Jaewon Jung
email: <jwjung@u-cergy.fr>.

Jean Mercenier
email: <jean.mercenier@u-paris2.fr>.

A Simple Model of Offshore Outsourcing, Technology Upgrading and Welfare

Keywords: Offshore outsourcing, Globalization, Skill upgrading, Technology upgrading, Firm heterogeneity JEL Classification: F16

We adapt Yeaple's (2005) heterogeneous agents framework to model firms in the North as making explicit offshore outsourcing decisions to cheap-labor economies.

Globalization results from a lowering of the set-up costs incurred when engaging in offshore activities. We highlight how firms' technology transformations due to globalization will induce skill upgrading in the North, increase aggregate productivity, average wages and therefore total welfare at the cost of increased wage inequalities. We analytically derive mild conditions under which all consumers-including lower-skilled workers-will nevertheless gain from the surge of offshore outsourcing. A parameterized version of the model roughly calibrated on U.S. data is then numerically explored and confirms our positive welfare predictions.

Introduction

Recent revolutionary advances in transportation and communication technologies coupled with institutional progress in many cheap labor countries have provided firms in the North with strong new incentives to extensively adopt offshore outsourcing strategies and transfer larger parts of their production activities to the South. Though the transferred activities are bound to be dominantly low-tech manufacturing shifting the demand for production workers at home, some low-skilled white collar jobs that were previously protected from foreign competition are now threatened by this new factor-cost savings prospect. This is a rather new phenomenon in international trade. For this reason, the prospect of massive offshoring in white collar services and its potential consequences on welfare in the North has surged as a major political issue in the previous US presidential campaign. As convincingly argued by Mankiw and Swagel (2006), the extent of outsourcing to low-wage countries is currently less than one might infer from media reports, and the idea that U.S. firms are shipping products back to the United States and disrupting the U.S. labor market simply does not line up with the data. But the phenomenon is recent, and possibly not yet perceptible in the data: in view of the new international environment, it is hard to exclude the possibility that firms in the North could massively turn multinational and switch to cost-saving offshore outsourcing practices, transferring large parts of their labor-intensive activities to the South. Assuming this does happen, and that U.S. firms start shipping products back to the United States, will that disrupt U.S. labor markets? Addressing this issue, Mankiw and Swagel (2006) note that, though there exists a large theoretical literature on the positive aspects of offshore outsourcing focusing on the factors influencing firms' choices of organizational structure and location of production, relatively little normative analysis is available on the welfare impact of offshoring. Most existing papers tend to suggest that offshore outsourcing is a modern form of trade, and that it will therefore almost inevitably imply that there are winners and losers-the curse of Stolper-Samuelson-but that the gains from the first are large enough to compensate for the latter. [START_REF] Becker | Offshoring and the Onshore Composition of Occupations, Tasks and Skills[END_REF] Our paper contributes to qualify this perception: offshoring need not lead to lower welfare for domestic factors competing with foreign factors. The argument is that, by making profitable expensive-to-set-up but cheap-to-operate technologies, globalization induces domestic-only firms to turn multinational and switch to more efficient technologies and hence induces a potentially large subset of workers in the North to relocate to more productive activities. There is ample evidence that this mechanism is of empirical relevance. Indeed, it is well established that multinationals use better technologies and are therefore more efficient than their purely domestic competitors. Furthermore, Head and Ries (2002) have investigated the influence of offshore production by Japanese multinationals on domestic skill intensity, using firm-level data. They find that additional foreign affiliate employment in low income countries raises skill intensity at home, but that this effect falls as investment shifts towards high income countries. This is clearly consistent with vertical specialization, and provides evidence that vertical specialization by multinationals contributes to skill upgrading domestically. [START_REF] Broda | Globalization and the Gains from Variety[END_REF] To model this mechanism, we adapt Yeaple (2005) to a North-South setting: workers are heterogeneous in their absolute and comparative advantages in different technologies and firms are ex ante identical but endogenously adopt different technologies. Two complementary activities within a firm, which we refer to as "repetitive" and "conceptual" enter in the production process, respectively of "intermediates" and "headquarter services"; these tasks may be separated geographically: conceptual tasks are exclusively performed in the North while the South can produce the intermediates at lower cost than the North. Headquarter services can be produced via a high fixed-cost low marginal-cost technology or from a low fixed-cost high marginal-cost technology. Workers sort into production activities in equilibrium. The ablest workers produce headquarter services and the less able intermediate goods. Among non-production workers employed in headquarter services, the most able use the high-fixed cost technology. Since offshoring involves a fixed cost, a firm must have sufficiently large sales volumes for this activity to increase profits.

Hence, only those firms that choose the high fixed-cost low marginal-cost technology in-vest abroad, substituting cheap foreign labor for domestic labor in the production of the intermediates. Globalization is interpreted as a reduction in the fixed cost of offshoring.

We demonstrate that this inevitably induces some firms to adopt better technologies and some workers to skill upgrade, in particular among blue-collars, who move to less repetitive more efficient activities. This rise in the economy's global productivity can benefit the least paid factor owners. Furthermore, the consecutive market size increase makes a greater variety of products available to consumers, in particular to the less-skilled. [START_REF] Conyon | The Productivity and Wage Effects of Foreign Acquisition in the United Kingdom[END_REF] We show that, under mild conditions, real wages rise even at the low-end of the skill ladder.

We next proceed to explore numerically a parameterized version of the model roughly calibrated on U.S. data and show that our theoretical results indeed bear some realism: globalization generates positive welfare gains for all.

We are obviously not the first to reach such a conclusion, though we use a very different approach. An early paper by Feenstra and Hanson (1996) develops a Heckscher-Ohlin type model without factor-price equalization. They then show that outsourcing leads to a productivity increase for firms which will lower the prices for final goods; this reduction in consumer prices, they stress, could exceed the fall in wages of the less-skilled workers.

More recently, Grossman and Rossi-Hansberg (2006a, b) also demonstrate that, depending on demand parameters, productivity growth induced by increased offshoring opportunities can benefit the factor intensely used in the sector with decreasing offshoring costs. An innovative aspect of their analysis is to focus, in a perfectly competitive environment, on the nature of tasks performed on the job; this, they advocate, is more relevant for a job's propensity to be offshored than either the skill-intensity of the occupation or the education level of the worker. The conceptual shift may prove extremely important (in particular for empirical investigations, see e.g., Becker et al. (2008)) but complexifies the theoretical analysis. [START_REF] Davidson | Globalization and Firm Level Adjustment with Imperfect Labor Markets[END_REF] In contrast with the previous authors, we acknowledge the role of increasing returns to scale and make imperfect competition an indispensable ingredient in the shaping of the new global economy. [START_REF] Deardorff | Gains from Trade and Fragmentation[END_REF] Furthermore, we explicitly consider the effect of globalization on four firm-level decisions: entry, technology choice, whether or not to offshore outsource and the type of workers to employ so that we account for the observed fact that, to take advantage of the new low-cost opportunities, some firms upgrade technologically. [START_REF] Deardorff | Comment on Mankiw and Swagel, "The politics and economics of offshore outsourcing[END_REF] Our model remains nevertheless extremely simple and the results quite intuitive. As we shall argue in the paper, the highlighted characteristics of firms that engage in offshore outsourcing is consistent with empirical evidence.

The paper is organized as follows: the model is laid down in Section 2, and the effects of globalization are analyzed in Section 3. Numerical results are reported in Section 4 from a calibrated version of the model. The paper closes with a conclusion, followed by technical appendices.

The model 2.1 Households

Households have Dixit-Stiglitz preferences over a continuum of varieties:

X = i∈N x(i) ρ di 1 ρ , 0 < ρ < 1 (1)
from which consumption demands are derived:

x(i) = P X p(i) σ X (2)
P X = i∈N p(i) 1-σ di 1 1-σ (3) with σ = 1/(1 -ρ).
Domestic households also supply labor from a continuum of workers with unit mass, differentiated by skill level z with cumulative distribution G(z) on support [0, ∞).

Firms

Each final-good variety is produced by a single firm. Output x(i) of any variety requires combining two type of activities within a firm: we refer to the first as conceptual activities MNEs under increasing returns to scale and imperfect competition. [START_REF] Deardorff | Comment on Mankiw and Swagel, "The politics and economics of offshore outsourcing[END_REF] See e.g., Navaretti et al. (2006) for a discussion on technological upgrading related to firms switching from national to multinational. associated with headquarter services, and to the second as repetitive tasks associated with the production of intermediate components, respectively in amount y(i) and m(i). These tasks may be separated geographically. We assume a Leontief production function with units chosen so that:

x(i) = y(i) = m(i). (4)
Both activities are performed by workers using Ricardian technologies. Headquarter services can only be produced in the home country, the North, using a high-(H) or a low-(L)

technology. Technology H is more expensive to set-up but cheaper to operate than L so that F L < F H and C L > C H , where F j and C j denote respectively the set-up and marginal costs involved by the use of technology j = L, H. Though born identical, firms will sort in equilibrium between these two types: this is one source of endogenously generated firm heterogeneity.

Firms also choose where to produce their intermediate goods: domestically with an M technology, at marginal cost C M , or in the South where unit production cost θC M is lower: θ < 1. Offshore outsourcing however involves specific set-up costs F I so that only the most productive firms will turn multinational. There is considerable evidence that multinational (MN) firms use more productive technologies than non-MNs,7 so we choose F I and θ such that only firms using the H technology find it profitable to offshore outsource the production of their intermediate inputs. (The conditions for this to be satisfied will be given later.) We define for future use θ j = 1, θ for j = L, H.

Finally, firms differ from one another by the skill level of the domestic workers they hire.

Let ϕ j (z) denote the productivity of a worker of skill z when working with technology j ∈ {M, L, H}. We assume ϕ j (z) continuous and increasing in z, so that, for any technology considered, a higher skilled worker is absolutely more productive than a less skilled one.

We characterize comparative advantages as follows:

0 < ∂ϕ M (z) ∂z 1 ϕ M (z) < ∂ϕ L (z) ∂z 1 ϕ L (z) < ∂ϕ H (z) ∂z 1 ϕ H (z) , (5)
with ϕ M (0) = ϕ L (0) = ϕ H (0) = 1, so that a higher skilled worker is relatively more productive with more efficient technologies. In equilibrium, workers will sort between the three technology types according to their respective comparative advantage. [START_REF] Feenstra | Foreign Investment, Outsourcing and Relative Wages[END_REF] Let z 1 and z 2 be equilibrium skill thresholds with 0 < z 1 < z 2 . Then, the least skilled with z ∈ [0, z 1 ] will be employed to perform repetitive tasks, whereas the intermediate (with

z ∈ [z 1 , z 2 ])
and most talented (with z ∈ [z 2 , ∞)) workers will be hired to perform conceptual activities in headquarters, respectively with low-and high-tech. See Figure 1. [START_REF] Feenstra | Global Production Sharing and Rising Inequality: A Survey of Trade and Wages[END_REF] Log of productivity

1 z 2 z Ln ( ) M z ϕ Ln ( ) L z ϕ Ln ( ) H z ϕ z Figure 1: The technologies
In a competitive labor market, worker z earns a wage w(z) that reflects both its talent and the technology of the firm that employs him. The competitive wage distribution will satisfy:

w(z) =          C M ϕ M (z) 0 ≤ z ≤ z 1 C L ϕ L (z) z 1 ≤ z ≤ z 2 C H ϕ H (z) z 2 ≤ z (6)
8 For ease of exposition, we assume in what follows that all three types of technologies are used in equilibrium. [START_REF] Feenstra | Global Production Sharing and Rising Inequality: A Survey of Trade and Wages[END_REF] We assume in this figure a log-linear form for the productivity functions ϕ j (z), though this is not the only functional form that satisfies condition (??).

with the marginal skill owners being indifferent, so that

C M ϕ M (z 1 ) = C L ϕ L (z 1 ) C L ϕ L (z 2 ) = C H ϕ H (z 2 ) (7)
as illustrated in Figure 2.

1 z 2 z z Ln Ln ( ) H H C z ϕ + Ln Ln ( ) L L C z ϕ + Ln Ln ( ) M M C z ϕ + Ln ( ) w z
C M = 1 C L = C M ϕ M (z 1 ) ϕ L (z 1 ) (8)
C H = C L ϕ L (z 2 ) ϕ H (z 2 ) .
Observe from (??) that C L and C H are decreasing respectively in z 1 and z 2 .

We are not interested in unrealistically extreme cost differences between North and South in manufacturing [START_REF] Feenstra | Globalization and Its Impact on Labor, presented at the Global Economy Lecture[END_REF] , and shall restrict our attention to cases where

C H < C L < θC M < C M . (9)
Empirical evidence on the level of the fixed costs is scarce but it seams reasonable to assume that the total fixed costs of a vertically fragmented firm is less than twice the fixed costs of a domestic firm:

(C H + θC M ) (F H + F I ) < 2(C L + C M )F L ( 10
)
where for convenience, fixed costs take the form of unsold final goods.

Multinationals and non-multinationals compete on the output market. We assume monopolistic competition to prevail so that firms charge a constant mark-up rate over their marginal production costs:

p j = σ σ -1 (C j + θ j C M ) j = L, H. (11)

Equilibrium

All domestically performed repetitive tasks are done within low-tech firms, so that:

z 1 0 ϕ M (z)dG(z) = z 2 z 1 ϕ L (z)dG(z). (12)
Free entry ensures zero profits for both firm types, so that mark-up revenues exactly cover fixed costs:

1 σ p j x j = (C j + θ j C M ) • (F j + δ j F I ) j = L, H (13)
with δ j = {0, 1} for j ∈ {L, H}.

Finally, we ensure balance of payment equilibrium by conveniently assuming that labor costs in the South are paid by multinationals in units of the consumption basket (??).

Incomes then follow from employment:

Inc = C M z 1 0 ϕ M (z)dG(z) + C L z 2 z 1 ϕ L (z)dG(z) + C H ∞ z 2 ϕ H (z)dG(z) (14)
Inc * = θC M ∞ z 2 ϕ H (z)dG(z) (15)
with P X X = Inc + Inc * , which completes the model. 11

1 1
We have assumed that F I and θ are such that only high-tech firms engage in offshore outsourcing.

To exclude the possibility for a low-tech firm to turn multinational requires that the mark-up revenue it would then earn not be large enough to cover the set-up costs, that is:

1 σ p * L x * L < (C L + θC M ) • (F L + F I )
where

p * L = σ σ-1 (CL + θCM ) and x * L = P X p * L σ
X, using respectively (??) and (??).

Globalization

We now analyze the effects on the home country of globalization which we shall quite naturally interpret as a fall in F I , making offshore outsourcing increasingly attractive.

The wage distribution

We start by showing how the cut-off skill levels z 1 and z 2 are affected by globalization.

Totally differentiating equilibrium condition (??), we get:

dz 1 dz 2 = ϕ L (z 2 )dG(z 2 ) ϕ M (z 1 )dG(z 1 ) + ϕ L (z 1 )dG(z 1 ) , (16)
an expression that is unambiguously positive so that z 1 and z 2 move in the same direction. [START_REF] Grossman | Trading Tasks: A Simple Theory of Offshoring[END_REF] The reason is transparent: less labor used domestically for repetitive tasks (dz 1 < 0) can only imply a contraction of aggregate activity by non multinationals (dz 2 < 0) and, therefore, an expansion of total employment in multinational firms. Consider next the revenue ratio between a MN and a non-MN firm: from equilibrium condition (??) we have:

p H x H p L x L = (C H + θC M ) • (F H + F I ) (C L + C M ) • F L
where prices and output can be substituted out with (??) and (??); rearranging, we get:

C H + θC M C L + C M = F H + F I F L -1/σ . ( 17
)
We learn from this equality that the equilibrium marginal-cost gap between MNs and non-MNs will narrow as F I is reduced. Making use of (??) we obtain

ϕ M (z 1 ) ϕ L (z 1 ) ϕ L (z 2 ) ϕ H (z 2 ) + θ ϕ M (z 1 ) ϕ L (z 1 ) + 1 = F H + F I F L -1/σ . ( 18
)
From our characterization (??) of technologies and the fact that, see (??), z 1 and z 2 move in the same direction, it is easy to check that the only possibility is for both C H and C L to increase, the first more than the second as the two cut-off skill levels move leftward. Globalization therefore affects the equilibrium wage distribution in this economy as illustrated in Figure 3.

z ' 1 Z ' 2 Z 1 Z 2 Z ' L LnC ' ' H LnC L LnC ' H LnC H LnC
Ln ( ) w z Two qualitatively different skill-upgrading mechanisms operate as aggregate employment in non-MNs contracts. Firstly, workers with abilities z ∈ (z ′ 1 , z 1 ) , initially employed by domestic-only firms at repetitive tasks are moved to more productive conceptual activities within the same firm type and therefore earn better wages. All the more talented domestic workers (those with z > z 1 ) benefit from this increased demand for skills and see their wages rise, as is apparent from the equal log-rise of marginal costs C L and C H (respectively to C ′ L and C ′ H ). Secondly, the best workers in the non-MN firms-those with z ∈ (z ′ 2 , z 2 )-move to high-tech jobs within multinationals and earn more, their new wages matching their improved productivity. All those with talent levels above z 2 benefit from this, their skill premium rising equally as reflected by the log-increase of the marginal production costs within multinationals from C ′ H to C ′′ H . In this economy, globalization unambiguously rises overall productivity, average wages, as well as wage inequalities.

Observe that, even though average wages decline in the low-skill activities, this is only a composition effect: for individual workers who remain in blue-collar jobs, wages remain unchanged in terms of the numeraire.

Individual firm behavior and industry concentration

We first consider non-multinational firms. It immediately follows from mark-up pricing (??) and free entry (??) that the individual non-MN's supply of final goods is proportional to fixed costs (expressed in real terms) and therefore remains constant: x L = (σ-1)F L . We know from the leftward move of z 1 , and from the technology, that aggregate blue-collar employment, and therefore aggregate output, of non-multinationals decrease. As one would have expected, fewer firms will survive to globalization without transferring their blue-collar activities to cheap labor countries. We know that those firms that survive do so by increasing the price of their output, skill upgrading some of their blue-collar workers to more conceptual tasks for which they earn better wages. Consider next multinationals. We know from the previous discussion that the individual MN's output scale is unaffected by changes in marginal costs -since x H = (σ -1)(F H + F I )and that smaller fixed costs reduce the equilibrium firm size. We also know that the skill threshold z 2 moves left which implies that the aggregate output of MNs increases. It therefore follows that the number of firms that outsource offshore has unambiguously increased. Clearly, globalization has induced a number of national firms to turn multinational, adopting the H -technology, skill upgrading their workers z ∈ (z ′ 2 , z 2 ), operating at larger scale and selling final goods at cheaper prices than their purely domestic competitors.

Hence, globalization implies both creation and destruction of firms. We now show that the net effect on the total number of firms is positive. From (??) and our definition of fixed costs in units of the firm's unsold final good, we have:

∞ z 2 ϕ H (z)dG(z) = N H (x H + F H + F I ) = N H σ (F H + F I ) (19)
z 1 0 ϕ M (z)dG(z) = N L σF L
where N j denotes the equilibrium number of type-j firms. Totally differentiating N = N L + N H and making use of (??)

yields dN dz 2 = ϕ M (z 1 )dG(z 1 ) σF L (F H + F I ) dz 1 dz 2 (F H + F I ) - ϕ H (z 2 ) ϕ L (z 2 ) F L (1 + ϕ L (z 1 ) ϕ M (z 1 ) ) ;
using (??) one then obtains:

dN dz 2 = ϕ M (z 1 )dG(z 1 ) σF L (F H + F I ) dz 1 dz 2 (F H + F I ) - C L + C M C H F L . (20)
Our assumptions (??) and (??) on technologies ensure that the term in brackets is always negative. We conclude that the number of product varieties available to consumers increases unambiguously.

Welfare

We know from Figure 4 that all workers see their wages increase in terms of the numeraire except those who remain attached to their blue-collar jobs within non-MN firms. It is shown in Appendix 1 that the purchasing power of the average wage increases unambiguously, so that would be losers -the low-skilled-could always be compensated for by transfers from those who benefit from the new international environment.

We are more interested in the conditions under which lower-skilled workers will benefit from globalization even in absence of redistributive policies. For this purpose, we need focus our attention on the consumption price index (??) only. We have shown that, because of higher wages paid to workers with skill z > z ′ 1 , dC H > dC L > 0 so that, from (??), both p L and p H increase: this "distribution effect" acts negatively on the welfare of those that remain in blue-collar activities.

A number of domestic firms, however, turn multinational and produce intermediates with cheaper labor: for this subset of firms, the total effect on output prices is uncertain given that dC H > 0. It is easy to derive a sufficient condition for this price to fall. This will happen if

p 1 H p 0 L = C 1 H +θC M C 0 L +C M
< 1 where superscripts 0/1 refer to the firm's pre-and post-mutation variables respectively. Using (??), we know that

C 1 H + θC M < 2θ so that p 1 H < p 0 L if θ < C 0 L +C M 2
, that is, if international wage disparities of low-skilled labor is not too small, presumably the case that concerns us most. This "selection effect" works to everyone's benefit, in particular to the less-skilled.

Finally, globalization has a positive effect on the size of the market, which by making available a larger number of product varieties contributes positively to welfare. We infer from Krugman (1981) that the more final goods are differentiated, the more it is likely that market-size expansion gain will outweigh the distribution loss: the "selection effect" contributes to make this requirement on preferences less stringent, and the positive welfare outcome more likely: see Appendix 2 for an expression that relates P X to σ.

To conclude, we have shown that in this economy, the surge of offshore outsourcing needs not inevitably induce losers: even workers employed in activities that are most easily moved offshore may gain. [START_REF] Hansson | Skill Upgrading and Production Transfer within Swedish Multinationals in the 1990s[END_REF] How realistic are the conditions for these welfare gains to materialize? We address this question in the next section by exploring numerically a parametrized version of the model roughly calibrated on US data. there is no skill upgrading and no "selection effect" on welfare. From (??) we know that dp H > 0 and from (??) that dN H > 0: we have nothing more than the standard opposition between a "distribution loss" and a "market-size expansion gain". In this specific case, it is easy to see that the net effect is always positive. For this, rewrite (??) as

P x = p 1-σ L N L + p 1-σ H N H 1 1
-σ ; make use of (??) to substitute out NL, NH and then substitute out FH + FI using (??); it follows that: PX = σ σ-1

1 σF L z 1 0 ϕ M (z)dG(z) [CL + CM ] 1-σ + 1 σF L ∞ z 2 ϕ H (z)dG(z) [CL + CM ] -σ [CH + θCM ] 1 1-σ .
As FI is reduced, everything remains constant in the RHS except θ that rises: PX therefore unambiguously falls.

A numerical appraisal 4.1 Calibration

In this section, we use a calibrated version of the model to assess some orders of magnitude and sensitivity with respect to key parameters. From Industry Statistics published by the U.S. Census Bureau (2002, Table 5, p.54) we choose

z 1 = 70% , (22)
as the ratio of the number of production workers to the total number of employees in Manufacturing in year 2002; from the same source, we pick the share of non-production activities in total value added from labor as:

C L z 2 z 1 ϕ L (z)dG(z) + C H ∞ z 2 ϕ H (z)dG(z) C M z 1 0 ϕ M (z)dG(z) + C L z 2 z 1 ϕ L (z)dG(z) + C H ∞ z 2 ϕ H (z)dG(z) = 42% ; (23)
we approximate the share of total production that is due to MN firms as the output share of establishments with 2500 or more employees:

∞ z 2 ϕ H (z)dG(z) z 2 z 1 ϕ L (z)dG(z) + ∞ z 2 ϕ H (z)dG(z) = 14% . (24)
We have little guidance from empirical evidence on the fixed costs, which we choose somewhat arbitrarily within the ranges consistent with the constraints: 14

F L = 1.0; F H = 1.186; F I = 0.75 . ( 25
)
1 4 In addition to constraints mentioned in a previous footnote, the theoretical consistency of the model imposes that: (a) some L-type firms exist, so that 1 σ pLxL ≥ (CL + CM ) FL; (b) all H-type firms adopt offshore outsourcing strategies so that 1 σ pH xH ≥ (CH + θCM ) (FH + FI ) and

1 σ p * H x * H ≤ (CH + CM ) FH where p * H = σ σ-1 (CH + CM ) and x * H = P X p * H σ
X, using respectively (??) and (??). The value of FH is actually chosen so that, at the initial equilibrium,

1 σ p * H x * H = (CH + CM ) FH .
We assume log-linear technologies (consistently with our graphical representations in previous sections) and a uniform distribution of talents G(z). Finally, we set

σ = 4 (26)
as the benchmark value for the differentiation elasticity in preferences.

With this set of functional forms and parameter values, it is straightforward to calibrate the model: Figure 4 displays the three calibrated technologies. 15

( ) j z ϕ z ( ) H z ϕ ( ) L z ϕ ( ) M z ϕ 0 1 2 3 4 5 0 1
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 reported for various values of the differentiation elasticity σ, and confirm our theoretical analysis. [START_REF] Helpman | Export versus FDI with Heterogeneous Firms[END_REF] We see that for realistic values of σ (i.e. for values not too different from 4) 1 5 Appendix 3 reports the calibrated benchmark equilibrium values. 1 6 Changing the values of σ obviously implies recalibrating the model. Doing this, we maintain conditions (??) to (??) and the values of FL and FI unchanged so as to keep z2 and the marginal costs CM , CL, CH unchanged; the two cost parameters that are affected are θ and FH. To σ ∈ [3, 8] globalization comes with positive welfare gains even at the low-end of the skill ladder. 17

( ) j z ϕ z ( ) H z ϕ ( ) L z ϕ ( ) M z ϕ 1

Conclusion

It is widely believed both in academic and in policy circles, that globalization and massive offshore outsourcing to cheap labor countries will benefit some-mainly high-skilledworkers within the North, at the expense of the others. Yet, up to now, empirical investigation fails to provide evidence in support of this view. [START_REF] Krugman | Intraindustry Specialization and the Gains from Trade[END_REF] Furthermore, in a country-Japan-where offshore outsourcing has been extensively practiced for decades, there seems to be strong evidence that vertical specialization by local multinationals has induced skill upgrading domestically (Head and Ries, 2002) with blue-collar workers being moved to more productive white-collar jobs within the same firms. This observed efficiencyimproving reallocation of factors suggests that globalization need not be associated with falling real wages at the low-end of the skill ladder. Based on Yeaple (2005), we have developed a simple general equilibrium model with endogenously induced heterogeneity of firms from exogenously heterogeneous labor; these firms make explicit decisions on whether or not to fragment geographically their production so as to take advantage of favorable cost-conditions offshore. As globalization proceeds, making increasingly profitable the displacement of manufacturing activities to low-cost countries, workers in the North are endogenously moved to less repetitive more productive tasks. We have shown analytically that, under mild conditions, real wages will rise even at the low-end of the skill ladder. Numerical exploration of the model roughly calibrated on U.S. data confirms that those conditions are far from being unrealistic. The basic mechanism, technology upgrading at the individual firm level and skill upgrading of workers, is simple enough to be plain to, and deemed reasonable by, any citizen. Yet, the implications are unlikely to be easily understood by non economists because they hinge on general equilibrium effects that are far more abstract. We believe that the simplicity of the model will render those powerful GE effects transparent enough so that our paper will contribute to change the perception that globalization is a threat rather than an opportunity for all.

Needless to say, the model abstracts from important elements of the real world such 1 8 There is ample evidence highlighting how globalization has increased wage inequality between skilled and unskilled workers; see Feenstra and Hanson (2003) for an excellent survey on this literature. However, as Feenstra (2007) argues, so far there is no evidence that real wages of unskilled (production) workers are negatively impacted by outsourcing.

as labor market imperfections: introducing rigidities could presumably inverse the conclusions. [START_REF] Mankiw | The politics and economics of offshore outsourcing[END_REF] But then the policy implication would clearly be that government action has to aim at reducing those imperfections, not at opposing to globalization as is often suggested: [START_REF] Markusen | Multinational Firms and the Theory of International Trade[END_REF] more rigid labor markets can only enhance the attractiveness to firms of offshore options. Rather than thwarting adjustment, public action should aim at protecting workers rather than jobs: this, in particular, calls for extensive and flexible re-training programs that could indeed be costly to set-up. But it is clear that such public action would in any case stand as a top priority even in absence of globalization, in view of the ongoing aging of populations in the North. From (??) and making use of (??), we have :

[ρP X ] 1-σ = 1 σF L z 1 0 ϕ M (z)dG(z)(C L + C M ) 1-σ + 1 σ(F H +F I ) ∞ z 2 ϕ H (z)dG(z)(C H + θC M ) 1-σ . (32)
Totally differentiating the RHS of (??) with respect to z 2 and making use of (??), we obtain:

dRHS(??) dz 2 = (1 -σ) 1 σF L z 1 0 ϕ M (z)dG(z)(C L + C M ) -σ d( ϕ M (z 1 ) ϕ L (z 1 ) ) dz 1 + 1 σ(F H +F I ) ∞ z 2 ϕ H (z)dG(z)(C H + θC M ) -σ d( ϕ M (z 1 ) ϕ L (z 1 ) ) dz 1 ϕ L (z 2 ) ϕ H (z 2 ) dz 1 dz 2 + 1 σF L ϕ M (z 1 )dG(z 1 )(C L + C M ) 1-σ dz 1 dz 2 - 1 σ(F H +F I ) ϕ H (z 2 )dG(z 2 )(C H + θC M ) 1-σ +(1 -σ) 1 σ(F H +F I ) ∞ z 2 ϕ H (z)dG(z)(C H + θC M ) -σ ϕ M (z 1 ) ϕ L (z 1 ) d( ϕ L (z 2 ) ϕ H (z 2 ) ) dz 2 . (33)
With (??) and (??) , the first bracket-term simplifies to:

(1 -σ)(C L + C M ) -σ N L + N H C H + θC M C L + C M -σ C H C L d( ϕ M (z 1 ) ϕ L (z 1 ) ) dz 1 dz 1 dz 2
which, making use of (??) simplifies further to:

(1 -σ)(C L + C M ) -σ N L + N H F H + F I F L C H C L d( ϕ M (z 1 ) ϕ L (z 1 ) ) dz 1 dz 1 dz 2 . ( 34
)
From (??) and (??), dz 1 dz 2 can be written as:

dz 1 dz 2 = C H C L C M C L + C M ϕ H (z 2 )dG(z 2 ) ϕ L (z 1 )dG(z 1 )
; substitution in the second bracket-term of (??) yields:

1 σF L (C L +C M ) 1-σ C H C L C M C L + C M ϕ M (z 1 ) ϕ L (z 1 ) - F L F H + F I C H + θC M C L + C M 1-σ ϕ H (z 2 ) dG(z 2 ),
which, from (??) and (??), simplifies to:

-θC M 1 σF L (C L + C M ) -σ ϕ H (z 2 )dG(z 2 ). (35)
Making use of (??) and (??), the third bracket-term of (??) can be written as:

(1 -σ) 1 σF L (C L + C M ) -σ ∞ z 2 ϕ H (z)dG(z) C L C M d( ϕ L (z 2 ) ϕ H (z 2 ) ) dz 2 . ( 36
)
Finally, making use of (??), (??) and (??) to rearrange (??), we obtain:

dRHS(??) dz 2 = (1 -σ)(C L + C M ) -σ N L + N H F H +F I F L C H C L d( ϕ M (z 1 ) ϕ L (z 1 ) ) dz 1 dz 1 dz 2 + (C L +C M ) -σ σF L (1 -σ) ∞ z 2 ϕ H (z)dG(z) C L C M d( ϕ L (z 2 ) ϕ H (z 2 ) ) dz 2 -θC M ϕ H (z 2 )dG(z 2 ) . Given that d( ϕ M (z 1 ) ϕ L (z 1 ) ) dz 1
< 0 and that dz 1 dz 2 > 0, the first term is unambiguously positive; given that

d( ϕ L (z 2 ) ϕ H (z 2 ) ) dz 2
< 0, the second term will be negative if σ is not too large. The impact of globalization on P X will therefore be negative only if there is enough product differentiation, that is, if σ is small enough.

Figure 2 :

 2 Figure 2: The wage distribution

Figure 3 :

 3 Figure 3: Wage distribution shift as F I decreases

1 3

 3 Our model abstracts from labor market adjustments in the South. Endogenizing wages in the South would only mitigate the welfare conclusions. To see this, consider the extreme case of inelastic labor supply abroad: as F I decreases, wages in the South -and therefore θ-adjust upward constraining the aggregate volume of offshore production by MN firms to remain constant. z 1 and z 2 remain unchanged:

  I.L.O. (2007) provides us with unit labor costs (relative to U.S.) in manufacturing for a number of cheap labor countries, from which we choose θ = 0.82 , (21) a value between those of Mexico and of the new EU Member States (Czech Republic, Hungary and Poland) in year 2002.

Figure 4 :Figure 5

 45 Figure 4: The three techologies

  are associated the calibrated values of θ ∈ [.757, .919] and FH ∈ [1.136, 1.407]. FH is recalibrated so that 1 σ p * H x * H = (CH + CM ) FH , as explained in footnote (??). See Appendix 3 for more information on which of the equilibrium variables are unaffected by changes in σ.

Figure 5 :

 5 Figure 5: Impact of globalization on P X (sensitivity w.r. to σ)

See Deardorff (2005,

2006) for an illuminating discussion on this.

[START_REF] Hansson | Skill Upgrading and Production Transfer within Swedish Multinationals in the 1990s[END_REF] reaches similar conclusions on Swedish MNEs during the years 1990-97. The period is particularly interesting because it covers the years after the iron curtain was lifted: Swedish MNEs have extensively taken advantage of the large supply of cheap labor in the immediate neighborhood which the processes of transition in the CEECs has given rise to. He finds a non-trivial, significantly positive, impact on skill upgrading in Swedish MNE parents of the increased employment share in their affiliates in non-OECD countries.

See Broda and Weinstein (2006) for an empirical investigation of the gains from trade for the U.S. due to the worldwide expansion of available varieties of goods.

The result also hinges on an assumption on technical progress that raises questions: see[START_REF] Taylor | Comment on Grossman and Rossi-Hansberg[END_REF].

Even though we focus on offshore outsourcing, our model could be seen as closely related to the traditional vertical FDI literature. See[START_REF] Helpman | A Simple Theory of International Trade with Multinational Corporations[END_REF] andMarkusen (2002, Ch.9) for modeling of vertical

It is widely documented that affiliates of multinationals are more productive than national firms; see for example Doms and Jensen (1998), Conyon et al. (2002). In addition, Helpman et al. (2004) highlight also that MNEs are substantially more productive than non-MNE exporters which outperform significantly purely domestic ones.

0 See I.L.O. (2007) for unit labor cost comparisons.

[START_REF] Broda | Globalization and the Gains from Variety[END_REF] Observe that this is not the case in[START_REF] Yeaple | A simple model of firm heterogeneity, international trade, and wages[END_REF] when opening his economy to trade.

[START_REF] Doms | Comparing Wages, Skills, and Productivity between Domestically and Foreign-Owned Manufacturing Establishments in the United Sates[END_REF] How much of these results is due to the selection effect, and how much to the market-size expansion effect? We can get a rough idea of this by computing from (??) the impact on NH of a reduction of FI keeping z2 constant, and comparing this with the equilibrium number of new MNs. In all the performed simulations, this ratio is below 10%.

Appendix 1: Effect of globalization on the average wage Let w be the average wage per worker; from (??) and (??):

making use of (??), w becomes:

The consumption price index (??) can we written as:

so that, making use of (??) and rearranging, we get:

Differentiating the RHS of this expression with respect to z 2 , and making use of (??), (??), (??) and (??) yields:

an expression that is unambiguously negative since w -N L σF L > 0 from (??) and (??),

< 0 and dz 1 dz 2 > 0. We have therefore shown that d( w P X

) dz 2 < 0 that is, globalization unambiguously improves aggregate welfare.


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 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