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On the Determinants of Exports Survival

Discussion paper by Marco Fugazza, Cristina Molina, 2011

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This study series paper examines the exporter's survival in foreign markets through an empirical investigation of possible determinants of exports survival rates such as fixed and sunk cost to export. It contains descriptive statistics of stylized facts that qualifies trade duration across groups of countries. The results are shown by region. The final part contains the results showing that the overall trade relationships with either higher average or initial trade values face lower hazard rate which have important policy implications.

UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT







POLICY ISSUES IN INTERNATIONAL TRADE AND COMMODITIES


STUDY SERIES No. 46







ON THE DETERMINANTS OF EXPORTS SURVIVAL


by





Marco Fugazza
UNCTAD, Geneva



and



Ana Cristina Molina


Graduate Institute of International Studies, Geneva








UNITED NATIONS 


New York and Geneva, 2011 





ii


Note 


The purpose of this series of studies is to analyse policy issues and stimulate discussions in the
area of international trade and development. The series includes studies by UNCTAD staff, as well as
by distinguished researchers from academia. In keeping with the objective of the series, authors are
encouraged to express their own views, which do not necessarily reflect the views of the UNCTAD
secretariat or its member States.


The designations employed and the presentation of the material do not imply the expression of
any opinion whatsoever on the part of the United Nations Secretariat concerning the legal status of any
country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or
boundaries.


Material in this publication may be freely quoted or reprinted, but acknowledgement is
requested, together with a reference to the document number. It would be appreciated if a copy of the
publication containing the quotation or reprint were sent to the UNCTAD secretariat at the following
address:



Chief


Trade Analysis Branch
Division on International Trade in Goods and Services, and Commodities


United Nations Conference on Trade and Development
Palais des Nations
CH-1211 Geneva


Switzerland



Series editor:
Khalilur Rahman


Chief, Trade Analysis Branch





UNCTAD/ITCD/TAB/47







UNITED NATIONS PUBLICATION


ISSN 1607-8291





© Copyright United Nations 2011
All rights reserved





iii


Abstract






The aim of this paper is to explore the patterns of trade duration across regions and to identify
its determinants. Using an extended Cox model, we evaluate the effects of country and product
characteristics, as well as of trade costs on the duration of trade relationships from 96 countries from
1995 to 2004. First, the duration of trade relationships increases with the region level of development:
trade relationships from richer economies face lower hazard rates (i.e. longer duration). Second, trade
relationships involving differentiated products show a hazard rate that is 6% to 14% lower than trade
relationships involving homogeneous goods. Third, high export costs systematically increase the
probability of export failure but the effect diminishes with time, thus suggesting that export experience
plays a role. Finally, the size of exports also matters: the larger the transaction, the higher the
probability of survival. This is true whether we take average or initial values of exports. This would be
evidence of hysteresis in the export status if trade values are seen to reflect sunk costs to export.





Key words: Duration, Trade, Fixed Costs


JEL Classification: F1, C41










iv






Acknowledgements



We are grateful to Olivier Cadot for helpful comments and suggestions. We also thank
seminar participants at the Geneva Graduate Institute, the University of Geneva and
UNCTAD for valuable comments.


Any mistakes or errors remain the authors’ own.










v


Contents




1. Introduction..........................................................................................................................1




2. Duration, trade and development .......................................................................................3




2.1. Trade duration: a first mapping....................................................................................4


2.2. Duration and trade values.............................................................................................5


2.3. Duration, trade and development .................................................................................6




3. Empirical analysis ................................................................................................................8




3.1. Empirical strategy ........................................................................................................8


3.2. Data ..............................................................................................................................9


3.3. Control variables ........................................................................................................10




4. Results .................................................................................................................................12




4.1. Whole sample.............................................................................................................12


4.2. Group specificities .....................................................................................................14


4.3. Trade protection .........................................................................................................16


4.4. Robustness and extensions.........................................................................................18




5. Conclusions .........................................................................................................................21






Appendices ....................................................................................................................................23




References ....................................................................................................................................30






vi


List of figures




Figure 1. Trade matrix composition (1995-2004) ...................................................................... 4
Figure 2. Average trade values (1995-2004) .............................................................................. 5
Figure 3. Duration and trade values by countries ....................................................................... 7





List of tables

Table 1. Cox proportional hazard ratios estimates (all countries)........................................... 13
Table 2. Cox proportional hazard ratios estimates
(Developing South – Emerging South – North)........................................................ 15
Table 3. Cox proportional hazard ratios estimates (all countries, manufactures) ................... 17
Table 4. Cox proportional hazard ratios estimates (all countries, Robustness Checks I)........ 19
Table 5. Cox proportional hazard ratios estimates (all countries, Robustness Checks II) ...... 20






1


1.  Introduction  


Trade duration (along with its determinants) has been most of the time overlooked in both
theoretical and empirical literature. This is rather surprising considering that the length of trade
relationships remains the main driver of the intensive margin, which is the most influential component
of export growth (see inter alia Eaton et al., 2008; Besedes and Prusa, 2007; Brenton and Newfarmer,
2007; Helpman et al., 2008; Felbermayr and Kohler, 2006; Evenett and Venables, 2002).1 Our data
show that the number of times trade is disrupted after a short period of time is considerably large.2 On
average 3 out of 5 new trade relationships fail within our period of investigation (i.e. 10 years),
implying that improving survival rates is a key component of a country’s export strategy. But why do
trade relationships fail? And what are the determinants of their persistence? These are the main
questions that this paper attempts to answer.


In a prominent theoretical contribution, Rauch and Watson (2003) explore the duration of trade
relationships through a search model. The authors study the creation and evolution of partnerships
between buyers (in developed countries) and suppliers (in less developed countries). The model
proceeds in three stages: search, investment (deepening), and rematch (abandon current relationship
and search for another supplier). In this framework, buyers, i.e. importers, start with small purchases
because of the uncertainty surrounding the supplier. Orders increase only if the seller delivered and
complied with his clients’ expectations. The model predicts that the length of a trade relationship is
positively correlated with the initial amount of the transaction, and that the propensity to start low
value transactions increases with the cost of search and decreases with reliability. Besedes and Prusa
(2006a, 2006b) as well as Besedes (2008) test some of the main predictions of the Rauch-Watson
model using data on imports from the United States at the TS (Tariff scheduled) 7-digit level and at the
HS 10-digit level. In Besedes and Prusa (2006a, 2006b) the authors find that duration of trade
relationships is longer for differentiated goods than for homogeneous goods. Their results also suggest
that short trading relationships tend to be low-valued. Besedes (2008) also finds that duration increases
with the initial value of exports. In addition his results highlight that many trade relationships begin
with small initial values and are essentially short lived. However, another explanation for low export
values at the beginning of the export activity could be related to the “traditional” product cycle:
discovery, rapid growth, maturation and decline (Shepherd, 2007).3


In Besedes and Prusa (2007), the authors use non-parametric survival techniques (Kaplan-
Meier estimator) to analyze the duration of exports to the United States from 46 countries at the SITC
4-digit level between 1975 and 2003. They observe higher survival rates for developed and successful
developing countries. These results are consistent with those found in Nitsch (2008), who analyses the
duration of German imports and its determinants at the 8-digit level from 1995 to 2005. In his analysis,
the majority of trading relationships are of short duration and very often last only between one and
three years. He also finds that duration depends on exporter and product characteristics, and on the size
of the transaction.


All the authors cited above emphasize the role of the type of product and of trade values in
determining the duration of trading relationships, but ignore the role of fixed costs whether the latter
are sunk or paid in each period in order to operate in foreign markets. Yet, one possible explanation for
trade stability (i.e. persistence of export status) goes back to the hysteresis trade literature of the 80’s
(Baldwin, 1988 and 1990, Baldwin and Krugman, 1989 and Dixit, 1989). Inspired by the effects of the



1 Trade expansion can occur via two channels: the intensive and the extensive margin. Via the intensive margin
countries increase exports of existing products with existing partners. Via the second channel countries expand
their exports by introducing a new product in a new market, an existing product in a new market or a new product
in an existing market.
2 That is the number of times trade amounts to zero.
3 In his study, Shepherd argues that most of the new products do not get into the maturation stage. Poor survival
of low-valued trading relationships probably reflects the sensitivity to external supply or demand shocks.





2


dollar overvaluation between 1980 and 1985, these models explain the persistence (i.e. hysteresis) of
firms’ export participation as a consequence of the sunk costs associated with the entry into new
markets.4 Following the dollar appreciation, foreign firms entered the United States market (while
United States firms exited some markets), but since they incurred entry costs they did not necessarily
exit once the exchange rate went back to its initial value. Market entry is generally costly: firms have to
meet market-specific standards and regulations, adapt their packaging, establish distribution channels,
accumulate information about foreign markets, etc.


The key point in these models is that entry fixed costs can have an impact on firm’s export
status and therefore on trade duration. Based on these models, the empirical literature on export and
firm performance has looked at the role of entry costs in the export decision process. In particular
Roberts and Tybout (1997) and Bernard and Jensen (1999, 2004) investigate the presence of sunk costs
and its influence on firms’ market participation. Both studies use lagged export status as a proxy for
sunk costs and find that they play a significant role in the decision to export. Roberts and Tybout
(1997) employ a dynamic probit model to analyze the entry and exit decision patterns of a panel of
Colombian manufacturing firms from 1981 and 1989. In their model, each firm has to pay a fixed cost
before entering the export market. Following entry, firms only bear variable costs. They introduce
dummies to control for the firm’s past export status and show that exporting history matters. Bernard
and Jensen (2004) use a linear probability framework to investigate the role and magnitude of sunk
costs using a sample of continuously operating United States plants from 1984 to 1992. They also find
that the entry costs are significant and that the probability of being an exporter today increases by 36
per cent the probability of being an exporter tomorrow. These papers identify the importance of entry
fixed costs for export status, thereby providing evidence that they should also be included when
explaining the duration of trading relationships.


Additional insights can be found in Irarrazabal and Opromolla (2009), who introduce
uncertainty (firms’ productivity evolves stochastically as a Brownian motion) in a trade model with
heterogeneous firms. In addition, fixed export costs are decomposed into sunk and per-period
components. In this context higher sunk costs imply higher initial export values. The authors define
and characterize their model like in Dixit (1989) and test using simulations how a cut in per-period
fixed costs and sunk costs could affect exporters and non-exporters’ status. They find that history-
dependent export decisions are a salient feature when export fixed costs are sunk upon entry in the
foreign market. It is not necessarily the case when fixed costs are paid on a per-period basis. Moreover,
the implications for the persistence of the export status are different. A reduction in per-period fixed
costs increases persistence in export status for exporters and decreases persistence in non-export status
for non-exporters. The logic behind this result is that, as fixed costs decline, the probability that an
exporter would be able to cover his fixed costs increases and the probability to start exporting for
domestic producers because of a positive shock increases. Empirically, we could then expect a negative
relationship between per-period fixed cost and survival rates. On the other hand a reduction in sunk
costs decreases the persistence in export status of exporters and non-exporters. This result is also found
in other studies presenting a dynamic version of the export decision and export path in the presence of
sunk costs to exporting such as Roberts and Tybout (1997), Das, Roberts and Tybout (2007),
Constantini and Melitz (2007) and Eaton et al. (2008). We should then observe a positive empirical
relationship between sunk costs and survival rates.


Following the empirical strategy adopted in Besedes and Prusa (2006b), we explore the
patterns and determinants of trade duration for a set of 96 countries over the 1995-2004 period. To this

4 In a more general framework, hysteresis can happen when the effect of any negative supply or demand shock
persists even when the shock has vanished. Hysteresis models where demand factors play a role also predicts that
new entrants initially facing low demand in foreign markets will not exit the market once the shock has vanished.
In the next period, since consumers in the foreign market have tried their products, firms will now face higher
demand curves. Consequently after the shocks vanished it is possible that not all the new entrants will be forced
out. Essentially the shock leads to a lasting change in the information set of consumers and this structural change
leads to hysteresis (Baldwin, 1988).





3


end, we analyse the sequence of export status at the HS 6-digit level using the semi-parametric Cox
survival model controlling for factors possibly influencing export survival. We do not only extend
Besedes and Prusa’s analysis to a matrix of bilateral trade relationships but we also augment the list of
duration determinants using recent data on export costs. In particular, we take a closer look at the role
of countries development level, the type of product, the size of exports and the role of export costs.


Our empirical results first show that trading relationships involving developed and emerging
economies face lower hazard rates, i.e. lower risk of “failure”, than those involving developing
countries. Second, they show that the relationship between trade duration and the type of product
portrays the degree of competition/information patterns characterizing traded products. Third, initial
export value appears to be positively correlated with export survival. Finally, export fixed costs affect
positively trade duration, but their effect decreases with time and with the initial size of exports. Hence,
our results support most predictions of recent theoretical contributions and are in line with existing
empirical findings included those based on firm-level data. An important exception, however, is the
estimated direct impact of fixed costs to exports.


The rest of the paper is organized as follows. The next section describes the raw data and
identifies a series of stylized facts related to the duration of trade relationships. Section 3 presents the
empirical strategy adopted. Empirical results are summarized in Section 4. Section 5 concludes.




2.  Duration, trade and development  


In this section we look at the features of trading relationships among 96 developed and
developing countries in order to sketch trade duration patterns across regions. The data presented here
are also used to carry out the empirical analysis. Our data are extracted from BACI, a trade database
maintained by CEPII.5 Based on the United Nations’ COMTRADE database, BACI provides
harmonized bilateral trade data6 at the HS 6-digit level for a total of 5,017 categories.7 Its main
advantage is that by applying different harmonization procedures (see Gaulier and Zignago (2007) for
details), BACI reconciles mirror flows, thus providing a more complete and refined geographical
coverage. Therefore, BACI achieves a greater accuracy of the zeros (i.e. absence of trade) in the trade
matrix, which is of particular importance in the present case, as it directly enters in the definition of
trade duration.


For the purpose of our analysis, we define three groups of countries: North (30 countries),
Emerging South (22 countries) and Developing South (44 countries).8 This broad categorization
reflects only major differences in economic development but already permits a relevant
characterization of trade duration. We define a trading relationship as the combination of an exporter,
an importer and a product. Based on this definition, we identify 7,114,784 trading relationships9 over
the 1995-2004 period, 762,622 (ca. 17,000 per country) of which involve exporters from the
Developing South (DS) group, 2,106,814 (ca. 96,000 per country) involve exporters from the

5 BACI is the French acronym for Base pour l’Analyse du Commerce International: Database for International Trade
Analysis. CEPII stands for Centre d’Etudes Prospectives et d’Informations Internationales.
6 Different procedures have been developed to harmonise the data: the evaluation of the quality of country declarations to
average mirror flows, the evaluation of CIF rates to reconcile import and export declarations, the conversion in tonnes of
other units of quantities exchanged.
7 In our analysis, we can not distinguish the number of exporting firms since we use product level data. However, the absence
of trade in one category allows inferring that no firm exports, and a positive trade value allows concluding that at least one
firm exports the product. This implies that aggregation does smooth firms’ entry-exit sequences but only partially.
8 Appendix 1 contains the complete list of countries included in the sample as well as their group affiliation. Our
classification follows the one in Akın and Kose (2007), who divide developing countries into two groups based on the extent
of their integration into the global economy. The emerging economies group roughly corresponds to the economies included
in the MSCI Emerging Economies index.
9 We exclude trading relationships with values below $1,000 and trade relationships involving oil products.





4


Emerging South (ES) group and 4,245,348 (ca. 141,000 per country) involve exporters from the North
(N) group.




2.1. Trade duration: a first mapping


We first look at the extensive margin for each group of countries. New trade relationships10
represent 81 per cent of total trade relationships recorded for the Developing South group. The figure is
62 per cent and 47 per cent for the Emerging South and North group respectively.


We then qualify trade failure patterns, by counting the number of the trade relationships that
disappeared during the period under consideration. Failure happens when a trading relationship
disappears until the end of the period under consideration. The data show that 68 per cent of the trade
relationships initiated by the Developing South’s exporters failed within that period. In the case of the
Emerging South 57 per cent of the new trading relationships failed, while in the North group failure
affected 62 per cent of the new trading relationships.


Finally, we investigate the patterns and differences in trade duration, i.e. the length of a trade
relationship, across regions. The duration can be simply assessed by counting the number of years, not
necessarily consecutive, an exporter has served a market. Besides recording errors, the approach is
unavoidably subject to right and especially left censoring due to the limited and relatively short period
of time covered by the analysis. Despite these drawbacks, and leaving statistical methods used to
correct for them to the econometric analysis presented in section 4, we believe that a glance at the data
remains relevant to identify any specific patterns in trade duration possibly related to differences in
economic development or any other characteristic. We sort trading relationships based on their
durations and report the results in Figures 1a and 1b.




Figures 1a and 1b. Trade matrix composition (1995-2004)






First, we observe that the duration of trading relationships varies strongly across regions.
Second, trading relationships are mostly of short duration. One and two-years old relationships account



10 A trade relationship is assumed to be “new” in our sample if it appeared in 1996 or latter. We also conducted the analysis
including as new only those relationships appeared in 1997 or after. In the latter case results in relative terms are only
marginally modified.


0
20


40
60


80
10


0
sh


ar
e


in
th


e
to


ta
l n


um
be


r t
ra


de
re


la
tio


ns
hi


ps


N ES DS


Trade matrix composition (1995-2004)


1 to 2 years
3 to 6 years
7 to 9 years
10 years


Duration


0
1,


00
0


2,
00


0
3,


00
0


4,
00


0
nu


m
be


r t
ra


de
re


la
tio


ns
hi


ps


N ES DS


Trade matrix composition (1995-2004)


1 to 2 years
3 to 6 years
7 to 9 years
10 years


Duration


These graphs do not include values below $1,000.





5


for at least one third of the total number of trading relationships in each region. The share is the largest
in the case of the Developing South, with 67 per cent of total trading relationships. On the other hand
trading relationships with no interruption, i.e. with 10-year duration, account only for a small share in
the trade matrix: 32 per cent in the case of the North, 20 per cent in the case of the Emerging South and
4 per cent in the case of the Developing South group. The distribution of other durations i.e. durations
longer than two years and shorter than 10 years, exhibits a remarkably similar pattern across regions as
shown in Figure 1b. These figures show that although trade failure affects regions in a similar way; the
time until failure varies strongly across regions.




2.2. Duration and trade values


Another important feature of a trade relationship is its value. All existing models dealing with
trade duration generate a positive relationship between initial trade values and the length of a trade
relationship. Such result unambiguously leads to a positive correlation also between the yearly average
of the value and the duration of a trade relationship. The following graphs are based exclusively on
average trade values but similar results are obtained using initial trade values. However, the use of
average limits the potential bias due to either reporting errors or multiple spells relationships.


In order to sketch the distribution of trade values across regions, we first compute for each
trading relationship its average trade value. The latter is the sum of the trade values in each year
divided by the number of years of service. We then classify trade relationships according to their
average trade value. Figures 2a and 2b show the results. The most striking fact is that between 55 per
cent (North) and 75 per cent (Developing South) of the total number of trade relationships generate less
than $50,000 on average per year. Trade relationships with an average value between $50,000 and
$500,000 per year account for around 30 per cent in the North and Emerging South, and for 20 per cent
in the Developing South. Trade relationships with an average value of more than $500,000 per year are
rare, representing less than 15 per cent in all three regions.




Figures 2a and 2b. Average trade values (1995-2004)




These graphs do not include values below $1,000.


0
1,


00
0


2,
00


0
3,


00
0


4,
00


0


nu
m


be
r o


f
tra


de
re


la
tio


ns
hi


ps
in


th
ou


sa
nd


s


N ES DS


Average trade value by trade relationships


trade<10
10<trade<50
50<trade<100
100<trade<500
500<trade


Average trade
in US thousands


0
20


40
60


80
10


0


sh
ar


e
in


th
e


to
ta


l n
um


be
r o


f
tra


de
re


la
tio


ns
hi


ps


N ES DS


Average trade value by trade relationships


trade<10
10<trade<50
50<trade<100
100<trade<500
500<trade


Average trade
in US thousands








6


These figures show that the majority of the trade relationships (bottom-bars) are low-valued
across regions. To get a sense of how average trade values vary with trade duration, we classify trade
relationships according to their average trade and according to their duration. For accuracy purposes
we exclude from our sample the one-year old trade relationships observed in 1995 (since we don’t
know if it started before) and in 2004 (for we don’t know if they continue), as well as the ones
concerning transportation equipment goods which are often a one-year-only transaction involving high
trade values.11 Results for each region are plotted in Appendix 2. Across regions more than half of the
trade relationships that last for only one year have an average trade value lower or equal to $10,000 per
year. This is also the case for around half of trade relationships that last for two years. From two years
on, the majority of trade relationships have an average trade value larger than $10,000. In other words,
as duration increases, the share of low-valued trade relationships (less than $10,000) decrease i.e.
bottom zone shrinks. At the other end most of the relationships that lasted for 9 to 10 years have an
average trade value that is larger than $50,000 per year.



2.3. Duration, trade and development


So far we have characterized each trade relationship by its duration and average trade value. In
this subsection, we take a closer look and examine how these characteristics vary across regions. For
each country, we compute the median duration and the median average trade value. The results are
plotted in Figures 3a, 3b and 3c. For countries in the North and in the Developing South, we find a
positive relationship between the median duration and the median average trade value. Countries like
Germany, the United States, Italy and France show the highest export performance in the Northern
region in terms of duration. Eastern European economies and Greece show the lowest median duration
and median average trade values.


As for the Emerging South, China is heading the group with the highest median duration
(seven years) and average trade per year ($59,000). Contrary to the North and the Emerging South,
none of the countries in the Developing South show a median duration longer than three years and a
median average trade value larger than $20,000. The Developing South is also the only region to
comprise countries with a median duration of one year. Indeed, the majority of countries in the
Developing South are in the bottom-left part of the graph. Interestingly, these figures point to different
stages of development and export performance across countries (in terms of duration and trade values).
Moreover, average trade values associated with a given duration are consistent across regions. For
instance, a two-year median duration in the North includes countries with median trade values between
$10,000 and $15,000. In the case of the Emerging South, the median trade values of a median duration
of two years range from $13,000 to $19,000. Trade relationships in the Developing South with a
median duration of two years have a median average trade value that ranges from $8,000 to $19,000.
The consistency of these numbers across regions reveals a possible trade threshold. Among the 96
countries, trade relationships with a median average trade value larger than $18,000 will last for at least
three years in most of the cases. Countries whose median average trade value is between $20,000 and
$40,000 per year will tend to export for four to seven years. Countries with a median trade value larger
than $40,000 per year will tend to export more for more than eight years. The only exception is China,
whose median average trade value is almost $60,000 and median duration, is seven years. These
figures show that the patterns of trade duration portray countries’ level of development.





11 These correspond to HS 2-digit codes: 86 to 89.





7


Figures 3a, 3b and 3c. Duration and trade values by countries




EstoniaLatvia
GreeceLithuania


AustraliaIreland
NorwayHung ryNew Zealand
PortugalSlovakiIsrael
Sl venia
Cana a Finland


Poland


Czech RepublicDenmark


SpainBelgium
Austria


Sweden
Netherlands


Switzerland


United Kingdom
Japan


United States


France
Italy


Germany


0
20


40
60


80
m


ed
ia


n
tra


de
v


al
ue


b
y


ye
ar


(U
S


$
th


ou
sa


nd
s)


2 4 6 8 10
median duration


North trade relationships


VenezuelaEgyptColombiaPeru
ChilMoroccoJordan
Pakistan South Africa


Mexico


Indonesia
Argentina
Philippines


TurkeyIndiaMal ysia
Brazil Hong Kong, ChinaThailand


Singapore Taiwan, China


China


0
20


40
60


80


2 4 6 8 10
median duration


Emerging South trade relationships


Nigeria


Nicaragua
SenegalMauritania


Liberia
Tanzania
Tri idad and Tobago
CameroonGhanaBoliviaUgandJamaica


Cote d'Ivoire


Q tarKuwait


Angola


Zambia


B h m s


Sudan


Y men


Algeria
Bahrain


United Arab Emirates
Sri LankaIran


OmanSaudi Arabia


Mauritius


Paraguay


Bangladesh


Uruguay


Panama


Dominican Republic
Lebanon
Kenya


Ho dur sGuatemala


TunisiCambodia


EcuadorZimbabwe
Cost Rica


El Salvador


Viet Nam


0
5


10
15


20


2 4 6 8 10
median duration


Developing South trade relationships




m
ed


ia
n


tra
de


v
al


ue
b


y
ye


ar
(U


S
$


th
ou


sa
nd


s)
m


ed
ia


n
tra


de
v


al
ue


b
y


ye
ar


(U
S


$
th


ou
sa


nd
s)










8


3.  Empirical analysis 


This section sketches the empirical model implemented to identify the main determinants of
trade duration echoing the stylized facts identified in the previous section.



3.1. Empirical strategy


As previously mentioned, the length of trade relationships can be examined using survival
analysis techniques.12 Hazard rate and hazard ratios are at the heart of this type of analysis. The hazard
rate ( )th is the ratio of the probability of failure to the probability of survival.


( ) ( )( )tS
tfth =


In the continuous time case it can be interpreted as the risk of an event to happen (i.e.
instantaneous rate of occurrence) by t, while in the discrete time case it is simply seen as the
conditional probability that the event will occur in time t, given that it has not occurred before. We are
interested in understanding how certain factors may affect the survival time of trading relationships.
There is a large family of survival models that can be used for continuous or discrete time cases. We
use the semi-parametric Cox (1972) model. This type of model has the advantage that it does not
require the specification of the distribution of the duration dependency and it is therefore appropriate to
assess the impact of explanatory variables on the hazard rate. The hazard rate in the Cox model is given
by:


( ) ( ) ixi ethth '0 β=


where ( )th0 is the baseline hazard function13, which in the Cox model is assumed to be
unknown and left unparametrized, ix is a vector of covariates representing the characteristics of
individual i, β is a vector of coefficients, accounting for the effect that those characteristics. By taking
the natural logarithm, we obtain the additive log-linear model to be estimated:


( )
( ) ii xth
th 'log


0


β=⎟⎟⎠


⎜⎜⎝


(1)




The estimates of the covariates in Cox models are obtained by the estimation of the partial
likelihood.14 In our case since the data shows ties i.e. proper to non-continuous cases, the partial
likelihood can only be approximated. As for the interpretation of the exponentiated coefficients, a
value larger than one indicates a positive effect on the hazard rate, while a value between zero and one
implies a negative effect on this latter. A value equal to one means the covariate does not have any
effect on the hazard rate. A last point concerns the assumptions related to the model, the Cox model is

12 Appendix 3 presents a general formulation of this survival analysis.
13 The term ( )th0 represents the risk at time t when ( ) 0=txi .
14 The partial-likelihood approach is used to estimateβ without specifying the form of the baseline hazard
function ( )th0 .





9


a proportional hazard rate model, which means that the ratio of two hazard rates is a fixed proportion
across time. We carried out the Schoenfeld test15 based on the regression residuals to assess the validity
of this assumption. The overall result pointed to the rejection of the proportional assumption. This is
common, especially when time-varying covariates are included in the model, which is the case in the
present study (i.e. GDP, trade value, competition, exchange rate).16 To take into account of the time
dependency of certain covariates vary with time, we took their average over the life period of a
relationship, so that the variables GDP per capita, trade value, competition and exchange rate are spell-
specific (i.e. period specific). As described below, we also use measures of export fixed costs (e.g. the
time spent on export procedures) whose effects on trade duration are assumed to change over time.
Indeed, it is reasonable to think that once exporters have learnt how to proceed, the time required to
export in the next period would be lower. To account for such possibility we allow for non
proportionality also on the export costs by adding an interaction term between the fixed costs and the
time duration of a relationship (i.e. number of years, in logs).


In our analysis, we then implement an extended version of the Cox model that relaxes the
proportionality hypothesis by including time-dependent covariates and time interaction terms.17



3.2. Data


A number of caveats in our dataset need to be highlighted. First and, as already mentioned,
observations are likely to be subject to left and/or right censoring. In the case of left censoring we don’t
know if trading relationships with a positive value in 1995 began that year or any year before. For
accuracy purposes we exclude possibly left censored relationships and keep only the ones that were
established strictly after 1995. This reduces our sample by 19 per cent in the case of the Developing
South (i.e. 619’218 trading relationships remain), 38 per cent in the case of the Emerging South (i.e.
1’310’746 trading relationships remain) and by 53 per cent in the case of the North (i.e. 2’003’678
trading relationships remain). As for right censoring, it involves trading relationships observed in 2004,
for which we don’t know if 2004 was the exit year. Unlike left censoring, right censoring can be easily
handled by survival methods.


Second, there is the issue of multiple spells (see Appendix 4): a trading relationship can stop
and be re-established once or several times over our 10-year period, after an interruption of one or
more years.18 In our dataset 13 per cent (in the case of the Developing South) to 20 per cent (in the case
of the North) of the trading relationships show multiple spells.19 In this exercise, we look at the
duration of first spells only, while controlling for the existence of multiple spells.20


Finally, trade data can suffer from measurement errors. This is particularly important in the
case of multiple spells. However if the interval between spells is just one year, the probability that this
is due to misreporting is very high i.e. no trade recorded when in reality there was trade. Overlooking



15 Under the null hypothesis, the proportional hazard ratio is accepted.
16 In this case, the usual procedure is to model time dependency by introducing an interaction effect between some
function of time and the covariate that does not comply with the proportionality assumption. By doing so, we
relax the assumption that the hazard ratios are proportional across time for the covariate in question.
17 Brenton et al. (2009) address the possible issue of heterogeneity by estimating a Prentice Gloeckler model
incorporating a gamma mixture distribution. Their results suggest that individual heterogeneity is likely to bias
results in a standard Cox model. However, as far as our analysis is concerned, the application of Brenton et al.
(2009) approach would not allow us to identify the impact of costs to exports data as they would be absorbed by
the treatment of individual effects.
18 In other words, a multiple spell is composed by more than one spell, each of them separated by one or several
years of non-service i.e. no trade.
19 These figures refer to the situation after we corrected for the possibility of measurement errors.
20 We reiterated the analysis counting as a single spell any multiple spell trade relationship. Estimates are only
marginally affected when affected in most specifications.





10


this issue could lead to the underestimation of the duration of the first spell. In order to correct for this
possibility we assume that a one–year gap is a measurement error and thus merge into one all the spells
with a one-year gap.



3.3. Control variables


Our main interest is to identify the factors that could explain the duration of trading
relationships across countries with possibly some regional specificity. To do so, we estimate equation
(1), where the dependent variable is the hazard of a trade relationship, i.e. the rate of occurrence of a
trading relationship exiting a market after t years, and where the vector of control variables is
composed essentially of “gravity”, product type and export costs variables. Sources and details for each
variable used are provided in Appendix 5.




Gravity covariates


Variables used in standard gravity specifications retained for our analysis are: GDP per capita
(in log), distance (in log), landlocked, border, common language and colonial links. The rationale is
that these variables not only affect trade volumes, but also the occurrence of trade and thus its duration.
As argued in Rauch (1999), proximity, common language and colony ties facilitate the establishment
and increase the probability that a trade relationships succeed. As for the GDP per capita, it is a proxy
for countries level of development as well as for (foreign) markets potential. The GDP per capita is an
average over the period of service and is included in its log form for both exporting and importing
countries.




Products characteristics


We include dummies by type of product. We follow the classification used by Rauch (1999) in
which products are classified according to their degree of differentiation: commodities or reference
priced goods, homogeneous products and differentiated products. The first category of goods refers to
goods that are traded on organized exchange markets and that involve specialized traders that centralize
prices. Homogeneous goods are goods that are not traded in organized exchange but have a reference
price (for instance quoted in trade publications). Finally heterogeneous goods are “branded” goods. We
expect that trade relationships based on differentiated goods will exhibit longer duration as they face
lower competition.




Per-period fixed costs


To control for the fixed costs that exporters face each time they sale abroad, we use data from
the Doing Business (DB) project, namely the time required to export.21 This variable refers to the time



21 Time is recorded in days. The time calculation for a procedure starts from the moment it is initiated and runs
until it is completed. The procedures include preparation of bank documents, customs declaration and clearance
documents, port filing documents, import/export licenses and other official documents exchanged between the
concerned parties. Logistic procedures are also included; these range from packing the goods at the factory to
their departure from the port of exit, like for instance the time to load a cargo. This implies that this variable,
although it refers to fixed costs, its impact on business could decrease across time as a result of learning effects.





11


(in days) necessary to comply with all procedures required to export. 22 In our analysis, we prefer this
time variable to the number of documents required to export (also provided by the DB database), which
we consider less accurate: countries with the same number of procedures can require a different amount
of time to complete them (Appendix 6).23 We also consider the time required to import as a proxy for
import costs and the time to start a business as a control for the business environment. The Doing
Business project provides data for all the 96 countries in our sample but only from 2004 onwards in the
case of the “Starting a Business” variables and from 2006 onwards in the case of the “Trade across
Borders” variables. To deal with the lack of data between 1995 and 2003, we construct a set of
dummies. For each cost variable we first compute the median cost across the whole sample. The
associated dummy takes the value of 1 if the cost value is higher than the median time to export (which
is 20 days) and 0 otherwise. In doing so, we assume that countries that were in the upper half (lower
half) of the cost distribution between 2004 and 2008, were also in the upper half (lower half) of the
cost distribution between 1995 and 2004. This assumption is based on the observation that variation of
costs over the period 2004-2008 is relatively low. Therefore the probability of a country switching
from one half to the other over the 1995-2004 period will also be low. At the same time a change in the
ranking within one half does not affect the value of the dummy and thus of the results. We construct
three cost related variables in the same way: one for the export costs, one for the import costs and
finally one for the costs related to starting a business.




Sunk costs


As previously discussed, the existence of sunk costs is expected to affect the duration of a trade
relationship. Higher sunk costs reduce exporters drop out and as such increase the length of positive
export spells. Theoretically, higher sunk costs are associated with both higher initial and higher
average export values. Figure 4 shows positive and significant unconditional correlations between
average trade values and duration. We use both measures (separately as they are highly correlated) to
check whether the sign and significance of the relationship are maintained in the presence of other
controls.




Additional control variables


We control for the size of the importing market by including the average number of countries
that export the same product to this market. The average is computed over the years for which the
relationship existed. We test for the impact of the macroeconomic environment by accounting for
variation in the exchange rate with respect to the United States Dollar. We control for multiple spells
by adding a dummy that is 1 whenever a relationship has more than one spell. By doing so, we want to
control for the possibility that the first spell in a multiple-spell relationship is systematically shorter
than single-spell relationships. 24 If that was the case and uncontrolled for that could bias the results.25
Finally, regional dummies are included whenever relevant.





22 Other authors have also identified time as a trade barrier, although they have only focus on the time associated
to transport (Hummels, 2001; Djankov S., Freund C. and Pham C. S., 2006).
23 Figures in Appendix 6 show first that there is positive relationship between time and the number of documents
required. Secondly, they show that the variability across countries is the largest for the time variable.
24 First spells with only one year of service in a multiple-spell relationship accounted for 67.5 per cent of the total
number of trading relationships, and first spells with less than three years accounted for 92 per cent of the total
number of trading relationships.
25 See for instance Hamerle (1989) for discussion and empirical illustration.





12


4.  Results 


We first estimate the model in equation (1) for the whole sample of countries. Results are
reported in Table 1. Second, we estimate the survival equation for each group of countries separately:
Developing South, Emerging South and the North (Tables 2a, 2b and 2c). Third, we estimate the model
for all countries including a measure of volatility in trade policy in importing countries. Due to limited
data availability only observations on manufactured goods are included in the estimation (Table 3).
Tables 1 and 3 are organized in a similar manner. Two different specifications are considered. The first
one is the baseline model where only export sunk costs are considered. Per-period export fixed costs
are introduced in the second specification. The first two columns of each table show the results
obtained using initial trade values as a proxy for sunk costs. The last two show the results obtained
using average trade values as a proxy for sunk costs.26 In case of multiple spells, the average trade
value is the one of the first spell. Table 2 reports only results for specifications including all cost
variables that is two for each regional country group.


As a last step, we do some robustness checks (Table 4). We first introduce an interaction term
between trade values indicators and per-period fixed export costs variables. Second, we further interact
export costs with the log of duration of the trade relationship.


All coefficients are presented in their exponential form. A value lower than one indicates that
the effect of changes in the covariate on the hazard rate is negative (higher values of the covariate
decrease the hazard rate). A value larger that one indicates that the effect of changes in the covariate on
the hazard rate is positive (higher values of the covariate increase the hazard rate). For dummies, a
coefficient in its exponential form can be easily transformed in a percentage change.27




4.1. Whole sample


Generally speaking, whether we use average trade value or initial value as a control variable it
does not affect the sign of other coefficients estimates. In terms of magnitude, the variation is at
maximum 5 per cent. We also obtain that our measures of export fixed costs are mostly orthogonal to
other covariates.


The relationship between average trade values and duration observed in section 2 is confirmed
by our empirical results: relationships with higher average trade values face lower hazards. This is also
true when initial trade values are considered. However, the magnitude of the impact is much stronger
with average trade values. Doubling the initial value would lead to a reduction of 4.4 per cent in the
hazard rate. The same increase in average trade value would decrease the hazard rate by slightly more
than 12 per cent. Average trade values are likely to reflect other elements than simply sunk costs. This
is also true for initial trade value but perhaps in a less prominent manner. Higher average trade values
could reflect for instance relatively lower arrival rates of shocks because of more maturity in the trade
relationship or high quality products.





26 Results appearing in the last two columns are taken from Fugazza and Molina (2009), where only the average
values where used as covariate.
27 Details are presented in the second section of Appendix 3.





13


Table 1. Cox proportional hazard ratios estimates (all countries)


Variables reg1 reg2 reg3 reg4


Exporter GDP (log) 0.953a 0.917a 0.933a 0.902a

Importer GDP (log) 1.003a 0.968a 1.001 0.972a

Initial trade value (log) 0.938a 0.939a

Average trade value (log) 0.828a 0.829a

Common language 0.987a 0.985a 0.985a 0.982a

Border 0.923a 0.939a 0.923a 0.937a

Colonial link 0.957a 0.949a 0.939a 0.935a

Landlocked 1.102a 1.097a 1.082a 1.080a

Distance (log) 1.105a 1.108a 1.089a 1.093a

Change_ER 0.969a 0.966a 0.960a 0.957a

Average_competition 0.983a 0.982a 0.986a 0.985a

Multiple_spells 2.052a 2.046a 1.988a 1.974a

Differentiated goods 0.983a 0.984a 0.930a 0.931a

Homogeneous goods 1.052a 1.049a 1.083a 1.079a

Business (time) 0.950a 0.931a

Export costs (time) 0.908a 0.936a

Import costs (time) 0.885a 0.903a

Region DS 1.333a 1.360a 1.221a 1.254a

Region ES 0.909a 0.920a 0.919a 0.931a

Countries 96 96 96 96
Observations 3’517’835 3’517’835 3’517’835 3’517’835


Estimation with robust standard errors. Significance level: a p<0.01, b p<0.05, c p<0.1.


Estimated coefficients of the export fixed costs are to a large extent in contrast with existing
theoretical and empirical evidence. Other things equal, countries with higher export fixed costs,
measured by the time required to export show lower hazard rates (i.e. longer duration). The effect is
even stronger for import costs in destination countries. Figures are -10 per cent and -12 per cent
respectively, when the control is initial trade. They become -7 per cent and -10 per cent respectively,
when the control is average trade. This would contradict theoretical predictions in most papers such as
Irarrazabal and Opromolla (2009).


A sound business environment (captured in our case by the Doing Business variable measuring
the time-cost of starting a business) is also found to be synonymous of lower hazard rates. Other results
are in line qualitatively with those presented in the existing literature.






14


They suggest that a doubling in the GDP/capita of the exporting country reduces the hazard by
between 7 per cent (i.e. 0.902log(2) -1 ) and 3 per cent (i.e. 0.953log(2) -1 ).28 This result is consistent with
the figures shown in the first part of this study, in which trade relationships from the North and in the
Emerging South tend to have longer duration than the ones from the Developing South. The impact of
a rise in partner size is not uniformly signed but in general remains very small in magnitude.


As for the type of products, we choose reference priced goods as the base category. Our results
show that the hazard rate for differentiated goods is 1.6 per cent (initial trade) to 6.9 per cent (average
trade) lower than that of reference priced goods. In the case of homogeneous goods the hazard rate is 5
per cent (initial trade) to 8 per cent (average value) higher than the one for reference priced goods.
These results are comparable to those of Besedes and Prusa (2006b), although the magnitude differs. 29
Differentiated products survive the longest, followed by reference priced goods and then by
homogeneous goods. These estimates suggest that trade duration increases as products become more
differentiated. Indeed, poor duration could result from strong competition in international markets:
exporters of homogeneous products like primary goods are likely to face fiercer competition and
therefore lower survival.


Trade relationships that are disrupted at least once and are re-established face a hazard rate that
is on average twice as much as the hazard of single spells trade relationships. In other words, the first
spell of a multiple spell trade relationships will be systematically shorter than single spells trade
relationships.


Both fiercer competition in destination markets and depreciating currencies with respect to the
United States dollar are associated with lower hazard rates. The former result may indicate that a higher
number of competitors not only reflect markets with larger capacity of absorption but also deeper
maturity and as such less turnover across time. The latter result may reflect the impact of maintained
international competitiveness due to a weaker currency. It could be argued that the inclusion of the
United States in sample biases the results as their currency is the reference. We thus reran all
regressions by excluding the United States from the sample with no significant impact on any
coefficient estimates.


The coefficient on the region dummies DS and ES are both statistically significant (with the
North as the base region). According to the results, the hazard of trading relationships from the
Developing South is always more than 25 per cent higher than the ones involving exporters from the
North. In the case of Emerging South, trading relationships have a lower hazard rate (between 7 per
cent and 9.1 per cent depending on the specification) than the one involving exporters from the North.
Then, compared to the North, trade relationships from the Developing South are shorter while the ones
from the Emerging South last longer.




4.2 Group specificities


In the second set of regressions reported in Table 2, we estimate survival equations separately
for each region. The sample of exporters is then a region-specific sub-set of the original sample and the
sample of destination markets remains the original one.30 Only specifications that include our fixed
costs variables are reported. The set of first columns (Reg1) again refer to specifications with initial



28 In 2004, the country in our sample with the lowest GDP/capita was Zimbabwe (with $202 (PPP)) and the
largest were Norway (with $45,154 (PPP)) and Qatar (with $68,166 (PPP)).
29 The authors find that the hazard for differentiated products is 18 per cent lower than for the reference priced
products and 25 per cent lower than homogeneous goods.
30 The scope of the exercise is to identify possible region specific features keeping in mind however likely
statistical constraints and caveats.





15


trade values as control while the set of second columns (Reg2) refer to specifications with average
trade values as control.


One important feature of the results is the consistency of the coefficients on trade values across
regions. Estimates obtained for each sample are similar to those obtained for the full sample. Hence,
the expectation that sunk costs and the maturity of a trade relationship positively affect its duration is
always verified and appears to be a general property.


Table 2. Cox proportional hazard ratios estimates
(Developing South – Emerging South – North)



Developing Emerging North


Variables Reg1 Reg2 Reg1 Reg2 Reg1 Reg2

Exporter GDP (log) 0.959a 0.931a 0.955a 0.965a 0.558a 0.578a

Importer GDP (log) 0.965a 0.971a 0.993a 0.985a 0.960a 0.975a

Trade values (log) 0.930a 0.847a 0.939a 0.825a 0.947a 0.835a

Common language 1.022a 1.035a 1.022a 0.976a 0.969a 0.963a

Border 0.948a 0.952a 1.030a 1.000 0.814a 0.824a

Colonial link 1.032a 1.016a 1.117a 1.122a 0.892a 0.820a

Landlocked 1.140a 1.067a 1.052a 1.044a

Distance (log) 1.093a 1.076a 1.113a 1.085a 1.149a 1.146a

Change_ER 0.987a 0.990a 0.840a 0.845a 1.715a 1.816a

Average_competition 0.991a 0.992a 0.981a 0.985a 0.968a 0.972a

Multiple_spells 1.517a 1.694a 2.129a 2.149a 1.854a 1.770a

Differentiated goods 0.991c 0.935a 0.963a 0.902a 0.996 0.948a

Homogeneous goods 0.940a 0.989 1.077a 1.107a 1.071a 1.093a

Business (time) 0.993 0.885a 0.851a 0.891a 0.894a 0.964a

Export costs (time) 0.961a 1.046a 0.870a 0.887a 0.974a 0.977a

Import costs (time) 0.882a 0.878a 0.884a 0.887a 0.911a 0.938a

Countries 44 44 22 22 30 30
Observations 562’419 562’419 1’176’586 1’176’586 1’778’830 1’778’830


Estimation with robust standard errors. Significance level: a p<0.01, b p<0.05, c p<0.1.


Coefficient estimates on export and import costs variables show a similar pattern across
country groups. The pattern is also similar to the one shown in Table 1. Magnitudes vary across groups
with larger effects on hazard rates found for the Emerging South group. The Developing South group is
characterized by larger effects of import costs and smaller of export costs always compared to
coefficients estimated for the whole sample. In this case the sign of the impact (coefficient becomes
larger than one) is even reverted when the sunk costs control variable becomes the average trade value.
Actually the latter would be the only case that reconciles, although not completely as the impact of
import costs goes the opposite direction, our empirical results and existing theoretical predictions.





16


Other results are comparable to those obtained in estimations with the full sample reported in
Table 1,31 except for some gravity variables and product characteristics.


Domestic and foreign market sizes are associated with lower hazard rates in all cases.
However, the impact of domestic market size is much more pronounced for the Northern countries.
This result is most probably due to a strong composition effect, which reflects the lower hazard rates
that Northern countries face in average.


As to other gravity controls, common language behaves differently across country samples and
across specifications in the case of Emerging South. A common language would unambiguously
increase hazards for the Developing South group and Emerging South group when initial trade is the
control. In the second specification, results for Northern and Emerging south countries are in line with
those in the full sample.


Contrasting results between the Developing and Emerging south groups on one hand and the
North group (and full sample) on the other are also obtained for the colonial link variable. As for the
Developing South region, results are again linked to the fact that countries governing the ones in the
variable are characterized by relatively higher hazard rates. As far as the Emerging South group is
concerned, the result is essentially a China effect.


In the case of the Developing South, coefficients on the homogenous goods variable are now
below unity. Those obtained for the two other country groups are similar to those obtained in full-
sample estimations. Then, while homogenous goods face lower hazard rates than reference priced
goods for the Developing South sample, they face higher hazard rates for the Emerging South and
North samples. This is essentially a consequence of the fact that amongst developing south countries a
certain number of them are exporters of few homogenous goods only but in which they have a strong
comparative advantage that translated into significantly lower than average hazard rates.


Coefficients estimates for the exchange rate variable are below unity for both the Developing
and Emerging South country groups. The fact that they stand strongly above unity for the North
country group is the reflection of its very definition, as discussed in the previous section. Again,
removing the variable in any country sample does not affect the rest of the results and thus is not
critical to their interpretation.



4.3 Trade protection


In the third set of regressions, we include import tariffs from the data base “Trade, Production
and Protection” (Nicita and Olarreaga, 2007) to control for the impact of trade policy in importing
countries on trade duration. Trade policy could be expected to affect both trade values (especially
average trade value) and hazard rates. As such, its exclusion from estimations could introduce an
omitted variable bias. As indicated previously we consider the coefficient of variation of tariffs across
destination markets for a given product. Because of data availability, we had to restrict our sample to
manufactured products.


Results are presented in Table 3. Again similarity to figures in Table 1 is a feature common to
most estimated coefficients despite a drop of more than quarter in the number of observations. We also
observe that the drop of observations does not show any specific group pattern and can be seen to a
large extent as a random draw. Rerunning the whole set of estimations without our tariffs variable leave
all results unchanged. This seems to underline strongly the non relevance of a possible omitted variable
bias in previous results.



31 We also try a specification with strata by type of product (HS 4-digit level). The results remain very similar and
are not presented here.





17


The coefficient estimated for our trade policy variable is statistically significant and below
unity. We also apply the average tariff (results not shown) over the period instead of the coefficient of
variation. No major difference is found: the coefficient remains below unity and is statistically
significant. Then higher volatility in protection across destination countries or higher protection on
average are both associated with longer duration. Following Besedes and Prusa (2006b) argument,
higher tariffs (or higher tariff variation across countries) lower hazards as it implies less competition
for incumbent firms. From a purely theoretical point of view, in models with heterogeneous firms,
destinations with relatively higher tariffs or in general markets characterized by relatively higher
protection across countries will be served only by the most productive firms of each source country.
Such a feature can be easily related to longer lasting export status.




Table 3. Cox proportional hazard ratios estimates (All countries, manufactures)


Variables reg1 reg2 reg3 reg4


Exporter GDP (log) 0.951a 0.915a 0.928a 0.897a

Importer GDP (log) 1.000 0.960a 1.000 0.970a

Initial trade value (log) 0.938a 0.938a

Average trade value (log) 0.826a 0.826a

Common language 0.976a 0.975a 0.976a 0.973a

Border 0.904a 0.921a 0.903a 0.917a

Colonial link 0.964a 0.958a 0.946a 0.945a

Landlocked 1.093a 1.090a 1.075a 1.075a

Distance (log) 1.099a 1.103a 1.082a 1.086a

Change_ER 0.966a 0.963a 0.955a 0.952a

Tariff change 0.984a 0.985a 0.986a 0.986a

Average_competition 0.983a 0.982a 0.986a 0.985a

Multiple_spells 2.092a 2.084a 2.013a 1.997a

Differentiated goods 0.983a 0.984a 0.925a 0.926a

Homogeneous goods 1.057a 1.052a 1.083a 1.079a

Business (time) 0.949a 0.927a

Export costs (time) 0.912a 0.940a

Import costs (time) 0.874a 0.903a

Region DS 1.374a 1.400a 1.245a 1.277a

Region ES 0.913a 0.923a 0.921a 0.932a

Countries 96 96 96 96
Observations 2’716’330 2’716’330 2’716’330 2’716’330


Estimation with robust standard errors. Significance level: a p<0.01, b p<0.05, c p<0.1.





18


4.4 Robustness and extensions


To check the robustness of our results, we first interact the fixed costs variables i) with the
sunk costs and with ii) a time measure. Secondly, we also used a more detailed classification of our
export cost variable. Tables 4 and 5 present the results obtained with initial trade values as a control for
sunk costs. Estimates with average trade are similar but are not shown for a matter of clarity.


The first column of Table 4 is our benchmark specification and is taken from Table 1. Column
two of Table 4 shows estimates obtained with the inclusion of interaction terms between the sunk costs
and the fixed costs variables. The third column of Table 4 includes an additional interaction term
between the fixed export costs variable and the log of the duration (in years) of the trade relationship.
In the Cox model, the dependent variable is time until an event occurs, this is why we do not include
the variable “Time” when it is interacted with other covariates in the model. If we include time as a
covariate, we would obviously be explaining time until event occurs with a time counter. However, the
latter interaction is a straightforward but consistent method to relax the assumption of proportionality.32


The inclusion of an interaction term between fixed costs variables and the initial trade value
(column two) does not affect any coefficient estimates but those for the fixed costs variables. However,
only the coefficient for the export cost variable varies significantly. The direct impact of the latter on
the hazard rate remains negative but its magnitude is reduced. Results on interaction terms suggest that
sunk costs and export fixed costs have complementary effects on the hazard rate. The reverse is true if
we consider sunk costs and import fixed costs in destination countries. A possible explanation for such
results is that sunk and per period fixed costs faced by exporters in their home country are unavoidable
and as such can be expected to increase export status duration. Import fixed costs are destination
specific and as such can simply be lowered by “picking up” the right destinations. As a consequence
exporters will aim at reducing the time spent on higher cost level destinations and shift to lower cost
ones as soon as possible assuming that sunk costs are not destination specific.


Introducing the interaction between export fixed costs and the log of duration affects most
coefficients more or less significantly but never dramatically. The coefficient representing the direct
effect of export fixed costs on the hazard rate has turned positive and the effect of the interaction term
is strongly negative. This means that the effect of fixed costs to export on the hazard rate decrease with
the duration of a trade relationship. Results could indicate that exporters face a learning curve in
dealing with per period fixed costs. As administrative procedures are likely to be the same across
periods, it is very plausible that exporters become more efficient with time in treating them. This is
equivalent to say that the effective cost is exporter specific and decreases with persistence in the export
status of exporters.


Table 5 reports results obtained for a three–level export cost covariate rather than a two-level
cost covariate. In other words we define three dummies for the export costs variable instead of two:
LowCost (cat1) equals 1 when the number of days to export ranges from 1 to 16 and zero otherwise,
MiddleCost (cat2) equals 1 when the number of days to export ranges from 17 to 23 and zero
otherwise, finally HighCost (cat3) equals 1 when the number of days to export is larger than 23 and
zero otherwise. We do this to have a more accurate idea of the level of the fixed costs in each country
and allow for greater flexibility in the model. Our based category is category 1.



32 See for instance Kovacevic and Georgia (2007), Ezell et al. (2003), Box-Steffensmeier and Zorn (2002) for a
detailed discussion and application.





19


Table 4. Cox proportional hazard ratios estimates (All countries, Robustness Checks I)


Variables reg1 reg2 reg3


Exporter GDP (log) 0.917a 0.917a 0.932a

Importer GDP (log) 0.968a 0.968a 0.970a

Initial trade value (log) 0.939a 0.940a 0.930a

Common language 0.985a 0.984a 0.983a

Border 0.939a 0.939a 0.972a

Colonial link 0.949a 0.950a 0.923a

Landlocked 1.097a 1.097a 1.056a

Distance (log) 1.108a 1.109a 1.104a

Change_ER 0.966a 0.966a 0.978a

Average_competition 0.982a 0.982a 0.984a

Multiple_spells 2.046a 2.046a 1.820a

Differentiated goods 0.984a 0.983a 0.986a

Homogeneous goods 1.049a 1.049a 1.042a

Business (time) 0.950a 0.946a 0.968a

Export costs (time) 0.908a 0.939a 1.541a

Import costs (time) 0.885a 0.876a 0.894a

Init. trade value(log)*
business(time) 1.002 1.000

Init. trade value(log)*exp.
costs(time) 0.985a 1.066a

Init. trade value(log)*imp.
costs(time) 1.005a 1.002a

Inter_time 0.309a

Region DS 1.360a 1.359a 1.235a

Region ES 0.920a 0.920a 0.948a

Countries 96 96 96
Observations 3,517,835 3,517,835 3,517,835


Estimation with robust standard errors. Significance level: a p<0.01, b p<0.05, c p<0.1.





20


Table 5. Cox proportional hazard ratios estimates (All countries, Robustness Checks II)


Variables Reg1 Reg2 Reg3


Exporter GDP (log) 0.930a 0.930a 0.941a

Importer GDP (log) 0.968a 0.968a 0.970a

Init. trade value(log) 0.938a 0.940a 0.924a

Common language 0.986a 0.986a 0.988a

Border 0.938a 0.937a 1.002

Colonial link 0.955a 0.955a 0.925a

Landlocked 1.106a 1.106a 1.064a

Distance (log) 1.108a 1.108a 1.091a

Change_ER 0.969a 0.969a 1.001a

Average_competition 0.982a 0.982a 0.987a

Multiple_spells 2.048a 2.048a 1.708a

Differentiated goods 0.984a 0.984a 0.989a

Homogeneous goods 1.048a 1.049a 1.043a

Business (time) 0.943a 0.945a 0.989a

Import costs (time) 0.884a 0.876a 0.905a

Export cost(cat2) 0.975a 0.989a 1.755a

Export cost(cat3) 0.954a 0.971a 1.610a

Init. trade value (log)*exp.
cost(cat2) 0.993a 1.080a

Init. trade value (log)*exp.
cost(cat3) 0.992a 1.078a

Time (log)*exp. cost(cat2) 0.277a

Time (log)*exp. cost(cat3) 0.288a

Init. trade value(log)*
business(time) 0.999 0.996a

Init. trade value(log)*imp.
costs(time) 1.004a 0.999

DS 1.355a 1.355a 1.210a

ES 0.924a 0.924a 0.963a
Countries 96 96 96
Observations 3’517’835 3’517’835 3’517’835


Estimation with robust standard errors. Significance level: a p<0.01, b p<0.05, c p<0.1.






21


The first specification includes the business, imports and new exports costs variables. In the
second specification, we control for the interaction between the initial trade value of the transaction and
the export costs variables. Finally, we add the time interaction term as described above.


Overall comments applying to Tables 1 and 4 also apply to Table 5. In particular we do find
that increasing export fixed costs are associated lower hazard. However, column one also shows that
the transformation of our export fixed cost variable translates into weaker impact on the hazard rate.
Complementarities between per period and sunk export fixed costs are also found to be weaker (almost
absent) as shown in column 2. The last column reveals that learning about exporting procedures
remains a plausible feature of exporter status. Learning appears to be sharper for intermediate levels of
costs (cat2). Coefficients also indicate that the direct positive impact on hazard rates is likely to
dominate the learning through time impact in the first two periods.




5.  Conclusions 


Exporters’ survival in foreign markets is essential to achieve sustained export growth. This
paper presents an empirical investigation of possible determinants of exports survival rates following
insights from recent theoretical developments and empirical findings, in particular those related to the
role of fixed and sunk cost to export. Our analysis is based on disaggregated bilateral trade data for a
sample of 96 developed and developing countries over the 1995-2004 period.


Descriptive statistics reveal a series of stylized facts that qualifies trade duration across groups
of countries: (a) more advanced countries are involved in a larger number of trade relationships than
less advanced countries; (b) the extensive margin of trade is more prominent in trade relationships for
less advanced countries; (c) failure rates are not dramatically different across groups of countries and
are even larger on average for more advanced countries; (d) very short duration characterizes trade
relationships in less advanced countries; (e) across regions the majority of trade relationships have an
average trade value lower than $50,000 (f) trade relationships with low average trade values (less than
$10,000) tend to have shorter durations and (g) trade duration portrays countries’ level of development.


Some of these unconditional properties are confirmed by the results of survival analysis. Our
estimation strategy is primarily based on the canonical version of the Cox model. However, we also
implement an extended version of this model that relaxes the proportionality hypothesis by including
time-dependent covariates and time interaction terms. We find that export status increases with the
level of development of both the origin and destination countries measured by their GDP per capita.
Moreover, our results by region suggest that the effect of an increase in the GDP/capita on trade
duration is larger for higher levels of GDP. We also have that the duration of trade varies with the type
of product, which is in line with previous studies (Rauch, 1999; Besedes and Prusa, 2006b). Our results
further suggest that this effect is very similar across regions. Trade relationships involving
differentiated goods show a probability of failure that is 6 per cent to 14 per cent lower than the one
obtained for trade relationships involving homogeneous goods. As for trade costs, high export costs
increase the hazard in all regions but by less in the North and the Emerging South. This is not
surprising given that exporters in the Emerging South and in the North face on average lower fixed
costs than exporters in the Developing South. However, this result is in clear contrast with recently
established theoretical predictions. Results obtained with the augmented version of the Cox model
further indicate that the effect of fixed costs on hazard rates falls over time, suggesting the existence of
learning effects (i.e. export experience matters).


Finally, our results also show that overall trade relationships with either higher average or
initial trade values face lower hazard rates. The effect is highly consistent across regions and has
important policy implications.





22


Our results suggest that one way of improving export survival rates will be to implement
policies that aim to increase export revenues. As listed in Das et al. (2007), these policies can range
from having preferential access to inputs, credits, insurance to policies that reduce transports costs or
any other variable cost that firms face. According to their findings on a sample of Colombian
manufacturing producers, subsidies on export earnings have a more significant impact on export
revenues (per dollar spent) than subsidies that aim to reduce the entry (sunk) costs or entry fixed costs
faced by new exporters. Indeed, such policies would not only help incumbent exporters to increase
their profits and therefore to improve their survival rates, but also encourage the entry of firms into the
export market.


Further investigation will explore to what extent poor survival prevents developing economies
from diversifying into new products or new markets.












23




Appendix 1 


exporter region exporter region exporter region
Algeria DS Trinidad and Tobago DS Australia N
Angola DS Tunisia DS Austria N
Bahamas DS Uganda DS Belgium N
Bahrain DS United Arab Emirates DS Canada N
Bangladesh DS United Rep. of Tanzania DS Czech Republic N
Bolivia (Plurinational DS Uruguay DS Denmark N
State of) Viet Nam DS Estonia N
Cambodia DS Yemen DS Finland N
Cameroon DS Zambia DS France N
Costa Rica DS Zimbabwe DS Germany N
Côte d'Ivoire DS Greece N
Dominican Republic DS Hungary N
Ecuador DS Ireland N
El Salvador DS Argentina ES Israel N
Ghana DS Brazil ES Italy N
Guatemala DS Chile ES Japan N
Honduras DS China ES Latvia N
Iran (Islamic Rep. of) DS Colombia ES Lithuania N
Jamaica DS Egypt ES Netherlands N
Kenya DS Hong Kong, China ES New Zealand N
Kuwait DS India ES Norway N
Lebanon DS Indonesia ES Poland N
Liberia DS Jordan ES Portugal N
Mauritania DS Malaysia ES Slovakia N
Mauritius DS Mexico ES Slovenia N
Nicaragua DS Morocco ES Spain N
Nigeria DS Pakistan ES Sweden N
Oman DS Peru ES Switzerland N
Panama DS Philippines ES United Kingdom N
Paraguay DS Singapore ES United States N
Qatar DS South Africa ES N
Saudi Arabia DS Taiwan Province of China ES N
Senegal DS Thailand ES N
Sri Lanka DS Turkey ES N
Sudan DS Venezuela (Bolivarian Republic of) ES N










24


Appendix 2 






Figure A1a. Average trade values by duration in the North


0
50


0
1,


00
0


1,
50


0


to
ta


l n
um


be
r o


f t
ra


de
re


la
tio


ns
hi


ps
in


th
ou


sa
nd


s


1 2 3 4 5 6 7 8 9 10


N: Average trade values by duration


trade<10
10<trade<50
50<trade<100


100<trade<500
500<trade


Average trade in US thousands










Figures A1b and A1c. Average trade values by duration in the South


0
10


0
20


0
30


0
40


0


to
ta


l n
um


be
r o


f t
ra


de
re


la
tio


ns
hi


ps
in


th
ou


sa
nd


s


1 2 3 4 5 6 7 8 9 10


DS: Average trade values by duration


trade<10
10<trade<50
50<trade<100


100<trade<500
500<trade


Average trade in US thousands


0
10


0
20


0
30


0
40


0


to
ta


l n
um


be
r o


f t
ra


de
re


la
tio


ns
hi


ps
in


th
ou


sa
nd


s


1 2 3 4 5 6 7 8 9 10


ES: Average trade values by duration


trade<10
10<trade<50
50<trade<100


100<trade<500
500<trade


Average trade in US thousands













25


Appendix 3. Duration models  


Survival or duration methods were initially applied in medical and biological research to study
the effect of certain independent variables on the occurrence of an event. Today duration models are
also applied in labour economics (i.e. employment /unemployment duration), development economics
(i.e. duration in poverty) and very recently in trade economics with the analysis of the duration of
export activity.


General framework


In our framework, the event of interest is the “death”, i.e. failure, of a trading relationship.
Duration models assume there is a random continuous (general case) variable T, whose distribution is
specified by:


• a cumulative distribution function (cdf): ( ) ( )tTtF <= Pr ,which gives the probability of
the event taking place by time t and


• a probability density function (pdf): ( ) ( )
dt


tdFtf =
The survival function S(t) is defined as the complement of the cdf and thus gives the


probability of being alive at duration t:


( ) ( )tTtS ≥= Pr


( ) ( )tFtS −= 1
Another key component in duration models is the Hazard function h(t), also called


instantaneous rate of occurrence of the event. It is given by


( ) { }
dt


tTdtTTt
th


dt


>+<<= →
Pr


lim
0






Which can be written (after a few computations) as:


( ) ( )( )tS
tfth =


The hazard rate corresponds to the ratio of the probability of failure to the probability of
survival. In the continuous time case, it can be interpreted as the risk of an event to happen (i.e.
instantaneous rate of occurrence) by t, while in the discrete time case it is simply seen as the
conditional probability that the event will occur in time t, given that it has not occurred before. There is
a large family of survival models that can be used for continuous or discrete time cases to analyze the
effect of certain covariates on the hazard rate. The most general version of the hazard rate model is
given by:


( )( ) ( ) ( ) ( )txtii iethtxth '0, β=





26




Where ( )txi is a vector of time-varying covariates representing the characteristics of
individual i at time t, ( )tβ is a vector of time-dependent coefficients, accounting for the effect that
those characteristics have at time t (i.e. the effect of covariates varies across time). Within this family
of survival models, the Cox (1972) model has the advantage that it does not need to specify the
distribution of the duration dependency and so it is appropriate when we assess the impact of
explanatory variables on the hazard rate. The hazard rate in the Cox model is given by:


( ) ( ) ixi ethth '0 β=
Where ( )th0 is the baseline hazard function, which is assumed to be unknown and left


unparametrized. The term ( )th0 represents the risk at time t when ( ) 0=txi and ix'β are time-
independent covariates.




Interpretation of the coefficients of the explanatory variables


The interpretation of the coefficients of the explanatory variables depends on the model
specification and do not have the same interpretation as in a linear model. The sign of the coefficient
indicates the direction of the effect of the covariate on the risk of experiencing the event by t. In other
words, the sign indicates whether some particular variable increases or decreases the hazard rate. The
percentage change in the risk of experiencing the event in the case of a dichotomous covariate is given
by:


( ) ( )1*100*100% 1*0* 0*1* −=−=Δ kk kk ee eeth ββ
ββ




In the case of a continuous covariate the percentage change in the hazard rate for a δ unit
change in the explanatory variable x is given by:




( ) ( )


( )1*100
*100*100%


*


*


***


*


**


−=


−=−=Δ
+


δβ
β


βδββ
β


βδβ


k


k


kkk


k


kk


e
e


eee
e


eeth x
xx


x


xx




A value larger than one indicates a positive effect, a value between zero and one a negative
effect on the hazard rate. A value equal to one means the covariate does not have any effect on the
hazard rate.






27


Appendix 4 






































One-spell


One-spell


One-spell


One-spell


Multiple spells


One-spell


Trading
relationship


year
1995 1999 2004


1


2


3


4


5


6





28


Appendix 5 




Variables Description Source


GDP per capita In US PPP for the 1994-2005 period IMF


Distance Distance in km between the two largest cities in each country CEPII


Border Dummy variable, equals 1 if common border CEPII


Landlocked Dummy variable, equals 1 if country is landlocked CEPII


Common language Dummy variable, equals 1 if common language CEPII


Colonial link Dummy variable, equals 1 if colonial relationship CEPII


Depreciation rate World Development Indicators


The change in the exchange rate by spell. The exchange
rate is the nominal value of national currency per United
States dollars for the 1994-2005 period


Competition Author’s calculations



Average number of countries that export product X,
over the spell


Tariffs
Weighted applied tariff from the database “Trade,
Production and Protection, 1976-2004”, 3-digit level
(ISIC, rev2)



World Bank, Nicita A.
and M. Olarreaga
(2006)


Business costs Doing Business, WB


(Entry regulations)


Include the number of procedures and the time until the
process is complete before a business can be established.
(2004-2008)


Export costs Doing Business, WB


(Trading across borders)
Include the number of export procedures and time until
the procedures are completed (2006-2008)


Import costs Doing Business, WB


(Trading across borders)
Include the number of import procedures and time until
the procedures are completed (2006-2008)






29


Appendix 6 






AGO


ARE


ARG


AUS


AUTBEL


BGD


BHR


BHS


BOLBRA


CANCHE


CHL


CHN


CIV


CMR


COL


CRI


CZE


DEUDNK


DOM


DZA


ECU


EGY


ESP


EST


FIN


FRA


GBR


GHA


GRC


GTM


HKG


HND


HUN


IDN


IND


IRL


IRN


IRQ


ISR ITA


JAM


JOR


JPN


KEN


KHM


KWT


LBN


LBR


LKA


LTULVA


MAR


M X


MRT


US


MYS


NGA


NIC


NLDOR


NZL


OMN


PAK


PAN


PER


PHL


POL


PRT


PRY


QATSA


SDN


SEN


SGP


S V


SVKSVN


SW


THA


TTOTUN


TUR


TWN


TZA


UGA


URY


U A


VEN


VNM YEM


ZAF


ZMB


ZWE


2
4


6
8


10
12


nu
m


be
r o


f p
ro


ce
du


re
s


be
fo


re
e


xp
or


t,
av


er
ag


e


0 20 40 60 80 100
time in days, average


Export fixed costs








AGO


ARE


ARG


AUS


AUT


BEL


BGD
BHR BHS


BOL


BRA


CAN


CHE


CHL


CHN


CIV


MRCOL


CRI


CZE
DEU


DNK


DOM


DZA ECU


EGY
ESP


EST


FIN


FRAGBR


GHA


GRC
GTM


HKG


HND


HUN


IDN
IND


IRL


IRN


IRQ


ISR


ITA


JAM


JOR


JPN


KEN


KHM


KWT


LBN


LBR


LKALTU


LVA


MAR


MEX


MRT


MUS


MYS NGA


NICNLD


NOR


NZL


OMN
PAK


PAN


PER


PHL


POL PRT


PRY


QAT


SAU


SDN SEN


SGP


SLV


SVK SVN


SWE


HA
TTO


TUN


TUR
TWN


TZA


UGA


URY


USA


VEN


VNM
YEM


ZAF


ZMB


ZWE


0
5


10
15


20
nu


m
be


r o
f p


ro
ce


du
re


s,
a


ve
ra


ge


0 50 100 150
time in days, average


Starting a Business costs






Note: Country abbreviations used are ISO ALPHA-3 codes.





30


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No. 18 Bijit Bora, Aki Kuwahara, Sam Laird, Quantification of non-tariff measures, 2002, 42


p. Sales No. E.02.II.D.8.

No. 19 Greg McGuire, Trade in services – Market access opportunities and the benefits of


liberalization for developing economies, 2002, 45 p. Sales No. E.02.II.D.9.

No. 20 Alessandro Turrini, International trade and labour market performance: Major


findings and open questions, 2002, 30 p. Sales No. E.02.II.D.10.

No. 21 Lucian Cernat, Assessing south-south regional integration: Same issues, many metrics,


2003, 32 p. Sales No. E.02.II.D.11.

No. 22 Kym Anderson, Agriculture, trade reform and poverty reduction: Implications for Sub-


Saharan Africa, 2004, 30 p. Sales No. E.04.II.D.5.

No. 23 Ralf Peters and David Vanzetti, Shifting sands: Searching for a compromise in the WTO


negotiations on agriculture, 2004, 46 p. Sales No. E.04.II.D.4.

No. 24 Ralf Peters and David Vanzetti, User manual and handbook on Agricultural Trade


Policy Simulation Model (ATPSM), 2004, 45 p. Sales No. E.04.II.D.3.

No. 25 Khalil Rahman, Crawling out of snake pit: Special and differential treatment and post-


Cancun imperatives, 2004.

No. 26 Marco Fugazza, Export performance and its determinants: Supply and demand


constraints, 2004, 57 p. Sales No. E.04.II.D.20.

No. 27 Luis Abugattas, Swimming in the spaghetti bowl: Challenges for developing countries


under the “New Regionalism”, 2004, 30 p. Sales No. E.04.II.D.38.

No. 28 David Vanzetti, Greg McGuire and Prabowo, Trade policy at the crossroads – The


Indonesian story, 2005, 40 p. Sales No. E.04.II.D.40.

No. 29 Simonetta Zarrilli, International trade in GMOs and GM products: National and


multilateral legal frameworks, 2005, 57 p. Sales No. E.04.II.D.41.

No. 30 Sam Laird, David Vanzetti and Santiago Fernández de Córdoba, Smoke and mirrors:


Making sense of the WTO industrial tariff negotiations, 2006, Sales No. E.05.II.D.16.

No. 31 David Vanzetti, Santiago Fernandez de Córdoba and Veronica Chau, Banana split: How


EU policies divide global producers, 2005, 27 p. Sales No. E.05.II.D.17.

No. 32 Ralf Peters, Roadblock to reform: The persistence of agricultural export subsidies,


2006, 43 p. Sales No. E.05.II.D.18.

No. 33 Marco Fugazza and David Vanzetti, A South-South survival strategy: The potential for


trade among developing countries, 2006, 25 p.





35


No. 34 Andrew Cornford, The global implementation of Basel II: Prospects and outstanding
problems, 2006, 30 p.



No. 35 Lakshmi Puri, IBSA: An emerging trinity in the new geography of international trade,


2007, 50 p.

No. 36 Craig VanGrasstek, The challenges of trade policymaking: Analysis, communication and


representation, 2008, 45 p.

No. 37 Sudip Ranjan Basu, A new way to link development to institutions, policies and


geography, 2008, 50 p.

No. 38 Marco Fugazza and Jean-Christophe Maur, Non-tariff barriers in computable general


equilibrium modelling, 2008, 25 p.

No. 39 Alberto Portugal-Perez, The costs of rules of origin in apparel: African preferential


exports to the United States and the European Union, 2008, 35 p.

No. 40 Bailey Klinger, Is South-South trade a testing ground for structural transformation?,


2009, 30 p.

No. 41 Sudip Ranjan Basu, Victor Ognivtsev and Miho Shirotori, Building trade-relating


institutions and WTO accession, 2009, 50 p.

No. 42 Sudip Ranjan Basu and Monica Das, Institution and development revisited: A


nonparametric approach, 2010, 26 p.

No. 43 Marco Fugazza and Norbert Fiess, Trade liberalization and informality: New stylized


facts, 2010, 45 p.

No. 44 Miho Shirotori, Bolormaa Tumurchudur and Olivier Cadot, Revealed factor intensity


indices at the product level, 2010, 55 p.


No. 45 Marco Fugazza and Patrick Conway, The impact of removal of ATC Quotas on
international trade in textiles and apparel, 2010, 50 p.



No. 46 Marco Fugazza and Ana Cristina Molina, On the determinants of exports survival, 2011,


40 p.





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