ESSAYS ON THE THEORY OF AUCTIONS AND ECONOMIC RENTS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
The Degree Doctor of Philosophy in the Graduate
School of the Ohio State University
By
Ilgaz T. Arikan, MBA
The Ohio State University 2004
Dissertation Committee: Approved by
Professor Oded Shenkar, Adviser
Professor Jay Barney
Professor John Kagel ______Professor Karen Wruck Adviser
Professor Richard Makadok Business Administration Graduate Program
Professor Konstantina Kiousis
Copyright by
Ilgaz T. Arikan
2004
ABSTRACT
Transaction cost economics focuses on when firms should buy resources, whereas
resource based view focuses on what resources firms should buy. This dissertation focuses on how firms should buy resources in factor markets to create competitive advantages.
In the first chapter, I model resource acquisition by operationalizing pricing as an endogenous component of competitive strategy and compare negotiation, auction and posted price mechanisms. I identify 5 factors, which affect a firm's choice between each market mechanism. Using a hypothetical entrepreneurial firm, I model the sale of a unique resource (a patent), and argue that the question of "how a firm should buy/sell
resources" is critical for our understanding of firm competitiveness.
In the second chapter, I apply the model predictions of my first essay to market
for firms, and study how an entrepreneurial firm should be sold. Using initial public
offerings as auctions and mid-market mergers and acquisitions as negotiations, I examine
the performance effects of the discrete choices entrepreneurial firms make when they sell
their firm (e.g. a bundle of resources).
In the third chapter, I study an application of the auction theory in practice and I
investigate business-to-business online auctions and auctioneers, and their effects on firm
ii competitiveness using an in depth analysis of intermediary firms and exchange rules by analyzing 4 online auctioneers.
In my dissertation I focus on how firms should buy resources in factor markets to create competitive advantages. When competing in factor or product markets to acquire resources or sell goods, firms often have to make strategic decisions whether to use spot market transactions with posted prices, negotiation markets with bargaining, or auction markets with bidding. Given these three different market mechanisms, what are the firm and industry specific factors that determine different selling/buying devices to occur simultaneously in the market? By endogenizing pricing as a strategic variable, managers can choose among different market mechanisms in pursuit of rents. In this paper I model dynamic resource acquisition in equilibrium, simultaneously taking into account the characteristics of factor markets from both the sellers' and the buyers' perspectives.
Auctions, negotiations and spot markets are compared given heterogeneity of expectations, bargaining power of the participants, market thickness, risk propensity and search costs.
I empirically investigate my thesis and find strong support for my predictions.
Based on the theoretical work by Campbell and Levin (2001) and Arikan (2002), I use predictions from the theory of auctions and negotiations to explain the optimal choice between market mechanisms in an entrepreneurial context. Two major markets exist for the sale of an entrepreneurial firm: initial public offering (IPO) versus mergers and
iii acquisitions (M&A) markets. This paper argues that choosing between these two market
mechanisms is not serendipitous. I argue that the discrete choice between choosing to auction off a company through an IPO or to negotiate its sale as a privately held target
rests on five factors: bargaining power, resource value, market thickness, risk propensity
and search costs. Using a nested logit model, I test this general discrete choice using a
sample of IPOs and M&As of privately held entrepreneurial firms between 1975-1999. I find that entrepreneurial firms strongly follow the theoretical predictions developed in
Arikan (2002). All else being equal, entrepreneurial firms with high bargaining power are more likely to choose negotiations (M&A) versus auctions (IPO). Firms that represent high private values (e.g. in high-tech industries) are more likely to be sold through auctions versus negotiations. As the market thickness increases, the likelihood of entrepreneurial firms being sold through M&A decreases. However, this finding is reversed for firms with higher private values. For firms with high debt ratios, the likelihood of M&A increases compared to IPOs. I find that as venture capital activity in the focal industry increases, the likelihood of M&As increases.
I further investigated the use of auctions in buying and selling resources in the context of business-to-business online marketplaces. I constructed a proprietary dataset consisting of major market makers and industry players that are involved in industrial parts and machinery trade. I identified the different auction formats and rules, as well as the market structures.
iv ACKNOWLEDGMENTS
I wish to thank my adviser Oded Shenkar, and my committee members Jay
Barney, John Kagel, Karen Wruck, Konstantina Kiousis and Richard Makadok for their intellectual support, encouragement, and enthusiasm which made this thesis possible.
I am grateful to Asli Arikan for her continued support and stimulating discussions on all aspects of my research interests.
I benefited a lot from discussions with Juan Alcacer, Adam Brandenburger, Colin
Camerer, Russ Coff, Boris Groysberg, Florian Heiss, David Hirshleifer, Anne-Marie
Knott, Josh Lerner, Dan Levin, Joe Mahoney, and Bernie Yeung for their helpful comments and discussions. All the errors remain mine.
I also wish to thank Reed Foster at Ravenswood Winery, Richard Langdale at
NCT Ventures and various analysts at W.R. Hambrecht. They gave me great many insights from practitioners’ perspective.
I gratefully acknowledge financial support from the Center for International
Business Studies Research and the OSURF both at the Ohio State University.
v VITA
May 5, 1971 ……………………Born – Manisa, Turkey
1994 ……………………….…..BA/BS University of Marmara, Istanbul Turkey
1997 ………………………….….MBA University of North Carolina
2003-Current …………………….Instructor, Boston University
PUBLICATIONS
Research Publication
Arikan, I. (2003) “Exit decisions of entrepreneurial firms: IPOs versus M&As” In New Venture Investment: Choices and Consequences, (Eds.) A. Ginsberg, and I. Hasan. North-Holland, Elsevier
Arikan, I., and Meredith, M. (2003) “Doing Research in International Management: Use of the Internet” In 2nd Edition of Handbook of International Management Research, (Eds.) B. J. Punnett, and O. Shenkar. University of Michigan Press.
FIELDS OF STUDY
Major Field: Business Administration Minor Field: Economics
vi TABLE OF CONTENTS
Page Abstract...... ii Acknowledgments...... v Vita...... vi List of Tables ...... ix List of Figures...... xi
Chapters:
1. Introduction...... 1
2. Theoretical Background...... 4
2.1 How Do Managers Create Economic Rents?...... 8 2.2 Strategic Factor Markets...... 11 2.3 How Can Firms Create Market Imperfections?...... 13 2.4 Market Mechanisms For Resource Acquisition...... 14 2.5 Spot Markets And Posted Prices...... 16 2.6 Negotiation Markets And Bargaining...... 18 2.7 Auctions And Strategic Bidding ...... 21 2.8 Comparing Market Mechanisms For Rent Generation...... 25 2.9 Discussion...... 32
3. Auctions Versus Negotiations...... 37
3.1 Market Mechanisms...... 39 3.2 Determinants Of Mechanism Choice...... 41 3.3 Ipos As Auctions...... 47 3.4 M&As As Negotiations...... 50 3.5 Empirical Analysis...... 55 3.5.1 Empirical Design ...... 55 3.6 Data And Sample Description ...... 56 3.7 Variables ...... 59 3.7.1 Explanatory Variables...... 60 3.7.2 Control Variables...... 68 vii 3.8 Statistical Method ...... 70 3.9 Results...... 76 3.10 Discussion...... 85
4. Economic Rent Generation In Online Auctions ...... 93
4.1 Auctions ...... 96 4.2 Design And Conduct Of Auction Institutions...... 100 4.3 Business-To-Business (B2b) Online Auctions ...... 101 4.4 Market Makers In Online Exchanges ...... 104 4.5 Decision Making And Bidding Capability ...... 112 4.5 Discussion...... 116
List of References ...... 128
viii LIST OF TABLES
Table Page
1. Market Mechanisms and Rents (Seller)...... 155
2. Market Mechanisms and Rents (Buyer) ...... 156
3.. Industry Classification - 2-Digit SIC Codes ...... 157
4. Summary of Empirical Predictions for the Nested Model for the Choice of Auctions
vs. Negotiation ...... 161
5. Number of Sellers for Mid-Market M&A Acquisitions ...... 162
6. Average Ratio of Price Paid over Total Assets for Mid-Market M&A...... 163
7. Financial Information for Private Company IPOs...... 165
8. IPO issues by Private Firms...... 168
9. Manufacturing Sector by 2 Digit SIC Codes ...... 169
10. Manufacturing Sector by Target Nation ...... 170
11. Price Range for the IPO and M&A Deals...... 171
12. Descriptive Statistics...... 172
13. Logistic Regression of the Mechanism Choice (IPO=1, M&A=0) ...... 173
14. Logit Model Coeff. Estimates For The Choice of Auctions (IPOs) vs. Not IPO* . 174
15. Logit Model Coeff. Estimates For the Choice of Auctions (IPOs) vs. Negotiations
(M&As)...... 175 ix 16. Conditional Logistic Regression...... 176
17. Multinomial Logistic Regression...... 177
18. Nested Logit-Part 1...... 178
19. Nested Logit-Part 2...... 179
x LIST OF FIGURES
Figure: Page
1. Empirical Model of Auction-Negotiation Mechanism Choices ...... 160
xi CHAPTER 1
INTRODUCTION
The central question in strategy is how do firms create competitive advantages
and outperform their rivals? Three main perspectives offer explanations to guide firm
strategies: structure-conduct-performance paradigm focuses on which industries firms
should enter into, transaction cost economics focuses on when firms should buy
resources, whereas resource based view focuses on what resources firms should buy. In
this paper, I focus on how firms should buy resources in factor markets to create competitive advantages. When competing in factor or product markets to acquire
resources or sell products, firms often have to make strategic decisions whether to use spot market transactions with posted prices, negotiation markets with bargaining, or auction markets with bidding. Therefore I examine the conditions under which firms will prefer one market mechanism over another.
In the strategy literature, two theories have sought to explain the heterogeneity in firm performance: structure-conduct-performance (SCP) paradigm based on industrial organization economics, and resource-based view (RBV) of the firm, based on a combination of Penrosian economics, Austrian economics, and the evolutionary theory of the firm. SCP researchers looked at product market competition and applied classical 1 industrial organization tools to explain firm performance differences and to utilize firms'
strategic actions to command prices through gaining monopoly positions. RBV studies
focused on factor market dynamics but implicitly assumed firms had to take prices as
given. In the RBV literature, it is well established that when firms acquire rare, inimitable
and valuable resources from factor markets they gain competitive advantage, and this
competitive advantage may be sustainable. However, the mechanisms by which firms acquire these resources have received little attention. The purpose of this paper is to
develop a general model of strategic factor markets and firm performance.
How do firms outperform their rivals when acquiring scarce factors of production
in strategic factor markets? In this paper, using heterogeneity and imperfect mobility of
resources, pricing is operationalized as a strategic variable that transforms price-taking
firms into market makers. In strategic factor markets, if markets are perfectly competitive
and efficient, the information released among players will reflect the performance of those resources traded in the exchange. Since markets will anticipate the value of these resources, their prices will be bid up, and the economics rents will dissipate. In fact, if a
firm's strategies result in imperfect product markets, these still cannot be a source of
economic rents. So how can firms create imperfections in resource markets? Firms can
generate heterogeneous expectations about resources and create market imperfections by
transforming themselves into market makers instead of price takers. In this context, pricing mechanism becomes a strategic variable and can generate economic rents in factor markets. Thus, market mechanisms such as auctions should be studied in a comparative context to understand the dynamics of rent generation and appropriation in factor markets. This chapter is organized as follows. First, a theoretical background of
2 factor market economics is provided outlining the SCP, TCE, and RBV arguments.
Second, based on Barney (1986) and Peteraf (1993) a resource-based model of factor
markets is presented. Third, the basic tenets of three market mechanisms in relation to
factor markets are introduced. Specifically, spot markets and posted prices, negotiation
markets with bargaining, and auctions with strategic bidding are modeled. In the final section, these three market mechanisms are compared given a hypothetical case.
Managerial implications and extensions of optimal acquisition strategies are discussed.
3 CHAPTER 2
THEORETICAL BACKGROUND
The traditional approach to resource acquisition has been various applications of
the neoclassical economic theory of the firm, which treats firms as price takers. In
competitive markets, firms operate in complete markets1 and perfectly competitive
equilibrium requires that rents converge to zero. In strategic factor markets, if markets are
perfectly competitive and efficient, information released among players will reflect the
performance of those resources traded in the exchange. Since markets will anticipate the value for these resources, their prices will be bid up, and economic rents will dissipate
(Barney, 1986). In fact, if a firm's strategies result in imperfect product markets, these
still cannot be a source of economic rents. In strategic factor markets, rents are created
through heterogeneous expectations. Heterogeneous expectations are caused by mark
1The central hypothesis of the Arrow-Debreu model is that there is a market for every good produced and consumed in every possible future contingency (Arrow, 1953; Debreu, 1959). In other words, there is a complete set of contingent markets, and if economic agents had full information of future events, and unlimited powers to compute benefits from all potential courses of action, and if the society could effortlessly and costlessly monitor these actions and commitments, markets could efficiently allocate resources.
4 imperfections, and market imperfections are caused by information asymmetries Most of
the theories in strategy implicitly assume that firms are price takers. The main reason is
the earlier dominance of product market strategies (Porter, 1980). RBV shifted the
emphasis to strategic factor markets. and resource immobility. I argue that how parties
exchange in factor markets (spot markets, negotiations, auctions) will have an impact on
rent generation and appropriation.
General equilibrium models describe a market economy for factors of production,
capital goods, and money. Originally developed by Walras (1874), later on modified by
Pareto (1909), general equilibrium models developed first and second welfare theorems.
First welfare theorem argues for a ``Walrasian equilibrium'' that satisfies the same price
ratio for all agents as well as the same marginal rates of substitution, which is a Pareto
efficient allocation. In other words, both agents (buyers and sellers) end up at the same allocation point when they maximize their preferences. The second welfare theorem rules out the increasing returns to scale assumptions by arguing that firms are small relative to the market. Hence, any Pareto allocation can be utilized by a market mechanism given a set of right prices and an appropriate redistribution of income among economic agents.
Arrow (1951), and Debreu (1952, 1959) transformed this equilibrium into an axiomatic form with the following assumptions: there are no information asymmetries between agents, no agent can exert market power and firms are small relative to the market, there are no increasing returns to scale and competitive markets allocate resources efficiently. In Walrasian equilibrium, it is not clear “who” sets the prices, or
“how” the market operates. Similarly “money” does not exist; prices are measured in
5 some numeraire. Instead, there exists a “Walrasian auctioneer2”, a fairly unrealistic
mechanism personified as an individual who stands in front of the markets and calls out
prices, allowing agents to prefer and consume goods and resources (Kreps, 1990). This is
a continuous auction and at some efficient allocation point, all markets clear.
Industrial Organization (IO) theory of the firm, relaxes the first welfare theorem
assumptions, and the most important concern remains as the social planner's role to attain
competitive equilibrium (Lippman and Rumelt, 1982). The nature of the goods can be
either public or private, and externalities and informational asymmetries may exist among
agents about products and resources. These assumptions result in managers being strategy makers and strategic pricing of resources, and untruthful bidding govern exchanges. An extension of these conditions results in incomplete contracts (Holmström and Tirole,
1987). Costs associated with such contracts are first discussed by Coase (1937) and developed by Williamson (1975) as transaction cost economics (TCE).
While TCE explains how firms choose similar governance mechanisms among various forms under similar economic situations, this only augments the homogeneity of firms (Barney and Hesterly, 1996), and does not explain why some firms outperform their rivals, and why they are different. In RBV, a resource must be valuable, rare, imperfectly imitable, and unsubstitutable to be a source of sustainable competitive advantage (Barney, 1991). But, if all agents value the same resource the same way, and the performance of the same resource is identical among all interested parties, even
2I would like to thank Axel Leijonhufvud who has coined the term for helpful insights. He created this anthropomorphism in order to compare the Walrasian auctioneer to Maxwell’s demon in a paper entitled “Keynes and the Keynesians: A Suggested Interpretation” in the American Economic Review, 1967.
6 though the resource is valuable, rare, inimitable, and unsubstitutable, the bidding among the parties would drive the price of the resource high enough to erase economic rents. In other words, if all agents have homogenous expectations about the productive capabilities of a resource, its value would be common across bidders, and competitive bidding would drive up the prices, wasting economic rents during the process. Therefore, unless a firm has a better use for the performance of a resource acquired in the factor market, it will overbid its value and be subject to winner's curse. Winner's curse occurs when winning bidders overestimate the true value of the item being traded (Levin et al., 1996; Kagel and Levin, 1986). In formal auction theory, the price of a product reflects two values: common and private values (Milgrom, 1989).
Common value component of a product is unknown at the time of bidding, but in fact its value is predetermined, through a continuous demand-supply adjustment in the market. Private value component is unique to each bidder and depends on each party's rent generation potential. Since the potential rents are not identical, the winner is not subject to winner's curse, and cannot overbid its value in private value auctions. The equilibrium prediction is that as public information about the value of the resource being auctioned increases, winning bids rise. When there is more uncertainty about the value, bidders discount their private information. Therefore in a market at equilibrium, when the uncertainty about the true value is reduced, agents discount their private information and cause winning bids to rise (Roth, 1995). Thus, I argue that firms that have generated heterogeneous expectations about a resource may create and appropriate economic rents through auctions.
7 Even if factor markets are perfectly competitive, auction mechanisms can create
Ricardian rents in equilibrium. Perfect competition in factor markets require prices to
reflect all information about the goods being traded. Buyers are indifferent across product
choices given goods’ undifferentiated qualities. Both sale and resale transactions are
conducted without costs3. Equilibrium in perfectly competitive markets is attained when
for a given price, all goods being offered are consumed, the producers maximize profits,
and there is no unfulfilled demand (Arrow and Debreu, 1954). Under such conditions, the
economic rents generated through auctions can only result from efficiency and
productivity differences among factors of production. If the productivity of these
resources were known, this information would be reflected in their prices. However, if the acquirer has a heterogeneous expectation about the productivity of the resource,
Ricardian rents can be generated. In this sense, pricing becomes a strategic variable and
transforms price-taking firms into market makers.
Proposition 1: In perfectly competitive factor markets, auction mechanisms can create Ricardian rents in equilibrium.
2.1 How Do Managers4 Create Economic Rents?
In the resource based view literature, two mechanisms have been proposed to
explain how managers generate economic rents: resource selection, and capability
building. Resource selection is a Ricardian perspective where the productivity of
3For an interesting demonstration of the concept, please refer to the HBS case #9-384-034 reprinted in Porter (1983). 4I use firms and managers interchangeably in the context of this paper.
8 resources are heterogeneously distributed among firms (Peteraf, 1993; Wernerfelt, 1984), and managers outsmart the factor markets by selecting resources based on their future values (Barney, 1986). The alternative Schumpeterian perspective is capability building, a mechanism that depends on the deployment of resources to affect a desired end (Amit and Schoemaker, 1993; Mahoney, 1995). While capability building requires managers to develop a capacity to manage firm-specific tangible and intangible processes, the resource selection mechanism demands managers to accurately assess expectations about the future value of resources.
One argument is that when resources are selected, economic rents are created before the firm acquires those resources, and after if the firm develops capabilities based on firm specific processes. The distinctions between these two mechanisms have important theoretical, empirical and practical implications (Makadok, 2001). Makadok assumes that rents are embedded on a resource, and existence of rents triggers firms' to select or pick this resource. Furthermore, due to the common value characteristics of the resource, the bidding process dissipates rents. On the other hand, the capability building approach is more in line with creation of private values for a resource, since the developed resource is more firm specific; hence, rents that are created are harder to bid away ex ante.
I argue that because all resources incorporate both common and private values, both the resource picking and capability building perspectives are integral to rent generation. If economic rents were embedded on a resource, then firms would try to pick resources with embedded economic rents and prices would be bid up, dissipating all
9 above normal returns. Similarly, if all rents were embedded in internally developed capabilities, then, these firm specific assets would have a very low common value when traded or transferred, resulting in no rent appropriation. The question then becomes:
Under what conditions will these two mechanisms be preferred to each other? And why are some firms better at picking resources versus some other that are good in developing capabilities?
Some empirical and practical cases display the characteristics of one mechanism dominating over the other. For example in the market for firms, mergers and acquisition activities require managers to value tangible and intangible assets of targets (Hitt et al.,
1990). It can be argued that tangible assets are easier to value due to their common value characteristics, and intangible assets being assessed more subjectively due to their private value components5. When managers overestimate the productivity of these assets, they are subject to winner’s curse (Arikan, 2002). On the other hand, research and development firms might find it more advantageous to develop capabilities internally
(Hitt et al., 1991).
If strategic factor markets are perfectly competitive, the acquisition of resources in those markets will anticipate the performance those resources will create when used to implement product market strategies. Therefore, even if firms are successful in creating imperfectly competitive product markets, those resources cannot be a source of economic
5For example, let us assume that a firm acquires a metal stamping plant. The productivity of stamping machines may be well established based on technical aspects of the machinery, their working conditions, etc. On the other hand, it is somewhat more difficult to establish the productivity of the engineering capability embedded in the firm.
10 rents (Barney and Arikan, 2001). How can managers create imperfections in strategic factor markets? Two ways are suggested: first, firms can be lucky in the face of uncertainty, and second, firms can have unusual insights about the future value of a resource (Barney, 1986; Demsetz, 1973). The role of managers is to coordinate the use of productive resources and entrepreneurial skills (Penrose, 1959). While luck cannot be a systematic source of competitive advantage, developing a productive skill set to assess expectations and valuations about a resource can be repeated. Such a skill set may be inimitable, rare and valuable, non-substitutable and therefore create rents. While it is clear that according to resource selection model economic rents are created before firms acquire these resources in strategic factor markets, it is not clear how firms acquire these resources, and whether the mechanism by which those resources are acquired would affect rents.
2.2 Strategic Factor Markets
A strategic factor market is a market where the resources necessary to implement a strategy are acquired (Barney, 1986). The economic foundations of RBV strongly rest on several early contributions on the dynamics of competition on product and resource markets by firms (Wernerfelt, 1984), and on the ability of firms to generate and appropriate economic rents (Rumelt, 1984). Differences between firms are in the relative efficiency by which they extract and process homogeneous goods. This in fact results in the birth of the ``strategic firm,'' which can be characterized ``by a bundle of linked and idiosyncratic resources and resource conversion activities'' (Rumelt, 1987). These early 11 contributions explained the dynamics of entrepreneurial activities, market entry and exit,
economic rent generation and appropriation through the processes of isolation, variation and selection (Rumelt, 1997). Rumelt identified sources of rents and isolating mechanisms, which help explain heterogeneity in an equilibrium framework. By
definition, economic rents are payments to owners of a factor of production in excess of
the minimum required to induce that factor into employment (Hirshleifer, 1980). The
major influence on the evolution of the RBV came from Ricardian Economics (Barney
and Arikan, 2001). Although Ricardian rents were paid off to those that owned higher-
quality factors of production with inelastic supply and left little or no role for managers,
firms that own such resources may be able to earn economic rents by exploiting them
(Barney and Arikan, 2001).
The mechanisms by which firms acquire or develop these resources in efforts to
create heterogeneity are seminal in strategic factor markets theory (Barney, 1986). The
concept of a strategic factor market is probably the very essence of the RBV, especially
in terms of rent generation and earning above normal firm performance arguments. “If
strategic factor markets are perfectly competitive, the acquisition of resources in those
markets will anticipate the performance those resources will create when used to
implement product market strategies.” This suggests that, if strategic factor markets are
perfectly competitive, even if firms are successful in implementing strategies that can
create imperfectly competitive product markets, those strategies will not be a source of
economic rents'' (Barney and Arikan, 2001). The economic insight in creation of
imperfection in factor markets follows Demsetz (1973): firms being lucky, and firms
having unusual insight about the future value of a resource. A general model of resources 12 and firm performance in the context of competitive advantage in RBV was later on
developed by Peteraf (1993) who argued for four conditions: resource heterogeneity, ex
post limits to competition, imperfect resource mobility, and ex ante limits to competition.
These four “cornerstones” are critical for the generation and appropriation of Ricardian or
monopoly rents.
2.3 How Can Firms Create Market Imperfections?
The potential for rent generation in factor markets are based on two assumptions:
resource heterogeneity (Barney, 1991), and resource immobility (Dierickx and Cool,
1989; Peteraf, 1993; Rumelt, 1987). These two assumptions are firmly established in the
RBV literature for the existence of economic rents. (Barney and Arikan, 2001). Resource heterogeneity ensures that productive factors have intrinsically differential levels of
“efficiency”, which may result in Ricardian rents (Peteraf, 1993). What results in efficiency based rents is the scarcity of these superior resources. If the availability of resources were not limited, rents would dissipate and homogeneous producers would earn normal returns. Heterogeneity arguments are also consistent with monopoly rents where homogeneous firms can earn above normal returns when they display Cournot competition (Caves and Porter, 1977). Imperfect mobility of resources is linked to firm specificity of productive factors. Both TCE and RBV explanations augment this condition, and because imperfectly mobile resources have lower value to other users, the
economic rents cannot be bid away by competitors.
13 Therefore, resources that are immobile, by definition are context specific, and cannot have economic rents embedded on themselves. When firms acquire such resources, rents are created ex post. Based on the resource heterogeneity and imperfect mobility of resources assumptions, the potential for rents arises. However, prices for these resources are assumed to be given by the market, and mechanisms by which firms acquire these resources are not discussed. This paper addresses this gap in the RBV literature by developing a framework for valuation and acquisition of resources in strategic factor markets. A formal model of resource pricing is proposed for resource acquisition mechanisms. The assumption that firms are price takers in perfect competition suggests that the pricing mechanism is not important because it is either exogenous and instantaneous, or endogenous but equifinal. However, in factor market arguments we require that firms buy resources in imperfectly competitive markets by luck or by heterogeneous expectations (Peteraf, 1993).
Proposition 2: If firms have heterogeneous expectations of a particular resource, then they may use pricing mechanisms to create competitive advantage.
Proposition 3: Resource immobility creates market imperfections for firms with heterogeneous expectations of a particular resource and allow differences in rent generation by using various market mechanisms.
2.4 Market Mechanisms for Resource Acquisition
Broadly speaking, there are three market mechanisms: spot market transactions at posted prices, negotiation markets with bargaining (bilateral or multilateral), and auction markets with strategic bidding. In the RBV literature, although not explicitly discussed,
14 spot market transactions at fixed prices are assumed.6 However, at the core of strategic
factor markets theory, there is an implied common value auction mechanism illustrating the conditions under which rents dissipate rather than being created. “In the long run, firms with more accurate expectations will usually be able to avoid economic losses associated with buying overpriced strategic resources. Firms that do acquire these overpriced resources suffer from the ‘winner’s curse,’ that is, the fact that they successfully acquire the resources in question suggests that they overbid” (Barney, 1986:
1233). This argument asserts that the value of the resource is, or will be, the same to all bidders after the acquisition. Hence, if there is a winner, then it is most likely the case
that she has paid too much. In other words, assuming the markets are efficient, through
her bidding all rents would be dissipated. This is probably one of the most interesting
issues in strategic factor market thinking. In this sense, the emphasis is not on what resource a firm buys, but how it buys that particular resource.
We have to think of the pricing mechanism as an endogenous component of firms’ competitive strategy. Given three different market mechanisms, the relevant question becomes: what determines different selling/buying devices to occur
simultaneously in the market? In the next section, I will briefly discuss these market
mechanisms.
6There are several recent exceptions such as Makadok, 2001, and Makadok and Barney, 2001. In these two studies, auction markets are utilized in factor markets.
15 2.5 Spot Markets and Posted Prices
Posted price mechanisms allow for market exchanges of immediately delivered
commodities between consumers and producers without negotiations. Neoclassical economic theory defines two entities party to a market exchange: firms and consumers. A firm has an objective and profit function that it maximizes, given resource and budget constraints. Similarly, a consumer has an objective and utility function, which she also maximizes given a different set of resource and budget constraints. These are known as technological and market constraints. Technological constraints affect production functions, and market constraints affect prices (Varian, 1992). These neoclassical models
of spot exchanges between firms and consumers are also extended to exchanges between
firms.
When there are many sellers of a homogeneous good and many well-informed
buyers who can costlessly search and acquire resources, we have perfectly competitive
markets. Buyers and sellers are fully informed about each others’ preferences, prices and
utilities, and entry and exit is unconstrained (Baumol et al., 1982). The prices are set to
be fixed, and in this sense consumers, suppliers and firms are price takers. The market
clearing prices that are set by the continuous auctioning action of the Walrasian
auctioneer, allow the customer to buy a certain amount of the homogeneous good. If the
prices were any higher than what is known to be the market clearing level, there would be
no transaction. Similarly, if the products were at all diversified, then customers would
have an incentive to compare quality, price and features of these resources.
16 If factor markets displayed a frictionless Walrasian framework, and firms did not
have to search for resources, all transactions would be conducted in spot markets, and economic rents could not be generated from the acquisition function (or from resource
selection alone). In such factor markets, any other market mechanism would introduce inefficiency due to high search and coordination costs. Since the inputs of one market is most often the outputs of another, symmetry would also hold for product markets and product market competition. Under such conditions, the only way to generate economic rents would be linked to a process of input to output conversion, consistent with the black
box characterization of firms (and this is similar to capability building).
For a firm, the simplest kind of market behavior is the price taking behavior, and
perfectly competitive firms are assumed to be price takers. This approach naturally
assumes the homogeneity of prices, firms, and managers. When resources and products
are standardized and market clearing prices are stable, posted price markets work highly
efficiently (Milgrom, 1989). When information costs are low, and long-term
commitments and contractual relations are not required, spot market transactions can be
optimal (Alchian and Demsetz, 1972). Price setting in neoclassical economics is through
the Walrasian auctioneer. He sends out price signals, pˆ and prices adjust depending on
demand D( pˆ) and supply S( pˆ ) . After these adjustments, the market price pw equates supply and demand Q w = S(p w ) = D(p w ) , where Q w is the market clearing quantity.
In order for markets to clear, and the law of one price to hold, the above
mentioned extreme conditions must be met. But most resources are not homogeneous and
17 search is costly both in terms of actual costs associated with locating appropriate resources, and in terms of opportunity costs associated with foregone opportunities.
Moreover, trade frictions give firms various degrees of market power, and one party often has an advantage over the other. This advantage could be based on information asymmetries, differences between firm specific resources and capabilities, or result strictly from coordination problems between firms. In such markets, market makers are needed to act as intermediaries7 to coordinate transactions and clear markets (Spulber,
1996a, 1996b). Because search is costly and less efficient for individual buyers, intermediaries may specialize in various resource searches, and provide expertise (Stigler,
1961).
Proposition 4: In factor markets with high search costs, heterogeneous valuations and information asymmetries, firms looking for resources conduct their exchanges through intermediaries.
Proposition 5: The use of intermediaries allows firms to extract and appropriate some of the rents that would otherwise go to costly search activities.
2.6 Negotiation Markets and Bargaining
Negotiation markets are best described by the actions of economic players committing themselves voluntarily to various courses to resolve conflicts (Roth, 1977;
Rubinstein, 1982). Ultimately, situations in which each party is guided mainly by his
7Intermediaries are negotiators who receive no utility from consuming the good they trade. Their incentive is to generate arbitrage by decreasing search costs and risks associated with goods having unknown quality. The arbitrage return intermediaries receive is the price they charge for reducing the adverse selection problem. For a detailed review of the role of intermediaries in search and bilateral bargaining, please refer to Rubinstein and Wolinsky (1987), and Biglaiser (1993).
18 expectations of what the other will accept constitute elements for pure bargaining.
Because each party knows the other has expectations, expectations get compounded, and a “bargain is struck when somebody makes a final concession” (Schelling, 1956).
Firms negotiate to create an opportunity for the involved parties to collaborate for mutual benefit in more than one way, given that the actions taken by individuals cannot affect the well being of the opposing parties without their consent (Nash, 1950). The possible solutions to such games consist of allocations that are Pareto efficient and that do not make either of the parties worse off from what is consensually agreed (Arrow and
Hahn, 1971). Generally, bargaining under symmetric information results in efficient outcomes, whereas information asymmetries might result in ex post trade inefficiencies
(Coase, 1960). Information asymmetries give incentives to the parties to contract ex ante to avoid or limit these inefficiencies (Tirole, 1988) and the contracting dynamics depend on the ex ante relative bargaining power of the competitors (Williamson, 1975).
Bargaining power is the power to bind an opponent. This suggests that an advantage can be gained by accumulating some features, that the opponent does not have access to
(Schelling, 1956). These advantages can vary from what a negotiator might have or manipulate, to the power to fool and bluff (Morgan, 1949). Given a range of indeterminate but all possible solutions, when the cost of disagreement is the deciding factor, the bargaining power of the party with less cost is high (Hicks, 1932; Pigou,
1932).
19 Simplest form of negotiations can be represented as bilateral bargaining models where two economic agents try to allocate a surplus between them. Many bilateral bargaining models have been developed and applied to various situations such as labor unions versus management, criminals versus district attorneys, etc. (Horn and Wolinsky,
1988). In many ways, it is possible to argue that bilateral bargaining is the most basic form of exchange. Expanding on this basic form, negotiations involving more than two agents (multilateral bargaining) can be developed based on the same principle: the division of a surplus common to all parties to an exchange (Krishna and Serrano, 1996).
In negotiation markets, the market-clearing price p c is dependent on the utility of feasible allocations for parties to the exchange8. If agent i faces all feasible
′ ′ allocations with utility ui , [i = (1,..., n)] the equilibrium would be set at ui , where ui
would be preferable to all other ui . Assuming a rational individual will not accept a
c ′ c bargain with a better p ui f p ui , the alternative will be the Pareto optimal allocation,
c ′ and the equilibrium would be (p ,ui ) . Given these, can negotiations repair externalities? Put it in another way; are negotiations efficient ways of transacting? Coase
(1960) claimed that if the market outcome is inefficient, then people would get together and negotiate their way to efficiency.
8 We can substitute Pi with Π i and make the same argument for a firm. That is, instead of an agent negotiating for a preferable and feasible allocation that will maximize her utility, a manager will negotiate a preferable and feasible allocation that will maximize firm's profits.
20 If the posted prices do not exist, and if voluntary negotiation cannot lead to fully efficient outcomes9, can the markets still complete the exchange through a market mechanism? I argue that auctions and strategic bidding help solve these before mentioned inefficiencies under certain conditions. These conditions are strongly affected by the bargaining power of the parties to an exchange. Heterogeneous expectations and resource immobility would also affect the choice between negotiations versus auctions.
2.7 Auctions and Strategic Bidding
Auctions can be broadly defined as any competition over a resource with a deadline and clear rules. Changing the rules of competition determines what type of auction is in place, and firms bid their valuations to acquire the item. There are close analogies between auction theory, negotiation markets, and price theory of market exchanges (Bulow and Klemperer, 1996). For example in an English auction where bidders increase their bids until one buyer remains and the seller is required to accept the bid, the sale price equals the lowest competitive price at which supply equals demand.
Similarly, in auction markets the seller is treated as a monopolist who determines the minimum sale price and the rules, in order to maximize her revenues. This assigns all bargaining power to the seller. The argument can easily be extended to games with
9Experimental findings show that the market behavior is still more closely approximated by the competitive equilibrium model than any other. Selten (1970), Plott (1982), Holt (1985) found that posted prices facilitate the maintenance of prices at higher than competitive equilibrium levels and when there was imperfect information prices converged to near the competitive equilibrium. With full information about opponents' choices, negotiations resulted in higher prices.
21 multiple players, covering a variety of market-clearing resource allocations and prices.
Since these market players conduct exchanges without using posted prices, they are called ‘price makers’, and can be viewed as players in noncooperative games (Marschak and Selten, 1978). Auctions have three components: information structures of the parties, institutional mechanisms (rules) that govern the exchange, and risk preferences of the parties.
Informational structure rests strongly on the efficiency of prices to communicate information. If markets could effectively transmit information, the market design would not matter; the markets could solve the problem of externalities, adverse selection would disappear, and managers would not have to worry about selecting resources10. But markets are rarely efficient in transmitting information, at least for brief periods. Hence, the information structure of markets have a direct effect on managers’ risk preferences11.
Auction theory provides explicit models for price formation and by eliminating the negotiation component, allows the competitive equilibrium be reached more efficiently. For example the trading floors of stock exchanges and commodity exchanges characterize open outcry markets. In ascending auctions, competition among buyers forces the bids upward until a contract occurs. The rules governing sellers is symmetric to
10Hayek (1945) argued for such an efficient mechanism, which was later on developed by Arrow (1959). Both models would fail to explain Akerlof’s (1970) lemons market where private information caused market failures.
11Due to page limitations, I will not include risk aversion-neutrality arguments. Please refer to Kahneman and Lovallo (1993) for a detailed review.
22 that of buyers, and with the descending auctions. The markets converge to the competitive equilibrium even with very few traders (Smith, 1962; 1965). The direction of convergence to equilibrium reveals important insights for economic rent generation and appropriation12.
In strategic factor markets, managers who can form accurate expectations about the firm-specific value of resources can create economic rents. Such valuations are dependent on three factors: pre-existing stocks of resources, information gathering capability and information processing capability (Makadok and Barney, 2001; Barney and Arikan, 2001). In addition to these three factors, managers must choose among market mechanisms for exchanges (spot market, negotiation, and auction), which in turn also affects their initial expectations.
The second component in auctions is institutions that set the rules to govern exchanges. There are many types of auctions based on a variety of bidding rules
(Cassady, 1967). These can be grouped under four broad categories: the English auction
(also called the oral, open, or ascending-bid auction); the Dutch (or descending bid) auction; the first-price sealed-bid auction; and the second-price sealed-bid (or Vickrey) auction (McAfee and McMillan, 1987). After a manager chooses auctions as a market
12Some general empirical properties are known, especially in financial markets. However, a compelling theory of dynamic adjustment does not exist. While it is observed that markets converge almost instantaneously, the influence of basic economic conditions on adjustment paths are yet to be explored to provide generalizations. For detailed discussions, please refer to Easley and Ledyard (1992), and Friedman (1984).
23 mechanism, then she is faced with a second question: what type of auctions best suit the firm characteristics, and conditions of the factor markets? This is especially important since different auction designs will not yield same expected revenues and economic rents
(Kagel and Levin, 1993; Levin et al., 1996).
Finally, risk preferences of both the seller and the buyer play a crucial role in resource valuation13. There are two extreme cases: the private values model, and the common values model. In private values model, bidder i knows her value exactly, and she draws other bidders' (and seller's) value from some probability distribution function
F . Therefore each bidder's value would be vi (i = 1,..., n), and the seller would
observe all the bidders given the distribution Fi . Most often items purchased for own use and not for resale such as antiques, resources acquired through internal development, rare and inimitable items fit this description. The common values model is when the value of the item is same to every bidder ex post, but unknown at the time of bidding
(Wilson, 1977). If V is the unobserved true value, each vi would be drawn from a
distribution H known to all bidders, that is H (vi |V ) , and bidders get signals from the market to estimate the value. Since the signals differ among individuals based on their
13Bidders and sellers are assumed to be either risk neutral or risk averse individuals. Each bidder is assumed to know the number of bidders, their risk attitudes, and the probability distributions of valuations, and everyone knows that each other knows this, and so on. In other words, it is assumed that bidders' subjective beliefs about unknown parameters are mutually consistent (Harsanyi, 1968). Arguments on overconfidence (Camerer, 1997) and bounded rationality (Conlisk, 1996) in auctions are omitted here for the sake of brevity.
24 risk preferences in common value auctions, it is especially useful to know the other bidders' valuation and preferences (Milgrom and Weber, 1982).
Proposition 6: Sellers with no bargaining power are better off using auction mechanisms to compete in strategic factor markets with sellers who have market power when quality, quantity, or prices are concerned.
Proposition 7: Sellers with some degree of market power are better off negotiating contracts versus selling at posted market prices.
2.8 Comparing Market Mechanisms for Rent Generation
Let us consider an entrepreneurial firm selling a new resource such as a new technology, a process, or a prototype where n risk neutral firms would be interested in
14 acquiring it . Bidders' signals xi are identically independently distributed,
∗ xi ~ iid, F(•) on []0,1 . Any bid bi that is at least as high as the reservation price b
∗ of the seller is acceptable, bi ≥ bmin ≥ b , and bids are a vector of prices. The entrepreneurial firm is faced with three alternatives. First, it can sell the resource
(technology, process, or prototype) to an interested firm through bilateral negotiations,
14Due to symmetry, I will focus on one risk neutral bidder without loss of generality. Also for simplicity, the resource put on the market is assumed to be a single, indivisible item. There is an important distinction between n bidders in auctions and n competitors in posted price markets. As the number n in auctions increase, the market dynamics approach to the competitive market conditions. If one were to correlate the number of bidders in common and private value auctions, the number of bidders would be positively correlated with common value components, and negatively correlated with private value components of resources. For example, a 1931 Bugatti Type 41 Royale Coupe was sold in 2001 for $17 Million, and the auctioneer had sent out 100 brochures to potential buyers. Conversely, a 2000-2001 model Chevy Cavalier (or a fleet of them) would not be sold at an auction, but its posted price would be equal to a common value auction price with relatively infinite number of buyers.
25 hence it can be acquired. If there are more than one potential clients, the entrepreneurial firm can bid potential acquirers against each other in multilateral negotiations. Second, the firm can sell this unique resource through an auction, by setting up clear rules and a time limit for the exchange15. Finally, the firm could establish a price for the value of the resource, and announce it was for sale. The question therefore becomes, under what conditions should the entrepreneur choose among auctions, posted prices, and negotiations in order to generate and appropriate economic rents?
First, let us consider the auctioning of this resource. Assuming a Bayes-Nash
equilibrium exists, every bidder bids as a function of xi , and the derivative is strictly larger than zero:
′ Bi (xi ) > 0,∃B(x) = Bi (x)∀i s.t.B′(x) > 0
The expected revenue for the seller with n risk neutral bidders with first-price private value signals would be:
1 n−1 ERs = n (vf (v) + F(v) −1)F(v) dv ∫b∗
For the bidder, the tension is about her value and the potential profits she will gain from the auction. The profit margin would be dependent on the probability of winning the auction. Conceptually, the expected gain to the buyer is equal to reservation value times the probability of winning minus expected payments (Riley and Samuelson, 1981). In this auction setup, the seller would have to pay to get information about the bidder's
15If the resource were to be the firm itself, it would put itself on the auction block, and conduct an IPO.
26 valuation. Maximizing with respect to b∗ would give us the optimal reservation price as shown in.
1 n−1 max n ∗ (vf (v) + F(v) −1)F(v) dv = 0 b∗ ∫b − n(b∗ f (b∗ ) + F(b∗ ) −1)F(b∗ ) n−1 = 0 1− F(b∗ ) ⇒ b∗ = ∗ f (b )
In the first-price private value auctions, economic rents would equal to the difference between the winner's true value, and the highest bid. The bidder maximizes
[F(σ (B)) n−1 (x − B)] 16 . If the design is second price ascending auction, the winning bid
∗ would be b = bi +δ = v , and the highest bidder would pay the second highest price, and the dominant strategy would be to bid your true value v (Vickrey, 1961).
If the resource in question had an equal probability of generating the same economic rents for all firms interested in acquiring it, the valuation of this resource would change from private values to common values. In common value auctions, the conceptual representation of the bidder's expected payoff in an auction is π e = (expected gain -
expected payment) x probability of winning. The surplus would be si = vi /n − ci where
16Assuming a symmetric solution, and we take the derivative of the profit function in equilibrium from an exogenous value ( x ). All derivations are omitted for the sake of brevity, and are available from the author upon request.
27 vi is the common value signal, there are n bidders, the bidder's surplus is s , and the
cost of acquiring the resource is ci . In common value auctions, as the number of bidders
n approaches infinity, si = ci , auction outcomes are going to be equal to posted price outcomes in spot markets. In an English auction, the equilibrium would be:
e π = E(V − ci | si = x,Ym = x) − E(Ym − yi | si = x,Ym = x)
where Ym is the maximum surplus, yi is the rest of the bidders, and x is the
rent. Similarly, in a Vickrey auction, E(V − ci | si = x, yi = x ) the equilibrium point would be the dominant strategy for the private value second-price auction.
So far, the above specified models assumed pure common values versus pure private values. However in practice it is very rare that firms be in a position to acquire such ‘pure value’ resources. In fact, resources will have both common and private value components, and a dominant strategy may no longer exist. Instead, firms would have to look for optimal strategies to bid according to their expected values. While in common value auctions overbidding one’s true value results in negative rents due to winner’s curse, firms with more accurate expectations than other firms about a resource's future value, will generate rents through auctions. On the other hand, in posted price markets, the firm pays the same price as all the other firms that acquire the resource with inaccurate valuations. Resources that have uniformly steeper marginal revenue curves
28 provide more incentives for economic rent generation through auction mechanisms when compared to posted price markets17.
When auction mechanisms are compared to negotiation markets, the number of competitors become a crucial criteria (Bulow and Klemperer, 1996). Intuitively, in private value auctions, the existence of more bidders raise prices. On the other hand, this intuition does not hold for common value auctions. Bulow and Klemperer (2002) show that restricting bidding (reducing the number of buyers) lowers the bargaining power of the seller. Therefore firms need to examine the number of serious bidders, their affiliated signals, and their bargaining position under two separate cases: private value auctions and risk neutral bidders versus common value auctions and risk averse bidders.
Proposition 8: Risk averse buyers with no bargaining power increase their rent generating potential using auction mechanisms than negotiating contracts.
Proposition 9: Risk neutral buyers with bargaining power increase their rent generating potential using negotiation mechanisms than using auction mechanisms.
Proposition 10: Sellers with no bargaining power increase their rent generating potential using auction mechanisms than negotiating contracts if there are competing sellers.
Proposition 11: Sellers with bargaining power increase their rent generating potential using negotiation mechanisms than using auctions if there are competing sellers.
17For a detailed discussion of global steepness of the marginal revenue curve and its association with the distribution of buyer's valuations, please refer to Bulow and Roberts (1989).
29 Let us consider the example of Scios Inc. Scios worked on the development of its
Natrecor drug for acute congestive heart failure with Bayer. Natrecor was the first new treatment for the deadly disease in 14 years, and the FDA rejected the drug and did not provide approval for sale. The development partner, Bayer, dissolved its ties with Scios.
After two more years of additional clinical trials financed by Scios, the drug was approved in 200118. The question is how much should Scios charge for the patent of
Natrecor? From the seller's perspective, the entrepreneur would not want to sell this unique resource in the spot market, since the price is not yet determined. Instead, given the unique characteristics of the resource, a price revealing market mechanism would be needed. The entrepreneur needs to decide between auctioning off the patent, versus negotiating with interested parties (possibly including Bayer).
As presented in Table 1, Scios would prefer to negotiate given its high bargaining power, and recently announced favorable market conditions. Scios would have preferred the auction mechanism to negotiations prior to the FDA approval. As presented in Table
II, Bayer on the other hand, would prefer to negotiate after the FDA approval, and strictly try to avoid entering an auction where it would compete with others. Scios would not want to negotiate with Bayer, since Bayer would have had better information with respect to other competitors, and therefore higher bargaining power. Bayer's competitors are better off entering an auction as potential buyers of Natrecor. The effects of the next
18Please refer to BusinessWeek, March 11, 2002 for more information on this case.
30 alternative, negotiating with multiple bidders, are dependent on the credible revelations of the negotiating parties’ offers. Without credible revelation of rivals' offers, multilateral negotiations would resemble first-price auctions (Thomas and Wilson, 2002). Search costs related to the drug are already high (with no immediate substitutes). It is difficult to comment on the risk propensity of bidders and the seller in this specific example.
These arguments are strongly embedded in the four conditions developed by
Peteraf (1993): resource heterogeneity, ex ante limits to competition, imperfect resource mobility, and ex post limits to competition. First, common versus private values of a resource are strongly determined by the heterogeneity of expectations of the acquirer. For example salicylic acid, a common chemical component widely used in pharmaceutical, food, dye and many other industries has a publicly known price in equilibrium. When an exogenous shock is introduced to the system, market clearing levels will shift. For pharmaceutical firms producing aspirin, food conservers producing tomato paste, or dye manufacturers producing ink, this resource provides heterogeneous returns. What mechanism should the buyers use to acquire salicylic acid which is in limited supply?
Depending on the sellers’ bargaining power, market thickness, and the public information about the exogenous shock, sellers will either negotiate or auction off contracts the good. Simply put, the bidder with the higher marginal return will overbid its competition and acquire the factor, and its performance will be determined by ex post competition conditions. Second, the existence of scarcity in markets creates the need for heterogeneity of expectations and private values. Both in pre-shock and post-shock markets, economic rents will be determined by the mechanisms through which firms acquire resources, rather than the productive capabilities of the resource itself. 31 2.9 Discussion
Firms will anticipate and exploit any opportunity for above normal returns in strategic factor markets (Barney, 1986). A firm can generate above normal returns by having a superior understanding of a strategy's return potential, and by acquiring the resource (capability) to implement that strategy using a price mechanism that will best allow for rent appropriation. Therefore, it is not only what the firm acquires, but also how it acquires that we should be concerned with. Returns that are unexpected are by definition, manifestations of firm's good luck. Strategies relying on the repetition of such
‘marvelous and serendipitous’ coincidences, is highly questionable. However, strategies that a firm develops for the acquisition of resources in factor markets can be a systematic source of competitive advantage. By applying various market mechanisms to their acquisition strategies, firms can distinguish strategic differences among posted price markets, negotiation (bargaining) markets and auction markets, and take advantage of their dynamics. In factor markets, prices for resources do not have to be assumed given, and therefore mechanisms by which firms acquire these resources can be a source of economic rents.
In this chapter, the market mechanisms by which firms can generate economic rents in strategic factor markets are discussed. More specifically, spot market transactions with posted prices are compared to negotiation markets with bargaining and to auction markets with strategic bidding. One cannot rightfully conclude that one market mechanism is superior or optimal over another, since the conditions need to be clearly identified under which these three market mechanisms are more efficient for parties to an
32 exchange. Based on the conditions that affect information between parties, their bargaining power and costs of transacting, several propositions are developed.
Throughout the chapter, the unit of analysis is the transaction. Unlike the TCE, these transactions make up a set of ‘choices’ for firms. This distinction is crucial for the arguments provided for factor and product markets, and especially for establishing the differences between TCE and RBV.
Alchian (1950) explained that firms would be replaced by their rivals if profit- maximizing characteristics were hindered in product markets. Williamson extended the analysis to capital markets and argued that firms would be replaced by their rivals if they did not maximize profits (Williamson, 1975). RBV provides an explanation for the interaction of factor markets and firms: firms would not create economic rents if they did not acquire valuable, inimitable and rare resources from imperfect capital markets
(Wernerfelt, 1984; Barney, 1986; 1991). The assumption that firms are price takers in perfect competition suggests that the pricing mechanism is not important because it is exogenous and instantaneous. In contrast, the strategic factor market argument requires just the opposite. Firms have to buy resources in imperfectly competitive markets by luck or by heterogeneous expectations. These heterogeneous expectations also affect the pricing mechanism used in such markets.
What is the most profitable way to buy (sell) resources in strategic factor markets? Does it pay to buy (sell) through an auction, or does it pay to use bargaining power? How effective are informational asymmetries on market mechanisms? The auction mechanisms work for a buyer that does not have market power. This way, the firm with no market power can compete with the firm that has market power, when 33 quality, quantity, or prices are held constant. However, if the firm already has some degree of market power, it pays off to take advantage of its bargaining power and negotiate contracts and deter entry into auction markets. The implications of bargaining power will be different for buyers and sellers when acquiring resources in strategic factor markets. In auction mechanisms, bilateral negotiation between the parties does not exist.
Instead, prices are lowered (or increased depending on the auction format) due to the competition between bidders. It is important to note that although auction mechanisms can be a source of competitive advantage and can generate economic rents, not all firms can benefit from auction strategies uniformly.
Information asymmetries play an important role in auction mechanisms. When two parties enter an exchange, one side often knows something more than the other regarding the transaction. Hayek (1945) argued that individuals do not have symmetric information, and the information asymmetries are the reason why price system is an efficient mechanism for communicating information. That is to say that when a party needs to make a decision, he considers the vector of prices. This is in fact a criticism of the Arrow-Debreu model of perfect information. Taking this argument further, Smith
(1982) finds empirical support for the “Hayek hypothesis”: strict privacy together with the trading rules is sufficient to produce competitive market outcomes at nearly perfect efficient levels. Information asymmetries are reflected in a broad body of research such as the principle-agent models, which led to the agency models, and the Akerlof's market for lemons argument. Auction mechanisms help us understand the missing explanation of
Arrow and Debreu's (1954) argument: in a model of many small buyers and sellers, the
34 market prices end up as ‘given’, through the Walrasian auctioneer's bargaining mechanism.
There are several important extensions. For some firms, using different market mechanisms will not yield economic rents. Rather, these firms rely strongly on long-term relations with their exchange partners. Through repeated exchanges, such firms establish patterns, which might be hard to change. The information dynamics between the buyers are of interest as well. In traditional procurement mechanisms, the buyers in the same industry do not have full information about each other's costs. This applies to the sellers as well. In many ways, they do compete with sealed bids and history plays a role. The more you do business with a customer, the more you have access to its confidential information and build idiosyncratic interfirm ties that increase the likelihood of future similar exchanges. With the emergence of electronic marketplaces, the informational asymmetries are diminished to a great extent. Depending on the format of the bid reporting, both the buyers and sellers can see the bids online, in real time. This might lead to participating firms to loose opportunities for building idiosyncratic relational ties with customer/suppliers that might be a source of competitive advantage.
The competition depicted in this chapter is rather simplistic. For future research, these simple assumptions should be revisited. For example, what if we do not have many sellers and buyers, and what if prices do not reflect perfect information? In practice, industries do not experience a radical influx of new entrants. Mostly, incumbents exchange resources among themselves. Some firms choose to develop these resources, others prefer to trade them, and it is difficult to homogenize the winning strategy. When the players are limited to a small number, what conditions determine the choice between 35 negotiations versus auctions? Another implication is what if the traded commodity is an intangible resource, such as human capital? Would the market better value the resource through an auction mechanism because a private value would emerge, or would the value be efficiently reflected in market prices? Most likely solution to this problem will be through theoretically separating economic rent generation and rent appropriation mechanisms.
36 CHAPTER 3
AUCTIONS VERSUS NEGOTIATIONS
We had a very strong name and a great brand, but needed financing. We approached a big investment bank to IPO the winery. They said ``In the world of finance, these are just rounding errors''. There were companies that wanted to acquire us but we did not want that then. Therefore we went ahead and did an OpenIPO. Following the IPO, for one year, if you put our stock price to a medical chart, you would only see a horizontal line. We had the EKG of a potato. Only after these problems did we negotiate the sale of our company. Reed Foster, Co-founder of Ravenswood Winery, 2002
There are three main types of market exchanges and firms often have to make strategic decisions regarding which market mechanism to use when competing in factor markets to acquire or sell resources: auctions with bidding, negotiations with bargaining and spot markets with posted prices. For a market mechanism to be used during an exchange, it should be optimal for both the seller and the buyer, otherwise the exchange would occur using a different market mechanism, or would not occur at all. The choice of market mechanism affects both the buyers' and sellers' rent generation and appropriation potential. Hence, while the buyer is concerned with how he/she should buy a resource, the seller is concerned with how he/she should sell it. While it is well established that firms should acquire rare, inimitable and valuable resources from factor markets to gain competitive advantages, the mechanisms by which firms acquire these resources have received little attention. In this paper, I study the factors that affect firms' decisions to prefer one market mechanism to another. In factor or product markets, it is 37 convenient to use a resource or a product as a tradable unit. What if the resource or the product is the whole firm itself? Firms have been characterized as a bundle of linked and idiosyncratic resources and resource conversion activities (Rumelt, 1987). When a firm is sold, its new owners have a right to its future cash flows. Therefore, the dynamics of competition on product and resource markets by firms (Wernerfelt, 1984), and the ability of firms to generate and appropriate rents is fully applicable to the market for firms, where firms are exchanged through various market mechanisms. I argue that for private firms an IPO is an auction, and an M&A is a negotiation; where, one unit of a firm's stock of equity represents a future cash flow opportunity, and a numeraire resource.
Two major markets exist for the sale of an entrepreneurial firm: initial public offering (IPO) versus mergers and acquisitions (M&A) markets. This paper argues that choosing between these two market mechanisms is not serendipitous. Based on the theoretical work by Campbell and Levin (2001) and Arikan (2002), I argue that the discrete choice between choosing to auction off a company through an IPO or to negotiate its sale as a privately held target rests on five factors: bargaining power, resource value, market thickness, risk propensity and search costs.
The purpose of this chapter is to test a general model of market mechanisms in strategic factor markets (Arikan, 2002) and to determine the conditions under which sellers will prefer one to the other. In order to test the performance differences of market mechanisms for the sellers, I use the market for firms as a factor market and study the propensity to choose one mechanism over another by looking at the choice of IPOs versus M&As for private firms. In the paragraphs that follow, I review the literature linking market mechanisms and entrepreneurial choices. Hypotheses are developed on
38 the basis of these arguments. I then describe the IPO and M&A data used in this study and provide a description of measures. The section on statistical methods is followed by a discussion of results and implications.
3.1 Market Mechanisms
What makes an entrepreneur decide to sell his/her company is beyond the scope of this study19. Only recently two formal explanations have been offered for going public: life cycle theory and market-timing theory. According to the life-cycle theory, entrepreneurs who would rather just run their firms, due to cash considerations, will go public when they grow sufficiently large because it is more optimal (Zingales, 1995;
Chemmanur and Fulghieri, 1999). The market-timing theory is based on an asymmetric information model where firms decide when to exercise the sale depending on the favorable pricing of the stock, and the existence of competitors in the market (Lucas and
McDonald, 1990). Entrepreneurs can in fact use timing as a strategy to signal growth opportunities (Schultz, 2000).
For the purposes of this study, I assume that founders and other initial shareholders desire to raise equity capital for the firm, and convert some of their wealth into cash at a future date. Also, while some entrepreneurs might have non-financial
19In the literature despite a burgeoning windfall of studies on long-run and short run performance of IPOs, there is a lack of studies on the choice of whether an entrepreneur should sell his company or not.
39 reasons that are driven by semirational considerations, it is assumed here that they will be inclined to sell their shares after public market valuations have reached optimal levels
(Ritter and Welch, 2002). Hence, I consider the cases when the entrepreneur or the venture capitalist gets rewarded for his/her initial efforts through selling his company to public or private investors either through auctions (IPO) or negotiations (M&A).
Entrepreneurs use both IPOs and acquisitions to raise capital. The magnitude and scope of these activities are highly sensitive to time trends, and show great variation across countries20.
Firms use both acquisitions and IPOs to sell their companies. Privately owned companies are also subject to takeovers, where small entrepreneurial firms choose to divest the entire firm21. In this paper, I focus only on privately held entrepreneurial firms.
In privately held firms, the agency conflicts between managers and nonmanagerial shareholders are negligible (Field and Karpoff, 2002), since the rents generated from the firm's sale accrue to a relatively small number of principals who internalize both costs and benefits. I have excluded the sale of existing state-owned enterprises in privatization
20Please refer to Gompers and Lerner (2001) for a detailed discussion on time trends, and to La Porta et al. (1997) for IPO activity differences across countries.
21An asset sale is a partial divestiture. The exogenous factors that affect a firm's decision to divest have been discussed in the literature as industry characteristics (Mitchell and Mulherin, 1996), market timing variables (DeLong et al., 1990), and liquidity related factors in takeovers (Leland and Pyle, 1977).
40 efforts22, spin-offs, and recapitalization of already offered firms from the sample. Also, I have not focused on firms that sell additional shares at full value after the initial IPO
(over-allotment options), and firms that seek new financing within three years of their
IPO23. Specifically, I test the model predictions developed in Arikan (2002) controlling for the exogenous variables discussed in the literature. In the sections that follow, first I discuss the factors that affect the choice between market mechanisms. Then I examine
IPOs as auctions and extend the analysis to acquisitions as negotiations.
3.2 Determinants of Mechanism Choice
Firms’ choice of a market mechanism is determined by both endogenous and exogenous factors. These factors in turn affect rent generation and appropriation potential. I identify five factors24: bargaining power of the parties, resource’s value to each bidder, the market thickness for both the demand and the supply side, risk taking propensity of the parties, and the existence of search costs. The existence of scarcity in factor markets creates the need for heterogeneous expectations and private values, which
22Furthermore, although similar arguments could be made for the sale of treasury securities markets, where both negotiations and auctions are dominant mechanisms in the sale of notes, bills and bonds; these are not included in the scope of this study.
23Please refer to Michaely and Shaw (1994), Benveniste and Spindt (1989), and Hansen et al. (1987) for detailed discussions on these areas.
24 For a detailed discussion, please refer to the following manuscript: Economics of Strategic Factor Markets, Arikan (2002).
41 signal higher marginal returns if acquired. I argue that, if a bidder is to generate rents, this is determined by the mechanisms by which the good is acquired, in addition to the productive capabilities of the good itself. Similarly, if a seller is to generate rents, the optimal mechanism choice might inhibit or enhance rent generation potential.
Bargaining power is the power to bind an opponent, and parties to an exchange try to gain advantages that their counterpart does not have access to (Schelling, 1956).
Information structures play a major role in determining the magnitude and the direction of bargaining power. Bargaining under symmetric information results in efficient outcomes, whereas asymmetric information results in ex post trade inefficiencies (Coase,
1960). Since both search and bargaining are time consuming and discount future utilities, both parties have an incentive to avoid or limit these inefficiencies (Tirole, 1988), and the contracting dynamics depend on relative bargaining power of the parties involved. When parties with bargaining power choose to auction, they might be wasting a valuable opportunity to extract a larger portion of the surplus by entering into negotiations.
Hypothesis 1: Entrepreneurs with low bargaining power are more likely to choose auctioning their firms through an IPO rather than negotiating the sale through an M&A.
Hypothesis 2: Entrepreneurs with some degree of bargaining power are more likely to choose negotiating the sale of their firms though an M&A rather than auctioning off through an IPO.
Resource value is represented by two components: private and common values.
The value of a pure common value item is the same to all bidders, but the bidders do not know the value at the time they bid. Instead, they receive signals related to the value of
42 the item. In pure private value auctions, each bidder knows with certainty the value of the item to him/her but only has probabilistic information about the value of the object to other agents (Kagel et al., 1987). In the context of RBV, the private value component of resources leads to heterogeneous expectations about the productive uses of those resources. The heterogeneous expectations are going to be sources of economic rents for both the seller and the buyer depending on the exchange mechanism of auctions vs. negotiations25.
The problem in private value bidding is `strategic'. Depending on the mechanism design, the winning bid either pays the highest value, or the second, and if there is overbidding, it has a small effect on the expected payoffs to the bidders (Roth, 1995). On the other hand, in common value auctions, inexperienced bidders are subject to systematic failure to account for adverse selection. Although each bidder receives unbiased estimates of the item's value and have homogeneous bid functions; they assume they have the highest signal value (Kagel and Levin, 1986), and overbid their value. Most often this will result in winner’s curse.
Hypothesis 3: As the private value component increases, the entrepreneurial firm's probability of being sold through an auction (IPO) increases.
Hypothesis 4: As the common value component decreases, the entrepreneurial firm's probability of being sold through an auction (IPO) increases.
25Based on only resource value, it is not enough to conclude which market mechanism should be used. However, with all certainty, we can rule out spot markets with posted prices in the context of private value resources, ceteris paribus.
43 Market thickness is determined by the number of buyers and sellers in a market, and the number of sellers and buyers party to an exchange have a very strong effect on the outcomes of auctions or negotiations. For example if there is only one bidder, and the sale of a resource is negotiated, the seller is always better off inviting a second potential buyer and holding an auction without a reservation price26.
The restriction of qualified bidders to enter the bidding, has a direct effect on the seller's expected revenues. Entry or restriction into markets can be induced by the use of reservation prices, or simply by endogenous choices27. As discussed earlier, the opening price for an issue may be regarded as the reservation price, and is inhibitive in independent private value auctions, where unrestricted entry is optimal. In common value auctions on the other hand, sellers will want to discourage entry (Kagel, 1995). In first- price independent private value auctions, increasing the number of bidders results in more aggressive bidding. In situations where the number of potential bidders is unknown, auction or negotiation outcomes are strongly affected by bidders' risk neutrality or risk aversion28.
26Krishna (2002) shows that when facing many buyers, by setting a discriminatory reserve price the seller allocates the buyer with the highest marginal revenue, hence the highest virtual valuation. However, this is applicable to the optimal take-it-or-leave-it offers, and an auction without a reservation price finds the bidder with the highest valuation, as long as the bidder’s values come from the same distribution.
27When Ravenswood Winery decided to sell the firm two years after their initial public offering, the owners held an auction, and invited 5 potential bidders. After a period of due diligence, 3 bidders dropped out. Theoretically, we know that with 5 bidders the first price auction would generate revenues about the same as a multilateral negotiation (Thomas and Wilson, 2002). However, once the number of bidders dropped to two, negotiations would be more favorable to first-price auctions. Ravenswood at this time released the information about the competitors' bids and terms for the acquisition, creating a multilateral negotiation, hence maximizing its revenues from the sale.
44
Hypothesis 5: Entrepreneurial firms are more likely to choose auctions as market thickness and private value component increases.
Hypothesis 6: Entrepreneurial firms are more likely to choose negotiations as market thickness decreases, and the common value component increases.
Risk propensity assumptions, risk aversion and risk neutrality of sellers (as well as the bidders), are seminal for conclusions regarding the expected revenues. From the sellers' perspective, an entrepreneur when choosing a mechanism will probably consider whether alternatives will result in equivalent revenues. If the expected revenues were equivalent, he/she would be indifferent. However, the mechanisms will rarely have equivalent outcomes, which makes the mechanism choice more salient. The ownership and debt structure of the entrepreneurial firm provide important signals about the risk propensity of the owners/managers. Varying degrees of ownership and the incentive mechanisms in place alter the risk level in the firm.
From the buyers perspective, if the bidders are assumed to be risk neutral, then we would expect them to maximize their expected profits, whereas, risk averse bidders would maximize their expected utilities. With risk neutral bidders, the expected revenues from different auction mechanisms will be equivalent (Vickrey, 1961; Riley and
Samuelson, 1981). With risk averse bidders, they tend to bid above the risk neutral Nash equilibrium levels, and first-price and Dutch auctions generate more revenues than the
28Please refer to Harstad et al. (1990), and McAfee and McMillan (1987) for a detailed analysis of equilibrium bidding strategies in different auctions with varying risk propensities.
45 English or second-price auctions (Kagel et al., 1987). The revenue equivalence theorem also fails in private value auctions: first-price auctions generate higher revenues than
Dutch, and second-price auctions generate higher revenues than English auctions.
Hypothesis 7: As the level of institutional ownership increases, risk averse owners will prefer auctions (IPOs) to negotiations (M&As).
Hypothesis 8: As the level of institutional ownership decreases, risk neutral owners will prefer negotiations (M&A) to auctions (IPOs).
Search costs exist due to information asymmetries among parties to an exchange.
For each buyer, there is a cost associated with switching from one seller to another, therefore each search is costly. In spot markets, there is no reason to search for resources, since all that is available is priced the same, and the prices reflect all relevant information about a good. The market reaches a competitive equilibrium under perfect information where search costs are negligible, search is costless but delaying the bargaining is (Arrow and Debreu, 1954).
However, market for firms is far from this frictionless Walrasian space, and price formation for firms, and buyers' search strategies are interdependent (Rothschild, 1973).
Each seller and each product is different, and the buyers search for other outside options if one bargain fails. Within industry effects are instrumental in the search cost arguments.
Although information is released to all potential investors, some information is valuable or less costly to industry insiders, while the same information may have higher information costs associated with for outsiders. Hence, although the rents are low, some
46 buyers might stay in the market to negotiate or bid for a seller in order to avoid information processing and search costs (Salop and Stiglitz, 1977).
Hypothesis 9: The higher the search costs, an entrepreneurial firm will be more likely to be sold though an auction (IPO).
3.3 IPOs as Auctions
When an entrepreneur decides to go public through an equity offering, he/she is faced with two choices: bookbuilding versus fixed price contracts. In the bookbuilding model, the entrepreneur picks its IPO team, consisting of an investment bank
(underwriter), an accountant, and a law firm, in order to establish a detailed financial report history, going back at least two years before the offering. The team works on a prospectus, that includes all the financial data of the company for the past five years, information on the management team, and a description of the company's target market, competitors, growth strategy, etc. The prospectus is filed with the Securities and
Exchange Commission (SEC) and the National Association of Securities Dealers
(NASD) and reviewed for accuracy. The lead underwriter assembles a syndicate of other investment banks, allocates a certain number of shares to sell to their clients, and takes the entrepreneurial firm on a road show. This trip involves meetings with large institutional investors and banks. The underwriter tries to come up with a demand schedule for various price levels to determine the offering price.
If the entrepreneur chooses the fixed price method, the goal is to generate information cascades among early and late investors (Welch, 1992). This type of IPO
47 mechanism allows sequential selling in the market and this enables early investors to be informative for the late investors. The early investors have greater market power and can generate information cascades through their investments (Benveniste and Busaba, 1997).
The fixed price method has the potential to exploit the market by initially pricing the offering low enough to lure investors, hence causing a buying frenzy. When subscription decisions are made simultaneously, the winner's curse is avoided. The differences between the two IPO processes are major sources of variance for entrepreneurial equity financing in the world capital markets29.
The fixed price offering is a common value auction, and it is less attractive for
IPOs involving private values because the mechanism does not allow the seller to discover bidder's valuations. Historically the dominant approach in most of Europe, and especially in the UK (and the British colonies) has been the fixed price method
(Benveniste and Busaba, 1997). The fixed price method tries to discover the price without first soliciting investor interest. Investors make decisions based on correlated pieces of information about the true value of the stock which is revealed after the offering
(Welch, 1992). Since the bidders are unclear about the value of the item at the time of
29In fixed price offerings, share allocations can be either discretionary, or nondiscretionary. Especially in international markets, the rules for allocating shares vary greatly. For example in the UK, share allocations can be non-discretionary, in Germany discretionary, and in the US, best-efforts contracts. A best-effort contract is where the lead underwriter only agrees to do their best to sell the shares to the public. In a bought-deal, or firm-commitment, the underwriter buys all the shares from a company and becomes financially responsible for selling them. It is also up-to the underwriter to cancel an offering if all the shares are not subscribed for. For a detailed discussion on differences between international share allocation processes, please refer to Rock (1986) and Loughran et al. (1994). Also Sherman (2002) reviews the use of bookbuilding versus the posted price IPOs across markets. While both are technically auctions, the rules for the exchange are significantly different in both methods.
48 bidding, but receive signal values that are related to (affiliated with) the value of the item, judgment failures result in winner's curse (Kagel, 1995). An investor's purchasing decision is informative about his signal, and impacts the reservation prices of late-mover investors. When an investor ignores his reservation price, and purchases the stock, a cascade is formed. If the selling price is higher than first-mover's signal, a negative cascade develops, and all investors refrain from investing (Benveniste and Busaba, 1997).
To avoid this, the issues are more underpriced initially when compared to bookbuilding pricing.
The bookbuilding method on the other hand aims to secure honest responses from investors on the offer price and allocations. The underwriter tries to set an offer price that better reflects the aggregate market valuation. This aggregate information is revealed to potential investors; therefore no one individual investor can have an effect on the overall market valuation. Since the underwriter solicits non-binding indications of interest from institutional investors and other investment banks, the IPOs conducted through bookbuilding mechanism can be referred to as Vickrey auctions, where the bidders' best strategy is to bid their true values.
The marketing method used to auction off entrepreneurial firms - whether the issuer uses bookbuilding or fixed price - will have an effect over the outcomes. The choice between these two forms are dependent on the resource value, risk propensity of the entrepreneurial firm, the issue size, and the marketing costs. Bookbuilding generates higher expected proceeds, and provides an opportunity to sell additional shares at full price after the IPO, but exposes the issuer to a greater risk. On the other hand, the fixed price offerings guarantee the issuer a certain level of proceeds, but at a lower level when
49 compared to bookbuilding (Benveniste and Busaba, 1997). Neither strategy dominates, since the issuer acts on behalf of the entrepreneur, whose choice may be influenced in the first place by future financing concerns, considerable costs of the IPO, and the market trends which are highly cyclical.
Hypothesis 10: As the common value component increases, an entrepreneurial firm is less likely to be sold through the bookbuilding method.
Hypothesis 11: As the private value component increases, an entrepreneurial firm is more likely to be sold through the bookbuilding method.
3.4 M&As as Negotiations
Many management scholars treat mergers and acquisitions as auctions, where several bidders strategically bid to acquire another firm. Moreover, the US competition authorities use auction models to evaluate the impact of proposed mergers and determine the antitrust implications (Baker, 1997), and yet technically M&A activities are either bilateral or multilateral negotiations, and parties bargain to buy-sell corporations.
Furthermore, as stated before auctions are disadvantageous when there are bidders with interdependent valuations, which is the case for the majority of M&As considered as auctions. To avoid confusion and mislabels, I would like to define the terminology I will use in this context.
50 When an entrepreneurial firm puts itself on the M&A block, several interested potential buyers approach the firm, and bid to acquire the controlling rights of the target.
The seller has to negotiate a deal with the potential buyers, and this is called a multilateral negotiation30. If there is only one buyer and one seller, this negotiation is called a bilateral negotiation (Thomas and Wilson, 2002), which might be the case when the firm is so specialized in its assets, resource base or functions such that only one buyer can appropriate the full value given its unique characteristics.
The experimental results show that multilateral negotiations closely resemble first-price auctions and are outcome-equivalent with a large number of bidders, whereas transaction prices are higher with only two bidders in negotiations than in auctions
(Thomas and Wilson, 2002). In negotiations involving less than four buyers, multilateral negotiations yield higher revenues for the seller than first price auctions. However, when the number of bidders reaches four or more, there are no statistical differences with first price auction outcomes and negotiation outcomes. Two issues in M&A activities are critical for my analysis: uncertainty regarding the outcome of the acquisition, and uncertainty regarding the value of the target firm.
Negotiation outcomes are uncertain and bargaining is indeterminate (Roth, 1995).
From a cooperative game theory perspective there are a number of Pareto optimal outcomes that can be achieved, and from a non-cooperative perspective, parties to an exchange can reach a number of feasible outcomes based on their expected utilities
30 When two or more parties bargain over the division of a common surplus, this is called multilateral bargaining (Krishna and Serrano, 1996). For example, a merger or an acquisition as a result of a renegotiation between parties to a joint venture would in fact be an outcome of a bilateral (or multilateral) bargaining.
51 (Nash, 1950). In practice, firms may negotiate the sale of assets and/or equity stock, and the decision to sell or not may depend on a variety of factors. For example Seth and
Easterwood (1993) talk about managerial and shareholder interest alignment in the case of management buyouts.
Acquisition outcomes are also altered in the case of takeovers. For example, in the case of initial partial equity ownership (toehold), overbidding is an optimal rational strategy, but can lead to an inefficient outcome (Burkart, 1995; Hirshleifer and Titman,
1990). A toehold makes a bidder more aggressive, and it increases the winner's curse for a non-toeholder. But, owning a toehold helps a bidder win cheaply only if there are no other toeholds. If there are other toeholds of equal or larger sizes, prices will be higher, and bidders sometimes exceed their ex ante expected profits that would occur in ex post acquisition (Bulow et al., 1999). The uncertainty surrounding the potential outcomes of bargaining behavior increases the winner's curse potential for some acquirers. Such uncertainty also discourages risk averse owners/managers to choose negotiations as a mechanism.
Hypothesis 12: Entrepreneurial firms with partial equity owners are more likely to be sold through negotiations (M&As) than auctions (IPOs).
Empirical evidence suggests that markets for buying and selling companies are reasonably competitive, and an acquirer pays approximately the discounted present value of the target firm. If any, all above normal returns go to stockholders of the acquired firms (Porter, 1980). Firms can only generate above normal returns if the cost of
52 resources to implement product market strategies is significantly less than their economic value (Barney, 1986). This requires firms to exploit competitive imperfections in factor markets. In other words, valuation of a target firm requires heterogeneous expectations about its future cash flow.
There is an important difference between bidding a premium and overbidding. If the bidder overestimates the value of the firm after the acquisition when incorporating the ex post synergistic gains, he/she will be subject to winner's curse. Two problems need to be addressed: valuation error and managerial hubris. Assuming decision makers are rational31, those bidders that repeat acquisitions will learn from their past mistakes. It is also possible that `markets learn' as well as the individuals, as bankruptcies drive out aggressive bidders (Kagel, 1995) and individuals can learn by observing and adjusting their bids. However in bilateral bargaining games, observational learning is not possible since the bidders can only see the outcome of their own choices (Garvin and Kagel,
1994).
The hubris arguments on the other hand are strongly related to the assertion that all markets (product, financial and labor) are strong-form efficient (Roll, 1986). This may be true, especially for the takeover markets of public targets, where the bidder receives a value from the market, and all he/she has to do is to determine whether this value represents the true value or not. However the market for privately held targets is expected
31Tversky and Kahneman (1981) and Kahneman et al. (1982) show that decision makers are not always rational. While this may be true, I assume that grossly irrational individuals exit the market either by bankruptcy or by the owners.
53 to have strong information asymmetries due to the existence of private information among targets' insiders. The privately held target firm will accept at least its reservation price, which is a lower bound. If the bidder expects that there will be potential synergies after the acquisition, he/she makes an offer. If this valuation is too high, and in error, the markets observe only the right hand side tail of the distribution; since the left side is never revealed (the error is always in the same direction). In the case of private targets, the hubris arguments do not apply in full effect because most of the bidders do not have full information about the target at the time of negotiations.
Related to the above discussion, one might ask what exactly do targets sell and bidders buy. In essence, ownership and control rights between the entrepreneur and the acquirers exchange hands. Control rights matter because it is impossible to write comprehensive contracts, and as new opportunities come along, it makes a difference that makes investment decisions. Furthermore, the control rights over assets and equity influence the ex post division of surplus (Aghion and Bolton, 1992; Hart, 1988). Equity sale for the control of cash flow rights places the responsibility for immediate decisions on the hands of the entrepreneur (or manager); where as ownership rights over assets shift the ex post rent appropriation to the acquirer. The capital structure for raising funds are strongly influenced by volatility of cash flows, and the net present value of the projects the firm is undertaking. If the assets that are currently held by an entrepreneurial firm have similar foreseeable economic rents among a number of potential bidders, then the entrepreneur is more likely to sell the assets. Unless these assets or resources generate
54 heterogeneous expectations among the potential bidders, it is not possible to generate economic rents from the sale.
Hypothesis 13: As the common value component increases, an entrepreneurial firm is more likely to sell assets (ownership rights).
Hypothesis 14: As the private value component increases, an entrepreneurial firm is more likely to sell equity (residual cash flow rights).
3.5 Empirical Analysis
3.5.1 Empirical Design
The empirical predictions for the market mechanism choice can be described as the choice tree depicted in Figure 1, and are summarized in Table 1. It is argued here that an entrepreneur will choose to auction off his/her company to public investors, or negotiate to private buyers if five conditions provide a suitable environment for the transaction. Once the decision is made, the entrepreneur follows the deal through, or he/she can back down, but all actions will have transaction costs associated with them. If the entrepreneur negotiates the sale, the deal is either realized or not; if he/she decides to auction off the firm, he can choose either the bookbuilding methods or the fixed price contract method, but these choices are non-sequential. Whether an entrepreneur wants to sell his/her company using a specific market mechanism is an endogenous decision, but who buys the firm is not. In other words, for the seller deciding whether to auction the firm using an IPO or to negotiate through an acquisition is a deliberate choice. The choice whether the sale is to a public or private buyer depends partially on the seller's disclosure
55 preferences, as well as other exogenous factors. In the sections that follow, I will discuss the data, the variables, and the methodology.
3.6 Data and Sample Description
I constructed a panel dataset containing information on initial public offerings of private firms and mergers and acquisitions of private targets, across time, industry and country, using four different databases. For the initial public offerings, I used the Global
New Issues Database maintained by the SDC Thompson Financial Securities. The data from this source covered private companies that issued common stocks between 1971-
2000 in the US, 1991-2000 in Continental Europe both Euro and foreign common stock.
Unit offers, ADRs, closed-end funds, REITs, bank and S&L IPOs are excluded from the sample.
From SDC, I obtained the M&A data from Mergers & Acquisitions Database for both US and Foreign targets, with private sellers (targets). Recapitalizations, privatizations, self-restructurings, and leverage-buyouts are excluded from the sample. In practice, although some deals are announced, it is sometimes the case that these deals are not realized; hence I checked the accuracy of the SDC data using the DoneDeals
Database from NVST, Inc. (Private Equity Network). The DoneDeals data is available from 1994 to 2000, so for the period 1991-1994, I used the LexisNexis search engine to randomly verify the announcements. The DoneDeals data reports mid-market M&As of consummated transactions with transaction values between $1Million-$250Million. The database provides both private and public sellers, but public sellers are excluded from the sample.
56 From the Global New Issues Database, for the following regions, initial public offering information is provided (Table 4): The United States market-US domestic public offerings of common stock for private companies, 1970-2000. The EuroMarket-common stocks sold in the EuroMarket, including foreign market issues, 1983-2000. The
Continental European market-common stocks sold in most major Continental European nations, 1991-2000. The countries included are: Austria, Belgium, Bulgaria, The Czech
Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy,
Luxemburg, Netherlands, Norway, Poland, Portugal, Slovakia, Spain, Sweden, and
Switzerland. The International market (1983-2000), and the Domestic UK market for the public offerings of common stock in England, 1989-2000. Rest of the World - common stock sales of private companies from Australia and Asia Pacific, 1984-2000.
For the mergers and acquisitions of private companies of both US targets and non-US targets, I used the Worldwide M&A database (Table 3). The domestic US targets sample covers firms between 1979-2000, and for non-domestic targets, between 1985-
2000. I separated the M&A dataset into two samples: stock sale and asset sale samples. A firm is considered to conduct a stock sale if: a combination of business takes place or
100% of the stock of a private company is acquired (merger), a deal in which 100% of a company is spun off or split off (acquisition), the acquirer holds less than 50% of the company's stock, and seeks to acquire 50% or more but less than 100% of the total number of stocks (acquisition of majority interest), the acquirer holds less than 50% of the company's stock, and seeks to acquire less than 50% or less than 100% of the total number of stocks (acquisition of partial interest), the acquirer holds over 50% of the stocks, and seeks to acquire 100% of the rest (acquisition of the remaining interest).
57 For the asset sale sample, a firm is considered to conduct an asset sale if: the assets of a company are acquired (acquisition of assets), a certain portion of the total assets is acquired (acquisition of certain assets). From both samples leveraged recapitalization such as one-time dividends, preferred stock or debt securities sales are excluded. Also, if a company buys back its equity securities or converts its securities on an open market (either through private negotiations or through a tender offer), these transactions are excluded from the sample.
Finally, I obtained the patent data from the National Bureau of Economic
Research (NBER) for the period 1963-1999. The dataset (PAT63_99) is complied by
Hall et al. (2001). I matched the patent data with the Compustat data using the assignee
(number). The patent dataset includes all utility patents in the USPTO's TAF database, and has 2,923,922 observations. Apart from the ten original variables issued by the
USPTO, I also used ten other variables constructed by Hall and colleagues (Table 5). The sample does not cover minor patent categories (design, reissue, and plant). In the data there are seven categories of assignees: unassigned (not yet granted, 18.4%), US corporations (47.2%), Non-US corporations (31.2%), US individuals (0.8%), Non-US individuals (0.3%), the US government (1.7%), non-US governments (0.4%). I excluded firms without cusip numbers, all US government and non-US government patents.
The majority of the transactions both in the M&A and IPO markets appear to have less than $300 Million in value (Table 6-PanelC). The majority of the mid-market M&A transactions involve private targets (82.5%) (Table 2-Panel A). The manufacturing sector has the second highest number of transactions (27.8%) after the services sector. These private targets on average have significantly lower total assets in $Million when
58 compared to public targets and subsidiaries (Table 2-PanelB). This is consistent with the argument that entrepreneurial firms are more likely to be smaller in size. In mid-market
M&A transactions involving private firms, the acquirer pays a higher premium for purchasing equity as opposed to buying the underlying assets (Table 3-Panel B). On average, acquirers of pay a price that is 7.58 times larger than the total assets of the target when the transaction involves equity. On the other hand, acquirers pay a price of 4.89 times larger than the total assets of the target when the transaction involves assets.
For the manufacturing sector the median offering price is similar to the median offering prices observed in other sectors except in mining (higher) and wholesale trade
(lower) between 1975-2000 (Table 4 A-B-C). Additionally, there is evidence of cyclical time trend regarding the average offering price in the manufacturing sector. The period between 1981-1990 experienced lower average offering prices when compared to the
1990s. In the final sample, I focused on manufacturing sector since approximately 32% of all IPOs are in manufacturing, followed by 25% in services. The majority of the IPOs are from the US (93.3%). The countries in the sample are listed in (Table 6-Panel B). The top three manufacturing industries represented in the sample are electronic and other electric equipment (20.6%), instruments and related products (17.2%), and chemical and allied products (16.3%).
3.7 Variables
In examining the discrete choice of the entrepreneur between the market types and mechanisms, in this section I focus on explanatory and control variables. The explanatory
59 variables are bargaining power, market thickness, resource value, risk propensity and search costs. The control variables are industry related characteristics, market-timing factors, other deal-specific factors, and financing factors. In the following subsections, I discuss the possible impact of these factors on the mechanism choice of the seller.
3.7.1 Explanatory variables
Bargaining power. Entrepreneurial firms gain bargaining power as they gain expertise and experience in the market, and as they accumulate assets. These firms also gain advantages in negotiations and signal uniqueness if they hold a patent. Especially in the biotech industry, firms that have formed alliances are more likely to negotiate a deal with their alliance partners. However, this does not mean that an entrepreneurial firm will always chose to be acquired. If the firm's goal is to generate capital, hence sell a minority equity stake, an IPO may be more feasible. In this case, firms that have formed alliances will have a higher propensity to IPO than firms that have not formed alliances (Stuart et al., 1999). Hence patents, prior related deals and the same industry dummy are used to establish bargaining power of the entrepreneur.
I use the number of patents prior to the deal to identify entrepreneurial firms' bargaining power32. I expect a positive relation between the number of patents and the bargaining power of the seller. Second, the same industry dummy shows if the target and the acquirer are in different 2-digit industry codes. If the target's industry is different than
32Patents grant temporary monopoly rights in exchange for disclosure, and this clearly results in increased bargaining power for the owner of the patent. Some firms sell their patents through intermediary firms. These firms provide a secondary market for patents that are not used by their inventors, both through auctions or negotiations. One of these intermediary firms (yet2.com) commented in an interview that the majority of the acquisitions were conducted through negotiations. 60 the acquirer, I expect the target's bargaining power to be higher. Finally, prior relationships (toehold by the bidder, and related prior deal dummy) are used: if the acquirer has a toehold, then the target has higher bargaining power. The prior deal dummy (yes or no) will have the same positive relationship. To account for firms' ex ante business performance, return on assets (ROA) is also used to proxy bargaining power33.
Market thickness. From the perspective of the entrepreneur, the number of bidders
(buyers) is critical. In most of the auction literature, the number of bidders n is determined exogenously. If the seller prefers one institution and fixes n , the bidders have less incentives to enter the auction in the first place (Levin and Smith, 1994). When the entry varies stochastically (Harstad et al., 1990) or is endogenously determined, this has an impact on the coordination costs associated with incentives. Experimental results provide interesting comparative static predictions when the number of bidders is varied and is common knowledge, and when the number of bidders is unknown. With constant or decreasing absolute risk averse bidders, expected revenues increase if the number of bidders is concealed. In second price auctions, the number of bidders in the market has no effect on expected revenues, and in third-price auctions result in lower expected revenues for the seller (Kagel and Levin, 1993). Also, even if bidders are bidding for a resource based on their private values, if they are within the same industry and they repeatedly bid
33The choice of selling for the firm hence will be strongly affected by the prior history of forming alliances, and its innovative performance. If an entrepreneurial firm has operating experience (Baum and Ingram, 1998), competitive experience (Barnett et al., 1994), or collaborative experience (Anand and Khanna, 2000), it will gain skills to coordinate and manage them (Sampson, 2002), and these will be a source of bargaining power.
61 for the same resources, their values are going to be correlated, hence the private value component would decline34.
I use the number of firms in target industry prior to the deal, the number of offers considered and the number of offers sought to measure how thick the market was for the sale.
Resource value. Empirically, it is very difficult to find pure common or private value goods. Instead, goods will have a combination of both common and private values.
For the purposes of this paper, I will refer to a resource with more common values than private, a common value resource, and more private values than common, a private value resource. I group firms in high tech versus not35, and proxy the private component of the firms' innovative capability. Firms in high-tech industries such as biotech, computer equipment, electronics, communications, and general technology accumulate specific know-how and intellectual capital that are unique in their industries regardless of their patents. Most of these patents in high tech industries will have private value components then common. Low tech industries on the other hand, report R&D spending, which is not
34With risk neutral bidders, small amounts of correlation in joint distribution of private information among competitors renders private information useless, and these cannot be a source of rents. Please refer to McAfee and Reny (1992) for a detailed discussion.
35The industry type of the firms also affect market reaction. Especially with high tech firms, it is shown that an IPO is more likely regardless of any existing positive earnings (Maksimovic and Pichler, 2001). Hence, firms are categorized as high tech versus low tech using classifications provided by the SDC. High tech industries would be biotechnology, chemicals, communications, computers, defense, electronics, medical, and pharmaceuticals.
62 a good measure of the output of their R&D activities (Griliches, 1990). This grouping is consistent with the argument that firms that are in `complex product industries' (high- tech) use patents to force rivals into negotiations, whereas, firms in `discrete industries'
(non-high tech) use patents to block the development of substitutes by rivals (Cohen et al., 2000). By definition, common value components would be most easily replicated or replaced by competitors.
One of the measures I use to determine a firm's resource value is Tobin's Q.
Tobin's Q has been shown to measure capitalized value of monopoly rents (Ross, 1981), and was correlated to intangible capital (Griliches, 1981; Cockburn and Griliches, 1988).
Intangible capital represents the major source of private values in firm valuation since outsiders will have heterogeneous expectations about a firm. This is especially the case with small firms in high-tech industries (Himmelberg and Peterson, 1994; Megna and
Klock, 1993).
Nature of the patents provide a very unique opportunity to proxy for the value components of a resource. I use originality of the patent, generality of the patent, and the industry cluster to measure the resource value. In the sample, the `originality' versus
`generality' of patented innovation, provides basis for the resource's common and private value components. An original patent will have heterogeneous expectations associated with it, and the interested bidders most likely will have diverse uses, whereas with a general patent, the signals about the true value of the patent will be closely affiliated36.
36The generality component of the patent refers to forward citations as indicative of the impact of the patent. The originality component refers to citations made to; indicative of the depth and breadth of technologies it uses (Hall et al., 2001). This is similar to the weighted patent citation Sampson (2002) uses. She assigns a weight to each patent using citations made by later patents to identify the technological 63 Hence I use the Trajtenberg et al. (1997) measures. According to this measure, if a patent is generalizable, it will have a widespread impact by influencing subsequent innovations37.
The originality of the patent will render it only applicable to a narrow set of technology applications (Hall et al., 2001). Generality measure is given by:
Generality 1 n i s2 i j ij ; where sij is the percentage of citations received by patent i that belong to patent class j , out of ni patent classes. The aggregate is the
Herfindahl concentration index (Hall et al., 2001). Due to the count nature of the underlying data, generality and originality measures are downward biased when the citations for a patent are small (e.g. for patents granted later in the dataset between 1963-
1999). I use the unbiased estimators for both measures correcting for the bias as provided in Hall et al. (2001: 43-46).
Risk Propensity. In an entrepreneurial firm, if the owner's or managers' reputations or wealth are affected by firm performance, their actions will be influenced by their attitudes towards risk; and their propensity to take risks (or avoid them) signal other firms and investors information about the value of the firm, and its future prospects
lineage of the invention. An alternative approach to this valuation problem is by studying the number of subclasses into which the patent office assigns the patent as in Lerner (1994). Lerner found that an increasing scope of patent is associated with higher valuations. In other words, a lot of firms found the patent valuable, and applicable to their existing resources and capabilities. Thus, if they were to bid for this resource, they would be bidding for a common value resource, and the highest bidder would most likely be subject to winner's curse.
37Firm's innovative performance is commonly measured via its citation count (e.g. Sampson, 2002). The citations of previous patents identify technological lineage, and there is strong correlation between the value of the invention and its citation count (Hall et al., 1998).
64 (Blazenko, 1987). When the fractional ownership of a firm is retained by the entrepreneur, this is a signal of firm quality38. As the fraction increases, the value of the firm increases, and if the entrepreneurial ownership is sufficiently great, increases in ownership are associated with increased debt (Leland and Pyle, 1977). In other words, if managers are sufficiently risk averse, they signal low firm value with equity, and high firm value with debt. In the Modigliani-Miller paradigm where capital markets are frictionless, all individuals have homogeneous expectations and can borrow and lend at a risk-free rate, they do not pay taxes and bankruptcy costs do not exist; shareholders are indifferent to capital structure but managers prefer equity (Blazenko, 1987). Risk averse managers will try to avoid debt because increased debt would increase the total risk of share ownership. Owners (if they are not managing the firm), can tie the managerial performance to equity performance, and risk averse managers will prefer equity over debt.
To measure risk propensity of the firm, I use the debt/total assets and venture capital involvement. If there is investor involvement, the number and the concentration of shareholders will also influence risk propensity of the owners/managers, thus the mechanism choice. If the manager is the owner, and a tender offer is made to takeover the company, an acquisition is more likely to occur (Buchholtz and Ribbens, 1994). When the entrepreneur is also the manager, the principal and the agent are the same, and the
38Moral hazard problems that are associated with asymmetric information structure between the firm and the investors are discussed in detail in Grossman and Hart (1982), and in agency perspective (Jensen and Meckling, 1976).
65 nature of incentives change. Further, if the number of shareholders are small, the owner's incentive to accept an acquisition is not fully aligned with the small number of shareholders (Alchian and Demsetz, 1972). Small number of shareholders would prefer to offer the stocks through an auction.
In a general equilibrium setting, it was argued that given uncertainty, risk neutral and risk seeking individuals would become entrepreneurs, and risk averse individuals would become laborers (Kihlstrom and Laffont, 1979). This assertion assumes that all potential entrepreneurs are equally able and industrious, which is a highly restrictive assumption. Relaxing this assumption, it was argued that risk averse entrepreneurs would sell to risk neutral investors if they thought they would get full value for their ideas (Amit et al., 1990). This raises the issue on how the entrepreneurial firm is financed, and what kind of signals this structure has on the expected revenues. Firms that signal quality and high future returns through venture capital funding and endorsements are more likely to conduct IPOs (Shane and Stuart, 2002).
I intentionally use entrepreneurial firms and the entrepreneur as the decision maker and study entrepreneurship as a function of actions39. Consistent with the agency perspective, an individual will shy away from risky modes, hence, in the case of selling his company, he/she will prefer auctions to negotiations since negotiation outcomes are
39This approach is consistent with Busenitz and Barney (1996). Further, a distinction between an ‘individual’ versus ‘team entrepreneurs’ needs to be made. It is argued that the team entrepreneurial forms will exhibit more variation in performance than will individual entrepreneurial forms, ceteris paribus because of risk aversion (Mosakowski, 1998). This argument is closely tied to risk aversion (Fama and Jensen, 1983) and shirking (Jensen and Meckling, 1976).
66 difficult to determine ex ante. On the other hand, as the ownership claims decline, entrepreneurs will decrease their efforts, unless the owner disperses hierarchy (and the residual risk) to a small number of team entrepreneurs. Ownership and debt structure variables are used to test the effects of risk propensity on the market mechanism choice.
Search costs. In this paper, search costs are lower for IPO selling than the acquisition option. When an entrepreneurial firm is to be acquired both the search for the firm and the time to negotiate are cost items, whereas, the IPO process reveals more information about the firm to all interested potential buyers publicly. Related to both bargaining power and search costs, empirical evidence on biotech firms show that biotech-pharma alliances are subject to both dynamics. While small entrepreneurial firms do come up with new innovations, it is more likely to pass the clinical trials if they form alliances with pharmaceutical firms with heavy discounts; and that these discounts are rational (Nicholson et al., 2002). Further, these biotech firms receive substantially higher valuations from venture capitalists and the public equity market after the alliance. Hence, an alliance in the first period lowers the search costs of the sellers and the buyers, and increases rents of the pharmaceutical firm which has a higher bargaining power. In the second period, the formed alliance increases the bargaining power of the entrepreneurial firms and they are more likely to send a positive signal to investors.
Another item that the entrepreneurs use to signal quality and value of their firm
(thus lower the search costs of the buyers) is the investment bank they use as underwriters. IPOs underwritten by high prestige investment bankers yield smaller initial underpricing and less negative long-run returns than the IPOs underwritten by lower
67 reputation investment banks (Michaely and Shaw, 1994). Similarly, IPOs backed by venture capital outperform nonventure backed offerings in the long-run (Brav and
Gompers, 1996). To incorporate the fixed effect of the lead underwriter, I use the Carter-
Manaster (1990) rankings for the investment banks40.
I proxy for search and coordination costs by using the number of advisors of the target, the lead underwriter, and whether the industry of the focal firm is one where venture capital firms are actively involved or not.
3.7.2 Control variables
Industry related factors. The model used in this paper will be tested using manufacturing sector. Industry membership is relevant because of the regulatory environment or industry concentration level. Therefore I use an industry dummy to control for industry specific effects. In the risk propensity measures above, I use firm's debt level as a proxy for risk preferences of the owner/managers. To control for industry wide effects, leverage ratios of firms across the same industry are proxied to give a normal debt level (Harris and Raviv, 1990). This is important to distinguish the differences between high debt/equity firms from the rest across the industry. Some industries are more appealing for IPOs whereas others are targets for acquisitions. Similar to the leverage ratios, the industry market-to-book (M/B) ratio is calculated to control for
40Several proxies for underwriter reputation exists in the IPO literature, e.g. Logue (1973); Beatty and Ritter (1986); Johnson and Miller (1988); Megginson and Weiss (1991). Due to its comprehensive nature and explanatory power for initial returns, I use the Carter-Manaster measure for 117 underwriters. Please refer to Carter et al. (1998) for a comprehensive discussion on investment bank reputation measures.
68 industry characteristics as a baseline for each firm within the same group. In industries with high M/B, IPOs are more likely to occur (Pagano et al., 1998), whereas low M/B attracts takeovers (Brau et al., 2001).
Market-timing factors. In market timing theories of Lucas and McDonald (1990), and Choe et al. (1993), firms delay their initial public offerings, either because they are undervalued or because there are other competing IPOs being issued with favorable pricing. Apart from these rational models of market timing, Ritter and Welch (2002) provide an alternative explanation for semirational perspective without asymmetric information, incorporating the lag the entrepreneurs use to adjust their valuations. On the other hand, the cyclical nature of the public offerings results in a time trend, where entrepreneurs cluster the offers predominantly over the same time frame (Ritter, 1984;
Lowry and Schwert, 2002). To proxy for the market cycle, I use the return on the stock market, and the volume of public offerings versus acquisitions to establish the hot versus cold markets.
Other deal-specific factors. Firm and deal size are two factors that need to be controlled for various reasons. First, firm size is argued as a clear indication whether a firm can compete after its initial offering (Pagano and Roell, 1998). Second, the process of initial public offering involves very high fixed costs (Ritter, 1987). If the firm is pursuing a bookbuilding method, it involves further costs for trips to potential institutional investors41. Small private firms will prefer less costly alternatives to high
41Several studies have demonstrated the trade-off between the costs and benefits of going public (Pagano, 1993). On average, registration and underwriting costs are 14%, and the underpricing costs are 15% of the funds raised during an IPO (Ritter, 1987). Further, Jensen and Meckling (1976) and Zingales (1995) discuss costs resulting from agency problems associated with the separation of ownership and control. 69 cost alternatives (Holmström and Tirole, 1993). Third, insider ownership and changes in the ownership-control structure has a potential effect on the choice of entrepreneurial firm's sale type.
In an acquisition, the controlling stake changes hands, but in an IPO, the entrepreneur still controls the firm if the owners design the offering or acquisition as such. Owners will prefer public offerings for smaller liquidity, and acquisitions for maximum liquidity and completely cash out (Zingales, 1995). To proxy for firm and deal size, three measures are used to accommodate different industry norms. For example in the biotechnology industry, most firms do not reach the marketing stage of their innovations; hence the number of employees is adequate to establish firm size (Shan,
1990). On the other hand, some industries invest heavily in their initial setup especially in manufacturing and transportation industries, hence proxies for scaled transaction values on the total assets are more appropriate measures for firm size (Pagano et al., 1998).
Hence, in the sample firms are controlled for the total assets, number of employees, and sales.
Other control variables. Deal year, and nation dummies are used to control for time and institutional environment fixed effects. In some models lagged deal year dummies are used to control for market adjustment lag effects.
3.8 Statistical Method
When an entrepreneur decides to sell his/her company, he is faced with a series of single decisions among two or more alternatives. Each of these alternatives are mutually
70 exclusive and are unordered. In other words, the focus of this empirical study is the decision after the entrepreneur decides to sell, and when he sells, he/she can either sell to another firm through negotiations, or he/she can auction off the firm to a number of public investors. Hence, I use a modified unordered conditional logit model. In a standard conditional logit model, it is assumed that the variability in scores for one variable is roughly the same at all values of the other variable, which is related to normality. When we relax the homoscedasticity assumption, we can group the alternatives into subgroups with their variance differing across and maintain the independence of irrelevant alternatives assumptions42 (IIA) within the groups. This slight modification of the stochastic specification in the original conditional logit model defines a nested logit model (Greene, 1997).
Originally developed by McFadden (1979, 1981), the nested logit model maintains the dependence among alternatives between levels of the nest, with the equal pattern of dependence occurring within a level of the nest43. This approach links different but interdependent decisions, and also decomposes a single decision so that the potentially restricted condition of cross-alternative substitution is minimized. The nested logit model is built around an inclusive value whose parameter provides a basis of
42The IIA assumptions implies that the odds-ratio between two alternatives does not change by the inclusion of any other alternative.
43In estimation of discrete choice models the major issues arise due to computational intractability for more than three alternatives (Maddala, 1983; McFadden, 1981). Nested structures solve these problems for single choices around many alternatives, and can be estimated either sequentially, or simultaneously. Please refer to Hensher (1986) for a detailed discussion on nested structure estimation.
71 identifying the behavioral relationship between choices at each level of the nest and also registers as a test of the consistency of the structure with utility maximization (Hensher,
1986). The alternative method to the nested model is an unordered choice model motivated by a random utility function, which works if and only if the choices are independently and identically distributed with Weibull distribution (McFadden, 1973).
Although there are theoretical differences between the two methodologies and the distributions at the extreme ends of the two tails, the choice of one over the other makes no difference in practice44 (Greene, 1997).
In order to construct the nested logit model, I start with L alternatives (auction vs. negotiation) divided into J subgroups (asset or stock sale, bookbuilding or fixed- price contracts). The entrepreneur first makes the L choice, and then makes specific choices within the set. The choice set is:
c1,...... ,cJ c11 ,.....,cJ11 ,...... ,c1L,...... ,cJLL . The data consists of observations on the attributes of the choices x jl (variables for explaining mechanisms in the second level) and attributes of the choice sets zl (attributes of markets in the first level). First, using the unconditional probability, we define an inclusive value for the l th branch:
44Both probit and logit use the same scores and the Hessian matrices. The estimator differences depend on how the probability functions differ, hence the tails are differentiated. Please refer to McKelvey and Zavoina (1975) for a detailed analysis of the ordered probit model.
72 e xjl zl Probmechanismj,market typel Pjl L Ji e xjl zl l1 j1
Jl L e xjl e zl x z e jl e l j1 l1 Pjl PjlPl Jl L L Jl e xjl e zl e xjl zl j1 l1 l1 j1
Jl xjl Inclusive value for the lth market Il ln e j1
45 When the term is simplified using a parameter l, the nested model is constructed. When the parameter l equals 1, the nested logit collapses to the original conditional logit model. The choice tree depicted in Figure 1 does not necessarily imply that firms make market mechanism choices sequentially. Instead, this figure reflects correlation patterns among unobservable variables across market mechanisms. Following
45The simplification follows the general rule: Pjl PJ L PL.
L Ji L xjl zl e xjle zl e zl P e ; e lIl jl Ji Pl L xjl z l1 j1 l1 e e llI l j1 l1
73 Greene's notation (1997), the derivatives describe covariation of the attributes and probabilities;
∂ ln Prob[mechanism ,market ] j l = ∂x(k) in mechanism J and market L
{1(l = L)[1( j = J ) − P ] +τ [1(l = L) − P ]P }β j | l L L J | L k
Nested logit models can be estimated using either limited information, two-step maximum likelihood approach (SML), or by full information maximum likelihood method (FIML). Since I am using a large sample that accommodates the parameters, the sequential approach was not used. Also, since the information matrix is not block diagonal in and , the FIML estimation was more efficient than sequential approach (Greene, 1997).
n ln L lnProbmechanism market Probmarketi i1
One critical drawback of the nested logit models is that when partitioning the choice set, the results might be dependent on the branches. Although this issue with the model can be resolved by relaxing the homoscedasticity assumption of equal variances, it is not a concern in this study46. This is mainly because the partitions are very clear, and
46Please refer to Bhat (1995) and Allenby and Ginter (1995) models that address this issue.
74 the choices are publicly announced. Even when the transaction involves private targets and acquirers, all choices are mutually exclusive, and recapitalizing firms are excluded from the sample.
The objective function Ve, is value maximization by the seller. For private entrepreneurial firms, the actual maximization function is equivalent to the maximization of entrepreneur's private value of the firm; where ai are equity financing alternatives, s C s1,...... ,sS are selling alternatives, N are the number of firms in the sample,
V is the value, firm f , entrepreneur e , financing alternative a, V e,s is a function of firm characteristics, and e,s is the error term which absorbs omitted firm characteristics and idiosyncrasies.
Ve,s V e,s e,s for all e e1,....eN
The selling choices can be partitioned into n disjoint subsets at two levels:
i `main' market type. Auction (IPO) versus negotiation (M&A), l L, and
ii `sub-method' market mechanism. Bookbuilding, posted-price contract, asset sale, stock sale, j J; such that a particular lower-level method is denoted as:
se,l,j L J and Ve,l,j V e,l,j e,l,j
75 I assume that V e,l,j is linear in firm characteristics and motives, and is additively separable into two parts. One part represents the value of a particular market type (auction vs. negotiation) and the other part represents the value from a particular market mechanism (bookbuilding, posted-price, asset sale, stock sale). I solve:
Ve,l,j Ae,l Be,l,j e,l,j
where and are parameter vectors to be estimated. A is the vector of explanatory and control variables for the main market type (auction vs. negotiation) and
B is the vector of explanatory and control variables for each market mechanism
(bookbuilding, posted price, asset sale, and stock sale).
3.9 Results
The choices discussed in this paper are discrete and not continuous, the error terms are heteroscedastic, and the dependent variable (the choice) is not normally distributed. I assume that the decision makers choose the alternative from which they derive the highest utility; therefore I use the random utility maximization (RUM) model.
The RUM outcome probabilities are based on utility difference only, and since the models normalize the utilities, the scales need not to be identified (Heiss, 2002). First, I ran three separate regressions: a standard logit, a conditional logit, and a multinomial logit regression between possible decision combinations for robustness checks and to verify the statistical conditions for using the nested logit model. The main results of these models are presented in Tables 8, 9 and 10.
76 To run nested logit tests, I generated two categorical variables to identify first- level alternatives (IPO, M&A), and the second level alternatives (book, fixed, asset, stock). The results of the nested logit model with multiple specifications for robustness are presented in Table 11-Panels A and B. I have discussed earlier the theoretical reasons why the separation of first and second level alternatives were required. From an econometric view, this separation is important especially for the auction markets. If there is unobserved auction heterogeneity, the predictions regarding auction markets would be inaccurate. This problem arises when the researcher ignores the possibility or the fact that some of the bidders at the time of the auction has additional and private information available (Krasnokutskaya, 2002). When this argument is applied in the context of IPOs versus M&As, it becomes necessary to differentiate IPO mechanisms as bookbuilding versus fixed price, because of differential information structures. A similar distinction can be made for asset sale versus stock sale. In M&As, sources of value creation vary depending on the acquisition strategies and types (Seth, 1990). An unobserved heterogeneity in acquisition types arises due to differences in information structures of the bidders such that information available to potential bidders will be asymmetrical.
In the conditional logit model the branches are assumed to be not correlated, and the main effects are due to the choice-specific attributes. In the multinomial logit model individual-specific set of probabilities for a set of choices are considered. The multinomial logit is testing single choices involving many alternatives, and this is resulting in a cross-alternative substitution (Hensher, 1986). As in the conditional logit model, the assumption of IIA should also hold for the multinomial logit model to have unbiased estimates. Furthermore, due to the globally concave likelihood function, the
77 maximum likelihood maximization is straightforward, assuming independently distributed error terms. In other words, when the error terms for alternatives are correlated, the conditional logit estimates are biased. Hence by using a nested-choice approach, I am decomposing a single decision and minimizing cross-alternative substitution.
In the multinomial logit regression, the IIA is tested using Hausman and Small-
Hsiao tests47, for the null hypothesis that the odds for outcome j vs outcome k are independent of other alternatives. In both multinomial and conditional logit regressions, we cannot reject the null, and therefore cannot collapse the decision tree. For all the models of multinomial logit regressions (Table 10) additional Wald tests for combining outcome categories are conducted (not reported). The Wald tests for all pairs are conducted to test the null hypotheses of all coefficients except intercepts associated with given pair of outcomes are zero (i.e., categories can be collapsed) and all the hypotheses are rejected at the 1% significance level.
The results of the multinomial logit are presented in Table 10. For the model estimates to be unbiased, the IIA assumption should hold. Although the estimates and therefore the model have significant explanatory power, based on the theory discussed in this paper, we would expect the mechanisms to be strongly correlated into nests (IPO
47In the Small-Hsiao (1985) test for the IIA assumption, if the IIA condition holds, the maximized log- likelihood for the restricted choice set will not be too different from the log-likelihood computed over the restricted choice set using parameters obtained from the full choice set. The test statistics is asymptotically chi-square distributed with degrees of freedom equal to the number of parameters. On the other hand, Hausman-McFadden (1984) test requires that the parameters of the restricted set model is approximately the same as those of the full choice set model.
78 versus M&A). Overall, the test results suggest that the null hypothesis of IIA should be rejected at the 1% significance level. This result also warrants the use of a more complicated nested model that does not restrict the choices to be uncorrelated.
Two problems need to be addressed when working with large panels of cross section data: missing data and self selection (Griliches et al., 1978). In this dataset, a number of observations were missing from the manufacturing sector sample. Since the observations were missing randomly, using a subsample resulted in unbiased but inefficient estimates. To test for omitted variables and the inclusion of irrelevant variables, I conducted a likelihood ratio test. The test showed that the model has a strong fit. Furthermore, the Wald statistics showed a strong model fit as well. To test for multicollinearity I checked the correlation coefficients. In a nested logit, the presence of multicollinearity does not lead to biased coefficients, but the standard errors of the coefficients will be inflated. To interpret the coefficients of the continuous independent variables, I used the odds ratio and tested significance by using Wald statistic. Consistent with the nested logit method, I used McFadden's likelihood ratio index (LRI), which is the pseudo R 2 .
The results of the logit models for the choice of auctions versus negotiations are presented in Tables 8-Panels A, B, and C. If we look at Table 8-Panel A, which presents the results of the logit model for the choice between IPO versus M&A, both Tobin's q and high-tech dummy are negative and statistically significant. This result fails to supports hypothesis 3 such that as the private value component increases, the entrepreneurial firms' probability of being sold though an auction increases. Although the
79 sign of the Tobin's q stays the same in the conditional logit models presented in Table 9, the sign changes in the multinomial models as well as the nested logit models presented in Tables 10 and 11 (Panel A and B) supporting hypothesis 3.
According to the multinomial logit results (Table 10), an increase in Tobin's q increases the likelihood of choosing fixed price over bookbuilding, as well as asset sale over stock sale. According tot he nested model results (Table 11), high-tech dummy has a positive and significant coefficient for the fixed price, which suggests that for firms in high-tech industries, the probability of choosing fixed over bookbuilding is higher than the firms in low-tech industries.
In Table 8-Panel B presents the base logit model estimates for the discrete choice of auctions versus negotiation. Patent dummy variable is used to proxy the bargaining power of the entrepreneurial firm. The coefficient estimate of the variable patent dummy is statistically significant with the p-value <0.001. Entrepreneurial firms' probability of being sold through an auction (IPO) is decreased if the firm has a patent. This result is consistent with the theoretical prediction that firms that have some degree of bargaining power will choose to negotiate the sale of their firms through an M&A.
As presented in Panel C, the coefficient of patent originality as a proxy of private value component of the resource value is statistically significant with p-value <0.0001 in explaining the probability of choosing auctions to negotiations. As the originality of the patent increases, the probability that the entrepreneurial firm will choose auctions to sell the company through an IPO increases. Therefore, hypothesis 3 is supported. On the other hand, the coefficient of patent generality as a proxy for the common value component of the resource has a negative sign as expected, and is significant with p-
80 value=0.0007. As the generality of the patent increases, the probability that the entrepreneurial firm will choose auctions to sell the company through an IPO decreases.
Therefore, hypothesis 4 is supported. In both models, as expected, the time fixed effects are statistically significant. This finding supports the established literature on the cyclicality of the IPO and M&A activities. Also, the findings using patents are consistent with Gans and Stern (2000), where they look at when technology will be commercialized via licensing versus a startup venture. They get a similar result that having a patent makes licensing (acquisition) more likely than startup (IPO).
Table 9-Model D provides the better conditional logit model with log-likelihood of 885.718 . Explanatory variables associated with the resource value are Tobin's q, and high tech dummy. In both models Tobin's q is significant for all mechanism types.
However, increase in Tobin's q decreases the likelihood of choosing the fixed price mechanism over bookbuilding, whereas the same variable increases the likelihood of choosing either asset or stock sale over bookbuilding. High tech dummy variable is positive and significant for all mechanisms in Model D, but insignificant as an explanatory variable for the likelihood of choosing fixed price over bookbuilding in
Model C.
Bargaining power as proxied by ROA for the whole sample is negative and statistically significantly associated with the likelihood of choosing asset or stock sales over bookbuilding as presented in the conditional logit model results in Table 9. ROA has no statistically significant explanatory power in explaining the likelihood of choosing fixed price versus bookbuilding. Overall increase in ROA is associated with an increase in the likelihood of choosing IPO over M&A as presented in the logit model exhibits in 81 Table 8-Panel A. However, according to the nested logit model results the coefficient of return on assets is insignificant but negative for explaining the choice between fixed versus bookbuilding. According to the multinomial logit regressions in Table 10-Model
D, ROA has negative and significant coefficient estimates for the choice between fixed price IPOs versus M&A. Overall, the results suggests that ROA is not a good predictor for the first level choices, but provides a strong support for the theoretical distinctions between fixed price offerings versus M&As; hence support for hypothesis 1 and 2.
Entrepreneurs with low bargaining power are more likely to choose IPO rather than
M&A. An increase in bargaining power is more likely to increase the likelihood of choosing M&A. The nested logit model results suggest that an increase in ROA increases the likelihood of choosing fixed price over bookbuilding, conditional on the firm choosing IPO over M&As.
Market thickness is proxied by the number of firms in the focal (target) firm's
2digit SIC industry segment in the deal year. According to conditional logit results in
Table 9, as the number of firms in the market increases the likelihood of choosing asset sale or stock sale over bookbuilding decreases. However, inclusion of industry specific control variables makes the coefficient insignificant. Also, according to the results in
Table 8-Panel A, the logit model estimate for the number of firms in the industry is positively associated with the likelihood of choosing IPOs over M&As: as the number of firms increases, the likelihood of choosing IPOs increases. This result is consistent with the market power arguments in the literature. As the number of firms in the high tech industries increases, the likelihood of choosing asset sale over stock sale increases. On the other hand, as the number of targets in low tech industries increases, the likelihood of
82 choosing fixed price over bookbuilding increases conditional on the choice of IPO over
M&A (Table 11). Nested logit model results are consistent with multinomial and conditional logit results. Results provide supporting evidence for hypotheses 5 and 6 which suggests that entrepreneurial firms are more likely to choose auctions as market thickness and private value components increase.
As summarized in hypotheses 7 and 8, discussion on risk propensity suggests that increase in risk aversion corresponds to increase in the likelihood of choosing IPOs. Risk averse owners (managers) are more likely to have lower debt/asset ratios in their firms, which suggests that as the ratio of debt/assets increases the likelihood of choosing M&As increases. According to the conditional logit estimates, as the debt/asset ratio increases, the likelihood of choosing asset sale or stock over bookbuilding decreases. However such an increase is negatively associated with the likelihood of choosing IPOs versus M&As as presented in Table 8. According to the results of the nested logit, increase in debt/asset ratio corresponds to the decrease in the likelihood of choosing fixed price over bookbuilding conditional on IPO being chosen over M&A. Moreover, an increase in debt to asset ratio increases the likelihood of choosing stock sale while decreases the likelihood of asset sale conditional on M&A being chosen. These results provide support for hypotheses 7 and 8. Venture capital involvement also proxies risk propensity for firms choosing IPOs. According to the nested logit model results, if the firm has venture capital involvement, the likelihood of choosing fixed price over bookbuilding decreases. When the probability of choosing IPO versus M&A is accounted for, the firms with VC involvement are more likely to choose bookbuilding over fixed price.
83 Hypothesis 9 suggests that as search costs increases, an entrepreneurial firm will be more likely to sold though an IPO. If the focal firm's industry is one where VC firms are active, then the search costs are expected to be lower for potential buyers and sellers as discussed previously. VC activity in the industry of the focal firm decreases the likelihood of choosing asset sale or stock sale over bookbuilding (Table 9). This suggests that VC activity in the focal firm's industry decreases the likelihood of choosing IPO versus M&A. The results of the nested logit model are in accordance with the results of the conditional as well as multinomial logit models.
The nested model results show that as the common value component increases
(Hypothesis 10), an entrepreneurial firm is less likely to be sold through the bookbuilding method. Hypothesis 11 suggests that as the private value component increases, an entrepreneurial firm is more likely to be sold through the bookbuilding method. Based on the nested logit results presented in Table 11, as patent generality increases, the likelihood of being sold through fixed price increases, whereas patent originality is mostly insignificant. These results provide support for hypothesis 10 but not for 11.
As the initial toehold of the acquirer increases, the target firm's likelihood of selling assets versus stocks decreases (the logit results available upon request). However, the results are insignificant according to the nested logit models, which fail to provide support for hypothesis 12. According to the nested logit results, for low tech firms, the likelihood of stock sale decreases, conditional on being acquired. As the private value component proxied by Tobin's q increases the likelihood of choosing asset sale over stock sale decreases. However, the effect is reversed if the focal firm is US based. According to the nested logit results, conditional on choosing M&A, Tobin's q is not a strong variable
84 in identifying a differential effect between asset sale versus stock sale, since it is positive and significant for both. However in all three specifications of multinomial logit, conditional logit, and nested logit, the magnitude of the Tobin's q effects on the choice of stock as opposed to assets are larger. On the other hand, for a high tech firm, the likelihood of being sold through a stock sale as opposed to bookbuilding is decreased while being sold though an asset sale as opposed to bookbuilding is increased. These results provide weak support for hypotheses 13 and 14. In the context of private M&As, it could be argued Tobin's q and high-tech dummy variables have opposite effects.
3.10 Discussion
How should an entrepreneur sell his/her firm48? Traditional theories of the firm such as TCE and RBV have focused on the choices between markets and hierarchies, and hybrid forms in between. However, when the question is `how' one should sell a resource, a commodity, or a firm to maximize economic rents, these theories fail to provide adequate explanations regarding the different market mechanisms available to decision makers. In this paper, I study two of these market mechanisms based on the theoretical
48Why firms want to go public, or private, is beyond the scope of this paper. However, it was an implied assumption that when entrepreneurial firms grew large enough, they would need capital to finance more operations and they would go public (Kaplan, 1991). This propensity to offer stocks to public especially became visible in the mid 1980s, but the trends changed to neo-private companies when the leveraged buyout (LBO) trend picked up. This trend too changed when these neo-private companies became public again within seven years of LBOs. The reason why the trends have shifted in this fashion has to do with the rights to control an entrepreneurial firm (ownership), and the rights to have access to future cash flows (shareholding).
85 arguments developed in Arikan (2002). An entrepreneur will choose either to negotiate the sale of his/her company through M&A, or auction it off to the public through an IPO.
Since a value (price) is not yet revealed, selling an entrepreneurial firm through the posted price mechanism in spot markets is not feasible.
When an entrepreneur decides to sell his/her privately-held firm, the choices are mutually exclusive and have direct implications for rent appropriation process. In fact, the rents generated through the sale can be higher or lower, depending on which market mechanism is used to conduct the exchange. The determinant of rent generation hence is not only what resource should one buy, or what type of governance choice should one adopt; but also which market mechanism should one use. This choice is dependent on the factors discussed in this paper. Specifically, I empirically study an entrepreneur's propensity to choose among different market mechanisms influenced by five conditions: bargaining power, market thickness, resource value, risk propensity, and search costs.
I find that entrepreneurial firms strongly follow the theoretical predictions developed in Arikan (2002). All else being equal, entrepreneurial firms with high bargaining power are more likely to choose negotiations (M&A) versus auctions (IPO).
Firms that represent high private values (e.g. in high-tech industries) are more likely to be sold through auctions versus negotiations. As the market thickness increases, the likelihood of entrepreneurial firms being sold through M&A decreases. However, this finding is reversed for firms with higher private values. For firms with high debt ratios, the likelihood of M&A increases compared to IPOs. I find that as venture capital activity in the focal industry increases, the likelihood of M&As increases.
86 Following the empirical analysis, I conducted a small sample field work and interviewed a number of entrepreneurs, venture capitalists and investment bankers49.
Interviewees provided several factors that affected their choices when they made decisions about choosing a particular market mechanism which are consistent with the implications of the empirical findings presented here.
First, some entrepreneurial firms choose a market mechanism for the sale without considering their bargaining position, the competition in the market, their risk structure, their product, or the search costs. These firms follow advice and pay little attention to firm-specific factors that should have been considered. One reason might be attributed to the decision maker's inexperience and the perceived informational advantages (or disadvantages) with respect to the venture capitalists and/or potential acquirers. As a result, entrepreneurs face difficulty in determining their bargaining position, and the timing of their actions (Chemmanur and Fulghieri, 1999).
After the IPO, we used the cash flow to expand the business, and did very well. But, we waited too long to sell the firm. If we had sold it earlier, the buyers would have an incentive to compete.
When Company B approached to buy us out, we hired a consultant. He said the offer was too low, and pushed us to seek an alliance partner. Now, why should we have done that I still do not understand. All we needed was to decide whether we should have done an IPO or be acquired.
49The names of the executives and the firms are concealed to provide anonymity per interviewees' requests.
87 Second, this paper introduces an exit strategy for entrepreneurial firms based on five factors. The key component of this strategy is the uncertainty surrounding the valuation of entrepreneurial firms. For established firms with track record of activities, both accounting and economic variables can provide a fairly comprehensive picture of firm value; hence their valuation is less problematic when compared to valuations of entrepreneurial firms. From a seller's point, as in the case of the entrepreneurial firms, and from a buyer's point, as in the case of established firms bidding for small entrepreneurial firms, managers need to consider the private versus common value components of their firms, and their firms' potential for rent generation and appropriation.
Our company is small; we cannot afford to make mistakes. I think we are sitting on a gold mine, if we only can bring the idea to the market. Unfortunately we have not reached our full potential yet. If we seek financing, we will loose control. If we don't, we are not worth half as much as how much we should be worth. Sometimes I think I should not have started this firm in the first place.
Third, from a managerial perspective, results show that most often firms within the same industry tend to follow the same market mechanism for the sale of their firms, regardless of significant firm heterogeneity given these five factors. One explanation is that managerial talent tend to follow the herd most of the time, without paying close attention to firm specific conditions (Bikhchandani et al., 1998; Brandenburger and
Polak, 1996). Although entrepreneurs (or managers) receive superior information about their firms, they might still be inclined towards making decisions that are consistent with cascades and herding. There are several other explanations. First, entrepreneurs might try to limit their potential losses by applying generic strategies to the firm's sale decision.
88 Second, they might choose one mechanism over another due to the advice they follow from various consultants specializing in one mechanism. Finally, and maybe more often true than not, some entrepreneurs might be making emotional decisions about letting go of control and ownership of their firms.
We started and then sold eight companies so far. In all but one we thought of doing an IPO. I mean we have a good thing going with acquisitions. We are good in negotiating with buyers. IPO would only be a distraction, it would be a waste of time and money. Suppose we did an IPO. What then? Why would I want to check the stock price of my company everyday to see how I am doing?
We did not want to be acquired before realizing all the potential of the firm. When you put your life in to a company, selling it to some stranger is the last thing on your mind. I sold my first house 30 years ago after living in it for 4 years, and I still go and visit it from time to time. It is an emotional thing. Imagine now selling your company you built from scratch with your hands. Selling to some other firm could only be the last option.
Fourth, in the case of repeat entrepreneurs, these firms may be applying the same rules over and over with few changes. Such generic strategies may work in a number of cases, assuming the same entrepreneur starts new ventures along the same ideas as the previous ones. The causality relationship in this instance is not clear. For example, one entrepreneur simply might become an expert in one market mechanism due to successful repeated tries. However, it might also be the case those who try and fail have to exit the market50.
50Bernardo and Welch (2001) refer to some entrepreneurs who are more likely to explore their environment and imitate their peers less as overconfident. These individuals follow their own information, downweighting the information from the herd. When these individuals overestimate the quality of their own information and irrationally ignore the actions of other individuals, often make mistakes, thus exit the market.
89
We follow a simple checklist when we decide whether to sell a firm or not. One - make sure you give value to your associates; these are the people you will work with the next time around. Two - Make sure the company you sell will generate some value to the buyer; they will be your next clients. Three - We must at least double our money, or gain 25% annual return on our initial investment.
Finally, entrepreneurial firms have a hard time taking certain types of risks mainly because if they fail they will incur heavy losses. This might be evident in the small number of initial public offerings compared to the mergers and acquisition markets. An alternative approach is to look at the risk propensity of entrepreneurs and proxy risk aversion by founder's age. Although there are many problems with this argument, it might be the case with a number of firms. In the literature entrepreneurial firms' success and survival has been linked to entrepreneurs' tenure (Eisenhardt and Schoonhoven,
1990), and further investigation in explaining the reasons for changes in risk propensities of entrepreneurs might be useful.
We had a hard time finding a firm who would accept our new IPO mechanism. They (entrepreneurial firm) kept asking ``Are you sure this method will work?'', we knew it would work, it had to work. It is coming from theory. There was no way we could fail. But, it took a long time to convince the client.
We spent a lot of time building a brand. When it was time to cash out, my partner said let's do an IPO and retire rich. If you play on the (stock) market, you might be rich one day, poor the next. Maybe when you are young you can take that risk. Not me... I will retire in Florida after I sell it to Company X. Money in the bank is sweeter.
90 The major shortcoming of this paper is that because I have used a large sample of manufacturing sector to test the arguments in an empirical setting, an in-depth analysis for these industries and companies could not be provided. By preferring breadth, I had to sacrifice details on several levels. Although implications of this study could be applicable to firms in general, a closer study of entrepreneurial firms are needed. A further analysis of firm-specific examples using a number of cases might provide useful insights as in
Graebner and Eisenhardt (2002).
Furthermore, since this study covers multiple industries, the measures for bargaining power and resource value are somewhat crude, mostly due to the well-known limitations of the patent data. Therefore, a natural extension of this research is a follow up industry-specific study on how these firms choose a specific mechanism, using the survey method. This would enable us to develop better and more precise measures. As mentioned above, the question in this paper is ‘how’ entrepreneurs should sell their firms, but ‘why’ they sell is equally interesting. For example, if the full value of the entrepreneurial firm could be realized with combined resources of the acquirer, the target would choose to be acquired if the marginal resources were too costly to acquire or would require time to develop internally. Following this logic, an entrepreneur would choose auctions (IPO) if they had all the resources needed to realize the full value, or negotiate the sale (M&A) if they did not have the resources, and could not develop them.
For future research, two venues show promise: network effects, and performance implications. I believe the effects of cooperative strategies and network ties should be emphasized. More specifically, I expect the entrepreneurial choices to be strongly influenced by the network effects they might be a part of. Two questions are interesting.
91 First, do network ties affect the propensity to choose one market mechanism over another? Second, do entrepreneurial firms learn from each other's experiences in networks?
In this paper I find the propensity scores for firms which choose a market mechanism to sell their firms given bargaining power, resource value, risk propensity, market thickness and search costs. A natural extension is to look at the performance implications of these five factors that guide the optimal market mechanism choice. In other words, what would have happened if the entrepreneur had chosen the other mechanism? Hence, further research regarding the optimal market mechanism choices will contribute to the discussion on rent appropriation.
We believed in our company and product. We had a great technology. We did an IPO because it was the right thing to do at that time. Now when I look back, I guess we should have sold it to Company Y. We would have made a lot more; they would have made a lot more
92 CHAPTER 4
ECONOMIC RENT GENERATION IN ONLINE AUCTIONS
Electronic commerce in procurement practices has decreased transaction costs between exchange partners and enabled small and/or startup firms to provide products and services to the traditional large buyers competitively. As a result, entry barriers due to market power arguments are no longer the pivotal explanation in successful procurement strategies. In addition to increased attention to online procurement, another traditional pricing mechanism has gained popularity; auctions. In the past, auction mechanisms would be considered ‘appropriate’ for such goods and services where potential buyers individually valued the product asymmetrically, for if the good in question had a common value, the winner would have paid too much in the first place; a common winner’s curse argument. However, if this were the whole story, firms in multiple industries would not have adopted auctions mechanisms for the procurement of inputs with common values. Theoretically, we aim to solve this contradiction. In this paper, we are looking at the first component of value creation, attainment of resources and factors of production for both manufacturing and service industries. We are examining the business-to-business (B2B) transactions in industrial parts industry and the procurement practices of several firms. Combining this business phenomenon with the
93 auction theory, bidding mechanisms, and the strategic factor markets argument, we propose strategies for firms to create economic rents using auction mechanisms.
We have three arguments. First, firms may acquire rare, inimitable and valuable resources through auction markets and gain competitive advantage, and this competitive advantage may be sustainable. Second, bidding behavior of firms both for the supplier and the buyers will be different if the product/service offering is a single unit versus a multiunit procurement. Finally, depending on whether the market is a buyers or a sellers market, firms can position themselves and adjust their bidding strategies to maximize their profits and utilities and create economic rents. We find that the creation of a centralized market generates rents for buyers. More specifically, rather than what you buy; how you buy it, becomes important. What is especially important in B2B online auctions is that the market players set the rules for the exchanges, and we have to think of the pricing mechanism as an endogenous component of firms’ competitive strategy. The relevant question is: what determines different selling devices to occur in the market?
Firms can be conceptualized as collection (bundles) of productive (Rumelt, 1984;
Penrose, 1959; 24). Resources include all assets (e.g. capital, labor), capabilities (e.g. innovativeness), and organizational attributes (e.g. level of bureaucracy) that is controlled by the firm to conceive of and implement efficiency enhancing strategies (Wernerfelt,
1984; Barney, 1991). According to Demsetz (1973), setting anti-competitive arguments aside, a firm that enjoys sustainable superior performance, may do so either because it is lucky, or because it is more competent in addressing the consumer needs (creating consumer surplus). Resource-base view of the firm makes two main assumptions based on the previous related literature (Barney, 1997: 142). The first one is extended from
94 Penrose's argument that firms are bundles of productive resources (1959: 24), and there is cross-sectional heterogeneity in these firm-specific bundles of productive resources. In this framework, sustainable performance differences are attributed to the firm-specific bundles of linked and idiosyncratic resources and resource conversion activities (or capabilities) (Barney, 1986; Conner, 1991; Rumelt, 1984). There is also an implicit assumption that these idiosyncratic resources are priced by the market, and therefore has economic value. The second assumption deals with resource immobility, which says that some of these idiosyncratic firm-specific resources are scarce and/or costly to imitate by the competitors leading to possible abnormal returns. The conclusion based on these assumptions is that if a resource or capability is valuable, rare (not many competitors have it at a particular time t), and costly to imitate, exploiting this resource would result in a sustained (long-run) competitive advantage (Barney, 1997: 164) measured as the above normal economic rents. In this framework, a firm has to develop, maintain, accumulate, and employ firm-specific resources that are valuable, rare, and hard to imitate to create sustainable competitive advantage and generate above average economic returns. Therefore, it is important to think about resource acquisition.
There are three major components of value creation to generate economic rents in both manufacturing and service industries. First, acquiring resources and components that go into the production or process, second, the actual conversion of resources into products or services that are marketable and tradable, and finally the exchange of the product or the service with a valuable commodity, hence the payoff. The difference between the value of inputs and the value of outputs is the economic rents generated, and these rents have been the topic of voluminous research in the economics literature. The
95 management literature, has approached the value creation concept from another perspective, and has studied various components that change the competitive nature of industries and firms. These components are mechanisms that alter the homogeneous characteristics of the firms, and differentiate one from the other, hence result in competitive advantage. In this paper, we are looking at the first component of value creation, attainment of resources and factors of production for both manufacturing and service industries. This paper is organized as follows. In the first half of this paper, an overview of auctions literature and various auction designs are given. Several propositions are developed to link economic rent generation and formal auctions logic. In the second half of the paper, business-to-business online auctions are discussed and specifically four market makers (auctioneers) are described in detail. Strategic and managerial implications of both sellers’ and buyers’ bidding behavior are discussed.
4.1 Auctions
An auction is a “market institution with an explicit set of rules determining resource allocation and prices on the basis of bids from the market participants” (McAfee and McMillan, 1987: 701). The kind of goods sold at auctions varies almost without any predictable pattern: artwork, agricultural produce, US treasury bills, precious metals, industrial parts and finished goods etc. Historically, Herodotus gives one of the earliest reports of an auction in which he describes the sale of brides in Babylonia. Among the early examples, auctioning off religious relics and artifacts, trade of slaves, and wheat, corn and barley during war times can be counted (Cassady, 1967). Now, there is a twist: instead of a Walrasian auctioneer, the market players set the rules for the exchanges, and 96 we have to think of the pricing mechanism as an endogenous component of firms’ competitive strategy. The relevant question is: what determines different selling devices to occur in the market? There are two fundamental answers to this question that actually define why we have auction mechanisms and why we have posted fixed price selling mechanisms for the same product at different times.
The first answer is that some products have no standard value. This way, you can get the highest price for your products in an auction setting. Their value change from individual to individual, and from market to market. The private values and the first and second best uses of the commodity dictate to a degree, its market value. Any fluctuations in the supply of this material will create an imbalance among the potential customers, and apart from its common value, customers now have private values attached to this commodity. When we translate these arguments into auction logic, we need to talk about valuation and risk aversion in an economic sense. A product (or service) has two values: private and common value. Common value is predetermined, most probably through continuous adjustments and is common to all participants (Milgrom, 1989). For example, the value of gold in a precious metal exchange market at a particular time t is fixed. The price is determined by the demand and supply for the good, and it should be equal in all the markets, given the law of one-price (Ise, 1940; Russell and Thaler, 1985; Spulber,
1996). The markets would correct the unequal prices if there were arbitrage possibilities, by supplying from higher supply (lower price) market to lower supply (higher demand) markets till prices in both markets equalized. However, private value is unique to each participant of an exchange. For example, while a jeweler uses the same gold to make a necklace, a pharmaceuticals manufacturer may use the gold as an ingredient in a
97 medicine, an electronics manufacturer can coat the connector cables for better conductivity and thus each manufacturer would value it differently given the unique properties and multiple uses of a product (in this case, gold). Whichever firms’ rent generation possibility is higher; it will value the gold differently and will outbid the rest of the buyers if the supply of gold is limited and scarce. This could also mean that the firm might relinquish all future profits, if the scarcity of the item drives prices up such that economics rents are dissipated. If however firms in the same industry with identical production functions were competing for the purchase of this gold, then the winner would have overbid in order to acquire the product. The second highest price of the product would be its true value; assuming second highest bidder is rational.
The second answer is that in order to discover the worth of a good, the demand and supply conditions at a specific moment in time must be considered. At two extremes, if there is only one buyer (monopsony) or only one seller (monopoly) the sellers or buyers have to reach an optimum point for transacting. When the product carries some unique features or characteristics that cannot be substituted, the demand or supply changes from the optimum point and this shift causes one party to demand the product more. In the monopsonist’s case, the buyer has the possibility of acquiring the resource at a lesser value than what it would have paid, if the supply were decreased due to some reason. On the other hand, the monopolist would have the possibility of selling the good for much more, if the demand was high and potential buyers were bidding to acquire the resource. Literature has not distinguished the effects of these two components from each other. Most of the times, the goods have a specific value not only due to their unique properties but also due to market demand and supply at a particular point in time.
98 Traditionally, governments mostly use auctions to conduct their procurement practices.
However, today, we are seeing a changing trend in auction markets. Businesses have started to use auction mechanisms more and more for their procurement practices, especially after the online procurement practices took off in the late 1990s.
Auctions are not a new pricing mechanism. Nor are the procurement practices of companies that create competition among their suppliers, or conversely, among their customers. However, B2B online auction markets are a new means of connecting suppliers and buyers. Federal Trade Commission defines B2B electronic marketplaces as
“software systems that allow buyers and sellers of similar goods to carry out procurement activities using common, industry-wide computer systems” (FTC, 2000). What makes the analysis of such online auctions and procurement practices interesting is the fact that these mechanisms are targeted to increase producers’ power in attainment of resources previously was available at a higher cost, while endogenizing the pricing mechanism. It is not limited to a specific example of B2B online auctions of industrial parts procurements.
It is increasingly becoming popular among all industries, and we are seeing fierce competitors get together for the common goal of creating a competitive procurement environment (Dasgupta and Spulber, 1990; Dana and Spier, 1994). Another application of these auction mechanisms can be observed in the mergers and acquisitions markets, and in the online markets where resources of production are traded between businesses.
Given this setup, in online auction markets, firms are bidding for common components, or for items with common values. We argue that these common value items cannot be a source of competitive advantage, or a source for rent generation due to their standardized nature, unless firms outsmart the resource market (Barney, 1986; Makadok 2001). Only
99 systematic and accurate expectation development can lead to sustained competitive advantages.
4.2 Design and Conduct of Auction Institutions
Can the selection of appropriate auction institutions be a source of competitive advantage, given the firm’s industry context, resources, capabilities, and strategies, and if so, which forms are used in which settings? There are many forms of auction institutions, but only some of these institutions have become widely used and recognized. First, let us look at some of these commonly used auction institutions.
The English auction is an auction with many variants. It is an ascending, progressive, open, and oral auction where the auctioneer calls for successively higher bids until one willing bidder remains in the market. The bidders are known publicly, and the bids are also known openly. Conversely, the Dutch auction starts at a high price, and drops as the bidders claims the contract or product at the lowest possible price. The auction institution for the sealed bid auctions can be organized according to the choice of the decision rule. If the decision rule is that the winning bid is the highest price offered, then the auction is first-price sealed bid institution. If the decision rule is that the highest bidder pays the second highest bid, then this institution is a second-price sealed auction.
However, second-price sealed auction does not necessarily result in lower revenues for the seller since the buyers in this setting usually place higher bids then they would normally bid in the first-price auctions (Milgrom and Weber, 1982). In a sealed bid auction, each bidder has a private value assigned to the good. The dominant strategy is to bid your value (Vickrey, 1961), and the bidder who values the good most wins the bid. 100 The Dutch auction is seemingly different, where the auctioneer calls up a high starting bid, and then the bidders start bidding successively lower values and the lowest offer wins the bid. This is mainly because the bidder has to value “paying as low as possible” as well as the contract content. Theoretical results for the independent private value auction is that English, Dutch, first and second price auctions yield the same expected revenue for risk neutral bidders (Kagel and Levin, 1993). This equivalence is known as the Revenue-equivalence Theorem, and is fundamental due to its two assertions: First,
English and second-price auctions are strategically equivalent as well as the first-price and Dutch auctions. In other words, these institutions yield the same expected revenue
(Riley and Samuelson, 1981). However, experimental research in testing the equivalence of these institutions has shown a systematic failure in both the second-price and English auctions, and first-price and Dutch auctions (Kagel, Harstad, and Levin, 1987; Cox,
Roberson, and Smith, 1982; Levin, Kagel, and Richard, 1996). In these experimental settings, the revenue equivalence has not taken place. There are several reasons why this might have taken place. Theoretically, the bidders in both auction institutions might be affected by the competitors’ bids at varying degrees. Practically, we would think that sellers would want to compete against each other in English auction mechanisms, and the buyers would favor the Dutch auction settings. These predictions signal that price concerns drive the auction mechanism choices among participants. Of course heterogeneity in preferences creates the opportunity of economic rents.
4.3 Business-To-Business (B2B) Online Auctions
The traditional procurement practices of firms are creation of competitive markets 101 where several sellers try to sell to a buyer, and the transaction is governed by a contract.
If the buyer is a government, usually the form of competitive market creation is through sealed bidding, and the lowest cost provider or highest revenue recipient is the winner
(Rob, 1986). The B2B procurement practices have changed significantly in the last 10 years, especially after the online integration of businesses and their purchasing patterns.
Today, there are three major markets where online auctions are taking place, and the possibilities are seemingly endless. The market for industrial parts is a $5 trillion global market, where 35 cents of one-dollar worth of good sold has to be acquired through this market (Fortune, 2000). The B2B online auctions are not limited to the commodity like products that are exchanged through the industrial parts market. Ford Motors, General
Motors, and Daimler Chrysler have joined to start a vast supply-chain network that features online auctions. One last example is the E2open.com, which is a joint venture between IBM Nortel Networks, Toshiba, Motorola, Nokia, and Ericsson. These firms are planning to link their billions of dollars in annual purchasing in what could be the biggest business-to-business Internet auction marketplace yet (USA Today, 2000).
The auctioneer of these markets is not limited to a single company, FreeMarkets,
CommerceOne, Ariba are only a few of the auctioneers whose primary task is to govern the contracts that are submitted to each bidding. These auctioneers act as contract quality inspectors. While they standardize the item characteristics and make it a level playing field for the bidders, they lower the search and governance costs associated with comparing unequal, but functionally identical goods or services. The characteristics of auctioneers are also important in analyzing the online markets. In a traditional setting, an auction house specializes on one trait, for example, authentication of precious artifacts. In
102 online auctions, the auctioneer oversees the fulfillment of the bids to the contracts.
Hence, the auctioneer establishes a common value for the goods that are bid. It is up to the bidders to create private values for their goods. Since private values for goods represent heterogeneous expectations, this creates rent possibility in perfectly competitive markets.
Proposition 1: If B2B online auction mechanisms for goods for common value goods create a private value for the buyers, then these procurement strategies can be a source of economic rents.
In the traditional sense, the industrial goods markets are a negotiation market where firms compete with each other to win the order on many levels, not only on price and shipment terms. Bulow and Klemperer (1996) look at what the most profitable way to sell to a company is. Does it pay to sell through an auction, or does it pay to use their
(seller’s) bargaining power subject to informational constraints? The auction mechanisms work for a firm that does not have market power. This way, the firm with no market power can compete with the firm that has market power, when quality, quantity, or price is concerned. However, if the firm already has some degree of market power, it pays off to take advantage of its bargaining power and negotiate contracts and deter entry into online auction markets by smaller firms. Implications of bargaining power will be different for buyers and sellers when acquiring resources in strategic factor markets. In auction mechanisms, bilateral negotiation between the parties does not exist. Instead,
103 prices are lowered (or increased depending on the auction format) due to the competition between bidders51.
Proposition 2a: Sellers with no bargaining power are better off using auction mechanisms to compete in strategic factor markets with sellers with market power when quality, quantity, or price is concerned.
Proposition 2b: Sellers with some degree of market power are better off negotiating contracts with fixed market prices.
Proposition 2c: Buyers with bargaining power will be indifferent to auctions versus posted market prices.
Proposition 2d: Buyers with no bargaining power are better off using auction mechanisms then negotiating contracts with posted market prices.
4.4 Market Makers in Online Exchanges
Market makers are innovative companies that provide confidence, convenience and immediacy for their customers and suppliers. In fact, these institutions coordinate exchange between partners and develop a network of suppliers and buyers. There are three basic market structures that reflect the extent of market centralization: auction
51 In B2B online auctions, auctioneers that facilitate the exchange have started using various softwares, which allows firms to send and receive messages to each other during the actual bidding process. These messages might be considered as bilateral negotiation, however, in principle, auctions replace bilateral negotiation between the buyer and the seller.
104 markets, dealer markets and search markets (Spulber, 1998: 79). The advent of Internet and online exchanges has facilitated these market makers who bridged the gap between partners and decreased the costs associated with transactions. We see an increase in the number of auction markets and market makers for various goods. Four examples of such market makers (FreeMarkets, CommerceOne, Ariba, and ePurchasing Agent) are discussed and their exchange mechanisms are compared. First, for each firm, market conditions will be discussed, and second, implications for suppliers and buyers will be discussed.
FreeMarkets is one of the first market makers in B2B online exchanges. It is established in 1995 as a global marketplace. It has offices in 13 countries and brings together buyers and sellers in more than 30 languages. About 100 buyers and 11,100 suppliers from 64 countries have participated in FreeMarkets online exchanges. Since
1995, it has driven over 11,500 online auctions, totaling more than $16.6 billion.
Reported savings of the buyers is $3.2 billion. FreeMarkets allows for both English auctions and Dutch auctions, and the buyer decides the auction format52. Duration of the auction, whether it will be a continuous-automatic extension format or fixed time format, is also at the discretion of the buyer. All the transactions between the market maker
FreeMarkets and the buyer(s) is governed by contracts that are based on 4 principles:
52 In an ascending auction (English) many buyers place bids to acquire a resource from one single seller. The prices ascend, and when only one bidder remains, the seller sells the item to the winner. In descending auctions (i.e. reverse or Dutch), there are many sellers, and they compete to win the contract of the single buyer. In B2B online auctions, although the auctioneer allows the buyer to decide on the auction format, nearly all of the time, the buyer selects the descending Dutch format, where many bidders (sellers) compete to win the contract. On eBay and similar peer-to-peer auction houses, the format is mostly English, where the seller posts the item up for sale, and many buyers compete to win the product.
105 trading rules, quality of the participants, global reach and technology. In some exchanges, the buyer pays a fixed rate, in others, a percentage of the final price53.
In some cases, it is reported that suppliers pay a fixed rate to qualify to participate in auctions. Bidders can bid for each item, or for a bundle, and they can “lock” certain portions of the bid to prevent them from going below a certain amount. The buyer decides the lot structures, the product bundles or bidding for each item separately.
FreeMarkets uses a proprietary technology (software) to govern the exchanges,
BidWare®. BidWare® provides a feedback to suppliers after an auction has ended. For example, bidders can view which suppliers have bid, for how much, and in what order.
Most common practice is to assign random numbers to each bidder as “identities”, and to display their bids as a rank54. The bidders do not see actual bids, but the higher or lower bids, depending on the auction type, are displayed. This point raises another question: what is the difference in auction outcomes when the auctioneer posts ranks and not actual bids? Would the revenue maximization be different if actual bids were displayed? Would the bidding strategy of bidders be different? There are 6 types of dynamic pricing formats in FreeMarkets.
53 Generally, FreeMarkets gets paid a percentage of the auction price on a sliding scale. On trades less than $25 million, FreeMarkets charge 2% fees, and the percentage decreases as the price goes up.
54 In Roth and Ockenfels (2000), eBay applied a “proxy bidding” system, where the bidders’ bids would be entered with small increments regardless of the actual bid. One would bid as high as he would like, but the current bid would be registered only by a small increment above the next lowest bid. In fact, in B2B auctions described here, the actual bid would not be displayed, but the random code given to each bidder would appear if it were lower than the current bid.
106 1. BidWare® Transformation Auction: A range of total cost factors is adjusted to ensure a level playing field between bidders. For example, quality, transportation, production process, equipment requirements are considered as some of the cost factors. 2. BidWare® Multi-currency Auction: Suppliers can bid in the currency of their choice, and the software converts these based on pre-specified conversion rates. 3. BidWare® Index Auction: Bidding is conducted relative to an index. The baseline is considered zero, and bids are negative or positive increments, depending on whether a premium or discount is offered. 4. BidWare® Net Present Value Auction: Prices are adjusted over time. All bids are represented in today’s dollars, and a series of payments to be made in the future are calculated in relation to all other bids. 5. BidWare® Multiple Offerings: Bidders can submit two or more offerings separately in bidding, for the same auction, to the same buyer. 6. BidWare® Rank-only Bidding: Bidders can only see their numeric rank in bidding, but they cannot see the actual dollar values of the items bid.
FreeMarkets provide two important services. First, it provides their proprietary bidding software. This software allows suppliers to send quotations to dynamic markets created by FreeMarkets. Second, as an intermediary FreeMarkets identifies and assesses suppliers. In fact, the firm groups related suppliers to enter an auction, provides post-bid analysis, and pre-bid assistance. Strategic implications of some of these products are not significant. For example, Multi-currency Auction (#2) is somewhat trivial given the fact that the only contribution of the market maker is the adjustment of foreign currencies.
Similarly, Net Present Value Auction (#4) is another feature where firms can calculate the NPV of contracts on their own. Although these products and services are helpful and convenient, they are not critical or crucial. However, the remaining products and services 107 are economically significant, and a lot more interesting. For example, Transformation
Auction (#1) allows the buyer to compare competing goods on a standardized platform.
Suppliers submit contracts for goods that perform at some minimum acceptable level, and the buyer is given the option to choose among the many products that conform to their specifications and maybe even exceed technical requirements. This feature closely affects resource bundling and Multiple Offerings (#5) service, where firms can submit bids on functionally identical products. Submitting a bid for a close substitute, or offering a differentiated product might suit the buyer’s needs, and create a competitive advantage for the supplier. In the Index Auction (#3), the buyer declares its current contract price, and that is set as a benchmark. If the current market value is below the current contract price, the bidders are most likely to bid down and the index is revealed as a discount.
Perhaps the most critical product is the Rank-only Bidding (#6). In this format, the sellers can only see each others’ randomly generated codes, and not the actual bid price. In every new session, the codes given to suppliers are regenerated, and only the buyer can see the actual names of the firms. If the buyer chooses revealing lowest bidders’ ranks or code numbers, this clearly creates asymmetric information among bidders. Information asymmetry between bidders and the buyer is ever present in auctions.
Proposition 3: Buyers that use rank-only bidding are going to generate more economic rents than those firms that allow revealing actual bid prices.
108 Ariba is a close competitor to FreeMarkets. Its main clients are DuPont, Federal
Express, Chevron, and Honda. Ariba serves about 30,000 suppliers. Its proprietary technology Ariba® Marketplace™ works along the same principles as FreeMarkets’ processes. More specifically, Ariba offers basically 4 products:
1. Ariba Buyer™: Software that manages the exchange transaction from requisition to payment, based on buyer’s existing technology architecture. 2. Ariba Sourcing™: Suppliers can enter network activities to provide buyers quotes at no fee. 3. Ariba Marketplace™: Brings suppliers and buyers together to buy, sell, share information on subscription fees, transaction fees, and other transaction costs, online. 4. Ariba Dynamic Trade™: This is the actual online auction site of Ariba. Depending on the buyers’ specifications, transactions are dynamically conducted on all factors, not just on price. The auction formats again vary according to buyers’ specifications.
Obvious outcomes from Ariba auctions are a) seller maximize sales revenues and reach more buyers to utilize their capital assets and surplus inventory; and b) buyers save money and time when procuring goods with volatile prices or a fragmented supplier base.
A distant competitor CommerceOne follows similar market making principles, and applies “customized” auction format with “customized” and negotiated fee structure.
A closer look at ePurchasingAgent (ePA) is required at this point. While
FreeMarkets, Ariba, CommerceOne are providing flexible auction formats and customized services to buyers, ePurchasingAgent follows a more rigid structure. ePA is an online market maker that uses the reverse-auction model. In the ePA model, there is no charge to the Buyer, and all the fees and commissions are paid by the suppliers, based 109 on a fixed rate. All these commissions are added to the Supplier’s bid placed during an auction. All bids posted during the live auction are shown including a transaction fee55.
The payment schedule can be summarized as follows:
Transaction Amount Transaction Fee Below $50,000 5% $50,000-$99,999.99 4% $100,000-$499,999.99 3% $500,000 or greater 2%
ePA does not sell its software, it is a web-based application unlike Ariba,
FreeMarkets and CommerceOne’s proprietary softwares. ePA provides Product
Expert/Procurement Consultant(s) to buyers and sellers upon request who are specialized in certain industry niches. The suppliers are chosen upon the request of buyers. Buyers submit a list of potential suppliers, and ePA contacts these suppliers. The buyer also determines the duration of the bidding. The buyer can set a fixed date and time, or most commonly, the deadline is automatically extended for 5 minutes if there is activity. The buyer does not have to choose the lowest priced bidder; they can award the contract to any one of the bidders they wish. Also, similar to the FreeMarkets’ multiple bidding formats, ePA allows and encourages suppliers to bid for alternate products and services.
While the actual auction compares identical products, an alternative report is provided to buyers for alternative products and services that might lower buyers’ costs. Interestingly, during an auction ePA allows all participants to see the entire bid amounts. The bidders
55 The Buyer is required to inform ePA which supplier they have chosen to award the contract. This supplier then automatically, upon issuance of a legal and binding agreement from the Buyer, extends its irrevocable permission to be liable for the transaction fee. The transaction fee is not subject to inflation or other causes that may affect the overall value of the contract. 110 are assigned random numbers to conceal their identity during an auction. When the auction is complete, bidders are revealed to the buyer by their actual names. Also, they can view the lowest bids of all the other suppliers, but the history of their bids is not revealed56.
From the perspective of the suppliers, there is no charge to participate in an auction. Only the winning supplier pays the fixed transaction fee. ePA’s proprietary software “Filter and Match” matches suppliers to buyers’ requests. The minimum number of sellers to start an auction is four, and most often auctions are open to all willing and qualified parties. In all bids, suppliers’ identities are kept confidential from each other, and only the buyer can see the actual identity of the bidders. Before or during the auction, bidders can contact the buyer through an online bulletin board (Q&A Page). Answers from the buyer are also posted here, and all the interested parties are free to view these replies. Finally, the buyer reserves the right to disclose its identity. If the buyer gives full disclosure, the suppliers will know who the buyer is, and if the buyer chooses to limit access of suppliers, ePA arranges the web site accordingly. The buyer determines actual duration of the auction, and all auctions automatically extend for an additional five minutes if there is still activity when the clock runs out.
56 History of bidding is a chronological list of all the bids in actual dollar terms. ePA does not disclose bidding history to sellers, but discloses only the lowest bid each has placed during the auction.
111 4.5 Decision Making and Bidding Capability
Who makes the final decision to bid low (or lower) in a B2B online auction? Is it a team of “experts”, or is the final decision left at the discretion of one person who is in charge? Since in firms it is not common to see “auction departments” and a “manager of auctions”, it is assumed here that a team of individuals who are experts in various aspects of the product makes the decisions in B2B online auctions. In psychology literature voluminous research has been conducted, and among the pioneers of this line of research,
Shaw (1932) studied the performance of individual and group problem solving57. It was demonstrated that since groups are able to check for errors, they were better problem solvers than individuals,. On the other hand, it was later observed that groups do not always check for errors, and in fact, in some cases, groups would make more errors.
Empirical evidence suggests that groups make less errors on problem-solving tasks, and yet more errors on decision-making58 tasks (Tindale, 1993).
What are the decision characteristics in B2B online auctions? First, the decision environment contains elements of time pressure, complex and multicomponent decisions, high information ambiguity, rapidly changing and evolving information, high short-term memory demands. These characteristics are in fact what Cannon-Bowers, Salas, and
57 For a more recent and thorough review of group decision-making and performance, please refer to Davis (1992), and Kerr, MacCoun, and Kramer (1996).
58 Decision-making problems are further studied in two broad categories: process problems and content problems. These problems can occur as a function of errors such as slips and mistakes (Norman, 1988), and cognitive biases (Kahneman, Tversky, and Slovic, 1982). A more detailed analysis is available in Duffy (1993). 112 Converse (1993) describe the need for complex system decision makers. They refer to cockpit crews, surgery teams, fire fighters and military teams in their study, and yet, I argue that B2B online auctions require similar coordination and individual expertise for the efficient functioning of decision-making teams. Definitely suppliers who bid for contracts are under a time pressure, whether the auctions have a fixed or automatic extension rule. Similarly, information asymmetries between suppliers are evident, given the fact that they cannot identify the bidders, or they cannot identify the actual bids themselves. If a supplier wants to win the auction, it is assumed that the team of experts already knows the lowest bid they can submit, but, this condition is subject to change if there is more than one item being bid for. In other words, if bundling were possible, team members would have to work together to make the decisions. This would increase the coordination needs among team members. Multiple proposal bidding conditions in fact facilitates this complex situation. Can firms facilitate rent generation in exchanges by strategically managing auctions? This can be linked to the existence of a managerial capability in acquiring resources in strategic factor markets. If there exists such a capability, we need to differentiate it based on the pricing mechanism used.
In B2B auctions there are multiple factors to consider such as budget constraints.
Here we need to make a distinction between firm-bidding versus individual-bidding.
Individuals who go on the Internet and participate in auctions to acquire goods are involved in peer-to-peer (P2P) or sometimes referred to as consumer-to-consumer (C2C) auctions. In P2P auctions, some individuals are better informed than others. This may be due to their professional experience, talent, or education. Roth and Ockenfels (2000) demonstrated that in eBay and Amazon auctions less informed individuals seek
113 information about the correct value of an item by considering who the other bidders are and how they are bidding. If these uninformed bidders assume that a particular bidder is an “expert” in arts, and if this expert is bidding for an item, naive bidders assume that the expert is trying win the auction for the purposes of reselling the item in the spot market.
This forms the basis of naive bidders’ true value and reservation price, that is, the highest price they are willing to bid. However, in some items, the advantages of being an expert disappear, for example when bidding for a computer monitor, or a printer. As an added bonus or a helpful service, such auction houses display the “brand new market price” of the item being auctioned off next to the bidding window. A surprising and puzzling behavior to many, it is sometimes possible to find bidders overbidding for an item even though the price of a brand new price of the product at the spot market is well below what they bid. B2B auctions should not be subject to such irrational behavior. There are two reasons for this: first, all parties send experts to bidding, and second, if firms overbid their values and above spot market prices, they will generate negative profits and exit the market in the long run. Do we need to study a more micro level bilateral bargaining structure in B2B auctions? While micro-level social psychology literature deals with the dynamics between parties (Rubin and Brown, 1975), understanding the effect of individuals on the final outcomes in B2B auctions is important. On eBay and Amazon, while individuals can afford to overbid their values to avoid their competitors winning the auction, firms cannot continuously overbid their value, assuming firms seek to maximize profits by minimizing costs, and individuals might have other motives when bidding.
114 Proposition 4: In B2B auctions overbidding true value to avoid a competitor winning
the contract will not be observed.
Returning back to the earlier question: which design of auction is likely to yield the highest price for a good, or highest revenue? As described above, the auction houses, or “market makers” are in the position of suggesting a mechanism design to buyers. This inherently results in asymmetric incentives, while some mechanisms maximize revenue for some; it is not an optimal outcome for others. In that case, a more normative approach is required to analyze the information problem (Molho, 1997). A related question is, what are the possible agency conflicts? This might be an important explanation for internalizing the auction function. Large firms such as GM, Toyota, and Chrysler are internalizing the online auctions to create economies of scale, and yet, an alternative explanation exists from this perspective. Information asymmetry discussion can be extended to individuals versus group argument outlined above. In an auction, parties gather information about other parties to the exchange, and assuming that the game is not a one-shot event, they build history. While short-run advantages are important, there are long-run effects, and these contribute in strategic information building. From this perspective, buyers better off in the long run knowing whom the bidders are, and this contradicts the market makers’ incentive to withhold information.
115 4.6 Discussion
Strategic implications are based on the interactions among the participants in these auction markets. Decreased cost of opportunism due to the role of auctioneer and lessened informational asymmetries enables more complete contracts to be devised. The contracts have to be more elaborate and complete, since each seller has to complete forms to participate in the online exchange markets, which are administered by the auctioneers.
In these electronic markets, the bidders (sellers) are establishing a history and reputation with the auctioneer. Therefore, market power over the buyers is potentially decreased.
Auctioneers are standardizing interfirm contractual relations between sellers and buyers.
The question remains to be: Is it still possible to develop relations with firm’s buyers and sellers that are unique and hard to imitate which would create economic rents?
Intermediation theory of the firm shows that intermediary firms are formed when they increase net gains from trade relative to direct exchange (Spulber, 1999: 345). These intermediaries, or market makers in the B2B online exchanges can effectively compete with decentralized-exchange alternative. We are observing an increased demand for putting procurement activities online, and utilization of auction mechanisms to purchase goods and services. The obvious reason that “internet decreases the transaction costs” is not an adequate answer. There are more significant incentives to conduct B2B auctions.
Being on the Internet only allows a faster response rate with lower administrative costs.
The issue of having auctions between firms is a more pressing matter.
Should firms use intermediaries which act as market makers in online auctions?
This question is a vertical integration and internalization matter. The auction houses
116 mentioned above, FreeMarkets, CommerceOne, Ariba, and ePurchasingAgent are competing in various industries such as: aerospace, automotive, consumer products, diversified manufacturing, energy and energy processing, high-technology, public sector services, and retail. While Roth and Ockenfels (2000) looked at only eBay and Amazon, and at 2 goods, representative of common value and private value auctions, B2B exchanges are inherently more complicated and require more research.
Market makers are creating centralized markets for various goods. The major contribution of the market makers is their certification service, or “standardization” function. This way bidders are competing with each other on pre-specified factors, while the auctioneer is keeping all the other factors constant. Who should negotiate on all these other factors? If the auctioneer negotiates, this will increase the cost of transactions, and if the firm negotiates, this will affect the efficiency of the auction mechanism. In an actual exchange, this ideal condition has not been fully met, and factors critical for decisions such as availability of goods, variations in product quality, shipping, packaging and other logistics details were unavailable to buyers. FreeMarkets conducts a very comprehensive report for each bidder and each participant has to clearly indicate such factors, but this is costly. Another solution is a better and newer technology, which allows buyers, and suppliers to negotiate many of the important transaction details online, including delivery dates and payment methods. Ariba’s Dynamic Trade 2.0 and
117 CommerceOne’s Auction Services 4.0 are such software that able parties to negotiate during the auction59.
What are the advantages and disadvantages of online auctions for buyers? Buyers ultimately have a lot more power and control over the auctions then sellers. In many of the described designs, the sellers are competing for the buyer’s contract, and in some cases, negotiating with the buyer on terms and conditions of an exchange. From this perspective, any buyer is advantageous, given the fact that any outcome will be better than its current offer in the posted price market governed by the conventional contract, mainly because auction mechanisms induce competition between sellers for the buyer’s contract. Even if the auction is not made known publicly, and given the fact that the participants are all invited bidders, the prices are assumed to be more favorable for the buyer, and the bidders have to comply with the factors outlined per bidding contract. The question from the perspective of the bidders is not that clear cut. When more than one bidders participate, the prices are going to go down due to competition, and the buyer is better off. It is not clear however, how the sellers benefit due to information asymmetries created by the design. While uncertainty does not exist with the affiliated private value auctions, the common value auctions have a major uncertainty associated with them since the bidders actually cannot see the other bidders’ offers, but only can see the relative rankings.
59 In the mean time, some independent exchanges such as Hologix, Moai, OpenSite, and Siebel Systems specialize in negotiation software, but lack the market reach and technical services of major online auctioneers.
118 Another aspect is the joining of buyers to act as market makers. When few buyers get together to form a joint venture to conduct online auctions, this form will also have an effect on prices. The question is, will buyers who choose to conduct auctions on their own without few other alliance partners be better off? That is, would GM be better off if the other automakers did not join an alliance to conduct auctions online? I would assume that bidders would be indifferent in placing bids to either one of the buyers given their products are compatible in these auctions. And yet, some people have argued that bidders would want to supply to a particular buyer because of its market power or reputation60.
Examples of internalizing auction mechanisms are numerous. Recently, Bristol-Myers
Squibb (BMS) purchased Transora to internalize online auctions for its pharmaceutical product sourcing (Gilbert, 2001). Will BMS outperform its competition in procurement practices because several others have an alliance in consumer pharmaceuticals industry procurement? Antitrust concerns are also a major issue. When buyers pool their resources, they are at a more advantageous position with respect to the smaller suppliers.
Are auction house alliances against principles of competitive markets?
60 Personal communication with Jay Barney. The argument is that a seller always wants to supply a major buyer. Although it is true that some firms may prefer to supply to a larger buyer, a seller in fact has an incentive not to supply to a major buyer due to strategic considerations, given that it can supply to a smaller buyer at a higher price since the larger buyer locks in the price at a more advantageous level for itself. In auctions literature, two formal mechanism designs aim to yield optimal strategies: competitive auctions versus discriminatory auctions (Holt, 1980). In competitive auctions, bidders sequentially lower (or increase) proposed bid prices until only one-bidder remains. The final contract price is the market-clearing price. In discriminatory auctions, bidders submit sealed bids and the final price is the lowest bid price. If the bidders are risk averse, the uncertainty in discriminatory auctions results in higher revenues for buyers (higher economic rents), and lower expected procurement costs compared to equivalent competitive auctions.
119 Strategic management theory should guide practitioners on pricing and purchasing strategies of firms. It is important to note that auction mechanisms can be a source of competitive advantage and can generate economic rents. There are several important extensions. Not all firms can benefit from auction strategies. For some firms, using different mechanisms will not yield economic rents. For example some firms strongly rely on long-term relations with their exchange partners. Through repeated exchanges, such firms establish patterns, which might be hard to change. The information dynamics between the buyers are of interest as well. In traditional procurement mechanisms, the buyers in the same industry do not have full information about each other’s costs. This applies to the sellers as well. In many ways, they do compete with sealed bids, and history plays a role. The more you do business with a customer, the more you have access to its confidential information and build idiosyncratic interfirm ties that increase the likelihood of future similar exchanges. With the electronic marketplace, the informational asymmetries are diminished to a great extent. Depending on the format of the bid reporting, both the buyers and sellers can see the bids online, in real time. This might lead to participating firms to lose opportunities for building idiosyncratic relational ties with customer/suppliers that might be a source of competitive advantage.
Information asymmetries play an important role in auction mechanisms. When two parties enter an exchange one side often knows something more than the other regarding the transaction. Hayek (1945) argued that individuals do not have symmetric information, and the information asymmetries are the reason why price system is an efficient mechanism in communicating information. That is to say that when a party needs to make a decision, he considers the vector of prices. This is in fact a criticism of
120 the Arrow-Debreu model of perfect information. Taking this argument further, Smith
(1982) finds empirical support for the “Hayek hypothesis”: strict privacy together with the trading rules is sufficient to produce competitive market outcomes at nearly perfect efficient levels. Information asymmetries are reflected in a broad body of research such as the principle-agent models, which led to the agency models, and the Akerlof’s market for lemons argument. Auction mechanisms help us understand the missing explanation of
Arrow and Debreu’s (1954) argument: in a model of many small buyers and sellers, the market prices end up as ‘given’, through the Walrasian auctioneer’s bargaining mechanism. What if we do not have many sellers and many buyers, and what if prices do not reflect perfect information? This leads to another implication: what if the traded commodity is an intangible resource, such as human capital? Would the market better value the resource through an auction mechanism because a private value would emerge, or would the value be efficiently reflected in market prices?
An auctioneer can control the auction rules, but in B2B online auctions, the market makers are allowing buyers to set the rules for the exchange to a great extent. In the reverse auction formats discussed here, information and valuations of the bidders are guided considerably by auction design. It is therefore critical to understand the outcomes of a particular auction structure before entering into this exchange. There are two important components to auctions: valuation and strategic bidding. Theoretically, an item’s value is determined by its common value, and its private value. Common value component is the same for every bidder, that is, each bidder receives the same amount of value for the good, but he or she may not know the value at the time of bidding. Most often, goods that are auctioned off for resale purposes represent common values. In
121 economics literature, oil, mineral, treasury bills are given as examples for goods that represent common values for bidders. Private values are unique for each product, and such values vary from bidder to bidder. Through strategic bidding in auctions, firms can generate economic rents. If bidders overvalue a good, they will overbid the true value of a good, and win the contract, but most likely be subject to winner’s curse. In fact, auctions for common value goods are most often under the threat of winner’s curse; a good example is the US Treasury Securities markets (Bikhchandani & Huang, 1993). In private value auctions (for goods with high private values), winner’s curse dissipates.
When we consider the neoclassical theory of the firm, and the industrial organization theories of the firm, level of aggregation is quite different. In neoclassical analysis, firms take prices as given, decisions of firms and consumers are aggregated to obtain total demand and supply, and the objective is to explain market equilibrium outcomes. In a frictionless world that is free of transaction costs, markets clear miraculously, thanks to the Walrasian auctioneer, through a continuous adjustment of prices and quantities, hence the market remains at equilibrium without unemployment, excess capacity, or shortages (Spulber, 1999). Industrial organization theories of the firm has eliminated the Walrasian auctioneer, and introduced strategic pricing models.
According to this perspective, competitors choose prices, products or investments, and managers are strategy makers. The IO perspective has not integrated organizational issues and ignored the intra-industry differences among firms (Rumelt, 1984). Transaction cost perspective has linked the firm and its trading partners. The individual contract is a result of negotiations and it is endogenously determined between parties. Vickrey (1960) presented the following question: “Is there a way to sell a parcel that will eliminate
122 strategic (untruthful) bidding, to ensure that the parcel will be sold to the buyer with the highest valuation?” (Makowski and Ostroy, 2001: 516) Vickrey offered the solution as offering the parcel to the highest bidder, but making him pay only the second highest bid.
This way, strategic misrepresentation of true values will disappear; hence, the dominant strategy remains to be bidding one’s true value. If the theory prediction is this straightforward, why do we not see one dominant auction mechanism? Furthermore, when factors of production are scarce, how can firms outperform their rivals and attain these resources?
Makadok (2001) is a good starting point. Building on Barney (1986), Amit and
Schoemaker (1993), and Mahoney and Pandian (1992), Makadok illustrates how rent generation differs in resource-picking and capability-building mechanisms using an example that utilizes an English auction format. Makadok (2001) points out to three stringent assumptions in his analysis: perfect inelasticity in the supply of the resource, single resource (a patent) in question, and two-bidder English auction. On these three assumptions this paper and Makadok (2001) differ. First, he assumes that supply of the resource in question is perfectly inelastic, and hence represents rarity, inimitability, and unsubstitutability. Although it is true that some resources display these three qualities, functionally identical resources are available in markets. Second, firms rarely enter auction markets for procurement of single items. Usually, substitutes and complements
(bundles) are procured using the same mechanism. The B2B auctioneers described in this paper often facilitate and encourage multiple items to be bid as close substitutes or bundles. A combined comment to assumptions one and two is that a patent is a very specific item, and most of the time, it is hard to find a lot of buyers for such specific and
123 unique products. Small firms, inventors and entrepreneurs might find it more efficient and feasible to sell such unique products through negotiated contracts. Finally, the
English auction format is not a common format for B2B buyers. Most of the buyer driven markets can be characterized for oligopsonies, where reverse auction mechanisms are used. While eBay and Amazon might fit the model parameters in Makadok (2001) for individual sellers, B2B auctions characterized in this paper focus on rent generation opportunities of buyers.
Barney’s strategic factor markets argument (1986) rests strongly on rent generation potential of buyers by creating heterogeneous expectations about a resource. If market bidders had similar expectations, the winners of contracts would have paid too much, and diminish all rents during the course of bidding. Hence, Ricardian rents arise in auction markets where private values create heterogeneous expectations about the future value of resources. This paper focuses on which auction mechanisms should be used to generate economic rents, while Makadok (2001) focuses on when firms should pursue resource picking versus capability building. From this perspective, they are complementary. There is a great deal to pursue in the field of B2B auctions. In this paper several strategies for firms to create economic rents using auction mechanisms were proposed by endogenizing the pricing mechanism as a component of firm’s competitive strategy. There are still many unanswered questions. For example, from a managerial perspective, how should a firm manage its procurement function? Should it be internalized, or outsourced? From an organizational perspective, how do trust, reputation and network ties fit the overall picture?
124 The extent, to which a seller's expected revenues are maximized given informational constraints in the environment, is the efficiency of the market. Therefore, when we are studying which market mechanism is going to generate rents for the entrepreneur more efficiently, it is important to consider several criteria that affect pricing of issues. From the auction literature, we know how different auction forms affect expected revenues both theoretically (Milgrom and Weber, 1982) and empirically (Holt,
1995). In practice, the efficiency of market design has been a central concern in the auctioning of treasury bills, securities, telecommunication licenses. In the case of entrepreneurial firms, the principle features are similar to established financial markets, with one important difference: the firm is better informed than the market (Myers and
Majluf, 1984). In a competitive IPO market, the stocks are priced such that the investors break even and the entrepreneurs decide whether to sell or not. If they think this price is fair, they sell, but if the anticipated price is not achieved, the underpricing implies a market failure, and investment opportunities are wasted (Giammarino and Lewis, 1989).
In this context, two issues are at the center of efficiency arguments: underpricing, and undersubscription.
In a bargaining setting, where the bookbuilding method provides a negotiation market during the roadshow, the entrepreneur and the institutional investors try to determine a demand schedule. The efficiency of this process is often criticized for intentional underpricing, and for the information asymmetries between the firm and the underwriters (Sherman and Titman, 2001). Compared to the bookbuilding negotiations, the posted price mechanism (fixed-price contracts) with nonbinding communication maximizes the seller's expected revenue given informational constraints. If the
125 entrepreneur selects among the potential investors who announce higher reservation prices, and restricts allocations, he/she can eliminate underreporting in the first stage of bookbuilding negotiations (Spatt and Srivastava, 1991). This mechanism would be a second price auction where the potential bidders would report their true values. In the case of initial underpricing, the early investors would appropriate the rents, and in the posted price with communication, the entrepreneur would generate and appropriate the rents (if any). In the case of underpricing, the secondary markets would be the fair market. The role of the underwriters in these types of markets can be characterized as intermediaries for ex post Pareto-inefficient agreements, who try to expand the pie in an incomplete information setting61.
The number of shares sold is higher in bookbuilding than in a posted price IPO. In this context, the entrepreneurial firm may be vulnerable to undersubscription, selling far less if the set reservation price is high, or leaving money on the table if the price is set too low (Ritter and Welch, 2002). These problems are less severe in the US and more profound in a large number of countries (Ljungqvist et al., 2001). The bookbuilding allows the underwriter to choose the bidders, whereas the posted price auction does not provide such a mechanism. The Dutch auction IPO, although it is very early to generalize, addresses these shortcomings: by building the demand schedule before the auction it aims to eliminate severe underpricing and undersubscription, and by the
61Early game-theoretic analysis assumed that bargaining for a negotiated agreement would be efficient, and Nash (1950) solved the problem using Pareto optimality. In practice, some entrepreneurs walk away from bargains, or settle for ex-post Pareto inefficient agreements to signal quality (from the interview with WRH). Ritter (2002) discusses these types of signals where high-quality forms demonstrate that they are high quality by throwing money away (leaving money on the table).
126 distribution of shares, tries to prevent informed investors from gaining windfall profits and large orders.
127
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153
APPENDIX A
TABLES AND FIGURES
154 Seller’s Perspective
Factors and Choices That Affect Rent Generation in Factor Markets – Panel A Spot Auctions Negotiations Factors Markets* Conditions Rents Conditions Rents Conditions Rents Private + Resource Value Private + Common 0 Common -
Bargaining Power of Low 0 Low + High + Seller High +
Few + Number of Buyers Few + Many 0 Many 0
Number of Sellers Few + Few + Many +
Risk Propensity Averse + Neutral + Irrelevant N/A
Search Costs High + Moderate + Low +
The conditions presented here are applicable to all firms that sell resources in factor markets. However, for simplicity, an entrepreneurial firm with a unique patent in a biotechnology field is assumed. This firm wants to maximize the economic rents it can generate from the sale of a particular resource. Basically, 5 factors are identified as determinants that affect the choice between the three market mechanisms which in turn affect rent potential: resource’s value to each bidder (common values versus private values), bargaining power of the seller (uniqueness of the patent, and whether substitutes exists), the thickness of the market for both the demand (number of potential buyers), and supply (number of potential sellers), risk taking propensity of the seller (overconfidence is intentionally omitted from the analysis), and the existence of search costs. Economic rents generated using various market mechanisms are shown as “+, 0, -”. While firms will not purposefully enter transactions that will earn them negative rents, in this table these represent opportunity costs. Furthermore, distinctions between the ‘types’ of economic rents are not provided here. *Unless otherwise stated, existence of many buyers in spot markets is the default.
Table 1. Market Mechanisms and Rents (Seller)
155
Buyer’s Perspective
Factors and Choices That Affect Rent Generation in Factor Markets – Panel B Spot Auctions Negotiations Factors Markets* Conditions Rents Conditions Rents Conditions Rents Private + Resource Value Private + Common 0 Common -**
Bargaining Power of Low + High + Low 0 Buyer
Few + Number of Buyers Few + Few + Many 0
Number of Sellers Few + Many + Many 0
Neutral 0 Neutral + Risk Propensity Averse 0 Averse + Averse 0
Search Costs High + Moderate + Low +
The conditions presented here are applicable to all firms that acquire resources in factor markets. However, for simplicity, a pharmaceutical firm is assumed. This firm wants to maximize the economic rents it can generate from the acquisition of a particular resource. Basically, the same 5 factors (as in Panel A) are identified as determinants that affect the choice between the three market mechanisms, which in turn affect rent generation. To avoid agency problems and information asymmetries, it is assumed that the pharmaceutical firm only bids for one unit of an indivisible asset, and the transaction is completed in a single period. If there are synergies and/or externalities, these are realized in the first period. *Unless otherwise stated, existence of many sellers in spot markets is the default. ** A buyer is often subject to winner’s curse when acquiring a common value resource in auctions. In such cases, the rents are at best 0, or nonpositive.
Table 2. Market Mechanisms and Rents (Buyer)
156
2-Digit Industry Industry 2-Digit SIC Industry Industry Classif. Classification SIC Code Code Hotels & Other Lodging 01 Agricultural Production- Crops 70 Places Agricultural Production- 02 72 Personal Services Agriculture Livestock , Forestry, 07 Agricultural Services 73 Business Services & Fishing Auto Repair, Services, & 08 Forestry 75 Parking Miscellaneous Repair 09 Fishing, Hunting, & Trapping 76 Services 60 Depository Institutions 78 Motion Pictures Services Amusement & Recreation 61 Non-depository Institutions 79 Services Security & Commodity 62 80 Health Services Finance, Brokers Insurance, 63 Insurance Carriers 81 Legal Services & Real Insurance Agents, Brokers, & 64 82 Educational Services Estate Service Museums, Botanical, 65 Real Estate 84 Zoological Gardens Holding & Other Investment 67 86 Membership Organizations Offices
Continued
Table 3. Industry Classification - 2-Digit SIC Codes
157
Table 3 continued
Engineering & 20 Food & Kindred Products 87 Management Services 21 Tobacco Products 88 Private Households Services, Not Elsewhere 22 Textile Mill Products 89 Classified Apparel & Other Textile 23 40 Railroad Transportation Products Local & Interurban 24 Lumber & Wood Products 41 Passenger Transit 25 Furniture & Fixtures 42 Trucking & Warehousing 26 Paper & Allied Products 43 U.S. Postal Service 27 Printing & Publishing Transportation 44 Water Transportation & Public Manuf. 28 Chemical & Allied Products 45 Transportation by Air Utilities Pipelines, Except Natural 29 Petroleum & Coal Products 46 Gas Rubber & Miscellaneous 30 47 Transportation Services Plastics Products 31 Leather & Leather Products 48 Communications Electric, Gas, & Sanitary 32 Stone, Clay, & Glass Products 49 Services Public Executive, Legislative, & 33 Primary Metal Industries 91 Administration General Justice, Public Order, & 34 Fabricated Metal Products 92 Safety Industrial Machinery & Finance, Taxation, & 35 93 Equipment Monetary Policy
Continued
158 Table 3 continued
Electronic & Other Electric Administration of Human 36 94 Equipment Resources Environmental Quality & 37 Transportation Equipment 95 Housing
Administration of 38 Instruments & Related Products 96 Economic Programs Miscellaneous Manufacturing National Security & 39 97 Industries International Affairs Building Materials & Nonclassifiabl Non classifiable 52 99 Gardening Supplies e Est. Establishments 53 General Merchandise Stores 10 Metal, Mining 54 Food Stores 12 Coal Mining Automotive Dealers & Service 55 Mining 13 Oil & Gas Extraction Retail Stations Nonmetallic Minerals, Trade 56 Apparel & Accessory Stores 14 except Fuels Furniture & Home Furnishings General Building 57 Construction 15 Stores Contractors Heavy Construction, 58 Eating & Drinking Places 16 Except Building 59 Miscellaneous Retail 17 Special Trade Contractors Wholesale Trade- Durable 50 Wholesale Goods Trade Wholesale Trade- Non-durable 51 Goods
159
Entrepreneur Private firms
Sell
Bargaining power Market thickness Resource value Risk propensity
M&A IPO Negotiation Auction
Asset Sale Stock Sale Bookbuilding Fixed-Price Ownership of Ownership of Control Cash Flow Private Value Common Rights Rights Value
Generic Model
Choice L Branch 1 Branch
C1│1 C2│1 C1│2 C2│2 C3│2 J
L: Market = auctions versus negotiations J: Mechanism = types of IPO and mid-market M&As
Figure 1. Empirical Model of Auction-Negotiation Mechanism Choices
160 Variables Auction vs. Negotiation Fixed vs. Bookbuilding Asset vs. Stock IPO vs. M&A Common vs. Private Control vs. Cash Flow Prediction for... Probability of Auction Probability of Fixed Probability of Stock Patent originality No prediction/Not included - No prediction/Not included Patent generality No prediction/Not included + No prediction/Not included Tobin's Q + - + High-tech dummy (Hi-Tech=1) + - + Patent dummy No prediction/Not included - No prediction/Not included Same industry dummy No prediction/Not included No prediction/Not included - Return on Assets (ROA) - - + Prior relationships - No prediction/Not included - # of firms in target's industry + + - # of offers sought No prediction/Not included No prediction/Not included - # of offers considered No prediction/Not included No prediction/Not included - Debt/total assets - - + VC involvement + - No prediction/Not included # of advisors of the target No prediction/Not included No prediction/Not included + Lead underwriter reputation No prediction/Not included - No prediction/Not included VC activity in focal industry - - + # of firms x High-tech + - No prediction/Not included # of firms x Low-tech - No prediction/Not included -
Table 4. Summary of Empirical Predictions for the Nested Model for the Choice of Auctions vs. Negotiation
161
Panel A. Number of Sellers for Mid-Market M&A PRIVATE PUBLIC SUBSIDIARY Total Agriculture, Forestry, & Fishing 11 0.23% Construction 49 2 1.08% Finance, Insurance, & Real Estate 310 48 42 8.49% Manufacturing 971 105 235 27.81% Mining 70 13 11 1.99% Retail Trade 167 26 19 4.50% Services 1726 74 138 41.11% Transportation & Public Utilities 329 24 46 8.46% Wholesale Trade 257 14 27 6.32% Total 82.52% 6.49% 10.99%
Panel B. Average of Total Assets of The Seller for Mid-Market M&A ($mil) PRIVATE PUBLIC SUBSIDIARY Median Agriculture, Forestry, & Fishing 21.91 21.91 Construction 12.39 33.25 22.82 Finance, Insurance, & Real Estate 91.43 261.42 133.05 133.05 Manufacturing 16.55 60.35 39.55 39.55 Mining 15.42 58.83 23.68 23.68 Retail Trade 20.18 126.15 32.31 32.31 Services 8.51 43.18 25.50 25.50 Transportation & Public Utilities 20.18 31.53 57.19 31.53 Wholesale Trade 16.70 80.06 38.05 38.05 Median 16.70 59.59 38.05 Data Source: DoneDeals Jun 1994-Dec 2000 id-market M&As refer to transactions involving targets with $1M-$250M USD.
Table 5. Number of Sellers for Mid-Market M&A Acquisitions
162
PRIVATE PUBLIC SUBSIDIARY Industry Category (2-digit SIC Stock Asset Stock Asset Stock groupings) Asset Sale Mixed Mixed Mixed Percent Sale Sale Sale Sale Sale Agriculture, Forestry, & Fishing 3 3 6 0.25% Construction 5 33 11 1 1 1.07% Finance, Insurance, & Real Estate 29 142 140 26 22 5 25 12 8.39% Manufacturing 173 477 321 7 54 44 106 69 60 27.42% Mining 2 22 78 1 4 21 5 1 21 3.24% Retail Trade 56 50 62 4 15 7 9 5 5 4.45% Services 269 908 553 17 37 20 49 55 34 40.61% Transportation & Public Utilities 55 116 158 9 5 10 15 19 12 8.34% Wholesale Trade 45 127 85 3 7 4 13 10 4 6.23% Percent 13.32% 39.27% 29.57% 0.88% 3.09% 2.70% 4.22% 3.85% 3.09% 100.00% Sub-group Total Percent 82.2% 6.7% 11.2%
Continued
Table 6. Average Ratio of Price Paid over Total Assets for Mid-Market M&A
163 Table 6 continued
Panel B. Average Ratio of Price Paid over Total Assets for Mid-Market M&A PRIVATE PUBLIC SUBSIDIARY Industry Category (2-digit Asse Stoc Asse Stoc Asse Stoc Mixe Mixe Mixe Media SIC groupings) t k t k t k d d d n Sale Sale Sale Sale Sale Sale Agriculture, Forestry, & Fishing 1.63 0.93 2.25 1.63 Construction 1.18 2.77 1.17 1.02 0.93 1.17 Finance, Insurance, & Real Estate 7.85 2.86 34.75 6.773.88 0.87 0.45 0.28 3.37 Manufacturing 4.89 7.58 2.28 1.94 2.06 1.25 1.58 1.41 1.60 1.94 Mining 2.46 4.35 2.60 1.43 1.34 1.27 1.48 1.84 1.13 1.48 Retail Trade 2.04 2.10 2.23 1.51 0.91 1.45 1.84 1.19 1.00 1.51 21.1 Services 5.89 8 3.86 1.61 5.06 19.28 6.15 3.45 5.52 5.52 Transportation & Public Utilities 3.43 7.11 3.47 2.09 1.33 3.08 2.00 2.16 2.37 2.37 Wholesale Trade 1.79 4.85 2.23 0.99 1.51 0.91 1.37 2.25 0.73 1.51
Median 2.46 4.35 2.28 1.51 1.51 1.36 1.58 1.84 1.13 Sub-group Median 2.77 1.48 1.58
164
Construction Finance, Insurance, & Real Estate Average Average Offering Average Year Average Average Offering Revenue price Revenue Debt/Equity Debt/Equity price ($per ($per share- ($per ($per share- (LTM) (LTM) share) LTM) share) LTM) 1975 14.63 1976 10.55 1977 9.21 30.20 7.80 1978 5.25 10.10 0.71 1979 95.41 1980 10.00 9.42 1981 5.00 6.60 4.52 7.77 1982 10.00 11.95 1.80 26.98 1983 8.00 11.80 82.90 11.45 23.35 21.33 1984 7.00 9.33 21.43 25.48 1985 4.71 12.00 32.82 36.17 1986 8.81 10.50 31.96 10.87 33.01 32.86 1987 8.59 9.87 10.95 13.46 1988 8.50 9.82 16.79 9.41 1989 13.33 10.14 10.60 12.11 1990 17.00 3.20 40.50 11.44 11.54 21.28 1991 11.00 12.95 13.49 19.10 1992 11.24 9.57 25.38 13.65 44.96 13.08 1993 13.45 6.10 69.38 15.94 40.23 12.58 1994 11.04 6.65 106.01 14.52 26.91 78.60 1995 5.75 15.00 20.25 12.04 1996 9.22 4.95 19.62 12.58 20.39 11.10 1997 14.33 14.70 28.86 15.61 26.05 10.89 1998 10.10 8.41 32.13 13.28 11.98 10.92 1999 18.00 31.20 76.35 14.76 19.01 12.97 2000 9.25 4.40 1.71 13.29 14.88 13.93 Median 10.00 7.53 32.05 11.98 20.25 13.08 LTM: Most recent 12 months prior to going public
Continued
Table 7. Financial Information for Private Company IPOs
165 Table 7 continued
Manufacturing Mining Average Average Offering Year Offering Average Average Revenue Revenue price price ($per Debt/Equity Debt/Equity ($per ($per share- ($per share) (LTM) (LTM) share- LTM) share) LTM) 1975 16.20 14.90 90.02 1976 13.61 3.14 30.03 6.49 1977 13.22 5.80 24.20 11.30 1978 9.98 6.10 11.92 0.18 1979 9.77 3.03 14.67 7.77 33.45 0.89 1980 10.00 5.65 12.32 6.62 1981 8.09 3.53 251.21 8.04 11.70 5.23 1982 9.03 4.82 11.56 3.05 1983 9.85 6.48 22.95 6.56 6.20 16.38 1984 6.60 8.32 13.60 10.17 6.30 3.84 1985 7.35 8.09 19.27 20.20 0.69 1986 8.52 7.91 17.21 12.80 1987 8.42 7.72 15.54 13.58 1988 7.66 39.83 19.52 14.92 10.50 13.50 1989 8.26 9.67 12.91 14.42 7.05 39.74 1990 8.58 3.98 11.60 12.21 9.28 10.11 1991 10.65 14.61 270.25 11.21 1992 10.64 6.88 13.92 10.38 4.10 6.30 1993 10.87 8.19 13.99 14.82 8.49 14.94 1994 10.16 9.74 13.07 15.38 4.90 16.64 1995 12.58 33.33 9.97 16.53 4.88 19.72 1996 10.90 85.08 8.50 13.38 3.14 8.16 1997 11.25 12.51 10.79 15.01 7.95 4.40 1998 12.77 8.51 11.50 11.88 4.40 5.45 1999 14.91 7.06 18.05 14.80 9.90 11.24 2000 15.89 4.68 4.05 13.79 7.04 6.34 Median 10.08 7.82 13.95 12.21 7.05 8.16 LTM: Most recent 12 months prior to going public
Continued
166 Table 7 continued
Transportation & Public Utilities Wholesale Trade Average Average Year Offering Average Offering Average Revenue Revenue price ($per Debt/Equity price ($per Debt/Equity ($per ($per share- share) (LTM) share) (LTM) share- LTM) LTM) 1975 1976 9.50 1977 7.33 1978 12.10 0.88 1979 10.33 13.00 1980 8.57 5.30 1981 7.29 20.20 23.70 5.95 3.70 73.33 1982 7.50 13.95 12.81 9.38 1983 9.98 13.07 35.01 7.20 11.20 34.05 1984 7.40 11.53 23.92 4.96 1985 6.25 13.70 14.35 6.43 10.90 15.46 1986 10.62 5.68 24.74 7.06 7.17 126.46 1987 10.56 44.60 29.34 6.73 6.66 44.99 1988 12.13 8.50 28.66 4.57 14.60 68.42 1989 18.16 29.60 4.73 7.32 13.77 31.50 1990 10.81 11.03 19.86 8.04 16.97 34.08 1991 13.52 14.95 26.45 8.81 4.00 40.19 1992 10.62 40.22 20.89 8.86 3.60 48.48 1993 13.25 29.58 37.22 9.45 5.85 55.26 1994 13.24 16.50 21.24 12.14 17.48 54.28 1995 12.94 15.06 14.34 10.61 5.85 74.91 1996 14.11 28.70 18.66 10.67 6.77 46.57 1997 14.97 16.92 13.91 9.65 8.48 35.34 1998 15.30 11.68 17.02 9.19 9.78 56.75 1999 17.16 21.95 7.13 11.71 11.93 10.94 2000 82.28 27.20 3.58 9.00 Median 10.81 15.78 20.38 8.81 8.48 46.57 LTM: Most recent 12 months prior to going public
167
Finance, Retail Wholesale Year Construction Insurance, Manufacturing Mining Total Trade Trade Real Estate
1975 1 5 6 1976 5 22 3 3 39 1977 7 14 2 1 32 1978 5 19 2 4 2 38 1979 12 26 8 1 1 62 1980 1 18 64 29 4 7 148 1981 2 21 152 47 18 15 346 1982 1 14 61 4 11 2 149 1983 8 123 326 9 82 39 842 1984 3 79 157 8 30 25 474 1985 5 110 127 9 42 24 437 1986 9 251 247 7 75 33 863 1987 11 181 215 16 52 40 706 1988 1 136 101 5 17 13 336 1989 3 72 100 9 18 11 286 1990 2 81 89 22 13 11 285 1991 4 95 166 8 41 27 500 1992 10 173 276 12 74 27 767 1993 10 281 313 28 75 34 969 1994 11 194 309 14 48 36 859 1995 2 90 276 17 33 29 725 1996 13 96 342 23 69 56 1074 1997 12 118 266 17 43 48 846 1998 16 161 151 2 36 25 642 1999 2 121 116 12 46 13 739 2000 8 42 285 18 33 7 911 Total 134 2487 4225 331 869 525 13081 Fraction 1.02% 19.01% 32.30% 2.53% 6.64% 4.01% 100%
Table 8. IPO issues by Private Firms
168
IPO M&A SIC N Fraction% N Fraction% 20 169 4.04 3795 11.24 21 7 0.17 60 0.18 22 51 1.22 849 2.51 23 91 2.18 866 2.57 24 41 0.98 657 1.95 25 36 0.86 728 2.16 26 67 1.60 962 2.85 27 120 2.87 3403 10.08 28 681 16.30 3179 9.42 29 18 0.43 239 0.71 30 78 1.87 1424 4.22 31 27 0.65 231 0.68 32 48 1.15 1365 4.04 33 111 2.66 1256 3.72 34 100 2.39 1987 5.89 35 649 15.53 4200 12.44 36 859 20.56 3538 10.48 37 168 4.02 1635 4.84 38 718 17.19 2399 7.11 39 139 3.33 988 2.93 Total 4178 100 33761 100
Table 9. Manufacturing Sector by 2 Digit SIC Codes
169
IPO M&A Country N Fraction% N Fraction% Canada 46 1.10 1090 3.23 China 3 0.07 357 1.06 France 13 0.31 2442 7.23 Germany 9 0.22 2528 7.49 Hong Kong 18 0.43 169 0.50 Israel 55 1.32 92 0.27 Italy 6 0.14 1335 3.95 Japan 4 0.10 396 1.17 Mexico 13 0.31 167 0.49 Netherlands 19 0.45 683 2.02 Singapore 8 0.19 154 0.46 Sweden 9 0.22 414 1.23 Switzerland 5 0.12 432 1.28 United Kingdom 14 0.34 5207 15.42 United States 3898 93.30 11858 35.12 Other 58 1.39 6437 21.10 Total 4178 100 33761 100
Table 10. Manufacturing Sector by Target Nation
170
IPO Panel: Public offerings of common stock for private companies in the manufacturing sector during 1970-2000 from the Global New Issues Database. M&A Panel: The mergers and acquisitions of private companies of Both US targets (in 1979-2000) and non-US targets (between 1985-2000) from the Worldwide M&A database. Value range represents the focal firm's transaction value in US$ Millions. M&A Panel IPO Panel Ranking Proceeds Value inc. Market Amount + Market Value Range Net Debt of Share Overallotment Share Target Sold ($ Mil) ($ Mil) % ($ Mil) % 0.0 - 199.9 242797.1 44.5 284494.2 59.2 200.0 - 399.9 77757.4 14.2 69238.9 14.4 400.0 - 599.9 48868.0 9.0 34557.4 7.2 600.0 - 799.9 29031.4 5.3 13821.7 2.9 800.0 - 999.9 20440.8 3.7 16562.1 3.5 1000.0 - 1199.9 14272.6 2.6 10768.8 2.2 1200.0 - 1399.9 16744.6 3.1 7694.8 1.6 1400.0 - 1599.9 10240.9 1.9 4418.3 .9 1600.0 - 1799.9 5013.3 .9 4862.8 1.0 1800.0 - 1999.9 9775.7 1.8 5718.3 1.2 2000.0 - 2199.9 8436.4 1.5 6209.9 1.3 2200.0 - 2399.9 6901.8 1.3 2368.7 .5 2400.0 - 2599.9 - - 4976.1 1.0 2600.0 - 2799.9 2600.0 .5 10828.1 2.3 2800.0 - 2999.9 - - - - 3000.0 - 3199.9 6032.1 1.1 - - 3200.0 - 3399.9 6596.4 1.2 - - 3400.0 - 3599.9 6871.2 1.3 - - 3600.0 - 3799.9 - - - - 3800.0 - 3999.9 - - 3841.3 .8 4200.0 - 4399.9 4300.7 .8 - - 5800.0 - 5999.9 5900.0 1.1 - - 6400.0 - 6599.9 6448.0 1.2 - - 6800.0 - 6999.9 6916.1 1.3 - - 10200.0 - 10200.0 10200.0 1.9 - - Industry Total 546144.6 100.0 480361.5 100.0
Table 11. Price Range for the IPO and M&A Deals
171 Table 7. Mean and Median of Explanatory Variables IPO Panel: A ll the firm s in the sam ple are original IPO s of private firm s. Their com m on stock has never traded publicly in any m arket and is offered in its initial public offering. Patent originality and generality are from H all et al. 2001. Tobin’s Q is defined as the sum of the book value of assets and the market value of equity net of the book value of equity over the book value of assets. I use the m iddle price, in the original filing price range, at w hich the issuer expects securities to be offered. Firms’ grouping under high-tech dummy is based on SDC definitions of the high tech industry in which the issuer is involved as its primary line of business. Patent dummy is constructed by matching the NBER Pat63_99 dataset with the Compustat assignee number, excluding all firms without cusips. The # of firms in target’s industry in the deal’s year are determined using the Compustat given 2 digit SIC codes. Return on assets to control for profitability, (earnings from total operations divided by the total assets) is expressed as a percentage. The debt/total assets ratio is the fiscal year total debt divided by the total assets, expressed as a percentage. Institutional ownership is the percentage of common stock held by all reporting institutions on the corresponding institutional holdings date. VC involvement is total investment received by company to-date, and is measured b y the sum of all rounds of financing a company has received throughout its lifetime. The # of advisors of the target are the number of book managers that maintain records of activity for the syndicate and underwrites the largest portion of the securities. For the lead underwriter reputation, Carter-M anaster (1990) rankings with Jay Ritter’s modifications is used. Debt/Equity ratio is the fiscal year long-term debt divided by the total common equity. M&A Panel: A ll the firm s in the sample are completed deals involving private target firms. For related prior deals, I use the dummy for the type of the deal that is related to the merger transaction, e.g. JV. Same industry dummy is used using targets’ and acquirer’s 2-digit SIC codes. The toehold in the firm is shares owned after the acquisition minus shares acquired. The number of offers sought is the number of offers made by the acquirer to the target to be considered. The number of offers extended is the number of times, which the offer was extended of common, or common equivalent, shares outstanding sought by the acquirer. Revenue per share equals the revenue divided by the most recent common shares outstanding. Panel A: Initial Public Offerings (IPOs) All IPOs Bookbuilding Fixed-Price Variables n Mean Median n Mean Median n Mean Median Patent originality 372 0.36 0.37 95 0.36 0.42 277 0.36 0.37 Patent generality 382 0.43 0.41 103 0.40 0.40 279 0.44 0.42 Tobin's Q 2820 1.04 1.00 338 1.13 1.00 2482 1.03 1.00 High-tech Dummy (High=1, Other=0) 2820 0.36 0.00 338 0.44 0.00 2482 0.35 0.00 Patent Dummy (Patent=1, No patents=2) 2820 1.86 2.00 338 1.69 2.00 2482 1.89 2.00 # of firms in target's 2-digit SIC industry 2820 787.74 1006.00 338 751.74 1006.00 2482 792.64 1006.00 Debt/total assets (%) 2820 84.07 100.00 338 86.18 100.00 2482 83.26 100.00 Number of Institutional Owners 58 148 138.50 29 161.00 221.00 29 135.00 73.00 Institutional Ownership (%) 58 51.66 54.35 29 57.40 73.00 29 45.92 40.20 VC Backed dummy (VC Backed=1, Not=0) 2820 0.48 0.00 338 1.00 1.00 2482 0.41 0.00 Lead underwriter reputation 2557 1.41 4.10 215 1.17 4.10 2342 1.43 4.10 VC activity in focal industry 2820 0.76 1.00 338 0.74 1.00 2482 0.76 1.00 Debt/Equity 807 0.57 0.59 91 0.51 0.48 716 0.58 0.59 Number of Employees 737 964 164 23 17.89 21.10 714 657.67 63.18 Return on Assets (LTM) 828 -14.17 -1.00 92 -8.37 1.25 736 -14.90 -1.40 Number of observations in the full sample 2820 337 2477 Panel B: Mergers and Acquisitions (M&As) All M&As Asset Sale Stock Sale Variables n Mean Median n Mean Median n Mean Median Tobin's Q 34109 1.06 1.00 15243 1.87 1.00 18866 1.39 1.00 High-tech Dummy (High=1, Other=0) 34109 0.69 1.00 15243 0.72 1.00 18866 0.66 1.00 Same industry dummy 34109 0.35 0.00 15243 0.35 0.00 18866 0.36 0.00 Prior deals Dummy (Yes=1, No=2) 31326 1.95 2.00 14068 1.98 2.00 17258 1.93 2.00 # of firms in target's 2-digit SIC industry 34109 590.48 412.00 15243 552.40 343.00 18866 621.25 412.00 # of offers sought 30868 1.01 1.00 14059 1.00 1.00 16809 1.02 1.00 # of offers considered 30786 1.14 1.00 14049 1.11 1.00 16737 1.16 1.00 Debt/total assets (%) 793 19.03 11.19 512 16.60 10.67 281 23.45 12.54 # of advisors of the target 1973 1.04 1.00 1356 1.03 1.00 608 1.04 1.00 VC activity in focal industry 34109 0.55 1.00 15243 0.74 1.00 18866 0.40 0.00 Debt/Equity (%) 688 31.00 29.04 440 30.01 28.25 225 32.95 31.50 Toehold of the buyer (%) 23026 0.81 0.00 11131 0.00 0.00 11895 1.58 0.00 Number of Employees 2301 385.67 120 1309 259.81 100.00 992 551.74 165.00 Return on Assets (LTM) 1279 -2.76 3.11 848 2.81 3.10 431 -13.71 3.25 Number of observations in the full sample 34109 15243 18866
Table 12. Descriptive Statistics
172
Logistic Regression Mechanism Type Choice (IPO=1, M&A=0)
Tobin's Q -0.895 -0.833 -0.815 -0.678 6.97*** 5.88*** 5.83*** 6.34*** VC Industry 0.667 0.376 0.428 0.933 0.591 0.732 0.632 2.77*** 1.45 1.62 4.18*** 3.16*** 3.00*** 2.41** Hi-Tech Industry -1.962 -2.072 -1.728 -2.409 8.94*** 8.92*** 8.23*** 15.57*** Number of Firms in Industry 0.002 0.001 0.001 0.001 0.001 7.52*** 3.78*** 2.08** 3.62*** 2.10** Debt/assets 0.633 0.546 0.67 1.082 3.99 1.336 1.447 1.92* 1.74* 2.11** 3.33*** 12.79*** 3.70*** 3.74*** ROA 0.003 0.004 0.004 0.004 0.002 0.003 0.004 2.51** 2.77*** 2.86*** 3.00*** 1.91* 3.40*** 4.18*** Industry Market/Book Value of Equity -0.793 0.145 -0.435 -0.054 -0.52 -0.538 -0.389 14.25*** 7.51*** 1.59 0.21 2.76*** 2.11** 1.44 Industry Debt/Equity -1.466 -1.03 -1.343 -0.039 0.772 -0.718 -4.085 -4.149 11.51*** 8.14*** 10.27*** 0.06 1.19 1.35 7.60*** 7.02*** Industry Tobin's Q 1.894 0.524 1.283 0.942 1.376 2.643 2.378 18.66*** 14.51*** 2.34** 1.77* 3.68*** 5.22*** 4.44*** 1-year Lagged S&P Index Return 2.533 2.407 2.451 -1.314 -1.33 -0.442 -0.945 9.763 15.11*** 14.30*** 14.61*** 2.31** 2.43** 0.99 1.85* 6.59*** 1-year Lagged Nasdaq Index Return -3.846 -3.372 -3.583 0.713 1.631 0.905 1.222 -5.915 13.75*** 12.00*** 12.75*** 0.63 1.51 1.09 1.2 3.88*** 1-year Lagged Total Value of IPOs over M&As 0.32 0.376 0.384 0.093 0.273 0.198 0.028 1.976 12.48*** 14.94*** 15.24*** 0.7 1.98** 1.94* 0.25 5.44*** Number of Firms in Hi- Tech Industry -0.002 -0.002 8.84*** 8.43*** Number of Firms in Industry x Tobin's Q -0.001 -0.001 9.62*** 9.34*** 1-year Lagged Nasdaq Index Return for Top Decile -11.138 5.52*** 1-year Lagged S&P Index Return for Top Decile 3.809 1.88* Constant 0.828 2.428 -2.86 -3.574 -3.337 2.293 -1.259 0.157 -0.82 2.28** 0.430*** 34.45*** 52.83*** 43.40*** 3.97*** 3.23*** 0.33 1.49 N 1597 1597 36812 36812 36812 1569 1569 1569 1569 1569 LR Chi-squared 1475.67 1561.24 1303.88 1087.8 943.43 1544.48 1528.83 914.65 1398.69 1492.27 Prob>Chi-squared 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Log Likelihood -368.032 -325.245 -9023.288 -9131.325 -9203.509 -315.004 -323.132 -629.915 -387.894 -341.105 2 Pseudo R 0.667 0.706 0.067 0.056 0.049 0.710 0.703 0.420 0.643 0.686
Table 13. Logistic Regression of the Mechanism Choice (IPO=1, M&A=0) 173
Probability Modeled is Mechanism=IPO Model 1 Model 2 Variable Estimate Wald Chi-Square Estimate Wald Chi-Square Intercept -- - - Bargaining Power Patent Dummy Patent=1 6.569 48474.427*** 3.377 184795.002*** No Patent=0 Resource Value High tech Dummy High tech = 1 -- -- Other = 0 Patent Originality - - -0.109 51.885*** Patent Generality - - 0.236 254.000*** Exogenous Factor Year Dummy Mixed Significance Wald Test of Model Fit Chi-Square 77626.113 Prob>ChiSq <0.0001 R-Square 0.618 0.669 Number of Observations IPO 112664 61321 Not IPO 112317 1873391 Log-Likelihood 216271.16*** 2138360.32*** Hosmer-Lemeshow Test 6267.561*** 23.545*** * Not IPO category includes private target firms that are acquired and private firms that continued to be privately held. Private firms that are acquired do not become public, hence their Cusips do not match the Cusips at Hall et. al. (2001) patent database.
Table 14. Logit Model Coefficient Estimates For The Choice of Auctions (IPOs) vs. Not IPO*
174
Probability Modeled is Mechanism=IPO Model 1 Model 2 Model 3 Variable Estimate Wald Chi-Square Estimate Wald Chi-Square Estimate Wald Chi-Square Constant 236.780 3.54* -206.963 113.65*** 239.000 3814.280*** Bargaining Power Patent Dummy Patent=1 -5.898 31.71*** - - -5.540 30197.282*** No Patent=0 Resource Value High tech Dummy High tech = 1 - --- -0.438 310.759*** Other = 0 Patent Originality - - 1.047 52.015*** - - Patent Generality - - -0.490 11.544*** - - Exogenous Factor Year Dummy Mixed Significance Mixed Significance Mixed Significance Wald Test of Model Fit Chi-Square 46810.690 31142.780 34694.957 Prob>ChiSq <0.0001 <0.0001 <0.0001 R-Square 0.625 0.513 0.547 Number of Observations IPO 112664 61321 61740 M&A 112317 2656 111230 Log-Likelihood 46810.692*** 3537.845*** 136863.63***
Table 15. Logit Model Coefficient Estimates For the Choice of Auctions (IPOs) vs. Negotiations (M&As)
175
Model A BC D Coef. z Coef. z Coef. z Coef. z Fixed -0.357 2.88*** -2.031 6.93*** -0.785 4.20*** -1.982 6.59*** Tobin's Q x Asset 0.319 3.72*** 0.388 3.66*** 0.336 3.68*** 0.388 3.59*** Stock 0.327 3.81*** 0.396 3.74*** 0.342 3.74*** 0.396 3.66*** Fixed 1.290 5.86*** -0.069 0.21 0.798 3.11*** -0.096 0.28 VC Industry x Asset -0.793 2.79*** -0.698 1.73* -0.660 1.99** -0.686 1.65* Stock -0.716 2.44** -0.546 1.31 -0.745 2.20** -0.642 1.50 Fixed 0.664 2.18** -0.019 0.06 0.549 1.69* 0.052 0.15 Hi-Tech Industry x Asset 3.009 8.67*** 3.919 9.05*** 3.614 9.04*** 3.960 8.83*** Stock 1.787 5.04*** 2.720 6.19*** 2.180 5.42*** 2.746 6.06*** Fixed 0.001 4.97*** 0.000 0.23 0.001 2.71*** 0.000 0.12 Number of Firms in Asset -0.001 1.83* 0.000 0.31 0.001 1.24 0.001 0.88 Industry x Stock -0.001 3.16** 0.000 0.39 0.000 0.19 0.000 0.01 Fixed 0.479 1.49 -0.823 2.59*** -0.108 0.34 -0.755 2.33** Debt/assets x Asset -2.696 5.36*** -2.148 4.21*** -2.090 3.98*** -1.988 3.88*** Stock -1.829 3.52*** -1.116 2.23** -1.406 2.57** -1.047 2.07** Fixed -0.002 1.06 -0.002 1.32 -0.002 1.14 -0.002 1.11 ROA x Asset -0.007 2.86*** -0.007 3.09*** -0.007 2.99*** -0.008 2.91*** Stock -0.007 2.80*** -0.007 3.00*** -0.007 2.94*** -0.007 2.86*** Fixed 0.210 0.74 -0.238 0.78 Industry Market/Book Asset -0.249 0.63 -0.031 0.07 Value of Equity x Stock -0.479 1.17 -0.075 0.17 Fixed 1.721 1.90* -0.183 0.18 Industry Debt/Equity x Asset 0.816 0.74 0.507 0.42 Stock -0.001 0.00 0.072 0.06 Fixed -0.062 0.11 0.393 0.65 Industry Tobin's Q x Asset -0.055 0.07 -0.197 0.23 Stock 0.293 0.36 -0.010 0.01 Fixed 2.133 1.89* 0.921 0.76 1-year Lagged S&P Index Asset -0.801 0.50 -0.499 0.29 Return x Stock -0.402 0.23 0.909 0.47 Fixed -0.702 1.35 -0.605 1.11 1-year Lagged Nasdaq Asset 0.146 0.17 0.133 0.15 Index Return x Stock -0.413 0.43 -0.892 0.84 1-year Lagged Total Fixed 0.602 3.13*** 0.300 1.74* Value of IPOs over Asset -0.644 2.36** -0.624 2.33** M&As x Stock 0.221 0.85 0.369 1.59 Fixed 4.971 7.87*** 4.644 5.85*** Constant x Asset -1.622 2.34** -0.746 0.81 Stock -1.454 2.17** -1.799 1.89* N 6016 6016 5916 5916 LR Chi-squared 2179.89 2337.44 2250.14 2329.22 Prob>Chi-squared 0.0000 0.0000 0.0000 0.0000 Log Likelihood -995.04 -916.26 -925.261 -885.718 Pseudo R2 0.5607 0.5605 0.549 0.568
* significant at 10%; ** significant at 5%; *** significant at 1% The number of firms in target's industry is based on 2 digit SIC codes. Absolute value of z statistics are reported.
Table 16. Conditional Logistic Regression
176
Model AB C D Comparisons Coef. z Coef. z Coef. z Coef. z Tobin's Q -0.374 3.10** 0.302 2.42** 0.539 3.57*** 1.632 6.09*** ROA 0.003 1.84* 0.002 1.03 0.002 1.30 0.002 1.13 Debt/assets -2.535 7.65*** -0.770 2.21** -0.187 0.56 0.831 2.54** H-Tech -0.752 2.52** -0.705 2.35** -0.195 0.60 Industry M/B of equity -0.691 2.59*** -0.278 2.86*** 0.247 0.81 Industry Tobin's Q 0.724 1.34 -0.423 0.71 Fixed vs Bookbuilding # of Firms -0.001 2.66*** -0.001 1.82* 0.000 0.00 VC industry -1.274 5.73*** 0.040 0.12 Industry Debt/Equity 0.308 0.31 1-yr Lagged S&P rtrn -1.011 0.84 1-yr Lagged Nasdaq rtrn 0.753 1.39 IPO/M&A (total value) -0.323 1.80* Constant -4.219 5.50*** Tobin's Q 0.252 9.38*** 0.659 6.29*** 0.875 6.16*** 1.955 7.27*** ROA -0.001 0.73 0.004 3.04*** -0.004 3.14*** -0.004 2.93*** Debt/assets -4.907 16.91*** -2.792 7.05*** -2.080 5.26*** -1.033 2.50** H-Tech 1.936 8.78*** 2.125 9.36*** 3.008 10.53*** Industry M/B of equity -0.506 1.91* -0.573 5.17*** 0.492 1.62 Industry Tobin's Q -0.573 1.01 -1.344 2.21** Fixed vs Asset # of Firms -0.001 2.72*** -0.001 2.36** 0.000 0.79 VC industry -1.541 6.74*** -0.412 1.43 Industry Debt/Equity 0.520 0.69 1-yr Lagged S&P rtrn -1.050 0.80 1-yr Lagged Nasdaq rtrn 1.452 2.02** IPO/M&A (total value) -0.768 3.90*** Constant -4.225 5.93*** Tobin's Q 0.237 8.83*** 0.668 6.38*** 0.884 6.22*** 1.963 7.30*** ROA 0.000 0.29 -0.040 2.68*** -0.004 2.65*** -0.004 2.54** Debt/assets -4.902 16.81*** -1.231 3.38** -0.598 1.74* 0.107 0.30 H-Tech 0.556 2.49*** 0.642 2.84*** 1.346 5.05*** Industry M/B of equity -0.467 1.76* -0.455 4.06*** 0.476 1.58 Industry Tobin's Q -0.334 0.58 -1.347 2.20** Fixed vs Stock # of Firms -0.002 5.24*** -0.002 4.93*** -0.001 3.53*** VC industry -1.296 5.62*** -0.303 1.02 Industry Debt/Equity -0.271 0.36 1-yr Lagged S&P rtrn -1.294 0.97 1-yr Lagged Nasdaq rtrn 1.869 2.72*** IPO/M&A (total value) 0.188 1.47 Constant -4.453 6.33*** N 1597 1597 1597 1569 Log Likelihood -1390.419 -1116.364 -1086.956 -997.800 Wald Test 1646.990 2195.100 2253.910 1739.670 Prob>Chi2 0.000 0.000 0.000 0.000 Pseudo R2 0.372 0.496 0.509 0.466 Omitted Chi2 Omitted Chi2 Omitted Chi2 Omitted Chi2 Small-Hsiao tests of IIA 2 27.14** 2 29.62 2 87.05*** 2 17.46 Assumption+ 3 10.62 3 21.42 3 115.94*** 3 43.05*** 4 13.70 4 43.15*** 4 95.90*** 4 30.48*** Omitted Chi2 Omitted Chi2 Omitted Chi2 Omitted Chi2 Hausman tests of IIA 2 656.55*** 2 8.71 2 28.232*** 2 -53.116 assumption+ 3 422.23*** 3 97.06*** 3 -89.61 3 27.977 4 246.23*** 4 178.18*** 4 106.542*** 4 0.058 +Ho: Odds(Outcome-J vs Outcome-K) are independent of other alternatives.The number of firms in target's industry is based on 2 digit SIC codes. Absolute value of z statistics are reported.* significant at 10%; ** significant at 5%; *** significant at 1%
Table 17. Multinomial Logistic Regression
177
Mod el 1234567 Variable x M echanism Tobin's q x Fixed -0.89 6.105 6.83*** 3.69*** Asset 1.651 0.97 5.022 4.99*** 5.20*** 3.88*** Stock 1.024 5.082 5.45*** 3.91*** VC Industry x Fixed 3.56 -6.468 11.13*** 3.21*** Asset -1.171 -0.701 -5.823 2.12** 0.98 3.25*** Stock -0.549 -5.796 0.56 3.05*** High-tech Dummy x Fixed -2.422 -3.666 6.55*** 2.29** Asset 6.44 9.767 6.379 11.19*** 5.74*** 2.92*** Stock -5.194 -8.386 2.25** 2.85*** # of targets x Fixed 0.003 -0.009 10.50*** 2.93*** Asset 0.0002 0.002 -0.006 0.68 2.29** 2.29** Stock -0.002 -0.01 1.73* 3.36*** Debt/Asset x Fixed 3.837 -10.188 7.98*** 2.11** Asset -2.348 -9.943 -18.134 2.90*** 5.23*** 4.34*** Stock 6.778 -2.501 3.77*** 0.5 ROA x Fixed 0.009 0.015 0.0006 2.86*** 2.14** 0.61 Asset -0.01 -0.014 0.001 3.21*** 3.19*** 0.1 Stock 0.004 0.015 0.85 2.01** # of targets x Low tech x Fixed -0.001 0.001 4.51*** 1.99** # of targets x Low tech x Stock # of targets x High tech x Fixed -0.003 -0.0001 0.0005 6.16*** 1.37 1.5 V C involvement x Fixed -0.284 -1.089 4.20*** 2.46** Number of patents x Fixed
Patent originality x Fixed 0.527 0.969 1.73* 1.17 Patent generality x Fixed 0.931 1.177 (.) (.) Invsetment bank rep. x Fixed -0.001 0.003 0.20 0.27 Same Industry x Stock Estimated probality of type choice from clogit x IPO 4.276 (.)
Estimated probality of mechanism choice from mlogit 8.474 18.46*** Type dummy (Ipo=1, M&A=0) -14.655 4.30*** Constant (IV for IPO) 3.787 102.876 13.845 13.479 -0.766 0.203 0.436 12.20*** 4.27*** 3.19*** 2.94*** -4.53*** 4.37*** 2.31** Correlation within IPO 0.41 0.96 0.81
Constant (IV for M&A) 5.285 97.297 19.416 -5.606 -1.848 -26.374 -28.529 14.83*** 4.09*** 4.36*** -2.57** -4.32*** -0.03 -0.01 Correlation within M & A LR test of homeskedaticity (IV=1) Chi2 446.28*** 942.17*** 118.95*** 128.40*** 125.55*** 116.36*** Observations 6016 6016 6016 6016 6016 1180 1180 Number of groups 1504 1504 1504 1504 1504 295 295 Log Likelihood -1137.58 -1251.61 -1185.33 -1082.08 -118.79 -100.68
Table 18. Nested Logit-Part 1
178 M o d el 9 10 11 12 13 14 15 Variable x M echanism Tobin's q x Fixed -0.182 -0.526 -0.399 -1.874 -0.904 1.68* 1.01 1.57 2.40** 2.76*** Asset
Stock
VC Industry x Fixed 0.303 0.188 0.884 0.445 2.191 1.243 1.134 2.10** 1.83* 2.44** 2.55** 1.73* 2.05** 2.92*** Asset Stock High-tech Dummy x Fixed 0.39 0.244 1.457 0.725 2.254 1.63 1.52 2.49** 2.60*** 1.64 Asset Stock
# of targets x Fixed 0.0001 0.0001 0.48 0.28 Asset
Stock
Debt/Asset x Fixed -0.674 0.052 -0.104 1.1 0.13 0.2 Asset Stock ROA x Fixed -0.003 -0.002 -0.01 0.42 0.39 1.21 Asset Stock # of targets x Low tech x Fixed 0.001 0.0001 0.003 0.001 0.002 0.001 0.001 5.61*** 2.11** 5.29*** 5.41*** 1.61 1.09 1.79* # of targets x Low tech x Stock
# of targets x High tech x Fixed 0.0001 0.0001 0.0001 0.0001 -0.002 0.84 0.7 0.72 0.28 1.28 VC involvement x Fixed -1.362 -0.553 -2.622 -1.38 -1.828 -1.325 -2.108 (.) 2.50** (.) (.) 1.67* 2.24** (.) Number of patents x Fixed 00 1.14 1.28 Patent originality x Fixed -0.007 0.062 1.692 0.02 0.28 2.09** Patent generality x Fixed 1.205 0.597 1.371 2.461 1.651 1.177 2.86*** (.) 3.36*** (.) (.) 1.69* Invsetment bank rep. x Fixed Same Industry x Stock Estimated probality of type choice from clogit x IPO
Estimated probality of mechanism choice from mlogit
Type dummy (Ipo=1, M&A=0) Constant (IV for IPO) 0.447 -54.584 0.997 0.483 0.606 0.42 0.582 6.71*** 2.50** 5.48*** 3.43*** 19.51*** 2.51** 4.35*** Correlation within IPO 0.80 0.01 0.77 0.63 0.82 0.66
Constant (IV for M&A) -45.461 0.196 -103.443 -56.066 -68.33 -86.668 -54.27 0 13.70*** 51.90*** -6.44*** -1.89* -3.71*** -2.33** Correlation within M &A 0.96 LR test of homeskedaticity (IV=1) Chi2 217.41*** 222.46*** 238.45*** 103.07*** 104.32*** 101.33*** Observations 1424 1424 1440 1480 584 576 576 Number of groups 356 356 360 370 146 144 144 Log Likelihood -161.191 -166.27 -168.17 -56.35 -57.64 -54.27
Table 19. Nested Logit-Part 2 179