Download Firms` Heterogeneity in Accessing Foreign Markets

Firms’ Heterogeneity in Accessing Foreign Markets:
A Network Analysis
Shibi He
∗
September 30, 2016
Abstract
This paper uses network analysis to provide a simple explanation for the heterogeneity in firm’s ability to access foreign markets. Starting from exploring the world
trade network from 2007 to 2013, I identify the most central countries in the global
market by calculating different centrality measures. Next, using Colombia’s firm-level
export data, I find the centrality of a firm’s current contacts plays a critical role in
improving the firm’s probability of entering new markets in the future. Specifically,
if the average (eigenvector) centrality of a firm’s current contacts increases by 1, the
probability for this firm to enter a new market in the next period would increase by
2.5%. This result suggests that connecting to the central countries is very important
for an individual firm’s development and expansion in the global market.
KEYWORDS: World Trade Network, Network Analysis, and Centrality.
JEL Classification Number: F14
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Introduction
It is a well-documented fact in the trade literature that exporting firms differ largely in
their ability to access foreign markets. Most firms do not export at all and of those which
do, only few export to a large number of countries. However, the source of the heterogeneity in firm’s ability to enter foreign markets remains largely unexplained.
The existing literature have provided several possible explanations for firm’s heterogeneity
in accessing foreign markets. The classic gravity model predicts that the bilateral trade
between two countries should be positively related to their economic sizes (measured by
∗
Department of Economics, Indiana University, 100 S. Woodlawn, Bloomington, IN 47405. Email:
[email protected]
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GDP or GNP) and negatively related to the distance between the two countries. Though
the gravity model shows us that economic sizes and distance are two critical factors influencing the trade value, it only considers the aggregated country-level trade and may
not be applicable to the firm-level export decisions. Another explanation is proposed by
Melitz in 2003. He argues that the differences in firms’ ability to enter foreign markets
are driven by the differences in their productivities. His model assumed that entering a
foreign market is very costly. The more productive firms are more likely to cover the entry
cost and therefore more likely to enter a new market. But for the less productive firms,
they may not be able to cover the entry cost and thus continue to produce only for the
domestic market. However, some follow-up studies, such as Armenter and Koren (2015),
point out that productivity differences can only explain a fraction of the heterogeneity in
firms’ ability to access foreign markets. There are lots of idiosyncratic noises, such as managerial quality, geographical location and industry-level heterogeneity, have to be added to
Melitz’s model in order to empirically match the firm level exports data.
In this paper, I provide a new explanation for firms’ different abilities to access foreign markets based on the approach of network analysis. The idea of treating international trade
as a network has been accepted by economists since long ago and it’s getting increasingly
popularity nowadays. The applicability and importance of the network analysis in international trade have been well-summarized in Benedictis et al.(2014): “Network Analysis
is a useful tool to describe bilateral trade relations among countries when interdependence
matters, and when trade relations are characterized by high dimensionality and strong
heterogeneity.” The strong heterogeneity in trade relations largely result from the strong
heterogeneity among the trading countries themselves. One convenient way to quantify the
country heterogeneity in world trade network is through the use of centrality measures.
Centrality, one of the most commonly used summary statistics in the network analysis,
indicates the level of importance of each node in a system. Countries with high centrality
scores are considered to be more connected and occupy a relatively more central position in
the world trade network. These high-centrality countries serve as the centers of commodity purchases and hubs of information exchange. Intuitively, we would think trading with
these central countries is very beneficial for an individual firm to learn information about
trade opportunities, to advertise its products, to meet potential partners and ultimately,
to enter new markets. In this paper, I provide evidence to show that the high centrality of
a firm’s current trading partners indeed increases the firm’s chance to enter new markets
in the future.
The main contribution of this paper is the discovery of the critical role played by centrality
in improving firm’s probability of entering new markets. Using Colombia’s firm level data,
this paper shows that if the average (eigenvector) centrality of a firm’s current contacts
increases by 1, the probability for this firm to enter a new market in the next period would
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increase by 0.025. Second, this paper explores the world trade network from 2007 to 2013
and identifies the most central countries by calculating different centrality measures. The
identification of the central countries will facilitate future research on related topics.
The rest of the paper proceeds as follows. In section 2, I discuss the related literature.
Section 3 provides an introduction to the network basics and the definition of centrality
measures. Section 4 is a detailed description of the dataset I use and section 5 discusses
the empirical models. In section 6, I present the results and conduct several robustness
checks. Finally, section 7 concludes.
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Literature Review
This paper contributes to the literature on network analysis of international trade. Nowadays more and more international economists have noticed the network nature of world
trade and apply the tools of network analysis to their studies. Benedictis and Tajoli (2011)
conduct a detailed analysis on the structure and properties of the world trade network
from 1950 to 2000. The results suggest that trade integration at the world level has been
increasing but it is still far from being complete. Moreover, network indices are used in
a gravity model regression and the results indicates that WTO members are more closely
connected than the rest of the world. Benedicts, Nenci, Santoni, Tajoli, and Vicarelli
(2014) explore the BACI-CEPII database using network analysis. Starting from the visualization of the world trade network, they then define and describe the topology of the
network, calculate and discuss some of the commonly used network’s statistics and finally
they conclude that “network approach can be fruitfully applied in several contexts of international trade analysis.” The above two papers have clearly demonstrated the network
nature of the international trade but only focused on the aggregated country-level trade.
In this paper, I take the network analysis to a more disaggregated level to study how the
network of trading partners would affect each individual firm’s likelihood of entering a new
market.
A somewhat similar work has been done in Thomas Chaney (2014), where he developed a
theoretical model of the formation of an international network of importers and exporters
and show how this network matters for firm-level trade patterns. In his model firms are
allowed to meet foreign importers both randomly and through their networks of existing
foreign contacts. Using French firm-level export data, the author shows that where a firm
currently exports to affects which new market it enters subsequently: if a French firm
exports to country C 0 at time t, it is subsequently more likely to enter any country C
that is closely related to country C 0 , either in the sense that it is geographically close to
C 0 , or that it trades intensively with C 0 . Based on Thomas Chaney’s idea that a firm
can search for new trading partners through its existing network of contacts, I expected
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that the centralities of an individual firm’s contacts also matter. Centrality is a commonly
used index in network analysis and identifies the most important and connected nodes in a
network. Intuitively, if an individual firm has contacts with the more connected or central
countries, this firm is more likely to enter new markets as it benefits from the large network
of contacts of its trading partners. I will discuss more about this idea and compare the
results I get with that of Thomas Chaney (2014) in section 6.
This paper is complementary to the literature on network effects in international trade.
Rauch and Trindade (2002) find ethnic Chinese networks can promote bilateral trade
through trust and information. On one hand, the ethnic Chinese networks can provide
community enforcement of sanctions that deter violations of contracts in a weak international legal environment. On the other hand the networks promote bilateral trade by
providing market information that help to match buyers and sellers. Combes, Lafourcade,
and Mayer (2005) use data on migrations and multiplant firms to infer a measure of social
and business linkages and show that social and business networks facilitate trade between
regions within France. Using Spanish data, Garmendia et al. (2012) show that social and
business networks have a stronger impact on the extensive margin than on the intensive
margin of trade. All these papers corroborate the trade-promoting effects of networks and
my paper is complementary to these literature in the sense that in addition to the intensive
margin of trade, I show that the network also has positive effects on the extensive margin
of trade. The evidence from Columbian firm-level data suggests that if the average eigenvector centrality of a firm’s contacts increases by 1, the probability for this firm to enter a
new market in the next period will increase by 2.5%.
This paper is also relateted to a growing literature on the expansion at firm level. Albornoz
et al. (2012) and Defever, Heid, and Larch (2011) both present simple models of learning
about a firm’s potential in a foreign market. They use Argentine and Chinese firm data to
show evidence of the “sequential exporting”, meaning that where a firm already exports
influences where it enters next. Morales, Sheu, and Mahler (2013) use a moment inequality
estimation procedure to estimate a similar model of sequential export choice, and argue
that exports tend to be history dependent. If a firm exports to a particular country, it is
subsequently more likely to export to other similar countries. The existing literature seem
to focus on the geographical location or economic masses with regard to the importance of
a firm’s current destination, but none of them have ever discussed the critical role played
by the centrality. This paper is the first one to study the effects of existing contacts’ centralities on firm’s probability of entering a new market.
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3
Network Basics and Centrality Measures
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For the purpose of this study, we can easily define the world trade network as a simple directed graph: N = (V, L). The first component V = {1, ..., n} is a set of vertices
(countries) in the network. Since we have a fixed sample of countries (n = 156) in the
dataset, the set V doesn’t change overtime. The second component L = {1, ..., m} is a set
of links (trade flows) between pairs of vertices, where m is the total number of existing
links in the network and changes year by year. In 2007, the link-dimension of the network is L = {1, 2, ..., 14858} and it increases to L = {1, 2, ..., 15703} in 2010. The links
are directed, originating from the exporting country i, to the importing country j, and
Lij = {0, 1}. Sepcifically, Lij = 1 indicates the existence of a trade link between i and j
and Lij = 0 denotes the absence of a trade link.
In this paper I try to analyze how an individual firm’s network of trading partners would
affect its probability of entering new markets. If a firm acquires a network of foreign contacts, the firm can use this network to search for new trading partners, but the probability
of success should depend on the existing contacts’ positions or importance in the world
trade network. Network analysis offers several indicators to assess the importance of a
node, the most commonly used one is centrality. Here, I give a brief review of different
centrality measures, each providing a different type of information.
3.1
Degree Centrality
Historically first and conceptually simplest centrality measure is degree centrality, which is
defined as the number of links that a node is connected to in the network. Let Cid denote
the degree centrality of node i, then we have
Cid =
n
X
Lij ,
j6=i
where n is the total number of nodes (countries) in the network and Lij is binary, indicating the existence (Lij = 1) or non-existence (Lij = 0) of an exporting link from country i
to country j.
The first column in Table 1 presents the degree centralities for the top 15 countries. In
2007, Colombia has the highest degree centrality of 154, meaning that Colombia exports
to 154 countries that year, but it drops out of the top 15 countries since 2010. Most of the
high centrality countries are the developed European countries, such as France, Germany,
Italy and Denmark. In general, the list of high centrality countries doesn’t change much
overtime.
1
The network basics and the definition of centrality measures are summarized and modified based on
Benedictis, Nenci, Santoni, Tajoli and Vicarelli (2013), section 2 and 3.
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3.2
Closeness Centrality
Closeness centrality tells us how close (in terms of geodistance) a node is with respect to all
other nodes. In the network analysis, the concept of distance doesn’t refer to the physical
distance between two nodes, but the number of steps needed for a node to reach another
node in the network. The shortest path between node i and node j is called the geodistance
between i and j. For example in a network, node i has to go through at least another node
k in order to reach node j, then the geodistance between i and j is 2, as there are at least
two steps needed for node i to reach node j: first from i to k and then from k to j. Let
Cic denote the closeness centrality of node i, then
Cic
=
X
n
−1
dij
,
j6=i
where dij is the geodistance (i.e. the shortest path) between i and j. Expressed as the
inverse of the the sum of geodistance, the closeness centrality shows us how quickly or how
easily a node can be reached by others and gives high centrality scores to nodes that are
located closer to the set of reachable nodes.
The second column in Table 1 lists the closeness centralities for the top 15 countries. The
closeness centrality has a high correlation of 0.94 with the degree centrality, meaning that
the countries with high degree centrality scores tend to also have high closeness centrality
scores. Again, most of the high centrality countries come from the Europe, as they are
easily reachable by each other.
3.3
Betweenness Centrality
Betweenness centrality describes how important a node is in terms of connecting other
nodes. Specifically, it quantifies the number of times a node acts as a bridge along the
shortest path between two other nodes. Let Cib denote the betweenness centrality of node
i and it is defined as:
X gjk (i)
Cib =
,
gjk
j6=k
where gjk is the total number of shortest paths between nodes j and k and gjk (i) is the
number of these paths that passes node i.
Nodes with high betweenness centralities are considered to be very influential in the network as they connect two nodes and “control” the flows passing through them. In fact,
betweenness centrality is an especially useful measure in the cases when a node is important as an intermediary, for example, in a communication network. The transmission of
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information will be interrupted or must make longer path if the critical node stops working
or disappears from the network. However, this measure seems not very applicable to the
study of trade data because the intermediary property doesn’t hold with the aggregated
bilateral trade. In the world trade network, suppose country i is not directly connected to
country j, but there is a path connecting these two countries through a third country k,
we cannot simply interpret the role of country k as an intermediary because we have no
evidence to say that there are trade flows pass through k from i to j. Put simply, in the
world trade network, the connectivity captured by the betweenness centrality simply has
no economic meaning.
For the purpose of comparison, I still presents the betweenness centralities for the top 15
countries in the third column of Table 1. Compared with other centrality measures, betweenness centralities vary a lot for different countries. The United States has the highest
betweenness centrality in 2007 and dropped to the fourth in 2010 and the fifth in 2013.
China has kept its second place in the ranking over the period from 2007 to 2013. Surprisingly, India’s betweenness centrality grew from 98.018 in 2007 to 110.829 in 2010 and
soared to 146.448 in 2013 and becomes the highest all over the world.
3.4
Eigenvector Centrality
Eigenvector centrality associates a node’s centrality to the node neighbors’ characteristics.
In other words, it matters who you are connected to. The assumption is that each node’s
centrality Cie is the sum of the centrality values of the nodes that it is linked to. That is:
n
Cie =
1X
Lij Cje .
λ
j6=i
Rewrite the system of equations into matrix form, we have LC~e = λC~e , where λ is the
eigenvalue and L is the trade adjacency matrix. Lij is the element (i, j) in the trade adjacency matrix L, where i is the row-indicator corresponding to exporting countries, and j
is the column-indicator corresponding to importing countries. Lij = 1 if there is an export
flow from country i to country j and Lij = 0 otherwise.
From the definition of eigenvector centrality we can see that, a node’s centrality is high
if its neighbors’ centralities are high. Thus eigenvector centrality actually captures the
extent of connecting to important nodes. Considering a country C with high eigenvector
centrality, it suggests that this country is connected to other important exporters. This
country C, together with its contacts, are the important, central, and influential ones in
the world trade network. If an exporting firm plans to make access to new foreign markets
and expand in the global market, it would be better to trade firstly with this high centrality
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country C in order to take advantage of its powerful network.
The eigenvector centralities are summarized in column (4) in Table 1. The European
countries are ranked high and have similar centrality scores, suggesting each of them is
connected to important and central neighbors, meaning that they form a cohesive and
tightly clustered neighborhood. This is possibly due to the affiliation to the European
Union, which makes the whole Europe to become one of the centers of the world trade.
In a brief conclusion, various centrality measures can be used to identify the most important nodes in a system, but each emphasizes a different aspect of a node’s position in
the network. Degree centrality measures how a node is connected to others. Closeness
centrality shows how easily a node can be reached by other nodes. Betweenness centrality describes how important a node is in terms of connecting other nodes. Eigenvector
centrality captures the extent of connecting to important nodes or how central, or tightly
clustered a node’s neighbors are. All of these centrality measures, except the betweenness
centrality, can be well applied to the study of international trade data. Countries with
high centrality scores occupy a relatively more central position in the world trade network
and can be regarded as the centers of the world trade.
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Data Description
In this paper, I try to explain the heterogeneity in firms’ abilities to access foreign markets
by tools of network analysis. Specifically, I want to study whether trading with a central
or a more connected country would increase an individual firm’s probability of entering
new foreign markets in the next period. I mainly use two sources of data. The first one
is the bilateral trade data from the World International Trade Solutions (WITS) over the
period 2007-2013, which I use to calculate different centrality measures for each country
and identify the central ones. Second, I explore the firm-level export data of Colombian
exporters over the same period to observe how the current network of existing contacts
would subsequently affect the likelihood of entering new markets. This dataset is obtained
from the Colombian government and it contains very rich information. For each firm and
each year, I observe the set of countries to which a firm exports, the export value and
product HS code. Table 2 presents the summary statistics of this dataset.
As showed in Table 2, there are between 9,481 (in 2011) and 11,141 (in 2008) exporters
in the dataset. Those firms export to a total number of 156 different foreign countries for
which I have additional information on size and distance. On average, Colombian firms
export to between 2.52 (in 2008) and 2.79 (in 2011) different foreign countries. However,
these firms’ abilities to enter foreign markets vary a lot. For example, in 2012, the maximum
number of destinations a firm exports to is 52 while the minimum number is only 1. The
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seventh column in Table 2 presents the percentage of firms that export to the top 20 most
central
countries (in terms of Eigenvector centrality). In general, more than 67% of the firms would
choose to export to the most connected countries with a highest percentage of 80.97% in
2010. The last column shows the percentage of trade values with the 20 most central countries in the world. For example, 55.19% of Colombian’s export goes to the 20 most central
countries in 2007. This percentage keeps increasing to nearly 70% in 2010 and slightly
decreases afterwards.
In addition to the bilateral trade data and Colombian firms’ export data, I also use information on the the size of countries, their distance from Colombia and from each other.
The size of a country is measured by GDP, collected from Penn World Tables. The simple
distance and population weighted distance between capital cities are obtained from CEPII.
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Empirical Model
I use a Probit model to test the following four predictions. First, firms with more foreign
contacts are more likely to enter an additional market subsequently. This is based on
Thomas Chaney’s remote search idea that firms can meet foreign importers through their
existing contacts. If a firm has a lot of foreign contacts, this large network of contacts
would bring even more new contacts, which potentially could be the firm’s future trade
partners, thus increasing the firm’s probability of entering new foreign markets.
Second, I want to test whether a firm benefits from the location of its existing contacts. In
other words, whether a firm is more likely to export to a destination C that is geographically close to its current destination C 0 .
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Third, a firm should benefit from the trade growth of its contacts. Here, I simply use the
aggregated trade flows between country C and C 0 as proxy for the intensity of communication between these countries. If there is a large trade growth between country C and
country C 0 , it implies the communication and information exchange are intensive between
them and it might increase the chance that C 0 introduces firm i to C and consequently
increases the probability for firm i to enter country C.
At last, firms should benefit from the high centralities of its existing contacts. Countries
with high centrality scores are the most important, influential and central ones in the world
trade network. Connecting with these central countries would help a firm to meet new potential trade partners, gather information about trade opportunities, business customs,
and consumers’ preferences, all increasing the firm’s likelihood of entering a new market.
The following graph helps to explain these predictions more clearly. The two solid arrows
indicate there is an export flow from a Colombian firm i to its current destination C 0 and
an export relationship between country C 0 and country C, respectively. The dashed arrow
depicts the export relationship in question, i.e. whether the Colombian firm i would be
subsequently more likely to export to a country C, compared to all other countries, if (i)
firm i has a lot of these contacts C 0 ; (ii) country C is geographically close to country C 0 ;
(iii) there is an export growth between country C 0 and country C and (iv) the current
destination C 0 has high centrality scores.
In order to test these predictions, I need to construct several variables for each individual
firm i. Let
X
N contactsi,t =
1[Exporti,c0 ,t > 0]
c0
be the number of contacts that firm i has at time t, that is, the number of countries firm
i exports to at time t. 1[Exporti,c0 ,t > 0] is an indicator function that equals 1 if firm i
export to country C 0 at time t and equals 0 otherwise.
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Next, the average proximity between firm i’s contacts and country C at time t is defined
as
P
0 1[Exporti,c0 ,t > 0]g(Distc0 ,c )
,
Disti,c,t = c
N contactsi,t
where g(Distc0 ,c ) = Dist1 0 is the proximity between country C 0 and C. I summed the
c ,c
proximities over firm i’s contacts and divided it by the number of contacts, giving me the
average proximity.
T radeGrowthi,c,t =
X
1[Exporti,c0 ,t > 0]
c0
∆Exportc0 ,c,t
Exportc0 ,c,t
measures the sum of export growth between firm i’s contacts and country C, where
∆Exportc0 ,c,t
0
Exportc0 ,c,t is the growth of aggregate exports from country C to country C between
years t and t + 1. At last,
P
0 1[Exporti,c0 ,t > 0]Centralityc0 ,t
Centrality i,t = c
N contactsi,t
captures the average centrality of firm i’s contacts at time t, where Centralityc0 ,t measures
the centrality of country C 0 at time t.
Next, I use the following Probit regression to test the above four predictions:
P rob(Exporti,c,t+1 > 0) = Φ(αN contactsi,t + β1 g(DistCOL,c )
+ β2 Disti,c,t + γT radeGrowthi,c,t
+ λCentrality i,t + η1[Exporti,c,t > 0] + δGDPc,t )
The dependent variable is the probability for firm i to enter country C at time t + 1.
The coefficient α controls for impact of the number of countries a firm exports to on the
likelihood it enters new markets subsequently. α > 0 would mean that the more markets a
firm exports to today, the more likely it is to enter new markets in the future. g(DistCOL,c )
is the inverse of distance (proximity) between Colombia and country C, thus the coefficient
β1 controls for the direct impact of proximity on trade. The coefficient β2 controls for the
indirect impact of proximity on trade and β2 > 0 would mean that if a firm exports to
countries which are close to C, it is subsequently more likely to enter that country C. The
coefficient γ captures the effects of contacts’ trade growth on the probability of a firm to
enter a new market. γ > 0 would suggest that if a firm already exports to countries whose
exports to C grow, it is subsequently more likely to enter that country. λ is the coefficient
of most interest to us. It reflects the impact of the centralities of a firm’s contacts. If
λ > 0, the more connected existing contacts have beneficial effects on the firm’s entry
to a new market. η controls for the export status of firm i in the previous year, and the
possibility that a firm loses foreign contacts. At last, the coefficient δ account for the effects
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of economic sizes since firms are mechanically more likely to export to a large country than
to a small one. I would expect all the coefficients to be greater than 1 as each of them
should have positive effects on firm’s probability of entering additional markets.
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Estimation Results and Robustness Checks
I estimate the Probit regression several times, with different centrality measures. For the
convenience of interpretation, I present the estimated marginal effects in Table 3. I also
include Thomas Chaney (2014)’s results in the first column for the purpose of comparison,
where he uses French firms’ export data.
Compared to the French exporters in Thomas Chaney’s paper, all variables tend to have
smaller effects for Colombian firms. This is possibly due to the relatively less competence of
Colombian firms in the global market. It is generally much more difficult for a Colombian
exporter to enter any new market, compared to the French firms. Therefore, we would
expect the coefficients and marginal effects to be smaller for Colombian firms.
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The estimates for Colombian firms are presented in column (2)-(5). All estimates have the
expected signs except for T radegrowth, which is insignificant in all cases. Specifically, having one more contact would increase an individual firm’s probability of entering a foreign
market by 0.06%. The geographical proximity between Colombia and country C, as well
as the proximity between firm i’s contacts and country C have positive effects on firm’s
entry to a foreign market. Moreover, if a firm already exports to country C in the previous
period, it would largely increase the firm’s probability to enter that market by around 56%
in the next period. GDP of the destination country also has positive effects, but it’s too
small and considered to be negligible.
The most interesting finding is that trading with more central countries would increase a
firm’s probability to enter additional markets. Among the four centrality measures, betweenness centrality has statistically insignificant effects, this might be caused by the fact
that betweenness centrality is not a suitable measure for importance in the world trade
network as I discussed in section 3. The eigenvector centrality seems to have the greatest
effects on firms’ likelihood to enter foreign markets. If the average eigenvector centrality
of a firm’s contacts increases by 1, the probability for this firm to enter a new market in
the next period would increase by 2.5%. Similarly, a unit increase in the degree centrality
and closeness centrality would lead the probability of entry to increase by 0.002% and
0.19%, respectively. The empirical results confirm my conjecture that connecting to the
more central countries would increase a firm’s chance to enter new markets. In the world
trade network, these central countries serve as the centers of goods and services purchases
and hubs of information exchange. Apparently, the exporting firms would benefit from
the important position and the large network of these central countries in their process to
enter new markets.
Moreover, Disti,c,t measures the average distance of a firm’s existing contacts to a new
market C. Centrality i,t calculates the average centrality of a firm’s existing contacts. Both
variables capture the importance of a firm’s existing contacts and can be used to measure
the network effects. One typical analysis of the network effects is to see whether the
network affects homogeneous goods and differentiated goods differently. In general, the
network should have a more pronounced effect for the trade of differentiated goods rather
than the homogenous goods. This is because the trade of differentiated goods involves more
uncertainty and requires more information. Buyers and sellers tend to rely more on the
networks to communicate with each other and exchange information. Therefore, networks
should play a more critical role in the trade of differentiated goods. In order to test this
conjecture, I treat the homogeneous goods and differentiated goods separately and rerun
the Probit regression. Results are summarized in Table 4.
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Surprisingly, as showed in Table 4, the marginal effects of both Disti,c,t and Centrality i,t
are larger for homogenous goods rather than differentiated goods in all cases. This result suggests that network effects tend to be stronger for the trade of homogeneous goods
rather than differentiated goods, which seems to be a little counter-intuitive. This might
be because Disti,c,t and Centrality i,t only capture part of the network effects and far from
complete. Some other mechanisms and channels of the network effects are overlooked here.
Next, I carry out several robustness checks of my main results presented in Table 3. First,
in Colombian firm’s export data, I observed very frequent entry and exit of the firms.
Below is a table shows the percentage of firms that enter and exit the exporting market
every year.
Each year, the total number of firms remains roughly the same. But the entry and exit
are very frequent and on a large scale. About 40% of firms enter the market and another
40% of firms exit each year. Some of the firms appear only one year and stop exporting
thereafter. For these firms, it’s inappropriate to discuss the probability of entering new
markets in the next period. Some other firms exit the market for a while and restart
to export for a couple of years. In this robustness check, I construct a sub-sample by
dropping the firms that appear only one year in the market (1,271,865 firms) and focus-
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ing on firms that export for at least two consecutive years. Results are presented in Table 6.
Comparing the marginal effects in Table 6 with that estimated from the whole sample in
Table 3, we can see that all variables tend to have similar effects. Again, Betweenness
centrality and T radegrowth have insignificant effects on firms’ probability to enter new
markets. Other variables, such as geographical proximity, export status in previous year,
and the average centralities of current contacts, have positive effects on an individual firm’s
probability to access new foreign markets, but are slightly stronger than in the case of using
the whole sample. For example, if the average eigenvector centrality of a firm’s current
contacts increases by 1, the probability for that firm to enter an additional market in the
next period would increase by 2.7%, slightly greater than 2.5%, which is estimated from the
whole sample. Dropping the firms that appear only one year in the dataset and focusing on
firms that export for at least two consecutive years didn’t change my results much, which
provides a confirmation to my conjecture that trading with more central countries would
increase a firm’s chance to enter new markets.
16
Next, I address the concern that an individual firm’s contacts may not be equally important
to this firm. Recall that the average centrality of a firm’s contacts is defined as:
P
0 1[Exporti,c0 ,t > 0]Centralityc0 ,t
Centrality i,t = c
.
N contactsi,t
Here, I simply treat each contact equally and calculate the simple average of these contacts’
centrality scores. However, different contacts means differently to an individual firm. For
example, the United States, which is the destination of 95% of a Colombian firm’s exports,
should have a different influence on this firm, compared to India, South Korea and Brazil,
which share the rest 5% of the firm’s exports. Using trade value as weights, I re-define
Centrality i,t as the trade-weighted average of the centrality scores, taking each contact’s
different importance into consideration. Specifically,
X Exporti,c0 ,t Centrality i,t =
Centralityc0 ,t ,
Exporti,t
0
c
where Exporti,c0 ,t is the trade value that firm i exports to country C 0 at time t and Exporti,t
is firm i’s total export value at time t. The ratio of these two gives the trade weight, and
also the importance weight, to each of the individual firm’s contacts. Note that the weight
equals 0 if firm i does not export to country C 0 at time t. I then re-run the Probit regression
with this different definition of the average centrality of a firm’s contacts and present the
results in Table 7.
The estimated marginal effects remain quantitatively and qualitatively similar to the baseline results in Table 3. The results confirm that trading with more central countries, in
terms of the trade-weighted average centrality, is beneficial for a firm’s entry to the new
markets.
7
Conclusion
This paper applies network analysis to the study of international trade and provides a simple explanation for firms’ heterogeneity in accessing foreign market. Starting from exploring
the world trade network, I use different centrality measures, such as degree, closeness, betweenness, and eigenvector, to identify the most important and central countries in the
global market (Table 1). Then I utilize a Probit model and the firm-level export data
from Colombia to show that: the average centrality of a firm’s exiting contacts plays a
statistically positive and economically significant role in improving that firm’s probability
of entering a new market. Specifically, If the average (eigenvector) centrality of a firm’s
contacts increases by 1, the probability for this firm to enter a new market in the next
period would increase by 2.5%. This is a very huge improvement and offers an important
implication for the exporting firms: if a firm plans to enter a new foreign market, which has
17
high trade barriers or is hardly reachable, it would be better to firstly trade with other
high centrality countries and enjoy the network effects.
This paper uncovered some other interesting findings as well. The empirical results suggest
that firms with more foreign contacts are more likely to enter a new market in the next
period. Moreover, a firm benefits from the location of its existing contacts. That is, a
firm is more likely to export to a new destination that is geographically close to its current
destinations. Separating homogenous goods and differentiated goods, I found the network
effects seem to be stronger for the trade of homogeneous goods.
To summarize, this paper suggests that network analysis can be fruitfully applied to the
contexts of international trade study and shows that an exporting firm’s choice of trading
partners matters a lot for this firm to access new markets and to develop in the global
market.
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References
[1] Albornoz, Facundo, Hector F. Calvo Pardo, Gregory Corcos, and Emanuel Ornelas
(2012):“Sequential Exporting.”Journal of International Economics.
[2] Armenter, Roc and Miklos Koren (2015):“Economics of Scale and the Size of Exporters.” Journal of the European Economic Association.
[3] Benedictis, Luca, and Lucia Tajoli (2011):“The World Trade Network.” The World
Economy.
[4] Benedictis, Luca, Silvia Nenci, Gianluca Santoni, Lucia Tajoli, and Claudia Vicarelli
(2014):“Network Analysis of World Trade Using the BACI-CEPII Dataset”. Banque de
France Working Paper No.471.
[5] Bernard, Andrew, Andreas Moxnes, and Karen Ulltveit-Moe (2014):“Two-Sided Heterogeneity and Trade”. NBER Working Paper No.20136.
[6] Chaney, Thomas (2014):“The Network Structure of International Trade.” American
Economic Review.
[7] Combes, Pierre-Philippe, Miren Lafourcade, and Thierry Mayer (2005):“The TradeCreating Effects of Business and Social Networks: Evidence from France.” Journal of
International Economics.
[8] Defever, Fabrice, Benedikt Heid, and Mario Larch (2011):“Spatial Exporters.” CESifo
Working Paper Series No.3672.
[9] Garmendia, Aitor, Carlos Llano, Asier Minondo, and Francisco Requena (2012):“Networks and the Disappearance of the International Home Bias.” Economics Letters.
[10] Kamal, Fariha and Asha Sundaram(2014):“Buyer-Seller Relationships in International
Trade: Do Your Neighbors Matter?” CES Working Paper No.14-44.
[11] Melitz, Marc (2003):“The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity.” Econometrica.
[12] Morales, Eduardo, Gloria Sheu, and Andres Zahler (2015):“Gravity and Extended
Gravity: Estimating a Structural Model of Export Entry.” Unpublished.
[13] Rauch, James (1999):“Networks Versus Markets in International Trade.” Journal of
International Economics.
[14] Rauch, James and Vitor Trindade (2002):“Ethnic Chinese Networks in International
Trade.” Review of Economics and Statistics.
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