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ERASMUS UNIVERSITY ROTTERDAM Reprint Prohibited Erasmus School of Economics Master Thesis
The influence of relatedness on corporate diversification.
Alexander Lunev 345203 Under supervision of Dr. F. Neffke Rotterdam, 2011
1 Abstract
Corporate diversification is often associated with growth, success and development of the company. There is much research for the motives of diversification; however connection between corporate diversification and relatedness is quite new. This paper investigates influence of human capital based and resource based relatedness measures on three aspects of corporate diversification (diversification into secondary activities, diversification through the market, and choice of the industry entry mode). The success of external diversification through market is measure by stock price reactions. The research is based on the custom dataset created with use of Zephyr, Orbis, Eurostat datasets and dataset created by Neffke and Henning (2010).
Keywords: diversification, relatedness, mergers and acquisitions, joint ventures, stock prices.
2 Contents
1. Introduction
2. Theoretical background
2.1. Firm diversification
2.2. Why do firms diversify?
2.3. Relatedness measures
2.4. Diversification modes
2.5. Market response on diversification
2.6. Disadvantages of diversification
2.7. Hypothesis
3. Data and methodology
3.1 Dataset
3.2 Description of the variables
3.3 Methodology
4. Empirical research and results
4.1 Descriptive statistics
4.2 Hypothesis 1.1
4.3 Hypothesis 1.2
4.4 Hypothesis 1.3
4.5 Hypothesis 2
5. Limitations and directions for further research
6. Conclusions and policy implications
7. References
8. Appendix
3 1. Introduction
Corporate diversification is closely associated with company’s success, performance and future prospects. Studying the motives behind corporate diversification and factors which influence the process of diversification is crucial in addressing the issues of business development strategy. This paper studies the process of external and internal corporate diversification as well as market reaction on corporate diversification moves.
Firm diversification strategy directly affects long run performance and thus makes crucial to study the motives behind diversification. The most recent issue discussed by researchers is the influence of relatedness on firm diversification and performance. Teece et al. (1994) stated that there is an effect on firm performance by diversifying into somehow related activities. Relatedness can be measured using value chain method created by Fan and Lang (2000), by using classification codes (e.g. NACE or SIC) and by using human capital relatedness measure developed by Neffke and Henning (2010). Neffke and Henning (2010) found evidence for Sweden that diversification into skill-related industry has higher probability then diversification into value chain or classification related industries. The research was done only for diversification by internal development; external diversification remained untouched by researchers. Diversification through market tends to occur in less related industries which lead to the first research question:
What type of relatedness has an effect on firm diversification?
Understanding what type of relatedness plays bigger role in firm diversification strategy is crucial for setting up the pattern for future diversification moves. Defining more influential relatedness measure makes firm diversification strategy choice easier and more beneficial. However, answer to the first research question does not provide insight on the success of diversification move. Pennings et al. (1994) stated that diversification into related activities through mergers and acquisitions or joint ventures are more successful. Pennings et al. (1994) measured success as the endurance of the expansion, but there are many ways to measure firm’s success. Positive investors’ reactions to the diversification move are often treated as a success of firm’s expansion. Best way to see investors’ reactions is to look at the stock prices fluctuations. This brings up the second research question:
Does diversification into related activities has a greater influence on the firm’s value?
4 In order to answer research questions the paper begins with the review of existing theories of motives behind corporate diversification. Literature review continues with discussion about different industry relatedness measures, which can be used to explain diversification. This paper investigates corporate diversification process from three perspectives: diversification into secondary activities, diversification through market, and choice of market diversification instrument. Market diversification can be achieved by establishing joint venture or by making mergers and acquisitions. Disadvantages and advantages of each method are discussed in the literature analysis as well. Influence of the diversification strategy on firm’s value is discussed in the theoretical part and the investigated empirically based on stock prices fluctuations.
The paper is organized in the following way: chapter 2 describes relevant theoretical background in the field of corporate diversification, measures of relatedness between industries and market reaction on diversification strategy. Than it follows up with the hypothesis based on theoretical background described earlier. Chapter 3 describes the construction of the dataset for this research and elaborates on the methodology used for empirical analysis. Chapter 4 is devoted to empirical analysis and discusses the results. Chapter 5 discusses limitation of the research and presents some guidelines for the future research. Conclusion and possible implication of the results are made in chapter 6.
5 2. Theoretical background
2.1 Firm diversification
Company diversification is the process of making a portfolio of industries which are different from the primary industry of the firm. The higher the amount of industries in company’s portfolio, the higher is company’s diversification. Usually the process of diversification is driven by the growth of the company because the company enters new industries seeking for more market space. Despite that, the advantages and disadvantages of corporate diversification are not clear. Motives for corporate diversification differ greatly as well. In the following chapter theories, which explain motives for diversification and its consequences will be discussed.
2.2 Why do firms diversify?
In a perfect world, with no restrictions and complete information any firm diversification move will have no effect on firm’s cash flows and no additional value will be created or destroyed. This makes crucial to study the motives behind firm diversification, to understand why firms diversify, which pattern they follow and what effect can be observed.
Theories of corporate diversification:
Agency theory
Most of the firms nowadays have a differentiation between owners and managers. This differentiation causes well known problem of principals and agents. Principals delegate their powers to managers in order to achieve given aim but without certain amount of control and motivation agents behave themselves to maximize their own benefits. Stockholders are principles and managers are agents in the firm perspective. Morck, Shleifer and Vishny (1990) suggested that managers with an insignificant amount of equity owned in the company use corporate assets for their own benefits and not for the benefits of stockholders. Diversification can be one of the strategies for managers to enlarge their own wealth at the expense of stockholders. Managers are usually willing to reinvest earnings of the firm. At the youth stage of company’s lifecycle there is a plenty of opportunities to reinvest in a profitable way. When the company reaches mature state, these opportunities distinct and managers seek for new ways of reinvesting earnings. The solution is acquisitions, but as Jensen (1986) argued they are likely to be low-benefit or even value destroying. The motives for such managerial behavior can be value
6 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
7 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 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
8 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.
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
9 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 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
10 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.
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
11 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 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.
12 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 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
13 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. 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
14 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 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
15 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.
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.
16 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.
Generally Montgomery (1994) suggests, that unrelated diversification is valued less by the market than related diversification. Although Jones and Hill (1988) argued that related diversification can imply higher administration costs than unrelated diversification and thus can be a motive for unrelated diversification.
17 2.7 Hypothesis
According to the review of the previous research in the field of company diversification, the debate concerning usage of different relatedness measures as a pattern to describe company diversification is still open. Firm diversification relatedness measures developed from the basic ones based on industry classification codes to more sophisticated ones based on value chain and human capital similarities. However there is lack of empirical research which investigates the role of related diversification on diversification in general. The first research question of this paper is
What type of relatedness has an effect on firm diversification?
According to the literature review, corporate diversification can be external and internal. Process of internal diversification is very hard to measure at the moment of diversification, but it could be measure ex post by investigating the number of secondary industries. Internal diversification is highly associated with “make or buy” decision and turns out to be the solution when high transaction costs arise on the market. High transaction costs arise when high assets specificity is present. Input-output relatedness measure deals with production chain assets specificity, while skill relatedness measure is connected with human capital specificity. First hypothesis tests influence of relatedness measures on firm diversification into secondary activities, without taking into account the external or internal origin of the diversification:
Hypothesis 1.1: Input-output and skill-related activities are more likely to be present as secondary activities in the firm’s portfolio.
External diversification process can be measured on the spot for publically listed companies. Basic “make or buy” decision can be developed into more complicated structure. First, company can buy the asset. Second, it can make the asset in house by creating new product line. Third, the company can buy another company, which makes this asset and now make it in house. In this case external diversification is motivated exactly the same as internal, and relatedness of industries should play significant role. Second hypothesis tests the influence of related diversification on the external diversification through the market.
Hypothesis 1.2: Company is more likely to diversify into input-output and skill-related activities through market.
Based on the first and second hypotheses the question about influence of related diversification on firm diversification can be answered. Additionally it is possible to make judgments to what extent different types of relatedness influence firm diversification. However, this paper is more
18 dedicated to study the relationship between related diversification and external diversification through the market, that’s why additional third hypothesis is also tested. As it was shown in the review of theoretical background, the choice of the market entry mode could be critical to the firm and its performance. Due to the presence of management cost, described in the theoretical part, human capital based relatedness measure should be more likely to occur in mergers and acquisitions, while input-output relatedness measure shouldn’t have any effect.
Hypothesis 1.3 Diversification into skill-related activities is more likely to occur in form of mergers and acquisitions than joint ventures.
The second research question is more orientated on the investigation of the success factors for diversification moves. There are plenty of instruments to check if certain action had a positive effect on the firm, however the most representative and intuitive is to see the change of the firm’s value caused by this action. The second research question of this paper is aimed to draw some light on the success of the related diversification:
Does diversification into related activities has a greater influence on the firm’s value?
Previous research found week positive effect of related diversification on the company’s value. For a public listed company firm value is very closely connected to the stock price, thus the stock price fluctuations are used to analyze the influence of related diversification. This leads us to the second hypothesis of this paper:
Hypothesis 2: Diversification into input-output or skill-related activities is valued positively by the market.
The results drawn from investigation of these four hypotheses provide vital information about firm diversification strategy and its valuation by the market. The core task is to estimate the influence of related diversification and thus will enable to imply findings of this research to develop recommendations and patterns for corporate diversification.
19 3. Data and methodology
3.1 Dataset
Dataset for this research was constructed using 4 separate databases. The core database is Zephyr, which is provided by Bureau Van Dijk. Zephyr database cover international 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.
20 3.2 Description of the variables
Stock price change (p_react)
Zephyr database provides information for stock prices of acquirer and target firms 3 months prior to rumor, prior to rumor, prior to announcement, after the completion and 3 months after the completion. Ideal period to highlight the price jump as a result of corporate diversification is between the day before the rumor and the day after announcement. In this case both rumor and announcement affect price fluctuations, making the effect the most significant. Unfortunately, Zephyr database does not provide data for stock prices after announcement. The end of the period should be the date after completion of the deal, while the start of the period could be date prior to rumor or prior to announcement. In this paper date prior to announcement is chosen because if the date prior to rumor is chose the period becomes too long. Long period has negative effect on estimations because of high stock price fluctuations caused by enormous amount of factors on this period. The longer is the period, the harder is to see the actual price reaction on corporate diversification.
In order to test the reaction of the stock market on the diversification of the company stock price change (p_react) variable was constructed. It is based on stock prices of the acquirer firm and is calculated according to the formula:
Stock price 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.
Diversification of the firm through secondary activities (div_sec)
21 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.
Financial data (capitalization, roa, tassets, cap_int)
22 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:
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:
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 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.
23 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:
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 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
24 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
obs amount of industry specific knowledge. Let Fij represent the observed labour flow between two
pred industries and Fij 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:
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:
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:
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,
25 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:
where
P is the probability of a particular outcome, x1 – xn are independent variables, and β0 – βn are the coefficients estimated by the model. The dependent variable can take values 1 if certain outcome occurred and 0 if it didn’t. The model estimates probability of outcome (P) which lies between 1 and -1. Dependent variables (xi) can be distributed between negative and positive infinity. For each dependent variable model estimates coefficient (βi) which characterizes the influence of the dependent variable on the probability of the outcome. If coefficient (βi) is positive, than probability of the outcome increases with the increase of the dependent variable, if it is negative, 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:
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
26 coefficients (β0 – βn) provide detailed information about the direction of the variable’s influence (positive or negative) and the power of influence (coefficient size).
27 4. Empirical research and results
4.1 Descriptive statistics
All control variables were log transformed in order to reduce skewness of the distribution. Additionally log transformation of control variables helps with interpretation of the effects, which now can be interoperated as elasticity (increasing log transformed variable by one unit corresponds to multiplying the untransformed variable by e. Table 1 provides summary statistics for independent variables showing number of observations, mean, standard deviation and skewness. Numbers of observations differ across the variables and age has the least amount of observations. Skewness higher than 5 is shown only by return on assets (roa). Full summary statistics for all the variables can be found in appendix in table 2.
Table 1. Summary statistics, independent variables.
Variable Obs Min Max Mean St. Dev. Variance Skewness Kurtosis a_sr 134232 -1 .9943836 -.7860958 .5413002 .2930059 2.269861 6.435041 inout 134232 0 .7447964 .0311508 .117646 .0138406 4.670814 23.26282 ln_cap 127840 6.938255 17.81981 13.02376 2.891565 8.361146 .1632628 1.853577 ln_capint 129908 .0426572 6.382078 2.039882 1.343552 1.805133 .8274746 2.792834 ln_beta 133480 -.912331 5.686178 .4036486 .4499034 .202413 4.07431 55.0075 ln_tassets 133292 2.782056 17.65182 13.26181 2.724718 7.424086 .0401974 2.320843 ln_roa 129156 -3.635095 .3185791 -.0199402 .2511896 .0630962 -13.02388 187.4249 ln_age 72568 -1.789708 4.700219 2.942635 1.186609 1.40804 -.4592952 2.970278
Summary statistics for dependent variables are presented in table 3. Diversification through secondary activities (div_sec) and diversification through market (div_market) variables show high skewness of over 20. Stock price reaction on the diversification move (p_react) has the least amount of observations.
Table 3. Summary statistics, dependent variables.
Variable Obs Min Max Mean St. Dev. Skewness div_market 134232 0 1 .00149 .0385713 25.84882 div_sec 134232 0 1 .0023541 .0484624 20.53746 deal_type 134232 0 1 .8935574 .3084044 -2.552226 p_react 46248 -.7482014 .8903229 .0029246 .1251869 .9406055
28 Correlation matrix on table 4 shows high correlation (over 0.6) only between total assets (ln_tassets) and market capitalization (ln_cap) which is predictable because both variables represent the size of the company. Correlation of around 34% is present between beta (ln_beta) and market capitalization (ln_cap), as well as between beta (ln_beta) and total assets (ln_tassets). Market capitalization (ln_cap) and total assets (ln_tassets) are the measures of the company’s size. Positive correlation between size and risk means that larger companies tend to be more risky.
Table 4. Correlation matrix of all variables.
div_sec div_market a_sr inout ln_tassets ln_beta ln_cap ln_capint ln_roa ln_age div_sec 1.0000 div_market 0.2115 1.0000 a_sr 0.0122 0.0230 1.0000 inout 0.0502 0.0303 0.1873 1.0000 ln_tassets -0.0152 -0.0074 0.0303 0.0196 1.0000 ln_beta -0.0097 -0.0012 0.0540 0.0520 0.3466 1.0000 ln_cap -0.0116 -0.0074 0.0240 0.0053 0.9374 0.3406 1.0000 ln_capint -0.0111 -0.0038 -0.0348 0.0084 0.0074 0.1860 -0.0224 1.0000 ln_roa 0.0058 0.0048 0.0029 0.0035 0.1753 -0.0139 0.1855 -0.0108 1.0000 ln_age -0.0053 -0.0103 -0.0029 0.0049 0.1921 0.1150 0.1184 -0.1010 0.0377 1.0000
Observations with absence of diversification through market were removed from the dataset in order to test hypothesis 1.3 and hypothesis 2. It was done by omitting cases when diversification through market (div_market) variable equals 0. Full summary statistics for adjusted to hypothesis 2 dataset are presented in the table 5. As it can be seen from the table, stock price reaction on the diversification move (p_react) has the least amount of observations.
Table 5. Summary statistics when market diversification is present.
Variable Obs Min Max Mean St. Dev. Variance Skewness Kurtosis 9.69680 p_react 370 -.2692307 .4310345 .0241029 .114722 .0131611 2.077292 4
12.3609 deal_type 1000 0 1 .93 .2557873 .0654271 -3.370606 8 2.21499 a_sr 1000 -1 .9856153 -.5096493 .7558768 .5713497 1.030272 4 inout 1000 0 .6537724 .2197098 .2858054 .0816847 .6243699 1.4187
ln_cap 970 7.052721 17.81981 12.72818 2.549267 6.498762 .2948607 2.09071 ln_capint 955 .3384914 5.667214 2.128907 1.333063 1.777057 .4454116 1.8884 64.7067 ln_beta 990 -.912331 5.686178 .4337431 .4963532 .2463665 5.777765 1 3.11368 ln_tassets 990 -1.470067 21.205 13.71006 3.739044 13.98045 .0374052 5
29 121.574 ln_roa 955 -.7236587 2.130044 .0156227 .1408894 .0198498 7.365647 6 3.09052 ln_age 545 -1.790392 4.58007 2.53568 1.312383 1.72235 -.3486765 4 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 1.0000 ln_tasset s -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.
Table 8. Results for hypothesis 1.1, dependent variable div_sec.
VARIABLES (1) (2) (3) (4) (5)
30 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 Observations 67,680 67,680 67,680 67,680 61,476 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
31 Model (1) consists only of control variables, model (2) includes control variables and 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 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
32 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 MargEfct a_sr 0.0001 0.0001 0.0001 0.0000 0.0001 inout 0.0094 0.0329 0.0033 0.0002 0.0014 ln_capint -0.0005 -0.0002 -0.0001 -0.0001 -0.0001 ln_tassets -0.0016 -0.0079 -0.0001 -0.0002 -0.0001 ln_age -0.0001 -0.0000 -0.0000 -0.0000 -0.0000 ln_beta -0.0005 -0.0001 -0.0001 -0.0001 -0.0001 ln_roa 0.0015 0.0367 0.0031 0.0004 0.0014
0 1
Pr(yx) 0.9997 0.0003
ln_capin a_sr inout t ln_tassets ln_age ln_beta ln_roa x= -.785106 .0285 1.78346 12.1298 3.0395 .286612 -.030538 sd_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 relatedness measure are the highest, reaching 0.0014. This effect is significant, because for each industry there are 188 possible secondary industries meaning that average probability of secondary industry choice is 0.005319. Marginal effect of value chain based relatedness measure is 26% of the average probability of diversification into secondary activities.
33 In conclusion, hypothesis 1.1 is partly rejected (concerning skill-relatedness measure) and partly not rejected (concerning input-output relatedness measure). Value chain based relatedness measure has a strong positive effect on the probability of diversification into secondary industries, which is in line with theoretical background. Among control variables largest effect was shown by return on assets.
4.3 Hypothesis 1.2
Hypothesis 1.2 (Company is more likely to diversify into input-output and skill-related activities through market.) tests the influence of relatedness measures on probability of market diversification. Similar to Hypothesis 1.1, five models were created using method of logistic regression, where dependent variable is diversification through market (div_market), taking value of 1 if diversification occurred and 0 otherwise. Table 10 provides results for all five models.
Table 10. Results for hypothesis 1.2, dependent variable div_market.
VARIABLES (1) (2) (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) 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)
34 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 67,680 Standard 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- 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
35 3.7% in model (2)) if the value-chain relatedness measure is substituted by the skill-relatedness measure, however the difference with model (1) is outstanding. Human capital-based relatedness measure is significant on 1% significance level in model (3) with positive coefficient of 0.850. Return on assets turned out to be significant on 1% significance level (compared to 5% in models (1) and (2)), and the coefficient reaches 3.036. All other controls variables show similar results to model (2) with only marginal change in coefficients. Full model (4) shows pseudo 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 MargEfct a_sr 0.0023 0.0015 0.0007 0.0003 0.0006 inout 0.0048 0.0098 0.0030 0.0003 0.0024 ln_capint -0.0003 -0.0001 -0.0000 -0.0001 -0.0000 ln_tassets -0.0015 -0.0008 -0.0001 -0.0003 -0.0001 ln_age -0.0015 -0.0003 -0.0002 -0.0002 -0.0002 ln_beta 0.0011 0.0001 0.0001 0.0001 0.0001 ln_roa 0.0027 0.0193 0.0040 0.0008 0.0029
0 1 Pr(yx) 0.9990 0.0010
a_sr inout ln_capin ln_tassets ln_age ln_beta ln_roa
36 t x= -.785106 .0285 1.78346 12.1298 3.0395 .286612 -.030538 sd_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 observations. Year dummies were omitted because of the small sample size. Table 10 shows the results for four models used to test hypothesis 1.3.
37 Table 12. Results for hypothesis 1.3, dependent variable deal_type.
VARIABLES (1) (2) (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 435 435 435 435 Standard 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 shows increase in pseudo R-squared compared to model (1) and model (2) which now reaches 35.49%. The increase in pseudo R-squared is mainly due to significance level (p<0.1) of the skill-relatedness measure. The coefficient is 1.306 meaning that if skill relatedness is increasing,
38 probability of using merger and acquisition instead of joint venture is increasing as well. The significance level of control variables remains equal to model (1) and model (2) with slight change in coefficients. Full model (4) shows marginal change in pseudo R-squared level, which now is 35.62% as model (3). Input-output relatedness measure is insignificant while 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 0.0434 inout -0.0135 -0.0241 -0.0197 -0.0045 -0.0195 ln_cap -0.9431 -0.0000 -0.0356 -0.0773 -0.0344 ln_capin t 0.0311 0.0110 0.0086 0.0106 0.0085 ln_age 0.0321 0.0055 0.0042 0.0054 0.0042 ln_beta 0.0793 0.0217 0.0245 0.0159 0.0241 ln_roa 0.1219 0.0377 0.5368 0.0193 0.2352
0 1 Pr(yx) 0.0352 0.9648
ln_capin a_sr inout ln_cap t ln_age ln_beta ln_roa x= -.444581 .122342 11.4734 1.57847 2.78582 .312533 .010554 sd_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 significant compared to average probability of mergers and acquisitions choice. Market
39 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.
(4 VARIABLES (1) (2) (3) )
- 0. 0 3 2 a_sr -0.0337 1
40 (0 .0 2 4 (0.0236) 0) 0. 0 2 5 inout 0.0360 8 (0 .0 5 4 (0.0546) 8) 0. 0 0 0 3 9 ln_beta -0.00501 -0.00601 0.00141 4 (0 .0 2 3 (0.0231) (0.0232) (0.0233) 6) 0. 0 1 3 9* ln_cap 0.0146** 0.0136* 0.0146** * (0 .0 0 6 (0.00674 9 (0.00679) (0.00696) ) 2) - 0. 0 0 8 1 ln_capint -0.00185 -0.00112 -0.00902 6 (0 .0 1 3 (0.0119) (0.0120) (0.0128) 1) ln_roa 0.0575 0.0689 0.0412 0. 0 5
41 0 1 (0 .2 9 (0.290) (0.291) (0.287) 0) 0. 0 8 0 deal_type 0.0874 0.0799 0.0855 2 (0 .0 6 4 (0.0632) (0.0645) (0.0627) 1) - 0. 2 4 1* Constant -0.243** -0.232** -0.249** * (0 .1 1 (0.112) (0.113) (0.111) 3)
3 5 Observations 355 355 355 5 0. 1 2 R-squared 0.094 0.101 0.124 7 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
42 10% and coefficient is decreased to 0.136. All other control variables are insignificant. Model (3) shows increase in R-squared compared to model (1) and model (2), reaching 12.4%. However, human capital-based relatedness measure is not significant. Market capitalization changes significance level to 5% and the coefficient to 0.0146. Full model (4) has R-squared of 12.7% which is only marginally bigger, compared to model (3). Skill-relatedness measure is significant is not significant, neither is input-output relatedness measure. The only significant control variable is market capitalization with 5% significance level and positive coefficient 0.0139. No support for hypothesis 2 was found meaning that it is rejected. Possible explanation for these results is the length of the time period between the date prior to announcement of the diversification move and the date of completion. Stock prices are affected by huge number of external and internal effects, making estimations, based on long time range, unreliable because of the uncontrolled noise. Time range between the rumor of the deal and after the announcement could show different results, compared to the ones found in this paper. Unfortunately, the dataset with such information wasn’t available.
43 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.
44 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.
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49 8. Appendix
Table 2. Detailed summary statistics of all variables.
Variable Obs Min Max Mean St. Dev. Variance Skewness Kurtosis . 669.161 div_market 134232 0 1 .00149 0385713 .0014877 25.84882 5 422.787 div_sec 134232 0 1 .0023541 .0484624 .0023486 20.53746 2 . 7.51385 deal_type 134232 0 1 .8935574 3084044 .0951133 -2.552226 9 19.3046 p_react 46248 -.7482014 .8903229 .0029246 .1251869 .0156718 .9406055 7 551.814 sr 134232 0 355.1014 1.110879 5.899515 34.80428 17.34482 3 6.43504 a_sr 134232 -1 .9943836 -.7860958 .5413002 .2930059 2.269861 1 23.2628 inout 134232 0 .7447964 .0311508 .117646 .0138406 4.670814 2 5.87078 capitalisation 127840 1029.97 5.48e+07 7450999 1.34e+07 1.79e+14 1.945264 7 1.85357 ln_cap 127840 6.938255 17.81981 13.02376 2.891565 8.361146 .1632628 7 cap_int 129908 .0435801 590.155 22.1393 50.82168 2582.843 4.984237 37.3325 2.79283 ln_capint 129908 .0426572 6.382078 2.039882 1.343552 1.805133 .8274746 4 354.250 beta 134232 -1.507058 293.7649 1.370227 15.50517 240.4102 18.78486 2
ln_beta 133480 -.912331 5.686178 .4036486 .4499034 .202413 4.07431 55.0075 6.12732 t_assets 133292 16.1522 4.64e+07 7316698 1.32e+07 1.74e+14 2.039818 2 2.32084 ln_tassets 133292 2.782056 17.65182 13.26181 2.724718 7.424086 .0401974 3 184.565 roa 129532 -2.867063 .3751724 -.0114694 .1541165 .0237519 -11.03545 3 187.424 ln_roa 129156 -3.635095 .3185791 -.0199402 .2511896 .0630962 -13.02388 9 2.48263 age 84600 1.015743 110.0151 39.62984 33.30048 1108.922 .9648917 5 2.97027 ln_age 72568 -1.789708 4.700219 2.942635 1.186609 1.40804 -.4592952 8
Table 6. Summary statistics when market diversification is present.
Variable Obs Min Max Mean St. Dev. Variance Skewness Kurtosis p_react 370 -.2692307 .4310345 .0241029 .114722 .0131611 2.077292 9.696804 deal_type 1000 0 1 .93 .2557873 .0654271 -3.370606 12.36098 sr 1000 0 33.30198 2.314693 5.268077 27.75263 3.630177 19.48613 a_sr 1000 -1 .9856153 -.5096493 .7558768 .5713497 1.030272 2.214994 inout 1000 0 .6537724 .2197098 .2858054 .0816847 .6243699 1.4187 capitalisation 970 1155 5.48e+07 4392088 9337436 8.72e+13 2.861801 12.26567 ln_cap 970 7.052721 17.81981 12.72818 2.549267 6.498762 .2948607 2.09071 cap_int 955 .4028296 288.2278 19.54588 32.32839 1045.125 3.973982 27.98984 ln_capint 955 .3384914 5.667214 2.128907 1.333063 1.777057 .4454116 1.8884 beta 1000 -1.353376 293.7649 2.025064 20.73906 430.1085 14.02298 197.7656 ln_beta 990 -.912331 5.686178 .4337431 .4963532 .2463665 5.777765 64.70671
50 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.
MargEfc min->max 0->1 =-1/2 =-+sd/2 t ln_capint -0.0007 -0.0003 -0.0002 -0.0002 -0.0002 ln_tassets -0.0018 -0.0046 -0.0001 -0.0003 -0.0001 ln_age -0.0002 -0.0000 -0.0000 -0.0000 -0.0000 ln_beta -0.0003 -0.0001 -0.0001 -0.0000 -0.0001 ln_roa 0.0025 0.0629 0.0051 0.0007 0.0022
0 1 Pr(yx) 0.9995 0.0005
ln_capint ln_tassets ln_age ln_beta ln_roa x= 1.78346 12.1298 3.0395 .286612 -.030538 sd_x= 1.37764 2.12873 1.14592 .484961 .284802
Table 16. Marginal effects for Hypothesis 1.1, Model (2), dependent variable div_sec.
MargEfc min->max 0->1 =-1/2 =-+sd/2 t inout 0.0109 0.0394 0.0037 0.0002 0.0015 ln_capint -0.0005 -0.0002 -0.0001 -0.0002 -0.0001 ln_tassets -0.0016 -0.0076 -0.0001 -0.0002 -0.0001 ln_age -0.0002 -0.0000 -0.0000 -0.0000 -0.0000 ln_beta -0.0004 -0.0001 -0.0001 -0.0001 -0.0001 ln_roa 0.0015 0.0335 0.0029 0.0004 0.0014
0 1 Pr(yx) 0.9997 0.0003
inout ln_capint ln_tassets ln_age ln_beta ln_roa x= .0285 1.78346 12.1298 3.0395 .286612 -.030538 sd_x= .110136 1.37764 2.12873 1.14592 .484961 .284802
51 Table 17. Marginal effects for Hypothesis 1.1, Model (3), dependent variable div_sec.
MargEfc min->max 0->1 =-1/2 =-+sd/2 t a_sr 0.0010 0.0007 0.0003 0.0002 0.0003 ln_capint -0.0007 -0.0002 -0.0001 -0.0002 -0.0001 ln_tassets -0.0017 -0.0050 -0.0001 -0.0003 -0.0001 ln_age -0.0001 -0.0000 -0.0000 -0.0000 -0.0000 ln_beta -0.0004 -0.0001 -0.0001 -0.0000 -0.0001 ln_roa 0.0025 0.0785 0.0054 0.0007 0.0021
0 1 Pr(yx) 0.9996 0.0004
a_sr ln_capint ln_tassets ln_age ln_beta ln_roa x= -.785106 1.78346 12.1298 3.0395 .286612 -.030538 sd_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- MargEfc >max 0->1 =-1/2 =-+sd/2 t ln_capint -0.0005 -0.0001 -0.0001 -0.0001 -0.0001 ln_tassets -0.0013 -0.0005 -0.0001 -0.0003 -0.0001 ln_age -0.0022 -0.0004 -0.0002 -0.0003 -0.0002 ln_beta 0.0027 0.0003 0.0002 0.0001 0.0002 ln_roa 0.0030 0.0163 0.0041 0.0009 0.0031
0 1 Pr(yx) 0.9988 0.0012
ln_capint ln_tassets ln_age ln_beta ln_roa x= 1.78346 12.1298 3.0395 .286612 -.030538 sd_x= 1.37764 2.12873 1.14592 .484961 .284802
52 Table 19. Marginal effects for Hypothesis 1.2, Model (1), dependent variable div_market.
min- MargEfc >max 0->1 =-1/2 =-+sd/2 t inout 0.0095 0.0225 0.0050 0.0004 0.0034 ln_capint -0.0004 -0.0001 -0.0001 -0.0001 -0.0001 ln_tassets -0.0014 -0.0007 -0.0001 -0.0003 -0.0001 ln_age -0.0019 -0.0003 -0.0002 -0.0002 -0.0002 ln_beta 0.0017 0.0002 0.0002 0.0001 0.0002 ln_roa 0.0027 0.0156 0.0038 0.0008 0.0028
0 1 Pr(yx) 0.9990 0.0010
inout ln_capint ln_tassets ln_age ln_beta ln_roa x= .0285 1.78346 12.1298 3.0395 .286612 -.030538 sd_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- MargEfc >max 0->1 =-1/2 =-+sd/2 t a_sr 0.0037 0.0026 0.0009 0.0005 0.0009 ln_capint -0.0003 -0.0000 -0.0000 -0.0001 -0.0000 ln_tassets -0.0014 -0.0007 -0.0001 -0.0003 -0.0001 ln_age -0.0017 -0.0003 -0.0002 -0.0002 -0.0002 ln_beta 0.0015 0.0002 0.0002 0.0001 0.0002 ln_roa 0.0029 0.0215 0.0044 0.0009 0.0031
0 1 Pr(yx) 0.9990 0.0010
a_sr ln_capint ln_tassets ln_age ln_beta ln_roa x= -.785106 1.78346 12.1298 3.0395 .286612 -.030538 sd_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- MargEfc >max 0->1 =-1/2 =-+sd/2 t a_sr 0.0020 0.0013 0.0006 0.0003 0.0006 inout 0.0044 0.0089 0.0028 0.0002 0.0022 ln_capint -0.0002 -0.0000 -0.0000 -0.0000 -0.0000 ln_tassets -0.0014 -0.0008 -0.0001 -0.0003 -0.0001
53 ln_age -0.0013 -0.0002 -0.0001 -0.0002 -0.0001 ln_beta 0.0013 0.0001 0.0001 0.0001 0.0001 ln_roa 0.0028 0.0243 0.0044 0.0009 0.0029 d1 0.0019 0.0019 0.0011 0.0003 0.0011 d2 0.0007 0.0007 0.0005 0.0001 0.0005 d3 0.0009 0.0009 0.0006 0.0001 0.0006 d4 -0.0002 -0.0002 -0.0002 -0.0001 -0.0002 d5 0.0006 0.0006 0.0005 0.0001 0.0005 d6 0.0007 0.0007 0.0005 0.0001 0.0005 d7 0.0001 0.0001 0.0001 0.0000 0.0001 d8 0.0003 0.0003 0.0003 0.0001 0.0003 d9 0.0013 0.0013 0.0009 0.0002 0.0008 d10 0.0007 0.0007 0.0006 0.0001 0.0006 d11 0.0003 0.0003 0.0002 0.0000 0.0002 d12 0.0013 0.0013 0.0008 0.0001 0.0008 d13 0.0007 0.0007 0.0006 0.0001 0.0005 d14 0.0008 0.0008 0.0006 0.0002 0.0006
0 1 Pr(yx) 0.9991 0.0009
a_sr inout ln_capint ln_tassets ln_age ln_beta ln_roa x= -.785106 .0285 1.78346 12.1298 3.0395 .286612 -.030538 . sd_x= .544345 110136 1.37764 2.12873 1.14592 .484961 .284802
x= d1 d2 d3 d4 d5 d6 d7 . sd_x= .066667 033333 .044444 .058333 .097222 .072222 .130556 . .249446 179507 .206082 .234374 .296262 .258857 .336916
x= d8 d9 d10 d11 d12 d13 d14 . sd_x= .088889 047222 .063889 .025 .016667 .033333 .113889 . .284585 212115 .244557 .156126 .12802 .179507 .317679
54 Hypothesis 1.3
Table 22. Marginal effects for Hypothesis 1.3, Model (1), dependent variable deal_type.
min- MargEfc >max 0->1 =-1/2 =-+sd/2 t ln_cap -0.9019 -0.0000 -0.0410 -0.0862 -0.0401 ln_capint 0.0239 0.0068 0.0060 0.0074 0.0060 ln_age 0.1179 0.0212 0.0125 0.0161 0.0124 ln_beta 0.0143 0.0024 0.0024 0.0016 0.0024 ln_roa 0.1393 0.0520 0.5167 0.0232 0.2836
0 1 Pr(yx) 0.0490 0.9510
ln_cap ln_capint ln_age ln_beta ln_roa x= 11.4734 1.57847 2.78582 .312533 .010554 sd_x= 1.97984 1.23229 1.2915 .656025 .081344
Table 23. Marginal effects for Hypothesis 1.3, Model (2), dependent variable deal_type.
min- MargEfc >max 0->1 =-1/2 =-+sd/2 t inout -0.0349 -0.0675 -0.0481 -0.0108 -0.0466 ln_cap -0.9061 -0.0000 -0.0404 -0.0853 -0.0395 ln_capint 0.0194 0.0053 0.0048 0.0059 0.0048 ln_age 0.1245 0.0224 0.0128 0.0166 0.0128 ln_beta 0.0323 0.0061 0.0062 0.0041 0.0062 ln_roa 0.1077 0.0496 0.3722 0.0184 0.2256
0 1 Pr(yx) 0.0475 0.9525
inout ln_cap ln_capint ln_age ln_beta ln_roa x= .122342 11.4734 1.57847 2.78582 .312533 .010554 sd_x= .231062 1.97984 1.23229 1.2915 .656025 .081344
55 Table 24. Marginal effects for Hypothesis 1.3, Model (3), dependent variable deal_type.
min- MargEfc >max 0->1 =-1/2 =-+sd/2 t a_sr 0.0647 0.0146 0.0473 0.0358 0.0447 ln_cap -0.9414 -0.0000 -0.0357 -0.0774 -0.0345 ln_capint 0.0345 0.0128 0.0097 0.0119 0.0096 ln_age 0.0314 0.0053 0.0041 0.0054 0.0041 ln_beta 0.0694 0.0186 0.0206 0.0134 0.0204 ln_roa 0.1459 0.0385 0.6508 0.0222 0.2691
0 1 Pr(yx) 0.0355 0.9645
a_sr ln_cap ln_capint ln_age ln_beta ln_roa . 11.473 01055 x= -.444581 4 1.57847 2.78582 .312533 4 . 1.9798 08134 sd_x= .773784 4 1.23229 1.2915 .656025 4
56