The Impact of R&D on Innovation and Productivity Professor Derek Bosworth Intellectual Property Research Institute of Australia Melbourne University

Methodologies Case studies of the impact of R&D (i.e. studies of agricultural research laboratories) Surveys of the effects of R&D and innovation (i.e. Community Innovations Survey) Statistical analyses of large scale, micro (company level) data sets (i.e. Griliches and Halls work using COMPUSTAT)

Statistical method I Market valuation function MV = f (tangible assets,intangible assets, …) MV = f (tangible assets,past R&D, …) Answers question: what value does the market place on the intangible assets of the company? what value does the market place on the activities which generate intangible assets?

Statistical method II Production function output = f (capital,labour,past R&D, …) TFP = f (past R&D, …) Answers question: what role does R&D play in driving total factor productivity? Methods I and II are consistent and give similar empirical results

Data used in statistical studies Panel data large number of companies long periods of time Explanatory variables tangible inputs (capital stock, labour) past R&D or the R&D stock patents or the patent stock pool of R&D

Overview: effects of R&D Past R&D has a positive and large impact on productivity R&D assets are valued by financial markets The effects of R&D change systematically over time

Overview: role played by IP Patents play a role (above and beyond R&D) in explaining firm performance, although the correlation is much weaker Citation-weighted patents are slightly more informative than patents Trade marks and other forms of IP are valued by the market

Overview: other key results Competitor R&D reduces the returns to the companys own R&D[?] Companys own R&D improves the ability of the company to innovate using inventions produced by others The R&D pool is at least as important as the companys own R&D

Average gross rate of return to R&D Firm-level estimates 27% - USA and Japan (average, 24 studies) 34% - Belgium, Canada, France and Germany (average, 6 studies) Industry-level estimates 26% - USA and Japan (average, 24 studies) 34% - Belgium, Canada, France, Germany and UK (average, 6 studies)

Risk and uncertainty Results from large panel data sets are broadly consistent and robust However, they tend to be much less consistent and robust in smaller samples for particular sectors for particular firms Reason is that risk and uncertainty become important at the individual firm level

Sources and nature of risk Risk comes from many sources Each of the following has a probability distribution R&D will find anything the patent office will grant patent a new or revised product will result market accepts/adopts the new product a higher quality competitor product is launched

Skewed nature of value Each probability distribution may be quite simple (say normally distributed) Probabilities at each stage interact - a highly skewed overall distribution of values of inventions Empirical results demonstrate most inventions are worth very little and a few are worth very large amounts

Normal distribution Value % of population

Skewed distribution of values % of population Value

Inequality in the Value of Patented Inventions Cumulative value Cumulative number of patents: ranked from least to most valuable