Shadow prices for intangible resources
| DOI | https://doi.org/10.1108/JIC-02-2020-0031 |
| Published date | 29 January 2021 |
| Date | 29 January 2021 |
| Pages | 666-686 |
| Subject Matter | Information & knowledge management,Knowledge management,HR & organizational behaviour,Organizational structure/dynamics,Accounting & finance,Accounting/accountancy,Behavioural accounting |
| Author | Elena Shakina,Iuliia Naidenova,Angel Barajas |
Shadow prices for
intangible resources
Elena Shakina
International Laboratory of Intangible-driven Economy,
National Research University Higher School of Economics,
Saint Petersburg, Russian Federation
Iuliia Naidenova
International Laboratory of Intangible-driven Economy,
National Research University Higher School of Economics,
Perm, Russian Federation, and
Angel Barajas
International Laboratory of Intangible-driven Economy, Department of Finance,
School of Economics and Management,
National Research University Higher School of Economics,
Saint Petersburg, Russian Federation
Abstract
Purpose –Focusing on managerial problems related to the measurement of intangibles, this paper develops
and validates a hedonic-pricing methodology for the evaluation of the intangible resources of companies
obtaining their shadow prices.
Design/methodology/approach –The paper adapts a hedonic-pricing methodology developed primarily
for markets in real estate and secondhand cars to define how much intangibles may contribute to companies’
market value. A certain calibration of the original tool has been developed to make this methodology
appropriate for interpretation and practical use. The main advantage of this approach is that it allows for an
evaluation of the shadow prices of intangible resources. These prices can be interpreted as the market value of
the intangible resources which are not reflected on the balance sheet.
Findings –The results of this study demonstrate that hedonic pricing with a self-selection correction
generates robust estimates. As one can see, the positive contribution of a high endowment of intangibles for all
shadow prices is confirmed through estimations using two different techniques. Meanwhile, the negative effect
of a low endowment is even moreevident for the baseline model. This model shows consistent negative shadow
prices for the majority of underinvested intangibles. Brands have the highest shadow prices in the introduced
models; humancapital, as measured by the qualification of top management and investments in employees, has
likewise demonstrated high prices. However, most structural resources seem to be not reflected to a large
degree in companies’market value.
Practical implications –This paper brings new opportunities to obtain the monetary value of intangible
resources based on estimated market prices of a corporation’s resource portfolio. These prices may be used for
several purposes –for example, benchmarking for performance management, capital budgeting or knowledge-
management practices. Moreover, by having methodological value, this study opens ways to evaluate any
other intangibles which are not explicitly discussed in the empirical test of this particular study.
Originality/value –This study primarily contributes to the methodological advancement of evaluation of
corporate intangible resources. It departs from the conventional hedonic-pricing mechanism to identify cogent
estimates to intangibles in monetary terms. Importantly, this mechanism implies individual shadow prices for
JIC
23,3
666
The authors thank the researchers of the ID Lab (NRU Higher School of Economics –Perm Campus) for
their useful comments and remarks during preparations and discussions of the paper. They also thank
the assistants of the ID Lab for their work creating the database and their support. The authors thank
Jeff Downing for his proofreading on the manuscript.
This article is an output of a research project implemented as part of the Basic Research Program at
the National Research University Higher School of Economics (HSE University).
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 11 February 2020
Revised 18 July 2020
22 August 2020
21 October 2020
Accepted 6 January 2021
Journal of Intellectual Capital
Vol. 23 No. 3, 2022
pp. 666-686
© Emerald Publishing Limited
1469-1930
DOI 10.1108/JIC-02-2020-0031
specific intangible resources which makes the contribution of this study unique for the existing literature,both
within resource-based and value-based views.
Keywords Shadow prices, Evaluation, Intangibles, Value-creation, Selection correction
Paper type Research paper
1. Introduction
The monetary evaluation of intangibles is one of the main challenges in the concept of
intellectual capital (Lev, 2001); it is a growing issue in corporate strategic management and is
becoming one of the most important problems of integrated reporting (Abhayawansa et al.,
2019). The fact that intangibles are of particular importance is not questioned anymore.
Nevertheless, approaches and toolkits for their measurement still generate hot debates. Both
scholars in the vast literature in resource-based and value-based theories and business
representatives are striving for validated metrics of intangibles along with empirical
confirmation of intangible-driven value creation. However, intangibles, despite being a key
counterpart of tangible resources displayed in traditional financial reports, are not yet
sufficiently disclosed for companies’stakeholders. This lack of disclosure distorts the
decision-making process and aggravates agency conflict due to information asymmetries.
This study proposes one possible solution for determining intangibles’value, expressed in
monetary terms. It departs from the conventional hedonic-pricing mechanism to identify
cogent estimates to intangibles in monetary terms. Hedonic pricing, which was developed for
the evaluation of tangible assets such as cars and real estate, allows likewise for the
identification of a model for the portfolio of corporate resources which do not obligatory have
material embodiment. The hedonic price applied for evaluation is treated as the share of final
value brought by each of the units of the relevant features (Rosen, 1974;Li and Brown, 1980;
Gillard, 1981;Walden, 1990;Sirpal, 1994;Reis and Santos Silva, 2006;Requena-Silvente and
Walker, 2007).
The measurement of intangibles, especially their monetary evaluation, is one of the main
challengesin the conceptof intellectualcapital (IC),as stated by Lev (2001). In the resource-based
view (Barney 1991), a lack of methods for evaluating intangibles is seen as one of the main
obstacles to discover their strategic impact and contribution to corporate performance.
Although the relevance of the evaluation of intangibles is evident, there is no solidtheoretical
and methodologicalground for measuringintangibles. Thisgap has to be explored and bridged.
Thispaper deliversa theory-drivenmethodology forevaluation of separateintangibleresources
by adopting the well-known hedonic pricing. Importantly, this mechanism has not been
intensely practiced for intangible resources for a number of reasons such as low identification
opportunities of all essential parameters of the model, insufficient statistical power of the
estimated individual prices due to limited data and high degree of endogeneity of available
specification. The proposed methodology seeks to overcome these essential restrictions,
providingthe best matching withthe original hedonic-pricing procedure.These restrictionsare
eliminated both by theoreticalreasoning and framing and specific statistical elaborations.
The empirical part of the paper is based on a setting of more than 1,600 listed European
companies (UK, Germany, France, Spain, Italy) observed from 2004 to 2015. This setting has
been chosen to try to mitigate problems with non-efficient markets, which can be the case for
less-developed financial markets. The data set consists of indicators of intangibles clustered
according to the three-component structure (human, relational and structural capital) and
measured by the indicators described in Table 1. Descriptive statistics of the sample indicate
that companies appear to be very heterogeneous in terms of intangible endowment and
performance. Moreover, only those companies that create value can be examined for the
purpose of this study. Two techniques that allow for the mitigation of self-selection bias are
employed to estimate shadow prices: the Heckman procedure and endogenous treatment
regression. Both statistical techniques correct for self-selection bias, since value creation for a
Shadow prices
for intangible
resources
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