Abstract
This work studies the linkages between spatially bound knowledge spillovers, internal research, and innovation (R&I) activities and firm productivity. Spillovers are modeled to emanate from intra- and extra-sectoral R&I activities in the firms’ regional business environments. We specifically test for non-linearities in the complex relationship between these internal and external knowledge sources and quantify their joint marginal effect on firm productivity. Our empirical results for a large panel of German manufacturing firms (1) underline the overall importance of knowledge spillovers in driving productivity and (2) point at distinct interactions between the included knowledge sources: First, we find that intra-sectoral knowledge spillovers only have a statistically significant effect on firm productivity when extra-sectoral spillovers are sufficiently large. Secondly, the link between knowledge spillovers and productivity varies with the level of the firms’ internal R&I activities.
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Notes
We still include labor input in Eq. (1) to allow for potential deviations from linear homogeneity when modeling output with respect to factor inputs capital and labor (Frantzen 2002). Assessing the statistical significance of \( \rho \) allows us to test for such deviations. The implicit coefficient for labor input is \( \beta = \varrho - \alpha - \gamma + 1. \)
In Germany, AMADEUS is provided by Bureau van Dijk and Creditreform. For the purpose of this study, AMADEUS updates 88-184 are used. A recent review by Carboni and Medda (2018) has shown that the AMADEUS database is increasingly used for research endeavors at the micro level.
Note that we will relax this assumption when we account for geographical neighborhood effects in a robustness check.
The OECD RegPAT database presents patent data that have been linked to regions through the addresses of applicants. The database derives from PATSTAT data and is documented in Maraut et al. (2008). For the purpose of this analysis, the RegPAT version January 2014 is used.
Please note that although the three sectors with the highest numbers of firm-year observations are NACE 28, 25, and 26, we have decided to replace NACE 25 by NACE 10 (ranked fourth) since the spatial patterns for NACE 28 and NACE 25 appear to be very similar. As a matter of course, the underlying map displaying the spatial distribution of regional patent activities and sample firms for NACE 25 can be obtained from the authors upon request.
Obviously, it would be advisable to control for other latent influencing factors; i.e., TFP shocks, as proposed in the literature on structural estimators for micro-level production functions (see, for instance, Olley and Pakes 1996). While these structural estimators provide a suitable means to sidestep endogeneity concerns under ideal data settings, Eberhardt and Helmers (2016) have recently found that data imperfections (e.g., unbalanced panel with missing observations) may significantly hamper the bootstrapping procedures required for inference in these structural estimators and may thus lead to significant estimation biases.
The reader should note that the choice of estimator is unaffected by the inclusion of spatial lags for intra- and extra-sectoral patent activities in the estimation of Eqs. (2)–(4). In the spatial econometric literature the resulting specification is also referred to as the spatial lag of X (SLX) model; see, e.g., Halleck Vega and Elhorst (2015) for details.
We have also tested for changes in the effects once we include spatial lags in the multiplicative interaction terms. As for the additive specifications, the empirical results remained unaffected, though. The specific estimation results can be obtained from the authors upon request.
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Mitze, T., Makkonen, T. When interaction matters: the contingent effects of spatial knowledge spillovers and internal R&I on firm productivity. J Technol Transf 45, 1088–1120 (2020). https://doi.org/10.1007/s10961-019-09729-w
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DOI: https://doi.org/10.1007/s10961-019-09729-w