Does Experience Matter?Productivity Effects of ICT in the German Service Sector

Thomas Hempell

Centre for European Economic Research (ZEW), Mannheim

February 2002

Abstract

In this paper, it is argued that ICT investment is closely linked with complementaryinnovations and most productive in firms with innovative experience. In an analysisbased on firm–level panel data covering the period 1994–99, system GMM estimates foran extended production function framework reveal significant productivity effects ofICT in the German service sector. Moreover, there is strong support for the hypothesisthat the experience gained from past process innovations is a specific complement thatmakes ICT investment more productive. The results suggest that ICT may have beencontributing to productivity differentials both between firms and countries.

Keywords: Information and Communication Technologies, Services, Production Func-tion Estimation, Panel Data

JEL–Classification: C23, D24, O32

Address: Centre for European Economic Research (ZEW)Research Group of Information and Communication TechnologiesP.O. Box 10 34 43D–68034 MannheimGermany

Phone: +49/621/1235–233Fax: +49/621/1235–225E-Mail: hempell@zew.de

1 Introduction

In spite of the current economic woes and emerging disillusions about a ‘New Economy’,productivity effects of information and communication technologies (ICT) continue toplay a key role in assessing the prospects and growth potentials of both firms and wholeeconomies. In fact, the economic downturn currently experienced by some countriesshows that ICT are far from being a panacea that yields permanent growth and the endof business cycles, as some analysts suggested at the peak of the hype. Rather, there isgrowing support for the view that it is the specific economic circumstances that determinethe success of exploiting the productivity potentials induced by new technologies.

ICT have been compared to other important historical inventions like the steamengine or the electric motor (David and Wright, 1999). These inventions are interpreted as‘general purpose technologies’ (GPT) with three main characteristics: they are pervasive,they carry an inherent potential for technical improvements and they lead to innovationalcomplementarities and scale economies (Bresnahan and Trajtenberg, 1995). Accordingto this view, GPT should be understood primarily as ‘enabling technologies’, “openingup new opportunities rather than offering complete, final solutions” (Bresnahan andTrajtenberg, 1995:84).

Various empirical studies seem to support the ‘general purpose’ hypothesis. Microe-conomic investigations by Brynjolfsson and Hitt (1995; 1996) and Lichtenberg (1995)found significantly positive contributions of ICT investment to output and productivityat the firm level. A macroeconomic study by Schreyer (2000) concludes that ICTinvestment has fostered output and productivity growth in the OECD countries duringthe 90s. Similarly, Colecchia and Schreyer (2001) find evidence that a broad applicationof ICT within an economy plays a key role in this context. They conclude that arapid diffusion of ICT depends less on the existence of an ICT producing sector butrather on the flexibility of product and labour markets as well as the business environment.

The ICT boom at the end of the 90s was accompanied by many exaggerations. Thestock markets soared, the market capitalization of various dot.com firms spectacularlyexceeded the value of several giants of the ‘Old Economy’. Bank credits and venturecapital were given to new firms on the basis of fashionable ideas rather than soundbusiness plans. In the meanwhile, about two years after the end of the ‘hype’ in 2000,both managers and economists have become less enthusiastic about the emergence of a‘New Economy’. On the one hand, numerous of the once highly praised dot.com firmshave declared bankruptcy. Moreover, many firms of the ‘Old Economy’ have not survivedthe wave of innovation and increased globalization and competition induced by the newtechnologies. On the other hand, some brick–and–mortar firms have been able to use

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ICT to boost productivity and market power. Obviously, not all firms have been equallyable to benefit from the productivity potentials of ICT.

More recent studies have tried to explain these differences in the productivityeffects between firms. Brynjolfsson and Hitt (2000) emphasize the role of organizationalchanges that are necessary to reap the potential benefits of ICT. Brynjolfsson and Yang(1999) and Yang and Brynjolfsson (2001) point to the role of intangible assets thatare complementary to the firm’s use of ICT. Bresnahan, Brynjolfsson and Hitt (2001)find evidence for significant synergies between ICT, workplace reorganization and newservices and products. These studies have an important message in common: in order toassess the impact of ICT on firm performance it is crucial to investigate the firm–specificcircumstances in which ICT are used. Hence, complementary factors are essential for theICT to unfold its productivity impacts as a ‘general purpose technology’.

The purpose of this paper is to shed more light on the factors that determinethe success of improving a firm’s performance by ICT use. Unlike previous studiesof the topic, the theoretical model proposed in this study stresses the importance ofICT being part of the innovation process within a firm. With reference to evolutionaryapproaches in innovation theory it is argued that firms that have introduced innovationsin the past are better prepared for ICT–induced innovations, like process improvementsand organizational restructuring, than firms without any innovation experience. As aconsequence, the model predicts productivity effects of ICT to be higher in experiencedfirms.

The empirical analysis of this hypothesis is based on a sample of firms from arepresentative survey in the German business–related and distribution service sectorcovering the period 1994 to 1999. In this regard, the study distinguishes itself frommost previous studies that have concentrated on the manufacturing sector or on samplesof large firms only.1 However, there are several reasons to draw more attention to theservice sector. Firstly, in the service sector, ICT investment is most dynamic and mostextensive in volume (OECD, 2000a; EITO, 2001). Moreover, as pointed out in OECD(2000b), the service sector — and in particular business–related services — have been themost important driver of economic growth over the last decades in most industrializedcountries.2 Finally, recent macroeconomic studies — for example by Baily and Lawrence(2001) and McKinsey Global Institute (2001) — conclude that services have accounted

1See for example Pilat (2001), Brynjolfsson and Yang (1996) and Brynjolfsson and Hitt (2000) forsurveys of the empirical literature.

2In Germany, the fraction of value added contributed by financial services and business services rosefrom 24,1% in 1991 to 30,3% in 1999 (Sachverstandigenrat zur Begutachtung der gesamtwirtschaftlichenEntwicklung, 2001).

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for a substantial part of productivity acceleration in the U.S. during the 90s. Therefore,in order to better assess the impact of ICT on the economy as a whole, a more detailedunderstanding of the underlying processes in the service sector seems to be particularlyimportant.

A further advantageous feature of the sample employed in this study is that it coversfirms of all size classes instead of only large firms. This broader focus is likely to increasethe variation of characteristics and implementation strategies between firms and therebyhelps to identify the role of the firm characteristics. Moreover, the broader focus en-hances the likelihood of receiving a more representative picture for the economy as a whole.

Even though the German economy continues to play a key role within the EuropeanUnion as well as within the OECD, few attempts have been made to quantitatively assessthe productivity effects of ICT use in German firms. To the knowledge of the author,the only related study is the paper by Bertschek and Kaiser (2001) who find significantcontributions of ICT investment in a cross–section analysis of business–related servicesfirms.3 Hence, a further objective of this paper is to shed more light on the impact ofICT use in the German economy.

The paper is organized as follows. In section 2, the theoretical background isdiscussed and the hypothesis of innovative experience playing a key role in efficient ICTuse is implemented into a production function framework. Section 3 gives an overviewof the data employed, that is the Mannheimer Innovation Panel in the Service Sector(MIP-S), and describes the methods used to construct different stock values for ICT andconventional capital separately. Section 4 discusses the econometric issues and presentsthe empirical results for both a simple ICT–extended production function framework andthe more specific model about the role of innovative experience. Section 5 concludes withsome comments on the implications of the findings concerning theoretical, methodologicaland policy issues.

2 Methodological and Theoretical Framework

Like many other important inventions in history, the rapid technological progress of thesemi-conductor industry and the fast expansion of the internet evoked high expectationsabout their impact on productivity and growth. The adoption and diffusion of thesenew technologies were expected to boost productivity substantially. However, for along time anecdotal evidence of computerized workplaces did not show up in aggregate

3Licht and Moch (1999) use qualitative data to assess the impact of ICT on the performance of Germanfirms. They find that the main impact of ICT use are quality improvements.

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productivity statistics, which led to the proclamation of the “productivity paradox” ofICT. While investment in ICT started to grow rapidly in the U.S. since the 1970s, labourproductivity growth slowed down substantially and remained at a low level until thebeginning of the 90s (Jorgenson and Stiroh, 1995). The service sector was at the heartof this paradox. Around 1990, the U.S. service sector accounted for nearly 80 percent oftotal IT investment in the U.S. but experienced productivity growth only slightly abovezero (Brynjolfsson and Yang, 1996).

In this regard, theoretical and empirical approaches that focus on the firm level ratherthan aggregate level appear very promising. Theoretically, the productivity impact of ICTis likely to vary substantially between firms. Like in the case of many other innovationwaves in economic history, some firms are better prepared than others to take productiveadvantage of new technologies. For the particular case of ICT, it has been argued thatcomplementary factors like organizational restructuring and intangible assets play akey role for ICT to unfold its largest benefits (Brynjolfsson and Hitt, 2000; Yang andBrynjolfsson, 2001). By treating all firms equally (assuming equal input coefficients in aproduction function framework, for example), the true impact of ICT on “well–prepared”firms may be understated. Therefore, in order to assess the determinants of successfullICT use, it may be useful to explicitly model the influence of various firm characteristicson the efficient use of new technologies.

A further — rather empirical — source of the productivity paradox is that officialstatistics do not adequately reflect quality changes. As pointed out by Brynjolfsson (1994)and Licht and Moch (1999), quality improvements — in particular improved customerservice — are an especially important goal for ICT investment decisions. Griliches (1994)shows that the problem of unmeasured quality improvements is especially importantin the case of ‘unmeasurables’ services like trade and F.I.R.E. (finance, insurance, realestate) where ICT investment has grown most rapidly. As a consequence, the contributionof ICT to real output growth inferred from aggregate data are likely to be biased towardszero. For applications to firm–level data, this problem will be less severe. If a firminvests in ICT in order to improve the quality of a product while its competitors continueto offer their old products, the innovating firm will try to charge a higher price for itsnew product. If the quality improvement is approved by customers, they will in factbe willing to pay a higher price for the good and the value added of the innovatingfirm will increase accordingly.4 Consequently, Brynjolfsson and Hitt (2000) argue thatmicroeconomic studies will capture this effect and variations in output quality willcontribute to measuring a higher output elasticity of ICT investment. This relationship

4A firm level study by Brynjolfsson and Hitt (1995) did not find any significant differences in IT produc-tivity between “measurable” and “unmeasurable” sectors, indicating that appropriate quality measurementis mainly a problem at the aggregate level.

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is independent of the price deflator employed for the corresponding industry. Moreover,firm–level data sets are particularly suitable for empirical assessment of productivityeffects if — apart from input and output data — they contain information on additionalfirm characteristics. These data may allow to identify the determinants of success in ICTadoption. On the contrary, this identification will be much more difficult with aggregatedata since differences between firms will even out by aggregation.

For these reasons — both theoretical and empirical —, firm level analysis seems to bethe appropriate approach for a more detailed investigation of productivity effects of ICTinvestments. In the following first subsection, it is argued on the basis of evolutionaryapproaches of innovation that a distinction between firms according to their innovativeexperience may play an important role for the benefits of ICT use in services. In thesecond subsection, this distinction is formalized within an extended microeconomicproduction function framework that is implemented empirically in the following.

2.1 Innovation in Services: The case of evolutionary approaches

In earlier studies on innovation, the service sector has been characterized as a mereapplier of technological innovations developed in the manufacturing sector (see forexample the influential taxonomy by Pavitt, 1984). Recent studies, however, tend toconfirm a more active role of the service sector in the process of technological change(see Blind et al., 2000; Sirilli and Evangelista, 1998). Though new technologies of themanufacturing sector play an important role in initiating innovation processes in theservice sector, the restructuring of processes and improvement of existing services oftenleads to the development of completely new services (Barras, 1986). Therefore, likein the manufacturing sector, differences in technological opportunities, appropriabilityconditions, and cumulativeness of innovative capabilities may lead to differences in theinnovation paths between firms.

As pointed out by Cohen and Levinthal (1990, p. 128), “the ability of a firm torecognize the value of new, external information, assimilate it, and apply it to commercialends” is critical to its innovative capabilities. They argue that this ‘absorptive capacity’ islargely a function of the level of prior related knowledge.5 Firstly, the absorptive capacity

5Building on studies in cognitive and behavioral sciences, Cohen and Levinthal (1990) state that atthe level of individuals, “learning is cumulative, and learning performance is greatest when the object oflearning is related to what is already known” (131). At the level of organizations, for example firms, theabsorptive capacity is not just the sum of absorptive capacities of its individual employees but also dependson the transfer of knowledge across and within subunits. As a consequence, there is a trade off betweenthe aim of highly diversified capacities of the individuals in order to recognize the value of various newexternal information on the one hand, and the need of specialization or a “shared language” to ensure thetransfer of knowledge within the organization. Therefore, the effect of specialization on the learning effect

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accumulated in a particular area in one period will permit a more efficient accumulationin the next. Secondly, ‘experienced’ firms will be able to better predict the nature andcommercial potential of technological advances in an uncertain environment. Both aspectsof absorptive capacity — cumulativeness and the impact on expectation formation —“imply that its development is domain–specific and is path– or history–dependent” (136).

There are further studies that stress evolutionary aspects of innovation in a similarway. Mansfield (1968) and Stoneman (1983) argue that a firm’s innovative successenhances its technological opportunities and thereby makes further success more likely.This ‘success breeds success’ hypothesis is supported by an empirical study by Flaig andStadler (1994). They derive a stochastic dynamic optimization model with intertemporalspill–overs of innovations within firms. In their empirical application, they find thatfirms that have introduced innovations in the past are indeed more likely to innovate insubsequent years.

Similar to these arguments and findings, innovative experience may be an explanationfor substantial differences in firms’ ability to boost productivity by the application ofICT. As emphasized by Bresnahan et al. (2001), the use of ICT is closely linked toinnovations within the firm. Case studies show that organizational changes and processreengineering play a key role as complementary innovational efforts (Brynjolfsson andHitt, 2000). Both the ‘absorptive capacity’ and the ‘success breeds success’ hypothesessuggest that firms that have introduced such innovations in the past are probably betterprepared than non-innovative firms to reap the potential benefits of ICT.

There are several more specific reasons supporting this view. Firstly, managersare likely to have learned from past mistakes.6 They are better prepared to assess thepotentials and limits of introducing major changes within their company, being aware ofpossible reactions of their employees and their traditional customers. In short, they havevaried experience concerning the optimal speed of innovation of their firm. Secondly,innovative firms will have more experience in how to implement innovations. In mostcases, the efficient introduction of ICT requires complementary changes of organizationalstructures and processes as pointed out by Brynjolfsson and Hitt (2000). Innovativefirms might be more successful in training and motivating their employees to take partactively in the innovation process. This argument suggests, in fact, that it will mainlybe experience in process and organizational innovations that increase the ability of

at the level of firms is ambigous.6The returns to innovations are far from being safe. However, the likelihood of a success may increase

with the experience gathered from past innovations. In this regard, the learning process in introducinginnovations may be compared to the search model proposed by Nelson (1982) for the case of R&D. In-novation is modelled as a search process in which knowledge helps to lower the search costs by focussingsearch on more relevant alternatives.

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exploiting the productivity potentials of ICT successfully. Thirdly, firms that are moreinnovative may have a higher share of employees that accept an intensive use of ICT attheir workplace. This may be due to both special recruitment strategies and to specificexperience and greater flexiblity of the workforce from former innovation activities.7

Finally, innovative firms may have gained some degree of ‘innovative reputation’ orbranding in new business areas. In particular, it might be easier for them to attract newcustomers by exploiting ICT–based sales channels (B2B, B2C) or by introducing newICT-based services or products.8 Since innovative reputation can most likely be acquiredby offering innovative products, this argument would imply that past product innovationsraise the ability of benefiting from ICT.

Taken together, these arguments imply that the returns to ICT investment arehigher in innovative firms than in firms with little or no innovative experience. In theempirical testing of this hypothesis it will be important to distinguish between differentkinds of innovative experience, in particular between product and process innovations. Inthe following formal derivation of the model, however, this distinction is abstracted fromand left for the empirical implementation in section 4.

2.2 The Model

In this part, a theoretical framework of production is proposed focussing on two specificquestions raised in the previous part. Firstly, is innovative experience a crucial prereq-uisite to implement ICT efficiently? And if so, secondly, is ICT capital ‘special’ in thesense that other capital inputs are much less dependent on innovative experience?

In order to assess these questions, the traditional Cobb-Douglas production setup isused as a starting point:

Yi = F (Ai,Ki, Li) = AiLγ1i Kγ2

i (1)

where Yi is value added of firm i, Ki is conventional (non-ICT) capital, Li is effectivelabour and Ai is the multifactor productivity of firm i.9 ICT capital does not enter

7Nelson and Winter (1982) suggest that much of the knowledge of a firm’s organizational routines andobjectives is tacit. They summarize this observation by claiming that organizations ‘remember by doing’.As a consequence, innovational capabilities are difficult to raise by hiring new personnel. Also in thisregard, the firm’s innovative history plays a key role (see Cohen and Levinthal, 1990).

8Smith and Brynjolfsson (2001) find that brand is an important determinant of consumer choice ininternet transactions. They find that “consumers use brand as a proxy for retailer credibility in non–contractible aspects of the product and service bundle, such as shipping reliability” (541). Interestingly,in the study the strongest brand effect was found for amazon, which has gained reputation as one of theleading internet retailers.

9F (•) may be such that Y exhibits constant returns to scale in K and L (γ1+γ2 = 1), but not necessarily.

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the production function directly, but rather affects multifactor productivity jointly withinnovative experience as a complement:

Ai = A(ICTi, Ei,mi) (2)

where Ai represents firm’s i (time–invariant) multifactor productivity, ICTi rep-resents its amount of ICT capital, Ei innovative experience and mi collects otherunobserved efficiency parameters of firm i like management skills, location, branding,etc. Furthermore, the following properties of function A(•) are imposed: (1) ∂Ai

∂ICTi≥ 0,

(2) ∂Ai∂Ei

≥ 0, (3) ∂2Ai

∂ICT 2i≤ 0. That is, a firm’s productivity depends positively on both

ICT input (1) and innovative experience (2). Marginal contributions to productivity areassumed to decrease with the amount of ICT employed (3). That is, the productivitypotentials of ICT are limited. Finally — and most importantly — it is predictedthat the potential benefits from ICT are higher for firms with innovative experience.More specifically, the productivity contributions of ICT are increasing in the degree ofinnovative experience: (4) ∂2Ai

∂ICTi∂Ei≥ 0. A very convenient functional form satisfying

these properties is the following specification:

Ai = C ·mi · ICTx(Ei)i (3)

with C as a common scale factor and x(•) being a strictly monotone function of Ei

such that ∂x(•)∂Ei

> 0 and x(•) ∈ [0, 1]. Past innovation activities are considered as a proxyof a firm’s innovative experience such that Ei = 1 if firm i has been an innovator andEi = 0 otherwise. Denoting x′ = x(1) and x(0) = x, it follows that x′ = x + ∆x with∆x > 0. Then equation (3) becomes:

Ai = C ·mi · ICT x+∆x·Ei (4)

Inserting equation (4) into (1) and taking logs then yields the following extendedproduction function equation:

ln Yi = ln C + ln mi + x ln ICTi + ∆x(Ei · ln ICTi) + γ1 lnLi + γ2 ln Ki (5)

Thus, the model corresponds to an ICT–extended Cobb–Douglas function except thatthe coefficient of ICT is predicted to be different for experienced firms (x′ = x + ∆x) andunexperienced firms (x). One implication of this framework is particularly noteworthy.The theoretical property that innovative firms exhibit a higher elasticity of outputwith respect to ICT (∆x > 0) does not necessarily imply the marginal product of ICTto be higher in innovating firms since the marginal product depends also on the ICT

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intensity (ICTi/Yi) of the individual firm.10 What a higher elasticity does imply isthat in innovating firms the marginal output contributions of ICT decrease less rapidlywhen ICT–input is raised. In the context here, this is equivalent to saying that thereare more potential benefits to be exploited by the use of ICT within innovative firms.In the extreme case, innovative experience is an essential condition for ICT to yieldbenefits at all. If so, the output elasticity of ICT would be zero for non–experienced firms(x = 0) or even negative11, whereas experienced firms exhibit positive returns to ICTcapital (∆x > max(0,−x)). Under the assumption that marginal products of ICT (MPI)are equal across all firms (that is ICT is earning equal returns), the implication is thatexperienced firms will produce their services with a higher ICT intensity ICT

Y = x(E)MPI and

a lower intensity in other factors.

Finally, equation (5) is transformed into the following econometrical model:

ln Yit = c + ηi + β1 ln Lit + β2 ln ICTit + β3 ln Kit

+β4(ln ICTit · Ei) + [β5(lnKit · Ei)] + εit (6)

with c = ln C, ηi = lnmi, β1 = γ1, β2 = x, β3 = γ2, β4 = ∆x and εit as a normallydistributed disturbance term. In this framework, the answer to the question whetherinnovative experience influences the output contributions of ICT depends crucially onwhether the coefficient β4 turns out to be significantly positive or not. As far as thesecond initial hypothesis is concerned, the effect of experience on ICT is contrasted by thecomparable effect on non–ICT capital (see the β5-term in brackets). If ICT is a ‘special’capital input by its dependence on past innovations, β5 is expected to be zero and β4 tobe positive in a simultaneous estimation of both coefficients.

3 The Data

In order to implement the production framework empirically, data from the MannheimerInnovation Panel in Services (MIP-S) are employed. This survey is conducted by the Cen-tre of European Economic Research (ZEW) on behalf of the German Federal Ministry forEducation and Research (bmb+f). The data has been being collected annually since 1994in a representative survey of innovation activities in the German business related-serviceand distribution sector and includes information of more than 2000 firms (Janz et al.

10Formally, the marginal returns to ICT (MPI) are the product of the output elasticity of ICT and theinverse ratio of ICT capital in output: MPIi = ∂Yi

∂ICTi= x(Ei) · Yi

ICTi. Therefore, the MPI increases with

the output elasticity x(Ei) but decreases with the share of ICT capital in output ICTiYi

.11A negative coefficient would imply that the introduction of ICT leads to efficiency losses, for example

due to high costs of internal restructuring or long lags in ICT causing measurable productivity effects.

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2001). It has an (unbalanced) panel structure in important key variables for the years since1994. The survey methodology is closely related to the guidelines proposed in the Oslo-Manual on innovation statistics (OECD/Eurostat, 1997). Furthermore, the 1997 waveof the survey in the service sector formed part of the Community Innovation Survey (CIS).

For the particular purpose of the empirical analysis, the MIP-S data set containsannual data on sales, number of employees (full-time equivalents), skill structure,expenditures for gross investment and for ICT-capital (hardware, software and telecom-munication technology). Furthermore, firms were asked whether they had introduced atechnologically new or substantially improved products (product innovation) or processes(process innovation) within the last three years.

In order to estimate the model equation, some data transformations were necessary.Most importantly, capital stocks for ICT capital and conventional (non-ICT) capitalwere constructed separately. For this purpose, investment on conventional capital wasdefined as total investment expenditures minus ICT expenditures.12 In order to derivethe corresponding real investment, conventional investment is deflated by the deflator ofthe German Statistical office.13 As far as deflators for ICT goods are concerned, Germanofficial price statistics on ICT goods tend to understate the real price decline of thisproduct class (Hoffmann, 1998). Therefore, the price deflators for total ICT in the U.S.,which are calculated on a hedonic basis (see Jorgenson, 2001), are used. Additionally,these prices were corrected for exchange rate changes, multiplying the U.S. deflators bythe annual averages of the $/DM exchange rates (taken from German Bundesbank, timeseries wj5636).

Given the deflated investments for both types of capital, the perpetual inventorymethod with constant, linear depreciation,14 was applied to construct the capital stocks

12Some firms reported investment expenditures in ICT that exceeded total investment (leading to anegative gross investment in conventional capital). These inconsistencies were most frequent for the years1994, 1997 and 1998 (6.3%) but almost absent in 1995 and 1996 (0.2%). The most likely explanation seemsto be questionnaire design: for the years 1995 and 1996, the question on total investment was immediatelyfollowed by the question on ICT investment, thereby drawing the respondent’s attention to the consistencyissue. The most likely underlying source for the inconsistent answers seems to be that respondents had notincluded ICT investment in total investment expenditures. In particular, expenditures for software mayhave been left out. In order to avoid the loss of many observations, the inconsistent cases were interpretedas having equated total investment with conventional investment only.

13The index of the producer prices, investment goods, from the Statistical Yearbooks with 1996 as thebase year, was employed for this purpose.

14It may be argued that especially for the case of ICT capital it might be more adequate to apply avintage model in which computers maintain their productive efficiency over the lifetime of computers (seefor example Jorgenson and Stiroh, 1995). However, the definition of ICT includes a very broad range oftechnologies. Furthermore, the length of the time series available is very short. The assumed life cycleof ICT capital (1/δ2 = 4 years) would exceed the time series available for the vast majority of the firms.

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for ICT and non-ICT. Accordingly, capital Kt in period t with investment It−1 is definedas:

Kkt = (1− δk)Kk,t−1 + Ik,t−1 (7)

with k = 1 for conventional and k = 2 for ICT capital and investment.

There are two potential problems in this approach. Firstly, reasonable values for thedepreciation rates of both types of capital have to be found. Secondly, since no informationis available on the level of capital stocks, initial capital stocks have to be constructed forall individual firms. Therefore, the method proposed by Hall and Mairesse (1995) for theconstruction of an R&D stock was followed since the problems in both contexts are verysimilar. Under the assumption that investment expenditures in capital good k have grownat a similar, constant average rate gk in the past for all firms, by backward substitutionequation (7) can be rewritten for period t = 1 (1994) in the following way:15

Kk1 = Ik0 + (1− δk)Ik,−1 + (1− δk)2Ik,−2 + . . . (8)

=∞∑

s=0

Ik,−s(1− δk)s = Ik0

∞∑

s=0

[1− δk

1 + gk

]s

=Ik1

gk + δk

Constant linear depreciation rates are assumed for conventional capital (δ1 = 0.06)and ICT capital (δ2 = 0.25) correspondingly. In particular, with δ1 < δ2 it is taken intoaccount that the fast technological progress in ICT implies more frequent replacementof ICT inventory than of conventional capital (including buildings and office furnitureamong others). In order to derive the initial capital stocks, assumptions about pre-periodgrowth rates of both type of investments must be made. For non-ICT investmentexpenditures, an annual growth rate of approximately 5% (g1 = 0.05) is assumed.16 ForICT investment, no time series are available for Germany. In order to get a rough ideaof the evolution of ICT investments during the last decades, U.S. data are referred to as

Therefore, there is little gain in trying to exploit the potential advantages of the vintage approach in thiscontext.

15In fact, the initial value of investment in conventional capital I1,1 was replaced by the average of theobserved values of conventional investment for each firm. With this “smoothing” it was aimed to correctfor cyclical effects which might have affected the estimated capital stock due to different initial years inthe unbalanced panel. The underlying assumption is that long term growth of investment in conventionalcapital (g1 = 0.05) is relatively low compared to cyclical variations in this variable. On the contrary, thefirst observation on ICT capital was not replaced by the corresponding averages since long-term growth(g2 = 0.4) rates of ICT investment are more likely to dominate changes that are due to cyclical fluctuations.

16Calculations on capital data provided by Muller (1998) show that gross capital stock in German serviceshas grown on average by 4.8% annually between 1980 and 1991.

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a rough guideline. Jorgenson and Stiroh (1995) calculate an average annual growth rateof 44.3% in real computer investment and of 20.2% for OCAM (office, computing, andaccounting machinery) between 1958 and 1992 for the U.S. Since the share of computersin OCAM has been steadily rising and is more closely linked to ICT investment asdefined in the context here, an annual pre-period growth rate of — somewhat arbitrarily— g2 = 0.4 for ICT investment is assumed.17 Since there are time lags between theinstallation and productive contribution of capital goods, the capital stock at eachperiod’s beginning (or at the end of the corresponding fore-period) are taken as measuresfor ICT and conventional capital input.

For labour input, the annual average of the number of the firm’s employees (full-timeequivalences) is used. Moreover, for some firms, data on human capital — based on formaleducation — were available. Two particular variables are used to proxy human capital:the fraction of employees with vocational training (Berufs- or Fachschulabschluss) on theone hand and the fraction of employees with a university degree including universities ofapplied sciences (Hochschul- or Fachhochschulabschluss) on the other. However, there isa vast amount of item non-responses in these variables, leading to a substantial decrease(nearly 50%) in the number of firms with sufficient observation for panel analysis.In the remainder, this data set will be referred to as the “small sample”. Estimatesbased on this sample are just used to explore the effects of omitted human capital variables.

As far as output is concerned, a firm’s value added, deflated by prices at the industrylevel, would serve as an appropriate measure for output. However, the empirical analysishad to rely on information on the firm’s undeflated total sales only since the survey doesnot contain further information on intermediate inputs, which would allow to derive afirm’s value added. Furthermore, there are no official deflators available for output in theGerman service sector. Finally, there is no information on factor utilization available. Inthe next chapter, it is shown that under some quite weak assumptions it is still possible tocontrol for these influences econometrically. In order to make results comparable betweendifferent types of econometric techniques, only firms for which consistent informationon at least three consequent periods were available were included in the sample. Theresulting unbalanced sample consists of 1246 firms with a total of 5355 observations, thatis with an average of 4.3 observed periods per firm and is referred to as the “full sample”in the remainder.

Some firms reported a share of ICT investment in total investment expendituresequal to zero for all the periods surveyed. Since the econometric specification is in logs,

17In fact, later results in the production function estimates turned out to be robust to variations in bothg and d.

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these firms must be excluded from the full sample. However, there are reasons to assumethat ICT investment in these firms is not zero, in fact, but rather very low and rounded tozero by the respondents. In order to prevent potential biases in the results (in particularin those regressions discriminating between experienced and not experienced firms) theICT stock per worker in these firms was assumed to be equal to the correspondingindustry minimum and the corresponding values were imputed. This sample is referredto as the “extended sample” in the remainder.

Several variables of the MIP-S data set make it possible to distinguish between firmswith more and less innovative experience. In each wave of the survey, firms were askedwhether they had introduced new or significantly improved services on the one hand andnew processes on the other. These questions refer to the past 3-year-period of each wave.These variables were used to construct two different classifications of firms in order toproxy for its innovative experience. According to the first (broader) distinction, a firmis classified as a “panel product innovator” (PPD) or “panel process innovator” (PPC)if it had declared itself an innovator in one of the periods surveyed. On the contrary,according to the second, narrower distinction, a firm is classified as an “experiencedproduct innovator” or an “experienced process innovator” (EPD or EPC respectively)only if it has declared itself an innovator in the first period surveyed.18 By this morerestrictive definition, it is intended to focus more strongly on innovation experience asa history–oriented concept since the first definition also includes firms that have beenintroducing innovations in the course of implementing ICT technologies. Therefore, thebroader definition can be viewed rather as the firm’s characteristic of how ICT capitalgoods are implemented. By comparing the results for both of these classifications, it cantherefore be concluded if it is really experience that matters for ICT output contributionor rather the way the new technologies are implemented.

The statistics of the sample are summarized in the tables in the appendix. Table4 shows the (pooled) summary statistics of the logs of the variables on input, outputand innovation that are employed in the regressions, and Table 5 gives an overview overthe shares of innovating firms according to the different classifications. Tables 6 and 7show that the sample reflects industry and size structure of the German distributionand business–related services fairly well.19 Finally, in table 8, the medians20 of the firmsaverages of capital and output intensity are displayed for the full sample.21 The figures

18Note that an “experienced innovator” must also be a “panel innovator”.19Retail trade is slightly oversampled whereas traffic and postal services as well as software and telecom-

munication are slightly oversampled. As far as firm size is concerned, large firms are oversampled in theirmere number and undersampled in their respective share in sales (see last two columns of table 7)

20The corresponding mean values are substantially higher, since some firms — in particular real estate— display very high values for both inputs and output per employee.

21The corresponding intensities for the other samples (not reported) are very similar.

13

indicate that at the median, workplace in services is equipped with ICT capital worthslightly more than DM 3000, and with non–ICT capital worth about DM 55,000. Themedian turnover per employee is DM 200,000. These figures show that — as suggestedin Griliches (1994) — the small share of ICT input (less than 6% of the value of othercapital goods) together with measurement errors may make it difficult to determine theproductivity effects of ICT use. However, the columns 2 and 3 of table 4 indicate that,in fact, the distinction according to firms’ innovative experience may help to identifythe productivity effects. In fact, the median of the per capita value of ICT stock inexperienced firms (defined according to its narrowest definition) is more than twice ashigh than among non–experienced firms whereas sales and conventional capital hardlydiffer. These simple summary statistics coincide with the outlined hypotheses: if ICT useis more productive in experienced firms, these firms will tend to spend a higher amounton ICT per worker than other firms.

4 Empirical Results

Before the full model from equation 6 is implemented empirically, the data problemsdescribed in the previous section have to be addressed methodologically. Besides,potential biases from various sources are to be addressed by using different econometricapproaches. These investigations may also shed some light on the potential sources of the‘productivity paradox’.

To keep things simple, this empirical section is organized in the following way.Firstly, econometric issues arising from data characteristics are discussed. In the secondsubsection, estimation results abstracting from firms’ innovative experience are discussedin order to investigate the effects of various potential sources of biases on the estimates.Finally, the initial hypotheses about the role of ICT for a firm’s exploiting its “experiencebenefits” are analyzed in more detail.

4.1 Econometric Issues

One of the main drawbacks in the MIP-S data set is that it contains only informationon undeflated nominal sales as a measure of firms’ output instead of deflated valueadded (see section 3). If the deflators of output and the share of value added insales did not vary substantially, this lack of data would not cause any substantialbias: both the deflator and the common share of value added in sales would enter theconstant term in the logarithmic specification.22 However, the share of value added in

22If, furthermore, these factors were constant over time, they could be “differenced away”.

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sales varies substantially across industries. Industries that are typically at the end ofthe value chain (like wholesale and trade) will exhibit much lower shares than otherservice industries that are much less dependent on intermediate goods in quantitativeterms. Moreover, prices have evolved quite differently between industries in Germanyin the period concerned. Prices of telecommunication, for example, are likely to havedeclined to a larger extent than prices in other services have. Finally, different cyclicaleffects between industries are very likely to have induced different evolvements of in-tensity in factor utilization between industries over the observed period from 1994 to 1999.

To control for these potential distortions econometrically, it is assumed that the mostsubstantial variation in the share of value added, price deflators, and factor utilizationis due to differences (both in levels and temporal change) between industries.23 Morespecifically, firms are classified with respect to 7 industries (j = 1 . . . 7)24, such thatnominal sales Yijt of firm i from industry j at time t can be expressed as:

Yijt =PjtUjt

SjVit (9)

where Pjt is the price deflator for industry j in year t, Ujt is the corresponding indexof factor utilization, Sj the industry’s average share of value added in sales (equal to oneminus the share of intermediate goods and materials in sales), and Vijt is the value added offirm i belonging to industry j at time t. By taking logs, the industry- and time-dependentfactors can be separated from V :

ln Yijt = Djt + lnVit with Djt = ln

(PjtUjt

Sj

)(10)

Therefore, by introducing interacted industry and time dummies Djt, the potentialbiases induced by prices, business cycles and differences in the share of value addedin sales can be controlled for. In fact, the allowance for interacted dummies might beparticularly important for analyzing the output contribution of ICT. ICT use tends tohave increased especially strongly in industries for which substantial price declines duringthe past decades can be observed, such as telecommunications and software. If the price

23Similar, but more restrictive assumptions were made by Lichtenberg (1995). He corrected for differencesin the share of value added in sales between industries by introducing industry dummies on the one hand andcontrolled for price movements by introducing common time dummies on the other. However, differencesin price movements and in business cycles between industries cannot be captured with this specification.In the discussion of the empirical results, the empirical relevance of including interacted dummies will beconsidered separately.

24These are (with the corresponding nace-codes in brackets): wholesale trade (51), retail trade (50,52), transport and postal services (60-63, 64.1), electronic processing and telecommunications (72, 64.2),consultancies (74.1, 74.4), technical services (73, 74.2, 74.3), and other business-related services (70, 71,745-748, 90). Since there are no output data available for banking and insurance (only the balance sheettotal and insurance premiums respectively), these industries must be excluded from the analysis.

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declines in these industries are not taken into account, the increase in real output forfirms of these industries will be understated. As a consequence, the output contributionof ICT in general will be underestimated. Furthermore, variations in factor utilizationdue to cyclical effects may add substantial ‘noise’ to the residual variation in output.Measuring the output contributions of ICT, however, has been compared to looking for”the needle in the haystack” (Griliches, 1994). Even though real quantities of ICT usedby firms have grown dramatically over the last decade, ICT still represents a relativelysmall portion of overall inputs. In the U.S., where ICT has diffused much more rapidlythan in Europe, the share of ICT services in GDP has risen from 4.4% in 1994 to amodest 5.5% in 1999 (Jorgenson, 2001). Griliches (1994) points out that the small shareof ICT combined with poor measurement in output and deflators makes it difficult todistinguish the output contributions of ICT from stochastic events. Thus, by omittingindustry specific cyclical effects and price deflators, the stochastic noise may be too strongto identify the contributions of ICT econometrically.25

4.2 Evidence from the ICT–augmented production function

In order to explore the effects of different potential biases, the ICT-augmented productionfunction is estimated firstly in a simple pooled OLS regression.26 In the regressionequation, interacted industry and time dummies as well as a dummy variable for firmslocated in East Germany are included. This last variable is expected to be significantlynegative since the transformation process in the Eastern part of Germany is still laggingbehind in both productivity and wages when compared to West Germany. The results forthe pooled regression are summarized in the first column of table 1. The coefficients ofall three inputs are significantly different from zero at the one percent level. The outputelasticity of labour takes the reasonable value of 61%.27 What is most striking in theresults is that the point estimate of the coefficient of ICT capital (24.2%) exceeds thecoefficient of conventional capital (12.6%).28 Given that the share of the average invest-

25However, the costs of including interacted time and industry dummies are substantial as well. Thereare n = J ·T−1 = 7·6−1 = 41 additional variables that must be included additionally, with J representingthe number of industries and T the number of years.

26All estimations were computed with the DPD98 programme developed by Arellano and Bond (1998)running in GAUSS. For all the results, heteroskedasticity–consistent standard errors are reported.

27Under the assumption of constant returns to scale and perfect competition, the income share of labourin an economy must equal its labour coefficient in the production function. For the German economy asa whole, the average share of labour payments in national income between 1994 and 1999 amounted to72.4% (Statistisches Bundesamt, 2001).

28Similar results have been found in cross section regressions by Bertschek and Kaiser (2001) for a sampleof firms in the business-related service sector taken from a different survey. On the contrary, Brynjolfssonand Hitt (1995) report point estimates of the coefficients of ICT capital (10.9%) that were only about halfof the value for non–ICT capital (20.9%) in the pooled regression for a sample drawn from the Fortune500 Manufacturing and Service listings. There are two main reasons for these differing results. First,

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ment expenditure in conventional capital exceeds the average IT-investment, and giventhat the average of the estimated non-ICT stock per worker exceeds the correspondingvalue for ICT capital by a factor close to twenty, these results would imply that firmsheavily underinvest in ICT capital goods. However, a more reasonable explanation is thatthe results from both pooled and simple cross–section regressions are biased. There arefive potential sources that will be considered step by step in the empirical exploration:unobserved heterogeneity between firms, simultaneity of the decisions about inputs andoutput, measurement errors in the input variables, autocorrelation of exogenous shocks,and biases from omitted variables.

Unobserved heterogeneity may bias the results if the investment strategies of highlyproductive firms are systematically different from their less productive competitors withinthe same industry.29 In particular, if highly productive firms tend to invest more innew technologies than firms with low productivity do, the ICT coefficient will be biasedupwards in a pooled or cross section OLS regression.30 Brynjolfsson and Hitt (1995) findthat unobserved heterogeneity may explain as much as half of the productivity effectsattributed to ICT in their pooled regressions.

In order to control for the firm–specific effects, the within–estimator was used.31 Inthe second column of table 1, the corresponding results are summarized. The figuresindicate that once unobserved heterogeneity is controlled for, the output contributions ofboth types of capital are no longer significantly different from zero whereas the labourcoefficient rises slightly (67.6%).32 The figures show that all the output contributionsassigned to both types of capital in the pooled regression were in fact due to unobservedheterogeneity. These results coincide with very similar findings by Black and Lynch (2001)and Wolf and Zwick (2002) for production function estimates with one type of capital only.

Brynjolfsson and Hitt (1995) referred to a very different sample of large firms only. They drew theirsample from the Fortune 500 listings of both the manufacturing and the service sector which consists oflarge firms only. Second, they constructed the estimate of the firms’ ICT capital stock in a very differentmanner. While the ICT capital in the study of Bertschek and Kaiser (2001) and this paper ultimatelybuilds upon ICT investment data, Brynjolfsson and Hitt (1995) used data on the stock of mainframes andPCs and converted these data into corresponding estimates of their market values. In particular, theirmeasure of ICT capital is much narrower than the measure employed in this study in that it excludesfactors such as telecommunication hardware, peripherals and software. With this background, the muchhigher ICT coefficient in the results presented here comes at no surprise.

29Productivity differences between different industries are captured by the industry dummies.30In fact, the highly significant first– and second–order autocorrelation in the errors of the pooled

regression indicate that fixed effects exert a strong effect on the results.31More specifically, the orthogonal deviations from the corresponding firm’s mean of the variables were

employed.32Since there is no variation in the East dummy over time, this variable is excluded from the within

estimation.

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Table 1: Results for the ICT-augmented production function

Dep. Variable: Salesproduction inputs pooled within GMM SYS–GMM SYS–GMM (2) SYS–GMM (3)constant 1.230*** -0.005 -0.025 0.615 0.456 0.551*

(0.136) (0.022) (0.028) (0.299) (0.293) (0.294)labour 0.610*** 0.676*** 0.549*** 0.665*** 0.675*** 0.664***

(0.019) (0.063) (0.099) (0.066) (0.066) (0.066)ICT capital 0.242*** -0.016 0.040 0.075** 0.050 0.041*

(0.018) (0.021) (0.053) (0.036) (0.037) (0.023)non-ICT capital 0.146*** -0.002 0.102 0.233*** 0.230*** 0.232***

(0.014) (0.072) (0.234) (0.043) (0.044) (0.042)East -0.101*** — — -0.396*** -0.397*** -0.404***

(0.041) (0.046) (0.047) (0.045)observations: 5355 4109 4109 5355 5355 5529number of firms: 1246 1246 1246 1246 1246 1292R-square 0.848 0.315 0.211 0.851 0.848 0.840joint significance,Wald-test [df]: 6906[4]*** 125.5[3]*** 35.1[3]*** 567.8[4]*** 573.1[4]*** 601.0[4]***Sargan (p–values): — — 0.043 0.091 0.073 0.046AR(1) of errors 0.000 0.000 0.002 0.001 0.001 0.001(p–values)AR(2) of errors 0.000 0.362 0.400 0.291 0.279 0.271(p–values)

***,**,*=significant on the 1,5 and 10 per cent levelAll regressions except SYS-GMM (2) contain industry dummy variables interacted with year dummyvariables. Heteroscedasticity consistent standard errors reported.

The unplausibly low estimates of the capital coefficients in the within estimates maybe due to a downward bias of the point estimates due to measurement errors as arguedby Griliches and Hausman (1986). Measurement errors, however, are very likely to besubstantial in both types of capital stocks. First, since there is no information availableabout the share of expansion investment in total investment expenditures, commondepreciation rates were assumed for all firms. This may have induced a significant(though presumably not systematic) measurement error into the construction of thecapital stocks. Second, there was no initial value of the capital stocks available in thedata employed here. For the calculation of approximate values for the initial stocks,however, both the depreciation and the pre–sample growth rates of the capital stocks hadto be assumed equal across firms. Again, deviations from this assumption are very likelyand will add much noise to the calculated values of ICT– and non–ICT stocks. On thecontrary, the measurement errors for labour input will be less severe, even though thetransformation of part–time workers into full–time equivalents may — apart from thewell–known problem of overtime accounting — add some measurement error here as well.

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On the other hand, the downward bias due to measurement error may be offsetby a second type of error which is simultaneity. If input and output are determinedsimultaneously, exogenous demand shocks result in an increase of both input and output.In this case, the output elasticities of the corresponding factors would be biased upwards.33

In order to correct for these two potential biases, the GMM estimator is appliedfor the production function in first differences. Similar to the proceeding proposed inMairesse and Hall (1996), all three inputs are assumed to be predetermined and the(log) levels of their lagged values xt−2, xt−3, ..., x0 are used to instrument the input indifferences ∆xt = xt − xt−1.34 The corresponding results in column 3 of table 1 showthat in this specification the point estimates for the capital coefficients increase while thelabour elasticity decreases as compared to the within estimator. These findings indicatethat the measurement error bias in the capital coefficients exceeds the counteractingsimultaneity bias.35 However, the capital coefficients remain insignificant from zero inthis specification. Furthermore, the low p–value of the Sargan test rejects the validity ofthe instruments employed in this specification.

A further estimation issue related to the simultaneity issue is the potential au-tocorrelation of the errors. If the exogenous shocks (demand shocks, cyclical effectsetc.) are autocorrelated and this effect is not taken into account, the estimates willnot be consistent. Therefore, the p–values are reported for the corresponding AR(1)–and AR(2)–tests of the errors in the corresponding specification. In the specification infirst differences of the variables, however, the first–order correlation of the errors will beinduced by the data transformation.36 Therefore, the relevant test for equations in firstdifferences is whether the corresponding errors are AR(2) or not. As shown in table 1,autocorrelation of the errors can be rejected for all specifications except simple pooledregression.

A possible reason for the insignificant capital coefficients found in the GMMregressions is the small power of the instruments used.37 Blundell and Bond (1998b)

33The simultaneity bias might apply in particular to those factors that can be adjusted rapidly whichis not so much the case for capital stocks. For a simple formal derivation of the origin of the simultaneitybias, see Griliches and Mairesse (1995).

34This means that the firms’ corresponding fixed effect are eliminated by explaining output growth bythe growth rates of the inputs.

35These findings coincide with similar results in Black and Lynch (2001) for estimates of the productionfunction with one type of capital only.

36It is easy to see that if the errors εit are i.i.d. with variance σ2 their corresponding first differenceswill be AR(1): E(∆εit ·∆εi,t−1) = E((εit − εi,t−1)(εi,t−1 − εi,t−2)) = −σ2.

37Since capital stocks within firms are highly persistent over time, the correlation of the first differenceswith the second lag in level is close to zero. Formally, this can be illustrated by assuming Kt being

19

show that this may result in substantial finite–sample biases when the GMM estimatorin first differences is used. In a specific application to production function estimation,Blundell and Bond (1998a) argue that the weak instruments will bias the differencedGMM estimates in the direction of the within group estimation, that is towards zeroin the case of the capital coefficients. They use an (extended) SYS–GMM estimatorin which both the equation in differences is instrumeted by suitably lagged differences(like in the simple GMM–estimation) and the equation in levels is instrumented bysuitably lagged differences additionally. These two specifications are then estimatedsimultaneously. This SYS–GMM estimator was originally proposed by Arellano andBover (1995). The additional moment conditions required for the equation in levels arenot very restrictive. As shown by Blundell and Bond (1998b), only weak assumptionsabout the initial distribution of the variables used are necessary. In particular, thejoint stationarity of the dependent and the independent variables is a sufficient, yetnot necessary condition for the validity of the moment conditions for the equation in levels.

The corresponding results for the SYS–GMM estimation are given in column 4 oftable 1. In this specification, all three factor inputs are significantly positive. The outputelasticity of labour amounts to two thirds which is consistent with the share of incomefrom labour in the aggregate statistics (see footnote 27). The coefficients of ICT andnon–ICT capital amount to 7.5% and 23.3% respectively, which coincides with the incomeshare from total capital goods of roughly one third. The null–hypothesis of constantreturns to scale (CRS) cannot be rejected at the 1%–level (not reported). A furthervery robust result is that East–German firms in services are significantly less productivethan their West–German counterparts. The coefficient of the East–Dummy (roughly-0.4) implies that the multifactor productivity in East–German firms is still only abouttwo–thirds of the West–German level. This finding coincides with aggregate statistics onproductivity differentials in Germany. The corresponding Sargan–statistic (p = 0.093)does not reject the validity of the instruments at the 5%–level. These robust results in-dicate that there are substantial output contributions of ICT in the German service sector.

In order to further investigate the sources of potential biases in assessing theproductivity effects of ICT, the effect of allowing for different business cycles andinflation rates between industries by including interacted time and industry dummieshas also been analyzed. In order to illustrate the importance of this procedure, theSYS–GMM estimation with simple (not–interacted) time and industry dummies were

AR(1): Kt = ρKt−1 + εt with ε ∼ i.i.d. If Kt is weakly autocorrelated (|ρ| ¿ 1 and ρ 6= 0), the pastlevels are correlated with the contemporaneous levels. For the first available instrument Kt−2, this is:E(∆Kt · Kt−2) = E((Kt − Kt−1) · Kt−2) = E(Kt · Kt−2) − E(Kt−1 · Kt−2) = ρ2 − ρ. However, if theevolution of Kt resembles a random walk (ρ ≈ 1), the correlation between the variable in differences andits past values in levels will disappear (ρ2− ρ ≈ 0) and the instruments will therefore turn out to be weak.

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repeated. The corresponding results reported in column 5 (“SYS–GMM [2]”) of table 1show that the coefficient of ICT capital is substantially affected by this change in theestimation specification. The corresponding point estimate reduces to roughly 5% and,more importantly, is not even significant at the 10% level. In contrast, the coefficients ofthe other explanatory variables do not exhibit any remarkable changes.38 These resultssuggest that the inclusion of interacted industry and time dummies is an importantprerequisite for assessing the contributions of ICT correctly. In fact, the impossibilityof controlling for these effects might be an important methodological reason for theinsignificant output contributions of ICT and the ‘productivity paradox’ found in earlierstudies.

Another source of distortion is considered in the last column of table 1 (SYS–GMM[3]). The corresponding results are based on exactly the same estimation method ascolumn 4 (SYS–GMM). However, the sample was extended by those 46 firms that havereported zero ICT investment for all years observed (‘extended sample’, see section3). The inclusion of these firms substantially lowers the point estimate for ICT (4.1%)compared to the values reported for the original sample (SYS–GMM). Moreover, theICT coefficient is significantly positive at the 10% level only. These results appear veryreasonable if one considers that firms may differ in their output elasticities. Those firmswith a low output elasticity of ICT are maximizing profits with a lower share of ICTcapital in output; excluding these firms might therefore overstate the ICT coefficient dueto sample selection bias.39

Finally, an important issue of estimating the productivity effects of ICT in theproduction function framework is the potential bias in the estimates from omittedvariables that are potentially complementary to the firm’s use of ICT. In particular,recent studies find that human capital plays an important role in this regard (Bresnahanet al., 2001). Furthermore, Brynjolfsson and Yang (1999) argue that the use of ICTis strongly complementary to intangible assets. On the one hand, ignoring thesecomplementary factors might lead to an overestimation of the true impacts of ICTon production if the output contributions of these factors are wrongly assigned to themere use of ICT. On the other hand, a firm’s human capital and intangible assetsare likely to be quite persistent. If these complementary assets hardly vary over time,their effect will not be distinguishable from other factors like management skills etc.which are controlled for as unobserved heterogeneity between firms. In this case, no dis-tortions are to be expected from the omission of these variables in the estimation equation.

38Again, the Sargan statistic does not reject the validity of the instruments (p = 0.073).39The Sargan statistic of the extended sample, however, indicates that the validity of the instruments is

rejected at the 5%–level (p = 0.046).

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Table 2: The effects of including human capital

Dep. Variable: log salesproduction inputs full small small w. skillconstant 0.615 0.426 0.637

(0.299) (0.418) (0.303)labour (log) 0.665*** 0.653*** 0.584***

(0.066) (0.101) (0.061)ICT capital (log) 0.075** 0.019 0.087**

(0.036) (0.045) (0.039)non-ICT capital (log) 0.233*** 0.244*** 0.172***

(0.043) (0.070) (0.051)East-Germany -0.396*** -0.364*** -0.363***

(0.046) (0.063) (0.058)% university – – 0.472***

(0.170)% vocational – – 0.318***

(0.106)observations: 5355 2060 2060number of firms: 1246 639 639R-square 0.851 0.826 0.835joint significance,Wald-test [df]: 567.8[4]*** 196.6[4]*** 364.9[6]***Sargan (p-values): 0.091 0.679 0.206

***,**,* = significant at the 1, 5 and 10 per cent levelAll regressions are based on SYS-GMM and contain industry dummy variables interacted with yeardummy variables. Heteroscedasticity consistent standard errors reported.

In order to assess the potential biases from omitting the probably most importantsource, human capital, the list of independent variables was extended by the share ofemployees with vocational training and with university degree or equivalent correspond-ingly. As discussed in section 3, the resulting ‘small sample’ consists of only 639 firms.The results of the corresponding regression are surveyed in table 2. The first columnreplicates the “SYS–GMM” results obtained from the large sample (with interacted timeand industry dummies, fourth column of table 1) to facilitate comparisons while thesecond column displays the corresponding results for the small sample. The most strikingresult is that while the coefficients of labour and non–ICT remain more or less unaffectedby the sample reduction, the point estimate of the ICT coefficient decreases substantiallyand becomes insignificant. This is likely to be the result of the information loss due tothe much smaller number of observations. The effect of including the proxies for humancapital in the regression becomes obvious from the results displayed in the third column

22

of table 2. Including the human capital variables reduces the coefficients of both labourand non–ICT capital but leads to a substantial increase in the estimated ICT elasticity.These quite astonishing results seem to imply a complementary relationship betweenhuman capital and non–ICT capital rather than ICT input. Most importantly for theexplorative analysis here, however, the omission of human capital obviously induces anunderestimation rather than an overestimation of the productivity impacts of ICT.

Taken together, the findings of this subsection indicate that unobserved hetero-geneity, measurement errors and industry–specific time effects may lead to substantialdistortions in assessing the productivity impacts of ICT. Ignoring heterogeneity maylead to a substantial overestimation of these impacts while measurement error and theomission of industry–specific cyclical effects and price deflators work in the oppositedirection. The potential upward bias from simultaneity issues is – at least compared tothe measurement error bias – relatively low. When all these effects are controlled for inan adequate SYS–GMM estimation (column 4 of table 1), reasonable results are obtainedthat are consistent with the rough income shares of the different inputs in aggregatestatistics. A further explorative analysis shows that the omission of human capital doesnot lead to an over– but rather an underestimation of the productivity impacts of ICT.

4.3 The role of innovative experience

After assessing the methodological robustness of the SYS–GMM estimator for theproduction function framework, this estimator is applied to the regression equation 6. Inorder to investigate the role of innovative experience for the success in implementing newtechnologies, ICT capital interacted with innovator dummies is included in the regressionas an additional variable (see section 2.2). A potential bias in favour of the hypothesesto be tested may arise by the mere fact that innovators will be more productive thanother firms due to the returns to their (intangible) R&D or innovation capital that islikely to be higher for firms with innovative experience. In order to control for this effect,the corresponding innovator dummy was included as a further variable that captures thedirect productivity contribution of innovations.40 The results are based on the extendedsample in order not to exclude firms with potentially low output elasticities of ICT (seecomments in the previous subsection).

In the first column, of table 3, the results are replicated for the specification inwhich a ‘surplus’ of ICT contributions (∆x) is allowed for in firms with experience inintroducing process innovations according to the narrow definition (epz). The most

40Both product and process innovations may lead to an increase in productivity by either raising thesales due to improved service quality or by lowering input costs due to more efficient processes.

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Table 3: The role of innovative experience

Dep. Variable: salesproduction inputs (epc) (epd) (ppc) (epc) (epd) epc w/o impconstant 0.717*** 0.495* 0.497* 0.749*** 0.616** 0.794***

(0.238) (0.266) (0.257) (0.233) (0.257) (0.254)labour 0.607*** 0.658*** 0.670*** 0.582*** 0.641*** 0.611***

(0.238) (0.063) (0.060) (0.051) (0.057) (0.057)ICT capital 0.032 0.012 0.027 0.051** 0.038 0.066

(0.022) (0.025) (0.029) (0.022) (0.026) (0.037)non-ICT capital 0.237*** 0.246*** 0.248*** 0.226*** 0.240*** 0.230***

(0.041) (0.042) (0.039) (0.055) (0.059) (0.043)innovator 0.188* 0.087 0.006 0.296 -0.007 0.127

(0.098) (0.101) (0.124) (0.223) (0.135) (0.110)ICT capital 0.087** 0.061* 0.015 0.060* 0.012 0.074(innovator) (0.039) (0.033) (0.035) (0.034) (0.042) (0.047)non-ICT capital — — — -0.008 0.018 —(innovator) (0.074) (0.078)East-Germany -0.390*** -0.396*** -0.403*** -0.368*** -0.397*** -0.383***

(0.043) (0.042) (0.043) (0.045) (0.042) (0.044)

observations: 5529 5529 5529 5529 5529 5355number of firms: 1292 1292 1292 1292 1292 1246R-square 0.842 0.842 0.841 0.834 0.842 0.852joint significance,Wald [df]: 1135[6]*** 919.4[6]*** 901.0[6]*** 1098[7]*** 1102[7]*** 1076[6]***Sargan (p-values): 0.085 0.069 0.124 0.046 0.123 0.194

***,**,* = significant at the 1, 5 and 10 per cent level. All regressions are based on SYS-GMM andcontain industry dummy variables interacted with year dummy variables. All variables in logs, exceptinnovator and East-Germany dummies. Heteroscedasticity consistent standard errors reported. Thedefinition of innovating firms (‘innovator’) varies between columns according to the abbreviation given inthe top row of each column (see text).

striking result is that the ICT coefficient, in fact, differs significantly between experiencedand not experienced firms. For epz–firms an implicit output elasticity of x′ = 11.9%is found.41 that is significantly higher than the coefficient of the non–experienced(p–value of the difference: 0.024). On the contrary, the corresponding ICT coefficientfor not experienced firms is quite low (3.2%) and insignificant. Interestingly, the dummyfor process innovation experience is highly significant as well. Obviously, beyond theimportance for ICT use there are direct benefits from new processes introduced in thepast as well. The significant coefficient of 0.188 of the epz–dummy implies that, on

41Note that the estimated ICT coefficient for innovators represents the difference between the elasticitiesof experienced and non–experienced firms ∆x (see equations 5 and 6). The implicit ICT elasticity of theexperienced firms x′ = x + ∆x is just the sum of the two ICT coefficients in each regression.

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average, firms with process innovation experience are about 20% more productive thanother firms.42

In the second column of table 3, the results are reported for the correspondingdistinction of firms according to their experience of product innovations (epd). The resultsare very similar to those in column 1. However, the coefficient marking the difference inICT contributions is significant only at the 10% level and the implicit output elasticityfor epd–firms (7.3%) is considerably smaller than in the regression with the classificationaccording to process innovation experience (epc).43 Given that more than 70% of theproduct innovators are also experienced in process innovation (see table 5), this differencemay be a consequence of the simultaneous process innovation experience rather than theexperience collected in the turn of product innovations. As a first preliminary result,it may be concluded that past process innovations have the biggest impact on a firm’sproductive use of ICT.

In column 3, the results for the less restrictive differentiation between firms with someprocess innovation (“panel process innovators”) are replicated. As discussed in section 3,this wider definition captures both firms with innovative experience and firms that have in-troduced innovations at a later point in time during the period observed. Therefore, apartfrom ‘experience’, this classification variable also includes implementation strategies. Inthe application of this definition, there is no significant difference between innovators andother firms regarding the productivity of ICT use. This finding suggests that it is indeedthe experience generated by a firm’s innovation history that facilitates an efficient ICT use.

Obviously, ICT productivity is higher in firms with experience in process innovation.But is this a special feature of ICT as opposed to conventional capital? In order toaddress this second question, non–ICT capital interacted with the dummy for experiencedprocess innovators has been added to the regressors in a further specification (col. 4in table 3). The results show that, in fact, there is a remarkable difference betweenboth types of capital. While ICT capital — like in column 1 — continues to be moreproductive in experienced firms (at the 10%–level however), no such difference can beobserved in the case of conventional capital. In the corresponding specification for productinnovators (column 6), however, there is no significant difference between both types offirms for either kind of capital. Taken together, these findings indicate that the effectsof innovation experience are obviously very specific with respect to two aspects: it ismainly experience from process innovations that matters, and it is the productivity of

42Compared to the SYS–GMM results in table 1, the other coefficients are hardly affected by the addi-tional variables included in the regression, which points to the robustness of the results.

43A further difference to the preceding results is that there are no significant direct productivity effectsresulting from past product innovations.

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ICT capital that is essentially affected.

Finally, the robustness of the findings of the first specification (process innovationexperience) was investigated with respect to changes in the sample. If firms that havereported zero ICT investments are excluded from the regression (that is if the ‘full’instead of the ‘extended’ sample is used; see column 6 of table 3), the difference in ICTproductivity between experienced and non–experienced firms becomes only marginallysignificant (p=0.110, not reported). However, the point estimates of the coefficients donot change greatly, indicating that the exclusion of firms with a potentially low ICTproductivity mainly affects the precision of the distinction between the two groups offirms analyzed.44

To sum up, the results for the empirical model specified in equation 6 deliver strongsupport in favour of the initial hypotheses. Firstly, innovative experience is found tosignificantly influence the productivity potentials of ICT use in services. Secondly, thisdependence on a firm’s innovative history apparently is a feature that distinguishes ICTfrom other capital goods. Beyond this, the findings suggest that it is experience collectedfrom past process innovations which is particularly worthwhile for the efficient use ofICT. This finding is in line with other studies that emphasize the close link between ICTuse on the one hand and organizational change and restructuring of business processeson the other. Because of this close link, experience from past innovations may reducemistakes and will improve expectation formation with regard to the costs and benefits ofICT–induced changes.

44In some further excercises to validate the robustness of the results, only insignifcant differences betweenthe ICT coefficients of experienced and not experienced firms were found for the small sample of 639 firmsthat includes information on human capital. This result was independent of the actual inclusion of humancapital. Furthermore, in the small sample specification without human capital, the result yielded nosignificant output contribution of ICT at all. These findings can be interpreted as an indicator thatthe precision loss due to a smaller sample size is an important issue in this empirical framework. Notsurprisingly, the need for a large sample for more precise estimates is most important for the capital inputsfor which measurement error was found to be an important issue.

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5 Conclusions

In this paper, the productivity effects of ICT use in the German business–related anddistribution services are analyzed with firm–level data. Using a production functionframework, two types of models are analyzed. Firstly, for the simple Cobb–Douglasspecification with labour and two types of capital as inputs, a highly significant outputelasticity of ICT–capital of 7.5% is found, indicating substantial productivity effectsof ICT in the service sector. These estimates are based on a SYS–GMM estimatorthat controls for a variety of potential estimation biases, like unobserved heterogeneity,simultaneity issues and measurement errors. Secondly, based on a theoretical model, theproduction function framework has been extended to allow productivity contributions ofICT capital to vary between firms. This more detailed analysis reveals that firms thathave introduced process innovations in the past — labelled ‘experienced’ firms — areespecially successful in ICT–use. The output elasticity of ICT in these firms amountsto about 12% and is significantly higher than for non–experienced firms (3%). On thecontrary, no such difference can be observed for conventional capital. These findingssupport the hypotheses developed in this paper which assign ICT the role of a ‘special’capital input: unlike other capital goods, the productive use of ICT is closely linkedto innovations in general and the re-engineering of processes in particular. Firms withexperience in process innovations are therefore predicted to exploit the potential benefitsof new technologies more successfully than other firms.

There are several implications of these findings concerning theoretical, methodologicaland policy issues. At the theoretical level, the results contribute to a clarification of therole of ICT as a general purpose technology (GPT). In spite of the diverse uses and therapid diffusion of ICT throughout all industries, the productivity effects of ICT are farfrom self–enforcing but rather demand an active implementation strategy within firms.The role of innovative experience found in this paper indicates that the determinantsfor the efficient use of ICT are in the range of a firm’s long–term strategies rather thancharacteristics that can be changed easiliy in the short term. Innovative experience islikely to be acquired within years rather than months.

Furthermore, the role of innovative history found at the micro level may also beuseful to shed more light on the differences of ICT–induced productivity effects foundbetween countries. In fact, the competitive and innovative business environment in theU.S. may be one reason that helps explain why the productivity impact of ICT has beenmuch higher there than in continental Europe. The higher innovation pressure in theU.S. over the last decades may have led firms to collect much more diverse innovativeexperience than the more protected firms in Europe. This may have enabled firms in theU.S. to recognize the productive value of the wave of ICT–induced innnovations faster

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and to react more flexibly with an appropriate restructuring and re-engineering of theinternal processes in order to reap high benefits from the use of ICT. In this respect, ICTmay have led to a further widening of the productivity gap between the U.S. and Europe.

As far as the empirical methodology is concerned, the findings of this paper illustratethat unobserved heterogeneity, measurement errors and omitted variables (includingdifferences in price and quality evolutions over time) are important sources of estimationbiases in assessing the impacts of ICT within a production function framework. Whileunobserved heterogeneity may result in a substantial overestimation of ICT impacts,measurement errors forcefully work into the opposite direction and may lead to anapparent affirmation of the ‘productivity paradox’. Furthermore, as shown in this paper,particular firm characteristics may play a key role for the potential impact of ICT onfirm performance. The results suggest that firm–level studies form a promising basis ofanalysis that may add more detailed insights and further complementarities between ICTand firm characteristics in future research.

As far as economic policy is concerned, the findings of this paper point to theimportance of an innovative business environment that is needed to lay the fundamentalsfor an efficient use of ICT. New technologies like ICT may be compared to the inventionof a new fertilizer in farming: though its potential uses may be very general and its costsquite low, a sound climate, a cultivated soil and a gifted farmer will still be needed toactually increase crop yield. Unlike the case of farming, however, the climate in economicsmay be favoured to a large extent by sound policies. The results of this study suggestthat the enhancing of competition and innovation incentives may serve as an importantdriver of both the rapid diffusion and a productive use of ICT.

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Appendix

Table 4: Summary statistics of the variables

full extended smallmean std. mean std. mean std.

log(output*) 2.329 1.899 2.291 1.895 2.127 1.698log(labour**) 3.885 1.693 3.842 1.700 3.717 1.550log(ICT stock*) -1.999 2.115 -2.294 2.673 -2.236 1.929log(non–ICT stock*) 1.049 2.607 1.021 2.594 0.863 2.473East 0.420 0.494 0.420 0.494 0.437 0.496# firms 1246 1292 639# observations 5355 5529 2060*measured in million DM, ** measured in # employees

Table 5: Summary statistics on firms’ innovative experience

share of innovatorsfull extended small

total (# firms) 1246 1292 639ppc 72.8% 71.7% 74.2%ppd 79.4% 78.6% 81.7%epc 45.8% 44.7% 48.5%epd 56.5% 55.5% 61.0%both epc & epd 40.3% 39.3% 42.9%

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Table 6: Comparison of sample and population by industries

full sample population*industry nace–digit # firms share (%) share (%)wholesale trade 51 177 14.2 10.6retail trade 50, 52 197 15.8 31.3transport and postal services 60–63, 64.1 222 17.8 11.7electronic processing and telecom. 72, 62.2 102 8.2 3.4consultancies 74.1, 74.4 444 8.3 12.1technical services 73, 74.2, 74.3 152 12.2 10.7other business–related services 70, 71, 74.5-.8, 90 292 23.4 20.3total 1246 100 100*German service firms with 5 and more employees in 1999.

Source: German Statistical Office, ZEW and own calculations

Table 7: Comparison of sample and population by size classes

full sample population*size class(# employees) # firms firms (%) firms (%) sales (%)

5–9 212 17.0 57.6 9.410–19 202 16.2 24.0 9.920–49 260 20.9 11.7 9.750–99 162 13.0 3.5 6.9100–199 169 13.6 1.6 6.0200–499 106 8.5 1.0 7.0500 and more 135 10.8 0.6 51.1total 1246 100 100 100*German service firms with 5 and more employees in 1999.

Source: German Statistical Office, ZEW and own calculations

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Table 8: Median of capital and output intensity of production for the full sample

all firms experienced (epc) not experiencedICT per worker 3,189 4,514 2,186non-ICT per worker 54,560 54,417 55,213sales per worker 200,140 195,960 207,100

# firms 1246 571 675

Values in DM. The figures are calculated as the median of the unweighed firms’ means over time, basedon the full sample of 1246 firms.

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