Abstract
We provide novel evidence about the innovation–employment nexus by decomposing it by R&D intensity in a continuous setup and relaxing the linearity assumption. Using a large international firm-level panel data set for OECD countries and employing a flexible semi-parametric method—the generalised propensity score—allows us to recover the full functional relationship between the R&D-driven innovation and firm employment as well as address important econometric issues, which is not possible in the standard estimation approach used in the previous literature. Our results confirm that the relationship between innovation and employment entails important nonlinearities responsible for significant differences in employment response to innovation at different R&D intensity levels.
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Notes
For an accessible presentation of the logic underlying the propensity-score matching, see Heinrich et al. (2010).
According to Bia et al. (2011), the estimated dose–response function is robust to the choice of a semi-parametric approach, but it is sensitive to a parametric specification.
Companies which do not disclose figures for R&D investment or which disclose only figures which are not material enough were also omitted from our analysis.
Note, however, that data reported by the Scoreboard companies do not inform about the actual geographic distribution of the number of employees. A detailed geographic analysis should take into account the location of subsidiaries of the parent Scoreboard companies as well as the location of other production activities involved in the value-chains.
These are not shown in the regression output table in order to save the space.
Higher-order power transformations of the GPS variable turned out to be insignificant and therefore were omitted them from the model specification for the sake of parsimony.
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The authors acknowledge helpful comments from Francesco Bogliacino and Daria Ciriaci, Mihails Hazans, Enkelejda Havari, Giuseppe Piroli, the editor of the journal, two anonymous referees, participants of the conferences on Counterfactual Methods for Policy Impact Evaluation in Rome and Milan, the Scottish Economic Society Annual Conference in Perth and the 57th Meeting of the Italian Economic Association in Milan 10th Nordic Econometric Meeting in Stockholm and 22nd Annual Conference on Global Economic Analysis in Warsaw as well as participants of the research seminars at the European Commission, Bank of Latvia, University of Leuven and Stockholm School of Economics in Riga. We are grateful to Pietro Moncada-Paterno-Castello and the IRI team for granting access to the EU Industrial R&D Investment Scoreboard data. This article follows the methodological framework of Kancs and Siliverstovs (2019). The authors are solely responsible for the content of the paper. The views expressed are purely those of the authors and may not under any circumstances be regarded as stating an official position of the European Commission or the Bank of Latvia. Computations were performed in R (R Development Core Team 2008).
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Kancs, d., Siliverstovs, B. Employment effect of innovation. Empir Econ 59, 1373–1391 (2020). https://doi.org/10.1007/s00181-019-01712-6
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DOI: https://doi.org/10.1007/s00181-019-01712-6