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Measuring EU Trade Integration within the Gravity Framework
Andrea Molinari
INTRODUCTION ...................................................................................................................................... 2
CHAPTER I. ECONOMIC HISTORY AND TRADE STYLISED FACTS .................................... 4
CHAPTER II. TRADE INTEGRATION AND GRAVITY MODELS: SURVEY AND SOME THEORETICAL ASPECTS ..................................................................................................................... 5
II.1. GRAVITY MODELS.......................................................................................................................... 5 II.1.1. What do they measure?.......................................................................................................... 5 II.1.2. On the Theoretical Foundations of Gravity Models .............................................................. 7
CHAPTER III. THE MODEL .............................................................................................................. 8 III.2.2. Data and Proxies .................................................................................................................. 9
III.2.2.1. The Baseline Gravity Variables ....................................................................................................10 III.2.2.2. The Integration (institutional) Effects...........................................................................................11 III.2.2.3. Unobservable Time Effects and Country Characteristics .............................................................12
III.4. THE RESULTS ............................................................................................................................. 15 III.4.1. SURE Estimation ................................................................................................................ 16
III.4.1.1. Baseline gravity variables .............................................................................................................16 III.4.1.2. The integration effects ..................................................................................................................20
III.5. MAIN FINDINGS.......................................................................................................................... 23 CHAPTER IV. FURTHER RESEARCH EXTENSIONS AND CONCLUSIONS........................ 24
APPENDIX A. WELFARE EFFECTS OF REGIONAL TRADE AGREEMENTS..................... 25
REFERENCES ......................................................................................................................................... 27
2
Introduction
This thesis focuses on trade integration among the countries that belong to a regional
trade agreement. Increase in intra-bloc trade is one of the most crucial ingredients for
prosperous economic integration process such as the one implemented by the
European Union (EU). We are especially interested in applying this issue to the EU
because of the high economic integration reached by its members since the post-war
period. Hence, our aim is to assess the contribution of the EU institutional framework
and its evolution to the intra-EU trade deepening process.
There is no doubt that intra-EU trade has increased after the creation of the European
Economic Community (ECC) in the late 1950s. There are many potential explanations
for this growth. On the one hand, EU members have experienced, as have most OECD
countries, an increase in their economic size and wealth, which may well have
stimulated their exports and imports to each other. In addition, doing business has
become easier through the past decades due to the growing globalisation process.
Proximity among EU members also operates as an important positive determinant of
the countries’ trade flows, given the lower transport and transaction costs that physical
closeness entails. Another determinant of the observed rise in trade flows among EU
countries may have been the ‘institutional’ effect entailed on the creation of this
regional trade agreement.
The well-known gravity framework provides us with the necessary tool to test
whether the institutional creation of the EEC in itself has had a significant impact on
intra-EU trade. Gravity models have been widely used in the literature to find the
determinants of bilateral trade flows. Their baseline specification includes the size of
the trading countries, their common characteristics, and the distance between them.
By adding a dummy variable for joint membership to a certain regional trade
agreement, it is possible to account for EU’s institutional incidence in bilateral trade
flows.
Given that we try to reflect the evolution through time of the integration process, it is
necessary to control for unobservable time effects in the baseline gravity equation.
These effects capture further trade determinants that are difficult to measure, such as
the globalisation process, and therefore cannot be included into the equation explicitly.
Along similar lines, it is also important to capture countries’ specific characteristics.
Thus, with a gravity model that controls for the baseline trade determinants (called
‘natural’ by Frankel, Stein and Wei, 1993) and for the unobservable time and
3
individual effects we are able to isolate the ‘institutional’ effects of the EU. This then
allows us to assess whether they were important in determining intra-EU trade flows.
Although the gravity framework’s lack of theoretical foundations provokes criticism, it
also provides some flexibility for application to various samples of countries and
years. However, the gravity framework has two intrinsic problems. First, it includes
time-invariant regressors, such as distance and common characteristics between
countries, which can make estimating their effects on trade more difficult. Moreover,
explaining trade with the partners’ economic sizes generates endogeneity problems.
This means that a country’s exports both depend on and determine its economic size.
We estimate our gravity model by three methods. In order to differentiate EU
members from a broader sample of countries with similar characteristics, and to
analyse the evolution through time of the institutional EU effect, we estimate our
gravity specification for a sample of developed OECD countries over the period 1960-
1997.
To account for the time correlation between the cross-section gravity equations, we
first estimate a Seemingly Unrelated Regression (SURE) model. We then consider a
panel data General Least Squares (GLS) method to control for unobservable individual
effects in the sample. Although this method might provide inconsistent estimators,
which are probably due to the mentioned endogeneity problem, GLS is preferable to
the within-group, since this sweeps out the effects of time-invariant regressors. Our
third method is an Instrumental Variables (IV) GLS method suggested by Hausman
and Taylor (1981), a useful way of accounting for both the unobservable individual
and time effects and the endogeneity issues. The IV-GLS method uses internal
instruments to control for the mass endogeneity and allows for the inclusion of time-
invariant regressors (such as ‘geographical’ distance and common characteristics).
Our main findings indicate that the EU effect decreases when controlling for
unobservable individual effects and mass endogeneity. The EU estimate is also
sensitive to globalisation and to the three external enlargement effects. In addition, we
find a positive long-run external effect for the first and third enlargements on bilateral
exports and a negative medium-run external effect for the first enlargement. This is
mainly due to a ‘learning process’ entailed in the accession of new members, UK’s
joining, and the first oil shock. Finally, our results show a positive globalisation effect
on bilateral exports.
4
To conclude, we suggest some ideas to include ‘economic’ distance measures and to
disaggregate the model into economic sectors. Finally, we begin analysing possible
ways to solve the lack of labour market mobility among EU countries by exploring the
wages-trade link.
Our work is organised as follows: Chapter I summarises the most relevant post-war
developments for the OECD countries, focussing mainly on the European integration
process and on stylised facts for intra-EU trade patterns. Chapter II introduces the
gravity model, discussing some of the theoretical aspects involved in adopting this
framework, and surveying the main studies that measure EU trade integration.
Chapter III derives a simple gravity model from an imperfect competition setup to
then specify and estimate our model to measure the evolution of trade integration
among the EU members by three different methods (SURE, GLS and IV-GLS). Finally,
Chapter IV poses further issues and ideas to be developed in relation to finding an
‘economic’ distance measure, sectoral gravity equations and labour market integration.
Chapter I. Economic History and Trade Stylised Facts
This chapter presents a brief historical overview and some trade stylised facts in order
to define the general settings of the period analysed in our model and to understand
the main results of our estimations. Section I.1. focuses on some general aspects of the
economic history of industrial countries.1 The next section describes the main post-
war multilateral and regional agreements reached by European countries. The last
part of the chapter shows stylised facts of the intra-European trade as well as trade
patterns between Europe and the rest of the world. 2
1 Given that most OECD members are industrial (or developed) countries, these terms will be used interchangeably. 2 This comprises third countries that do not form part of the respective agreement.
5
Chapter II. Trade Integration and Gravity models: Survey and Some Theoretical Aspects
We will use the gravity model as a tool to measure trade integration among EU
countries. The first part of this chapter explains the gravity framework and highlights
some theoretical issues. The second part of the chapter surveys the main studies that
measure trade integration.
II.1. Gravity models
This section introduces the main tool we are going to use for measuring EU trade
integration, the gravity model. We first explain the idea behind it and then describe
some of the main theoretical issues posed in the literature.
II.1.1. What do they measure?
Gravity models derive their inspiration from Newton’s law of gravity, which
recognises that all material particles, and the bodies that are composed of them, have a
property called gravitational mass. This property causes any two particles to exert
attractive forces upon each other that are directly proportional to the product of the
masses and inversely proportional to the square of the distance between the particles.
Gravity models are used to explain bilateral trade links between countries as directly
proportional to their size and inversely related to the distance between them. Most
models also include some common idiosyncratic characteristics of these countries,
such as the sharing of a common language or membership in certain preferential trade
agreements. They have proved to be empirically robust and consistent with the
observed data and offer a systematic framework for measuring bilateral trade patterns
around the world. As Eichengreen and Irwin (1998) assert, “Few aggregate economic
relationships are as robust”3.
Baldwin (1994) makes a useful and intuitive analogy of an individual family’s pattern
of purchases to explain the idea behind the gravity model:
A family lives near two shopping areas. Factors influencing how much the family buys at each shopping area may be divided into those that concern the family’s characteristics and those that relate to the particular shopping area’s traits. For instance, the richer the family becomes per capita, the more they will tend to spend on goods from both shopping
3 Eichengreen and Irwin (1998), page 34.
6
areas. Similarly, holding constant the per capita income of the family but increasing the family’s total income, and thereby the size of the family, would increase the amount bought at both sites. The division of purchases between the two shopping areas would depend primarily on the various characteristics of the shopping areas themselves. It is likely that the family would buy relatively more from the area that offered the wider selection of goods. Also, other things being equal, the family will tend to do more of their shopping at the nearby shopping area.4
In an international trade setup, the richer and bigger the country, the higher its
purchases of foreign goods; i.e. a country’s imports increase with its per capita and
total income. The volume and the variety of goods produced and available resources
will also be greater as a country grows in size and becomes richer. In other words, an
exporting country’s per capita and total GDP (called ‘mass’ in the gravity framework)
should be positively correlated with its exports. Finally, the greater the goods
transportation cost between two countries, the smaller the quantity of trade; i.e.
distance (or any other determinant of transaction costs) dampens trade.
This positive correlation between exports and GDP is possible as long as greater
production is evenly distributed across all goods and services. It is possible that a
country’s economy responds to an increase in GDP only by expanding its non-traded
sector. In that case, there would not be such a correlation between trade and size.
Moreover, an argument can be made for bigger countries to be self-sufficient.
During the 1990s, gravity models became widely used as a tool to explain bilateral
trade flows among countries or regions. Some of their applications incorporate trade
blocs dummies to test how ‘natural’ those blocs are (Frankel, Stein and Wei, 1993 and
1998). A second group of studies includes both internal and external trade and
distance proxies (Wei, 1996; Helliwell, 1996, 1997 and 1998; Nitsch, 1999) to measure
the width of the borders. The gravity setup has also been used to measure the
potential trade of developing countries, such as Eastern Europe EU accession (Wang
and Winters, 1991 and 1994; Hamilton and Winters, 1992; Baldwin, 1994), or within the
Southern Africa region (Foroutan and Pritchett, 1993; Cassim and Hartzenburg, 2000).
Due to our main interest in EU trade integration, our survey focuses on the first two
lines of study as well as other studies that measure EU trade integration outside the
gravity framework.
4 Baldwin (1994), pages 82 and 83.
7
II.1.2. On the Theoretical Foundations of Gravity Models
The origins of the gravity model to explore the determinants of bilateral trade flows go
back to Linnemann (1966), who proposed to consider the importer’s demand, the
exporter’s supply and the trade costs between them.
Since then, the theoretical foundations of gravity models have been questioned. Due
to their intuitive specification, gravity models have always been considered to work
fairly well in empirical grounds. However, most concerns centre on whether it is
possible to derive this model from various theoretical frameworks that adopt different,
and sometimes contradictory, assumptions. The critics point out that the gravity
framework is compatible both with the perfect competition models, such as
Heckscher-Ohlin, and with trade theories that assume imperfect competition.
More specifically, Wang and Winters (1991) point out that a simple Cobb-Douglas
expenditure system (such as Anderson’s, 1979), is not appropriate to derive a gravity
specification. As Anderson shows, introducing “(...) stochastic errors and/or multiple
commodities, (...) the log-linear relationship between aggregates is difficult to
support”5.
Moving to more comprehensive functional forms, Bergstrand (1989) obtains a gravity
equation that explains bilateral trade flows from a general equilibrium model with two
differentiated products and two factors. The representative consumer is assumed to
maximise a ‘nested Cobb-Douglas-CES-Stone-Geary’ utility function subject to a
budget constraint, whereas the firms produce in a ‘Chamberlinian monopolistic
competition’ setup. Bergstrand shows that, under this framework, demand depends
upon relative prices and domestic income. Hence, the gravity equation ‘fits in’ with
both the Heckscher-Ohlin model of inter-industry trade and the Helpman-Krugman-
Markusen intra-industry trade models.
Deardoff (1995) shows that, starting from a Heckscher-Ohlin model, the gravity model
can be derived assuming either frictionless trade or imperfect competition. In the first
case, preferences need to be identical and homothetic or demands have to be
uncorrelated with supplies. In the context of countries producing differentiated goods,
preferences can be either Constant Elasticity of Substitution (CES) or the special case
Cobb-Douglas.
However, this argument has been discussed by later work. For example, Helliwell
(1998) finds that a model of comparative advantage limited by trade barriers would
8
not seem to predict any influence of average incomes on the size of border effects, 6
which he estimates using a gravity model. Moreover, some authors consider this lack
of theoretical incompatibility as an attractive feature that gives flexibility to explain
“(...) bilateral trade flows across a wide variety of countries and periods”7.
In sum, some studies have shown that the gravity model can be derived from two
‘opposite’ theoretical models. However, this only indicates that gravity models cannot
be used to test rival trade theories, and hence we believe that the theoretical flexibility
of gravity models is an advantage rather than an obstacle for explaining bilateral trade
flows.
Chapter III. The Model
One of the main objectives of an economic union is to increase trade among its
members. The trade intensity between countries depends mainly on the explicit and
implicit barriers that each imposes on its partners. These barriers generally take the
form of transport costs, tariffs, and non-tariff restrictions. Declining costs of transport
and communication reduce the economic distance between communities, regardless of
which country the communities belong to. These cost reductions are likely to
strengthen both domestic and economic linkages, which are necessary for the
increased economic integration among the member countries.
The stylised facts described in Chapter I show that trade between European Union
countries has grown considerably since the creation of the EEC in the late 1950s.
However, the simple observation of intra-EU trade patterns does not shed light upon
the determinants that caused this significant increase in bilateral exports. These may
involve the EU8 treaties (i.e. ‘institutional effects) or may simply reflect the growth, the
wealth and the reduction of countries’ transport costs attained by trading with their
neighbours. A further determinant of intra-EU trade might be the impact of
globalisation on trade flows, which reduces international (and national) transaction
costs.
5 Wang and Winters (1991), page 7. 6 The border effect measures the extent to which domestic sales of a country are greater than its external trade, after allowing for the effects of economic size, distance, and alternative trading opportunities. 7 Eichengreen and Irwin (1998), pages 33 and 34. 8 For simplicity, we will use the terms EEC, EC and EU interchangeably all throughout this chapter, if this distinction is not fundamental.
9
Hence, we need a method that will control for the so-called ‘natural’ determinants of
trade and at the same time capture the ‘institutional’ effect of the EU integration
process, time and country-specific effects. We accomplish this by estimating a gravity
equation. As mentioned in Chapter II, the gravity model is a useful tool to explain
bilateral trade as a proportion of the product of both countries’ masses and inversely
related to the distance between them. The baseline gravity model that we consider
includes mass of the trading countries, their wealth and dissimilarity, and the distance,
adjacency and common cultural linkages between them.
It is important to clarify that we do not intend to derive economic welfare results. As
Viner (1950) noted, there can be are positive and negative welfare effects when
creating a customs union (CU). We are just analysing the bilateral trade determinants
of intra-EU trade integration.
We add variables to the baseline gravity model that will help us explain the effects of
EU and EFTA on bilateral trade flows. As noted in Chapter II, some gravity models
introduce internal and external trade and distance proxies to measure the width of the
borders, and others include trade bloc dummies to test how ‘natural’ those blocs are.
In this paper, we take a combined approach that tries to measure the width of the
borders, which we call ‘institutional effects’, by also controlling for the specific time
and individual effects of our sample. Our approach adds another dimension to the
surveyed studies by estimating a panel data model and uses other techniques in order
to account for some of the problems generated by this estimation. More specifically,
we estimate an Instrumental Variables General Least Squares (IV-GLS) model
developed by Hausman and Taylor (1981). This will allow us to instrument for some
endogenous time-varying determinants within a setup that includes time-invariant
regressors, which would otherwise be swept out from the estimation.
III.2.2. Data and Proxies
Our sample contains annual data from 1960 to 1997 for twenty-one developed OECD
countries9 and the RW. We included the latter as the twenty-second country to
consider the total trade flows of each country in our sample. As the common feature
in gravity models, our dependent variable is bilateral exports between these twenty-
9 The OECD countries are the same used in Chapter I: Australia, Austria, Brussels + Luxembourg, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the UK, and the US.
10
two countries. Thus, we are working with 462 pairs of countries per year, which
without considering missing data becomes a total of 15,178 observations.
Bilateral exports and exports price indices are provided by the Directions of Trade
Statistics (IMF), and the constant mass measures by the World Development Indicators
(World Bank). Pairwise great circle distance measures were taken from Frankel, Stein
and Wei (1995) and Wei and Frankel (1995). We created the dummy variables.
Our model is based upon four main determinants of bilateral exports: (i) the baseline
gravity variables; (ii) the integration effects; (iii) the time effects; (iv) the individual
countries characteristics. Each of them comprises a set of regressors. Meanwhile the
first two sets capture the main economic and ‘institutional’ determinants of bilateral
exports flows; (iii) and (iv) allow us to explore further the stylised facts described in
Chapter I.
III.2.2.1. The Baseline Gravity Variables
Like most of the studies within the gravity framework, our specification includes the
following baseline variables:
• Mass of both importer and exporter countries. In order to capture the exporter’s
production we take its GDP, and to proxy the importing country’s absorption we
use GNP. They are estimated separately to explain exports, as opposed to trade
flows, and there is no prior reason to impose a common coefficient between them.
• Wealth of both exporting and importing countries, as a means of reflecting each
country’s prosperity. The proxies used are GDP (GNP) per capita for the exporter
(importer). 10
• Dissimilarity between trading countries, to reflect the relative wealth between them.
We measure it as the absolute difference between the wealth of exporting and
importing countries. This variable is partly included to control for the potential
multicollinearity between the mass and wealth measures. 11
• Transport costs, proxied by the great circle physical distance between the capital
cities12 of the trading countries and an adjacency dummy variable.
10 Including global per capita GDP together with GDP is equivalent to taking the latter with population, as Brun, Guillaumont and de Melo (1998) show. 11 One of the ways of solving this problem is to formalise a relationship among these regressors. 12 As will be discussed in the next chapter, a better way of capturing the transport costs involved in trade would be to find a measure of ‘economic’ distance.
11
• Linguistic ties, given that having a language in common may have an impact on
transaction costs and increase the transborder contacts and information flows.
III.2.2.2. The Integration (institutional) Effects
As mentioned in Chapter II, some of the literature uses the gravity equation to
measure the width of the borders between two countries. However, this approach
needs not only external but also internal bilateral trade and distance measures. For
most countries, internal trade and distance proxies are difficult to find, and thus
require ad-hoc calculations. Another way of measuring trade integration effects is to
estimate a gravity equation for international trade flows, including dummies to
account for the determinants of regionalisation among countries. We consider the
latter approach more accurate and less sensitive to the calculations adopted, given that
it only relies on observed data.
In order to test for the EU trade integration process itself, i.e. to distinguish between
globalisation and European integration effects, we add the following variables to the
baseline gravity model:
• An EU and an EFTA dummy variables to reflect the time-average effect of the trade
between two EU or EFTA member countries. They include the countries joining
these blocs at each point in time, and thus vary both through time and across
individuals.
The idea is to consider the EU (EFTA) as one single country (with the bilateral
‘internal’ trade given by the individual members), and measure the width of its border
with non-EU (EFTA) countries. Some of our panel data specifications include a time-
interacting EU dummy to analyse the evolution of the EU ‘institutional’ effect over
time.
• For capturing the ‘external’ effect13 of the EU enlargements, three types of
dummies for exporting countries were included:
− Enlargement: indicate the long-run external effects of each EU enlargement.
− New member: to capture the medium-run external effects of a country being a
new EU partner.
− Joining country: to look at the short-run (or first-year impact) of joining EU
members.
12
III.2.2.3. Unobservable Time Effects and Country Characteristics
As described in Chapter I, along the period 1960-1997 economic conditions have
changed considerably. Hence, accounting for unobservable time effects is useful to
capture certain trade determinants that could bias our estimates of EU integration.
Time effects can be partly due to the globalisation process.
Depending on the specification of the model, we proxy globalisation effects with a
time trend or time dummies. The former measures the average effects and the latter is
useful to analyse the time effects evolution at each point in time and to identify
structural time breaks in the sample.
Following Brun, Guillaumont and de Melo (1998), we include a linear time trend. We
also tried to include a quadratic time trend, 14 but this was not significantly different
from zero.
Country individual characteristics can be proxied by including n-1 exporting country
dummies, taking as base country the RW. Hence, these dummies measure the relative
impact of each exporter to the bilateral trade flows with respect to the RW’s. The
inclusion of these country-specific effects is important to capture any determinant not
accounted for in the mass and wealth measures. This not the same as estimating panel
data individual effects, since given our ‘individuals’ are pairs of countries.
III.2.3. Parameters of Interest
Incorporating the four sets of regressors described in the previous section, the general
log linear specification for our gravity model is:
(III.10)
where:
x, m exporting and importing countries, respectively.
Xxmt natural logarithm of real bilateral exports between x and m in year t.
Mxt and Mmt natural logarithms of real masses of x and m (respectively) in year t.
Mpcxt and Mpcmt natural logarithms of real wealth of x and m (respectively) in year t.
DISIMxmt natural logarithm of dissimilarity between x and m in year t.
13 Eichengreen and Irwin (1998) add this dummy for only one of the two countries participating in the trade arrangement to test for the ‘external’ effect of the grouping on trade with nonmembers.
13
DISTxm natural logarithm of distance between x and m.15
Dxm is a partitioned matrix of dummy variables to control for adjacency (ADJxm) and
linguistic ties (LINGTIExm) between x and m:
, and hence
EUxmt and EFTAxmt dummy variables for common EU or EFTA membership in t.
Ext a partitioned matrix to account for enlargement external effects:
, and hence
where:
− ENLARjxt = 1 from the t that x joined the EU onwards, if x joined the EU in the jth.
enlargement.
− NEWjxt = 1 from the t that x was a new member of the EU until the following
enlargement, if x joined the EU in the jth. enlargement.
− JOINjxt = 1 on the year of joining the EU if x joined in the jth. enlargement.
for (j = 1, 2, 3) and zero otherwise.
t linear time trend.
Tt set of T-1 time dummies, with 1960 as the base year.
Cx set of n-1 country dummies, with the RW as the base country.
εxmt error term.
Let us first briefly describe the baseline parameters, i.e. the (i) set of regressors.
Income (wealth) elasticities of bilateral exports for exporting and importing countries
are α1 and α2 (α3 and α4), respectively. A positive α5 indicates that two countries trade
more with each other the more their wealth differs and could be interpreted as
favouring comparative advantage inter-industry trade theory. Conversely, a negative
coefficient for DISIMxm, suggesting that two countries with similar endowments trade
more, would support an intra-industry trade explanation. Given that we take DISTxm,
ADJxm and LINGTIExm as proxies for transportation and transaction costs, we expect α6
14 They expect a concave evolution of the time trend, mainly due to the 1970s’ oil shocks and the contra-shocks of 1985 and late 1990s. 15 Although the Newtonian formula indicates that square distance enters the equation, we follow the usual approach of including a linear distance, since the former was not statistically significant.
14
<0, and the vector α7 to have positive elements. In other words, the greater the
distance between two countries, the smaller their trade (ceteris paribus), and the
greater the link between two countries (either geographical or cultural), the greater the
amount of bilateral trade between them.
Our main interest is in the β coefficients, i.e. the ‘institutional’ effects of the creation of
the EU and EFTA on bilateral exports. These dummies take the value of one if both
countries in the trading pair belong to the corresponding trade agreement in year t.
For example, the pair France-Italy will have a 1 for the whole 1960-1997 period,
whereas France-Spain will only have a one since 1986, when the latter became a
member of the EU, and similarly for EFTA. Hence, the base categories for the EU
(EFTA) dummy is composed by the pairs of non-EU (EFTA) with non-EU (EFTA)
countries and those of EU (EFTA) with non-EU (EFTA) members. The definition of
our EU dummy differs with that used in Frankel (1997), where the EC bloc dummy
does not vary over time.
In a panel data model, the EU effect is composed of β1 and β3. The latter is the EU
effect at each point in time. If the former were not included, the EU estimate would be
biased (i.e., we allow for a non-zero origin of the trended EU coefficient). These semi-
elasticities indicate that two EU members trade more with one another than predicted
by their ‘natural’ trade determinants and the average behaviour of the developed
OECD countries. In other words, it suggests an increase in intra-bloc trade.
We look at the ‘external’ effects of the three EU enlargements with the vector of
coefficients β4. To differentiate between the speeds of adjustment of each group of
joiners, we define three types of ‘external’ effects of each enlargement depending on its
persistence over time. The long-run (ENLARjxt) measures the effect of joiners since
they became EU members; the medium-run effect (NEWjxt) shows the period during
which joiners are ‘new’ members of the EU; and the short-run (JOINjxt) captures the
one-year effect of joining the EU. In order to facilitate the interpretation of these
enlargement dummies, it is convenient to clarify the base countries of these dummies:
− The base countries for ENLAR1xt are all but the UK, Denmark and Ireland for all t,
and all countries for all t < 1973. ENLAR2xt has all countries other than the
Mediterranean new members for all t, Greece for all t < 1981, and Portugal and
Spain for all t < 1986 as base. For ENLAR3xt the base are all countries but Austria,
Finland and Sweden for all t, and all countries for all t < 1995.
15
− The base countries for NEW1xt are all but the three first joiners for all t, and all
countries for all t < 1973 and t ≥ 1981. In NEW2xt, all except the Mediterranean
countries for all t, Greece for all t < 1981 and t ≥ 1995, and Portugal and Spain for
all t < 1986 and t ≥ 1995; and for ENLAR3xt the non-EU15 and the EC12 members
for all t, and all countries for all t < 1995.
− The base countries for JOIN1xt are all but the first joiners for all t, and all countries
for all t ≠ 1973; for JOIN2xt, all countries except the Mediterranean for all t, Greece
for all t ≠ 1981, and Spain and Portugal for all t ≠ 1986; and for ENLAR3xt all
countries except the last joiners for all t, and all countries for all t ≠ 1995.
Hence, for example, a positive and significant indicates that Denmark, Ireland
and the UK increased their bilateral exports relatively more than the other EU and
OECD countries. A negative indicates a medium-run decrease in the bilateral
exports due to Greece, Portugal and Spain joining the EC. A positive shows a
short-run increase in the bilateral exports of Austria, Finland and Sweden after joining
the EU.
In addition, a positive ϕ indicates that on average, globalisation (among other
unobservable time effects) increases bilateral exports. 16 Similarly, a negative γt for a
given period indicates that unobservable time effects decreased bilateral exports
compared to the beginning of the period. Finally, a negative coefficient for a particular
exporting country (δx ) suggests that this has a smaller impact on bilateral trade flows
than the RW’s.
We will interchange some of the proxies defined in (ii)-(iv) according to the different
dimensions of the alternative methods estimated, without any loss of generality or
change in the interpretation of the results obtained.
III.4. The Results
This section presents the results for the three methods described in III.3. In sum,
SURE, accounts for the time correlation between the gravity cross-sections, whereas
GLS controls for unobserved time and individual specific effects. Finally, IV-GLS
16 This could also be indicating a positive globalisation effect lower, in absolute value, than the other unobservable time effects.
16
solves the endogeneity problem together with estimating the marginal effects of the
time-invariant regressors.
III.4.1. SURE Estimation
The first step towards estimating our gravity model is to run cross-section estimations.
This allows us to test for the baseline model predictions, together with the additional
trade determinants included in our model. Following Wei (1996) and Helliwell (1996,
1997), we estimate a system of cross-section equations of the form of (III.11) for each
time period of our sample.
The Breusch-Pagan test rejects the null hypothesis of a diagonal variance-covariance of
the estimated errors matrix, thus confirming the presumed time correlation between
the countries cross-sections for different years. This means that it is appropriate to
estimate a SURE system of gravity cross-section equations, since it links them through
their error terms. These reflect, among other things, the time differences between the
cross sections. Although estimating cross sections is not the most efficient way of
analysing the effects of different regressors on bilateral trade flows, they allow us to
look at the time paths of the estimated export elasticities. Moreover, in this setting we
can test for structural change in the coefficients.
The overall fit of the model is similar to the ones reported in previous studies. An
increasing Adjusted R-squared, with a mean of 86%, shows the good joint significance
of the regressors in explaining the bilateral exports.17 For convenience, we present
here the estimated coefficients in graphs. The values for these coefficients and the
result of the Breusch-Pagan test can be found in Appendix B.
III.4.1.1. Baseline gravity variables
Graphs III.1., III.2. and III.3. show the estimated elasticities and semi-elasticities of
bilateral exports with respect to the baseline gravity variables. These are the
coefficients of the estimated SURE model for the masses of exporting and importing
countries, the distance between them, their wealth and their common characteristics.
The mass elasticities of the bilateral exports predicted by our model are similar to
those found in the literature. We find that on average, a marginal increase in a
17 The number of observations decreases when estimating a SURE, given that availability of observations around the whole time period is imposed. However, estimating a SURE for less years, and then getting more observations per year, gave smaller Adjusted-R2.
17
country’s real size (GDP) or in its real absorption (GNP) generates a significant18
average increase of 0.7% in both its real19 exports to and its imports from the other
developed OECD countries and the RW. If two countries were twice as apart from
each other as from a third country, their trade is on average 0.9% lower. Graph III.1.
shows the evolution through time of these elasticities.
Graph III.1.
Wald tests indicate a significant change over the period for GDPx and for DISTxm.
The significantly decreasing relative importance of the exporting country’s mass
measure, indicates that the size of a country has lost some relevance in explaining its
exports. Moreover, size has become a relatively better determinant for the imports of a
country, thus indicating a higher preference for imported goods of industrial
countries.
In order to verify some of the facts described in Chapter I, we performed various Wald
tests to account for the significant structural change in the coefficients over time.20 We
found that the break-up of Bretton Woods (1971) has increased significantly the
exporter’s income elasticity of exports for OECD countries. The drop in this elasticity,
which may be partly due to the first oil crisis, is also significant. We also find that the
second oil crisis has apparently increased significantly the sensitivity of exports to the
importer’s income.
The relatively stationary pattern around a significantly decreasing trend of the
distance elasticity of exports might be due to a shrinking in the transport costs during
18 Unless otherwise stated, this refers to a significantly different from zero at a 1% level. 19 From now on we will refer to real economic variables. 20 The null hypothesis for these tests is that the corresponding coefficient did not change from one year to the following.
18
the last four decades. Moreover, this finding may also be indicating a miss-
specification problem because of only including a ‘geographical’ (constant) distance
measure, which we tackle in Chapter IV.
Graph III.2. shows that the estimated exporting country’s wealth effect has been steady
until the early 1980s, when it begins to decline. Moreover, this coefficient only
becomes non-significantly different from zero towards the end of the period.
Throughout the period considered, a one-percentage change in the wealth of a country
increases its exports by almost the same amount, whereas decreases its imports by
0.8% on average.
Graph III.2.
Wald tests indicate a significant change over the period for GDPpcx (at a 1% level) and for GDPpcm (at a 5% level).
The negative (and generally significant) elasticity of the bilateral exports with respect
to the importer’s wealth seems to contradict the common finding of a positive
correlation between a country’s wealth and its imports. This may be due to a higher
‘domestic income’ effect, which makes people consume relatively more domestically
produced goods the wealthier they are.
As mentioned above, to control for multicollinearity of wealth and mass measures, we
include DISIMxm. While its mean increases considerably over time, its elasticity is
rather small compared to the wealth effects. We find that if two countries become 1%
more similar, their trade is on average a 0.1% lower. In addition, the significance of
the coefficient varies over time.
The positive coefficient of DISIMxm supports the hypothesis of high correlation
between GDP per capita differences and differences in factor endowments. This
19
correlation leads us to conclude that smaller differences between countries could
reduce trade, especially inter-industry trade driven by comparative advantage. Hence,
our results would be more in favour of a Heckscher-Ohlin explanation of trade flows.
Moreover, this result opposes the Linder Hypothesis, where trade is higher between
countries with similar living standards, given that they share a broader range of goods
to trade.
Testing for yearly structural breaks in these variables we found that the second oil
crisis has increased significantly the sensitivity of exports to the importer’s wealth and
its dissimilarity with respect to its partner.
Graph III.3. shows the path of the adjacency (ADJxm) and linguistic ties (LINGTIExm)
effects on two countries exports.21 While a marginal effect of being next to the
importing country would make the exports of a country an average of 0.4% greater,
sharing a common language would increase its exports by an average of 0.6%.
However, these results should be considered with care, given that these coefficients on
both dummies are in general not significantly different from zero, even at a 10% level.
Graph III.3.
Wald tests indicate that neither ADJxm nor LINGTIExm have experienced a significant change, even at a 10% level.
Although not too significant, the trend seems to indicate that sharing a common
language has become less important for doing business during the last four decades.
This may be due to the widespread usage of English as the international business
language. The fact that having a common border is slightly becoming a better
determinant of bilateral trade patterns is perhaps related to a decrease in the
transportation costs over the period considered. Given that our time-invariant
20
distance measure does not capture these effects, we would expect to account for them
by finding a measure of economic distance that varies through time.
III.4.1.2. The integration effects
The semi-elasticity of bilateral trade with respect to the EU dummy allows us to
measure the so-called border effect of the European Union trade with non-EU
countries.
Graph III.4. shows the evolution of the implied integration elasticities (or estimated
coefficients) for EU and EFTA blocs.22 We find that on average EU membership has
increased bilateral exports by 26%,23 whereas this has decreased for two members of
the EFTA by approximately 16% (although this is not too significantly different from
zero).24 The decreasing path of the EUxm coefficient since the late 1960s may be partly
due to the consolidation of the bloc after each enlargement, thus leaving less and less
scope to trade with the new joining countries. However, we will see later that
unobservable time and individual effects account for much of this trend.
Graph III.4.
Wald tests indicate that only EFTAxm changed significantly over the period (at a 1% level).
Although, as far as we are aware, there are no studies that estimate the evolution of the
EU integration, our findings coincide with other results found in the literature, for
somewhat different set-ups and models.
21 In this case, we refer to the vector of coefficients in equation (III.11). 22 External effects of exporting EU countries were not significantly different from zero at a 10%, and hence were not included. 23 In this case, it is more convenient to work with 100% changes of the explanatory variables. 24 To a smaller extent, EFTA dummy depends inversely on the enlargements of the EU, since countries like Denmark, the UK and Finland left it as were joining the EU.
21
Nitsch (1999) estimates a SURE system of gravity equations using internal and external
measures of trade, masses and distance, for the first twelve EU members25 over the
period 1979-1990. Bearing in mind the different methodologies and data employed,
our results do not differ from his findings of a decreasing home bias.26
Frankel (1997) estimates gravity cross-section equations for every-five years and finds
that the EU effect is not significant until 1985, when it attains a 20%, which then rises to
30% in 1990.
The positive trend of our ‘EU integration’ indicator during the 1960s reflects the
increase in trade integration of the EEC6 countries. This was mainly favoured by the
stability of having their currencies fully convertible at fixed, but adjustable, exchange
rates ruled by the Bretton Woods System. Our indicator also shows a positive and
significant27 response to the completion of the customs union in 1968.
The benefit of the EU effect slowed in 1970, mainly due to the inflationary pressures on
the stable fixed exchange rates system, which drove to the adoption of flexible
exchange rates regimes by 1971. De Grauwe (1988) finds that, although with relatively
stable exchange rates, EEC6 members experienced a strong decline in their trade-
integration process since the 1970s.
In the early 1970s, EU integration seemed to be recovering its upward path. However,
the first oil shock of 1973 exacerbated the very strong inflationary pressures and
plunged most oil importing countries into massive trade deficits. This led most OECD
countries in general to adopt national protectionist measures, thus reducing their
trade. Given the relatively high trade integration achieved by the EEC6, this change in
policies might have dampened their intra-EEC trade even further.
Moreover, EU effect’s main drops coincide with the three enlargements of the
European Union.28 This finding can be explained by three different motives,
concerning the trade orientation of joiners, their size and the external economic
circumstances of each period.
25 Given the period of his sample, Nitsch does not consider the third enlargement. 26 Although Nitsch employs a different methodology, this variable is capturing the EU effect, comparable with our EU dummy. 27 We ran Wald tests to check for structural changes on the EU coefficients, and rejected the null of equal coefficients at a 1% level. 28 This is the other reason why Frankel (1997) adopts as time-invariant EU dummy. However, given that our arguments are plausible with the stylised facts described in Chapter I, we will keep a time-varying EU dummy.
22
The trade orientation cause entails a ‘learning process’ for the trade patterns among
the old and the new EU countries when the latter first enter the union. This process
partly determines the extent to which the integration coefficient falls and then
recuperates.29 By ‘learning process’ we mean the adjustment that new members
necessarily do in order to ‘catch up’ with the degree of integration achieved by the old
EU countries. For example, in the first enlargement, the three new members had to go
through a transitional process to finally approach the degree of integration achieved in
fifteen years by the EEC6. Moreover, specially UK’s stronger ties with non-EEC6
countries also contributed to its ‘learning process’.
The second and third causes can also be better observed taking the first enlargement.
The significant30 decrease in the EU coefficient may also be picking up the arrival of a
big country, the UK, into the EU group. Finally, the temporary increase in the weight
of imports from OPEC countries during the 1960s may have contributed to lowering
the intra-EEC trade share during the early 1970s. In other words, the first oil shock,
together with the inclusion of three countries that handled their economic policies
differently to the EEC6’s, might also have affected trade patterns between the EEC9.
Other results of the yearly-structural change Wald tests on the EU estimated
coefficients indicate that neither the establishment of the EMS (1979) nor the inclusion
of Greece (1981) significantly affected EU integration. Spain and Portugal EU only had
a significant negative effect on EU trade integration one year after joining this bloc
during the same year that the SEA was adopted (1987). The late 1980s sought a change
in trade patterns in favour of intra-EEC trade.
The early 1990s significant recovery may have been partly favoured by the
repercussion of the SEA (1987) and the Maastricht treaty of 1991. EU trade integration
appears to recover a slightly positive trend in 1993, with a smaller but significant
‘learning process’ negative effect after the third enlargement.
Finally, the decrease of the EFTA coefficient since the first EU enlargement, which
coincided with the loss of two important member countries, indicates an increase in
the relative importance of the EU over the other European free trade agreement. This
could be partly explained by the more limited agreements taken by EFTA members, as
explained in Chapter I. Conversely, EU countries, by adopting a customs union by
29The decrease in the significance of this dummy after the mid-1990s does not allow us to observe a reversion of the trend after the third enlargement. 30 Wald test rejected at a 1% level.
23
signing the Treaty of Rome, and then an economic union with the Maastricht Treaty,
compromised committed themselves to a higher degree of economic integration.
III.5. Main Findings
This chapter has focused on three methods to measure EU trade integration. Given
that there is a significant time-correlation between the errors of each cross-section
gravity equation, we have first estimated a SURE model for a system of thirty-eight
cross-sections. We find that being in the EU increases bilateral exports by 26%.
Because of the need to control for unobserved individual effects without sweeping out
the effects of observable time-invariant regressors, our second chosen method is a
panel data GLS. Given the panel data time dimension, we add a globalisation proxy
and the high degrees of freedom allow us to incorporate long, medium and short-run
external effects of the three EU enlargements. However, GLS seems to derive
inconsistent estimators, may be due to an endogeneity problem between masses and
exports. Hence, we finally adopt the HT method that allows us to instrument the mass
variables by using internal instruments and is able to estimate the marginal effects of
time-invariant regressors. Rejecting the equality between the estimated coefficients of
HT and GLS methods, we can conclude that the former derives (at least) ‘more’
consistent estimators.
Our main general result is that not accounting for unobservable individual effects and
for the endogeneity of mass measures biases upwards the EU effect on bilateral trade.
Since HT, unlike SURE, controls for unobserved individual effects and because,
converse to both SURE and GLS, it solves the endogeneity problem, we next analyse in
greater detail the EU integration effects results from the HT method.
EU integration was positively affected by globalisation: not including time effects
(model A) gives us an EU coefficient of 12%, whereas doing so (model B) leads to an
EU estimate of 20%. In addition, accounting for the ‘external’ effects of enlargements
(model C) take much of the EU effect: it goes down to 3%. Our results are consistent
with Frankel’s (1997), who finds an EEC12 bloc effect of 16%, and a decrease in the EC
bloc when including ‘openness’ effects.
With regard to the significant enlargement effects, our findings coincide with the
evolution of the EU effect for the SURE method. The first and third enlargements
show positive long-run external effects on bilateral exports of 48% and 32%,
24
respectively. Frankel (1997) also finds a positive effect of the first EU enlargements of
30%, but he also gets a significantly positive second enlargement effect on trade.
Finally, the only significant medium-run external effect is that of the first enlargement,
which has decreased exports in 18% on average.
We argue that trade orientation and the size of the first EU joiners, together with
special economic circumstances that affected OECD countries during the early 1970s
are the main causes for the negative medium-run effect. More specifically, the first
enlargement entailed what could be called a ‘learning process’ for the three new
members, given the high degree of integration achieved in fifteen years by the EEC6.
In addition, the accession of the UK made the integration effect to pick up the arrival
of a big country into the EU group. Finally, the first oil shock, in a context of
voluminous imports from the OPEC countries, may also have contributed to lowering
the intra-EEC trade share. The positive long-run enlargement effects can reflect the
end of the ‘learning process’ and improved economic conditions towards the end of
the period.
Chapter IV. Further Research Extensions and Conclusions
This last chapter focuses on further issues to be explored within the gravity
framework. The first section discusses the measurement of ‘economic’ (as opposed to
‘geographic’) distance. Then we consider the advantages and disadvantages of
estimating sectoral gravity models. Thirdly, we explore the link between wages and
trade among the EU countries. Finally, we describe some interrelations between these
three topics and state some final remarks.
25
Appendix A. Welfare effects of regional trade agreements
Regional trade agreements have both static and dynamic welfare consequences. This appendix briefly describes the welfare implications of the formation of a customs union and of an increase in trade in general.
In an ideal world, most economists would consider a complete absence of trade barriers between countries as the first best. This would allow consumers to buy the best products at the lowest prices anywhere and everywhere. The broader welfare gains from eliminating trade barriers are not the static effects once they have been removed, but the dynamic efficiencies. World competition, by its pressure on restructuring, investment and innovation have a direct impact on growth and employment. These welfare effects are the driving forces for multilateral negotiations that attempt to liberalise international trade between all countries.
Although many of these negotiations have been fruitful, trade between countries is far from being completely free of barriers. Countries still impose all sorts of barriers to trade in goods, services and factors. Broadly speaking, the barriers generally imposed to trade are:
• Trade in goods is restricted by: standard border measures (tariffs, quantitative restrictions, ‘grey area measures’31); contingent protection (safeguards, anti-dumping measures and countervailing subsidies);32 and non-tariff-non-quota barriers (environmental and health standards, human rights and labour laws concerns).
• Trade in services can be hindered by restrictions on the establishment of foreign firms in the local market; professionals’ accreditation and services; discriminatory tax treatment; state monopoly in public services.
• Barriers to trade in productive factors can affect labour, capital or technological mobility. Labour mobility barriers include outright ban or strict quotas on immigration. Capital mobility concerns direct prohibitions on foreign ownership of domestic assets, taxation of or restrictions on the use of profits earned in the local market. 33 Finally, the barriers to technology take the form of patents, copyrights, and trademarks.
Given that free world trade is not considered a feasible alternative, at least in the short run, there are some arguments to consider regional trade agreements as a second best solution. However, it is worth noting briefly the pros and cons of regionalism as opposed to multilateralism.
The increasing resorts devoted to regional arrangements, continuing friction in the system, and conflict of trade rules that may result from membership in overlapping agreements are considered some of the dangers entailed in regionalism. On the other hand, proponents of regionalism as complementary to multilateralism argue that the regional trade agreements can be established more easily, can provide a more manageable structure in the international trade system, and can help to speed up multilateral negotiation processes.
31 These include: voluntary export restraints (VERs), voluntary restraint agreements (VRAs), and orderly marketing arrangements (OMAs). 32 Safeguards are temporary impositions of import protection when one industry suffers due to increased imports; anti-dumping measures are unfair trade laws and predatory pricing protection; countervailing subsidies are duties to offset subsidies paid to the exporter. 33 These were very common up to the 1992 Single Market programme.
26
Although the risk that regionalism might undermine the possibility of ideal world free trade should be kept in mind, our premise throughout this paper is that the adoption of a regional trading area undermines protectionism and reinforces the movement toward liberalisation. In other words, we consider regionalism as our preferred second-best solution. Some of the ways in which this may be manifested are by a lock-in and mobilising regional solidarity, efficiency of negotiating between larger units, competitive liberalisation, and political building blocs to further trade liberalisation.
The classical ‘static’ distinction to evaluate the desirability of a customs union is due to Viner’s (1950) theory. Viner distinguished between the harmful trade-diverting effects if members reorient their trade away from low-cost sources (outside the CU) to higher-cost sources (inside the CU), and their beneficial trade-creating effects as sources shift from high-cost domestic production to lower-cost CU partner production.
The dynamic welfare effects of forming a regional trade agreement are concerned with the removal of domestic entrance barriers, the elimination or reduction of (national) monopolies, and market deregulation and liberalisation. Competition, reinforced by an increase in the scale of production, is expected to lead to a fall in production costs, and thus to efficiency gains and price reductions. 34 The main idea is that the internal market would eventually affect economic structures that will, in turn, produce an accelerated rate of growth among member countries.
Many studies try to estimate the welfare effects of regional trade agreements. Krugman (1991) derived a model in which if every regional bloc pursues an optimal tariff, three regional blocs may minimise world welfare. In an imperfect competition setup, Frankel and Stein (1994) simulate the magnitudes of trade creation and trade diversion in order to answer to the question of whether FTAs raise the welfare of the representative consumer, finding that, under many plausible parameter values, the first effect is bigger. Finally, Grossman and Helpman (1995) find that a FTA is most likely to be adopted when trade diversion outweighs trade creation in a lobbing framework.
34 The Treaty of Rome explicitly identifies a number of common competition, external trade, taxation and social security policies, mainly meant to compensate for ‘market failure’ at the EU level.
27
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