Paper_Patenting, Innovative Training and Firm Performance performance


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NES 20th Anniversary Conference, Dec 13-16, 2012
Article "Patenting, Innovative Training and Firm Performance" presented by Maksim Belitski at the NES 20th Anniversary Conference.
Authors: Maksim Belitski, SPEA, Indiana University, USA; International Business School, Anglia Ruskin University, UK; Yulia Rodionova, Leicester Business School, De Montfort University, UK

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Paper_Patenting, Innovative Training and Firm Performance performance

  1. 1. PATENTING, INNOVATIVE TRAINING AND FIRM PERFORMANCEi Maksim Belitski a,b a SPEA, Indiana University, USA b International Business School, Anglia Ruskin University, UK Email:; Yulia Rodionova, Leicester Business School, De Montfort University, UK Email: AbstractThis study assesses the returns to patenting and training for a panel of 4049 innovators in theUK during 2002-2009 and quantifies the incentives that patent protection provides forinvestment in training. When controlling for firm- and industry-specific characteristics, patentand training premiums are positive; however, returns to training vary across firm age andtime. Our findings contradict the common-place assumption that there is inducement toknowledge expenditure from patent protection. These results further the understanding ofmanagers and policy-makers on the importance of knowledge expenditure, and demonstratethat the majority of innovations are not protected by patents.JEL classification: L20, L26, O31, O34, O38Keywords: Innovation, Patenting, Training, Patent propensity, Firm performance
  2. 2. Introduction Patent protection and knowledge expenditure, which includes R&D, training, andeducation have been argued to be a crucial resource for success in entrepreneurial ventures(Sexton and Upton, 1985; Florin et al., 2003; Florin, 2005). As the number of patentapplications has increased in Europe, Japan and the US (Kortum et al., 2003; EPO AnnualReport, 2010) and importance of the entrepreneurs’ experiences and knowledge linked to thefirm potential has increased (Stuart and Abetti, 1990); policy-makers argue that the modelsestimating the value of knowledge using patent applications, and number of grants, asoutcome variables are no longer satisfactory. The models and indicators used byentrepreneurship researchers do not always agree with the data available. This may not allowextracting at least approximate returns to patenting and knowledge expenditure. As a result itmay become more difficult for managers to decide on filing a patent and/or investing intraining, given their resource constraints. Generating innovation and protecting it is important because it provides a competitiveadvantage to companies. Innovation demands continuous investment in human capital. Alongwith investments in R&D, acquisition of machinery, equipment and software, different formsof design firm’s knowledge (including management skills and experience) is the mostfrequently used selection criteria for venture capitalists (Zacharakis and Meyer, 2000) as theycontribute to a firm’s performance as intangible assets (Haskel et al., 2011). The literature on knowledge expenditure and human capital investment relate it to ventureperformance (e.g., Van der Sluis et al., 2005; Aguinis and Kraiger, 2009; Unger et al., 2011).The returns of this relationship, however, remain unknown. While some authors argue thatthe relationship between knowledge, skills and performance is overemphasized (Baum andSilverman, 2004), others question the magnitude of the effect of knowledge on the
  3. 3. entrepreneurial process (Haber and Reichel, 2007), revealing a disagreement about the sizeand importance of knowledge investment in entrepreneurship research. Additionally, therelationship between knowledge expenditure in training and patent protection within firmsremains under-investigated. Our study is the first to examine this link in the context ofentrepreneurial performance. The purpose of this study is to estimate the private returns to patenting and innovativetraining using a panel of 4,049 UK innovators over the period 2002-2009, and also examinethe incentives that patent protection offers for further investment in innovative training andeducation. There have been studies on identifying the returns to patenting and training(Kortum et al., 2003; Schankerman, 1998; Pakes and Simpson, 1989; Arora et al., 2008;Leiponen and Byma, 2009), the returns to R&D and intellectual property protection of UKinnovators (Haskel et al., 2011; Hall et. al., 20111; Arora et al., 2012), role of human capitalfor venture performance (Chandler and Hanks, 1994, 1998; Davidsson and Honig, 2003;Unger et al., 2011). These studies have applied the concepts of innovation and knowledge inthe context of entrepreneurship (Chandler and Hanks, 1994, 1998; Davidsson and Honig,2003; Audretsch ae al., 2008; Unger et al., 2011), However, the returns to innovative trainingand patenting for entrepreneurial firms have not yet been precisely identified. Neither havebeen the incentives that patent protection provides for investment in knowledge (Artz et al.,2010). While Arora et al. (2012) attempt to estimate the interval of patent premium for UKinnovators using Community Innovation Survey (CIS) UK data for 1997-2006 and found it tovary between 40 and 287% , they had to rely on an ad-hoc assumption about a firms’ patentpropensity. Identifying patent propensity for innovators is crucial. It will enable managersand policy-makers to calculate the patent returns more precisely and provide a betterunderstanding for constructing intellectual property rights policy.
  4. 4. In this study, we aim to quantify the level of patent propensity for UK innovators, which isa proportion of innovations for which patent protection was sought. Firms’ patentpropensities vary widely across industries. Moreover, within each industry, there aresignificant discrepancies between the number of pending patents and the number ofinnovative products launched to the market. Some products are protected by multiple patents,while certain patents are never embodied into tangible products (Branzei and Vertinsky,2006). The model offered in this study is generalizable to non-UK settings to measure theindicators of interest.. We also discuss main determinants of knowledge expenditure andinnovation outcomes (proxied by the new product revenue, NPR) other than patent protectioncontributing to the training literature (Bishop, 1991, 1997; Parker and Coleman, 1999; Galiaand Legros, 2004) There are two main contributions of this study to the entrepreneurship literature:methodological and empirical. Modifying the model developed by Arora et al. (2012), weemploy a new approach to estimation of patent premium, patent propensity and returns toinnovative training for a firm. Unlike previous studies, we are able to estimate the returns totraining precisely because we use data on the amount of training expenditure as opposed to adummy variable on the incidence of training commonly used in the literature. Our first empirical contribution is in quantifying the patent propensity and patent premiumfor UK innovators using the most recent micro-level panel and cross-sectional data availableat the Office of National Statistics UK (ONS UK) since October 2011. Our second empiricalcontribution is in estimating the implied increment to innovative performance due toexpenditure on training and education.. Our third empirical contribution is in estimating theincentive that patent protection provides for additional knowledge expenditure.Acknowledging Branzei and Vertinsky (2006) and Unger et al. (2011), who argue the returns
  5. 5. to human capital investments are higher for young businesses compared to old businesses, weshow this is also true for returns to patenting. 2. Theoretical Background and Hypothesis The following subsections present a chronologically organized literature review on thereturns to patenting and innovative training. 2.1. Returns to patenting Firms use various methods to protect their inventions such as patents and different formsof the first mover advantage (e.g., Levin et al., 1987; Cohen et al., 2001). Instruments ofprotection and the nature of innovation vary across industries and firms of different size(Branzei and Vertinsky, 2006; Cohen and Klepper, 2006). Patents serve to protect the firm’stechnological knowledge, embody an exclusion right and provide an incentive for the firm toinvest in innovation, knowledge and marketing activities (Greenhalgh and Rogers, 2006).This study opens a discussion about a link between the legal protection of innovation andfurther investment in knowledge. Scherer’s (1983) analyzes the relationship between R&D and invention patenting by 4,274lines of business in 443 U.S. industrial corporations. He has shown that the number of patentstends to rise most frequently in proportion to R&D, and that it exhibits diminishing returns.Horstmann et al. (1985) first discuss the costs of disclosure which can more than offset theprivate gains from patenting with an effect of “stronger” patents on incentives to innovate.The private returns to patent protection were explored by Pakes (1986), Pakes and Simpson(1989) and Pakes and Schankerman (1984) in their examinations of European firms patentrenewal decisions. In the early 1990s Harabi (1995) stresses the economic returns to technicalinnovations as an important factor for driving inventors. Since economic returns on technical
  6. 6. innovations were difficult to measure directly, many researchers have attempted to investigatethem indirectly through qualitative techniques and by examining the effectiveness of variousmeans of protection of invention. Patent protection per se yields monetary value and providesan incentive for more research expenditure including training and educational programs thatgenerate the underlying inventions (Schankerman, 1998). The value of a patent is representedby the incremental returns generated by holding that patent, above and beyond the returnsthat could also be earned by using the second-best means. Leiponen and Byma (2009)examine small firms’ strategies for capturing the returns to investment in innovation andestablish a small firms’ strategy, which turn out to be qualitatively different from those foundin earlier studies of both small and large firms. The authors conclude that most of the smallfirms use informal means of protection, such as speed to market or secrecy that prove to bemore important than patenting. Only firms with university cooperation and large firms werelikely to identify patents as the most important method of protecting their innovation. Greenhalgh and Rogers (2006) estimate the value of innovation and its link withcompetition, R&D and intellectual property. This is the first study to use data on marketvaluations of UK companies and their knowledge expenditure. More recent research on thereturns to patenting has been conducted by Bulut and Moschini (2009), Acosta et al. (2009)and Artz et al (2010). Bulut and Moschini (2009) study US universities that have increasedtheir involvement in patenting and licensing activities through their own technology transferoffices. Artz et al (2010) analyze two innovative outcomes on a sample of 272 firms in 35industries and find that knowledge spending increases the number of patents; however theinverse relationship between the patent protection and knowledge spending had not beenexamined, leaving a gap in the field. As for returns to patenting, consistent with theirprevious work, Artz et al (2010) find a negative relationship between patents and both returnson assets and sales growth. On the contrary, a positive relationship is found between patents
  7. 7. and new product announcements. While these findings are unexpected, they are intriguing. Patel and Ward (2011) estimate annual measures of Tobins q using data on changes inpatent citations related to the area of science where firm patents. Finally, Arora et al. (2011)utilize the CIS and Business Survey Database (BSD) to estimate the returns to intellectualproperty protection. Their main assumption is that firms can earn larger revenues and profits(due to patenting), although the data is limited in terms of cross-section structure and lacksinformation on patent propensity for UK businesses. This does not allow them to estimatepatent premiums precisely and calls for further research. Overall, the high importance of patent protection to venture performance leads us topropose that holding a patent increases new product revenue for a firm. Hypothesis 1: All else equal, new product revenue is higher for business that holds apatent. 2.2. Returns to training and training determinants Maier (1965) opened an extensive discussion on abilities, aptitudes, skills and training.He defined two kinds of abilities: those that arise without training (aptitudes) and thoseintroduced by training (achievements). In the context of management literature, Herron andRobinson (1993) expressed Maier’s formulation of achievements as skills equal aptitudestimes training. The Maier’s word “abilities” gives way to the words skills and training as anintegral component of abilities. Skills needed for “win-win” strategies are the result of bothnatural aptitudes and training. Herron and Robinson (1993) argue that “training” may meaneither experience or formal training whenever skills are exercised. Possession of skills isexpected to affect the motivation to use them; for instance, entrepreneurial characteristics andskills are expected to affect entrepreneurial behavior and, eventually, business performance.
  8. 8. Training may also affect psychological characteristics of entrepreneurs by providing moremotivation through skill acquisition (Begley and Boyd, 1987). A resource-constrainedmanager would be interested in finding out how much extra revenue could be generated fromadditional investments in training. One comprehensive review of training literature during the 80s and 90s is done by Bartel(2000). More recently, Aguinis and Kraiger (2009) review the training literature focusing onthe benefits of training and development for individuals and teams, organizations, and societyduring the 2000s. Authors call for further empirical research regarding organizational-levelbenefits of training saying it is “not nearly as abundant as the literature on individual- andteam-level benefits.” They further contend that “not only have there been few empiricalstudies showing firm-level impact of training, but those studies use unclear causal link backto training activities.” Existing empirical studies analyzing the impact of training on firmperformance concentrate on general measures of training, rather than on the expenditure ontraining specifically for innovation (Marotta et al., 2007; Acemoglu 1997). A summary ofempirical research on the impact of training (broadly defined) on productivity related to ourstudy is presented in Table 1 and presents mixed evidence. Based on these arguments associated with the returns to investment in training we expectthat innovative outcomes are positively affected by increase in the knowledge expenditure,because of the specific nature of training (Thornhill, 2006; Hansson, 2007). Thus, we posit: Hypothesis 2: All else equal, investment in innovative training increases innovativeoutcomes. 2.3. Patent-training relationship and training determinants We start the discussion on patent-knowledge investment relationship with a recent work of
  9. 9. Rosenbusch et. al (2011) on venturing approach to innovation. They argue that ‘venturingapproach reflects the widespread assumption that in order to be successful, the entrepreneurneeds to have an innovative edge to compete against bigger, well-established incumbents’Rosenbusch et. al (2011 p.441). In doing so the entrepreneur will use different forms of legaland strategic protection of their innovation, looking to increase the investment in knowledgeif intellectual property rights allow for effective protection of innovative outcomes. Yet, thereis no sufficient empirical evidence to support a direct link between protection of innovationand further investment in knowledge (e.g. training expenditure, R&D, market research). Recently, using survey data for the U.S. manufacturing sector Arora et al., (2008) analyzedthe effect of patenting on R&D with a model linking a firms R&D effort with its decision topatent. Their study recognizes that R&D and patenting affect one another and are both drivenby many of the same factors. ‘Patent protection stimulates R&D across all manufacturingindustries, albeit with the magnitude of that effect varying substantially’ Arora et al., (2008:p.1153). Almeida and Teixeira (2007) found patents positively impact on knowledge intensityfor the set of less developed countries whereas no statistically significant effect emerges inthe case of ‘higher developed converge clubs’. No work has been done on investigating patent - innovative training link being a part ofknowledge expenditure and our study aims to bridge this gap. We hypothesize: Hypothesis 3: All else equal, patent protection has a positive impact on firms’ innovativetraining. Regarding the drivers of training, our paper employs standard controls as found in much ofthe literature (e.g., Bishop, 1991, 1997; Galia and Legros, 2004; Baldwin and Johnson, 1995),subject to their availability in our data, including firm size; global nature of activities; numberof competitors in the industry; cooperation with universities, public and government research
  10. 10. bodies; ownership type; adoption of a patent; and industry dummies (e.g., Parker andColeman, 1999; Barrett and O’Connell, 2001).
  11. 11. Table 1. Existing estimates of the impact of training on firm’s performance (sorted by year of study). Study (Year) Dataset Method Performance measure Data type/ Sample size Results Longitudinal Manpower survey Fixed /random Earnings of white and non-white While women are found to benefit significantly from manpower Bassi (1984) Worker earnings (1975-1978) effects males and females training programs, no such effect was found for menIchniowski et al. Interviews of 45 steel finishing 2190 observations from 36 lines Positive effect of high and low incidence of training on OLS, Fixed effects Productivity (1987) lines in the US owned by 17 steel companies productivity in steel finishing lines Survey by the Nat. Center for Cross-sectional Returns on investment on 100 hours of new hire training ranged Bishop (1991) Productivity growth 2594 firms Research in Vocat. Educational OLS and difference from 11% to 38%. Holzer et al. Doubling of worker training reduces scrap rates by 7%; this is Survey of Michigan firms Fixed effects Scrap rates 157 firms (1993) worth $15,000. Firms operating at less than expected labour productivity Bartel (1994) Columbia HR Survey (1986) OLS, Probit Value added per worker 155 US enterprises in 1986 implemented training which resulted in 6% higher productivity 2 Tan and Batra World Bank survey Predicted training has positive effect on value added; effects range OLS; Probit Log of Value added 300-56,000 firms by country (1995) from 2.8% to 71% per year 1992 survey of human resource Cross-section, as Tobin’s Q and gross rate High performance practices had significant effect in cross-sectionsHuselid (1995) 968 firms practices well as Fixed effects of return on capital but disappeared in the fixed effects study Dollar value of sales, 617 firms, matched with the Census Per cent of formal off-the job training in manufacturing, as well as Black and National Employers Survey Cross-sectional receipts or shipments in Bureau’s Longitudinal Research computer training in non-manufacturing sector is positively related Lynch (1996) (1994) OLS 1993 Database for the panel study to productivity in the cross-section. Black and EQW National Employers Panel, First Productivity Panel data for 1987 to 1993 Number of workers trained in a firm is not linked to productivity.Lynch (2001) survey (1987-1993) differences Barrett and Surveys of enterprises in OLS and First Surveys of enterprises in Ireland in General and all training is positively related to productivity; O’Connell Productivity Ireland in 1993 and 1996-7 differencing panel 1993 and 1996-7 specific training has no significant impact. (2001) Guerrero and Guerrero and Barraud- Performance, employee 1530 human resource directors 4.6% of the variance in financial performance was explained byBarraud-Didier Interview Didier questionnaire productivity working in large companies in France training (via social and organizational performance) (2004) Cassidy et al. Total Factor Productivity Panel data fixed Total Factor Foreign-owned and indigenous Irish Plants engaged in training have a TFP advantage of 0.3 (2005) Survey (1999 – 2002) effects estimation Productivity manufacturing with > 10 workers Per cent, ceteris paribusUbeda Garcıa Level of satisfaction; 78 Spanish firms with more than 100 Training programs oriented toward human capital development are Ubeda Garcıa questionnaire Interview (2005) labor productivity employees. related to employee, customer, business performance Survey of Canadian Weighted Heckman, Innovation; Revenue Training is not statistically significant for either group; TrainingThornhill (2006) 845 firms Manufacturing firms Logit, OLS growth positive significant for innovation the top 10%; upper 5,824 private-sector firms in 26 Positive relationship between the number of employees receivingHansson (2007) The Cranet survey OLS, Probit /lower half; profitability. countries training and being in top 10% of profitability among other firms. Source: Bartel (2000), Aguinis and Kraiger (2009) with the authors’ additions and compilation.
  12. 12. 2.3. Theoretical Model. As the starting point of our analysis we modify a theoretical model developed byArora et al. (2012) which is used to analyze the private returns to patenting and inducementfor R&D incorporating the trade-offs of holding a patent postulated by Schankermann (1998).From the CIS we first create a measure of the total revenue from new products (NPR) whichis total revenue (TR) times a share of revenues from new products. We consider as newproducts those products that are new to the industry – and not just to the firm. TR  P1Q1 (1.1) where P1 = average price of products and Q1 = average quantity of products. Weassume that TR=P1Q1= PQ (1-) + PQ (1.2) where P is the price of products and Q is the quantity of products sold. This equationsays that the total revenue is a weighted average by  of revenue created with and withoutpatent protection, and that the revenue for items with a patent protection is greater followingSchankerman (1998).  is the share of products for which patent protection was sought, i.e.patent propensity; its estimates are not available at ONS UK and Intellectual Property OfficeUK (IPO UK) data, because of no special surveys undertaken; and  is the patent premium. We assume a production function linking the share of new product innovations toinvestment in innovative training, N1 = f(T) (Black and Lynch, 1996). Note that T is the
  13. 13. amount of money spent on training for product innovation, not the total training expenditure.Combining with (1.2) and (1.1), we get (1.3). Taking logs, and transforming the model (1.3)into econometric form we get (1.4), where lowercases denote natural logs: NPR = N1 P Q (1 -  + ) = f(T) P1Q (1 -  + ) (1.3) npr = p + q + ln(1-  + ) + ln(f(T)) + εi (1.4) where f(T) is thought of as an analogue of total factor productivity in a growth model.We assume f’(T)>0 which means that NPR is an increasing function of training. Now we can estimate (1.4) as a non-linear least squares (where  is not known and is a parameter to be estimated). The econometric model of (1.4) becomes (1.5), where A =p+q + intercept. For simplicity we assume f(T)=T. npri = A + b1 ln(Ti) + ln(1- i + i) + εi (1.5) There are two issues. First, (1.5) imposes a specific non-linear specification, albeitone that naturally follows. Second, T is endogenous. In particular, it will depend uponunobserved firm specific differences in price and quantity. Put differently, demand shocks(which affect p and q) will also affect innovative training expenditure. This can easily be seenby writing p = p+ , where p is the average (across firms) price and  is a firm specificcomponent of price. All else equal, if  is high, T will be higher too. The obvious way out isto find an instrument for T. A natural instrument for (1.5) is any variable that affects cost ofinputs, provided those are independent of demand shocks. We have explored measures from
  14. 14. the CIS, such as the importance of increased capacity for production or service provision toproduct (good or service) and/or process innovations introduced scaled (0-3); and theimportance of knowledge factors as constraints to innovation activities or influencing adecision not to innovate, scaled (0-3). We also attempted to find the Arellano-Bond typeinstruments (e.g., Arellano and Bover, 1995) i.e. the first lagged values of innovative trainingexpenditure; however the sample has considerably decreased increasing the selection bias. We modify the original model (1.5), given our data constraints and the limitedinformation available in the following way: npri = A+ b1ln(Ti) + ln(1- i *(1-))+ εi = A + b1ln(Ti) + i (-1)+ εi (1.6) where the last equality holds since in the vicinity of x=0, y=ln(1+x) can beapproximated by y=x. Since patent propensity i is observed (equals 1 for a firm holding a patent and zerowhen patent protection is not used) we can quantify the returns to patenting in addition toestablishing a direction of a relationship between patent protection (holding a patent) and theNPR. We assume innovative firms to be identical and therefore  can be interpreted as theaverage patent propensity for the entire firm population. Thus, for each firm i to computeNPRi we can use the average propensity to patent from the population of firms . Now wecan rewrite (1.6) as the reduced form npri = A + B1ln(Ti) + B2xi + ei (1.7)Therefore, xi= i and 0<i<1 and B2= (-1)   = B2+1 (1.8)
  15. 15. Assuming firms choose their innovative training investments to maximize returns, so thatactual NPR and T are jointly determined by underlying firm and industry characteristics(denoted by X) thus the estimating equation becomes: Ti = C1 + Xi i + Bixi+ e2 (1.9) npri = C2 + Xi i + B1ln(Ti) + B2xi+ e2 (1.10) where C1 , C2 are vectors of intercept terms in equations (1.9) and (1.10) respectively, iis a vector of unknown coefficients of the exogenous variables in equation (1.9), i is a vectorof unknown coefficients of the exogenous variables in equation (1.10), Xi is a vector ofexogenous variables (controls) in both equations; npr is new product revenue that serves asdependent variable in equation (1.10); T is innovative training expenditure is endogenousvariable in equation (1.10) and therefore a dependent variable in the first stage of 2SLSestimation in equation (1.9). Note that (1.10) is similar to (1.7). However, by estimating (1.9)and (1.10) together in a cross section, we accomplish two objectives. First, we improve theefficiency of the estimate, because parameters are estimated together in the two equations.Second, we are able to estimate the incentives offered for innovative training due to patentprotection and the other factors. The econometric model of equation (1.10) based on the paneldata is as follows: nprit = C + Xit  + B1ln(Tit) + B2xit+ eit (1.11)
  16. 16. eit =vi + uit (1.12) where i denotes a reporting unit (i=1, …,N) and t - the time period (t=1,..,T); C is avector of intercept terms, it is a vector of unknown coefficients of the exogenous variables,Xit is a vector of exogenous variables (controls); Tit and xit are the variables of interest:training expenditure and patent protection of a firm i in period t. The error term eit consists ofthe unobserved individual-specific effects, vi and the observation-specific errors, uit. Our study is subject to certain limitations. We do not analyze all different ways thatpatenting might affect innovation; however, we do analyze NPR due to the existence ofpatent protection and for different enterprise age. Given our main focus is on studying theprivate returns to innovative training. Thus, while we control for training spillovers includingpatenting, we do not model the impact of training on those spillovers. Nor do we consider theimpact of training on entry and associated innovation. 3. Data and Methodology 3.1. Identification Strategy In general, many indices could be used to measure innovation (Acs and Audretsch, 1987a,1987b; Arora et al., 2008). Commonly used indicators of innovation outcome based on theCIS data include percentage sales of products that are new to the market or to the firm orsignificantly improved compared to sales of other products. A review of the advantages and
  17. 17. disadvantages of such indicators and some of the studies that employ them is provided byVásquez-Urriago et al. (2011). Their main advantages are that they provide a measure of theeconomic success of innovations, are applicable to all sectors, allow types of innovations tobe distinguished, and allow the definition of continuous variables, which contribute to thedevelopment of econometric analyses (Negassi, 2004). Their limitations are that they aresensitive to product life cycles and markets, which may differ in the context of competingcompanies (Kleinknecht et al., 2002; Frenz and Ietto-Gillies, 2009). The number or a share ofproducts in the market gauged the success of firms in developing and introducing newproducts is used as a substitute for a share of new products and therefore, new productrevenue. This measure was among the most widely used indicators of the firm’s innovativeoutputs (Deeds and Hill, 1999; Harmon et al., 1997; George et al., 2002). New products wereviewed as the forerunners of a company’s future market offerings, and key stakeholders werelikely to weigh this variable heavily in determining the company’s viability (George et al.,2002). For the robustness check in this study two indicators are explored: sales of productsthat are new to the market per employed (in 000s £) and new product revenue per employee 3. We define patent premium as the additional revenue from been able to protect itsinnovation on the assumption that firms earn more per unit on innovations that are protectedby patents (Arora et al., 2008). Training premium is defined as the additional revenue fromknowledge expenditure in a form of innovative training and education aimed to improvepersonnel skills, abilities and productivity of the innovative companies. Innovative trainingand training for innovation in our study are used interchangeably.
  18. 18. Regarding the cross-section estimation methodology (equation 1.9 and 1.10) we employparametric techniques including Two-stage least squares (2SLS) and Tobit estimation toevaluate the training premium and returns to patenting. First, 2SLS is used to deal withpotential endogeneity of training expenditure. Second, our dependent variable is doublecensored, as firms can have none or all sales from products that are new to the market (new tothe market products per employee). There are several different ways of estimating such avariable using parametric techniques (e.g., Wooldridge, 2003; p. 565). A double censored IVTobit model will account for this fact. This is used in several of the empirical analyses(Negassi, 2004; Faems et al., 2005; Laursen and Salter, 2006). Tobit approach does notinvalidate 2SLS estimation, however it allows estimating the effect of training expenditurefor those firms whose NPR is strictly greater than zero and in terms of propensity changesrather than elasticities. In effect, tobit estimation models a dual decision making process: inour case, firms’ that have NPR equal zero and non-zero; and, if non-zero, how much to sell.In this way, tobit estimation addresses the potential endogeneity of our independent variablesthat would arise if the self-selection of firms into innovative product sales were to be omittedfrom the model. In panel data estimation (equation 1.11) we employ both non-instrumented (Pooled OLS,Random and Fixed effects, Maximum-likelihood estimation) and instrumented approaches(IV Random and Fixed effects and Baltagi Random Effects) with training expenditure beinginstrumented. We use both instrumented and non-instrumented approaches with variouseconometric estimation techniques as a robustness check of our results.
  19. 19. 3.2. Data and variable description The dataset used in this paper is based on two independent, albeit mergeable, datasets,which is the CIS5 conducted bi-annually and BSD conducted annually by the ONS UK. Wefurther discuss several particularities of the data. First, since the survey is CIS-based, thestudy can be replicated in the other 27 European Union Members, which will enable thedevelopment of stylized facts. Our study could also be useful for North America todemonstrate the analyses of data available for researchers on innovation and R&D (e.g.Branzei and Vertinsky, 2006). Second, there is an inconsistency in the survey questionsbetween CIS4-5 and CIS6 on patent protection. Data on patent protection is available only forthe period of 2002-2006. Third, we use panel data estimation with a split by venture age todeal with unobserved heterogeneity across the firms of different age and sectors. Thedefinition of a new venture (firm) varies across studies (Zahra, 1996; Rosenbusch et al.,2011). Within the scope of this analysis, we use an average age of 10 years as a cut-off pointbetween young and mature firms. Fourth, the instruments chosen are treated with caution asthe integrated effect can moderate the relationship between training expenditure and firminnovative outcome (Zhuang et al., 2009)4. To date there have been four rounds of CIS taken place with the latest in 2009. CIS 4covers the period 2002-2004 and includes 24.93% matched firms from 16240 firms originallyavailable from ONS. The CIS 5 and 6 cover 2004-2006 and 2007-2007 periods and result28% merge from about 14000 originally available on CIS5-6 surveys. Top 5 sectorspresented in CIS4-6 panel data presented in Table 2 and the venture size - in Table 3.
  20. 20. Table 2: Top 5 sectors included in the CIS4-6 panel dataset (CIS split) SIC 92 sector Number of reporting. Units Other business activities 1939 Construction 959 Wholesale trade and commission trade, except of motor vehicles and 895 motorcycles Wholesale trade and commission trade, except of motor vehicles and 819 motorcycles Hotels and restaurants 659 Source: Office of National Statistics, UK Table 3: Firm size composition by CIS CIS4 CIS5 CIS6 Number of Number of Number of Size of Enterprise reporting. % reporting. % reporting. % Units Units Units Small - 10-49 employees 2040 50.38 1989 49.12 1927 47.59 Medium - 50-249 999 24.67 1018 25.14 1068 26.38 employees Large - 250+ employees 1010 24.94 1042 25.73 1054 26.03 Total 4049 100 4049 100 4049 100 Source: Office of National Statistics, UKTable 4 below shows the list of variables used in the analysis, sources and the way they wereconstructed. Table 5 shows the descriptive statistics of the variables.
  21. 21. Table 4: Variables used in the study Variable name Source of the data Measure description and constructionDependent New product revenue NPR is obtained by multiplying firm’s share of products introduced that were new to firm’s CIS 4-6 (q810, q2420)variables (NPR) in £000 market by the firm’s turnover. Measure included was ln(1+NPR) NPR divided by the number of listed employees in £000. Measure is reported as (1+NPR) / NPR per employee CIS 4-6 (q810, q2420, q2520) q2520 taken in logsEndogenous Training expenditure is company-financed training unit expenditures in £000. We transform Training (T) CIS 4-6 (q1450) variable measure in ln(1+T). This variable is also a dependent variable in equation (1.9). Rivals BSD (2002-2009) Number of rivals in the industry calculated by 2 digit SIC (92) sector taken in logs Dummy variable=1 if the enterprise sells goods and/or services overseas (Other Europe and all Global CIS 4-6 (q230, q240) other countries except the UK). Public BSD (2002-2009) Dummy variable=1 if the enterprise is a publicly traded company. Foreign BSD (2002-2009) Dummy variable=1 if the parent firm is located abroad (USA or other). Dummy variable=1 if the co-operation partner (e.g., Universities or other higher education CIS 4-6 (q1861, q1862, Cooperation institutions; Government or public research institutes) is located locally/ regionally within the q1871, q1872) UK or a partner is a UK national. Reporting unit level Dummy variable=1 if the unit used patents to protect its innovation; zero – if patent protection Patents CIS 4-6 (q2130) has not been used. Data is unavailable for CIS6 due to changes in reporting the survey question. Reporting unit level Number of employees educated to degree level in science and engineering. Measure included Scientists (S) CIS 4-6 (q2610, q2520) was ln(1+S) Small firm Dummy variable=1 if the unit’s number of employees less or equal 50; zero – otherwise. CIS 4-6 (q2520) Reporting unit level
  22. 22. Large firm Dummy variable=1 if the unit’s number of employees more or equal 250; zero – otherwise. CIS 4-6 (q2520) Reporting unit level Biotech and Dummy variable=1 if the if 3 digit SIC(92) is sic244 or/ and sic241 or/and sic247; zero CIS 4- 6 (SIC92, SIC2003) pharmaceutical otherwise Computers & electronic Dummy variable=1 if the if 3 digit SIC(92) is sic721 or/ and sic723 or/ and sic724 or/and CIS 4- 6 (SIC92, SIC2003) equipment sic300 or/ and sic722; zero otherwise Dummy variable=1 if the if 3 digit SIC(92) is sic343 or/ and sic292 or/ and sic295 or/and Machinery CIS 4- 6 (SIC92, SIC2003) sic341 or/and sic353 or/and sic296 or/and sic291; zero otherwise Dummy variable=1 if the if 3 digit SIC(92) is sic294 or/and sic332 or/and sic333 or/and sic334; Instruments CIS 4- 6 (SIC92, SIC2003) zero otherwise Dummy variable=1 if the if 3 digit SIC(92) is sic602 or/and sic601 or/and sic603 or/and sic611 Transportation CIS 4- 6 (SIC92, SIC2003) or/and sic621 or/and sic623; zero otherwise Medical instruments CIS 4- 6 (SIC92, SIC2003) Dummy variable=1 if the if 3 digit SIC(92) sic331 Reported the importance of increased capacity for production or service provision for the product Firm’s capacity CIS4-6 (q1250) (good or service) and/or process innovations. Four mutually exclusive responses (0 - Not used; Instruments for 1-Low; 2 - Medium; 3 - High). Training Reported the importance to enterprise the lack of information on markets as a factor which expenditures Market info CIS4-6 (q1907) constraints innovation activities. Four mutually exclusive responses (0 - Not used; 1-Low; 2 - Medium; 3 - High).Source: Office of National Statistics, UK
  23. 23. Table 5: Descriptive statistics CIS4 (2002-2004) CIS5 (2004-2006) CIS6 (2007-2009) Panel CIS4-6 (2002-2009) Variable Obs. Mean Std. Dev. Obs. Mean Std. Dev. Obs. Mean Std. Dev. Obs. Mean Std. Dev. NPR 4049 1.51 3.90 4049 1.20 3.53 4049 1.12 3.41 12147 1.28 3.61 NPR per employee 3668 0.98 2.44 3763 0.76 2.17 3521 0.78 2.21 10805 0.77 2.20 Rivals 4049 6.19 0.97 4049 6.19 0.96 4049 6.20 0.95 12147 6.19 0.95 Global 4049 0.19 0.40 4049 0.20 0.40 4049 0.19 0.39 12147 0.19 0.39 Public 4049 0.88 0.32 4049 0.88 0.32 4049 0.88 0.32 12147 0.88 0.32 Foreign 4049 0.13 0.33 4049 0.13 0.33 4049 0.13 0.33 12147 0.12 0.33 Cooperation 4049 0.06 0.23 4049 0.04 0.21 4049 0.07 0.26 12147 0.05 0.23 Patents 3942 0.21 0.41 3662 0.24 0.43 4049 . . 11653 0.22 0.42 Scientists 4049 2.38 3.28 4049 2.44 3.31 4049 2.27 3.24 12147 2.36 3.28 Small firms 4049 0.50 0.50 4049 0.49 0.50 4049 0.48 0.50 12147 0.49 0.50 Large firms 4049 0.25 0.43 4049 0.26 0.44 4049 0.26 0.44 12147 0.26 0.44 Biotech and pharmaceutical 4049 0.00 0.07 4049 0.00 0.07 4049 0.01 0.08 12147 0.01 0.07Computers & electronic equipment 4049 0.02 0.14 4049 0.02 0.14 4049 0.02 0.14 12147 0.02 0.14 Machinery 4049 0.04 0.19 4049 0.04 0.19 4049 0.04 0.20 12147 0.04 0.19 Instruments 4049 0.01 0.10 4049 0.01 0.11 4049 0.01 0.11 12147 0.01 0.11 Transportation 4049 0.06 0.23 4049 0.06 0.23 4049 0.06 0.23 12147 0.06 0.23 Medical instruments 4049 0.00 0.06 4049 0.00 0.05 4049 0.00 0.06 12147 0.00 0.06 Firm’s capacity 3566 0.94 1.14 3881 0.42 0.92 3750 0.67 1.05 11197 0.68 1.04 Market info 2102 1.34 0.66 1805 1.17 0.76 2283 1.18 0.73 6190 1.23 0.72 Training 4049 0.90 1.50 4049 0.77 1.38 4049 0.41 1.07 12147 0.70 1.35 Training (total)* 4049 23.09 171.80 4049 27.49 797.14 4049 23.27 799.73 12147 24.62 659.37 Note: Training expenditure is taken in levels, 000s £ Source: ONS UK
  24. 24. 4. Results The results of the analysis are presented in Appendices A-C. Both H1 and H2 are supported(failed to be rejected) by the estimation results. H3 is not supported (rejected). Although differentestimation techniques were used, our results have low variation across time and estimationmethod which proves the robustness of our results across all three cross-sections and in the paneldata. 4.1. New product revenue and returns to patenting Our returns to patenting measure = B2+1 means that, as a firm gets a patent, NPR increasesby 1+1.64=2.64 for CIS4 and by 1+0.59=1.59 for CIS5 (Appendix A). The results from the paneldata estimation using instruments are more precise: 1.92-2.01 (Appendix B). Although ourfindings on H2 is consistent with the lower bound estimates by Arora et al. (2012), assuming thatthe patent propensity is 1/3, the method of obtaining these results is different and narrows downthe vague interval assumed by Arora et al. (2012). While they used cross-section estimation andassumed various levels of patent propensity from 1/3 to 2/3, and their patent premium to NPR isderived from the marginal effect of patent effectiveness on NPR (importance of patents as aninstrument of legal protection), in our case, a patent propensity for the firm is given: it is eitherone or zero, depending on whether the firm holds a patent. Our results enable us to choose fromthe range of assumed patent premium offered by Arora et al. (2001); those that overlap with the
  25. 25. range 1.92-2.01 are the patent propensity of 1/3 or less. These estimates of patent propensity aresimilar to those in the US manufacturing sector (0.28-0.32) calculated by Arora et al. (2008), butare marginally lower for UK innovators. Our results show that UK innovators patent a third orless of their innovations, which can also be established from the descriptive statistics - the meanof ‘holding a patent’ dummy. UK innovators may choose to use other methods of protection fortheir innovation such as secrecy, speed and others. Partly, this may happen because of the lack ofinformation on patent returns which ensure up to 200% extra new product revenue. Interpreting other determinants of new product revenue, they are similar for both thepropensity of firms to have innovative sales in a particular period (i.e., the likelihood of havingnew product revenue at all) and the extent of new product revenue by those firms that do trade innew products in a particular period. With regards to patent protection, as the unconditionalmarginal effects show, higher effectiveness of patents increases a likelihood of higher newproduct revenues for those firms with non-zero NPR and the propensity of having non-zero NPRfor those firms with zero NPR. In this case, the tobit model provides consistent and unbiasedestimates. We split the sample into two in Appendix C. One instrumented sample consists of 520 youngfirms called “start-ups” (<11 years) and 4,824 mature firms (>10 years). The patent premium ispositive both for young (2.86) and mature firms (1.87) and significant for both types. Theseresults suggest that holding a patent increases NPR of young firms on average by 286% andmature firms by 187% (depending upon which CIS round we use for coefficient values). Thisfollows Rosenbusch et al. (2011) who emphasized that innovation has a stronger impact in
  26. 26. younger firms than in more established SMEs. Their finding indicates that new firms possessunique capabilities to create and appropriate value through innovations. Higher returns to patenting may discourage young firms from investment in innovativetraining and education, if they are able to restrict the access of competitors and significantlyincrease their innovative outcomes by holding a patent. Holding a patent could become asubstitute for investment in innovative training and education, which may affect the youngcompany in the longer run. This is a message to policy makers and young (start-ups) companymanagers. 4.2. New product revenue and returns to trainingOur estimates combine both the direct effect and indirect effects from training expenditure onNPR analogously to returns to patenting (Holzer et al., 1993). We estimate the returns to trainingby quantifying the change in the new product revenue due to change in training expenditure,which is elasticity. We find that the elasticity of new product revenue with respect to trainingexpenditure is within the range of 3-5 % for the 2SLS estimates across three CIS waves. Tobitestimation shows that the greater the expenditure on training the higher the expected revenuefrom new products (15-36%). The results indicate that as firm’s expenditure on training growsthere is a higher propensity for firms with zero NPR start selling new products as well as forthose NPR performers to increase their revenues from new products.When estimating the same equations on the panel data, the corresponding elasticity of NPR totraining expenditure is 0.25-0.32 % for the linear panel data non-instrumented regressions
  27. 27. (Pooled OLS, random and fixed effects, maximum-likelihood estimation), and 3.2-5.0 % for theinstrumented estimations. Thus, we note that our 2SLS results (excluding Tobit results havingdifferent interpretation) are very robust and consistent both across cross-section and the panelestimation. The elasticity is lowest for the CIS4 and the highest for the CIS6, which falls during theeconomically constrained times 2007-2009. The potential explanation is linked with the impactof economic crisis, in a way that companies starting with the same level of training may yieldhigher returns from their inputs in various ways: improving the quality of services provided,putting additional pressure on workers, cutting material and input costs. Workers during thecredit crunch years are often expected to put in more effort for the same or even lowercompensation, and may be afraid of layoffs which may increase their productivity. Furthermore,a consistently growing demand for new products given the lower level of inputs (includingtraining expenditure) may increase the returns to training in terms of NPR. Given same level ofinputs (innovative training and education), a company would attempt to achieve higher resultsduring economically constrained times. When splitting the sample into two (Appendix C) we find that the difference in trainingpremium between the start-ups and mature firms is respectively 2.8 and 3.3%6. We are notattempting to calculate the training premium for start-ups and mature companies separately,although we can conclude that there are significant and positive returns, which are about 15-20%higher for the mature firms (>10 years).
  28. 28. 4.3. The inducement for training from patent protection The most interesting finding linked to managerial policy is related to estimating the effect ofpatent protection in inducing increases in training and education expenditure. Equation (1.9), byincrementing training expenditures, enables us to compute the implied elasticity of training topatent protection (ET). What would happen with training expenditure if a company chooses toprotect its innovation by patenting and why? First stage results (in Appendix A) show thatholding a patent does not imply more investment in training. This effect does not change acrossthe CIS4 and CIS5 for the same companies. The result goes contrary to the perception of patentsand training being complements. We contribute to the discussion opened by Rosenbusch et. al.,(2011), Almeida and Teixeira (2007) and Arora et al., (2008) on the impact of patent protectionon R&D and knowledge expenditure. This shows that the effect of patents is different forinnovative training, from that of R&D. Comparing both returns on patenting and training, onecould understand that the returns to patenting outweigh the returns to training. Although we arenot claiming that investment in training and education is not important, it is however not thepriority for those companies who are able to extract higher benefits on innovative sales once theyacquire a patent. Patent premiums earned on innovation protection discourage or have zero-effecton additional training expenditure for the firms that have higher patent propensity. Conversely,companies with a lower patent propensity or those that do not hold patents tend to spend more onother forms of formal protection such as design registration, confidentiality agreements,copyright, as well as forms of informal protection such as secrecy, lead-time advantage oncompetitors, design complexity, markets information and additional training. Existence of other
  29. 29. forms of innovation protection may drive knowledge investment in training out of those marketswhere the protection has already been granted. This calls for further research. We reject H3 anddo not find any impact of patenting on investment in innovative training concluding on bothinnovative activities to be independent. 4.4. New product revenue, training expenditure and their driversMost of the controls in Appendix A are significant in at least two waves of the CIS data.Consistent with most of the literature (e.g., Baldwin and Johnson, 1995; Bryan, 2006; Aguinisand Kraiger, 2009) relating training and firm size, we find that small firms’ training expenditureis 19-39% less than that of the medium-sized firms, while for the large firms it is 13-58% higher(Hansson, 2007). The explanation can be viewed from the resource based perspective. Bryan(2006, p. 635) explains that ‘small firms are less likely to train employees than larger firms,because they suffer higher labour turnover and higher failure rates, and they tend to have shallowhierarchies that limit long-term career prospects’.The number of competitors has a positiveimpact on training expenditure, which suggests that firms may use their training policy as astrategy against their industry rivals. Interestingly, cooperation between firms and universities orresearch institutes has a strong positive impact on training, the presence of such cooperationincreases training expenditure by 46-61%. Global scope of operations (exporting activities) isfound to be not related to training with only the CIS4 result being negative and significant. Theshare of degree-educated scientists among the firm’s employees is positive and significantconsistently across all three waves. Ours is the first study that employs this variable as a driver of
  30. 30. training (as opposed to the share of worker with higher education in general). Ownership type(public or foreign-owned)7 is not significantly related to innovative training, which is in contrastto, e.g., Korber and Muravyev (2008) who find that state ownership has a positive effect ontraining.Our finding also contrasts Parker and Coleman (1999) who found a positive impact of foreignownership on training expenditure for UK firms. Notably, however, Parker and Coleman (1999)do not find the differences in percentage of establishments (UK vs. Foreign) who cite‘implementing new technology’ and ‘updating staff on new products and services’ being ‘veryimportant’ factor motivating training. These factors are attributed to innovative training motives.We also find that training expenditure tend to be 45-53% higher in the computer and electronicequipment industries, 40-61% higher in industries that produce medical instruments, and 30% intransportation industry, but the latter result is obtained only for the CIS4 data. 5. Discussion This study estimates the patent premium to be between 192-201% and the training premiumto be between 3.4-5.1% (Appendix A, B). It also quantifies a propensity to patent which is 1/3.This means that UK innovators patent only a third of their innovations and use other methods ofprotection for the rest of their innovation such as secrecy, lead-time advantage on competitors,technical advantage, know-how. Patent premiums are positive for both young and mature firms,although patent premiums for young companies are higher as expected since they can benefit
  31. 31. more from investment in knowledge (Branzei and Vertinsky, 2006; Unger et al. 2011) and, thusprotection of their knowledge investment. Returns to innovation and innovation inputs arelimited in mature firms due to greater impediments to innovation , where pursuing innovation ischaracterized by greater difficulties when contrasted with flexible and fast-moving new firms(Rosenbusch et. al., 2011). Companies experience lower returns from training than they do from patenting. These couldbe because of important factors necessary for successful training practices to be furtherinvestigated. Managers and shareholders may reconsider those factors such as: an organisationalculture which supports learning, mechanisms to link training to the business and organizationalstrategy and mechanisms to link training to workplace change (Dawe, 2003). The gap in returnsis even more striking for young ventures experiencing lower returns from training (2.78%) thanthey do from patenting (186%) as opposed to mature firms that receive 3.32% and 87%accordingly. Thus, young firms have even lesser incentives to invest in training. Our results mayguide practitioners in their policy development, especially for young businesses, and may resolvesome of the controversies surrounding investment in training decisions. In order to maximizeinnovative outcomes, managerial decision making should focus on those relevant factors thatexplain training expenditure. These are the number of rivals in an industry, cooperation with thegovernment and universities, share of employees with scientist and engineering degrees. SMEshaving on average lower training expenditure could be motivated by various government trainingschemes and waivers. For instance, using the example of Michigan manufacturing firms Holzeret al (1993) show that obtaining a job training grant has a strong positive effect of hours of
  32. 32. training Moreover, small ventures may benefit from these grants via investing in the acquisitionof task-related knowledge (Unger et. al., 2011). Addressing endogeneity of training expenditure using a system of equations (1.9-1.10)allowed us to estimate the main determinants of training as well as to test H3. Rejecting H3 hasan important implication for policy makers as our findings contradict the common-placeassumption that patent protection results in higher knowledge expenditure. Government agenciesand Intellectual Patent Offices may be interested in interpreting this result as there is no increasein knowledge expenditure for firms, once they acquire patent protection. In fact, governmentagencies interested in stimulating training and education expenditure by innovators shouldencourage inventors to consider non-patent instruments which could stimulate training. Theyshould not also expect high knowledge intensity of in businesses once patents are granted aslegal protection from patents neither encourages nor discourages knowledge expenditure. Acknowledging a positive relationship between training expenditure, innovative outcomesand cooperation with universities, authors would like to advise practitioners to initiate projectsthat encourage cooperation between firms and universities facilitating knowledge spillover ofentrepreneurship (Acs et. al., 2009; Agarwal et. al., 2010). The cooperation could also link otherhigher educational institutions as well as the Government or public research institutes locatedeither locally or regionally. Additionally, helping companies recruit and educate potentialemployees holding an advanced degree in science and engineering will not only increaseknowledge expenditure, but also result in innovative outcomes. Both policy instruments could beconsidered a main priority while developing firm’s innovation policy.
  33. 33. Finally, patenting appears to be especially useful for predicting higher innovative outcomes ofyoung businesses. However, no link between patent protection and knowledge expenditure forthese firms indicates that young businesses may benefit more by restricting market access viapatent protection, than by investing in additional training and education. This paper calls for efficient policy formulation on intellectual property rights protection andknowledge investment. As such, information on the patent propensity of UK firms could beuseful in developing measures that increase this propensity. Comparing patent propensity of UKfirms to that of overseas innovators may provide important insights about the effectiveness ofintellectual property rights protection in these countries. This may also help to design measuresto increase patent propensity and create knowledge spillovers from making innovation publiclyavailable, thus benefiting society as a whole (Audretsch et al., 2008). Additionally, intellectualproperty rights protection and training should be aligned within a venture’s performancemanagement system to motivate employees to do innovation and increase productivity whichmay not happen if certain restrictions are enforced by the firm (Aguinis, 2009). Our study provides some directions for future research on returns from patenting and training,which is required for various industries , firm age and organizational types (such as social andgreen entrepreneurship). For instance the researchers may want to compare the returns fromknowledge investment by aggregated industrial sectors (e.g. manufacturing, machinery, transport,retail, computers and software, pharmaceutical and biotech).. The relevant questions could be:Are the returns from patenting and innovative training different across firms of various sizes,locations and industries? What is a patent propensity of the UK innovators by industry, firm size
  34. 34. and firm age? How the change in patent propensity or effectiveness of patent protection mayimpact final innovative outcomes and firm’s innovative performance? Is there a link betweenpatent protection and investment in knowledge expenditure by firm size ownership, exportorientation, spatial location and industry? 6. Conclusion This study develops and estimates model which enables to quantify the increase in firms’innovation outcomes due to investment in training and patent protection. While, returns areestimated for UK innovators, this approach can be replicated to ventures in any country usingvarious indicators of innovative outputs, knowledge expenditure and intellectual propertyprotection. These findings bring an important contribution to the entrepreneurship literature.First, we develop a model framework to assess returns to patenting, innovative training anddetermine patent propensity of a firm despite the limitations in survey data. Second, using ourmodel we link innovation outcomes with patent protection and training to estimate the additionalnew product revenues from having a patent and training expenditure. Third, we estimate theimpact of patent protection on further investment in training. This study unveils other influentialdeterminants of innovation and training, and makes suggestions for managers and policy makersinterested in increasing firms’ propensity to patenting, innovation and investment in knowledge.More research is required for better understanding of how firms’ heterogeneity effect returnsfrom patenting and innovative training and their link with firms’ innovation success.
  35. 35. Footnotes 1. Hall, B., Helmers, C., Rogers, M, Sena, V. 2011. The choice between formal andinformal intellectual property: a review. Accessible at: 2. Tan, H. W., Batra, G., 1995. Enterprise Training in Developing Countries: Incidence,Productivity Effects and Implications. Unpublished paper. World Bank. 3. The results obtained by using the new product revenue per employee as a dependentvariable in the model (1.9) and (1.10) confirmed the results reported in the paper. Thesignificance and the direction of relationship between the innovative outcome, patent protection,training and other control variables remained stable across various the estimation methods. Thisis also explained by the correlation coefficient between two innovative measures (sales ofproducts that are new to the market per employed (in 000s £) and new product revenue peremployee) which is 0.98. 4. Zhuang, Y., Berkowitz, D., and Y.-Q. Bao. 2009. Integrated effects on R&D compositeinput: China manufacturing firms practices. 2009 International Conference on ManagementScience and Engineering - 16th Annual Conference Proceedings, ICMSE 2009: 1739-1746. 5. For more information on CIS and what these datasets contain see:http://nswebcopy/StatBase/Source.asp?vlnk=926&More=Y 6. This result is obtained using instrumented estimation (Baltagis EC2SLS random-effectsestimator) described in Baltagi (2008) which has proved to fit better than non-instumented andfixed effects method in (than?) the estimated model when the number of waves is small. ALikelihood-ratio test of Sigma u=0 is rejected at 1% level in favour of random effects and the F-
  36. 36. test of all firm dummies jointly equal zero is rejected which confirms the presence of randomeffects. Although we do not use Tobit estimation in panel data analysis we ensure theconsistency between the 2SLS estimations in Appendices A and B. 7. Domestic private firms are not listed here because it is a base category. Acknowledgements We would like to thank Professors David Audretsch, Herman Aguinis, Giorgio Barba-Navaretti, Davide Castellani, Anthony Ferner and Furio Rosati as well as the participants of theseminar at the Institute of Development Strategies seminar series at Indiana University onNovember 10th, 2011, the Royal Economic Society 2012 conference in Cambridge University onMarch 22-24th, 2012 and BAFA2012 conference in Brighton University on April 30th, 2012 forcomments and suggestions. We are grateful to Sowmya Kypa for excellent research assistance.Yulia Rodionova gratefully acknowledges funding from De Montfort University ECR Scheme.
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  47. 47. Appendix A. Training premium equation: cross-section estimation by CIS roundDep. Var.: NPR in 000s £, CIS4 (2002-2004) CIS5 (2004-2006) CIS6 (2007-2009) log Estimation method OLS 2SLS IV Tobit OLS 2SLS IV Tobit OLS 2SLS IV Tobit 0.28*** 3.45*** 20.6*** 0.33*** 3.22*** 14.8*** 0.50*** 5.14*** 36.4*** Training (0.05) (0.58) (3.43) (0.06) (0.47) (2.29) (0.09) (0.74) (5.51) -0.14** -0.50*** -2.77*** -0.18*** -0.51*** -2.52*** -0.17*** -0.15 -0.78 Rivals (0.06) (0.18) (0.98) (0.07) (0.18) (0.88) (0.06) (0.15) (1.09) 0.60*** 1.20*** 6.18*** 0.84*** 1.12*** 4.64** 1.02*** 0.34 1.49 Global (0.20) (0.41) (2.23) (0.18) (0.39) (1.84) (0.18) (0.37) (2.57) 0.29** 0.81 6.84** 0.31** 1.00 5.55* 0.23** 0.01 -0.69 Public (0.12) (0.56) (3.47) (0.12) (0.63) (3.33) (0.11) (0.49) (3.63) -0.43 -0.91 -5.02 -0.40 -0.40 -2.37 0.033 0.81* 6.20* Foreign (0.27) (0.57) (3.08) (0.27) (0.57) (2.79) (0.24) (0.49) (3.45) 2.36*** 0.60 -3.45 2.85*** 0.63 -1.25 2.13*** -1.33* -15.9*** Cooperation (0.39) (0.65) (3.45) (0.46) (0.69) (3.01) (0.34) (0.72) (4.98) 2.08*** 1.62*** 6.43*** 1.24*** 0.59* 2.94* Patents (0.21) (0.35) (1.93) (0.18) (0.35) (1.68) 0.11*** -0.16** -1.27*** 0.11*** -0.080 -0.32 0.14*** -0.25*** -1.97*** Scientists (0.02) (0.08) (0.44) (0.02) (0.06) (0.31) (0.02) (0.08) (0.57) 0.15 1.24*** 7.86*** 0.39*** 1.65*** 8.99*** 0.31*** 1.15*** 9.05*** Small firm (0.13) (0.42) (2.39) (0.13) (0.44) (2.19) (0.12) (0.36) (2.60) 0.11 -1.99*** -12.8*** 0.11 -1.31** -7.02*** -0.13 -0.57 -6.12** Large firm (0.19) (0.57) (3.25) (0.18) (0.52) (2.50) (0.15) (0.38) (2.78) Biotech and -1.33 -3.40* -15.3 -0.72 -0.11 -0.052 -0.30 -0.72 -3.02 pharmaceutical (0.89) (1.89) (10.52) (1.01) (1.77) (8.11) (0.83) (1.80) (12.36)Computers and electronic 0.32 -0.75 -5.98 0.94* -0.69 -3.22 0.39 0.44 2.79 equipment (0.51) (1.05) (5.57) (0.55) (1.08) (4.80) (0.48) (0.88) (6.03) 0.20 -0.69 -4.36 -0.096 -0.95 -5.08 0.30 -0.11 -2.43 Machinery (0.39) (0.74) (4.01) (0.38) (0.69) (3.25) (0.34) (0.64) (4.41) 0.91 0.50 -0.058 1.11 -0.21 -5.30 1.99*** -0.71 -12.4 Instruments (0.81) (1.24) (6.51) (0.73) (1.34) (5.84) (0.75) (1.13) (7.63) -0.53*** -1.21* -8.61* -0.21 0.01 -7.40 -0.14 -0.17 -2.04 Transportation (0.15) (0.73) (4.47) (0.15) (0.77) (5.34) (0.15) (0.60) (4.72) 1.98 2.36 14.0 1.67 2.28 7.84 2.45** 0.64 -1.81 Medical instruments (1.21) (2.33) (12.47) (1.59) (2.31) (10.18) (1.07) (2.08) (13.87) 0.89* -0.13 -27.3*** 0.79 -0.33 -24.9*** 0.98** -0.025 -29.5*** Constant (0.47) (1.31) (7.51) (0.49) (1.42) (7.03) (0.44) (1.17) (8.57) Obs. 3942 1779 1779 3662 1413 1413 4049 2152 2152 R-square 0.170 -0.976 0.164 -0.734 0.164 -1.406 F statistics 26.24 10.45 20.69 9.36 20.33 10.85 Sargan J-statistics 0.001 0.028 0.049Sargan J stat. p-value 0.96 0.86 0.82Anderson-Rubin chi-sq 86.83 100.15 143.53 Kleibergen-Paap LM 0.00 0.00 0.00 statistic p-value Uncensored obs. 307 268 360 Wald test chi2(1) 39.95 36.39 34.16 First stage estimates: Dep. Variable: Training expenditure, log 0.090** 0.081* -0.013 Rivals (0.04) (0.04) (0.03) -0.16* -0.10 0.062 Global (0.09) (0.10) (0.06)