Innovation‐Related Diversification and Firm Value

AuthorZhao Rong,Sheng Xiao
Date01 June 2017
Publication Date01 June 2017
Innovation-Related Diversication and
Firm Value
Zhao Rong
Research Institute of Economics and Management, Southwestern University of Finance and
Economics, 55 Guanghuacun Street, Chengdu, Sichuan, 610074, China
Sheng Xiao
Bill and Vieve Gore Business School, Westminster College, 1840 S 1300 E, Salt Lake City,
UT 84105, USA
We examine a novel determinant of corporate diversication and its valuation
effect: corporate innovations. We nd consistent evidence that corporate
innovations increase the extent of diversication. To establish causality, we
estimate the rm xed effects, 2SLS and GMM models. The 2SLS model uses the US
state-level R&D tax credits as an instrumental variable for corporate innovations.
We also nd that a rm is more likely to diversify into an industry where it has
more applicable innovations. Further, such innovation-related diversication is
associated with signicantly higher rm value. Our results are robust to various
measures of corporate innovations.
Keywords: innovation, diversification, firm value
JEL classification: G34, O32
1. Introduction
What motivates companies to pursue industrial diversication? Why do some
industrial diversication efforts enhance rm value while others lower rm value?
The authors thank John Doukas (Editor), three anonymous referees, as well as Kai Li,
Peter Thompson and Yoram Barzel for helpful comments. The authors also thank
participants of the EFMA Conference 2014 in Rome, Academy of Management Annual
Meeting 2014 in Philadelphia and Paris Financial Management Conference 2013 for
helpful discussions. Sheng Xiao gratefully acknowledges financial support from the
University of Minnesotas single-semester leave fund, Westminster Colleges Gore course
release fund and Naomi Fallentine Weyher Endowment. Zhao Rong gratefully acknowl-
edges financial support from Florida International University Dissertation Year Fellowship.
European Financial Management, Vol. 23, No. 3, 2017, 475518
doi: 10.1111/eufm.12110
© 2016 John Wiley & Sons, Ltd.
These are arguably the two most important questions in industrial diversication
research (Montgomery, 1994). For example, for the motivation of diversication,
Khanna and Tice (2001) and Maksimovic and Phillips (2007) examine synergies
in internal capital markets, Tate and Yang (2015) study synergies in internal
labour markets, and Anjos and Fracassi (2015) scrutinise synergies in internal
information markets. For the valuation effect of diversication, Lang and Stulz
(1994), Berger and Ofek (1995), Lins and Servaes (1999) and Lamont and Polk
(2002) discover a diversication discount, while Whited (2001), Campa and Kedia
(2002), Villalonga (2004) and SColak (2010) nd that the diversication discount
drops and sometimes even turns into a premium, after addressing measurement errors
or endogeneity.
We propose a novel determinant of industrial diversication and its valuation effect:
corporate innovations. Specically, a rms innovative activities may result in
innovations that are not applicable to its existing business lines. To fully capture the
returns on these innovations, the rm may choose to diversify into an industry where
such innovations can be utilised. We therefore hypothesise that a rms innovations
have signicant and positive effects on its extent of subsequent diversication. In
addition, we hypothesise that a diversifying rm is more likely to enter an industry where
its innovations are more applicable. Inspired by the models of Penrose (1959),
Matsusaka (2001), Maksimovic and Phillips (2002) and Gomes and Livdan (2004), we
regard diversication as a process to explore the opportunities for productivity
improvements. Since innovative rms continuously innovate, it is natural to expect that
these rms continuously search for good matches between their innovations and business
lines through diversication. Such innovation-related diversication is expected to
improve the match between a rms innovations and its business lines, resulting in a
more efcient allocation of resources. We thus hypothesise that when a diversifying rm
has more applicable innovations in the industry it enters, such a good match should
enhance its rm value.
3M, one of the largest 100 companies in the world, provides a case in point. It is a very
active innovator, and has diversied into a myriad of industries. It les about 500 new
patents per year, and produces over 55,000 products in multiple industries such as
aerospace, architecture, automotive, healthcare, electronics, food, beverages and
transportation, etc. As 3M states in a brochure titled A Culture of Innovation,3Ms
business model is to foster organic growth by inventing new products that previously did
not exist. This business model has led not only to new products, but also the creation of
new industries.
3Ms business model seems to have worked well. For example, its
share price rose by 223.96% between 31 December 1999 and 31 December 2015, while
the S&P 500 index gained only 40.15%. 3Ms case shows that when a rm diversies
into a related industry after achieving proprietary innovations, its rm value may rise.
To examine whether 3Ms success is a general case that can be applied to other
companies, or just a special case that holds true only for a particular company, we
conduct a large-sample analysis. We rst examine whether a rms innovations inuence
Another relevant case is given by Kazuo Suzuki (1993), Chairman of Toppan Printing Co.
Ltd. in Japan, who describes how, through innovation-related diversication, his company
grew rapidly into a US$90 billion business with 19 ofces overseas employing about 2,200
people in the US, Europe and Asia.
© 2016 John Wiley & Sons, Ltd.
476 Zhao Rong and Sheng Xiao
its extent of subsequent diversication. We measure a rms innovations by the number
of patents that the rm has accumulated, weighted by the number of citations that these
patents receive. Our main measure of a rms extent of diversication is the number of
industries (at the 4-digit SIC level) in which the rm is operating, but we also use other
measures for robustness checks. When we examine the effects of innovations on
diversication, endogeneity is a major concern. For example, a third variable may affect
both diversication and innovation. Also, reverse causality is possible. For example,
Seru (2014) nds that diversication signicantly affects innovations. To tackle these
endogeneity issues, we utilise three main empirical strategies: (1) the rm xed effects
model with one-year lagged innovations as the key independent variable; (2) the 2SLS
(two-stage-least-squares) model using the state-level R&D tax credits as the instrument
for innovations; and (3) the dynamic panel GMM (Generalised Method of Moments)
model. As a robustness check, we also use the Abadie-Imbens matching method.
Our rm xed effects model successfully controls for the time-invariant rm-specic
factors that are correlated with both innovations and diversication. Furthermore, using
lagged innovations helps us mitigate the potential endogeneity issue to some degree. In
this model, we nd that a rms innovations have signicant and positive effects on the
extent of its subsequent diversication.
However, if innovations exhibit strong autocorrelation, using lagged innovations
would be less effective to mitigate potential endogeneity. To deal with endogeneity more
effectively, we resort to the 2SLS technique. Motivated by Wilson (2009), we use an
exogenous policy shock, i.e., the staggered implementation of state-level R&D tax
credits as the instrument for innovations. It is a valid instrument because the
implementation of R&D tax credits should affect rmsinnovations, but it should not
directly affect rmsdiversication decisions. Indeed, Wilson (2009) nds that the
implementation of state-level R&D tax credits signicantly boosts rmsR&D
expenditures, and many studies document that higher R&D expenditures are associated
with more innovations (e.g., Fang et al., 2014). Using this exogeneous policy shock, our
2SLS estimation provides evidence on the positive causal effect of a rms innovations
on its subsequent diversication.
One potential problem with the above instrument is that it only varies at the state-year
level, so it may fail to address the possibility that the time-variant rm-specic
unobservables affect both innovations and diversication. To deal with this dynamic
endogeneity issue, we follow Acemoglu et al. (2008), Hoechle et al. (2012), Wintoki
et al. (2012), OConnor and Rafferty (2012) and Flannery and Hankins (2013)s methods
and estimate the dynamic panel GMM model. Doing so enables us to use rm-level
internal instrumentsto tackle dynamic endogeneity. Our GMM estimation results
provide further evidence on the positive causal effect of innovations on diversication.
Thispositive relationshipbetween a rms innovationsand its subsequentdiversication
is consistent with the following two scenarios. Suppose a rm has some innovations
applicableto industry A but none of the rms innovations is applicableto industry B, and
both industriesare potential industriesthe rm may enter. Scenario (1) isthat the rm then
diversiesinto industryA, while scenario (2) is thatthe rm then diversies intoindustry B.
In both scenarios, the rms innovations are positively correlated with the extent of its
subsequent diversication. If scenario (1) is true, our hypotheses will receive strong
support. However, if scenario (2) is true, it will cast serious doubt on our hypotheses.
To distinguish between these two scenarios, we need to examine the following
question: given that a rm diversies into one of these two industries (A or B), and that its
© 2016 John Wiley & Sons, Ltd.
Innovation-Related Diversication and Firm Value 477

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