SUPPORTING STATEMENT FOR INFORMATION COLLECTION REQUEST FOR

WILLINGNESS TO PAY SURVEY FOR §316(B) EXISTING FACILITIES COOLING WATER INTAKE 

STRUCTURES: INSTRUMENT, PRE-TEST, AND IMPLEMENTATION
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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..................................................................................................................... 13

3(d) Effects of Less Frequent Collection.................................................................................. 14

3(e) General Guidelines............................................................................................................ 14

3(f) Confidentiality .................................................................................................................. 14

3(g) Sensitive Questions........................................................................................................... 15

4. The Respondents and the Information Requested ............................................................ 15

4(a) Respondents...................................................................................................................... 15

4(b) Information Requested...................................................................................................... 17

(I) Data items, including recordkeeping requirements .......................................................... 17

(II) Respondent activities........................................................................................................ 21

5. The Information Collected - Agency Activities, Collection Methodology, and 

Information Management.............................................................................................................. 21

5(a) Agency Activities.............................................................................................................. 21

5(b) Collection Methodology and Information Management................................................... 22

5(c) Small Entity Flexibility..................................................................................................... 23

5(d) Collection Schedule .......................................................................................................... 24
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6. Estimating Respondent Burden and Cost of Collection ................................................... 24

6(a) Estimating Respondent Burden......................................................................................... 24

6(b) Estimating Respondent Costs ........................................................................................... 25

6(c) Estimating Agency Burden and Costs .............................................................................. 25

6(d) Respondent Universe and Total Burden Costs ................................................................. 26

6(e) Bottom Line Burden Hours and Costs.............................................................................. 26

6(f) Reasons for Change in Burden ......................................................................................... 26

6(g) Burden Statement.............................................................................................................. 26

PART B OF THE SUPPORTING STATEMENT ....................................................................... 28

1. Survey Objectives, Key Variables, and Other Preliminaries............................................ 28

1(a) Survey Objectives............................................................................................................. 28

1(b) Key Variables.................................................................................................................... 28

1(c) Statistical Approach.......................................................................................................... 29

1(d) Feasibility.......................................................................................................................... 29

2. Survey Design................................................................................................................... 30

2(a) Target Population and Coverage....................................................................................... 30

2(b) Sampling Design............................................................................................................... 30

(I) Sampling Frames .............................................................................................................. 30

(II) Sample Sizes..................................................................................................................... 31

(III) Stratification Variables ..................................................................................................... 31

(IV) Sampling Method.............................................................................................................. 32

(V) Multi-Stage Sampling ....................................................................................................... 33

2(c) Precision Requirements .................................................................................................... 33

(I) Precision Targets............................................................................................................... 33

(II) Non-Sampling Errors........................................................................................................ 34

2(d) Questionnaire Design........................................................................................................ 36

3. Pretests and Pilot Tests..................................................................................................... 38

4. Collection Methods and Follow-up .................................................................................. 38

4(a) Collection Methods........................................................................................................... 38
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4(b) Survey Response and Follow-up ...................................................................................... 39

5. Analyzing and Reporting Survey Results......................................................................... 39

5(a) Data Preparation................................................................................................................ 39

5(b) Analysis............................................................................................................................. 40

5(c) Reporting Results.............................................................................................................. 57

REFERENCES ............................................................................................................................. 59

ATTACHMENTS

Attachment 1: Full Text of Regional Stated Preference Survey Component ............................... 68

Attachment 2: Full Text of National Stated Preference Survey Component................................ 88

Attachment 3: Federal Register Notice....................................................................................... 108

Attachment 4: Description of Statistical Survey Design ............................................................ 114

Attachment 5: Description of Internet-based Sampling Alternative........................................... 119

Attachment 6: Telephone Screener Script .................................................................................. 123

Attachment 7: Cover Letter to Mail Survey Recipients.............................................................. 130

Attachment 8: Post Card Reminder to Mail Survey Recipients ................................................. 131

TABLES

Table A1: Geographic Stratification Design................................................................................. 16

Table A2: Schedule for Survey Implementation .......................................................................... 24

Table A3: Total Estimated Bottom Line Burden and Cost Summary .......................................... 26

Table B1: Number of Households and Household Sample for Each EPA Study Region ............ 34

Table B2: Illustration of an External Scope Test......................................................................... 55
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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) Existing Facilities Cooling Water Intake 

Structures: Instrument, Pre-test, and Implementation

1(b) Short Characterization (Abstract)

On February 16, 2004, the U.S. Environmental Protection Agency (EPA) took final 

action on the Phase II rule governing cooling water intake structures at existing facilities that are 

point sources; that, as their primary activity, both generate and transmit electric power or 

generate electric power for sale to another entity for transmission; that use or propose to use 

cooling water intake structures with a total design intake flow of 50 MGD or more to withdraw 

cooling water from waters of the United States; and that use at least 25 percent of the withdrawn 

water exclusively for cooling purposes. See 69 FR 41576 (July 9, 2004). Industry and 

environmental stakeholders challenged the Phase II regulations. On judicial review, the Second 

Circuit (Riverkeeper, Inc. v. EPA, 475 F.3d 83, (2d Cir., 2007)) remanded several provisions of 

the Phase II rule. Some key provisions remanded are as follows: EPA improperly used a costbenefit analysis as a criterion for determining Best Technology Available (BTA), and EPA 

inappropriately used ranges in setting performance expectations. In response, EPA suspended the 

Phase II regulation in July 2007 pending further rulemaking. The U.S. Supreme Court granted 

Entergy Corporation’s petition for writ of certiorari, solely on the question of whether EPA had 

the authority under §316(b) of the Clean Water Act to consider costs and benefits in decisionmaking. On April 1, 2009, the Court, in Entergy Corp. v. Riverkeeper Inc., decided that “EPA 

permissibly relied on cost-benefit analysis in setting the national performance standards ... as 

part of the Phase II regulations.” EPA is now taking a voluntary remand of the rule, thus ending 

Second Circuit review.

On June 1, 2006, EPA promulgated the 316(b) Phase III Rule for existing manufacturers, 

small flow power plants (facilities that use cooling water intake structures with a total design 

intake flow of less than 50 MGD to withdraw cooling water from waters of the United States; 
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and that use at least 25 percent of the withdrawn water exclusively for cooling purposes), and 

new offshore oil and gas facilities. Offshore oil and gas firms and environmental groups 

petitioned for judicial review, which was to occur in the Fifth Circuit, but was stayed pending the 

Supreme Court decision on the Phase II case. EPA has petitioned the Court for a voluntary

remand of the existing facilities portion of the Phase III rulemaking. In developing the Phase III 

regulation, EPA began, but did not complete, a similar stated preference survey effort. The 

current effort builds on that earlier work.

EPA is now combining the two phases into one rulemaking covering all existing 

facilities. EPA will develop regulations to provide national performance standards for controlling 

impacts from existing cooling water intake structures (CWIS) under Section 316(b) of the Clean 

Water Act (CWA). 

Under Executive Order 12866, EPA is required to estimate the potential benefits and 

costs to society for economically significant rules. To assess the public policy significance or 

importance of the ecological gains from the section 316(b) regulation for existing facilities, EPA 

requests approval from the Office of Management and Budget to conduct a stated preference

survey. Data from the stated preference survey will be used to estimate the values (willingness 

to pay, or WTP) derived by households for changes related to the reduction of fish losses at 

CWIS, and to provide information to assist in the interpretation and validation of survey

responses. As indicated in the prior literature (Cummings and Harrison 1995; Johnston et al. 

2003a, 2005), it is virtually impossible to justify, theoretically, the decomposition of empirical 

estimates of use and non-use values. The survey will provide the flexibility, however, to 

estimate nonuser values, using various nonuser definitions drawn from responses to survey

question 10. The structure of choice attribute questions will also allow the analysis to separate 

value components related to the most common sources of use values—effect on harvested 

recreational and commercial fish. In summary, the survey will provide estimates of total values

(including use and nonuse), will allow estimates of value associated with specific choice 

attributes (following standard methods for choice experiments), and will also allow the flexibility

to provide some insight into the relative importance of use versus non-use values in the 316(b) 

context.

Within rulemaking, among the most crucial concerns is the avoidance of benefit (or cost) 

double counting. Here, for example, the WTP estimates will include use and non-use values 

among a representative population sample. These may overlap—to a potentially substantial 
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extent—with use value estimates that might be provided through some other methods, including 

revealed preference methods that might be used to estimate use values of recreational anglers for 

fish kill reductions (i.e., through related improvements in fishing quality). While using the 

proposed stated preference value estimates for benefit estimation, particular care will be given to 

avoid any possible double counting of values that might be derived from alternative valuation 

methods. In doing so, EPA will rely upon standard theoretical tools for non-market welfare 

analysis, as presented by authors including Freeman (2003) and Just et al. (2004). From a purely 

mechanistic perspective, survey results will be used to derive total values following standard 

practice for choice experiments (Adamowicz et al. 1998).

The target population for this stated preference survey is all individuals from continental 

U.S. households who are 18 years of age or older. The population of households will be 

stratified into four study regions: Northeast, Southeast, Inland, and Pacific. The Northeast survey

region includes the North Atlantic and Mid Atlantic 316(b) benefits regions, the Southeast 

survey region includes the South Atlantic and Gulf of Mexico 316(b) benefits regions, the 

Pacific region includes states on the Pacific coast, and the Inland region includes all non-coastal 

states. In addition, EPA will administer a national version of the survey that does not require 

stratification. Respondents will be recruited using a random digit dialing (RDD) sample 

(overlapping dual frame; land and cell phone numbers) and a brief telephone questionnaire. 

Participation in the survey is voluntary. If the person in the household agrees, the recruited 

respondent will be asked to complete the survey questionnaire by mail. EPA intends to 

administer the mail survey to 5,720 households in order to achieve 2,288 completed responses 

assuming a 40% completion rate for households agreeing to participate. EPA is also considering 

implementing the proposed survey as an internet-based, choice experiment questionnaire. This 

alternative approach is described in Attachment 5.

For the selection of households, the population of households in the 48 states and the 

District of Columbia will be stratified by the four study regions. The sample is allocated to each 

region in proportion to the total number of households in that region with the restriction that we 

get at least 288 completed responses in each region. A sample of 288 households completing the 

national survey version would be distributed among the study regions based on the percentage of 

regional survey sample to ensure that respondents to the national survey version are distributed 

across the continental U.S. Non-response bias has the potential to occur due to households 

declining to participate in the mail survey when contacted by phone or households which fail to 
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return a completed survey after agreeing to participate. To conduct a non-response study, EPA 

will use a brief telephone questionnaire to collect sample characteristics in the course of the 

telephone contact. EPA will analyze the characteristics of the completed and non-completed 

cases from the random digit dialing sample to determine whether there is any evidence of 

significant non-response bias in the completed sample. This analysis will suggest whether any

weighting or statistical adjustment is necessary to minimize the non-response bias in the 

completed sample.

As part of the testing of the survey instrument, EPA conducted a series 7 focus groups 

with 8-10 participants per focus group, with approval from the Office of Management and 

Budget (OMB control # 2090-0028). The Agency conducted focus groups in several regions to 

account for the potentially distinct information relevant to survey design. 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). One 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 for 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). Transcripts from these seven focus 

groups can be found in the docket for this ICR (ICR # 2402.01). EPA revised the survey based 

on the findings of the seven focus groups. These seven focus groups were conducted in addition 

to focus groups conducted previously by EPA under ICR #2155.01 to test the draft survey for the 

Phase III benefits analysis. Transcripts from the previously conducted focus groups for the 

Phase III analysis can be found in the docket for EPA ICR #2155.02 (Besedin et al., 2005). 

Findings from these previous focus groups were also incorporated into the development of the 

current survey. 

The total national burden estimate for all components of the survey is 1,938 hours. The 

burden estimate is based on 9,533 participants in telephone screening interviews and 2,288 

respondents to the 5,720 mailed questionnaires. EPA assumes an average burden estimate of 5 
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minutes per telephone screening participant and 30 minutes per mail survey respondent including 

the time necessary to complete and mail back the questionnaire. Given an average wage rate of 

$20.42, the total respondent cost is $39,583.

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 project is exploring how public values for fishery resources are affected by fish 

losses from impingement and entrainment (I&E) mortality at cooling water intake structures. 

Understanding total public values for fishery resources, including the more difficult to estimate 

non-use values1, is necessary to determine the full range of benefits associated with reductions in 

I&E mortality losses, and whether the benefits of government action to reduce I&E mortality

losses at existing facilities are commensurate with the costs of such actions. Because non-use 

values may be substantial, failure to recognize such values may lead to improper inferences 

regarding benefits and costs. The findings from this study will be used by EPA to improve 

estimates of the economic benefits of the section 316(b) regulation for existing facilities as 

required under Executive Order 12866.

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 existing facilities. Specifically, the Agency will use the 

survey results to estimate total values for preventing losses of fish through I&E mortality at 

CWIS, following standard practices outlined in the literature (Freeman 2003; Bennett and 

Blamey 2001; Louviere et al. 2000; U.S. EPA 2000). 

 

1The stated preference format was chosen because it is the only method that allows the estimation of non-use values, 

which are potentially significant in this case.
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3. Non-duplication, Consultations, and Other Collection Criteria

3(a) Non-duplication

There are many studies in the environmental economics literature that quantify benefits 

or willingness to pay (WTP) associated with various types of water quality and aquatic habitat 

changes. However, none of these studies allows the isolation of non-market WTP associated with 

quantified reductions in fish losses for forage fish. Most available studies estimate WTP for 

broader, and sometimes ambiguously defined, policies that simultaneously influence many 

different aspects of aquatic environmental quality and ecosystem services, but for which WTP 

associated with fish or aquatic life alone cannot be identified. Other studies provide benefit 

estimates associated with improvements in fish (or aquatic) habitat, but do not link this to welldefined and quantified changes in affected or supported organisms. Still other studies address 

willingness to pay for changes in charismatic or recreational species that have little relationship 

to the types of forage fish that are the vast majority of species affected by cooling water intake 

structures.

For example, choice experiment studies such as Hanley et al. (2006a, 2006b) and 

Morrison and Bennett (2004) estimate WTP for aquatic ecosystem changes that affect fish, but 

the effects on fish are quantified and valued solely in terms of the presence/absence of different 

types of fish species. This approach renders associated results unsuitable for 316(b) benefit 

estimation. Also, many of these studies were conducted outside the U.S. (e.g., the European 

Union or Australia), making their use for benefit transfer to a U.S. policy context more 

challenging.

Other studies have estimated the value of changes in catch rates or populations of select 

recreational and commercial species, charismatic species such as salmon, or changes in water 

quality that affect fish, but none 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 non-anglers, to determine their WTP for doubling 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 in the United States and would provide values for the full range of 
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forage, recreational, and commercial species affected by 316(b) regulations, instead of valuing a 

few recreational species in one specific geographical area.

Among available studies, the most closely related is Johnston et al. (2010), which 

estimates total willingness to pay (WTP) for multi-attribute aquatic ecosystem changes related to 

improvements in forage fish in Rhode Island. Unlike other studies, the choice experiment data of 

Johnston et al. (2010) allow estimation of WTP associated with quantified changes in forage fish 

(e.g., WTP per fish or percentage change in fish), holding other ecological effects constant. That 

is, unlike results provided by other studies in the literature, WTP estimates of Johnston et al. 

(2010) are not confounded with values for other changes including water quality, habitat, overall 

ecological condition, charisma of species, etc. In addition, the choice experiment of Johnston et 

al. (2010) addresses species such as alewife and blueback herring that are neither subject to 

recreational or commercial harvest in Rhode Island, nor are charismatic species. Hence, the 

species affected are a close analog to the forage fish affected in the 316(b) policy context.

Although the methods and data of Johnston et al. (2010) allow estimation of total values 

associated with specific improvements in forage and/or recreational fish, the policy context and 

scale of the survey prevent its direct use for analysis of national benefits of the 316(b) regulation. 

Specifically, Johnston et al. (2010) estimate Rhode Island residents’ preferences for the 

restoration of migratory fish passage over dams in the Pawtuxet and Wood-Pawcatuck 

watersheds. Hence, the case study is for a watershed-level policy with statewide welfare 

implications. In contrast, 316(b) policies would have nationwide implications, both on 

ecosystems and on affected facilities. 

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 July 21, 2010, announcing that the survey

questionnaire and sampling methodology were available for comment. A copy of the first 

Federal Register notice is attached at the end of this document (EPA ICR #2402.01.02) (See 

Attachment 3). EPA received a number of comments on the proposed information collection, 

which are summarized in the following paragraphs. Also see docket # EPA-HQ-OW-2010-0595. 

EPA considered relevant comments on the draft survey when developing the survey

questionnaire and sampling methodology for the current survey for existing facilities. 
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Some commenters expressed concern that the draft survey questionnaire and sampling 

methodology would not provide accurate estimates of WTP. Some stated that the proposed 

stated preference survey would overestimate WTP to prevent fish losses. Another 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 stated preference survey and supporting documentation for the July 21, 2010 Federal Register 

notice required revision. Following OMB approval of the focus groups, EPA conducted seven 

focus groups (including one set of cognitive interviews) to pretest the draft survey 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 were 

made to the draft survey that significantly improved its reliability and reduced its potential for 

bias. Hence, many of the survey design elements on which commenters took issue have already 

been removed or changed. In many other instances, however, focus groups showed little cause 

for concern, suggesting that many of the speculative claims raised by commenters have little

value to the population being surveyed. EPA also notes that number of focus groups and 

interviews conducted for this survey—and the draft survey tested in 2005-06—far exceeds the 

number conducted for most stated preference surveys found in the published literature. 

EPA notes that the survey proposed in this ICR is different in many ways from the draft 

survey for the Phase III benefits analysis that was peer reviewed in 2005 (Versar 2006). While 

findings from pre-testing of the previous draft survey were considered when developing the 

present survey, the present survey has undergone various revisions based on additional analysis 

and the results of recent focus groups. Due to these differences, EPA notes that many of the peer 

review comments received on the Phase III draft survey are no longer relevant for the survey

proposed in this ICR. The Agency, however, takes all peer review comments very seriously, and 

has accounted for these comments in all survey revisions and in the development of the present 

stated preference survey.

One commenter argued that in conducting the stated preference survey, EPA should use 

the willingness-to-accept (WTA) metric in place of or in addition to WTP. EPA notes that good 

practice guidelines for stated preference surveys almost universally indicate the use of WTP 

elicitation mechanisms over WTA elicitation mechanisms. This is due to the potential for biases 

in WTA stated preference surveys that can be ameliorated by the use of the WTP format. WTP 
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is also considered to be the more conservative choice, but in most cases, the divergence between 

WTA and WTP, as predicted, by theory, should be very small. EPA follows standard practice in 

proposing a WTP format in order to avoid these biases, comply with guidance and practice in the 

stated preference literature, and ensure a conservative benefit estimate.

Some commenters argued that hypothetical bias in the survey questionnaire would inhibit 

respondents ability to provide meaningful survey responses. EPA agrees that hypothetical bias is 

an important concern with stated preference surveys—and has taken this very seriously in survey

development and testing. EPA does not agree that this inhibits respondents or that the literature 

suggests that hypothetical bias is unavoidable; in fact, the published literature includes 

approaches to mitigate such biases. The Agency followed the published literature in designing 

mitigation strategies to eliminate or at a minimum reduce the potential for hypothetical bias. 

This includes explicitly designing the survey to maximize the consequentiality of choice 

experiment questions through direct linkages to proposed EPA regulatory efforts. Moreover, the 

survey explicitly incorporates elements such as certainty follow-up question to enable mitigation

of any remaining hypothetical bias (Ready et al. 2010). Focus group and interview transcripts 

show that, when asked explicitly, respondents almost universally indicated that their answers to 

choice questions in the survey instrument would be identical if the same questions were 

encountered in a binding referendum. This indicates that there is little evidence of hypothetical 

bias within the draft survey. 

A commenter argued that the respondents to the survey would be informed and 

conditioned based on the information included in the survey and that this would lead to the 

creation of preferences where none existed before. EPA believes that this result is entirely 

expected, and is consistent with the academic literature. The Agency emphasizes that the 

sensitivity of values (and behavior) to information is true of both market and non-market values 

(Bateman et al. 2002, p. 298), and in no way invalidates the proposed non-market values that 

would be estimated through the proposed stated preference approaches. It is common practice in

such surveys to provide substantial information to survey respondents. The survey information 

was pretested extensively during the seven focus group sessions and EPA revised or removed 

informational elements which respondents found confusing or misleading.

Some commenters argued that the survey results will be unreliable because the survey

questionnaire contains inaccurate statements and comparisons which overstate the resource 

impacts from baseline I&E mortality and the regulatory options presented in the survey. EPA 
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recognizes the importance of accurately characterizing resource and regulatory impacts and notes 

that the survey is based on the best biological and engineering data available. Despite the 

general observation that I&E impacts are small compared to other effects, I&E has been shown 

to have measurable impacts on local fish populations and communities. Importantly, increases in 

fishery sustainability and fish population values presented in the survey instrument are small. 

Overall, the Agency rejects the claims from commenters, based largely on feedback from focus 

group participants, that impacts are misrepresented, and emphasizes that the survey is explicitly

designed to provide respondents with an understanding of the proposed policies that is as 

accurate as possible given the best available ecological science. 

Commenters also questioned whether survey respondents would have sufficient 

comprehension of the issues in order to provide meaningful responses to the survey’s valuation 

questions. EPA agrees that in order to receive meaningful responses, a stated preference survey

should provide information to respondents about the hypothetical commodity so that they 

understand and accept it and can give meaningful answers to the valuation questions. Given the 

importance of commodity comprehension, EPA devoted considerable attention to comprehension 

of the hypothetical commodity and related issues (e.g., understanding of payment vehicle, 

understanding of the ecological scores) during the recent focus groups and cognitive interviews 

and focus groups conducted for the original version of the Phase III survey instrument in 2005.

Focus groups participants showed no difficulty understanding the format of the payment vehicle

and that selecting Options A or B would result in increased costs to their household. They also 

correctly understood this cost as ongoing. 

In addition to the comments regarding the payment vehicle, commenters expressed 

concern regarding respondent comprehension of the ecological scores used in the survey. EPA 

emphasizes that the reaction and understanding of likely respondents, as opposed to experts, is 

crucial when testing the communication of ecological information in stated preference surveys. 

The survey has undergone substantial changes in the way it communicates ecological 

information based on the results of the seven focus group sessions. For example, in the revised 

survey, EPA provides more precise information regarding the definition of “young adult fish” on 

Page 4, which currently states: “After accounting for the number of eggs and larvae that would 

be expected to survive to adulthood, scientists estimate that the equivalent of about 1.1 billion 

young adult fish (the equivalent of one year old) are lost each year in Northeast coastal and fresh 

waters due to cooling water use.” Overall, focus group and interview participants’ statements 
11

implied different opinions about the importance of preventing fish losses versus increasing fish 

population or improving the condition of aquatic ecosystems. Also, there did not appear to be 

any confusion over the fact that scores of 100 for various attributes are generally unattainable 

through reductions in CWIS fish losses alone. One commenter argues that the stated preference 

survey fails to account for effects on a number of non-fish species as well as effects on 

threatened, endangered, and other protected species. In response to this comment, EPA agrees 

but notes that focus group respondents suggested that additions to the survey’s length should be 

avoided. Thus EPA will not be able to use these survey results to represent absolutely complete 

benefits estimation, but will be able to say that a potentially substantial category of benefits, nonuse benefits, has been included. As is common in surveys, EPA has chosen to present policy

scenarios in simplified form to facilitate respondent comprehension, and to encourage 

respondents to focus on the most important policy characteristics related to fish losses. Such 

simplification of the survey helps to balance the provision of detailed policy information against 

respondents’ cognitive abilities to consider a large number of attributes simultaneously (Louviere 

et al. 2000). 

Some commenters stated that EPA did not sufficiently emphasize the uncertainty

associated with effects and costs of the proposed policies presented within the survey. EPA 

agrees that there is uncertainty regarding the number of fish killed annually, as well as the effects 

and costs of the regulatory policies presented within the survey. Additionally, EPA does note 

uncertainty within the current existing facilities survey. For example, the following statements 

are included in the current survey version for the Northeast region: “Although scientists can 

predict the number of fish saved each year, the effect on fish populations is uncertain. This is 

because scientists do not know the total number of fish in Northeast water and because many 

factors – such as cooling water use, fishing, pollution, and water temperature – affect fish 

populations”, and “Policy costs and effects depend on many factors.” EPA has also included 

debriefing questions in the survey instrument that are designed to identify individuals whose 

responses are based on incorrect interpretation of the environmental changes described in the 

survey, including the uncertainty of the expected changes. 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. Furthermore, 

when asked, focus group respondents indicated that they were comfortable making decisions in 

the presence of uncertainty.
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Another commenter questioned various components of EPA proposed sampling 

methodology, experimental design, and methods for accounting for non-response bias. In 

response, EPA emphasizes that methods for WTP estimation from ecological choice experiment 

data—of exactly the type proposed by EPA—are very well established in the published 

literature. More broadly, when designing the proposed methods, EPA closely followed accepted 

contemporary methods in the published literature for the estimation of WTP distributions under 

statistical uncertainty. In the absence of concrete and established alternatives for the choice of 

sampling weights, EPA has proposed a more conservative approach of reliance on accepted 

and standard methods from the stated preference literature. EPA believes—following guidance 

in the literature and its own guidance documents (Arrow et al. 1993; US EPA 2000) for a 

weighting and extrapolation approach—that established stated preference methods are capable 

of estimating reliable and accurate welfare measures, if surveys and approaches are 

appropriately designed. Regarding the potential for non-response bias, EPA has proposed 

standard approaches for non-response assessments and calibrations in proposing tests and 

corrections for non-response based on a small number of attitudinal and behavioral questions, 

combined with demographic characteristics. These currently reflect standard practice within the 

literature.

Another commenter argued that although the Supreme Court held that Clean Water Act 

section 316(b) does not prohibit the consideration of costs in relation to benefits of proposed rule

it did not find that cost benefit analysis is required (Entergy Corp. v. Riverkeeper, Inc.). EPA has 

the authority to decide whether to conduct cost benefit analysis of proposed rule options. The 

commenter, however, recognized that Executive Order 12866, “Regulatory Planning and 

Review,” requires EPA to estimate potential costs and benefits to society of proposed rule 

options. In response to the commenter’s claim about utility of cost-benefit analysis in 

environmental context, EPA notes that cost-benefit analysis is only one tool that can be used to 

inform policy decisions. EPA is conducting this survey because of Executive Order 12866, 

“Regulatory Planning and Review,” which requires Federal Agencies to conduct economic 

impact and cost-benefit analysis for all major rules. Furthermore, cost-benefit analysis requires a 

comprehensive, estimate of total social benefits, including non-use values. The current 

information collection would provide valuable information regarding total social benefits of the 

316(b) regulation for existing facilities, thus enabling the Agency to perform cost-benefit 
13

analysis for the regulation, if it should choose to, without ignoring a potentially important 

category of benefits (non-use values), and to satisfy the requirements of Executive Order 12866.

For a more detailed discussion of the issues raised by commenters on this ICR, see EPA’s 

response to pubic comments on the Federal Register notice published on November 23, 2004 (69 

FR 68140). For a discussion of the issues raised by commenters on the previous Phase III survey

ICR, see EPA’s response to pubic comments on the Federal Register notice published on June 9, 

2005 (70 FR 33746). For a discussion of issues raised by commenters on the previous Phase III 

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

The Principal Investigator for the stated-preference portion of this effort is Dr. Robert 

Johnston. Dr. Johnston is assisted by Dr. Elena Besedin, a Senior Economist at Abt Associates 

Inc. Dr. Erik Helm at the U.S. Environmental Protection Agency serves as the project manager 

and a contributor to this research. 

Robert J. Johnston is Director of the George Perkins Marsh Institute and Professor of 

Economics at Clark University. He is President-elect of the Northeastern Agricultural and 

Resource Economics Association (NAREA), on the Program Committee for the Charles Darwin 

Foundation, the Science Advisory Board for the Communication Partnership for Science and the 

Sea (COMPASS), and is the Vice President of the Marine Resource Economics Foundation. 

Professor Johnston has published extensively on the valuation of non-market commodities 

(goods, services, and resources), benefit cost analysis, and resource management. His recent 

research emphasizes coordination of ecological and economic models to estimate ecosystem 

service values, with particular emphasis on the role of aquatic ecological indicators. He has also 

worked extensively in methodologies for benefit transfer, including the use of meta-analysis. 

Professor Johnston’s empirical work on non-market valuation and benefit transfer has 

contributed to numerous benefit cost analyses conducted by federal, state and local government 

agencies in the US, Canada and elsewhere. 

Elena Y. Besedin, a senior economist at Abt Associates Inc., specializes in the economic 

analysis of environmental policy and regulatory programs. Her work to support EPA has 

concentrated on analyzing economic benefits from reducing risks to the environment and human 

health and assessing environmental impacts of regulatory programs for many EPA program 
14

offices. She has worked extensively on valuation of non-market benefits associated with 

environmental improvements of aquatic resources. Dr. Besedin’s empirical work on non-market 

valuation includes design and implementation of stated and revealed preference studies and 

benefit transfer methodologies. Her recent work has focused on developing integrated 

frameworks to value changes in ecosystem services stemming from environmental regulations. 

EPA notes that the current survey instrument is built upon an earlier version that was peer 

reviewed in January 2006. It incorporates recommendations received from the first peer review 

panel. Because the final product of this study meets the major technical work criteria specified 

in the Peer Review Handbook (U.S. EPA 2006) the Agency also plans to convene a peer-review 

panel to review the entire survey process, including the survey instrument, study results, and 

EPA’s final estimated results for the 316(b) Existing Facilities rulemaking, after the survey is 

completed. 

3(d) Effects of Less Frequent Collection

The survey is a one-time activity. Therefore, this section does not apply.

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.

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 detailed survey questionnaire will not ask 

respondents for personal identifying information, such as names, phone numbers, or addresses. 

Addresses will be collected during telephone screening, but the telephone screening and mail 

survey databases will not be matched or combined. Prior to taking the survey, respondents will 

be informed that their responses will be held strictly anonymous. The survey data will be made 
15

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 

The target population for the Stated Preference Survey is all individuals from continental 

U.S. households who are 18 years of age or older. Survey participants are recruited randomly

through random digit dialing. EPA will identify a random stratified sample of 5,720 adults who 

express their willingness to participate in a mail survey when contacted by telephone through 

random digit dialing. All of these recruits will be sent a copy of the mail survey. Approximately

2,288 of the adults of the 5,720 adults sent a survey are expected to return a completed survey.

For the selection of households, the population of households in the 48 states and the 

District of Columbia will be stratified by four study regions. There are a total of seven study

regions for purposes of evaluating the 316(b) existing facilities rule benefits. For the purposes of 

the stated preference survey implementation, EPA uses four geographic regions: Northeast, 

Southeast, Inland, and Pacific. The Northeast region includes the North Atlantic and Mid 

Atlantic regions, the Southeast region includes the South Atlantic and Gulf of Mexico regions, 

the Pacific region includes states on the Pacific coast, and the Inland region includes all noncoastal states. 

A sample of 2,000 households would complete a version of the survey which specifically

addresses policies within their region. The total sample completing regional survey versions is 

allocated to each region in proportion to the total number households in that region with the 

restriction that at least 288 persons respond in each region. This is the number required to 

estimate the main effects and interactions under an experimental design model. The total sample

size for each region is much larger then the minimum sample size required for model estimation 

for all but one region (Pacific). An additional sample of 288 households will receive a national 

survey version which addresses policies at the national scale. This sample would be distributed 
16

among the study regions based on the percentage of regional survey sample (as shown in Table 

A1) to ensure that respondents to the national survey version are distributed throughout the 

continental U.S. Part B of this document provides detail on sampling methodology.

Table A1 shows the stratification design for the geographic regions covered by the 

sample for this survey. More detail on planned sampling methods and the statistical design of 

the survey can be found in Part B of this supporting statement.

Table A1: Geographic Stratification Design

Region States Included

Sample

Sizea

Percentage 

of Sample

Northeast CT, DC, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT 417 21%

Southeast AL, FL, GA, LA, MS, NC, SC, TX, VA 562 28%

Inland AR, AZ, CO, ID, IA, IL, IN, KS, KY, MI, MN, MO, MT, NM, 

OK, ND, NE, NV, OH,TN, SD, UT, WI, WV, WY

732 37%

Pacific CA, OR, WA 288 14%

Total for Regional 

Surveys Versions

U.S. (excluding AK and HI) 2,000 100%

National Survey

Version

U.S. (excluding AK and HI) 288 -

a

 Sample sizes presented in this table include only the 2,288 individuals returning completed mail surveys.
17

4(b) Information Requested

(I) Data items, including recordkeeping requirements

Individuals who agree to participate in the survey will be mailed a copy of the survey. 

The full text of the regional version of the mail survey for the Northeast region is provided in 

Attachment 1 and the full text of the national version of the mail survey is provided in 

Attachment 2. EPA revised the survey based on the findings of a series of seven focus groups

conducted as part of survey instrument development (OMB control # 2090-0028). Additional 

information regarding focus group implementation is provided in Section 5(b). 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.

Relative Importance of Issues Associated with Industrial Cooling Water. The first survey

question asks respondents to rate the general importance of (a) preventing the loss of fish caught 

by humans, (b) preventing the loss of fish not caught by humans, (c) maintaining ecological 

health in rivers, lakes, and bays, (d) keeping the cost of goods and services low, (e) making sure 

there is enough government regulation on industry, and (f) making sure there is not too much 

government regulation on industry. This question is designed to elicit the respondent’s general 

preferences for regulation, reductions in fish losses, and ecological health. It also places 

respondents in the mindset where they are cognizant of the range of issues associated with the 

use of cooling water by industrial facilities.

Concern for Policy Issues. The second survey question asks respondents to rate the general 

importance of protecting aquatic ecosystems compared to other issues that the government might 

address. This question is designed to remind respondents that there are other issues (such as 

public safety, education, and health) to which the government could 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 that there are substitute goods and policy

issues to which they might direct their scarce household budgets.

Relative Importance of Effects. Question 3 asks the respondent to rate the importance of each 

of the effects captured by the five scores: (a) commercial fish populations (b) fish populations 

(for all fish), (c) fish losses prevented, (d) condition of aquatic ecosystems, and (e) cost to my
18

household. This question is designed to promote understanding of the scores by placing 

respondents in a mindset where they consider the meaning of each score and consider their 

general preferences for effects prior to considering specific policy options. The question also 

promotes understanding by telling the respondent that they can return to previous pages for 

reminders of what the scores mean. 

Voting for Regulations to Prevent Fish Losses in the Respondent’s Region. 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 “status quo” choice to reject both options) for waters 

within the respondents’ region (e.g., Northeast waters). Each of the multi-attribute options is 

characterized by (a) commercial fish populations (in 3-5 years) (b) fish populations (all fish; in 

3-5 years), (c) fish saved per year (out of [total] fish lost in water intakes), (d) condition of 

aquatic ecosystems (in 3-5 years), and (e) 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 is 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; 2002b, 

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 vary

the order in which the policy option attributes are presented across respondents, such as 

presenting household cost first or presenting fish saved per year lower in the list of choice 

question attributes. While complete randomization is impractical for the mail survey, the change 

in order would allow for a potential test of ordering effects.

Reasons for Voting “No Policy”. Question 7 is a follow-up to the prior voting questions, and 

asks respondents to identify the primary reason for voting no, if they always voted for “no 

policy” in questions 4-6. It is designed to identify respondents whose “no policy” responses are 

based on their budget constraint, respondents who do not consider fish losses important enough 

to vote for a policy, or respondents who ignored information presented in the survey and 

answered questions based on their general convictions and principles. In an electronic survey
19

format, respondents who voted for a policy would not see this question, potentially reducing 

burden.

Respondent Certainty and Reasons for Voting. Questions 8 and 9 are follow-up questions to 

the prior voting. Question 8 assesses the certainty that respondents feel in their choice 

experiment responses, following methods of Champ et al. (2004; 2009), Akter et al. (2009), 

Kobayashi et al. (2010), and others. It is designed to identify respondents whose responses are 

based on incorrect interpretation of the resource changes and the uncertainty of ecological 

outcomes from policy options. EPA will evaluate respondent understanding when completing

stated preference surveys using methods and approaches discussed in Boyle (2003), Kaplowitz et 

al. (2004), Bateman et al. (2002), Powe (2007) and others. Question 9 asks respondents to rate 

the effect of factors on their choices, and why they voted for or against the regulatory programs. 

Responses to such questions have been used in the literature to successfully control for 

hypothetical bias. 

Recreational Experience. Questions 10 asks respondents how often they participate in specific 

types of water-related recreational activities within the last year. This question can be used to 

identify non-users of the fishery resource—thereby allowing the estimation of non-user values 

for I&E mortality reductions.

2

Examples of this approach to estimation of non-user values are 

provided by Johnston et al. (2005a), 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 11-21 ask respondents to provide basic demographic information, 

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

location, employment status, and household income. This information will be used in the 

analysis of survey results, as well as in the nonresponse analysis.

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

The Agency will modify the survey instrument for each region relative to the regional 

survey shown in Attachment 1 for the Northeast region as follows:

 Cover – The text on the cover reads “A Survey of Northeast Residents (CT, DC, DE, 

MA, MD, ME, NH, NJ, NY, PA, RI, VT)”. “Northeast Residents” and the list of 

included states will be changed to match the respondent’s region.

 

2 Non-user values are by definition non-use values. Users can also hold non-use values. However, user’s non-use 

values may differ from non-users values due to familiarity with the resource. Thus, non-user values are not 

necessarily representative of average non-use values. 
20

 Cover – The photo on the cover is of a forage species (silversides) in found in the 

Northeast region. If that species isn't found in other regions, EPA will replace the cover 

photo with a similar substitute photo for a forage species relevant to that region.

 Page 1 – The survey states that it “asks for your opinions regarding policies that would 

affect fish and habitat in the Northeast U.S.” “Northeast U.S.” will be replaced with the 

respondent’s region or simply, “the U.S.” for the national version.

 Page 1 – Includes the statement that “Northeast fresh and salt waters support billions of 

fish.” “Northeast” will be replaced with the respondent’s region or “the U.S.” for the 

national version and “salt water” will be removed from the Inland and Great Lakes 

regions.

 Page 2 - The survey states that “Cooling water use affects fresh and salt waters 

throughout the Northeast US, but almost all fish losses are in salt waters such as coastal 

bays.” For the Southeast and Pacific regions, which include both salt water and 

freshwater facilities, reference to the Northeast region would be replaced with the name 

of the respondent’s region or “the U.S.” for the national version of the survey. For the 

Inland region, which only include freshwater facilities, reference to salt water will be 

removed.

 Page 2 – A map of the Northeast region and facility locations is presented. A comparable 

map will be produced for each region or the U.S., and map included in the survey will 

correspond to the respondent’s region or the U.S..

 Page 4 - This page provides an example for two Northeast coastal species and includes a 

figure comparing estimates of young fish lost to the total number of adult fish. The 

estimate of total losses will be modified to describe species and losses occurring within 

the respondent’s region. 

 Page 5 - The total losses (1.1 billion) included within the survey corresponds to losses 

from facilities in Northeast coastal waters. This total would be replaced with the total 

losses for the respondent’s region or total losses for the U.S. for the national version. The 

pie charts will be updated based on the regional (national) total while maintaining the 

25% and 95% reductions used in the example.

 Page 7 - The table text describing each score will be changed to include the current scores 

for the respondent’s region.
21

 Page 8 - Text describing the commercial fish populations score and fish populations (all 

fish) score will be changed to describe the current score for the respondent’s region 

relative to its maximum. 

 Page 10 – The included figure illustrates the location of “Facilities Using Cooling Water 

Intake”. The figure will be replaced with a map showing facility locations within the 

respondent’s region or in the U.S. for the national version. 

 Pages 11-14 – Regional references within the table headings in Questions 4, 5, and 6 

(e.g., “Policy Effect NE Waters”, “Option A NE Waters”) will be modified to refer to the 

policy effects and options for the respondent’s region. The “Fish Saved per Year” score 

includes a note reminding the respondent of total fish lost within the region due to I&E 

(e.g., 1.1 billion); this number will be changed to reflect total losses within the 

respondent’s region. The values describing the current situation and Options A and B 

within experiment questions 4, 5, and 6 will also vary across regions.

(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 beginning of the survey document. 

 Complete the survey questionnaire and return it by mail.

A typical subject participating in the mail survey is expected to take 30 minutes to 

complete the survey. These estimates are derived from focus groups and cognitive interviews in 

which respondents were asked to complete a survey of similar length and detail to the current 

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 is 

funded by EPA contract No. EP-C-07-023 which provides funds for the purpose of analyzing the 
22

economic benefits of the proposed rule for existing facilities subject to the section 316(b) 

regulation. Agency activities associated with the survey consist of the following:

 Develop the survey questionnaire and sampling design.

 Printing of questionnaires.

 Telephone screening of respondents.

 Mailing of questionnaires.

 Mailing of postcard reminders.

 Follow-up with non-respondents to the mail survey/resending of questionnaire if needed.

 Data entry and cleaning of data file.

 Analysis of survey results. 

Although not covered under this ICR, EPA will primarily use the survey results to 

estimate the social value of changes in I&E mortality losses of forage, recreational, and 

commercial species of fish, as part of the Agency’s analysis of the benefits of the 316(b) rule for 

existing facilities. If reliable environmental data were to be developed for population changes 

and other ecosystem impacts, social values for these benefit types may also be assessed for the 

rulemaking using survey results.

5(b) Collection Methodology and Information Management

To pretest the survey questionnaire, EPA conducted a series of seven focus groups, 

including one using cognitive interview methodologies under a different ICR (OMB control # 

2090-0028). Focus groups provided valuable feedback which allowed EPA to iteratively edit 

and refine the 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 a mail choice experiment questionnaire. 

First, EPA will use a telephone screener to solicit respondents to receive the mail questionnaire. 

A dual frame, random digit dial design will be used, including both landline and cell phone 

numbers. This ensures that even cell-phone-only households have an opportunity to participate 

in the survey to reduce potential bias. The telephone screener, shown in Attachment in 6,
23

includes not only an invitation to participate in the mail survey, but also some attitudinal and 

behavioral questions that are also included in the mail survey, as well as demographic 

characteristics. EPA will use this information for the non-response analysis to compare 

respondents and non-respondents to the mail questionnaire.

It is anticipated that approximately 60 percent of respondents to the telephone screener 

will agree to participate in the mail survey. EPA will then send the mail survey to those who 

agree to participate, along with a cover letter explaining the purpose of the survey. All of these 

respondents will receive a reminder postcard approximately one week after the initial 

questionnaire mailing. The cover letter and postcard reminder are included as Attachments 7 and 

8, respectively.

Approximately three weeks after the reminder postcard, all those who have not responded 

will receive a second copy of the questionnaire with a revised cover letter. The following week, 

another reminder postcard will be sent. Based on this approach to mail data collection, it is 

anticipated that approximately 40 percent of those who agree to participate in the mail survey

will actually do so. Since the desired number of completed surveys is 2,288, it will be necessary

to recruit 5,720 respondents by telephone who agree to participate (Dilman 2000).

Data quality will be monitored by checking submitted surveys for completeness and 

consistency, and by asking respondents to assess their own responses to the survey. Question 8 

asks respondents to rate their understanding of the survey and their confidence in their responses. 

Questions 7 and 9 are designed to assess the presence or absence of potential response biases by

asking respondents to indicate their reasoning and rate the influence of various factors on their 

responses to the choice experiment questions. 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 I&E mortality by section 316(b) existing 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. 

Alternatively, EPA is considering implementing the proposed survey as an internetbased, choice experiment questionnaire. This alternative method is described in Attachment 5.

5(c) Small Entity Flexibility
24

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:

Table A2: Schedule for Survey Implementation

Activity

Duration of 

Each Activity

Printing of questionnaires Weeks 1 to 2 

Telephone screening of respondents Weeks 1 to 5

Mailing of questionnaires Weeks 3 to 8

Postcard reminder (one week after initial questionnaire mailing) Weeks 4 to 9

Follow-up with non-respondents to the mail survey/resending of 

questionnaire if needed (4 weeks after initial questionnaire mailing)

Weeks 6 to 13

Data entry Weeks 4 to 16

Cleaning of data file Week 17

Delivery of data Week 18

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 mail survey to 5,720 households, the 

national burden estimate for all respondents is 1,938 hours assuming that 9,533 households will 

participate in telephone screening and 2,288 respondents complete and return the survey, 

EPA estimates that telephone screening interviews will take 5 minutes (0.08 hours) per 

interview for each of the 9,533 screened households. Based on pretests conducted in focus 

groups, EPA estimates that on average each respondent mailed the survey will spend 30 minutes 

reviewing the introductory materials and completing the survey questionnaire. Thus, the average 

burden per respondent is 30 minutes (0.5 hours) for these 2,288 respondents. 
25

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 $20.42 (2009$) (U.S. Department of Labor 2009). Assuming an 

average per-respondent burden of 0.08 hours for each of the 9,533 screening participants and an 

average hourly wage of $20.42, the average cost per screening participant is $1.70. Therefore 

the total cost to participants in the telephone screening phase would be $16,223. Assuming an 

average per-respondent burden of 0.5 hours for individuals mailed the survey and an average 

hourly wage of $20.42, the average cost per respondent is $10.21. Of the 5,720 individuals 

receiving the mail survey, 2,288 are expected to return their completed survey. The total cost for 

all individuals that return surveys would be $23,360.

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

6(c) Estimating Agency Burden and Costs

This project will be undertaken by Abt Associates Inc. with funding of $446,000 from 

EPA contract EP-C-07-023, which provides funds for the purpose of analyzing the economic 

benefits of the proposed rule for existing facilities subject to the section 316(b) regulation. Abt 

Associates Inc. staff is expected to spend 1,155 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 $77,000. In addition to the effort expended by EPA’s 

contractors, EPA staff is expected to spend 320 hours managing and reviewing this project and 

contributing to the analysis at a cost of $31,000. Agency and contractor burden is 1,475 hours, 

with a total cost of $108,000 excluding the costs of survey printing and mailing and telephone 

screening. Telephone screening is expected to take 8,400 hours and cost $247,000 while mailing 

and printing of the survey is expected to take 161 hours and cost $122,000, including the cost of 

survey printing and mailing. Thus, the total Agency and contractor burden would be 10,036

hours and would cost $477,000.
26

6(d) Respondent Universe and Total Burden Costs

EPA expects the total cost for survey respondents to be $39,583 (2009$), based on a total 

burden estimate of 1,938 hours and an hourly wage of $20.42.

6(e) Bottom Line Burden Hours and Costs

The following table presents EPA’s estimate of the total burden and costs of this 

information collection:

Table A3: Total Estimated Bottom Line Burden and Cost Summary

Affected Individuals Total Burden Total Cost (2009$)

Participants in Telephone Screening 794 hours $16,223

Mail Survey Respondents 1,114 hours $23,360

Total for Survey Respondents 1,938 hours $39,583

EPA Staff 320 hours $31,000

Telephone Screening 8,400 hours $247,000

Survey Printing and Mailing 161 hours $122,000

EPA’s Contractors 1,155 hours $77,000

Total Burden and Cost 11,974 hours $516,583

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 

mail survey will average 0.5 hours per respondent (i.e., a total of 1,144 hours of burden divided 

among 2,288 survey respondents). Households included in telephone screening are expected to 

average 0.08 hours per screening interview participant (i.e., a total of 794 hours of burden 

divided among 9,533 screening interview participants). This results in a total burden estimate of 
27

1,938 hours including both the telephone screening interviews and mail survey. 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; 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. EPA-HQ-OW-2010-0595, which is available for online viewing at 

www.regulations.gov, or in person viewing at the Office of Water Docket in the EPA Docket 

Center (EPA/DC), EPA West, Room 3334, 1301 Constitution Ave., NW, Washington, DC. The 

EPA/DC 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 Office of Water Docket is 202-566-1752. 

Use www.regulations.gov to obtain a copy of the draft collection of information, submit 

or view public comments, access the index listing of the contents of the 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, EPA-HQ-OW-2010-0595. 
28

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 public values (including non-use values) 

for fish and aquatic organisms are affected by I&E mortality at cooling water intake structures 

(CWIS) located at existing 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 total values, including non-use values, that individuals place on 

preventing losses of fish and other aquatic organisms caused by CWIS at existing 316(b) 

facilities.

 To understand how much individuals value preventing fish losses, increasing fish 

populations, improvements in aquatic ecosystems, 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 for fish resources lost to I&E mortality is necessary to 

determine the full range of benefits associated with reductions in impingement and entrainment 

losses at existing 316(b) facilities. Because non-use values may be substantial, failure to 

recognize such values may lead to improper inferences regarding policy benefits (Freeman 

2003).

1(b) Key Variables

The key 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) I&E 
29

mortality losses of fish, (b) commercial fish sustainability, (c) long-term fish populations, and (d)

condition of aquatic ecosystems3. More specifically, the choice experiment framework allows 

respondents to view pairs of multi-attribute policies associated with the reduction of I&E 

mortality 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 

sustainability of commercial fishing, the change in fish populations, and the condition of aquatic 

ecosystems. Other important variables include whether or not the respondent is a user of the 

affected aquatic resources, household income, and other respondent demographics.

1(c) Statistical Approach

EPA believes that a statistical survey approach is appropriate. 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 loss reductions for the 316(b) case. Thus, a statistical survey is the most 

reasonable approach to satisfy EPA’s analytic needs for the 316(b) regulation benefit analysis.

EPA has retained Abt Associates Inc. (55 Wheeler Street, Cambridge, MA 02138) as a 

contractor to assist in questionnaire design, sampling design, and analysis of the survey results. 

1(d) Feasibility

 

3 The environmental attributes to be compared against the cost of living increases where designed based on the 

Johnston et al. (2009) Bioindicator-Based Stated Preference Valuation (BSPV) method which was developed to 

promote ecological clarity and closer integration of ecological and economic information within SP studies. In 

contrast to traditional SP valuation, BSPV employs a more structured and formal use of ecological indicators to 

characterize and communicate welfare-relevant changes. It begins with a formal basis in ecological science, and 

extends to relationships between attributes in respondents’ preference functions and those used to characterize 

policy outcomes. Specific BSPV guidelines ensure that survey scenarios and resulting welfare estimates are 

characterized by: (1) a formal basis in established and measurable ecological indicators, (2) a clear structure linking 

these indicators to attributes influencing individuals’ well-being, (3) consistent and meaningful interpretation of 

ecological information, and (4) a consequent ability to link welfare measures to measurable and unambiguous policy 

outcomes. The welfare measures provided by BSPV method can be unambiguously linked to models and indicators 

of ecosystem function, are based on measurable ecological outcomes, and are more easily incorporated into benefit 

cost analysis. This methodology was developed in part to address the EPA Science Advisory Board’s call for 

improved quantitative linkages between ecological services and economic valuation of those services.
30

The survey instrument was repeatedly pre-tested during a series of seven focus groups

(conducted under a different ICR with OMB control # 2090-0028), in addition to the twelve 

focus groups conducted for the Phase III survey (EPA-HQ-OW-2004-0020), and it will be

subject to peer 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 a mail survey, it will be easily accessible 

to 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. EP-C-07-23) 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 final benefits analysis for the 316(b) existing facilities 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 recruited by random digit 

dialing (over-lapping dual frame; land and cell) to participate in a mail survey. The overall 

sampling frame from which these individuals would be selected 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 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 telephone screening will use an overlapping dual frame design of landline and cell 

phone numbers to increase the coverage of the sample. The inclusion of cell phone numbers will 

allow EPA to reach potential respondents who only have cell phones and would be missed if 
31

only a sample of landlines was selected. The dual frame design also avoids bias because the cellphone-only population skews heavily towards lower-income and younger individuals. The cell 

and landline samples will be weighted to correct for each individual’s probability of being 

included in the survey. People who live in a household with more than one person are less likely

to be chosen for the survey, and people with more than one telephone line (e.g., both cell and 

landline) are more likely to be chosen for the survey. All landline respondents to the telephone 

screener will be asked the number of eligible respondents in their household and every

respondent (cell and landline) will be asked whether they have a landline only, a cell phone only, 

or both. Next, the survey data will be weighted to match national data (U.S. Census and 

National Health Interview Survey) by a set of demographic variables (e.g., age and gender) as 

well as phone usage (cell-only users, landline-only users and people who use both cell phones 

and landline phones). For January through June 2009, the National Health Interview Survey

indicated that 15.5 percent of households are landline only, 22.7 percent are cell phone only, and 

59.4 percent have both landline and cell phone service. About two percent of households have 

no telephone service. It is anticipated that estimates for landline and cell phone service from the 

January to June 2010 National Health Interview Survey will be available for weighting purposes 

by the time the survey would be implemented.

For discussion of techniques that EPA will use to minimize non-response and other nonsampling errors in the survey sample, refer to Section 2(b)(II), below.

(II) Sample Sizes

The intended sample size for the survey is 2,288 households including only households 

providing completed mail surveys. 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 (see section 2(c)) 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
32

The survey sample will be selected using a stratified selection process. For the selection 

of households, the population of households in the contiguous 48 states and the District of 

Columbia will be stratified by the geographic boundaries of four study regions: Northeast, 

Southeast, Inland, and Pacific. As described previously, the Northeast region includes the North 

Atlantic and Mid Atlantic 316(b) benefits regions, the Southeast region includes the South 

Atlantic and Gulf of Mexico 316(b) benefits regions, the Pacific region includes states on the 

Pacific coast, and the Inland region includes all non-coastal states. The sample is allocated to 

each region in proportion to the total number of households in that region, with at least 288 

completed surveys in each region. This is the number required to estimate the main effects and 

interactions under an experimental design model as described in Section 4(a) of Part A. To 

accommodate this requirement the sample sizes in other regions will be slightly reduced. A 

sample of 288 households completing the national survey version would be distributed among 

the study regions based on the percentage of regional survey sample (as shown in Table A1) to 

ensure that respondents to the national survey version are distributed across the continental U.S. 

(IV) Sampling Method

Using the stratification design discussed above, respondents will be randomly selected. 

First, a larger sample of households will be selected through random digit dialing in each of the 4

regions. The household to which the selected telephone number belongs will be called and the 

respondent will be asked whether he or she is willing to provide information required by the 

survey. If the person in the household agrees, then a questionnaire will be mailed to that 

household. If it is assumed that 40% of the households which agreed to provide information 

actually return a completed mail survey (completion rate) then 5,720 questionnaires will need to 

be mailed to households.4

If we assume that 60% of the households that are called say “yes” they are willing to 

receive a questionnaire (the screening response rate), then we need to screen 9,533 households. 

A dual frame random digit dialing design, including both landline and cell phone numbers, will 

be used for telephone screening. If we assume that 40% of the households called will participate 

in the telephone screener, then we will need to call 23,833 numbers. The actual number of 

telephone numbers selected through random digit dialing sampling will be larger than the 

 

4 The assumed response rate is based on a similar stated preference studies conducted by Johnston et al. (2010) in 

the Northeast U.S. Actual response rates could vary across study regions. 
33

number of households called. This is because we are expecting that 40% of the telephone 

numbers initially selected will be screened out as non-working or business numbers. Therefore 

the actual number of telephone numbers selected will be around 39,722. The quantity of 

telephone numbers that we need to select in each region can be obtained by allocating the total 

number in proportion to the total households in that region. 

For obtaining population-based estimates of various parameters, each responding 

household will be assigned a sampling weight. This weight combines a base sampling weight 

which is the inverse of the probability of selection of the household and then an adjustment for 

non-response. The weights will be used to produce estimates that are generalizable to the 

population from which the sample was selected (e.g., percent of population participating in 

water-based recreation such as fishing and shellfishing). Proportional allocation of the sample to 

regions ensures an equal probability sample. To estimate total WTP for the quantified 

environmental benefits of the 316(b) existing facilities rulemaking data will be analyzed 

statistically using a standard random utility model framework. 

(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 target samples sizes for both the U.S. (excluding Alaska and 

Hawaii) and each of the four EPA study regions. At the regional level, a sample of 2,000 

households (completed surveys) will provide estimates of population percentages with a margin 

of error ranging from 3.6 to 5.8 percentage points at the 95% confidence level. A sample of 288 

household for the national survey version (completed surveys) will provide an estimate of 

population percentages with a margin of error no greater than 5.8 percentage points at the 95% 

confidence level.
34

Table B1: Number of Households and Household Sample for Each EPA Study Region

Region Household Population Household Sample

Northeast 23,281,296 417

Southeast 31,378,122 562

Inland 40,852,983 732

Pacific 16,158,206 288

Total for Regional Survey Versions 111,670,607 2,000

National Survey Version 111,670,607 288

Source: The number of households in each region was obtained based on the estimated population size 

and average household size from the 2006-2008 American Community Survey (ACS).

(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 I&E 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 (over the earlier 

contingent valuation format).

A different type of non-sampling error is non-response bias. Non-response rates in this 

survey are affected by non-response at each stage of sampling. There are two types of nonrespondents to this survey: respondents unwilling to complete a questionnaire when initially

contacted by phone and respondents who agree but do not complete the questionnaire. EPA will 

conduct several additional activities to minimize the potential for non-response bias in the 

current survey:
35

(1) The telephone interviews will be conducted by trained interviewers who are 

experienced in encouraging response. Calls will be placed on different days of the 

week and at different times to maximize response. 

(2) The mail survey will be designed to maximize response. A mail survey will be sent 

to all those who agree to participate. Then, one week later, a postcard reminder will 

be sent. Three weeks later, a second copy of the questionnaire with a revised cover 

letter will be sent to non-respondents. As final follow-up, another postcard reminder 

will be sent one week later.

(3) Response rates will be tracked on a daily basis for both the telephone screener and 

mail survey. If any unexpected declines are encountered, corrective action can 

immediately be undertaken.

(4) EPA will undertake non-response bias analysis as detailed in the following section. 

If necessary, EPA will use appropriate weighting or other statistical adjustment to correct the 

bias because of non-response.

Non-response Interviews

To determine whether there is any evidence of significant non-response bias in the 

completed sample, EPA will compare the characteristics of the completed and non-completed 

cases from the random digit dialing sample. There are two main stages of non-response: nonresponse to the telephone survey and non-response to the mail survey.

To assess the potential for telephone non-response bias, EPA will collect sample 

characteristics in the course of the telephone survey as follows: (1) prior to household contact, 

(2) at respondent selection for the survey, and (3) after beginning the interview. EPA will then 

analyze the data available for respondents and non-respondents prior to household contact and at 

respondent selection. Because the telephone instrument is very short, we do not expect many

people prematurely terminating the call after the selected household member has started the 

interview.

The drawn sample of random digit dialing numbers from the landline frame that are 

released to telephone interviewers for contact attempts can be coded for certain contextual 

characteristics. The “hundreds bank” from which the RDD number is drawn (e.g., 301-608-

38xx) is classified geographically according to the unit from which the majority of listed 

numbers in that bank are located. This permits us to classify all of the numbers drawn by EPA 
36

region, State, and urbanicity (central city, metro remainder, and non-metropolitan area). It also 

permits us to classify those numbers by demographic characteristics, such as percentage of 

African-American and Hispanic, based on Census data linked to telephone exchanges. EPA will 

compare the characteristics of all dialed residential numbers in which an interview had not been

completed with those that had been completed using these variables to determine whether nonresponse bias is present.

Once contact is achieved with a household, one adult is selected in each household as the 

designated respondent. If there is more than one eligible respondent per household, then a 

random selection is done for the individual with the most recent/next birthday. EPA will identify

the age and gender of this individual if callbacks are necessary. This information permits 

additional comparisons of any differences in the age and gender of respondents in the completed 

sample and those in the non-completed sample after household contact and respondent selection 

have been made. 

The telephone screener includes not only an invitation to participate in the mail survey, 

but also some attitudinal (relative importance of various government activities) differences and 

behavioral differences (recreational activities), as well as demographic characteristics (see 

Attachment 6 for detail). EPA will use the telephone screening data to compare mail survey

respondents and non-respondents. The items of information collected during telephone screening 

will help determine the type of person that is likely to not respond to the survey and may help in 

forming weighting classes for adjusting weights of respondents to account for non-response and 

minimize the bias because of non-response.

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 draft questionnaire for the Northeast region is provided 

in Attachment 1 and the full text of the draft questionnaire for the national survey version is 

provided in Attachment 2. 

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

 Relative Importance of Issues Associated with Industrial Cooling Water. EPA 

included this section to prepare respondents to answer the stated preference questions by
37

motivating respondents to consider the relative importance of key issues associated with 

the use of cooling water by industrial facilities.

 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.

 Relative Importance of Effects. This section was included to promote understanding of 

the metrics included in the stated preference questions by asking them to consider their 

relative importance prior to evaluating policy options and by encouraging respondents to 

re-read previous pages for reminders if necessary.

 Voting for Regulations to Prevent Fish Losses in the Respondent’s Region (or 

Nationally). The questions in this section are the key part of the survey. Respondents’ 

choices when presented with specific fish-related resource changes within their region

and household cost increases are the main data that allow estimation of willingness-topay. 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 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 “No Policy”. This question provides information that will be used 

by EPA to identify protest responses.

 Respondent Certainty and Reasons for Voting. This section is designed to identify

respondents who incorrectly interpreted the choice questions or the uncertainty of 

outcomes. Responses to these questions are important to successfully control for 

hypothetical bias. 

 Recreational Experience. This question elicits recreational experience data to test if 

certain respondent characteristics influence responses to the referendum questions. This 

question will also allow EPA to identify resource non-users, for purposes of estimating 

non-user WTP (to gauge the relative importance of non-use values to overall benefits).

 Demographics. Responses to these questions will be used to estimate the influence of 

demographic variables on respondents’ voting choices, and ultimately, their WTP to 
38

prevent I&E mortality losses of fish. This information will allow EPA to use regression 

results to estimate WTP for populations in different regions affected by the 316(b) rule 

for existing 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 conducted extensive pretests of the survey instrument during a set of seven focus 

groups (EPA ICR # 2090-0028), in addition to the twelve focus groups conducted for the Phase 

III survey. These focus groups 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. Particular emphasis in these survey pretests 

was 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). Based on focus group and cognitive interview responses, EPA 

made various improvements to the survey questionnaire including changes to ameliorate and 

minimize these biases in the final survey instrument.

4. Collection Methods and Follow-up

4(a) Collection Methods

The survey will be administered as a mail survey. Respondents identified by random 

digit dialing who agree to participate will be mailed a copy of the survey questionnaire. 

Respondents will be asked to mail the completed survey back to EPA. 
39

4(b) Survey Response and Follow-up

The target response rate for the mail survey is 40 percent. That is, 40 percent of 

households which agreed to participate in the mail survey are expected to return a completed 

survey. However, the actual response rate compared to the general population is lower, given 

that the telephone screening process is expected to have a response rate of 60 percent among 

contacted households. The resolution rate (determining the status of a telephone number), the 

screener response rate and the survey completion rate will contribute to the overall response rate 

in the survey. The actual number of telephone numbers selected for inclusion will be much 

larger to account for the exclusion of non-working and business numbers. The number of 

telephone numbers needed for selection in each region can be obtained by allocating the total 

number in proportion to the number of households in that region. 

To improve the response rate, all of these respondents will receive a reminder postcard 

approximately one week after the initial questionnaire mailing. Then, approximately three weeks 

after the reminder postcard, all those who have not responded will receive a second copy of the 

questionnaire with a revised cover letter. The following week, another reminder postcard will be 

sent. 

As noted in Section 2(b), the survey sample will be selected using a stratified selection 

process. For the selection of households, the population of households in the contiguous 48 

states and the District of Columbia will be stratified by the geographic boundaries of four EPA 

study regions. In addition, EPA will administer a national version of the survey that does not 

require stratification. We will keep track of the response rates for each of regional surveys and 

the national version of the survey to ensure that the rates are reasonable. We will also look at the 

frame characteristics of non-respondents to determine if there are any substantial biases in the 

estimates because of an imbalance in the distribution of certain important subgroups in the 

sample. 

5. Analyzing and Reporting Survey Results

5(a) Data Preparation

Since the survey will be administered as a mail survey, survey responses will manually

entered into an electronic database after they are returned. After all responses have been entered, 
40

the database contents will be converted into a format suitable for use with a statistical analysis 

software package. The mail survey, database management, and data set conversion will be 

conducted by Abt Associates Inc.

All survey responses will be vetted 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, such as transitivity. 

Responses which satisfy transitivity exhibit relational relationships when separate choices among 

policy options are compared. For example, if values for policy 1 are greater than policy 2, and 

values for policy 2 are greater than Policy 3, then values for policy 1 should also be greater than 

values for Policy 3.

5(b) Analysis

Once the survey data has been converted into a data file, it will be analyzed using 

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 I&E mortality losses, 

EPA defines a standard utility function Ui(

.

) that includes environmental attributes of an I&E 

reduction plan and the net cost of the plan to the respondent. Following standard random utility
41

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) + εi

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;

εi = 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. In this case, the respondent’s choice set 

of potential policies also includes maintaining the status quo. The random utility model presumes 

that the respondent assesses the utility that would result from each I&E reduction plan i

(including the status quo), 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) + εi) ≥ (v(Xk, D, Y-Fk) + εk)  k≠i.

If the εi 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 
42

Plan), and results in an econometric (empirical) estimate of the systematic component of utility

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 

independent of other choice questions, 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 and 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 the 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

techniques, as described by Train (1998), Greene (2002) and others. Mixed logit model 

performance of alternative specifications will be assessed by EPA using standard statistical 

measures of model fit and convergence, as detailed by Greene (2002, 2003) and Train (1998).
43

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 [contingent valuation]” (Bateman et al. 2002, p. 271). Advantages of choice 

experiments include a capacity to address choices over a wide array of potential policies, 

grounded 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 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 
44

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, ecosystem condition, and 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). For the purpose of stated preference survey implementation,

EPA will use four geographic regions: Northeast, Southeast, Inland, and Pacific. The Northeast 

regional survey is included in this ICR as Attachment 1. In addition, EPA will administer a 

national version of the survey that is included as Attachment 2. EPA emphasizes that the survey

versions included in this ICR are for illustration only; they are but two of what will ultimately be 

a large number of different survey versions covering a wide range of potential policy outcomes

as described in Attachment 4. 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 the choice experiment 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 were subjected to extensive development and pretesting in focus groups to 
45

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 scope of 

the resource changes under consideration, and the availability of substitute environmental 

resources. 

As noted above, survey pretests 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 improvements to the survey questionnaire. Of particular emphasis in these survey

pretests was 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). Based on focus group and 

cognitive interview responses, EPA made various improvements to the survey questionnaire 

including changes to ameliorate and minimize these biases in the final survey instrument. Results 

from focus groups and cognitive interviews provided evidence that respondents answer the stated 

preference survey in ways appropriate for stated preference WTP estimation, and that their 

responses generally do not reflect the biases noted above. 

The number of focus groups used in survey design, seven (excluding the 12 focus groups 

conducted for the Phase III survey), exceeds the number of focus groups used in typical 

applications of stated preference valuation. Moreover, EPA incorporated cognitive interviews as 

detailed by Kaplowicz et al. (2004). We note that the current survey instrument is built upon an 

earlier version that was peer reviewed in January 2006 (Versar 2006) and it incorporates 

recommendations received from that peer review panel. 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 conducted for the previous Phase III survey are discussed by EPA in a prior ICR 

(#2155.01).
46

Econometric Specification

Based on prior focus groups, expert review, and attributes of the policies under 

consideration, EPA anticipates that four attributes will be incorporated in the vector of variables 

describing attributes of an I&E reduction plan (vector Xi), in addition to the attribute 

characterizing unavoidable household cost Fi.

5

 These attributes will characterize the annual 

reduction in I&E losses (x1), anticipated effects on fish populations (all fish) (x2), anticipated 

effects on commercial fish populations (x3), and anticipated effects on aquatic ecosystem 

condition (x4). 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 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 

 

5 EPA plans to complete focus group testing of the instrument before the second Federal Register notice of this 

information collection request. The inclusion of all four attributes is an important aspect of focus group testing. If 

focus groups find this number cognitively challenging, the number will be reduced, and if cognitive issues are 

minimal as identified by randomly selected focus group participants, all four will remain.
47

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 discussed 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).

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(Fish Saved) + 2(Change in Populations of All Fish) + 3(Change in 

Commercial Fish Populations) + 4(Change in Condition of Aquatic Ecosystem) + 5(Cost)

+ 6(Fish Saved)(Baseline) + 7(Change in Populations of All Fish)(Baseline) + 8(Change in 

Commercial Fish Populations)(Baseline) + 9(Change in Aquatic Ecosystem)(Baseline) + 

10(Cost)(Baseline) + 11(Fish Saved)(Change in Populations of All Fish) + 12(Fish 

Saved)(Change in Commercial Fish Populations) + 13(Fish Saved)(Change in Aquatic 

Ecosystem) + 14(Change in Populations of All Fish)(Change in Commercial Fish Populations)

+ 15(Change in Populations of All Fish)(Change in Aquatic Ecosystem) + 16(Change in 

Commercial Fish Populations)(Change in Aquatic Ecosystem)

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 populations of all fish, long-term effects on 

commercial fish populations) on utility, effects on aquatic ecosystem condition, 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
48

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 

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 the 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 Abt Associates Inc. 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 4.

Each treatment (survey question) includes two choice Options (A and B), characterized 

by four attributes and a cost variable that vary across the two choice options (Commercial Fish 

Populations, Fish Populations (all fish), Fish Saved per Year, Condition of Aquatic Ecosystems, 

and Increase in Cost of Living of Your Household). Hence, there are a total of ten attributes for 

each treatment. Based on focus groups and pretests, and guided by realistic ranges of attribute 

outcomes, EPA allows for three different potential levels for Commercial Fish Populations, Fish 

Populations (all fish), Fish Saved per Year, and Condition of Aquatic Ecosystems, and allows for 

six different levels of annual Household Cost for the regional or national choice questions. The 
49

survey versions also include two separate treatments of location, or geographic scope; the first 

version refers to policies only within the respondent’s region and the second version refers to 

national policies. 

The number of combinations for each attribute may be summarized as follows:

 LocationA, LocationB (2 levels)

 Commercial Fish PopulationsA, Commercial Fish PopulationsB (3 levels)

 Fish Populations (all fish)A, Fish Populations (all fish)B (3 levels)

 Fish Saved per YearA, Fish Saved per YearB (3 levels)

 Condition of Aquatic EcosystemsA, Condition of Aquatic EcosystemsB (3 levels)

 CostA, CostB (6 levels)

Beyond the levels specified above, each question will include a “no policy” option, characterized 

by baseline levels for each attribute including a household cost of $0.

Following standard practice, EPA constrained the design somewhat in response to 

findings in seven focus groups and the prior literature. For example, the 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 72 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 mail surveys, these questions will be randomly assigned 

to survey respondents, with each respondent considering three questions. 

Information Provision
50

According to Arrow et al. (1993, p. 4605), if “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 the provided information 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 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 mortality losses and potential impacts on aquatic life, the 

survey will include introductory figures to aid respondents’ comprehension of the goods and 

policies addressed by the survey instrument, and to encourage appropriate neoclassical tradeoffs 

in responding to choice experiment questions. 

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 graphics used in 

the draft mail survey, to ensure that these graphical elements were not prejudicial, and that they

did not bias responses. Graphics judged to be prejudicial or confusing to respondents during the 

seven focus groups and cognitive interviews were revised or replaced. EPA acknowledges that 

certain types of graphics can be prejudicial in certain contexts—and hence all graphical elements 

were pretested extensively. EPA found that focus group respondents endorsed the use of 

graphics in the survey booklet and indicated that they helped them to visualize how fish are 

entrained and impinged, technological solutions, facilities locations, and ecosystem effects. 

Participants made such statements as, “Yeah, I’d rather have them” and “I like on page 2 the 

graph and illustration because the adage a picture is worth a thousand words”. EPA also 

emphasizes that there is no precedent or support in the literature for the total elimination of 

graphics 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), Duke and Ilvento (2004), Opaluch et al. (1993), Johnston et al. (1999, 2002a, 
51

2002b), and Mazzotta et al. (2002). Bateman et al. (2002) also includes examples of various 

types of survey materials including pictures and graphical elements. 

Amelioration of Hypothetical Bias

EPA considers the amelioration of hypothetical bias to be a paramount concern in survey 

design. However, the agency acknowledges—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). 

To obtain reliable estimates of WTP, the Agency tested and designed all survey elements 

to promote incentive compatible preference elicitation mechanisms. Incentive compatible stated 

preference surveys provide no incentive for non-truthful preference revelation (Carson and 

Groves 2007). The literature is clear regarding the importance of incentive compatibility in stated 

preference value elicitation and the role of both question format and scenario consequentiality in 

ensuring this property (Carson et al. 2000; Carson and Groves 2007; Collins and Vossler 2009; 

Herriges et al. 2010; Johnston 2006; Vossler and Evans 2009). It has been established that 

referendum-type stated preference choices are incentive compatible given that certain conditions 

are met, including the condition that responses are believed by respondents to be consequential, 

or potentially influencing public policy decisions (Carson and Groves 2007; Herriges et al. 

2010). 

The survey is explicitly designed to emphasize the importance of the budget constraint 

and program cost. For example, the survey asks respondents to compare protecting aquatic 

ecosystems to other policy issues which the government could potentially ask households to pay

costs. The survey itself includes 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) explicitly mentioning that this survey is 

associated with assessment of proposed policies that are being considered, b) numerous details 

provided in the survey concerning specifics of the proposed policies, and c) emphasis that the 
52

type of policy enacted will depend in part on survey results and that their vote is important. 

Johnston and Joglekar (2005) show the capacity of such information to eliminate hypothetical 

bias in choice-based stated preference WTP estimation. 

Focus groups and cognitive interviews indicated 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 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 “No. [My vote] would have 

been the same actually” and “If I believed that it was gonna affect regulations, I think I would 

have voted the exact same way.”

EPA does not anticipate significant hypothetical bias in the proposed survey based on 

focus group results. Focus groups respondents took the survey questions seriously and indicated 

that they though that their choices would actually influence policy. 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 – and cheap talk is not included in the survey.

Amelioration of Symbolic Biases and Warm-Glow Effects

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 
53

reflect symbolic or warm glow concerns (Mitchell and Carson 1989). 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, such as asking 

respondents to reflect on importance of attributes before making selections, and using a payment 

vehicle that doesn’t raise trust issues (cost of living increase rather than an electric bill increase). 

The focus group and cognitive interview results indicated that most participants answered the 

choice questions based on the effects discussed in the survey, not on a desire to help the 

environment in general.

The survey includes clear language to instruct respondents only to consider the specific 

attributes in the survey, and not to base answers on broader environmental concerns including 

the statement that:

“Scientists expect that effects on the environment and economy not shown explicity will 

be small. For example, studies of industry suggest that effects on employment will be 

close to zero.”

This is also consistent with the statement from Arrow et al. (1993) that a referendum-type format 

may limit the warm-glow effect. Some focus group participants indicated that they were inclined 

to support environmental causes and would like “to do a good thing” but still considered the cost 

and effects under the policy options. For example, respondents stated, “[...] if we can do 

something to help as long as the price is right, then do it” and “I feel if it’s going to be benefit 

everyone and be better for the economy, I’m OK with paying a little bit more.” 

This evidence notwithstanding, EPA believes that it is important to include follow-up 

questions to ensure that responses do not reflect symbolic biases. Question 9 in the survey

instrument—which addresses the rationale for choice responses given earlier in the draft 

survey—explicitly tests 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 (CV) (Arrow et al. 1993, p. 

4605) states clearly that if “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)...” 
54

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 CV literature (e.g., Mitchell and Carson 1989; Louviere et al. 2000). Among the 

reasons for this guidance is that such descriptions tend to encourage appropriate framing and 

sensitivity to scope.

Following Arrow et al. (1993), Mitchell and Carson (1989), and others, while noting the 

clear limitations in scope tests discussed by Heberlein et al. (2005), EPA believes that it is 

important that survey responses in this case show sensitivity to scope. This is one of the primary

reasons for the use of the choice experiment methodology, which is better able to capture WTP 

differentials related to changes in resource scope (Bateman et al. 2002). Unlike open-ended 

questions, in which scope insensitivity is a primary concern, EPA emphasizes that choice 

experiments generally have shown much less difficulty with respondents reacting appropriately 

to the scope and scale of resource changes. Moreover, as clearly noted by Bennett and Blamey

(2001, p. 231), “internal scope tests are automatically available from the results of a [choice 

modeling] exercise.” That is, within choice experiments, sensitivity to scope is indicated by the 

statistical significance and sign of parameter estimates associated with program attributes 

(Bennett and Blamey 2001). Internal scope sensitivity will therefore be assessed through model 

results for the variables Commercial Fish Populations, Fish Populations (all fish), Fish Saved per 

Year, and Condition of Aquatic Ecosystems. Statistical significance of these variables—along 

with a positive sign—indicates that respondents, on average, are more likely to choose plans with 

larger quantities of these variables. 

In addition to internal scope tests implicit in all choice experiment statistical analysis, 

EPA will also conduct external scope tests (cf. Giraud et al. 1999). The primary difference 

between internal and external tests is that the former assess sensitivity to scope across choices of 

a single respondent, while the latter involves split-sample assessments across different 

respondents. Within a choice modeling context, external scope tests are generally considered 

“stronger,” although also more likely to be confounded by differences in the implied choice 

frame (Bennett and Blamey 2001). A variety of options for external scope tests exist, depending 

on the structure of the stated choice questions under consideration.

In the present case, attribute-by-attribute external scope tests will be conducted over a 

split sub-sample of respondents considering a specific set of choices, with all held constant 
55

across the considered choices except the scope of the attribute for which the test is to be 

conducted. For example, to conduct an external scope test for reductions in annual fish losses, 

one would consider a set of choices that is identical over two respondent groups, except that one 

considers a choice with a greater reduction in fish losses. Assessing the choices over this split 

sample allows for an external test of scope. To illustrate this test, consider the following stylized 

choice between Option A and Option B. The generic labels “Level 0”, “Level 1”, and “Level 2” 

are used to denote attribute levels, where for all attributes Level 2 > Level 1 > Level 0.

 

Table B2: Illustration of an External Scope Test

Variable Option A Option B

Fish Saved per Year Sample 1: Fish Saved Level 1

Sample 2: Fish Saved Level 2

Fish Saved Level 0

Commercial Fish Populations Commercial Fish Populations

Level 0

Commercial Fish Populations

Level 0

Fish Populations (all fish) Population (all fish) Level 0 Population (all fish) Level 0

Condition of Aquatic Ecosystems Aquatic Ecosystem Condition 

Level 0

Aquatic Ecosystem Condition 

Level 0

Increase in Cost of Living for Your 

Household

Cost Level 1 Cost Level 0

In the above example, only Fish Saved per Year and Cost vary across the choice options. 

Because both Fish Saved per Year (at Level 1 and Level 2) and Cost are higher in Option A than 

in Option B, neither option is dominant. In the illustrated split-sample test, respondent sample 1 

views the choice with Fish Saved per Year at Level 1, while respondent sample 2 views an 

otherwise identical choice with Fish Saved per Year at Level 2, where Level 2 > Level 1. If 

responses are externally sensitive to scope in Fish Saved per Year, this will manifest in a greater 

proportion of sample 2 respondents choosing Option A than sample 1 respondents. This 

hypothesis may be easily assessed using a test of equal proportions across the two sub-samples, 

and provides a simple attribute-by-attribute test of external scope. Analogous tests may be 

conducted for all attributes within the choice experiment design, using parallel methods. EPA 

emphasizes that the formal applicability of the above-noted scope test is contingent upon the 

specific choice frame implied by levels of other attributes in the choice question. This is a 
56

characteristic of nearly all external scope tests applied in choice experiment frameworks (Bennett 

and Blamey 2001).

Split-sample tests such as those proposed above often require the addition of question 

versions to the experimental design, to accommodate the specific structural needs of the 

attribute-by-attribute external scope test. Otherwise, confounding effects of other varying 

attributes (including demographic information) can render results of scope tests ambiguous. In 

the present case, the proposed tests would require the addition of up to six unique question 

versions to the experimental design, enabling scope tests for the three non-cost attributes within 

the 316(b) choice experiment scenarios. If scope tests in additional question frames are desired 

(e.g., the same scope test illustrated above, but given Level 1 for commercial fish populations, 

fish population (all fish), and aquatic ecosystem condition attributes), still additional question 

versions would be added. While small numbers of questions added to the experimental design 

should have minimal impacts on overall efficiency (e.g., orthogonality of the design), larger 

numbers may have a more significant impact. Hence, given constraints on the total number of 

survey respondents, there is a potential empirical tradeoff between the number of external scope 

tests that may be conducted and the efficiency of the experimental design and statistical analysis.

Communicating Uncertainty to Respondents

EPA believes that the role of risk and uncertainty is an important issue to be addressed in 

the development of benefits estimates, and points out that the literature provides numerous 

examples of cases in which appropriate survey design, including focus groups, was used to 

successfully address such concerns. For example, as stated by Desvousges et al. (1984), “using 

contingent valuation to estimate the benefits of hazardous waste management regulations 

requires detailed information on how and the extent to which respondents understand risk (or 

probability) and how government regulatory actions might change it... Using focus groups 

helped make this determination...” EPA also emphasizes that all regulatory analyses involve 

uncertainty of some type (Boardman et al. 2001). 

The ecological outcome of I&E reductions is subject to considerable uncertainty. EPA 

believes that it is important that survey respondents be aware of this uncertainty, and that their 

responses reflect the knowledge that the resource changes reflected in the survey are scientific 

estimates. However, EPA is also aware of the clear advice from the choice modeling literature 

(e.g., Bennett and Blamey 2001; Louviere et al. 2000) to avoid cognitive burden on respondents. 
57

Hence, the proposed survey materials clearly indicate the uncertainty involved with the described 

resource changes in choice modeling scenarios, yet do so in a way designed to minimize 

cognitive burden.

For example, prior to answering choice experiment questions, respondents are told:

“Although scientists can predict the number of fish saved each year, the effect on fish 

populations is uncertain. This is because scientists do not know the total number of fish in 

Northeast waters and because many factors – such as cooling water use, fishing, pollution 

and water temperature – affect fish populations.”

This statement clearly indicates the uncertainty involved with scientific estimates of the 

outcomes of I&E regulations. This is followed by a further reminder of uncertainty:

“Depending on the type of technology and other factors, effects on fish and ecosystems 

may be different – even if the annual reduction in fish losses is similar.”

Focus groups and cognitive interviews participants understood that the ecological changes 

described in the survey were uncertain, and most participants were comfortable making decisions 

in the presence of this uncertainty. Their responses indicated that they understood this 

uncertainty based on the information presented in the introductory material and considered it 

when evaluating policy options. Respondents made such statements as: “My guess is that it did 

come from studies but I have a healthy dose of skepticism about the accuracy of it. I don’t think 

it’s been in any way skewed purposefully, but I know that this is a best guess, reasonable guess 

perhaps”, “it shows me that they are being honest for the most part. You know, you can't 

obviously be accurate on everything, but this is a kind of a best guess”, “[...] They had more 

numbers on the commercial fish population. The rest was more of a guesstimate”, and “you don’t 

know the exact number and nobody knows.”

In previous focus groups conducted for the Phase III survey, EPA tested alternative 

versions of the Phase III survey instrument in which choice experiment attributes were presented 

as 90% confidence ranges, rather than as point estimates. Focus group respondents were 

explicitly asked whether the ranges were helpful in understanding the uncertainty of estimates 

presented in the choice question or whether they were a source of confusion. Seven out of the 

eight respondents interviewed on that occasion indicated that the use of ranges was more 

confusing than the use of point estimates. Furthermore, respondents were comfortable making 

decisions in the presence of this uncertainty. 

5(c) Reporting Results
58

The results of the survey will be made public as part of the benefits analysis for the 

316(b) regulation for existing facilities. Provided information will include summary statistics for 

the survey data, extensive documentation for the statistical analysis, and a detailed description of 

the final results. The survey data will be released only after it has been thoroughly vetted to 

ensure that all potentially identifying information has been removed.
59

REFERENCES

Aadland, D., and A.J. Caplan. 2003. “Willingness to Pay for Curbside Recycling with Detection 

and Mitigation of Hypothetical Bias.” American Journal of Agricultural Economics

85(2): 492-502.

Adamowicz, W., P. Boxall, M. Williams, and J. Louviere. 1998. “Stated Preference Approaches 

for Measuring Passive Use Values: Choice Experiments and Contingent Valuation.” 

American Journal of Agricultural Economics 80(1): 64-75.

Akter, S., R. Brower, L. Brander, and P. Van Beukering. 2009. “Respondent Uncertainty in a 

Contingent Market for Carbon Offsets.” Ecological Economics 68(6): 1858-1863.

Arrow, K. , R. Solow, E. Leamer, P. Portney, R. Rander, and H. Schuman. 1993. “Report of the 

NOAA Panel on Contingent Valuation.” Federal Register 58 (10): 4602-4614.

Bateman, I.J., R.T. Carson, B. Day, M. Hanemann, N. Hanley, T. Hett, M. Jones-Lee, G. 

Loomes, S. Mourato, E. Ozdemiroglu, D.W. Pierce, R. Sugden, and J. Swanson. 2002. 

Economic Valuation with Stated Preference Surveys: A Manual. Northampton, MA: 

Edward Elgar.

Bennett, J. and R. Blamey, eds. 2001. The Choice Modelling Approach to Environmental 

Valuation. Northampton, MA: Edward Elgar.

Bergstrom, J.C. and J.R. Stoll. 1989. “Aplication of experimental economics concepts and 

precepts to CVM field survey procedures.” Western Journal of Agricultural Economics 

14(1): 98-109.

Bergstrom, J.C., J.R. Stoll, and A. Randall. 1989. “Information effects in contingent markets.” 

American Journal of Agricultural Economics 71(3): 685-691.

Besedin, Elena, Robert Johnston, Matthew Ranson, and Jenny Ahlen, Abt Associates Inc. 2005. 

“Findings from 2005 Focus Groups Conducted Under EPA ICR #2155.01.” Memo to 

Erik Helm, U.S. EPA/OW, October 18, 2005. See docket for EPA ICR #2155.02

Blamey, R.K., J.W. Bennett, and M.D. Morrison. 1999. “Yea-saying in Contingent Valuation 

Surveys.” Land Economics 75: 126-141.

Boardman, A.E., D.H. Greenberg, A.R. Vining, and D.L. Weimer. 2001. Cost-Benefit Analysis: 

Concepts and Practice, 2nd edition. Upper Saddle River, NJ: Prentice Hall.

Boyle, K.J. 2003. “Contingent valuation in practice.” In A Primer on Nonmarket Valuation.

Edited by P.A. Champ, K.J. Boyle, and T.C. Brown, Kluwer Academic Publishers.
60

Brown, T. C., I. Ajzen, and D. Hrubes. 2003. “Further Tests of Entreaties to Avoid Hypothetical 

Bias in Referendum Contingent Valuation.” Journal of Environmental Economics and 

Management 46(2): 353-361.

Bunch, D.S., and R.R. Batsell. 1989. “A Monte Carlo Comparison of Estimators for the 

Multinomial Logit Model.” Journal of Marketing Research 26: 56-68.

Cameron, T.A., and D.D. Huppert. 1989. “OLS versus ML Estimation of Non-market Resource 

Values with Payment Card Interval Data.” Journal of Environmental Economics and 

Management 17: 230-246.

Carson, R.T., and T. Groves. 2007. “Incentives and informational properties of preference 

questions.” Environmental and Resource Economics 37(1): 181-210.

Carson, R.T., T. Groves, and M.J. Machina. 2000. “Incentive and Informational Properties of 

Preference Questions.” Working Paper, Department of Economics, University of 

California, San Diego.

Champ P.A., and R.C. Bishop. 2001. “Donation Payment Mechanisms and Contingent 

Valuation: An Empirical Study of Hypothetical Bias.” Environmental and Resource 

Economics 19(4): 383-402.

Champ, P.A., R.C. Bishop, T.C. Brown, and D.W. McCollum. 1997. “Using Donation 

Mechanisms to Value Non-use Benefits from Public Goods.” Journal of Environmental 

Economics and Management 33(2): 151-162.

Champ, P.A., R. Moore, and R. C. Bishop. 2004. “Hypothetical Bias: The Mitigating Effects of 

Certainty Questions and Cheap Talk.” Selected paper prepared for presentation at the 

American Agricultural Economics Association Annual Meeting, Denver, Colorado.

Champ, P.A., R. Moore, and R.C. Bishop. 2009. “A Comparison of Approaches to Mitigate 

Hypothetical Bias.” Agricultural and Resource Economics Review 38(2): 166-180.

Collins, J.P., and C.A. Vossler. 2009. “Incentive compatibility tests of choice experiment value 

elicitation questions.” Journal of Environmental Economics and Management 58(2): 226-

235.

Croke, K., R.G. Fabian, and G. Brenniman. 1986. “Estimating the Value of Improved Water 

Quality in an Urban River System.” Journal of Environmental Systems 16(1): 13-24. 

Cronin, F.J. 1982. “Valuing Nonmarket Goods Through Contingent Markets.” Pacific Northwest 

Laboratory, PNL 4255, Richland, WA.
61

Cummings, R.G., and G.W. Harrison. 1995. “The Measurement and Decomposition of Non-use 

Values: A Critical Review.” Environmental and Resource Economics 5: 225-247.

Cummings, R. G., G.W. Harrison, and L.L. Osborne. 1995. “Can the Bias of Contingent 

Valuation Surveys Be Reduced?” Economics working paper, Columbia, SC: Division of 

Research, College of Business Administration, Univ. of South Carolina.

Cummings, R.G., P.T. Ganderton, and T. McGuckin. 1994. “Substitution Effects in CVM 

Values.” American Journal of Agricultural Economics 76(2): 205-214.

Cummings, R.G., and L.O. Taylor. 1999. “Unbiased Value Estimates for Environmental Goods: 

A Cheap Talk Design for the Contingent Valuation Method.” American Economic 

Review 89(3): 649-665.

Desvousges, W.H., and V.K Smith. 1988. “Focus Groups and Risk Communication: the Science 

of Listening to Data.” Risk Analysis 8: 479-484.

Desvousges, W.H., V.K. Smith, D.H. Brown, and D.K. Pate. 1984. “The Role of Focus Groups 

in Designing a Contingent Valuation Survey to Measure the Benefits of Hazardous Waste 

Management Regulations.” Research Triangle Institute: Research Triangle Park, NC.

Dillman, D.A. 2000. Mail and Internet Surveys: The Tailored Design Method. New York: John 

Wiley and Sons.

Duke, J.M., and T.W. Ilvento. 2004. “A Conjoint Analysis of Public Preferences for Agricultural 

Land Preservation.” Agricultural and Resource Economics Review 33(2): 209-219.

Entergy Corp. v. Riverkeeper Inc., 129 S. Ct. 1498, 1505 (2009)

Freeman, A.M., III. 2003. The Measurement of Environmental and Resource Values: Theory 

and Methods. Washington, DC: Resources for the Future.

Giraud, K.L., J.B. Loomis, and R.L. Johnson. 1999. “Internal and external scope in willingnessto-pay estimates for threatened and endangered wildlife.” Journal of Environmental 

Management 56: 221-229.

Greene, W.H. 2002. NLOGIT Version 3.0 Reference Guide. Plainview, NY: Econometric 

Software, Inc.

Greene, W.H. 2003. Econometric Analysis. 5th ed., Prentice Hall, Upper Saddle River, NJ. 

Haab, T.C., and K.E. McConnell. 2002. Valuing Environmental and Natural Resources: The 

Econometrics of Non-market Valuation. Cheltenham, UK: Edward Elgar.

Hanemann, W.M. 1984. “Welfare Evaluations in Contingent Valuation Experiments with 

Discrete Responses.” American Journal of Agricultural Economics 66(3): 332-41.
62

Hanemann, W.M., and B. Kanninen. 1999. “The Statistical Analysis of Discrete-Response CV 

Data.” In Valuing Environmental Preferences: Theory and Practice of the Contingent 

Valuation Method in the US, EU, and Developing Countries. Edited by I.J. Bateman and 

K.G. Willis, Oxford University Press, Oxford, UK.

Hanley, N., S. Colombo, D. Tinch, A. Black, and A. Aftab. 2006a. "Estimating the benefits of 

water quality improvements under the Water Framework Directive: are benefits 

transferable?," European Review of Agricultural Economics 33(3):391-413.

Hanley, N., R. E. Wright, and B. Alvarez-Farizo. 2006b. “Estimating the Economic Value of 

Improvements in River Ecology using Choice Experiments: An Application to the Water 

Framework Directive.” Journal of Environmental Management 78(2):183-193.

Heberlein, T.A., M.A. Wilson, R.C. Bishop, and N.C. Schaeffer. 2005. “Rethinking the Scope 

Test as a Criterion in Contingent Valuation.” Journal of Environmental Economics and 

Management 50(1): 1-22.

Heckman, J. J. 1979. “Sample Selection Bias as a Specification Error.” Econometrica 47(1): 

153-161.

Hensher, D. A., and Barnard, P. O. (1990). "The Orthogonality Issue in Stated Choice Designs." 

In Fischer, M., Nijkamp, P., and Papageorgiou, Y. (eds.), Spatial Choices and Processes. 

North-Holland, Amsterdam, 265-278.

Herriges, J., C. Kling, C. Lieu, and J. Tobias. 2010. “What are the consequences of 

consequentiality?” Journal of Environmental Economics and Management 59(1): 67-81.

Hoehn, J. P. 1991. “Valuing the Multidimensional Impacts of Environmental Policy: Theory and 

Methods.” American Journal of Agricultural Economics 73(2): 289-299.

Hoehn, J.P., F. Lupi, and M.D. Kaplowitz. 2004. Internet-Based Stated Choice Experiments in 

Ecosystem Mitigation: Methods to Control Decision Heuristics and Biases. In 

Proceedings of Valuation of Ecological Benefits: Improving the Science Behind Policy 

Decisions, a workshop sponsored by the US EPA National Center for Environmental 

Economics and the National Center for Environmental Research.

Hoehn, J.P., and A. Randall. 2002. “The Effect of Resource Quality Information on Resource 

Injury Perceptions and Contingent Values.” Resource and Energy Economics 24: 13-31.

Horne, P., P.C. Boxall, and W.L. Adamowicz. 2005. “Multiple-use management of forest 

recreation sites: a spatially explicit choice experiment.” Forest Ecology and 

Management 207(1/2): 189-99.
63

Johannesson, M. 1997. “Some Further Experimental Results on Hypothetical Versus Real 

Willingness to Pay.” Applied Economics Letters 4: 535-536.

Johnston, R.J., E.T. Schultz, K. Segerson and E.Y. Besedin. 2010. “Bioindicator-Based Stated 

Preference Valuation for Aquatic Habitat and Ecosystem Service Restoration”, In 

Bennett, J. ed. International Handbook on Non-Marketed Environmental Valuation. 

Cheltenham, UK: Edward Elgar, forthcoming.

Johnston, R.J. 2006. “Is Hypothetical Bias Universal? Validating Contingent Valuation 

Responses Using a Binding Public Referendum.” Journal of Environmental Economics 

and Management 52(1):469-481.

Johnston, R.J., and D.P. Joglekar. 2005. “Validating Hypothetical Surveys Using Binding Public 

Referenda: Implications for Stated Preference Valuation.” American Agricultural 

Economics Association (AAEA) Annual Meeting, Providence, July 24-27.

Johnston, R.J., J.J. Opaluch, M.J. Mazzotta, and G. Magnusson. 2005. “Who Are Resource Nonusers and What Can They Tell Us About Non-use Values? Decomposing User and Nonuser Willingness to Pay for Coastal Wetland Restoration.” Water Resources Research

41(7), doi:10.1029/2004WR003766.

Johnston, R.J., E.Y. Besedin, and R.F. Wardwell. 2003a. “Modeling Relationships Between Use 

and Non-use Values for Surface Water Quality: A Meta-Analysis.” Water Resources 

Research 39(12): 1363.

Johnston, R.J., S.K. Swallow, T.J. Tyrrell, and D.M. Bauer. 2003b. “Rural Amenity Values and 

Length of Residency.” American Journal of Agricultural Economics 85(4): 1000-1015.

Johnston, R.J., G. Magnusson, M. Mazzotta, and J.J.Opaluch. 2002a. “Combining Economic and 

Ecological Indicators to Prioritize Salt Marsh Restoration Actions.” American Journal of 

Agricultural Economics 84(5): 1362-1370.

Johnston, R.J., S.K. Swallow, C.W. Allen, and L.A. Smith. 2002b. “Designing Multidimensional 

Environmental Programs: Assessing Tradeoffs and Substitution in Watershed 

Management Plans.” Water Resources Research 38(7): IV1-13.

.Johnston, R.J., S.K Swallow and T.F. Weaver. 1999. “Estimating Willingness to Pay and 

Resource Trade-offs With Different Payment Mechanisms: An Evaluation of a Funding 

Guarantee for Watershed Management.” Journal of Environmental Economics and 

Management 38(1): 97-120.
64

Johnston, R.J., T.F. Weaver, L.A. Smith, and S.K. Swallow. 1995. “Contingent Valuation Focus 

Groups: Insights From Ethnographic Interview Techniques.” Agricultural and Resource 

Economics Review 24(1): 56-69.

Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy: A 

Practical Approach to Project and Policy Evaluation. Edward Elgar Publishing, 

Cheltenham, UK and Northampton, MA.

Kaplowicz, M.D., F. Lupi, and J.P. Hoehn. 2004. “Multiple Methods for Developing and 

Evaluating a Stated-Choice Questionnaire to Value Wetlands.” Chapter 24 in Methods 

for Testing and Evaluating Survey Questionnaires, eds. S. Presser, J.M. Rothget, M.P. 

Coupter, J.T. Lesser, E. Martin, J. Martin, and E. Singer. New York: John Wiley and 

Sons.

Kobayashi, M.K. Rollins, and M.D.R Evans. 2010. “Sensitivity of WTP Estimates to Definition 

of 'Yes': Reinterpreting Expressed Response Intensity.” Agricultural and Resource 

Economics Review 39(1): 37-55.

Kuhfeld, W.F. 2009. “Experimental Design: Efficiency, Coding and Choice Designs.” SAS 

Institute. http://support.sas.com/techsup/tnote/tnote_stat.html#market. 

Layton, D.F. 2000. “Random coefficient models for stated preference surveys.” Journal of 

Environmental Economics and Management 40(1): 21-36.

List, J.A. 2001. “Do Explicit Warnings Eliminate the Hypothetical Bias in Elicitation 

Procedures? Evidence from Field Auctions for Sportscards.” American Economic Review

91(5): 1498-1507.

Loomis, J., T. Brown, B. Lucero, and G. Peterson. 1996. “Improving Validity Experiments of 

Contingent Valuation Methods: Results of Efforts to Reduce the Disparity of 

Hypothetical and Actual Willingness to Pay.” Land Economics 72(4): 450-461.

Louviere, J.J., D.A. Hensher, and J.D. Swait. 2000. Stated Preference Methods: Analysis and 

Application. Cambridge, UK: Cambridge University Press.

Maddala, G.S. 1983. “Limited-Dependent and Qualitative Variables in Econometrics.” 

Econometric Society Monographs No. 3, Cambridge University Press, Cambridge.

Mazzotta, M.J., J.J. Opaluch, G. Magnuson, and R.J. Johnston. 2002. “Setting Priorities for 

Coastal Wetland Restoration: A GIS-Based Tool That Combines Expert Assessments 

And Public Values.” Earth System Monitor 12(3): 1-6.
65

McConnell, K.E. 1990. “Models for Referendum Data: The Structure of Discrete Choice 

Models for Contingent Valuation.” Journal of Environmental Economics and 

Management 18(1): 19-34.

McFadden, D., and K. Train. 2000. “Mixed Multinomial Logit Models for Discrete Responses.” 

Journal of Applied Econometrics 15(5): 447-470.

Mitchell, R.C., and R.T. Carson. 1981. An Experiment in Determining Willingness to Pay for 

National Water Quality Improvements. Preliminary draft of a report to the U.S. 

Environmental Protection Agency. Resources for the Future, Inc., Washington.

Mitchell, R.C., and R.T. Carson. 1984. A Contingent Valuation Estimate of National Freshwater 

Benefits: Technical Report to the U.S. Environmental Protection Agency. Washington, 

DC: Resources for the Future.

Mitchell, R.C., and R.T. Carson. 1989. Using Surveys to Value Public Goods: The Contingent 

Valuation Method. Resources for the Future, Washington, D.C.

Morrison, M., and J. Bennett. 2004. Valuing New South Wales rivers for use in benefit transfer. 

Australian Journal Of Agricultural And Resource Economics 48(4): 591-611.

Murphy, J.J., T. Stevens, and D. Weatherhead. 2004. “Is Cheap Talk Effective at Eliminating 

Hypothetical Bias in a Provision Point?” Working Paper No. 2003-2. Department of 

Resource Economics, University of Massachusetts, Amherst.

Olsen, D., J. Richards, and R.D. Scott. 1991. “Existence and Sport Values for Doubling the Size 

of Columbia River Basin Salmon and Steelhead Runs.” Rivers 2(1): 44-56.

Opaluch, J.J., T.A. Grigalunas, M. Mazzotta, R.J. Johnston, and J. Diamantedes. 1999. 

Recreational and Resource Economic Values for the Peconic Estuary. Prepared for the 

Peconic Estuary Program. Peace Dale, RI: Economic Analysis Inc. 124 pp.

Opaluch, J.J., S.K. Swallow, T. Weaver, C. Wessells, and D. Wichelns. 1993. “Evaluating 

impacts from noxious facilities: Including public preferences in current siting 

mechanisms.” Journal of Environmental Economics and Management 24(1): 41-59.

Poe, G. L., J.E. Clark, D. Rondeau, and W.D. Schulze. 2002. “Provision Point Mechanisms and 

Field Validity Tests of Contingent Valuation.” Environmental and Resource Economics

23: 105-131.

Poe, G.L., M.P. Welsh, and P.A. Champ. 1997. “Measuring the Difference in Mean Willingness 

to Pay when Dichotomous Choice Contingent Valuation Responses are not Independent.” 

Land Economics 73(2): 255-267.
66

Powe, N.E. 2007. Redesigning Environmental Valuation: Mixing Methods within Stated 

Preference Techniques. Cheltenham, UK: Edward Edgar.

Powe, N.A., and I.J. Bateman. 2004. “Investigating Insensitivity to Scope: A Split-Sample Test 

of Perceived Scheme Realism.” Land Economics 80(2): 258-271.

Ready, R.C., P.A. Champ, and J.L. Lawton. 2010. “Using Respondent Uncertainty to Mitigate 

Hypothetical Bias in a Stated Choice Experiment.” Land Economics 86(2): 363-381.

Ready, R.C., J.C. Whitehead, and G.C. Blomquist. 1995. “Contingent Valuation When 

Respondents are Ambivalent.” Journal of Environmental Economics and Management 

29(2): 181-196.

Smith, V. K., and C. Mansfield. 1998. “Buying Time: Real and Hypothetical Offers.” Journal of 

Environmental Economics and Management 36: 209-224.

Schkade, D.A. and J.W. Payne. 1994. “How People Respond to Contingent Valuation Questions: 

A Verbal Protocol Analysis of Willingness to Pay for an Environmental Regulation.” 

Journal of Environmental Economics and Management 26: 88-109.

Train, K. 1998. “Recreation Demand Models with Taste Differences Over People.” Land 

Economics 74(2): 230-239. 

U.S. Department of Labor, Bureau of Labor Statistics. 2009. Table 1: Civilian workers, by 

major occupational and industry group. September 2009. 

http://www.bls.gov/news.release/ecec.t01.htm.

U.S. EPA. 2000. Guidelines for Preparing Economic Analyses. (EPA 240-R-00-003). U.S. EPA, 

Office of the Administrator, Washington, DC, September 2000.

U.S. EPA. 2006. Peer Review Handbook 3rd Edition. (EPA 100-B-06-002). U.S. EPA, Science 

Policy Council, Washington, DC, 2006.

Versar. 2006. Comments Summary Report: Peer Review Package for "Willingness to Pay

Survey Instrument for §316(b) Phase III Cooling Water Intake Structures.” Prepared by 

Versar Inc., Springfield, VA.

Viscusi, W. Kip, Joel Huber, and Jason Bell. 2008. “The Economic Value of Water Quality.” 

Environmental and Resource Economics 41(2): 169-187.

Vossler, C.A., and M.F. Evans. 2009. “Bridging the gap between the field and the lab: 

Environmental goods, policy maker input, and consequentiality.” Journal of 

Environmental Economics and Management 58(3):338-345.
67

Vossler, C.A., and J. Kerkvliet. 2003. “A Criterion Validity Test of the Contingent Valuation 

Method: Comparing Hypothetical and Actual Voting Behavior for a Public 

Referendum.” Journal of Environmental Economics and Management 45(3): 631-649.

Whitehead, J.C., G.C. Blomquist, T.J.Hoban, and W.B. Clifford. 1995. “Assessing the Validity

and Reliability of Contingent Values: A Comparison of On Site Users, Off Site Users, 

and Non-users.” Journal of Environmental Economics and Management 29(2): 238-251.

Whitehead, J.C., and P.A. Groothuis. 1992. “Economic Benefits of Improved Water Quality: a 

Case Study of North Carolina's Tar Pamlico River.” Rivers 3: 170-178.
68

Attachment 1: Full Text of Regional Stated Preference Survey Component (Northeast 

Regional Example)
69

Fish and Aquatic Habitat

A Survey of Northeast Residents

(CT, DC, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT)

The public reporting and recordkeeping burden for this collection of information is estimated to average 30 minutes per 

response. Send comments on the Agency's need for this information, the accuracy of the provided burden estimates, and 

any suggested methods for minimizing respondent burden, including through the use of automated collection techniques 

to the Director, Collection Strategies Division, U.S. Environmental Protection Agency (2822T), 1200 Pennsylvania Ave., 

NW, Washington, D.C. 20460. Include the OMB control number in any correspondence. Do not send the completed 

survey to this address.

OMB Control No. 2040-XXXX 

Approval expires XX/XX/XX 
70

HUMAN ACTIVITIES, AQUATIC HABITAT 

AND FISH

This survey asks for your opinions regarding policies that would affect fish and habitat 

in the Northeast U.S. Your answers will help the government decide which policies will 

be enacted. Background information in this survey was provided by the National 

Marine Fisheries Service, U.S. Environmental Protection Agency, U.S. Geological 

Survey and other state and federal offices.

Northeast fresh and salt waters support billions of fish. These include fish that are used 

by humans, as well as forage fish that are not used by humans, but serve as food for 

larger fish, birds, and animals. 

This survey concerns proposed policies that would reduce fish losses caused by

cooling water use by industrial facilities, including factories and power plants.

These policies would benefit aquatic ecosystems but would increase the costs 

of some goods and services you buy, including electricity and common 

household products.

1

1

Natural factors such as weather have 

always influenced fish, but in recent years 

human activities have had an increasing 

effect.

Activities that affect fish include fishing, 

pollution, commercial and residential 

development, and the extraction of cooling 

water at industrial facilities. 

Declines in fish can affect the condition of 

ecological systems, food webs, and related 

human uses such as fishing. Food Web

Birds/Mammals

Fish

Microorganisms
71

HOW DOES COOLING WATER 

AFFECT FISH?

The water that industrial facilities use to cool equipment is pumped from bays, rivers, 

and lakes. The largest amount is used by power plants that produce electricity.

The equipment that pumps the cooling 

water kills small fish and fish eggs.

Juvenile fish and eggs move through 

screens and into the cooling system 

where they are killed by high 

temperature.

Large fish may be injured or killed 

against screens or filters. 

Pumping warm water back into the 

environment (called thermal discharge) 

also affects ecological systems.

Cooling Water 

Intake Screen

2

Cooling water use affects fresh 

and salt waters throughout the 

Northeast US, but the majority 

of all fish losses are in salt

waters such as coastal bays. 

How Fish Are Affected by Water Intake
72

WHAT KINDS OF FISH ARE AFFECTED?

Cooling water use is not the largest cause of fish loss in most areas (fishing causes 

greater losses), but has affected some fish populations.

About 1/6 of the fish lost are species caught by commercial and recreational 

fishermen. Examples include striped bass, flounder, and cod.

The other 5/6 of the fish lost are forage species not caught by humans but are 

part of the food web. Examples include killifish, silverside, and stickleback.

Question 1. When thinking about how industrial facilities use cooling water, please 

rate the importance of the following to you. Check one box for each.

Not 

Important

Somewhat 

Important

Very

Important

1. Preventing the loss of fish that

are caught by humans 1 2 3 4 5

2. Preventing the loss of fish that 

are not caught by humans 1 2 3 4 5

3. Maintaining the ecological health 

of rivers, lakes and bays 1 2 3 4 5

4. Keeping the cost of goods and 

services low 1 2 3 4 5

5. Making sure there is enough 

government regulation of industry 1 2 3 4 5

6. Making sure there is not too 

much government regulation of 

industry

1 2 3 4 5

3
73

HOW MANY FISH ARE AFFECTED?

After accounting for the number of eggs and larvae that would be expected to 

survive to adulthood, scientists estimate that the equivalent of about 1.1 billion 

young adult fish (the equivalent of one year old) are lost each year in Northeast 

coastal and fresh waters due to cooling water use. 

Scientists can predict the number of these fish that will be saved under different 

policies. This number ranges from 0.3 to 1.0 billion fish saved per year. 

For commercial fish species, losses of young fish in cooling water intakes vary

by species, from the equivalent of less than 0.1% to about 10% of total 

populations.

Yearly effects on fish species are between these levels. For example, the number of 

young fish lost in cooling water intakes relative to the total number of fish in the water 

is relatively high for some species, but low for others.

Although scientists can predict the number of fish saved each year, the effect on 

fish populations is uncertain. This is because scientists do not know the total 

number of all fish in Northeast waters and because many factors – such as 

cooling water use, fishing, pollution and water temperature – affect fish 

populations.

The following page provides information on policies that would be required to reduce 

these fish losses.

Largest Fish Losses per Year

Total Fish Population

Losses

10%

Smallest Fish Losses per Year

Losses

< 0.1%

Total Fish Population

4
74

NEW REGULATIONS ARE BEING 

PROPOSED TO PROTECT FISH

 

Current Situation

1.1 Billion Fish Lost per Year 

(0% saved)

 

With Advanced Filters

0.8 Billion Fish Lost per Year

( 25% Saved)

0.3 Billion Fish 

Saved per Year

 

With Closed Cycle Cooling

<0.1 Billion Fish Lost per Year

(95% saved)

>1.0 Billion Fish 

Saved per Year

Advanced filters and closed cycle cooling are already in use at many facilities and are 

proven technologies. New regulations would require a mix of advanced filters and 

closed cycle cooling at all facilities—with reductions in fish losses between 25% 

and 95%.

The government is considering policies that 

would require new measures to protect fish.

One policy would require advanced filters

that block fish from entering cooling water 

facilities. Requiring advanced filters could 

reduce fish losses about 25%. 

Another possibility is closed cycle cooling

that recycles and reuses cooling water, so 

that less water is needed. Requiring closed 

cycle cooling could reduce fish losses by 

95% and also reduces thermal discharge. 

However, costs are higher than for advanced 

filters.

5
75

HOW IMPORTANT ARE THESE ISSUES TO 

YOU?

While these policies would reduce fish losses, they would also increase the 

costs of producing many goods and services — these costs would be passed on 

to consumers like you.

Question 2. Compared to other issues that the government might address—such as 

public safety, education and health—how important is protecting aquatic ecosystems to 

you? Check one box.

Not 

Important

Somewhat 

Important

Very 

Important

Protecting aquatic ecosystems is 1 2 3 4 5

The government needs to know whether households are willing to pay the costs 

of these new policies. 

This survey will ask you to compare policies with different effects on cooling 

water use, fish, and costs to your household. You will be asked to vote for the 

options you prefer.

You will also have the opportunity to support the current situation, with no new

policies, and no new costs to your household.

6
76

THIS SURVEY IS SIMILAR TO A 

PUBLIC VOTE

The next part of this survey will ask you to consider different types of policies to protect 

fish, and indicate how you would vote. Effects of each possible policy will be described 

using the following scores:

Effect of 

Policy What It Means

Commercial Fish 

Populations

(Fish Used by 

People)

A score between 0 and 100 showing the overall health of commercial 

and recreational fish populations. Higher scores mean more fish and 

greater fishing potential. A score of 100 means that these fish 

populations are at a size that maximizes long-term harvest; 0 means no 

harvest. The current score in Northeast waters is 43.

Fish Populations

(All Fish)

A score between 0 and 100 showing the estimated size of all fish 

populations compared to natural levels without human influence. A 

score of 100 means that populations are the largest natural size 

possible; 0 means no fish. The current score in Northeast waters is 

31.

Fish Saved

(per Year)

A score between 0 and 100 showing the reduction in young fish 

lost because of the new policy. A score of 100 means that no fish 

are lost in cooling water intakes (all fish are saved). The current 

score in Northeast waters is 0; this represents the status quo 

with no new policies.

Condition of

Aquatic 

Ecosystems

A score between 0 to 100 showing the ecological condition of 

affected areas, compared to the most natural waters in the Northeast. 

The score is determined by many factors including water quality and 

temperature, the health of aquatic species, and habitat conditions. 

Higher scores mean the area is more natural. The current score in 

Northeast waters is 48.

$

Cost per Year

How much the policy will cost your household, in unavoidable price 

increases for products and services you buy, including electricity

and common household products.

7
77

HOW WOULD YOU RATE THE 

IMPORTANCE OF THESE EFFECTS?

Question 3. When considering policies that affect how facilities use cooling water, how 

important to you are effects on each of the following scores? Check one box for each. 

(For reminders of what the scores mean, please see page 7).

Not 

Important

Somewhat 

Important

Very 

Important

1. Effect on commercial fish 

populations 1 2 3 4 5

2. Effect on the fish 

populations (for all fish) 1 2 3 4 5

3. Effect on fish saved 1 2 3 4 5

4. Effect on the condition of

aquatic ecosystems 1 2 3 4 5

5. Effect on cost to my 

household 1 2 3 4 5

The next questions will ask you to choose between different policy options that would 

affect fish losses in cooling water systems. You will be given choices and asked to vote 

for the choice you prefer by checking the appropriate box. Questions will look similar to 

the sample on the next page. 

8
78

SAMPLE QUESTION

Questions will look like the sample below.

Policy Effect

Current 

Situation

(No policy)

Option A Option B

Commercial Fish 

Populations

(in 3-5 Years)

43%

(100% is populations 

that allow for maximum 

harvest)

45%

(100% is populations 

that allow for maximum 

harvest)

47%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

31%

(100% is populations 

without human 

influence)

34%

(100% is populations 

without human 

influence)

38%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 1.1 billion fish lost 

in water intakes)

0%

No change in status quo

25%

0.3 billion fish saved

50%

0.6 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

48%

(100% is pristine 

condition) 

48%

(100% is pristine 

condition)

50%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$24

per year

($2 per month)

$36

per year

($3 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

If you prefer Option A, 

check this box

If you do not want A

or B, check this box

If you prefer Option B,

check this box

X X X

9
79

AS YOU VOTE PLEASE REMEMBER

 The map below shows the facilities and areas that would be affected by the 

proposed policies.

 The policy options (A and B) given to you each require a different mix of 

advanced filters and closed cycle cooling in different areas, so effects on fish 

are different. 

 You will be shown different questions, with different combinations of 

technology and different costs

 Depending on the policies chosen, costs to your household could range from 

$0 per month to a maximum of $6 per month.

 Depending on the type of technology required and other factors, effects on 

fish and ecosystems may be different—even if the annual reduction in fish 

losses is similar.

 Consider each pair of policy options separately—do not add them up or 

compare programs from different pages.

 Scientists expect that effects on the environment and economy not shown 

explicitly will be small. For example, studies of industry suggest that effects 

on employment will be close to zero.

 Your votes are important. Answer all questions as if this were a real, binding 

vote.

10
80

Question 4. Assume that Options A and B would require a different mix of 

filters and closed cycle cooling in different areas. Assume all types of fish 

are affected. How would you vote?

Policy Effect

NE Waters

Current 

Situation

(No policy)

Option A

NE Waters

Option B

NE Waters

Commercial Fish 

Populations

(in 3-5 Years)

43%

(100% is populations 

that allow for maximum 

harvest)

44%

(100% is populations 

that allow for maximum 

harvest)

45%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

31%

100% is populations 

without human 

influence)

32%

(100% is populations 

without human 

influence)

35%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 1.1 billion fish 

lost in water intakes)

0%

No change in status quo

25%

0.3 billion fish saved

50%

0.6 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

48%

(100% is pristine 

condition)

49%

(100% is pristine 

condition)

50%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$24

per year

($2 per month)

$36

per year

($3 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

11
81

POLICIES COULD REQUIRE DIFFERENT 

COMBINATIONS OF TECHNOLOGY 

Now you will be asked to consider a new set of policy options for Northeast 

waters. As you vote, please remember—

 Questions 5 and 6 present new sets of policy options. These options require a 

different mix of technologies in different areas. 

 Each question is a separate vote. Questions 5 and 6 cannot be directly 

compared to each other, or to Question 4.

 Do not add up effects or costs across different questions.

 Policy costs and effects depend on many factors. Saving more fish does not

necessarily mean that all effects will improve.

12
82

Question 5. Assume that Options A and B would require a different mix of 

filters and closed cycle cooling in different areas. Assume all types of fish 

are affected. How would you vote?

Policy Effect

NE Waters

Current 

Situation

(No policy)

Option A

NE Waters

Option B

NE Waters

Commercial Fish 

Populations

(in 3-5 Years)

43%

(100% is populations 

that allow for maximum 

harvest)

45%

(100% is populations 

that allow for maximum 

harvest)

47%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

31%

(100% is populations 

without human 

influence)

33%

(100% is populations 

without human 

influence)

36%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 1.1 billion fish 

lost in water intakes)

0%

No change in status quo

25%

0.3 billion fish saved

75%

0.8 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

48%

(100% is pristine 

condition)

48%

(100% is pristine 

condition)

52%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$36

per year

($3 per month)

$60

per year

($5 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

13
83

Question 6. Assume that Options A and B would require a different mix of 

filters and closed cycle cooling in different areas. Assume all types of fish 

are affected. How would you vote?

Policy Effect

NE Waters

Current 

Situation

(No policy)

Option A

NE Waters

Option B

NE Waters

Commercial Fish 

Populations

(in 3-5 Years)

43%

(100% is populations 

that allow for maximum 

harvest)

50%

(100% is populations 

that allow for maximum 

harvest)

47%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

31%

(100% is populations 

without human 

influence)

39%

(100% is populations 

without human 

influence)

36%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 1.1 billion fish 

lost in water intakes)

0%

No change in status quo

95%

1.0 billion fish saved

50%

0.6 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

48%

(100% is pristine 

condition)

52%

(100% is pristine 

condition)

52%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$72

per year

($6 per month)

$24

per year

($2 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

14
84

Question 7. If you always voted for NO POLICY in questions 4-6, what was the 

primary reason? Check one. (Skip this question if you voted for Option A or B in any 

question above.)

____ The cost to my household was too high

____ Preventing fish losses is not important to me

____ I do not trust the government to fix the problem

____ I would rather spend my money on other things

____ I did not believe the choices were realistic

____ Since the problem was created by private facilities, they should fix it 

without passing costs on to consumers

Question 8. Indicate how strongly you agree with the following statements about 

questions 4 - 6 and the information provided. Check one box for each.

Strongly

Disagree

Disagree Neutral Agree Strongly

Agree

The survey provided enough information for 

me to make informed choices 1 2 3 4 5

I feel confident about my answers 1 2 3 4 5

Information in the survey was easy for me to 

understand 1 2 3 4 5

Information in the survey was fair and 

unbiased 1 2 3 4 5

Questions were easy for me to answer 1 2 3 4 5

I would vote the same way in an actual 

public vote 1 2 3 4 5

The effect of the proposed policies depends 

on many factors 1 2 3 4 5

Future ecological conditions are never 100% 

guaranteed 1 2 3 4 5

15
85

Question 9. How much did the following factors affect your answers to questions 4 –

6? Check one box for each row.

Effect on my answers to questions 4-6

Very Small 

Effect

Moderate 

Effect

Very Large 

Effect

.

Wanting to reduce taxes or costs 

to my household. 1 2 3 4 5

Wanting to prevent the loss of 

industrial jobs. 1 2 3 4 5

Wanting to preserve fish for 

commercial fishing. 1 2 3 4 5

Wanting to send a message that 

all environmental issues are 

important regardless of cost.

1 2 3 4 5

Wanting to preserve fish for 

recreation (fishing, etc.). 1 2 3 4 5

Wanting to preserve fish to benefit 

aquatic ecosystems. 1 2 3 4 5

Wanting to know that fish exist in 

local lakes, rivers and bays. 1 2 3 4 5

Wanting to pay my fair share for 

government programs. 1 2 3 4 5

Wanting to sustain the 

competitiveness of US business 1 2 3 4 5

Wanting to preserve fish as a 

source of food for people. 1 2 3 4 5

Wanting to preserve fish and 

ecosystems for future generations. 1 2 3 4 5

16
86

Question 10. How many days did you participate in the following during the last year? 

For trips longer than one day, please count each day separately. Check one box for 

each row.

Number of days you did the activity during the past year

0 1-5 6-10 11-15 16+

Boating / Canoeing / Kayaking 1 2 3 4 5

Swimming / Going to the Beach 1 2 3 4 5

Recreational Fishing (Fresh Water) 1 2 3 4 5

Recreational Fishing (Salt Water) 1 2 3 4 5

Shellfishing / Crabbing 1 2 3 4 5

Scuba Diving / Snorkeling 1 2 3 4 5

18

17
87

The following questions ensure that all groups are fairly represented. 

All answers are anonymous and confidential.

11.What is your age?___ years

12.What is your gender?  Male  Female

13.What is the highest level of education that you have completed?

Less than high school One or more years of college

High school or equivalent Bachelor’s Degree

High school + technical school Graduate Degree

14. How many people live in your household? ______________

15. How many of these people are 16 years of age or older? ____

16. How many of these people are 6 years of age or younger? ____

17.What is your zip code? _____________________________

18.What town and state do you live in? Town: _____________ State:____

19.Are you currently employed? ___Yes _____ No

20.What category comes closest to your total household income?

 Less than $10,000  $60,000 to $79,999

 $10,000 to $19,999  $80,000 to $99,999

 $20,000 to $39,999  $100,000 to $249,999

 $40,000 to $59,999  $250,000 or more

21. If you have any comments on this survey, please write them below:

Thank you for your participation in this important survey!

18
88

Attachment 2: Full Text of National Stated Preference Survey Component
89

Fish and Aquatic Habitat

A Survey of US Households

The public reporting and recordkeeping burden for this collection of information is estimated to average 30 minutes per 

response. Send comments on the Agency's need for this information, the accuracy of the provided burden estimates, and 

any suggested methods for minimizing respondent burden, including through the use of automated collection techniques 

to the Director, Collection Strategies Division, U.S. Environmental Protection Agency (2822T), 1200 Pennsylvania Ave., 

NW, Washington, D.C. 20460. Include the OMB control number in any correspondence. Do not send the completed 

survey to this address.

OMB Control No. 2040-XXXX 

Approval expires XX/XX/XX 
90

HUMAN ACTIVITIES, AQUATIC HABITAT 

AND FISH

This survey asks for your opinions regarding policies that would affect fish and habitat 

in the U.S. Your answers will help the government decide which policies will be 

enacted. Background information in this survey was provided by the National Marine 

Fisheries Service, U.S. Environmental Protection Agency, U.S. Geological Survey and 

other state and federal offices.

Northeast fresh and salt waters support billions of fish. These include fish that are used 

by humans, as well as forage fish that are not used by humans, but serve as food for 

larger fish, birds, and animals. 

This survey concerns proposed policies that would reduce fish losses caused by

cooling water use by industrial facilities, including factories and power plants.

These policies would benefit aquatic ecosystems but would increase the costs 

of some goods and services you buy, including electricity and common 

household products.

Natural factors such as weather have 

always influenced fish, but in recent years 

human activities have had an increasing 

effect.

Activities that affect fish include fishing, 

pollution, commercial and residential 

development, and the extraction of cooling 

water at industrial facilities. 

Declines in fish can affect the condition of 

ecological systems, food webs, and related 

human uses such as fishing. Food Web

1

1

Birds/Mammals

Fish

Microorganisms
91

HOW DOES COOLING WATER 

AFFECT FISH?

The water that industrial facilities use to cool equipment is pumped from bays, rivers, 

and lakes. The largest amount is used by power plants that produce electricity.

The equipment that pumps the cooling 

water kills small fish and fish eggs.

Juvenile fish and eggs move through 

screens and into the cooling system 

where they are killed by high 

temperature.

Large fish may be injured or killed 

against screens or filters. 

Pumping warm water back into the 

environment (called thermal discharge) 

also affects ecological systems.

Cooling Water 

Intake Screen

2

Cooling water use affects fresh and salt waters throughout the US, but almost all fish 

losses are in salt waters such as coastal bays. 

How Fish Are Affected by Water Intake
92

WHAT KINDS OF FISH ARE AFFECTED?

Cooling water use is not the largest cause of fish loss in most areas (fishing causes 

greater losses), but has affected some fish populations.

About 1/3 of the fish lost are species caught by commercial and recreational 

fishermen. Examples include striped bass, flounder, and cod.

The other 2/3 of the fish lost are forage species not caught by humans but are 

part of the food web. Examples include killifish, silverside, and stickleback.

Question 1. When thinking about how industrial facilities use cooling water, please 

rate the importance of the following to you. Check one box for each.

Not 

Important

Somewhat 

Important

Very

Important

1. Preventing the loss of fish that 

are caught by humans 1 2 3 4 5

2. Preventing the loss of fish that 

are not caught by humans 1 2 3 4 5

3. Maintaining the ecological 

health of rivers, lakes and 

bays

1 2 3 4 5

4. Keeping the cost of goods 

and services low 1 2 3 4 5

5. Making sure there is enough 

government regulation of 

industry

1 2 3 4 5

6. Making sure there is not too 

much government regulation 

of industry

1 2 3 4 5

3
93

HOW MANY FISH ARE AFFECTED?

After accounting for the number of eggs and larvae that would be expected to 

survive to adulthood, scientists estimate that the equivalent of about 2.7 billion 

young adult fish (the equivalent of one year old) are lost each year in U.S. 

coastal and fresh waters due to cooling water use. 

Scientists can predict the number of these fish that will be saved under different 

policies. This number ranges from 0.7 to 2.6 billion fish saved per year. 

For commercial fish species, losses of young fish in cooling water intakes vary

by species, from the equivalent of less than 0.1% to about 10% of total 

populations.

Yearly effects on fish species are between these levels. For example, the number of 

young fish lost in cooling water intakes relative to the total number of fish in the water 

is relatively high for some species, but low for others.

Although scientists can predict the number of fish saved each year, the effect on 

fish populations is uncertain. This is because scientists do not know the total 

number of all fish in U.S. waters and because many factors – such as cooling 

water use, fishing, pollution and water temperature – affect fish populations.

The following page provides information on policies that would be required to reduce 

these fish losses.

Largest Fish Losses per Year

Total Fish Population

Losses

10%

Smallest Fish Losses per Year

Losses

< 0.1%

Total Fish Population

4
94

NEW REGULATIONS ARE BEING 

PROPOSED TO PROTECT FISH

 

Current Situation

2.7 Billion Fish Lost per Year

(0% Saved)

 

With Advanced Filters

2.1 Billion Fish Lost per Year

(22% Saved)

0.6 Billion Fish 

Saved per Year

 

With Closed Cycle Cooling

0.1 Billion Fish Lost per Year

(95% Saved)

2.6 Billion Fish 

Saved per Year

Advanced filters and closed cycle cooling are already in use at many facilities and are 

proven technologies. New regulations would require a mix of advanced filters and 

closed cycle cooling at all facilities—with reductions in fish losses between 25% 

and 95%.

The government is considering policies that 

would require new measures to protect fish.

One policy would require advanced filters

that block fish from entering cooling water 

facilities. Requiring advanced filters could 

reduce fish losses about 25%. 

Another possibility is closed cycle cooling

that recycles and reuses cooling water, so 

that less water is needed. Requiring closed 

cycle cooling could reduce fish losses by 

95% and also reduces thermal discharge. 

However, costs are higher than for advanced 

filters.

5
95

HOW IMPORTANT ARE THESE ISSUES TO 

YOU?

While these policies would reduce fish losses, they would also increase the 

costs of producing many goods and services — these costs would be passed on 

to consumers like you.

Question 2. Compared to other issues that the government might address—such as 

public safety, education and health—how important is protecting aquatic ecosystems to 

you? Check one box.

Not 

Important

Somewhat 

Important

Very 

Important

Protecting aquatic ecosystems is 1 2 3 4 5

The government needs to know whether households are willing to pay the costs 

of these new policies. 

This survey will ask you to compare policies with different effects on cooling 

water use, fish, and costs to your household. You will be asked to vote for the 

options you prefer.

You will also have the opportunity to support the current situation, with no new

policies, and no new costs to your household.

6
96

THIS SURVEY IS SIMILAR TO A 

PUBLIC VOTE

The next part of this survey will ask you to consider different types of policies to protect 

fish, and indicate how you would vote. Effects of each possible policy will be described 

using the following scores:

Effect of 

Policy What It Means

Commercial Fish 

Populations

(Fish Used by 

People)

A score between 0 and 100 showing the overall health of commercial 

and recreational fish populations. Higher scores mean more fish and 

greater fishing potential. A score of 100 means that these fish 

populations are at a size that maximizes long-term harvest; 0 means no 

harvest. The current score in U.S. waters is 43.

Fish Populations

(All Fish)

A score between 0 and 100 showing the estimated size of all fish 

populations compared to natural levels without human influence. A 

score of 100 means that populations are the largest natural size 

possible; 0 means no fish. The current score in U.S. waters is 31.

Fish Saved

(per Year)

A score between 0 and 100 showing the reduction in young fish 

lost because of the new policy. A score of 100 means that no fish 

are lost in cooling water intakes (all fish are saved). The current 

score in U.S. waters is 0; this represents the status quo with no 

new policies.

Condition of

Aquatic 

Ecosystems

A score between 0 to 100 showing the ecological condition of 

affected areas, compared to the most natural waters in the U.S.. The 

score is determined by many factors including water quality and 

temperature, the health of aquatic species, and habitat conditions. 

Higher scores mean the area is more natural. The current score in 

U.S. waters is 48.

$

Cost per Year

How much the policy will cost your household, in unavoidable price 

increases for products and services you buy, including electricity

and common household products.

7
97

HOW WOULD YOU RATE THE 

IMPORTANCE OF THESE EFFECTS?

Question 3. When considering policies that affect how facilities use cooling water, how 

important to you are effects on each of the following scores? Check one box for each. 

(For reminders of what the scores mean, please see page 7).

Not 

Important

Somewhat

Important

Very 

Important

1. Effect on commercial fish 

populations 1 2 3 4 5

2. Effect on the fish 

populations (for all fish) 1 2 3 4 5

3. Effect on fish saved 1 2 3 4 5

4. Effect on the condition of

aquatic ecosystems 1 2 3 4 5

5. Effect on cost to my 

household 1 2 3 4 5

The next questions will ask you to choose between different policy options that would 

affect fish losses in cooling water systems. You will be given choices and asked to vote 

for the choice you prefer by checking the appropriate box. Questions will look similar to 

the sample on the next page. 

8
98

SAMPLE QUESTION

Questions will look like the sample below.

Policy Effect

Current 

Situation

(No policy)

Option A Option B

Commercial Fish 

Populations

(in 3-5 Years)

53%

(100% is populations 

that allow for maximum 

harvest)

55%

(100% is populations 

that allow for maximum 

harvest)

57%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

39%

(100% is populations 

without human 

influence)

42%

(100% is populations 

without human 

influence)

42%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 2.7 billion fish lost 

in water intakes)

0%

No change in status quo

25%

0.7 billion fish saved

50%

1.4 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

46%

(100% is pristine 

condition) 

46%

(100% is pristine 

condition)

48%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$24

per year

($2 per month)

$48

per year

($3 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

If you prefer Option A, 

check this box

If you do not want A

or B, check this box

If you prefer Option B,

check this box

X X X

9
99

AS YOU VOTE PLEASE REMEMBER

 The map below shows the facilities and areas that would be affected by the 

proposed policies.

 The policy options (A and B) given to you each require a different mix of 

advanced filters and closed cycle cooling in different areas, so effects on fish 

are different. 

 You will be shown different questions, with different combinations of 

technology and different costs

 Depending on the policies chosen, costs to your household could range from 

$0 per month to a maximum of $6 per month.

 Depending on the type of technology required and other factors, effects on 

fish and ecosystems may be different—even if the annual reduction in fish 

losses is similar.

 Consider each pair of policy options separately—do not add them up or 

compare programs from different pages.

 Scientists expect that effects on the environment and economy not shown 

explicitly will be small. For example, studies of industry suggest that effects 

on employment will be close to zero.

 Your votes are important. Answer all questions as if this were a real, binding 

vote.

10
100

Question 4. Assume that Options A and B would require a different mix of 

filters and closed cycle cooling in different areas. Assume all types of fish 

are affected. How would you vote?

Policy Effect

US

Current 

Situation

(No policy)

Option A

US

Option B

US

Commercial Fish 

Populations

(in 3-5 Years)

53%

(100% is populations 

that allow for maximum 

harvest)

55%

(100% is populations 

that allow for maximum 

harvest)

57%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

39%

100% is populations 

without human 

influence)

40%

(100% is populations 

without human 

influence)

43%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 2.7 billion fish 

lost in water intakes)

0%

No change in status quo

25%

0.7 billion fish saved

50%

1.4 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

46%

(100% is pristine 

condition)

47%

(100% is pristine 

condition)

48%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$24

per year

($2 per month)

$48

per year

($3 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

11
101

POLICIES COULD REQUIRE DIFFERENT 

COMBINATIONS OF TECHNOLOGY

Now you will be asked to consider a new set of policy options for U.S. waters. As 

you vote, please remember—

 Questions 5 and 6 present new sets of policy options. These options require a 

different mix of technologies in different areas. 

 Each question is a separate vote. Questions 5 and 6 cannot be directly 

compared to each other, or to Question 4.

 Do not add up effects or costs across different questions.

 Policy costs and effects depend on many factors. Saving more fish does not

necessarily mean that all effects will improve.

12
102

Question 5. Assume that Options A and B would require a different mix of 

filters and closed cycle cooling in different areas. Assume all types of fish 

are affected. How would you vote?

Policy Effect

US

Current 

Situation

(No policy)

Option A

US

Option B

US

Commercial Fish 

Populations

(in 3-5 Years)

53%

(100% is populations 

that allow for maximum 

harvest)

55%

(100% is populations 

that allow for maximum 

harvest)

57%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

39%

(100% is populations 

without human 

influence)

41%

(100% is populations 

without human 

influence)

43%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 2.7 billion fish lost 

in water intakes)

0%

No change in status quo

25%

0.7 billion fish saved

75%

2.0 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

46%

(100% is pristine 

condition)

46%

(100% is pristine 

condition)

50%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$36

per year

($3 per month)

$60

per year

($5 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

13
103

Question 6. Assume that Options A and B would require a different mix of 

filters and closed cycle cooling in different areas. Assume all types of fish 

are affected. How would you vote?

Policy Effect

US

Current 

Situation

(No policy)

Option A

US

Option B

US

Commercial Fish 

Populations

(in 3-5 Years)

53%

(100% is populations 

that allow for maximum 

harvest)

60%

(100% is populations 

that allow for maximum 

harvest)

57%

(100% is populations 

that allow for maximum 

harvest)

Fish Populations

(all fish)

(in 3-5 Years)

39%

(100% is populations 

without human 

influence)

47%

(100% is populations 

without human 

influence)

44%

(100% is populations 

without human 

influence)

Fish Saved per Year

(Out of 2.7 billion fish lost 

in water intakes)

0%

No change in status quo

95%

2.6 billion fish saved

50%

1.4 billion fish saved

Condition of Aquatic 

Ecosystems 

(in 3-5 Years)

46%

(100% is pristine 

condition)

50%

(100% is pristine 

condition)

50%

(100% is pristine 

condition)

$

Increase in Cost of 

Living for Your 

Household

$0

No cost increase

$96

per year

($6 per month)

$48

per year

($2 per month)

HOW WOULD 

YOU VOTE?

(CHOOSE ONE 

ONLY)



I would vote for

NO POLICY



I would vote for

OPTION A



I would vote for

OPTION B

14
104

Question 7. If you always voted for NO POLICY in questions 4-6, what was the 

primary reason? Check one. (Skip this question if you voted for Option A or B in any 

question above.)

____ The cost to my household was too high

____ Preventing fish losses is not important to me

____ I do not trust the government to fix the problem

____ I would rather spend my money on other things

____ I did not believe the choices were realistic

____ Since the problem was created by private facilities, they should fix it 

without passing costs on to consumers

Question 8. Indicate how strongly you agree with the following statements about 

questions 4 - 6 and the information provided. Check one box for each.

Strongly

Disagree

Disagree Neutral Agree Strongly

Agree

The survey provided enough information for 

me to make informed choices 1 2 3 4 5

I feel confident about my answers 1 2 3 4 5

Information in the survey was easy for me to 

understand 1 2 3 4 5

Information in the survey was fair and 

unbiased 1 2 3 4 5

Questions were easy for me to answer 1 2 3 4 5

I would vote the same way in an actual 

public vote 1 2 3 4 5

The effect of the proposed policies depends 

on many factors 1 2 3 4 5

Future ecological conditions are never 100% 

guaranteed 1 2 3 4 5

15
105

Question 9. How much did the following factors affect your answers to questions 4 –

6? Check one box for each row.

Effect on my answers to questions 4-6

Very Small 

Effect

Moderate 

Effect

Very Large 

Effect

.

Wanting to reduce taxes or costs 

to my household. 1 2 3 4 5

Wanting to prevent the loss of 

industrial jobs. 1 2 3 4 5

Wanting to preserve fish for 

commercial fishing. 1 2 3 4 5

Wanting to send a message that 

all environmental issues are 

important regardless of cost.

1 2 3 4 5

Wanting to preserve fish for 

recreation (fishing, etc.). 1 2 3 4 5

Wanting to preserve fish to benefit 

aquatic ecosystems. 1 2 3 4 5

Wanting to know that fish exist in 

local lakes, rivers and bays. 1 2 3 4 5

Wanting to pay my fair share for 

government programs. 1 2 3 4 5

Wanting to sustain the 

competitiveness of US business 1 2 3 4 5

Wanting to preserve fish as a 

source of food for people. 1 2 3 4 5

Wanting to preserve fish and 

ecosystems for future generations. 1 2 3 4 5

16
106

Question 10. How many days did you participate in the following during the last year? 

For trips longer than one day, please count each day separately. Check one box for 

each row.

Number of days you did the activity during the past year

0 1-5 6-10 11-15 16+

Boating / Canoeing / Kayaking 1 2 3 4 5

Swimming / Going to the Beach 1 2 3 4 5

Recreational Fishing (Fresh Water) 1 2 3 4 5

Recreational Fishing (Salt Water) 1 2 3 4 5

Shellfishing / Crabbing 1 2 3 4 5

Scuba Diving / Snorkeling 1 2 3 4 5

18

17
107

The following questions ensure that all groups are fairly represented. 

All answers are anonymous and confidential.

11.What is your age?___ years

12.What is your gender?  Male  Female

13.What is the highest level of education that you have completed?

Less than high school One or more years of college

High school or equivalent Bachelor’s Degree

High school + technical school Graduate Degree

14. How many people live in your household? ______________

15. How many of these people are 16 years of age or older? ____

16. How many of these people are 6 years of age or younger? ____

17.What is your zip code? _____________________________

18.What town and state do you live in? Town: _____________ State:____

19.Are you currently employed? ___Yes _____ No

20.What category comes closest to your total household income?

 Less than $10,000  $60,000 to $79,999

 $10,000 to $19,999  $80,000 to $99,999

 $20,000 to $39,999  $100,000 to $249,999

 $40,000 to $59,999  $250,000 or more

21. If you have any comments on this survey, please write them below:

Thank you for your participation in this important survey!

18
108

Attachment 3: Federal Register Notice

ENVIRONMENTAL PROTECTION AGENCY

[EPA-HQ-OW-2010-0595; FRL - ] 

Agency Information Collection Activities; Submission to OMB for Review and Approval; 

Willingness to Pay Survey for §316(b) Existing Facilities Cooling Water Intake Structures

(New), EPA ICR No. 2402.01, OMB Control No. 2040-NEW

AGENCY: Environmental Protection Agency (EPA).

ACTION: Notice.

SUMMARY: In compliance with the Paperwork Reduction Act (PRA) (44 U.S.C. 3501 et seq.), 

this document announces that an Information Collection Request (ICR) has been forwarded to 

the Office of Management and Budget (OMB) for review and approval. This is a request for a 

new collection. The ICR, which is abstracted below, describes the nature of the information 

collection and its estimated burden and cost. 

DATES: Additional comments may be submitted on or before [insert date 30 days after 

publication in the Federal Register]. 

ADDRESSES: Submit your comments, identified by Docket ID No. EPA-HQ-OW-2010-0595

to: 

(1) EPA by one of the following methods:

 www.regulations.gov: Follow the on-line instructions for submitting comments.

 Email: OW-Docket@epa.gov, Attention Docket ID No. EPA-HQ-OW-2010-0595

 Mail: Water Docket, Environmental Protection Agency, Mailcode: 28221T, 1200 Pennsylvania 

Ave., NW., Washington, DC 20460, Attention Docket ID No. EPA-HQ-OW-2010-0595. Please 

include a total of 3 copies.

 Hand Delivery: Water Docket, EPA Docket Center, EPA West, Room 3334, 1301 
109

Constitution Ave., NW., Washington, DC, Attention Docket ID No. EPA-HQ-OW-2010-

0595. Such deliveries are only accepted during the Docket’s normal hours of operation 

and special arrangements should be made.

(2) OMB by mail to: Office of Information and Regulatory Affairs, Office of Management and 

Budget (OMB), Attention: Desk Officer for EPA, 725 17th Street, NW, Washington, DC 20503.

FOR FURTHER INFORMATION CONTACT: Erik Helm, Office of Water, Office of 

Science and Technology, Engineering and Analysis Division, Economic and Environmental 

Assessment Branch, 4303T, Environmental Protection Agency, 1200 Pennsylvania Ave., NW, 

Washington, DC 20460; telephone number: 202-566-1049; fax number: 202-566-1053; email 

address: Helm.Erik@epa.gov.

SUPPLEMENTARY INFORMATION: EPA has submitted the following ICR to OMB for 

review and approval according to the procedures prescribed in 5 CFR 1320.12. On July 21, 2010 

(74 FR 42438), EPA sought comments on this ICR pursuant to 5 CFR 1320.8(d). EPA received 

five comments during the comment period, which are addressed in the ICR. Any additional 

comments on this ICR should be submitted to EPA and OMB within 30 days of this notice.

EPA has established a public docket for this ICR under Docket ID No. EPA-HQ-OW2010-0595 which is available for online viewing at www.regulations.gov, or in person viewing at 

the Water Docket in the EPA Docket Center (EPA/DC), EPA West, Room 3334, 1301 

Constitution Ave., NW, Washington, DC. The EPA/DC 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-

566-1752.
110

Use EPA’s electronic docket and comment system at www.regulations.gov, to submit or 

view public comments, access the index listing of the contents of the docket, and to access those 

documents in the docket that are available electronically. Once in the system, select “docket 

search,” then key in the docket ID number identified above. Please note that EPA’s policy is 

that public comments, whether submitted electronically or in paper, will be made available for 

public viewing at www.regulations.gov as EPA receives them and without change, unless the 

comment contains copyrighted material, CBI, or other information whose public disclosure is 

restricted by statute. For further information about the electronic docket, go to 

www.regulations.gov. 

Title: Willingness to Pay Survey for Section 316(b) Existing Facilities Cooling Water Intake 

Structures: Instrument, Pre-test, and Implementation

ICR numbers: EPA ICR No. 2402.01, OMB Control No. 2040-NEW. 

ICR status: This ICR is for a new information collection activity. 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 in title 40 of the CFR, after appearing in the Federal Register when approved, are 

listed in 40 CFR part 9, are displayed either by publication in the Federal Register or by other 

appropriate means, such as on the related collection instrument or form, if applicable. The 

display of OMB control numbers in certain EPA regulations is consolidated in 40 CFR part 9.

Abstract: Section 316(b) of the Clean Water Act (CWA) requires EPA to ensure that the 

location, design, construction, and capacity of cooling water intake structures (CWIS) reflect the 

best technology available (BTA) to protect aquatic organisms from being killed or injured by

impingement or entrainment. At question here is the regulation of the existing steam electric and 

manufacturing facilities. 
111

Under Executive Order 12866, EPA is required to estimate the potential benefits and 

costs to society of proposed rule options of significant rules. To assess the importance of the 

ecological gains from the section 316(b) regulation, EPA requests approval from the OMB to 

conduct a stated preference survey. Data from the associated stated preference survey will be 

used to estimate values (willingness to pay, or WTP) derived by households for changes related 

to the reduction of fish losses at CWIS, and to provide information to assist in the interpretation 

and validation of survey responses. EPA has designed the survey to provide data to support the 

following specific objectives: [a] the estimation of the total values that individuals place on 

preventing losses of fish and other aquatic organisms caused by 316(b) facilities; [b] to 

understand how much individuals value preventing fish losses, increasing fish populations, and 

increasing commercial and recreational catch rates; [c] 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; and [d] to understand how such values vary

with respect to individuals’ economic and demographic characteristics. 

The key elicitation questions ask respondents whether or not they would vote for policies 

that would increase their cost of living, in exchange for specified multi-attribute changes in [a] 

impingement and entrainment losses of fish, [b] commercial fish populations, [c] long-term 

populations of all fish, and [d] condition of aquatic ecosystems. The respondents’ stated 

preferences with respect to levels of environmental goods and cost to households, when used in 

conjunction with other information collected in the survey on the use of the affected aquatic 

resources, household income, and other demographics, can be analyzed statistically (using a 

mixed logit framework) to estimate total WTP for the quantified environmental benefits of the 

316(b) rulemaking. Data analysis and interpretation is grounded in a standard random utility

model.
112

The welfare values that can be derived from this stated preference survey along with 

those that are estimated apart from the survey effort will offer insight into the composition of the 

value people place on the 316(b) environmental impacts. WTP estimates derived from the 

survey may overlap - to a potentially substantial extent - with estimates that can be provided 

through some other methods. Therefore, particular care will be given to avoid any possible 

double counting of values that might be derived from alternative valuation methods.

Burden Statement: The annual public reporting and recordkeeping burden for this collection of 

information is estimated to average 5 minutes per telephone screening participant and 30 minutes 

per mail survey respondent including the time necessary to complete and mail back the 

questionnaire. 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; adjust the existing ways to 

comply with any previously applicable instructions and requirements which have subsequently 

changed; 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. 

The ICR provides a detailed explanation of the Agency’s estimate, which is only briefly

summarized here:

Respondents: Individuals from US households

Estimated total number of potential respondents: 9,533 for telephone screening and 2,288

for mailed questionnaires.

Frequency of response: one-time response.
113

Estimated total average number of responses for each respondent: one-time response.

Estimated total burden hours: 1,938 hours.

Estimated total costs: $ 39,583. EPA estimates that there will be no capital and operating 

and maintenance cost burden to respondents.

Dated: ________________ 

__________________________________

John Moses, Director, Collection Strategies Division.
114

Attachment 4: Description of Statistical Survey Design

The following represents an anticipated experimental design for survey implementation, 

along with the associated number of completed surveys that will be required. Part B of this 

supporting statement provides detail on the sampling design. The proposed design and sampling 

plan is based on standard design and sampling theory for choice experiments and population 

surveys, as outlined by Louviere et al. (2000), Kuhfeld (2009) and Dillman (2000). EPA notes 

that the anticipated experimental design described here is preliminary and it may be subject to 

refinements during design evaluations to account for issues such as dominate or dominated pairs, 

ecological feasibility, and to remove attribute combinations which do not provide information for 

estimation.

The purpose of the 316(b) survey is to calculate average per household parameters (e.g., 

willingness to pay and choice probabilities) within a given survey population. No sub-population 

estimates are required. The anticipated experimental design for the choice experiment includes 

two multi-attribute choice options or alternatives, A and B, together with a fixed status quo or 

“no policy” option. Options A and B are characterized by levels for the following six attributes:

1. Location in A and B (x1A; x1B) – 2 possible levels

2. Fish Saved per Year in A and B (x2A; x2B) – 3 possible levels

3. Commercial Fish Populations in A and B (x3A; x3B) – 3 possible levels

4. Fish Populations (all fish) in A and B (x4A; x4B) – 3 possible levels

5. Aquatic Ecosystem Condition in A and B (x5A; x5B) – 3 possible levels

6. Cost in A and B (x6A; x6B) - 6 possible levels

This implies an experimental design characterized by [2×3

4

×6] for each alternative, or 

[22×3

8

×6

2

] for alternatives A and B combined.

To construct a preliminary main effects design that is sufficiently flexible to estimate 

alternative specific main effects and response patterns (i.e., a non-generic design), we begin with 

the smallest available 100% efficient linear main effects plan for a full 223

8

6

2

design. This treats 

each attribute of each alternative as a separate design element. The result is a design with 72 

profiles, with attributes labeled following the above notation, and levels indicated by integers 

1...N, where N for each attribute is the number of levels identified above:
115

Table 1: Set of 72 Design Profiles

Obs x1A x2A x3A x4A x5A x6A x1B x2B x3B x4B x5B x6B

1 1 1 1 1 1 1 1 1 1 1 1 1

2 1 1 1 1 2 2 2 2 2 2 2 2

3 1 1 1 3 1 6 2 2 3 3 1 3

4 1 1 1 3 3 5 2 1 1 2 3 2

5 1 1 2 2 1 3 1 1 3 2 2 3

6 1 1 2 2 2 1 2 3 1 1 2 4

7 1 1 2 2 3 4 1 2 2 2 1 1

8 1 1 2 2 3 6 1 3 2 3 3 6

9 1 1 3 1 1 2 1 3 2 3 2 5

10 1 1 3 1 2 3 2 1 3 1 3 6

11 1 1 3 3 2 4 1 2 1 3 3 4

12 1 1 3 3 3 5 2 3 3 1 1 5

13 1 2 1 1 2 4 2 3 3 2 1 6

14 1 2 1 2 2 5 1 3 3 3 2 1

15 1 2 1 2 3 2 1 3 1 1 3 3

16 1 2 1 3 1 3 2 3 2 2 3 4

17 1 2 2 1 1 5 2 1 2 3 1 4

18 1 2 2 1 3 1 2 2 3 2 3 5

19 1 2 2 3 1 2 1 2 1 1 1 6

20 1 2 2 3 2 6 1 1 1 2 2 5

21 1 2 3 1 3 4 1 1 1 3 2 3

22 1 2 3 2 1 1 1 2 3 3 3 2

23 1 2 3 2 2 6 1 1 2 1 1 2

24 1 2 3 3 3 3 1 2 2 1 2 1

25 1 3 1 1 3 6 2 2 3 1 2 4

26 1 3 1 2 1 4 2 1 2 1 3 5

27 1 3 1 2 2 3 1 2 1 3 1 5

28 1 3 1 3 3 1 1 1 2 3 2 6

29 1 3 2 1 2 5 2 2 2 1 3 3

30 1 3 2 1 3 3 2 3 1 3 1 2

31 1 3 2 3 1 4 2 3 3 1 2 2

32 1 3 2 3 2 2 2 1 3 3 3 1

33 1 3 3 1 1 6 1 3 1 2 3 1

34 1 3 3 2 1 5 2 2 1 2 2 6

35 1 3 3 2 3 2 1 1 3 2 1 4

36 1 3 3 3 2 1 2 3 2 2 1 3

37 2 1 1 1 1 1 2 1 1 1 1 1

38 2 1 1 1 2 2 1 2 2 2 2 2

39 2 1 1 3 1 6 1 2 3 3 1 3

40 2 1 1 3 3 5 1 1 1 2 3 2

41 2 1 2 2 1 3 2 1 3 2 2 3

42 2 1 2 2 2 1 1 3 1 1 2 4

43 2 1 2 2 3 4 2 2 2 2 1 1

44 2 1 2 2 3 6 2 3 2 3 3 6

45 2 1 3 1 1 2 2 3 2 3 2 5

46 2 1 3 1 2 3 1 1 3 1 3 6
116

47 2 1 3 3 2 4 2 2 1 3 3 4

48 2 1 3 3 3 5 1 3 3 1 1 5

49 2 2 1 1 2 4 1 3 3 2 1 6

50 2 2 1 2 2 5 2 3 3 3 2 1

51 2 2 1 2 3 2 2 3 1 1 3 3

52 2 2 1 3 1 3 1 3 2 2 3 4

53 2 2 2 1 1 5 1 1 2 3 1 4

54 2 2 2 1 3 1 1 2 3 2 3 5

55 2 2 2 3 1 2 2 2 1 1 1 6

56 2 2 2 3 2 6 2 1 1 2 2 5

57 2 2 3 1 3 4 2 1 1 3 2 3

58 2 2 3 2 1 1 2 2 3 3 3 2

59 2 2 3 2 2 6 2 1 2 1 1 2

60 2 2 3 3 3 3 2 2 2 1 2 1

61 2 3 1 1 3 6 1 2 3 1 2 4

62 2 3 1 2 1 4 1 1 2 1 3 5

63 2 3 1 2 2 3 2 2 1 3 1 5

64 2 3 1 3 3 1 2 1 2 3 2 6

65 2 3 2 1 2 5 1 2 2 1 3 3

66 2 3 2 1 3 3 1 3 1 3 1 2

67 2 3 2 3 1 4 1 3 3 1 2 2

68 2 3 2 3 2 2 1 1 3 3 3 1

69 2 3 3 1 1 6 2 3 1 2 3 1

70 2 3 3 2 1 5 1 2 1 2 2 6

71 2 3 3 2 3 2 2 1 3 2 1 4

72 2 3 3 3 2 1 1 3 2 2 1 3

Following common examples in the environmental economics literature, we anticipate 

three choice questions per survey. This will allow the 72 profiles to be included (orthogonally 

blocked) in 24 unique survey booklets. Monte Carlo experiments indicate that approximately 6 

completed responses are required for each profile in order to achieve large sample statistical 

properties for choice experiments (Louviere et al. 2000, p. 104, citing Bunch and Batsell 1989). 

Following this guidance, the above design will require 24×6 = 144 completed surveys, or 6 

completed surveys for each unique survey booklet. This will provide a total of 432 profile 

responses.

Sample Sizes for Maximum Acceptable Sampling Error

The goal of the choice experiment is to estimate regression coefficients from mixed or 

conditional logit models that may be used to estimate willingness to pay for multi-attribute 

policy alternatives, or the likelihood of choosing a given multi-attribute alternative, following 

standard random utility modeling procedures (Haab and McConnell 2002). Required sample 
117

sizes to estimate population parameters with given degrees of freedom are drawn from standard 

statistical theory, as described by Dillman (2000).

The maximum acceptable sampling error for predicting response probabilities (the 

likelihood of choosing a given alternative) in the present case is ±10%, assuming a true response 

probability of 50% associated with a utility indifference point. Given the survey population size, 

this level of precision requires a minimum sample size of approximately 96 observations. The 

number of observations (completed surveys) required to obtain large sample properties for the 

choice experiment design provide more than sufficient observations to obtain this required 

precision for population parameters. The choice experiment design requires 432 completed 

profile responses from 144 surveys. Statistical theory shows that 400 assumed-independent 

profile responses will allow a 50% choice probability to be estimated within ±4.9 percent, at a 

95% confidence level. With only a single choice question per survey (144 responses), the same 

probability and confidence level can be obtained within approximately ±8.2 percent; this is also 

within acceptable limits.

Experimental Design Allowing Two-Way Interactions with Location (x1A; x1B)

In order to estimate a parallel design that allows for interactions with policy location, and 

insulates main effects from these interactions, we follow the suggested design strategies of 

Louviere et al. (2000). First, we estimate the smallest available 100% efficient linear main 

effects plan for non-location factors (X2 – X6) across both alternatives A and B, [38×6

2

]. This 

results in a main effects design with 36 profiles:

Table 2: Set of 36 Design Profiles

Obs x2A x3A x4A x5A x6A x2B x3B x4B x5B x6B

1 1 1 1 1 1 1 1 1 1 1

2 1 1 2 2 2 2 2 2 1 2

3 1 1 2 3 4 3 2 1 1 6

4 1 1 3 2 6 3 1 2 2 4

5 1 2 1 1 4 2 1 3 2 5

6 1 2 2 2 3 1 3 1 3 5

7 1 2 2 3 5 3 3 2 3 1

8 1 2 3 3 2 1 1 3 1 3

9 1 3 1 2 6 3 3 1 2 3

10 1 3 1 3 3 2 2 3 3 4

11 1 3 3 1 1 2 3 2 3 6

12 1 3 3 1 5 1 2 3 2 2

13 2 1 1 1 5 2 3 1 1 4
118

14 2 1 2 2 5 2 1 3 3 3

15 2 1 3 2 1 3 2 3 3 5

16 2 1 3 3 3 1 3 1 2 2

17 2 2 1 1 3 3 2 2 1 3

18 2 2 2 3 1 1 1 2 2 4

19 2 2 3 2 4 2 2 1 2 1

20 2 2 3 3 6 2 3 3 1 6

21 2 3 1 2 2 1 1 1 3 6

22 2 3 1 3 4 3 1 2 3 2

23 2 3 2 1 2 3 3 3 2 1

24 2 3 2 1 6 1 2 2 1 5

25 3 1 1 3 2 2 3 2 2 5

26 3 1 1 3 6 1 2 3 3 1

27 3 1 2 1 3 3 1 3 2 6

28 3 1 3 1 4 1 3 2 3 3

29 3 2 1 2 1 3 3 3 1 2

30 3 2 1 2 5 1 2 2 2 6

31 3 2 2 1 6 2 1 1 3 2

32 3 2 3 1 2 3 2 1 3 4

33 3 3 2 2 4 1 3 3 1 4

34 3 3 2 3 1 2 2 1 2 3

35 3 3 3 2 3 2 1 2 1 1

36 3 3 3 3 5 3 1 1 1 5

This design is then repeated for each of the four possible combinations of values for x1A

and x1B (i.e., 1,1; 2,2; 1,2; 2,1) creating a candidate design of 36×4 = 144 profiles. The resulting 

design fully insulates all main effects (eliminates aliasing) from interactions with location 

attributes x1A and x1B, and allows associated location interactions to be estimated independent of 

main effects. The number of required survey versions (48) is calculated by dividing the 144 

profiles by the three choice questions per survey. The total number of completed surveys 

required (288) is based on a requirement of six completed surveys per survey version (48) to 

estimate the main effects and interactions. As above, the number of observations (completed 

surveys) required to obtain large sample properties for the choice experiment design provide 

sufficient observations to obtain required precision for population parameters.1 Also as above, 

this candidate design may be subject to refinements based on design evaluations, as described by 

Louviere et al. (2000) and Kuhfeld (2009), but this is unlikely to alter design dimensions.

 

1 Alternatively, the smallest possible main effects plan for all attributes (72 profiles) may be folded over to make 

another 72 profiles, for a combined set of 144 profiles. This protects main effects from all unobserved 2-way 

interactions, but does not guarantee that specific interactions may be estimated. The required sample size is the 

same as that which would guarantee that location interactions can be estimated.
119

Attachment 5: Description of Internet-based Sampling Alternative

EPA is also considering the use of an internet-based (i.e., web-based) sampling approach 

as an alternative to the mail survey described in Part B of the supporting statement. The survey

content, pre-testing, review process, and data analysis would be identical to the mail survey

described in Parts A and B of the supporting statement. To support implementation of the webbased alternative, EPA would retain a vendor that maintains a preexisting representative national 

panel. This attachment provides a brief summary of the web-based alternative, focusing on

differences from the mail survey approach described in Part B of the supporting statement.

Sampling Frame

The sampling frame for the web-based survey is the panel of individuals previously

recruited by a vendor to participate in online surveys. The overall sampling frame would be 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 panel most likely be recruited using a list-assisted 

random digit dialing telephone methodology, thus providing a probability-based starting sample 

of U.S. telephone households. Panels of this type are usually routinely supplemented to account 

for panel attrition. The preexisting panel would have to closely tracks the U.S. population on 

age, race, Hispanic ethnicity, geographical region, employment status, and other demographic 

elements. If differences between the panel and the U.S. population do exist, sample weights 

using U.S. Census demographic benchmarks could be developed to reduce error due to noncoverage of non-telephone households and to reduce bias due to non-response and other nonsampling errors. 

Sample Size

The intention is to obtain 2,288 completed web-based surveys from the preexisting panel. 

The same geographically stratified design will be implemented following the same the same 

regions and sample sizes as described in Part B of the supporting statement under the mail survey 

approach. Within each region, the sample will be stratified by demographic variables including 

age, education, Hispanic ethnicity, race, gender, and household income. The actual number of 

panel members contacted for this survey will be determined by the vendor’s knowledge of within 

panel response rates. Most likely, the stratification design used in the web-based survey will 
120

systematically oversamples groups that tend to have lower response and consistency rates so that 

the demographic characteristics of respondents who provide valid completed surveys will mirror 

the Census demographic benchmarks more closely. This potential oversampling correction 

technique will reduce error due to non-coverage of non-telephone households in the original 

panel member recruitment process, and will also reduce bias due to non-response and other nonsampling errors.

Survey Response

The target response rate for the survey among panel members that are sampled is 80 

percent. However, the actual response rate compared to the general population is lower, given 

that the telephone recruitment process for a preexisting panel has an expected response rate in 

the 30 to 40 percent range. This will produce a lower cumulative response rate. Panel members 

who are selected for the survey will be contacted to inform them that the survey is available 

online and reminders will be sent to panel members who do not complete the survey within a 

short period of time. 

Although the cumulative response rate for this survey may be low compared to response 

rates typically achieved using other survey methods (e.g., phone and mail surveys), the webbased format facilitates geographic and demographic stratification, which will substantially

improve the quality of the survey.

Non-response and Other Non-Sampling Error Corrections

Approaches to minimize non-response bias in internet surveys are established in the 

literature (Dillman 2000) and include initial RDD probability sampling, careful panel 

management, and effective survey sampling procedures. The representativeness of preexisting 

panel survey sample is generally comparable to those telephone surveys sponsored by various 

Federal agencies. Nonetheless, EPA would conduct several additional activities to minimize the 

potential for non-response bias if the survey were implemented through the use of a preexisting 

panel including: (1) use of a stratified survey sample to mitigate the effect of non-response bias, 

(2) use of non-response follow-up interviews to increase the overall response rate, and (3) 

statistical correction for unobserved heterogeneity that might be present in the data from 

respondents interviewed for this study
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Non-response Interviews

In order to minimize non-response bias from individuals who chose not to respond during 

some stage of panel recruitment, EPA will conduct non-response 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 preexisting panel, who agreed to join the panel but did not connect 

to the vendors web-site, 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 of this effort will be a direct 

measurement of non-response bias and EPA will use the information to increase the effective 

cumulative response rate by using a weighted response rate formulation. 

Statistical Correction for Unobserved Heterogeneity

EPA is also considering the use of a statistical technique to correct for unobserved 

heterogeneity, based on work by Heckman (Heckman, 1979). This correction would be 

conducted as a complement to a non-response 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. 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 interviewing of the panel respondents. 

The technique could be used to produce both adjusted and unadjusted estimates of WTP. 

Because the technique yields regression estimates that explain survey outcomes using 

subpopulation data related to neighborhood characteristics, it can be used to identify the sources 

of differences between the adjusted and unadjusted estimates.

As described above, EPA will use statistical techniques to correct for unobserved 

heterogeneity in the survey sample and non-response follow-up interviews to increase the 

effective survey response rate. Combined, these techniques will reduce error due to noncoverage of non-telephone households in the original panel recruitment process, and also reduce 

bias due to non-response and other non-sampling errors. 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. (2008). 
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Data Preparation

Since the survey will be administered as an online survey, survey responses will be 

automatically 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 in 

EPA’s statistical analysis. 
123

Attachment 6: Telephone Screener Script

Fish and Aquatic Habitat Telephone Screener

Hello, this is _________________calling from the Abt Associates. We are conducting an 

important survey of U.S. residents for the Environmental Protection Agency, or EPA. This is not 

a sales call. 

SL1 [ASK IF SAMPLE=LANDLINE]

Could you please tell me how many people, age 18 and older, live in this household?

IF NEEDED: This study will help us to better understand the value of environmental 

protection and public programs. Any answers you give are kept strictly confidential.

IF ASKED: This is a short survey which should take no more than five minutes.

0 None THANK AND SCREEN OUT

1 One

__ Number of persons 16+ SKIP TO SL1c

98 Refused (VOL) THANK AND TERMINATE – Soft Refusal

99 Refused (VOL) THANK AND TERMINATE – Hard Refusal

SL1b [ASK IF SAMPLE=LANDLINE]

May I speak with that person?

1 Rspn on line SKIP TO SL2

2 Rspn called to phone SKIP TO SL1d

3 Rspn unavailable SCHEDULE CALLBACK

8 Refused THANK AND TERMINATE – Soft Refusal

9 Refused THANK AND TERMINATE – Hard Refusal

SL1c [ASK IF SAMPLE=LANDLINE]

In order to select just one person to interview, may I please speak to the person in 

your household, age 18 or older, who (has had the most recent/will have the next) 

birthday?

1 Rspn on line SKIP TO SL2

2 Rspn called to phone

3 Rspn unavailable SCHEDULE CALLBACK

8 Refused THANK AND TERMINATE – Soft Refusal

9 Refused THANK AND TERMINATE – Hard Refusal

SL1d [ASK IF SAMPLE=LANDLINE]
124

Hello, this is _________________calling from the Abt Associates. We are 

conducting an important survey of U.S. residents for the Environmental 

Protection Agency, or EPA. Could we begin now?

IF ASKED: This is a short survey which should take no more than five minutes.

1 Yes

2 No time SCHEDULE CALLBACK

8 Refused THANK AND TERMINATE – Soft Refusal

9 Refused THANK AND TERMINATE – Hard Refusal

SL2 [ASK IF SAMPLE=LANDLINE]

Do you have a cell phone in addition to the line on which we’re speaking right now?

1 Yes, also have cell phone

2 No, this is only phone SKIP TO SA2

8 (VOL) Don’t know THANK AND END, screen out

9 Refused THANK AND END, soft refusal

SC1 [ASK IF SAMPLE = CELL]

Are you in a safe place to talk right now?

IF NEEDED: Are you Driving? [IF RESPONDENT ANSWERS YES, DRIVING, 

SCHEDULE CALLBACK AND TERMINATE - CODE AS 2]

IF ASKED: This is a short survey which should take no more than five minutes.

1 Yes, safe place to talk

2 No, call me later SCHEDULE CALLBACK

3 No, CB on land-line RECORD NUMBER, schedule call back

4 Cell phone for business only THANK & END - BUSINESS#

8 Refused THANK AND TERMINATE – Soft Refusal

9 Refused THANK AND TERMINATE – Hard Refusal

SC2 [ASK IF SAMPLE=CELL]

Are you at least 18 years old?

IF NEEDED: This study will help us to better understand the value of environmental 

protection and public programs. Any answers you give are kept strictly confidential.

1 Yes

2 Yes, no time SCHEDULE CALLBACK

3 No SCREEN OUT
125

8 Refused THANK AND TERMINATE – Soft Refusal

9 Refused THANK AND TERMINATE – Hard Refusal

SC3 [ASK IF SAMPLE=CELL]

Do any other people age 18 or older regularly ANSWER your cell phone, or just you?

INTERVIEWER: This question refers to the physical phone and not to their calling plan

1 Yes, others

2 No, just respondent SKIP TO SC4

9 Don’t know/Refused SKIP TO SC4

SC3b [ASK IF SAMPLE=CELL]

How many other people age 18 or older regularly answer your cell phone? 

__ [ENTER NUMBER 1-10]

99 Don’t know/Refused

SC4 [ASK IF SAMPLE=CELL]

Not counting any that are used strictly for business purposes, are there other cell phones 

that you use regularly, or is it just the one? 

1 Yes, use other cell phones

2 No SKIP TO SC5

9 Don’t know/Refused SKIP TO SC5

SC4b [ASK IF SAMPLE=CELL]

How many other cell phones do you use regularly, excluding those used only for 

business purposes? 

__ [ENTER NUMBER 1-10]

99 Don’t know/Refused

SC5 [ASK IF SAMPLE=CELL]

Not counting (this/these) cell phone(s), do you also have a regular land-line phone at 

home?

1 Yes, has a regular phone at home

2 No, cell is only phone SKIP TO SA2

7 Don’t know (VOL) THANK AND TERMINATE – Screen out

8 Refused (VOL) THANK AND TERMINATE – Soft Refusal

9 Refused (VOL) THANK AND TERMINATE – Hard Refusal
126

SA1 [ASK BOTH SAMPLES]

Of all of the phone calls that you or your family receives, are...(Read List) 

1 all or almost all calls received on cell phones,

2 some received on cell phones and some received on land lines, or 

3 very few or none on cell phones.

8 (VOL) Don’t know

9 (VOL) Refused 

SA2 [ASK BOTH SAMPLES]

Record gender from observation. (Ask only if Necessary) 

1 Male

2 Female

Q1 [ASK BOTH SAMPLES]

First, compared to other issues that the government might address – such as public safety, 

education and health – how important is protecting aquatic ecosystem to you, on a scale of 

1 to 5 where 1 is “not important” and 5 is “very important”? (MATCHES QUESTION 2 IN 

THE MAIL SURVEY) 

1 2 3 4 5 (VOL) Don’t Know (VOL) Refused

Q2 [ASK BOTH SAMPLES]

People have ideas about the extent to which the government should be involved in 

protecting the environment. On a scale of 1 to 5 where 1 is “not at all involved” and 5 is 

“highly involved”, how involved do you think the government should be in environmental 

protection? 

1 2 3 4 5 (VOL) Don’t Know (VOL) Refused
127

Q3 [ASK BOTH SAMPLES]

Could you please tell me if you participated in each of following activities during the last 

year? (DO NOT ROTATE)

1 Yes

2 No

8 (VOL) Don’t Know

9 (VOL) Refused

Boating, canoeing, or kayaking 1 2 8 9

Swimming/going to the beach 1 2 8 9

Fresh Water Recreational fishing 1 2 8 9

Salt Water Recreational fishing 1 2 8 9

Fresh Water Recreational fishing 1 2 8 9

Shell fishing or crabbing 1 2 8 9

Scuba diving or snorkeling 1 2 8 9

Now, I have just a few questions for classification purposes. 

D1 [ASK BOTH SAMPLES]

What is your age?

________ Years Don’t Know Refused

D2 [ASK BOTH SAMPLES]

What is the highest level of education that you have completed? Is it... (READ LIST)

1 Less than high school

2 High school or equivalent

3 High school and technical school

4 One or more years of college

5 Bachelor’s degree

6 Graduate degree

D3 [ASK BOTH SAMPLES]

Including everyone living in your household, which of the following categories best 

describes your total household income before taxes? Is it ... (READ LIST)

1 $10,000 or Less,

2 Between $10,001 and $20,000,

3 Between $20,001 to $35,000,

4 Between $35,001 to $50,000,
128

5 Between $50,001 to $75,000,

6 Between $75,001 to $100,000, or

7 More than $100,000

8 (VOL) Don’t know

9 (VOL) Refused

D4 [ASK BOTH SAMPLES]

Are you of Hispanic or Latino origin?

1 Yes

2 No

8 (VOL) Don’t know

9 (VOL) Refused

D5 [ASK BOTH SAMPLES]

Which of the following racial categories describes you? You may select more than one. 

Would it be... (READ LIST – MULTIPLE RECORD)

1 American Indian or Alaskan Native,

2 Asian,

3 Black or African American,

4 Native Hawaiian or Other Pacific Islander, or

5 White

6 (VOL) Hispanic / Latino

8 (VOL) Other

9 (VOL) Refused

D6 [ASK BOTH SAMPLES]

Do you... (READ LIST)

1 Rent your home or apartment

2 Own your own home

3 Live with family or friends and pay part of the rent or mortgage

4 Live with family or friends and do not pay rent

7 (VOL) Other, Specify

8 (VOL) Other

9 (VOL) Refused

RECRUITMENT:

In addition to this telephone survey, we are also conducting a mail survey to help U.S. 

policymakers to better understand your priorities for the environment and the use of public 

funds. This survey will be mailed to households along with a free reply envelope

The survey takes most people about 30 minutes to complete.
129

I1 [ASK BOTH SAMPLES]

Would you be willing to participate in this mail survey?

1 Yes

2 No THANK FOR PARTICIPATION AND TERMINATE

3 Don’t know THANK FOR PARTICIPATION AND TERMINATE

4 Refused THANK FOR PARTICIPATION AND TERMINATE

I2 [ASK BOTH SAMPLES]

To what name and address should we mail the survey? (RECORD NAME, ADDRESS, 

CITY, STATE, AND ZIP CODE AND REPEAT BACK)

I3 [ASK BOTH SAMPLES]

And is this the best number to reach you at if we should have a problem mailing your 

survey?

1 Yes SKIP TO CLOSING

2 No

I4 [ASK BOTH SAMPLES]

What is the best number to reach you? (RECORD NUMBER AND REPEAT BACK)

CLOSING:

Thank you. The survey will be sent to you in approximately one to two weeks. The packet 

will include information about the study, how to return your survey, and additional contact 

information in case you have questions. Thank you very much for your time, and have a 

great evening/day.
130

Attachment 7: Cover Letter to Mail Survey Recipients

<date>

<given name> <surname> 

<address>

<city>, <state> <zip code>-<zip+4>

Dear <title> <surname>:

A few days ago you agreed to take a short survey regarding environmental protection and 

government regulations in the Northeast U.S. Thank you for agreeing to participate—the survey 

booklet is enclosed with this letter. Your answers to the survey will help officials from the 

Environmental Protection Agency (EPA) to better understand the value of policies which would 

affect the future of fish and aquatic habitat in the Northeast. By filling out this survey, you will 

be participating in an important study that will help government officials understand your 

priorities for the environment and regulations.

Your responses to this survey are very important. Over time, human activities have caused many 

changes in Northeast’s rivers, streams and bays. The Environmental Protection Agency is 

considering policies that could impact the quality of fish and aquatic habitat in these areas. 

These policies can have different effects and costs. Because of this, it is important to know what 

types of policies are supported by Northeast residents.

All answers to the survey are confidential. Once we have received your survey, we will delete 

your name from all lists, so that your responses can never be traced back to you. Of course, your 

participation is voluntary and you may refuse to answer any or all questions. 

We hope that you find this survey important and interesting, and thank you for your assistance in 

this important project. We would greatly appreciate if you could return the survey in the near 

future.

Sincerely,

Mary T. Smith, Director

U. S. Environmental Protection Agency

Engineering and Analysis Division
131

Attachment 8: Post Card Reminder to Mail Survey Recipients

FRONT

BACK

Abt SRBI

Government Services Divison

8403 Colesville Road, Suite 820

Silver Spring, MD, 20910

Last week a survey was mailed to you concerning environmental protection 

and government regulations in the Northeast U.S. If you have already 

returned your completed survey, please accept our sincere thanks. 

If you have not yet completed your survey, we ask that you please do so 

today. You are one of a select few who have been chosen to participate—

your answers will help us understand your priorities for the environment and 

regulations in the Northeast U.S.

If you have misplaced your survey, please contact Ryan Stapler at 

(617) 520-3524 or ryan_stapler@abtassoc.com for a replacement. 

Regards,

Mary T. Smith

Environmental Protection Agency