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

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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.

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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

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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?

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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.

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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

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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 buyer-

seller 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.

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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

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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

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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.

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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.

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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.

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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.

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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 company-

level 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.

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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 t he completion−stock price priour ¿announcement ¿stock price prior ¿

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 IncomeTotal 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∫¿

Total AssetsSales 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−r f

rm−r f

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. Skill-

relatedness measure is then constructed as the ratio of these labour flows:

SRij=F ij

obs

F ijpred

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:

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input relatedness(i , j)= ¿(i , j)∑

i¿(i , j)

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)∑

jout (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 output-

relatedness 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= 11+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

26

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,

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 X i 1+ β2 X i 2+…+βn X¿+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).

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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

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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_secdiv_market a_sr inout

ln_tassets ln_beta ln_cap ln_capint ln_roa ln_age

div_sec 1.0000div_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 .33849145.667214 2.128907 1.333063 1.777057 .4454116 1.8884

ln_beta 990 -.912331 5.68617 .4337431 .4963532 .2463665 5.777765 64.70671

29

8

ln_tassets 990 -1.470067 21.205 13.71006 3.739044 13.98045 .0374052 3.113685

ln_roa 955 -.72365872.130044 .0156227 .1408894 .0198498 7.365647 121.5746

ln_age 545 -1.790392 4.58007 2.53568 1.312383 1.72235-.3486765 3.090524

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 ln_age

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 1.0000ln_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 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.

30

Table 8. Results for hypothesis 1.1, dependent variable div_sec.

VARIABLES (1) (2) (3) (4) (5)

a_sr 0.665*** 0.203 0.166(0.195) (0.208) (0.211)

inout 5.096*** 4.919*** 4.965***(0.494) (0.532) (0.542)

ln_capint -0.349** -0.374** -0.336** -0.369** -0.370**(0.167) (0.164) (0.167) (0.165) (0.172)

ln_tassets -0.303*** -0.369*** -0.315*** -0.373*** -0.371***(0.0834) (0.0916) (0.0836) (0.0910) (0.100)

ln_age -0.0566 -0.0729 -0.0486 -0.0637 0.0269(0.141) (0.131) (0.139) (0.132) (0.151)

ln_beta -0.124 -0.415 -0.213 -0.442 -0.816*(0.385) (0.420) (0.404) (0.422) (0.477)

ln_roa 4.850*** 4.643*** 5.173*** 4.748*** 4.599***(1.353) (1.327) (1.382) (1.339) (1.394)

Constant -3.035*** -2.658** -2.475** -2.482** 1.462*** (0.964) (1.071) (0.996) (1.086) (0.551)

d1 0.0637(0.847)

d3 -0.109(0.735)

d5 -0.935(1.101)

d6 -0.0706(0.640)

d7 0.333(0.647)

d8 0.896(0.700)

d9 0.925(0.652)

d10 0.751(0.859)

d11 0.275(1.121)

d12 -0.523(1.108)

d13 0.0644(0.653)

d14 -3.001**(1.274)

Pseudo R-squared 0.0483 0.1331 0.0599 0.1342 0.1539

31

Observations 67,680 67,680 67,680 67,680 61,476Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Model (1) consists only of control variables, model (2) includes control variables and input-

output 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

32

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

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 0->1 =-1/2 =-+sd/2 MargEfcta_sr 0.0001 0.0001 0.0001 0.0000 0.0001inout 0.0094 0.0329 0.0033 0.0002 0.0014ln_capint -0.0005 -0.0002 -0.0001 -0.0001 -0.0001ln_tassets -0.0016 -0.0079 -0.0001 -0.0002 -0.0001ln_age -0.0001 -0.0000 -0.0000 -0.0000 -0.0000ln_beta -0.0005 -0.0001 -0.0001 -0.0001 -0.0001ln_roa 0.0015 0.0367 0.0031 0.0004 0.0014

0 1Pr(yx) 0.9997 0.0003

a_sr inout ln_capint ln_tassets ln_age ln_beta ln_roax= -.785106 .0285 1.78346 12.1298 3.0395 .286612 -.030538sd_x= .544345 .110136 1.37764 2.12873 1.14592 .484961 .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

33

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.

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) (3) (4) (5)

a_sr 0.850*** 0.669*** 0.645***(0.134) (0.143) (0.144)

inout 3.229*** 2.496*** 2.470***(0.446) (0.482) (0.485)

ln_capint -0.0720 -0.0724 -0.0440 -0.0494 -0.0281(0.0852) (0.0843) (0.0847) (0.0840) (0.0872)

ln_tassets -0.117** -0.137** -0.137** -0.152*** -0.153***(0.0543) (0.0569) (0.0550) (0.0570) (0.0583)

ln_age -0.194** -0.191** -0.181** -0.172* -0.165*(0.0916) (0.0895) (0.0893) (0.0884) (0.0902)

ln_beta 0.209 0.167 0.159 0.131 0.153(0.160) (0.179) (0.170) (0.185) (0.206)

ln_roa 2.639** 2.705** 3.036*** 2.987*** 3.271***(1.149) (1.120) (1.164) (1.139) (1.136)

34

Constant -4.589*** -4.554*** -3.895*** -3.987*** 1.212** (0.648) (0.684) (0.672) (0.701) (0.498)

d1 0.588(0.715)

d2 0.696(0.587)

d3 -0.242(0.824)

d4 0.505(0.546)

d5 0.566(0.584)

d6 0.127(0.544)

d7 0.318(0.577)

d8 0.950*(0.568)

d9 0.619(0.584)

d10 0.249(0.824)

d11 0.908(0.831)

d12 0.611(0.709)

d13 0.676(0.513)

d14 -4.573***(0.802)

Pseudo R-squared 0.0117 0.0370 0.0359 0.0511 0.0599

Observations 67,680 67,680 67,680 67,680 67,680Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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 R-35

squared 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

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 R-

squared 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 0->1 =-1/2 =-+sd/2 MargEfcta_sr 0.0023 0.0015 0.0007 0.0003 0.0006inout 0.0048 0.0098 0.0030 0.0003 0.0024ln_capint -0.0003 -0.0001 -0.0000 -0.0001 -0.0000ln_tassets -0.0015 -0.0008 -0.0001 -0.0003 -0.0001ln_age -0.0015 -0.0003 -0.0002 -0.0002 -0.0002ln_beta 0.0011 0.0001 0.0001 0.0001 0.0001

36

ln_roa 0.0027 0.0193 0.0040 0.0008 0.0029

0 1Pr(yx) 0.9990 0.0010

a_sr inout ln_capint ln_tassets ln_age ln_beta ln_roax= -.785106 .0285 1.78346 12.1298 3.0395 .286612 -.030538sd_x= .544345 .110136 1.37764 2.12873 1.14592 .484961 .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

37

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.

Table 12. Results for hypothesis 1.3, dependent variable deal_type.

VARIABLES (1) (2) (3) (4)

a_sr 1.306* 1.277*(0.739) (0.743)

inout -1.029 -0.574(1.732) (1.989)

ln_cap -0.860*** -0.874*** -1.007*** -1.014***(0.285) (0.290) (0.326) (0.328)

ln_capint 0.129 0.106 0.281 0.251(0.386) (0.380) (0.416) (0.419)

ln_age 0.267 0.282 0.121 0.124(0.252) (0.257) (0.290) (0.287)

ln_beta 0.0510 0.137 0.595 0.710(0.998) (1.313) (1.617) (1.682)

ln_roa 6.084 4.986 7.854 6.925(8.663) (8.322) (9.207) (9.551)

Constant 11.81*** 12.10*** 14.39*** 14.54***(3.675) (3.805) (4.445) (4.527)

Pseudo R-squared 0.2933 0.2988 0.3549 0.3562

Observations 435 435 435 435Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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

38

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,

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 skill-

relatedness 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 0->1 =-1/2 =-+sd/2 MargEfct

a_sr 0.0629 0.0145 0.0457 0.0347 0.0434inout -0.0135 -0.0241 -0.0197 -0.0045 -0.0195ln_cap -0.9431 -0.0000 -0.0356 -0.0773 -0.0344ln_capint 0.0311 0.0110 0.0086 0.0106 0.0085ln_age 0.0321 0.0055 0.0042 0.0054 0.0042ln_beta 0.0793 0.0217 0.0245 0.0159 0.0241ln_roa 0.1219 0.0377 0.5368 0.0193 0.2352

0 1Pr(yx) 0.0352 0.9648

a_sr inout ln_cap ln_capint ln_age ln_beta ln_roax= -.444581 .122342 11.4734 1.57847 2.78582 .312533 .010554sd_x= .773784 .231062 1.97984 1.23229 1.2915 .656025 .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

39

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.

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 (1) (2) (3) (4)

a_sr -0.0337 -0.0321(0.0236

)(0.0240

)inout 0.0360 0.0258

(0.0546 (0.0548

40

) )

ln_beta-

0.00501-

0.00601 0.001410.00039

4(0.0231

)(0.0232

)(0.0233

)(0.0236

)

ln_cap0.0146*

* 0.0136*0.0146*

*0.0139*

*(0.0067

9)(0.0069

6)(0.0067

4)(0.0069

2)

ln_capint-

0.00185-

0.00112-

0.00902-

0.00816(0.0119

)(0.0120

)(0.0128

)(0.0131

)ln_roa 0.0575 0.0689 0.0412 0.0501

(0.290) (0.291) (0.287) (0.290)deal_type 0.0874 0.0799 0.0855 0.0802

(0.0632)

(0.0645)

(0.0627)

(0.0641)

Constant-

0.243**-

0.232**-

0.249**-

0.241**(0.112) (0.113) (0.111) (0.113)

Observations 355 355 355 355R-squared 0.094 0.101 0.124 0.127

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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

41

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.

42

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.

43

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. Skill-

relatedness 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 input-

output related industries.

44

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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 0355.1014 1.110879

5.899515 34.80428 17.34482

551.8143

a_sr 134232 -1.9943836 -.7860958

.5413002 .2930059 2.269861

6.435041

inout 134232 0.7447964 .0311508 .117646 .0138406 4.670814

23.26282

capitalisation 127840 1029.975.48e+07 7450999

1.34e+07 1.79e+14 1.945264

5.870787

ln_cap 127840 6.93825517.81981 13.02376

2.891565 8.361146 .1632628

1.853577

cap_int 129908 .0435801 590.155 22.139350.82168 2582.843 4.984237 37.3325

ln_capint 129908 .0426572

6.382078 2.039882

1.343552 1.805133 .8274746

2.792834

beta 134232 -1.507058293.7649 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 16.15224.64e+07 7316698

1.32e+07 1.74e+14 2.039818

6.127322

ln_tassets 133292 2.78205617.65182 13.26181

2.724718 7.424086 .0401974

2.320843

roa 129532 -2.867063.3751724 -.0114694

.1541165 .0237519 -11.03545

184.5653

ln_roa 129156 -3.635095.3185791 -.0199402

.2511896 .0630962 -13.02388

187.4249

age 84600 1.015743110.0151 39.62984

33.30048 1108.922 .9648917

2.482635

ln_age 72568 -1.7897084.700219 2.942635

1.186609 1.40804 -.4592952

2.970278

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

48

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 -.3037236 .3751724 .0053675 .0710634 .00505 1.116267 12.10798

ln_roa 955 -.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

Marginal effects

Hypothesis 1.1

Table 15. Marginal effects for Hypothesis 1.1, Model (1), dependent variable div_sec.

min->max 0->1 =-1/2 =-+sd/2MargEfct

ln_capint -0.0007 -0.0003 -0.0002 -0.0002 -0.0002ln_tassets -0.0018 -0.0046 -0.0001 -0.0003 -0.0001ln_age -0.0002 -0.0000 -0.0000 -0.0000 -0.0000ln_beta -0.0003 -0.0001 -0.0001 -0.0000 -0.0001ln_roa 0.0025 0.0629 0.0051 0.0007 0.0022

0 1Pr(yx) 0.9995 0.0005

ln_capint ln_tassets ln_age ln_beta ln_roax= 1.78346 12.1298 3.0395 .286612 -.030538sd_x= 1.37764 2.12873 1.14592 .484961 .284802

Table 16. Marginal effects for Hypothesis 1.1, Model (2), dependent variable div_sec.

min->max 0->1 =-1/2 =-+sd/2 MargEfctinout 0.0109 0.0394 0.0037 0.0002 0.0015ln_capint -0.0005 -0.0002 -0.0001 -0.0002 -0.0001ln_tassets -0.0016 -0.0076 -0.0001 -0.0002 -0.0001ln_age -0.0002 -0.0000 -0.0000 -0.0000 -0.0000ln_beta -0.0004 -0.0001 -0.0001 -0.0001 -0.0001ln_roa 0.0015 0.0335 0.0029 0.0004 0.0014

0 1Pr(yx) 0.9997 0.0003

inout ln_capint ln_tassets ln_age ln_beta ln_roax= .0285 1.78346 12.1298 3.0395 .286612 -.030538

49

sd_x= .110136 1.37764 2.12873 1.14592 .484961 .284802

Table 17. Marginal effects for Hypothesis 1.1, Model (3), dependent variable div_sec.

min->max 0->1 =-1/2 =-+sd/2 MargEfcta_sr 0.0010 0.0007 0.0003 0.0002 0.0003ln_capint -0.0007 -0.0002 -0.0001 -0.0002 -0.0001ln_tassets -0.0017 -0.0050 -0.0001 -0.0003 -0.0001ln_age -0.0001 -0.0000 -0.0000 -0.0000 -0.0000ln_beta -0.0004 -0.0001 -0.0001 -0.0000 -0.0001ln_roa 0.0025 0.0785 0.0054 0.0007 0.0021

0 1Pr(yx) 0.9996 0.0004

a_sr ln_capint ln_tassets ln_age ln_beta ln_roax= -.785106 1.78346 12.1298 3.0395 .286612 -.030538sd_x= .544345 1.37764 2.12873 1.14592 .484961 .284802

Hypothesis 1.2

Table 18. Marginal effects for Hypothesis 1.2, Model (1), dependent variable div_market.

min->max 0->1 =-1/2 =-+sd/2

MargEfct

ln_capint -0.0005 -0.0001 -0.0001 -0.0001 -0.0001ln_tassets -0.0013 -0.0005 -0.0001 -0.0003 -0.0001ln_age -0.0022 -0.0004 -0.0002 -0.0003 -0.0002ln_beta 0.0027 0.0003 0.0002 0.0001 0.0002ln_roa 0.0030 0.0163 0.0041 0.0009 0.0031

0 1Pr(yx) 0.9988 0.0012

ln_capint ln_tassets ln_age ln_beta ln_roax= 1.78346 12.1298 3.0395 .286612 -.030538sd_x= 1.37764 2.12873 1.14592 .484961 .284802

50

Table 19. Marginal effects for Hypothesis 1.2, Model (1), dependent variable div_market.

min->max 0->1 =-1/2 =-+sd/2

MargEfct

inout 0.0095 0.0225 0.0050 0.0004 0.0034ln_capint -0.0004 -0.0001 -0.0001 -0.0001 -0.0001ln_tassets -0.0014 -0.0007 -0.0001 -0.0003 -0.0001ln_age -0.0019 -0.0003 -0.0002 -0.0002 -0.0002ln_beta 0.0017 0.0002 0.0002 0.0001 0.0002ln_roa 0.0027 0.0156 0.0038 0.0008 0.0028

0 1Pr(yx) 0.9990 0.0010

inout ln_capint ln_tassets ln_age ln_beta ln_roax= .0285 1.78346 12.1298 3.0395 .286612 -.030538sd_x= .110136 1.37764 2.12873 1.14592 .484961 .284802

Table 20. Marginal effects for Hypothesis 1.2, Model (3), dependent variable div_market.

min->max 0->1 =-1/2 =-+sd/2

MargEfct

a_sr 0.0037 0.0026 0.0009 0.0005 0.0009ln_capint -0.0003 -0.0000 -0.0000 -0.0001 -0.0000ln_tassets -0.0014 -0.0007 -0.0001 -0.0003 -0.0001ln_age -0.0017 -0.0003 -0.0002 -0.0002 -0.0002ln_beta 0.0015 0.0002 0.0002 0.0001 0.0002ln_roa 0.0029 0.0215 0.0044 0.0009 0.0031

0 1Pr(yx) 0.9990 0.0010

a_sr ln_capint ln_tassets ln_age ln_beta ln_roax= -.785106 1.78346 12.1298 3.0395 .286612 -.030538sd_x= .544345 1.37764 2.12873 1.14592 .484961 .284802

Table 21. Marginal effects for Hypothesis 1.2, Model (5), dependent variable div_market.

min->max 0->1 =-1/2 =-+sd/2 MargEfct

a_sr 0.0020 0.0013 0.0006 0.0003 0.0006inout 0.0044 0.0089 0.0028 0.0002 0.0022

51

ln_capint -0.0002 -0.0000 -0.0000 -0.0000 -0.0000ln_tassets -0.0014 -0.0008 -0.0001 -0.0003 -0.0001ln_age -0.0013 -0.0002 -0.0001 -0.0002 -0.0001ln_beta 0.0013 0.0001 0.0001 0.0001 0.0001ln_roa 0.0028 0.0243 0.0044 0.0009 0.0029d1 0.0019 0.0019 0.0011 0.0003 0.0011d2 0.0007 0.0007 0.0005 0.0001 0.0005d3 0.0009 0.0009 0.0006 0.0001 0.0006d4 -0.0002 -0.0002 -0.0002 -0.0001 -0.0002d5 0.0006 0.0006 0.0005 0.0001 0.0005d6 0.0007 0.0007 0.0005 0.0001 0.0005d7 0.0001 0.0001 0.0001 0.0000 0.0001d8 0.0003 0.0003 0.0003 0.0001 0.0003d9 0.0013 0.0013 0.0009 0.0002 0.0008d10 0.0007 0.0007 0.0006 0.0001 0.0006d11 0.0003 0.0003 0.0002 0.0000 0.0002d12 0.0013 0.0013 0.0008 0.0001 0.0008d13 0.0007 0.0007 0.0006 0.0001 0.0005d14 0.0008 0.0008 0.0006 0.0002 0.0006

0 1Pr(yx) 0.9991 0.0009

a_sr inout ln_capint ln_tassets ln_age ln_beta ln_roax= -.785106 .0285 1.78346 12.1298 3.0395 .286612 -.030538sd_x= .544345 .110136 1.37764 2.12873 1.14592 .484961 .284802

x= d1 d2 d3 d4 d5 d6 d7sd_x= .066667 .033333 .044444 .058333 .097222 .072222 .130556

.249446 .179507 .206082 .234374 .296262 .258857 .336916

x= d8 d9 d10 d11 d12 d13 d14sd_x= .088889 .047222 .063889 .025 .016667 .033333 .113889

.284585 .212115 .244557 .156126 .12802 .179507 .317679

52

Hypothesis 1.3

Table 22. Marginal effects for Hypothesis 1.3, Model (1), dependent variable deal_type.

min->max 0->1 =-1/2 =-+sd/2

MargEfct

ln_cap -0.9019 -0.0000 -0.0410 -0.0862 -0.0401ln_capint 0.0239 0.0068 0.0060 0.0074 0.0060ln_age 0.1179 0.0212 0.0125 0.0161 0.0124ln_beta 0.0143 0.0024 0.0024 0.0016 0.0024ln_roa 0.1393 0.0520 0.5167 0.0232 0.2836

0 1Pr(yx) 0.0490 0.9510

ln_cap ln_capint ln_age ln_beta ln_roax= 11.4734 1.57847 2.78582 .312533 .010554sd_x= 1.97984 1.23229 1.2915 .656025 .081344

Table 23. Marginal effects for Hypothesis 1.3, Model (2), dependent variable deal_type.

min->max 0->1 =-1/2 =-+sd/2

MargEfct

inout -0.0349 -0.0675 -0.0481 -0.0108 -0.0466ln_cap -0.9061 -0.0000 -0.0404 -0.0853 -0.0395ln_capint 0.0194 0.0053 0.0048 0.0059 0.0048ln_age 0.1245 0.0224 0.0128 0.0166 0.0128ln_beta 0.0323 0.0061 0.0062 0.0041 0.0062ln_roa 0.1077 0.0496 0.3722 0.0184 0.2256

0 1Pr(yx) 0.0475 0.9525

inout ln_cap ln_capint ln_age ln_beta ln_roax= .122342 11.4734 1.57847 2.78582 .312533 .010554sd_x= .231062 1.97984 1.23229 1.2915 .656025 .081344

53

Table 24. Marginal effects for Hypothesis 1.3, Model (3), dependent variable deal_type.

min->max 0->1 =-1/2 =-+sd/2

MargEfct

a_sr 0.0647 0.0146 0.0473 0.0358 0.0447ln_cap -0.9414 -0.0000 -0.0357 -0.0774 -0.0345ln_capint 0.0345 0.0128 0.0097 0.0119 0.0096ln_age 0.0314 0.0053 0.0041 0.0054 0.0041ln_beta 0.0694 0.0186 0.0206 0.0134 0.0204ln_roa 0.1459 0.0385 0.6508 0.0222 0.2691

0 1Pr(yx) 0.0355 0.9645

a_sr ln_capln_capint ln_age ln_beta ln_roa

x= -.44458111.4734 1.57847 2.78582 .312533 .010554

sd_x= .7737841.97984 1.23229 1.2915 .656025 .081344

54