David Bronwyn Toole 2000

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.Research Policy 29 2000 497529www.elsevier.nl r locate r econbase

Is public R&D a complement or substitute for private R&D? Areview of the econometric evidence

Paul A. David a,c,1 , Bronwyn H. Hall b,d,e, ) , Andrew A. Toole c,f a All Souls College, Oxford, UK

b Oxford Uni ersity, Oxford, UK c Stanford Uni ersity, Stanford, CA, USA

d Nuffield College, Oxford, UK e Uni ersity of California at Berkeley, Berkeley, CA, USA

f

Stanford Institute for Economic Policy Research, Stanford Uni ersity, Stanford, CA, USA

Abstract

Is public R&D spending complementary and thus additional to private R&D spending, or does it substitute for andtend to crowd out private R& D? Conflicting answers are given to this question. We survey the body of availableeconometric evidence accumulated over the past 35 years. A framework for analysis of the problem is developed to helporganize and summarize the findings of econometric studies based on time series and cross-section data from various levels

.of aggregation laboratory, firm, industry, country . The findings overall are ambivalent and the existing literature as a whole .is subject to the criticism that the nature of the experiment s that the investigators envisage is not adequately specified.

We conclude by offering suggestions for improving future empirical research on this issue. q 2000 Elsevier Science B.V.

All rights reserved.

JEL classification: O30; O38; H40; H54; L10Keywords: R&D; Fiscal policy; Government subsidy; Technology policy

1. Introduction

The opening of the new millennium finds a na-tional public-sector civilian research enterprise whosescale and scope in most of the worlds countriessurpasses that of any previous period of their history.

Among the leading industrial nations this may beseen as the outcome of a long historical process

)

Corresponding author. Department of Economics, Universityof California at Berkeley, 549 Evans- Hall a 3880, Berkeley, CA94720-3880, USA. E-mail: [email protected]

1 Also corresponding author. E-mail:[email protected].

initiated with state patronage during the scientificrevolution of the seventeenth century. But, only sincethe closing decades of the nineteenth century haveorganized research and development activities begunto make appreciable claims upon the productive re-sources of those societies. 2 Since then, however, the

fraction of real gross national product being directedby both private and governmental agencies towardexpanding the base of scientific and technologicalknowledge for non-defense purposes has trended up-

2 . .See, e.g., David 1998a; b , Lenoir 1998 , and sources citedtherein.

0048-7333 r 00r $ - see front matter q 2000 Elsevier Science B.V. All rights reserved. .PII : S0048-7333 99 00087-6


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( )P.A. Da id et al. r Research Policy 29 2000 497529498

wards halting at times, yet never reversing signif-icantly.

Most of the growth in the relative importance of this intangible form of capital accumulation has comewithin the past half-century: even under the stimulusof military preparations during the 1930s, total R&Dexpenditures in countries such as the US, the UK andJapan remained in the range between two-thirds andone-quarter of one percentage point of their respec-tive national product figures. 3 In the aftermath of World War II, the belief that organized research anddevelopment could stimulate economic growth andcontribute to improving economic welfare led to thecreation of many new public institutions supportingcivilian science and engineering, and pushed the civilR&D fraction upwards towards the one percentagepoint level in a growing number of countries. TheCold War Era fostered a further expansion of gov-ernment agency research programs in non-defense aswell as military technologies, and established modelsfor the performance of government-funded R&D byprivate sector contractors. Thus, accompanying theinstitutional expansion in public sector production of scientific and technological knowledge, there wereenormous increases in the scale of public financialobligations for R&D activities performed primarilyby non-governmental agents. Added to these, a vari-ety of tax and subsidy measures was introduced withthe intention of encouraging private firms to under-

take R&D projects at their own expense.4

Although most people believe that governmentR&D activities contribute to innovation and produc-

3 .See the estimates in Edgerton 1996 , Table 5.8, but note thatthe R&D fraction shown for Japan as 0.22% of national income

.in 1934 actually refers to share of GNP and is based on theresults of a 1930 survey; a Japanese survey taken in 1942 returnedexpenditures on the order of 1.5% of GNP. See Odagiri and Goto .1993 , p. 84.

4 For an historical account focusing upon the US in the twenti-

.eth century, see Mowery and Rosenberg 1989 and the contribu-tions dealing with particular sectors and industries in Nelson .1982 . For the post-Cold War climate affecting government

.support, especially in the US, see Cohen and Noll 1997 . Nelson .1993 brings together profiles of the evolution of the nationalinnovation systems of a variety of industrialized and some devel-

.oping countries, mainly since the 1960s; Soligen 1994 providesa broader, internationally comparative treatment of relations be-tween scientific research and the twentieth century state.

tivity, many economists and policymakers havegrown frustrated with the paucity of systematic sta-tistical evidence documenting a direct contributionfrom public R&D. The burden of econometric find-ings concerning the productivity growth effects of R&D seems to be that there is a significantly posi-tive and relatively high rate of return to R& Dinvestments at both the private and social levels. Yet,quite generally, privately funded R&D in manufac-turing industries is found to yield a substantial pre-mium over the rates of return from own productiv-ity improvements derived from R&D performedwith government funding. 5 In a recent survey,

.Griliches 1995, p. 82 , suggests that the especiallypronounced differential over the returns on tangiblecapital investments observed at the private level mayreflect individual firms perceptions of especiallyhigh private risk in the case of R& D. The latterwould, of course, lead to the imposition of higherhurdle rates of return for firms individual fundingdecisions; whereas, by comparison, government-funded industrial R&D projects would be seen as

.carrying less private risk, especially as much of itis devoted to product innovation for outputthat eventually is to be sold back to the governmentprocurement agency under the terms of cost pluscontracts. In such circumstances there is little basisfor expecting that the R&D it performed with publicmonies would have a substantial direct impact on the

contracting firms own productivity.

1.1. The issue: substitution s. complementarity in public and pri ate R&D in estments

Having a direct impact on innovation that showsup as industrial productivity growth, however, is notthe only way in which public R&D may enhance

5 . .See Griliches 1995 and Hall 1996 for recent surveys.Negative findings on the productivity growth payoff from govern-ment expenditures for industrial R&D emerged from an earlier

.econometric studies by Griliches 1980 , Griliches and Lichten- . . .berg 1984 , Bartelsman 1990 and Lichtenberg and Siegel 1991 ,

some of which obtained coefficients on federally funded R&Dthat were close to zero as well as statistically insignificant.


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ing for, or actually crowding-out private R&Dinvestment, it obviously is hard to justify such ex-penditures on the grounds that they exerted an imme-diate net positive impact upon industrial innovationand productivity growth. 11

Simply counting up the numbers of findings proand con that have accumulated on the issue of publicprivate R & D complementarity since themid-1960s, however, cannot be very informative.Our approach instead will be to survey the availablebody of econometric work systematically, and insome detail, from an analytical perspective. Al-though we take notice of a number of time-seriesstudies that have been carried out at the macroeco-nomic level, most of this inquest is concerned withresearch that focuses on the impact of public R&Dcontracts and grants upon private R&D investmentby manufacturing firms and industries. It is there thatthe bulk of R&D expenditures by the worlds devel-oped economies continues to be concentrated. Ourpurpose in this is to assess the reliability of thestatistical findings and to arrive at a better under-standing of the reasons for the persisting lack of aclear-cut empirical consensus in the literature.

Three quite restricted questions will be askedregarding those investigations. First, is the design of the statistical analysis such that it can yield anyreliable findings on the question of whether govern-ment R&D expenditures do or do not have a signifi-

cant and economically palpable impact upon theirprivate sector counterparts? Secondly, where thefindings are credible, may we conclude that govern-ment subsidy programs do not displace private R&Dinvestment, but instead have the complementary ef-fect of inducing additional company-funded R&Dactivities? Thirdly, how can the econometric findingsbe reconciled with those of other well-designed stud-ies that addressed ostensibly the same question, yetarrived at different conclusions?

At this time, the econometric results obtainedfrom careful studies at both the micro- and macro-

11 It remains conceivable, however, that some special features of the government-sponsored projects create capabilities in the per-forming firms that are conducive in the longer run to increasedprivate R&D investment, to higher marginal innovation yields, orto both.

levels tend to be running in favor of findings of complementarity between public and private R&Dinvestments. But, that reading is simply an un-weighted summary based upon some 30 diverse stud-ies; it is not a conclusion derived from a formalstatistical meta-analysis, and in no sense is itoffered here as a judgement that would pretend tosettle the issue definitively. To formally weigh up

and aggregate the available and still-growing array.of statistical analyses seems to us a virtually impos-

sible task in this case. We are not dealing withstatistical results that have been generated by prop-erly designed experiments, where provision wasmade in the policy process for replication and con-trols. Instead, we are dealing with ex-post in-quiries, and the results reported by many of theindividual papers that constitute the literature on thistopic reflect a convolution of many counterbalancingeffects that are further compounded with the effectsof a varying mix of public funding and other incen-tives for R&D activities. The ability of the econome-tricians to impose ex-post statistical controls varieswidely among these studies. Moreover, they aredistributed over differing time periods, and across avariety of scientific and technological fields, as wellas diverse sectors and different economies.

Inasmuch as the spheres of investigation as wellas the findings considered here are far from uniform,it is difficult to see what good would be served by

striving for a broad empirical generalization thatmight mask clear-cut instances, however few, wherepublicly funded R&D is found substantially to dis-place private investment. Indeed, the better way of proceeding would seem to lie in trying more pre-cisely to identify and delineate the characteristics of the circumstances in which substitution effectspredominate. Policy making in this area of growinglong-term importance calls for more specific empiri-cal support and guidance if it is to advance beyondgeneral theoretical arguments, intuitive practical

judgements, and political rhetoric.

1.2. Cautions regarding the sur eys scope and limi -tations

It should be made explicit at the outset that thepresent review is addressed to only one aspect of the


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broader empirical picture that is of interest for publicpolicy formation. For one thing, we do not examinethe large body of evidence on the relative productiv-ity impacts of public and private R&D. 12 A secondissue that we do not treat in any detail is the otherside of the interdependence of public and privateR&D spending, namely, the latters impact upon theformer. Many of the micro-level empirical studieswe have surveyed treat public R&D either explicitlyor implicitly as an exogenous influence on privateR& D within an investment framework. Conse-quently, in a number of instances we do find itnecessary to point out the econometric consequencesof ignoring the existence of latent variables that may jointly effect both public- and private-sector deci-sions to allocate R&D funding to specific industrialareas, and to have the work performed by particular .rather than randomly drawn firms. Our discussionrecurrently touches on this point, arguing that moreattention to structural modeling of governmentagency behavior as well as industrial R& D re-sponses is needed for a proper interpretation of theoverall, reduced-form findings. 13 This may be seenas an instance of the more general case that David

.and Hall 1999 advance for taking an explicit struc-tural modeling approach to mitigate the frequency of apparent contradictions and ambiguities in theeconometric literature.

1.3. Organization

The remaining presentation is organized as fol-lows. Section 2 presents a conceptual framework forunderstanding the net effects of public R&D uponprivate R& D investment activities. Section 3 re-views and critiques the available econometric re-search findings, beginning with studies carried outusing data for the line-of-business and laboratory

.level Section 3.1 , and progressing upwards to those

12 More recent work on this question is surveyed by Klette et al. .2000 .

13 .The studies by Leyden and Link 1991 and Leyden et al. .1989 are noticed as having set a good example for future work in this regard.

concerned with effects at the level of the firm Sec-. .tion 3.2 , the industry Section 3.3 and the aggre-

.gate economy Section 3.4 . Because the bulk of theeconomic research on this question looks at publiclyfunded R& D that is being performed under theterms of government contracts with commercialfirms, the main focus of discussion in this surveyfalls upon programs of that kind. In Section 3.5,however, notice is taken also of a small body of econometric studies that examine the impacts onprivate R&D of publicly funded r publicly performedresearch or publicly funded r non-profit performedresearch. 14 We conclude in Section 4 with severalmethodological observations and suggestions for fu-ture research in this area of perennial policy rele-vance.

2. Net private R&D effects of public R&D: aconceptual framework

Before proceeding further, it is appropriate topause to ask what modern technology policy mea-sures have been meant to achieve. Presumably thecentral rationale for government support of R&D isthe correction of the market failures in the produc-tion of scientific and technological knowledge, aris-ing from the incomplete private appropriability

.problems identified by Nelson 1959 and Arrow .1962 . Economists have indicated two main policyresponses to the resulting tendency towards under-provision of knowledge-based innovative effort onthe part of profit-seeking business entities: directprocurement and r or production in public facilities,and incentives for a greater amount of private invest-ment. We have here eschewed issues concerning thefirst of these, primarily those involving the perfor-mance of public research institutes and national labo-

14 .Recent studies by Jaffe and Trajtenberg 1996 and Jaffe et al. .1998 use patent citations to investigate the flow of knowledgeand commercial technology out of federal labs, but our discussionis restricted to papers that explicitly deal with the impacts uponprivate R&D investment.


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ratories, and have restricted our review exclusivelyto the second class of policy responses.

2.1. Tax incenti es s. direct subsidies for R&D

Under that heading two main policy instrumentsmay be identified: tax incentives that reduce the costof R&D, and direct subsidies that raise the private

.marginal rate of return MRR on investment in suchactivities. Although not strictly necessary, the pri-mary difference in execution between these two pol-icy instruments is that the former typically allows theprivate firms to choose projects, whereas the latterusually is accompanied by a government directedproject choice, either because the government spendsthe funds directly or because the funds are dis-tributed via grants to firms for specific projects orresearch areas.

The effectiveness of R&D tax credits in increas-ing R&D in private firms is surveyed in Hall and

.van Reenen 1999, this issue . Because a tax creditdirectly reduces the marginal cost of R& D, onewould not expect to see crowding out effects onindustrial R&D, spending except via the channel of

an increase in the real cost of R &D if the inputs are.in inelastic supply . This implies that crowding out

of nominal private-sector R&D expenditures wouldnot be observed, even though it is entirely possiblethat there could be displacement of private realR&D investment if the prices of the inputs increasedsufficiently. 15 Tax credits, however, do not leavethe composition of R&D unaffected. As firms ex-pand their R&D activity in response to linked taxoffsets against earnings, the incentives are likely tofavor projects that will generate greater profits in theshort-run. Consequently, projects with high socialrates of return, and long-run exploratory projects andresearch infrastructure investments in particular,may be less favored by the expansion of private

funding. In this way, rather weaker spillover ben-efits to other firms and industries would be generated

15 .See David and Hall 1999 for a full discussion of the impactof inelastic R&D inputs on the effects of R&D subsidies.

by the private response to extensive reliance uponthis particular pro-R&D policy instrument.

By contrast, direct funding of R& D programsdesignated by government agencies allows publicR&D subsidies to be targeted toward projects thatare perceived to offer high marginal social rates of return to investments in knowledge. At least in prin-ciple, such funding could be concentrated in areaswhere there was a large gap between the social andthe private rate of return. For this reason, directR& D subsidies or government spending on basicresearch activities should not be expected to displaceprivate real R&D investment, except via its genericimpacts on the price of research and developmentinputs that are in inelastic supply. Yet, the possibilityremains that in the politics of technology policyformation, there will be strong pressures to providesubsidies for projects with high private marginalrates of return possibly to assure the appearanceof successful public launch aid, or simply be-cause the prospective private payoffs make lobbyingfor subsidies an attractive undertaking. In such cir-cumstances, it is more likely that increased directgovernment funding for industrial R& D projectswould enable firms correspondingly to reduce theirown outlays. This form of investment displace-ment arises primarily because R&D activities areheterogeneous rather than homogeneous, and it isdistinguishable from, and additional to the form of

.crowding out identified by David and Hall 1999as operating through the R&D input market effectsof public expenditures.

2.2. The need for a structural framework

When considered as a whole, the literature underreview here may be characterized as predominantlyinductive in its approach to considering the effects of government R&D funding upon the level of busi-ness R&D investment behavior. That is not simplyto say that the orientation of this work has beenprimarily empirical rather than analytical, but, ratherthan the empirical approach pursued is essentiallydescriptive in nature, aiming at establishing the signand magnitude of the overall or net effect inquestion. Few studies in this area have been espe-


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cially concerned to delineate the different channelsof influence that may connect R&D resource alloca-tion in the two spheres, and fewer still attemptstructural estimation in order to ascertain the charac-ter and strength of the underlying effects associatedwith each such channel.

Although all these empirical studies acknowledgebeing motivated by the same over-arching policyissue, the widespread tendency to eschew explicitstructural modeling has reinforced the common fail-ing of econometric work in this field to specifyadequately what experiment the investigators im-plicitly envisage conducting. In other words, suppos-ing one could vary government policy, what ob-

.served pairings of policy action s and sequelaewould establish whether public and private R& Dwere complements or substitutes? Taken to-gether with the fact that a large number of verydifferent empirical experiments at various levels

.of aggregation are contemplated in effect by theresearch literature, this lack of specification con-tributes to the difficulties of interpreting the individ-ual findings and reconciling the seeming contradic-tions among them.

For the foregoing reasons, rather than out of anymethodological precommitment to favor structuralmodeling approaches in econometric analysis, webelieve it will be best to review the evidence pre-sented by the individual studies only after having set

out a general conceptual framework that identifiesthe array of hypothesized micro-level determinantsof private sector R&D investment, and relates thesein turn to relationships that hold and manifest them-selves at the macro-level. That is our task in thepresent section.

2.3. Determinants of pri ate R&D in estment at themicro-le el

A useful framework for understanding how publicR&D affects R&D funding decisions in the privatesector is provided by an adaptation of a familiar,rather elementary model of firm-level investmentbehavior. To our knowledge, such a framework wasfirst employed for this purpose by Howe and

.McFetridge 1976 . It postulates that, at each point in .time or for each planning period , an array of

Fig. 1.

potential R&D investment projects is available. 16

The firm is assumed to rationally consider the ex-pected cost and benefit streams for each project, inorder to calculate its expected rate of return. Undercertain conditions, these can be thought of as internalrates of return and therefore used by the firm inquestion to rank the associated projects in descend-ing order of anticipated yield, thereby forming itsMRR schedule.

A downward-sloping schedule of this kind ap-pears in Fig. 1, where the marginal yield and the

.marginal cost of capital is plotted on the vertical

axis, and the horizontal axis gives the cumulatedamount of investment required as one proceeds downthe list of projects. Following expositional conven-

tion, each project is implicitly taken as being finelydivisible, so that the resulting MRR schedule is

.continuous and continuously differentiable . Under

16 This formulation abstracts from important issues concerningthe determinants of the firms access to the scientific and engi-

neering knowledge base that is relevant for formulating plausiblyfeasible R&D projects, and estimating the time distribution of thecosts and benefits of the innovations they would generate. There isa well-known recursion problem here, inasmuch as among theresearch projects that a rational decision process would need toconsider is the project for gaining the knowledge required toconstruct and evaluate its current innovation possibility set.But, in a full dynamic specification, it is straightforward analyti-cally to treat the latter as a lagged endogeneous variable.


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this construction, as one moves along a given sched-ule describing the distribution of projects in thefirms prevailing technological innovation possibil-ity set, there is no alteration in the constellation of other variables that would influence the rates of return on the array of R&D projects in the firmspotential portfolio. The net impact of any and allalternations in those other conditions, therefore, mustshow up as shifts in the MRR schedule.

As also may be seen from Fig. 1, the firm faces a .marginal cost of capital MCC schedule, which

reflects the opportunity cost of investment funds atdifferent levels of R&D investment. 17 The upwardslope of this schedule over its full range is at-tributable to the fact that as the volume of R&Dinvestment is increased the firm will have to movefrom financing projects with internally generated

.funds i.e., retained earnings to calling upon exter- .nal equity and debt funding. Use of retained earn-

ings for R&D accounts for the flat range at the leftof the MCC schedule, whereas the firms increasedrecourse to external financing would tend to push itsmarginal costs of capital upwards. 18

It should be apparent that as the MCC schedule inFig. 1 describes the opportunity cost of capital, itwould slope upwards eventually. This must be soeven were it the case that all of the firms R&Dinvestment remained financed out of retained earn-

17 This implicitly holds constant the amount of other, tangiblecapital formation expenditures that the firm has scheduled for theplanning period in question. Although the assumption of risk neutrality on the part of the firm is implied by the use of theexpected MRR as a sufficient statistic to describe each project inthe portfolio, it should be recognized that in practical capital-budgeting exercises firms add premia to their marginal costs of capital, forming hurdle rates of return that allow for theriskiness of various classes of investment. Although, for exposi-tional simplicity it has been supposed that the tangible capitalformation budget has been predetermined, at the margin R&D

should compete with all other capital projects on the basis of theirrisk-adjusted internal rates of return.

18 Obviously, in the case of R&D intensive start-ups thereare no retained earnings upon which to draw. But, the possibilityexists of borrowing capital from employees by paying them withstock option, which may keep the marginal cost of capital downso long as there is an adequate supply of qualified personnel whoalso happen to have a high tolerance for risk, although issuance of stock options does have a cost due to its effect in diluting equity.

ings; at the margin, expansion of the R&D invest-ment budget would force the firm to turn to externalfinancing for its tangible capital acquisition projects.

The foregoing simplified schema can be repre-sented by the following equations:

MRR s f R, X , 1 . .MCC s g R , Z , 2 . .

where R is the level of R&D expenditure, and X and Z are vectors of other shift variables thatdetermine the distribution of project rates of returnand the associated marginal costs of capital, respec-tively. The X -variables reflect:

.i The technological opportunities governingthe ease with which it is possible to generate

.innovations relevant to the firms market area ; .ii The state of demand in its potential marketarea or line-of-business; .iii Institutional and other conditions affecting theappropriability of innovation benefits.

Correspondingly, the Z -variables include: .i Technology Policy measures that affect the

private cost of R& D projects such as the taxtreatment of that class of investment, R&D subsi-dies, and cost-sharing programs of government

.procurement agencies ;

.ii Macroeconomic conditions and expectationsaffecting the internal cost of funds, via the generalstate of price-earnings ratios in equity markets; .iii Bond market conditions affecting the externalcost of funds; .iv The availability and terms of venture-capitalfinance, as influenced by institutional conditions .such as the development of IPO markets and thetax treatment of capital gains.As is depicted by Fig. 1, in the firms profit

maximizing equilibrium the optimal level of R&Dinvestment is found at RU , where the MRR and themarginal cost of funds are equalized:

RU s h X , Z . 3 . .

Several points are now obvious about the relation-ship between private R&D investment and public


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R& D funding. First, if we take the provision of public funds to be exogenous, the effects of suchshocks would show up as a shift of either thefirms MRR schedule or its MCC schedule, or of both. For example, direct R&D subsidies, and cost-sharing arrangements by public agencies, by reliev-ing the firm of some joint costs of research anddevelopment activities would be tantamount to shift-ing the position of its MCC schedule to the right.Had the firm initially been facing increasing marginalcosts of capital, this change would permit the under-taking of additional projects with its own money other things being equal, of course. 19 To cite an-other specific illustration, the award of governmentR&D contracts to a small firm also might have theeffect of lowering the recipients capital costs at themargin; especially in the case of start-up enterprises,where this could act as a signal for external fundingsources to apply a smaller risk premium when settingtheir lending terms.

A number of other potential positive micro-leveleffects of government contracts for industrially per-formed R&D, also have been noticed in the litera-

.ture following Blank and Stigler 1957 . Each of thefollowing three factors would appear as an outwardshift of the MRR schedule in Fig. 1.

.a Publicly subsidized R &D activity can yieldlearning and training effects that acquaint the enter-prise with the latest advances in scientific and engi-

neering knowledge, and so enhance its efficiency inconducting its own R&D programs. .b Where public funds are made available for

construction of test facilities and the acquisition of durable research equipment, and also pay the fixedcosts of assembling specialized research teams, thefirm involved may be able to conduct further R&D

.projects of its own at lower incremental cost, andthereby derive higher expected internal rates of re-turn on its R&D investments.

.c Government contract R&D, by signaling fu-ture public sector product demand, and private sector

19 It should be obvious, however, that were the firm facing acompletely inelastic MCC constraint, the public contract fundingwould not have any positive incremental impact upon the level of company-funded R&D.

demand in markets for dual-use goods and services,may raise the expected marginal rates of return onproduct or process innovation targeted to those mar-kets.

There is a distinct possibility that in the case of . .the above-mentioned effects a and c , the techno-

logical knowledge and market information associatedwith publicly funded R&D performed by one firmcould result in spillovers. The latter would, simi-larly, raise the expected marginal rates of return forother firms in the same industry, and also for firmsin other industries. Public R&D performed in aca-demic and other non-profit institutions, includinggovernment laboratories, also could have correspond-ingly positive spillover effects. This is so particularlywhere the research resulted in the development of infrastructural knowledge general principles,research tools and techniques, and skill acquisitionthat raised the expected rates of return on commer-cially oriented, applied R&D projects. 20

2.4. Distinguishing between go ernment R&D con -tracts and grants

When considering the potential net effects of public R&D activities, it is likely to be important todistinguish between public contracts and grants.Government R&D contracts in most instances are

financial outlays to procure research results that areexpected to assist the public agency in better defin-ing and fulfilling its mission objectives. Such con-tracts are the largest component of public awardsmade to private for-profit firms, and also include allarrangements to purchase R&D- intensive publicgoods. This category covers much of the publicaerospace and defense expenditure. Public grants, onthe other hand, are usually competitive financialawards that do not carry any future public commit-ment to purchase. They are the primary mechanism

20 . .Leyden and Link 1991 and David et al. 1992 suggest avariety of spillover channels through which the infrastructure-forming aspects of so-called basic research funded and insome cases performed in the public sector has a complemen-tary impact upon private sector R&D investment.


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for funding exploratory research for the advancementof knowledge and fostering emerging technologies.

Since the path-breaking work of Blank and Stigler .1957 , four channels have been identified in theliterature through which public contracts for industri-ally performed R&D could stimulate complemen-tary private R&D expenditures in the short run:

.i Public R&D contracts increase the efficiencyof the firms R&D by lowering common cost orincreasing absorptive capacity; .ii Public R&D contracts signal future demand; .iii Public R& D contracts may improve thechances for success on the firms other projects; .iv Public R &D contracts allow firms to over-come fixed R &D startup costs pump-prim-ing.These imply that public subsidies either shift the

.firms marginal returns schedule in Fig. 1 out to theright, andr or that the firms opportunity cost of capital schedule is shifted to the right, thereby even-tually lowering the firms MCC for the higher levelsof R&D expenditure. The first three of the foregoingputative effects would shift the firms MRR schedulerightwards either by increasing the expected rev-enues or decreasing the expected costs of the firmsavailable projects. The fourth effect, by decreasing afirms fixed costs, lowers the opportunity cost of capital and shifts this curve out to the right. All of these complementarity effects suggest that public

R& D contracts stimulate additional private R& Dinvestment.From the preceding discussion, it might appear

that the micro-level impact on private R&D invest-ment of both government contracts for industrialR&D and grants awarded to non-profit organizationswould be unambiguously positive. But, there are twosorts of countervailing influences, both of which arelikely to operate more strongly in the case of con-tracts. First, the performance of contract-specifiedlines of R&D with public funding may simply sub-

.stitute for some if not all of the investment that theperforming firms otherwise would have been pre-pared to undertake in order to be in a position to bidsuccessfully for related government procurementcontracts.

Secondly, publicly funded R&D also may mili-tate against private sector investments in the sametechnological areas, because the expected rates of

return to investments by firms that do not receivecontracts tend to be lowered by the prospect thatgovernment contractors would succeed in producingcommercially exploitable innovations. Doing socould leave them well positioned to enter the finalproduct market with significant first-mover advan-tages. Non-contract receivers also might be discour-aged from undertaking their own R&D by the antici-pation that the government procurement agency inquestion would have an incentive to disseminatecost-saving and quality-enhancing innovations, as ameans of enabling entry and greater competition inthe end-product market. When viewed from the latterperspective, dual-use programs of governmentprocurement of R&D-intensive goods take on theappearance of a two-edged sword. 21

Both the direct and the indirect displacementeffects just considered may be conceptualized asaltering the shape of the firms respective MRRschedules, reducing expected marginal returns onR&D projects belonging to particular technologicalareas that were targeted for public contract sup-port. Although it is clear in principle that the policyprescription should be for the government to selectprojects for subsidization that the private sector isnot likely to undertake, or not undertake in sufficientvolume, matters may be otherwise in actual practice.Pressures within public agencies for high successrates in contract awards may lead to the use in

R&D funding decisions of selection criteria that putheavy weight on factors that are correlated positivelywith high expected rates of return to private R&Dfunding. Therefore, when investigating the net ef-fects of government-funded R&D at the micro-level,it is important to distinguish between programs that

21 Government procurement costs may be reduced by takingadvantage of spillovers from industry-funded R&D directed to-

wards civilian products. But the potential for the R&D performedunder cost-plus procurement contracts to have spillover effects oncompany financed R&D might, correspondingly, be nullified bythe heightened anticipation of competitive entry into the businessof exploiting dual-use designs. Such opportunities may exist wherenew high-tech systems required by government have components,or utilize methods applicable to the production of goods forprivate purchasers. On the benefits of dual-use technology pro-

.grams, see Branscomb and Parker 1993 .


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provide grant funding and those that involve con-tracts. Likewise, when dealing with questions con-cerning aggregate level effects of changes in policiesaffecting public R&D, one should make allowancefor the effects of any significant alterations in thedistribution of funding between those two modes.

Although government grants typically do not havea final product demand-increasing component such

as is frequently present in the case of public con-.tracts for R& D performance , they may cause the

MRR schedule to be shifted upwards nonetheless.This would occur because a program of grant-fundedresearch had raised firms R&D efficiency, or hadimproved the risk-return pattern on other projects.The convention in the literature, however, has beento abstract from the ways in which these effects of grant-type funding for industrial R&D would im-pinge upon the shape or position of firms MRRschedules, and so to identify whatever effects ensueas produced by shifts in the MCC schedule.

Three main analytical cases have been delineated.In the first, it is assumed that the firm is asset-con-

.strained and thus faces a perfectly inelastic verticalMCC schedule at its current level of R&D invest-ment. The award of a subsidy in the form of a publicgrant then shifts the MCC curve to the right, increas-ing the firms performance of R&D by just the fullamount of the subsidy. The second case postulatesthat the public grant shifts an upward sloping MCC

curve to the right, so that the amount of the incre-mental increase in the amount of R& D the firmundertakes increases by less than the grant award.The third case considers that the MCC schedule is

.perfectly elastic horizontal at the pre-grant equilib-rium, but is shifted downwards because the signal toequity holders provided by the public grant awardlowers the firms internal cost of funds. The magni-tude of the increase in private R&D investment willthen depend on both the strength of the signal,which is likely to vary directly with the relative sizeof the grant, and on the slope of the firms MRRschedule. Other things being equal, the flatter isthe MRR schedule, the greater will be the increase inthe induced amount of private R&D investment.

Thus, only the last of these speculative situationsenvisages the possibility of a complementarity effectof public grants for industrial R&D, i.e., one thatwould elicit additional private R&D expenditures.

Grants to firms for R&D are likely to be used inways that assure greater private appropriability of thebenefits than is the norm for grant-funded research inacademic institutions, and similar non-profit researchinstitutes. There consequently may be justificationfor having presumed, in the foregoing analysis, thatcontracts yield no positive spillover effects that wouldinduce significantly increased private R&D invest-ment. But, by the same token, the same presumptionshould not be extended to considerations of theimpact of all public sector grant funding.

2.5. Short-run net impacts on pri ate R&D: frommicro- to macro-le el effects

In general, the likely direction of net effects of public R&D contracts on private R&D investmentremains ambiguous. The previous discussion has re-viewed an array of channels at the micro-levelthrough which public contracts as well as grantswould have positive effects on the level of privatelyfunded R& D activity. On the other side of theledger, however, two principal arguments have beenadvanced on behalf of supposing that public expendi-tures for industrial R&D would exert a crowdingout effect on private R&D investment. The first of these is simply the micro-level displacement of fund-ing, previously discussed. This would occur wherecontracts are targeted in areas of technological devel-

opment that firms otherwise would still find it worth-while undertaking; the resulting alteration in theshape of the MRR schedule may be such as to pushit downwards and to the left.

The second argument introduces macro-level con-siderations. There is likely to be upward pressure onthe prices of R& D inputs when the provision of funding to a particular firm or group of firms occursin the context of an expanded government R& Dprogram that absorbs substantial scientific and engi-neering personnel, along with other specialized mate-

rials and facilities. The resulting increased costsassociated with the array of potential private R&Dprojects implies a lowering of the MRR schedule;and, other things being unchanged, that translatesinto a reduced level of business R&D investment.

Where will the balance be struck between theopposing forces arising from increased public sectorR&D expenditures at higher levels of aggregation


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i.e., in technologically related industry groups,and the economy as a whole? 22 This question re-cently has been examined analytically by David and

.Hall 1999 . Their basic proposition is simple: when-ever the market supply of R&D inputs is less thaninfinitely elastic, as is likely to be the case in theshort-run, increased public sector demands for thoseresources must displace private R&D spending, un-less it gives rise to spillovers that also raise theaggregate private derived demand for R&D inputs.In the simple two-sector model developed by David

.and Hall 1999 , the nature of the macro-level rela-tionship between private and public R& D invest-ment depends upon four parameters of the system.Complementarity, rather than substitution effects arelikely to dominate where the relative size of thepublic sector in total R& D input use is smaller,where the elasticity of the labor supply of qualifiedR&D personnel is higher, where the grantcontractmix of public outlays for R& D performance isskewed more towards the former, and where the rateat which the private marginal yield of R& D de-creases more gradually with increased R&D expen-ditures.

Without having fully specified both the magni-tudes of the elasticities, and the shifts in schedulesdue to spillover effects, in an analogous manner forthe microeconomic framework depicted by Fig. 1,the foregoing review of the static qualitative argu-

22 Attention should be called to the analytical difficulty of passing explicitly from the micro-level framework of the previoussubsections to the macro-level. In principle it would be possible toconstruct an aggregate private marginal efficiency of R&D invest-

ment schedule, from the union of all the projects each with their.individually perceived expected internal rates of return . As that

might well involve duplicative investments in some projects, itshould be evident that the private expectations would generallynot be realized. The consistent aggregate private marginal effi-ciency of investment schedule would be lower, even with spillovers

it is likely to lie below the aggregate social marginal efficiency of investment schedule. But the real difficulty lies in passing fromthe aggregate private MRR schedule to the aggregate demand forR&D investment when firms are realistically heterogeneous. Thedistribution of projects is not identical across firms, and neither dothey all face the same MCC schedule. This means that there isnothing to guarantee that the same ranking of all the projectswould be selected by a central profit-maximizing agent chargedwith allocating the private sectors total R&D funding.

ments for and against complementarity leaves oneunable to determine the sign of the net impact of public subsidies on the level of business R&D ex-penditures. The general point that the foregoing dis-cussion does bring out clearly, however, is the pres-ence of identification problems due to the fact thatthe MRR and MCC curves may be shifting simulta-neously. This must be dealt with if econometricstudies are to succeed either in providing reliableestimates for the critical underlying elasticity param-eters, or in simply ascertaining the sign of the neteffect of public R&D contracts. It has been pointedout that both fixed costs associated with R& Dstartup, and resource constraint effects on input pricesthat are correlated with individual firms receipts of funding, may be shifting the MCC schedule. Byholding those effects essentially constant by the useof an appropriate econometric specification, includ-ing proper instrumental variables, it should be possi-ble to evaluate the net impact of public R&D con-tracts on private R& D investment demand. Theidentified effect would measure the net movement of the firms MRR schedule, holding fixed the opportu-nity cost of capital.

2.6. Dynamic or long-run effects of R&D subsi -dies

Even though most of the empirical literature hasbeen devoted to quantifying these presumed short-run .static effects, we should recognize at least twodynamic or long-run effects that are partially theoutcome of public R&D funding. First, informa-tional spillovers from the advance of public scienceand engineering knowledge, much of which is madepossible by government R&D activities, will likelyshift the firms MRR schedule outward over time.Since new knowledge is the main source of newtechnological opportunities, the outward shift of theMRR cur e assumes that these opportunities takethe form of higher project returns. Of course, sucheffects would be felt with variable lags and are likelyto be localized among some subset of the underlyingprojects. So, the relevant schedules would undergochanges in shape as well as position and the impactswill not necessarily be felt symmetrically throughoutthe population of firms.


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A second dynamic effect stems from the trainingof new scientists and engineers. There is a strong andimportant emphasis in the US and UK public re-search enterprise on training, particularly within theresearch universities. Due to the trend toward in-creasingly heavy reliance upon foreign graduate stu-dents as research assistants on grant-funded aca-demic research projects, one must not simply pre-sume the existence of tight coupling between train-ing activities and the future availability of qualifiedresearch personnel in the labor markets where suchtraining occurred. But, insofar as there is a laggedinput supply response from expanded public funding

.of R &D grants , this could show up at the microe-conomic level as an outward drift of the MRRschedule over time. 23 But, if one considers thesituation in the market for industrial R&D person-nel, it is more natural to conceptualize the aggregateeffect in terms of a downward shift of the laborsupply schedule. The latter would thus be a factormitigating such demand-driven upward pressures onreal unit costs of R&D that were set in motion bythe expansion of government funding. In general,then, the balance of the long-run dynamic effectsseems to favor the emergence at higher levels of aggregation of net complementarities, rather than arelationship dominated by crowding out, or thesubstitution of public for private R&D investment.

2.7. Endogeneity, and common latent ariables ef - fects

Using this simple investment framework to clarifythe expected channels of influence helps to formulate

23 On the economic significance of the rising numerical impor-tance of foreign graduate students and post-doctoral fellows in

.university research systems, see Dasgupta and David 1994 .There is substantial evidence, most of it accumulated from surveysof company executives responsible for research and development,

that firms actively search for recent trainees of university depart-ments that have successfully drawn public funding for basicresearch; that they regard recent graduates as an important sourceof practical knowledge about the use of new techniques. See, e.g.,

. .Levin et al. 1987 and Pavitt 1991 . It remains unclear to whatextent this public goods spillover effect via researcher training isequally characteristic of university-based research supported byindustry funds, and conducted under restrictions typical of propri-etary R&D.

and evaluate the empirical tests that exist in theliterature. It does not, however, allow us to addressthe possible mutual interdependence of public andprivate R& D expenditures. This may present anissue for econometric analysis, either because of simultaneity and selection bias in the funding pro-cess, or because there are omitted latent variablesthat are correlated with both the public and privateR&D investment decisions. Endogeneity due to se-lection biases in R&D grants and subsidized loans tosmall and medium size firms has been addressed

.recently in the work of Busom 1999 and Wallsten .1999 , whereas the possibility of omitted time-con-stant firm effects in the awarding of governmentR&D contracts was pointed out many years ago by

.Lichtenberg 1984 . Lichtenberg also remarked onthe problem presented by the fact that firms under-take a significant amount of preparatory R&D inorder to qualify for government contracts and grants:this may create a situation in which the firms MRRschedule already has shifted outward in anticipationof a public R&D contract or grant, and consequentlythe firms response to the award may be more diffi-cult to detect in the data.

More generally, it may well be that there arestrong selectivity biases that lead firms which have arecognized competence for certain kinds of R&D toreceive public contracts as well as to fund suchactivities with their own money. Beyond that, in

cross-section analyses at the industry level, a distinctbut related econometric problem may arise whereboth private and public investment decisions areresponding to the same latent variable, namely, theinter-industry variation in the technological oppor-tunity set. The possibility of exogenous changes inthe state of the opportunities created for commer-cially attractive innovation such as those openedup by developments in fiber optics, high-temperaturesuperconductivity, or the availability of restrictionenzyme techniques for gene-splicing may con-found efforts to identify the causal impact of publicR&D allocations upon the pattern of private invest-ment.

Even though the framework presented here hasnot undertaken to formalize these and other, morepolitically implicated sources of endogeneity inpublic R&D expenditures, it has assisted us in un-derscoring the need for empirical studies to be ex-


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plicit about their identifying assumptions, and toinclude proper control variables. The foregoingdiscussion also has highlighted the point that publicR&D contracts are likely to have a much strongerimmediate effect on the firms marginal returnsschedule than is the case with public R&D grants.For that reason alone, public contracts are moredifficult to evaluate because those effects are readilyconfounded with the many other factors that shift theMRR schedule, including the nature of changes inthe production and product technology, appropriabil-ity conditions, the type of product market competi-tion, and so on.

As the previous discussion of identification prob-lems may have suggested, to undertake to estimatethe magnitude of the effect of public R&D spendingupon private R&D investment at different levels of aggregation is tantamount to conducting rather widelydiffering experiments in the hope of determiningthe response. Some considerable doubt must sur-round the very idea that there is a universal relation-ship of that kind, and so it will be better to avoidcasual comparisons and juxtapositions of findings,striving to compare like with like where that isfeasible. Macro-level time-series studies have to con-sider feedback effects operating through price move-ment in the markets for R& D inputs, whereas inmicro-level analyses the findings should reflectreal rather than nominal expenditure relationships

between public and private R& D. At least thatwould be so once controls had been entered for time .effects in the form of year dummies . Of course, in

saying this we assume that there is substantial poten-tial mobility on the part of R&D personnel amongfirms and industries. In other words, when theexperiment under analysis involves the provision of a subsidy for R&D conducted by a particular firm, itis reasonable to assume that the firm faces a highlyelastic supply of R& D inputs, and therefore thatinput prices and unit costs cannot be materiallyaffected by the subsidy. 24 The implication is thatthe observed effect on the level of private R& D

24 This is precisely true only for the loglog specification,where the aggregate price effects are in the constant term, or whenthe number of firms receiving subsidies is a small fraction of thetotal so that there is no price effect in the cross-section.

spending should be somewhat weaker at the microe-conomic level than that found by studies conducted

using aggregate data since the effects in the former.case are real rather than nominal .

Nevertheless, at the lower level of aggregationthere are likely to be additional complications due tothe presence of significant cross-sectional differencesin technological opportunity or innovation capabili-

ties competences in the terminology preferred by.the recent management literature . Some controls for

fixed effects may be appropriate in such cases.But, in studies based on firm- and industry-levelpanel data, over time the innovation opportunitysets may be undergoing differential alterationsamong the various technological and market areas.Simple, fixed effects methods are then likely toprove inadequate to the task, and more complexeconometric tactics would seem to be called for.

Thus alerted to the numerous underlying complex-ities that beset those in search of a straightforwardempirical answer to the question of whether com-plementarity or substitution prevails in this do-main, we may now be in a position to criticallyexamine the econometric findings which the litera-ture presently has to offer.

3. The empirical literature: review and critique

Our survey is organized in subsections, distin-guishing among the econometric studies according totheir choice of the unit of observation, and by thetype of data analyzed. Four types of observationalunits have been studied: line-of-business or labora-

.tory, firm, industry, and national or domestic econ-omy. The typical econometric approach is to regresssome measure of private R&D on the governmentR&D, along with some other control variables.When a positive coefficient on the public R& Dvariable is found, this is interpreted as revealing thepredominance of complementarity between publicand private investments. On the flip-side, a negativecoefficient is taken to imply that public R&D andprivate R&D are substitutes. In several studies, theauthors use the magnitude of the estimate to makestatements to the effect that a one dollar increase inpublic R&D funding leads to an X dollar increase


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.decrease in private R& D investment. On thewhole, however, the magnitudes of the publishedestimates are very diverse. 25 In some instances, theyare very difficult to compare directly, owing tovariations in the specification of the estimated equa-tion, and the absence of collateral statistical informa-tion needed to calculate dimension-free parameterestimates, such as elasticities. Our summary tablespresent the estimated elasticity of private R&D withrespect to public R&D wherever it has been possibleto obtain this magnitude.

To achieve appropriate comparability among thefindings, we divide the studies into four main groups,according to the nature of the statistical observationsused and the econometric approach complied withthe following.

.1 Pure cross-section studies at the micro level,where firms or industries with different levels of government R&D funding are compared. Here it iscrucial to control for differences in demand condi-tions, technological opportunity, and appropriability.

.2 Panel data studies at the micro level within agiven industry, in which there are controls for time-invariant differences among firms each firm ineffect serving as its own control, so that the resultsreflect individual firms time-series responses tochanges in government funding.

.3 Aggregate or macroeconomic studies, wherethe response is identified by changes in private R&D

funding over time as a function of government R&Dfunding. Here it is important to control for macroe-conomic influences that may be driving both vari-ables. In addition, it is likely that results based onthese studies will contain R&D input supply effects

.of the sort that are identified by Goolsbee 1998 and .David and Hall 1999 .

.4 Studies, whether micro or macro, that attemptto control for the simultaneity between private andpublic R&D spending using instrumental variables.It is probable that the results from these studies will

25 .For example, Wallsten 1999 concludes that there is a one-for-one crowding-out of private investment, whereas Robson .1993 concludes that there is a one-for-one stimulus of privateR&D investment. To be sure, these studies were not analyzing thesame dataset.

differ from those in the other studies if commonomitted variables are a problem.

The discussion in Section 2 urges caution wheninterpreting the results of some of these regressionstudies. For example, when we look across firms orindustries in a cross-section, we are seeing a set of

Uequilibrium choices for the level of R&D R in.Fig. 1 , rather than tracing out the derived demand

curve for R&D as a function of the position of theMCC curve. In fact, each of the firms and industriesin question is likely to face a different demand curvefor R&D investment, as well as a different MCCschedule. In addition, some of the effects of thepublic support for R&D will be to shift or otherwisechange that curve. Many, but not all, researchershave attempted to control for the variability in theMRR curve when estimating the relationship, and inwhat follows we will try to assess the success of theapproaches that they have taken in doing so.

.Blank and Stigler 1957 suggested in their origi-nal formulation of the question that the most effi-cient and direct way to test for a complementary orsubstitution relationship is to analyze a sample of research programs over time within a suitablesample of companies. Due to data limitations, how-ever, they were forced to rely on a cross-section of manufacturing firms in their analysis. Their methodwas to compare the ratios of scientific workers to allemployment for firms with and without government

contracts. For a total sample of 1564 firms in 1951,they found that firms with public contracts also hadlower scientific personnel intensity working on pri-vate research. This result supports substitution, atleast in the sense that public contracts were associ-ated with fewer research personnel on private projects. When Blank and Stigler changed the samplinguniverse, however, and took it to be US manufactur-ing firms engaged in any R& D, public and r orprivate, they reported that in general substitution isnow almost absent. 26 Finally, using their most

reliable data for firms with more than 5000 employ-ees, they found evidence for complementarity. Theseresults are consistent with the view that althoughmany individual firms may find it attractive to sub-

26 .Blank and Stigler 1957 , p. 61.


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stitute government funding for their own R&D bud-gets, the large firms are better able to take advantageof complementarities, due to knowledge spilloversand pump-priming effects. The latter may operateacross lines-of-business and even standard industrialclassification lines, so that the size of those firmsmay really be a surrogate for the product diversifica-tion that enables them to appropriate benefits fromthe less predictable range of their R&D projects.Blank and Stigler, however, warned that their esti-mate based on variation across firms and industriesat a point in time could be seriously biased by othersources of heterogeneity, for example, variations intechnological conditions faced by different firms.

3.1. Line of business and laboratory studies

.The subsequent research of Scott 1984 and Ley- . .den et al. 1989 ; Leyden and Link 1991 , together

with very recent work using Norwegian data by .Klette and Moen 1998 , are the only studies that

follow the recommendation of Blank and Stigler .1957 to look below the firm level. For each of these studies, Table 1 lists the sample used, theeconometric methodology and type of data, the formof the private R&D variable to be explained, theform of the public R& D explanatory variable, aswell as the net findings reported by the authors.

.Scott 1984 performs a cross-sectional analysis

on FTC line-of-business data for 437 firms in 259four-digit industries. He finds that private R&D ispositively associated with government financed R &Dusing both a restricted intensity version of the rela-tionship, and a log-level version. In both cases, hisestimates are robust to the inclusion of firm dummyvariables and four-digit industry dummy variables.The results also hold up whether or not firms thathave zero company financed R&D are includedfrom the sample. As is the case for many of thestudies surveyed here, his analysis may be biasedbecause of endogeneity between public and privateR&D, due to omitted variables that drive both setsof funding decisions. At least part of the below-firm variation across lines-of-business probably isattributable to variation in technological opportuni-ties and appropriability conditions that are affectingthe marginal returns. Scotts use of industry dum-mies should capture some of these omitted variables.

.But, the recent works of Trajtenberg 1989 and .Toole 1999a; b suggest that variation in technologi-

cal opportunities is very important even at the prod-uct class level.

. .Leyden and Link 1991 and Leyden et al. 1989develop a more elaborate structural model in order toprovide insight into the empirical relationship be-tween public and private R&D. Since the approachof these two studies is very similar, we will focus on

.the empirical results of Leyden and Link 1991 . Theauthors estimate a three-equation system using

.three-stage least squares 3SLS , using a cross-sec-tion of firm R&D laboratory data for 1987. Theirendogenous variables are laboratory total private R&D budget, laboratory knowledge sharing effort, andgovernment total spending to acquire technicalknowledge. For each lab, total government spendingis defined broadly to include government contracts,grants, and the value of government financed equip-ment and facilities.

Leyden and Links reduced form equations in- .clude the following predetermined variables: 1 a

dummy variable indicating that the lab has coopera- .tive sharing arrangements; 2 a dummy variable

.indicating that the lab does basic research; 3 adummy variable indicating that the lab is oriented

.toward biological or chemical research; 4 the two- .digit industry level R& D r sales ratio; and 5 an

interaction term between the R&D r sales ratio and

the presence of cooperative sharing arrangements.The excluded exogenous variables in the privateR & D equation are essentially the dummy of bior chem research and the dummy for basic re-

search the dummy for cooperative sharing arrange-ments is present in the equation, multiplying theindustry R& D to sales, so it is not adding any

.identification .The regression model allows government R&D to

influence the private R&D budget directly, as wellas indirectly through the labs knowledge-sharing

activities. Further, the authors account for potentialsimultaneity of private and public funding decisionsby using the predicted values from a first stagereduced form regression as instrumental variables.They find a positive and significant coefficient onthe government R& D in both their private R& Dand knowledge sharing equations. Accounting forvarious feedback effects, this implies an elasticity of


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Table 1Line of business and laboratory studiesSee text for details.OLS s Ordinary least squares; 3SLS s three-stage least squares; FE s Fixed effects.Where the regression is in levels, the elasticity is derived using the mean levels of R&D spending for the sample.

Author Time Data type Number of Explained variable Explanatory variable Controls . . period observations private R&D public R&D

. . . Scott 1984 1974 LB cross-section 3338 log private R&D Log Government R&D Size, firm or

industry dummiLeyden et al. 1987 Lab. cross-section 120 US$ Private US$ Government R&D Size, lab K -sha . 1989 lab budget funding to lab D R&D industLeyden and 1987 Lab. cross-section 137 US$ Private US$ Government R&D R r S, lab K -sh

. Link 1991 lab budget and equipment D chem r bio , . D basic R

Klette and Moen 19821995 Panel within industry 192 = 3.6 US$ Private US$ Government Sales, sales sq., . . .1998 Norway Mach., elec., inst. R&D Log R&D subsidy log cash flow,

. . private R&D Government R&D time dummies


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mate a reduced form model of private R&D inten-sity on government incentive grants and other vari-ables. They use a sample of 81 firms over the period19671971 separated into three industry groups. As

.in Hamberg 1966 , this helps protect against biasedestimates stemming from inter-industry differencesin product characteristics, technology, appropriabil-ity, and other factors. They find a positive coefficienton public incentive grants for all three industries;however, it is significant only for the electrical in-dustry.

. .Higgins and Link 1981 and Link 1982 analyzehow the composition of private R&D investmentresponds to public R&D support. Using a cross-sec-tion of 174 manufacturing firms in 1977, Higgins

.and Link 1981 find that the percentage of privateR& D investment devoted to research falls as thefirm receives more public R& D funds. Althoughthey interpret this as evidence for crowding-out, itshould be remembered that this is only one portionof a firms R&D budget. In fact, in an expanded

.version of this study, Link 1982 finds that federalR&D stimulates total private R&D intensity. Whenprivate R&D investment is decomposed into basic,applied, and development components, Link findsthat public R&D reduces private basic research in-tensity. Public funds, however, stimulate private de-velopment intensity and have no significant effect onprivate applied research intensity. While both Hig-

. .gins and Link 1981 and Link 1982 include con-trol variables accounting for firm profits, diversifica-tion, and high-technology orientation, their resultsare probably affected by unobserved inter-industryvariation. Nonetheless, these studies attest to theimportant point that the type of public R&D supportmatters for the private R&D investment decision.The distinction made between public R&D contractsand grants in Section 3 is based on the same observa-tion.

.Lichtenberg 1984; 1987; 1988 has stressed theeconometric issues involved in the analysis of therelationship between public contract R&D and pri-vate R&D investment. He has employed a demand

and supply framework similar in spirit to the.micro-level investment framework presented here in

order to make two basic points: first, that publicR&D contracts should be treated as endogenous atthe firm level; second, that sales to government are

more R&D intensive and have an effect on privatemarginal returns that should be included in the speci-fication. In his 1984 paper, Lichtenberg also suggeststhat the failure of previous studies to account fortime constant and unobserved firm characteristicshas led to an upward bias on the coefficient esti-mates. Using data on a cross-section of firms in threedifferent time periods, he presents regression resultsfor private R&D intensity in both level and first-dif-

ference specifications i.e., estimates that control for.permanent differences across firms . The mostly pos-

itive and significant coefficients on public R& Dbecome negative and significant in the latter regres-sions, which implies that the observed complemen-tarity was due to firm-level differences in R& Dintensities that are correlated with the award of public R& D contracts. Increases or decreases inpublic R&D funding within the firm were not asso-ciated with increases or decreases of private funding.As in all such differenced regressions, however, thepotential for downward bias due to measurementerror in the independent variable must be kept inmind.

The subsequent papers of Lichtenberg 1987;.1988 address the issues posed by the tendency of

federal procurement demand to concentrate on moreR& D-intensive products, and the potential endo-geneity of federal contracts. His 1987 paper com-bines firm data from Compustat and the Federal

Procurement Data System to construct a panel of 187firms over the period 19791984. He demonstratesthat the value of government contracts variable be-comes insignificant once government purchases areincluded separately in the regression equation.

Using a sample of 169 firms from the same .database, Lichtenberg 1988 proceeds to further par-

tition federal contracts into competitive andnon-competitive. While his results show a posi-tive and significant government R&D coefficient forthe total and within OLS regressions, the coeffi-

cient becomes negative and significant when he ac-counts for endogeneity using as an instrumental vari-able potential government contracts for products thatthe firm produces. It is important to note that thegovernment sales variable in the regression includesthe value of government contracts. As he makesclear, his results imply that there is no additionaleffect of government contracts beyond their impact


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Table 2 . .Firm-level studies a US data; b Data from other countries

.Definitions: E s employment. CS s Cross-section. FE s Fixed effect within or firm dummies . FD s First differences. IV s Instru


( )a . .Hamberg 1966 1960 Firm CS within 8 = ; 20 Private R&D E r US$ Government Size, depreciatio

industry Total E contracts r Assets investment, lag R&D E

.Shrieves 1978 1965 Firm CS across 411 Log % Government- Size, prod mkt, .industry private R&D E finaced R&D tech opportunity

.Carmichael 1981 19761977 Firm CS within 46 = 2 US$ Private R&D US$ Government Size industry expenditure R&D contracts .transprotation

Higgins and 1977 Firm CS across 174 % Research in US$ Government- Profit r S, dive . .Link 1981 industry private R&D financed R&D D hitech .Link 1982 1977 Firm CS across 275 Private R&D r sales Government- Profit r S, dive

industry financed C4, D governaR&Dr sales

.Lichtenberg 1984 1967, 1972, Firm CS across 991 Change in private Change in Size 1977 industry R&D r sales Government

R&Dr sales .Lichtenberg 1987 19791984 Panel across 187 = 6 US$ Private US$ Government- Year dummies, industry R&D expenditure financed R&D sales to governm

. Lichtenberg 1988 19791984 Panel across 167 = 6 US$ Private US$ Government- Year dummies, industry R&D expenditure financed R&D sales to governm

.Wallsten 1999 19901992 Firm CS across 81 US$ Private R&D Number of SBIR Age, size, patents industry experienced awards, total value R&D exp. 1990

in 1992 of SBIR awards D never applyindustry andgeography dum


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( )b .Howe and 19671971 Firm panel 6 = 44 US$ Private US$ Government Size poly. , profi

McFetridge within industry R&D Expenditure R&D grants deprec, HHI, . . .1976 Canada D foreign

. . Holemans and 19801984 Firm CS across 5 = ; 47 Log private R&D log Government Size, divers., HHI. . Sleuwaegen and within R&D grants D for. , log

. . .1988 Belgium industry royalties .Antonelli 1989 1983 Firm CS within 86 Private R&D % Government- Size, profit,

. . .Italy industry MM lire , log financed R&D, log D div , share for . private R&D government R&D r Sales, foreign R.total R&D

. Busom 1999 1988 Firm CS across 147 Private R&D D participation in Size, patents, . .Spain industry expenditure, R&D subsidy loan program export share

per employee industry DummieToivanen and 1989, 1991, Panel across 133 = 3 US$ Private US$ Government- Investment, cash Niininen 1993 industry R&D expenditure financed R&D flow, interest, rate, . . . 1998 Finland loans and subsidies current and one la

of all variables


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on the firms marginal returns. As opposed to substi-tution, it would appear that what Lichtenberg hasidentified is that the primary impact of governmentcontracts on private R&D investment works thoughtheir effect on the firms private marginal returns,rather than on the firms marginal cost of fundsschedule.

. .Toivanen and Niininen 1998 , Busom 1999 and .Wallsten 1999 are the most recent firm-level stud-

ies on the public r private R&D relationship. Wall-sten focuses on the effects of the US Small Business

.Innovation Research Program SBIR . This is a com-petitive grant program designed to target R&D sub-sidies to smaller businesses, and Wallsten was ableto collect data on firms that have won SBIR awards,firms that applied but were rejected, and firms thatwere eligible to apply but did not apply. His sampleof awardees comes from the data systems of elevendifferent governmental agencies and covers a varietyof industrial areas over the 19901992 time period.In order to correct for the potential endogeneity of the public funding decision on the firms R& Dresponse, Wallsten uses a three-equation system in-tended to model the award granting process as wellas the firms response. Estimating the system by3SLS and using the total SBIR budget from whichfirms could have won awards as his instrument, hefinds that the number of SBIR awards won by a firmhas a positive but insignificant effect on firm em-

ployment. In a separate set of regressions, he findsthat the number and value of SBIR awards signifi-cantly reduces firm R&D expenditure. In these latterregressions, however, Wallsten is forced to analyzeonly publicly owned firms and this reduces his sam-ple of firms from 481 observations to 81 observa-tions. Based on the typical size of an SBIR award,Wallsten concludes that public grants displace pri-vate R&D investment on a nearly one-for-one basis,although it must be noted that this finding pertainsonly to the publicly owned recipients of such fund-ing.

.Busom 1999 analyzes the effect of Spanish gov-ernment subsidized loans on private R&D expendi-ture and employment using a sample of Spanish

.firms. Like Wallsten 1999 , Busom is careful toaddress the potential endogeneity of the public fund-ing decision stemming from selection bias in thegrant process. She explores this problem by imple-

menting a two-step procedure that predicts the prob-ability of participation in the program in the firststep, and includes a correction for selection in thesecond step. Using a sample of 147 Spanish firms in1988, Busom finds that the hypothesis of no selec-tion bias cannot be rejected, even thought, the legiti-macy of pooling participant and non-participant firmsis rejected by a Chow test. Unfortunately, her datadoes not include the amount of the R&D subsidy,only the fact that it exists; so she is unable to providea quantitative estimate of the complementarity orcrowding-out effect. She finds, that on average, re-ceiving a government R&D subsidy induces moreprivate R&D effort than would be predicted on thebasis of an R&D effort equation for the controls-

.firms those that did not receive a subsidy , whencorrections are made for sample selection biases. For30% of the firms, however, full crowding-out re-mained a possibility that could not be ruled out bythe data. It will be interesting to see results based ondata from this Spanish program in the future, espe-cially those that reveal the amount as well as thepresence of the R&D subsidies.

In addition to the typical econometric formula-tions that regress some measure of private R&D onpublic R&D, an increasing number of alternativeand more indirect approaches have been pursued inrecent years. Since these papers are less direct thanthose considered above, the following brief resumes

report only the bottomline from these efforts. Theinterested reader is urged to consult the originalstudies for more detail. First, Irwin and Klenow .1996 analyze a sample of US semiconductor firmsto find out how private R&D investment respondswhen firms participate in a government supportedR&D consortium. By finding that membership re-duces private R&D investment, the authors suggestthat membership eliminates the need for duplicative

.R&D. Lerner 1999 looks at the long-run impact of the SBIR program on firm sales and employment. He

finds that these government grants have only a lim-ited positive impact, except in those areas wherethere also is substantial venture capital activity. Thespecification, however, does not allow one to distin-guish whether the presence of venture capital fund-ing exerts its effect through shifting the MCC sched-ule, or whether it is really a surrogate for technologi-cal opportunity set differences, i.e., proxying the


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attraction of biotechnology and software innovationsthat can be pursued by small firms.

.Cockburn and Henderson 1998 analyze the co-authorship of scientific papers between public andprivate institutions in the US. They find that privatefirm organization and research productivity are posi-tively related to the fraction of co-authorship withacademic institutions, which are largely supported byfederal funds. In a closely related study, Narin et al. .1997 examine the contribution of public science toindustrial technology using patent citation measures.Their research finds a strong link between industrialpatents and publicly supported research. Feldman

.and Lichtenberg 1998 report finding that for asample of European Union member countries there isa strong positive relationship between the number of private institutions that specialize in a particularscientific field and the number of public institutionsspecializing in the same scientific field. They inter-pret this result as evidence of complementarity be-tween public and private investments in R&D.

3.3. Industry-le el studies

There have only been a handful of industry levelstudies on the relationship between public R&D andprivate R&D investment, probably because at thislevel of aggregation the absence of a clear experi-

ment is most glaring. The most important proxi-mate source of variation in R& D intensity acrossfirms is differences in industry, and this is as true of public R& D as of private R& D expenditures.Therefore we should not be surprised if industry-levelstudies show complementarity between the two, norshould we conclude anything other than the fact thatsome industries have greater technological opportu-nity than others.

The industry studies are summarized in Table 3. . .The Globerman 1973 and Buxton 1975 papers

examine industry cross-sectional variation and findcomplementarity. However, these studies use very

.small samples, 15 data points in Globerman 1973 .and 11 data points of Buxton 1975 . Larger samples

and better specifications can be found in Goldberg . .1979 , Lichtenberg 1984 , and Levin and Reiss .1984 . Each of these studies use NSF data arrangedas a panel with observations on a cross-section of

.industries over time. Goldberg 1979 , following aneoclassical investment approach, regresses privateR&D per unit of output on both current and laggedfederal R&D per unit of output, plus industry dum-mies and other control variables. He finds that cur-rent federal R& D has a negative and significantcoefficient while federal R& D lagged one periodhas a positive and significant coefficient. The sum of these coefficients indicates a small complementarityeffect of federal R&D on private R&D investment.

.Lichtenberg 1984 , on the other hand, finds thatpublic R&D reduces private R&D investment andemployment at the industry level. His preferred spec-ification regresses the change in private R& D in-

.vestment or employment on the change in contem-poraneous and lagged federal R&D plus industrydummies, and time dummies. For federal R&D, heconcludes that an additional dollar crowds out eightcents of private R&D investment. Introducing bettercontrols for cross-industry variation in technologicalopportunity and appropriability, however, would tendto remove the upward bias in the estimated coeffi-cient and thus reinforce the indications of crowding-out in this study.

.The paper of Levin and Reiss 1984 stands out asthe most ambitious industry level study in the litera-ture. They develop a structural equation system thatrelates an industrys concentration, R&D and adver-tising intensities to the industrys structure of de-

mand, technological opportunities, and appropriabil-ity conditions. In their model, government R& Dintensity enters both as a measure of technologicalopportunity and as a determinant of appropriabilityconditions. Rather than treat government R&D asexogenous, however, the authors specify a reducedform equation that models government R&D inten-sity as a function of the government share of indus-

.try sales both defense and non-defense and of theextent of R&D borrowing via capital purchasesfrom other industries, along with the opportunity and

appropriability variables. Using two stage leastsquares, Levin and Reiss find that government R&Dintensity has a positive and significant effect onprivate R&D intensity. In all specifications, the au-thors find a complementary relationship with a mag-nitude that implies each additional dollar of publicfunds stimulates from seven to seventy-four cents of private R&D investment.


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Table 3Industry-level studiesAll studies use US data unless otherwise noted.See Table 2 for definitions.


. Globerman 1973 19651969 Cross-section 15 R&D E r Government R&D r D tech opp .Canada Total E sales % foreign, i

conc., sales .Buxton 1975 1965 Cross-section 11 Private R&D r Government R&D r C4, Divers.

.United Kingdom Gross output Gross output entry barriers . Goldberg 1979 19581975 Panel 18 = 14 Log private R&D r Government R&D r sales Industry Du

. .output sum of lag 0 and 1 price of Rlag private

.Lichtenberg 1984 19631979 Panel 12 = 17 Change in Change in Year dummiprivate R&D Government R&D industry dum

Levin and Reiss 1963, 1967, Panel 20 = 3 Private R&D

Documents

David Bronwyn Toole 2000