SMALL FIRMS, BIG PATENTS? ESTIMATING PATENT VALUE USING DATA ON START-UPS' FINANCING ROUNDS

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1 Paper to be presented at the DRUID-DIME Academy Winter 2010 PhD Conference on Comwell Rebild Bakker, Aalborg, Denmark, January 21-23, 2010 SMALL FIRMS, BIG PATENTS? ESTIMATING PATENT VALUE USING DATA ON START-UPS' FINANCING ROUNDS Gili Greenberg Bocconi University, Economics Department Abstract: This paper estimates the value of patents to technological start-ups by using data on VC-backed Israeli ventures. In the age of technology, estimating the value of patents is extremely important. It assists in measuring the return on R&D, correctly rewarding inventors inside firms, assessing the worthiness of ventures, facilitating the sale and licensing of IPR and determining the salvage value of firms that fail. The new data and analysis employed in this paper enable a real monetary estimation of the value of patents. Furthermore, by separately estimating the significance of both granted patents and patent applications to firms s, two important issues are addressed: 1. The role of patent-granting institutions and the functions of the patent system 2. The value of more recent inventions compared to older ones (innovation decay) The bulk of previous literature estimates patent value either by deducing it from inventors behavior or by disentangling it from large firms market-cap values. The novel approach used here differs from that literature in several important ways. First, it uses new data about young start-up ventures that, unlike large public firms, have very few assets apart from their IPR and their founders skills. Secondly, it examines the value assigned to those ventures by investors, in particular VCs, who are an important and experienced audience for innovation signals, and are more objective about the value of inventions than the inventors themselves. Thirdly, by employing 15 year long panel data comprised of ventures multiple financing rounds, I am able to control for firms unobserved heterogeneity and measure more accurately the change in their pre-money as associated with their patents as opposed to other determinants. Finally, since the data on patents have been hand collected, they include information about both granted patents and pending patent applications, the latter not being available from datasets provided by the NBER. The data employed consist of 1158 financing rounds of 387 companies in seven technological sectors, which took place between 1994 and A fixed-effects (FE) regression of pre-money s on the cumulative stock of patent applications and a set of controls yields the following interesting results. A doubling of patent application stock is associated with a 22% increase in start-ups s, which translates to an average of $2.9 million per application. Furthermore, a set of FE regressions that include both applications and grants, applications alone, grants alone and pending applications, reveals that the increase in s is related solely to applications, while the coefficient on granted patents is insignificant in all regressions. Moreover, the significant coefficient on applications is found to be due entirely to recent applications that are still pending. These findings suggest that while a recent patent application is a strong signal to investors, the final scope of the patent (e.g. claims), or more generally, the attribution of property rights on the invention, as established by the grant, does not add much to a company's. In JEL - codes: O31, -, -

2 Small Firms, Big Patents? Estimating patent value using data on Israeli start-ups' financing rounds Gili Greenberg December 2009 Preliminary and incomplete

3 ABSTRACT This study estimates the value of patents by examining new data on the patenting and venture financing activities of 369 Israeli technological start-ups that received more than 1000 rounds of financing between 1994 and By using this 15 year long panel data, I control for firms' unobserved heterogeneity and measure the change in companies' s as associated with changes in their patent stock. Separate estimates for the value of pending patent applications and of granted patents are provided for the entire sample and for the non-software and software sectors. My results reveal significant and positive associations between pending and granted patents and companies' s for the entire sample and for the non-software sector. These associations are stronger for younger firms, for which patents can act as powerful quality signals to investors. When translated into monetary terms these results show a substantial increase in the value of an invention once it becomes protected by a patent, implying that investors attribute value to patents not only for their signaling effect but also for their productive effects, which come into play once a patent is granted. In the case of software companies, I find no significant association between patents and financing, suggesting that investors view patents as less important in an industry where inventions can be protected by other means such as copyright. 2

4 INTRODUCTION Finding an estimate for the economic value of patents has become increasingly important over the last two decades, as trade in disembodied technologies has dramatically surged and the number of patent applications in Europe, the US and Japan has constantly grown. In the age of technology, the correct estimation of patent values is as important as the estimation of land values in the 19th century. The of IPR facilitates trade and licensing of technology and the appraisal of projects and companies. It helps to measure the return on investment in R&D and to correctly reward inventors inside firms. For knowledge-intensive young firms, whose assets are largely intangible, patents facilitate obtaining financing from investors and affect salvage value in case of failure. Patent value is made up of two components: 1) the value of the underlying invention that is protected by the patent 2) the value of the patent right itself, named the 'patent premium' by Arora et al. (2008), which is the private incremental value provided by the IP protection, above and beyond the profits generated by the unprotected invention. Empirically, two broad strategies are normally used to measure the value of patents. The first uses a regression of firm market value on a measure of patents owned by the firm (possibly weighted by citations) and various other firm characteristics. The second strategy estimates patent value by observing the actions of the inventor or the assignee with respect to the patent. Such actions can include the owner's willingness to pay patent renewal fees (Pakes (1986), Schankerman and Pakes (1986)), her decision to register the patent in multiple countries (Putnam (1996)) or her answers to surveys which elicit a private estimate of value (Gambardella et al. (2008)). The renewal fee approach's premise is that patent owners choose to pay renewal fees to maintain valuable patents 'alive', while they let their rights on less valuable ones expire. Pakes (1986), and Schankerman and Pakes (1986) find that only around 10% of the patents issued in Germany, France and the UK `live' for the full statutory life time. Based on this approach they report estimates of the means and medians of the patent value distribution for patents issued in 1970 in Germany, France, and the UK. The median values in these countries are, respectively, $17,329, $847 and $1861 (all in 1980 prices). 3

5 The distribution means are substantially higher than the median in France and the UK ($6656 and $6963, respectively), and slightly higher in Germany ($19,124). The findings can be summarized as confirming several points: 1) the distribution of patent right value is skewed; 2) chemical and pharmaceutical patents are worth more on average, followed by electronics, computers and communications equipment 3) most learning by the patentee about the value of his patent takes place in the first 5 years or so. This approach however, only provides an estimate of the 'patent premium' and one must recall that even in the case where a patent is not renewed, the invention may still be practiced by the former owner (Harhoff et al. (2003)). Another possible drawback of the renewal fee approach is that patent renewal fees are usually too low to reveal much about the truly valuable patents, those which are always renewed and which create most of the value in the system. It is also prone to underestimating the value of 'early bloomers'- patents that are very valuable in the first few years of their lives, but may become obsolete later. Another estimate of patent value is provided by Serrano (2005), who uses data on the commercial transfer of patent rights, as registered at the USPTO. He applies a model that resembles the Pakes (1986) model, in that it follows the value of the patent over its lifetime. In addition to the decision to renew the patent, however, the Serrano model also includes an option to sell the patent. Using estimated model parameters, Serrano estimates the median value in his sample of patents to be equal to $27,895 and the mean equal to $86,782 (both 2003 prices). These values include patent premium and invention value, that is, they represent total patent value, however, his sample contains only patents applied for by `small innovators' (firms that have no more than five patent). Gambradella et al. (2008) base their results on the large-scale PatVal European survey, which asks inventors what is the minimum price for which they would sell the rights to their patent. They find that the distribution of values is extremely skewed, with the mean patent value equal to 3.4 million euros (in mid 1990s euros) and the median equal to a tenth of that. The s obtained from this survey are subjective and depend on the inventors' perceptions of their patent's worth. The market value or portfolio approach relates the financial market of a firm to its assets: tangible (plant, equipment etc.) and intangible (knowledge capital, 4

6 patent stocks, reputation, etc.). The coefficients in these regressions are the shadow value of the various assets in the market. Financial markets value patents both as indicators of a successful innovation and as instruments that secure returns to that activity by excluding competitors. This approach therefore measures a combination of part of the value of the underlying inventions and the patent rights associated with them. There is a rich literature that uses this approach and focuses mainly on the financial markets of the US, UK, Australia, Canada and Japan. The results show that patents are valued beyond the R&D done by a company and that the value of patent protection is higher in pharmaceuticals and possibly somewhat higher in chemicals, computers and machinery (Hall et al. 2005; Bessen 2006). A typical quantitative result is that of Hall et al (2005), who find that one additional patent per million (1992) dollars of R&D increases a firm's market value by 3 per cent. This paper uses an entirely different approach to estimate the value of patents. It considers patents as quality signals to investors, in particular, Venture-Capitalists (VCs) and relates panel data on start-ups' pre-money s, which are the values assigned to ventures by investors prior to a financing round, to companies' stock of patent applications and grants. This method has several advantages. First, VCs are an important audience for quality signals and they assign non-subjective s to new ventures. Second, the ability to observe changes in start-up s before a liquidity event such as an acquisition or an IPO gives the advantage of observing intermediate start-up s, otherwise unobservable. Third, by using panel data, I am able to control for unobserved firm heterogeneity, which is highly likely to influence s. Young start-ups normally rely on their technology to secure financing and since they have little tangible assets it is easier to estimate the value of their IP among other factors. A drawback to this approach, however, is that my sample only contains start-ups that are VC-backed and had at least two financing rounds (or a financing round and an exit round). This entails a problem of sample selection to some extent, since I am using a sample of start-ups which exceed a minimum threshold of quality. There are several papers that examine the link between the patenting behavior of start-ups and VC funding. Hall and Ziedonis (2001) report evidence from interviews with 5

7 individuals in the hi-tech sector who view patent applications as high on the list of questions asked by a prospective VC investor. Haeussler, Harhoff et al. (2009) show that patent applications, especially those of high quality, speed the arrival of VC financing for British and German biotechnology start-ups. They also find that patent opposition increases the likelihood of financing, but that ultimate grant decisions do not spur financing. Hsu (2004) found that having no patents reduced a start-up's pre-money by about 17% to 20%. Cockburn and MacGarvie (2007) confirm that having patents pending (granted or not) significantly raises the probability that a firm will obtain initial funding. However, they cannot tell whether it is the patent or the technology that is creating the value. Mann and Sager (2007) examine software firms that received their first financing round during 1997, 1998 or 1999, count their number of granted patents before December 2004 and relate it to the total investment received by a company before January They find that only one in four VC backed software firms have patents, even several years into their existence, compared with half of biotechnology firms, and that the rate of patenting differs within sub-sectors. Having patents is significantly related to the firm's progress in terms of number of financing rounds, longevity and total investment. (The size of the patent portfolio does not seem to matter). They find that an increase of 1 in the total number of patents is related to an increase of $2.7m ($3.04 in 2008 prices) in total investment. Value of patents for software startups first becomes significant as they reach the stage at which they begin to generate revenues. T. Hall (2006) interviewed 351 managers of technological start-ups that received their seed round funding between 1998 and He uses a dummy for whether a venture has any patents and a second dummy to control for patents which are "useful" for generating barriers to entry, as defined by the start-ups managers. He finds that about 30% of the companies in his sample had "useful" patents and another 10% had patents that were not considered "useful". His regression analysis shows that the presence of patents does not significantly enhance : controlling for their usefulness, the coefficient on patent possession is actually negative, and often significant, in various specifications. But the usefulness of patents is robustly and positively related to and to the amount raised, especially for expansion stage firms. One interpretation of this result is that firms 6

8 with patents but without useful patents potentially wasted resources on technologies that did not help the firm's prospects, resulting in lower s and in less money raised. In Hsu and Ziedonis (2008)'s fixed-effects regression analysis of 813 financing rounds by 269 American semiconductor firms they found that a doubling of a company's application stock is associated with a 28% increase in pre-money s which translates to a value of $2.3 million per patent in 2008 prices. They also find that the signaling value of patents is greater in earlier financing rounds. In her research paper on 'The Use and Value of Patent Rights' (2009), B. Hall states that: "although consistent and very interesting in itself, the accumulated evidence (on the link between patents and VC funding) is in some ways not surprising. Having applied for patents is a signal that a new or young firm has some technology worth protecting, but we don't know whether it is the patent or the technology that is creating the value". It is on this important issue that the present study sheds some light. By using data on both granted patents and non-granted patent applications I am able to separately estimate the value of the technology (together with the discounted value of an eventual grant), as embodied in the application, and the value of the technology once it is actually protected by the patent right. THE DATA The data were collected from the Israel Venture Capital (IVC) online database, which contains information about approximately 6000 Israeli technological start-ups, out of which 1409 are backed by VCs. The information found in the database is disclosed by the companies on a voluntary basis and includes details about the company's technology, its date of establishment, its founders, its investors, its stage of development, number of financing rounds, total investment per round, exit status, eventual "fate" (whether the company is ongoing, ceased to exist, had an IPO, was merged or acquired), pre-money s and acquisition amounts. Since all the information is not always available for each company, I was able to collect data on 369 companies who provided information on two pre-money s or a pre-money and an IPO or acquisition amount. The sample size is of 1106 financing or exit rounds, which took place between 1994 and 7

9 July I hand-matched this data with data on patent applications and grants taken from the US Patent and Trademarks Office (USPTO), which contains information about all patents granted in the last two hundred years and about published patent applications since The USPTO grant rate is very high and is estimated to be between 90% and 95% (Quillen and Webster (2001)). This is because the United States is unique in permitting patent applicants to re-file their patent applications as continuation and continuation-in-part applications, claiming the benefit of the filing date of the initial application, and restart the examination process all over again. Quillen and Webster's (2001) analysis of the data for continuing applications for the USPTO's fiscal years in conjunction with the USPTO Annual Report statistics for the same fiscal years shows that the number of utility, plant and reissue (UPR) applications allowed in fiscal years was 95% of the number of original UPR applications filed in fiscal years For this reason, I am not very concerned about "missing" patent applications observations which date prior to 2001 since it is very likely that they were granted, in some scope, by the time I collected the observations in The 369 companies in the dataset belong to 6 technological sectors: Semiconductors, Communications, Life-Sciences, Cleantech, IT & Enterprise Software and Internet. Since these sectors are heterogeneous and differ in their patenting behavior, I separated them into 2 broad technological sectors: software based industries and nonsoftware based technologies. Software based industries include IT & Enterprise Software, Internet and Communications Applications. Non-software based technologies include Semiconductors, Communications Infrastructure & Hardware, Life-Sciences and Cleantech. Companies in both broad sectors are young: 75% of the companies in Software were established after 1996 and 50% of them after Among the companies in Non-Software, 75% were established after 1995 and 50% after Statistics about the patenting behavior in the different sectors are found in Table 1. 8

10 Table 1 Patenting Statistics by Sector no. of companies no. of observations mean applications per company mean grants per company % of companies with at least one application (sample period) % of companies with at least one application (before July 09) Life sciences % 90% Semiconductors % 90% Communications % 72% Cleantech % 100% Non-Software Technolgies % 84% Software % 60% Comms Software % 54% Internet % 48% Software Technologies % 56% Total % 72% The last two columns of the table show the propensity to patent of companies in different sectors, during the sample period and post sample period. The propensity to patent is, as expected, very high in life-sciences, semiconductors and cleantech, and lowest in internet. It is recognized that software companies have traditionally tended to patent less for several reasons. First, software can be protected by copyright. Second, when filing for a patent, one has to disclose and publish one's technology. Software codes are fairly easy to copy and make slight changes to and as a result, difficult to defend in litigation. Therefore, trade-secrecy (possibly accompanied by copyright) is more widely used by software companies. Although there has been a shift towards patenting in software following a series of court decisions in the US during the 1990s that enabled the patenting of "pure" software, the largest software patenters, accounting for 50% of software patenting, are still electronics multinationals. 1 1 Bessen and Hunt (2004) find that only 5%-7% of software patents are issued to firms in the software sector and 75% to firms in the manufacturing sector. Hall et al. (2007), find that over 60% of such patents 9

11 DATA ANALYSIS My main set of regressions estimates the effect of pending patents and granted patents on the s of startups across funding and exit rounds, holding unobservable time invariant effects constant via start-up fixed effects ( i ). More specifically, I estimate the following type of equation for firm i in funding round t: log( ) log(1 age) 3 it log(1 patents pending) it 0 (company stage) 4 i 1 it log(patents granted) (round type) (funding year) 5 it it 2 6 it it it (1) Explanation about the variables used in the analysis is found in Table 2. in the US and the EU are taken out by firms in electrical machinery and electronics including computers and only 2 per cent by software firms. 10

12 Valutaion Patent Applications Table 2 Summary Statistics and Variable Definitions VC pre-money (share price*shares outstanding prior to venture round) of the focal round or acquisition amount, deflated to 2008 prices Cumulative patent application stock at the time of the funding round Non-Software Software STD. MEAN MED. DEV MEAN MED. STD. DEV Patents Pending Patent Applications - Patent Grants Patent Grants Start-up age at time of VC round Early stage round Late stage round Acquisition/merger round IPO round Seed stage R&D stage VARIABLE Cumulative patent stock at the time of the funding round Age of the start-up in year at the time of the VC funding round Dummy=1 if funding round is classified as Seed, 1st of 2nd round Dummy=1 if funding round is classified as 3rd round or above Dummy=1 if the focal funding round involved an acquisition/merger Dummy=1 if the venture achieved an initial public offering Dummy=1 if the venture is in its early days of product development and fund raising Dummy=1 if the venture is discov ering new knowledge and applying it to create new and improved products that fill market needs Initial revenues stage Dummy=1 if the venture has revenues which do not exceed $10 million dollars Revenue growth stage Funding year controls DEFINITION Dummy=1 if the venture has revenues which exceed $10 million dollars and a double digit yearly growth rate A dummy=1 for each of the years when the financing round occurred between 1996 and 2008 The distribution of s in very skewed, with few high values, and a very large difference between s' mean and median. This can be clearly seen in Figure 1, which plots the density function of s, excluding the highest 12 observations which are above $500 million. 11

13 Figure 1 Valuations Density Function Density s in millions of USD Table 2 also shows a noticeable difference in s amounts and number of patents between software and non-software industries, but little difference in other variables. After removing observations for which I had no information about the company's stage at the time of the financing round, I remain with a total sample of 317 companies and 968 observations. The results of the estimation of several variations of equation (1) are presented in tables 3 to 6. Table 3 presents the results of the estimation of equation (1) for the entire sample, using a FE regression. Table 4 presents the results of the estimation of the same equation with interaction terms of pending and granted patents with software and nonsoftware dummies. Table 5 presents the results of separate estimations of equation (1) for software and non-software sectors. Table 6 shows the results of the separate estimations for software and non-software companies, including interaction terms of pending and granted patents with "young" companies (of age less than 4 years) and "older" companies (of age above 4 years) dummies. 12

14 Table 3 Valuation Fixed-Effects OLS Regression IPO Round 13

15 Table 4 Valuation Fixed-Effects OLS Regression with Sector Dummies IPO Round 1.199*** (0.33) Observations R-squared Number of id 14

16 Table 5 Separate Valuation FE Regressions for the Software and Non-Software Sectors Dependent Variable: Log Valuation IPO Round 15

17 Table 6 Valuation FE Regression with Age Dummies, Non-Software Sector Dependent Variable: Log Valuation IPO Round 16

18 Sampling weights Since information disclosure is voluntary, it is uncertain whether the sample of companies used in the analysis is representative of the population of 1409 VC-backed companies found in the database. It is possible that under-performing companies were more reluctant to disclose information and that companies that ceased to exist in their early days did not have the opportunity to have more than one financing round. As an extension to the preceding analysis, I constructed sampling weights which reflect the distribution of companies in the population according to their fate (whether they had an IPO, were acquired, merged, are still ongoing or ceased to exist), under the assumption that the best proxy for a company's general quality is what became of it. A detailed explanation about the construction of the sampling weights and the tables presenting the results of the various weighted FE regressions are found in Appendix A. Table 1 of the Appendix shows that the sample used has a relatively higher share of acquired and IPO companies and lower shares of ongoing and ceased companies, which is unsurprising, since the IPO or acquisition amounts provide a second observation and ongoing and ceased companies might have been too young to have more than a single financing round. Nonetheless, the regression results presented in Tables 2-5 of the Appendix are very similar to the ones in the non-weighted regression. DISCUSSION OF RESULTS Several interesting insights about the relationship between start-ups' patents and s arise from the preceding regressions. In the first estimation of the entire sample in Table 3, we see a significant and positive relationship between pending and granted patents and s. A doubling of pending patent stock is associated with a 24% increase in companies' s and a doubling of granted patents stock is associated with 21% increase in s. The coefficients on the other covariates in the regression are as we would expect them to be. The coefficient on age is positive and significant. It is fairly large, since the age variable is likely to proxying for other variables that are not included in the regression and are highly correlated with it such as experience and company size. The coefficients on the different company stages are positive and significant, relative to the missing category which is the seed stage. The coefficient on 17

19 early financing round is negative, since firms are likely to get less financing in earlier rounds compared to later ones. Acquisition is a liquidity event which might be positive or negative and accordingly, the coefficient on acquisition rounds is not significant. The coefficient on IPO round is positive and significant since reaching an IPO is normally a positive liquidity event. Among the year dummies, the coefficient on the year 2000 is particularly large and significant, reflecting the hi-tech boom which took place around that time. Having established that there is a positive association between patents and s, I was interested to see whether this association differed between non-software and software companies. As mentioned in the introduction, the software industry has traditionally relied on trade-secrecy and copyright protection as means for protecting inventions. Software patents were also thought by some to be susceptible to being "low quality" and held invalid if challenged (Hall and MacGarvie (2006) and references therein). Conversely, studies of the semiconductor industry (Shapiro (2001)), for example, suggest significant barriers to efficient technological trade in the absence of enforceable IP rights. In my sample, I find the propensity to patent and the average number of patent applications and grants to be much lower in software than in other industries. In the regression presented in Table 5, I find that the coefficients on the interaction terms of pending and granted patents with the software sector dummies are not significant, while the ones for the non-software sector are significant and similar in size to those of Table 3, suggesting that patents do not contribute to software companies. Table 5 presents separate regressions results for the software and non-software sectors. For non-software industries, doubling of the pending or granted patents stock are associated with 38.5% or 34.2% increase in s, respectively. In the software industry, neither pending nor granted patents are associated with a change in. The coefficient on age in software is much larger than in the non-software sector, possibly because experience and size of the company are more important signals in this sector. Finally, the regressions in Table 6 inquire whether patents serve as stronger signals to investors when a company is young relative to when it is older. Approximately half the financing rounds in the sample took place when companies were younger than 4 18

20 years and that is why I chose to divide the sample in this way. The results presented in the first column of Table 6 show that the coefficients on both pending and granted patents are much higher for younger companies in the non-software industries, suggesting that, since younger companies can probably point to little else in terms of revenues, experience or assets, their IP serves as a more powerful signal to investors. For software companies, the coefficients are not significant. The monetary values of pending and granted patents, calculated using the different regressions' coefficients are presented in Table 7. The impact of patents on mean and median s is computed according to the moments of the log-normal distribution in the following way: V Impact of patent on mean coefficient P 2 log( V ) Vmean exp( log( V ) ) 2 2 log( P 1) Pmean exp( log( P 1) ) 2 V Impact of patent on mean coefficient P V median exp( log( V ) ) mean mean median median P median exp( log( P 1) ) where V is, P is the cumulative stock of patents (pending or granted), log(v ) and are the mean and variance of the log of s and log( P 1) and 2 log(v ) 2 log( P 1) are the mean and variance of the stock of patents plus one. 19

21 Table 7 Monetary Impact of Patents on Valuations in millions of USD coefficient on pending coefficient on granted Impact of pending on mean Table 3: entire sample Impact of granted on mean Impact of pending on median Impact of granted on median Table 4: entire sample non-software dummy Table 5: non-software companies Table 6: "Young" nonsoftware companies Table 6: "Older" nonsoftware companies In all cases, the value of a granted patent exceeds that of a pending patent application. The monetary impact on median might be a better indication of patent value than the impact on the mean, due the extremely skewed nature of the distribution of s, as presented in Figure 1. When looking at the entire sample or at the non-software sub-sample (results of Tables 3-5), the median values appear to be in same order of magnitude as Hsu and Ziedonis's (2008) estimate of $2.4 million and Gambardella et al.'s (2008) estimate of 3.4 million euros. For young non-software companies, the values of both pending and granted patents are higher than for older companies, in particular the value of granted patents. However, since the sample contained only 10 companies who had any granted patents in their first 4 years, these estimates should be interpreted with caution. All monetary estimates are fairly high because, similarly to the sample used by Hsu and Ziedonis (2008), they pertain to innovations that entrepreneurs considered worthwhile commercializing by creating start-ups. In addition, these start-ups were then considered good enough by investors to merit at least two rounds of financing. The monetary impact of patents, as estimated from the results of the FE regression using the sampling weights (Table 6 of Appendix A) are approximately 20% lower. This is 20

22 probably because the weights eliminate some of the sample s bias towards better companies that were acquired or had an IPO. Magnitudes are also likely to be affected by asset variations within the product development stage or because of contracts with large clients, both of which may be related to patents. It might therefore be more instructive to look at the relative magnitudes of the impacts of pending and granted patents. The general finding that the value of a granted patent is higher than that of a pending application is not surprising. Despite the high probability that a patent would eventually be granted, its exact scope and claims are only determined upon grant. Gans, Hsu and Stern (2008) find that the grant of IPR impacts the timing of cooperation or licensing between technological start-ups and established firms and therefore has an intrinsic value. The actual acquisition of IPR plays a particularly important role for technologies with longer technology life cycles or that lack alternative appropriation mechanisms such as copyright. The exact addition in value to a pending application by its grant cannot however, be determined from these results. Since there is a substantial lag of 3-4 years between the time of application and the time of grant, pending applications are likely to refer to more recent inventions while granted patents refer to older ones. Innovation decay for the technological industries in the sample tends to be quite high and therefore pending applications benefit in value thanks to their novelty, while granted patents' value is decreased by the invention's depreciation. Unfortunately, since virtually all new inventions are still patent pending and almost all old inventions have already been granted a patent, there are too few periods in which there is enough overlap to get significant coefficients for both pending and granted patents. This is shown by the distribution of the number of observations for which there exist any pending or granted patents, whose application date is t-k, where t is the year of the funding round and k is the number of years that elapsed since the application year. 21

23 Figure t t-1 t-2 t-3 t-4 t-5 t-6 t-7 t-8 t-9 t-10 pending granted The median time it takes for an application to become granted, in my sample, is 3.2 years and the mean time is 3.5 years. In order to get a better estimate of the exact incremental value added to a pending application by the granting of a patent, I use an exponential yearly innovation depreciation rate, δ, and the median time it takes for an application to get granted to calculate the addition in value in the following way: Value of Value of P Grant (1 ) 3.2 granted Value of P pending Some estimates of δ are found in the literature. Hsu and Lim (2009) and Macher and Boerner (2006) use a δ of 20% in their research of pharmaceutical companies and Hall et al. (2005) use a δ of 15% to depreciate R&D capital stock. I estimate δ using a fixed effects non-linear regression of companies s on the number of applications by application year (not their cumulative stock by that year), between year t and year t-10, and a set of controls. The equation estimated is: log( ) (company stage) 3 it 0 it i (1 ) log(1 patent applications) (round type) (funding year) k 0 it t k 5 it it it-k log(1 age) The results of this regression are found in Table 1, in the Appendix B. The estimated δ for the entire sample is equal to 22% and for the non-software sector it is equal to 21%. I 2 it (2) (3) 22

24 therefore choose to use a 20% depreciation rate for my calculations of the monetary estimates of Grant, according to equation (2), presented in Table 8. Table 8 Monetary estimates of the impact of Grant on s, millions of USD Impact of pending on mean PV impact of granted on mean Impact of Grant on mean Impact of pending on median Table 3: entire sample PV impact of granted on median Impact of Grant on median Table 4: entire sample non-software dummy Table 5: non-software companies Table 6: "Young" nonsoftware companies Table 6: "Older" nonsoftware companies These results suggest that a hypothetical comparison of two innovations dating from the same period, one of which is patent-pending while the other already patented, would reveal the difference in value between the two to be substantial, with the patented innovation being worth more than twice the value of the patent-pending one. Since most patent applications are eventually granted, this means that the actual grant has a value which is substantially greater than the value of the mere expectation of an eventual grant, which is associated with the patent application. CONCLUSIONS This study provides new evidence on the separate effects of pending and granted patent applications on start-ups' s. I find that both patent-pending applications and granted patents have positive and significant value for companies whose technology is not software based, and that this value is greater in the case of younger firms. I find no value associated with patents in the software industry. 23

25 Once quantified, a patent-pending innovation appears to be worth substantially less than a patented innovation, dating from the same period. This suggests that investors perceive an invention to be worth more once it becomes protected by IPR. The value of patents relates to both productive effects (exclusion of competitors and/or trade in technology) and to signaling effects. While a clean separation is not possible this paper improves upon previous efforts in estimating the value associated with pending applications, which is likely to be more reflective of the signaling effect, and the additional value associated with the grant, which is likely to reflect the patent s productive effects. This study finds both effects to be fairly large and important for companies seeking finance from VCs. 24

26 REFERENCES Arora, Ashish, Marco Ceccagnoli and Wesley Cohen, 2008, "R&D and the patent premium". International Journal of Industrial Organization Bessen, J. E Estimates of Firms' Patent Rents from Firm Market Value. Boston, MA: Boston University School of Law Working Paper Series Cockburn, I.M., and M. MacGarvie "Patents, Thickets, and the Financing of Early- Stage Firms: Evidence from the Software Industry". Cambridge, MA:NBER Working Paper No Gambardella, Alfonso, Dietmar Harhoff and Bart Verspagen, 2008, "The value of European patents". European Management Review 5: Gans, J.S., Hsu, D. H. and Stern, S "The Impact of Uncertain Intellectual Property Rights on the Market for Ideas: Evidence from Patent Grant Delays". Management Science 54: Haeussler, C., D. Harhoff, and E. Mueller To be financed or not...- the role of patents for venture capital financing. Mannheim, Germany: ZEW Discussion Paper No Hall, B. H "The Use and Value of Patent Rights". Research Paper. Economic Value of Intellectual Property Forum. Hall, B. H., A. B. Jaffe, and M. Trajtenberg Market Value and Patent Citations.Rand Journal of Economics 36,

27 Hall, B. H. and R. H. Ziedonis The patent paradox revisited: an empirical study of patenting in the U.S. semiconductor industry, RAND Journal of Economics 32, Hall, B. H. and M. Macgarvie The Private Value of Software Patents. Cambridge, MA: NBER Working Paper No , revised Hall, T "Law, Finance, and Venture Capital: The Cost of Capital for High-Tech Firms". Christopher Newport University, Newport News, VA. Harhoff, Dietmar, Frederic Scherer and Katrin Vopel, 2003, "Citations, family size, opposition and the value of patent rights -- evidence from Germany". Research Policy, 32: Hsu, D. H What Do Entrepreneurs Pay for Venture Capital Affiliation? Journal of Finance LIX (4), Hsu, D. H., and K. Lim The Antecedents and Innovation Consequences of Organizational Knowledge Brokering Capability. Working Paper. Hsu, D. H., and R. H. Ziedonis "Patents as Quality Signals for Entrepreneurial Ventures". Academy of Management Best Paper Proceedings Macher, J. and C.S. Boerner Experience and Scale and Scope Economies: Tradeoffs and Performance in Development, Strategic Management Journal, 27: Mann, R. J., and T. W. Sager "Patents, Venture Capital, and Software Startups". Research Policy 36, Pakes, Ariel, 1986, "Patents as options: Some estimates of the value of holding European patent stocks". Econometrica, 54:

28 Putnam, J The Value of International Patent Protection, Ph.D. Thesis. Yale. Quillen Jr., C.D., and O.H. Webster "Continuing Patent Applications and Performance of te U.S. Patent Office". Federal Circuit Bar Journal, 11: Schankerman, Mark and Ariel Pakes, 1986, "Estimates of the value of patent rights in European countries during the Post-1950 period". Economic Journal, 97: Serrano, Carlos, 2005, "The market for intellectual property: Evidence from the transfer of patents". Unpublished manuscript. University of Toronto, Shapiro, C Prior User Rights. American Economic Review 96,

29 APPENDIX A RESULTS OF WEIGHTED REGRESSIONS Appendix A, Table 1 Construction of Sampling Weights according to companies types Sample with 2 observations or more Complete dataset Type # of companies # of observations % out of total number of observations # of companies % out of total number of companies Weight for each observation in the sample Acquired % % 0.48 Ongoing % % 1.41 IPO % % 0.50 Ceased % % 2.74 Merged % % 0.78 Total % % 28

30 Appendix A, Table 2 Valuation Fixed-Effects Weighted OLS Regression IPO Round 29

31 Appendix A, Table 3 Valuation Fixed-Effects Weighted OLS Regression with Sector Dummies IPO Round Observations R-squared Number of id 30

32 Appendix A, Table 4 Separate Valuation FE Weighted OLS Regressions for the Software and Non-Software Sectors Dependent Variable: Log Valuation IPO Round 31

33 Appendix A, Table 5 Valuation FE Weighted OLS Regression with Age Dummies, Non-Software Sector Dependent Variable: Log Valuation IPO Round 32

34 Table 6 Weighted Monetary Impact of Patents on Valuations in millions of USD Table 2: entire sample coefficient on pending coefficient on granted Impact of pending on mean Impact of granted on mean Impact of pending on median Impact of granted on median Table 3: entire sample non-software dummy Table 4: non-software companies Table 5: "Young" nonsoftware companies Table 5: "Older" nonsoftware companies Table 7 Weighted Monetary estimates of the impact of Grant on s, millions of USD Impact of pending on mean PV impact of granted on mean Impact of Grant on mean Impact of pending on median PV impact of granted on median Impact of Grant on median Table 2: entire sample Table 3: entire sample non-software dummy Table 4: non-software companies Table 5: "Young" nonsoftware companies Table 5: "Older" nonsoftware companies

35 APPENDIX B Appendix B, Table 1 Results of the Non-Linear LS Estimation of Innovation Depreciation Dependent Variable: Log Valuation IPO Round 34