Abstract
Research and Development (R&D) is a key component behind technological development and economic growth; therefore, understanding the drivers of R&D is crucial. An interesting question is the role of technology spillovers, transferred by trade, and their impact on firm R&D. Here we analyze not only how international and domestic inter- and intra-industry technology spillovers affect firm R&D but also the relatively unexplored issue of how relationship-specific interactions between buyer and seller affect such spillovers. We find international technology spillovers to be larger and more significant than domestic inter- and intra-industry spillovers. Moreover, relationship-specific interactions between seller and buyer enhance technology spillovers in general and international spillovers in particular.
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Notes
Arguments for localized knowledge are characterized as five ‘stylised facts’ by Dosi (1988) and further developed by Feldman (1994a, b) as well as Baptista and Swann (1998). The spatial dimension of economic growth is highlighted by Amiti (1998) and Hanson (1998). Studies on trade, technology spillovers and R&D include Griliches (1992), Coe and Helpman (1995), Fagerberg (1995), Keller (1997, 2000) and Cohen and Levinthal (1989). For a survey, see Keller (2004).
For example, it may be crucial to control for firm-level heterogeneity, but such a control is difficult when using aggregated data.
Aghion and Howitt (1999) demonstrate how a positive correlation between productivity growth and entry and exit of firms can be established.
See, e.g., Stoneman (1983).
The annual response rate for firms with at least 50 employees in the financial statistics is approximately 97%.
An alternative to the FS R&D data is the bi-annually collected Research Statistics (RS), based on all firms in the FS with at least 200 employees and on a sample of firms with 50–200 employees, and given that these firms report R&D expenditures of at least 200 000 SEK to the FS. Regarding statistical reliability, the bi-annually collected “Research Statistics” is of higher quality but has less coverage. The RS and FS data generate very similar results, but the RS reduces the sample size with more than 50%, and we therefore focus on results from the FS.
Examples of industries not intensive in relationship-specific interactions include poultry processing, flour milling, petroleum refineries and corn milling; conversely, automobile, aircraft and computers are examples of industries intensive in relationship-specific investments.
It might be argued that spillovers are endogenous and/or that spillovers are realized with an impact lag. We therefore follow the assumption of strong exogeniety (Hendry 1995) and apply the spillover variables with one lag. An alternative is to use external instruments, which was not feasible for our research. In addition, as shown by Bound et al. (1995), using weak instruments may amplify the bias.
The significance of both tests for independent equations and the Mills ratio indicates that a selection procedure is appropriate. We find no contradictions between the selection and target equation, though we notice generally lower significance in the selection.
Note that the Inverse Mills Ratio (IMR) is a nonlinear function of the variables included in the first-stage probit and that the target equation can be identified from this nonlinearity alone. The nonlinearity of IMR arises from the assumption of normality. However, identification is aided by adding a variable to the selection equation that is closely related to the decision to undertake R&D. As discussed above, firms’ profit fits these requirements and is therefore applied.
The maximum number of observations is 15821, including firms with and without R&D. The selection equation accounts for a slight drop in observations. Results for the OLS model, Heckman target equation and the selection-adjusted FEVD reflects the number of R&D-performers. The Negative binomial model includes firms with zero observations where the fe calculation of the dispersion parameter accounts for loss of observations. See, e.g., Guimarães (2007) and Hilbe (2007).
One explanation for the negative results found regarding particular intra-industry spillovers in interaction intensive industries may be the extent that R&D might be outsourced; however, this is likely to be most pronounced in the home industry where personal interactions are common.
The correlation matrix in Table 7 indicates that though there is no severe multicollinearity, though we cannot exclude that multicollinearity might affect results when all spillover variables are considered.
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Tingvall, P.G., Poldahl, A. Determinants of Firm R&D: The Role of Relationship-Specific Interactions for R&D Spillovers. J Ind Compet Trade 12, 395–411 (2012). https://doi.org/10.1007/s10842-011-0112-7
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DOI: https://doi.org/10.1007/s10842-011-0112-7