Barriers to Entry, Trade Costs, and

Export Diversification in Developing Countries

Allen Dennis and Ben Shepherd*, †

The World Bank1818 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 tofocus 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: adennis@worldbank.org or bshepherd@worldbank.org. † 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

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 (8-

digit) 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

2

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).

3

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.

4

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.)

5

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.

6

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

11

1

11

Vv

dvvqU , where v represents each variety and V is the full set of varieties. Each

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

7

parameter k and support 00 aa (e.g., Chaney, 2006; Eaton and Kortum, 2002). That is,

k

a

aaG

0

. It is the stochastic nature of this process which results in firms having different

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 dF , while exporting entails xF . 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 dx FF . 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 dF , and those firms will therefore

maximize profits by not producing at all. For intermediate marginal cost draws, firms will be

able to cover dF but not xF , and will therefore produce for the domestic market only. Only

those firms with low marginal cost draws will be able to support xF , 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.

8

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

xd aa . 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

111 dx FF such that it ranges between zero (infinite fixed or variable trade costs) and

unity (free trade with dx FF ), and using 1

k to simplify notation, allows us to express

those solutions as follows:

k

x

ix F

Faa

1

0 1

1

(1)

k

d

id F

Faa

1

0 1

1

(2)

1

1

dF

Ln

(3)

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

9

equilibrium proportion of exporters in the total mass of firms will therefore be simply

k

d

x

d

x

a

a

aG

aG

, 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:

1

1

xd

xx F

Ln

aG

aGn

(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:

01

xx FdF

d (5)

01

d

d(6)

011

1

dxd FFdF

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

10

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 dx FF —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 xF , τ and dF :3

01

x

x

x

x

F

n

dF

dn (8)

01

1

xx n

d

dn(9)

0

111

1

1

xdxx

d

x

FFFn

dF

dn (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 d

x

F

F

1

, 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.

11

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.

12

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-by-

country 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

13

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, 6-

digit 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).

14

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

w

e

Ji

wi

wi

Ji

wi

wi

e qp

qp

lambda , 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.

15

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

quantities q):

J

iJ

jjj

iie

qp

qpindexhh

1

2

1

_ .

16

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).

17

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.

18

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).

19

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

20

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 non-

negative 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:

!2_

exp2_

2_

es

cnlineseses

eses cnlinescnlinesf

esX (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

21

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

i

ie

ieeeses controldostsexternal_ccostsinternal_cbentryaf

1

loglogloglogexp (12)

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 0dL

dnx .

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.

22

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).

23

(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.

24

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).

25

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

26

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

27

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.)

28

To ensure that our results are robust to the choice of diversification measure, we therefore re-

estimate 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

29

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

30

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

31

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.

32

References

Amin Gutiérrez de Piñeres, Sheila, and Michael Ferrantino, 1997, “Export Diversification and

Structural Dynamics in the Growth Process: The Case of Chile”, Journal of Development

Economics, 52(2), 375-391.

Amurgo Pacheco, Alberto, 2006, “Preferential Trade Liberalization and the Range of Exported

Products: The Case of the Euro-Mediterranean FTA”, Working Paper No. 18/2006, Graduate

Institute of International Studies (Geneva).

Baldwin, Richard E., 2000, “Regulatory Protectionism, Developing Nations, and a Two-Tier

World Trading System” in Susan Collins and Dani Rodrik (eds.) Brookings Trade Forum,

Washington, D.C.: The Brookings Institution.

Baldwin, Richard E., 2005, “Heterogeneous Firms and Trade: Testable and Untestable Properties

of the Melitz Model”, Working Paper No. 11471, NBER.

Baldwin, Richard E., and Virginia Di Nino, 2006, “Euros and Zeros: The Common Currency

Effect on Trade in New Goods”, Working Paper No. 12673, NBER.

Baldwin, Richard E., and Rikard Forslid, 2006, “Trade Liberalization with Heterogeneous

Firms”, Working Paper No. 12912, NBER.

Bernard, Andrew B., J. Bradford Jensen, Stephen J. Redding, and Peter K. Schott, Forthcoming,

“Firms in International Trade”, Journal of Economic Perspectives.

Bernard, Andrew B., Stephen J. Redding, and Peter K. Schott, 2007, “Comparative Advantage

and Heterogeneous Firms”, Review of Economic Studies, 74, 31-66.

33

Brainard, William C., and Richard N. Cooper, 1968, “Uncertainty and Diversification in

International Trade” in Studies in Agricultural Economics, Trade, and Development, Stanford:

Stanford University.

Broda, Christian, and David E. Weinstein, 2006, “Globalization and the Gains from Variety”,

The Quarterly Journal of Economics, 121(2), 541-585.

Cameron, A. Colin, and Pravin K. Trivedi, 2001, “Essentials of Count Data Regression” in Badi

H. Baltagi (ed.), A Companion to Theoretical Econometrics, Malden, Ma.: Blackwell.

Chandra, V., J. Boccardo, and I. Osorio, 2007, “Export Diversification and Competitiveness in

Developing Countries”, Working Paper.

Chaney, Thomas, 2006, “Distorted Gravity: Heterogeneous Firms, Market Structure and the

Geography of International Trade”, Working Paper, University of Chicago.

Cox, Nicholas J., 2006, “MLOWESS: Stata Module for Lowess Smoothing with Multiple

Predictors”, http://fmwww.bc.edu/repec/bocode/m/mlowess.ado.

De Rosa, Dean A., 1992, “Increasing Export Diversification in Commodity Exporting Countries:

A Theoretical Analysis”, International Monetary Fund Staff Papers, 39(3), 572-595.

Dixit, Avinash K., and Joseph E. Stiglitz, 1977, “Monopolistic Competition and Optimum

Product Diversity”, The American Economic Review, 67(3), 297-308.

Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer, 2002, “The

Regulation of Entry”, The Quarterly Journal of Economics, 117(1), 1-37.

Eaton, Jonathan, and Samuel Kortum, 2002, “Technology, Geography and Trade”,

Econometrica, 70(5), 1741-1779.

34

Feenstra, Robert C., 1994, “New Product Varieties and the Measurement of International

Prices”, The American Economic Review, 84(1), 157-177.

Feenstra, Robert C., and Hiau Looi Kee, 2006, “Export Variety and Country Productivity:

Estimating the Monopolistic Competition Model with Endogenous Productivity”, Mimeo.

Fontagné, Lionel, Michael Freudenberg, and Nicolas Péridy, 1998, “Intra-Industry Trade and the

Single Market: Quality Matters”, Discussion Paper No. 1959, CEPR.

Funke, Michael, and Ralf Ruhwedel, 2001a, “Product Variety and Economic Growth: Empirical

Evidence for the OECD Countries”, IMF Staff Papers, 48(2), 225-242.

Funke, Michael, and Ralf Ruhwedel, 2001b, “Export Variety and Export Performance”, Journal

of Asian Economics, 12(4), 493-505.

Gourieroux, C., A. Monfort, and A. Trognon, 1984, “Pseudo Maximum Likelihood Methods:

Applications to Poisson Models”, Econometrica, 52(3), 701-720.

Greene, William, 2004, “The Behavior of the Maximum Likelihood Estimator of Limited

Dependent Variable Models in the Presence of Fixed Effects”, Journal of Econometrics, 7, 98-

119.

Harding, Torfinn, and Beata Smarzynska Javorcik, 2007, “Note on the Effect of FDI on Export

Diversification in Central and Eastern Europe”, Working Paper.

Hausman, Jerry, Bronwyn H. Hall and Zvi Griliches, 1984, “Econometric Models for Count Data

with an Application to the Patents-R&D Relationship”, Econometrica, 52(4), 909-938.

Hausmann, Ricardo, Jason Hwang, and Dani Rodrik, 2006, “What You Export Matters”,

Working Paper, http://ksghome.harvard.edu/~drodrik/hhr.pdf.

35

Helpman, Elhanan, Marc Melitz, and Yona Rubinstein, 2007, “Trading Partners and Trading

Volumes”, Working Paper,

http://post.economics.harvard.edu/faculty/helpman/papers/TradingPartners.pdf.

Helpman, Elhanan, Marc J. Melitz, and Stephen R. Yeaple, 2004, “Export Versus FDI With

Heterogeneous Firms”, The American Economic Review, 94(1), 300-316.

Henry de Frahan, Bruno, and Mark Vancauteren, 2006, “Harmonization of Food Regulations and

Trade in the Single Market: Evidence from Disaggregated Data”, European Review of

Agricultural Economics, 33(3), 337-360.

Hummels, David, and Peter J. Klenow, 2005, “The Variety and Quality of a Nation’s Exports”,

The American Economic Review, 95(3), 704-723.

Imbs, Jean and Romain Wacziarg, 2003, “Stages of Diversification”, The American Economic

Review, 93(1), 63-86.

Jansen, Marion, 2004, “Income Volatility in Small and Developing Economies: Export

Concentration Matters”, Discussion Paper No. 3, World Trade Organization.

Klinger, Bailey, and Daniel Lederman, 2004, “Discovery and Development: An Empirical

Exploration of New Products”, Policy Research Working Paper No. 3450, The World Bank

Klinger, Bailey, and Daniel Lederman, 2006, “Diversification, Innovation and Imitation Inside

the Global Technological Frontier”, Policy Research Working Paper No. 3872, The World Bank

Laborde, David, Houssein Boumelassa, and Maria C. Mitaritonna, Forthcoming, “A Consistent

Picture of the Protection Across the World in 2004: MAcMap HS6 V2”, Working Paper, CEPII.

Manchin, Miriam, 2006, “Preference Utilization and Tariff Reduction in EU Imports from ACP

Countries”, The World Economy, 29(9), 1243-1266.

36

Mayer, Thierry, and Soledad Zignago, 2006, “Notes on CEPII’s Distance Measures”, Working

Paper, CEPII.

Melitz, Marc J., 2003, “The Impact of Trade on Intra-Industry Reallocations and Aggregate

Industry Productivity”, Econometrica, 71(6), 1695-1725.

Moulton, Brent R., 1990, “An Illustration of a Pitfall in Estimating the Effects of Aggregate

Variables on Micro Units”, Review of Economics and Statistics, 72(2), 334-338.

Prebisch, Raúl, 1959, “Commercial Policy in the Underdeveloped Countries”, The American

Economic Review, 49(2), 251-273.

Santos Silva, J.M.C. and Silvana Tenreyro, 2006, “The Log of Gravity”, Review of Economics

and Statistics, 88(4), 641-658.

Simcoe, Timothy, 2007, “XTPQML: Stata Module to Estimate Fixed-Effects Poisson Quasi-ML

Regression with Robust Standard Errors”, http://fmwww.bc.edu/repec/bocode/x/xtpqml.ado.

Wilson, John S., Catherine L. Mann, and Tsunehiro Otsuki, 2005, “Assessing the Benefits of

Trade Facilitation: A Global Perspective”, The World Economy, 28(6), 841-871.

Winkelmann, Rainer, 2000, Econometric Analysis of Count Data, 3rd edition, Berlin: Springer.

Wooldridge, Jeffrey M., 1997, “Quasi-Likelihood Methods for Count Data” in M.H. Pesaran and

P. Schmidt (eds.), Handbook of Applied Econometrics, Vol. 2, Oxford: Blackwell.

Wooldridge, Jeffrey M., 1999, “Distribution-Free Estimation of Some Nonlinear Panel Data

Models”, Journal of Econometrics, 90, 77-97.

37

Tables

Table 1: Data description and sources.Variable Description Units Year Source

Agriculture % GDP

Size of the agricultural sector in the exporting country. Proxied by agriculture value added as a percent of GDP.

%2005 or most recent

World Development Indicators

Customs CostsOfficial customs clearance and technical control fees levied on a 20 foot container leaving the exporting country.

USD 2006 Doing Business

DistanceDistance 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.

Km NAMayer and Zignago (2006)

Document CostsOfficial document preparation costs in respect of a 20 foot container leaving the exporting country.

USD 2006 Doing Business

Entry CapitalPaid-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.

USD 2006 Doing Business

Entry CostOfficial cost of starting up and formally operating an industrial or commercial business in the exporting country. Converted from percent GNI per capita to USD.

USD 2006 Doing Business

Exchange RateUSD 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

2005 or most recent

World Development Indicators

GDP Deflator Inflation in the exporting country. Proxied by the GDP deflator. %2005 or most recent

World Development Indicators

GDP/capita Per capita gross domestic product of the exporting country.Constant 2000 USD

2005 or most recent

World Development Indicators

38

Variable Description Units Year Source

HH_Index Herfindahl-Hirschman index for exporter e =

J

iJ

jjj

ii

qp

qp

1

2

1

NA 2005 Eurostat

HH_Index_CN2 Herfindahl-Hirschman index for exporter e in sector s =

s

s

J

iJ

jjj

ii

qp

qp

1

2

1

NA 2005 Eurostat

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

LambdaFeenstra’s relative diversification measures for exporter e =

w

e

Ji

wi

wi

Ji

wi

wi

qp

qp

, where Jw is the

set of products exported by the world and Je is the set of products exported by country e.

NA 2005 Eurostat

Lambda_CN2Feenstra’s relative diversification measures for exporter e in sector s =

ws

es

Ji

wi

wi

Ji

wi

wi

qp

qp

, where

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).

NA 2005 Eurostat

LinesCount of the total number of product lines in which a country has strictly positive exports to the EU.

NA 2005 Eurostat

Lines_CN2Count of the number of product lines in a 2-digit sector for which a country has strictly positive exports to the EU.

NA 2005 Eurostat

Lines_CN2_100kCount 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.

NA 2005 Eurostat

39

Variable Description Units Year Source

Lines_CN2_1mCount 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.

NA 2005 Eurostat

Lines_CN2_ManCount 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).

NA 2005 Eurostat

Manufacturing % GDP

Size of the manufacturing sector in the exporting country. Proxied by manufacturing value added as a percent of GDP.

%2005 or most recent

World Development Indicators

Port Costs Official terminal handling charges for a 20 foot container leaving the exporting country. USD 2006 Doing Business

TariffsSimple average applied tariff by HS2 sector, including ad valorem equivalents of specific tariffs.

% ad valorem

2005 WITS-TRAINS

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.

Table 2: Country group definitions.Country Group

Members

OECDAustralia, 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 States

EU-25Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, United Kingdom

High IncomeAntigua and Barbuda, Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, China, Iceland, Ireland, Israel, Italy, Japan, Korea, Kuwait, Netherlands, New Zealand, Norway, Portugal, Saudi Arabia, Singapore, Slovenia, Spain, Sweden, Switzerland, United Arab Emirates, United Kingdom, United States

Upper Middle Income

Argentina, Belize, Botswana, Chile, Costa Rica, Croatia, Czech Republic, Dominica, Equatorial Guinea, Estonia, Gabon, Grenada, Hungary, Latvia, Lebanon, Lithuania, Malaysia, Mauritius, Mexico, Oman, Palau, Panama, Poland, Romania, Russia, Seychelles, Slovakia, South Africa, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Trinidad and Tobago, Turkey, Uruguay, Venezuela

Lower Middle 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, Iran, Iraq, Jamaica, Jordan, Kazakhstan, Kiribati, Lesotho, Macedonia, FYR, Maldives, Marshall Islands, Micronesia, Moldova, Morocco, Namibia, Nicaragua, Paraguay, Peru, Philippines, Samoa, Serbia, Sri Lanka, Suriname, Swaziland, Syria, Thailand, Tonga, Tunisia, Ukraine, Vanuatu

Low Income

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, 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

40

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

1 China 8053 69 Jamaica 4232 India 6449 70 Georgia 3793 Brazil 5477 71 Suriname 379

4 Romania 5148 72 Uganda 3775 Thailand 5073 73 Gabon 369

6 Hong Kong, China 5062 74 Azerbaijan 3647 Israel 4830 75 Mongolia 350

8 South Africa 4806 76 Paraguay 3459 Russia 4451 77 Uzbekistan 316

10 Bulgaria 4297 78 Cape Verde 31511 Malaysia 4071 79 Congo, Rep. 314

12 Indonesia 4059 80 Yemen 30813 Singapore 3970 81 Lao PDR 296

14 Croatia 3807 82 Mali 28115 United Arab Emirates 3454 83 Armenia 279

16 Morocco 3396 84 Mauritania 25817 Tunisia 3297 85 Burkina Faso 257

18 Argentina 3158 86 Togo 24919 Ukraine 3077 87 Mozambique 248

20 Egypt 3000 88 Guinea 24621 Philippines 2858 89 Sudan 223

22 Vietnam 2806 90 Benin 21723 Pakistan 2612 91 Zambia 212

24 Chile 2032 92 Botswana 21025 Saudi Arabia 1993 93 Sierra Leone 206

26 Iran 1935 94 Afghanistan 20027 Sri Lanka 1876 95 Papua New Guinea 200

28 Bosnia and Herzegovina 1815 96 Fiji 19429 Lebanon 1682 97 Seychelles 192

30 Colombia 1680 98 Nicaragua 18831 Peru 1666 99 Antigua and Barbuda 184

32 Macedonia, FYR 1528 100 Swaziland 16433 Syria 1491 101 Kyrgyz Republic 160

34 Belarus 1439 102 Niger 15835 Mauritius 1262 103 Iraq 154

36 Bangladesh 1225 104 Equatorial Guinea 15337 Nigeria 1155 105 Malawi 152

38 Kenya 1118 106 Guyana 13639 Albania 1087 107 Gambia 132

40 Ecuador 1052 108 Maldives 13141 Jordan 1050 109 Belize 118

42 Algeria 1028 110 Rwanda 11843 Venezuela 1018 111 Haiti 115

44 Uruguay 970 112 St. Lucia 112

41

Rank Country Name Lines Rank Country Name Lines

45 Moldova 937 113 Chad 9846 Oman 905 114 Dominica 96

47 Dominican Republic 895 115 Tajikistan 9348 Kuwait 894 116 Eritrea 88

49 Ghana 876 117 Djibouti 8650 Madagascar 858 118 Marshall Islands 83

51 Costa Rica 843 119 St. Kitts and Nevis 8152 Nepal 813 120 Central African Republic 71

53 Côte d'Ivoire 764 121 St. Vincent and the Grenadines 7154 Senegal 746 122 São Tomé and Principe 66

55 Panama 685 123 Burundi 5456 Kazakhstan 678 124 Lesotho 51

57 Cameroon 642 125 Vanuatu 4958 Guatemala 611 126 Grenada 45

59 Tanzania 552 127 Guinea-Bissau 4460 Bolivia 505 128 Kiribati 43

61 Zimbabwe 475 129 Bhutan 4062 Angola 472 130 Samoa 38

63 Honduras 456 131 Tonga 3664 Trinidad and Tobago 446 132 Solomon Islands 35

66 El Salvador 440 133 Comoros 2765 Namibia 440 134 Micronesia 12

67 Cambodia 439 135 Palau 968 Ethiopia 432c) 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).

42

Table 4: Export diversification as measured by Feenstra’s lambda.Rank Country Name Lambda Rank Country Name Lambda

1 China 0.84 69 Iraq 0.162 Brazil 0.82 70 Uzbekistan 0.15

3 Russia 0.82 71 Honduras 0.154 India 0.81 72 El Salvador 0.15

5 South Africa 0.72 73 Namibia 0.156 Romania 0.71 74 Uganda 0.15

7 Israel 0.68 75 Nepal 0.148 Thailand 0.67 76 Armenia 0.14

9 United Arab Emirates 0.66 77 Equatorial Guinea 0.1410 Ukraine 0.66 78 Bolivia 0.14

11 Singapore 0.66 79 Zimbabwe 0.1412 Hong Kong, China 0.64 80 Cape Verde 0.13

13 Malaysia 0.64 81 Zambia 0.1314 Indonesia 0.64 82 Mozambique 0.13

15 Bulgaria 0.63 83 Mali 0.1216 Croatia 0.62 84 Paraguay 0.12

17 Tunisia 0.60 85 Benin 0.1218 Argentina 0.59 86 Sierra Leone 0.12

19 Egypt 0.58 87 Antigua and Barbuda 0.1120 Saudi Arabia 0.57 88 Chad 0.11

21 Morocco 0.54 89 Togo 0.1122 Iran 0.53 90 Suriname 0.11

23 Philippines 0.46 91 Seychelles 0.1124 Algeria 0.44 92 Afghanistan 0.11

25 Bosnia and Herzegovina 0.43 93 Cambodia 0.1126 Vietnam 0.43 94 Botswana 0.10

27 Chile 0.43 95 Mongolia 0.1028 Pakistan 0.42 96 Mauritania 0.10

29 Kuwait 0.41 97 Sudan 0.1030 Venezuela 0.41 98 Lao PDR 0.10

31 Colombia 0.40 99 Guinea 0.1032 Belarus 0.39 100 Burkina Faso 0.09

33 Lebanon 0.39 101 Maldives 0.0834 Macedonia, FYR 0.36 102 Dominica 0.08

35 Nigeria 0.36 103 Nicaragua 0.0836 Albania 0.36 104 Malawi 0.08

37 Syria 0.36 105 Papua New Guinea 0.0838 Jordan 0.34 106 Rwanda 0.07

39 Côte d'Ivoire 0.34 107 Guyana 0.0740 Oman 0.33 108 Kyrgyz Republic 0.07

41 Peru 0.32 109 Fiji 0.0742 Sri Lanka 0.32 110 Tajikistan 0.07

43 Mauritius 0.31 111 St. Vincent and the Grenadines 0.0744 Kazakhstan 0.31 112 Belize 0.06

45 Kenya 0.30 113 Haiti 0.06

43

Rank Country Name Lambda Rank Country Name Lambda

46 Cameroon 0.29 114 Gambia 0.0647 Panama 0.29 115 St. Lucia 0.05

48 Moldova 0.28 116 Djibouti 0.0549 Uruguay 0.27 117 Swaziland 0.05

50 Gabon 0.27 118 Central African Republic 0.0551 Costa Rica 0.26 119 Eritrea 0.05

52 Guatemala 0.26 120 Burundi 0.0453 Bangladesh 0.26 121 Marshall Islands 0.04

54 Ecuador 0.26 122 Vanuatu 0.0455 Angola 0.25 123 Grenada 0.04

56 Senegal 0.25 124 São Tomé and Principe 0.0457 Ghana 0.24 125 St. Kitts and Nevis 0.03

58 Dominican Republic 0.24 126 Lesotho 0.0359 Azerbaijan 0.23 127 Kiribati 0.03

60 Trinidad and Tobago 0.23 128 Comoros 0.0361 Madagascar 0.21 129 Bhutan 0.02

62 Jamaica 0.19 130 Samoa 0.0263 Tanzania 0.19 131 Tonga 0.02

64 Congo, Rep. 0.19 132 Guinea-Bissau 0.0165 Georgia 0.19 133 Solomon Islands 0.01

66 Yemen 0.18 134 Micronesia 0.0167 Niger 0.17 135 Palau 0.01

68 Ethiopia 0.16a) 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).

44

Table 5: Export diversification as measured by the Herfindahl-Hirschman index.Rank Name HH_Index Rank Name HH_Index

1 China 0.01 69 Uganda 0.212 Croatia 0.01 70 Yemen 0.21

3 Romania 0.01 71 Panama 0.214 India 0.01 72 Senegal 0.21

5 Hong Kong, China 0.01 73 Algeria 0.236 Indonesia 0.01 74 Namibia 0.23

7 Bulgaria 0.01 75 Honduras 0.238 Pakistan 0.01 76 Costa Rica 0.23

9 Thailand 0.01 77 Mongolia 0.2310 Vietnam 0.02 78 Belarus 0.24

11 Tunisia 0.02 79 Micronesia 0.2512 Sri Lanka 0.02 80 Cameroon 0.25

13 Lebanon 0.02 81 Ghana 0.2514 Bosnia and Herzegovina 0.02 82 El Salvador 0.26

15 Ukraine 0.02 83 Dominica 0.2616 Macedonia, FYR 0.03 84 Congo, Rep. 0.26

17 Israel 0.03 85 Afghanistan 0.2718 Malaysia 0.03 86 Samoa 0.27

19 Brazil 0.03 87 Armenia 0.2820 South Africa 0.04 88 Russia 0.28

21 Singapore 0.04 89 Palau 0.2822 Albania 0.04 90 Guyana 0.28

23 Uruguay 0.04 91 Central African Republic 0.2824 Philippines 0.04 92 Suriname 0.29

25 Moldova 0.05 93 São Tomé and Principe 0.3026 Egypt 0.05 94 Sudan 0.30

27 Lao PDR 0.05 95 Comoros 0.3028 Peru 0.06 96 Maldives 0.30

29 Madagascar 0.06 97 Belize 0.3130 Kyrgyz Republic 0.06 98 Oman 0.31

31 Morocco 0.07 99 Grenada 0.3332 Bangladesh 0.07 100 Nicaragua 0.34

33 Zimbabwe 0.07 101 Mali 0.3434 Kenya 0.08 102 Ethiopia 0.35

35 Tanzania 0.09 103 Jamaica 0.3736 Burkina Faso 0.09 104 Togo 0.40

37 Benin 0.10 105 Chad 0.4438 Tonga 0.10 106 Marshall Islands 0.45

39 Mauritius 0.10 107 Guinea 0.4740 Dominican Republic 0.10 108 Swaziland 0.48

41 Argentina 0.11 109 Mauritania 0.4842 Georgia 0.11 110 Solomon Islands 0.49

43 Eritrea 0.11 111 Kuwait 0.4944 Zambia 0.12 112 St. Lucia 0.51

45 Djibouti 0.12 113 Kiribati 0.51

45

46 Gambia 0.12 114 St. Kitts and Nevis 0.5247 Malawi 0.13 115 Paraguay 0.54

48 Trinidad and Tobago 0.13 116 Saudi Arabia 0.5549 Uzbekistan 0.13 117 Vanuatu 0.56

50 Colombia 0.13 118 Antigua and Barbuda 0.6151 Bolivia 0.13 119 Nigeria 0.61

52 Gabon 0.13 120 Equatorial Guinea 0.6253 Chile 0.14 121 Rwanda 0.67

54 Cambodia 0.14 122 Mozambique 0.6855 Jordan 0.14 123 Kazakhstan 0.68

56 Guinea-Bissau 0.14 124 Angola 0.6857 Côte d'Ivoire 0.14 125 Syria 0.70

58 Papua New Guinea 0.15 126 Sierra Leone 0.7459 Tajikistan 0.15 127 Iran 0.75

60 Guatemala 0.15 128 St. Vincent and the Grenadines 0.7561 Cape Verde 0.15 129 Azerbaijan 0.76

62 Bhutan 0.16 130 Niger 0.7763 Haiti 0.17 131 Fiji 0.79

64 Ecuador 0.19 132 Burundi 0.9365 Nepal 0.19 133 Botswana 0.94

66 Venezuela 0.20 134 Lesotho 0.9467 United Arab Emirates 0.20 135 Iraq 0.99

68 Seychelles 0.21a) 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).

46

Table 6: Estimation results.Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

0.002 0.019*** -0.029*** -0.029*** -0.023*** -0.024***Entry Capital

0.003 0.002 0.002 0.002 0.003 0.005-0.097*** -0.259*** -0.168*** -0.089*** -0.078*** 0.121***

Entry Cost0.010 0.016 0.010 0.009 0.011 0.019-0.250*** -0.895*** -0.921*** -0.285*** -0.356*** -0.146***

Export Cost0.017 0.020 0.035 0.016 0.022 0.026-0.463*** -0.413*** -0.455*** -0.472*** -0.545***

Distance0.031 0.039 0.028 0.034 0.036

0.321*** -0.062*** -0.086*** -0.041*Tariffs

0.027 0.019 0.019 0.0240.515*** 0.453*** 0.453*** 0.494***

GDP0.016 0.013 0.013 0.0150.032* 0.067*** 0.025 -0.257***

GDP/cap.0.018 0.021 0.018 0.0330.330*** 0.331*** 0.566***

Mftg. % GDP0.025 0.024 0.0660.007 -0.054*** -0.061***

Ag. % GDP0.015 0.014 0.016-0.256*** -0.519***

GDP Defl.0.017 0.029

0.731***Ex. Rate

0.0710.019** -0.084***

Int. Rate0.008 0.021

Obs. 10088 12804 8286 8210 7849 2997No. Sectors 97 97 96 96 96 96

No. Countries 104 (Balanced) 132 (Balanced)86.3(Unbal. ave.)

85.5(Unbal. ave.)

81.8(Unbal. ave.)

31.2(Unbal. ave.)

Wald Chi2 3437.19*** 2459.90*** 1280.66*** 2531.63*** 3471.62*** 4721.38***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 7: Estimation results using broken down export costs.Model 7

-0.001Entry Capital

0.003-0.072***

Entry Cost0.009-0.050***

Customs Costs0.005-0.010**

Document Costs0.005-0.001

Inland Transport Costs0.0060.039***

Port Costs0.004-0.451***

Distance0.0310.531***

GDP0.0150.042**

GDP Per Capita0.0180.369***

Manufacturing % GDP0.025-0.008

Agriculture % GDP0.015-0.238***

GDP Deflator0.0190.025***

Interest Rate0.008

Observations 10088No. of Sectors 97

No. of Countries 104 (Balanced)Wald Chi2 4704.43***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.

48

Table 8: Robustness checks—estimation methodology.OLS Tobit Neg. Bin. Poisson (Aggregate)

-0.009*** -0.008** -0.003*** -0.002Entry Capital

0.003 0.004 0.003 0.015-0.069*** -0.098*** -0.098*** -0.086

Entry Cost0.010 0.014 0.011 0.055-0.136*** -0.192*** -0.183*** -0.262***

Export Cost0.014 0.022 0.020 0.091-0.314*** -0.445*** -0.409*** -0.469***

Distance0.016 0.019 0.016 0.0760.388*** 0.520*** 0.507*** 0.515***

GDP0.005 0.007 0.005 0.0230.048*** 0.067*** 0.044*** 0.032

GDP Per Capita0.012 0.017 0.014 0.0600.161*** 0.248*** 0.291*** 0.305***

Manufacturing % GDP0.013 0.019 0.016 0.0940.041*** 0.051*** 0.046*** 0.010

Agriculture % GDP0.012 0.016 0.012 0.068-0.223*** -0.188*** -0.272*** -0.197***

GDP Deflator0.012 0.018 0.015 0.061-0.019** 0.033*** 0.021** 0.004

Interest Rate0.010 0.013 0.010 0.039-3.638 -5.454*** -6.748*** 0.506

Constant0.239 0.337 0.276 1.154

Observations 10088 10088 10088 104No. of Sectors 97 97 97 NA

No. of Countries 104 (Balanced) 104 (Balanced) 104 (Balanced) 104Wald Chi2/F 1457.81*** 21601.40*** 13598.69*** 1140.68***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.

49

Table 9: Robustness checks—country sample.Low +Middle Income

Low +Lower Middle Income

Low Income

0.002 0.011*** -0.005Entry Capital

0.002 0.004 0.011-0.148*** -0.171*** -0.293***

Entry Cost0.012 0.012 0.042-0.240*** -0.234*** -0.302***

Export Cost0.015 0.026 0.052-0.442*** -0.383*** -0.379**

Distance0.030 0.038 0.1640.500*** 0.505*** 0.506***

GDP0.016 0.017 0.023-0.016 -0.002 0.240***

GDP Per Capita0.015 0.017 0.0870.450*** 0.384*** 0.413***

Manufacturing % GDP0.048 0.051 0.090-0.077** -0.073 0.141**

Agriculture % GDP0.032 0.048 0.063-0.284*** -0.228*** -0.074**

GDP Deflator0.016 0.018 0.0340.017* -0.016 -0.080**

Interest Rate0.010 0.013 0.034

Observations 9603 7372 3007No. of Sectors 97 97 97No. of Countries 99 (Balanced) 76 (Balanced) 31 (Balanced)

Wald Chi2 2836.71*** 2666.85*** 1077.42***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.

50

Table 10: Robustness checks—diversification measure.>$100k >$1m Manufactures HH Index Lambda

-0.007* -0.013* 0.004 0.005** -0.001*Entry Capital

0.004 0.007 0.003 0.002 0.001-0.141*** -0.160*** -0.091*** 0.032*** -0.012***

Entry Cost0.019 0.034 0.011 0.008 0.002-0.397*** -0.422*** -0.263*** 0.047*** -0.006*

Export Cost0.063 0.102 0.019 0.013 0.004-0.621*** -0.636*** -0.501*** 0.153*** -0.046***

Distance0.060 0.083 0.032 0.012 0.0030.697*** 0.750*** 0.526*** -0.185*** 0.063***

GDP0.031 0.035 0.019 0.004 0.001-0.034 -0.075 0.016 0.018* 0.004

GDP Per Capita0.045 0.054 0.020 0.011 0.0030.492*** 0.545*** 0.338*** -0.098*** 0.030***

Manufacturing % GDP0.029 0.041 0.029 0.012 0.0030.001 -0.009 -0.022** 0.020* -0.002

Agriculture % GDP0.023 0.030 0.010 0.011 0.003-0.391*** -0.396*** -0.248*** 0.110*** -0.034***

GDP Deflator0.039 0.082 0.018 0.011 0.0030.023 0.003 0.024*** -0.011 0.003

Interest Rate0.022 0.038 0.009 0.008 0.002

1.567*** -0.744***Constant

0.206 0.056

Observations 10088 10088 7592 6675 6695No. of Sectors 97 97 73 97 97

No. of Countries 104 (Balanced) 104 (Balanced) 104 (Balanced)68.8(Unbal. ave.)

69(Unbal. ave.)

Wald Chi2 1925.73*** 1113.07*** 3087.39*** 340.60*** 546.86***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.

51

FiguresFigure 1: Nonparametric regression results for entry costs.

02

000

400

06

000

800

0lin

es

0 1 2 3 4 5ent_cost

bandwidth = .8

Lowess smoother

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.

02

000

400

06

000

800

0lin

es

0 1000 2000 3000 4000exp_cost

bandwidth = .8

Lowess smoother

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.

52

Figure 3: Nonparametric regression results for entry costs and export costs together.

-200

00

200

04

000

600

0lin

es

0 1 2 3 4 5ent_cost

-20

000

20

004

000

60

008

000

line

s

0 1000 2000 3000 4000exp_cos t

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).

Figure 4: Kernel densities of lines_cn2 and predicted values from Model 1 (Table 3).

0.0

5.1

.15

0 200 400 600 800x

kdensity lines_cn2 kdensity lines_cn2_hat