Estimating the Value of Patent Rights in Australia€¦ · Estimating the Value of Patent Rights in...

Estimating the Value of Patent Rights in Australia∗

Changtao WangSchool of Economics, UNSW, Sydney 2052, Australia

Abstract

This study consolidates the original patent renewal data in Australia, whichis essential for estimating patent values using the patent renewal framework ini-tiated by Pakes and Schankerman (1984). Also, improvements to the model havebeen made to fit the data characteristics and ensure quality of results. Finally,it successfully estimates the value of patent rights in Australia, and finds evi-dence of inter-cohort, international and inter-sector differences in patent values,and structural changes in the patterns of patent values over time among sectors.As a secondary achievement, this study demonstrates a way of estimating thedepreciation rate for intangibles in Australia.

∗I would like to thank Professor Kevin Fox for his helpful suggestions and supports. Errors andomissions are my own.

i

ii Value of patent rights in Australia

PREFACE

Thesis title: The economics of Innovation and intellectual property rightsSupervisors: Professor Kevin FoxInnovation plays a fundamental role in driving economic growth. And the intellectual propertyrights are important in offering firms incentives to innovate. This thesis will sudey the IPRsin different perspectives. First, analyzing the economical significance of patent protectionsin different origins and technology sectors. Second, examining the importance of IPRs at anmacroeconomic level study. And finally, an microeconomic study on the firm’s propensity ofusing formal and informal IPRs. The thesis consists of the following studies:

1. Estimating the value of patent rights in AustraliaThe patent value provides an important economic and policy indicator. One way toachieve the patent value is using the patent renewal framework, which estimates thepatent value based on the patentee’s renewal behavior. The results will yield importantinsights in the international and inter-sectoral difference in patent values, and structuralchanges in patenting activity across different sectors over time.

2. The long-run relationship between IPRs (patents and trademarks) and growthThe new growth theory emphasizes the importance of innovation in stimulating theeconomic growth. There are many advantages of using the IPRs statistic ( patents andtrademarks) over R&D data as an indicator of innovation. The long-run relationshipbetween IPRs statistics and the growth in seven major OECD countries will be explored.

3. The choice between formal and informal intellectual propertyThe survey has revealed that firms use a wide range of means to protect their assets andsecure returns, and that informal methods, such as secrecy are used more often thanformal methods such as patents. The empirical investigation will be carried out to findthe determinants of a firms’s choice of an IP protection strategy (patent or secrecy) inAustralia.

The thesis will take the following structure:Chapter I: IntroductionChapter II: The value of patent rights in AustraliaChapter III: Intellectual Property rights, Innovation and GrowthChapter IV: Patents versus Secrecy: the case of Australian innovative firmsChapter V: ConclusionsThis paper is a part of Chapter II that estimates the value of patent rights in Australia basedon the patentee’s incentive of renewing the patent.

Value of patent rights in Australia 1

1 Introduction

Patenting activity is one of the most commonly used strategies by firms to protect theirinventions. The successful applications are offered with exclusive powers on the use ofthe underlying inventions augmented with additional patent rents, also known as thevalue of patent rights. The patent system has thus been praised as adding “the fuel ofinterest to the fire of genius, in the discovery and production of new and useful things”by the former US president Abraham Lincoln.In the recent decades, the estimation of patent value has attracted more and moreattention by economists because of its usefulness as an economic and policy indicator.As an alternative to the R&D expenditure, many researchers treat the patent countas a proxy for intangible assets by taking the advantage of a much longer time seriesfeatured by the patent data. However, there has been concern of potential bias bysimply using patent count data due to the high inequality in value among patents.And the patent value can be used as the weights in aggregating patent data. As thepatent value to some extent measures the value of successful inventions, it thus providesuseful information on measuring the level of productivity and efficiency of R&D. Asa measure of the additional reward to the inventors by the patent system, it providesimportant information on the strictness and performance of the current policy.The patent renewal framework has been proven to be one of the most effective way ofachieving the patent value, and been employed frequently in the research of estimat-ing the patent value for many different countries, ever since its creation by Pakes andSchankerman (1984). So far, the results from adopting this framework are availablefor many countries at an aggregate level, such as for European countries (Schankermanand Pakes, 1986), USA (Bessen, 2008) and India (Fikkert and Luthria, 1996), or somedisaggregate by nationality of ownership or by technology (Schankerman, 1991, 1998;Deng, 2007).Despite the numerous attentions to the patent value and its estimationusing patent renewal framework, there have not been many studies done in Australia,and the patent renewal approach in particular has not been attempted.1 The lack ofevidence from Australian studies makes it difficult to draw the same picture of theAustralian patent system or make international comparisons, and it is surely necessaryto fill this gap.At first, this study consolidates the original patent renewal data for Australia by the

1Some studies estimate patent value through surveying and/or the market value approach, such asJensen et al. (2011).

2 Value of patent rights in Australia

nationality of ownerships and by technology sectors. The data collection is fairly timeconsuming, but which is compulsory for estimating the value of patent rights. In ad-dition, improvements to the basic patent renewal framework have been made in orderto fit with the characteristics of the Australian data, and ensure the results are able toreveal unique properties for patents in different cohorts (filing year) and groups (originsand sectors). Moreover, the value of patent rights in Australia at disaggregate levelshave been estimated with alternative estimation strategy to previous studies. Further-more, the evidence of structural changes on patent values across different technologysectors over time are found, which provides a useful explanation for disagreement aroseby Schankerman (1998) and Bessen (2008), those studies were based on different patentcohorts that are two decades apart. Finally, this study provides an estimate for thedecay rate as a secondary product. The structure of this study is organized as fol-lows. Some related research is reviewed in Section 2. It is followed by a descriptionof the Australian patent renewal data collected for this study in Section 3. Section 4explains the specifications of various empirical models in detail. The empirical resultsare reported in Section 5 along with the discussions on the implications, and Section 6concludes.

2 Background

The earliest interest in the patent renewal data by the economists can be traced backto Nordhaus (1969) (Lanjouw et al., 1996). The data has become more attractivesince Pakes and Schankerman (1984) initiated the patent renewal model that allowsusing the renewal data (including the shares and costs of patent renewal) to obtain thevalue of patent protection. With some constructive improvements, the updated ver-sion, Schankerman and Pakes (1986), formed the foundation in this area. The patentrenewal framework assumes that patents in every cohort (defined by the year the appli-cation was filed) are endowed with a distribution of initial returns, which are assumedto decay deterministically at an annual rate δ. To keep patents active, the patenteesmust pay an annual renewal fee that usually increases in the age of the patent, and therenewal fee schedule also undergoes frequent adjustments over time. A patentee maxi-mizes the (discounted) net returns to patent protection and pays the renewal fee at aget only if the current return is in excess of the cost. By specifying a functional form forthe distribution for the initial returns (e.g. lognormal), Pakes and Schankerman (1984)


demonstrated that the parameters of the assumed distribution function and the decayrate can be estimated. These estimates provide sufficient information to characterizethe distribution of the value of patent rights and the behavior over time. Limited bythe data, earlier work, including studies by other authors, are only for the three majorEuropean countries and at aggregate levels.The early studies particularly highlight the usefulness of conducting similar studiesat disaggregate levels. This was not carried out until Schankerman (1991), with thepublished version being Schankerman (1998). His research applied French disaggregatedata by nationality of ownership and by technology sector to the patent renewal frame-work, and showed the evidence of inter-sector and international differences in patentvalues. Specifically, he showed patents with the Japanese and French ownership arerelatively more valuable than their counterparts. In addition, the estimates by indus-try indicate that the electronic and mechanical patent values are higher than the otherindustries. Fascinated by the rich implications obtained by Schankerman, researchersshowed considerable interest in the disaggregate studies. Another important work thatof Lanjouw (1998), which focused on German patents in computer and pharmaceuticalsectors. The more recent evidence for the United States by Bessen (2008) finds that thechemical and pharmaceutical patents filed in 1991 are substantially more valuable thanthe electrical and electronic counterparts, which contradicts the results of Schankerman(1998).

3 Data

3.1 Patent renewal rates

Patent renewal data are essential for estimating patent values using the patent renewalframework. There has been a considerable amount of effort expected by the authorin collecting and consolidating the dataset from original data sources. The datasetconsists of two components: patent renewal rates Pjt and patent renewal costs Cjt.The first part is constructed based on the patent count data generated by AusPatfrom Australian Patent Office (APO) official web site.2 The data cover most of thepatent grants in Australia filed in period 1980-1994 and the patent renewals that oc-curred in period 1982-2010.3 The patents are categorized according to the filing year

2AusPat is the online search engine for patents filed in APO.3Refer to appendix table 6 for detailed descriptions of the patent renewal data.


(cohort), the nationality of ownership and technology sector. In addition, the datasetcontains the number of patents granted during each of the years subsequent to thecohort date, and the number of patents dropped out at each age up to the statutorylimit for each cohort-age-nationality and cohort-age-sector cell. The Pjt could there-fore be divided into two groups, which allow estimating patent values across variousnationalities of ownerships and technology sectors, respectively. The proportion ofdropouts at a certain age is simply the number of dropouts in each three-dimensioncohort-age-nationality (cohort-age-sector) cell divided by the number of patent grantsfor the corresponding cohort-nationality (cohort-sector) pair. Under the same cohort-nationality (cohort-sector) pair, this dropout rate at age t is exactly the decrementof the renewal rate from the previous age and the patent renewal rate for each three-dimension cell can be calculated accordingly. The patent renewal rates are describedbelow, followed by the renewal cost.This study focuses on the top six nationalities of ownership in the number of patentgrants in Australia: United States, Australia, Great Britain, Japan, Germany andFrance, which comprise approximately 81 percent of all patent grants in Australia onaverage over period 1980-1994. In fact, there is only a small share (13.5%) of thosebelonging to the residents, whereas the majority is held by foreigners. Americans inparticular take place over one third of the share. Figure 1 shows the average renewalproportions by nationality of ownership in the fifteen-year period. The renewal ratesvary across cohorts and nationalities. It is obvious that the Japanese patents in generalexperienced significantly higher renewal rates at all ages compared with the others. Itis followed by the American and European counterparts, with relatively smaller dispar-ities between them. As the patents approach to the maximum age, there is a tendencyfor convergence of these curves. It can also be observed that the Pjt for the domesti-cally owned patents are among the lowest, especially for the early ages.One of the difficulties for collecting data by technology sectors arises due to the

incompatibility of the different standards used between the patent and industrial clas-sifications. The patents are assigned by the examiners in the patent office to one ofthe categories defined by the International Patent Classification (IPC), which is clas-sified based on the function of the invention, rather than industry criteria. To solvethis problem, the data is consolidated into groups based on the type of technologiesaccording to the MERIT IPC-ISIC (rev. 2) concordance table.4

4Refer to Verspagen et al. (1994).


Figure 1: Patent renewal rates by nationality of ownerships, 1980-1994

Figure 2: Patent renewal rates by technology sectors, 1980-1994

Four of the most heavily patented technology sectors are chemical, pharmaceutical,electronics (excluding computer hardware and software) and electrical machinery, andthe patterns of average renewing proportions in each of these four sectors are shownin figure 2. The renewal rates for the electronics patents are among the highest inthese four sectors except for older ages. They are followed closely by the chemicaland pharmaceutical patents, and the patterns of renewal proportions between thesetwo sectors are very similar. A rather interesting observation is that the renewal ratesfor pharmaceutical patents aged over sixteen years surpass the other sectors. Thereare rather large gaps between the electrical machinery patents and those of the otherthree sectors. In general, the differences of the patterns of proportions are minor ascompared with the groups based on nationalities above, and sometimes the lines in thefigure 2 cross.


Figure 3: Average real patent renewal costs

3.2 Patent renewal costs

The patent renewal fees under different fee schedules are collected from the varies ver-sions of the Patent Regulations Amendments under the Patents Acts (1952 and 1991)published in a series of annual Statutory Rules dating back to 1979. The (nominal)renewal fees within the same fee schedule rise monotonically with age, and the sched-ule has been adjusted sixteen times between 1979 and 2010. However, only the latestschedule is applied to all patents regardless of cohort, nationality or sector. It meansthat the renewal cost depends both on the age and the year that patent reaches thatage (cohort plus age). In term of dimensionality, the renewal costs Cjt are featuredwith 2 dimensions, and they are unique in each cohort-age pair.To deflate the nominal cost, many existing studies followed the initial work by Schanker-man and Pakes (1986) by using the GDP deflator. However, as argued by Diewert(2002), the GDP deflator is unsuitable as a general index of inflation because the tradecomponents (both imports and exports) in the formation of the GDP deflator are likelyto cause fluctuations and inconsistencies, and the Consumer Price Index (CPI) is pre-ferred in this situation. Therefore, the costs are deflated using the CPI and the pricesare measured in constant 2008/09 Australian dollars.For each cohort, there is an unique set of costs depending on the patent age. To presentthe costs over fifteen cohorts effectively, they are broke down into three consecutivefive-year segments, and the graphs of the average of each five-year period are shown


in figure 3. The average real patent renewal costs are quite similar among three pe-riods, although some disparities are observed when the age is large. And the averagereal costs increase over ages ranging from approximately 80 to 600 dollars (A$2008/9).Also, there is roughly a quadratic relationship between the average real renewal costsand the renewal age.

4 METHODOLOGY

The estimation of patent values in this study mainly consists of two procedures, namelyestimation and simulation. The former involves building up the estimation model,such that it features a large sample, compatible dimensionality of the data, and moreimportantly, it is able to capture the unique values of the patents across differentcohorts, origins or sectors. By specifying a distribution function that is parameterizedby θ for the initial returns and initiating a decay rule (with decay rate δ), it allows thepatent renewal model to estimate the parameter (of the distribution function) alongwith the decay rate. With the estimates of the parameter θ and decay rate δ fromthe estimation stage, the distributions of initial returns and the corresponding patentvalues can be drawn by the simulation in the second stage, and the distinctions inpatent values among different cohort-nationality or cohort-sector pairs can be clearlyobserved. The technical detail of the model specification and analysis are discussedbelow.

4.1 Estimation of the patent renewal model

The estimation model in this study has been developed based on the original patentrenewal model initiated by Pakes and Schankerman (1984) and Schankerman and Pakes(1986) with additional improvements, and it is also comparable with the setups inSchankerman (1998). The models in these earlier studies assume that the patenteechooses the optimal number of years, which the patentee wants the granted patent toremain in force, T, in order to maximize the value of the patent rights, V(T). This isformulated as the expected discounted value of the net returns (current return minus


renewal cost)

MaxT∈{1,··· ,T̄}V (T ) =T∑t=1

ρt(Rjt − Cjt), (1)

where Rjt and Cjt respectively denote the real annual return from patent protectionand the real renewal cost of a single patent from cohort j at age t, ρ is the discountfactor, and T̄ is the statutory limit for the patent. The patent is ceased permanentlysoon after discontinuing the payment of the annual renewal fee.Two assumptions have to be made on the distribution of the initial returns Rj0 anddecay rules for Rjt(t > 0) respectively to model the diverse preferences in choosing theoptimal lifespan for the patents and the behavior of the profit flow as the patent ages.With consideration for simplification of the modeling process and approaches utilizedby earlier studies, two assumptions are imposed here. Specifically, (1) the initial returnsRj0 are distributed log-normally and (2) Rjt(t > 0) are assumed to diminish over age ata constant decay rate δ ∈ (0, 1), with the decay rate taken as common across cohorts(although distinct across nationalities and sectors). The lognormal distribution hasbeen formally tested by earlier studies including Schankerman and Pakes (1986), andit is generally believed to provide the best fit for patent value. It has been employedfrequently in the similar research. One may be concerned that the assumption of deter-ministic decay is rather unrealistic. A more sophisticated alternative is the stochasticdecay rule, which is technically involved without guaranteeing better results; see Pakes(1986).Under the constant decay assumption, Rjt can be written as an equation of the initialreturn Rj0 and the decay rate:

Rjt = Rj0

t∏τ=1

(1− δτj) = Rj0(1− δ)t. (2)

In addition, this assumption also implies that Rjt is non-increasing in age t, and giventhe fact that the renewal cost Cjt is monotonically increasing in t, it implies that thesequence {Rjt − Cjt}(t = 1)T̄ is non-increasing in t. Thus, the unique optimal ageT ∗ ∈ {1, 2, · · · , T̄} is the age when Rjt − Cjt switches sign from positive to negative.In the case that Rjt − Cjt remains positive even when the age reaches the statutorylimit, T ∗ = T̄ . Intuitively, there is incentive to renew the patent as long as the currentreturns at least cover the renewal cost. Formally, the renewal criterion for patents incohort j at age t is given by Rjt ≥ Cjt, which can be rewritten as (3) by substituting


(2) and taking the logarithm, where the lower case represents the logarithmic form ofthe associated upper case letter,

rj0 ≥ cjt − t× ln(1− δ). (3)

Moreover, under the first assumption (i.e. Rj0 is log-normally distributed), rj0 followsa normal distribution with mean µj and standard deviation σj, respectively, i.e. rj0 ∼N(µj, σ

2j ). Then (4) is the standardized version of (3) with (rj0 − µj)/σj ∼ N(0, 1):

rj0 − µjσj

≥ cjt − t× ln(1− δ)− µjσj

. (4)

In fact, it can be stated that the proportion of patents in cohort j that are renewedat age t (Pjt) is equal to the probability that renewal condition (4) has been satisfied.Given that (rj0 − µj)/σj ∼ N(0, 1), the statement above can be expressed by,

Pjt = Pr(rj0 − µjσj

≥ cjt − t× ln(1− δ)− µjσj

) = 1− Φ(cjt − t× ln(1− δ)− µj

σj), (5)

where Φ(·) is standard normal cumulative density function (CDF). By inverting (5), itgives the basic patent renewal model as below,

yjt = Φ( − 1)(1− Pjt) =cjt − t× ln(1− δ)− µj

σj, (6)

where yjt = Φ(−1)(1−Pjt) is the inverted standard normal CDF of the renewal propor-tions.Let n denote the nationality, n ∈ N = {AU,US,GB,DE, FR, JP}, where N con-sists of six nations: Australia, the United States, Great Britain, Germany, France andJapan; and the set of technology sectors S consists of electrical machinery (EM), elec-tronics (EL), chemicals (CH) and pharmaceutical (PH), s ∈ S = EM,EL,CH,PH.Furthermore, there are three considerations to be addressed in constructing the mod-els: First, the unique characteristic of data needs to be accommodated. The renewaldata (either by nationality or by sector) has a dimensionality of three (cohort-age-nationality or cohort-age-sector) that is distinct from both the 2-dimension aggregatedata (cohort-age) used by Schankerman and Pakes (1986) and the 4-dimension disag-gregate data (cohort-age-nation- sector) by ?. Secondly, the models are built in such amanner to ensure that the sample size of the data is sufficiently large, which is necessary


for attaining consistent estimators. As stated in appendix table 6, there are only 259observations for each individual nationality or sector. One solution to achieve a largersample is to pool across groups in each case, which can enlarge the sample size by sixand four times respectively. One disadvantage of this pooling strategy is that the decayrate is then an average over groups, which fails to differentiate decay rates betweengroups. In particular, the decay rates are expected to be different between sectors.Fortunately, the evidence from ? shows that those disparities are practically small,and the restriction on the decay rate is unlikely to cause large problems in estimatingthe patent values. For the nationality case, it does not lose generality by holding δ fixedacross nations provided that the technological formations of patents between differentnationalities do not differ too much. The final thought is to maximize the flexibilityof the model specifications to allow unique distributions for each cohort-nationality orcohort-sector groups, and to be able to capture the structural changes between sectorsover time. The pooled models are incorporated with the cohort and nationality (sector)specific dummy variables, which allow inter-cohort, and international (inter-sector) dif-ferences in both µ and σ. In another words, there is a unique lognormal distributionfunction associated with each cohort-nationality or cohort-sector pair. The modifiedversion of the estimation models are formulated respectively as follows,

ynjt =cjt − (µnbase

i +∑

n6=nbaseβnDn +

∑j 6=i βjDj)− t× ln(1− δ)

σnbasei +

∑n6=nbase

γnDn +∑

j 6=i γjDj

+ unjt, (7)

ysjt =cjt − (µsbasei +

∑s 6=sbase βsDs +

∑j 6=i βjDj)− t× ln(1− δ)

σsbasei +∑

s 6=sbase γsDs +∑

j 6=i γjDj

+ usjt, (8)

where nbase ∈ N (sbase ∈ S) is the base nation (sector); i ∈ 1980, · · · , 1994 is thebase cohort; Dj, Dn, Ds are the cohort specific, nationality specific and sector specificdummies; unjt (usjt) is the error terms for each model.Finally, there are still two more econometric issues to be tackled, namely nonlinearityand heteroskedasticity. The estimation models shown in (7) and (8) above are clearlynonlinear in parameters, which could be estimated using nonlinear least squares (NLS).As shown by Davidson and MacKinnon (2004), the NLS estimators of the parametersare consistent with a large sample size. However, they are not efficient when theerror terms unjt (usjt) is heteroskedastic. To account for the heteroskedasticity when theheteroskedastic function is actually unknown, feasible generalized least squares (FGLS)


has been applied (Wooldridge, 2009).5

4.2 Simulation of patent values

The distribution for the initial returns of patent rights R0 in each cohort-nationality(cohort-sector) pair is obtained by 50,000 random draws from the lognormal distri-bution parameterized by the estimates of the associated µ and σ. Given an array of50,000 generated values for R0 and decay rate estimated in the previous step, the valueof patent rights for a single patent in a certain cohort is computed using

V =T ∗∑t=1

ρt[R0(1− δ)t − Ct], (9)

where Ct is the real renewal cost for the patent at age t; T ∗ is the optimal age; ρ isthe discount factor.6 Equation (9) shows that each draw of R0 is associated with avalue of patent rights V , and therefore there has been potentially 50,000 patent valuesgenerated. By following these procedures, the entire distribution of patent values foreach cohort-nationality (cohort-sector) pair is constructed, and the descriptive statisticscan be calculated accordingly.

5 RESULTS

The empirical results for the patent renewal models pooling across nationalities andsectors in the base cohort are shown in tables 1 and 2 respectively, where [1a] in-cludes all the dummy variables defined in (7) or (8); [1b] keeps only those dummieswith statistically significant coefficients and [2] (only for the second case) includesthe cohort-sector interaction terms in addition to (8) in order to capture the possiblestructural changes. With concerns regarding possible the inferior data quality for theearly 1980s, the latest cohort with complete observations (i.e. 1990/1) is chosen asthe base cohort; Australia (AU) and electrical machinery (EM) are used as the basenationality and sector, respectively. Instead of directly showing the estimates of themodels that consist of parameters for the base cohort-nationality (cohort-sector) pairand coefficients on the group specific and cohort specific dummy variables as indicated

5Refer to Chapter 8 Heteroskedasticity, pp283-284.6A commonly assumed value for ρ of 0.9 is used here.


in (7) and (8), only the estimates (and standard errors) of the parameters µ and σ fordifferent groups in cohort 1990/1 are calculated and presented.

5.1 Estimates of patent renewal models

5.1.1 By nationality of ownership

The estimates of coefficients on the nationality specific dummies (β̂n,γ̂n) those are notreported, are both jointly statistically significant (with the p value approximating zero),and individually significant at the 1% significance level. It shows that the parameterµ and σ those determine the patent values, differ dramatically across different originsgiven any cohorts.As for cohort specific dummies, although there is a fraction of the coefficients thatare not individually significant even at 10% significant level, they are jointly signifi-cant at 1% level. T1 ∼ F (14, 1521) tests the joint significance of the fourteen cohortspecific dummies βj in the numerator of (7) that equals to 3.97, as compared with acritical value of 1.70 at the 5% significance level or 2.09 at the 1% level. Similarly,T2 ∼ F (14, 1514) examines the joint significance of the coefficients on the cohort spe-cific dummies γj in the denominator of (4), which is an even larger measure of 5.76.These statistical results provide evidence for inter-cohort differences in the parameterµ and σ, respectively.Since the parameter µ determines the size (mean and median) and σ decides the varia-tion (variance and level of skewness) of the initial return Rj0, and therefore the patentvalues due to model setups, the results imply that the value of the patents on averagetend to be distinct across different origins, and vary over time. Also, the level of vari-ations and skewness for the value distribution is likely to be unique patents in eachnationality and cohort.The estimates of the parameter µ and σ (shown in table 1) for all nationalities, com-puted using the estimates of the bases µ̂AU1991, σ̂AU1991 and of coefficients on the correspond-ing dummies,are statistically significant at 1% significant level in both model [1a] and[1b]. The estimated decay rates measured at average levels (across six origin countries)are 10.1 and 11 percent respectively in two models. They are within the range of theestimates in the existing studies from other countries, which generally lie between 5and 20 percent. Also, among the R&D studies when the Perpetual Inventory Method


is applied to generate the stock of knowledge, the depreciation rate is often assumedat 10-15 percent, which also agrees with the estimated decay rates in this study.For model selection, the Akaike information criterion (AIC) and Bayesian informationcriterion (BIC) are also reported (Akaike, 1974; Schwarz, 1978).7 As seen in table 1,model [1b] is preferred by both criteria (smaller measures of both). Consequently, theresults in [1b] will be applied to simulate patent values in the next stage.

5.1.2 By technology sector

The estimates of parameters for different technology groups shown in table 2 are cal-culated in the same manner as those in table 1. Model [1b] excludes the statisticallyinsignificant cohort specific dummies from [1a] and the excluded dummies are ten co-hort specific dummies γj and four βj. There are twice as many cohorts that are notstatistically different from the base cohort in either parameter µ or σ, as comparedwith the previous case. Despite half of the cohort dummies being excluded, the esti-mated results are reasonably insensitive to the change. Inspired by the possibility ofstructural change over time among sectors as stressed earlier, the interaction terms ofcohort specific and sector specific dummies are added in addition to (8), and only thestatistically significant dummies are retained in model [2].As shown in table 5, the inter-sector differences for the parameters µ and σ are muchsmaller as compared with the nationality case, which are as expected based on thegraphs of the renewal patterns presented in Section 3. The estimated decay rates indifferent models are in the narrow range of 11.3 to 11.4 percent, similar to the results intable 4, although they represent the decay rates in different perspectives: the averageacross four technology sectors.T3 ∼ F (14, 1014) and T4 ∼ F (14, 1014) test for the inter-cohort differences in the pa-rameters µ and σ in (8), respectively. Both T3(5.62) and T4(2.16) are greater than thecritical values at the 1% significance level, showing evidence for inter-cohort differencesin the µ and σ, and therefore the Rj0 as well as the patent values.Unlike the previous case, the two information criteria lead to different considerationsregarding model selection. The AIC clearly prefers model [2], while the BIC (penalizingmore heavily the number of parameters) is in favor of [1b], with of a smaller number ofparameters than [2]. However, the difference in the BIC between these two models isvery tiny. As model [2] is clearly superior in terms of flexibility, it is thus the preferred

7Note that The R2 is not useful when FGLS is used (Wooldridge, 2009).


model.

5.2 Value of patent rights

5.2.1 By nationality of ownership

The descriptive statistics for the value of patent rights by origins in 1990/1 cohort areshown in table 1. There is much evidence of differences in the descriptive statistics anddistributions of patent values.The findings can be summarized into three main points. First, the patents with do-mestic ownerships are generally less valuable than the foreign counterparts based onboth mean and median measures. This result is similar to many studies using thepatent renewal framework. For example, a French study by Schankerman (1998), aUS study by Bessen (2008), and Finnish study by Gronqvist (2009). Second, thereare large disparities in the patent values depending on the nationality of ownership.In particular, the mean patent value held by Japanese patentees are 50-100% morevaluable than those from other origins, which is also as expected after observing simi-lar conclusions from earlier research. i.e. Schankerman (1998) and Bessen (2008) alsofound the superiority of Japanese patents in attracting patent rents, except they hadeven larger disparities. Third, a rather unexpected result is that the mean value ofthe German owned patents in Australia appears to be the lowest among all the foreigngroups. This result seems in conflict with the fact that Germany is indeed one of themost competitive and technologically advanced countries in the world. However, it isconsistent with the conclusion in Schankerman (1998), in which the German ownedpatents appear to be relatively inferior in value among all French patents.Some explanations for these findings are discussed below. First, due to the higher riskand uncertainty involved in the foreign market, a new invention is normally initiatedand examined in the domestic market before marketing overseas. The underlying in-ventions of the associated foreign patents are most likely to be the ones that succeededin the initial stage and relatively more superior than the others, whereas the domes-tically owned patents without the “selection effect” are a mixture of both high andlow qualities. As shown in table 1, the foreign to domestic ratios of the median areabout 2-7 times larger than the same ratios in the top one percentile, indicating thatthe lower mean for the domestic patents is to a large extent attributable to the largershare of the relatively inferior patents.


In addition, differences in patent values by nationality may also arises as a result ofdiverse trade patterns determined by the level of compatibility between the prefer-ences of domestic consumption and the composition of the industries in the foreignnations. Australia has been characterized as a small economy with a limited capacityfor demanding some of the most sophisticated German inventions. As a result, theunderlying inventions of the German patents in Australia are likely under-representingthe average level of all German inventions. Whereas Japanese inventions appear to bemore successful in occupying the Australian market overall.Moreover, the incentives of patenting activities in securing the achievements in techno-logical advances against the competitors generally play an important role in enhancingthe patent values. The Japanese manufactory industries are arguably the most com-petitive in the world throughout the 1980s with exceptionally large market shares overmany countries worldwide. Surveys show that Japanese firms are relatively more eagerfor patents than firms from other developed countries (Hall, 2009).Furthermore, it is possible that the distinct levels of strictness in the patent systemsamong countries play some roles in the patent value differentiation. Despite the factthat Germany has the highest ’capacity for innovation’, the number of German patentsper capita has never been higher than those of comparable countries (Schull, 2011).This is mainly because of the strict patent system in Germany that involves rigorousexamination procedures for all patent applications and for judgments in the case ofinfringements, which leads to a relatively lower quantity and shorter life expectancyfor the German patents. It is expected to cause low patent renewal rates in generalfor the German patents and thus a low estimated average patent value in the researchusing the patent renewal framework.Finally, there is a possibility of overvaluing the foreign patents due to the assumptionof constant decay rates over time regardless of nationality of ownership, which is prob-ably not realistic for the foreign patents. As argued by Bessen (2008), there is sometime delay expected for an invention to enter into the foreign market after selling do-mestically, such that the depreciation rate of profit flow for the foreign patentees maybe not constant. It may be possible to increase the flexibility of the model by takingaccount of the different depreciation rates for the foreign patents before entering theAustralian market that depends on the market structure of the holding country, whichis expected to be a challenging task and beyond the scope of this study.Besides the magnitude, some comments can be made regarding the distribution ofpatent values. The results in this study verify the conclusions made by other studies


evaluating patent value (either through estimating or surveying) that the distributionfor the value of patent rights is highly skewed. This can be seen by the fact that themean of patent values for each nationality of ownership ranges between approximately5-18 times the median. More to the point, the level of skewness for the patent valuedistributions differ substantially between nationalities, with the domestic group beingthe most skewed and the Japanese counterpart being the least skewed. By only ac-counting for patents valued in the top one percent, there is a much smaller differencebetween the domestic and foreign patents, and between the foreign ownerships.

Percentiles Australia USA UK Germany France Japan

25 111 242 209 189 292 93650 506 1,193 1,010 835 1,329 3,74375 3,149 5,783 5,248 4,077 5,843 13,09395 32,068 47,308 42,291 31,174 38,981 71,80499 136,670 180,410 165,420 120,060 132,400 231,650

Mean 9,379 12,332 11,224 8,326 9,498 18,466

Table 1: The value of patent rights (2008/9 A$), by nationality of ownership, 1990/1cohort.

5.2.2 By technology sector

The results by technology sectors in 1991 cohort are shown in table 2. With regard tothe mean, the four sectors are roughly divided into two groups: the pharmaceutical andchemical patents on average receive approximately A$15-18 thousand in patent rents(in A$2008/9) from the patent system, which are more than the other two counterpartsvalued at less than A$12 thousand. Also, these inter-sector differences are relativelysmaller than the international differences discussed previously.Despite the fact that the mean value of electronic patents is only about two thirds ofthe pharmaceutical counterpart, the median value of the former is about fifteen percentmore than the latter, signifying the value distribution for the electronic patents is theleast skewed among four sectors. In contrast, the pharmaceutical patents are top inthe term of skewness. If comparisons are made only on the top one percentile, theelectronics patent has the lowest value among the four sectors, which is less than sixty


percent of the pharmaceutical counterpart. It indicates that the relatively lower meanvalue of the electronics patents is mainly due to the relatively poorer performances ofthe leading patents.The results for the 1990/1 cohort are not necessarily the representative of other cohorts.In other words, there have been structural changes over the sample period, which canbe seen from the mean across different cohorts illustrated in figure 4. The electronicspatents have not always been less value able, the mean for which was up to 80 per-cent higher than the other sectors in the early 1980s, which then experienced a rapiddownturn thereafter. The other three sectors followed opposite trends for most of the1980s. The pharmaceutical patents in particular experienced value growth just afterthe mid-1980s, and remained at the top among four sectors for the rest of the sampleperiod. The electrical machinery sector remained at the bottom in every cohort, asexpected according to the renewal patterns plotted in the data Section.There are many factors contributing to these inter-sector and inter-temporal differencesin the patent values. Two of the rather important ones are thought to be the inventor’slevel of preferences for using patents over other means of protection and the politicalsupport for the patent system.8 The average patent value for each sector is reflectingthe incentives for the patenting activity in that sector, which depend on the simplicityof identifying infringements and the effectiveness of the enforcement. A patent wasan effective means of protection for the electronics industry prior to the early 1980s,when there was a relative lack of competition and the electronic products were onlybased on a few patented ideas. The electronics sector has since experienced significantevolutionary growth and technological progress in the following decades, which has ledto not only an increase in the intra-industry competition, but also substitutability andcomplexity of the electronic products. Nowadays, most electronics are drawn on a largenumber of patented ideas owned by different entities. As a result, there has been areduction in the importance of the individual patent, and more importantly the com-plex interdependent relationships among electronics producers force them to considerother more efficient means of securing returns. On the other hand, it is still possiblenowadays that the pharmaceutical or chemical products are based on only a few oreven a single patent owned by one entity, and the patenting is relatively more efficientthan for electronics because the process of identifying and prosecuting the infringementwill be much easier when it is needed.

8Other factors probably play lesser roles, including the different intensities of protection, the marketsize and trade structure differentiation between sectors.


The other key factor that impacts the inter-sector patent value differences is that thepharmaceutical substances were subject to a five years’ longer term than other cate-gories since mid 1980s. Benefiting from this exclusive political assistance, the phar-maceutical patents remained the most valuable on average since the mid of the 1980s.Although the privilege of a longer statutory limit over other substances plays littlerole for the patent failed to remain in force way before reaching the maximum age, itallows the top pharmaceutical patents to enjoy longer lives and generate extraordinaryreturns. This explains the phenomenon that the value of pharmaceutical patents arehigher in the mean, but lower in the median (as compared with the electronics) as beingmainly attributable to the outstanding performance of the top performing patents.

Percentiles Electrical Machinery Electronics Chemistry Pharmacy

25 285 449 294 30150 1,366 1,997 1,483 1,64675 6,250 7,849 7,123 8,09695 44,225 48,878 53,795 66,77099 165,480 161,060 203,690 273,220

Mean 11,731 11,765 15,066 17,817

Table 2: The value of patent rights (2008/9 A$), by technology sectors, 1990/1 cohort.

Figure 4: The mean patent value for all technology sectors for cohorts 1980-1992.


5.2.3 International Comparisons of patent values

As one of the few studies estimating value of patent rights in Australia, the recent workby Jensen et al. (2011) develops a completely different method using the AustralianInventor survey data. The estimated median patent rent in their study as about onequarter million dollars, which is much larger than the estimates in this study. Twoof the reasons that help in explaining this divergence are discussed below. First, thedifferent approaches being attempted in these two studies are the main cause of dif-ferences. Jensen, Thomson, and Yong’s results are based on expected patent valuesreported by the inventors in the survey, and the estimated premium rate for success-ful applications above those unsuccessful ones. There are possibly some incentives foroverstating the value by the inventors, in which case it could lead to overestimating thepatent premium. Also, as outlined by Jensen, Thomson, and Yong, the inventors hold-ing the granted patents (those are relatively more valuable) are more likely to respondto the survey, than those being rejected. As a consequence, using survey data couldpotentially misrepresent and overestimate the values of all the patents in general.On the other hand, there might be concerns about the bias in the estimated patentvalues caused by the lognormal assumption in this study. In fact, the lognormal isgenerally believed to be well suited to the distribution of patent values, except for theright tail that the latter tend to be even more skewed. By imposing a lognormal struc-ture, it is possible to underestimate the estimates of the top valued patent. However,it can affect the mean, but will have little influence on the median. Hence, the lowermedian value achieved cannot be attributed to this assumption.Also, the patents studies in these two papers are based on different sets of patents andsample periods. The patent values found in their paper are associated with the patentfiled in a mixture of years over a 20 years’ period (1986-2005) and only a few percentof all patents are included, those can hardly be considered as representative of all thepatents. In contrast, over 80% of all patents filed between 1980 and 1992 in Australiaare studied here, and the results contain the estimation of patent values for individualcohorts.To gain further insights into the properties of the patents in Australia, it is informa-tive to make international comparisons with similar studies. The results at either theaggregate or sectoral level from selected studies are shown in table 7. It is observedthat at the aggregate level the mean patent value in Australia is less than two third ofthose European counterparts and only around ten percent of those patents granted in


the US. It is not surprising since it has been proven that the patent values are largelydetermined by the market size (Schankerman and Pakes, 1986), and the AustralianGDP is small relative to the GDP of the major European nations, or about five per-cent of the GDP of the United States.The published research at disaggregate levels is not available until a decade after theinitiation of the patent renewal framework. Table 7 reveals considerable disparities inthe inter-sector ranking orders for the mean patent values across these studies. Forexample, the results for four major sectors in a French study for the 1970 cohort bySchankerman (1998) indicate the top position for the electronic patent, while the chem-ical and pharmaceutical counterparts in the same cohort are much less valuable. Quitethe opposite, the results for the US patents (filed in year 1991) by Bessen (2008) showan inverse ranking, i.e. the chemical and pharmaceutical patents are significantly morehighly valued than the electronics patents. A possible explanation can be the structuralchanges among industries over time as there is a time lag of over two decades betweenthese two subject cohorts. It is reasonable to believe that Australia has been expe-riencing similar situations as these countries over the same period because the majorshare of the Australian granted patents are in fact owned by these nations, thereforethe evidence of structural changes during the 1980s found in this study are somewhathelpful for explaining the divergences among these papers. Being more specific, theFrench results in 1970 by Schankerman are similar to the findings in early 1980s in thisstudy, whereas Bessen’s results are directly comparable with the findings here for thesame period (i.e. 1991). This study has thus successfully linked these earlier studiesby showing that their different conclusions are not necessarily in conflict, but ratherare a function of the structural change that has been identified.

5.3 Equivalent subsidy rate

The patent value alone without taking account of the underlying costs of innovationfails to provide a good measure for the importance of the patenting activity. The betterway to measure the contributions of the patent protections relative to other sources ofreturns to R&D is the “equivalent subsidy rate” (ESR) as suggested by Schankerman(1998). Besides the exclusive institutional rights, the patent system also offers theinventors with complementary patent rents as rewards, which is equivalent to a kind


of R&D cash subsidy.9 As defined by Schankerman, the ESR is simply the ratio ofpatent value to the corresponding R&D expenditure, and it is calculated by dividingthe aggregated estimated value of patents over each sector by the corresponding R&Dexpenditures in that sector. The aggregate annual R&D data at the national levelis directly available from Australian Bureau of Statistics (ABS), whereas the data atindustrial levels are only available occasionally for a few years, and they are classifiedaccording to ASIC that differs from standard (ISIC, rev 2) used here. Fortunately,the concordances can be found in ABS database to unify these industrial standards,although there is no guarantee of exact matching.10 Due to the fact that the R&Ddata mainly cover the domestic innovations, only those patents owned by Australianresidents are considered in the computation of the national level ESR. 11

The computed ESR at the aggregated level in the cohort 1990/1 is approximately0.65%, which indicates that the patent protection has limited effect on returns toR&D, although it is unquestionably that patenting activity is an important source ofprotection. In addition, as shown in figure 5, there has been rapid decrement in theaggregated level ESRs in the first half of the 1980s i.e. halved in just five years, andthe trend remains stable ever since. There seems to be a decrease in the importanceor effectiveness of using patents in the late 1980s as compared with a decade ago.TheESRs in cohort 1990/1 (in table ??) diverge among different sectors. The ESRs forpharmaceutical and chemical patents are quite similar at around 2%, indicating thatthe innovations in these two sectors enjoy significantly higher rates of returns to R&Dfrom the patent system than the other sectors. The lowest ESR has been observed forthe electronics sector that is just one fourth of a percentage point, which indicates therelatively less active in patenting.It is also of interest to see how the ESR in each sector varies over time.12 The resultsshow that there have been various levels of improvements in the ESRs in three out ofthe four sectors, except for the electronics that remains stable over the entire sample

9The cash subsidy required to induce firms to make the same investment in R&D.10Since there is no direct ISIC-ASIC concordance, it is forced to combine the uses of the ISIC-ANZIC

and the ANZIC-ASIC concordances, both of which are available from ABS.11The national aggregated value of patents is calculated by multiplying the estimated mean value

of patents for domestic patents by the number of domestic patent grants. However, the aggregatedpatent value at the sectoral level is less straightforward because the estimated mean patent value ineach sector consists of both domestic and foreign patents. This is solved by multiplying the meanpatent value in each sector by the ratio of the domestic average to the weighted average across allorigins within the same cohort.

12Please refer to the table 8.


period. Surprisingly, despite that the average patent values of electrical machinerieshas always been lower than the electronics counterpart in every cohorts especially inthe early 1980s, the ESR for the former ranges up to twice as large as the latter.Ones may question about whether or not these results are too small to be reasonable.Supports to these findings could be found through studying the innovation surveys andcomparing the results with the similar works. There are a group of innovation surveyssummarized by Hall (2009). One of the questions asks the R&D managers to rankthe importance of means they secure returns to the innovative activity, from a menuthat consists of different types of formal and informal IP.13. These surveys consistentlyshow the facts that most of the firms consider the informal IPs to be more importantthan patents. It thus no surprise to see the results that only a few percentage points ofthe returns to innovation attribute to the patent. Another survey question studies thedifferent preferences of using patents across sectors. The patent is the most preferredby the pharmaceutical sector in most if not all of the nations, followed by the chemicalsector. The electrical machinery occasionally appears in the list.14 And the electronicsdoes not show up in the table, which means there has always been a better choice forthe electronics firms regardless of their nationalities, which is consistent with a lowESR.Several earlier studies (Schankerman and Pakes, 1986; Schankerman, 1998; Lanjouw,1998) calculate the ESRs by dividing the aggregated value of patents from all originsby the aggregated R&D expenditures, both of those are the aggregated national data.These studies obtain the estimates of ESRs for patent cohorts back to 1970 that rangefrom 4 to 35 percent, and with an average of around 18 percent, those are much largerthan those in this study. However, their results are certainly overestimated to a largeextent, since the major share of patent grants in these countries are with foreign owner-ships, while the associated foreign R&D expenditures are not captured by the nationaldata. Bessen (2006) attempts to recalculate these ESRs and concludes that the ESRsfor the European countries in these studies should actually be in the range of 0.2 to2.6 percent. The ESR in the United States is approximately 2.9 percent that is higherthan those for the European nations after the adjustment. It can be concluded thatthe ESRs among these countries are positively associated with market sizes. In fact,the ESRs for Australia in this study are well within the reasonable range suggested by

13They include patents, trademarks, trade secrecy, lead time, superior sales and service and so on14It is partially integrated in other groups in these surveys, including transport equipment and

special machines


Bessen (2006).

Industry (1) (2) (3) (4) ESR

Electrical Machines 188 1.73 252.1 0.74 0.69%Electronics 112 1.03 392.6 0.29 0.27%Chemical 427 5.05 248.7 1.72 2.03%

Pharmaceutical 251 3.51 176.7 1.42 1.99%Aggregate (domestic) 2247 21.1 3264.4 0.69 0.65%

(1) Domestic patent grants. (2) Aggregated Patent value (mil A$08/9).(3) R&D expenditure (million A$2008/9). (4) Patent Intensity (grants/million R&D).

Table 3: The ESR of domestically owned patents and at each sector (1990/1).

Figure 5: The trend of ESRs for the domestically owned patents over cohorts of patents.

6 Conclusion

This study consolidates the patent renewal data in Australian and estimates the valueof patent rights in Australia by nationality of ownership and technology sector, us-ing the improved version of the patent renewal framework pioneered by Pakes andSchankerman.There are several useful findings in this study: First, the patents owned by the domes-tic patentees are generally less valuable than the foreign counterparts. Particularly, the


patents with Japanese ownership outperform the other foreign patents. These findingsare consistent with the similar French and US studies.Second, there is evidence of structural change in the pattern of patent values amongsectors over time, which suggests an useful explanation for the distinct conclusionsmade between the existing studies on the ranking orders of patent values across sec-tors.Third, this study demonstrates another way to achieve the estimate of the deprecationrate for intangible assets. The average decay rate (equivalent to the deprecation rate)for the patent values over time is within the range 11.0 to 11.4 percent, which providesuseful information for the future research whenever the depreciation rate is needed.Moreover, the ESR for the domestically owned patents in Australia at the aggregatelevel is ranged between 0.51 and 1.35 percent, much smaller as compared with the ma-jor countries. And there has been evidence of reduction in the importance of patentssince the early 1980s. The small ESRs achieved here are comparable with the otherstudies.Furthermore, there is evidence of divergence in the levels of preferences for using patentsas means of securing returns to innovative activity among sectors. The ESRs are muchhigher in the pharmaceutical and chemical sectors those consider the patents as theprimary channel of protection.

Beside these findings, the patent values achieved in this study also build up a foun-dation for further research. For example, in the research evaluating the return toinnovation, these patent values can be well applied as one type of weights wheneverthe patent data is used.There are also further improvements or possible extensions that can be attempted inaddition to the work done in this study, including exploring alternative model spec-ifications (in particular, relaxing the assumption of constant decay), bringing in thedeterminants of patent values, consolidating patent renewal data with higher dimen-sionality, and more sophisticated and detailed international comparisons.


References

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Bessen, J. (2006). The value of us patents by owner and patent characteristics. Workingpaper.

Bessen, J. (2008). The value of us patents by owner and patent characteristics. ResearchPolicy 37.

Davidson, R. and J. G. MacKinnon (2004). Econometric Theory and Method. NewYork: Oxford University Press.

Deng, Y. (2007). Private value of european patents. European Economic Reviews 51.

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Fikkert, B. and M. Luthria (1996). Estimates of the private value of patent protection.University of Maryland: Mimeo.

Gronqvist, C. (2009). The private value of patents by patent characteristics: evidencefrom finland. J Tech Transf 34.

Hall, B. H. (2009). The use and value of ip rights. UK IP ministerial forum on theeconomic value of intellectual property.

Jensen, P. H., R. Thomson, and J. Yong (2011). Estimating the patent premium:evidence from the australian inventor survey. Strategic Management Journal 32.

Lanjouw, J., A. Pakes, and J. Putnam (1996). How to count patents and value intel-lectual property: uses of patent renewal and application data. NBER working paperNo. 5741. NBER.

Lanjouw, J. O. (1998). Patent protection in the shadow of infringement: simulationestinations of patent value. The Review of Economic Studies (65), 671–710.

Nordhaus, W. D. (1969). Invention, growth and welfare: a theoretical treatment oftechnological change. Cambridge: M.I.T. Press.


Pakes, A. and M. Schankerman (1984). The rate of obsolescence of patents, researchgestation lags, and the private rate of return to research resources. In Criliches (Ed.),R&D, patent and productivity. Chigago: University of Chicago Press.

Schankerman, M. (1991). The private value of patent rights in france, 1969-87: Anempirical study of patent renewal data. NBER working paper No. 3780. NBER.

Schankerman, M. (1998). How valuable is patent protection? estimates by technologyfield. RAND Journal of Economics 29(1), 77–107.

Schankerman, M. and A. Pakes (1986). Estimates of the value of patent rights ineuropean countries during the post 1950 period. The Economic Journal 96, 1052–1076.

Schull, G. (2011). Germany patent litigation: the rejected cornerstone?, Volume 111.Cohausz & Florack.

Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics 6(2),461–464.

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27

Appendices

Parameters [1a] [1b]

µAU 5.404 (0.071) 5.467 (0.074)µUS 6.074 (0.108) 6.149 (0.110)µGB 5.939 (0.111) 6.006 (0.113)µDE 5.797 (0.086) 5.860 (0.089)µFR 6.160 (0.098) 6.236 (0.101)µJP 6.951 (0.135) 7.076 (0.140)

σAU 2.078 (0.147) 2.149 (0.151)σUS 1.889 (0.106) 1.975 (0.109)σGB 1.918 (0.120) 2.007 (0.124)σDE 1.834 (0.137) 1.914 (0.141)σFR 1.733 (0.131) 1.816 (0.135)σJP 1.609 (0.136) 1.672 (0.142)

δ 0.101 (0.013) 0.110 (0.013)

df 1507 1514T1 : F (14, 1521) 3.97T2 : F (14, 1521) 5.76

AIC -2319.20 -2496.30BIC -2100.06 -2314.58

Table 4: Estimates of the patent renewal model for cohort 1990/1, by nationality ofownership.

28

Parameters [1a] [1b] [2]

µEM 6.204 (0.147) 6.228 (0.139) 6.268 (0.136)µEL 6.545 (0.149) 6.568 (0.142) 6.570 (0.156)µCH 6.369 (0.130) 6.392 (0.141) 6.356 (0.142)µPH 6.353 (0.145) 6.375 (0.141) 6.350 (0.142)

σEM 1.848 (0.151) 1.822 (0.136) 1.889 (0.137)σEL 1.835 (0.151) 1.808 (0.137) 1.741 (0.129))σCH 1.908 (0.159) 1.884 (0.144) 1.958 (0.145)σPH 1.969 (0.164) 1.947 (0.151) 2.026 (0.150)

δ 0.113 (0.016) 0.113 (0.015) 0.114 (0.015)

df 1000 1014 980T1 : F (14, 1014) 5.62T2 : F (14, 1014) 2.16

AIC -2190.10 -2226.00 -2385.83BIC -2007.21 -2112.31 -2109.01

Table 5: Estimates of the patent renewal model for cohort 1990/1, by technology sector.

Data Characteristic

Range of cohorts (patent filing year) 1980-1994Maximum patent age 20 15

Minimum patent age 1980-1989/1990-1994 3/4

Average No. of patent grants/cohorts 13,594Number of cohorts 15Sample size each nation/sector 259

Number of ownership nationalities 6Coverage over total grants (15 years average) 81%

Number of technology sectors 4Coverage over total grants 70%

Domestic share patent grants (15 years average) 0.135

Table 6: The characteristics of the patent renewal data for Australia, 1980-94.

29

Study Cohort Nation Industry Mean Value ($09)

Schankerman and Pakes (1986) 1970UK Aggregate 14,578

France Aggregate 15,191Germany Aggregate 43,645

Schankerman (1998) 1970

France Pharm. 9,843Chemical 11,341Mechanical 34,508Electronics 45,273

Bessen (2008) 1991 USA

Aggregate 111,623Chem. 710,001Pharm. 171,959

Electrical/Electronics 97,759Mechanical 122,855

This study 1985 Australia

Aggregate 8,743Electrical Mach. 7,236

Electronics 14,679Chemical 10,719Pharm. 12,762

This study 1991 Australia

Aggregate 10,760Electrical Mach. 10,558

Electronics 10,589Chemical 13,559Pharm. 16,035

Table 7: The estimated mean patent values from other studies, in 2009 US$.

30

Cohort Tech. field (1) (2) (3) (4) ESR

1981/82

EM 121 0.80 131.0 0.92 0.61%EL 43 0.55 186.8 0.23 0.29%CH 156 1.47 134.7 1.16 1.09%PH 66 0.63 40.6 1.63 1.56%

1984/85

EM 157 0.87 215.6 0.73 0.40%EL 59 0.66 259.6 0.23 0.26%CH 202 1.66 174.2 1.16 0.95%PH 96 0.94 85.5 1.12 1.10%

1986/87

EM 174 1.40 312.3 0.56 0.45%EL 81 1.09 348.0 0.23 0.31%CH 260 3.13 236.5 1.10 1.32%PH 127 1.74 122.5 1.04 1.42%

1988/89

EM 191 1.47 300 0.64 0.49%EL 113 0.88 350.6 0.32 0.25%CH 319 3.08 229.7 1.39 1.34%PH 200 2.18 184.8 1.08 1.18%

1990/91

EM 188 1.73 252.1 0.74 0.69%EL 112 1.03 392.6 0.29 0.26%CH 427 5.05 248.7 1.72 2.03%PH 251 3.51 176.7 1.42 1.99%

(1) Domestic patent grants. (2) Aggregated Patent value (mil A$08/9).(3) R&D expenditure (million A$2008/9). (4) Patent Intensity (grants/million R&D).

Table 8: ESR for each sector in year 1982, 1985, 1987, 1989 and 1991 respectively.

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