Innovation and economic performance in
services: a firm-level analysis
Giulio Cainelli, Rinaldo Evangelista and Maria Savon...
A common feature of these streams of the literature is their explicit or implicit focus on
the manufacturing sector. Servi...
The paper is structured as follows. Section 2 identifies the key links between innovation
and economic performance. Section...
innovation. In later work, Schumpeter argued that the increasingly scientific base of
economic activities had caused innova...
Geroski and Walters found empirical support for the Schmooklerian hypothesis. Geroski
and Walters focused on the role of d...
to technological change and industrial dynamics. Such an approach, starting with the
pioneering contribution of Nelson and...
are drawn form CIS II; the second group measures the economic performance of firms over
the period 1993–98.
3.1 The innovat...
Along with R&D, the CIS takes into account other fundamental sources of innovation,
such as activities related to the desi...
indicators allow us to identify which of these different innovation inputs are the most
important in explaining the econom...
The basic descriptive statistics of the indicators used in the econometric estimates are
presented in Table 3.
4. The econ...
of know-how; (ii) development or acquisition of software; and (iii) acquisition of new
capital equipment. The reasons for ...
structural associations between innovation and past and future economic performance. In
this sense, the estimates of our e...
expected, the software industry (COMP) and the S&T-based business services
(RDCONS) show positive and much higher coefficie...
Table 5. The impact of economic performance on the innovation intensity
Explanatory var. Dependent variables
[1] [2] [3] [...
OTHBUS ÿ1.688** ÿ0.176 ÿ2.040** ÿ0.913* ÿ1.910** ÿ0.302 ÿ0.938** 0.142
[0.271] [0.311] [0.499] [0.545] [0.332] [0.335] [0....
in technologically new capital equipment (INV)). The analysis of these coefficients shows
that labour productivity in the p...
5.2 From innovation to economic performance (mechanism A)
The results of the estimates of equation (2) are presented in Ta...
The specifications in Table 6 refer to the use of different explanatory variables:
the introduction of innovation (INN); to...
systematically higher than coefficient b1, confirms that the relationship between innovation
and economic performance is dyn...
competition models prevailing in this important part of the economy. As we stated in the
introduction, this is a relativel...
pointed to the need to develop a unified theoretical framework to analyse innovation in both
the service and manufacturing ...
Dosi, G. 2004. On some statistical regularities in the evolution of industries: evidence,
interpretation, and open questio...
Kleinknecht, A. and Verspagen, B. 1990. Demand and innovation: Schmookler re-examined,
Research Policy vol. 19, 387–94
Klo...
Schumpeter, J. A. 1942. Capitalism Socialism and Democracy, New York, Harper
Schmookler, J. 1962. Economic sources of inve...
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  1. 1. 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’ 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: cainelli@idse.mi.cnr.it; Rinaldo Evangelista, IRPPS-CNR, Rome and University of Camerino, Italy; email: r.evangelista@irpps.cnr.it; 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 Poˆle Europe´en de Gestion et d’Economie, Strasbourg, France; email: savona@cournot.u-strasbg.fr * 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. atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  2. 2. 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 and Miles, 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 and productivity slowdown in the advanced economies, which has primarily been imputed by such authors to the structural 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). 436 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  3. 3. 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 a mechanism, 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 der Wiel (2001), Loof and Heshmati (2001), van Leeuwen and van der Wiel (2003) and Evangelista and Savona (2003). Innovation and economic performance in services 437 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  4. 4. 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- mance might be more 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’ sparked much 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 Schmooklerian hypothesis as a co-presence, and mutual interaction between technology-push and demand-pull mechanisms, which in the post-Schmooklerian literature had been considered to be mutually exclusive. We look at this issue in considering mechanism C. 438 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  5. 5. 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 that innovation activities are in fact triggered by unfavourable economic conditions which put pressure on firms to invest more effort and resources into the innovation process. According to this view, the pace of technical change 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 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  6. 6. to technological change and industrial dynamics. Such an approach, starting with the pioneering contribution of Nelson and Winter (1982), has further developed the micro- foundations of the Schumpeterian model of competition and growth (Winter, 1984; Dosi, 1988; Dosi and Nelson, 1994; Dosi et al., 1995; Nelson and Winter, 2002). In an evolutionary framework, innovation is seen as the most important competitive weapon for firms in an economic and technological context characterised by high uncertainty, bounded rationality and path dependency. Such features leave room for a broad variety of (best and worst) innovative behaviours and learning processes which tend to create wide asymmetries in both the technological and economic performances of firms. Technological and economic asymmetries reflect (along with chance) differences in the ‘level’ and ‘quality’ of past innovation activities and competence building processes, with market forces eventually identifying the most successful. Given the highly cumulative and path- dependent nature of such processes, it is likely that asymmetries in both innovation capabilities and economic performances are not temporary, but will tend to persist and be reinforced over time. Compared with the Verdoorn–Kaldorian laws, the evolutionary approach has a more explicit and robust micro-foundation. Therefore, and in line with the empirical agenda of this paper, we label the cumulative mechanism linking economic and innovation performance at the firm level ‘Evolutionary’. The lack of longitudinal firm-level data on innovation and economic performance already referred to has hampered a proper empirical testing of the evolutionary hypothesis. The presence of virtuous circles and long-lasting relationships between the innovativeness and economic performance of firms has been demonstrated so far mainly through case studies and qualitative evidence.1 Furthermore, the literature has focused mainly on the evolutionary trajectories of manufacturing industries and firms. As far as services are concerned, we know very little about the degree of ‘endogeneity’ of technological change or the relevance of models of competition and selection mechanisms based on innovation. 3. The dataset and indicators Before describing the model and the results of the econometric estimates of mechanisms A, B and C sketched above, it is worth examining the main characteristics of the dataset and indicators used in the empirical analysis. Our investigation is based on a new and original longitudinal firm-level dataset built up by matching data drawn from two different statistical sources: the Italian Community Innovation Survey (CIS II) and the System of the Enterprise Accounts (SEA). The resulting sample of this merging consists of 735 service firms with 20 or more employees for which a wide set of innovative data for the period 1993–95, and a selected number of economic performance indicators for the period 1993–98, are available. The statistical representativeness of our sample can be assessed by comparing it with the CIS II population in Table 1. From this table, it can be seen that our sample closely resembles the entire CIS II population in terms of both percentage of innovative firms in total firms and overall structure. The exception is the trade sector, which is slightly underrepresented in our sample. Also, our sample shows a slight bias towards innovative firms. The sector of financial services is not covered because it is not included in the SEA. The indicators used in the econometric estimations are presented in Table 2. The first group of indicators measures different dimensions of firms’ innovation performance and 1 Among the few exceptions, see Marsili (2001). 440 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  7. 7. are drawn form CIS II; the second group measures the economic performance of firms over the period 1993–98. 3.1 The innovation performance indicators As already pointed out, compared with the technological indicators more traditionally used in this field of research, CIS II data provide us with a much richer range of information on firms’ innovation activities and performances. The most basic information provided by CIS is whether the firm introduced an innovation in the period covered by the survey (1993–95) and what type of innovation it was (product/service or process innovation). This information allows us first to link the economic performance of firms to the mere presence of innovation (INN) and second to verify whether product and process-oriented strategies (INSERV, INPRO) lead to different economic outcomes (mechanism A). The distinction between a product and a process innovation has long been recognised in the economics of innovation literature as being crucial in order to identify the different strategies of firms. Product innovations are usually associated with more radical and proactive technological strategies, which are expected to bring high economic returns. Process innovations generally prevail in traditional industries and signal the presence of a more defensive technological strategy, often associated with ration- alisation and restructuring processes. Most of the empirical evidence supporting this view relates to the manufacturing industry. In the case of services, the economic outcomes of these two types of strategies might be less obvious and require proper empirical testing. In fact, in the case of services, product and process innovations are closely intertwined (Miles, 1995; Gallouj and Weinstein, 1997). Furthermore, it is argued that, in many service industries, it is the introduction of a process innovation that opens the way to improvements in the quality of the service delivered, or even to a completely new set of services (Barras, 1986, 1990). Table 1. A comparison between CIS II (Italy) and the sample used in the empirical analysis CIS II population Selected sample Service sectors Total firms % % Innovating firms to total firms Total firms % % Innovating firms to total firms Trade 8,310 43.7 29.3 216 29.4 50.0 Hotel & restaurants 2,186 11.5 19.6 43 5.9 41.9 Transport 2,828 14.9 29.6 217 29.5 49.3 Waste disposal 255 1.3 27.8 19 2.6 31.6 Software & related 972 5.1 54.3 53 7.2 90.6 R&D, engineering, technical consultancy 435 2.3 55.4 36 4.9 77.8 Legal & marketing 677 3.6 34.9 22 3.0 63.6 Security, cleaning, other business services 2,069 10.9 19.3 128 17.4 28.9 Post & telecommunication 55 0.3 10.9 1 0.1 100.0 Financial services 1,237 6.5 61.9 0 0.0 0.0 Total 19,024 100 31.3 735 100 49.9 a Financial services are not covered by the Italian System of the Enterprise Accounts. Innovation and economic performance in services 441 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  8. 8. 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 introduced at least one innovation in 93–95 INPROC Dichotomous variable equal to 1 for firms which have introduced at least one process innovation in 93–95 INSERV Dichotomous variable equal to 1 for firms which have introduced at least one 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 sales PROD 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 employees D100–249 Firms with more than 100 and less than 250 employees D250 Firms with more than 250 employees 442 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  9. 9. 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 of current prices; thus they may be subject to price change effects. In order to account for this, we should need appropriate sectoral deflators, which unfortunately were not available. However, the use of constant prices is not 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 in some industries. The choice of the size dummies was based on a purely numerical criterion, that is, we attempted to preserve homogeneity in the distribution of firms across the different size classes. Innovation and economic performance in services 443 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  10. 10. The basic descriptive statistics of the indicators used in the econometric estimates are presented in Table 3. 4. The econometric analysis 4.1 Specification of the model In order to test mechanisms B and A empirically, already discussed in Section 2, we estimated the following two ‘reduced-form’ equations, respectively Yi;t ¼ a0 þ a1 ÁXi;tÿ1 þ a#3 ÁZi þ ei;1 ð1Þ Xi;tþ1 ¼ b0 þ b1 ÁYi;t þ b#3 ÁZi þ ei;2 ð2Þ where Yi,t denotes the innovative performance of firm i at time t, and Xi,tÿ1 and Xi,tþ1 respectively, denote the economic performance of firm i at time tÿ1 and tþ1, and Zi is a vector of sector and size dummies.1 ei,j is a normally distributed error term. Equation (1) aims to test whether growth and productivity differentials across firms in the period 1993–95 are associated with differentials in the propensity to innovate in the same period, and the amount of resources devoted to innovation in 1995. The hypothesis underlying equation (1) is in line with mechanisms B1 and B2 discussed in Section 2. More particularly, firms with higher levels of productivity or those experiencing faster (than average) growth rates are expected to be more profitable and to have greater financial resources. Both these factors would be expected to act as an incentive to innovate (mechanism B1). We also assume that high growth rates (in sales) and labour productivity levels hint at the presence of a demand-pull incentive to innovate (mechanism B2). We estimated seven different specifications of equation (1), each using a different innovation indicator from those listed in Table 2. These included: the probability that a firm will introduce an innovation; probability of it being a process innovation; proba- bility of it being a service innovation; total innovation expenditure per employee; innovation expenditure (per employee) devoted to: (i) R&D, design activities, acquisition Table 3. Economic and innovation indicators—descriptive statistics Variables N obs. Mean Std. dev. Min. Max. Innovation performance indicators TOTEXP 300 0.710 1.804 ÿ4.227 5.858 RD-DES 148 0.315 2.140 ÿ4.166 5.430 ICT 204 ÿ0.776 1.483 ÿ4.700 2.957 INV 213 0.075 1.685 ÿ4.227 5.729 Economic performance indicators SALES9395 735 0.111 0.117 ÿ1.094 1.034 SALES9698 735 0.059 0.296 ÿ2.36 1.848 PROD9395 735 5.1 1.3 1.8 9.3 PROD9698 735 5.2 1.3 2.5 9.6 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 dummies in order to reduce the degree of unobserved heterogeneity. 444 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  11. 11. of know-how; (ii) development or acquisition of software; and (iii) acquisition of new capital equipment. The reasons for choosing these indicators were discussed in the previous section. Here, it suffices to restate that the use of the indicators listed above allows us to explore in depth the endogenous nature of technological change in services and, in particular, to identify which type of innovation (product or process) and type of innovation activity is spurred by firms’ economic performance and demand factors. The economic performance indicators used in equation (1) are rate of growth of sales in the periods 1993– 95 (SALES9395) and 1996–98 (SALES9698), expressed in logarithms (SALES), and labour productivity in the periods 1993–95 (PROD9395) and 1996–98 (PROD9698). Equation (2) estimates the impact of firms’ innovation activities on their economic performance (mechanism A). The aim is to verify ‘what really boosts’ the productivity and economic growth of service firms. In other words, to find out whether just being an innovator is what matters, or whether it is the type of innovation introduced and the specific knowledge input used that is important. As explanatory variables, we use—in separate estimations—all the innovation indicators listed in Table 2. Finally, we would expect there to be a virtuous circle between innovation, economic performance and enhanced competitiveness, which according to mechanism C discussed in Section 2 would boost innovation through a dynamic self-reinforcing mechanism. In order to test empirically for the presence of such a mechanism, we estimated the following equation Xi;tþ1 ¼ g0 þ g1 Á ^Yi;t þ g3 ÁZi þ ei;3 ð3Þ In equation (3), Yˆ i,t is the Yi,t variable estimated in equation (1) and can therefore be interpreted as firms’ innovation activity ‘induced’ by their past economic performance. By this means, we intend to account for the cumulative effect of past economic performance through ‘induced’ innovation on the economic performance in the sub- sequent period. In other words, in estimating equation (3), we aim to verify whether the evolutionary metaphor (mechanism C) is effective to depict models of competition and selection mechanisms in services. Also, in this case, the use of different innovation indicators will allow us to identify the technological factors sustaining the long-term performance of firms, and the kind of knowledge inputs that have lasting effects on the growth and productivity of service firms. It would be interesting from this point of view to compare the role of ICT vis a` vis other types of knowledge inputs (R&D and Design) as alternative determinants of technological and economic asymmetries among service firms. 4.2 Some econometric issues Before describing the empirical findings of our analysis, it is worth discussing two statistical and econometric issues related to the characteristics of the database used in the econometric analysis. These issues are: (i) the lag structure between innovation and economic performance and the causality direction between these variables, and (ii) the potential bias related to the sample selection problem. First, the characteristics of the database used in the empirical analysis, and described in Section 3, are not those of a panel. While the indicators of economic performance refer to the whole time-span (1993–98), matching with the CIS II only allows us to dispose of innovation indicators for the year 1995. This constrains the possibility of using a proper lag structure between innovation and performance. However, we believe that, given the constraints related to the characteristics of the database, which do not allow us to test for Granger causal links, the data are adequate to conduct a sound test for the existence of Innovation and economic performance in services 445 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  12. 12. 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. 446 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  13. 13. expected, the software industry (COMP) and the S&T-based business services (RDCONS) show positive and much higher coefficients compared with the more traditional service sectors (Hotels and restaurants, Transport, Other business), though such differences mainly refer to service innovations. Also large firms were found more likely to innovate than small firms, though this finding holds with reference to process innovations only. Table 5 reports the ‘robust’ Heckit estimations for the impact of past economic performance on firms’ financial commitment to innovation, and particularly on the amount of resources devoted to R&D and other disembodied technological inputs (design and know-how (RD-DES), software development and acquisition (ICT) and investments Table 4. The impact of economic performance on the propensity to innovate Explanatory var. Dependent variables [1] [2] [3] INN INPROC INSERV [a] [b] [a] [b] [a] [b] Estimation Method Logit Logit Logit Logit Logit Logit Constant 0.445** ÿ2.555** 0.436** 0.193 ÿ0.322 0.206 [0.188] [0.606] [0.250] [0.801] [0.246] [0.809] SALES9395 1.010** – 1.695** – 0.464 – [0.501] [0.605] [0.661] PROD9395 – 0.500** – 0.063 – ÿ0.075 [0.095] [0.119] [0.121] TRADE Ref. Ref. Ref. Ref. Ref. Ref. HOTELS ÿ0.638** 0.264 ÿ0.386 ÿ0.214 ÿ1.232** ÿ1.360** [0.360] [0.393] [0.553] [0.601] [0.664] [0.690] TRANSP ÿ0.316 0.494** 0.489* 0.574 ÿ0.401 ÿ0.537 [0.206] [0.249] [0.293] [0.353] [0.297] [0.363] WASTE ÿ1.028** ÿ0.241 0.140 0.279 0.287 0.164 [0.507] [0.532] [0.944] [0.938] [0.837] [0.873] COMP 2.149** 2.872** 0.949** 1.030** 1.207** 1.096** [0.487] [0.497] [0.410] [0.452] [0.366] [0.408] RDCONS 1.238** 1.772** 0.237 0.156 1.730** 1.590** [0.402] [0.403] [0.481] [0.472] [0.515] [0.526] LEGMKT 0.390 0.996 1.190* 1.251* ÿ0.112 ÿ0.227 [0.518] [0.614] [0.675] [0.697] [0.620] [0.654] OTHBUS ÿ1.193** 0.064 ÿ0.410 ÿ0.160 ÿ0.039 ÿ0.213 [0.248] [0.333] [0.401] [0.497] [0.392] [0.513] D20–99 ÿ1.175** ÿ1.236** ÿ1.051** ÿ0.946** ÿ0.559 ÿ0.514 [0.222] [0.223] [0.326] [0.315] [0.343] [0.345] D100–249 ÿ0.340* ÿ0.415** ÿ0.464* ÿ0.432* ÿ0.157 ÿ0.126 [0.182] [0.188] [0.259] [0.261] [0.254] [0.257] D250 Ref. Ref. Ref. Ref. Ref. Ref. N obs. 735 735 367 367 367 367 Pseudo R2 0.112 0.139 0.062 0.05 0.087 0.087 ** 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 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  14. 14. Table 5. The impact of economic performance on the innovation intensity Explanatory var. Dependent variables [1] [2] [3] [4] TOTEXP RD-DES ICT INV [a] [b] [a] [b] [a] [b] [a] [b] Estimation method Heckit Heckit Heckit Heckit Heckit Heckit Heckit Heckit Second stage eq. Constant 0.879 ÿ2.216** 2.176** ÿ0.759 0.699 ÿ3.407** ÿ1.590** ÿ4.201** [2.389] [0.646] [0.557] [1.052] [0.317] [0.539] [0.616] [1.122] SALES9395 0.330 – 0.320 – 0.221 – 0.604 – [0.411] [0.423] [0.549] [0.458] PROD9395 – 0.573** – 0.445** – 0.624** – 0.430** [0.090] [0.132] [0.079] [0.122] TRADE Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. HOTELS ÿ1.033** ÿ0.057 ÿ1.378** ÿ0.732 ÿ0.811** 0.271 ÿ0.560 0.072 [0.368] [0.361] [0.519] [0.583] [0.256] [0.279] [0.504] [0.513] TRANSP ÿ0.648** 0.326 ÿ1.078** ÿ0.400 ÿ1.013** ÿ0.098 ÿ0.231 0.449 [0.244] [0.261] [0.396] [0.459] [0.226] [0.238] [0.322] [0.383] WASTE ÿ0.479 0.624 – – ÿ0.773** 0.387 0.677 1.518** [0.546] [0.506] [0.292] [0.289] [0.596] [0.612] COMP 0.952** 1.892** 1.515** 2.240** 0.259 1.357** 0.459 1.078** [0.270] [0.274] [0.392] [0.461] [0.268] [0.297] [0.317] [0.347] R&DCONS 2.226** 3.046** 3.197** 4.111** 0.253 1.045** 1.123** 1.394** [0.410] [0.477] [0.470] [0.510] [0.277] [0.275] [0.478] [0.527] LEGMKT 0.513 1.366** 0.251 1.014 ÿ0.083 1.015** 0.171 0.897** [0.332] [0.287] [0.594] [0.671] [0.589] [0.434] [0.331] [0.372] 448G.Cainelli,R.EvangelistaandM.Savona atUniversidadCarlosIIIonOctober25,2010 cje.oxfordjournals.org Downloadedfrom
  15. 15. OTHBUS ÿ1.688** ÿ0.176 ÿ2.040** ÿ0.913* ÿ1.910** ÿ0.302 ÿ0.938** 0.142 [0.271] [0.311] [0.499] [0.545] [0.332] [0.335] [0.344] [0.434] D20–99 0.9 1.168** 2.781** 2.815** 1.435** 1.295** 0.699** 0.686* [1.131] [0.290] [0.552] [0.539] [0.305] [0.272] [0.382] [0.415] D100–249 0.208 0.236 0.923** 0.790** 0.667** 0.510** 0.078 0.013 [0.454] [0.206] [0.405] [0.402] [0.251] [0.228] [0.261] [0.248] D250 Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref. N obs. 668 668 516 516 572 572 581 581 Censored obs. 368 368 368 368 368 368 368 368 Uncensored obs. 300 300 148 148 204 204 213 213 Likelihood ÿ987.56 ÿ967.72 ÿ570.64 ÿ565.51 ÿ692.8 ÿ667.79 ÿ764.75 ÿ758.24 ** Significant at 5%; * significant at 10%; robust standard errors in brackets. Innovationandeconomicperformanceinservices449 atUniversidadCarlosIIIonOctober25,2010 cje.oxfordjournals.org Downloadedfrom
  16. 16. in technologically new capital equipment (INV)). The analysis of these coefficients shows that labour productivity in the period 1993–95 is positively related to all types of innovation expenditure made in 1995. However, a comparison among the different elasticity coefficients in Table 5 reveals that highly productive firms are more likely to re-invest their revenues in internal development or acquisition of software. The growth rate of sales in 1993–95 does not seem to have a statistically significant positive impact on innovation. The coefficients associated with the variable PROD9395 (specification b) provide the value of coefficient a1 in equation (1). This coefficient shows values ranging from 0.430 for capital equipment expenditure, to 0.624 for ICT expenditure per employee. The co- efficient a1 can then be compared with the value of the elasticity g1 (equation 3), to test for the presence of a cumulative effect (mechanism C), which will be discussed in Section 5.3. To sum up, past economic performance does affect both the propensity for service firms to innovate and the amount of resources devoted to innovation activities. Somewhat surprising is the result that past economic performance has an impact on process innovation rather than on the introduction of new services. This might be a peculiarity of services. It has already been pointed out that, in some of service sectors, process innovation takes the form of heavy investment in costly technological infrastructures (both tangible and intangible), while service innovations might consist of quality improvements carried out on a more continuous basis. Our results provide indirect support for the hypothesis that high growth rates, large profits and substantial cash flows might be a precondition for process innovation activity in services. Further, our estimates provide support for the hypothesis that past economic performance strengthens firms’ commit- ment to make investments in ICT—both hardware and software. The endogenous nature of innovation seems therefore to have a process-oriented connotation, and this is likely to be a peculiar feature of services. However, it should be recalled that, in the case of services, process innovation strategies do not necessarily follow a cost-cutting objective. Both process innovations and ICTs could be introduced to enhance the quality and performances of the services delivered. Indeed, sales growth rates and levels of labour productivity measured as a ratio between sales and number of employees might be considered as a proxy for final demand, with high rates of sales growth and labour productivity levels being a symptom of favourable and sustained demand conditions. Although, as pointed out above, our analysis is not able to prove a Granger causality between economic performance and innovation activity, we can nevertheless argue that the presence of a positive structural association between the two variables does support the idea of a Schmooklerian type of mechanism in operation in service firms, which implies that favourable and sustained conditions of demand are a positive incentive to innovate and increase the amount of innovation expenditure. These findings for services are in line with most of the empirical evidence in the post- Schmooklerian tradition, discussed in Section 2, but hitherto exclusively confined to manufacturing activities (see Kleinknecht and Verspagen, 1990; Geroski and Walters, 1995; Brower and Kleinknecht, 1999, among others). Further, the stronger link found between past economic performance and the level of innovation expenditure devoted to ICTs compared with other types of innovation expenditure, is in line with most of the empirical literature on innovation in services. According to this body of work, service innovation is mainly incremental in nature and more likely to be related to specific applications of ICT as a general purpose technology (Helpman, 1998; Freeman and Soete, 1997) and arguably highly dependent on the positive response of destination markets as well as favourable demand conditions. 450 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  17. 17. 5.2 From innovation to economic performance (mechanism A) The results of the estimates of equation (2) are presented in Table 6. Only the results relating to the effects of innovation on productivity are presented, since the rate of growth in the period 1996–98 was not found to be associated (with statistically significant coefficients) with any of the innovation variables considered in this study. Table 6. The impact of innovation on productivity Explanatory var Dependent variables PROD9698 [a] [b] [c] [d] [e] Estimation method OLS Heckit Heckit Heckit Heckit Second stage eq. Constant 6.191** 5.622** 7.263** 5.564** 5.421** [0.089] [0.094] [0.325] [0.246] [0.133] INN 0.363** – – – – [0.073] TOTEXP – 0.166** – – – [0.034] RD-DES – – 0.105** – – [0.039] ICT – – – 0.211** – [0.072] INV – – – – 0.139** [0.037] TRADE Ref. Ref. Ref. Ref. Ref. HOTELS ÿ1.774** ÿ1.469** ÿ1.654** ÿ1.480** ÿ1.538** [0.104] [0.128] [0.310] [0.202] [0.137] TRANSP ÿ1.683** ÿ1.828** ÿ1.614** ÿ1.529** ÿ1.955** [0.101] [0.145] [0.262] [0.307] [0.184] WASTE ÿ1.513** ÿ1.542** – ÿ0.825** ÿ1.584** [0.173] [0.103] [0.374] [0.101] COMP ÿ1.361** ÿ1.565** ÿ1.718** ÿ1.539** ÿ1.554** [0.109] [0.107] [0.199] [0.123] [0.132] R&DCONS ÿ1.134** ÿ1.676** ÿ1.770** ÿ1.347** ÿ1.198** [0.136] [0.212] [0.252] [0.424] [0.184] LEGMKT ÿ0.959** ÿ1.545** ÿ1.526** ÿ1.557** ÿ1.668** [0.240] [0.157] [0.309] [0.159] [0.164] OTHBUS ÿ2.497** ÿ2.291** ÿ2.474** ÿ2.146** ÿ2.435** [0.085] [0.120] [0.348] [0.139] [0.134] D20–99 0.178** ÿ0.655** 0.072 ÿ0.725** ÿ0.830** [0.082] [0.159] [0.276] [0.214] [0.206] D100–249 0.150** ÿ0.137 0.233 ÿ0.202 ÿ0.188 [0.079] [0.126] [0.198] [0.175] [0.157] D250 Ref. Ref. Ref. Ref. Ref. N obs. 735 668 516 572 581 Censored obs. – 368 368 368 368 Uncensored obs. – 300 148 204 13 Adj. R2 0.539 – – – – Likelihood – ÿ797.97 ÿ470.28 ÿ588.58 ÿ619.83 ** Significant at 5%; * significant at 10%; robust standard errors in brackets. Innovation and economic performance in services 451 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  18. 18. The specifications in Table 6 refer to the use of different explanatory variables: the introduction of innovation (INN); total innovation expenditure per employee (TO- TEXP); and the relevance of the three types of innovation activities carried out by firms (RD-DES, ICT, and INV). The picture provided by Table 6 suggests that innovation is likely to be a key factor in the economic performance of firms. Innovation activities undertaken in 1995 were shown to have a positive impact on productivity levels in the subsequent three years. Productivity levels are in fact associated with both the presence of innovation activities and the amount of resources devoted to innovation. It is also interesting to look at the effects of the different types of innovation activities undertaken by firms. The estimated coefficients in the specifications from [b] to [e] in fact provide the value of coefficient b1 in equation (2). This should be interpreted as the elasticity of economic performance with respect to different types of innovation activities. As analysis of Table 6 shows, coefficient b1, associated with the different innovation variables, presents values ranging from 0.105 in the case of expenditure on R&D, Design and Know How, to 0.211 in the case of firms’ innovation expenditure on ICTs. It is therefore the acquisition and internal development of software that has the greatest impact on firms’ productivity. These results not only support the widespread view regarding the centrality of ICT in explaining the aggregate performance of services, but they also demonstrate that this new technological regime is shaping the innovation strategies of service firms and represents the ‘competence area’ on which firms build their competitive advantage. Although this finding comes as no surprise, it is nonetheless a relevant research outcome, because it is not based on simple common sense nor on specific case studies, but for the first time is based on statistically robust micro-data. What is surprising, however, is that noassociation was found between the innovation performance of firms in the period 1993–95 and the rate of economic growth (in terms of sales) in the following three years (the results of these estimations are not shown). Some possible explanations for this finding are proposed in the next section. 5.3 Dynamic link between innovation and economic performance (mechanism C) In this section, the hypothesis that the link between innovation and economic performance is cumulative and self-reinforcing over time is empirically tested by estimating equation (3). The results of the econometric estimations are shown in Table 7. The fitted values of the innovative indicators estimated by equation (1) are used here as independent variables. This allowed different specifications of equation (3) to be tested. The dependent variable in all the estimations is the average level of labour productivity in the period 1996–98. In this way, we aim to capture the economic impact of different types of innovative activity which are ‘induced’ by past economic performance. Sectoral and size dummies (not shown in the table) were included in the regressions. The estimates of regression [a] reveal the presence of the cumulative mechanism mentioned above, which dynamically links productivity and the overall financial commit- ment of service firms to innovation, as measured by total innovation expenditure per employee. Such a link is confirmed for all three different types of innovation activities considered in our analysis. The highest coefficients, however, were found in the case of ICT and capital expenditures. This finding suggests that investment in ICTs (both hardware and software) plays a dominant role in explaining the virtuous circle between innovation and economic performance in the service sector. R&D activities, on the other hand, are confirmed as being a much weaker competitive factor in services. The fact that coefficient g1—which can be read in terms of cumulative elasticity of the innovation ‘induced’ by past economic performance on future economic performance—gives results 452 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  19. 19. systematically higher than coefficient b1, confirms that the relationship between innovation and economic performance is dynamically self-reinforcing. These results confirm and further reinforce those obtained with the estimations of equations (1) and (2) especially with reference to the key role played by ICTs in their process-oriented connotation. As was the case in the estimation of equation (2), the statistical significance of the links between innovation and economic performance disappears when the latter is measured in terms of sales growth rates. This might be due to two related factors. The first is that, owing to the cumulative nature of technological change and learning processes, the relationship between innovation and economic growth takes place over, and should be explored in, the long run. This argument and our results in turn support the view that the dynamic relationship between innovation and economic performance has a structural nature or, in others words, shows a high degree of persistence (Cefis, 2003).1 This is confirmed to some extent by the wide variation found (both across firms and over time) in the case of all indicators measuring annual ‘growth rates’ of sales and employment. Second, the high volatility of these indicators is also likely to reflect weaknesses in the balance-sheet data as well as erratic factors governing the short-term performance of firms. Both these features might be further accentuated in the case of services. 6. Summary of the findings and conclusions In this paper, we have attempted to answer the question of whether innovation plays a role in explaining the economic performance of service firms and more generally the Table 7. The relationship between economic performance and innovation intensity Explanatory var. Dependent variables PROD9698 [a] [b] [c] [d] Estimation method Heckit Heckit Heckit Heckit Second stage eq. Constant 4.426** 5.027** 5.690** 7.651** [0.080] [0.272] [0.066] [0.304] TOTEXP_F 0.590** – – – [0.045] RD_F – 0.319** – – [0.034] ICT_F – – 1.097** – [0.083] INV_F – – – 1.308** [0.088] N obs. 735 735 735 735 Censored obs. 368 368 368 368 Uncensored obs. 367 367 367 367 Likelihood ÿ1031.5 ÿ1065.5 ÿ930.89 ÿ990.48 ** Significant at 5%; * significant at 10%; robust standard errors in brackets. 1 This finding is consistent with the statistical regularities presented in Dosi (2004). Innovation and economic performance in services 453 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  20. 20. competition models prevailing in this important part of the economy. As we stated in the introduction, this is a relatively unexplored area dominated by evocative views rather than robust empirical evidence. This paper has attempted to correct this by exploring, both conceptually and empirically, the two-way relationship between innovation and economic performance in services at the firm level. The complex nature of this relationship has been stylised and modelled empirically. Three different ‘mechanisms’ have been identified, mainly inspired by the seminal contributions of Schumpeter and the literature in the Schumpeterian tradition. In addition to assessing the impact of innovation on the economic performance of service firms, we also explored the reverse relationship and looked for the presence of a self-reinforcing virtuous circle between innovation and economic performance at the firm level. The results presented in Section 5 show that innovation has a positive impact on the economic performance of firms (mechanism A). Innovating firms out-perform non- innovating firms in terms of both productivity and economic growth. Furthermore, productivity in services is associated not only simply with the presence of innovation, but also with the level of financial commitment to innovation and the type of innovation activity performed. Productivity differentials among firms and sectors emerge as being affected by the innovation efforts of firms and, crucially, by the amount of resources devoted to the internal generation and adoption of ICTs (both software and hardware). The reverse relationship (mechanism B) also seems to be at work in services: better performing firms are more likely to innovate and to devote more of their resources to innovation. In particular, firms that have achieved high levels of labour productivity and experienced high rates of sales growth show above average innovation expenditure and concentrate their innovativeness towards investment in ICTs, both hardware (capital equipment) and software. These results confirm that, even in services, embarking on long- lasting, costly and risky innovation projects requires a ‘healthy’ economic structure, and is facilitated by fast-growing markets. These results can also be seen as supporting the presence of a demand-pull effect on innovation (Schmooklerian-type mechanism). Al- though our data do not allow us to test for the presence of ‘cycles’ of economic activity leading to ‘cycles’ of innovation activity in services, they nevertheless hint at the presence of a positive association between the two. The presence of a cumulative and self-reinforcing mechanism linking firms’ productivity and innovation was found. The evidence presented shows that the process of market selection in services is shaped by the cumulative nature of innovation. Asymmetries across firms in labour productivity and innovation performance not only tend to persist over time, but reinforce each other. Such a cumulative mechanism underlies the ability of firms to exploit the opportunities offered by ICTs. This is likely to be a peculiarity of services. In summary, the evidence presented in this paper gives two important messages. The first is that innovation in the service sector emerges as a truly endogenous process in so far as the technological activities of firms are affected by their past economic performance and demand conditions. The second is that the evolutionary metaphor is able to depict some essential dynamic properties of service industries and, in particular, the role that innovation plays in driving models of competition and selection mechanisms in this part of the economic system. These two points have certain implications. On a theoretical basis, the results presented suggest that the demarcation between services and manufacturing loses most of its meaning, at least in terms of the basic tool-box needed to analyse the determinants and economic effects of the innovative behaviour of firms. This supports those contributions that have 454 G. Cainelli, R. Evangelista and M. Savona atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
  21. 21. 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. The message we want 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. Bibliography Aghion, P. and Howitt, P. 1992. A model of growth through creative destruction, Econometrica, vol. 60, 323–51 Andersen, B., Howells, J., Hull, R., Miles, I. and Roberts, J. (eds) 2000. Knowledge and Innovation in the New Service Economy, Cheltenham, Edward Elgar Archibugi, D. and Michie, J. (eds) 1998. Trade, Growth and Technical Change, Cambridge, Cambridge University Press Barras, R. 1986. Towards a theory of innovation in services, Research Policy, vol. 15, 161–73 Barras, R. 1990. Interactive innovation in financial and business services: the vanguard of service revolution, Research Policy, vol. 19, 215–37 Baumol, W. J. 1967. Macroeconomics of unbalanced growth: the anatomy of an urban crisis, American Economic Review, vol. 57, 415–26 Baumol, W. J. 2002. Services as leaders and the leader of services, in Gadrey, J. and Gallouj, F. (eds), Productivity, Innovation and Knowledge in Services: New Economic and Socio-economic Approaches, Cheltenham, UK/Northampton, MA, Edward Elgar Baumol, W. J., Blackman, S. A. B. and Wolff, E. 1989. Productivity and American Leadership: The Long View, Cambridge, MA, MIT Press Bresnahan, T. and Trajtenberg, M. 1995. General purpose technologies: ‘engines of growth’?, Journal of Econometrics, vol. 65, 83–108 Brower, E. and Kleinknecht, A. 1999. Keynes-plus? Effective demand and changes in firm-level R&D: an empirical note, Cambridge Journal of Economics, vol. 23, 385–91 Cefis, E. 2003. Is there persistence in innovation activities?, International Journal of Industrial Organization, vol. 21, 489–515 Crepon, B., Duguet, E. and Mairesse, J. 1998. Research, innovation, and productivity: an econometric analysis at the firm level, Economics of Innovation and New Technology, vol. 7, 115–58 Cohen, W. 1995. Empirical studies of innovation activities, in Stoneman P. (ed.), Handbook of the Economics of Innovation and Technical Change, Oxford, Blackwell Cohen, S. S. and Zysman, J. 1987. Manufacturing Matters—The Myth of Post-industrial Economy, New York, Basic Books Coombs, R. and Miles, I. 2000. Innovation, measurement and services: the new problematique, in Metcalfe, J. S. and Miles, I. (eds), Innovation System in the Service Economy. Measurement and Case Study Analysis, Boston, Kluwer Djellal, F. and Gallouj, F. 1999. Services and the search for relevant innovation indicators: a review for national and international surveys, Science and Public Policy, vol. 26, no. 4, 218–32 Dosi, G. 1988. Sources, procedures and microeconomic effects of innovation, Journal of Economic Literature, vol. 26, 1120–71 Innovation and economic performance in services 455 atUniversidadCarlosIIIonOctober25,2010cje.oxfordjournals.orgDownloadedfrom
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