Not Your Stepping Stone: Collaboration and the Dynamics of Industry Evolution in Biotechnology
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DATA SOURCESWe began assembling a database in 1990, using BioScan, an independent industry directory, founded in 1988 and published six times a year, that covers a wide range of organizations in the life sciences field. We attend to those companies that are independently operated, profit-seeking entities involved in human therapeutic and diagnostic applications of biotechnology. This focus results in a sample that covers 482 firms, of which as many as 180 are in existence in all years over the 12-year period, 1988-99. During this period, 229 firms were founded and entered the database, and 91 exited, due to failure, departure from the industry, or merger. The database, like the industry, is heavily centered in the U.S., although in recent years there has been expansion in Europe. In 1999, eighty percent of the companies were located in the U.S. and ten percent in Europe. We stress that our focus is on dedicated human biotech firms. We do not include companies involved in veterinary and agricultural biotech, which draw on different scientific capabilities and operate in a much different regulatory climate. We do not include large pharmaceutical corporations, health care companies, hospitals, universities, or research institutes in our primary database; these participants enter the database as partners that collaborate with dedicated biotech firms. We also exclude companies that are wholly owned subsidiaries of pharmaceutical or chemical corporations. We do, however, include BFs that have minority or majority investments in them by other firms, as long as the company continues to be independently traded on the stock market. We observe and study the process by which some independent companies are acquired. Our rationale for excluding both small biotech subsidiaries and large, diversified chemical, medical, or pharmaceutical corporations is that the former do not make decisions autonomously and that biotechnology may represent only a minority of the activities of the latter. Both circumstances generate serious data ambiguities. The reference source BioScan reports information on a firm’s ownership, financial history, formal contractual linkages to collaborators, products, and current research. Firm characteristics reported in BioScan include founding data, employment levels, financial history, and for firms that exit, whether they were acquired or failed. The data on interorganizational agreements cover the time frame and purpose of the relationship. Our database draws on BioScan’s April issue, in which new information is added for each calendar year. Hence the firm-level and network data are measured during the first months of each year. When information was missing from BioScan, we consulted other sources including various editions of Genetic Engineering and Biotechnology Related Firms Worldwide, Dun and Bradstreet’s Who Owns Whom?, and Standard and Poor’s. In addition, we utilized annual reports, Securities and Exchange Commission filings, and, when necessary, made phone calls to companies. We have constructed a relational database that allows us to examine biotech firms, their ties to other participants, and the evolving network structure of the field. Thus the database contains separate files for 1.) Biotech firms, 2.) The formal agreements involving biotech, and 3.) The parties to these agreements. We treat each agreement as a tie, and code each tie for its purposes and duration, using an implicit logic of production to classify them into categories: R&D, clinical trials, manufacturing, marketing, sales, and so forth. Table 1 describes each type of tie, and provides illustrations of typical participants. The “partner” datafile for all organizations that appear as partners on any tie with a biotech firm is large, expanding annually (numbering more than 2900 organizations active by 1999), and exceptionally diverse in both form and nationality, including multinational corporations, government agencies, hospitals, universities, and pharmaceutical companies. Data on financial performance are available only for publicly held firms, and were obtained from COMPUSTAT, a widely used electronic data service operated by Standard and Poor. COMPUSTAT contains information compiled from public records filed by firms listed on NYSE, AMEX, or NASDAQ. We obtained annual performance data for 154 biotech firms listed on COMPUSTAT. MEASURESWe utilize a variety of measures of firm characteristics, interorganizational relations, and financial performance. Descriptive statistics are presented in Table 2 for the measures described below. Dependent variables: Hypothesis 1-4 predict the subsequent depth, configuration, and R&D intensity of a firm’s network. To measure the depth of a firm’s collaborations, we use the total number of ties, the number of types of activity, and the number of complex ties. The means for these variables are 8.5 collaborations, 2.4 types of collaboration, and 1.1 complex collaborative arrangements. To measure the configuration of a firm’s collaborations, we use the number of ties for each type of activity and the number of kinds of partners for each activity. To capture R&D involvement after initial success, we use the number of R&D agreements. Independent variables: Our predictions are based on firm size, measured by the number of employees, calendar age, and performance, captured by positive earnings. The mean for size is 153 employees, but the distribution is highly skewed as the largest firm has 7500 employees. The average age is 7.75 years, though the oldest firm is 42. The earnings measure is taken from the firm’s income statements and is the difference between operating income and expenses before dividends. We form an indicator variable that is 1 if a firm’s earnings are positive and 0 if otherwise, thus identifying those firms that have generated sufficient funds for reinvestment in new activities. Twenty one out of 154 firms have showed positive earnings. Download 154.14 Kb. Share with your friends:
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