export survival in lac - world...
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WHAT DRIVES EXPORT SURVIVAL?
AN ANALYSIS OF EXPORT DURATION IN LATIN AMERICA
Tibor Besedes Juan Blyde Georgia Institute of Technology Inter-American Development Bank
January 2010
ABSTRACT
Statistical techniques from survival analysis have recently been applied to explore the duration of trade relationships (Besedes and Prusa, 2006a, 2006b, 2007). Among the findings from this new literature is that trade relationships are remarkably brief. In this paper we provide evidence confirming that export relationships are in general short-lived but that significant differences across regions exist with Latin America exhibiting lower export survival rates than the US, the EU and East Asia, among others. Counterfactual exercises show that raising export survival rates in Latin America to the levels observed in other regions can produce fairly large increases in exports over the long run. In the second part of the paper we test a battery of possible correlates of trade duration to analyze what factors help explain the differences in survival rates. The findings point to a number of aspects with potential policy implications for the countries in Latin America. JEL No. F1 Key word: Survival, duration, export growth, specialization
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I. Introduction
In recent years, a growing body of reserach has stressed the importance of export discovery for
developing countries. Hausmann and Rodrik (2003) argue that developing countries facing
shortcomings in learning what they are good at producing (or “self-discovery”) is an important
explanation for their limited export success. Hausmann, Rodriguez and Wagner (2006) present
evidence showing that the degree of flexibility of the economy – measured by its capability to move
production to new goods – is significantly correlated to the length of its economic crises which tend
to be longer in the developing world.
Given its relevance to export growth and development, several studies have been devoted to
better understand the process of export discovery. Hausmann and Klinger (2006) explain differences
in the rate of structural transformation and diversification across countries as a function of input
linkages between sectors. Klinger and Lederman (2006) show that empirical measures of entry costs
affect patterns of new export discovery.
Although the lack of export discovery can be an important factor behind the lack of export
growth in developing countries, some authors have argued that the main reason behind the lack of
export growth in developing countries is not necessarily the failure to discover new export activities
but rather the inability to maintain export relationships (see Besedes and Prusa, 2007). The two
concepts, however, are not necessarily independent. For instance, some of the short-lived export
episodes that we observe might be trials and errors in which the exporter experiments with different
prototypes of the good or in different markets before “discovering” the new successful export
activity.
Stepping aside from this discussion, what is clear is that there is abundant evidence
describing new export attempts in developing countries that fail to survive after a few years of
service. A recent Inter-American Development Bank research project, for example, presents
information about exporters in Latin America that initially succeed in penetrating foreign markets
but fail to maintain their trade relationships after some time (IDB, 2007). Some examples cited in
the project are exports of mangoes in Colombia, office furniture in Brazil, flowers in Ecuador
(during the sixties) and segments of the electronic industry in Uruguay. Episodes in which a country
exports a product for a few years and then stops seem to be far from unusual. This prompts the
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question of what drives export survival. In other words, why do some export relationships persist
over time while others are only short-lived?
Studies of export duration are a relatively recent phenomenon for several reasons. In most
models of international trade, explicit considerations of the duration of trade are normally absent.
The implicit assumption, however, is that once a trade pattern is established it will last for long time.
In most models shifts in the patterns of specialization due, for example, to differences in factor
accumulation, the diffusion of technology or the life cycle of a product, tend to occur only gradually.
Even in models of hysteresis in trade (Baldwin and Krugman, 1989) which explicitly consider the
duration of trade, the stability of trade partnerships is the common assumption. Accordingly, the
general perception has been that trade relationships are long-lived.
Besedes and Prusa (2006a, 2006b), however, found that most US import relationships did
not survive for long. More than half of all trade relationships are observed for only one year and
approximately 80% are observed for less than five years. Short-lived trade relationships have been
found in other studies: Eaton, Eslava, Kugler and Tybout (2007) for the case of Colombian exports,
Volpe and Carballo (2007) for Peruvian exports, and Nitch (2007) for German imports. The
literature on trade duration is still incipient,1 but the empirical findings so far suggest that short-lived
trade episodes are far more common than initially thought and clearly not limited to the developing
world. This is also confirmed by a recent study of manufacturing exports of 46 developed and
developing countries which shows the median survival to be just 1-2 years (Besedes and Prusa,
2007). Particularly relevant is the finding that although trade relationships are short-lived in general,
there are differences among regions. The hazard rates of Latin American exports are normally higher
than the corresponding hazard rates in other countries. Another relevant finding is that even small
differences in survival rates could account for large differences in export growth in the long-run.
The possibility that a low survival rate may be at the core of the mediocre overall export
performance of Latin American countries relative to other countries deserves a closer. In this study
we compare duration of exports of Latin American economies to those of several other countries
and examine the determinants of differences in survival.
The rest of the analysis is divided as follows. Section II describes data and presents some
relevant statistics. Section III sketches the basic concepts of survival analysis applied to trade data
and shows the empirical strategy that is followed throughout the paper. Section IV shows the
1 There is also a recent related literature that studies the duration of pricing of traded goods. Gopinath and Rigobon (2006), for instance, analyze the stickiness of pricing of US imports and exports.
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typology of export survival in Latin America and presents benchmark comparisons. Section V
performs counterfactual exercises to calculate the potential growth rate of exports in Latin America
under alternative survival rates. Section VI proceeds to estimate several regression models for
survival data to analyze what factors help predict the hazard rate of exporting. The decomposition of
the regional differences in export hazard rates among the covariates is also presented in this section.
Finally, section VII concludes with a discussion of the results and their implications.
II. Data Description
The choice of data aggregation is particularly important for any analysis of duration of trade.
In general, periods of continued trade tend to become longer the more aggregated the data is
because the wider the range of products that are covered by an industry classification, the higher is
the probability that at least one product is traded in that category for a given year (see Besedes and
Prusa 2006a). On the other hand, at a very detailed level of product disaggregation, even a minor
change of product specifications may lead to a reclassification of an otherwise identical product,
which would result in a recorded failure of a trade relationship. Potential modifications of product
codes over the years may affect the results more strongly when using highly disaggregated data.
Finally, concerns about data quality and the presence of missing values, particularly for developing
countries and for early decades, become more of an issue with more disaggregated data. Given these
considerations, we use data recorded at the 4-digit level of SITC revision 1 between 1975 and 2005.
This provides us with the longest time series of data as well as higher quality than the 5-digit level.2
Accordingly we analyze the period 1975-2005. We focus on manufacturing and not agriculture
because the most important implications for growth strategies in Latin American countries
fundamentally target the manufacturing sector. We restrict our attention to the following SITC
industries Chemicals (SITC=5), Manufactured Materials (6), Machinery (7), and Miscellaneous
Machinery (8). Data come from the UN Commodity Trade Statistics Database (UN Comtrade). We
also perform the analysis with a more disaggregated data recorded at the 6-digit HS level. Since the
Harmonized System did not become widely used before 1991 or 1994 it gives us a shorter time
series and we use it as a robustness check only.
II.A. Preliminary Look at the Data
2 Especially for the early years in our sample it is often the case that there are more observations at the 4-digit than the 5digit level of SITC.
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Our benchmark database consists on bilateral trade flows for 47 exporting countries and 157
importing countries over the period 1975-2005. The entire dataset comprises a total of 12,994,907
observations.3 For the HS-6 digit level of aggregation the dataset consists on 44 exporting countries
(the same exporting countries of the 4-digit dataset except for Barbados, Jamaica and Panama that
were eliminated because of lack of data) and the same 157 importing countries. This dataset
comprises a total of 16,674,816 observations. Table 1 presents a preliminary look at the data. In
1975, for example, Peru exported to 56 countries a total of 181 4-digit level activities. The total
number of country-activities pairs (export relationships) that Peru had during this year was 679. In
2005, the corresponding numbers were 115 countries, 343 activities and 5,845 export relationships.
A fundamental part of duration analysis is to measure the length of the export relationship.
That is, the length (in years) that a country exports a particular good to another country. The key
step involves converting the annual raw data into spells of service for each export relationship. For
instance, if a country exports the same good to the same country during ten years, we take this as
one spell of service with a length of 10. Also, if the country exports the same good to the same
country in two (or more) distinct non-overlapping spells of service, for example, during 1985-1994
and then again during 2000-2003, we treat this as two independent spells.4 Our data consists on a
total of 2,580,724 spells. The last column in Table 1 shows the number of spells for each country. In
the next section we discuss in more detail all the statistical techniques of survival analysis applied in
this study.
Table 2 provides some preliminary insights on the issue of export survival. The first column
shows the growth of total exports by countries and regions for the period 1975-2005.5 It is easy to
see that the growth of exports in LAC has been relatively weak, particularly when compared to East
Asia. Column (2) shows the growth rate of export relationships. Once again, the growth rate in LAC
3 The selection of the countries is dictated by data limitations. For example, for the case of the exporting countries, we could not include some of the small countries in Latin America (most of the small islands in the Caribbean, for example) because of a lack of data covering the entire period. Nevertheless, we include 20 countries of Latin America representing more than 90% of the region’s GDP. The other exporting countries serve as comparators, which include nations from the EU, East Asia, Oceania, Africa and North America. Regarding the importing countries, we would have liked to add all countries. However, some countries had to be eliminated from the analysis because of the particular changes that they experienced that impede us to track their import flows over time. For example, Comtrade reports data on U.S. exports to Georgia, an ex-Soviet Republic, starting from 1992. Before 1992, Comtrade reports U.S. exports to the entire “Soviet Union.” As it is not possible to know what part of exports from the US to the Soviet Union went to Georgia between 1975 and 1992, Georgia is eliminated from the analysis. Likewise, we eliminated all destinations for which it was not possible to track the flows of imports during the entire period 1975-2005. The 157 destination countries that we include in the analysis comprise more than 90% of total exports for any of the 47 exporting countries. 4 We also use alternative methods for handling multiple spells which will be shown later in the analysis. 5 To avoid the presence of outlier years, the growth rate between 1975 and 2005 is calculated using the average of 1975-77 for the beginning of the sample and 2003-05 for the end.
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pales relative to East Asia.6 This is suggestive of the meager efforts in the region to discover new
export activities and/or new markets. Hausmann and Rodrik (2003) have indeed argued that the
shortcomings at the discovery stage are at the center of the limited export success of developing
countries.
The lack of dynamism in export discovery, however, does not seem to be the only problem
in Latin America. The third column in Table 2 shows the ratio of the growth of exports to the
growth of export relationships. For the US and the EU this ratio is many times larger than for Latina
America. Even for East Asia, a region with an impressive growth rate of export relationships (the
denominator in this ratio), the growth of exports relative to the growth in export relationships is
much larger than for Latin America. A potential explanation for this finding is that export
relationships in East Asia survive for longer periods of time than in Latin America, thus allowing
East Asian relationships to grow more. As we will discuss later, differences in duration account for
very large differences in export performance across these two regions.
III. Empirical Strategy
Using statistical techniques of survival analysis, duration can be modeled as a sequence of
conditional probabilities that a trade relationship continues after t periods given that it has already
survived for t periods. This is the essence of the survivor function. In a similar fashion, the hazard
function is the probability that the trade relationship fails after t periods given that it has survived up
to that point.7
More formally, let T be a non-negative random variable denoting the time to a failure event.
The survivor function of T is:
)Pr()( tTtS (1)
The function is equal to one at t = 0 and decreases toward zero as t goes to infinity. The
hazard function, also known as the instantaneous failure rate, is:
) |Pr()( tTtTth (2)
6 The industrial countries experienced lower growth rates of export relationships during this period than Latin American countries. However, in 1975 these countries already had a stock of discoveries that was larger than Latin America by several orders of magnitude: the EU had a stock of discoveries that was 11 times larger and the U.S. had a stock of discoveries 20 times larger (see Table 1, column 3). Note that it is relatively more difficult to increase the number of export relationships during a given period if a country starts with a large stock of export relationships than with a very low base. At the limit, a country that already exports all 4-digit activities to all countries of the sample cannot expand its export relationships any further, and even if all relationships remain active, by definition, the growth rate would be equal to zero. 7 See Kiefer (1988) for a detailed description of duration analysis.
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The hazard function can vary from zero (meaning no risk at all) to infinity (meaning the
certainty of failure at that instant).
In practice, the survivor and hazard functions are estimated (non-parametrically) by
computing the number of spells that survive (fail) as a fraction of the total number of spells that are
at risk after t periods. For a dataset with observed failure times, t1…., tk, where k is the number of
distinct failure times observed in the data, the Kaplan-Meier product limit estimator of the survivor
function is:
ttj j
jj
jn
dntS
|
)(ˆ (3)
where nj is the number of spells at risk at time tj and dj is the number of failures at time tj. The
product is over all observed failure times less than or equal to t. Similarly, a non-parametric
estimation of the hazard function is given by the ratio of spells who fail to the number of spells at
risk in a given period t.
j
j
n
dth )(ˆ (4)
The survivor and the hazard functions are alternative ways to express the same underlying
process. We will use both measures throughout the paper.
A common issue in survival analyses is the presence of censored observations. These are the
observations for which there is uncertainty regarding either the beginning or the ending date (or
both) for some trade relationships. In our case, for example, export relationships observed in 1975
are left-censored as they may have started in 1975 or before. Relationships observed in 2005 are
right-censored as the may have truly ended in that year or at a later unobserved time. Both types of
censoring are common in duration analysis and are accounted for in the analytical tools that we use
in the study.
The empirical strategy of the paper is as follows: in section IV, we estimate export survival
functions for several different countries in order to compare how Latin America fares relative to
other regions. In section VI we estimate regression models for survival data to analyze what factors
help explain the differences in the duration of exports. The regression models are normally specified
around the hazard function as these models are more easily grasped by observing how covariates
affect the hazard. In particular we use the semiparametric Cox proportional hazards model proposed
by Cox (1972). The model asserts that the hazard rate for the ith subject in the data is
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xitxi ethxth )(
0 )() |( (5)
where )(tx contains potentially time-varying covariates, and the regression coefficients x are to be
estimated from the data. In the Cox model the baseline hazard, )(0 th , is left unestimated. This is an
advantage of the Cox model as no assumptions about the shape of the hazard have to be made,
assumptions that might lead to misspecification and mislead results about x .8 The Cox model is by
far the most popular of choices in regressions models for survival data.
IV. Typology of Export Duration
Table 3 presents a first view of the length of export relationships by regions. It is
immediately evident that export relationships are in general brief. The median length of a spell in the
US is 2 years and only 1 year for all the other regions. More nuances appear in survival functions. In
the US 60.7% of export relationships survive the first year, 32.8% survive the first 5 years and only
22.2% survive by the end of 15 years. As low as these numbers may look, they are higher than the
corresponding survival rates in Latin America by more than 10 percentage points. In the Latin
American region, only 47.3% of the export relationships survive passed the first year. This survival
rate is lower than corresponding rates in the US, the EU and East Asia by 13.4, 5.5 and 5.6
percentage points respectively. By the end of 15 years, the differences in survival rates between Latin
America and the US, the EU and East Asia are 12.3, 6.0 and 8.9 percentage points respectively.
The fact that regional differences across survival functions exist can be tested systematically
through standard tests for equality of survivor functions. Table 4 shows the Cox tests in which the
null hypothesis is that Latin America has the same survival function as the US, the EU and East
Asia, respectively.9 The results for our benchmark data is presented in the first row. All three
hypotheses are easily rejected indicating that there are statistically significant differences between
Latin American survival function and those from the US, the EU and East Asia.
Figure 1 shows the graphical representations of the Kaplan-Meier estimated survival
functions for the four regions. The Latina American survival function is always below that of the
other regions. Note that all survival functions are similar in shape; that is, the survivor functions are
relatively steeper in the early years and relatively flatter later on. This indicates that export
8 The cost, however, is a loss in efficiency; if we knew the functional form of )(0 th , we could do a better job of estimating x . 9 Other tests for the equality of the survivor functions are: Long-rank, Wilcoxon-Breslow-Gehan, Peto-Peto-Prentice and Tarone-Ware. Note that the regional data have been weighted so that each country has the same weight within its own region. The consequence of this weighting is that only the Cox test that can be used to test equality of survivor functions.
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relationships experience higher risks of failure when they are fairly new with a decreasing the risk as
relationship age. In the case of Latin America, the rate of survival decreases by 15 percentage points
between years 1 and 2, by 7 percentage points between years 2 and 3 and by 4 percentage points
between years 3 and 4. Starting from year 6, the risk of survival decreases by less than 2 percentage
points every year and beginning in year 9 only by less than 1 percentage point. The risks of failure
decrease with age and after some time the risks change only marginally. The result might have some
important policy implications. For instance, any mechanism designed to support export activities
would be particularly important during the early years of service when the risks of failure are high.
After some time, the risks of failure substantially drop and the support might no longer be needed.
Returning to regional comparisons, Figure 1 shows that regardless of the age of the export
relationship, the differences in survival rates between Latin America and other regions persist and
sometimes even increase. The difference in survival rates between Latin America and the EU
increase with the year of service. The same is true of the difference between Latin America and East
Asia.
IV.A. Robustness Checks
We now check whether the results shown in Figure 1 hold under alternative data checks. First, as
mentioned before, some exports relationships reappear, exhibiting multiple spells of service. A
country may export a good to another country, exit the market, then re-enter and possibly exit again.
So far we have treated these multiple spells as different spells of service. However, we can explore
alternative treatments. There is a possibility that some of the reported multiple spells are not
different episodes but the result of a measurement error. For example, if we observed a relationship
between 1980 and 1991 and then again between 1993 and 1995 we treated them as two different
spells of service. However, it could have been the case that the reason we did not observe the
relationship in 1992 was because there was a measurement error. Therefore interpreting the initial
spell as ‘ending’ in 1991 was inappropriate. It is more adequate to interpret the two spells as one
longer spell lasting from 1980 to 1995. This type of error would be particularly problematic if there
is more misreporting in developing countries, particularly in LAC, than in the benchmark regions
because we might be concluding that export survival rates in LAC are lower than what they really
are.
To allow for such misreporting, we follow Besedes and Prusa (2006a) and assume that a one-
year gap between spells is an error, merge the individual spells, and adjust the spell length
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accordingly. Gaps of two or more years are assumed to be accurate and result in no adjustments.
While more elaborate methods could be employed to adjust the data, this approach is the most
appealing one due to its simplicity particularly when more information on the exact nature of
misreporting is not available. The results are shown in the second row of Table 4, labeled “Adjusted
gap”. The Cox tests performed with the adjusted data indicate that in all the three cases, the null
hypotheses of equality of survivor functions are once again rejected.
Another check we perform is to consider whether the results shown in Figure 1 hold if we
use only the new export activities. We mentioned in Section III that the survival methods used in
this paper address the issues of censoring. Nevertheless, by focusing the analysis on the new exports
activities, we take an extra step in making sure that issues of censoring (in this case, left-censoring)
are not affecting the results in any significant way. We adjust our dataset to consider only new export
relationships that begin in 1976 or later. The third row of Table 4, labeled “New exports” shows the
results of the Cox tests. The three hypotheses of equality of survivor functions are rejected once
again.
We can adjust the data to consider trade flows above some minimum threshold. Minimum
thresholds have been used before to eliminate minor errors in the data or to get rid of values that
might not be meaningful enough to be counted as a trade flow. For instance, in order to explore
whether or not a nation imported a good in a given period, Evenett and Venables (2002), use
minimum thresholds to get rid of what they call “economically unimportant levels of imports”.
Balza, Caballero, Ortega and Pineda (2007) also used minimum thresholds for the same purpose. In
this paper we apply minimum thresholds to explore how robust our results are. Evenett and
Venables use a cutoff value equal to $50,000, meaning that recorded trade flows below $50,000 are
treated as if there was no trade at all. We use three alternative thresholds: $10,000, $30,000 and
$50,000. This means that we drop spells with initial trade smaller than the corresponding threshold.
In the fourth row of Table 4 (labeled “Minimum threshold”) we report the results with the most
stringent threshold, $50,000. As indicated by the Cox tests, all the hypotheses are once again
rejected. Results with the other thresholds are qualitatively similar.
Finally, we report the results when we use the 6-digit HS data. Figure 2 shows the K-M
survival functions for the four regions and the last column of Table 4 presents the results of the
corresponding Cox tests. Even with this alternative dataset we find once again that Latin American
export survival rates are significantly lower than those of the comparators.
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IV.B. Latin American sub-regions
Table 5 shows the Kaplan-Meier estimates of the survivor function disaggregated by Latin American
sub-regions and countries. Within Latin America, the region that shows the largest survival rates is
the Southern Cone followed by the Andean countries, Central America, and finally the Caribbean.10
The relatively better performance of the Southern Cone is no consolation when compared to our
non-Latin American benchmarks. All four sub-region’s survival rates are lower than the survival
rates of any of the benchmarks. Figure 3, for example, shows the K-M estimated survivor functions
of the four sub-regions and includes the survival function of East Asia for comparison. It is clear
that the four survivor functions lie below the East Asian survival function at any year of service.
Table 6 shows the Cox tests of equality when using East Asia as a reference. All hypotheses of
equality are easily rejected not only with benchmark data but also when adjusting for 1-year gaps,
minimum thresholds, new exports only, and using the 6-digit HS data. We also arrive to similar
conclusions when using the EU or the US as reference points (not shown in the table).
In Table 7 we show the Cox tests for equality of survival functions for each Latin American
country. According to the tests, all the countries in the region have survival functions with implicit
hazard rates that are statistically significantly higher than that of any of the comparators. The
exception to this is Brazil. Brazil has a survival function that does not lie below the survival function
of either East Asia or the EU. Indeed, according to the Cox tests, the survival function in Brazil has
an implicit hazard rate that is statistically significantly lower than that of East Asia or the EU. This is
not the case, however, when Brazil is compared to the US. Relative to the US, Brazil still compares
unfavorably. All other countries in Latin America have survival functions that are significantly lower
than the survival functions of the three benchmark regions.
Having found that LAC survival rates are consistently below that of other parts of the world,
the natural question to ask is what drives these gaps. One possibility is differences in specialization
patterns. The export baskets of LAC are in general biased towards homogeneous goods. Therefore,
the average export survival rate in the region could be lower than in other regions because of a
relative specialization in these goods. Besedes and Prusa (2006b), for example, show that the hazard
rate is at least 23 percent higher for homogeneous goods than for differentiated products. We will
analyze in detail the determinants of survival rates in section VI, but here we provide a brief
preliminary look at this issue.
10 A Cox test of equality of survival functions indicates that the four functions are indeed statistically different at 1% level.
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We aggregate all 4-digit level activities in 3 categories according to Rauch’s (1999)
classification: homogenous, differentiated and an intermediate category called reference products.11
Then we estimate separate Kaplan-Meier survivor functions for each product category. Figure 4
shows the results. The probability of survival is always the highest for differentiated products and
always the lowest for homogeneous goods. The hypothesis that these three functions are equal is
rejected at the 1% significance level with the Cox test. This indicates that there are indeed
differences in survival rates across sectors categories. Regarding the biases of the export baskets, our
data shows that exports of differentiated products represent 84% of total exports in East Asia while
73% in LAC.
Given the above two results, one is tempted to conclude that the lower average export
survival rate in Latin America is the result of its specialization patterns with relatively lower export
shares in differentiated products. However, although this is in part true, there is more than meets the
eye here. In Table 8 we report again the export survival rates by product classification but also by
regions. The main finding is that export survival rates in East Asia are higher than in Latin America
for all three types of goods.12 East Asia has sustained higher survival rates than Latin America in
differentiated products, in referenced and in homogeneous goods. Therefore, even if East Asia
would have had the same specialization pattern as Latina America, its average export survival rate
would have still been higher. The way we interpret this result is that differences in the average
survival rates across the regions might not be solely the result of differences in specialization
patterns but that other factors might be playing a role as well. As mentioned before, we will address
this issue in detail in section VI where we investigate the determinants of export survival rates with
the help of an econometric model.
Before turning to the determinants of export survival, we would like to get an estimate on
what Latin America is likely to gain in terms of export growth if export survival rates were to
increase to levels attained in other parts of the world.
V. The Impact of Export Survival on Export Growth
We have shown that export survival rates in Latin America are lower than in other parts of
the world. The US, for example, exhibits average survival rates 13 percentage points higher than in
Latin America. The survival rates in the EU and East Asia are also higher by around 6 and 7
percentage points, respectively. We have shown that these differences are statistically significant. We
11 For more on this see Rauch (1999)
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would like to know how economically important these differences are. We calculate the impact of
differences in survival rates on export growth. More specifically, we perform counterfactual
exercises to ask the following question: what would have been the growth rate of exports in Latin
American countries if they had exhibited the survival rates of benchmarks regions, everything else
the same. The exercise is based on Besedes and Prusa (2007) which relies on a decomposition of
exports that is described below.
Consider tV the value of exports in year t, tn the number of export relationships during that
year and tv the average value per export relationship. Then, the total value of exports in year t is
ttt vnV (6)
Denote with ts the export relationships that survive from t-1 to t, and with te the new
export relationship, so that ttt esn . The change in exports from t to t+1 takes the following
expression
tttttt vnvnVV 111 (7)
which can be written as
11111 ttttttttt vevdvvsVV (8)
where td is the number of export relationships that end in t. This expression can be rewritten in
terms of the hazard rates as follows:
111111 1 ttttttttttt vevnhvvnhVV (9)
where 1th denotes the hazard rate of an export relationship in t+1. The final step involves
accounting for the fact that hazard rates may vary over time. In section IV in fact we saw that hazard
rates are normally higher on new export relationships than on established ones. Therefore the above
decomposition has to account for the fact that hazard rates may differ depending on the year of
service. Taking this into account, the final expression for the decomposition becomes:
I
itt
it
it
it
it
it
it
ittt vevnhvvnhVV
1
0111111 1 (10)
where subscript i denotes the year of service, I denotes the maximum potential year of service
and 01tv is the value of new export relationships in year t+1. Our counterfactual exercise consists of
12 Cox tests show that the differences in each product category between LAC and East Asia are statistically significant at 1% level.
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substituting ith 1 from the above expression in each Latin American country by iCF
th ,1 , the
corresponding hazard rate of the benchmark country or region.
Table 9 shows the results of this exercise in which export hazard rates for Latin American
countries have been substituted by the average hazard rate in East Asia. The table shows that for the
typical LAC country (average of the sample) the annual growth rate of exports would have increased
by 1.5 percentage points. That is, the annual growth rate would have increased from 8.6% to 10.1%.
Over the long 1975-2005 horizon, this higher annual growth would have mapped into a large
increase in exports, from 1601% to 2006%.13
Figures 5.1 to 5.20 show the actual and counterfactual levels of exports for each Latin
American country. Although there is some heterogeneity within the region with many countries
experiencing a substantial gap between the actual series and the counterfactual and a few
experiencing only minor deviations, the general result is that most of the export performances in
Latin America would have improved substantially. Figure 6 ranks the countries in terms of the
increase in their annual growth rates of exports. For countries like Panama, Nicaragua, Bolivia,
Barbados and Jamaica, the increases in the annual growth rates are fairly substantial, around 3
percentage points. Over the course of three decades, these higher rates would have generated huge
increases in exports more than doubling their levels by 2005. Other countries exhibiting very large
increases in the annual growth rate of exports are Honduras, Paraguay, Uruguay, Venezuela and
Ecuador.
We also repeat the exercise but instead of using the average hazard rate of East Asia, we use
the hazard rates of this region calculated separately for each of the three type of goods according to
the Rauch’s classification (homogenous, reference and differentiated). The results are shown in the
columns 5 to 7 of Table 9. The conclusions do not change in any significant way. Differences in
export survival rates between Latin America and East Asia generate large differences in export
growth over the long run.
VI. What Lies Behind the Length of a Trade Relationship?
In this section we test a battery of possible correlates of trade duration. As explained in
section III, we employ the Cox proportional hazard model to this end.
15
There are several candidates that can affect the length of a trade relationship. Following
Besedes and Prusa (2006b) and Besedes (2008), we start by including the standard determinants of
bilateral trade volumes typically used in the gravity model. In its most basic form, the gravity model
says that trade between two countries is proportional to the product of their GDPs and inversely
related to the distance between them. The gravity variables have been very successful in explaining
trade volumes; therefore, they might have a role in explaining the duration of trade as well. For
instance, by exporting to a larger market, the chances of disrupting a trade flow might be lower as
the presence of a larger pool of potential buyers increases the opportunities to accommodate
fluctuations in demand more easily. Distance might also play a role as it increases the time and the
costs of delivering a product to the market. The longer the distance covered by the shipment, the
higher the chances of potential interruptions or delays which might prompt cancellations of
subsequent orders. We expect the hazard rate to decrease with the size of the import market and to
increase with distance. We also include in the specification two additional variables from the gravity
literature: common border and common language.
Besides distance, there are other factors that may influence the size of transport costs. For
instance, the quality of onshore infrastructure and the degree of competition among shipping firms
have been found to be important determinants of shipping charges (see for example, IDB, 2008;
Limao et al., 2001; Clark et al., 2005; Fink et al., 2002; and Hummels et al., 2007). These factors
might also affect the odds of export survival. For instance, congested ports or onshore facilities with
inadequate auxiliary services might significantly delay the delivery of a product or even damage the
merchandise while waiting on land. New exporters that fail to meet the buyer’s requirements for
time and quality might not get their contracts renewed for subsequent years. Anecdotal evidence
from case studies supports these arguments. For example, a study on new export activities in
Colombia indicates that the deficiencies inherent to the Colombian transportation system were at
the roots of the short-lived episode of mango exports (Arveláez, Meléndez, León, 2007, p.45-46):
“…entrepreneurs had to deal with problems related to transportation and the distances between production and shipment sites… Poor road conditions made for a long trip to the port. As the trip time increased, the possibility of maintaining an adequate temperature was minimized. In addition, commercial ports did not have freezing facilities or cold rooms. To make matters worse, at the time there were no cranes for lifting heavy containers (temperature-maintenance equipment was very heavy) … [These] issues imposed such
13 It is worth mentioning that these are partial equilibrium counterfactuals. For instance, the exercise does not account for any effect on the export growth of country i that could have been derived from a higher export growth (and consequently a higher GDP growth) in country j.
16
constraints on mango producers that their projects failed. Many containers filled with mangos were lost at port and in transit. Mango growers reduced their efforts and interest in exporting this good…”
The above case expresses quite vividly how an export attempt can be short-lived due to deficiencies
in the transportation system of a country.
To assess the role of these transport-related factors we include an ad-valorem measure of
transport costs that we compute as the difference between the CIF and FOB value of a trade flow
divided by its FOB value. The variable is computed at the SITC 4-digit level. Note that since the
specification already includes distance, this new variable will capture all the other components that
affect transport costs besides distance. It is worth mentioning that measuring transport costs by
matching CIF and FOB values presents some shortcomings. The technique relies on independent
reports of the same trade flow that may differ for reasons other than shipping costs (Hummels and
Lugovskyy, 2006). Direct measures of transport costs would have been more appropriate but
unfortunately very few countries report detailed information on shipping costs as part of their trade
statistics. Therefore, the results from this variable should be interpreted with some caution.
The length of a trade partnership might also be affected by the evolution of relative prices.
An overvalued currency, for example, reduces the competitiveness of exports for the supplier
country. Conversely, an undervalued currency reduces the purchasing power of the buyer in the
importing country. Accordingly, exchange rate misalignments can potentially deter (or boost) the
length of a trade relationship. We include the misalignment of the real bilateral exchange rate that we
compute as the percentage difference between the actual bilateral real exchange rate between the
two partners and the trend exchange rate, the latter calculated with a Hodrick-Prescott filter.14 The
misalignment variable is constructed so that higher values represent a more undervalued
(overvalued) currency for the exporting (importing) country. Therefore, we should expect a negative
coefficient for this variable.
Another potential determinant of duration is the industry level tariff rate. For a given
product, an increase in the tariff should lead some foreign firms to exit since higher tariffs raise the
cost of servicing the market. Unfortunately, we do not have data on tariff rates to cover the extent
of the dataset. We include, however, memberships in trade agreements. Signing a trade agreement
eliminates the costs of servicing the market imposed by the tariff. Additionally, trade agreements
restrict competition from countries outside the agreement thereby making the partnership more
stable. Therefore, trade agreements should reduce the hazard. We include a dummy variable that is
14 This methodology is based on Goldfajn and Valdés (1997).
17
equal to 1 if the two countries have a free trade agreement (or if they share a common membership
in a regional trade agreement). We expect the coefficient for this variable to be negative.
Duration of trade might depend on product characteristics. One product characteristic that
we specifically control is the elasticity of import demand. The intuition is that trading goods whose
import demands are less sensitive of prices changes (more inelastic) might be less prone to suffer
disruptions than trading goods with high import demand elasticities. We include the elasticity of
import demand at the 4-digit SITC level. This variable is taken from Broda and Weinstein (2006).
Note that our specification also includes product fixed-effects. This serves two purposes. First,
product fixed-effects will account for product-specific effects beyond those from the elasticity of
import demand. Second, the estimates will be used to analyze how much differences in hazard rates
are explained by differences in specialization patterns.
We include the value of the exports in the first year of service. Very often trade relationships
begin in a state of uncertainty where there is some doubt about the prospect of success. Empirical
evidence indicates that in such an environment, the partners tend to start small (see for example,
Egan and Mody 1992, Rauch and Watson 2003, Besedes 2008). Buyers placing large orders with new
suppliers usually need to make substantial investments in training them so they can meet the buyer’s
requirements. Therefore, by starting with a small relationship, the buyer can discover the supplier’s
capability and willingness to learn to deliver a good to the buyer’s specifications on time before
incurring in the costs of training. The main cost of starting small, however, is the delay for the buyer
in realizing the profits from larger orders as larger orders normally generate greater surplus for the
buyer (Rauch and Watson, 2003). This implies that relationships that start with large orders are likely
to be the ones in which a considerable amount of trust is established by both partners from the start.
In such an environment, the buyer is willing to make investments in training thereby making the
relationship more lasting. Accordingly, we expect initial size to be negatively correlated with the
hazard.
The ability to maintain trade relationships might also be correlated to various characteristics
of the exporting country. For instance, an underdeveloped financial system in which firms are
unable to tap resources particularly in times of stress can set companies out of business terminating
their trade relationships. Another aspect is the quality of institutions. For example, a firm making
clothes to sell abroad might find it hard to deliver its orders if institutions at home do not support
contract enforceability with its suppliers. There is empirical evidence showing that poor contract
enforceability affects the volume of trade (Ranjan and Lee, 2007). To account for the role of these
18
country characteristics we include two additional variables in the model. We add a proxy for the
level of financial development that consists on the sum of the domestic credit to the private sector
as a proportion of GDP and the market capitalization of listed companies as a proportion of GDP.
A similar proxy of financial development has been used, in a different context, by Rajan and
Zingales (1998). The degree of contract enforceability in the country is proxied by an index of the
rule of law provided by the International Country Risk Guide (ICRG) database.15
Table 10 reports the results of Cox regressions. Besides the variables described above, the
regressions also include country fixed effects. Regression (1) shows the most basic form of the
gravity equation with country size (in terms of GDP) and distance. Trade partnerships among larger
countries face lower hazards while a larger distance between the countries increases the hazard rate.16
Regression (2) includes the common border and language dummies. Neighboring countries and
countries that share the same language exhibit hazards that are about 9% and 11% lower.
In regression (3) we include the ad-valorem transport costs. The coefficient is positive and
significant. Doubling the ad-valorem freight rate increases the hazard by around 17 percentage
points. The impact is substantial. This result implies that besides distance, other factors related to
transport costs play a significant role in the duration of a trade relationship. We mentioned before
that examples of such factors could be the level of port efficiency or the degree of competition in
trading routes. The impact of transport costs on the volume and the diversification of trade are well
documented in a recent Inter-American Development Bank report on transport costs (IDB, 2008).
The findings from this section provide further evidence that transport costs can significantly distort
trade by reducing the odds of export survival.
In regression (4) we include the misalignment of the exchange rate. The estimate is negative
as expected. Maintaining a depreciated exchange rate is shown to reduce the odds of failure. The
role of a membership in a trade agreement is assessed in regression (6). The effect is significant.
Countries that share a trade agreement exhibit hazard rates that are around 7% lower than countries
not sharing a trade agreement. In regression (7) we find there is a positive relationship between the
elasticity of import demand and the hazard rate. This supports prior findings that goods whose
demands are very sensitive to changes in prices face higher hazard rates (see Nitsch, 2007).
15 These data are available since 1980. An inspection of the data, however, indicates that the index is very stable over the long run; therefore, we complete the period 1975-79 using a linear tendency that is calculated from 1980 to 2005. 16 Similar results are found in Nitsch (2007).
19
In regression (8) we test the hypothesis that initial export values are associated with duration.
The finding indicates that starting with a large relationship increases the likelihood of survival.17 As
we described above, this might reflect that partnerships that start large are likely to be those in which
a considerable amount of trust is established from the beginning.
Regressions (9) and (10) include the two variables that proxy characteristics of the exporting
country. In regression (9) we measure the role of financial development and in regression (10) we
measure the impact of institutions, in particular, the rule of law – our proxy for contract
enforceability. The odds of export survival are higher in countries with more developed financial
systems and in countries in which the rule of law is observed.
In regression (12) we include all the covariates together. The results show that all the
estimates conserve their original signs and remain statistically significant at the 1% level.
VI.A. Robustness Checks
We perform a series of tests to analyze robustness of regression results. We first use data adjusted
for one-year gaps. The regression is presented in Table 11 column (2). The results from the original
Cox regression are shown in column (1) for comparison purposes. The estimates indicate one-year
gap adjustments do not alter the results in any significant way.
Our second test involves focusing on new export activities only. The results are shown in
column (3). The results are very much in line with those of the original Cox regression. The third
test involves using the dataset that has been adjusted for minimum thresholds. This is presented in
column (4). Once again, the results are very similar to those of the original regression.
Finally, we split the sample in the three broad sector categories that we used before:
agriculture, minerals and manufactures and estimate the model for each subsample.. This allows us
to check whether the impact of the covariates differ by sectors. The results are presented in columns
(4)-(6) of Table 11. There are only some minor changes that are worth commenting.
The coefficient on the level of financial development becomes insignificant for agriculture.
A detailed analysis of how the level of financial development affects the duration of trade escapes
the scope of this paper, but the result suggests that the link is much stronger in manufacturing goods
and in minerals than in agricultural products.
17 Similar findings are found in Besedes and Prusa (2005), Nitsch (2007) and Volpe and Carballo (2007)
20
The coefficient for FTA becomes insignificant in minerals while it increases in manufactures.
This suggests that trade duration is particularly sensitive to trade agreements when it comes to trade
in manufacturing goods but not very sensitive with respect to minerals.
VI.B. Decomposition of Hazard Rate Differences
We can use the results of the econometric model to decompose the differences in the hazard rates
between Latin America and other regions into its various determinants. The objective is to identify
the most important factors behind differences in hazard rates. To get the contribution of, for
example, the level of financial development to the difference in the hazard rates between Latin
America and East Asia, we calculate the following:
EASIALAC
EASIALACfindev
hh
xx
ˆlnˆln
)ln(lnˆ
(11)
where findev is the estimated coefficient for financial development, LACx and EASIAx are the sample
averages of the levels of financial development in Latin America and in East Asia respectively, and
LACh and EASIAh are the predicted hazard rates for Latin America and East Asia respectively. We
proceed in a similar way for each explanatory variable. Table 12 shows the results when comparing
Latin America to East Asia and the EU.
The first row presents the hazard rate of Latin America relative to East Asia and the EU
while the rest of the rows show the contribution of each factor. Consider the comparison with East
Asia (first column): Latin America’s average hazard rate is 56% higher than the average hazard rate
in East Asia. Of this, 26% comes from differences in specialization patterns18, 12% comes from a
weaker contract enforceability environment, 10% comes from lower levels of financial development,
9% is due to lower initial trade values in the region; 5% comes from differences in country sizes
(Latin American countries and their partners tend to be smaller to those in East Asia), 2% is due to
less adequate transport systems, and 2% comes from Latin American countries exporting goods with
higher import demand elasticities. Differences in exchange rate misalignments and in free trade
18 This effect is calculated as follows:
EASIALAC
j
jEASIA
jLACj
hh
ss
ˆlnˆln
)(ˆ
, where j is the product fixed-effect of good j estimated from
the Cox regression, andj
LACs and j
EASIAs are the average shares of trade in good j over total trade for LAC and East Asia
respectively.
21
agreements play no role in explaining differences in hazard rates between the two regions. Finally,
common border, common language and distance tend to play in favor of Latin America. That is,
relative to East Asia, a larger percentage of Latin America’s total trade takes place between
neighbors, between countries that share the same language and between countries that are closer to
each other. Accordingly, these factors tend to reduce the difference in hazard rates; however, their
combined effect is only marginal and does no compensate for the opposite effect generated by other
factors.
The model was also estimated using country-fixed effects. In the final row of Table 12 we
show the contribution of the country effects after taking their averages for each region. The result
indicates that 48% of the higher hazard rates in Latin America relative to East Asia are explained by
variables outside the model. With respect to the EU, this effect is much smaller, equal to 1%.
When Latin America is compared to the EU (second column) similar results arise with
differences in specialization patterns, contract enforceability, financial development, initial export
values and country sizes explaining the bulk of the differences in the hazard rates between the
regions. Contrary to East Asia, however, common border, distance and FTA play in favor of the
EU.
It is clear that the main driver of the positive wedge between the hazards rates in Latin
America and East Asia or the EU comes from differences in specialization patterns. We saw in
section IV that Latin American exports are more biased towards agricultural products and natural
resources and that these products tend to exhibit higher hazard rates than manufacturing goods. A
somewhat related finding is presented in Besedes and Prusa (2006b) who show that the hazard rate
for homogenous goods is around 20% higher than in differentiated products. They argue that
homogenous and differentiated products differ by the extent of search and investment costs that a
buyer must spend before a supplier can deliver an order. The assumption is that differentiated
products involve higher costs than homogenous goods. Higher search costs then result in longer
lasting relationships while low search costs (or their absence) result in less stable relationships.
Following Besedes and Prusa’s argument, it is possible that the specialization patterns of Latin
America involve the production of goods with low search and investment costs resulting in higher
hazard rates, but analyzing this in more detail goes beyond the scope of this paper.
Regardless of what causes hazard rates to differ across product categories, it is clear that
specialization patterns are important, but are not the only important factor. This is both good news
and bad news. The bad news is that the policy implications of this finding are not very clear.
22
Changing the specialization patterns of the region is easier said than done. How one would go about
it or whether this should be done at all are discussions among economists that are not likely to be
settled any time soon. The goods news, however, is that beyond specialization patterns there are
other factors, with more tangible policy implications, that significantly contribute to explain the
regional differences in survival rates. The broad areas identified in this exercise are: strengthening
contract enforceability between exporters and their suppliers, addressing market imperfections in
trade financing, improving transport efficiency and logistics systems and finding mechanisms to
reduce the uncertainty of new trade relationships. With respect to the latter, helping firms obtain
ISO 9000 certifications, for example, could be a way to signal the quality of the firm’s management.
It is interesting, for example, that in 2005, the number of ISO 9000 certificates per industrial worker
in East Asia and in Europe were higher than in Latin America by 7 and 12 times respectively.19
Better transportation and logistic systems are also shown to close the gap in hazard rates between
Latin America and other regions.
VII. Conclusions
During the last three decades, the growth rate of exports in Latin America has been relatively
weak particularly when compared to other regions like East Asia or the EU. Evidence from case
studies indicates that episodes in which exporters succeed in penetrating foreign markets but fail to
maintain their trade relationships beyond a few years of service are far from unusual. This suggests
that low export survival rates could be at the core of the mediocre export performance.
We apply use duration analysis to study regional differences in export survival episodes. The
findings show export relationships are in general short-lived but that significant differences across
regions exist with Latin America exhibiting lower export survival rates than the US, the EU, and
East Asia. In Latin America, for example, 48% of the export relationships survive the first year, 19%
survive after 5 years, and only 10% survive by the end of 15 years. These survival rates are lower
than the corresponding rates in the US, the EU, and East Asia by an average of 11, 5, and 6
percentage points respectively. Within LAC, the region that shows the largest export survival rates is
the Southern Cone followed by the Andean countries, Central America, and the Caribbean.
However, even the performance of the Southern Cone is poor when compared to the non-Latin
American benchmarks.
19 Calculated using the ISO Survey of Certification 2006 and data on employment in industry from the World Development Indicators of the World Bank.
23
Counterfactual exercises show that raising export survival rates to the levels observed in
other regions can produce fairly large increases in exports over the long run. For the typical LAC
country, for example, a survival rate equal to that of East Asia would have generated an increase in
the annual growth rate of exports of 1.4 percentage points. Over the long 1975-2005 horizon, this
higher annual growth rate would have mapped into a large increase in exports from 670% to around
900%, almost matching the growth rate of the EU during the same period. The increases in the
growth of exports would have been particularly large for Barbados, Honduras, Nicaragua, Panama
and Venezuela and very significant for Bolivia, Ecuador, El Salvador, Guatemala, Jamaica, Paraguay,
Trinidad and Tobago and Uruguay.
We test a battery of possible correlates to analyze which factors help explain the hazard rate
of exporting. We find that the hazard increases with the distance between partners, the ad-valorem
transport costs (a proxy for the efficiency of transportation systems), and with the elasticity of
import demand of the goods traded. At the same time, partners that are large in size, share a
common border or language and have a free trade agreement tend to exhibit lower hazard rates. The
initial size of the export value is also associated with a higher probability of survival suggesting that
large initial exports are synonymous of some degree of trust established by both partners from the
start thereby making the relationship more lasting. The econometric findings also show that
depreciated exchange rates increase the odds of export survival. Finally, exporters in countries with
more developed financial systems and with institutions that support contract enforceability tend to
maintain their export relationships longer.
Using the results from the econometric model, we decompose differences in hazard rates
between Latin America and other regions into its various determinants and find that the main driver
to be the difference in specialization patterns. The bad news is that the policy implications of this
finding are not very clear as changing specialization patterns of the region is not trivial. How one
would go about it or whether this should be done at all are discussions among economists that are
not likely to be settled any time soon. The goods news, however, is that beyond specialization
patterns there are other factors with more tangible policy implications that significantly contribute to
explain the regional differences in survival rates. The broad areas we identify are strengthening
contract enforceability between exporters and their suppliers, addressing market imperfections in
trade financing, improving transport efficiency and logistic systems and finding mechanisms to
reduce the uncertainty of new trade relationships.
24
It is worth mentioning that the list of factors affecting export survival that we explore in this
paper is not exhaustive as it is limited to the type of data that we used which is at the product level.
Similar analyses could be done, for example, with firm level data which opens up the possibility to
explore the role of other variables on export survival like the firm’s age, size or type of ownership.
One example of such analysis is Volpe and Carballo (2007) in which the authors explore the role of
export promotion activities on the rate of export survival of Peruvian firms. Further research in this
direction could be particularly fruitful in uncovering additional factors or policies affecting export
survival that are more specific to the firm. Also, using data that allows exploiting the regional
variation that exists within countries might be particularly useful to explore how aspects such as
agglomeration or cluster formation influence the chances of surviving in international markets.
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27
Appendix
A. Data Sources
Variable Description Source
Trade Flows Exports and Imports: 4-digit level SITC Rev. 1 UN Comtrade
GDP Gross Domestic Product, PPP (international $)
World Development Indicators. World Bank
Distance Great-circle distance between capitals
Centre D’Etudes Prospectives Et D’Informations Internationale (CEPII)
Common Border Dummy equal to 1 if countries share a common border, 0 otherwise Rose and Glick (2002)
Common Language
Dummy equal to 1 if countries share a common language, 0 otherwise
Rose and Glick (2002)
Exchange Rate Real Bilateral Exchange Rate
Own calculation using current exchange rate and CPI indexes from IFS, IMF
FTA Dummy equal to 1 if countries have a free trade agreement or if they share a membership in a regional trade agreement, 0 otherwise
World Trade Organization (WTO)
Elasticity of Import Demand
Elasticity of Import Demand: 4-digit level SITC Rev.1
Adapted from Broda and Weinstein (2006)
Domestic Credit Domestic credit to private sector (% of GDP) World Development Indicators. World Bank
Market Capitalization
Market capitalization of Listed domestic companies (%GDP)
World Development Indicators. World Bank
Rule of Law Rule of Law Index International Country Risk Guide (ICRG)
28
Table 1: Data Overview
Country Country
destinations in 1975
Exporting industries in
1975
Export relations in
1975
Country destinations
in 2005
Exporting industries in
2005
Export relations in
2005
No. of spells
(1) (2) (3) (4) (5) (6) (7) US 134 357 27579 155 362 31758 128909 Canada 117 251 9603 153 366 20901 100863 EU average 134 351 15270 151 361 20253 - Austria 134 331 11409 149 362 18313 92959 Denmark 145 369 12719 155 364 16601 92085 Finland 124 343 5395 145 351 11727 62710 France 147 377 28662 154 365 29354 129440 Germany 148 373 30509 155 363 32603 127952 Greece 100 282 2749 141 354 8333 48559 Ireland 125 341 3738 151 354 8573 55148 Italy 148 376 23184 155 366 29323 137418 Netherlands 144 362 17250 155 363 23989 120863 Portugal 115 342 4881 144 358 12025 62936 Spain 121 361 12832 152 365 25212 107776 Sweden 136 340 13190 152 363 18157 92266 UK 150 367 31988 155 362 29079 149035 East Asia average 86 295 3251 144 363 16190 - Indonesia 46 165 579 151 366 14040 49285 Malaysia 79 339 2619 150 358 12947 57709 Singapore 89 361 5763 114 364 16092 76639 Korea 122 336 4977 153 363 18905 83618 Thailand 95 276 2316 151 363 18967 68863 LAC average 53 192 1318 93 300 4616 - Argentina 99 312 3150 130 353 9950 44460 Barbados 36 191 792 81 280 2153 15286 Bolivia 23 36 95 71 269 1701 7070 Brazil 124 344 6417 147 363 18342 77619 Chile 51 233 838 104 336 5546 27691 Colombia 85 295 2410 118 349 6799 29580 Costa Rica 41 154 665 103 322 3596 15326 Ecuador 32 97 296 89 294 2676 11466 El Salvador 43 192 655 76 302 2463 10470 Guatemala 44 218 905 86 344 4502 16201 Honduras 24 90 258 66 294 1766 8413 Jamaica 59 223 1108 70 243 1538 11227 Mexico 100 326 4190 137 362 12518 57518 Nicaragua 23 150 467 62 270 1267 6690 Panama 33 124 335 47 119 409 5304 Paraguay 20 22 68 61 219 1018 4345 Peru 56 181 679 115 343 5845 23236 T&T 64 254 1411 93 317 3501 18631 Uruguay 43 139 460 97 282 2311 11327 Venezuela 57 253 1170 105 334 4410 27798 India 124 337 7342 155 364 25764 102409 Africa average 59 168 667 106 292 3530 - Algeria 39 146 416 61 220 913 6080 Egypt 73 147 781 125 310 4741 24783 Morocco 76 175 892 122 321 5008 22790 Tunisia 47 202 579 117 317 3456 20610 Oceania average 111 344 5584 146 359 12278 - Australia 132 355 7968 148 362 14421 79322 New Zealand 89 332 3199 144 355 10134 50039
29
Table 2: Export Performance
Growth of Exports
(2005-1975) Growth in Exports
Relations (2005-1975)
Growth of Exports / Growth in Export
Relations (1) (2) (3) = (1) / (2) US 812% 13% 6361% EU 1245% 58% 2136% East Asia 8161% 699% 1167% LAC 1601% 311% 515% Argentina 962% 173% 556% Barbados 212% 150% 141% Bolivia 87% 953% 9% Brazil 1959% 172% 1139% Chile 845% 382% 221% Colombia 1446% 138% 1047% Costa Rica 2614% 328% 798% Ecuador 2802% 619% 453% El Salvador 417% 195% 214% Guatemala 889% 253% 352% Honduras 627% 470% 133% Jamaica 123% 21% 586% Mexico 11671% 154% 7575% Nicaragua 10% 130% 8% Panama 352% 22% 1593% Paraguay 675% 935% 72% Peru 753% 436% 173% T&T 1903% 126% 1511% Uruguay 414% 234% 177% Venezuela 3255% 321% 1013%
Table 3: Export Duration by Regions
Observed spell
length (years)
Estimated K-M survival rate
Country/Region Median 1 year 5 years 15
years
US 2 0.607 0.328 0.222 EU 1 0.528 0.245 0.159 East Asia 1 0.529 0.257 0.188 LAC 1 0.473 0.186 0.099
30
Table 4: Cox Test for Equality of Survivor Functions Between LAC and Comparators
LAC versus:
US EU
East Asia
Benchmark data 10336*** 5363*** 5304*** Adjusted gap 12439*** 7858*** 7139*** New exports 1081*** 247*** 4470*** Minimum threshold 4972*** 3593*** 1559***
6-digit level 10834*** 8153*** 2609***
*, **, ***, significant at 10%, 5% and 1% level respectively
Table 5: Export Duration by LAC’s Sub-regions
Estimated K-M survival rate
LAC sub-region 1 year 5 years 15 years Caribbean 0.426 0.152 0.077 Barbados 0.422 0.138 0.066 Jamaica 0.407 0.137 0.060 T&T 0.451 0.185 0.109 Central America 0.470 0.185 0.092 Costa Rica 0.517 0.224 0.136 El Salvador 0.497 0.228 0.134 Guatemala 0.534 0.243 0.125 Honduras 0.461 0.164 0.067 Mexico 0.519 0.227 0.138 Nicaragua 0.447 0.169 0.051 Panama 0.374 0.098 0.033 Andean 0.480 0.188 0.104 Bolivia 0.456 0.165 0.093 Colombia 0.519 0.229 0.140 Ecuador 0.477 0.182 0.102 Peru 0.521 0.223 0.124 Venezuela 0.448 0.158 0.076 Southern Cone 0.504 0.209 0.120 Argentina 0.525 0.233 0.143 Brazil 0.562 0.270 0.172 Chile 0.472 0.198 0.123 Paraguay 0.488 0.177 0.076 Uruguay 0.500 0.192 0.099
31
Table 6: Cox Test for equality of Survivor Functions Between LAC’s Sub-regions and East Asia East Asia versus:
Caribbean Central America
Andean Southern Cone
Benchmark data 4096*** 2820*** 1899*** 809*** Adjusted gap 4558*** 3851*** 2355*** 1753*** New exports 4162*** 2346*** 1317*** 700*** Minimum threshold 331*** 460*** 1113*** 519*** 6-digit level 128*** 1567*** 1809*** 400***
*, **, ***, significant at 10%, 5% and 1% level respectively
Table 7: Cox Test for equality of Survivor Functions Between LAC countries and comparators
Latin American country versus: East Asia EU US Argentina 150*** 14*** 1458*** Barbados 2332*** 1844*** 3892*** Bolivia 467*** 339*** 1114*** Brazil 72*** (a) 422*** (a) 541*** Chile 513*** 242*** 1802*** Colombia 177*** 43*** 1154*** Costa Rica 163*** 61*** 908*** Ecuador 474*** 308*** 1328*** El Salvador 227*** 123*** 869*** Guatemala 152*** 58*** 936*** Honduras 1084*** 854*** 2174*** Jamaica 1602*** 1230*** 2875*** Mexico 466*** 159*** 2226*** Nicaragua 780*** 613*** 1707*** Panama 1917*** 1580*** 2835*** Paraguay 412*** 313*** 1040*** Peru 269*** 119*** 1362*** T&T 992*** 679*** 2390*** Uruguay 460*** 300*** 1494*** Venezuela 2562*** 2020*** 4732***
*, **, ***, significant at 10%, 5% and 1% level respectively (a) The relative hazard rate is lower in Brazil than in the comparator
32
Table 8: Export Duration by Rauch’s Product Classification and Regions
Estimated K-M survival rate
1 year 5 years 15 years
East Asia Homogeneous 0.471 0.195 0.118 Reference 0.525 0.251 0.187 Differentiated 0.536 0.264 0.194 LAC Homogeneous 0.457 0.163 0.069 Reference 0.478 0.186 0.096 Differentiated 0.474 0.188 0.102
33
Table 9: Export Performance in LAC with East Asian Survival Rates
Counterfactuals
Counterfactuals adjusted by type of good survival rate (Rauch
classification)
Actual annual
growth rate of exports (1975-2005)
Potential annual
growth rate of exports (1975-2005)
Potential growth rate of exports (1975-2005)
Potential exports in
2005 / Actual
exports in 2005
Potential annual
growth rate of exports (1975-2005)
Potential growth rate of exports (1975-2005)
Potential exports in
2005 / Actual
exports in 2005
(1) (2) (3) (4) (5) (6) (7) LAC 8.6% 10.1% 2006% 167% 9.7% 2000% 141% Argentina
9.6% 10.0% 1219% 112% 10.0% 1231% 108%
Barbados 5.3% 8.4% 726% 254% 8.1% 662% 227% Bolivia 3.2% 6.3% 303% 257% 4.5% 170% 148% Brazil 11.3% 11.4% 2215% 105% 11.4% 2218% 102% Chile 8.9% 9.4% 1027% 117% 8.7% 1005% 97% Colombia
10.5% 10.9% 1688% 110% 10.9% 1752% 105%
Costa Rica
11.9% 12.4% 3325% 116% 12.4% 3336% 113%
Ecuador 12.3% 13.3% 3542% 133% 13.2% 3367% 125% El Salvador
6.6% 6.9% 508% 109% 6.9% 508% 106%
Guatemala
9.9% 10.1% 1114% 108% 10.1% 1097% 106%
Honduras
7.7% 9.9% 1380% 194% 9.3% 1175% 163%
Jamaica 3.0% 5.6% 379% 218% 3.6% 189% 121% Mexico 16.5% 16.6% 11995% 102% 16.6% 12932% 100% Nicaragua
1.1% 5.0% 250% 322% 4.2% 180% 247%
Panama 6.2% 10.4% 1207% 356% 10.0% 1120% 314% Paraguay 8.6% 10.5% 1468% 176% 9.9% 1308% 149% Peru 9.1% 9.7% 930% 120% 9.1% 931% 100% T&T 10.5% 10.9% 2183% 113% 10.8% 2116% 104% Uruguay 7.4% 9.0% 832% 161% 8.9% 791% 153% Venezuela
13.0% 14.4% 3820% 155% 14.2% 3905% 136%
35
Table 10: Determinants of the Hazard, Cox Estimates
Covariates (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GDPi x GDPj (in logs) -
0.0709*** -
0.0757***-
0.0957*** -
0.0724*** -
0.0753*** -
0.0805*** -
0.0620*** -
0.0760***-
0.0760*** -0.0766***
Distance (in logs) 0.1819*** 0.1708*** 0.1926*** 0.1651*** 0.166*** 0.1790*** 0.1605*** 0.1720*** 0.1699*** 0.1809***
Common Border -
0.0879***-
0.0702*** -
0.0814*** -
0.0851*** -
0.0969*** -
0.0781*** -
0.0895***-
0.0885*** -0.0731***
Common Language -0.1109***
-0.1199***
-0.1053***
-0.1103***
-0.1174***
-0.1213***
-0.1107***
-0.1111***
-0.1239***
Ad-valorem Transport Costs (in logs)
0.1697*** 0.0841***
Exchange Rate Misalignment -0.0101***
-0.0161***
FTA -
0.0703*** -0.0303***
Elasticity of Import Demand (in logs)
0.0184*** 0.03811***
Initial Export Value (in logs) -
0.1205*** -0.1361***
Financial Development (in logs) -0.0323***
-0.0354***
Rule of Law Index -
0.0374*** -0.0434***
Number of Observations 15950049 15635151 11109865 13979230 15635151 14317034 15635151 15328185 15591470 9219425
Number of Subjects (spells) 3406332 3313300 1824658 2993345 3313300 2987584 3313300 3253020 3296373 1522503
All regressions include country (exporter) and product fixed effects (not reported)
***, **, * significant at the 1%, 5% and 10% level respectively
36
Table 11: Robustness Checks
Covariates Cox Adjusted-
gap New
Exports Adjusted-threshold
Agriculture Minerals Manufactures
(1) (2) (3) (4) (5) (6) (7)
GDPi x GDPj (in logs) -0.0766*** -0.0997*** -0.0638*** -0.1039*** -0.0524*** -0.0721*** -0.0867***
Distance (in logs) 0.1809*** 0.2286*** 0.1412*** 0.2286*** 0.1288*** 0.2010*** 0.2034***
Common Border -0.0731*** -0.0981*** -0.0848*** -0.1160*** -0.0963*** -0.1548*** -0.0569***
Common Language -0.1239*** -0.1589*** -0.1043*** -0.1598*** -0.0887*** -0.1614*** -0.1296***
Ad-valorem Transport Costs (in logs)
0.0841*** 0.0671*** 0.0616*** 0.2880*** 0.1070*** 0.1063*** 0.0760***
Exchange Rate Misalignment -0.0161*** -0.0150*** -0.0133*** -0.0122** -0.0092** -0.0105** -0.0233***
FTA -0.0303*** -0.0204** -0.0124** -0.0309*** -0.0216*** -0.0008 -0.0499***
Elasticity of Import Demand (in logs)
0.03811*** 0.0451*** 0.0275*** 0.0655*** 0.0126*** 0.1284*** 0.0356***
Initial Export Value (in logs) -0.1361*** -0.1412*** -0.0926*** -0.1312*** -0.1229*** -0.1161*** -0.1424***
Financial Development (in logs) -0.0354*** -0.0408*** -0.0264*** -0.0378*** -0.0004 -0.0568*** -0.0428***
Rule of Law Index -0.0434*** -0.0534*** -0.0197*** -0.0711*** -0.0389*** -0.0429*** -0.0444***
Number of Observations 9219425 9219425 4928000 4276867 1298840 1347359 6572626 Number of Subjects (spells) 1522503 1162451 1266326 409754 253162 212907 1056434
All regressions include (exporter) country and product fixed effects (not reported) ***, **, * significant at the 1%, 5% and 10% level respectively
38
Table 12: Decomposing Differences in Hazard Rates Between LAC and Comparators
East Asia EU
Hazard rates: iLAC hh ˆ/ˆ (i = EU, East Asia) 156% 173%
Contribution to differences in fitted values:
Specialization Patterns 26% 23%
Contract Enforceability (rule of law) 12% 21%
Financial Development 10% 7%
Initial Export Value 9% 6%
Size (GDP) 5% 14%
Transport Efficiency (ad-valorem transport costs)
2% 1%
Elasticity of Import Demand 2% 3%
Exchange Rate Misalignment 0% 0%
FTA 0% 2%
Common Language -1% -4%
Common Border -2% 1%
Distance -10% 25%
Country fixed effect 48% 1%
Source: own calculations based on results from regression (10), Table 10.
39
Figure 1: Export Duration by Regions Figure 2: Export Duration by Regions Kaplan-Meier Survival Estimates using 6-digit HS Level Data
Kaplan-Meier Survival Estimates
0.0
00
.25
0.5
00
.75
1.0
0
Pro
babi
lity
of s
urv
iva
l
0 5 10 15
Time
US EU-15East Asia LAC
0.0
00
.25
0.5
00
.75
1.0
0
Pro
babi
lity
of s
urv
iva
l
0 5 10 15
Time
US EUEast Asia LAC
Figure 3: Export Duration by LAC Figure 4: Export Duration by Sub- regions Rauch’s Product Classification
Kaplan-Meier Survival Estimates Kaplan-Meier Survival Estimates
0.0
00
.25
0.5
00
.75
1.0
0
Pro
babi
lity
of s
urv
iva
l
0 5 10 15
Time
East Asia SouthAndean CentralCaribbean
0.0
00
.25
0.5
00
.75
1.0
0
Pro
babi
lity
of s
urv
iva
l
0 5 10 15
Time
Homogeneous ReferenceDifferentiated
40
Figure 5.1: Argentina Figure 5.2: Barbados
05
000
100
001
5000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
01
002
003
004
00
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
Figure 5.3: Bolivia Figure 5.4: Brazil
05
001
000
150
0
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
02
0000
400
006
0000
800
00
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
Figure 5.5: Chile Figure 5.6: Colombia
05
000
100
001
5000
200
00
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
02
000
400
06
000
800
0
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
41
Figure 5.7: Costa Rica Figure 5.8: Ecuador
01
000
200
03
000
400
05
000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
05
001
000
150
0
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
Figure 5.9: Guatemala Figure 5.10: Honduras
01
000
200
03
000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
02
004
006
00
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
Figure 5.11: Jamaica Figure 5.l2: Mexico
500
100
01
500
200
02
500
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
05
0000
100
000
150
000
200
000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
42
Figure 5.13: Nicaragua Figure 5.14: Panama
01
002
003
004
00
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
01
002
003
00
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
Figure 5.15: Peru Figure 5.16: Paraguay
02
000
400
06
000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
01
002
003
004
00
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
Figure 5.17: El Salvador Figure 5.18: Trinidad and Tobago
02
004
006
008
001
000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
01
000
200
03
000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
43
Figure 5.19: Uruguay Figure 5.20: Venezuela
05
001
000
150
0
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
02
000
400
06
000
800
01
0000
MM
US
$
1970 1980 1990 2000 2010
Year
Actual Counterfactual
Figure 6: Increase in the Annual Growth Rate of Exports Assuming East Asian Survival Rates
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
Pan
ama
N
icara
gua
Bol
ivia
Bar
bados
Ja
maic
a
H
ondura
s
Par
aguay
Uru
guay
Ven
ezuela
Ecu
ador
Per
u
Chile
Cos
ta R
ica
T&
T
Arg
entin
a
Col
ombia
El S
alvad
or
Guat
emala
Bra
zil
M
exico