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Deriving Biodiversity Option Value Within a Model of Biotechnology Research and Development

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The purpose of this project is to derive formulas for computing biodiversity option values within a dynamic model of biotechnological innovation. The approach employs formal economic models and rigorous methods of analysis to clarify the economic effects of introducing new goods, the role of genetic materials as an input to the research and development process, and the imputed option value of the existing stock of genetic resources. The analysis is based on the view that biodiversity conservation - the preservation of genetic materials - and biotechnology research and development - the incorporation of genetic materials into goods providing economic utility - are components of a single problem, that of determining the optimal use of genetic resources for economic benefit. The claim is that, given a general theory for valuing innovations, the value of options on potential innovations can be analyzed using tools well-established in the literature on finance. The product of the research will be an analytic framework, including explicit formulas, that relate biodiversity option value to other economic fundamentals (e.g., interest rates). This framework could then be applied in empirical studies to estimate the option values associated with genetic resources, and could be expanded to analyze the effect of economic incentives facing agents involved with biodiversity conservation and biotechnology innovation.

Metadata

EPA/NSF ID:
R824707-010
Principal Investigators:
Rausser, Gordon C.
Technical Liaison:
Research Organization:
California at Berkeley, University of
Funding Agency/Program:
EPA/ORD/Valuation
Grant Year:
1995
Project Period:
October 1, 1995 - September 30, 1997
Cost to Funding Agency:
$80,000
Project Status Reports:
For the year 1997
Objective: The goal of this project is to develop a feasible method for computing the potential value of biodiversity in its role as a source of intellectual property. The focus is on the role of ecological and taxonomic knowledge in the process of biodiversity prospecting. We analyze a sequential-search model of biodiversity prospecting in which genetic materials are usefully differentiated by prior information. The analysis shows that, as prior information allows for the differentiation of biological habitats according to their potential as sources of new drug leads, bioprospecting values increase in some areas, while declining in others. When search procedures are optimized to take account of this information, areas of especial promise may have a high value. Information creates value both by increasing the chance of making a discovery, and by lowering the average cost of conducting searches.

Our work represents a conceptual departure from previous economic models of biodiversity prospecting (e.g., Simpson, Sedjo and Reid, 1996; Polasky and Solow, 1995) in two ways. First, we argue that the proper unit of analysis in such work is not the species, but the physical location. Relevant decisions that bear on bioprospecting ? conservation, fundamental systematics, and goal-driven search projects ? are generally made at the site level. Second, we argue that, once we adopt the site as our unit of analysis, we open the door to the possibility that searches can be guided by observable ecological and taxonomic data. The success of bioprospecting projects depends on the identification of previously unknown complex compounds. Current ecological science suggests that biochemical "creativity" ? the propensity of organisms to generate the complex organic compounds that provide leads for new drugs ? may be correlated with such observables (Dreyfuss and Chapela, 1994). Indeed, Sandoz Pharmaceuticals, Inc., has recently launched a Biolead Project that will attempt to identify observable factors that correlate with microbial creativity.

In the model, a bioprospecting firm conducts a search for a compound that will make possible the development of a lucrative new product. There are a large number N of sites where the compound might be found. Sites are tested sequentially, at a cost c per site. A test of the nth site is treated as a Bernoulli trial with probability pn of scoring a success (or "hit"). The hit probabilities of different sites are assumed to be independent. In order to avoid trivial cases, we assume that no site contains the desired compound with certainty (pn < 1 for all n). Without loss of generality, we can assign labels to sites in order of decreasing hit probability, so that 1 > p1 ? ? ? pN. When a test is successful, a payoff is realized. Multiple hits are redundant. It is shown that the pharmaceutical firm maximizes the payoff of its search program by testing the most promising sites first and, therefore, that the probability ordering (p1, p2,..., pN) is also the order in which sites are examined (up to a permutation of equi-probable sites). Using this principle, a value function is derived that elucidates the expected payoff of the search at each stage, conditional on results at previous stages. Let Vn denote the ex post expected value of continuing the search, after n-1 sites have been tested unsuccessfully. This continuation value is characterized by the recursive relationship:

Vn = pnR+(1 - pn)Vn+1-c , n=1,...,N

where VN+1 = 0. An expression for the expected incremental contribution of the nth site follows:

vn = an[pn(R-Vn+1)-c],

where an = P n-1i=1 is the probability that the search is carried to the nth stage; i.e., the probability of failure in each of the first n-1 tests. Analysis of this formula yields a fundamental insight into the relationship between information resources and the bioprospecting value of genetic resources:

Proposition: Let {pn}Nn=1 be a sequence of hit probabilities on a collection of research leads, index in order of decreasing probability. Let the incremental value vn of the nth lead be defined as above. Then vn can be decomposed into components vn = vnI +vN, where

and where vN = aN(pNR-c) is the value of a marginal lead.

The components vnI and vN are referred to as the information rent and the scarcity rent, respectively, of lead n. Analyzing the model, we find that sites toward the front of the search queue add more to the project's expected return than do those further back. This result is due to a combination of two factors. First, the early, high-probability sites contribute more than the others to the chance of a successful outcome. As repeated failures push investigators to pick through lower-grade ore, it becomes increasingly unlikely that a hit will ever be scored. Second, even if a hit is made eventually, the shift to low-quality sources implies an increase in the expected number of trials required to make the discovery and, therefore, an increase in the expected costs of continuing the search. Since an early success obviates the need for continued (and costly) search, sites toward the front of the queue are valuable for their capacity to reduce total search costs, in expectation. In sum, when search procedures can be optimized to incorporate useful prior information, high-probability sites command information rents associated with their expected contribution to the chance of success and to the avoidance of search costs.

To demonstrate the approach, we apply our formula to Myers' (1988, 1990) data on several biodiversity "hot spots," using the density of endemic species as a proxy for site quality (see Figure 1). Several insights emerge. Information values can be several orders of magnitude larger than the "scarcity value" of the material itself, and can be substantial even when scarcity values are negligible. Indeed, the values associated with the highest-quality sites (on the order of $4,000/hectare in our simulation, for rainforest in Western Ecuador) can be large enough to motivate conservation activities. These results are robust over large ranges of parameter values.

The valuation approach advanced here could be the basis of a technique for assigning an expected bioprospecting value to a habitat or parcel. Such a technique would take advantage of available scientific knowledge, and could be sharpened as new information emerges about relevant relationships. This includes information on the relationship between habitat, ecology, and the creativity of micro-organisms, and on how microbial communities are affected by various forms of environmental disturbance. The expected bioprospecting value of a parcel or region could be incorporated into benefit/cost studies, as an aid to policy decision making for cases in which development could disturb or imperil microbial communities. Future work in this area should include an examination of how bioprospecting values vary with changes in the institutional environment.

Project Reports:
Final Report:
Project Description:

Objective: The goal of this project is to develop a feasible method for computing the potential value of biodiversity as a source of intellectual property. The focus is on the complementarities between knowledge resources and genetic resources in applied biotechnological innovation. The approach employs formal economic models and rigorous methods of analysis to c larify the economic effects of introducing new goods, the role of genetic materials as an input to the research and development process, and the imputed option value of the existing stock of genetic resources. Research products include an analytic framework from which explicit formulae are derived that relate biodiversity option value to other economic fundamentals (e.g. interest rates). This framework has also been extended to analyze the effect of economic incentives facing agents involved with biodiversity conservation and biotechnology innovation, and shows promise for use in applied in empirical studies to estimate genetic resource option values.

Summary/Accomplishments: The economic study of biological resources has focused traditionally on the role of living creatures as sources of material, such as food and fiber. An emerging literature, however, has begun to focus on the economic role of biological materials as sources of information. As new techniques of genetic manipulation lead to enormous growth in biotechnology, these bioinformatic resources promise to play an increasingly important role as inputs to innovation. Partly as a consequence, national and other organizations are moving increasingly to appropriate the economic value of genetic resources, asserting claims of intellectual property rights over materials that had, until recently, been viewed as the common heritage of humanity. The project examined a set of questions that are raised by this "enclosure" of the genetic commons.

The first component considered the question, Under what conditions can the privatization of genetic resources create significant incentives for biodiversity conservation? To address this question, a sequential-search model of biodiversity prospecting was constructed in which genetic materials are usefully differentiated by prior information. Model analysis shows that, as prior information allows for the differentiation of biological habitats according to their potential as sources of new product leads, bioprospecting values increase in some areas, while declining in others. When search procedures are optimized to take account of this information, areas of special promise can have high value. Information creates value both by increasing the chance of making a discovery, and by lowering the average cost of conducting searches.

The work represents a conceptual departure from previous economic models of biodiversity prospecting in two fundamental ways. First, we argue that the proper unit of analysis in such work is not the species, but the physical location. Relevant decisions that bear on bioprospecting -- conservation, fundamental systematics, and goal-driven search projects -- are generally made at the site level. Second, we argue that, once we adopt the site as our unit of analysis, we open the door to the possibility that searches can be guided by observable ecological and taxonomic data.

In the model, a bioprospecting firm conducts a search for a compound that will make possible the development of a lucrative new product. There are a large number N of sites where the compound might be found. Sites are tested sequentially, at a cost c per site. A test of the nth site is treated as a Bernoulli trial with probability pn of scoring a success (or "hit"). The hit probabilities of different sites are assumed to be independent. In order to avoid trivial cases, we assume that no site contains the desired compound with certainty (pn < 1 for all n). Without loss of generality, we can assign labels to sites in order of decreasing hit probability, so that 1 > p1 ( ... ( pN. When a test is successful, a payoff is realized. Multiple hits are redundant. It is shown that the pharmaceutical firm maximizes the payoff of its search program by testing the most promising sites first and, therefore, that the probability ordering (p1, p2,..., pN) is also the order in which sites are examined (up to a permutation of equi-probable sites). Using this principle, a value function is derived that elucidates the expected payoff of the search at each stage, conditional on results at previous stages. Let Vn denote the ex post expected value of continuing the search, after n-1 sites have been tested unsuccessfully. This continuation value is characterized by the recursive relationship:

Vn = pnR+(1 - pn)Vn+1-c , n=1,...,N

where VN+1 = 0. An expression for the expected incremental contribution of the nth site follows:

vn = an[pn(R-Vn+1)-c],

where an = (n-1i=1 is the probability that the search is carried to the nth stage; i.e., the probability of failure in each of the first n-1 tests. Analysis of this formula yields a fundamental insight into the relationship between information resources and the bioprospecting value of genetic resources:

Proposition: Let {pn}Nn=1 be a sequence of hit probabilities on a collection of research leads, index in order of decreasing probability. Let the incremental value vn of the nth lead be defined as above. Then vn can be decomposed into components vn = vnI +vN, where

and where vN = aN(pNR-c) is the value of a marginal lead.

The components vnI and vN are referred to as the information rent and the scarcity rent, respectively, of lead n. Analyzing the model, we find that sites toward the front of the search queue add more to the project's expected return than do those further back. This result is due to a combination of two factors. First, the early, high-probability sites contribute more than the others to the chance of a successful outcome. As repeated failures push investigators to pick through lower-grade ore, it becomes increasingly unlikely that a hit will ever be scored. Second, even if a hit is made eventually, the shift to low-quality sources implies an increase in the expected number of trials required to make the discovery and, therefore, an increase in the expected costs of continuing the search. Since an early success obviates the need for continued (and costly) search, sites toward the front of the queue are valuable for their capacity to reduce total search costs, in expectation. In sum, when search procedures can be optimized to incorporate useful prior information, high-probability sites command information rents associated with their expected contribution to the chance of success and to the avoidance of search costs.

To demonstrate the use of this valuation approach, we apply our formula to Myers' (1988, 1990) data on several biodiversity "hot spots," using the density of endemic species as a proxy for site quality. Several insights emerge. Information values can be several orders of magnitude larger than the "scarcity value" of the material itself, and can be substantial even when scarcity values are negligible. Indeed, the values associated with the highest-quality sites (on the order of $9,000/hectare in our simulation) can be large enough to motivate conservation activities. These qualitative conclusions are robust over large ranges of parameter values. Such figures lend support to claims that bioprospecting could be used, in certain cases, as the basis for financing biodiversity conservation.

The bioprospecting model was extended to address contracting relationships between suppliers and consumers of genetic resources, in the context of a monopolistically competitive market in research leads. A functional market in genetic resources is shown to be supported only when the state of scientific knowledge is sufficiently advanced. The stock of genetic resources conserved in a market will not, in general, be "efficient," in the narrow sense that total economic surplus is maximized in expectation.

An empirical investigation was also launched of the option value of plant genetic resources in the agricultural biotechnology industry. Data were gathered from the US Patent and Trademark Office, the US Department of Agriculture, and the annual reports of significant firms. Development was begun of a new structural model of the agrobiotechnology, plant breeding and seed industry. A valuation function derived from the model will be used to evaluate potential gains from any combination of assets, such as would be realized by a merger of existing firms. This initiative is currently active, and will be concluded with supplementary support from outside funds.

Conclusions: Previous work has suggested that the scarcity rents accruing to genetic resources are likely be vanishingly small. Our analysis that these earlier results depend sensitively on a counter-factual assumption---that prospectors are constrained to use brute-force search procedures. Indeed, when scientific models are sufficiently rich to provide useful guides to the search process, promising materials can command significant information rents. Information creates value not so much by increasing the likelihood of a lucrative discovery, but by decreasing search costs in expectation. Consequently, an increase in the payoff to research success has virtually no effect on genetic resource rents. Furthermore, improvements in search technology actually lower the value of promising leads. Results of a numerical simulation suggest that bioprospecting information rents could, under reasonable assumptions, be large enough to finance meaningful biodiversity conservation.
The valuation approach we advance could be the basis of a technique for assigning an expected bioprospecting value to a habitat or parcel. Such techniques take advantage of available scientific knowledge, and can be sharpened as new information emerges about relevant relationships. This includes information on the relationship between habitat, ecology, and the creativity of micro-organisms, and on how microbial communities are conditioned by their external environment. The expected bioprospecting value of a parcel or region could be incorporated into benefit/cost studies, as an aid to policy decision making for cases in which environmental disturbance could impact microbial communities.

Papers:
Rausser, Gordon C. and Arthur A. Small, "Bioprospecting With Prior Ecological Information." Under consideration for journal publication. Available as Giannini Foundation Working Paper No. 819, Dept. of Agricultural and Resource Economics, University of California at Berkeley.
Rausser, Gordon C. and Arthur A. Small, "The Economic Value of Patents, Licenses, and Plant Variety Protection Certificates." Forthcoming in proceedings volume, Building Partnerships for Commercializing University Research, Tahoe City, California, May, 1996. Available as Giannini Foundation Working Paper No. 797, Dept. of Agricultural and Resource Economics, University of California at Berkeley.
Rausser, Gordon C. and Arthur A. Small, "The Market for Genetic Resources: Prior Information and Conservation Incentives." In preparation.
Rausser, Gordon C. and Arthur A. Small, "The Option Value of Intellectual Property in Agricultural Biotechnology." In preparation

Presentations:
Rausser, Gordon C. and Arthur A. Small, "Bioprospecting With Prior Ecological Information." Presented at: National Science Foundation/Environmental Protection Agency Workshop, Valuation and Environmental Policy, April 7-8, 1997; Dept. of Agricultural and Resource Economics, University of California at Berkeley, Sept. 5, 1997; Dept. of Economics, Georgia State University, Jan. 14, 1998; School of Economics, Georgia Institute of Technology, Jan. 15, 1998; Dept. of Economics, University of Washington, Jan. 27, 1998; Graduate School of Business, Columbia University, Jan. 30, 1998; Dept. of Agricultural Economics and Marketing, Rutgers University, Feb. 3, 1998; Dept. of Economics, Iowa State University, Feb. 6, 1998; Dept. of Economics, Stanford University, June 1, 1998.
Rausser, Gordon C. and Arthur A. Small, "The Economic Value of Patents, Licenses, and Plant Variety Protection Certificates." Conference, Building Partnerships for Commercializing University Research, Tahoe City, California, May, 1996.
Rausser, Gordon C. and Arthur A. Small, "The Impact of Genetic Engineering on the Value of Ex Situ Collections," Invited presentation to CIMMYT-Stanford conference, Building the Basis for Economic Analysis of Genetic Resources in Crop Plants, Palo Alto, California, August 18-19, 1997.
Rausser, Gordon C. and Arthur A. Small, "The Market for Genetic Resources: Prior Information and Conservation Incentives," Economic Theory Seminar, Dept. of Economics, University of California, Berkeley, Nov. 26, 1997.
Rausser, Gordon C. and Arthur A. Small, "The Option Value of Intellectual Property in Agricultural Biotechnology," Graduate School of Business, Columbia University, Feb. 24, 1998.
Rausser, Gordon C., Arthur A. Small and Seung Jick Yoo, "Valuation of Plant Genetic Resources in Agricultural Biotechnology," Natural Resources Seminar, Dept. of Agricultural and Resource Economics, University of California at Berkeley, Oct. 23, 1996.
Rausser, Gordon C. and Arthur A. Small, "Valuing Biodiversity," Invited presentation at conference Managing Human-dominated Ecosystems, Missouri Botanical Garden, St. Louis, Missouri, March 26-29, 1998.

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