1. Service Lives of R&D Assets: Comparing survey and patent based approaches Daniel Ker UNECE Conference of European Statisticians Geneva, 7 th May 2014

2. Overview  Measuring and capitalising R&D – brief intro  How long is R&D useful for and why does this matter?  Estimating R&D service lives  Results  Conclusions

3. Measuring and Capitalising R&D  R&D: creative, systematic work to produce knowledge, use of this knowledge for new products or processes of production  Can be bought in, usually produced by user  Treated as investment in SNA08

4. Measuring and Capitalising R&D 3 key questions to answer: 1.How much R&D is there?  Sales for specialists  Sum-of-costs for non-market, own-account  expenditure data from ‘Frascati Manual’ sources 2.Who uses it?  Funders of R&D (from FM sources)  Exports of R&D (from ITIS)  Survey questions on intended owners 3.How long is it useful for?

5. How long is R&D useful for? Service life: ‘the total period during which [the asset] remains in use, or ready to be used, in a productive process’ The period over which the R&D is used in:  Products sold  Licences granted  Policies implemented  Research papers published Not infinite:  Superseded by new R&D  obsolescence  Gradually becomes ‘common knowledge’

6. Why do R&D service lives matter?  Knowledge capital thought to explain differing economic performance ( between countries, industries)  2 key determinants of knowledge stock:  the amount of knowledge produced (i.e. R&D output)  how long it remains in the stock “the accuracy of capital stock estimates derived from a PIM is crucially dependent on service lives” (OECD 2009) “ Specifying a service life of 10 years rather than 5 years would make a huge difference to estimates of capital measures. Net capital stock would be approximately double, and with a typical scenario of strong growth, consumption of fixed capital would be appreciably smaller.” (OECD 2010)

7. Estimating R&D asset lives Not practical to gather information on each individual asset  Need representative (average, max, min) service lives 1.Estimate from questions on R&D surveys  ‘general’ approach – ‘over how many years would the business expect to benefit from a typical investment in R&D?’  ‘specific’ approach (USA) – identify a specific product which embodied R&D; over how many years did the business sell this product? 2.Estimate from data on patent renewals  Assume patents protect the results of R&D  In each year the patent renewal fee is paid, the R&D must be worth at least as much as the renewal fee  Examine number period over which patents are renewed (‘patent lives’)

8. Strengths and limitations Survey approach + specifically targets R&D performed by the respondent + more timely + can distinguish different types of R&D + linked to different industries (via respondent NACE codes) - may be challenging for respondents – response burden - delay while responses are collected Patent approach + readymade administrative source + direct observations for large population of patents - assumes patent lives are representative of R&D lives - have to wait to observe patent ‘death’ - assumes patents only renewed if of value - ‘artificial’ maximum life due to patent rules - industry breakdown requires linking to information on owner

9. Different methods Mean or Median average lives  Frequency distribution of lives highly positively skewed  median preferable, less prone to bias  But mean common in literature Weighted or un-weighted averages  Desirable to give greater weight to:  Survey responses from firms which perform the most R&D  Patents of highest value

10. Different methods Survival analysis (patents only)  patents can be renewed for up to 21 years (in the UK)  patent data covered the 24 years between 1986 and 2010  so relatively few patents had the opportunity to reach the maximum age of 21 years (only those filed before 1989)  downward bias in average life  Many cases are “censored”  Observe many patents surviving a number of years (e.g. from 1990 to 2010 = 20 years)  BUT do not get to observe the time of death (as it is after 2010) Kaplan-Meier survival analysis uses the information we have about these patents to produce improved estimates of average patent lives

11. R&D survival profiles

12. Illustrative impact of different service lives on R&D stocks These are all average R&D lives from the above sources and methods! Large spread (14 years):  shortest = 6 years…un-weighted median from survey  longest = 20 years…from Kaplan-Meier analysis of patent lives

13. Conclusions  R&D service lives are a key determinant of knowledge stocks and hence economic performance  Countries face choices over the data sources used to estimate R&D asset lives  Countries also face choices of the methods applied to these data  Different choices will introduce artificial variation in R&D service lives and reduce the international comparability of R&D stock statistics