Some Surprising Facts About the Concentration of Trade Across Commodities and Trading Partners

Some Surprising Facts About theConcentration of Trade Across

Commodities and Trading PartnersArvind Panagariya1 and Nitika Bagaria1,2

1Columbia University, New York, USA and 2London School of Economics, London, UK

1. INTRODUCTION

THE literature on the theory of the patterns of international trade and specialisation

proceeds along three alternative but related lines. First, it relates the characteristics of a

country in terms of relative factor endowments to the technology as defined by factor intensi-

ties of the goods the country exports and imports. The dominant theory here is the Heckscher

–Ohlin theory. In its two-good, two-factor and two-country version, it predicts that each coun-

try exports the product using its relatively abundant factor more intensively and imports the

product using its relatively scarce factor more intensively. In the multigood, multicountry and

two-factor version of the theory, a chain proposition obtains under a set of specified condi-

tions (Deardorff, 1979). According to this proposition, letting capital and labour be the two

factors, the capital–labour ratio of exported commodities declines with the capital–labourendowment ratio of the country.

The second closely related strand of the literature connects the factor endowments of the

counties to the factor content of exports and imports. The key proposition here is that under a

specified set of conditions, each country’s content of a factor in the net export vector is posi-

tive provided it is better endowed with that factor relative to the rest of the world. The propo-

sition is derived within a multicommodity, multicountry and multifactor world (Leamer,

1980).

The third and final strand of the literature focuses on intra-industry trade. The main

thrust of this literature is that economies of scale and product differentiation rather than

factor endowments are the central determinants of the patterns of trade. This literature

(Krugman, 1979; Helpman, 1981) focuses on specialisation within rather than across prod-

ucts whereby countries export and import different varieties of the same product. A related

line of enquiry (Falvey, 1981) introduces differentiation in terms of quality rather than

variety. It assumes higher quality of a product requires higher capital intensity in produc-

tion. This latter approach marries the intra-industry trade literature to the Heckscher–Ohlintheory with capital abundant countries exporting high-quality (and therefore capital-

intensive) varieties of a product while importing low-quality (and therefore labour

intensive) varieties of it.1

We thank Alan Deardorff for comments on an earlier draft of the paper.

1 Helpman and Krugman (1985) provide a different approach to integrating the two theories. They allowfor product differentiation and economies of scale (as in the original models of intra-industry trade) aswell as differences in factor endowments and factor intensities in the production of different commodi-ties (as in the traditional Heckscher–Ohlin theory).

© 2013 John Wiley & Sons Ltd 1165

The World Economy (2013)doi: 10.1111/twec.12060

The World Economy

In the last two decades, the international trade literature has seen a shift away from

national patterns of trade to firm-level patterns. The impetus to this shift has come from

empirical evidence that is at odds with the assumptions underlying the theory focusing on the

patterns of trade at the national level. A key assumption of the conventional theory including

the Krugman–Helpman model is that firms are homogeneous. The recent empirical literature,

which began to grow in the 1990s and is neatly summarised in Bernard et al. (2007), shows

however that firms are heterogeneous, especially as between those that export and those that

sell exclusively at home, in ways that matters. Exporting firms are larger, employ more work-

ers and exhibit higher value added per worker than those selling exclusively in the domestic

market. No matter how one measures, exporting firms are more productive than the latter.

And the balance of evidence favours the hypothesis that the causation between exports and

productivity runs from the latter to the former: more productive firms enter exports rather than

exporting firms becoming more productive by ‘learning from exports’. Finally, firm heteroge-

neity turns out to be behind a large part of the increase in industry-level productivity follow-

ing trade liberalisation. Liberalisation results in the expansion of more productive exporting

firms and contraction or even exit of the less productive domestically focused firms.

Responding to these empirical observations, trade theorists in the last decade have paid

increasing attention to firm heterogeneity with contributions by Eaton and Kortum (2002) and

Melitz (2003) along two alternative approaches. In the Melitz framework, firms within an

industry, which are hypothesised to produce a differentiated product, are born with differential

productivities. Those with very low productivity fail to generate non-negative profits and are

forced to exit. With fixed costs of exporting, only the more productive of the surviving firms

export. Of course, the more productive firms also end up larger relative to their less

productive counterparts.

While theory has, thus, caught up with some of the earlier empirical findings, recent

research has produced yet more surprising empirical patterns. One such pattern relates to con-

centration in international trade at the level of the firms. Using the transactions-level data of

the United States manufacturing firms for 2000, Bernard et al. (2009) find a very high degree

of concentration in trade across firms. It turns out that the top 1 per cent of the firms that

engaged in international trade (exports or imports) in 2000 accounted for more than 80

per cent of the value of total trade. The top 10 per cent of the firms accounted for over 95

per cent of the value of total trade. In a similar vein, firms exporting to five or more destina-

tions accounted for just 13.7 per cent of the exporting firms, but 92.2 per cent of the export

value. At the other extreme, 64 per cent of the firms exported to a single destination country

with their exports accounting for only 3.3 per cent of the total exports.

This paper is in the spirit of this concentration in trade. Whereas Bernard et al. (2009)

document concentration at the level of the firm and only in exports, we document it at the

level of nations and in both exports and imports. As these authors discuss, the firm-level con-

centration in exports is relatively easy to explain in terms of models of heterogeneous firm

models with entry costs in both the domestic and foreign markets. Sufficiently high market-

specific entry costs will confine the firms barely able to export to exporting small amounts

and to just one market, while highly productive firms will export to multiple markets and in

large volumes. But as we shall see, the concentration at the level of the nation, especially in

imports if not exports, turns out to be far more difficult to explain.

We show pervasive concentration in export and import values both across products and

across trading partners. We systematically find that a small proportion of the exported

products account for the bulk of the value of exports of every country we examine, and much

© 2013 John Wiley & Sons Ltd

1166 A. PANAGARIYA AND N. BAGARIA

more surprisingly, a small proportion of the imported products account for the bulk of the

value of imports. Likewise, a small number of destination countries account for the bulk of

the value of exports of a country, and a small number of source countries account for the bulk

of the import value. Remarkably, we do not see the kind of specialisation across productseven on the export side that the conventional theories often predict. For example, small coun-

tries are observed to export almost as many products as the large countries. A similar pattern

holds for imported products.

To give some flavour of the high degree of concentration of the products with strictly posi-

tive export values, top 2 per cent of the 10-digit products in terms of the Harmonised System

(HS) of classification accounted for nearly 50 per cent of the total value of the United States

exports in 2001. The bottom 50 per cent of the products accounted for less than 3 per cent

of the total export value the same year. Even more surprisingly, just the top 1 per cent HS

10-digit products accounted for 50 per cent of the US import value, and the bottom 50 per

cent of the products, for just 1 per cent of the total import value in 2001. This degree of

concentration is not unique to the United States. It turns out to be pervasive with virtually no

exceptions.

The direction of trade is also highly concentrated: frequently, top five partners account for

50 per cent or more of the total value of exports as well as imports. In many cases, the figure

exceeds 60 per cent and, in some cases, reaches as high as 80 per cent. Similar concentration

is found to exist when we look at exports to and imports from individual trading partners or

consider the direction of exports and imports of individual products. There is concentration in

trade along all dimensions.

There are probably two main insights related to specialisation across products that can be

gleaned from the existing literature, although neither gets to the key issue of concentration at

the national level. First, theory tells us that countries are likely to export fewer products than

they import. This result emerges from the two-factor multicommodity model as well as mod-

els based on scale economies. Second, smaller countries would export fewer products and

import many more products than their larger counterparts. While data are consistent with

these predictions in a literal sense, the differences along these dimensions are truly minimal.

Thus, the difference between the number of products exported and those imported is often

very small. For instance, in terms of the HS classification, the United States exported 4,921

HS 6-digit products and imported 4,940 products in 2001. Smaller countries do not look very

different: Sweden exported 4,627 HS 6-digit products and imported 4,833 products the same

year. Likewise, the numbers of products exported by other small countries such as Belgium

and Austria across HS 6-digit products at 4,799 and 4,484, respectively, are not much smaller

than those by such large countries as the United States.

The explanation of concentration of trade across trading partners can perhaps be provided

in terms of a combination of size, distance and entry cost variables commonly used in the

gravity models. But even this explanation is at best partial. For example, it does not explain

why the direction of trade of countries of similar size but diverse locations would exhibit

remarkably similar degrees of concentration. For example, the degree of concentration of

imports by source and of exports by destination countries is remarkably similar for Belgium,

which is located in the heart of Europe, and New Zealand, which is relatively more isolated.

We examine the top exports and imports of a few large countries in search of clues to why

trade may be concentrated in these commodities. But other than the facts that some commodi-

ties such as aircraft and automobiles are associated with very high unit values and that oil can

be a significant import or export commodity in value terms for at least some of the countries,


TRADE ACROSS COMMODITIES AND TRADING PARTNERS 1167

we do not find a clear explanation for the high degree of concentration. We are unable to find

a satisfactory answer to the question why countries with quite different patterns of exports

and imports show similar and high degree of concentration in exports and imports. For exam-

ple, why should the exports of Belgium and Austria, which export neither aircraft nor oil, be

concentrated? Of all the patterns we document, however, we are most surprised by the same,

often higher, degree of concentration in imports compared with exports. Almost all our

theories predict much greater diversity in imports than in exports.

We may note that the patterns we describe have an important implication for the measure-

ment of the gains from increased variety made possible by trade. The bias resulting from

ignoring new varieties has been shown to depend on their share in total expenditure as well

as the elasticity of substitution between varieties (Feenstra, 1994). In so far as the bottom half

of the import products (by value) uniformly account for less than 5 per cent of the expendi-

ture on imports, any calculations of the gains must take into account this reality. Except in

countries that go from near autarkic policies to highly open import policy, ignoring this fact

would result in biased estimates of the gains from trade.

In the remainder of the paper, we present the evidence showing the concentration of trade

across various dimensions. We calculate the degree of concentration across products and trading

partners using methodology similar to Gini coefficients. We first consider the United States data

in detail since they are available in terms of the highly disaggregated HS 10-digit classification.

We then shift to the HS 6-digit classification since this is the most disaggregated level at which

data are available for a large number of countries. A comparison between the results for the Uni-

ted States at 10- and 6-digit levels of disaggregation for the United States suggests that the latter

adequately captures the concentration properties on which we focus.

2. CONCENTRATION OF TRADE IN HS 10-DIGIT DATA FOR THE UNITED STATES

Throughout this paper, we work with trade data relating to year 2001. This choice of the

year is entirely arbitrary.2 Throughout the paper, including in the introduction, the HS 10-digit

data for the United States come from the Center for International Data at UC Davis and all of

the rest of the data, which are at HS 6-digit level, from the United Nations COMTRADE

database. The latter data are extracted for 33 countries. For robustness, we have performed

similar calculations for 1990 and found that the patterns we present below do not change.

In Figure 1, we plot the cumulative export values of US exports using HS 10-digit data along

the Lorenz curve familiar from the income distribution literature. We represent the percentage

of export products along the horizontal axis and cumulative value of exports of those products

along the vertical axis. Along the horizontal axis, we arrange products in the ascending order of

export value. The plot of cumulative export values as more and more products are added in the

ascending order of the value of exports, represented by curve OCO’, is the Lorenz curve.

To explain the Lorenz curve, consider a point such as A on it. By construction, bottom

OA proportion of the products account for AC proportion of the total value of exports. The

top AE products account for the remaining FO’ per cent of the value of exports. The ratio of

the area between the diagonal and the Lorenz curve to that of triangle OEO’ equals the Gini

coefficient. By construction, the Gini coefficient varies between zero and one, with a higher

2 The first author of the paper had previously worked with the HS 10-digit U.S. trade data ranging from1989 to 2001, and we simply picked the last of these years.



value indicating a higher degree of concentration. If export values were exactly equally dis-

tributed across products, the Lorenz curve would coincide with the diagonal OO’. This is

because the first 10 per cent of the products would then account for 10 per cent of the value

of exports, 20 per cent products for 20 per cent of the value of exports and so on. With the

Lorenz curve coinciding with the diagonal, the area between the two would be zero, and the

Gini coefficient would also be zero. At the other extreme, if the entire export value was con-

centrated in a single product, the Lorenz curve would coincide with triangle OEO’. This is

the case of most unequal distribution (greatest concentration) and the Gini coefficient would

be 1. When the Lorenz curve lies between these two extremes, the Gini coefficient is strictly

between 0 and 1. Ceteris paribus, the farther away the Lorenz curve from the diagonal, the

more concentrated the distribution and the higher the Gini coefficient.

In Figure 1, as already noted, the Lorenz curve is drawn using the actual 10-digit export

data for the United States for the year 2001. Being very far away from the diagonal, it indi-

cates a very high concentration of exports values in the top export products and very small

values of exports of a large proportion of the products at the bottom. The Gini coefficient

associated with this curve is 0.827, which is much closer to 1 than 0.

To give further idea of the degree of concentration, we present some details of the fre-

quency distribution in the top left half of Table 1. The top 86 10-digit products, which repre-

sent just 1 per cent of the total number of products exported, account for a gigantic 36.3 per

cent of the total value of US exports. The top 2 per cent of the products already account for

nearly half (46 per cent) of the total value of exports. At the other extreme, bottom 50 per

cent of the products account for less than 3 per cent of the value of exports. A vast number

of products are exported in tiny volumes.

As we noted earlier, models of heterogeneous firms with entry costs can explain the kind

of concentration Bernard et al. (2009) find at the level of the firm. But the kind of concentra-

tion we have noted across products at the national level is harder to explain. Even if a few

firms drive exports, why should these firms export only a few of the products in large vol-

umes? Asymmetric entry costs such that the vast majority of the products is subject to extre-

mely high entry costs do not explain this pattern either since these would imply the absence

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Cumulative Products (%)

Cum

ulat

ive

Exp

ort V

alue

(%

)

O

O'

E

C

A

F

FIGURE 1The Lorenz Curve Based on 2001 United States Harmonised System (HS) 10-digit Exports



of any exports. High entry costs would likely imply that once a firm has entered the market,

it would export the product in large, not small, volumes. No doubt, the demand conditions

have something to do with this outcome, but it is not immediately obvious precisely what

properties must be assigned to it to rationalise the observed pattern.

The mystery is deepened and surprise magnified when we consider the pattern of imports

across products. As already noted, the concentration of the value of imports across products

in the United States turns out to be even heavier. Figure 2, which depicts the Lorenz curve

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Cumulative Products (%)

Cum

ulat

ive

Impo

rt V

alue

(%

)

O

O'

FIGURE 2The Lorenz Curve Based on the US HS 10-digit Imports in 2001.

HS, Harmonised System

TABLE 1Concentration of 2001 US HS 10-digit Exports and Imports

Per cent ofProducts WithProductsArranged in theDecliningOrder of Value

AbsoluteNumber ofProducts inDecliningOrder ofValue

Per cent ofTotal ExportValueAccounted forby TheseProducts

Per cent ofProducts WithProductsArranged in theDecliningOrder of Value

AbsoluteNumber ofProducts inDecliningOrder ofValue

Per cent ofTotal ExportValueAccounted forby TheseProducts

All exports Two-way traded (exports)1 86 36.3 1 54 29.732 171 46 2 109 40.115 428 61.4 5 272 56.7320 1,712 85.8 20 1,089 83.5650 4,281 97.3 50 2,724 96.94100 8,562 100 100 5,447 100Gini 0.8269 Gini 0.8926

All imports Two-way traded (imports)1 164 49.22 1 54 45.672 328 59.27 2 109 55.25 819 73.32 5 272 68.7720 3,276 92.17 20 1,089 89.2450 8,190 99.05 50 2,724 98.3100 16,380 100 100 5,447 100Gini 0.8926 Gini 0.8656



associated with HS 10-digit US imports in 2001, demonstrates this phenomenon. The Gini

coefficient associated with this Lorenz curve is 0.893, higher than that associated with

exports. Not only do the US imports turn out to be highly concentrated, but they are more

concentrated than exports!The bottom left half of Table 1 shows further numerical details of the frequency distribu-

tion of import values across products. Surprisingly, the top 1 per cent of the products account

for virtually half of the total value of imports. The top 5 per cent of the products account for

nearly three-fourths of total imports. Symmetrically, as much as half of the products at the

bottom account for less than 1 per cent of the total value of imports.

We will later show that a very partial explanation of concentration in both exports and

imports may lie in the presence of some high-unit-value products in large quantities including

aircraft and automobiles and parts thereof. But this explanation is far from sufficient. While

this is true for the United States, it does not explain the concentration of exports of countries

such as India and China that do not export any of these items. On the import side, petroleum

and petroleum products are often big contributors, but they are far from sufficient to explain

the phenomenon we note since high concentration will be ameliorated but not disappear if we

were to exclude these products.

A related hypothesis is that much of trade involves the exchange of raw materials and

components for final products and that the final demand is concentrated in a handful of high-

value final products. A country may then import a large volume of a small number of inputs

required to produce a large volume of a small number of export products. Decline in the costs

of services trade and technological advances that have allowed previously vertically integrated

production processes to fragment into various components with each component produced in

its least costly location as analysed by Jones and Kierzkowski (2000) may add to such con-

centration. Intraproduct specialisation across countries may mean each country specialising in

a few components or the assembly of a few final products. If data classification system then

lumps the inputs associated with a final product (for example, auto parts) into a single cate-

gory, we would observe concentration in both exports and imports. The problem with this

hypothesis is that it is not clear that demand is concentrated in the way it is assumed. More-

over, even taking the demand assumption at face value, it can explain the concentration in

imports and exports in rare cases of countries such as China, which heavily engage in pro-

cessing exports.

As a matter of curiosity, we may ask whether the degree of concentration varies signifi-

cantly among products that are traded two-way relative to those that are not. To check this,

we separated the products that are traded in both directions. The Gini coefficient associated

with the exports and imports of these products is not vastly different from those obtained for

all exports and imports: 0.897 and 0.866, respectively. Exports are now slightly more concen-

trated than imports, but both are heavily concentrated in a few products.

3. PERVASIVENESS OF THE CONCENTRATION ACROSS PRODUCTS AND TRADE PARTNERS

The obvious question we may ask is whether the United States is unique in terms of hav-

ing a high concentration in exports and imports. Therefore, we now present the results from a

large number of countries and show that the phenomenon of concentration is pervasive across

small and large countries and across developed and developing countries. These calculations

require us to switch to the United Nations COMTRADE data, which are available for nearly

all countries. A limitation of these data relative to those used in the previous section is that



they allow us to disaggregate maximally to HS 6-digit classification. But as we will see

from a comparison of the results for the United States, for which we have the results at HS

10-digit disaggregation, our findings are not particularly sensitive to the switch to 6-digit

disaggregation.

Table 2 reports the Gini coefficient of the distribution of export values and import values

across products for a large number of countries. In the case of exports, the minimum and

maximum values of the Gini are 0.821 for Italy and 0.981 for Iceland, respectively. The Gini

exhibits a value exceeding 0.9 for as many as 18 of 33 countries included in Table 2. For the

United States, the value using the HS 6-digit data is 0.848 compared with 0.827 found for the

HS 10-digit data in the previous section. Turning to imports, the minimum and maximum

TABLE 2Concentration Across Products Within Exports and Within Imports

Country Exports Imports

Gini Total Numberof Products

Gini Total Numberof Products

Australia 0.945 4,596 0.849 4,738Austria 0.881 4,484 0.820 4,857Belgium 0.864 4,799 0.837 4,896Brazil 0.930 4,305 0.871 4,641Canada 0.922 4,663 0.842 4,931China 0.848 4,792 0.875 4,832Czech Republic 0.867 4,662 0.828 4,861Denmark 0.889 4,390 0.813 4,758Finland 0.935 4,377 0.848 4,756France 0.845 4,745 0.805 4,936Germany 0.835 4,775 0.833 4,912Greece 0.920 3,930 0.859 4,751Hungary 0.897 3,353 0.854 4,358Iceland 0.981 1,355 0.859 4,096India 0.904 4,592 0.925 4,504Ireland 0.966 3,748 0.894 4,732Italy 0.821 4,793 0.825 4,914Japan 0.908 4,570 0.878 4,841Korea 0.918 4,354 0.886 4,803Luxembourg 0.953 3,364 0.886 4,528Mexico 0.929 4,398 0.855 4,788Netherlands 0.867 4,690 0.847 4,828New Zealand 0.945 3,883 0.838 4,582Norway 0.973 3,868 0.839 4,748Poland 0.843 3,116 0.798 4,159Russia 0.971 4,362 0.855 4,717Slovakia 0.915 4,140 0.846 4,726Spain 0.850 4,809 0.820 4,895Sweden 0.901 4,627 0.840 4,833Switzerland 0.895 4,621 0.836 4,893Turkey 0.901 4,389 0.869 4,577UK 0.880 4,810 0.843 4,878USA 0.848 4,921 0.871 4,940



values of the Gini are 0.798 for Poland and 0.925 for India, respectively. For the United

States, the import Gini in Table 2 is 0.871 compared with 0.893 based on the HS 10-digit

data reported in the previous section.

Almost all theories would predict greater concentration in exports than imports. This turns

out to be true for 29 of 33 countries in Table 2, the exceptions being China, India, Italy and

the United States. While this pattern is somewhat reassuring, the small difference between the

Gini associated with exports and that associated with imports for almost every country

remains puzzling. The largest value of the difference between the export and import Gini is

associated with Norway, which exhibits an export Gini of 0.973 and import Gini of 0.839.

These are both very large values.

Setting aside the issue of large absolute values, there appears some evidence that smaller

countries, as measured by the GDP, exhibit greater concentration in exports. The six most

concentrated countries in the descending order of the Gini are Iceland, Norway, Russia, Ire-

land, Luxemburg and Australia, while the six least concentrated (most diversified) ones in the

ascending order of the Gini are Italy, Germany, Poland, France, China and the United States.

This pattern is, of course, purely suggestive since the stability of the evidence must be

checked using a much larger sample of countries as well as for a variety of years, an exercise

that is beyond the scope of our paper. Moreover, the theory behind such concentration in any

case is not clear. The conventional theory suggests specialisation across fewer products by

smaller countries, but not necessarily greater concentration of the value of exports among

fewer products from among the products exported.

The pattern of concentration is less clear when we consider imports. The eight most con-

centrated countries in the descending order of the Gini are India, Ireland, Luxemburg, South

Korea, Japan, China, Brazil and the United States, which represent a mix of large and small

economies. Symmetrically, at the bottom end, the eight least concentrated countries in the

ascending order of the Gini are Poland, France, Denmark, Austria, Spain, Italy, Czech Repub-

lic and Germany. Once again, we have a mix of large and small countries.

Let us next turn to the degree of concentration in the direction of trade. Our findings are as

follows. In Tables 3 and 4, we report the Gini coefficient associated with the distributions of

the values of exports and imports across destination and source countries, respectively. The

minimum and maximum values of the Gini associated with exports are 0.842 for Russia and

0.979 for Canada, respectively. As many as 21 of the 33 countries shown exhibit values of the

Gini exceeding 0.9 across destinations of their exports. The story is similar in terms of sources

of imports. The minimum and maximum values of the Gini are 0.855 for Iceland and 0.962 for

Switzerland, with as many as 18 countries having the Gini in excess of 0.9.

Tables 3 and 4 also report the concentration of trade across top five partners. For exports,

the top five destination countries account for more than 50 per cent of the total value for 26

of 33 countries. Of the remaining seven, only one, Russia, exhibits a top-five-partner concen-

tration ratio of less than 40 per cent. The ratio exceeds 90 per cent in the case of Canada and

Mexico. The sources of imports are slightly more diversified. Top five sources account for

more than 50 per cent of imports for 23 of 33 countries. Of the remaining 10 countries, only

one, Germany, shows a concentration ratio of less than 40 per cent.

To some degree, gravity variables such as distance and incomes of partner countries may

explain this concentration of source and destination countries. This may be particularly true

for countries whose proximate partners also happen to be large in terms of the GDP. But this

explanation is perhaps only a part of the story since we find high levels of concentration for

countries with all kinds of partner country GDP and distance vectors. Gravity model perhaps



does not explain why the concentration for countries such as Australia and New Zealand

should be similar to that for the United States.

4. INDIVIDUAL PRODUCTS AND TRADE PARTNERS

We next provide illustrative evidence showing the concentration of exports and imports

across trading partners for individual products. For this purpose, we take four top exports and

four top imports at HS 6-digit level for the United States. In Table 5, we report the degree of

concentration of top four exports across destination countries and in Table 6 that of top four

imports across source countries. The pattern observed for exports and imports as a whole

largely repeats at the product level with two closely related differences. First, the number of

destination countries for exports is typically much larger than the number of source countries

TABLE 3Concentration of Exports Across Destination Countries

Country Gini Total Numberof Countries

Concentration Ratio(Top 5 Partners)

Australia 0.901 215 48.9Austria 0.910 203 56.6Belgium 0.919 217 63.5Brazil 0.872 206 45.9Canada 0.979 213 92.1China 0.903 208 63.2Czech Republic 0.925 195 62.6Denmark 0.909 226 51.7Finland 0.934 205 69.6France 0.882 224 51.4Germany 0.888 227 42.5Greece 0.924 209 66.6Hungary 0.920 182 60.7Iceland 0.883 103 60.1India 0.920 212 67.8Ireland 0.931 203 64.5Italy 0.866 217 49.3Japan 0.916 214 56.1Korea 0.887 219 54.1Luxembourg 0.928 180 69.8Mexico 0.976 181 91.7Netherlands 0.924 231 65.1New Zealand 0.891 193 55.4Norway 0.929 202 59.5Poland 0.893 170 55.0Russia 0.842 175 32.8Slovakia 0.933 182 66.5Spain 0.891 210 59.6Sweden 0.895 212 45.1Switzerland 0.907 227 55.8Turkey 0.861 193 47.6UK 0.901 226 53.1USA 0.901 221 53.9



for imports. Second, top five partners generally account for much larger share of total imports

than that of total exports at the level of individual products.

Finally, we turn to the degree of concentration across products in trade with specific trade

partners. For this purpose, we consider the United States trade with its three largest partners

in exports as well as imports: Canada, Mexico and Japan. The summary data on concentration

are provided in Table 7. Predictably, the Gini is consistently high, exceeding 0.85 in exports

and 0.90 in imports in the case of all three trading partners. Top 200 products account for

more than 50 per cent of every flow shown. Perhaps, the most surprising aspect of the flows

is the consistently higher value of the Gini in imports than in exports. For reasons not clear

to us, the US bilateral imports tend to concentrate more than its bilateral exports across

products.

TABLE 4Concentration of Imports Across Source Countries

Country Gini Total Numberof Countries

Concentration Ratio(Top 5 Partners)

Australia 0.912 199 51.2Austria 0.924 210 60.6Belgium 0.908 197 61.6Brazil 0.891 190 52.8Canada 0.955 210 78.5China 0.897 185 54.8Czech Republic 0.921 210 53.8Denmark 0.913 204 53.8Finland 0.889 172 47.3France 0.896 228 48.9Germany 0.886 226 37.8Greece 0.868 178 43.6Hungary 0.903 178 51.9Iceland 0.855 124 47.2India 0.889 178 51.1Ireland 0.936 213 65.3Italy 0.876 206 44.9Japan 0.903 206 48.1Korea 0.910 212 53.8Luxembourg 0.950 135 82.1Mexico 0.960 194 81.1Netherlands 0.899 214 52.2New Zealand 0.901 161 60.8Norway 0.894 178 49.9Poland 0.883 162 52.0Russia 0.871 189 45.1Slovakia 0.959 191 80.4Spain 0.889 209 53.1Sweden 0.912 191 50.6Switzerland 0.962 215 78.2Turkey 0.923 180 68.7UK 0.891 219 44.9USA 0.902 219 55.3



5. WHICH PRODUCTS?

We finally look at descriptions of the top exports and imports of a small number of coun-

tries with the view to explore the reasons behind the high degree of concentration in interna-

tional trade. We choose three large developed and two large developing countries. For

purposes of brevity, we limit ourselves to presenting the top 25 products in each case.

Tables A1–A5 in the Appendix report the top 25 export items with corresponding export

values of the United States, Germany, Japan, China and India in 2001. Tables A6–A10 report

the top 25 import items with their corresponding import values for the same countries in the

same year.

Only two possible explanations emerge from the lists neither of which seem adequate to

explain the pervasiveness of the concentration, especially in imports. First, the tables bring

out a classification issue. In each of the tables, five to eight of 25 entries appear with the

qualification ‘nes’, which stands for ‘not elsewhere specified’. This suggests that some of the

concentration is purely a statistical anomaly: unrelated items of various kind have been

lumped together into a single 6-digit category. While there is some truth in this explanation,

its importance must not be exaggerated. Items with this qualification are not catch-all catego-

ries that lump together unrelated items. A category of latter description titled ‘commodities

not specified according to kind’ numbered 999999 does exist, but we have already excluded it

TABLE 5Concentration of Value Across Destination Countries in Top 4 US Exports

Product Number of DestinationCountries in the DescendingOrder of Value of Exports

Per cent of Exports Accountedfor by These DestinationCountries

H1-854213: metal oxidesemiconductor

5 49.210 80.320 96.450 99.9

All (153) 100Gini 0.9146

H1-880240: fixed wing aircraft,unladen weight >15,000 kg

5 44.210 61.720 80.650 99.1

All (61) 100Gini 0.6376

H1-847330: parts andaccessories of data processingequipment nes

5 48.410 70.420 89.950 98.6

All (192) 100Gini 0.9066

H1-880330: aircraft parts nes 5 4710 6820 85.550 98.5

All (181) 100Gini 0.8914



from our lists. The ‘nes’ categories included in the tables relate to very specific category of

items. For example, first four ‘nes’ items to appear in Table A1 are as follows: parts and

accessories of data processing equipment nes; aircraft parts nes; motor vehicle parts nes; and

parts and accessories of bodies for motor vehicles nes. All these categories refer to parts or

accessories items associated with very narrowly defined products. An additional point to

remember is that ‘nes’ categories appear with approximately equal frequency on the export

and import lists. Therefore, even if we take the view that the choice of classification exagger-

ates the degree of concentration, we will be left to explain why the degree of concentration is

as high – sometimes higher – in imports as in exports.

TABLE 6The Concentration of Value Across Source Countries in Top 4 US Imports

Product Number of Source Countries Per cent of ImportsAccounted for by TheseSource Countries

H1-270900: petroleum oils, oils frombituminous minerals, crude

5 6610 87.920 97.7

All (44) 100Gini 0.7743

H1-870323: automobiles, spark-ignition engine of 1,500–3,000 cc

5 89.810 98.920 100

All (28) 100Gini 0.8381

H1-870324: automobiles, spark-ignition engine of >3,000 cc

5 96.110 99.520 100

All (34) 100Gini 0.8992

H1-271000: petroleum oils and oilsobta

5 43.410 59.720 79.650 97.3

All (89) 100Gini 0.7326

TABLE 7Concentration of US Exports and Imports Across Top Three Trade Partners

Partner Exports Imports

Gini Total Numberof ProductsExported

Share ofTop 200Products

Gini Total Numberof ProductsImported

Share ofTop 200Products

Canada 0.853 4,927 58.43 0.925 4,430 73.14Mexico 0.876 4,883 63.13 0.935 3,565 79.79Japan 0.895 4,510 69.66 0.932 3,843 53.44



The second observation from the tables is that at least for the three developed countries

shown, exports and imports both contain several high-unit-value products that are exported or

imported in large volumes. Items related to three broad products – aircraft, automobiles and

petroleum – are of particular importance. But the items relating to China and India are quite

different, and they are not high-unit-value products. This contrast suggests that the underlying

explanation for the concentration, especially in imports, must lie elsewhere. Unfortunately, so

far, we have not been able to zero in on it.

6. THE WORLD TRADE

For completeness, we also report the degree of concentration of world trade as a whole. In

view of our results above, it should not be surprising that world trade is also highly concen-

trated in terms of both commodity composition and direction. Conceptually, the world exports

and world imports are the same, but it is well known that these often differ by wide margins

in the sense that the exports of a commodity to China reported by the United States usually

do not match the imports of the same commodity from the United States reported by China.

The total exports across commodities also do not match the total imports across all commodi-

ties. Nevertheless, the ranking of commodities by export value and import value seems simi-

lar. We compared the top 20 HS 6-digit export and import commodities and found 18 of

them to be in common.

The main point to note is that both with respect to the commodity composition and direc-

tion of trade, the concentration of world trade is high but uniformly lower than every one of

the country calculations we have performed. The Gini coefficient of the HS 6-digit world

commodity exports and imports turns out to be the same: 0.7886. This is less than each Gini

coefficient shown in Table 3. In the commodity composition, the items that dominate in the

top 20 list are similar to those in the lists of the larger countries: oil- and petroleum-related

products; aircraft and parts thereof; and automobiles and parts thereof.

The bottom line that follows from these observations is that while the number of products

exported and imported is very large at the HS 6-digit classification, the bulk of trade takes

place in very small proportion of these products. The bottom half of the products accounted

for less than 5 per cent of the world exports in 2001. Likewise, the larger countries account

for the bulk of the world trade. The bottom half of the countries account for just 0.92 per cent

of the world exports in 2001.

7. SUMMARY OF RESULTS

The main message of this paper is that there is a high concentration of international trade

along all dimensions. The main findings can be enumerated as:

� Only a small proportion of commodities account for much of the value of a country’s

exports and imports. Typically, the bottom half of the commodities account for less than

5 per cent of a country’s exports and imports.� Most surprisingly, the degree of concentration is as high in imports as in exports. Indeed,

for many countries, imports are more concentrated than exports. This observation is con-

trary to the existing theories that predict specialisation by a country in a small subset of

the traded commodities. Accordingly, we expect the import basket to be much more

diversified that export basket.



� Even in terms of the number of commodities, with a small number of exceptions, diver-

sification in exports turns out to be only slightly less than that in imports. For example,

the number of import products exceeded the number of HS 6-digit export products by

just 0.4 per cent in the United States in 2001. In three countries, India, Italy and China,

the number of export products was slightly larger than the number of import products.� Contrary to the predictions of some theories, small countries such as Belgium are almost

as diversified in exports as Germany and France and only tiny bit less diversified than

the United States.� Like the commodity composition, the direction of trade also exhibits high degree of con-

centration. Frequently, the top five partners account for half of the exports and imports,

while the bottom half account for less than 1 per cent of the trade in either direction.� The same concentration also characterises the bilateral commodity composition of

exports and imports as well as the direction of trade for individual commodities.

8. CONCLUDING OBSERVATIONS

The traditional explanation for the concentration of exports in a limited number of products

says that trade leads countries to specialise in relatively few products that it exports while

importing all others. This simplistic explanation clearly fails since countries produce and

export an extremely large number of products but export only a small proportion of those in

large volumes. One possible explanation we have suggested for the observed phenomenon is

that countries export some high-unit-value products such as automobiles and aircraft and parts

thereof. This explanation applies at least partially to countries such as the United States and

Japan. On the import side, the presence of large volumes of petroleum and petroleum prod-

ucts, automobiles, aircraft and auto parts helps explain the concentration in some of the coun-

tries but once again only partially. Exclusion of these high-unit-value or high volume

products does not make the concentration phenomenon go away.

A complementary explanation for the concentration in trade is in terms of international

specialisation in the production of components that has been made possible by the fragmenta-

tion of previously vertically integrated production processes. A country may specialise in the

exports of a few final products that it assembles using the components that it imports. This

will produce some concentration in both exports and imports. But this explanation is also par-

tial since it must then import the final products in which the other countries specialise. Clearly

more work is required. A complete explanation would minimally require a three-country

model so that the concentration in the source countries of imports and destination countries of

exports can also be addressed. In addition, it will likely require a structure that explicitly

allows trade in components, possibly with heterogeneous firms. Evidently, this is a major task

that requires a separate paper, and therefore, we make no attempt to undertake it here.

REFERENCES

Bernard, A. B., J. B. Jensen, S. J. Redding and P. K. Schott (2007), ‘Firms in International Trade’,Journal of Economic Perspectives, 21, 3, 105–30.

Bernard, A. B., J. B. Jensen and P. K. Schott (2009), ‘Importers, Exporters and Multinationals:A Portrait of Firms in the U.S. That Trade Goods’, in T. Dunne, J. B. Jensen and M. J. Roberts(eds.), Producer Dynamics: New Evidence From Micro Data (Chicago, IL: University of ChicagoPress), 513–56.



Deardorff, A. V. (1979), ‘Weak Links in the Chain of Comparative Advantage’, Journal of InternationalEconomics, 9, 2, 197–209.

Eaton, J. and S. Kortum (2002), ‘Technology, Geography, and Trade’, Econometrica, 70, 5, 1741–79.Falvey, R. (1981), ‘Commercial Policy and Intra Industry Trade’, Journal of International Economics,

11, 4, 495–511.Feenstra, R. C. (1994), ‘New Product Varieties and the Measurement of International Prices’, American

Economic Review, 84, 1, 157–77.Helpman, E. (1981), ‘International Trade in the Presence of Product Differentiation, Economies of Scale

and Monopolistic Competition: A Chamberlin–Heckscher–Ohlin Approach’, Journal of InternationalEconomics, 11, 3, 305–40.

Helpman, E. and P. Krugman (1985), Market Structure and Foreign Trade: Increasing Re-turns,Imperfect Competition and the International Economy (Cambridge, MA: MIT Press).

Jones, W. R. and H. Kierzkowski (2000), ‘A Framework for Fragmentation’, in S. Arndt and H. Kierz-kowski (eds.), Fragmentation: New Production Patterns in the World Economy (Oxford: OxfordUniversity Press), 17–34.

Krugman, P. (1979), ‘Increasing Returns, Monopolistic Competition, and International Trade’, Journal ofInternational Economics, 9, 4, 469–79.

Leamer, E. E. (1980), ‘The Leontief Paradox, Reconsidered’, Journal of Political Economy, 86, 3,495–503.

Melitz, M. J. (2003), ‘The Impact of Trade on Intra-industry Reallocations and Aggregate IndustryProductivity’, Econometrica, 71, 6, 1695–725.

TABLE A1Top 25 Export Products and Their Values: United States

CommodityCode

Commodity Description Trade Value($Billion)

H1-854213 Metal oxide semiconductor 26.48H1-880240 Fixed wing aircraft, unladen weight >15,000 kg 23.98H1-847330 Parts and accessories of data processing equipment nes 19.37H1-880330 Aircraft parts nes 13.52H1-870899 Motor vehicle parts nes 9.90H1-870829 Parts and accessories of bodies nes for motor vehicles 8.40H1-870323 Automobiles, spark-ignition engine of 1,500–3,000 cc 8.36H1-870324 Automobiles, spark-ignition engine of >3,000 cc 8.14H1-854230 Monolithic integrated circuits 7.56H1-847180 Units of auto data process 7.38H1-841191 Parts of turbo-jet or turbo-propeller engines 7.04H1-300490 Medicaments nes, in dosage 6.59H1-271000 Petroleum oils and oils 6.41H1-120100 Soya beans 5.45H1-840734 Engines, spark-ignition reciprocating, over 1,000 cc 5.42H1-851790 Parts of line telephone/telegraph equipment, nes 5.34H1-847989 Machines and mechanical appliances nes 5.14H1-847149 Digital auto data process units 4.74H1-852520 Transmit–receive apparatus for radio, TV, etc. 4.73H1-100590 Maize except seed corn 4.56H1-841112 Turbo-jet engines of a thrust >25 KN 4.36H1-852990 Parts for radio/tv transmit/receive equipment, nes 4.31H1-710812 Gold in unwrought forms non-monetary 4.19H1-901890 Instruments, appliances for medical, etc. science, nes 3.99H1-843143 Parts of boring or sinking machinery 3.98

Note:(i) nes, not elsewhere specified.



TABLE A2Top 25 Export Products and Their Values: Germany

CommodityCode

Commodity Description Export Value($Billion)

H1-870323 Automobiles, spark-ignition engine of 1,500–3,000 cc 30.38H1-870332 Automobiles, diesel engine of 1,500–2,500 cc 17.09H1-880240 Fixed wing aircraft, unladen weight >15,000 kg 13.46H1-870324 Automobiles, spark-ignition engine of >3,000 cc 13.25H1-300490 Medicaments nes, in dosage 11.50H1-852520 Transmit–receive apparatus for radio, TV, etc. 8.31H1-854213 Metal oxide semiconductors 5.78H1-847330 Parts and accessories of data processing equipment nes 5.23H1-870899 Motor vehicle parts nes 4.71H1-271000 Petroleum oils and oils 4.03H1-870322 Automobiles, spark-ignition engine of 1,000–1,500 cc 3.78H1-870829 Parts and accessories of bodies nes for motor vehicles 3.24H1-841112 Turbo-jet engines of a thrust >25 KN 2.98H1-880330 Aircraft parts nes 2.93H1-840999 Parts for diesel and semi-diesel engines 2.82H1-847989 Machines and mechanical appliances nes 2.76H1-870421 Diesel powered trucks weighing <5 tonnes 2.70H1-870333 Automobiles, diesel engine of >2,500 cc 2.62H1-844319 Offset printing machinery nes 2.60H1-870840 Transmissions for motor vehicles 2.50H1-847160 I/O units w/n storage units 2.41H1-271129 Petroleum gases and gaseous hydrocarbons nes, as gas 2.38H1-870120 Road tractors for semi-trailers (truck tractors) 2.31H1-392690 Plastic articles nes 2.24H1-382490 Chemical prep, allied 2.19


TABLE A3Top 25 Export Products and Their Values: Japan

CommodityCode


H1-870323 Automobiles, spark-ignition engine of 1,500–3,000 cc 31.65H1-854213 Metal oxide semiconductor 14.14H1-870324 Automobiles, spark-ignition engine of >3,000 cc 12.29H1-847330 Parts and accessories of data processing equipment nes 9.60H1-852540 Still image video camera 6.22H1-847989 Machines and mechanical appliances nes 5.84H1-890190 Cargo vessels other than tanker or refrigerated 5.81H1-870899 Motor vehicle parts nes 4.74H1-870840 Transmissions for motor vehicles 4.63H1-847150 Digital process units 3.95H1-847160 I/O units w/n storage units 3.77H1-870322 Automobiles, spark-ignition engine of 1,000–1,500 cc 3.69



TABLE A3 Continued

CommodityCode


H1-854230 Monolithic integrated circuits 3.59H1-840991 Parts for spark-ignition engines except aircraft 3.37H1-852990 Parts for radio/tv transmit/receive equipment, nes 3.20H1-900990 Parts and accessories for photocopying apparatus 3.05H1-851790 Parts of line telephone/telegraph equipment, nes 2.97H1-847170 Storage units 2.81H1-870829 Parts and accessories of bodies nes for motor vehicles 2.78H1-854389 Electrical machines and 2.36H1-890120 Tankers 2.36H1-840734 Engines, spark-ignition reciprocating, over 1,000 cc 2.30H1-870333 Automobiles, diesel engine of >2,500 cc 2.27H1-854290 Parts of electronic integrated circuits etc. 2.22H1-900912 Electrostatic photocopiers, indirect process 2.22


TABLE A4Top 25 Export Products and Their Values: China

CommodityCode


H1-847330 Parts and accessories of data processing equipment nes 7.98H1-847160 I/O units w/n storage u 6.84H1-852520 Transmit–receive apparatus for radio, TV, etc. 4.58H1-847170 Storage units 3.21H1-852990 Parts for radio/tv transmit/receive equipment, nes 2.87H1-640399 Footwear, sole rubber, plastics uppers of leather, nes 2.64H1-270112 Bituminous coal, not agglomerated 2.42H1-852290 Parts and accessories of recorders except cartridges 2.25H1-640299 Footwear, outer soles/uppers of rubber or plastic, nes 2.25H1-950390 Toys nes 2.22H1-860900 Cargo containers designed for carriage 2.20H1-420212 Trunks, suitcases, etc., outer surface plastic/textile 2.18H1-271000 Petroleum oils and oils obta 2.12H1-852190 Video record/reproduction apparatus not magnetic tape 2.02H1-420310 Articles of apparel of leather or composition leather 2.00H1-850440 Static converters, nes 1.74H1-611030 Pullovers, cardigans etc. of man-made fibres, knit 1.69H1-852731 Radio-telephony receiver, with sound reproduce/record 1.62H1-610910 T-shirts, singlets and other vests, of cotton, knit 1.60H1-392690 Plastic articles nes 1.56H1-853400 Electronic printed circuits 1.52H1-620342 Men’s, boys’ trousers and shorts, of cotton, not knit 1.43H1-950341 Stuffed toys – animals or non-human creatures 1.41H1-852812 Colour television receiver 1.41




TABLE A5Top 25 Export Products and Their Values: India

CommodityCode


H1-710239 Diamonds (jewellery) worked but not mounted or set 5.62H1-271000 Petroleum oils and oils 2.06H1-711319 Jewellery and parts of precious metal except silver 1.05H1-030613 Shrimps and prawns, frozen 0.81H1-100630 Rice, semi-milled or wholly milled 0.61H1-620520 Men’s, boys’ shirts, of cotton, not knit 0.60H1-610910 T-shirts, singlets and other vests, of cotton, knit 0.53H1-300490 Medicaments nes, in dosage 0.49H1-620630 Women’s, girls’ blouses and shirts, of cotton, not knit 0.48H1-294200 Organic compounds, nes 0.45H1-630492 Furnishing articles nes, of cotton, not knit, crochet 0.43H1-420310 Articles of apparel of leather or composition leather 0.40H1-230400 Soya bean oil cake and other solid residues 0.38H1-610510 Men’s, boys shirts, of cotton, knit 0.37H1-080132 Cashew nuts, shelled dri 0.36H1-260111 Iron ore, concentrate, not iron pyrites, unagglomerated 0.35H1-520511 Cotton yarn >85% single uncombed >714 dtex, not retail 0.31H1-520710 Cotton yarn (except sewing thread) >85% cotton, retail 0.29H1-630790 Made-up articles (textile) nes, textile dress patterns 0.26H1-100190 Wheat except durum wheat, and meslin 0.25H1-847330 Parts and accessories of data processing equipment nes 0.25H1-380810 Insecticides, packaged for retail sale 0.25H1-640610 Footwear uppers and parts thereof, except stiffeners 0.24H1-030379 Fish nes, frozen, whole 0.24H1-620442 Women’s, girls’ dresses, of cotton, not knit 0.23


TABLE A6Top 25 Import Products and Their Values: United States

CommodityCode

Commodity Description Import Value($Billion)

H1-270900 Petroleum oils, oils from bituminous minerals, crude 79.29H1-870323 Automobiles, spark-ignition engine of 1,500–3,000 cc 53.25H1-870324 Automobiles, spark-ignition engine of >3,000 cc 51.53H1-271000 Petroleum oils and oils 26.14H1-847330 Parts and accessories of data processing equipment nes 25.08H1-854213 Metal oxide semiconductor 19.80H1-847160 I/O units w/n storage units 16.21H1-271121 Natural gas in gaseous state 15.36H1-847170 Storage units 13.43H1-870431 Spark-ignition engine trucks weighing <5 tonnes 12.76H1-852520 Transmit–receive apparatus for radio, TV, etc. 12.45H1-710239 Diamonds (jewellery) worked but not mounted or set 10.05H1-300490 Medicaments nes, in dosage 10.03



TABLE A6 Continued

CommodityCode


H1-870899 Motor vehicle parts nes 9.29H1-880240 Fixed wing aircraft, unladen weight >15,000 kg 7.61H1-847130 Portable digital data processor 7.49H1-440710 Lumber, coniferous (softwood) thickness <6 mm 6.78H1-640399 Footwear, sole rubber, plastics uppers of leather, nes 6.65H1-880230 Fixed wing aircraft, unladen weight 2,000–15,000 kg 6.58H1-852812 Colour television receiver 6.54H1-840734 Engines, spark-ignition reciprocating, over 1,000 cc 6.52H1-611020 Pullovers, cardigans etc. of cotton, knit 6.05H1-847180 Units of auto data processing 5.94H1-293390 Heterocyclic compounds with N-hetero-atom(s) only, nes 5.84H1-293490 Heterocyclic compounds, nes 5.69


TABLE A7Top 25 Import Products and Their Values: Germany

CommodityCode

Commodity Description Import Value(Billion$)

H1-270900 Petroleum oils, oils from bituminous minerals, crude 19.16H1-271121 Natural gas in gaseous state 11.11H1-870323 Automobiles, spark-ignition engine of 1,500–3,000 cc 10.72H1-847330 Parts and accessories of data processing equipment nes 9.26H1-271000 Petroleum oils and oils obta 8.65H1-880240 Fixed wing aircraft, unladen weight >15,000 kg 7.72H1-854213 Metal oxide semiconductor 7.31H1-293390 Heterocyclic compounds with N-hetero-atom(s) only, nes 6.69H1-870332 Automobiles, diesel engine of 1,500–2,500 cc 5.77H1-880330 Aircraft parts nes 5.40H1-847160 I/O units w/n storage units 5.01H1-300490 Medicaments nes, in dosage 4.77H1-847170 Storage units 4.64H1-870322 Automobiles, spark-ignition engine of 1,000–1,500 cc 4.59H1-852520 Transmit–receive apparatus for radio, TV, etc. 4.46H1-870899 Motor vehicle parts nes 3.59H1-840734 Engines, spark-ignition reciprocating, over 1,000 cc 3.34H1-870829 Parts and accessories of bodies nes for motor vehicles 2.32H1-847150 Digital process units wh 2.27H1-854230 Monolithic integrated circuits 2.12H1-841112 Turbo-jet engines of a thrust >25 KN 2.05H1-851750 Apparatus for carrier-cu 2.03H1-854430 Ignition/other wiring sets for vehicles/aircraft/ship 2.02H1-847130 Portable digital data pr 1.89H1-851790 Parts of line telephone/telegraph equipment, nes 1.87




TABLE A8Top 25 Import Products and Their Values: Japan

CommodityCode


H1-270900 Petroleum oils, oils from bituminous minerals, crude 38.76H1-271111 Natural gas, liquefied 13.13H1-854213 Metal oxide semiconductor 10.85H1-847330 Parts and accessories of data processing equipment nes 7.35H1-270112 Bituminous coal, not agglomerated 5.94H1-271019 Light petroleum distillates nes 5.10H1-847170 Storage units 3.93H1-870323 Automobiles, spark-ignition engine of 1,500–3,000 cc 3.77H1-847150 Digital process units wh 3.68H1-271112 Propane, liquefied 3.13H1-847160 I/O units w/n storage units 2.97H1-760110 Aluminium unwrought, not alloyed 2.77H1-260111 Iron ore, concentrate, not iron pyrites, unagglomerated 2.75H1-020329 Swine cuts, frozen nes 2.38H1-030613 Shrimps and prawns, frozen 2.28H1-440710 Lumber, coniferous (softwood) thickness <6 mm 2.16H1-870324 Automobiles, spark-ignition engine of >3,000 cc 2.09H1-847180 Units of auto data process 2.06H1-260300 Copper ores and concentrates 2.03H1-240220 Cigarettes containing tobacco 2.00H1-852990 Parts for radio/tv transmit/receive equipment, nes 1.95H1-100590 Maize except seed corn 1.94H1-852812 Colour television receiver 1.87H1-300490 Medicaments nes, in dosage 1.85H1-854230 Monolithic integrated circuits 1.75


TABLE A9Top 25 Import Products and Their Values: China

CommodityCode


H1-270900 Petroleum oils, oils from bituminous minerals, crude 11.66H1-854230 Monolithic integrated circuits 7.40H1-847330 Parts and accessories of data processing equipment nes 6.63H1-854219 Monolithic integrated circuits, except digital 4.05H1-271000 Petroleum oils and oils obta 3.75H1-852990 Parts for radio/tv transmit/receive equipment, nes 3.39H1-847989 Machines and mechanical appliances nes 3.07H1-851750 Apparatus for carrier-cu 2.83H1-120100 Soya beans 2.81H1-854240 Hybrid integrated circuits 2.58H1-880240 Fixed wing aircraft, unladen weight >15,000 kg 2.34H1-851790 Parts of line telephone/telegraph equipment, nes 2.24H1-260111 Iron ore, concentrate, not iron pyrites, unagglomerated 2.16



TABLE A9 Continued

CommodityCode


H1-853400 Electronic printed circuits 1.93H1-852290 Parts and accessories of recorders except cartridges 1.89H1-854213 Metal oxide semiconductor 1.87H1-847170 Storage units 1.84H1-852520 Transmit–receive apparatus for radio, TV, etc. 1.60H1-854040 Data/graphic display tub 1.58H1-390110 Polyethylene – specific gravity <0.94 in primary forms 1.56H1-870899 Motor vehicle parts nes 1.52H1-390330 Acrylonitrile–butadiene–styrene (ABS) copolymers 1.46H1-291736 Terephthalic acid, its salts 1.39H1-390210 Polypropylene in primary forms 1.36H1-740311 Copper cathodes and sections of cathodes unwrought 1.35


TABLE A10Top 25 Import Products and Their Values: India

CommodityCode


H1-270900 Petroleum oils, oils from bituminous minerals, crude 12.87H1-710812 Gold in unwrought forms non-monetary 4.28H1-710231 Diamonds (jewellery) unworked or simply sawn, cleaved 3.81H1-271000 Petroleum oils and oils obta 1.08H1-270119 Coal except anthracite or bituminous, not agglomerated 0.95H1-847330 Parts and accessories of data processing equipment nes 0.68H1-280920 Phosphoric acid and polyphosphoric acids 0.64H1-710813 Gold, semi-manufactured forms, non-monetary 0.51H1-270799 Coal tar distillation products nes 0.43H1-151110 Palm oil, crude 0.41H1-710691 Silver in unwrought forms 0.39H1-520100 Cotton, not carded or combed 0.39H1-151190 Palm oil or fractions simply refined 0.38H1-150710 Soya bean oil crude, whether or not degummed 0.37H1-440399 Logs, non-coniferous nes 0.36H1-710239 Diamonds (jewellery) worked but not mounted or set 0.36H1-260300 Copper ores and concentrates 0.32H1-720449 Ferrous waste or scrap, nes 0.30H1-480100 Newsprint 0.28H1-852499 Recorded media for sound 0.27H1-310420 Potassium chloride, in packs >10 kg 0.26H1-847170 Storage units 0.26H1-852520 Transmit–receive apparatus for radio, TV, etc. 0.25H1-854219 Monolithic integrated circuits, except digital 0.22H1-281410 Anhydrous ammonia 0.20




Documents

Some Surprising Facts About the Concentration of Trade Across Commodities and Trading Partners