Equity Links and Information Acquisition in Biotechnology Alliances Darren Filson and Rosa Morales Claremont Graduate University Forthcoming, Journal of Economic Behavior & Organization Morales is a PhD candidate at Claremont Graduate University

Introduction We develop and test a simple model of the determinants of equity links in biotechnology alliances Successful innovation requires several ingredients; individual firms may lack particular ingredients, so alliances can help Allies use several contractual devices to organize exchange including asset purchases, cross-licenses, equity links, licenses, loans, options, sublicenses, and termination clauses We follow the previous literature and focus on whether allies use a minority equity link A minority equity link is formed when a client buys less than 50% of the R&D firm’s equity

The Goal: Test a simple model that predicts when minority equity links are used in strategic alliances; the previous literature lacks models 1.Introduce a model with one client, one R&D firm, and one project – an equity link facilitates information acquisition; it helps the client learn about the R&D firm and the project 2. Use the model to generate testable hypotheses about the determinants of equity links; many of these hypotheses are new to the literature 3.Test the hypotheses using a large sample of biotechnology alliances – one of the main industries where alliances are used – the biotechnology industry is growing in importance due to continuing scientific advances – large data set is available – biotechnology alliances typically have a clear R&D firm and client

Key Assumption We assume that an equity link facilitates monitoring and learning This assumption is based on findings in the incomplete contracts literature Contracts allow some monitoring but permit opportunism because they are incomplete Integration (ownership) creates closer ties and facilitates information flows; we assume partial ownership through an equity link also has this effect Research that supports this assumption includes Williamson (1979, 1985) and Allen and Phillips (2000)

Anticipating the Results The model generates ten testable hypotheses An equity link is less likely to be used when: 1.The R&D firm has more previous alliances 2.This effect is stronger if the previous alliances involve the same technology as the current one 3.This effect is stronger if the previous alliances are with the current client An equity link is more likely to be used when: 4. The project tasks involve uncertain outcomes 5. The client’s estimated money investment in the project is larger 6. The project involves more collaboration 7. The R&D firm’s value is low 8. The R&D firm is publicly traded 9. The terms of the alliance are modified after the initial contract is signed 10. The alliance is formed during a stage that involves high investments from the client, high risks, or both

A Qualification Even though we find support for the hypotheses our model generates, we cannot be sure that the mechanism emphasized in our model is the cause of the empirical relationships we observe A stronger analysis would find some way to measure information flows with and without equity links to determine whether the mechanism in our model is operating The degree of contractual incompleteness, the closeness of ties, the extent of monitoring, and the quality of information flows are all difficult to measure All studies of equity links suffer from a similar problem; it is difficult to check explanations against alternatives that imply the same relationships between observable variables

The Model A client, C, an R&D firm R, and a project. C chooses whether to acquire a minority equity stake in R at the beginning of the project C must invest I in the project, and C receives V if the project succeeds Two sources of uncertainty affect the likelihood of project success: R is either good or bad and the project is either good or bad Both must be good in order for the project to succeed, but C cannot observe these types before investing some funds. C’s prior belief that R is good is p and C’s prior belief that the project is good is q, where p,q > 0 C’s expected utility from investing in the project without any further information is Uc = pqV – I(1)

Equity Links Before investing I, C can acquire an equity link in R at a cost of e + t, where e is an investment and t is a transaction cost. The equity purchase accomplishes two objectives: 1.The transfer of e allows the project to begin 2.The equity link facilitates monitoring. The role of monitoring is to resolve C’s uncertainty about R and the project before I – e must be spent. Two extreme assumptions simplify computations without any loss in intuition: 1.Monitoring is impossible without an equity link 2.Monitoring resolves uncertainty completely. This implies that C invests I – e only if it observes that both R and the project are good. Given these assumptions, if C purchases an equity link then C’s expected utility is Uc = pq [V – (I – e)] – e – t(2)

Testable Hypotheses Compare (1) and (2) to see that purchasing the equity link is worthwhile if and only if (I – e)(1 – pq) is greater than or equal to t(3) Simple partial derivatives show that Inequality (3) is more likely to hold when I is high and p, q, e, and t are low We use Inequality (3) to construct testable hypotheses by considering factors that affect I, p, q, e, and t

Factors that affect p, C’s belief about R’s type: Assume that C’s belief about R’s ability in the current project is positively correlated with R’s success in its previous projects. Hypothesis 1: An equity link is less likely to be used when R has more previous alliances. Hypothesis 2: The effect of Hypothesis 1 is stronger if more of R’s previous alliances involve the same technology as the current alliance (because the information C obtains from observing these is more pertinent). Finally, C can learn from its own previous experience with R and can avoid selecting R as a partner if R did not perform well in the past Hypothesis 3: The effect of Hypothesis 1 is stronger if more of R’s previous alliances are with the current client

Factors that affect q, C’s belief about the project’s type: Hypothesis 4: An equity link is more likely to be used when the project tasks involve uncertain outcomes, as is the case for R&D projects and technology exchanges Factors that affect I, C’s investment in the project: Hypothesis 5: An equity link is more likely to be used when C’s estimated money investment in the project is larger More generally, I includes non-monetary transfers of knowledge and other resources Hypothesis 6: An equity link is more likely to be used when the project involves more collaboration

Factors that affect e and t, the amount of the equity purchase and the transaction cost: R’s firm value determines how expensive it is to acquire a large enough share of its equity to facilitate monitoring Hypothesis 7: Equity links are more likely to be used when R’s value is low The transaction cost is likely to be low if R’s equity is easier to trade Hypothesis 8: Equity links are more likely to be used if R is publicly traded

Dynamic Considerations The formal model assumes p and q are constants, but it is more reasonable to allow them to evolve over the life of the project. Further, I could change as milestones were reached. Hypothesis 9: An equity link is more likely to be used if the terms of the alliance are modified after the initial contract is signed Hypothesis 10: An equity link is more likely to be used when the alliance is formed during a stage that involves high investments from C, high risks, or both

The Data The unit of observation is an alliance The sample includes as many biotechnology alliances as possible from the mid 1970s until May, 2001 Data on the client, R&D firm, date the alliance is formed (and modified), tasks, contract terms, amount of funds the client invests, intended application, technology the partners will use, and stage the project is at when the alliance is formed provided in online summaries by Recombinant Capital Corporation (ReCap, Data on the R&D firm’s market capitalization is provided by CRSP Additional information on IPO dates used to determine whether the R&D firm is public is provided by Yahoo! Finance

Table 3, Equation 1 (4344 obs., 16% ones) Probit Model. Dep. Variable is one if there is an equity link Hypotheses 1-3: Previous Alliances Hypothesis 6: Collaboration Different Tcch and Client -.021***Co-development.50*** Same Technology and Different Client -.034***Co-marketing.22** Current Client-.12*Collaboration.15** Hypothesis 4: TasksHypothesis 9: Research.12*The Contract has been Modified.71*** Development.32*** Technology.59* Supply.11 Manufacturing-.49*** Marketing-.059

Table 3, Equation 2 (2694 obs.) Probit Model, same dep. Var. Hypotheses 1-3: Previous Alliances Hypothesis 6: Collaboration Different Tcch and Client-.024***Co-development.41*** Same Technology and Different Client -.041***Co-marketing.24** Current Client-.21**Collaboration.083 Hypothesis 4: TasksHypothesis 9: Research.11The Contract has been Modified.70*** Development.23*** Technology.78*Hypothesis 10: Supply.071Lead Molecule.13 Manufacturing-.52**Pre-Clinical.44*** Marketing.020Formulation.046 Phase Phase 2.43*** Phase 3.60*** PLA/NDA filed-.11 Approved-.25

Table 3, Equation 3 (1031 obs.) Probit Model, same dep. Var. Hypotheses 1-3: Previous Alliances Hypotheses 7-8: Mkt Cap and Private Different Tcch and Client-.016*R&D firm’s Market Cap.-.50** Same Technology and Different Client -.044***R&D firm is not publicly traded.16 Current Client-.21* Hypothesis 9: Hypothesis 4: TasksThe Contract has been Modified.46*** Research-.12 Development.0034Hypothesis 10: Technology.057Lead Molecule-.047 Supply-.084Pre-Clinical.31* Manufacturing-.16Formulation.32 Marketing-.12Phase Phase 2.21 Hypothesis 5-6: Size and Collaboration Phase 3.45* Size1.70***PLA/NDA filed-.16 Co-development-.13Approved-.79** Co-marketing.17 Collaboration.12

Marginal Effects To interpret the effects of previous alliances, size, and market capitalization, consider increasing each variable by approximately one standard deviation: Increasing the number of previous alliances with a different tech/client by 9 decreases the likelihood of observing an equity link by as much as 5.3 percentage points Increasing the number with the same tech by 5 decreases the likelihood by as much as 8 percentage points Each additional alliance with the current client decreases the likelihood by as much as 7.7 percentage points Increasing size by $46 million increases the likelihood by 29 percentage points Increasing the firm’s mkt cap by $2.13 billion decreases the likelihood by 40.5 percentage points

Marginal Effects The marginal effects of the dummy variables are also important If an alliance involves research, the likelihood of observing an equity link rises by 2.5 percentage points If it involves development, 6.8, technology exchanges, 12, co- development, 11 If the contract has been modified, 15 The stage of signing also has important effects: Pre-clinical, 11 Phase 2, 10 Phase 3, 15

Robustness Consider how many previous alliances of each type involve equity links Include only alliances described in an SEC file to ensure accurate reporting of the presence or absence of an equity link Include firm effects for firms with at least ten alliances to control for earnings management and other firm-specific reasons for using equity links Report conditional means for the independent variables to ensure the results are not due to the particular specification we use Allow for diminishing effects of previous alliances by including quadratic terms

Conclusion A simple model in which an equity link creates closer ties, which help facilitate monitoring and learning Monitoring and learning helps the client learn more about the R&D firm and the project before committing more resources Tests of the model’s hypotheses receive empirical support Future work could explore the determinants of contractual devices other than equity links Future work could also develop the links between an R&D firm’s previous alliances and its current ones to examine factors such as timing, selection of partners, and the growth of industry networks