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Employment Effect of Innovation

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Abstract

The present paper estimates and decomposes the employment e?ect of innovation by R&D intensity levels. Our micro-econometric analysis is based on a large international panel data set from the EU Industrial R&D Investment Scoreboard. Employing ?exible semi-parametric methods – the generalised propensity score – allows us to recover the full functional relationship between the R&D investment and ?rm employment, and to address important econometric issues, which is not possible in the standard estimation approach used in the previous literature. Our results suggest that modest innovators do not create and may even destruct jobs by raising their R&D expenditures. Most of the jobs in the economy are created by innovation followers: increasing innovation by 1% may increase employment up to 0.7%. The job creation e?ect of innovation reaches its peak when the R&D intensity is around 100% of the total capital expenditure, after which the positive employment e?ect declines and becomes statistically insigni?cant. Innovation leaders do not create jobs by further increasing their R&D expenditures, which are already very high.

Suggested Citation

  • d'Artis Kancs & Boriss Siliverstovs, 2017. "Employment Effect of Innovation," KOF Working papers 17-428, KOF Swiss Economic Institute, ETH Zurich.
  • Handle: RePEc:kof:wpskof:17-428
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    File URL: http://dx.doi.org/10.3929/ethz-a-010852605
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    References listed on IDEAS

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    Cited by:

    1. Kancs, d’Artis & Siliverstovs, Boriss, 2016. "R&D and non-linear productivity growth," Research Policy, Elsevier, pages 634-646.
    2. Persyn, Damiaan & Torfs, Wouter & Kancs, d’Artis, 2014. "Modelling regional labour market dynamics: Participation, employment and migration decisions in a spatial CGE model for the EU," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 29, pages 77-90.
    3. Francesco Di Comite & D'Artis Kancs, 2015. "Macro-Economic Models for R&D and Innovation Policies - A Comparison of QUEST, RHOMOLO, GEM-E3 and NEMESIS," JRC Working Papers JRC94323, Joint Research Centre (Seville site).

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • F23 - International Economics - - International Factor Movements and International Business - - - Multinational Firms; International Business
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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