Innovation and economic performance in services: a firm-level analysis. By Giulio Cainelli, Rinaldo Evangelista and Maria Savona

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.

Key words: Technological innovation, Economic performance, Service sectorJEL 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:

[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).

Cambridge Journal of Economics 2006, 30, 435–458doi:10.1093/cje/bei067Advance Access publication 8 August, 2005

� The Author 2005. Published by Oxford University Press on behalf of the Cambridge Political Economy Society.

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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).

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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).

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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.


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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).


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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).


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are drawn formCIS II; the second groupmeasures 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 sectorsTotalfirms %

% Innovatingfirms to

total firmsTotalfirms %

% Innovatingfirms to

total firms

Trade 8,310 43.7 29.3 216 29.4 50.0Hotel & restaurants 2,186 11.5 19.6 43 5.9 41.9Transport 2,828 14.9 29.6 217 29.5 49.3Waste disposal 255 1.3 27.8 19 2.6 31.6Software & related 972 5.1 54.3 53 7.2 90.6R&D, engineering,technical consultancy

435 2.3 55.4 36 4.9 77.8

Legal & marketing 677 3.6 34.9 22 3.0 63.6Security, 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

aFinancial services are not covered by the Italian System of the Enterprise Accounts.


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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


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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.


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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 �Xi;t�1 þ a#3 �Zi þ ei;1 ð1Þ

Xi;tþ1 ¼ b0 þ b1 �Yi;t þ b#3 �Zi þ ei;2 ð2Þ

where Yi,t denotes the innovative performance of firm i at time t, and Xi,t�1 and Xi,tþ1

respectively, denote the economic performance of firm i at time t�1 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 �4.227 5.858RD-DES 148 0.315 2.140 �4.166 5.430ICT 204 �0.776 1.483 �4.700 2.957INV 213 0.075 1.685 �4.227 5.729

Economic performance indicators

SALES9395 735 0.111 0.117 �1.094 1.034SALES9698 735 0.059 0.296 �2.36 1.848PROD9395 735 5.1 1.3 1.8 9.3PROD9698 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 dummiesin order to reduce the degree of unobserved heterogeneity.


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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 �Yi;t þ g3 �Zi þ ei;3 ð3Þ

In equation (3), Yi,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


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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.


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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** �2.555** 0.436** 0.193 �0.322 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 – �0.075[0.095] [0.119] [0.121]

TRADE Ref. Ref. Ref. Ref. Ref. Ref.HOTELS �0.638** 0.264 �0.386 �0.214 �1.232** �1.360**

[0.360] [0.393] [0.553] [0.601] [0.664] [0.690]TRANSP �0.316 0.494** 0.489* 0.574 �0.401 �0.537

[0.206] [0.249] [0.293] [0.353] [0.297] [0.363]WASTE �1.028** �0.241 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* �0.112 �0.227

[0.518] [0.614] [0.675] [0.697] [0.620] [0.654]OTHBUS �1.193** 0.064 �0.410 �0.160 �0.039 �0.213

[0.248] [0.333] [0.401] [0.497] [0.392] [0.513]D20–99 �1.175** �1.236** �1.051** �0.946** �0.559 �0.514

[0.222] [0.223] [0.326] [0.315] [0.343] [0.345]D100–249 �0.340* �0.415** �0.464* �0.432* �0.157 �0.126

[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 367Pseudo 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.


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Table 5. The impact of economic performance on the innovation intensity


[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 �2.216** 2.176** �0.759 0.699 �3.407** �1.590** �4.201**[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 �1.033** �0.057 �1.378** �0.732 �0.811** 0.271 �0.560 0.072[0.368] [0.361] [0.519] [0.583] [0.256] [0.279] [0.504] [0.513]

TRANSP �0.648** 0.326 �1.078** �0.400 �1.013** �0.098 �0.231 0.449[0.244] [0.261] [0.396] [0.459] [0.226] [0.238] [0.322] [0.383]

WASTE �0.479 0.624 – – �0.773** 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 �0.083 1.015** 0.171 0.897**[0.332] [0.287] [0.594] [0.671] [0.589] [0.434] [0.331] [0.372]

448

G.Cainelli,

R.Evangelista

andM

.Savona

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OTHBUS �1.688** �0.176 �2.040** �0.913* �1.910** �0.302 �0.938** 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 �987.56 �967.72 �570.64 �565.51 �692.8 �667.79 �764.75 �758.24

**Significant at 5%; * significant at 10%; robust standard errors in brackets.

Innovatio

nandeconomic

performancein

services

449

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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.


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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 �1.774** �1.469** �1.654** �1.480** �1.538**

[0.104] [0.128] [0.310] [0.202] [0.137]TRANSP �1.683** �1.828** �1.614** �1.529** �1.955**

[0.101] [0.145] [0.262] [0.307] [0.184]WASTE �1.513** �1.542** – �0.825** �1.584**

[0.173] [0.103] [0.374] [0.101]COMP �1.361** �1.565** �1.718** �1.539** �1.554**

[0.109] [0.107] [0.199] [0.123] [0.132]R&DCONS �1.134** �1.676** �1.770** �1.347** �1.198**

[0.136] [0.212] [0.252] [0.424] [0.184]LEGMKT �0.959** �1.545** �1.526** �1.557** �1.668**

[0.240] [0.157] [0.309] [0.159] [0.164]OTHBUS �2.497** �2.291** �2.474** �2.146** �2.435**

[0.085] [0.120] [0.348] [0.139] [0.134]D20–99 0.178** �0.655** 0.072 �0.725** �0.830**

[0.082] [0.159] [0.276] [0.214] [0.206]D100–249 0.150** �0.137 0.233 �0.202 �0.188

[0.079] [0.126] [0.198] [0.175] [0.157]D250 Ref. Ref. Ref. Ref. Ref.N obs. 735 668 516 572 581Censored obs. – 368 368 368 368Uncensored obs. – 300 148 204 13Adj. R2 0.539 – – – –Likelihood – �797.97 �470.28 �588.58 �619.83



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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).Thepicture provided byTable 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 associatedwith 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 coefficientb1 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

robustmicro-data.What is surprising, however, is that no associationwas foundbetween 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


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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


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 735Censored obs. 368 368 368 368Uncensored obs. 367 367 367 367Likelihood �1031.5 �1065.5 �930.89 �990.48


1 This finding is consistent with the statistical regularities presented in Dosi (2004).


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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 andmanufacturing losesmost of itsmeaning,

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


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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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