Martin F, 1998 Economic Impact University R&D

Ž .Research Policy 27 1998 677–687

The economic impact of Canadian university R&D

Fernand Martin )

Faculte des arts et des sciences, Departement de sciences economiques, UniÕersite de Montreal, C.P. 6128, succ. Centre-Õille, Montreal,´ ´ ´ ´ ´ ´Quebec H3C 3J7, Canada´

Received 21 July 1997; revised 1 October 1997; accepted 24 June 1998

Abstract

Canadian university research expenditures and those of graduate students produce a gross static economic impact uponŽ .the Canadian gross domestic product GDP which considerably overestimates the net static impact of university research.

Moreover, the net static impact, because it keeps constant the economic structure and the productivity of factors ofproduction, does not account for the principal beneficial impact of university research—the building of human capital and

Ž .the transfer of knowledge technology . All of this leads to an increase in productivity and, thus, in the size of the GDP. Thiseffect is called the dynamic impact. This article, besides comparing succinctly the gross impact with the net static impact,develops a practical method to measure the dynamic impact of university research. In a way, this method provides anotherbenchmark, besides the peer review, to judge the relevance of university R&D. q 1998 Elsevier Science B.V. All rightsreserved.

Keywords: R&D; University; Impact; Growth theory

1. Introduction

There is a large literature on the economicimpact 1 of universities, either at the regional levelor at the national level, which establishes the contri-bution of university R&D to the Gross Domestic

) Tel.: q1-514-343-7216; Fax: q1-514-343-7221; E-mail:[email protected]

1 Economic impact studies should be distinguished from cost–Ž .benefit analyses CBA . An economic impact study determines the

change in the composition of the GDP that can be traced to aproject, but where the project does not necessarily increase theGDP in real terms. Furthermore, it does not deal with efficiency.Only a CBA, using shadow prices, the consumer surplus andincorporating externalities, can determine the efficiency of a pro-ject and compute a social rate of return.

Product GDP. 2 The purpose of these studies is tojustify government support for universities. Yet,

Žmany of these studies or consulting reports commis-.sioned by universities themselves are in part inade-

quate or incomplete. First, their computations usuallyoverstate the static economic impact of universityresearch activities, mainly because the static multi-plier assumes a world where there are no constraintsin terms of primary resources, price changes, etc.Ž .Poole, 1993, p. 8 . Second, some of the studies lackor do not integrate correctly the dynamic impact,which is the principal impact of university activities,

2 There is also a literature that evaluates university R&D by thejudgment of peers, i.e., through the number of articles, citations,etc. This article supplements these evaluation methods.

0048-7333r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved.Ž .PII: S0048-7333 98 00083-3

( )F. MartinrResearch Policy 27 1998 677–687678

especially R&D. The purpose of this article is toclear up some misunderstandings about the signifi-cance of the studies that limit themselves to the staticapproach, and more importantly to develop a practi-cal method to measure the dynamic 3 impact ofuniversity activities, especially R&D.

2. Static and dynamic approaches for measuringthe economic impact of university R&D

As alluded to above, there are two main ap-proaches for measuring the economic impact of uni-

Ž .versity activities: i the static approach which isŽbased upon simulations through an input–output I–

. 4 Ž .O model or a crude regional multiplier, and iithe dynamic approach which corresponds to the shareof university research in the real increase in GDP

Žimputable to the generation of knowledge technol-. 5ogy .

2.1. The static approach to measure the economicimpact of uniÕersity R&D

The static impact is obtained by feeding an I–Omodel, representing the country’s economy, withuniversity R&D expenditures. In its ‘gross’ version,all expenditures are considered new. Furthermore,the results are sometimes ‘enriched’ by the inducedeffect which accounts for the respending of earnedincomes. But, as is well known, the ‘gross’ eco-nomic impact, to which many studies limit them-selves, is an overstatement because it includes a lotof substitution effects. In the case of universityR&D impact studies, the substitution effects consistof subsistence expenditures of graduate students, the

3 Although a better measure of the economic impact of univer-sity research, the ‘dynamic’ impact must still, as will be seenbelow, be supplemented by a CBA for the determination ofsubsidies and policies regarding university research.

4 Note that most of the existing computable general equilibriumŽ .models are also static Ben Ali and Martens, 1993, p. 110 .

5 Ž .Nelson and Romer 1996 define scientific knowledge as theŽknow-how embodied either in software or in ‘wetware’ human

.capital which is not patentable, and technology as the softwarewhich is patentable. This applies to both internal and externalsources of knowledge, tacit knowledge, etc.

Žabsence of foregone supplementary expenditures thesupplementary disposable income graduate studentswould have earned over the one attainable by a firstuniversity degree, minus the subsistence level of

.expenditures just mentioned , the negative impact ofŽgovernment subsidies governments must tax or can-

.cel projects to finance university subsidies , andfinally ‘induced’ effects which are too uncertain tobe included.

In the case of Canadian university R&D, the netŽ . 6the ‘gross’ minus overstatements static annualeconomic impact, $1.5 billion, is only one third ofthe ‘gross’ economic impact.

2.2. The concept of the dynamic economic impact ofuniÕersity R&D

Not only do universities have a static economicimpact like other economic agents, but through theirgraduates and the research of their star professors,

Ž .they with other economic agents also have a dy-namic 7 impact upon the size and sources of acountry’s GDP.

This benefit to society has been historically andempirically recognized. Historically, especially in theUnited States, a large number of state universitieswere created for utilitarian purposes. Later, somepublic and private universities gave rise to well-known scientific-industrial complexes, e.g., SiliconValley, the Research Triangle, and Route 128. To-day, because of the acknowledged role they playedin the definitive success of physical and natural

Ž .sciences e.g., biotechnology and computing , andthe present movement toward the knowledge econ-omy, these universities may be so much in demandthat they may have difficulties in maintaining theirliberal education tradition.

6 Ž .Source: Martin 1997 . Yet, even in its ‘net’ version, thenature of the static multiplier stays the same; a positive impactdoes not necessarily mean an increase in a country’s welfare.

7 ‘Dynamic’ is defined here in opposition to ‘static’. In thestatic approach, university R&D expenditures do not change thecoefficients of the I–O table. But through the education andknowledge that universities provide, they affect the productivity ofthe factors of production. Eventually, this modifies the coeffi-cients of the I–O table. That is the ‘dynamic economic impact’, orthe retroactive effect of university research upon the economy.

( )F. MartinrResearch Policy 27 1998 677–687 679

Empirically, the ‘usefulness’ of university re-search is first measured by the fact that in somecountries, e.g., Canada, universities do 30% of allresearch with a portion of it financed by industry.

Ž . Ž .Second, studies by Jaffe 1989 p. 968 , Berman´Ž . Ž . Ž .1990 , Acs et al. 1992 and others show that: iuniversity research has important spillovers in terms

Ž .of innovations; ii it stimulates industrial R&D; andŽ .iii these effects are localized near universities. Arecent US compilation reinforces this opinion byshowing that 53% of scientific papers cited on indus-trial patents have a university source; the percentagegoes up to 73% for all publicly funded researchŽ .Narin et al., 1997 . Though not strictly comparable,this is much more than what previous research hadfound. No wonder then that university R&D is now

wviewed as an endogenous input to growth StephanŽ . x1996 gives five reasons why this is so .

2.3. Measuring the dynamic impact of uniÕersityR&D

The measurement of the dynamic impact of uni-versity R&D is based upon aggregate data 8 pro-duced largely by the neo-classical growth theory. 9

This credible source furnishes, among other things,Ž . 10an estimate of the total factor productivity TFP ,

an expression of the Solow residual which resultsfrom known and less known factors.

Yet, somehow, this surplus can be traced toŽchanges in knowledge or in R&D capital Bernstein,

1996; Organisation de Cooperation et de Developpe-´ ´´ment Economique, 1992, p. 187; Bayoumi et al.,

.1996, pp. 12 and 20 . This is so because it is

8 Especially the work done on the G-7 countries by the Organi-´ Ž .sation de Cooperation et de Developpement Economique 1996 .´ ´

ŽThis is different from data obtained by a sample of firms Mans-. Ž .field, 1991 or by interviews Faulkner and Senker, 1995 . Yet,

the experimental data provided by the authors just mentioned anda host of others are congruent with the aggregate approach of thispaper.

9 Our computations also appeal to a variant of the new growthŽ .theory produced by Bayoumi et al. 1996 .

10 There is no denying that the quantification of the TFP, and itsallocation among various sources, is difficult. Consequently, ourresults only correspond to what econometrics can offer at themoment.

possible to link R&D expenditures to increases inŽ .knowledge or technology , then to increases in pro-

Ž .ductivity Bernstein, 1996, p. 391 , and finally tochanges in GDP. 11 This linear research model corre-sponds to the product life cycle approach or to thetechnological trajectory. 12 But since we do not needto distinguish basic research from applied research, 13

our computations are also compatible with today’sinterlocking research continuum where the phasesare interwoven, blurred, etc. This is because so-called‘basic research’ is now directed work in solvingquestions arising late in a product-development cycleŽ .Vandendorpe, 1997 . In turn, this multiplies thechannels used by researchers of innovative firms toaccess scientific and technological inputs providedby university and other public research centersŽ .Faulkner and Senker, 1995, pp. 90–91 .

From the aggregate point of view, the pressure toŽ .produce new knowledge technology comes from

within the economic system. This is so because ofimperfect competition, where firms must continu-ously generate or have access to new knowledge to

Žreestablish their competitive position Stephan, 1996;.Romer, 1994, p. 13 , and also because knowledge

depreciates with the passage of time. 14

Whatever the model of innovation used, the roleof R&D expenditures can be captured by assimilat-ing ‘R&D capital to a factor of production and as aspillover source in determining output and productiv-

11 These relationships are pretty well assured. See for instanceŽ .Fagerberg 1994 who mentions 30 studies that relate GDP per

capita to a set of variables including innovations. Yet, although itis expected that an increase in productivity will increase GDP,there are simulations which make room for at least an initial dropin GDP following the rapid introduction of ‘general purpose

Ž .technologies’ such as computers Howitt and Aghion, 1997 . ForŽ .an historical perspective, see David 1990 .

12 The linear model of innovation is still prevalent in someŽindustries such as the pharmaceutical industry Faulkner and

.Senker, 1995, p. 211; Vandendorpe, 1997 .13 Ž .As do Nelson and Romer, 1996, pp. 17 and 19 who feel that

it is not only difficult to separate these two notions, but also that itis less necessary to do so today.

14 Since our goal is to disaggregate the GDP already produced,this article does not deal with the adjustment mechanisms orprocesses at work. Similarly, we do not have to examine in detailthe links between industry and universities as do Faulkner and

Ž .Senker 1995 .


Ž .ity growth’ Bernstein, 1996, p. 393 . Total factorŽ .productivity TFP growth corresponds, then, to:

DTFPs r y1 a lqb mqd k qu qmrqc s.Ž .Ž .g t151Ž .

This equation shows that with constant returns toscale, a plausible hypothesis in the case of CanadaŽ .Bernstein, 1996, p. 403 ,

DTFPsu qmrqc s. 2Ž .t

DTFP is consequently a function of various sourcesof knowledge or technology. 16

The sources of the stock of knowledge, R, avail-able locally are:

Rs f D ,T , I 17 3Ž . Ž .s

15 Ž . Ž .Source: adapted from Bernstein 1996 p. 395 where DTFPschange in total factor productivity. r san index of returns toscale, e.g., r s1 means constant returns to scale. a ß,d s1

production elasticities of rates of growth of factors of production:l,m,ks factors of production: labour, intermediate inputs andphysical capital. u s the time trend that represents the rate oftechnological change that is not immediately attributable to R&D

Ž .capital and spillovers Bernstein, 1996, p. 409, footnote 4 . r,ssrates of growth of scientific capital and its spillovers, m and c

their production elasticities.16 Ž .Although u is a separate variable in Eq. 1 , in the long run

Ž .its role though circumspect Bernstein, 1996 is not divorced fromknowledge. Indeed, trend productivity can be defined as ‘thecapacity to absorb new technology and production practices devel-

Ž .oped and used elsewhere, . . . and . . . changes in the quality ofŽ . Žinputs, . . . such . . . as better educated workers’ Economic

.Council of Canada, 1992, p. 22 . But education, experience orŽtrend productivity are a function of R&D activity past and

.present since you need to be a competent and involved researcherto ‘understand, interpret and appraise knowledge that has been put

Ž Ž . .on the shelf’ Rosenberg cited by Pavitt 1991 , p. 112 , whetherŽyou want to innovate or simply imitate Fagerberg, 1994, p.

.1161 . So that in a fundamental sense, all economic growth, evengrowth that is directly caused by capital accumulation, can ulti-

Žmately be attributed to technological change Nelson and Romer,.1996, p. 14 . That means that ‘without knowledge, there would be

Ž .none of the other things’ Lipsey and Carlaw, 1996, p. 256 . Thisis why DTFP can be attributed to the production of knowledge.Admittedly, this conclusion is due to the choice of the variable

Žknowledge which was chosen to play the role of the residual i.e.,the variable that accounts for the increase in productivity notdirectly attributable to the other factors of production in a given

.function . But, as said above, this choice is logical given today’sparadigm where growth is said to be increasingly driven byknowledge generation.

And the undepreciated direct local contribution tothe stock of knowledge is:

y118Ds R&D 1yd 4Ž . Ž .Ž .Ý j

yn

2.4. The nature of the link between R&D and GDP

The link between R&D and GDP has two charac-teristics.

Ž .1 R&D is a generic term that covers the contri-bution of the chain of R&D activities leading to newproducts or processes, whether the innovation pro-

Žcess is linear basic research is followed by applied.research or an interlocking continuum. Here,

spillovers from one firm to another, or from oneindustrial sector to another 19 are automatically in-corporated since we are dealing with all R&D ex-penditures.

Ž . Ž2 The contribution of R&D expenditures later.measured by their rate of return , is governed by the

facilitating structure 20 so that what is attributed to

17 Rsstock of knowledge available locally. Dsdomestic stockof knowledge accumulated through previous R&D expendituresŽcontributors to this stock include private, government and univer-

.sity R&D . T sstock of knowledge obtained from abroad byinternational trade or multinational corporations; I sspillovers ofs

Žinternational scientific capital obtained by means other than.trade . The above are the three components of the IMF’S MULTI-

Ž .MOD model that Bayoumi et al. 1996 use to quantify the impactof R&D expenditures.

18 Ýy1 R&D sdomestic R&D expenditure of country j doneyn j

from year y n to y1. ds rate of obsolescence of knowledge.19 So that even if only a few Canadian manufacturing firms do

Žmuch of the research, they have an impact measured by their.spillovers across the whole economy. For instance, the non-

manufacturing sector is a heavy user of innovations produced byŽ .the manufacturing sector Mohnen, 1994, p. 19 . Furthermore, Eq.

Ž .3 shows that the source of knowledge is not exclusively local.20 The facilitating structure or ‘milieu’ includes variables at the

Žfirm, industry and economy levels Lipsey and Carlaw, 1996, 256.ff pp. , such as the financial and institutional organization of the

production unit, the degree of concentration of the industry, publicsector institutions such as private property, infrastructures, demo-graphics, regulations concerning labour markets, international trade

Žregulations, the quality of the labour force, etc. see also Organisa-´tion de Cooperation et de Developpement Economique, 1996, p.´ ´

.34 . All this corresponds to unobserved factors. Many of theseŽfactors explain the Solow paradox Organisation de Cooperation´

´ . Žet de Developpement Economique, 1996 . Grossman and Help-´.man, 1994, p. 31 also acknowledge the role of a ‘facilitator’.


R&D supposes andror includes the indirect contri-bution of the ‘milieu’. Consequently, the effective-ness of R&D expenditures is maintained only solong as the facilitating structure keeps pace with thelevel of R&D.

3. The quantification of the contribution of do-mestic R&D expenditures

ŽThe quantification of the contribution in an ac-counting sense, see Organisation de Cooperation et´

´ .de Developpement Economique, 1996, p. 29 of´domestic R&D expenditures to the Canadian GDP isbased upon the model presented in Section 2 aboveand upon OECD computations. The procedure be-gins by allocating a portion of the average rate of

Ž .growth period 1971 to 1993 of the Canadian GDPŽ .to the TFP. Then, according to Eq. 3 , it allocates

the TFP to its contributors: domestic R&D, foreignR&D and foreign trade.

3.1. The importance of TFP in Canadian GDP

With the average growth rate of GDP at 3.25%Ž . Žin real terms and TFP being 20% of DGDP the

.rest being allocated to L and K , the contribution ofTFP or knowledge to the Canadian GDP of 1993 is

Ž . 21$73 billion in current Can$ .

3.2. Allocating to domestic R&D a portion of thecontribution of TFP to the growth of GDP

The further disaggregation of the GDP growthdue to various sources of knowledge into its compo-nents presents even more problems, but a consider-able amount of econometric work has already been

Ž .done: i OECD suggests that 42% of the CanadianTFP originates outside Canada for the 1980sŽOrganisation de Cooperation et de Developpement´ ´´ .Economique, 1996, graph 2.6 . Presumably, that

Ž .covers foreign R&D spillovers and trade. ii Bay-

21 More precisely: $73.085304 billion. See Appendix A forcomputations. This is a minimum; with the Divisia method thecontribution could reach $91.36 billion.

Ž . Ž . Žoumi et al. 1996 Table 3 propose for all indus-.trial countries 21% of TFP originating in foreign

Žcountries with a negligible contribution from for-. Ž . Ž . Ž .eign trade . iii Mohnen 1994 p. 25 mentions

25% to 65% as the contribution of foreign countriesto TFP.

Missing is the contribution of Canadian R&D toforeign countries or Canadian R&D done in foreigncountries. This is an asset since intra-firm transac-tions over national borders begets reciprocity. Fur-thermore, let us note that the above percentages,arrived at through direct R&D expenditures, do notgive full credit to the fundamental role of domesticR&D since, as said before, foreign R&D becomes

Ž .usable albeit by incurring transaction costs only aslong as there exists competent local researchersthemselves engaged in R&D activities and which

Žare at proximity Grossman and Helpman, 1994, p..39 . Otherwise, the spillovers do not materialize. In

the knowledge sphere, they remain a mass of innocu-ous bits of information, and the potential of newmachines is not fully perceived or exploited.

For all of these reasons, we recognize the criticalrole of domestic R&D by setting the foreign contri-bution in R&D to the Canadian TFP at only 29%Žthe average of the values of two of the sources

.mentioned above plus the minimum of Mohnen plusŽ .2% for foreign trade Bayoumi et al., 1996 , for a

total of 31%. The portion of the difference in Cana-dian GDP between 1971 and 1993 that should beallocated to Canadian R&D is consequently $50billion. 22

In other words, $50 billion of the Canadian GDPŽ .for 1993 $712.9 billion at market prices is derived

from knowledge produced in Canada. For manypeople, attributing 7% of Canadian GDP for 1993 tolocal knowledge is exaggerated. They hold ‘that theresources spent on commercial research and develop-ment are too small for business-generated technolog-ical improvements to be the driving force behind

Ž .growth’ Grossman and Helpman, 1994, p. 31 . Inthe case of Canada, the objection is how can 1.5% ofGDP spent on R&D accounts for 7% of GDP in-crease GDP?

22 That is, 73.085304=0.69s50.428859 in current Can$.


The answer lies in the recourse to the social rateof return of R&D which includes the return toinnovators and the economic impact on users, thoseaffected through spillovers, and a host of unknownindirectly affected beneficiaries.

There is an abundant literature 23 measuring thesocial rate of return by country, by group of indus-tries and by group of firms, etc. Figures vary consid-erably, but a rate of return of 30% seems a reason-able approximation. It also seems to fit the Canadiancase since a simulation involving a yearly R&Dinvestment of 1.5% of GDP starting in 1971, a socialrate of return of 30% and a useful life of 7 years for

Ž .innovations, makes the GDP grow in real terms upto 1993, at a rate of 3.25%, which is exactly whathas happened. Note that the social rate that connectsthe initial R&D expenditures to the eventual GDPincrease, takes for granted the contribution of re-

Žsources provided by the ‘milieu’ stimulated by R&D.expenditures , i.e., users, those affected by spillovers

and the rest of the economy. R&D expenditures arethen seen as the catalyst to growth. Provided that itssocial rate of return holds, 24 the R&DrGDP ratiocan be used as a benchmark against which individualfirms or countries compare their own level of R&Dexpenditures and eventually make decisions. 25 It isthen part and parcel of the decision-making process.Yet, in no way is it implied that by simply changingthe level of R&D expenditures will sales or the GDPchange by the benchmark multiplier, since the im-pact depends upon an a priori unknown social rate ofreturn. 26

23 Ž . Ž .For example, Mansfield et al. 1977 , Mohnen 1994 , Bern-Ž .stein 1996 , etc.

24 Being an average measure of impact, the ratio covers bothsuccessful and unsuccessful R&D projects. Thus, the multiplierderived from the ratio cannot be used for a single project.

25 In many industries, there is an R&Drsales ratio common toalmost all the firms of the industry; e.g., in computer communica-tion equipment it is 12.1%, in pharmaceutical 11.6%, in aircraft3.4%. The ratios of R&D to value added are even higher, respec-

Ž .tively 21%, 16.9%, 18.5%. Source: Schonfield et al. 1996 . It isthe value added that is compatible with GDP.

26 Ž .This is illustrated by the simulation of Bayoumi et al. 1996Ž .Table 1 , where, for a sustained increase of 1r2 of 1% in the

ŽR&DrGDP ratio for the US, there follows an increase compared.to the baseline of 4.2% of GDP after 15 years and of 7.1% after

35 years. They implicitly assume that the previous social rate ofreturn prevails during the simulation.

4. Quantifying the contribution of university R&D

Having established that $50 billion of the 1993GDP can be imputed to Canadian generated knowl-edge, the task is now to allocate a portion of thisamount to university R&D.

ŽBecause there are others contenders private and.government laboratories , the usefulness of univer-

sity research must first be established, at least inŽ .general terms since: i few academic results, directly

Ž .and immediately, contribute to innovation, and iiuniversity R & D produces by-and-large non-ap-

Žpropriable knowledge, e.g., new ideas Howitt, 1996,.pp. 15–16 . No wonder then that this contribution is

Ž .labelled ‘minor’ Faulkner and Senker, 1995, p. 206 .At the same time university R&D is highly valued

Ž .and even considered important by: i lobbyists ofthe business sector who urge the government togenerously finance university research, especially the

Ž . Ž .risky and exploratory kind Vandendorpe, 1997 ; iithe business sector which finances 31.3% of univer-

Ž .sity research in Quebec, Canada in 1993–1994 ;Ž .and iii the innovation studies that ‘confirm that

academic and government laboratories make a signif-icant knowledge contribution to innovation’Ž .Faulkner and Senker, 1995, p. 41 .

ŽIndeed, universities make knowledge basic and.applied available to the Canadian economy, thus

Ž .increasing its productivity by: i ameliorating theŽ . 27 Ž .supply of human capital graduate students ; ii

Ž . Ž .their own research fundamental or applied ; and iiithe consulting activities of their teachers and star

Ž .researchers Mansfield and Lee, 1996, p. 1056 . Bydoing so they attract, retain and generate high-techindustries.

Second, the quantification per se uses the twochannels that connect university R&D with the GDP:Ž . Ž1 The enhanced productivity of human capital the

27 ŽAs is often said, the ‘primary role . . . according to Martin.and Irvine, 1981 . . . of universities is in the education of qualified

Ž .scientists’ Faulkner and Senker, 1995, p. 20 . This is echoed byŽ . Ž .Nelson and Romer 1996 pp. 19–20 and by Vandendorpe

Ž .1997 . Yet, this does not confine universities to the classroomsince teaching and research are not separate activities. Indeed,highly trained graduates cannot be developed without the facultyitself being highly involved in research.


. 28differential income of university graduates . Thisdifferential output 29 is part of the $50 billion of

Ž .GDP imputed to Canadian knowledge. 2 The en-hanced productivity of the rest of the economy: firmsthat finance a good portion of university research,spin-offs and firms that benefit freely from spillovers.

4.1. Measurement of the contribution of uniÕersityR&D through the enhanced productiÕity of humancapital

The enhanced productivity of university graduatesŽ . Ž . 30higher degrees is put at $7.7 billion Can$ .However, this amount cannot be entirely credited to

Žuniversities since this increase in productivity due to.the generation and transfer of knowledge is the

result of two contributing factors:Ž .a students who contribute tuition fees, subsis-

Žtence expenditures and foregone income for those.that study full time ;

Ž .b university expenditures.By weighting the expenditures of each contributingfactor, the share of universities amounts to 35%. 31

That means that the contribution of universityR&D through the differential productivity of theirgraduates to the Canadian GDP is:

$7.7=0.35s$2.7 billion Can$ .Ž .

4.2. Measurement of the contribution of uniÕersityR&D through the enhanced productiÕity of otherfactors of production

From Section 3.2, the portion of the CanadianGDP derived from access to knowledge produced in

28 Here we are only considering the added productivity stem-ming from university research, i.e., the difference between the

Ž .output income of graduates with higher degrees and the outputof those with only a first degree. As said before, we suppose thathigher degrees cannot be produced without university researchand adequate facilities. Note that measuring the contribution ofuniversity R&D through the differential income of its graduates

Ž .somewhat underestimates its contribution for two reasons: i thesubstitution of new technologies for old ones requires more

Ž . Ž .education Nelson, cited by Fagerberg, 1994, p. 1153 , ii gradu-Ž .ates higher degrees produce spillovers that ameliorate the pro-

Ž .ductivity of other employees Glaeser, 1996, p. 73 .29 This contribution is net of the necessary remuneration of the

other cooperating inputs.30 See Appendix B.31 See Appendix C.

Canada having been set at $50.4 billion and havingalready allocated $7.7 billion of this output to uni-versity graduates, a reasonable but imperfect basis toallocate the remainder to university research is to useits relative importance in all R&D in Canada, i.e.,30%. 32 The share of university R&D is then:

$42.693711=0.30s$12.808113

4.3. The total contribution of uniÕersity R&D to the( )change in Canadian GDP 1993

Its contribution to the human 2.707302Ž .capital billion Can$

Its contribution to other economic 12.808113Ž .agents billion Can $Ž .Total billion Can$ 15.515415

5. Conclusion

It is commonplace to say that university researchhas the potential to produce breakthrough advancesthat can fundamentally alter economic growth, evenif not all of the research leads to world-changingresults.

This article has shown that the stream of newideas and technologies stemming from universitiestranslates, when its economic impact is measuredthrough the dynamic approach, into an appreciablegrowth in GDP and employment.

32 Strictly, the relative importance of university expenditures is26.36% since direct university R&D is $2.0513 billion, while total

ŽCanadian R&D is $7.7828 billion PPA US$ Organisation de´Cooperation et de Developpement Economique, 1994, pp. 52 and´ ´

.54 . We have marginally increased this percentage to 30% for thesame reasons that we have also marginally augmented the relative

Žimportance of Canadian general R&D expenditures in GDP Sec-.tion 3.2 . One reason is that the less the knowledge is appropri-

Žable, the greater the spillovers i.e., the less the expenditures.approach accounts for the real impact . This is especially the case

of fundamental research where big payoffs are eventually tracedŽ .to such research Murphy, 1996, p. 4 . This is complicated by the

fact that only a very small portion of the basic knowledgeproduced by universities is either patented or patentableŽ .Trajenterberg et al., 1992, p. 18 .


Furthermore, since university research acceleratesat least the development of new products, countriesthat support it secure a favourable position in aknowledge-intensive, globally competitive market-place.

Acknowledgements

This article is based upon a study made for theAssociation of Universities and Colleges of CanadaŽ .A.U.C.C. . The author has benefited from docu-ments and advice provided by Herb O’Heron ofA.U.C.C. and from Ronald Rioux of StatisticsCanada. My colleagues Robert Lacroix, Marcel Da-genais and Francois Vaillancourt have also helpedme. Peter Hanel of the Universite de Sherbrooke and´Pierre Mohnen of UQAM have criticized the paper.Of course, none of the above mentioned people areresponsible for any errors or omissions.

Appendix A. Computation of the portion of theCanadian GDP 1993 accounted for by total factorproductivity

The computations are based upon the average rateof growth of the GDP from 1971 to 1990 compiledby OECD. It is assumed that this rate of growthapplies also up to 1993, thus for 22 years:

1.032522 =286.998 billion Can$ 1986, for theŽyear 1971 s580.04253 billion $ 1986 for the. Žyear 1993 ..

Sources: Organisation de Cooperation et de Develop-´ ´´ Ž . Ž .pement Economique 1996 p. 29 and StatisticsŽ .Canada Cat. 11210XPB, p. 7 .

The increase in GDP from 1971 to 1993 in 1986 $is:

580.04253y286.998s293.04453 billion $ 1986 .Ž .Transformed in $ 1993:

293.04453=1.247 the GDP price index of 1993Žin terms of $ 1986.s365.42652 billion Can$ 1993 .Ž .

ŽSource of price index: Statistics Canada Cat..11210XPB, p. 42 .

The portion allocated to TFP is then:

365.42652=0.20s73.085304 billion $ 1993Ž .

where 0.20 is inferred from graph no. 2.1 asŽTFPrDGDP source: Organisation de Cooperation et´

´ .de Developpement Economique, 1996, p. 29 or´Ž .from a statement on p. 28. ibid. . That means that in

the GDP of 1993, $73.1 billion is attributable to theŽcumulative effect of TFP since 1971. We do not

credit TFP for any influence before 1971; we areconsequently underestimating the influence of tech-

.nological progress .ŽThis is also a minimum Organisation de

´Cooperation et de Developpement Economique,´ ´.1996, p. 28 , since with the Divisia method the

Ž .contribution would be 25% ibid., p. 273 . Thatmeans that the contribution of TFP to GDP could be

Ž .as high as 91.36 billion $ 1993 .

Appendix B. Measurement of the additional pro-ductivity of university graduates

ŽThe additional productivity of graduates higher.degrees is measured by their differential income. InŽCanada source: Statistics Canada, 1994, Cat. 13-

.217 , the average income of a person with a univer-sity degree is $58,691ryear for men, and

Ž$41,730ryear for women, for an average computed.by the author of $51,787ryear. On the other hand,

the average income of those people in the categoryjust below a university degree is $36,711ryear. Theincrease in income due to a university degree is then$15,076ryear.

Ž .The increase considering the working life isgreater for higher and professional degrees than forBA degrees, and for men compared to women. For

Ž . Ž . Žmen used as an index , Hecker 1995 p. 4, Table.1 puts the increase in income of a BA at $4000 less

than the average increase in income for all degrees.For MA degrees, the increase in income is $3000more than the average for all degrees. It is of courseconsiderably more for PhD and professional degrees,but there are fewer people in this category. Theirrelative importance is thus small and as such ne-


glected. Something similar affects the income ofwomen with degrees.

In the absence of more information we put thedifferential of income of MAs, PhDs and profes-

Žsional degrees at $4924 to eventually have round.numbers over the average differential in income due

to a university degree. Thus, the average income ofMAs, PhDs and professional degrees is 36,771qŽ .15,076q4924 s$56,711.

The average income of BA degree holders iscomputed as a residue in the following computations.

Step one: The differential income of all universityŽ .graduates BA q MA q PhD q professionals is:

$15,076 33 =2,419,750 34 s$ 36,479,751,000.Step two: Already the MA, PhD and professional

degrees account for $16,679,500,000. 35 That meansthat the remaining 1,585,775 BA degrees account

Ž .for 36,479,751,000 – 16,679,500,000 , i.e.,19,800,251,000. Then, 19,800,251,000%1,585,775

Ž .yields $12,500 in round numbers per BA degree, oran income of 36,711q12,500s$49,211. 36

A higher degree provides, then, $7500 of yearlyincome more than a BA degree. That represents thenet contribution of graduate studies and at the sametime the R&D knowledge transmitted to these de-gree holders.

For the BA degree, even if a R&D basis is notabsolutely necessary to transmit the required knowl-edge, it remains that without being nourished byresearch BA teaching would stagnate. That means

Žthat this teaching is still to a small extent for.instance, one fourth tributary to research. Indeed,

many teachers teach at both the undergraduate andgraduate levels. Furthermore, undergraduates profit

33 The average increase in income due a university degreeŽ .computed before .

34 According to Statistics Canada, Cat. 93329, 1991, there are inCanada 833,975 MA and higher degrees, and 1,585,775 BAdegrees, for a total of 2,419,750 degree holders.

35 Ž .833,975= 15,076q4924 s$16,679,500,000.36 Ž .The Canadian data seem to give for BA graduates relatively

Ž .higher numbers than the US data as provided by Hecker 1995 . Itis the contrary for MA and PhD degrees. Besides the differentlabor markets, one explanation for the discrepancy is the fact thatHecker dealt with the year 1991, and Stat. Can. with 1993.Because of lack of data, and because our interest is mainlymethodological, our aim is only to produce satisfactory figures.

from better libraries, laboratories, equipment, etc.The ranking of universities testifies to that point.

Consequently, the way to account for the contri-bution of R&D activities to BA degrees is to credituniversities with 25% of the increase in incomeŽ . Žproductivity of a BA degree $12,500 to R&D,

.i.e., $3125 .For Canada, as a whole, the yearly increase in

productivity originating in university graduates isthen:

Ž833,975 graduates with 6,254,812,500.higher degrees =$7500s

Ž1,585,775 graduates with 4,955,546,875.BA degrees only =$3125s

Total 11,210,359,375

But as shown in Section 3.2, approximately 69%of this amount can be imputed to local R&D, since31% of knowledge is from foreign sources. Conse-quently, the change in GDP that can be attributed toa change of knowledge through graduate studentsoriginating in universities is:

$11,210,359,375=0.69s7.73514796875billion Can $ .Ž .

Appendix C. Share of universities in the differen-(tial productivity of university graduates higher

)degrees

The share of universities in the provision of ahigher university degree comes from the costs theyincur compared to the costs supported by the stu-dents themselves. The total cost of a degree is thesum of both sources of costs.

Ž .a The costs incurred by the university for atypical graduate student 37 are as follows.

The average annual cost incurred by the univer-Žsity to train a student of a higher degree a weighted

.average of MA and PhD degrees was, for the year1993–1994, $16,928.

Since the average length of studies for a higherdegree is 5.0118 semesters, and the typical studentuses the university services during 2.25 semesters

37 The figures come from the Universite de Montreal, presum-´ ´Ž .ably an average university see Martin, 1996 .


Ž .per year, the cost non-actualized to the universityis:

$16,928= 5.0118r2.25 s$37,707 per studentŽ .Ž .b The opportunity cost incurred by a student for

Ž .a higher degree: i tuition fees: $1400rsemester=Ž . Ž .5.0118 semesters per student s$7016. ii The op-

portunity cost in terms of lost income is based uponŽ .three hypotheses. 1 As a BA graduate, the student

could have earned $33,000ryear or $2750rmonth,had he not pursued his studies at the MA or PhD

Ž .level. 2 His affiliation with the university wouldhave lasted:

5.0118 semesterss2.227466 years or

2.25 semestersryear

26.729599 months.Ž .3 The student takes either short vacations or worksa small portion of hisrher time while affiliated withthe university, so that only 84.37496% of the periodrepresents lost income. Hisrher opportunity cost isthen: 26.729599 = 0.8437496 = $2750 s $62,021

Ž . Žplus iii supplementary incidental expenditures s. Ž .631 , total: $69,668 c . The share of the university

in the total cost of a higher degree:

37707s0.3511 rounded at 0.35.

37707q69668

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