June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 1

Date June 5, 2012 

To The Section 316(b) Existing Facilities Rule Record 

From Erik Helm, U.S. Environmental Protection Agency

Subject 316(b) Stated Preference (SP) Survey – Survey Methods and Model Results

In this memorandum, EPA is documenting preliminary data, analysis, and results for the stated preference 

survey it conducted as part of the Final 316(b) Existing Facilities Rulemaking. This memorandum 

expands on the data, analysis and results of the survey presented in EPA’s Notice of Data Availability. 

Stated preference surveys are a type of non-market valuation method used, in this case, to measure values 

associated with ecosystem improvements. The values individuals hold for ecosystem improvements are 

estimated by analyzing the selections that respondents make between hypothetical policy options and 

current conditions. The use of a stated preference survey reflects recent EPA guidelines for benefits 

analysis (USEPA 2010, p. 7-41) that recognize “advantages of [stated preference] methods include[ing] 

their ability to estimate non-use values and to incorporate hypothetical scenarios that closely correspond 

to a policy case.” 

EPA’s estimated benefits of the proposed rule were partial estimates only. Specifically, the proposal 

included only a partial estimate of non-use values (e.g., values that people may hold for an environmental 

improvement that are not tied to any use of the resource such as recreation. For the proposed 316(b) rule, 

EPA employed a benefits transfer approach using an existing stated preference study conducted by 

Johnston et al. (2011a, b) which is closely related to the 316(b) policy context. Benefits transfer is the 

“practice of applying nonmarket values obtained from primary studies of resource or environmental 

changes undertaken elsewhere to the evaluation of a proposed or observed changes that is of interest to 

the analyst” (Freeman 2003, p.453). EPA used the best available scientific and economic methodologies 

for the benefit transfer but could only partially monetize benefits. The non-use values estimated through 

benefits transfer were limited to two of the seven benefits regions due to limitations of the study used for 

benefits transfer; EPA was unable to estimate non-use values for the other five regions. The estimates 

were also based on an assessment of a limited number of species within the two regions due to data 

limitations at the time of the proposal. 1 EPA is conducting the stated preference study (EPA ICR # 

2402.01) to estimate total (use plus non-use) benefits of the ecological gains from the regulation of 

cooling water intake structures at NPDES-permitted facilities. EPA did not have sufficient time before the 

notice of proposed rulemaking to fully develop and deploy this survey and thus, derive estimates of the 

monetary value of reducing impingement and entrainment (I&E) mortality impacts. 

This memorandum describes the methods EPA used to develop and implement the 316(b) stated 

preference survey and presents preliminary regional and national regression models based on data from 

the respondents. The preliminary results presented in this memorandum are estimated based on a choice 

experiment. Choice experiments, also called choice models, are a stated preference technique in which 

individual’s values are estimated based on their choices over a set of hypothetical but realistic policy 

options. EPA undertook the Northeast version in advance of the other versions as a pilot study to inform 

potential changes to other survey versions, as described in the ICR for the 316(b) stated preference survey 

(EPA ICR # 2402.01) and as recommended in published guidance for stated preference survey design 

(Arrow et al. 1993; Bateman et al. 2002). At the time this memorandum was prepared, EPA had finished 

 

1

 Refer to Chapter 8 of the Environmental and Economic Benefits Analysis (EEBA) (USEPA 2011a) for additional description of 

the benefits transfer approach used for the proposed rule. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 2

fielding all five versions of the main mail survey (four regional and one national). EPA used regression 

models to produce preliminary estimates of annual household willingness-to-pay (WTP) (or implicit 

price) for a one percentage point improvement in environmental attributes included in the survey (fish 

saved, commercial fish populations, fish populations (all fish), and aquatic ecosystem condition). EPA 

also describes how it would use these implicit prices to estimate the benefits of regulatory options. 

EPA is conducting a non-response study for each version of the survey, to learn whether respondents are 

fundamentally different from non-respondents. For the Northeast region, EPA used the non-response 

study results to develop weights that correct for any differences in respondent and non-respondent 

populations. The results enabled EPA to reduce the weight placed on overrepresented respondent groups, 

while increasing the weight placed on any underrepresented respondent groups. EPA presents separate 

implicit prices for the Northeast region based on the unweighted and weighted models. EPA currently is 

still fielding the non-response studies for the other three regional and national versions of the survey. 

Thus, EPA estimated only unweighted models for the Pacific, Inland, Southeast and National versions of 

the survey. EPA intends to complete weighted models for these three regions and the national surveys. 

After completing the non-response studies for all regions, reviewing public comment, and conducting 

additional scope and validity testing, EPA will present a more complete set of stated preference survey 

materials and model results for an external peer review. EPA will then determine what role, if any, the 

monetized benefits based on the stated preference survey should play in EPA’s assessment of benefits for 

the final rule, and will include the complete results in its assessment as appropriate.2

1 Choice Experiment Framework 

Stated preference surveys generally ask questions that elicit individuals’ stated values for carefully 

specified changes in an environmental amenity (Freeman 2003). This value is typically estimated in 

terms of WTP, defined as the maximum amount of money (or some other commodity) that an individual 

or household would be willing to give up in exchange for a specified environmental change, rather than 

go without that change. Various question formats have been used in the stated preference literature to 

elicit WTP. Some types of stated preference surveys ask respondents to reveal their WTP using openended questions, payment cards, or bidding games. Increasingly, however, these original types of stated 

preference surveys have been replaced in the literature by methods grounded in random utility models 

(Hanemann 1984), in which respondents express their WTP through choices over hypothetical policy 

options. Advantages of these choice-based methods include similarity to familiar referenda or market 

choice contexts, in which individuals choose among alternative policy options or commodities at different 

costs (Freeman 2003), although responses to hypothetical choice questions are still not actual market 

transactions or referenda. Appropriately designed choice-based stated preference methods may reduce 

hypothetical biases that can result from asking survey questions versus assessing WTP through market 

transactions or binding referenda. Substantial research has been conducted over the past two decades on 

hypothetical bias in stated preference surveys. While many studies have found evidence of hypothetical 

bias (List and Gallet 2001), a recent meta-analysis indicates that “hypothetical bias in SP studies may not 

be as important” as some have argued previously (Murphy et al. 2005), mirroring similar findings in prior 

studies that compare hypothetical and actual referenda (see discussion in Johnston 2006). The 316(b) 

survey is designed as a choice experiment following established choice experiment methodology and 

 

2

 EPA notes that the benefit estimates based on the stated preference survey, even when completed and peer reviewed, cannot be 

viewed as additive to those benefit values estimated at proposal because they represent both use and non-use willingness-topay. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 3

format (Adamowicz et al. 1998; Louviere et al. 2000; Bennett and Blamey 2001; Bateman et al. 2002). 

Choice experiments, also called choice models, are a stated preference technique in which individual’s 

values are estimated based on their choices over a set of hypothetical but realistic policy options. Under 

the choice experiment (or choice modeling) format, respondents are presented with a set of multi-attribute 

alternatives and asked to select their preferred alternative, much as one might choose a preferred option in 

a public referendum. This format has been applied to assess WTP for ecological resource improvements 

of a type similar to those at issue in the 316(b) policy case (e.g., Bennett and Blamey 2001; Hanley et al. 

2006a, b; Hoehn et al. 2004; Johnston et al. 2002, 2011a, b; Milon and Scrogin 2006; Morrison and 

Bennett 2004; Morrison et al. 2002; and Opaluch et al. 1999). 

Advantages of these choice-based methods include similarity to familiar referenda or market choice 

contexts, in which individuals choose among alternative policy options or commodities at different costs 

(Freeman 2003). Choice experiments allow survey respondents’ to express WTP for a wide range of 

different potential outcomes of 316(b) policies, differentiated by their attributes.3

 This enables EPA to 

isolate the marginal effects of different possible policy outcomes on stated choices, and hence on 

estimated WTP, thereby estimating benefits for a wide range of potential policy outcomes. This is a 

primary factor distinguishing choice experiments from older forms of stated preference analysis, in which 

estimated WTP is typically contingent upon a single specification of ecological effects. The goal of the 

choice experiment is to collect data which can be used to estimate regression coefficients from mixed or 

conditional logit models for estimating WTP 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; Train 2009). 

Following standard choice experiment (or choice modeling) format (Adamowicz et al. 1998; Bennett and 

Blamey 2001), each respondent was asked to consider three potential policies, or choice options, (i.e., 

Option A, Option B, No Policy (current situation))—choosing the option that is most preferred, that is, 

provides the highest utility. Figure 1 is an example of a choice experiment question from the Northeast 

survey. Respondents may also choose to reject both policies and retain the status quo. The “no policy” or 

status quo option is included in the visible choice set following guidance from the literature, to ensure that 

WTP measures are well-defined (Louviere et al. 2000). The underpinning theoretical model is adapted 

from a standard random utility specification in which household h chooses among three choice options, 

(j=A,B,N), including two multi-attribute policy options (A, B) and a fixed “no policy” status quo (N) that 

includes no policy changes and zero household cost. Each choice option reflects a hypothetical but 

feasible outcome under various 316(b) regulatory alternatives. Following standard practice (Day et al. 

2012; Poe et al. 1997; Layton 2000), respondents are presented with more than one choice question within 

the same survey. Other questions in the survey elicit information including whether the respondent is a 

user of the affected aquatic resources, household income, and other respondent demographics. 

As shown in Figure 1, the effects of the policy options are described in terms of an annual household cost 

incurred indefinitely and four environmental endpoints, or attributes: (a) commercial fish populations, (b) 

fish populations (all fish), (c) fish saved, and (d) condition of aquatic ecosystems. Values are reflected in 

the survey by individuals’ willingness to “vote” for policies that would increase their cost of living, in 

exchange for specified changes in the four environmental attributes. The definitions of the five attributes 

used to characterize policies are presented in Table 1. Table 1 also presents the baseline (status quo) 

attribute values included across survey versions. The regional versions (Northeast, Southeast, Inland, and 

 

3

 Choice experiments following the random utility model 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). 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 4

Pacific) present policy options and attibute values specific to the respondent’s region, while the National 

survey presents policy options and attribute values for all U.S. waters. 

The four environmental attributes were designed based on the Johnston et al. (2011a, b; 2012) 

Bioindicator-Based Stated Preference Valuation (BSPV) method which was developed to promote 

ecological clarity and closer integration of ecological and economic information within stated preference 

studies. Johnston et al. (2011a, b; 2012) was an EPA Science to Achieve Results (STAR) grant project. 

The BSPV method’s focus on improved ecological valuation is an EPA priority as described in findings 

of EPA’s Science Advisory Board Committee on Valuing the Protection of Ecological System and 

Services (USEPA 2009). In contrast to traditional stated preference valuation, BSPV employs a more 

structured and formal use of ecological indicators to characterize and communicate welfare-relevant 

changes. 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. It also provides a means to estimate values for ecological 

outcomes that individuals might value, even though they may not fully understand all relevant ecological 

science. 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 aim to 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. 

 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 5

Figure 1 – Example Choice Experiment Question from the Northeast Survey 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 6

Table 1—Definitions of Policy Attributes and Baseline (Status Quo) Values Included 

Across Survey Versions 

Attribute Definition Northeast Southeast Pacific Inland National 

Commercial Fish 

Populations 

A score between 0 and 100 percent 

showing the overall health of 

commercial and recreational fishing 

populations. High 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. 

42% 39% 56% 39% 51% 

Fish Populations 

(All Fish) 

A score between 0 and 100 percent 

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. 

26% 24% 32% 33% 30% 

Fish Saved (per 

Year) 

A score between 0 and 100 percent 

showing the reduction in young fish lost 

compared to current levels. A score of 

100 would mean that no fish are lost in 

cooling water intakes (all fish would be 

saved because of the new policy). 

0% 0% 0% 0% 0% 

Condition of 

Aquatic 

Ecosystems 

A score between 0 and 100 percent 

showing the ecological condition of 

affected areas, compared to the most 

natural waters in the region. The score is 

determined by many factors including 

water quality and temperature, the health 

of aquatic species, and habitat 

conditions. 

50% 68% 51% 42% 53% 

Cost per Year How much the policy will cost your 

household, in unavoidable ongoing 

prices increases for products and 

services you buy, including electricity 

and common household products. 

$0 $0 $0 $0 $0 

EPA estimated the commercial fish population score based on the average ratio of fish population to 

maximum sustainable yield (MSY) among commercially harvested species including commercially 

harvested species with stock assessment reports conducted by a reputable body such as NOAA or the 

Atlantic States Marine Fisheries Commission. For commercially targeted fish, “natural” population was 

calculated as a scalar multiple of MSY; typically, an unharvested population is believed to be 

approximately three times as large as MSY. The score was calculated by comparing the baseline 

population to this estimate of natural populations averaged across all species to obtain regional values. 

Changes in scores under regulatory options can be calculated by modeling commercial fish populations 

with implementation of the rule and comparing to natural populations. 

The baseline value for “fish saved” is 0% for all regions, which reflects the status quo level of I&E 

mortality before the implementation of regulatory options. The estimates of “fish saved” due to 316(b) 

facilities at baseline are based on EPA’s estimate of age-one-equivalent (A1E) losses, a metric used by 

EPA to standardize all I&E mortality losses into equivalent numbers of 1-year-old fish. This conversion 

allows losses to be compared among species, years, facilities, and regions. To obtain regional I&E 

mortality estimates, EPA extrapolated loss rates from facilities for which I&E mortality data are available 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 7

(referred to as model facilities), to all in-scope facilities within the same region. Refer to Section 3 of the 

Environmental and Economic Benefits Analysis (EEBA) of the proposed rule (USEPA 2011a) for 

additional detail on EPA’s assessment of baseline A1E losses and reductions in A1E losses under 

regulatory options. The introductory materials describe the age classes impacted due to cooling water 

intakes and the “fish saved” attribute is defined as “young fish lost compared to current levels.” While the 

A1E terminology is not used specifically within the stated preference survey, pre-testing during focus 

groups and cognitive interviews indicated that participants understood the “fish saved” attribute and the 

concept of “young fish” as reflecting initial losses of eggs and other juvenile life stages. 

The baseline fish populations (all fish) score was calculated in a fashion similar to commercial fish 

population score based on species with population estimates published in the literature including 

commercially or recreationally harvested species with stock assessment reports and threatened species 

with assessment reports. Current populations were compared to estimates of natural population size 

“without fishing”. For regulatory options, changes in fish populations can be modeled with compliance 

and compared to natural populations. 

EPA estimated the baseline values for the aquatic ecosystem score, by identifying studies in a region that 

apply or define various multimetric indices of water quality, such as Indices of Biotic Integrity (IBIs) 

from the published literature or from state reports. EPA took a wide geographic sampling of these indices, 

converted output values to scores between 0 and 100, and averaged across all outputs within the region to 

obtain regional values. Changes under the regulatory options can be evaluated based on changes in the 

multrimetric indices. 

2 Experimental Design 

Following established practices, EPA used an experimental design to generate multiple unique 

combinations of policy options for different respondents to compare. Respondents were presented with 

three separate policy questions in the survey, each with a unique combination of policy options. The 

experimental design specifies how attribute levels were “mixed and matched” within choice questions, 

thereby developing an empirical data framework with appropriate statistical properties to allow for 

analysis of respondent’s choices (Louviere et al. 2000). 

EPA applied a fractional factorial experimental design representing a subset of all possible combinations 

of environmental attributes and household cost, allowing for efficient estimation of particular effects of 

interest (Louviere et al. 2000) and reducing the cognitive burden faced by respondents (Holmes and 

Adamowicz 2003). It was used to construct choice questions with an orthogonal (independent) array of 

attribute levels, with questions randomly divided among distinct survey versions (Louviere et al. 2000). 

The fractional factorial experimental design was generated using a D-efficiency criterion for main effects 

estimation (Kuhfeld 2010; Kuhfeld and Tobias 2005). A more efficient design enables model coefficients 

(and hence estimated willingness to pay) to be estimated with greater precision (i.e., lower standard errors 

or variability) for any given number of observations. It also minimizes correlation between attributes 

across survey questions (i.e., attributes do not “move together” across different survey questions), so that 

the unique effect of each attribute on respondents’ choices can be isolated.4

 

4

 EPA removed dominated pairs where one option is superior to the other in all attributes. Focus groups showed that respondents 

react negatively and often protest when offered dominated pairs. Given that such choices provide negligible statistical 

information compared to choices involving non- dominated pairs, they are typically avoided in choice experiment statistical 

designs. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 8

The attribute levels included across options pairs in the survey versions are summarized below in Table 2. 

As described in Section 1, each choice question includes two choice options (Option A and Option B), 

characterized by the five attributes in Table 1, with values differing between the two choice options. The 

resulting experimental design is characterized by 72 unique Option A vs. Option B pairs, each 

corresponding to a choice question defined by an orthogonal array of attribute levels for the two policy 

options. Following guidance from the literature, EPA designed the attribute to illustrate 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). Choice scenarios represent each ecological attribute 

in relative terms with regard to upper and lower reference conditions (i.e., best and worst possible in the 

affected area), as defined in survey informational materials. The survey also presents the cardinal basis 

for relative scores where applicable, e.g., change in fish saved per year is illustrated both in terms of 

numbers of age-one equivalent fish and in terms of a percentage of current estimated mortality. Relative 

scores represent percent progress towards the upper reference condition (100%), starting from the lower 

reference condition (0%). This approach is based on BSPV methods of Johnston et al. (2011a, b; 2012). 

As described above, the experimental design for each the four regional and national surveys is 

characterized by 72 unique A vs. B option pairs. Each pair represents a unique choice modeling question, 

with a unique set of attribute levels distinguishing options A and B. It is standard practice to include more 

than one choice question in each survey, increasing the information obtained from each respondent (Poe 

et al. 1997; Layton 2000); this has been described as a “fundamental element of ... choice experiments” 

(Day et al. 2012, p. 73). EPA randomly assigned the 72 option pairs to 24 distinct survey booklets for 

each of the four regional and the national surveys, with three option pairs (i.e., choice questions) per 

survey booklet. All 72 profiles included in each of the four regional and national survey versions are 

presented in Appendix A. The 24 versions of the booklets for each of the regional and the national 

surveys were randomly assigned to households in the mail sample. 

Focus groups showed that respondents react negatively and often protest when offered choices in which 

one policy option dominates the other in all attributes. Following guidance from the literature (Hensher 

and Barnard 1990), EPA constrained the design to eliminate such dominant/dominated pairs. EPA also 

eliminated non-credible pairs where one of the options offers both a greater reduction in fish losses and a 

smaller increase in the population in order to avoid protest bids and confusion among respondents 

(Bateman et al. 2002). 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 9

Table 2—Attribute Levels Assigned Across Policy Options and Survey Versions 

Attribute Baseline 

(Status Quo)a

Max Change 

Assigned Attribute Levels Assigned to Option A vs. Option B Pairs 

1 2 3 4 5 6 

Commercial Fish Populations (Score showing the overall health of commercial and recreational fish populations) 

Northeast 42% 6% 43% 45% 48% - - - 

Southeast 39% 6% 40% 42% 45% - - - 

Pacific 56% 6% 57% 59% 62% - - - 

Inland 39% 6% 40% 42% 45% - - - 

National 51% 6% 52% 54% 57% - - - 

Fish Populations (all fish) (Score showing the estimated size of all fish populations compared to natural levels without human 

influence) 

Northeast 26% 4% 27% 28% 30% - - - 

Southeast 24% 4% 25% 26% 28% - - - 

Pacific 32% 4% 33% 34% 36% - - - 

Inland 33% 4% 34% 35% 37% - - - 

National 30% 4% 31% 32% 34% - - - 

Fish Saved per Year (Score showing the reduction in young fish lost compared to current levels) 

Northeast 0% 95% 5% 50% 95% - - - 

Southeast 0% 90% 25% 55% 90% - - - 

Pacific 0% 95% 2% 50% 95% - - - 

Inland 0% 95% 55% 75% 95% - - - 

National 0% 95% 25% 55% 95% - - - 

Aquatic Ecosystem Condition (Score showing the ecological condition of affected areas, compared to the most natural waters 

in the region) 

Northeast 50% 4% 51% 52% 54% - - - 

Southeast 68% 4% 69% 70% 72% - - - 

Pacific 51% 4% 52% 53% 55% - - - 

Inland 42% 4% 43% 44% 46% - - - 

National 53% 4% 54% 55% 57% - - - 

Household Costs (The increase in annual household cost, in unavoidable price increases) 

Northeast $0 $72 $12 $24 $36 $48 $60 $72 

Southeast $0 $72 $12 $24 $36 $48 $60 $72 

Pacific $0 $72 $12 $24 $36 $48 $60 $72 

Inland $0 $72 $12 $24 $36 $48 $60 $72 

National $0 $72 $12 $24 $36 $48 $60 $72 

a Each question includes a “no policy” option, characterized by the baseline levels for each attribute and a household cost of $0. 

3 Pretests and Pilot Tests 

Following recommended methods for stated preference survey design (cf. Arrow et al. 1993; Bateman et 

al. 2002; Bennett and Blamey 2001; Kaplowicz et al. 2004), EPA extensively pre-tested the survey during 

six focus groups, with 8 to 10 participants each, and a set of eight one-on-one cognitive interviews (EPA 

ICR # 2090-0028). Each focus group was 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). Each cognitive interview included one participant, allowing 

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). 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 10

Focus groups and cognitive interviews also included questions following the verbal protocols suggested 

by Schkade and Payne (1994), in which respondents were asked to talk through the process used to 

answer choice questions. They were conducted in several regions to account for the potentially distinct 

information relevant to survey design. Transcripts from these seven focus groups can be found in the 

docket for the ICR (ICR # 2402.01). 

Participants in focus groups and cognitive interviews completed draft survey questionnaires and provided 

comments and feedback on concerns such as whether (1) questions and survey information were readily 

understood, (2) respondents were interpreting questions similarly to how EPA interprets them, (3) 

responses or survey interpretations showed any evidence of heuristics or survey biases, including 

hypothetical bias, (4) respondents were addressing choice questions in a manner commensurate with 

utility maximization and neoclassical WTP estimation, and (5) respondents were following instructions 

provided in the survey instrument and responding to questions accordingly. Responses to the survey 

choice questions from participants in the focus groups and cognitive interviews could not be included in 

model estimation because the draft surveys completed during pre-testing differ somewhat from the final 

survey. EPA modified the survey several times based on the results of these pre-tests to minimize 

potential biases and to ensure shared and accurate interpretation of survey language among the 

respondents. Results from focus groups and cognitive interviews provided evidence that respondents 

answer the stated preference survey questions in ways appropriate for stated preference WTP estimation, 

and that their responses generally do not reflect the biases noted above.5

 The amount of pre-testing 

conducted for stated preference surveys varies within the literature and tends to be related to the 

complexity of the survey instrument (i.e., more complex survey instruments addressing complex 

ecological issues require more pre-testing and subsequent revisions). EPA believes that the amount of 

time and number of focus groups applied in survey design compares favorably to stated preference 

analyses in the peer reviewed literature. 

Stated preference surveys also require the provision of information to enable respondents to comprehend 

the potential implications of their hypothetical choices. For example, in this case, respondents may not be 

aware that cooling water intake structures potentially can kill large numbers of fish, eggs, and larvae, or 

that the vast majority of those organisms are species that provide no consumptive use (e.g., commercial or 

recreational fishing) to humans. Even if they are aware of this issue in a general way, it is unlikely that 

most of them will have previously considered what preserving those species is worth to them. Elicitation 

of informed responses requires the provision of background information to respondents including the 

general context and scope of the issue. Following standard practices in stated preference survey design, 

EPA pretested the information provided to respondents in focus groups and cognitive interviews to 

determine what quantity and types of information respondents needed to feel confident and well-informed 

in their responses (DCN 11-4710). For example, EPA explains in the introductory materials 

accompanying the four regional and national survey versions that the number of “young adult fish” lost in 

coastal and fresh waters due to cooling water use (also called “age-one-equivalents”) includes eggs and 

larvae. Without this educational material, survey respondents might not realize that reported effects on 

“fish saved per year” in the valuation questions partially result from reduced mortality of eggs and larvae. 

The presentation of this type of background information, if not properly vetted in the survey instrument 

development process, could result in focusing respondent attention on particular environmental amenities 

 

5

 For example, participants took the survey questions seriously, indicating that hypothetical bias may not be a significant design 

issue. Many participants were confident when asked whether their choices would be different if they knew the vote was 

binding; one participant stated that “No. It would have been the same actually.” The transcripts can be found in the docket 

for the ICR (ICR # 2402.01) 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 11

to the exclusion of other market and non-market goods that may also be important to some respondents’ 

decision making with regard to the choice questions. 

The final survey instrument is built upon an earlier version initially developed as part of the Phase III 

316(b) rulemaking. Twelve focus groups were conducted for the Phase III survey, which was peer 

reviewed in January 2006 (Versar 2006). See EPA ICR #2155.01 for details. The current survey 

incorporates both the results of prior focus groups and recommendations received from that peer review 

panel.6

 

Consistent with established best practices for stated preference surveys, EPA has sought to minimize 

possible biases by careful and thorough construction and testing of the survey instrument. The Agency 

recognizes that potential biases may still remain and may influence the results of the study. While EPA 

believes that the study incorporates current best professional practice in the conduct of stated preference 

studies, EPA acknowledges that the results of any empirical study depend on the methodology applied. 

4 Sampling Design and Sample Frame 

The stated preference study was designed as a mail survey sent to households in different regions 

throughout the country. The target population for the stated preference survey is all individuals from 

continental U.S. households who are 18 years of age or older. The population of households is stratified 

into four survey regions: Northeast, Southeast, Inland, and Pacific. These regions are defined by state 

boundaries and differ from the 316(b) benefits regions used in the EEBA for the proposed rule. Alaska 

and Hawaii were excluded because they include only four in-scope non-recirculating facilities, represent a 

small percentage of overall household population, and are separated geographically from the states which 

comprise each region. EPA is also administering a national version of the survey that does not require 

stratification. See Table 3 for a list of states included in each survey region. 

The survey instrument and sampling were designed to maximize the response rate and minimize the 

potential non-response bias following Dillman’s mail survey approach (Dillman et al. 2009). Dillman et 

al. (2009) is among the most definitive sources for survey logistics management. Under this approach 

households selected for the mail survey sample were sent a series of mailings: 

1. Preview letter: respondents receive a preview letter that notifies the household that it has been 

selected and briefly describes the survey; 

2. First survey mailing: the survey booklet was sent to selected households one to two weeks after 

the preview letter; 

3. Postcard reminder: a postcard reminder was sent one week after the first survey mailing; 

4. Second survey mailing: the survey booklet was sent to those households who did not respond to 

the first mailing three weeks after the first survey mailing; 

5. Second reminder: a follow up letter was sent one week after the second survey mailing. 

EPA developed target sample sizes for each region to provide statistically robust results while minimizing 

the cost and burden of the survey to individual respondents.7

 The target sample sizes refer to completed

 

6

 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). 

7

 EPA included three choice questions within each survey, to increase information obtained from each respondent. It is standard

practice within choice experiment and dichotomous choice contingent valuation surveys to include more than one choice 

question in each survey (Poe et al. 1997; Layton 2000). Including more than three choice questions may have negatively 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 12

mail surveys. They are presented in Table 3 along with the number of households selected to receive a 

survey. EPA selected a total target sample of 2,000 completed surveys across all four regional surveys. 

These 2,000 surveys were allocated across the four regions based on the number of households in each 

region relative to the total number of household in the continental U.S. In addition, a minimum number 

of completed surveys were required for each region. Monte Carlo experiments indicate that approximately 

6 to 12 completed responses are required for each profile (unique set of choice options) in order to 

achieve large sample statistical properties for choice experiments (Louviere et al. 2000, p. 104, citing 

Bunch and Batsell 1989). As described previously, the experimental design includes 72 option profiles. 

Following this guidance, the experiment design will require 12 completed surveys for each of the 72 

profiles for a total of 864 profile responses per region (72×12=864). A minimum of 288 completed 

surveys are required for each region because each survey version includes 3 profiles (864÷3=288). Based 

on this allocation, the sample sizes range from a high of 732 to a low of 288 households. If we are 

estimating a population percentage, then the margin of error for the sample percentage based on these 

sample sizes ranges from 3.6 to 5.8 percentage points at the 95% confidence level.8

The allocation of the 2,000 completed surveys across the four regions resulted in target sample sizes of 

417 for the Northeast version, 562 for the Southeast version, 289 for the Pacific version, and 732 for the 

Inland version. EPA is also implementing a national version of the mail survey with a target sample size 

of 288 completed surveys. EPA mailed the survey to 7,840 households in total, anticipating a response 

rate of 30 percent.9

 The sample for the national survey version is being 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. Table 3 presents the states included in each 

region, the total number of households in each region, the target number of completed surveys, and the 

number of surveyed households for each region. Households were randomly selected from the U.S. Postal 

Service Delivery Sequence File (DSF).The DSF covers over 97% of residences in the U.S. including 

city‐style addresses and PO boxes, and covers single‐unit, multi‐unit, and other types of housing 

structures. Responses to the mail survey are discussed in the following section. 

 

affected the response rate by increasing burden on respondents and including fewer would have increased survey costs by 

requiring additional households. 

8

 Margin of error was calculated assuming that the population percentage selecting a specific answer (e.g., “yes”) in a binary 

question is 50% (i.e., worst case scenario). The range of the margin of error (3.6 to 5.8%) is based on the sample sizes for 

each region. For example, the sample percentage selecting a specific response to a binary question based on a sample of 732 

households has a margin of error of plus or minus 3.6% at a 95% confidence level whereas the sample percentage selecting a 

specific response based on a sample of 288 households will have a margin of error of plus or minus 5.8%. 

9

 The number of intended completed questionnaires for each survey region was rounded up so that the same number of 

households were sent each of the 24 survey versions. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 

Table 3—Target Sample Sizes and Geographic Stratification Design Survey Region States Included Household Population Target Sample Size a,b NNortheast CT, DC, DE, MA, MD, ME, NH, NJ, NY, 

PA, RI, VT 

23,281,296 417 

Southeast AL, FL, GA, LA, MS, NC, SC, TX, VA 31,378,122 562 Inland AR, AZ, CO, ID, IA, IL, IN, KS, KY, MI, MN, 

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

40,852,983 732 

Pacific CA, OR, WA 16,158,206 289 Total for Regional Surveys Versions 

U.S. (excluding AK and HI) 111,670,607 2,000 

National Survey Version U.S. (excluding AK and HI) 111,670,607 288 a Target sample sizes presented here refer to completed mail surveys. b The sample is allocated to each region in proportion to the total number of households in that region, with at least 288 completed 

region, the number required to estimate the main effects and interactions under an experimental design model. 

c The number of intended completed questionnaires for each survey region was rounded up so that the same number of households 

24 survey versions. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 14

5 Mail Survey Responses 

At the time this memorandum was written, EPA had received a total of 2,313 completed mail surveys 

across all versions. Error! Reference source not found. summarizes the number of completed surveys 

received and the response rate (minus undeliverable surveys across the survey versions). The average 

response rate across all versions was 33 percent. This response rate is comparable to various other recent 

mail surveys in the stated preference literature (e.g., Hanley et al. 2006a; Johnston and Duke 2009; 

Johnston and Bergstrom 2011; Boyle and Ozdemir, 2009). Responses to the mail survey and nonresponse survey were entered into an electronic database suitable for use with a statistical analysis 

software package. Table 5 provides demographic characteristics of survey respondents in each survey 

region. 

The survey data indicate that respondents appear to have been evaluating trade-offs between costs and 

benefits of policy options presented to them, not simply selecting “no policy” or the same policy option 

for all three choice questions. The survey data also indicate that WTP is strongly responsive to scope (i.e., 

the quantity of environmental improvements across different attributes). Responses also reveal, as 

suggested in focus groups, that respondents appear to understand and distinguished between different 

types of outcomes from 316(b) regulation. As with any mail survey, some respondents did not complete 

all questions. However, about 90% responded to the choice experiment questions (questions 4, 5, and 6 of 

the survey). As desired for a choice experiment of this type, EPA does not see evidence of clear 

correlations between respondents’ choices across policy questions. About 17% of respondents selected 

“no policy” (i.e., status quo) for all three choice questions and about 56% of respondents selected a new 

policy (either Option A or Option B) for all three choice questions. Thus, more than a quarter of 

respondents made selections of both policy and status quo in their trio of choice questions. Results of this 

nature indicate that the experimental design (the selection of the attribute values and household costs) 

correctly spans and brackets the values respondents hold for this resource. This feature allows for robust 

estimates of WTP based on the survey results. If the survey had used lower monthly household costs, it’s 

more likely that a larger group of respondents would have selected Options A or B, making it difficult to 

distinguish between respondents with positive, but low, WTP and high WTP. On the other hand, if EPA 

had used higher monthly costs and most respondents had selected the status quo, it would be difficult to 

distinguish between respondents with zero WTP and those with positive WTP that is below the levels 

implied in the choice question. 

Table 4—Completed Surveys Received and Response Rates by Survey Version 

Survey Version Households 

Surveyed

Completed Surveys 

Received a,b Response Rate a

Northeast 1,440 421 31%

Southeast 1,920 506 30%

Pacific 1,040 311 32%

Inland 2,480 787 36%

National Survey Version 960 288 34%

a

 The number of undeliverable surveys was subtracted from surveys mailed when calculating the response rate 

for each survey region. Undeliverable surveys are those surveys that were returned to sender. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 15

Question 8 of the survey asked respondents to rate their understanding of the survey material, confidence 

in their responses, and thoughts on potential bias. Responses to Question 8 are summarized in Table 6 and 

Figure 2. The vast majority of respondents selected “strongly agree”, “agree”, or “neutral”, with a small 

minority selecting “disagree” or “strongly disagree”. These responses indicate that for the most part, 

respondents understood the survey materials and were confident in their responses. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 16

Table 5—Demographic Characteristics of Survey Respondents by Survey Region a

Statistic Northeast Southeast Pacific Inland National 

Average age of respondents 54.6 54.3 52.8 53.7 54.2 

Percent of respondents under 65 b 74.6% 74.1% 76.1% 76.3% 72.7% 

Percent male respondents 63.9% 62.3% 62.7% 64.6% 60.4% 

Percent currently employed 63.6% 59.2% 65.0% 64.4% 60.2% 

Percent employed under age 65 76.9% 75.0% 80.3% 76.9% 72.5% 

Highest Level of Education 

Less than High School 4.2% 4.4% 1.7% 1.8% 4.7% 

High School or Equivalent 15.7% 16.0% 13.6% 16.8% 17.0% 

High School +Technical School 10.2% 11.4% 7.5% 13.8% 9.4% 

One or More Years of College 23.9% 24.1% 26.4% 24.5% 22.0% 

Bachelor’s Degree 22.7% 25.8% 28.8% 22.4% 30.7% 

Graduate Degree 23.2% 18.3% 22.0% 20.7% 16.2% 

Hispanic or Latino Origin 5.1% 9.9% 13.3% 3.4% 7.0% 

Racial Categoryc 

American Indian or Alaskan 

Native 

3.1% 3.9% 3.6% 3.2% 3.0% 

Black or African American 7.5% 14.7% 3.6% 6.6% 10.2% 

Native Hawaiian or Other 

Pacific Islander 

1.3% 0.0% 0.4% 0.5% 1.5% 

Asian 5.7% 2.6% 10.9% 2.8% 4.5% 

White d 86.6% 82.3% 84.7% 91.0% 83.4% 

Average Household Size 2.5 2.5 2.6 2.5 2.7 

Number of household members 

16 or older 

2.0 1.9 1.9 1.9 1.9 

Total Household Income e 

Average $88,880 $75,588 $96,144 $73,567 $79,496 

Standard Deviation $69,309 $62,618 $71,282 $57,261 $60,972 

Consume commercially caught fish 

or seafood 

91.9% 89.3% 90.4% 89.7% 92.0% 

Consume recreationally caught fish 

or seafood 

46.4% 59.5% 50.5% 61.0% 57.7% 

Notes: 

a

 Respondents did not answer a given demographic question were excluded when calculating percentages. 

b

 Compares to 83.21% for 18+ population nationally (excluding HI and AK) based on Census 2010. 

c

 Racial percentages sum to more than 100% due to some individuals checking more than one racial category box. 

The survey did not have a box specifically indicating two or more races. 

d

 Compares to 74.9% nationally (excluding HI and AK) based on Census 2010. However, the racial categories 

presented in this table are different from the census categories. Unlike the census, EPA does not present a separate 

category for respondents selecting more than one race. The census also includes an “other category” which was not 

included in the 316(b) stated preference survey. 

e

 The survey asked respondents to select one of eight categories for annual household income. The average and 

standard deviation reported here were calculated using the midpoint of each range. $250,000 was used for the highest 

income category included in the survey (“$250,000 or more”). 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 17

Table 6—Summary of Responses to Questions Regarding Survey Understanding and 

Bias Across All Survey Regions 

Question Strongly 

Disagree Disagree Neutral Agree Strongly Agree 

The survey provided enough 

information for me to make 

informed choices 

5.5% 12.0% 26.9% 43.8% 11.8% 

I feel confident about my answers 1.2% 4.2% 21.7% 46.5% 26.5% 

Information in the survey was easy 

for me to understand

3.9% 9.7% 22.4% 43.7% 20.3% 

Information in the survey was fair 

and unbiased 

4.8% 8.1% 38.7% 35.3% 13.0% 

Questions were easy for me to 

understand 

2.9% 10.9% 20.9% 45.0% 20.3% 

I would vote the same way in a 

public vote 

0.9% 1.8% 18.8% 45.0% 33.4% 

The effect of the proposed policies 

depends on many factors 

0.9% 1.2% 10.7% 46.7% 40.5% 

Future ecological conditions are 

never 100% guaranteed 

0.8% 1.5% 8.5% 39.8% 49.3% 
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June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 19

deterministic) while the random component reflects researcher uncertainty about the choice (i.e., to 

capture all factors that influence the choices that are observed or known by the respondents but 

unobserved by the researcher) (Holmes and Adamowicz 2003). 

The random utility models for all four regions and the national survey were estimated using maximum 

likelihood mixed logit. The mixed logit model is an approach for modeling preference heterogeneity 

based on the assumption that individual’s preferences are randomly distributed and that heterogeneity in 

population preferences can be captured by estimating the mean and variance of the random parameter 

distributions (Holmes and Adamowicz 2003). As described by Hensher and Greene (2003), “the mixed 

logit model offers an extended framework within which to capture a greater amount of behavioral choice 

making. Broadly speaking the mixed logit model aligns itself much more with reality than most discrete 

choice models with every individual having their own inter-related systematic and random components 

for each alternative in their perceptual choice set(s)” (p. 170). It is a highly flexible model that “obviates 

the three limitations of standard logit by allowing for random taste variation, unrestricted substitution 

patterns, and correlation in unobserved factors over time” (Train 2009, p.134). The mixed logit model 

allows for the possibility of preference heterogeneity but cannot attach specific parameter values to 

particular individuals. That is, the mixed logit (ML) model relaxes the assumption of respondents being 

identical (required for multinomial logit estimation), replacing it with less restrictive assumption that 

respondents are identically distributed. The theory and methods of mixed logit modeling are wellestablished (Train 2009), and it has now become standard practice in many areas of research (Hensher 

and Greene 2003). These models allow for coefficients on attributes to be distributed across sampled 

individuals according to a set of estimated coefficients and researcher-imposed restrictions. The model is 

evaluated numerically using random draws because choice probabilities take the form of an integral over 

a mixing distribution which does not have a closed form (Train 2009). The likelihood simulation for the 

models used 300 Halton (random) draws. 

Economic theory provides guidance regarding some, but not all, aspects of model specification for mixed 

logit models within stated preference choice experiments. For example, the parameter on program cost is 

expected to have a negative sign, reflecting a positive marginal utility of income. To allow for this, 

preliminary models included specifications in which the coefficient on cost was modeled as (1) fixed, (2) 

lognormal, and (3) bounded triangular. These are among the most common specifications that impose the 

desired sign on the cost coefficient (Hensher and Greene 2003; Johnston and Bergstrom 2011). 

Coefficients on all variables except that on program cost (cost) are specified as random with a normal 

distribution. 

EPA tested various statistical models based on survey data for the Northeast region. Comparison of model 

outputs for the Northeast survey suggest that the best overall model fit and greatest robustness of results 

(or stability across model specifications) is achieved when cost is modeled as fixed. The Northeast model 

with a lognormal distribution of cost and free correlation of random parameters converged with a slightly 

lower overall model χ

2

 (512.36, d.f. = 27) than the model with a fixed cost coefficient and free 

correlation. In addition, resulting WTP estimates are sensitive to assumptions made regarding 

observations in the tails of the distribution (cf. Hensher and Greene 2003; Johnston and Bergstrom 2011). 

Given that mixed logit models such as these are nonlinear and estimated using simulated likelihood 

methods, it is possible for a seemingly less constrained model (e.g., with a lognormal cost distribution) to 

converge with a lower log likelihood χ

2

 than an otherwise parallel model with a fixed cost coefficient. The 

Northeast model, in which the parameter on cost is specified with a bounded triangular distribution, also 

converges with a lower model χ

2

. This is likely due in part to additional necessary constraints imposed 

on this model to ensure a parameter estimate in the positive domain (e.g., the model is constrained such 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 20

that the parameter estimate is equal to the spread; because of this constraint, free correlation among 

random parameters is disallowed). 

Although similar results and WTP estimates are generated with all preliminary model specifications, the 

illustrated model leads to somewhat more conservative WTP estimates than other model variants. The 

model allows free (unrestricted) correlation among all model parameters. The results of the models for 

each region are presented in Table 8 through Table 12. The definition of variables included the model are 

presented in Table 7. Mixed logit model statistics suggest good statistical fit across the survey versions. 

Across the models the χ

2 values ranged from 389.97 to 989.68 (all with d.f. = 21, p<0.0001) and pseudo 

R2

 ranged from 0.22 to 0.29. See Table 8 through Table 12 for detail on the significance of the model 

estimated for each survey version. Direct comparisons of statistical fit measures across different choice 

experiments in the literature can be misleading and should be viewed with extreme caution. Many 

measures of model fit are not directly comparable across different datasets or models. Nonetheless, the 

overall statistical fit of the model appears broadly similar to choice experiments found in the published 

literature addressing environmental improvements both worldwide and in the U.S. Johnston et al. 

(2011a,b), in a similar survey of ecological improvements, report a χ

2

 of 533.62 (d.f. = 12, p<0.0001) and 

a pseudo R2

 of 0.30. By way of comparison using a commonly reported measure of model fit (pseudo or 

McFadden R2

), Campbell et al. (2009) report a pseudo R2 of 0.20; Carlsson et al. (2003) report pseudo R2 

values between 0.12 and 0.27; Do and Bennett (2009) report pseudo R2 between 0.07 and 0.18; and 

Colombo and Hanley (2008) report values between 0.16 and 0.36. Other measures of fit are also similar—

although again caution must be exercised when drawing conclusions from any such comparisons across 

models. 

Table 7—Variables Included with the Preliminary Random Utility Models for the 

Regional and National Surveys 

Question Variable Definition 

Random parameters in utility functions

CONSTANT Alternative specific constant (ASC) associated with the status quo, or choice of neither 

plan. 

COM_FISH Score showing the overall health of commercial and recreational fish populations. 

FISH_POP Score showing the estimated size of all fish populations compared to natural levels without 

human influence. 

FISH_SAV Score showing the reduction in young fish lost compared to current levels. 

AQUATIC Score showing the ecological condition of affected areas, compared to the most natural 

waters in the region (e.g., Northeast). 

Nonrandom parameters in utility functions

COST The increase in annual household cost, in unavoidable price increases for products and 

services, including electricity and common household products. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 21

Table 8—Preliminary Results for the Northeast Region a,b

Variable Coefficient c Standard Error Coefficient/St. 

Error 

P-value 

P[|Z| > z] 

Random parameters in utility functions 

CONSTANT -0.08269 0.36139 -0.229 0.8190 

COM_FISH 0.20338 0.05351 3.801 0.0001 

FISH_POP 0.07493 0.08347 0.898 0.3693 

FISH_SAV 0.03044 0.00519 5.868 0.0000 

AQUATIC 0.20973 0.08991 2.333 0.0197 

Nonrandom parameters in utility functions 

COST -0.02792 0.00447 -6.239 0.0000 

Derived standard deviations for parameter distributions

sdCONSTANT- 0.03823 1.05647 0.036 0.9711 

sdCOM_FISH- 0.17539 0.28451 0.616 0.5376 

sdFISH_POP- 0.17880 0.31559 0.567 0.5710 

sdFISH_SAV- 0.05615 0.01159 4.845 0.0000 

sdAQUATIC- 0.26680 0.33937 0.786 0.4318 

a

 The mixed logit results have a χ

2

 of 518.40 (d.f. = 21, p<0.0001) and a pseudo R2 of 0.22. 

b

 EPA received a total of 421 completed surveys for the Northeast region. Not all of these observations were 

data entered at the time of model estimation for this memorandum due to time constraints. The preliminary 

Northeast model was estimated based on 394 observations. The additional survey responses will be included in 

the analysis for the final rule. 

c

 For random parameters in utility functions, coefficients represent the estimated means of random parameter 

distributions. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 22

Table 9—Preliminary Results for the Southeast Region a,b

Variable Coefficient c Standard Error Coefficient/St. 

Error 

P-value 

P[|Z| > z] 

Random parameters in utility functions 

CONSTANT 0.08004 0.34193 0.234 0.8149 

COM_FISH 0.12104 0.04574 2.646 0.0081 

FISH_POP 0.12531 0.07138 1.756 0.0791 

FISH_SAV 0.02568 0.00536 4.788 0.0000 

AQUATIC 0.23161 0.07885 2.937 0.0033 

Nonrandom parameters in utility functions 

COST -0.03487 0.00351 -9.925 0.0000 

Derived standard deviations for parameter distributions

sdCONSTANT- 0.02489 1.16784 0.021 0.9830 

sdCOM_FISH- 0.09525 0.16377 0.582 0.5608 

sdFISH_POP- 0.09301 0.13918 0.668 0.5040 

sdFISH_SAV- 0.06804 0.00952 7.146 0.0000 

sdAQUATIC- 0.12186 0.12918 0.943 0.3455 

a

 The mixed logit results have a χ

2

 of 683.29 (d.f. = 21, p<0.0001) and a pseudo R2 of 0.24. 

b

 EPA received a total of 506 completed surveys for the Southeast region. Not all of these observations were 

data entered at the time of model estimation for this memorandum due to time constraints. The preliminary 

Southeast model was estimated based on 470 observations. The additional survey responses will be included 

in the analysis for the final rule. 

c

 For random parameters in utility functions, coefficients represent the estimated means of random parameter 

distributions. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 23

Table 10—Preliminary Results for the Pacific Region a,b

Variable Coefficient c Standard Error Coefficient/St. 

Error 

P-value 

P[|Z| > z] 

Random parameters in utility functions 

CONSTANT 0.24593 0.54100 0.455 0.6494 

COM_FISH 0.10278 0.10424 0.986 0.3241 

FISH_POP 0.14215 0.15130 0.940 0.3475 

FISH_SAV 0.05323 0.01141 4.664 0.0000 

AQUATIC 0.20853 0.16636 1.254 0.2100 

Nonrandom parameters in utility functions 

COST -0.02226 0.00483 -4.606 0.0000 

Derived standard deviations for parameter distributions

sdCONSTANT- 0.02585 1.92718 0.013 0.9893 

sdCOM_FISH- 0.20354 0.15901 1.280 0.2005 

sdFISH_POP- 0.14019 0.36490 0.384 0.7008 

sdFISH_SAV- 0.13881 0.05278 2.630 0.0085 

sdAQUATIC- 0.18715 0.50826 0.368 0.7127 

a

 The mixed logit results have a χ

2

 of 455.32 (d.f. = 21, p<0.0001) and a pseudo R2 of 0.29. 

b

 EPA received a total of 311 completed surveys for the Pacific region. Not all of these observations were data 

entered at the time of model estimation for this memorandum due to time constraints. The preliminary Pacific 

model was estimated based on 274 observations. The additional survey responses will be included in the 

analysis for the final rule. 

c

 For random parameters in utility functions, coefficients represent the estimated means of random parameter 

distributions. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 24

Table 11—Preliminary Results for the Inland Region a,b

Variable Coefficient c Standard Error Coefficient/St. 

Error 

P-value 

P[|Z| > z] 

Random parameters in utility functions 

CONSTANT -0.25032 0.35323 -0.709 0.4785 

COM_FISH 0.09898 0.03205 3.088 0.0020 

FISH_POP 0.11198 0.05370 2.085 0.0370 

FISH_SAV 0.02470 0.00427 5.788 0.0000 

AQUATIC 0.08669 0.06599 1.314 0.1890 

Nonrandom parameters in utility functions 

COST -0.03186 0.00272 -11.709 0.0000 

Derived standard deviations for parameter distributions

sdCONSTANT- 0.02650 2.01575 0.013 0.9895 

sdCOM_FISH- 0.07322 0.16944 0.432 0.6656 

sdFISH_POP- 0.06925 0.20775 0.333 0.7389 

sdFISH_SAV- 0.05182 0.00773 6.706 0.0000 

sdAQUATIC- 0.52674 0.40309 1.307 0.1913 

a

 The mixed logit results have a χ

2

 of 989.68 (d.f. = 21, p<0.0001) and a pseudo R2 of 0.22. 

b

 EPA received a total of 787 completed surveys for the Inland region. Not all of these observations were data 

entered at the time of model estimation for this memorandum due to time constraints. The preliminary Inland 

model was estimated based on 752 observations. The additional survey responses will be included in the 

analysis for the final rule. 

c

 For random parameters in utility functions, coefficients represent the estimated means of random parameter 

distributions. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 25

Table 12—Preliminary Results for the National Survey Version a,b

Variable Coefficient c Standard Error Coefficient/St. 

Error 

P-value 

P[|Z| > z] 

Random parameters in utility functions 

CONSTANT -0.06065 0.60390 -0.100 0.9200 

COM_FISH 0.17407 0.06579 2.646 0.0082 

FISH_POP 0.25141 0.12384 2.030 0.0423 

FISH_SAV 0.03938 0.01084 3.631 0.0003 

AQUATIC 0.14043 0.13630 1.030 0.3029 

Nonrandom parameters in utility functions 

COST -0.03539 0.00461 -7.674 0.0000 

Derived standard deviations for parameter distributions

sdCONSTANT- 0.02280 1.82792 0.012 0.9900 

sdCOM_FISH- 0.13724 0.52054 0.264 0.7921 

sdFISH_POP- 0.11719 0.56426 0.208 0.8355 

sdFISH_SAV- 0.10766 0.05600 1.922 0.0545 

sdAQUATIC- 0.16083 1.21828 0.132 0.8950 

a

 The mixed logit results have a χ

2

 of 389.97 (d.f. = 21, p<0.0001) and a pseudo R2 of 0.24. 

b

 EPA received a total of 288 completed surveys for the National version. Not all of these observations were 

data entered at the time of model estimation for this memorandum due to time constraints. The preliminary N 

model was estimated based on 270 observations. The additional survey responses will be included in the 

analysis for the final rule. 

c

 For random parameters in utility functions, coefficients represent the estimated means of random parameter 

distributions. 

The variable for fish saved (fish_sav) is significant in all five models, commercial fish populations 

(com_fish) is significant in four of the five models, fish populations (all fish) (fish_pop) is significant in 

three of the five models, and aquatic ecological condition (aquatic) is statistically significant in two of 

the five models. The significance of these attributes suggests positive implicit prices (WTP for changes in 

the attributes alone). Analogous outcomes are common in choice experiments across the literature 

addressing aquatic ecological improvements, with the substantial majority of choice attributes found to 

have statistically significant impacts (e.g., Johnston et al. 2011a,b; Carlsson et al. 2003; Do and Bennett 

2009). The alternative specific constant (ASC) (constant) is a fixed coefficient estimated within choice 

experiments that is designed to capture “systematic but unobserved information about why respondents 

chose a particular option (that is, unrelated to choice set attributes)” (Bennett et al. 2001). The ASC was 

not significant in any of the five models, which should be viewed as a desirable result. ASCs only become 

statistically significant in choice experiment models when elements other than the independent variables, 

or choice attributes, in the model influence respondents’ choices (Kerr and Sharp 2006). Here, no such 

statistically significant effects were found. In other words, respondents are not willing to pay a positive 

or negative amount for a regulation that has no effects on ecological attributes. EPA views this as a 

desirable result. 

As noted above, all variables except cost represent percent progress towards the upper ecological 

reference condition (100%). Hence, these coefficients may be directly interpreted as the relative marginal 

utility derived from a one percentage point change in each ecological attribute. In the estimated Northeast 

model, for example, marginal utility is greatest (per percentage point change) for increases in aquatic 

ecological condition (aquatic), with lower (but still statistically significant) impacts associated with 

changes in commercial fish populations (fish_pop) and the number of fish saved (fish_sav). As noted 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 26

above, the percentage differences across the options presented were much larger for the number of fish 

saved (fish_sav) than for the other variables. Following recommended practice in stated preference 

valuation, these variations correspond with realistic ecological and policy expectations for regulatory 

outcomes (Bateman et al. 2002). 

EPA notes that these model results are preliminary. At the time this memorandum was written, EPA had 

not completed the fielding and analyzing data from the non-response studies for the Southeast, Pacific, 

Inland and National survey versions. The models summarized in Table 8 to Table 12 do not include any 

weighting based on non-response studies. EPA intends to complete the non-response follow up studies for 

the other regions to estimate weighted models that would correct for potential non-response bias if 

needed. EPA also intends to apply a number of validity tests to examine the robustness and theoretical 

consistency of estimated WTP equations after the non-response studies have been completed for all 

versions. When these efforts are completed, EPA intends to post these findings on its website10, and 

subject the complete survey results and analysis, including scope and validity testing, to external peer 

review. 

6.2 Model Sensitivity to Scope 

The role of external scope tests within choice modeling has been given relatively little attention in the 

literature (cf., Heberlein et al. 2005). Unlike open-ended contingent valuation questions, choice 

experiments provide a direct mechanism for respondents to react to the scope and scale of resource 

changes, by enabling respondents to compare policy options with different levels for each attribute. 

Hence, as noted by Bennett and Blamey (2001, p. 231), “internal scope tests are automatically available 

from the results of a [choice modeling] exercise.” Within a choice modeling context, external scope tests 

may also be confounded by differences in the implied choice frame (Bennett and Blamey 2001). These 

caveats aside, an external scope test can provide some insight into response patterns, and many view these 

tests as a “stronger” form of validation than internal scope tests. EPA therefore implemented some 

preliminary external scope tests to evaluate this form of validity using the Northeast survey data. 

The illustrated split-sample external validity test for the Northeast mail survey data is based on the 

concept that, if all else is orthogonal (effectively equal), a choice option with a greater number of fish 

saved should be chosen more often than a choice option with a lesser number of fish saved. To 

distinguish this from the “internal” scope tests automatically performed by choice experiments, it is 

implemented using a split sample of choice options viewed in isolation. To implement the test, we first 

create a dataset only of observations on Option A for all survey responses, along with the dummy (0-1) 

variable choice, indicating whether that option was chosen. We then further split this sample into three 

sub-samples: (1) observations on Option A when fish_sav = 95%, (2) observations on Option A when 

fish_sav = 50%, and (3) observations on Option A when fish_sav =5%. Because of the near orthogonal 

nature of the experimental design, all other attribute levels should be approximately equal across each of 

these three sub-samples. Given this split sample, we would expect to observe the greatest proportion of 

respondents choosing Option A in sub-sample (1), followed by sub-sample (2) and then (3). This order 

would establish external sensitivity to scope. We then repeat the same test for Option B. 

Results of the test are presented below in Table 13, which illustrates means and standard deviations for 

choice and attributes over each observation of Option A and Option B. The values of other choice 

attributes (com_fish, fish_pop, aquatic, and cost) are approximately equal over the split samples as one 

 

10 http://water.epa.gov/lawsregs/lawsguidance/cwa/316b/ 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 27

would expect, given the experimental design. The proportion of respondents choosing Option A (choice) 

declines from 0.45 to 0.42 to 0.25 as the percentage of fish saved (fish_sav) declines from 95% to 50% to 

5%. Option B exhibits a similar decline in choice with fish_sav. This external scope tests for split 

samples of both Options A and B demonstrate scope sensitivity, as indicated by economic theory. 

Table 13—Results of the Split-Sample External Validity Test for the Northeast 

Survey Data 

Variable Option A Option B 

Mean Std. Dev. Mean Std. Dev. 

Fish Saved = 95% 

CHOICE 0.4538 0.4986 0.4854 0.5005 

CONSTANT 0.0000 0.0000 0.0000 0.0000 

COM_FISH 45.3866 2.0736 45.3077 2.0187 

FISH_POP 28.0560 1.1647 28.5199 1.2591 

FISH_SAV 95.0000 0.0000 95.0000 0.0000 

AQUATIC 52.3305 1.2441 52.2334 1.2500 

COST 42.3866 20.8902 44.1804 21.7200 

Fish Saved = 50%

CHOICE 0.4212 0.4945 0.4313 0.4960 

CONSTANT 0.0000 0.0000 0.0000 0.0000 

COM_FISH 45.0630 1.9802 45.4469 2.0762 

FISH_POP 28.2493 1.2169 28.4187 1.2742 

FISH_SAV 50.0000 0.0000 50.0000 0.0000 

AQUATIC 52.2808 1.2323 52.2906 1.2569 

COST 41.8109 20.4754 41.6250 20.6614 

Fish Saved = 5%

CHOICE 0.2514 0.4344 0.2452 0.4308 

CONSTANT 0.0000 0.0000 0.0000 0.0000 

COM_FISH 45.2938 2.1432 45.3333 2.1172 

FISH_POP 28.4379 1.3350 28.3278 1.2054 

FISH_SAV 5.0000 0.0000 5.0000 0.0000 

AQUATIC 52.4407 1.2474 52.3636 1.2077 

COST 40.0678 19.6789 38.9752 19.8014 

7 Estimation of Implicit Prices and WTP 

EPA used the preliminary results of the random utility models described in Section 6.1to estimate the 

marginal annual WTP (or implicit price) for a one percentage point change in each of the four 

environmental attributes within each survey region. This represents WTP per household, per year, for a 

one percentage point change in the corresponding choice model attribute. For example, one could 

calculate the marginal WTP for each additional percentage increase in fish saved, holding all else 

constant. If utility is modeled as a linear function of attributes, implicit prices may be calculated as IPa = 

௔ߚ

ߚ ௡ൗ , where βa is the estimated coefficient on a non-monetary attribute (e.g., change in fish saved), and 

βn is the coefficient on program cost. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 28

Assuming a linear preference function as estimated above, compensating surplus (or household WTP) for 

any given policy option may be calculated as:11

஺ ௔ܲܫ ∑ = CS

 ,ሻ௔݁ݐݑܾ݅ݎݐݐܣ∆ሺ כ ୀଵ௔

where a=1...A represents the number of distinct non-price attributes in the model, and ∆ݎݐݐܣܾ݅ݐݑ݁ ௔is the 

change in attribute a resulting from the policy. Relevant attributes are com_fish, fish_pop, fish_sav and 

aquatic (see Table 21 for variable definitions). Compensating surplus for any non-status quo policy 

option is calculated as: 

כ ௖௜௧௔௨௔௤ܲܫሺ ൅ ሻݒܽݏ_݄ݏ݂݅∆ כ ௩௔௦௛_௦௙௜ܲܫሺ ൅ ሻ݌݋݌_݄ݏ݂݅∆ כ ௣௢௣_௛௦௙௜ܲܫሺ ൅ ሻ݄ݏ݂݅_݉݋ܿ∆ כ ௛௦௖௢௠_௙௜ܲܫሺ = CS

ሻ݅ܿݐܽݑݍܽ∆

where the delta (∆) represents a change in the attribute in question. That is, total WTP for a policy change 

is calculated as the sum of the product of implicit prices and corresponding attribute changes. The implicit 

price for the ASC (constant) was excluded when calculating compensating surplus because it was found 

to be insignificant during model estimation. See Section 6.1 for additional discussion of the ASC and its 

interpretation. 

EPA notes that ecological systems are typically characterized by correlation among many processes and 

outcomes. In the context of impingement and entrainment losses, for example, a reduction in A1E losses 

(fish_sav) maybe correlated with changes in fish populations (fish_pop), aquatic ecosystem condition 

(aquatic), and commercial fish populations (com_fish). Were the stated preference survey scenarios to 

incorporate the same correlations, it would be difficult to determine which attribute(s) caused respondents 

to choose one scenario over another. For example, if large reductions in I&E losses always accompany 

large positive effects on fish populations and large positive effects on ecosystem condition within survey 

scenarios (and vice versa), it would be difficult to estimate the relative influence of each effect on 

respondents’ choices. 

The experimental design used in the stated preference survey breaks this correlation, allowing different 

survey attributes to vary independently. This enables different respondents to view many different 

possible policy outcomes, each with different combinations of fish_sav, fish_pop, aquatic and com_fish. 

While some of the resulting scenarios might be unlikely in actual aquatic systems, they are not 

ecologically impossible. For example, the experimental design allows respondents to consider scenarios 

in which large reductions in fish losses accompany small changes in fish populations and aquatic 

condition (positive changes in fish_sav in some questions are also paired with no change in the population 

or aquatic condition metrics). Because attributes vary independently across the 72 different choice 

questions presented to respondents in each survey region, it is possible to estimate the unique effects of 

each attribute on individuals’ choices and therefore, values. By breaking the correlation between these 

attributes present in ecosystems, the choice experiment design allows estimation of the independent effect 

of each attribute on choices and willingness to pay. 

These attributes have almost zero correlation in the resulting experimental design. It is thus possible to 

obtain precise (i.e., efficient) estimates of each effect, without concerns that these estimates are 

confounded by correlations among the ecological outcomes in the survey. This allows WTP for each 

ecological effect to be estimated, independent from all other effects. 

 

11 EPA excluded the ASC when estimating the benefits of regulatory options. The sign of the mean implicit price for the ASC 

varies across the survey regions. It is negative for Northeast, Inland, and National versions and positive for the Southeast 

and Pacific versions. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 29

Because the mixed logit model includes random coefficients, EPA estimates implicit prices using the 

welfare simulation approach of Johnston and Duke (2007; 2009) following the framework outlined by 

Hensher and Greene (2003).12 The procedure begins with a parameter simulation following the parametric 

bootstrap of Krinsky and Robb (1986), with R=1000 draws taken from the mean parameter vector and 

associated covariance matrix. For each draw, the resulting parameters are used to characterize 

asymptotically normal empirical densities for fixed and random coefficients. For each of these R draws, a 

coefficient simulation is then conducted for each random coefficient, with S=1000 draws taken from 

simulated empirical densities. Here, all coefficient simulations draw from a normal distribution except for 

that on cost, which is fixed. WTP measures are calculated for each draw, resulting in a combined 

empirical distribution of R×S observations from which summary statistics are derived. All implicit prices 

are modeled as the WTP for a one percentage point change in the ecological attribute all else being 

constant. The resulting empirical distributions accommodate both the sampling variance of parameter 

estimates and the estimated distribution of random parameters. We follow Hu et al. (2005) and simulate 

welfare estimates as the mean over the parameter simulation of mean WTP calculated over the coefficient 

simulation (i.e., mean of mean WTP). 

As shown in Table 14, the estimated implicit prices are uniformly larger for commercial fish populations, 

fish populations (all fish), and aquatic ecosystem condition than fish saved. In the Northeast, for example, 

households value a one percentage point increase in commercial fish populations or aquatic ecosystem 

condition about seven times more than a one percentage point increase in fish saved. Although the 

discussion in this section refers to WTP for a percentage point increase in fish saved, it is important to 

note that this variable represents a one percentage point reduction relative to the baseline mortality (e.g., 

the Northeast survey booklet indicated a baseline loss of 1.1 billion fish). This relationship between the 

percentage point reduction and cardinal fish losses was specified clearly in survey questions, and the same 

relationship was maintained throughout each survey version. Again, using the Northeast survey as an 

example, EPA is presenting the WTP for a percentage point reduction of mortality which is associated 

with a specific absolute quantity of fish out of 1.1 billion fish, rather than a general, relative reduction of 

one percent from an unspecified level of I&E mortality. The regional and national surveys have different 

baseline fish losses. EPA expected survey responses to vary across the regions, both because residents 

might have different values, and because baseline losses differ. The implicit prices reflect this expected 

variation. EPA plans to explore whether respondents are more sensitive to the representation of fish saved 

in percentage terms, or in absolute numbers. 

 

12 This is the same approach applied by Johnston et al. (2011a; 2012). 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 30

Table 14— Estimated Implicit Prices for a One Percentage Point Change in Each 

Attribute , WTP per household, per Year (2011$) 

Region/Attribute Implicit 

Price a

Standard 

Deviation 

90% Confidence Interval 

5th 95th

Northeast

ASC (CONSTANT) -$2.42 $13.39 -$22.52 $19.88 

Commercial Fish Populations (COM_FISH) $7.35 $2.15 $4.14 $11.14 

Fish Populations (all fish) (FISH_POP) $2.66 $3.13 -$2.32 $7.71 

Fish Saved (FISH_SAV) $1.12 $0.22 $0.76 $1.50 

Aquatic Ecosystem condition (AQUATIC) $7.66 $3.40 $2.35 $13.45 

Southeast

ASC (CONSTANT) $2.47 $10.02 -$13.51 $18.97 

Commercial Fish Populations (COM_FISH) $3.49 $1.42 $1.30 $5.89 

Fish Populations (all fish) (FISH_POP) $3.57 $2.08 $0.27 $6.98 

Fish Saved (FISH_SAV) $0.75 $0.18 $0.46 $1.04 

Aquatic Ecosystem condition (AQUATIC) $6.61 $2.12 $3.00 $10.00 

Pacific

ASC (CONSTANT) $11.72 $26.76 -$28.73 $54.17 

Commercial Fish Populations (COM_FISH) $4.67 $5.26 -$3.16 $13.35 

Fish Populations (all fish) (FISH_POP) $6.43 $7.32 -$5.12 $17.61 

Fish Saved (FISH_SAV) $2.52 $0.80 $1.39 $3.90 

Aquatic Ecosystem condition (AQUATIC) $9.50 $7.67 -$3.03 $22.38 

Inland

ASC (CONSTANT) -$7.82 $11.37 -$26.28 $10.93 

Commercial Fish Populations (COM_FISH) $3.10 $1.07 $1.43 $4.83 

Fish Populations (all fish) (FISH_POP) $3.48 $1.72 $0.75 $6.30 

Fish Saved (FISH_SAV) $0.78 $0.14 $0.55 $1.01 

Aquatic Ecosystem condition (AQUATIC) $2.70 $2.11 -$0.84 $6.16 

National

ASC (CONSTANT) -$2.09 $17.57 -$30.91 $25.01 

Commercial Fish Populations (COM_FISH) $4.93 $2.03 $1.84 $8.34 

Fish Populations (all fish) (FISH_POP) $7.04 $3.56 $1.35 $12.78 

Fish Saved (FISH_SAV) $1.13 $0.32 $0.59 $1.68 

Aquatic Ecosystem condition (AQUATIC) $3.94 $4.00 -$2.72 10.56 a

 The implicit prices are per percentage point increase from the specified baseline (reference) levels. They are not 

directly transferable to scenarios with alternative baseline levels.

8 Method for Estimating Regional and National Benefits 

The implicit prices presented in Table 14 could be used to estimate annual monetized benefits for the 

survey regions and total U.S. households under regulatory options. Annual WTP per household for a 

regulatory option can be calculated by multiplying changes in environmental attributes due to the 

regulation (fish saved, commercial fish populations, fish populations (all fish), and aquatic ecosystem 

condition) by their respective implicit prices, or WTP per percentage point change. The models use a 

linear approximation for utility, so the implicit price of each attribute is constant. 

The annual household WTP under each regulatory option could be estimated solely based on changes in 

fish saved (fish_sav) by multiplying its implicit price by the percentage change in fish saved. EPA has 

calculated the marginal change in fish saved (%) under each regulatory option based on the percentage 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 31

reduction in A1E losses relative to baseline A1E losses within the survey region.13 The resulting estimates 

of A1E reductions and percentage fish saved for each survey region and the United States are presented in 

Table 15. Confidence intervals for annual household WTP would be calculated based on the 5th and 95th 

percentile from the empirical distribution of the implicit price for fish saved (see Table 14). Changes in 

commercial fish populations, fish populations (all fish), and aquatic ecosystem condition could also factor 

into the calculation of household WTP; however, EPA has not modeled changes in these drivers of WTP. 

Within the context of the model, using only changes in fish saved would result in conservative estimates 

of benefits, in that it assumes zero impacts on all other choice model attributes. EPA notes that while 

monetized benefits based solely on fish saved would be conservative within the framework of the survey 

results and model, there are other reasons why the WTP estimates might be biased, as discussed 

previously. 

As described previously, the regions for the stated preference survey differ from the benefits regions used 

for the analysis of the proposed rule.14 EPA estimated the A1E reductions in Table 15 by applying statelevel data for facility actual intake flow (AIF) to regional A1E reductions from the proposed rule. EPA 

believes that the use of flow to extrapolate A1E reductions is appropriate for this regional and national 

level analysis. It is important to note that the generalized relationship between flow and A1E described, as 

well as any resulting estimates of WTP for percent of fish saved, which are based on specific regional 

levels of baseline losses, may not hold at specific sites around the country. 

 

13 For its 316(b) analyses, EPA standardized all I&E mortality losses into equivalent numbers of 1-year-old fish, or A1E. This 

conversion allows losses to be compared among species, years, facilities, and regions. Refer to Section 3 of the 

Environmental and Economic Benefits Analysis (EEBA) of the proposed rule (USEPA 2011a) for additional detail on 

EPA’s assessment of baseline A1E losses and reductions in A1E losses under regulatory options. 

14 See Table 3 for the list of states included in each survey region. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 32

Table 15—Reduction in A1E Losses and Fish Saved (%) by Survey Version and 

Regulatory Option 

Survey Version and Regulatory Option Reduction in A1E Losses Fish Saved (%) 

Northeast 

Eliminating Baseline I&E Mortality Losses a 964.87 100.00

Option 1 78.31 8.12

Option 2 880.70 91.28

Option 3 893.73 92.63

Option 4 77.29 8.01

Southeast 

Eliminating Baseline I&E Mortality Losses 722.97 100.00

Option 1 271.41 37.54

Option 2 642.28 88.84

Option 3 651.70 90.14

Option 4 265.86 36.77

Pacific b

Eliminating Baseline I&E Mortality Losses 385.99 100.00

Option 1 1.78 0.46

Option 2 33.28 8.62

Option 3 34.74 9.00

Option 4 1.73 0.45

Inland 

Eliminating Baseline I&E Mortality Losses 462.29 100.00

Option 1 263.48 56.99

Option 2 425.29 92.00

Option 3 433.38 93.75

Option 4 257.54 55.71

National Version 

Eliminating Baseline I&E Mortality Losses 2536.13 100.00

Option 1 614.97 24.25

Option 2 1981.55 78.13

Option 3 2013.55 79.39

Option 4 602.42 23.75 a This hypothetical scenario reflects the benefits that would be achieved if all I&E mortality losses were eliminated. EPA 

includes it to allow comparison of regulatory option benefits to total baseline I&E mortality losses. This scenario was 

listed as “Baseline I&E Losses” in the EEBA for the proposed rule. b

 The calculation of Fish Saved (%) for the Pacific survey region includes reductions in A1E losses at Hawaii facilities. 

This approach is consistent with the EEBA which included Hawaii facilities in the California region. EPA did not apply 

household WTP to Hawaii households when estimating regulatory benefits because Hawaii households were not included 

in the mail survey sample. 

Regulatory Scenarios: Option 1 = IM limitations based on modified traveling screens for all facilities with flow greater 

than 2 million gallons per day (MGD); Option 2 = Intake flow commensurate with closed-cycle cooling for facilities that 

have a design intake flow of greater than 125 MGD and IM limitations based on modified traveling screens for all 

facilities with flow greater than 2 MGD; Option 3 = Intake flow commensurate with closed-cycle cooling for all facilities 

and IM limitations based on modified traveling screens for all facilities with flow greater than 2 MGD; Option 4 = IM 

limitations based on modified traveling screens for all facilities with flow greater than 50 million gallons per day (MGD).

If the results of the stated preference survey are to be used for the final rule, EPA would calculate the total 

annual WTP within each survey regions under each regulatory option by multiplying the annual 

household WTP by the number of households in the survey region. See Table 3 for the number of 

households in each survey region. Annual WTP for each survey region would then be discounted based 

on the regulatory compliance schedule. The compliance schedule is a time profile that reflects when 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 33

benefits from each facility will be realized. It is based on the anticipated timeline of rule implementation 

and biological considerations. EPA estimated a separate compliance schedule for each 316(b) region for 

the proposed rule. The compliance schedule for each survey region would be calculated by weighting 

each 316(b) regional schedule based on its contribution to total regional A1E reductions under regulatory 

options. Refer to Appendix D of the EEBA for the proposed rule (USEPA 2011a) for additional 

description of the compliance schedule. Since the compliance schedule moves the reductions in A1E into 

the future, this step will reduce the present value of the total benefits accrued to households decreasing 

annualized benefits estimates. 

Annualized national benefits could be calculated as the sum of regional benefits or based on the national 

survey version. If the results are to be used for the final rule, EPA would conduct additional analysis to 

compare the results and evaluate the validity of these two approaches for estimating benefits to U.S. 

households.15 As described previously, the model estimates a constant marginal WTP (or implicit price) 

per percentage point reduction in baseline mortality, regardless of how much progress has already been 

made in reducing I&E mortality. EPA notes that this simplification, while common in many types of 

benefit analysis, does not allow one to quantify or test theoretically-expected effects such as diminishing 

marginal WTP, i.e., that the more of a good an individual has, the less she is willing to pay for an 

additional unit. Relative to a diminishing marginal utility form, the linear functional form likely produces 

lower estimates of WTP at low percentages of fish saved, and higher estimates of WTP at high 

percentages of fish saved. This fact could affect the relative attractiveness of regulatory options from a 

cost-benefit viewpoint; therefore, EPA will also investigate alternative non-linear functional forms that 

relax the assumption of constant utility. As noted earlier, EPA has not yet completed fielding the nonresponse studies and analyzing the non-response data for the Southeast, Pacific, Inland, and National 

versions. The implicit prices presented in Table 14 do not include any adjustments or weighting based on 

the non-response studies. EPA expects to improve the accuracy of preliminary implicit prices by 

incorporating the results of the non-response studies for all survey versions. EPA will post these results 

on the Agency’s website16 once they become available. EPA will also make adjustments to the estimation 

methodology that may be necessary to address issues of representativeness that arise as a result of the 

Agency’s continuing efforts at scope and validity testing, public comment that results from the NODA, 

and comments from the planned external peer review. As already noted, EPA will carefully consider 

those comments and results before determining what role quantified benefits based on this stated 

preference survey should play in the benefits analysis for the final rule. The Northeast non-response study 

has been completed and a weighted model and implicit prices based on its results are discussed in Section 

9.3. 

EPA notes that benefits estimated based on the stated preference survey should not be viewed as additive 

to those benefit values estimated at proposal. Values estimated using the stated preference survey would 

represent both use and non-use willingness-to-pay. As a result, if EPA includes the quantified benefits 

derived from the stated preference survey in the final benefits analysis, those benefits will supplant the 

previous benefit estimates. 

 

15 EPA notes that the preliminary benefit estimates generated using the implicit prices presented in Table 14 should not be viewed 

as additive to those benefit values estimated at proposal. As a result, if EPA includes quantified benefits derived from the 

stated preference survey in the final benefits analysis, those benefits will supplant the previous benefit estimates. 

16 http://water.epa.gov/lawsregs/lawsguidance/cwa/316b/ 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 34

9 Non-Response Study and Preliminary Results Using a Weighted 

Model for the Northeast Region 

9.1 Non-Response Study 

EPA is also conducting a follow-up study of households that did not return a completed mail survey to 

identify whether survey non-respondents are fundamentally different than survey respondents by asking 

them a few demographic and attitudinal questions. Their responses are compared statistically to the main 

mail survey sample to indicate whether there is any evidence of significant non-response bias in the 

completed mail survey sample. The follow-up study includes a set of key attitudinal and sociodemographic variables that are thought to be associated with WTP for reducing fish mortality from 

cooling water intake systems (CWIS). EPA anticipates that respondents will need approximately five 

minutes to complete the short questionnaire. 

EPA’s target sample across all regions for the non-response study is 600 completed questionnaires. This 

sample size will permit EPA to reject the hypothesis of no difference in population percentages between 

respondents and non-respondents in characteristics of interest (yes/no type) with 80 percent power when a 

two-sided statistical test shows a difference of 12 percentage points. The subsample of non-respondents is 

being contacted using a combination of Priority Mail and telephone. In total, EPA planned to achieve 400 

completed surveys in the Priority Mail subsample and 200 completed questionnaires in the telephone 

subsample. EPA allocated the target non-response completed surveys to each survey region in proportion 

to the mail survey sample size of each region (see Table 3). The resulting non-response targets are 

presented in Table 16. 

As described previously, EPA implemented the Northeast mail survey and non-response study in advance 

of other regions. EPA has not yet completed the non-response studies for the Southeast, Pacific, Inland 

and the national survey versions. For 109 total non-response contacts in the Northeast region, the initial 

target sample sizes were 73 and 36 for the Priority Mail and telephone subsample, respectively. EPA 

implemented the Priority Mail component in advance of the telephone component. Questionnaires were 

sent to 146 non-responding households based on an anticipated 50% response rate (73/0.5). The 

anticipated response rate was based on prior studies that administered surveys via Priority Mail. The 

selected households were sent a preview letter one week in advance of the questionnaire, which included 

a $2 incentive. EPA actually received 48 completed questionnaires from the Priority Mail subsample, for 

a 33% response rate (48/146). Because the Priority Mail response was lower than expected, the target 

number of telephone completed surveys was increased to obtain the desired number of responses. 

EPA randomly selected 331 households for the telephone survey from the subset of households with 

matched telephone numbers that did not complete the main mail survey or Priority Mail questionnaire. 

Fifty-one of the households had been previously sent a completed Priority Mail questionnaire but did not 

return it. The other 280 households (330-51) were sent a preview letter including a $2 incentive one week 

before the first telephone contact attempt. EPA made up to 12 attempts to achieve telephone contacts with 

the selected households. EPA stopped telephone calls after reaching 63 completed questionnaires within 

the 331 selected households, for a response rate of 19%. 

Responses to the Northeast non-response questionnaires were compared statistically to the responses of 

the main mail survey to determine whether weighting or statistical adjustment was necessary to minimize 

non-response bias in the main mail survey sample. See Section 9.2 for additional detail on the nonresponse analysis. Refer to the ICR (EPA ICR# 2402.01) for additional details regarding the non-response 

study and sampling design. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 35

9.2 Results of the Northeast Non-Response Study 

EPA received 111 completed responses to the short questionnaire administered to sampled households in 

the Northeast survey region that did not respond to the main survey. Descriptive statistics for the nonresponse study include: 

3⁄4 Number of households contacted: 426 households (280 received only the telephone call , 95 

received only the Priority Mail survey, and 51 received both the telephone call and Priority 

Mail survey). 

3⁄4 Number of complete responses received: 111 households (48 by mail and 63 by telephone). 

3⁄4 Priority Mail response rate: 33% (48 completed surveys out of 146 households). 

3⁄4 Telephone response rate: 19% (63 completed surveys out of 331 households contacted). 

Table 17 provides demographic characteristics for participants in the Northeast non-response study. EPA 

tested for statistical differences between respondents and non-respondents to the main mail survey for a 

set of eight key demographic characteristics. Table 18 presents the results of hypothesis testing for age, 

gender, education, employment, the importance of aquatic ecosystems, Hispanic or Latino origin, race, 

and income. T-testing was used for age, the only cardinal variable in this group, and the Mann Whitney U 

Test and χ

2 Test of Proportions were used for categorical and ordinal variables. Gender and education 

were found to be statistically different across the respondent and non-respondent populations. For other 

variables, we do not reject the null hypothesis of equal means across populations. 

The non-response survey included two attitudinal questions asking participants to rank the importance of 

protecting aquatic ecosystems and to rank how involved government should be in environmental 

regulation. Responses to these two questions are summarized in Figure 3. The question regarding the 

importance of protecting aquatic ecosystems was included in both the main mail survey and the nonresponse survey. As shown in Table 18, hypothesis testing indicated that rankings for the importance of 

protecting aquatic ecosystems were not statistically different for respondent and non-respondent samples. 

The question about how involved government should be in environmental protection was worded 

differently between the main and non-response survey so we did not compare those questions in this 

document. 

Table 16—Target Sample Sizes for the Non-Response Study 

Survey Region

Completed Questionnaires Target

Priority Mail 

Subsample

Telephone 

Subsample Total

Northeast 73 36 109 

Southeast 98 49 147 

Inland 128 64 192 

Pacific 51 25 76 

Total for Regional Surveys 

Versions 

350 175 524 

National Survey Version 50 25 76 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 36

Table 17—Demographic Characteristics of the Main and Non-Response Samples for the 

Northeast Survey Region 

Statistic 

Value/Percent of Samplea

Main Mail Sampleb Non-Response 

Sample 

Average age of respondents 54.6 53.7 

Percent of respondents under 65 c 74.6% 73.9% 

Percent male respondentsd 63.9% 44.5% 

Percent currently employed 63.6% 62.7% 

Percent employed under age 65 76.9% 79.3% 

Highest Level of Education 

Less than High School 4.2% 4.5% 

High School or Equivalent 15.7% 27.3% 

High School +Technical School 10.2% 4.5% 

One or More Years of College 23.9% 17.3% 

Bachelor’s Degree 22.7% 27.3% 

Graduate Degree 23.2% 19.1% 

Hispanic or Latino Origin 5.1% 5.6% 

Racial Categorye 

American Indian or Alaskan Native 3.1% 2.9% 

Black or African American 7.5% 11.4% 

Native Hawaiian or Other Pacific Islander 1.3% 0.0% 

Asian 5.7% 2.9% 

White 86.6% 85.7% 

Average Household Size 2.5 2.6 

Number of household members 16 or older 2.0 2.1 

Total Household Income f 

Average $88,880 $81,480 

Standard Deviation $69,309 $68,486 

Consume commercially caught fish or seafood 91.9% 84.7% 

Consume recreationally caught fish or seafood 46.4% 47.7% 

Notes: 

a

 Respondents did not answer a given demographic question were excluded when calculating percentages. 

b

 The demographic statistics presented here for the main mail sample are based on all 421 observations. Note all 

EPA received a total of 421 completed mail surveys for the Northeast region. The demographic statistics presented 

here in this table are based on all 421 observations. Not all of these observations were data entered at the time 

respondent and non-respondent characteristics were statistically compared for this memorandum due to time 

constraints. The statistical comparisons and the preliminary Northeast model were estimated based on 394 

observations. The additional survey responses will be included in the analysis for the final rule. 

c

 Compares to 79% for 18+ population based on Census 2010 

d

 The population is 48.6% male averaging across Northeast states, based on the American Community Survey 2005-

2009. 

e

 Racial percentages sum to more than 100% due to some individuals checking more than one racial category box. 

The survey did not have a box specifically indicating two or more races. f

 The survey asked respondents to select one of eight categories for annual household income. The average and 

standard deviation reported here were calculated using the midpoint of each range. $250,000 was used for the 

highest income category included in the survey (“$250,000 or more”). 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 37

Table 18—Results from Comparison of Demographic Characteristics of Survey 

Respondents and Non-Respondents for the Northeast Regiona

Variable T-test Mann Whitney U Test χ

2

 Test of 

Proportions 

P-value Two-sided Pr >|Z| Probability 

Age 0.5404 - - 

Gender - 0.0001 0.0001 

Education - 0.2532 0.0360 

Employment - 0.9841 0.9837 

Importance of Aquatic Ecosystems - 0.6205 0.9010 

Hispanic/Latino - 0.6825 0.6814 

Race - 0.7004 0.5335 

Income - 0.3993 0.9781 

Notes: 

a

 Hypothesis testing was used to compare the respondent and non-respondent samples and identify statistical 

differences. T-testing was used for age, the sole cardinal demographic variable assessed. The Mann Whitney U 

Test (or Wilcoxon rank-sum test) and χ

2

 Test of Proportions are preferred for comparing samples of categorical 

and ordinal variables. The null hypothesis for all three tests is equality of means across respondents and nonrespondents. 
June 5, 201

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316(b) SP Su

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| pg 38
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 39

EPA conducted additional analysis with the unweighted Northeast model to evaluate the need to include 

weights to account for differences in gender and education. EPA did this by re-estimating the unweighted 

random utility model with interactions for two dummy variables: (1) college, which identifies college 

education respondents, and (2) female, which identifies female respondents. This random utility model 

with interactions is presented in Table 19. Results from the unweighted model with interactions are 

inconclusive overall. Mixed logit model statistics indicate a statistical fit that is comparable to the model 

without interactions, with a model χ

2

 of 510.93 (d.f. = 33, p<0.0001) and a pseudo R2 of 0.22. The 

interaction variables college*com_fish, college*fish_sav, and female*fish_sav are individually statistically 

significant. However, taken as a group the set of interactive terms does not improve the model and we 

fail to reject the null hypothesis of zero joint influence. The result of the joint test of significance indicate 

that a weighted model may not be needed, but individual significance of some of the interacted education 

and gender variables point to the possible need to develop a weighted model. 

Table 19—Results for the Preliminary Unweighted Model for the Northeast Survey 

Region Including Interactions for Education (college) and Gender (female) 

Variable Coefficient Standard 

Error 

Coefficient/St. 

Error 

P-value 

P[|Z| > z] 

Random parameters in utility functions 

CONSTANT -0.11591 0.44143 -0.263 0.7929 

COM_FISH 0.18027 0.06949 2.594 0.0095 

FISH_POP 0.07348 0.10314 0.712 0.4762 

FISH_SAV 0.02767 0.00857 3.228 0.0012 

AQUATIC 0.17045 0.11677 1.460 0.1444 

Nonrandom parameters in utility functions 

COST -0.02720 0.00560 -4.854 0.0000 

COLLEGE*CONSTANT 0.21781 0.56578 0.385 0.7003 

COLLEGE*COM_FISH 0.13960 0.07728 1.806 0.0708 

COLLEGE*FISH_POP 0.01544 0.12576 0.123 0.9023 

COLLEGE*FISH_SAV 0.04490 0.00830 5.410 0.0000 

COLLEGE*AQUATIC 0.13699 0.12766 1.073 0.2832 

COLLEGE*COST -0.00679 0.00692 -0.981 0.3268 

FEMALE *CONSTANT -0.23947 0.53138 -0.451 0.6522 

FEMALE *COM_FISH 0.01361 0.07840 0.174 0.8622 

FEMALE *FISH_POP 0.07421 0.12132 0.612 0.5407 

FEMALE *FISH_SAV 0.01836 0.00791 2.320 0.0203 

FEMALE *AQUATIC 0.05411 0.12544 0.431 0.6662 

FEMALE*COST -0.00317 0.00746 -0.424 0.6714 

Derived standard deviations for parameter distributions

sdCONSTANT 0.03890 0.65397 0.059 0.9526 

sdCOM_FISH 0.13270 0.09846 1.348 0.1777 

sdFISH_POP 0.13294 0.21716 0.612 0.5404 

sdFISH_SAV 0.08309 0.01321 6.289 0.0000 

sdAQUATIC 0.28535 0.31368 0.910 0.3630 

EPA decided to estimate a Northeast model with weights for gender and education based on the results of 

hypothesis testing described in this section and the individual significance of some of the interacted 

education and gender variables presented in Table 10. The estimated WTP values for reducing I&E 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 40

presented in Section 8 are based on this weighted model. Weighting to reduce non-response biases adjusts 

the weight given to individual observations in a statistical analysis. This adjustment is implemented so 

that the weight given to a particular subgroup of individuals within the analyzed sample matches the 

weight for the same subgroup in the desired population (Yansaneh 2003). Subgroups in this case are 

defined using demographic data on income and education, although they may be defined using any 

observable variable. The weighting process is as follows: 

3⁄4 Non-response adjustment weight for the i

th subgroup is given by wi = w1i*w2i, where wi is the 

final adjusted weight for the observation, w1i is the initial (unadjusted) weight, and w2i is the 

non-response adjustment factor. 

3⁄4 The default initial weight on each observation, w1i, is equal to either 1 or 1/N, where N is the 

number of observations. 

3⁄4 The representation (or proportion) of subgroup i in the target population is known, and is 

given by Ri. The representation (or proportion) of that subgroup in the sample is given by Si. 

3⁄4 The adjustment factor w2i is then calculated as ܴ௜

ܵ௜ ൗ . The final weight given to each 

observation is then given by wi = ݓ1݅ ൈ

ܴ݅

ܵ݅ ൗ . 

3⁄4 Subgroups are defined as individuals characterized by a particular combination of 

demographic or other attributes. The number of subgroups is generally equal to ∏ݎ ݎܦ, 

where Dr is the number of possible outcomes (levels) for the r

th attribute. For example, nonresponse weighting according to gender (male vs. female) and education (no bachelor’s 

degree vs. bachelor’s degree or higher), has four subgroups (male without a bachelor’s 

degree, male with a bachelor’s degree, female without a bachelor’s degree, and female with a 

bachelor’s degree). 

Table 20 summarizes the four subgroups included in the model and presents the proportion of respondents 

which fall in each subgroup. The proportions for the overall Northeast population were calculated using 

data from the 2010 American Community Survey. The preliminary results from the weighted model are 

presented in Table 21 below. EPA will continue to explore potential non-response bias and weighting as 

the results of the other regions and national survey results become available. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 41

Table 20—Subgroups Included within the Weighted Model 

Subgroup Definition Sample Proportiona Population 

Proportionb

M1 Male, age 18+, without a bachelor’s 

degree 

0.35 0.33 

M2 Male, age 18+, with bachelor’s degree 

of higher

0.30 0.14 

F1 Female, age 18+, without a bachelor’s 

degree 

0.19 0.37 

F2 Female, age 18+, with bachelor’s 

degree of higher

0.16 0.16 

Notes: 

a

 The proportion of the sample in each subgroup was calculated based on respondents that provided both 

educational attainment and gender information. 

b

 The proportion of the 18+ population within in each subgroup was calculated using 5-yr estimates of 

educational attainment within Northeast states from the 2010 American Community Survey. 

Mixed logit model statistics for the weighted model suggest good statistical fit, with a model χ

2

 of 478.67 

(d.f. = 21, p<0.0001) and a pseudo R2

of 0.22. These statistics compare favorably to similar models in the 

published literature. The mixed logit model also shows significant improvements in fit over the 

comparable conditional logit model. See Section 6.1 for a discussion of the statistical fit across different 

choice experiments in the literature. 

Results for the preliminary Northeast model with weights for education and gender are presented in Table 

21 (refer to Table 7 for the definitions of variables included in the weighted model). Signs of all 

statistically significant variables are as expected. All model variables except for fish_pop (changes in fish 

populations) and constant (the alternative specific constant (ASC) for the status quo) are statistically 

significant. This implies that statistically significant and positive WTP is associated with increases in fish 

saved (fish_sav), commercial fish populations (com_fish), and aquatic ecological condition (aquatic). 

Stated another way, EPA can statistically reject the premise that the fish saved have no value, or that their 

value is zero. However, EPA did not find statistically significant WTP for increases in “all fish 

populations,” ceteris paribus, at least within the range presented in survey scenarios. These patterns are 

robust across a wide range of model specifications. As noted above, all variables except cost represent 

percent progress towards the upper ecological reference condition (100%). Hence, these coefficients may 

be directly interpreted as the relative marginal utility derived from a one percentage point change in each 

ecological attribute. In the estimated model, for example, marginal utility is greatest (per percentage 

point change) for increases in aquatic ecological condition (aquatic), with lower (but still statistically 

significant) impacts associated with changes in commercial fish populations (fish_pop) and the number of 

fish saved (fish_sav). 

Within the presented weighted model, four out of five primary choice attributes (80%)—fish saved 

(fish_sav), commercial fish populations (com_fish), aquatic ecological condition (aquatic), and program 

cost (cost)—are statistically significant at p<0.052 or better, which suggests positive implicit prices (WTP 

for changes in the attributes alone). These patterns are robust across a wide range of model specifications. 

By comparison, six out of seven primary attributes (86%) are statistically significant at the same level in 

Johnston et al. (2011b). Carlsson et al. (2003) report five out of six coefficients on choice attributes as 

similarly significant. Do and Bennett (2009) also report four out of five primary choice attributes as 

statistically significant, and (similar to the present analysis) find the choice attribute associated with 

broader fish species to be statistically insignificant. Analogous outcomes are common in choice 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 42

experiments across the literature addressing aquatic ecological improvements, with the substantial 

majority of choice attributes found to have statistically significant impacts. Similarly, mixed findings with 

regard to the statistical significance of diagonal and below diagonal values in the Cholesky matrix are 

common—both statistically significant and insignificant effects are common.17

EPA notes that these weighted model results are preliminary. At the time of this memorandum, EPA has 

finished fielding the Northeast version of the mail survey, the first of five such versions (four regional and 

one national). EPA implemented the Northeast version in advance of the other versions as a pilot study to 

inform potential changes to other survey versions. EPA intends to apply a number of validity tests to 

examine the robustness and theoretical consistency of estimated WTP equations after other survey 

versions have completed fielding. 

Table 21— Preliminary Results for the Northeast Model Weighted For Education 

and Gender a,b

Variable Coefficient c Standard 

Error 

Coefficient/St. 

Error 

P-value 

P[|Z| > z] 

Random parameters in utility functions 

CONSTANT -0.19167 0.34295 -0.559 0.5762 

COM_FISH 0.14850 0.04921 3.018 0.0025 

FISH_POP 0.09356 0.07931 1.180 0.2381 

FISH_SAV 0.02712 0.00548 4.947 0.0000 

AQUATIC 0.18299 0.09398 1.947 0.0515 

Nonrandom parameters in utility functions 

COST -0.02017 0.00393 -5.137 0.0000 

Derived standard deviations for parameter distributions

sdCONSTANT 0.03763 0.93582 0.040 0.9679 

sdCOM_FISH 0.17587 0.14002 1.256 0.2091 

sdFISH_POP 0.18098 0.21738 0.833 0.4051 

sdFISH_SAV 0.05255 0.01532 3.430 0.0006 

sdAQUATIC 0.24411 0.43106 0.566 0.5712 

a

 The mixed logit results have a χ

2

 of 478.67 (d.f. = 21, p<0.0001) and a pseudo R2 of 0.22. 

b

 EPA received a total of 421 completed surveys for the Northeast region. Not all of these observations 

were data entered at the time of model estimation for this memorandum due to time constraints. The 

preliminary Northeast model was estimated based on 394 observations. The additional survey responses 

will be included in the analysis for the final rule. 

c

 For random parameters in utility functions, coefficients represent the estimated means of random 

parameter distributions. 

 

17 Within mixed logit estimation, the estimated Cholesky matrix characterizes the correlated distributions of random parameters; 

it may be used to calculate implied standard deviations random parameter distributions, as well as correlations among 

different random parameters. As described by Hensher and Greene (2003, p. 162), the Cholesky matrix “decompose[s] the 

standard deviation parameters into their attribute-specific ... and attribute-interaction ... standard deviations.” 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 43

9.3 Implicit Prices and Annualized Benefits for the Northeast Region using the 

Weighted Model 

EPA used the results of the weighted Northeast model to estimate the marginal annual WTP (or implicit 

price) for a one percentage change in each of the four environmental attributes within the Northeast U.S. 

The resulting implicit prices are presented in Table 22. For the Northeast region, the implicit prices are 

higher when using the weighted results. This increase is the net result of increases and decreases from 

weighting to decrease the impact of overrepresented groups and increase the impact of underrepresented 

groups. Based on the survey results, women and the college educated tend to express greater WTP for 

improvements in the environmental attributes. Weighting based on population proportions for gender and 

education (see Table 20) decreases the impact of the college educated thereby reducing WTP, and 

increases the importance of female respondents thereby raising WTP. Overall, these changes resulted in a 

net increase in estimated implicit prices. EPA will analyze the non-response data for Southeast, Pacific, 

Inland, and national surveys using an approach consistent with that of the Northeast non-response data. 

See Section 7 for a description of how EPA would use estimated implicit prices to estimate benefits under 

regulatory options. 

Table 22— Estimated Implicit Prices for a One Percentage Point Change in Each 

Attribute in the Northeast, WTP per household, per Year based on the Weighted 

Model (2011$) 

Attribute Implicit 

Price a

Standard 

Deviation 

90% Confidence Interval 

5th 95th

ASC (CONSTANT) -$8.61 $17.70 -$34.53 $20.23 

Commercial Fish Populations 

(COM_FISH) 

$7.55 $3.01 $3.37 $12.76

Fish Populations (all fish) 

(FISH_POP) 

$4.75 $4.32 -$1.84 $11.62

Fish Saved (FISH_SAV) $1.40 $0.38 $0.85 $2.08

Aquatic Ecosystem condition 

(AQUATIC) 

$9.34 $5.03 $1.35 $18.14

a

 The implicit prices are per percentage point increase from the specified baseline (reference) levels. They 

are not directly transferable to scenarios with alternative baseline levels.
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 44

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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. 

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. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 48

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.E. 2009. Discrete Choice Methods with Simulation. Cambridge, UK: Cambridge University 

Press. 

U.S. Environmental Protection Agency (USEPA). 2011a. Environmental and Economic Benefits Analysis 

for the Proposed Section 316(b) Existing Facilities Rule. Office of Science and Technology, 

Engineering and Analysis Division. EPA 821-R-11-002. March 28. 

U.S. Environmental Protection Agency (USEPA). 2011b. Supporting Statement for Information 

Collection Request for Willingness to Pay Survey §316(b) Existing Facilities Cooling Water 

Intake Structures: Instrument, Pre-test, and Implementation. EPA ICR # 2402.01. 

U.S. Environmental Protection Agency (USEPA). 2010a. Guidelines for Preparing Economic Analyses. 

EPA 240-R-10-001. U.S. EPA, Office of the Administrator, Washington, DC, December (prepublication edition). 

U.S. Environmental Protection Agency (USEPA). 2010b. Supporting Statement: Request to Conduct 

Focus Groups, 316(b) Benefits Survey. June 17, 2010. EPA ICR #2090-0028. 

U.S. Environmental Protection Agency (USEPA). 2009. Valuing the Protection of Ecological Systems 

and Services: A Report of the EPA Science Advisory Board. Office of the Administrator, Science 

Advisory Board. EPA-SAB-09-012. 

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. 

Yansaneh, I.H. 2003. Construction and use of sample weights. United Nations Secretariat Statistics 

Division, ESA/STAT/AC.93/5. 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 49

Appendix A: Experimental Design for the Survey Regions 

Table A-1—Experimental Design for the Northeast Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

1 1 45% 30% 5% 52% $48 48% 27% 5% 54% $48 

1 2 48% 28% 50% 51% $60 48% 30% 95% 52% $72 

1 3 48% 27% 50% 52% $72 45% 27% 50% 51% $12 

2 1 48% 30% 5% 52% $48 43% 28% 50% 52% $12 

2 2 45% 28% 50% 54% $24 48% 27% 50% 51% $36 

2 3 43% 27% 5% 54% $36 45% 27% 95% 51% $24 

3 1 48% 30% 5% 51% $48 48% 27% 5% 54% $72 

3 2 45% 28% 95% 54% $60 45% 28% 95% 52% $12 

3 3 43% 28% 5% 54% $12 43% 30% 50% 54% $24 

4 1 45% 28% 95% 54% $72 43% 30% 5% 51% $72 

4 2 48% 30% 95% 52% $36 45% 28% 50% 54% $36 

4 3 45% 27% 50% 51% $60 43% 27% 95% 52% $48 

5 1 48% 27% 50% 52% $24 45% 30% 50% 54% $48 

5 2 45% 27% 5% 51% $36 43% 28% 5% 51% $36 

5 3 43% 30% 50% 54% $12 45% 28% 5% 51% $60 

6 1 43% 28% 95% 52% $36 43% 30% 5% 54% $72 

6 2 48% 27% 95% 54% $60 48% 28% 95% 52% $36 

6 3 43% 28% 50% 51% $48 45% 27% 50% 51% $24 

7 1 43% 30% 50% 54% $48 43% 30% 95% 51% $12 

7 2 48% 28% 95% 51% $24 45% 27% 5% 54% $60 

7 3 45% 27% 5% 51% $36 48% 28% 50% 52% $72 

8 1 45% 30% 5% 51% $36 45% 30% 95% 54% $72 

8 2 43% 28% 95% 51% $60 48% 28% 50% 51% $72 

8 3 45% 28% 50% 52% $60 43% 28% 5% 52% $24 

9 1 48% 27% 5% 54% $72 48% 28% 50% 51% $48 

9 2 43% 30% 50% 51% $24 45% 30% 95% 52% $72 

9 3 43% 28% 95% 52% $12 43% 27% 95% 54% $48 

10 1 48% 30% 50% 51% $60 43% 30% 50% 51% $24 

10 2 45% 28% 95% 54% $48 45% 30% 95% 52% $48 

10 3 43% 28% 50% 52% $36 48% 28% 95% 54% $72 

11 1 45% 28% 5% 51% $12 48% 30% 95% 52% $60 

11 2 48% 30% 95% 54% $60 43% 27% 5% 54% $24 

11 3 43% 30% 50% 52% $36 45% 28% 5% 51% $12 

12 1 48% 28% 95% 51% $36 48% 30% 50% 51% $48 

12 2 43% 27% 5% 54% $60 45% 27% 95% 52% $60 

12 3 43% 30% 50% 52% $72 43% 27% 50% 54% $72 

13 1 48% 27% 95% 54% $24 48% 30% 5% 52% $24 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 50

Table A-1—Experimental Design for the Northeast Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

13 2 45% 27% 50% 51% $12 43% 28% 95% 54% $48 

13 3 43% 30% 5% 52% $60 45% 30% 5% 51% $60 

14 1 45% 30% 50% 54% $36 48% 28% 95% 54% $24 

14 2 48% 28% 5% 52% $60 45% 27% 95% 52% $72 

14 3 48% 27% 95% 51% $48 43% 30% 50% 51% $60 

15 1 43% 27% 5% 54% $36 45% 30% 50% 52% $12 

15 2 45% 27% 50% 52% $48 48% 27% 5% 52% $24 

15 3 45% 30% 95% 51% $72 43% 28% 50% 54% $60 

16 1 43% 28% 5% 54% $48 48% 28% 50% 54% $60 

16 2 48% 30% 50% 54% $48 45% 28% 5% 51% $36 

16 3 45% 27% 5% 52% $72 43% 30% 95% 51% $24 

17 1 45% 27% 95% 54% $48 45% 28% 95% 51% $72 

17 2 45% 27% 95% 52% $24 45% 30% 50% 54% $12 

17 3 48% 28% 5% 54% $12 48% 27% 50% 52% $60 

18 1 43% 27% 95% 52% $48 43% 28% 5% 52% $36 

18 2 45% 27% 50% 54% $12 48% 30% 5% 54% $72 

18 3 43% 30% 5% 51% $24 48% 27% 95% 51% $12 

19 1 43% 30% 95% 51% $12 48% 30% 5% 52% $36 

19 2 45% 27% 95% 52% $60 43% 27% 95% 51% $36 

19 3 48% 28% 5% 52% $72 45% 28% 5% 52% $24 

20 1 43% 30% 5% 52% $60 48% 30% 5% 51% $48 

20 2 45% 30% 95% 51% $12 45% 28% 50% 52% $24 

20 3 48% 28% 50% 54% $36 43% 27% 5% 52% $12 

21 1 48% 27% 95% 51% $72 48% 27% 5% 54% $12 

21 2 45% 30% 5% 54% $24 43% 27% 50% 52% $36 

21 3 43% 28% 95% 52% $24 48% 28% 95% 51% $24 

22 1 43% 27% 50% 51% $60 43% 28% 5% 54% $12 

22 2 43% 30% 95% 54% $72 45% 27% 50% 52% $48 

22 3 48% 27% 5% 52% $12 43% 30% 95% 51% $36 

23 1 43% 27% 5% 51% $24 43% 28% 5% 52% $48 

23 2 48% 27% 95% 52% $12 48% 30% 95% 51% $12 

23 3 48% 28% 50% 51% $48 45% 30% 95% 54% $36 

24 1 45% 28% 50% 52% $12 48% 30% 50% 54% $60 

24 2 43% 27% 50% 54% $72 45% 27% 5% 51% $36 

24 3 48% 30% 5% 54% $24 43% 28% 95% 54% $72 

Notes: 

“Com.fish” is an abbreviation for commercial fish populations score. 

“Fish Pop.” is an abbreviation for the fish populations (all fish) score. 

“Aq. Cond.” is an abbreviation for the aquatic ecosystem condition score. 

 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 51

Table A-2—Experimental Design for the Southeast Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

1 1 42% 28% 25% 70% $48 45% 25% 25% 72% $48 

1 2 45% 26% 55% 69% $60 45% 28% 90% 70% $72 

1 3 45% 25% 55% 70% $72 42% 25% 55% 69% $12 

2 1 45% 28% 25% 70% $48 40% 26% 55% 70% $12 

2 2 42% 26% 55% 72% $24 45% 25% 55% 69% $36 

2 3 40% 25% 25% 72% $36 42% 25% 90% 69% $24 

3 1 45% 28% 25% 69% $48 45% 25% 25% 72% $72 

3 2 42% 26% 90% 72% $60 42% 26% 90% 70% $12 

3 3 40% 26% 25% 72% $12 40% 28% 55% 72% $24 

4 1 42% 26% 90% 72% $72 40% 28% 25% 69% $72 

4 2 45% 28% 90% 70% $36 42% 26% 55% 72% $36 

4 3 42% 25% 55% 69% $60 40% 25% 90% 70% $48 

5 1 45% 25% 55% 70% $24 42% 28% 55% 72% $48 

5 2 42% 25% 25% 69% $36 40% 26% 25% 69% $36 

5 3 40% 28% 55% 72% $12 42% 26% 25% 69% $60 

6 1 40% 26% 90% 70% $36 40% 28% 25% 72% $72 

6 2 45% 25% 90% 72% $60 45% 26% 90% 70% $36 

6 3 40% 26% 55% 69% $48 42% 25% 55% 69% $24 

7 1 40% 28% 55% 72% $48 40% 28% 90% 69% $12 

7 2 45% 26% 90% 69% $24 42% 25% 25% 72% $60 

7 3 42% 25% 25% 69% $36 45% 26% 55% 70% $72 

8 1 42% 28% 25% 69% $36 42% 28% 90% 72% $72 

8 2 40% 26% 90% 69% $60 45% 26% 55% 69% $72 

8 3 42% 26% 55% 70% $60 40% 26% 25% 70% $24 

9 1 45% 25% 25% 72% $72 45% 26% 55% 69% $48 

9 2 40% 28% 55% 69% $24 42% 28% 90% 70% $72 

9 3 40% 26% 90% 70% $12 40% 25% 90% 72% $48 

10 1 45% 28% 55% 69% $60 40% 28% 55% 69% $24 

10 2 42% 26% 90% 72% $48 42% 28% 90% 70% $48 

10 3 40% 26% 55% 70% $36 45% 26% 90% 72% $72 

11 1 42% 26% 25% 69% $12 45% 28% 90% 70% $60 

11 2 45% 28% 90% 72% $60 40% 25% 25% 72% $24 

11 3 40% 28% 55% 70% $36 42% 26% 25% 69% $12 

12 1 45% 26% 90% 69% $36 45% 28% 55% 69% $48 

12 2 40% 25% 25% 72% $60 42% 25% 90% 70% $60 

12 3 40% 28% 55% 70% $72 40% 25% 55% 72% $72 

13 1 45% 25% 90% 72% $24 45% 28% 25% 70% $24 

13 2 42% 25% 55% 69% $12 40% 26% 90% 72% $48 

13 3 40% 28% 25% 70% $60 42% 28% 25% 69% $60 

14 1 42% 28% 55% 72% $36 45% 26% 90% 72% $24 

14 2 45% 26% 25% 70% $60 42% 25% 90% 70% $72 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 52

Table A-2—Experimental Design for the Southeast Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

14 3 45% 25% 90% 69% $48 40% 28% 55% 69% $60 

15 1 40% 25% 25% 72% $36 42% 28% 55% 70% $12 

15 2 42% 25% 55% 70% $48 45% 25% 25% 70% $24 

15 3 42% 28% 90% 69% $72 40% 26% 55% 72% $60 

16 1 40% 26% 25% 72% $48 45% 26% 55% 72% $60 

16 2 45% 28% 55% 72% $48 42% 26% 25% 69% $36 

16 3 42% 25% 25% 70% $72 40% 28% 90% 69% $24 

17 1 42% 25% 90% 72% $48 42% 26% 90% 69% $72 

17 2 42% 25% 90% 70% $24 42% 28% 55% 72% $12 

17 3 45% 26% 25% 72% $12 45% 25% 55% 70% $60 

18 1 40% 25% 90% 70% $48 40% 26% 25% 70% $36 

18 2 42% 25% 55% 72% $12 45% 28% 25% 72% $72 

18 3 40% 28% 25% 69% $24 45% 25% 90% 69% $12 

19 1 40% 28% 90% 69% $12 45% 28% 25% 70% $36 

19 2 42% 25% 90% 70% $60 40% 25% 90% 69% $36 

19 3 45% 26% 25% 70% $72 42% 26% 25% 70% $24 

20 1 40% 28% 25% 70% $60 45% 28% 25% 69% $48 

20 2 42% 28% 90% 69% $12 42% 26% 55% 70% $24 

20 3 45% 26% 55% 72% $36 40% 25% 25% 70% $12 

21 1 45% 25% 90% 69% $72 45% 25% 25% 72% $12 

21 2 42% 28% 25% 72% $24 40% 25% 55% 70% $36 

21 3 40% 26% 90% 70% $24 45% 26% 90% 69% $24 

22 1 40% 25% 55% 69% $60 40% 26% 25% 72% $12 

22 2 40% 28% 90% 72% $72 42% 25% 55% 70% $48 

22 3 45% 25% 25% 70% $12 40% 28% 90% 69% $36 

23 1 40% 25% 25% 69% $24 40% 26% 25% 70% $48 

23 2 45% 25% 90% 70% $12 45% 28% 90% 69% $12 

23 3 45% 26% 55% 69% $48 42% 28% 90% 72% $36 

24 1 42% 26% 55% 70% $12 45% 28% 55% 72% $60 

24 2 40% 25% 55% 72% $72 42% 25% 25% 69% $36 

24 3 45% 28% 25% 72% $24 40% 26% 90% 72% $72 

Notes: 

“Com.fish” is an abbreviation for commercial fish populations score. 

“Fish Pop.” is an abbreviation for the fish populations (all fish) score. 

“Aq. Cond.” is an abbreviation for the aquatic ecosystem condition score. 

 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 53

Table A-3—Experimental Design for the Pacific Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

1 1 59% 36% 2% 53% $48 62% 33% 2% 55% $48 

1 2 62% 34% 50% 52% $60 62% 36% 95% 53% $72 

1 3 62% 33% 50% 53% $72 59% 33% 50% 52% $12 

2 1 62% 36% 2% 53% $48 57% 34% 50% 53% $12 

2 2 59% 34% 50% 55% $24 62% 33% 50% 52% $36 

2 3 57% 33% 2% 55% $36 59% 33% 95% 52% $24 

3 1 62% 36% 2% 52% $48 62% 33% 2% 55% $72 

3 2 59% 34% 95% 55% $60 59% 34% 95% 53% $12 

3 3 57% 34% 2% 55% $12 57% 36% 50% 55% $24 

4 1 59% 34% 95% 55% $72 57% 36% 2% 52% $72 

4 2 62% 36% 95% 53% $36 59% 34% 50% 55% $36 

4 3 59% 33% 50% 52% $60 57% 33% 95% 53% $48 

5 1 62% 33% 50% 53% $24 59% 36% 50% 55% $48 

5 2 59% 33% 2% 52% $36 57% 34% 2% 52% $36 

5 3 57% 36% 50% 55% $12 59% 34% 2% 52% $60 

6 1 57% 34% 95% 53% $36 57% 36% 2% 55% $72 

6 2 62% 33% 95% 55% $60 62% 34% 95% 53% $36 

6 3 57% 34% 50% 52% $48 59% 33% 50% 52% $24 

7 1 57% 36% 50% 55% $48 57% 36% 95% 52% $12 

7 2 62% 34% 95% 52% $24 59% 33% 2% 55% $60 

7 3 59% 33% 2% 52% $36 62% 34% 50% 53% $72 

8 1 59% 36% 2% 52% $36 59% 36% 95% 55% $72 

8 2 57% 34% 95% 52% $60 62% 34% 50% 52% $72 

8 3 59% 34% 50% 53% $60 57% 34% 2% 53% $24 

9 1 62% 33% 2% 55% $72 62% 34% 50% 52% $48 

9 2 57% 36% 50% 52% $24 59% 36% 95% 53% $72 

9 3 57% 34% 95% 53% $12 57% 33% 95% 55% $48 

10 1 62% 36% 50% 52% $60 57% 36% 50% 52% $24 

10 2 59% 34% 95% 55% $48 59% 36% 95% 53% $48 

10 3 57% 34% 50% 53% $36 62% 34% 95% 55% $72 

11 1 59% 34% 2% 52% $12 62% 36% 95% 53% $60 

11 2 62% 36% 95% 55% $60 57% 33% 2% 55% $24 

11 3 57% 36% 50% 53% $36 59% 34% 2% 52% $12 

12 1 62% 34% 95% 52% $36 62% 36% 50% 52% $48 

12 2 57% 33% 2% 55% $60 59% 33% 95% 53% $60 

12 3 57% 36% 50% 53% $72 57% 33% 50% 55% $72 

13 1 62% 33% 95% 55% $24 62% 36% 2% 53% $24 

13 2 59% 33% 50% 52% $12 57% 34% 95% 55% $48 

13 3 57% 36% 2% 53% $60 59% 36% 2% 52% $60 

14 1 59% 36% 50% 55% $36 62% 34% 95% 55% $24 

14 2 62% 34% 2% 53% $60 59% 33% 95% 53% $72 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 54

Table A-3—Experimental Design for the Pacific Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

14 3 62% 33% 95% 52% $48 57% 36% 50% 52% $60 

15 1 57% 33% 2% 55% $36 59% 36% 50% 53% $12 

15 2 59% 33% 50% 53% $48 62% 33% 2% 53% $24 

15 3 59% 36% 95% 52% $72 57% 34% 50% 55% $60 

16 1 57% 34% 2% 55% $48 62% 34% 50% 55% $60 

16 2 62% 36% 50% 55% $48 59% 34% 2% 52% $36 

16 3 59% 33% 2% 53% $72 57% 36% 95% 52% $24 

17 1 59% 33% 95% 55% $48 59% 34% 95% 52% $72 

17 2 59% 33% 95% 53% $24 59% 36% 50% 55% $12 

17 3 62% 34% 2% 55% $12 62% 33% 50% 53% $60 

18 1 57% 33% 95% 53% $48 57% 34% 2% 53% $36 

18 2 59% 33% 50% 55% $12 62% 36% 2% 55% $72 

18 3 57% 36% 2% 52% $24 62% 33% 95% 52% $12 

19 1 57% 36% 95% 52% $12 62% 36% 2% 53% $36 

19 2 59% 33% 95% 53% $60 57% 33% 95% 52% $36 

19 3 62% 34% 2% 53% $72 59% 34% 2% 53% $24 

20 1 57% 36% 2% 53% $60 62% 36% 2% 52% $48 

20 2 59% 36% 95% 52% $12 59% 34% 50% 53% $24 

20 3 62% 34% 50% 55% $36 57% 33% 2% 53% $12 

21 1 62% 33% 95% 52% $72 62% 33% 2% 55% $12 

21 2 59% 36% 2% 55% $24 57% 33% 50% 53% $36 

21 3 57% 34% 95% 53% $24 62% 34% 95% 52% $24 

22 1 57% 33% 50% 52% $60 57% 34% 2% 55% $12 

22 2 57% 36% 95% 55% $72 59% 33% 50% 53% $48 

22 3 62% 33% 2% 53% $12 57% 36% 95% 52% $36 

23 1 57% 33% 2% 52% $24 57% 34% 2% 53% $48 

23 2 62% 33% 95% 53% $12 62% 36% 95% 52% $12 

23 3 62% 34% 50% 52% $48 59% 36% 95% 55% $36 

24 1 59% 34% 50% 53% $12 62% 36% 50% 55% $60 

24 2 57% 33% 50% 55% $72 59% 33% 2% 52% $36 

24 3 62% 36% 2% 55% $24 57% 34% 95% 55% $72 

Notes: 

“Com.fish” is an abbreviation for commercial fish populations score. 

“Fish Pop.” is an abbreviation for the fish populations (all fish) score. 

“Aq. Cond.” is an abbreviation for the aquatic ecosystem condition score. 

 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 55

Table A-4—Experimental Design for the Inland Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

1 1 42% 37% 55% 44% $48 45% 34% 55% 46% $48 

1 2 45% 35% 75% 43% $60 45% 37% 95% 44% $72 

1 3 45% 34% 75% 44% $72 42% 34% 75% 43% $12 

2 1 45% 37% 55% 44% $48 40% 35% 75% 44% $12 

2 2 42% 35% 75% 46% $24 45% 34% 75% 43% $36 

2 3 40% 34% 55% 46% $36 42% 34% 95% 43% $24 

3 1 45% 37% 55% 43% $48 45% 34% 55% 46% $72 

3 2 42% 35% 95% 46% $60 42% 35% 95% 44% $12 

3 3 40% 35% 55% 46% $12 40% 37% 75% 46% $24 

4 1 42% 35% 95% 46% $72 40% 37% 55% 43% $72 

4 2 45% 37% 95% 44% $36 42% 35% 75% 46% $36 

4 3 42% 34% 75% 43% $60 40% 34% 95% 44% $48 

5 1 45% 34% 75% 44% $24 42% 37% 75% 46% $48 

5 2 42% 34% 55% 43% $36 40% 35% 55% 43% $36 

5 3 40% 37% 75% 46% $12 42% 35% 55% 43% $60 

6 1 40% 35% 95% 44% $36 40% 37% 55% 46% $72 

6 2 45% 34% 95% 46% $60 45% 35% 95% 44% $36 

6 3 40% 35% 75% 43% $48 42% 34% 75% 43% $24 

7 1 40% 37% 75% 46% $48 40% 37% 95% 43% $12 

7 2 45% 35% 95% 43% $24 42% 34% 55% 46% $60 

7 3 42% 34% 55% 43% $36 45% 35% 75% 44% $72 

8 1 42% 37% 55% 43% $36 42% 37% 95% 46% $72 

8 2 40% 35% 95% 43% $60 45% 35% 75% 43% $72 

8 3 42% 35% 75% 44% $60 40% 35% 55% 44% $24 

9 1 45% 34% 55% 46% $72 45% 35% 75% 43% $48 

9 2 40% 37% 75% 43% $24 42% 37% 95% 44% $72 

9 3 40% 35% 95% 44% $12 40% 34% 95% 46% $48 

10 1 45% 37% 75% 43% $60 40% 37% 75% 43% $24 

10 2 42% 35% 95% 46% $48 42% 37% 95% 44% $48 

10 3 40% 35% 75% 44% $36 45% 35% 95% 46% $72 

11 1 42% 35% 55% 43% $12 45% 37% 95% 44% $60 

11 2 45% 37% 95% 46% $60 40% 34% 55% 46% $24 

11 3 40% 37% 75% 44% $36 42% 35% 55% 43% $12 

12 1 45% 35% 95% 43% $36 45% 37% 75% 43% $48 

12 2 40% 34% 55% 46% $60 42% 34% 95% 44% $60 

12 3 40% 37% 75% 44% $72 40% 34% 75% 46% $72 

13 1 45% 34% 95% 46% $24 45% 37% 55% 44% $24 

13 2 42% 34% 75% 43% $12 40% 35% 95% 46% $48 

13 3 40% 37% 55% 44% $60 42% 37% 55% 43% $60 

14 1 42% 37% 75% 46% $36 45% 35% 95% 46% $24 

14 2 45% 35% 55% 44% $60 42% 34% 95% 44% $72 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 56

Table A-4—Experimental Design for the Inland Survey Region 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

14 3 45% 34% 95% 43% $48 40% 37% 75% 43% $60 

15 1 40% 34% 55% 46% $36 42% 37% 75% 44% $12 

15 2 42% 34% 75% 44% $48 45% 34% 55% 44% $24 

15 3 42% 37% 95% 43% $72 40% 35% 75% 46% $60 

16 1 40% 35% 55% 46% $48 45% 35% 75% 46% $60 

16 2 45% 37% 75% 46% $48 42% 35% 55% 43% $36 

16 3 42% 34% 55% 44% $72 40% 37% 95% 43% $24 

17 1 42% 34% 95% 46% $48 42% 35% 95% 43% $72 

17 2 42% 34% 95% 44% $24 42% 37% 75% 46% $12 

17 3 45% 35% 55% 46% $12 45% 34% 75% 44% $60 

18 1 40% 34% 95% 44% $48 40% 35% 55% 44% $36 

18 2 42% 34% 75% 46% $12 45% 37% 55% 46% $72 

18 3 40% 37% 55% 43% $24 45% 34% 95% 43% $12 

19 1 40% 37% 95% 43% $12 45% 37% 55% 44% $36 

19 2 42% 34% 95% 44% $60 40% 34% 95% 43% $36 

19 3 45% 35% 55% 44% $72 42% 35% 55% 44% $24 

20 1 40% 37% 55% 44% $60 45% 37% 55% 43% $48 

20 2 42% 37% 95% 43% $12 42% 35% 75% 44% $24 

20 3 45% 35% 75% 46% $36 40% 34% 55% 44% $12 

21 1 45% 34% 95% 43% $72 45% 34% 55% 46% $12 

21 2 42% 37% 55% 46% $24 40% 34% 75% 44% $36 

21 3 40% 35% 95% 44% $24 45% 35% 95% 43% $24 

22 1 40% 34% 75% 43% $60 40% 35% 55% 46% $12 

22 2 40% 37% 95% 46% $72 42% 34% 75% 44% $48 

22 3 45% 34% 55% 44% $12 40% 37% 95% 43% $36 

23 1 40% 34% 55% 43% $24 40% 35% 55% 44% $48 

23 2 45% 34% 95% 44% $12 45% 37% 95% 43% $12 

23 3 45% 35% 75% 43% $48 42% 37% 95% 46% $36 

24 1 42% 35% 75% 44% $12 45% 37% 75% 46% $60 

24 2 40% 34% 75% 46% $72 42% 34% 55% 43% $36 

24 3 45% 37% 55% 46% $24 40% 35% 95% 46% $72 

Notes: 

“Com.fish” is an abbreviation for commercial fish populations score. 

“Fish Pop.” is an abbreviation for the fish populations (all fish) score. 

“Aq. Cond.” is an abbreviation for the aquatic ecosystem condition score. 

 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 57

Table A-5—Experimental Design for the National Survey 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

1 1 54% 34% 25% 55% $48 57% 31% 25% 57% $48 

1 2 57% 32% 55% 54% $60 57% 34% 95% 55% $72 

1 3 57% 31% 55% 55% $72 54% 31% 55% 54% $12 

2 1 57% 34% 25% 55% $48 52% 32% 55% 55% $12 

2 2 54% 32% 55% 57% $24 57% 31% 55% 54% $36 

2 3 52% 31% 25% 57% $36 54% 31% 95% 54% $24 

3 1 57% 34% 25% 54% $48 57% 31% 25% 57% $72 

3 2 54% 32% 95% 57% $60 54% 32% 95% 55% $12 

3 3 52% 32% 25% 57% $12 52% 34% 55% 57% $24 

4 1 54% 32% 95% 57% $72 52% 34% 25% 54% $72 

4 2 57% 34% 95% 55% $36 54% 32% 55% 57% $36 

4 3 54% 31% 55% 54% $60 52% 31% 95% 55% $48 

5 1 57% 31% 55% 55% $24 54% 34% 55% 57% $48 

5 2 54% 31% 25% 54% $36 52% 32% 25% 54% $36 

5 3 52% 34% 55% 57% $12 54% 32% 25% 54% $60 

6 1 52% 32% 95% 55% $36 52% 34% 25% 57% $72 

6 2 57% 31% 95% 57% $60 57% 32% 95% 55% $36 

6 3 52% 32% 55% 54% $48 54% 31% 55% 54% $24 

7 1 52% 34% 55% 57% $48 52% 34% 95% 54% $12 

7 2 57% 32% 95% 54% $24 54% 31% 25% 57% $60 

7 3 54% 31% 25% 54% $36 57% 32% 55% 55% $72 

8 1 54% 34% 25% 54% $36 54% 34% 95% 57% $72 

8 2 52% 32% 95% 54% $60 57% 32% 55% 54% $72 

8 3 54% 32% 55% 55% $60 52% 32% 25% 55% $24 

9 1 57% 31% 25% 57% $72 57% 32% 55% 54% $48 

9 2 52% 34% 55% 54% $24 54% 34% 95% 55% $72 

9 3 52% 32% 95% 55% $12 52% 31% 95% 57% $48 

10 1 57% 34% 55% 54% $60 52% 34% 55% 54% $24 

10 2 54% 32% 95% 57% $48 54% 34% 95% 55% $48 

10 3 52% 32% 55% 55% $36 57% 32% 95% 57% $72 

11 1 54% 32% 25% 54% $12 57% 34% 95% 55% $60 

11 2 57% 34% 95% 57% $60 52% 31% 25% 57% $24 

11 3 52% 34% 55% 55% $36 54% 32% 25% 54% $12 

12 1 57% 32% 95% 54% $36 57% 34% 55% 54% $48 

12 2 52% 31% 25% 57% $60 54% 31% 95% 55% $60 

12 3 52% 34% 55% 55% $72 52% 31% 55% 57% $72 

13 1 57% 31% 95% 57% $24 57% 34% 25% 55% $24 

13 2 54% 31% 55% 54% $12 52% 32% 95% 57% $48 

13 3 52% 34% 25% 55% $60 54% 34% 25% 54% $60 

14 1 54% 34% 55% 57% $36 57% 32% 95% 57% $24 

14 2 57% 32% 25% 55% $60 54% 31% 95% 55% $72 
June 5, 2012 316(b) SP Survey Methods and Preliminary Model Results| pg 58

Table A-5—Experimental Design for the National Survey 

Survey 

Version 

Choice 

Question 

Option A Option B 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

Com. 

Fish. 

Fish. 

Pop 

Fish 

Saved 

Aq. 

Cond. Cost 

14 3 57% 31% 95% 54% $48 52% 34% 55% 54% $60 

15 1 52% 31% 25% 57% $36 54% 34% 55% 55% $12 

15 2 54% 31% 55% 55% $48 57% 31% 25% 55% $24 

15 3 54% 34% 95% 54% $72 52% 32% 55% 57% $60 

16 1 52% 32% 25% 57% $48 57% 32% 55% 57% $60 

16 2 57% 34% 55% 57% $48 54% 32% 25% 54% $36 

16 3 54% 31% 25% 55% $72 52% 34% 95% 54% $24 

17 1 54% 31% 95% 57% $48 54% 32% 95% 54% $72 

17 2 54% 31% 95% 55% $24 54% 34% 55% 57% $12 

17 3 57% 32% 25% 57% $12 57% 31% 55% 55% $60 

18 1 52% 31% 95% 55% $48 52% 32% 25% 55% $36 

18 2 54% 31% 55% 57% $12 57% 34% 25% 57% $72 

18 3 52% 34% 25% 54% $24 57% 31% 95% 54% $12 

19 1 52% 34% 95% 54% $12 57% 34% 25% 55% $36 

19 2 54% 31% 95% 55% $60 52% 31% 95% 54% $36 

19 3 57% 32% 25% 55% $72 54% 32% 25% 55% $24 

20 1 52% 34% 25% 55% $60 57% 34% 25% 54% $48 

20 2 54% 34% 95% 54% $12 54% 32% 55% 55% $24 

20 3 57% 32% 55% 57% $36 52% 31% 25% 55% $12 

21 1 57% 31% 95% 54% $72 57% 31% 25% 57% $12 

21 2 54% 34% 25% 57% $24 52% 31% 55% 55% $36 

21 3 52% 32% 95% 55% $24 57% 32% 95% 54% $24 

22 1 52% 31% 55% 54% $60 52% 32% 25% 57% $12 

22 2 52% 34% 95% 57% $72 54% 31% 55% 55% $48 

22 3 57% 31% 25% 55% $12 52% 34% 95% 54% $36 

23 1 52% 31% 25% 54% $24 52% 32% 25% 55% $48 

23 2 57% 31% 95% 55% $12 57% 34% 95% 54% $12 

23 3 57% 32% 55% 54% $48 54% 34% 95% 57% $36 

24 1 54% 32% 55% 55% $12 57% 34% 55% 57% $60 

24 2 52% 31% 55% 57% $72 54% 31% 25% 54% $36 

24 3 57% 34% 25% 57% $24 52% 32% 95% 57% $72 

Notes: 

“Com.fish” is an abbreviation for commercial fish populations score. 

“Fish Pop.” is an abbreviation for the fish populations (all fish) score. 

“Aq. Cond.” is an abbreviation for the aquatic ecosystem condition score.