∗ July 9, 2009 The distance puzzle: disentangling the black boxes Iván Arribas

The distance puzzle: disentangling the black boxes
Iván Arribas†
Abel Fernández‡
∗
Emili Tortosa-Ausina§
July 9, 2009
Abstract
International trade costs are key to the understanding of some of today’s macroeconomic puzzles, and the persistence of the distance effect is one the most elusive
issues. We address this “distance puzzle” through a two-step approach. In the first
step, we obtain year-by-year robust elasticity estimates for the distance effect over a
long period (1967–2007), showing a deep decline (–26%) throughout the first 25 years
and a stagnation (–2%) during the last 15. We argue that the increasing regional integration may have counterbalanced the improvements in transport and communication
technologies, since bilateral trade costs must be measured against the average costs
each country faces with the rest of the world—the so-called multilateral resistances.
These multilateral resistances act like “black boxes” in cross-section models, capturing all country-specific information and making its identification impossible. In our
second step we disentangle these “black boxes”, explaining their variability in terms of
country-specific geographic and economic characteristics. In doing so, we propose a
measure for the assessment of regional bloc’s effect: the size of the bloc the country
belongs to. We find that the increase of the regional bloc’s size explains an important part of their member’s trade growth and is responsible for the persistence of the
distance effect.
Keywords: Distance, Gravity, Multilateral Resistance, Trade
JEL Classification: F02, F15, Z13
Communications to:
∗
This paper is a result of the FBBVA-Ivie Research Program. All authors acknowledge the excellent
research assistance by Rodrigo Aragón.
†
Universitat de València and Ivie.
‡
Ivie.
§
Universitat Jaume I and Ivie.
1.
Introduction
One of the most robust results in international trade is the diminishing effect of distance
over trade. The gravity literature has been long time studying this effect, along with other
factors that impede international trade flows. Conceptually, distance is correlated and
accounts for a wide array of factors, such as transport costs, the likelihood of technical and
non-technical barriers to trade, cultural and linguistic differences or information and transaction costs, among others. The advance of globalization is supposed to have diminished
the importance of these obstacles, given the advances in transport logistics or the improvement in communication technologies. Even though one of the expected consequences of
these changes was the reduction of the distance effect, this reduction has not occurred, a
fact that has been labeled as the “distance puzzle”.
The importance of the distance effect is not trivial at all. Closer countries are more
likely to share language, culture and harmonized trade legislations than distant countries.
Thus, distance acts as a proxy not only for shipment costs, but for a host of other trade
costs, and, as Obstfeld and Rogoff (2000) stated, trade costs are key for the understanding
of several of the major macroeconomic empirical puzzles. However, as recognized by the
literature on international trade, the standard gravity models that are usually estimated
in the log-linear form are unable to capture the significant decline in trade costs brought
by globalization of the world economy. The literature has adopted different ways to refer
to this apparent puzzle. For instance, one may find some studies using the expression
“the missing globalization puzzle” (Coe et al., 2002, 2007); other authors refer to it as
“the conservation of distance in international trade” (Berthelon and Freund, 2008), or “the
puzzling persistence of the distance effect on bilateral trade” (Disdier and Head, 2008).
The question is even more strongly posed by Brun et al. (2005) (“has distance died?”) or
Carrere and Schiff (2005), who state that (distance) “is alive and well”. The number of
studies on the issue is now remarkable, and the meta-analysis by Disdier and Head (2008)
presents a helpful summary. It concludes that the estimated negative impact of distance
on trade rose around the middle of the twentieth century and has remained high since
then, and this result holds even after controlling for sample heterogeneity and estimation
techniques.
1
Coe et al. (2002) groups the different explanations into four explanations: (i) those
focusing on the decline in average costs relative to marginal costs of trade over time; (ii)
those highlighting the increased dispersion of economic activity; (iii) those emphasizing
the changing composition of trade; (iv) and those who stress the importance of relative
rather than absolute costs in determining bilateral trade. Since then, some additional
explanations have been proposed in the literature. Buch et al. (2004) argue that changing
distance costs are largely reflected in the constant term and, therefore, “little can actually
be learned with regard to changes in distance costs from comparing distance coefficients
for different time periods”, which is common practice. Siliverstovs and Schumacher (2007)
argue that it is important to use disaggregated trade flows, as for 25 three-digit ISIC
Rev.2 categories they find a substantial decline in the value of the distance elasticity in
most manufacturing industries. Coe et al. (2007) find evidence of “globalization” (declining
trade costs) because of the advantages of the nonlinear specification of the gravity model
over the standard log-linear specification. However, Márquez-Ramos et al. (2007) argue
that linear specifications may be satisfactory once we control for the fact that distance has
a different effect for developed and developing countries.
This paper addresses the “distance puzzle” from a new perspective, estimating a wide
year-by-year (1967–2007) set of distance elasticities, imposing no structure on its evolution.
The estimation techniques of the distance elasticities have advanced significantly over the
last years. Melitz (2003) showed that the decision to export includes some discrete choice
at firm-level. Following this idea and the fact that many countries do not trade with
each other, the empirical literature has steadily shifted to the use of Tobit-like estimation
methods in order to deal with the abundant zero observations. Moreover, Anderson and
van Wincoop (2003) showed that consistent estimates of trade costs between two countries
needed to take into account the role of the multilateral resistances each country faces,
i.e., the average barriers to trade with the rest of the world. We take into account these
developments to obtain consistent estimates of the year-by-year distance elasticity, which,
as we show, falls during the first years of the period but stagnates after the middle 80’s.
What is responsible for this evolution? If international trade has largely increased
during the last two decades, why hasn’t the distance evolved in less of an obstacle? A
2
recurrent hypothesis lies on the role of the increasing regional integration the world has
seen the last decades, an issue closely related to the multilateral resistances described by
Anderson and van Wincoop. Tariffs, technical and non-technical barriers to trade between
many of the world’s biggest economies have been disappearing. As bilateral trade costs
must be measured against the average trade costs a country faces, the increasing regional
integration may have counterbalanced the reductions in trade costs associated with distance
between distant countries. The literature has studied the effect of regional integration on
trade through the use of gravity equations. Most studies have assessed this effect with
the help of country-pair dummy variables controlling whether trading partners shared a
common regional trade agreement (RTA)1 (see, for instance ??). ?, citematyas.1997 or ?
have criticized the use of such dummy variables in the gravity equation, since they lead to
an econometric mis-specification.
In this paper we propose an alternative way of measuring the trade effects of regional
integration. In addition to the econometric problem, we argue that the use of common
RTA dummy variables fails to deal with country size heterogeneity and the evolution of
the economic size of the RTAs. A dummy variable measuring the effect of two countries
belonging to the same RTA imposes an equal effect on all bilateral relations for that RTA,
while it is evident that the effect depends on the size of each country: the Netherlands
benefits from a much more large relative market size by sharing RTA with Germany than
the opposite case. Thus, we argue that the size of the RTA to which a country belongs plays
a key role on the multilateral resistance it faces, and that increasing regional integration
may be counterbalancing the reduction of trade costs associated with distance.
The economic size of a RTA a country belongs to is a country-specific variable. Due
to perfect multicolinearity, it is impossible to identify its effect on a cross-section gravity
equation in the presence of country-specific dummies controlling for multilateral resistance,
as it is for other country-specific variables plausibly related to trade, such as economic and
geographic size, productive specialization or other geographic characteristics, like being
landlocked. We overcome this problem with a second stage regression, in which we explain
1
As indicated by Greenaway and Milner (2002), there is a wide range of forms of integration agreements
including free trade areas, custom unions, and preferential trade areas. Following their criterium, we will
use RTA as a generic descriptor, since we will not focus on the specific differences among the different
types.
3
the variability of the country specific dummies through the set of country specific variables
mentioned before. These dummies can be interpreted as the inverse of the multilateral
resistances, and have a greater value for countries that are more open to international
trade, i.e., those that face lesser average trade costs with the rest of the world.
The results show that Free Trade Areas have a significant impact on countries’ inverse
multilateral resistance. A back-of-the-envelope calculation shows that just the increase in
the size of the European Union is responsible for approximately 20% of Germany’s exports
between 1986 and 2006.
The rest of the paper is organized as follows. Section 2 explains the theoretical framework, the method of estimation and the data. Section 3 presents the data used in the
study. Section 4 presents and discusses the results of both steps of the analysis. Section 5
concludes.
2.
Methodology
The economic literature has long ago identified a strong and robust negative relationship
between distance and trade. The seminal work by Anderson (1979) laid the first theoretical
stone, relating bilateral trade between two countries to the economic size of both countries
and the costs that impede trade through the so-called gravity equation. This workhorse
model has been widely used to study different aspects of international trade, such as the
effects of border barriers, their economic significance (Evans, 2003), the role of commercial
policy and, ultimately, the relationship between trade and growth (see, for instance Frankel
and Romer, 1999; Rodríguez and Rodrik, 2001).
Theoretical literature in this area has advanced significantly since the first studies
were conducted. Particularly, the famous McCallum’s (1995) puzzle—an apparently huge
border effect between Canada and the US—helped discovering a miss-specification problem
in the econometric estimation of the original model that led to further developments in
the gravity literature. Anderson and van Wincoop (2003) showed that bilateral trade
between two countries is affected not only by distance and both countries’ income, but
also by their respective level of prices. The price indexes summarize all the weighted trade
costs each country faces, and are referred to as multilateral resistances. These resistances
4
play a key role in the model: since they are plausibly correlated with the economic size
of the different countries, their omission leads to biased estimates of the distance effect.
Other authors, such as Eaton and Kortum (2002) and Helpman et al. (2008), have used
different approaches and have further developed the existing knowledge on the determinants
of international trade, but one of the basic findings holds still through all the empirical
studies: distance is one of the most important factors affecting international trade.
The widely used gravity model by Anderson and van Wincoop (2003) develops from
a general equilibrium model of international trade for J countries, in which each country
produces a single and differentiated good. Consumers enjoy variety and maximize their
utility, which follows a CES demand form and is assumed to be identical across countries.
Trade costs are considered exogenous and drive up the destination price of traded products,
being tij = 1 the bilateral trade cost factor, expressed as one plus the tariff equivalent.
The micro-founded gravity equation derived from the model relates bilateral trade xij
between countries i and j to both countries’ income, yi and yj , to the bilateral trade costs
tij , and to the average outward (Πi ) and inward (Pj ) multilateral resistances (respectively
equivalent to countries’ i and j price indexes):
yj
tij 1−σ
xij = yi w
y
Πi Pj
(1)
where σ > 1 is the elasticity of substitution across goods. The price indexes P ii and Pj
can be expressed as follows, being θj the world income share of country j, yj /y w :
=
Π1−σ
i
Pj1−σ =
J
j=1
J
i=1
Pjσ−1 θj t1−σ
ij
(2)
Πσ−1
θi t1−σ
i
ij
(3)
The model implies that bigger countries trade more with each other, and that trade
costs decrease international trade but have to be measured in terms of the inward and
outward multilateral resistances, Πi and Pj . E.g., an exogenous rise of a country’s trade
costs with a set of partners increases bilateral trade between that country and a third one
5
(not included in that set) because of the falling relative trade costs between them, being
everything else equal.
Much has been written about the estimation of the gravity model and the functional
form and possible elements of the trade costs function. Here, we follow one of the estimation
methods proposed by Feenstra (2004) and Anderson and van Wincoop (2004), who use a set
of outward and inward country-specific dummies to control for the multilateral resistances,
a method that gives unbiased estimators of the trade cost function. Another problem
to obtain consistent estimates lies on how to deal with the zero trade flows present in
the data. While several authors have chosen to ignore the zero trade observations, this
approach excludes a lot of information, suffers from potential selection bias, and makes the
year-by-year results non-comparable. Ignoring the missing trade observations would bias
the distance estimates, since zero trade flows are associated with higher bilateral distances
(Coe et al., 2007). Moreover, if two countries do not engage in bilateral trade on a given
year t but do so in year t + 1, both cross-sections must include that bilateral information
in order to be comparable, since the t year distance estimate would not have taken into
account the fact that the two countries were not trading at all between them before year
t + 1. In order to deal with this problem, we express the dependent variable as xij + 1.
For high levels of flows, ln(1 + xij ) ≈ ln(xij ); for non-existent trade flows, ln(xij + 1) = 0.
This simple transformation allows us to include the information of zero-trade observations
through the estimation of a Tobit model. As the resulting estimates cannot be directly
interpreted as the elasticities, we follow McDonald and Moffitt (1980) procedure to obtain
the elasticity estimates at sample means.
By now, we have described the cross-section basics of the gravity estimation, but the
goal of this paper is to study the evolution and determinants of the distance effect over the
last four decades in order to address the “distance puzzle”. There are two ways we can treat
time-varying data, which ranges from 1967 and 2007: (i) we can estimate 41 separate cross
section models and look at the evolution or (ii) we can use a panel data approach. Even
though the latter method would seem more efficient, since it would use all the information
available to estimate the unknown parameters, it would be too strong of an assumption for
trade costs to be fixed over time. In fact, we need the model to allow for variable costs over
6
time in order to test our hypothesis, so the panel data approach would need a more flexible
functional form to allow for it. Moreover, since the multilateral resistances are a function
of the observed trade costs, we also need to allow them to vary over time. We argue that
simple ways of introducing variability over time, such as making all the parameters interact
with a linear or quadratic time trend, impose too much structure on the model. On the
other hand, letting the model estimate a different parameter for each year would essentially
be the same as estimating a different cross section model for each year. Thus, we will use
this latter approach and estimate 41 separate cross section equations.
The standard estimation of cross-country gravity equations faces another problem.
The inclusion of the country-specific dummies, necessary to control for the multilateral
resistances, makes the identification of all the country-specific variables difficult. Even the
identification of both countries’ GDPs effect is difficult to assess, since they are countryspecific in a cross-section approach, so we follow Anderson and van Wincoop (2004) and
let the multilateral resistance dummies capture their effect, along with the rest of countryspecific variables. The identification of all the country-specific variables is left to the
second-step regression. In the first step, we will estimate the standard gravity equation
including the most common variables used in the literature: bilateral distance (measured
as the great circle distance between countries’ capital cities), the existence of a common
border between i and j and a dummy controlling for a common language.
Thus, being
tij = dλij eδbij +γLij
(4)
the trade cost function, where dij is distance, bij the existence of a common border, and
Lij a dummy variables for the existence of a common language, the empirical equation to
be estimated through the Tobit procedure is expressed as follows:
ln(xij ) = λ(1 − σ)lndij + δ(1 − σ)bij + γ(1 − σ)Lij +
J−1
i=1
αi Πi +
J−1
βj Pj + εij
(5)
j=1
where Πi and Pj are the country-specific outward and inward dummies controlling for
the multilateral resistances and the country-specific information, λ, δ, αi and βi are the
parameters to be estimated and εij is an error term, assumed to be independent and
7
normally distributed. Also note that the estimated parameters will include the effect
of (1 − σ), impossible to separate through this method of estimation. As a measure of
robustness, we will also report results using a set of dummies controlling whether both
countries belong to the same Regional Trade Agreement (RTA). We have included the
world’s four most relevant RTAs in terms of their member’s GDPs: European Union,
ASEAN, NAFTA and Mercosur.
In the second step we will estimate a panel data model, being the multilateral resistances estimated in the first step the dependent variables, in order to address the role of
the country-specific variables: whether the country is landlocked or an island, its GDP, its
geographic size, its degree of productive specialization and the economic size of the FTA
to which it belongs.
2.1.
Second-stage estimation
In our second step we address the role of the country-specific variables on the estimated
proxies for the inverse of the multilateral resistances: whether the country is landlocked,
its geographic size (expressed by the internal distance, the radius of the area-equivalent
circle), its degree of productive specialization, its GDP and the economic size of the RTA
to which it belongs. The inclusion of the landlocked variable is common in the estimation
of gravity equations and needs little explanation: a landlocked country cannot make use
of the low cost maritime transport and is dependent on road, railway or air transport, so
the expected sign of its estimate is positive.
The inclusion of the geographic size and the degree of specialization requires a more
detailed explanation. One of the most critical advances in international trade theory was
the discovery of the role of the returns to scale. Firms located in bigger countries have
access to a larger market size and can thus achieve scale efficiency relatively easier relying
just on inner demand. On the other hand, firms located in small countries tend to be
more outward-oriented in order to survive, since they need a larger market to achieve
an acceptable degree of scale efficiency. As a result, small countries tend to be more
specialized, focusing on the production of a smaller range of products and relying on
imports to satisfy the inner demand for variety. Failing to control for this issue is perhaps
8
the main drawback from studies using the traditional openness measure, which cannot
explain why the Netherlands has a 60.3% exports openness (X/GDP ) while the US has
just a 9.8% in 2007. Ignoring this problem would lead us to biased GDP estimates, given
its correlation with geographic size and specialization. Figure 1 shows the relationship
between exports openness and our estimates for the “outward multilateral resistance”, (Πi )
for our sample in 2007. As expected, open countries face a smaller multilateral resistance,
which indicates the necessity to control for the size and specialization effect in the second
step of our analysis. The expected signs are straightforward: bigger countries trade less
and more specialized countries trade more.
The measure we propose to capture the RTA effect addresses again some issues related
to the heterogeneity in size. The usual procedure of adding dummies for the common RTA
imposes an equal weight on all observations from countries belonging to the same RTA.
But, considering the NAFTA agreement, it is obvious that the benefit in decreasing trade
costs have a very different effect for Canadian firms than for US ones. While Canadian
firms have an easier access to a market 10.3 times larger than its own, US firms just gain
access to a 0.18 bigger demand. Moreover, RTA’s size increases over time, not just by
the economic growth of its members, but also through the addition of new countries to
the agreements. A dummy variable would fail to capture these different situations, so we
choose the adjusted economic size of the RTA as our relevant variable, being its expected
sign positive.
The second stage results will be presented for both the outward and inward inverse
multilateral resistances, Πi and Pj . While the outward resistance summarizes the average
trade costs a country faces when exporting goods, the inward resistance captures the
average costs imposed on the rest of the world. We have added year dummies in order to
control for year-specific spikes and slumps in international trade. On the other hand, we
do not include country-specific dummies since our dependent variables already come from
the gravity equation country dummies. Thus, the equation to be estimated is as follows:
IM Ri,t = α0 +α1 LLi +α2 log(GDPi,t )+α3 log(IDi )+α4 log(HHIi,t )+α5 log(RT ASizei,t )+ui,t
(6)
9
where IM R stands for Inverse Multilateral Resistance, recovered from the first-stage results, LL controls whether a country is landlocked, GDP is the gross domestic production,
ID is the country’s internal distance (measured as the radius of the circle-equivalent area),
HHI is the Herfindahl-Hirschman Index that controls for specialization and RT ASize is
the adjusted size of the RTA the country belongs to, if any. ui,t is an independent and
identically distributed disturbance term.
3.
Data
We use the data set CHELEM (Comptes Harmonisés sur les Echanges et l’Economie Mondiale,
or Harmonised Accounts on Trade and The World Economy) provided by CEPII.2 There
are other available databases on trade flows. However, a nice feature of CHELEM is that
it also contains some additional information, different to trade flows, that we will use in
our study (distances, both external and internal, GDP, etc.). In addition, this database is
frequently updated.
Indeed, our database is composed by 71 countries accounting for 86.9% of world output
and 89.9% of international trade, and we analyze a long period of 41 years (1967–2007).
The data set contained information for more countries and years. However, in case we had
an interest in increasing the number of countries in the sample we would have to drop some
years, and in case we wanted more years the list of countries had to be reduced. Therefore,
we decided to select a reasonable balance between number of countries and years.
We restrict our analysis to trade in goods only. This constitutes a bias, since specialization patterns vary markedly across countries. However, this problem has long been
recognized by the literature (see, for instance Mirza and Nicoletti, 2004; Kimura and Lee,
2006). However, in our particular case it would be difficult to extend the analysis to account for trade in services, due to the non-existence of a services equivalent to the matrix
of trade in goods between country pairs.
2
See URL http://www.cepii.fr/anglaisgraph/bdd/chelem.htm.
10
4.
Results
4.1.
First-stage results
Figure 2 shows the evolution of the estimated distance effect for five different specifications considered. The levels of significance cannot be reported, since we have estimated 205
models, but descriptive statistics on the evolution of the estimates and their variance are
presented in ??. In summary, most variances lie below the 0.01 level for the distance effect
and most variables present a high degree of significance. The common language presents
a high and significant positive sign across the five specifications. The lower distance effect
belongs to the specification without a common border, which presents an unexpected negative sign across some specifications. The same happens to the RTA dummies, included
for robustness purposes and to test their inadequacy: the estimates show that countries
belonging to the EU and the NAFTA trade less with its partners, everything equal. Furthermore, the RTA estimates sometimes fall below the level of significance. This should not
be viewed as a causal effect, but as a misspecification warning: given their closeness, size
and common shared languages, the EU and NAFTA members trade less than expected.
This result calls for another approach on the RTA effect, addressed in the second step of
this study.
The country omitted for the estimation of the inward resistance variables was Vietnam,
which has been dropped from the second step results because of non-available data on
disaggregated production.
More importantly, the evolution of the distance effect shows that the distance puzzle
arises at the beginning of the 1990 decade. During the 1967–1993 frame, the distance effect
declined at a 1.07% rate (–24.3%), while for the rest of the period it stagnated, decreasing
at a rate of just 0.15% (–2.3%). Meanwhile, as ?? shows, the estimated multilateral
resistances remained stable until the beginning of the 1990 decade but soared after year
1992 (+49.2% from 1992 to 2007).
As Anderson and van Wincoop (2003) showed, trade costs must be measured against
the multilateral resistances and there is a perfect substitution relationship between them.
Exports from country i to j fall as average trade costs from country i fall. Thus, an
11
increasing regional integration—i.e., a reduction of average costs with RTA’s increasing
members—, along with other country-specific variables, may be easily counterbalancing
the observed reduction in costs associated with international trade.
4.2.
Second-stage results
This section presents results only for 1985 through 2006 due to the lack of disaggregated
production data for the preceding period for too many countries of our sample. This does
not threaten the consistency of the results, since the first step estimations were independent cross-sections. Moreover, the 1985–2006 period the last years of the distance effect
reduction, along with the whole stagnation years. The results from the five different specifications of Equation (5) are presented in tables 1 through 5. We have added year dummies
in order to control for year-specific spikes and slumps in international trade. On the other
hand, we do not include country-specific dummies since our dependent variables already
come from the gravity equation country dummies.
Although we provide estimations for all possible specifications in the first stage, we will
focus our comments on specification C. We consider this model as the most reliable. Its
explanatory variables were distance, the common border and the common language but
not the RTA dummies, which, as we have stated, are suspicious of being mis-specified.
Anyway, as the reader can corroborate through tables 1–5, results were mostly coincident
for all five specifications in the first stage.
The country-specific right-hand-side variables in this second-stage analysis are the landlocked dummy (LL), the economic size (GDP ), the geographic size (IntDist), the specialization indexes (HHI and CV ) and the size of the RTA the country belongs to (RT ASize)
All the parameters present their expected sign along all possible specifications, with
the minor exception of the landlocked dummy, which presents a positive sign in the first
specification (column 1 in tables 1–5). The landlocking dummy’s is non-significant through
models 2–4 (columns 2–4 in tables 1–5), though. The estimates for GDP , IntDist and
RT ASize effects show the expected sign and are robust and highly significant (1%) for
all combinations of first-stage and second-stage models. The geographic distance seems to
play a significant role in the determination of the average costs a country faces. Bigger
12
countries face bigger implicit average costs, as shown by the estimates in the ] − 1.9, −1.8[
range in columns 2–4 (tables 1–5). On the other hand, cæteris paribus, the richer the
country the less average costs it faces, thus trading more.
The relationship between both GDP and IntDist estimates must also be interpreted
with care. The first specification, column 1 in tables 1–5, omits the countries’ geographic
size. The GDP estimate for those columns is very close to 1. That value is the theoretical
prediction for the GDP effect in the standard gravity equation; moreover, it is a standard
result in most empirical applications. What we find interesting here is that, when the
geographic size enters the model, the GDP estimate rises a full 50%, robust for the rest
of the specifications (see columns 2–4 in tables 1–5). This result suggests that the income
elasticity to trade, once size is controlled for, might be bigger than 1. This hypothesis
is grounded on theory see, for instance..., as explained in Section 2. Bigger countries
have a larger internal market in which national firms can rely to achieve scale efficiency.
On the other hand, firms located in small countries must export to survive. Thus, the
Netherlands can be comparable to the US in terms of per capita wealth but trades six
times more, showing then much lower relative average trade costs. Failing to control for
this size effect biases downwards the GDP estimate, since the economic and the geographic
size are positively correlated.
The adjusted size of the RTA is significant throughout all the specifications, and robust
once the geographic size (IntDist) has entered the model, with an elasticity of approximately 3.3%. Although this might not seem a big effect at first glance, one must take
into account that the number and, ultimately, size of RTAs has increased greatly over the
last decades: the EU and the NAFTA together account for half the wealth in the world
and have been either constituted or consolidated in the 90’s decade. A simple calculation
shows that only the growth of the European Union and ASEAN account, respectively, for
a 20.7% and a 16.8% growth in trade for Germany and Indonesia. It has to be pointed
that this method of estimation does not enable us to assess directly that RTAs cause trade
diversion, but we can asses that the RTAs explain a significant part of the average trade
costs (MTR) reduction. And, due to the substitution relationship between multilateral
resistances and the trade costs, we can also asses that the growth of the RTAs has been
13
counterbalancing the reduction of international trade costs.
5.
Conclusions
Over the last few years, a topic thoroughly investigated in international economics has
been the failure of declining trade-related costs to be reflected in estimates of the standard
gravity model of bilateral trade. Some authors who studied the topic in early stages labeled
it as the “missing globalization puzzle” (Coe et al., 2002). Since then, there have been
several initiatives analyzing different aspects of this topic, proposing different solutions—
not incompatible among them. The ways to refer to the topic have also varied, but most
of them coincide in labeling it as the “distance puzzle”.
As indicated in the introduction, the literature has proposed different partial solutions
to this puzzle which contribute to address the question. Some of these studies suggest that
underlying the conservation of distance one may find the fact that, over the last few years,
many countries have been negotiating separated preferential trade treaties among themselves in the form of GATT-sanctioned free trade areas, or custom unions. As indicated
by Greenaway and Milner (2002), “since the GATT’s inception, well over 100 agreements
have been notified under Article XXIV Article I”.
Under these circumstances, the questions that the literature has attempted to answer
are multiple, including whether there is a regional bias to trade (i.e., whether RTAs are a
natural feature of international trade because countries tend to trade with near neighbors),
whether there is an identifiable RTA effect, which is the trade potential associated with
integration, or whether there is a “domino effect” of RTAs on non-members, i.e., whether
an RTA results in less trade with non-members, thereby increasing the likelihood of them
joining an RTA. Greenaway and Milner (2002) review the body of literature providing
answers to these questions.
However, among the studies reviewed, none has apparently analyzed two relevant issues
that the use of common RTA dummy variables is unable to capture: (i) which are the trade
effects of the varying economic sizes of the RTA members; and (ii) which are the trade
effects of the evolution of the economic size of the different RTAs—which differ remarkably
among different RTAs.
14
As indicated in the introduction, a dummy variable that tries to capture the effect on
two countries affiliated to the same RTA will impose an equal effect on all bilateral trade
for that RTA, ignoring the role of size of its trading partner and its evolution over time. A
measure that seeks to explain the role of the RTAs in international trade should take this
issues into account.
On the methodological side we argue that, in order to study the evolution of the
distance effect over time, we should impose as little structure as possible on the distance
estimates. The same can be said about the multilateral resistance dummies: countries’
specific circumstances can vary greatly over time (think about Estonia, South Korea or
Spain), so imposing the same coefficient on the multilateral resistance dummies would lead
to unreliable measures; moreover, they are a direct function of the trade costs, so they also
need to vary over time. For those reasons, we avoid for the panel data approach and run
41 independent cross-sections, follow a Tobit procedure to include the zero observations
and conclude that the distance effect falls steadily (–26%) between 1967 and 1992 but then
stagnates (–2%) from 1993 to 2007. This method of estimation makes the identification
of country-specific variables impossible, since the country-specific dummies act as black
boxes, capturing relevant information. This problem calls for a second stage panel data
analysis, in which we disentangle the multilateral resistance variables.
In that second stage we address relevant issues related to the countries’ size, along
with the RTA measurement, regressing the estimated multilateral resistance dummies on
a set of country-specific economic and geographic variables. First, we argue that omitting
a country’s geographic size can bias the GDP estimates. Firms located on big countries
can rely on internal demand to achieve the needed scale efficiency, while firms located
in small countries grow more outward-oriented for that same reason. The same can be
said about productive specialization; while bigger countries can produce a wider range of
products, smaller ones must focus on a more reduced set, relying on imports to satisfy
internal demand for product variety. Failing to take this issue into account would bias our
GDP estimate, since size is highly correlated with total GDP.
The results show a robust and significant role of the geographic and economic size on
the determination of the multilateral resistances, i.e., the average costs a country faces.
15
Bigger countries trade much less, whereas wealthier countries trade much more; omitting
the geographic size biases the GDP estimate downwards. We also show a positive and
robust RTA effect. While the estimated elasticity seems low (3.3%), one must remember
that the last decades have seen a huge growth in size and RTAs members throughout the
world. As an example, our estimates imply that approximately 20% of the trade growth
in Germany since 1986 is due to the growth of the total economic size of the EU. Given
the substitution relationship between the multilateral resistances and the trade costs in
the gravity equation, we argue that the surge of the RTAs around the world explains part
of the distance effect persistence.
16
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19
Table 1: Estimated Inverse Multilateral Resistance (from A specification in first stage)
Model 1
∗∗∗
−4.075
(0.472)
0.690∗∗∗
(0.201)
1.013∗∗∗
(0.037)
0.095∗∗∗
(0.008)
(Intercept)
LL
GDP
RT ASize
IntDist
HHI
Model 2
∗∗∗
1.051
(0.308)
−0.177
(0.124)
1.458∗∗∗
(0.025)
0.020∗∗∗
(0.005)
−1.802∗∗∗
(0.038)
CV
R2
R̄2
σ
F
p
Log-likelihood
Deviance
AIC
BIC
N
∗
,
∗∗
and
∗∗∗
0.773
0.769
2.001
190.380
0.000
−2869.177
5363.134
5790.353
5926.026
1364
0.916
0.914
1.221
580.980
0.000
−2195.104
1996.054
4444.208
4585.099
1364
Model 3
Model 4
∗∗∗
1.181
(0.323)
−0.162
(0.125)
1.481∗∗∗
(0.029)
0.020∗∗∗
(0.005)
−1.801∗∗∗
(0.038)
0.162
(0.113)
0.916
0.914
1.221
556.681
0.000
−2191.278
1991.350
4438.556
4584.624
1362
0.688
(0.367)
−0.154
(0.125)
1.485∗∗∗
(0.029)
0.020∗∗∗
(0.005)
−1.801∗∗∗
(0.038)
0.259
(0.147)
0.916
0.914
1.221
557.165
0.000
−2190.735
1989.763
4437.470
4583.538
1362
denote significance at 10%, 5%, and 1% significance levels, respectively.
20
Table 2: Estimated Inverse Multilateral Resistance (from B specification in first stage)
Model 1
∗∗∗
−4.307
(0.468)
0.731∗∗∗
(0.199)
1.028∗∗∗
(0.037)
0.094∗∗∗
(0.008)
(Intercept)
LL
GDP
RT ASize
IntDist
HHI
Model 2
Model 3
∗
∗
0.761
(0.306)
−0.127
(0.123)
1.468∗∗∗
(0.024)
0.020∗∗∗
(0.005)
−1.781∗∗∗
(0.038)
CV
R2
R̄2
σ
F
p
Log-likelihood
Deviance
AIC
BIC
N
∗
,
∗∗
and
∗∗∗
0.778
0.774
1.983
195.404
0.000
−2856.451
5263.991
5764.903
5900.575
1364
0.917
0.915
1.214
589.911
0.000
−2186.625
1971.390
4427.249
4568.140
1364
Model 4
0.819
(0.322)
−0.118
(0.124)
1.480∗∗∗
(0.029)
0.020∗∗∗
(0.005)
−1.781∗∗∗
(0.038)
0.079
(0.113)
0.917
0.915
1.214
564.548
0.000
−2183.549
1968.878
4423.099
4569.167
1362
0.557
(0.365)
−0.113
(0.124)
1.483∗∗∗
(0.029)
0.020∗∗∗
(0.005)
−1.781∗∗∗
(0.038)
0.142
(0.146)
0.917
0.915
1.214
564.757
0.000
−2183.319
1968.211
4422.637
4568.705
1362
denote significance at 10%, 5%, and 1% significance levels, respectively.
21
Table 3: Estimated Inverse Multilateral Resistance (from C specification in first stage)
Model 1
∗∗∗
−3.990
(0.486)
0.727∗∗∗
(0.206)
1.003∗∗∗
(0.038)
0.111∗∗∗
(0.009)
(Intercept)
LL
GDP
RT ASize
IntDist
HHI
Model 2
∗∗∗
1.406
(0.305)
−0.187
(0.122)
1.472∗∗∗
(0.024)
0.033∗∗∗
(0.005)
−1.897∗∗∗
(0.038)
CV
R2
R̄2
σ
F
p
Log-likelihood
Deviance
AIC
BIC
N
∗
,
∗∗
and
∗∗∗
0.764
0.760
2.061
181.054
0.000
−2909.141
5686.799
5870.283
6005.955
1364
0.919
0.918
1.209
607.419
0.000
−2180.888
1954.876
4415.775
4556.666
1364
Model 3
Model 4
∗∗∗
1.052∗∗
(0.363)
−0.165
(0.124)
1.499∗∗∗
(0.029)
0.032∗∗∗
(0.005)
−1.896∗∗∗
(0.038)
1.534
(0.320)
−0.172
(0.123)
1.495∗∗∗
(0.029)
0.033∗∗∗
(0.005)
−1.896∗∗∗
(0.038)
0.160
(0.112)
0.919
0.917
1.209
582.126
0.000
−2176.994
1950.015
4409.987
4556.055
1362
0.252
(0.145)
0.919
0.917
1.208
582.586
0.000
−2176.500
1948.602
4408.999
4555.067
1362
denote significance at 10%, 5%, and 1% significance levels, respectively.
22
Table 4: Estimated Inverse Multilateral Resistance (from D specification in first stage)
Model 1
∗∗∗
−4.198
(0.482)
0.765∗∗∗
(0.205)
1.016∗∗∗
(0.038)
0.110∗∗∗
(0.009)
(Intercept)
LL
GDP
RT ASize
IntDist
HHI
Model 2
∗∗∗
1.144
(0.303)
−0.139
(0.122)
1.481∗∗∗
(0.024)
0.032∗∗∗
(0.005)
−1.878∗∗∗
(0.037)
CV
R2
R̄2
σ
F
p
Log-likelihood
Deviance
AIC
BIC
N
∗
,
∗∗
and
∗∗∗
0.768
0.764
2.044
185.113
0.000
−2897.938
5594.145
5847.876
5983.549
1364
0.920
0.918
1.203
614.204
0.000
−2174.308
1936.108
4402.617
4543.508
1364
Model 3
Model 4
∗∗∗
0.935∗∗
(0.362)
−0.126
(0.123)
1.496∗∗∗
(0.028)
0.032∗∗∗
(0.005)
−1.877∗∗∗
(0.037)
1.206
(0.319)
−0.131
(0.123)
1.493∗∗∗
(0.029)
0.032∗∗∗
(0.005)
−1.877∗∗∗
(0.037)
0.084
(0.112)
0.920
0.918
1.203
587.939
0.000
−2171.138
1933.319
4398.275
4544.343
1362
0.145
(0.145)
0.920
0.918
1.203
588.149
0.000
−2170.913
1932.682
4397.826
4543.894
1362
denote significance at 10%, 5%, and 1% significance levels, respectively.
23
Table 5: Estimated Inverse Multilateral Resistance (from E specification in first stage)
Model 1
∗∗∗
−4.269
(0.478)
0.748∗∗∗
(0.203)
1.022∗∗∗
(0.038)
0.109∗∗∗
(0.009)
(Intercept)
LL
GDP
RT ASize
IntDist
HHI
Model 2
∗∗
0.994
(0.303)
−0.143
(0.122)
1.480∗∗∗
(0.024)
0.033∗∗∗
(0.005)
−1.850∗∗∗
(0.037)
CV
R2
R̄2
σ
F
p
Log-likelihood
Deviance
AIC
BIC
N
∗
,
∗∗
and
∗∗∗
0.772
0.768
2.023
188.908
0.000
−2884.004
5481.009
5820.008
5955.680
1364
0.920
0.918
1.201
612.988
0.000
−2172.275
1930.344
4398.550
4539.441
1364
Model 3
Model 4
∗∗∗
0.795∗
(0.361)
−0.130
(0.123)
1.495∗∗∗
(0.028)
0.033∗∗∗
(0.005)
−1.849∗∗∗
(0.037)
1.052
(0.318)
−0.135
(0.123)
1.491∗∗∗
(0.029)
0.033∗∗∗
(0.005)
−1.849∗∗∗
(0.037)
0.080
(0.112)
0.920
0.918
1.202
586.726
0.000
−2169.140
1927.657
4394.281
4540.349
1362
0.138
(0.145)
0.920
0.918
1.201
586.919
0.000
−2168.935
1927.077
4393.871
4539.938
1362
denote significance at 10%, 5%, and 1% significance levels, respectively.
24
Figure 1: Exports’ degree of openness vs. outward (inverse) multilateral resistance
Ϭ͘Ϭϲ
ĐĞŶ
Ăƚ Ϭ͘Ϭϱϱ
ŝƐƐ
ĞZ Ϭ͘Ϭϱ
ůĂ
ƌĞ
ƚĂ Ϭ͘Ϭϰϱ
ŝůƚů
Ƶ
D
Ě Ϭ͘Ϭϰ
ƌĂ
Ϭ͘Ϭϯϱ
ƚǁ
Ƶ
K
Ϭ͘Ϭϯ
Ϭ
Ϭ͘Ϯ
Ϭ͘ϰ
Ϭ͘ϲ
Ϭ͘ϴ
džƉŽƌƚƐĚĞŐƌĞĞŽĨŽƉĞŶŶĞƐƐ;yͬ'WͿ
25
ϭ
ϭ͘Ϯ
1.9
1.8
1.7
1.6
1.5
Distance coefficient
2.0
2.1
Figure 2: Evolution of the distance effect
1970
1980
1990
2000
Year
A
B
C
D
E
Independent variables included in each model:
Model A: dij and bij ;
Model B: dij and Lij ;
Model C: dij , bij and Lij ;
Model D: dij , bij and RTA dummies;
Model E: dij , bij , Lij and RTA dummies.
26
18
16
14
12
1/Πi
20
22
Figure 3: Inverse Outward Multilateral Resistances (unweighted average)
1980
1985
1990
1995
2000
Year
Π1
Π2
Π3
27
Π4
Π5
2005