1. No category

The Positive Effects of the Schengen Agreement
on European Trade1
Dane Davis
CRU Group
Thomas Gift
Department of Political Science
Duke University
Forthcoming at The World Economy
Abstract: Enacted in 1985, the Schengen Agreement is widely heralded as both a symbol and major
institutional advancement of the European project. By eliminating passport requirements for workers,
the compact ostensibly produces gains from travel, ease of market access, and economies of scale. Yet
despite these optimistic predictions, scholars know little about the actual effects of Schengen on trade.
We fill this void by identifying why labor mobility should expand the cross-country exchange of goods
and services and then test our theory with data from Europe spanning the period 1980 to 2011. We
argue that labor mobility resulting from Schengen yields positive effects on trade by increasing demand
for foreign goods, improving awareness of low-cost producers abroad, and lowering the risks associated
with buying and selling outside the country. Using the gravity model of trade, we show empirically that
Schengen membership makes European states more robust trading partners.
Introduction
ers the world over had high hopes that a unified
European market would produce gains from travel,
ease of market access, and economies of scale. Yet
while Schengen has undoubtedly become a vivid
symbol of the European project—representing solidarity and harmony between nations—what impact
has it really had on regional economic integration?
Our article constitutes an original attempt to
shed light on how Schengen affects trade on the
continent. We argue that the immigration policies
established by Schengen promote cross-border commerce for three central reasons. First, immigrants
bring a preference for goods specific to the country from which they emigrated, thereby raising demand for familiar products. Second, immigrants
have the potential to recognize the savings from
trade because of their knowledge of foreign low-cost
producers. Finally, immigrants are plugged into social networks that lower the risks of importing and
exporting. Employing the well-established gravity
model of trade, we show that migratory flows stemming from Schengen are positively and significantly
related to cross-national exchanges in goods and
services over the period 1980 to 2011.
We find that the effects of Schengen on European trade are considerable in substantive terms.
When two countries are members of Schengen, total trade between them increases by approximately
.10 percent every year. Furthermore, a net increase
of immigrants from one country to another by just
1 percent annually can expand trade between those
two nations by an almost equivalent amount. When
Along the banks of the River Moselle in Luxembourg on June 14th, 1985, European leaders signed
the Schengen Agreement, an unprecedented step in
the drive toward continental integration. Supplemented by the 1990 Convention Implementing the
Schengen Agreement, the compact promised a new
era of cooperation among European states in a variety of policy areas, most noticeably in regional
border control. In particular, it created the eponymous Schengen Area, a 4.3 million square kilometer area that eliminated passport requirements for
cross-country European travel and work. Today,
the more than 400 million citizens residing within
the 26 member nations of the Schengen Agreement
can visit, work, and live in any other member state
without restriction.
When originally conceived, Schengen was billed
not only as a crucial step for regional border control, but also as a major advancement in the goal
of creating a single European polity. Predating the
European Union by eight years, the zone was to
be instrumental in erecting the first large-scale international labor market. In addition, the fluidity
of labor enshrined by Schengen was heralded as the
perfect complement to the free movement of capital
and goods implemented by previous treaties. Lead1 Gift gratefully acknowledges financial support from
the National Science Foundation Graduate Research Fellowship Program. Contact: dane.davis@crugroup.com /
thomas.gift@duke.edu.
1
viewed in the context of overall trade, this can make
a large imprint on the amount of cross-border commercial transactions that take place throughout Europe. For example, trade totals between Italy and
Spain amounted to nearly $53 billion in 2011. Accordingly, a growth of .10 percent in this figure
would generate more than $50 million worth of extra trade between these states every year.
Our article’s main finding—that the world’s
largest free labor compact has a positive influence on European integration through trade—is
germane to both academics and policymakers. It
advances the ongoing scholarly debate over the nature of the relationship between labor mobility and
factor endowments, including trade in product and
service markets. Given the growing prominence of
regional, integrative organizations throughout the
globe, it also has salient implications for states considering joining Schengen or other similarly styled
compacts. In the wake of a global financial crisis that has left the European Union teetering on
the brink of collapse, it is particularly timely to
probe the forces that may help save—or ultimately
shatter—the largest experiment of pooled national
sovereignty in history.
The rest of our article proceeds in six sections.
Section I puts Schengen in historical context and
discusses its intended objectives. Section II reviews
the relevant economics literatures on the link between labor mobility and trade. Section III presents
our theory for why immigration stemming from
Schengen should have a positive influence on economic integration in Europe by expanding transnational trade. Section IV outlines the research design
and empirical models. Section V highlights the key
findings and applies a series of robustness checks
that gauge the explanatory power of our analysis.
Section VI concludes, offers an agenda for future research, and derives implications for EU integration
in the coming years.
halting unregulated labor mobility. World War II
and its aftermath left Europe divided, with travel—
let alone labor mobility—nearly impossible between
East and West.
Cross-country labor mobility, however, was not a
complete anomaly during much of the mid- to late
twentieth century. Prior to the enactment of Schengen, there were several smaller-scale attempts to
construct a liberalized labor mobility regime. Belgium, the Netherlands, and Luxembourg, for instance, devised a novel plan in 1948 to eliminate
border controls among the three countries. This effort was followed in 1952 by the formation of the
Nordic Passport Union, which permitted free travel
and eliminated border controls among Denmark,
Sweden, Iceland, Norway, and Finland. The predecessor to the European Union, the European Economic Community, also partially vouchsafed the
free movement of labor in the 1957 Treaty of Rome.
But it was not until 1985, when France, West
Germany, and the three members of the Benelux
Economic Union debated, drafted—and ultimately
signed—the Schengen Agreement, that labor mobility truly took hold in contemporary Europe. The
treaty, which represented a sea change in how workers traveled in Europe, was not only confined to
eliminating border controls, but also covered a variety of related fields, such as common agreements
on security, police, and personal identification data.
The 1997 Treaty of Amsterdam would later subsume the Schengen Agreement into the European
Union’s acquis communautaire, meaning that all
member states of the EU, with the exception of
the United Kingdom and Ireland, were obligated
to implement the compact.
Today, Schengen is widely regarded as the
quintessence of European collaboration.
The
director-general for research of the European Parliament has hailed Schengen as “the most notable
example of multilateral immigration policy harmonisation” (EU 1997). Kinga Goncz, former
prime minister of Hungary, has similarly declared
that “besides the euro, Schengen is the most im1 Historical Context
portant acquis of the European Union.... [F]or
Although early data on European population flows many people, it is the most visible sign of the exisare limited, evidence suggests that from the 1800s tence of the European Union” (European Parliato the mid-1900s, it was relatively easy for citizens ment 2011).1 Yet despite its undoubted imporon the continent to travel from one country to an- tance, remarkably little is known about the actual
other. The main driver of labor mobility then, as impact of increased migration flows on European
now, was the search for gainful employment. The
First World War, however, abruptly ended this situ1 Not everyone is so enamored with unimpeded travel
ation of laissez-faire labor mobility. Fear of foreign within Europe. One British MP, for example, has scathingly
spies and other looming security concerns led to raised the prospect that “the Schengen agreement represents
ambition of the hard core of the European Union to
more stringent border control. Meetings in 1920, the
do away with internal frontiers” and thus any semblance of
1926, and 1927 by the League of Nations formal- sovereignty at the nation-state level (United Kingdom Parized the issuance of passports and visas, effectively liament 1995).
2
cohesion. In particular, how does Shengen affect
European economic integration through trade?
country’s exports and a .14 percent increase for the
host country’s imports given a 1 percent boost in
immigrant stock.
2
What distinguishes our study from prior research is its focus on how a particular freedom of
labor compact—the Schengen Agreement—affects
the link between worker flows and trade in Europe. As far as we are aware, no existing study
has examined how a large-scale free labor compact
influences trade across member and non-member
countries over time. We argue that labor mobility
between states, all other things equal, should produce a net rise in trade. A region that trades more
constitutes a more integrated economic market. In
the following section, we outline why migration resulting from Schengen should have positive effects
on integration. Using data encompassing all major
European nations from 1980 to 2011, we then test
our hypotheses using the gravity model of trade.
Limitations of Existing Literature
Over the past two decades, a burgeoning economics
literature has examined the link between immigration and trade. These studies can be broadly
lumped into three groups, based on their scope and
units of analysis. The first group explores how
domestic labor flows affect trade among a country’s political or economic subunits, such as states,
provinces, or other local jurisdictions (e.g., Bardhan and Guhathakutra 2004; Dunlevy 2006; Wagner, Head, and Ries 2002). Scholars have conducted
several such analyses in advanced industrialized
economies, including the United States, Canada,
and Spain. In general, results suggest that immigration has a positive effect on trade. Yet these
analyses raise concerns of external validity. Factors
like a similar culture or low transportation costs
may not apply across countries, making it hard to
generalize about migration-trade linkages to other
parts of the world.
A second group of studies investigates the impact
of bilateral trade flows of a single host country (e.g.,
Helliwell 1997; Ching and Chen 2000). Specifically,
these studies focus on how labor flows from the immigrant’s origin country to the host country affect
trade patterns between the selected nations. This
research again shows that the effect of labor flows
on trade tends to be positive and statistically significant. Such analyses, however, are also subject
to questions that they do not demonstrate how immigration and trade are connected across a larger
sample of countries. If there is something unique
about the host country being investigated, the positive relationship between people flows and trade
may not hold across a large set of nations or regions.
The last group of studies—and which most
closely resembles our analysis—investigates the link
between trade and people flows among multiple
host countries using panel data (e.g., Dolman 2008;
Lewer 2006; Konecny 2007). This research, which
provides an international view of how immigration
influences the volume and direction of trade, concentrates largely on the OECD and other wealthy
market economies. To the best of our knowledge,
however, only Parsons (2005) measures the impact
of labor mobility on trade exclusively within the
European context. His work, comprising the EU15 and the EU-15 expansion countries from 1994
to 2004, finds a .12 percent increase for the host
3
Theory
We hypothesize that continental worker flows emanating from Schengen should increase trade in Europe for three central reasons.
3.1
Tastes and Preferences
The first is the tastes and preferences that migrants
bring for goods unique to their home country (e.g.,
Dunlevy and Hutchinson 1999; White 2007). All
nations specialize in certain products and services,
many of which reflect the cultural, topographical,
or historical characteristics of their society. Moreover, many of these countries have specific brands
or geographic-specific versions of said products. For
example, French workers living in Germany may
demand Bordeaux wines, while German workers residing in France may desire Frankfurt sausages. If
immigrants cannot find perfect (or even suitable)
substitutes, one of two outcomes is likely. First,
expatriates will import these items. This practice
has become increasingly common in recent decades
with the rise of the Internet. The second is that new
businesses will either form or relocate that cater
to these workers. Sometimes, these products proliferate and become mainstream, so it is not just
(or even primarily) expatriates who purchase them.
In either case, such burgeoning demand for foreign
goods should expand trade.
3
3.2
Awareness of Low-cost Produc- 4
ers Abroad
Research Design
Below, we present our research design to examine
The next reason why increased migration in Eu- the influence of Schengen on European trade.
rope should bolster trade is that foreign workers
possess privileged information about low-cost pro- 4.1 Case Selection
ducers abroad (e.g., Bryant, Genc, and Law 2004;
Girma and Yu 2002; Gould 1994). When opening a All major countries that might reasonably be conconfines of Europe are
business or working for a corporation, immigrants sidered in the geographic
2
represented
in
our
study.
This
includes EU memcan use this knowledge by patronizing companies in
ber
states,
so-called
EU
“fast-track”
nations, and
their homeland. Imagine, for instance, an Italianother
countries
that
could
conceivably
be slated for
born tailor living in Spain, who knows of a boutique
future
EU
membership.
By
extending
the universe
Milan-based company from which to buy low-cost,
of
cases
beyond
the
Schengen
Zone,
we
can exploit
high-quality linens. By patronizing this company,
variation
in
compact
membership
while
still focusthe tailor will save money on the wholesale price
ing
on
Europe
as
the
unit
of
analysis.
In total,
and boost sales by passing savings onto consumers.
we
include
36
countries
in
our
empirics,
spanning
This process can also work in reverse. If an immigrant finds that his or her host country has better the years 1980 to 2011, for which comparable data
or lower cost producers of certain products, then were available: Albania, Austria, Belgium, Bosnia,
the immigrant can take advantage of this arbitrage Bulgaria, Croatia, Cyprus, Czech Republic, Denby exporting goods to his or her home country. The mark, Estonia, Finland, France, Germany, Greece,
Hungary, Iceland, Ireland, Italy, Latvia, Lithuaresult will again be expanded trade.
nia, Luxembourg, Macedonia, Malta, Montenegro,
Netherlands, Norway, Poland, Portugal, Romania,
Serbia, Slovakia, Slovenia, Spain, Sweden, Switzer3.3 Minimizing Risks of Trade
land, and the United Kingdom.
Finally, we predict that migration in Europe will
positively affect trade because foreign workers lower
the risks associated with buying and selling abroad
(e.g., Rauch and Trindade 2002; Tadesse and White
2008). This occurs in three ways. First, immigrants
who seek out products from their home country
generally do so because they believe in the reliability of a product. This makes them more inclined
to buy goods from outside their country. Second,
trading in a foreign country necessitates trust not
just in the product or good, but also in the suppliers
or consumers. Negotiating cross-border contracts is
inherently risky, and foreign-born employees may
reduce barriers that dissuade companies from penetrating international markets. Third, immigrants
are often better positioned to negotiate with foreign
producers. Because workers remain plugged into
social networks in their native country, they can
pressure foreign producers if they renege on contracts. This makes buying from producers abroad
less of a gamble and increases trade.
3.4
4.2
Dependent Variable
Our dependent variable is bilateral trade flows in
a given year among country dyads. This is computed as the sum of freight on board imports and
exports, measured in constant US dollars. In addition to looking at aggregate trade flows, we also
unpack this variable into its two constituent parts:
1) imports from country i to country j ; and 2) exports from country i to country j. This enables us
to examine whether our theory holds for all types
of trade, just imports, just exports, or none of the
above.3 Data on trade come from the International
Monetary Fund’s Direction of Trade Statistics.
2 We exclude a number of small countries because of data
constraints.
3 Previous studies reveal that the causes of trade may operate differently depending on whether the dependent variable is imports or exports, since immigrants often affect one
market but not the other (e.g., Girma and Yu 2002). Consider, for instance, a scenario whereby the host country for
an immigrant group produces mainly commodities and their
source country produces primarily consumer goods, with little importation of unfinished products. We would expect,
based on these patterns, for the immigrant group to have a
larger impact on imports to the host country than exports
from the origin nation.
Main Hypothesis
• Increased labor flows between countries resulting from the Schengen Agreement should
strengthen trade, and hence, bolster European
economic integration.
4
4.3
Key Independent Variables
N etExportsijt = f (GDPit , GDPjt , Distanceij )
We have two main independent variables. The first
is whether both countries in a dyad are members of
the Schengen Agreement. We code a dyad as “1”
if both nations are members and “0” otherwise. A
total of 26 nations within Europe are now part of
Schengen.4 Our second independent variable is total migration, which comes from Eurostat and measures immigration to the host country based on the
immigrant’s citizenship.5 The migration variable
represents the actual amount of people flows from
one country to another, while the Schengen variable captures the opportunity for workers to change
domiciles.
where N etImportsijt constitutes country i ’s imports from j in year t and N etExportsijt denotes
country i ’s exports from j in year t.
In each case, GDP should be positively related
to overall trade, imports, and exports, since larger
and wealthier countries produce more goods and
services to trade and also have populations that
demand more items from abroad. Distance should
be negatively related, since trading across long distances is inconvenient and raises transportation
costs.
For many years, economists generally estimated
gravity models via a log-log ordinary least squares
regression. In such a setup, a unit percentage
change in the independent variables is associated
with a unit percentage change in trade. The basic
estimator can be written as:
4.4
Methodological Considerations
We employ the gravity model of trade to assess
the relationship between our independent and dependent variables. The gravity model dates back
more than 50 years (Tinbergen 1962), but it remains one of the most effective tools for analyzing
the causes of imports and exports between countries.6 The intuition behind this model is simple:
Trade between two nations is chiefly determined
by their geographic space apart (i.e., distance) and
relative economic masses (i.e., national incomes).
Additional independent and control regressors can
then be added to isolate the causes of cross-country
trade. The most basic specification of the gravity
model is:
ln(N etT radeijt ) = B0 + ln(B1 (GDPit )) +
ln(B2 (GDPjt )) + ln(B3 (Distanceij )) + E
But recent scholarship shows this strategy introduces two problems: 1) “Jensen’s inequality” (i.e.,
in estimating ln(N etT radeijt ), the expected value
of the natural log of a random variable 6= the natural log of its expected value), which biases estimates amid heteroskedasticity; 2) the existence of
zero values on the dependent variable, which are
undefined as logs.
Following the advice of Silva and Tenyro (2006),
most economists now estimate a multiplicative discrete probability distribution model with a Poisson
pseudo-maximum likelihood estimator (PPML).
This estimator, first derived by Gourieroux, Monfort, and Trogon (1984), solves the challenge of
modeling zero trade flows and overcomes Jensen’s
inequality by obviating the need for logging the dependent variable.7
This specification can be expressed as:
N etT radeijt = f (GDPit , GDPjt , Distanceij )
where N etT radeijt denotes country i ’s net trade
totals from country j in year t; GDPit and GDPjt
are the total national incomes of countries i and
j, respectively, in year t (measured in constant US
dollars); and Distanceij is the distance (measured
in kilometers) between the countries’ capital cities.
Net trade can also be disaggregated into imports
and exports:
N etT radeijt = exp[B0 + ln(B1 (GDPit )) +
ln(B2 (GDPjt )) + ln(B3 (Distanceij ))] + E
N etImportsijt = f (GDPit , GDPjt , Distanceij )
In recent years, scholars have proffered many
alternatives
to the PPML (e.g., gamma pseudocountries are: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, maximum-likelihood (GPML), nonlinear least
4 These
Hungary, Iceland, Italy, Latvia, Liechtenstein, Lithuania,
Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland.
5 While our theory bears more on worker flows specifically, rather than migration flows generally, there do not
exist—as far as we know—harmonized data on labor mobility for European countries over an extended period of time.
For a discussion of data limitations on European immigration, see Raymer, de Beer, and van der Erf (2011).
6 For reviews of the gravity model, see Anderson (2011)
and Leamer and Levinsohn (1995).
7 One notable challenge comes from Martin and Pham
(2008), who argue that PPML estimators still suffer from
some bias in the presence of zero trade flows. Yet this is
only problematic if the dependent variable has an extremely
large number of zeroes, which should not occur in our data
of all European countries. In the official trade data, we identify what appears to be occassionally inconsistent reporting
between zeros and missing data (e.g., jumps from zero trade
flows one year to large trade figures the next). To minimize
such problems, we code all zeros as missing.
5
of cheese, retailers in each nation would have an incentive to buy from domestic producers, thereby
reducing trade.
squares (NLS) estimator, feasible generalized least
squares (FGLS), etc.).8 Yet as far as we are aware,
most operate effectively only under very narrow circumstances or perform at roughly the same level
as the PPML in terms of minimizing bias. Thus,
we concur with Silva and Tereyo’s contention that
PPML “has all the characteristics needed to make it
a promising workhorse for the estimation of gravity
equations and, more generally, constant elasticity
models” (Include citation).
To improve confidence in our regressions, we employ the PPML estimator. For robustness checks,
however, we also reestimate all of our full models using OLS. One advantage of this approach is
that the coefficient parameters are directly interpretable, since the underlying model is linear. For
consistency with prior studies that do not employ
MLE techniques, the OLS regressions also provide
a point of reference for comparing the magnitude
of each coefficient. We append to all the models our two key independent variables: 1) whether
both countries in a trade dyad are signatories to the
Schengen Agreement; and 2) total migration flows.
We also include two other explanatory variables,
which have been robustly linked to cross-national
trade.
The first control is a binary variable for whether
two nations are geographically adjacent. Direct
proximity should increase trade by facilitating special partnerships between countries that share borders. Due to their distance, we would expect, for
instance, Croatia and Slovenia, whose capital cities
are just 118 kilometers away (about a 90 minute
drive), to trade more than Cyprus and Iceland,
whose capital cities are 4,778 kilometers apart (a
several hour plane ride, across both continental Europe and the North and Norwegian Seas). But
we may also expect these countries to trade more
because sharing a border often implies having increased diplomatic and commercial linkages.
We also include a dichotomous variable for
whether both nations in a dyad have a common
official language. On the one hand, this may boost
trade if two countries with similar ethno-linguistic
traits enjoy enhanced social and economic relations.
For example, both Germany and Austria share the
German language, so the ties between their populations may be especially strong. On the other
hand, if such countries already produce similar
products, this variable may be negatively related to
trade since there exists less opportunity to capitalize on comparative advantage. If, for instance, two
French-speaking countries already produce a surfeit
4.5
Empirical Model
The final specification of our gravity model, including both the independent and control variables, is
the poisson estimation of:
N etT radeijt = exp[B0 + ln(B1 (GDPit )) +
ln(B2 (GDPjt )) + ln(B3 (Distanceij )) +
ln(B4 (Immigrationijt )) + B5 (Schengenij ) +
B6 (Adjacentij ) + B7 (CommonLanguageij ) + E
5
Results and Discussion
(Table 1 about here)
(Table 2 about here)
(Table 3 about here)
(Table 4 about here)
We begin by testing whether cross-country immigration and membership in Schengen affect overall
trade within country dyads. Models 1-5 of Table 1
present the results of the PPML regressions, introducing the controls in stepwise fashion. We find
strong support for our theory that people flows
have a statistically significant and positive effect
on trade. In addition, when two countries are both
signatories to Schengen, they trade more. These
results hold in both Models 2 and 3, which include
geographic distance and economic mass as the control variables. They remain significant in Model
5, the full regression with all the explanatory regressors. The coefficients on both Immigration and
Schengen suggest that these effects are nontrivial
in economic terms, with elasticities of .09 and .14,
respectively.
Models 8-12 of Table 2 reestimate our main models using OLS regression. Consistent with our expectations, country dyads where both states belong to Schengen and which possess higher immigrant flows enjoy more trade. The elasticities mirror those from the PPML regressions. According to
Model 12, which includes all the controls, a 1 percent increase in immigration between two countries
increases their bilateral trade by .10 percent. Moreover, when two countries are members of Schengen,
the net trade between country i and country j is on
average .09 percent higher per year. Such a figure
8 For a summary of alternative approaches, see Martinezhas large economic significance. For example, trade
Zarzoso (2013).
totals between Germany and France amounted to
6
$231 billion in 2011, so a .09 percent increase in this 5.1 Additional Robustness Checks
figure would equate to almost $208 million worth of
additional trade between these two countries every To ensure that our findings are not partially a funcyear.
tion of some specific qualities underlying our data
Next, we see whether immigration and mutual (i.e., violations of i.i.d. assumptions, unit heteromembership in Schengen influence imports, not just geneity, and/or omitted variable bias), we apply
total trade, within country dyads. Models 14-18 of several robustness checks to our models. First, we
Table 3 summarize our PPML regressions, again reestimate all of our main linear estimations with a
adding the control variables in stepwise order. The non-parametric bootstrapping method (Models 13,
positive and significant coefficients on both Immi- 26, 39). Following a Monte Carlo approach, we
gration and Schengen suggest that labor flows are treat the sample as a population and then, based
associated with greater imports between country i on 500 replications, randomly resample from the
and country j. This finding holds in the baseline re- observed data with replacement. Because each of
gressions, Models 15 and 16, which only include the the samples is unique, the simulations allow us to
people flows and Schengen variables, as well as ge- see how the standard errors vary where standard
ographic distance and GDP. In the regression with distributional assumptions might not hold (Efron
all the controls, Model 18, the elasticity on Immi- and Tibshirani 1993). We find that the coefficients
gration is .09, while for Schengen it is .15. The and their significance levels closely conform to those
directions and magnitudes of these effects are sim- in our original regressions.
ilar to those regressions that employ overall trade
We also apply spatial and temporal fixed effects
as the dependent variable.
to our main PPML models to account for unobserved unit heterogeneity in our data. While our
control variables are standard in gravity models of
trade, omitted variables owing to the particular circumstances of a nation or time period could still
skew our results. Country fixed effects, for instance,
might pick up specific properties of states that affect trade, such as tariffs, subsidies, or geographical constraints (e.g., landlocked status, etc.). Year
fixed effects may control for time sensitive factors,
such as macroeconomic upturns and downturns, exogenous shocks (e.g., natural disasters, etc.), and
the diffusion of treaties dealing with European integration. In no instance do our primary results
change after controlling for either country (Models 6, 19, 32) or temporal (Models 7, 20, 33) fixed
Finally, we investigate exports. As demonstrated effects.
by Models 27-31 of Table 5, both Immigration and
Finally, we employ a lagged dependent variable
Schengen are positive and statistically significant in all our PPML models (results not shown). While
in the PPML regressions. In the regression with it is a matter of debate in the literature, some scholall the controls, Model 31, the coefficient is .09 on ars suggest that failing to do so might cause errors
Immigration and .14 on Schengen. As seen in Mod- when countries exhibit irregular trading patterns
els 34-38 of Table 6, these results are similar using due to factors not explicitly captured by the model
OLS. According to the main regression, Model 38, (e.g., Eichengreen and Irwin 1996). Others sugwhen both countries in a dyad are signatories to gest that lagging the dependent variable may unSchengen, exports rise by an average of .14 per- duly inflate the size of the coefficients because it
cent. Furthermore, a 1 percent rise in immigration permits immigration to affect trade in two ways:
is associated with a .11 percent increase in exports. directly (through immigration’s effect on trade in
To put this in context, in 2011, the total exports the current year), and indirectly (by influencing
from France to Spain totaled roughly $44 billion. trade in the past, which influences trade in the
A .11 percent growth in this amount would ex- present) (e.g., Wagner, Head, and Ries 2002). Uspand French exports by about $48 million annually. ing a lagged dependent variable, we discover no subTaken collectively, our results suggest that Schen- stantive differences in the signs and significance of
gen, and the immigration that it promotes, have Immigration or Schengen on overall trade, imports,
positive and sizeable impacts on European trade.
or exports.
We then reestimate our import regressions using OLS in Models 21-25 of Table 4. While Immigration stays positive and significant in all of the
regressions, we discover a slight difference when it
comes to Schengen. The complete model, Model 25,
indicates that, controlling for other factors, Schengen has a positive, yet not statistically significant,
impact on trade. By comparison, when looking at
actual immigration, and not just the potential for
migrating via Schengen, the impact is large and statistically significant. Specifically, a 1 percent jump
in immigration is linked to a .09 percent increase
in imports. This aligns with our hypothesis that
countries that promote greater exchanges of people
also reap gains from imports.
7
6
Concluding Remarks
also made clear in the explicit rejection of European
political integration within the stated platforms of
many of these parties.
Immigration might also trigger resentment in migration donor states through “brain drain” of educated and talented workers away from feeble domestic markets. The loss of a state’s most productive
citizens can adversely affect economic activity in
that country, resulting in losses to innovation and
international competitiveness. The size and extent
of this phenomenon is a topic of considerable research, and questions linger about the magnitude
of its actual impact within Europe. But to the degree that it does occur, there is reason to suspect
that free flows of labor promoted by Schengen may
fuel backlash among poorer European nations that
lag behind in human-capital-intensive jobs.
In short, labor mobility supported by Schengen
should yield both hope and caution for European
observers. Hope, because our results indicate that
increasing cross-national labor mobility can fuel
trade and economic growth. Europe, then, has a
possible source for relieving its current economic
malaise. Caution, because there are many other
factors besides trade that matter for regional integration. These realities suggest a need for tempered
claims when it comes to how Schengen will shape
the future trajectory and long-term viability of the
EU. It also highlights the necessity of probing more
deeply the varied, and perhaps cross-cutting, repercussions of Schengen. Only by tackling the issue
from multiple angles will scholars gain a more complete profile of how immigration affects Europe’s
future.
Our paper began by asking a simple question:
What are the effects of labor mobility stemming
from the Schengen Agreement on European trade?
Building on insights from international economics,
we hypothesized that the fluid movement of migrants within Europe should bolster trade for three
central reasons: It shifts out the demand curve for
foreign goods by consumers, informs retailers about
low-cost producers abroad, and enables firms to
hedge their risks associated with buying and selling outside the country. Using the gravity model,
we showed that labor mobility enhances trade, all
other things equal. Over the whole of Europe, this
boost in immigration results in hundreds of millions
of dollars of increased trade every year.
Our study represents an initial attempt to quantify the effects of Schengen on trade and also contributes to a burgeoning literature regarding the
impacts of labor mobility on cross-border commerce. Given the battery of factors that shape
trade within Europe, we are hesitant to offer bold
policy implications from our findings. Still, at a
time when many have predicted a future end-game
for the EU, it is worth highlighting how one aspect
of European policy seems to be binding the continent closer together. For advocates of the European
project, as well as policymakers concerned about
what the demise of the EU would mean for monetary stability and the economic balance of power,
this news should prove encouraging.
Of course, just as our results can be seen as a
boon for Schengen and prospects for the further
“deepening and widening” of modern Europe, it
is important to underscore that our results only
speak to one aspect of regional integration. Looking ahead, it may well prove fruitful to explore
how Schengen influences other dimensions of integration, including political support for pooled
sovereignty via supranational institutions. After
all, just because Schengen brings the continent
closer together economically, it does not necessarily
follow that labor mobility will lead to the automatic
integration of the EU from a political vantage, as
well. Indeed, the two might not go hand-in-hand.
One reason to be wary is that labor mobility
tends to harden domestic attitudes toward immigration in general and often leads native workers to
perceive that foreigners are poaching scarce domestic jobs. These mindsets might affect preferences
for political parties, shifting the partisan center of
gravity to the right. Anecdotal evidence of this can
be observed through a groundswell in the election
of far-right political parties, which increasingly oppose the concentration of power in Brussels. It is
8
7
References
Racism.” Directorate-General for Research. Working document. Public Liberties Series LIBE 102
Anderson, James E. 2011. “The Gravity Model.” EN.
Annual Review of Economics 3(1): 133-160.
Girma, Sourafel, and Zhihao Yu. 2002. “The
Bardhan, Ashok D., and Subhrajit Guhathakurta. Link between Immigration and Trade: Evidence
2004. “Global Linkages of Subnational Regions: from the United Kingdom.” Weltwirtschaftliches
Coastal Exports and International Networks.” Archiv 138(1): 115-130.
Contemporary Economic Policy 22(2): 225-236.
Gould, David M. 1994. “Immigrant Links to
Bryant, John Robert, Murat Genc, and David Law. the Home Country: Empirical Implications for
2004. “Trade and Migration to New Zealand.” U.S. Bilateral Trade Flows.” Review of Economics
and Statistics 76(2): 302.
New Zealand Treasury Working Paper.
Gourieroux, C., A. Monfort, and A. TROGNON.
1984. “Pseudo Maximum Likelihood Methods:
Theory.” emphEconometrica 52(3): 681.
Ching, Hsianghoo S., and Li-Lu Chen. 2000.
“Links between Emigrants and the Home Country:
The Case of Trade between Taiwan and China.”
In Regional Cohesion and Competition in the Age
of Globalization. Eds. Hirotada Kohno, Peter
Nijkamp, and Jacques Poot. Cheltenham, UK:
Edward Elgar.
Harden, Jeffrey J. 2011. “A Bootstrap Method for
Conducting Statistical Inference with Clustered
Data.” State Politics & Policy Quarterly 11(2):
223-246.
Dolman, Ben. 2008. “Migration, Trade and
“National Borders,
Investment.” Australian Government Productivity Helliwell, John F. 1997.
Trade and Migration.” Pacific Economic Review
Commission Staff Working Paper. Canberra.
3(3): 165-185.
Dunlevy, James A. 2006. “The Impact of Corruption and Language on the Pro-trade Effect of Konecny, Tomas 2007. “Can Immigrants Hurt
Immigrants: Evidence from the American States.” Trade?” CERGEI-EI Working Paper Series 329.
Review of Economics and Statistics 88(1): 182-186.
Leamer Edward E., and James Levinsohn. 1995.
Dunlevy, James A., and William K. Hutchin- “International Trade Theory: The Evidence.” In
son. 1999. “The Impact of Immigration on Handbook of International Economics, Vol. 3.
American Import Trade in the Late Nineteenth Eds. Gene M. Grossman and Kenneth Rogoff.
and Early Twentieth Centuries.” Journal of Eco- Amsterdam: Elsevier Sci. B.V.
nomic History 59(4): 1043-1062.
Lewer, Joshua J. 2006. “The Impact of ImEfron, Bradley, and Robert Tibshirani. 1993. migration on Bilateral Trade: OECD Results
An Introduction to the Bootstrap. Boca Raton, from 1991-2000.” Southwestern Economic Review.
33(1): 9-22.
FL: CRC Press.
Martin, Will, and Cong S. Pham. 2008. “Estimating the Gravity Model When Zero Trade Flows Are
Frequent.” Deakin University, Australia. Mimeo.
Eichengreen, Barry and Douglas A. Irwin (1997).
“The Role of History in Bilateral Trade Flows.” In
The Regionalization of the World Economy. Ed.
Jeffrey A. Frankel. Chicago: University of Chicago
Press.
Martinez-Zarzoso, Inmaculada.
2013.
“The
Log of Gravity Revisited.” Applied Economics
45(3): 311-327.
European Parliament. 2011. “Changes to Schengen (Debate).” Available at http://www.euro
parl.europa.eu/sides/getDoc.do?pubRef=//EP//TEXT+CRE+20110706+ITEM-017+DO
C+XML+V0//EN
Gourieroux, C. A., A. Monfort, A. Trogon.
1984.
Pseudo maximum likelihood methods:
Applications. Econometrica. 52: 701-720.
European Union.
1997.
“European Union Rauch, James E., and Vitor Trindade. 2002.
Anti-Discrimination Policy: From Equal Oppor- “Ethnic Chinese Networks in International Trade.”
tunities between Women and Men to Combating Review of Economics and Statistics 84(1): 116-130.
9
Raymer, James, Joop de Beer, and Rob van
der Erf. 2011. “Putting the Pieces of the Puzzle
Together: Age and Sex-Specific Estimates of
Migration amongst Countries in the EU/EFTA,
2002-2007.” European Journal of Population 27(2):
185-215.
Silva, J.M.C. Santos and Silvana Tenreyro,
2009. “Further Simulation Evidence on the Performance of the Poisson Pseudo-maximum Likelihood
Estimator.” Economics Discussion Papers 666,
University of Essex, Department of Economics.
Silva, J. M. C. Santos, and Silvana Tenreyro.
2006. “The Log of Gravity.” Review of Economics
and Statistics 88(4): 641-658.
Tadesse, Bedassa, and Roger White.
2011.
“Emigrant Effects on Trade: Re-examining the
Immigrant-trade Link from the Home Country
Perspective.” Eastern Economic Journal 37(2):
281-302.
Tinbergen, Jan.
1962.
Shaping the World
Economy: Suggestions for an International Economic Policy. New York: Twentieth Century
Fund.
United Kingdom Parliament. 1995. Transcript of
the House of Commons. March 9. Available at
http://www.publications.parliament.uk/pa
/cm199495/cmhansrd/1995-03-09/Orals-1.html
Wagner, Don, Keith Head, and John Ries.
2002. “Immigration and the Trade of Provinces.”
Scottish Journal of Political Economy 49(5):
507-525.
White, Roger. 2007. ”Immigrant-Trade Links,
Transplanted Home Bias and Network Effects.”
Applied Economics 39(7-9): 839-852.
10
8
Tables
Table 1: Effect of Immigration and Schengen on Trade: PPML Models
loggdpreali
loggdprealj
logdistance
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Country FE
Model 7
Year FE
0.800***
(0.004)
0.803***
(0.004)
-1.219***
(0.010)
0.766***
(0.010)
0.764***
(0.007)
-1.151***
(0.016)
0.078***
(0.007)
0.750***
(0.010)
0.749***
(0.007)
-1.122***
(0.015)
0.087***
(0.007)
0.143***
(0.018)
0.750***
(0.010)
0.749***
(0.007)
-1.120***
(0.019)
0.086***
(0.007)
0.143***
(0.018)
0.004
(0.027)
-11.749***
(0.169)
0.921
25360
-10.813***
(0.312)
0.926
8204
-10.326***
(0.309)
0.931
8204
-10.333***
(0.317)
0.931
8204
0.748***
(0.009)
0.748***
(0.007)
-1.126***
(0.020)
0.088***
(0.007)
0.141***
(0.019)
0.006
(0.027)
-0.031
(0.035)
-10.241***
(0.308)
0.931
8204
0.304***
(0 .025)
0.756***
(0 .006)
- 1.088***
(0 .020)
0.131***
(0 .008)
0.170***
(0 .016)
- 0.034*
(0 .021)
0.209***
(0 .026)
1.189*
(0 .631)
0.956
8204
0.763***
(0.009)
0.768***
(0.007)
-1.144***
(0.019)
0.077***
(0.006)
0.199***
(0.017)
-0.027
(0.024)
-0.006
(0.032)
-11.167***
(0.299)
0.941
8204
logimmigrationij
schengen
adjacent
officiallanguage
cons
r2
N
* p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed test)
Standard errors are given in parentheses
11
Table 2: Effect of Immigration and Schengen on Trade: OLS Models
loggdpreali
loggdprealj
logdistance
Model 8
Model 9
Model 10
Model 11
Model 12
0.911***
(0.004)
0.890***
(0.004)
-1.400***
(0.011)
0.794***
(0.008)
0.878***
(0.006)
-1.393***
(0.016)
0.102***
(0.006)
0.785***
(0.008)
0.868***
(0.006)
-1.389***
(0.016)
0.104***
(0.006)
0.103***
(0.022)
0.789***
(0.008)
0.868***
(0.006)
-1.317***
(0.019)
0.098***
(0.006)
0.092***
(0.022)
0.289***
(0.040)
-15.941***
(0.157)
0.854
25360
-13.235***
(0.268)
0.879
8204
-12.847***
(0.280)
0.880
8204
-13.432***
(0.291)
0.880
8204
0.789***
(0.008)
0.869***
(0.006)
-1.323***
(0.019)
0.099***
(0.006)
0.094***
(0.022)
0.330***
(0.042)
-0.206***
(0.053)
-13.408***
(0.291)
0.881
8204
logimmigrationij
schengen
adjacent
officiallanguage
cons
r2
N
* p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed test)
Standard errors are given in parentheses
12
Model 13
Bootstrap
0.789***
(0 .010)
0.869***
(0 .007)
- 1.323***
(0 .020)
0.099***
(0 .006)
0.094***
(0 .018)
0.330***
(0 .036)
-0.206***
(0 .053)
-1 3.408***
(0 .378)
0.881
820 4
Table 3: Effect of Immigration and Schengen on Imports: PPML Models
loggdpreali
loggdprealj
logdistance
Model 14
Model 15
Model 16
Model 17
Model 18
Model 19
Country FE
Model 20
Year FE
0.790***
(0.005)
0.807***
(0.005)
-1.206***
(0.013)
0.753***
(0.013)
0.769***
(0.009)
-1.138***
(0.020)
0.079***
(0.009)
0.738***
(0.013)
0.754***
(0.009)
-1.108***
(0.019)
0.087***
(0.009)
0.142***
(0.022)
0.739***
(0.013)
0.754***
(0.009)
-1.116***
(0.026)
0.088***
(0.009)
0.144***
(0.023)
-0.013
(0.037)
-12.385***
(0.204)
0.888
25635
-11.447***
(0.422)
0.885
8219
-10.964***
(0.415)
0.890
8219
-10.937***
(0.427)
0.890
8219
0.740***
(0.012)
0.754***
(0.009)
-1.110***
(0.025)
0.087***
(0.009)
0.146***
(0.023)
-0.016
(0.037)
0.029
(0.038)
-11.026***
(0.403)
0.891
8219
0.247***
(0 .032)
0.774***
(0 .008)
- 1.229***
(0 .027)
0.121***
(0 .010)
0.237***
(0 .021)
- 0.131***
(0 .027)
0.196***
(0 .033)
2.593***
(0 .809)
0.922
821 9
0.756***
(0.012)
0.774***
(0.009)
-1.129***
(0.025)
0.076***
(0.009)
0.209***
(0.023)
-0.050
(0.034)
0.054
(0.036)
-11.544***
(0.393)
0.900
8219
logimmigrationij
schengen
adjacent
officiallanguage
cons
r2
N
* p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed test)
Standard errors are given in parentheses
13
Table 4: Effect of Immigration and Schengen on Imports: OLS Models
loggdpreali
loggdprealj
logdistance
Model 21
Model 22
Model 23
Model 24
Model 25
0.866***
(0.004)
1.045***
(0.004)
-1.426***
(0.013)
0.771***
(0.011)
1.047***
(0.008)
-1.479***
(0.022)
0.099***
(0.008)
0.767***
(0.011)
1.043***
(0.008)
-1.477***
(0.023)
0.100***
(0.008)
0.042
(0.031)
0.772***
(0.011)
1.042***
(0.008)
-1.398***
(0.027)
0.093***
(0.008)
0.030
(0.031)
0.320***
(0.056)
-19.414***
(0.194)
0.811
25635
-17.306***
(0.373)
0.819
8219
-17.149***
(0.391)
0.819
8219
-17.803***
(0.407)
0.819
8219
0.771***
(0.011)
1.043***
(0.008)
-1.403***
(0.027)
0.094***
(0.008)
0.031
(0.031)
0.353***
(0.058)
-0.166**
(0.074)
-17.783***
(0.407)
0.819
8219
logimmigrationij
schengen
adjacent
officiallanguage
cons
r2
N
* p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed test)
Standard errors are given in parentheses
14
Model 26
Bootstrap
0.771***
(0 .013)
1.043***
(0 .010)
- 1.403***
(0 .025)
0.094***
(0 .008)
0.031
(0 .026)
0.353***
(0 .047)
-0.166**
(0 .064)
-1 7.783***
(0 .475)
0.819
821 9
Table 5: Effect of Immigration and Schengen on Exports: PPML Models
loggdpreali
loggdprealj
logdistance
Model 27
Model 28
Model 29
Model 30
Model 31
Model 32
Country FE
Model 33
Year FE
0.811***
(0.005)
0.798***
(0.005)
-1.231***
(0.013)
0.777***
(0.011)
0.760***
(0.008)
-1.164***
(0.021)
0.077***
(0.007)
0.761***
(0.011)
0.744***
(0.008)
-1.134***
(0.021)
0.086***
(0.007)
0.144***
(0.024)
0.760***
(0.011)
0.744***
(0.009)
-1.123***
(0.025)
0.085***
(0.007)
0.142***
(0.024)
0.019
(0.028)
-12.508***
(0.216)
0.885
25519
-11.563***
(0.350)
0.891
8217
-11.070***
(0.365)
0.895
8217
-11.108***
(0.357)
0.895
8217
0.756***
(0.011)
0.743***
(0.008)
-1.141***
(0.027)
0.088***
(0.007)
0.136***
(0.024)
0.026
(0.027)
-0.087**
(0.044)
-10.856***
(0.349)
0.895
8217
0.355***
(0 .029)
0.741***
(0 .007)
- 0.963***
(0 .024)
0.139***
(0 .008)
0.110***
(0 .019)
0.056***
(0 .020)
0.222***
(0 .030)
- 1.501**
(0 .755)
0.949
821 7
0.770***
(0.011)
0.762***
(0.008)
-1.157***
(0.026)
0.078***
(0.006)
0.190***
(0.024)
-0.005
(0.025)
-0.062
(0.041)
-11.768***
(0.336)
0.906
8217
logimmigrationij
schengen
adjacent
officiallanguage
cons
r2
N
* p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed test)
Standard errors are given in parentheses
15
Table 6: Effect of Immigration and Schengen on Exports: OLS Models
loggdpreali
loggdprealj
logdistance
Model 34
Model 35
Model 36
Model 37
Model 38
1.085***
(0.005)
0.841***
(0.005)
-1.510***
(0.014)
0.902***
(0.009)
0.818***
(0.007)
-1.490***
(0.019)
0.105***
(0.007)
0.890***
(0.009)
0.804***
(0.007)
-1.484***
(0.019)
0.108***
(0.007)
0.149***
(0.026)
0.892***
(0.009)
0.804***
(0.007)
-1.438***
(0.022)
0.104***
(0.007)
0.142***
(0.026)
0.183***
(0.047)
-19.232***
(0.201)
0.805
25519
-14.550***
(0.309)
0.852
8217
-13.989***
(0.323)
0.853
8217
-14.359***
(0.336)
0.853
8217
0.892***
(0.009)
0.805***
(0.007)
-1.447***
(0.022)
0.106***
(0.007)
0.144***
(0.026)
0.240***
(0.048)
-0.284***
(0.061)
-14.325***
(0.336)
0.853
8217
logimmigrationij
schengen
adjacent
officiallanguage
cons
r2
N
* p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed test)
Standard errors are given in parentheses
16
Model 39
Bootstrap
0.892***
(0 .011)
0.805***
(0 .007)
- 1.447***
(0 .025)
0.106***
(0 .007)
0.144***
(0 .023)
0.240***
(0 .040)
-0.284***
(0 .061)
-1 4.325***
(0 .396)
0.853
821 7