+ All Categories
Home > Documents > Measuring EU Trade Integration within the Gravity...

Measuring EU Trade Integration within the Gravity...

Date post: 09-Aug-2018
Category:
Upload: vonhu
View: 216 times
Download: 0 times
Share this document with a friend
30
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
Transcript
Page 1: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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 

Page 2: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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

Page 3: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 4: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 5: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 6: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 7: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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

Page 8: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 9: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 10: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 11: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 12: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 13: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 14: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 15: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 16: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 17: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 18: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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

Page 19: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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

Page 20: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 21: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 22: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 23: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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%,

Page 24: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 25: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 26: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

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.

Page 27: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

27

References

Adam, C. (1999). ‘MSc Development Economics. Lectures in Quantitative Methods’. Oxford University, Hilary Term.

Aitken, N.D. (1973). ‘The Effect of the EEC and EFTA on European Trade: A Temporal Cross-Section Analysis’. American Economic Review, vol. 63 no.5, December, pages 881-892.

Anderson, J.E. (1979). ‘A Theoretical Foundation for the Gravity Equation’. American Economic Review, vol. 69, pages 106-116.

Arellano, M. and S. Bond (1991). ‘Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations’. Review of Economic Studies; 58(2), April, pages 277-97.

Balassa, B. (1967). ‘Trade Creation and Trade Diversion in the European Common Market’. The Economic Journal, vol. LXXVII, March, pages 1-23.

Balassa, B. and L. Bauwens (1988). ‘The determinants of intra-European Trade in manufactured goods’. European Economic Review 32, pages 1421-1437.

Baldwin, R. (1994). Towards an Integrated Europe. Centre for Economic Policy Research, United Kingdom.

Baltagi, B.H. (1995). Econometric Analysis of Panel Data. John Wiley ed., West Sussex, England.

Barro, R. and V. Grilli (1994). European Macroeconomics. The Macmillan Press Ltd., London, England.

Bayoumi, T. and B. Eichengreen (1995). ‘Is regionalism simply a diversion? Evidence from the evolution of the EC and EFTA’. Centre for Economic Policy Research Discussion Paper no. 1294, November.

Bergstrand, J.H. (1989). ‘The Generalised Gravity Equation, Monopolistic Competition, and the Factor-Proportions Theory in International Trade’. Review of Economics and Statistics, vols. 71, pages 143-153.

Blundell, R. and S. Bond (1998). ‘Initial Conditions and Moment Restrictions in Dynamic Panel Data Models’. Journal of Econometrics 87(1), November, pages 115-43.

Boltho, A. (1982). The European Economy: Growth and Crisis. Oxford University Press, United Kingdom.

Breusch, T. S., G. E. Mizon and P. Schmidt (1989). ‘Efficient Estimation Using Panel Data’. Econometrica, vol. 57, No. 3 , May, pages 695-700.

Brun, J.F., P. Guillaumont and J. de Melo (1998). ‘La distance abolie? Critères et mesure de la mondialisation du commerce extérieur’. Centre d’Études et de Recherhces sur le Développement International, Série Etudes et Documents, Décembre.

Cassim, R. and T. Hartzenburg (2000). ‘Trade related aspects of regional integration is Southern Africa’. African Economic Research Consortium, unpublished.

Chamberlin, E.H. (1933). The Theory of Monopolistic Competition. Harvard University Press, Cambridge, Massachusetts.

Deardoff, A. (1995). ‘Determinants of Bilateral Trade: Does Gravity Work in a Neoclassical World?’. National Bureau of Economic Research Working Paper 5377, December.

De Grauwe, P. (1988). ‘Exchange Rate Variability and the Slowdown in Growth of International Trade’. International Monetary Fund Staff Papers vol. 35, pages 64-84.

Direction of Trade Statistics, International Monetary Fund, Washington D.C., USA.

Page 28: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

28

Dixit, A. K. and V. Norman (1980). Theory of International Trade. Cambridge Economic Handbooks, Cambridge University Press, Great Britain.

Eichengreen, B. and D.A. Irwin (1998). ‘The Role of History in Bilateral Trade Flows’. In Frankel (ed.), The Regionalisation of the World Economy. National Bureau of Economic Research. The University of Chicago Press, Chicago and London.

Engel, C. and J.H. Rogers (1996). ‘How Wide is the Border?’. American Economic Review, vol. 86, no.5, December.

Epstein, G.A. and H. Gintis (1995). ‘Macroeconomic policies for sustainable growth’. In Epstein and Gintis (eds.), Macroeconomic policies after the conservative era. Cambridge University Press, Great Britain.

Feenstra, R.C., J. R. Markusen and A. K. Rose (1998). ‘Understanding the Home Market Effect and the Gravity Equation: the Role of Differentiating Goods”. Centre for Economic Policy Research Discussion Paper no. 2035, December.

Fontagné, L., M. Freudenberg and M. Pajot (1999). ‘Le potentiel d’échanges entre l’Union européenne et les PECO. Un réexamen’. Revue Economique, vol. 50, No.6, Novembre, pages 1139-1168.

Foroutan, F. and L. Pritchett (1993). ‘Intra-Sub-Saharan African Trade. Is It Too Little?’. The World Bank Policy Research Working Paper 1225, November.

Frankel, J. (1997). Regional Trading Blocs in the World Economic System. Institute for International Economics, Washington, October.

Frankel, J. and D. Romer (1996). ‘Trade and Growth: An Empirical Investigation’. National Bureau of Economic Research Working Paper 5476, March.

Frankel, J. and E. Stein (1994). ‘The Welfare Implications of Trading Blocs in a Model with Transport Costs’. Pacific Basic Working Paper Series no. PB94-03. San Francisco: Federal Reserve Bank, May.

Frankel, J., E. Stein and S-J. Wei (1998). ‘Continental trading blocs: are they natural, or super-natural?’. In Frankel (ed.), The Regionalisation of the World Economy. National Bureau of Economic Research. The University of Chicago Press, Chicago and London.

Frankel, J., E. Stein and S-J. Wei (1995). ‘Trade Blocs and the Americas: The Natural, The Unnatural, and the Super-Natural’. Journal of Development Economics, June.

Frankel, J., E. Stein and S-J. Wei (1993). ‘Continental trading blocs: are they natural, or super-natural?’. National Bureau of Economic Review Working Paper no. 4588,` December.

Glyn, A. (1995). ‘Stability, inegalitarianism, and stagnation: an overview of the advanced capitalist countries in the 1980s’. In Epstein and Gintis (eds.), Macroeconomic policies after the conservative era. Cambridge University Press.

Gremmen, H.J. (1985). ‘Testing The Factor Price Equalisation Theorem in the EC: An Alternative Approach’. Journal of Common Market Studies, vol. XXIII, no. 3, March, pages 277-286.

Grossman, G. and E. Helpman (1995). ‘The Politics of Free trade Agreements’. American Economic Review, vol. 85, September, pages 667-90.

Hamilton, C.B. and L.A. Winters (1992). ‘Opening up international trade with Eastern Europe’. Economic Policy: A European Forum, April, pages 77-104/111-116.

Hausman, J.A. and W.E. Taylor (1981). ‘Panel data and unobservable individual effects’. Econometrica, vol.49, November, pages 1377-1398.

Helliwell, J. (1998). How Much do National Borders Matter?. Brookings Institution Press, Washington DC.

Page 29: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

29

Helliwell, J. (1997). ‘National Borders, Trade and Migration’. Pacific Economic Review, 2: 3, pages 165-185.

Helliwell, J. (1996). ‘Do National Borders Matter for Quebec’s Trade?’. Canadian Journal of Economics 29, pages 507-522.

Helpman, E. and P.R. Krugman (1985). Market Structure and Foreign Trade. Increasing Returns, Imperfect Competition and the International Economy. Wheatsheaf Books Ltd, Brighton, United Kingdom.

Hsiao, C. (1986). Analysis of Panel Data. Econometric Society Monographs, Cambridge University Press.

Kennedy, P. (1998). A Guide to Econometrics. Fourth Edition, Blackwell Publishers.

Krugman, P. (1991). ‘Is Bilateralism Bad?’ in Helpman E. and A. Razin, eds., International Trade and Trade Policy. MIT Press, Cambridge, Massachusetts.

Krugman, P. (1980). “Scale Economies, Product Differentiation, and the Pattern of Trade”. American Economic Review 70, 950-959.

Krugman, P. and M. Obstfeld (1994). International Economics. Theory and Policy. Third Edition, Harper Collins College Publishers, New York.

Linder, S. B. (1961). An Essay on Trade and Transformation. Wiley, New York, USA.

Linnemann, H. J. (1966). An Economic Study of International Trade Flows. Amsterdam.

McCallum, J. (1995). ‘National Borders Matter: Canada-US Regional Trade Patterns’. American Economic Review, vol.85 no.3, June.

Neal, L. and D. Barbezat (1998). The Economics of the European Union and the Economies of Europe. Oxford University Press, New York and Oxford.

Nitsch, V. (1999). ‘National Borders and International Trade: Evidence from the European Union’. Bankgesellschaft, Berlin, Germany.

Ostry, S. (1998). The post-cold war trading system. Who’s on first? A twentieth century fund book. The University of Chicago Press, Chicago and London.

Rauch, J.E. (1999). ‘Network versus markets in international trade’. Journal of International Economics 48, pages 7-35.

Schmiedel, F. (1998). ‘The Geographical Orientation of Chinese Merchandise Imports. Where do European Exporters stand? A Gravity Model Approach Based on Panel Data’. Centre d’Études et de Recherhces sur le Développement International, Série Etudes et Documents.

Temin, P. (1999). ‘Globalization’. Oxford Review of Economic Policy, vol. 15 no. 4, Winter.

Temple, J. (1999). ‘The new growth evidence’. Journal of Economic Literature, 37(1), March, pages 112-156.

Tovias, A. (1982). ‘Testing Factor Price Equalization in the EEC’. Journal of Common Market Studies, vol. XX, no. 4, June, pages 375-388.

Viner, J. The Customs Union Issue. Carnegie Endowment for International Peace, New York, USA.

Wang, Z.K and L.A. Winters (1991). ‘The Trading Potential of Eastern Europe’. Centre for Economic Policy Research Discussion Paper no.610, November.

Wei, S-J (1996). ‘Intra-National versus International Trade: How stubborn are nations for Global Integration?’. National Bureau of Economic Review Working Paper no. 5531, April.

Wei, S-J and J. Frankel (1995). ‘Open Regionalism in a World of Continental Blocs’. Unpublished, Harvard University and University of California-Berkeley.

World Development Indicators. World Bank, Washington D.C., USA.

Page 30: Measuring EU Trade Integration within the Gravity Frameworkecaths1.s3.amazonaws.com/economiaint/35496569.Molinari (2000).pdf · Measuring EU Trade Integration within the Gravity Framework

30

Young, A. (1991). ‘Learning by doing and the Dynamic Effects of International Trade’. The Quarterly Journal of Economics, May.


Recommended