A rather empty world: the many faces of distance and

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Cambridge Journal of Regions, Economy and Society 2008, 1, 439–458 doi:10.1093/cjres/rsn019 Advance Access publication 20 August 2008

A rather empty world: the many faces of distance and the persistent resistance to international trade

Gert-Jan M. Lindersa, Martijn J. Burgerb and Frank G. van Oortc a

Despite the rapid growth of world trade over the past several decades, international trade patterns remain very much affected by high trade costs. In this paper, we emphasize the importance of distance between countries in a proper explanation of the persistent resistance to trade. We find that both formal trade barriers (tariffs, transport costs, etc.) and informal trade barriers (related to cultural and institutional distances) between countries have retained their importance to trade. Not only are these tangible and intangible barriers critical in explaining the volume of trade between countries but they also help to explain the absence of trade between many countries. Keywords: international trade, distance, trade barriers JEL Classifications: F11, F12, F18.

Introduction Every other decade, there seems to be room for a popular and provocative book arguing that communication technologies and increasing individualism open the world’s economic opportunities to everyone. In 1980, Alvin Toffler foresaw the Third Wave post-industrial society in which advances in information technologies enabled optimal contact opportunities, reducing all places on earth to a ‘global village’. In 1997, Frances Cairncross published the bestseller, The Death of Distance, in which she claimed that geography and distance

are no longer critical to interaction opportunities, in an economic sense. In his recent book, The World is Flat, Thomas Friedman (2005) argues that economic globalization renders the world a level economic playing field, leaving opportunity for economic convergence of developing countries, companies and individuals (p. 11), ‘potentially lifting large numbers of people out of poverty’ (p. 437). Friedman acknowledges that ‘when opting for a big metaphor, you trade a certain degree of academic precision for a much larger degree of explanatory power’ (p. x). However, what if countries

Ó The Author 2008. Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved. For permissions, please email: [email protected]

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Department of Spatial Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, NL-1081 HV Amsterdam, the Netherlands. [email protected] b Department of Applied Economics and Erasmus Research Institute of Management, Erasmus University Rotterdam, PO Box 1738, NL-3000 DR Rotterdam, the Netherlands. [email protected] c Department of Economic Geography, Utrecht University, PO Box 80115, Utrecht NL-3508 TC, the Netherlands. [email protected]

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tion introduces an empirical model that is used to investigate the effects of these barriers on bilateral trade patterns, simultaneously taking note of some important specification issues (omitted-variable bias, log-normality and zero flows). The paper moves on to describe the data and the sample studied, and discusses the findings that result from implementing this model. The final section concludes.

Growth in trade: a shrinking world? International trade increases economic efficiency for trading countries. Nations vary in terms of their relative productivity across different industries and in the availability of production factors, such as specific types of labour and physical capital goods, which are required in differing proportions for each industry. International trade allows a country to specialize and concentrate its resources in those industries in which it has comparative natural advantages (Feenstra, 2004). This is known as the factor proportions theory of trade or the Heckscher–Ohlin theorem. Gains from international trade arise not only from comparative advantages. The larger market that is opened up by international trade also generates gains due to the increased scale of production and specialization (Helpman and Krugman, 1985; Krugman, 1979) from more intense competition, from R&D spillovers between countries (Coe and Helpman, 1995; Lejour and Nahuis, 2005) and from the availability of increased diversity of products for customers (Dixit and Stiglitz, 1977; Krugman, 1979). Perhaps the most obvious advantage of being open to international trade is that participating countries gain access to products that cannot be produced domestically. Most notably, this applies to natural resources such as oil and mineral ores (with which few countries are richly endowed), but a similar argument applies to the introduction of new products developed abroad (Romer, 1994). Figure 1 illustrates that since the Industrial Revolution in the late 18th and 19th centuries, international trade has shown rapid growth compared to world output (as measured by countries’ combined Gross Domestic Product (GDP)).

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do not live up to such expectations of economic democracy? Richard Florida (2005) demonstrated that innovation and scientific renewal—as measured by the number of patents issued to residents and scientific citations—increasingly concentrate in only a few places on earth, leaving the world looking more spiky than flat. Perhaps in the spaces between these ‘excellence regions’ (cf. Frenken et al., 2007), a level playing field does emerge, but certainly not between these and more regionally focused, less innovative regions (cf. Hoekman et al., 2008). This paper investigates the determinants of persistent resistance to bilateral and international trade patterns, as patents and citations do not necessarily measure, or properly value, economic interactions. In particular, distance is interpreted as a multidimensional concept that keeps countries apart and generates transaction costs for international trade. The contribution of this paper to the recent literature on determinants of trade is 3-fold. First, using gravity modelling, we not only focus on tangible barriers to trade (tariffs, transport costs) but also on intangible barriers to trade, such as institutional distance and cultural unfamiliarity between countries (cf. Loungani et al., 2002; Obstfeld and Rogoff, 2000). Second, we address the impact of these tangible and intangible barriers to trade on both the volume of bilateral trade and the existence of a bilateral trade relationship between pairs of countries. Third, by applying a Hurdle Poisson-Logit model, we simultaneously deal with a number of specification issues of the gravity model of trade that proved to be problematic in previous research: omitted country-specific variables’ bias, zero flows and the log-normality problem (see Santos Silva and Tenreyro, 2006). This paper is organized as follows. In the next section, we briefly review the gains of international trade in the context of comparative advantages. We also present some evidence from which it becomes clear that despite such advantages, countries trade far less than would be justified by exploitable economies of scale, or by differences in technological resources (the so-called ‘mystery of missing trade’). The third section analyses barriers to international trade in light of the multidimensional facets of distance, both tangible and intangible. The next sec-

A rather empty world 20 18 16 14 12 10 8 6 4 2 1820

1870

1913

1929

1950

1973

1998

Figure 1. World merchandise exports as share of GDP. Source: Maddison (2001), based on Table F-5 (p. 363).

The fact that worldwide GDP began to grow at a historically unprecedented rate at the same time implies that growth in trade was even more unprecedented, as it outpaced the growth of national economies (see Maddison, 2001). Growth in trade was stimulated by advances in transport and communication technologies such as the introduction of steamships, railroads, canal systems, telegraph and by policy changes in many countries toward openness. At the end of the 19th century, these advances led to the first wave of globalization (Crafts and Venables, 2003). As protectionism was revived in the first half of the 20th century, however, the Great Depression and both World Wars caused a slowdown in trade. Since the 1960s, acceleration in the growth of world trade has been renewed (see Figure 1), consistently outpacing worldwide growth in GDP (Baier and Bergstrand, 2001). In the European Union (EU), foreign trade rose by 730% in real terms over the period 1960–2000, and intra-EU trade rose by 1,200% over that period (see CPB, 2005, 152). This wave of international integration is related to several factors. Baier and Bergstrand (2001) convincingly showed that, respectively, the growth in GDP, the reduction in tariffs (spurred by multilateral agreements and organizations such as the General Agreement on Tariffs and Trade and its successor organization, the World Trade Organization (WTO))1 and declines in transportation costs were the main sources of trade growth. Further evidence shows that in recent decades, trade growth

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has also been related to increased outsourcing of production processes across borders (vertical specialization, or fragmentation of production); this represents a change in the organization of production that is linked to reductions in costs and tariffs for transportation and communication (Yi, 2003). Rapid growth of world trade in recent decades has resulted in substantial efficiency gains in those countries that have been able to participate in the change. Expressed in monetary terms, Hufbauer and Grieco (2005) state that an average American household enjoys annual benefits worth about $10,000 United States Dollars (USD) from ‘shrinking distances’ (due to advances in shipping an Information and Communication Technology (ICT)) and increasingly relaxed policy barriers to trade and investment in recent decades. Similarly, Badinger (2005) estimates that EU countries would have had 20% lower income per capita, on average, in the absence of international economic integration in the post-war era. Despite the fast pace of growth in world trade during the past several decades, barriers of physical geography, culture and economic policy still obstruct trade between some countries. Comparing theoretical expectations with actually observed trade patterns, it is clear that countries trade far less than would be expected, taking into consideration only the potential motivation to exploit scale effects caused by differences in resource endowments, technology and variety of goods produced among countries (Loungani et al., 2002). In an empirical analysis of trade patterns, Eaton and Kortum (2002) argue that if trade were frictionless, trade volume would be five times as great as presently observed. Trefler (1995) argues that home bias in consumer preferences may be an important factor in explaining the large deviations in actual trade patterns from those predicted by trade theory. Barriers to trade that are not easily measured may provide an explanation for home bias, consistent with widely documented evidence, starting with Bro¨cker (1984) and McCallum (1995) who assert that trade falls sharply when crossing international borders. See Feenstra (2004) for an overview on this so-called border effect, which shows that the effect is smaller, but still present, when correcting for country-specific

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Barriers to international trade: the multidimensional role of distance Persistent resistance to international trade indicates that trade costs remain high. To explain trade costs, we must inspect the barriers that lead to these costs.

Figure 2. Power law of bilateral trade (1996–2000).

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Trade barriers may be defined as obstacles in space or time that impede a smooth, frictionless transfer of products or information (cf. Nijkamp et al., 1990). In addition to policy-induced obstacles such as import tariffs and non-tariff barriers (e.g. import quotas and voluntary export restrictions), spatial separation imposes a multitude of barriers to trade. Previous research has shown that geographic distance between countries strongly affects trade, and that the impact of physical distance has not diminished appreciably over time (e.g. Berthelon and Freund, 2004; Disdier and Head, 2008). To illustrate the importance of distance to trade, consider how distance affects the typical export pattern for The Netherlands and the UK. For a set of selected countries, Figure 3A and B plots Dutch and UK bilateral exports against the geographic distance to each trading country (adjusted for the destination country level of GDP)2. The results clearly show that trade falls sharply as distance increases, after correcting for country size. They also illustrate the importance of physical distance for explaining the observed intensity of bilateral trade between countries. Geographic distance is therefore an important parameter to provide an explanation for resistance

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omitted variables. The ‘home-bias-in-trade’—one of the main puzzles in international economics identified by Obstfeld and Rogoff (2000)—illustrates that trade barriers are persistent and important determinants of the volume and patterns of trade across countries. Evidently, some countries face more resistance to international trade than other countries. Examining the distribution of international trade across country pairs (see Figure 2), it becomes evident that the majority of all trade is taking place between only a few countries (most notably, the G-7). In fact, the data trend toward a power-law distribution, where many country pairs have a low volume of trade (the long tail) and even more country pairs have no trade at all (the greater long tail). Although some countries are larger and richer than others, economic and demographic differences alone would not justify such disproportionality in the trade network.

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Figure 3. (a) Dutch bilateral exports (1996–2000). (b) British bilateral exports (1996–2000).

to trade. Distance causes transport costs to be incurred for the delivery of traded goods from the exporting country to the importing country. Geographic distance also serves as a proxy for the time required for shipment, which is especially important to the trade of perishable goods. Despite technological improvements in transport and ICT, an almost ‘immutable effect of physical distance’ on

trade is observed (cf. Poot, 2004). Furthermore, the sharp downward correlation between distance and trade is too large to be attributed exclusively to the effect of transport costs (Wonnacott, 1998). Recent evidence supports this conclusion, showing that geographic distance reduces international transactions in financial assets to a comparable extent for merchandise trade, even though transport 443

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Wincoop (2004, 692–693) suggest that transportation costs contribute the equivalent of a 21% tax, of which 9% is related to time costs. This leaves the equivalent of a 44% tax related to trade barriers, of which 8% is due to policy barriers. Anderson and Van Wincoop conclude that unobservable (i.e. inferred) border-related trade costs appear to be more important than transportation and trade policy costs in explaining the mark-up on prices arising from international trade barriers. Similarly, Deardorff (2004) and Obstfeld and Rogoff (2000) argue for the importance of unobservable trade costs in understanding patterns of trade. International trade patterns thus seem to depend more on unobservable trade costs related to intangible barriers to trade than on tangible barriers. Furthermore, intangible barriers to trade are important because they are ‘likely to affect the amount of trade generated by trade liberalization . with implicit consequences for the welfare and growth effects of trade liberalization’ (WTO, 2004, 176). Hence, closer empirical inquiry into intangible barriers to trade is needed. The remainder of this paper will operationalize three intangible dimensions of transactional distance, namely, institutional distance, cultural unfamiliarity and distance, as well as pure economic distance. We investigate the empirical effects of these barriers, both on the decision whether to trade and on patterns of observed bilateral trade, while controlling for the other distinct effects of spatial distance and tangible trade barriers. Beyond the question of whether these intangible dimensions of distance are important for explaining the absence of bilateral trade between many country pairs, and for understanding the variations in observed trade patterns, we are also interested in knowing whether the inclusion of these barriers in the analysis will open the black box of the large-distance trade decay effect typically correlated with spatial distance. This analysis will combine recent advances in empirical modelling of trade patterns, which corrects for various specification issues identified in the relevant literature. The next section introduces this model.

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costs are not a factor in the exchange of financial resources (Portes et al., 2001). In this respect, geographic distance stands as a proxy for a variety of barriers related to incomplete information and cultural unfamiliarity (Frankel, 1997; Loungani et al., 2002). Spatial separation comes with institutional, mental, psychological and cultural separation that may also be barriers to trade (e.g. Boschma, 2005; Van Houtum, 1998). A multidimensional concept of transactional distance (Obstfeld and Rogoff, 2000) would properly incorporate all barriers to trade that increase the effective distance between countries and impose transaction costs on bilateral trade. These barriers include both tangible and intangible barriers to trade. ‘Tangible barriers’ are directly observable in terms of their effect on the costs or quantity of trade. Examples are transport barriers and trade policy barriers (tariffs, and import and export quotas). Second, we can identify ‘intangible barriers to trade’, which cannot be measured or observed directly in terms of a monetary or quantitative restriction. Intangible barriers to trade include incomplete information barriers, cultural barriers and institutional barriers across countries (Anderson and Van Wincoop, 2004). Due to incomplete and asymmetric information, cultural differences and variations in institutional environments, it is more costly to successfully cooperate across national boundaries. Because international trade involves multiple legal and political systems, it is more complicated to secure property rights and commitment to contracts (Dixit, 2004; Rodrik, 2000). Both tangible and intangible barriers to trade are important to understanding variations in trade patterns. For the purpose of explaining bilateral trade patterns, it is critical to note that both categories of bilateral barriers increase trade costs and act as mark-ups on import prices. Anderson and Van Wincoop (2004, 692) summarized empirical evidence of the effect of trade barriers on trade prices in developed countries and they report a 74% tariff equivalent for all combined barriers to international trade. Although direct evidence is difficult to ascertain, Anderson and Van

A rather empty world

Analyzing patterns of international trade The gravity model of trade

b

Iij = K

b

Mi 1 Mj 2 b

dij 3

;

ð1Þ

where Iij is the interaction intensity, or the volume of trade between areas i and j; K is a proportionality constant; Mi is the mass of the country of origin; Mj is the mass of the country of destination; dij is the physical distance between the two countries; b1 is the potential to generate flows; b2 is the potential to attract flows, and b3 is an impedance factor, reflecting the rate of increase of the friction of physical distance. This basic model can easily be augmented to include other variables, such as whether countries i and j share borders, speak the same language or are members of the same trading bloc. Taking logarithms on both sides of the equation, the multiplicative form (1) can be converted into a linear stochastic form to arrive at the following testable Equation (2):

where eij is assumed to be independent and identically distributed. This specification (2) is better known in the trade literature as the conventional, unconstrained log-normal gravity model.

Specification of the gravity equation Recent formulations of the gravity model that derive from general equilibrium modelling of bilateral trade patterns state that the traditional specification of the gravity model suffers from omitted-variable bias because it does not take into account the role of relative prices on trade patterns (Eaton and Kortum, 2002; Feenstra, 2004). As shown by Anderson and Van Wincoop (2003), bilateral trade intensity not only depends on bilateral trade costs (affected by spatial distance, language differences, trade restrictions and the like) but also on GDP-share weighted multilateral trade costs (affecting the prices of imported competing goods in the destination country and exported opportunities in the origin country). Omitting these terms from the specification may result in an omitted-variable bias for the remaining parameter estimates in the gravity model. Essentially, this extension of the gravity model corresponds to earlier literature in regional science, which motivates singly or doubly constrained gravity models that satisfy the constraints on total country-specific inflows and/or outflows in a spatial system (see Bro¨cker, 1989; Fotheringham and O’Kelly, 1989). As shown by Anderson and Van Wincoop (2003) and Feenstra (2004), and in analogy with the regional science literature, a fixedeffects estimation of the gravity equation is in accordance with the theoretical concerns about the correct specification of the model and it also yields consistent parameter estimates. In the cross-section setting, this implies including country-specific exporter and importer dummies in the specification. Outside of the trade theory literature, Egger (2005) and Matyas (1998) made a similar point, motivated by econometric theory. Another strand of research has focused on the deficiencies of the log-normal formulation of the 445

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This section introduces an empirical gravity model that is used to investigate the effect of tangible and intangible trade barriers on bilateral trade patterns. The model simultaneously takes into consideration important specification issues that have emerged in the recent literature on gravity modelling of trade: omitted-variable bias, lognormality and zero flows. Spatial interaction patterns, such as international trade, can be predicted and elucidated in analogy with Newton’s law of universal gravitation. The gravity model—which dates back to the work of Isard (1954), Tinbergen (1962) and Ullman (1954)—hypothesizes that the gravitational force between two objects is positively dependent on the mass of the objects and negatively dependent on the physical distance between them. Over the years, this model has become popular in international economics for analysing the pattern of trade flows between countries (Eichengreen and Irwin, 1998).3 In its most elementary form, the gravity model can be expressed by Equation (1):

ln Iij = ln K + b1 ln Mi + b2 ln Mj b3 ln dij + eij ; ð2Þ

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This Poisson specification of the gravity model deals adequately with the problems highlighted above. First, as the linking function is log-linear instead of log-log, the Poisson regression model generates estimates of Iij and not of ln Iij, thereby avoiding under-prediction of large trade flows or of the total volume of trade. In addition, because the Poisson regression model is estimated by a maximum likelihood method, the estimates are adapted to actual data, which means that the sum of the predicted values is virtually identical to the sum of the input values. Second, Poisson regression estimates operate consistently in the presence of heteroscedasticity and are reasonably efficient, especially for large samples (King, 1988). Third, due to its multiplicative form, the Poisson specification provides a natural way to deal with zero-trade flows. We are not only interested in explaining the ‘volume’ of trade but also in explaining the ‘absence’ of trade, assuming that the two are, in fact, different processes. To do so, we employ a Poisson-Logit PML Hurdle fixed-effects specification (Mullahy, 1986), which models two different processes, namely: (i) whether trade between countries takes places and (ii) given that there is trade between countries, the magnitude of the volume of trade.

Tangible and intangible barriers to trade Data To determine to what extent tangible and intangible barriers obstruct trade between countries, we focus on trade patterns from 1996 to 2000, for a set of 138 countries, listed in Table A1 (the World Trade Database, based on the UN COMTRADE database, made consistent by Feenstra et al., 2005). Excluding domestic trade, this leaves 18,906 (138 3 137) possible individual trade flows for the 138 countries of origin (exporters) and 138 countries of destination (importers). We use the average yearly bilateral exports for 1996–2000, expressed in millions of dollars, as indicators of the volume of trade such that each country pair yields two observations, one as exporter and one as importer. We use reported

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gravity model and estimation by the use of ordinary least squares, which has until recently been the most common way to investigate bilateral trade flow in international economics (Santos Silva and Tenreyro, 2006). It is argued, however, that this results in three serious problems with such estimation (Flowerdew and Aitkin, 1982).4 First, the logarithmic transformation can lead to the underprediction of large trade flows and the underprediction of the total trade flow. Second, when there are a large number of cases with small observed and expected flows, the homoscedasticity condition of equal variances of the error terms can be violated. Thirdly, the log-normal model cannot handle trade flows with the value zero because the logarithm of zero is undefined. Traditionally, the most common strategies for circumventing the ‘zero problem’ in the analysis of trade flows are as follows: omit all trade flows with value zero or arbitrarily add a small positive number (usually 0.5 or 1) to all trade flows in order to take the logarithm (Linders and De Groot, 2006). However, by deleting all zero-valued flows, important information on low trade levels is omitted from the model (Eichengreen and Irwin, 1998). More specifically, when zero values are non-randomly distributed, results may be biased. Likewise, the strategy of substituting zeros with a small positive constant for all trade flows is inadequate. King (1988) demonstrates that any desired parameter estimate can be generated by adjusting the size of the constant. To overcome these problems, we make use of a Poisson pseudo-maximum likelihood (PPML) fixed-effects model (also known as the Anderson and Van Wincoop specification). Applying this Poisson specification to the gravity model of trade, it can be hypothesized that the observed volume of trade between countries i and j has a Poisson distribution with a conditional mean ðlÞthat is a function of the independent variables (the spatial, economic and cultural distances between countries). More formal treatment of the Poisson specification of the gravity model of trade can be found in Burger et al. (2008), Flowerdew and Aitkin (1982) and Santos Silva and Tenreyro (2006).

A rather empty world ity model covariates made available online by CEPII (see http://www.cepii.fr). The determination of whether pairs of countries take part in common regional integration agreements has been made on the basis of Organisation for Economic Co-operation and Development (OECD) data about major regional integration agreements. The FTA dummy indicates whether the importing and exporting countries are both members of the same free trade area. Because information is only available for a limited sample (7173 country pairs), bilateral tariffs and trade restrictiveness are only included in the sensitivity analyses. ‘Bilateral tariffs’ are measured as trade-weighted applied bilateral importer tariffs. Tariff data are from the TRAINS tariff database (adapted for the World Bank WITS database). We include the commonly used logarithmic specification for the bilateral importer tariff (ln(1 + tariffij)). The ‘low tariff and non-tariff barriers dummy’ is based on overall trade restrictiveness indices (for 2000) by Kee et al. (2008). For 96 out of 138 countries in our sample, information on these dimensions was available. The dummy takes the value of one if (i) the importer generally imposes low tariff and non-tariff barriers and the exporter generally faces low tariff and nontariff barriers, (ii) the importer generally imposes average tariff and non-tariff barriers and the exporter generally faces low tariff and non-tariff barriers or (iii) the importer generally imposes low tariff and non-tariff barriers and the exporter generally faces average tariff and non-tariff barriers.

Table 1. Summary statistics of variables in the gravity equation

Yearly average volume of trade (1996–2000) Physical distance (ln) Contiguity dummy Common language dummy Common history dummy FTA dummy Institutional distance Economic distance Sectoral complementarities Cultural distance Bilateral tariff Low tariff and non-tariff barriers dummy

Mean

Standard deviation

Minimum

Maximum

N

270.1 8.685 0.012 0.132 0.023 0.054 2.014 2.320 2.000 2.000 1.592 0.166

2884 0.800 0.140 0.339 0.151 0.226 1.931 1.727 1.590 1.582 1.140 0.372

0 4.007 0 0 0 0 0.002 0 0.004 0 0 0

189,000 9.897 1 1 1 1 11.14 10.05 15.71 11.88 5.142 1

18,906 18,906 18,906 18,906 18,906 18,906 18,906 18,906 18,906 8372 7173 9120

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exports rather than reported imports because the former provides better coverage. As can be seen from the standard deviation (2,834) in relation to the mean, and also from the skewness (34.99) and kurtosis (1,706), the volume of trade across trade flows strongly deviates from normal. In fact, over 50% of all bilateral trade flows have a zero value. The explanatory variables entered in the gravity model include bilateral data from various sources related to the different dimensions of distance between countries. We differentiate between the tangible (physical distance, trade agreements and tariffs) and intangible (cultural, historical, and institutional distance, economic distance and differences in the production structure) barriers to trade. Table 1 provides summary statistics of the variables used in the model. For tangible barriers to trade, we include physical distance, contiguity, free trade agreements (FTAs) and tariff and non-tariff barriers as explanatory variables.5 ‘Physical distance’ is measured as the greatcircle (as the crow flies) distance between countries, using the capital of each country as its centre of gravity. Thus, the distance between the two centres of gravity of neighbouring countries is likely to overestimate the average distance of trade between them. Therefore, we include a ‘contiguity dummy’ to control for measurement error in calculating the relevant distances. If two countries are adjacent, the contiguity dummy takes the value of one. These data have been taken from the extensive set of grav-

Linders, Burger and van Oort

 2 6 Iki  Ikj 1X IDij = ; 6 k =1 Vk

ð3Þ

where Iki indicates country i’s score on the k-th dimension and Vk is the variance of this dimension across all countries. Institutional distance reflects the fact that a higher difference in institutional effectiveness raises adjustment costs and may decrease bilateral trust at the same time (De Groot et al., 2004).6 Traders from countries with very different levels of guarantee for property rights and the enforceability of contracts are frequently unfamiliar with the other country’s formal or informal procedures for doing business. Economic distances are small when countries have similar levels of per capita income, similar preferences and a similar output mix (Linder, 1961). Apart from the direct effects of similar preferences and output structure increasing bilateral intra-industry trade, there is an additional effect related to transactional distance and intangible barriers. Loungani et al. (2002) argue that trade networks that form to reduce information barriers in trade may be characterized by economies of scale due to network externalities. If networks form between selected, similar countries for reasons described by Linder’s (1961) theory of intra-industry 448

trade, the negative effect of economic distance may be further increased. The Linder (1961) effect of ‘economic distance’ is reflected in the gravity equation by including the absolute difference between the logarithms of per capita GDP as an explanatory variable. The data on per capita GDP come from the World Bank WDI database (in current USD). Note that, in accordance with the Heckscher–Ohlin theorem, we would expect a negative correlation: countries that are economically distant would trade more because they produce different goods and are specialized in different sectors. Thus, economic distance can be beneficial, providing comparative advantages. Ultimately, the balance will be decided by the relative importance of information barriers, intra-industry trade versus comparative advantages, as well as inter-industry trade. To more precisely capture the traditional factor proportions theory of trade, we also include differences in production structure in our model, which we label ‘sectoral complementarities’. These are estimated in a similar fashion as institutional distance, but with the Kogut–Singh index (Equation 3) estimated using the differences in share from six broad sectors in the total economy of countries i and j (agriculture, manufacturing, construction, wholesale, transport and services). Information on the production structure of the countries used in the sample was obtained from the UNCTAD database. Cultural familiarity between countries reduces information asymmetries and increases bilateral trust. On the other hand, cultural distance—in terms of norms and values—decreases trust and increases adjustment costs in exchange (Parkhe, 1991; Elsass and Veiga, 1994). Cultural barriers to trade are measured by language and common history dummies. To assess whether two countries have the same official language, we used the CEPII database. A ‘language dummy’ variable reflects whether or not two countries have a common language. The ‘history dummy’ takes the value of one if the two countries had, or have, a colonial relationship or if they were ever part of the same country. This variable is also constructed on the basis of CEPII data. In the international business literature (e.g. Barkema and Vermeulen, 1997), the cultural

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We include common language and history dummies, institutional and economic distance, sectoral complementarities and cultural distance as explanatory variables to reflect intangible dimensions of transactional distances. Our measure of ‘institutional distance’ is based on Kaufmann’s six dimensions of governance quality (Kaufmann et al., 2004). These dimensions include voice and accountability, political stability, effectiveness of government, quality of regulation, rule of law and control of corruption. All these indicators are constructed on the basis of factor analysis, and reflect different aspects of the quality of governance. A more detailed description of these dimensions can be found in Kaufmann et al. (2004). We measure the institutional distance between country pairs by means of the index developed by Kogut and Singh (1988):

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Empirical results: disentangling the probability and magnitude of trade This paper extends the literature on trade barriers by including measures of two dimensions of intangible barriers, specifically, institutional and cultural distance in a gravity model consistent with the most recent theoretical insights, which controls for a host of other tangible and intangible dimensions of transactional distance. Before we turn to discuss our estimation results, Table 3 presents a selection of results found in earlier studies on various dimensions of trade barriers. We distinguish four barriers typically controlled for in gravity studies of trade patterns: physical distance, language barriers, historical barriers and trade policy barriers. The table presents estimates (where available) on variables that represent these barriers. The table intends to put our primary estimation results into perspective. We do not, however, aim to provide an exhaustive review of the gravity literature. For such overviews, see Oguledo and MacPhee (1994), Frankel (1997), and Anderson and Van Wincoop (2004). We restrict ourselves to one representative estimate per study. We consider the selected studies representa-

tive for some of the issues dealt with in this paper (estimation, types of barriers considered), and comparable to our paper in their focus on gravity parameter estimates. This means, for example, that we do not include key studies like Anderson and Van Wincoop (2003, 2004), since they focus on inferring tariff equivalents of total border barriers from regression estimates on border dummies. We have already discussed some of those relevant findings earlier in this paper. Specification (1) in Table 4 uses the PPML estimator introduced by Santos Silva and Tenreyro (2006). Overall, it can be inferred that—in accordance with the trade literature—most variables have the expected sign and are highly statistically significant. With respect to the tangible barriers to trade, trade volume decreases with geographical distance: an increase in distance by 1% leads to a decrease in the volume of trade by 0.55%. In addition, adjacent countries trade substantially more than nonadjacent countries (by 99%) while an FTA raises trade between countries by 72%. There is somewhat weaker evidence with respect to the intangible barriers to trade. Having the same language and institutional distance do not affect the volume of trade by a statistically significant amount. Having a common history positively affects the volume of bilateral trade. Nevertheless, the observed effect (i.e. 26% higher volume of trade) is much weaker when compared to the effect of the tangible barriers on trade volume. With respect to economic distance and sectoral complementarities, we find confirmation of the Heckscher–Ohlin factor proportions theorem and no evidence for the Linder hypothesis. Differences in GDP per capita, per country do not significantly affect the volume of bilateral trade. However, countries specializing in different economic sectors substantially trade more with each other. Compared to the estimates of distance decay in the trade literature, as shown in Table 3, our own estimate of the distance effect is a bit lower, in absolute terms. For example, our estimate in Specification (1) can be compared to the estimates in Santos Silva and Tenreyro (2006), who also use the PPML estimator, which turns out to be lower. Also, compare our estimates of around 0.55 to the 449

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distance between countries is often more directly assessed through the dimensions of national culture identified by Hofstede (1980, 2001). Analyzing survey data obtained from 116,000 IBM employees in 40 countries, Hofstede identified four dimensions in which national cultures differ: power distance, uncertainty avoidance, individualism/collectivism and masculinity/femininity. Hofstede assigned each country a score between 0 and 100 for each cultural dimension; this was used to indicate how people from different cultures feel about each societal issue. The ‘Hofstede cultural distance’ between countries was again computed using the index from Equation (3). Since the intangible barrier measures intend to capture aspects other than tangible barrier measures, the variables should show sufficient independent variation. Table 2 shows that this is indeed the case—the correlations between the explanatory variables are always below 0.35 (in absolute terms).

Linders, Burger and van Oort

450 Table 2. Correlations of the intangible barrier variables Physical distance Physical distance

Contiguity

Language

History

Free Trade Agreement

Institutional distance

Economic distance

Sectoral complement.

Cultural distance*

Bilateral tariff*

Low trade restrictiveness*

1.0000

Contiguity

0.3453

1.0000

Language

0.1400

0.1496

1.0000

History

0.1989

0.3247

0.2340

Free Trade Agreement

0.2788

0.3145

0.1679

0.1809

1.0000

Institutional distance

0.0617

0.0789

0.0119

0.0027

0.0836

1.0000

Economic distance

0.0813

0.0502

0.0020

0.0463

0.0922

0.2111

1.0000

Sectoral complement.

0.0637

0.0680

0.0434

0.0432

0.1027

0.1643

0.1043

1.0000

Cultural distance*

0.0310

0.0703

0.1338

0.0023

0.0057

0.3847

0.0981

0.1001

1.0000

Bilateral tariff*

0.2415

0.1221

0.0421

0.1139

0.2823

0.2495

0.1221

0.0971

NA

1.0000

Low trade restrictiveness*

0.0139

0.0154

0.0415

0.0040

0.0525

0.0570

0.0442

0.0047

NA

NA

1.0000

*

Calculated for the representative sample.

1.0000

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Table 3. Representative estimates from gravity model research on trade barriers Study

Distance/border

FTA

Language

History

Notes

Estimate

Frankel (1997)

0.61**/0.57**

0.03 to 1.97**

0.57**

NA

Table 5.1, column 1

Hutchinson (2002)

0.99**/

NA

0.13** and 0.17**

NA

Loungani et al. (2002)

0.78**/

NA

0.18**

NA

Me´litz (2002)

0.6**/0.73**

1.12**

0.73**

0.75 and 2.02**

Guiso et al. (2004)

0.61**/0.65**

NA

0.58**

0.34

Santos Silva and Tenreyro (2006) Baier and Bergstrand (2007)

0.75**/0.37**

0.38**

0.38**

0.08

1.01**/0.38**

0.27** to 0.46**

0.58**

NA

Helpman et al. (2008)

0.81**/0.87**

0.12

0.03

0.85**

Separate trade blocs reported English as first and second language; US trade with rest of the world Controlling for bilateral telephone traffic (affects language and distance downward) Distance controls for remoteness; additional indicators of language distance not reported; history refers to political union and colonial link, respectively Country fixed effects; trade at industry level (common parameters); language is percentage of people speaking same language; history is common origin of law Poisson regression with country fixed effects Panel estimation; second FTA coefficient from panel model with bilateral, and country-year fixed effects, correcting for endogeneity of FTAs Probit-non-linear least squares selection-regression model for zero flows and non-zero trade; country fixed effects

Table 1, column 1

Table 1, column 2

Table 3, column 5

Panel A, column 5

Table 5, column 6 Table 4, column 2 Second FTA coefficient: Table 5, column 1

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451

** and * indicates statistical significance at 1% and 5%. Reported results reflect effect on trade volumes. Unless otherwise indicated, ‘language’ refers to a 0/1 indicator regressor variable on common language and ‘history’ to a 0/1 indicator variable on colonial ties.

A rather empty world

Table 2, column 3

Linders, Burger and van Oort Table 4. PPML and Hurdle Poisson-Logit PML on average yearly trade from 1996 to 2000 Hurdle Poisson-Logit PML (2)

Tij

Logit

Tij > 0

0.557** (0.031) 0.686** (0.073) 0.544** (0.053) 0.113 (0.061) 0.231** (0.082) 0.000 (0.015) 0.010 (0.015) 0.173** (0.038) 18,906 YES YES 8.24 3 105 1.65 3 106

1.125** (0.062) 0.304 (0.273) 0.191 (0.205) 1.287** (0.111) 0.016 (0.082) 0.083** (0.026) 0.314** (0.061) 0.024 (0.042) 18,906 YES YES

0.550** (0.031) 0.687** (0.073) 0.556** (0.053) 0.109 (0.061) 0.230** (0.079) 0.013 (0.015) 0.032* (0.015) 0.173** (0.038) 9128 YES YES 8.05 3 105 1.61 3 106

Robust standard errors between parentheses.

average estimate of 0.9 found by Disdier and Head (2008) in a meta-analysis of a set of findings across the literature (quantitative literature review). Our estimate, however, is within the range reported around this average. As argued previously, the distance effect in trade not only reflects tangible transport barriers but also provides an indication of the importance of intangible trade barriers. Albeit lower than some of the representative findings from previous studies, the estimated distance decay remains economically and statistically substantial, even after controlling for a host of tangible and intangible barriers that may be correlated to physical distance. Since the pairwise correlation between the different indicators is low-to-moderate, physical distance still may be expected to have a clear, independent effect on bilateral trade, even after extending the gravity equation. Specification (2) in Table 4 estimates a Hurdle Poisson-Logit PML, in which two different processes are modelled simultaneously, namely, (i) whether or not trade between countries takes place and (ii) given that there is trade between countries, the volume of trade. This splitting of two processes turns out to be important in describing and explaining trade patterns, since relations come to the fore that otherwise are hidden in the un-split estima452

tion.7 For example, geographical proximity is not only important in explaining the volume of trade but it is also essential in explaining selection into trade. This effect is captured most clearly by the physical distance variable. Every 1% increase in distance multiplies the probability of trade by a factor of about 0.99. Although the direction of most of the observed effects is the same for the two different processes, there are also some apparent differences— both between the two processes and between the processes compared to the single equation estimation. Institutional distance and the absence of a common language are important determinants for the absence of trade. Sharing a common language increases the probability of trade by a factor of 2.6. If we decrease institutional distance by one standard deviation (see Table 1), the probability of trade is multiplied by a factor of 1.17.8 However, countries that do trade, and those that have a short institutional distance, do not trade substantially more than trading countries that lack institutional similarities. The effect of common language on bilateral trade remains positive, but statistically insignificant at 5%. On the contrary, we find that countries that trade under an FTA and those that share a common history trade substantially more than trading partners without these characteristics.

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Physical distance Contiguity dummy FTA dummy Language dummy History dummy Institutional distance Economic distance Sectoral complementarities Observations Importer fixed effects Exporter fixed effects 2 log likelihood Akaike’s information criterion

PPML (1)

A rather empty world Table 5. Robustness tests for cultural distance, bilateral tariffs and trade restrictiveness PPML (4)  Tij

PPML (5)  Tij

0.551** (0.032) 0.703** (0.073) 0.056 (0.062) 0.156 (0.080) 0.548** (0.053) 0.029 (0.015) 0.009 (0.016) 0.191** (0.042) 0.031* (0.016)

0.513** (0.031) 0.647** (0.068) 0.170** (0.060) 0.132 (0.075) 0.416** (0.063) 0.030* (0.014) 0.022 (0.013) 0.201* (0.045)

0.624** (0.038) 0.653** (0.075) 0.060 (0.067) 0.353** (0.087) 0.525** (0.056) 0.000 (0.017) 0.029* (0.015) 0.196** (0.043)

0.296** (0.059) 0.589** (0.189) 8372 YES YES 6.79 3 108 1.36 3 109

However, FTAs and historical linkages do not affect the ‘probability’ of trade. Concerning economic distance, we find interesting, though seemingly contradictory results for the selection into trade and trade volume. In line with the Heckscher–Ohlin theorem, we find that countries with large differences in per capita GDP are more likely to trade. However, in line with the Linder hypothesis and information network effects, countries that do trade and those that have more similar GDP per capita, trade more. The information network-externality effect in trade, captured by the economic distance variable, thus receives partial support. Differences in production structure leads to increased trade, as indicated by the sectoral complementarities variable. Controlling for the factor proportions effect, network effects appear to increase the concentration of bilateral trade among countries with similar levels of per capita income. However, they do not inhibit the formation of bilateral trade relationships per se. The positive effect of economic distance on the probability of trade may be attributed to factor proportions theory, although the sectoral complementarities measure does not show an independent effect to support this. Specifications (3)–(5) in Table 5 extend the specification with cultural distance, tariff barriers and

7173 YES YES 4.33 3 108 8.67 3 109

9120 YES YES 5.04 3 108 1.01 3 109

trade restrictiveness, respectively, as well as test for the robustness of the results found in Specification (1). According to Specification (3), cultural distance—in terms of norms and values— negatively affects the volume of trade between countries. This measure of cultural distance, though widely used in international business studies on foreign direct investment, has only recently been applied to international trade (see Linders et al., 2005). Previous findings were often insignificant or even positive. Using PPML fixed-effects regression, we find that bilateral trade decreases by 5% on average if we increase cultural distance by one standard deviation. Following Specification (4), bilateral tariffs statistically reduce the volume of bilateral trade. A 1% increase in the applied bilateral tariff rate decreases the volume of trade between countries by 0.25%. Using a different indicator of trade policy—trade restrictiveness—Specification (5) indicates that low tariff and non-tariff barriers increase the volume of trade between countries. Note that the effect of the FTA dummy is only slightly lower when including these measures of trade policy. This suggests that membership in a trade agreement primarily benefits trade by lowering non-tariff and intangible barriers (by harmonizing regulation and strengthening (sometimes pre-existing) trade 453

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Physical distance Contiguity dummy Language dummy History dummy FTA dummy Institutional distance Economic distance Sectoral complementarities Cultural distance Bilateral tariff Low trade restrictiveness (tariff and non-tariff barriers) Observations Importer fixed effects Exporter fixed effects 2 log likelihood Akaike’s information criterion

PPML (3)  Tij

Linders, Burger and van Oort

Discussion and conclusions According to Thomas Friedman’s book, the world is flat: technological progress has steadily eroded the economic importance of geographic location: everyone is now a player, no matter where on earth he or she resides. Although Friedman warns us ‘not to get caught up in measuring globalization patterns by purely economic phenomenon like trade statistics, because these miss the impact of change’ (p. x), we rebelled against this warning, and found that, in agreement with Florida (2005), economic activity clusters and fosters in only a few locations, at least with respect to trade. In particular, we interpret distance as a multidimensional concept that drives a core of already trading countries together, but pushes other countries apart, on average generating positive overall transaction costs for international trade. We applied the idea of transaction costs to a number of dimensions of intangible barriers between countries, namely, spatial distance, institutional distance and cultural differences between countries. We introduced an empirical model (PPML) to analyse the effect of these trade barriers on bilateral trade patterns. This model is designed to deal specifically with several commonly acknowledged specification problems, namely those of omitted-variable bias, log-normality and zero flows. As a complementary extension to the recent literature on determinants of trade, we introduce a hurdle Poisson model to deal with the impact of transactional distances on the volume of bilateral trade, especially in the absence of a bilateral trade relationship between pairs of countries. 454

Using these models, some important conclusions came to light. First, the results on intangible barriers were mixed. Institutional distance does not appear to have a statistically significant effect on trade. The negative effect of economic distance—capturing the Linder effect, and related information network externalities—is partially supported. Cultural distance was found to have a negative effect on trade. We control for more conventional measures of intangible barriers, such as language and historical ties, which showed results in line with the literature, although the importance of common language did not receive strong statistical support. More traditional explanations of trade patterns—such as tangible policy barriers (captured by an FTA variable and bilateral import tariffs) as well as comparative advantages and factor proportion differences (captured by our measure of sectoral complementarities)—appear to remain at least as important for explaining trade patterns, statistically speaking, as do these additional measures of intangible barriers. Perhaps the most traditional measure of trade barriers—physical distance—stands firm, in spite of many extensions of the gravity equation. This may imply that tangible transport barriers remain important, or alternatively, that our selected measures for intangible barriers do not capture all intangible cultural and informational barriers related to transactional distance. Second, modelling two different processes simultaneously (namely, whether or not trade between countries takes place, and given that there is trade between countries, the magnitude of this trade) adds considerable value to the estimation. Different explanations are attached to the propensity to trade and the magnitude of trade. Institutional distance and the absence of a common language, for instance, appear to be important determinants for the absence of trade (probability effect). Trading countries that have a FTA and that share a common history trade substantially more than those that do not have these things in common (magnitude effect). Physical distance is an important conditioning factor for both stages of the trade decision. The world is not flat. Bilateral trade is still unevenly and disproportionately distributed across countries. Dealing carefully with model specification

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networks). Overall, research results suggest that most explanatory variables are robust with respect to cultural distance, bilateral tariffs and trade restrictiveness. Introducing import tariffs to the model has a more notable effect, however. The effects of shared language and institutional distance are now positive and significant. A possible explanation for these discrepancies is sample selection, as there is very little information available on applied bilateral tariffs for countries that do not trade with one another.

A rather empty world issues related to omitted-variable bias, log-normality and zero flows, we show that various tangible and intangible dimensions of transactional distance are crucial to interpreting differences between countries in both the propensity and magnitude of bilateral trade. This analysis helps to distinguish why parts of the world remain empty and why some countries do not live up to their trade expectations, even once they are linked up to the trade network.

are not directly observable in the market, such as contracting costs, monitoring costs, regulatory costs, expropriation risks and other uncertainties and adjustment costs related to differences in the quality of the institutional settings. 7

8

Endnotes 1

The process of multilateral liberalization has co-existed with many initiatives for closer regional economic integration, which has resulted in many preferential trade agreements, free trade areas (such as the North American Free Trade Agreement) and customs unions (like the EU). For an overview, see Frankel (1997).

The coefficients on institutional and cultural distance are semi-elasticities. To interpret the impact of institutional distance on the probability of trade suggested by the estimate in specification (2), we assume that institutional distance decreases by one standard deviation. The probability of trade is then multiplied by a factor e0.08331.931 where 1.931 is the standard deviation reported in Table 1.

2

Countries whose exports equal zero have been excluded from the figure. Bilateral trade is scaled by the GDP of the importing country, because the potential for trade is higher between the Netherlands and any given foreign economy that is larger. 3

See Anderson and Van Wincoop (2004) and Feenstra (2004) for the theoretical rationale behind the gravity model of trade.

4

A more detailed discussion of these issues can be found in Burger et al. (2008).

5

However, note that physical distance also serves as a proxy for various intangible barriers to trade, related to things such as cultural unfamiliarity and incomplete or asymmetric information. We try to capture cultural barriers by including specific variables, but do not include direct measures of transport barriers. Though direct measurement of transport barriers has been attempted in the literature—either by studying variation in cif/fob price ratios or by studying port efficiencies and actual shipping trajectories rather than simple distances (distance as the crow flies)—these approaches remain limited by data availability and data quality (see Frankel, 1997). 6

Though conceptually helpful for highlighting the importance of less tangible dimensions of trade barriers, it is sometimes hard to separate tangible and intangible barriers empirically. Institutional barriers are identified as intangible barriers to trade, although in principle some of the costs related to institutions are directly observable (e.g. legal costs). Most of the transaction costs related to institutions

References Anderson, J. E. and Van Wincoop, E. (2003) Gravity with gravitas: a solution to the border puzzle. American Economic Review, 93: 170–192. Anderson, J. E. and Van Wincoop, E. (2004) Trade costs. Journal of Economic Literature, 42: 691–751. Badinger, H. (2005) Growth effects of economic integration: evidence from the EU member states. Review of World Economics, 141: 50–78. Baier, S. and Bergstrand, J. H. (2001) The growth of world trade: tariffs, transport costs and income similarity. Journal of International Economics, 53: 1–27. Baier, S. and Bergstrand, J. H. (2007) Do free trade agreements actually increase members’ international trade? Journal of International Economics, 71: 72–95. Barkema, H. G. and Vermeulen, F. (2001) What differences in the cultural backgrounds of partners are detrimental for international joint ventures? Journal of International Business Studies, 28: 845–864. Berthelon, M. and Freund, C. (2004) On the Conservation of Distance in International Trade, World Bank Policy Research Working Paper, no. 3293. Washington, DC: World Bank. Boschma, R. A. (2005) Proximity in economic interaction. Regional Studies, 39: 41–47. Bro¨cker, J. (1984) How do international trade barriers affect interregional trade? In A.E. Andersson, W. Isard, and T. Puu (eds.). Regional and Industrial Development Theories, Models and Empirical Evidence, pp. 219–239. Amsterdam, North–Holland.

455

Downloaded from http://cjres.oxfordjournals.org/ at Institute of Social Studies on May 9, 2013

This paper is not the first to introduce an equation for explaining selection into trade. There are a limited number of studies that introduce a probit, or two-stage, model. See, for example, Hillberry (2002) and Helpman et al. (2008). These studies, however, do not use Poisson regression to deal with the problems of log-normality, and some do not include country fixed effects.

Linders, Burger and van Oort

456

Working Paper 11040. Cambridge, MA: National Bureau of Economic Research. Florida, R. (2005) The world is spiky. Globalization has changed the economic playing field, but hasn’t levelled it, The Atlantic Monthly, October 2005 pp. 48–51. Flowerdew, R. and Aitkin, M. (1982) A method of fitting the gravity model based on the Poisson distribution. Journal of Regional Science, 22: 191–202. Fotheringham, A. S. and O’Kelly, M. E. (1989) Spatial Interaction Models: Formulations and Applications. Dordrecht, Germany: Kluwer Academic Publishers. Frankel, J. A. (1997) Regional Trading Blocs in the World Economic System. Washington, DC: Institute for International Economics. Frenken, K., Hoekman, J. and Van Oort, F. G. (2007) Towards a European Research Area. Rotterdam and The Hague: NAi Publishers. Friedman, T. L. (2005) The World is Flat: A Brief History of the Twenty–First Century. New York: Farrar, Strauss and Giroux. Guiso, L., Sapienza, P. and Zingales, L. (2004) Cultural biases in economic exchange. Cambridge, MANBER Working Paper, no. 11005. Helpman, E. and Krugman, P. (1985) Market Structure and Foreign Trade. Cambridge, MA: MIT Press. Helpman, E., Melitz, M. and Rubinstein, Y. (2008) Estimating trade flows: trading partners and trading volumes. Quarterly Journal of Economics, 123: 441– 487. Hillberry, R. H. (2002) Aggregation bias, compositional change, and the border effect. Canadian Journal of Economics, 35: 517–530. Hoekman, J., Frenken, K. and Van Oort, F. G. (2008) The geography of collaborative knowledge production in Europe. CESPRI Working Papers 214. Hofstede, G. (1980) Culture’s Consequences: International Differences in Work–Related Values. Beverly Hills, CA: Sage. Hofstede, G. (2001) Culture’s Consequences: Comparing Values, Behaviors, Institutions, and Organizations across Nations. Thousand Oaks, London, New Delhi: Sage Publications. Hufbauer, G. C. and Grieco, P. L. E. (2005) The Payoff from Globalization, Washington Post, July 6, 2005. Hutchinson, W. K. (2002) Does ease of communication increase trade? Commonality of language and bilateral trade. Scottish Journal of Political Economy, 49: 544– 556. Isard, W. (1954) Location theory and trade theory: short run analysis. Quarterly Journal of Economics, 68: 305–322. Kaufmann, D., Kraay, A. and Mastruzzi, M. (2004) Governance matters III: governance indicators for 1996, 1998, 2000, and 2002. World Bank Economic Review, 18: 253–287.

Downloaded from http://cjres.oxfordjournals.org/ at Institute of Social Studies on May 9, 2013

Bro¨cker, J. (1989) Partial equilibrium theory of interregional trade and the gravity model. Papers of the Regional Science Association, 66: 7–18. Burger, M. J., Linders, G. J. M. and Van Oort, F. G. (2008) On the Specification of the Gravity Model of Trade: Zeros, Excess Zeros and Quasi–Poisson Estimation. Working Paper, Erasmus University Rotterdam. Cairncross, F. (1997) The Death of Distance. How the Communications Revolution Will Change our Lives. London: Texere Publishing. CPB (Centraal Planbureau). (2005) Macroeconomische Verkenningen 2006 (Macroeconomic Outlook 2006). The Hague: CPB. Crafts, N. and Venables, A. J. (2003) Globalization in history: a geographical perspective. In M. Bordo, A. Taylor, and J. Williamson (eds.). Globalization in Historical Perspective, pp. 323–364. Chicago: University of Chicago Press. Coe, D. T. and Helpman, E. (1995) International R&D spillovers. European Economic Review, 39: 859–887. Deardorff, A. V. (2004) Local comparative advantage: trade costs and the pattern of trade, Research Seminar in International Economics Discussion Paper, University of Michigan, no. 500, Ann Arbor. De Groot, H. L. F., Linders, G. J. M., Rietveld, P. and Subramanian, U. (2004) The institutional determinants of bilateral trade patterns. Kyklos, 57: 103–123. Disdier, A. C. and Head, K. (2008) The puzzling persistence of the distance effect on bilateral trade. Review of Economics and Statistics, 90: 37–41. Dixit, A. K. (2004) Lawlessness and Economics: Alternative Modes of Governance, The Gorman Lectures in Economics. Princeton and Oxford: Princeton University Press. Dixit, A. K. and Stiglitz, J. E. (1977) Monopolistic competition and optimal product diversity. American Economic Review, 67: 297–308. Eaton, J. and Kortum, S. (2002) Technology, geography, and trade. Econometrica, 70: 1741–1779. Eichengreen, B. and Irwin, D. A. (1998) The role of history in bilateral trade flows. In J.A. Frankel (ed.). The Regionalization of the World Economy, pp. 33–57. Chicago: University of Chicago Press. Egger, P. (2005) Alternative techniques for the estimation of cross–section gravity models. Review of International Economics, 13: 881–891. Elsass, P. M. and Veiga, J. F. (1994) Acculturation in acquired organizations: a force–field perspective. Human Relations, 47: 431–453. Feenstra, R. C. (2004) Advanced International Trade: Theory and Evidence. Princeton, NJ: Princeton University Press. Feenstra, R. C., Lipsey, R. E., Deng, H., Ma, A. C. and Mo, H. (2005) World trade flows 1962–2000, NBER

A rather empty world Obstfeld, M. and Rogoff, K. (2000) The six major puzzles in international macroeconomics: is there a common cause? NBER Macroeconomics Annual, 15: 339–390. Parkhe, A. (1991) Interfirm diversity, organizational learning, and longevity in global strategic alliances. Journal of International Business Studies, 22: 579–601. Oguledo, V. I. and MacPhee, C. R. (1994) Gravity models: a reformulation and an application to discriminatory trade arrangements. Applied Economics, 26: 107–120. Poot, J. (2004) Peripherality in the global economy. In J. Poot (ed.). On the Edge of the Global Economy, pp. 3–26. Cheltenham, UK: Edward Elgar. Portes, R., Rey, H. and Oh, Y. (2001) Information and capital flows: the determinants of transactions in financial assets. European Economic Review, 45: 783–796. Rodrik, D. (2000) How far will international integration go? Journal of Economic Perspectives, 14: 177–186. Romer, P. (1994) New goods, old theory, and the welfare costs of trade restrictions. Journal of Development Economics, 43: 5–38. Santos Silva, J. C. M. and Tenreyro, S. (2006) The log of gravity. The Review of Economics and Statistics, 88: 641–658. Tinbergen, J. (1962) Shaping the World Economy. New York: The Twentieth Century Fund. Trefler, D. (1995) The case of missing trade and other mysteries. American Economic Review, 85: 1029–1046. Ullman, E. L. (1954) Geography as spatial interaction. In D. Rezvan and E.S. Engelbert (eds.). Interregional Linkages, pp. 63–71. Berkeley, CA: UCLA, Proceedings of the Western Committee on Regional Economic Analysis. Van Houtum, H. (1998) The Development of Cross– Border Economic Relations, CentER Dissertation, no. 40. Tilburg: Tilburg University. Wonnacott, P. (1998) Comment on ‘The role of history in bilateral trade flows’. In J.A. Frankel (ed.). The Regionalization of the World Economy, pp. 59–62. Chicago: University of Chicago Press. World Trade Organization (WTO). (2004) World Trade Report 2004. Geneva: WTO. Yi, K. M. (2003) Can vertical specialization explain the growth of world trade? Journal of Political Economy, 111: 52–102. Received on March 31, 2008; accepted on July 7, 2008

457

Downloaded from http://cjres.oxfordjournals.org/ at Institute of Social Studies on May 9, 2013

Kee, H. L., Nicita, A. and Olarreaga, M. (2008) Estimating Trade Restrictiveness Indices. World Bank Policy Working Paper, no. 3840, Washington, DC, World Bank. King, G. (1988) Statistical models for political science event counts: bias in conventional procedures and evidence for the exponential Poisson regression model. American Journal of Political Science, 32: 838–863. Kogut, B. and Singh, H. (1988) The effect of national culture on the choice of entry mode. Journal of International Business Studies, 19: 411–432. Krugman, P. (1979) Increasing returns, monopolistic competition, and international trade. Journal of International Economics, 9: 469–479. Lejour, A. M. and Nahuis, R. (2005) R&D spillovers and growth: specialisation matters. Review of International Economics, 13: 927–944. Linder, S. B. (1961) An Essay on Trade and Transformation. New York: John Wiley and Sons. Linders, G. J. M., Slangen, A. H. J., De Groot, H. L. F. and Beugelsdijk, S. (2005) Cultural and institutional determinants of bilateral trade flows, Tinbergen Discussion Paper TI2005-074/3. Amsterdam-Rotterdam. Linders, G. J. M. and De Groot, H. L. F. (2006) Estimation of the gravity equation in the presence of zero flows, Tinbergen Institute Discussion Paper, 2006–072/3. Loungani, P., Mody, A. and Razin, A. (2002) The global disconnect: the role of transactional distance and scale economies in gravity equations. Scottish Journal of Political Economy, 49: 526–543. Maddison, A. (2001) The World Economy: A Millennial Perspective. Paris: OECD. Matyas, L. (1998) The gravity model: some econometric considerations. The World Economy, 21: 397–401. McCallum, J. (1995) National borders matter: Canada– U.S. regional trade patterns. American Economic Review, 85: 615–623. Me´litz, J. (2002) Language and foreign trade. CEPR Working Paper, no. 3590, London. Mullahy, J. (1986) Specification and testing of some modified count models. Journal of Econometrics, 33: 341–365. Nijkamp, P., Rietveld, P. and Salomon, I. (1990) Barriers in spatial interactions and communications: a conceptual exploration. Annals of Regional Science, 24: 237–252.

Linders, Burger and van Oort

Appendix Table A1 Countries included in the analysis Gabon Gambia Germany Ghana Greece

Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus

Guatemala Guinea Haiti Honduras Hungary India Indonesia

Belgium-Luxembourg Belize

Iran Ireland

Bermuda Bolivia Bosnia Herzegovina Brazil Bulgaria Burkina Faso Burundi Cameroon Canada Central African Republic Chad Chile China

Israel Italy Jamaica Japan Jordan Kazakhstan Korea Republic Kenya Kuwait Laos

Colombia Congo Costa Rica Cote D’Ivoire Croatia

Lithuania Macau Madagascar Malawi Malaysia

Cuba Cyprus Czech Republic Denmark Djibouti Dominican Republic Ecuador Egypt

Mali Malta Mauritania Mauritius Mexico Morocco

El Salvador Estonia Ethiopia Fiji Finland France-Monaco

458

Latvia Lebanon Libya

Mozambique Netherlands Antilles-Aruba Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria

Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Republic Moldova Romania Russian Federation Rwanda Saudi Arabia Senegal Singapore Slovakia Slovenia South Africa Spain Sri Lanka Sudan Suriname Sweden SwitzerlandLiechtenstein Syria Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Kingdom United Arabian Emirates Uruguay USA Uzbekistan Venezuela Vietnam Yugoslavia Zambia Zimbabwe

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Albania Algeria Angola Argentina Australia