Specialization Patterns in International Trade
Walter Steingress∗
January 22, 2013
Abstract
I document new facts on the pattern of specialization by looking at export and import concentration. As a result of international trade, countries normally specialize in a few sectors/varieties,
which tend to get exported, and diversify the importing sectors/varieties. To measure specialization, I compute concentration indexes for the value of exports and imports and decompose the
overall concentration into the extensive product margin (number of products traded) and intensive product margin (volume of products traded). Using detailed product-level trade data for 160
countries, I find that exports are more concentrated than imports, specialization occurs mainly
on the intensive product margin, and larger economies have more diversified exports and imports because they trade more products. Based on these novel facts, I evaluate the ability of the
Eaton-Kortum model, the workhorse model of modern Ricardian trade theory, to account for the
observed patterns. The results show that specialization through comparative advantage induced
by technology differences explains the qualitative and quantitative facts. Also, I evaluate the role
of the key determinants of specialization: the degree of comparative advantage, the elasticity of
substitution and geography.
Keywords: Ricardian Trade Theory, Comparative Advantage, Specialization, Import Concentration, Export Concentration
∗I
thank Andriana Bellou, Rui Castro, Jonathan Eaton, Stefania Garetto, Ulrich Hounyo, Joseph Kaboski, Raja Kali,
Baris Kaymak, Michael Siemer, Ari Van Assche and Michael Waugh for their useful comments and suggestions. This
paper also benefited greatly from comments by seminar participants at Boston University, Carleton University, HEC
Montreal, the University of Montreal and the Fall 2012 Midwest Macroeconomics Meetings. All errors are my own.
Contact: Department of Economics, Université de Montréal, C.P.6128, succ. Centre-Ville, Montréal (Québec) H3C 3J7,
Canada (e-mail: walter.steingress@umontreal.ca).
1
1
Introduction
The pattern of specialization is at the core of international trade theory. A consequence of international trade is that countries are not required to produce all their consumption goods, instead they
can specialize in the production of certain goods in exchange for others. Trade theory offers different explanations of how countries specialize in the number and sales volume of goods. Assessing
the empirical relevance of the underlying theory is of vital interest since it not only allows to evaluate the gains from trade through specialization but also informs how trade affects the structure
of an economy.
My contribution is twofold. First, I uncover new facts on the pattern of specialization by looking at export and import concentration. I decompose the overall level of concentration into a
measure for the extensive and intensive product margins and document concentration levels for
exports and imports on all margins. The extensive product margin indicates the degree of specialization in the number of goods traded. The concentration index on the intensive margin measures
specialization in the volume of goods traded. The second contribution consists of testing the Eaton
and Kortum (2002) model’s ability to account for the observed specialization patterns. Specifically, I test the model based on three basic questions about specialization: What explains the level
of specialization in exports and imports? What determines the gap between specialization in exports and imports? Does specialization occur on the intensive or extensive product margin?
To start with, I establish a new set of qualitative and quantitative facts of observed crosscountry specialization patterns. Based on detailed product-level trade data for 160 countries, the
results show that, on average, countries specialize more in exports relative to imports, with Gini
coefficients of 0.98 and 0.91 respectively. The decomposition reveals that specialization occurs predominately on the intensive margin. Countries concentrate their exports and imports in the value
of few products and, at the same time, trade a fairly wide range of goods. The difference between
the concentration levels of exports and imports is explained by the number of products traded.
Countries specialize in exporting few goods and diversify on imports by acquiring various products from abroad. Focusing on cross-country differences, I find that larger economies have more
diversified imports and exports. This is mostly along the extensive margin, i.e. large economies
export and import a wider product range.
Having documented the observed specialization pattern, I employ a standard Ricardian trade
model developed by Eaton and Kortum (2002) to evaluate its ability to reproduce the stylized facts.
My analysis relies on the Alvarez and Lucas (2007) general equilibrium extension of the Eaton and
Kortum model. A key implication of this model is that it uncovers how comparative advantage
due to technology differences determines specialization endogenously on both the extensive and
the intensive product margins. Furthermore, it identifies geography together with the degree of
comparative advantage and the elasticity of substitution as the main determinants of specializa-
2
tion. The degree of comparative advantage heightens the sensitivity of concentration to changes
in unit costs, thereby dictating specialization on both margins. Trade costs decrease comparative
advantage and increase specialization on the extensive and intensive margin. A higher elasticity
of substitution provides for better substitution between intermediate goods and consequently increases concentration on the intensive margin. The model characterizes import specialization on
all margins.
To calibrate the model, I follow Waugh (2010) and use data and the structure of the model
to infer trade costs, technology and the elasticity of substitution. Not surprisingly, the simulated
results show that the model produces the observed specialization pattern qualitatively with countries being specialized in exports and diversified in imports on all margins. More importantly, the
simulated model also reproduces the degree of specialization on the extensive versus the intensive margin for both, exports and imports. However, the obtained concentration levels for exports
are too high in comparison to the data. Focusing on the variation across countries, the simulated
model replicates the fact that larger economies are more diversified but fails to account for the
observed cross-country pattern of imports.
This paper contributes to the international trade literature that analyses the relationship between the pattern of trade and specialization in commodities. Leamer (1984), who tested whether
the structure of trade can be explained by the availability of resources, started an empirical literature on specialization relating the pattern of trade to factor endowments motivated by the
Hechscker-Ohlin theorem, see, for example, Bowen, Leamer, and Sveikauskas (1987), Trefler (1995)
and Schott (2003). On the other hand, MacDougall (1951), Balassa (1963), Golub and Hsieh (2000)
and Costinot, Donaldson, and Komunjer (2012) use trade data to test the Ricardian prediction that
countries export relatively more of the commodities they are relatively more productive in. Unlike these papers, the analysis in this paper does not intend to explain why countries specialize
in a certain commodity or group of commodities, rather it uses the level of concentration in trade
data to shed light on the factors that specify specialization in the number and the volume of goods
traded by disregarding the underlying properties of the commodity. The levels of concentration
in each trade direction contain information on the pattern of trade and as such they provide a new
quantitative test of the extent of specialization observed in the data.
The analysis presented in this paper is also related to a growing literature in quantifying the
importance of Ricardian comparative advantage in explaining trade patterns using the EatonKortum framework, see, for example, Chor (2010), Shikher (2011), Levchenko and Zhang (2011)
and Costinot, Donaldson, and Komunjer (2012). These papers specify a multi-sector Ricardian
model with both inter- and intra-industry trade in order to derive implications on the sectorial
level. In contrast, I abstract from intra-industry trade and attach a sectoral interpretation to the
continuum of traded goods within the standard Eaton-Kortum framework. Given this notion, the
number of traded sectors arises endogenously and is not assumed to be fixed as in the previous
3
papers. While the standard model has been primarily used to explain bilateral trade flows and
trade volume, (see, for example, Eaton and Kortum (2002), Alvarez and Lucas (2007) and Waugh
(2010)), I focus on the models implications on the pattern of trade and analyze how trade induces
countries to specialize in narrow sectors. In particular, I characterize the models predictions on
export and import concentration on the intensive and extensive product margin and highlight
the implications on the specialization pattern. A merit of this approach is that it uncovers how
preferences, technological differences as well as geography effect specialization.
At this point, it is important to note that the Ricardian model shares with other models of international trade, most notably monopolistic competition models based on Krugman (1980) and
Armington models like Anderson and van Wincoop (2003), the ability to develop quantitative
predictions about specialization patterns on the intensive and extensive margin. However, the
underlying mechanisms of generating the specialization pattern differ. In monopolistic competition and Armington models, tradable goods are differentiated by location of production since each
country is the sole producer of a good. Thus, countries are completely specialized in disjoint sets
of goods. In the Ricardian model of Eaton and Kortum (2002), production competes with imports
because countries produce and export the same goods. As such, the Eaton and Kortum model
generates incomplete specialization.
Finally, my investigation relates to the empirical growth literature analyzing the relationship
between economic growth and trade patterns on the intensive and extensive margins, see Hummels and Klenow (2005) and Cadot, Carrèère, and Strauss-Kahn (2011). Like the previous papers,
I study cross-country differences by decomposing the trade pattern into extensive and intensive
margins. However, my focus is not to analyze cross-country linkages between exports and economic growth by quantifying the contribution of each export margin to explain GDP differences.
Instead, I apply the decomposition to both exports and imports and use the resulting empirical evidence to test Ricardian trade theory based on the Eaton-Kortum model. My analysis shows that
the Eaton-Kortum model offers a structural framework that can reconcile Hummels and Klenows
finding that larger economies export more goods and Cadot, Carrèère, and Strauss-Kahns result
that cross-country concentration differences in exports are predominately driven by the extensive
margin. The novel approach of linking cross-country variation of export and import concentration
to test the Eaton-Kortum model sheds light on how the interaction between preferences, technology and geography establishes trade patterns on the intensive and extensive margin. As such, the
Eaton-Kortum framework can provide theoretical guidance for future work.
The rest of the paper is organized as follows. Section 2 describes the data and presents the empirical evidence of import and export concentration. Section 3 lays out the theoretical framework.
Section 4 describes the simulation results and the resulting pattern of specialization. Section 5
concludes.
4
2
Empirical evidence and data
The starting point of my analysis is an empirical assessment of the observed specialization patterns in world trade using detailed product level trade data. Before describing the data and the
empirical evidence, I illustrate the properties of the concentration measurements used, which form
the basis of the qualitative and quantitative tests of the model.
2.1
Concentration measurements
I compute two measures of specialization for product level sales, the Gini coefficient and the Theil
index. The Theil index has the advantage of being decomposable into an extensive and intensive
product margin measure. For concreteness, I focus on exports - concentration measures for imports are entirely analogous. The two measurements are defined as follows. Let k index a product
among the N products in operation in the world economy, let Rk be the corresponding export sales
revenue, say, in a given country. The export Gini in this country is defined as :
2
G = ¯ Cov ( R, F ( R))
R
(1)
where Cov is the covariance between export revenues R and the cumulative distribution of export revenues F ( R). R¯ denotes the average export revenue. A Gini coefficient of zero expresses
complete diversification across trade revenues, i.e. (1) a country exports all products and (2) the
revenues are the same across them. An index of one expresses complete specialization in which
case export revenues stem from one product only. The Theil index is a weighted average of the
log difference from the mean export revenue ( R¯ ) and defined by the following formula
1
T=
N
∑
k∈ N
Rk
ln
R¯
�
Rk
R¯
�
(2)
The index takes the value of zero in the case of complete diversification and ln( N ) in the case of
complete specialization. Cadot, Carrèère, and Strauss-Kahn (2011) decompose the Theil index into
a measure for the intensive and extensive product margin, T = T ext + T int . The extensive Theil
index ( T ext ) captures the concentration in the number of products (extensive product margin)
whereas the intensive Theil ( T int ) measures the concentration in the sales volume of products
(intensive product margin). The intensive Theil index is given by:
T int =
1
Nex
∑
k ∈ Nex
Rk
R
ln( ¯ k )
¯
Rex
Rex
(3)
and the extensive Theil index is
T
ext
= ln
�
N
Nex
�
(4)
Nex denotes the number of exported products and R¯ ex represents the mean value of exported
products.
5
2.2
Data
To build my empirical evidence, I use the Comtrade data set collected by the United Nations and
choose the 6 digit HS 1992 product classification scheme as the preferred level of disaggregation.
I follow (Hummels and Klenow, 2005) and refer to import flows of the same 6-digit product from
different trading partners to different varieties of the same product. I assume that the tradable
goods sector corresponds to manufactures defined to be the aggregate across all 34 BEA manufacturing industries, see Feenstra, Lipsey, and Bowen (1997).1 Using a correspondance table provided
by the United Nations, I identify 4529 tradable manufacturing products. The baseline sample covers 160 countries representing all regions and all levels of development between 1992 and 2009
(16 years), including 129 developing countries, defined by the World Bank as countries with per
capita GDP under $16,000 in constant 2005 PPP international dollars. After I take out missing-year
data, the sample consists of 2880 observations (country-years).
Note the data includes trade within 6 digit product categories. The model I am testing is Ricardian and does not feature trade between varieties of the same product. To establish a mapping
between the model and the data, I net out the within product component by considering net trade
flows instead of gross trade flows. To measure the importance of trade between products and
trade between varieties, I follow Grubel and Lloyd (1975) and calculate the percentage share of
trade between products with respect to total trade. I find an average value of 81 percent across
countries. The overall share of total net trades flows with respect to total gross trade flows is 65
percent. Both findings suggest that in this sample the majority of trade flows between countries is
across products.2
Based on net trade flows at the product level, I calculate first the corresponding concentration
indexes on all margins for each year and then take the average over the whole sample period.
Since the concentration indexes employed are independent of scale, the calculation on a year-toyear basis avoids the need to deflate the data. Figure 1 plots the mean export against the mean
import concentration for each country together with the 45 degree line. In terms of overall concentration, see Figures 1(a) and 1(b), the vast majority of observed levels lie above the 45 degree
line highlighting the fact that exports are more concentrated than imports for almost all countries.
The notable exceptions are the United States, China, Germany, India, Italy, Japan and Afghanistan.
On the intensive product margin, see Figure 1(c), the specialization level of exports is similar to
imports with slightly higher levels of concentration for exports. Figure 1(d) plots the results for
the extensive product margin. With the exception of China and Germany, who lie below the 45
1 This is a simplification, but it is reasonable as a first-order approximation to reality because, for all countries in the
sample, this represents on average 76 percent of all merchandise imports; the median is 91 percent.
2 In the appendix I present an alternative approach to account for observed intra-industry trade in the data. The basic
idea is to develop a measurement device that enables the model to characterize trade within and across products. The
suggested procedure converts the measurement of product units in the model to product units in the data and allows
examining specialization patterns based on gross trade flows. In the rest of the paper, I follow the net trade flow approach
and present the estimation and results of the alternative procedure in the appendix.
6
Table 1: Mean concentration indexes over 2676 country-year pairs
Gini
Level of
concentration
% share of overall
concentration
Theil Exports (X)
Theil Imports (M)
Exports
Imports
Extensive
Margin
Intensive
Margin
Total
Extensive
Margin
Intensive
Margin
Total
0.98
0.91
2.60
2.13
4.73
1.10
1.61
2.71
55%
45%
40%
60%
degree line, countries export fewer products than they import.
Table 1 summarizes the sample statistics with the average year-by-year indices over the 2880
country-year pairs. As implied by Figure 1, exports are more concentrated than imports on all
margins. In terms of overall concentration, the summary statistics reveal high levels of export and
import concentration with a Gini coefficient of 0.98 for exports and 0.91 for imports. A possible
explanation for the high concentration levels might be that countries export and import very few
products between each other and hence specialization is driven by the extensive margin. In the
case of imports, the decomposition favors the alternative explanation. Countries import a fairly
wide range of products but concentrate their trade in the value of few products. The share of
the intensive margin with respect to overall concentration is 60 percent. On the export side, concentration is dominated by the extensive margin with a share of 55 percent in terms of overall
concentration. Focusing on the gap between export and import concentration, Table 1 shows that
differences between exports and imports are primarily driven by the number of products traded.
Countries export few and import a lot of products. In fact, the Theil index of 1.10 on the extensive
margin of imports implies that, on average, a country imports a 33.3 percent of all products. On
the other side, the extensive Theil index of 2.13 implies that a country exports 7.4 percent of the
product range.
Turning the attention to cross country differences, the empirical evidence shows that larger
economies diversify more than smaller economies. Figure 2 plots the log of the mean levels of
concentration as a function of market size including the best linear fit for all margins. Market size
is measured by the log of the average relative GDP to the United States (USA = 0) over the periods
1992 to 2009. As Figures 2(a) and 2(b) show, the overall Theil index is decreasing with respect to
relative GDP, i.e. smaller economies specialize more. This relationship is more pronounced for
exports than for imports with an R square of 0.58 compared to 0.41. The decomposition reveals
that specialization on the intensive margin does not vary with market size for both exports and
imports, see Figures 2(e) and 2(f). The main driver of specialization differences across countries
is the extensive margin. Particularly robust is the linear relationship on the extensive margin for
exports with an R square of 0.75. Bigger economies are more diversified because they export
7
more products. The relationship between market size and specialization on the extensive margin
of imports follows a L shape pattern. As the size of an economy increases, countries diversify
on imports until reaching a certain market size after which concentration is roughly equal across
countries.
At this point, the key qualitative and quantitative facts have been established. First, exports are
more specialized than imports on all margins. Second, overall concentration is primarily driven by
specialization on the intensive rather than the extensive product margin. Third, the target levels
of concentration are displayed in Table 1. Fourth, the cross-country patterns imply a negative
relationship between market size and specialization caused by the extensive margin. The rest
of the paper evaluates the Ricardian models ability to account for these stylized facts. I start
by presenting the relevant parts of the Alvarez and Lucas (2007) extension of the Eaton-Kortum
framework.
3
Model
The Eaton–Kortum model is Ricardian, with a continuum of goods produced under a constantreturns technology. The Alvarez and Lucas (2007) extension builds on the Eaton Kortum framework and introduces a non-tradable sector to establish the general equilibrium. I derive the relevant theoretical predictions on the pattern of trade and evaluate the importance of the key model
parameters for specialization of imports and exports.
Consider a world economy with I countries, where each country produces tradable intermediate goods as well as non-tradable composite and final goods. Following Alvarez and Lucas (2007),
define x = ( x1 , ..., x I ) as a vector of technology draws for any given tradable good and refer to it
I . The production of an intermediate good in country i is defined by:
as “good x” with x ∈ R+
qi ( xi ) = xi−θ si ( xi ) β qmi ( xi )1− β .
Technology xi differs between goods and is drawn independently from a common exponential
distribution with density φ and a country specific technology parameter λi , i.e. xi ∼ exp(1/λi ).
Denote the wage by wi and the price of the intermediate aggregate good by pm,i . The intermediate
good sector is perfectly competitive. Intermediate good producers minimize input costs and sell
the tradable intermediate good at price
β 1− β
pi ( xi ) = Bxiθ wi pmi .
where B = β− β (1 − β)−(1− β) . The continuum of intermediate input goods x enters the production
of the composite good qi symmetrically with a constant elasticity of substitution (η > 0)
8
qi =
�ˆ
0
∞
q( x )
1−1/η
φ( x )dx
�η/(1−η )
.
The produced aggregate intermediate good qi can then be allocated costless towards the production of final goods or being used as an input in the production of intermediate goods. Similarly,
labor, as the only primary factor input, can be used either to produce intermediate or final goods.
Finally, consumers draw their utility linearly from the final good. All markets are perfectly competitive. Since these features are not central to the implications I derived in this paper, I omit them.
The interested reader is refereed to Alvarez and Lucas (2007) for the full description of the model.
3.1
General equilibrium
Once a country opens to international goods markets, the intermediate goods are the only goods
traded. Final goods and labor do not move between countries. Due to trade costs, factor prices
will not equalize across countries. The intermediate goods needed to produce the composite good
will be acquired from the producer of good x in the country that operates at lowest unit costs.
Trading these intermediate goods between countries is costly. We define “Iceberg” transportation costs for good x from country i to country j by κij where κij < 1 ∀ i �= j and κii = 1 ∀i.
As in Alvarez and Lucas (2007), we also consider tariffs. ωij is the tariff charged by country i on
goods imported from country j. Tariffs distort international trade but do not entail a physical loss
of resources. Incorporating the trade costs, composite good producers in country i will buy the
intermediate good x from country j that offers the lowest price

pi ( x ) = B min 
j
β 1− β
w j pmj
κij ωij

x θj  .
(5)
Equation 5 shows that whether country i specializes in the production of good x depends on the
productivity realizations, factor prices and trade costs. The set of goods where country i obtains
I . If country i does not offer a good at lowest
the minimum price at home is denoted by Bii ⊂ R+
costs in the local market, the good is imported. Following Alvarez and Lucas, the resulting price
index of tradable goods in country i is

I


pmi = ( AB)  ∑ 
j =1
β 1− β
w j pmj
κij ωij
−1/θ

−θ

λj 
(6)
where A = Γ(1 + θ (η − 1)) is the Gamma function evaluated at point (1 + θ (η − 1)). Next, we
calculate the expenditure shares for each country i. Let Dij be the fraction of country i’s per capita
spending pmi qi on tradables that is spent on goods from country j. Then, we can write total spending of i on goods from j as
9
pmi qi Dij =
ˆ
Bij
pi ( x )qi ( x )φ( x )dx
where Bij defines the set of goods country j attains a minimum in equation 5. Note that Dij is
simply the probability that country j is selling good x in country i at the lowest price and calculated
to be

Dij = ( AB)−1/θ 
β 1− β
w j pmj
pmi κij ωij
−1/θ

λj.
(7)
Equation 7 shows that in this model the sensitivity of trade between countries i and j depends
on the level of technology λ, trade costs ω, geographic barriers κ and the technological parameter θ (reflecting the heterogeneity of goods in production) and is independent of the elasticity of
substitution η. This result is due to the assumption that η is common across countries and does
not distort relative good prices across countries. Note also that by the law of large numbers, the
probability that country i imports from country j is identical to the share of goods country i imports from j. In this sense, trade shares respond to costs and geographic barriers at the extensive
margin: As a source becomes more expensive or remote it exports/imports a narrower range of
goods. It is important to keep in mind that the amount of input industries into the production
of the composite good is fixed. Each country uses the whole continuum of intermediate goods to
produce composite goods. There are no gains of trade due to an increased number of varieties.
Welfare gains are realized through incomplete specialization. Domestic production competes with
imports and countries specialize through the reallocation of resources made available by the exit
of inefficient domestic producers.
Finally, to close the model, we impose that total payments to foreigners (imports) are equal to
total receipts from foreigners (exports) for all countries i
Li pmi qi
I
I
j =1
j =1
∑ Dij ωij = ∑ L j pmj q j Dji ω ji
(8)
The previous equation implies an excess demand system which depends only on wages. Solving this system, describes the equilibrium wages rate for each country together with the corresponding equilibrium prices and quantities. Next, I describe the models predictions on export
and import concentration on both margins.
3.2
Concentration of exports and imports
In the model, the pattern of trade is established by domestic producers competing with importers
for selling intermediate goods in the local market. If foreign producers selling a particular good at
a lower price than domestic ones, the good will be imported from the cheapest source. Given the
10
equilibrium price, p( x ), and quantity, q( x ), the total expenditure that country i spends (c.i.f.) on
imported good x, RiM ( x ), is:
x∈
/ Bii
RiM ( x ) = Li pi ( x )qi ( x )
Equivalently, domestic producers export the good to all the foreign markets where they obtain
the minimum price. The set of exporting goods is simply a collection of the set of goods country
i exports to any destination j, x ∈ ∪ jI�=i B ji . As a result, (f.o.b.) export revenue sales of good x,
Ri,X ( x ), are given by :
RiX ( x ) =
I
∑ Lk pk (x)qk (x)κki ωki
k � =i
x ∈ ∪ jI�=i B ji
Given the described pattern of trade, the concentration index for imports is identified. To show
this, I decompose the overall concentration into a concentration measure for the intensive and
extensive product margin. Using equation 3, the Theil index for the concentration of imports on
the intensive production margin can be written as:
int
TiM
=
ˆ
x∈
/ Bii
RiM ( x )
ln
R¯ iM
�
RiM ( x )
R¯ iM
�
φ( x )dx
In the appendix I show that the distribution of import expenditures follows a Fréchet distribution
with shape parameter 1/θ (η − 1) and scale parameter si . Solving the integral, the intensive Theil
index of imports for country i becomes:
int
TiM
= ln (Γ(1 + θ (1 − η ))) −
ˆ
0
1
�
�
ln u(−θ (1−η )) e−u du
(9)
where Γ(.) stands for the Gamma function. Import specialization on the intensive margin is independent of equilibrium prices, trade costs, geography and the level of technology λ. It is solely
determined by preferences (i.e the elasticities of substitution) and heterogeneity in production (i.e.
the degree of comparative advantage). A higher elasticity of substitution (η ) increases specialization by allowing producers in the composite intermediate good sector to better substitute less
for high productive goods. Similar, an increase in the degree of comparative advantage (θ ), which
corresponds to a higher variance of productivity realizations across products, heightens the degree
of specialization.
To compute the concentration of imports on the extensive margin, I use the fact that the set
of goods produced is disjoint form the set of goods imported. Consequently, we can express the
share of goods imported as 1 minus the share of goods produced, (1 − Dii ). The Theil index for
the extensive margin of imports is equal to :
ext
TiM
= ln
�
N
NiM
�
= − ln(1 − Dii )
11
(10)
where
Dii = ( AB)−1/θ
�
wi
pmi
�− β/θ
λi
and depends on the level of technology and equilibrium prices.
To assess the level of specialization in exports, I simulate the model within a discrete product
space. I calculate the export concentration index on the intensive margin according to equation 3.
The extensive Theil index on the extensive margin is given by the inverse share of the number of
goods exported, NiX , with respect to the total number of simulated goods, N.
ext
TiX
= ln
�
N
NiX
�
Having outlined the pattern of trade and the corresponding implications on the specialization
pattern of exports and imports, the next section discusses the simulation of the model. It contains
special cases of equilibria designed to spell out step-by-step the main implications of the model
on export and import concentration and in further instance on specialization.
4
Calibration and simulation
To simulate the theoretical model, which assumes an infinite amount of goods, I "discretize" the
Fréchet distribution of total factor productivity and calculate the respective trade value for each
product x. Concerning the remaining parameters of the model, I use the same values as Alvarez
and Lucas (2007). I assume a variance of individual productivities θ = 0.12, an elasticity of substitution of intermediate goods η = 2, an efficient labor share in the production of non-tradable
final goods α = 0.75 and an efficient labor share in the tradable goods sector of β = 0.5. I simulate I = 160 countries. In the following subsections, I analyze import and export concentration in
special cases of the equilibrium by assuming different trading schemes. Doing so builds intuition
of how taste, technology and geography determine specialization. To illustrate the impact of each
factor separately, it is instructive to start the analysis by assuming symmetric countries and introduce heterogeneity across countries afterwards. Finally, I show that for a particular configuration
of trade costs the Eaton-Kortum model is able to replicate the specialization patterns observed in
the data.
4.1
Symmetric countries
All countries are identical with respect to their size Li = L and technology parameter λi = λ.
Trade costs are symmetric and set to κij = κ ∀ i �= j with κii = 1 and ωij = 1 ∀i, j. I solve
for the equilibrium wage in each country, all good prices and the value of imports and exports.
12
Due to symmetry, wages and composite good prices equalize across countries. Trade costs distort
international trade. In comparison to free trade, firms will not be able to buy good x from the
cheapest producer world wide and rely more on home production. The corresponding trade share
matrix D is symmetric and the (i, j) element is given by:
Di,j =
(κ )1/θ
1
∀i �= j and Di,i =
1 + ( I − 1)(κ )1/θ
1 + ( I − 1)(κ )1/θ
where Dij is the set of goods country j exports to country i and Dii the set of goods country i
produces at home. In free trade, κ = 1, each countrys intermediate good producers specialize in
a distinct set of goods equal to the relative size of the economy and export all products produced,
Dii = Dij = 1/I. The corresponding share of imported products is 1 − Dii = ( I − 1)/I. Hence,
the more countries participate in international trade, the more countries specialize in exports and
diversify in imports. In this case, Ricardian specialization forces are strongest and the gap between
export and import concentration reaches a maximum.
Concentration on the Extensive Margin Including trade costs the concentration index of
imports equals the share of goods country i imports from all countries in the world and is given
by:
ext
TiM
= − ln((1 − Dii )) = ln(1 + ( I − 1)(κ )1/θ ) − ln(( I − 1)(κ )1/θ )
Concentration at the extensive margin of imports increases with trade barriers κ and decreases
with the number of trading partners I − 1 and the degree of comparative advantage θ. Regarding
exports, the extensive Theil index is given by the number of products exported to any destination
divided by the total number of products in the world. Note that the randomness of the productivity distribution implies that in this model there is no fixed hierarchy of export destinations as in
Melitz (2003), i.e. goods that are exported to the k + 1 “most popular” destinations are not ncessairily exported to the k most popular destinations.3 To count the number of products exported,
define the set of products exported as the union of the set of products exported to each destinations, Uex = ∪ jI�=i B ji . Because some of the products exported to destination j are also exported to
destination k, B ji ∩ Bki �= ∅, I apply the Inclusion Exclusion principle to avoid double counting. As
I show in the appendix, under the assumption of symmetry, the extensive Theil index of exports
is given by:
ext
TiX
= − ln
�
I −1
∑ (−1)k−1
k =1
�
I
k
�
ak
�
where the share of products exported to k destinations, ak , is given by:
3 In
the basic version of the Melitz (2003) model exported goods obey a hierarchy, see Eaton, Kortum, and Kramarz
(2011). Any good sold to the k + 1st most popular destination is necessarily sold to the kth most popular destination as
well. In that model the total number of exported goods would be simple all the products exported to the most popular
destination, i.e. the destination with the lowest trade costs.
13
ak =
(κ )1/θ
k + ( I − k )(κ )1/θ
The concentration of exports at the extensive margin increases with geographical barriers, the
degree of comparative advantage and the number of trading partners. In general, a larger number
of trading partners increases competition between production and imports in the domestic market resulting in the production of fewer goods at home and an increase in the number of goods
imported. Also, more trading partners increase competition among exporters in foreign markets
forcing countries to specialize more on the extensive margin of exports. Impediments to trade, i.e.
a reduction in κ, and a higher degree of comparative advantage, θ, reduce import competition and,
as a result, fewer goods are exported and imported. Notice that in the special case of free trade
all goods produced are exported and concentration of production equals concentration of exports.
With trade costs, countries export a subset of produced goods leading to more concentration of
exports relative to production.
Concentration on the Intensive Margin
As noted previously the distribution of imports
follows a Fréchet distribution where the concentration indexes depend on the elasticity of substitution (η) and the degree of comparative advantage (θ). Consequently, given θ and the concentration
of imports observed in the data, I can pin down the elasticity of substitution. Concerning the distribution of export revenues across products, the simulation shows that it depends positively on
the elasticity of substitution (η), the degree of comparative advantage (θ) and geographical barriers (κ ). The number of trading partners has non-monotone effects on the concentration of exports
at the intensive margin. Few trading partners increase concentration because high revenue generating exports sell in more markets. However, as the number of trading partners increases, the
degree of competition in the export markets increases and low revenue generating products do
not sell in foreign markets anymore. Thus, after a threshold level, concentration among export
revenues reduces with the number of trading partners. In the case of free trade, countries export all their goods to all destinations and, given that preferences are identical, export and import
concentration on the intensive margin equalize.4
The results presented in Table 2 show that the free trade calibration of Alvarez and Lucas (2007)
is able to replicate the qualitative fact that, overall, exports are more concentrated than imports.
4 The
intuition behind this result is that preferences are such that the import expenditure distribution is the same
for each trading partner. In the appendix, I show in detail that the expenditure distribution of bilateral trade, Eij (qp),
between importer i and exporter j is the same for each source country j, i.e. Eij (qp) = Ei (qp), ∀ j ∈ I. Furthermore, the
bilateral import expenditure distribution, Ei (qp), is Fréchet with common shape parameter 1/(θ (η − 1)) and country
specific scale parameter si . The shape parameters are identical because preferences are common across countries. Note
that the bilateral import expenditure distribution of country i, Ei (qp), equals the export revenue distribution of country j.
In free trade, the exporting country ships the exact same goods to all countries. As a consequence, overall export revenue
distribution of country j is equal to the import expenditure distribution of each country scaled up by the number of
trading partners. Since the concentration indexes are independent of scale, the concentration of exports and imports on
the intensive margin equalize.
14
Table 2: Simulated export and import concentration indexes for benchmark parameters.
Gini
Symmetric
countries
Theil Exports (X)
Parameters
Exports
Imports
Extensive
Intensive
Margin
Margin
Theil Imports (M)
Total
Extensive
Intensive
Margin
Margin
Total
(η = 2,κ = 1)
0.99
0.09
5.01
0.01
5.02
0.01
0.01
0.02
(η = 7.1,κ = 1)
0.99
0.72
5.01
1.91
6.92
0.01
1.61
1.62
(η = 7.1, κ = 0.7)
0.99
0.77
5.04
1.18
6.22
0.10
1.61
1.71
(η = 7.1, κ = 0.7, NT=10)
0.98
0.86
2.47
2.45
4.92
1.09
1.61
2.70
Data
0.98
0.91
2.60
2.13
4.73
1.10
1.61
2.71
While the simulated overall level of export concentration attains the degree of specialization observed in the data, in the benchmark free trade parametrization countries diversify excessively
in imports. Focusing on the decomposition reveals the underlying reason: countries import too
many goods and the value of those goods is too evenly distributed. Using the fact that for a given
value of θ, import concentration on the intensive margin can be determined by the elasticity of
substitution, I calibrate η = 7.1 to match the level observed in the data5 . As row 2 of Table 2
shows, this allows composite good producers to better substitute between intermediate inputs
and alters the level of import concentration.
To reduce the gap between export and import concentration caused by the extensive product
margin, I introduce 42 percent symmetric trade costs to all trading partners, κ = 0.7. Row 3 of
Table 2 features the results. Impediments to trade reduce the number of products exported and
imported and concentration on the extensive margin increases for both. Note that higher trade
costs lower the level of concentration on the intensive margin of exports. Due to the increase in
trade costs, only efficient producers remain exporters allowing them to distribute their export revenues more evenly across products and trade partners. Although the gap between export and
import concentration narrows slightly, the difference is still substantial. The reason is that the degree of competition countries face in export and domestic markets is too high. Creating trading
blocks by introducing infinite trade costs with countries outside of the block limits the number of
trading partners (NT) and reduces competition in all markets. The fit of the model improves on
all dimensions, see the fourth row of Table 2. Less competition in the domestic market increases
the survival rate of domestic producers and reduces the amount of goods imported. Infinite trade
costs reduce the number of countries competing in a particular market and increases the probability to export to any of them. As a result, the gap between export and import concentration diminishes. Note that revenues of exporting industries are now geographically more concentrated and
hence specialization on the intensive margin of exports intensifies.
In sum, with the introduction of symmetric trade costs, the model can replicate the mean levels
5 This
value is consistent with previous ones found in the literature, see Broda and Weinstein (2006).
15
of concentration observed in the data. The key parameters are the elasticities of substitution η and
the trade cost function κ. In particular, by creating trade blocks, which amounts to introduce zeros
in the bilateral trade matrix, we can calibrate the model to explain the pattern of specialization at
the mean.
4.2
Asymmetric countries
In this section I analyze the effects of cross-country heterogeneity on specialization. The empirical
facts imply a negative relationship between specialization and market size. For this reason I introduce heterogeneity in technology λi and size Li to reflect the observed GDP differences in the
data. To start with, consider the models free trade equilibrium relationship between wages, size
and technology:
wi =
�
λi
Li
�θ/( β+θ )
(11)
Using equation 11, I back out the level of technology, λi = (wi Li )( β+θ )/θ Li
− β/θ
, as a function of
GDP (wi Li ) and labor endowment ( Li ). To calibrate λ, I use GDP and endowment data from the
Penn World table. I proxy labor endowment with data on the countrys level of population and
normalize the obtained parameters for λi and Li relative to the United States.
Concentration on the Extensive Margin Plugging in the equilibrium wage into equation 7,
I get the corresponding trade share matrix D with the (i, j) element given by:
D ji =
wi L i
I
∑ k =1 w k L k
∀j
(12)
Equation 12 shows that under the assumption of free trade countryi’s share of number of
products exported is equal to its relative level of GDP with respect to world GDP. Hence, larger
economies export more products and import less products compared to small economies. This result is at odds with the empirical evidence. In the data, larger economies export and import more
products. In the next section, I introduce trade costs and argue that they have to be asymmetric in
order to replicate the empirical observations.
Concentration on the Intensive Margin On the intensive margin, under the assumptions of
homogenous tastes across countries and free trade, export and import concentration equalize. In
this case, export and import concentration on the intensive margin depend only on θ and η and
are unaffected by the introduction of heterogeneity in technology and country size.
4.3
Asymmetric trade costs
To reconcile the cross-country concentration differences for imports, I introduce asymmetric trade
costs. In particular, I consider trade costs as a function of a fixed export cost (ex j ) or a fixed import
16
cost (imi ). The next paragraphs show the different implications of each effect.
Importer fixed effect
Under the assumption of a fixed import cost, country i faces the same cost of importing independent of the origin country j. The trade cost matrix becomes κi,j = imi ∀ j �= i and κi,i = 1 ∀ j = i.
Due to asymmetric trade costs, wages and composite good prices do not equalize. The trade share
matrix is asymmetric and given by:
D ji = ( AB)
−1/θ
�
β 1− β
wi pmi
pmj im j
�−1/θ
λi ∀i � = j
and
Dii = ( AB)−1/θ
�
wi
pmi
�− β/θ
λi
Focusing on the expression for the share of goods that country j imports from country i, D j,i , shows
that higher import costs (imi ↓) reduce the number of goods country j imports from i. Solving
for the equilibrium and assuming that price differences across countries are approximately equal
to the import cost differences, one can show that the corresponding share of goods imported is
approximately:
(1 − Dii ) ≈
�
−1
1 − C1 imi θ wi Li
�
(13)
where C1 is a constant independent of country i. Equation 13 shows that an importer fixed effect
can counterbalance the fact that larger economies import less under the assumption that they
face lower costs to import. Lower import costs increase the share of goods imported, (∂(1 −
Dii )/∂imi > 0), and lead to a reduction in the unit cost of production through a lower price
index of tradable goods.
Exporter fixed effect
In the case of an exporter fixed effect, each country pays a country specific cost to export, which
is independent of the importing country i, κi,j = ex j ∀ j �= i and κi,i = 1 ∀ j = i. The trade share
matrix is asymmetric and given by:
D ji = ( AB)
−1/θ
�
β 1− β �−1/θ
wi pm,i
pmj exi
λi ∀i � = j
and
Dii = ( AB)
−1/θ
�
wi
pm,i
�− β/θ
λi
Here the expression for D ji implies that a higher export cost (exi ↓) reduces the number of goods
country i exports to any destination j. Solving for the equilibrium and assuming that compos-
ite good prices across countries are approximately equal, one can show that the share of goods
imported is approximately given by:
�
−1
θ
(1 − Dii ) ≈ 1 − C2 exi wi Li
17
�
(14)
Table 3: Simulated export and import concentration indexes for asymmetric countries.
Gini
Theil Exports (X)
Extensive
Intensive
Margin
Margin
0.73
5.75
1.91
0.99
0.85
8.08
0.98
0.85
2.59
0.98
0.91
2.60
Parameters
Exports
Imports
(η = 7.1, κ = 1)
0.99
Asymmetric
(η = 7.1, κ = ex)
countries
(η = 7.1, κ = ex, NT=10)
Data
Theil Imports (M)
Extensive
Intensive
Margin
Margin
7.66
0.007
1.61
1.62
1.68
9.76
1.09
1.61
2.70
2.67
5.26
1.10
1.61
2.71
2.13
4.73
1.10
1.61
2.71
Total
Total
where C2 represents a constant independent of country i. Equation 14 shows that the share of
goods imported is decreasing in the country specific exporting costs, (∂(1 − Dii )/∂exi > 0). Lower
exporting costs lead to higher domestic wages, increase unit costs of production and result in a
larger share of imported goods. Hence, an exporter fixed effect can reconcile the fact that larger
economies import more by assuming that (1) larger economies face lower costs to export and (2)
the effect of GDP on the export cost is more pronounced than the effect of GDP on the share of
goods imported, (∂(1 − Dii )/∂wi Li > 0) ⇒ ((∂(1 − Dii )/∂exi ) (∂exi /∂wi Li ) > ∂(1 − Dii )/∂wi Li ).
The main difference between the import cost and the export cost in terms of import concentration lies in the implication on the price level of tradable goods. The export cost implies a nearly
constant price level of tradable goods across countries. As a result, unit cost differences between
countries are driven predominantly by wage differences. On the contrary, the import cost leads
to large cross-country price level differences with smaller economies facing a higher price level.
In this case, unit cost differences are driven by wage and price level differences. Based on Waugh
(2010)’s results that countries have similar price levels of tradable goods, I focus in the rest of my
analysis only on the case of a country specific export costs.
In sum, the introduction of asymmetric trade costs in form of a country specific cost to export or import allows the model to replicate the import specialization pattern across countries,
in particular when larger economies face relative lower costs to either export or import. Waugh
(2010) argues that trade costs have to be asymmetric, with poor countries facing higher costs to
export relative to rich countries, in order to reconcile bilateral trade volumes and price data. While
both our approaches highlight the importance of asymmetric trade costs in explaining trade data,
our analysis differs. Waugh uses the Eaton Kortum model to explain bilateral trade volumes and
price data whereas I look on the models implications on the specialization pattern of exports and
imports. In this respect, the results presented in this paper provide further evidence on the importance of asymmetry in trade costs when studying trade volumes and trade patterns across
countries.
Row 1 of table 3 presents simualtions results in the case of asymmetric countries and free trade.
18
Note that in relation to the symmetric country case introducing technology differences increases
the mean level of concentration for exports and decreases the level of concentration for imports.
The underlying reason is the technology distribution being skewed towards less productive countries and these countries export fewer and import more goods. Beside these changes, the results
are similar to the symmetric case. While exports are more concentrated than imports, the simulated level of concentration for exports (imports) is too high (low) compared to the data. The
reason is excessive specialization (diversification) on the extensive margin of exports (imports).
In terms of cross country differences, calibrated GDP differences in combination with zero or
symmetric trade costs lead to the false prediction that larger economies import less goods. As discussed to reconcile the empirical evidence I introduce a country specific cost to export with larger
economies facing relative lower export costs. I calculate the implied export cost from equation
14 by replacing the share of goods produced at home by the extensive Theil index of imports obExt ). Row 2 of table 3 shows the results of the corresponding
served in the data , Dii = 1 − exp(− TM
mean concentration levels. While the model matches the cross country concentration pattern, the
mean level of export concentration is twice as high as in the data. The reason being excessive competition in the export markets that leads to high levels of concentration on the extensive margin of
exports. To counter the competition effect, I create trading blocks between countries (i.e. number
of trading partners NT = 10) by introducing infinite trade costs. In addition, I assume that countries within a block trade with countries whose market size is similar.6 Trade blocks conditional
on market size improve the fit of the model. The obtained concentration levels match the data on
all dimension. Row 3 of Table 3 presents the results. Countries are more concentrated in exports
than in imports on all margins, the intensive margin dominates in terms of overall concentration
and the simulated concentration levels are close to the target levels. In terms of the cross country pattern, Figure 3 plots the simulated (in red) and the empirical (in blue) concentration levels
against GDP for both margins. The figure shows that the country specific export cost in combination with technology and endowment differences can replicate the across country evidence on all
margins. Bigger economies are more diversified because they import/export more products and
concentration patterns on the intensive margin are insensitive to market size.
In the previous section I analyzed special cases of the equilibrium to study the different factors
that determine specialization in the Eaton Kortum model. The key determinants are the degree
of comparative advantage, the elasticity of substitution and asymmetric trade costs. However, I
treated trade costs as free parameters and showed that for a particular configuration of trade costs,
the model is able to reproduce concentration levels at the mean as well as the cross-country specialization pattern for both exports and imports. In the next section, I estimate trade cost and technology parameters based on bilateral trade shares using the models structure and check whether
for given trade shares, the model is able to generate the observed specialization pattern in the
data.
6 The
precise trade cost configuration is given in the appendix.
19
5
Estimating trade costs from bilateral trade shares
The starting point of the estimation of technology and trade costs is a structural log-linear “gravity” equation that relates bilateral trade shares with trade costs and structural parameters of the
model. To derive the relationship, simply divide each country i’s trade share from country j, see
equation 7, by country i’s home trade share. Taking logs yields I − 1 equations for each country i :
log
�
Dij
Dii
�
= S j − Si +
1
1
log(κij ) + log(ωij )
θ
θ
(15)
− β/θ −(1− β)/θ
pmi
λ i ).
in which Si presents the structural parameters and is defined as Si = log(wi
In
order to estimate trade costs κ and technology λ implied by equation 15 I use data on bilateral
trade shares across 160 countries. I calculate the corresponding bilateral trade share matrix by the
ratio of total gross imports of country i form country j divided by absorption Absi
Dij =
Mij
Absi
where Mij represents total imports of of country i from country j. Absorption is defined as total
GDP plus total imports Mi minus total exports Xi . Note there are only I 2 − I informative moments
and I 2 parameters of interest. Thus, restrictions on the parameter space are necessary. To create
them, I follow Eaton and Kortum (2002) and assume the following functional form of trade costs.
� �
log κij = bij + dk + ωij + ex j + �ij
Trade costs are a logarithmic function of distance (dk ) a shared border effect between country i
and j (bij ), a tariff charged by country i to country j and an exporter fixed effect (ex j ). Tariff (ωij )
represents the weighted average ad valorem tariff rate applied by country i to country j. The
distance function is represented by a step function divided into 6 intervals. Intervals are in miles:
[0, 375); [375, 750); [750, 1,500); [1,500, 3,000); [3,000, 6,000); and [6,000, maximum]. �ij reflects
barriers to trade arising from all other factors and is orthogonal to the regressors. The distance and
common border variables are obtained from the comprehensive geography database compiled by
CEPII.
ˆ and strucTo recover technology, I follow Waugh (2010) and use the estimated trade costs, κ,
ˆ
tural parameters, S, to compute the implied tradable good prices, pˆ m , by rewriting equation 6 in
ˆ
terms of S:
pˆ mi = ( AB)
�
I
∑e
Sˆj
j =1
�
κˆ ij ωij
�1/θ
�−θ
From the obtained prices and the estimates Sˆi , I get the convolution of wages and technology,
− β/θ
log(wi
λi ). Then, given the bilateral trade shares, Di,j , and the balanced trade condition in
20
Table 4: Estimation Results
Summary Statistics
Observations
9649
Geographical barriers
Barrier
[0,375)
[375,740)
[750,1500)
[1500,3000)
[3000,6000)
[6000,max)
Shared border
Tariff
TSS
2,60E+05
SSR
4,67E+04
R2
0.82
Paremeter estimate
-4,89
-5,76
-6,78
-7,98
-9,05
-9,81
1,37
0,23
Standard error
0,10
0,06
0,04
0,03
0,02
0,03
0,09
0,10
% effect on cost
79,93%
99,60%
125,62%
160,66%
196,42%
224,64%
-15,19%
-5,47%
equation 8, I follow Alvarez and Lucas (2007) and calculate equilibrium wages according to the
following equation.
L i wi (1 − s f i ) =
I
∑ Lj
j =1
w j (1 − s f j )
D ji ω ji
Fj
where s f i is the labor share in the production of final goods
sfi =
α(1 − (1 − β) Fi )
(1 − α) βFi + α(1 − (1 − β) Fi )
and Fi is the fraction of country i spending on tradable goods net of tariff expenses.
Fi =
I
∑ Dji ω ji
j =1
The obtained equilibrium wages together with tradable good prices, determine the implied technology levels λˆ for each country given the structural estimates of the gravity equation.
Table 4 summarizes the regression outcome of the gravity equation. In terms of fitting bilateral
trade flows, I obtain an R2 of 0.82 slightly lower than the R2 of 0.83 reported by Waugh. The
obtained coefficients on trade costs are consistent with the gravity literature, where distance and
tariffs are an impediment to trade. The magnitudes of the coefficients reported in Table 4 are
similar to those in Eaton and Kortum (2002) and in Waugh (2010), which consider a similar sample
of countries without tariffs. The overall size of the trade costs in terms of percentage are similar to
those reported in Anderson and Van Wincoop (2004).
21
Table 5: Simulated concentration level with exporter fixed effect
Gini
Model
Simulation
Data
Theil Exports (X)
Exports
Imports
0.99
0.89
0.98
0.91
Extensive
Intensive
Margin
Margin
4.83
3.32
59%
41%
2.60
2.13
55%
45%
Theil Imports (M)
Total
8.15
4.73
Extensive
Intensive
Margin
Margin
0.84
1.61
34%
66%
1.10
1.61
40%
60%
Total
2.45
2.71
Having identified trade costs and technology, see Table 6 for the estimated technology parameters, I simulate the Eaton and Kortum model to test whether the calibrated version can replicate
the concentration levels observed in the data. Table 5 presents the mean concentration levels for
the simulated countries. The results show that the calibrated model replicates the fact that countries are more specialized in exports than in imports on all margins. Focusing on the obtained
concentration levels reveals that countries concentrate excessively on exports with respect to the
data. The concentration levels of exports are almost twice as high as the ones observed in the data.
Mean export (import) concentration on the extensive margin is 4.83 (2.60) compared to 0.84 (1.10)
in the data. This implies that simulated countries export (import) 0.8% (43.2%) of the product
space compared to 7.4% (33.3%) in the data.
Figure 4 plots the corresponding cross country pattern for simulated and empirically observed
concentration levels against the log of GDP. The model replicates the empirical pattern with export concentration decreasing in market size. However, the simulated concentration levels on
the extensive margin are too high, particularly for smaller economies. Countries specialize excessively on the number of products exported. On the importing side, the calibrated model is unable
to replicate the L shape relationship between market size and concentration. The relationship is
cloudy and countries tend to import too many goods. Turning the attention to the intensive margin, see figures 4(e) and 4(f), the results show that consistent with the data the model predicts
no relationship between concentration and market size. Overall, the calibrated model is able to
replicate the qualitative pattern for exports but produces excessive concentration levels relative to
the data, particularly on the extensive margin.
The underlying reasons for the excessive concentration in exports lies in the structure of the
model. While the model reproduces the bilateral trade volumes, it fails to capture the underlying distribution of trade volumes across products. To shed light on why countries trade too few
products, I plot the share of the number of exported and imported products against the number
of exporting and importing countries and compare it with the data. Figure 5 shows the results.
In the case of exports, simulated countries export their goods to too many destinations. The assumed productivity distribution generates so extremely efficient producers that even firms facing
22
high trade costs can sell their products to many destinations in the world. As a consequence, the
number of exporting countries per product is small. In the data (in blue) more than a third of the
products are exported by 25 or more countries. In the simulation (in red) no product is exported
by more than 25 countries. Turning the attention to imports, Figure 5(b) shows that, contrary to exports, the simulated distribution of the number of countries importing a product is closely related
to the empirical one. The distributions are similar at the mean, however, the empirical distribution
is more dispersed. The average number of importing countries per product is 70 in the data and
75 in the model.
5.1
Discussion of results
There are several potential reasons why the model is not able to reproduce the cross country pattern of import concentration on the extensive margin. Note that the model implies that expenditure shares equal to product shares in the tradable sector, i.e. in the model the share of expenditure
that country i spends on goods from country j equals the share of products country i imports from
country j. Figure 6 plots the empirical relationship between import product shares and import
expenditure shares. The red line marks the 45 degree line where the two shares are equal. Notice
that countries below the 45 degree import a lot of goods and spend relative little on those goods,
whereas countries above the 45 degree line import few goods and spend a lot on them.
One potential reason why expenditure shares do not equal product shares in the tradable sector
could be that not all manufacturing products are tradable. When calculating expenditure shares,
then ideally one wants to use absorption of the tradable sector instead of absorption of the manufacturing sector. If the size of the non-tradable sector varies between countries with countries
below the 45 degree line having a relative larger non-tradable sector in manufacturing, then the
resulting downard bias in the measurement of import expenditure shares can explain why those
countries spend relative little on imported goods. In addition to potential size differences of the
non-tradable sector, relative prices of non-tradables differ across countries. For example, suppose that trade increases productivity but that productivity gains, in accordance with the BalassaSamuelson hypothesis, are greater in the tradable than in the nontradable sector. Then, relatively
greater productivity gains in the tradable sector lead to a rise in the relative price of nontradables. As a result, import expenditure shares with respect to tradables are in fact higher than
computed import shares with respect to expenditure on tradables and non-trabables. Alcalá and
Ciccone (2004) argue that computing import expenditure shares with respect to GDP using real
GDP instead of nominal GDP eliminates distortions due to cross-country differences in the relative
price of nontradable goods. For this reason, I experiment with computing absorption with respect
to manufacturing production by multiplying gross manufacturing production by the Purchasing
Power Parity index from the Penn World table. The obtain results show that indeed countries that
lie below the 45 degree line have a higher PPP index. However, the resulting concentration pattern
23
of imports does only change slightly.
The previous argument cannot reconcile the fact that some countries lie above the 45 degree
line, i.e. those countries that import relative few goods and spend a lot on them. One reason may
be that not all countries make use of all intermediate goods. When calculating the share of goods
imported, I divide the total number of net products imported by the total number of HS codes,
which is common to all countries. If a countries do not make use of all intermediate goods (for example they do not have the underlying technology to use a particular intermediate good), then the
calculated import product shares for those countries are downward biased. Ethier (1982) argues
that countries may differ in the number of intermediate goods used for manufacturing production
due to increasing specialization in the production process. He supposes that the production of intermediate goods features increasing returns to scale external to the firm and these returns depend
upon the level of technology and the size of the market. Thus, larger, more advanced economies
have a higher degree of specialization with a greater number of inputs in the production of manufacturing goods in comparison to less developed, smaller economies. This argument may explain
why predominately low income countries are above the 45 degree line.
Non-homothetic preferences may represent an alternative explanation for the fact that some
countries spend on average relatively more on few imported goods. Consider the equality between expenditure shares and product shares. If I multiply both sides by total expenditure of
tradables and divide both sides by the total number of products imported, the equality implies
that average import expenditure equals average expenditure in the tradable sector. Under the
assumption of homothetic preferences, the ratio of average import expenditure with respect to
average tradable expenditure should be one. Plotting this relationship against income per capita
reveals a negative correlation of -0.6, meaning that richer economies tend to spend on average less
per imported good. This evidence is consistent with non-homothetic preferences, where poorer
countries spend relative more per imported good as rich ones, and reconciles that fact that poorer
countries are predominately above the 45 degree line.
A fourth potential reason why import expenditure shares do not equal import product shares
may be due to the presence of fixed costs to enter a destination market. Arkolakis (2010) formalizes a model where producers selling to export markets have to incur market penetration costs,
for example in the form of advertising or marketing costs, to reach consumers in the destination
market. Eaton, Kortum, and Kramarz (2011) embed the fixed cost to export into a general equilibrium version of Alvarez and Lucas (2007) by assuming that producers pay the fixed cost to export
in terms of labor in the destination market. The resulting import expenditure shares, see equation
(44) in Eaton, Kortum, and Kramarz (2011) adopted to the notation in this paper, is
�
�
β 1− β −1/θ
λi wi pmi
1 − Dii = 1 −
�
�−1/θ
β 1− β
(ηFik )−(θ −(η −1))/(η −1)
∑kN=1 λk wk pmk κik
24
where Fik is the fixed cost that country k has to pay when exporting to destination i. Note that
the parameter restriction 1+θ (1 − η ) > 0 implies that high market penetration costs into country
i decrease the import expenditure share. As a result, market penetration costs can explain why in
countries below the 45 import expenditure per good is low. These countries import lots of goods
but exporters to these markets have to hire a fair amount of local workers to pay for the high fixed
costs. Therefore the average import expenditure per good is low.
6
Robustness checks
The first part addresses concerns on the robustness of the empirical observed concentration indexes. In particular, the level of disaggregation as well as the classification scheme chosen may
affect the empirical concentration measures and the decomposition of the intensive and extensive
margin. For this reason, I re-calculated the concentration indexes on all margins by defining a
product to correspond to a 4 digit SITC code instead of a 6 digit HS code. The implied product
space changes significantly as it comprises only 642 products compared to 4529 products using 6
digit HS codes. Also, the SITC classification scheme differs from the HS classification scheme by
grouping products based on economic functions rather than their material and physical properties. The empirical estimates of the SITC industry classification are very similar to the 6 digit HS
code sample. The correlation coefficient between the SITC and HS concentration indexes is 0.9 for
exports and 0.7 for imports. The obtained concentration levels are slightly lower because of the
higher level of aggregation. However, the core results remain the same. Exports are more concentrated than imports on all margins and the intensive margin dominates concentration for imports
and the extensive margin for exports. The obtained shares of the intensive margin in terms of
overall concentration are almost identical to the standard sample with 55 percent for exports and
66 percent for imports. Also, the cross country concentration patterns feature a negative log linear
relationship between concentration on the extensive margin and market size for both exports and
imports. In sum, the obtained results on the 4 digit SITC level support the empirical evidence
based on the 6 digit HS classification and highlight the level of generality my results apply.
Finally I want to address the discrepancy of the product space between the data and the model
caused by intra-industry trade. In the main part of the paper I establish correspondence between
the model and the data by netting out within product trade and considering only trade across
products. This approach leaves valuable information unused and may bias the results because
intra-industry trade flows occur predominantly between OECD countries and to a lesser extend
between developing countries. In an alternative approach, I deal with intra-industry trade by developing a “measurement device” that enables the model to characterize trade within and across
products. The basic idea is that in reality the true state of the world is indeed Ricardian, i.e. varieties are in fact products, but the data are not sufficiently disaggregated to capture the true product
level. Instead, these “Ricardian products” are aggregated into sectors according to a classification
scheme, i.e. HS codes. The suggested procedure converts the measurement of product units in the
25
model to product units in the data and allows to examine gross trade flows. Because the classification scheme is unobserved, I assume that varieties are randomly assigned to an HS code following
a Poisson process. Using the structure of the model, I can then estimate the Poisson parameter and
characterize the “measurement device” completely. I obtain a value of 6 for the Poisson parameter
implying that, on average, 6 “Ricardian products” comprise an HS product category. Based on this
result, I apply the Poisson process to group simulated Ricardian products randomly into artificial
HS codes for which I calculate the implied concentration indexes. The results, presented in detail
in the appendix, show that this approach leads to similar results as the net trade flow approach. In
particular, it implies a similar value for the elasticity of substitution, η = 8 (compared to η = 7.1
in the net trade sample), and an exporter fixed effect to reconcile the cross country concentration
pattern on the intensive margin. However, mean trade costs are with κ = 0.6 significantly higher
than the κ = 0.7 mean trade costs in the net trade flow case.
7
Conclusion
I have argued that export and import concentration in combination with a decomposition into
an extensive and intensive product margin concentration measure provide new quantitative and
qualitative evidence on specialization patterns in world trade. Based on detailed trade data, my
calculations show that exports are more concentrated than imports on all margins and specialization is dominated by the extensive product margin for exports and by the intensive product
margin for imports. The extensive product margin explains the gap between export and import
concentration and drives specialization differences across countries. Larger economies diversify
more because they export and import more products. Furthmore, I show that the Eaton Kortum
model is consistent with the observed patterns and replicates the stylized facts as well as the crosscountry differences qualitatively and quantitatively. Overall, my results stress the importance of
the role that comparative advantage and geography play in determining the pattern of specialization.
Finally, I want to point out that my analysis can be readily applied to other trade theories as
well. In this paper I study specialization patterns in the Ricardian framework assuming that specialization patterns emerge through comparative advantage induced by technological differences.
Other models of international trade, most notably monopolistic competition based on Krugman
(1980) and Armington models, also develop quantitative predictions about specialization patterns.
However, in both types of models, goods are differentiated by location of production and countries completely specialize in disjoint sets of goods requiring an adaption of the product space in
the empirical analysis. Nevertheless, it would be interesting to compare the performance of both
types of models to the results in this paper in order to gain further insights on the relevance of
each trade theory.
26
Figures
Gini index − HS 6 digit
Total Theil index − HS 6 digit
Mean of the index from 1992−2009
Mean of the index from 1992−2009
6
4
2
Export Concentration
.95
.9
.85
ITA
GER
NGA
TCD
YEM
OMN
GAB
COG
IRN
KWT
GNB
SAU
QAT MLI
COM
ARE
BFA WSM
BWA AZEBDI
RWA
KIR
BMU
BEN
SDN MWI
GIN
STP
CAF
VEN DZA ZMB MRT
NER
VUT
GMB
CUB
JAM
ECUSYR
MOZ
PNG
ETH
UGA
SLEBHS
ATG
CMR
GUY
CPV
ERI
GHA
PRY MNG
NOR TTO
TGO ARM
BHR
KAZ
DJI
BOL
ISL
BLZ MLT
BRB
KGZ
TZA
MUS
CRI
COL
ZWE
FJI GEO
SEN
NIC
KHM
RUS
PER
PAN
KEN
SWZ
CHL
MDGNPL
JOR
HND
EGY
SLV
LBN
LVA
GTM
BGD
CYPPHL
ALB
VNM
AUS
DOM
IRL
ARG
LTU MDA
BIH
ISR
MEX URY
AFG
MYSBLR
CAN
NZL
MKD
TUN
MAR
ZAF
GRC
LKA
EST
PAK
BRA
FIN
UKR
SRB
HRV
SVK KOR
PRT
HUN
IDN
ROM
BGR
DNK
SWE
THA
ESP
SVN
IND
TUR
POL
CHE
FRA
GBR
CZE
BEL
JPN
AUT NLD
USA
CHN
GER
ITA
0
.75
.8
Export Concentration
8
TCD
GAB
KWT
COM
YEM
COG
GNB
QAT
BDI
KIR
RWA
MLI
MRT
WSM
DZA
OMN
GIN
BMU
CAF
BEN
BWANGA
VUT
SDN
SAU ARE
STP
MWI
BFA
PNG
NER
CUB
GMB
ZMB
AZE
CPV
MOZ
BHS
CMR
GUY
IRN
ETH
JAM
ERI
GHA
VEN
ECU
UGA
TTO
PRY
MNG
TGO
BHR
BOL
ISL
DJI
SLE
ATG
BRB
ARMATG
ARM
SEN
KAZ
TZA
BLZ
NIC
MLT
MUS
KGZ
FJI
KHM
SYR
GEO
MDG
ZWE
HND
JOR
PER
NORCOL
CRI
SWZ
CHL
BGD
KEN
SLV
LBN
DOM
PAN
ALB
GTM
CYP
MDA NPL
URY
RUS
LVA
EGY
IRL
BIH
AUS
ARG
NZL
LTU
MARVNM
MKD
PHL
GRC
LKA BLR
AFG
PAK
MYS
MEX TUN
EST
SRB
CANHRV
ISR
ZAF
FIN
PRTROM
BRA UKR
SVK
HUN
BGR KOR
DNK
IDN
POLSVNSWE
TUR
THA
ESPCHE
AUT
CZE
BEL NLD
GBR
IND
FRA
JPN
USA
CHN
1
8
.75
.8
.85
.9
.95
1
0
2
4
Import Concentration
(a) Gini coefficient
8
(b) Theil index
Mean of the index from 1992−2009
6
Extensive Theil index − HS 6 digit
Mean of the index from 1992−2009
6
Intensive Theil index − HS 6 digit
4
0
PAN
KIR
TCDSTP
GNB
WSM ERI
CPV
BDI
VUT
RWA
BMU
BENGMB CAF
DJI
GAB
COG
MRT ATG
BFA
GIN
MWI
SDN
BWA BHS PNG
MLI
MOZNER
ETH
DZA
GUY
BRB
CUB
UGA
QAT
YEM
MNGARM
BLZ TGO
ZMB
KWT CMR
JAM
SEN AZE
NGA
GHA
PRY
NIC
SLE
BOL
KHM
TZA
BHR
FJI
ISL
TTO
OMN
ALB GEOKGZ
MLT
MDG
LBN
NPL
KAZ
MUS
CYP
AFG
JOR
SAU
SLV
VEN
ECU
HND
DOM SWZ
BIH
MDA
BGD
URY
GTM
ZWE
CRI
MKD
KEN
PAN
GRC
LVA
IRN
PER
HRV
LKA
SRB
CHL
MAR
SYR
LTU
TUN
ARE
BLR
EST
NOR
EGY
COL
NZL
VNM
ARG
PRT
ISR
IRL
PHL
ROM
AUS
PAK
UKR
FIN
MEX
SVK
SVN
CAN
BGR
HUN
DNK
POL
RUS
MYS
TUR
AUT
CHE
SWE
ESP
CZE
BRA
BEL
ZAF
GBR
IDN
THA
KOR
NLD
FRA
USA
IND
JPN
ITA
GER
CHN
2
4
2
Export Concentration
COM
IRN
ARE
NGA
OMN
SAU
YEM
SYR
KWT
VEN
NOR
QAT
ECU
MLI
AZE
RUS
GAB
COG
BWA
COL
ZMB
BFA
DZA
JAM
TCD
SLE KAZ
SDN
MWI
CRI
TTO
GIN
ZWE
CHL
PER
CMR
NER
CUB
EGY
GHA
PRY
KEN
AUS
MRT
MOZ
IRLMYS
BEN
VNM
BHR
MLT PHL
ETH
UGA
MEX
PNG
MUS
ARG
ISL BRA
ISRZAF
BDI
GUY
CAF
SWZ
CAN
BOL
MNG
BHS
LVA
BMU
KGZ
RWA
GNB WSM
TGO
KORIND
JOR
HND
NPL
GTM
ARM
TZA
LTU
IDN
GEO
FJISLV
BGD
BLR
GMB
ATG
MDG
NZL
PAK
FIN
THAJPN
HUN
FRA
UKR
BLZ
SVK
KHM
NIC
SEN
TUN
URY
MDA
LBN CYP
SWE
DOM
ESP
MAR
STPBIH
GBR
PRT
VUT
NLD
BRB
EST
CHE
DNK
TUR
CHN
MKD
LKA
BGR
GRC USA
CZE
BEL
ALB
POL
GER
ROM
COM
SVN
SRB
KIR
HRV
AFG
DJI AUT
ERI CPV
ITA
0
Export Concentration
6
Import Concentration
0
2
4
6
0
Import Concentration
2
4
6
Import Concentration
(c) Intensive margin
(d) Extensive margin
Figure 1: Average export versus import concentration for the period 1992 to 2009 for 151 countries
27
8
6
STPKIR
4
6
4
VUT BMU
AFG
PAN
ATG
GNB
ERI BHS
COMWSM
ARM
DJI CAF
SLE TCD
IND
NER
MRT
TGO
BEN
BFA
KHM
GEO
KGZKHM
KGZ
GMB BDI
MLI
RWA
GIN
MNG
PHL
MWI
MLT
MDA
UKR
NPL
JPN
CPV ZWE
YEM BLR
KOR
FJI COG
USA
SEN
MOZ
AZE
BHR
UGA
CYP
GUY
ZMB
MDG
ETH
LTU
ZAF
BLZ
ITAGER CHN
TZA
QAT
THA
PRY
SWZ
PNG
SDNCUB
LBN
JAM
KEN
MYS
BRBNIC
OMN
PAK
GHA
ALB
CMR
BIH
JOR
BGR
BGD
TTO
HND
DOM
ISR
SVK
NLD
GAB
SYR
URY
LKA
HUN
BRA
KAZ
KWT
VNM
IRL
MUS
SRB
MAR
SLV
FIN
TUR
CHE
FRA
EST
DZA
IDNESP
BWA
SWE
EGY
GRC
BEL
BOL
CHL
ISLMKD
GTM
CRI
CZE
IRN
NGA
LVA
ROM
PER
NZL
ARE
ECU
PRT
TUN
SVN
AUT
HRV
GBR
SAU
POL
RUS
AUSCAN
MEX
COL
NOR
DNK
VEN
ARG
0
2
Import Concentration
NGA
TCD
YEM
OMN
GAB
COGMLI
IRN
GNB
QAT KWT ARE SAU
COM
WSM BDI
BFA BWA AZE
RWA
BMU
BEN
SDN DZA
GIN
MWI
MRTNER
CAF
ZMB
VUT
GMB
CUB VEN
JAM
ECU
MOZ
PNG UGA
SYR
ETH
BHS
SLE
ATG
CMR
CPV GUY
ERI MNG
GHA
PRY
NOR
TTO
TGO ARM
KAZ
DJIBLZ
ISL BHRBOL
MLT
BRB
KGZ
TZA
MUS
CRI
COL
ZWE
FJISWZ
SEN
NIC
GEO
KHM
RUS
PER
PAN
NPL
KEN
CHL EGY
MDG
JOR
HND
SLV
LBNGTM BGD
LVA
CYP
ALB
VNM
DOM
IRL
MDA
ARGAUSMEX
LTU BLR ISRPHL
BIHURY
AFG
CAN
NZL MYS
MKD
TUN
MAR
ZAF
GRC
LKA
EST
PAK
BRA
FIN
UKR
SRB
HRV
SVK HUN
PRT
KOR
IDN
ROM
BGR
DNKCHE
SWE
THA
SVN
IND
TUR ESPFRA
POL
GBR
CZE
BEL
JPN
NLD
AUT
USA
GER CHN
ITA
STPKIR
2
8
Total Theil index − HS 6 digit
Mean of the log index from 1992−2009
0
Export Concentration
Total Theil index − HS 6 digit
Mean of the log index from 1992−2009
−10
−5
0
−10
Log of GDP relative to the US
−5
0
Log of GDP relative to the US
R2=0.39
R2=0.38
(a) Overall concentration of exports
(b) Overall concentration of imports
Mean of the log index from 1992−2009
6
Extensive Theil index − HS 6 digit
Mean of the log index from 1992−2009
6
Extensive Theil index − HS 6 digit
4
Import Concentration
USA
0
4
2
KIR
STP
2
TCD
BDI
ERI RWA
BMU
GMB
BEN GAB
DJI CAF
COG
MRT GIN
ATG
BFAZAR
MWI
SDN
BWA
BHS
NER
PNG
MLI
MOZ
ETH
GUY
BRB
UGA
QAT CUBDZA
YEM
MNG
BLZ TGO
ARM
ZMB JAM
LAO
KWT
AZE
CMR
SEN
NGA
GHA
PRY
NIC
BOL
KHM
TZAOMN
BHR
FJI
ISL
KGZ
TTO
GEO
ALB
UZB
MLT MDG NPLLBN
MUSAFG
CYP
JOR
ECUKAZ VEN SAU
HNDSLV DOM
SWZ MDA BIH
BGD
URY
GTM
ZWE
CRI
MKDPAN
GRC IRN
LVA KEN
HRV
LKA
SRB
CHL
MAR PER
SYR
TUN
BLR
EST LTU
NOR
EGY
COL
NZLARE
VNM
ARG AUSMEX
PRT
ISR
PHL
ROM
PAK
UKR
FIN
SVKIRL
SVNBGR
CAN
HUN
DNK
POL
RUS
MYS
TUR ESP
AUT
CHE
SWE
CZE
BRA
BEL
ZAF
GBR
IDN
THA
KOR FRA
NLD
IND JPN
ITA
GERCHN
GNB
CPV
VUT
VUT
GNB
ERI TCD AFG
COM
WSM
SLE
DJI CAF
ATG
BMU
ARM
GMB MRT RWA
KGZ
COG
NER
CPV BDI
BEN
MWI
MNG
BFAGEO
TGO
MOZ
MLI
KHMNPL
FJI GIN
GUY
PNGBIH AZE
ZWE
BLZ
YEM
MDA
BRB
ZMB
ALB
SWZ
UGA
CMR
GAB
SEN
SDNCUB
QAT
MDG
SYR
KAZ
BHR
ETH
BHS
CHN
TZA
GHA
BLR
NIC
EST
GERJPN
KEN
PAK
LVA
MKD
DOM
HND
KWTBGD
MLT
LTU
OMN
ITAIND
VNM
JAM
MUS
PRY
BGR
TTO
UKR
PAN
BOL
LKA
URY
SLV
JOR
USA
IRN
NGA
LBN
ISL CYP
THA
SVN
IDN
NLD
FRA
KOR
SVK HUN
BRA
GTM
ZAF
CZE
TUR
PMYS
PHL
HL
PER
TUN
SWE
GBR
POL
ECU
RUS
ISR
ROM
BWA CRI
BEL
MAR
CHE
ARE
EGY
AUT
ESP
ARG
DNK
SRB
FIN
COL
HRV
DZA
NZL
IRL
CHL
MEX
PRT
VEN
NOR
GRC
SAU AUSCAN
0
Export Concentration
PLW COM
−10
−8
−6
−4
−2
0
−10
Log of GDP relative to the US
R2=0.75
(d) Extensive margin of imports
Mean of the log index from 1992−2009
6
Intensive Theil index − HS 6 digit
Mean of the log index from 1992−2009
PAN
BHS
BMU
IND
PHL
KOR
UKR
MLT CYP
JPN
USA
ZAF
MYS
THA
BLRIRL
LBN
LTU
ISR
ITA
GRC
PRY
JAM
ATG
SVK
BHR
HUN
SRB
SEN
FIN EGY
MDA
DZA
OMN
MAR
JOR
AFG
CHL
ARM
ESP
BRAGER CHN
CHE
KEN
KHM
TUR
BGD
BWA
PAK
BGR
SWE
ETH
BEL
TZA
MDG
TTO
SAUNLD
URY
NZL
PRT
GEO
YEM
TGO
UGA
LKA
HND
BFA
ROM
IDNMEXFRA
GTM
MLI
DOM
CZE
ARE
GIN
NIC
SLV
QAT
BEN
PER
HRV
GHA
AUS
CRI
ECU
CUB
VNM
ISL
ZMB
NER
MUS
AUTPOL
SDN
KWT
TUN
NOR
MNG
MKD
NGA
BOL
AZE
VUT ZWE
IRN
GBR
COL
VEN
RUS
SWZ
MRT
SVN
FJI
MWI
KGZ
DNK
NPL
CMR
CAN
BRB
SYR
EST
BLZ
LVA
ALB
ARG
KAZ
DJI GUY
GAB
MOZ
BDI
GMB
PNG
BIH
RWA
CPV
CAF
COG
STP
COMWSM
SLE
ERI TCD
GNB
KIR
0
MLI
RUS
COG GAB
BWA
COL
ZMB
BFA
JAM SDN DZA
TCD
SLE MWI
KAZ
CRI
TTO
GIN
ZWE NER
CHL
PER
CMR
EGY AUS
PAN
GHA
PRY
KEN CUB
PHL
MRT MLT
MOZBHR
IRL
MYS
BEN
VNM
ZAF
ETH
UGA
MEX
PNG LVA
MUS
ARG
ISL
ISR
BDI SWZ
GUY
CAF
CAN
BOL
MNG
BHS
BRA
KGZ
RWA
GNB
KOR
WSM BMUTGO
JOR
HND
NPL
ARM
TZA
IND
LTUGTM
IDN
GEO
BGD
GMBBLZFJI
BLR
ATG
MDG
NZLHUN
PAK
SLV
FIN UKR
THA
JPN
SVK
KHM
NIC
SEN
TUN
URY
MDA
LBN
SWE NLD ESPFRA
DOM
MAR
CYP
STP
GBR CHN
PRT
VUT
BRB
EST
CHE
DNK
BIH
TUR
MKD
LKA
BGR
USA
GRC
CZE
BEL
ALB
POL
GER
ROM
SVN
SRB
KIR COM DJI
AUT
HRV
AFG
ERI
ITA
CPV
4
Import Concentration
4
IRN
ARE
NGA
OMN
SAU
YEM
SYR
KWT VEN
NOR
QAT
AZE ECU
2
6
Intensive Theil index − HS 6 digit
0
2
0
R2=0.56
(c) Extensive margin of exports
Export Concentration
−5
Log of GDP relative to the US
−10
−5
0
−10
Log of GDP relative to the US
−5
0
Log of GDP relative to the US
R2=0.01
R2=0.14
(e) Intensive margin of exports
(f) Intensive margin of imports
Figure 2: Average export and import concentration versus the log of average relative GDP with respect to the
United States (log( GDPUS ) = 0) for the period 1992 to 2009 for 151 countries.
28
Total Theil Index
Total Theil Index
Data versus simulation
Data versus simulation
12
8
Red − Simulated Data
Blue − Empirical Data
11
Import Concentration
Export Concentration
9
8
PLW
PLW
7
6
5
4
3
2
NGA
TCD
YEM
GAB OMNKWT
COG
IRN
MLI
GNB
SAU
QAT
BDI
COM
ARE
BFABWAAZE
RWA
MLI
BMU
BEN
SDN DZA
GIN
MWI
MRT
CAF
COM
GNB
ZAR
NER
VEN
CPV
ZMB
DJI
VUT
BDI
GMBBMU
CUB
JAM
ECU
MOZ
PNG
SYR
MRT
NER
ETH
UZB
ATG
BHS
ERI
UGA
CMR
CPV GUY
ERI
CAF
GHA
PRY
NOR
MNG
COG
TTO
MLT
BLZ
VUT
TGO
GMB
FJI
MWI
ARM
MOZ
PNG
BHR
GUY
KAZ
PAN
BRA
DJI
BOL
ISL
MNG
SEN
MLT
BLZ BRB
ZMB
GEO
KGZ
JOR
LVA
GIN
LAO
TCD
TZA
BHS
BFA
NIC
ARM
YEM
MUS
POL
CRI
TZA
COL
ZWE
UKR
ZAF
FJI
TUN
SEN
LBN
TGO
MDA
NIC
ZAR
GEO
EST
SWZ
ALB
KHM
RUS
GER
URY
UZB
PAN
NPL
RWA
KEN
LTU
SWZ
CHLCOL
BGR
DOM PER
MDG
JOR
BWA
BRB
LAO
JAM
HND
BEN
ISL
MUS
GBR
ETH
NLD
THA
GAB
EGY
KGZ
ARG
VEN
MDG
CRI
OMN
SLV
MKD
HUN
KWT
BHR
AFG
IRN
KHM
TUR
LBN
LVA
NOR
GHA
CYP
BIH
GTM
BLR
PER
HRV
BGD
UGA
ECU
IDNESP
AUT
CMR
CYP
ALB
PRY
KAZ
TTO
VNM
BGD
AUS
PAK
DOM
PHL
GTM
BOL
IRL
MDA
QAT
CHE
SVN
CHL
SLV
ARE
AZE
IND
ARG
ISR
SWE
BEL
LKA
CAN
LTU
EGY
URY
SDN
GRC
BIH
ROM
SAU
ISR
SVK
CUB
MEX
SRB
SYR
ITA
BLR
NZLVNM
AFG
MAR
MYS
FRA
CZE
CAN
PRT
NGA
MEX
NZL
DNK
MKD
MAR
FIN
IRL
KOR
MYS
RUS
DZA
ZAF
GRC
LKA
EST TUN
PAK
BRA
FIN
UKR
SRB
HRV
CHN
SVKHUN
PRT
KOR
JPN
IDN
BGR ROM
DNK
SWE
THA
SVN
IND
TURESPFRA
POL
CHE
GBR
CZE
BELNLD
JPN
AUT
USA
GERCHN USA
ITA
1 −6
10
−4
−2
10
6
VUT
5
PLW
4
PLW
3
2
10
−4
10
0
10
Log of GDP relative to the US
(a) Overall concentration of exports
(b) Overall concentration of imports
Extensive Theil Index
Extensive Theil Index
Data versus simulation
Data versus simulation
6
6
Red − Simulated Data
Blue − Empirical Data
PLW
COM
PLW
TCD
GNB
CPV BDI
VUT
ERI RWA
COM
BMU
GNB
GMB
VUT
DJICAF BEN
GAB
COG
MRT
ATG
BFA
GIN
ZAR
ATG
MWI
BWA SDN
BHS
NER
PNG
CPV
MLI
MOZ UGA
ETH
DZA
GUY BRB
QAT CUB
YEM
GMB
MNG
BLZ TGO
ARM
ZMB AZE KWT
LAO
DJI
JAM
BMU
CAF
BDI
FJI
CMR
ERI
SEN
MRT
NGA
GUY
ZWE
BLZ
GHA
NIC
PRY
TGO
BOL
KHM
SWZ
TZA
BHR
FJI
MWI
KGZ
ISLGEO
COG
TTO
TCD
RWA
OMN
GIN
MNG
MLT
BRB
BEN
NER
LAO
ARM
ALB
BFA
BHS
UZB
MOZ
MDA
MLT
KGZ
MDG
MLI
NIC
PNG
ISL
MUS
LBN KAZ
ALB
AFG
SEN
ZAR
MDG
ZMB
GAB
NPL
GEO
EST
MKD
MUS
CYP
AFG
KHM
BIH
BHR
JOR
CYP
SLV DOM
ECU VEN SAU
HND
UGA
GHA
CMR
NPL
HND
LVA
JOR
PAN
BIH
TZA
JAM
TTO
SWZMDA BWA
PRY
YEM
BOL
UZB
URY
BGD
QAT
OMN
SLV
ETH
AZE
KEN
LTU
SVN
URY
LBN
GTM
ZWE
CRI
TUN
SYR
HRV
SDN
KWT
LKA
DOM
MKD
BLR
BGR
SRB
SVK
KEN
PAN
CUB
GTM
ECU
GRC IRN
LVA
MAR
PER
HRV
LKA
SRB
NZL
CHL
BGD
KAZ
VNM
MAR
SYR
PRT
NGA
CZE
TUN
DNK
PHL
ROM
ARE
FIN
BLR
VEN
EST LTU
NOR
CHL
UKR
ARG
AUT
IRL
PER
MYS
THA
SWE
PAK
BEL
DZA
ZAF
CHE
EGY
POL
COL
ISR
SAU
HUN
NZL
ITA
GRC
TUR
GBR
IRN
IDNMEX
NLD
AUS
FRA
VNM
ARG
PRT
GER
ESP
ISR
IRL
PHL
ROM
RUS
AUS
IND
KOR
CAN
PAK
UKR
FIN
BRA
MEX
SVKHUN
SVN
CAN
BGR
DNK
POL
RUS JPN
MYS
TURESP
AUT
CHN
CHE
SWE
CZEBEL
BRA
ZAF
GBR
IDN
THA
KOR FRA
NLD
IND
JPN USA
ITA
GERCHN
USA
4
3
2
1
0 −6
10
−4
−2
10
Red − Simulated Data
Blue − Empirical Data
5
Import Concentration
5
Export Concentration
−2
10
Log of GDP relative to the US
VUT
GNB
3
PLW
2
0 −6
10
10
ERI TCDAFG
COM
1
0
10
PLW
4
DJICAF
COM
VUT
GNB
ATG
ATG
LAO
BMU
CPV
GMB
DJI
BLZ
ZAR
ZWE
RWA
ARM
GMB
GUY
CAF
BDI
MRT
KGZ
COG
NER
CPV BMU
ERI
BDI
NER
COG
TGO
BEN
FJI
BRB
MWI
MNG
MRT
SWZ
BFA
NPL
RWA
TGO
GEO
GIN
BHS
MOZ
MLI
GIN
KGZ
TCD
LAO
MWI
KHM UZB
BEN
MDA
FJI MLT
MLI
MUS
GUY
MOZ
BFA
PNG
ALB
GAB
MDG
ARM
ISL
AZE
ZMB
MKD
PNG
ZWE
AFG
NIC
ZAR
SEN
BLZ
YEM
GEO
BIH
KHM
MDA
BHR
BIH
EST
TTO
PRY
BRB
BWA
NPL
HND
JOR
ZMB
ALB
SWZ
CYP
UGA
GHA
AZE
PAN
UGA
LVA
ETH
CMR
JAM
BOL
KEN
GAB
TZA
SEN
SDNKAZ
QAT
SLV
URY
CRI
YEM
MDG
LBN
LTU
UZB
SYR
BHR
ETH
BHS
CHN
CUB
QAT
OMN
SDN
GHA
LKA
BLR
BGR
SVN
NICESTTZA
TUN
DOM
HRV
GTM
GER
BLR
ECU
KEN
PAK
KAZ
LVA
SRB
IND
MKD
DOM
ISR
HND
KWT
HUN
MLT
LTU
OMN
PER
ITA
CUB
JAM
VNM
MUS
PRY
BGR
MAR
JPN USA
TTO
UKR
KWT
SVK
IRL
PAN
BOL
DZA
LKA
URY
SLV
BGD
NZL
JOR
IRN
CHL
NGA
VNM
LBN
ROM
USA
ISLCYP
DNK
FIN
ARE
UKR
CZE
THA
CRI
PHL
SVN
MYS
IDN
VEN
NLD
FRA
COL
NOR
KOR
PAK
PRT
EGY
SVKHUN
BRA
CHE
GRC
GTM
ZAF
CZE
AUT
SWE
TUR
PER
PHL
GBRJPN
POL
ECU
RUS
ISR
ROM
BWA TUN
BEL
MAR
MYS
CHE
ARE
EGY
AUT
THA
ESP
DNK
ARG
SRB
KOR
FIN
IRN
COL
SAU
POL
HRV
DZA
IDN
CAN
CHN
NLD
ESP
NZL
IRL
TUR
CHL
MEX
PRT
VEN
CAN
NOR
AUS
BRA
GER
IND
GRC
SAU
RUS
FRA
GBR
ITA
−4
10
0
10
Log of GDP relative to the US
(c) Extensive margin of exports
(d) Extensive margin of imports
Intensive Theil Index
Intensive Theil Index
Data versus simulation
Data versus simulation
8
8
Red − Simulated Data
Blue − Empirical Data
7
OMN
4
3
PLW
PLW
Import Concentration
5
ARE
NGA
Red − Simulated Data
Blue − Empirical Data
7
6
2
−2
10
Log of GDP relative to the US
Export Concentration
AFG
BMU
PAN
ATG
GNB
ERI BHS
COM
ARM
TCD
VUT
COM
DJI
GNB
ATG
CPV
GMBCAF
IND
NER
MRT
DJI
BLZ
LAO
TGO
BEN
BFA
KHM
GEO
KGZ
CAF
ZWE
GUY
GMB
MLI
RWA
GIN
BDI
MNG
FJI
ERI
PHL
BMU
BDI
ZARNPL
MWI
MLT
MDA
UKR
BHS
ALB
RWA
JPN
MRT
ARM
MDG
PNG
TCD
KGZ
BFA
BRB
MNG
CPV ZWE
YEM
MWI
KOR
TGO
NER
COG
UZBBLR
EST AZE
GIN
BEN
FJI
USA
SEN
LAO
MLI
MDA
NIC
MOZ
COG
BHR
BWA
MUS
MLT
BIH
GAB
UGA
CYP
GUYSWZ
ISL
GEO
KHM
NPL
MKD
ZMB
MDG
ETH
AFG
LTU
ZAF
BLZ
ITA
TZA
TTO
QAT
GERCHN
THA
CMR
PRY
SWZ
ZAR
PNG
SDN
LBN
BOL
JAM
KEN
UGA
MYS
BRB
SEN
UZB
QAT
ZMB
PAK
PAN
LVA
SVN
CUB
OMN
GHA
HND
NIC
ALB
ETH
CMR
BIH
SLV
PRY
JOR
AZE
BGR
LTU
LBN
CRI
TTO
BGD
HND
DOM
ISR
SVK
JOR
NLD BRA
GAB
SYR
TZA
URY
LKA
YEM
HUN
KAZ
KWT
DOM
VNM
SYR
ECU
IRL
MKD
BLR
MUS
SRB
MAR
SLV
FIN
TUR
CHE
FRA
ESP
EST
TUN
DZA
IDN
BGR
BWA
SWE
LKA
EGY
HRV
SDN
GRC
BOL
BELIRN
CHL
ISL LVA
PHL
GTM
CRIGTM
MYS
CZE
NGA
ROM
NZL
PER
ARE
BGD
ECU
DZA
DNK
KAZ
HUN
PRT
TUN
CHL
SVN
ISR
CZE
NGA
AUT
HRV
COL
MAR
PER
GBRJPN
SAU
IRL
GRC
POL
RUS
VNM
FIN
ROM
AUS
SVK
CUB
MEX
UKR
COL
KWT
NOR
EGY
DNK
ARE
NZL
GERCHN USA
VEN
IND
CHE
PAK
ZAF
ARG
BEL
SWE
THA
AUT
SAU
CAN
ITA
IRN
FRA
GBR
POL
KOR
TUR
IDN
NLD
MEX
AUS
ESP
BRA
CAN
RUS
1 −6
10
0
10
Red − Simulated Data
Blue − Empirical Data
7
10
IRN
SAU
YEM
SYR
KWT VEN
MLI
BRA
NOR
QAT
MLI
AZE ECU
RUS
COG GAB
UZB
BWA
NER
POL
GER
DJI BDI
COL
ZAF
ZMB
UKR
BFA
COL
MRT
DZA
JAM
PAN
BMU
TCD
SDN
ERICOG
GBR
MWI
NLD
KAZ
MLT
CRI
CPV ZWE
TTO
TUN
GIN
CHL
PER
NER
CMR
CUB
PNG
JOR
IRN
LVA
THA
HUN
EGY
PAN
MOZ
GHA
ESP
TUR
ARG
LBN
YEM
PRY
VEN
KEN
CAF
AUS
SEN
PHL
BGR
ZAR
MRT
MOZ
TZA
IDN
MYS
DOMIRL
BLZ
MWI
BEN
VNM
GEO
NOR
KEN
ZMB
PER
BHR
ZAF
CAN
MLT
ETH
UGA
LTU
MEX IND
PNG
MNG
URY
UZB
MUS
AUT
ARGAUS
NPL
FJI
ISL
ISR
BDI
GUY
CAF
SWZ
PAK
CAN
BOL
CHE
MNG
BHS
LVA
PHL
NIC
BRA
CRI
HND
BWA
BFA
KWT
JAM
KAZ
GIN
ETH
ISR
GRC
EST
ARM
TCD
KGZ
CHL
RWA
ECU
ZAR
EGY
GNB
BLR
BGD
ATG
SAU IDN
TGO
SWE
BEL
KOR
OMN
HRV
ALB
JOR
HND
NPL
ARE
ITA
GTM
ARM
GMBBMU
MDA
TZA
IND
FRA
MEX
KOR
LTU
ROM
CHN
RUS
GNB
GEO
FJI
BGD
BLR
GMB
ATG
MDG
NZL
PAK
SLV
ZWETGO
FIN
GHA
GAB
ISL
THA
MUS
BHR
MKD
HUN
COM
NZL
MDG
RWA
UKR
BRB
LKA
JPN
BLZ
CZE
CUB
PRY
CMR
CYP
SVN
SWZ
UGA
LAO
NGA
QAT
SDN
PRT
SVK
TTO
VNM
KHM
BEN
DNK
BIH
BOL
NIC
SRB
IRL
SEN
AFG
MYS
TUN
SLV
KGZ
DZA
MAR
URY
AZE
FIN
MDA
SWENLDESPFRA
LBN
SYR
DOM
MAR
CYP
GBR CHN USA
PRT
VUT
BRB
EST
CHE
DNK
BIH
TUR
MKD
LKA
BGR ROM
USA
GRC
CZE
BEL
ALB
POL
GER
VUT
COM
SRB
AUT
HRV
AFG SVN
DJI ERI
ITA
CPV
1
6
5
PAN
4
BMU
3
2
PLW
1
BHS
IND
PHL
KOR
UKR
MLT CYP
JPN USA
ZAF
MYS
THA
BLR
LBN
EST
LTU
ISR
ITA
ARM
GRC
IRL
CYP
BWA
ALB
PRY
MDG
SVN
QAT
PNG
JAM
ATG
BHR
SVK
BFA
PHL
MYS
SEN
SRB
HUN
NPL
FIN
MDA
UZB
DZA
OMN
MAR
NLD
GER
AFG
JOR
CHL
ARM
IND
NIC
ESP
BRA
ITA
CHE
CMR
BIH
KEN
BOL
JAM
CHN
JPN
KHM
TUR
BGD
FRA
GBR
BWA
ECU
EGY
MOZ
PAK
BHS
SRB
BGR
ATG
TCD
BLR
SWE
ETH
FJI
BEL
MWI
LTU
MDG
TZA
KGZ
LBN
GRC
TTO
DOM
SAU
URY
COL
CHN
NZL
CPV
BGD
PRT
MLI
LVA
SLV
DNK
GEO
CZE
YEM
FRA
TGO
UGA
ISL
RWA
LKA
PAN
GAB
CRI
BEN
ETH
HND
BFA
GTM
ROM
IDNMEX
VUT
MKD
BDI
ERI
CHL
NGA
DOM
MDA
LAO
URY
DZA
MLT
ARG
GMB
MRT
ARE
GNB
AFG
TUN
FIN
SAU
GIN
COM
HND
NIC
SLV
BEN
QAT
SYR
PER
HRV
GHA
YEM
AUS
CAF
AZE
ROM
EGY
CRI
BGR
RUS
ECU
CUB
BRB
UKR
THA
VNM
GUY
ZAF
ISL
TZA
USA
ZMB
LKA
IRN
BMU
VEN
NER
MUS
AUT
CHE
SDN
ZAR
ZWE
KWT
BEL
BLZ
IRL
HUN
TUR
BRA
TGO
TUN
DJI
PRY
MAR
NOR
POL
SWE
MNG
ISR
ARE
PAK
MKD
SEN
NLD
PER
IDN
NGA
KAZ
KOR
BOL
AZE
VUT
IRN
JOR
NER
GBR
SVK
COL
ESP
VEN
ZMB
POL
RUS
SWZ
GTM
CAN
MRT
KWT
CUB
NZL
SVN
FJI
MWI
KGZ
NPL
DNK
CMR
CAN
BRB
SYR KAZ
BLZ
EST
LVAUZB
ALB
ARG
GUY
DJI
GAB
MOZ
BDI COG
GMB
PNG
BIH
RWA
CPV
CAF
LAO
COG
ZAR
COM
ERI TCD
GNB
PLW
0 −6
10
−4
10
−2
10
0 −6
10
0
10
Log of GDP relative to the US
−4
10
−2
10
0
10
Log of GDP relative to the US
(e) Intensive margin of exports
(f) Intensive margin of imports
Figure 3: Simulated (in red) and empirical observed (in blue) export and import concentration versus GDP
across 151 countries. The simulation uses parameterized trade costs to match the data using a country specific
export cost.
29
Total Theil Index
Total Theil Index
Data versus simulation
Data versus simulation
20
8
Red − Simulated Data
Blue − Empirical Data
ATG
18
Red − Simulated Data
Blue − Empirical Data
7
PLW
Import Concentration
Export Concentration
16
KIR
14
PLW
STP
BMU
GNB
CPV
ERI
BDISYC SEN UZB
TCD
WSM
COM
GEO
SLE
SDN
AZE
MNG
IRQ
CAF
NGA
CUB
VUT MLI
ARM
BIH
NER
TKM
MLT
ALB
NPL
MRTFJI
MUS
MKD DOM
ZAR
DJI AFG BRB
SUR
MWI
LBY
DZA
ETH
ECU COL
JAM
COG
CMR
KGZ
GNQ
CYP
VEN
TZA
RWATGO
MDG
ISL
SLV
TTOHRV
BHR
GHA
HTI OMN
BLZ
HND
CIV
QAT
SYR IRN
MDA
BFA
BOL
NIC
PRY
NZL
SAU
GMB
LAO
AGO
SRB
BEN YEM
KHMLKA
LBN
PAN
LTU
MOZ
KAZ
KEN
ZWE
ARE
MAR
EST
AGO
ZMB
LVA
TUN
PER
SVK
GNQ
SWZ
COM
JOR
UGA
PHLZAF
WSM
SYC
SUR
MOZ
CRI
ZAR LUX
MLI
KIR
BMU
NER
CAF
ZMB
BDI
BLR
PLW
ROMAUS
VNM
MRT
KWT
BGD
JAM
GTM
VUT
GMB
COG
ISR
TCD
UKR
GNB
EGY
SVN
BGR
SDN
ERIRWA
CMR
BRA
CPVSLE
TTO
GRC
MEX
HUN
BEN
GHA
IRQ
KWT
BFA
CUB
QAT
LBY
ARM
KGZ
STP ATG
UGA
TUR
URY
CHL
ETH
MNG
ISL
IND ITA
TGO
NOR
BOL
DZA
TZA
SEN
DJI
LAO
TKM
YEM
PRY
ECU
MLT
UZB
BHR
MWI
PAK
NGA
POL
PER
ARG
BLZ
CHL
NIC
HTI
CZE
ZWE
BRB
MDG
NPL
SWE
AZE
KHM
KAZ
JOR
MUS
CIV
GEO
RUS
AUT
SAU
CRI
THA
HND
LBN
SWZALB
FJI
IRN
DNK
SGP
PRT
ARE
OMN
BEL
BGD
CYP
DOM
VEN
PHL
ARG
IDN
MDA
FIN
URY
IRL
SLV
PAN
ISR
CHE
KEN
IRL
GTM
BIH
NLD
LVA
AUS
LUX
CAN
KOR
NZL
ZAF
MYS
MAR
MKD
VNM
AFG
CHN
GRC
ESP
TUN
BLR
NOR
SYR
LKA
PAKMEX
RUS
COL
LTU
EST
FIN
UKR
SRB
EGY
HRV
HUN
PRT
SVK
JPN
BRA
GBR
KOR
ESP
THA
SWE
DNK
BGRROM
TUR
CAN
SVN
FRA
IND
POL
CHE
IDN
CZE
GBR
BEL
SGP
AUT
JPN
FRA
GER
MYS
NLD
USA
CHN
GER
ITA
USA
12
10
8
6
4
2
0 −8
10
−6
−4
10
−2
10
0
10
10
6
5
PLW
4
3
2
1 −8
10
2
10
−6
10
Log of GDP relative to the US
−2
0
10
10
2
10
(b) Overall concentration of imports
Extensive Theil Index
Extensive Theil Index
Data versus simulation
Data versus simulation
14
6
Red − Simulated Data
Blue − Empirical Data
ATG
8
6
4
2
0 −8
10
5
PLW
KIR
STP
COM BMU
IRQ
CPV
VUT
AFG
WSM
TCD
MNG
ERI
GNB
SYC
SUR
BRB
DJI
SLETKM
HTI
ISL
NPL
BIH
RWA
GMB
BDIBLZ
ALB
BFA
MLI
MWIGNQ
KGZ
DZA
BOL
NER
FJILAO
SDN
BEN
CUB
PRY
YEM
MDA
MLT
ARM
LBY LKA
GEO
CAF
AGO
MDG
AZE
MRT
ZAR MKD
LUX
BHR
TGO
COM
CYP
KHM
LBN
MUS
UZB
JAM
PLW
COG
KIR
SYR
UGA
KWT
TTO
GNQ
MOZ
GNB
ECU
TCD
HRV
BGD
BDI
VUT
TZA
LVA
NIC
STPETH
WSM
SWZ
SRB
ERIRWA
AGO
CPV
LTU
EST
MAR
PHL
IRQ
ZMB
BMUJOR
CMR
SVN
NGA
BEN
GMB
SYC
CAF
NZL
VEN
SLV
DJI
BLR
DOM
KEN
OMN
COG
KAZ
URY
ZAR
SDN
BFA
SEN
TUN
MRT
MWI
ATG
PAN
LBY
SUR
ETH
IRN
GTM
HND
TKM
MOZ
PER
BGR
SVK
NER
EGY
DZA
QAT
MLI
UGA
CUB
YEM
ZWE
ZMB
TGO
BRB
HTI
QAT
MNG
BLZ
LAO
JAM
NGADNK
ARM
KWT
GHA
AZE
CMR
GHA
COL
SEN
TZA
CIV
VNM
NIC
BOL
PRY
GRC
CHL
ISL
CZE
HUN
SLE
TTO
CHE
KHM
FJI KGZ
BHR
MDG
ROM
NOR
OMN
UZB
ALB
GEO
CRI
AUS
KAZ
CIV
LBN
PRT
MLT
CYP
SAU
ECUVEN
NPL
JOR
HND
MUS
SLV
AFG
DOM
UKR
IRL
BEL
BIH
MDA
GTM
SWZ
ZWE
NLD
BGD
URY
CRI
KEN
AUT
POL
IRN
CHL
GRC
MKD
PER
FIN
KOR
PAK
PAN
LVA
LKA
HRV
MAR
SRB
SYR
SAU
LTU
LUX
EGY
TUN
ARE
NOR
EST
BLR
NZL
COL
ISR
TUR
VNM
ARG
ARE
AUS
SGP
PRT
ISR
RUSCAN
IRL
PHL
ROM
ZAF
IDN
PAK
UKR
MEX
CAN
FIN
SVK
BGR
HUN
SWE
SVN
POL
DNK
RUS
TUR
MYS
ARG
AUT
ZAF
BRA
ESP
IND
SWE
CZE
CHE
BEL
IDN
GBRFRA
THA
SGP
MEX
BRA
NLD
KOR
JPN
FRA
ESP
USA
IND
THA
ITA
ITA
JPN
GER
CHN
GBR
MYS
GER
CHN
USA
10
−6
−4
10
−2
10
Red − Simulated Data
Blue − Empirical Data
PLW
Import Concentration
12
Export Concentration
−4
10
Log of GDP relative to the US
(a) Overall concentration of exports
0
10
10
PLW
4
KIR
3
2
1
0 −5
10
2
10
VUT
AFG
GNB
ERI TCD
GNQ
COM
WSM SLE
DJI CAF
ATG
HTI CMR
BMU GNQ
LAO
BRA
IRQ
IRN
ZAR
ARM
GMB MRT RWA
SYR
DOM PER
AUS
MWI
KGZ TKM
VEN
CPV BDI
NER
COG
ZAF
SLV
TKM
TCD
BEN
MNG
NPL
MWI
SYC SUR
TGO
BFA
JPN
UZB NZL COL
TTOAGO
MOZ
MLIGEO
KHM
FJI
ERI
AZE KWT
BHR
AGO
CIV
ZWE
AZE
BLZ
MDG
BLR ISR
RUS
YEM
CHL ARG
BDI
GTM
BIH KEN
MDA
YEM
ARM
ECU
BRB
SDN
HTI
SWZ
RWA
ALB
BOL
LBYKAZ
ZMBUGA
SRB
USA
ROM
UGA
MOZ
QAT
CMR
CRI
SEN
PHLSAUTUR
CAF
SDN
BGD
KGZ
LKA
SUR MUS
ETH
BHR
SYR
KAZ
CIV
MDG
TZA
ETH
GER CHN
CUB
EGY
IND
BLR
UKR
NPL
BEN
GHA
SEN
NIC
EST
HUN
BIH
DZA
MAR
ITAIND JPN
WSM
PAK
LVA
CUB
FIN
KEN
MKD
KWT
CPV
DOM
MLT
JOR
LTU
OMN
HND
MLI
ALB
MUS
POL
BGR
JAM
SYC SLE NER
VNM
TTO
UKR
PRY
USA
PAN
NGA
GNB
LBY BGD
LKA
URY
BOL
SLV
JOR
SWE
ITA
LBN
PRY
IRN
KOR
GRC
NGA
URY
ISL KHM
FRA
SVN
NLD
THA
BGR
MEX
PAK
IDN
CRI
BRB
LTU
GHA
CZE
SVK
GBR
LUX
BRA
ESP
SWE
CYP
CHE
ZAR
BEL
GTM
ZAF
HRV
JAM
POL
AUT
RUS
PHL
TUR
LVA
MYS
HUN
TUN
LAO
ROM
OMN
PER
ISR
PRT
ECU
ARE
ESP
DNK
QAT
MAR
FIN
EGY
TGO
HND
SRB
KOR
NOR
ARG
COL
COG
HRV
LBN
KIR COM GMB MRT
IRL
DZA
MKD
MDA
SVN
NZL
PRT
MEX
MLT
ZMB
CAN
NOR
SGP
VEN
CYP
CHL
GEO
AUT
AUS
CZE
GRC
SAU
VUT
GBR
GER
SVK
IRQ VNM
THA
STP
PAN
EST
DNK
DJIBLZ
BMU MNG AFG
IRL
MYS
SGP
LUX
ATG
CHE
BELNLDCANFRA
STP
−4
10
Log of GDP relative to the US
−2
−1
10
10
0
10
(d) Extensive margin of imports
Intensive Theil Index
Intensive Theil Index
Data versus simulation
Data versus simulation
10
6
Red − Simulated Data
Blue − Empirical Data
9
SEN
7
NGA
UZB
Red − Simulated Data
Blue − Empirical Data
5
Import Concentration
8
SAU
COL
ARE
DOM
GEOGHACIV
ZAF
AZE
CAF
CMR VEN
QAT
HND
SDNOMN
BDI ETH
MEXBRA
SLV
MUS
ARM
ITA
ECU IRN ISR
MRT TZA CUB
IND
NZL
ZWE
MKD
PAN
ZAR
CRIHRV
AUS
MLT
COG JAM
NER
THA
ARG
TUR
TTO
KAZ
ROM
GNB MLI
NIC
UKR
CHN
TGO
CYP SRB
LBY
SYC
PER
FJI KEN
SVK
VNM SWE
SLE
TUN
LTU
BIH
ERI
ZMB
ZMB
ALB
MAR
SUR
SYR
RUS
MOZ
JAM
MLI
EST
BHR
SLE
NER
PER
TCD
AGOPAK
POL
SGP
MDG
CHL
KIR CPV
TTO
DZA
ESP
HUN
NPL
ZAR
SYC
MOZ
PHL
GBR
GRC
IDN
WSM MWIGNQ
ZAF
ZWE
KGZ
LVA
IRL
CMR
MYS
PHL
ECU
MRT
GTM
ARE
KGZ
BLR
MLT
ISR
ARG
CHL
AUT
CAF
ISL
GHA
MNG
NOR
CAN
KWT
CRI
LBN
STP
JOR
IRN
SWZ
JPN
UZB
KHM
BMU
SWZ
ARM
EGY
BHR
NPL
AUS
WSM
COG
MUS
KOR
JOR
QAT
BGR
MEX
RUS
BOL
MDA
TKM
FIN
IND
GER
KAZ
PAN
HND
TZA
SAU
PRY
SEN
GMB
MNG
CIV
CZE
GNQ
NZL
BGD
VNM
BMU
FIN
BRA
LUX
PAK
CUB
HUN
FRA
MDG
THA
BEL
LBN
UKR
LVA
LAO
JPN
URY
KHM
GEO
MYS
SDN
PLW
AGO
TGO
ESP
NIC
MDA
NGA
BRB
UGA
ATG
KEN
SWE
SVK
PRY
BLR
PRT
MAR
DOM
TUN
KOR
NOR
BDI
LBY
PRT
BOL
GTM
UGA
VEN
LKA
CYP
BLZ
COL
CHE
FJI
SLV
BIH
GBR
TUR
IDNNLD
IRL
RWA
MKD
CHN
DNK
EST
FRA
ETH
BFA
HTI
SVN
DJIVUT
SYR
YEM
SGP
USA
BEL
CZE
ALB
GRC
LTU
AZE
DZA
OMN
VUT
BGR
POL
SVN
BGD
EGY
LKA
YEM
ROM
URY
NLD GER
BLZ
BRB
TKM
BFA
SRB
AUT
SUR
HRV
CAN
BEN
COM
IRQ BEN
COM
KIR
LAO
KWT
AFG
IRQ
MWI
DNK
TCD
ERI
ITA
CPV
CHE
ISL
PLW
DJI RWA
HTI
GNB
LUX
AFG
GMB
STP
6
ATG
5
4
3
2
1
0 −8
10
−3
10
Log of GDP relative to the US
(c) Extensive margin of exports
Export Concentration
AFG
VUT
BMU
PAN
KIR ATG GNQ
STP
GNB
COM ERITCD
WSM ARM IRQ
GNQ
SLECMR
CAF
HTI
DJI
SYR
IRN
NER
MRT
KHM
BEN
GMB
AUSBRA
SYC
TKMPERPHL
MLT
GEO
LAO
RWA
IND
BDI
MWITKM
KGZ
COG
JPN
BFA
DOM
CPV TGO
VEN ZAF
UZB
ZAR
TCD
UZB
MWI
AGO
MLI
BHR
ERI BFA
MOZ
NPL
MNG
FJI
YEM
NZL
AZE
KWT
SLV
QAT
MYS RUS
YEM
AGO
SVN
CIV
ISR
COL
TTO
SUR
KAZ
OMN
BHR
CIV
SEN
KGZ
AZE
ROM
ZWE
CHL
ARG
SRB
CYP
USA
BIH
TUN
MDG
SAU
SDN
ZMB
SDN
ZWE
GTM
CHN
BDI CAF
CMR
BLR
CHN
USA
ARM
LBY
PHL
SWZ
BOL
KEN
BLZ
ALB
UGA
FJI
ITA
UGA
BGD
ETH
GER
TUR
KWT
RWA
DZA
PRY
BIH
ISR
LKA
MDG
ECU
IND
BRB
TZA
SWZ
SGP
NPL
EGY
KAZ
MDA
HTI
GHA
LUX
LBY
ETH
PAK
KEN
HUN
HRV
THA
LBN
MOZ
GNB
TTO
SYR
AUT JPN
IRL
NIC
BENSLE ALB
TZA
BGD
MUS
MLT
CHE
NLD
SEN
HND
SUR
ARE
CRI
WSM
MAR
JOR
DOM
JOR
DZA
BEL
CPV
GRC
FIN
JAM
UKR
MLI
GBR
EST
PAK
VNM
SLV
HUN
IRN
BOL
QAT
CUB
KOR
LKA
NGA
ISL
OMN
BGR
KHM
MUS
LTU
UKR
BRA
SYC
MKD
PRY
ARE
MKD
SAU
LVA
LAO
BLR
ZAF
SVK
NER NGA
EGY
ECU
RUSKOR
FRA
NZL
CHE
POL
CRI
IDN
GRC
BGR
NIC
URY
AUT
SWE
DNK
GHA
SWE
CYP
URY
NOR
LBN
MEX
CZE
FIN
TUN
AUS
GTM
COL
MNG
TUR
ZAR
TGO
COM
ESP
CUB
VEN
IDN
KIR
VNM
CAN
SVN
PER
ESP
STP
FRA
ISL LTU
PRT
GMB
CZE
SVK
MAR
ARG
DJI
COG
MDA
BRB
IRQ
CHL
ITA GER
SRB
VUT
JAM
POL
NOR
PRT
ZMB
GBR
ROM
DNKNLD
BEL
AFG MRT
THA
GEO
LUX
MEX
HNDLVA HRV
IRL
BLZ
PAN EST
SGP
MYS
ATGBMU
CAN
−6
10
−4
10
−2
10
0
10
PAN
4
3
BMU
PHL
MLT
SVN
IND
MYS
CYP
TUN SGP
ATG MLT OMNIRL
ISR AUT
CHE FRA
USA
LUX
HRV
AFG
DZA
SAU
KHM
BIH
MNG
ARE
CHE
PRY
GRC
QAT
THA
MKD
BHR
CIV
LBY
DJI GNB
KWT
QAT
LBN
BEL
STP
BEL
HUN
VNM
ITA
AFG
ARE
OMN
KOR
NLD
NZL
VUT
GBR
LUX
CYP
BGD
AUS
KGZ
SYC
DZA
SEN
BEN
NOR
EGY
SVK
TTO
ARM
DNK
MEX
LAO
IRQ
NLD
BRA
GRC
ECU
ALB
PAK
CZE
IRQ
VEN
CHN
ZAF
KHM
KEN
PAK
KAZ
YEM
LBN
GEO
JOR
RUS
SLE
ROM
AGO
SDN
CAN
GMB
DNK
BGR
VUT
GER
IRN
KOR
ISL
YEM
NGA
COL
FIN
GBR
SVK
NER
BHR
MRT
JPN
CHN
KIR
SRB
SLV
AUT
CMR
PRT
HND
BOL
NPL
THA
GHA
TKM
ETH
KWT
GER
IND
TZA
EST
LKA
TUN
TGO
PRY
CHL
CAF
AZE
SWE
HUN
JPN
ISR
PHL
JAM
EGY
SGP
JOR
IRL
CRI
SAU
MDG
ERI
MDA
GTM
DOM
COM
ZMB
GHA
IDN
TUR
BFA
VNM
COG
MLI
UZB
BGD
ARG
TGO
ESP
CPV
BLZ
MYS
ZMB
SYR
KAZ
PER
ZAR
CZE
WSM
USA
ETH
NIC
ATG
MRT
UGA
UKR
MAR
FIN
GEO
MAR
SYC
LKA
SDN
ESP
BEN
MUS
NER
URY
NGA
PAN
CIV
MLI
RUS
LBY
MUS
FJI
CHL
TUR
AUS
LTU
NOR
SRB
HRV
SEN
BOL
UZB
SWE
SWZ
ZWE
SYR
TZA
ALB
KEN
LVA
MOZ
GNQ
CMR
ARG
HTI
ZAF
URY
TCD
PRT
IDN
AGO
BFA
GTM
MDG
GMB
ISL
NZL
UGA
JAM
POL
MKD
POL
SVN
BDI
ARM
LTU
EST
ROM
RWA
MWI
BGR
BMU
COG
TKM
LVA
PER
UKR
GNQ
IRN
BRB
FRA
HND
BDI
BRA
SWZ
BLR
AZE
MWI
MOZ
KGZ
SUR
CPV
ZWE
CAN
NPL
BRB
CUB
VEN
HTI
COL
MNG
BLZ
ECU
CAF
CRI
ITA
BIH
TTO
RWA
NIC
BLR
MDA
CUB
MEX
DOM
SUR
SLV
PLW
COM
WSM
DJIFJI LAO
ZAR
STP
SLE
ERITCD
GNB
KIR
2
1
PLW
0 −8
10
2
10
Log of GDP relative to the US
−6
10
−4
10
−2
10
0
10
2
10
Log of GDP relative to the US
(e) Intensive margin of exports
(f) Intensive margin of imports
Figure 4: Simulated (in red) and empirical observed (in blue) export and import concentration versus GDP
across 160 countries. The simulation is based on estimated trade costs form bilateral trade shares including an
exported fixed effect.
30
Share of products per number of exporters
Share of products per number of importers
Data versus simulation
Data versus simulation
0.12
0.018
Red − Simulated Data
Blue − Empirical Data
Red − Simulated Data
Blue − Empirical Data
0.016
0.1
Share of products
Share of products
0.014
0.08
0.06
0.04
0.012
0.01
0.008
0.006
0.004
0.02
0.002
0
0
10
20
30
40
0
0
50
Number of exporting countries
50
100
150
Number of importing countries
(a) Share of products per exporting country
(b) Share of products per importing country
Figure 5: The simulated (in red) and empirical observed (in blue) share of the number of products traded
against the number of trading countries.
31
Import expenditure versus product share
1
Import expenditure share (1−D)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
AFG ATG
LUX
VUT DJI
HND
SGP
PAN
BMU
ZMB
ZAR
EST
COG
JAM
MKD
MLT
MRT
SVK
GEO
IRQ TGO
QAT GHA
SYC
BIHALB
STP
CHE
MDA
COM GMB
NER
KIR
BLZ
MYS IRL
OMN NLDCYP
LVA
BRB
BEL
AGO
WSM
ARE
NIC
LAO SUR
CAF
NGA TUN
HTI CPV FJI
MDG
KHM
MNG
MEX
MLI
MUS
ISL
LKA
TZA LTU LBN
ARMBEN
THA PHL
LBY ETH
HUNHRV
KGZ MOZ
CRI
KWT JOR
PRY SVN AUT
ISR
UGA
GNB
VNM
NPL
DNKDZA
KAZ
UZB ZWE SEN
CZE
SWZ BHR
SAU
GTM
YEM
BOL
GNQ
BGR
SDN
SLE
BDI
MAR CAN
ERI
SLV
KEN
NOR
BLR
MWI
CHL
GRC
SWE
AZE
CIV
RWA
DOMBGD
SRBPRT
ROM
ECU
TKM
TTO
URYIDN
GBR
BFA
FIN NZL
UKR
EGY
POL
GER PAK
COL
ESP
FRA TUR
PER VEN
TCD
CMRCUB
ZAF
RUSARG AUS
ITA
IRN
KOR
SYR IND USA
CHN
BRA
JPN
0.1
0
0
PLW
0.2
0.4
0.6
0.8
1
Import product share (1−π)
Figure 6: The import expenditure share versus the import product share.
32
9
Tables
Table 6: Country-Specific Technology and Trade Costs estimates
Country
USA
AFG
AGO
ALB
ARE
ARG
ARM
ATG
AUS
AUT
AZE
BDI
BEL
BEN
BFA
BGD
BGR
BHR
BIH
BLR
BLZ
BMU
BOL
BRA
BRB
CAF
CAN
CHE
CHL
CHN
CIV
CMR
COG
COL
COM
CPV
CRI
CUB
CYP
CZE
DJI
DNK
DOM
DZA
ECU
EGY
ERI
ESP
EST
ETH
FIN
FJI
FRA
GBR
GEO
Exporter FE
Standard error
Precent cost
Si
Standard error
(λUS /λi )θ
6,36
-0,46
-1,96
-3,31
2,98
2,19
-3,14
1,12
3,29
2,03
-3,41
-3,45
5,53
-3,11
-4,45
0,96
0,05
-0,83
-3,57
-1,40
-0,26
-1,26
-1,84
3,17
-1,49
-2,05
4,10
4,79
2,13
5,11
-0,12
-2,10
0,87
-0,04
-3,06
-3,16
0,32
-1,47
0,61
1,13
-1,23
2,57
-1,12
-2,29
-0,18
0,42
-4,87
3,76
1,75
-1,73
1,77
-1,88
4,56
4,86
-0,54
0,18
0,25
0,23
0,23
0,19
0,19
0,22
0,45
0,19
0,19
0,22
0,24
0,18
0,23
0,23
0,2
0,19
0,32
0,24
0,21
0,26
0,41
0,22
0,19
0,23
0,26
0,18
0,19
0,2
0,18
0,2
0,2
0,23
0,19
0,29
0,32
0,21
0,2
0,19
0,19
0,28
0,18
0,2
0,2
0,2
0,19
0,26
0,18
0,21
0,2
0,19
0,25
0,18
0,18
0,21
-53,47
5,53
26,79
48,53
-30,12
-23,01
45,55
-12,28
-32,75
-21,83
50,25
51,47
-48,63
45,41
70,73
-10,19
-0,59
10,52
53,34
18,12
3,53
16,48
24,88
-31,64
20,33
28,12
-38,98
-43,90
-22,51
-45,86
1,13
28,96
-9,83
0,63
44,10
46,15
-3,62
19,41
-6,89
-12,91
16,32
-26,76
14,60
31,68
2,25
-4,75
79,16
-36,45
-19,23
23,10
-19,42
25,42
-42,30
-44,32
6,46
0,84
-3,06
-0,97
-0,12
-0,71
1,54
0,2
-3,72
0,98
1,24
1,12
-0,45
-0,89
-0,38
0,6
0,27
1,01
0,26
1,1
2,1
-1,77
-1,91
0,39
1,71
-0,91
-1,11
0,43
-0,76
0,48
1,57
0,06
0,78
-2,63
1,13
-1,78
-0,66
0,06
0,86
-0,44
1,37
-2,99
0,97
0,65
0,61
0,57
0,83
0,12
0,81
-1,36
-0,6
1,73
-0,36
1,05
0,57
-1,25
0,13
0,19
0,16
0,16
0,14
0,14
0,16
0,3
0,13
0,13
0,16
0,16
0,13
0,15
0,15
0,14
0,14
0,23
0,17
0,15
0,18
0,28
0,15
0,13
0,16
0,19
0,13
0,13
0,14
0,13
0,14
0,14
0,17
0,13
0,19
0,2
0,15
0,14
0,14
0,13
0,2
0,13
0,14
0,13
0,14
0,13
0,19
0,13
0,14
0,13
0,13
0,17
0,13
0,13
0,15
1
193,42
23,5
9,63
2,6
2,24
9,61
12,93
1,2
0,77
12,2
49,84
0,85
45,53
36,85
10,69
2,79
1,87
6,66
2,17
8,17
5,66
10,01
2,22
5,95
21,31
0,99
0,9
2,34
2,22
8,73
8,12
16,64
5,89
42,22
16,29
3,43
9,57
3,51
1,04
50,23
0,8
3,72
17,61
6,51
9,14
43,83
1,19
2,27
70,73
0,59
5,57
0,8
1,06
15,46
33
Table 7: Country-Specific Technology and Trade Costs estimates - cont.
Country
GER
GHA
GMB
GNB
GNQ
GRC
GTM
HND
HRV
HTI
HUN
IDN
IND
IRL
IRN
IRQ
ISL
ISR
ITA
JAM
JOR
JPN
KAZ
KEN
KGZ
KHM
KIR
KOR
KWT
LAO
LBN
LBY
LKA
LTU
LUX
LVA
MAR
MDA
MDG
MEX
MKD
MLI
MLT
MNG
MOZ
MRT
MUS
MWI
MYS
NER
NGA
NIC
NLD
NOR
NPL
NZL
OMN
Exporter FE
Standard error
Precent cost
Si
Standard error
(λUS /λi )θ
4,74
1,14
-1,69
-3,13
-3,99
0,73
-1,41
1,26
-0,60
-3,14
0,43
4,30
3,76
3,90
-1,18
-3,12
0,08
1,26
3,96
0,76
-0,60
4,91
-0,28
-0,24
-3,04
-2,22
-2,77
4,42
-1,70
-3,15
-0,31
-1,81
0,98
-0,24
1,44
-0,64
0,73
-1,11
-0,95
3,42
-1,04
-2,42
0,30
-2,60
-1,13
-0,58
0,95
-3,87
5,40
-1,89
0,15
-1,13
5,66
1,83
-3,03
2,54
0,39
0,18
0,2
0,24
0,38
0,28
0,19
0,21
0,24
0,19
0,32
0,19
0,19
0,18
0,18
0,2
0,3
0,21
0,19
0,18
0,21
0,2
0,18
0,21
0,2
0,24
0,29
0,39
0,18
0,2
0,29
0,19
0,24
0,2
0,2
0,25
0,21
0,19
0,23
0,22
0,19
0,23
0,25
0,22
0,27
0,21
0,24
0,2
0,23
0,19
0,23
0,21
0,22
0,18
0,19
0,23
0,19
0,21
-43,54
-12,49
23,14
45,90
61,56
-8,59
18,61
-13,99
7,28
45,77
-5,15
-40,26
-36,12
-37,55
15,38
44,88
-1,09
-14,17
-38,00
-8,32
7,39
-44,65
3,08
3,22
43,58
30,65
38,98
-41,23
23,09
46,04
3,77
24,19
-11,03
2,69
-16,07
7,78
-8,11
14,13
12,10
-33,56
13,26
33,82
-3,45
36,44
14,84
7,23
-10,48
59,69
-47,75
25,67
-1,37
14,67
-49,44
-19,87
44,04
-26,41
-4,70
1,17
-1,78
-1,99
-0,89
0,39
0,93
0,41
-2,49
0,92
-0,5
1,49
0,21
1,03
-0,47
1,94
-1,13
-0,18
1,11
1,27
-1,7
0,24
1,95
1,08
-0,06
0,39
0,71
-1,68
1,4
0,84
0,54
-0,23
0,27
-0,37
0,6
-0,65
0,3
0,39
-0,33
-0,93
-0,1
-0,73
-0,45
-0,68
-0,51
-0,55
-2,13
-0,98
0,29
-0,74
-1,35
-1,19
-0,78
-0,88
1,02
0,37
0,58
-0,59
0,13
0,14
0,17
0,27
0,19
0,13
0,14
0,17
0,13
0,23
0,13
0,13
0,13
0,13
0,15
0,21
0,15
0,14
0,13
0,15
0,14
0,13
0,15
0,14
0,16
0,21
0,29
0,13
0,14
0,23
0,14
0,17
0,14
0,14
0,2
0,15
0,14
0,16
0,15
0,13
0,15
0,17
0,16
0,19
0,14
0,17
0,14
0,15
0,14
0,16
0,14
0,15
0,13
0,13
0,16
0,14
0,15
0,65
17,47
30,89
34,18
3,92
2,5
6,43
9,19
2,53
26,46
1,26
4,69
6,78
0,78
7,13
224,32
1,15
1,26
0,8
6,45
5,19
0,48
4,17
20,53
10,95
10,91
20,97
0,73
3,69
11,92
7,97
8,88
7,75
2,6
0,86
2,97
5,1
8,17
20,18
3,27
5,07
43,51
1,64
10,12
18,96
21,41
3,68
34,15
1,64
39,78
57,57
10,6
1,02
0,9
18,27
1,27
6,51
34
Table 8: Country-Specific Technology and Trade Costs estimates - cont.
Country
PAK
PAN
PER
PHL
PLW
POL
PRT
PRY
QAT
ROM
RUS
RWA
SAU
SDN
SEN
SGP
SLE
SLV
SRB
STP
SUR
SVK
SVN
SWE
SWZ
SYC
SYR
TCD
TGO
THA
TKM
TTO
TUN
TUR
TZA
UGA
UKR
URY
UZB
VEN
VNM
VUT
WSM
YEM
ZAF
ZAR
ZMB
ZWE
Exporter FE
Standard error
Precent cost
Si
Standard error
(λUS /λi )θ
1,59
2,82
0,47
2,33
-9,10
0,87
1,76
-1,36
0,60
0,18
1,98
-3,73
1,34
-2,46
-0,69
6,66
-0,49
-1,74
-1,84
-2,21
-1,59
1,67
-0,38
2,74
-0,81
-1,17
-3,28
-5,68
-1,07
5,42
-4,02
-1,00
0,44
1,92
-0,25
-1,79
0,91
0,76
-2,14
-0,46
2,46
-0,93
-2,40
-2,67
3,49
1,02
1,85
-1,06
0,19
0,23
0,2
0,2
0,4
0,19
0,19
0,22
0,21
0,19
0,19
0,23
0,19
0,2
0,21
0,19
0,49
0,21
0,21
0,33
0,26
0,2
0,19
0,18
0,24
0,28
0,21
0,26
0,23
0,18
0,26
0,22
0,19
0,18
0,2
0,21
0,19
0,21
0,24
0,2
0,19
0,34
0,3
0,22
0,19
0,27
0,26
0,22
-17,08
-28,76
-5,45
-24,40
197,33
-9,93
-19,23
17,79
-7,14
-2,06
-21,33
57,16
-14,98
34,35
8,70
-55,17
5,65
23,31
24,59
29,84
21,12
-18,39
4,59
-28,26
10,26
15,16
48,21
98,20
14,05
-47,82
61,53
12,96
-4,70
-20,57
3,46
24,15
-10,55
-8,63
29,20
5,82
-25,57
11,75
32,82
37,58
-34,26
-11,18
-19,62
13,97
0,9
-2,16
1,17
0,07
4,52
1,78
0,7
0,6
-0,62
1,73
1,89
-0,15
0,36
-0,12
-0,57
-2,19
-0,94
0,42
1,36
-1,89
-0,75
-0,18
1,09
1,41
0,15
-1,46
2,26
0,93
-1,56
-0,68
1,08
0,46
0,01
1,38
-0,88
-0,32
1,75
0,44
0,68
1,35
0,24
-2,46
-1,26
0,39
0,48
-2,97
-2,55
0,16
0,14
0,17
0,14
0,14
0,32
0,13
0,13
0,17
0,15
0,13
0,13
0,15
0,13
0,13
0,14
0,13
0,33
0,15
0,15
0,24
0,18
0,14
0,13
0,13
0,19
0,2
0,15
0,18
0,15
0,13
0,19
0,15
0,14
0,13
0,13
0,14
0,14
0,15
0,18
0,14
0,13
0,23
0,22
0,15
0,13
0,2
0,17
0,16
8,28
6,67
3,64
4,89
0,19
1,57
1,47
7,95
2,69
2,39
2,15
67,74
4,12
41,79
14,97
0,98
22,08
4,95
3,15
30,19
3,21
1,73
1,01
0,66
3,87
3,51
7
52,38
32,81
2,93
12,48
2,04
3,76
2,55
30,26
37,54
3,01
2,72
12,54
4,18
6,16
16,57
9,02
31,04
2,25
58,54
18,9
9,86
35
References
A LCALÁ , F., AND A. C ICCONE (2004): “Trade and productivity,” The Quarterly Journal of Economics,
119(2), 613–646.
A LVAREZ , F., AND R. J. L UCAS (2007): “General equilibrium analysis of the Eaton Kortum model
of international trade,” Journal of Monetary Economics, 54(6), 1726–1768.
A NDERSON , J., AND E. VAN W INCOOP (2003): “Gravity with gravitas: a solution to the border
puzzle,” American Economic Review, 93(1), 170–192.
A NDERSON , J., AND E. VAN W INCOOP (2004): “Trade costs,” Journal of Economic Literature, 42(3),
691–751.
A RKOLAKIS , C. (2010): “Market penetration costs and the new consumers margin in international
trade,” Journal of Political Economy, 118(6), 1151–1199.
B ALASSA , B. (1963): “An empirical demonstration of classical comparative cost theory,” The Review of Economics and Statistics, 45(3), 231–238.
B OWEN , H., E. L EAMER , AND L. S VEIKAUSKAS (1987): “Multifactor, multicountry tests of the
factor abundance theory,” American Economic Review, 77(5), 791–809.
B RODA , C., AND D. W EINSTEIN (2006): “Globalization and the Gains from Variety,” Quaterly
Journal of Economics, 121(2), 541–585.
C ADOT, O., C. C ARRÈÈRE , AND V. S TRAUSS -K AHN (2011): “Export Diversification: What’s behind the Hump?,” The Review of Economics and Statistics, 93(2), 590–605.
C HOR , D. (2010): “Unpacking sources of comparative advantage: A quantitative approach,” Journal of International Economics, 82(2), 152–167.
C OSTINOT, A., D. D ONALDSON , AND I. K OMUNJER (2012): “What goods do countries trade? A
quantitative exploration of Ricardo’s ideas,” Review of Economic Studies, 79, 581–608.
E ATON , J., AND S. K ORTUM (2002): “Technology, Geography, and Trade,” Econometrica, 70(5),
1741–1779.
E ATON , J., S. K ORTUM , AND F. K RAMARZ (2011): “An anatomy of international trade: Evidence
from French firms,” Econometrica, 79(5), 1453–1498.
E THIER , W. (1982): “National and international returns to scale in the modern theory of international trade,” The American Economic Review, pp. 389–405.
F EENSTRA , R. C., R. E. L IPSEY, AND H. P. B OWEN (1997): “World Trade Flows, 1970–1992, with
Production and Tariff Data,” National Bureau of Economic Research Working Paper, 5910.
36
G OLUB , S., AND C. H SIEH (2000): “Classical Ricardian theory of comparative advantage revisited,” Review of International Economics, 8(2), 221–234.
G RUBEL , H., AND P. L LOYD (1975): Intra-industry trade: The theory and measurement of international
trade in differentiated products, vol. 12. Macmillan London.
H UMMELS , D., AND P. K LENOW (2005): “The Variety and Quality of a Nations Exports,” American
Economic Review, 95(3), 704–23.
K RUGMAN , P. (1980): “Scale Economies, Product Differentiation, and the Pattern of Trade,” The
American Economic Review, 16(4), 670–675.
L EAMER , E. (1984): Sources of comparative advantage. MIT Press„ Cambridge, Mass.
L EVCHENKO , A., AND J. Z HANG (2011): “The evolution of comparative advantage: Measurement
and welfare implications,” Discussion paper, National Bureau of Economic Research.
M AC D OUGALL , G. (1951): “British and American exports: A study suggested by the theory of
comparative costs. Part I,” The Economic Journal, 61(244), 697–724.
S CHOTT, P. (2003): “One size fits all? Heckscher-Ohlin specialization in global production,” The
American Economic Review, 93(3).
S HIKHER , S. (2011): “Capital, technology, and specialization in the neoclassical model,” Journal of
International Economics, 83(2), 229–242.
T REFLER , D. (1995): “The case of the missing trade and other mysteries,” The American Economic
Review, pp. 1029–1046.
WAUGH , M. E. (2010): “International trade and income differences,” American Economic Review,
100(5), 2093–2124.
37