Intangibles and innovation-labor-biased technical change
Piekkola, Hannu (2020-06-11)
Emerald
11.06.2020
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020072447603
https://urn.fi/URN:NBN:fi-fe2020072447603
Kuvaus
vertaisarvioitu
©2020 the author(s). Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.
©2020 the author(s). Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.
Tiivistelmä
Purpose - This paper analyzes the productivity effects of structural capital such as research and development (R&D) and organizational capital (OC). Innovation work also produces innovation-labor-biased technical change (IBTC) and knowledge spillovers. Analyses use full register-based dataset of Finnish firms for the period 1994–2014 from Statistics Finland.
Design/methodology/approach - Intangibles are derived from the labor costs of innovation-type occupations using linked employer-employee data. The approach is consistent with National Accounting and offered as one method in OECD (2010) and applied in statistical offices, e.g. in measuring software. The EU 7th framework Innodrive project 2008–2011 extended this method to cover R&D and OC.
Findings - Methodology is implementable at firm-level and offers way to link personnel reporting to intangible assets. The OC-IBTC as well as total resources allocated to OC are relevant for productivity growth. The R&D stock is relatively higher but R&D-IBTC is smaller than OC-IBTC. Public policy should, besides technology policy, account for OC and OC-IBTC and related knowledge spillovers in the industries that are most important among the SMEs (low market-share-firms).
Research limitations/implications - The data are based on remote access to Statistics Finland; the data cannot be disseminated.
Originality/value - Intangible assets are measured from innovation work that encompasses not only R&D work. IBTC is proxied in production function estimation by relative compensations on IA work. The non-competing nature of IAs is captured by IA knowledge spillovers. The sample sizes are much higher than in earlier studies on horizontal knowledge spillovers (such as for SMEs,) thus bringing additional generality to the results.
Design/methodology/approach - Intangibles are derived from the labor costs of innovation-type occupations using linked employer-employee data. The approach is consistent with National Accounting and offered as one method in OECD (2010) and applied in statistical offices, e.g. in measuring software. The EU 7th framework Innodrive project 2008–2011 extended this method to cover R&D and OC.
Findings - Methodology is implementable at firm-level and offers way to link personnel reporting to intangible assets. The OC-IBTC as well as total resources allocated to OC are relevant for productivity growth. The R&D stock is relatively higher but R&D-IBTC is smaller than OC-IBTC. Public policy should, besides technology policy, account for OC and OC-IBTC and related knowledge spillovers in the industries that are most important among the SMEs (low market-share-firms).
Research limitations/implications - The data are based on remote access to Statistics Finland; the data cannot be disseminated.
Originality/value - Intangible assets are measured from innovation work that encompasses not only R&D work. IBTC is proxied in production function estimation by relative compensations on IA work. The non-competing nature of IAs is captured by IA knowledge spillovers. The sample sizes are much higher than in earlier studies on horizontal knowledge spillovers (such as for SMEs,) thus bringing additional generality to the results.
Kokoelmat
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