1. Empirical analysis of the effects of R&D on productivity: Implications for productivity measurement? OECD workshop on productivity measurement and analysis Bern, Switzerland 16-18 October 2006 Empirical analysis of the effects of R&D on productivity: Implications for productivity measurement? Dean Parham

2. 2 Motivation n 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 n 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 n Is there a problem here?

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

4. 4 1.Formation of R&D capital stocks n R&D outputs are largely unobservable n 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 n Accumulated via the perpetual inventory method (PIM)

5. 5 Business R&D capital stocks: levels

6. 6 Business R&D capital stocks: annual growth

7. 7 Domestic and foreign business R&D stocks 0 20 40 60 80 100 120 19681976198419922000 Australia -5% 0% 5% 10% 15% 19681976198419922000 Australia Foreign

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

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

10. 10 Estimation of standard models n Models with limited controls mis-specified n 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 n Dynamics and lags  little improvement n Sensitivity testing on PIM depreciation rate  Variation in implied returns, but no improvement in precision

11. 11 Further exploration n Specification in growth form  elements of endogenous growth  continuation of mixed results n 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. 12 Summary n 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 n Some reasonable models and robust explanation from other factors

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

14. 14 Measurement: Use of constructed variable to proxy R&D knowledge asset n Smoothness of change. Contributed by two principal assumptions n 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. n Constant (or at least steady change in) depreciation rates

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

16. 16 4.Capitalisation of R&D in the national accounts n Same essentials  use of the PIM n Open to similar criticisms  concerns about accuracy of measurement of R&D-based knowledge stocks n 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. 17 Criticisms n Doubtful accuracy  ‘Conservative’ but not accurate n R&D not the only form of knowledge accumulation n Different views on how knowledge relates to productivity  not just like a physical asset

18. 18 Doesn’t look good, but …. n Problems in current procedures  R&D expensed  underestimate value added  particular relationship between R&D and productivity is imposed by default n 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. 19 5.Concluding remarks n Capitalisation may be lesser of the two evils n But that does not make it right n Transparency to assist users  limitations  assumptions  choice? n Communication to improve broader understanding