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Making enduring choices: Uncertainty and public policy
Energy Economics 28 (2006) 667 – 676 www.elsevier.com/locate/eneco
Making enduring choices: Uncertainty and public policy David J. Bjornstad a,⁎, Michael McKee b b
a The Oak Ridge National Laboratory Department of Economics, University of Tennessee
Available online 12 June 2006
Abstract This paper provides an experimental test of the theory of investment under uncertainty and applies the theory to a group of policy decisions. Investment under uncertainty describes a class of problems, characterized by uncertainty that resolves itself over time, sunk costs that cannot be fully recovered, and the ability of the decision maker to wait without loss of strategic advantage. It also implies a “bad news principle,” under which decision makers are motivated by expected costs, but not expected benefits. The theory is illustrated as a two-period model for which the decision maker can either invest in the first period (and receive a known current return plus an uncertain future return) or defer until the second period when uncertainty is resolved. By foregoing current period benefits a “cost” that is sometimes referred to as an option value is incurred. Parameter values are specified and an experimental test, the results of which closely track the predictions of the theory, is described. Three cases, the structuring of contingent valuation questionnaires, the operalization of the precautionary principle in risk analysis, and the development of public policy to stimulate private investments (as for energy-saving products), are used to illustrate potential public policy applications. © 2006 Elsevier B.V. All rights reserved.
1. Introduction Enduring choices are similar to investments in the sense that a flow of services, or damages, extends from the point at which the decision is made until some time into the future. When choices are reversible, the point at which benefits or damages cease is endogenous, and the issues of whether or when to commit are less serious than when choices are irreversible and the decision maker must live with the costs of the decision. Some such choices are irreversible for financial reasons; aftermarkets may fail to return some or all sunk costs. Other choices are irreversible for technical reasons; once extinct, a species cannot generally be regenerated. To the extent that these outcomes are well understood, they are not particularly troublesome. But when outcomes affecting ⁎ Corresponding author. E-mail addresses: [email protected] (D.J. Bjornstad), [email protected] (M. McKee). 0140-9883/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.eneco.2006.05.005
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key components of an irreversible choice are uncertain the decision maker is faced with a choice that has variously been described as investment under uncertainty, irreversible investment theory, or the options theory of investment.1 This paper describes a behavioral test of these theories and supplies examples of how they could be applied to the making of durable choices for public policy. No attempt has been made to provide a thorough review of a list of applications that is rapidly growing. Rather, the goal of this brief paper is to capture the importance of the behavioral underpinnings of the choice process described by this body of theory for public policy and to provide examples of the broad range of topics to which the theory might be applied. The material is presented as follows. The following section provides a discussion of the issues at hand and why this approach to decision making can provide insights that differ from those of standard benefit-cost methodologies, which take the point of analysis as coterminous with the decision point. Included is a description of the general form of the theory and the types of predictions that it makes. Next, a behavioral test of a simplified form of the theory is summarized. This test makes use of the methodology of experimental economics and considers the ability to reproduce theoretical predictions under controlled predictions, rather than inferring the validity of the theory from econometric tests. This is of critical importance because many of the behavioral predictions of the theory cannot be easily observed from accessible secondary data. Subsequently, three quite different applications are described. These include: (1) policies to encourage consumers to purchase new energy efficient technologies, (2) decisions over the point at which regulators should permit new, uncertain productions to penetrate markets, and (3) how the scenario used to describe contingent valuation choices can bias the data generated by stated preference surveys. The paper concludes with a summary of the implications of this work for enduring public policy choices and research that might add to its utility. 2. Theory The theory of investment under uncertainty follows the logic of financial analysis tools developed for evaluating the values of financial options. A financial option is a derivative financial instrument that permits its holder to purchase some principal financial instrument or asset, like a stock, at a fixed price during some clearly specified future period of time. The value of the option is derived from the value of the principal asset, and when the principal asset exceeds some trigger price, it is optimal for the investor to exercise the option. However, specified in its financial form, as reflected, for example, in the Black–Scholes formulation, this approach places significant constraints on the analysis.2 An alternative format, referred to as the real options approach follows a more straightforward optimization format that is more akin to a simple benefit-cost model, and we shall concentrate on this methodology. The real options model assumes that (1) an investment can be made today or can be costlessly postponed until some point in the future, (2) the investment entails fixed costs (and perhaps benefits) that cannot be fully recovered, and (3) there are one or more uncertain elements that drive the profitability of the investment, the values of which will become known over time. The assumption of postponability differs from classical benefit-cost analysis which essentially assumes that a decision must be made today or is foregone. Thus, this formulation changes the question 1
A standard reference is Dixit and Pindyck (1994). See also Pindyck (1991), Dixit (1992), and Hubbard (1994). Application of financial options models is not, however, impossible. For an example, see Morel et al. (2003), which we discuss further below. 2
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from whether to invest to whether and when to invest. The assumption of fixed costs that cannot be fully recovered introduces irreversibility into the analysis in a very general manner. A business investment can be irreversible in the sense that if economic conditions change and the original investor incurs losses, other investors will likewise find the investment unattractive and will not bid on it. Of course the value of business investments may be relative. An initial developer may overextend himself and go bankrupt only to find that the next owner, having acquired the investment at a lower price, can operate profitably. Irreversibility can also be absolute. To return to the example given above, when an endangered species goes extinct, or when a unique habitat is lost, the outcome is irreversible. The assumption that uncertainty resolves itself over time supplies a general way in which to insert the role of information into the model, though the literature contains examples of endogenous information. The model can be summarized as follows: at what point in time is the present value of an investment opportunity both positive and at a maximum? This can be thought of as carrying out a series of benefit-cost studies, one for each possible point in the future at which an investment could be undertaken, discounting these back to the present to obtain a series of present values, and choosing the largest positive value. Alternative approaches might consider the future as unfolding continuously, or dichotomously, now, tomorrow, or never. The particular specification of the model depends on the analyst's beliefs as to the nature of the uncertainty driving the model. For example, owing to its financial roots, real options models are frequent specified in continuous time and solved as dynamic, stochastic optimization models. One such form is Max FðV Þ ¼ max E½VT −I e−rT
ð1Þ
where F(V) is the present value of a future choice, VT is the net return from point of choice T, and r is the discount rate. This model is typically solved using an assumption of Brownian motion to describe the time path of the variable that is uncertain, basically a random walk process that is often used to describe the behavior of price paths. Thus, an investment might be deemed contingent upon the time path of interest rates, a stock option might be contingent on the time path of the stock price, and an energy using appliance purchase might be contingent on the time path of energy prices.3 An essential result from this model is that the point at which the value of the maxima turns positive is not the optimal point at which to purchase. Instead, optimal choice calls for sacrificing some benefits until the investment reaches the point at which the present value of the stream of net benefits exceeds the value of irreversible costs. The foregone net benefits are referred to as the option value of waiting. By making assumptions about parameter values, one can use the analytical solution of this model to evaluate any number of real world choices, some of which will be discussed below. However, not all uncertainty need follow this path. An alternative, simpler approach, which we test below, is the “today, tomorrow or never” approach which assumes that uncertainty will be fully resolved at some discreet future point. Assume that a choice can be made today in period 0 or at T, a single point in the future. The irreversible investment is I. Between today and T a choice to invest today would yield a net present value of X. At T a random draw is made, such that for a high rate of return a present value of H is obtained and for a low rate of return a present value of L is obtained. H occurs with probability p, and L occurs with probability (1 − p). We then have two potential outcomes; invest today, or wait 3
Mathematical descriptions of this model may be found in Dixit and Pindyck (1994).
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until T. For convenience, we assume L b I b H, so that if the investor waits until T, she will invest if H occurs and will not invest if L occurs. Thus we have two possible outcomes, Invest now in time period 0 FðV0 Þ ¼ X þ pH þ ð1−pÞL−I :
ð2Þ
Or wait until T and invest if the high return occurs FðVT Þ ¼ pH−pI :
ð3Þ
Optimization implies choosing the larger of the two values such that to choose to invest in period 0, F(V0) must be greater than F(VT), and simplifying X þ ð1−pÞðL−IÞN0 :
ð4Þ
Here the irreversible loss is (L − I), low return less investment costs, which occurs with probability (1 − p). The option value (X) is earnings foregone by waiting until T. To invest in period 0 the earnings foregone by waiting must exceed the expected value of irreversible losses. This approach encompasses the traditional benefit-cost assumption of invest today in Eq. (2) and is thus more general. Note that this decision rule does not contain the expected value of high returns, implying that the timing of the decision to invest is determined by downside risks, a result referred to in the literature as the “Bad News Principle.” Note also the p may be the perception of the investor concerning the likelihood of future events, rather than a true state of nature, and thus may be a behavioral rather than a technical parameter. To summarize, the utility of the options approach to investment decision making is that it restates the decision of whether to invest as a decision of whether and when to invest. It focuses attention on specific elements of uncertainty and provides a menu of modeling approaches, depending on the assessment of the analyst about mechanisms of uncertainty. 3. A behavioral test of the options model We have tested the model predictions contained in Eq. (4) using the methodology of experimental economics.4 In our experimental laboratory market individual subjects are asked to choose between two investment contracts. Under one contract the investment is undertaken in the current period (period 0) while under the alternate contract, the investment decision is delayed until the second period (period T). Each subject faces a series of such decisions with each decision being independent. A detailed description of the experiments and their design is presented elsewhere.5 This section supplies an overview of the elicitation of agents' decisions. The parameter values used in all contracts are described, expected values F(V0) and F(VT) implied by each contract's set of parameters, and the associated OM prediction for each contract. Finally, the various series of experiments conducted are described. The choice posed to the agent, in written form, may be summarized as follows: “I will buy this contract for (I) before a bingo cage is spun. I will immediately receive a payoff (X), and after the cage is spun I will get an additional payoff (H) if a red ball is drawn or an additional (L) if a white ball is drawn.” The values are then repeated and summarized. “OR, I will wait until after the bingo cage is spun to see whether or not a red or white ball is drawn. If the ball is red I will buy the contract 4 5
Bjornstad et al. (1996). See also Bjornstad et al. (2002). ibid.
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and earn (H–I). If a white ball is drawn, since (L) is less than (I), I will not buy and I will avoid the loss.” The agents are fully informed as to the proportion of red and white balls in the bingo cage, and of the values that will accrue to each choice. A total of seventeen choices is presented to each agent, with each choice yielding an observation for analysis. Agents are paid earnings in dollars at a conversion rate, such that average earnings for each agent range from $25–$35. Parameter values for each choice are shown in Table 1. Assuming risk neutrality, when p = .35, contracts G, N, P, and Q are rationally chosen in period 0, for .50, G, N, P, I and K are chosen, and for .65, G, N, P, I, K, C, D, F, and P are rationally chosen. Because of the unique context, we expect agents to have non-neutral risk preferences and are more interested in agents' response to different parameter values than to simple yes or no choices. We also recognize that not all agents will fully recognize their dominant strategy such that some proportion of agents may simply adopt heuristic strategies, e.g., always choose the first contract. Regarding sensitivities, the results show that while X is raised, holding the other parameters constant, proportions choosing the first contract tend to increase. The same result obtains for increases in L. That is, as the downside risk falls (L increases) more subjects choose to invest in the initial period. For changes in I the inverse relationship holds. All of these results are consistent with the theory. For changes in H, no consistent pattern is evident, and, in fact, for each probability, the proportion choosing the first contract is lower for the highest value than for the lowest value. This is consistent with the theory and fully replicates the “Bad News Principle.” These data are presented in Table 2. Formal tests using regression analysis comport with these findings, but highlight the presence of risk aversion, such that relationships for X and L, while consistent, are disproportionate, reflecting the discount placed on uncertain outcomes. In general, we can say that the subjects of our experiments applied logic consistent with the theory of investment under uncertainty, but were generally risk averse. So, what does this mean? We should assume that the financial community, long accustomed to making intricate calculations, would behave in the manner most beneficial to its pecuniary success.
Table 1 Contracts, parameters, and expected values for options Parameter value
F(V0)
F(VT)
Contract
X
H
L
I
p = .35
p = .5
p = .65
p = .35
p = .5
p = .65
A B C D E F G H I J K L M N O P Q
2 2 2 3 2 3 2 3 3 2 5 2 2 8 2 3 6
16 20 16 16 22 16 16 16 16 16 16 24 16 11 18 16 16
1 1 1 4 1 3 1 1 5 1 1 1 1 3 1 6 1
10 10 5 10 10 10 4 10 10 8 10 10 6 10 10 10 10
− 1.75 − 0.35 3.25 1.20 .35 .55 4.25 − 0.75 1.85 .25 1.25 1.05 2.25 3.80 − 1.05 2.50 2.25
0.50 2.50 5.50 3.00 3.50 2.50 6.50 1.50 3.50 2.50 3.50 4.50 4.50 5.00 1.50 4.00 n.a.
2.75 5.35 7.75 4.80 6.65 4.45 8.75 3.75 5.15 4.75 5.75 7.95 6.75 6.20 4.05 5.50 n.a.
2.10 3.50 3.85 2.10 4.20 2.10 4.20 2.10 2.10 2.80 2.10 4.90 3.50 0.35 2.80 2.10 2.10
3.00 5.00 5.50 3.00 6.00 3.00 6.00 3.00 3.00 4.00 3.00 7.00 5.00 0.50 4.00 3.00 n.a.
3.90 6.50 7.15 3.90 7.80 3.90 7.80 3.90 3.90 5.20 3.90 9.10 6.50 0.65 5.20 3.90 n.a.
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Table 2 Contracts with varying X (H = 16, L = 1, I = 10) Participants choosing option 1 when p = .35
p = .50
p = .65
Contract
Value of X
Number
Percent
Number
Percent
Number
Percent
A H K Q
$2 3 5 6
(n = 110) 5 4 26 43
5% 4 24 39
(n = 123) 14 8 37 –
11% 7 30 –
(n = 102) 18 5 36 –
18% 15 36 –
Contracts with varying L (X = 3, H = 16, I = 10) Participants choosing option 1 when p = .35 Contract F D I P
Value of L
Number
3 4 5 6
(n = 110) 14 19 40 60
p = .50 Percent
Number
13 17 36 54
(n = 123) 15 19 38 54
p = .65 Percent
Number
Percent
12 15 31 44
(n = 102) 22 27 40 53
22 27 40 52
Contracts with varying I (X = 2, H = 16, L = 1) Participants choosing option 1 when p = .35
p = .50
p = .65
Contract
Value of C
Number
Percent
Number
Percent
Number
Percent
A J M C G
$10 8 6 5 4
(n = 110) 5 3 16 29 61
5% 3 14 26 55
(n = 123) 14 7 15 32 58
11% 6 12 26 47
(n = 102) 18 23 29 43 51
18% 23 29 42 50
Contracts with varying H (X = 2, L = 1, I = 10) Participants choosing option 1 when p = .50
p = .35 Contract A O B E L
Value of H
Number
$16 18 20 22 24
(n = 110) 5 1 2 4 3
Percent
Number
5% 1 2 4 3
(n = 123) 14 3 18 6 7
p = .65 Percent
Number
Percent
11% 3 15 5 6
(n = 102) 18 13 21 19 17
18% 13 21 19 17
For studies of industry financing, a behavioral test of this nature is therefore of modest interest. For household behavior, however, the work is somewhat of a breakthrough, particularly in light of a stream of publications documenting differences between behavior predicted by theory and
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observed in the experimental laboratory.6 Of special interest is the replication of the Bad News Principle. If household decision making agents respond asymmetrically to prospective gains and losses it presages important implications for application of the theory to public policy. Displays of risk aversion can reinforce these findings. Thus, public policy making over durable decisions should benefit from these results. 4. Applications The basic message contained in the Bad News Principle is that decision-making agents respond differently to matters of loss and gain when aspects of the decision are uncertain, but better information may become available. Here are three examples of relevance to integrated assessment of climate change: resource valuation, regulation of uncertain technologies, and promotion of technological change. For resource valuation the question addressed is: Does the wording of contingent valuation (CV) scenarios trigger “option-motivated” individuals to respond in predictable manners? In particular, given the admonition of human subjects protocols to avoid misleading subjects, scenarios may “hedge” on presentation of facts thereby inducing feelings of uncertainty in subjects. Scenarios may also describe similar outcomes as benefits or costs, based on choice of context. Consider a program to protect the environment from an irreversible loss that is uncertain, for example, loss of an endangered species from a local habitat. The scenario states that we are uncertain whether or not the species may be found in other habitats but that research will identify the facts over time. Developing the habitat would yield substantial, relatively certain benefits, and the results from the CV will be used in a benefit-cost study to evaluate the development. The CV questionnaire requires a current response in the form of a dollar value for the habitat. If the respondent wishes to wait, an option not offered but implicitly logical given the scenario, he or she can simply add a premium to the dollar value, in the limit a large option that can be interpreted as a “protest response.” This response, moreover, may be lost if the researcher dutifully “trims” the response set to eliminate extreme values, despite the fact that these values may be validly drawn from the respondent's preference set. Do CV scenarios contain such language? A review of one of our own scenarios revealed the following language. Discussing rain forests (emphasis added): The rain forests cover only seven percent of the Earth's surface, but 50 to 70% of the different types of plants animals, and insects can be found in the rain forests. We rely on this abundance of plant and animal life through a plan that only recently are we beginning to understand…Half of all the medicines that we use come from plants and animals, and, at the present time, the plants and animals of the rain forests are being studies for this purpose. New remedies are going to emerge from plants and animals that have not even been discovered yet…many species of plants and animals are being devastated and run the risk of beginning to
6 Colin Camerer (Science 2003) has recently suggested that game theory be divided into three approaches: rational game theory, useful for modeling firms and countries that analyze games carefully (for example, bidding for telecommunications spectrum in auctions); behavioral game theory, useful for explaining what normal people do and how they learn; and evolutionary game theory which explains equilibration in animal populations by natural selection, and imitation among humans (social selection). See also Camerer (Princeton University Press, 2003).
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disappear and eventually become extinct. But given that all living things on the planet are related, the loss of a species can affect others in ways that are difficult to predict… (Cummings et al., 1996). The implications of these results for the CV practitioner are straightforward. Many, if not most, environmental resources are potentially subject to irreversible damages. Often the nature or extent of these damages is uncertain and wording may make it more uncertain. Outcomes may be stated as either benefits or losses. Waiting is not explicitly allowed. If the CV scenario frames a resource valuation question in a manner in which the respondent can reasonably conclude that additional information, pertinent to the resource decision, will become available in the foreseeable future, he or she may add an option premium to the CV query that appears to overstate a “rational value,” essentially imposing an ability to wait by decreasing the probability that the loss will be incurred. Such responses may or may not pass scope tests and other tests of internal consistency. Yet they can logically be argued to recover the parameters of the individual's preference function. Regulatory decision making would also appear to be a logical candidate for application of the options model. At present, regulators rely on science when making decisions for which uncertainty is low and a variety of ad hoc procedures when uncertainty is high. One such procedure is termed the precautionary principle and directs regulators to prohibit the use of a new, uncertain product or process until it can be determined that there are no harms likely from releasing the product or process.7 Morel et al. suggest that the precautionary principle might be made operational through the use of options analysis. The essence of the real options portion of their analysis is that even if a choice meets the positive net benefit test of benefit-cost analysis it may be optimal to wait until the point at which the benefits forgone by waiting exceed the irreversible costs. This provides their definition of the precautionary principle. It is when the maximum expected loss is offset by the opportunity costs of postponing the benefit. This definition is unique and potentially controversial, but it is also operational. Their approach uses the dynamic formulation and creates a timepath for net value, a function for the optimal switch point, and parameterizes the model to create a comparison with an actual decision made in the 1990s, whether or not to permit the use of Bt corn. Bt corn is genetically modified to produce Bt toxin, a naturally occurring toxin that acts as a pesticide to a number of lepidopteron pests. Bt corn is more productive than natural corn because the net effectiveness of planting the pest resistant corns dominate the use of natural corn, even combined with spraying a Bt pesticide. However, widespread use of Bt corn, virtually guarantees that a regime of Bt-resistant pests will emerge. The implication of this is that the value of the Bt corn will depreciate over time for its users. However, non-Bt corn growers will also suffer externalities because the pests will be resistant to sprayed application of Bt pesticides. There is a lot going on in the analysis. Using a creatively developed data base the authors are able to determine that whereas a simple benefit-cost test would call for releasing the crop to use (which in fact happened) an options approach would have called for waiting before permitting release. Finally, consider the case of the adoption of new energy-saving technologies by households. Energy security, carbon reduction, and other national goals are enhanced by widespread adoptions of these technologies, provided the technologies are not excessively costly. However, applications of the normal benefit-cost test seem to imply either that individuals ignore cost-effective energy 7
Perhaps more accurately, the precautionary principle is a family of technical that tends to downplay benefits and emphasize potential risks. Some are rigid and ignore benefits while other more closely resemble benefit-cost analysis.
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investment opportunities or that internal rates of return on applied by households are so high as to appear irrational.8 Hassett and Metcalf (1993) dispute these findings, applying a real options model to energy price paths that suggests that hurdle rates for energy might reasonable exceed normal rates of return four-fold. However, energy prices paths may not supply the greatest uncertainty to the purchase of energy efficient technologies. Often these technologies are highly innovative and unfamiliar to households contemplating the purchase. Issues of sunk costs (I) are therefore important, as are issues of risk and irreversibility. Sometimes these new technologies presuppose infrastructure developments that may or may not occur. To predict behavior, it may be appropriate to use options modeling approach. Moreover, policies to promote market penetration of these technologies might also be based on options reasoning. The Bad News Principle suggests that policies compensating overall rates of return (such as with investment tax credits or energy taxes) fail to address the behavioral implications of downside risks. More affective policies might address behavior directly by providing warrantees, product buybacks, performance guarantees, or contingency provisions. The private sector recognizes the importance of tailoring risk management practices to consumers' risk preferences. Hence, automobiles are sold with a menu of guarantees: multi-year drive train, short term bumper to bumper, warrantee extensions, and the like. Policy actions may protect against such consequences rather than simply subsidizing the adoption of the technology. In some cases, the private sector has undertaken just such protections when new technologies are introduced. General Motors only permitted leasing of a recent generation of electric automobile. Thus, consumers were protected if the technology failed or if a new generation technology resulted in making the generation of electric cars obsolete. It is noteworthy that a key element of success for such programs is identifying sources of uncertainty accurately. Other programs might key on (and study) information provision aimed at specific consumer needs, such as innovative demonstration programs, R&D that targets reliability, and energysavings labels that reflect a range of household lifestyles. More generally, options reasoning based on behavioral considerations calls for a better understanding of how consumers actually make investment decisions about energy using products, rather than assertions over how they should make these decisions. 5. Conclusions The behavioral evidence reviewed in this paper provides generic support for the options model as an explanation of individual behavior. However, the available experimental results are limited, sample sizes are small and the parameter ranges are narrow. Tests using real world data are focused on energy price time series analysis whereas uncertainty due to technical aspects of the technologies, infrastructure, and related support services may be as or more important. Our own research agenda will address the influence of induced uncertainty and gains vs. losses on CV responses. And, we would expect the regulatory requirements of controversial emerging technologies, such as recombinant DNA medicines, bionanotechnoloiges, and universal individual medical records to call forth new approaches to regulatory analysis that would include real options approaches. An additional issue of particular interest lies in the normative relationship between information, uncertainty, and market failure. Stated differently, what should be the role of government in 8
Hausman (1979) makes estimates of these internal rates of return. Carlsmith et al. (1990) supply an example of the rejection of cost-effective technologies.
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correcting information inadequacies, in reducing asymmetric responses to gains and losses, or in generally taking account of the growing literature that describes strategic behavioral responses by market players in virtually all circumstances? Earlier dialogue over the role of government focused on ability of the price system to coordinate the behavior of buyers and sellers and dealt with public goods, externalities, decreasing costs, and the like. We are awaiting the seminal piece that responds to Stiglitz's (2003) comment and similar comments by a range of observers that there is only one way for information to be complete and an infinite number of ways for it to be incomplete.9 Acknowledgement We thank Ronald G. Cummings and Paul Brewer for their contributions to our joint work on the experiments described in this paper. This work is cited elsewhere in the paper as Bjornstad et al. (2002). Neither of our colleagues is responsible for errors or omissions in the present paper. The work was sponsored in part by the Department of Energy's Office of Biomedical and Environmental Research, through its Integrated Assessment and Human Genome programs. References Bjornstad, David J., January 2003. Economic Incentives in the Purchase and Use of Energy Using Products: Past Practices and New Developments, Joint Institute for Energy and Environment, JIEE 2003-1. Bjornstad, David J., Paul Brewer, Ronald Cummings, Dec. 9, 1996. Investment Behavior in the Case of Irreversibility and Decreasing Uncertainty: An Experimental Laboratory Investigation, A Final Report to the Global Climate Change Research Program, Office of Energy Research, U.S. Department of Energy. Bjornstad, David J., Brewer, Paul, Cummings, Ronald, McKee, Michael, 2002. An experimental test for options value: relevance for contingent valuation elicitation. In: List, John, de Zeeuw, Aart (Eds.), Recent Advances in Environmental Economics. Edward Elgar Publishing, pp. 340–364. Camerer, Colin F., 2003. Strategizing in the brain. Science 300, 1673–1675. Camerer, Colin F., 2003. Behavioral Game Theory: Experiments in Strategic Interaction. Princeton Univ., Princeton, NJ. Carlsmith, Roger, W. Chandler, J. McMahon, D. Santino, 1990. Energy Efficiency: How Far Can We Go? Unpublished, Oak Ridge National Laboratory. Cummings, Ronald B., Melanie B. Williams, David J. Bjornstad, Dec. 9, 1996. The Value of the Global Environment: A Cross-Cultural Analysis Employing the Contingent Valuation Methodology in Support of Integrated Policy Analysis, A Final Report to the Global Climate Change Research Program, Office of Energy Research, U.S. Department of Energy. Dixit, Avinash, 1992. Investment and hysteresis. Journal of Economic Perspectives 6 (1), 107–132. Dixit, Avinash K., Pindyck, Robert S., 1994. Investment Under Uncertainty. Princeton University Press, Princeton, NJ. Hassett, Kevin A., Metcalf, Gilbert E., 1993. Do consumers discount the future correctly? Energy Policy, 710–716. June. Hausman, Jerry A., 1979. Individual discount rates and the purchase and utilization of energy using durables. Bell Journal of Economics 10 (1), 33–54 (Spring). Hubbard, R. Glenn, 1994. Investment under uncertainty: keeping one's options open. Journal Economic Literature 32, 1816–1831. Morel, Benôit, R., Farrow, Scott R., Wu, Felecia, Casman, Elizabeth A., 2003. Pesticide resistance, the precautionary principle, and the regulation of Bt corn. In: Laxminarayan, Ramanan (Ed.), Battling Resistance to Antibiotics and Pesticides. Resources for the Future Press, Washington, D.C. Pindyck, Robert S., 1991. Irreversibility, uncertainty, and investment. Journal of Economic Literature 29, 1110–1148. Stiglitz, Joseph A., 2003. Information and the change in paradigm in economics. American Economic Review 92 (3), 460–501.
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See also Bjornstad (2003).
Making enduring choices: Uncertainty and public policy David J. Bjornstad a,⁎, Michael McKee b b
a The Oak Ridge National Laboratory Department of Economics, University of Tennessee
Available online 12 June 2006
Abstract This paper provides an experimental test of the theory of investment under uncertainty and applies the theory to a group of policy decisions. Investment under uncertainty describes a class of problems, characterized by uncertainty that resolves itself over time, sunk costs that cannot be fully recovered, and the ability of the decision maker to wait without loss of strategic advantage. It also implies a “bad news principle,” under which decision makers are motivated by expected costs, but not expected benefits. The theory is illustrated as a two-period model for which the decision maker can either invest in the first period (and receive a known current return plus an uncertain future return) or defer until the second period when uncertainty is resolved. By foregoing current period benefits a “cost” that is sometimes referred to as an option value is incurred. Parameter values are specified and an experimental test, the results of which closely track the predictions of the theory, is described. Three cases, the structuring of contingent valuation questionnaires, the operalization of the precautionary principle in risk analysis, and the development of public policy to stimulate private investments (as for energy-saving products), are used to illustrate potential public policy applications. © 2006 Elsevier B.V. All rights reserved.
1. Introduction Enduring choices are similar to investments in the sense that a flow of services, or damages, extends from the point at which the decision is made until some time into the future. When choices are reversible, the point at which benefits or damages cease is endogenous, and the issues of whether or when to commit are less serious than when choices are irreversible and the decision maker must live with the costs of the decision. Some such choices are irreversible for financial reasons; aftermarkets may fail to return some or all sunk costs. Other choices are irreversible for technical reasons; once extinct, a species cannot generally be regenerated. To the extent that these outcomes are well understood, they are not particularly troublesome. But when outcomes affecting ⁎ Corresponding author. E-mail addresses: [email protected] (D.J. Bjornstad), [email protected] (M. McKee). 0140-9883/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.eneco.2006.05.005
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key components of an irreversible choice are uncertain the decision maker is faced with a choice that has variously been described as investment under uncertainty, irreversible investment theory, or the options theory of investment.1 This paper describes a behavioral test of these theories and supplies examples of how they could be applied to the making of durable choices for public policy. No attempt has been made to provide a thorough review of a list of applications that is rapidly growing. Rather, the goal of this brief paper is to capture the importance of the behavioral underpinnings of the choice process described by this body of theory for public policy and to provide examples of the broad range of topics to which the theory might be applied. The material is presented as follows. The following section provides a discussion of the issues at hand and why this approach to decision making can provide insights that differ from those of standard benefit-cost methodologies, which take the point of analysis as coterminous with the decision point. Included is a description of the general form of the theory and the types of predictions that it makes. Next, a behavioral test of a simplified form of the theory is summarized. This test makes use of the methodology of experimental economics and considers the ability to reproduce theoretical predictions under controlled predictions, rather than inferring the validity of the theory from econometric tests. This is of critical importance because many of the behavioral predictions of the theory cannot be easily observed from accessible secondary data. Subsequently, three quite different applications are described. These include: (1) policies to encourage consumers to purchase new energy efficient technologies, (2) decisions over the point at which regulators should permit new, uncertain productions to penetrate markets, and (3) how the scenario used to describe contingent valuation choices can bias the data generated by stated preference surveys. The paper concludes with a summary of the implications of this work for enduring public policy choices and research that might add to its utility. 2. Theory The theory of investment under uncertainty follows the logic of financial analysis tools developed for evaluating the values of financial options. A financial option is a derivative financial instrument that permits its holder to purchase some principal financial instrument or asset, like a stock, at a fixed price during some clearly specified future period of time. The value of the option is derived from the value of the principal asset, and when the principal asset exceeds some trigger price, it is optimal for the investor to exercise the option. However, specified in its financial form, as reflected, for example, in the Black–Scholes formulation, this approach places significant constraints on the analysis.2 An alternative format, referred to as the real options approach follows a more straightforward optimization format that is more akin to a simple benefit-cost model, and we shall concentrate on this methodology. The real options model assumes that (1) an investment can be made today or can be costlessly postponed until some point in the future, (2) the investment entails fixed costs (and perhaps benefits) that cannot be fully recovered, and (3) there are one or more uncertain elements that drive the profitability of the investment, the values of which will become known over time. The assumption of postponability differs from classical benefit-cost analysis which essentially assumes that a decision must be made today or is foregone. Thus, this formulation changes the question 1
A standard reference is Dixit and Pindyck (1994). See also Pindyck (1991), Dixit (1992), and Hubbard (1994). Application of financial options models is not, however, impossible. For an example, see Morel et al. (2003), which we discuss further below. 2
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from whether to invest to whether and when to invest. The assumption of fixed costs that cannot be fully recovered introduces irreversibility into the analysis in a very general manner. A business investment can be irreversible in the sense that if economic conditions change and the original investor incurs losses, other investors will likewise find the investment unattractive and will not bid on it. Of course the value of business investments may be relative. An initial developer may overextend himself and go bankrupt only to find that the next owner, having acquired the investment at a lower price, can operate profitably. Irreversibility can also be absolute. To return to the example given above, when an endangered species goes extinct, or when a unique habitat is lost, the outcome is irreversible. The assumption that uncertainty resolves itself over time supplies a general way in which to insert the role of information into the model, though the literature contains examples of endogenous information. The model can be summarized as follows: at what point in time is the present value of an investment opportunity both positive and at a maximum? This can be thought of as carrying out a series of benefit-cost studies, one for each possible point in the future at which an investment could be undertaken, discounting these back to the present to obtain a series of present values, and choosing the largest positive value. Alternative approaches might consider the future as unfolding continuously, or dichotomously, now, tomorrow, or never. The particular specification of the model depends on the analyst's beliefs as to the nature of the uncertainty driving the model. For example, owing to its financial roots, real options models are frequent specified in continuous time and solved as dynamic, stochastic optimization models. One such form is Max FðV Þ ¼ max E½VT −I e−rT
ð1Þ
where F(V) is the present value of a future choice, VT is the net return from point of choice T, and r is the discount rate. This model is typically solved using an assumption of Brownian motion to describe the time path of the variable that is uncertain, basically a random walk process that is often used to describe the behavior of price paths. Thus, an investment might be deemed contingent upon the time path of interest rates, a stock option might be contingent on the time path of the stock price, and an energy using appliance purchase might be contingent on the time path of energy prices.3 An essential result from this model is that the point at which the value of the maxima turns positive is not the optimal point at which to purchase. Instead, optimal choice calls for sacrificing some benefits until the investment reaches the point at which the present value of the stream of net benefits exceeds the value of irreversible costs. The foregone net benefits are referred to as the option value of waiting. By making assumptions about parameter values, one can use the analytical solution of this model to evaluate any number of real world choices, some of which will be discussed below. However, not all uncertainty need follow this path. An alternative, simpler approach, which we test below, is the “today, tomorrow or never” approach which assumes that uncertainty will be fully resolved at some discreet future point. Assume that a choice can be made today in period 0 or at T, a single point in the future. The irreversible investment is I. Between today and T a choice to invest today would yield a net present value of X. At T a random draw is made, such that for a high rate of return a present value of H is obtained and for a low rate of return a present value of L is obtained. H occurs with probability p, and L occurs with probability (1 − p). We then have two potential outcomes; invest today, or wait 3
Mathematical descriptions of this model may be found in Dixit and Pindyck (1994).
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until T. For convenience, we assume L b I b H, so that if the investor waits until T, she will invest if H occurs and will not invest if L occurs. Thus we have two possible outcomes, Invest now in time period 0 FðV0 Þ ¼ X þ pH þ ð1−pÞL−I :
ð2Þ
Or wait until T and invest if the high return occurs FðVT Þ ¼ pH−pI :
ð3Þ
Optimization implies choosing the larger of the two values such that to choose to invest in period 0, F(V0) must be greater than F(VT), and simplifying X þ ð1−pÞðL−IÞN0 :
ð4Þ
Here the irreversible loss is (L − I), low return less investment costs, which occurs with probability (1 − p). The option value (X) is earnings foregone by waiting until T. To invest in period 0 the earnings foregone by waiting must exceed the expected value of irreversible losses. This approach encompasses the traditional benefit-cost assumption of invest today in Eq. (2) and is thus more general. Note that this decision rule does not contain the expected value of high returns, implying that the timing of the decision to invest is determined by downside risks, a result referred to in the literature as the “Bad News Principle.” Note also the p may be the perception of the investor concerning the likelihood of future events, rather than a true state of nature, and thus may be a behavioral rather than a technical parameter. To summarize, the utility of the options approach to investment decision making is that it restates the decision of whether to invest as a decision of whether and when to invest. It focuses attention on specific elements of uncertainty and provides a menu of modeling approaches, depending on the assessment of the analyst about mechanisms of uncertainty. 3. A behavioral test of the options model We have tested the model predictions contained in Eq. (4) using the methodology of experimental economics.4 In our experimental laboratory market individual subjects are asked to choose between two investment contracts. Under one contract the investment is undertaken in the current period (period 0) while under the alternate contract, the investment decision is delayed until the second period (period T). Each subject faces a series of such decisions with each decision being independent. A detailed description of the experiments and their design is presented elsewhere.5 This section supplies an overview of the elicitation of agents' decisions. The parameter values used in all contracts are described, expected values F(V0) and F(VT) implied by each contract's set of parameters, and the associated OM prediction for each contract. Finally, the various series of experiments conducted are described. The choice posed to the agent, in written form, may be summarized as follows: “I will buy this contract for (I) before a bingo cage is spun. I will immediately receive a payoff (X), and after the cage is spun I will get an additional payoff (H) if a red ball is drawn or an additional (L) if a white ball is drawn.” The values are then repeated and summarized. “OR, I will wait until after the bingo cage is spun to see whether or not a red or white ball is drawn. If the ball is red I will buy the contract 4 5
Bjornstad et al. (1996). See also Bjornstad et al. (2002). ibid.
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and earn (H–I). If a white ball is drawn, since (L) is less than (I), I will not buy and I will avoid the loss.” The agents are fully informed as to the proportion of red and white balls in the bingo cage, and of the values that will accrue to each choice. A total of seventeen choices is presented to each agent, with each choice yielding an observation for analysis. Agents are paid earnings in dollars at a conversion rate, such that average earnings for each agent range from $25–$35. Parameter values for each choice are shown in Table 1. Assuming risk neutrality, when p = .35, contracts G, N, P, and Q are rationally chosen in period 0, for .50, G, N, P, I and K are chosen, and for .65, G, N, P, I, K, C, D, F, and P are rationally chosen. Because of the unique context, we expect agents to have non-neutral risk preferences and are more interested in agents' response to different parameter values than to simple yes or no choices. We also recognize that not all agents will fully recognize their dominant strategy such that some proportion of agents may simply adopt heuristic strategies, e.g., always choose the first contract. Regarding sensitivities, the results show that while X is raised, holding the other parameters constant, proportions choosing the first contract tend to increase. The same result obtains for increases in L. That is, as the downside risk falls (L increases) more subjects choose to invest in the initial period. For changes in I the inverse relationship holds. All of these results are consistent with the theory. For changes in H, no consistent pattern is evident, and, in fact, for each probability, the proportion choosing the first contract is lower for the highest value than for the lowest value. This is consistent with the theory and fully replicates the “Bad News Principle.” These data are presented in Table 2. Formal tests using regression analysis comport with these findings, but highlight the presence of risk aversion, such that relationships for X and L, while consistent, are disproportionate, reflecting the discount placed on uncertain outcomes. In general, we can say that the subjects of our experiments applied logic consistent with the theory of investment under uncertainty, but were generally risk averse. So, what does this mean? We should assume that the financial community, long accustomed to making intricate calculations, would behave in the manner most beneficial to its pecuniary success.
Table 1 Contracts, parameters, and expected values for options Parameter value
F(V0)
F(VT)
Contract
X
H
L
I
p = .35
p = .5
p = .65
p = .35
p = .5
p = .65
A B C D E F G H I J K L M N O P Q
2 2 2 3 2 3 2 3 3 2 5 2 2 8 2 3 6
16 20 16 16 22 16 16 16 16 16 16 24 16 11 18 16 16
1 1 1 4 1 3 1 1 5 1 1 1 1 3 1 6 1
10 10 5 10 10 10 4 10 10 8 10 10 6 10 10 10 10
− 1.75 − 0.35 3.25 1.20 .35 .55 4.25 − 0.75 1.85 .25 1.25 1.05 2.25 3.80 − 1.05 2.50 2.25
0.50 2.50 5.50 3.00 3.50 2.50 6.50 1.50 3.50 2.50 3.50 4.50 4.50 5.00 1.50 4.00 n.a.
2.75 5.35 7.75 4.80 6.65 4.45 8.75 3.75 5.15 4.75 5.75 7.95 6.75 6.20 4.05 5.50 n.a.
2.10 3.50 3.85 2.10 4.20 2.10 4.20 2.10 2.10 2.80 2.10 4.90 3.50 0.35 2.80 2.10 2.10
3.00 5.00 5.50 3.00 6.00 3.00 6.00 3.00 3.00 4.00 3.00 7.00 5.00 0.50 4.00 3.00 n.a.
3.90 6.50 7.15 3.90 7.80 3.90 7.80 3.90 3.90 5.20 3.90 9.10 6.50 0.65 5.20 3.90 n.a.
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Table 2 Contracts with varying X (H = 16, L = 1, I = 10) Participants choosing option 1 when p = .35
p = .50
p = .65
Contract
Value of X
Number
Percent
Number
Percent
Number
Percent
A H K Q
$2 3 5 6
(n = 110) 5 4 26 43
5% 4 24 39
(n = 123) 14 8 37 –
11% 7 30 –
(n = 102) 18 5 36 –
18% 15 36 –
Contracts with varying L (X = 3, H = 16, I = 10) Participants choosing option 1 when p = .35 Contract F D I P
Value of L
Number
3 4 5 6
(n = 110) 14 19 40 60
p = .50 Percent
Number
13 17 36 54
(n = 123) 15 19 38 54
p = .65 Percent
Number
Percent
12 15 31 44
(n = 102) 22 27 40 53
22 27 40 52
Contracts with varying I (X = 2, H = 16, L = 1) Participants choosing option 1 when p = .35
p = .50
p = .65
Contract
Value of C
Number
Percent
Number
Percent
Number
Percent
A J M C G
$10 8 6 5 4
(n = 110) 5 3 16 29 61
5% 3 14 26 55
(n = 123) 14 7 15 32 58
11% 6 12 26 47
(n = 102) 18 23 29 43 51
18% 23 29 42 50
Contracts with varying H (X = 2, L = 1, I = 10) Participants choosing option 1 when p = .50
p = .35 Contract A O B E L
Value of H
Number
$16 18 20 22 24
(n = 110) 5 1 2 4 3
Percent
Number
5% 1 2 4 3
(n = 123) 14 3 18 6 7
p = .65 Percent
Number
Percent
11% 3 15 5 6
(n = 102) 18 13 21 19 17
18% 13 21 19 17
For studies of industry financing, a behavioral test of this nature is therefore of modest interest. For household behavior, however, the work is somewhat of a breakthrough, particularly in light of a stream of publications documenting differences between behavior predicted by theory and
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observed in the experimental laboratory.6 Of special interest is the replication of the Bad News Principle. If household decision making agents respond asymmetrically to prospective gains and losses it presages important implications for application of the theory to public policy. Displays of risk aversion can reinforce these findings. Thus, public policy making over durable decisions should benefit from these results. 4. Applications The basic message contained in the Bad News Principle is that decision-making agents respond differently to matters of loss and gain when aspects of the decision are uncertain, but better information may become available. Here are three examples of relevance to integrated assessment of climate change: resource valuation, regulation of uncertain technologies, and promotion of technological change. For resource valuation the question addressed is: Does the wording of contingent valuation (CV) scenarios trigger “option-motivated” individuals to respond in predictable manners? In particular, given the admonition of human subjects protocols to avoid misleading subjects, scenarios may “hedge” on presentation of facts thereby inducing feelings of uncertainty in subjects. Scenarios may also describe similar outcomes as benefits or costs, based on choice of context. Consider a program to protect the environment from an irreversible loss that is uncertain, for example, loss of an endangered species from a local habitat. The scenario states that we are uncertain whether or not the species may be found in other habitats but that research will identify the facts over time. Developing the habitat would yield substantial, relatively certain benefits, and the results from the CV will be used in a benefit-cost study to evaluate the development. The CV questionnaire requires a current response in the form of a dollar value for the habitat. If the respondent wishes to wait, an option not offered but implicitly logical given the scenario, he or she can simply add a premium to the dollar value, in the limit a large option that can be interpreted as a “protest response.” This response, moreover, may be lost if the researcher dutifully “trims” the response set to eliminate extreme values, despite the fact that these values may be validly drawn from the respondent's preference set. Do CV scenarios contain such language? A review of one of our own scenarios revealed the following language. Discussing rain forests (emphasis added): The rain forests cover only seven percent of the Earth's surface, but 50 to 70% of the different types of plants animals, and insects can be found in the rain forests. We rely on this abundance of plant and animal life through a plan that only recently are we beginning to understand…Half of all the medicines that we use come from plants and animals, and, at the present time, the plants and animals of the rain forests are being studies for this purpose. New remedies are going to emerge from plants and animals that have not even been discovered yet…many species of plants and animals are being devastated and run the risk of beginning to
6 Colin Camerer (Science 2003) has recently suggested that game theory be divided into three approaches: rational game theory, useful for modeling firms and countries that analyze games carefully (for example, bidding for telecommunications spectrum in auctions); behavioral game theory, useful for explaining what normal people do and how they learn; and evolutionary game theory which explains equilibration in animal populations by natural selection, and imitation among humans (social selection). See also Camerer (Princeton University Press, 2003).
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disappear and eventually become extinct. But given that all living things on the planet are related, the loss of a species can affect others in ways that are difficult to predict… (Cummings et al., 1996). The implications of these results for the CV practitioner are straightforward. Many, if not most, environmental resources are potentially subject to irreversible damages. Often the nature or extent of these damages is uncertain and wording may make it more uncertain. Outcomes may be stated as either benefits or losses. Waiting is not explicitly allowed. If the CV scenario frames a resource valuation question in a manner in which the respondent can reasonably conclude that additional information, pertinent to the resource decision, will become available in the foreseeable future, he or she may add an option premium to the CV query that appears to overstate a “rational value,” essentially imposing an ability to wait by decreasing the probability that the loss will be incurred. Such responses may or may not pass scope tests and other tests of internal consistency. Yet they can logically be argued to recover the parameters of the individual's preference function. Regulatory decision making would also appear to be a logical candidate for application of the options model. At present, regulators rely on science when making decisions for which uncertainty is low and a variety of ad hoc procedures when uncertainty is high. One such procedure is termed the precautionary principle and directs regulators to prohibit the use of a new, uncertain product or process until it can be determined that there are no harms likely from releasing the product or process.7 Morel et al. suggest that the precautionary principle might be made operational through the use of options analysis. The essence of the real options portion of their analysis is that even if a choice meets the positive net benefit test of benefit-cost analysis it may be optimal to wait until the point at which the benefits forgone by waiting exceed the irreversible costs. This provides their definition of the precautionary principle. It is when the maximum expected loss is offset by the opportunity costs of postponing the benefit. This definition is unique and potentially controversial, but it is also operational. Their approach uses the dynamic formulation and creates a timepath for net value, a function for the optimal switch point, and parameterizes the model to create a comparison with an actual decision made in the 1990s, whether or not to permit the use of Bt corn. Bt corn is genetically modified to produce Bt toxin, a naturally occurring toxin that acts as a pesticide to a number of lepidopteron pests. Bt corn is more productive than natural corn because the net effectiveness of planting the pest resistant corns dominate the use of natural corn, even combined with spraying a Bt pesticide. However, widespread use of Bt corn, virtually guarantees that a regime of Bt-resistant pests will emerge. The implication of this is that the value of the Bt corn will depreciate over time for its users. However, non-Bt corn growers will also suffer externalities because the pests will be resistant to sprayed application of Bt pesticides. There is a lot going on in the analysis. Using a creatively developed data base the authors are able to determine that whereas a simple benefit-cost test would call for releasing the crop to use (which in fact happened) an options approach would have called for waiting before permitting release. Finally, consider the case of the adoption of new energy-saving technologies by households. Energy security, carbon reduction, and other national goals are enhanced by widespread adoptions of these technologies, provided the technologies are not excessively costly. However, applications of the normal benefit-cost test seem to imply either that individuals ignore cost-effective energy 7
Perhaps more accurately, the precautionary principle is a family of technical that tends to downplay benefits and emphasize potential risks. Some are rigid and ignore benefits while other more closely resemble benefit-cost analysis.
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investment opportunities or that internal rates of return on applied by households are so high as to appear irrational.8 Hassett and Metcalf (1993) dispute these findings, applying a real options model to energy price paths that suggests that hurdle rates for energy might reasonable exceed normal rates of return four-fold. However, energy prices paths may not supply the greatest uncertainty to the purchase of energy efficient technologies. Often these technologies are highly innovative and unfamiliar to households contemplating the purchase. Issues of sunk costs (I) are therefore important, as are issues of risk and irreversibility. Sometimes these new technologies presuppose infrastructure developments that may or may not occur. To predict behavior, it may be appropriate to use options modeling approach. Moreover, policies to promote market penetration of these technologies might also be based on options reasoning. The Bad News Principle suggests that policies compensating overall rates of return (such as with investment tax credits or energy taxes) fail to address the behavioral implications of downside risks. More affective policies might address behavior directly by providing warrantees, product buybacks, performance guarantees, or contingency provisions. The private sector recognizes the importance of tailoring risk management practices to consumers' risk preferences. Hence, automobiles are sold with a menu of guarantees: multi-year drive train, short term bumper to bumper, warrantee extensions, and the like. Policy actions may protect against such consequences rather than simply subsidizing the adoption of the technology. In some cases, the private sector has undertaken just such protections when new technologies are introduced. General Motors only permitted leasing of a recent generation of electric automobile. Thus, consumers were protected if the technology failed or if a new generation technology resulted in making the generation of electric cars obsolete. It is noteworthy that a key element of success for such programs is identifying sources of uncertainty accurately. Other programs might key on (and study) information provision aimed at specific consumer needs, such as innovative demonstration programs, R&D that targets reliability, and energysavings labels that reflect a range of household lifestyles. More generally, options reasoning based on behavioral considerations calls for a better understanding of how consumers actually make investment decisions about energy using products, rather than assertions over how they should make these decisions. 5. Conclusions The behavioral evidence reviewed in this paper provides generic support for the options model as an explanation of individual behavior. However, the available experimental results are limited, sample sizes are small and the parameter ranges are narrow. Tests using real world data are focused on energy price time series analysis whereas uncertainty due to technical aspects of the technologies, infrastructure, and related support services may be as or more important. Our own research agenda will address the influence of induced uncertainty and gains vs. losses on CV responses. And, we would expect the regulatory requirements of controversial emerging technologies, such as recombinant DNA medicines, bionanotechnoloiges, and universal individual medical records to call forth new approaches to regulatory analysis that would include real options approaches. An additional issue of particular interest lies in the normative relationship between information, uncertainty, and market failure. Stated differently, what should be the role of government in 8
Hausman (1979) makes estimates of these internal rates of return. Carlsmith et al. (1990) supply an example of the rejection of cost-effective technologies.
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correcting information inadequacies, in reducing asymmetric responses to gains and losses, or in generally taking account of the growing literature that describes strategic behavioral responses by market players in virtually all circumstances? Earlier dialogue over the role of government focused on ability of the price system to coordinate the behavior of buyers and sellers and dealt with public goods, externalities, decreasing costs, and the like. We are awaiting the seminal piece that responds to Stiglitz's (2003) comment and similar comments by a range of observers that there is only one way for information to be complete and an infinite number of ways for it to be incomplete.9 Acknowledgement We thank Ronald G. Cummings and Paul Brewer for their contributions to our joint work on the experiments described in this paper. This work is cited elsewhere in the paper as Bjornstad et al. (2002). Neither of our colleagues is responsible for errors or omissions in the present paper. The work was sponsored in part by the Department of Energy's Office of Biomedical and Environmental Research, through its Integrated Assessment and Human Genome programs. References Bjornstad, David J., January 2003. Economic Incentives in the Purchase and Use of Energy Using Products: Past Practices and New Developments, Joint Institute for Energy and Environment, JIEE 2003-1. Bjornstad, David J., Paul Brewer, Ronald Cummings, Dec. 9, 1996. Investment Behavior in the Case of Irreversibility and Decreasing Uncertainty: An Experimental Laboratory Investigation, A Final Report to the Global Climate Change Research Program, Office of Energy Research, U.S. Department of Energy. Bjornstad, David J., Brewer, Paul, Cummings, Ronald, McKee, Michael, 2002. An experimental test for options value: relevance for contingent valuation elicitation. In: List, John, de Zeeuw, Aart (Eds.), Recent Advances in Environmental Economics. Edward Elgar Publishing, pp. 340–364. Camerer, Colin F., 2003. Strategizing in the brain. Science 300, 1673–1675. Camerer, Colin F., 2003. Behavioral Game Theory: Experiments in Strategic Interaction. Princeton Univ., Princeton, NJ. Carlsmith, Roger, W. Chandler, J. McMahon, D. Santino, 1990. Energy Efficiency: How Far Can We Go? Unpublished, Oak Ridge National Laboratory. Cummings, Ronald B., Melanie B. Williams, David J. Bjornstad, Dec. 9, 1996. The Value of the Global Environment: A Cross-Cultural Analysis Employing the Contingent Valuation Methodology in Support of Integrated Policy Analysis, A Final Report to the Global Climate Change Research Program, Office of Energy Research, U.S. Department of Energy. Dixit, Avinash, 1992. Investment and hysteresis. Journal of Economic Perspectives 6 (1), 107–132. Dixit, Avinash K., Pindyck, Robert S., 1994. Investment Under Uncertainty. Princeton University Press, Princeton, NJ. Hassett, Kevin A., Metcalf, Gilbert E., 1993. Do consumers discount the future correctly? Energy Policy, 710–716. June. Hausman, Jerry A., 1979. Individual discount rates and the purchase and utilization of energy using durables. Bell Journal of Economics 10 (1), 33–54 (Spring). Hubbard, R. Glenn, 1994. Investment under uncertainty: keeping one's options open. Journal Economic Literature 32, 1816–1831. Morel, Benôit, R., Farrow, Scott R., Wu, Felecia, Casman, Elizabeth A., 2003. Pesticide resistance, the precautionary principle, and the regulation of Bt corn. In: Laxminarayan, Ramanan (Ed.), Battling Resistance to Antibiotics and Pesticides. Resources for the Future Press, Washington, D.C. Pindyck, Robert S., 1991. Irreversibility, uncertainty, and investment. Journal of Economic Literature 29, 1110–1148. Stiglitz, Joseph A., 2003. Information and the change in paradigm in economics. American Economic Review 92 (3), 460–501.
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See also Bjornstad (2003).