Efficiency, R&D and ownership – some empirical evidence

Int. J. Production Economics 83 (2003) 85–94

Efficiency, R&D and ownership – some empirical evidence

Mogens Dilling-Hansena,c, Erik Str�jer Madsenb,c, Valdemar Smithb,c,*aSchool of Economics and Management, University of Aarhus, Aarhus, Denmark

bDepartment of Economics, Aarhus School of Business, Fuglesangs Alle 20, 8210 Aarhus V, DenmarkcCentre for Industrial Economics, University of Copenhagen, Copenhagen, Denmark

Received 1 October 2001; accepted 25 June 2002

Abstract

Investments in R&D create an immaterial capital asset, which is expected to have a positive influence on firms’

efficiency and consequently on their profit. Nonetheless, the short-term effects of current investments in R&D are

difficult to prove. Therefore, this paper focuses on analyses based on the technical efficiency methodology using a

stochastic frontier approach in order to investigate the influence of R&D activities on the technical efficiency of the

firms. Using a sample of 2370 Danish firms, the analysis suggests that R&D-active firms are significantly more efficient

than other firms. The legal form of ownership also affects the efficiency significantly, whereas firms owned by large

shareholders – who are expected to exert a disciplinary influence on the managers of the firm – do not seem to be more

efficient than other firms.

r 2002 Elsevier Science B.V. All rights reserved.

Keywords: Efficiency; R&D; Block owners; Productivity

1. Introduction

The earnings capacity of a particular firm is asine qua non for its continued presence in themarket place. Thus, among several prerequisites ofa firm’s superior earnings capacity is a highproductivity level or when compared to otherfirms a high efficiency level. Several studies haveverified that both external factors (competition,market structure, etc.) and internal factors (man-agement, financial resources, ownership, innova-

tion, etc.) affect productivity (see, e.g. Nickell et al.,1997; Dilling-Hansen et al., 1998).In line with the studies of firm productivity,

there is a wide-ranging interest in determiningwhich factors enhance a firm’s efficiency level. Theaim of this paper is to analyse whether relativeefficiency in some companies is due to a strategyinvolving R&D investment. Furthermore theinfluence from ownership concentration is ana-lysed. First, companies that emphasize R&D as atool of enhancing their competitiveness becomepresumably relatively efficient unless the researchis centred exclusively around product developmentor basic research, in which case there will only bean influence on efficiency in the long run. Next, thegrowing literature within corporate governanceemphasizes that managers and owners can have

*Corresponding author. Department of Economics, Aarhus

School of Business, Prismet, Silkeborgvej 2, DK-8000 Aarhus

C, Denmark. Tel.: +45-89486670; fax: +45-89486197.

E-mail address: [email protected] (V. Smith).

0925-5273/02/$ - see front matter r 2002 Elsevier Science B.V. All rights reserved.

PII: S 0 9 2 5 - 5 2 7 3 ( 0 2 ) 0 0 3 0 2 - X

different objectives for the company and thereforethe company’s productivity and/or efficiencymight be affected.The terms productivity and efficiency are

frequently used interchangeably, which is some-what simplistic. Both concepts relate to the firm’sability to transform input to output. A highlyproductive firm with comparatively low produc-tion costs will frequently do better than othercompanies within the same line of business, and itis precisely its relative position within its line ofbusiness that forms the basis of determining afirm’s efficiency. Thus, efficiency will always beclosely related to any definition of productivity.Farrell (1957) gave a definition of total economic

efficiency, which he decomposed into two com-ponents, technical and allocative efficiency. Therelation between these concepts and the produc-tion function is illustrated in Fig. 1, which shows asimple relation between one output, y, and oneinput, x. Any combination of input and output onthe bold line, the production function, will showthe maximum output for any given input – or interms of input, the minimum consumption of inputfactors for any given output. All companies thatmove on the production function or – in otherwords – on the production frontier, are said to betechnically efficient: The companies that are at thepoints C, A and D are technically efficient becausenone of them is able to produce more at the input

levels xj ; xi and xk: The firm that produces at thepoint B, on the other hand, is inefficient becausethe point A can be reached using the same input,xi; or the same output can be produced by lessinput xj : While all the points on the marginrepresent technically efficient companies, it is onlythe companies that simultaneously minimize theircosts that can be said to exhibit allocative

efficiency. To illustrate this point we assume thatthe production function has two inputs; techni-cally efficient production means the above men-tioned output maximisation on the productionfrontier for any combination of the two inputs, butonly the point on the frontier where costs areminimised are at the same time allocativelyefficient. Cost minimising production will on theother hand be any combination of the two inputs,where the marginal costs of the two factors areequal.In the simple case with one output and one

input, the relation between productivity andtechnical efficiency is illustrated by the movementfrom point C over A to D.1 According to Fig. 1,productivity can be measured by the slope of theline from the origo through the point, and it will beseen that despite efficient production, productivitywill keep rising until the point A, then it will fall tothe C level in point D.The rest of the paper is disposed as follows. The

next section includes a discussion of factorsaffecting firm productivity. This leads to formula-tion of an empirical model, which is presented inSection 3. A description of the data that have beenused is found in Section 4, and Section 5 shows theestimation results. Section 6 concludes.

2. Which factors affect efficiency?

The relation between total production andresource input is normally analysed by use ofmodels based on a total factor productivityapproach (see, e.g. Nickell, 1996). This methodol-ogy specifies a parametric relation between outputand the production factors used. In the case of

Input, X

Out

put,

Y

yi

yj

xj x i

A

C B

0

D

xk

Fig. 1. Technical efficiency and productivity.

1Note that the allocative efficient level of production requires

information on prices.

M. Dilling-Hansen et al. / Int. J. Production Economics 83 (2003) 85–9486

a Cobb–Douglas specification with k inputs thefollowing log transformed version is found:2

yi ¼Xk

j¼1

bjxij þ ei; ð1Þ

where yi is the log of the output and xij is the log ofinput j but also contains other relevant factors

affecting y: This parametric relation can beestimated by means of simple OLS, with estimatedresiduals distributed around the regression line.If some companies are not technically efficient,i.e. are operating below the production frontierin Fig. 1, the OLS will yield biased estimatesof the coefficients. These problems are discussedin Section 3, which presents the empiricalmodel.Besides the factor input of labour and capital

the x-matrix contains explanatory variables ofownership dispersion, financial pressure, the com-petitive situation and R&D activities in the firm.These factors are important for the efficiency offirms and below we discuss their effects.The dispersion of ownership in large corpora-

tions and the effects on their efficiency have beenexamined in a lot of studies. For a perennialdiscussion of the influence (see, e.g. Short, 1994)and for a survey of the empirical results, seeGugler (2001). This discussion is based on theassumption that owners and managers of largecorporations have different objectives for thecompany. The owners want a high long-termreturn of their investment, whereas the managerswant a high growth rate and large fringe benefitsto the leaders. In large and widely hold corpora-tions the individual owner has no incentive tocontrol the managers of the company as only asmall fraction of the benefit from his controllingeffort will belong to himself. This separation ofownership and control of the firm has a demitrialeffect on firm efficiency as the managers canpursue their own goals without any control fromthe owners. The general hypothesis drawn fromthese corporate theories is that increased owner-ship control has a positive effect on firm efficiency.

Typically, information about type (Limitedliability companies, LTDs, vs. personally ownedcompanies) and spread of ownership has been usedto describe this influence. On the one hand, alimited number of owners are expected to exertmore direct control on the firm (and thus increaseproductivity). However, one big (inside) ownermay favour himself and capture wealth form(outside) small owners, see Morck et al. (1988).Therefore, our analysis introduces an alternativemeasurement of ownership control, i.e. the con-cept of ‘‘block ownership’’. This term means that aparticular shareholder is in possession of a majoramount (>5%) of shares and consequently thestockholder is assumed to have influence on andknowledge about the firm’s strategy. The existenceof ‘‘block owners’’ – especially if they are externalowners – is therefore expected to exert a disciplin-ary influence on the day-to-day running of thefirm, and consequently a growing number of blockowners – up to a certain limit – is expected to havea positive influence on productivity. However, ifthe number of ‘‘block owners’’ becomes large,owner control decreases and inefficiency increases,see above. Consequently, a nonlinear relationshipis expected and in the empirical part we use aU-shaped specification.The financial situation or structure of the

company is also important for its efficiency. Firmswith a high debt ratio, i.e. lower solvency (equitycapital in relation to total assets) have a higher riskof bankruptcy. As the management team often willlose their jobs if the firm goes bankrupt, they willput more effort into the management of the firmand they will have to pursue a policy to increasethe profitability of the firm in order to reduce therisk of bankruptcy. In other words, the idea is thathigher debts will have a disciplinary effect causingnon- or weakly profitable activities to be cut away.Investing in projects that do not contributeimmediately to the firm’s earnings is left out eventhough the management might wish to go on withthe investment for other reasons.Another argument for this relation is the free

cash flow theory of Jensen (1986). Normally, theinterest to be paid on loan capital is higher thanthe alternative interest which the firm managementwill impute on the firm’s own available funds.

2The coefficients of the input, the vector of b’s, can beinterpreted as output elasticities and are constant irrespective of

input level.

M. Dilling-Hansen et al. / Int. J. Production Economics 83 (2003) 85–94 87

Therefore, projects which are financed exclusivelyby internal funds from the free cash flow tend to benot quite as profitable as projects financed by loancapital which has to meet the rate of interest.Because low-solvency companies are compelled togo to the money market, these companies willengage in projects that are presumably slightlymore profitable on average and hence moreproductive than projects usually undertaken bysolvent companies. Accordingly, the liquidityconstraint has an effect on the firm’s efficiency.The competitive situations of the firm may also

have a disciplinary effect on the management ofthe firm and its performance. If a firm has amonopolistic position in the market, it enjoys aprotected environment which may lead to ineffi-ciency, cf. the x-inefficiency theory, see Leibenstein(1966). Thus a number of researchers argue thatincreased competition in a market will increasefirm productivity (see, e.g. Nickell, 1995) and thisargument is supported by the fact that companiesnot coming up against competition will tend to payless attention to traditional financial goals such asproductivity. A positive relation is expectedbetween competition and firm efficiency, i.e. thehighest degree of efficiency is expected in low-concentrated industries.3

However, market concentration is not a perfectmeasure of the competitive environment in theindustries (see Hay and Liu, 1997). To control forthe internationalization of market competitions,the export shares of the firms are included in ouranalyses below. We expect a higher efficiency inexporting firms due to the international competi-tiveness. Further, conflicting arguments are in-voked by the Schumpeter hypothesis. Schumpeter(1942) argues that declining competition due togrowing monopolization provides large companieswith profits which allow them to innovate and thusincrease their efficiency.Finally, a firm’s innovative activities and more

specifically its investments in research and devel-opment, R&D, can be expected to have a positiveeffect on their efficiency. A study based on Danishdata of the relation between productivity and

R&D is found in Smith et al. (2000). In thisanalysis, the influence of firm investments in R&Dis based on a capital approach in which total netinvestments in R&D have been accumulated overtime into a R&D capital stock. Based oninformation of about 200 manufacturing compa-nies, all of them having made investments in R&D,the paper finds a stable positively significant returnon R&D capital. Measured by the output elasticityof the R&D capital, the effect is in the range of9–12%, which is in agreement with similar interna-tional studies, see Griliches (1998) for a survey.The present study uses a slightly different

measurement of the R&D concept as the compa-nies are grouped into R&D-active companies andcompanies without investments in R&D. Of thesetwo groups, the R&D-active companies areexpected to be the most efficient, i.e. all otherthings equal these companies create more valueadded than other companies.4

3. Firm efficiency models

The preceding section presented key theoreticalarguments in explaining differences in firm pro-ductivity. Turning to the concept of firm efficiency,it becomes clear that a number of specific factorsdetermine a firm’s ability to exploit the mostmodern and most productive production facilities(adoption of new technology, implementation ofinnovative activities, etc.); but apart from thesespecific factors, it seems clear that the same factorsthat influence productivity will also have an effecton efficiency.Model (1) can be estimated immediately using

simple OLS, and as mentioned before this estima-tion method assumes all firms to be technicallyefficient, i.e. being on the production frontier inFig. 1, cf. Coelli et al. (1998). The aim of efficiencymodels is by contrast to identify technicallyinefficient companies, and therefore a stochastic

frontier model is formulated as an alternative to theOLS approach. Stochastic frontier models, whichwere proposed almost simultaneously by Aigner

3Like in other studies, the concept ‘‘competition’’ is

measured by market concentration in the respective industries.

4 In principle, the added value may be due to both product

and process-specific investments in R&D.


et al. (1977) and Meeusen and van den Broeck(1977), use the same functional relation as in (1),with one important difference in the specificationof the residual, ei;

ei ¼ vi � ui; ð2Þ

where viBNð0;s2vÞ and ui is a non-negativecomponent, typically assuming a truncated normalor half normal distribution. The non-negativecomponent ui is introduced by Meeusen and vanden Broeck (1977) as a measure of inefficiency, andeven though the interpretation of the two compo-nents in (2) is being widely debated (see Aigneret al., 1977; F�rsund et al., 1980; Coelli et al.,1998) the subsequent analyses have been based onthe traditional ‘‘Farrell interpretation’’ of theresidual: The two components in (2) are indepen-dent of each other, and the first component isinterpreted as normal ‘‘white noise’’ (accidents,external factors such as luck, weather conditions,special equipment, etc.) while the other componentis a non-negative expression of technical ineffi-ciency in the firm. These models are estimated byML-based methods, and several test statistics forpresence of inefficiency are available. In this study,the relative size of inefficiency and ‘‘white noise’’ istested by the following expression:

l ¼su

sv

: ð3Þ

l is non-negative and is estimated and tested in thesame way as the other estimated coefficients in thelikelihood function. In the estimated model, apositively significant l means that deviation fromthe regression line is beyond the ‘‘white noise’’.A significant l is therefore an indicator of thestochastic frontier approach being superior tosimple OLS-approach.If the stochastic frontier model is significant, the

next step is to identify the determinants of theinefficiencies. The inefficiency component ui foreach firm in (2) is transformed to technicalinefficiency by the simple transformationexpð�uiÞ; and technical inefficiency is positiveand less than one (efficient firms having the valueone). However, as the inefficiency effect, ui; isunobservable, it has to be estimated by a ML-based procedure as described above. Typically,

two strategies for estimation of stochastic frontiermodels and explaining the inefficiency are selected.The first method uses a two-step approach, whichfirst estimates the inefficiency model in (1) and (2)and then the technical efficiency is estimated andexplained by taking the truncated form intoaccount – typically using a Tobit model (see, e.g.Hay and Liu, 1997). The other method, presentedin Battese and Coelli (1995), simultaneouslyestimates the stochastic frontier model, (1) and(2), and the explanatory model of the inefficiency:

ui ¼ zidþ wi: ð4Þ

Modelling the inefficiency effects uses a simplelinear framework and the (1�M) vector zi isa set of explanatory variables, the (M � 1)vector d is the set of parameters to be estimatedand wi is defined by the truncation of a normaldistribution.In both cases, the explanatory variables in the

production function (1) can be used in theinefficiency model (4), too, due to the twoindependent error components. This study usesthe first two-step method to explain inefficiencies.5

4. Data

Table 1 shows descriptive statistics for thevariables used in the empirical analysis of thepaper. The figures are based on data from differentsources, in general from 1997. Thus, the data havebeen collected from Statistics Denmark, Køb-

mandsstandens Oplysningsbureau (private danishdata collecting firm) and The Danish Institute for

Studies in Research and Research Policy. Data forvalue added, employment, capital, legal type offirm, exports and solvency are from the officialfirm statistics in Statistics Denmark. This alsoapplies to the construction of the market concen-tration measurement, which is a standard 4-firmconcentration index showing the share of anindustry’s value added produced by the top

5This choice is due to the simple fact that the data used in the

analyses come from Statistics Denmark. These data can only be

accessed on technical equipment that, for the time being, does

not support the special programs required to estimate the model

presented in Coelli et al. (1998).


4 companies.6 The industry has been delimited ata 4-digit level according to the NACE, Rev. 1nomenclature. Data about the number of ‘‘blockowners’’, i.e. owners holding a minimum of 5% ofthe stock in any given firm as explained above,have been processed from information collectedfrom Købmandsstandens Oplysningsbureau.7

In addition, the data set provides informationabout the companies’ research activities drawnfrom the 1995 and 1997 R&D statistics from The

Danish Institute for Studies in Research and

Research Policy covering the private sector. Table1 shows that 21% of the companies in the samplewere R&D active, which is a little below the sharereported in the official R&D statistics (30%), butthere is no reason to believe that there is any biasamong the three data sources estimated on thebasis of the R&D statistics.It is seen in Table 1 that the companies are

relatively large by Danish standards resulting in a

right-skewed distribution. The selected two-stepprocedure determines the production frontierbased on the full sample of 3677 observations,but in the subsequent efficiency analyses theeffective number of observations fall to around2370, due to lack of information on R&D, export,solvency, ownership and market concentration.

5. Results

Table 2 reports the estimated relationshipbetween firms value added and production factors,cf. Eq. (1). In order to control for industry-specificdifferences in the production function, the stan-dard Pavitt taxonomy has been applied, i.e. addingdummy variables for industries as explanatoryvariables.8 Besides its generally very high explan-atory power, R2-adjusted=0.93, the estimated

Table 1

Descriptive statistics for the firms, 1997

Variable Number of obs. Average Std. dev.

Value added-y (full sample) 3677 37,899.8 214,255.4

Value added-y 2370 49.622.3 256,970.2

Number of employess-L (full sample) 3677 85.3 354.3

Number of employess-L 2370 110.4 415.1

Capital-K (full sample) 3677 37,426.3 393,052.5

Capital-K 2370 47,385.3 429,410.7

Efficiency in percent 2370 79.95 5.934

Market concentration 2370 0.3513 0.220

Dummy for LTD companies 2370 0.6139 —

Dummy for R&D activity 2370 0.2093 —

Financial solvency 2370 0.2990 0.102

Dummy for export 2370 0.3337 —

Number of block owners (>5%) 2370 1.364 0.986

Notes: Financial solvency is defined as the share of equity capital in total assets. Market cocentration is measured by the 4-firm index,

CR4. Export intensity is defined as export in percent of total sales. A ‘‘block owner’’ is defined as an owner with more than 5% of the

assets. Only 626 out of the sample of 2303 firms have reported information on owners with more 5% of the assets.

6The concentration index used is a so-called unadjusted index,

i.e. no allowances have been made for the import penetration of

the industries under investigation.7At the experimental stage of this project, data about firm

earnings and measurements of minimum-efficient-scale, i.e. the

production level using the lowest possible average costs, were

used. In both cases, the source was K�bmandsstandens

Oplysningsbureau and no significant difference was found.

8Pavitt (1984) categorizes industries according to their

specialisation patterns, i.e. whether they are ‘Supplier domi-

nated’, Scale Intensive’, ‘Specialized Suppliers’ or ‘Science-

based’, assuming that they have different technologies, see also

Laursen (2000). The impact on the production technology of

belonging to one of these groups is included in the model as

fixed effects. As a consequence, by including Pavitt dummy

vaiables the final calculation of the efficiency variables takes

account of industry-specific differences in technology.


model also indicates that there is constant returnsto scale. A simultaneous increase in both inputs ofe.g. 1% would result in a 1% increase in valueadded. This result is in line with previous studies(see, e.g. a Danish study in Smith et al. (2000))except that using value added instead of turnoverwill increase the output elasticity for capital with acorresponding decrease in the output elasticity oflabour. The estimated firm efficiency is based onthe full sample using the information from all 3677firms. However, as noted above, the efficiencyestimation in the second part of the empiricalanalysis can only be made using information for2370 firms. According to Table 1, the average sizeof the firms in the large sample is smaller than inthe sample including only 2370 observations.There is, however, only minor changes in theestimated parameters using the smaller sample9

indicating that the sample used in the followinganalysis is representative.Output from the stochastic frontier model is

reported in the last column in Table 2. Thesignificant estimate of l suggests that the stochas-tic frontier approach is superior to simple OLS-approach in this model. Therefore, the stochasticfrontier model is used for calculating the relative,

technical efficiency for each firm used in the nextstep of the analysis.Next, the relation between firm efficiency and

internal and external factors has been estimatedusing both a standard OLS approach and a Tobitmodel with lognormal distribution10 with technicalefficiency restricted to the interval [0;1]. Table 3shows the results of selected models, and it is seenthat the estimated coefficients are stable in signsand magnitude across the different specificationsof the model and estimation methods.A characteristic feature of the analyses is the

positive effect of firms’ investments in R&D onefficiency. A dummy variable has been used forR&D activities, and the coefficient is seen to begenerally positive and significant. Thus, firms whohave decided to use ressources for R&D are moretechnically efficient than other firms.11

The estimations also show that the sales marketconcentration is statistically significant and that ithas a negative effect on efficiency. This result is inaccordance with the theoretical arguments, i.e.

Table 2

Total factor productivity models (standard Cobb-Douglas in logs) using an OLS (assuming technical efficiency) and Stochastic

Frontier approach on the cross-section sample-dependent variable is value added in 1997.

Variable Fixed-effect modela Fixed-effect modela,b Stochastic frontier modela

Number of employees – log(L) 0.363* 0.408* 0.368*

(0.009) (0.012) (0.006)

Capital – log(K) 0.639* 0.605* 0.637*

(0.008) (0.010) (0.004)

l — — 0.826*

(0.049)

Number of observations 3677 2370 3677

R2-adjusted 0.93 0.94

Log(L) �1721.4 — �1711.2

aModels are estimated as fixed-effect models controlling for industry difference using a Danish implementation of the Pavitt

taxanomy.bSample used in efficiency models in Table 3. The constant and dummies for different industries are not shown. Standard errors are

reported in brackets.

*Indicates significance at 1% level.

9The various experiments based on different sub-samples of

the data are not shown in this paper.

10The estimated efficiency variable, which is restricted to the

interval [0;1], is negatively skewed. Consequently a lognormal

distribution has been preferred. The estimations were per-

formed using LIMDEP, version 7.0, see Greene (1998).11This phenomenon has also been seen in a large number of

studies of firm R&D behaviour, see Dilling-Hansen et al. (1998)

for a survey.


firms operating in industries where competition isrelative high (less concentrated industries) aretechnically more efficient.12

As expected, higher financial solvency has anegative influence on firm efficiency, i.e. compa-nies with limited or no financial constraints are lessefficient than other firms. This relation is quitestable and highly statistically significant, suggest-ing that firms financing their activities by borrow-ing money experience a higher pressure to beefficient.Looking at the export characteristics of the firm,

the results give only weak support to the (expectedpositive) relation between efficiency and inter-

national orientation as measured by a firm-specificexport dummy variable, see columns 3 and 4 inTable 3.13

The effect of firm ownership/type of organiza-tion has been analysed by information on thenumber of major owners and a dummy for limitedliability companies. Generally, limited liabilitycompanies seem to be more efficient than othercompanies, and the reason may be that for suchcompanies the commercial risk is limited to theshare capital itself. Consequently, this type ofcompanies can take higher risks with expectedhigher returns on average projects. Therefore,limited liability companies actually surviving willin general have higher returns on projects and willprobably be more efficient compared to other

Table 3

Models explaining technical efficiency using OLS and Tobit approaches

Variable Basic OLS

model

Tobit model with

lognormal

distribution

Tobit model

lognormal

distribution

Tobit model with

lognormal

distribution

Constant 0.811* �0.212* �0.213* �0.210*(0.004) (0.0053) (0.0053) (0.0072)

Market concentration �0.0287* �0.0438* �0.0462* �0.0462*(0.0057) (0.0078) (0.0079) (0.0079)

LTD-dummy 0.0215* 0.0273* 0.0256* 0.0264*

(0.0026) (0.0035) (0.0037) (0.0037)

R&D-dummy 0.0106* 0.0141* 0.0134** 0.0137*

(0.0030) (0.0041) (0.0041) (0.0042)

Financial solvency �0.0562* �0.0654* �0.0679* �0.0666*(0.0123) (0.0167) (0.0167) (0.0168)

Export-intensity or dummy — — 0.0060 0.0059

(0.0037) (0.0037)

Number of block owners — — — �0.0031(0.0057)

(Number of block owners)2 — — — 0.0002

(0.0009)

Number of observations 2370 2370 2370 2370

R2-adjusted (column 1)/

scale parameter (col. 2,3,4)

0.045 0.079

(0.001)

0.079

(0.001)

0.079

(0.001)

Notes: Standard errors are reported in brackets. *Indicates significance at the 1% level, ** at the 5% level.

12At the experimental stage of this project, proxies for large-

scale advantages were tried, e.g. minimum efficient scale (MES).

The higher the value of MES, the better protected are

incumbent firms against the penetration of new companies,

and this lack of competition may result in falling efficiency in

the long run. Unfortunately, correlation problems between the

concentration variable and MES obstruct their simultaneous

use in the estimation form.

13Using standard OLS on the models specified in columns 3

and 4 results in a slightly higher level of significance, i.e. the

influence from export activity is reported positive and

significant at the 10% level of significance.


companies. Table 3 shows that the positive effect issignificant and quite stable.In Section 2, it was argued that the existence of

major owners, block owners, may influence firmefficiency. Companies with a single owner or fewowners will typically tend to be family ownedcompanies, consequently with identical ownersand managers. If there is only a single dominatingowner, he/she will typically be the founder of thefirm, and hence the owner and the manager willpursue identical goals even in the long run. Severalowners do not necessarily enhance efficiency. Butwhen the number of major owners grows beyond acouple, there will typically be external blockshareholders, i.e. investors that do not have anypersonal or family ties to the firm’s management,e.g. institutional investors. Traditionally, severalowners have been looked at as an expression ofdispersed ownership and hence less control of thefirm’s management and perhaps lower efficiency.But when each of these block owners holds at least5% of the share capital, such major shareholders(e.g. pension funds and other institutional inves-tors) may conversely exercise their influence byplacing board members, etc.; this will typically be arequirement when they acquire stock, especially inunlisted companies. Therefore, external blockowners are considered to be strong owners.Column 4 includes the number of block share-

holders as an explanatory variable. However theeffect on efficiency is not significant.14 In theexperimental phase of the project, a dummyvariable controlling in a more crude way for theexistence of more than one owner was included inthe model. Still, the estimated coefficient was notreported significant. Furthermore, log-transforma-tion of the block-owner variable did not result inany change of the significance. Consequently, theempirical evidence from Danish firms suggests thatthe legal form of the firm (the risk argument) ismuch more important than the number of largeowners for its technical efficiency.

6. Summary

The purpose of this article is to analyse theinfluence of ownership and R&D activities on theefficiency of Danish firms. Compared to standardproductivity analyses, the present study uses aslightly different R&D concept as the firms aregrouped into R&D-active firms and into firmswithout investments in R&D. As R&D investmentis considered as a high risk/high return investment,firms investing in R&D are expected to be moreefficient than other firms, i.e. all other things beingequal these companies create more value addedthan other companies. Using a stochastic frontiermodel and the Pavitt taxonomy to constructindustry fixed effects, the relative efficiency foreach firm is computed. In the second part of theanalysis it is shown that being R&D active yieldssignificant, stable and positive effects on theefficiency in all model specifications. The policyimplication of this result seems to be that in orderto increase the general level of efficiency amongprivate-sector firms, the public research fundsdesigned for stimulation of the private R&Dinfrastructure could be allocated in a way thatmakes more firms consider R&D investment as asuitable strategy.The analyses also introduce the concept of block

ownership. In accordance with the theoreticalarguments, the empirical specification allows fora nonlinear relationship, i.e. firms with one singleor several block shareholders are expected to berelatively more efficient than firms with a fewdominating owners. Though the estimated para-meters are correctly signed indicating a U-shapedinfluence on efficiency, the effect is insignificant.The most important influence from ownershipcomes from the legal form, i.e. LTDs versuspersonal ownership, with the former type of firmsbeing more efficient than other firms. This effect isstable and significant across the various estimationforms presented. Thus, what matters seems to beelimination of risk, i.e. LTDs actually survivingbecome the most efficient firms.Finally, other factors are included in the

analysis. Less financial solvency (more financialpressure) of the firm has a positive influence on thefirm’s efficiency. This result is in accordance with

14The squared expression – allowing for a U-shaped influence

– indicates declining efficiency when the number of block

owners grows to about 8, and after that point efficiency will

start increasing again. However, as mentioned above the effect

is insignificant.


the free cash flow theory. In addition, morecompetition at the market, as measured byincreasing values of the sales market concentrationrate, seems to enhance the firm efficiency, thusconfirming the x-inefficiency theory by Leibenstein.

Acknowledgements

The authors thank two anonymous referees fortheir helpful comments on this paper. We are alsograteful to the participants in the 28th EARIEConference held in Dublin, August 30–September2, 2001, to the participants in the 23rd Symposiumon Applied Statistics held in Copenhagen, January22–24, 2001, and to colleagues at the Departmentof Economics, the Aarhus School of Business, andat The Danish Institute for Studies in Researchand Research Policy in Aarhus for helpful com-ments. All errors and omissions are our own.

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Efficiency, R&D and ownership – some empirical evidence