Persistence of innovative activities, sectoral patterns of innovation and international technological specialization

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International Journal of Industrial Organization 15 (1997) 801-826

Industrial Omanization

Persistence of innovative activities, sectoral patterns of innovation and international technological specialization 1 a

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F r a n c o M a l e r b a " , L u i g i O r s e n l g o , Pietro Peretto b aUniversity of Brescia and Cespri, Bocconi University, Via Sarfatti, 25, 20136 Milan, Italy ~Department of Economics, Duke University, Durham, NC 27708, USA

Abstract In this paper, we focus on the role of persistence and heterogeneity of innovative activities at the level of the firm in determining the patterns of technological change in different industries and countries. We ask: are persistence and heterogeneity associated with higher degrees of concentration in innovative activities, stability in the ranking of innovators, and lower degrees of entry and exit in the population of innovators? Or, do the patterns of innovation depend on other variables like firm size and industrial concentration? Moreover, what are the relationships between the patterns of innovative activities, their determinants, and the technological specialization of countries? We compute indicators of persistence and heterogeneity using the OTAF-SPRU patent database at the firm level for five European countries over the period 1969-1986 for 33 technological classes. Then, we estimate the relationships between our indicators of the sectoral patterns of innovative activities and international technological specialization on the one hand, and our indicators of persistence, heterogeneity and market structure on the other. Results show that persistence and asymmetries are important (and strongly related) phenomena that affect the patterns of innovative activities across countries and sectors, while the role of market structure variables is less clear. Finally, international technological specialization is associated to a competitive core of persistent innovators. © 1997 Elsevier Science B.V.

Keywords: Innovation; Persistence; International technological specialization JEL classification: O31; L10; F14 *Corresponding author. Tel.: (+39) 2 58363391; fax: (+39) 2 58363399; E-mail: franco.malerba@ uni-bocconi.it We thank Cristiano Antonelli, Stephen Martin and an anonymous referee for helpful suggestions that significantly contributed to improve the paper. Support from the Italian National Research Council (CNR) and from the Italian Ministry of University and Scientific and Technological Research (40% Fund) is gratefully acknowledged. 0167-7187/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved. PII S0167-7 187(97)00012-X

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I. Introduction In this paper, we focus on the role of two dimensions of the process of innovation, persistence and heterogeneity of innovative activities at the level of the firm in determining the patterns of technological change in different industries and countries. We ask: are persistence and heterogeneities associated with higher degrees of concentration in innovative activities, stability in the ranking of innovators, and lower degrees of entry and exit in the population of innovators? Or do the patterns innovation depend on other variables like firm size and industrial concentration? Moreover, what are the relationships between the patterns of innovative activities, their determinants, and the technological specialization of countries? Ever since Schumpeter (1912), (1942), one finds in the literature two conceptualizations of the process of technological change. The difference between the two rests on some fundamental assumptions about the properties of technology and of the innovative process (for a more detailed discussion of this point see Pavitt, 1988; Pavitt and Patel, 1994; Dosi, 1988). At the cost of oversimplification, one can summarize the first conceptualization as viewing technological change as a process of 'creative destruction' (or Schumpeter Mark I model, as the literature labels the interpretive model that Schumpeter discussed in The Theory o f Economic Development). This is an uneven and random process, driven by a population of homogeneous firms fishing in a pool of technological opportunities which are accessible to everybody. Innovation generates monopoly power which is at best only temporary, since it is quickly challenged and eventually eroded by the innovative success of competitors in the following period. Moreover, since the relevant knowledge base is easily accessible, challengers may come from every quarter. As a consequence, new innovators systematically substitute for incumbents at the frontier of technology. Under these conditions, one would expect to find that typical innovators are small, newly established firms.2 In contrast, the second conceptualization emphasizes that technological change is a process of 'creative accumulation' (or Schumpeter Mark II model, as the literature labels the interpretive model that Schumpeter discussed in Capitalism, Socialism and Democracy). This view emphasizes that technical knowledge has a strong tacit component and is highly specific to individual firms and applications. As a consequence, innovation results from the in-house accumulation of technological competencies by heterogenous firms. Moreover, firm-specific technical

2 As an illustration of such a process, drawn from the modem macroeconomicliterature, consider quality-ladders models of endogenous innovation, all of which embody some more or less simplified specification of this basic representation (see, e.g., Grossman and Helpman, 1991, Ch. 4; Aghion and Howitt, 1992 and Barro and Sala-i-Martin, 1995, Ch. 7).

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change is cumulative in the sense that accumulated competencies significantly constrain the future technological performance of the firm. Over time, the firmspecific, tacit, and cumulative nature of the knowledge base builds higher barriers to entry. As a consequence, the role of new innovators is limited and a few (large) firms eventually come to dominate the market in a stable oligopoly. Disruption of the leadership of established innovators at the technological frontier requires drastic changes in the relevant technological paradigm to make accumulated competencies obsolete. In the theoretical literature, only a few models are able to generate either creative destruction or creative accumulation or, more interestingly, both. Models as different in inspiration as the ones in Nelson and Winter (1982) and Ericson and Pakes (1992) show that these two alternative representations of the patterns of technological change can be interpreted as two faces of the stochastic process which drives technological accumulation at the firm level and thereby drives the dynamics of the industry.3 Very little is known about the relative empirical relevance of the two characterizations of technological progress. In this paper, we offer some preliminary evidence that partially fills this gap. There are a number of interesting questions that one can ask. We chose to focus on a few that we consider particularly important. First, is it possible to observe in the data patterns of technological change that more closely resemble the Schumpeter Mark I or the Schumpeter Mark II model? Second, what are the determinants of the observed patterns of innovative activities and what kind of dynamic process can generate those patterns? Third, are these observed patterns related to technological performance in any meaningful way? For instance, is the technological specialization of countries associated with creative destruction or creative accumulation? Answers to these questions have important implications for our understanding of the determinants of the patterns of technological change and thereby for the theory of industrial dynamics, growth and trade. Furthermore, the quality of the debate about alternative policy prescriptions largely depends on resolving these issues. In previous papers (Malerba and Orsenigo, 1995, 1996), two of us provided some empirical evidence concerning the first question: are the Schumpeter Mark I and Mark II models actually observed in the data? We examined the patterns of innovative activities at the sectoral level in six countries (United States, Japan, United Kingdom, Germany, France and Italy) using different sets of patent data (the OTAF-SPRU database on patents granted in the USA and the CESPRI database on patent applications at the European Patent Office). We calculated and compared indicators of relevant dimensions of the patterns o f technological change, such as concentration of innovative activities, stability in the hierarchy of innovators, size of the innovating films, and rates of technological entry and exit.

3See also Pakes and McGuire, 1993.

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We found the alternative models of creative destruction and creative accumulation in different technological classes, with relatively little variation across countries. We speculated that the patterns of innovative activities are linked to the nature of the relevant technological regime, defined by opportunity and appropriability conditions, degrees of cumulativeness of technological advances, and the nature of the knowledge base (for further discussions of the notion of technological regimes, see, e.g., Nelson and Winter, 1982; Winter, 1984; Dosi, 1988; Malerba and Orsenigo, 1990, 1993). In this paper, we develop further this line of inquiry, by providing some evidence concerning the other two questions, respectively about the determinants of the sectoral patterns of innovation and about the relationships between the latter and the technological specialization of countries. In particular, we focus on the role of cumulativeness, imperfectly proxied by the notion of persistence of innovative activities, and of heterogeneity in the population of innovators. In a strongly empirical and descriptive attitude, we ask whether and how different degrees of persistence in innovative activities are linked to the observed patterns of innovative activities at the sectoral level. In Section 2, we briefly discuss the notions of persistence and heterogeneity. In Section 3, we describe the data and the methodology of analysis. In Section 4, we discuss our results. In Section 5 we examine the relationships between the sectoral patterns of innovation and international technological specialization. We conclude in Section 6.

2. The notions of persistence and heterogeneity Consider, to begin with, innovation as a purely random shock in a firm's technological domain. In the simplest statistical interpretation, the notion of innovative persistence can be defined as the conditional probability that innovators at time t will innovate at time t + 1. More precisely, one can think of persistence as the degree of serial correlation in innovative activities and consider innovation as a purely random process that the firm does not control. Innovation, however, results from the actions of economic agents and it is affected by opportunities and constraints that are defined by the characteristics of technologies and markets. Thus, persistence of innovative activities is likely to be generated by the properties of the process of accumulation of technological competencies and by market forces. In its simplest economic interpretation, the notion of innovative persistence can be related to the Schumpeterian intuition that critical market feedbacks link R&D investment, technological performance and profitability. For instance, persistence may be simply the outcome of 'success-breeds-success' processes like those used in the Nelson and Winter (1982) models: innovative success yields profits that can be reinvested in R&D, thereby increasing the probability to innovate again.

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In its simplest technological interpretation, the notion of innovative persistence can be related to the notion of technological cumulativeness, i.e., the idea that technical change is gradual and incremental, since it builds on accumulated competencies in the firm's technological domain. Thus, specific innovation generates a stream of subsequent innovations, which improve gradually upon the original one. In this perspective, persistence may be the outcome of the intrinsically cumulative nature of learning processes (Rosenberg, 1976; Nelson and Winter, 1982). The generation of new knowledge builds upon what has been learned in the past, not only in the sense that past knowledge constrains current research, but also in the sense that knowledge generates questions which, in turn, generate new research. Moreover, research is typically characterized by dynamic increasing returns in the form of leaming-by-doing and learning-to-learn, and today's research generates tomorrow's new opportunities (Klevorick et al., 1993; Cohen and Levinthal, 1989). Innovative persistence may derive also from organizational features at the firm level. For instance, persistence might be generated by the establishment of R&D facilities at a fixed cost, which produce a relatively stable flow of innovations. More generally, persistence is likely to be originated by firm-specific technological and organizational capabilities, which can be improved only gradually over time and thus define what a firm can do now and what it can hope to achieve in the future. In this perspective, persistence of innovative activities is likely to be related to qualitative heterogeneity in the population of innovators. Heterogeneous agents, characterized by different competencies in different technological domains, show different innovative capabilities that, through the kind of cumulative processes we have in mind, are likely to persist over time. Thus, not only do we expect heterogeneity to lead to persistence, in the sense that firms that are ahead in specific fields will tend to stay ahead in those fields, we also expect persistence to reproduce initial asymmetries, generating further heterogeneity and, over time, widening the quantitative and qualitative dispersion of firms' capabilities. Serial correlation, technological cumulativeness and economic (market) feedbacks constitute different aspects of the same phenomenon. Technological cumulativeness and market feedbacks relate, respectively, to the cognitive and to the economic aspects of the innovative process. Serial correlation, on the other hand, captures the observable statistical properties of the process. In practice, it may be very difficult to distinguish the technology-specific, the firm-specific and the market-specific sources of serial correlation in innovative activities. To the extent that cumulativeness and market feedbacks are not observable, firm-level serial correlation can be considered as an indicator of the persistence of innovative activities generated by technology and market processes. Various theoretical models have examined the effects of serial correlation in innovative activities upon the patterns of technical change and industrial dynamics, but rarely in a systematic way. Moreover, persistence has been specified in different ways in the few models that address this issue (see, e.g., Nelson and

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Winter, 1982; Winter, 1984; Dosi et al., 1995). These models predict that higher degrees of persistence in innovative activities tend to generate, ceteris paribus, higher levels of concentration, higher rates of innovation, higher average age and size of survivors, and lower degrees of turbulence, i.e., lower entry, lower exit and less frequent rank reversals in market shares. The precise mechanisms through which these results are obtained are less clear, however. Clearly persistence tends to reproduce over time initial differences in innovative capabilities across firms. Whether persistence also implies a widening gap between firms depends on other additional assumptions on the form of the innovation and market processes. Very little is known about these phenomena from an empirical perspective. In this vein, this paper provides some preliminary evidence on the empirical relevance of persistence and heterogeneity, and on their relationships with the observed patterns of firm-level innovative activities across sectors and countries.

3. Data and m e a s u r e s

3.1. Data

The analysis is based on patent data. We use the OTAF-SPRU database which contains information on patents granted in the United States to firms and institutions from all over the world 4 Criticisms of the use of patent data are well known. Not all innovations are patented by firms. Patents cannot be distinguished in terms of relevance unless specific analyses on patent renewals or patent citations are done. Finally, different technologies are differently patentable and finns may have highly diverse propensities to patent. However, patents represent a very homogeneous measure of technological advance across countries and are available for long time series. They also provide very detailed data at the firm and the technological class levels. As a consequence, they are an invaluable and unique data source on innovative activity. As Griliches (1991, p. 1702) has pointed out: "patent statistics remain a unique resource for the analysis of the process of technical change. Nothing else even comes close in the quantity of available data, accessibility, and the potential industrial, organizational, and technological detail". The OTAF-SPRU database has been elaborated at the firm level for five European countries: Germany (the former Federal Republic), France, United Kingdom, Italy and Sweden. These countries are rather heterogeneous, with some at the technological frontier and others lagging behind, and with some large countries and some small ones.

4 We wish to thank Keith Pavitt and Pari Patel of the Science Policy Research Unit, University of Sussex, who supplied the data.

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The database covers the period 1969-1986 for the 33 technological categories of the O T A F / S P R U classification (see Table 1). Class 33 (Others, which include ammunitions, road structure, plant and animal husbandry, and others) has been considered despite the fact that is composed of miscellaneous items. Firm-level economic data concern only firm size as measured by the number of employees in 1984. The lack of economic information strongly constrains the scope of the data set. However, the data are reasonably good for the specific purpose of this paper. They provide information on patenting activity at the firm-level for 18 years in 33 technological classes and 5 countries, Thus, it is possible to exploit the micro-level information contained in the data to calculate

Table 1 Technological classes (OTAF/SPRU database) 1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Inorganic chemicals Organic chemicals Agricultural chemicals Chemical processes Hydrocarbons, mineral oils, fuel, igniting devices Bleaching, dyeing and disinfecting Drugs and bio-affecting Plastics and rubber products Non-metallic minerals, glass and other materials Food and tobacco (processes and products) Metallurgical and other mineral processes Apparatus for chemical, food and glass etc. General industrial equipment (non electrical) General industrial apparatus (electrical) Non-electricalspecialized and misc. industrialequipment Metallurgical and metal working equipment Assembling and material handling apparatus Nuclear reactors and systems Power plants Road vehicles and engines Other transport equipment (excluded aircraft) Aircraft Mining and wells machinery and processes Telecommunications Semiconductors Electrical devices and systems Calculators, computers, other office equipment Image and sound equipment Photography and photocopy Instruments and controls Miscellaneous metal product Textile, clothing, leather, wood products Other (ammunitionsand weapons, road structure, bridges and plant and animal husbandry)

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aggregate indicators of the patterns of technological change across sectors and countries. 3.2. Measures

Unless otherwise specified, the indicators we discuss below refer to the technological class in each country. We drop the sector and country indexes to save notation. Our basic variable is the firm's flow of patenting activity, the number of patents granted to firm i in year t, Pit, where i = 1.... N and t = 1.... T. In this notation, N is the number of firms in the sample, t = 1 is the year 1969 and t = T is the year 1986. Since it focuses on patenting activity and not on economic activity, by construction the database is composed of balanced panels. In the data, technological death is revealed by the appearance of a string of zeros in the firm's record. The firm, however, might still be alive in economic terms. Similarly, technological birth is revealed by the interruption of a string of zeros in the finn's record. The firm, however, might have been economically active prior to the first recorded innovation. Since we have the dates of economic entry, the legal start-up of the firm, for a significant subsample of firms, we can show that this is indeed the case most of the time. Moreover, patenting is a stochastic process characterized by relatively few blips in a time path with many zeros. Thus, our measures of technological birth and death are relative to sample size, not to the lifespan of the firm. As a consequence, the appearance of a string of zeros in patenting activity cannot be strictly interpreted as technological death, since the firm's record might exhibit a new blip in the process some time after its presumed death. For these reasons, the data are organized in balanced panels where the zeros in patenting activity mean that the firm has not innovated and not necessarily that it exited the business. Only if the firm has really exited the industry is it considered dead. For the purposes of this paper, however, it does not make any difference whether the firm exhibits a string of zeros because it is economically dead or simply because it is not patenting. This might be an issue in the long run, concerning the asymptotic properties of the stochastic process generating the data. Within the time span of our sample, the question is irrelevant. Thus, we balance the panels by treating all firms as if they existed and consider the zeros in their records as realizations of the event 'the firm did not patent'. A first group of indicators measures technological performance. The average patent stock in 1986, AVSTOCK, measures the firm-level average intensity of cumulated patenting activities. This indicator measures the end-of-period level of technological progress: it measures the firm's technological performance over the 18 years covered by the sample. The index of revealed technological advantage, RTA, is calculated as the world share of the country's patent stock in 1986 held in a technological class, divided by the country's share of the world's total patent stock in 1986 in all technological classes. This is an index of international

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technological specialization, calculated in the same way as the index of revealed comparative advantages used in empirical tests of international trade theory (Soete, 1981). We can consider only two economic indicators: the average number of employees in 1984, AVEMPL, that measures the average size of the firms, and the Herfindhal concentration index calculated on the number of employees in 1984, HERFEMPL, that measures the degree of economic concentration. Next, we calculate indicators of the aggregate characteristics of the firms' patenting activities in terms of their asymmetries in achieved levels of technological progress, the stability of the hierarchy of innovators, and rates of technological entry and exit. The Herfindhal concentration index for the stock of patents in 1986, HERFSTO, is a measure of technological concentration. This indicator measures the asymmetries in the cumulated patent stock, not the flow of patenting activities. The patent stock is a better measure of the firms' technological performance and of their technological strength at a moment in time. Furthermore, since we are interested in exploring the relation between the characteristics of the flow of patenting activities and the characteristics of the end-of-period distribution of the levels of technological progress, the patent stock is the proper variable to use. The Herfindhal concentration index captures some broad characteristics of the shape of the distribution of the levels of innovative activities across firms. A complementary and quite relevant feature of this distribution is the degree of flux and the rate of relative movement in the technological position of firms. To obtain a measure of this characteristic, we rank firms in terms of their patent stock and calculate two indicators of the stability of the hierarchy of innovators. SPEATOT is the Spearman rank correlation coefficient between cumulated patent stocks in 1976 and in 1986 for all firms in the sample. SPEACORE is the Spearman rank correlation coefficient between cumulated patent stocks in 1976 and in 1986 for firms that patented both in the 1969-76 period and in the 1977-1986 period. This definition is quite arbitrary but it serves the purpose of distinguishing between firms that consistently innovated over the whole period and those who did not. Thus, it helps to identify a core of consistent innovators. A final set of measures of the degree of turbulence of innovative activities is given by measures of technological entry and exit. MORFIRM is calculated as the number of firms that patented in 1969-1976 and did not patent in 1977-1986, divided by the total number of firms in 1977-86. This index measures technological exit, not economic exit. More precisely, it is a measure of the relative technological mortality, or the rate of exit from the ranks of the industry's innovators. Similarly, NATFIRM, is calculated as the number of firms that did not patent in 1969-1976 and patented in the period 1977-1986, divided by the total number of firms in 1969-76. This index measures relative technological natality, or the rate of entry into the ranks of the industry's innovators. MORPAT provides a measure of relative technological mortality in terms of the relevance of the dying innovators as measured by their share of the aggregate patent stock in the period in

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which they have been active. Thus, MORPAT is calculated as the patent stock of firms that patented in 1969-1976 and did not patent in the following period, divided by the patent stock of all firms in 1977-1986. Similarly, NATPAT is calculated as the patent stock of firms that did not patent in the first period and patented in 1977-86, divided by the patent stock of all firms in 1969-76. This is a measure of relative technological natality in terms of the share of the aggregated patent stock held by new innovators and gives information about the relevance of the new innovators in driving the industry's performance. Finally, we calculated indicators of firm-level innovative persistence, ALPHA and heterogeneity, SIGMAB. We construct these indicators from the microeconomic information contained in the data. For each technological class in each country (165 regressions in all), we estimate the dynamic panel data model with variable intercept Pi, = fli + ° l P i t - 1 + Uit' O[ ~ (-- 1,1), i = 1,..,N and t = 1,..,T

(1)

where N is the number of firms for each technological class in each country, and T is the number of years in the panel, 18. The parameter a is a time-invariant, sector-specific coefficient of firm-level autocorrelation in the patenting process. The parameter /3i is a time-invariant, firm-specific random effect. We make standard assumptions on the disturbances and the firm-specific random effects (see, e.g., Hsiao, 1986, Chap. 4). Namely, the disturbances ui, satisfy: E u i t = 0 a n d EuitUjs

= o-u2 lft • . = j and t =

s, EuitUjs =

0 otherwise.

(2)

The random effects satisfy: E ~ i : O and E~i~j = trt32 l•f t• = j , E f l i f l j --- 0

otherwise.

(3)

The instrumental variable (IV) estimators for the autocorrelation coefficient and the variance of the firm-specific random effects are our ALPHA and SIGMAB indicators. We use this procedure because it is relatively simple and we do not have strong priors on the initial conditions in the dynamic panel. The IV estimator t~ is consistent regardless of the initial conditions when N or T or both tend to infinity, while the IV estimator 6-~ is consistent only when N goes to infinity. In our case, T is reasonably large for this type of exercise and N is larger than 20 or 30 in most cases.5

When N is low, less than 10 (as in technological Class 3, Agricultural Chemicals, Class 18, Nuclear Power and Systems, and Class 19, Aircraft), we are willing to go ahead despite this because it probably does not affect our results in a crucial way. Moreover, excluding these sectors yields results similar to those in the text. We consider IV estimators only for simplicity, since we have to run 165 regressions. In addition, we do not really need to implement maximum likelihood (ML) estimators, since we do not make any specific hypothesis on the finn-level patenting process and therefore we do not want to test any.

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Eq. (1) produces an additional measure of the properties of the patenting ^2 process. The estimated variance of the disturbances, o-u, provides an estimate of the variability in the firms' patenting successes that is independent of firm-specific ^2 factors• More precisely, o"u, termed SIGMAU, measures firm-specific uncertainty due to random shocks in the patenting process• Thus, SIGMAU captures the cross-firm variability in patenting activity that is linked to the intrinsic randomness of the technological environment• To distinguish this source of asymmetry across firms from the role played by the random fixed-effects discussed above, we shall refer to SIGMAU as randomness and to SIGMAB as heterogeneity• The database does not provide enough economic information to support any specific assumption about the patenting process. Moreover, an equation like Eq. (1) is not derived from a fully specified theoretical model• This equation might be thought of as a linearized reduced form derived from some model of the determinants of innovative activities. Yet, in the absence of a fully specified model predicting an equation like Eq. (1), we do not want to stick to this interpretation too tightly• A behaviourally founded interpretation of Eq. (1) is not critical to this paper• The equation is useful in constructing measures of innovative persistence and heterogeneity from the firm-level information in the data. These measures can then be exploited at the sectoral level to examine how they correlate with the other indicators of the patterns of innovation across sectors and countries. Thus, this formulation seems adequate for calculating rough approximations of the microeconomic dimensions of persistence and heterogeneity, to the extent that we are not testing alternative theories of the patterns of technical change, but we are only providing some evidence on the broad characteristics of the latter and of the critical correlations in the database. Admittedly, our ALPHA, SIGMAB and SIGMAU indicators are, at best, very crude proxies of the micro properties of the innovative process that we are trying to measure. We are aware of the caveats that this limitation imposes on our results • 6 and therefore we emphasize the exploratory nature of the exercise. This paper is not on measuring persistence and heterogeneity or on testing alternative models. More humbly, we provide preliminary evidence on the patterns of correlation between some micro-level properties of the innovative process and the characteristics of the macro-level patterns of patenting activity. We believe that our approach is interesting precisely because the data allow us to construct from highly disaggregated information on the patenting of individual firms over a long period of time some variables that might prove quite important in explaining the aggregate patterns on innovative activity across sectors and countries. In this perspective, the paper contains the seeds of an important contribution despite its current, hopefully temporary, limitations. At the same time, our preliminary 6 We are in the process of constructing more precise measures of persistence and heterogeneity by using more sophisticated (and appropriate) econometric methods.

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exploration sheds light on what directions for future research seem to be particularly promising. Improving the quality of our estimates of persistence and heterogeneity is the first item in the agenda. We are currently pursuing this line of inquiry.

4. Persistence, heterogeneity and the sectoral patterns of innovation 4.1. The determinants o f the patterns o f innovation

In this section, we examine the relationships between persistence, heterogeneity and the observed patterns of innovative activities across sectors and countries. We run cross-country and cross-sector regressions using two specifications. Specification (a) is a simple pooled regression, while Specification (b) controls for country and sector fixed effects. Our dependent variables are the index of technological concentration, HERFSTOCK, and the two indexes of stability of the rank of innovators, SPEATOT for the whole sample and SPEACORE for the firms that innovated consistently over the entire period. We also consider the indicators of the relevance of technological entrants and exiters in terms of patents, NATPAT and MORPAT. The independent variables include our indicators of persistence and heterogeneity: ALPHA, SIGMAB and SIGMAU. In addition, we include as explanatory variables the relative rates technological natality and mortality, NATFIRM and MORFIRM. The latter are considered here as exogenous variables, although they might be determined by the same factors which determine technological concentration and stability in the rank of innovators. Technological entry and exit in terms of number of firms, however, are likely to depend on many other factors that can be considered exogenous to the dynamics we are trying to capture. Unfortunately, we do not have measures for these factors. To a first approximation, NATFIRM and MORFIRM can be interpreted as proxies for these exogenous forces. Finally, we consider as independent variables our measures of market structure, AVEMPL and ERFEMPL. These variables give some indications on whether persistence and heterogeneity in the patenting process are correlated in some systematic way to market structure. Given the characteristics of our data, we cannot say much about the direction of causation between the three sets of variables discussed above. We cannot claim unambiguously that persistence, or average firm size, generates technological concentration or that it is the latter that leads to higher degrees of persistence and/or larger average firm size. On the one hand, it seems highly plausible that the patterns of technical change are determined by persistence and heterogeneity, especially if the latter were interpreted as primitives determined by the very nature of the technology. As mentioned in Section 2, there exist various models that make

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these assumptions and these predictions. On the other hand, there are arguments supporting the idea that there might also exist a reverse causation from the characteristics of the observed structure of innovative activities to the degree of persistence of innovative activities and firms' heterogeneity. For instance, high technological concentration may lead to faster and persistent innovation through economies of scale in R&D, cumulativeness of learning processes, and 'successbreeds-success' economic feedbacks. This argument is particularly appealing if one interprets Eq. (1) as the reduced form of some behaviourally founded model. In order to disentangle these effects we would need a fully specified dynamic model and a more sophisticated analysis of our data. We leave these extensions to future research. In the meantime, we try to provide some preliminary evidence about the broad patterns of correlation in the data.

4.2. T e c h n o l o g i c a l c o n c e n t r a t i o n ( H E R F S T O C K )

Table 2 reports the results for the regressions explaining technological concentration. HERFSTOCK. The coefficient of ALPHA is positive and significant, showing that persistence in patenting activities is positively related to end-of-period concentration of the patent stock. Heterogeneity in firms technological capabilities, SIGMAB, is strongly related to technological concentration. This result is consistent with the role of persistence. In pure statistical terms, this evidence is quite intuitive. Autocorrelation and heterogeneity in the patenting process give rise over time to a highly asymmetric distribution of the patent stock. However, the coefficient of the variable SIGMAU is negative and significant. At first sight, this suggests that the randomness of the patenting process tends to reduce the end-of-period asymmetries between firms. This result is counterintuitive, since one would think that randomness should be a source of asymmetry complementary to heterogeneity. We could only speculate in order to try to explain the result. To avoid overinterpreting our regression, we simply conclude that the role of the two sources of asymmetry needs further exploration. The variables measuring technological entry, NATFIRM, and exit, MORFIRM, are not significant, implying that end-of-period concentration bears no relation to the processes of technological natality and mortality which occur mainly in the fringe. Finally, a concentrated industry structure, HEREFEML, is positively associated with technological concentration. Although it is very difficult to infer any direction of causality, the result is intuitively appealing. However, the coefficient of AVEMPL, average number of employees, is negative and significant at the 10% level. This result is puzzling; it implies that end-of-period concentration in innovative activities is higher when the average size of innovative firms in the sector is low. This might simply mean that when the average size of innovative firms is low, the industry is typically composed of a few large firms and many

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Table 2 Regression results for Herfindahl index of concentrationin innovativeactivities (HERFSTOCK) Dependent Variable: HERFSTOCK Specifications A Independent variables INTERCEPT ALPHA SIGMA B SIGMA U NATFIRM MORFIRM HERFEMPL AVEMPL No. of observations Degrees of freedom R-Square Adj R-Square F Value Prob> F Root MSE

- 0.118 (0.087) 0.622"* (0.249) 0.00007*** (0.00001) -0.002*** (0.0007) 0.035 (0.112) 0.255** (0.106) 0.643*** (0.066) - 0.000001** (0.0000009) 164 157 0.4658 0.442 19,557 0.0001 0.09258

B

With country (C1-C4) and sector-specific (S1-$32) effect - 0.057 (0.102) 0.715"** (0.237) 0.00008*** (0.00001) -0.003 (0.0008) 0.072*** (0.125) 0.111 (0.114) 0.320*** (0.108) - 0.000002* (0.000001) 164 121 0.7325 0.6374 7,704 0.0001 0.07463

*Significant at the 10% level; **significantat the 5% level; ***significantat the 1% level; standard errors in parenthesis.

small companies: if large firms innovate in absolute terms more than small firms, technological concentration tends to be high. The observation of a positive and significant correlation between average innovative intensity, AVSTOCK, and average size of innovative firms, AVEMPL, provides some indirect support for this speculation. A qualification to this explanation might be that small firms tend not to innovate less than large firms, but that they tend to patent less systematically over time. Again, this would result in a lower end-of-period absolute number of patents and hence in higher technological concentration as we measure it. The inclusion of country and sector fixed effects greatly improves the goodnessof-fit of the regression, but does not change the signs and the significance of the coefficients. We conclude that technological concentration is strongly correlated with persistence and heterogeneity in the patenting process, but is independent of firm size and of the rates of entry and exit.

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4.3. Stability in the ranks o f innovators ( S P E A T O T and SPEACORE) Results for the stability o f the ranks o f innovators w h e n the entire sample o f finns is considered, S P E A T O T , are reported in Table 3. T h e r e is a positive association b e t w e e n rank stability on the one hand, and persistence and heterogeneity on the other. Both A L P H A and S I G M A B h a v e positive and significant coefficients, w h i l e S I G M A U has a n e g a t i v e and marginally insignificant coefficient. In this case, the opposite signs that S I G M A B and S I G M A U take are quite intuitive. H e t e r o g e n e i t y should lead to a s y m m e t r i e s that persist o v e r time. In contrast, r a n d o m n e s s should facilitate rank reversals. Clearly, the difference b e t w e e n the two is that we m o d e l heterogeneity as time-invariant (random) fixed effects and r a n d o m n e s s as time-variant disturbances. As expected, indicators o f entry and exit, M O R F I R M and N A T F I R M , are

Table 3 Regression results for index of stability of the ranking of innovators (SPEATOT) Dependent Variable: SPEATOT Specifications A Independent variables INTERCEPT ALPHA SIGMA B SIGMA U NATFIRM MORFIRM HERFEMPL AVEMPL No. of observations Degrees of freedom R-Square Adj R-Square F Value Prob > F Root MSE

1.954"* * (0.103) 0.554* (0.294) 0.00003** (0.00001 ) - 0.001 (0.0009) -2.741"** (0.132) - 2.867*** (0.125) 0.145 * (0.077) 0.0000008 (0.000001 ) 164 157 0.8455 0.8386 122,721 0.0001 0.1093

B

With country (C1-C4) and sector-specific (S1-$32) effect 2.174"* * (0.144) 0.528 (0.332) 0.00003 (0.00002) - 0.001 (0.001) -2.813"** (0.175) - 3.114"** (0.160) - 0.146 (0.151) 0.000002 (0.000002) 164 121 0.8907 0.8518 22,928 0.0001 0.10471

*Significant at the 10% level; **significant at the 5% level; ***significant at the 1% level; standard errors in parenthesis.

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negatively and significantly correlated with rank stability. Finally, rank stability is correlated with industrial concentration, HERFEMPL. The inclusion of country and sector fixed effects changes the results drastically. Whilst the goodness-of-fit increases slightly, only the variables measuring entry and exit, MORIFIRM and NATFIRM, remain significant, with negative signs. In sum, the processes of entry and exit of firms in the fringe account for almost all of the rank reversals that occur in a given technological class. Compare these results with those reported in Table 4 concerning the rank stability within the core of innovators, SPEACORE. The indicator of persistence, ALPHA, is now positively associated with stability within the core of innovators. The coefficients of the measures of heterogeneity, SIGMAB, and randomness, SIGMAU, become significant with, respectively, a positive and a negative sign. Finally, neither the variables measuring market structure, HERFEMPL and

Table 4 Regression results for index of stability of the ranking of continuous innovators(SPEACORE) Dependent Variable: SPEACORE Specifications A Independent variables INTERCEPT ALPHA SIGMA B SIGMA U NATFIRM MORFIRM HERFEMPL AVEMPL No. of observations Degrees of freedom R-Square Adj R-Square F Value Prob> F Root MSE

0.407 (0.272) 2.222*** (0.723) 0.00006 (0.00004) -0.002 (0,002) - 0.241 (0.349) 0,069 (0.345) 0.166 (0.253) 0.0000007 (0.000002) 152 145 0.1117 0.0688 2,604 0.0147 0.25404

B

With country (C1-C4) and sector-specific (S1-$32) effect 0.805"* (0.402) 1.836422** (0.782) 0.0002*** (0.00006) -0.007*** (0,002) - 0.449 (0,499) - 0.316 (0,467) 0.226 (0.475) 0.000005 (0.000005) 152 109 0.4406 0.2199 1,996 0.0021 0.23252

*Significantat the 10% level; **significantat the 5% level; ***significantat the 1% level; standard errors in parenthesis.

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AVEMPL, nor the indicators of entry and exit, MOREFIRM and NATFIRM, are correlated with rank stability among the core innovators. Thus, the stability of the ranking of core innovators appears to be associated to persistence, heterogeneity, and, negatively, to randomness, whilst industrial concentration, firm size and (quite obviously) turnover in the fringe do not bear any role. We conclude that stability in the ranking of innovators is influenced by two different, albeit related, forces. First, persistence of the patenting process, coupled with heterogeneity and randomness, determines the rank stability of the core innovators. Second, turnover in the fringe, as measured by the rates of entry and exit, determines rank stability for all firms in the industry. It is interesting that when the entire sample of firms is considered most of the rank reversals within the sample of innovators occur in the fringe through entry and exit. This form of turbulence makes the effect of persistence, heterogeneity and randomness insignificant. Within the core, in contrast, the effects of entry and exit are removed. Stability is much higher, and higher degrees of persistence and heterogeneity make rank reversals less likely, unless large and frequent shocks in the individual firms' patenting processes occur.

4.4. Rates o f entry and exit in terms o f patents (NATPAT and MORPAT)

Results for the relative rate of technological entry measured in terms of patents, NATPAT, are reported in Table 5. The relevance of new innovators in terms of patents is strongly and positively correlated with the rates of entry and exit in terms of number of firms. Not surprisingly, the technological relevance of entrants is associated with high degrees of turnover. The results for the other variables are more interesting, though, because they confirm the relevance of persistence and heterogeneity. Persistence, ALPHA, and asymmetries among firms, SIGMAB, are negatively and significantly correlated with the relevance of technological entry. Conversely, randomness, SIGMAU, has a positive and significant effect. This suggests that persistence and asymmetries make it very difficult to make the transition from non-innovator to innovator and, a fortiori, from non-innovator to significant innovator. Finally, the variables for market structure are not related to technological entry. Concentration and large average firm size do not constitute a significant barrier to entry of new innovators. The results of the regression for technological mortality in terms of patents, MORPAT, reported in Table 6, are quite similar to those for technological natality, confirming that the same factors that prevent entry also make exit less likely. In particular, persistence implies that large innovators have lower probabilities of suddenly ceasing to innovate (and vice versa). This confirms that entry and exit tend to concentrate in the fringe of small innovators. More generally, these results suggest that persistence and asymmetries are

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Table 5 Regression results for the rate of technologicalentry (NATPAT) Dependent Variable: NATPAT Specifications A Independent variables INTERCEPT ALPHA SIGMA B SIGMA U NATFIRM MORFIRM HERFEMPL AVEMPL No. of observations Degrees of freedom R-Square Adj R-Square F Value Prob>F Root MSE

- 0.766*** (0.115) - 1.448"** (0.328) -0.00007*** (0.00001) 0.002*** (0.001) 1.865"** (0.147) 1.351"** (0.139) 0.138 (0.086) - 0.OOOO01 (0.00OO01) 164 157 0.692 0.6782 50,384 0.0OOl 0.12168

B

With country (C1-C4) and sector-specific (S1-$32) effect - 0.642*** (0.158) - 1.294"* (0.365) -0.00009*** (0.00002) 0.003*** (0.001) 1.567"** (0.193) 1.225*** (0.176) 0.034 (0.167) - 0.0000008 (0.000002) 164 121 0.7876 0.7121 10,435 0.0OOl 0.11509

,

*Significant at the 10% level; **significantat the 5% level; ***significantat the I% level; standard errors in parenthesis.

important factors in determining the relevance of turnover in terms of patents, along with concentration and stability of the hierarchy of innovators. It is worth emphasizing, however, that technological mortality and natality are not simply two faces of the same phenomenon. As opposed to technological entry, technological exit is correlated negatively to the industry average size of innovative firms, AVEMPL, and positively to industrial concentration, HERFEMPL. Thus, market structure exerts an asymmetric effect on technological natality and mortality. Industries characterized by large firms do not discourage entry and make exit less likely. Conversely, industries populated by small firms are more likely to generate technological exit but not necessarily to attract entrants. In other words, when average firm size is small, opportunities for entry are not necessarily higher, and small innovative firms are likely to be occasional rather than persistent innovators.

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Table 6 Regression results for the rate of technological exit (MORPAT) Dependent Variable: MORPAT Specifications A Independent variables INTERCEPT ALPHA SIGMA B SIGMA U NATFIRM MORFIRM HERFEMPL AVEMPL No. of observations Degrees of freedom R-Square Adj R-Square F Value Prob>F Root MSE

- 0.946*** (0.122) - 0.528 (0.350) -0.00003* (0.00001) 0.001 (0.001) 1.475*** (0.157) 1.986'** (0.149) 0.204'* (0.092) -0.000004*** (0.000001) 164 157 0.6649 0.6499 44,497 0.0001 0.12978

B

With country (C1-C4) and sector-specific (S1-$32) effect - 0.866*** (0.154) - 0.593* (0.356) -0.00006** (0.00002) 0.002** (0.001) 1.420*** (0.187) 1.731"** (0.171) 0.245 (0.162) -0.000008*** (0.000002) 164 i 21 0.8076 0.7392 11,808 0.0001 0.11203

*Significant at the 10% level; **significant at the 5% level; ***significant at the 1% level; standard errors in parenthesis.

Indirectly, this result suggests that size does not necessarily determine the firm's innovative intensity, but rather the continuity of its innovative activity. This finding is then consistent with the negative relationship between concentration in innovative activities, H E R F S T O C K , and average firms size, AVEMPL, that we discussed in Section 4.2. If, for a given distribution of firm sizes and innovative intensity, small firms are more likely to stop patenting, their end-of-period share of patents will be smaller. This increases concentration, provided that exiters are not much smaller or much bigger in terms of patents than average. Finally, this result is broadly consistent with similar findings in industrial demography showing that the probability of exit is negatively related to firm size (and age) and that variables traditionally thought o f as barriers to entry are better understood as barriers to survival (see, e.g., Acs and Audretsch, 1992; Baldwin, 1995; Dunne et al., 1988, 1989; Evans, 1987).

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4.5. I n n o v a t i v e intensity ( A V S T O C K )

In this regression, we analyze the relationships between persistence, heterogeneity and market structure, and a (highly imperfect) measure of technological performance: the sectoral intensity of innovative activities at the firm-level as measured by the average number of patents per firm, AVSTOCK. The analysis of the relationship between market structure and innovation was the main concern of the debate on the so-called 'Schumpeterian hypotheses' (Kamien and Schwartz, 1982; Cohen and Levin, 1989). Here, we widen this strand of analysis in two ways: first, we fully recognize the importance of the insight that sector- and technology-specific variables may be very important factors in determining the intensity of innovative activities (see, e.g., Cohen and Levin, 1989); 7 second, we try to capture some key characteristics of the technological environment by using our measures of persistence and heterogeneity as well as additional indicators of innovative entry and exit. 8 As mentioned above, different theoretical models have predicted that higher persistence and asymmetries are associated with higher rates of innovation. However, we cannot but emphasize again that we are not testing any model here, but just looking at meaningful patterns of correlation among variables. Finally, our dependent variable is not really an indicator of the rates of innovation, but only of the intensity of end-of-period patenting activities, AVSTOCK. This indicator is obviously influenced by the total number of patents, but also by the total number of firms within a technological class. Thus, similar values of AVSTOCK might reflect simply different structures of the patterns of innovation, e.g., one characterized by a large number of patents and a large number of firms and the second by a small number of patents and a small number of innovators. Results are reported in Table 7. The intensity of innovative activities is positively associated to persistence, ALPHA, heterogeneity, SIGMAB, and randomness SIGMAU. Both our indicators of turbulence, NATFIRM and MORFIRM, have a negative sign. Finally, innovative intensity is positively associated with industrial concentration, HERFEMPL, but this result disappears when country and sector fixed effects are introduced. These results suggest that innovative intensity is linked to a core of heterogeneous firms which innovate persistently over time, i.e., to creative accumulation, although coupled with large and frequent shocks on firms' capabilities, i.e., with elements of creative destruction. Market structure variables are not significantly related to innovative intensity.

7 Indeed, the consideration of variables like opportunityand appropriability conditions has proved to be extremely important for the explanation of the inter-sectoral variation in innovative activities (see, for instance, Levin et al., 1985 or Levin et al., 1987). 8 It is important to emphasize again the caveats suggested previously about the interpretation of the direction of causation between the variables.

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Table 7 Regression results for the index of innovativeintensity (AVSTOCK) Dependent Variable: AVSTOCK Specifications A Independentvariables INTERCEPT ALPHA SIGMA B SIGMA U NATFIRM MORFIRM HERFEMPL AVEMPL No. of observations Degrees of freedom R-Square Adj R-Square F Value Prob>F Root MSE

15.017"** (2.265) 18.708*** (6.464) 0.003*** (0.0003) 0.131 (0.019) - 16.919"** (2.902) - 16.869'** (2.751) 5.222*** (1.709) 0.00001 (0.00002) 164 157 0.8682 0.8623 147,714 0.0001 0

B With country (C1-C4) and sector-specific (S1-$32) effect 4.589" (2.535) 18.376*** (5.842) 0.004*** (0.0004) 0.050** (0.021) - 6.497** (3.081) -7.065** (2.812) 0 (2.666) - 0.000003 (0.00003) 164 121 0.9402 0.919 44,248 0.0001 0

*Significant at the 10% level; **significantat the 5% level; ***significantat the 1% level; standard errors in parenthesis.

5. The sectoral patterns of innovation and international technological specialization We now turn to the analysis of the relationships between the patterns of innovative activities and international technological specialization of countries, as measured by the index of revealed technological advantages, RTA. Various studies have tried to determine whether revealed technological advantages bear any relationship with concentration in innovative activities, or economic concentration and firm size, with rather inconclusive results. Here, we develop this line of inquiry by asking whether other important characteristics of the observed patterns of innovation influence the relative technological performance of countries. In particular, we focus on the variables that measure dynamism and turbulence as potential determinants of technological specialization. In a Schumpeterian perspective, it may well be that what matters for the construction of technological advantages is the existence of a large pool of potential innovators,

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that compete fiercely by exploiting latent technological opportunities. Entry of new innovators and fierce competition among incumbents will then disrupt the hierarchy of innovators. Conversely, technological advantages might be linked to systematic and continuous processes of accumulation of technological capabilities at the firm-level. In this case, technological specialization would be correlated with high degrees of stability in the ranking of innovators and low degrees of turbulence. In the following regressions, the index of revealed technological advantages, RTA, is the dependent variable. The independent variables include our indicators of the patterns of innovative activities: innovative concentration, HERFSTOCK, stability in the ranking of innovators, SPEATOT, and entry and exit in terms of number of firms, NATFIRM and MORFIRM. 9 We included also the number of firms in a technological class, FIRMS, to control some implications of our results for the role of concentration of innovative activities. In a second specification, we added the indicators of market structure: industrial concentration, HERFEMPL, and average firm size, AVEMPL. We do not consider regressing RTA on our indicators of persistence, heterogeneity and randomness because we think of our regression as the reduced form of a more complex structural model. Indeed, we expect revealed technological advantages to be influenced by ALPHA, SIGMAB and SIGMAU indirectly, through their effects on the sectoral patterns of innovation, rather than directly. Results are reported in Table 8. The specification that includes only the indicators of the patterns of innovative activities performs very badly. In the simple pooled regression, the index of revealed technological advantages, RTA, is associated positively only to the index of rank stability, SPEATOT, and all other variables are not significant. Even when controlling for country and sector fixed effects, the results do not improve. The index of the stability of the hierarchy of innovators, SPEATOT, loses its significance, while the coefficients of innovative concentration, HERFSTOCK, and of exit, MORFIRM, become significant, respectively, with a positive and a negative sign. When the variables measuring market structure are included, the regression becomes more interesting. The index of revealed technological advantages is positively associated to innovative concentration, HERFFSTO, and, when country and sector fixed effects are included, negatively to turnover, NATFIRM and MORFIRM, whilst stability in the rank of innovators, SPEATOT, loses its significance. In addition, the coefficients of industrial concentration, HERFEMPL, and, with fixed effects, average firm size, AVEMPL, become significant, with, respectively, a negative and positive sign. 9 We did not use entry and exit in terms of patents (NATPATand MORPAT) because they might give rise to spurious correlation, since the same term, the number of patents of country i in sectorj in the period 1977-1986, appearsin the numerator of the index of revealed technological advantagesand in the denominator of the variables measuring entry and exit.

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Table 8 Regression results for the index of relative technological advantages (RTA) Dependent Variable: RTA Specifications A Independent variables INTERCEPT HERFSTOCK SPEATOT NATFIRM MORFIRM FIRMS

0.735 (0.483) 0.271 (0.290) 0.411 ** (0.200) 0.269 (0.575) -0.431 (0.471 ) 0.0002 (0.0002)

HERFEMPL AVEMPL No. of observations Degrees of freedom R-Square Adj R-Square F Value Prob > F Root MSE

164 159 0.0712 0.042 0 0.0368 0

B With country (C1-C4) and sector-specific (S1-$2) effect

C Independent variables

D With country (CI-C4) and sector (S1-$32) specific fixed effect

1.935"** (0.706) 1.199"** (0.406) 0.292 (0.224) - 0.855 (0.814) - 1.710"* (0.672) 0.00005 (0.0005) - 1.471 *** (0.342) 0.000004 (0.OOOOO3) 164 123 0.2985 0.0646 0 0.18878 0.38457

1.233"* (0.498) 0.834*** (0.306) 0.179 (0.198) - 0.1 (0.583 ) -0.769* (0.462) 0.000003 (0.0002)

2.786*** (0.691) 1.678"** (0.396) - 0.056 (0.225) - 1.786** (0.792) -2.171"** (0.641 ) 0.0006 (0.0005) - 2.184 (0.565) 0.00001 0.OOOOO7 164 121 0.3949 0.1799 1,836 0.0053 0.36011

164 157 0 0.00 4,694 0.0001 0

*Significant at the 10% level; **significant at the 5% level; ***significant at the 1% level; standard errors in parenthesis. T h e s e results suggest that relative technological advantages are higher in sectors characterized by the existence o f a c o m p e t i t i v e core o f large finns w h i c h innovate systematically o v e r time in c o m p e t i t i v e industries. T h e s e findings are consistent with those obtained in the regression for i n n o v a t i v e intensity. W e c o n c l u d e that in our data there is e v i d e n c e for creative a c c u m u l a t i o n ' c o r r e c t e d ' by e l e m e n t s of creative destruction.

6. Conclusion In this paper, we p r o v i d e d exploratory e v i d e n c e on the determinants o f the patterns o f i n n o v a t i v e activities. W e focused attention on the role o f persistence and h e t e r o g e n e i t y at the firm level, f o l l o w i n g the insights o f that part o f the literature on the e c o n o m i c s o f i n n o v a t i o n that e m p h a s i z e s the c u m u l a t i v e and

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firm-specific nature of technological change. Our results can be summarized as follows. (a) Persistence and asymmetries are important (and strongly related) phenomena that affect the patterns of innovative activities across countries and sectors, thereby generating processes of creative accumulation and creative destruction. In particular, persistence and heterogeneity generate concentration, stability in the ranks of innovators, and low turnover in the population of innovators. Randomness partially offsets these tendencies. It reduces stability and concentration and increases turnover. (b) Turnover in the fringe is an important determinant of the patterns of innovative activities. Technological entrants and exiters are typically small, occasional innovators which operate in the fringe of the industry. Turnover in the fringe affects the rank stability of innovators as well as the relevance of entrants and exiters. It does not affect, however, innovative concentration. (c) The role of market structure variables is not clear. Industrial concentration does not have an overwhelming influence and is significantly associated only with innovative concentration. Firm size, conversely, plays a more important role: it is negatively related to the relevance of firms exiting the population of innovators and to innovative concentration. The first result is interesting, because it suggests that firm size may not be directly related to innovativeness, but to the continuity of the firm's innovative activities. Moreover, this finding is consistent with the negative relationship between average firm size and innovative concentration. If small firms have higher probabilities to stop innovating than large companies, they will not be able to continue to accumulate patents over time and their end-ofperiod share will be smaller. If the industry is composed of many small firms and few large companies (and the average size of innovators is small), this will increase concentration. (d) Technological performance (as proxied by two different measures, innovative intensity and revealed technological advantages) is associated with a competitive core of persistent innovators. Innovative intensity is positively associated with persistence, heterogeneity and randomness, and negatively to turnover. Revealed technological advantages are negatively related to turnover and industrial concentration, and positively to innovative concentration and average firm size. This suggests that innovative performance is higher in sectors where a stable core of innovators emerges, but where the innovative process exhibits a high degree of heterogeneity across firms due to firm-specific (random) fixed effects and firmspecific uncertainty.

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