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Heterogeneity of innovation strategies and firms’ performance

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Abstract

This work deals with two main issues: first, the possibility of identifying differences in firm economic returns (operating profit margins) for different groups of innovation strategy and second, the possibility of checking for factors explaining the probability of being within the best performers for each group of innovation strategy. It is an empirically based analysis using descriptive statistics (first part) and a probit econometric analysis (second part) where data are collected at firm level from two CIS surveys matched with economic accountability data for 902 Italian manufacturing firms for the period 1998-2000. The distribution analysis of profit margins by different populations of firms shows a better economic performance for groups characterized by more complex innovation strategies. Unexpectedly, the risk associated to economic returns is lower for groups where returns’ mean is higher. In this case skewness is higher too suggesting that reaching “excellence” is more difficult. The probit regressions account for the role played by different (market and firm) factors on the probability of being the best positioned for each firm population. This work gives two main messages: first, when studying the impact of R&D activity (both on firm productivity or competitiveness) it is worth to distinguish among different kinds of innovation strategy rather than limiting the analysis to aggregated results and second, it appears quite clear that competition awards more complex innovation strategies than simple R&D intra-muros activity.

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Bibliographic Info

Paper provided by Institute for Economic Research on Firms and Growth - Moncalieri (TO) in its series CERIS Working Paper with number 200706.

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Length: 29 pages
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:csc:cerisp:200706

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Keywords: profitability; strategic heterogeneity; R&D and innovation; probit regression;

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References

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  1. Orietta Marsili & Ammon Salter, 2005. "'Inequality' of innovation: skewed distributions and the returns to innovation in Dutch manufacturing," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(1-2), pages 83-102.
  2. Jacques Mairesse & Mohamed Sassenou, 1991. "R&D Productivity: A Survey of Econometric Studies at the Firm Level," NBER Working Papers 3666, National Bureau of Economic Research, Inc.
  3. Elena Cefis & Matteo Ciccarelli, 2005. "Profit differentials and innovation," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(1-2), pages 43-61.
  4. Dosi, Giovanni, 1988. "Sources, Procedures, and Microeconomic Effects of Innovation," Journal of Economic Literature, American Economic Association, vol. 26(3), pages 1120-71, September.
  5. Newey, Whitney K., 1987. "Efficient estimation of limited dependent variable models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 36(3), pages 231-250, November.
  6. Jacques Mairesse & Pierre Mohnen, 2002. "Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application," American Economic Review, American Economic Association, vol. 92(2), pages 226-230, May.
  7. Cohen, Wesley M. & Levin, Richard C., 1989. "Empirical studies of innovation and market structure," Handbook of Industrial Organization, in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 2, chapter 18, pages 1059-1107 Elsevier.
  8. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
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Cited by:
  1. Fabio Cerina & Francesco Mureddu, 2010. "Is Agglomeration really Good for Growth? Global Efficiency, Interregional Equity and Uneven Growth," DEGIT Conference Papers c015_022, DEGIT, Dynamics, Economic Growth, and International Trade.

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