SUPPORTING STATEMENT FOR INFORMATION COLLECTION REQUEST FOR WILLINGNESS TO PAY SURVEY FOR § 316( B) PHASE III COOLING WATER INTAKE STRUCTURES: INSTRUMENT, PRE TEST, AND IMPLEMENTATION i TABLE OF CONTENTS PART A OF THE SUPPORTING STATEMENT....................................................................... 1 1. Identification of the Information Collection..................................................................... 1 1( a) Title of the Information Collection .................................................................................. 1 1( b) Short Characterization ( Abstract) .................................................................................... 1 2. Need For and Use of the Collection................................................................................. 5 2( a) Need/ Authority for the Collection ................................................................................... 5 2( b) Practical Utility/ Users of the Data ................................................................................... 5 3. Non duplication, Consultations, and Other Collection Criteria......................................... 6 3( a) Non duplication .............................................................................................................. 6 3( b) Public Notice Required Prior to ICR Submission to OMB............................................... 7 3( c) Consultations................................................................................................................. 11 3( d) Effects of Less Frequent Collection............................................................................... 12 3( e) General Guidelines ........................................................................................................ 12 3( f) Confidentiality .............................................................................................................. 13 3( g) Sensitive Questions ....................................................................................................... 13 4. The Respondents and the Information Requested........................................................... 13 4( a) Respondent.................................................................................................................... 13 4( b) Information Requested .................................................................................................. 14 ( I) Data items, including record keeping requirements........................................................ 14 ( II) Respondent activities ................................................................................................... 17 5. The Information Collected  Agency Activities, Collection Methodology, and Information Management .......................................................................................................... 18 5( a) Agency Activities.......................................................................................................... 18 5( b) Collection Methodology and Information Management ................................................. 18 5( c) Small Entity Flexibility ................................................................................................. 19 ii 5( d) Collection Schedule....................................................................................................... 19 6. Estimating Respondent Burden and Cost of Collection.................................................. 20 6( a) Estimating Respondent Burden...................................................................................... 20 6( b) Estimating Respondent Costs ........................................................................................ 20 6( c) Estimating Agency Burden and Costs............................................................................ 21 6( d) Respondent Universe and Total Burden Costs ............................................................... 22 6( e) Bottom Line Burden Hours and Costs ........................................................................... 22 6( f) Reasons for Change in Burden ...................................................................................... 22 6( g) Burden Statement .......................................................................................................... 22 PART B OF THE SUPPORTING STATEMENT..................................................................... 24 1. Survey Objectives, Key Variables, and Other Preliminaries ......................................... 24 1( a) Survey Objectives ......................................................................................................... 24 1( b) Key Variables ............................................................................................................... 24 1( c) Statistical Approach ...................................................................................................... 25 1( d) Feasibility ..................................................................................................................... 25 2. Survey Design ............................................................................................................... 26 2( a) Target Population and Coverage.................................................................................... 26 2( b) Sampling Design ........................................................................................................... 26 ( I) Sampling Frames........................................................................................................... 26 ( II) Sample Sizes ................................................................................................................. 27 ( III) Stratification Variables .................................................................................................. 27 ( IV) Sampling Method.......................................................................................................... 27 ( V) Multi Stage Sampling ................................................................................................... 28 2( c) Precision Requirements ................................................................................................. 28 ( I) Precision Targets........................................................................................................... 28 ( II) Non Sampling Errors ................................................................................................... 29 2( d) Questionnaire Design ................................................................................................... 31 iii 3. Pretests and Pilot Tests.................................................................................................. 32 4. Collection Methods and Follow up................................................................................ 33 4( a) Collection Methods ....................................................................................................... 33 4( b) Survey Response and Follow up.................................................................................... 34 5. Analyzing and Reporting Survey Results....................................................................... 35 5( a) Data Preparation............................................................................................................ 35 5( b) Analysis ........................................................................................................................ 35 5( c) Reporting Results .......................................................................................................... 53 REFERENCES......................................................................................................................... 54 ATTACHMENTS Attachment 1: Informational Slide Show.................................................................................. 63 Attachment 2: Slide Show Script............................................................................................ 125 Attachment 3: Full Text of Stated Preference Survey Component .......................................... 132 Attachment 4: Federal Register Notice ................................................................................... 145 Attachment 5: Description of Statistical Survey Design ......................................................... 154 LIST OF TABLES Table A1: Reviewers................................................................................................................ 12 Table A2: Geographic Stratification Design.............................................................................. 14 Table A3: Schedule for Survey Implementation ....................................................................... 20 Table A4: Total Estimated Bottom Line Burden and Cost Summary ........................................ 22 Table B1: Confidence Intervals for Binary Survey Variables, by Region.................................. 29 Table B2: Illustration of an External Scope Test ...................................................................... 51 1 PART A OF THE SUPPORTING STATEMENT 1. Identification of the Information Collection 1( a) Title of the Information Collection Willingness to Pay Survey for Section 316( b) Phase III Cooling Water Intake Structures:

Instrument, Pre test, and Implementation 1( b) Short Characterization ( Abstract)

The U. S. Environmental Protection Agency ( EPA) is in the process of developing new regulations to provide national performance standards for controlling impacts from cooling water intake structures ( CWIS) for Phase III facilities under section 316( b) of the Clean Water Act ( CWA). Phase III under Clean Water Act section 316( b) regulations applies to facilities that withdraw water for cooling purposes from rivers, streams, lakes, reservoirs, estuaries, oceans, or other waters of the United States, and that are either existing electrical generators with cooling water intake structures that are designed to withdraw 50 million gallons of water per day ( MGD)

or less, or existing manufacturing and industrial facilities. The regulation also establishes section 316( b) requirements for new offshore oil and gas extraction facilities. EPA has previously published final section 316( b) regulations that address new facilities ( Phase I) on December 18,

2001 ( 66 FR 65256) and existing large power producers ( Phase II) on July 9, 2004 ( 69 FR 41576). See 40 CFR Part 125, Subparts I and J, respectively.

As required under Executive Order 12866, EPA is conducting economic impact and costbenefit analyses for the section 316( b) regulation for Phase III facilities. Cost benefit analysis should include a comprehensive estimate of total social benefits, including both use and non use values. " Non use values, like use values, have their basis in the theory of individual preferences and the measurement of welfare changes. According to theory, use and non use values are additive" ( Freeman, 2003). It is generally accepted in the economic literature that non use values may be substantial in some cases. Additionally, when small per capita non use values are held by a substantial fraction of the population, they can be very large in the aggregate.

Therefore, failure to recognize such values may lead to improper inferences regarding policy benefits. As stated by Freeman ( 2003, p. 138), "[ i] f non use values are large, ignoring them in 2 natural resource policymaking could lead to serious errors and resource misallocations." With regard to non use values, the literature also advises against the use of ad hoc, expert assumptions regarding the magnitude of these values   particularly assumptions that non use values are trivial. Because values are inherently subjective, Bateman et al. ( 2002, p. 75) emphasize that " it would be wrong for experts to assume that one resource is a perfectly good substitute for another" [ and hence that non use values are trivial or small]. As further noted by Bateman et al.

( 2002, p. 75), " there are, therefore, no easy rules for determining at the outset" whether non use values are likely to be significant or non significant. Based on this clear guidance, EPA believes that empirical analysis should be use to determine the magnitude of non use values for the section 316( b) Phase III rulemaking.

Consideration of potential non use values is particularly important in the case of the section 316( b) regulation because nearly all ( 96 percent of) impingement and entrainment losses at CWIS consist of either forage species, or non landed recreational and commercial species that do not have direct uses. Although individuals do not use these resources directly, they may nevertheless be affected by changes in the resource status or quality, such that they would be willing to pay to maintain these resources. Although economic theory clearly allows for the possibility of significant non use values in this case, it does not shed light on whether such values are large or small in the aggregate. Whether the associated nonmarket values   including non use values   are significant is an empirical question.

Many public comments on the proposed section 316( b) regulation for Phase II facilities and the Phase II Notice of Data Availability ( NODA) suggested that a properly designed and conducted stated preference, or contingent valuation ( CV), survey would be the most appropriate and acceptable method to estimate total ( including use and non use) benefits of the 316( b) rule ( U. S. EPA, 2004a). Stated preference surveys use carefully designed questions to elicit respondents' willingness to pay ( WTP) for particular ecological improvements, based on their responses to either discrete choice or open ended questions regarding hypothetical resource improvements or programs. Such improvements may include increased protection of aquatic habitats or species with particular attributes. Stated preference survey methodology is the generally accepted means to estimate total ( including use and non use) resource values.

Moreover, stated preference survey methods " are likely to offer the only feasible approaches to estimating non use values" ( Freeman, 2003, p. 154). Although the peer reviewed literature is highly skeptical of stated preference surveys that attempt to decompose use and non use values, 3 there is general conceptual acceptance that the total values of non users can be used to approximate non use values for all individuals ( Cummings and Harrison 1995, Johnston et al.

2003a).

To assess public policy significance or importance of the ecological gains from the section 316( b) regulation for Phase III facilities, EPA requests approval from the Office of Management and Budget to conduct a stated preference study to measure total benefits of reduced fish losses at CWIS due to the regulation. The study would focus on a broad range of aquatic species, including forage fish and a variety of fish species harvested by commercial and recreational fishermen. The results of the survey would be used to estimate the total benefits of the proposed 316( b) regulation, but would also be of interest to economists and policy makers studying changes in fish populations and aquatic habitat improvements, since past studies have focused only on a few select fish species such as salmon and striped bass.

Survey subjects will be randomly selected from a representative national panel of respondents maintained by Knowledge Networks, an online survey company. Subjects will be asked to complete a web based questionnaire. Participation in the survey is voluntary. EPA intends to administer the survey to 5,000 persons, including 500 persons who will take part in an initial survey pilot, 3,900 persons who will participate in the main survey effort, and 600 persons who will take part in nonresponse follow up interviews. EPA chose a web based survey format because it is the most cost effective method available to conduct a large statistically based survey covering a wide geographic region in a relatively short time frame. As highlighted by Dillman ( 2000), internet surveys are increasingly used within a wide range of research contexts,

based on the many potential advantages of web based survey methods. Among these are the ability to provide significant amounts of information in ways impossible in telephone or mail survey instruments, combined with an ability to reach a broader, nationwide audience more costeffectively than is typically possible using in person survey methods. Moreover, in recent years clear guidance has emerged regarding the appropriate design and use such survey approaches ( Dillman 2000). Web based surveys are increasingly used in stated preference research, with recent examples by Hoehn et al. ( 2004), Li et al. ( 2005), and Berrens et al. ( 2004).

To avoid potential sampling biases associated with the web based survey methodology,

the survey sample will be stratified by geographical region, and within each region, by demographic variables including age, education, Hispanic ethnicity, race, gender, and household income. The number of respondents in each demographic stratification group will be inversely 4 proportional to the historical response rates of individuals in that group for similar types of surveys. By oversampling groups that tend to have lower response and consistency rates, the demographic characteristics of respondents who provide valid completed surveys will mirror U. S. Census Bureau demographic benchmarks more closely. Thus, this design will reduce error due to non coverage of non telephone households in the original Knowledge Networks recruitment process, and will also reduce bias due to nonresponse and other non sampling errors.

This bias may occur due to panel attrition at several stages, including recruitment, maintenance,

and final survey implementation. EPA will undertake two sets of activities to reduce the potential for nonresponse bias: ( 1) use of 600 nonresponse follow up data collection interviews to increase the overall response rate; and ( 2) statistical correction for unobserved heterogeneity that might be present in the data from respondents interviewed for this study.

To assist in the development of this stated preference survey, EPA has previously requested and obtained approval from the Office of Management and Budget to conduct a series of twelve focus groups with a total of 96 respondents. These focus groups were conducted following standard, accepted practices in the stated preference literature, as outlined by Mitchell and Carson ( 1989), Desvousges et al. ( 1984), Desvousges and Smith ( 1988) and Johnston et al.

( 1995). Some of the focus groups incorporated individual cognitive interviews, as detailed by ( Kaplowicz et al. 2004). The focus groups and cognitive interviews allowed EPA to better understand the public's perceptions and attitudes concerning fishery resources, to frame and define survey questions, to pretest draft survey questions, to test for and reduce potential biases that may be associated with stated preference methodology, and to ensure that both researchers and respondents have similar interpretations of survey language and scenarios. In particular,

cognitive interviews allowed in depth exploration of the cognitive processes used by respondents to answer survey questions, without the potential for interpersonal dynamics to sway respondents' comments ( Kaplowicz et al. 2004). Detailed documentation for all focus groups,

which were conducted by EPA under ICR # 2155.01, can be found in the docket for EPA ICR # 2155.02 ( Besedin et al., 2005).

The total national burden estimate for all components of the survey is 3,383 hours. The burden estimate is based on administration of survey questionnaires to 5,000 respondents. EPA assumes an average burden estimate of 41 minutes per respondent, based on burden estimates of 40 minutes per respondent for completion of the pretest and main survey, and 45 minutes per 5 respondent for completion of nonresponse follow up interviews. Given an average wage rate of $ 17.71, the total respondent cost is $ 59,919.

2. Need For and Use of the Collection 2( a) Need/ Authority for the Collection The project is being undertaken pursuant to Section 104 of the Clean Water Act dealing with research. Section 104 of the Clean Water Act authorizes and directs the EPA Administrator to conduct research into a number of subject areas related to water quality, water pollution, and water pollution prevention and abatement. This section also authorizes the EPA Administrator to conduct research into methods of analyzing the costs and benefits of programs carried out under the Clean Water Act.

This research project is exploring how public values for fishery resources ( including use and non use values) are affected by fish losses from impingement and entrainment at CWIS.

Understanding total public values   including non use values for fishery resources lost to impingement and entrainment   is necessary to determine the full range of benefits associated with reductions in impingement and entrainment losses. Because non use values may be substantial in some cases, failure to recognize such values may lead to improper inferences regarding policy benefits ( Freeman 2003). Although the findings from this study will primarily be used by EPA to improve estimates of the economic benefits of the section 316( b) regulation for Phase III facilities as required under Executive Order 12866, these findings are also expected to be useful to state and local regulatory agencies dealing with fishery resources and fish habitat,

and to the research community.

2( b) Practical Utility/ Users of the Data EPA plans to use the results of the survey to improve estimates of the economic benefits of the section 316( b) regulation for Phase III facilities. Specifically, the Agency will use the survey results to estimate total values for preventing losses of fish through impingement and entrainment at CWIS, following standard practices outlined in the literature ( Freeman 2003;

Bennett and Blamey 2001; Louviere et al. 2000; U. S. EPA 2000). State and local agencies that deal with fishery resources may also use the results of this survey to assist in activities such as 6 permit writing. Furthermore, the academic community, particularly researchers studying changes in fish populations and aquatic habitat improvements, may find the results of this study to be of substantial academic interest.

3. Non duplication, Consultations, and Other Collection Criteria 3( a) Non duplication EPA has not identified any previous studies that would allow estimation of the social benefits of nationwide changes in populations of fish species ( including forage, recreational, and commercial species) affected by the proposed 316( b) regulation. Moreover, many studies that provide values for arguably similar resources reflect research conducted over a decade ago, using methods that have since been significantly improved in the literature. Although many previous studies have estimated the value of changes in catch rates or populations of selected ( often high value) recreational and commercial species, or of changes in water quality that affect fish, no studies have specifically valued changes in forage fish populations. For example, Olsen et al.

( 1991) conducted a survey of Pacific Northwest residents, including both anglers and nonanglers to determine their willingness to pay ( WTP) for a doubling of the size of the Columbia River Basin salmon and steelhead runs. EPA's proposed survey approach differs from this study and others like it ( such as Cameron and Huppert, 1989) in that it would include respondents from various geographic regions throughout the United States and would provide values that encompass a variety of forage, recreational, and commercial species, instead of valuing a few high value recreational species in one specific geographical area.

Some previous studies do provide values for changes in water quality that affect all fish species, including forage fish. However, the resulting values for water quality improvements from these studies also include values for changes in other factors such as water clarity, pollution levels, species diversity, etc. For example, Magat et al. ( 2000) asked North Carolina and Colorado residents to value an increase in water quality under which fresh water in their states would become safer for swimming, fish could be eaten safely, and the number and diversity of plants and aquatic organisms would increase. EPA's proposed survey approach differs from this study and other similar studies ( such as Mitchell and Carson, 1981; or Whitehead and Groothuis,

1992) in that it will value only changes in fish populations, not changes in general water quality that affect a variety of ecological services provided by a water body. Hence, it will represent the 7 only study available in the literature allowing the evaluation of economic values associated with changes in large scale fish populations ( without changes in other environmental quality characteristics), where these changes involve significant increases or decreases in forage fish.

3( b) Public Notice Required Prior to ICR Submission to OMB In accordance with the Paperwork Reduction Act ( 44 U. S. C. 3501 et seq.), EPA published a notice in the Federal Register on June 9, 2005, announcing that the survey questionnaire and sampling methodology were available for comment. A copy of the second Federal Register notice is attached at the end of this document ( EPA ICR # 2155.02). EPA received a number of comments on the proposed information collection, which are summarized in the following paragraphs.

Several commenters stated that the survey would provide inaccurate estimates of WTP to prevent fish losses. One commenter argued that the presentation materials prepared by EPA contain inaccurate statements and invalid comparisons that will lead respondents to believe that the benefits of increased regulation of cooling water intakes would be substantially greater than is actually the case. A different commenter argued the opposite: that the proposed contingent valuation survey is biased against protecting ecosystems, and will drastically undervalue non use benefits. EPA agrees that certain details of the survey and presentation materials provided in the supporting documentation for the June 9 Federal Register notice required revision. Since issuing the June 9 Federal Register notice, EPA has conducted four focus groups and two cognitive interview sessions to improve the Agency's understanding of the public's perceptions and attitudes concerning fishery resources, to pretest draft survey questions and presentation materials, to test for and reduce potential biases that may be associated with stated preference methodology, and to ensure that both researchers and respondents have similar interpretations of survey language and scenarios. As a result of this extensive pre testing, a number of revisions have been made to the survey that have significantly improved its reliability and reduced its potential for bias.

One commenter argued that the survey does not sufficiently emphasize the uncertainty in the estimates of the fish losses that would be prevented by the proposed policies. EPA points out that debriefing sessions during focus groups and cognitive interviews showed that respondents clearly understood that the ecological changes described in the survey were uncertain. 8 Furthermore, respondents were comfortable making decisions in the presence of this uncertainty.

Additionally, EPA has modified the survey and slide show to emphasize the uncertainty of the expected environmental changes. For example, the following statement has been added to the survey: " You will be shown different policy options   with different effects on fish. This is because scientists are still working to determine what the exact effect on fish will be, so it is important to know how you would react to a wide range of possible outcomes. Common sense indicates that preventing the loss of fish eggs and young fish will mean more adult fish in future years, but at this point there is still significant uncertainty regarding the exact size of these future effects." EPA also added debriefing questions to the survey instrument that are designed to identify respondents whose responses are based on incorrect interpretation of the environmental changes described in the survey, including the uncertainty of the expected changes.

The same commenter also argued that the benefits of regulating cooling water intake structures for recreationally and commercially significant species are already reflected in the EPA's RIA for the proposed rule for Phase III facilities. EPA disagrees with the commenter's statement. As stated by Freeman, " Non use values, like use values, have their basis in the theory of individual preferences and the measurement of welfare changes. According to theory, use and non use values are additive" ( Freeman, 2003). EPA notes that the non use benefits of the proposed regulation are a completely separate category of value from the commercial and recreational welfare effects of the proposed rule. Furthermore, results from the focus groups and cognitive interviews indicated that some individuals hold substantial non use values for protecting fish species with direct uses, in addition to any use values that they have for those species.

The commenter also argued that the survey is unnecessary because in independently conducted verbal protocol interviews with 15 individuals, respondents did not demonstrate meaningful values for marginal changes in forage fish populations. EPA notes that this finding contradicts the results of the EPA focus groups and cognitive interviews conducted under ICR # 2155.01. These focus groups and interviews provided strong evidence that many individuals value forage fish and are willing to pay to prevent losses of all fish species including forage fish.

Participants in the EPA focus groups and interviews cited a variety of motivations for preventing fish losses, including the satisfaction of knowing that the fish exist, the desire to bequeath healthy fish populations to future generations, and the desire to protect the functioning of aquatic ecosystems. Furthermore, EPA notes that the economic literature advises against the use of ad 9 hoc, expert assumptions regarding the magnitude of non use values   particularly assumptions that non use values are trivial. Because values are inherently subjective, Bateman et al. ( 2002, p.

75) emphasize that " it would be wrong for experts to assume that one resource is a perfectly good substitute for another" [ and hence that non use values are trivial or small]. As further noted by Bateman et al. ( 2002, p. 75), " there are, therefore, no easy rules for determining at the outset" whether non use values are likely to be significant or non significant. Based on this clear guidance, EPA believes that empirical analysis should be used to determine the magnitude of non use values in the 316( b) regulation for Phase III facilities.

The commenter argued that because the survey materials are based on unreliable population and fish losses data, the survey results will also be unreliable. In response, EPA first notes that the survey materials are based on the best biological and engineering data available.

Second, even if this data is uncertain, the stated preference survey will still produce results that are meaningful in the context of the scenarios that are presented to respondents. Different versions of the survey will show a range of different baseline and resource improvement levels,

where these levels are chosen to almost certainly bound actual levels. Different respondents will be asked to make choices over all possible policy scenarios where impingement and entrainment reductions range from 0% ( no policy) to 98%. Given that there will almost certainly be some biological uncertainty regarding the specifics of the actual baselines and improvements, the resulting valuation estimates will allow flexibility in estimating WTP for a wide range of different circumstances.

Finally, the commenter expressed the opinion that the survey should be peer reviewed by an independent panel of experts. EPA agrees with the commenter's opinion, and the Agency plans to convene two peer review panels. The first panel will review the results of the focus groups, the instrument and the planned survey sampling design, and the proposed willingness to pay estimation methodology, before the survey is fielded. The second peer review panel will review the entire survey process, including EPA's final estimated results for the 316( b) Phase III rulemaking, after the survey is completed.

Another commenter argued that neither the Clean Water Act nor section 316( b) require or allow EPA to use monetized cost benefit analysis to determine regulatory standards for cooling water intake structures. EPA is conducting this survey because of Executive Order 12866,

" Regulatory Planning and Review," which requires Federal Agencies to economic impact and cost benefit analysis for all major rules. Furthermore, cost benefit analysis requires a 10 comprehensive, estimate of total social benefits, including both use and non use values. The current information collection would provide valuable information that includes non use benefits of the 316( b) regulation for Phase III facilities1, thus enabling the Agency to perform cost benefit analysis for the regulation and to satisfy the requirements of Executive Order 12866.

In addition to the comments received in response to the June 9 Federal Register notice, a number of the comments received by EPA in response to the previous focus group information collection request ( EPA ICR # 2155.01) are relevant to the current ICR. Most of these comments addressed the more general topic of resource valuation and stated preference surveys. Many of the claims made in the submitted comments represent empirical questions that were appropriately addressed within the survey design process. Some commenters argued that non use benefits in the Phase III policy context are likely to be trivial. EPA points out that ( 1) these claims are unsubstantiated by the empirical literature and ( 2) that the magnitude of non use benefits in the Phase III context is an empirical issue that can only be addressed with appropriate research methods. Even if non use values for fish are small on a per capita basis, they may be large in the aggregate.

Commenters also questioned the reliability of the empirical estimates of total benefits of the Phase III regulation and thus the practical value of the information provided by the focus groups and stated preference survey. However, EPA believes, following guidance in the literature, that stated preference methods are capable of measuring total values ( including use and non use) of fish affected by impingement and entrainment, if surveys and approaches are appropriately designed. EPA's own guidance explicitly permits the use of stated preference methods for use and non use value estimation ( US EPA 2000), following prior findings of the NOAA Blue Ribbon Panel on Contingent Valuation ( Arrow et al. 1993). EPA recognizes the difficulties faced in the design of appropriate stated preference survey instruments [ focus groups], but does not consider these difficulties to be insurmountable. As stated by Freeman ( 2003, p. 154), stated preference survey methods " are likely to offer the only feasible approaches to estimating non use values." Furthermore, although the peer reviewed literature is highly skeptical of stated preference surveys that attempt to decompose use and non use values, there is general conceptual acceptance that the total values of non users can be used to approximate nonuse values for all individuals ( Cummings and Harrison 1995, Johnston et al. 2003a).

1 Note that by definition, total values for non users are non use values. 11 Finally, one commenter noted that if EPA is to consider non use value effects on the benefit side of the regulation, then the Agency should also consider non market and non use value effects on the cost side of the regulation, such as values for effects on mining, the efficiency of energy production, and the use of depletable resources. While EPA acknowledges the theoretical validity of this point, EPA knows of no precedents to guide design of a survey on non use costs, nor does EPA expect economic impacts as significant as closures that would warrant further consideration of non use costs. The same commenter also argued that experimental economics may offer a superior method for studying non use values. EPA points out that there is no evidence in the literature that suggests that experimental economics methods provide a suitable alternative for the measurement of non use values, or a substitute for qualitative evidence that may be gathered from appropriately moderated focus group sessions.

For a more detailed discussion of the issues raised by commenters on the Federal Register notice published June 9, 2005, and of issues raised by commenters on the previous focus group ICR, see EPA's response to public comments on the Federal Register notice published on November 23, 2004 ( 69 FR 68140).

3( c) Consultations During development of the survey through focus groups already approved by OMB,

EPA's Office of Water has engaged in consultations with focus group and cognitive interview participants, as well as internal Agency reviewers. Following standard guidance from the economic literature ( e. g., Arrow et al. 1993; Mitchell and Carson 1989; Johnston et al. 1995;

Desvousges et al. 1984; Desvousges and Smith 1988; Kaplowicz et al. 2004; Opaluch et al.

1993), participants in focus groups and cognitive interviews primarily provided input on the survey questionnaire design and content, including the introductory materials, the survey format,

the sequence and content of questions, and relationships between questionnaire design and their interpretation and cognitive processing of survey questions. Cognitive interviews ( Kaplowicz et al. 2004) have also been conducted during focus group sessions to assess respondents cognitive processing of stated preference survey questions, and to ensure that questions are answered in a way that corresponds to neoclassical requirements for value estimation.

Internal Agency reviewers provided and will continue to provide input on the content and format of the focus groups, questionnaire design issues, and issues related to survey sampling 12 design and methodology. This has to date included comments on the appropriateness of the survey design for measuring fish resources affected by cooling water intake structures ( CWIS)

for Phase III facilities, and also appropriate correspondence between proposed survey approaches and economic theory underlying benefit cost analysis.

Table A1: Reviewers Reviewer Organization Telephone Number Kelly Maguire U. S. EPA, NCEE 202 566 2273 Nicole Owens U. S. EPA, NCEE 202 566 2302 Natalie Simon U. S. EPA, NCEE 202 566 2347 Christopher Miller U. S. Forestry Service 801 517 1034 Michael Dennis ( survey statistician) Knowledge Networks 650 289 2160 Additionally, as part of the final survey, EPA plans to conduct a survey pilot on a sample of 500 individuals. After this pilot has been conducted, EPA will solicit additional input from Agency and academic reviewers on the preliminary performance of the survey questionnaire and sampling design. Based on the results of the survey pilot and comments received from these reviewers, EPA may make necessary changes to the questionnaire or sampling methodology before implementing the survey with 3,900 individuals in the main sample and 600 individuals in the non response follow up sample.

3( d) Effects of Less Frequent Collection The survey is a one time activity. Therefore, less frequent collection is not practical.

3( e) General Guidelines The survey will not violate any of the general guidelines described in 5 CFR 1320.5 or in EPA's ICR handbook. 13 3( f) Confidentiality All responses to the survey will be kept strictly anonymous. To ensure that the final survey sample includes a representative and diverse population of individuals, the survey questionnaire will elicit basic demographic information, such as age, household size,

employment status, and income. However, the survey questionnaire will not ask respondents for personal identifying information, such as names, phone numbers, or addresses. Prior to taking the survey, respondents will be informed that their responses will be held strictly anonymous.

The survey data will be made public only after it has been thoroughly vetted to ensure that all potentially identifying information has been removed.

3( g) Sensitive Questions The survey questionnaire will not include any sensitive questions pertaining to private or personal information, such as sexual behavior or religious beliefs.

4. The Respondents and the Information Requested 4( a) Respondents EPA will recruit survey participants through Knowledge Networks, a national consumer information firm. The firm will recruit participants by selecting a random stratified sample of 4,400 adults from a panel of individuals who have previously indicated their willingness to participate in internet based surveys. The sample selection process will be designed to result in a set of participants who are representative of the adult U. S. population.

The individuals in the Knowledge Networks panel are recruited randomly through random digit dialing. They represent the broad diversity and key demographic dimensions of the U. S. population. The panel tracks closely to the U. S. population on age, race, Hispanic ethnicity,

geographical region, employment status, and other demographic elements. The differences that do exist are small and can be corrected statistically in the survey data ( i. e., by nonresponse adjustments). Knowledge Networks provides all panel members with free internet access, and in return, panel members are asked to participate in internet surveys three to four times a month. 14 Table A2 shows the stratification design for the geographic regions covered by the sample for this survey.

Table A2: Geographic Stratification Design Region States Included Sample Sizea Percentage of Sample Northeast CT, DC, DE, MA, MD, ME, NH, NJ, NY, RI, VT 665 17%

Southeast AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX, VA, WV 1,305 33%

Great Lakes IA, IL, IN, MI, MN, MO, OH, PA, WI 984 25%

Inland Region CO, ID, KS, MT, NE, ND, SD, UT, WY 206 5%

Pacific Mountain AZ, CA, NM, NV, OR, WA 739 19%

Total, All Regions All states except AK and HI 3,900 100%

a Sample sizes presented in this table include only the 3,900 individuals asked to participate in the final survey. An additional 500 individuals will be included in a pre test of the survey, and an additional 600 individuals will be included in the nonresponse follow up interviews.

In addition to the 4,400 respondents recruited for the web based survey panel, an additional 600 individuals will be recruited for nonresponse follow up interviews. These individuals will include randomly sampled respondents who chose not to participate at some stage of the panel recruitment and sampling process, i. e., respondents who were contacted but did not join the Knowledge Networks web panel, who joined the panel but did not connect their WebTVs, who connected but did not complete the first survey, who completed the first survey but did not complete any following surveys, and who completed following surveys but did not complete the 316( b) survey.

More detail on planned sampling methods and statistical design of the survey can be found in Part B of this supporting statement.

4( b) Information Requested ( I) Data items, including record keeping requirements.

Individuals who agree to participate in the web based survey will be provided with a link to online versions of the supporting materials and survey. The supporting materials, including 15 the PowerPoint presentation and the slide show script, are provided in Attachments 1 and 2. The full text of the stated preference component of the survey is provided in Attachment 3. EPA has determined that all questions in the survey are necessary to achieve the goal of this information collection, i. e., to collect data that can be used to support an analysis of the total benefits of the 316( b) regulation.

The following is an outline of the major sections of the survey.

Concern for Policy Issues. The first survey question asks respondents to rank the general importance of a range of public policy issues, including both environmental and nonenvironmental issues. This question is designed to highlight the many important policy issues and substitute concerns to which respondents might choose to direct funds, rather than spending these funds to prevent fish losses. Such questions are commonly used in introductory sections of stated preference surveys ( e. g., Mitchell and Carson 1984), in order to place respondents in a mindset in which they are cognizant of substitute goods and policy issues to which they might direct their scarce household budgets.

Testing for Appropriate Understanding of Introductory Materials. Questions 2 and 3 are questions designed to assess respondents' understanding of the material provided in the introductory slide show presentation. These questions will allow EPA to identify those respondents who do not   based on answers to these questions   provide evidence that they have understood key areas of introductory information. Question 2 addresses the relative importance of CWIS losses on fish populations   a topic addressed clearly by the introductory materials.

Question 3 addresses the general purpose of the survey   an issue also clearly addressed by the introductory materials. Incorrect answers to these questions will indicate respondents with an inappropriate understanding of the provided introductory materials, and allow such responses to be either removed or isolated statistically in the final analysis. These questions are included following the guidance of the NOAA Blue Ribbon Panel ( Arrow et al. 1993), to assess whether respondents understand the survey materials as presented.

Voting for Regulations to Prevent Fish Losses. Questions 4, 5, and 6 are " choice experiment"

or " choice modeling" questions ( Adamowicz et al. 1998; Bennett and Blamey 2001), and ask respondents to choose how they would vote, if presented with two hypothetical regulatory options ( and a third choice to reject both options). Each of the multi attribute options is characterized by [ a] changes in annual impingement and entrainment losses of fish and other 16 organisms, [ b] effects on long term fish populations, [ c] effects on recreational and commercial catch, and [ d] an unavoidable cost of living increase for the respondent's household. Following standard choice experiment methods, respondents choose the regulatory options that they prefer,

based on their preferences. Respondents always have the option to vote for neither option   providing the status quo option necessary for appropriate welfare estimation ( Adamowicz et al.

1998). Advantages of choice experiments, and the many examples of the use of such approaches in the literature, are discussed in later sections of this ICR. Following standard approaches ( Opaluch et al. 1993, 1999; Johnston et al. 2002a, 2003b), respondents are instructed to answer each of the three choice questions independently, and not to " add up or compare programs across different pages." This is included to avoid biases associated with sequence aggregation effects ( Mitchell and Carson 1989). EPA will also randomize the order in which the policy option attributes are presented across respondents. This would allow a statistical test of potential ordering effects.

Reasons for Voting. Questions 7   13 are follow up questions to the prior voting questions, and ask respondents how certain they feel about their answers, whether their responses are based on resource changes reflected in the survey, whether they are aware that the ecological outcomes of the 316( b) regulations are subject to considerable uncertainty and what factors affected their choices, and why they voted for or against the regulatory programs. Question 7 assesses the certainty that respondents feel in their choice experiment responses, following methods of Champ et al. ( 2004) and others. Responses to such questions have been used in the literature to successfully control for hypothetical bias. Questions 8 10 are designed to identify respondents whose responses are based on incorrect interpretation of the resource changes described in the survey or respondents who ignored information presented in the survey and answered questions based on their general convictions and principles. Questions 9 and 11 13 are also designed to allow EPA to identify those respondents whose answers to questions 4 6 reflect symbolic or warm glow biases ( Arrow et al. 1993; Mitchell and Carson 1989), and respondents who did not consider their budget constraint when answering the choice questions.

Affiliations and Recreational Experience. Questions 14 and 15 ask respondents whether they are members of environmental, sporting, or industry groups, and whether there are any reasons that they might be directly affected by regulations that affect fish abundance. Questions 16, 17 and 18 ask respondents whether they participate in specific types of water related recreational activities, and whether those activities would be affected by changes in fish abundance. 17 Question 19 asks respondents how far they have to travel to reach the nearest water body that supports fish or shellfish. These questions can be used to identify non users of the fishery resource   thereby allowing the estimation of non user values for I& E reductions. Examples of this approach to estimation of non user values are provided by Johnston et al. ( 2005), Whitehead et al. ( 1995), Croke et al. ( 1986), Olsen et al. ( 1991), Cronin ( 1982), Whitehead and Groothuis ( 1992), and Mitchell and Carson ( 1981).

Demographics. Questions 20 29 ask respondents to provide basic demographic information,

including age, gender, highest level of education, household size, household composition,

location, employment status, and household income.

Comments. The survey offers respondents a chance to comment on the survey.

( II) Respondent activities EPA expects individuals to engage in the following activities during their participation in the contingent valuation survey.

  Review the background information provided in the online supporting materials.

  Complete the online survey questionnaire.

A typical subject from the Knowledge Networks panel that is recruited for the survey will spend approximately 15 minutes reviewing the online background materials. The participant will then spend 25 minutes completing the online survey questionnaire. These estimates are derived from focus groups and cognitive interviews in which respondents completed both tasks.

The survey administration format will be designed to ensure that individuals who agree to participate must review all background information before completing the survey.

EPA expects individuals recruited for nonresponse follow up interviews to engage in the following activities:

  Respond to telephone recruitment.

  Review the background information provided in the online supporting materials.

  Complete the online survey questionnaire. 18 A typical individual who is recruited for the nonresponse follow up survey will spend approximately five minutes responding to telephone recruitment, 15 minutes reviewing the online background materials, and 25 minutes completing the online survey questionnaire. The survey administration format will be designed to ensure that individuals who agree to participate in the follow up interviews must review all background information before completing the survey.

5. The Information Collected  Agency Activities, Collection Methodology, and Information Management 5( a) Agency Activities The survey is being developed, conducted, and analyzed by Abt Associates Inc. and Knowledge Networks and is funded by EPA contracts No. 68 C99 239 and No. 68 W 01 039,

which provide funds for the purpose of analyzing the economic benefits of the proposed rule for Phase III facilities subject to the section 316( b) regulation. Agency activities associated with the survey consist of the following:

  Pre test the survey on a sample of 500 individuals.

  Modify the survey questionnaire and sampling design, if necessary.

  Conduct the survey.

  Conduct nonresponse follow up interviews.

  Analyze the survey results.

Although not covered under this ICR, EPA will use the survey results to estimate the social value of changes in impingement and entrainment losses and populations of forage,

recreational, and commercial species of fish, as part of the Agency's analysis of the benefits of the 316( b) rule for Phase III facilities.

5( b) Collection Methodology and Information Management To pretest the survey questionnaire, EPA has conducted a series of 12 focus groups,

including some using cognitive interview methodologies ( covered under EPA ICR # 2155.01).

Focus groups have provided valuable feedback that allowed EPA to iteratively edit and refine the 19 questionnaire, and eliminate or improve imprecise, confusing, and redundant questions. Focus groups and cognitive interviews were conducted following standard approaches in the literature,

as outlined by Desvousges et al. ( 1984), Desvousges and Smith ( 1988), Johnston et al. ( 1995),

Schkade and Payne ( 1994), Kaplowicz et al. ( 2004), and Opaluch et al. ( 1993).

EPA plans to implement the proposed survey as an internet based, choice experiment questionnaire. Data quality will be monitored by checking submitted surveys for completeness and consistency, and by asking respondents to assess the accuracy of their own responses. Data quality will also be assessed using responses to questions 2, 3, and 7 through 13   questions designed to assess the presence or absence of potential response biases, as discussed above.

Responses to the survey will be stored in an electronic database. This database will be used to generate a data set for a regression model of total values for reductions in fish impingment and entrainment by section 316( b) Phase III facilities.

To protect the confidentiality of survey respondents, the survey data will be released only after it has been thoroughly vetted to ensure that all potentially identifying information has been removed the general public will not be given access to the survey data.

5( c) Small Entity Flexibility This survey will be administered to individuals, not businesses. Thus, no small entities will be affected by this information collection.

5( d) Collection Schedule The schedule for implementation of the survey will be as follows: 20 Table A3: Schedule for Survey Implementation Activity Duration of Each Activity Total Elapsed Time Period Following OMB Approval Make questionnaire available to pilot survey subjects 7 days 10 days Tabulate pilot survey responses, solicit input from Agency and academic reviewers on performance of survey, and make necessary changes to survey questionnaire or sampling methodology. 7 days 17 days Concurrent: Make questionnaire available to survey subjects Conduct nonresponse follow up interviews 21 days ( 14 days) ( 21 days) 38 days Tabulate survey responses and analyze survey data 14 days 52 days 6. Estimating Respondent Burden and Cost of Collection 6( a) Estimating Respondent Burden Subjects who participate in the survey and follow up interviews will expend time on several activities. Based on the administration of the main survey to 4,400 respondents and follow up interviews to 600 respondents, the national burden estimate for all respondents is 3,383 hours.

Based on pretests conducted in focus groups, EPA estimates that on average each respondent to the main survey will spend 15 minutes reviewing the provided background materials and 25 minutes completing the survey questionnaire. Thus, the average burden per respondent is 40 minutes ( 0.67 hours) for these 4,400 respondents.

EPA estimates that on average, each follow up interview respondent will spend 5 minutes responding to phone recruitment, as well as 15 minutes reviewing the provided background materials and 25 minutes completing the survey questionnaire. The average burden per respondent is 45 minutes ( 0.75 hours) for these 600 respondents.

These burden estimates reflect a one time expenditure in a single year.

6( b) Estimating Respondent Costs According to the Bureau of Labor Statistics, the average hourly wage for private sector workers in the United States is $ 17.71 ( 2004$). Assuming an average per respondent burden of 21 0.67 hours for respondents to the pretest and main survey and 0.75 hours for respondents to the nonresponse follow up interviews, and an average hourly wage of $ 17.71, the average cost per respondent is $ 11.81 and $ 13.28 for respondents to the pretest/ main survey and nonresponse follow up interviews, respectively. Since 4,400 individuals are expected to take the pretest/ main survey and 600 individuals are expected to participate in the nonresponse follow up interviews,

the total average cost for all respondents is $ 11.98 per respondent. This cost does not take into account an incentive payment of approximately $ 5 to $ 10 per respondent for completing the survey, or the compensation that respondents who are panel members receive in the form of free internet access provided by Knowledge Networks. Internet access is provided to make it easy for respondents to complete surveys, and the incentive payment is provided to encourage respondents to complete this survey, which is somewhat longer than surveys Knowledge Networks participants are typically asked to complete.

EPA does not anticipate any capital or operation and maintenance costs for respondents.

6( c) Estimating Agency Burden and Costs This project is being undertaken by Abt Associates Inc. and Knowledge Networks with funding of $ 287,114 from EPA contract No. 68 C99 239 and No. 68 W 01 039, which provide funds for the purpose of analyzing the economic benefits of the proposed rule for Phase III facilities subject to the section 316( b) regulation. The total cost of this project includes labor costs, as well as costs related to use of the Knowledge Networks panel. Abt Associates Inc. and Knowledge Networks staff are expected to spend 1,143 hours pre testing the survey questionnaire and sampling methodology, conducting the survey, and tabulating and analyzing the survey results. The cost of this contractor time is $ 117,856, and the estimated direct panel cost is $ 169,258. In addition to the effort expended by EPA's contractors, EPA staff are expected to spend 650 hours managing and reviewing this project and contributing to the analysis. The cost of this EPA staff time is $ 19,614. Thus, total agency and contractor burden is 1,793 hours, with a total cost of $ 306,728. 22 6( d) Respondent Universe and Total Burden Costs EPA expects the total cost for survey respondents to be $ 59,919 ( 2004$), based on a total burden estimate of 3,383 hours and an hourly wage of $ 17.71.

6( e) Bottom Line Burden Hours and Costs The following table presents EPA's estimate of the total bottom line burden and costs of this information collection:

Table A4: Total Estimated Bottom Line Burden and Cost Summary Affected Individuals Total Burden Total Cost ( 2004$)

Survey respondents 3,383 hours $ 59,919 EPA staff 650 hours $ 19,614 EPA's contractorsa 1,143 hours $ 287,114 Total Burden and Cost 5,176 hours $ 366,647 a The total cost listed for EPA's contractors includes an incentive fee of approximately $ 10 for each survey respondent. Respondents also receive additional compensation from Knowledge Networks in the form of free internet access.

6( f) Reasons for Change in Burden The survey is a one time data collection activity.

6( g) Burden Statement EPA estimates that the public reporting and record keeping burden associated with the survey will average 0.68 hours per respondent ( i. e., a total of 3,383 hours of burden divided among 5,000 survey respondents). Burden means the total time, effort, or financial resources expended by persons to generate, maintain, retain, or disclose or provide information to or for a Federal agency. This includes the time needed to review instructions; develop, acquire, install,

and utilize technology and systems for the purposes of collecting, validating, and verifying information, processing and maintaining information, and disclosing and providing information; 23 adjust the existing ways to comply with any previously applicable instructions and requirements;

train personnel to be able to respond to a collection of information; search data sources; complete and review the collection of information; and transmit or otherwise disclose the information. An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB control number. The OMB control numbers for EPA's regulations are listed in 40 CFR part 9 and 48 CFR chapter 15.

To comment on the Agency's need for this information, the accuracy of the provided burden estimates, and any suggested methods for minimizing respondent burden, including the use of automated collection techniques, EPA has established a public docket for this ICR under Docket ID No. OW 2005 0006, which is available for public viewing at the Water Docket in the EPA Docket Center ( EPA/ DC), EPA West, Room B102, 1301 Constitution Ave., NW,

Washington, DC. The EPA Docket Center Public Reading Room is open from 8: 30 a. m. to 4: 30 p. m., Monday through Friday, excluding legal holidays. The telephone number for the Reading Room is ( 202) 566 1744, and the telephone number for the Water Docket is ( 202) 566 2426. An electronic version of the public docket is available through EPA Dockets ( EDOCKET) at http:// www. epa. gov/ edocket. Use EDOCKET to submit or view public comments, access the index listing of the contents of the public docket, and to access those documents in the public docket that are available electronically. Once in the system, select " search," then key in the docket ID number identified above. Also, you can send comments to the Office of Information and Regulatory Affairs, Office of Management and Budget, 725 17th Street, NW, Washington,

DC 20503, Attention: Desk Office for EPA. Please include the EPA Docket ID number ( OW

2005 0006) in any correspondence. 24 PART B OF THE SUPPORTING STATEMENT 1. Survey Objectives, Key Variables, and Other Preliminaries 1( a) Survey Objectives The overall goal of this survey is to explore how the public values ( including use and non use values) for fish and aquatic organisms are affected by impingement and entrainment at cooling water intake structures ( CWIS) located at Phase III 316( b) facilities, as reflected in individuals' willingness to pay for programs that would prevent such losses. EPA has designed the survey to provide data to support the following specific objectives:

  To estimate the use and non use values that individuals place on preventing losses of fish and other aquatic organisms caused by CWIS at Phase III 316( b) facilities.

  To understand how much individuals value preventing fish losses, increasing fish populations, and increasing commercial and recreational catch rates.

  To understand how such values depend on the current baseline level of fish populations and fish losses, the scope of the change in those measures, and the certainty level of the predictions.

  To understand how such values vary with respect to individuals' economic and demographic characteristics.

Understanding total public values   including non use values for fish resources lost to impingement and entrainment   is necessary to determine the full range of benefits associated with reductions in impingement and entrainment losses at Phase III facilities. Because non use values may be substantial in some cases, failure to recognize such values may lead to improper inferences regarding policy benefits ( Freeman 2003).

1( b) Key Variables The key elicitation questions in the survey ask respondents whether or not they would vote for policies that would increase their cost of living, in exchange for specified changes in [ a]

impingement and entrainment losses of fish, [ b] long term fish populations, and [ c] recreational 25 and commercial catch. More specifically, the choice experiment framework allows respondents to view pairs of multi attribute policies associated with the reduction of I& E losses.

Respondents are asked to choose the program that they would prefer, or to choose to reject both policies. This follows well established choice experiment methodology and format ( Adamowicz et al. 1998; Louviere et al. 2000; Bennett and Blamey 2001; Bateman et al. 2002). Important variables in the analysis of the choice questions are how the respondent votes, the amount of the cost of living increase, the number of fish losses that are prevented, the change in long term fish populations, and the change in commercial and recreational catch. Other important variables include whether or not the respondent is a user of the affected aquatic resources, other recreational activities in which the respondent participates, household income, and other respondent demographics.

1( c) Statistical Approach EPA believes that a statistical survey approach is necessary to ensure that inferences and analyses based on the resulting data are as statistically unbiased and as precise as practicable. A census approach is impractical because contacting all households in the U. S. would require an enormous expense. On the other hand, an anecdotal approach is not sufficiently rigorous to provide a useful estimate of the total value of fish. Thus, a statistical survey is the most reasonable approach to satisfy EPA's analytic needs for the 316( b) regulation benefit analysis.

To support implementation of the survey, EPA has retained two contractors. Abt Associates Inc. ( 55 Wheeler Street, Cambridge, MA 02138) will assist in questionnaire design,

sampling design, and analysis of the survey results. Knowledge Networks, Inc ( 1350 Willow Road, Suite 102, Menlo Park, CA 94025) will assist in sampling design, recruitment of subjects,

and implementation of the survey.

1( d) Feasibility The survey instrument has been repeatedly pre tested during a series of twelve focus groups, and has been and will continue to be subject to review by reviewers in academia and government, so EPA does not anticipate that respondents will have difficulty interpreting or responding to any of the survey questions. Additionally, since the survey will be administered as 26 a web based survey, it will be easily accessible to all respondents. Thus, EPA believes that respondents will not face any obstacles in completing the survey, and that the survey will produce useful results. EPA has dedicated sufficient funding ( under EPA contracts No. 68 C99

239 and 68 W 01 039) to design and implement the survey. Given the timetable outlined in Section A. 5( d) of this document, the survey results will be available for timely use in the benefits analysis for the Phase III 316( b) rule.

2. Survey Design 2( a) Target Population and Coverage The target population for this survey includes individuals from continental U. S.

households who are 18 years of age or older. The sample will be chosen to reflect the demographic characteristics of the general U. S. population.

2( b) Sampling Design ( I) Sampling Frames The sampling frame for this survey is the panel of individuals previously recruited by Knowledge Networks to participate in online surveys. The overall sampling frame from which Knowledge Networks selects these individuals is the set of all individuals in continental U. S.

households who are 18 years of age or older and who have listed phone numbers. Individuals in the Knowledge Networks panel are recruited using a list assisted random digit dialing telephone methodology, thus providing a probability based starting sample of U. S. telephone households.

The panel is routinely supplemented to account for panel attrition. Panel sample weights are adjusted to U. S. Census demographic benchmarks to reduce error due to non coverage of nontelephone households and to reduce bias due to nonresponse and other non sampling errors. The panel closely tracks the U. S. population on age, race, Hispanic ethnicity, geographical region,

employment status, and other demographic elements, and the differences that do exist are small and can be corrected statistically in the survey data. For discussion of techniques that EPA will use to minimize nonresponse and other non sampling errors in the survey sample, refer to Section 2( b)( II), below. 27 ( II) Sample Sizes The intended sample size for the survey is 5,000 households, including 500 households for the pre test and 600 households for the non response follow up interviews. This sample size was chosen to provide statistically robust regression results while minimizing the cost and burden of the survey. Given this sample size, the level of precision achieved by the analysis will be more than adequate to meet the analytic needs of the benefits analysis for the 316( b)

regulation. For further discussion of the level of precision required by this analysis, see Section 2( c)( I) below.

( III) Stratification Variables The survey sample will be selected from the Knowledge Networks panel using a stratified selection process. The panel will be stratified by geographical region, and within each region, by demographic variables including age, education, Hispanic ethnicity, race, gender, and household income. Two factors will be used to determine the number of respondents included in the sample for each stratification grouping. First, to increase the demographic similarities between members of the sample who respond to the survey and the U. S. Census population benchmarks, sample sizes for each demographic stratification group will take into account each group's estimated response rate to Knowledge Network's initial telephone sample of U. S. phone numbers. Sample sizes will be larger for under represented groups. Second, sample sizes for each demographic stratification group will factor in the historic response rates of individuals to similar types of surveys   in particular, historic tendencies to give consistent and valid answers to conjoint and valuation questions. These estimates for providing consistent/ valid answers,

while initially based on past experience, will be refined as a result of experience gained during the pretest. Sample sizes will be larger for groups that tend to provide inconsistent or invalid answers.

( IV) Sampling Method Using the stratification design discussed above, respondents will be randomly selected from the Knowledge Networks panel sampling frame. As noted in the previous paragraph, the 28 stratification design systematically oversamples households from certain demographic groups.

By oversampling groups that tend to have lower response and consistency rates, the demographic characteristics of respondents who provide valid completed surveys will mirror the Census demographic benchmarks more closely. Thus, this design will reduce error due to non coverage of non telephone households in the original Knowledge Networks recruitment process, and will also reduce bias due to nonresponse and other non sampling errors.

( V) Multi Stage Sampling Multi stage sampling will not be necessary for this survey.

2( c) Precision Requirements ( I) Precision Targets Table B1, below, shows the expected level of precision for this survey's statistical design,

at both regional and national levels. The sample design effect ( i. e., the ratio of the design based variance estimate divided by the variance estimate that would have been obtained from a simple random sample of the same size) for the region level estimates is 1.3, which takes into account unequal probabilities of selection arising from the panel recruitment stage and probabilistic selection from the panel for the survey. The national sample design effect is also assumed to be 1.3. The confidence intervals presented in Table B1 take into account the reduction in the effective sample size resulting from these sample design effects.

The table shows 90 percent confidence intervals for a typical binary survey variable with a sample mean of 50 percent ( for example, the number of respondents who would vote for a particular policy option). The confidence intervals range from +/ 6.6 percentage points in the Inland region, which has a sample size of 206, to +/ 2.7 percentage points in the Southeast region, which has a sample size of 1,305. For the 3,900 respondents in the total national main survey sample, the 90 percent confidence interval is +/ 1.5 percentage points. 29 Table B1: Confidence Intervals for Binary Survey Variables, by Region Region Sample Size 90 Percent Confidence Intervala Northeast 665 +/ 3.7%

Southeast 1,305 +/ 2.7%

Great Lakes 984 +/ 3.0%

Inland 206 +/ 6.6%

Pacific Mountain 739 +/ 3.5%

All Regions 3,900 +/ 1.5%

a Represents a 90 percent confidence interval for a binary survey variable ( for example, whether or not respondents would visit a new fishing site) with a sample mean of 0.5 ( or 50 percent).

( II) Non Sampling Errors One issue that may be encountered in stated preference surveys is the problem of protest responses. Protest responses are responses from individuals who reject the survey format or question design, even though they may value the resources being considered ( Mitchell and Carson 1989). For example, some respondents may feel that any amount of impingement and entrainment is unacceptable, and choose not to respond to the survey. To deal with this issue,

EPA has included several questions, including an open ended comments section, to help identify protest responses. The use of such methods to identify protest responses is well established in the literature ( Bateman et al. 2002). Moreover, many researchers ( e. g., Bateman et al. 2002)

suggest that a choice experiment format, such as that proposed here, may ameliorate such responses.

A different type of non sampling error, which may result from use of panel data, is nonresponse bias. The Knowledge Networks web enabled panel involves several stages of recruitment, maintenance, and survey implementation. Because attrition occurs at each of these stages, the cumulative response rate for a survey using the panel is lower than would be obtained in a simple cross sectional survey using random digit dialing. However, approaches to minimize nonresponse bias in internet surveys are established in the literature ( Dillman 2000). Because of the use of these techniques ( such as initial RDD probability sampling, careful panel management, and effective survey sampling procedures), the representativeness of Knowledge 30 Networks' survey samples is generally comparable to those telephone surveys sponsored by various Federal agencies. Nonetheless, EPA will conduct several additional activities to minimize the potential for nonresponse bias in the current survey:

( 1) EPA will use a stratified survey sample to mitigate the effect of nonresponse bias.

( 2) EPA will use nonresponse follow up interviews to increase the overall response rate.

( 3) EPA will correct statistically for unobserved heterogeneity that might be present in the data from respondents interviewed for this study Stratified Survey Sample EPA will use a stratified survey sample to mitigate the effect of nonresponse bias. By varying the sample sizes of various demographic groups, this survey will control for the differential propensity of individuals in different demographic groups to respond to the original Knowledge Networks recruitment process and to this survey.

Nonresponse Follow up Interviews In order to minimize nonresponse bias from individuals who chose not to respond during some stage of panel recruitment, Knowledge Networks will conduct nonresponse follow up interviews within the initial sample for all stages of panel attrition. Respondents will include individuals who were contacted but did not join the Knowledge Networks web panel, who joined the panel but did not connect their WebTVs, who connected but did not complete the first survey, who completed the first survey but did not complete any following surveys, and who completed following surveys but did not complete the 316( b) survey. The end result is a direct measurement of nonresponse bias and an increase in the effective cumulative response rate using a weighted response rate formulation. The nonresponse follow up study will include interviews with 600 individuals, using a self administered, computer assisted mode of data collection that is consistent with the main sample. These follow up interviews will be used to supplement the responses from the main study data set, and will be identified by sample grouping ( e. g., panel recruitment non responder, panel drop off case, etc.).

Statistical Correction for Unobserved Heterogeneity EPA also plans to use a statistical technique to correct for unobserved heterogeneity,

based on work by Heckman ( Heckman, 1979). This correction may be conducted as substitute or 31 as complement to a nonresponse study effort. The technique will provide the probabilities of participation for each subpopulation grouping ( as defined by Census long form data, voting data,

and other data sources) included in the initial sample frame. Importantly, these probabilities of participation will demonstrate the extent of self selection bias in the actual survey sample. The selection correction technique will take fully into account each stage of the panel recruitment and sampling process: that is, all stages in between construction of the sample frame for panel recruitment and web interviewing of the panel respondents.

The selection correction technique will be applied to the key estimate of interest for this survey: respondents' WTP for environmental changes resulting from the 316( b) regulation,

including reduction in fish mortality from impingement and entrainment and expected changes in fish population and commercial and recreational catch. The technique will be used to produce both adjusted and unadjusted estimates of WTP. Because the technique yields regression estimates that explain survey outcome data using subpopulation data related to neighborhood characteristics, it can be used to identify the sources of differences between the adjusted and unadjusted estimates.

2( d) Questionnaire Design The information requested by the survey is discussed in Section 4( b)( I) of Part A of the supporting statement. The full text of the questionnaire is provided in Attachment 3.

The following bullets discuss EPA's reasons for including the questions in the survey:

  Concern for Policy Issues. EPA included this section to prepare respondents to answer the stated preference questions by motivating respondents to think about the relative importance of different policy issues.

  Voting for Regulations to Prevent Fish Losses. The questions in this section are the key part of the survey. Respondents' choices when presented with specific fish related resource changes and household cost increases are the main data that allow estimation of willingness to pay for resource changes. The questions are presented in a choice experiment ( A, B, or neither) format because this is an elicitation format that has been successfully used by a number of previous valuation studies ( Adamowicz et al. 1998;

Bateman et al. 2002; Bennett and Blamey 2001; Louviere et al. 2000; Johnston et al.

2002a, 2005; Opaluch et al. 1993). Furthermore, many focus group participants indicated 32 that they have some previous experience making choices within a framework in which they are asked to vote for one of a series of options, and are comfortable with this format.

  Reasons for Voting. These questions provide information that will be used to determine which aspects of the resource change were important to respondents. These questions also allow EPA to identify protest responses and responses that reflect symbolic or warm glow biases, following Mitchell and Carson ( 1989).

  Affiliations and Recreational Experience. These questions elicit affiliation and recreational experience data to test if certain respondent characteristics may heavily influence responses to the referendum questions. These questions will also allow EPA to identify resource non users, for purposes of estimating non user WTP.

  Demographics. Responses to these questions will be used to estimate the influence of demographic variables on respondents' voting choices, and ultimately, their WTP to prevent I& E losses of fish. This information will allow EPA to use the regression results to estimate WTP for populations in different regions affected by the 316( b) rule for Phase III facilities.

  Comments. This section is primarily intended to help identify protest responses, i. e.

responses from individuals who rejected the format of the survey or the way the questions were phrased.

3. Pretests and Pilot Tests EPA has conducted extensive pretests of the survey instrument during a set of 12 focus groups ( EPA ICR # 2155.01). These focus groups have also included individual cognitive interviews with survey respondents ( Kaplowicz et al. 2004), and think aloud or verbal protocol analyses ( Schkade and Payne 1994). Individuals in these focus groups completed draft survey questionnaires and provided comments and feedback about the survey format and content, their interpretations of the questions, and other issues relevant to stated preference estimation. Based on their responses, EPA has made a number of improvements to the survey questionnaire.

Particular emphasis in these survey pretests was placed on testing for the presence of potential biases associated with poorly designed stated preference surveys, including hypothetical bias,

strategic bias, symbolic ( warm glow) bias, framing effects, embedding biases, methodological misspecification, and protest responses ( Mitchell and Carson 1989). Focus groups and cognitive 33 interviews led to numerous changes to ameliorate and minimize these biases in the final survey instrument and introductory materials. Results from the final two focus groups ( one including cognitive interviews) provided convincing evidence that the great majority of respondents answer the stated preference survey in ways appropriate for stated preference WTP estimation,

and that their responses do not reflect the biases noted above ( Besedin et al., 2005; see docket for EPA ICR # 2155.02). Moreover, survey pretests have demonstrated the ability of follow up survey questions ( i. e., after the primary choice questions) to clearly identify the respondents who's answers to the choice questions display the influence of bias, or do not understand the survey context or information.

Additionally, as part of the current information collection, EPA plans to conduct a survey pilot on a sample of 500 individuals. After this pilot has been conducted, the Agency will solicit additional input from Agency and academic reviewers on the preliminary performance of the survey questionnaire and sampling design. Based on the results of the survey pilot and comments received from these reviewers, EPA may make necessary changes to the questionnaire or sampling methodology before implementing the survey with 3,900 individuals.

4. Collection Methods and Follow up 4( a) Collection Methods The survey will be administered as an online survey through the Knowledge Networks electronic distribution system. Selected Knowledge Network panel members will be sent a personalized e mail notifying them that the background materials and survey questionnaire are available online. The email notification also contains a hyperlink to the survey. Because this hyperlink includes a unique identifier, there is no possibility that any respondent could submit more than one response. Furthermore, since no one except the selected panel members has access to the e mail from Knowledge Networks, panel members are the only individuals who will be able to respond to the survey.

Respondents will receive an incentive fee of $ 5 to $ 10 for completing this survey. All members of the Knowledge Network panel are provided with home internet access as part of their agreement to participate in the panel. 34 4( b) Survey Response and Follow up The target response rate for the survey is 80 percent. This response rate represents the fraction of the Knowledge Network sample members that are expected to complete the survey.

However, the actual response rate compared to the general population is lower, given that the Knowledge Network telephone recruitment process has a 36 percent response rate. Knowledge Network panel members who are selected for the survey will be contacted by e mail to inform them that the survey is available online. To improve the response rate, e mail reminders will be sent to panel members who do not complete the survey within a short period of time.

Additionally, an incentive fee of $ 5 to $ 10 will be provided to respondents who complete the survey.

Although the cumulative response rate for this survey may seem low compared to response rates typically achieved using other survey methods ( e. g., phone and mail surveys), the web based format facilitates geographic and demographic stratification, which will substantially improve the quality of the survey. As noted in Section 2( b), the stratification design systematically oversamples households from certain demographic groups that historically have lower response and consistency rates, which helps to ensure that the demographic characteristics of respondents who provide valid completed surveys will mirror the Census demographic benchmarks more closely. Also, as noted in Section 2( c)( II), EPA will use statistical techniques to correct for unobserved heterogeneity in the survey sample, as well as nonresponse follow up interviews to increase the effective survey response rate. Combined, these techniques will reduce error due to non coverage of non telephone households in the original Knowledge Networks recruitment process, and also reduce bias due to nonresponse and other non sampling errors. Furthermore, practical limitations including time and budget constraints prevented use of other survey methodologies for this survey. For an example of a similar research effort that successfully used this web based survey approach to evaluate WTP for changes in water quality,

see Viscusi et al. ( 2004). 35 5. Analyzing and Reporting Survey Results 5( a) Data Preparation Since the survey will be administered as an online survey, survey responses will automatically be entered into an electronic database at the time they are submitted. After all responses are submitted, the database contents will be converted into a format suitable for use with a statistical analysis software package. The online survey, database management, and data set conversion will be conducted by Knowledge Networks and Abt Associates Inc.

All survey responses will be vetted by EPA for completeness. Additionally, respondents'

answers to the choice experiment questions will be tested to ensure that they are internally consistent with respect to scope and other expectations of neoclassical preference theory.

5( b) Analysis Once the survey data has been converted into a data file, it will be analyzed using stateof the art statistical analysis techniques. The following section discusses the model that will be used to analyze the stated preference data from the survey.

Analysis of Stated Preference Data The model for analysis of stated preference data is grounded in the standard random utility model of Hanemann ( 1984) and McConnell ( 1990). This model is applied extensively within stated preference research, and allows well defined welfare measures ( i. e., willingness to pay) to be derived from choice experiment models ( Bennett and Blamey 2001; Louviere et al.

2000). Within the standard random utility model applied to choice experiments, hypothetical policy alternatives are described in terms of attributes that focus groups ( Johnston et al. 1995;

Adamowicz et al. 1998; Opaluch et al. 1993) reveal as relevant to respondents' utility, or wellbeing One of these attributes would include a mandatory monetary cost to the respondent's household.

Applying this standard model to choices among policies to reduce entrainment and impingement ( I& E) losses, EPA defines a standard utility function Ui(.) that includes attributes of an I& E reduction plan and the net cost of the plan to the respondent. Following standard 36 random utility theory, utility is assumed known to the respondent, but stochastic from the perspective of the researcher, such that ( 1) Ui(.) = U( Xi, D, Y Fi) = v( Xi, D, Y Fi) +  i where:

Xi = a vector of variables describing attributes of I& E reduction plan i;

D = a vector characterizing demographic and other attributes of the respondent.

Y = disposable income of the respondent.

Fi = mandatory additional cost faced by the household under plan i;

v(.) = a function representing the empirically estimable component of utility;

 i = stochastic or unobservable component of utility, modeled as an econometric error.

Econometrically, a model of such a preference function is obtained by methods designed for limited dependent variables, because researchers only observe the respondent's choice among alternative policy options, rather than observing values of Ui(.) directly ( Maddala, 1983;

Hanemann, 1984). Standard random utility models are based on the probability that a respondent's utility from a policy Plan i, Ui(.), exceeds the utility from alternative Plans j, Uj(.),

for all potential plans j  i considered by the respondent. More specifically, the random utility model presumes that the respondent assesses the utility that would result from each I& E reduction plan i, and chooses the plan that would offer the highest utility.

When faced with k distinct plans defined by their attributes, the respondent will choose plan i if the anticipated utility from plan i exceeds that of all other k 1 plans. Drawing from ( 1),

the respondent will choose plan i if ( 2) ( v( Xi, D, Y Fi) +  i)   ( v( Xk, D, Y Fk) +  k)   k  i.

If the  i are assumed independently and identically drawn from a type I extreme value ( Gumbel) distribution, the model may be estimated as a conditional logit model, as detailed by Maddala ( 1983), Greene ( 2003) and others. This model is most commonly used when the respondent considers more than two options in each choice set ( e. g., Plan A, Plan B, Neither Plan), and results in an econometric ( empirical) estimate of the systematic component of utility 37 v(.), based on observed choices among different policy plans. Based on this estimate, one may calculate welfare measures ( willingness to pay) following the well known methods of Hanemann ( 1984), as described by Freeman ( 2003) and others. Following standard choice experiment methods ( Adamowicz et al. 1998; Bennett and Blamey 2001), each respondent will consider questions including three potential choice options ( i. e., Plan A, Plan B, Neither Plan)   choosing the option that provides the highest utility as noted above. Following clear guidance from the literature, a " neither plan" or status quo option is always included in the visible choice set, to ensure that WTP measures are well defined ( Louviere et al. 2000).

EPA also anticipates that respondents will consider more than one choice question within the same survey, to increase information obtained from each respondent. This is standard practice within choice experiment and dichotomous choice contingent valuation surveys ( Poe et al. 1997; Layton 2000). While respondents will be instructed to consider each choice question as an independent, non additive choice, it is nonetheless standard practice within the literature to allow for the potential of correlation among questions answered within a single survey, by a single respondent. That is, responses provided by individual respondents may be correlated even though responses across different respondents are considered independent, identically distributed ( Poe et al. 1997; Layton 2000; Train 1998).

There are a variety of approaches to such potential correlation. Following standard practice, EPA anticipates the estimation of a variety of models to assess their performance.

Models to be assessed include random effects and random parameters ( mixed) discrete choice models, now common in the stated preference literature ( Greene 2003; McFadden and Train 2000; Poe et al. 1997; Layton 2000). Within such models, selected elements of coefficient vector are assumed normally distributed across respondents, often with free correlation allowed among parameters ( Greene 2002). If only the model intercept is assumed to include a random component, then a random effects model results. If both slope and intercept parameters may vary across respondents, then a random parameters model is estimated. EPA anticipates that such models will be estimated using standard maximum likelihood for mixed conditional logit,

as described by Train ( 1998), Greene ( 2002) and others. Model performance for alternative specifications of mixed logit will be assessed by EPA using standard statistical measures of model fit and convergence, as detailed by Greene ( 2002), Greene ( 2003), and Train ( 1998). 38 Advantages of Choice Experiments Choice experiments following the random utility model outlined above are favored by many researchers over other variants of stated preference methodology ( Adamowicz et al. 1998;

Bennett and Blamey 2001), and may be viewed as a " natural generalization of a binary discrete choice CV" ( Bateman et al. 2002, p. 271). Advantages of choice experiments include a capacity to address choices over a wide array of potential policies, grounding in well developed random utility theory, and the similarity of the discrete choice context to familiar referendum or voting formats ( Bennett and Blamey 2001). Compared to other types of stated preference valuation,

choice experiments are better able to measure the marginal value of changes in the characteristics or attributes of environmental goods, and avoid response difficulties and biases ( Bateman et al.

2002). For example, choice experiments may reduce the potential for ` yea saying' and symbolic biases ( Blamey et al. 1999; Mitchell and Carson 1989), as many pairs of multi attribute policy choices ( e. g., Plan A, Plan B, Neither) will offer no clearly superior choice for a respondent wishing to express solely symbolic environmental motivations. For similar reasons choice experiments may ameliorate protest responses ( Bateman et al. 2002). An additional advantage of such methods is that they permit straightforward assessments of the impact of resource scope and scale on respondents' choices. This will enable EPA to easily conduct scope tests and other assessments of the validity of survey responses ( Bateman et al. 2002, p. 296 342). Finally, such methods are well established in the stated preference literature ( Bennett and Blamey 2001).

Additional details of choice experiment methodology ( also called choice modeling) are provided by Bennett and Blamey ( 2001), Adamowicz et al. ( 1998), Louviere et al. ( 2000) and many other sources in the literature.

An additional advantage of choice experiments in the present application is that they are commonly applied to assess WTP for ecological resource improvements of a type quite similar to those at issue in the 316( b) policy case. Examples of the application of choice experiments to estimate WTP associated with changes in aquatic life and habitat include Hoehn et al. ( 2004),

Johnston et al. ( 2002b), and Opaluch et al. ( 1999), among others. EPA has drawn upon these and other examples of successful choice experiment design to provide a basis for survey design in the present case.

A final and key advantage of choice experiments in the present application is the ability to estimate respondents WTP for a wide range of different potential outcomes of 316( b) policies,

differentiated by their attributes. The proposed choice experiment survey versions will allow 39 different respondents to choose among a wide variety of hypothetical policy options, some with larger and other with very small changes in the presented attributes ( annual fish losses, long term fish populations, recreational and commercial catch, household cost). That is, because the survey is to be implemented as a choice experiment survey, levels of attributes in choice scenarios will vary across respondents ( Louviere et al. 2000). The experimental design will also explicitly allow for variation in baseline population and harvest levels, following standard practice in the literature ( Louviere et al. 2000; Bateman et al. 2002).

Aside from providing the capacity to estimate WTP for a wide range of policy outcomes,

it also frees EPA from having to predetermine a single policy outcome for which WTP will be estimated. Given the potential biological uncertainty involved in the 316( b) policy case, the ability to estimate values for a wide range of potential outcomes is critical.

The ability to estimate WTP for a wide range of different policy outcomes is a fundamental property of the choice experiment method ( Bateman et al. 2002; Louviere et al.

2000; Adamowicz et al. 1998). EPA emphasizes that the survey version included in this ICR is for illustration only; it is but one of what will ultimately be a large number of survey versions covering a wide range of potential policy outcomes. The experimental design ( see below) will allow for survey versions showing a range of different baseline and resource improvement levels,

where these levels are chosen to ( almost certainly) bound the " actual" levels. Given that there will almost certainly be some biological uncertainty regarding the specifics of the " actual"

baselines and improvements, the resulting valuation estimates will allow flexibility in estimating WTP for a wide range of different circumstances. Additional details on the statistical ( experimental) design of choice experiments is provided in later sections of this ICR.

Comment on Survey Preparation and Pretesting Following standard practice in the stated preference literature ( Johnston et al. 1995;

Desvousges and Smith 1988; Desvousges et al. 1984; Mitchell and Carson 1989), all survey elements and methods have been subjected to extensive development and pretesting in focus groups to ameliorate the potential for survey biases ( cf. Mitchell and Carson 1989), and to ensure that respondents have a clear understanding of the policies and goods under consideration, such that informed choices may be made that reflect respondents' underlying preferences. Following the guidance of Arrow et al. ( 1993), Johnston et al. ( 1995), and Mitchell and Carson ( 1989),

focus groups were used to ensure that respondents are aware of their budget constraints, the 40 scope of the resource changes under consideration, and the availability of substitute environmental resources.

As noted above, survey pretests have also included individual cognitive interviews with survey respondents ( Kaplowicz et al. 2004), and think aloud or verbal protocol analyses ( Schkade and Payne 1994). Individuals in these pretests completed draft survey questionnaires and provided comments and feedback about the survey format and content, their interpretations of the questions, and other issues relevant to stated preference estimation. Based on their responses, EPA made numerous improvements to the survey questionnaire. Of particular emphasis in these survey pretests is testing for the presence of potential biases including hypothetical bias, strategic bias, symbolic ( warm glow) bias, framing effects, embedding biases,

methodological misspecification, and protest responses ( Mitchell and Carson 1989). Focus groups and cognitive interviews led to numerous changes to ameliorate and minimize these biases in the final survey instrument and introductory materials. Results from focus groups and cognitive interviews used to test the final survey version provided convincing evidence that the great majority of respondents answer the stated preference survey in ways appropriate for stated preference WTP estimation, and that their responses do not reflect the biases noted above.

Moreover, survey pretests have demonstrated the ability of follow up survey questions ( i. e., after the primary choice questions) to clearly identify the respondents who's answers to the choice questions display the influence of bias, or do not understand the survey context or information.

The number of focus groups used in survey design ( 12) exceeds the number of focus groups used in typical applications of stated preference valuation, and is approximately equal to the number of focus groups used by Johnston et al. ( 2002b) and Opaluch et al. ( 1999) in their development of choice experiment surveys addressing generally similar types of ecological resources. Moreover, unlike these prior analysis, EPA also incorporated cognitive interviews as detailed by Kaplowicz et al. ( 2004). Given this extensive effort in survey design   applying the most state of the art methods available in the literature   EPA believes that survey design far exceeds standards that are typical in the published literature. The details of focus groups to be used in survey design are discussed by EPA in a prior ICR (# 2155.01).

Econometric Specification Based on prior focus groups, expert review, and attributes of the policies under consideration, EPA anticipates that three attributes will be incorporated in the vector of variables 41 describing attributes of I& E reduction plan ( vector Xi), in addition to the attribute characterizing unavoidable household cost Fi. These attributes will characterize the annual reduction in I& E losses ( x1), anticipated long term effects on fish populations ( x2), and anticipated long term effects on recreational and commercial harvest ( x3). These variables will allow respondents'

choices to reveal the potential impact of both annual fish losses and long term population effects on utility. Based on results of prior focus groups and expert opinion, these will be presented as averages across identified aggregate species groups. The survey will also allow for changes in baseline population levels, to assess whether WTP depends on the " starting point" of fish populations.

Although the literature offers no firm guidance regarding the choice of specific functional forms for v(.) within choice experiment estimation, in practice linear forms are often used ( Johnston et al. 2003b), with some researchers applying more flexible ( e. g., quadratic) forms ( Cummings et al. 1994). Standard linear forms are anticipated as the simplest form to be estimated by EPA, from which more flexible functional forms ( able to capture interactions among model variables) will be derived and compared. Anticipated extensions to the simple linear model include more fully flexible forms that allow for systematic variations in slope and intercept coefficients associated with demographic or other attributes of respondents. Such variations may be incorporated by appending the simple linear specification with quadratic interactions between variables in vector D and the variables Xi and Fi ( cf. Johnston et al. 2003b).

One may also incorporate quadratic interactions between policy attributes Xi and Fi, ( cf.

Johnston et al. 2002b). Such quadratic extensions of the basic linear model allow for additional flexibility in modeling the relationship between policy attributes ( including cost) and utility, as suggested by Hoehn ( 1991) and Cummings et al. ( 1994). EPA anticipates estimating both simple linear specifications, as well as more fully flexible quadratic specifications following Hoehn ( 1991) and Cummings et al. ( 1994), to identify those models which provide the most satisfactory statistical fit to the data and correspondence to theory. EPA anticipates estimating all models within the mixed logit framework outlined above. Model fit will be assessed following standard practice in the literature ( e. g., Greene 2003; Maddala 1983). Linear and quadratic functional forms here, as they are common practice in the literature, are presented and discussed in many existing sources ( e. g., Hoehn 1991, Cummings et al. 1994, Johnston et al. 1999, and Johnston et al. 2003b). 42 For example, for each choice occasion, the respondent may choose Option A, Option B,

or Neither, where " neither" is characterized by 0 values for all attributes ( except Baseline population levels). Assuming that the model is estimated using a standard approximation for the observable component of utility, an econometric specification of the desired model ( within the overall multinomial logit model) might appear as:

v( ) =  0 +  1( Loss Reduction) +  2( Population Change) +  3( Catch Change) +  4( Cost) +   5( Loss Reduction)( Baseline) +   6( Population Change)( Baseline) +   7( Catch Change)( Baseline) +   8( Cost)( Baseline) +   9( Fish Saved)( Population Change) +   10( Fish Saved)( Catch Change) +   11( Population Change)( Catch Change)

Main effects are in bold. Interactions are in italics. This sample specification   one of many to be estimated by EPA   allows one to estimate the relative " main effects" of policy attributes ( annual reduction in I& E losses, long term effects on fish populations, and long term effects on recreational and commercial harvest) on utility, as well as interactions between these main effects. This specification also allows EPA to assess the impact of baseline fish populations on the marginal value of changes in other model attributes. In sum, specifications such as this allow WTP to be estimated for a wide range of potential policy outcomes, and allow EPA to test for a wide range of main effects and interactions within the utility function of respondents. Such flexible utility specifications for stated preference estimation are recommended by numerous sources in the literature, including Johnston et al. ( 2002b), Hoehn ( 1991), and Cummings et al.

( 1994), and follow standard practice in choice modeling outlined by Louviere et al. ( 2000) and others.

Experimental Design Experimental design for the choice experiment surveys will follow established practices.

Fractional factorial design will be used to construct choice questions with an orthogonal array of attribute levels, with questions randomly divided among distinct survey versions ( Louviere et al.

2000). Based on standard choice experiment experimental design procedures ( Louviere et al.

2000), the number of questions and survey versions will be determined by, among other factors:

a] the number of attributes in the final experimental design and complexity of questions, b] the extent to which estimation of interactions and higher level effects is desired, and c] pretests revealing the number of choice experiment questions that respondents are willing/ able to answer 43 in a single survey session, and the number of attributes that may be varied within each question while maintaining respondents' ability to make appropriate neoclassical tradeoffs.

Based on models proposed above and recommendations in the literature, EPA anticipates an experimental design that allows for an ability to estimate main effects, quadratic effects, and two way interactions between policy attributes ( Louviere et al. 2000). Choice sets ( Bennett and Blamey 2001), including variable level selection, will be designed by EPA based on the goal of illustrating realistic policy scenarios that " span the range over which we expect respondents to have preferences, and/ or are practically achievable" ( Bateman et al. 2002, p. 259), following guidance in the literature. This includes guidance with regard to the statistical implications of choice set design ( Hanemann and Kanninen 1999) and the role of focus groups in developing appropriate choice sets ( Bennett and Blamey 2001).

Based on these guiding principles, the following experimental design framework is proposed by EPA. The experimental design will be conducted by Donald Anderson, President of StatDesign, Inc., a statistician with significant experience in experimental designs for choice experiments ( e. g., Johnston et al. 2003b; Newell and Swallow 2002). The experimental design will allow for both main effects and selected interactions to be efficiently estimated, based on a choice experiment framework. For a more detailed discussion of the experimental design, refer to Attachment 5.

Each treatment ( survey question) includes two choice Options ( A and B), characterized by four attributes that vary across the two choice options ( Fish Saved, Population Change, Catch Change, and Household Cost). Hence, there are a total of eight attributes for each treatment.

Based on focus groups and pretests, and guided by realistic ranges of attribute outcomes, EPA allows for four different potential levels for Fish Saved, Population Change, and Catch Change,

and allows for five different levels of monthly Household Cost.

These attributes and levels may be summarized as follows:

Fish SavedA, Fish SavedB ( 4 possible levels each: 25, 50, 75, and 95%)

Population ChangeA, Population ChangeB ( 4 possible levels each: 0, 5, 8, and 10%)

Catch ChangeA, Catch ChangeB ( 4 possible levels each: 0, 2, 5, and 10%)

CostA, CostB ( 5 possible levels each; $ 1, $ 2, $ 3, $ 4, $ 6)

The subscripts ( A, B) denote to the attributes in Option A and B, respectively. The available or potential levels for each attribute are balanced across A and B. In addition, there is a context variable which indicates the starting point of fish population, denoted Baseline. This context 44 attribute is the same across Options A and B for each question, but may influence the marginal utility of the other attributes through interactions in the utility function.

BaselineAB ( 4 possible levels; 40, 50, 60, and 70%)

Beyond the levels specified above, each question will include a " neither plan" option,

characterized by zero values for all attributes except for the Baseline.

Following standard practice, EPA constrained the design somewhat in response to findings in focus groups and the prior literature. For example, focus groups showed that respondents react negatively and often protest when offered choices in which one option dominates the other in all attributes. Given that such choices provide negligible statistical information compared to choices involving non dominant/ dominated pairs, they are typically avoided in choice experiment statistical designs. For example, Hensher and Barnard ( 1990)

recommend eliminating profiles including dominating or dominated profiles, because such profiles generally provide no useful information. Following this guidance, EPA constrained the design to eliminate such dominant/ dominating pairs. EPA also constrained the design to eliminate the possibility of pairs in which, when looking across two options, one of the options offers both a greater reduction in fish losses and a smaller increase in the population. The elimination of such nonsensical ( or non credible) pairs is common practice, and is done to avoid protest bids and confusion among respondents ( Bateman et al. 2002).

The resulting experimental design is characterized by 64 unique A vs. B option pairs,

where attribute levels for option A and B differ across each of the pairs. Each pair represents a unique choice modeling question   with a unique set of attribute levels distinguishing options A and B. Following standard practice for internet surveys, these questions will be randomly assigned to survey respondents, with each respondent considering three questions.

Information Provision According to Arrow et al. ( 1993, p. 4605), "[ i] f CV surveys are to elicit useful information about willingness to pay, respondents must understand exactly what it is they are being asked to value." It is also well known that information provision can influence WTP estimates derived from stated preference survey instruments and that respondents must be provided with sufficient information to make an informed assessment of policy impacts on utility ( e. g., Bergstrom and Stoll 1989; Bergstrom et al. 1989; Hoehn and Randall 2002). As stated 45 clearly by Bateman et al. ( 2002, p. 122), "[ d] escribing the good and the policy context of interest may require a combination of textual information, photographs, drawings, maps, charts and graphs.  [ V] isual aids are helpful ways of conveying complex information  while simultaneously enhancing respondents' attention and interest." Given that many respondents may not be fully familiar with the details of programs to reduce I& E losses and potential impacts on aquatic life, it is anticipated that the survey will follow the approach of Opaluch et al. ( 1993),

Johnston et al. ( 1999), and Johnston et al. ( 2002a), each of whom used short slide shows and/ or video presentations to provide information to survey respondents [ see also Horne et al. ( 2005),

Ready et al. ( 1995), Powe and Bateman ( 2004), and Duke and Ilvento ( 2004)]. Focus groups in all cases demonstrated that the use of such multimedia methods was able to substantially increase respondents' comprehension of the goods and policies addressed by the survey instrument, and to encourage appropriate neoclassical tradeoffs in responding to choice experiment questions ( e. g., Opaluch et al. 1993). This finding is consistent with previous valuation studies such as Bateman et al. ( 2002, p. 122), who indicate that that photographs can be the " best way" of depicting certain types of policy changes, as long as they are " pre tested in the same way as textual descriptions."

Following this guidance of Bateman et al. ( 2002) and prior examples of Opaluch et al.

( 1993) and Johnston et al. ( 2002a), among others, EPA extensively pretested all photographs and graphics used in the introductory materials and survey itself, to ensure that these graphical elements were not prejudicial, and that they did not bias responses. Photographs or graphics judged to be prejudicial based on focus groups and/ or cognitive interviews were removed and replaced. EPA acknowledges that certain types of graphics and photographs can be prejudicial in certain contexts   and hence has pretested all graphical elements extensively. However, EPA also emphasizes that there is no precedent or support in the literature for the total elimination of pictures in survey instruments. To the contrary, the literature explicitly indicates that pictures and graphics may be necessary and useful components of survey instruments in many cases ( Bateman et al. 2002). EPA highlights that numerous peer reviewed surveys described in the literature include pictures and graphics both in survey instruments and in introductory materials such as slide shows. For example, see Horne et al. ( 2005), Ready et al. ( 1995), Powe and Bateman ( 2004) and Duke and Ilvento ( 2004), Opaluch et al. ( 1993), Johnston et al. ( 1999,

2002a, 2002b), and Mazzotta et al. ( 2002). Bateman et al. ( 2002) also includes examples of various types of survey materials including pictures and graphical elements. 46 Amelioration of Hypothetical Bias EPA considers the amelioration of hypothetical bias to be a paramount concern in survey design. However, the agency also considers   based on prior evidence from the literature   that hypothetical bias is not unavoidable. For example, not all research finds evidence of hypothetical bias in stated preference valuation ( Champ and Bishop 2001; Smith and Mansfield 1998; Vossler and Kerkvliet 2003; Johannesson 1997), and some shows that hypothetical bias may be ameliorated using cheap talk, certainty adjustments, or other mechanisms ( Champ et al.

1997; Champ et al. 2004; Cummings and Taylor 1999; Loomis et al. 1996).

EPA emphasizes that it has been established that referendum type SP choices are incentive compatible given that certain conditions are met, including the condition that responses are considered by respondents to be consequential, or potentially influencing public policy decisions ( Carson et al. 2000). Hence, choice based surveys should provide no incentive for non truthful preference revelation ( or hypothetical bias), as long as choices are considered consequential. Induced value laboratory experiments verify this result, showing incentive compatibility in both hypothetical and real referenda, and an equivalent ability to elicit valid demand information ( Taylor et al. 2001).

Both the introductory materials and the survey itself are explicitly designed to emphasize the importance of the budget constraint and program cost. For example, the introductory slide show presentation contains four distinct reminders concerning the importance of program cost and the budget constraint. The survey itself includes an additional two explicit reminders of program cost and the budget constraint.

The survey has also been explicitly designed to maximize the consequentiality of choice experiment questions, thereby maximizing incentive compatibility ( i. e., reducing strategic and hypothetical biases), following clear guidance of Carson et al. ( 2000). Elements specifically designed to maximize consequentiality include a] explicit mention of the agency involved, b]

explicit mention that this survey is associated with considerations of actual policies that are being considered by US EPA, c] numerous details provided in the slide show and survey concerning specifics of the proposed policies, d] emphasis that some sort of policy will be enacted by EPA   and that the type of policy enacted will depend in part on survey results. Johnston and Joglekar ( 2005) show the capacity of such information to eliminate hypothetical bias in choicebased stated preference WTP estimation. 47 Focus groups and cognitive interviews provided clear evidence that respondents viewed choices as consequential, that they considered their budget constraints when responding to all questions, and that they would answer the same way were similar questions to be asked in a binding referendum. When asked if they thought about the program cost in the same way as " money coming out of their pocket," the vast majority of focus group and interview respondents indicated that they treated program costs the same way that they would have if there were actual money consequences. For example, respondents made statements such as " If this is just an exercise to see how I would react, I'd still answer the same as if the government really used it to create policy," and " I would be very certain that I would answer in a real referendum the way I did it here."

Given this evidence from focus groups and cognitive interviews, and clear indications of consequentiality in the survey, EPA finds little evidence to suggest that hypothetical bias should be significant in the proposed survey instrument. Regarding the potential use of cheap talk mechanisms or other devices to further address the potential for hypothetical bias, the Agency emphasizes that the literature is mixed as to their performance. For example, the seminal work by Cummings and Taylor ( 1999) shows that cheap talk is able to reduce hypothetical biases.

Similar results are shown by Aadland and Caplan ( 2003). However, other authors ( e. g.,

Cummings et al. 1995; List 2001; Brown et al. 2003) find that a cheap talk script is only effective under certain circumstances, and for certain types of respondents. For example, Cummings et al.

( 1995) find that a relatively short cheap talk script actually worsens hypothetical bias, while a longer script appears to ameliorate bias. Brown et al. ( 2003) finds cheap talk only effective at higher bid amounts   a result mirrored by Murphy et al. ( 2004). Still other authors find no effect of cheap talk, including Poe et al. ( 2002). Given the clearly mixed experiences with such mechanisms, EPA is not convinced that cheap talk scripts are likely to provide a panacea for hypothetical bias in the present case   although they appear to reduce bias in a limited set of circumstances. More importantly however, given the already lengthy slide show, the addition of a cheap talk script would likely detract from the respondents' ability to remember other critical information. Hence, the incorporation of a cheap talk script would involve significant tradeoffs,

for which the benefits are unclear.

Amelioration of Symbolic Biases and Warm Glow Effects 48 Following clear guidance of Arrow et al. ( 1993) and others, EPA has taken repeated steps to ensure that survey responses reflect the value of the affected fish resources only, and do not reflect symbolic or warm glow concerns ( Mitchell and Carson 1989). Focus groups and cognitive interviews indicated that respondents were aware of the ecological role of fish, and in many cases grounded their preferences in the role that fish play in larger aquatic ecosystems ( e. g., as food for other organisms, etc.). However, these pretests showed that the overwhelming majority of focus group respondents answered questions based on the specific changes in resources described in the survey, and not based on symbolic concerns.

Following explicit guidance of the NOAA Blue Ribbon Panel on Contingent Valuation ( Arrow et al. 1993, p. 4609), EPA has explicitly designed all elements of the survey to " deflect the general ` warm glow' of giving or the dislike of ` big business' away from the specific program that is being valued." This was done in a variety of ways, based on prior examples in the literature. For example, following the general examples of Opaluch et al. ( 1993), the survey introductory materials clearly indicate that facilities owners and investors would " do their fair share" to bear the costs of the new technology, and that reductions in I& E losses could not be achieved without the imposition of costs on households. As noted in the proposed slide show:

Slide 26: " While these policies would reduce fish losses, they would also increase the production costs of commercial facilities. While a significant proportion of these costs would be absorbed by the facility owners and investors, it is unavoidable that some would be passed on to consumers. This would increase the cost of living for all Northeast households, including yours."

Similar techniques are clearly illustrated in the survey of Mitchell and Carson ( 1984), to ameliorate symbolic concerns. Focus groups clearly showed that this language allowed respondents to answer choice questions based on the attributes provided, while at the same time deflecting " the general ` warm glow' of giving or the dislike of ` big business'" discussed by Arrow et al. ( 1993). Moreover, tests of survey versions that did not include this language revealed significant and obvious protest responses related to the perception that " big business"

was not doing their fare share to address the problem. This concern did not arise in survey pretests once the above noted language was inserted into the survey.

The survey and introductory materials also include clear language to instruct respondents only to consider the specific attributes in the survey, and not to base answers on broader environmental concerns.

Slide 52: " When considering these policies, you should only consider effects on fish and the cost to your household. This is because scientists expect no other significant environmental or economic impacts, other than those described in the survey " 49 Focus groups and cognitive interviews clearly showed that the vast majority of focus group survey responses did not reflect symbolic or warm glow concerns. This is also consistent with the statement from Arrow et al. ( 1993) that a referendum type format may limit the warm glow effect. For example, one focus group participant stated, " You know, if you were telling me you were saving spiders, who cares? You know what I mean? So, it is not just to do the right thing.

I think that fish are important." Another participant stated, " No, I don't have any specific fish in mind. I think every fish will be equally important for one thing or another, whether it's for food or I guess some fish are food for other fish. Or even just to enjoy, you know. They have a right to live like anything else, you know?"

This evidence notwithstanding, EPA believes that it is important to include follow up questions to ensure that responses do not reflect symbolic biases. Questions 9,11,12, and 13 in the survey instrument   which address the rationale for choice responses given earlier in the survey   explicitly test for the presence of symbolic or warm glow biases. Follow up questions such as these are common in stated preference survey instruments, to assess the underlying reasons for the observed valuation responses ( e. g., Mitchell and Carson 1984).

Assessing Scope Sensitivity The NOAA Blue Ribbon Panel on Contingent Valuation ( Arrow et al. 1993, p. 4605)

states clearly that " if CV surveys are to elicit useful information about willingness to pay,

respondents must understand exactly what it is they are being asked to value ( or vote upon) "

They further indicate that surveys providing " sketchy details" about the results of proposed policies call " into question the estimates derived there from," and hence suggest a high degree of detail and richness in the descriptions of scenarios. Similar guidance is provided by other key sources in the CVM literature ( e. g., Mitchell and Carson 1989; Louviere et al. 2000). Among the reasons for this guidance are that such descriptions tend to encourage appropriate framing and sensitivity to scope.