Innovation and economic performance in services: a firm-level analysis Giulio Cainelli, Rinaldo Evangelista and Maria Savona* This paper explores the two-way relationship between innovation and economic performance in services using a longitudinal firm-level dataset which matches data from the second Community Innovation Survey, CIS II (1993–95), against a set of economic variables provided by the System of Enterprise Accounts (1993–98). The results presented show that innovation is positively affected by past economic performance and that innovation activities (especially investments in ICTs) have a positive impact on both growth and productivity. Furthermore, productivity and innovation act as a self-reinforcing mechanism, which further boosts economic performance. These findings provide empirical support for the endogenous nature of innovation in services and the presence in this sector of competition models and selection mechanisms based on innovation. Key words: Technological innovation, Economic performance, Service sector JEL classifications: O31, O33, L80 1. Introduction It is widely acknowledged that technological change and innovation are the major drivers of economic growth and are at the very heart of the competitive process. Over the last few decades, a large body of literature on economic growth has attempted to account both theoretically and empirically for such a major issue in economic theory, although from different perspectives and with different approaches. A major theoretical duel is the one between the neoclassically inspired ‘New Growth Theory’ and the neo-Schumpeterian ‘evolutionary’ approach 1 (see Verspagen, 2005 for a recent reassessment of this debate). Manuscript received 10 March 2003; final version received 6 June 2005. Addresses for correspondence: Giulio Cainelli, University of Bari, and CERIS-CNR, Milan, Italy; email: [email protected]; Rinaldo Evangelista, IRPPS-CNR, Rome and University of Camerino, Italy; email: [email protected]; and Maria Savona, SPRU, Science and Technology Policy Research, University of Sussex (UK) and BETA, Bureau d’Economie The ´orique et Applique ´e UMR CNRS 7522 Po ˆle Europe ´en de Gestion et d’Economie, Strasbourg, France; email: [email protected]* University of Bari, and CERIS-CNR, Milan; IRPPS-CNR, Rome and University of Camerino; and SPRU, UK, and BETA, Strasbourg, respectively. The authors thank Giulio Perani (Italian National Institute of Statistics—ISTAT), coordinator of a research group on ‘Technological innovation in services’, who provided the firm-level dataset used for the empirical analysis. The authors are also grateful to Daniele Archibugi, Nick von Tunzelmann, Roberto Zoboli and three anonymous referees for their valuable comments on a previous draft of this paper. 1 In the field of New Growth theory, see, among others, Romer (1990), Grossman and Helpman (1991), Bresnahan and Trajtenberg (1995), Helpman (1998), Aghion and Howitt (1992), Griliches (1984, 1995, 1998), in the Schumpeterian stream see, among others, Nelson and Winter (1982, 2002), Dosi et al. (1988), Silverberg and Soete (1994), Nelson (1995), Stoneman (1995), Freeman and Soete (1997) and Archibugi and Michie (1998). Cambridge Journal of Economics 2006, 30, 435–458 doi:10.1093/cje/bei067 Advance Access publication 8 August, 2005 Ó The Author 2005. Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved. at Universidad Carlos III on October 25, 2010 cje.oxfordjournals.org Downloaded from
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Innovation and economic performance inservices: a firm-level analysis
Giulio Cainelli, Rinaldo Evangelista and Maria Savona*
This paper explores the two-way relationship between innovation and economicperformance in services using a longitudinal firm-level dataset which matches datafrom the second Community Innovation Survey, CIS II (1993–95), against a setof economic variables provided by the System of Enterprise Accounts (1993–98).The results presented show that innovation is positively affected by past economicperformance and that innovation activities (especially investments in ICTs) havea positive impact on both growth and productivity. Furthermore, productivity andinnovation act as a self-reinforcing mechanism, which further boosts economicperformance. These findings provide empirical support for the endogenous natureof innovation in services and the presence in this sector of competition modelsand selection mechanisms based on innovation.
It is widely acknowledged that technological change and innovation are the major drivers of
economic growth and are at the very heart of the competitive process. Over the last few
decades, a large body of literature on economic growth has attempted to account both
theoretically and empirically for such a major issue in economic theory, although from
different perspectives and with different approaches. A major theoretical duel is the one
between the neoclassically inspired ‘New Growth Theory’ and the neo-Schumpeterian
‘evolutionary’ approach1 (see Verspagen, 2005 for a recent reassessment of this debate).
Manuscript received 10 March 2003; final version received 6 June 2005.Addresses for correspondence: Giulio Cainelli, University of Bari, and CERIS-CNR, Milan, Italy; email:
[email protected]; Rinaldo Evangelista, IRPPS-CNR, Rome and University of Camerino, Italy; email:[email protected]; and Maria Savona, SPRU, Science and Technology Policy Research, University ofSussex (UK) and BETA, Bureau d’Economie Theorique et Appliquee UMR CNRS 7522 Pole Europeen deGestion et d’Economie, Strasbourg, France; email: [email protected]
*University of Bari, and CERIS-CNR, Milan; IRPPS-CNR, Rome and University of Camerino; andSPRU, UK, and BETA, Strasbourg, respectively. The authors thank Giulio Perani (Italian National Instituteof Statistics—ISTAT), coordinator of a research group on ‘Technological innovation in services’, whoprovided the firm-level dataset used for the empirical analysis. The authors are also grateful to DanieleArchibugi, Nick von Tunzelmann, Roberto Zoboli and three anonymous referees for their valuablecomments on a previous draft of this paper.
1 In the field of New Growth theory, see, among others, Romer (1990), Grossman and Helpman (1991),Bresnahan and Trajtenberg (1995), Helpman (1998), Aghion and Howitt (1992), Griliches (1984, 1995,1998), in the Schumpeterian stream see, among others, Nelson and Winter (1982, 2002), Dosi et al. (1988),Silverberg and Soete (1994), Nelson (1995), Stoneman (1995), Freeman and Soete (1997) and Archibugiand Michie (1998).
A common feature of these streams of the literature is their explicit or implicit focus on
the manufacturing sector. Services for a long time have been seen as technologically
backward, with innovation playing only a marginal role in explaining the aggregate
performance of this sector and the competitive strategies of firms. The ‘old’ debate over the
long-term growth of services has been dominated since the late 1960s by Baumol’s (1967)
‘cost disease’ argument, according to which the growth of service activities is the main
reason for the productivity slowdown that has affected the advanced countries in the last
few decades.1
It was not until quite recently, with the growth potentiality linked to the new information
and communication technologies (ICTs), that this attitude began to change. Over the last
decade, a new stream of contributions to the literature has in fact begun to challenge the
old view of services as being technologically backward or passive adopters of technology
(Miles, 1993, 1995; Miles et al., 1995; Andersen et al., 2000; Metcalfe and Miles, 2000;
Gadrey and Gallouj, 2002; Tether, 2003).
There is an increasing amount of empirical evidence to support this new perspective.
OECD data show that service industries in the advanced countries perform up to one-third
of total business R&D (BERD) and account for more than 50% of the R&D embodied in
intermediate inputs (ICT hardware) and capital equipment (OECD, 2000A,B,C). The
results of the second Community Innovation Survey (CIS II) confirm that innovation
activities do occur in the services sector, though to differing extents and in various forms
across industries (Evangelista, 2000; EUROSTAT, 2001).
Although more is known about the varieties of innovation in services, investigation of its
economic impact has been largely ignored, particularly in terms of firm-level analyses. The
small number of firm-level studies can to some extent be explained by the difficulty
involved in accessing micro-data, which in the case of services is even greater. There are
also data constraints and methodological problems related to the availability of appropriate
indicators to measure innovation activities in services. Those traditionally used in the
manufacturing sector, e.g., R&D and patents—are not at all appropriate for services
(Evangelista and Sirilli, 1995; Djellal and Gallouj, 1999; Coombs andMiles, 2000). Thus,
to study the relationship between technological change and economic performance in
services requires different and more comprehensive measures of firms’ innovation
activities. The CIS collected data not just on R&D, but on a much wider spectrum of
firms’ innovation activities (OECD-EUROSTAT, 1997). Despite the potential offered by
this data source, only a very few studies have so far used CIS data to explore the
relationship between innovation and economic performance at the firm level.Most existing
studies have focused on the manufacturing sector (Crepon et al., 1998; Klomp and van
Leeuwen, 1999; Evangelista, 1999; Kremp et al., 2004).
This paper explores the links between innovation and economic performance in services
using longitudinal firm-level data based on CIS II (1993–95) and a set of economic
performance indicators drawn from the Italian System of Enterprise Accounts (1993–98).
These data are used to discover whether innovation has a real impact on the economic
performance of service firms and find the extent to which innovation activities are spurred
by a firm’s economic performance.
1 A whole stream of literature has emerged since then, mainly concerned with de-industrialisation andproductivity slowdown in the advanced economies, which has primarily been imputed by such authors to thestructural change of the employment composition towards service activities (Fuchs, 1968, 1969; Petit, 1986,2002; Cohen and Zysman, 1987; Baumol et al. 1989; Baumol, 2002; Wolff, 2002).
The paper is structured as follows. Section 2 identifies the key links between innovation
and economic performance. Section 3 provides a brief description of the dataset and
indicators used in the empirical analysis. Section 4 presents the model, and Section 5
presents the empirical results of the econometric analysis. Finally, Section 6 synthesises the
main empirical findings and draws some conclusions.
2. The links between innovation and economic performance at the firm level
The empirical literature on the relationship between innovation and economic perfor-
mance has mostly focused on the economic impact of technological change, and tends to
overlook the ‘reverse’ relationship, that is the extent to which innovation is spurred by past
economic performance. This section aims to re-establish the two-way nature of this
relationship.
2.1 Mechanism A: Innovation as a determinant of economic performance (Schumpeter I)
The key role played by innovation in explaining the dynamic properties of firms, industries
and economic systems has been acknowledged since the origin of economic thought, as is
clear from the works of Smith and Marx, and is nowadays part of the general consensus
among economists. The issue was further developed by Joseph Schumpeter, who put
innovation at the core of his first major contribution, The Theory of Economic Development
(Schumpeter, 1934). In this work, the role of innovation is fully endogenised and
conceived first and foremost as an ‘entrepreneurial fact’ which is the core of competition
and the dynamic efficiency of firms and industries. Whatever the primary source of
scientific advance and even of technological change, it is the (successful) introduction of
product, process and organisational innovations that allows firms to override the pre-
existing conditions of markets and industries, and to grow and gain market shares at the
expense of non-innovating firms. Dynamic rather than static efficiency is what matters in
the process of creative destruction brought about by innovation. Innovation allows the firm
to build up monopolistic rents which tend to be progressively eroded alongside the
imitative diffusion of new products and processes. The importance of this mechanism is
nowadays acknowledged by neo-Schumpeterian scholars and increasingly by neoclassical
economists (Verspagen, 2005).We can summarise the characteristics of such amechanism,
linking firms’ economic performance to innovation, by labelling it Schumpeterian I
(Freeman, 1982).
As far as the manufacturing sector is concerned, previous studies found positive effects of
innovation on economic performance and more especially on productivity (Griliches, 1995,
1998; Loof and Heshmati, 2001; Crepon et al., 1998; Klomp and van Leeuwen, 1999;
Evangelista, 1999;Kremp et al., 2004).What requires to be empirically tested iswhether such
a mechanism governs the dynamics of firms and industries in the service sector, for which, as
already mentioned, the empirical evidence is still very limited.1
2.2 Mechanism B: Economic performance as a determinant of innovation activity
2.2.1 Schumpeter II
Another seminal contribution from Schumpeter, which has become part of our common
understanding of innovation, emphasised the costly, risky and uncertain nature of in-
novation activities and the crucial issue of the ‘appropriability’ of the economic benefits of
1 Among the few contributions including services, see van derWiel (2001), Loof andHeshmati (2001), vanLeeuwen and van der Wiel (2003) and Evangelista and Savona (2003).
Innovation and economic performance in services 437
innovation. In later work, Schumpeter argued that the increasingly scientific base of
economic activities had caused innovation to become more and more costly, as a result of
indivisibilities and significant economies of scale and scope (Schumpeter, 1942). In the
presence of barriers to entry and weak appropriability conditions, large firms and ex ante
monopolistic power might be more conducive to innovation than fully competitive markets
populated by small firms (Freeman, 1982; Cohen, 1995; Freeman and Soete, 1997). Some
of the insights provided by Schumpeter have important implications for the relationship
between innovation and economic performance, especially in terms of its direction of
causality. The funding of risky, long-term and large-scale innovation projects requires
substantial financial resources and is facilitated by healthy economic track records from
firms that are associated with high growth rates, large profits and healthy cashflows.1
Although this line of reasoning mainly refers to manufacturing sectors and technologies, it
might also hold for the service industries. However, innovation activities in services are
believed to take place on an informal basis and be less dependent on technological
breakthroughs. Both these features might reduce the importance of past economic
performance as a determinant of innovation. However, innovation activities in some service
sectors such as telecommunications, transports and finance are associated with the
establishment of expensive technological infrastructures, which requires large financial
resources and high demand. Therefore, for firms in these sectors, past economic perfor-
mancemight bemore relevant as a basis for their overall financial commitment to innovation
but, also in this case, there is no empirical evidence showing the presence and strength of
such a link.
2.2.2 Schmooklerian
The endogenous nature of innovation has been pointed too with reference to the role
played by ‘demand’ conditions on the overall pace of technological change and as an
incentive for firms to invest in innovation.Markets in the early phases of their life cycle and/
or benefiting from a favourable economic environment, experience sustained growth in
demand, which acts as an incentive for the entry of new firms and the growth of
incumbents. Both these conditions, coupled with expectations of positive market growth,
might act as an important stimulus for innovation activity. The hypothesis that technical
change is mainly ‘demand-pulled’ was proposed by Schmookler (1962, 1966). This
hypothesis was empirically supported by the positive correlation found between cycles of
inventive effort (proxied by patents, ‘a tolerable assumption’; Schmookler, 1962, p. 119)
and cycles of output across industries producing capital goods. The shape of the long-term
trend of these two indicators showed that cycles of output were leading cycles of relevant
patenting activity in the capital goods industries. Schmookler’s claim that technical
progress was ‘dependent’ on ‘economic phenomena’ sparkedmuch debate about the actual
determinants of technical progress. Many scholars tried to test Schmookler’s hypothesis
empirically at different levels of analysis (among them Scherer (1965, 1982), Mowery and
Rosenberg (1979), Stoneman (1979), Walsh (1984) and, more recently, Kleinknecht and
Verspagen (1990), Geroski and Walters (1995), Brower and Kleinknecht (1999)).2
However, these contributions produced controversial results. Kleinknecht et al. and
1 See Hao and Jaffe (1990) and Cohen (1995) for a review of empirical studies.2 Among these attempts, Kleinknecht and Verspagen tried to test the Schmooklerian hypothesis empirically
at the firm-level of analysis, using Dutch firm-level CIS data. The authors ‘re-read’ the Schmooklerianhypothesis as a co-presence, andmutual interaction between technology-push and demand-pull mechanisms,which in the post-Schmooklerian literature had been considered to be mutually exclusive. We look at thisissue in considering mechanism C.
Geroski and Walters found empirical support for the Schmooklerian hypothesis. Geroski
and Walters focused on the role of demand to determine whether innovation is more likely
to be pro-cyclical or counter-cyclical.1 It emerges that the direction of the causal
relationship is from variations in demand to variations in innovative activity and not the
reverse. Brower and Kleinknecht reached the same conclusion, but based on cross-
sectional rather than panel data.
Once again, all these contributions are confined to the manufacturing sector, leaving
a gap in the empirical analysis of the role of market demand as an incentive for innovation
activity in services. This is somewhat surprising insofar as most of the literature on
innovation in services tends to emphasise the ‘co-terminality’, that is the close interaction
between production and consumption of services (Miles et al., 1995; Gallouj and
Weinstein, 1997) and, also, the importance of user–producer links in determining the
financial effort devoted to innovation by service firms. Further, some scholars have referred
to the importance of distinguishing between radical and incremental innovations (Barras,
1986, 1990), with the latter expected to be more sensitive to demand and market
conditions. Given that innovation in services is more likely to be incremental in nature and
to consist of specific applications of a general purpose technology such as ICT (Helpman,
1998; Freeman and Soete, 1997; Freeman and Loucxa, 2001), the absence of any empirical
investigation on the role of demand as an incentive for service firms to innovate is even
more striking. A fairly large body of literature has in fact related the increasing importance
of services in modern economies to the paradigmatic change brought about by the ‘ICT
revolution’ (Freeman and Soete, 1997; Freeman and Loucxa, 2001; Perez, 2002).Overall, the empirical studies of the Schmooklerian mechanism in the domain of service
firms and industries are still at an embryonic stage, and generally ignore the role of demand
levels and growth, and demand expectations as determinants of innovation investments
and activities. The present empirical study is a first step towards filling this gap.
2.3 Mechanism C: Two-way dynamic link between innovation and
economic performance (evolutionary)
Mechanisms A and B above cannot be considered to be mutually exclusive. On the
contrary, in a dynamic perspective, they work in tandem, reinforcing each other over time.
This might be a general dynamic property of an economic system or might hold (and be
particularly strong) only in certain contexts: particular sectors, markets, stages of de-
velopment of industries and technologies, historical periods. In all the cases in which such
a phenomenon occurs, the relationship between innovation and economic performance
should be conceptualised as being two-way as well as possibly cumulative. The strength of
such a mechanism could also be enhanced by the presence of increasing returns to scale,
and occurring in sectors and technological regimes characterised by the ‘Verdoorn–
Kaldorian laws’. These latter dynamically link—albeit mainly at sectoral and macro-
economic levels—labour productivity performance with scale of economic activities and
investments (Verdoorn, 1949; Kaldor, 1975, 1978).
The presence of a two-way self-reinforcing relationship between innovation and
economic performance at firm level is also fully consistent with the evolutionary approach
1 The idea of innovation as being counter-cyclical was supported by Mensch (1975), who argued thatinnovation activities are in fact triggered by unfavourable economic conditions which put pressure on firms toinvest more effort and resources into the innovation process. According to this view, the pace of technicalchange accelerates in the proximity of a business cycle downturn. See also the works of Kleinknecht (1984,1987, 1990).
Innovation and economic performance in services 439
Along with R&D, the CIS takes into account other fundamental sources of innovation,
such as activities related to the design of new services, software development, the
acquisition of know-how, investment in new machinery (ICT hardware) and training.
Firms were asked to provide quantitative figures on the financial resources devoted to these
different activities. These data are particularly important in the case of services, since
several studies have already shown that R&D activities and assets play only a marginal role
in this sector of the economy and patents are rarely taken out by service firms to protect
their innovative output from imitation (Evangelista, 2000; EUROSTAT, 2001). In most
service sectors, innovation activities are incremental in nature, require substantial human
capital investment and rely upon the acquisition and internal development of ICT. Thus,
we built four additional innovation performance indicators which capture: the overall
innovative efforts of firms (i.e., total innovation expenditure per employee: TOTEXP); the
resources devoted, out of total innovation expenditures, to: (i) R&D, design activities and
the acquisition of know-how (RD-DES); (ii) the development or acquisition of new
software (ICT); and (iii) innovative investments in capital equipment (INV). These four
Table 2. List of variables used in the econometric estimates
Acronym Variable
Innovation performance indicators
INN Dichotomous variable equal to 1 for firms which have introducedat least one innovation in 93–95
INPROC Dichotomous variable equal to 1 for firms which have introduced at leastone process innovation in 93–95
INSERV Dichotomous variable equal to 1 for firms which have introduced at leastone service innovation in 93–95
RD-DES R&D, Design, Know How expenditure per employee (Log variable)ICT ICT (software) expenditure per employee (Log variable)INV Capital equipment and ICT hardware expenditure per employee
(Log variable)TOTEXP Total innovative expenditure per employee (Log variable)
Economic performance indicators
SALES Average annual growth rate of salesPROD Average level of productivity (sales per employee) (Log variable)
Sector dummies NACE two and three digit classification equivalent
TRADE Trade and repair of motor-vehicles (50), Wholesale trade (51),Retail trade (52)
HOTELS Hotels and Restaurants (55)TRANSP Land transport (60), Sea transport (61), Air transport (62), Travel
and transport agencies (63)WASTE Waste and disposal (90)COMP Software and related (72)R&DCONS R&D (73), Engineering (74.2) and Technical consultancy (74.3)LEGMKT Legal and Accounting (74.1) and Marketing (74.4)OTHBUS Security (74.6), Cleaning (74.7) and Other Business (74.8)
Size dummies
D20–99 Firms with more than 20 and less than 100 employeesD100–249 Firms with more than 100 and less than 250 employeesD250 Firms with more than 250 employees
indicators allow us to identify which of these different innovation inputs are the most
important in explaining the economic performance of firms (mechanism A) and what kinds
of innovation activity are spurred by firms’ past economic performance and demand
factors (mechanisms B1 and B2).
3.2 The economic performance indicators
The economic performance indicators used in our econometric investigation are in line
with most of the empirical literature referred to in the previous section. We employ two
particular economic performance indicators: (i) the average growth rate of sales at current
prices over the two sub-periods 1993–95 and 1996–98, expressed in natural logarithms
(SALES); and (ii) the ratio between ‘sales’ at current prices and ‘number of employees’,
used as a proxy for labour productivity at current prices.1 The latter was computed as the
natural logarithm of the average values of the ratio in the sub-periods 1993–95 and 1996–
98 (PROD9395 and PROD9698).
While the rationale behind the use of (i) is straightforward, we need to justify our use of
the ratio between sales and the number of employees. This indicator is used to measure
both the impact of innovation on the firm’s economic performance (mechanism A) and the
impact of economic performance on innovation (mechanism B). Innovation can have
a positive impact on the sales per employee ratio through either enlarging the numerator or
decreasing the denominator. The introduction of new or improved services allows firms to
increase their sales in quantitative terms or via a price increase for the service delivered; the
introduction of process innovations increases the ratio by reducing the labour content of
the service produced and delivered. Using the ratio between sales and employees also
seems an obvious way to capture the impact of economic performance on innovation. It is
a good proxy for the total amount of resources that a firm has available to finance its
innovation activity.
Moreover, the use of a ‘level’ indicator turns out—given the time-span of the data at our
disposal—to be a more reliable proxy for structural differences in economic performance
across firms. In fact, the level of productivity tends to capture not only the firm’s static
efficiency, but also its dynamic efficiency, which in turn results from the technological
investments made in the past. In other words, the innovative activity of a firm is likely to be
reflected in its level of productivity rather than in the short-term rate of growth of this
variable, which is affected by the state of the business cycle or by the contingent behaviours
of firms.
3.3 Dummy variables
The last group of indicators in Table 2 includes a set of dummies. These were selected to
capture sector-specific technological regimes as well as structural differences between
sectors and firm-size classes in terms of funding and conducting innovation activities, and
also in terms of economic performance. Great care was taken in the empirical identification
of the sectoral dummies which were identified on the basis of earlier work that used the full
set of data provided by CIS to explore the different dimensions of innovation in services
(Evangelista, 2000; Savona, 2002; Evangelista and Savona, 2003).2
1 The economic performance indicators such as sales/employees and sales growth are expressed in terms ofcurrent prices; thus they may be subject to price change effects. In order to account for this, we should needappropriate sectoral deflators, which unfortunately were not available. However, the use of constant prices isnot relevant here, because the time span considered in the analysis is quite short.
2 In some instances, the choice of sectoral dummies was dictated by the small number of cases observed insome industries. The choice of the size dummies was based on a purely numerical criterion, that is, weattempted to preserve homogeneity in the distribution of firms across the different size classes.
Innovation and economic performance in services 443
1 Given the nature of the dataset, we are not able to take fixed effects into account in our investigation.Therefore, as already stated, we paid close attention to the empirical identification of sectoral and size dummiesin order to reduce the degree of unobserved heterogeneity.
structural associations between innovation and past and future economic performance. In
this sense, the estimates of our equations should be regarded as purely descriptive, and not
as causality tests between the independent and the dependent variables. In other words, as
we shall show, the empirical tests suggest that firms that performed better in the past tend
to carry out more innovative activities (equation (1)) and that firms that were engaged in
innovation activities in the past tend to perform better in the future (equation (2)). In order
to perform a ‘true’ causality test between innovation and performance, a panel dataset
would be needed.
The second econometric issue concerns the (potential) presence in our data of a sample
selection bias. In order to overcome this potential bias, we estimated equations (1), (2) and
(3) using the Heckman two-step procedure. The first step consists of estimating a Probit
model of a dummy variable. In our case, the latter takes the value 1 if the service firm has
introduced a technological innovation and 0 otherwise, and is ‘explained’ by a set of
variables available for all the firms in the sample (innovative and non-innovative).1 The
residuals of this regression were used to construct a selection bias control factor, which is
equivalent to the Inverse Mill’s Ratio (Greene, 2000). This factor accounts for the effects
of all unmeasured characteristics which are related to the selection variable. The Inverse
Mill’s Ratio is then introduced as an extra explanatory variable in the second stage of the
Heckman procedure. The second step of the procedure consists in estimating the
maximum likelihood of equations (1), (2) and (3) using the selection bias control factor
as an additional independent variable. In this way, we obtain efficient and consistent
estimates of the unknown coefficients of the equations.
5. The empirical results
In this section, we present the results of the empirical estimation of the model described in
the previous section.
5.1 From economic performance to innovation (B mechanisms)
Table 4 presents the results of a set of ‘robust’ Logit regressions estimating the impact of
economic performance on respectively the probability of introducing an innovation (INN)
[1], the probability of introducing a process innovation (INPRO) [2] and the probability of
introducing a service innovation (INSERV) [3]. Each specification in turn considers the
effects on the binary dependent variable of the average growth rate of sales over 1993–95
(SALES9395) [a] and the average level of labour productivity for the same period
(PROD9395) [b]. The Logit models also include the complete set of sectoral and size
dummies. In Table 4 (and all subsequent tables) the statistical significance of the variables
under investigation has been measured in terms of t-ratios, corrected for the potential
presence of data heteroscedasticity.
Table 4 shows that the best performing firms in terms of both sales growth and labour
productivity levels in the period 1993–95 are more likely to introduce innovations in that
same period (estimation 1). However, these will be process innovations (estimation 3). Past
economic growth (SALES9395) seems to be a greater stimulus for innovation than
productivity levels (PROD9395). The coefficients of the sectoral dummies reveal the
presence of wide differences across industries in the average propensity for firms to
innovate, which are associated with different levels of technological opportunity. As
1 The independent variables used in the first step are the following: a constant term, two size dummies,a geographical dummy (North-West) and a dummy for whether or not the firm belongs to a business group.
**Significant at 5%; * significant at 10%; robust standard errors in brackets.Equation [1] estimates on total sample; equations [2] and [3] on the sub-sample of innovative firms.
Innovation and economic performance in services 447
pointed to the need to develop a unified theoretical framework to analyse innovation in both
the service and manufacturing industries (Evangelista, 2000, Miles, 2002; Miozzo and
Miles, 2002; Tether, 2003). The suggestion to work towards an integrated approach also
applies to innovation policies which so far have been directed mainly towards the
manufacturing industry. This is because the service sector has always been depicted as
technologically backward, with innovation playing a very marginal role in explaining both
the aggregate performance of this part of the economy and service firms’ individual
competitive strategies. This view needs to undergo a radical change, and our results provide
more evidence for the necessity of broadening the target and scope of existing innovation
policies to include the service sector. Policies currently directed towards the service sector
have, in fact, a clear focus on deregulation and liberalisation schemes. Themessage wewant
to be conveyed through our analysis is that much stricter attention should be paid to
introducing measures to enhance the innovation dynamism of service firms and exploiting
the full potential of the service industries to be generators and users of ICTs.
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