ECONOMICS

THE GRAVITY OF RESOURCES AND THE TYRANNY OF DISTANCE

by

Peter E Robertson Business School

University of Western Australia

and

Marie-Claire Robitaille The University of Nottingham

Ningbo China

DISCUSSION PAPER 15.01

The Gravity of Resources and theTyranny of Distance

Peter E. Robertson ∗

The University of Western Australia

Marie-Claire RobitailleThe University of Nottingham Ningbo China

November 2014

Abstract

Falling transport costs and the rise of global production networks have reshapedworld trade. But endowments still determine production locations for fuels andminerals. Moreover, because they are often bulky or difficult to store, unit transportcosts for natural resources may still be very large. To what extent, therefore,does geography remain an important determinant of comparative and absoluteadvantage in these markets? We estimate gravity models and show that someminerals and fuels, particularly Iron Ore and Gas, have very high elasticities oftrade with respect to distance. We then consider counterfactuals, how trade woulddiffer if location advantages were eliminated. We find that for a few resourceintensive countries, distance barriers have a large impact of their market share andare equivalent to a 70-100% ad valorem export tax relative to an average country.Similar implicit subsidies apply for a large number of well located countries.

Keywords: International Trade, Energy, Resources, Gravity Model, Geogra-phy.

JEL: F14, Q02

∗Robertson acknowledges the hospitality of St. Antony’s College Oxford and The Centre for theStudy of African Economies (CSAE) in the Department of Economics at The University of Oxford. Weare grateful for comments from Peter Hartley and the participants at the Asia Pacific Trade Seminars(APTS) at Southeast University, Nanjing, China, 2013 and the International Workshop on Distanceand Border Effects in Economics, Loughborough University, January 2014. Corresponding Author;Peter E. Robertson, Economics, School of Business, University of Western Australia, Perth. Email:peter.robertson@uwa.edu.au. Phone: +61 6488 5633.

DISCUSSION PAPER 15.01

1 Introduction

Historically only the most precious commodities, such as silk and spices, were traded

over very long distances, due to high transport costs. Remote antipodean colonies thus

suffered from a “tyranny of distance”.1 Technological changes, however, have allowed

dramatic reductions in transport costs. Consequently, world manufacturing is now char-

acterized by global production networks and intra-industry trade in product varieties.

The importance of endowments as a source of comparative advantage is thus believed

to have been eroded and preferences and institutions are argued to now be critical in

understanding trade patterns (Romalis 2004, Levchenko 2007, Chor 2010).

But for resource exporting countries, supply is indelibly linked to that country’s endow-

ments. Moreover many resources, such as iron ore and coal, are bulky and have high

unit transport costs. Others, such as as fresh food and gas, have high storage costs.

These two facts – the link between endowments and supply and the high transport costs

– suggest that geography remains a very important factor in explaining the pattern of

resources trade as well as the sources of absolute advantage, or “competitiveness”, in

resource sectors.

Understanding these issues is important since the impact of distance on each country’s

absolute advantage, will influence the degree of market power wielded by that country.

Likewise it will influence the impact of tax and other policies aimed at resource companies

in terms of their competitiveness and ability to seek alternative host countries. It is also

important for understanding how future changes in technology that effect transport costs

might affect world trade patterns and country’s competitiveness in international markets.

The aim of this paper, therefore, is to quantify the impact of distance on world food,

minerals and fuels trade patterns. Specifically we show how world commodity trade

patterns would differ if the natural barriers due to country location did not exist. We

estimate gravity models for several energy and resource commodities and then consider

a counterfactual set of distance values that removes any advantage or disadvantage of

distance for any single exporting country. Thus we consider what would trade patterns in

food, minerals and fuels would look like, if no country had an advantage or disadvantage

in terms of geographical location.

We find that several resource intensive economies’ exports are significantly suppressed

1The term is due to Blainey (2001). He notes that the net inflow of diggers during the gold rush inAustralia provided empty ships in Melbourne which could be used for wool exports. Blainey argues thatthe availability of empty ships reduced transport costs enough to make wool exports, which also had lowperishability and a high value to weight ratio relative to wheat, a feasible export.

2

by their geographical disadvantage. Specifically there are five countries in which exports

would be 35-50% higher if their location were an average distance from their markets. For

these countries transport costs are the equivalent of a 70-100% ad valorem resource export

tax relative to the average country in terms of geographical location. Moreover there are

many countries that enjoy similar, or larger, implicit “subsidy” due to their location.

The difference between being well located or remote is therefore very substantial. These

differences are therefore very important in explaining the global pattern of resources

trade, tax decisions of governments and location decision of firms.

The remainder of this paper is organized as follows. In Section 2 we discuss the geography

of world demand for different commodities. In Section 3 we consider gravity models for

food, different raw materials and fuels. Sections 4 and 5 then consider the implications

of a counterfactual experiment where distance barriers are equalized across countries and

Section 6 concludes.

2 The Geography of World Resources Demand

Our first task is to characterize the geography of world demand patterns for resources.

This differs substantially by commodity. For example, although the USA is the world’s

largest importing country overall, China is the world’s largest importer of Minerals fol-

lowed by Japan. Likewise, with respect to Coal, Japan is by far the largest importer

accounting for almost a quarter of world import demand while China is only the 10th

largest country, behind countries like Italy and India.

Thus measures of the distance of a country from their export market will differ by com-

modity. Table 1 summarizes the import shares for selected resource commodities based

on COMTRADE/WITS data, following the Standard International Trade Classification

1 (SITC-1), for 2006 while Figures 1 to 7 (Panel A) map the major importers for selected

commodity groups.

It can be seen that overall world import demand is largest in Europe-Central Asia (44%)

followed by roughly equal shares from East Asia (23%) and North America (22%).2

However, with respect to Minerals, world import demand is roughly split between Europe-

Central Asia (38%) and East Asia (46%). Within this category world demand for Iron

Ore is predominantly from East Asia (70%), of which China alone accounts for two thirds

(48%). Thus East Asia’s world share of Minerals imports is more than twice as large as

2The list of countries included in each region is provided in Table A.1 while the definitions of thecommodity groups are provided in Table B.1.

3

its overall world import share while in North America’s Minerals import share is a mere

third of its overall world import share.

Likewise world Coal demand is mostly split geographically between East Asia and Europe-

Central Asia, with relatively little demand from North America. However, for Gas and

Petroleum, world demand is more evenly divided between Europe-Central Asia (35 and

36%), North America (27 and 25%) and East Asia (29 and 29%).

Thus the world import demand is very biased toward East Asia for Minerals, especially

Iron Ore, and for Coal. Conversely North American demand for these same commodities

is much lower than its total trade shares. With respect to fuels other than Coal, however,

world import demand shifts toward North America. Europe and Central Asia, in contrast,

has a very large demand for Manufactured goods and Food, but relatively little demand

for Minerals, except Coal.

Finally as can be seen in Table 1, Food and Raw materials import patterns are similar

to Manufacturing patterns and the overall average, though Europe can be seen to be a

relatively large importer of Food and Raw Materials, while North America and Asia tend

to import less food relative to other commodities.

To what extent, therefore, does geography account for world trade patterns in these

resources? One way of addressing this is to consider what the world pattern of trade

would be if there was no location advantage or disadvantage for any country. This is our

aim in the rest this paper.

[Table 1 and Figures 1-8 about here]

3 The Gravity Model

3.1 Estimation Strategy and Data

To estimate the impact of distance on the patterns of trade and specialization, we use

the gravity model. Given our focus on commodities, we adopt Santos Silva and Tenreyro

(2006) specification of the gravity model as our preferred specification. This has the ad-

vantage of handling the numerous zero trade flow observations, which are highly prevalent

in the Minerals and Fuel sectors. Specifically in our sample, 61% of the country-pair-year

do not trade Minerals, but only 18% do not trade Manufactured Goods.

Our data set is constructed from COMTRADE/WITS yearly data for the 104 economies

4

for which all variables are available, following the SITC-1 classification for the period

1998-2003 and 2006.3, 4 We use import data to measure trade flows between country-pair

as imports data are believed to be less at risk of double-counting and misreporting of the

country of origin/destination than exports data (Athukorala 2009).

There are two problems with the traditional log-linearization of the gravity model equa-

tion. The first is that the data usually contain many zero values. This may arise

from missing values or represent genuine instances of zero trade between country-pairs.

The common solution of omitting the zero trade observations leads to selection bias

(Santos Silva and Tenreyro 2006, Disdier and Head 2008). Santos Silva and Tenreyro

(2006) also point out that the log linearization of the gravity equation leads to biased co-

efficient estimates in the presence of heteroscedasticity. They propose the use of a Poisson

Pseudo-Maximum-Likelihood (PPML) model which, by avoiding log-linearization, thus

avoids the problem of zeroes and bias.5

Thus following Santos Silva and Tenreyro (2006), we estimate the gravity model using

the PPML estimator.

Xcijt = αGRAV γg

g εFEf δf (1)

with Xcijt the trade volume in million USD (constant 2000 USD) between exporting

country i and importing country j for commodity c in year t.

GRAV is a matrix including the standard gravity variables. From the World Develop-

ment Indicators (WDI), we include the log of GDP of the exporting country (ln gdp) and

of the importing country (p ln gdp), the log of the population of the exporting country

(ln pop) and of the importing country (ln p pop) as well as the log of the land area of the

country-pair (ln land paire). From the Centre d’Etudes Prospectives et d’Informations

3As in Greenaway, Mahabir and Milner (2008), we include Hong Kong’s exports with China’s exportsas the two economies are closely integrated. As pointed out by Greenaway et al. (2008) many exportsoriginating from those two countries combine management and distribution skills from Hong Kong andlabour from China.

4Those years and classification system have been selected so as to include, as far as possible, all largenatural resources exporters.

5An alternative approach is to use the Tobit model. However, as pointed out by Linders and Groot(2006) this approach relies on assumptions on the data generating process that do not hold in the gravitymodel of trade. More precisely, the Tobit model assumes that the data suffer from rounding, which ishighly uncommon for trade data, or that the desired outcome may not be measured by the actualoutcome, for example, negative value, again a characteristic not applicable to trade data. Helpman,Melitz and Rubinstein (2008) similarly consider a selection model where only the most productive firmsexport. We find that their model is compelling for understanding manufacturing trade but less convincingfor resources trade.

5

Internationales (CEPII) data set, we take the log of the weighted great-circle distance

between the country-pair, with the weight depending on the population distribution

within both partner countries (ln distwces), a set of dummy variables classifying the

pair of country as none is landlocked (reference category), one country is landlocked

(landlocked 1) and two countries are landlocked (landlocked 2), a dummy variable tak-

ing the value of one if the two countries in the country-pair are contiguous (contig), a set

of dummy variables classifying the country-pair into none of the countries is an island

(reference category), one country in the country-pair is an island (island 1), and the

two countries in the country-pair are islands (island 2), a dummy variable taking the

value of one if at least one language is spoken by at least 9% of the population in both

countries (comlang ethno) and, finally, a dummy variable taking the value of one if the

two countries in the country-pair have ever been in a colonial relationship (colony).

We also include the log of human capital level in the exporting country (hum k) and

of the importing country (p hum k).6 Finally, FE is a set of fixed effects for exporting

countries (COUNTRY ), for importing countries (PCOUNTRY ) and for years (YEAR).

The standard errors are adjusted for clustering at the country-pair level.

The commodities have been classified into broad categories, that is, All, Food, Raw

Materials, Minerals, Coal, Petrol, Gas and Manufactured Goods. Given its importance

in world trade, we also estimate a model for Iron Ore (24% of Minerals exports in 2006).

The exact SITC-1 categories included into each element of this classification are presented

in Table B.1.

3.2 Trade-Distance Elasticities by Commodity

The results are given in Table 2. We find that an increase of 1% in distance leads to a

drop of approximately 0.77% in total exports and a 0.75% fall in Manufacturing trade.

These results are consistent with the existing literature (Disdier and Head 2008). It can

be seen, moreover, that distance is highly significant for all of the different commodity

groups. Nevertheless it can also be seen that the elasticities for some commodities differ

substantially from Manufacturing. They range from -0.86 for Raw Materials to -1.96 for

Iron Ore and -2.56 for Gas. In general the trade elasticities for Iron Ore and fuels are

substantially larger than the elasticities of other commodities and Manufacturing.

Interestingly we do not find much evidence than geography matters in other respects

6Human capital is measured using the Mincerian relationship e0.15s where s is the average years ofschooling in the labour force (Barro and Lee 2010).

6

than distance and the contiguous border dummy. Sharing a border increases the volume

of trade by 26%. Moreover the impact of sharing a border is particularly large in the

case of Minerals and Gas, increasing the volume of trade by approximately 60%.

[Table 2 about here]

3.3 Quantifying the Impact of Distance

To quantify the implications of geographical location in determining the pattern of world

trade we consider a counterfactual experiment, where each export market is at the same

distance from every country. In designing this experiment, we keep the average distance

to importing country the same so as to keep the geography of demand constant.

More precisely, let denote the actual distance from exporting country i to import country

j as dij. We then replace all of the dij with a common value d̄j where d̄j is the average

distance for all exporters to destination market j, that is d̄j =∑

i dij/(n−1), where n is

the number of countries. One way to think of this is that we impose a destination specific

export tax, or subsidy, on all countries in proportion to their distance from the export

market, such that that no country has any transport cost advantage or disadvantage due

to the distance from its export markets.

Thus we calculate the average distance of all countries in our sample to each destination

country. We hence have an average value specific to each importing country. These values

are presented in Table C.1. We then use these counterfactual values d̄j to recalculate

total world trade for each commodity group using the coefficients from (1).

4 Geography and Global Resource and Energy Ex-

port Shares

The aggregate change in trade implied by this experiment for each commodity group is

shown in Table 3. It can be seen that although average trade weighted distance only

increases by 5%, total world trade in the experiment falls by 34%.7

7The average trade weighted distance is calculated for the year 2006 using the following formula. LetJ be the set of world markets and J−i be the set of export markets for country i ∈ J . We define theaverage distance to market for country i for commodity c as Di,c =

∑j dijsjc, j ∈ J−i, where sjc is

country j’s share of imports for all countries j ∈ J−i. Likewise the average distance to market impliedby the counterfactual distances is D′i,c =

∑j d̄jsjc, where it will be recalled that d̄j is the counterfactual

common distance between all exporters and the destination market j.

7

Thus as we redistribute distance equally across countries, but preserve total distance be-

tween all country pairs, trade volumes fall substantially. Hence, despite keeping the total

average distance constant, the total trade costs have increased. This reflects the fact

that neighboring countries trade much more with each other than with distant countries,

and that the relationship between trade and distance is non-linear. In particular Europe

consists of many large countries with a lot of trade, while many remote southern hemi-

sphere countries are small. Effectively by breaking up Europe, we reduce world trade in

the counterfactual.

Nevertheless there are significant differences by commodity. For example it can be seen

that equalizing export distance across countries causes Gas trade to collapse – falling by

78%. In contrast Iron Ore trade increases by 48%.

A detailed visual description of the changes in each market is given in panel B of Figures

1-8. Table 4 summarizes the actual and counterfactual exports share by broad region

while Table 5 presents the actual and counterfactual export shares by country.

[Tables 3 to 5 about here]

4.1 Food

First we consider the pattern of change in world Food supply. As Europe and Central

Asia alone accounts for over 50% of world Food imports with the two other major regions,

East Asia and North America, have, respectively, a world share of 16% and 19% (Figure

2, Panel A), European and Central Asian countries have a clear locational advantage

when it comes to food exports.

Thus, in our experiment, European countries lose their world share of Food exports,

which falls from 48% to 25% (Table 4 and Figure 2, Panel B). Most other regions gain

exports shares but particularly South America and East Asia and the Pacific. Thus in

this market there is a very clear locational advantage for the European and Central Asian

region. As we shall see, this last result is very important when we come to look at how

distance affects individual country’s overall advantage or disadvantage in world natural

resources markets.

8

4.2 Minerals

Recall from the preceding discussion that Minerals imports are focused geographically

on East Asia, particularly China, Japan and South Korea (Figure 4, Panel A). Panel B

of Figure 4 shows the actual and counterfactual export patterns. It can be seen that

Australia and the Americas (Brazil, Chile, Canada and the USA) dominate the world

supply of Minerals, accounting for 42% of world Minerals exports.

In the counterfactual there are large increases in exports in the southern hemisphere –

South America, Australia and South Africa – roughly neutral impact in North America

and a collapse of exports in Europe-Central Asia and East Asia. The shares of Australia,

Brazil and Chile increase dramatically with the collective share doubling from 27% to

44% of world exports of Minerals (see Tables 4 and 5).

Hence in the world Minerals market there is a relatively large dispersion between pro-

ducers and their main destination markets and this dispersion has a very significant cost

on the southern Minerals giants such as Australia and Brazil. This also suggests that

these regions still stand to make substantial gains in terms of world market shares with

new transport technologies and reduction in transport costs.

4.3 Iron Ore

Iron Ore is a subset of Minerals that, as noted above, is mainly imported by East Asia,

especially China. It is dominated on the supply side by Brazil and Australia, which

mutually account for 61% of world exports (Figure 5). Because of this concentration of

demand in East Asia, Brazil is much farther from the world’s largest Iron Ore importers

than Australia. So in relative terms Australia is close to the Iron Ore market.8

In the counterfactual, because of its relative proximity to East Asia, Australia market

share falls by approximately one third to 23% of world trade. Likewise European based

exporters such as Sweden and Ukraine are driven out from the market. Brazil however

almost doubles its export share from 31% to 59% of world exports. Similar results are

found for the other Latin American countries – Peru, Venezuela and Chile – though their

shares of world trade are very small. Moreover total Iron Ore trade flows increase in the

counterfactual, suggesting that Brazil’s distance from China is an important impediment

to world Iron Ore trade.

8In absolute terms the Iron Ore market is the most dispersed with trade weighed distance falling by19% in our counterfactual experiment, as shown in Table 3.

9

The large changes in world Iron Ore trade shares reflect not only Brazil’s rather large

distance from its market but also the very large elasticity of Iron Ore trade with respect

to distance of -1.96. The experiments thus endorse the popular view that Australia

has benefited from its locational advantage to China and that Brazil is particularly

disadvantaged, at least in the Iron Ore market.

4.4 Coal

Whereas the world Coal market is relatively dispersed across northern hemisphere coun-

tries in Europe-Central Asia and East Asia, Panel B of Figure 6 shows that supply is

very concentrated in the South with one country, Australia, accounting for 28% of the

world exports. Removing any distance disadvantage increases Australia’s share of world

exports substantially to 47%.

Hence with respect to Coal the popular notion that Australian resource exports have

benefited from its proximity to Asia can be seen in a somewhat different light. For Coal,

Australia’s relative proximity to Japan and China does not offset the cost of remoteness

from Europe. Moreover, unlike Iron Ore, there are apparently no major South Amer-

ican suppliers that would be in a position to expand substantially supply if distance

disadvantages were removed.

The results also show that South Africa faces a similar geographical disadvantage as

Australia - though its Coal exports are substantially smaller. The two second largest

exporters, however, China and Russia, have significant geographical advantages being

relatively close to both East Asian and European-Central Asian markets.

4.5 Petroleum

The demand for imported Petroleum is almost equally split between Europe-Central

Asia (36%), North America (25%) and East Asia (29%) while the supply of Petroleum

is essentially split equally between the Middle-East (32%) and Europe-Central Asia,

including Russia, (34%) (Figure 7).

The European-Central Asian exporters are therefore strongly advantaged by their geo-

graphical location within the largest Petroleum import market. This is confirmed by the

counterfactual results which show that Saudi Arabia’s share of world Petroleum exports

increases from 15 to 25% and Kuwait’s share increases from 4 to 7% in the counterfactual

case. Overall the Middle-East share rises to 46%, while Europe and Central Asia’s share

10

falls to 25% (Table 4). Thus the European-Central Asian exporters have a significant

advantage relative to the Middle-East. The exporters on the American continent, though

small on the world market, nevertheless are also shown to be gaining significantly from

their proximity to the USA.

4.6 Gas

It can be seen immediately in Figure 8 that the Gas market lacks the South-North pattern

seen in the preceding commodity markets due to significant Gas exporters in the northern

hemisphere. The exception is the Australia-Indonesia-Malaysia East Asia LNG corridor

which accounts for 17% of world Gas exports.

Central to the Gas market is the issue of the delivery mode. Over a short distance

pipelines are the cheapest mode of transport, while over long distances, shipping liquified

gas (LNG) is the only viable solution. Indeed, estimating the model separately for LNG

and for Gaseous gas, we find that the elasticity of distance for Gaseous Gas (-5.7) is

approximately double the LNG elasticity (-2.7) (Table 6).9 In either case however the

elasticity of trade to distance is much higher than for other commodities.

Thus countries that share a border with a large Gas importer benefit significantly from

their geographical location, with notably Canada and the USA market and, to a smaller

extent, Algeria and the European market. At the other extreme the Middle-Eastern

countries, in particular Saudi Arabia and Qatar, but also Australia, suffer from their

incapacity to be connected via pipelines to large import markets.

Our counterfactual of removing distance advantages or disadvantages would lead to major

changes in the Gas market, with current big players, such as Canada and Algeria, to be

almost completely eliminated from the market. Conversely Australia’s world exports

share rise from 3% to 13% and Saudi Arabia and Qatar’s world share would more than

double. This is of interest given that there have been recent technological advances that

have lowered the cost of transporting LNG (Ruester 2010). Consequently there is also

evidence that the elasticity of trade to distance has started dropping significantly in the

LNG sector (see Table 7).

[Tables 6 and 7 about here]

9To maximise the sample size and to ensure that as many major natural resource exporters areincluded in our sample, we use SITC-1 classification throughout this paper. However, for Gas, asthe SITC-1 classification does not distinguish between LNG and Gaseous Natural Gas, we use SITC-3classification for this set of results.

11

5 The Tyranny of Distance

Given the preceding discussion it is clear that a country’s location has an important effect

on its volume of resource trade. Brazil probably exports much more Petroleum and much

less Minerals than it would if it were located elsewhere. Australia is close to Asia and

this is typically regarded as being very advantageous in terms of resource exports. We

have found that this is true for Iron Ore but Australia still suffers a tyranny of distance

in terms of Coal.

How then do these costs and benefits of location add up for each country? Specifically,

which countries face the greatest disadvantage of location in resources? Table 5 reports

the total percentage change in resources trade for each country as a result of removing

the relative distance barriers across countries. The countries with the largest gains are

thus the countries that currently suffer the greatest competitive disadvantage from their

geographical location. For each country we also report the composition of this change by

commodity, so we can see which particular markets contribute to that country’s remote-

ness.

Figure 9 shows this information for all the countries where the total increase in trade

exceeds 10%. There are 11 such countries out of a sample total of 104. It can be seen

that the countries at the greatest disadvantage are the antipodean countries: Australia,

New Zealand and the South American and South African resource exporters.

For the 5 most disadvantaged countries the losses are very significant, exceeding 30%.

Chile and New Zealand are particularly affected with a loss of around 50%. That is Chile

and new Zealand’s resources trade flows are predicted to be around 50% larger if their

distance to exports markets were at the world average for each commodity.10

To convert these quantity changes to ad valorem tax equivalents, a useful rule of thumb

is that the elasticity of demand is approximately -1/2 across a wide range of commodities

(Deaton 1974, Clements 2008). Thus the change in export quantities of around 50% for

Chile and New Zealand implies that the cost disadvantage of distance, relative to the

average country, is approximately the equivalent of a 100% export tax. Likewise the

magnitude for South Africa, Brazil and Australia is similar, with a handicap equivalent

to approximately a 70% export tax across their resources trade as a whole.

10These values are all percentage changes. In terms of absolute changes the countries that stand themost are Australia in terms of Minerals, Brazil in terms of Coal and the USA in terms of everything else.Thus the absolute gains and losses tend to reflect the country’s size in the market. In general Brazil,Australia and the USA are the biggest remote countries.

12

These values however are relative to a country that suffers no particular advantage or

disadvantage. Kenya is an example of such a country (Table 5). The disadvantage of these

antipodean countries relative to Kenya is much smaller than their disadvantage relative

to the least remote countries. For convenience Figure 10 thus shows the 20 countries

that experience the greatest fall in their resource trade volumes in the counterfactual.

These countries have the opposite of a tyranny of distance, that is, the “benevolence of

proximity”.

It can be seen that, in our counterfactual, trade volumes fall by around 70% in these coun-

tries. Thus we can reconsider the disadvantage of distance implied by our experiments

by comparing for example New Zealand with a well-located economy such as France. In

France the total percentage loss in trade is 70% whereas for New Zealand the total gain

is 46%. This suggests that the combination of implicit “tax” on New Zealand produc-

ers and an implicity “subsidy” to French producers adds up to a 106 percent change in

volumes and a 212% ad valorem tax disadvantage for New Zealand producers relative to

France. In general the results show that many country, mostly European, benefit from

an enormous locational advantage over more remote countries. This advantage is often

in the dimension of a “subsidy” of over 100%.

It can also be seen that the reasons behind these changes differs by country. For New

Zealand the key factor is its distance from European Food markets. For Chile and

Brazil the distance from world Minerals demand is the key component. As we have

seen above this is due to their distance from Asia relative to other countries. Likewise

Australia and South Africa have a very similar pattern in terms of the composition of

their distance disadvantage with Food, Minerals and also Coal being similarly important

for each country. It can be seen further that Argentina is not as remote overall as, for

example, Chile and Brazil. This is in part because it exports Petroleum and Gas to North

America, and in part because minerals is a relative small component of its exports, so it

is not affected by the distance to Asia in the same way Brazil is.

Finally for the least remote countries we can see in, Figure 9, that their advantage is

mostly in Food and Petroleum. Thus the Slovak Republic, Korea, Algeria, Norway and

Mexico are shown to have particular luck in terms of having both Petroleum endowments

and proximity to large markets. In each case the benevolence of proximity adds around

30-50% to their Petroleum export sales. Two exceptions from the general pattern are

Poland and the Czech Republic which also benefit considerably from having coal deposits

and being within Europe. Similarly Canada and Algeria have significant advantages in

terms of the world Gas market.

13

[Figures 9 and 10 about here]

6 Conclusion

While a broad literature exists on the importance of geography in explaining the volume

of trade across countries, little is known on the importance of geography in explaining

the predominance of some countries in resource exports. In contrast to manufacturing

trade we have find that geography plays a very important role in explaining trade in

commodities. This is due to both the relatively high transports costs and the fact that

resource export supply is limited by natural endowments.

We show first that many resources, particularly Iron-Ore and Gas, have very large elas-

ticities of trade with respect to distance. However the impact of geography also depends

the geographic separation of the export sources and the geographic distribution of de-

mand. Thus we also show show that equalizing distances to markets would have large

effects in some markets and on a country shares of world resource exports.

In particular we found that the several southern resource exporting countries are sig-

nificantly disadvantaged by their location. However Southern Food producers such as

New Zealand were also found to have a large disadvantage, while many Latin American

petroleum exporters were found to have a smaller total disadvantage due to the proximity

to the USA.

Finally, we also show that the impact of distance on exports is very large. The counter-

factual resulted in an increase in export sales in the range of 35-50% for several countries.

This implies the equivalent of an export tax in the order of 70-100%. Likewise for many

countries the location advantage is very large. Especially the changes in trade volumes in

the counterfactual for many countries are also consistent with subsidies exceeding 100%.

Consequently, we conclude that the ability of many countries to be competitive in the

world resource markets derives, to a large extent, from their location. This suggests that

the responsiveness of extraction firms to national policies, for example with respect to

resource taxation and regulation, may be quite low, particularly in Europe. Nevertheless

the results also show that this advantage differs by sector, due to the differing geographical

distribution of world demand by commodity. Finally the results also suggest that future

technological changes that reduce transport costs have the potential to substantially alter

country market shares in world resource markets.

14

Appendix A: Country Coverage by Region

[Table A.1 about here]

Appendix B: Definition of Variables

[Table B.1 about here]

Appendix C: Average Distance

[Table C.1 about here]

Figure 1: Trade Shares: All

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 2: Trade Shares: Food

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 3: Trade Shares: Raw Materials

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 4: Trade Shares: Minerals

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 5: Trade Shares: Iron Ore

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 6: Trade Shares: Coal

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 7: Trade Shares: Petroleum

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 8: Trade Shares: Gas

Import Shares

Export Shares

Source: WITS/COMTRADE data for selected countries in 2006, SITC-1 classification. Authors’ calculation.

Figure 9: The Tyranny of Distance: Percentage Change of Export Values when Location Advantages/Disadvantages Are Removed

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Food Raw Minerals Coal Petrol Gas

Figure 10: The Benevolence of Proximity: Percentage Change of Export Values when Location Advantages/Disadvantages AreRemoved

-80

-60

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Food Raw Minerals Coal Petrol Gas

Table 1: Regional Share of World Imports in 2006

Europe and North America South America Middle-East East Asia and South Asia Sub-SaharanCentral Asia the Pacific Africa

All 44.23 21.78 5.57 2.51 23.26 1.56 1.09Food 52.96 16.15 5.42 4.65 18.80 0.58 1.44Raw 41.92 13.61 5.25 3.10 31.95 3.02 1.16Min. 38.30 6.72 3.52 1.12 46.30 3.76 0.28Iron Ore 22.51 2.89 2.35 1.03 70.91 0.20 0.11Coal 40.62 6.83 5.03 2.14 37.38 7.44 0.55Petrol 35.88 25.33 3.61 1.48 29.22 3.21 1.27Gas 34.57 26.85 5.64 1.96 29.06 1.81 0.11Manuf. 44.89 22.13 6.02 2.54 22.14 1.26 1.03Source: COMTRADE/WITS data. Authors’ calculation.

Table 2: PPML Results

(1) (2) (3) (4) (5) (6) (7) (8) (9)All Food Raw Min. Iron Ore Coal Petrol Gas Manuf.

ln gdp 1.502*** 0.479*** 0.458** -0.406* -0.008 0.792** 0.904*** 0.212 1.638***(0.000) (0.000) (0.025) (0.093) (0.991) (0.016) (0.000) (0.796) (0.000)

p ln gdp 1.289*** 0.803*** 1.553*** 2.384*** 3.032*** 1.089*** 1.006*** -0.240 1.342***(0.000) (0.000) (0.000) (0.000) (0.000) (0.006) (0.001) (0.746) (0.000)

ln pop 0.330 -1.274*** -1.214*** 2.109*** 2.117 0.115 -0.925*** 1.029 -0.828**(0.249) (0.000) (0.008) (0.005) (0.268) (0.942) (0.002) (0.150) (0.028)

p ln pop -0.846*** -0.126 -0.863* -1.714** -1.754 0.176 0.926 6.935** -1.004***(0.002) (0.703) (0.083) (0.021) (0.119) (0.863) (0.264) (0.022) (0.000)

ln distwces -0.767*** -0.968*** -0.864*** -0.959*** -1.964*** -1.220*** -1.372*** -2.557*** -0.752***(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

ln land paire -0.090 -0.027 -0.141** 0.043 -0.135 0.082 0.095 0.110 -0.139***(0.112) (0.620) (0.018) (0.613) (0.572) (0.742) (0.397) (0.540) (0.004)

landlocked 1 0.790 -0.410 3.827* 8.455*** 8.077* 2.707 3.806 9.772 0.679(0.554) (0.802) (0.083) (0.007) (0.065) (0.553) (0.255) (0.202) (0.602)

landlocked 2 1.918 -0.178 8.302* 17.431*** 15.205* 7.225 9.652 19.490 1.571(0.474) (0.957) (0.061) (0.005) (0.081) (0.430) (0.149) (0.205) (0.547)

contig 0.264*** 0.248*** 0.358*** 0.590*** 0.839** 0.361 0.158 0.596* 0.194**(0.000) (0.006) (0.000) (0.000) (0.013) (0.139) (0.348) (0.051) (0.011)

island 1 -1.519 -0.378 0.437 3.789 8.866 1.848 6.708 25.882* -2.306(0.359) (0.843) (0.876) (0.381) (0.163) (0.757) (0.139) (0.058) (0.158)

island 2 -2.717 -0.559 0.790 7.965 17.876 4.215 13.254 52.465* -4.346(0.408) (0.882) (0.887) (0.358) (0.159) (0.723) (0.146) (0.055) (0.179)

comlang ethno 0.305*** 0.362*** 0.115 0.236* 0.169 0.006 0.549*** -0.087 0.306***(0.000) (0.001) (0.298) (0.055) (0.556) (0.979) (0.004) (0.746) (0.000)

colony -0.101 0.179 0.101 0.411*** 1.734*** 0.382* 0.228 0.419 -0.142(0.324) (0.147) (0.430) (0.005) (0.000) (0.073) (0.241) (0.201) (0.162)

hum k 0.209*** 0.177*** 0.096* 0.041 0.203 0.189* 0.029 0.350 0.197***(0.000) (0.000) (0.088) (0.456) (0.306) (0.089) (0.716) (0.266) (0.000)

p hum k 0.093*** 0.015 0.068 0.180*** -0.016 0.004 -0.038 -0.117 0.118***(0.000) (0.487) (0.244) (0.000) (0.854) (0.968) (0.417) (0.400) (0.000)

Constant -61.765*** -13.704** -31.129*** -49.403*** -73.869** -45.247* -39.501*** -19.879 -59.360***(0.000) (0.017) (0.000) (0.000) (0.022) (0.052) (0.000) (0.473) (0.000)

Observations 74,984 74,984 74,984 74,984 74,984 74,984 74,984 74,984 74,984Pseudo R2 0.952 0.908 0.897 0.870 0.932 0.892 0.859 0.902 0.963

Notes: Country, partner country and year fixed effects are controlled for. Standard-errors are adjusted for clustering at the country-pairlevel. ***, p-value< 0.01; **, p-value< 0.05; *, p-value< 0.10.

Table 3: Distance and Total Trade

Unweighted Distance Weighted Distance Trade ValueActual Count. Change in % Actual Count. Change in % Actual Count. Change in %

All 7726 7730 0.05 7310 7703 5.37 8245530 5445163 -33.96Food 7374 7381 0.09 7216 7385 2.34 493876 310746 -37.08Raw 7762 7768 0.07 7943 7748 -2.46 193021 140350 -27.29Min. 7927 7939 0.15 8858 7932 -10.45 156250 133808 -14.36Iron Ore 8594 8617 0.26 10589 8618 -18.61 37322 55200 47.90Coal 7859 7866 0.08 8896 7856 -11.69 52327 51711 -1.18Petrol 8086 8087 0.00 7462 8084 8.33 867712 444992 -48.72Gas 8061 8062 0.01 7475 8054 7.75 113589 24685 -78.27Manuf. 7714 7717 0.04 7251 7687 6.00 6191161 4122514 -33.41

Table 4: Regional Share of World Exports in 2006: Actual and Counterfactual

Europe and North America South America Middle-East East Asia and South Asia Sub-SaharanCentral Asia the Pacific Africa

Actual Count. Actual Count. Actual Count. Actual Count. Actual Count. Actual Count. Actual Count.All 41.40 28.98 14.93 16.41 6.16 7.58 4.21 4.91 31.14 38.78 1.28 1.77 0.88 1.57Food 47.82 25.24 15.50 19.03 14.48 24.94 1.45 1.24 16.68 23.20 1.67 2.19 2.41 4.16Raw 35.25 19.91 25.90 29.68 11.00 19.92 1.12 0.90 23.28 25.18 1.06 1.00 2.39 3.41Min. 29.15 12.70 15.18 15.41 19.77 35.08 3.19 2.25 24.51 25.03 3.63 2.64 4.57 6.89Iron Ore 10.55 0.99 6.17 3.26 34.41 65.04 0.29 0.06 31.87 23.09 12.57 2.26 4.13 5.31Coal 20.11 5.69 13.52 12.21 6.49 8.20 0.22 0.08 51.42 61.50 0.02 0.01 8.22 12.30Petrol 34.07 25.15 6.39 3.48 12.91 12.42 32.06 45.74 12.47 9.45 0.67 0.92 1.43 2.85Gas 24.86 13.42 27.70 4.86 4.63 6.81 24.99 41.10 17.78 33.51 0.01 0.01 0.04 0.29Manuf. 42.50 29.59 15.04 16.75 4.33 4.79 1.22 1.33 35.15 44.81 1.26 1.76 0.51 0.96

Table 5: Percentage Change of Export Values When Locational Advan-tages/Disadvantages Are Removed

Rank Country Food Raw Minerals Coal Petrol Gas Total

1 Chile 14.61 5.31 30.53 0.01 0.13 -0.21 50.38

2 New Zealand 33.48 10.24 0.64 1.40 0.73 0.00 46.50

3 South Africa 8.41 1.85 12.57 12.77 2.38 0.19 38.17

4 Brazil 12.61 6.97 16.31 0.02 2.24 0.01 38.15

5 Australia 9.35 2.36 6.19 14.48 3.31 -0.71 34.99

6 Paraguay 6.93 19.61 0.55 0.00 -0.25 -0.05 26.79

7 Uruguay 16.68 7.07 0.38 0.00 -0.75 -0.03 23.35

8 Mauritius 22.35 0.32 0.23 0.00 0.03 0.01 22.93

9 Peru 4.24 1.36 21.03 0.01 -3.73 -0.11 22.80

10 Zambia 4.28 2.94 6.65 0.00 0.15 0.05 14.07

11 Argentina 12.43 7.12 2.23 0.00 -6.10 -3.71 11.97

12 Guyana -1.35 0.67 8.46 0.00 0.00 0.00 7.77

13 Gabon -0.01 0.23 0.57 0.00 5.33 0.00 6.13

14 Tanzania 2.59 0.14 0.77 0.00 0.30 0.00 3.79

15 Burundi 0.58 -0.02 0.36 0.00 -0.03 0.00 0.90

16 Kenya -0.34 -0.51 0.00 0.00 1.23 0.01 0.40

17 Cameroon -1.23 -1.01 0.00 0.00 0.83 0.00 -1.42

18 Uganda -3.07 -0.72 0.00 0.00 0.03 0.00 -3.76

19 Maldives -3.67 -0.28 -0.31 0.00 0.00 0.00 -4.26

20 Ghana -5.19 -0.25 0.47 0.00 -0.10 -0.03 -5.10

21 CAF -1.15 -4.50 -0.02 -0.02 0.00 0.00 -5.69

22 Cote d’Ivoire -4.85 -0.15 0.03 0.00 -1.07 -0.02 -6.06

23 Benin -1.62 -2.93 0.01 0.00 -1.93 -0.17 -6.64

24 Mali -0.76 -7.51 -0.02 0.00 -0.03 0.00 -8.32

25 Yemen -0.65 -0.06 -0.07 0.00 -7.53 -0.06 -8.36

26 Senegal -6.93 -0.73 0.15 0.00 -2.46 -0.18 -10.15

27 Jamaica -11.02 0.00 4.97 0.00 -4.38 -0.03 -10.46

28 Sudan -1.38 -1.31 -0.07 -0.01 -8.80 -0.06 -11.61

29 Gambia -10.54 -2.15 0.06 0.00 -0.12 0.00 -12.74

30 United States -4.21 0.01 -0.54 -0.02 -6.84 -2.85 -14.44

31 Costa Rica -15.01 0.52 0.24 0.00 -0.31 -0.13 -14.68

32 Bolivia 3.02 3.75 8.29 0.00 -2.50 -29.52 -16.97

33 Kuwait -0.04 -0.01 -0.18 0.00 -18.29 -1.97 -20.50

34 Niger -1.04 -0.21 -0.14 0.00 -19.75 -0.06 -21.20

35 Saudi Arabia -0.09 -0.02 -0.16 0.00 -19.14 -2.02 -21.45

36 Colombia -2.34 0.49 0.21 4.69 -24.50 -0.07 -21.53

37 Ecuador -0.36 0.70 0.09 0.00 -22.62 -0.24 -22.42

38 Honduras -23.17 -0.43 0.79 0.00 -0.16 -0.34 -23.31

39 Nicaragua -18.97 -0.01 0.29 0.00 -4.99 0.00 -23.70

40 Qatar -0.01 0.00 -0.08 0.00 -13.16 -13.57 -26.82

41 India -6.18 -2.69 -10.48 -0.01 -7.88 -0.02 -27.25

42 Panama -9.72 0.09 0.70 0.07 -17.58 -1.35 -27.79

43 El Salvador -23.01 -0.07 0.34 0.00 -4.91 -0.95 -28.60

44 Pakistan -13.33 -6.07 -2.21 -0.02 -7.34 0.00 -28.98

45 Guatemala -21.35 -0.59 0.21 0.00 -8.14 -0.13 -30.00

46 Iran -0.69 -0.18 -0.53 0.00 -28.08 -0.88 -30.36

47 Belize -24.54 -0.14 0.04 0.00 -8.31 -0.03 -32.99

48 Thailand -14.70 -6.66 -1.40 0.00 -9.62 -1.03 -33.41

49 Barbados -2.25 0.06 0.16 0.00 -31.71 -0.09 -33.82

50 Philippines -11.76 -5.97 -12.73 0.00 -5.74 -0.92 -37.11

Rank Country Food Raw Minerals Coal Petrol Gas Total

51 Egypt -4.99 -1.81 -1.95 -0.82 -19.35 -10.07 -38.99

52 Indonesia -2.23 -2.83 -3.93 -0.40 -18.00 -12.10 -39.49

53 Iceland -37.09 -1.45 -0.80 -0.01 -0.15 -0.01 -39.51

54 Kyrgyz Rep. -11.94 -9.36 -18.62 -0.01 -0.39 0.00 -40.32

55 Kazakhstan -1.57 -0.58 -4.79 -1.43 -30.64 -1.44 -40.47

56 Israel -20.06 -7.03 -6.90 -0.01 -10.06 -0.01 -44.07

57 Trinidad -0.25 0.01 0.15 0.00 -26.21 -17.89 -44.20

58 Venezuela -0.13 0.01 0.59 0.23 -44.40 -0.92 -44.62

59 Syria -3.80 -1.83 -0.69 -0.01 -38.84 0.00 -45.17

60 Jordan -10.01 -1.23 -33.90 0.00 -0.80 -0.16 -46.10

61 Morocco -26.23 -1.95 -12.08 0.00 -5.76 -0.24 -46.26

62 Cyprus -29.34 -2.69 -10.47 -0.30 -6.93 -0.46 -50.18

63 Vietnam -10.27 -1.50 -0.80 -1.22 -36.58 -0.01 -50.38

64 China -18.76 -4.13 -4.28 -8.91 -15.17 -0.08 -51.33

65 Turkey -29.50 -4.26 -8.34 -0.03 -8.67 -0.53 -51.33

66 Russia -1.88 -2.04 -2.11 -1.88 -37.54 -7.08 -52.53

67 Malta -9.93 -1.31 -1.69 -0.02 -40.09 -0.38 -53.43

68 Portugal -24.10 -11.80 -7.29 -0.10 -9.81 -0.52 -53.62

69 Greece -28.53 -9.09 -4.51 0.00 -10.76 -1.64 -54.54

70 Finland -7.43 -22.56 -3.72 -0.19 -20.60 -0.10 -54.61

71 Spain -37.19 -5.95 -3.29 -0.42 -5.78 -2.39 -55.03

72 Japan -4.80 -10.78 -17.33 -1.93 -19.89 -0.58 -55.31

73 Ukraine -15.22 -6.49 -15.03 -3.76 -12.85 -2.34 -55.69

74 Singapore -2.59 -1.06 -1.24 -0.05 -51.32 -1.29 -57.55

75 Tunisia -8.89 -7.94 -5.32 -0.01 -35.56 0.00 -57.71

76 Romania -10.51 -13.33 -11.30 -0.10 -24.36 -0.54 -60.14

77 Albania -16.56 -16.16 -18.62 -0.02 -9.16 0.00 -60.51

78 Bulgaria -23.50 -8.42 -8.59 -0.16 -21.70 -0.09 -62.46

79 Latvia -5.69 -15.09 -1.46 -2.04 -38.03 -0.19 -62.50

80 Estonia -8.57 -9.64 -4.41 -2.28 -38.18 -0.02 -63.09

81 Sweden -12.18 -20.59 -8.58 -0.14 -21.26 -0.45 -63.21

82 Macao -27.85 -9.11 -23.09 0.00 -3.72 0.00 -63.77

83 Lithuania -12.82 -5.17 -5.10 -1.04 -38.48 -1.52 -64.13

84 Italy -37.11 -6.37 -3.16 -0.11 -17.93 -0.81 -65.49

85 Malaysia -4.30 -10.75 -1.28 0.00 -36.43 -12.76 -65.51

86 United Kingdom -16.96 -2.25 -3.55 -0.17 -40.24 -3.49 -66.66

87 Mexico -9.90 -0.45 -0.23 0.00 -56.56 -0.24 -67.37

88 Norway -4.88 -0.66 -1.27 -0.14 -53.55 -7.31 -67.81

89 Canada -10.19 -6.99 -2.03 -0.62 -26.30 -22.35 -68.48

90 Ireland -56.54 -2.55 -7.25 -0.66 -2.49 -0.15 -69.63

91 France -48.12 -5.80 -4.43 -0.12 -10.74 -1.53 -70.73

92 Croatia -22.16 -15.57 -9.67 -0.09 -18.11 -5.32 -70.92

93 Slovenia -34.31 -21.99 -12.85 -0.13 -2.79 -0.08 -72.14

94 Poland -32.49 -5.61 -6.11 -20.37 -7.75 -0.06 -72.38

95 Denmark -40.90 -6.30 -2.02 -0.05 -21.98 -1.34 -72.59

96 Hungary -41.99 -8.55 -4.57 -0.58 -17.81 -0.46 -73.98

97 Algeria -0.08 -0.02 -0.31 0.00 -46.48 -27.33 -74.22

98 Germany -38.64 -9.15 -6.68 -0.62 -14.55 -4.62 -74.25

99 Korea -8.80 -6.34 -2.07 -0.02 -57.93 -1.05 -76.20

100 Austria -37.57 -18.70 -9.26 -0.07 -9.30 -1.56 -76.46

101 Netherlands -38.09 -10.54 -4.13 -0.37 -22.83 -2.06 -78.02

102 Czech Rep. -25.42 -15.76 -9.73 -15.60 -12.22 -0.43 -79.16

103 Switzerland -39.48 -8.95 -15.75 -2.13 -12.89 -0.96 -80.17

104 Slovak Rep. -14.20 -8.87 -6.45 -0.43 -52.82 -0.15 -82.92

Table 6: PPML Results: Natural Gas

(1) (2) (3)Nat. Gas LNG Gaseous

ln gdp 0.184 1.331 -3.303(0.873) (0.235) (0.156)

p ln gdp 0.390 1.774 0.126(0.772) (0.224) (0.956)

ln pop 0.836 -0.052 -3.576(0.349) (0.949) (0.648)

p ln pop 8.388** 11.381* 4.609(0.044) (0.094) (0.463)

ln distwces -3.713*** -2.672*** -5.674***(0.000) (0.000) (0.000)

ln land paire 0.110 -0.332 0.324(0.699) (0.183) (0.624)

landlocked 1 11.216** 16.958*** 10.299(0.013) (0.003) (0.226)

landlocked 2 22.200** 39.743*** 19.520(0.015) (0.000) (0.253)

contig 0.780 0.709 -0.708(0.150) (0.605) (0.204)

island 1 27.179** 38.292* 18.686(0.023) (0.051) (0.297)

island 2 54.999** 76.786** 42.732(0.021) (0.050) (0.232)

comlang ethno 0.145 -0.312 0.545(0.754) (0.514) (0.484)

colony 0.558 1.471* -0.912(0.241) (0.096) (0.211)

hum k 0.291 -0.318 0.029(0.453) (0.327) (0.936)

p hum k -0.057 -0.654 0.091(0.739) (0.144) (0.594)

Constant -30.152 -97.625** 79.478(0.455) (0.018) (0.335)

Observations 52,876 52,876 52,876

Notes: WITS/COMTRADE DATA using SITC-3 clas-sification, for the period 1999-2003 and 2006. Coun-try, partner country and year fixed effects are controlledfor. Standard-errors are adjusted for clustering at thecountry-pair level. ***, p-value< 0.01; **, p-value< 0.05;*, p-value< 0.10. To allow the impact of distance tovary over time, interaction variables between distanceand time are included.

Table 7: PPML Results: Natural Gas over Time

(1) (3) (4) (5)All Nat. Gas LNG Gaseous

ln gdp 1.203*** -0.100 0.764 -2.145(0.000) (0.928) (0.393) (0.283)

p ln gdp 0.821*** 0.688 -0.160 -0.137(0.000) (0.625) (0.928) (0.950)

ln pop -0.260*** 0.711 -0.263 -3.133(0.000) (0.420) (0.700) (0.673)

p ln pop -0.016 9.210* 29.756*** 5.187(0.845) (0.062) (0.003) (0.419)

ln distwces -0.782*** -3.696*** -3.185*** -5.583***(0.000) (0.000) (0.000) (0.000)

ln land paire -0.084 0.109 -0.329 0.329(0.142) (0.699) (0.184) (0.620)

landlocked 1 0.438 12.825** 34.200*** 9.856(0.544) (0.038) (0.002) (0.231)

landlocked 2 1.180 25.417** 74.251*** 18.631(0.416) (0.041) (0.001) (0.260)

contig 0.237*** 0.779 0.605 -0.699(0.001) (0.149) (0.633) (0.211)

island 1 0.257** 30.586** 96.711*** 19.781(0.032) (0.046) (0.004) (0.282)

island 2 0.876*** 61.813** 193.615*** 44.877(0.000) (0.044) (0.004) (0.220)

comlang ethno 0.306*** 0.144 -0.351 0.540(0.000) (0.755) (0.461) (0.489)

colony -0.105 0.555 1.399* -0.902(0.304) (0.244) (0.093) (0.217)

hum k 0.172*** 0.342 0.218 0.056(0.000) (0.408) (0.610) (0.892)

p hum k 0.119*** -0.072 -0.046 0.037(0.000) (0.688) (0.935) (0.856)

dist y2000 0.018*** 0.030 0.137 0.043(0.000) (0.586) (0.220) (0.751)

dist y2001 0.008 -0.143 0.129 -0.115(0.151) (0.131) (0.361) (0.563)

dist y2002 -0.008 -0.095 0.201 -0.029(0.343) (0.283) (0.188) (0.901)

dist y2003 -0.002 -0.033 0.288 0.114(0.847) (0.783) (0.122) (0.688)

dist y2006 0.006 0.064 0.849** -0.310(0.671) (0.777) (0.011) (0.552)

Constant -38.905*** -33.642 -108.177*** 60.250(0.000) (0.439) (0.001) (0.422)

Observations 52,876 52,876 52,876 52,876

Notes: WITS/COMTRADE DATA using SITC-3 classification.Country, partner country and year fixed effects are controlled for.Standard-errors are adjusted for clustering at the country-pair level.***, p-value< 0.01; **, p-value< 0.05; *, p-value< 0.10.

Table A.1: Country Coverage by Region

Europe and North America South America Middle-East East Asia and South Asia Sub-SaharanCentral Asia the Pacific AfricaAlbania Canada Argentina Algeria Australia India BeninAustria United States Barbados Egypt China Maldives BurundiBulgaria Belize Iran* Indonesia Pakistan CameroonCroatia Bolivia Israel Japan CAFCyprus Brazil Jordan Korea Cote d’IvoireCzech Rep. Chile Kuwait Macau GabonDenmark Colombia Malta Malaysia GambiaEstonia Costa Rica Morocco New Zealand GhanaFinland Ecuador Qatar Philippines KenyaFrance El Salvador Saudi Arabia Singapore MaliGermany Guatemala Syria* Thailand MauritiusGreece Guyana Tunisia* Viet Nam* NigerHungary Honduras Yemen* SenegalIceland Jamaica South AfricaIreland Mexico SudanItaly Nicaragua TanzaniaKazakstan Panama Tunisia**Kyrgyzstan Paraguay UgandaLatvia Peru ZambiaLithuania TrinidadNetherlands UruguayNorway VenezuelaPolandPortugalRomaniaRussiaSlovakiaSloveniaSpainSwedenSwitzerlandTurkeyUkraineUnited KingdomNotes: * Countries available only in SITC-1. ** Country available only in SITC-3.

Table B.1: Variables’ Definition

Variable Definition SourceDependent VariablesAll SITC-1: 00-96. COMTRADE/WITSFood SITC-1: 00-09 and 11-12. COMTRADE/WITSRaw SITC-1: 21-26, 29 and 41-43. COMTRADE/WITSMinerals SITC-1: 27-28. COMTRADE/WITSIron Ore SITC-1: 281. COMTRADE/WITS

Coal SITC-1: 32. COMTRADE/WITSPetrol SITC-1: 33. COMTRADE/WITSGas SITC-1: 34. COMTRADE/WITSNat. Gas SITC-3: 343. COMTRADE/WITSLNG SITC-3: 3431. COMTRADE/WITSGaseous SITC-3: 3432. COMTRADE/WITS

Manuf. SITC-1: 51-89. COMTRADE/WITSIndependent Variablesln gdp and p ln gdp Log of GDP. WDIln pop and p ln pop Log of population (in millions). WDIln land paire Log of total land area of country pair. WDIln distwces Weighted great-circle distance (based on population distribution) between country pair. CEPIIlandlocked 1 1: 1 country in the country pair is landlocked, 0 otherwise. CEPIIlandlocked 2 1: 2 countries in the country pair are landlocked, 0 otherwise. CEPIIcontig 1: countries are continguous, 0 otherwise. CEPIIisland 1 1: 1 country in the country pair is an island, 0 otherwise. CEPIIisland 2 1: 2 countries in the country pair are islands, 0 otherwise. CEPIIcomlang ethno 1: a language is spoken by at least 9% of the population in both countries, 0: otherwise. CEPIIcolony 1: country pair has ever been in a colonial relationship, 0: otherwise. CEPIIhum k and p hum k Exponent of 0.15 times the average years of schooling among the 25+ years old. Barro and Lee (2010)Note: The subscript p indicates that the variable refers to the partner country.

Table C.1: Average Distance

Country Distance Country Distance Country DistanceNew Zealand 14508 Guyana 8498 Jordan 5776Australia 13390 Trinidad 8483 Portugal 5774Chile 10948 Canada 8379 Morocco 5769Indonesia 10938 Barbados 8301 Syria 5757Philippines 10743 India 7966 Israel 5746Argentina 10482 Zambia 7677 Sweden 5732Singapore 10446 Tanzania 7384 Latvia 5706Uruguay 10369 Pakistan 7384 Egypt 5699Malaysia 10311 Kyrgyz Rep. 7234 United Kingdom 5667Peru 10244 Kenya 7136 Cyprus 5654Japan 10235 Burundi 7053 Ukraine 5643Bolivia 9997 Kazakhstan 6985 Lithuania 5642Mexico 9994 Uganda 6898 Denmark 5617Paraguay 9961 Gabon 6706 Spain 5587Macao 9959 Yemen 6646 Turkey 5557Vietnam 9957 Gambia 6581 Netherlands 5552Ecuador 9930 Cote d’Ivoire 6571 Algeria 5510Korea 9834 Senegal 6557 Poland 5486Guatemala 9754 Qatar 6542 France 5483El Salvador 9709 Ghana 6479 Germany 5477Thailand 9666 CAF 6462 Romania 5446Nicaragua 9611 Iceland 6420 Tunisia 5431Costa Rica 9610 Cameroon 6389 Greece 5427Honduras 9564 Benin 6373 Bulgaria 5426Belize 9514 Mali 6323 Czech Rep. 5425Panama 9454 Iran 6304 Switzerland 5423China 9419 Kuwait 6279 Malta 5414Colombia 9323 Sudan 6233 Slovak Rep. 5410Brazil 9030 Saudi Arabia 6232 Hungary 5392Mauritius 9012 Russia 6100 Austria 5384Jamaica 8924 Niger 6072 Albania 5373United States 8861 Finland 5899 Slovenia 5365Venezuela 8788 Norway 5827 Croatia 5358South Africa 8612 Estonia 5814 Italy 5353Maldives 8596 Ireland 5800

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