Much of the early research of theeconomics of science and technology is centered around the search for variablesto be used as some sort of indicators of input to innovation and some measureof the output of innovation. Researchers have found it difficult to find anycatch-all measure of innovation. Perhaps the best measure of the input ofinnovative activity is Research and Development (R&D) expenditure, and thebest measure of innovative output is patents. Data on patents and R&Dexpenditure is readily available for most developed countries, collected ataggregate and firm level allowing for a wide scope analysis. Early research focuses ondetermining whether patents are a valuable measure of inventive output as wellas the causality between patents (output) and R&D (input). An early surveywritten by Zvi Griliches (1990) is an excellent compilation of the researchdevoted to determining the value of patents as an economic indicator.
Patentstatistics were being used as early as the 1950’s and 1960’s as an index ofinventive output. In the following decades, an“attempt to ‘validate’ patents as an economic indicator,” (Griliches, 1990) ledto increased research into the relationship between R&D and patentstatistics. Major research contributions contemporary with Griliches surveyinclude Bound et.
al (1984), Hall, Griliches, and Hausman (1986), Pakes andGriliches (1984), Scherer (1983), and Acs and Audretsch (1989).These studies mostly focus on cross-sectional analysis between countries orstates. This prevailing literature came to the main conclusion that there is astrong relationship between patenting and R&D expenditure, paving the wayfor extensive use of patent and R&D statistics in economic analysis.
Withthe conclusion that patent and R&D statistics can provide valuable insightsinto the economy, more modern research efforts focus on the underlying legaland regulatory aspects on world patent systems. This part of the literaturefocuses questions such as, the way specific patent laws and restrictions andintellectual property rights affect the inventive output of firms andcountries. An important survey by Hall and Harhoff (2012) provide an detailedexplanation of these ideas. This is an important area of research in thisfield, but is not the focus of this research project, so I will leave this as anote for interest in future research. Oneof the many areas of interest within the patent-R&D nexus is the idea ofR&D spillovers.
A spillover being, the ability of firms to exploit theinvestment in R&D made by other firms or entities. Adam B. Jaffe, startingwith his thesis work (1985) and subsequent papers (1986, 1989), was one of thefirst to research the effects of R&D investment spillover for inventiveoutput of firms. Of interest to this research proposal is Jaffe’s workinvolving R&D spillover of academic research (1989). This paper providesthe inspiration for the research question that is asked in this proposal: Whatis the impact of higher education research on inventive output? Where Jaffeasks what this impact is at the state level (in the United States), thisproposal asks what this impact is at the national level, using cross countrydata for OECD member countries.
Theimpact of education research on inventive output will measure the real effectsthat university level research has on the inventive output of privatecorporations. This is what Jaffe, and others in the literature call“spillover”. The degree to which university research spills over into theprivate domain to help promote innovation. The motivation behind the researchquestion is to determine if universities have a positive effect on promotinginnovation, and if they do it is worthwhile to invest more research intounderstanding how to best maximize universities research potential. This paperhopes to update the work done by Jaffe, with a model inspired by his, using asignificantly different dataset, and therefore explain this spillover effect ata national level instead of state-level. Therefore,I will propose a methodology similar to Jaffe’s. The paper will be empirical innature and use regression analysis to explain the impact of university R&Don innovation. The dependent variable will be some measure of innovative output.
Although somewhat contended (Griliches, 1990), patents provide a readilyavailable measure of innovative output. One such argument against patents as ameasure of output is that not all inventions are patented, but this still maybe the best measure available for such a study. The main independent variableswill be R&D expenditure. Firstly, R&D expenditure follows the earlyliterature that shows strong linkages between patenting and R&D, thereforeit is expected that R&D will have a positive relationship with totalpatents. Secondly, this R&D expenditure will be split into privatecorporation R&D and higher education R&D.
Business R&D is expectedto have a positive impact on innovative output. Based on Jaffe’s results,higher education R&D is also expected to have a positive impact oninnovative output, this being the main interest of the research proposed. Itwill also be interesting to compare the impacts of business R&D anduniversity R&D.
Also, following Jaffe and previous literature otherindependent variables will be considered as controls, and are likely to includevariables such as population, as a control for size of the potential researchpopulation, and also some measure of the amount of universities in the country,as a control for the research potential of higher education. It is expectedthat as population and university size grow the amount of innovative outputwill grow. Therefore, to summarize, the model can be visualized as follows, PATENTSi,t= BusinessR&Di,t + UniversityR&Di,t + POPi,t+ UniSizei,t + ei,t Thedata is available through the OECD “Main Science and Technology Indicators”database which provides various measures of patenting, R&D, technologytrading and other statistics for OECD member countries. The data is alsoavailable for the last six available years, which allows for either across-sectional analysis or to extend the analysis to include the timedimension using panel data estimation methods. It is likely that both thecross-sectional and panel analysis will be done to provide a clear analysis ofthe available data and increase the number of observations.