Innovation Science Technology - UNU-MERIT...Innovation Policy and Sustainable Development: Can...

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EDITOR: PATRIES BOEKHOLT

Innovation Policy and Sustainable Development:Can Innovation Incentives make a Difference?

Contributions to a Six Countries Programme Conference,February 28 - March 1, 2002, Brussels

IWT-Studies

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InnovationScience

Technology

Institute for the Promotion of Innovationby Science and Technology in Flanders

IWT-Studies is published by IWT-Vlaanderenas part of the work programme of the IWT-Observatory. However, the authors arepersonally responsible for the standpointsadopted in the development of these studies.

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IWT-ObservatoryJan Larosse, Co-ordinatorDonald Carchon, Information systemAnn Van den Bremt, SecretariatHenri Delanghe, Policy analysis

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Phone: 02/209 09 00Fax: 02/223 11 81E-mail: [email protected]: http://www.iwt.beRegistration number: D/2002/7037/5Published in August 2002

COLOFON

Luke Georghiou (1955) is Professor ofScience and Technology Policy and Mana-gement and Director of PREST (Programmeof Policy Research in Engineering, Scienceand Technology) University of Manchester.He is responsible for management ofresearch and postgraduate institute of 25staff and 28 doctoral researchers. LukeGeorghiou completed his PhD from theUniversity of Manchester in 1982. The thesisis called “Technical Characteristics and Inter-Fuel Substitution” and was supervised by of Professor Michael Gibbons. He has pub-

lished extensively in the area of technologypolicy and evaluation. Publications includenumerous invited conference presentationsand keynote addresses, academic journalarticles, book chapters and reports. Otheractivities in the past include MemberQuinquennial Review of the ResearchCouncils (2001), Chair Advisory Committeeon Evaluation of Science Foundation Ireland(2000), Chairman Strategic Review Panel of EUREKA Initiative and Chairman 5 YearAssessment Panel of EU FrameworkBiotechnology Programmes (1996).

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LUKE GEORGHIOU

Policy Research in Engineering, Science and Technology (PREST),University of Manchester, United Kingdom

IMPACT AND ADDITIONALITY OF INNOVATION POLICY

Impact and Additionality of Innovation Policy

1. INTRODUCTION

The design of innovation policy is both apractical art and one which draws upon cur-rent theories to provide a rationale, a justifi-cation and some specific principles ofdesign. Just as success in innovation itselfcomes from the ability to sustain a series ofimprovements over time rather than froman achievement at a fixed point in time, sothe design of innovation policies needs to beadaptive to circumstances and to build onlessons learned in practical situations.Informal feedback is always present butpolicy evaluation provides the most systema-tic approach to learning. In this paper, a keydimension of evaluation, the additionalityof the intervention, is reviewed and linkedto different perspectives on and rationalesfor innovation policy. This is set in the con-text of the evolving behaviour of innovativefirms. Finally some brief conjectures aredrawn about the implications choice ofpolicy rationale would have for addressingthe issue of sustainability in the context ofinnovation policy.

2. ADDITIONALITY

It is tempting for the policymaker to compiledossiers replete with “success stories” whichmay be used to justify continuation orexpansion of the policy measure in question.Even better if some proxy for rate of returnor economic activity generated can be inte-grated. However, it has long been realizedthat the critical question that an evaluationneeds to ask must go beyond the level ofeffects achieved by the beneficiaries of apolicy and pursue the issue of the contribu-tion to those effects made by the existenceof the public intervention. In an earlierpaper, the author and colleagues encapsu-lated this question in terms of what differ-ence is made by the policy, or to use the ter-minology of evaluation, the additionality ofthe support measure (Buisseret et al, 1995).

Conceptually, additionality appears rela-tively simple on superficial examination. Itinvolves comparison with the null hypothe-sis or counterfactual – what would have

happened if no intervention had takenplace. The framework we developed treatedadditionality in three manifestations:

a) Input additionality: a concern withwhether resources provided to a firm areadditional, that is to say whether for everyEuro provided in subsidy or other assis-tance, the firm spends at least an addi-tional Euro on the target activity. Twoother scenarios are worthy of note, thefirst being that the firm does not increaseits expenditure on the activity in questionby an amount equal to or greater than thesubsidy, thus diverting the public funds toenhance its profits. This is an outcome eas-ily possible when a fiscal incentive for R&Dis introduced which allows the wholespend rather than incremental spend to beoffset against the allowance. The secondscenario is that the firm accepts the subsidyfor an activity that it would have carriedout anyway and spends the resources onanother project, thus rendering the sub-sidy as “deadweight”. This is a typical sce-nario in grant-funding for R&D where it isvery difficult for the policymaker to judgethe firm’s initial intentions. The incremen-tal spend in R&D may be achieved but thepolicymaker is funding a different project(normally the marginal one which the firmwould have undertaken if additionalresources were available). The advantageto the firm in a situation of competition forgrants is that it can put forward a moreattractive project which (obviously) prom-ises higher returns than the marginalproject.

b) Output additionality: At its simplest thismeasure concerns the proportion of out-puts which would not have been achievedwithout public support. It has appeal forevaluators as they are more interested inassessing the achievement of programmegoals than in auditing the expenditure ofparticipants. However, the evaluator isfaced with two serious impediments inoperationalising this concept. The first isthat the outputs of an innovative projectin receipt of support (say reports, patents,prototypes, business plans, new part-nerships) are essentially intermediate

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achievements. They do not in themselvesconstitute innovation. However, if theconcept is shifted in the direction of out-come additionality (improved businessperformance as a result of new orimproved products, processes or services)it becomes increasingly difficult to attrib-ute the effect to the intervention.Multiple competences need to be appliedto realize an innovation and separatingthem is non-trivial. Furthermore, if out-come additionality is the issue in questionthen outcomes beyond the economic per-formance of the beneficiary become rele-vant. These include unintended effects(including negative ones) and all types ofspillovers, Jaffe (1996). The timing of theassessment is also relevant – there aremany different time-profiles over whichoutcomes are manifested. These includeshort-term effects which terminateabruptly in the face of changed marketconditions or corporate strategy. At theother end of the scale is the “sleeper tech-nology” which finds its applications someyears later, perhaps because complemen-tary technologies have been developed orsimply because a new application hasbeen perceived.

c) Behavioural additionality: this is a cate-gory we introduced following the observa-tion that a common effect of innovationpolicy was not to alter a stop-go decisionby the firm in respect of the project butrather to modify in some way the way inwhich the project was carried out. The UKDepartment of Trade and Industry hasarticulated these changes in three sub-divi-sions – scale additionality when the activityis larger than it would otherwise havebeen as a result of government support(perhaps creating economies of scale);scope additionality, where the coverage ofan activity is expanded to a wider range ofapplications or markets than would havebeen possible without government assis-tance (including the case of creating a col-laboration in place of a single-companyeffort); and acceleration additionalitywhen the activity is significantly broughtforward in time, perhaps to meet a marketwindow.

As Bach and Matt (2002) have correctlyobserved, behavioural additionality has afurther dimension in capturing permanentor persistent changes in firm behaviour as aresult of the policy intervention. This can beat the strategic level (incentivising the firmto move into a new area of activity, or toalter its business processes) or at the level ofacquired competences. These changes arepotentially more significant in the long runthan the short-term boost to resourcesafforded by a subsidy. The two types ofintervention are not wholly separable – sup-port for collaborative R&D often has arationale of overcoming firms’ natural resist-ance to collaboration through informationfailures or lack of the necessary compe-tences to manage a partnership.

Bach and Matt (2002) have sought toextend this dimension of additionality byintroducing a further category, that of cog-nitive capacity additionality. With this per-spective they see the key question as beingwhether the policy action changes the cog-nitive capacity of the agent. They arguethat if output additionality reflects a neo-classical perspective, then cognitive capacityadditionality occupies the equivalent posi-tion in an evolutionary-structuralist per-spective.

Since the introduction of the behaviouralconcept some further evidence has accumu-lated on this topic. Davenport and Grimes inassessing the effects of company support inNew Zealand found that the behaviouraladditionality concept provided the bestexplanation for their findings. They discusshow managers and policy administrators canexploit the occurrence of behavioural addi-tionality to maximize the impact of aresearch policy, on the basis that modifiedbehaviour is likely to strengthen a policy'slatent ability to influence the creation ofoutput additionality. In such circumstances,the study suggests that managers and pol-icy-makers should be identifying those inter-ventions that lead to sustained improve-ments in managerial practice, and incompetitiveness, and should be managingtheir diffusion within firms and throughoutindustries.

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On the other hand Luukkonen has criticisedthe additionality concept on the groundsthat it is insufficient to reveal the usefulnessof public support. She cites empirical evi-dence to show that projects deemed as triv-ial by firms at the time of support may in thelong run turn out to have been highly sig-nificant in their impacts, for examplebecause they may build capacity in areaswhere firms have suffered from whatSalmenkaita and Salo (2001) have subse-quently labeled “anticipatory myopia”. Inresponse to this criticism it may be arguedthat whether a project is additional or not isa separate question from that of the successof a project. Indeed high additionality mayeasily be associated with an increased risk offailure because the intervention hastempted a firm to move beyond its compe-tences or to undertake a project which wasmore risky than usual. Both of these may bepositive effects overall. There is also the caseof high additionality where the policymakerincentivised the firm to move in the wrongdirection because the policymaker mis-judged the direction of technology or themarket. Empirical evidence from Hervik(1997) in a study of successive policies inNorway found a clear trade-off betweenadditionality and economic impact probablyfor the first reason given above.

How can the different types or manifesta-tions of additionality be reconciled with cur-rent thinking on rationales for innovationpolicy? The market failure rationale needslittle explanation here. Following Arrow(1962) the argument follows the generalline of positive spillovers, non-appropriabil-ity and uncertainty creating a situation inwhich there is under-investment in research(and by implication in other knowledge-based innovative activities) in comparisonwith the socially desirable level. As arguedpreviously (Metcalfe and Georghiou, 1998)the market failure perspective has beenhighly successful in providing a generalrationale for policy intervention but it isinherently unable to provide specific guid-ance on policy prescriptions. Lipsey and Carlaw (1998) in a study aimingto show that neo-classical and structuralistevolutionary policies lead one to different

conclusions in a technology policy evalua-tion, engage in a discussion of how addi-tionality (or in Canadian terminology, incre-mentality) would be assessed under eachperspective. They argue that a neo-classicalapproach would insist at least on what theyterm a “narrow test of incrementality”being that “some technology is developedor installed that would not have been pro-duced in the absence of the policy or pro-gramme under consideration”. This corre-sponds to output additionality as discussedabove. They argue that the neo-classicalapproach could also go further to demand atest of “ideal incrementality” in which thepolicy is demonstrated to be an optimal useof government expenditure. This invokes aseries of tests attributed to the Canadianeconomist Dan Usher:• The project must be the least costly way to

undertake the desired level of R&D invest-ment;

• Social benefits must exceed the subsidy(including transaction costs, deadweightand other leakages); and

• Discounted benefits must exceed dis-counted costs of intervention.

It is clear that the information requirementsof these test far exceed what is likely to beavailable in any practical situation and may inthemselves place undue transaction costsupon the subsidy. The crucial criticism whichLipsey and Carlaw make is that the structural-ist/evolutionary perspective would apply onlya “weak test of incrementality”, defined as“something the policy makers are trying to dohas happened as a result of their expenditureof funds”. The difference from the neo-classi-cal perspective is that, with no attempt atoptimality, the desired effects are less clearlyspecified (to allow for inherent variabilitybetween firms) and the effects looked forinclude structural changes and enhancementsof firms’ capabilities. For innovation policiessuch as R&D subsidies where the main aim isto provide resources to the firm it seems rea-sonable to expect both kinds of effect to beevident (the targeted product and the longerterm enhancements). However, when wecome to consider innovation policies which donot involve the provision of finance this dis-tinction becomes crucial.

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It is noticeable that the operational level foradditionality tests is the firm. Indeed thewhole structure is founded upon conjec-tures about firms’ decision-making andintentions. This sits comfortably with poli-cies anchored in market failure but if weshift to the systems failure rationale forinnovation policy, the level of analysis andthe range of actors in the frame bothbroaden. Consider for example Smith’sarticulation of systems failure (Smith, 2000)with the four types of manifestation, failurein infrastructure provision, failure toachieve transitions to new technologicalregimes, failure from lock-in to existingtechnological paradigms, and institutionalfailure (regulation, standards and policy cul-ture), it can be seen the first and fourthexist only at the system level while transi-tion and lock-in failures are manifested atboth firm and system levels.

As we have seen, innovation policy alsoworks both at the systemic level and at thelevel of the firm. The preceding discussionof additionality is clearly cast at the level ofthe firm. It can be seen that discussions ofinput additionality and of output addition-ality are dealing with the questions raisedin a market failure context. Hence, the pol-icy measure presumes to correct a failure ofthe firm to allocate sufficient resources andthe additionality question is whether it nowallocates those resources. For output or out-come additionality the question is whetherthe socially desirable innovation has beenachieved.

For behavioural and cognitive capacityevaluation, the issues at the level of thefirm are closer to the systems failure per-spective. A typical goal for a technologyprogramme is to direct firms towards atransition (see for example the strenuousefforts most governments are now pursu-ing to stimulate an interest in nanotechnol-ogy). Larger public initiatives, involvingcoordination in the establishment of newstandards (for example in mobile communi-cations) can be seen as directed towardslock-in failures and again are aiming tomodify the firms capabilities and behaviourin a particular direction.

3. BEHAVIOUR OF INNOVATIVE F IRMS

3.1 The Project Fallacy

The situation for R&D support is furthercomplicated by what we term the “projectfallacy” whereby the policymaker concludesa contract with a firm to perform a set ofwork packages which it considers to be theinnovation project. The fallacy lies in theassumption that the contract and packageof associated deliverables against which thefirm will be monitored is the real innovationproject. Empirical evidence has establishedthat for firms, the real project often startsbefore the contracted work, continues afterit, and integrates the contract work with asuite of other innovative activities which areprivately funded (or even draw upon othersources of public support). In effect the firmreduces its overall innovation costs by scan-ning public programmes for funding oppor-tunities and matching relevant parts of itsactivities to cause the release of funds. Thissituation becomes relevant when evalua-tions seek evidence on socio-economiceffects. The project outputs which constitutethe deliverables were never intended tolead to such effects in isolation. The realevaluation question is what did the publiclysupported contract contribute to the widereffort? The answer requires a much deeperunderstanding of the strategic positioningof the project within the firm than wouldnormally be sought by an evaluation.

While this concept was developed todescribe a situation in large firms, the proj-ect fallacy concept can also be applied tosmall firms and academic research. A deter-mined innovator in a small firm can often gofrom one source of funding to another tokeep a development programme going,with successive supporters each believingthat they are supporting the phase beforecommercialization. The academic analogueis the (normal) situation where a researcheror research team is pursuing a long termagenda and writes proposals to a variety ofsources over time to maintain funding forthe programme through a succession ofshorter term grants. Key findings may onlyemerge after several funding rounds.

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3.2 Implications of Trends in Industrial R&D

We have earlier stressed the close linkbetween policy design, additionality andconceptions of how firms make decisions.Following this argument it is worth askingwhat have been the major trends in indus-trial R&D and what implications have thesefor innovation policy and its evaluation.Quantitatively, there has been a forwardsurge (35% growth in real terms in OECDcountries between 1993 and 1998) and com-panies are doing their best to maintain R&Dlevels during the present economic down-turn (Butler and Giles, 2001). The growth wasin part fuelled by a large increase in venturecapital funding in the USA (Sheehan, 2001)though this has declined dramatically fromits peak in 2000. This increase in R&D couldbe ascribed to a range of explanations:

• A wider range of technological opportuni-ties;

• Increases in R&D productivity throughavailability of new tools and methods;

• Increased competitive pressure to inno-vate; greater ability to afford R&D infavourable economic conditions; and

• Increased returns from stronger andbroader intellectual property rights.

All have their advocates but, in a discussionof R&D policy, few would ascribe a large roleto direct government support which wasgenerally declining as a proportion of BERD(from 12.6% in 1993 to 9.9% in 1998 forOECD countries and from 11.3% to 9% ofBERD in the EU). However, qualitativechanges in industrial R&D provide a closerlink to policy. A similar list (Coombs andGeorghiou, 2002) includes:

• Increased acquisition of technology richsmaller companies emerging from the ven-ture capital sector;

• Growth in outsourcing of R&D to specialistfirms and universities;

• Globalisation making R&D facilities more“footloose”; and

• Continuing high significance of technolog-ical alliances.

All of these trends constitute a changedindustrial ecology. The common factor is

that all stress relationships between compa-nies or between companies and other inno-vation actors; put more broadly a distrib-uted innovation system (Coombs et al,2001). Even globalisation can be seen aspartly driven by the desire to find moreeffective linkages.

Strengthening such relationships and pro-viding the framework conditions in whichthey can thrive is nowadays seen as a cen-tral thrust of innovation policy. A list of suchpolicies includes fostering industry-aca-demic links, commercialization of publicsector research and promotion of venturecapital, particularly in circumstances whereit is perceived to be deficient, notably at theseed funding stage.

What does the concept of additionalitymean in such circumstances? Policies are lessconcerned with market failures than withcreating markets for knowledge that didnot previously exist and with introducinginnovation actors to new relationships. Theaim of most such policies is not to redress atemporary problem, the failure to invest ina given project, but rather to introduce newcapabilities which will, if successful lead to aself-sustaining cycle of investment in inno-vation. Par excellence this is the territory ofbehavioural additionality.

3.3 Policies for SMEs and TraditionalIndustries and the Measurement of Additionality

A similar line of argument can be applied toinnovation policies directed at SMEs and attraditional industries with low levels of inno-vative activity. In addition to innovationfinancing and support for networking, thepolicies of the last decade have focused onthe provision of advice, information andinfrastructure. Government and its agentsshift from the role of provider to that of bro-ker (Metcalfe and Georghiou, 1998) with theaim of guiding firms to suitable partners orfinanciers, technology acquisitions or man-agement capabilities. Broader infrastructuralinitiatives address educational deficiencies,barriers to mobility and improvement of reg-

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ulatory frameworks governing innovation.Such policies certainly consume public finan-cial resources and hence can be and are eval-uated. But on which criteria?

The desire for evidence-based policy and thecurrent vogue for performance indicatorsmakes it likely that at least the advisory andinformation schemes will have objectives.Many of these will be essentially activityrelated (eg the number of firms assisted).Policymakers still wish to know what effectstheir schemes are having upon the innova-tive performance of firms and ideally therelative effectiveness of different measures.This latter goal is complex to pursue for tworeasons:

• Comparison of widely varying schemes ishighly dependent upon the choice andweighting of criteria; and

• Comparison often presumes independ-ence when in fact the range of schemesshould be considered as a portfolio, cover-ing a range of deficiencies in the systemand often interacting with each other.

Taking the simpler task of effects at the levelof the firm, the additionality questionbecomes hard to pursue by direct investiga-tion. Reconstruction of the consequences ofan advisory or training input is much harderthan the same process for a large grant.While the grant will usually involve dedi-cated staff and a detailed accountabilitytrail, this is unlikely for the “softer” meas-ures. Evaluations in this domain tend to relyon econometric comparisons of matchingsamples of companies (see for example

Shapira et al, 1996 for a review of evalua-tions of manufacturing extension pro-grammes). Such approaches have an implicitmodel of additionality built into them whichsubstitutes the counterfactual by presumingthat the matched firm will behave in theway that the original firm would have with-out the intervention. The model is that ofoutcome additionality. Leaving aside theproblem of how satisfactory the matchingprocess is, the concerns which arise areclearly those of attribution. Findings of thiskind should always be combined with real-time case study evidence to allow someinsight into the causality mechanisms.

Taking the issue of additionality measure-ment more generally there are three mainapproaches, all of which have their prob-lems:

• The matched sample approach, as just dis-cussed always raises questions of just howsimilar the firms are. From the early daysof innovation studies Project Sappho illus-trated the difficulties of achieving truematching.

• The second approach is a variant on thematched sample, involving the use offailed applicants for assistance. This isgenerally criticized on the grounds that“failures” are by definition different. Thisproblem is reduced if there is significantover-subscription and the failures can beperceived as close equivalents in terms ofthe selection criteria. Only random selec-tion above a quality threshold wouldguarantee this approach. However, thisapproach raises another difficulty. Even

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RES

EAR

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INNOVATION

Attribution

Timing

Problems in calculating returns to R&D

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the process of application can be seen as apolicy intervention and it is often the casethat raising the issue with the firm, orcausing it to expend thought andresources on an application, will lead it toact in the direction of the policy. The com-parison is then not with an unaffectedstate of events.

• The third approach comes closest to thecore of additionality, by asking the firmdirectly about the counterfactual – wouldit have done the work without assistanceand if so how would it have been differ-ent. This presumes that the respondent ishonest (usually they are) but more impor-tantly that they are capable of performingthe counterfactual analysis themselves.Ideally the intentions of the firm shouldbe explored before the decision to apply.This begins to approach the situation ofthe Usher tests where the transactioncosts of evaluative information exceed thebenefit.

CONCLUSIONS

Faced with a barrage of methodologicaland practical difficulties the policymakerand evaluator are faced with a choice.Pursuit of input and output/outcome addi-tionality can be treated ex ante as a designcriterion and ex post as an area where someevidence can be collected but where fullmeasurement may be impossible and in anycase is not justified in resource terms (or interms of the disruptive effect on intendedbeneficiaries).On the other hand, if thebehavioural perspective is adopted then amore pragmatic strategy is possible. Again,other forms of additionality can be meas-ured within the bounds of practicality butthe primary interest will be in the qualita-tive changes which have been stimulated inthe innovation system. Empirical work thenfocuses less upon the events during andimmediately following a project (a meas-urement of flow) and instead switches to adesire to understand the present capacitiesof the innovation system and the players init (a measurement of stock).This perspective also has implications for a

conference which is interested in exploringhow innovation could be influenced in thedirection of sustainability. A project-basedapproach would suggest that efforts shouldbe targeted on clean technologies. Thiswould provide incentives to specialists inthe field and aim to draw in others to pro-duce cleaner technologies than they wouldotherwise contemplate. A regulatoryapproach would set standards with whichinnovations would have to comply. Practicalexperience has shown that there are signif-icant failings possible with this approach.Evaluation findings have often indicatedthat firms (especially small ones) fail at thecommercial stage because they havewrongly anticipated the level of regulation(in either direction) (see for exampleOrmala et al, 1993).

A systems-perspective would start from adifferent position by questioning whatcapabilities and incentives a firm wouldneed to engage in sustainable innovation.This would include better information andinteraction with regulation, better under-standing of the social context in which theinnovation would perform and a commandof the necessary technological skills, whichcould include a Smith-style transformationto a new technological regime based on dif-ferent materials or processes. A shift of thiskind requires coordination. Measures tostimulate R&D and innovation in this con-text need to be designed as public-privatepartnerships rather than as support mecha-nisms. The partnership element is one inwhich both sides contribute and both bene-fit. Government provides resources andinformation and industry produces innova-tions in an area which it might not other-wise have done and feeds back into theinformation and regulatory process. Mostdeveloped societies face a much greaterchallenge in stimulating innovation in pub-lic goods and services than they have in thepresent wave of consumer and businessinnovations. To address these it is necessaryto employ flexible and pragmatic rationaleswith the underpinning aim of achieving apersistent shift in capabilities and hence inbehaviour.

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REFERENCES

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Buisseret TJ, Cameron H and Georghiou L(1995) “What difference does it make?Additionality in the public support of R&D inlarge firms”, International Journal ofTechnology Management, Vol.10, Nos 4/5/6pp587-600

Butler D and Giles J, (2001) Nature 413, 448

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