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AIM Research Working Paper Series Measuring Competition Rachel Griffith Jan Boone Rupert Harrison 022-Au g gus t t-200 5 5 ISSN: 1744-000 9 9

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Page 1: Waitrose 5 Forces

AIM Research Working Paper Series

Measuring Competition

Rachel Griffith Jan Boone

Rupert Harrison

002222--AAuugguusstt--22000055IISSSSNN:: 11774444--00000099

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Measuring competition

Jan Boone∗, Rachel Griffith†, Rupert Harrison†

August 13, 2005

Abstract

We investigate the empirical usefulness of a new measure of the degree of competitionin a market, proposed by Boone (2000). This measure is based on the reduction in profitsthat firms experience as a result of cost inefficiencies. We compare this with measurescommonly used by policy makers and in empirical work - market shares, concentrationindices, the Herfindahl index and price cost margins. Using simulated data we show thatin markets where goods are symmetrically differentiated, and where firms differ in theirmarginal cost, traditional measures can be poor indicators of the degree of competition,while the new relative profits measure performs better. Using accounting data on UKfirms we investigate the relationship between the relative profits measure and traditionalmeasures. Our results suggest that concentration-based measures perform worst, whilethe relative profits measure may provide a useful empirical complement to price-costmargins in both policy and econometric analysis.

1 Introduction

Despite the ubiquity of the concept of competition in economic analysis we in fact onlyhave very poor measures of the degree of competition in a market. Common measures usedby anti-trust authorities and empirical researchers include market shares, the concentrationindex, the Herfindahl index and the price-cost margin. However, as shown in Boone (2000)and discussed below, these measures can sometimes give an incorrect view of the degreeof competition in a market. They are not particularly robust from a theoretical point ofview, in the sense that they can incorrectly show an increase in competition, when in factcompetition has declined, and vice versa.

Boone (2000) suggests an alternative measure, based on relative profits, which is morerobust to the different ways in which competition can be parameterized in theory. Theintuitive idea behind the relative profits measure is that in a more competitive industry,firms are punished more harshly in terms of profits for cost inefficiency. Put differently,comparing two firms in an industry, where one is more efficient than the other, the moreefficient firm will have higher profits than the less efficient firm. As the industry becomesmore competitive, for given efficiency levels of the firms, the profits of the more efficientfirm go up relative to the profits of the less efficient firm. This can happen because theprofits of the more efficient firm actually increase, or if the more efficient firm’s profits fall,

∗Tilburg University†Institute for Fiscal Studies, University College London and Advanced Institute for Management Research

(AIM)

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then the profits of the less efficient firm fall by more. While in theory this relative profitsis more robust to capturing changes in the degree of competition, it has not been testedempirically. This paper provides an empirical comparison of commonly used measures withBoone’s proposed relative profits measure, using both simulations and actual accountingdata.

The structure of the paper is as follows. We first write down a simple model of competi-tion in which we can explore the properties of the measures. Boone (2000) shows that theseresults hold up in more general models of competition. In section three we discuss an ex-ample to illustrate how the measures capture competition. We then provide more extensiveanalysis, using simulated data, of how the measures perform for different parameterisationsof competition and for different distributions of costs. Finally, we use accounting data tosee how the measures perform. We first look at firms in two industries - pharmaceuticalsand supermarkets - and also show the patterns and correlations of the Herfindahl, price-costmargin and relative profits measures across a large range of industries and over time. Indoing this we do not have a good indicator of firms’ market and we use standard industrialclassification (SIC) codes, which are well known to be a poor reflection of firm’s actualmarket. However, they are the only information we have to classify a large number of firms,and we believe that the comparison provides some interesting results.

We find that the relative profits measure performs well. It is significantly positivelycorrelated with the price-cost margin in about half of the industries we examine. TheHerfindahl is generally uncorrelated with either the price-cost margin or the relative profitsmeasure in both within and between dimensions. The fact that the relative profits measureis correlated with the price-cost margin, but behaves differently in a significant numberof cases, suggests that theoretical concerns about the price-cost margin as a measure ofcompetition may have practical importance. We also find some evidence that the relativeprofits measure is less affected by cyclical changes than the price-cost margin.

2 Model

To introduce the relative profits (RP) measure, we use a standard I.O. model with lineardemand. Although the results do not depend on this particular framework, using this simplemodel we can explain why price-cost margin (PCM) is not a robust measure of industrycompetition from a theoretical point of view. We further show that RP is a more robustmeasure of competition. For an analysis of RP in a broader set of models, the reader isreferred to Boone (2000).

Consider an industry where each firm i produces only one symmetrically differentiatedproduct, faces a demand curve of the form

p(qi, q−i) = a− bqi − dXj 6=i

qj ,

and has constant marginal costs ci. The parameter a capture the size of the market, theparameter b captures the market elasticity of demand and the parameter d captures theextent to which consumers see the different products in a market as close substitutes foreach other. Firm i chooses output qi to solve

maxq≥0

{(a− bq − dXj 6=i

qj)q − ciq},

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where we assume that a > ci > 0 and 0 < d ≤ b. The first order condition for a CournotNash equilibrium can be written as

a− 2bqi − dXj 6=i

qj − ci = 0. (1)

Assuming N firms produce positive output levels, one can solve the N first order conditions(1). This yields

q (ci) =

¡2bd − 1

¢a−

¡2bd +N − 1

¢ci +

PNj=1 cj

(2b+ d(N − 1))¡2bd − 1

¢ . (2)

We define a firm’s variable profits as π (ci) = (a − bq (ci) − dP

j 6=i q (cj))q (ci) − ciq (ci).These are variable in the sense that they do not include the entry cost γ. In other words,a firm with marginal costs ci enters the industry if and only if π (ci) ≥ γ in equilibrium. Itis routine to verify that the variable profits can be written as

π (ci) = b [q (ci)]2 .

For our analysis, we consider two ways in which competition can change within themodel. First, we consider the case where goods become more or less substitutable, that isd (< b) increases or decreases. Second, we consider the effect of a reduction in entry costsγ. We analyze the effects of these changes in competition on market shares, the Herfindahlindex, the industry price cost margin and relative profits. These variables are defined asfollows:

si =piqiPNj=1 pjqj

H =NXi=1

s2i

PCM =NXi=1

sipi − cipi

RP (ci, cj) =π (ci)

π (cj)

where si denotes the market share of firm i in the industry and pi denotes firm i’s equilibriumprice. Price-cost margins are weighted by market share for the following reason. Supposethere are two firms with a price cost margin equal to 0.1 who share the market equally.Now a third firm enters who charges a very high price and has a price cost margin equal to2. Because of the high price, this firm has a market share close to zero. Hence, not muchhappens to the weighted price cost margin, which is correct, as the competitive situation inthe industry has not changed. However, if we calculated the unweighted industry price costmargin, we would get the incorrect impression that competition went down considerably inthe industry. The Herfindahl index H is defined in the usual way and relative profits aredefined for any pair of firms with differing efficiency levels ci and cj .

The following lemma shows the output reallocation effect, which turns out to be impor-tant in understanding which measures work well as indicators of competition, and whichmeasures do not.

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Lemma 1 The effect of an increase in d on relative variable costs with ci < cj is

∂³ciq(ci)cjq(cj)

´∂d

> 0.

The effect of a fall in γ which allows firm N + 1 to enter the industry on relative variablecosts with ci < cj is

∂³ciq(ci)cjq(cj)

´∂N

> 0.

Note that because we allow for the possibility that goods are heterogenous (i.e. b 6= d),we cannot consider q(ci)

q(cj)as that involves dividing apples by oranges. A simple way to make

the output levels comparable is to express them in terms of money and multiplying bymarginal costs does exactly that. Hence we consider relative variable cost levels and notrelative output levels.

The lemma shows that an increase in competition (either through a rise in d or afall in γ) reallocates output from inefficient firms to more efficient firms. This outputreallocation effect has two immediate consequences for the competition measures that weare considering. First, the effect makes clear why the Herfindahl index is not a good measureof competition. The standard intuition of the Herfindahl index is based on a Cournot modelwith symmetric firms. In that model a fall in entry barriers reduces H. However, with firmsthat differ in efficiency an increase in competition through a rise in d reallocates output tothe more efficient firms (that had higher output levels to start with). Hence, the increasein competition raises H. Second, because π (ci) = b [q (ci)]

2 the lemma implies that anincrease in competition raises profits of a firm relative to a less efficient firm (or reducesthe profits of the more efficient firm by less). Hence, relative profits is a robust measure ofcompetition. Any change in competition intensity that reallocates output from less efficientto more efficient firms increases the profits of a firm relative to a less efficient firm.

The implications of the output reallocation effect on the industry PCM are less clearcut. In the model presented here, an increase in competition indeed reduces each firm’sprice cost margin.1 But the output reallocation effect increases the weight (the marketshare) of the more efficient firms, which have higher PCM ’s. Hence it can happen thatan increase in competition raises the industry PCM . We illustrate this in the simulationexercises below.

3 Comparing the performance of the measures using simu-lations

In this section we compare the performance of various measures of competition by simulatingthe above model for different distributions of efficiency and different parameterisations ofcompetition. We use simulations for two main reasons. First, the results are instructiveabout the economic mechanisms at work and help to illuminate some important intuitions.

1Note that this is not always the case. Theoretical papers showing that an increase in competition canraise a firm’s price cost margin include Amir and Lambson (2000), Boone (2000), Bulow and Klemperer(2002) and Stiglitz (1987).

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Secondly, the results can be used to generate predictions about when the different measuresmight be expected to correctly pick up a change in competition. These predictions can thenbe tested empirically.

3.1 Measures

Market share:Market share is the firms sales divided by total industry sales,

si =yiPi yi

.

where yi = piqi, is firm revenues.Herfindahl:The Herfindahl index is the sum of the squared market shares, which captures infor-

mation about the number of firms in the industry and the distribution of their marketshares,

H =Xi

s2i . (3)

Price-cost margin:The weighted (by market share) price-cost margin, or the gross profit margin, is

PCM =Xi

siyi − TV Ci

yi, (4)

where i indexes firms and TV Ci = ciqi is total variable costs to the firm, which includeslabour and intermediate costs.

Relative profits:The relative profits measure is the difference (or change) in profits over the difference

(or change) in average variable costs. This is most easily measured by bβ from a regressionof the form

lnΠi = α+ βAV Ci + ei, (5)

where profits are revenues minus labour costs and intermediates, Πi = (yi − TV Ci), andaverage variable costs are labour costs and intermediates divided by revenue, AV Ci =TV Ci/Yi, giving

ln (yi − TV C)i = α+ β

µTV C

y

¶i

+ ei.

Why do we implement the relative profits measure in this way? As shown above, an increasein competition raises the profits of a more efficient firm relative to a less efficient firm.Hence, one way to implement the RP measure would be to estimate the relation betweenrelative profits and marginal costs ci. However, marginal costs ci are generally not directlyobservable. We can normally obtain reasonable measure of total revenues piqi and total(variable) costs ciqi. Therefore, we can approximate a firm’s (in)efficiency by dividingvariable costs by revenue, ciqi

piqi. Since qi cancels out this is equivalent to ci

pi= 1 − PCMi.

Since more efficient firms have higher price-cost margins they also have lower values of cipi.

There is thus an increasing one-to-one relationship between the unobservable marginal costs

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and our empirical proxy for a firm’s efficiency cixipixi

= cipi. We call this measure "variable

costs". It will lie between zero and one for firms making positive profits.Instead of investigating the relation between relative profits of firm i and some reference

firm πiπ1and variable costs, we estimate log profits as a function of variable costs from the

following OLS regressionlnπi = α+ β

cipi+ εi.

This is equivalent to estimating the relationship using the log of profits relative to somereference profit π1, since ln( πiπ1 ) = lnπi− lnπ1. In practice it can be problematic to specifythis reference profit, and so in this specification it is absorbed into the constant term α.The relative profits measure of competition is captured by the estimated coefficient β, whichmeasures the extent to which less efficient firms are punished with lower relative profits.The interpretation of β is as follows: it measures the percentage decrease (increase) in firmi’s profits if its variable costs (i.e. marginal costs relative to price) increase (decrease) byone percentage point. For example, an estimated β of −2 would suggest that a firm withone percentage point higher variable costs than another (more efficient) firm would have 2percent lower profits than the more efficient firm.

3.2 Simulations

The set-up for the simulations is as follows. For each simulation we have 15 potentialentrants with marginal costs drawn from a log normal distribution. We select startingparameter values for the demand function (a, b, d) and for entry costs (γ). We then computethe equilibrium market outcome (number of firms in the market, prices and quantities) andcalculate the various measures of competition using information on revenues and costs. Wethen change the degree of competition by altering one of the parameters of the model, andsolve again for the equilibrium outcome. The parameters that we use to capture changesin competition include: (i) d, the degree of product differentiation in the market, i.e. theextent to which consumers see products in a market as close substitutes for each other, and(ii) γ, entry costs relative to market size.

3.3 An example

An example helps to illustrate the idea. Consider an industry as described above with thefollowing parameters: a = 10, b = 2, d = 1.6, γ = 0.2. We have 15 potential entrants withmarginal costs drawn from a log normal distribution with underlying mean equal to 1 andunderlying variance equal to 0.05. We discuss the empirical relevance of these values below.In this section we discuss an example draw where the marginal cost of the least efficientpotential entrant is about 2.8 times that of the most efficient potential entrant, althoughit turns out that for our chosen parameters the least efficient firm does not choose to enterthe market.

We examine the effect of a reduction in competition, as captured by decreasing sub-stitutability between products, by reducing d from 1.6 to 0.8. This represents a decreasein the extent to which consumers see the products in the market as close substitutes foreach other. The lowest possible value for d is zero, which corresponds to monopoly, andthe highest possible value is 2, which corresponds to the case where products are perfect

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substitutes. Thus the change we consider represents a significant reduction in competition.We can simulate the effect of this change on the equilibrium market outcomes.

It is worth discussing a few characteristics of the market before the change (with dequal to 1.6). More efficient firms (those with lower marginal costs) have lower prices,higher market shares, higher price-cost margins and higher profits. The number of firmsthat enter the market is determined by how many can earn sufficient profits to meet theentry costs.

We reduce the substitutability between products by setting d equal to 0.8. This corre-sponds to less intense competition. Equilibrium prices for all firms are higher. The marketshares of the most efficient firms, those with the lower price, are reduced, because consumersnow see the products as less close substitutes. In this example the price-cost margins andprofits of all firms are increased, and as a result 5 more firms enter the market since theycan now cover their entry costs, increasing the number of firms from 7 to 12.

Figures 1 to 4 illustrate this example. In all cases marginal cost is on the horizontalaxis, with the marginal cost of the most efficient firm re-scaled to 1 to aid comparability.Thus we can see that the least efficient firm to enter the market in the more competitivecase has marginal costs about 60% higher than the most efficient firm. In Figure 1 we plotprices on the vertical axis. We can see that the reduction in competition is associated withhigher prices for all firms, with larger increases for the least efficient firms. The verticalaxis has been re-scaled so that the lowest price in the less competitive case is equal to 1, sowe can see that the highest price is just over 15% higher than the lowest price.

In Figure 2 we plot market shares on the vertical axis. We can see that the reduction incompetition actually reduces the market shares of all incumbent firms, as more firms enterthe market. In addition, the market shares of the most efficient firms are reduced the most.This immediately indicates that measures of competition based on market shares will pointin the wrong direction for this type of change in competition. In this case the Herfindahldecreases from 1587 to 890 and the market share of the top firm decreases from 21% to justover 12%, both misleadingly indicating an increase in competition. The important pointis that markets in which consumers can switch easily between products reward more effi-cient firms with higher market shares, and so a reduction in product substitutability allowsless efficient firms to survive with higher market shares. Thus a reduction in competitionreallocates output towards the less efficient firms.

In Figures 3 and 4 we plot gross profit-margins and total profits on the vertical axis,where total profits are rescaled so that the profit of the most efficient firm in the lesscompetitive case is equal to 1. We can see that more efficient firms have higher profitmargins and profits. The profits of all firms are higher after a reduction in competition,resulting in entry by less efficient firms that can now cover their fixed costs. Because theentrants are less efficient they have lower profit margins than incumbents. In addition, theprofits of the most efficient firms are increased the least by the reduction in competition (orequivalently reduced the least by an increase in competition).

Combined with the fact that a reduction in competition reallocates output towardsthe less efficient firms, these changes in profit margins indicate why the weighted industryprice-cost margin can move in a misleading direction. In this example, the reduction incompetition actually reduces the weighted industry price-cost margin from 36% to 33%.This is because the reallocation of output towards less efficient firms, which have lowerprice-cost margins, outweighs the increase in each individual firm’s price-cost margin.

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So how well does the relative profits measure capture the change in competition in thisexample? Recall that we implement the relative profits measure by estimating log profitsas a function of variable costs from the following OLS regression

lnπi = α+ βcipi+ εi.

Figure 5 plots log profits against variable costs. Variable costs lie between zero and one andlog profits are decreasing in variable costs (firms with higher costs earn lower profits). Thesteepness of the fitted line is equal to β. A steeper slope (or a more negative β) indicateshigher competition. In this case, the reduction in competition is associated with a reductionin the steepness of the fitted line, with the slope increasing from -5.8 to -5.0. Thus, in thiscase, unlike the Herfindahl index and the price-cost margin, the relative profits measurecorrectly indicates a reduction in competition.

Figures 6 to 10 show the same outcomes except this time we reduce the level of compe-tition by increasing entry costs, γ, from 0.2 to 0.6. The substitutability between productsis set at a constant level between the two levels in the previous example, with d = 1. Wecan see that in this case the reduction in competition is associated with a reduction in thenumber of firms (from 11 to 7) as the least efficient firms are no longer able to cover theincreased entry costs. The reduction in competition is again associated with an increase inprices, although this time the absolute increase is similar across firms. As some firms leavethe market, the market shares of the remaining firms rise. Thus, in this case, measuresbased on market shares or concentration will point in the correct direction. In this examplethe reduction in competition is associated with an increase in the Herfindahl from 988 to1484.

The price-cost margins of all firms rise, and output is reallocated towards the moreefficient firms, which have higher price cost margins, so the overall effect on the weightedindustry price-cost margin is unambiguous - it rises from 32% to 41%. Thus, the weightedprice-cost margin also points in the correct direction, indicating a reduction in competition.

Finally, the relative profits measure also correctly points to a reduction in competitionin this case, with the fitted line in Figure 10 becoming less steep as entry costs rise, andthe slope increasing from —5.4 to —4.2. So in the case where we change competition byincreasing entry costs all three measures correctly indicate a reduction in competition.

3.4 More general simulations

We now look more systematically at how the measures perform for different distributionof costs and at different parameter values. For any given change in competition we runthe simulation 100 times, for each change drawing marginal costs from the same underlyingdistribution. We then calculate the proportion of draws for which each measure moves in the"correct" direction. For example, if the simulated change is an increase in substitutabilitybetween products, then we look at the proportion of draws for which each measure indicatesan increase in competition. By changing the characteristics of the distribution of marginalcosts we can examine in which types of industries the different measures are likely to performbadly or well. For example, a larger variance in the cost distribution will result, on average,in a more concentrated industry dominated by a few firms with high efficiency levels.

Figure 11 shows a three-dimensional plot of the success rate of the weighted price-cost margin in capturing the change in competition correctly for different levels of product

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substitutability and different levels of variation in costs between firms within the industry(as captured by the variance of the underlying log normal distribution from which we drawmarginal costs). The change in competition in each case is an increase in d of 0.1. Alongthe back of the surface (in light green) we see that for low variance in costs the price-cost margin is accurate most of the time, over any level of substitutability. As the varianceincreases, the price-cost margin shows an incorrect picture of the change in competition morefrequently. At higher variance the price-cost margin gets it wrong more frequently whensubstitutability is high than when it is lower, reaching a minimum success rate below 40%.In other words the price-cost margin is least successful at capturing changes in competitionwhen the variance of the underlying cost distribution is larger and when the initial level ofcompetition is higher.

The intuition behind this result is as follows. Higher variance in costs is associated withindustries that are dominated by a few low-cost firms. In these industries the reallocationeffect from changing substitutability between products is more likely to result in the changein the price-cost margin pointing in the wrong direction. This reallocation effect is strongestwhen the initial degree of substitutability between products is high.

Figure 12 shows the same three-dimensional plot of the success rate of the relativeprofits measure. The success rate is higher here, and never falls below 80 percent. Whenthe relative profits measure does get it wrong, it appears to be driven by the exit or entry ofinefficient firms that have a large effect on the slope of the estimated fit. If we estimate therelative profits measure only on continuing firms, the measure is correct every time. Therelevance of this effect empirically is a subject for future research.

4 Applying to accounting data

In this section we look at how these measures behave using actual accounting data. We startby looking at two industries - pharmaceuticals and supermarkets. These illustrate some ofthe points brought out in the simulations. We also show the patterns and correlations of theHerfindahl, price-cost margin and relative profits measures across a large range of industriesand over time. In doing this we do not have a good indicator of firms’ market, so we usestandard industrial classification (SIC) codes, which are well known to be a poor reflectionof firm’s actual market. However, they are the only information we have to classify a largenumber of firms, and we believe that the comparison provides some interesting results. Wefind that the relative profits measure performs well. It is significantly positively correlatedwith the price-cost margin in about half of the industries we examine. The Herfindahl isgenerally uncorrelated with either the price-cost margin or the relative profits measure inboth within and between dimensions (except, surprising, in one of our example industries,supermarkets).

4.1 Data and measurement issues

The data comes from the annual report and accounts filed by firms listed on the LondonStock Exchange over the period 1986 to 1999. We consider all 3-digit SIC codes that have atleast five firms present in every year. In making our cross-industry comparisons we consider43 SIC codes (shown in Table 17).

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4.2 Pharmaceuticals

We consider all firms in the 3-digit industry Pharmaceutical (257), listed in Table 1 with theyears they are in our sample and their average market share over the period. Pharmaceuti-cals has high fixed costs, mostly in R&D, and our measure of variable costs includes R&Dlabour costs and intermediate costs. Ideally R&D costs should not be part of the variablecosts measure, however, with the data that we use, we cannot separate R&D labour fromother (production) labour.

The relative profits measure (the βt coefficients from (5)) is shown in the first column ofTable 2, with standard errors in the second column. In all years this is negative and in mostyears it is significantly different than zero (t-statistics less than -1.96). The third columnshows the price-cost margin and the final column the Herfindahl. The Table reports thesemeasures for every year. Towards the bottom of the table we report the values averagedover two five year periods either side of the 1991-92 recession. The F-test of the statisticaldifference between the value of the relative profits measure in (1986-1990) and (1995-1999)has a value of F(1,16)=3.34 with p-value 0.0862, suggesting they are statistically differentat a 10% confidence level. Competition goes up over this period according to all threemeasures.

The correlation between the three measures is shown in Table 3. The "beta" and theprice-cost margin are correlated with a coefficient of 0.5403, which is statistically significant.This says that around half of the variation in beta is mirrored in the price-cost margin (buthalf is not). Beta and the Herfindahl index and the price-cost margin and the Herfindahlindex are not significantly correlated.

Figures 13 to Figure 16 plot log variable profits against variable costs for the years 1986,1990, 1994 and 1999, with the individual firms labeled. The slope of the fitted line in eachyear shows the estimated beta. The slope becomes more negative over the period, especiallybetween 1990 and 1994. This suggests an increase in competition over the period.

We can see that it is not the same group of firms over the whole period, so it is not abalanced panel. In theory the relative profits measure does not require us to have all firmsin the market in all years. It simply requires us to be sure that the firms that are included inany year are all in the same market. In practice it may be the case that firms entering andleaving the sample between years affects the estimated beta. They also affect the price-costmargin. However, the price-cost margin is less likely to be affected by the entry and exit ofsmall firms from the sample, since it is weighted by sales. This is not true of beta (as wehave calculated it), where every firm has the same weight.

The slope becomes more negative over time and the fit becomes better. If we comparethe latter two graphs to the first two there are fewer outlying firms. In other words thereare fewer inefficient firms making large profits and fewer efficient firms making small profits.GlaxoSmithkline is the most efficient firm throughout the period.

Figure 17 shows the estimated beta over time with 95% confidence intervals (the dottedlines). These show the range within which we can be 95% confident the true beta lies.

Figure 18 plots the price-cost margin and beta over time. Beta is labeled on the left handaxis, price-cost margin on the right hand axis. Interestingly, in this industry, they seem totell a similar story over time: an increase in competition over 1990-1994, and again at theend of the period. However, the price-cost margin dates the initial increase in competitionabout two years later than beta (ending in 1994 compared to 1992 for beta.) The correlation

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coefficient between the two over time is 0.54 and is significant at the 5% level.Figure 19 adds the Herfindahl index to Figure 18. All the axes are now on the left hand

side. The Herfindahl is much more variable over time, and is not very correlated with theother two measures. In particular it shows a big reduction in competition between 1993and 1995, and does not show any increase in competition at the end of the period. Thecorrelation between the Herfindahl and the price-cost margin is only 0.17, and with beta itis 0.15. Neither is significantly different from zero.

4.3 Supermarkets

Supermarkets are classified in the 3-digit industry Food Retailing (641). There are 13firms listed in that industry over the sample period.2 However, not all of these firms aresupermarkets. The Competition Commission (2002) report into alleged anti-competitivepractices in the supermarket industry identifies the firms listed in Table 4 as operatingin the supermarket industry. The first column gives the market shares published by theCompetition Commission. The firms in the top half of the table are the big five supermarketfirms in the UK. The firms in the bottom half of the table are mostly not observed in ourdata.3 The second column gives the market share of the top five only, recalculated using theCC’s numbers. The final columns give the market shares that we calculate from Datastreamdata. They are remarkably similar, with Sainsbury’s being slightly overstated and Safeway’sbeing slightly understated. We use a balanced panel of these top five firms in the analysisthat follows.

As with pharmaceuticals, the relative profits measure is shown (the βt coefficients from(5)) in the first column of Table 5, with t-statistics in the second column. In all years exceptthe last two this is negative, however, it is only significantly different from zero in 1992 and1993. The third column shows the price-cost margin and the final column the Herfindahl.The Table reports these measures for ever year. Towards the bottom of the table we reportthe values averaged over two five year periods either side of the 1991-92 recession. The F-test of statistical difference between the value of the relative profits measure in (1986-1990)and (1995-1999) has a value of F(1,4)=0.95 with p-value 0.3855, suggesting the decreasein competition is not statistically different. None of the measures suggests an increase incompetition.

The correlation between the three measures is shown in Table 6. The beta and price-cost margin are not significantly correlated, nor is the price-cost margin and the Herfindahl.However, beta and the Herfindahl index are significantly correlated with a coefficient of0.8026.

Figures 20 to 23 plot log variable profits against variable costs for the years 1986, 1990,1994 and 1999, with the individual firms labeled. The slope of the fitted line in eachyear shows the estimated beta. Unlike in the pharmaceutical case we have not includedall firms in the 3-digit SIC code “food retailing”. We have included only the “big five”supermarkets (Tesco, Sainsbury, Asda, Safeway and Morrison) that are singled out in the2000 Competition Commission report. The slope is fairly constant over time, until 1997

2 In addition to the big five there is Alldays, BEJAM, GATEWAY CORP., Greggs, Kwik Save Group,LOW,WILLIAM, M & W, Shoprite Group.

3Waitrose is privately owned, Aldi, Lidl, Netto are foreign owned and Marks and Spencer is observed butsupermarket activities not measured separately from clothes retailing.

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when it begins to flatten out and even becomes positive in 1998 and 1999.Figure 24 shows the estimated beta and confidence intervals over time for supermarkets.

This confirms that beta is fairly constant until 1997 when it begins to increase. As we sawbefore, this shows that beta is significantly different from zero in only two years: 1992 and1993. The first three are fairly similar, apart from a general increase in the level of profitsover time. The last picture is very different. There seem to be two main reasons: first Tescoand Sainsbury, who have the highest profits, appear to become less efficient in that year.Secondly, Morrison appears to increase its efficiency without increasing its profits, such thatit is an outlier in 1999. Indeed, if it weren’t for Morrison, the fitted slope in 1999 wouldprobably be quite similar to 1994.

Figure 25 plots the price-cost margin and beta over time. Unlike pharmaceuticals, thetwo measures tell quite different stories. Both indicate a reduction in competition overthe period as a whole, but the price-cost margin shows this happening over 1986-1991,while beta shows it happening later, between 1992 and 1999. Indeed the price-cost marginactually shows an increase in competition over these years. Figure 26 adds the Herfindahl.Unlike pharmaceuticals, in this case it shows a very strong correlation with beta, but betaand the Herfindahl are very different from the price-cost margin. The correlation betweenbeta and the Herfindahl is 0.80 and is very significant (at the 1% level). The correlations ofbeta and the Herfindahl with the price-cost margin are —0.20 and 0.16 respectively. Neitheris significantly different from zero.

In summary then, whereas in pharmaceuticals beta and the price-cost margin followeda very similar pattern that was different to that of the Herfindahl, the reverse is true inSupermarkets: beta and the Herfindahl follow a very similar pattern that is different tothat of the price-cost margin. At this stage the reasons for these differences are not clear.

4.4 Comparisons within all industries

Table 8 shows the within industry correlation between the measures for all the industries.Beta and the price-cost margin are significantly positively correlated in 20 out of the 43industries, and are never significantly negatively correlated. Beta and the Herfindahl aresignificantly positively correlated in only 10 industries, and also significantly negativelycorrelated in 10 industries. Finally, the price-cost margin and the Herfindahl are significantlypositively correlated in only 3 industries, while they are significantly negatively correlatedin 10 industries.

Table 10 shows the beta estimates for the two time periods (1986-1990) and (1995-1999), while the final column shows a test of whether values for the two time periods arestatistically different. In eight industries the beta estimates suggest that the degree ofcompetition changed significantly at the 5% confidence level, in six of these it increased. Ina further three it increased significantly at the 10% confidence level.

4.5 Cross industry comparisons

So far we have looked at correlations between the measures within an industry over time.In other words, we were asking whether the three measures told similar stories about whathappened to the level of competition within a particular industry. Now we investigatecorrelations between the three measures across industries. So now we are asking whether thethree measures lead to the same conclusions about which industries are more competitive.

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Making cross-industry comparisons raises a number of complications that are not of suchconcern when making time series comparisons within industry, including measurement errorand the fact that many 3-digit SIC industries do not correspond to economic markets.

Table 7 shows each of the three measures. In all but four the beta is negative on averageover the period.

Table 9 shows the between industry correlations for each year. Beta and the price-cost margin are significantly correlated across industries in all years except 1991 and 1992.Interestingly these two years were those most affected by a serious recession in the UK. Wewill show later that there is some evidence that the price-cost margin is significantly morecyclical than Beta, which may help to explain the fact that they are not correlated acrossindustries in these years. Neither measure is particularly correlated with the Herfindahlacross industries.

Figure 27 shows the cross-industry correlations between the three measures over time.The highest correlation is between beta and the price-cost margin in every year apart from1991 and 1992, when the correlation between the Herfindahl and the price-cost margin ishigher. The correlation between beta and the price-cost margin is significant (at the 5%level) in every year apart from these two recession years and 1999. In 1999 it is significantat the 10% level.

Beta and the Herfindahl are never significantly correlated across industries. The price-cost margin and the Herfindahl are not significantly correlated across industries apart fromin 1995.

4.6 Cyclical behaviour of Beta and the PCM

Figure 28 shows the average beta and price-cost margin across all industries over time. Theprice-cost margin appears to be strongly pro-cyclical, falling during the years of recessionand rising during the recovery. Beta appears to be less cyclical.

In a regression of the industry price-cost margin on industry dummies and dummies forthe years 1991 and 1992 (not reported), the year dummies are significantly negative (thet-values are -2.7 and -4.2 respectively). In contrast, this is not the case for beta (the t-valuesare 0.8 and -0.1). This provides some evidence that the price-cost margin was significantlymore affected by the cyclical downturn than beta. This seems intuitively likely, since aproportional reduction in all firms’ profits will result in a reduction in the measured price-cost margin, while it will not affect beta as we have estimated it.

5 Summary and discussion

In this paper we have considered a new measure of the degree of competition in an industrythat is based on relative profits. This measure is theoretically attractive as it provides amore robust representation of competitiveness. We have used simulations and actual dataon UK firms to consider the empirical usefulness of this measure.

Several conclusions emerge from the empirical analysis. First, the Herfindahl performsthe worst of the measures we consider. In general it is not correlated with the other twomeasures in the simulations, over time or across industries. This raises questions about itusefulness as a measure of competition.

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Second, the relative profits measure is generally positively correlated with the price-cost margin both over time and across industries. However, there are a significant numberof cases where this is not the case, and the relative profits measure exhibits substantialindependent variation. Without a priori information about the "true" degree of competitionin an industry it is difficult to say whether the relative profits measure is empirically betterthan the price-cost margin. However, given that it is a theoretically preferable measureand that the simulations suggest that it "gets it right" more often, our results suggest thatconcerns about the price-cost margin may have practical importance.

Finally, we have found some evidence that the relative profits measure is less affected bycyclical changes than the price-cost margin. This provides another reason why the relativeprofits measure may provide at least a useful empirical complement to the price-cost marginin both policy and econometric analysis.

Appendix A: Proof of results

Proof of lemma 1Differentiating ciqi

ckqkwith respect to d yields the following

∂³ciqickqk

´∂d

=cick

µ( 2bd −1)a−(

2bd+N−1)ci+ N

j=1 cj

( 2bd −1)a−(2bd+N−1)ck+ N

j=1 cj

¶∂d

=cick

2b

d2(ck − ci)

PNj=1 (a− cj)³¡

2bd − 1

¢a−

¡2bd +N − 1

¢ck +

PNj=1 cj

´2 > 0

for ci < ck because every firm with positive output levels has a− ci > 0.Now consider the effect of an increase in N where firm N +1 enters with marginal cost

level cN+1:

∂³ciqickqk

´∂N

=cick

µ( 2bd −1)a−(

2bd+N−1)ci+ N

j=1 cj

( 2bd −1)a−(2bd+N−1)ck+ N

j=1 cj

¶∂N

=cick

(ck − ci)h¡2bd − 1

¢a−

¡2bd +N

¢cN+1 +

PN+1j=1 cj

i³¡

2bd − 1

¢a−

¡2bd +N − 1

¢ck +

PNj=1 cj

´2 > 0

for ci < ck because q (cN+1) > 0 implies thath¡2bd − 1

¢a−

¡2bd +N

¢cN+1 +

PN+1j=1 cj

i> 0.

Q.E.D.

References

Aghion, Bloom, Blundell, Griffith and Howitt (2002) “Competition and Innovation: Aninverted U relationship”, Quarterly Journal of Economics vol. 120(2), pages 701-728

Amir, R. and V.E. Lambson, (2000), ’On the effects of entry in Cournot markets’,Review of Economic Studies, 67 (2): 235-254.

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Boone, J. (2000): "Measuring Product Market Competition", CEPR Working Paper2636

Bulow, J. and P. Klemperer, (2002), ’Prices and the winner’s curse’, RAND Journal ofEconomics Vol. 33 (1): 1-21.

Competition Commission (2002) SupermarketsCorts, K., 1999, Conduct parameters and the measurement of market power, Journal of

Econometrics, 88, 227-250.Hall R. E. (1988) “The relationship between price and marginal cost in U.S. industry”

Journal of Political Economy, vol. 96, 921-47.Hall R. E. (1990) “Invariance properties of Solow’s productivity residual” in P. Diamond

(ed.), Growth, Productivity, Unemployment, MIT Press (Cambridge, US.).Martins, J, Scarpetta, S and Pilat, D (1996) “Markup-up ratios in manufacturing in-

dustries: estimates for 14 OECD countries”, OECD Working Paper No. 162Nickell, S. (1996) "Competition and Corporate Performance", Journal of Political Econ-

omy, 104, 724-746.Roeger, W. (1995) “Can Imperfect Competition Explain the Difference Between Primal

and Dual Productivity Measures? Estimates for US Manufacturing” Journal of PoliticalEconomy 103, 316-330

Stiglitz, J., (1987), ’Imperfect information in the product market’, in R. Schmalensee andR. Willig (ed.), Handbook of industrial organization, Volume I, Elsevier Science Publishers.

Vickers, J., 1995, Entry and competitive selection, Mimeo Oxford University.Wolfram, C., 1999, Measuring duopoly power in the British electricity spot market,

American Economic Review 89 (4), 805-826.

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Table 1: Pharmaceutical firms Company First year Last year Average market share Beecham Group 1986 1988 44.1SmithKline Beecham 1989 1999 38.7GlaxoSmithkline 1986 1999 32.4AstraZeneca 1993 1999 25.9Wellcome 1986 1993 16.1Macarthy 1987 1991 3.9 Amersham 1986 1997 2.0 Medeva 1990 1999 1.2 AIR CALL 1986 1989 0.7 Whatman 1986 1999 0.5 SSL International 1991 1999 0.4 Bespak 1986 1999 0.3 VDC 1994 1998 0.3 Meconic 1996 1999 0.2 Ransom (William)& Son 1986 1999 0.1 SkyePharma 1988 1995 0.1 Treatt 1989 1999 0.1 Table 2: Pharmaceutical industry beta, price-cost margin and herfindahl by year Coefficient (t-statistic) PCM Herfindahl 1986 -14.697 (-3.30) 0.127 35721987 -10.061 (-2.13) 0.136 31401988 -6.4472 (-0.90) 0.136 28951989 -10.739 (-1.56) 0.128 36091990 -13.002 (-1.82) 0.143 34691991 -19.753 (-3.47) 0.130 33721992 -26.210 (-6.91) 0.146 34711993 -25.125 (-5.96) 0.130 25871994 -23.530 (-5.30) 0.101 30871995 -24.115 (-3.60) 0.098 33731996 -19.095 (-6.94) 0.099 31251997 -19.865 (-5.59) 0.102 30851998 -22.189 (-4.98) 0.096 31711999 -31.544 (-6.56) 0.087 3194 1986-1990 -10.567 (-1.94) 1995-1999 -22.360 (-5.87) Mean -19.340 0.118 3216 Notes: year dummies included, 152 observatations, standard errors clustered at the firm level.

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Table 3: Correlation between beta, price-cost margin and herfindahl in pharmaceutical

PCM Herfindahl

Beta 0.5403 (0.0461)

0.1475 (0.6149)

Herfindahl 0.1721 (0.5563)

Table 4: Supermarket market shares, 1998/1999 Market share (CC) Market share amongst

top five implied by CCMarket share using

Datastream Tesco 23.0 33.2 33.1Sainsbury 18.7 27.0 31.7Asda 12.2 17.6 15.8Safeway 11.5 16.6 14.5Morrison 3.9 5.6 4.9sub total 69.2 100.0 100.0 Somerfield 9.8 Marks & Spencer 4.9 Waitrose 3.0 Aldi 1.4 Lidl 0.9 Netto 0.7 Budgens 0.7 Iceland 3.0 Booth 0.2 Co-ops 6.4 Herfindahl 1324 2447 2585 Source: CC Table 5.6; authors’ calculations

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Table 5: Supermarkets industry beta, price-cost margin and herfindahl by year Coefficient (t-statistic) PCM Herfindahl 1986 -44.165 (-1.34) 0.036 24841987 -50.807 (-1.43) 0.041 24811988 -51.051 (-1.15) 0.043 24621989 -50.809 (-1.57) 0.044 25121990 -52.409 (-1.43) 0.046 24981991 -61.172 (-1.59) 0.050 24641992 -92.806 (-3.03) 0.049 24841993 -72.556 (-2.04) 0.051 24971994 -55.598 (-1.60) 0.049 25011995 -52.394 (-1.56) 0.050 25101996 -61.200 (-1.44) 0.049 25301997 -10.216 (-0.76) 0.047 25351998 3.211 (0.12) 0.047 25901999 24.851 (0.37) 0.045 2585 1986-1990 -49.734 (-1.66) 1995-1999 -32.516 (-1.82) Mean -44.794 0.046 2510 Notes: year dummies included, 70 observatations, standard errors clustered at the firm level. Table 6: Correlation between beta, price-cost margin and herfindahl in supermarkets PCM Herfindahl

Beta -0.2023

(0.4880) 0.8026 (0.0006)

Herfindahl 0.1578 (0.5899)

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Table 7: Beta, price-cost margin and herfindahl by industry, average 1986-1999 Beta Price-cost margin Herfindahl 248 Refractory and ceramics -19.145 0.068 3725250 Chemical industry -12.151 0.096 2218257 Pharmaceutical products -19.027 0.119 3226316 Finished metal goods -15.864 0.066 3473320 Mechanical engineering -10.684 0.058 2540324 Processing machinery -18.884 0.061 4310328 Other machinery -15.858 0.081 2021340 Electrical and electronics -15.484 0.072 1785344 Telecomm equipment -5.405 0.086 2668345 Other electronic equipment -20.727 0.089 5531353 Motor vehicle parts -15.502 0.068 3163364 Aerospace equipment 3.768 0.088 4035427 Brewing and malting 2.663 0.111 6685430 Textiles -25.477 0.053 4293453 Clothing -10.283 0.061 1578467 Wood furniture -5.136 0.080 1765475 Newspapers books periodicals -11.939 0.105 1593483 Plastic -4.855 0.087 1393500 General construction -2.091 0.042 1863501 Construction and repair -4.785 0.103 1343502 Civil engineering -12.731 0.054 3848613 Wholesale of building material -25.743 0.044 3279614 Wholesale of machinery -10.062 0.070 2841616 Wholesale of textiles -44.279 0.051 3138617 Wholesale of food and drinks -9.956 0.054 2916619 Other wholesale distribution 6.180 0.053 3194640 Retail distribution -17.495 0.048 1983641 Food retailing -44.794 0.046 2510645 Clothing retailing -14.153 0.061 5973651 Motor vehicle retailing -21.221 0.028 1084654 Specialised retail (non-food -2.174 0.076 5063656 Mixed retail businesses -21.569 0.075 2037660 Hotels and catering -12.610 0.102 6234662 Public houses and bars 0.465 0.130 5563665 Hotel trade -4.847 0.121 6075723 Road haulage -10.875 0.057 4188770 Transport services and storage -4.464 0.054 2742837 Professional services n.e.s. -12.906 0.082 2013838 Advertising -5.427 0.052 2441839 Computing business service -2.491 0.099 1407974 Radio and television service -6.309 0.139 2851979 Sport and recreational service -6.727 0.101 5663998 Personal services n.e.s. -4.297 0.092 1573 Total -11.985 0.076 3205

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Table 8: Within industry correlations (correlation in movements over time within industries) Beta, PCM PCM, H Beta, H 248 Refractory and ceramics 0.537 ** -0.307 -0.494 * 250 Chemical industry 0.745 ** 0.038 0.003 257 Pharmaceutical products 0.540 ** 0.172 0.147 316 Finished metal goods 0.706 ** -0.808 ** -0.617 ** 320 Mechanical engineering 0.520 ** -0.496 * -0.621 ** 324 Processing machinery -0.155 0.038 0.150 328 Other machinery 0.629 ** 0.222 -0.081 340 Electrical and electronics 0.526 * -0.364 -0.535 * 344 Telecomm equipment 0.267 -0.800 ** -0.274 345 Other electronic equipment 0.839 ** 0.309 0.297 353 Motor vehicle parts 0.664 ** -0.321 -0.769 ** 364 Aerospace equipment -0.171 -0.131 -0.605 ** 427 Brewing and malting 0.308 0.487 * 0.874 ** 430 Textiles 0.870 ** -0.335 -0.283 453 Clothing 0.622 ** -0.791 ** -0.584 ** 467 Wood furniture 0.033 -0.457 0.581 ** 475 Newspapers books periodicals -0.201 0.033 0.133 483 Plastic 0.694 ** -0.580 ** -0.322 500 General construction 0.172 0.338 -0.463 * 501 Construction and repair 0.787 ** 0.246 0.703 ** 502 Civil engineering 0.763 ** 0.720 ** 0.710 ** 613 Wholesale of building material 0.111 0.341 -0.374 614 Wholesale of machinery 0.397 -0.679 ** 0.021 616 Wholesale of textiles 0.078 -0.137 -0.332 617 Wholesale of food and drinks 0.771 ** 0.453 0.809 ** 619 Other wholesale distribution 0.060 0.535 ** -0.056 640 Retail distribution 0.095 -0.758 ** 0.304 641 Food retailing -0.202 0.158 0.803 ** 645 Clothing retailing 0.046 0.115 -0.118 651 Motor vehicle retailing 0.302 0.408 -0.105 654 Specialised retail (non-food 0.223 0.009 0.499 ** 656 Mixed retail businesses 0.600 ** -0.191 -0.467 * 660 Hotels and catering 0.095 -0.379 0.395 662 Public houses and bars 0.772 ** 0.688 ** 0.289 665 Hotel trade -0.201 -0.896 ** 0.353 723 Road haulage 0.716 ** -0.540 ** -0.666 ** 770 Transport services and storage 0.567 ** 0.180 0.112 837 Professional services n.e.s. 0.933 ** -0.327 -0.315 838 Advertising 0.305 0.111 0.893 ** 839 Computing business service 0.112 0.074 0.633 ** 974 Radio and television service 0.266 -0.259 -0.360 979 Sport and recreational service 0.452 -0.505 * -0.348 998 Personal services n.e.s. -0.090 0.052 0.874 ** * significant at 10% level, ** significant at 5% level

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Table 9: Between industry correlations Beta, PCM Beta, Herfindahl PMC, Herfindahl 1986 0.4120 **

(0.0061) 0.1167 (0.4560)

0.1961 (0.2077)

1987 0.3533 ** (0.0201)

0.0290 (0.8536)

0.0451 (0.7741)

1988 0.4052 ** (0.0070)

-0.0626 (0.6901)

0.0539 (0.7313)

1989 0.4497 ** (0.0025)

0.0855 (0.5855)

0.1440 (0.3570)

1990 0.4435 ** (0.0029)

-0.1297 (0.4071)

0.2242 (0.1484)

1991 0.2150 (0.1662)

0.0439 (0.7797)

0.2519 (0.1032)

1992 0.1885 (0.2260)

0.1808 (0.2458)

0.2299 (0.1380)

1993 0.3194 ** (0.0368)

0.1635 (0.2948)

0.2004 (0.1977)

1994 0.3918 ** (0.0094)

0.1746 (0.2629)

0.2586 * (0.0940)

1995 0.3992 ** (0.0080)

0.0161 (0.9186)

0.3340 ** (0.0286)

1996 0.3996 ** (0.0079)

0.1958 (0.2083)

0.1238 (0.4289)

1997 0.3867 ** (0.0104)

0.0347 (0.8250)

0.2938 * (0.0559)

1998 0.3777 ** (0.0125)

-0.0024 (0.9881)

0.2356 (0.1282)

1999 0.2573 * (0.0957)

0.1125 (0.4726)

0.1593 (0.3077)

Note: number in () is the p-value for significance; * significant at 10% level, ** significant at 5% level

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Table 10: change in beta (1986-1990) to (1995-1999)

beta(1986-1990) beta(1995-1999)

P-value for difference between

beta(1986-1990) and beta(1995-1999)

248 Refractory and ceramics -19.108 -15.648 0.717 250 Chemical industry -5.217 -13.819 0.196 257 Pharmaceutical products -10.567 -22.360 0.086 * 316 Finished metal goods -9.853 -17.611 0.523 320 Mechanical engineering -6.490 -13.188 0.324 324 Processing machinery -23.476 -21.153 0.927 328 Other machinery -13.019 -15.690 0.535 340 Electrical and electronics -12.825 -16.245 0.442 344 Telecomm equipment -2.483 -8.001 0.274 345 Other electronic equipment -11.009 -29.776 0.050 ** 353 Motor vehicle parts -1.887 -17.396 0.522 364 Aerospace equipment 6.728 -11.055 0.095 * 427 Brewing and malting -13.300 13.475 0.236 430 Textiles -18.114 -29.760 0.014 ** 453 Clothing -6.583 -13.922 0.212 467 Wood furniture -6.797 -5.771 0.919 475 Newspapers books periodicals -11.289 -13.482 0.438 483 Plastic -4.816 -1.980 0.594 500 General construction -2.794 -0.733 0.730 501 Construction and repair -0.421 -7.131 0.015 ** 502 Civil engineering -25.914 -3.264 0.109 613 Wholesale of building material -8.780 -27.551 0.210 614 Wholesale of machinery -11.178 -8.325 0.618 616 Wholesale of textiles -39.957 -46.362 0.596 617 Wholesale of food and drinks -11.100 -1.803 0.044 ** 619 Other wholesale distribution 4.421 4.302 0.990 640 Retail distribution -20.743 -19.687 0.770 641 Food retailing -49.733 -32.516 0.386 645 Clothing retailing -14.593 -19.447 0.499 651 Motor vehicle retailing -22.684 -13.856 0.403 654 Specialised retail (non-food 0.181 -2.436 0.801 656 Mixed retail businesses -24.046 -25.730 0.853 660 Hotels and catering -7.103 -18.820 0.202 662 Public houses and bars -16.544 8.474 0.020 ** 665 Hotel trade -2.165 -9.525 0.220 723 Road haulage 0.284 -28.675 0.049 ** 770 Transport services and storage -8.462 -3.181 0.506 837 Professional services n.e.s. -3.430 -20.294 0.009 ** 838 Advertising -7.870 -2.490 0.184 839 Computing business service -0.719 -3.215 0.085 * 974 Radio and television service -3.130 -8.693 0.009 ** 979 Sport and recreational service -9.333 -5.585 0.120 998 Personal services n.e.s. -0.602 -9.026 0.060 * * significantly different at 10% level, ** significantly different at 5% level

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