draft of this manuscript.

Abstract:
We investigated how childhood education and experiences helped to form noncognitive skills and later, trade policy preferences. We used individual-level datawith approximately 10,000 observations collected July 2016. Using the instrumental
variables (IV) method, with sporting experience and informal education in thechildhood as exogenous IV, we found that (1) sporting experiences and informal
education lead people to have positive subjective views about the role of group
work,
competition, reciprocity, patience, and generalized trust and (2) positive views
about the role of group work, competition, reciprocity, patience, and generalized trust
leads people to prefer the Trans-Pacific Strategic Economic Partnership Agreement(TPP).
Key words: Trade policy; Policy preferences; TPP; Informal school curriculum;Childhood sporting experience; Social capital

JEL classification: F13; D83; I21; Z13; Z29

 yamaei@seinan-gu.ac.jp
 tsutsui@center.konan-u.ac.jp

11. Introduction
A 2016 referendum in the United Kingdom resulted in a striking change in its rolein Europe—its withdrawal from the European Union (Brexit). Furthermore, in thatsame year, Donald Trump was elected president of the United States and thenannounced its secession from the Trans-Pacific Strategic Economic Partnership
Agreement (TPP) and the Paris agreement. In a number of European countries,
individual political parties increased their popularity and therefore their influenceon economic policy.
Thus, unilateralism appears pervasive in developed countries, although thesecountries are expected to promote globalization by seeking market integration
among countries. The views of economic researchers on policies systematically differ
from those of ordinary people (Caplan, 2002). Researchers of international economics
consider it important that people understand the mutual benefit of international
trade between countries to gain their support for globalization. However, it isunknown whether people support globalization and international cooperation evenwhen aware that economists promote the benefits of participating in the European
Union and TPP. Educated people are observed to have a tendency to preferinternational trade and immigration (Mayda and Rodrik, 2005; Mayda, 2006)1. Thisis congruent to the argument that a lack of basic knowledge on economics leads to
unexpected outcomes and people should therefore learn about economics (Caplan,2007).
However, people’s views about globalization appear to depend not only on logicalthinking but also on emotions or perceptions. As observed in Mayda and Rodrik(2005), even after controlling for cognitive skills captured by schooling years, noneconomic factors such values and attachments play an important role in determining
trade preferences. That is, people’s non-cognitive skills including their values andperception are possibly key factors in avoiding unilateralism. The issue of how noncognitive skills are formed in the field of behavioral and education economics has
become a hot topic (e.g., Algan et al., 2013; Heckman et al., 2010a; 2010b; Hryshko
et al., 2011; Kawaguchi and Miyazaki, 2009; Fehr et al., 2008). Despite this, little is
Sasaki et al. (2017) found that the effect of education on perceptions about immigrants
varied according to country and various specifications. In Japan, South Korea, andSingapore, people with higher education levels tend to discriminate against foreigners
while the opposite results are observed in Germany. In contrast, Tomiura et al. (2017)
found that more educated people are more likely to prefer immigrants as well as imports.
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known about how the process of preference formation in childhood influences tradepreferences in adulthood, although a number of studies have analyzed thedeterminants of international trade preferences. The contribution of this paper is to
examine the trade preference formation by bridging educational economics andinternational economics from the viewpoint of behavioral economics.
The purpose of this paper is to examine how childhood experiences form noncognitive skills and in turn trade policy preferences. We examined several types ofchildhood experiences, and we used proxies for bridging and bonding social capitalin childhood (primary school age). Here, sporting experiences are considered as
bridging social capital while participation in community activities is treated as
bonding social capital. In addition, the informal “hidden curriculum” in school is also
considered. In July 2016, we collected individual level data from all over Japan, witha sample size of approximately 10,000 observations. Thus, under the condition where
there is conflict between unilateralism and globalism, we examined how childhood
experiences influence the trade preference.
We used the variables of experiences and types of education in childhood as
exogenous instrumental variables (IV) to control for the endogeneity of key
independent variables. This will capture non-cognitive skills when the formation of
trade preferences is examined. The key findings of this study are that sporting
experiences and informal education formed non-cognitive skills, which in turn had a
crucial influence on the preference for international trade. The remainder of thisarticle is organized as follows. Hypotheses are proposed in Section 2 and Section 3
explains the data. Section 4 provides the empirical method used and we present andinterpret the estimation results in Section 5. The final section offers someconclusions.
2. Related Literature and Hypotheses
According to the traditional and standard economic theory, international trade
increases productivity and then economic benefits. International trade is promoted
by interdependence among countries. Competitive pressure increases as aconsequence of international trade. In the long run, a country’s benefit results in thebenefit of other counties through trade. Therefore, international trade is notregarded as a zero-sum game. However, in reality, not everyone supports free trade,instead seeking the protection of domestic industries. Based on the established trade
theory, existing research that highlights the reasons why domestic markets should3

be protected focus on human capital (e.g, Blonigen, 2011; Mayda and Rodrik, 2005;
Scheve and Slaughter, 2001) and the characteristics of the sectors where individuals
are employed (e.g., Beaulieu, 2002a; Ito 2015).2 More recently, as determinants oftrade preferences, researchers have considered other factors such as task routineness,gender (Blonigen and McGrew, 2014), and status quo bias (Tomiura et al., 2016),

which can be categorized as issues in the field of behavioral economics.3 However,existing studies have not considered the process regarding how trade preferences areformed. Investigating preference formation during childhood can be considered a hot
issue within behavioral economics (e.g., Kawaguchi and Miyazaki, 2009; Fehr et al.,2008). Thus, we attempted to investigate whether childhood experiences formed
people’s non-cognitive skills, which in turn influenced their trade preferences inadulthood. As is illustrated in Figure 1, the shaded arrows are examined in thispaper.4
Trade is considered to increase mutual benefits. Therefore, interaction andcooperation with other individuals are critical to understand trade benefits. The
theoretical model has indicated that trade liberalization, as a cooperative relationship,
evolves gradually in a non-cooperative environment (Chisik, 2003). Basically,

constructing reciprocal relations with others is thought to be a key factor to maintainstable trade networks. International trade basically increases the economic gains as
a larger number of countries participate in trade. Therefore, Hypothesis 1 ispostulated:

Hypothesis 1: Those who understand that benefits emerge from group work andreciprocity prefer free trade.
The networks can be roughly divided into two types. Bonding social capitalrefers to connections between individuals within a group, while bridging social
capital unites heterogeneous individuals belonging to different groups (Putnam,
2000). There are various obstacles for international trade. For instance, in the real
In a similar way, the determinants of votes for trade liberalization bills were
investigated (Kaempfer and Marks, 1993; Baldwin and Magee, 2000; Beaulieu, 2002b).
Candidates in close elections are more likely to be protectionist because of electoralpressure in Japan (Ito, 2015).
3 The role of morals in international trade is examined within the framework thatcountries incur “psychological costs” when they renege on formal international trade
agreements (Furusawa, 2009).
4 We also show that the direct effect of childhood experiences on trade preference isgenerally weak, so that the former variables are appropriate for IV.
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world, there is often insufficient market information provided. Furthermore, thereis the possibility of breaching the contract. If so, the transaction may fail because it
is unknown whether the anonymous trading partner is reliable in the market.
Bonding social capital, such as the network within an ethnic group, can function intrade (e.g., Rauch, 2000; Rauch and Trnidade, 2002). However, as observed in ahistorical case study about Mediterranean trade, the closed trade network has been
replaced with the opened trade network (Greif, 1994). Thus, in the current era ofglobalization, trading with unfamiliar individuals seems to be more effective toincrease benefits. Therefore, bridging social capital is more effective and importantin gaining benefits from international trade. Trust in others in an anonymous society
is a fundamental key element to increase benefits and economic development
throughout the world (e.g., Zak and Knack, 2001; Bjørnskov, 2012; Bjørnskov andMéon, 2015). Generalized trust enhances the extending of economic exchanges tooutside the closed society, and is therefore a key factor in enhancing international
trade. Here, we propose Hypothesis 2:

Hypothesis 2: Those who trust and have bridging social networks preferinternational trade.
Market competition enhanced by free trade is expected to result in economic
benefits in the long run by fostering new industries or by giving industries acomparative advantage. This is despite the fact that international trade can change
the industry structure, which harms industries that do not have a comparativeadvantage. International trade often reduces the short-run benefits of industries
without comparative advantages, which in turn generates unemployment. If thelabor market functions well, unemployed people will find jobs in the export sector.
That is, international trade causes the reallocation of labor between sectors, andeach moving worker must pay adjustment costs (Furusawa and Lai, 1999). However,from a long-term viewpoint, after paying an adjustment cost, the industry structure
becomes more efficient and the demand for labor increases and unemployment isthen reduced. Therefore, patient people have a more positive view on free trade. Here,
we raise Hypothesis 3:

Hypothesis 3: Those who consider that competition generates benefits and consider
patience to be important prefer international trade.

5The effects of non-cognitive skills on trade preferences have been formalized in
Hypotheses 1 to 3. We now proceed to a hypothesis regarding the determinants of
non-cognitive skills. We focus on the effects of childhood experiences on non-cognitive
skills.
Early childhood education has been found to be effective in forming noncognitive skills, which play a crucial role in creating positive outcomes in adulthood(Heckman et al., 2010a; 2010b; 2013). More specifically, an increasing number ofstudies have explored how specific features of education (such as teaching practices)formed preferences and views about society (e.g., Aspachs-Bracons et al., 2008;
Hryshko et al., 2011; Milligan et al., 2004)). Algan et al. (2013) investigated whether
teaching practices at school changed students’ beliefs and found a positive causal
relationship between “working in groups” and students’ beliefs in cooperation andtrust. Informal school education is considered to influence non-cognitive skills (Alganet al., 2013; Ito et al., 2014). Social participation extends personal relationships insociety and thereby teaches the importance of interaction between people andcollective action, which accumulates non-cognitive skills. However, informal school
curriculums and social participation during childhood are unlikely to influence tradepreferences directly, partly because primary school pupils are unable to understandthe notion of international trade. That is, informal curriculums and social
participation only affect trade preference via the channel in which these experiences
influence non-cognitive skills. In Japan, there are some differences in informal
curriculum among primary schools. Group work is adopted in some schools but notothers. Based on egalitarianism, in some primary schools teachers do not rank thefinishing order of pupils in running races. Ito et al. (2014) found that group work andpro-competition curriculums foster non-cognitive skills such as mutual reciprocity
and cooperation.5 Thus, we propose Hypothesis 4:

Hypothesis 4: Those who have experienced social participation in a community andcompetition and group work in school tend to cooperate and trust others.
Experiences of community participation are expected to lead people to form
interpersonal networks within a community (Putnam, 2000). Furthermore,generalized trust seems to be fostered not only by the closed community within aGlaeser et al (2007) constructed a model where schooling teaches people to interact
with others, which increases the benefits of civic participation. Following this line,
education possibly leads people to prefer group work and reciprocity.
56community but also by exchanges with children from other schools and towns. People
seem to learn and form their values from experiences outside of the closed personal
relations within a school. In number of studies, sports were found to play a criticalrole in forming not only cognitive but also non-cognitive skills (e.g., Cabane et al.,2016; Lechner and Sari, 2105; Lechner, 2009; Light, 2010; Pfeifer and Cornelissen,
2010; Rees and Sabia, 2010). Those who join team sports are expected to learn how
to improve team performance. Furthermore, they are thought to learn that theperformance of team can be improved by cooperation and trust among team members.
They also had the opportunity to play with those from other schools and towns, andtherefore interchanged with them. Through such experiences, they learned how tobridge network with other groups. Consequently, they were more likely to growaccustomed to strangers and thereby trust others. Hence, we raise Hypothesis 5:

Hypothesis 5: Those who experience team sports in childhood tend to cooperate andtrust others.
3. Data
To explore peoples’ experiences in childhood, their current non-cognitive skills,
and their current trade preferences, we collected individual-level data via an online
survey in July 2016. We commissioned the Nikkei Research Company (a company
with significant experience in academic research) to conduct the web survey. The
survey was conducted with randomly selected people aged 20 to 66 years from allover Japan, and was to run until approximately 10,000 observations had beengathered. Consequently, we gathered 9,997 observations.
The definition and mean values of the key variables used for the estimations areshown in Table 1. Trade preference was measured by the degree of support for the
TPP, which ranges from 1 (strongly disagree) to 5 (strongly agree).
Non-cognitive skills was captured by the following 5 statements, which also range
from 1 (strongly disagree) to 5 (strongly agree):(1) Group work leads to a better outcome than working individually.
(2) Competition results in benefits for all.
(3) If someone does me a favor, I am prepared to return it.
(4) I should contain my craving for success in the far distant future.
(5) I generally trust others.
Statements (1) and (3) were used to test the effect of understanding the importanceof cooperation and interdependence. In terms of economics, the outcome of7

competition can be considered as follows. In the short term, competition produces
winners and losers, resulting in inequality among people. However, in the long term,competition possibly generates benefits for all. Thus, the difference in time
preference possibly leads to a difference in views about competition. Hence, views
about international trade depend on whether people consider the outcome ofinternational trade from a long- or short-term perspective (time preference). The
variable from statement (4) is considered to capture the time preference. The
variables (2) and (4) are used to test the effect of understanding the importance ofcompetition and the long-term view. Trusting others extends market exchanges
outside of the closed society. Statement (5) was used to test the effect of trustingothers. Furthermore, to consider the various facets of these variables as a whole, wesummed these five variables into a synthetic index of non-cognitive skills, which isexpressed as NON_COG_SKIL. We also used variables for cognitive skills obtained
through formal education such as EDU and ECONOMIC.
Experiences in childhood can be divided into formal and informal education andsocial experiences. Informal education was known as the “hidden curriculum” in aschool (Ito et al., 2014). Following Ito et al. (2014), we used a dummy, GROUP PRIM,
taking 1 if there is group work in the primary school, otherwise 0. In addition, thedummy COMPET PRIM was used, taking 1 if the teacher ranked the finishing orderin running races at primary school. From experiences outside of school, children havethe opportunity to develop their non-cognitive skills. Experiences of community
participation in childhood are also important childhood social experiences, teaching
children how to understand the benefits of collective action. Sporting experiences
have also been observed to improve life outcomes in adulthood (e.g., Cabane et al,
2016; Lechner and Sari, 2105; Lechner, 2009; Light, 2010; Pfeifer and Cornelissen,
2010; Rees and Sabia, 2010). Furthermore, we included the sporting experiences of
the respondents as school-aged children. The experiences were then divided into
team and individual sporting experiences. Team sports seem to be more effective indeveloping interpersonal relations and understanding the benefits of team work.
Through sporting matches, children have the chance to meet other children fromother schools and communities. Therefore, participation in sports enables children
to interact with people from different communities and to extend their world tooutside that of their own school and community. This in turn helps individuals tolook at the world with a wider field of vision.
4. Framework and Method8

In the baseline model, to assess the non-cognitive skills on trade preferences, theestimated function takes the following form:
TRADE PREFi = α0 + α1 Cognitive Skill i + α2 EDU i + α3 ECONOMICi+ α4 AGEi + α5 AGESQi + α6 MALEit + Xi B + u i.

(1)

The key variables are cognitive skills. We included V_GROUP, V_COMPET,
V_RECIPRO, V_PATIENT, and V_TRUST separately in different estimations.
Hypotheses 1–3 state that understanding the benefit from mutual dependence,cooperation, competition, long-term view (time preference), and generalized trust
seems to be critical for people to prefer international trade. Hence, the non-cognitive
skills variables are expected to be positive. To decompose human capital into noncognitive skills and cognitive skills, we included EDU and ECONOMIC as
independent variables. EDU captures schooling years considered as the quantitative
effect. As observed by Caplan (2002; 2007), those with knowledge about economics
have systematically different views about economic policy. ECONOMIC is included
to capture this qualitative effect.
Views about international trade may be related to age and its relation is possibly
non-linear. Hence, we incorporate not only AGE but also its square AGESQ.
Furthermore, the vectors of the control variables are denoted by Xi, and B is thevector of their coefficients. These control variables are 17 income dummies and 19
occupation dummies.
There is the possibility of endogenous bias in the baseline model because thecausality between trade preference and non-cognitive skills is not clear. To control
for this, childhood experiences are used as exogenous IV to conduct an IV model
estimation.
In the first-stage estimation of the IV model, to exogenously determine the noncognitive skills, the estimated function takes the following form:
Cognitive Skill i = β0 + β1 TEAM_SPORTS i + β2 INDI_SPORTSi + β3 GROUP_PRIMi + β4 COMPET_PRIM i + β5 SOCIAL_PRIM i + Yi C + e i.

(2)

According to Hypothesis 4 regarding the informal school curriculum, thecoefficients of GROUP_PRIM, COMPET_PRIM, and SOCIAL_PRIM are expected to
be positive. Childhood sporting experiences are observed to form non-cognitive skills
9through social learning (Light, 2010). However, childhood sporting experiences areunlikely to be related to trade preference. Therefore, the proxy for sporting
experiences can be considered as an exogenous IV. Team sports are thought to betterhelp children learn about the importance of interpersonal cooperation to beat
opponents than individual sports. Based on Hypothesis 5, we predicted that thecoefficient of TEAM_SPORTS will be positive.
Furthermore, the age at which people experience sports is also thought to beimportant because early childhood education has a great impact on life outcomes(Heckman et al., 2010a; 2010b; 2013). To compare the effects of team and individual
sports according to age groups, we asked respondents in the survey whether they
had experienced team and/or individual sports as primary school pupils (ages 6–12),
junior high students (ages 13–15), and high school students (ages 16–18). We thenmade six dummy variables for team and individual sports experiences across thethree schooling periods: TEAM SPORTS_PRIM, TEAM SPORTS_JUNIOR, TEAMSPORTS_HIGH, INDI SPORTS_PRIM, INDI SPORTS_JUNIOR, and INDI
SPORTS_HIGH. We also aggregated these six dummies to make the proxies for teamand individual sports experiences; for example, TEAM SPORTS and INDI SPORTS,
which range from 0 (never participated in any sport) to 3 (participated in sport
throughout the time at school). In an alternative model, as exogenous IV, we
incorporate these six dummies separately.
5. Results
5.1.

Baseline OLS model

The estimation results of the baseline OLS model are presented in Table 2. The
results show that non-cognitive skills such as V_GROUP, V_COMPET, V_RECIPRO,
V_PATIENT, and V_TRUST are positive and are significant at the 1% level. Thus,
the results of V_GROUP and V_RECIPRO imply that those with a positive view
about group work and who consider mutual benefit to be important tend to prefertrade. These results are consistent with Hypothesis 1. Furthermore, the results of
V_TRUST can be interpreted to state that those who trust others are inclined toprefer trade. Thus, fostering generalized trust is important to enhance international
trade. This is consistent with Hypothesis 2. The results of V_PATIENT show thatpatient people who consider the issue from a long-term view tend to prefer trade.
This is consistent with Hypothesis 3. NON_COG_SKILL, which combines these factors,
also shows a significant positive value. This suggests that, overall, non-cognitive skills
lead people to prefer international trade.
10Let us now turn to the results of the control variables. The results of the income
dummies suggest that people with higher earnings are more likely to prefer trade,which is consistent with existing studies (e.g., Blonigen, 2011; Mayda and Rodrik,
2005; Tomiura et al., 2016). Regarding the job dummies, only the estimates on fourjob dummies are exhibited because they were the only ones to show statisticalsignificance and are treated as key variables in Tomiura et al. (2016). Among these,
the result for JOB_AGRI is the most interesting. It shows a significant negative
coefficient, suggesting that agriculture is the most sensitive and import-competing
sector in Japan. This is consistent with previous research (Ito 2015, Tomiura et al.
2016).
ECONOMIC shows a significant positive coefficient, which is consistent with
Caplan (2002, 2007). This suggests that those who learn about economics atuniversity tend to support free trade. In contrast, the coefficient of EDU is positive
but not significant. The estimates of AGE and AGESQ show negative and positive
values, respectively, and are statistically significant. This indicates that younger
people prefer free trade, although this tendency decreases as one become older.
5.2.

IV model.

We used various sets of IV for robustness checks and report the results of the IVmodels in Tables 3, 4, and 5. Furthermore, to check the relation between exogenous
IV variables and trade preference, we exhibit the estimation results in the Appendix
when the variables of the informal curriculum and sporting experience are added to
the OLS Baseline model (1).
We now turn to the results of the IV model in Table 3. In the first stage, an overidentification test shows the validity of the estimation with the exception of column(1). Not only the degree of team sport experiences under the age of 18 years but alsothat of individual sports improving non-cognitive skills shows a significant positive
value, with the exception of V_PATIENT being the dependent variable. Furthermore,
the coefficients of TEAM_SPORTS are larger than those of INDI_SPORTS when
V_GROUP and V_TRUST are dependent variables. This convincingly suggests thatteam sports have a greater effect on positive views about group work and generalized
trust. In contrast, the coefficients of INDI_SPORTS are larger than those of
TEAM_SPORTS when V_COMPET and V_RECIPRO are dependent variables. This
indicates that individual sports experience leads more people to have positive view
about competition because interpersonal relations are less likely to play a role inunderstanding the meaning of competition. Regarding the coefficients of11

GROUP_PRIM and COMPET_PRIM, they are significant and positive with the
exception of COMPET_PRIM when V_TRUST is the dependent variable. The
coefficients of GROUP_PRIM are larger than those of COMPET_PRIM when
V_GROUP and V_TRUST are dependent variables. Thus, GROUP_PRIM (with its
relatively larger effect) can be interpreted similarly to TEAM_SPORTS, as the latterhas a larger effect than INDI_SPORTS. With respect to social participation, thecoefficients of SOCIAL_PRIM are significant and positive in all columns. All in all,and consistent with our predictions, childhood experiences and education have aclear influence on non-cognitive skills. These results are consistent with Hypotheses
4 and 5.
In the second-stage results of Table 3, the coefficients of V_GROUP, V_COMPET,
V_RECIPRO, V_PATIENT, V_TRUST, and NON_COG_SKILL are all positive andsignificant at the 1% level. Therefore, even after controlling for endogeneity bias,
non-cognitive skills formed in childhood lead people to prefer trade. There is,however, the possibility that the parents’ characteristics influenced the formation of
non-cognitive skills. If parents’ characteristics are not controlled, the estimation
results are biased. To avoid this, the father’s and mother’s schooling years areincluded in the model in Table 3 and the results are shown in Table A1 of the
Appendix, although its sample size is reduced to approximately 8,500. The results ofTable A1 are similar to those of Table 3. Hence, these results strongly support
Hypotheses 1, 2, and 3.
To further check the validity of the exogenous instruments, we included IV
variables into Equation (1). The results are shown in Table A2 of the Appendix,
which suggests that the coefficients of TEAM_SPORTS and COMPET_PRIM are
significant and positive with the exception of column (2). Hence, TEAM_SPORTS
and COMPET_PRIM are possibly invalid as exogenous instruments. Thus, in thespecification exhibited in Table 4, we exclude TEAM_SPORTS and COMPET_PRIM
from the set of exogenous instruments.
In the first stage of Table 4, from the results of the over-identification test, theset of the exogenous instruments are valid in all columns. Regarding the results ofthe second stage, in all columns, we can see significant and positive coefficients forthe proxies for non-cognitive skills: V_GROUP, V_COMPET, V_RECIPRO,
V_PATIENT, V_TRUST, and NON_COG_SKILL. The coefficient of V_PATIENT is0.33, which is larger than those of the other proxies for non-cognitive skills.
Therefore, the time preference formed in childhood experience is the most influential
factor in making people prefer international trade. The coefficients of V_COMPE and12

V_RECIPRO are approximately 0.20 and those of V_GROUP and V_TRUST areapproximately 0.16. These results imply that a 1-point increase in these variables on
the 5-point scale leads to a 0.20- and 0.16-point increase in the international tradepreference. As for TEAM_SPORTS and COMPET_PRIM, they do not show consistentstatistical significance and therefore their direct effect on trade preference is not as
robust.
In Table 5, sporting experience variables are decomposed into three categories
according to the schooling age groups. Thus, there are six variables: TEAM SPORTS
PRIM, TEAM SPORTS JUNIOR, TEAM SPORTS HIGH, INID SPORTS PRIM,
INDI SPORTS JUNIOR, and INDI SPORTS HIGH. These are used in the first stage
to consider how the effects of sporting experiences differ according to age and thekinds of sports. We see from Table 5 that TEAM SPORTS PRIM is statisticallysignificant in all columns. Therefore, as a whole, team sports experience at a younger
age has a larger effect on non-cognitive skills. In contrast, it is interesting to observethat participating in individual sports at primary school age has no effect while theopposite is true for experiences at an older age. In our interpretation, before
establishing the self, experiences with others are critical, whereas individual
experiences became more important as people grow older to establish the self.
Sportspeople possibly form tightly knit groups, which are closed to outsiders. If so,they share a conservative view and oppose trade liberalization to protect domestic
industries. In this case, sporting experience accumulates bonding social capitalrather than bridging social capital. Hence, an empirical investigation is required totest whether sporting experiences lead people to prefer international trade. Our
results clearly show that childhood sporting experiences foster

bridging social

capital, which can lead people to prefer trade liberalization in adulthood.
Our observations thus far suggest that the influence of non-cognitive skills on
trade preferences is robust to alternative specifications even after controlling for
endogenous bias. Furthermore, childhood education and sporting experiences play
an important role in the formation of non-cognitive skills. From this we derive theargument that policymakers should put place a greater emphasis on fostering noncognitive skills to promote international trade and economic development.
There are many examples of anecdotal evidence about the influence of sporting
experiences on the formation of non-cognitive skills. For example, Shinya Yamanaka(2012 Nobel Prize winner) participated in both judo and rugby at school days, andsuffered broken bones on more than ten occasions. He learned much from theseexperiences (Nihon Keizai Newspaper 2008). His time playing sports as a teenager
13

led Yamanaka to believe that his successes were the result of cooperation with many
people while his failures were due to his own mistakes (Yamanaka, 2017). He
originally trained as a plastic surgeon, but was not good at performing operations
and experienced repeated setbacks. He then decided to change careers and spent 3
years as a Post-Doctoral Fellow in the United States (Nihon Keizai Newspaper 2015).
Before winning the Nobel Prize, Yamanaka gave the following advice to a young
student, “it is important to continue to challenge to improve one’s best” and“inscrutable are the ways of heaven” (Nihon Keizai Newspaper 2008). A further
example is that of Kazuhiro Takei, a top corporate lawyer (Nihon Keizai Newspaper2015). Takei played football in early childhood, and then tennis from junior high until
university (Minamiyama, 2017). He walked a hard road in his experience of sports.
Even with considerable practice he could not improve his tennis game. However, he
endured hours of rigorous training and withstood adversity.6 This experience led
Takei to consider the importance of patience and to believe that fortune awaits
honest toil and earnest endeavor (Minamiyama 2017, 6). Thanks to the formation of
non-cognitive skills through sporting experiences as a teenager, Takei later studied
abroad at Harvard Law School and an MBA course at Oxford to obtain a wider legal
perspective and knowledge. These cases are in line with the findings in this paper.
6. Conclusion.
As shown in the results of various political elections in 2016, unilateralism appears
pervasive in developed countries such as the United States and the United Kingdom,
although these countries have an initiative to drive globalization by promoting
market integration. It is important for people to understand the benefits ofinterdependence between countries to promote international trade because those
who understand it are more likely to support international trade policies such as
TPP at election time. Existing studies have analyzed the determinants ofinternational trade preferences. However, little is known about the process ofpreference formation in childhood.
This paper empirically examined how international trade preferences are formed
in a long-term process. We treated childhood experience and education as exogenous
IV to form non-cognitive skills. We then examined the relation between such skills
and international trade preferences, as learning experiences from the far distantYasuyuki Todo is Japanese researcher in the field of international and development
economics, and was a schoolmate of Takei. According to Todo, classmates attached anhonorific suffix to Takei’s name because he was well-known by his earnest character. In
Japanese, he was called “Takei-san” by fellow pupils.
6

14

past are unlikely to be related to trade preferences but are considered to be
determinants of non-cognitive skills. Furthermore, we controlled for cognitive skills
measured by schooling years and a dummy for learning about economics atuniversity. The key findings are: (1) sporting experiences, group learning, and social
participation in childhood resulted in people having positive subjective views about
the role of group work, competition, reciprocity, patience, and generalized trust, and(2) greater non-cognitive skills makes people prefer free trade.
The findings imply that not only group learning within school classes but also
experiences that extend children’s interpersonal relations to others outside of theirschool are important to help people view matters from a broader perspective andthen to understand the benefits of international trade.
The data used in this paper are limited to Japan and so it is unknown whether
our argument holds true in other countries. Thus, to generalize the discussion, we
should examine our hypotheses using data from other countries with different
economic and social conditions. The findings of this paper are based on cross-section
survey data. The experiment was not conducted in a natural setting and the validityof the exogenous instruments is not completely acceptable, even though we
attempted to use the appropriate method for estimation under the constraints.
Therefore, future research should include a natural experiment or field experiments
should be conducted to scrutinize the role of childhood experiences and tradepreferences.

15

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19

Non-cognitive Skill

Childhood Experiences

Trade Preference

Figure 1. Effect of childhood experiences on trade preferences through the formation of non-cognitive skills
Note: Shaded arrows denote the focus in this study. The Appendix shows that the direct effect is generally weak, so that childhood
experiences are appropriate for the IVs.

20

Table 1. Basic statistics and definitions of key variables used for estimation
Variables
TRADE PREF
V_GROUP
V_COMPET
V_RECIPRO
V_PATIENTV_TRUSTNON_COG_SKILLTEAMSPORTS
PRIM
TEAM
SPORTSJUNIORTEAM
SPORTSHIGHTEAM SPORTS
INIDI
SPORTS
PRIM
INIDISPORTSJUNIOR

Definition
Degree of support for the TPP
1 (strongly disagree) – 5 (strongly agree)Group work leads to better outcomes than working individually
1 (strongly disagree) – 5 (strongly agree)
Competition results in benefits for society
1 (strongly disagree) – 5 (strongly agree)
If someone does me a favor, I am prepared to return it
1 (strongly disagree) – 5 (strongly agree)
I should contain my craving for success in the far distant future
1 (strongly disagree) – 5 (strongly agree).
I generally trust others
1 (strongly disagree) – 5 (strongly agree).
Sum of V_GROUP, V_COMPET, V_RECIPRO, V_PATIENT and V_TRUST.
Proxy variables for investment in bridging social capital
Takes 1 if the respondent participated in team sports at primary school age(about 6–12 years old), other wise 0.
Takes 1 if the respondent participated in team sports at junior high age (about
13–15 years old), other wise 0
Takes 1 if the respondent participated in team sports at high school age (about
16–18 years old), other wise 0.
Sum of TEAM SPORTS PRIM, TEAM SPORTS JUNIOR,
and TEAM SPORTS HIGH.
Takes 1 if the respondent participated in individual sports at at primary schoolage (about 6–12 years old), other wise 0.
Takes 1 if the respondent participated in individual sports at junior high age(about 13–15 years old), other wise 0.
21

Mean
3.18

Max
5

Min
1

3.35

5

1

3.84

5

1

4.21

5

1

3.41

5

1

3.36

5

1

17.7

25

5

0.24

1

0

0.28

1

0

0.17

1

0

0.70

3

0

0.12

1

0

0.29

1

0

INIDISPORTSHIGHINIDI SPORTS

0.18

1

0

0.60

3

0

2.10

3

1

0.43

1

0

0.89

1

0

EDU

Takes 1 if the respondent participated in individual sports at high school age(about 16–18 years old), other wise 0.
Sum of INDI SPORTS PRIM, INDI SPORTS JUNIOR,
and INDI SPORTS HIGH
Proxy variable for investment in bonding social capital
Degree of participating in community activities as a primary school student
1 (not at all), 2 (only participated in main community events), 3 (participatedin all community events)
Informal school curriculum to learn from others and competition
Takes 1 if there was a task in which students worked together as a group at
primary school, otherwise 0.
Takes 1 if there were running races during sporting events at primary school
and teachers ranked the finishing order, otherwise 0.
Cognitive skills
Schooling years

14.7

18

6

ECONOMIC

Takes 1 if the respondent majored in economics at university, otherwise 0.

0.11

1

0

AGE

Control variables
Respondents’ ages

44.9

66

18

AGESQ

Square of respondents’ ages

2166

4356

324

MALE

Takes 1 if the respondent is male, otherwise 1

0.53

1

0

INCOM
DUMMIESJOB ORDINARY
EMP

12 household income dummies (income level) are included

-----

----

---

Takes 1 if the respondent is a rank-and-file employee, otherwise 1

0.28

1

0

SOCIAL PRIMGROUP PRIMCOMPET PRIM

22

JOB MANAGER

Takes 1 if the respondent works in a managerial position, otherwise 1

0.08

1

0

JOB TEACHER

Takes 1 if the respondent works as a teacher, otherwise 1

0.02

1

0

JOB MEDICAL

Takes 1 if the respondent works in the medical service sector, otherwise 1

0.02

1

0

JOB AGRI

Takes 1 if the respondent works in primary industry, otherwise 1

0.004

1

0

Note: Apart from the job dummies indicated, 15 other job dummies were included in the estimation model.

23

Table 2. Baseline estimation (OLS model): Dependent variable is TRADE PREF

V_GROUP(1)
0.09***(9.04)

V_COMPET

(2)

(3)

(4)

(5)

0.22***(21.4)

V_RECIPRO

0.11***(8.04)

V_PATIENT

0.09***(7.14)

V_TRUST

0.07***(8.78)NON_COG_SKILLEDUECONOMIC
AGE
AGESQ
MALE

0.01*
(1.68)
0.13***(4.27)−0.01**(−2.03)0.0002***(2.80)
0.17***(9.75)

0.01(1.08)
0.13***(4.42)−0.02**(−2.66)0.0002***(3.41)
0.18***(10.3)−0.08(−1.51)−0.07(−1.37)

INCOM<100
INCOM_100<200

(6)

0.01*
0.01*
(1.97)(1.70)
0.13***
0.13***(4.23)(4.25)−0.02***−0.02***(−2.89))(−2.72)0.0002***0.0002***(3.53)(3.41)
0.20***
0.17***(11.6)(9.70)
Reference group−0.07(−1.45)−0.08(−1.61)24

0.01(1.63)
0.13***(4.31)−0.01**(−2.06)0.0002**(2.66)
0.20***(11.4)

0.04***(13.6)
0.01(1.42)
0.13***(4.24)−0.01*(−1.97)0.0002**(2.62)
0.19***(10.6)−0.08(−1.50)−0.08(−1.51)INCOM_200<400
INCOM_400<600
INCOM_600<800
INCOM_800<1000
INCOM_1000<1200
INCOM_1200<1400
INCOM_1400<1600
INCOM_1600<1800
INCOM_1800<2000
INCOM>=2000
JOB
ORDINARYEMP
JOB MANAGERJOB TEACHERJOB MEDICALJOB AGRI

0.01(0.27)
0.07(1.53)
0.11**(2.21)
0.16***(3.26)
0.23***(3.10)
0.35***(6.62)
0.16*(1.96)
0.23**(2.17)
0.24**(2.47)
0.36***(3.82)
Default−0.003(−0.07)0.04(0.80)
0.07(1.41)
0.12**(2.33)
0.18**(2.35)
0.30***(5.11)
0.12(1.41)
0.18(1.67)
0.15(1.65)
0.30***(3.34)
Default

0.01(0.12)
0.06(1.22)
0.10*(1.93)
0.16***(3.02)
0.22***(2.86)
0.34***(6.27)
0.16*(1.97)
0.22**(2.06)
0.20**(2.11)
0.36***(3.79)
Default

0.01(0.29)
0.07(1.51)
0.11**(2.22)
0.17***(3.22)
0.23***(3.12)
0.36***(6.64)
0.17*(1.97)
0.22**(2.14)
0.22**(2.23)
0.36***(3.83)
Default

0.01(0.12)
0.06(1.21)
0.10*(1.85)
0.15***(2.85)
0.20***(2.74)
0.34***(6.23)
0.15*(1.81)
0.21*(1.90)
0.10*(1.89)
0.35***(3.72)
Default

0.004(0.08)
0.05(1.14)
0.09*(1.77)
0.14***(2.79)
0.20***(2.71)
0.33***(6.28)
0.15*(1.79)
0.20*(1.92)
0.20**(2.07)
0.34***(3.71)
Default

0.21***(6.29)−0.20***(−2.72)−0.14**(−2.37)−0.82***

0.20***(6.05)−0.18**(−2.66)−0.13**(−2.15)−0.82***

0.22***(6.69)−0.21***(−3.00)−0.15**(−2.49)−0.83***

0.23***(6.76)−0.22**(−3.11)−0.15**(−2.56)−0.83***

0.21***(6.20)−0.23**(−3.25)−0.14**(−2.52)−0.83***

0.20***(6.07)−0.22**(−3.10)−0.15**(−2.56)−0.83***

25

Other job dummies,
Constant
R-squareObservations(−3.69)Yes(−3.82)Yes(−3.67)Yes(−3.72)Yes(−3.68)Yes(−3.69)
Yes

0.06
9,997

0.08
9,997

0.05
9,997

0.05
9,997

0.05
9,997

0.06
9,997
Notes: ***, **, and *denote statistical significance at the 1%, 5%, and 10% levels, respectively. T-values are calculated based on robust standard errors
clustered on prefectures. “Yes” means that those variables are included as independent variables.

26

Table 3. Estimation (IV model): Dependent variable is TRADE PREF
Second stage(1)
V_GROUP

(2)

(3)

(4)

(5)

(6)

0.27***(5.41)

V_COMPET

0.30***(6.70)

V_RECIPRO

0.26***(5.90)

V_PATIENT

0.54***(5.56)

V_TRUST

0.29***(4.40)NON_COG_SKILL

0.07***(6.00)EDUECONOMIC

0.01

0.003

0.01*

0.002

0.002

0.01

(1.10)(0.76)(1.96)(0.48)(0.55)(1.06)

0.13***

0.13***

0.13***

0.12***

0.12***

0.12***(4.16)(4.48)(4.09)(3.64)(4.10)(4.16)First stageEDU
ECONOMIC

0.01*

0.02***−0.01

0.01**

0.02***

0.05***(1.84)(3.07)(−1.10)(2.51)(3.90)(2.92)

0.01

0.01

0.02

0.02

0.05

0.10

(0.31)(0.33)(0.53)(0.60)(1.25)(0.08)

Exogenous
27

IV
TEAM SPORTS
INDI SPORTS
SOCIAL PRIMGROUP PRIMCOMPET PRIM

0.08***

0.05***

0.04***

0.01

0.07***

0.29***(9.72)(6.25)(5.73)(1.12)(6.36)(11.2)

0.03***

0.06***

0.06***

0.01

0.05***

0.18***(2.96)(4.19)(5.93)(1.30)(4.33)(4.97)

0.08***

0.07***

0.08***

0.04**

0.13***

0.42***(5.71)(7.71)(7.99)(2.26)(7.78)(9.79)

0.20***

0.07***

0.08***

0.08***

0.07**

0.64***(11.3)(3.82)(5.80)(2.76)(2.64)(10.2)

0.15***

0.36***

0.42***

0.16***

0.05

0.94***(5.48)(15.3)(12.6)(6.53)(1.32)(11.6)

Under-

320.5

309.5

356.1

90.5

203.3

468.7

identification test

P = 0.00

P = 0.00

P = 0.00

P = 0.00

P = 0.00

P = 0.00

Over-identification

85.9

2.66

2.97

5.31

6.62

4.87

test

P = 0.07

P = 0.61

P = 0.57

P = 0.25

P = 0.16

P = 0.30

9,997

9,997

9,997

9,997

9,997

9,997

(HansenJ-

stat)
Observations

Notes: ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively. T-values are calculated based on robust standard errors clustered on
prefectures. Under-identification test is based on Kleibergen-Park rk LM statistics. Over-identification test is based on Hansen Jstatistics. Other control variables included in Table 2 are also included in the first and second stage of the model.

28

Table 4. Estimation where exogenous IV variables are restricted (IV model): Dependent variable is TRADE PREF
Secondstage(1)
V_GROUP

(2)

(3)

(4)

(5)

(6)

0.14**(2.11)

V_COMPET

0.22**(2.46)

V_RECIPRO

0.20**(2.38)

V_PATIENT

0.33**(2.53)

V_TRUST

0.16**(2.07)NON_COG_SKILL

0.04**(2.28)EDUECONOMICTEAM SPORTS
COMPET PRIM

0.01

0.005

0.01*

0.004

0.005

0.01

(1.43)(1.05)(1.93)(0.05)(1.02)(1.37)

0.13***

0.13***

0.13***

0.12***

0.12***

0.13***(4.11)(4.31)(4.00)(3.80)(4.04)(4.08)

0.02

0.02

0.02*

0.03**

0.02

0.02(1.31)(1.65)(1.78)(2.09)(1.32)(1.41)

0.08***

0.02

0.01

0.05

0.09***

0.06*(2.75)(0.47)(0.28)(1.26)(3.31)(1.87)First stage
29

Under-

172.1

117.9

141.8

90.5

134.9

266.6

identification test

P = 0.00

P = 0.00

P = 0.00

P = 0.00

P = 0.00

P = 0.00

Over-identification

1.40

0.40

0.24

5.31

0.10

0.49

test

P = 0.49

P = 0.81

P = 0.88

P = 0.25

P = 0.95

P = 0.78

9,997

9,997

9,997

9,997

9,997

9,997

(HansenJ-

stat)
Observations

Notes: ***, **, and * denote statistical significance at the 1%, 5%, and 10 % levels, respectively. T-values are calculated based on robust
standard errors clustered on prefectures. Under-identification test is based on Kleibergen-Park rk LM statistics. Over-identification test
is based on Hansen J-statistics. Results of the first stage are not reported but are available upon request from the corresponding author.
Other control variables included in Table 2 are also included in the first and second stage of the model.

30

Table 5. Estimation where exogenous IV variables are restricted (IV model): Dependent variable is TRADE PREF

V_GROUP(1)
0.27***(5.31)

V_COMPET

Second stage
(2)
(3)

(4)

(5)

0.30***(6.38)

V_RECIPRO

0.27***(5.80)

V_PATIENT

0.52***(5.47)

V_TRUST

0.29***(4.31)NON_COG_SKILLEDUECONOMICTEAM
SPORTS
PRIM
TEAM
SPORTSJUNIORTEAM
SPORTS
HIGH

(6)

0.01
(1.11)
0.13***(4.17)
0.08**(2,37)
0.06**(2.27)
0.12***(2.91)

0.003
0.01*(0.76)(1.96)
0.13***
0.13***(4.48)(4.09)First stage exogenous IV
0.07***
0.05***(3.53)(3.48)
0.01
0.05**(0.56)(2.70)
0.07**
0.02(2.69)(1.11)

0.003(0.54)
0.12***(3.67)

0.002(0.56)
0.12***(4.09)

0.07***(5.86)
0.01(1.06)
0.12***(4.16)

0.04**(2,28)
0.02(0.52)−0.01(−0.42)0.10***(3.96)
0.03(1.16)
0.09***(2.75)

0.35***(4.35)
0.22***(2.71)
0.35***(3.44)

31

INIDISPORTS
PRIM
INIDISPORTSJUNIORINIDISPORTSHIGH
SOCIAL PRIMGROUP PRIMCOMPET PRIM
Underidentification test
Over-identification
test (Hansen Jstat)
Observations

0.01(0.51)
0.03(1.12)
0.05**(2.40)
0.08***(5.64)
0.19***(11.1)
0.15***(5.47)
322.4
P = 0.00
12.5
P = 0.13

0.03(1.60)
0.05**(2.01)
0.09***(4.29)
0.07***(7.74)
0.07***(3.78)
0.36***(15.4)
317.3
P = 0.00
11.6
P = 0.17−0.01(0.26)
0.10***(5.47)
0.05**(2.47)
0.08***(7.95)
0.08***(5.83)
0.42***(12.6)
362.7
P = 0.00
11.8
P = 0.18−0.04(−1.48)0.05**(2.14)−0.004(−0.28)0.04**(2.20)
0.08***(2.80)
0.16***(6.35)
97.9
P = 0.00
10.9
P = 0.20

0.02(0.52)
0.05*(1.70)
0.07**(2.21)
0.14***(7.74)
0.07**(2.63)
0.05(1.30)
207.7
P = 0.00
10.5
P = 0.22

0.002(0.02)
0.25***(3.76)
0.22***(3.04)
0.42***(9.68)
0.64***(10.7)
0.93***(11.5)
475.5
P = 0.00
10.2
P = 0.24

9,997

9,997

9,997

9,997

9,997

9,997

Notes: ***, **, and * denotestatistical significance at the 1%, 5%, and 10% levels, respectively. T-values are calculated based on robust standard errors clustered on
prefectures. Under-identification test is based on Kleibergen-Park rk LM statistics. Over-identification test is based on Hansen Jstatistics. Other control variables included in Table 3 are also included but their results are not reported.

32

Appendix. Table A1. Adding parent’s years of schooling to the model in Table 3 (IV model)
Second stage(1)
V_GROUP

(2)

(3)

(4)

(5)

(6)

0.24***(4.03)

V_COMPET

0.31***(5.82)

V_RECIPRO

0.28***(4.94)

V_PATIENT

0.51***(4.30)

V_TRUST

0.26***(3.10)NON_COG_SKILL

0.07***(4.51)

Father’s EDU
Mother’s EDUEDUECONOMIC−0.003−0.003−0.004−0.005−0.003−0.003(−0.87)(−0.86)(−0.97)(−1.11)(−0.82)(−0.97)0.0001−0.00030.0002−0.0001−0.0004

0.0005(0.03)(−0.04)(0.32)(−0.16)(−0.07)(0.08)

0.007

0.005

0.01**

0.01

0.01

0.01

(1.19)(1.00)(2.13)(1.06)(0.86)(1.23)

0.14***

0.14***

0.14***

0.13***

0.13***

0.14***(4.88)(5.22)(4.82)(4.17)(4.69)(4.86)

0.004

0.003

0.02*

First stage
Father’s EDU

0.002

0.002

0.004

33

Mother’s EDU
EDUCONOMIC(0.56)(0.72)(1.00)(1.13)(0.67)(1.83)−0.003−0.0006−0.01**

0.001−0.001−0.02(−0.57)(−0.13)(−2.12)(0.24)(−0.08)(−0.92)0.01**

0.02***

−0.01

0.01*

0.02***

0.05***(2.24)(3.00)(−1.27)(1.69)(2.75)(2.74)

0.01

0.01

0.01

0.03

0.05

0.11

(0.51)(0.27)(0.39)(0.84)(1.22)(1.12)

Exogenous
IV
TEAM SPORTS
INDI SPORTS
SOCIAL PRIMGROUP PRIMCOMPET PRIM

0.08***

0.05***

0.03***

0.01

0.07***

0.28***(8.93)(6.05)(3.73)(0.55)(5.80)(8.83)

0.03**

0.06***

0.04***

0.01

0.05***

0.16***(2.31)(3.60)(4.35)(0.90)(4.23)(3.67)

0.08***

0.06***

0.07***

0.03*

0.14***

0.39***(5.18)(6.12)(5.26)(1.89)(8.14)(8.95)

0.20***

0.07***

0.08***

0.09**

0.07**

0.64***(10.2)(3.58)(5.35)(2.57)(2.25)(9.14)

0.13***

0.35***

0.41***

0.15***

0.03

0.84***(3.93)(10.8)(12.2)(5.75)(0.59)(9.35)

Under-

251.3

230.7

247.2

62.0

167.2

357.0

identification test

P = 0.00

P = 0.00

P = 0.00

P = 0.00

P = 0.00

P = 0.00

Over-identification

7.91

2.49

3.37

7.62

6.03

4.51

test

P = 0.09

P = 0.64

P = 0.49

P = 0.25

P = 0.19

P = 0.34

8,536

8,536

8,536

8,536

8,536

8,536

(HansenJ-

stat)
Observations

34

Notes: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. T-values are calculated based on robust
standard errors clustered on prefectures. Under-identification test is based on Kleibergen-Park rk LM statistics. Over-identification test
is based on Hansen J-statistics. Father’s EDU and Mother’s EDU are the father and mother’s schooling years, respectively. Other control
variables included in Table 2 are also included in the first and second stage of the model.

35

Table A2. Checking the exogeneity of IV variables (OLS model): Dependent variable is TRADE PREF

V_GROUP(1)
0.08***(7.75)

V_COMPET

(2)

(3)

(4)

(5)

0.21***(20.9)

V_RECIPRO

0.10***(7.22)

V_PATIENT

0.09***(6.64)

V_TRUST

0.07***(8.20)NON_COG_SKILLEDUECONOMICTEAM SPORTS
INDI SPORTS
SOCIAL PRIMGROUP PRIM

(6)

0.01
(1.54)
0.09***(3.73)
0.02*(1.79)
0.004(0.44)
0.01(1.07)−0.0003(−0.02)0.01(1.08)
0.13***(4.25)
0.02(1.63)−0.01(−0.52)0.01(0.53)
0.001(0.04)

0.01*(1.85)
0.13***(4.02)
0.03**(2.04)
0.01(0.16)
0.01(1.05)
0.01(0.53)

0.01(1.53)
0.13***(4.03)
0.03**(2.33)
0.01(0.62)
0.02(1.43)
0.01(0.60)
36

0.01(1.48)
0.13***(4.09)
0.02*(1.99)
0.004(0.40)
0.01(0.91)
0.01(0.71)

0.04***(12.1)
0.01(1.35)
0.13***(4.08)
0.02(1.48)−0.002(−0.03)0.004(0.34)−0.01(−0.54)

COMPET PRIM
Other
controlvariables in Table 2.
R-squareObservations

0.09***(3.73)
Yes

0.02(0.87)
Yes

0.06**(2.18)
Yes

0.09***(3.53)
Yes

0.10***(3.91)
Yes

0.06**(2.65)
Yes

0.06
9,997

0.08
9,997

0.06
9,997

0.06
9,997

0.06
9,997

0.06
9,997

Notes: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. T-values are calculated based on robust
standard errors clustered on prefectures. “Yes” means that those variables are included as independent variables.

37