R&D Impact on Productivity Measurement: Empirical Analysis and Challenges

1. OECD workshop on productivity measurement and analysisBern, Switzerland16-18 October 2006Empirical analysis of the effects of R&D on productivity: Implications for productivity measurement? Dean Parham

2. Motivation • Empirical uncertainty about magnitude of R&D’s effect on productivity • Shanks & Zheng (2006), Econometric Modelling of R&D and Australia’s Productivity, Productivity Commission Staff Working Paper • Not just this study. Widespread through other studies/countries • Certainty about magnitude of effects will be implicit in national accounts if proposals to capitalise R&D are implemented • Canberra II group recommendations • R&D capital would be incorporated into productivity estimates • Is there a problem here?

3. Outline • Formation of R&D capital stocks • The Shanks & Zheng study • Why the empirical uncertainty? • Capitalisation of R&D in the national accounts • Concluding remarks

4. 1. Formation of R&D capital stocks • R&D outputs are largely unobservable • Knowledge assets measured by use of R&D inputs • Implicit assumption of constant relationship between R&D inputs and R&D outputs  ie constant productivity of R&D • Accumulated via the perpetual inventory method (PIM)

5. Business R&D capital stocks: levels

6. Business R&D capital stocks: annual growth

7. 120 15% 100 Australia 10% 80 60 5% Foreign Foreign 40 Australia 0% 20 0 -5% 1968 1976 1984 1992 2000 1968 1976 1984 1992 2000 Domestic and foreign business R&D stocks

8. Characteristics • Generally smooth • Timing and extent of growth in domestic v. foreign stocks • R&D tax concession • Change in structure of R&D •  business • shift to services • firm entry

9. 2. The Shanks & Zheng study • Conventional framework • Cobb-Douglas specification • ‘Two step’ transformation

10. Estimation of standard models • Models with limited controls mis-specified • Models with extended controls OK • returns to R&D  point estimates of 60%, but imprecise (include zero) • negative coefficient on either domestic or foreign stock commonly found • other explanators more robust  human capital, ERAs, communications infrastructure, ICT, decentralised wage bargaining • Dynamics and lags • little improvement • Sensitivity testing on PIM depreciation rate • Variation in implied returns, but no improvement in precision

11. Further exploration • Specification in growth form • elements of endogenous growth • continuation of mixed results • Two equation specification • separate specifications for determinants of domestic R&D and for determinants of productivity • showed more promise • indications that foreign R&D had positive effect via domestic R&D as well as directly

12. Summary • Effect of R&D on productivity hard to pin down • Mis-specification in standard models • Imprecise estimates • Sensitive to reasonable changes in model and variable specification • Some reasonable models and robust explanation from other factors

13. 3. Why the empirical uncertainty? • Generic • limited degrees of freedom • multi-collinearity • measurement problems • Country and period specific • shocks to R&D and to productivity • policy changes and ‘phantom’ effects of the R&D tax concession

14. Measurement: Use of constructed variable to proxy R&D knowledge asset • Smoothness of change. Contributed by two principal assumptions • Constant productivity of R&D • across projects  single price deflator on R&D inputs subsumes differences in value of R&D outputs • across time  same real input use generates same increment to stock in all periods. • Constant (or at least steady change in) depreciation rates

15. Criticisms • R&D outputs highly heterogeneous. Not same price/value • Productivity of R&D affected inter-temporally by: • technological opportunities • organisation of R&D • policy changes in Australia • Depreciation of knowledge • diversity in depreciation rates • changes in R&D composition affect average depreciation • interactions lead to increasing returns and discontinuities

16. 4. Capitalisation of R&D in the national accounts • Same essentials  use of the PIM • Open to similar criticisms • concerns about accuracy of measurement of R&D-based knowledge stocks • Flow-on effects • R&D capital enters capital input measure in derivation of productivity estimates • deterministic effect on productivity • smooth effect on productivity growth  smooth change in R&D stocks • small effect?  relative size of R&D capital and conventional productive capital stock, relatively high rental price weight

17. Criticisms • Doubtful accuracy • ‘Conservative’ but not accurate • R&D not the only form of knowledge accumulation • Different views on how knowledge relates to productivity • not just like a physical asset

18. Doesn’t look good, but …. • Problems in current procedures • R&D expensed • underestimate value added • particular relationship between R&D and productivity is imposed by default • Choose between the ‘lesser of two evils’ • current: incorrect MFP, errors related to size of current R&D expenditure and to its expensing in the accounts • proposed: inaccurate but ‘smoothed’ effect on MFP, errors related to mismeasurement of knowledge and rental prices and to limitations of specification of relationship between knowledge and productivity

19. 5. Concluding remarks • Capitalisation may be lesser of the two evils • But that does not make it right • Transparency to assist users • limitations • assumptions • choice? • Communication to improve broader understanding