Capacity Utilisation and Excess Capacity: Theory, Evidence, and Policy

Review of Industrial Organization16: 69–87, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

69

Capacity Utilisation and Excess Capacity: Theory,Evidence, and Policy∗

CIARAN DRIVER1

Imperial College Management School, University of London, U.K.

Abstract. This paper models capacity utilisation as a cyclical variable that reflects both the valueof precautionary capacity and the desire to hold strategic excess capacity. Business unit data fromtheProfit Impact on Marketing Strategy(PIMS) database of large, predominantly US, companies areused. Separate estimation is carried out for a number of SIC industry groups. Panel data estimation infirst difference instrumental variable form is employed. Unlike many previous studies the utilisationvariable is well determined. The results show significance for precautionary and strategic effects inparticular industries. The paper discusses the reasons for the industry specificity of the results. Policyimplications are discussed.

JEL Classification: E22; L1; L4; L60.

I. Introduction

This paper sets out to explain variation in capacity utilisation and excess capacityusing data on business units of large manufacturing firms. Excess capacity mayarise simply because of demand shocks and for this reason we need to model itconditional on cyclical variables. Excess capacity may also be planned and herewe distinguish two reasons. First capacity may be held as a buffer, i.e., for precau-tionary reasons, particularly where demand is volatile or uncertain. Second, excesscapacity may be used as a means of deterring entry and of signalling a readinessto respond to that threat with price cuts. In the paper an econometric model isestimated with panel data to explore the relative importance of these reasons for

∗ This research was partly financed by the U.K. Economic and Social Research Council (ResearchGrant R000232176) and was facilitated by a sabbatical at the Research School of Social Sciences,Australian National University, Canberra. I am indebted to Steve Dowrick and to referees for helpfulcomments. Paul Yip and Nazera Dakhil helped with software. Advice on accessing and interpret-ing the PIMS data was kindly provided by the London office of the Strategic Planning Institute,in particular Keith Roberts, Tony Clayton and Iain Brown. The paper has been presented at theAtlantic Economic Conference and the annual Conference of the European Association for Researchin Industrial Economics.

1 The author is a Reader in Economics at Imperial College Management School, University ofLondon, 53 Princes’ Gate, Exhibition Rd., London SW7 2PG, U.K. Telephone 0171-594-9123; Fax:071-823-7685; E-mail: [email protected]

70 CIARAN DRIVER

carrying excess capacity. The estimation is carried out for twelve separate industrygroups. Variation in the cross-industry results allows us to identify conditions underwhich either of the two hypothesised reasons for carrying planned excess capacitymay dominate.

The paper also explores the policy issue of investment subsidies and the suf-ficiency of privately financed investment under risk (Meltzer, 1979; Greenwaldand Stiglitz, 1993). For individual firms and for the economy as a whole, a highdegree of cyclically-adjusted capital utilisation is double-edged. On the one handit signals high capital productivity. On the other hand, it implies a low margin ofspare capacity, which under stochastic demand may imply a greater probability ofcapital shortage, i.e., an inability to supply profitably due to inadequate capital for-mation. Investment subsidies exist in most industrialised countries to counter anydownward bias imparted to the capital stock due either to demand risk or to a failureto appropriate at firm level the full gains from new capital formation. One aim ofthese subsidies is to encourage the building of precautionary capacity. However,since policymakers and officials do not distinguish between precautionary capacityand strategic capacity, there must be a risk that such subsidies might encouragestrategic entry-deterring behaviour with attendant waste of resources. Thus, theresults of the paper may inform the policy discussion on investment subsidies.

Most previous studies of capacity utilisation have been carried out on industry-level data. This is a severe limitation because capacity is planned at the level of thefirm and inter firm differences will provide important clues to how capacity is set.In this paper we study the determinants of capacity utilisation using panel data onbusiness units, mainly with head offices in the U.S.A., during the period 1970–84.Observed capacity utilisation is the ratio of a realisation of stochastic demand toplanned capacity. To study the variable of interest – planned capacity utilisation– the data has to be purged of demand shocks by estimating observed utilisation,conditional on cyclical variables; thus we are, in effect, estimating planned capacityutilisation. In Section 2 of the paper we set out a model of planned utilisationthat takes into account both precautionary and strategic motives for excess capac-ity. Section 3 surveys previous empirical work on capacity utilisation. Section 4specifies an econometric model. The results are reported in Section 5. Section 6concludes with a policy evaluation.

II. Planned Excess Capacity

1. CAPACITY AS PRECAUTIONARY BUFFER

When demand is uncertain the firm has to choose the level of precautionary excesscapacity. The deciding factor in this will be the balance between cost of stock-outand the carrying cost of unused capacity. The cost of stock out is often modelledas the cost of non-supply, e.g., loss of goodwill and future orders when the firmresorts to rationing. Alternatively, where the firm tries to guarantee supply, the costof stock-out is the cost of inefficient production beyond “full” capacity. We now

CAPACITY UTILISATION AND EXCESS CAPACITY 71

show formally that the firm will have more incentive to hold excess capacity, themore severe the cost penalty of inefficient production and the higher the cost ofnon-supply.

The following symbols apply: Output price (p); capacity (y); capacity cost perunit of production (c); demand under certainty (D0(p)); stochastic shift parameter(a). Setting marginal cost with respect to y equal to expected marginal revenue,with an absolute capacity constraint aty = aD0,

c = p.prob(aD0(p) > y)

1− c/p = prob(aD0(p) < y) = prob(a < y/D0(p)) = F(Z)whereZ is the ratio of capacity to mean demand− is an inverse indicator ofexpected utilisation. Note thatZ > 1 implies that the firm plans to hold excesscapacity.

For a symmetrical distribution ofa, F(1) = 1/2. It then follows that:

p > 2c⇒ Z > 1.

Not surprisingly, a distribution with larger tails will for any givenc/p ratio have anoptimalZ that is further away from unity. For example, ifp > 2c, planned excesscapacity rises with a mean-preserving spread ofa.

We now replace the capacity constraint above by a cost penalty for inefficientproduction, assuming that demand can be met profitably. Where this cost penaltyis greater than the cost of non-supply, we assume that the firm will not supply; theunit cost of non-supply may be the price or some multiple of the price that takesinto account loss of goodwill, etc. In the latter case, the unit cost of non-supplymay depend on the extent of rationing. Focusing first on the case where the firmchooses to supply, we treat normal production cost as negligible for simplicity andmodel total cost as the sum of capacity costs and additional production costs whenfull capacity is exceeded.

Total cost fora > Z is then given byyc +D0(p)(c1.(a − Z)).The expected extra cost of inefficient production beyond “full” capacity is thengiven by:

D0(p)

∫ u

Z

c1.(a − Z)f (a)da, whereu is the upper support fora.

The expected return from a marginal change inZ is given by the negative of thederivative of this w.r.t.Z which, using Leibnitz’s theorem, may be expressed as:

D0(p)

∫ u

Z

c1f (a)da.

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Introducing the choice of non-supply with a unit lost profit ofp1.(a − Z), we maywrite the return from a marginal change inZ as:

D0(p)

{∫ u

Z

min[c1, p1]f (a)da}.

The above expression is the expected marginal benefit of a change inZ and foroptimality must be equated with the marginal cost of changingZ which is cD0.IncreasingZ makes the expression for expected marginal benefit smaller. It is clearthat the heavier the cost penalty of inefficient production (represented by the termc1), the larger mustZ be in order to maintain the equality and thus, the larger theexpected excess capacity will be. Furthermore, the heavier the penalty for non-supply (represented by the termp1), the larger mustZ be. Planned excess capacitywill thus be directly related to the cost of inefficient production beyond full capacityand to the cost of non-supply.

It is arguable that the cost of inefficient production or production beyond “full”capacity is related to firm size as measured by the size of the customer base. Large-businesses, being diversified across product lines and markets, will be able to meetdemand surges by reallocating across production lines – using linear programmingetc. – at least unless demand shocks are perfectly correlated. Smaller firms arelikely to be in niche markets with less scope for reallocating production.

To see the advantage of large market share in this respect, consider the expectedvalue to the firm of a marginal unit of capacity in two cases. In the first case weconsider two identical single-plant firms serving markets which are differentiatedby a single variable such as geographical location or product attribute. This iscontrasted with a second case where the firms have merged. For simplicity weignore stocks and assume that the demand buffer is held in the form of a margin ofspare capacity. In the first case, the expected marginal value of capacity for eachfirm is, as before:

D0(p)

{∫ u

Z

min[c1, p1]f (a)da}. (a)

Following the merger of the two firms, the expected marginal value of capacity inany one plant is:

D0(p)

{∫ u

Z

min[c1, p1, c∗]f (a)da

}, (b)

wherec∗ is the additional cost incurred when capacity of plant i is used to meetdemand normally filled from plantj or vice versa.2

2 For simplicity we have not dealt with the change in the demand distribution that results fromthe merger, but the same conclusions follow if this is done.


Clearly (b)≤ (a), with the strict inequality requiringc∗ < min[c1, p1] for somerealisation of demand.

More specifically, the strict inequality will apply where

(i) demand fori, j are not perfectly correlated. This implies thatc∗ may be lessthanc1 at some output levels, where one plant is capacity constrained and theother not.

(ii) for some output levelc∗ < p1, making it economic to supply from the “wrong”plant rather than turn down the order.

Where the strict inequality applies, the previous model shows that it is optimalfor the merged firm to adopt a lowerZ value, i.e., to carry less spare capacity inrelation to expected demand. Thus it appears that firms with larger market shares,where cross-supply is more possible, could operate optimally with higher levels ofexpected capacity utilisation than firms with smaller market shares.3

The practical import of the above model depends on assumptions (i) and (ii)as they imply that it is economic to substitute the capacity normally dedicated toone region (or product) to supply another. This flexibility will characterise largemarket share firms that operate in several markets. In the literature this type offlexibility is considered one of the most important . . . “if sales fall off in oneproduct and increase in another, it is possible to maintain a high degree of capacityutilisation of the plant and equipment by shifting production from one product toanother” (Carlsson, 1989, p. 191). Firms with lower market shares will find this lesseasy. Although it is possible in principle even for a single product firm to switchproduction to a different product and back again, the advantage will clearly lie witha firm that already produces a variety of lines or serves a variety of markets.4 Wetherefore expect that for any given demand variance, capacity utilisation will bepositively correlated with market share.5

3 The cost of non-supply is also likely to be less for large firms since these firms will be ableto choose which customers to ration and so minimise loss of goodwill. Smaller firms with fewercontracts may find this difficult. In any event, the fixed costs of a rationing system will entail higherunit set-up costs for these firms. These considerations imply that the optimal buffer stock for largefirms is smaller for comparable demand volatility and so observed utilisation should be higher onaverage.

4 Carlsson’s study was based on interviews with U.S. engineering, vehicle and metalworkingfirms. Other forms of flexibility include the ability to vary throughput on a single line and the abilityto convert a whole plant to alternative use – see Bresnahan and Ramey (1993) for an example of thelatter. Upton (1995) finds that larger operations have an advantage in changeover time and also thatchangeover time is not reduced where the plant produces a smaller range of products.

5 An empirical test is available using the main business survey data bank for the U.K., the CBIindustrial trends survey. We compare the mean capacity utilisation for 80 quarters up to 1995 relativeto the variance of the residual of output growth from estimated values based on an autoregressivemodel of order one. The statistics for this ratio are 0.89 for the medium-size firms in the sample(between 500 and 5000 employees) and 0.52 for the large firms (>5000), where size can be construedas an imperfect correlate of market share. Thus it appears that the larger firms are indeed carryingless of a buffer relative to their variability in demand than the medium-size ones.

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2. STRATEGIC DETERRENCE

Planned excess capacity may also arise for strategic reasons, e.g., to add credibilityto the threat to expand output in the face of entry (Lyons, 1986). More generallyexcess capacity may constitute mobility barriers to counter aggressive undercuttingby rivals (Caves and Porter, 1977; Saloner, 1985; Gilbert, 1989). Much theoreticaleffort has gone into oligopoly models of entry-deterring capacity creation and thestudy of two-stage investment-output strategies with sunk costs (e.g., Dixit, 1980).Such formal models of entry deterrence have difficulty in showing that excesscapacity is optimal. A necessary condition for excess capacity is that fixed costs belower than needed to blockade entry but high enough not to make entry preclusionimpossible. Potential entrants have to be excluded either by a credible threat toexpand output or by capacity and output being expanded to the paint where entrywould be unprofitable. The theoretical difficulty is that the threat in the formercase is not credible whereas the latter strategy (the Dixit model) does not make ex-cess capacity optimal for the incumbent (see for instance Martin, 1993). However,excess capacity can reappear in the Dixit model under some modifications suchas constant elasticity demand curves (Bulow et al., 1985) or a queue of potentialentrants (Lyons, 1988).

A necessary condition for excess strategic capacity to be effective is the exis-tence of high set-up costs. This suggests that, unless entry is blockaded, excesscapacity may be positively related to concentration. (Caves et al., 1979). Fur-thermore, concentration facilitates the information-sharing needed to sustain priceco-ordination and facilitates participation in price wars that are a structural featureof price-co-ordination (Green and Porter, 1984). We will therefore take as a nullhypothesis that capacity utilisation is negatively related to concentration.6 Our priorexpectation is, however conditioned by the knowledge that concentration does notalways imply excess capacity, particularly if other methods of entry deterrence areavailable.

III. Previous Empirical Work

Previous empirical work on capacity utilisation has mostly been concerned withstrategic deterrence only and has been on industry-level rather than firm-leveldata. Using a single cross-section of industry time-averages Esposito and Esposito(1974) and Caves et al. (1979) report that excess capacity is positively related tomoderate levels of concentration (40%< C4 < 70%). One problem with suchstudies is that cross section estimation using OLS is likely to yield severely bi-ased estimates if there are omitted variables correlated with the regressors (Hsiao,1986). A number of papers use indirect econometric tests of the excess capacity

6 Kirman and Masson (1986) argue that excess capacity may prove more effective in a looseoligopoly and we have tested for this by allowing the concentration variable to enter non-linearly:the data fail to discriminate between functional forms, possibly because our sample is composedprimarily of firms in tight oligopolies.


hypothesis. Haskel and Martin (1994) using industry panel data find a positive in-fluence of capacity constraints on profit margins when the former is interacted withconcentration. They argue that this is inconsistent with entry-deterrence, though itwould be interesting to see if this result is robust across broad industry groupsbecause theory does not suggest that concentration has a one-to-one relationshipwith excess capacity, even for a given level of concentration.7

A possible criticism of the Haskel and Martin approach is that the incidenceof capacity constraint is not related to the planned capacity utilisation decision offirms and thus influenced, as in the Newsboy model by unit profitability.

The econometric evidence on capacity utilisation has tended to focus on the useof excess capacity as a competitive weapon, rather than modelling capacity choicein relation to stochastic demand as in the precautionary holding model. Becauseof the likelihood of omitted variable bias, any conclusions must be tentative butthe evidence is largely unsupportive of the idea that excess capacity is generallyplanned to deter entry.8 This is in line with direct evidence; a study of U.S. chemicalcompanies identified only two or three out of 38 products with strategic excesscapacity (Lieberman, 1987). Survey evidence from the U.S. and the U.K. suggestthat only a minority of managers place emphasis on excess capacity as a com-petitive response to entry or mobility (Smiley, 1988; Singh et al., 1991a, 1991b,1992). However, the U.K. sample was biased towards small and medium sizedfirms. When the sample is split into firms who regard themselves as strategists(roughly 1/3) and non-strategists, or between major players (market share>25%)and minor players, the use of excess capacity as a competitive weapon is a highlysignificant discriminator with strategists and major players far more likely to ratethis action highly. We now proceed to specify an estimating equation which testsfor both the precautionary and the strategic motives for excess capacity.

IV. Data, Specification and Estimation

The main data source used in this paper is the PIMS data base of large firms.PIMS (Profit Impact on Marketing Strategy) was established in 1972 at HarvardUniversity and achieved a reporting base of 3000 business units representing 450companies. The PIMS programme is described in Buzzell and Gale (1987). ThePIMS database is maintained by the Strategic Planning Institute (SPI). The dataitself is prepared by managers of each business unit under detailed guidance fromSPI. Firms subscribe to PIMS as a way of benchmarking performance in differentbusinesses: a digest of the results in ratio form is returned to firms to allow them to

7 Masson and Shaanan (1986) using a much smaller sample failed to observe a relationship be-tween profits and excess capacity, possibly because of their different specification. These authors findthat excess capacity reduces entry, but as the excess capacity equation is so poorly determined, theysuggest that the finding may not reflect strategic capacity choice.

8 Reynolds (1986) is an exception – he claims support for entry deterrence in the Aluminiumindustry. A further exception is the finding that expected capacity utilisation has tended to deterentry into the U.S. Titanium metal industry (Mathis and Koscianski, 1997).

76 CIARAN DRIVER

compare indicators such as R&D intensity, capacity utilisation, or profitability. Thebusiness units correspond to narrow market segments at least as fine as four-digitSIC. The full database contains about 500 variables and is collected in five-yearblocks: it has been arranged in time-series form (the SPI-Yearly database) for asubset of the variables for the period 1970–1984, during which period the vastmajority of the firms were based in the U.S.A. The data is disguised by beingscaled using a constant term specific to each business unit. The data has all thevirtues and shortcomings of any survey-based sample. In particular the data arelikely to be consistent since they are collected from the same source. On the otherhand, they are only as reliable as the reporting managers choose to be.

The theoretical discussion in Section 2 is summarised in a matrix showing thehypothesised direction of influence on capacity utilisation (and inversely on excesscapacity).

INFLUENCES ON CAPACITY UTILISATION

VARD SIZE HCON CYCLE FIRM

BUFFER CAPACITY − + +/−DETERRENCE − +/−SHOCK/RESPONSE − + +/−where VARD is the variance of demand about trend. SIZE is proxied by the ownfirm share of its perceived served market, SHARE, which represents the relativesize of the firm in terms of its customer base.9 This variable is expected to capturethe desirability of precautionary excess capacity. The PIMS database contains onlyscaled variables in order to disguise the identity of the reporting firms and thus theonly available measure of size is a relative one. This is less of a problem if theanalysis is carried out for individual industries.

HCON is the Herfindahl index of concentration in the served market perceivedby the firm itself. This is based on information on the firm’s own share and the threeleading rivals. Together these companies account for about 80% of served marketsales in most industries with the remainder always supplied by companies with lessthan a 10% share.10 CYCLE is capacity utilisation in U.S. manufacturing – 95% ofPIMS units have their main markets in North America. FIRM is the firm-specificterm for the PIMS business units described later.

The first row of the matrix shows influences on utilisation arising from pre-cautionary capacity. Demand variability (VARD) negatively influences utilisationvia a higher requisite buffer; the size (SHARE) variable expresses the ability of

9 The PIMS form states: “Share of market” is defined as the sales billed by a business as apercentage of the total sales to your “served market”. “Served market” is defined as “total salesof the market actively served by your business”

10 The Herfindahl measure is used because it also captures the coefficient of variation of the shares.When this is high it is likely to reflect non-blockaded entry given that there are almost always threeor four major firms in the industry.


larger firms to operate with proportionately smaller buffer capacity. The secondrow expresses the hypothesis that concentrated firms may carry excess capacity todeter entry or mobility. The final row represents cyclical changes and adjustment tonormal utilisation levels. These shocks and reactions depend on demand variance(VARD) and aggregate utilisation (CYCLE).

The above consideration imply a specification of the form:

CUit = a0+ a1(L)CYCLEt+ a2HCONit+ a3SHAREit(+) (−) (+)

+ b(L)CUit + vt + wi + uit . (1)

CU is the firm’s own evaluation of capacity utilisation in the relevant line of busi-ness.11 L is a lag operator;vt andwi represent time specific, firm specific effects;anduit is expected to be white noise. Time dummies may be included to modifythe CYCLE term as an alternative to lagged values. VARD has been subsumed inthe firm-specific effects as it does not have a time dimension given the short paneldata.

The regressors in (1) may be correlated with the firm-specific error terms in-dicating that either within-group or first difference estimation is required (Hsiao,1986). The lagged dependent variable in (1) was generally insignificant for within-group estimation, though the coefficient here suffers from downward bias (Nickell,1981). Omission of a lagged dependent variable if appropriate would removeone source of bias in within-group estimation. However, as the regressors HCONand SHARE are unlikely to be exogenous, estimation in first-difference form isnecessary (Blundell et al., 1987). Denoting a first difference by D, we obtain:

DCUit = b0 + b1 (L)DCYCLEt + b2 DHCONit + b3 DSHAREit(+) (−) (+)

+ eit . . . (2)

The lagged dependent variable is omitted because it is never significant in thisspecification. The error term is now expected to show first order negative auto-correlation, given the differenced form. Time dummies are included where jointlysignificant at the 25% level. The SHARE variable is instrumented by a laggedvalue given that it may reflect the same shocks that raise capacity utilisation. Con-centration is also instrumented by a lagged value – it may depend on the cycle orprofitability (Cowling, 1980; Davies, 1988; Schmalensee, 1989).Other instruments

11 The exact question on the PIMS form is: “What percentage of the standard (rated) capacitywas used, on average, during each year. Include production for inventory. If your business sharesoperating facilities with other businesses, indicate the overall capacity utilisation”. The PIMS forminstructs companies to include only facilities normally in operation and with current constraints(technology, workrules, and labor practices).

78C

IAR

AN

DR

IVE

R

Table I. Industry mean descriptive statistics

PLAST ORCHEM ALEXTR ALSHEE MINPRO HTOOLS COMAC METAC GENMAC DVICES ELINAP PRFOOD

cu 0.8 0.77 0.68 0.76 0.77 0.66 0.75 0.77 0.79 0.67 0.68 0.77

share 23.53 34.09 21.35 26.92 30.89 33.71 26.92 20.57 32.17 33.5 22.11 27.85

sha 27.41 25.73 26.49 38.18 26.28 27.25 31.49 23.10 19.56 24.69 28.06 32.16

shb 15.97 13.91 13.54 17.23 13.97 12.17 13.28 14.94 12.23 12.71 15.28 14.06

shc 9.69 8.8 9.04 8.95 8.38 7.36 8.07 9.74 8.24 8.47 9.43 8.16

conc 76.6 82.53 70.42 91.28 79.52 80.49 79.76 68.35 72.2 79.37 74.88 82.23

dprod 0.28 0.43 – 0.15 0.36 0.03 0.09 0.26 – 0.42 0.28 0.42

dproc 0.37 0.64 – 0.10 0.37 0.07 – 0.08 0.05 0.40 0.37 0.31

search 0.25 0.62 – – 0.20 0.39 0.87 0.44 0.98 0.93 0.85 –

life 2.50 2.71 2.97 2.79 2.89 3.07 2.71 2.79 2.99 2.68 2.82 3.0

entry 0.3 0.36 0.72 0.20 0.18 0.3 0.16 0.09 0.31 0.25 0.13 0.33

exit 0.19 0.3 0.51 0.07 0.21 0.1 0.08 0.18 0.05 0.16 0.16 0.06

rvif 1.95 1.85 2.08 2.22 2.14 1.93 1.84 2.11 2.06 2.10 1.99 2.03

rvib 2.08 1.9 2.52 2.00 2.21 1.96 1.84 2.09 2.03 1.96 1.65 2.19

cu: own capacity utilisation; share: own share; sha, shb, shc: shares of main competitors; conc: sum of own and top three shares (%); dprod, dproc: significantpatent protection for product process (0/1 dummies); search: proxy for product differentiation – see text; life: stage of lifecycle (dummy 1–4); entry (exit):major entry (or exit) to or from served market in previous 5 years (0/1 dummy); rvif (rvib): relative vertical integration forward (backward) (1–3 dummy).

CA

PA

CIT

YU

TIL

ISA

TIO

NA

ND

EX

CE

SS

CA

PA

CIT

Y79

Industry mnemonics for Table I. List of 3-digit SIC industries with PIMS observations>180

Mnemonic SIC code No. of original observations Short title

PLAST 282 286 Plastics materials and synthetics

ORCHEM 286 215 Industrial organic chemicals

ALEXTR 3354 205 Aluminium extruded products

ALSHEE 3353 250 Aluminium sheet, plate and foil

MINPRO 329 (1 + 2) 185 Abrasives; asbestos products

HTOOLS 342 210 Cutlery, handtools and hardware

CONMAC 353 193 Construction and related machinery

METMAC 354 189 Metalworking machinery

GENMAC 356 262 General industrial machinery

DVICES 382 201 Measuring and controlling devices

ELINAP 362 251 Electrical industrial apparatus

PRFOOD 203 193 Preserved fruit and vegetables

80 CIARAN DRIVER

include DCYCLE and DCYCLE(-1) as well as a set of structural variables in levelsform, viz the last eight variables listed in Table 1 and described below.12 The latterare included because these structural variables, representing technology or marketstructure, may be expected to enter a full model that includes the endogenousvariables.

The panel estimation was carried out separately for different SIC industrygroups because the cyclical and production flexibility terms may differ by tech-nology and market and because market share is a better indicator of size ofcustomer base within a narrowly defined industry. Industries will differ also inscale economies which reduce the degree of excess capacity that is necessary todeter entry (Lyons, 1986). Based on previous work we do not expect to find the de-terrence effect in all industries. In particular we expect to find a role for deterrenceonly where there is (a) a high risk of entry and (b) little scope for cheaper forms ofentry barriers. We proxy the risk of entry by mean output growth over firms in eachindustry (Smith, 1981). Preference for other forms of entry barriers will be inferredfrom the degree of product differentiation. Further discussion is postponed to theresults section.

The PIMS business units typically have market shares greater than twenty per-cent and operate in tight oligopolies. For convenience we refer to these businessunits as firms. Most three-digit industries in the PIMS database did not have suffi-cient data to generate reasonable degrees of freedom for our estimation. However,by confining ourselves to the SIC groups with more than 180 initial observationswe obtained a set of twelve industries where the final sample – after differencingand discarding missing data records – ranged from 67 to 103 observations with amean of about 90.13 The sample of industries used here is the same as that usedin the fixed investment study reported in Driver et al. (1996). Descriptive data onthese industries is given in Table I. Data is available in time blocks of varyinglength over the interval 1970–84. The data are thus panel data but unbalanced withunequal numbers of observations for different time periods.

The table is largely self explanatory with key variables described in the ta-ble footnote. The first five variables refer to utilisation, shares and concentration.The next eight variables are structural variables specific to the business unit orline of business. Technology variables (prod and proc) are (0/1) dummies indi-cating whether managers perceive patent protection as significant for products orprocesses. The product characteristic variable (search) is binary with 1 = high-search and is constructed using the dichotomy between high search and low searchproduct groups as defined by PIMS end-user orientation; it is a proxy for product

12 The number of structural instruments varies from case to case because in some cases there is novariation in the data e.g. all the firms in an industry may be “mature” with “life” = 3.

13 We could have extended the sample to include industries with lower numbers of initial obser-vations – but the few industries with between 150 and 180 initial observations all had fewer than 25firms in the sample and IV estimation reported on later is only consistent when the number of firmsis large. The median number of firms in our sample of industries was 30.


differentiation as discussed in Hagerty et al. (1988). The Life-cycle variable (life) isa (1–4) dummy, defined as the categories new; growth; maturity; and decline. Mostindustries are in the mature stage of their life cycle as may be seen by mean scoresclose to 3. Major entry (entry) is defined as a new competitor having now attaineda market share of at least 5%; entry via acquisition is excluded from this. . . . Majorexit (exit) is defined analogously. Considering that most industry groups containtight oligopolies, major entry and exit are surprisingly common. Mean capacityutilisation ranged from 67% to 80%.

V. Results

The results of estimating Equation (2) for first differenced capacity utilisation(DCU) are given in Table II. The estimation was carried out in IV form using theDynamic Panel Data programme developed by Arellano and Bond (1988, 1991).

The diagnostic tests are encouraging across the twelve industries. The Waldtests confirm joint significance of the variables. The Sargan tests for the validityof the instrument set are satisfactory in all cases. The m1 statistic testing for firstorder autocorrelation is always negative as expected, given the differenced form,while the m2 statistic suggests second order autocorrelation only in the case ofMINPRO.14 The constant terms are generally insignificant indicating the lack ofa trend in the utilisation level. Exceptions are the positive trend for ELINAP andnegative trends for DVICES and PRFOOD.

The CYCLE variables are signed as expected and show strong significanceeither for the contemporaneous term which is always entered or the lagged termwhich is included where it is significant at the 5% level (construction machinery;measuring devices; and food). The magnitude of the coefficients – which are some-what similar across industries – imply that the company’s cycle is nearly alwaysmore variable - up to a multiple of 2.5 – than that of the manufacturing sector.15

Of the twelve industries, three – plastics; aluminium extrusion and mineralproducts show strong significance for the HCON variable indicating a strategic rolefor excess capacity.16 Two others – hand tools and cutlery, i.e., fabricated metals;and construction machinery show a significant effect for the SHARE variable witha weak indication of support for the inclusion of HCON. None of the other indus-tries are of interest in respect of these variables. This might be though surprisingin view of the argument in Section 3 that SHARE represents the ease with whichcompanies can reallocate capital across sub-markets. It may be that the engineering

14 In that case re-specification with a lagged dependent variable was unsuccessful. The m1 statisticis based on equation (8) of Arrelano and Bond (1991) and the m2 statistic is based on equation 10 ofthe same paper.

15 The DCU variable in Table II is in percentage form while the DCYCLE variable is 100 timesthe percentage.

16 Note that the correlation between HCON and SHARE for these industries is always less than0.7.

82C

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Table II. First difference IV, dependent variable DCU

PLAST ORCHEM ALEXTR ALSHEE MINPRO HTOOLS CONMAC METMAC GENMAC DVICES ELINAP PRFOOD

CONST −0.003 −0.035 0.045 0.019 0.016 0.002 −0.018 0.001 −0.016 −0.06 0.038 −0.02

(0.11) (1.40) (1.45) (0.51) (0.33) (0.65) (1.55) (0.02) (0.94) (2.42) (1.90) (1.86)

DCYCLE 0.015 0.025 0.022 0.016 0.015 0.005 0.007 0.023 0.008 0.008 0.014 0.004

(2.11) (3.00) (5.69) (3.62) (1.48) (3.25) (2.58) (2.86) (1.99) (1.46) (2.48) (1.41)

DCYCLE – – – – – – 0.009 – – 0.011 – 0.005

(−1) . . . (3.37) . (2.09) .(2 67).

DHCON −0.031 −0.001 −0.012 0.003 −0.019 −0.007 −0.017 0.004 0.014 0.002 0.013 0.005

(2.14) (0.68) (2.50) (0.47) (2.35) (1.28) (1.80) (0.28) (1.68) (0.36) (1.17) 0.72

DSHARE 0.015 0.005 0.009 −0.004 0.013 0.015 0.021 0.009 −0.001 −0.003 0.014 0.008

(1.39) (0.36) (2.56) (0.58) (1.76) (3.50) (2.71) (0.70) (0.20) (0.55) (1.02) (0.86)

N 103 98 79 82 98 75 75 94 110 77 101 67

WALD 13.1 (3) 11.2 (3) 38.0 (3) 16.5 (3) 13.7 (3) 46.0 (3) 24.0 (4) 10.3 (3) 9.48 (3) 8.1 (4) 13.8 (3) 11.8 (4)

M1 −1.81 −1.65 −1.26 −0.46 −0.94 −3.46 −1.91 −0.88 −1.22 −1.11 −1.47 −0.79

M2 −0.49 0.23 −0.92 −0.96 −2.61 −0.24 1.01 −1.89 −1.57 −0.72 −0.73 0.87

SARGAN 8.2 (8) 15.5 (8) 10.5 (5) 8.4 (6) 7.67 (6) 15.7 (6) 16.5 (13) 10.0 (7) 2.8 (8) 8.6 (7) 11.7 (8) 15.2 (9)

The prefix D indicates a difference;t-statistics in parentheses; CYCLE = U.S. manufacturing capacity utilisation (Conference Board data); HCON =Herfindahl index based on top 4 shares; SHARE = own share; Industry mnemonics are given in Table I. Instruments used are DCYCLE; DCYCLE(−1); DSHARE (−1); DHCON (−1) and the following variables are listed in Table I; prod, proc, search, life, entry, exit, rvif, rvib.Diagnostics. The Wald Test reported is for overall significance and is asymptotically distributed X2 with the d.f. provided. The M1 and M2 variablestest for first order and second order autocorrelation and are asymptotically distributed as standard normal variables. First differencing will induce anMA(1) serial correlation where the levels error term is white noise so M1 is expected to be negative in that case. An acceptable M2 statistic couldindicate either white noise or random walk error in the levels equations (see Arellano and Bond, 1988). The Sargan test of over−identifying restrictionsis distributed asymptotically asχ2.


and food industries have more production flexibility independently of their marketshare. Typically their plant and equipment may be multi-purpose even for smaller-share companies.17 Returning to the results for HCON, it was suggested in Section4 that this variable should be significant only (a) where output growth is high –otherwise the risk of entry is low or (b) where differentiation is low – otherwisethere are cheaper forms of entry barriers.

By and large these observations are borne out in the results. Mean output growthis above 5% in five industries, including three in which HCON was significant:METAL; PLAST; ORCHEM; ALEXTR and MINPRO: no other industry had meangrowth in excess of 3.5%. Product differentiation, reflected in the search variable(Table I, row 9) tends to be high for the engineering, while the least differentiatedindustries are ALEXTR; ALSHEET; PLAST; MINPRO and FOOD. The three in-dustries showing a significant HCON effect form the intersection set of the highgrowth group and the low differentiation group.18

For completeness, further estimation tests were carried out for the three indus-try groups PLAST, ALEXTR, and MINPRO. First, the time-dummies included inTable II are removed as there may be some concern that they are multicollinearwith the CYCLE term. It was found that the coefficients remain broadly stableand remain significant. Second, a cyclical variable is entered specific to the firm’sserved market (GSMit). This is formed by differencing the log of served marketoutput where the latter is estimated as the quotient of the firm’s own output andSHARE. This variable is not generally significant at the 5% level. Third, allowinglags on HCON and/or SHARE did not generally improve the results.

17 Bresnahan and Ramey (1993) report that auto plants were converted to produce different sizecars size within a period of months on three occasions in the 1970s. It is also possible that industriessuch as electricals aim to meet demand surges by sub-contracting rather than by carrying excesscapacity (Singh Utton and Waterson, 1991a). Backlogging of orders may also be more acceptable inthese industries.

18 Some further observations may explain the contrasts between the behaviour of similar industrialgroups – ORCHEM and PLAST and ALEXTRUD and ALSHEET. ORCHEM contains an unusualdispersion of high and low growth sectors. When HCON is interacted with a dummy for sectors inthe highest growth quartile the results are similar to PLAST with a significant negative effect. Onepossibility in regard to ALSHEET is that the result here may be due to the unusually subordinateposition of the firms in the PIMS sample – on average the firms sampled in this industry are onlythree-quarters the size of their largest rivals (Table I, rows 2 and 3). These smaller-share firmsmay rely on the dominant firms – excluded from our sample – to carry the burden of preventingentry – entry deterrence can be seen as a form of public good (Lieberman, 1987). To test for this adummy was created for firms with rival shares no larger than twenty percent bigger than own share– roughly half the firms. When the concentration variable for Aluminium sheeting is interacted withthis dummy the results are more in line with the extrusion industry with significance just outside 5%for a negative concentration effect. An alternative explanation is that the oligopoly is unusually tight(average four firm concentration ratio>90%) and growth is only moderate so that there is little fearof price agreements unravelling.

84 CIARAN DRIVER

VI. Evaluation and Concluding comments

Our results suggest thatprecautionarycapacity is least for high-share firms – thiseffect is significant in four or five of the twelve industries studied.Entry-retardingexcess capacityseems to characterise only three industries, those characterised byhigh growth and low differentiation.

The estimated effects for precautionary capacity might be thought to reflectthe turbulent period to which the data applies (1970–84). This encompasses twomajor oil shocks when uncertainty and volatility was unusually high. Utilisationwill be positively related to the cycle and negatively (via required buffer stock) onthe variance of the shocks as argued in Section 4. However, In this study we havelargely exploited thecross-sectionvariation in the sample, using first-differencedestimation and conditioning on the cycle and time dummies. Thus, the resultsshould not be unduly influenced by the sample period.

The industry pattern of our results is relevant to the policy stances current in theliterature. One position argues that growth (allied to free import conditions) givesrise to sufficient competitive pressures; concentration may thus be beneficial, e.g.,in engineering where new technology adoption may depend on scale (Jacquemin,1990). An opposite view is that concentration in declining industries with weakmargins is inevitable; the real danger is lack of competition in innovation intensiveindustries (Aiginger and Leo, 1992).

Our results suggest that the particular industries that might benefit from concen-tration, e.g., engineering industries do not appear to use excess capacity as a barrierto entry, perhaps preferring to rely on product differentiation, which is superiorfrom a welfare perspective.

The magnitude of the HCON and SHARE effects on capacity utilisation can beworked out using the mean values of Table I and the coefficients in Table II. Thepercentage point changes in utilisation arising from a 10% reduction in HCON andSHARE are given below for the three significant industries.

PLAST ALEXTR MINPRO

HCON +5.1% +1.7% +3.6%SHARE −3.5% −1.9% −4.0%

It is clear that a reduction in concentration could have a substantial effect onutilisation in these industries since the SHARE effect will largely be netted outin the aggregate. It is for the reader to judge how important this is for policy: thebottle is either quarter full or three-quarters empty. In any event the results pinpointthe type of sector where case-studies of excess capacity should be directed, i.e.,undifferentiated, fast-growing oligopolies.

A quite separate policy question raised at the outset of this paper arises in re-spect of capital stock growth. While this has been reasonably robust in the US inthe last decade, fixed investment has continued to disappoint in Europe, particularlyin manufacturing for a number of complex reasons. Attempts to stimulate capital


investment are justified if it can be shown that there is a divergence between privateand social rates of return. However, there is concern that the encouragement offixed capital may be counterproductive in leading to stocks of anti-competitiveexcess capacity. This study has shown that concern over excess capacity is notvalid for most of the engineering sector where concern over levels of investmentis probably greatest. In general, a pro-investment stance seems unlikely to resultin large-scale rent-seeking through excess capacity, though the danger of that doesexist in some specific sectors.

Perhaps the most interesting point to emerge from the study is that it is unwiseto estimate capacity utilisation equations without accounting for precautionary,planned, excess capacity. Precautionary excess capacity is observed in more in-dustries than is strategic excess capacity. This provides valuable background topolicy discussion in respect of incentives for increased capacity.

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Capacity Utilisation and Excess Capacity: Theory, Evidence, and Policy