INFORMS Annual MeetingNashville, TNNovember 13, 2016

Technology-push, Demand-pull, and StrategicR&D Investment

Benjamin D. LeibowiczAssistant ProfessorGraduate Program in Operations Research and Industrial EngineeringDepartment of Mechanical EngineeringThe University of Texas at Austin

OutlineBackground

Model

Numerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions

OutlineBackground

Model

Numerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions

Two Types of Technology PolicyI Technology-push

– Reduce the private cost of engaging in innovation– ExamplesI Public R&DI Government funding for private R&DI Support for higher education to enlarge pool of innovators

I Demand-pull– Create or expand markets to increase the payoff tosuccessful innovation– Examples

I Subsidies for consumer purchasesI Direct government procurementI Stronger intellectual property protection

Retrospective AnalysesI Empirical and case study literatures indicate that eachpolicy type is generally ineffective if used alone.I Technology-push

– Mod program for wind turbines, U.S., 1970sI Loiter and Norberg-Bohm (1999)

– GAVE program for biofuels, Netherlands, 1998–2002I Suurs and Hekkert (2009)

I Demand-pull– Tax credits for wind installations, California, 1980s

I Nemet (2009)– Tax credits for solar installations, California, 1970s–present

I Taylor (2008); Wiser et al. (2007)

Portfolio ApproachI Technology-push and demand-pull policies arecomplementary, and the ideal technology policy portfolioshould include policies of both types (Gallagher et al., 2012).I The relative importance of technology-push anddemand-pull varies across applications (Pavitt 1984).

In this study, a bilevel optimization model is developed todetermine the optimal balance of technology-push anddemand-pull policies for a given technology policyapplication.

OutlineBackground

Model

Numerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions

Key Model Features 1I Unique features

– Includes a policymaker and firms as separatedecision-making agents– Represents both technology-push and demand-pull– Represents both process and product R&D– Captures uncertainty in R&D outcomesI Three market failures

– Incomplete appropriability of innovation– Imperfect competition– Negative production externality

1 Contrast these to how energy technology R&D is incorporated into IAMs (Bosetti et al., 2009).

Strategic Innovation in an Oligopoly

The Model

OutlineBackground

Model

Numerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions

OutlineBackground

Model

Numerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions

Three Different Motivations for Stimulating InnovationI Case 1: Combat negative externality

– The new good is a close and expensive substitute for theexsiting good, but the latter has a negative productionexternality (e.g. nuclear fission to fusion).I Case 2: Reduce cost

– The new good is a close substitute for the existing good, butcan ultimately be produced at lower cost (e.g. crystallinesilicon to organic PV).I Case 3: Create demand

– Developing the new good is expensive, but it has a largepotential demand that is not being met by the existing good(e.g. new energy-consuming end-use appliance).

Case 1: Combat Negative Externality

Case 2: Reduce Cost

Case 3: Create Demand

OutlineBackground

Model

Numerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions

Product R&D

Expected Profit

Expected Welfare

Optimal Technology-push Policy

OutlineBackground

Model

Numerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions

ConclusionsI Process and product R&D are substitutes.I If innovation serves to combat a negative externality,technology policy should emphasize technology-push, butit is difficult to enhance welfare through technology policy.I Firms perform less product R&D under stronger spillovers,but expected welfare is higher.I Each firm performs less product R&D under greatercompetition, but total industry R&D rises.I Expected welfare decreases with competition in theno-policy case, but increases with competition if optimaltechnology policies are imposed.

AcknowledgmentsI Previous institutions where this work took place

– International Institute for Applied Systems Analysis– Stanford UniversityI Individuals who offered guidance

– Lawrence Goulder– Arnulf Grubler– Charles Kolstad– Volker Krey– James Sweeney– John WeyantI Funding source

– DOE Office of Science PIAMDDI grant

Thank You for Listening!

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BackgroundModelNumerical SimulationsMotivations for Stimulating InnovationMarket Failure Sensitivity Analysis

Conclusions