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  • 1. Innovation Premium and the Survival of Entrepreneurial Firms∗ Elena Cefisa University of Bergamo and Utrecht School of Economics, Utrecht University Orietta Marsilib Rotterdam School of Management, Erasmus University October 2005 Abstract: This paper examines the difference in survival probability between innovators and non-innovators(the ‘innovation premium’), for different types of firms and technological environments. Inparticular, we compared the innovation premiums for high-tech and low-tech manufacturing todiscover whether innovation plays a different role as a strategy for survival depending on thetechnological environment. In addition, we analysed whether different patterns emerge when wecontrast entrepreneurial firms and established firms. We estimated survival probabilities with anapproach based on TPM, using data from the Business Register of the population of manufacturingfirms in the Netherlands and the CIS-2. Among established firms, the highest premium in survival(whether an innovator or not) lies in being active in a high-tech sector. Thus, to increase survivalprobability, innovation must be complemented by firm specific organisational and commercialcapabilities. For entrepreneurial firms it is crucial to either be an innovator or at least to be active ina high-tech sector; in a low-tech sector, innovative activity is a “matter of life or death”. Indeed,innovation increases the survival probability of entrepreneurial firms in low-tech sectors by 58 percent compared to non-innovative firms. This is the highest innovation premium amongst all thecategories of firms and sectors studied.Keywords: Firm Survival; Innovation; Entrepreneurship; High technology industries.JEL codes: L11; O30; D21; C14; L25∗ We would like to thank, Michael Dahl, Giovanni Dosi, Toke Reichstein, Ammon Salter, and participants at ESSID2005, Cargese. Elena Cefis gratefully acknowledges the financial support of the University of Bergamo (grant ex 60%,n. 60CEFI04, Department of Economics). The empirical part of this research was carried out at the Centre for Researchof Economic Microdata at Statistics Netherlands. The views expressed in this paper are those of the authors and do notnecessarily reflect the policies of Statistics Netherlands.a Corrisponding author: Elena Cefis, Utrecht School of Economics, Utrecht University, Vredenburg 138, 3511 BGUtrecht, The Netherlands. Tel. +31 (0)30 2539856; Fax +31 (0)30 2537373 e.cefis@econ.uu.nlb Orietta Marsili, RSM, Erasmus University, PO box 1738, 3000 DR Rotterdam, The Netherlands.Tel. +31.010.4081979; Fax +31.010.4089015
  • 2. 1. Introduction This study explores the effects of innovation on the survival of manufacturing firms indifferent technological environments in the Netherlands. Previous studies have related thesurvival of firms to firm-specific characteristics and industry features. Several studies identifyfirm size and age as determinants of survival. It is accepted as a stylised fact that increasingage and size exert a positive effect on the likelihood that firms will survive (Geroski, 1995).Other studies have focused on the role of innovative activities, looking at the intensity ofR&D expenditure (Hall, 1987; Esteve Perez et al., 2004) and indicators of innovativeperformance (Cefis and Marsili, 2005). In general, these studies find that innovative activitiesare beneficial for firm survival, regardless of the industry in which firms are active. Acrosssectors, a number of variables have been associated with the likelihood of survival, such asmarket size and growth rates (Mata and Portugal, 1994), the characteristics of technology(Audretsch, 1995; Agarwal, 1996; Agarwal, 1998) and the life cycle (Agarwal and Audretsch,2001). These studies emphasise the heterogeneity of sectors and how the effects of firmspecific characteristics vary across them (Audretsch et al., 1999). In this study we analyse the determinants of firms’ survival probability by combiningfirm level and industry level features. We examine the role of innovation within the firm inshaping its survival probability, and contrast this effect across different technologicalenvironments; specifically we compare innovative and non-innovative firms in high- and low-tech industries. In addition, we control for the characteristics of the firm, size and age, whichare generally pointed to in the literature as being important. Thus, we distinguish betweenentrepreneurial and established firms in an industry. The empirical analysis combines economic and demographic data from the BusinessRegister of all firms active in the Netherlands with data on innovation derived from the
  • 3. second Community Innovation Survey (CIS-2). Integration of these two datasets produced asample of 3,275 firms for which information on innovation, number of employees, date ofentry, date of exit and industrial sector were available. The survival probability of a firm wasestimated using an approach based on Transition Probability Matrices. We then statisticallytested for the significance of differences in survival probability between different categoriesof firms. Our results show that, as expected, entrepreneurial firms are more exposed to the risk offailure than established firms, confirming earlier results of the effects of firm age and size onthe likelihood of survival. However, entrepreneurial firms benefit relatively more thanestablished firms from a technology rich environment, which in general favours survival.Also, in low-tech industries entrepreneurial firms that innovate have significantly higher(58%) chances of survival than non-innovative firms. In other words, the innovation premiumfor survival is highest for entrepreneurial firms in low-tech industries. This study is organised as follows. Section 2 focuses on the determinants of firm survivalidentified in the related literature. Sections 3 and 4 respectively present the data used in theempirical analysis, and the survival analysis methodology. We discuss the results of theanalysis in Section 5. Section 6 concludes.2. The determinants of firm survival It is well known that the survival probability of firms varies across industrial sectors(Geroski, 1995; Audretsch et al., 1999; Audretsch et al., 2000). The survival differencesacross industrial sectors vary less over time when compared to more volatile entry rates. Thisfact has been interpreted as evidence that barriers to survival are more effective than barriersto entry (Geroski, 1995). These barriers to survival have been related to traditional market 3
  • 4. structure variables, such as the presence of scale economies, other cost advantages ofestablished firms, and the growth rate of sector specific demand (Audretsch, 1991; Audretschand Mahmood, 1994; Dunne and Hughes, 1994; Mata and Portugal, 1994; Wagner, 1994).Some studies have highlighted the role of technological conditions in an industry as adeterminant of firm survival (Audretsch, 1991; 1995; Agarwal, 1998). There are two interpretations of the relationship between the level of technologicalintensity in a sector and the survival probability of firms active in that sector (Agarwal, 1996).One argument maintains that a fast changing environment hampers firm survival. Geroski(1995) argues that it is the ability of new firms to learn about the environment and to adapttheir strategies to changes in it, which ultimately determine their chances of survival. Becauseof the uncertainty associated with innovation (Ericson and Pakes, 1995), the risk of exit ishigher for firms in high-tech sectors. Consistent with this interpretation, Mahmood (1992)observes greater hazard rates for new establishments in high-tech industries than in low-techindustries. Also, in high-tech industries the hazard rate is more sensitive to external factorssuch as scale economies and the intensity of R&D expenditure in the sector. Thus, high levelsof innovative activity in a sector render the survival probability of a firm lower and moreconstrained by structural barriers, such as scale economies. The second argument sees the technological activities of a sector as being a source ofopportunity for innovation for new entrants. Highly innovative sectors may enable new firmsto introduce new products and successfully compete with established firms. This increases thelikelihood of survival of new firms. In addition, in a fast changing environment thecumulative processes of learning within established firms may be less relevant, and mayfacilitate the survival of new firms (Agarwal, 1996). In this interpretation, it is not only theexistence of a general pool of technological opportunities that matters for survival, but also 4
  • 5. the ease with which these opportunities can be exploited by new or established firms (Winter,1984). The ‘evolutionary’ approach to industrial dynamics characterises these conditions interms of a ‘technological regime’. Following Schumpeter’s insight, Nelson and Winter (1982)distinguish two opposing regimes: an entrepreneurial regime (also labelled Schumpeter MarkI) in which new firms are the main drivers of innovation, and a routinised regime (orSchumpeter Mark II) in which established firms are the main sources of innovation. Inelaborating Nelson and Winter’s model, Marsili (2001) shows that, in general, the chances ofsurvival of established firms increase with the level of technological opportunity within anindustry; survival prospects are lower when opportunities can be exploited by entrant firms(that is, when technological entry barriers are low). In addition, the effect is conditional on theage of the firm: survival of young firms increases and survival of older firms decreases as thelevel of technological opportunity for entrants increases (Dosi et al., 1995). Following the ‘evolutionary’ approach, Audretsch and Mahmood (1994; 1995), in theirstudy of US manufacturing, separate the two components by taking the total innovation rate inan industry (as a measure of the general level of technological opportunity) and the relativeinnovation rate of small firms (as indicative of the presence of a an ‘entrepreneurial’ asopposed to a ‘routinised’ regime). After controlling for the latter effect, they show that thelikelihood of failure of new establishments is greater in highly innovative environments,confirming earlier results where this factor was not controlled for. Thus, these results supportthe hypothesis that high-tech sectors, because of their high degree of uncertainty, have anegative effect on the survival probabilities of firms. Audretsch (1995) refines this evidence by demonstrating that the effects of technologicalconditions vary with firm age. In more innovative industries, new firms have a lowerprobability of survival within a limited period after entry; but, after a certain number of years 5
  • 6. (8 years in the cited study) after entry, their probability of survival increases. Similar effectscan be found for the level of innovation of small firms in the industry, which approximatesthe ‘entrepreneurial’ or ‘routinised’ nature of the innovation regime. When based on firms’experience, the effects of technology on survival are the opposite to those for newly createdfirms. Technological opportunities influence firm survival relative to whether the firm is anentrepreneurial or an established firm. High-tech industries negatively affect the survival ofnewly created firms, but favour the survival of incumbents (Audretsch et al., 2000). However, Agarwal’s (1996) empirical study of US manufacturing produces contrastingresults to Audretsch (1995). Agarwal classifies a set of industries into the five stages of theproduct life cycle (PLC) and into technical and non-technical industry categories, based onthe intensity of R&D expenditure. She finds that intense technological activities favour thesurvival of new firms in the 4 years after entry, while this advantage tends to disappear by 12years after entry. In addition, she shows there is a non-linear relationship between firm age,technological activity and survival. Across all five stages of the PLC, ‘infant’ firms (6 or lessyears old) are exposed to greater risk of failure in non-technical industries. In contrast,‘incumbent’ firms (older than 6 years) encounter greater risk of failure in technical industries.Focusing on firm size, Agarwal (1998) observed that small firms have greater likelihood ofsurvival in high-tech than low-tech industries. In sum, technological activities seem toenhance the survival probability of new and small firms, while they tend to limit the survivalchances of incumbent firms (Agarwal, 1996; Agarwal, 1998). Despite the role attributed to the technological environment for a firm’s survival, thereare few studies linking survival to the innovative activities carried out by the firm.Introducing new products and processes is considered to be a key source of competitiveadvantage in the market (Schumpeter, 1942; Baumol, 2002). With regard to the innovative 6
  • 7. effort of firms, investing more intensively in R&D activities positively influences theirsurvival prospects (Hall, 1987; Esteve Perez et al., 2004). With regard to innovativeperformance, in an earlier study (Cefis and Marsili, 2005), we show that innovation doesindeed enhance the survival probability of a firm and for young and small firms in theNetherlands, this effect is especially evident. Our contribution to this field of the literature is to analyse the interaction between firmspecific effects of innovation on survival according to the different levels of technologicalintensity in the external environment.3. DataThis study is based on two micro-economic databases collected by the Central Bureau ofStatistics Netherlands (CBS): the Business Register database and the CIS-2 in theNetherlands. By combining these datasets, we were able to integrate at firm level,comprehensive data on innovation and the demography of firms. The Business Register database consists of all firms registered in the Netherlands forfiscal purposes. The database reports number of employees, sector of activity and the dateexpressed as month of entry and exit in the datasets. Because the dataset includes allregistered firms in the population, these dates can be considered as close approximations ofthe actual dates of entry and exit of firms. For compatibility with the CIS-2, we considered allmanufacturing firms present in the Business Register at year 1996. The number of firms in theBusiness Register at 1996, including firms with zero employees, that is self-employment, is61,177. The second wave of the CIS provides information on innovation activities in theNetherlands for the period 1994–96. This survey includes private sector firms with at least 10employees. The CIS firms were extracted from those present in the Business Register in order 7
  • 8. to constitute a stratified random sample, based on size class, region and industrial sector at the2-digit Standard Industrial Classification (SIC) code level. The number of manufacturingsector respondents to CIS-2 was 3,299 firms, with a response rate of 71 per cent. Our variable of interest is the survival probability of a firm. To estimate this variable, weuse the date of exit of a firm from the population of manufacturing firms in the BusinessRegister. The exit date is expressed in months and ranges from January 1996 to December2003. The dataset thus covers 96 months of possible existence. For each month, we built adummy variable that was equal to 1 if the firm existed in the database and 0 otherwise. The first condition that we want to relate to survival probability is the technologicalenvironment in which a firm is active. On the basis of the SIC code, each firm is assigned totwo broad areas of activity: high-tech manufacturing or low-tech manufacturing. We thusadopt the OECD (2001) classification of industries based on level of technological intensity. Given some broad technological conditions, our interest is to establish whether theinnovative activity of a firm enhances its chances of survival, and whether such anenvironment shapes this effect. To measure innovation at firm level, we use the CIS-2 for theNetherlands. For this CIS sample of firms, we distinguish between innovators and non-innovators. An innovator is defined as a firm that has introduced in the period 1994–1996either a product or a process innovation. These variables reflect the respondent’s subjectiveperception of ‘being an innovator’ and may lead to an overestimation of the actual innovativeactivity of a firm. As we assume that technology, and the innovative activity may have different impacts onthe survival probability, depending on the age and size of a firm, we built two oppositecategories of firms. We define ‘entrepreneurial’ firms as those that, at 1996, are between 0and 4 years old (where the age 0 identifies the firms that have entered during the year 1996), 8
  • 9. and have less than 50 employees. They are both young and small firms. We define‘established’ firms those that, at 1996, are 5 or more years old and have more than 50employees. They are both old and large firms. Firm age and size are derived from theBusiness Register of the population. The estimate of firm age relies on the entry date, whilefirm size is measured by number of employees, including the 0 value of self-employment. By matching the two datasets, we are able to link innovation at the firm level (from theCIS-2 dataset) to firm performance, namely the survival probability (as estimated from theBusiness Register of the population), while also controlling for the effects of differenttypologies of firms (entrepreneurial and established) and industries (high-tech and low-techmanufacturing) (both derived from the Business Register). The resulting dataset comprises3,275 firms. Table 1 reports the descriptive statistics of the different sets of firms used in the analysis.Firms in low-tech sectors are on average (both mean and median) smaller than firms in high-tech sectors independent of the category of firms reported in Table 1. Innovators are of largersize than non-innovators, both in low-tech and high-tech industries. These characteristics arein line with the more general observation that innovators are larger than non-innovators (Cefisand Marsili, 2004). --- Insert Table 1 ---4. Methodology We used a non-parametric approach based on Transition Probability Matrices to analysethe survival probabilities among different groups of firms. We measure survival probability asthe firm probability of remaining in the state in which the firm actually exists, while the 9
  • 10. probability of exiting the market is given by the probability to go from the state of existenceto the one of non-existence. Among the firms that were in existence when the second wave of CIS data werecollected, namely 1996, this being the finite population of firms, at each point in time (after1996) there is a cross-section distribution of firms that exist and firms that have ceased toexist. The objective is to describe the evolution of this distribution over time, to enable theintra-distribution mobility of firms to be analysed. The intra-distribution mobility givesinformation about the firm’s relative situation, and its movement over time (Cefis, 2003). To study the evolution of this distribution it is necessary to hypothesise a law of motionfor the cross-section distribution within a more formal structure. Let Ft denote the distributionof firms at time t; and let us describe the evolution of the distribution using the law of motion: Ft+m = P · Ft (1)where P maps one distribution into another, and tracks where points in Ft end up in Ft+m.Equation (1) is a useful first step for analysing the dynamics of { Ft }. Operator P of equation(1) can be approximated by assuming a finite state space for firms S={s1 s2 ... sr}, wheresi(i=1,...,r) are the possible states. In this case P is simply a Transition Probability Matrix(TPM). P encodes the relevant information about mobility and persistence of firms within thecross section distributions. Therefore, the one-step transition probability is defined by: pij = P ( X t + m = j X t = i ) (2)where t denotes discrete moments of time and m different discrete transition periods. The TPM P is the matrix with pij as elements measuring the probability of moving fromstate i to state j in one period m. (Hoel P.G. et al., 1987). 10
  • 11. The focus of our analysis is the probability of firm survival. Therefore, we consider astate space constituted by two states identified by the condition of existence of the firm.More precisely, the first state is defined as the non-existing state (state 0), in which firms arenon-active in the market (they have in fact exited the market), and the second is defined as theexisting state in which firms are actually present in the market (state 1). The transitionprobabilities between the two states provide information useful for analysing survival sincethey measure the probability that a firm remains in existence or exits the market in a particularperiod. We estimate the following probabilities: P ( X t + m = 1 X t = 1) = p ˆ ˆ (3) P( X t + m = 1 X t = 0) = 1 − p ˆ ˆ (4) The probabilities 3 and are computed on different period lengths (for different m, rangingfrom 12 to 84 months). These different transition periods allow us to capture the dynamics ofthe survival probability of firms, and to study how it evolves over time. It should be noted that in order to perform the persistence analysis we have assumed thatfirms are homogeneous. In this context, heterogeneity among firms, due to belonging todifferent sectors or having different sizes or ages, is accounted for by breaking down theoverall sample into sub-samples according to industrial classification, and size and ageclasses. Nevertheless, in our sub-samples firms are assumed to be homogeneous and, usingour methodology, it is not possible to control for heterogeneity at the firm level. 11
  • 12. We conducted the analysis dividing the sample into entrepreneurial and establishedfirms, and according to whether they were categorised as high-tech or low-techmanufacturing. In order to test whether the differences between two estimated probabilities were ˆ ˆstatistically significant, we applied the following test. Let P and P2 be the survival 1probabilities estimated in the samples of size n1 and n2 (for example the survivalprobabilities for innovators and non-innovators in the CIS) drawn from their respectivepopulations with probabilities p1 and p 2 . The null hypothesis is that there is no differencebetween the survival probabilities of the populations, that is H 0 : p1 = p 2 , and thus thesamples are really drawn from the same population with survival probability p . The teststatistic is the difference in the estimated probabilities: P − P2 . Given the fact that the size of ˆ ˆ 1our samples is sufficiently large (with at least n > 80 ), under the null hypothesis, thestandardised variable P − P2 − 0 ˆ ˆZ= 1 is approximately distributed N(0,1), where σ P −P ˆ ˆ 1 2 p1 (1 − p1 ) p2 (1 − p2 )σ P −P = ˆ ˆ + 1 2 n1 n2Given that p1 and p 2 . are unknown and for the null hypothesis p1 = p 2 = p , the estimator ofσ P − P is ˆ ˆ 1 2 ⎛1 1⎞S P − P = P (1 − P) ⎜ + ⎟ ˆ ˆ ⎝ n1 n2 ⎠ 1 2where P is the estimator of the survival probability of the population given by the arithmeticweighted average of p1 and p 2 : 12
  • 13. n1 P1 + n 2 P2P= . n1 + n 2We tested the differences in the survival probabilities of different sub-groups of firms and thetest results (in parentheses) are reported in Tables 2 and 3.5. Empirical Results We first examine the differences in survival probability of the firms that responded to theCIS-2 of high-tech manufacturing and low-tech manufacturing. Here we focus on the general‘structural’ conditions for the survival of entrepreneurial and established firms independent oftheir specific innovative behaviour. As expected, in general survival probability is higher for established than forentrepreneurial firms. Indeed, the percentage differences in survival probability betweenestablished and entrepreneurial firms increases from 3.1 in the first year to 22.2 in the lastyear. This shows that firm age and firm size have a positive effect on the survival probability.An earlier study (Cefis and Marsili, 2004) carried out for the entire Dutch manufacturingsector that explored the relationship between firm survival, age and size in more detail,produced similar evidence. --- Insert Figure 1--- As Figure 1 shows, survival probability is higher in high-tech than in low-tech industries.In general, this holds for both entrepreneurial and established firms, and more particularly, thedifference between high tech and low-tech industries increases over time and is higher forentrepreneurial firms than for established firms. Survival probability for an entrepreneurialfirm in the high-tech sector is 3.3 per cent higher than in the low-tech sector over a one-yearperiod, and 19.8 per cent higher over a 7 year period. For an established firm, the differences 13
  • 14. are smaller, 1.7 per cent and 12.7 per cent respectively (see Table 2 for the statistical test ofsignificance for the differences in survival probability between high-tech and low-techindustries)1. Therefore, while the survival probability for entrepreneurial firms is generallylower than for established firms, entrepreneurial firms benefit more from a high intensitytechnological environment than do established firms. --- Insert Table 2 --- We next examine survival in relation to innovators versus non-innovators. In particular,we are interested in comparing the innovation premium between high-tech and low-techmanufacturing, and whether the role of innovation as a strategy for survival differs accordingto technological intensity of the environment. In addition, we contrast entrepreneurial firmsand established firms to see whether a different pattern emerges. At first glance, innovation can be seen to have a positive effect on survival probability.However, its magnitude varies according to the technological characteristics of theenvironment. --- Insert Figure 2 --- Figure 2 depicts the effects of the technology on the survival probabilities of innovatorsand non-innovators. The survival probabilities of entrepreneurial and established firms areplotted according to their innovative status in low-tech and high-tech industries. Among theentrepreneurial firms, innovators do not seem to be affected by sectoral differences in theirsurvival probability, while non-innovators in high-tech sectors have better survival probabilitythan those in low-tech sectors. It should be noted that for entrepreneurial innovators in the1 Table 2 reports the difference in the level of survival probability and its statistical significance. In the text, wealso refer to the percentage variation in probability. 14
  • 15. high-tech sector, the chances of survival decrease over the long term, falling to below thelevel for low-tech sectors. For established firms, those firms in high-tech sectors have higherchances of survival regardless of whether they are innovators or non-innovators. --- Insert Figure 3 --- The next step in the analysis is to establish whether technology environments shape, notonly the survival probabilities of firms, but also the magnitude of the effect of innovation onthese probabilities. For this purpose, we calculate the innovation premium (the difference inthe survival probabilities between innovators and non-innovators) for entrepreneurial andestablished firms according to the different technological environments. In low-techindustries, innovation is crucial for enhancing survival chances. The innovation premium isparticularly relevant for entrepreneurial firms, and is shown to be positive and increasing overthe seven year period. From Figure 3 it can be seen that differences in the levels ofprobabilities are 0.060 over one year, and 0.282 over seven years (see Table 3 for thestatistical significance of these differences). This shows that the survival probability is 6.7 percent higher for innovators than for non-innovators in the first year, and 58.0 per cent higher inthe last year of observation. The effect of innovation is always positive, but less pronouncedfor established firms and follows a non-monotonic increase along the years. --- Insert Table 3 --- In high-tech sectors, the survival probabilities of innovators and non-innovators arebarely distinguishable , either for established or entrepreneurial firms. Figure 3 depicts thetwo curves of the differences in the levels of probabilities fluctuating around zero, meaningthat the innovation premium is very small in the case of both an entrepreneurial and an 15
  • 16. established firm. Indeed, these differences are not statistically significant, except for thelongest transition period in which the difference is negative and significant at 1% (see Table3). In fact, the innovation premium for entrepreneurial firms becomes negative in the ‘longrun’. As Figure 3 shows, over 6 and 7 years, in high-tech sectors non-innovators have higherchances of survival than innovators. This counterintuitive result might be due to the fact thatentrepreneurial innovative firms are more likely to be acquired by other firms thanentrepreneurial non-innovative firms. This effect appears over the long run because it canrequire a certain period of observation to determine whether a new and small firm issuccessful or not. Therefore, the acquiring firm may ‘wait and see’ before making thedecision to acquire a new and innovative firm. Because we are not able to distinguish amongdifferent modes of exit in our current dataset, we cannot conclude if the negative sign of thedifference in probabilities reflects a real negative premium of innovation or is instead theeffect of the acquisition process.6. Conclusions In this study we examined the difference in survival probability between innovators andnon-innovators, which we label the ‘innovation premium’ for survival, for different types offirms and technological environments. In particular, we compared the innovation premiumsfor high-tech and low-tech manufacturing to discover whether innovation plays a moreimportant role as a strategy for survival in a rapidly changing environment than in a slowlychanging environment. In addition, we analysed whether different patterns emerge when wecontrast entrepreneurial firms and established firms, and assuming that innovation plays adistinct role for the two categories of firms. We carried out an empirical analysis of the survival probabilities with an approach basedon TPM. Using data from the Business Register of the population of manufacturing firms in 16
  • 17. the Netherlands and the CIS-2, we estimated the survival probabilities for different categoriesof firms. These categories were based on firm age and size, firm specific innovativeperformance, and industry specific technology intensity. Among the group of established firms, we observed that firms with the highest survivalprobabilities are those active in technology intensive environments, regardless of whetherthey are innovators or non-innovators. On the other hand, in low-tech sectors, being aninnovator is a decisive factor in firm survival increasing survival probability by 7.2 per cent.This result suggests that the highest premium in survival for an established firm (whether aninnovator or not) lies in being active in a high-tech sector. Thus, to increase survivalprobability, innovation must be complemented by firm specific organisational andcommercial capabilities. Our results show that for entrepreneurial firms it is crucial to either be an innovator or atleast to be active in a high-tech sector; in a low-tech sector, innovative activity is a “matter oflife or death”. Indeed, innovation increases the survival probability of entrepreneurial firms inlow-tech sectors by 58 per cent compared to non-innovative firms. This is the highestinnovation premium amongst all the categories of firms and sectors studied. 17
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  • 21. Table 1. Descriptive statistics of the number of employees of manufacturing firms at 1996 by sample andindustry group N % Mean Std Dev Skewness Kurtosis MedianAll firms in BR Low-tech 49119 80.8 14.2 105.4 53.1 5205.9 2 High-Tech 11673 19.2 27.3 395.2 82.6 7880.3 2All firms in BR with at least 10 employees Low-tech 9161 74.7 66.4 237.0 24.4 1067.4 21 High-Tech 3099 25.3 96.7 762.8 43.0 2124.3 27All firms in CIS-2 Low-tech 2279 69.6 108.9 228.4 9.9 146.7 52 High-Tech 996 30.4 136.0 322.8 7.6 72.2 59Innovators in CIS-2 Low-tech 1322 63.7 132.7 249.9 9.2 128.2 69 High-Tech 753 36.3 151.4 327.4 6.3 47.7 67Non innovators in CIS-2 Low-tech 957 79.8 76.1 190.0 11.7 194.0 33 High-Tech 243 20.3 88.2 303.9 12.9 185.5 35Note:Datasets: Business Register (BR), Second Community Innovation Survey (CIS-2).
  • 22. Figure 1. Survival probabilities in high- and low-tech industries Entrepreneurial firms Established firms 1 1 0.8 0.8Prob Prob 0.6 0.6 0.4 0.4 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Years Years Low-tech High-tech Low-tech High-tech
  • 23. Table 2. Differences in survival probability between high-tech and low-tech industries(z-values in parentheses) Number of months 12 24 36 48 60 72 84Entrepreneurial firm 0.028 0.053 0.073 0.082 0.105 0.115 0.124 (1.76) (3.46) (4.99) (5.84) (7.87) (9.05) (10.43)Established firm 0.016 0.038 0.057 0.072 0.089 0.101 0.098 (2.70) (6.21) (9.58) (2.36) (15.71) (18.31) (18.36)Note:|z| > 2.58 statistically significant at 1 per cent|z| > 1.96 statistically significant at 5 per cent|z| > 1.64 statistically significant at 10 per centDifferences signicant at 10 per cent are in bold 24
  • 24. Figure 2. Survival probabilities of established and entrepreneurial firms by innovation. Entrepreneurial firms/ Innovators Entrepreneurial firms/ Non-innovators 1 1 0.8 0.8 Prob Prob 0.6 0.6 0.4 0.4 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Years Years Low-Tech High-Tech Low-Tech High-Tech Established firms/ Innovators Established firms/ Non-innovators 1 1 0.8 0.8 Prob Prob 0.6 0.6 0.4 0.4 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Years Years Low-Tech High-Tech Low-Tech High-Tech 25
  • 25. Figure 3. Innovation premium of established and entrepreneurial firms. Difference Innovators/ Non-innovators in Difference Innovators/ Non-innovators in 0.3 Low-Tech 0.3 High-Tech 0.2 0.2Prob Prob 0.1 0.1 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 -0.1 Years Years Entrepreneurial Established Entrepreneurial Established 26
  • 26. Table 3. Differences in survival probability between innovators and non-innovators(z-values in parentheses)High-tech Number of months 12 24 36 48 60 72 84Entrepreneurial firm -0.008 -0.010 0.008 0.017 0.014 -0.026 -0.051 (-0.36) (-0.44) (0.35) (0.78) (0.64) (-1.25) (-2.62)Established firm 0.002 0.003 -0.008 -0.016 -0.011 0.013 0.015 (0.13) (0.24) (-0.62) (-1.28) (-0.94) (1.04) (1.25)Low-tech Number of months 12 24 36 48 60 72 84Entrepreneurial firm 0.060 0.112 0.141 0.157 0.205 0.247 0.282 (3.08) (5.93) (7.91) (9.39) (12.86) (16.53) (20.11)Established firm 0.010 0.027 0.042 0.066 0.063 0.067 0.053 (1.06) (2.89) (4.62) (7.52) (7.49) (8.19) (6.72)Note:|z| > 2.58 statistically significant at 1 per cent|z| > 1.96 statistically significant at 5 per cent|z| > 1.64 statistically significant at 10 per centDifferences signicant at 10 per cent are in bold 27