Download European Economic Integration in Econometric Modeling –

European Economic Integration in Econometric Modeling –
Concepts and Measures
Christiane Krieger-Boden, Rüdiger Soltwedel,
The Kiel Institute for the World Economy, Kiel, Germany
Abstract:
Processes of regional economic integration have been shaping the economic relations between
countries significantly during the last decades. In addition, an increasing integration of the
national economies into the global economy has affected these economic relations, too. In an
effort to operationalize these integration processes for the purpose of empirical analyses, this
paper reviews concepts and actual measures of European integration and globalization. In
particular, it discusses how to separate the effects of European integration from those of
globalization. The paper searches for regional integration and globalization indices that avoid
the endogeneity problem, and it discusses collinearity between them. Operational indicators
are suggested that are then used for analysis in an illustrative gravity model.
1
Introduction ........................................................................................................................ 1
2
Understanding regional integration and globalization ....................................................... 2
2.1
2.2
Concepts of regional integration and globalization.................................................... 3
Distinguishing EU integration from globalization ..................................................... 7
3
Measuring regional integration and globalization .............................................................. 9
3.1
Measures of European integration............................................................................ 11
3.2
Measures of globalization ........................................................................................ 13
3.3
Correlations between the EU integration and the globalization indices .................. 20
4
Illustration: A gravity model for the European Union ..................................................... 25
4.1
Empirical model and data ......................................................................................... 25
4.2
Results ...................................................................................................................... 27
5
Conclusion ........................................................................................................................ 29
References ................................................................................................................................ 30
Appendix .................................................................................................................................. 33
 The authors like to thank Eckhardt Bode for intense discussions on the paper, numerous fruitful suggestions,
and substantial help particularly with regard to section 4 of the paper. They also like to thank the participants
of the Tinbergen Institute (TI) Workshop in Amsterdam, June 2007, for valuable comments. The usual
disclaimer applies.
1
1
Introduction
Processes of regional economic integration have been shaping the economic relations between
countries significantly during the last decades, prominent examples being the European Union
(EU), the North American Free Trade Agreement (NAFTA), and the Asia Pacific Economic
Cooperation (APEC). In addition, an increasing integration of the national economies into the
global economy has affected these economic relations, too. Both kinds of economic
integration reduced transactions costs, though perhaps in different ways and to different
degrees, and among a given regional subset of countries in the one case, and among all
countries worldwide in the other. Conventional wisdom has it, and economic theories of trade
and growth show it, that both kinds of integration processes are likely to have had a
tremendous impact on the intensity of trade and the division of labor between the countries
involved, as well as on wages and incomes within them.
However, empirical studies trying to quantify the impact of these integration processes face
major problems: Generally, it is by far not trivial to determine indicators that operationalise
integration processes in order to use such indicators as explanatory variables for various
aggregate economic variables in econometric models (cf. Bosker, Garretsen 2007). More
particularly, it is even more demanding to distinguish separate indicators for the two kinds of
integration processes, regional integration and globalization, where the effects of both may be
observational equivalent.
In the empirical literature, to the best of our knowledge, integration indicators have not yet
been used for quantifying effects of regional integration. Various studies have instead used a
simple before-after dummy variable (e.g., Bun and Klaassen 2007; Rose 2000; Glick and
Rose 2002), a time trend (e.g., Traistaru, Nijkamp, Longhi 2002), or changes of physical
transportation costs (e.g., Glaeser, Kohlhase 2005; Schürmann, Talaat 2000). However, such
approaches neglect the gradual non-monotonous nature of the integration process over a long
period of time and as a result of several subsequent small integration steps, or they neglect the
numerous facets by which integration processes manifest. Empirical studies that would take
into account this gradual and multifaceted nature could be expected to yield more accurate
estimates of the effects of integration processes than those that do not. The present paper
investigates to what extent the available statistical indices that aim at quantifying the progress
of regional integration can be used as explanatory variables in econometric models.
Moreover, most empirical studies also neglect the problem of observational equivalence
between regional integration processes and globalization. There is a good chance that effects
of globalization are misinterpreted as effects of the regional integration processes, if they are
not controlled for explicitly in time-series or panel regression analyses. The present paper
investigates to what extent the available statistical indices that aim at quantifying the progress
of globalization can be used as control variables in panel regressions that aim at assessing the
economic effects of regional integration processes.
2
To adequately specify indicators of regional integration and globalization requires getting
straight which purpose the indicators are supposed to serve. In particular, analyses adopting a
microeconomic perspective, i.e., assessing the effects of economic integration on economic
agents, require detailed indicators that take different values for different agents. In this paper,
however, a macroeconomic perspective is adopted, and such analyses, assessing the effects of
economic integration on countries, require aggregate indicators that take only one value for a
whole country.
Besides being adequately specified and being able to distinguish between regional integration
and globalization, operational indicators of regional integration and globalization should
satisfy further, practical requirements. The preferred indices should be exogenous to the
response variable in respective empirical investigations. If the appropriate indices cannot be
assumed to be exogenous, the additional problem of finding appropriate instruments for these
indices arises. The present paper will search for regional integration and globalization indices
that avoid the endogeneity problem. Also, the paper will discuss the collinearity between
potential indicators.
The paper is organized as follows. Section 2 surveys concepts of regional integration
processes and globalization in order to distil operational indicators for econometric models. It
investigates to what extent regional integration processes can be distinguished conceptually
from global integration processes. Section 3 surveys empirical approaches of measuring
regional integration and globalization, and investigates by means of a correlation analysis to
what extent indices of regional integration can be distinguished empirically from those of
globalization. Section 4 presents an illustrative estimation of a panel gravity model that uses
the indices of regional integration and globalization to identify the effects of the EU
integration on intra-European trade intensities. Finally, Section 5 concludes.
2
Understanding regional integration and globalization
Several concepts of regional integration and globalization exist in the literature, and usually
both terms are defined rather vaguely. This section shortly goes over such concepts in order to
arrive at indicators of regional integration and of globalization that are operational for
econometric models. In a first part, the high complexity of these processes is demonstrated.
For meaningful econometric analysis, it would be highly desirable to have this complexity
reflected in respective integration indicators. As this paper adopts a macroeconomic
perspective, the required indicators need to reflect this complexity in one value for a whole
country. In a second part, we will investigate the differences between regional and global
integration processes. Only to the extent that the two processes differ conceptually from each
other, their differences can be exploited in a meaningful way in empirical studies.
3
2.1
Concepts of regional integration and globalization
Pure models of trade theory (particularly NEG models, e.g. Fujita et al 1999, Baldwin et al.
2003) analyse the impact of economic integration on trade, wages and the division of labor by
defining economic integration to be the inverse of transportation costs. 1 In these models,
integration is assumed to reach from autarky (no integration at all) to unrestricted freeness of
trade (complete integration). Usually, no distinction is made between regional integration and
globalization. Moreover, in such models, integration usually refers to the freeness of
exchanging goods and services only.
When analysing the effects of economic integration empirically, however, this definition turns
out to be too narrow to capture the complexity of the actual phenomenon of economic
integration, and in fact there are several much broader concepts of economic integration.
Thus, following Balassa (1961) and Oman (1996), economic integration is often understood
as a process, which is initiated by some driving forces and results in a state where all sorts of
economic transactions beween countries have increased, or in other words, following
Cairncross (1997), where the economic distance between countries has declined. In this
broader sense, the integration process consists of three constitutive elements: the forces
driving the process of economic integration, the transmission channels through which
economic integration affects the economies of the integrating countries and the consequences
of economic integration (Figure 1). Following Oman (1996) and van Liemt (1998), the
driving forces include technological progress in information, communication and
transportation (ICT) technologies (that are interpreted to take place autonomously) as well as
institutional progress by deliberate national or supranational policy measures. Moreover, there
may be other driving forces such as innovations in industrial organization that direct the focus
of business activities towards global rather than local (national) markets and locations, or
innovations on financial markets that increase the volatility of financial flows. The impetus
from these driving forces is transmitted via reductions of transaction costs, both spatial
transaction costs and natural or administrative border impediments between one country and
other countries. And following Bhagwati (2004) and Schulze and Ursprung (1999), these
reductions of transaction costs affect trade of goods and services, capital flows, flows of
information or knowledge, or migration of workers. The effects of lower transaction costs on
trade or migration then finally yield the consequences in the form of increased trade intensity,
of adjustments in the international division of labor, and of changes of income, employment
and growth in the countries involved in the process.
1 On the problems of the rather narrow iceberg-type definition of any distance-related costs in NEG models cf.
McCann 2005.
4
Figure 1: Schematic relationship of driving forces and consequences in processes of
economic integration
Driving forces
Transmission
Progress in
ICT
technology
Other forces
Reduction of transaction costs
spatial transaction costs
Consequences
Institutional
progress
border impediments
Change of response variables
(e.g., volume of transactions, wages, division of labor,
employment, income, growth)
Source: Own illustration.
The transaction costs are at the core of the process of economic integration, linking driving
forces and consequences, and being closest to the concept of economic integration in pure
models of trade theory. To precisely determine these transaction costs would represent a
major improvement for assessing the effects of economic integration. However, this is
difficult to achieve given the high degree of heterogeneity of transaction costs. Transaction
costs vary considerably according to the various dimensions they include. To list but a few of
such dimensions: First, as mentioned above, transaction costs differ according to the objectsto-be-moved: Moving goods, people, capital, or information requires different means of
transport with different amounts of costs. Second, transaction costs differ according to the
distance dependencies incorporated:2 Some costs are closely related to all forms of bridging
geographical distance (spatial transaction costs). Others are independent of distance (one-stop
border impediments); they may arise from tariffs or non-tariff barriers, technical standards,
market and product regulations, consumer preferences, differences in law, culture and
language, or other institutional settings. Third, transaction costs differ according to different
components included: The material component relates to the costs of physically moving
objects from one place to another, which in itself may vary according to the transport means
employed. The non-material component relates to costs of, e.g., getting permissions and
allowances, paying tariffs, closing contracts between the agents involved (potentially under
2 For expositional convenience, Figure 1 displays only the division of transaction costs into these two
dimensions of distance-dependent and distance-independent costs. This distinction is of particular interest for
econometric models in that some models include the one and some the other.
5
different legal and cultural systems and in different languages), insuring freight, processing
payments, opening up foreign markets for the objects in question.
Fourth, transaction costs differ according to the spatial validity of the rules under which they
accrue, e.g., within a country, within a regional integration zone like the EU, or worldwide.
Costs for land transport, for instance, often depend on the specific rules within each country;
certain cross-border transaction costs depend on rules agreed upon bilaterally or within a
regional integration zone; marine or air transport costs for goods and persons depend on the
rules of global markets. Each country has, in principle, the sovereignty of deciding to what
extent it participates in such bilateral or multilateral rules. It may take measures to unilaterally
reduce the transaction costs of international trade or factor flows to a greater extent than other
countries, participate in multilateral agreements on jointly reducing transaction costs, or
exempt itself from economic integration processes.3 Each country may even prevent its
economic agents from adopting new ICT technologies and thus shape its transaction costs.
Fifth, transaction costs differ according to time: Usually they are assumed to have declined
continuously and gradually over at least the last fifty years, yet there may also be sudden
leaps such as the fall of the iron curtain, or drawbacks. 4
As each of these dimensions may occur in combination with almost each other, they span a
whole range of different possible transaction costs (not all combinations are equally likely,
however). In a strict sense, transaction costs are thus to be defined for a specific transaction
between a specific pair of countries at a specific point in time only. Also, any reductions of
transaction costs thus refer to specific transactions between a specific pair of countries and for
a specific time interval only.
When analysing empirically the effects of economic integration, a reflection of this immense
heterogeneity of transaction costs in respective integration indicators would be highly
desirable. This would assure that the effects of integration are specified adequately in time
series and panel regression estimates. However, obviously, it is hard to imagine operational
indicators that would allow for all dimensions of transaction costs. Moreover, for
macroeconomic analyses, all information has to be condensed into aggregate country-specific
and year-specific indices. In search for operational indicators, attention is therefore drawn to
the driving forces of economic integration processes that underlie the transaction cost
3 The examples of countries like the United Kingdom (UK), which has chosen to not fully participate in the
EU, or North Korea, which has largely isolated itself from the global economy, indicate that national
governments do, in fact, have a choice.
4 All in all, transaction costs are often said to have dropped substantially over the last century, internationally
as well as intranationally; some authors already claimed the “death of distance” (Cairncross 1997). However,
there is also some objection to this common knowledge as markets remain far from being universally
integrated (e.g., Anderson, van Wincoop 2004; Carrère, Schiff 2005; Brakman, Garretsen and Schramm
2006). Persisting high trade costs are assumed to explain the existence of several home bias “puzzles” that
have attracted considerable attention in international trade theory (e.g., Trefler 1995; Obstfeld, Rogoff 2000).
This makes the question all the more important, how to conceptualize and to measure transaction costs.
6
reductions. These driving forces may serve as proxies of transaction costs facilitating the
procedure of determining adequate indicators. Moreover, they are more likely to be
exogenous to the response variables of respective empirical investigations than any
transaction cost indicators.
The driving forces of economic integration intervene into all combinations of transaction
costs at different points. The progress in ICT technologies, for instance, is expected to extend
the opportunities to interact with other countries by bridging distance. Major improvements of
this sort are, e.g., the invention of automobiles and expressways, of container traffic, or of the
telecommunication technology from the telegraph to the internet. Primarily, the progress in
ICT technologies can be expected to reduce the material component of the transaction costs
and to influence distance-dependent costs. However, progress in ICT technologies,
particularly of telecommunication technologies, may also facilitate the handling of legislative
regulations, of administrative and cultural border barriers, etc., thereby reducing also nonmaterial, one-stop transaction costs.
Institutional progress by deliberate government action may be implemented between
countries as well as within each country. Between countries, bilateral or multilateral
agreements on trade and cooperation remove border barriers of all sorts, such as tariffs,
quotas, and non-tariff barriers, impediments resulting from differing currencies and
differences in technical standards, trade and migration barriers, and political, legislative,
cultural and social differences. Within countries, liberalizations and deregulations may foster
a competitive environment, and investment in infrastructure may extend infrastructure
facilities. Most directly, these government actions can be expected to reduce non-material,
distance-independent one-stop transaction costs. Via the increase of competition on ICT
markets and via the improvement of ICT infrastructure, they can also be expected to reduce
material, distance-dependent transaction costs. Other driving forces such as innovations in
industrial organization, or innovations on financial markets may influence all sorts of
transaction costs in a similar way.
Generally, these driving forces seem closely related to the heterogeneity of transaction costs
and their changes. Operational indicators of the driving forces may thus approximate
sufficiently the changes of transaction costs and provide adequately specified indicators of
economic integration.
A problem, however, arises from the fact that the driving forces are interrelated horizontally
in complex ways. For example, innovations in ICT technologies that reduce the international
transaction costs may trigger policies of liberalizing goods and factor markets because they
raise the opportunity costs of protection. They may also trigger business strategies of
developing new products or processes, or of relocating the production and other corporate
functions to places that offer higher comparative advantages for these functions. And they
may trigger financial innovations providing the necessary accompanying financial flows.
7
Liberalizations of goods and factor markets, in turn, may call for improved ICT technologies
or new corporate strategies. And financial innovations may call for new international
institutions and regulations in order to stabilize markets. In addition, there may be vertical
feedbacks between the driving forces, transaction cost changes and consequences of economic
integration. For instance, the decrease of wages or incomes (on the level of consequences)
may motivate policies of liberalizing goods or factor markets (on the level of driving forces).
Or increasing turmoil on various markets in response to supranational shocks may call for
policies to secure the success of economic integration in terms of welfare and growth. Still, to
make economic integration operational, it is necessary to abstract to some extent from these
horizontal interdependencies between the driving forces of economic integration, and in
particular from the vertical feedbacks between the driving forces, transaction cost changes and
consequences.
We define, for the sake of simplicity, economic integration as a unidirectional, causal
relationship between its driving forces, transaction cost changes and consequences. And we
suggest the driving forces to be sufficient specifications of the whole process of integration
and as adequate indicators in econometric analyses. Moreover, the analysis of the
heterogeneity of transaction cost changes and of underlying driving forces enables
distinguishing European integration (as an example of regional integration) from
globalization.
2.2
Distinguishing EU integration from globalization
European integration and globalization concern different changes of transaction costs and can
be traced to different underlying driving forces (Table 1).5 These differences can, in our view,
be used as characteristics distinguishing between European integration and globalization.
The processes of European integration and globalization affect different transaction cost
dimensions in different ways. Most obviously, the European integration process reduces
transaction costs between European countries only, whereas globalization reduces transaction
costs between most countries worldwide (except those that exempt themselves from this
reduction); hence the spatial validity of both processes differs significantly. Moreover, the
European integration process can be said to reduce primarily non-material, distanceindependent transaction costs, since its major field is the removal of border impediments. This
is not withstanding some reductions of material and distance-dependent transaction costs due
to improved transport infrastructure (e.g., by the TEN program) that seem to us, however, of
minor importance compared to the other transaction cost reductions. By contrast, the process
of globalization, in our view, reduces both material, distance-dependent costs (e.g., by the
introduction of container traffic, internet) and non-material, distance-independent costs (e.g.,
5 European integration and globalization also yield different first consequences, as the former leads to trade
creation and trade diversion, and the latter to trade creation only.
8
by reductions of tariffs and quotas, legal agreements for safeguarding transactions). And
European integration can be defined precisely in terms of the time period concerned, whereas
the respective time period for globalization is indeterminate.
Table 1 — Distinctive characteristics of EU integration and globalization
EU integration
Globalization
Transaction cost dimensions concerned
Objects to be moved
All
All
Distance dependency
Independent costs (primarily)
All
Components
Non-material (primarily)
All
Spatial validity
All EU member states (with
exemptions),
discriminates partly against third
countries
Most countries worldwide
Time
EU6: since 1957;
countries acceding later: since start
of negotiations
indeterminate
Underlying driving forces
Institutional progress by government Institutional progress by government
action
action
& autonomous progress of ICT
technologies
& further autonomous driving forces
Source: Own compilation.
These different specifications of transaction costs reflect the different driving forces that
underlie European integration and globalization. The process of European integration is
driven primarily by institutional progress, fostered by actions undertaken by national
governments. These government actions are necessarily selective in terms of the choice of the
partner countries, and they are guided in particular by the common goal of overcoming the
confrontations from World War II and the Cold War. The government actions strive for
reducing the political, social and economic barriers between European countries – from the
creation of a Free Trade Area to the completion of the Single Market and the introduction of
the European Monetary Union, and the instalment of the Acquis Communautaire as a
common legal framework for all EU member states.
The process of globalization, too, is driven by institutional progress fostered by government
actions. These government actions are more complex. On the one hand, there are actions like
those taken in accordance with WTO or UN agreements that are less selective in terms of the
choice of the partner countries than those regarding European integration, and that are guided
by a common understanding among (almost) all countries in the world to reap the benefits of
9
enlarged markets. On the other hand, however, there are numerous bilateral actions, such as
bilateral double-taxation agreements, that are highly selective
e in terms of the partner countries. Yet, taken together, they form a whole “noodle bowl”
(Baldwin 2006) of mutual agreements between countries, which may be taken as being part of
the globalization process, too.
Another major cause of economic integration, the progress of ICT technology, however, is
assumed to evolve autonomously and as a worldwide phenomenon and hence to be part of
globalization, but not to be specific to European integration. We consider the latest ICT
technologies to be available ubiquitously in principle. A national government may, however,
restrict the access of its firms and citizens to these technologies. Similarly, other driving
forces like the changes in industrial organisation or the changes on financial markets are
understood to evolve autonomously as a worldwide phenomenon, and hence to be part of
worldwide globalization.
We conclude that addressing these driving forces of European integration on the one hand,
and globalization on the other, could provide indicators that are at the same time exogenous,
adequately specified and conceptually distinct from one another and that are thus operational
for empirical research.
3
Measuring regional integration and globalization
We now turn to reviewing and assessing actual approaches to measure regional integration
and globalization, with the aim of detecting measures that are apt to serve as adequate and
operational indicators in econometric models.
In the literature, there exist several approaches for measuring the changes of spatial
transaction costs. Mostly, they do not discriminate between regional integration and
globalization processes (Table 2). The simplest measure is a time trend. The time trend is
exogenous to any response variable but insensitive to fluctuations over time in the progresses
of regional integration or globalization. Another measure is trade volume. Trade volume is a
catch-all variable that is sensitive to fluctuations over time in the progresses of regional
integration or globalization but endogenous to many response variables. In addition, the
measure of trade volumes does not allow discriminating between the effects of any forms of
economic integration and factors that are unrelated to economic integration.
Still another group of measures aims at quantifying the material trade costs, for instance, by
means of the share of the transport industry in a country’s total value added or employment,
by means of the ratio of a country’s freight bill over GDP, or by means of the product of
traveling time and the freight charges. In another approach, trade costs are derived as distance
decay functions where the parameters are estimated in the context of computable NEG
models. These trade cost measures generally focus on the distance-dependent material trade
10
costs thereby largely neglecting all other kinds of transaction costs. In addition, like the
measure of trade volumes, they do not allow discriminating between the effects of any forms
of economic integration and factors that are unrelated to economic integration. All these
measures are not suited for the purpose of the present paper because they do not allow
discriminating between European integration and globalization processes, although they may
be useful for assessing the aggregate effects of any form of economic integration.
Table 2 — Overview of approaches to measure European integration and globalization
Approaches
Measures not distinguishing between EU integration and globalization
Broad measures
Time trend (e.g., Traistaru, Nijkamp, Longhi 2002)
World trade volume
Specific measures
Material transportation costs:
–
transport industry share in total economy (Glaeser, Kohlhase 2005)
–
freight bills over GDP (Glaeser, Kohlhase 2005)
–
traveling time × freight charges (Combes, Lafourcade 2005)
–
inference from trade flows (e.g., Head, Ries 2001, Bosker, Garretsen
2007)
–
accessibility indicator derived from gravity approach on basis of
traveling times (Schürmann, Talaat 2000)
Trade cost functions with distance decay (e.g., Anderson, Wincoop 200;
Redding, Venables 2004; Hanson2005; Brakman, Garretsen and Schramm
2006)
Measures distinguishing between EU integration and globalization
Counterfactuals
Before-after dummy (e.g., Bun and Klaassen 2007; Rose 2000; Glick and Rose
2002; Egger, Pfaffermayr 2002)
Difference-in-differences analysis (e.g., Hanson, Xiang 2002; Slaughter 2001)
Measures of EU
integration
ECB index (Dorrucci et al. 2002; Dorucci 2005)
Modified ECB index (this paper)
Measures of
globalization
EFW Fraser Institute index (Gwartney and Lawson 2006)
KOF index (Dreher 2006)
A.T.Kearney index (A.T. Kearney, Inc. 2006)
OECD globalization indices (OECD 2005)
UNCTAD-TRAINS Database
Source: Own compilation.
Other analyses try to infer the changes of trade costs from creating counterfactuals. Various
studies have used a simple before-after dummy variable. In a panel gravity regression
framework, Bun and Klaassen (2007), for example, use a dummy variable to assess the effects
of the Euro on trade intensities. The dummy variable is set to one if both trading countries
have the Euro in the respective year, and zero else. A similar dummy variable is used
frequently to assess the effects of free-trade-areas on trade intensities (e.g., Rose 2000; Glick
and Rose 2002). Using a before-after dummy variable requires assuming that the progress of
11
economic integration is taking place in a single step at a given point in time. Most regional
integration processes are, however, continuous processes that are characterized by gradual
reductions of spatial transaction costs or border impediments over longer periods of time.6
Difference-in-differences analyses study the effects of integration events (“before” vs. “after”,
“included” vs. “non-included”). The evolution over time of the response variable in question
may be compared econometrically to the evolution over time of the same response variable in
a “control group” of countries outside Europe (e.g., South-East Asia), which is, like the
European countries, affected by the globalization but is unaffected by the European
integration. Other studies estimate transaction costs from the difference between actual trade
volumes versus trade volumes predicted on the base of gravity models, or from the mark-up
of actual prices from prices predicted on the base of price models. Creating counterfactuals is,
however, not always possible, particularly not in the case of measuring globalization; it is also
very demanding with respect to the database. Another major problem with these approaches is
controlling for any systematic differences between the EU and the control group that is not
associated but may interfere with the European integration.
This leaves some measures explicitly designed to approximate the process of European
integration and the process of globalization, respectively, as most promising approaches for
the purpose of the present paper. Yet, these indices also bear some disadvantages: On the one
hand, some of them include a mix of sub-indices that are partly related to the driving forces,
partly to the consequences of the integration process. On the other hand, they measure the
institutional progress as the only driving force. The neglect of other driving forces like the
progress of ICT technology represents a disadvantage particularly in the case of the
globalization indices.
3.1
Measures of European integration
The European integration process is described by an index developed at the European Central
Bank (ECB; Dorrucci et al 2002). Dorrucci et al. construct a numerical composite index of
institutional integration that accounts for institutional changes from 1957 to 2001 (Figure 2).
This index assigns scores to the level of integration at a given point in time for five subindices, each of which refers to one stage of integration, as defined by Balassa (1961):
(i) a free trade area (FTA) where internal tariffs and quotas among member countries are
abolished,
(ii) a customs union (CU) where common external tariffs and quotas are set up,
(iii) a common market (CM) where restrictions on internal factor movements are abolished,
6 The Single Market of the EU, for example, was not established or completed at the end of 2002, as was
announced in official EU documents. It has rather been established gradually by a large number of single
steps since the mid-1980s, and is still not completed by today.
12
(iv) an economic union (EUN) where a significant degree of policy co-ordination and law
harmonization is achieved, and
(v) a total economic integration (TEI) where economic policies are conducted at a supranational level.
Each single event in the process of European integration is attributed a certain score where all
scores obtainable for each stage reach a maximum total of 20. Thus, the scores for each stage
range from 0 (no integration) to 20 (full integration). They add up to the composite integration
index, which thus ranges from 0 to 100.
Figure 2 —
ECB indices of institutional integration among the EU15 countriesa
100
90
80
70
60
50
40
30
20
10
19
57
19
59
19
61
19
63
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
0
Austria
France
Italy
Spain
Belgium
Germany
Luxembourg
Sw eden
Denmark
Greece
Netherlands
United Kingdom
Finland
Ireland
Portugal
EU6
a The index for the EU6 was compiled by the ECB (Dorrucci et al. 2002). The indices for the remaining nine
member states are based on own compilations, following the method used by Dorrucci et al. (2002) but timesmoothing the indices during the pre- and post-accession periods.
Source: Dorrucci et al. 2002. - Own supplements.
The ECB index is originally defined for the EU6 founding members only but has been
supplemented by the authors in 2005 to include scores for all countries acceding to the EU in
1973-1995 (Dorrucci 2005). In these supplemented indices, the accessions of the nine
countries are reflected by one-step increases in the official accession year from zero to the
contemporary level of the integration index for the incumbent EU6 countries. This method
does, however, not account for the fact that the integration of a new member state into the EU
is a gradual process that extends over several years both before and after the formal accession
date.
13
Therefore, to pay due tribute to this gradual process, we have compiled alternative indices for
these nine countries (Figure 2). In contrast to Dorrucci (2005), our indices have been timesmoothed during the respective pre- and post-accession period, beginning with the official
start of the accession negotiations and ending with the agreed end of the transition period. The
integration of any new member during this transition period is modelled as linear increases of
the indices for simplicity, unless distinct information on accelerations or drawbacks in this
process is available. Such accelerations or drawbacks are events such as, on the one hand,
advanced unilateral tariff reductions as in Spain 1979, or, on the other hand, the new
installation of state-owned enterprises as in Greece 1983 (this installation has, however, been
withdrawn in 1990-1992).7 EFTA membership and the exemptions of some EU countries like
the UK or Denmark from the Schengen process and the European Monetary Union are also
explicitly taken into account.
The ECB index has the advantage of keeping close track with the events that are at the roots
of the European integration process. Although the scoring may be a bit arbitrary, the index
offers an original and convincing way of measuring the progress of institutional integration.
To the econometric modelling of the effects of integration, it offers an explaining variable that
is purely exogenous, related to the driving forces of the process and available for a long time
period (1957-2001). Moreover, the index is clearly distinct from the globalization process, as
it is defined for institutional progress only, and rests upon events that are purely European.
3.2
Measures of globalization
A variety of globalization indices have been proposed in the literature (see Table 2). The
present paper focuses only on three of these indices: (i) the world trade volume as a
percentage of the world GDP as an example of a simple index that focuses on the
consequences of globalization; (ii) the “Economic Freedom of the World” (EFW) index by
the Fraser Institute (Gwartney and Lawson 2006) as an example of a purely institutional
index; (iii) the KOF index by Dreher (2006) as an example of a mixed index that captures
both indicators of the institutional determinants of a country’s integration into the world
economy as well as those of a country’s actual integration into the world economy.8
A simple, comprehensive index of globalization is the aggregate world trade volume as a
share of the aggregate world real GDP (see Figure 3). This index is easy to calculate and
available on an annual basis. It may, however, not be exogenous to the response variable in an
econometric study. Moreover, this index is sensitive to the number and size distribution of the
7 Information on such events have been drawn in particular from OECD, Economic Surveys, var. countries,
var. years.; moreover, from Schrader and Laaser 1994, Laaser 1997, Preston 1997, Vanthoor 2002.
8 The A.T. Kearney index is available (on a yearly base) only from 1991 onwards. The OECD globalisation
indicators offer lots of valuable information yet without weighing them into ready-for-use indices. The same
is true for the UNCTAD-Trains database, which offers an impressive amount of detailed information on
tariffs and NTBs.
14
countries in the world. It will, e.g., tend to increase, if a country is split into several countries;
the formerly intra-national trade will turn into international trade, while the real GDP will be
unaffected.
Figure 3 —
Volume of world trade as a share of world real GDP
0,25
0,20
0,15
0,10
0,05
0,00
1970
1975
1980
1985
1990
1995
2000
Source: World Bank.
The Fraser Institute’s EFW (“Economic Freedom of the World”) index is a country-specific
composite index that captures a country’s integration into the world economy fairly
comprehensively. The index, which is available from 1970 to 2000 on a five year basis and
from 2000 on an annual basis, is defined as an unweighted average of five sub-indices, each
of which ranges between 0 and 10. The sub-indices capture various issues related to
globalization (Table 3). A detailed list of the characteristics entering the sub-indices is given
in Table A1 in the Appendix.
Table 3 — EFW Index of Globalization 2007 - Variables
1: Size of Government
A. Government consumption spending
B. Transfers and subsidies
C. Government enterprises and investment
D. Top marginal tax rate
2: Legal Structure and Property Rights
A. Judicial independence
B. Impartial courts
C. Protection of intellectual property
D. Military interference in law and politicy
E. Integrity of the legal system
3: Access to Sound Money
A. Growth of money supply
Source: Gwartney and Lawson (2006).
B. Standard inflation variability
C. Recent inflation rate
D. Foreign currency bank accounts
4: Freedom to Trade Internationally
A. Taxes on international trade
B. Regulatory trade barrier
C. Size of trade sector
D. Off. exchange rate ./. black market rate
E. International capital market controls
5: Regulation of Credit, Labor, Business
A. Credit Market Regulations
B. Labor Market Regulations
C. Business Regulations
15
Figure 4 – “Economic Freedom of the World” indices for the EU 15 countries 2006
10
9
8
7
6
5
4
3
2
1
0
1970
1975
1980
Austria
France
Italy
Spain
1985
Belgium
Germany
Luxembourg
Sw eden
1990
1995
Denmark
Greece
Netherlands
United Kingdom
2000
2005
Finland
Ireland
Portugal
World average
Source: Gwartney and Lawson (2006).
For each sub-index, indices are calculated from raw data, and ratings between 1 to 10 are
attributed to them accordingly. Composite ratings for an area are calculated as unweighted
means of the ratings for belonging sub-indices. The summary index is calculated as
unweighted mean of the area-ratings. In contrast to this raw index, the chain-linked index
includes corrections for missing data (see Gwartney and Lawson, 2006, for details). Figure 4
plots the index values for the EU15 countries of the 2006 issue of the “chain-linked” index.
One advantage of the EFW index is its focus on institutional factors. For example, the 4th
sub-index, which assesses the freedom to trade internationally, evaluates trade restrictions
resulting from tariff and non-tariff barriers to trade, politically induced exchange rate
distortions, or capital market controls, rather than just using some measure of the trade
volume. These institutional determinants of a country’s openness are, to our opinion, well
suited for estimating the effects of the globalization through transport costs on some
macroeconomic indicators because they are – or can for simplicity be assumed to be –
exogenous to the macroeconomic indicators. A disadvantage of the EFW index is that it is
available only on five years’ intervals. Its use in a panel data analysis with annual data will
require interpolations.
The KOF index developed at the ETH Zürich (Dreher 2006; Table 4; Figure 5) is a countryspecific composite index explicitly designed to measure the globalization degree of each
country. It ranges from 0 to 100 and is available on an annual basis. The index is a weighted
16
average of three sub-indices, which focus on economic (weight: 36%), social (38%), and
political (26%) aspects of the globalization. Each sub-index comprises of several components.
For example, the sub-index of economic globalization (A in Table 4) comprises two
components, one of which focuses on the consequences of a country’s integration into the
world economy by capturing the actual intensities of foreign trade, foreign direct investments
(FDI) and transfers (A.i), while the other focuses on more fundamental, institutional
determinants of a country’s integration into the world economy by capturing the institutional
barriers to trade and capital flows (A.ii).
Table 4 — KOF Index of Globalization 2007 - Indices and Variables
A. Economic Globalization
i) Data on Actual Flows
Trade (% of GDP)
FDI flows (% of GDP)
FDI stocks (% of GDP)
Portfolio Investment (% of GDP)
Income Payments to Foreign Nationals (% of
GDP)
ii) Data on Restrictions
Hidden Import Barriers
Mean Tariff Rate
Taxes on International Trade (% of current
revenue)
Capital Account Restrictions
C. Political Globalization
Embassies in Country
Membership in International Organizations
Participation in U.N. Security Council Missions
B. Social Globalization
i) Data on Personal Contact
Outgoing Telephone Traffic
Transfers (% of GDP)
International Tourism
Telephone Average Cost of Call to US
Foreign Population (% of total population)
ii) Data on Information Flows
Internet Hosts (per capita)
Internet Users (share of population)
Cable Television (per capita)
Daily Newspapers (per capita)
Radios (per capita)
iii) Data on Cultural Proximity
McDonald's and Ikea (per capita)
Trade in books (% of GDP)
Source: Dreher (2006). - KOF online 2007 (http://globalization.kof.ethz.ch/).
The KOF index is very convenient for panel data regressions because it is available for a long
period of time, starting in 1970. It may, however, give rise to serious endogeneity concerns
because the actual trade and capital flows entering the sub-index of economic globalization as
well as several components of the sub-index of social globalization cannot be assumed to be
exogenous to response variables like the trade intensity, wages, or income. Moreover, the
weights assigned to the sub-indices and variables in order to derive the composite indicator
are highly arbitrary.
Still, the KOF index also facilitates separating out the institutional sub-indices, which are
publicly available. We construct an institutional KOF sub-index by combining the sub-index
of economic restrictions (A.ii), which is assigned a weight of 18% in the aggregate index, and
the sub-index of political globalization (C; 26%). The evolution of this combined institutional
sub-index for the EU15 countries is displayed in the lower graph of Figure 5. The variety of
institutional determinants of a country’s integration into the global economy covered by this
institutional sub-index is more limited than that of the EFW index. Still, this lack of
17
potentially relevant information will not reduce the usefulness of the institutional KOF subindex too seriously from a practical standpoint, if it can be shown to be highly correlated with
the EFW index.
Figure 5 —
KOF Index of Globalization, 2007 issue
Total index
100
90
80
70
60
50
40
30
20
10
Austria
France
Italy
Spain
Belgium
Germany
Luxembourg
Sw eden
Denmark
Greece
Netherlands
United Kingdom
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
19
88
19
86
19
84
19
82
19
80
19
78
19
76
19
74
19
72
19
70
0
Finland
Ireland
Portugal
World average
18
Institutional sub-index
100
90
80
70
60
50
40
30
20
10
Austria
France
Italy
Spain
Belgium
Germany
Luxembourg
Sw eden
Denmark
Greece
Netherlands
United Kingdom
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
19
88
19
86
19
84
19
82
19
80
19
78
19
76
19
74
19
72
19
70
0
Finland
Ireland
Portugal
World average
Source: Dreher (2006); KOF online 2007 (http://globalization.kof.ethz.ch/).
Assessing in how far the various globalization indices can be substituted one by the other
yields a rather mixed picture. Table 5 and Figure 6 show (Pearson) correlation coefficients
between the EFW index, the KOF index and the institutional KOF sub-index across time and
across countries for the EU15 countries; for convenience, the correlations with the world trade
volume and with the time trend are also provided. Table 5 reports the means and the standard
deviations of all country-specific across-time correlations (upper triangle) and the means and
the standard deviations of all year-specific across-country correlations (lower triangle). Figure
6 displays detailed results (upper graph: across time for each EU15 country; lower graph:
across countries for each year since 1970).
All these indices prove to be highly correlated across time for most of the EU15 countries.
Also all indices are highly correlated with the world trade volume and even with the time
trend. These correlations reflect that all indices analysed show an upward trend and that this
upward trend is rather continuous. By contrast, the indices are only poorly correlated across
countries in individual years.9 Although all countries reveal upward trends for all indices, the
paces of these trends obviously vary considerably for each country with regard to each index.
9 The correlation coefficients for the world trade index and the time trend are zero because the world trade
volume does not vary across countries.
19
Table 5 — Correlations between different indices of globalization across time and across
countries, 1970-2001a
Across time
EFW
Across countries
KOF
KOF_
Inst
World Trade
Time Trend
EFW
0.85
(.11)
0.83
(.08)
0.81
(.11)
0.83
(.12)
0.92
(.07)
0.94
(.06)
0.95
(.07)
0.91
(.04)
0.90
(.05)
KOF
0.53
(.18)
KOF_Inst
0.36
(.24)
0.84
(.05)
World Trade
—
—
0.98
(.00)
—
a
The upper triangle reports, in each cell, the mean and standard deviation over the 15 EU countries of the
country-specific Pearson correlation coefficients between the indices given in the column and the row heads
across the time 1970–2001. The lower triangle reports, in each cell, the mean and standard deviation over the
years 1970–2001 of the year-specific Pearson correlation coefficients between the indices given in the column
and the row heads across the15 EU countries.
Source: Gwartney and Lawson (2006). - Dreher (2006). - KOF online 2007. - World Bank data. – Own calculations.
Figure 6 — Correlations between different indices of globalization across time and across
countries, 1970-2001
Correlations across time, by countrya
Spain
Finland
Italy
France
Germany
Ireland
Portugal
UK
EFW vs. KOF
M EA N
EFW vs. KOF_Inst
Austria
Netherlds
Belgium
Greece
Sweden
Denmark
Luxemb
-1
-0,75
-0,5
-0,25
0
0,25
0,5
0,75
1
Pearson correlation coefficient
a
Data are lacking for the KOF_Inst index in the cases of Germany, Ireland, UK, Denmark and Luxemburg.
20
Correlations across countries, by year
1
Pearson correlation coefficient
0,9
0,8
0,7
0,6
0,5
0,4
0,3
EFW vs. KOF
0,2
EFW vs. KOF_Inst
KOF vs. KOF_inst
0,1
0
1970
1975
1980
1985
1990
1995
2000
2005
Source: Gwartney and Lawson (2006). - Dreher (2006). - KOF online 2007.– Own calculations.
Over time, however, the cross-country correlations show a marked trend: they are weakly
positive in the early 1970s, increase slightly until 1990 and decrease since. A bit surprisingly,
the correlations are always a little higher between the EFW index and the general KOF index
than between the EFW and the institutional KOF index. For econometrical purpose, the
institutional KOF index also bears the disadvantage that it does not provide data for all EU15
countries (whereas the composite KOF index does).
All in all, the EFW index seems to be a comprehensive and adequately specified indicator of
the institutional side of globalization. The institutional KOF sub-index seems actually a lesssuited substitute of the EFW index and, due to its limited coverage of the variety of
institutional determinants, possibly also a less-suited indicator of a country’s integration into
the global economy.
3.3
Correlations between the EU integration and the globalization indices
In Section 2, we argue that it is possible to distinguish European integration from
globalization in conceptual terms. Now we turn to the question whether it is also possible to
distinguish both in empirical terms. The effects of European integration cannot be identified
separately from those of globalization in econometric models, if the respective indices are
perfectly correlated. The indices must, at least to some extent, carry differing pieces of
information that allow identifying the effects of European integration.
A cursory inspection of the Figures 2 to 5 above already indicates that all indices are likely to
be highly correlated across time. All indices tend to increase more or less continuously across
21
time in all countries. This visual impression is confirmed by Pearson correlation coefficients
given in Table 6 and Figure 7. Table 6 (upper row) reports, in each cell, the means and the
standard deviations over the 15 EU countries of the country-specific correlations across the
time 1980–2001 between the ECB index and the globalization indices given in the row heads.
Figure 7 (upper graph) displays the country-specific results in detail.
Table 6 — Correlations between indices of European integration and globalization across time
and across countries, 1970-2001a
EFW
KOF
KOF_
Inst
World
Trade
Time
Trend
Across time
0.84
(.11)
0.93
(.05)
0.91
(.07)
0.94
(.02)
0.95
(.03)
Across countries
0.13
(.39)
0.18
(.35)
0.19
(.38)
—
—
ECB versus
a
The upper line reports, in each cell, the mean and standard deviation over the 15 EU countries of the countryspecific Pearson correlation coefficients between the ECB index and the indices given in the column head across
the time 1970–2001. The lower line reports, in each cell, the mean and standard deviation over the years 1970–
2001 of the year-specific Pearson correlation coefficients between the ECB index and the indices given in the
column head across the15 EU countries.
Source: Dorucci et al. 2002. - Gwartney and Lawson (2006). - Dreher (2006). - KOF online 2007. - World Bank data. – Own
calculations.
Figure 7 — Correlations between the ECB index of European integration and selected
globalization indices across time and across countries, 1970-2001
Correlations across time, by countrya
Luxemb
Austria
Netherlds
Italy
Spain
Sweden
Finland
UK
ECB vs. EFW
France
ECB vs. KOF
M EA N
ECB vs. KOF_Inst
Portugal
Ireland
Belgium
Germany
Denmark
Greece
-1
-0,75
-0,5
-0,25
0
0,25
0,5
0,75
1
Pearson correlation coefficient
a
Data are lacking for the KOF_Inst index in the cases of Germany, Ireland, UK, Denmark and Luxemburg.
22
Correlation across countries, by year
1
ECB vs. EFW
Pearson correlation coefficient
0,8
ECB vs. KOF
ECB vs. KOF_Inst
0,6
0,4
0,2
0
1970
1975
1980
1985
1990
1995
2000
2005
-0,2
-0,4
-0,6
Source: Dorucci et al. 2002. - Gwartney and Lawson (2006). - Dreher (2006). - KOF online 2007. – Own calculations.
The time series of the ECB indices and the globalization indices (EFW, KOF, world
trade/GDP) are almost perfectly correlated. The correlation coefficients are >0.8 for the EU
on average and for most countries. The correlations are high where the globalization indices
are focusing on institutional aspects that can be considered reflecting the sources of
globalization, such as the EFW index, but the correlations are even higher where the
globalization indices are focusing on the volumes and intensities of economic transactions
that can be considered reflecting the consequences of globalization, such as the composite
KOF index and the world trade volume relative to world GDP. Relatively low correlations are
only found between the ECB index and the EFW index in the cases of Greece and Denmark,
Belgium and Germany (Figure 7, upper graph). On aggregate, we therefore conclude that the
time dimension alone will not contribute much to distinguishing the effects of the European
integration from those of the globalization in empirical studies.
Across the EU15 countries, the ECB indices are, by contrast, virtually unrelated to those of
the globalization indices, the average of the year-specific correlations varying between 0.19
(to the institutional KOF index) and 0.13 (to the EFW index; Table 6).10 However, this
aggregate picture masks an interesting time trend (Figure 7, lower graph). For the 1970s and
1980s, the year-specific indices of European integration and of globalization appear to be
positively related to each other. The EU countries most integrated within the EU also tended
to be the most globalized countries. The intensity of this positive correlation decreased over
time, however, and eventually turned negative, such that, in 2001, the EU countries most
10 The correlation coefficients for the world trade index and the time trend are zero because the world trade
volume does not vary across countries.
23
integrated within the EU tended to be the least globalized countries, and vice versa. 11 This is
true for the two types of indices, the institutions-based indices (EFW; including the driving
forces of globalization only), and the mixed, institutions- and transactions-based ones (KOF;
including both the driving forces and consequences of globalization).
These decreasing correlations between European integration and globalization seem to
highlight different policies of the EU15 countries with respect to these two ways of
integration: Whereas some countries seem to emphasize their integration into the EU, others
seem to emphasize their integration into global markets. Figure 8 plots the co-movement of
the EU integration and globalization indices for various EU countries during the observation
period 1970-2000. It shows for each country the combinations of ECB index and EFW index
in five-year intervals (each point marks one of the years 1970, 1975, 1980, 1985, 1990, 1995,
2000). All original EU6 member states start in the first observation year 1970 at a European
integration level slightly below the value of 50, all countries acceding later at a European
integration level close to zero. The level of globalization in the first observation year 1970
varies between values little above 5 (Sweden) and above 7 (Germany).12
From these starting points, some countries with an emphasis on European integration show an
evolution into a primarily upward direction (upper graph of Figure 8). That is to say, their
position improves relatively fast with regard to European integration, less so with regard to
globalization. All of these countries but one reach the highest European integration level
attainable in 2000 (close to a value of 90), but they reach a relatively low globalization level
of around 7. These countries include the EU6 member states Belgium, Germany, Luxemburg
and the Netherlands, moreover the accession countries Austria, Finland, Spain and Sweden.
They seem to follow a policy strategy where the main focus lies on being part of the European
integration process, and less on integrating into global markets.
On the contrary, other countries with an emphasis on global integration show an evolution not
only in an upward direction but also towards the right-hand side (lower graph of Figure 8).
Their position improved not so fast with regard to European integration yet relatively fast
with regard to globalization. Several of them do not reach the highest European integration
level attainable in 2000, but they reach relatively high globalization levels between 7 and
more than 8. These countries include the EU6 member states France and Italy, moreover the
accession countries Denmark, Greece, Ireland, Portugal and the UK. According to these
indices, these countries seem to be a bit less engaged in the European integration process, and
more in the process of globalization, compared to the other EU15 countries.
11 The outliers in the correlations between ECB index and institutional KOF sub-index for 1999 and 2000
cannot be traced to any single underlying cause, but rather seem to be the random outcome of several causes.
12 For all countries, the globalization level decreases between 1970 and 1975 according to the EFW index (cf.
Figure 4). This is a specialty of the EFW index, it is not observed for the KOF indices (cf. Figure 5).
24
Figure 8 – Co-movement of EU integration and globalization indices, 1970-2000
Countries with emphasis on European integration
90
Development of EU integration and globalisation
(1970-1975-1980-1985-1990-1995-2000)
80
EU integration (ECB indicator)
70
60
50
40
30
20
10
0
7
6
5
4
3
9
8
Globalisation (EFW indicator)
Austria
Netherlands
Luxemburg
Sweden
Germany
Spain
Belgium
Finland
Countries with emphasis on globalization
90
80
Development of EU integration and globalisation
(1970-1975-1980-1985-1990-1995-2000)
EU integration (ECB indicator)
70
60
50
40
30
20
10
0
3
4
5
6
7
8
Ireland
Portugal
9
Globalisation (EFW indicator)
France
Italy
Denmark
Greece
Source: Dorucci et al. 2002. - Gwartney and Lawson (2006). – Own illustration.
UK
25
Several implications can be drawn from these empirical results, on the relationship between
driving forces and consequences as well as on the relationship of the driving forces among
themselves. First, driving forces and consequences of the globalization process appear to be
closely related: Among each other, the globalization indices are highly correlated irrespective
of whether they represent driving forces or actual transactions (as first consequences). Also,
the correlations between the ECB index and the various globalization indices do not differ
much, irrespective of whether the globalization indices focus on the driving forces or on
actual transactions. Accordingly, there seems to be a close relationship between the driving
forces and the consequences of globalization. And this relationship holds even though the
indices representing the driving forces cover only part of these driving forces, namely
institutional progress, and neglect other, in particular the progress of ICT technology. Second,
the driving forces among themselves, the institutional progress on the one hand and the
progress in ICT technology on the other, may also be closely related: The empirical result of a
high correlation between one driving force of globalization and the general consequences
suggests that the other driving force(s) might either be irrelevant for the consequences or
impact similarly on them. Moreover, the conceptual considerations in section 2 suggest that
all driving forces are mutually interdependent where the institutional progress induces new
ICT technology and better ICT technology challenges further institutional progress. As a
consequence, a globalization index based solely on the institutional progress may still be a
sufficient proxy for all driving forces, and an additional ICT-technology-based index may be
dismissible.
To sum up, the ECB index as an indicator of European integration is empirically distinct from
the EFW and KOF indices as indicators of globalization. In particular, viewed across time, the
indices carry differing pieces of information for each country, as the countries seem to follow
different policy strategies with respect to European integration and globalization.
4
4.1
Illustration: A gravity model for the European Union
Empirical model and data
To illustrate the approach discussed in this paper, we use as a baseline model a simple panel
gravity model of trade among the EU15 countries (cf. Anderson and van Wincoop, 2004, for a
recent review of the gravity literature). We augment this baseline model by assuming that the
border costs are a function of the degree of integration among the EU15 countries, their
integration into the global economy, and technological progress. The baseline gravity model
is
ln X ijt  1 ln GDPit   2 ln GDPjt   3 ln POPit   4 ln POPjt
  ln Dij   ij  DIC   ijt
,
(1)
26
where Xij denotes exports from country i to country j, (i, j = 1, …, N), GDP and POP the
(nominal) gross domestic product and the population size, Dij the bilateral distance, ij a set of
country-pair fixed effects, and ijt an idiosyncratic error term. The country-pair fixed effects
account for the effects of time-independent fundamental characteristics of both the individual
countries, and the country pairs on trade intensities.13 These effects include, on the one hand,
the individual countries’ exogenous comparative advantages (e.g., resource endowments) and
their consumers’ preferences. The effects include, on the other hand, the average trade
intensities between the pairs of countries.14 To account for a possible structural break in the
intra-EU trade intensities due to the fall of the iron curtain in 1989, we add a dummy variable,
DIC to the model that is one for all years before the fall of the iron curtain, and 0 after its fall.
We augment this baseline model by adding, separately for the respective exporting and
importing countries, the ECB index (Section 3.1; see Figure 2) as our indicator of the
integration of the countries into the EU, and the EFW index (Section 3.2; see Figure 4) as our
indicator of the institutional integration of the countries into the global economy. We prefer
indices that reflect the institutional integration into the EU and the global economy to avoid
endogeneity problems as far as possible. We assume both the ECB and the EFW index to be
exogenous to the trade intensities between the countries. We prefer the EFW index over the
institutional KOF subindex because it measures the institutional characteristics of
globalization more comprehensively, as was noted in Section 3.2. In addition to the EU
integration and globalization indices, we add a time trend as our indicator of global
technological progress to prevent the parameters of the EU integration and globalization
indices from picking up the additional opportunities for trade generated by the global
technological progress. The model to be estimated is
ln X ijt  1 ln GDPit   2 ln GDPjt   3 ln POPit   4 ln POPjt   ln Dij   ij  DIC
 1 I it   2 I jt   3Git   4G jt   5Tt   ijt .
(2)
Iit and Ijt denote the levels of institutional EU integration of the two trading countries i and j
into the EU, Git and Gjt their levels of institutional integration into the global economy, and Tt
the indicator of the technology. A country that is more integrated into the EU is expected to
13 We refer to the dependent variable as reflecting trade intensities rather than export values because the GDP
and population variables on the right-hand side of the regression equation can be interpreted as controlling
for the effects of the absolute country sizes or per-capita income levels.
14 We ignore for simplicity the product of the unobservable price indices of traded goods in the exporting and
importing countries. Based on a simple trade model with product variety and monopolistic-competition,
Anderson and van Wincoop (2004: eq. 12) derive at a gravity equation that differs from (1) in that it
comprises as explanatory variables the real instead of the nominal incomes of the trading countries as well as
two additional variables, the CES price index of exported goods in the exporting country and the CES price
index of imported goods in the importing country. (1) will be approximately equivalent to the model (12) in
Anderson and van Wincoop, if the ratios of the price indices for traded goods and all goods do not change
significantly over time. Investigating the effects of the changes of these ratios over time on trade is left to
future research.
27
have greater opportunities to trade with other EU countries because exporters and importers
face lower institutional border impediments for intra-EU trade. A country that is more
integrated into the global economy is expected to have greater opportunities to trade with nonEU countries. This may affect the intensity of its trade with EU countries either positively or
negatively, depending on whether the trade with non-EU countries complements or substitutes
for the trade with EU countries. The global technological progress, finally, is expected to
extend the opportunities to trade with other EU countries by generating new traded goods and
making non-tradable goods tradable.
Equation (2) is estimated in first differences to eliminate the country-pair fixed effects. This
first differencing also eliminates the distance term, Dij, because the distances are fixed over
time. And it removes the time trend from the EU integration and globalization indices,
thereby reducing multicollinearity induced by their high correlation over time (see Section
3.3). We estimate the augmented gravity model (2) for the period 1970–2000. Since the EFW
index is available only for every fifth year, we have seven observations in time, ranging from
1970 to 2000, or six observations in the first-differenced model. The data on trade (exports)
among the EU15 countries (variable Xijt), nominal GDP (GDPit, GDPjt) and population
(POPit, POPjt) are available from the OECD. Exports and GDP are in current-year US-$.
Belgium and Luxembourg are merged to one country because trade data are available only for
the aggregate of both countries for several years.15 The ECB index is divided by 10 to get
parameter estimates comparable in magnitude to the EFW index. Both indices range from 0 to
10. The descriptive statistics for the explanatory variables are depicted in Table A2 in the
Appendix.
4.2
Results
The results of the estimation of equations (2) in first differences by panel OLS are reported in
column (1) of Table 7. The estimates for the indicators of the GDP and population of the
trading countries have the correct signs and are of plausible magnitudes. The parameters of
the two population variables are not significantly different from zero, however, which is not
too surprising because the per-capita incomes (GDP/POP) do not vary too much across the 15
EU countries. The positive and significant parameter of the dummy for the iron curtain
indicates that the trade intensities were, ceteris paribus, higher in the 1970s and 1980s. Some
trade between the Western European EU members has apparently been diverted towards
Eastern Europe after the fall of the iron curtain.
15 Data on exports from and to Belgium and Luxembourg are not available for the years before 1997.
28
Table 7: Regression results
(1)
parm Std.dev
ln(GDP exporter)
ln(GDP importer)
ln(POP exporter)
ln(POP importer)
dummy iron curtain
0.341
0.677
-0.309
-0.432
0.136
0.056***
0.056***
0.334
0.268
0.037***
EU integr. (ECB) exporter
EU integr. (ECB) importer
globalization (EFW) exporter
globalization (EFW) importer
technology (time trend)
0.033
0.016
-0.065
0.071
0.123
0.012***
0.012
0.029**
0.028**
0.020***
R-squared
Adj. R-squared
SSR
No of obs
0.644
0.640
95.7
1014
(2)
parm Std.dev
0.368
0.689
0.329
-0.150
0.129
(3)
parm Std.dev
0.057***
0.058***
0.382
0.257
0.030***
0.372
0.721
1.415
0.863
0.052
0.062 0.011***
0.044 0.011***
—
—
—
—
—
—
—
—
0.628
0.623
99.9
1014
0.058***
0.058***
0.472***
0.288***
0.031*
0.600
0.595
108.4
1014
Panel OLS regressions in first differences; dependent variable: logged exports 1970–2000. White-robust
standard deviations (Std.dev); *, **, ***: significant at the 10%, 5%, 1% level.
A higher institutional integration of a country into the EU fosters its exports to other EU
countries to a greater extent than its imports from other EU countries, according to the
estimates. The parameter of the ECB index of the exporting country is estimated to be 0.033;
that of the importing country only 0.016. Moreover, the parameter for the importing country
appears to be not significantly different from zero. This is, however, due to multicollinearity
between the two ECB indices.16 The estimated parameters of the two EU integration indices
suggest that an increase of a country’s ECB index by 20 (e.g., from 50 to 70) raises, ceteris
paribus, its exports to other EU countries by 6.6% (2*0.033*100) and its imports from other
EU countries by 3.2%. A parallel increase of the ECB index for the exporting and importing
country by 20 raises the trade intensity between these two countries consequently by 9.8%
(6.6% + 3.2%). An increase of the ECB index by 20 is equivalent to completing one stage of
integration, as defined by Balassa (1961), i.e., moving from no integration to a free trade area,
from a free trade area to a customs union, and so on. These estimated trade effects of a freetrade area, implied by the estimates reported in Table 7, are fairly close to the estimates
reported in Bun and Klaassen (2007) who find the trade intensities to be 6% higher, if both
trading countries belong to the Euro zone. The estimates differ, however, significantly from
16 If the product of the two indices, Iit x Ijt, is specified as a single variable, a higher EU integration of the
exporting and importing country taken together is estimated to be 0.098 and highly significant. The estimates
for this model, which are not reported here, are available from the authors upon request.
29
those reported by Rose (2000) and Glick and Rose (2002), who find the trade intensities to be
100% higher, if both trading countries belong to the Euro zone. Bun and Klaassen argue that
these high estimated trade effects of free trade areas are mainly due to omitted country-pairspecific time trends in Rose (2000) and Glick and Rose (2002). In the present study, the
country-specific time trends are part of the EU integration and globalization indices, and are
eliminated by first-differencing.
A higher institutional integration of a country into the global economy reduces its exports to
other EU countries but fosters its imports from other EU countries, according to the estimates.
The parameter of the EFW index of the exporting country is estimated to be –0.065; that of
the importing country 0.071. Both parameters are, in spite of some multicollinearity,
significant at the 5% level. This indicates that a country’s higher integration into the global
economy diverts some exports away from the EU, possibly towards non-EU countries, but
generates additional imports from other EU countries. EU countries that focus more on
removing barriers to trade with other EU countries than on opening up towards the rest of the
world apparently still participate indirectly from global openness of other EU countries by
selling more to these countries.
The global technological progress, finally, which is proxied by a time trend, is estimated to
impact strongly on trade intensities. The additional trade facilitated by global technological
progress is estimated to be 12.3% in five years, or 2.3% annually. Notice that this figure
comes close to an increase in real exports because the GDP variables tend to work as
deflators.
Columns (2) and (3) of Table 7 report control regressions. Column (2) shows that the effects
of the EU integration will be overstated significantly, if the effects of the globalization and the
global technological progress are not controlled for explicitly. The parameters of the two ECB
indices are twice as high as those in column (1). As expected, they pick up at least a part of
the effects of the globalization and the technological progress on trade intensities. Column (3)
shows that ignoring the effects of EU integration, globalization and technological progress
completely yields implausible results. The parameters of the two population variables assume
positive and highly significant values, which implies that the trade intensities decrease with
increasing per capita income. In addition, the iron-curtain dummy looses much of its
influence.
5
Conclusion
This paper serves the purpose to determine operational indicators for analyzing empirically
the effects of European integration on trade, the division of labor, income, employment and
growth, and it addresses two major problems: to define an informative indicator adequately
describing the European integration process, and to distinguish the effects of European
integration from similar effects of globalization.
30
Conceptually, we suggest distinguishing European integration from globalization by
addressing the specific driving forces that are behind the integration processes. With the ECB
index (Dorucci et al 2002) and the EFW index (Gwartney and Lawson 2006), there are
indicators of European integration and globalization available that describe comprehensively
the institutional progress specific to each of these processes.
Empirically, we find the ECB index of European integration to be distinct from the EFW and
other indices of globalization. In particular, viewed across time, the indices carry differing
pieces of information for each country, as the countries seem to follow different policy
strategies with respect to European integration and globalization (in particular, Finland and
Germany seem to place more emphasis on European integration, whereas, in particular, the
UK and Ireland seem to place more emphasis on their integration into global markets).
The illustrative gravity model demonstrates that including indicators for the processes of
European integration, globalization and technological progress in empirical analyses may
improve the quality and plausibility of the results decisively.
According to the findings of this paper the ECB index and the EFW index are considered
suitable measures to assess the impact of EU integration and globalization, respectively.
Future research may explore by sensitivity tests in how far the existing integration indices
depend on their arbitrary weights, and in how far such dependencies bear the danger of
misinterpretations. Also, future research may focus on finding more adequate concepts and
measurements of the progress in ICT technology as another major driving force of economic
integration, particularly with respect to globalization.
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Appendix
Table A1 — EFW Index 2006 - Fields and Components
1: Size of Government: Expenditures, Taxes, and Enterprises
A. General government consumption spending (% of total consumption)
B. Transfers and subsidies (% of GDP)
C. Government enterprises and investment (% of total investment)
D. Top marginal tax rate (and income threshold to which it applies)
i. Top marginal income tax rate (and income threshold at which it applies)
ii. Top marginal income and payroll tax rate (and income threshold at which top marginal rate
applies)
2: Legal Structure and Security of Property Rights
A. Judicial independence
B. Impartial courts
C. Protection of intellectual property
D. Military interference in rule of law and the political process
E. Integrity of the legal system
3: Access to Sound Money
A. Growth of money supply minus growth of real GDP (average annual rates in the last 5/10 years)
B. Standard inflation variability (last five years)
C. Recent inflation rate
D. Freedom to own foreign currency bank accounts domestically and abroad
4: Freedom to Trade Internationally
A. Taxes on international trade
i. Revenue from taxes on international trade(% of exports plus imports)
ii. Mean tariff rate
iii. Standard deviation of tariff rates
B. Regulatory trade barriers
i. Non-tariff trade barriers
ii. Compliance cost of importing and exporting
C. Size of trade sector (actual compared to expected)
D. Difference between official exchange rate and black market rate
E. International capital market controls
i. Foreign ownership/investment restrictions
ii. Index of controls for capital exchange among 13 IMF categories
5: Regulation of Credit, Labor, and Business
A. Credit Market Regulations
i. Ownership of banks: deposits held in privately owned banks (%)
ii. Competition in domestic banking from foreign banks
iii. Extension of credit: percentage of credit extended to private sector
iv. Avoidance of interest rate controls and regulations that lead to negative real interest rates
v. Interest rate control on bank deposits and/or loans: freely determined by the market?
B. Labor Market Regulations
i. Impact of minimum wages
ii. Hiring and firing practices determined by private contract
iii. Centralized collective bargaining (% of labor force concerned)
iv. Unemployment Benefits: the unemployment benefits system preserves the incentive to
work
v. Use of conscripts to obtain military personnel
C. Business Regulations
i. Price controls: freeness to set own prices
ii. Burden of regulation
iii. Government bureaucracy: time spending of senior management with it
34
iv. Starting new business: easiness
v. Irregular payments for permits and licenses
Source: Gwartney and Lawson (2006).
Table A2 – Descriptive statistics for variables used in the illustrative regression
Variable (first differences)
Mean
lnXijt
Iit(ECB index exporter)
Ijt(ECB index importer)
Git, (EFW index exporter)
Gjt (EFW index importer)
time trend
lnGDPit
lnGDPjt
lnPOPit
lnPOPjt
dummy iron curtain
0.546
1.072
1.108
0.203
0.213
1
0.401
0.403
0.024
0.025
-0.167
Std.dev Minimum Maximum
0.515
0.862
0.876
0.544
0.562
0
0.440
0.434
0.031
0.032
0.373
-1.153
-0.400
-0.400
-2.235
-2.235
1
-0.380
-0.299
-0.014
-0.014
-1
2.388
3.600
3.600
1.804
1.804
1
1.088
1.088
0.255
0.255
0