Intangibles and innovation-labor-biased technical change
| DOI | https://doi.org/10.1108/JIC-10-2019-0241 |
| Date | 11 June 2020 |
| Published date | 11 June 2020 |
| Pages | 649-669 |
| Author | Hannu Piekkola |
| Subject Matter | Information & knowledge management,Knowledge management,HR & organizational behaviour,Organizational structure/dynamics,Accounting & Finance,Accounting/accountancy,Behavioural accounting |
Intangibles and innovation-
labor-biased technical change
Hannu Piekkola
Department of Economics, University of Vaasa, Vaasa, Finland
Abstract
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.
Keywords Intangible capital, R&D, Skill-biased technical change, Innovative work
Paper type Research paper
1. Introduction
This paper introduces an analysis of all Finnish industries from 1995 to 2013 using a
production function that covers innovation-labor-biased technical change (IBTC),
accumulating the intangible assets (IAs) of the firms within the industries and knowledge
spillovers. The production function for different firm types is evaluated in the NACE 3-digit
level industries. IBTC accounts for the direct effect of innovative work on technology
(Hellerstein et al., 1999;Ilmakunnas and Piekkola, 2014). The contribution in this paper is a
different method that accounts for relative IA compensations rather than the IA labor share
Intangibles
and IBTC
649
JEL Classification —O33, O32, O30, J30, J42, M12
© Hannu Piekkola. 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.
This paper is funded as part of the EU Horizon 2020 project, GLOBALINTO (‘Capturing the value of
intangible assets in micro data to promote the EU’s growth and competitiveness’, 2019–2022), project
number 822229. The author thank participants for valuable comments from the GLOBALINTO
workshop on the Special Issue for JIC in July 2020 as part of the IC15 –The World Conference on
Intellectual Capital for Communities Conference, Paris 11-12.7.2019 at UNESCO, and coorganised by the
European Chair on Intangibles, Universit
e Paris-Saclay.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1469-1930.htm
Received 21 October 2019
Revised 17 December 2019
1 February 2020
24 March 2020
Accepted 20 April 2020
Journal of Intellectual Capital
Vol. 21 No. 5, 2020
pp. 649-669
Emerald Publishing Limited
1469-1930
DOI 10.1108/JIC-10-2019-0241
as proxy for the quality of labor creating IBTC. IBTC also creates horizontal knowledge
spillovers. Another novelty is the inclusion of IA capital accumulation in the same model
(Corrado, Haskel, and Jona-Lasinio, 2017). Two types of IAs are constructed, i.e., the
organizational capital (OC) related to marketing and management, and research and
development (R&D). The novelty here is that they are measured in a comparable way to
observe the large differences in the ways they improve productivity. Each individual
contributes to a certain type of IA so that the alternative IAs are not overlapping. The IA
measurement follows the EU 7th framework project Innodrive, see Piekkola et al. (2011) and
the guidelines in OECD (2010).
We measure the own-account IA, which is assumed to be produced with a similar share
of factor inputs as are used in the production of purchased IAs from IA producing
industries (business services). Much of the purchased IAs may also consist of intermediates
used in the production of own-account IAs and may currently be categorized as
intermediate inputs from business and ICT services. This is our way of measuring the
“overhead”of the innovation-type labor costs mentioned in the OECD (2010) approach. IAs
are similar to tangible capital and require additional intermediates and physical capital to
transform it into accumulating capital.
Organizational capital is evaluated in the management and marketing work, and is an
important part of economic competence in Corrado et al. (2012). Economic competence
accounts for 38% of all intangible capital in the EU countries, see also Roth and Thum (2013);
Haskel and Westlake (2017). The measure IBTC is a good approximation of technological
improvement together with related knowledge spillovers. The analysis obtains support from
A~
n
on Hig
on, G
omez, and Vargas (2017), who find that R&D, human capital, and design all to
contribute to total factor productivity (TFP), but human capital influences it to higher degree
than R&D. We find OC type IBTC to be important especially among large firms but it is
negatively related to the amount of OC. R&D plays an important role in accumulating IAs and
R&D type IBTC complements it. Knowledge spillovers are large and this non-rival part of IA
can be a considerable share of what is usually considered as marketable IA. Especially SMEs
are important sources of knowledge spillovers to all kinds of firms. The IA analysis also
covers micro firms with less than ten employees, which have somewhat different roles
compared with large SMEs.
Section 2 presents the literature review on IAs. Section 3 models the intangibles in
upstream industries or the self-production of IAs. Section 4 describes the data and the IA
measurement. Section 5 conducts empirical research on how IAs affect technical change and
accumulate. Section 6 concludes.
2. Literature
IAs, or intellectual capital with a wider context, has gained considerable interest since the
1990s (Cheng et al., 2010;Phusavat et al., 2011). Thum-Thysen et al. (2017) defines IAs as all
types of strategic investments in the long-run growth of individual companies and the
economy as a whole. Corrado, Hulten, and Sichel (2005,2009) advanced the research on
intangible assets in the economics field by introducing a broad division of intangibles that
covers computerized information (digitalization with software and database) and economic
competences acquired through management, investments in branding and through
purchases of management and consulting services. In accounting, different extensions
have been used, such as including customer relations; for other definitions, see Hejazi et al.
(2016).Andrews and De Serres (2012) analyze network externalities as a way to benefit from
returns to scale; this view can be extended to several types of information and communication
technologies (ICTs) that are not considered here.
Organization for Economic Co-operation and Development (OECD) (2005, annex B pp.
149–154) sets forth the guidelines for innovation surveys to cover a wider set of intangibles
JIC
21,5
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