Innovation and Productivity in KnowledgeIntensive Business Services

Antonio Musolesi and Jean-Pierre HuibanCESAER, UMR1041, INRA Dijon

This version 30/03/2007

Abstract

This paper analyses a two-equation structural model derived from the socalled �knowledge production function�. We estimate simultaneously an in-novation function and an �augmented�production function where innovationenters as a measure of Hicks neutral technical change. We focus on FrenchKnowledge Intensive Business Services and make use of the third CommunityInnovation Survey, CIS3 (1998-2000). We also use CIS2 (1994-1996) in orderto introduce a dynamic behaviour of both innovation and production func-tions. The results show that di¤erent types of innovations rely on varioussources and have di¤erent e¤ects on �rm productivity. From the estimationof the dynamic model we �nd that past innovation does not enhance directlyproductivity. However we �nd an indirect e¤ect of innovation on produc-tivity via the innovation function which presents a signi�cant autoregressivecomponent.

1 Introduction

The relationship between innovation and the productivity of the �rm hasreceived considerable attention, as surveyed by Griliches (1998). Followingthis research line, our paper presents two speci�c interests: its empirical �eldand its econometric modelisation.Most of the previous studies concern manufacturing. One knows that

there is now a well-established relationship between innovation and produc-tivity in the case of these industries. Our study is devoted to service ac-tivities, and more precisely to what is called Knowledge-Intensive-Business-Activities (KIBS thereafter). Innovation in such activities has not been sooften studied, with some recent exceptions as works by Cainelli et al.(2005),or Vang et al. (2005). Moreover, there is a discussion about the validity ofsome usual analytical framework, as the well-known knowledge production,when applied to services. Some authors, as Gallouj (2002 ), consider thatboth the nature of output and the innovation process in services invalidate theuse of such tools. A �rst task within this paper is to use the Hill�s typology(Hill, 1999). Following this author, we show that KIBS should be consideredas intangible goods instead of services in the typical sense. Then the usualmodel, involving the innovation as an input in the production function, stillapplies.So, we shall use the Duguet (2006) econometric framework. Our model is

composed of two parts: the innovation equation and the production function.We do not use R&D or patents as proxies for innovation, but a direct mea-sure, provided by the CIS survey (namely CIS2 and CIS3). Our results �rstshow that innovation in KIBS is as frequent as in manufacturing whateverkind of innovation is considered. This survey also provides us informationabout the sources of innovation. Innovation in KIBS does not use exactly thesame channels as in manufacturing. The in�uence of R&D activities seemsto be as high as in the case of manufacturing. But external R&D plays avery important role, particularly in the case of product innovation. Inno-vation can also be issued from less formalized sources of knowledge: publicsector sources seem to be particularly used, much more than market sources.Of course the respective importance of sources depends on which kind ofinnovation is considered. Technological innovation is more often issued fromR&D than non-technological one. All this could suggest a di¤erence in thenature of innovation according to the industry which is considered, but thisis a complex, non-hierarchical one. In the second part of the model, inno-vation is introduced as an input in the production function. This leads tothe achievement of a signi�cant estimate for this variable, while the esti-mated elasticity is about 0.3. Thus innovation exists in KIBS and has a deep

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in�uence on the productivity of the �rm .A last interest of this paper is the use of a dynamic model. Two surveys,

CIS2 and CIS3 are available. Then one can build a model where productivityis a function of the contemporary but also of the past value of innovation.The paper results �rst con�rm the well-known assumption of innovation per-sistency. The innovation propensity at a given period (1998-2000 in the caseof CIS3) is signi�cantly and positively a¤ected by the existence of innovationat the previous period (1994-1996 in the case of CIS2). By the other hand theestimation of the dynamic model does not exhibit any signi�cantly positivee¤ect of past innovation on present productivity. All this could suggest thatthe past innovation e¤ect is only an indirect one: it does not directly a¤ectthe present productivity but favour the present innovation which has itselfan important e¤ect on productivity.The paper is structured as follows. Section II presents the empirical �eld:

services, and more precisely KIBS. Section III introduces the model. Dataare presented in section 4. Section 5 presents the results. Section 6 concludesand suggests some further improvements.

2 The empirical �eld: KIBS

This section focuses on our empirical �eld: KIBS. We �rst recall what areservice activities, using both the well-known �technical�distinction betweengoods and services and the Hill�s (1999) taxonomy. The Hill�s approach usesthe concept of intangible goods and seems very useful in order to introduceand conceptualize KIBS. Finally, once KIBS have been de�ned, we exam-ine their characteristics in terms of innovation and productivity. We thenconclude that the empirical models derived from the so called �knowledgeproduction function�, which are generally used to study innovation in man-ufacturing, can be reasonably applied to KIBS activities.

2.1 Services: Towards a de�nition

All start with the de�nition of the service sector. According to the OECD(2000, p. 7), services are:"a diverse group of economic activities not directly associated with the

manufacture of goods, mining or agriculture that include high-technology,knowledge-intensive sub sectors, as well as labour-intensive, low-skill areas."This is a broad and "negative" de�nition, indicating what services are

not. This de�nition also suggests the great heterogeneity which exists withinservices. According to the two-digit NACE revised 1.1 classi�cation, market

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services include the following wide range of economic activities (with the as-sociated code between brackets): wholesale (50, 51), retail trade (52), hotelsand restaurants (55), transportations (60-63), post and telecommunications(64), Financial activities and insurances (65-67), real estate (70), renting(71), computer and related activities (72), R&D (73), and other businessactivities (74).In spite of this heterogeneity many authors have attempted to de�ne

what are service activities. A �rst strand of literature, starting with classicaleconomists, points out the main particular characteristic of the service out-put, which is its intangibility. While Adam Smith (1776, book 2, ch. 3, p.136) de�nes it as a product which "perishes in the very instant of its produc-tion", Alfred Marshall (1920, p.47) mentions "Services and other goods, (...)pass out of existence in the same instant that they come into it". Accordingto this view of goods and services, the �nal output of an economic activity ismaterial if it takes the form of tangible things that have an existence in timeand space that is independent from that of their producers, their consumersand the production process that lead to their creation.Later, Fuchs (1968) introduces a second characteristic with the notion of

co-production or co-terminality suggesting that a close interaction betweenproducer and consumer takes place in order to achieve the desired serviceoutcome. This characteristic should make the distinction between productand process innovation di¢ cult. Finally, a third characteristic that distin-guishes services from goods is the fact that they cannot be neither held instock nor transported.This "technical" approach insists on the speci�city of service activities,

with respect to manufacturing. However, this approach does not really helpto analyze the heterogeneity which exists within the �eld of service sectors. Asecond strand of literature provides some useful tools in order to understandand characterize such an heterogeneity. Hill (1977) proposes the followingde�nition of services, widely adopted in the literature:"a service may be de�ned as a change in the condition of a person or

a good belonging to some economic unit, which is brought about as a resultof the activity of some other economic unit, with the prior agreement of theformer person or economic unit."This de�nition assumes the existence of a technical process, conducting

to a "change" in the state of nature and also includes social relationships,as an intervention request. Even when there is no material input, a concreteresult (a change in the state of nature) can be observed1. Twenty years afterhis �rst contribution, in a very signi�cant and appealing paper, Hill (1999)

1For a development of this concept, see Delaunay and Gadrey (1992).

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proposes a new de�nition, stating that "the habit of describing services asintangible products is an invention of economists" (Hill, 1999, p. 427) andthat "most goods are material objects, but goods do not necessarily have tobe material or tangible. Intangible entities exist which have all the economiccharacteristics of goods" (Hill, 1999, p. 427). Consequently he argues thatthe traditional dichotomy between goods and services should be replaced bya new taxonomy which introduces three output categories: tangible goods,intangible goods and services. Hill (1999, p. 437) de�nes a good as:"an entity over which ownership rights may be established and from which

its owner(s) derives some economic bene�t".According to this de�nition, when a good is produced (some goods can

also be found in nature) its production has two characteristics that are notshared with services. Firstly, the entire output of the production process isthe property of the producer. Second, the use of a good can be separatedfrom its production and take place afterwards. The separability of use andproduction is an important characteristic while it supposes that goods canbe held in stock.The main Hill�s original contribution is that such a de�nition of goods

allows for the existence of immaterial goods ("intangible entities"). Hillde�nes such "originals", as the fruit of creative activities (as a new scienti�cformula, a new computer program, a new original song...). Ownership rightscan be established which are of economic value for its owner. Such outputscan also be stored, recorded and transported, while they are not tangible inthe physical sense. Finally, the third category, services, di¤er from goods,both tangible and intangible. They are not entities and thus cannot bestocked and no property rights can be established over them. A symphonyis an intangible good while its performance, at a given place on a given day,is a service, involving proximity between producer and consumer, with nostorage possibility.This has practical and important consequence for our topic as, according

to Hill, "the traditional assimilation of intangibles with services has unfor-tunate consequences for the statistical system, standards and classi�cationsunderlying the data used for economic analysis" (Hill, 1999, p. 444). Moreprecisely, while some service industries still correspond to the usual represen-tation (as theatre or health, for instance), some others are more concernedby the Hill�s characterization of intangible goods. We shall see in the nextsection that KIBS are in such a situation.

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2.2 The Knowledge-Intensive Business Services: someintangible goods?

As suggested by their name, KIBS have two main characteristics. First,as "business services", they are connected with other private �rms and noteither to households or to public institutions. Secondly, they are "knowledge-intensive". However, giving an exact de�nition of this latter characteristiccould be a quite complicated issue. Some answers can be found in the lit-erature as by Grishaw and Miozzo (2006, p. 1), "these services involve theintensive use of high technologies, specialized skills and professional knowl-edge", or by Miles (1994, p.7), "knowledge intensive business-services involveeconomic activities which intended to result in the creation, accumulation ordissemination of knowledge".A broad de�nition of KIBS is given by Miles (2001, cited on Grishaw and

Miozzo, 2006, p.2), "KIBS encompass all those business services foundedupon technical knowledge and/or professional knowledge". Such a de�ni-tion capture both social/institutional knowledge of many traditional busi-ness services and the technological/technical knowledge of "high-tech" ser-vices. However, when applying this general de�nition, many sectors are stilldi¢ cult to classify, and some di¤erent methodological choices can be made.As an example, Grishaw and Miozzo (2006) include the following industriesin their KIBS de�nition: Computer science and related activities (NACE72), R&D activities (NACE 73), Other business activities (NACE 74). How-ever they are not totally satis�ed by this choice, while considering that somecomponents of "Other Business Activities", as "labour recruitment and pro-vision of personnel" or "investigation and security activities" are typicallynon-real KIBS activities. Consequently, we have to be very pragmatic whenempirically de�ning what is part of KIBS and what is not.However, whatever empirical de�nition of KIBS is used, we can reason-

ably assume that a large part of outputs produced by these activities possessthe characteristics of an intangible good, as it has been de�ned by Hill (op.cit.). Ownership rights can be established, outputs can be stored, recordedand transferred, even after the production process. This has some impor-tant consequences when considering, see below, the study of innovation insuch activities. First, considering goods rather than services means thatthe distinction between product and process innovation remains signi�cant.Second, it means that variables capturing protection for property rights canbe considered in our econometric model like in models adopted to studymanufacturing. Third, even when considering some output as services, itstechnological content makes adequate the standard technological de�nitionof innovation.

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2.3 Innovation and productivity in KIBS

Many quantitative studies focusing on innovation and its impact on produc-tivity have been conducted in the past years. The focus was the manufactur-ing sector and the econometric modelisation was derived from the neoclassicaltheory. In the early stages as in Minisian (1962) it was common to considera production function in which the R&D capital stock �accounting for theunobserved knowledge capital - enters as an additional separable factor ofproduction. More recently, applied works, as in Crépon et al. (1998) or asin Duguet (2006), were founded on the conceptual schema of the "knowledgeproduction function" (Griliches, 1979; Griliches and Pakes, 1984). This kindof model is composed at least of two equations. In the �rst one, innovation isthe output, which level depends on di¤erent sources (as R&D, for instance).In the second one, innovation (or another measure for unobserved knowledgecapital) enters the neoclassical production function.An important question is then to understand if this kind of approach,

commonly used in the case of manufacturing, remains adequate when study-ing KIBS.On one hand, services have been often considered as little productive and

innovative activities which makes the analysis of innovation and productivitynot very interesting from an economic point of view. On the other hand someother authors as Gallouj (2002) suggests that the usual measurement of pro-duction and productivity (see Griliches, 1992), as well as the technologicalde�nition of innovation, derived from the Frascati and Oslo Manual, wouldbe inadequate. However, they contest the vision of non-innovative activitiesarguing that the utilization of these usual measures of innovation and pro-ductivity would be at the origin of the poor reputation of services in termsof innovation propensity.We try now to answer these questions. First, with respect to the issue of

de�nition and measurement of innovation and productivity, it seems possi-ble to argue that the usual de�nition and measurement of productivity andinnovation raise of course important problems, when considering recreative,health or education services, but not in the case of KIBS, or at least notmuch more than in the case of manufacturing. This assumption is justi�edboth applying the Hill�s taxonomy and considering the technological contentsof KIBS as outlined in the previous section.Second, with respect to the issue of the innovation propensity and pro-

ductivity in services, there is now an increasing amount of works �conceptualand empirical ones- supporting the idea that service activities are often inno-vative, productive and use formal R&D (Baumol, 1967; Baumol et al. 1985,Miozzo and Soete , 2001; OECD, 2000, 2005; Wol�, 2005).

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For example, it has be said that the famous �cost disease� assump-tion does not apply for all services as for example telecommunication be-ing highly innovative activities (Baumol et al., 1985). Moreover, Miozzoand Soete (2001) propose a technological taxonomy of services in whichScience-based sectors are assimilated to the handful of manufacturing sec-tors -pharmaceutical and electronics � in terms of innovative activities antechnological progress.Indeed, some empirical works of the OECD (OECD, 2000, 2005; Wol�,

2005) support this perspective. According to Wol� (2005, p.54), "Severalservice industries are characterized by factors that drive productivity growth.This is notably the case for transport, storage and communications servicesand �nancial intermediation. These services (...) are important contributorsto overall business R&D or use new, productivity enhancing technologies suchas ICT."Table 1 illustrates the growth rate of labour productivity (Value Added

per Employee) for France and Germany. The whole service sector (NACE 50-99) exhibits a productivity growth rate signi�cantly lower than the manufac-turing. However some services as telecommunication (64) for both countriesor computer and related activities (72) for Germany have known a very highincrease of their productivity, signi�cantly higher than in Manufacturing.Indeed, we can also consider the results in terms of innovation propensity,

as provided by the CIS3 survey (OECD, 2005). The share of innovative �rmsin the EU was everywhere lower in services than in manufacturing. However,this was no more true when considering the business services2. While 48%of European manufacturing �rms were considered as innovative, the sharewas lower in wholesale and retail trade sector (38%), or Transports (29%),but signi�cantly higher in Business Services (62%). Japanese �rms werealso faced with the the same situation: 25% of manufacturing �rms declareto be innovative, while the ratio reaches 40% in Business Service sectors.Another interesting statistics to mention is the share of innovative �rmsmaking intramural R&D which was almost 75% for business services and lessthan 60% for manufacturing (OECD, 2005, EU average from CIS3).According to the above mentioned works, it clearly emerges that KIBS,

are not low-productive and few innovative sectors, even when using classicalconcepts and indicators. Consequently, we can reasonably argue that when

2According to OECD, Business Services includes the renting of machine and equip-ment, computer and related activities, R&D and other business services (NACE 71-74).Market services include wholesale and retail trade, hotels and restaurants, transports andcommunications, �nancial intermediation and real estate and business services (NACE 50-74). Total services include market services plus public administration, health, educationand other community, social and personal services (NACE 50-99).

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analyzing KIBS, it makes sense to study the sources and e¤ects of innovationby using the same analytical tools than in manufacturing. This does notimply, of course, that we expect to obtain similar results.

3 The model

Our model is consistent with the conceptual framework of the �knowledgeproduction function�proposed by Pakes and Griliches (1984) and with theequations systems�econometric modelisation by Crépon et al. (1998) and byDuguet (2006). As in Duguet (2006) we use a direct indicator of innovationrather than some proxies as the number of patents (which is an output ofinnovation) or the R&D expenses (one inputs an input of innovation).We suppose that the innovation activity is the main source of techno-

logical progress. As long as the analysis is conducted at the �rm level, theterm technological progress is used as a synonym of �relative e¢ ciency�of aparticular �rm.Let f : Rm+1 ! R1; a twice di¤erentiable production function whose

image is:

y = f (x; I)

where x is a m dimensional vector of inputs like capital, labor, etc., yrepresents output like added value and I is a measure of innovation andaccounts for technological progress. f is assumed to be quasiconcave ininputs, x, and non decreasing in technological progress I.We suppose Hicks neutral (HN) technological progress which requires

that the marginal rate of substitution between each pair of inputs be inde-pendent from technological progress. This de�nition of technological progressis equivalent to the separability of x from I in f (Leontief, 1947; Goldmanand Uzawa, 1964), namely:

f (x; I) � l (g (x) ; I)Assuming an input homogeneous technology, the HN technological progress

is equivalent to a situation in which the production function can be multi-plicatively decomposed into a function of inputs only and another functionof technological progress only (Blackorby et al. 1976):

f (x; I) � A (I) g (x)This situation implies that the rate of disembodied technological progress

can be written as:

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@ ln f (x; I)

@I� 1

f (x; I)

@f (x; I)

@I� A0(I)

A(I)= �(I):

By analyzing the speci�cation of the function �(I) we can also analyze thestructure of the function A(I). The two polar cases are: i) �(I) = 0 whichcorresponds to the absence of technological progress because A0(I)=A(I) =0 ) A(I) = A; ii) �(I) is a nonconstant function of I. We assume that therate of technological progress is constant, namely that �(I) = �. A0(I)=A(I) =� implies that A (I) = Ae�I :For estimation purposes, the explicit technology of a �rm i = 1; :::; n is

assumed to be a Cobb-Douglas type which has the usual multiplicative form:

yi = Ae(�Ii)

Qmj=1 x

�jji

Taking natural logarithmic and adding a disturbance term for estimationwe get:

ln yi = lnA+ �Ii +Pm

j=1 �j lnxji + "i

where "i is the usual white noise error term. The term A accounts fora �macroeconomic� level of technology, being constant across �rms. Alter-natively, A can be be replaced with industry dummies measuring industrydi¤erences in the level of technology.The main focus of this paper is the assessment of the impact of innova-

tive output on the productive e¢ ciency of the �rm. The innovative output ishowever endogenous depending on innovative inputs like formal internal andexternal R&D, informal knowledge and other determinants like size, mar-ket pull, technology push, etc. For this reason we specify an �innovationproduction function�which relates the innovative inputs to the innovativeoutputs.We suppose that it exits an unobservable latent innovation variable, I�,

which represents a continuous measure of the innovation potential, being afunction of z;I� = �(z); where z is an l dimensional vector of innovativeinputs and � is a function � : Rl ! R1. Supposing linearity and adding adisturbance term for estimation purposes, we obtain:

I�i = c+Pl

k=1 �kzki + ui

and an e¤ective innovation is observed when this latent variable cross athreshold &:

Ii =

�1 if I�i > &0 if I�i � &

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Before obtaining the system of equations to be estimated we have to doa further assumption about the e¤ect of innovation on �rm e¢ ciency. Weassume here that only practical implementations of innovation (I) matter forimproving technological progress rather than the innovative potential (I�).This hypothesis is consistent with Duguet (2006) as well as with the endoge-nous growth models where innovation is represented as succession of produc-tivity shifts (Aghion and Howitt, 1998). The �nal model to be estimated isthus:

ln yi = lnA+ �Ii +Pm

j=1 �j lnxji + "i (1)

Ii = 1(I�i >&)(2)

I�i = c+Pl

k=1 �kzki + ui (3)

The use of two CIS surveys, namely CIS2 (1994-96) and CIS3 (1998-200) allows the introduction in the model of some dynamic behavior in theinnovation process. First we are interested in measuring and testing the�long-run�(4 years) impact of innovation on productivity. It can be done byintroducing the past innovation in the equation explaining current output:

ln yi = lnA+ �Ii + �Ii(�1) +Pm

j=1 �j lnxji + "i

where the script (-1) indicates that the variable under consideration refersto the CIS2 survey while the absence of such script indicate that the variablerefers to the CIS3 survey. As innovation is widely considered to be a keylong-term driving force for economic growth, then answering to the issue ofthe time-length of the e¤ect of innovation on �rm productivity can be helpfulboth for the empirical understanding of long-run growth and for theoreticalworks.Secondly, we introduce a dynamic behavior in the �innovation production

function�. According to the theoretical literature there are some potentialreasons explaining the time dependence in the innovation production as the(i) hypothesis of success breeds success (Mans�eld, 1968) and the ii) hy-pothesis of dynamic increasing returns involved by the innovation activity(Nelson and Winter, 1982). Moreover, this research question plays an impor-tant role in the context of the "competitive" endogenous growth models byRomer (1990) and by Aghion-Howitt (1992)3. Consequently we specify theequations system in our dynamic model as:

3More recently, Aghion et al. (1997, 2001) show that the persistence of innovation isadmitted and could be justi�ed by di¤erent microeconomic assumptions.

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ln yi = lnA+ �Ii + �Ii(�1) +Pm

j=1 �j lnxji + "i (4)

Ii = 1(I�i >&1)(5)

I�i = c+ Ii(�1) +Pl

k=1 �kzki + ui (6)

Ii(�1) = 1(I�i (�1)>&2)(7)

I�i (�1) = d+Pl

k=1 �kizk(�1) + vi (8)

where equations (5) to (6) represent the dynamic innovation productionfunction while equations (7) to (8) account for the CIS2�s static innovationproduction function.In order to estimate the model, we follow Duguet (2006) and use both a

two step methodology and GMM. In the two-step methodology after assum-ing that the error term in the innovation function has a logistic distribution,the innovation function can be estimated by LM using the dichotomous vari-able I (�rst step):

E (Ii j zi) = exp�c+

Plk=1 �kzki

� h1 + exp

�c+

Plk=1 �kzki

�iIn the second step, the production function is estimated by replacing I

with the innovation predicted probability bE (I j zi). We correct the two-step standard errors using the heteroskedastic consistent covariance matrixof White (1980).We also estimate the augmented production function with GMM using

the vector of innovation inputs, z, as instruments for I. In this context theGMM reduces to the two-stage instrumental variable of White (1982). TheGMM is more general than the two-step procedure in the sense that it doesnot impose to the error term of the innovation equation to be logistic butit has a lower interpretative power estimating only the production functionequation.

4 Data

4.1 Sources

We use two main sources. The Community Innovation Surveys -CIS2 andCIS3 - provide us a measurement of innovation and related variables. Wealso use the SUSE (Système Uni�é de Statistiques d�Entreprises) data set forthe other arguments of the production function (capital, labour and value-added). Both data set are at the level of the �rms. CIS2 and CIS3 surveys

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refer to the period 1994-1996 and 1998-2000, respectively and the variablesobtained from the SUSE data set cover the period 1994-2000.

4.2 Industry coverage

The �rst Community Innovation Survey, CIS1, was carried out in 1991 on thebasis of �rst version of the Oslo Manual and covers only the manufacturingsector. Services were included since the second wave of the survey, CIS2which covers some non-personal market services (NACE 60, 61, 62, 64.2) andsome business services (NACE 72, 74.2), while CIS3 covers only computerand related activities (NACE 72), other business activities (NACE 74.2, 74.3)and post and telecommunications (NACE 64).When estimating the static model using CIS3 data, our empirical def-

inition of KIBS is thus: computer and related activities (NACE 72), Ar-chitectural and engineering activities (NACE 74.2), checking and techniquesanalyses (NACE 74.3) and telecommunications (NACE 64.2). The inclu-sion of telecommunications - which is not a business activity � is relatedto the technological and innovative contents of such activities. It is worthmentioning that our empirical de�nition of KIBS seems very close from thetheoretical de�nition outlined earlier. When we analyze the dynamic model,we should concentrate on the industry intersection between the two surveyswhich corresponds to the following sectors of activities: NACE 64.2, NACE72 and NACE 74.2.

4.3 De�nition and measurement of Innovation

The surveys�methodology and de�nitions of innovation are consistent withthe concept of innovation developed in the Oslo Manual. Innovation is de�nedas "a new or signi�cantly improved product (goods or services) introduced tothe market or the introduction within the enterprise of a new and signi�cantlyimproved process" (OECD, 2005, 11).It is worth mentioning, however, that some di¤erences exist between the

two surveys. The CIS3 follows the indications of the 1997 revised version ofthe Oslo Manual and takes into account organizational changes and providesa distinction between process and product innovations, while CIS2 does not.When estimating the static model on CIS3 data, we can analyze several

kinds of innovation. A �rst distinction is related to the object of innovation(process or product) while a second distinction concerns its nature4 (tech-

4One could easily recognize the Schumpeterian distinction between radical and incre-mental innovation. Unfortunately such distinction is not exploited in the frame of this

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nological or non technological). precisely we use the following innovationindicators:- I1: Binary variable (yes/no) for any kind of innovation- I2: Binary variable (yes/no) for product innovation- I3: Binary variable (yes/no) for process innovation- I4: Binary variable (yes/no) for technological innovation- I5: Binary variable (yes/no) for non technological innovation

4.4 Innovation: some determinants

A great number of works has been made related to the determinants of in-novation, as surveyed by Cohen et al. (1989). In the present study, while aninnovation production function is considered, one may distinguish betweenthe innovation inputs on one hand and, by the other hand, some other vari-ables that could infer with the propensity of the �rm.In a simple sense, inputs are factors which are used in order to produce in-

novation, namely knowledge under its di¤erent forms. The usual distinctionis between formal sources of knowledge, issued from R&D activities and non-formal sources, using di¤erent channels as new equipment, public researchor employers know-how. Another distinction is between internal and exter-nal sources. Combining both distinction, the CIS3 survey provides severalvariables that we label "knowledge inputs":

z1 =�r1; r2; r3; k1; ::::; k5

�with:- r1: R&D internal activity;- r2: R&D external subcontracted activity;- r3: Other expenditures associated to some knowledge acquisition (patents,

licenses, training...);- k1: Internal sources of knowledge (at the �rm level);- k2: Internal sources of knowledge (at the corporate level);- k3: External sources of knowledge, linked to the market (suppliers,

customers, competitors);- k4: External sources of knowledge, issued from Public research (Univer-

sity...);- k5: Other External sources of knowledge (Conferences, Articles, Meet-

ings).

survey.

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Moreover, one knows that, for a given level of knowledge, the actualproduction of innovation depends on some other variables, Cohen et al. (op.cit.) using the general term of absorption capacity in order to qualify suchcharacteristics. Some are speci�c to the �rm. The size of the �rm, sinceSchumpeter (1912) itself, has been traditionally considered as one of themost important determinant of the �rm propensity to innovate. Anotherimportant dimension is the organizational status of the �rm and its stability:the relation between innovation and change into organizational design is oftenassumed. Market structure variables are often taken into account, throughthe size of the market or the industrial concentration. A classical distinction(Schmookler, 1962) is made between two determinants of innovation whichre�ect more the general context (in both technological and economic sense):the demand pull (where innovation is issued from the customers�will) and thetechnology push (where innovation is permitted by the progress of science andtechnology). As a �nal point, the industry has to be introduced as a proxy fora number of activity characteristics, all linked to innovation (as the intensityof technology use, as approximated by the capital intensity....). According tothis line, we use the following vector of �absorptive capacity factors�:

z2 =�l96; �

1; � 2; � 3; a;m; o�

with:- � 1: the demand pull e¤ect ;- � 2: the technology push e¤ect ;- � 3: the non-technological knowledge push e¤ect ;- m : the market level of the �rm (region, country, Europe, world-wide);- o change of the status or organization of the �rm (creation, mergers,

functional out-sourcing or new management methods implementation .- l96: the size of the �rm measured through the number of employees in

1996;- s: the industry a¢ liation of the �rm, according to three digits NACE�s

classi�cation (as speci�ed in our appendix).

Using both kinds of inputs our vector z = (z1; z2) of inputs is de�nedas:

z = (z1; z2) =�l96; �

1; � 2; � 3; a;m; o; r1; r2; r3; k1; ::::; k5�

4.5 Some statistical facts from our data

According to our empirical de�nition of KIBS, the number of KIBS �rms inFrance is about 2300 (SESSI). The CIS3 survey questionnaire was addressedto a strati�ed sample of 630 �rms and the rate of return reached 72%. Once

14

we have merged the CIS3 and the SUSE �les, we obtain a dataset of 416�rms.A �rst set of informations is related to the innovation propensity. Among

these �rms 42% declared to be innovative (i.e. have implemented an innova-tion during the 1998-2000 period). This share signi�cantly varies accordingto the sector which is considered within KIBS, as it goes from 9% in Mainte-nance and Repair of O¢ ce and Computer Machinery (NACE 72.5) to 56% inSoftware Consultancy and Supply (NACE 72.2). Table 3 shows that KIBS�rms generated more product (33%) than process (23%) innovations andmore technological (31%) than non technological (17%) innovations. Theseresults are very similar to those obtained for manufacturing where 40% (re-spectively 33% and 23%) of �rms produce innovation (resp. product andprocess innovation). Moreover, the technological part of innovation in KIBSis slightly higher than in manufacturing.When looking at the sources of innovation, one can observe that KIBS

�rms primarily recur to intramural R&D (63%) and training (52%). Thevalues obtained for these expenditures are of the same order as those formanufacturing. Moreover, more than one-third of KIBS �rms resorts to theacquisition of external knowledge as patents, licences or programs while onlyone-tenth of manufacturing �rms resorts to this kind of expenditure. Ex-tramural R&D and the acquisition of machinery and equipment are utilizedrarely �and less than in manufacturing - from KIBS �rms.Both in KIBS and manufacturing the main sources of knowledge are is-

sued from the �rm itself (87% and 84%, respectively), from customers (80%and 82%, respectively) and suppliers (78% and 72%, respectively). An otherimportant source is issued from competitors. This last source is more fre-quently used in manufacturing (74%) than in KIBS (57%). This is also thecase for the resorting to research institutes (34% and 25%). To the contrary,the corporate sources of knowledge and the conferences are more utilized inKIBS (40% and 73%) than in manufacturing (25% and 53%).Given the goal of this paper, it is worth having a look to the economic

characteristics of the �rms. Table 3bis presents some summary indicators(median and Qrange) for several variables: �rm size (as measured by thenumber of employees), turnover and added value, both in level and growthrate. One can �rst notice that KIBS is mainly composed of small (less than50 employees) and productive �rms. In addition, innovating �rms have alarger size, are more productive and exhibit a higher growth rate of addedvalue than non-innovating �rms.In order to obtain a more informative picture, we plot the estimated kernel

densities for every variable (�g. 1 to 4) for �rms introducing innovationagainst non-innovative �rms. The Epanechnikov Kernel function and the

15

width of the kernel are chosen in order to minimize the mean integratedsquared error. The distributions of productivity both in terms of addedvalue per employee (�g. 2) and turnover per employee (�g. 3) have a verysimilar shape - presenting also similar tales - for both kinds of �rms (seeParisi et al., 2006 for a similar investigation of the Italian manufacturing).However the estimated densities of the number of employees and of the

growth rate of added value have quite di¤erent shapes according to the inno-vative activity of the �rms. The estimated density of the number of employ-ees for innovative �rms is more �at than for non-innovative �rms. However,these two densities have similar modal values (about thirty) but the densityof non-innovative �rms is much concentrated on low values...

5 Estimations Results

5.1 The static model

5.1.1 The innovation function

The logit regressions results are presented in table 7. Five regressions areperformed, which correspond to di¤erent kinds of innovation: either (I1),product(I2), process(I3), technological(I4) and non technological(I5).When considering the �rst column, devoted to either kind of innovation, a

�rst result appears, which is consistent with the related literature: the signif-icant and positive e¤ect of intramural R&D on innovation with an estimatedvalue (0.89) of the same order than in Duguet (2006) .An important result regards the coe¢ cient associated to both extramural

R&D and other expenditures (as acquisition of patents, equipment...) whichare higher than that associated to internal R&D. These two results clearlyshow the importance of transfer of formalized knowledge for KIBS, whichseems to be higher than for manufacturing.The comparison between the di¤erent kinds of innovation indicates that

external R&D plays the main role in the case of process innovation, whileproduct innovation is more linked to internal R&D or other channels for theacquisition of formalized knowledge (as patents). Thus KIBS would producetheir own formalized knowledge in order to conceive their new products,while they would contract (or bene�t without contracting) with other �rmsto improve innovation within their production process.Looking at of the last two columns, one can observe a well-known re-

sult: R&D matters for improving the probability to implement technologicalinnovation, while non-technological innovations is not a¤ected by the R&De¤ort.

16

When entering the non-technological conception of innovation, one canobserve the importance of two kinds of sources: internal sources and publicsources. Other sources of knowledge, as corporate or market, do not seem toplay any role. The �rst result, related to the importance of internal sources,is a well-known one, common to all industries, while it is not clearly the casefor the latter, which was less expected. It seems justi�ed to assume thatthe importance of public sources of knowledge could be due to the small sizeof �rms, joint to their high human capital. In such �rms, the connectionwith public R&D, for instance, would be at the same time, necessary andpossible. One more time, this situation is mostly due to process innovation,while product innovation is not concerned. KIBS appear to be singular intheir way to innovate within their production process, but not in the waythey conceive their new products.As expected, non-technological innovations are linked to internal less-

formalized sources of knowledge, rather than to R&D or external sources.According to table 7, the test of the theoretical distinction between demand-pull and technology-push e¤ects leads to very poor empirical results, withnon signi�cant estimates almost always. The market level at which the �rmoperates, plays also a role, but only at the world-wide level and for generaland product innovation. As noticed in a number of previous works (Tiddet al., 1997), organizational change is closely linked to innovation, while theestimated parameter is signi�cant and about 0.5 . This result mostly concernsproduct and non-technological innovations. By the end, while a numberof corresponding estimates (not reported here) are strongly signi�cant, theintroduction of industry dummies appear to be necessary in order to capturesome unobserved activity e¤ects and to prevent from biases which wouldpossibly a¤ect the other estimates.A last result is common to all kinds of innovation: there is no signi�cant

e¤ect of the �rm size. Several reasons can be found to explain this result,while the opposite one can also be found in the literature. The �rst oneis the very small variability of size observed within our sample: one-half ofthe �rms has a size ranging from 18 (�rst quantile) and 77 employees (thirdquantile). Thus, Cainelli et al. (2006) introduce two size dummies in theirinnovation function ("less than 100 employees", "from 100 to 249", "250 andmore" being the reference), but we are not allowed to do the same thing, justbecause such "large" �rms (more than 250 or even more than 100 employees)are not signi�cantly present in our sample. Another reason is suggested bythe exam of the Kernel distributions of innovative and noninnovative �rmsdi¤er (Fig. 1). Even if the average size of innovative �rms is a little bithigher than the average size of non-innovative, the two distribution domainsare as large in the two cases: some small �rms innovate, while some big

17

does not. Moreover the last reason could be the way the model is built.One could think that the e¤ect of size on the innovation propensity is anindirect one: greater �rms perform more often R&D and R&D has an e¤ecton innovation propensity. Because a great number of explanatory variables isstill included in the model, only the pure e¤ect of size e¤ect remains, whichis not apparently so signi�cant in the case of KIBS.

By the end, the two main features can be pulled out of these results.First, KIBS determinants of innovation di¤er from those for manufactur-ing. Secondly, the distinction between product and process innovations is animportant one. The di¤erences between KIBS and manufacturing does notcome from the impact of R&D, which appears to be strongly signi�cant inboth case. It comes more from the respective e¤ect of internal and externalR&D, the last one being very high in the case of KIBS. This result is directlyissued from the single case of process innovation. For such a kind of innova-tion, external R&D plays the main role: KIBS �rms subcontracts with other�rms (or institutions) in order to improve their own production process. Inthe case of product innovation, a more usual landscape is described: newproducts are conceived by the mean of internal R&D or acquired throughpatents or licenses.

5.1.2 The production function

The results obtained from estimating the production function in eq.1, usingthe two-step method and GMM, are presented in table 3. As standard inputof the production function we use the number of employees for measuringlabour. However we do not dispose of a time series for investments and wecannot calculate the stock of capital using the permanent inventory method.Consequently we use the investments as proxy for capital input.In the regression (i) an innovation (either kinds) variable is added to the

standard production function, while in regressions (ii) and (iii), two kinds ofinnovation are introduced simultaneously. Regression (ii) includes productand process innovations, while technological an non-technological innovationsare included in regression (iii).First, the estimates of the usual labour and capital inputs exhibit some

values which are classical ones in the frame of such regressions, using �rmlevel data and concerning small sized production units. The estimated para-meter (all kinds) of innovation, which represents here the rate of disembodiedtechnological change, equals to 0,28 when using two-steps method and 0.24when using GMM, both estimates being signi�cant at the 1% level. In otherwords, giving the framework of Hicks neutral technological change, an 1%

18

increase of the innovation propensity (in this case, the probability to inno-vate) increases the productivity of other inputs of 0,28% (0.24%). Giving theassumption of almost constant returns to scale of the estimated productionfunction, the increase of the output will be of almost 0,28% (0.24%).This result should be compared with those from the corresponding litera-

ture, even if it is quite di¢ cult to build such comparison, due to the diversityof the models which are used5. Cainelli et al. (2006) use older data (relatedto innovation during the 1993-1995 period), concerning All Italian services,but uses the same dependent and explanatory variables. Then, they obtain a0.363 value for the innovation estimate, not very far from this present study�sresult. Loof and Heshmati (2006) use a similar model but with a di¤erentde�nition of innovation (log innovation sales per employee), also include hu-man capital, and obtain some values of 0.121 for manufacturing and 0.093for services, both in the Swedish case. Summarizing, our results seem to beconsistent with the literature and located in the upper part of the obtainedrange of values.Product and process innovations (as shown by regression ii) lead to quite

similar e¤ects on �rm performance. The estimate is slightly higher in the caseof product innovation: 0.31 versus 0.26 in the case of two-steps method, and0.33 versus 0.24 in the case of GMM. This di¤erence is consistent with theLoof and Heshmati�s results, which use a "Process Innovation" dummy, theyfound it to be slightly signi�cant and negative. Using the other distinction,one can observe that technological innovations have a stronger impact on�rm productivity than non-technological ones: 0.49 versus 0.21 in the caseof two-steps method, and 0.44 versus 0.19 in the case of GMM. Such a resultappears to be logical with the strong technological dimension contained inthe output indicator, labour productivity, which is used.

5.2 The dynamic model

This section provides the results of the dynamic model, as de�ned by eq. (4)to (8). One has to keep in mind that the estimations are now performed onthe population of �rms which were both surveyed by CIS2 and CIS3. Beforethe estimations results, the sample (labelled CIS) which results from thismerging is presented, then some descriptive statistics are introduced.

5By instance, some works as by Duguet [2006] use total factor productivity instead oflabour productivity as dependent variable.

19

5.2.1 Merging CIS2 and CIS3: the CIS sample

As shown by Table 5, only 79 �rms composed the CIS sample. One hasto care about two econometric problems. The �rst one is directly linked tothe small number of observation units which increases - for a given numberof explicative variables - the probability of quasi complete separation in thelogistic regression model. Consequently a small number of explanatory vari-ables is introduced in the model. Selection bias is a second possible problem.Thus, it is important to compare the two populations, namely CIS3 andCIS, in the case of a certain number of indicators, as innovation propensity,the sources of innovation and economic characteristics, as �rm size or addedvalue. Almost identical results in terms of innovation, associated expendi-tures and sources of knowledge, can be observed. However, the average sizeand added value are higher in the case of CIS sample.A feature of innovative activities which is worth analyzing at this stage is

whether innovative activities persist over time and whether any di¤erencesexists between the di¤erent sources of innovation. To investigate this table 6provides transition probabilities obtained from transitional matrices. We �nda lot of persistence in almost all activities related to innovation. In fact, 64%(70%) of �rms doing (not doing) innovation at the time t-1 (1994-96) is foundto do (not to do) innovation at time t (1998-2000). These results are verysimilar to those obtained by Parisi et al., (2006), for the Italian manufacturingand by Peters, (2006), for the German service sector: innovation seems topersist over time.The following results in Table 6 di¤er according to the source of inno-

vation. Persistency clearly concerns R&D expenses. Firms that performR&D activity during the period t� 1 clearly exhibit a higher probability toperform again such activities during period t. The case of external R&D isabout the same but to a lesser degree. A lower persistence is then foundfor the acquisition of external technology like licences, patents or programs.Indeed for this variable, the chi-square independence test is not signi�cant.A high degree of persistency can be found again in the case of training. Suchresults are not surprising when related to the �rm employment structure.In order to perform R&D activities, the �rm needs to employ a high skilledlabour force, whose skill need to be maintained. All this can explain whyboth strategies, R&D and training, cannot be considered as one-shot gamesbut requires time and stability, which appear through our persistency indi-cators. On the contrary, the acquisition of patents or licences do not requirestructural investments for the �rm and can be considered as non-repeatedoperations.When focusing on the �sources of knowledge�one can �nd a quite di¤erent

20

pattern. All things considered, it still exists a strong �positive�persistency: a�rm which uses a given source of knowledge during the �rst period has greaterchance to do the same in the next period. However some low values appear inthe case of �negative�persistency, namely a large share of �rms which do notuse a given source at time t� 1 but who nevertheless use this source duringperiod t. These results indicate that the �access�to knowledge sources otherthan formalized R&D is more easy and requires less investments.

5.2.2 Estimation Results

Table 9 provides the estimation results of the dynamic innovation equation 6,where determinants are internal and external R&D, plus past innovation, aslagged-dependent variable. The main new result is that the past (1994-1996)innovation coe¢ cient is statistically signi�cant. In other terms, innovationpresents a signi�cant autoregressive component. Present innovation is bothdetermined by some contemporary factors (as internal R&D) and by pastinnovation.Two explanations can be provided. The �rst one is that once a �rm has

innovated, its innovation capacity is increased in the context of a learningby doing process (Arrow, 1962). With more details, �rst �rm knows how touse the knowledge inputs in the best possible way, while its knowledge ab-sorption capacity is higher. Secondly, �rm is more able to really implementinnovation, i.e. to transform knowledge in new products or new processes. Asecond explanation is an econometric one. Present innovation would be theobserved result of two e¤ects: the observed e¤ect of the variables explicitlyincluded in the model but also an individual �rm e¤ect, issued from unob-served characteristics. If these characteristics are time-persistent, they wouldproduce the same results in terms of innovation, at time t and at time t� 1.In this way, past innovation could be used as a proxy for such an unobservede¤ect.Table 10 presents the results of the dynamic production function as given

by equation (4). The contemporary variable coe¢ cients exhibit some valuesthat are close to those found in the static regressions: they range between 0.73and 0.77 for the labour coe¢ cient, between 0.21 and 0.25 for the capital oneand between 0.21 to 0.24 for contemporary innovation. Beside this expectedresult, one can observe that the coe¢ cient for past innovation is clearly notsigni�cant. Past innovation (between 1994 and 1996) does not exert anysigni�cant e¤ect on present (2000) level of added value.This result can be explained by two reasons. The �rst one concerns the

delay which separate the period when innovation occurs (between 1994 and1996) and the period when the �rm productivity is measured (2000). One

21

can reasonably conceive that innovation exerts some lagged e¤ects, within,say a 2 or 3 years delay, but a 4 to 6 years delay seems a (too) long de-lay, particularly in the case of activities which are intensively innovative asKIBS. A second explanation can be found within the results of the dynamicinnovation equation. As there is a strong relation between past and presentinnovation, the eventual e¤ect of past innovation on a variable Y could becaptured by the e¤ect of present innovation on the same variable Y.By the end, the main conclusion is that past innovation has a positive

e¤ect on present innovation, but not on present productivity. The only e¤ecton present productivity is thus an indirect one, which does not mean a neg-ligible one: past innovation helps the �rm to innovate in the present periodand this present innovation greatly increases �rm present performance.

6 Conclusions

This paper follows the Duguet (2006) approach and proposes a structuralmodel, which includes the estimation of both innovation and production func-tions. Data are issued the second and third Community Innovation Surveys(CIS2 and CIS3). A direct measurement is provided for innovation, whichprevent from using the usual proxies, as R&D expenditures or the num-ber of patents. The empirical �eld is a part of services, namely the KIBS(for Knowledge-Intensive-Business-Activities). As shown by simple statistics,such service activities are far from the usual representation of low innovativeand productive sectors, sometimes used to characterize the service activities.Innovation propensity is signi�cantly high and reaches about the same levelthan in manufacturing. The kind of innovation di¤er as non-technologicalinnovation is more present.A two stage estimation method is then used. One of the main results is

that within KIBS the innovation channels are not always (nor often) linkedto the existence of internal R&D activities. This result also depends on thekind of innovation which is considered. As an example, product innovationis more often issued from internal R&D than process innovation which isclosely related to external R&D. At the same time, the implementation ofa technological innovation is often linked to most formalized R&D activity,while non-technological -ie organizational- change mostly issues from internalsources of knowledge. The estimate of the productivity function indicates astrong e¤ect of innovation. This e¤ect is greater in the case of technologi-cal innovations than in the non-technological one. The distinction betweenprocess and product does not lead to any apparent di¤erences in terms ofproductivity. The use of a dynamic model provides a clear result. Past in-

22

novation does not a¤ect directly productivity, but it does within an indirectchannel. Past innovation greatly improves the present innovation propensityand by this indirectly a¤ect the present productivity level.Following this, one can �rst consider that the use of the general technical

change framework is not unappropriated when studying KIBS. The usualsources explain a signi�cant part of observed innovation process. This doesnot mean that the same results are obtained. In that sense, KIBS are in-novative sectors, as well as manufacturing, but the sources and the form ofinnovation are not the same. Technological innovation and the associatedsources of formal knowledge, as R&D, are less present than in manufactur-ing. This should lead to a deeper exploration of other sources of knowledge,as a �rst direction in order to improve the study. Another director wouldconsist in using a larger and richer dataset, including the recent CIS4 survey,which covers years 2002 to 2004. This could be very interesting, particularlyin a dynamic perspective. Our results con�rm the well-known assumption ofinnovation persistency. It is then important to validate this result with thehelp of a more recent period and a larger sample.

23

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Table 1Growth Value Added per Employee,

Average Annual Growth Rate, 1990-2001All Manuf. 50-99 50-74 64 72 73 74

France 1.0 3.5 0.2 0.2 4.6 0.3 -1.4 -1.5Germany 1.7 2.4 1.2 1.4 12.2 3.5 1.5 -2.6

Stan, OECD Database.

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Table 2Sectors covered by the CIS surveys

NACE Activity CIS2 CIS360.1 Transport via Railways *60.2 Other Land Transports *61.1 Sea and Coastal watertransport *61.2 Inland water transports *62.1 Scheduled air transports *62.2 Non Scheduled air transports *64.1 Post and Courier activities *64.2 Telecommunications * *72.1 Hardware Consultancy * *72.2 Software Consultancy and supply * *72.3 Data Processing * *72.4 Data Base activities * *72.5 Maintenance and repair of o¢ ce * *

and computing machinery72.6 Other Computer related activities74.1 Legal, accounting and other auditing

business activities74.2 Architectural and engineering activities * *74.3 Technical testing and analysis *74.4 Advertising74.5 Labour recruitment and provision of personnel74.6 Investigation and security activities74.7 Industrial Cleaning74.8 Miscellaneous business activities

29

Table 3Share of �rms introducing an innovation or adopting a source of knowledge

Variable KIBS Manufacturing

INNOVATIONInonovation (general sense) 42 40Product innovation 33 33Technological product innovation 25 17Process innovation 23 23Technological process innovation 16 14Technological innovation 31Non technological innovation 17

INNOVATION EXPENDITURESInternal R&D 63 69External R&D 17 25Acquisition of machinery and equipment 34 47Acquisition of other external technology 27 11Training 52 46

SOURCES OF KNOWLEDGEInternal sources of knowledge 87 84Corporate sources of knowledge 40 25Suppliers 78 72Clients 80 82Competitors 57 74Universities 35 37Research institutes 25 34Conferences, meeting 73 53Fairs, exhibitions 62 65

30

Table 3 bisDescriptive statistics: size, production and productivity

Variable All �rms Non Innovating InnovatingMedian Qrange Median Qrange Median Qrange

Employees 33 59 26 40 42 91Turnover 19459 40135 15014 26796 25219 73035Added value 17966 39349 14138 25311 24069 68119Turnover/employees 538 395 516 411 569 391Added value/employees 498 366 470 358 528 360

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Table 4Comparing �rms surveyed in CIS3 with �rms surveyed both in CIS3 and

CIS2Variable CIS3 CIS

(406 obs.) (79 obs.)INNOVATION�

Inonovation (general sense) 42 46

INNOVATION EXPENDITURES�

Internal R&D 63 55External R&D 17 19Acquisition of machinery and equipment 34 39Acquisition of other external technology 27 34Training 52 55

SOURCES OF KNOWLEDGE�

Internal sources of knowledge 87 87Corporate sources of knowledge 40 53Suppliers 78 78Clients 80 72Competitors 57 55Universities 35 32Research institutes 25 20Conferences, meeting 73 75Fairs, exhibitions 62 58

SIZE, PRODUCTION AND PRODUCTIVITY��

Employees 33 48Turnover 19459 46690Added value 17966 44570Turnover per employee 538 689Added value per employee 498 631growth rate of added value .068 .071

Notes.*: categorical variables espressed in %.**: real variables. Median values

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Table 5Persistence in Innovation: transition probabilities

Variable positive negative Chi-square(p-value)

INNOVATIONInonovation (general sense) .64 .7 9.75(.002)

INNOVATION EXPENDITURESInternal R&D .82 .71 13.57(.000)External R&D .66 .64 2.74(.095)Acquisition of machinery and equipment .63 .73 5.62(.017)Acquisition of other external technology .50 .73 2.31(.126)Training .79 .55 4.36(.036)

SOURCES OF KNOWLEDGEInternal sources of knowledge .96 .25 2.49(.114)Corporate sources of knowledge .58 .47 .06(.792)Suppliers .91 0 .35(.543)Clients .85 .25 .46(.492)Competitors .64 .38 .021(.885)Universities .71 .77 5.57(.018)Research institutes .60 .96 10.84(.001)Conferences, meeting .90 .33 2.57(.108)Fairs, exhibitions .69 .43 .5133(.474)

Notes."Positive" and "negative" transition probabilities are calculated using

tables of contingencies. "Positive" ("negative") is de�ned as the share of�rms doing (not doing) innovation - or using an innovation inputs - at thetime t (1998-2000) have doing (noot doing) innovation at time t-1 (1994-96)."Chi-square" indicates the chi-square independence test.

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Table 6The Innovation production function

Innovation Either Product Process Techno. Non Tech.

Inputs

Internal R&D 0.89*** 0.64*** 0.23 0.80*** 0.26External R&D 1.33** 0.38 0.86** 0.81*** 0.42*Other (patents...) 1.11*** 0.97*** 0.50** 0.09*** 0.28

Internal Sources 0.68** 0.46 0.97** 0.67** 0.89***Corporate Sources -0.07 0.06 0.16 0.18 0.18Market Sources -0.12 -0.19 0.41 0.01 -0.43Public Sources 0.42** 0.24 0.36** 0.18 0.16Other External -0.31 -0.09 -0.48** -0.56** 0.26Sources

Other Variables

Demand Pull 0.22 0.28 -0.37 -0.32 0.53Technology Push -0.30 -0.53* 0.57* 0.31 -0.17Non Techno. Push -0.29 -0.03 0.27 0.10 0.01

Organizational change 0.54** 0.61** 0.26 0.15 0.68***Size 0.00 0.00 0.01 0.00 0.00

MarketWorld-Wide 1.01** 1.02** -0.10 0.61 0.31Europe -0.23 -0.73 -0.27 -0.04 -0.37France -0.26 -0.16 0.17 0.04 -0.11(regional=ref )

Industry Dummies included but not reported here

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Table 7Estimated production function

(dependent variable: ln(value added), Eq1)Method Two-step GMM

(i) (ii) (iii) (i) (ii) (iii)ln(number of employees) .84*** .84*** .84*** .81*** .81*** .81***

ln (investments) .14*** .14*** .13*** .18*** .18*** .18***

Innovation (either) .28*** .24***

Product innovation .31*** .33***

Process innovation .26** . .24***

Technological innovation .49*** .44***

Non technological innovation .21*** .19***Notes.**: signi�cant at the 5% level; ***: signi�cant at the 1% level.Two-step standard errors are calculated using the heteroskedastic consistent variancematrix of White (1980)

Dummy Industries coe¢ cients are not reported

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Table 8Dynamic innovation functions

I1 - eq. (6)l94 : employees 1994

l96 : employees 1996 0,008

r1: R&D internal activity; 0,57***

r2: R&D external activity; 0,12

I(-1): past innovation (CIS2) 0 ,38**

Notes.**: signi�cant at the 5% level; ***: signi�cant at the 1% level.ML estimations of logit models.Sample of 79 �rms

36

Table 9Estimated dynamic production function (dependent variable: ln(value

added)) -eq. (4)Two-step GMM(i) (i)

ln(number of employees) .77*** .73***

ln (capital) .21*** .25***

I1 : current innovation (CIS3) .24*** .21***

I1(�1) : past innovation (CIS2) .02 .02

Notes.**: signi�cant at the 5% level; ***: signi�cant at the 1% level.Two-step standard errors are calculated using theheteroskedastic consistent variance matrix of White (1980).Sample of 79 �rms.

37

0.0

05.0

1.0

15.0

2ke

rnel

 den

sity

0 200 400 600 800 1000Number of employees

No innovation Innovation

Figure 1.Kernel density estimation: number of employees.

Epanechnikov kernel function.The width of the kernel has been chosen minimizing the mean integrated

squared error.

38

0.0

005

.001

.001

5.0

02ke

rnel

 den

sity

0 1000 2000 3000 4000 5000Value added per employee

No innovation Innovation

Figure 2.Kernel density estimation: value added per employee.

Epanechnikov kernel function.The width of the kernel has been chosen minimizyng the mean integrated

squared error.

39

0.0

005

.001

.001

5ke

rnel

 den

sity

0 1000 2000 3000 4000 5000Turnover employee

No innovation Innovation

Figure 3.Kernel density estimation: turnover per employee

Epanechnikov kernel function.The width of the kernel has been chosen minimizyng the mean integrated

squared error.

40

01

23

4ke

rnel

 den

sity

­.2 0 .2 .4 .6growth rate of value added

No innovation Innovation

Figure 4.Kernel density estimation: growth rate of value added.

Epanechnikov kernel function.The width of the kernel has been chosen minimizyng the mean integrated

squared error.

41