Mélise Jaud

Olivier Cadot

Akiko Suwa-Eisenmann

Akiko Suwa Eisenmann+

Do food scares explain supplier concentration? An analysis of EU agri-food imports

Keywords: European Union, import concentration, sanitary risk, food, agricultural trade European Union, import concentration, sanitary risk, food, agricultural trade JEL classification codes: F1, O13, Q17, Q18

This paper documents a decreasing trend in the geographical concentration of EU agrofood imports. Decomposing the concentration indices into intensive and extensive margins components, we find that the decrease in overall concentration indices results from two diverging trends: the pattern of trade diversifies at the extensive margin (EU countries have been sourcing their agri-food products from a wider range of suppliers), while geographical concentration increases at the intensive-margin (EU countries have concentrated their imports on a few major suppliers). This leads to an increasing inequality in market shares between a small group of large suppliers and a majority of small suppliers. We then move on to exploit a database of food alerts at the EU border that had never been exploited before. After coding it into HS8 categories, we regress the incidence of food alerts by product on determinants including exporter dummies as well as HS8 product dummies. Coefficients on product dummies provide unbiased estimates of the intrinsic vulnerability of exported products to food alerts, as measured at the EU border. We incorporate the product risk coefficient as an explanatory variable in a regression of geographical concentration and show that concentration is higher for risky products.

Introduction

After a series of highly publicized food scares (bovine spongiform encephalopathy or dioxin-contaminated chickens to name but two), publichealth concerns have started to loom large in the buying policies of EU foodstuff distributors. These concerns have the potential to affect the evolution of EU foodstuff imports, and therefore the access that developing countries-in particular the poorest ones, who find it most difficult to comply with stringent sanitary standards-enjoy on EU markets [START_REF] Maskus | The cost of compliance with product standards for firms in developing countries: an econometric study[END_REF]. This is particularly important for products like fruit & vegetables or fisheries products, which represent a growing share of EU food imports and are also of particular concern to the least developed countries [START_REF] Jaffee | Standards and Agro-Food Exports from Developing Countries: Rebalancing the Debate[END_REF], World Bank 2005).

The impact of sanitary concerns on industrial-country foodstuff imports has been studied extensively, essentially by sticking standards as explanatory variables in gravity equations (see e.g. Moenius 1999[START_REF] Maskus | Quantifying the Impact of Technical Barriers to Trade. A framework for Analysis[END_REF], Otzuki and Wilson 2001, Sheperd and Wilson 2007). Estimation of such models has highlighted the trade restrictiveness of such standards [START_REF] Fontagné | Estimating the Impact of Environmentally -Related Non-Tariff Measures[END_REF], Disdier, Fontagné et al. 2008).

We differentiate ourselves from the existing literature in two ways. First, we shift focus from gravity modelling to an analysis of the geographical concentration of EU foodstuff imports, using conventional and nonconventional concentration measures (similar approaches can be found in Imbs and Wacziarg 2003 for production or in Cadot, [START_REF] Carrère | Export Diversification: What's Behind the Hump?[END_REF][START_REF] Carrère | Export Diversification: What's Behind the Hump?[END_REF]Dutt, Mihov andvan Zandt 2008 for exports). We propose a decomposition of Theil's entropy index between active and potential suppliers with the property that variations in the index's within-and between-group components map directly into intensive-and extensive-margin variations. We also propose a variant of Hummels and Klenow's intensive and extensive margins (which they developed for the analysis of the product-wise concentration of exports) adapted to imports and to geographical concentration.

Our product-level analysis shows that, over the last two decades, EU foodstuff imports have concentrated, geographically, at the intensive margin. That is, on average, at the product-line (HS8) level, the market shares of active suppliers have diverged. However, we also observe a trend toward diversification at the extensive margin. That is, again at the product-line level, the EU sources its foodstuffs from an increasing number of exporting countries. These two observations appear, at first sight, to be contradictory. Using our adaptation of Hummels and Klenow's intensive and extensive margins, we show that the number of suppliers used by the EU is indeed increasing, but by addition of a fringe of small-volume exporters. Thus, EU foodstuff imports gradually evolve toward a two-tier distribution with a small number of increasingly dominant suppliers and a growing fringe of marginal ones.

Second, we shift focus from standards, which affect trade flows ex-ante, to alerts, which affect them ex-post. What we call here a "food alert" is the notification of a contaminated foodstuff shipment at the external border of an EU member state. Food alerts have the power to alter buyer perceptions of the quality of particular suppliers. They could either lead to reinforced concentration if buyers react by eliminating fringe suppliers perceived as dubious by analogy with the culprit, or, alternatively, to the destruction of dominant positions if they affect dominant suppliers. This is what we explore, using an original database constructed from the European Commission's Rapid Alert System for Food and Feed (RASSF) database. Technically, the EU Commission classifies contaminated-shipment notifications into two types: "informations", which lead to the destruction or re-routing of the concerned shipment, or "alerts" stricto sensu which lead to the destruction or re-routing of all shipments from the same exporting country at all EU borders. Since 2001, all informations (about 19'000 of them) have been recorded in a detailed database, which has never been used. We coded that database into HS8 product categories to make it compatible with trade data, generating a population of events each defined at the (product × exporter × year) level.

The RASFF database shows substantial heterogeneity in the incidence of food alerts across exporting countries. This implies that a raw count of alerts by product cannot give a correct proxy for product-specific sanitary risk. For instance, a product imported overwhelmingly from a country with weak quality standards would appear as risky even though other exporters might have managed to make the product safe. In addition, the incidence of notifications is likely to be correlated with the frequency of controls. Those controls may not be purely random: they may reflect a particular exporter's past performance or hidden protectionism. Thus, regressing concentration indices on the frequency of notifications at the product level would say nothing without controlling for other factors.

In order to get an unbiased estimate of product-specific sanitary risk, we rely on a two-step procedure. In the first step, we estimate product-specific sanitary risk with a regression of the count of food alerts at EU borders over the sample period, using an original database described in the next section. The unit of observation is a product × exporter pair where alerts are summed over all years in the sample period. The regressors are exporting-country characteristics and product dummies. Estimated coefficients on those product dummies give us an estimated measure of product risk. In a second step, we regress the evolution of geographical concentration indices on our measure of product risk and time dummies.

Overall, we find that except for fisheries products no chapter stands out as having particularly high risk levels. Incorporating our constructed measure of product risk as an explanatory variable in a regression of geographical concentration confirms that product riskiness affects sourcing concentration. Product riskiness leads to reinforced concentration at the intensive margin and reinforced diversification at the extensive margin. Thus, the distribution of EU suppliers for riskiest agrofood imports is converging towards a pattern of increasingly dominant suppliers with a growing fringe of small-scale ones.

The paper proceeds as follows. The next Section analyses the trend in the geographical concentration of EU agro-food imports both at the intensive and extensive margin. We then outline the EU "Food Alerts" Database in Section 3, contrast it with previous data collection efforts, and present some descriptive results. Section 3 then explores the impact of product riskiness on the patterns of concentration. Section 4 concludes.

Agri-food supplier concentration

2.1 Overall diversification

The data

We use EUROSTAT agri-food import data covering EU-12 member states1 (France, Belgium-Luxembourg, the Netherlands, Germany, Italy, Ireland, United Kingdom, Denmark, Greece, Portugal and Spain) between 1988 and 2005 at the HS8 level (the highest level of disaggregation available, as Eurostat does not make 10-digit data available to researchers). Agri-food products, excluding beverages and animal feed, are in chapters 1 to 21 of the HS system, which represent 3'073 potential export lines. With 146 partner countries (exporters) including 122 developing countries, we have a four-dimensional panel where the unit of observation is a product imported by an EU member state from an extra-EU partner in a given year.2 For some calculations, however, we aggregate import data across EU member countries, reducing the panel's dimension to three (product × exporter × year).

At the HS8 level, reclassifications are frequent. Five types of reclassification can be distinguished: (i) creation of a new code corresponding to a new product; (ii) creation of several new codes by splitting a former one; (iii) creation of a new code by merging several former ones; (iv) creation of new codes resulting from a change in the coding system (HS harmonizations in 1988(HS harmonizations in , 1996(HS harmonizations in and 2002)); and finally (v) termination of old codes. Of the 3'073 HS8 codes available in our dataset, only 37.7% are unaffected by reclassification between 1988 and 2005. Of the remainder (62.3%), 1.6% are new products (type i), and 0.7% are terminated codes (type v). This leaves 60% of reclassifications of "continued" products. Among those, half (30%) are type-ii reclassifications (splittings) and half are type-iv (system changes).

In order to reduce the inconsistencies introduced by type-iii and type-iv reclassifications to a minimum, we used EUROSTAT's documentation to rereclassify new codes into initial ones; in case (iii), where a new code was made out of several old ones, we used the first parent's code in the HS order. This gives us a consistent database using the initial nomenclature throughout the sample period.

Trade relationships at the product level

Table 1 shows the evolution of the structure of EU agri-food imports between 1988 and 2005. The share of developing countries, already dominant at 68% at the start of the sample period (1988), grew even more dominant, reaching 77% in 2005. Interestingly, this rise is not attributable to a rise in imports of traditional tropical products (coffee, cocoa, sugar, etc.) which, as a share of imports from developing countries, shrank from 25.2% to 15.7% over the sample period. Rather, it is due to a spectacular rise in imports of horticulture and fisheries products, whose share rose from 21.2% to 30% of EU agri-food imports from developing countries. This is remarkable given that fisheries and horticulture products are, in general, fairly sensitive to sanitary and phytosanitary issues, and the ability to meet stringent SPS standards is, in general, correlated with exporter income. Figure 1 shows the distribution of EU imports by number of source countries.

The distribution is strikingly skewed, with single-supplier-meaning source country; there may be several suppliers per exporting country-accounting for 30% of the total number of product lines. 2 shows the proportion of products imported from a single country by HS chapter, and Panel B by importing country. A negative correlation -as expected -between importer size (total imports value) and proportion of single-supplier relationship is apparent, suggesting that exporters have fixed costs by destination market. Panel C shows the evolution over time: a decreasing trend clearly appears.

Concentration indices

We now turn to an analysis of the geographical concentration of sourcing, product by product. Our measures are standard ones: Herfindahl, Theil and Gini. The Herfindahl index for good k, normalized to range between zero and one, is

( ) 2 * 1 / 1 1 / i k k i k k s n H n - = - ∑ (1)
where

/ i i k k k s x x =
is the share of origin country i in EU imports of product k and k n is the total number of countries exporting good k (we will discuss in more detail below alternative definitions of the set of exporting countries).

Theil's entropy index (Theil 1972), again for good k, is given by

1 1 1 1 ln where k k n n i i i k k k k k i i k k k k x x T x n n µ µ µ = =   = =     ∑ ∑ (2)
For Gini indices, we use Brown's formula; that is, for each product and year, we first sort exporting countries, indexed by i, by increasing order of trade value x so that

1 i i k k x x + < . Cumulative export shares are 1 1 k n i i k k k X x x = = = ∑ ∑ ℓ ℓ ℓ ℓ (3)
and cumulative shares in the number of exporting countries are simply / k i n .

Brown's formula for the Gini coefficient is then

( )( ) 1 1 1 2 1 . k n i i k k k k i G X X i n - = = - - - ∑ (4)
All three indices are dependent on the definition of k n , the number of "potential exporters". Our baseline definition of the set of potential exporters is the simplest one: it is the set of all countries having exported good k to some destination in the world (not necessarily EU countries) at least two years in a row over the sample period. We impose the requirement of two consecutive years of exports instead of just one in order to ensure that the exporter is a successful one [START_REF] Besedes | Surviving the U.S. Import Market: The Role of Product Differentiation[END_REF]Prusa 2006a, 2006b show that two years is the median duration of export spells; only one year might signal failure rather than the capacity to export). This definition has the advantage of being time-and importer-invariant. We will discuss alternative definitions and decompositions in Section 2.2 below.

Table 3 shows some descriptive statistics for the three concentration indices.

The average number of potential suppliers is high, at around 74 suppliers. Concentration indices are very high, and this is consistent with our earlier observation that the distribution of the number of active suppliers is highly skewed. This has to do with the very detailed level of disaggregation. At the HS8 level, a large proportion of product lines are imported from a small number of suppliers. Figure 2 shows the evolution of simple averages, over all products, of our three concentration indices. A clear downward trend (diversification) is apparent for all indices, in particular after the mid-1990s.

Intensive vs. extensive margins 2.2.1 "Raw" margins

We now use the additive decomposability property of Theil's index to get a first cut at the respective roles of the intensive and extensive margins in overall concentration trends. Both margins are defined geographically supplier-wise instead of product-wise.

In each year, we decompose our sample of EU suppliers into two groups, holding the importing member-state constant and un-indexed in order to avoid cluttering the notation. Group 1 is composed of active suppliers, numbering EU kt n , and group 0 is made of potential but inactive ones, numbering

EU k kt n n - .
Using this partition, we can decompose Theil's index into group, between and within components called respectively

0 kt T , 1 kt T , B kt T and W kt T .
The between-groups component of Theil's index is given by

1 0 ln j j j B kt kt kt kt j k k k n T n µ µ µ µ =   =     ∑ . ( 5
)
That is, it is a weighted average of terms involving only group means (relative to the population mean). By L'Hôpital's rule,

0 0 0 0 lim ln 0 kt kt kt k k µ µ µ µ µ →     =         (6) so 1 1 1 ln . B kt kt kt kt k k k n T n µ µ µ µ   =     (7) As ( ) 1 1 1 / EU kt kt ikt i G n x µ ∈ = ∑ , ( )
1 / k k ikt i n x µ =
∑ and, by construction,

1 ikt ikt i G i x x ∈ = ∑ ∑ , it follows that ln B k kt EU kt n T n   =     . (8) As k n is time-invariant, it follows that , 1 ln B B B EU kt kt k t kt T T T n - ∆ = - = -∆ ; (9)
that is, changes in the between-groups components of Theil's index trace exactly percentage changes in the extensive margin defined as the number of suppliers.

We now turn to the within-groups component. The within-group Theil index is defined as

0,1 j j W j kt kt kt kt j k k n T T n µ µ = = ∑ . ( 10
)
As Theil's index is zero when all individuals have equal shares, 0 0 T = . As for group 1,

1 1 1 ln ikt ikt kt EU EU EU i G kt kt kt x x T n µ µ ∈   =     ∑ (11) With 0 0 T = , W kt T reduces to 1 . EU EU W kt kt kt kt k k n T T n µ µ = ( 12
)
where

/ EU EU kt ikt kt i x µ µ = ∑
. That is, the within component of Theil's index is equal to its group-1 sub-index (concentration among active suppliers).

In other words, given our partition of suppliers, the between-groups and withingroups components of Theil's index map directly into the extensive and intensive margins. Let us start the analysis with the within-groups component.

Figure 3 shows the evolution of simple averages over all products of Theil's within-group component. 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 In order to verify whether this trend is significant, and that it is not driven by between-product composition effects (i.e. a sectoral shift away from widelyprocured products toward narrowly-procured ones), Table 4 shows the results of regressions of concentration indices on time and its square using (importer × product) fixed effects. How about the extensive margin? Figure 4 shows the evolution of simple averages over all products of Theil's between-group component and EU's number of suppliers over time. 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 3.5 3.7

Theil

3.9

4.1 4.3 4.5 4.7 4.9

1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 Note: Simple averages of number of exporters to EU at the product (HS8) level.

N
The downward trend in the between component of the Theil index, along with the rising number of suppliers, now suggests diversification at the extensive margin. This is what one would have expected in view of declining transportation and trade costs (as suggested by the gravity literature), but it is somewhat conflicting with the rising concentration observed at the intensive margin.

A regression of the between component of the Theil index and the number of suppliers on a time trend (with, again, importer × product fixed effects) confirms the monotone increase. Table 5 reports the results for pooled and fixed-effects regressions of the between component of the Theil index and the number of suppliers to an EU member country on time and its square. The coefficients in column (5) imply that the number of E.U. suppliers rises by 1,5% a year. This is a slow rise: Given that the average number of suppliers per product is a little less than 5 for the representative E.U. member, it takes about 14 years for the E.U. to add one more supplier.

So far, thus, we get the following picture: on the one hand, supplier concentration is rising at the intensive margin, meaning that the largest existing suppliers get larger relative to the average. On the other hand, concentration is decreasing at the extensive margin, as more and more suppliers are addedalbeit slowly-to the EU's portfolio of suppliers. These observations can be reconciled as follows. Suppose first that a group of three incumbent suppliers each supply $3 to the E.U., with two more potential suppliers waiting in the wings with zero exports to the EU. The within Theil index for group 1 (active suppliers) is zero. Suppose now that one of the two potential suppliers gets in at a scale of $3. Group 1 enlarges to 4 members, group 0 shrinks to 1 member, and the within Theil index for group 1 stays at zero.

Consider now a different setup where the three initial incumbents have unequal export levels; say, $4, $3 and $2 respectively. The group Theil index for group 1 is 0.04. Suppose now that the entrant from group zero enters with exports of only $1. Then the within Theil index for group 1 rises to 0.2. The reason is that the group is now more unequal, as the largest exporter has 4/2.5 = 1.6 times the group average instead of 4/3 = 1.33 previously. Thus, there has been diversification at the extensive margin but the within-group Theil index calculated on active exporters is showing rising concentration.

That entrants enter small-scale is natural if they are being tested or if they themselves want to "try the market" small scale before taking big risks (on this, see e.g. [START_REF] Rauch | Starting Small in an Unfamiliar Environment[END_REF]. But is it the case that new entrants in the EU's portfolio of suppliers are small in world trade? In order to look at this, we now turn to an adaptation of Hummels and Klenow's definition of the intensive and extensive margins.

Using Hummels and Klenow's decomposition

We use a slightly different definition of the extensive margin due to Hummels and Klenow (2005, henceforth HK). They defined the intensive and extensive export margins of country i, product-wise, as

i i i k k i W k k X IM X ∈ℵ ∈ℵ = ∑ ∑ (13) and i W k k i W k k X EM X ∈ℵ = ∑ ∑ (14)
respectively. In words, country i's intensive margin is its share of world trade in what it exports (how big it is in what it exports), whereas its extensive margin is the share in world trade of the products that it exports (how important is what it exports). The difference between i EM and just counting the active export lines is that if country 1 exports, say, carrots and potatoes, whereas country 2 exports cars and computers, they have the same number of active lines, but the extensive margin measured à la HK would be higher for country 2 because what it exports is larger in world trade. It is easily verified that multiplying the extensive margin by the intensive one gives country i's share in world trade.

We adapt the concept to imports and to a geographical instead of product-wise measure. In our setting, the equivalents of HK's intensive and extensive margins are

, , k k i EU k i S k i W k i S M IM M ∈ ∈ = ∑ ∑ (15) and , , k i W k i S k i W k i M EM M ∈ = ∑ ∑ . ( 16
)
That is, product k's intensive margin for the EU is the EU's share of its suppliers' exports (how big it is in its suppliers' exports of good k), whereas its extensive margin is the share of its suppliers in world trade of good k (how important are its suppliers in good k). Whereas in HK's case, multiplying the intensive margin by the extensive margin gives the exporter's share in world trade, here it gives the importer's share in world imports of good k. Our extensive margin differs from counting the number of supplier countries as follows. If the EU imports wheat from the US and Australia, and rice from Niger and the Mali, both products are sourced from two countries. However the US and Australia are much larger in wheat trade than are Niger and Mali in rice trade. The extensive margin is, accordingly, higher for wheat than for rice.

Table 6 reports the results of pooled and fixed-effects regressions of the extensive and intensive margins on time and its square. The negative trend at the extensive margin is only significant with the introduction of importer × product fixed effects. The decrease in the intensive margin is significant and robust to the introduction of fixed effects. However, econometric results must be interpreted very cautiously here. Figure 5 shows fitted curves corresponding to columns (2) and ( 4) of Table 6. They head down only between 1988 and 1994-5, during which the COMTRADE database was progressively enlarging to new countries. Once COMTRADE reaches steady-state, there is no trend anymore. We take this to be the correct answer to our question.

Intensive Margin

IM E M

The flat trend of the extensive margin over 1995-2005 suggests that the combined share of EU suppliers in world trade is constant, even though their number is growing. That is, newcomers into EU supply chains are so small in world trade that they make no difference in the extensive margin. This confirms our interpretation of the rise in the group-1 (active-exporters) value of Theil's index: inequality is rising among EU suppliers, not because large suppliers acquire increasingly dominant positions, but because small suppliers keep on coming on a very small scale.

All in all, in spite of the rise in Theil indices among active suppliers, the picture we get is one of increasingly diversified geographical sourcing, albeit by addition of a fringe of very small exporters. We now turn to an analysis of the relationship between public-health concerns and the concentration and identity of EU suppliers.

Do food alerts cause supplier concentration?

Trade theory predicts that if trade costs go down or if productivity rises exogenously in a pool of potential suppliers with heterogeneous productivity levels, the number of suppliers will enlarge (Helpman, Melitz and Rubinstein 2008). An exogenous taste for variety, or a desire to limit monopoly positions, would also lead to a larger number of suppliers, although these forces are static.

In the presence of heterogenous quality, however, the dynamics of diversification/concentration can be different.

As new exporting countries get on the EU's list of suppliers of good k, they need to build a reputation of quality for their products. The value of information on the level of health risk of a good k drives the search for quality. There is then a trade-off between concentrating on top quality suppliers and keeping several suppliers in order to "test" them.

The food alert data

We use Eurostat's Rapid Alert System for Food and Feed (RASFF). The RASFF is a system of notification and information exchange on emergency sanitary measures taken at the border by EU member states in place since 1979. The database we use records all notifications (19'000 of them) between 2001 and 2008 with the identity of the importing EU member state, exporting country, product, hazard, type of notification, and type of measure. Notifications can be of two types: "information" or "alerts". In the former case, the hazard is deemed limited; the importing member state imposes a measure (e.g. destruction of the shipment) and informs the rest of the Community of the problem, but other members do not follow suit. In the latter case, the hazard is deemed sufficiently serious to warrant action at the Community level. Measures are thus taken simultaneously by all member states against the exporting country for the product concerned.

The database contains complete information regarding products, but in verbal form, as products are not coded into the HS system. We painstakingly coded all incriminated products into HS8 categories over the period 2001-2005 (8'895 observations), and created an entirely new database, which we now briefly describe.

Figure 6 shows the evolution over time of the number of notifications (including informations and alerts). Informations outnumber alerts by a ratio of more than four to one, and represent 82% of all notifications. Both informations and alerts show a sharply rising trend, although somewhat decelerating for informations after 2003. There is substantial prima-facie heterogeneity among notifying EU states in the frequency of notifications. Germany (25% of observations), Italy (21%) and Spain (17%) are the top notifying countries, while Ireland accounts for only 0.61% of them. Figure 7 shows that there is also heterogeneity in terms of products. Fishery products (30%), Fruit and Nuts (23%) and Coffee & Herbs and spices (10%) rank highest in terms of reported notifications. In terms of hazards, considering all years and importers, the main cause of notifications for agricultural products is contamination by mycotoxins (mainly aflatoxin), which alone accounts for 40% of the notifications. The second cause is contamination by residues of veterinary medicinal products (chloramphenicol, nitrofuran, and tetracycline) which together account for 13% of the notifications. Then comes the presence of pathogenic micro-organisms (10%) and contamination by pesticide residues (3%).

The dataset also details the action taken after each notification. Overall, 92.7% of notified shipments are stopped at the border. In 42.2% of the cases, they are destroyed. The rest (50.5%) are re-dispatched to other destinations; that is, they do not penetrate the EU market but will nevertheless end up in somebody's mouth. An additional 0.4% of imports are banned (Table 7). Thus, whether alerts or informations, notifications are extremely restrictive. The level of sanitary risk associated with imported agri-food products can result from (i) intrinsic product characteristics, as some products are more vulnerable than others to contamination, (ii) supplier characteristics, as some producers are more able than others to apply necessary controls, or a combination of both. Figure 10 shows a scatter plot of exporter shares in notifications against their share in EU imports, both in logs and averaged over years and products. Along the diagonal, China, Turkey and Brazil are most affected by SPS notifications, but they are also the EU's largest suppliers. Dispersion around the diagonal is substantial; countries like Poland, Hungary or the US are large exporters, but subject to relatively few notifications; at the other end of the spectrum, Vietnam, India and Indonesia suffer a disproportionate number of notifications given their relatively lower import shares. The dispersion around the diagonal suggests that important country-specific characteristics, over and above their sales volumes, affect the number of times their exports are affected by notifications. These characteristics include of course the product composition of their exports, but they may include as well characteristics of national production systems which must be taken into account when assessing the level of product-specific sanitary risk.

In the econometric analysis that follows, we combine the RASFF database with the EUROSTAT data on EU agri-food imports analyzed in the first part of this paper. The sample period is restricted to 2001-2005 where both trade and notification data are available.

Product risk and concentration

As explained in the introduction, we use a two-stage procedure where observed product riskiness, used as explanatory variable in the second-stage regression, is estimated in a first-stage auxiliary regression. The procedure goes like this:

Step 1. We also include k B , a dummy variable indicating whether exporter i is affected by a ban on product k during the sample period. k B controls for decreases in the incidence of notification resulting from reduced imports rather than reduced risk. Finally, the regression includes product and exporter effects k δ and i δ .

Formally, ( )

0 1 2 3 4 ik k k k k i k ik A f Q D B u α α τ α α α δ δ = + + + + + + + (17)
where ik u is an error term. Because the number of notifications is a count (with a huge proportion of zeroes), estimation is by Poisson or negative binomial. 4We also control for the initial value of EU imports of product k in the year 2000 (one year before the sample start), as products imported in large volumes are likely to be inspected (and therefore to fail inspections) more often than others.5

Step 2.

Estimated coefficients on product dummies, ˆk δ , are retrieved from Step 1 and used as explanatory variables in a panel regression of concentration indices, where the unit of observation is a product × year pair. 6 That is, the second-stage equation is

0 1 2 3 4 5 6 ˆkt k k k k t k t kt C Q D v β β τ β β β δ β δ β δ δ = + + + + + + + (18)
where kt C is a measure of concentration (within-and between-groups components of Theil's index, or simple number of suppliers) for good k in year t and other variables are as before, except for time effects t δ which enter the equation both linearly and interacted with product-risk estimates from Step 1.

Results

First-step regression results yielded over two thousand estimated product coefficients. The distribution of significant point estimates is shown in Figure 8. It can be seen that no chapter stands out as having particularly high risk levels, except for fisheries products, with mussels as an unsurprising outlier. It is also remarkable to see that traditional tropical products such as coffee and cocoa, whose share in EU foodstuff imports is, as noted, declining, are among the safest.

suppliers is higher. We already observed, in Section 2, that the distribution of EU suppliers was evolving precisely in that direction-concentration at the intensive margin and diversification at the extensive margin. Thus, combining our results in Section 2 with those of the present section, it seems that the distribution of EU suppliers is converging toward the pattern that we observe for the riskiest products-increasingly dominant suppliers with a growing fringe of small-scale ones.

Figure 9 shows the first and last quartile import share distribution in 2005 for risky versus safe products. Risky products are products with a positive risk coefficient. For the smallest partners ( last quartile of the import share distribution), the risky products distribution is shifted to the compared safe products. While for the biggest exporters ( first quartile of the import share distribution) the curves are very similar and no shift in either direction is observed. Thus, there is a rising polarization between the bottom and the top of the distribution for risky products.

Concluding remarks

This paper establishes a stylized fact on EU import concentration in agro-food products and a correlation with the degree of product safety. We have shown that EU imports of agro-food products over 1988-2005 show a pattern of concentration at the intensive margin and diversification at the extensive margin, the more so for products that EU deemed risky, as demonstrated by the number of food alerts that occurred on that product between 2001 and 2005.

While previous empirical work have focused on the ex-ante impact of standards on trade flows, this paper is, to our knowledge, the first to assess the impact of standards on trade flows ex-post. Using a new dataset -that has never been exploited before-on food alerts that can provide information on the effective (ex-post) implementation of SPS norms by EU importing countries, this paper contributes to the empirical debate about the evolution of geographical concentration of agrofood imports across time. The empirical results are clear. European importers tend to procure their agrofood products from an increasingly large portfolio of suppliers but large orders are concentrated on few among this pool of suppliers.

The policy implications of these results are of significant interest. Indeed as EU foodstuff distributors show growing concerns for food safety, the access to EU markets developing countries enjoy may be constrained by increasingly stringent sanitary requirements. While almost all papers address the issue of developing countries exports opportunities from the exporting country viewpoint, we consider it from the importer point of view. Developing countries export opportunities especially for high-value food products -fresh and processed fruits and vegetables, fish, meat, nuts, and spices-are shaped by importers requirements. Therefore understanding how the implementation of sanitary standards may affect importers suppliers selection is of critical importance for developing countries to maximise the magnitude of these opportunities.

  Figure 1 Number of partners, by product

Figure 2

 2 Figure 2 Geographical concentration indices, 1988-2005

  Figure 3Within-group component of Theil's index, 1988-2005

  There is clearly rising concentration among the EU's suppliers. The rising trend appears also if we calculate Gini and Herfindahl indices using EU kt n as the population size, which is the closest equivalent to Theil's within-group index.

  Columns (1) to (3) show that overall concentration indices, computed using the number of potential suppliers W k n , display a monotone downward trend. For within indices, computed using the number of active suppliers EU kt n , columns (4) to (6) show that coefficients on time and its square confirm the monotone rising trend. 3

  Figure 4 Extensive margin, 1988-2005

  averages of Theil-between components at the product (HS8) level.

  Figure 5 Predicted HK's intensive and extensive margins, 1988-2005

  Figure 6TotalSPS, Alerts and Information notifications, between 2001-2005

  Figure 7Main Sectors concerned by SPS notifications

  Figure 10Mains exporters concerned by SPS notifications, all years and products

  For a product k and an exporter i, the dependent variable is ik A , the combined count of notifications from all EU member states between 2001 and 2005. Thus, the unit of observation is an exporter × product pair and the regression is crosssectional. Regressors include ik S , exporter i's initial share in EU imports of product k; k τ , the ad-valorem equivalent of the EU's MFN tariff on product k; k Q , a dummy variable indicating whether product k is affected by a quota during the sample period; k D , a dummy variable indicating whether product k has been the object of a dispute at the WTO between the EU and any other country. Variables k τ , k Q , and k D control for a possible protectionist agenda.

  Figure 8Distribution of significant point estimates on product dummies

  Figure 9 Import share distribution for risky and safe products, 2005

Table 1

 1 Evolution of EU imports structure, between 1988 and[START_REF] Jaffee | Food Safety and Agricultural Health Standards: Challenges and Opportunities for Developing Country Exports[END_REF]

		Developping	Developped
		countries	countries	Total imports
		1988	2005	1988	2005	1988	2005
	Traditional tropical products
	Coffee, tea, mate and spices	16.6%	7.7%	0.1%	0.1%	16.7%	7.8%
	Lacs; gums, resins	0.5%	0.7%	0.1%	0.2%	0.7%	0.9%
	Sugars and sugar confectionery	3.0%	2.2%	0.3%	0.3%	3.2%	2.5%
	Cocoa and cocoa preparations	5.1%	5.1%	0.1%	0.1%	5.2%	5.2%
	Subtotal	25.2%	15.7%	0.6%	0.7%	25.8%	16.4%
	Tempered zone products
	Live animals	0.1%	0.1%	0.5%	0.9%	0.6%	1.0%
	Meat and edible meat offal	2.2%	3.3%	2.7%	2.4%	4.9%	5.7%
	Dairy produce; birds' eggs; natural honey 0.3%	0.3%	1.0%	0.5%	1.3%	0.9%
	Products of animal origin nes	1.0%	1.2%	0.7%	0.3%	1.7%	1.5%
	Cereals	1.0%	1.9%	2.9%	1.6%	3.9%	3.5%
	Subtotal	4.5%	6.8%	7.9%	5.7%	12.5%	12.5%
	Fish and Horticulture
	Fish and crustaceans	5.6%	10.9%	5.7%	5.0%	11.3%	16.0%
	Live trees and other plants	0.5%	1.6%	0.5%	0.4%	1.0%	2.0%
	Edible vegetables	5.2%	3.6%	1.3%	1.3%	6.5%	4.9%
	Edible fruit and nuts	9.5%	13.7%	3.8%	4.2%	13.3%	17.8%
	Vegetable plaiting materials	0.3%	0.2%	0.1%	0.0%	0.4%	0.2%
	Subtotal	21.2%	30.0%	11.3%	11.0%	32.5%	41.0%
	Others
	Products of the milling industry	0.1%	0.1%	0.0%	0.0%	0.1%	0.1%
	Oil seeds and oleaginous fruits	5.4%	6.5%	7.6%	2.6%	13.0%	9.1%
	Animal or vegetable fats and oils	3.9%	7.0%	1.1%	0.3%	5.0%	7.4%
	Preparations of meat, fish or crustaceans 3.0%	5.2%	1.6%	0.8%	4.6%	6.0%
	Preparations of cereals	0.1%	0.4%	0.2%	0.3%	0.2%	0.7%
	Preparations of vegetables, fruit, nuts 4.0%	5.2%	1.5%	0.5%	5.5%	5.7%
	Miscellaneous edible preparations	0.5%	0.5%	0.3%	0.7%	0.7%	1.2%
	Subtotal	16.9%	25.0%	12.2%	5.2%	29.2%	30.2%
	Total	68%	77%	32%	23%	100%	100%

Table 2

 2 Share of single-supplier products

	Panel A

Table 4

 4 Regression results, EU import concentration on time trend

		(1)	(2)	(3)	(4)	(5)	(6)
		Theil Overall Hrfal Overall Gini Overall Theil Within Hrfal Within Gini Within
	Time	0.001091	0.000992	-0.000778	0.006959	0.002601	0.002866
		[1.565]	[2.540]**	[7.984]***	[11.477]***	[4.836]***	[7.701]***
	Time^2	-0.000397	-0.000203	-0.000035	0.000037	-0.000006	0.000088
		[11.237]***	[10.268]***	[7.158]***	[1.194]	[0.226]	[4.675]***
	Constant	3.708313	0.724127	0.91898	0.374646	0.258379	0.307514
		[1269.443]*** [442.112]*** [2250.572]*** [147.390]*** [114.595]*** [197.090]***
	Turning Point	1988	1989	1976	1893	2204	1971
	Observations	155290	155290	155290	155334	155334	155334
	R-squared	0.768	0.628	0.842	0.71	0.407	0.694
	Fixed Effects	yes	yes	yes	yes	yes	yes

Table 6

 6 Regression results, Intensive and Extensive margins of EU country imports

		(1)	(2)	(3)	(4)
	Extensive	Extensive	Intensive	Intensive
	Margin	Margin	Margin	Margin
	Time	-0.000029 -0.007404 -0.020632	-0.020213
	[0.024]	[10.934]*** [23.982]*** [37.906]***
	Time^2	-0.000047	0.000309	0.000777	0.000745
	[0.830]	[9.884]*** [20.650]*** [30.260]***
	Constant	0.537564	0.567948	0.200292	0.200116
	[88.946]*** [170.718]*** [44.286]*** [76.385]***
	Observations	78665	78665	78665	78665
	R-squared	0	0.737	0.019	0.493
	Fixed Effects (Importer*product)	no	yes	no	yes
	* significant at 10%; ** significant at 5%; *** significant at 1%
	Absolute value of t statistics in brackets

Table 7

 7 Main actions taken following notifications, 2001Main actions taken following notifications, -2005

		2001		2002		2003		2004		2005
		Info Alert Info Alert Info Alert Info Alert Info Alert Total	%
	Physical treatment	0	0	1	0	6	0	27	2	61	0	97	1.3%
	Product seized &
	destroyed	264	66	614	131	616	109	410	222	451	353	3236 42.2%
	Product re-dispatched	120	31	197	28 1049	29 1189	44 1137	56	3880 50.5%
	Ban	0	0	1	2	1	0	4	5	7	12	32	0.4%
	Reinforced checking &
	screening	9	1	6	5	17	0	5	0	40	4	87	1.1%
	Others	6	0	44	6	54	10	106	11	72	36	345	4.5%
	Total	399	98 863	172 1743	148 1741 284 1768	461	7677 100%

We use this restrictive definition for consistency of time series, as EUROSTAT does not provide data on member states before their accession.

We drop intra-EU trade on the ground of the mutual recognition of standard. The principle ensures that a product lawfully produced in one Member State is acceptable without adaptation in another Member State, provided that both states pursue the same general objectives in health

For all estimations where the coefficient on time square is significant, the turning point is outside the sample period. We are on the downward-sloping part of the parabola for overall concentration indices and on its upward-sloping part for within indices.

This is largely inconsequential, as consistency of second-stage estimates does not depend on the correct specification of the first-stage equation.

This is done using the "exposure" option for count models in STATA, which is equivalent to including the initial volume of imports as a regressor with a coefficient constrained to be one.

When estimated coefficients were not significant at the 10% level, they were set equal to zero.

Second-step regression results are shown in Table 8. The dependent variable is the within-groups component of the Theil index (the intensive margin) in column (1), its between-groups component (the extensive margin) in column (2), and the number of suppliers in column (3). Coefficients of our constructed measure of product risk are all highly significant (at the 1% level), confirming that product riskiness seems indeed to affect sourcing concentration.

Thus, again, the evidence points in conflicting directions. On one hand, column (1) suggests that concentration is higher at the intensive margin for riskier products. On the other hand, column (2) suggests that concentration is lower at the extensive margin for those products, and column (3), that the number of


Mélise Jaud

Madina Kukenova
email: madina.kukenova@unil.ch

Martin Strieborny
email: martin.strieborny@unil.ch

Melise Jaud
email: melise.jaud@pse.ens.fr

Financial dependence and intensive margin of trade

Keywords: G1, F10, C41 financial development, financial dependence, trade duration Financial Development, Financial Dependence, Trade Duration JEL classi…cation: G1 F10 C41

. An exporter faces multiple obstacles when entering new markets: imperfect information about the market, quality requirements of the importing countries, trade and marketing costs etc. Only …rms with su¢ cient …nancial resources and high productivity can enter the international market. Therefore, one can expect exporters from a country with a well functioning …nancial markets to survive longer than exporters from a country where the …nancial markets are underdeveloped. In particular, we check if the exports of industries heavily dependent on external …nance survive longer in foreign markets when produced in countries with developed …nancial system.

Introduction

Exporting …rms face multiple obstacles when entering new markets: imperfect information about the market, quality requirements of the importing countries, trade and marketing costs etc. Only …rms with su¢ cient …nancial resources and high productivity can enter the international market. (Melitz 2003; [START_REF] Chaney | Liquidity constrained exporters[END_REF]Berman 2009).

Therefore, one can expect exporters from a country with a well functioning …nancial markets to survive longer than exporters from a country where the …nancial markets are underdeveloped.

Since Besedes and Prusa …rst paper -( 2006)-an increasing number of papers have looked into the dynamics of trade using survival analysis. Identifying the determinants of duration of trade relationships is at least as important as understanding the factors driving …rms'decision to export. This is particularly relevant for developing countries for whom access to international market is a mean to achieve higher economic growth. At the product level developing countries export growth is driven primarily by the intensive margin [START_REF] Besedes | A Search Cost Perspective on Duration of Trade[END_REF]Prusa 2007, Brenton and[START_REF] Newfarmer | Watching more than the Discovery channel to diversify export[END_REF]. [START_REF] Brenton | What explains the low survival rate of developing country export ‡ows ?[END_REF] show that higher export performance is to be expected from the securing (survival) and deepening of existing trade ‡ows rather than the creation of new ones. Therefore, identifying the determinants of the survival of trade ‡ows is a key issue.

This paper objective is to analyze the survival of developing countries exports using the methodology developed by [START_REF] Rajan | Financial dependence and growth[END_REF]. Building on the authors' work we investigate whether …nancial development facilitates country-product survival on the international market. In particular, we check if the exports of products from industries heavily dependent on external …nance survive longer in foreign markets when produced in countries with developed …nancial system.

Our paper relates to two strand of the literature. The …rst examines the link between …nancial development and growth. This literature's main drawback is the di¢ culty to establish a casual relationship between …nancial development and growth as they both might be driven by a common omitted variable. In addition, …nancial development is typically proxied by the level of credit or the size of the stock market, which may expand due to anticipation of future growth opportunities. In their seminal paper, Rajan and Zingales (1998) propose a methodology to overcome these problems by looking at a speci…c channel through which …nancial development may trigger economic growth.

They consider the di¤erentiated impact of …nancial development across industries characterized by di¤erent level of external …nance dependence. This methodology has been applied intensively in …nance and trade literature. Krozner, Laeven and Klingebiel (2007) analyze the e¤ect of banking crises on industry growth.

They show that industries dependent on external …nancing experience greater growth contraction of value added during the crisis. Levchenko, Ranciere and Thoening (2008) study the impact of …nancial liberalization on the industrial growth and volatility. They …nd that …nancial liberalization increases both growth and volatility of the sectors relying on external …nance. Pang and Wu (2009) study the e¤ect of …nancial development on capital allocation across industries. E¢ ciency improvement in capital allocation is more prominent in the industries that depend on external …nance. [START_REF] Manova | Credit Constraints, "Equity Market Liberalizations and International Trade[END_REF] applies Ralan Zingales methodology to international trade, studying the impact of equity market liberalization on export growth, she …nds that equity market liberalization a¤ect disproportionately more sectors intensive in external …nance. This paper also relates to the survival trade literature. Besedes and Prusa (2006a) show that prevalence of short spells in the US import is consistent with a matching model of trade formation. In a subsequent paper, [START_REF] Besedes | A Search Cost Perspective on Duration of Trade[END_REF]Prusa (2006b, 2007) show that survival rate of product level exports varies across types of goods (di¤erentiated, referenced and homogeneous) and across countries (developed and developing countries).

These empirical …ndings …nd theoretical foundation in the work of [START_REF] Rauch | Starting small in an unfamiliar environment[END_REF]. The authors develop a model where developed country buyers look for the suppliers from developing countries. The search is costly and there is uncertainty about the ability of suppliers from developing countries to deliver large orders according to buyers' speci…cation. The buyer learns about the quality of suppliers before making costly investment in their training, by making small orders which do not generate pro…t.

The model predicts that the chances to start a relationship small increase with the search costs. More recently, Araujo and Ornelas (2007) propose a model highlighting the importance of partners reputation in the environment with low contract enforcement. In their model, producers from developed countries look for a partnership with distributors in overseas markets where contracting institutions are weak. Incomplete information about distributors type and imperfect contract enforcement in the host country allows for opportunistic behavior of some distributors, thus depressing trade. The more producer trade with his partners, the more he learns about its level of commitment. This allows in turn to increase the volume of trade substituting for adequate contract enforcement.

Improvement in contract enforcement in the importing country rises the expected pro…t of foreign exporters by boosting the volume within existing partnership. On the other hand, it also reduces the frequency of defaults, slowing down the process of reputation building. The net e¤ect on trade volume within existing partnerships depends on both horizon of the analysis and the initial level of enforcement.

Our paper di¤ers from existing empirical papers as it focuses on a particular channel through which …nance may help trade relationship to survive. Financial markets and institutions help …rms overcome moral hazard and adverse selection problem by reducing their costs of raising money from outsiders. Therefore, …nancial development can bene…t the …rms or industries that rely on external funding to support their operations. It can also improve …rms' perspective in the promotion of their goods in the international market and increases the chance of surviving after trade is initiated.

The paper is organized as follows. Section (2) presents the empirical strategy and the estimation issues. In section (3) we present the country-product data used and show some descriptive statistics on country-product trade relationships survival. Section [START_REF] Alvarez | Determinants of Firm Export Performance in a Less Developed Country[END_REF] presents the results and section (5) concludes.

Empirical strategy

The duration of a country'export for a given product is de…ned as the time (measured in years) that a trade relationship has been in existence without interruption. We de…ne a trade relationship, the unit of observation, as a exporting-country*product*importingcountry triplet. Duration analysis rely on conditional probabilities. The hazard rate of a trade relationship is the probability it dies after t periods given it has survived up to that point. Formally, let T 0, denote the survival time ( length of a spell) of a given triplet with covariates x. Our trade ‡ow data being reported annually, we consider the discrete-time hazard rate h(t), given by: h(t) = P (T = tjT t; x) t = 1; 2; ::: The underlying assumption of the proportional hazard model is that the hazard rate of trade relationships only depends on time at risk, and on explanatory variables a¤ecting trade independently of time. The hazard function is parametrized as:

To
h(t; x; ) = h 0 (t) exp (x: )
the product of an arbitrary and unspeci…ed baseline hazard rate, h 0 (t), which is a function of time only and positive function, exp(x 0 ) independent of time, which incorporates a vector of explanatory variables x: are the parameters to be estimated.

The baseline hazard, h s0 (t); characterizes how hazard changes as a function of time and is allowed to di¤er across stratas, unlike the vector of parameters which is restricted to be the same for all stratas. The model is estimated by maximizing a partial likelihood function with respect to the vector of parameters without specifying the form of the baseline hazard function h 0 (t): Since baseline hazard function is not speci…ed, only the order of duration provides information about the unknown parameters. The estimated coe¢ cients indicate the relationship between the explanatory variables and the hazard function (i.e. the risk for a trade relationship to end).

There are several issues which we need to keep in mind when analyzing the duration of export ‡ows. First, observations may be right-censored. This is the case when trade relationships are still in progress in the …nal year of the sample period. One third of our observations are right censored. The Cox proportional hazard model can take care of right censoring of the data. Second, spells may be left-censored, which means that we cannot determine the date when they were initiated. In this situation, the actual length of the spells cannot be determined. To mitigate this problem, we estimate the model dropping left censored observations, that is the observations for which trade ‡ows were recorded already in 1995. Such observations represent around 10% of all observations. Third, some trade relationships re-occur, exhibiting what is referred to as multiple spells of service. A country will service a the market for a speci…c good, exit and re-enter the market. Such consecutive exits may be interdependent. The …rst exit may make the second one more likely to occur or inversely exporters might learn from their initial failure and manage to stay in a relationship afterwards. To account for this issue in the main estimation, we treat multiple spells as independent but use a dummy variable for higher order spells

The empirical model

Because entering and trading on international markets is costly, exporters must have su¢ cient …nancial resources to sustain. This is particularly true for industries that rely intensively on external …nancing2 . Considering a trade relationship as a country i export to country j of product k from industry s, our main assumption is that exporting country level of …nancial development has a positive impact on the duration of trade relationships involving products from industries highly dependent on external …nance. The external …nance intensive industries are identi…ed by Rajan and Zingales (1998) using US …rm level data. This measure of external …nance dependence captures technological demand for …nancing and is equal across countries. In their paper, the authors interact the level of …nancial development with the external …nance dependence of the industry controlling for industry and country e¤ects. This allows them to identify di¤erentiated e¤ect of …nancial development across industries within countries.

Following this methodology, our baseline hazard model writes:

h(t; x; ) = h 0 (t) exp ( 1 :F in_Dev jt 0 Ext_F in_Dep s + X l 2 l: x lt 0 + m +" ijst 0 ) (1)
where F in_Dev jt 0 is the exporting country j level of …nancial development at time t 0 ;initiation of the spell. We proxy the level of …nancial development by the ratio of private bank credit to GDP.We expect the coe¢ cient to be negative and signi…cant, meaning that …nancial development helps exports survival for external …nance dependent industries.

Ext_F in_Dep s is the Rajan and Zingales measure of external …nance dependence of ISIC-level industry s. The measure is time invariant.

m are various …xed e¤ects (importer, industry, year, importer-time, industrytime).

We estimate a strati…ed Cox model, we use the interaction between exporter and time as a strati…cation variable. Strati…cation is a way to accommodate …xed e¤ects. It enables a ‡exible estimation of the model, allowing the baseline hazard rate to vary across exporters and time. Following this methodology we are not able to identify the overall e¤ect of the …nancial development since it is absorbed by exporting country …xed e¤ects (strata).

In our baseline speci…cation we include a set of variables, x lt 0 ; that may help describe the bilateral trade relationship apart from features captured by a comprehensive set of country industry time …xed e¤ects: log initial_export ijkt 0 : the (log) initial trade relationship value, export of product k by country j to country i at time t 0 (initiation of the spell) in dollars. This stands as a proxy for the level of con…dence trading partners have in the pro…tability of their trade relationship; log bilateral_trade ijt 0 : the (log) value of aggregate bilateral trade to capture the exporter's knowledge of the import market; log total_export jkt 0 : the (log) value of total country j export of product k; captures the experience of country j in exporting product k abroad; Gravity variables are highly successful in explaining patterns of trade; they may also be relevant for the duration of trade. Transport costs are measured with the (log) bilateral distance between both partners (log dist ij ). These distances are extracted from the CEPII database and are calculated as the sum of the distances in kilometers between the biggest cities of both countries, weighted by the share of the population living in each city. We also include a dummy variable "Common border" (cbord) that equals one if both countries share a border. Bilateral trade can be fostered by countries' cultural proximity. Similarity in culture can indeed increase the quality of the match between varieties produced in country i and tastes of consumers in country j. We therefore control for this proximity by introducing two dummies, respectively equal to one if both countries share a language (comlang_of f ) or if both partners have had a colonial relationship in the past (col). Data come from the previously mentioned CEPII database. log GDP it 0 : the (log) value of GDP of the importing country i at initiation of the spell, t 0 . A larger destination market may allow exporters to accommodate demand shocks more easily; log GDP pc it 0 : the (log) value of per capita GDP of the destination market i at initiation of the spell, t 0 ; The variation in level of wealth may capture changes in preferences and tastes of the importing country, i.e. demand shocks that may reduce the probability to survive.

N _suppliers ikt 0 : the number of countries supplying product k to country i at time t 0 may proxy the degree of competition on this market. multiple_spell : the dummy variable is de…ned for an ijk triplet. It takes value one if the trade relationship was initiated, stopped and started again. The existence of previous attempts for a given trade relationship may re ‡ect either the ability (inability) of an exporting country to account for previous failures and learn from its mistakes. Both possible e¤ects …nd support in the literature. In Besedes (2006) multiple spells are an indicator of exporting country low reliability, while [START_REF] Brenton | What explains the low survival rate of developing country export ‡ows ?[END_REF] support the hypothesis that previous experience in exporting helps maintaining trade relationships.

Estimation issues 2.2.1 Endogeneity problem

Our speci…cation allows us to examine a speci…c channel through which …nancial development may a¤ect survival of trade relationships. However we may still face possible endogeneity problem. Using the level of credit to GDP as a proxy for …nancial development introduces a potential endogeneity bias, as quantity of credits in economy expands in anticipation of future growth opportunities. Furthermore we may face a reverse causality issue. As shown by [START_REF] Do | Comparative advantage, demand for external …nance, and …nancial development[END_REF], countries with comparative advantage in …nancially intensive goods have a higher demand for external …nance, and therefore, higher …nancial development. Finally unobservable shocks may a¤ect simultaneously the level of …nancial development and the duration of trade relationships.

For a given exporting country j, using the level of …nancial development at time t 0 , i.e. at the initiation of the spell, is likely to mitigate the endogeneity problem, but does not eliminate it completely. Therefore (i) we estimate our model using the level of …nancial development in 1996.

(ii) we estimate our model instrumenting the level of …nancial development with legal origin, following a two-step procedure: F in_Dev j = 1 :LO_f r j + 2 :LO_uk j + 3 :LO_de j + 4 :LO_sc j + 5 :LO_so j +latt j +" j [START_REF] Álvarez | Entry and exit in international markets: Evidence from Chilean data[END_REF] where LO_f r (_uk; _de; _sc; _so) is a dummy variable that takes value one whether country j legal system is based on French civil law ( British, German, Scandinavian or Socialist), zero otherwise, and latt j is the country latitude.

nd

Step: We retrieve the predicted value of …nancial development from step one and incorporate it in our Cox PH model. To account for measurement errors we estimate equation (1) bootstrapping the standard errors. h(t; x; ) = h 0 (t) exp ( 1 :

F in_Dev jt 0 Ext_F in_Dep s + X l 2 l: x lt 0 + m + " ijst 0 ) (3)

Omitted variables

In our speci…cation we control for the omitted variable bias: (i) we stratify by exporting country-time to account for any omitted country (country-time) characteristics (economic development, infrastructure, road, etc. . . ) that bias selection of countries into exporting. (ii) we include various …xed e¤ects. Technical constraints forced us to limit the number of …xed e¤ect variables. We therefore include only industry-speci…c, importer-speci…c e¤ects to account for unobserved time invariant characteristics. We do not interact importer …xed e¤ects or industry …xed e¤ects with time dummies3 .

Estimation procedure

All in all, the estimation procedure goes as follow: In these speci…cations we should control for importer-time and industry-time e¤ects.

However this would result in including 29*10 and 28*10 additional dummies, which is not technically possible given the size of our sample. Therefore we might still have omitted variable bias. To address this issue and obtain an unbiased coe¢ cient on our variable of interest we then :

(ii) estimate our baseline speci…cation for each year of the sample period including importer and industry …xed e¤ects (strata by exporter). In this case all …xed e¤ects dimensions are accounted for. Nevertheless slicing the dataset by year might not be optimal use of the rich structure of our database. First, we cannot estimate the model for the last year, 2005, since all observations are right censored for that year. Second, the length of the spell decreases over time, i.e., the maximum length for a spell initiated in 1995 is ten years while a spell initiated in 2003 can at maximum lasts two years.

It may not be a problem since we focus on the di¤erentiated e¤ect of our variable of interest across industries.

Results for speci…cations detailed in (i) are reported in section 4.2. Results for speci…cation detailed in (ii) are reported in section 4.3.

3 Data and descriptive statistics They recompute Rajan and Zingales measure for 3 digit ISIC level. Using concordance between ISIC and hs6, we are able to include this variable into the set of our regressors.

Given that hs6 product classi…cation is more disaggregated than the ISIC one, several hs6 product categories share the same value for external …nance dependence. The data on legal origin used in the …rst step of the instrumental regression procedure, comes from La Porta et al.(1998).

Descriptive statistics

Table 1 presents summary statistics on duration of exports for our data. After correcting for left censoring, the full sample data consists of 2 0 785 0 203 trade relationships (exporterproduct-importer triplets) corresponding to 4 0 287 0 659 spells over the sample period 1996-2005. The average spell duration is only about two years and the median duration is one year, con…rming previous …ndings of Besedes and Prusa (2006). About one third of the spells are right censored. When considering single spell trade relationships the average duration increases. In a similar manner dropping all spells with initial trade value inferior to 10'000 dollars (100'000, or 1'000'000 dollars) increase the average and median duration. 65% of spells start with trade values lower to 10'000 dollars, 7,5% are initiated with trade values higher than 100'000 dollars and only 0,9% start with initial trade values greater than 1'000'000 dollars.

[Table 1

: Descriptive Statistics]

Approximately 62% of trade relationships experience multiple spells. About 38% experience just two spells. Less than 20% have more than three spells (Table 2).

[Table 2: Description of exports duration data, 1996-2005]

4 Estimation results

Kaplan-Meier survival function

Before exploring the Cox estimations results, we begin characterizing duration of trade relationships non parametrically by estimating survival functions using the Kaplan-Meier estimator. In discrete time, the survivor function is de…ned as the probability that an individual survives at least to time t: S(t) = P (T t) t = 1; 2; :::

The Kaplan-Meier estimator of the survivor function at time t is de…ned as:

Ŝ(t) = t i t [n i d i =n i ]
where t i , i = 1; 2::: is the ordered failure times, n i denotes the number of spells alive (at risk) just before time t i , including those who will die at time t i . Let d i denote the number of failures (deaths) at time t i 4 .

Figure 1 We identify industries according to their external …nance dependence (using Rajan and Zingales measure). We rank industries by increasing order. We then, estimate the survival function of the …rst and last quartile of the industries distribution. The industry at the 25 th percentile (low dependence) is beverages while the industry at the 75 th percentile (high dependence) is machinery. Duration of exports varies with the degree of external …nance dependence. Industries which are less dependent on external …nance have higher survival rate (Figure 2 and3). This is to be expected as external …nance dependent sector are more vulnerable to liquidity shortages. This patterns is more accentuated for non OECD countries. We test for the equality of survival functions across industry types. The equality hypothesis is rejected for non OECD countries but we fail to reject it for OECD countries. Indeed, within the OECD sample, countries have similar level of …nancial development. Therefore, survival of …rms on the international market may not be limited by access to …nancial resources. While in the non OECD sample, variation in the level of …nancial development across countries allows for di¤erentiated survival of …rms based on their need for external …nance. 4 The conditional probability that a spell dies in the time interval from ti to ti, given survival up to time ti , is estimated as d i n i : The conditional probability that a spell survives beyond ti , given survival up to time ti , is estimated as n i d i n i :In the limit as ! 0,

n i d i n i
becomes an estimate of the conditional probability of surviving beyond ti given survival up to ti.

Baseline estimation

Table 3 details the estimation procedure to assess the e¤ect of …nancial development on duration of exports for non-OECD countries. In the …rst column we regress the length of trade relationships over a set of country and product speci…c covariates.

Length ijkt 0 = 0 + 1 F in_Dev jt 0 Ext_F in_Dep s + X l 2 l: x lt 0 + m + " ijst 0 (4)
where Length ijkt 0 is the duration of spell ijk initiated at t 0 . We include exportertime …xed e¤ects, importer and industry …xed e¤ects. We estimate equation ( 4) with conventional OLS. Results are reported in column (1). Although linear models are not appropriate for duration analysis, this gives us a …rst insight on the e¤ect of …nancial development on exports survival. The coe¢ cient on our intreaction term is positive and signi…cant; suggesting a positive e¤ect of …nancial development on the duration of exports.

Column (2) to (6) reports coe¢ cients obtained using a Cox semiparasitic proportional hazard model (equation(1)). We use exporter-time as the strati…cation variable.

Column (2) reports results for the full sample (all trade relationships between 1996-2005) using the level of …nancial development at the initiation of the spell. In column

(3) and ( 4) we report results adding consecutively industry and importer …xed e¤ects.

In the following speci…cations we always control for industry and importer speci…c characteristics. Column (5) reports results using the level of …nancial development in 1996.

Finally,we estimate equation (1) instrumenting the level of …nancial development by legal origin and the latitude of the exporting country, results are shown in columns [START_REF] Baldwin | Zeros, Quality, and Space: Trade Theory and Trade Evidence[END_REF].

In column ( 4) to ( 6) since the speci…cation controls for exporting country-speci…c characteristics and industry-speci…c characteristics the interaction between external …nance dependence and …nancial development captures the e¤ect that varies both across countries and industries. Through out all speci…cations, the coe¢ cient on the interaction term remains negative and signi…cant. Financial development helps exports survival, by reducing the costs of external …nance to …rms which rely on external funds to support their operations.

Regarding traditional gravity covariates, all variables have expected sign and are highly signi…cant, suggesting that they successfully explain patterns and duration of trade. Distance as a proxy for trade costs increases exports hazard rate. A common border decreases the hazard rate , everything else held constant. Cultural proximity variables, colonial ties and sharing a common language both increase the likelihood of sustaining bilateral trade ‡ows over time.

The GDP of the importing country have expected negative sign. Interestingly, results

suggests that holding GDP constant, exports to richer countries are on average shorterlived. As changes in the level of GDPpc capture changing tastes and preferences in the importing country, GDPpc negatively in ‡uences the probability of exports to survive over time.

Interestingly the hazard rate decreases with the number of competitors. A possible explanation for this …nding is as proposed by Nitsch (2007), that the number of competitors may just be a proxy for the size of the market. The multiple spells dummy variable increases the hazard rate. Suggesting as hypothesized by Besedes (2006), that multispell acts as an indicator of exporters poor reliability.

The initial export value plays a signi…cant role as implied by [START_REF] Rauch | Starting small in an unfamiliar environment[END_REF]. Duration increases with the transaction size, that is spell that start with higher initial value remain in existence for longer periods of time. The aggregate bilateral trade variable enters the regression negatively capturing the exporter's knowledge of the import market. An increase in total exports of a product irrespective of the destination market favours duration, suggesting exporting experience is product speci…c.

[Table 3: Exports survival estimation results for nonOECD countries]

Results for OECD countries are shown in Table 4. Qualitatively results are similar for all speci…cations.

[Table 4: Exports survival estimation results for OECD countries]

Estimation for each cohort

We now estimate our baseline speci…cation for each cohort in the sample. we de…ne a cohort as the group of spells initiated the same year, we therefore have eight cohorts.

Unlike in the baseline speci…cation, we are able to control for all dimensions including importer and industry …xed e¤ects. We use exporting country as the strati…cation variable. Results for non OECD (OECD) countries are reported in Table 5 (Table 6).

Results for both non OECD and OECD countries are qualitatively similar to our baseline results. The coe¢ cient on our main interaction term is negative and signi…cant but the e¤ect is three times smaller than in the baseline speci…cation (column (5) Tables 3 and4). This is not surprising as we estimate our model for each cohort . This suggests that we may overestimate our coe¢ cients in our baseline speci…cation when considering full sample due to the fact we do not control for all …xed e¤ects dimensions.

[Table 5: Cox proportional hazard estimates, nonOECD countries]

[Table 6: Cox proportional hazard estimates, OECD countries] Table 7 and Table 8 show results where the level of …nancial development is instrumented by legal origin and the latitude of the exporting country. Since we use estimated variable as a regressor we bootstrap standard errors. Results are qualitatively the same as in Table 5 and Table 6. All variables have expected sign and are all statistically signi…cant except for the interaction term coe¢ cient in the year 1997 for the OECD countries (column (2) Table 7) .

[

Concluding Remarks

In this paper we examine empirically the duration of 121 developing countries'exports to OECD countries at the product level (6-digit level) from 1995 to 2005. We focus on the duration of trade relationships, that is the intensive margin of trade, using semi parametric model. This results is robust to a variety of robustness checks. Table 5: Cox proportional hazard estimates, nonOECD countries Our measure of …nancial development is the private credit to GDP ratio.

(1)

(3)

(5) (1)

(6) (7) (8)
(3)

(5)

(6) (7) (8)
(2)

(3) (4)
(5) Table 8: Cox proportional hazard estimates, OECD countries Our measure of …nancial development is the private credit to GDP ratio, instrumented.

(6) (7)
( ( (1)

(3)

  establish whether …nancial development increases the survival of trade ‡ows we model the determinants of product-country survival and check whether the …nancial development interacted with the degree of external …nance dependence of the industry is a statistically signi…cant determinant of the exporting country's hazard of exiting a trade relationship. We examine the role of potential factors a¤ecting trade duration estimating a strati…ed Cox proportional hazard model 1 . The model is semi-parametric, i.e. no assumptions are made about the nature or shape of the underlying failure distribution.

1

  st Step: We regress cross countries the level of …nancial development over a dummies for legal origin and the latitude (see LaPorta et al., 1998; Beck and al 2002).

( i )

 i We begin estimating equation (1) including importer and industry e¤ects (strata by esporter-time): a-using the level of …nancial development at the initiation of the spell b-using the level of …nancial development in 1996 c-instrumenting the level of …nancial development using legal origin and the latitude of the country

3. 1

 1 SourcesTo assess the empirical relevance of our hypothesis, we use data on detailed product level trade ‡ows from the BACI database, developed by the CEPII (seeGaulier and Zignago 2008). Our dataset consists of highly disaggregated export data at the country-product level. The data covers export for 5010 products from 143 countries ( 114 developing countries ) to 30 OECD countries over the period 1995-2005. Each record includes the exporting country, the product code (6-digit HS), the country of destination and the export value in thousands US dollars.One disadvantage of using this database is the relatively short time coverage, however this disadvantage is compensated by the fact that we use very detailed product level trade data. This is crucial for survival analysis as Besedes and Prusa showed that aggregation of data may introduce considerable bias since it may hide failures.All bilateral gravity variables are taken from the CEPII database, GDP data comes from the WDI database. We use GDP reported in constant 2000 US dollars. The data for …nancial development is taken from Beck, Demirguc-Kunt, and Levine (2000) database which contains various indicators of …nancial development across countries and over time. We use the private credit to GDP as a proxy for country's …nancial development in our main speci…cation. As a robustness check we use the stock market capitalization divided by the GDP. This ration measures the organized trading of …rm equity as a share of national output and therefore should positively re ‡ect liquidity on an economy-wide basis. Finally we use a dummy for equity liberalization constructed by Bekaert, Harvey and Lundblad (2005) using detailed chronology of important …nancial, economic and political events in a broad range of developing and developed countries. Rather than using the external …nance dependence measure developed by[START_REF] Rajan | Financial dependence and growth[END_REF] which is calculated for a mix of three-digit and four-digit ISIC industries, we adopt the measure of external …nance dependence used by Klingebiel, Kroszner and Laeven (2002).

[Figure 2 :

 2 Kaplan Meier Survival Estimates across di¤erent industries, non OECD sample] [Figure 3:Kaplan Meier Survival Estimates across di¤erent industries, OECD sample]

  While …nancial development implication in …rms' decision to export, has been established both theoretically and empirically (Melitz, 2003; Chaney 2005; Berman 2009), our purpose is to assess the distinct e¤ect of …nancial development on the duration of exports. Following seminal work of Rajan and Zingales we propose a distinctive methodology to investigate a particular channel through which …nancial development may help exports survive longer, therefore, the causality is clearly identi…ed. By looking at the interaction term (between …nancial development and external …nance dependence) rather than the direct e¤ect of …nancial development and controlling for adequate country and industry …xed e¤ects, we reduce the number of variables that we rely on as well as possible omitted variable bias. Finally we control for possible endogeneity problem instrumenting the level of …nancial development following an two step procedure. The main result of the paper is that …nancial development helps exports survival by reducing the costs of external …nance to …rms. Firms with facilitated access to external …nance can easily accommodate shocks and survive longer on the international market.

Financial

  development is proxied by the private credit to GDP ratio. p<0.01, ** p<0.05, * p<0.1

Table 6 :

 6 p<0.01, ** p<0.05, * p<0.1 Cox proportional hazard estimates, OECD countries Our measure of …nancial development is the private credit to GDP ratio.

  p<0.01, ** p<0.05, * p<0.1

  p<0.01, ** p<0.05, * p<0.1

  parentheses, bootstrapped *** p<0.01, ** p<0.05, * p<0.1 Table A: Cox proportional hazard estimates, nonOECD countries Our measure of …nancial development is the stock market capitalisation to GDP ratio.

  p<0.01, ** p<0.05, * p<0.1 Table B: Cox proportional hazard estimates, OECD countries Our measure of …nancial development is the stock market capitalisation to GDP ratio.

  p<0.01, ** p<0.05, * p<0.1 Standard errors in parentheses

  p<0.01, ** p<0.05, * p<0.1 Table D: Cox proportional hazard estimates, OECD countries Our measure of …nancial development is the stock market capitalisation to GDP ratio, instrumented.

  presents the Kaplan Meier estimator of the survival function for OECD and non OECD countries. Di¤erences in duration of exporting in the OECD and non OECD sample are important and signi…cant. In line with previous studies we observe that OECD exports survive signi…cantly longer than non OECD exports. The equality of

the survival functions being rejected at a 1% level of signi…cance for all tests (Logrank, Cox, Wilcoxon, and Tarone Ware). Therefore in the remainder of the paper, we analyze separately OECD and non OECD samples'exports duration.

[Figure1: Kaplan Meier Survival Estimates]

Table 7 :

 7 Cox proportional hazard estimates, non OECD countriesIn order to test whether our results are driven by the choice of the …nancial development variable, we re-estimate all speci…cations using an alternative proxy for …nancial development, the ratio of stock market capitalization to GDP. Results are shown in Table

	Financial development instrumented]
	[Table 8: Cox proportional hazard estimates, OECD countries
	Financial development instrumented]
	5 Robustness checks
	5.1 Using stock market capitalization
	(A) to (D).
	Results are robust to the change in the …nancial development variable.

[Table A: Cox proportional hazard estimates, non OECD countries, using Stock market Capitalisation as a proxy for Financial development] [

Table B :

 B Cox proportional hazard estimates, OECD countries, Financial development

	instrumented]
	[Table C: Cox proportional hazard estimates, non OECD countries, using Stock
	market Capitalisation as a proxy for Financial development ]
	[Table D: Cox proportional hazard estimates, OECD countries, Financial development
	instrumented]

Table 3 :

 3 Exports survival estimation results for nonOECD countries Financial development is proxied by the private credit to GDP ratio.

	7 Figures and Tables
		Figure1: Kaplan Meier Survival Estimates
			OECD vs nonOECD
	1.00	Kaplan-Meier survival estimates
	0.75
	0.50
	0.25
	0.00
	0	2	4	6	8	10
			analysis time
			OECD = 0	OECD = 1
	Figure 2: Kaplan Meier Survival Estimates across di¤erent industries,
			non OECD sample
	1.00	Kaplan-Meier survival estimates
	0.75
	0.50
	0.25
	0.00
	0	2	4	6	8	10
			analysis time
			FD = 0	FD = 1
			FD = 2
		FD=1 25 th percentile (low dependence)
		FD=2 75 th percentile (high dependence)
	P values of the tests on equality of survival function ,Pr>chi2
	Variables/Tests log rank Wilcoxon-Breslow-Gehan Tarone Ware	Cox
	nonOECD	0.0000	0.0000		0.0000		0.0000

Table 4 :

 4 Exports survival estimation results for nonOECD countries

Table 7 :

 7 Cox proportional hazard estimates, nonOECD countriesOur measure of …nancial development is the private credit to GDP ratio, instrumented.

Table C :

 C Cox proportional hazard estimates, nonOECD countries

	Our measure of …nancial development is the stock market capitalisation to GDP ratio, instrumented.	(1)

The proportional hazards (PH) model was developed in order to estimate the e¤ects of di¤erent covariates in ‡uencing the times-to-failure of a system. The model has been widely used in the biomedical …eld and more recently has received incresing attention from trade economists.

These industries do not generate su¢ cient cash ‡ow to ensure their investment. In order to ensure …rm's production meet international quality standard …rms need to do big investment in production plant and machinery.

Generally the use of …xed e¤ects is not recommmended for duration models due to incidental parameters problem. In our case true …xed e¤ect would be exporter-importer-product-time e¤ects. Including country-speci…c and industry-speci…c e¤ects we can estimate consistently our equations using Cox proportional hazard model.

1998 1999 2000 2001 2002 2003 2004 Ext_Fin_Dep * Fin_Dev -0.091680*** -0.095916*** -0.087923*** -0.186117*** -0.145144*** -0.119170*** -0.157738*** -0.143700*** -0.087160*** (0.015304) (0.018394) (0.016682) (0.018344) (0.020663) (0.021484) (0.022409) (0.024736) (0.029197) log initial_export -0.081290*** -0.019780*** -0.017749*** -0.022699*** -0.026033*** -0.027815*** -0.033408*** -0.031156*** -0.025023*** (0.001534) (0.001927) (0.001942) (0.001913) (0.001709) (0.001757) (0.001774) (0.001722) (0.001866) log dist 0.226952*** 0.157938*** 0.155797*** 0.150673*** 0.145058*** 0.130916*** 0.114545*** 0.129636***

Appendices

Legal origin and other instrumehts Glaeser, Edward L., Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer, (2004)