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ABSTRACT FUNDAMENTAL ISSUES IN BENEFIT TRANSFER AND NATURAL RESOURCE DAMAGE ASSESSMENT


FUNDAMENTAL ISSUES IN BENEFIT TRANSFER AND NATURAL RESOURCE DAMAGE ASSESSMENT
James J. Opaluch and Marisa J. Mazzotta*

ABSTRACT
In this paper, we address three pertinent questions for benefit transfer. Can we reliably measure benefits within the original study context? To what extent can benefit measures that are reliable within the study context be transferred to provide reliable estimates for the policy site? How can we improve benefit transfers to make them more reliable under a wider set of conditions? Researchers must establish that the benefits estimates they are transferring are defensible themselves. Researchers should also test the adequacy of benefit transfers. by quantifying their accuracy. Finally, we need to improve our methods of transferring benefit estimates, perhaps by developing a wider range of calibration variables.

Benefit transfer estimates values in a policy context using available information from studies carried out in another context (the study context). For example, we may have an estimate of the value of recreational fishing derived from a study of coho salmon fishing in Oregon and attempt to transfer this result to estimate the value of king salmon fishing in Alaska. Participants at the AERE workshop agree that, for practical reasons, benefit transfer is a necessary component of policy analysis. In many situations the expense of carrying out an original study cannot be justified, or the funds or time simply aren’t available. Yet some information is needed to support decision making. In a sense, even site-specific studies are a form of information transfer, where data from the sample is transferred to a more general population. In many cases, the same kinds of issues arise (e.g., Loomis, 1987). For example, researchers must be careful that the sample is representative of the larger population. In cases where the sample is not representative, researchers frequently adapt results, often using socioeconomic characteristics of the sample and the population. Therefore, the relevant concern for economists is not whether to do benefit transfer; instead, we suggest three pertinent questions for benefit transfer. The first is whether we can reliably measure benefits within the original study context. We certainly shouldn’t consider transferring benefit estimates that are unreliable even within their own context. The second is to what extent benefit measures that are reliable within the study context can be transferred to
*University of Rhode Island, Department of Resource Economics.

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provide reliable estimates within the policy context. Reliability will depend on the extent to which values vary between the study and policy site, the extent to which we can explain and correct for these differences, and the standard of accuracy for benefit estimation. The third question is how we can improve benefit transfers to make them more reliable under a wider set of conditions. The answers to these questions will depend on the context of the benefit transfer, where different standards might be applied in different contexts. In many arenas, our institutions have set differing standards of accuracy or burdens of proof for different kinds of social decisions. For example, society has established the most rigorous burden of proof for criminal cases, requiring the evidence to prove the case “beyond a reasonable doubt.” This standard applies independent of associated penalty and holds for criminal fines, as well as loss of personal freedom through prison sentences or the death penalty. A weaker standard has been placed on other cases, such as in civil suits, where the standard is preponderance of the evidence. Here, the judge or jury will side with the stronger case. This standard will differ to some degree for cases that include a rebuttable presumption where a result is assumed to be correct unless a preponderance of evidence to the contrary exists. Finally, the weakest standard of proof exists for policy decisions, where the agency making the decision only needs to show that it is not being "arbitrary and capricious." An action by an agency is considered arbitrary and capricious when the agency has relied on factors which Congress has not intended it to consider, entirely failed to consider an important aspect of a problem, offered an explanation for its decision that runs counter to the evidence before the agency, or is so implausible that it could not be ascribed to a difference in view or the product of agency experience. (Motor Vehicle Manufacturers Association versus State Farm Mutual Insurance Company 463 U.S. 29, 43, 103 S.Ct. 2856, 2867, 77 L.Ed.2d 443, 458 [1983]) In contrast, an agency’s judgment will generally be accepted when it can show that it “examine[d] the relevant data and articulate[d] a reasoned basis for its decision" (NRDC v. Harrington 247 U.S. App. D.C. 340, 370, 768 F.2d 1355, 1385 [1985]). Hence, in developing regulations or in policy analysis, the agency is given considerable latitude for judgment and need not demonstrate, for example, a “preponderance of the evidence” or an absence of a "reasonable doubt” These legal doctrines may provide one basis for establishing different standards of accuracy or acceptability of benefit transfer within different contexts. An alternative standard may be provided by a form of benefit-cost analysis whereby a higher standard of accuracy might

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be required when the costs of making a bad decision are high. A lower standard of accuracy might be acceptable when costs are lower, such as when the information from the benefit transfer is only one of number of sources of information, or when benefit transfer is used as a screening device for the early stages of a policy analysis. Hence, the acceptability of benefit transfer depends not only on how appropriate the estimated value but also on the institutional context. A number that may be “good enough” to be used as part of agency judgment for a screening study may be judged to be inadmissible in a criminal case or even in a civil case, such as in litigation surrounding a natural resource damage assessment (NRDA). However, even for screening studies, we need to apply sensible standards of accuracy. We cannot allow ourselves to accept the proposition that “some number is better than no number,” particularly because an unreliable number may be given undue credibility. Benefit transfer will obstruct, rather than facilitate, rational planning and will lose credibility if we apply misinformation. In some cases we are better off acknowledging that we have no reliable estimate for a particular factor, and we can then either collect information to estimate this factor or account for it in qualitative terms. FRAMEWORK FOR EVALUATING BENEFIT TRANSFER The basic goal of benefit transfer is to estimate benefits for one context by adapting an estimate of benefits from some other context. Consider the example discussed above, where we have an estimate of the value of recreational fishing for coho salmon in Oregon, and we attempt to transfer this result to estimate the value of king salmon fishing in Alaska. The estimate of the value of coho fishing in Oregon may not accurately measure the value for king salmon fishing in Alaska for at least three reasons: The preferences of participants in Alaska may differ from the preferences of participants in Oregon. The characteristics of the king salmon fishing experience in Alaska may differ from the characteristics of the coho salmon fishing experience in Oregon. The estimated value of coho fishing in Oregon may not measure the true value of coho fishing in Oregon. The first two reasons imply that the value of fishing in Oregon differs from the value of fishing in Alaska, while the third implies that the estimated value in Oregon is incorrect.

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Figure 1 formalizes these three sources of variation. Individual variation denotes variation across people that might arise because of differences in preferences. The value of

"Mean" Value Fixed Component Random Component CL

Individual Variation I4

Commodity Variation PC

“Other” Variation CL0

El

EC

EO

Figure 1. Framework for Benefit Transfer

Alaskan king salmon fishing may differ from that of Oregon coho fishing because anglers in Alaska may have different preferences than anglers in Oregon. Some of this variation may be due to fundamental differences in tastes across individuals, while other components may be due to differences in socioeconomic characteristics like income or age. Commodity variation denotes variation across commodities that might arise because of differences in their characteristics. The value in Alaska may differ from the value in Oregon because of differences in the two experiences. For example, on average, differences may exist in catch rates, scenery, congestion, size of fish, or other characteristics. The third source of variation is meant to capture any variation that is independent of preferences or the commodity and thus includes “bias” or “error” in measuring value. Various sources of bias and error have been recognized in the literature. For example, using an incorrect functional form can bias regression results and subsequent value estimates. Similarly this form of variation may arise when survey respondents do not correctly express their values in a contingent valuation survey or when people act in ways that do not allow us to infer their true values through revealed preference methods. The issue of bias has been most carefully considered for contingent valuation (e.g., Mitchell and Carson, 1989). but important biases may also exist for revealed preference methods, such as the travel cost approach (e.g., Bockstael, 1984; Smith, 1989). Many studies have

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attempted to test reliability and validity of benefit estimation techniques within the context of a specific study (e.g., Bishop and Heberlein, 1979; Loomis, 1989). To our knowledge, Cummings, Brookshire, and Schulze (1986) are the only ones who have taken a broad look at the issue of accuracy. We are aware of two studies that look at accuracy within the context of benefit transfer (Loomis, 1992; Downing and Ozuna, 1992). In Figure 1, each source of variation is composed of “fixed” components and random components. The fixed components are the components of variation that in some cases can potentially be estimated and corrected for, while the random components cannot be explained. For example, the value of recreational fishing may vary systematically over individuals because of differences in age or income. By including age and income as explanatory variables in the demand function, these sources of differences in value between the study and policy contexts can be explained, predicted, and corrected. However, tastes may differ randomly and unexplainably across individuals between the study context and the policy context, The Oregon estimate will misrepresent Alaskan values if the distribution of these random components of participants' tastes is different in Alaska than in Oregon, after correcting for identifiable differences in the populations (e.g:, age, income). In some cases we may be able to place confidence intervals on this variation. For example, our Oregon data may allow us to estimate the variance in tastes over participants. However, we cannot guarantee that our policy site will fall within the confidence intervals because sources of variance may exist between the study and policy sites that we cannot observe within the data for the study site only. Thus, to the extent that values in the study and policy contexts differ because of random differences in tastes, we may be unable to adjust our benefit estimates to reflect these differences. A similar problem may arise if the contribution of “identifiable” factors differs across the two sites. That is, using the Oregon coho study, we may be able to estimate how age and income affect the value of fishing in Oregon. However, these characteristics may have different effects on the value of fishing in Alaska. For example, if the weather is colder during the fishing season in Alaska, age may be a more significant factor in Alaska. Similarly, two regions may have cultural differences, so that in one region participants in some age group would not be “caught dead" fishing, while this attitude may not be a factor in the other region. Again information obtained in one region may not be transferable to another region. Furthermore, if no studies are available in the policy region, we may have no systematic means of identifying or measuring these sources of variance.

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The three sources of variation may be viewed somewhat differently in terms of three components of benefit transfer. The first component consists of the “knowns,” or the differences between the study context and policy context for which information is available and that can be estimated. For example, we may be able to estimate how the value of fishing varies with income, age, and catch rates. Using the known (or knowable) information, such as demographic information and characteristics of the activity, we can adjust the estimates of value obtained for the study context to produce estimates for the policy context. The second component consists of the “known-unknowns,” which might include the random components of the individual or commodity variation. For example, preferences may differ in ways that we cannot explain, but we may nevertheless be able to estimate the variance due to these effects at the study site. Thus these random components may be accounted for by using confidence intervals on the estimates. Alternatively, known-unknowns might also arise because of variables that are known to affect value but for which no data are available at the policy site. We may know how income affects value, but we may not have data on income of participants at the policy site. One way of accounting for these effects would be to use sensitivity analysis to place plausible upper and/or lower bounds on these variables. Finally, “unknown-unknowns” could arise in the original study from the “other” sources of variation discussed above, or from unobservable or unknown differences between the two populations and/or commodities. Limiting the magnitude of the unknown-unknowns is crucial to the success of the benefit transfer. However, the magnitude of these sources of variance is, by definition, not known to the researcher, and the researcher generally has no way to quantitatively account for them in the transfer, short of carrying out a study at the policy site. In the case of unknown differences between the study context and policy context, one possible approach is to use a pilot study to assure that results appear to be transferable. Of course, the cost of carrying out such a study may negate much of the cost savings that may come from benefit transfer. Thus, the answers to the first two questions posed earlier-whether we can reliably measure benefits in the original study context and whether we can reliably transfer benefit estimates-depend on the types of variation that occur and their magnitudes and on how well we can identify and measure them. For benefit measures to be suitable for transfer, unexplained variation must be limited to an “acceptable” level. The margin of error that is “acceptable” will depend on the appropriate reliability standard, as described above.

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TESTING AND IMPROVING BENEFIT TRANSFERS We might judge our confidence in the soundness of a benefit transfer in several ways. Generally, statistical tests are used to evaluate the original study. These tests include statistical significance of explanatory variables, the equation R2, and the size of prediction intervals. The significance of important explanatory variables, such as the travel cost coefficient, is necessary but not sufficient proof of the validity of value estimates. In addition, the model must have acceptable explanatory power, which is indicated by the R2 and prediction intervals. Typically, economists focus on statistical significance and tend to ignore R2 and prediction intervals. These statistics suggest the relative magnitudes of the knowns and the known-unknowns, but they will not measure the effects of the unknown-unknowns. Therefore, we can only really be certain about negative test results. If a model has poor explanatory power for the study site, we will not have much confidence in its soundness for benefit transfer. However, a model may have good explanatory power, but extrapolation of its results outside of the original sample may imply that we cannot measure important components of the variance-the unknownunknowns-so that the model may not provide good benefit estimates for the policy site. Given the above problems, researchers should place greater emphasis on testing and calibrating benefit transfers. Socioeconomic variables, which are typically used as calibrating variables in transfers, often have very low explanatory power, implying that they are not good calibrating variables for benefit transfer. Consequently, we need to be more creative in the variables used for transfer and focus research efforts on finding variables that better explain variations in preferences. For example, attitude statements about the importance of an activity or the experience level of participants might be one type of variable that would improve the explanatory power of models and transfers. Travel cost and contingent valuation surveys could include a series of attitude statements, answered on a scale of one to ten, about the importance of an activity or the respondent’s experience level. If these variables have good explanatory power they could be used to calibrate the transfer. One reason that socioeconomic variables are generally used to calibrate transfers, despite their low explanatory power, is that these are the variables for which data are easily available. To use other calibrating variables researchers might conduct a small "calibration" survey for the policy site, where respondents are only asked to answer the same series of attitude/experience questions asked in the original study. The results from the original study could then be weighted by the attitude/experience values for the policy site to calibrate the transfer. Again, we should be

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Anchorage oil spill cost nearly $250,000 to conduct, but natural resource damages were estimated at $32,000 (Washington State Department of Ecology, 1987). In recognition of these issues, researchers have developed a variety of structures for benefit transfer. For example, the Department of Interior (DOI) has developed the Natural Resource Damage Assessment Model for Coastal and Marine Environments (NRDAM/CME), which is a structure for benefit transfer based on a computer model that simulates the physical fates of a spilled substance, the biological effects of this spill, and the resultant economic damages (e.g., Grigalunas, Opaluch, French and Reed, 1989; Jones, 1992). Alaska and Washington State have developed more ad hoc approaches for simplified damage assessment that use damage indexes based on the properties of the substance spilled and the environment in which the spill occurs. In formulating the regulations for the Oil Pollution Act (OPA), the National Oceanic and Atmospheric Administration (NOAA) is considering using compensation tables, the NRDAM/CME model, and other means of “expedited” damage assessment. Many damage assessments have been based on more “traditional” applications of benefit transfer, where available estimates of impacts, such as body counts or lost beach days, are combined with available estimates of the values of the resources to estimate damages (e.g., Washington State Department of Ecology, 1987). Most of the NRDA work by economists attempts to calculate the value of lost services due to a spill. This approach is based on the usual definition of Hicksian compensation: C = W, NR”, uo) - E(P, NRl, Uo, where C is monetary compensation required to make the individual whole, E(e) is the expenditure function, P is a vector of market prices, NRo is the without-spill vector of natural resources, NR1 is the with-spill vector of resources, and Uo is the without-spill level of utility. Thus, monetary compensation is the difference between the with-spill and without-spill levels of expenditure needed to achieve the fixed level of utility. The aggregate level of compensation required can be calculated by aggregating over all individuals. This level is often calculated by estimating compensation required by a "representative" individual and then multiplying by the size of the affected population. However, under CERCLA and OPA, a strong preference is expressed for making the public whole by restoring injured natural resources rather than providing monetary compensation (e.g., Mazzotta, Opaluch, and Grigalunas, 1992). Additionally, all funds collected, including (1)

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The cost-effective restoration program that makes the public whole is defined by Min C(R) R s.t. (3)

where C(R) is the cost associated with restoration program R, E is the expenditure function, and FGP is a factor of gross proportions, as described below. The first constraint requires that the public be made whole through resource restoration, R, and the second constraint requires that the cost of restoration not be “grossly disproportionate” to the value of the resource. This sort of constraint is implicit in the Ohio Decision, where the court suggests “the rule might for instance hinge on the relationship between restoration cost and use value (e.g., damages are limited to three-times the amount of use value)” (U.S. Court of Appeals, 1989, footnote 7. p.21). Thus, the Court’s suggestion for grossly disproportionate would be based on a factor of gross proportions (FGP) of 3. Equation system (3) is equivalent to the traditional expenditure minimization problem of utility theory with two exceptions. First, OPA and CERCLA’s restriction that the funds must be used to “replace, restore, rehabilitate or acquire the equivalent" implies that the resulting expenditure function is restricted in the commodities that can be purchased. This restriction is reflected in the fact that the minimization is over R, not over all possible commodities. Second, the purchases are constrained to those sets that are not “grossly disproportionate” to the value of the resource. In practical terms, the solution to this problem would progress in stages. For example, researchers could fast identify a number of feasible restoration plans and estimate the time path of recovery for various resources under each plan. Next, researchers could identify “equivalent” resources to restore, in terms of social preferences. Here, researchers could use standard discrete choice models, where a sample of respondents are presented with alternative programs for restoration, described in terms of the resources and time frame for each. The respondents would then be asked to choose the most preferred restoration programs or to rank alternative programs. Standard methods of discrete choice analysis (McFadden, 1973) could then be applied to determine the levels of restoration for

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compensate fully for the injury. Congress refusal to view use value and restoration cost as having equal presumptive legitimacy merely recognizes that natural resources have value that is not readily measured by traditional means. (U.S. Court of Appeals, 1989, p. 51) This quote suggests that Congress' intention was not to suggest that full restoration always be carried out. “regardless of cost and regardless of whether anybody cares,” but to make sure that the value of resources are not systematically understated. Although the intentions of Congress are not stated clearly, an alternative to Hanemann’s (1992) interpretation would allow for an anthropocentric approach such as that presented above, where the objective is to make the public whole in terms of maintaining the value to the public of the stock of resources and associated services, rather than to make the environment whole regardless of public values. This interpretation is also consistent with the idea of gross proportions. Duffield also addresses this issue, discussing the fact that requiring full restoration of injured resources is based on an equity goal and is likely to result in losses in economic . efficiency (Ward and Duffield, 1992). He states that Congress expressed a preference for full restoration, which will often be economically inefficient. Yet, the DOI proposed regulations under CERCLA do not require that damages be calculated as the cost of full restoration but that some combination of restoration and compensation be chosen. A “reasonable number” of alternatives must be considered, and these alternatives must include the possibility of "no actionnatural recovery." Although restoration is still the preferred goal, combining both economic efficiency and the idea of restoration is possible under the proposed regulations. Hanemann (1992) and others see restoration as based on the deontological principle so that “economic analysis plays only a minor role, associated with calculating restoration costs and cost-effectiveness. It is under the anthropocentric approach that economics moves to center stage” (p. 574). However, the notion of restoration as in-kind compensation is useful for framing the restoration problem as one of compensating the public in a cost-effective manner, while remaining compatible with the expressed Congressional preference for restoring damaged resources. If we view restoration from this anthropocentric viewpoint, economists play a critical role. SUMMARY AND CONCLUSIONS Benefit transfer is a necessary and important economic tool for practical policy analysis. However, to establish and improve the credibility of benefit transfer we need to place greater

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BENEFITING BENEFITS TRANSFER: INFORMATION SYSTEMS FOR COMPLEX SCIENTIFIC DATA Martin H. David*

ABSTRACT In this paper, I suggest reorganizing the science on which we build benefits estimates. I advocate developing a system for sharing data, creating support for archiving scientific measurements, and making data easier to use. This paper advocates reorganizing the science on which we build estimates of benefits. Reorganization implies four imperatives: 1. Build an effective system for sharing data, an Information System for Complex Data (ISCD). 2. Create necessary support for archiving scientific measurements. 3 . Begin now. Deploy existing computer and software capabilities to reduce learning time for secondary use of data, to increase scope of questions that can be addressed to existing data, and to anticipate the arrival of new generations of software and hardware. 4. Change incentives. WHY BUILD ISCD? Positive Reasons Benefits transfer is applied science, statistical science, implying the following: Estimates of benefit are simulations based on empirical fact Observations and models of those observations are used to simulate out-of-sample forecasts for benefits transfer. The procedure for benefits transfer must be reproducible. Reproductibility keeps the estimates out of court and away from accusations of fraud. Bounds on error in estimates are needed to tell us how good the estimate is. We have more certainty about the value of the salmon fishery, governed by market prices, than we have about the value of wilderness, whose nonmarket externalities preserve options, species, and perhaps even climate. Learning-by-doing. Over time we expect the error of benefit estimates to decline. The scope of estimates will increase as the range and quality of measurements increases.

*University of Wisconsin-Madison, Department of Economics.

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benefit estimation and will likely lose credibility as a policy tool. This potential effect suggests that we should focus our attempts at benefit transfer on obtaining a smaller number of studies that fit the policy context most closely, rather than obtaining the largest number of studies possible, where many may not fit the policy context. Other interesting issues regarding benefit transfer arise within the context of NRDA. Because the statutes express a strong preference for restoration of resources over monetary compensation, we need to develop methods to evaluate restoration alternatives. More specifically, we need to identify restoration programs that will make the public whole in the least costly manner and whose costs are not grossly disproportionate to the value of the injured resources. We also need to determine the extent to which these measures of compensation are transferable across contexts. REFERENCES Bockstael, Nancy E. 1984. “Valuing Natural Resource and Environmental Amenities: Can Economic Valuation Techniques be Made Defensible.” Journal of the Northeast Agricultural and Resource Economic Association 13(2). Bishop, Richard C., and Thomas A. Heberlein. 1979. ‘Measuring Values of Extra-Market Goods: Are Indirect Measures Biased?” American Journal of Agricultural Economics 61(5):926-930. Cummings, Ronald G., David S. Brookshire, and William D. Schulze. 1986. Valuing Environmental Goods: An Assessment of the Contingent Valuation Method. Totawa, NJ: Rowman and Allanheld. Downing, Mark, and Teofilo Ozuna, Jr. 1992. “Testing the Feasibility of Recreation Benefit Function Transferability.” Paper presented at AAEA Annual Meeting, August, 1992, Baltimore, Maryland. Grigalunas, Thomas A. James J. Opaluch, Deborah French, and Mark Reed 1988. “Measuring Damages to Marine Natural Resources from Pollution Incidents Under CERCLA: Application of an Integrated Ocean Systems/Economic Model." Journal of Marine Resources 5(1). Hanemann, W. Michael. 1992. “Natural Resource Damages for Oil Spills in California” In Natural Resource Damages: Law & Economics, Kevin M. Ward and John W. Duffield, eds. New York: Wiley Law Publications. Jones, C.A 1992. “Recreational Fishing Valuation: Application of the Type A Model." Paper presented at the 1992 AERE Benefits Transfer. Procedures, Problems, and Research Needs Workshop, Snowbird, UT, June 3-5. Loomis, John B. 1987. “‘Expanding Contingent Value Sample Estimates: Current Practices and Proposed Solutions.” Land Economics 63(4):396-402.

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Figure 1. An Information System for Complex Data

scientists call a domain specialist, a diagnostician who can solve problems in interpreting data in one-on-one consultation or who can alter the computational capability to teach many users to solve the problem for themselves. (This keeps cost down.) The knowledge resource integrates software and data in a system that can respond to inquiries from users. Pre-programmed artificial intelligence and carefully designed data structure (the database schema) speed the recovery of frequently used data and description. The knowledge resource includes several critical elements: a body of data accessible for statistical analysis (e.g., a SAS system file), descriptions of the data that provide necessary support, an archive of reports generated from the data, and bibliographic databases that can be searched for citations, data sources, and subject arc crucial to the process of learning about and using data. At Wisconsin many of these capabilities were organized in a relational database management system (RDBMS). The power of those systems is explained in the Appendix.

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- Conceptual level-When are the numbers real? When are numbers imputed? When are numbers randomly altered, “fuzzed” (to limit disclosure)? Consequences of using each datum: What unusual interpretations? When are zeroes null? When are values truncated? Inference from the data: How can honest inferences be made from the data? Does selection bias inference from reports? Does variance in the measurement process mask real world phenomena ? Is the measurement process biased by moral hazard? All of this information can be embedded in and linked to the RDBMS that contains the measurements. Table 1 contains a summary of the kinds of information that should be stored in an ISCD to provide necessary support for the secondary data researcher. WHAT PAYOFFS DO ISCD NECESSARY SUPPORT CREATE FOR BENEFITS TRANSFER? Three kinds of payoff follow from organizing data on economic benefits in an ISCD: discovering the state of the art of benefit measurement will be less costly, synthesizing benefit measurements will be easier, and incorporating superior methodology in estimating models on larger sets of data will be possible. State-of-the-Art Garner’s bibliographic database (circulated for the AERE conference) represents an important step towards an ISCD for benefit measurements. Citations to reports about what we already have discovered are accessible. The perspective of the ISCD, says Garner, should add one element to the bibliographic database-a title for each dataset exploited in each report Titling and citing datasets are the only ways to establish the empirical foundation for any analysis. The title concept is implemented in Roistacher et al. (1980) but has not found its way into accepted referencing for scientific publication or into bibliographic databases. The perspective of an ISCD implies that the database is complemented by electronically stored files containing each of the reports and articles cited. Electronic preservation of reports allows any user to review any document cited. Archiving scientific work in this way assures permanence for the published record. Because many excellent datasets are collected to pursue contractual obligations, reports containing important datasets are difficult to find-even in the contractor’s archives. This makes electronic archiving of reports critical.

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Synthesizing Measurements Smith and Huang (1991) undertook to synthesize measures of willingness to pay for air quality from data on the housing market in areas with differing levels of air quality. Their analysis searched over fifty statistical studies spanning two decades of observations. The response that Smith and Huang seek to estimate is the marginal rate of substitution between price paid for housing and particulate deposition. Underlying that measure of response is a model of the price paid by individuals for residential housing units in various urban areas of the U.S. Smith and Huang develop an excellent “meta-analysis” of models fit to the underlying data. Their work synthesizes past investigation but cannot recover much of the variability in underlying data (because each model is a projection of the underlying data into a small number of dimensions). Furthermore, the technique fails to recover any direct information about the dynamics of willingness to pay; all human response to pollution is inferred from differences in prices paid by similar people buying housing in different places. Crippling Problems This study faced extreme difficulties and I admire the authors for their perseverance: Assembly. The study required assembling 26 journal articles, 5 unpublished papers, 5 dissertations, and 1 edited volume. Incomplete estimates of response to air quality. Many studies did not contain responses to ozone, S$, and other indicators of air pollution The meta-analysis is confined to understanding response to particulates (arguably the most obvious aspect of air quality). Models that did not include particulates had to be excluded. Reuse of data. The same data were used to estimate several models, both within and between research teams. For that reason models estimated are not independent. Incomplete documentation. Some papers failed to describe either the data or estimating method in sufficient detail to permit meta-analysis. Contact with one researcher filled some lacunae. In other cases pollution data were augmented; still other models could not be included in the meta-analysis. Limitations of the Approach Some of the studies are based on measures of house value and air pollution that are aggregated over space (e.g., Census tracts). A second problem is that the studies use a variety of measures of willingness to pay: samples of sales data, samples of FHA-mortgaged properties, samples derived from the Census self-reported house values, and the Annual Housing survey.

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"documented " and "titled" Funding institutions can also require the deposit of completed publications in a data-oriented library. Funding institutions are in a position to provide necessary, resources and to enforce their agreements with data collectors. Funding institutions cannot proceed without support from the professions. Journals need to require citation of data sources. They also need to require datasharing that permits replication of published findings. Some journals have already adopted this point of view (AER, JHR, and JEEM). These changes in institutions and journals are easy. Change in our own professional conduct is also needed. While the computational capability to create low-cost datasharing is available on most desktops, social conventions need to be forged to support effective datasharing. Just as we have conventions to drive on the right and stop at the red signal, we need conventions on a common system for organizing shared data. Up to now, we have been unable to specify completely what is required for data-sharing. Concepts from computer science clarify what is needed at the same time that those concepts forged the technology that we can now use to organize data. Experience at Wisconsin shows the efficacy of ISCD. Computer science has given us the relational data model, which organizes data and necessary support for data in a common framework. The geniuses of Silicon valley have given us technology that makes the computational costs of ISCD trivial in comparison to the cost of professional time. So long as we do not implement ISCD, much professional time will be wasted in searching for the right data, the right model, and implementing the simulation required for benefits transfer. Do we really want to waste scarce resources for learning about the environment? REFERENCES Casella, George, and Edward L George. 1992. “Explaining the Gibb's Sampler.” American Statistician 46(3):167-174. Codd, E. F. 1985. “Is Your Database Really Relational?” Computerworld ID1-ID9. Also: “Does your Database Run by the Rules?” Computerworld 45-49ff. Date, C.J. 1987. An Introduction to Database Systems. Vol 1.4th Ed. Reading MA: AddisonWesley Publishing Co. David, Martin H. 1991. “The Science of Data Sharing: Documentation.” In Sharing Social Science Data: Advantages and Challenges. Joan E. Sieber, ed., Newbury Park, CA: Sage Publications.

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APPENDIX A RELATIONAL DATABASE MANAGEMENT SYSTEMS-RDBMS In the past dozen years computer scientists have discovered important principles for storing and retrieving large volumes of numerical data and smaller volumes of text. Their ideas culminated in relational database management systems (RDBMS) technology that is now available to every PC owner for about the same price as a spreadsheet program. RDBMS denotes a system with several essential features, starred in Table A-1. The systems were designed to meet needs for commercial “transactions processing," whose requirements are somewhat different from scientific statistical processing, although the commonalities are much greater than most social scientists understand. The systems are designed for multiple users-both multiple data suppliers (i.e., points of data entry) and multiple researchers. RDBMS are designed to support interactive use of the data at all times and maintain an unambiguous outcome for statistics (reports in the RDBMS jargon) that are generated at any point in time. This feature is called data concurrency. A mandatory requirement for RDBMS is dynamic independence. Adding new data to the system without restructuring the existing data must always be possible. For example, successive measures of pollution control and abatement expenditures (PACE) can be loaded into the system without knowing about or interfering with older data. Contextual data can be added to the system without determining the attributes used to link those data to individuals in advance. Thus interview data obtained from households can be loaded without knowing that their report of industry affiliation might subsequently be used to assign worker exposure to safety risks or that geography might later be used to assign prevalence of radon exposures. Data entry is controlled by logical rules that can draw on any part of the existing data to enforce consistency; consistency may be applied to individuals, households, firms, activities (other entities), and combinations of entities. Consistency rules are called integrity constraints on the database. Referential integrity implies that adjustments to the database do not leave garbage in the system. For example, if an individual is found to be associated with the wrong address, all traces of that individual are dissociated from that address when the address is corrected.

A-1

For researchers the most important property of RDBMS is its “query language.” Requests for information are written in the query language, which has a simple structure derived from concepts of mathematical logic. Query languages support any logical operation on any mathematical or lexical function of the attributes or variables in the database. Query languages are compact, and SQL has been adopted as an industry standard that will be supported by all database vendors.’ RDBMS provide permanent housekeeping that is essential when multiple points of entry and multiple users must be accommodated. Finally, the RDBMS support sophisticated security and reporting. Users can be restricted, from access to particularly sensitive data. Operations can be monitored continuously by reports on the capture of interviews, error-rates, outliers, and interviewer comments. The logic of RDBMS results in “flat files,” rectangular arrays that are easy to move outside of the RDBMS environment. Furthermore, the RDBMS encompass two capacities that aid a complex data collection through a nation-wide system. The databases support “distributed databases" whose parts may reside on different computers. For example, the database required for sampling can be separated from the data generated by interviewing. The second capability is “platform independence” that assures the system operates in the same manner on all hardware using identical programs or applications. The most important feature of RDBMS for a complex data collection is that it maintains a vocabulary of names for each measurement, each transformation, and each relationship encompassed in the database, no matter how many users are proceeding to make independent . uses of the data.2 Table A-1 lists aspects of RDBMS that are critical for successful processing of scientific data pertaining to the environment
RDBMS apply artificial intelligence to minimizing the cost of executing the query. Therefore, execution of particular requests does not proceed in the procedurally defined manner of scientific programming languages and statistical processors. This feature implies that embedding scientific programming languages in the database (and all RDBMS support such capabilities) causes poor performance. Understanding the strengths of the RDBMS, however, allows us to design interfaces to statistical processors (e.g., SAS and SPSS) that permit the RDBMS to locate required data efficiently while permitting the aggregation of data across entities to proceed equally efficiently. The merit of such interfaces is that inefficiencies of data management and storage in statistical processors can be eliminated without eliminating the finely tuned calculation of estimates that those processors support. This capability makes it possible to generate new databases from the existing metadata that describe the survey instrument and automate the evolution of the database as each panel proceeds and as new panels are created. The result is greater productivity in manipulating on-going change to the structure of the database and greater clarity in the documentation and diagnostics produced for the documentation of data-processing steps.

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APPENDIX A ECONOMIC ANALYSIS AND RESOURCE BRANCH ENVIRONMENTAL BENEFITS DATABASE

1983. Cenangent Valuation Surveys for Evaluating Evironmental Assets. Natural Resources Journal. 1984. Facts and Values in Risk Analysis in Environmental Toxicants, Risk Analysis. 1983. Second Annual Symposium on Environmental Epidemiology, Environmental Health Perspectives. 1989. Biological Habitat Reconstruction, ed. Buckley, G. P., Belhaven Press, London. 1984. Estimating Willingness to Pay to Reduce the Risk of Infertility: An Expolratory Inquiry, prepared by Charles River Associates, Inc. 1983. Benefits of Preserving Cultural Materials From Damages Associated with Acidic Deposition prepared by Charles River Associates, Inc. 1984. Assessing Cost-Benefit Assessments, Journal of Water Pollution Control Federation. 1986. An Economic Assessment of Marine Recreational Fishing in Southern California, prepared by National Marine Fisheries Service. 1991. Valuing the Environment: Six Case Studies, ed. Barde. J.P and Pearce, D.W., Earthscan Pub. Ltd., London AERE. 1987. Environmental Monitoring and Enforcement: Theory and Practice, prepared by AERE, EPA, NOAA for Association of Environmental and Resource Economists. AERE. 1990. Natural Resource Market Mechanisms, prepared by AERE, EPA, NOAA, USDA for Association of Environmental and Resource Economists. AERE. 1985. Recreation Demand Modeling, prepared by AERE. EPA, NOAA for Association of Environmental and Resource Economists. AERE. 1991. The Management of Non-Point Source Pollution, prepared by AERE, EPA, NOAA, USDA for Association of Environmental and Resource Economists. AERE. 1988. Fourth Annual AERE Workshop - Marine and Sport Fisheries - Economic Valuation and Management -Papers, Association of Environmental and Resource Economists. Abdalla, Charles. 1990. Measuring Economic Losses from Groundwater Contamination: An Investigation of Household Avoidance Cost, Water Resources Bulletin, Vol. 26, No. 3, pp. 451-463. Abel, Fred H., Dennis P. Tehansky, and Richard G. Walsh. 1975. National Benefits of Water Pollution Control. prepared by Washington Research Center for US Environmental Protection Agency (ORD). Abelson, Peter W. 1979. Property Prices and the Value of Amenities, Journal of Environmental Economics and Management, Vol. 6, pp. 11-28. Abt Associates, Inc. 1984. Air Pollution Damages to Cultural Materials. Adamowicz, W. L., and W. E. Phillips. 1983. A Comparison of Extra Market Benefit Evaluation Techniques, Canadian Journal of Agricultural Economics, Vol. 31, pp. 401-412. Adamowicz, Wiktor L., Jerald J. Fletcher, and Theodore Graham-Tomasi. 1989. Functional Form and the Statical Properties of Welfare Measures, American Journal of Agicultural Economics, Vol. 71, No. 2, pp. 414-421. Adams, R.L., R. C. Lewis, and B. H. Drake. 1973. An Economic Analysis of Outdoor Recreation, prepared by Bureau of Outdoor Recreation for US Department of Interior, Washington, DC.

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Anderson L.G. 1980. Estimating the Benefits of Recreation Under Conditions of Congestion: Comments and Extension. Journal of Environmental Economics and Managment Vol. 7, pp. 401-496. Anderson, Lee 1983. The Demand Curve for Recreational Fishing with an Application to Stock Enchancement Activities. Land Economics. Vol. 59. No. 3. pp. 279-286. Anderson, R.C. and R. C. Dower. 1980. Land Price Impacts of the Adirondack Park Land Use and Development Plan, American Journal of Agricultural Economics, Vol. 62. No. 3, pp. 543-548. Anderson, R. J. 1981. A Note on Option Value and the Expected Value of Consumer Surplus, Journal of Environmental Economics and Management Vol. 8, pp. 187-191. Anderson, R. J., and T. Crocker. 1972. Air Pollution and Property Values: A Reply, Review of Economics and Statics, Vol. 54, No. 4, pp. 470-473. Anderson, R. J., and T. Crocker. 1971. Air Pollution and Residential Property Values, Urban Studies, Vol. 8. pp. 171-180. Anderson, Robert, and Bart Ostro. 1983. Benefits Analysis and Air Quality Standards, Natural Resources Journal. Angelo, R. J., and L. G. Anderson. 1984. The Value of Fish and Fishing Days: A Partial Solution to Managing Recreational Fisheries with Stock Externalities, University of Delaware. Amdorfer, David J., and Nancy Bockstael. 1986. Estimating the Effects of King Mackeral Bag Limits on Charter Boat Captains and Anglers, prepared by Environmental Resources Management, North Central Inc. for NMFS Southeast Fisheries Center. Arrow, Kenneth, and Anthony C. Fisher. 1974. Environmental Preservation, Uncertainty, and Irreversibility. Quarterly Journal of Economics, Vol. 88, pp. 312-319. Asako, Kazumi. 1979. Environmental Pollution in an Open Economy, Vol. 55. No. 151. pp. 359-367. Ashford, Nicholas A., and Christopher T. Hill. 1982. Analyzing the Benefits of Health, Safety, and Environmental Regulations, prepared by Massachusetts Institute of Technology for US Environmental Protection Agency. Ashford, Nicholas, C. W. Ryan, and C. C. Caldard. 1983. A Hard Look at Federal Regulation of Formaldehyde: A Departure from Reasoned Decisionmaking, Harvard Environmental Law Review. Assaf, George B., Brent C. Kroetch, and Suboodh C. Mathur. 1986. Non-Market Valuation of Accidental Oil Spills: A Survey of Economic and Legal Principles, Marine Resource Economics, Vol. 2, No. 3, pp. 211-238. Atkinson, S. 1983. Marketable Pollution Permits and Acid Rain Externalities, Canadian Journal of Economics. Atkinson, Scott E, Thomas D. Crocker, and Herbert L. Needleman. 1983. The Economic Consequences of Elevated Body Lead in Children: A Proposed Study Framework, prepared by EPA Economic Analysis and Research Branch for US Environmental Protection Agency. Atkinson, Scott and T. H. Tietenberg. 1984. Approaches for Reaching Ambient Standards in Non-Attainment Areas: Financial Burden and Efficiency Considerations, Land Economics. Australia. 1992. RAC Forest and Timber Inquiry Final Report, Volume 2A, prepared by Resource Assessment Commission for Canebera, AGPS, pp. E20-E22. Avol, A. E., W. S. Linn, and T. G. Venet. 1983. Acute Respirator Effects of Los Angeles Smog in Continously Excersizing Adults, Journal of Air Pollution Control Association. Bailey, M. J. 1982. Risks, Costs and Benefits of Flourocarbon Regulation, American Economic Review.

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Barrick, Kenneth A. 1986. Option Value in Relation to Distance Effects and Selected User Characteristics for the Wasakie Wilderness, Northeast Wyoming, prepared by International Research Station. Ogden UT for US Forest Service, pp. 411-422. Barry, Gorgon. 1983. Law and Economics: Its Application to Air Quality Management in the United States. Isreal Ecological Society/et al Ecology & Environmental Qual. Bartelmus, P . C. Stahmer. and J. van Tongeren. 1989. Integrated Environmental and Economic Accounting -Framework for a SNA Sattelite System, Review of Income and Wealth. Bartik, Timothy J. 1987. The Estimation of Demand Parameters in Hedonic Price Models, Journal of Political Economy, Vol. 95, No. 1, pp. 81-88. Bartik, Timothy J. 1987. Estimating Hedonic Demand Parameters Using Single-Market Data: The Problems Caused by Unobserved Tastes, Review of Economics and Statistics, Vol. 69, No. 1, pp. 178-180. Bartik, Timothy J. 1988. Measuring the Benefits of Amenity Improvements in Hedonic Price Models, Land Economics, Vol. 64 pp. 172-183. Bartik, Timothy J., and V. Kerry Smith. 1987. Urban Amenities and Public Policy, North Holland Publishing Company, Amsterdam. Bartik, Timothy J. 1988. Evaluating the Benefits of Non-Marginal Reductions in Pollution Using Information on Defensive Expenditures, Journal of Environmental Economics and Management, Vol. 15, No. 1, pp. 111-127 Bartik, Timothy J. 1988. The Effects of Environmental Regulation on Business Location in the United States, Growth and Change, Vol. 19, No. 3. pp. 22-44. Bartlett. R. V., and W. F. Baber. 1987. Matrix Organization Theory and Environmental Impact Analysis: A Fertile Union?, Natural Resources Journal. Vol. 27, No. 3. pp. 605-615. Bateman, I. J., K. G. Willis, G. Garrod, and P. Doktor, et al. 1992. A Contingent Valuation Study of the Norfolk Broads, prepared by Environmental Appraisal Group, University of East Anglia for National Rivers Authority, England. Batie, S. S. 1985. Economics: Nonpoint Source Pollution Impacts, National Conference of Nonpoint Source Pollution, pp. 229-231. Batie, S. S.. and J. R Wilson. 1979. Economic Value Attributable to Virginia’s Coastal Wetlands and Inputs in Oyster Production, prepared by Department of Agricultural Economics for Virginia Polytech Institute, Blacksburg, VA Batie, S. S., R. B. Jensen, and L. G. Hogue. 1976. A Lancasterian Approach for Specifying Derived Demands for Recreational Activities, Southern Journal of Agricultural Economics, Vol. 8. pp. 101-107. Batie, Sandra S.. and James R Wilson. 1978. Economic Values Attributable to Virginia’s Coastal Wetlands as Inputs in Oyster Production, Southern Journal of Agricultural Economics, pp. 111-118. Batie, Sandra S., and Leonard A. Shabman. 1982. Estimating the Economic Value of Wetlands: Principles, Methods, and Limitations, Coastal Zone Management Journal, Vol. 10, No. 3, pp. 255-278. Batie, Sandra, and Carl Mabbs-Zeno. 1985. Opportunities Costs of Preserving Coastal Wetlands: A Case Study of a Recreational Housing Development, Land Economics, Vol. 61, No. 1. Baumol, William J. 1980. Theory of Equity in Pricing for Resource Conservation, Journal of Environmental Economics and Management, Vol. 7, pp. 308-320.

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Bergstrom, John C. and H. Ken Cordell. 1990. An Analysis of the Demand for and Value of Outdoor Recreation in the United States, University of Georgia. Bergstrom, John C., and H. Ken Cordell. 1989. Household Market Demand and Supply Comparisons for Outdoor Recreation. prepared by Southeastern Forest Experiment Station, Athens, GA. Bergstrom, John C., and John R. Stoll. 1986. Structure, Conduct and Performance in Contingent Markets, prepared by Texas A&M University for Texas A&M University. . Bergtrom, John C., and John R. Stoll. 1989. Application of Experimental Economics Concepts and Precepts to CVM Field Survey Procedures, Western Journal of Economics, Vol. 14, No. 1. pp. 98-109. Bergstrom, John C., John R. Stoll, and Alan Randall. 1990. The Impact of Information on Environmental Commodity Valuation Decisions, Journal of Agricultural Economics Research. Vol. 72, No. 3, pp. 614-621 Bergstrom, John C., John R. Stoll. and Alan Randall. 1989. Information Effects in Contingent Markets, American Journal of Agricultural Economics, Vol. 71, No. 3, pp. 685-691. Bergstrom, John C., John R. Stoll, John P. Titre, and Vernon Wright. 1990. Economic Value of Wetlands-Based Recreation, Ecological Economics, Vol. 2, pp. 129-147. Bergstrom, John C., and John R. Stoll. 1987. A Test of Contingent Market Bid Elicitation Procedures for Piecewise Valuation, Western Journal of Economics, Vol. 12, No. 2. Bianchi, Dennis. 1969. The Economic Value of Streams for Fishing, prepared by Water Resources Institute for University of Kentucky. Billings, Bruce R., and Donald E. Agthe. 1980. Rice Elasticities for Water: A Case of Increasing Block Rate, Land Economics, Vol. 56, No. 1. pp. 73-84. Bingham, Taylor, and Luanne Lohr. 1984. A Preliminary Assessment of the Benefits of Reducing Formaldehyde Exposures - Draft Rpt, prepared by Research Triangle Institute, Inc. for US Environmental Protection Agency EARB), Research Triangle Institute, Research Triangle Park, NC. Bingham, Taylor, Donald Anderson, and Phillip Cooley. 1987. Distribution of the Generation of Air Pollution, Journal of Environmental Economics and Management, Vol. 14, No. 1, pp. 30-40. Binkley, Clark S., and W. Michael Hanemann. 1978. The Recreation Benefits of Water Quality Improvement: Analysis of Day Trips in an Urban Setting, US Environmental Protection Agency. Biosystems Analysis. 1984. Methods far Valuation of Environmental Costs and Benefits of Hydroelectric Facilities: A Case Study of the Sultan River Project, The Office of Power and Resource Management, Oregon. Bishop, R. C. 1978. Endangered Species and Uncertainty: The Economics of a Safe Minimum Standard, American Journal of Agricultural Economics, Vol. 60. No. 1, pp. 10-18. Bishop, R. C. 1979. Endangered Species, Irreversibility and Uncertainty: A Reply, American Journal of Agricultural Economics, Vol. 61, No. 2, pp. 376-379. Bishop, R. C. 1981. Option Value and the Great Lakes: A First Assessment - Draft. Bishop, R C. 1982. Option Value: An Exposition and Extension, Land Economics, Vol. 58, pp. 1-15. Bishop, R C. 1986. Resource Valuation under Uncertainty: Theoretical Principals for Empirical Research, JAI Press, Inc., Greenwich, CT, pp. 133-158.

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Blumenterd, Karen, and Teresa M. Lynch. 1989. EPA's Economic Analyses and Regulatory Decision Making prepared by Alliance Technologies Corporation. Bockstael, Nancy E., and Catherine L. Kling. 1988. Valuing Environmental Quality: Weak Complementarity with Sets or Goods. American Journal of Agricultural Economics, Vol. 70, No. 3, pp. 654-662. Bockstael, Nancy E., and I. E, Strand. 1985. Distribution Issues and Non-Market Benefit Valuation, Western Journal of Agricultural Economics, Vol. 10. pp. 162-169. Bockstael, Nancy E., and I. E. Strand. 1987. The Effect of Common Sources of Regression Error on Benefit Estimates, Land Economics, Vol. 63. pp. 11-20. Bockstael, Nancy E., I. E. Strand, and W. Michael Hanemann. 1987. Time and the Recreational Demand Model; American Journal of Agricultural Economics, Vol. 69, NO. 2. pp. 293-302. Bockstael, Nancy E., I. E. Strand, and W. Michael Hanemann. 1984. Time and Income Constraints in Recreation Demand Analysis, University of Maryland. Bockstael, Nancy E., and K. E. McConnell. 1981. Theory and Estimation of the Household Production Function for Wildlife Recreation, Journal of Environmental Economics and Management, Vol. 8, pp. 199-214. Bockstael, Nancy E., and K. E. McConnell. 1980. Calculating Equivalent and Compensating Variation for Natural Resource Facilities, Land Economics, Vol. 56, No. 1, pp. 56-63. Bockstael, Nancy E., and K. E. McConnell. 1988. Welfare Effects of Changes in Quality: A Synthesis, University of Maryland. Bockstael, Nancy E., and K. E. McConnell. 1983. Welfare Measurement in the Household Production Function Framework, American Economic Review, Vol. 73, pp. 806-814. Bockstael, Nancy E., and K. E. McConnell. 1984. Implicit Market Methods for Benefit Estimation - Vol. I, prepared by University of Maryland for US Environmental Protection Agency (EARB). Bockstael, Nancy E., Kenneth McConnell, and Ivar E. Strand. 1987. Benefits from Improvements in Chesapeake Bay Water Quality - Vol.III, prepared by University of Maryland for US Environmental Protection Agency (EARB), US Government Printing Office, Washington, DC. Bockstael, Nancy E., Kenneth McConnell, and Ivar E. Strand. 1989. Measuring the Benefits of Improvements in Water Quality: The Chesapeake Bay, Marine Resource Economics, Vol. 6, No. 1, pp. 1-18. Bockstael, Nancy E., W. M. Hannemann, and I. E. Strand, Jr. 1984. Measuring the Benefits of Water Quality Improvements Using Recreation Demand Models - Vol. II, prepared by University of Maryland for US Environmental Protection Agency (EARB). Bockstael, Nancy E., W. Michael Hanemann, and Catherine L. Kling. 1985. Modeling Recreational Demand in a Multiple Site Framework Boulder, CO. Bockstael, Nancy E., W. Michael Hanemann, and Catherine L. Kling. 1987. An Evaluation of Multiple Site Model Development, University of Maryland, Water Resources Research, Vol. 3, pp. 951-960. Bockstael, Nancy E., W. Michael Hanemann, and Catherine L. Kling. 1987. Estimating the Value of Water Quality lmprovements in a Recreational Demand Framework, Water Resources Research, Vol. 23, No. 5, pp. 951960. Bohm, P. 1971. An Approach to the Problem of Estimating Demand for Public Goods, Swedish Journal of Economics, Vol. 73, No. 1, pp. 94-105.

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Boyle, Kevin. 1989. Commodity Specification and Framing Contingent Valuation Question, Land Economics, Vol. 65, No. 1. pp. 57-63. Boyle, Kevin. 1985. Essays on the Valuation of Non-market Resources: Conceptual Issues and Empirical Case Studies, University of Wisconsin. Boyle, Kevin J., Michael P. Welsh, and Richard C. Bishop. 1988. Using Scenarios of Unexperienced Environmental Condition in Contingent Valuation Studies. Boyle, Kevin J., Michael P. Welsh, and Richard C. Bishop. 1988. Validity of Welfare Measures of Welfare Change: Comment, Land Economics. Vol. 64, No. 1. pp. 94-98. Boyle, Kevin J., Michael P. Welsh, and Richard C. Bishop. 1985. Starting Point Bias in Contingent Valuation Bidding Games,. University of Maine, Land Economics, Vol. 61, No. 2, pp. 188-194. Boyle, Kevin J., Michael P. Welsh. and Richard C. Bishop. 1990. Contingent Valuation of Unexperienced Environmental Conditions, University of Maine, Journal of Environmental Economics and Management. Boyle, Kevin J., and Richard C. Bishop. 1987. Valuing Wildlife in Benefit-Cost Analyses: A Case Study Involving Endangered Species, University of Maine, Water Resources Research Vol. 23. No. 5. pp. 943-950. Boyle, Kevin J.. and Richard C. Bishop. 1988. Welfare Evaluations Using Contingent Valuation: A Comparison of Techniques, American Journal of Agricultural Economics, Vol. 70, No. 1, pp. 20-28. Boyle, Kevin J., and Richard C. Bishop. 1979. Toward the Total Valuation of Great Lakes Fishery Resources, Water Resources Research, Vol. 5, pp. 943-990. Boyle, Kevin J., and Richard C. Bishop. 1984. Economic Benefits Associated with Boating and Canoeing on the Lower Wisconsin River, prepared by Department of Agricultural Economics for University of Wisconsin. Boyle, Kevin J., and Richard C. Bishop. 1985. The Total Value of Wildlife Resources: Conceptual and Empirical Issues. Boyle, Kevin J., Stephen D. Reiling, and Marcia L. Phillips. 1990. Species Substitution and Question Sequencing in Contingent Valuation Surveys Evaluating the Hunting of Several Types of Wildlife, University of Maine, Leisure Science, Vol. 12, pp. 103. Boyle, Kevin J., and Trish Heekin. 1989. Benefits and Costs in Natural Resource Planning, prepared by Western Regional Research. Braccio, Ralph, Dan Pyne, Jean Tilly, and B. Hendricks. 1988. Results of the Preliminary Analysis of the Proposed Second Third Land Disposal Restrictions Rule, prepared by ICF, Inc. Braden, John B., and Randolph M. Lyon. 1985. Pollution Control Cost Analysis, prepaid by University of Illinois for US Environmental Protection Agency (EARB), Pollution. Braden, John B., and Charles D. Kolstad. 1991. Measuring the Demand for Environmental Quality, North-Holland. Bradford, D. F., and G. G. Hildebrandt. 1977. Observable Preferences for Public Goods, Journal of Political Economy, Vol. 8, pp. 111-132. Brady, G. L., and R. E. Morrision. 1984. Emissions Trading: An Overview of the EPA Policy Statement, International Journal of Environmental Studies.

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Brookshire, David S., Larry S. Eubanks, and Alan Randall. 1983. Estimating Option Prices and Existence Values for Wildlife Resources, Land Economics, Vol. 59, No. 1, pp. 1-15. Brookshire, David S., and V. Kerry Smith. 1987. Measuring Recreation Benefits: Conceptual and Empirical Issues. Water Resources Research, Vol. 23, No. 5, pp. 931-935. Brookshire, D. S., M.A. Thayer, J. Tscherhart, and W.D. Schulze. 1985. A Test of the Expected Utility Model: Evidence from Earthquakes, Journal of Political Economy, Vol. 93, No. 2. pp. 369-389. Brookshire, David D., and Don L. Coursey. 1987. Measuring the Value of a Public Good: An Empirical Comparison of Elicitation Procedures, American Economic Review, Vol. 77, No. 4, pp. 554-566. Brown, G. M., and H. O. Pollakowski. 1977. Economic Valuation of Shoreline, Review of Economics and Statistics. Vol. 59, pp. 272-278. Brown, Gardner, Jr., and Jon H. Goldstein. 1984. A Model for Valuing Endangered Species, Journal of Enviromental Economics and Management, Vol. 11, No. 4, pp. 303-309. Brown, Gardner, Jr.. and Ralph Johnson. 1984. Pollution Control by Effluent Charges: It Works in Germany, Why Not the U.S.?, Natural Resources Journal, Vol. 24, No. 4, pp. 929-966. Brown, Gardner, Jr., J. John Charbonneau, and Michael J. Hay. 1978. The Value of Wildlife Estimated by the Hedonic Approach, US Fish and Wildlife Service, Washington, DC. Brown, Gardner, Jr., J. John Charbonneau, and Michael J. Hay. 1979. Estimating the Values of Wildlife: Analysis of the 1975 Hunting and Fishing Survey, prepared by Division of Program Planning for US Fish and Wildlife Service, Washington, DC. Brown, Gardner, Jr., and Mark Plummer. 1979. Recreation Valuation: An Analysis of the Nontimber Uses of Forestland in the Pacific Northwest, Washington State University. Brown, Gardner, Jr., and Michael J. Hay. 1987. Net Economic Recreation Values for Deer and Waterfowl Hunting and Trout Fishing, 1980, prepared by USFWS Division of Policy and Directives Management for US Fish and Wildlife Service. Brown, Gardner, Jr., and Robert Mendelsohn. 1984. The Hedonic Travel Cost Method, University of Washington, Department of Economics, Review of Economics and Statistics, Vol. 66, No. 3, pp. 427-433. Brown, W. G., C. Sorhus, B. Chou-Yang, and J. Richards. 1983. Using Individual Observations to Estimate Recreation Demand Functions: A Caution, American Journal of Agricultural Economics, Vol. 65. No. 1, pp. 154-157. Brown, William, Colin Sorhus, and Kenneth Gibbs. 1980. Estimated Expenditure by Sport Anglers and Net Economic Values of Salmon and Steelhead for Specified Fisheries in the Pacific Northwest, prepared by Department of Economics for Oregon State University. Brown, William, D. M. Larson, R. S. Johnston, and R. J. Wahle. 1979. Improved Economics Evaluation of Commercially and Sport Caught Salmon and Steelhead of the Columbia River, prepared by Department of Economics for Oregon State University. Brown, William, and F. Nawas. 1975. Impact of Aggregation on the Estimation of Outdoor Recreation Demand Function, American Journal of Agricultural Economics, Vol. 55, pp. 246-249. Brown, William, F. Nawas, and Joe Stevens. 1973. The Oregon Big-Game Resource: An Economic Evaluation, prepared by Agricultural Experimental Station for Oregon State University.

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USEPA. 1990. Final Regulatory Impact Analysis and Summary and Analysis of Comments on the NPRM - Interm Control of Gasoline Volatility, prepare by EPA Office of Mobile Source for US Environmental Protection Agency. USEPA. 1987. Draft RIA - Control of Gasoline Volatility and Evaporative Hydrocarbon Emissions from New Motor Vehicles, prepared by EPA Office of Mobile Sources for US Environmental Protection Agency. USEPA. 1985. Economic Impact Analysts of Proposed Regulations to Control Volatile Synthetic Organic Chemicals in Drinking Water, prepared by EPA Office of Drinking Water for US Environmental Protection Agency. USEPA. 1988. Estimating Exposure to 2,3,7,8 - TCDD (Draft), US Environmental Protection Agency. USEPA. 1980. Regulatory Analysis and Environmental Impact of Final Emission Regulations for 1982 and 1983 model year High-Altitude Motor Vehicles, prepared by EPA Office of Mobile Sources for US Environmental Protection Agency. USEPA. 1979. Regulatory Analysts and Environmental Impact of Final Emission Regulations for 1984 and Later Model Year Heavy Duty Engines, prepared by EPA Office of Mobile Sources for US Environmental Protection Agency. USEPA. 1987. Regulatory Impact Analysis: Worker Protection Standards For Agricultural Pesticides, prepared by EPA Office of Pesticide Programs for US Environmental Protection Agency. USEPA. 1981. Measuring and Comparing the Cost-effectiveness of EPA Regulatory Efforts to Control Toxicsrelated Health Risks. Vol. I: Feasibility study. Vol. II: Cost-effectiveness Analysis of EPA Intermedia Prior, prepared by EPA Office of Pesticides and Toxic Substances for US Environmental Protection Agency. USEPA. 1987. Regulatory Impact Analysis in Support of Proposed Rulemaking Under Sections 322-323 of the Superfund Amendments and the Reauthorization Act of 1986, prepared by EPA Office of Pesticides and Toxic Substances for US Environmental Protection Agency. USEPA. 1985. Final Regulatory Impact Analysis - 40 CFR Part 191: Environmental Standards for the Management and Disposal of Spent Nuclear Fuel, High-level and Transuranic Radioactive wastes, prepared by EPA Office of Radiation Programs for US Environmental Protection Agency. USEPA. 1987. Low Level and NARM Radioactive Wastes - Draft Environmental Impact Statement- Vol. 2, prepared by EPA Office of Radiation Programs for US Environmental Protection Agency. USEPA. 1990. Sites for Our Solid Waste - A Guidebook for Effective Public Involvement, prepared by EPA Office of Solid Waste and Emergency Response for US Environmental Protection Agency. USEPA. 1990. Toxic Chemical Release Inventory Reporting Form R and Instructions, prepared by EPA Office of Toxic Substances far US Environmental Protection Agency. USEPA. 1989. Economic Report on Terephthalic Acid, prepared by EPA Office of Toxic Substances for US Environmental Protection Agency. USEPA. 1988. Regulatory Impact Analysis for Financial Responsibility Requirements for Petroleum Underground Storage Tanks, prepared by EPA Office of Underground Storage Tanks for US Environmental Protection Agency. USEPA. 1989. Perspectives on Non-Point Source Pollutions: Proceedings of a National Conference, prepared by EPA Office of Water for US Environmental Protection Agency, Washington, DC. USEPA. 1985. Pretreatment Implementation Review Task Force: final report, prepared by EPA Office of Water for US Environmental Protection Agency.

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Winston, Harrigan, Alan Krupnick, and Walter Spofford. 1985. The benefits of Preventing an Outbreak of Giardiasis Due to Drinking Water Contamination, prepared by Resources for the Future for US Environmental Protection Agency, Water. Wittman, D 1982. Efficient Rules in Highway Safety and Sports Activity, American Economic Review. Wo1ka, Keven K. 1988. Groundwater Contamination Benefit-Cost Analysis Methodology, Journal of Water Resources Planning and Management. Vol. 114, No. 2. pp. 210-222. Wong, S. T. 1972. A Model on Municipal Water Demand: A Case Study of Northeastern Illinois, Land Economics. Vol. 48, No. 1. pp. 34-44. Woodard, Duane, and Michael Hope. 1990. Natural Resource Damage Litigation Under the Comprehensive Environmental Response, Compensation, and Liability Act, Harvard Environmental Law Review, Vol. 14, No. 189. pp. 190-215. World Bank. 1981. An Annotated Bibliography of Environmental Economics, prepared by Office of Environmental Affairs for World Bank. Yang, Edward J., Roger C. Dower, and Mark Menefee. 1984. The Use of Economic Analysis in Valuing Natural Resource Damages, prepared by Environmental Law institute for National Oceanographic and Atmospheric Administration. Yardas, David, Alan J. Krupnick, Henry M. Peskin, and Winston Harrington. 1984. Directory of Environmental Asset Data Bases and Valutaion Studies, prepared by Resources for the Future for Washington, D.C. Yocum, John E., and Alexander R. Stankunas. 1980. A Review of Air Pollutant Damage to Materials, prepared by TRC, Inc. Young, C. Edwin. 1984. Perceived Water Quality and the Value of Seasonal Homes, Water Resources Bulletin. Young, John S., Dennis M. Donnelly, Cindy F. Sorg, and John B. Loomis, et al. 1987. Net Economic Value of Upland Game Hunting in Idaho, prepared by Rocky Mountain Forest and Range Land Experiment Station for US Forest Service. Young, Robert A. 1973. Price Elasticity of Demand for Municipal Water: A Case Study of Tucson, Arizona, Water Resources Research, Vol: 9, No. 4, pp. 1068-1072. Zeckhauser, R., and A. Fisher. 1976. Averting Behavior and External Diseconomics. Zeise, L., R. Wilson, and E. Crouch. 1984. Use of Acute Toxicity to Estimate Carcinogenic Risk, Risk Analysis. Ziemer, R. F., W. N. Musser, and R C. Hill. 1980. Recreation Demand Equations: Functional Form and Consumer Surplus, American Journal of Agricultural Economics, pp. 136-41. Zilberman, David, and Erik Lichtenberg. 1986. Framework - Case Study Design for a Risk Benefit Analysis of Pesticides in the Special Review Process, prepared by Western Consortium for the Health Professions, Inc. for US Environmental Protection Agency.

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APPENDIX B ATTENDEES LIST

Sergio Araila 7605 Heatherfon Ln. Potomac, MD 20854 Ross Arnold U.S. Forest Service 2285 Double Eagle Ct. Reston, VA 22091 John C. Bergstrom The University of Georgia Department of Agricultural and Applied Economics 301 Conner Hall Athens, GA 30602 Robert Berrens Oregon State University Department of Agriculture and Resource Economics Ballard Extension Hall, Room 322 Corvallis, OR 97331 Tayler Bingham Research Triangle Institute 3040 Cornwallis Rd. Research Triangle Park, NC 27709 Kevin Boyle University of Maine Department of Agricultural and Resource Economics Room 207 Winslow Hall Orono, ME 04469 David Brookshire University of New Mexico Department of Economics 1915 Roma St. NW Albuquerque, NM 87131-1101 Trudy Ann Cameron University of California-Los Angeles Department of Economics 405 Highland Ave. Los Angeles, CA 90024-1477 David Campbell National Wildlife Foundation 1400 16th St, NW Washington, DC 20036-2266

J. Lon Carlson Argonne National Laboratory EID/900 9700 South Cass Ave. Argonne, IL 60439 Lauraine Chestnut RCG/Hugler, Bailly, Inc. P.O. Drawer O Boulder, CO 80306 Randy Childs West Virginia University College of Business and Economics Morgantown, WV 26506-6108 Dr. Stephen Crutchfield 627 8th SL, NE Washington, DC 20002 Elizabeth David Wisconsin ONR 2300 19th St. NW Washington, DC 20009 Martin David Resources for the Future 1616 P St. NW Washington, DC 10009 Leland Deck Abt Associates, Inc. 4800 Montgomery Lane, Suite 500 Bethesda, MD 20814 Mike Denning Exxon 600 Jefferson St. Suite 947 Houston, TX 77095 William Desvousges Research Triangle Institute 3040 Cornwallis Rd. Research Triangle Park, NC 27709 Jerry Diamantides University of Rhode Island 10 Overhill Rd. Providence, RI 02906

B-1

Alan Krupnick Resources for the Future 1616 P St., NW Washington, DC 20036 Linda Langner U.S. Forest Service 820 N. Howard St., #202 Alexandria, VA 22304 Donna Lawson National Oceanic and Atmospheric Administration Damage Assessment Center 6001 Executive Blvd., Rm. 425 Rockville, MD 20852 Michael LeBlanc U.S. Department of Agriculture Economic Research Service 1301 New York Ave., NW Room 438 Washington, DC 20005-4788 Robert Leeworthy National Oceanic and Atmospheric Administration N/ORCA1 6001 Executive Blvd., Rm. 220 Rockville, MD 20852 Wade Martin Colorado School of Mines Mineral Economics Department Golden, CO 80401-1887 Kristy Mathews Research Triangle Institute 3040 Cornwallis Rd. Research Triangle Park, NC 27709 Marisa Mazzotta University of Rhode Island Resource Economics Lippitt Hall Kingston, RI 02881 Daniel McCollum U.S. Forest Service Rocky Mountain Station 240 West Prospect St. Ft. Collins, CO 80526-2098

Norman Meade National Oceanic and Atmospheric Administration Damage Assessment Center 6001 Executive Blvd., Rm. 425 Rockville, MD 20852 R. Gregory Michaels Abt Associates, Inc. 4800 Montgomery Lane, Suite 500 Bethesda, MD 20814 Edward Morey University of Colorado-Boulder Department of Economics Campus Box 256 Boulder, CO 80309-0256 Andrew Muller McMuster University Department of Economics Hamilton, Ontario 28S 4M4 Canada Stale Navrud Agricultural University of Norway Noragric P.O. Box 2, N-1432 Norway James Opaluch The University of Rhode Island Department of Resource Economics Kingston, RI 02881-0814 George Parsons University of Delaware College of Marine Studies Newark, DE 19716 Spencer R. Pearce Consultant P.O. Box 417 Elephant Butte, NM 87935 Richard Ready University of Kentucky Department of Agricultural Economics 318 Agriculture Engineering Bldg. Lexington, KY 40546-0276

B-3


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