Barriers to Entry, Trade Costs, and
Export Diversification in Developing Countries
Allen Dennis and Ben Shepherd*, †
The World Bank
1818 H St. NW
Washington D.C. 20433
Draft
April 24, 2007
Abstract:
We show that lower entry costs for firms, and lower internal and external trade
costs, are strongly and robustly associated with export diversification in
developing countries. Specifically, a 10% reduction in internal trade costs
increases the number of products exported by 2.5%, while a similar cut in entry
costs increases diversity by 1%. These results withstand numerous robustness
checks including changes in country sample, diversification measure, and
econometric methodology. Our analysis, using new Doing Business data, covers
the direct, official costs of market entry, as well as both internal trade costs
(document preparation, inland transport, customs, and port charges) and external
trade costs (distance and tariffs). In policy terms, our results suggest that one
effective way for developing countries to promote export diversification is to
focus regulatory reform efforts on making entry procedures simpler and less
expensive, as well as on trade facilitation measures.
Keywords:
International trade; Trade policy; Product variety; Diversification; Economic
development.
JEL Codes: F12; F13; 024.
*
The authors are respectively Economist (Doing Business) and Consultant (Development Research Group—Trade).
This work is part of a broader project on trade facilitation and development supported through a Trust Fund of the
U.K. Department for International Development. We are grateful to Simeon Djankov, Matthias Helble, Bernard
Hoekman, Will Martin, and Beata Smarzynska Javorcik for helpful comments on a previous draft. Correspondence
to: [email protected] or [email protected].
†
The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not
necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.
1
Introduction
Export diversification has long been a stated policy goal for many developing countries. In this
paper, we leave to one side the debate as to how far that goal should be pushed, or which sectors
and products should be given priority over others. We approach the question from a different
angle: taking the goal of export diversification as a given, what policies are available to
developing countries in order to pursue it at minimum economic cost? We show that one set of
policy measures that has not received much attention in this area, namely lower barriers to firm
entry and lower international trade costs, constitutes an important way in which developing
countries can help diversify their export baskets. In concrete terms, we find that 10% reductions
in trade costs and entry costs increase export product diversity by 2.5% and 1% respectively.
To establish these results, we draw on new data from the World Bank’s Doing Business
database, which provides a unique cross-country resource on firm entry restrictions and trade
costs. We also construct an original database on export diversity, using highly disaggregated (8digit) import data for the European Union. This dataset allows us to distinguish amongst more
than 10,000 different traded products, across 100+ developing countries. Using count data
econometric methods, we find that the association between lower barriers to entry and trade
costs, and greater export diversity, is extremely robust to model specification, country sample,
estimation technique, and choice of diversification metric.
Why has export diversification historically been important to developing countries? As is well
known, some of them depend on a relatively small number of products, normally agricultural
commodities, for a large proportion of their overall export earnings. Intuitively, it seems
plausible that there may be scope for welfare enhancing diversification as a means of reducing
1
price and terms of trade risk in the aggregate (Brainard and Cooper, 1968; De Rosa, 1991).
Optimal policy in such a framework is therefore a tradeoff between the traditional trade gains
from specialization, and a “hedging” gain from diversification. Alongside this approach to
diversification, there has also long been a view in development circles that in order to grow rich,
poor countries need to alter the composition of their exports so that it looks more like that of rich
countries. (Again, we leave to one side the validity of that argument, and the causal chain it
suggests.) Traditionally, that point of view has found expression in a preference for exporting
manufactures rather than raw materials (e.g., Prebisch, 1959), or more recently in the argument
that one way in which countries develop is by latching onto high productivity goods (Hausmann
et al., 2006). The view that we take of diversification in this paper is more closely related to the
first of these concerns (i.e., hedging) than to the second, since we do not privilege, a priori, one
sector or product group over another.
Against this background, a number of previous studies have examined the extent to which export
diversification is associated with particular economic outcomes. Funke and Ruhwedel (2001a)
find a positive link between export diversification and per capita GDP, as well as TFP growth,
amongst OECD countries.1 Feenstra and Kee (2006) find that greater export variety is associated
with a 4.5% productivity gain for exporters over the 1980-2000 period. There is also mounting
evidence of the importance of product variety as a determinant of aggregate trade values
(Hummels and Klenow, 2005; Funke and Ruhwedel, 2001b), and as a major source of importers’
gains from trade (Broda and Weinstein, 2006). Coming at the question from the opposite
1
For Chile, the evidence presented by Amin Gutiérrez de Piñeres and Ferrantino (1997) seems to point in the
opposite direction: lagged export specialization is found to be positively associated with growth in exports and GDP.
However, the authors’ interpretation is that their results “are consistent with the possibility that in the long run,
export diversification enhanced Chilean growth performance” (p. 390).
2
direction, Jansen (2004) finds evidence that export concentration is associated with greater
volatility both in the terms of trade and national income.
Surprisingly, there is much less empirical work on the determinants of export diversification,
which will be the focus of this paper.2 Chandra et al. (2007) follow Imbs and Wacziarg (2003) in
focusing attention on GDP per capita, as well as additional aggregate factors such as technology,
infrastructure, and openness. However, their results do not lend themselves to a direct policy
interpretation due to the rather broad nature of the policy variables they use. Feenstra and Kee
(2006) address the question of policy determinants using more detailed indicators, though in an
indirect way. They use an instrumental variables strategy to isolate exogenous shifts in export
variety, and thereby show that it is in part determined by tariffs, distance and resource
endowments. The signs on tariffs and distance are generally negative and statistically significant,
suggesting that variable trade costs play a role in determining export diversity. Finally, Klinger
and Lederman (2004, 2006) examine the factors underlying the rate of export “discoveries”, i.e.
instances when a country starts exporting a product that it has not previously exported. They
consider per capita income, historical exports, a proxy for the return to exporting relative to
domestic production, and Doing Business data on barriers to domestic market entry. They
conclude that the first three factors have significant impacts on discovery, but that the direct
effect of entry barriers is not significant or robust.
2
In related work, Imbs and Wacziarg (2003) show that production (not export) concentration and per capita income
appear to follow an inverse U relationship. However, their analysis using nonparametric techniques is primarily
descriptive and cannot be said to establish causality in a strict sense (although the authors discuss a number of
theoretical issues which could be developed to account for the relation they observe). In any case, their conclusions
cannot simply be extrapolated to the case of export diversification: production diversity is likely to be a poor proxy
for export diversity, since countries generally export only a small subset of the total range of goods they produce.
3
Our paper seeks to provide greater detail on the policy determinants of export diversity, and
thereby to contribute to the debate as to what might be an appropriate policy set for developing
countries seeking to promote export diversification. To do that, we extend the literature in three
main ways. Firstly, we embed our analysis in the well-known heterogeneous firms framework of
Melitz (2003). In doing so, we follow Feenstra and Kee (2006), as well as a different strand of
the literature that looks at the determinants of the extensive margin of trade (e.g., Baldwin and Di
Nino, 2006; Amurgo Pacheco, 2006), although it does not make an explicit link to export
diversification in a development context. Our version of the Melitz (2003) model suggests that
export diversification should be negatively associated with fixed and variable trade costs—
including components that are both internal and external to the exporting country—and with
domestic barriers to entry. We indeed find strong and robust evidence in favor of that hypothesis.
Secondly, our empirical approach treats export diversity as integer count data, namely the
number of product lines with positive imports into the EU-15 in 2005. Such a treatment
immediately suggests the use of econometric models specific to count data. This departs from the
gravity model framework more commonly used to look at the impact of various factors on the
extensive margin of trade via zero entries in the bilateral trade matrix (e.g., Helpman et al.,
2007). Doing so has a number of potential advantages. On the one hand, the estimation
problem—even with over 10,000 products and 100+ countries—is highly tractable. Moreover,
the Poisson quasi-maximum likelihood estimator that we use does not suffer from bias or lack of
consistency in the presence of fixed effects—a potential problem for some commonly used
estimators in the gravity literature, including Tobit and Heckit (see Greene, 2004, for a review of
this issue; cf. Santos Silva and Tenreyro, 2006, who apply a Poisson QML estimator to the
gravity model.)
4
The closest relative to our approach is to be found in the two papers by Klinger and Lederman
(2004, 2006) which we have previously referred to. However, our approach is fundamentally
different from theirs in that our dependent variable is not “new” products, but the full range of
exported products at a given time. Moreover, we expand on their approach by including a wider
range of policy variables motivated by the Melitz (2003) framework, in particular the fixed and
variable costs of international trade.
The third way in which our paper adds value is in terms of the datasets used. To construct our
measure of export diversification, we use highly detailed 8-digit import data for the European
Union. These data are largely unexploited in the literature, and offer nearly twice the level of
product detail that can be found in their more commonly used 6-digit counterparts. To measure
fixed and variable trade costs internal to the exporting country, we use new data from the World
Bank’s Doing Business database. It provides by far the most detailed and easily comparable
cross-country data on entry costs and internal trade costs. It allows us to distinguish amongst
different varieties of internal trade costs, such as transport, customs, port and terminal charges,
and document preparation. As far as we know, this is the first occasion on which these trade cost
data are used in empirical work.
The paper is set out as follows. The next Section presents a brief theoretical motivation for our
empirical work, in terms of the Melitz (2003) heterogeneous firms framework. Section 3 presents
our dataset, and discusses in detail the way in which we have measured export diversification,
trade costs, and barriers to entry. Our empirical results are in Section 4, and we conclude with
some policy implications and suggestions for further research in Section 5.
5
2
Theoretical Motivation
Since our contribution in this paper is primarily empirical, we do not present a full-blown
theoretical model. Rather, this Section provides a simple theoretical motivation for linking export
diversification with trade costs and barriers to entry. To do so, we use a simplified version of the
Melitz (2003) heterogeneous firms model, due to Baldwin (2005).
Our basic contention is simple, and follows directly from the model’s comparative statics. Lower
costs mean that domestic firms face lower hurdles when exporting. This enables less efficient
producers to start exporting, in addition to those firms already exporting. When each producer is
associated with a distinct product variety (as is the case in most monopolistic competition trade
models), this means that the country’s export structure becomes more diversified as costs fall, in
the sense of including a greater number of different products.
To fix ideas, we present a simple formalization following Baldwin (2005). Each of two identical
countries (Home and Foreign) is endowed with a single numéraire factor of production (labor)
and produces a continuum of goods in a single, differentiated sector with constant elasticity of
substitution σ. Trade costs are symmetric at all times. A representative consumer has utility
1
1

 1 1
1
U    qv   dv   , where v represents each variety and V is the full set of varieties. Each
vV

firm produces a single variety of the differentiated product, under increasing returns to scale.
On the production side, the dynamics of entry is broken down into three stages. First, each firm
pays a fixed innovation cost Fi, which can be likened to the research costs incurred in developing
a new variety. Once Fi is paid, each firm is assigned its marginal production cost a randomly, by
repeated draws from the distribution G[a], which is usually assumed to be Pareto with shape
6
parameter k and support 0  a  a0 (e.g., Chaney, 2006; Eaton and Kortum, 2002). That is,
k
a 
Ga     . It is the stochastic nature of this process which results in firms having different
 a0 
productivity levels.
The second and third stages of the process relate to specific market entry decisions. Selling to the
domestic market requires the firm to pay a fixed cost Fd , while exporting entails Fx . These can
be conceptualized as, for example, market-specific product adaptation costs (e.g., Baldwin,
2000), or the costs of setting up market-specific distribution and servicing networks (e.g.,
Helpman et al., 2004). We assume that Fx  Fd . One justification for that assumption is that
information on the overseas market (such as consumer tastes) is more costly to obtain than for
the domestic market. As will become clear below, it also provides a rationale for the wealth of
empirical evidence suggesting that only a small percentage of active firms in a country actually
export (see Bernard et al., Forthcoming, for a review).
The presence of these two fixed costs leads firms to sort into three groups. The present value of
firms with high marginal cost draws will be less than Fd , and those firms will therefore
maximize profits by not producing at all. For intermediate marginal cost draws, firms will be
able to cover Fd but not Fx , and will therefore produce for the domestic market only. Only
those firms with low marginal cost draws will be able to support Fx , and they will sell in both
markets. In productivity terms, the least efficient firms will exit and the most efficient will
produce for both the domestic and export markets, while an intermediate group will sell to the
domestic market only.
7
We can use this analysis to define marginal cost cutoffs for domestic production (ad) and export
(ax), by association with the corresponding levels of fixed costs. By symmetry, we assume
a d  a x . For a given cost distribution G[a], the mass of firms that produces in equilibrium will
be larger for higher ad. Similarly, the equilibrium mass of exporting firms is larger for higher ax.
Free entry by firms implies that the ex ante expected value of developing a new variety that can
be profitably produced must be equal to the ex ante expected fixed cost of doing so. Baldwin
(2005) shows that this condition makes it possible to derive explicit expressions for the
equilibrium domestic and export marginal cost cutoffs ad and ax, as well as the total number of
producers n, in terms of fixed costs (Fi, Fd and Fx), “iceberg” trade costs τ, the elasticity of
substitution σ, and the parameters of the marginal cost distribution (a0 and k). Defining openness
    1  Fx1  Fd 1 such that it ranges between zero (infinite fixed or variable trade costs) and
unity (free trade with Fx  Fd ), and using  
k
to simplify notation, allows us to express
 1
those solutions as follows:
1
 F   1  k
a x  a0  i

 Fx 1    
(1)
1
 F   1  k
a d  a0  i

 Fd 1    
(2)
L  1
Fd 1   
(3)
n
The marginal cost distribution G[a] drives the process that leads firms to self-select into
production for the domestic market only, or for the domestic and export markets jointly. The
8
equilibrium proportion of exporters in the total mass of firms will therefore be simply
Ga x   a x

Ga d   a d
k

 , where the equality follows from our assumption of a Pareto distribution for

marginal costs. This allows us to derive an explicit expression for the number of exporting firms
in equilibrium:
nx 
Ga x 
L  1
n
Ga d 
Fx 1   
(4)
Since each firm produces a distinct variety of the differentiated product, nx is also a measure of
export diversification. It is related to openness Ω, which suggests that export diversity will be
affected by changes in the fixed and variable costs of trading, as well as domestic market entry
costs Fd. To see this, we conduct some simple comparative statics (cf. Results 2-3 and 11 in
Baldwin, 2005). As a preliminary, we note that openness is decreasing in both fixed and variable
trade costs, but increasing in the fixed costs of domestic market entry:


d

 1     0
dFx
Fx
(5)

d   
  1     0
d

(6)

d    1
1 
  0
  1    
dFd
F
F
x
d


(7)
While there is nothing surprising about (5) and (6), the intuition behind (7) is not immediately
clear. In fact, the reason for the sign of the derivative in (7) is largely mechanical. The model is
of course highly stylized in its presentation of Fd and Fx, the only constraint being that the fixed
cost of exporting must be greater than the fixed cost of domestic market entry. It is therefore
9
legitimate to assume that Fx in fact includes the cost set contained in Fd, with some additional
amount to represent the added burden of foreign market entry. We therefore set dFx = dFd when
deriving (7). As a result, a given absolute change in Fd causes an identical absolute change in Fx
which—assuming as always Fx  Fd —changes the ratio of export to domestic entry costs. And
it is this ratio which matters for openness as we have defined it.
Using the above results, we now differentiate (4) sequentially with respect to Fx , τ and Fd :3


dn x
n    
 x
0
1    Fx
dFx



(8)


dn x n x  1   

0
 1   
d

(9)


 
  
 1     1
dn x
1  1


 nx


0
 1    Fx Fd  Fx 
dFd




(10)
Equations (8) through (10) directly support our contention that lower fixed and variable trade
costs, and lower barriers to entry, are associated with export diversification. The intuition behind
these results is simple. Within the framework of this model, exporters must pay both the costs of
domestic market entry and a supplementary cost related specifically to exporting. Hence, lower
barriers to domestic market entry translate directly into lower costs both for Home-only
producers, and for exporters. Similarly, lower fixed and variable trade costs also lower costs for
3
The sign of the derivative in (10) is ambiguous. It is negative provided
     Fx
  1 Fd
, which we interpret as a
condition that export costs not be “too high” with respect to domestic entry costs. In what follows, we assume that
that condition holds.
10
exporters. For given country technology (i.e., marginal cost distribution), lower costs for
exporters make it easier for firms to shift from producing for the domestic market only, to
producing for both the domestic and export markets. The mass of exporting firms therefore
increases, which mechanically leads to greater product variety in exports since the new exporters
make different varieties from existing ones—the country’s export bundle becomes more
diversified.
Conceptualizing the process of diversification in this way is important. It emphasizes that when
“new” products appear in export data, they do not necessarily represent pure innovation in the
exporting country. They can also arise from existing domestic producers who decide that it is
now profitable to begin exporting (cf. Klinger and Lederman, 2004 and 2006). Intuitively, we
would argue that this is an appealing feature of the model which reflects an easily recognizable
stylized fact.
3
Data
We have used a simplified Melitz (2003) framework to show that lower trade costs and barriers
to entry should promote export diversity. In this and the following Section, we take that
hypothesis to the data.
Our variables and sources are fully set out in Table 1, and the list of countries we consider is in
Table 2. A number of our data sources are standard, and do not require any particular discussion.
However, our dataset also contains two novel aspects that do require a more detailed
presentation. Firstly, we measure export diversity using largely unexploited 8-digit trade data for
the European Union. Secondly, we use data from the World Bank’s Doing Business report to
measure entry restrictions and trade costs. We now address each of those particularities in turn.
11
3.1
Measuring Export Diversification
The literature discloses a number of different ways of measuring export diversification. One set
of papers uses variations on the Herfindahl-Hirschman index (e.g., Amin Gutiérrez de Piñeres
and Ferrantino, 1997; Imbs and Wacziarg, 2003), while another follows the relative variety
approach of Feenstra (1994). Still another strand of the literature analyzes diversification
indirectly, by looking at zero entries in disaggregated trade data (Helpman et al., 2007; Baldwin
and De Nino, 2006).
We choose to take a more direct approach, by defining diversification in terms of a count of the
number of products exported, either by a country as a whole, or in a particular sector. Simple
counts have the advantage of operationalizing a transparent and natural definition of
diversification, which corresponds directly to the way in which diversity is conceptualized in our
theoretical framework presented in the previous Section.
Given sufficiently detailed trade data, it should be a simple matter to calculate country-bycountry counts of the number of product lines in which exports take place. But therein lies the
problem: the most detailed level of trade data available on a worldwide basis is at the HS 6-digit
level, which differentiates among 5,000 or so “products”. However, each of these 5000 codes in
fact aggregates together a number of individual varieties, and counts based upon them therefore
tend to understate the true level of diversity in exports.
To resolve this problem, we use detailed import data for the European Union. These data are
freely available through the Eurostat website (http://fd.comext.eurostat.cec.eu.int/xtweb/). They
distinguish amongst 10,000 or so individual “products” at the CN 8-digit level. While there is
still some degree of aggregation involved, it is considerably less than for the HS 6-digit
classification. We therefore expect count data constructed using this dataset to paint a more
12
accurate picture than could be obtained with 6-digit data. These data are comparable in detail to
the US 10-digit data used by, for instance, Feenstra and Kee (2006). To our knowledge, they
have not previously been used in empirical work on product variety in trade.4
The main objection that can be made to our use of these data is that they in fact capture diversity
of exports to the EU, rather than towards all trading partners. That is indeed the case. However,
the EU represents an important export outlet for many developing countries, particularly in
Africa, which suggests that we can be confident that we are capturing a significant part of their
overall export pattern. In any case, the only ready alternative—use of HS 6-digit export data—
has two major disadvantages which lead us to favor the approach we have just set out. Firstly, 6digit data is arguably too aggregated to serve as a reliable guide to export diversity. Secondly,
export data from developing countries are generally considered less reliable than “mirror” import
data from developed countries. We therefore believe that our dataset represents a defensible
compromise between accuracy and completeness. In any case, replication of our results using
alternative data is a potential avenue for future research.
Taking 2005 as our base year, we use these data to construct two measures of export
diversification for the trade partners of the EU-15 (aggregated to a single importer). We start
with a dataset of 470,035 observations across 246 countries and customs areas (including the
EU), and 10,753 distinct products. In this paper, we focus only on the developing country
component of that dataset, namely countries that are neither members of the EU-25 nor the
OECD. (We return to this definition in the context of robustness checks below.) Our first
measure of export diversification, lines, is a simple count of the number of CN 8-digit product
4
Indeed, these data are an underused resource more generally. The only published applications appear to be
Fontagné et al. (1998), Henry de Frahan and Vancauteren (2006), and Manchin (2006).
13
lines in which a given country exported to the EU-15 in 2005. It has one observation per country.
To provide greater detail, we also construct lines_cn2 following the same pattern as for lines, but
with counts by 2-digit sector rather than aggregated to the country level. Lines_cn2 therefore has
97 observations per country (the number of 2-digit Chapters in the CN classification). Given that
the CN 8-digit classification scheme is inconsistent in the level of detail (i.e., individual
“products”) it accords to each sector, we will need to take care to correct for this when using
lines_cn2 as an indicator of export diversification. We return to this issue below.
In Table 3, we provide a ranking of countries in terms of their level of export diversification as
measured by lines (i.e., at the aggregate, not sectoral, level). On average, the countries in our
sample—excluding the EU-25 and OECD members—exported 1,138 8-digit product lines to the
EU in 2005. However, the range is extremely wide: from 9 lines (Palau) to 8053 (China), out of a
possible maximum of 10,753. In broad terms, the country rankings accord with the sensible prior
that larger, more developed countries tend to have more diversified export bundles. Thus, we
find China, India, and Brazil at the top of the table, while Palau, Micronesia, and the Comoros
are at the opposite end. In order to gauge the robustness of our data against other diversification
measures that have been used in the literature, we also calculate Feenstra’s lambda measure of
relative variety,5 and the Herfindahl-Hirschman index of export concentration.6 Rankings across
5
Feenstra and Kee (2006) show that country e’s export variety relative to the world as a whole can be measured as
p

p
w w
i
i
lambdae
q
iJ e
iJ w
w w
i
i
q
, where the numerator is the total value of world exports in product lines exported by e, and
the denominator is the total value of world exports across all products.
14
all three measures are reasonably similar, and are quite close for lines and lambda. The sample
correlations with lines are 0.95 and -0.52 for lambda and hh_index respectively. In other words,
we can be confident that our measure of diversification squares well with alternative
approaches—but this is, in any case, a robustness issue that we return to below.
3.2
Trade Costs
Our theoretical presentation in Section 2 identified four distinct costs facing potential exporters:
the fixed costs of innovation, domestic entry and export market entry, and variable “iceberg”
trade costs. While these costs are well articulated in the theoretical model, their empirical content
is quite broad. One the one hand, they cover “internal” trade costs, i.e. those incurred by an
exporter between the factory gate and the exporting country dock. But they also include
“external” trade costs incurred between the exporting country dock and the importer. The breadth
of factors involved makes data difficult to come by on a cross-country basis. For instance, the
annual Global Competitiveness Reports published by the World Economic Forum have
previously been used to measure trade costs related to infrastructure and logistics performance
(e.g., Wilson et al., 2005). However, the indices included in the GCR are perception based and
are therefore subject to potentially significant measurement error. Secondly, data are not
collected for some developing countries, meaning that the sample size is likely to be
considerably reduced in a study like this one.
6
The index for exporter e is simply the sum of the squared market shares of each export product i (with prices p and
2




J
pi qi 

quantities q): hh _ indexe   J

 .
i 1
  p j q j 
 j 1

15
The World Bank Doing Business survey now provides a complement to existing perception
measures in its “Trading Across Borders” section. For its 2006 and 2007 reports, Doing Business
has included information on the number of procedures required for importing and exporting, as
well as the time taken to comply with them. (See Djankov et al., 2006, for an empirical
application.) For 2007, “Trading Across Borders” was expanded to include a trade costs
indicator as well. The Doing Business trade costs indicator captures the total costs for importing
and exporting a standardized cargo of goods (“Export Cost”), excluding ocean transit and trade
policy measures such as tariffs. The four main components of the costs that are captured are:
costs related to the preparation of documents required for trading, such as a letter of credit, bill of
lading, etc. (“Document Costs”); costs related to the transportation of goods to the relevant sea
port (“Inland Transport Costs”); administrative costs related to customs clearance, technical
controls, and inspections (“Customs Costs”); and ports and terminal handling charges (“Ports
Costs”). These trade costs are provided for both imports and exports on the basis of a standard
set of assumptions, including: the traded cargo travels in a 20ft full container load; the cargo is
valued at $20,000; and the goods do not require any special phytosanitary, environmental, or
safety standards beyond what is required internationally. Data are collected from local freight
forwarders, shipping lines, customs brokers, and port officials.
For the purposes of this study, we use the export component of the Doing Business trade cost
dataset, which covers 175 countries. To our knowledge, these data have not previously been used
in empirical work. They disclose a considerable range of country experiences: in some countries
these export operations cost as little as $300-$400 (Tonga, China, Israel, Singapore, and UAE),
whereas they run at nearly ten times that level in others (Gabon, and Tajikistan). On average, the
cost is around $1278 per container (excluding OECD and EU countries).
16
The broad definition of export costs used by Doing Business is useful in the present context since
it includes most additional cost components faced by domestic firms that seek to export. As the
above discussion suggests, some of these costs have both fixed and variable components: for
instance, export documentation needs to be agreed and drafted prior to any export activity taking
place (fixed cost), but then needs to be copied and slightly adapted for each shipment (variable
cost). However, Doing Business data do not cover two important aspects of trade costs, namely
tariffs in importing countries (the EU in this case) and the cost of shipping from the exporting
country to the importing countries. In what follows, we use WITS-TRAINS data on tariffs,7
while we proxy international transport costs using distance, as in the gravity model literature. We
take Germany to be the “center” of Europe, and therefore measure the distance between each
exporting country and Germany.
3.3
Barriers to Entry
Doing Business also provides information on the costs of domestic market entry in 175 countries,
through its “Starting a Business” section. (For a detailed discussion of methodology, see
Djankov et al., 2002.) It includes indicators on the costs, time, and number of procedures
required for an entrepreneur to start-up and formally operate a local limited liability company
with general industrial or commercial activities. This includes pre-registration, registration, and
post-registration activities legally required in the country (“Entry Cost”). Information on the
paid-in minimum capital required before starting a business is also recorded (“Minimum
Capital”). Only official costs are considered. The company law, commercial code, and specific
7
In future versions of this paper, we will replace TRAINS tariff data with applied tariffs from the MAcMap v2
database (Laborde et al., forthcoming). The advantage of MAcMap over WITS is that it fully accounts for
preferential agreements, and is fully disaggregated to the HS 6-digit level even when no trade is observed for a given
country pair-product combination.
17
regulation and fee schedules are used as sources for calculating costs. The information is
provided by incorporation lawyers and government officials. As far as we are aware, this is the
most comprehensive source of information on business start-up costs, and hence it is the dataset
best suited to being used as a measure of domestic market entry costs (i.e., Fd) in our study. It
has previously been used in related contexts by Klinger and Lederman (2004, 2006), and
Helpman et al. (2007).
4
Empirical Model and Results
Summarizing from Section 2, the Melitz (2003) framework leads us to expect that lower entry
barriers and trade costs (both fixed and variable) will be positively associated with export
diversification.
We start the empirical testing of that hypothesis by conducting some exploratory analysis using
nonparametric regression techniques (cf. Imbs and Wacziarg, 2003). Figures 1 and 2 show output
from locally weighted regression (Lowess smoothing) of lines on entry cost (in % GNI per
capita) and export cost (in US dollars).8 Both Figures show a clear, negative association between
export diversification, as measured by an aggregate country-level count of products exported,
and entry or trade costs. The relationship between the variables is not, however, a strictly linear
one. Visually, it would appear to be more consistent with a logarithmic relation. The plots
suggest that a small absolute reduction in very high trade or entry costs is associated with only a
relatively small absolute increase in export diversification, while a similar reduction starting
8
Each figure is obtained by running a separate regression of lines on the relevant trade costs variable at each data
point, using 80% of the total effective sample. For each regression, observations are downweighted according to
their distance from the central data point around which the regression takes place. All calculations are performed in
Stata 9.2SE. In this case, we use the mlowess module developed by Cox (2006).
18
from moderate cost levels can be expected to have a considerably greater impact. Although we
can only speculate at this point as to the reasons why the cost-diversification relationship takes
this form, it seems plausible that very high levels of costs are close to prohibitive, and in any
case are well in excess of the levels that would allow moderately productive firms to overcome
them and start producing and, perhaps, exporting. In terms of the Melitz (2003) framework we
have used above, very high absolute costs of entry and export place the domestic and export
market productivity cutoffs a long way to the right in terms of the productivity distribution of
domestic firms. We have assumed that that distribution is Pareto, which means that it is strongly
skewed towards the left. Small cost shifts that keep the productivity cutoffs in the right tail of the
distribution would therefore not be expected to have a large impact on the equilibrium number of
firms with sufficiently high productivity to meet them. In any case, a deeper investigation of
such questions could be a fruitful avenue for future research.
Suggestive though these results are, they do not yet account for the many other influences which
could simultaneously be acting on our outcome variable, namely export diversification. To try
and separate out the role that entry and trade costs play, we now move to a more traditional
parametric regression framework. As discussed in the previous Section, our measure of
diversification by sector (lines_cn2) is observed repeatedly across a number of countries. This
suggests that we can exploit both cross-country and cross-sectoral variation in estimating the
relationships of interest. Adopting a panel data framework also has the advantage of allowing us
to control for one dimension of variation using econometric methods rather than additional data.
Since the variables that are of main interest to us vary in the country dimension, we choose to
model cross-sectoral variation in terms of unobserved heterogeneity to be captured by a set of
fixed effects. This method will allow us to take proper account of factors which are specific to
19
sectors but common to all exporters, such as non-discriminatory trade barriers (e.g., MFN tariffs
and non-tariff barriers) and regulatory measures (e.g., product standards) imposed by the
European Union on exports from third countries. It also controls for differences in the level of
recorded 8-digit product detail within individual 2-digit sectors.
Our dependent variable, lines_cn2, displays an important characteristic that will need to be taken
into account in estimation: it is discrete (not continuous), and is composed exclusively of nonnegative integer values. Estimators built to deal with count data will be more appropriate than a
general estimator—such as OLS—that deals well with continuous, unbounded data. We
therefore turn to the workhorse model for count data, namely the Poisson estimator. (On Poisson
and other count data methods, see generally: Cameron and Trivedi, 2001; Wooldridge, 1997; and
Winkelmann, 2000).
Concretely, we postulate that lines_cn2, can be described by a Poisson process with mean and
variance equal to μes (indexing over e for exporters and s for sectors). Its density conditional on
the set of independent variables Xes is given by:
f lines _ cn2es X es  
exp es es
lines _ cn2es !
lines _ cn 2es
(11)
The conditional mean is itself assumed to be a function of the set of independent variables Xes.
We follow standard practice, and parameterize the conditional mean as an exponential function.
We include exporter barriers to entry (entry), internal and external trade costs (internal_costs and
external_costs), a full set of sectoral fixed effects (fs), and J additional exporter dimension
control variables (control). The first three elements come directly from our theoretical
presentation above, and are designed to capture entry costs, and fixed and variable trade costs in
terms of the Melitz (2003) model (i.e., Fd, Fx, and τ). We include additional exporter control
20
variables to account for other country-specific factors that might also influence export
diversification, such as the economy’s size, structure, and level of development, as well as
macroeconomic conditions.9 Finally, the fixed effects account for unobserved cross-sectoral
heterogeneity, as discussed above.
J


es  f s expa logentrye   b loginternal_costse   c logexternal_costse    d i logcontrolei  (12)
i 1


Conditional maximum likelihood techniques can be used to provide parameter estimates for the
model defined by (11) and (12), namely a, b, c, and the J control coefficients d (Hausman et al.,
1984). Given the functional form of (12), all coefficients can be interpreted as elasticities.10
It may seem overly restrictive to apply the Poisson model—which assumes equality of mean and
variance in lines_cn2—and indeed the general literature referred to above discloses a number of
alternative count data estimators. The most common is the negative binomial, which differs from
Poisson in that the assumed distribution for the dependent variable exhibits over-dispersion: i.e.,
its variance is larger than its mean (whereas the two are identical under Poisson). This might
appear to be a strong factor in favor of the negative binomial over Poisson in many empirical
settings, since real world data are often over-dispersed. However, Poisson has an important
property which, we would argue, makes it more appropriate as a workhorse model in this
context: it can be shown using quasi-maximum likelihood reasoning that Poisson estimates are
consistent under quite weak assumptions (Gourieroux et al., 1984). Indeed, the data do not have
9
We note in passing that larger countries should clearly be expected to export a more diversified product basket. By
inspection of (4), it is obvious that
dn x
 0.
dL
10
Prior to transforming the independent variables into logarithms, we add unity to those series containing zeros.
Where small negative values are observed—for instance, in the GDP deflator series—we replace them with zeros
prior to transformation, so as to conserve sample size.
21
to follow a Poisson process at all, and may be over- or under-dispersed. Essentially all that is
required for consistency is that the conditional mean function (12) be properly specified. The
negative binomial model, by contrast, is only consistent if the conditional distribution of the
dependent variable is in fact a negative binomial. As a result, the potential gain from preferring
the negative binomial over Poisson—increased efficiency if the data are over-dispersed—needs
to be balanced against the more restrictive conditions that have to be met to ensure consistency.
It is for this reason that we use the Poisson estimator to produce our baseline results, and use the
negative binomial as a robustness check only. (For a more detailed exposition of this reasoning,
see Wooldridge, 1997.)
Table 6 presents regression results using the fixed effects Poisson estimator.11 We estimate six
models in all, using different combinations of dependent variables. In terms of (12), we use the
following sets of variables (see Table 1 for a full description):
entrye = {Entry Capital, Entry Cost}
(13a)
internal_costse = {Export Cost}
(13b)
external_costse = {Distance, Tariffs}
(13c)
controle = {GDP, GDP per Capita, Manufacturing % GDP, Agriculture % GDP, GDP Deflator,
Exchange Rate, Interest Rate}
(13d)
Our preferred specification in Table 6 is Model 1. It includes all variables listed above except
Tariffs and Exchange Rate. The reason for excluding them is simply data availability: Model 1
represents in our view the best compromise between variable set and number of observations
11
We report robust quasi-maximum likelihood standard errors (Wooldridge, 1999), as computed by the xtpqml
module in Stata (Simcoe, 2007).
22
(10,088 across 104 countries if the two variables are excluded, versus 2,997 across 31 countries
on average if they are included). But in any event, little turns on our preference for Model 1 in
terms of the qualitative interpretation of our results: the main coefficients are remarkably stable
in terms of sign, significance, and even magnitude, across Models 1-6.
The first thing to note about Model 1 is that it represents an extremely good fit to the data: R2 is
0.97, and a comparison of the kernel densities for the dependent variable and its predicted values
shows that the two are virtually indistinguishable on the basis of visual evidence (see Figure 3).12
Addressing the results for Model 1 in detail, we see that the coefficient on entry costs is negative
(with elasticity -0.1) and statistically significant at the 1% level; the minimum capital
requirement has an unexpected positive sign, but is statistically insignificant at the 10% level.
Internal trade costs are also negative (elasticity = -0.25) and statistically significant (1%), as is
distance (elasticity = -0.46). From the estimated coefficients, we conclude that export
diversification appears to be particularly sensitive to international transport costs—as proxied by
distance—but is also significantly affected by internal trade costs and entry costs. These results
are clearly consistent with the theoretical framework developed in this paper.
Most of the control variables in Model 1 also carry the expected signs, and are statistically
significant at the 10% level. This is the case for aggregate and per capita GDP and the size of the
manufacturing sector (all of which enter positively), as well as for the GDP deflator (which
enters negatively). In other words, bigger and richer countries tend to produce more varieties, as
do those with more moderate rates of inflation, which could be associated with a stable
12
We are conscious that the standard R2 measure that we present (1-ESS/TSS) does not, strictly speaking, apply to
the Poisson model, since there is no residual as such. However, we follow Wooldridge (1997) and present this R2
nonetheless, since it provides a convenient summary indicator of model fit. Its interpretation in that sense, if
somewhat approximate, is more straightforward than the pseudo-R2 measures more commonly used for count data
models.
23
investment climate. The estimated coefficient on the size of the agricultural sector is positive, but
not statistically significant. Only the real interest rate coefficient is surprising, since it is positive
and statistically significant at the 5% level. One possible reason behind both of these less
encouraging results could be correlation amongst the dependent variables: the size of the
agricultural sector is strongly related to GDP per capita (ρ = -0.5), while the interest rate is
correlated with the GDP deflator (ρ = -0.8).
All in all, we conclude that the Poisson estimates of Model 1 provide strong evidence in favor of
our primary contention, namely that lower costs of entry and exporting are associated with
greater export diversification in developing countries. As already noted, these results are quite
insensitive to model specification: entry and trade cost variables are consistently negative and
significant at the 1% level in Table 6. The only exceptions are tariffs in Model 3, and entry costs
in Model 6; however, these variables are negative and statistically significant in all other
formulations. Results are more mixed for entry capital, which has the wrong sign in two models
and is not statistically significant in our preferred specification. We interpret that as indicating
that it is the direct costs of entry which represent the more serious constraint on potential
exporters.
Comparing results from Models 1 and 3-6 with those from Model 2—which includes only our
three core variables for entry and trade costs—shows that addition of control variables lowers (in
absolute value) our estimate of the sensitivity of export diversification to these costs, but does
not in any sense eliminate the qualitative result. Concretely, we find that adding controls reduces
the entry cost elasticity from -0.26 to -0.10, and the export cost elasticity from -0.90 to -0.25 (all
significant at the 1% level).
24
Thus far, we have analyzed the impact of trade and entry costs in a global sense. However, the
Doing Business dataset allows us to look in more detail at the individual components of our
internal trade costs measure, in order to gauge whether particular cost types have a stronger or
weaker impact on diversification. We therefore disaggregate internal trade costs into four
categories: document preparation, customs, inland transport, and terminal handling (ports). Table
7 shows results for Model 1 with export costs replaced by these four variables (which together
sum to the original export costs variable). Again, the qualitative conclusion to be drawn is clear:
entry costs and trade costs impact negatively on export diversification. In terms of the particular
cost categories we have identified, we find that customs and document preparation costs have the
strongest negative impacts (elasticities of -0.05 and -0.01 respectively). The coefficient on inland
transport costs is also negative, but is very small (elasticity = -0.001) and is statistically
insignificant. The only surprising result is that the estimated coefficient on port costs is positive
and statistically significant. Again, this might be related to correlation with other explanatory
variables, such as GDP (ρ = -0.3). While this question deserves further research in the future, we
would argue that it does not significantly detract from our results in an overall sense.
4.1
Robustness Checks
There are a number of potential criticisms of our results that we can deal with via simple
robustness checks. We divide these into three groups: estimation method, choice of country
sample, and construction of our variety measure lines_cn2.
We consider first the issue of estimation technique. Our preferred method is Poisson conditional
fixed effects. As discussed above, Poisson provides consistent estimates under relatively weak
assumptions. However, the negative binomial can provide efficiency gains under appropriate
circumstances. We therefore re-estimate Model 1 using the fixed effects negative binomial
25
estimator (Hausman et al., 1984). For comparative purposes only, we also present results for
simple OLS fixed effects and Tobit estimation. Finally, to take account of the fact that our main
variables of interest vary only at the country level, and not the sector level (cf. Moulton, 1990),
we re-estimate Model 1 using the lines variable which is calculated for each country based on
exports of all products (i.e., one observation per country.)
Table 8 shows clearly that none of the alternative estimation methods produces substantially
different results from those obtained via Poisson. Both the negative binomial and OLS estimates
suggest that all trade and entry cost variables—including the minimum capital requirement—
enter negatively, and are 1% significant. These coefficients are also negative using Tobit,
although entry capital is only significant at the 5% level; all other entry and trade costs variables
are 1% significant. Even the somewhat extreme step of aggregating the data to the country
level—and reducing our sample size from over 10,000 to just 104 observations—does not
substantially alter our results: all three entry and trade costs variables have the expected negative
signs and coefficients very close to those reported in Table 6. Export costs remain significant at
the 1% level, but entry costs are now only significant at the 15% level (prob. = 0.12).
Nonetheless, we would argue that this is a relatively minor loss in significance given the extreme
drop in sample size that this exercise imposes. It should not be seen as altering our conclusions.
A second set of potential criticisms relates to our country sample: all non-OECD and non-EU
countries represents a very broad definition of the developing country group, which could mask
heterogeneity according to income level. To address this criticism, Table 9 presents Poisson
estimation results for Model 1 using progressively narrower country samples. The first column
excludes, in addition to those countries already excluded from Model 1, all countries in the
World Bank’s high income group. The second column excludes both high income and upper
26
middle income countries, while the third column includes only low income countries. Our
conclusions emerge unaltered from this process: export costs and entry costs retain their negative
coefficients and 1% significance. Indeed, our conclusions are arguably strengthened, since there
is weak evidence that the elasticities on trade costs and entry barriers increase in absolute value
as group income falls. In other words, relatively poor developing countries would seem to have
the most to gain in terms of diversification from reductions in entry barriers and trade costs.
Finally, there are a number of grounds on which our metric of export diversification could be
called into question. On the one hand, it could be argued that our theoretical framework is more
appropriate to manufactured goods than to agricultural products, yet lines_cn2 includes both
groups. Alternatively, the same variable might be said to give too much weight to very small
export flows that do not represent diversification that is meaningful in a policy sense. Poisson
results in Table 10 show that neither criticism holds water. Our results are just as strong when we
construct a new dependent variable, lines_cn2_man, which only includes products in HS
chapters 25-97 (i.e., manufactured goods). The same applies when we construct lines_cn2_100k
and lines_cn2_1m which only count as exports product lines with a value to the exporting
country of at least €100,000 or €1 million in 2005.
Our use of simple product line counts in order to capture diversification could also be questioned
on the basis that the previous literature devotes greater attention to two alternative, and
potentially quite different, measures. On the one hand, the diversification literature privileges
measures of concentration such as the Herfindahl-Hirschman (HH) index, which take account of
both the number and value of products exported. The product variety and extensive margin
literature, on the other hand, prefers the measure of relative variety developed by Feenstra (1994)
based on a CES aggregator. (See above and Table 1 for the formulae relating to each measure.)
27
To ensure that our results are robust to the choice of diversification measure, we therefore reestimate Model 1 using alternately hh_index_cn2 and lambda_cn2. In light of our theoretical
framework, we expect trade and entry cost coefficients to be positive in the first formulation
(since diversification is decreasing in the HH index), and negative in the second.
As can be seen from Table 10, the change in diversification measure does not have any
significant impact on the qualitative interpretation of our results: entry costs and trade costs carry
the expected signs, have sensible magnitudes, and are statistically significant at the 10% level or
better in all five columns of the Table. Moreover, Table 10 makes clear that lines_cn2 has an
important potential advantage over the two other measures when applied at the sectoral level.
From the way in which both the HH Index and Feenstra’s lambda are constructed, they break
down when a country does not export any goods in a particular sector. This is why the last two
columns of Table 10 contain many missing observations—approximately one third of the sample
used in Table 6 (3,393 observations). Our count variable, on the other hand, codes these
observations quite naturally as zeros. As a result, our Poisson estimates take account of all
available information, including the fact that some countries doe not export any product lines at
all in particular sectors.
In sum, we conclude that our model survives very well in the face of extensive robustness
checks. Our results are, we would argue, unlikely to be an artifact either of the way in which we
measure diversification, the countries we analyze, or the econometric methods we apply.
5
Conclusions, Policy Implications, and Questions for Future Research
We have used highly disaggregated EU trade data along with new information from the Doing
Business database to show that export diversification is inversely related to trade costs and entry
28
restrictions. This association is found to hold using both parametric and nonparametric
techniques, and is very robust to changes in methodology and control variables. Moreover, we
would argue that our results should be given a causal interpretation: they sit well with the
theoretical motivation presented in Section 2, and our policy variables can reasonably be
regarded as exogenous to diversification. This suggests that developing countries can promote
export diversification by acting to reduce entry and trade costs. Trade costs here include both
those which are internal to developing countries themselves—customs and port charges, and
document preparation—and those which are external to them—distance and tariffs. This in turn
suggests that countries can act to reduce trade costs both unilaterally, by focusing on their
internal constraints, and regionally or multilaterally in terms of their external constraints.
Whilst our study does not constitute a full welfare analysis of the various policy options
available for promoting diversification, its conclusions are nonetheless suggestive of a simple,
but important, result: encouraging diversification does not have to equate with protecting
“infant” industries or pursuing a selectively active industrial policy. Rather, the same ends can be
achieved using different means, such as making it easier for firms to enter the market, and
making it cheaper for them to export once they are producing. In a static sense, we expect that
the net balance of costs and benefits from diversification through trade and entry cost reduction
is likely to be significantly more positive than for diversification through infant industry
protection. After all, one set of measures reduces distortions in the economy, while the other
increases them. Moreover, such policies are likely to be considerably more efficient in a dynamic
sense, since they reduce rents and sharpen competitive forces. Infants raised on such a diet are,
we expect, more likely to “grow up” than those which are over-protected. However, our
29
conclusions on this point are only suggestive, and we leave it to future research to elaborate
further on the costs and benefits of these various policy measures.
In concrete policy terms, developing countries have a number of means at their disposal by
which to pursue export diversification through lower entry barriers and trade costs. In terms of
trade costs, a starting point might be regulations governing customs procedures and
documentation: Table 7 suggests that export diversification is relatively sensitive to changes in
these costs. In broad terms, this can be seen as an additional incentive to make progress on trade
facilitation, i.e. the set of policies designed to reduce such costs. Similarly, a number of potential
policy measures are available for lowering domestic barriers to entry: reducing the number of
procedures required for company registration, applying a fixed registration fee rather than a
percentage of capital, and removing the need for pre-tax payments, are examples.
We can see many avenues for future research in this area. On the one hand, it will be important
to replicate these results using other data, and perhaps taking account of other potential
influences on diversification. In light of the cost-based approach we have adopted here, we
consider it likely that financial sector development and FDI might turn out to be important
factors in promoting diversification (Harding and Javorcik, 2007).
Second, our reasoning throughout this paper has been in partial equilibrium. Historically,
however, the general equilibrium aspect of diversification has also been important on a policy
level. The Melitz (2003) framework has been extended in a general equilibrium sense by, for
example, Bernard et al. (2007). However, it remains for future research to take the model’s
predictions to the data in the diversification context, as we have done here.
Finally, an important caveat to our research is that due to data limitations, we have not been able
to analyze the cost-diversification link in the time dimension. We are aware that diversifying a
30
country’s export base is unlikely to be an instantaneous process, and would not want to be
understood as implying such through our use of a cross-country regression in this paper. As more
data on trade costs and barriers to entry become available—Doing Business is updated
annually—we hope that researchers will return to the questions we have raised, and investigate
in more detail the dynamics of the processes underlying them. The flipside of this is that our
results will need to be supplemented with detailed work, including cost-benefit analysis, on
particular mechanisms that governments use to promote diversification over time. Given the
recent revival of interest in export diversification, we anticipate that future research in this area
has the potential to be particularly fruitful in policy terms.
31
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36
Tables
Table 1: Data description and sources.
Variable
Description
Agriculture %
GDP
Customs Costs
Distance
Document Costs
Entry Capital
Entry Cost
Units
Size of the agricultural sector in the exporting country. Proxied by agriculture value added
as a percent of GDP.
Official customs clearance and technical control fees levied on a 20 foot container leaving
the exporting country.
Distance of the exporting country from Europe. Proxied by the average of the great circle
distances between the main cities of the exporting country and Germany, weighted by
population shares.
Official document preparation costs in respect of a 20 foot container leaving the exporting
country.
Paid-in minimum capital requirement in the exporting country, i.e. amount an entrepreneur
must deposit with a bank prior to company registration. Converted from percent GNI per
capita to USD.
Official cost of starting up and formally operating an industrial or commercial business in
the exporting country. Converted from percent GNI per capita to USD.
Year
2005 or
most
recent
Source
World
Development
Indicators
USD
2006
Doing Business
Km
NA
Mayer and
Zignago (2006)
USD
2006
Doing Business
USD
2006
Doing Business
USD
2006
Doing Business
%
Exchange Rate
USD exchange rate for the exporting country. Proxied by the real effective exchange rate
index.
Index
(2000 =
100)
2005 or
most
recent
World
Development
Indicators
Export Cost
Official fees levied on a 20 foot container leaving the exporting country. Includes
document preparation costs, administrative fees for customs clearance and technical
control, terminal handling charges, and inland transit. Sum of customs costs, document
costs, inland transport costs, and port costs (below).
USD
2006
Doing Business
GDP
Gross domestic product of the exporting country.
Constant
2000 USD
GDP Deflator
Inflation in the exporting country. Proxied by the GDP deflator.
%
GDP/capita
Per capita gross domestic product of the exporting country.
Constant
2000 USD
2005 or
most
recent
2005 or
most
recent
2005 or
most
recent
World
Development
Indicators
World
Development
Indicators
World
Development
Indicators
37
Variable
Description
Year
Source
NA
2005
Eurostat
NA
2005
Eurostat
HH_Index


 pi qi
Herfindahl-Hirschman index for exporter e =  J

i 1
  p j q j
 j 1
HH_Index_CN2


Js
 pi qi
Herfindahl-Hirschman index for exporter e in sector s =   J
s
i 1
  p jq j

 j 1
Inland Transport
Costs
Official inland transit costs for a 20 foot container leaving the exporting country.
USD
2006
Doing Business
Interest Rate
Interest rate in the exporting country. Proxied by the real interest rate.
%
2005 or
most
recent
World
Development
Indicators
NA
2005
Eurostat
NA
2005
Eurostat
NA
2005
Eurostat
NA
2005
Eurostat
NA
2005
Eurostat
J






Units
2
p
Lambda
Feenstra’s relative diversification measures for exporter e =
w
i







2
q iw
iJ e
 p iw qiw
, where Jw is the
iJ w
set of products exported by the world and Je is the set of products exported by country e.
p
Feenstra’s relative diversification measures for exporter e in sector s =
Lambda_CN2
iJ s e
w
i
q iw
 p iw q iw
, where
iJ s w
Lines
Lines_CN2
Lines_CN2_100k
Jw is the set of products exported by the world and Je is the set of products exported by
country e (both in sector s).
Count of the total number of product lines in which a country has strictly positive exports
to the EU.
Count of the number of product lines in a 2-digit sector for which a country has strictly
positive exports to the EU.
Count of the number of product lines in a 2-digit sector for which a country has exports to
the EU of a value greater than €100,000.
38
Variable
Lines_CN2_1m
Lines_CN2_Man
Manufacturing %
GDP
Description
Count of the number of product lines in a 2-digit sector for which a country has exports to
the EU of a value greater than €1,000,000.
Count of the number of product lines in a 2-digit sector for which a country has strictly
positive exports to the EU. Limited to HS sectors 25-97 only (manufactured goods).
Size of the manufacturing sector in the exporting country. Proxied by manufacturing value
added as a percent of GDP.
Units
Year
Source
NA
2005
Eurostat
NA
2005
Eurostat
2005 or
most
recent
2006
World
Development
Indicators
Doing Business
2005
WITS-TRAINS
%
Official terminal handling charges for a 20 foot container leaving the exporting country.
USD
Simple average applied tariff by HS2 sector, including ad valorem equivalents of specific
% ad
Tariffs
tariffs.
valorem
a) Log transformations use ln(1+…) for series containing zero values.
b) Negative observations on the GDP deflator and the interest rate are coded as zero prior to taking the log transform.
Port Costs
Table 2: Country group definitions.
Country
Members
Group
Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan,
Korea, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovakia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United
OECD
States
Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania,
EU-25
Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, United Kingdom
Antigua and Barbuda, Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, China, Iceland, Ireland,
High Income Israel, Italy, Japan, Korea, Kuwait, Netherlands, New Zealand, Norway, Portugal, Saudi Arabia, Singapore, Slovenia, Spain, Sweden,
Switzerland, United Arab Emirates, United Kingdom, United States
Argentina, Belize, Botswana, Chile, Costa Rica, Croatia, Czech Republic, Dominica, Equatorial Guinea, Estonia, Gabon, Grenada, Hungary,
Upper
Latvia, Lebanon, Lithuania, Malaysia, Mauritius, Mexico, Oman, Palau, Panama, Poland, Romania, Russia, Seychelles, Slovakia, South Africa,
Middle
St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Trinidad and Tobago, Turkey, Uruguay, Venezuela
Income
Albania, Algeria, Angola, Armenia, Azerbaijan, Belarus, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Cameroon, Cape Verde, China,
Colombia, Congo, Rep., Djibouti, Dominican Republic, Ecuador, Egypt, El Salvador, Fiji, Georgia, Guatemala, Guyana, Honduras, Indonesia,
Lower
Iran, Iraq, Jamaica, Jordan, Kazakhstan, Kiribati, Lesotho, Macedonia, FYR, Maldives, Marshall Islands, Micronesia, Moldova, Morocco,
Middle
Namibia, Nicaragua, Paraguay, Peru, Philippines, Samoa, Serbia, Sri Lanka, Suriname, Swaziland, Syria, Thailand, Tonga, Tunisia, Ukraine,
Income
Vanuatu
Afghanistan, Bangladesh, Benin, Bhutan, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad, Comoros, Côte d'Ivoire, Eritrea,
Ethiopia, Gambia, Ghana, Guinea, Guinea-Bissau, Haiti, India, Kenya, Kyrgyz Republic, Lao PDR, Madagascar, Malawi, Mali, Mauritania,
Low Income
Mongolia, Mozambique, Nepal, Niger, Nigeria, Pakistan, Papua New Guinea, Rwanda, Senegal, Sierra Leone, Solomon Islands, Sudan, São
Tomé and Principe, Tajikistan, Tanzania, Togo, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, Zimbabwe
39
Table 3: Export diversification as measured by counts of the number of 8-digit products exported to the EU
in 2005 (lines).
Rank Country Name
Lines Rank Country Name
Lines
China
8053 69
Jamaica
423
1
India
6449 70
Georgia
379
2
Brazil
5477
Suriname
379
3
71
Romania
5148 72
Uganda
377
4
Thailand
5073 73
Gabon
369
5
Hong Kong, China
5062 74
Azerbaijan
364
6
Israel
4830 75
Mongolia
350
7
South Africa
4806 76
Paraguay
345
8
Russia
4451 77
Uzbekistan
316
9
Bulgaria
4297 78
Cape Verde
315
10
Malaysia
4071
Congo,
Rep.
314
11
79
Indonesia
4059 80
Yemen
308
12
Singapore
3970 81
Lao PDR
296
13
Croatia
3807 82
Mali
281
14
United Arab Emirates
3454 83
Armenia
279
15
Morocco
3396 84
Mauritania
258
16
Tunisia
3297 85
Burkina Faso
257
17
Argentina
3158 86
Togo
249
18
Ukraine
3077
Mozambique
248
19
87
Egypt
3000 88
Guinea
246
20
Philippines
2858 89
Sudan
223
21
Vietnam
2806 90
Benin
217
22
Pakistan
2612 91
Zambia
212
23
Chile
2032 92
Botswana
210
24
Saudi Arabia
1993 93
Sierra Leone
206
25
Iran
1935 94
Afghanistan
200
26
Sri
Lanka
1876
Papua
New
Guinea
200
27
95
Bosnia and Herzegovina 1815 96
Fiji
194
28
Lebanon
1682 97
Seychelles
192
29
Colombia
1680 98
Nicaragua
188
30
Peru
1666 99
Antigua and Barbuda
184
31
Macedonia, FYR
1528 100
Swaziland
164
32
Syria
1491 101
Kyrgyz Republic
160
33
Belarus
1439 102
Niger
158
34
Mauritius
1262
Iraq
154
35
103
Bangladesh
1225 104
Equatorial Guinea
153
36
Nigeria
1155 105
Malawi
152
37
Kenya
1118 106
Guyana
136
38
Albania
1087 107
Gambia
132
39
Ecuador
1052 108
Maldives
131
40
Jordan
1050 109
Belize
118
41
Algeria
1028 110
Rwanda
118
42
Venezuela
1018
Haiti
115
43
111
Uruguay
970
St. Lucia
112
44
112
40
Rank Country Name
Lines Rank Country Name
Lines
Moldova
937
Chad
98
45
113
Oman
905
Dominica
96
46
114
Dominican Republic
895
Tajikistan
93
47
115
Kuwait
894
Eritrea
88
48
116
Ghana
876
Djibouti
86
49
117
Madagascar
858
Marshall Islands
83
50
118
Costa Rica
843
St. Kitts and Nevis
81
51
119
Nepal
813
Central African Republic
71
52
120
Côte d'Ivoire
764
St. Vincent and the Grenadines 71
53
121
Senegal
746
São Tomé and Principe
66
54
122
Panama
685
Burundi
54
55
123
Kazakhstan
678
Lesotho
51
56
124
Cameroon
642
Vanuatu
49
57
125
Guatemala
611
Grenada
45
58
126
Tanzania
552
Guinea-Bissau
44
59
127
Bolivia
505
Kiribati
43
60
128
Zimbabwe
475
Bhutan
40
61
129
Angola
472
Samoa
38
62
130
Honduras
456
Tonga
36
63
131
Trinidad and Tobago
446
Solomon Islands
35
64
132
El Salvador
440
Comoros
27
66
133
Namibia
440
Micronesia
12
65
134
Cambodia
439
Palau
9
67
135
Ethiopia
432
68
c) Sample is limited to non-OECD and non-EU25 countries.
d) Calculation is based on 10,753 distinct products identified in the CN8 classification.
e) Countries ranked by lines in decreasing order of export diversification (higher lines indicates greater
diversification).
41
Table 4: Export diversification as measured by Feenstra’s lambda.
Rank Country Name
Lambda Rank Country Name
China
0.84
Iraq
1
69
Brazil
0.82
Uzbekistan
2
70
Russia
0.82
Honduras
3
71
India
0.81
El Salvador
4
72
South Africa
0.72
Namibia
5
73
Romania
0.71
Uganda
6
74
Israel
0.68
Nepal
7
75
Thailand
0.67
Armenia
8
76
United Arab Emirates
0.66
Equatorial Guinea
9
77
Ukraine
0.66
Bolivia
10
78
Singapore
0.66
Zimbabwe
11
79
Hong Kong, China
0.64
Cape Verde
12
80
Malaysia
0.64
Zambia
13
81
Indonesia
0.64
Mozambique
14
82
Bulgaria
0.63
Mali
15
83
Croatia
0.62
Paraguay
16
84
Tunisia
0.60
Benin
17
85
Argentina
0.59
Sierra Leone
18
86
Egypt
0.58
Antigua and Barbuda
19
87
Saudi Arabia
0.57
Chad
20
88
Morocco
0.54
Togo
21
89
Iran
0.53
Suriname
22
90
Philippines
0.46
Seychelles
23
91
Algeria
0.44
Afghanistan
24
92
Bosnia and Herzegovina 0.43
Cambodia
25
93
Vietnam
0.43
Botswana
26
94
Chile
0.43
Mongolia
27
95
Pakistan
0.42
Mauritania
28
96
Kuwait
0.41
Sudan
29
97
Venezuela
0.41
Lao PDR
30
98
Colombia
0.40
Guinea
31
99
Belarus
0.39
Burkina Faso
32
100
Lebanon
0.39
Maldives
33
101
Macedonia, FYR
0.36
Dominica
34
102
Nigeria
0.36
Nicaragua
35
103
Albania
0.36
Malawi
36
104
Syria
0.36
Papua New Guinea
37
105
Jordan
0.34
Rwanda
38
106
Côte d'Ivoire
0.34
Guyana
39
107
Oman
0.33
Kyrgyz Republic
40
108
Peru
0.32
Fiji
41
109
Sri Lanka
0.32
Tajikistan
42
110
Mauritius
0.31
St. Vincent and the Grenadines
43
111
Kazakhstan
0.31
Belize
44
112
Kenya
0.30
Haiti
45
113
42
Lambda
0.16
0.15
0.15
0.15
0.15
0.15
0.14
0.14
0.14
0.14
0.14
0.13
0.13
0.13
0.12
0.12
0.12
0.12
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.10
0.10
0.10
0.10
0.10
0.10
0.09
0.08
0.08
0.08
0.08
0.08
0.07
0.07
0.07
0.07
0.07
0.07
0.06
0.06
Rank Country Name
Lambda Rank Country Name
Lambda
Cameroon
0.29
Gambia
0.06
46
114
Panama
0.29
St. Lucia
0.05
47
115
Moldova
0.28
Djibouti
0.05
48
116
Uruguay
0.27
Swaziland
0.05
49
117
Gabon
0.27
Central African Republic
0.05
50
118
Costa Rica
0.26
Eritrea
0.05
51
119
Guatemala
0.26
Burundi
0.04
52
120
Bangladesh
0.26
Marshall Islands
0.04
53
121
Ecuador
0.26
Vanuatu
0.04
54
122
Angola
0.25
Grenada
0.04
55
123
Senegal
0.25
São Tomé and Principe
0.04
56
124
Ghana
0.24
St. Kitts and Nevis
0.03
57
125
Dominican Republic
0.24
Lesotho
0.03
58
126
Azerbaijan
0.23
Kiribati
0.03
59
127
Trinidad and Tobago
0.23
Comoros
0.03
60
128
Madagascar
0.21
Bhutan
0.02
61
129
Jamaica
0.19
Samoa
0.02
62
130
Tanzania
0.19
Tonga
0.02
63
131
Congo, Rep.
0.19
Guinea-Bissau
0.01
64
132
Georgia
0.19
Solomon Islands
0.01
65
133
Yemen
0.18
Micronesia
0.01
66
134
Niger
0.17
Palau
0.01
67
135
Ethiopia
0.16
68
a) Sample is limited to non-OECD and non-EU25 countries.
b) Lambda ranges between 0 and 1.
c) Countries ranked by lambda in decreasing order of export diversification (higher lambda indicates greater
diversification).
43
Table 5: Export diversification as measured by the Herfindahl-Hirschman index.
Rank Name
HH_Index Rank Name
China
0.01
Uganda
1
69
Croatia
0.01
Yemen
2
70
Romania
0.01
Panama
3
71
India
0.01
Senegal
4
72
Hong Kong, China
0.01
Algeria
5
73
Indonesia
0.01
Namibia
6
74
Bulgaria
0.01
Honduras
7
75
Pakistan
0.01
Costa Rica
8
76
Thailand
0.01
Mongolia
9
77
Vietnam
0.02
Belarus
10
78
Tunisia
0.02
Micronesia
11
79
Sri Lanka
0.02
Cameroon
12
80
Lebanon
0.02
Ghana
13
81
Bosnia and Herzegovina 0.02
El Salvador
14
82
Ukraine
0.02
Dominica
15
83
Macedonia,
FYR
0.03
Congo, Rep.
16
84
Israel
0.03
Afghanistan
17
85
Malaysia
0.03
Samoa
18
86
Brazil
0.03
Armenia
19
87
South Africa
0.04
Russia
20
88
Singapore
0.04
Palau
21
89
Albania
0.04
Guyana
22
90
Uruguay
0.04
Central African Republic
23
91
Philippines
0.04
Suriname
24
92
Moldova
0.05
São Tomé and Principe
25
93
Egypt
0.05
Sudan
26
94
Lao PDR
0.05
Comoros
27
95
Peru
0.06
Maldives
28
96
Madagascar
0.06
Belize
29
97
Kyrgyz Republic
0.06
Oman
30
98
Morocco
0.07
Grenada
31
99
Bangladesh
0.07
Nicaragua
32
100
Zimbabwe
0.07
Mali
33
101
Kenya
0.08
Ethiopia
34
102
Tanzania
0.09
Jamaica
35
103
Burkina Faso
0.09
Togo
36
104
Benin
0.10
Chad
37
105
Tonga
0.10
Marshall Islands
38
106
Mauritius
0.10
Guinea
39
107
Dominican
Republic
0.10
Swaziland
40
108
Argentina
0.11
Mauritania
41
109
Georgia
0.11
Solomon Islands
42
110
Eritrea
0.11
Kuwait
43
111
Zambia
0.12
St. Lucia
44
112
Djibouti
0.12
Kiribati
45
113
44
HH_Index
0.21
0.21
0.21
0.21
0.23
0.23
0.23
0.23
0.23
0.24
0.25
0.25
0.25
0.26
0.26
0.26
0.27
0.27
0.28
0.28
0.28
0.28
0.28
0.29
0.30
0.30
0.30
0.30
0.31
0.31
0.33
0.34
0.34
0.35
0.37
0.40
0.44
0.45
0.47
0.48
0.48
0.49
0.49
0.51
0.51
Gambia
0.12
St. Kitts and Nevis
0.52
46
114
Malawi
0.13
Paraguay
0.54
47
115
Trinidad and Tobago
0.13
Saudi Arabia
0.55
48
116
Uzbekistan
0.13
Vanuatu
0.56
49
117
Colombia
0.13
Antigua and Barbuda
0.61
50
118
Bolivia
0.13
Nigeria
0.61
51
119
Gabon
0.13
Equatorial Guinea
0.62
52
120
Chile
0.14
Rwanda
0.67
53
121
Cambodia
0.14
Mozambique
0.68
54
122
Jordan
0.14
Kazakhstan
0.68
55
123
Guinea-Bissau
0.14
Angola
0.68
56
124
Côte d'Ivoire
0.14
Syria
0.70
57
125
Papua New Guinea
0.15
Sierra Leone
0.74
58
126
Tajikistan
0.15
Iran
0.75
59
127
Guatemala
0.15
St. Vincent and the Grenadines 0.75
60
128
Cape Verde
0.15
Azerbaijan
0.76
61
129
Bhutan
0.16
Niger
0.77
62
130
Haiti
0.17
Fiji
0.79
63
131
Ecuador
0.19
Burundi
0.93
64
132
Nepal
0.19
Botswana
0.94
65
133
Venezuela
0.20
Lesotho
0.94
66
134
United Arab Emirates
0.20
Iraq
0.99
67
135
Seychelles
0.21
68
a) Sample is limited to non-OECD and non-EU25 countries.
b) HH_Index ranges between 0 and 1.
c) Countries ranked by hh_index in decreasing order of export diversification (lower hh_index indicates greater
diversification).
45
Table 6: Estimation results.
Model 1
0.002
Entry Capital
0.003
-0.097***
Entry Cost
0.010
-0.250***
Export Cost
0.017
-0.463***
Distance
0.031
Model 2
0.019***
0.002
-0.259***
0.016
-0.895***
0.020
Tariffs
GDP
GDP/cap.
Mftg. % GDP
Ag. % GDP
GDP Defl.
Model 3
-0.029***
0.002
-0.168***
0.010
-0.921***
0.035
-0.413***
0.039
0.321***
0.027
0.515***
0.016
0.032*
0.018
0.330***
0.025
0.007
0.015
-0.256***
0.017
Ex. Rate
Int. Rate
0.019**
0.008
10088
97
Model 5
-0.023***
0.003
-0.078***
0.011
-0.356***
0.022
-0.472***
0.034
-0.086***
0.019
0.453***
0.013
0.025
0.018
0.331***
0.024
-0.054***
0.014
Model 6
-0.024***
0.005
0.121***
0.019
-0.146***
0.026
-0.545***
0.036
-0.041*
0.024
0.494***
0.015
-0.257***
0.033
0.566***
0.066
-0.061***
0.016
-0.519***
0.029
0.731***
0.071
-0.084***
0.021
2997
96
31.2
(Unbal. ave.)
4721.38***
8286
8210
7849
96
96
96
86.3
85.5
81.8
No. Countries 104 (Balanced) 132 (Balanced)
(Unbal. ave.) (Unbal. ave.) (Unbal. ave.)
3437.19***
2459.90***
1280.66***
2531.63***
3471.62***
Wald Chi2
a) Dependent variable is lines_cn2. Independent variables are in logarithms.
b) Estimation is by Poisson QML with conditional fixed effects by 2-digit sector. Robust standard errors are in
italics under the parameter estimates.
c) Sample is limited to non-OECD and non-EU25 countries.
Obs.
No. Sectors
12804
97
Model 4
-0.029***
0.002
-0.089***
0.009
-0.285***
0.016
-0.455***
0.028
-0.062***
0.019
0.453***
0.013
0.067***
0.021
46
Table 7: Estimation results using broken down export costs.
Model 7
-0.001
Entry Capital
0.003
-0.072***
Entry Cost
0.009
-0.050***
Customs Costs
0.005
-0.010**
Document Costs
0.005
-0.001
Inland Transport Costs
0.006
0.039***
Port Costs
0.004
-0.451***
Distance
0.031
0.531***
GDP
0.015
0.042**
GDP Per Capita
0.018
0.369***
Manufacturing % GDP
0.025
-0.008
Agriculture % GDP
0.015
-0.238***
GDP Deflator
0.019
0.025***
Interest Rate
0.008
10088
Observations
97
No. of Sectors
104 (Balanced)
No. of Countries
4704.43***
Wald Chi2
a) Dependent variable is lines_cn2. Independent variables are in logarithms.
b) Estimation is by Poisson QML with conditional fixed effects by 2-digit sector. Robust standard errors are in
italics under the parameter estimates.
c) Sample is limited to non-OECD and non-EU25 countries.
47
Table 8: Robustness checks—estimation methodology.
OLS
Tobit
Neg. Bin.
Poisson (Aggregate)
-0.009***
-0.008**
-0.003***
-0.002
Entry Capital
0.003
0.004
0.003
0.015
-0.069***
-0.098***
-0.098***
-0.086
Entry Cost
0.010
0.014
0.011
0.055
-0.136***
-0.192***
-0.183***
-0.262***
Export Cost
0.014
0.022
0.020
0.091
-0.314***
-0.445***
-0.409***
-0.469***
Distance
0.016
0.019
0.016
0.076
0.388***
0.520***
0.507***
0.515***
GDP
0.005
0.007
0.005
0.023
0.048***
0.067***
0.044***
0.032
GDP Per Capita
0.012
0.017
0.014
0.060
0.161***
0.248***
0.291***
0.305***
Manufacturing % GDP
0.013
0.019
0.016
0.094
0.041***
0.051***
0.046***
0.010
Agriculture % GDP
0.012
0.016
0.012
0.068
-0.223***
-0.188***
-0.272***
-0.197***
GDP Deflator
0.012
0.018
0.015
0.061
-0.019**
0.033***
0.021**
0.004
Interest Rate
0.010
0.013
0.010
0.039
-3.638
-5.454***
-6.748***
0.506
Constant
0.239
0.337
0.276
1.154
10088
10088
10088
104
Observations
97
97
97
NA
No. of Sectors
104 (Balanced) 104 (Balanced) 104 (Balanced) 104
No. of Countries
1457.81***
21601.40***
13598.69***
1140.68***
Wald Chi2/F
a) Dependent variable is log(1+lines_cn2) in columns 1 and 2, lines_cn2 in column 3, and lines in column 4.
b) Independent variables are in logarithms in all models.
c) Estimation (column 1) is by OLS with fixed effects by 2-digit sector. Robust standard errors are in italics under
the parameter estimates.
d) Estimation (column 2) is by Tobit with fixed effects by 2-digit sector. Robust standard errors are in italics under
the parameter estimates.
e) Estimation (column 3) is by negative binomial ML with conditional fixed effects by 2-digit sector. Non-robust
standard errors are in italics under the parameter estimates.
f) Estimation (column 3) is by Poisson QML using aggregate country-level data. Robust standard errors are in
italics under the parameter estimates.
g) All samples are limited to non-OECD and non-EU25 countries.
48
Table 9: Robustness checks—country sample.
Low +
Low +
Low Income
Middle Income Lower Middle Income
0.002
0.011***
-0.005
Entry Capital
0.002
0.004
0.011
-0.148***
-0.171***
-0.293***
Entry Cost
0.012
0.012
0.042
-0.240***
-0.234***
-0.302***
Export Cost
0.015
0.026
0.052
-0.442***
-0.383***
-0.379**
Distance
0.030
0.038
0.164
0.500***
0.505***
0.506***
GDP
0.016
0.017
0.023
-0.016
-0.002
0.240***
GDP Per Capita
0.015
0.017
0.087
0.450***
0.384***
0.413***
Manufacturing % GDP
0.048
0.051
0.090
-0.077**
-0.073
0.141**
Agriculture % GDP
0.032
0.048
0.063
-0.284***
-0.228***
-0.074**
GDP Deflator
0.016
0.018
0.034
0.017*
-0.016
-0.080**
Interest Rate
0.010
0.013
0.034
9603
7372
3007
Observations
97
97
97
No. of Sectors
99 (Balanced)
76 (Balanced)
31 (Balanced)
No. of Countries
2836.71***
2666.85***
1077.42***
Wald Chi2
a) Dependent variable is lines_cn2. Independent variables are in logarithms.
b) Estimation is by Poisson QML with conditional fixed effects by 2-digit sector. Robust standard errors are in
italics under the parameter estimates.
c) The sample in column 1 is limited to low and middle income countries (non-OECD and non-EU25). In column
2, it is limited to low and lower middle income countries. In column 3, low income countries only are included.
49
Table 10: Robustness checks—diversification measure.
>$100k
>$1m
-0.007*
-0.013*
Entry Capital
0.004
0.007
-0.141***
-0.160***
Entry Cost
0.019
0.034
-0.397***
-0.422***
Export Cost
0.063
0.102
-0.621***
-0.636***
Distance
0.060
0.083
0.697***
0.750***
GDP
0.031
0.035
-0.034
-0.075
GDP Per Capita
0.045
0.054
0.492***
0.545***
Manufacturing % GDP
0.029
0.041
0.001
-0.009
Agriculture % GDP
0.023
0.030
-0.391***
-0.396***
GDP Deflator
0.039
0.082
0.023
0.003
Interest Rate
0.022
0.038
Manufactures
0.004
0.003
-0.091***
0.011
-0.263***
0.019
-0.501***
0.032
0.526***
0.019
0.016
0.020
0.338***
0.029
-0.022**
0.010
-0.248***
0.018
0.024***
0.009
Constant
Observations
No. of Sectors
10088
97
10088
97
7592
73
No. of Countries
104 (Balanced)
104 (Balanced)
104 (Balanced)
HH Index
0.005**
0.002
0.032***
0.008
0.047***
0.013
0.153***
0.012
-0.185***
0.004
0.018*
0.011
-0.098***
0.012
0.020*
0.011
0.110***
0.011
-0.011
0.008
1.567***
0.206
6675
97
68.8
(Unbal. ave.)
340.60***
Lambda
-0.001*
0.001
-0.012***
0.002
-0.006*
0.004
-0.046***
0.003
0.063***
0.001
0.004
0.003
0.030***
0.003
-0.002
0.003
-0.034***
0.003
0.003
0.002
-0.744***
0.056
6695
97
69
(Unbal. ave.)
546.86***
1925.73***
1113.07***
3087.39***
Wald Chi2
a) Independent variables are in logarithms.
b) Dependent variable in columns 1-3 is lines_cn2, adjusted to include only exports greater than €100,000 (column
1), exports greater than €1 million (column 2), or exports in HS chapters 25-97 only (column 3).
c) Dependent variables in columns 4-5 are the Herfindahl-Hirschman index, and Feenstra’s lambda respectively.
d) Estimation for columns 1-3 is by Poisson QML with conditional fixed effects by 2-digit sector. Columns 4-5 are
estimated by OLS with fixed effects by 2-digit sector. In all cases, robust standard errors are in italics under the
parameter estimates.
e) Sample is limited to non-OECD and non-EU25 countries.
50
Figures
Figure 1: Nonparametric regression results for entry costs.
0
2000
lines
4000
6000
8000
Lowess smoother
0
1
2
3
4
5
ent_cost
bandwidth = .8
a) Dependent variable is lines. Independent variable is entry cost.
b) Sample limited to non-OECD and non-EU25.
c) Observations (3) with ent_cost > 500% of GNI per capita excluded as outliers.
Figure 2: Nonparametric regression results for export costs.
0
2000
lines
4000
6000
8000
Lowess smoother
0
1000
2000
exp_cost
3000
4000
bandwidth = .8
a) Dependent variable is lines. Independent variable is export cost.
b) Sample limited to non-OECD and non-EU25.
c) Observations (3) with ent_cost > 500% of GNI per capita excluded as outliers.
51
6000
4000
lines
2000
0
-2000
-2000
0
2000
lines
4000
6000
8000
Figure 3: Nonparametric regression results for entry costs and export costs together.
0
1
2
3
ent_cost
4
5
0
1000
2000
3000
exp_cos t
4000
a) Sample limited to non-OECD and non-EU25.
b) Three observations with ent_cost > 500% of GNI per capita excluded as outliers.
c) Calculations performed in Stata using the MLOWESS module (Cox, 2006).
0
.05
.1
.15
Figure 4: Kernel densities of lines_cn2 and predicted values from Model 1 (Table 3).
0
200
400
x
kdensity lines_cn2
600
800
kdensity lines_cn2_hat
52