Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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. References A.T. Kearney, Inc. (2006) The Globalisation Index, Foreign Policy. http://www.atkearney.com/main.taf?p=5,4,1,127,1 Anderson, J.E., E. van Wincoop (2004). Trade Costs. Journal of Economic Literature 42(3): 691-751. Balassa, B. (1961), The Theory of Economic Integration. Homewood, Ill. Irwin. Baldwin, R., R. Forslid, P. Martin, G.I.P. Ottaviano, F. Robert-Nicoud (2003). Economic Geography and Public Policy. Princeton . Bhagwati, J. (2004). In Defense of Globalization. Brakman, S., H. Garretsen, M. Schramm (2006). Putting New Economic Geography to the Test: Freeness of Trade and Agglomeration in the EU Regions. Regional Science and Urban Economics 36(5): 613-635. Bosker, M., and H. Garretsen (2007). Trade Costs, Market Access and Economic Geography:Why the Empirical Specification of Trade Costs Matters. CESIfo Working Paper 2071. 31 Bun, M., and F. Klaassen (2007). The euro effect on trade is not as large as commonly thought. Oxford Bulletin of Economics and Statistics 69 (4): 473-496 Carrère, C., M. Schiff (2005). On the Geography of Trade : Distance is alive and well. Revue Economique 56: 1249–1274. Cairncross, F. (1997). The Death of Distance: How the Communications Revolution will Change our Lives. Boston, Mass.. Harvard Business School Publ. Combes, P.-P., M. Lafourcade (2005). Transport costs : measures, determinants and regional policy implications for France. Journal of Economic Geography 5: 319-349. Dorrucci, E. (ECB), An Institutional Index of Regional Integration for the European Union and Mercosur, Mimeo (2005) Dorruci, E., S. Firpo, M. Fratzscher, F.P. Monelli (2002). European Integration: What Lessons for other Regions? The Case of Latin America. ECB Working Paper 185. Frankfurt: European Central Bank. Dreher, A. (2006). Does Globalization Affect Growth? Empirical Evidence from a new Index, Applied Economics 38, 10: 1091-1110. Eastern Carribbean Central Bank, Submitted by Dr. June Soomer, Adviser, Strategic Policy and Planning Department, ECCB, as part of the Eastern Caribbean Currency Union’s Financial Literacy Month Programme Oct. 2003 - “Building Strong Economies Depends on You and Me”) http://www.eccb-centralbank.org/PDF/newspaper3.pdf Egger, P., M. Pfaffermayr (2002). The Pure Effects of European Integration on Intra-EU Core and Periphery Trade. University of Innsbruck Working Papers in Economics 2002/1. Fertig, M. (2003). The Impact of Integration on Employment - An Assessment in the Context of EU Enlargement. IZA Discussion Paper 919. Glaeser, E.L., J.E. Kohlhase (2005). Cities, Regions and the Decline of Transport Costs. Papers in Regional Science 83 (1): 197 – 228. Glick, R., and A.K. Rose (2002). Does a currency union affect trade? The time-series evidence. European Economic Review, 46 (6): 1125-1151. Gwartney, J., and R. Lawson (2006). Economic Freedom of the World: 2006 Annual Report. Vancouver: The Fraser Institute. Data retrieved from http://www.freetheworld.com Hanson, G. (2005). Market Potential, Incraesing Returns, and Geographic Concentration. Journal of International Economics 67 : 1-24. Hanson, G., C. Xiang (2002). The Home Market Effect and Bilateral Trade Patterns. NBER Working Paper 9076. Head, K., and J. Ries (2001). Increasing Returns versus National Product Differentiation as an Explanation for the Pattern of U.S. Canada Trade. American Economic Review 91(4): 858-876. Fujita, M., P. Krugman and A.J. Venables (1999). The Spatial Economy: Cities, Regions and International Trade. Cambridge (Mass.). KOF online 2007 (http://globalization.kof.ethz.ch/). Laaser, C.-F. (1997). Ordnungspolitik und Strukturwandel im Integrationsprozeß: das Beispiel Griechenlands, Portugals und Spaniens. Kieler Studie 287. Tübingen. Mohr. McCann, P. (2005). Transport Costs and New Economic Geography. Journal of Economic Geography 5: 305-318. 32 Mongelli, F.P., E. Dorucci, I. Agur (2005) What does European institutional integration tell us about trade integration? ECB occasional Paper series No 40. Frankfurt. Obstfeld, M., K. Rogoff (2000). The Six Major Puzzles in International Macroeconomics: Is There A Common Cause? NBER Working Paper W7777. OECD (2005), Measuring Globalisation: OECD Economic Globalisation Indicators. Paris. OECD, Economic Surveys, var. countries, var. years Oman, Ch. (1996). The Policy Challenges of Globalisation and Regionalisation. OECD Policy Brief No. 11. Paris. Preston, C. (1997). Enlargement and Integration in the European Union. London. Routledge. Redding, S., and A.J. Venables (2004). Economic Geography and International Inequality. Journal of International Economics 56: 97-118. Rose, A.K. (2000). Currency unions and international integration. Journal of Money, Credit and Banking 34 (4 ):1067-1089 Schrader, K., and C.-F. Laaser (1994), Die baltischen Staaten auf dem Weg nach Europa : Lehren aus der Süderweiterung der EG. Kieler Studie 264. Tübingen. Mohr. Schulze, G.G., H.W. Ursprung (1999). Globalisation of the Economy and the Nation State. World Economy 22(3): 295-352 Schürmann, C., A. Talaat (2000). Towards a European Peripherality Index. Final report. Berichte aus dem Institut für Raumplanung 53, Dortmund. Slaughter, M.J. (2001). Trade liberalization and per capita income convergence: a differencein differences analysis. Journal of International Economics 55: 203-228. With comment by D. Ben-David. 229-234. Stiglitz, J.E. (2006). Making Globalization Work. New York. Norton. Traistaru, I ., P. Nijkamp, S. Longhi (2002). Regional specialization and concentration of industrial activity in accession countries. ZEI policy paper : B ; 16/2002. Bonn. Trefler, D. (1995). The Case of the Missing Trade and Other Mysteries. American Economic Review 85 (5): 1029-1046. UNCTAD TRAINS (Trade Analysis and http://r0.unctad.org/trains_new/index.shtm Information System) Database van Liemt, G. (1998), Labour in the Global economy: Challenges, adjustment and policy responses in the EU. In: Memedovic, Kuyvenhoven, Molle (eds.), Globalisation of Labour Markets. Challenges, adjustment and policy responses in the EU. Dordrecht, Boston, London. Vanthoor, W.F.V. (2002). A Chronological History of the EU 1946-2001. Elgar. 33 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