discussion papers

FS IV 95 -30

Industrial Concentration and Endogenous Sunk Costs in the European Union

Bruce R. Lyons* Catherine Matraves**

* University of East Anglia

** Wissenschaftszentrum Berlin fur Sozialforschung

December 1995

ISSN Nr 0722 - 6748

Forschungsschwerpunkt Marktprozeß und Unter nehmensentwicklung

Research Unit Market Processes and Corporate Development

Zitierweise/Citation:

Bruce R. Lyons, Catherine Matraves, Industrial Concentration and Endogenous Sunk Costs in the European Union, Discussion Paper FS IV 95 - 30, Wissenschaftszentrum Berlin, 1995.

Wissenschaftszentrum Berlin für Sozialforschung gGmbH, Reichpietschufer 50, 10785 Berlin, Tel. (030) 2 54 91 - 0

ABSTRACT

Industrial Concentration and Endogenous Sunk Costs in the European Union

Much has been written about the Single European Market and the potential for "restructuring" EU industry. However, remarkably little is known about a central com­ponent of that structure: industrial concentration. First, we present new estimates for EU concentration and make comparisons with the US, Germany, France, UK and Italy for 100 3-digit manufacturing industries. Second, we test a model o f the determinants of concentration, based on Sutton (1991), and emphasizing the competitive roles of adver­tising and R&D. One striking finding is that the negative relationship between concentra­tion and market size breaks down at the EU level when firms compete in both types of endogenous sunk cost.

ZUS AMMENFAS SUNG

Industriekonzentration und endogene versunkene Kosten in der Europäischen Union

Viel ist über den gemeinsamen Markt und das Restrukturierungspotential der europäi­schen Industrie geschrieben worden. Um so bemerkenswerter ist es, wie wenig über die Industriekonzentration bekannt ist, ist sie doch eines der entscheidenden Merkmale der Marktstruktur. In diesem Beitrag werden neue Schätzungen der Konzentration für die Europäischen Union vorgestellt und mit Werten für die USA, Deutschland, Frankreich, Großbritannien und Italien verglichen. Die Konzentrationsmessung wurde für 100 Industriezweige der sogenannten Drei-Steller-Systematik durchgeführt. Außerdem wird ein Modell der Bestimmungsfaktoren der Konzentration überprüft, das sich auf Sutton (1991) stützt und in dem die entscheidenden Wettbewerbsparameter Werbeausgaben und Ausgaben für Forschung und Entwicklung sind. Ein überraschendes Ergebnis ist, daß die negative Beziehung, die normalerweise für den Zusammenhang von Konzentration und Marktgröße festgestellt wird, auf der Ebene der Europäischen Union nicht mehr festzu­stellen ist, wenn die Unternehmen sich im Konkurrenzkampf mit Werbe- und Forschungs- und Entwicklungsaktivitäten behaupten.

This work was funded by the CEC SPES initiative. Thanks are due to all the SPES team, includ­ing Jordi Gual, Rainer Feuerstack, Laura Rondi, Hans Schenk, Alessandro Sembenelli, Leo Sleuwaegen, and Reinhilde Veugelers. The EU concentration data used in this paper was gener­ated by collaboration across the whole team. A special debt is due to Steve Davies as many ideas in this project have evolved over long discussions together, and it is difficult to allocate owner­ship of particular research results.

1. Introduction

A great deal has been written about the Single European Market and the consequent potential for

the 'restructuring' of EU industry. However, remarkably little is known about one of the most

significant components of that structure: industrial concentration at the EU level. The general

expectation has been that the removal of non-tariff barriers to trade, beginning with the

ratification of the Single European Act in 1987 and (nearly) completed with the advent of the

Single Market in 1993, should have brought about a new equilibrium market structure (see

Emerson, 1988). The wider market was expected to bring more firms into competition with each

other, and the consequent reduction in prices would force mergers and exit, thus pushing up

concentration. Note an important ingredient in this story. Market structure is the outcome of the

competitive process. If competition is working at a particular geographic level, then market

structure will also be formed at that level; and if this is a one-to-one relationship, then by

investigating the relationship between market structure and size, it should be possible to draw

implications about the geographic level at which competition is effective. This is one of the

objectives of this paper.

Until recently, one empirical foundation to this story, the initial level of EU concentration, was

completely missing. Davies and Lyons (1996) set about filling that gap for 1987, by presenting

the first estimates of concentration in manufacturing industry at the EU level. However, they

look exclusively at the EU as a unit of analysis, and do not make comparisons with concentration

in individual member states. In section 3, we compare the EU level with that in the US and in

the four largest member states (Germany, France, the UK and Italy). The EU is very similar in

size to the US, yet it turns out that while member state average concentration is very close to the

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US level, EU concentration is much lower. The implication is that the Single European Market

would have a major impact if EU concentration levels were to rise to those in the US.

However, this simple interpretation of the data is potentially dangerous, because it fails to take

proper account o f the relationship between competition, economies o f scale, market size, and

concentration. For example, while the EU market is similar in size to the US, the size of even

the largest member state is very much smaller, so the simple idea that concentration is

determined by economies of scale relative to market size is clearly inadequate. Following Sutton

(1991), the theoretical framework is presented in Section 2. Sutton developed a general approach

to inter-industry studies which focuses on the concentration-size relationship, where the shape

of this relationship depends crucially on both the severity of price competition and the

predominant form of non-price competition in an industry. For example, in advertising intensive

industries, the incentive to compete in advertising increases with market size, and the consequent

increase in fixed costs per firm can substantially modify the concentration-size relationship.

However, although the framework was argued to be robust across industries, Sutton applied the

model to the closed national food industries. The Sutton analysis is extended in two ways in this

paper. First, we use evidence on the whole of manufacturing industry to assess the generality of

the theory. Second, we take into account the additional issues of firm expenditure on R&D and

the extent of market integration. The results of applying the model to EU and member state data

are provided in section 4, and section 5 concludes.

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2. Concentration and Market Size: Theory2

In this section, we establish the link between equilibrium market structure and market size, and

examine the properties o f this relationship when buyers are responsive to spending on an

endogenous fixed cost. Following Sutton (1991), but using Schmalensee’s (1992) terminology,

we define industries as one of two types. Type 1 industries are characterised by homogeneous

or horizontally differentiated products. Type 2 compete through spending on advertising or

R&D, both o f which raise the perceived product quality. Sections 2.1 and 2.2 summarise

Sutton’s ‘bounds approach’ to robust theorising about the concentration-market size relation.

In sections 2.3 and 2.4, we develop some implications of observing this relationship at two levels

of geographical aggregation - national (member state) and the aggregate EU - when only one is

appropriate for competition in a particular industry. The principal results are summarised in five

hypotheses to be tested in Section 4 on cross-section industry data.

2U TypeLLIndustries

First, assume product homogeneity and consider a class of 2-stage games. In the first stage,

firms decide whether to enter, which incurs a fixed cost o (i.e. the amount necessary to set up a

plant of minimum efficient scale); and in the second stage, firms compete in price or quantity.

Consider pricing in the second stage, where the number of firms, N, is pre-determined. All

plausible models of price/quantity competition have the characteristic that price is a non­

increasing function of N. Only in two extreme models is this function not generally decreasing.

First, in the Bertrand model with completely homogeneous products, price equals marginal cost

2 The sub-sections on Type 1 and Type 2 industries, and hypotheses H l and H2, are closely based on Sutton (1991) chapters 2 and 3.

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for all N>1. Second, if perfect collusion is possible, independent o f N, price never falls below

the monopoly level, even at very high N. Next consider the entry stage. Excluding the extreme

Bertrand case, any increase in market size (relative to o) will allow more firms to enter the

industry, with the market becoming arbitrarily fragmented as size, S, becomes very large (i.e. the

share of the largest firm approaches zero).3 This limit theorem (Shaked and Sutton, 1987) is

Sutton’s first robust result from this class of game theoretic models. Furthermore, if we also

exclude the extreme collusion model, margins will fall as N rises, so an increase in market size

has a less than proportionate effect on N - a doubling of market size results in a less than

doubling of N as each firm has to sell more in order to cover overheads. This negative (non­

proportional) relation between concentration and market size is the second robust result from this

class of models.

In simple, symmetric models, concentration is naturally measured as 1/N, and it will be

convenient if we define the elasticity of concentration in Type t industries as r|t = -[dN'VdS] S/N'1

= [dN/dS] S/N. Excluding the extreme cases, the general results from this class of models

suggest 0<t) ,<1, with strict inequalities. Somewhat loosely, but for ease of expression, this

elasticity will be referred to as the 'slope' of the concentration-market size relationship. As a

simple example, assume that the total value of market demand is fixed at S (i.e. there is a unit

elasticity of demand). Straightforward calculations show that, if all N firms have the same

constant marginal cost, and competition is Cournot, gross profit per firm will be k = S/N2. If

each firm must incur a fixed cost o f o, and ignoring the fact that N may not be an integer, the

3 In the Bertrand case, the market remains a natural monopoly äs long as there are fixed costs and marginal costs at the firm level are non-increasing in the long run.

5

free-entry zero-profit condition (ir = a) gives the long run equilibrium number o f firms:

N = [S/a]'/j (1)

Thus, as S - °°, N - <*>, and r |, = ‘A. Less tough price competition (i.e. if the impact of N on price

was smaller) would raise this elasticity, while tougher price competition (i.e. if N had a greater

impact on price) would reduce it.

Next, relax the assumption of product homogeneity, and consider the effect of horizontal product

differentiation. In general, this can have two effects as compared to the homogeneous product

model. First, the elasticity of demand facing each product is reduced, and this relaxation of price

competition reduces the concentration-size slope in a similar way to that already discussed, but

may also shift it down as market niches open up. Second, product differentiation allows the

possibility of multi-product firms, whose presence can generate multiple equilibria including

some with very high concentration at a given market size. As Sutton forcefully argues, these

considerations imply that the best we can hope to observe in simple empirical models is a lower

hound to the concentration-size relationship.

In summary, a very broad class of game theoretic models share the following predictions. Type

1 industries are expected to have a downward sloping lower bound relationship between

concentration and market size, with lower bound concentration approaching zero as market size

becomes very large. Thus, the real bite to the Type 1 theory comes in excluding the possibility

that an industry with small market size (relative to a) will also have low concentration.

However, the existence of multiple equilibria means that it remains possible for an industry with

a large market size to have high or low concentration.

6

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Firms in Type 2 industries have an extra decision variable, so we consider the class of 3 stage

games in which firms first choose to enter, then invest in product quality, and finally compete

in price or quantity. As is usual, the equilibrium is solved in reverse order of stages. The third

stage competition has the added twist that firms in the same industry may have vertically

differentiated products. However, it is in the second stage choice of quality that the fundamental

difference between Type 1 and Type 2 industries emerges. Shaked and Sutton (1987) show that

as long as the cost of perceived quality improvement is largely borne as a fixed as opposed to a

marginal cost, then as market size rises, the share of the largest firm will not approach zero (see

also Dasgupta and Stiglitz, 1980). This is a very general non-fragmentation result, and contrasts

sharply with the Type 1 proposition. Intuitively, a larger market size encourages firms to invest

more heavily in quality raising fixed costs, and these overheads act to discourage entry in the first

stage. The larger is the market, then the greater is the incentive to invest, and the result is that

no matter how large the market, concentration will remain bounded away from zero.

Interestingly, and also in contrast with Type 1 industries, the concentration-size relation need not

be monotonic (i.e. it is possible that p2<0). For example, it may not be worthwhile investing in

quality improvement if the market is very small; but as market size increases, there are

competitive advantages in investment. The escalation in fixed costs may be so rapid that this

fixed cost effect outweighs the direct market size effect, and so leaves room for fewer firms in

the market. O f course, this is only a reasonable possibility, and for particular models or

parameterisations, there may be a negative monotonic relation.

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As a simple example o f a Type 2 industry, Sutton (1991) assumes that a fixed amount, S, is spent

on the output of the industry; and allows consumer utility to depend not just on quantity

consumed, but on the product o f u and quantity, where u is an index of perceived product quality.

He also proposes the following relationship between endogenous sunk costs, E, and u4:

£(«)=[a/Y][«v-l] (2)

where y > 1, and a higher y corresponds to more rapidly diminishing returns to investment in the

endogenous sunk cost, a is a parameter reflecting the unit cost of investing. For our purposes,

E may be interpreted as either advertising or R&D expenditure. Given strictly positive E, the

symmetric subgame perfect Nash equilibrium with free entry generates

N + N -1 - 2 = -X[1-(O- - ) — ] (3)2 y S

This equation implicitly expresses N as a function of S. In the limit as S - % [N+N'1] -

[2+(y/2)j, which is a finite constant. This is the limit property of Type 2 equilibria.

Furthermore, sgn{dN/dS] = sgn{o - (a/y)}, which confirms the possibility that concentration

may actually rise with market size (i.e. if a < a /y \

These are the results developed and tested by Sutton (1991) for national markets in the food

industries, contrasting low-advertising Type 1 sectors with advertising-intensive Type 2 sectors.

4 We should distinguish clearly between endogenous sunk costs and endogenous fixed costs. Sunk costs are not recoverable and thus have commitment value in the present sequential game setting. However, as Schmalensee (1992) points out, the general idea of Type 2 industries is much more widely applicable including, for example, to industries in which advertising costs must be incurred regularly as endogenous fixed costs, because there is no 'capital' or 'goodwill' effect.

8

Without claiming quite the same level of generality for such wide classes of models, we can also

hypothesise that the escalation of fixed costs will have the general tendency to dampen the effect

of market size, so we expect ti2<t]1. This hypothesis is likely to be robust as long as the

price/quantity game is held constant, but it is clearly less general than the empirical hypotheses

proposed by Sutton. For example, we have already argued that tj j may be close to zero if

behaviour is collusive, while it remains possible that r| 2 may be non-negligibly positive if the

price game is more competitive and the sensitivity of u to E is slight.

In terms of the above example, the elasticity of N with respect to S is

n2=_0[A72yj_i -tv-M ms]

where

0=y[a-(a/Y)]

(4)

(5)

Since dr|2/dy > 0, and r|2 - ’/2 as y - % the sensitivity o f concentration to market size is less in

this Type 2 industry compared with an equivalent Cournot Type 1. However, this reduced

elasticity need no longer hold if, say, a Cournot Type 2 industry was compared with a Bertrand

Type 1.

Because of a probable correlation between horizontal and vertical differentiation, our earlier

comments with respect to horizontal product differentiation apply aTortiori in Type 2 industries.

Thus, it is possible that for 'small' market sizes populated by niche producers, the concentration

level for Type 2 industries may be less than for Type 1, even though the reverse must hold for

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'large' markets. Thus, once again, the only robust hypotheses relate to the 'slope' of the lower

bound.

In his empirical work, Sutton (1991) deliberately avoids industries which engage in R&D as well

as advertising.5 However, if we want to test theory across a wide range of industries, we have

to recognise the possible interactions between the two sources of endogenous fixed costs. If

advertising and R&D were perfect substitutes as ways of improving perceived quality, quite

obviously, the combination would have no effect beyond that of either one alone, and firms

would simply employ the cheaper option. If they are imperfect substitutes, then if both are used,

this is likely to raise the aggregate overhead costs incurred and so raise the equilibrium

concentration.6 If they were perfect complements, with one unit of each being necessary to make

an improvement in quality, then this would be equivalent to raising the cost of quality

improvement (a in the above example). This rise in the cost of quality improvement may either

raise or reduce the amount spent, and so could affect the limit concentration either way. In the

above example, however, the limit concentration is independent of 0; and a high a only raises the

market size threshold above which investment becomes profitable. A high a also reduces r)2 and

5 His more recent empirical work focuses on industries which engage in R&D, but not advertising.

6 For example, if we generalise equation (2) in the text to have two perceived utility enhancing expenditures, E, and E2, we have:

IZ = [1 +a b

In the limit as S-% [N + N '1] - [2 + (y ß/2(y+ß))] < [2 + (y/2)], so the limiting level of concentration is higher with two types of endogenous fixed cost. Also, since [y ß/(y+ß)] < y, p 2 is reduced by the presence of the second endogenous fixed cost. Finally, it is straightforward to show that 0 is more likely to be negative. Overall, the equilibrium market structure is even more ‘Type 2ish’.

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increases the likelihood of q2 < 0. Overall, when we observe advertising and R&D together, the

combination is likely to exacerbate the differences as compared with Type 1 industries. To

summarise, we have so far derived the empirically testable hypotheses below:

H l: For Type 1 industries, the lower bound to concentration tends to zero as market sizeincreases. For Type 2 industries, the minimal level of concentration is bounded away from zero, independent of market size. Furthermore, the slope of the concentration-size relationship is less steep in Type 2 industries.

H2: For Type 2 industries, the lower bound to concentration may be non-monotonic.

H3: Industries competing in both advertising and R&D will tend to have the highest limitinglevel of concentration, the shallowest slope to the concentration-size relationship and the greatest likelihood for concentration to increase with market size.

2.3 Geographical Aggregation: Type llndustries

We now consider the appropriate level of geographical aggregation. Suppose that the true forces

of competition were operating at the EU level, but we (wrongly) estimated the concentration-size

relationship at the national level. To focus on the aggregation issue, we ignore extra-EU trade

flows. Much depends on the international (within EU) distribution of production relative to

consumption. If firms are proportionately distributed across member states, such that Nk/N =

Sk?S = cck, where k subscripts refer to member states and absence of a subscript refers to the EU

aggregate, then given the true relationship N = f(S/a), we would be estimating Nk = ak f(Sk/a ka).

If f(.) is homogeneous of degree A, with A < 1, then this can be written as Nk = cck’'x f(Sk/o); and

member states would have a similar concentration-size relation to the EU as a whole, but shifted

upwards (the more so for smaller countries). The elasticity, tj ,, would be unchanged. Finally,

if ak is also constant across industries, then it would not particularly matter if the cross-section

relation was estimated at the national or the EU level, since only the constant would be different.

However, it takes a large number of assumptions to reach these invariance results, which are

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therefore not robust. Furthermore, if there are marginal cost differences between firms, then

market shares will differ, and in a truly integrated industry, the link between national

concentration and national production will be disrupted (except in the unlikely event that the

distribution of marginal costs is identical in each country). Thus, the concentration-size

relationship should fit better when estimated at the EU level.

Now suppose that the true relationship holds at the national level but is estimated incorrectly at

the EU level. Begin by supposing there are no firms with multinational production facilities.

If there is no integration by trade, each country will have its own equilibrium structure depending

on the size of its home market, indexed k: Nk = f(Sk/o). Aggregating to the EU level, N = Lk Nk

= Lk f(akS/a); and assuming homogeneity, N = E akz f(S/o). Once again, these assumptions are

sufficient to ensure invariance to cross-section analysis at the wrong geographical level, as long

as the index L a / is constant across industries. This index can be interpreted as an inverse

measure of international specialisation, ranging from K 1A, when there are K countries each of

equal size, to 1, when all production takes place in one country. Since international

specialisation generally does differ across industries (see Davies and Lyons, 1996, ch 5), the

concentration-size relation should now fit better when estimated at the national level.

Next, continue to suppose the true relationship holds at the national level, so o must be incurred

in each country, but allow for multinational firms.7 Then, N = m '1 Ek Nk = m '1 S a kA f(S/o), where

m is the average number of member states in which firms operate. Thus, if S a / /m is the same

7 If the forces of competition work at the EU level, multinational production is uninteresting in the present context. In that case, national boundaries would have no more significance for EU concentration than would local county boundaries.

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across industries, and the other assumptions such as homogeneity continue to hold, a cross-

section estimate of the size-concentration relationship at the EU level would fit well despite the

true relationship operating at the member state level.8 In general, however, we would expect

variance across industries, and a much noisier relationship will be observed if it is estimated at

the wrong geographic level.

Turning to Type 2 industries, the general points made about geographical aggregation in relation

to Type 1 industries carry through, but an important new issue is introduced.9 Does spending on

endogenous fixed costs in one country enhance willingness-to-pay in others (or at least raise the

effectiveness of such spending in other countries)? It is not possible to derive general results,

but the example used in section 2.2 allows an illustrative discussion. Assume that the same firms

operate in each member state and consider the interpretation of the concentration-size

relationship estimated at the EU level. We examine four possible degrees of integration formally

identified with whether production overheads, a, must be incurred in each country or not; and

whether the endogenous sunk costs, E, must be incurred in each country or not. For simplicity,

assume K identical sized countries and focus on the elasticity, r)2. This continues to be given by

equation (4), but the definition o f 6 needs modifying:

A. Full integration : 0A = y[o -(a/y)]: this is the base case already developed (see equation

8 Similarly for a time-series industry study if S a Vm is constant over time.

9 The effect of multinational firms in Type 2 industries is qualitatively the same as for Type 1 industries, though inasmuch as multinationals are the result o f specific assets associated with R&D and advertising, the extent of such activity will be greater for the former.

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(5))-

B. Integrated production; local E: 8B = Y[o-(aK/Y)]: since E must be spent in each country,

this is equivalent to multiplying the cost o f advertising/ R&D, a, by K (see equation (2)).

C. Local production; EU-wide benefits o f E: 0C = Y[Ko-(a/Y)]: since o must be incurred in

each country, this is equivalent to multiplying o by K.

D. Local production; local E: 0D = YK[a-(a/Y)]: this combines cases B and C.

Therefore, the market structure effects of the various levels of integration are indicated by

investigating the effect of raising a (case B), a (case C) or the two together (case D). To save

on complex sub-cases, we assume 0 > 0. Total differentiation of equation (3) gives dN/dtf > 0,

dN/do < 0 and dN/do|a=a < 0. Also, differentiation of equation (4), and substitution of the

derivatives just mentioned, gives dr)2/da < 0, dr|2/do > 0 and d-q2/do|a=a > 0.

Thus, in this example, the full integration case A exhibits higher EU concentration and a

shallower concentration-size slope than the entirely local case D.10 Integrated production alone

(case B) results in lower concentration than full integration but a shallower slope. This is

because localised endogenous costs raise the price of investing, which has the effect of reducing

real expenditures but raising the marginal returns. (This is also the case for which it is most

likely that 0 < 0 and so for the slope to be positive.) EU-wide benefits of E, but local production

(case C), make for higher concentration but a steeper slope than with full integration. Ranking

the four cases with 0 > 0, then N B > NA > ND > Nc, and t|b < t|a < r|D < n c-

10 This assumes that firms in case D are not multinational. Inasmuch as they are, then the issues discussed in the previous section apply.

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Which type of integration is likely to apply to the various categories o f Type 2 industry? The

fruit of R&D is generally easier to apply across national borders, than is advertising which is

typically dependent on national media, culture and language.11 Thus, R&D intensive industries

may approximate cases A and C while advertising industries may be closer to B and D.

However, there is also evidence that R&D intensive industries tend to be more geographically

specialised in production than are advertising intensive industries, though firms in the latter are

often multinational within the EU in their production (see Davies and Lyons, 1996). Thus, it is

expected that Type 2R industries more closely approximate case A, while Type 2A may be closer

to case D. Finally, because Type 2AR industries are also geographically specialised in

production, while requiring local E, they may exhibit a tendency to fall into case B. However,

the actual relationships are an empirical matter, although we may be able to draw implications

from the shape and position of the size-concentration relation about the nature o f integration.

Sections 2.3 and 2.4 have provided two further hypotheses.

H4: The lower bound to concentration is better defined at the EU level if competition in priceand/or endogenous fixed costs takes place at the EU level.

H5: International economies of scope suggest a stronger relationship at the EU level for R&Dintensive industries than for advertising intensive or Type 1 industries.

3. Industrial Concentration in the EU, its Member States, and the USA

Since there is no European census of production, we had to construct our own estimates of

industrial concentration. This was a huge task, because for each disaggregated industry, we had

11 The claim is not that there is no potential for international economies of scope in advertising (e.g. tradenames, reputation, marketing skills, etc), but rather that the scope is less than for R&D.

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to: estimate the size of EU production; identify the leading firms; and estimate their

disaggregated production. Following the tradition of census concentration figures, we based all

our calculations on production within the EU (i.e. excluding both imports and extra-EU

production by EU firms). We were able to construct concentration ratios for 100 manufacturing

industries, covering 98.9% of all manufacturing production. Brief details of all data

measurement are provided in the appendix, and a full explanation of the methodology is available

in Davies and Lyons (1996, ch 3). For comparative purposes, we also collected national

concentration data for Germany, Italy, France, the UK and USA.

It is predicted that endogenous costs such as advertising and R&D are likely to have a substantial

effect on the concentration process. Thus, we split our sample into the following industry types:

Type 1, Type 2A, Type 2R and Type 2AR. Type 2A industries engage in significant advertising,

Type 2R engage in significant R&D, Type 2AR are both advertising and R&D intensive, and

finally, Type 1 do neither. Data on advertising and R&D are not available at the EU level at the

required level of disaggregation, so we had to use UK advertising data, and UK and Italian R&D

data. Natural breaks in these data suggest that at these levels of aggregation, advertising or R&D

to sales ratios of 1% provide suitable criteria for classification. The results reported in this paper

are robust to changes in this cut-off point. Finally, we also use a measure of trade to assess the

impact of market integration on concentration levels.

The mean four-firm concentration ratios for the complete set of 3-digit manufacturing industries

and for the various industry types, are given in Table 1. To facilitate comparisons, the EU

sample has been restricted to the 96 industries for which there are reasonably comparable US

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data. No such restriction has been applied to the member states, but the general results remain

unaffected. Consider the full sample. The US 4-firm concentration ratio averages 33.4%, while

the EU averages just 20.1%. Concentration in the 'Big 4' member states averages 35.5%, with

Italy being somewhat less and the UK somewhat more concentrated, compared with Germany

and France. Thus, mean national concentration is much closer to the US level than EU

concentration is.

A simple prediction is that industries which compete in advertising or R&D will be more

concentrated than those which do not. Table 1 confirms the lower concentration in Type 1

industries for all countries. Moving down through the table, for every country (including the EU)

we can see a substantial difference in concentration between Type 1 and Type 2 industries. Type

2AR industries are the most concentrated in the US and the EU. Note that in France, average

Type 2AR concentration is considerably lower than in other member states. The ranking of Type

2A and Type 2R concentration differs interestingly across countries: for the UK, Type 2A are

higher; for the US and Italy, there is little difference; and for Germany, France, and especially

the EU, Type 2R are more concentrated. Taking account also of the levels of concentration in

the two industry types, this is consistent with R&D having a greater effect than advertising on

EU concentration. Finally, focusing on international differences, concentration in EU Type 2R

industries is relatively closer to the US level than in Type 1 or advertising intensive industries,

with Type 2AR industries exhibiting an intermediate gap. This hints that firms may be choosing

their expenditures on R&D, but not advertising, at the wider EU (or even global) level. These

descriptive statistics are highly suggestive, but they fail, however, to take account of potentially

crucial factors such as market size, or economies of scale. In order to better understand the

17

competitive process, we now formally test the model.

4. Concentration and Market Size: Results

The econometric problem is to estimate the lower bound to the concentration-size relation. There

are two main components to the problem: choice of functional form and choice of estimator. On

the first issue, we have followed Sutton (1991) in estimating for industry type t:

LCj = a, + bt SIZE; + ct INTEGRATION; (6)

where LC; = ln[CR4;) I (100-CR4;)] is the logistic transform of the 4-firm concentration ratio

in industry i;

SIZE; measures industry size adjusted for production economies of scale: SIZE; =

l/ln(MES/SIZE);12;

INTEGRATION; is measured by total intra-EU trade relative to EU production;

and, a ; , bt and c, are parameters specific to industry Type t, where t = 1, 2A, 2R, 2AR.

This functional form has the limit property that as S - °°, CR4 - exp(at)/[l+exp(at)]. It also

provides a good visual fit to the data. We differ from Sutton, however, in fitting a stochastic,

rather than a deterministic, lower bound to the data. It is not possible to claim that European

industrial structure was in long-run equilibrium in 1987, and we wanted to allow for a two-sided

(normal) error as well as for a (one-sided) distribution of observations above the lower bound.

Unfortunately, there is little theory to guide on the choice of appropriate one-sided distribution,

12 Sutton used the minimum capital requirement (MCR) instead of MES. We did try using a measure of capital intensity, based on UK data, to estimate the MCR, and this made no qualitative difference to our results. Given that the capital data was not well suited to our emphasis on European industry, we have reported only the results based on MES.

18

but we tried the standard exponential, half normal and truncated normal estimators, and they each

gave very similar results.13 We have reported the exponential results14. Assuming a similar error

structure for each industry type, the final estimating equation is:

LC; = a, + «tTYPEtl + b.SIZE; + £ t.i ßtSIZEti + £, c,INTEGRATIONti + v, + u. (7)

where f(u) = 0 exp(-0u)

and v - N(0, ov2)

Table 2 presents our econometric results, estimating equation (7) at the EU level (R l) and the

pooled national level (R2). Trade data are only available for 92 industries so as a preliminary,

the results are first presented for the full 100 industry observations and then, for the subset of 92

industries. Table 2 shows that the substantive results do not differ, indicating there is no

significant bias from the exclusion of eight data points. First, consider the concentration-size

relation at the EU level (Rl). The results show that the underlying negative concentration-size

relation for Type 1 industries is well established by the large, very significant, coefficient on

market size relative to economies of scale. The limiting concentration level for such industries

is just 1%. More interestingly, there is a strong R&D effect which both raises the limiting

concentration level and significantly reduces the slope of the concentration-size relation. This

provides strong support for Hl beyond Sutton's own work on narrowly defined advertising

intensive industries at the national level.

13 Though Sutton makes an interesting case for the Weibull.

14 Estimation was carried out using LIMDEP.

19

However, in contrast to Sutton, we do not find a significant difference between Type 1 and

advertising intensive industries, either for the slope or the limit concentration. This may be

because our industries are too broadly defined, but it may also be, as we earlier argued, because

advertising has only weak international economies of scope. This result provides an interesting

contrast with the simple Table 1 averages, which suggest a large gap between Type 1 and Type

2A concentration levels. There are two reasons for this. First, Type 2A industries tend to be

smaller relative to economies of scale, and this biases the simple averages upwards. Second,

there are more industries above the lower bound in the Type 2A sample, reflecting a greater

incidence of product differentiation and multi-product firms.

Even more striking, are our findings on the combined effect of advertising and R&D. Supporting

H3, they have a cumulative effect that is both statistically and quantitatively stronger than for

industries characterised by only one type of endogenous fixed cost. Furthermore, by summing

the slope coefficients, we find that the slope for Type 2AR industries is positive. Thus, as H2

suggested as possible, the lower bound to concentration actually rises with market size in such

industries. If industries are competing in both types of endogenous sunk costs, the Type 2 effects

are exacerbated. The limiting level of concentration in Type 2AR industries is 23% (15% for

Rlc).

Now consider the additional effects of trade integration. We observe that the marginal effect of

increasing trade integration is strongest in Type 2R industries, and there is also a significant trade

effect in Type 2AR industries. This implies that in R&D intensive industries, if trade is low,

these industries contain more firms than would exist in a fully integrated market. In turn, this

20

is consistent with each firm doing less R&D than would be achieved in a fully integrated market.

However, trade appears to have no effect in Type 1 or Type 2A industries. This may be because

Type 1 and Type 2A industries are the lowest trade industries (Davies & Lyons, 1996) so

marginal increments in trade have little effect on concentration, particularly if we also take into

account the fact that the localised effectiveness of advertising in Type 2A industries may lock

firms into national markets15.

Looking above the lower bounds, there exist 25 industries where concentration is at least 10

percentage points above the bound. There are many more R&D intensive industries (9 out of

22 Type 2R; 5 out of 9 Type 2AR) compared with other industry types (3 out o f 13 Type 2A; 8

out of 56 Type 1). This reinforces our econometric results on the crucial role of endogenous

sunk costs in forming industrial structure: the effect is felt in raising both the lower bound and

individual industries above that bound. Overall, the results suggest that the escalation in

endogenous costs is greatest in R&D intensive industries; the effect of firms competing in both

advertising and R&D is very strong. The slope to the concentration-size relation is steepest in

Type 1 industries, and shallowest (even slightly positive) in Type 2AR industries.

R2 reports the estimates based on national concentration and national size data. Once again we

find a strong negative underlying concentration-size relation. The ranking of both intercepts and

15 In Davies & Lyons (1996, ch 6), the dependent variable used is the Herfindahl index of concentration. Interestingly, the results differ. Although trade remains highly significant in Type 2R industries, it has no longer has a significant effect in Type 2AR industries. Also, there is a significant positive coefficient on trade in Type 1 industries, implying that low trade integration is associated with lower EU concentration; firms are competing at the national level.

21

slopes for the various industry types remains unaltered. However, there is a substantial decline

in the size and significance of the Type 2AR effect. For this industry type at least, this provides

evidence that competitive forces are working at the EU level. Thus, in accordance with H4, the

concentration-size relation should be estimated at the EU level. The similarity of the shallower

slope effects for Type 2R industries at both geographic levels may mean that an even broader,

global market is relevant.

The results also reveal some interesting national differences. Dummy variables are set up where

Germany is taken as the base case. Unlike the simple averages in Table 1, the coefficients on the

intercept dummies represent a lower bound effect and also correct for differences in national

industry size. Italian Type 1 industries are confirmed as being significantly less concentrated,

but having adjusted for industry size, the UK is no longer more concentrated than German

industry. French Type 1 industries are also significantly less concentrated than German industry,

although simple averages had suggested that the level of concentration is approximately the

same. Moving onto Type 2 industries, there is no difference in Type 2A or Type 2AR industries

across the member states, once we control for differences in industry size. Again, this is

interesting when observation of simple averages suggested that French 2AR industries were

considerably less concentrated than in Germany. However, both Italian and UK Type 2R

industries are shown to be significantly less concentrated than in Germany. Overall, the

prevalence of negative signs on the UK, Italian and French dummy variables does, however,

suggest that German industry tends to be more concentrated once industry size and above-lower-

bound effects are taken out. In the sense of Demsetz (1973), this is consistent with German firms

being more efficient.

22

Finally, consider the effects o f trade integration. As at the EU level, trade integration appears

to have little effect on concentration in Type 1 and Type 2A industries. However, trade

integration in R&D intensive industries has a significantly positive effect on the level of

concentration in member states. This indicates that the national lower bound to concentration

is shifted upwards in high trade R&D intensive industries. This, in turn, implies that in low trade

Type 2R industries, firms are still competing at the national level. Overall, the econometric

results imply that the appropriate geographic market for estimating the lower bound to

concentration in high trade Type 2R industries and Type 2AR industries is the EU.

5. Conclusion

In this paper, we have shown that average concentration is much lower in the EU than in the US.

This gap might reflect greater price competition in the US, if the EU is the relevant geographical

market. We have also shown that there are large differences in the levels o f concentration

depending on the degree o f advertising and R&D. Sutton's model of market structure was

applied to national and EU concentration data. Sutton showed the model worked well at a more

disaggregated level for advertising; we have shown that this model also applies for 3-digit R&D

intensive industries. The empirical results highlighted that, even in 1987, there was substantial

integration in high trade Type 2R and Type 2AR industries. Amongst Type 2AR industries,

there is no tendency for concentration to decline with market size. Concentration in advertising

intensive and Type 1 industries was shown to be unrelated to the degree of trade, which may

suggest that competition in these industries is operating at the national level.

23

REFERENCES

Davies, S.W. and B.R. Lyons (1996), IndustriaLOrganisationünThe-Europei

Strategy and the Competitive Mechanism, Oxford University Press

Demsetz, H. (1973), 'Industry structure, market rivalry and public policy'

Economics, 16, pp. 24-37.

Emerson, M., et al (1988), The Economics of 1992, Oxford University Press

Matraves, C. A. (1992), 'Endogenous Sunk Costs, Industry Size and Market Structure: A Four

Pratten, C. (1987) A Survey of Economies of Scale Report prepared for the European

Commission, Brussels

Ravenscraft, D. and F.M. Scherer (1985), M ergcrs,Sell-offs,andE corrom icEfficiency,

Brookings, Washington

Schmalensee, R. (1992), 'Sunk Costs and Market Structure: A Review Article' IoumaL_of

IndustriaiEfionomLcs, XL(2), pp. 125-134.

Sutton, J. (1991), Sunk Costs and Market Structure: Price Competition, Advertising, and the

EvolurimitTJD^mc^trafion, MIT Press

24

DF.FTNTTTONS AND DATA SOURCES

EU 4-firm Concentration Ratios

There is no EU census of manufactures, and in order to obtain estimates o f the sizes of the largest

firms in each industry, we had to identify candidate market leaders. The first step was a detailed

analysis o f the operations o f the 500 largest European firms, supplemented by firms from

industrial directories, and the institutional knowledge of the multinational research team.

Company accounts and other secondary sources were used to disaggregate each candidate firm's

operations by industry. Like most national concentration data, our estimates relate to production

within the EU, and are not adjusted for trade. Full details of the calculation of EU concentration

ratios, at the 3-digit NACE industrial classification level, are given in Davies and Lyons (1996,

ch 3).

EU Industry.Size

Published 3-digit Eurostat data [Structure and Activity o f Industry, 1987] are available for 'sales

of products manufactured by the Kind of Activity Unit and revenue from industrial services

rendered to others' [Eurostat code 19(KAU)] expressed in million ECUs. We have made

extensive use of the footnotes to make good the numerous gaps in the data. However, these data

still only refer to sales by firms employing at least 20 persons. Consequently, we have grossed

up these figures to take account of production by smaller firms, which can be significant in some

industries. No small firm EU data were collected for 1987, but in 1988 data were published at

the 2-digit level [Enterprises in Europe, Second Report, Eurostat, 1992]. Further data,

disaggregated to the 3-digit level, were kindly supplied by Directorate D of Eurostat. Full details

of the derivation of EU industry size are given in Davies & Lyons (1996).

25

Miuiurum_Efficieut_Size

MES is expressed in million ecu. The basic source for these engineering estimates of minimum

efficient size is Pratten (1987) - The Costs of Non-Europe, Vol.2. Unfortunately, many estimates

in that survey derive from studies dating back to the 1960's and 1970's. As far as is possible, our

estimates refer to technological production economies, and exclude economies of R&D,

marketing, etc. Although Pratten's is a comprehensive review of such estimates, there are

numerous gaps; some estimates are not representative of the 3-digit NACE industry, and often

the information is not provided as a sales value. Sometimes, we made use of additional data on

unit values from, for example, the UK Annual Abstract of Statistics, or case studies. Industries

were placed in 11 size classes for MES reflecting the 'typical' minimum efficient size. This was

felt to be as fine a categorisation as the data would allow. Very often, industries were classified

on the assumption that they would have a similar technology to another industry for which a

direct estimate was available (e.g. various types of industrial machinery). Some products (e.g.

tobacco, cars) have such high demand that they are able to use a distinct level of mass production

technology compared with other products of similar composition and complexity (e.g. processed

paper, tractors). Other product groups (e.g. instruments) tend to be so differentiated that

engineering estimates are small relative to apparently similar products (e.g. computers). This

suggests a warning that engineering estimates are not as exogenous as is sometimes claimed, and

in the long run they must be sensitive to demand conditions.

NatiouaLA-EiriuUouCfiutratiouRatiQS_aud_NatiouaTInduslry_Siz.es

USA: US four-firm concentration ratios are published only at the 4-digit level; the aggregation

problems are well known. The principal problem is that the extent of diversification by market

leaders across constituent 4-digit industries within a 3-digit industry is unknown. A standard

26

response is the 'average' method of aggregation. The maximum level of 3-digit concentration,

Cx3d, is given by assuming that the four market leaders are the same firm in each constituent

industry, and taking the shipments weighted average o f the 4-firm ratios, Cv4d. The minimum,

CNI3d, is given by assuming that the top four firms in each constituent industry are the same size

and that they are not diversified; so the single 4-digit industry with the largest top four provides

the numerator for the 3-digit industry. This can be modified to allow for a distribution of firm

sizes within the top four of each industry. We assumed a similar distribution to that within the

EU top four in the relevant 3-digit industry to estimate the sizes o f all 4-digit leaders; and again

assuming no diversification, we could isolate the four largest firms to provide the numerator to

CN23d. The simple average of Cx3d and one of the two 'minimum' estimates give Cvl3d and CV23d

respectively. In practice, the allowance for an unequal size distribution within the top four raises

the average estimates only slightly (on average, by 0.7% points); though the gap between the

maximum and minimum is large (on average, 13.2% points for Cv23d). In the text, we have used

r<v2C 3d-

Germany: Where necessary, 1987 concentration data were aggregated from 4-digit SYPRO to

3-digit NACE using a weighted average of the constituent industries using the same average

method as for the USA. In fact, the German SYPRO is at the 3-digit level for most industries.

Data was for CR3 and CR6 only. Pareto's Law was used to interpolate CR4 (see Italy below).

If only CR6 and/or CR10 were available, CR4 could still be extrapolated but there was obviously

a greater margin of error. Additional industry size data were derived from Statistisches

Bundesamt, 1987/88, converted from SYPRO/WZ1979 (German code) into NACE.

Eranee: 1987 concentration data were provided by INSEE, Paris. Industry size data were derived

from Eurostat.

27

UK: Adjustments to the Census of Production (1986) 5-firm sales concentration ratios were made

using detailed estimates o f market shares within the top 5 (using a similar methodology to the

EU market share estimates). Additional industry size data were taken from the Census of

Production, 1986: PA1002.

Ttaly: Derived from the ISTAT size distribution tables using employment (as information was

unavailable by sales). Typically, there were more than four firms in the largest size class.

Interpolations were then made by assuming a Pareto distribution of firm sizes within the top

class, and applying Van der Vijk's Law. Pareto's Law states that the number of firms of size

greater than s is given by N(s) = ß s'K for s > s° and a > 0. Van der Vijk's Law states that within

the largest size class of firms, s' = sL (cc/a-1) where s' is the average size of these leading firms

and sL is the class lower bound. This provides an estimate of a to substitute into the equation of

Pareto's Law, and thus provide an estimate of CR4. The 1987 CR4s were favourably assessed

against 1983 CR4s estimated by the CERIS institute, Turin. Additional industry size data were

taken from ISTAT size distribution tables, 1987.

OtherVariables

TYPE 2A*: Advertising data in the EU were only available for the UK, aggregating advertising

agency data provided by MEAL to the 3-digit level. These figures were expressed relative to UK

apparent consumption: national industry size minus exports plus imports (Source: 'Overseas

Trade Analysed in Terms of Industry' Business Monitor MQ10, 1987). If the advertising to

apparent consumption ratio was at least 1%, industries were classified as Type 2A*.

TYPE 2R*: RDSUK, the R&D to sales ratio for UK industries was derived from Business

Monitor MO14, CSO 1989. Some observations are at the 2-digit level, in which case they were

disaggregated to the 3-digit NACE level assuming the same R&D intensity among constituent

28

industries. RDSIT, the R&D to sales ratio for Italian industries, were provided by CERIS at a

slightly more aggregate level than the UK data, and disaggregated in the same way. In order to

use both sets of available data, and to make as much use of overlapping detail as possible, we

constructed the variable TYPE2R*=1 if RDSUK>1% or RDSIT>1%, and RDSUK>0.25% and

RDSIT>0.25% (else TYPE2R*=0). Our main results are not sensitive to this cut-off, as for

advertising.

TYPE 2A =1 if the industry is TYPE 2A* but not TYPE 2R* (=0 otherwise)

TYPE 2R =1 if the industry is TYPE 2R* but not TYPE 2 A* (=0 otherwise)

TYPE 2AR =1 if the industry is both TYPE 2R* and TYPE 2A* (=0 otherwise)

TRADE: Intra-EU trade relative to EU production. Trade data were provided by Eurostat.16

Dummy variables were then set up on the measure of trade for each industry type. For example,

Trade l=trade*Type 1, etc.

16 Average of measured intra-EC imports and intra-EC exports. These should be the same, but differ slightly due to measurement error. The actual difference is, for our purposes, relatively minor.

29

Table 1: Concentration by Country and Industry Type

Sample Country Mean St. Dev. Min. Max. Cases

Full US 33.4 16.4 7 87 96

EÜ 20.1 15.2 3 66 96

Big 4 35.5 22.3 3 100 393

G 35.8 20.9 8 91 98

Fr 34.9 23.5 4 100 99

UK 39.5 22.3 8 98 96

It 31.6 22.3 3 100 100

Type 1 US 26.0 13.4 7 74 53

EU 12.5 9.0 3 37 53

Big 4 29.7 21.4 3 100 218

G 29.9 18.6 8 91 55

Fr 28.3 22.8 4 95 55

UK 35.0 22.3 8 98 52,

It 25.4 21.9 3 100 56

Type 2A US 36.6 19.8 14 76 12

EU 21.9 11.2 9 46 12

Big 4 42.2 21.6 14 100 52

G 38.7 20.0 14 89 13

Fr 40.0 24.2 15 100 13

UK 50.6 23.0 17 94 13

It 39.3 19.2 19 87 13

Type 2R US 36.3 15.3 12 73 22

EU 31.5 17.4 9 66 22

Big 4 43.4 21.7 8 93 87

G 45.8 23.3 8 85 21

30

Sample Country Mean St. Dev. Min. Max. Cases

Fr 48.2 20.9 11 93 22

UK 40.4 22.0 9 86 22

It 39.2 20.5 11 86 22

Type 2AR US 44.6 19.3 24 87 9

EU 35.0 16.0 17 65 9

Big 4 41.8 20.3 11 92 36

G 44.3 20.0 20 75 9

Fr 35.0 20.0 11 82 9

UK 46.9 17.1 32 86 9

It 40.8 24.1 17 92 9

31

Table 2: Stochastic Lower Round For Concentration-Size Relationship

Variables R la R ib R lc R2a R2b R2c

Constant -4.578(-21.042)**

-4.677(-24.522)**

-4.556(-21.041)**

-2.654(-18.91)**

-2.805(-18.53)**

-2.793(-18.22)**

TYPE 2A 0.878(0.875)

0.900(0.924)

1.021(0.904)

0.368(1.134)

0.508(1.573)

0.507(1.195)

TYPE 2R 1.602(4.958)**

1.659(5.580)**

0.715(1.740)+

0.861(3.912)**

0.964(4.263)**

0.174(0.604)

TYPE 2AR 3.394(4.249)**

3.409(4.151)**

2.825(2.994)**

0.945(2.182)*

1.089(2.520)*

0.722(1.372)

SIZE -11.439(-9.136)**

-12.099(-11.538)**

-11.412(-8.518)**

-4.979(-19.58)**

-5.226(-20.21)**

-5.239(-20.04)**

SIZE*2A 2.533(0.475)

2.911(0.562)

2.992(0.546)

0.791(1.088)

1.086(1.502)

1.088(1.295)

SIZE*2R 5.023(3.045)**

5.644(3.874)**

4.677(2.512)*

2.073(4.266)**

2.322(4.922)**

2.431(5.341)**

SIZE*2AR 12.081(3.491)**

12.573(3.565)**

11.658(3.516)**

2.470(2.505)*

2.793(2.845)**

2.504(2.515)*

UK1 0.038(0.264)

0.160(1.056)

0.161(1.061)

UK2A 0.355(1.207)

0.351(1.228)

0.352(1.095)

UK2R -0.486(-2.150)*

-0.463(-2.107)*

-0.465(-2.022)*

UK2AR -0.220(-0.355)

-0.190(-0.314)

-0.209(-0.348)

IT1 -0.541(-3.73.8)**

-0.490(-3.135)**

-0.488(-3.156)**

IT2A -0.384(-1.195)

-0.381(-1.200)

-0.382(-1.169)

IT2R -0.446(-1.888)*

-0.427(-1.855)+

-0.394(-1.702)+

IT2AR -0.532(-1.291)

-0.516(-1.274)

-0.508(-1.158)

32

FR1 -0.365(-2.535)*

-0.370(-2.413)*

-0.369(-2.428)*

FR2A -0.204(-0.532)

-0.194(-0.524)

-0.196(-0.541)

FR2R -0.112(-0.493)

-0.086(-0.389)

-0.011(-0.049)

FR2AR -0.430(-1.255)

-0.426(-1.285)

-0.397(-1.050)

TRADE 1 0.484(1.238)

-0.004(-0.016)

TRADE2A 0.179(0.243)

-0.002(-0.003)

TRADE2R 2.033(4.756)**

1.636(4.393)**

TRADE2AR 1.399(2.023)*

0.460(1.081)

0 1.856(5.019)**

2.741(3.128)**

1.865(8.069)**

1.761(8.121)**

1.833(7.736)**

CTV 0.269(3.932)**

0.340(4.235)**

0.564(11.352)**

0.538(10.359)**

0.537(10.174)**

Number of observations

100 92 92 378 347 347

LogLikelihood

-78.37 -70.78 -62.50 -430.48 -393.74 -387.21

Notes: Figures in parentheses are t-ratios

** significant at 1% level (2-tailed test)* significant at 5% level (2-tailed test)+ significant at 10% level (2-tailed test)

Dependent variable is LC = ln[CR4 /(100-CR4)] (i.e. the logistic transform of the 4-firm concentration ratio). SIZE is the reciprocal o f the log of minimum efficient size relative to (EU or national) industry size. Note that 15 observations (3 German, 3 French, 4 UK, and 5 Italian) included in Table 1 have been excluded from the Big 4 frontier estimates because national industry size is less than engineering estimates of minimum efficient size (all had CR4>60%). This exclusion is inevitable given that our preferred functional form is discontinuous at S=1.

33