Abstract
Most studies investigating the determinants of R&D investment consider pooled estimates. However, if the parameters are heterogeneous, pooled coefficients may not provide reliable estimates of individual industry effects. Hence pooled parameters may conceal valuable information that may help target government tools more efficiently across heterogeneous industries. There is little evidence to date on the decomposition of the determinants of R&D investment by industry. Moreover, the existing work does not distinguish between those R&D determinants for which pooling may be valid and those for which it is not. In this paper, we test the pooling assumption for a panel of manufacturing industries and find that pooling is valid only for output fluctuations, adjustment costs and interest rates. Implementing the test results into our model, we find government funding is significant only for low-tech R&D. Foreign R&D and skilled labour matter only in high-tech sectors. These results suggest important implications for R&D policy.
Similar content being viewed by others
Notes
Maddala et al. (1997) argue that shrinkage estimators, whereby the individual estimates are shrunk towards the pooled estimate, thereby allowing for some but not complete heterogeneity or homogeneity, are superior to either heterogeneous or homogeneous parameter estimates, especially for prediction purposes. However, a number of case studies, based on demand for gasoline (Baltagi and Griffin 1997; Baltagi et al. 2003), cigarettes (Baltagi et al. 2000) and consumption of electricity and natural gas (Baltagi et al. 2002) conclude that pooled estimators outperform their heterogeneous as well as their shrinkage counterparts in terms of their out-of-sample forecasting performance based on mean squared forecast error. In this paper, we do not consider forecasting.
For a recent survey on the returns to R&D, see Hall et al. (2010).
However, the results reported in Sect. 3 are robust to treating LRR as exogenous, so any bias resulting from use of the constructed variable is small.
In an analysis of the effect of uncertainty on physical investment, Driver et al. (2004) suggest that interpreting the negative and significant effect they find for the pooled model as holding for all industries may be misleading, as the heterogeneous estimates vary considerably across industries.
Whilst the test can have low power, the Null hypothesis cannot be rejected even at the 50% significance level.
This of course also requires that some of the other coefficients are significantly different from zero, as holds in all results.
Repeating the analysis for the maximum time period for which earlier total industry R&D data were available, 1985–2000, drew a similar picture, with an explanatory power of the first principal component of only 39%. Interestingly, the explanatory power of the first (47%) and the first four (94%) principal components over the first half of the period, 1985–1992, are essentially the same as for the second half.
It is interesting to note here that when the tests of the pooling assumption suggest that two of the three industry groups can be pooled while the third one cannot, the group of two always involves the medium-tech sector. The high- and the low-tech industry effects are never similar to each other and at the same time significantly different from the effect of the medium group. This supports the notion that the industries in the latter group are indeed of a medium character, displaying characteristics from both of the two extremes.
Similarly to our results for industry R&D investment for the UK, a study on the effect of financial constraints on firm level investment for the US by Hsiao and Tahmiscioglu (1997) finds that the pooled estimate substantially underestimates the impact of liquidity, and that there are considerable differences across firms in terms of their investment behaviour.
This hypothesis finds support from the sub-sample regression results presented in Table 8 below, which suggest that the insignificance of the low-tech coefficient above reflects some correlation with high- or medium-tech terms. We explore to this in more detail below.
Jaffe (1989), using US data on research spending by university departments, finds a more significant and somewhat larger effect on commercial innovative activity, as measured by corporate patents, within technical industries such as drugs and chemicals than in the total across industries.
The results for the medium-tech sample are available upon request.
References
Abramovsky L, Simpson H (2011) Geographic proximity and firm university innovation linkages: evidence from Great Britain. J Econ Geogr 11:949–977
Abramovsky L, Harrison R, Simpson H (2007) University research and the location of business R & D. Econ J 117:C114–C141
Adams JD, Chiang EP, Starkey K (2001) Industry-university cooperative research centers. J Technol Transf 26:73–86
Adams JD, Chiang EP, Jensen JL (2003) The influence of federal laboratory R&D on industrial research. Rev Econ Stat 85:1003–1020
Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica 60:323–351
Aghion P, Bloom N, Blundell R, Griffith R, Howitt P (2005) Competition and innovation: an inverted U relationship. Quart J Econ 120:701–728
Baltagi BH, Griffin JM (1997) Pooled estimators versus their heterogeneous counterparts in the context of dynamic demand for gasoline. J Econom 77:303–327
Baltagi BH, Griffin JM, Xiong W (2000) To pool or not to pool: homogeneous versus heterogeneous estimators applied to cigarette demand. Rev Econ Stat 82:117–126
Baltagi BH, Bresson G, Pirotte A (2002) Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators. Some empirical evidence from US electricity and natural-gas consumption. Econ Lett 76:375–382
Baltagi BH, Bresson G, Griffin JM, Pirotte A (2003) Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption. Empir Econ 28:795–811
Banerjee A, Hendry DF (1992) Testing integration and cointegration: an overview. Oxford Bull Econ Stat 54:225–255
Becker B, Pain N (2008) What determines industrial R & D expenditure in the UK? The Manch Sch 76:66–87
Bloom N, Griffith R, Van Reenen J (2002) Do R&D tax credits work? Evidence from an international panel of countries 1979–97. J Pub Econ 85:1–31
Blundell R, Griffith R, Van Reenen J (1999) Market share, market value and innovation in a panel of British manufacturing firms. Rev Econ Stud 66:529–554
Bond S, Harhoff D, Van Reenen J (2003) Investment, R&D and financial constraints in Britain and Germany. Centre for Economic Performance Discussion Paper 0595
Busom I (2000) An empirical evaluation of the effects of R&D subsidies. Econ Innov New Technol 9:111–148
Caves RE (1985) International trade and industrial organisation. Problems solved and unsolved. Eur Econ Rev 28:377–395
Clemenz G (1990) International R&D competition and trade policy. J Int Econ 28:93–113
Cohen W (1995) Empirical studies of innovative activity. In: Stoneman P (ed) Handbook of the economics of innovation and technological change. Blackwell, Oxford
Cohen WM, Levin RC (1989) Empirical studies of innovation and market structure. In: Schmalensee R, Willig RD (eds) Handbook of industrial organisation II. North-Holland, Amsterdam, pp 1059–1107
Dasgupta P, Stiglitz J (1980) Industrial structure and the nature of innovative activity. Econ J 90:266–293
David PA, Hall BH, Toole AA (2000) Is public R&D a complement or substitute for private R&D? A review of the econometric evidence. Res Policy 29:497–529
Dixit AK (1988) International R&D competition and policy. In: Hazard HA, Spence AM (eds) International competitiveness. Ballinger, Cambridge
Driffield N (2001) The impact on domestic productivity of inward investment in the UK. Manch Sch 69:103–119
Driver C, Imai K, Temple P, Urga G (2004) The effect of uncertainty on UK investment authorisation: homogenous versus heterogeneous estimators. Emp Econ 29:115–128
Durbin J (1954) Errors in variables. Rev Int Stat Inst 22:23–32
Gerhaeusser K (1991) Firm size and R & D expenditure. A decomposition analysis for the West Germany. Econ Lett 37:459–463
Granger CWJ (1980) Long memory relationships and the aggregation of dynamic models. J Econ 14:227–238
Guellec D, Ioannidis E (1997) Causes of fluctuations in R&D expenditures: a quantitative analysis. OECD Econ Stud 29:123–138
Guellec D, Van Pottelsberghe B (2003) The impact of public R&D expenditure on business R&D. Econ Innov New Technol 12:225–244
Hall BH (2002) The financing of research and development. Oxf Rev Econ Policy 18:35–51
Hall BH, Van Reenen J (2000) How effective are fiscal incentives for R&D? A review of the evidence. Res Policy 29:449–469
Hall BH, Mairesse J, Branstetter L, Crepon B (1999) Does cash flow cause investment and R&D: an exploration using panel data for French, Japanese, and United States scientific firms. In: Audretsch D, Thurik SR (eds) Innovation, industry evolution, and employment. Cambridge University Press, Cambridge
Hall BH, Mairesse J, Mohnen P (2010) Measuring the returns to R&D. UNU-MERIT working paper 2010-006
Harhoff D (1998) Are there financing constraints for R&D and investment in German manufacturing firms? Annales d’Economie et de Statistique 49(50):421–456
Harman HH (1976) Modern factor analysis (3rd edn) University of Chicago Press. Section 8.3
Hausman JA (1978) Specification tests in econometrics. Econometrica 46:1251–1271
Henderson R, Jaffe A, Trajtenberg M (2005) Patent citations and the geography of knowledge spillovers: a reassessment comment. Am Econ Rev 95:461–464
Himmelberg CP, Petersen BC (1994) R&D and internal finance: a panel study of small firms in high-tech industries. Rev Econ Stat 76:38–51
Hsiao C, Tahmiscioglu AK (1997) A panel analysis of liquidity constraints and firm investment. J Am Stat Assoc 92:455–465
Jaffe AB (1989) Real effects of academic research. Am Econ Rev 79:957–970
Jaffe AB, Trajtenberg M, Henderson R (1993) Geographic localisation of knowledge spillovers, as evidenced by patent citations. Quart J Econ 108:577–598
Kamien MI, Schwartz NL (1982) Market structure and innovation. Cambridge University Press, Cambridge
Kamien MI, Mueller E, Zang I (1992) Research joint ventures and R&D cartels. Am Econ Rev 82:1293–1306
Kanwar S, Evenson R (2003) Does intellectual property protection spur technological change? Oxf Econ Pap 55:235–264
Kremers JJM, Ericsson NR, Dolado JJ (1992) The power of cointegration tests. Oxf Bull Econ Stat 54:325–348
Krugman P (1991) Geography and trade. MIT Press, Cambridge
Lewbel A (1992) Aggregation with log-linear models. Rev Econ Stud 59:635–642
Lewbel A (1994) Aggregation and simple dynamics. Am Econ Rev 84:905–918
Maddala GS, Trost RP, Li H, Joutz F (1997) Estimation of short-run and long-run elasticities of energy demand from panel data using shrinkage estimators. J Bus Econ Stat 15:90–100
Mamuneas TP, Nadiri MI (1996) Public R&D policies and cost behavior of the US manufacturing industries. J Pub Econ 63:57–81
Mulkay B, Hall BH, Mairesse J (2001) Investment and R&D in France and in the United States. In: Herrmann H, Strauch R (eds) Investing today for the world tomorrow. Springer, Berlin
Nickell S (1981) Biases in dynamic models with fixed effects. Econometrica 49:1399–1416
OECD (2005) Main Science and Technology Indicators. http://www.sourceoecd.org
Pagan A (1984) Econometric issues in the analysis of regressions with generated regressors. Int Econ Rev 44:223–258
Pesaran MH, Smith R (1995) Estimation of long-run relationships from dynamic heterogeneous panels. J Econ 68:79–114
Robertson D, Symons J (1992) Some strange properties of panel data estimators. J Appl Econ 7:175–189
Romer PM (1990) Endogenous technological change. J Polit Econ 98:S71–S102
Salop S (1977) The noisy monopolist: imperfect information, price dispersion, and price discrimination. Rev Econ Stud 44:393–406
Schumpeter JA (1939) Business cycles. Allen and Unwin, London
Schumpeter JA (1942) Capitalism, socialism, and democracy. Harper, New York
Stern S, Porter M, Furman J (2002) The determinants of national innovative capacity. Res Policy 31:899–933
Symeonidis G (2001) Price competition, innovation and profitability: theory and UK evidence. Centre for economic policy research discussion paper 2816
Thompson P, Fox-Kean M (2005a) Patent citations and the geography of knowledge spillovers: a reassessment. Am Econ Rev 95:450–460
Thompson P, Fox-Kean M (2005b) Patent citations and the geography of knowledge spillovers: a reassessment reply. Am Econ Rev 95:465–466
Tirole J (1988) The theory of industrial organisation. MIT Press, Cambridge, MA
Van Reenen J (1997) Why has Britain had slower R&D growth? Res Policy 26:493–507
Veugelers R, Cassiman B (2004) Foreign subsidiaries as a channel of international technology diffusion: Some direct firm level evidence from Belgium. Eur Econ Rev 48:455–476
Veugelers R, Vanden Houte P (1990) Domestic R&D in the presence of multinational enterprises. Int J Ind Organ 8:1–15
Wu D (1973) Alternative tests of independence between stochastic regressors and disturbances. Econometrica 41:733–750
Zietz J, Fayissa B (1992) R&D expenditures and import competition: some evidence for the U.S. Rev World Econ 128:52–66
Acknowledgments
We would like to thank Ron Borzekowski, Georges Bresson, Holger Gorg, Ron Smith, Christopher Snyder, Bruno Van Pottelsberghe de la Potterie, Michela Vecchi and Martin Weale for helpful comments and discussion. We also thank conference participants at the Applied Econometrics Association in Paris, the Econometric Society World Congress in London, the International Industrial Organization in Atlanta, the Royal Economic Society in Nottingham and a seminar at the National Institute of Economic and Social Research in London for their comments. Any remaining errors are our own. Financial support from the Economic and Social Research Council (ESRC) grant No. L138250122 is also gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Becker, B., Hall, S.G. Do R&D strategies in high-tech sectors differ from those in low-tech sectors? An alternative approach to testing the pooling assumption. Econ Change Restruct 46, 183–202 (2013). https://doi.org/10.1007/s10644-012-9122-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10644-012-9122-7