globe lecture slides week5 geographicsdistance

Globalization Note Series Pankaj Ghemawat

Copyright © 2014 Pankaj Ghemawat. This material was developed for students in the GLOBE course at IESE Business School and should not be cited or circulated without the authors’ written permission.

Geographic Distance: The Persistent Power of Proximity and Reasons for Regionalization

In 1944, George Orwell lamented how writers of his era continued repeating phrases such as “the abolition of distance,” which were fashionable before 1914.1 Orwell’s lament notwithstanding, some of the most popular books on globalization in recent decades followed the same theme, with titles such as The Borderless World (Ohmae, 1991), The Death of Distance (Cairncross, 1997), and The World is Flat (Friedman, 2005). Empirical evidence, however, paints a starkly different picture, one where geographic factors continue to exert a strong negative influence on international interactions. Surprisingly, research on merchandise trade suggests that the inhibiting effect of physical distance has not declined over the past five decades and may possibly even have increased. Trade economists refer this as the “distance puzzle.”2

The first three sections of this note examine and quantify the effects of physical distance on international interactions. The first section focuses on the geography of merchandise trade and introduces the calibration of distance effects using gravity models. The second section examines the question of why geography continues to exert such a strong influence on merchandise trade in spite of apparent declines in transportation costs. The third section broadens the discussion of how physical distance impacts international interactions beyond merchandise trade, covering services trade as well as selected capital, information, and people flows. The fourth section turns to an examination of regionalization, the empirical regularity that most international interactions take place within rather than between roughly continent-sized regions. The fifth section builds on the discussion of regionalization to explore how geography relates to the other dimensions of the CAGE framework: culture, administration, and economics.

I. Gravity and the Geography of Merchandise Trade

Germany was the world’s third largest merchandise exporter in 2012, and derived 41% of its GDP from merchandise exports, significantly more than the first ranked exporter (China, 25%) or the second (United States, 10%).3 Exhibit 1 provides a simple visualization of the impact of geography on Germany’s exports: a map in which all other countries are sized in proportion to Germany’s exports to them and shaded based on the share of their imports coming from Germany. The ranking of Germany’s export destinations shown on the left side of the map provides an initial hint of the importance of distance: France was Germany’s top export destination, even though France was only the world’s 5th largest economy. Other countries that share borders with Germany also appear much larger than they do on normal maps. Nonetheless, two of Germany’s top four export destinations are relatively distant countries: the United States (#2) and China (#4). These examples, the world’s two largest economies, illustrate the need to control for the size of export destinations when analyzing the effects of physical distance on export volumes, which is the purpose of the map’s shading scheme.

As indicated by the shading on the map, all of the countries where Germany’s exports make up more than 15% of destination country imports are located within Europe. And even within Europe, distance also exerts a significant influence on Germany’s exports. All of the countries where Germany’s exports account for more than 25% of imports are adjacent to Germany, with the sole exception of Hungary, which is only slightly more distant. Exhibit 2 extends the same concept illustrated in the coloring on the map by plotting the intensity of Germany’s exports (this time in relation to partner countries’ GDPs rather than their imports) against their distances from Germany in kilometers (with both transformed in natural logarithms).4 In this example, 59% of the variation in export intensity across destination countries is explained by physical distance. The slope of the regression line (-1.1) quantifies how the intensity of Germany’s exports vary with distance in the form of an elasticity. If distance from


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Germany increases by 1%, one should expect the intensity of Germany’s exports to decline by 1.1% (or more approximately 1%).

Germany represents, of course, only one country example, and the analysis presented thus far only covers the effects of physical distance and GDP, leaving out other factors that are known to impact trade patterns. Therefore, we proceed next to a more general characterization of the impact of geography on merchandise trade that controls for factors on all of the dimensions of the CAGE framework (cultural, administrative, geographic and economic) and calibrates effects based on data from large multi-country samples (typically covering all economies that are significant participants in international trade). The econometric model used to generate this analysis is the gravity model, so named because of its resemblance to Newton’s law of universal gravitation in physics: that the gravitational force between two bodies is directly proportional to the product of their masses and inversely proportional to the square of the distance between them. Gravity models in economics, similarly, relate trade (and other interactions) between countries to the sizes of their economies and the physical distance between them (as well as to other factors expected to promote or inhibit interactions).

Over the past 50 years, international economists have estimated literally thousands of gravity models of merchandise trade, and published hundreds of papers reporting their results. A 2013 paper by Keith Head and Thierry Mayer provides a summary of the results of 2508 gravity models, drawn from 159 papers. Across all of the models, the median elasticity of trade with respect to physical distance is roughly -1.5 This indicates that the rough summary conclusion from the German example is a good first order approximation of the global effect of physical distance on merchandise trade: from any given country, all else equal (including GDP), if one trade partner is half as far away as another, one should expect the exports to the closer country to be twice as large as the exports to the farther country.6

To illustrate the magnitude of the distance effects implied by this calibration, consider the intensity7 of Germany’s exports to the Netherlands, Spain, and Azerbaijan. The distance from Germany to the Netherlands (calculated based on a population-weighted average of distances between major cities in the two countries) is one-fourth as large as the distance from Germany to Spain. Therefore, the rough assumption that halving distance doubles exports intensity implies that for a distance one-fourth as far, exports intensity should quadruple (double twice: 2 x 2 = 4). Indeed, the intensity of Germany’s exports to Spain is one-fourth the intensity of Germany’s exports to the Netherlands. The distance from Germany to Azerbaijan is 8.5 times the distance from Germany to the Netherlands (and double the distance from Germany to Spain), implying that the effect should be doubled again (2 x 2 x 2 = 8). The basic pattern continues to hold with Germany’s exports to the Netherlands being 9 times more intense than Germany’s exports to Azerbaijan.

Distance is not the only geographic factor that is often incorporated as an explanatory variable in gravity models. The second most common geographic variable is contiguity: a dummy variable indicating whether or not two countries share a common border. Among the 2508 gravity models summarized by Head and Mayer, 1066 incorporated contiguity as an explanatory variable, and the median coefficient across them implied that a common border boosts merchandise trade by about 60%.8 As one would expect, gravity models that do not include contiguity tend to find larger (more negative) effects of physical distance.9 Other less common geographic variables include time zones, remoteness from large economic centers, land area, climate, whether or not a country is landlocked, and whether or not a country is an island.

Head and Mayer also compared distance effect estimates drawn from gravity models using data on merchandise trade across time periods ranging from 1960 to 2005 to analyze possible changes in the magnitude of the effects of distance on trade over time. While the results vary depending on the statistical methods employed, their analysis tends to suggest that effects of physical distance have risen rather than diminished over time. Since declining transportation and telecommunication costs would presumably lead one to expect these effects to have decreased rather than increased, this pattern is referred to as the “distance puzzle.”10

II. Transportation Costs and Merchandise Trade

The previous section presented evidence that physical distance continues to constrain merchandise trade. How can this finding be reconciled with the sense that transportation cost have


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declined as new transportation technologies have improved the efficiency with which goods can be transported over long distances? This section begins with a brief review of transportation cost trends, then relates transportation costs to trade patterns, and then goes on to begin introducing factors other than transportation costs that help explain the magnitude and persistence of distance effects on merchandise trade.

Roughly 90% of international trade by volume and 60-80% by value is transported by sea.11 Containerized shipping was introduced in the United States in the 1950s, on routes between the US and Europe/Japan starting in the late 1960s and into the 1970s, and then gradually expanded elsewhere.12 Containerization increased the productivity of dock labor more than 15 times13 and indirectly reduced shipping costs that vary with distance. It enabled ships to spend less time in ports (and more time at sea) and made it economically justifiable for shipping companies to invest in larger and faster ships (both effects reducing per ton-kilometer shipping costs). The growth of containerized shipping, however, took place amid rising port, fuel, and ship costs, complicating the assessment of its impact on sea freight rates. Thus, the evidence on actual declines in real sea freight rates is somewhat murky, with some analyses indicating declining trends while others do not. One study indicates that sea freight rates fell 70% from 1920 to 1960 (before containerization) but then remained fairly stable, rising modestly during the 1980s and then falling again during the 1990s.14 A regression analysis based on US data that does attempt to incorporate relevant control variables, however, estimates that doubling container usage lowers shipping costs by 13.4%.15

Air transportation costs declined more dramatically, contributing to its faster growth. Globally, air cargo has grown 2.6 times faster than ocean cargo from 1965-2004.16 Per ton-kilometer air freight rates fell 92% from 1955 to 2004. Roughly 80% of this decline, however, took place prior to 1975, as jet aircraft took over routes formerly handled by propeller-driven airplanes. Declines since 1975 have been fairly steady but more modest. Based on US data, the elasticity of air freight rates with respect to physical distance also declined 63% from 1974-2004.17 The per-kilogram cost of air freight into the US, however, does still average 6.5 times the cost of sea freight.18

The evidence across both sea and air transportation, thus, does indicate a broad pattern of declining transportation costs, but one where recent declines have been much more modest than earlier ones. Sea freight rates declined more sharply before the 1960s than after, and air cargo rates declined more steeply before the mid-1970s than after. This background helps temper expectations that dramatic declines in transportation costs should have reduced the sensitivity of merchandise trade to physical distance over the last few decades, but provides only—at best—a partial explanation for the persistence of those distance effects. A more complete explanation requires some perspective on transportation costs relative to other distance-related factors that inhibit trade.

A simple way to gain additional perspective on this question is to look at how much of the cost of traded goods is accounted for by international transportation. For goods transported by sea, international transportation costs average 6% of the value of the imported goods.19 For goods transported by air, a global ratio was not available, but U.S. data indicate international transportation costs average 3% of value. 20 (The lower ratio for air transportation presumably reflects the higher value of goods transported by air than those transported by sea.) International transportation costs are not a negligible contributor to the cost of imported goods—they do often exceed tariff rates, which currently average only 3% (down from 9% in 1980).21 However, transportation costs are relatively small both in comparison to the value of the goods traded and in comparison to other trade costs that do not vary with distance. Costs of documentation and customs processes associated with moving goods across national borders (once they have arrived there) are estimated at between 2% and 24% of the value of traded goods. 22

Head and Mayer provide a more sophisticated statistical analysis relating transportation costs to trade costs. They estimate that only 4-28% of distance-driven trade costs are attributable to transportation costs.23 Further support for the proposition that transportation costs are not the main or only factor explaining how distance impacts trade comes from the similar negative effects observed in gravity models of other types of international interactions (including those where no product needs to be transported) described in the next section of this note.

If transportation costs provide only a partial explanation of the negative impact of physical distance on merchandise trade, then what other factors must be (at least collectively) more important? Another geographic factor that tends to receive less attention than transportation costs is transportation time. David Hummels and Georg Schaur estimate that each day goods spend in transit is worth 0.6% to


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2.3% of the value of the traded goods (equivalent to ad-valorem tariffs at those rates). 24 This calibration implies that over inter-continental distances (where sea shipping typically requires at least 20 days), shipping time may be an even larger impediment to trade than shipping costs.25

A comparison of average distances across which goods are traded, as shown in Exhibit 3, also helps illustrate how transportation provides only a partial explanation for the distance sensitivity of merchandise trade. A casual examination of the figure does suggest some obvious transportation-related effects. Electricity and milk are traded over the shortest distances because of problems associated with transmission losses and spoilage (the latter another illustration of time playing an important role). However, other patterns are also apparent. Bulk commodities (e.g. soybeans, corn, sugar, coal) tend to be traded over longer distances than products where demand is more sensitive to cultural and economic differences (e.g. pasta, beer, cars).

The latter set of examples point to the fact that geographic factors alone do not provide a complete explanation for the persistent negative impact of distance on merchandise trade. Thus, a literature review in the Oxford Handbook of International Business sums up that, “the consensus is that the bulk of trading costs are due to trade-reducing factors such as differences in legal systems, administrative practices, market structures, networks, languages, and monetary regimes.”26 In other words, physical distance is also correlated with inhibitors of trade that are rooted in the cultural, administrative, and economic dimensions of the CAGE framework, the focus of the final section of this note.

III. Geography across Trade, Capital, Information and People Flows

The points covered in the previous two sections—that physical distance has a strong negative impact on trade flows and that transportation costs explain only a small part of that effect—cast doubt upon the commonplace notion that physical distance should not have any impact on interactions that do not require physical products to be transported. It has been asserted, for example, that foreign direct investment might be positively associated with physical distance, serving as a substitute for trade over long distances. Plummeting telecommunications costs (the cost of international phone calls have dropped 95% since 198027) imply that calling someone on the other side of the world is now almost as easy and cheap as calling someone next door. As this section will elaborate, physical distance, nonetheless, still exerts a significant negative effect on foreign direct investment, telephone calls, and a variety of other international interactions.

Exhibit 4 presents the results of a gravity model analysis that estimated the effects of distance on 10 types of international interactions using a common analytical specification across all types and data from 97 countries that account for 92% of the world’s GDP and 88% of its population. In addition to merchandise trade, it adds in services trade to account for all trade flows. It then covers three capital flows (FDI outward stocks, portfolio equity assets, and portfolio long term debt), two information flows (outgoing phone calls and printed publications exports) and three people flows (emigration, international student arrivals, and international tourist arrivals). This model controlled for the effects of other CAGE factors: common language (a cultural factor), colonial linkage, trade agreement, and regional bloc (administrative factors), common border (an additional geographic factor) and the ratio of countries’ per capita incomes (an economic factor).

Physical distance, even after incorporating the controls described above, had a statistically significant negative relationship with all of the types of international interactions covered in the analysis. While the magnitude of the effect of halving physical distance did vary from increasing portfolio long term debt by 78% to boosting trade in printed publications by 282%, the effects for 7 out of the 10 flows fell within the range between 78% and 129%, suggesting the broad rule of thumb that halving the distance between countries doubles the intensity of most types of international interactions between them. With respect to merchandise trade, this model found a larger negative effect than the median model in the Head and Mayer paper cited above: an elasticity of -1.55 (as compared to roughly -1 in Head and Mayer).28

While the gravity model estimates covered here do not incorporate online interactions, those will also be addressed here briefly to debunk the myth that distance does not matter on the internet. One recent study analyzed interactions on Twitter and estimated that the average distance between the original sender of a tweet and another twitter user who retweets a tweet is 1200 kilometers.29 Keeping in


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mind that the data on retweets include domestic interactions, this estimate still implies that retweets are more distance sensitive than, at least, international movements of most of the goods covered on Exhibit 3. While distance metrics are not available for other online interactions, research indicates only about 17% of all internet traffic crosses national borders and only 10-15% of Facebook friends are located in different countries.30 The distance sensitivity of online interactions reflects the fact that online communication media such as social networking applications are superimposed on a “social graph” which already exists and conforms closely to physical and political geography. Thus, researchers have concluded that Facebook has a “strongly modular network structure at the scale of countries.”31

IV. Regionalization

The sensitivity of international interactions to physical distance is also observed in the general pattern that more international interactions take place within rather than between roughly continent sized regions. If the world is divided into seven regions32 (East Asia & Pacific, Europe, Middle East & North Africa, North America, South & Central America & Caribbean, South & Central Asia, and Sub-Saharan Africa), 53% of merchandise exports in 2012 (by value) took place within rather than between regions. The regionalization of merchandise trade, of course, is directionally consistent with the evidence described above on distance effects. The average distance between two countries in the same region is 3155 km, roughly one third of the average distance between countries in different regions (9398 km). The distance effect estimate from the gravity model behind Exhibit 4 implies that tripling physical distance (shifting from the intra-regional to the inter-regional average distance) should cut merchandise trade by 82% (roughly 66% based on estimates in Head and Mayer).

Exhibit 5 tracks the intra-regional share of merchandise exports since 1960, and shows that this proportion was on a fairly consistent rising trend through 2003. That trend toward a higher proportion of trade taking place within regions is consistent with the possibility highlighted above that trade flows may have become more rather than less sensitive to physical distance during the second half of the twentieth century. The decline in the intra-regional share of exports since 2003 was driven by the rising share of East Asian emerging economies in total exports, driving down the global average even as their own exports became more regionalized.33

The pattern of regionalization applies not only to merchandise trade but broadly across types of international interactions, as shown in Exhibit 6. Tourism is the most regionalized of the interactions studied, with nearly 80% of international tourists remaining within their home regions. Services exports and printed publications exports are also among the most regionalized, both with close to 70% taking place within regions. At the other end of the spectrum, the majority of portfolio equity assets and international student arrivals do cross regional boundaries, but close to 40% of those flows are also intra-regional. Portfolio equity investment tends to be less distance sensitive than foreign direct investment (the other type of equity investment analyzed), presumably because most portfolio equity investment involves shares in public companies, traded on major stock exchanges where there are systems available to facilitate long-distance transactions. The relatively low intra-regional share of international student arrivals reflects the high proportion of those flows that involve students from emerging economies going to study in advanced economies, often crossing regional boundaries to do so. In 2010, 73% of outbound students came from emerging economies and 77% of them were studying in advanced economies (where most of the world’s top ranked universities are located).34

The pattern of regionalization also applies when one looks at firm-level rather than country-level data. As the Note on the Globalization of Firms: A Cross-Functional Perspective elaborates, the operations of the world’s largest multinational firms also tend to be regional rather than global. Between 2000 and 2007, firms in the Fortune Global 500, on average, derived 76% of their sales from their home regions and had 78% of their assets located in their home regions (including domestic sales and assets). Changes in these proportions over the eight years studied were not statistically significant at the 5% level.35

Many multinational firms with operations spread across multiple regions also incorporate regional levels of aggregation into their management structures. Multinationals, for example, utilize regional headquarters to spearhead business development efforts within a region, help the enterprise understand regional conditions, signal commitment to the region, coordinate activities within the region, and pool resources to take advantage of region-level scale economies.36 However, some large multinationals have found it more efficient to prioritize a set of key markets to report directly to


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headquarters, de-emphasizing the regional layer between these countries and headquarters. Sir Martin Sorrell, CEO of WPP, took this perspective, arguing that “regional management is very difficult to execute effectively.”37 In his view, the regional layer of management is costly and struggles to deal with the differences across countries and offices within regions.

Are distance effects and regionalization simply two different ways of expressing the same phenomenon, or does one give us greater insight than the other into the phenomenon of globalization? A comparison of the distance effect estimates shown in Exhibit 4 and the intra-regional shares of interactions in Exhibit 6 indicates that these values are clearly related, but that there also are important differences (correlation coefficient between the two is 0.46, implying that one only explains 21% of the variation in the other). One difference—perhaps the most important—is that the distance effect estimates shown in Exhibit 4 controlled for a variety of attributes along which countries that are located in the same region are more similar than countries in different regions, as described in the final section of this note. For now, the key point is that regionalization picks up on influences that are not fundamentally geographic but are correlated with geographic distance.

There are also other important advantages of distance effect calibrations over regionalization in the analysis of globalization. Important granularity is lost if the analysis does not delve deeper than the regional level of detail. Recall, again, how much more intensively Germany exports to its immediate neighbors as compared to more distant countries within Europe. More generally, ~23% of world trade is between countries that share a land border,38 implying that trade with immediate neighbors accounts for almost half of all intra-regional trade. Distance effects within regions are important (one of the reasons behind Sorrell’s view on regional management structure), and are missed entirely if focusing only on the proportion of interactions that cross regional boundaries.

Additionally, focusing on regionalization would be more attractive if levels of regionalization were clearly and consistently increasing. However, the merchandise trade analysis described above provides an example of a trend away from regionalization over the past decade. In summary, the analysis of distance effects via gravity models is preferable to simply relying on levels of regionalization in cases where data are available on country-by-country interactions. Where country-by-country data are unavailable, of course, calibrations of geographic effects based on levels of regionalization are much better than having no calibrations at all. Such data limitations are typically most prominent when working with firm-level data, as even the largest multinationals typically report their performance only at the level of regions, not countries.

V. Geography and the Rest of the CAGE Framework

The phenomenon of regionalization covered in the previous section is measured with respect to

geographic (regional) boundaries, but also reflects how geography relates to the other dimensions of the CAGE framework (cultural, administrative, and economic). Exhibit 7 compares similarities and differences across the CAGE framework between countries that are located in the same versus different regions. Across all of the factors shown on the exhibit, countries that are located in the same region are, on average, more similar than countries located in different regions. Starting at the top of the exhibit, for example, countries that are located in the same region are 2.5 times as likely as countries in different regions to share a common official language (32% of countries in the same region do as compared to only 13% in different regions). The control variable in the gravity model for common language tells us that a common language boosts merchandise trade by 119% (more than doubles it). Thus, one of the reasons behind the regionalization of merchandise trade is the pattern that countries in the same region are more likely to share a common language than countries in different regions.

The same basic pattern—greater similarity within as compared to between regions driving more intra-regional than inter-regional interactions—tends to apply across the factors shown in Exhibit 7. Countries in the same region are 42 times more likely to share a common currency, 5 times more likely to share a trade agreement 29 times more likely to have a common border, and also tend to have more similar levels of economic development. In the exhibit, the latter pattern is quantified based on the ratios of countries’ per capita GDPs (for each country pair, dividing the GDP per capita of the country that ranks higher on that metric by the GDP per capita of the country that ranks lower). Based on this metric, countries in the same region are 2.7 times more similar with respect to GDP per capita than countries in different regions. Some such non-geographic similarities are customarily incorporated as control


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variables in gravity models. The incorporation of these control variables, as suggested in the previous section, improves the precision of gravity-based distance effect calibrations relative to estimates of the extent of globalization that rely only on levels of regionalization.

The pattern that countries located in the same region are more similar—across multiple dimensions—than countries located in different regions applies more generally to distance rather than only to regionalization. Exhibit 8 provides an illustration of this more general pattern by plotting the proportion of countries that share a common trade agreement, regional trade bloc, official language, and colonial linkage against the distances between them (in percentiles). The figure shows that there is a steep drop in all four commonalities starting even over the shortest distances. This pattern, again, echoes what we saw in the case of Germany’s export intensities: similarities drop off significantly even within regions, and in some cases, by the time one reaches a regional boundary there is very little room left for further declines.

The same correlations also imply that physical distance can serve as a useful shorthand heuristic for composite distance across all four of the CAGE dimensions. Exhibit 9 presents the results of an analysis that systematically tested this intuition. A composite CAGE distance metric was generated for each of the types of international interactions covered in Exhibit 4, based on the results of the same general gravity model that produced the distance effect estimates shown in that exhibit. Those composite distance effect estimates were then correlated with physical distance. The correlation coefficients exceed 80%, except in the cases of the two types of portfolio investment.

Another illustration of this pattern is provided by ranking, for a given focal country, its largest partner countries across multiple types of interactions. For the United States, Canada ranks among the top 5 partners across all of the interactions covered in Exhibits 4 and 6. The United States is Canada’s top partner across all of the interactions except one (students). Similar patterns tend to apply for other focal countries, with most international interactions involving countries that are both geographically proximate and share similarities on other CAGE dimensions. In the absence of systematic data on similarities and differences across the CAGE dimensions, simple comparisons based on physical distance will often provide a reasonable approximation of composite CAGE distance effects.

Conclusion

Geography continues to exert a strong negative influence on international interactions. This note has shown distance effects to be both persistent over time and widespread across types of interactions. It has also shown how similarities along the non-geographic dimensions of the CAGE framework (cultural, administrative, and economic) also tend to more common among proximate countries than among distant ones. While the evidence presented here indicates that the effects of distance on international interactions are not diminishing, it would be a mistake to presume that the geographic aspects of international business remain entirely static. The rising share of economic activity taking place in emerging economies is bringing about a dramatic restructuring of the geography of trade and to a lesser extent the geography of capital, information, and people flows. Economic differences—particularly those between advanced and emerging economies—are the focus of the next note of this series on the economic dimension of the CAGE framework.


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Exhibit 1: World Map with Countries Sized in Proportion to Germany’s Merchandise Exports to them and Shaded Based on Germany’s Share of their Merchandise Imports, 2011

Exhibit 2: Scatterplot: Log of Intensity of Germany’s Merchandise Exports vs.

Log of Distance from Germany


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Exhibit 3: Average Distance Traversed by a Sample of Traded Goods

Exhibit 4: The General Effects of Halving Physical Distance (Gravity Model Estimates)

121%

282%

121%

151%

189%

129%

100%

78%

86%

193%

0% 50% 100% 150% 200% 250% 300%

Outgoing Phone Calls

Printed Publications Exports

Tertiary Students' Arrivals

Emigration

Tourists' Arrivals

FDI Outward Stocks

Portfolio Equity Assets

Portfolio Long Term Debt

Services exports

Good exports

Info

Peo

ple

Capital

Trade


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Exhibit 5: Intra-regional Share of Merchandise Exports by Region, 1960-2012

0%

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80%1960

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Europe

World

East Asia & Pacific

North America

S. & C. America, Caribbean

South & Central AsiaMiddle East & N. Africa

Sub‐Saharan Africa

Exhibit 6: Intra-regional Share of International Interactions by Type

0% 10% 20% 30% 40% 50% 60% 70% 80%

Outgoing Phone Calls

Printed Publications Exports

Tertiary Students' Arrivals

Emigration

Tourists' Arrivals

FDI Outward Stocks

Portfolio Equity Assets

Portfolio Long Term Debt

Services exports

Good exports

Info

Peo

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Capital

Trade


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Exhibit 7: Average CAGE Commonalities/Differences among

Countries in Same vs. Different Regions

Note: Ratios shown in italics refer to differences rather than similarities.

Exhibit 8: Correlations of Other CAGE Similarities with Physical Distance

0

0.1

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5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Distance Percentiles

Regional Bloc

Common Language

TradeAgreement

ColonialLinkage


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Exhibit 9: Correlations of Composite CAGE Distance with Physical Distance


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1 George Orwell, “As I Please,” Tribune, May 12, 1944, reprinted in CEJL 3: 173. 2 Keith Head and Thierry Mayer. "What separates us? Sources of resistance to globalization." Canadian

Journal of Economics/Revue Canadienne d'Economique 46.4 (2013): 1196-1231. 3 Pankaj Ghemawat and Steven A. Altman, “Depth Index of Globalization 2013.” 4 This figure is similar to one in Keith Head and Thierry Mayer. "What separates us? Sources of resistance

to globalization." Canadian Journal of Economics/Revue canadienne d'économique 46.4 (2013): 1196-1231, which the authors of that paper trace back to a graphic in Walter Isard and Merton J. Peck. “Location theory and international and interregional trade theory,” The Quarterly Journal of Economics (1954): 97-114.

5 Keith Head and Thierry Mayer, “Gravity Equations: Workhorse, Toolkit, and Cookbook,” in Handbook of International Economics Vol. 4, eds. Gopinath, Helpman, and Rogoff, Elsevier, 2013. [Note: The elasticity across all models in the sample is -0.89 and across the subset of “structural gravity” models is -1.1.]

6 The formula to calculate how many times more intensively a country is expected to export to a nearer country as compared to a farther country for any elasticity and combination of distances is: (Distance from focal country to nearer country ^ Elasticity) / (Distance from focal country to farther country ^ Elasticity). The elasticities appear in the exponents here because both distance and exports were transformed in natural logs in the gravity model regression.

7 Intensity here refers to focal country exports divided by destination country GDP. Thus, for example, the intensity of Germany’s exports to the Netherlands is calculated as the value of Germany’s exports to the Netherlands divided by the Netherlands’ GDP.

8 Keith Head and Thierry Mayer, “Gravity Equations: Workhorse, Toolkit, and Cookbook,” in Handbook of International Economics Vol. 4, eds. Gopinath, Helpman, and Rogoff, Elsevier, 2013.

9 Anne-Célia Disdier and Keith Head, "The puzzling persistence of the distance effect on bilateral trade," The Review of Economics and Statistics 90.1 (2008): 37-48.

10 Keith Head and Thierry Mayer. "What separates us? Sources of resistance to globalization." Canadian Journal of Economics/Revue Canadienne d'Economique 46.4 (2013): 1196-1231.

11 Moïsé, E. and F. Le Bris (2013), “Trade Costs - What Have We Learned?: A Synthesis Report”, OECD Trade Policy Papers, No. 150, OECD Publishing indicates 90% by volume and 80% by value. International Maritime Statistics Forum (http://www.imsf.info/seabourne_trade.html) indicates only 60% by value.

12 David Hummels, "Transportation costs and international trade in the second era of globalization," The Journal of Economic Perspectives 21.3 (2007): 131-154.

13 Keith Head and Thierry Mayer. "What separates us? Sources of resistance to globalization." Canadian Journal of Economics/Revue canadienne d'économique 46.4 (2013): 1196-1231.

14 United Nations Development Program, Human Development Report 1999, p. 30. 15 David Hummels, "Transportation costs and international trade in the second era of globalization." The

Journal of Economic Perspectives 21.3 (2007): 131-154. 16 David Hummels and Georg Schaur, “Time as a trade barrier,” National Bureau of Economic Research

No. w17758, 2012. 17 David Hummels, "Transportation costs and international trade in the second era of globalization." The

Journal of Economic Perspectives 21.3 (2007): 131-154. 18 David Hummels and Georg Schaur, “Time as a trade barrier,” National Bureau of Economic Research

No. w17758, 2012. 19 Moïsé, E. and F. Le Bris (2013), “Trade Costs - What Have We Learned?: A Synthesis Report”, OECD

Trade Policy Papers, No. 150, OECD Publishing. http://dx.doi.org/10.1787/5k47x2hjfn48-en 20 Value deplotted from Figure 5 of David Hummels, "Transportation costs and international trade in the

second era of globalization." The Journal of Economic Perspectives 21.3 (2007): 131-154.


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21 World Economic Forum, “Enabling Trade: Valuing Growth Opportunities,” 2013. 22 Moïsé, E. and F. Le Bris (2013), “Trade Costs - What Have We Learned?: A Synthesis Report”, OECD

Trade Policy Papers, No. 150, OECD Publishing. http://dx.doi.org/10.1787/5k47x2hjfn48-en 23 Keith Head and Thierry Mayer. "What separates us? Sources of resistance to globalization." Canadian

Journal of Economics/Revue canadienne d'économique 46.4 (2013): 1196-1231. 24 David Hummels and Georg Schaur, “Time as a trade barrier,” National Bureau of Economic Research

No. w17758, 2012. 25 Another factor that trade economists have analyzed is change over time in the composition of

the set of country pairs that are active in international trade. New “entrants” could result both from country-pairs that formerly did not trade starting to do so and from the addition of newly independent countries. Head and Mayer find a much more modest increasing trend in distance effects over time if they restrict their analysis to the same country pairs in every time period.

26 Michele Fratianni, “The Gravity Equation in International Trade,” in Alan M. Rugman, ed., Oxford Handbook of International Business, 2nd ed. Oxford: Oxford University Press, 2009.

27 Keith Head and Thierry Mayer. "What separates us? Sources of resistance to globalization." Canadian Journal of Economics/Revue canadienne d'économique 46.4 (2013): 1196-1231.

28 This elasticity implies that countries that are half as distant would trade 2.93 as much, countries that are 1/4 as distant would trade 8.6 times as much (2.93 * 2.93), and countries that are 1/8 as far would trade 25 times as much (2.93 * 2.93 * 2.93).

29 Kalev Leetaru, et al, "Mapping the global Twitter heartbeat: The geography of Twitter," First Monday 18.5, 2013. http://firstmonday.org/article/view/4366/3654

30 A published estimate in Jason Ugander, Brian Karrer, Lars Backstrom, and Cameron Marlow “The Anatomy of the Facebook Social Graph” Report, arXiv:1111.4503 [cs.SI], November 2011, puts this proportion at 16%, but newer unpublished research indicates a lower proportion.

31 Jason Ugander, Brian Karrer, Lars Backstrom, and Cameron Marlow “The Anatomy of the Facebook Social Graph” Report, arXiv:1111.4503 [cs.SI], November 2011.

32 The region classification employed here is based on the World Bank’s region classification scheme with modifications made to incorporate advanced economies and to bring the Europe category into closer (though not complete) alignment with the European Union. The North America category here refers only to the member countries of the North American Free Trade Agreement (Canada, Mexico, and United States). For more details, refer to Appendix B of Pankaj Ghemawat and Steven A. Altman, “Depth Index of Globalization 2013.”

33 For a more detailed explanation of this phenomenon, refer to Chapter 2 of Pankaj Ghemawat and Steven A. Altman, “Depth Index of Globalization 2013.”

34 Pankaj Ghemawat and Steven A. Altman, “Depth Index of Globalization 2013,” Chapter 5. 35 Alan M. Rugman and Chang Hoon Oh, “Why the home region matters: location and regional

multinationals,” British Journal of Management, 2012. 36 List adapted from Philippe Lasserre, "Regional headquarters: The spearhead for Asia Pacific

markets," Long Range Planning 29.1 (1996): 30-37. 37 Pankaj Ghemawat, Steven A. Altman, and Robert Strauss, “WPP and the Globalization of Marketing

Services,” IESE Business School Case Study SM-1600-E, June 2013. 38 David Hummels, "Transportation costs and international trade in the second era of globalization." The

Journal of Economic Perspectives 21.3 (2007): 131-154.

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