Patents, secret innovations and firm's rate of return : differential effects of the innovation leader

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This paper studies the dynamic interactions and the spillovers that exist among patent application intensity, secret innovation intensity and stock returns of a well-defined technological cluster of firms. We study the differential behavior when there is an Innovation Leader (IL) and the rest of the firms are Innovation Followers (IFs). The leader and the followers of the technological cluster are defined according to their patent innovation activity (stock of knowledge). We use data on stock returns and patent applications of a panel of technologically related firms of the United States (US) economy over the period 1979 to 2000. Most firms of the technological cluster are from the pharmaceutical-products industry. Interaction effects and spillovers are quantified by applying several Panel Vector Autoregressive (PVAR) market value models. Impulse Response Functions (IRFs) and dynamic interaction multipliers of the PVAR models are estimated. Secret patent innovations are estimated by using a recent Poisson-type patent count data model, which includes a set of dynamic latent variables. We show that firms’ stock returns, observable patent intensities and secret patent intensities have significant dynamic interaction effects for technologically related firms. The predictive absorptive capacity of the IL is the highest and this type of absorptive capacity is positively correlated with good firm performance measures. The innovation spillover effects that exist among firms, due to the imperfect appropriability of the returns of the investment in R&D, are specially important for secret innovations and less relevant for observed innovations. The flow of spillovers between followers and the leader is not symmetric being higher from the IL to the IFs.
Patent count data model, Stock market value, Secret innovations, Absorptive capacity, Technological proximity, Panel Vector Autoregression (PVAR), Impulse Response Function (IRF), Efficient Importance Sampling (EIS)
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