Heterogeneity of innovation strategies and firms’ performance
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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- 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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- 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.
- 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.
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