ABSTRACT
This paper examines how intangible assets contribute to firm-level productivity in the small open economies of Denmark and Finland from 2000 to 2013. We examine whether the role of intangible assets has changed over time, from the period of fairly stable growth prior to the crisis in 2008 to the more difficult period of recovery afterwards where intangible capital deepening decreased in 2008–2013 in many European countries. The productivity analysis is conducted in two stages. First, we derive total factor productivity (TFP), and second, we estimate the effects of intangible assets on total factor productivity. Our approach for measuring intangible assets is based on occupational classifications in a linked employer–employee dataset. We construct measures for three types of intangibles: broad R&D assets (R&D), organizational assets (OC) and information and communication technology assets (ICT). In both countries, the TFP effects of broad R&D increase slightly in the period after the crisis. For Finland, we also find that the TFP effects of OC increase after the crisis, while Denmark experienced a considerable increase in OC assets after financial crises in intangible intensive industries such as information, education and health industries, where productivity is lower.
Acknowledgements
We thank the editor and two anonymous reviewers for helpful comments on earlier versions of this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 See, in particular, the work undertaken on intangibles measurement at both the macro and micro level in the EU FP7 project, Innodrive (www.Innodrive.org) and the Horizon 2020 project, GLOBALINTO (www.globalinto.eu).
3 Another approach used by Ilmakunnas and Piekkola (Citation2014) measures intangibles investments with employees of Finnish firms, where work shares proxy intangible capital for three categories: organizational, RD and ICT.
4 The resulting final sample used includes all firms in the selected industries with over 10 employees and where data for key variables are available and positive (as the log values of these variables will be used in regressions).
5 In terms of NACE Rev. 2 2-digit code, the sample includes manufacturing (10–33), excludes other production (35–39), construction 41 and includes all services 45–74 with the exception of financial services (64–66).
6 We deflated values from Danish service industries with an aggregated producer price index for the entire service sector, as we did not find deflators for individual service industries for the sample from 1999 to 2013.
7 One finding of Awano et al. (Citation2010) is that the benefit of intangibles last longer in manufacturing than in service industries.
8 An exception here is Bontempi and Mairesse (Citation2015) who use a constant elasticity of substitution function.
9 The firm's exit decision can be a function of unobservable productivity shocks in an unbalanced panel like ours.
10 The beginning of year assets are the same as the assets at the end of the previous period. This means that depreciation and investments of year t has not happened but depreciation and investments of time t-1 have.
11 Results are essentially unchanged if the break is set at 2008 instead of 2009.
12 We have examined use of the ACF correction (suggested by Ackerberg, Caves, and Frazer Citation2015) to improve production function estimates of Olley and Pakes (Citation1996) and Levinsohn and Petrin (Citation2003), which might suffer from functional dependence problem for both countries but as it was unfeasible with industry dummies and did mostly affect dummy coefficients, we decided not to use the correction.
13 Higón, Gómez, and Vargas (Citation2017, 874) reports significant coefficients from 0.002 to 0.006 to R&D stock.