ERASMUS UNIVERSITY ROTTERDAM
Reprint Prohibited
Erasmus School of Economics
Master Thesis
The influence of relatedness on corporate diversification.
Alexander Lunev
345203
Under supervision of Dr. F. Neffke
Rotterdam, 2011
1
Abstract
Corporate diversification is often associated with growth, success and development of the
company. There is much research for the motives of diversification; however connection
between corporate diversification and relatedness is quite new. This paper investigates influence
of human capital based and resource based relatedness measures on three aspects of corporate
diversification (diversification into secondary activities, diversification through the market, and
choice of the industry entry mode). The success of external diversification through market is
measure by stock price reactions. The research is based on the custom dataset created with use of
Zephyr, Orbis, Eurostat datasets and dataset created by Neffke and Henning (2010).
Keywords: diversification, relatedness, mergers and acquisitions, joint ventures, stock prices.
2
Contents
1. Introduction
2. Theoretical background
2.1. Firm diversification
2.2. Why do firms diversify?
2.3. Relatedness measures
2.4. Diversification modes
2.5. Market response on diversification
2.6. Disadvantages of diversification
2.7. Hypothesis
3. Data and methodology
3.1 Dataset
3.2 Description of the variables
3.3 Methodology
4. Empirical research and results
4.1 Descriptive statistics
4.2 Hypothesis 1.1
4.3 Hypothesis 1.2
4.4 Hypothesis 1.3
4.5 Hypothesis 2
5. Limitations and directions for further research
6. Conclusions and policy implications
7. References
8. Appendix
3
1. Introduction
Corporate diversification is closely associated with company’s success, performance and future
prospects. Studying the motives behind corporate diversification and factors which influence the
process of diversification is crucial in addressing the issues of business development strategy.
This paper studies the process of external and internal corporate diversification as well as market
reaction on corporate diversification moves.
Firm diversification strategy directly affects long run performance and thus makes crucial to
study the motives behind diversification. The most recent issue discussed by researchers is the
influence of relatedness on firm diversification and performance. Teece et al. (1994) stated that
there is an effect on firm performance by diversifying into somehow related activities.
Relatedness can be measured using value chain method created by Fan and Lang (2000), by
using classification codes (e.g. NACE or SIC) and by using human capital relatedness measure
developed by Neffke and Henning (2010). Neffke and Henning (2010) found evidence for
Sweden that diversification into skill-related industry has higher probability then diversification
into value chain or classification related industries. The research was done only for
diversification by internal development; external diversification remained untouched by
researchers. Diversification through market tends to occur in less related industries which lead to
the first research question:
What type of relatedness has an effect on firm diversification?
Understanding what type of relatedness plays bigger role in firm diversification strategy is
crucial for setting up the pattern for future diversification moves. Defining more influential
relatedness measure makes firm diversification strategy choice easier and more beneficial.
However, answer to the first research question does not provide insight on the success of
diversification move. Pennings et al. (1994) stated that diversification into related activities
through mergers and acquisitions or joint ventures are more successful. Pennings et al. (1994)
measured success as the endurance of the expansion, but there are many ways to measure firm’s
success. Positive investors’ reactions to the diversification move are often treated as a success of
firm’s expansion. Best way to see investors’ reactions is to look at the stock prices fluctuations.
This brings up the second research question:
Does diversification into related activities has a greater influence on the firm’s value?
4
In order to answer research questions the paper begins with the review of existing theories of
motives behind corporate diversification. Literature review continues with discussion about
different industry relatedness measures, which can be used to explain diversification. This paper
investigates corporate diversification process from three perspectives: diversification into
secondary activities, diversification through market, and choice of market diversification
instrument. Market diversification can be achieved by establishing joint venture or by making
mergers and acquisitions. Disadvantages and advantages of each method are discussed in the
literature analysis as well. Influence of the diversification strategy on firm’s value is discussed in
the theoretical part and the investigated empirically based on stock prices fluctuations.
The paper is organized in the following way: chapter 2 describes relevant theoretical background
in the field of corporate diversification, measures of relatedness between industries and market
reaction on diversification strategy. Than it follows up with the hypothesis based on theoretical
background described earlier. Chapter 3 describes the construction of the dataset for this research
and elaborates on the methodology used for empirical analysis. Chapter 4 is devoted to empirical
analysis and discusses the results. Chapter 5 discusses limitation of the research and presents
some guidelines for the future research. Conclusion and possible implication of the results are
made in chapter 6.
5
2. Theoretical background
2.1 Firm diversification
Company diversification is the process of making a portfolio of industries which are different
from the primary industry of the firm. The higher the amount of industries in company’s
portfolio, the higher is company’s diversification. Usually the process of diversification is driven
by the growth of the company because the company enters new industries seeking for more
market space. Despite that, the advantages and disadvantages of corporate diversification are not
clear. Motives for corporate diversification differ greatly as well. In the following chapter
theories, which explain motives for diversification and its consequences will be discussed.
2.2 Why do firms diversify?
In a perfect world, with no restrictions and complete information any firm diversification move
will have no effect on firm’s cash flows and no additional value will be created or destroyed.
This makes crucial to study the motives behind firm diversification, to understand why firms
diversify, which pattern they follow and what effect can be observed.
Theories of corporate diversification:
Agency theory
Most of the firms nowadays have a differentiation between owners and managers. This
differentiation causes well known problem of principals and agents. Principals delegate their
powers to managers in order to achieve given aim but without certain amount of control and
motivation agents behave themselves to maximize their own benefits. Stockholders are
principles and managers are agents in the firm perspective. Morck, Shleifer and Vishny (1990)
suggested that managers with an insignificant amount of equity owned in the company use
corporate assets for their own benefits and not for the benefits of stockholders. Diversification
can be one of the strategies for managers to enlarge their own wealth at the expense of
stockholders. Managers are usually willing to reinvest earnings of the firm. At the youth stage of
company’s lifecycle there is a plenty of opportunities to reinvest in a profitable way. When the
company reaches mature state, these opportunities distinct and managers seek for new ways of
reinvesting earnings. The solution is acquisitions, but as Jensen (1986) argued they are likely to
6
be low-benefit or even value destroying. The motives for such managerial behavior can be value
driven or risk driven, which is discussed later on in the next part of this paper. Value driven
motives for managerial behavior were discussed by Shleifer and Vishny (1989). They argued,
that managers involve into firm diversification in order to build a structure, which will demand
his or her managerial skills more, although this diversification can be value-destroying.
Market power
Traditionally diversification was treated as tool to reduce competition. Edwards (1955)
suggested that the main motive behind firm diversification is acquiring market power. Firm can
defend or extend its market power not only following monopoly strategy but also through
activities on other markets. Reduction of competition and increase of market power can be
achieved through several tactics. First, diversified firm can transfer profits from one, more
successful market, to support its positions on the other market. Second, presence of large
diversified firms on the market closes it from entry of smaller competitors. And thirdly,
Bernheim and Whinston (1990) had shown that while competitors meet each other on a number
of markets, not just one, they compete less aggressively because they realize their
interdependence.
Information asymmetry
If markets were perfect all agents will have access to perfect information. Although real markets
suffer from a number of imperfection and information asymmetry is one of them. Information
asymmetry theory is often opposed in the literature to the agency costs theory. Scharfstein and
Stein (1997) stated that information asymmetry arise when managers fail to fully explain the
value of a firm or project to the external capital market through signals. The wrong perceptions
of investors about the project or company lead to under or over investments and thus, to
inefficiency. Managers and capital market can get rid of inefficiency of resource allocation
caused by information asymmetry with the help of diversification. In other words, external
capital market, or a part of it, can be turned into internal capital market. Hyland and Diltz (2002)
suggested that motives for corporate diversification come from manager’s incentive to create or
enhance an internal capital market. Internal capital market allows controlling investments’ flow
for all projects better than if each project was financed using external capital market. However
Williamson (1975) argued that creation of internal capital markets may cause agent-principal
7
problem and thus will have some negative effect. Williamson (1975) suggested that information
asymmetry affects company’s governance structure because with the rise of information
asymmetry managers tend to behave more and more opportunistically. Managers tend to follow
their own utility maximization strategy which is different from the owners’ but due to certain
amount of control and motivation owners can change mangers’ behavior. When information
asymmetry arises, control becomes more difficult to imply and opportunism increases.
Transaction costs
Companies can benefit from making operating synergies in a number of ways. Mostly benefits
com from transaction costs perspective. Transaction cost is any cost, which is caused by
existence of institutions as Cheung and Steven (1987) state. Benefits from lower transaction
costs differ between vertically and horizontally diversified firms. Diversification across buyerseller chain is called vertical diversification, for example if car assembler diversifies into engine
production industry. Horizontal diversification is made into competitive fields, for example car
manufacturer diversifies into motorcycle industry. Vertical diversification can provide
transaction costs reductions due to elimination of various contracts between customers and
suppliers.
Transaction costs usually arise with asset specificity. If two economic agents trade on a regular
basis goods and services with very low asset specificity they can use market mechanism. If the
supplier refuses to fulfill his part of the contract, the buyer switches immediately to another
supplier. The same situation may happen vice versa, if the buyer refuses to follow the contract,
the supplier can sell his goods or services to another buyer. This condition holds true until
problem of the assets specificity arises. If the assets are highly specific, agents cannot switch
easily. On one hand, for the buyer it will be hard to find new supplier of these highly specific
goods. On the other hand, the supplier will have troubles while trying to sell these goods.
Interdependence of the supplier and the buyer causes possibilities for opportunistic behavior.
One can put the other into unbeneficial circumstances by the fear of opportunistic behavior. This
problem can be solved by making a contract. With the rise of assets specificity, contracts need to
become more and fuller to eliminate any possibility of opportunistic behavior. Such contracts
require huge amounts of resources to be created. Contract can be omitted by the integration of
the supplier and the buyer. It is so-called “make or buy” decision, where agent decides, whether
it is more beneficial to buy the asset with possible transaction costs or to make it in house. With
the rise of asset specificity, probability of making the asset arises and this serves as a proxy for
corporate diversification.
8
Resource based perspective
Many economists devoted their attention to explanation of firm diversification from agency
theory or market power theory. The resource based theory wasn’t that popular among the
researches but it is one of underlying motivations for firm diversification. First paper from
resource perspective was done by Penrose (1959) and suggested that diversification may occur
when firm has an excess capacity of resources. Later on, Teece (1980) developed this theory by
arguing that diversification driven by economies of scope takes place only while some market
imperfections are involved. If the markets are perfect, firm can trade its resources trough the
market without being involved into conglomeration. However, market imperfections occur quite
recently. Some resources cannot be easily transferred between firms because they are deeply
involved in firm’s daily functioning or because there are contracting problems.
Firm resources mainly are divided into three types: tangible, intangible and financial. Tangible
resources consist of production and distribution facilities available inside the company, like
production plant, equipment, sales force and etc. Intangible resources were defined by Porter
(1987) as “core skills”. One of the differences of intangible resources from tangible is the ability
of intangible resources to be transferred with a low or no cost. Intangible resource are usually
represented by skills and if one firm develops new skill it can be easily transferred to the other
firm through the employees, for example outstanding marketing skills developed in one industry
can easily be adopted in the related industry (Porter (1987) uses example of beer and cigarettes
industries). Financial resources are excluded from tangible and intangible classification because
of an open debate on them. The main debate is between Porter and Chatterjee and Wernerfelt.
First, Porter (1985) classified financial resources as tangible. Chatterjee and Wernerfelt (1991)
suggested that financial resources are more flexible than tangible resources and they have direct
influence on the diversification. The reason for that is influence of the capital structure on the
choice of related or unrelated diversification. Chatterjee and Wernerfelt (1991) argued that
unrelated diversification is more likely to be financed by long-term debt or short-term liquid
assets. Related diversification is more likely to be financed by internal assets, but their results
show almost the same probability of financing with internal assets for related and unrelated
diversification.
Lippman and Rumelt (1982) suggest that competitive advantage can be gained if the resource
cannot be easily transferred from one firm to another because imitation of this resource by
9
competitors is difficult. Diversification allows transferring this competitive advantage from one
industry to another.
Other important characteristic of resources is specificity. Montgomery and Wernerfelt (1988)
argue that resource specificity influences firm diversification directly. On one hand, if the
resource is strongly specific to a particular activity, than it can be used only in a small number of
other activities, but if resource is standard, it can be used in large number of industries. On the
other hand, marginal returns increase with the increase of resource specificity. Firms with more
standard resources tend to be more diversified than firms with very specific resources, although
profits of firm with more specific resources can be higher due to high level of resource
specificity.
Most of the literature devoted to resource based view on corporate diversification considers
single resource rather than a combination of resources. Tsang (1997) argues that sometimes a
willingness to get desired combination of resources is the motive for diversification. A firm can
receive increased profits by building a scarce combination of resources, even if each of used
resources is not scarce. Tsang (1997) provides an example of a pharmaceutical company with
above average R&D intensity and a retail chain with well-located outlets. If considered
separately, none of the firms show any outstanding performance. If they form a joint venture or
merge together, pharmaceutical company can use the sales possibilities of retail chain which will
make a distinctive competitive advantage for the pharmaceutical firm and thus increase its
profits.
Taxes
Taxes can serve as strong motive for corporate diversification. Diversified firms are sometimes
faced with lower taxation than single activity firms. Despite agency theory or information
asymmetry approach, taxation approach to describe the motives behind firm diversification lacks
permanency. Taxation policies differ across the countries and countries change them time to time
as well. Motives for corporate diversification will be discussed in the following part based on
possible tax reductions which are available by the moment or were available back in time. As
taxation policy changes, new ways to benefit from diversification may occur. To understand the
influence of taxation on firm diversification two approaches were created: shareholder’s
perspective and company’s perspective. First, shareholder’s perspective will be discussed and
then company’s perspective.
10
Dividends are the income of shareholders which are paid from excessive amount of free cash
flows generated by the firm. If firm generates free cash flows it can either reinvest them or pay
shareholders as dividends. Baysinger, Kosnik and Turk (1991) argued that not all free cash
flows could be reinvested with profit and thus they should be allocated among shareholders,
which caused motives for corporate diversification. Motives for corporate diversification are
caused by tax implied on dividends. As Hoskisson and Hitt (1990) state, the tax rate on
dividends was higher than tax rate on personal income before 1980 in the United States of
America. Taxation difference motivated shareholders to force firm management to spend all free
cash flow on firm growth, especially on acquiring new companies. Shareholders benefited
because reduction in dividends was offset by the increase in stock prices, and trading stocks had
a lower taxation than dividends. Although, after taxation policy change in 1980s these motive
was no longer vital and shareholders had stopped considering diversification as tax reduction
strategy.
From the perspective of the firm diversification has other influence on taxation. Auerbach and
Reishus (1988) suggested that corporate diversification usually allows firms to increase levels of
depreciation and thus lowers taxable part of the income. To achieve tax reduction diversification
is usually done through acquisitions. The Tax Equity and Fiscal Responsibility Act issued in
1982 allowed General Motors to have $400 million tax reduction annually for five years due to
its acquisition over Electronic Data Systems. The acquisition value was $2.6 billion while it
allowed General Motors to claim $2 billion of depreciable assets. The Tax Reform Act of 1986
ended the possibility of tax reduction with the help of acquisitions in the United States. As
Grinblatt and Titman (1989) claim, it has also ended another possibility of tax gains caused by
diversification. Before the Act, companies were able to benefit from merger or acquisition with a
firm with past losses. After merger or acquisition past losses of one party served like a tax shield
for the profitable party and diversified firm could claim tax reduction compared to single
profitable firm. By the Act of 1986 the ability of the bidder to use past losses of the target to
reduce current or future profits was closed.
Risk
Risk hedging can be the motives for firms to diversify because diversified portfolio is less risky
than a single firm. This is a logical explanation of firm diversification, but Levy and Sarnat
(1970) argued that stockholders cannot benefit from risk reduction through firm diversification.
If the capital markets are in perfect state stockholders can hedge their risk on their own by
11
diversifying their own portfolio and not diversify assets of one single firm. Moreover, Amihud
and Lev (1981) state that even if transaction costs occur and capital market is not in a perfect
state stockholders don’t benefit from firm diversification because they can still hedge risk
through their own portfolio diversification at a low cost. Black and Scholes (1973) suggest that
diversification affects stockholders negatively by transferring wealth to bondholders. These
theories show unwillingness of stockholders to be involved in firm diversification but the motive
behind it can come from managers and not stockholders. Managers lack the opportunity to lower
their risk of losing a job or reputation by diversification as stockholders do. Amihud and Lev
(1981) suggest that firm diversification moves are manager driven in order to reduce their risk.
They found significant results that firms controlled by managers engage in more diversification
moves than firms controlled by the owner. This managerial motive of firm diversification can be
treated as risk driven or agency cost driven.
Concluding this section, companies have a number of reasons to get involved into diversification
process. Agency theory, transaction costs, resource based view, information asymmetry, market
power, risk and taxes are among them. However we focus on resource based perspective,
because most of industry relatedness measures are based on it. Further section discusses different
types of relatedness measures, their pros and cons.
2.3 Relatedness measures
The expression “related industries” is very broad and needs clarification and precise instruments
to measure relatedness. There are a plenty of ways to measure relatedness of one industry to
another, which have different underlying basis. The most basic and easy instrument to measure
relatedness between two industries is to look at their standard classification codes. There are
several industry classification systems, like European Nomenclature générale des Activités
économiques dans les Communautés Européennes (NACE) or American Standard Industrial
Classification (SIC), but they all based on the same algorithm. First they distinguish between a
number of broad industries (up to 10) and for each industry code 0 to 9 is recorded. Than for
each broad industry more specified sub industries are distinguished and encoded with 0 to 9
codes relatively. The algorithm is used until the necessary precision is achieved (usually 4 digit
codes in NACE system). Comparing the codes of two industries may tell the relatedness of these
industries by counting the number of first matching digits. This is very straightforward and easy
to apply method. However, it has a lot of limitations because industry classification is very
12
subjective. Classification system based measure lacks information about type of relatedness; it is
very vulnerable to classification errors and provides only discrete measure of relatedness. A
number of researchers were trying to develop relatedness measures based on classification codes
(Chang, 1996; Farjoun, 1998) but they failed to reach any identity in interpretations.
More sophisticated approach was developed by Teece et al. (1994) and relied on firm’s portfolio.
The idea is that if some activities or industries are present in firm’s portfolio then they are related
because they provide economies of scope. Finding relationships between industries in portfolios
creates relatedness measure. The main disadvantage of this method is it’s ex post nature; it
doesn’t investigate why industries co-occur in the portfolio but takes as presupposition. Thus,
nothing can be derived about the type of relatedness or the motives behind co-occurrence.
In order to investigate the types of industry relatedness resource based approach is the most
precise. The idea main ides behind this approach is to find similarities in resources used by
different firms and build relatedness measure based on this background. Intensity of specific
resources use differs between industries, so there is no ultimate measure. Resources can be
roughly divided into three main types: human capital, technology and materials. Resource based
approach in the scope of materials investigates the relatedness of two industries along the value
chain (Fan and Lang, 2000). The relatedness measure is built based on the amount of output of
firm x used by firm y and the amount of input of firm x served by firm y. This type of relatedness
is a proxy for vertical diversification in order to achieve economies on transaction costs.
Approach based on technology as a primary resource is based on patent analysis (Jaffe, 1989).
Relatedness measure is constructed by tracking origin industry of a patent which is used in
another industry. Materials and technology based relatedness measures are continuous and they
include information about the motives of firm diversification. However they share one
disadvantage, materials and technology based relatedness measures are very dependent on
industry type. Some industries are very technology intensive and some are very material
intensive which makes the estimations for the entire economy extremely biased. In that scope
human capital based approach stands out of the crowd. First developed by Farjoun (1994) and
then improved by Neffke and Henning (2010), this approach uses labor flows between industries
to create relatedness measure. The view on the firm resources has changed over time and now
knowledge is considered as the main firm’s resource (Grant and Spender, 1996). Firms’
investments into their employees’ human capital grew rapidly during past years. Time to time
employee switch their jobs and transfer their particular human resources from one firm to
another. Eventually employee gathers a set of specific skills which can be applied in an industry.
13
If two industries are able to share workforce without significant loses of human capital while
transferring from one industry to another makes this industries related. Neffke and Henning
(2010) created relatedness measure based on the difference between predicted labor flows and
observed. This relatedness measure has all the advantages of other resource-based measures,
mentioned above and lacks dependency on industry type because human capital plays crucial
role in all industries today.
2.4 Diversification modes
Motives for company’s diversification were described above. This part of the paper is devoted to
analysis of possible industry entry modes if the firm is motivated to diversify. Each industry
entry mode will be described in detail and its advantages/disadvantages will be discussed.
M&A versus JV
If the firm chooses to diversify through the market it has to make one more crucial decision:
whether to use mergers and acquisitions or joint ventures. These modes of industry entry are
very different from each other, each one has its own pros and cons. Joint ventures are often
treated as substitutes for mergers and acquisitions in the sense of entering a market. Lee and
Lieberman (2009) suggested that the choice of industry entry mode has a direct influence on the
success of an entry. The next part of the paper will discuss the advantages and disadvantages of
each industry entry mode using the theories of indivisible assets, management costs and
information asymmetry. Companies may imply joint ventures, acquisitions and mergers
simultaneously for different goals.
Indivisible assets
Hennart (1988) suggested that one possible explanation why joint ventures should be chosen
above mergers and acquisitions is indivisibility of some assets. The goal of firm diversification
may be to acquire specific asset of the other firm, but if it can’t be disentangled from other
assets, acquirer showed buy the whole company with many unneeded assets. In order to illustrate
this Hennart and Reddy (1997) give an example of biotechnology and pharmaceutical firms.
Biotechnology firm aims to acquire the sales force of pharmaceutical firm to introduce a new
drug. Pharmaceutical firm is usually a large vertically integrated firm with R&D, manufacture
14
and distribution stages, which cannot be acquired separately. For biotechnology firm acquiring
of such pharmaceutical firm will lead to a huge amount of expenses buying all the assets and
managing them. On the other hand, joint venture will allow biotechnology firm to access the
sales force of the pharmaceutical firm without being involved into managing all other assets.
Joint ventures work well when target assets cannot be subtracted from other firm’s assets
because acquisitions become very expensive in these conditions. This statement holds for the
cases, when desired assets can be separated from the others but with a great effort. When the
difficulty of assets separation lowers, acquisitions become more and more favorable. Assets’
indivisibility is often associated with the company size. Hennart and Reddy (1997) suggested
that the larger is the target firm, the more is the probability of joint venture creation. However, as
Kay, Robe and Zagnolli (1987) argued, it holds unless large firms don’t have a governance
structure of quasi-independent divisions, which can be acquired separately.
Management costs
Management costs are the stumbling block for mergers and acquisitions and for joint ventures as
well. First, let’s consider management costs for the case of mergers and acquisitions. When an
acquirer finishes the deal and overtakes target firm it gets, besides all other assets, all target’s
employees. As Jemison and Sitkin (1986) argued that managing target’s employees can be
extremely difficult due to cultural differences between the acquirer and target firms. Cultural
differences include country and industry differences between firms. For this case joint venture
can be a solution, because employees of all companies involved in a joint venture are motivated
to maximize profits of a joint venture. Kogut and Singh (1988) suggested that managing joint
ventures can be done through partner companies, which are experienced in managing particular
culture of employees. However, mergers and joint ventures may include more than two
companies. With the increasing number of parties involved, managerial cost rise dramatically for
the entry mode through mergers and acquisitions. At the same time, managerial costs also rise
for joint ventures. Powell (1990) stated that joint ventures experience difficulties because they
are based on hybrid governance structures which make creation of specific assets possible but
costly. Large number of companies involved in joint ventures makes coordination of hybrid
governance structures difficult, some companies can demonstrate opportunistic behavior and
incentives for investing in specific assets will be lowered. The choice of mergers and
acquisitions over joint ventures is made when the ability to invest in specific assets outweighs
higher management costs of target’s staff.
15
Information asymmetry
Information asymmetry reveals itself while it comes to assessing the value of the other firm or its
assets. Bidding firm often lack information about true value of a target firm or its desired assets.
Balakrishnan and Koza (1993) suggest that joint ventures should be used in such cases to reduce
informational asymmetry and thus lower the possible costs of over or under valuation of target’s
assets. Information asymmetry is an often case for industries which have high level of
differences because they cannot use particular knowledge about their own industry to valuate
another industry. Joint ventures are capable of sharing information between involved parties, and
thus makes them more preferable in the case of significant industry differences, as Balakrishnan
and Koza (1993) show in their research.
2.5 Market response on diversification
Previous literature analysis has shown that there are a number of motives for firms to diversify
and diversification can be related or unrelated. The basic rule behind firm diversification is that
benefits of diversified firm outweigh the costs of diversification. This information is crucial to
understand the process of diversification but draws no light on market response on
diversification. Jensen and Ruback (1983) made a research concerning market response to
acquisition announcement. They distinguish between stock prices of bidder firms and target
firms. Bidder firm’s stock prices show no response on the announcement of acquisition or
slightly drop after the announcement. Meanwhile target firm’s stock prices show substantial
increase in prices. The main criticism of Jensen’s and Ruback’s (1983) findings is addressed to
the lack of differentiation between related diversification and unrelated.
Morck, Schleifer and Vishny (1990) investigated differences in returns of diversification into
related activities and unrelated. Their findings show that for bidder firm diversification into
related activities had 45.6 percent of positive treatment by the market, compared to 32.2 percent
for diversification into unrelated activities. Interesting to point out, that these results applicable
for 1980s but not for 1970s.
16
Generally Montgomery (1994) suggests, that unrelated diversification is valued less by the
market than related diversification. Although Jones and Hill (1988) argued that related
diversification can imply higher administration costs than unrelated diversification and thus can
be a motive for unrelated diversification.
17
2.7 Hypothesis
According to the review of the previous research in the field of company diversification, the
debate concerning usage of different relatedness measures as a pattern to describe company
diversification is still open. Firm diversification relatedness measures developed from the basic
ones based on industry classification codes to more sophisticated ones based on value chain and
human capital similarities. However there is lack of empirical research which investigates the
role of related diversification on diversification in general. The first research question of this
paper is
What type of relatedness has an effect on firm diversification?
According to the literature review, corporate diversification can be external and internal. Process
of internal diversification is very hard to measure at the moment of diversification, but it could
be measure ex post by investigating the number of secondary industries. Internal diversification
is highly associated with “make or buy” decision and turns out to be the solution when high
transaction costs arise on the market. High transaction costs arise when high assets specificity is
present. Input-output relatedness measure deals with production chain assets specificity, while
skill relatedness measure is connected with human capital specificity. First hypothesis tests
influence of relatedness measures on firm diversification into secondary activities, without
taking into account the external or internal origin of the diversification:
Hypothesis 1.1: Input-output and skill-related activities are more likely to be present as
secondary activities in the firm’s portfolio.
External diversification process can be measured on the spot for publically listed companies.
Basic “make or buy” decision can be developed into more complicated structure. First, company
can buy the asset. Second, it can make the asset in house by creating new product line. Third, the
company can buy another company, which makes this asset and now make it in house. In this
case external diversification is motivated exactly the same as internal, and relatedness of
industries should play significant role. Second hypothesis tests the influence of related
diversification on the external diversification through the market.
Hypothesis 1.2: Company is more likely to diversify into input-output and skill-related activities
through market.
Based on the first and second hypotheses the question about influence of related diversification
on firm diversification can be answered. Additionally it is possible to make judgments to what
extent different types of relatedness influence firm diversification. However, this paper is more
18
dedicated to study the relationship between related diversification and external diversification
through the market, that’s why additional third hypothesis is also tested. As it was shown in the
review of theoretical background, the choice of the market entry mode could be critical to the
firm and its performance. Due to the presence of management cost, described in the theoretical
part, human capital based relatedness measure should be more likely to occur in mergers and
acquisitions, while input-output relatedness measure shouldn’t have any effect.
Hypothesis 1.3 Diversification into skill-related activities is more likely to occur in form of
mergers and acquisitions than joint ventures.
The second research question is more orientated on the investigation of the success factors for
diversification moves. There are plenty of instruments to check if certain action had a positive
effect on the firm, however the most representative and intuitive is to see the change of the
firm’s value caused by this action. The second research question of this paper is aimed to draw
some light on the success of the related diversification:
Does diversification into related activities has a greater influence on the firm’s value?
Previous research found week positive effect of related diversification on the company’s value.
For a public listed company firm value is very closely connected to the stock price, thus the
stock price fluctuations are used to analyze the influence of related diversification. This leads us
to the second hypothesis of this paper:
Hypothesis 2: Diversification into input-output or skill-related activities is valued positively by
the market.
The results drawn from investigation of these four hypotheses provide vital information about
firm diversification strategy and its valuation by the market. The core task is to estimate the
influence of related diversification and thus will enable to imply findings of this research to
develop recommendations and patterns for corporate diversification.
19
3. Data and methodology
3.1 Dataset
Dataset for this research was constructed using 4 separate databases. The core database is
Zephyr, which is provided by Bureau Van Dijk. Zephyr database cover international companylevel data concerning deals such as IPO’s, mergers, acquisitions, etc. This paper uses data for
mergers, acquisitions and joint venture types of deals for German for the last 10 years. Other
company information, such as name, primary industry code, secondary industry codes, etc was
subtracted from the database as well. The second database used for this research is Orbis which
is also provided by Bureau Van Dijk. Additional company-level data, such as date of
incorporation and company’s risk rate is used from Orbis database and added to the sample from
Zephyr dataset. For each diversification deal all possible industries were created and new
variables market diversification (div_market), taking value of 1 if possible industry of
diversification is equal to primary industry of the target firm, zero otherwise; and secondary
diversification (div_sec), taking value of 1 if possible industry of diversification is equal to
secondary industry of the acquirer firm, were created. Third database is developed by Neffke and
Henning (2010) providing relatedness measure between two industries based on Swedish
economy. The dataset uses industry classification codes NACE 1.1 on four digit level. Industries
were converted according to converter tables provided by Eurostat in order to make it compatible
with Bureau Van Dijk’s datasets, which are based on NACE rev.2 classification system. While
converting industries from NACE 1.1 to NACE rev.2 a number of missing values were generated
because the industries didn’t match due to differences in classification systems. Then the dataset
is merged with skill-relatedness measure dataset. Fourth database used for creating dataset for
this study was based on input and output matrixes provided by Eurostat for German economy for
NACE rev.2 two digit level classification system. Input-output relatedness measure was
constructed based on matrixes using Fang and Lang (2000) method. Industries in the working
dataset were limited to 2 numbers in order to merge with input-output relatedness measure
dataset.
20
3.2 Description of the variables
Stock price change (p_react)
Zephyr database provides information for stock prices of acquirer and target firms 3 months
prior to rumor, prior to rumor, prior to announcement, after the completion and 3 months after
the completion. Ideal period to highlight the price jump as a result of corporate diversification is
between the day before the rumor and the day after announcement. In this case both rumor and
announcement affect price fluctuations, making the effect the most significant. Unfortunately,
Zephyr database does not provide data for stock prices after announcement. The end of the
period should be the date after completion of the deal, while the start of the period could be date
prior to rumor or prior to announcement. In this paper date prior to announcement is chosen
because if the date prior to rumor is chose the period becomes too long. Long period has
negative effect on estimations because of high stock price fluctuations caused by enormous
amount of factors on this period. The longer is the period, the harder is to see the actual price
reaction on corporate diversification.
In order to test the reaction of the stock market on the diversification of the company stock price
change (p_react) variable was constructed. It is based on stock prices of the acquirer firm and is
calculated according to the formula:
𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑖𝑐𝑒 𝑎𝑓𝑡𝑒𝑟 𝑡ℎ𝑒 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑖𝑜𝑛 − 𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑖𝑐𝑒 𝑝𝑟𝑖𝑜𝑢𝑟 𝑡𝑜 𝑎𝑛𝑛𝑜𝑢𝑛𝑐𝑒𝑚𝑒𝑛𝑡
𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑖𝑐𝑒 𝑝𝑟𝑖𝑜𝑟 𝑡𝑜 𝑎𝑛𝑛𝑜𝑢𝑛𝑐𝑒𝑚𝑒𝑛𝑡
Stock price prior to announcement is the stock price of the acquirer firm just before the
announcement of the diversification move. Stock price after the completion is the stock price of
diversified firm after completion of the diversification process. This formula enables to present
changes in stock prices as percentage levels to the basis period stock prices and makes
comparison of different companies possible. In order to make estimations more précised and less
biased, a number of outliers were removed from the dataset. Diversification moves which was
followed by more than 50% of stock price increase (2 observations), and more than 50%
(1observation) of stock price decrease were removed. Additional check for extra long period
between the announcement day and the completion day was performed but no outliers were
found.
21
Diversification of the firm through secondary activities (div_sec)
Diversification of the firm through secondary activities is used to test hypothesis 1.1 concerning
influence of relatedness on different aspects of firm diversification. A number of all possible
secondary industries based on NACE rev.2 four-digit classification codes were created for each
company from the perspective of the primary industry of the company. Then diversification
through secondary activities variable was created, taking value of 1 if firm has diversified in the
secondary industry and 0 if the firm hasn’t. This variable is used as independent variable in
logistic regression to test the influence of relatedness measures and control variables on the
probability of firm diversification into secondary activities.
Diversification of the firm through market (div_market)
Diversification of the firm through market is used to test hypothesis 1.2. The variable is
constructed similar to the diversification through secondary activities variable. First, for each
deal, as there could be multiple deals for one firm, all possible industries to diversify were
created based on NACE rev.2 four-digit classification codes. Primary industry of the company is
considered as a starting point and any possible industry of diversification is considered as a
target point. Afterwards the diversification through market variable was created taking value of 1
if diversification was made in one or some of the possible industries, in other words, if acquirer’s
primary industry matches target’s primary industry and 0 otherwise. This variable is used as
dependent variable in hypothesis 1.2 concerning the diversification through market into related
activities.
Market entry mode (deal_type)
Market entry mode is a binary variable which has the value of 1 if diversification was made
through merger and acquisition and value of 0 if diversification was made by establishing joint
venture. This variable is used as dependent variable to examine the influence of relatedness
measures and control variables on market entry mode. Additionally it used as the control variable
for the testing hypothesis 2 concerning influence of relatedness on market reaction of
diversification.
22
Financial data (capitalization, roa, tassets, cap_int)
Companies in the dataset vary significantly by their size, structure and other specifics. To control
for possible influences of stated specifics, financial data control variables are introduced. First,
the size of the company is controlled by taking into account total assets of the firm (tasstes) and
market capitalization of the firm (capitalization). Theoretical review of previous researches
didn’t find any significant relationship between company size and diversification, but according
to the common sense larger companies should diversify more than smaller ones.
Lieberman and Lee (2009) suggested the use of a number of financial variables, such as market
to book ratio, to control for firm specifics. This paper follows the logic of Lieberman and Lee
(2009) and introduces two financial control variables, which may influence firm diversification
strategy: return on assets (roa) and capital intensity of the firm (cap_int). Return on assets (roa)
is the measure of firm’s profitability and it is constructed according to formula:
𝑟𝑜𝑎 =
𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
Return on assets (roa) is used to control for the effect that more successful firms may be
involved in less related diversification because they have abundant resources for investment.
Capital intensity (cap_int) variable is constructed according to formula:
𝑐𝑎𝑝_𝑖𝑛𝑡 =
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
𝑆𝑎𝑙𝑒𝑠 𝑅𝑒𝑣𝑒𝑛𝑢𝑒
On average, capital intensive firms tend to get involved into related diversification more often
because high capital usage acts as an industry entry barrier. Company needs to achieve certain
level of capital intensity to enter these industries, but when it has been achieved, company can
diversify in related industries without the need to build up new level of capital intensity.
Company age (age)
The control variable company age is introduced to control for any specifics caused by firm age.
Elder firm can be more experienced in diversification compared to younger firm; however this
experience can have a twofold effect. From one hand, more experience with diversification in
past leads to more diversification in future. From the other hand elder firms can stick to their
own way of diversification, being more conservative than the young firms, making their
diversification patterns totally unrelated to the market tendencies of the present and thus imply
23
negative noise on the estimations. The company age variable is constructed as the number of
years from company’s incorporation date to the announcement date of the diversification move.
Announcement date of diversification move (announcement_date)
Announcement date of the diversification move through market is used as a control variable in
hypothesis 1.2, 1.3 and 2. Diversification strategy may differ in time because of the market
environment. Some significant events can change firm diversification strategy dramatically and
influence both dependent variables (diversification through market, market entry mode, stock
price change) and independent variables such as input-out relatedness and skill-relatedness
diversification. Thus, announcement date of diversification move is used to control for any
market peculiarities of the diversification period. The variable is treated as year dummies for
years from 1997 to 2011 (d1 – d14) with the reference level of 2000.
Firm risk measure (beta)
Based on suggestions made by Levy and Sarnat (1970) firm owners cannot benefit from risk
reduction by firm diversification, because they can diversify their own portfolio. Amihud and
Lev (1981) developed this theory suggesting that managers will try to diversify company under
the influence of risk, because, unlike shareholders, they cannot hedge their own risk. As a
conclusion to that, managers will try to involve company in excessive diversification in case of
the presence of high risk. In order to control for this effect company risk measure variable is
introduced. It is the beta provided by the stock market. Beta is an index of firm risk compared to
the market risk, which is the German stock market in this research. The index is taken from the
Capital Assets Pricing Model (CAPM) according to the formula:
𝛽=
𝑟 − 𝑟𝑓
𝑟𝑚 − 𝑟𝑓
Where r is return of the stocks, rf is the risk-free rate and rm is the return on German market.
Skill-relatedness measure (sr)
Neffke and Henning (2010) developed a sophisticated measure of relatedness based on human
capital relatedness of industries. The underlining concept of the theory is that industries can be
24
called related if they share the same kind of specific human capital. Skilled employees can and
do switch between the industries if their specific human capital is applicable in other industry.
Neffke and Henning (2010) developed the measure based on Swedish economy. They used
company level data concerning labor flows between the industries. In order to avoid biased
estimations, low paid workers were omitted from the dataset, because their jobs do not require
specific human capital. For the same reason managers were removed from the dataset as well,
because managers can transfer easily between industries because they do not require significant
amount of industry specific knowledge. Let Fijobs represent the observed labour flow between
two industries and Fijpred is the predicted labour flow between these industries. The prediction of
labour flow is based on industry specifics, for example industry size and wage levels. Skillrelatedness measure is then constructed as the ratio of these labour flows:
𝑆𝑅𝑖𝑗 =
𝐹𝑖𝑗𝑜𝑏𝑠
𝐹𝑖𝑗𝑝𝑟𝑒𝑑
The skill-relatedness index equals to 1 suggests there is no skill relatedness between the
industries because observed labour flow is equal to predicted. Skill-relatedness index smaller
than one means skill-dissimilarity between the industries and skill-relatedness index greater than
1 suggests skill-relatedness. Skill-related index developed by Neffke and Henning (2010) is
based on Swedish four digits NACE 1.1 classification system. In order to make it applicable for
this paper it was converted to NACE rev.2 four digit classification system using transition tables
provided by Eurostat.
Input-output relatedness measure (inout)
Input-output relatedness measure is based on the approach developed by Fan and Lang (2000)
which uses value chain relations as a proxy for industry cohesion. It is a classic approach to
explain why industries cluster together and firms diversify into certain industries. The concept is
to measure the amount of output sourced from one firm to another and the amount of input of
one firm used by another. The data of input and output usage by different industries was
provided by Eurostat and is based on NACE rev.2 two digit classification codes. Neffke and
Henning (2010) introduced algorithm of construction input-output relatedness measure. First
input relatedness index for each possible pair of industries is constructed using given formula:
𝑖𝑛𝑝𝑢𝑡 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑛𝑒𝑠𝑠(𝑖, 𝑗) =
𝑖𝑛(𝑖, 𝑗)
∑𝑖 𝑖𝑛(𝑖, 𝑗)
25
where in(i,j) is the value of inputs sourced from industry i to industry j. Second output
relatedness index for each possible pair of industries is created according to the formula:
𝑜𝑢𝑡𝑝𝑢𝑡 𝑟𝑒𝑙𝑎𝑡𝑒𝑑𝑛𝑒𝑠𝑠(𝑖, 𝑗) =
𝑜𝑢𝑡(𝑖, 𝑗)
∑𝑗 𝑜𝑢𝑡(𝑖, 𝑗)
where out(i,j) is the value of output sold by industry i to industry j. The third step is to construct
the aggregate input-output relatedness index by making average of input-relatedness and outputrelatedness indices. The values are between 0, meaning industries are totally unrelated, to 1,
meaning all inputs and outputs are interchanged only between these industries, or perfect
relatedness in other words.
3.3 Methodology
Methodology of the paper is divided into two main sections: descriptive statistics and hypotheses
tests using the models of econometric regressions. Descriptive statistics provide general
overview of variables, their distribution, mean, standard deviation and kurtosis. In order to
describe the influence of the variables on each other correlation matrixes are used. Additionally
to show the influence of certain conditions on firm diversification the dataset is split according to
the presence of the diversification through market. Then descriptive statistics for each part of the
dataset are compared and some conclusions are made.
Logistic regression is used to predict the likelihood of an event by the values of a set of
attributes. First block of hypothesis is tested using logistic regression model:
𝑃=
1
1 + 𝑒 −(𝛽)
where 𝛽 = 𝛽0 + 𝛽1 𝑥𝑛 + 𝛽2 𝑥𝑛 + ⋯ + 𝛽𝑛 𝑥𝑛
P is the probability of a particular outcome, x1 – xn are independent variables, and β0 – βn are the
coefficients estimated by the model. The dependent variable can take values 1 if certain outcome
occurred and 0 if it didn’t. The model estimates probability of outcome (P) which lies between 1
and -1. Dependent variables (xi) can be distributed between negative and positive infinity. For
each dependent variable model estimates coefficient (βi) which characterizes the influence of the
dependent variable on the probability of the outcome. If coefficient (βi) is positive, than
probability of the outcome increases with the increase of the dependent variable, if it is negative,
26
the probability decreases. The size of the coefficient (βi) contributes to the size of the effect on
probability of the outcome, the greater is the coefficient the greater is the effect.
Hypothesis 2 is tested using Ordinary Least Squares method (OLS), which is commonly used for
estimation of the unknown parameter based on linear model. The model is:
𝑌 = 𝛽0 + 𝛽1 𝑋𝑖1 + 𝛽2 𝑋𝑖2 + ⋯ + 𝛽𝑛 𝑋𝑖𝑛 + 𝜀𝑖
Where Y is the dependent variable, Xi1 - Xin are independent variables, and β0 – βn are the
coefficients, estimated by the regression model. OLS method is based on minimization of the
sum of least squared distances between the observations and the predictions. Estimated
coefficients (β0 – βn) provide detailed information about the direction of the variable’s influence
(positive or negative) and the power of influence (coefficient size).
27
4. Empirical research and results
4.1 Descriptive statistics
All control variables were log transformed in order to reduce skewness of the distribution.
Additionally log transformation of control variables helps with interpretation of the effects,
which now can be interoperated as elasticity (increasing log transformed variable by one unit
corresponds to multiplying the untransformed variable by e. Table 1 provides summary statistics
for independent variables showing number of observations, mean, standard deviation and
skewness. Numbers of observations differ across the variables and age has the least amount of
observations. Skewness higher than 5 is shown only by return on assets (roa). Full summary
statistics for all the variables can be found in appendix in table 2.
Table 1. Summary statistics, independent variables.
Variable
Obs
Min
Max
Mean
St. Dev.
Variance
Skewness
Kurtosis
a_sr
134232
-1
.9943836
-.7860958
.5413002
.2930059
2.269861
6.435041
inout
134232
0
.7447964
.0311508
.117646
.0138406
4.670814
23.26282
ln_cap
127840
6.938255
17.81981
13.02376
2.891565
8.361146
.1632628
1.853577
ln_capint
129908
.0426572
6.382078
2.039882
1.343552
1.805133
.8274746
2.792834
ln_beta
133480
-.912331
5.686178
.4036486
.4499034
.202413
4.07431
55.0075
ln_tassets
133292
2.782056
17.65182
13.26181
2.724718
7.424086
.0401974
2.320843
ln_roa
129156
-3.635095
.3185791
-.0199402
.2511896
.0630962
-13.02388
187.4249
ln_age
72568
-1.789708
4.700219
2.942635
1.186609
1.40804
-.4592952
2.970278
Summary statistics for dependent variables are presented in table 3. Diversification through
secondary activities (div_sec) and diversification through market (div_market) variables show
high skewness of over 20. Stock price reaction on the diversification move (p_react) has the
least amount of observations.
Table 3. Summary statistics, dependent variables.
Variable
Obs
Min
Max
Mean
St. Dev.
Skewness
div_market
134232
0
1
.00149
.0385713
25.84882
div_sec
134232
0
1
.0023541
.0484624
20.53746
deal_type
134232
0
1
.8935574
.3084044
-2.552226
p_react
46248
-.7482014
.8903229
.0029246
.1251869
.9406055
28
Correlation matrix on table 4 shows high correlation (over 0.6) only between total assets
(ln_tassets) and market capitalization (ln_cap) which is predictable because both variables
represent the size of the company. Correlation of around 34% is present between beta (ln_beta)
and market capitalization (ln_cap), as well as between beta (ln_beta) and total assets (ln_tassets).
Market capitalization (ln_cap) and total assets (ln_tassets) are the measures of the company’s
size. Positive correlation between size and risk means that larger companies tend to be more
risky.
Table 4. Correlation matrix of all variables.
div_sec
div_market
a_sr
inout
ln_tassets
ln_beta
ln_cap
ln_capint
ln_roa
ln_age
div_sec
1.0000
div_market
0.2115
1.0000
a_sr
0.0122
0.0230
1.0000
inout
0.0502
0.0303
0.1873
1.0000
ln_tassets
-0.0152
-0.0074
0.0303
0.0196
1.0000
ln_beta
-0.0097
-0.0012
0.0540
0.0520
0.3466
1.0000
ln_cap
-0.0116
-0.0074
0.0240
0.0053
0.9374
0.3406
1.0000
ln_capint
-0.0111
-0.0038
-0.0348
0.0084
0.0074
0.1860
-0.0224
1.0000
ln_roa
0.0058
0.0048
0.0029
0.0035
0.1753
-0.0139
0.1855
-0.0108
1.0000
ln_age
-0.0053
-0.0103
-0.0029
0.0049
0.1921
0.1150
0.1184
-0.1010
0.0377
1.0000
Observations with absence of diversification through market were removed from the dataset in
order to test hypothesis 1.3 and hypothesis 2. It was done by omitting cases when diversification
through market (div_market) variable equals 0. Full summary statistics for adjusted to hypothesis
2 dataset are presented in the table 5. As it can be seen from the table, stock price reaction on the
diversification move (p_react) has the least amount of observations.
Table 5. Summary statistics when market diversification is present.
Variable
Obs
Min
Max
Mean
St. Dev.
Variance
Skewness
Kurtosis
p_react
370
-.2692307
.4310345
.0241029
.114722
.0131611
2.077292
9.696804
deal_type
1000
0
1
.93
.2557873
.0654271
-3.370606
12.36098
a_sr
1000
-1
.9856153
-.5096493
.7558768
.5713497
1.030272
2.214994
inout
1000
0
.6537724
.2197098
.2858054
.0816847
.6243699
1.4187
ln_cap
970
7.052721
17.81981
12.72818
2.549267
6.498762
.2948607
2.09071
ln_capint
955
.3384914
5.667214
2.128907
1.333063
1.777057
.4454116
1.8884
ln_beta
990
-.912331
5.686178
.4337431
.4963532
.2463665
5.777765
64.70671
ln_tassets
990
-1.470067
21.205
13.71006
3.739044
13.98045
.0374052
3.113685
ln_roa
955
-.7236587
2.130044
.0156227
.1408894
.0198498
7.365647
121.5746
ln_age
545
-1.790392
4.58007
2.53568
1.312383
1.72235
-.3486765
3.090524
29
Correlation matrix for adjusted dataset (table 7) shows high correlation (over 60%) only between
total assets (t_assets) and market capitalization (ln_cap), which is in line with results from the
full dataset. Correlation of around 42% is found between beta (ln_beta) and return on assets
(ln_roa), as well as between deal type (deal_type) and total assets (ln_tassets). It can be
concluded that riskier companies have less return on assets and larger companies tend to
diversify through establishing joint ventures.
Table 7. Correlation matrix of variables when market diversification is present.
deal_type
p_react
a_sr
inout
ln_tassets
ln_beta
ln_cap
ln_capint
ln_roa
deal_type
1.0000
p_react
0.3417
1.0000
a_sr
-0.0129
-0.1908
1.0000
inout
0.1055
0.2127
-0.1227
1.0000
ln_tassets
-0.4155
-0.1411
0.0004
-0.1594
1.0000
ln_beta
0.0243
-0.3423
0.2259
-0.0152
-0.1113
1.0000
ln_cap
-0.3601
-0.1332
0.1577
-0.1496
0.9329
-0.0922
1.0000
ln_capint
0.1428
-0.1126
-0.2803
-0.0327
-0.0887
0.1701
-0.1090
1.0000
ln_roa
-0.0983
0.2447
-0.0768
0.0494
0.2452
-0.4145
0.2941
-0.3314
1.0000
ln_age
0.1842
0.2405
0.1430
0.2385
0.3767
-0.0484
0.2944
0.1907
0.1432
ln_age
1.0000
4.2 Hypothesis 1.1
In order to test Hypothesis 1.1 (Input-output and skill-related activities are more likely to be
present as secondary activities in the firm’s portfolio) logistic regression model is used. The
dependent variable is diversification into secondary activities (div_sec), which takes value of 1 if
the firm has one or more secondary industries and 0 otherwise. Five models were constructed to
show the effect of each relatedness measure separately. Table 8 provides all the results for each
model.
Table 8. Results for hypothesis 1.1, dependent variable div_sec.
VARIABLES
a_sr
(1)
(2)
(3)
(4)
(5)
0.665***
0.203
0.166
30
(0.195)
inout
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
Constant
-0.349**
(0.167)
-0.303***
(0.0834)
-0.0566
(0.141)
-0.124
(0.385)
4.850***
(1.353)
-3.035***
(0.964)
5.096***
(0.494)
-0.374**
(0.164)
-0.369***
(0.0916)
-0.0729
(0.131)
-0.415
(0.420)
4.643***
(1.327)
-2.658**
(1.071)
-0.336**
(0.167)
-0.315***
(0.0836)
-0.0486
(0.139)
-0.213
(0.404)
5.173***
(1.382)
-2.475**
(0.996)
(0.208)
4.919***
(0.532)
-0.369**
(0.165)
-0.373***
(0.0910)
-0.0637
(0.132)
-0.442
(0.422)
4.748***
(1.339)
-2.482**
(1.086)
(0.211)
4.965***
(0.542)
-0.370**
(0.172)
-0.371***
(0.100)
0.0269
(0.151)
-0.816*
(0.477)
4.599***
(1.394)
1.462***
(0.551)
0.0637
(0.847)
-0.109
(0.735)
-0.935
(1.101)
-0.0706
(0.640)
0.333
(0.647)
0.896
(0.700)
0.925
(0.652)
0.751
(0.859)
0.275
(1.121)
-0.523
(1.108)
0.0644
(0.653)
-3.001**
(1.274)
0.1342
67,680
0.1539
61,476
d1
d3
d5
d6
d7
d8
d9
d10
d11
d12
d13
d14
Pseudo Rsquared
Observations
0.0483
0.1331
0.0599
67,680
67,680
67,680
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
31
Model (1) consists only of control variables, model (2) includes control variables and inputoutput relatedness measure, model (3) includes control variables and skill-relatedness measure,
model (4) is the full model with control variables and both relatedness measures and year
dummy variables are added in model (5). Model (1) has the lowest pseudo R-square of 4.83%,
meaning that all other models are fitted better. Total assets of the firm and return on assets have
the highest significance level (p<0.01) in the model. The effect is negative for total assets but
positive for return on assets. Other significant variable is capital intensity with 5% significance
level. Capital intensity has negative influence on the probability of diversification into secondary
activities. Model (2) adds value-chain relatedness measure to the model (1) which makes pseudo
R-squared triple reaching 13.31%. Input-output relatedness measure has a positive (5.096) and
significant on 1% significance level effect on the probability of diversification. All control
variables don’t change their significance levels compared to model (1) and coefficients change
very slightly. The results change dramatically if value-chain relatedness measure is substituted
by the skill-relatedness measure. Model (3) has only a slight increase in pseudo R-square
reaching 5.99% compared to 6.76% in model (1), while model (2) showed 13.31%. However
human capital relatedness measure turns out to be significant in model (3) on 1% significance
level and has small (0.665) positive effect. Control variables show no change in significance
levels and slight change in coefficients, compared to model (1). Full model (4) has very similar
results to model (2). Pseudo R-square is 13.42% (compared to 13.31% in model (2)) and the
significance levels of control variables are the same. Input-output relatedness measure is highly
significant (p<0.01) and has strong positive effect (4.919) on the probability of diversification
into secondary activities. Human capital-based relatedness measure is insignificant in this model.
In model (5) year dummies are added to control for year specifics, which could affect corporate
diversification process. Model (5) shows increased to 15.39% pseudo R-squared (compared to
13.42% in model (4). Only one dummy variable turns out to be significant. Year 1999 has
negative significant on 5% significance level effect on diversification into secondary activities.
Significance levels for control and independent variables show no change, compared to model
(4), and the coefficients change only marginally. Comparing all five models it is possible to
state, that value-chain relatedness measure plays strong positive role in probability of company’s
diversification into related activities, because it is significant in models (2), (4) and (5) with
positive coefficients. Adding skill-relatedness measure showed very slight increase in R-squared,
meaning that this variable has very small explanatory power. Significance of skill-relatedness
measure in model (3) can be explained by its cross-connection with value-chain based
relatedness measure (some activities can be both skill and input-output related), while full
models showed insignificance of this variable. Control variables showed different direction of
32
influence on the probability of diversification into secondary activities; return on assets has
positive effect, while capital intensity and total assets have negative effect, which is in line with
literature.
In order to show the size of the effect for each variable Table 9 provides detailed information for
marginal effects in model (4). Marginal effects for the rest four models can be found in the
appendix.
Table 9. Marginal effects for Hypothesis 1.1, Model (4), dependent variable div_sec.
min->max
a_sr
0.0001
inout
0.0094
ln_capint -0.0005
ln_tassets -0.0016
ln_age
-0.0001
ln_beta
-0.0005
ln_roa
0.0015
Pr(yx)
x=
sd_x=
0
0.9997
a_sr
-.785106
.544345
0->1
0.0001
0.0329
-0.0002
-0.0079
-0.0000
-0.0001
0.0367
=-1/2
0.0001
0.0033
-0.0001
-0.0001
-0.0000
-0.0001
0.0031
=-+sd/2
0.0000
0.0002
-0.0001
-0.0002
-0.0000
-0.0001
0.0004
MargEfct
0.0001
0.0014
-0.0001
-0.0001
-0.0000
-0.0001
0.0014
1
0.0003
inout
.0285
.110136
ln_capint ln_tassets ln_age
1.78346 12.1298
3.0395
1.37764 2.12873
1.14592
ln_beta
.286612
.484961
ln_roa
-.030538
.284802
Logit regression model was used to test hypothesis 1.1, so marginal effects differ for each X in
the model. First column provides increase in probability of diversification into secondary activity
if the independent variable is increased from its minimum to its maximum. Second column
shows increase of the diversification probability caused by increase of the independent variable
from 0 to 1. Than mean is calculated for each variable (x) and third column of table shows
changes in probability of diversification when the independent variable increases form mean –
0.5 to mean + 0.5. Fourth column shows change in probability if the independent variable grows
from mean – standard deviation to mean plus standard deviation. The last column shows the
marginal effect of the independent variable. Marginal effects of return on assets and input-output
relatedness measure are the highest, reaching 0.0014. This effect is significant, because for each
industry there are 188 possible secondary industries meaning that average probability of
secondary industry choice is 0.005319. Marginal effect of value chain based relatedness measure
is 26% of the average probability of diversification into secondary activities.
33
In conclusion, hypothesis 1.1 is partly rejected (concerning skill-relatedness measure) and partly
not rejected (concerning input-output relatedness measure). Value chain based relatedness
measure has a strong positive effect on the probability of diversification into secondary
industries, which is in line with theoretical background. Among control variables largest effect
was shown by return on assets.
4.3 Hypothesis 1.2
Hypothesis 1.2 (Company is more likely to diversify into input-output and skill-related activities
through market.) tests the influence of relatedness measures on probability of market
diversification. Similar to Hypothesis 1.1, five models were created using method of logistic
regression, where dependent variable is diversification through market (div_market), taking
value of 1 if diversification occurred and 0 otherwise. Table 10 provides results for all five
models.
Table 10. Results for hypothesis 1.2, dependent variable div_market.
VARIABLES
(1)
(2)
a_sr
inout
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
Constant
d1
d2
-0.0720
(0.0852)
-0.117**
(0.0543)
-0.194**
(0.0916)
0.209
(0.160)
2.639**
(1.149)
-4.589***
(0.648)
3.229***
(0.446)
-0.0724
(0.0843)
-0.137**
(0.0569)
-0.191**
(0.0895)
0.167
(0.179)
2.705**
(1.120)
-4.554***
(0.684)
(3)
(4)
(5)
0.850***
(0.134)
0.669***
(0.143)
2.496***
(0.482)
-0.0494
(0.0840)
-0.152***
(0.0570)
-0.172*
(0.0884)
0.131
(0.185)
2.987***
(1.139)
-3.987***
(0.701)
0.645***
(0.144)
2.470***
(0.485)
-0.0281
(0.0872)
-0.153***
(0.0583)
-0.165*
(0.0902)
0.153
(0.206)
3.271***
(1.136)
1.212**
(0.498)
0.588
(0.715)
0.696
(0.587)
-0.0440
(0.0847)
-0.137**
(0.0550)
-0.181**
(0.0893)
0.159
(0.170)
3.036***
(1.164)
-3.895***
(0.672)
34
d3
-0.242
(0.824)
0.505
(0.546)
0.566
(0.584)
0.127
(0.544)
0.318
(0.577)
0.950*
(0.568)
0.619
(0.584)
0.249
(0.824)
0.908
(0.831)
0.611
(0.709)
0.676
(0.513)
-4.573***
(0.802)
d4
d5
d6
d7
d8
d9
d10
d11
d12
d13
d14
Pseudo Rsquared
Observations
0.0117
0.0370
0.0359
0.0511
67,680
67,680
67,680
67,680
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
0.0599
67,680
Model (1) has just control variables, model (2) includes control variables and value-chain
relatedness measure, model (3) includes control variables and human capital relatedness
measure, model (4) is the full model with control variables and both relatedness measures and
year dummy variables are introduced in model (5). Model (1) has very low pseudo R-square of
1.17%. There are three significant variables: return on assets is significant on 5% significance
level and has a positive coefficient, total assets show significance level of 5% with the negative
coefficient and age has a negative effect with 5% significance level. The input-output relatedness
measure has outstanding effect of model estimations while added up. In the model (2) pseudo Rsquared increased more than three times reaching 3.7%, compared to model (1). Input-output
relatedness measure has a positive (3.229) and significant on 1% significance level effect.
Control variables don’t show any dramatic change neither in significance levels nor in
coefficients, compared to model (1). Pseudo R-squared slightly drops to 3.59% (compared to
35
3.7% in model (2)) if the value-chain relatedness measure is substituted by the skill-relatedness
measure, however the difference with model (1) is outstanding. Human capital-based relatedness
measure is significant on 1% significance level in model (3) with positive coefficient of 0.850.
Return on assets turned out to be significant on 1% significance level (compared to 5% in
models (1) and (2)), and the coefficient reaches 3.036. All other controls variables show similar
results to model (2) with only marginal change in coefficients. Full model (4) shows pseudo Rsquared level of 5.11%. Skill-relatedness measure and input-output relatedness measure are both
significant on 1% significance level, however input-output relatedness measure has greater
coefficient (2.496 compared to 0.669 for skill-relatedness measure). Return on assets doesn’t
show any change in significance level and coefficient, while significance level of total assets
increases to 1% (compared to 5% in previous models) and drops to 10% for age (compared to
5% in previous models). The coefficient of total assets is -0.152 and the coefficient of age is 0.172. In model (5) pseudo R-squared increases slightly to 5.99% (compared to 5.11% in model
(4)) while introducing year dummies. Year 1999 is significant on 1% significance level with
negative coefficient -4.573. Year 2008 is significant on 10% significance level with coefficient
0.95. These results show that in 1999 the environment for diversification through market wasn’t
pleasant in Germany while year 2008 shows positive effect on probability of diversification
through market. All independent variables show very similar results to model (4) with marginal
coefficient changes. The results for the control variables are in line with literature, return on
assets has positive effect, while age and total assets have negative effect.
Table 11 show marginal effects of the independent variables for model (4). Marginal effects for
other models for hypothesis 1.2 can be found in the appendix.
Table 11. Marginal effects for Hypothesis 1.2, Model (4), dependent variable div_market.
min->max
a_sr
0.0023
inout
0.0048
ln_capint -0.0003
ln_tassets -0.0015
ln_age
-0.0015
ln_beta
0.0011
ln_roa
0.0027
Pr(yx)
0
0.9990
a_sr
0->1
0.0015
0.0098
-0.0001
-0.0008
-0.0003
0.0001
0.0193
=-1/2
0.0007
0.0030
-0.0000
-0.0001
-0.0002
0.0001
0.0040
=-+sd/2
0.0003
0.0003
-0.0001
-0.0003
-0.0002
0.0001
0.0008
MargEfct
0.0006
0.0024
-0.0000
-0.0001
-0.0002
0.0001
0.0029
1
0.0010
inout
ln_capint ln_tassets ln_age
ln_beta
ln_roa
36
x=
sd_x=
-.785106
.544345
.0285
.110136
1.78346
1.37764
12.1298
2.12873
3.0395
1.14592
.286612
.484961
-.030538
.284802
The structure of the table is explained in the chapter 4.2. Both input-output based and human
capital base relatedness measures are highly significant in the model. When comparing the
effects on the probability of diversification through market they show dramatic differences. The
effect size is similar only for the range of standard deviation. For the rest ranges value chain
relatedness measure shows stronger effects than skill relatedness measure. Marginal effect of
input-output relatedness measure is 0.0024. The average probability of diversification is
0.005319 (for each industry there are 188 possible industries of diversification). Marginal effect
of value chain based relatedness measure is half of the size of average probability of
diversification, which is a very strong effect. Skill relatedness measure shows marginal effect of
0.006, which is comparatively low. High marginal effect (0.0029) is also shown by return on
assets.
In conclusion, value chain-based relatedness measure affects probability of diversification more
than human capital-based relatedness measure and the marginal effects differ greatly, but both
relatedness measures are significant. Based on this findings hypothesis 1.2 cannot be rejected.
4.4 Hypothesis 1.3
Logistic regression is used to test hypothesis 1.3 (Diversification into skill-related activities is
more likely to occur in form of mergers and acquisitions than joint ventures). The dependent
variable is type of industry entry mode (deal_type), which takes value of 1 if diversification
move was done by mergers and acquisitions and value of 0 if it was done through establishing
joint ventures. Mergers, acquisitions and joint ventures are only relevant to diversification
through market. In order to fulfill this condition, all the observations, where diversification
through market didn’t occurred (div_market = 0) were dropped from the dataset. Due to a
number of missing values in the variables, the final dataset for hypothesis 1.3 has 435
observations. Year dummies were omitted because of the small sample size. Table 10 shows the
results for four models used to test hypothesis 1.3.
37
Table 12. Results for hypothesis 1.3, dependent variable deal_type.
VARIABLES
(1)
(2)
a_sr
inout
ln_cap
ln_capint
ln_age
ln_beta
ln_roa
Constant
Pseudo Rsquared
Observations
-0.860***
(0.285)
0.129
(0.386)
0.267
(0.252)
0.0510
(0.998)
6.084
(8.663)
11.81***
(3.675)
-1.029
(1.732)
-0.874***
(0.290)
0.106
(0.380)
0.282
(0.257)
0.137
(1.313)
4.986
(8.322)
12.10***
(3.805)
(3)
(4)
1.306*
(0.739)
1.277*
(0.743)
-0.574
(1.989)
-1.014***
(0.328)
0.251
(0.419)
0.124
(0.287)
0.710
(1.682)
6.925
(9.551)
14.54***
(4.527)
-1.007***
(0.326)
0.281
(0.416)
0.121
(0.290)
0.595
(1.617)
7.854
(9.207)
14.39***
(4.445)
0.2933
0.2988
0.3549
435
435
435
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
0.3562
435
First model (1) includes just control variables. Value chain-based relatedness measure is added
in the model (2). Skill-relatedness measure and control variables are present in the model (3).
Model (4) is the complete model with both relatedness measures and control variables. Pseudo
R-squared in model (1) is 29.33%. Market capitalization is significant on 1% significance level
and has a negative coefficient meaning that it has positive effect on probability that joint venture
will be chosen. Introducing value chain-based relatedness measure in model (2) doesn’t have any
effect on the results, compared to model (1), despite the small increase in pseudo R-squared
(29.88% compared to 29.33% in model (1)) and relatedness measure itself is insignificant.
Coefficients and significance levels of the control variables change very slightly. Model (3) with
human capital-based relatedness measure instead of value chain-based relatedness measure
shows increase in pseudo R-squared compared to model (1) and model (2) which now reaches
35.49%. The increase in pseudo R-squared is mainly due to significance level (p<0.1) of the
skill-relatedness measure. The coefficient is 1.306 meaning that if skill relatedness is increasing,
38
probability of using merger and acquisition instead of joint venture is increasing as well. The
significance level of control variables remains equal to model (1) and model (2) with slight
change in coefficients. Full model (4) shows marginal change in pseudo R-squared level, which
now is 35.62% as model (3). Input-output relatedness measure is insignificant while skillrelatedness measure is significant (p<0. 1) and has positive coefficient of 1.277. The results for
control variables are identical to model (3). Market capitalization turns out to be significant and
has negative effect on the choice of mergers and acquisitions. This is not in line with previous
research, because increasing company size is considered to increase probability of merger and
acquisition.
Marginal effects for the variables in model (4) are shown in the table 13. Marginal effects for
other models for hypothesis 1.3 can be found in the appendix.
Table 13. Marginal effects for Hypothesis 1.3, Model (4), dependent variable deal_type.
min>max
a_sr
0.0629
inout
-0.0135
ln_cap
-0.9431
ln_capint 0.0311
ln_age
0.0321
ln_beta
0.0793
ln_roa
0.1219
0->1
0.0145
-0.0241
-0.0000
0.0110
0.0055
0.0217
0.0377
0
Pr(yx)
0.0352
x=
sd_x=
a_sr
-.444581
.773784
=-1/2
0.0457
-0.0197
-0.0356
0.0086
0.0042
0.0245
0.5368
=-+sd/2
0.0347
-0.0045
-0.0773
0.0106
0.0054
0.0159
0.0193
MargEfct
0.0434
-0.0195
-0.0344
0.0085
0.0042
0.0241
0.2352
1
0.9648
inout
ln_cap
ln_capint ln_age
ln_beta
.122342 11.4734 1.57847 2.78582 .312533
.231062 1.97984 1.23229
1.2915 .656025
ln_roa
.010554
.081344
The structure of the table is explained in the chapter 4.2. The average probability that merger and
acquisition will be chosen over joint ventures is 0.5, but in our sample the amount of joint
ventures is very small compared to the amount of mergers and acquisitions (see the probabilities
of 0 and 1). The marginal effect of skill relatedness measure is 0.0434, which is not dramatically
significant compared to average probability of mergers and acquisitions choice. Market
capitalization has the marginal effect of -0.0344 on the probability of mergers and acquisitions
choice, meaning that increase of market capitalization increases the probability of joint ventures.
39
Four models demonstrate that value chain-based relatedness measure has absolutely no effect on
market entry mode choice. Human capital-based relatedness measure has a positive effect on
probability that merger or acquisition will be chosen. With the increase of market capitalization
probability of establishing joint venture is increasing.
The hypothesis is not rejected; diversification into skilled related activities is more likely to
occur through mergers and acquisitions, while nothing can be stated concerning input-output
related activities.
4.5 Hypothesis 2
Results for hypothesis 2 (Diversification into input-output or skill-related activities is valued
positively by the market) are calculated using Ordinary Least Squares (OLS) method. The
dependent variable is stock price reactions on diversification (p_react). If price reaction variable
is positive, than market valuated diversification move as successful, while negative price
reaction is considered as a failure of the diversification move. The original dataset, constructed
for this research was developed to test hypothesis 2 by omitting all the observations where
diversification through market didn’t occur, or, in other words, div_market variable was equal to
zero. Considering missing values from other variable the working sample was limited to 335
observations. Because of the relatively small sample of observations year dummy variables are
not used. Four models were constructed to show the changes in model estimations while
introducing skill-relatedness measure, input-output relatedness measure or both. All the results
are provided in table 11.
Table 14. Results for hypothesis 2, dependent variable p_react.
VARIABLES
a_sr
inout
ln_beta
ln_cap
ln_capint
(1)
(2)
(3)
(4)
-0.0337
(0.0236)
-0.0321
(0.0240)
0.0360
0.0258
(0.0546)
(0.0548)
-0.00501 -0.00601 0.00141 0.000394
(0.0231) (0.0232) (0.0233) (0.0236)
0.0146** 0.0136* 0.0146** 0.0139**
(0.00679) (0.00696) (0.00674) (0.00692)
-0.00185 -0.00112 -0.00902 -0.00816
40
ln_roa
deal_type
Constant
Observations
R-squared
(0.0119)
0.0575
(0.290)
0.0874
(0.0632)
-0.243**
(0.112)
(0.0120)
0.0689
(0.291)
0.0799
(0.0645)
-0.232**
(0.113)
(0.0128)
0.0412
(0.287)
0.0855
(0.0627)
-0.249**
(0.111)
355
355
355
0.094
0.101
0.124
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(0.0131)
0.0501
(0.290)
0.0802
(0.0641)
-0.241**
(0.113)
355
0.127
Model (1) includes just the control variables; value chain-based relatedness measure is added in
the model (2). Model (3) includes control variables and human capital-based relatedness
measure. Model (4) is the complete model with control variables, human capital-based
relatedness and value chain-based relatedness measure. Model (1) has R-squared of 9.4% which
is high for predicting stock price fluctuations because they can be affected by huge amount of
conditions. Market capitalization is the only significant variable in the model, and the
significance level is 5%. The coefficient is positive, meaning that if diversification occurs, stock
prices grow with the growth of the market capitalization. Introducing input-output relatedness
measure don’t affect R-squared significantly, it reaches only 10.1%. Value chain-based
relatedness variable is insignificant in model (2). Market capitalization drops significant level to
10% and coefficient is decreased to 0.136. All other control variables are insignificant. Model (3)
shows increase in R-squared compared to model (1) and model (2), reaching 12.4%. However,
human capital-based relatedness measure is not significant. Market capitalization changes
significance level to 5% and the coefficient to 0.0146. Full model (4) has R-squared of 12.7%
which is only marginally bigger, compared to model (3). Skill-relatedness measure is significant
is not significant, neither is input-output relatedness measure. The only significant control
variable is market capitalization with 5% significance level and positive coefficient 0.0139. No
support for hypothesis 2 was found meaning that it is rejected. Possible explanation for these
results is the length of the time period between the date prior to announcement of the
diversification move and the date of completion. Stock prices are affected by huge number of
external and internal effects, making estimations, based on long time range, unreliable because
of the uncontrolled noise. Time range between the rumor of the deal and after the announcement
could show different results, compared to the ones found in this paper. Unfortunately, the dataset
with such information wasn’t available.
41
5. Limitations and directions for further research
Dataset incompatibility is the main issue in this paper. Great number of missing values was
generated while merging three datasets for this study. The main problem is classification system
mismatch since skill relatedness measure developed by Neffke and Henning (2010) is based on
NACE 1.1 four digit classification systems, input-output relatedness measure, constructed using
input-output Eurostat tables is based on NACE rev.2 two digit classification system and both
Bereau’s Van Dijk databases use NACE rev.2 four digit classification system. Estimations will
be more précised if they were done using skill relatedness measure based on NACE rev.2 four
digit classification systems for German economy and input-output relatedness measure based on
four digit classification instead of two. However the industries dropped while constructing the
dataset were random, so the working sample can be treated as representative.
Secondly, diversification strategy is usually affected by traditions and history of a certain region.
In that case applying the same methodology to other countries, United States of America for
example, may provide different results. Some regions tend to develop strong value chain
linkages of industries while others are extremely diversified.
Thirdly, this paper considers only publically listed companies because no data on diversification
strategy of privately owned companies is available. Diversification strategy of privately owned
companies could totally differ from public companies because of numerous factors like size,
financial characteristics, higher influence of owner’s personal traits, etc. Further researches may
compare company’s behavior of public and private owned companies and investigate the
differences in strategies and motives behind them.
Fourthly, market response on corporate diversification was estimated by the period starting prior
to announcement and ending after the completion date. This period is too vague and stock prices
can be affected by a number of external factors. The best period to highlight the market reaction
on stock prices is between the date prior to rumor and the date after the announcement.
Unfortunately such data wasn’t’ available.
Finally, no possibility of internal diversification as a substitute to external diversification is
considered in this paper. Company’s diversification strategy can be external, internal or
combination of internal and external. It is crucial to understand the factors which force company
to choose one method over another.
42
6. Conclusions and policy implementations
The paper tried to find the connection between company diversification and relatedness
measures. Empirical analysis of the hypothesis showed that both human capital-based and value
chain-based relatedness measures have influence on firm diversification strategy. However, each
relatedness measure is more influential in particular aspect of company’s diversification. Skillrelatedness measure turned out affect choice of market entry mode. Increasing skill-relatedness
between two activities leads to higher probability of choice in favor of mergers and acquisitions.
Both human capital-based relatedness measure and value chain-based relatedness measure have a
positive effect on probability of diversification through market, but value chain-based
relatedness measure coefficient is relatively bigger compared to human capital-based relatedness
measure coefficient and marginal effect is greater as well. Value chain-based relatedness
measure turns out to have positive effect on probability of diversification into secondary
activities. The size of the coefficient and marginal effect makes an insight that the effect is
dramatic. However, no relationship between stock price fluctuations and industry relatedness
measures was found in this paper. Possible explanation for it could be the length of considered
range of time, which was too long to filter out other effects on stock price fluctuations.
Considering these aspects of firm diversification process some policy implementations can be
drawn. Owners of the firm and mangers can adjust their diversification strategy to fulfill desired
outcomes or adjust firm’s characteristics to succeed in desired diversification strategy.
Government can imply certain regulations to enable industry cohesion in the region, for example
providing tax shields for firms diversified into skill-related industries. If the company is good at
taking over other companies, they might consider skill-related diversification. If local
governments want to form a portfolio of highly diversified companies, they need to attract inputoutput related industries.
43
7. References
1. Amihud, Y., Lev, B. (1981). Risk Reduction as a Managerial Motive for
Conglomerate Mergers, 12, (2).
2. Auerbach, A.J., Reishus, D. (1988). The Effects of Taxation on the Merger
Decision. Corporate Takeovers: Causes and Consequences, 157-190.
3. Balakrishnan, S., Koza, M.P. (1993). Information asymmetry, adverse selection
and joint-ventures. Theory and evidence. Journal of Economic Behavior &
Organization, 20 (1), 99-117.
4. Baysinger, B.D,Kosnik, R.D, Turk, T.A. (1991). Effects of Board and Ownership
Structure on Corporate R&D Strategy. The Academy of Management Journal, 34
(1), 205-214.
5. Bernheim, B.D., Whinston, M.D. (1990). Multimarket Contact and Collusive
Behavior. The RAND Journal of Economics, 21 (1), 1-26.
6. Black, F., Scholes, M. (1973). The Pricing of Options and Corporate Liabilities.
Journal of Political Economy, 81 (3), 637-654.
7. Chang, S.J. (1996). An evolutionary perspective on diversification and corporate
restructuring: entry, exit, and economic performance 1981-89. Strategic
Management Journal, 17, 587-611.
8. Chatterjee, S., Wernerfelt, B. (1991). The Link between
Resources and Type of
Diversification: Theory and Evidence. Strategic Management Journal, 12 (1), 3348.
9. Cheung, Steven, N.S. (1987). Economic organization and transaction costs. The
New Palgrave: A Dictionary of Economics, 2, 55–58.
10. Edwards, C.D. (1955).Conglomerate bigness as a source of power. University of
Virginia.
11. Fan, J.P., Lang, L.H. (2000). The measurement of Relatedness: An application to
Corporate Diversification. The Journal of Business, 629-660.
12. Farjoun, M. (1994). Beyond Industry Boundaries: Human Expertise,
Diversification and Resource Related Industry Groups. Organization science, 5
(2), 185-199.
13. Farjoun, M. (1998). The independent and join effects of the skill and physical
bases of relatedness in diversification. Strategic Management Journal, 19 (7), 611630.
44
14. Grant, R.M. (1996). Toward a knowledge-based theory of the firm. Strategic
Management Journal, 17, 109-122.
15. Grinblatt, M., Titman, S. (1989). Portfolio performance evaluation: Old issues
and new insights. Review of Financial Studies, 2, 391–421.
16. Hennart, J.F. (1988). A Theory of Multinational Enterprise. A Transaction Cost
Theory of Equity Joint Ventures. Strategic Management Journal, 9, 361-374.
17. Hennart, J.F., Reddy, S. (1997). The Choice between Mergers/Acquisitions and
Joint Ventures: The Case of Japanese Investors in the United States. Strategic
Mangement Journal.
18. Hoskisson, R.E., Hitt, M.A (1990). Antecedents and Performance Outcomes of
Diversification: A Review and Critique of Theoretical Perspectives. Journal of
Management, 16, 461-509.
19. Hyland, D.C., Diltz, J.D. (2002). Why Firms Diversify: An Empirical
Examination. Financial Management, 31 (1), 51-81.
20. Jaffe, A. (1989). Characterizing the "technological position" of firms, with
application to quantifying technological opportunity and research spillovers.
Research Policy, 18, 87-97.
21. Jemison, D.B., Sitkin, S.B. Corporate Acquisitions: A Process Perspective. The
Academy of Management Review, 11 (1), 145-163.
22. Jensen, M.C. (1986). Agency Costs of Free Cash Flow, Corporate Finance, and
Takeovers. The American Review, 76 (2).
23. Jensen, M.C., Ruback, R.S. (1983). The market for corporate control: The
scientific evidence. Journal of Financial Economics, 11 (1-4), 5-50.
24. Jones, G.R., Hill, C.W.L. (1988). Transaction Cost Analysis of Strategy Structure
Choice. The Strategic Management Journal.
25. Kay, N., Robe, J.P., Zagnoli, P. (1987). An approach to the analysis of joint
ventures. Working paper, European, University Institute.
26. Kogut, B., Singh, H. (1988). The Effect of National Culture on the Choice of
Entry Mode. Journal of International Business Studies, 19 (3), 411-432.
27. Lee, K., Lieberman, M.B. (2009). Acquisitions vs. Internal Development as
Modes of Market Entry. StrategicMangementJournal, 31, 140-158.
28. Levy, H., Sarnat, M. (1970). International Diversification of Investment
Portfolios. The American Economic Review, 60 (4), 668-679.
45
29. Lippman, S.A., Rumelt, R.P. (1982). Uncertain Imitability: An Analysis of
Interfirm Differences in Efficiency under Competition. The Bell Journal of
Economics, 13 (2), 418-443.
30. Montgomery, C.A. (1994). Corporate Diversification. The Journal of Economic
Perspectives, 8 (3), 163-170.
31. Montgomery, C.A., Wernerfelt, B. (1988). Diversification, Ricardian Rents, and
Tobin’s Q. The RAND Journal of Economics, 19 (4), 623-632.
32. Morck, R., Shleifer, A., Vishny, R.W. (1990). Do Managerial objectives Drive
Bad Acquisitions. The Journal of Finance, 45 (1), 31-48.
33. Neffke, F., Henning, M. (2010). Skill-relatedness and firm diversification. Papers
on Economics and Evolution.
34. Pennings, J.M., Barkema, H., Douma, S. (1994). Organizational Learning and
Diversification. The Academy of Management Journal, 37 (3), 608-640.
35. Penrose, E.T. (1959). The Theory of the Growth of the Firm. Basil Blackwell:
London, UK.
36. Porter, M.E. (1987). From competitive advantage to corporate strategy. Harvard
Business Review, 65 (3), 43-59.
37. Powell, W. (1990). Neither market nor hierarchy: Network forms of
organization. Research in organizational behavior, 12, 295-336.
38. Scharfstein, D.S., Stein, J.C. (1997). The Dark Side of Internal Capital Markets:
Divisional Rent-Seeking and Inefficient Investment. NBER Working Paper No.
5969.
39. Shleifer, A., Vishny, R.W. (1989). Management entrenchment: The case of
manager-specific investments.Journal of Financial Economics, 25 (1), 123-139.
40. Teece, D.J. (1980). Economics of scope and the scope of the enterprise. Journal of
Economic Behavior and Organization, 1, 223–247.
41. Teece, D.J., Rumelt, R., Dosi, G., Winter, S. (1994). Understanding corporate
coherence. Theory and evidence. Journal of Economic Behavior and
Organization, 23, 1-30.
42. Tsang, E.W.K. (1997). Transaction Cost and Resource-Based Explanations of
Joint Ventures: A Comparison and Synthesis. Organization Studies, 21 (1), 215242.
43. Williamson, O.E. (1975). Markets and Hierarchies: Analysis and Antitrust
Implications: A Study in the Economics of Internal Organization. University of
California, Berkeley - Business & Public Policy Group.
46
8. Appendix
Table 2. Detailed summary statistics of all variables.
Variable
Obs
Min
Max
Mean
St. Dev.
Variance
Skewness
Kurtosis
div_market
134232
0
1
.00149
.0385713
.0014877
25.84882
669.1615
div_sec
134232
0
1
.0023541
.0484624
.0023486
20.53746
422.7872
deal_type
134232
0
1
.8935574
.3084044
.0951133
-2.552226
7.513859
p_react
46248
-.7482014
.8903229
.0029246
.1251869
.0156718
.9406055
19.30467
sr
134232
0
355.1014
1.110879
5.899515
34.80428
17.34482
551.8143
-1
.9943836
-.7860958
.5413002
.2930059
2.269861
6.435041
a_sr
134232
inout
134232
0
.7447964
.0311508
.117646
.0138406
4.670814
23.26282
capitalisation
127840
1029.97
5.48e+07
7450999
1.34e+07
1.79e+14
1.945264
5.870787
ln_cap
127840
6.938255
17.81981
13.02376
2.891565
8.361146
.1632628
1.853577
cap_int
129908
.0435801
590.155
22.1393
50.82168
2582.843
4.984237
37.3325
ln_capint
129908
.0426572
6.382078
2.039882
1.343552
1.805133
.8274746
2.792834
beta
134232
-1.507058
293.7649
1.370227
15.50517
240.4102
18.78486
354.2502
ln_beta
133480
-.912331
5.686178
.4036486
.4499034
.202413
4.07431
55.0075
t_assets
133292
16.1522
4.64e+07
7316698
1.32e+07
1.74e+14
2.039818
6.127322
2.782056
17.65182
13.26181
2.724718
7.424086
.0401974
2.320843
-2.867063
.3751724
-.0114694
.1541165
.0237519
-11.03545
184.5653
-3.635095
.3185791
-.0199402
.2511896
.0630962
-13.02388
187.4249
1.015743
110.0151
39.62984
33.30048
1108.922
.9648917
2.482635
-1.789708
4.700219
2.942635
1.186609
1.40804
-.4592952
2.970278
ln_tassets
roa
133292
129532
ln_roa
age
129156
84600
ln_age
72568
Table 6. Summary statistics when market diversification is present.
Variable
Obs
Min
Max
Mean
St. Dev.
Variance
Skewness
Kurtosis
p_react
370
-.2692307
.4310345
.0241029
.114722
.0131611
2.077292
9.696804
deal_type
1000
0
1
.93
.2557873
.0654271
-3.370606
12.36098
sr
1000
0
33.30198
2.314693
5.268077
27.75263
3.630177
19.48613
a_sr
1000
-1
.9856153
-.5096493
.7558768
.5713497
1.030272
2.214994
inout
1000
0
.6537724
.2197098
.2858054
.0816847
.6243699
1.4187
capitalisation
970
1155
5.48e+07
4392088
9337436
8.72e+13
2.861801
12.26567
ln_cap
970
7.052721
17.81981
12.72818
2.549267
6.498762
.2948607
2.09071
cap_int
955
.4028296
288.2278
19.54588
32.32839
1045.125
3.973982
27.98984
ln_capint
955
.3384914
5.667214
2.128907
1.333063
1.777057
.4454116
1.8884
beta
1000
-1.353376
293.7649
2.025064
20.73906
430.1085
14.02298
197.7656
ln_beta
990
-.912331
5.686178
.4337431
.4963532
.2463665
5.777765
64.70671
t_assets
990
2366.925
4.64e+07
6076818
1.27e+07
1.61e+14
2.412428
7.668184
ln_tassets
990
-1.470067
21.205
13.71006
3.739044
13.98045
.0374052
3.113685
roa
955
.3751724
.0053675
.0710634
.00505
1.116267
12.10798
ln_roa
955
-.3037236
.7236587
2.130044
.0156227
.1408894
.0198498
7.365647
121.5746
age
545
4.016427
110.0151
34.973
31.97641
1022.491
1.162859
2.89122
ln_age
545
-1.790392
4.58007
2.53568
1.312383
1.72235
-.3486765
3.090524
47
Marginal effects
Hypothesis 1.1
Table 15. Marginal effects for Hypothesis 1.1, Model (1), dependent variable div_sec.
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
min->max
-0.0007
-0.0018
-0.0002
-0.0003
0.0025
0->1
-0.0003
-0.0046
-0.0000
-0.0001
0.0629
=-1/2
-0.0002
-0.0001
-0.0000
-0.0001
0.0051
0
1
ln_age
3.0395
1.14592
Pr(yx)
0.9995
0.0005
x=
sd_x=
ln_capint
1.78346
1.37764
ln_tassets
12.1298
2.12873
=-+sd/2
-0.0002
-0.0003
-0.0000
-0.0000
0.0007
MargEfct
-0.0002
-0.0001
-0.0000
-0.0001
0.0022
ln_beta
.286612
.484961
ln_roa
-.030538
.284802
Table 16. Marginal effects for Hypothesis 1.1, Model (2), dependent variable div_sec.
inout
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
min->max
0.0109
-0.0005
-0.0016
-0.0002
-0.0004
0.0015
0->1
0.0394
-0.0002
-0.0076
-0.0000
-0.0001
0.0335
=-1/2
0.0037
-0.0001
-0.0001
-0.0000
-0.0001
0.0029
0
1
ln_tassets
12.1298
2.12873
Pr(yx)
0.9997
0.0003
x=
sd_x=
inout
.0285
.110136
ln_capint
1.78346
1.37764
=-+sd/2
0.0002
-0.0002
-0.0002
-0.0000
-0.0001
0.0004
MargEfct
0.0015
-0.0001
-0.0001
-0.0000
-0.0001
0.0014
ln_age
3.0395
1.14592
ln_beta
.286612
.484961
ln_roa
-.030538
.284802
48
Table 17. Marginal effects for Hypothesis 1.1, Model (3), dependent variable div_sec.
a_sr
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
min->max
0.0010
-0.0007
-0.0017
-0.0001
-0.0004
0.0025
0->1
0.0007
-0.0002
-0.0050
-0.0000
-0.0001
0.0785
=-1/2
0.0003
-0.0001
-0.0001
-0.0000
-0.0001
0.0054
0
1
Pr(yx)
0.9996
0.0004
x=
sd_x=
a_sr
-.785106
.544345
ln_capint
1.78346
1.37764
ln_tassets
12.1298
2.12873
=-+sd/2
0.0002
-0.0002
-0.0003
-0.0000
-0.0000
0.0007
MargEfct
0.0003
-0.0001
-0.0001
-0.0000
-0.0001
0.0021
ln_age
3.0395
1.14592
ln_beta
.286612
.484961
ln_roa
-.030538
.284802
Hypothesis 1.2
Table 18. Marginal effects for Hypothesis 1.2, Model (1), dependent variable div_market.
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
min>max
-0.0005
-0.0013
-0.0022
0.0027
0.0030
0->1
-0.0001
-0.0005
-0.0004
0.0003
0.0163
0
=-1/2
-0.0001
-0.0001
-0.0002
0.0002
0.0041
=-+sd/2
-0.0001
-0.0003
-0.0003
0.0001
0.0009
MargEfct
-0.0001
-0.0001
-0.0002
0.0002
0.0031
ln_age
3.0395
1.14592
ln_beta
.286612
.484961
ln_roa
-.030538
.284802
1
Pr(yx)
0.9988
0.0012
x=
sd_x=
ln_capint
1.78346
1.37764
ln_tassets
12.1298
2.12873
49
Table 19. Marginal effects for Hypothesis 1.2, Model (1), dependent variable div_market.
inout
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
min>max
0.0095
-0.0004
-0.0014
-0.0019
0.0017
0.0027
0->1
0.0225
-0.0001
-0.0007
-0.0003
0.0002
0.0156
Pr(yx)
0
0.9990
1
0.0010
x=
sd_x=
inout
.0285
.110136
ln_capint
1.78346
1.37764
=-1/2
0.0050
-0.0001
-0.0001
-0.0002
0.0002
0.0038
=-+sd/2
0.0004
-0.0001
-0.0003
-0.0002
0.0001
0.0008
MargEfct
0.0034
-0.0001
-0.0001
-0.0002
0.0002
0.0028
ln_tassets
12.1298
2.12873
ln_age
3.0395
1.14592
ln_beta
.286612
.484961
ln_roa
-.030538
.284802
Table 20. Marginal effects for Hypothesis 1.2, Model (3), dependent variable div_market.
a_sr
ln_capint
ln_tassets
ln_age
ln_beta
ln_roa
min>max
0.0037
-0.0003
-0.0014
-0.0017
0.0015
0.0029
0->1
0.0026
-0.0000
-0.0007
-0.0003
0.0002
0.0215
0
=-1/2
0.0009
-0.0000
-0.0001
-0.0002
0.0002
0.0044
=-+sd/2
0.0005
-0.0001
-0.0003
-0.0002
0.0001
0.0009
MargEfct
0.0009
-0.0000
-0.0001
-0.0002
0.0002
0.0031
ln_age
3.0395
1.14592
ln_beta
.286612
.484961
1
Pr(yx)
0.9990
0.0010
x=
sd_x=
a_sr
-.785106
.544345
ln_capint ln_tassets
1.78346 12.1298
1.37764 2.12873
ln_roa
-.030538
.284802
Table 21. Marginal effects for Hypothesis 1.2, Model (5), dependent variable div_market.
a_sr
inout
ln_capint
ln_tassets
min>max
0.0020
0.0044
-0.0002
-0.0014
0->1
0.0013
0.0089
-0.0000
-0.0008
=-1/2
0.0006
0.0028
-0.0000
-0.0001
=-+sd/2
0.0003
0.0002
-0.0000
-0.0003
MargEfct
0.0006
0.0022
-0.0000
-0.0001
50
ln_age
ln_beta
ln_roa
d1
d2
d3
d4
d5
d6
d7
d8
d9
d10
d11
d12
d13
d14
-0.0013
0.0013
0.0028
0.0019
0.0007
0.0009
-0.0002
0.0006
0.0007
0.0001
0.0003
0.0013
0.0007
0.0003
0.0013
0.0007
0.0008
-0.0002
0.0001
0.0243
0.0019
0.0007
0.0009
-0.0002
0.0006
0.0007
0.0001
0.0003
0.0013
0.0007
0.0003
0.0013
0.0007
0.0008
0
-0.0001
0.0001
0.0044
0.0011
0.0005
0.0006
-0.0002
0.0005
0.0005
0.0001
0.0003
0.0009
0.0006
0.0002
0.0008
0.0006
0.0006
-0.0002
0.0001
0.0009
0.0003
0.0001
0.0001
-0.0001
0.0001
0.0001
0.0000
0.0001
0.0002
0.0001
0.0000
0.0001
0.0001
0.0002
-0.0001
0.0001
0.0029
0.0011
0.0005
0.0006
-0.0002
0.0005
0.0005
0.0001
0.0003
0.0008
0.0006
0.0002
0.0008
0.0005
0.0006
Pr(yx)
0.9991
1
0.0009
x=
sd_x=
a_sr
-.785106
.544345
inout
ln_capint
.0285
1.78346
.110136 1.37764
ln_tassets
12.1298
2.12873
ln_age
3.0395
1.14592
ln_beta
.286612
.484961
ln_roa
-.030538
.284802
x=
sd_x=
d1
.066667
.249446
d2
d3
.033333 .044444
.179507 .206082
d4
.058333
.234374
d5
.097222
.296262
d6
.072222
.258857
d7
.130556
.336916
x=
sd_x=
d8
.088889
.284585
d9
d10
.047222 .063889
.212115 .244557
d11
.025
.156126
d12
.016667
.12802
d13
.033333
.179507
d14
.113889
.317679
51
Hypothesis 1.3
Table 22. Marginal effects for Hypothesis 1.3, Model (1), dependent variable deal_type.
ln_cap
ln_capint
ln_age
ln_beta
ln_roa
min>max
-0.9019
0.0239
0.1179
0.0143
0.1393
0->1
-0.0000
0.0068
0.0212
0.0024
0.0520
0
=-1/2
-0.0410
0.0060
0.0125
0.0024
0.5167
=-+sd/2
-0.0862
0.0074
0.0161
0.0016
0.0232
MargEfct
-0.0401
0.0060
0.0124
0.0024
0.2836
ln_age
2.78582
1.2915
ln_beta
.312533
.656025
ln_roa
.010554
.081344
1
Pr(yx)
0.0490
0.9510
x=
sd_x=
ln_cap
11.4734
1.97984
ln_capint
1.57847
1.23229
Table 23. Marginal effects for Hypothesis 1.3, Model (2), dependent variable deal_type.
inout
ln_cap
ln_capint
ln_age
ln_beta
ln_roa
min>max
-0.0349
-0.9061
0.0194
0.1245
0.0323
0.1077
0->1
-0.0675
-0.0000
0.0053
0.0224
0.0061
0.0496
0
=-1/2
-0.0481
-0.0404
0.0048
0.0128
0.0062
0.3722
=-+sd/2
-0.0108
-0.0853
0.0059
0.0166
0.0041
0.0184
MargEfct
-0.0466
-0.0395
0.0048
0.0128
0.0062
0.2256
ln_capint
1.57847
1.23229
ln_age
2.78582
1.2915
ln_beta
.312533
.656025
1
Pr(yx)
0.0475
0.9525
x=
sd_x=
inout
.122342
.231062
ln_cap
11.4734
1.97984
ln_roa
.010554
.081344
52
Table 24. Marginal effects for Hypothesis 1.3, Model (3), dependent variable deal_type.
a_sr
ln_cap
ln_capint
ln_age
ln_beta
ln_roa
min>max
0.0647
-0.9414
0.0345
0.0314
0.0694
0.1459
0->1
0.0146
-0.0000
0.0128
0.0053
0.0186
0.0385
0
=-1/2
0.0473
-0.0357
0.0097
0.0041
0.0206
0.6508
Pr(yx)
0.0355
1
0.9645
x=
sd_x=
a_sr
-.444581
.773784
ln_cap
ln_capint
11.4734 1.57847
1.97984 1.23229
=-+sd/2
0.0358
-0.0774
0.0119
0.0054
0.0134
0.0222
MargEfct
0.0447
-0.0345
0.0096
0.0041
0.0204
0.2691
ln_age
2.78582
1.2915
ln_beta
.312533
.656025
ln_roa
.010554
.081344
53