Real Options, Patents, Productivity and Market Value November 2002

1. Real Options, Patents, Productivity and Market ValueNovember 2002 Nicholas Bloom (Institute for Fiscal Studies) John van Reenen (Institute for Fiscal Studies & UCL)

2. Summary Part 1:Patents Data • There is a consensus that technological advance is crucial in the “new economy” • Patents provide a powerful indicator of this technology • We hand-match patents from over 12,000 assignees to 450 UK parent firms. • Using this dataset we show a strong and significant effect of patents on • Productivity • Market Value • Patent citations are also shown to informative

3. Summary Part 2:Real Options • We use this data to test new “Real Options” theories • Embodying new technology requires heavy investment, training and marketing. • When firms patent technologies they have the option to see how market conditions develop • This generates patenting real options • Hence, higher uncertainty will lead to a more gradual technology take up • This turns out to be empirically significant

4. Previous Patenting Work • Toivanen, Stoneman and Bosworth (1998) and Bosworth, Wharton and Greenhalgh (2000) find patenting effects on market value in UK firms. • Griliches (1981), Hall (1993), and Hall, Jaffe and Tratjenberg (2001) report effects on market value in US firms. • Greenhalgh, Longland and Bosworth (2000) report a positive employment effect of patenting in UK firms.

5. Patents Data • We constructed the new IFS-Leverhulme dataset using patenting, accounting and financial data. • The patenting data was hand matched from the 12,000 largest US PTO patenting assignees to their UK parent companies. • The remaining 128,000 patenting subsidiaries were then computer matched – which is less accurate. • This provides reliable firm level patenting information from 1968 to 1993 on the UK and Overseas subsidiaries of about 200 UK firms

6. Patents Data

7. Patents Data The distribution of firms by total patents: 1968-96 The Top 8 UK Patenting Firms

8. Citations Data • Citations provide a proxy of patent values, which appear to be extremely variable. • This allows us to fine tune our raw patent counts

9. Citations Data The Five Most Cited Patents

10. Citations Data • But the lag between patenting and citing can lead to truncation biases when using citation weights

11. Citations Data • We correct for these truncation biases in citations data using a Fourier series estimator

12. The IFS-Leverhulme Dataset • We match patents with Datastream accounting data

13. Patenting & Productivity • Standard production models (see Griliches, 1990) usually assume Cobb-Douglas production • We proxy he knowledge stock using the stock of patents (PAT) built up using the perpetual inventory method. • This allows us to estimate “ ” – the return to patents • Using patent citations allow us to fine tune our knowledge stock measure where: G is knowledge stock, K is capital, and L is labour

14. Productivity Equation Results Notes: A full set of firm and time dummies is included. All coefficient marked * are significant at the 1% level All variables are in logs. Estimation covers 1968-1993.

15. Patenting and Market Value • The effect of patents on firm performance can also be measured using forward looking market values • Following Griliches (1981), Bosworth, Wharton and Greenhalgh(2000), and Hall et al (2000) we use a Tobin's Q functional form. where

16. Market Value Results Notes: A full set of firm and time dummies is included. All coefficient marked * are significant at the 1% level All variables are in logs. Estimation covers 1968-1993.

17. Patents and Real Options • Bertola (1988), Pindyck (1988), Dixit (1989) and Dixit and Pindyck (1994) first noted the importance of real options in generating investment thresholds for individual projects. • Abel and Eberly (1996) and Bloom (2000) extend this theory to show how real options lead firms to be cautious in responding to demand shocks. • This cautionary effect of real options on investment has been shown empirically by Guiso and Parigi (1999) and Bloom, Bond and Van Reenen (2001).

18. Modeling Patents & Real Options • To model this caution effect of real options we define “G” as the firms potential knowledge stock and “Ge” as its embodied knowledge • We can then define the elasticity of embodied to actual knowledge as • Higher uncertainty leads to a lower elasticity of embodiment – a slower pass through of patents into production

19. Modeling Patents & Real Options • We prove that the effect of total patents (PAT) will be positive • But the effect of new patents on productivity will be reduced by higher uncertainty - the caution effect • The direct effects of uncertainty will be ambiguous. • Interestingly, while this is true for productivity, market values are forward looking. • To investigate these effects we add in uncertainty levels and interaction effects.

20. Our Uncertainty Measure • Our uncertainty measure is the average daily share returns variance of our firms over the period • Using a firm specific time invariant uncertainty measure matches the underlying theory • This share returns uncertainty measure has been used before by Leahy and Whited (1998) and Bloom, Bond and Van Reenen (2001).

21. Our Uncertainty Measure Mean Daily Share Returns – our entire sample

22. Patent Real Options Results Notes: All coefficient marked * and ^ are significant at the 1% and 10% level All variables are in logs. Estimation covers 1968-1993.

23. Conclusion • Patents appear to play an important role in determining productivity and market value • But their impact on productivity is delayed when higher uncertainty reduces the rate of technological embodiment • Hence, micro and macro stability could play a large role in encouraging technological development.