### S UPPORTING 

### S TATEMENT FOR 

### I NFORMATION 

### C OLLECTION 

### R EQUEST FOR



### W ILLINGNESS TO 

### P AY 

### S URVEY FOR 

### C HESAPEAKE 

### B AY 

### T OTAL 

### M AXIMUM 

### D AILY



### L OAD S 

### : I NSTRUMENT 

### , P RE 

### - TEST 

### , AND 

### I MPLEMENTATION1 TABLE OF CONTENTS

PART A OF THE SUPPORTING STATEMENT

................................ ................................ .... 4
1. Identification of the Information Collection

................................ ................................ ....... 4 1(a) Title of the Information Collection

................................ ................................ ..................... 4 1(b) Short Characterization (Abstract)

................................ ................................ ....................... 4
2. Need For and Use of the Collection

................................ ................................ .................... 5 2(a) Need/Authority for the Collection

................................ ................................ ...................... 5 2(b) Practical Utility/Users of the Data

................................ ................................ ...................... 6
3. Non

- duplication, Consultations, and Other Collection Criteria

................................ .......... 6 3(a) Non

- duplication

................................ ................................ ................................ .................. 6 3(b) Public Notice Required Prior to ICR Submission to OMB

................................ .............. 10 3(c) Consultations

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

................................ ................................ .................. 17 3(e) General Guidelines

................................ ................................ ................................ ............ 17 3(f) Confidentiality

................................ ................................ ................................ .................. 18 3(g) Sensitive Questions

................................ ................................ ................................ ........... 18
4. The Respondents and the Information Requested

................................ ............................ 18 4(a) Respondents

................................ ................................ ................................ ...................... 18 4(b) Information Requested

................................ ................................ ................................ ...... 20
5. The Information Collected

- Agency Activities, Collection Methodology, and Information Management

................................ ................................ ................................ .. 26 5(a) Agency Activities

................................ ................................ ................................ .............. 26 5(b) Collection Methodology and Inform ation Management

................................ ................... 27 5(c) Small Entity Flexibility

................................ ................................ ................................ ..... 28 5(d) Collection Schedule

................................ ................................ ................................ .......... 28
6. Estim ating Respondent Burden and Cost of Collection

................................ ................... 30 6(a) Estimating Respondent Burden

................................ ................................ ......................... 30 6(b) Estimating Respondent Costs

................................ ................................ ........................... 31 6(c) Estimating Agency Burden and Costs

................................ ................................ .............. 31 6(d) Respondent Universe and Total Burden Costs

................................ ................................ . 322

6(e) Bottom Line Burden Hours and Costs

................................ ................................ .............. 32 6(f) Reasons for Change in Burden

................................ ................................ ......................... 33 6(g) Burden Statement

................................ ................................ ................................ .............. 33

PART B OF THE SUPPORTING STATEMENT

................................ ................................ ... 35
1. Survey Objectives, Key Variables, and Other Preliminaries

................................ ............ 35 1(a) Survey Objectives

................................ ................................ ................................ ............. 35 1(b) Key Variables

................................ ................................ ................................ .................... 35 1(c) Statistical Approach

................................ ................................ ................................ .......... 37 1(d) F easibility

................................ ................................ ................................ .......................... 37
2. Survey Design

................................ ................................ ................................ ................... 38 2(a) Target Population and Coverage

................................ ................................ ....................... 38 2(b) Sampling Design

................................ ................................ ................................ ............... 39 2(c) Precision Requirements

................................ ................................ ................................ .... 42 2(d) Questionnaire Design

................................ ................................ ................................ ........ 46
3. Pretests

................................ ................................ ................................ .............................. 49
4. Collection Methods and Follow

- up

................................ ................................ .................. 50 4(a) Collection Methods

................................ ................................ ................................ ........... 50 4(b) Survey Response and Follow

- up

................................ ................................ ...................... 51
5. Analyzing and Reporting Survey Results

................................ ................................ ......... 51 5(a) Data Preparation

................................ ................................ ................................ ................ 51 5(b) Analysis

................................ ................................ ................................ ............................. 52 5(c) Reporting Results

................................ ................................ ................................ .............. 60
REFERENCES

................................ ................................ ................................ ............................. 613

List of Attachments
Attachment 1

– Declining baseline version of survey
Attachment 2

– Constant baseline version of survey
Attachment 3

– Improving baseline version of survey
Attachment 4

– Federal Register Notices
Attachment 5

– Preview letter to mail survey recipi ents Attachment 6

– Cover letter to mail survey recipients
Attachment 7

– Postcard reminder to mail survey recipients
Attachment 8

– Cover letter to recipients of the second survey mailing
Attachment 9

– Reminder letter to recipients of the second surv ey mailing
Attachment 10

– Cover letter to recipients of the priority mail non

- response questionnaire
Attachment 11

– Non

- response follow

- up questionnaire
Attachment 12

– Description of statistical survey design
Attachment 13

– Responses to public comm ents4 PART A OF THE SUPPORTING STATEMENT

1. Identification of the Information Collection 1(a) Title of the Information Collection

Willingness to Pay Survey for Chesapeake Bay Total Maximum Daily Load s : Instrument, Pre

- test, and Implementation

1(b) Short Characterization (Abstract)

The Clean Water Act (CWA) directs EPA to coordinate Federal and State efforts to improve water quality in the Chesapeake Bay. In 2009, Executive Order (E.O.) 13508 reemphasized this

man date, directing EPA to define the next generation of tools and actions to restore water quality in the Bay and describe the changes to be made to regulations, programs, and policies to implement these
actions. In response, EPA is undertaking an assessment of the costs and benefits of meeting established pollution budgets, called Total Maximum Daily Loads ( TMDLs ), of nitrogen, phosphorus, and sediment for the Chesapeake Bay

. The Chesapeake Bay W atershed encompasses 64,000 square miles in parts of six states and the District of Columbia. While efforts have been underway to restore the Bay for more than 25 years, and significant progress has been made over that period, the TMDLs are necessary to

c ontinue progress toward the goal of a healthy Bay. The watershed states of New York, Pennsylvania, Delaware, West Virginia, Virginia, and Maryland, as well as the District of Columbia, have developed Watershed Implementation Plans (WIPs) detailing the ste ps each will take to meet its obligations under
the TMDLs

. EPA has begun a new study to estimate the costs of compliance with the TMDLs and the corresponding benefits. As an input to the TMDLs benefits study, EPA ’s National Center for
Environmental Econ omics (NCEE) is seeking approval to conduct a stated preference survey to collect data on households’ use of Chesapeake Bay and its watershed, preferences for a variety of water quality improvements likely to follow from pollution reduction programs, and d emographic information. If approved, the survey would be administered by mail , in two phases, to a total 9,140 residents living in the Chesape ake Bay states, Chesapeake Bay W atershed, and other east coast states.5

NCEE will use the survey responses to e stimate willingness to pay for changes related to reductions in nitrogen, phosphorous, and sediment loadings to the Bay and lakes in the Chesapeake Bay Watershed. The analysis relies on state of the art theoretical and statistical tools for non

- market wel fare analysis

. A non

- response follow

- up survey will also be administered to inform the interpretation and validation of survey responses

. The total national burden estimate for all components of the survey is 88 7 hours. The burden
estimate is based on 150 responses to 500 pretest surveys, 2,592 responses to 8,640 mailed main surveys, and 7 70 responses to the combined non

- response surveys for the pre

- test and main survey

. Assuming 18 minutes are needed to complete the mail survey and 5 minutes for the n on

- respo nse survey the total respondent cost comes to $ 2 0 , 68 2 using an average wage rate of $ 23.33 (United States Department of Labor, 2012)

.

2. Need For and Use of the Collection 2(a) Need/Authority for the Collection

Benefits from meeting the TMDLs for the Chesapeake Bay will accrue to those who live near the Bay or visit for recreation, those who live near or visit lakes in the watershed
, and those who live further away and /or may never visit the Bay but have a general concern for the environment. While benefits from the first two categories can be measured using hedonic property value methods , recreational
demand methods , and other revealed preference approaches, only stated preference methods can capture

non

- use benefits (i.e., benefits for those who do not use the resource.) The findings from this study will be used by EPA to estimate the total value of benefits of the nutrient and sediment TMDLs designed to meet the requirements of Executive Order 13508

. Specifically, t his project will explore how public values for ecosystem goods and services are affected by nutrient and sediment loading to the Chesapeake Bay . Understanding total public values for ecosystem resources , including the more difficult to estimate non

- use values, is necessary to determine the full range of benefits associated with reductions in nutrient and sediment loading

. Because non

- use values may be substantial, failure to estimate such values may lea d to improper inferences regarding
benefits and costs. States and their congressional representatives have expressed a need to know how practices to which policy

- makers have already committed for reducing nutrients and sediment will benefit their6

constit uents (see, for example, US Congress 2011). There are limited stated preference studies specifically on the Chesapeake Bay, and no studies specifically addressing the environmental improvements predicted under the TMDLs . This study will provide policy mak ers with information on how much the public would benef it in return for the cost of the programs. The project is being undertaken pursuant to section 104 of the Clean Water Act dealing with
research. Section 104 authorizes and directs the EPA Administra tor to conduct research into a number of subject areas related to water quality, water pollution, and water pollution prevention and abatement. This section also authorizes the EPA Administrator to conduct research into methods of analyzing the
costs and b enefits of programs carried out under the Clean Water Act.

2(b) Practical Utility/Users of the Data

EPA plans to use the results of the stated preference survey to estimate the net welfare impacts of the Chesapeake Bay TMDLs . Specifically, the Agency w ill use the survey results to estimate values for improvements in Bay and for reduced algae in watershed lakes under measures taken to meet the
TMDLs. In this work, the Agency will follow standard practices outlined in the literature (Freeman 2003; Bennett and Blamey 2001; Louviere et al. 2000; U.S. EPA 2000). The results of the study will be made available to state and local governments which they may use to better understand the preferences of households in their jurisdictions and the benefits they can expect as a result of meeting the TMDLs . Finally, stakeholders and the general public will be able to use this information to better understand the social benefits of improving water quality in the Chesapeake Bay in comparison to the cost information als o being developed by EPA.

3. Non

- duplication, Consultations, and Other Collection Criteria 3(a) Non

- duplication

There are many studies in the environmental economics literature that quantify benefits or willingness to pay (WTP) associated with various types of water quality and aquatic ecosystem changes. The Chesapeake Bay, as an iconic resource and the subject of a long history of restoration efforts is relatively well studied

. However, no study or set of studies provides a comprehensive estimate of non

use values or values associated with specific improvements likely to result from the TMDLs . Further,7

existing stu dies do not provide a sufficient basis for using benefits transfer to estimate the value of these improvements. The proposed survey is designed to fill this gap. The most recent review of valuation studies relevant to the Chesapeake Bay TMDLs is by Cropp er and Isaac (2011), who identify studies that estimate WTP for use and non

- use values associated with the Bay. Cropper and Isaac also consider how studies not associated with the Bay may be used in a benefits transfer exercise to value improvements resul ting from the TMDLs . While these studies all provide insights into particular categories of benefits associated with the Bay, none provides a comprehensive estimate of non

- use values or values associated with specific improvements likely to
result from t he TMDLs

. Three of the studies reviewed in Cropper and Isaac examine the residential amenity value of water quality (Leggett and Bockstael, 2000; Poor et al., 2007; Van Houtven, 2009). Leggett and Bockstael (2000) estimate the impact on housing prices from fecal coliform counts on waterfront properties, finding significant effects. Poor, et al. (2007) study the effect of dissolved inorganic nitrogen and total suspended solids on property values in St. Mary’s County, MD and also find significant
effects . These studies, however, reflect the benefits to homeowners on or near the Bay and not benefits for the general population

. Recreational benefits in the Chesapeake Bay have been relatively well

- studied, and Cropper and Isaac identify six original studi es that estimate the recreational activity value of improved water quality (Bockstael et al., 1988, 1989; Lipton and Hicks, 1999; Hicks and Strand, 2000; Lipton and Hicks, 2003; Lipton, 2004; Massey et al., 2006). Cropper and Isaac also identify three stu dies that use benefit transfer to estimate recreation benefits (Krupnick, 1988; Morgan and Owens, 2001; Van Houtven, 2009). The se nine studies focus on the benefits from recreational fishing, swimming or beach visits, and boating. The
limitations of thes e studies for estimating the benefits of the TMDLs are described in Cropper and Isaac. Key limitations include that only two species (striped bass and flounder) are examined in the recreational fishing studies, and the choice of water quality measure for swimming and boating studies. These limitations make it difficult to use the studies for benefit transfer. More importantly for this ICR, these studies, even taken together, look at a small subset of the kinds of benefits expected from the
TMDLs

. Commerc ial fishing benefits from improved water quality are the subject of three studies identified in Cropper and Isaac (Kahn and Kemp, 1985; Anderson, 1989; Mistiaen et al., 2003). While8

commercial benefits from the TMDLs are likely to be important, they are d istinct from the other types of benefits the proposed stated preference study will estimate. There is a large gap in the literature on the non

- use benefits of improved water quality in the Chesapeake Bay. Cropper and Isaac (2011) identified only two origi nal research studies that estimate

non

- use benefits of water quality improvements in the Chesapeake Bay. The first study, Bockstael, et al. (1988, 1989), estimates willingness to pay to make the Bay “swimmable” for those respondents who
considered that it was not acceptable for swimming. There is no clear way to link “swimmability” to the improvements expected from the TMDLs , making it difficult to use this study for valuation. Further, the study sample was limited to the Washington

- Baltimore area. A se cond study, Lipton et al. (2004), estimated willingness to pay of non

- users for restoring oyster reefs in the Chesapeake Bay. While oyster reefs are likely to benefit from the TMDLs , this is only a small part of the expected environmental improvements. Th e sample in this study was broader, including most mid

- Atlantic states, but did not include Pennsylvania and was not a random sample. An alternative to directly estimating Chesapeake Bay non

- use benefits is to use existing studies that are similar but focus on other locations. Van Houtven (2009) suggests this approach, based on a

meta

- analysis of stated preference studies that value cha nges in a water quality index. T his approach can produce value estimates for improvements in the index, and it may be feasible to map changes resulting from the TMDLs into a comparable index. However, as noted by Cropper and Isaac (2011),
the water bodie s used in the meta

- analysis differ greatly from the Chesapeake Bay, and the values reported from Van Houtven (2009) are substantially different from those in Bockstael, et al. (1988)
, raising questions about the accuracy of a benefits transfer exercise . T he limitations of existing Bay

specific studies, and the inability to effectively apply benefits transfer, suggest that an original study is
needed to estimate non

- use benefits from the TMDLs

. One additional group of researchers, Hicks et al. (2008), us ed a choice experiment to survey knowledgeable stakeholders (e.g., recreational anglers, charter boat operators) with close connection to the Chesapeake Bay and its restoration about their preferences for a hypothetical Bay restoration package in the conte xt of a prior Chesapeake Bay restoration effort, “Chesapeake 2000.” They account for a variety of outcomes related to reduced sediment and nutrient loads: number of seafood consumption advisories, number of beach closures, oyster biomass, blue crab biomass , shad population, and acres of wetlands. While perhaps the most closely related among available studies, the choice experiment setup a s well as the type and scale of the surveyed population prevent its direct use for9

analysis of benefits of the TMDL s. Spe cifically, cost was not included as an attribute in the choice experiment, preventing EPA from estimating WTP for restoration activities based on these results. Furthermore, only knowledgeable stakeholders were surveyed; preferences for water quality impro vements achieved under the Chesapeake Bay TMDLs among this group may substantially differ
from those of the general and/or non

- user population. The proposed stated preference study addresses one additional gap: benefits throughout the Chesapeake Bay W aters hed that result from TMDLs implementation. Based purely on proximity,
residents farther out in the watershed are less likely to have direct use value for in

- the

- Bay benefits of
TMDLs implementation, yet may face substantial implementation costs
, particularly in the upper portions of the watershed . BMPs designed to reduce nutrient and sediment loadings to the Bay will also improve water quality in thousands of lakes and reservoirs in the watershed; since many watershed residents have relatively mor e direct access to these amenities, not counting these benefits is likely to substantially underestimate total benefits of the TMDLs . These effects are not well

- considered in the existing economics literature. For example, Cropper and Isaac (2011) identi fied only one appropriate study (von Haefen, 2003) related to ancillary benefits from BMP implementation. To conclude, while specific aspects of uses of the Bay have been well studied, an original stated preference study is needed to properly estimate ben efits of the Chesapeake Bay TMDLs due to the following reasons: (1) valuation endpoints used in the existing studies tend to be incompatible with endpoints used to measure water quality improvement under the Chesapeake Bay TMDLs (e.g., water clarity); (2) necessary data to transfer benefits estimates from existing studies to the Chesapeake Bay are lacking, particularly regarding use and non

- use data for watershed ecosystems like lakes; (3) the overall population likely to benefit from action take under the Chesapeake Bay TMDLs is broader than the populations sampled in prior studies, which would likely result in incorrect value estimates; and (4) although studies separating use and non

- use components of total value estimate that non

- use value is
substantial , available studies are based on discrete endpoints (i.e., “swimmability” in the Bay itself, oyster reef acreage) do not reflect the full range of TMDL

- related Bay and watershed improvements (e.g., ancillary improvements in watershed lakes, Bay water clari ty, and Bay populations of fish and
shellfish ). While these concerns might be ameliorated by using results from environmental economics studies based in other estuaries of the United States, EPA believes that studies based in other estuaries are unlikely t o accurately or completely capture willingness to pay for TMDL

- related impr ovements in the Chesapeake Bay W atershed given the unique character of this water resource and the goods and10

services it provides. Cropper and Isaac reach a similar conclusion, writ ing, “ we strongly suggest that a new stated preference study be conducted to elicit willingness to pay for water quality improvements more closely linked to the TMDLs” (Cropper and Isaac, 2011, p. 20).

3(b) Public Notice Required Prior to ICR Submission t o OMB

In accordance with the Paperwork Reduction Act (44 U.S.C. 3501 et seq.), EPA published a notice in the Federal Register on May 24, 2012 , announcing EPA’s intent to submit this application for a new Information Collection Request (ICR) to the Office of Management and Budget (OMB), and soliciting comments on aspects of the information collection request. A copy of the Federal Register notice (77 FR 31006) is attached at the end of this document (See Attachment 4 ). Because certain supporting documents were not available in the docket for public review during the first 30 days of the comment period, EPA re

- opened the comment period for an additional 30 days beginning on July 26 (77
FR 43822 ; Attachment 4 )

. Also see docket # EPA

- HQ

- OA

- 2012

- 0033

. Three sets of substantive comments were received in response to the Federal Register Notice

-- one each from the Ut i lity Water Act Group (UWAG) , Food and Water Watch (FWW) , and a coalition of 18 organizations representing various business, construction, ma nufacturing, housing, agriculture, forestry and energy interests (C18)

. These organizations provided many useful comments that have
since been incorporated into the survey design. Complete comments and responses are in Attachment
13

. Brief response summ aries to the major comments are provided below:

Stated preference is not a generally accepted valuation approach (UWAG, C18)
Response

– Stated preference studies have been conducted by leading academic scholars at Harvard University, University of North Carolina, University of Maryland, Cornell University, University of California, Berkeley, UCLA, and other top research universities across the country and around the world. In addition, studies have been published in premier economic journals (see , for e xample, Cummings and Taylor 1999; Evans, Poulos and Smith 2011; Johnston 2006; Layton and Brown 2000; Parsons and Thur 2008; Viscusi, Huber, and Bell 2008 ). Stated preference studies are used by a wide range of federal agencies and countries including the U.S., Canada, the UK, and Australia to assess the benefits of regulations and federal activities (for U.S. examples see NOAA 2002; U.S. EPA 2008, 2009; U .S. Bureau of Reclamation 2012). 1 The use of stated preference studies is consistent with EPA’s peer

- reviewed Guidelines for Preparing Economic Analyses (U.S. EPA 2010) and OMB’s guidance on Regulatory Impact Analysis

1 Miller, et al. 2006 is a useful reference of the applica tion of stated preference methods to valuing health risk reductions across federal agencies.11

(Circular A

- 4 2003). While stated pr eference has its critics, EPA believes there is a wide range of general acceptance towards stated preference methods both within academic circles and for public policy analysis (see a recent issue of the Journal of Economic Perspectives for three papers on the topic, Carson 2012, Hausman 2012, and Kling, et al. 2012).

Stated preference surveys are subject to biases Hypothetical bias

– Refers to the fact that stated preference surveys pose hypothetical situations, and people are not actually asked to make the payments indicated in the survey. (UWAG, FWW, C18)

Response

– EPA recognizes that hypothetical bias is a potential concern in stated preference surveys and takes this concern seriously. In general, stated preference methods have “been tested and val idated through years of research and are widely accepted by federal, state, and local government agencies and the U.S. courts as reliable techniques for estimating nonmarket values” (Bergstrom and Ready 2009, p. 26). A recent meta

- analysis of the stated p reference literature also concludes that hypothetical bias may not always be a significant concern (Murphy, et al. 2005 ). To reduce the potential for hypothetical bias in this survey EPA has consulted with experts and drawn from peer reviewed literature t o address it in the survey de sign. For example
, the survey explicitly incorporates elements that allow mitigation of hypothetical bias, such as the use of reminders about budget constraints (akin to the cheap talk language in Cummings and Taylor 1999; List 2001)

. These features of survey design are shown to minimize hypothetical bias in experimental settings. The text used in this survey has undergone thorough testing with participants in focus group s and cognitive interviews. EPA believes that the s teps taken during survey development and testing have largely mitigated the potential for hypothetical bias. See Section 2d of Part B this ICR for more information on how we address hypothetical bias.

Non

- response bias

– Refers to situations in which people who choose to complete and return the survey are systematically different than those who do not. This could lead to an unrepresentative sample of respondents and bias the willingness to pay estimates upward. (UWAG, C18)

Response

– EPA rec ognizes the potential for non

- response bias and the impacts it could have on the data analysis. First, EPA is taking steps to obtain the highest possible response rate, thereby mitigating non

- response bias. Specifically, EPA is using focus group

- tested des ign choices to encourage participation. EPA is also following the Dillman tailored design method (Dillman 2008) for mail surveys which includes an introduction letter preceding the survey, a reminder post card, and second mailing of the survey, and a remi nder letter following the second survey.

EPA will also administer a non

- response follow

- up survey ( A ttachment 11) in both the pre

- test and full survey in order to examine whether or not respondents are systematically different from

non

- respondents (see OMB 2006). In the non

- response follow

- up survey, households that do not return the survey will be randomly sampled to receive a short questionnaire by priority mail. The questionnaire will elicit basic demographic information as well as a few short quest ions regarding awareness and the reasons they did not complete the survey. Responses to these12

questions will be used to examine whether respondents are systematically different from non

respondents. See Section 2c of Part B of this Information Collection Request for a description of the non

- response follow

- up survey.

Yea

- saying

– Yea

- saying refers to the tendency of respondents to agree with questions regardless of content. (UWAG)
Response

– Survey and study design choices can mitigate yea

- saying. The use of mail survey s rather than face

- to

- face interview s has been shown to decrease the social pressure that may influence a respondent to provide a response deemed desirable (Blamey and Bennett 2001, Dillman 200 8 ). This survey also employs a conjoint choice framework, where respondents must consider the trade

- offs between a status quo and two policy options. Respondents are asked to make a discrete choice among three unranked options rather than stating a simple “ yes ” or “ no

. ” These options vary in terms of the levels of five environmental attributes (plus cost). In this choice experiment framework it has been shown that the likelihood for yea

- saying and strategic responses is less prominent (Blamey and Bennett 20 01, Collins and Vossler 2009).
In addition, in order to identify such respondents EPA includes debriefing questions at the end of the survey to identify respondents who might believe that protecting the environment is important no matter the cost. The a ddition of debriefing questions is a suggested practice and regarded as an excellent opportunity to bring clarity to the respondents' choices (Krupnick and Adamowicz 2007). Sensitivity analysis will be used to examine these debriefing questions and determ ine if and how a yea

- saying phenomenon may have influenced responses . See Section 5b of Part B of this ICR for more information on how we address yea

- saying.

It is too d ifficult to quantify and measure complex environmental commodities (UWAG, FWW, C18)
Response

– EPA agrees that it challenging to measure complex environmental commodities. Standard survey design cognitive interviews were followed in developing the survey. EPA conducted 10 focus groups and 72 cognitive interviews with individuals with in and outside the Chesapeake Bay Watershed in order to test their level of understanding of the materials included in the survey (OMB Control Number 2090

- 0028). We used standard cognitive to identify the most salient environmental commodities that will b e affected by the TMDLs . Limiting the survey to those policy outcomes (i.e., water clarity, striped bass, oysters, blue crabs, and lake water quality) is conservative but we can be confident in the benefits we do capture from the survey.

Baseline scena rios are inaccurate (UWAG, C18)
Response: The estimates of the environmental conditions under the baseline and program scenarios are based on scientific models developed by EPA, NOAA and others, as cited in the survey. We recognize, however, the points that are raised by the reviewers and have made several modifications to the survey design. First, we label the programs as Option A, Option B, and Option C, where Option A represents the predicted conditions in 2025 without new programs , while Options B and C represent new programs with additiona l costs

. Second, in the13

experimental design respondents are randomly assigned to one of three different projections for the Option A (i.e., a status quo scenario): a decreasing baseline, an increasing baseline, or a constant baseline. As described in Se ction 5b of Part B of this Information Collection Request, these different baseline scenarios capture the potential range of possible future conditions, absent new programs. EPA wishes to emphasize that the responses to the conjoint choice experiments, and hence the baseline and policy scenarios themselves, do not directly estimate the benefits of the TMDLs

. Rather, the responses to the hypothetical choice options and baselines will be used to estimate a “benefits function” or curve. This benefits functi on reflects the benefits from generic improvements in the environmental and water quality attributes specified. The benefits function will then be used to estimate the incremental benefits of the TMDLs relative to the most accurate baseline as predicted by the EPA and NOAA models. An advantage of this benefits function approach is that the total benefits can be updated if the management practices chosen by the states to meet the TMDLs change in the future.
Impact of program on water quality is unknown (UWA G, C18)
Response: The choice experiment design in the stated preference survey allows EPA to estimate benefits from a range of policy outcomes. This will allow EPA to update the benefit estimates as the order of implementation of the management practices becomes known.
Surveys include improvement to lakes that are not in the Chesapeake Bay Watershed (C18)
Response: Thank you for raising this point. EPA never intended to include lakes outside the Chesapeake Bay Watershed in the benefit estimate. We have made several changes to the survey instrument to make i t clear that only lakes in the w atershed should be considered. First, we have enhanced the map at the beginning of the survey to identify major cities within and outsi de the

w atershed and added the Finger Lakes to the map (which are now clearly marked as being outside the watershed). This helps orient respondents who are considering whether or not they “use” (e.g., engage in recreation activities) l akes in the w atershed. S econd, we clearly describe the w atershed as including lakes, and state that water bodies outside of the w atershed will not be affected by the programs. One follow

- up question is specifically aimed to test the respondents’ level of understanding on this issue.
Respondents may not know if they l ive in the Chesapeake Bay Watershed (C18)
Response: In addition to pr oviding an enhanced map of the w atershed, EPA will identify respondents who reside within or outside of the watershed and explicitly inform them of this in the introductory cover letter s. See Attachments 5 and 6 for examples of the cover letters.
Adding benefits from the Stated Preference study to those from Revealed Preference studies will double

- count some benefits (C18)
Response: EPA agrees and does not intend to add the total m onetized benefit results from this study with results from other studies, such as those that use revealed preference methods. The14

results from this study can be used to isolate nonuse values or used alone as a measure of total monetized benefits.

Link between survey attributes and TMDLs is not established (UWAG, C18)
Response: In response to peer review comments from academic experts in stated preference methods, EPA is now only modeling willingness to pay for improvements in bay water clarity, stripe d bass, blue crab, oyster populations, and the quality of lakes in the watershed. This was previously referred to as the “endpoint” version of the survey. These attributes were chosen based on extensive focus groups and interviews as the environmental fea tures that are most salient to the general public. Furthermore, EPA and NOAA models predict that these features will be affected by the TMDLs . The stated preference survey outlined in this ICR does not estimate the benefits of the TMDLs directly; rather this survey is designed to value generic status quo and policy options that result in changes in the environmental attributes. As part of the experimental design, respondents are presented with hypothetical changes in these attributes an d cost. In other words, the hypothetical levels associated with each of the attributes and costs in the survey vary across respondents. This allows us to identify the parameters and estimate a range of values associated with different scenarios. The para meters estimated from respondents’ choices to these hypothetical scenarios will then be used to estimate the benefits of the TMDLs incremental to the baseline.

The costs of the options on the survey do not reflect the true costs of the TMDL ( UWAG )
Resp onse: The variation in costs across programs is not intended to reflect the costs of the TMDL, but rather the likely range of values respondents hold for the options, as found in extensive focus groups and interviews. The parameters estimated from respond ents’ choices to these hypothetical scenarios will then be used to estimate the benefits of the TMDL incremental to the baseline.
A complementary study of the costs of the TMDL is being conducted by EPA’s Chesapeake Bay Program Office and will be issued by EPA after a peer

- review is complete.
3(c) Consultations

Consultations with Scholars: Prior to commencing the survey design phase of this project, EPA co

sponsored a workshop on October 31 and November 1, 2011 at Resources for the Future on the costs and benefits of protecting and restoring the Chesapeake Bay. The main purpose of the works hop was to gather scholars who are working on estimating the costs and benefits of water quality improvements in the Bay to exchange ideas. The agenda included a wide range of presentations on the costs and benefits of improving water quality in the Bay and its watershed, the possibility of ecosystem service payments and trading to ameliorate some of the costs, ancillary benefits that may arise, water quality and ecological modeling for the Bay, and policy options for approaching protection and restoratio n.15

Although the workshop topics were clearly broader than stated preference techniques, the agenda did include presentations by EPA on the estimation of benefits using stated preference methods

. Participants included academics from major universities with in the Chesapeake Bay Watershed (MD, VA, DE, WV, PA); representatives from several NGOs including the Chesapeake Bay Foundation and the Chesapeake Bay Trust; as well as participants from several government agencies (NOAA, DOI , and USDA). On September 11, 2012, EPA participated in a quarterly meeting of the Chesapeake Bay Program’s (CBP) Scientific and Technical Advisory Committee (STAC). As described on its website, 2

STAC provides scientific and technical guidance to the Chesapeake Bay Program on measures to restore and protect the Chesapeake Bay and serves as a liaison between the region's scientific community and the CBP. Through professional and academic contacts and organizational networks of its members, STAC ensures close cooperation among and betwee n the various research institutions and management agencies represented in the Chesapeake Bay W atershed. EPA’s presentation of the progress made to date on the development of the stated preference survey and the goals of the project were well received.

Consultations with Respondents : As part of the planning and design process for this collection, EPA
conducted a series of 10 focus groups and 72 cognitive interviews . Eight of th e ten focus groups were in
venues located inside the Chesapeake Bay W atershed , in locations close to Bay itself and further upstream. Two other focus groups were conducted in North Carolina, a state that is entirely outside of the Chesapeake Bay W atershed. Of the cognitive interviews, 57 were conducted at locations in the
watersh ed, and 15 were conducted in parts of Pennsylvania that were outside the Chesapeake Bay
Watershed. While early focus group sessions were used to narrow the list of attributes to be highlighted in a survey and the kinds of information respondents would need to answer the questions, later sessions
and cognitive interviews were employed to test the draft survey materials. These consultations with potential respondents were critical in identifying sections of the questionnaire that were redundant and
lacked cl arity and in producing a survey instrument meaningful to respondents. The later focus group
sessions and the cognitive interviews were also helpful in estimating the expected amount of time respondents would need to complete the survey instrument. While completion times varied
, most participants required approximately 15 to 20 minutes to complete the final draft surveys. The focus group session s and cognitive interviews were conducted under OMB Control # 2090

- 0028.

2 http://www.chesapeake.org/stac/index.php16

Consultations with other Government Agencies: EPA has been working closely with ecosystem modelers in NOAA’s Chesapeake Bay Office and N ational M arine F isheries S ervice’s Office of Habitat
Conservation. Specifically, NOAA’s modelers have provided assistan ce with the ecosystem based fishery models "Ecopath with Ecosim" and "Atlantis." These consultations have been instrumental in examining the ecological impacts of reducing nutrient and sediment loads to the Bay . T he ecosystem

based fishery models have pr ovided useful background for the survey instrument itself and will allow EPA to more accurately estimate the values people place on the various attributes of the Chesapeake Bay highlighted in the survey.

. Consultations with Peer Reviewers: The survey ins trument has undergone peer review by three leading scholars specializing in stated preference surveys for estimating benefits associated with environmental improvements: Dr. Kevin Boyle, Professor, Department of Agricultural and Applied Economics, Virgini a Tech University ; Dr. John Whitehead, Professor and Chair, Department of Economics, Walker College of Business, Appalachian State University ; and Dr. Robert Johnston, Director, George Perkins Marsh Institute, Professor, Department of Economics, Clark Univ ersity

.

Survey Design Team: Dr. Christopher Moore at the U.S. Environmental Protection Agency serves as the project manager for this study. Dr. Moore is assisted by Dr. Chris Dockins, Dr. Dennis Guignet, Dr. Kelly Maguire
, Dr. Nathalie Simon and Ms. Sarah Marrinan , all with the U.S. EPA’s National Center for Environmental Economics. Dr. Alan Krupnick, Senior Fellow and Director, Center for Energy Economics and Policy at Resources For the Future, Dr. Maureen Cropper, Senior Fellow at Resources
for the Futur e and Professor of Economics at the University of Maryland, and Mr. William Isaacs,
Research Assistant at Resources for the Future , provid e re view of the survey development, focus group materials, and cognitive interview materials. Dr. Elena Besedin, Seni or Economist at Abt Associates Inc, and Ryan Stapler, M.S., Senior Analyst at Abt Associates Inc. provide contractor support.
Dr. Alan Krupnick is Director of Resources for the Future’s Center for Energy Economics and Policy , as well as Director of Research and a Senior Fellow at RFF, and specializes in analyzing environmental and energy issues, in particular, the benefits, costs and design of pollution and energy policies. He was lead author for Toward a New National Energy Policy: Assessing the Op tions study, examining the costs and cost

- effectiveness of a range of federal energy policy choices in both the17

transportation and electricity sectors. His primary research methodology is in the development and analysis of stated preference surveys. Dr. Kr upnick has been a consultant to state governments, federal agencies, private corporations, the Canadian government, the European Union, the World Health Organization, and the World Bank. He is a regular member of expert committees from the National Academy of Sciences and the U.S. EPA.
Dr. Maureen Cropper, P rofessor of E conomics at the University of Maryland and Senior Fellow at Resources for the Future, has made major contributions to environmental policy through her research, teaching, and public service . Her research has focused on valuing environmental amenities, estimating consumer preferences for health and longevity improvements, and the tradeoffs implicit in environmental regulations. Previously at the World Bank, her work focused on improving polic y choices in developing countries through studies of deforestation, road safety, urban slums, and health valuation. From 1994 through 2006, she served on the U.S. Environmental Protection Agency's Science Advisory Board, where she chaired the Advisory Cou ncil for Clean Air Act Compliance Analysis and the Environmental Economics Advisory Committee. She is a research associate of the National Bureau of Economic Research and a member of the National Academy of Sciences. Dr. Elena Y. Besedin, a senior economi st at Abt Associates Inc., specializes in the economic analysis of environmental policy and regulatory programs. Her work to support EPA has concentrated on analyzing economic benefits from reducing risks to the environment and human health and assessing environmental impacts of regulatory programs for many EPA program offices. She has worked extensively on valuation of non

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

- market valuatio n includes design and implementation of stated and revealed preference studies and benefit transfer methodologies.

3(d) Effects of Less Frequent Collection

The survey is a one

- time activity. Therefore, this section does not apply.

3(e) General Guidelines18

The survey will not violate any of the general guidelines described in 5 CFR 1320.5 or in EPA’s
ICR H andbook.

3(f) Confidentiality

All responses to the survey will be kept confidential to the extent provided by law. To ensure
that the final survey sample includes a representative and diverse population of individuals, the survey questionnaire will elicit basic demographic information, such as age, number of children under 18, type of employment, and income. However, the detailed survey questi onnaire will not ask respondents for personal identifying information, such as names or phone numbers. Instead, each survey response will receive a unique identification number. Prior to taking the survey, respondents will be informed that their response s will be kept confidential to the extent provided by law. The name and address of the respondent will not appear in the resulting database, preserving the respondents ’ identity. The survey data will be made public only after it has been thoroughly vetted to ensure that all other potentially identifying information has been removed.

3(g) Sensitive Questions

The survey questionnaire will not include any sensitive questions pertaining to private or personal information, such as sexual behavior or religious beliefs.

4. The Respondents and the Information Requested 4(a) Respondents Eligible respondents for this s tated p reference s urvey are individuals 18 years of age or older
who reside in one of 1 7 east coast U.S. states and the District of Columbia (hereafter states). Before
selection , the population of households in the 1 8 states will be stratified by three mutually

- exclusive study regions : states immediately bordering Chesapeake Bay (Maryland
, Virginia
, and the Distri ct of
Columbia ) ; states in the Chesapeake Bay W atershed but not immediately bordering the Bay itself (Delaware, New York, Pennsylvania, and West Virginia) ; and east ern states outside of the watershed ( Vermont, New Hampshire, New Jersey, Massachusetts, Conn ecticut, Rhode Island, Maine, North Carolina, South Carolina, Georgia, and Florida ) but within 100 miles of the Atlantic Ocean

. The last19

stratum is restricted to households in coastal states as residents in these areas are more likely to be
familiar with coastal estuar ine issues and may be aware of the relationship between the Bay and coastal
fisheries.
Households will be selected randomly from the U.S. Postal Service Delivery Sequence File (DSF), which covers over 97% of residences in the U.S. The survey households that will be sampled from the DSF include city

‐ style addresses and PO boxes, and covers single

‐ unit, multi

‐ unit, and other types of housing structures

. As described in Part B of this Supporting Statement, we assume that 92% of
the addresses wil l be eligible and will receive the survey

. EPA will send a copy of the mail survey to a random stratified sample of 9, 934 households in two phases

. The first phase, a pretest , will be sent to 543 addresses, resulting in 500 households receiving the surve y . The second phase, encompassing full survey administration, will be administered to an additional 9,391 addresses, resulting in 8 , 640
households receiving the survey . In each phase, we anticipate a response rate of 30 pe rcent, resulting in
150 and 2, 59 2 completed surveys, respectively.
Table A 1 shows the stratification design for the geographic regions included in this study

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

Table A1: Geographic Stratification Design

Region States Included Phase 1: P retest Phase 2: Full Survey Sample Size a Percentage

of Sample Sample

Size a Percentage

of Sample Chesapeake Bay MD, VA, DC 90 60 % 8 6 4 33%

Chesapeake Bay W atershed DE, NY, PA, WV 3 0 20 % 8 6 4 33%

Additional East Coast states CT, FL, GA, MA, ME, NC, NH, NJ, RI, SC, VT 3 0 20 % 8 6 4 33%

Total for Sample Regions 150 100% 2, 59 2 100%

a Sample sizes presented in this table include total expected completed surveys.20

150 responses are expected in the pretest and households will receive one of the three scenarios
described above. The p retest will be divided across regional samples as described in Table A1. An
additional 2, 59 2 responses are expected upon administratio n of the full survey for a total of 2, 74 2

responses. 2,592 responses to the full survey are required to estimate the main effects under an experimental design model. Part B of this document provides detail on sampling methodology. 4(b) Information Reque sted ( i ) Data items, including recordkeeping requirements EPA developed the survey based on the findings of a series of ten focus groups and 72 cognitive interviews conducted as part of survey instrument development (OMB control # 2090

- 0028 ). Focus
groups provided valuable feedback which allowed EPA to iteratively edit and refine the questionnaire, and eliminate or improve imprecise, confusing
, and redundant questions. In addition, later focus groups and cognitive interviews provided useful information on the approximate amount of time needed to complete the survey instrument. This information informed our burden estimates. Focus groups and cognitive interviews were conducted following standard approaches in the literature, as outlined by Desvousges et a l. (1984), Desvousges and Smith (1988), Johnston et al. (1995), Schkade and Payne (1994), Kaplowicz et al. (2004), and Opaluch et al. (1993). EPA has determined that all questions in the survey are necessary to achieve the goal of this
information collec tion, i.e., to collect data that can be used to support an analysis of the total benefits of Chesapeake Bay TMDLs implementation. Households randomly selected from the U.S. Postal Service DSF database will be mailed a copy of the survey. The total number o f households recruited from each stratum will be equally divided among three different versions of the survey. Each version of the survey presents a different baseline represent ation of ecological and water quality conditions in the Chesapeake Bay W atersh ed , which is
then used in the choice experiment (e.g., attributes). Evaluating a policy that is expected to be fully implemented more than 10 years from now raises some challenges in specifying baseline conditions. There is uncertainty regarding how popu lation growth and changes in land use patterns will affect water quality. There is also the question of what practices that are not in place now would be in place in 2025 if the TMDL were not implemented. Therefore, EPA is stratifying its sample design to reflect three possible scenarios representing future conditions without TMDLs implementation: (1) water quality
remains the same in the future as it does today (constant baseline) (2) water quality in the Bay would
decline in the future , relative to today (declining baseline), and (3) water quality in the Bay would21

improve in the future, relative to today (improving baseline)

. Given that there will be some uncertainty regarding the specifics of the “actual” baselines and improvements, the resulting valuati on estimates will allow flexibility in estimating WTP for a wide range of different circumstances. It also increases the potential that the results of the study can be used in other studies as part of a benefit transfer exercise,
consistent with EPA’s Gui delines for Preparing Economic Analyses (US EPA 2010). The full texts of Versions A (constant baseline), B (declining baseline), and C (improving baseline) of the mail survey are provided in Attachments 1 , 2, and 3.
There are no standard protocol s for defining the effects of ecological changes in stated preference surveys. However, recent analyses distinguish between “inputs” (environmental features or conditions that, via natural process are converted into different features) and “endpoints,” or biophysical outputs that are more “directly and economically meaningful” to firms and households (Boyd and Krupnick, 2009). This survey defines effects as outcomes . Changes in the Chesapeake Bay that result
from actions taken under the TMDLs are charact erized by expected changes in the populations of striped bass, blue crabs, and oysters, as well as changes in the clarity of Chesapeake Bay waters. Changes in lakes and reservoirs in the Chesapeake Bay W atershed are characterized by average algae levels, with lakes being classified as either “low” or “high” algae lakes. These attributes were found to be salient and meaningful to respondents in focus groups and interviews, and can be estimated from available
models. The following is an outline of the maj or sections of the survey. The survey versions are identical except for the baseline information provided.
Familiarity with the Chesapeake Bay Watershed and Watershed Issues

. This section provides a map and narrative description of the Chesapeake Bay and watershed and distinguishes between the Chesapeake Bay itself and lakes in the watershed. Question 1 asks respondents whether they have heard of the Chesapeake Bay prior to the survey. Question 2 asks how often respondents see the C hesapeake Bay and lakes in the w atershed, and question 3 asks respondents whether they have visited these water bodies to participate in recreational activities. This section also provides responden ts information about nutrient and sediment pollution in the Chesapeake Bay, thereby promoting understanding of the context for subsequent stated preference questions. Question 4 checks the respondent’s prior knowledge about the effect of these pollutants o n water quality. Information and question s in this section are designed to elicit the respondent’s familiarity with the Chesapeake Bay and watershed lakes and to identify respondents as recreational user s , other users, or non

- user s of the Chesapeake Bay and watershed lakes

.22

This section begins to prepare respondents to answer the stated preference questions by motivating respondents to consider how they use or do not use the Chesapeake Bay watershed

. R esponses to questions 1 throug h 4 can also be used to test if certain respondent characteristics influence response s to the referendum questions.
Current and Future Conditions in the Chesapeake Bay and Watershed Lakes

. The survey next provides information about how nutrients and sedim ents affect water clarity and populations of striped bass, blue crab, and oysters in the Chesapeake Bay. This information includes a chart with current levels
for these attributes and baseline levels predicted for the year 2025 under current programs to ma nage nutrients and sediment (i.e., if no further action is taken). This section also describes how different amounts of nutrients entering lakes can affect algae growth and therefore the appearance of these lakes and the types of fish those conditions fav or. The information is based on Carlson’s Trophic State Index (Carlson 1977; Carlson and Simpson 1996). Lakes are characterized as having algae levels that are either “low” or “high,” and a reduction in nutrients is associated with a larger percentage be ing in the low category, and information is provided about the number of lakes in the “low” category today and in the year 2025 under current programs. Respondents are asked in question 5 how predicted conditions for the Bay and w atershed l akes compare to their own expectations. This i nformation and associated questions are designed to promote understanding of the water quality attributes used in subsequent choice experiments . This section encourages respondents to consider the meaning of each at tribute and consider their general preferences for these qualities
, prior to considering specific policy options.

Additional Pollution Reduction Programs for the Chesapeake Bay Watershed

. This section informs respondents that federal and state agencies are considering additional programs to further reduce nutrients and sediment in the Chesapeake Bay Watershed. This information sets up the following choice experiment, putting respondents in the frame of mind that different programs will have different eff ects on the Bay. This section also describes how any additional programs would affect the respondent’s cost of living. The means by which cost of living would increase described i n the survey are not intended to reflect the true impacts of implementing the TMDL. The payment vehicle was chosen based on peer reviewed literature (e.g. Nielsen 2011 ) and focus group testing. Nor are the amounts of the hypothetical cost of living incre ases presented with each option intended to reflect the true household costs of the
TMDL. R espondents are reminded that there are other items on which their household could spend their income, rather than spending these funds to pay for changes required u nder pollution reduction

programs. This approach is commonly used in stated preference surveys ( see Mitchell and Carson 198 9 )23

to remind respondents that, given scarce household budgets, paying for pollution reduction programs reduces their ability to purc hase substitute goods. Question 6 asks respondents if they currently pay any environmentally

- related taxes or fees as part of their utility bill. This was found to be relevant in focus groups and the response can be used as a control variable in the sta tistical analysis.
Deciding Future Actions

. This section provides instructions and an example of how one responds to the choice questions. This section also provides text encouraging respondents to only consider the environmental outcomes presented in th e choice questions, thus minimizing the potential consideration of omitted variables, and reducing omitted variable bias. This page prepares the respondents for the choice questions that follow. It includes “cheap talk” text (e.g., Cummings and Taylor, 1999) emphasizing that even though this exercise is hypothetical, respondents should consider their choices as if they were real. Additional text is included to further emphasize the consequentiality of one’s choices. No questions are asked in this section

. Voting for Programs to Improve the Condition of the Chesapeake Bay and Lakes in the
Watershed. The questions in this section are the key part of the survey. Questions 7 , 8 , and 9 are “c hoice experiment” or “choice modeling” questions (Adamowicz et al. 1998; Bennett and Blamey 2001) that ask respondents how they would vote if presented with three hypothetical regulatory options

. One of these options is always a “status quo” choice (labeled “Option A” in the survey)

. Each of the
multi

- attribute options is characterized by (a) feet of average visibility in the Chesapeake Bay , (b) striped bass population
, (c) blue crab population, (d) oyster population, (e) number of watershed lakes
with relatively low algae, and (f) a permanent cost of living increase for the respondent’s household.
Environmental a ttribute levels for all three scenarios are described both in numerical terms and in percentage increase or decrease from present day conditions. The cost attribute is defined in terms of both annual cost and monthly cost. In the “declining baseline” versions of the survey, the “status quo” option references attribute levels that are lower than the present day. In the “constant baseline” versions
of the survey, the “status quo” option references attribute levels that are the same as present day ( i.e. , 0% change). In the improving baseline version of the survey, the “status quo” option references attribute levels that are higher than the present day.
Following standard choice experiment methods, respondents choose the regulatory options that they prefer based on their preferences. The status quo option is always available, something that is necessary for appropriate welfare estimation (Adamowicz et al. 1998). Advantages of choice24

experiments, and the many examples of the use of such approaches in the literature, are discussed in later sections of this ICR. Following standard approaches (Opaluch et al. 1993, 1999; Johnston et al. 2002a; 2002b, 2003b ), respondents are instructed to answer each of the three choice questions independently, and not to add up or compare programs across different pages. This is included to avoid biases associated with sequence aggregation effects (Mitchell and Carson 1989 ).
Debriefing Questions

. Questions 10 and 11 ask respondents to rate how their votes were affected by various factors, and why they voted for or against the regulatory programs. Question 10 asks respondents the extent to which they agree or disagree with a series of statements that may describe how they considered the information provided on the programs, confidence in their answers, and attitudes toward regulation and the environment. Question 1 1 asks respondents about potential reasons they might
have v oted for or against regulator programs. Consistent with established practice, the responses to these questions will be used to examine and control known survey issues, including hypothetical bias,

yea

- saying, scenario rejection, and protest responses. Th ey also provide insight into motivations for voting for or against programs.
Recreational Experience and T ime P references. Questions12, 13, and 14 ask about recreational experiences on the Chesapeake Bay and at other water bodies within the watershed. Q uestion 12 asks respondents how many times they have visited an outdoor recreation site on the Chesapeake Bay. Question 13 asks them the name of the site they have visited most often in the last 12 months, its location, how long it took to get there, and what kind of recreation they engaged in at the site. Question 14 asks about the number of visits to lakes, streams, or rivers within the Chesapeake Bay Watershed. These questions will be used to more clearly identify users of the Chesapeake Bay and othe r water bodies and to inform estimation of revealed preferences for recreation. In order to get information on discount rates, question 15 asks whether respondents would make a one

- time payment for a device that
saved them a fixed amount each month over a n extended time period. The one

- time payment will vary across surveys at values of $20, $50, $100, and $250.
Demographics. Questions 16

- 23 ask respondents to provide basic demographic information, including

sex, age, household composition (number of chil dren under 18)
, income, race and ethnicity, highest
level of education
, , and employment industry . This information will be used in the analysis of survey results, as well as in the non

- response analysis. Responses to these questions will be used to esti mate the25

influence of demographic variables on respondents’ voting choices, and ultimately, their WTP for water quality improvements.

The non

- response follow

- up survey will be administered to households who received the main survey but did not return it. The following is an outline of the major sections of the non

- response follow

- up
survey , each of which is very similar to the corresponding sections of the main survey d escribed above. Questions in this survey are also asked in the main survey in order to facilitate comparison of respondents and non

- respondents to the main survey. Familiarity with the Chesapeake Bay Watershed and Watershed Issues

. This section provides a map and narrative description of the Chesapeake Bay and watershed and distinguishes between the Chesapeake Bay itself and lakes in the watershed. The maps and information are identical to the main

survey , but the non

- response follow

- up survey does not p rovide information on nutrient and sediment pollution

. Questions 1 through four are identical to the main survey: Question 1 asks respondents whether they have heard of the Chesapeake Bay prior to the survey. Question 2 asks how often respondents see the C hesapeake Bay and lakes in the w atershed . Q uestion 3 asks respondents whether they have visited these water bodies to participate in recreational activities. Question 4 checks the respondent’s prior knowledge about the effect of thes e pollutants on wate r quality.

Attitudinal and Demographic Questions . There is only a single four

- part question in this section, question 5, that asks the extent to which the respondents agree or disagree with statements regarding the importance of improving waters in the Chesapeake Bay Watershed, the general role of government regulations and spending, whether they feel they should pay for programs to improve water quality, and the time they generally have available for surveys. These are all also asked on the main survey

.

Demographics. Questions 6

- 1 3 ask respondents to provide basic demographic information, including sex, age, household composition (number of children under 18), income, race and ethnicity, highest level of education, , and employment industry. This i nformation is identical to the demographic questions asked in the main survey.26

( ii ) Respondent activities EPA expects individuals to engage in the following activities during their participation in the valuation survey: Review the background information provided in the beginning of the survey document. Complete the survey questionnaire and return it by mail. A typical subject participating in the mail survey is expected to take 18 minutes to complete the survey.
These estimates are derived from focus groups and cognitive interviews in which respondents were asked to complete a survey of similar length and detail to the current survey.

Respondents engaged in the non

- response survey will: Review the short questionnaire provided via priority mail and return the completed survey by
mail. Response time for the non

- response survey is expected to be approximately 5 minutes. These estimates are based on agency experience with a similar survey.

5. The Information Collected

- Agency Activities, Collection Methodology, and Information Management 5(a) Agency Activities

The survey is being developed, conducted, and analyzed by EPA’s National Center of
Environment Economics with contract support provided by Abt Associates Inc. ( EPA contract No. EP

W

- 11

- 003 ). Agency activities associated with the survey consist of the following: Developing the survey questionnaire and related materials as well as sampling design. Randomly selecting survey participants from the U.S. Postal Servic e DSF database

. Printing of survey. Mailing of preview letter to notify the household that it has been selected. Mailing of surveys. Mailing of postcard reminders. Resending the survey to households not responding to the first survey mailing.27

Mailing the follow

- up letter reminding households to complete the second survey mailing. Conducting a f

ollow

- up study of non

- respondents to the mail survey using a priority mailing to reach non

- respondents. Data entry and cleaning. Analyzing survey results. Analyzin g the non

- response study results

. If necessary, EPA will use results of the non

- response study to adjust weights of respondents to
account for non

- response and minimize the bias. EPA will primarily use the survey results to estimate the social value of cha nges in ecosystem quality , as part of the Agency’s analysis of the benefits of the TMDLs

.

5(b) Collection Methodology and Information Management EPA plans to implement the proposed survey as a mail ed choice experiment questionnaire. First,
EPA will use the U.S. Postal Service DSF database to identify households which will receive the mail questionnaire. Prior to mailing the survey, EPA will send the selected households a preview letter notifying them that they have been selected to participate in the s urvey and briefly describing the purpose of the study. The survey will be mailed one to two weeks after the preview letter accompanied by a cover letter explaining the purpose of the survey. The preview and cover letters are included as
Attachments 5 and 6 , respectively

. EPA will take multiple steps to pr omote response. All households will receive a reminder postcard approximately one week after the initial questionnaire mailing. The postcard reminder is
included as Attachment 7 . Approximately three weeks after the first round of survey mailing, all households that have not responded will receive a second copy of the questionnaire with a revised cover letter (see Attachment 8 ). A week after the second survey is mailed, a letter will be sent to remind
house holds to complete the survey. The letter reminder is included as Attachment 9

. Based on this approach to mail data collection, it is anticipated that approximately 30 percent of the selected households will return the completed mail survey (Dillman 2008)

. Since the desired number of completed surveys is 2,592 , it will be necessary to mail surveys to 9,388 households Data quality will be monitored by checking submitted surveys for completeness and consistency, and by asking respondents to assess their ow n responses to the survey. Question 1 0 includes an inquiry of whether respondents were confident in their answers. Questions 1 0 and 11 are designed to assess the28

presence or absence of potential response biases by asking respondents to indicate their reas oning and rate the influence of various factors on their responses to the choice experiment questions. Responses to the survey will be stored in an electronic database. This database will be used to generate a data set for a regression model of total val ues for ecosystem improvements achieved under the Chesapeake Bay
TMDLs

. To protect the confidentiality of survey respondents, the survey data will be released only after it has been thoroughly vetted to ensure that all potentially identifying information h as been removed.

5(c) Small Entity Flexibility

This survey will be administered to individuals, not businesses. Thus, no small entities will be affected by this information collection.

5(d) Collection Schedule The schedule for implementation of the s urvey is shown in Table A2.29

Table A2: Schedule for Survey Implementation

Pretest Activit ies Duration of Each Activity Printing of questionnaires Weeks 1 to 3 Mailing of Preview Letters Week 4
Mailing of survey Week 5
Postcard reminder (one week after initial survey mailing) Week 6 Initial Data Entry Week7 Mailing of 2 nd survey to non

- respondents Week 9
Letter reminder (one week after 2 nd survey mailing) Week 10 Telephone non

- response interviews (if used) Weeks 12 to 13

P riority mail non

- response survey Week 12 Data entry Weeks 5 to 15 Cleaning of data file Week 16

Deli very of data Week 17

Full Survey Implementation Printing of questionnaires Weeks 22 to 24 Mailing of Preview Letters Week 25 Mailing of survey Week 26
Postcard reminder (one week after initial survey mailing) Week 27 Initial Data Entry Week28 Mailing of 2 nd survey to non

- respondents Week 29
Letter reminder (one week after 2 nd survey mailing) Week 30 Telephone non

- response interviews (if used) Weeks 32 to 33

P riority mail non

- response survey Week 32
Data entry Weeks 26 to 36 Cleaning of data file Week 37

Deli very of data Week 3830

6. Estimating Respondent Burden and Cost of Collection 6(a) Estimating Respondent Burden

Subjects who participate in the survey and follow

- up interviews during the pre

- test and main

surveys will expend time on several activities. EPA will use similar materials in both the pre

- test and main stages; it is reasonable to assume the average burden per respondent activity will therefore be the same for subjects participating during either pre

- test or main survey stages. Both the pre

- test and main survey stages will include the survey questionnaire and a non

- response follow

- up. Based on focus grou p s and cognitive interviews , EPA estimates that on average each respondent
mail ed the survey will spend 18 minutes (0. 3 hours) reviewing the introductory materials and completing the survey questionnaire. EPA will administer the pre

- test survey to 5 00 hous eholds; assuming that 150 respondents will complete and return the survey, the national burden estimate for respondents to the pre

- test survey is 45 hours. During the main survey stage, EPA will administer the mail survey to 8,640 households ; the national burden estimate for these survey respondents is 778 hours assuming that 2,592 respondents will complete and return the survey

. EPA plans to conduct a non

- response follow

- up s tudy (NRFU) that uses a short questionnaire sent by priority mailin g. Th is questionnaire is included as Attachment 11 . The sample for the priority mail NRFU will consist of households that did not respond to the survey questionnaire. It is anticipated that approximately 20 percent of households in the NRFU sample will ret urn the completed NRFU survey. EPA plans to complet e 50 total NRFU surveys during pre

- testing, and 720 total NRFU surveys during main imp lementation. Based on expected response rates, it will be necessary to invite approximately 250 households to participa te in the pre

- test NRFU study, and 3,600 households to participate in the main NRFU study.
EPA estimates that the short questionnaire will take 5 minutes to complete (0.08 3 hours) . Thus
the national burden estimate for all NRFU pre

- test respondents ( 5 0 pa rticipants) is 4.2 hours

. Using the

same average burden per respondent of 5 minutes
, t he national burden estimate for NRFU respondents in the main survey implementation stage is 60 hours for the anticipated 720 total NRFU participants.
These burden estimates reflect a one

- time expenditure in a single year.31

6(b) Estimating Respondent Costs ( i ) Estimating Labor Costs According to the Bureau of Labor Statistics, the average hourly wage for private sector workers in the United States is $ 23.33 (20 12 $) (U.S. Department of Labor
, 2012 ). Assuming an average per

respondent burden of 0. 3 hours ( 18 minutes) for individuals mail ed the survey and an average hourly wage of $ 23.33 , the average cost per respondent is $ 7.00

. Of the 9,140 individuals receiving the m ail

survey during either pre

- test or main implementation
, 2,742 are expected to return their completed

survey. The total cost for all individuals that return surveys would be $ 19,192

. Assuming an average per

- respondent burden of 0. 08 3 hours (5 minutes) for each non

- response study participant and an average hourly wage of $ 23.33 , the average cost per non

- response study participant is $1.94

. Of the 3,850 individuals receiving the non

- response follow up mailing during
either pre

- test or main implementation
, 770 are expected to return their completed survey. The total

cost for all participants in the two non

- response study phases would be $ 1,4 91

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

6(c) Estimating Agency Burden and Costs

Agency costs arise from staff costs, contractor costs, and printing costs. EPA staff ha ve expended 2, 2 00 hours developing and testing the survey instrument to date and is expected to spend and
additional 2,0 00 hours printing the survey instrument, analyzing data, writing reports, reviewing intermediate products, writing reports and managing the project more generally . The total EPA staff

costs are shown in Table A3 below and total $ 368,379

.

Table A3: Agency Burden Hours and Costs GS Level Hours Hourly Rate Hourly Rate with Benefits Total 12 700 $ 40.66 $ 65.06 $ 45,539 13 1400 $ 48.35 $ 77.36 $ 108,304 14 700 $ 57.13 $ 91.41 $ 63,986 15 1400 $ 67.21 $ 107.54 $ 150,550

-- 4,400

-- -- $ 368,379

Abt Associates will be providing contractor support for this project with funding of $490,955
from EPA contract EP

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- 003 , which provides funds for the purpose of analyzing economic benefits of the Chesapeake Bay TMDLs

. Abt Associates Inc. staff and its consultants are expected to32

spend 2,434 hours pre

- testing the survey questionnaire and sampling methodology, conducting the mail

survey, conducting the non

- response survey, and tabulating and analyzing the survey results. The cost of
this contractor time i s $ 490,955

. Agency and contractor burden is 6, 834 hours, with a total cost of $ 859,334 excluding the costs of survey printing

. P rinting and mailing of the survey is expected to take 133 hours and cost $ 8 4 , 7 2 4 . Thus, the total Agency and contractor burden would be 6,9 67 hours and would cost $ 944 , 058

.

6(d) Respondent Universe and Total Burden Costs

EPA expects the total cost for survey respondents to be $ 2 0,68 2 (2012$), based on a total burden
estimate of 88 7 (across both pre

- test and main stages, each with primary survey administration and non

response follow up surveys) at an hourly wage of $ 23.33

.

6(e) Bottom Line Burden Hours and Costs

The following table s present EPA’s estimate of the total burden a nd costs of this information collection for the respondents and for the Agency. The bottom line burden for these two together is $9 61,803

.

Table A3: Total Estimated Bottom Line Burden and Cost Summary for Respondents

Affected Individuals Burden (hours) Cost (2012$)
Pre

- test Survey Respondents 45 $ 1,050

Pre

- test NRFU Respondents 4 $ 9 7
Main Survey Respondents 778 $ 18,141
Main NRFU Respondents 60 $ 1,3 9 4
Total for All Survey Respondents 88 7 $ 2 0 , 68 233

Table A 5 : Total Estimated Burden and Cost Summary for Agency

Affected Individuals Burden (hours) Cost (2012$)
EPA Staff 4, 4 00 $ 36 8,379 Survey Printing 133 $ 8 4 ,72 4
EPA's Contractors for the Mail Survey 2,434 $ 490,955

Total Agency Burden and Cost 6 , 9 67 $9 4 4 , 058

6(f) Reasons for Change in Burden

This is a new collection. The survey is a one

- time data collection activity.

6(g) Burden Statement

EPA estimates that the public reporting and record keeping burden associated with the mail survey will average 0. 3 hours per respondent (i.e., a total of 823 hours of burden divided among 150 pre

test respondents and 2,592 main survey respondents). Households included in the non

- response study are expected to average 0.08 3 hours per mail survey participant (i.e., a total of 6 4 hours of burden divided among 5 0 pre

- test NRFU participants and 720 main NRFU participants). This results in a total
burden estimate of 88 7 hours across the pre

- test and main mail surveys and non

- response studies

. Burden means the total time, effort, or financial resources expended by persons to generate, maintain, retain, or disclose or provide information to or for a Federal agency. This includes the time needed to review instructions; develop, acquire, install, and utilize technology and systems for the purposes of collecting, valida ting, and verifying information, processing and maintaining information, and disclosing and providing information; adjust the existing ways to comply with any previously applicable instructions and requirements; train personnel to be able to respond to a c ollection of information; search data sources; complete and review the collection of information; and transmit or otherwise disclose the information. An agency may not conduct or sponsor, and a person is not required to respond to, a34

collection of informat ion unless it displays a currently valid OMB control number. The OMB control numbers for EPA's regulations are listed in 40 CFR part 9 and 48 CFR chapter 15. To comment on the Agency's need for this information, the accuracy of the provided burden
estimat es, and any suggested methods for minimizing respondent burden, including the use of automated collection techniques, EPA has established a public docket for this ICR under Docket ID No. EPA

- HQ

OA

- 2012

- 0033 , which is available for online viewing at www.reg ulations.gov, or in person viewing at the Office of Water Docket in the EPA Docket Center (EPA/DC), EPA West, Room 3334, 1301 Constitution Ave., NW, Washington, DC. The EPA/DC Public Reading Room is open from 8:30 a.m. to 4:30 p.m., Monday through Friday, excluding legal holidays. The telephone number for the Reading
Room is 202

- 566

- 1744, and the telephone number for the Office of the Administrator Docket is 202

- 566

- 1752. Use www.regulations.gov to obtain a copy of the draft collection of information, subm it or view public comments, access the index listing of the contents of the docket, and to access those documents in the public docket that are available electronically. Once in the system, select “search,” then key in the docket ID number, EPA

- HQ

- OA

- 2012

- 003335 PART B OF THE SUPPORTING STATEMENT

1. Survey Objectives, Key Variables, and Other Preliminaries 1(a) Survey Objectives

The overall goal of this survey is to examine the total value of benefits (including non

- use values) for improvements in water quality in the Chesapeake Bay and its Watershed. Water quality improvements are expected to follow nitrogen, phosphorous, and sediment load reductions set forth in recent Chesapeake Bay Total Maximum Daily Load (TMDL) requirements. EPA has designed t he survey to provide data to support the following specific objectives: To estimate the total values, including non

- use values that individuals place on improving water quality in the Chesapeake Bay and lakes in the Watershed.
To understand how individuals value improvements in the Chesapeake Bay and lakes in the Watershed, including: water clarity; populations of striped bass, blue crab, and oysters; and lake
conditions. To understand how the above values depend on the future baseline level of water qualit y in the Chesapeake Bay and its Watershed. To understand how values vary with respect to individuals’ attitudes, awareness, and demographic characteristics. Understanding total public values for water quality improvements is necessary to determine the full range of benefits associated with reductions in nutrient (nitrogen and phosphorous) and sediment loads to the Chesapeake Bay. While direct use values can be estimated using a variety of methods, non

- use values can only be assessed via stated preference s urvey methods. Because non

- use values may be substantial, failure to recognize such values may lead to improper inferences regarding policy benefits (Freeman 2003).

1(b) Key Variables

The key questions in the survey ask respondents whether or not they would vote for policies that would result in improvements in water quality in the Chesapeake Bay and lakes in the Watershed in36

exchange for an increase in their cost of living. The choice experiment framework allows respondents to view pairs of multi

- attri bute policies associated with total maximum daily loads to the Chesapeake Bay. Respondents are asked to choose one of three options. Two of these options correspond to “additional programs” that yield improvements in some or all of the environmental attrib utes specified, and the third option is the status quo (i.e., maintain current programs with no additional household costs). The survey design follows well

- established choice experiment methodology and format (Adamowicz et al. 1998; Louviere et al. 2000; Bennett and Blamey 2001; Bateman et al. 2002). The survey focuses on environmental and ecological “endpoints.” In other words, it asks respondents about changes in attributes that directly enter into their household production and utility function. Specifi cally, the survey presents changes in the following attributes: (a) water clarity, (b) striped bass population, (c) blue crab population, (d) oyster abundance, (e) conditions of freshwater lakes in the Watershed, and (e) the cost of living. As discussed by Boyd and Krupnick (2009), these endpoints are aspects of the environment that people experience, make choices about, and have a tangible meaning. The study design includes three treatment levels in which environmental conditions without
additional actio n are either declining (i.e., “declining baseline”), unchanged (i.e., “constant baseline”), or improving (“improving baseline”), relative to the conditions today. As discussed in Part A of this ICR, section 4(b) (i), the three survey versions are included because t here is uncertainty regarding how population growth and changes in land use patterns will affect water quality in the future . There is also the question of what practices that are not in place now would be in place in 2025 if the TMDL s were not i mplemented. . Given that there will be some uncertainty regarding the specifics of the “actual” baselines and improvements, the resulting valuation estimates will allow flexibility in estimating WTP for a range of different circumstances. It also increas es the potential that the results of the study can be used in other studies as part of a benefit transfer exercise, consistent with EPA’s Guidelines for Preparing Economic Analyses (US EPA 2010). The three versions of the survey are included in this
docum ent as Attachments 1 through 3. While EPA intends to administer all three baseline versions of the survey in all three geographic strata, budget considerations may cause that plan to be scaled back so that not all versions are
administered in all strata.
T he analysis of choice questions will use data on how the respondent votes, the amount of the cost of living increase, and the degree of improvement in the environmental attributes, to estimate values37

for changes in those attributes. Variables for socio

- ec onomic characteristics and attitudes will also be included in the analysis.

1(c) Statistical Approach

A statistical survey approach in which a randomly drawn sample of households is asked to complete the survey is appropriate for estimating the use and non

- use values associated with the Chesapeake Bay and its Watershed. A census approach is impractical because of the extraordinary cos t of contacting all households. The relevant population includes households not only residing near the Chesapeake Bay, but more distant states on the east coast. Specifically, the sample population includes households in the Bay states (MD, VA, DC), Wate rshed states (DE, NY, PA, WV) and other East Coast states (CT, FL, GA, MA, ME, NC, NH, NJ, RI, SC, VT). An alternative approach, where individuals
self

- select into the sample, is not sufficiently rigorous to provide a useful estimate of the total value of water quality and habitat improvements. Therefore the statistical survey is the most reasonable approach to estimate the total value of the Chesapeake Bay TMDLs. Much of the work in developing the survey instrument was conducted by the EPA, and EPA will also directly conduct much of the analysis of the survey results. EPA has retained Abt Associates Inc. (55 Wheeler Street, Cambridge, MA 02138) under EPA contract EP

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- 003 to assist in the questionnaire design, sampling design, administration of the su rvey, and analysis of the survey results.

1( d ) Feasibility

Following standard practice in the stated preference literature (Adamowicz et al. 1998; Batemen, et al, 2002; Bennett and Blamey 2001; Johnston et al. 1995; Louviere et al. 2000), EPA conducted a series of 10 focus groups and an initial set of 26 cognitive interviews (OMB control # 2090

- 0028). Based on findings from these activities, EPA made various improvements to the survey instrument to reduce the potential for respondent bias, reduce respon dent cognitive burden, and increase respondent comprehension of the survey materials. In addition, EPA solicited peer review of the survey instruments by three specialists in academia, as well as input from other experts (see section 3c in Part A).
Recomme ndations and comments received as part of that process have been incorporated into the design38

of the survey instrument and the revised survey was subsequently tested in an additional 46 cognitive
interviews. Because of the steps taken during the survey dev elopment process, EPA does not anticipate that respondents will have difficulty interpreting or responding to any of the survey questions. Furthermore, since the survey will be administered as a mail survey, it will be easily accessible to all respondents. EPA therefore believes that respondents will not face any obstacles in completing the survey, and that the survey will produce useful results. EPA has dedicated sufficient staff time and resources to the design and implementation of this survey, including funding for contractor assistance under EPA
contract No. EP

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- 003. Given the timetable outlined in Section A 5(d) of this document, the survey
results should be available for timely use in the final benefits analysis for the Chesapeake Bay TMDLs

.

2. Survey Design 2(a) Target Population and Coverage

To assess both use

- and non

- use values for improvements in Chesapeake Bay water quality, the target population is individuals who are 18 years of age or older and reside in the District of Columbia

or one of 17 east coast U.S. states: Maryland, Virginia, Delaware, New Jersey, New York, Pennsylvania, West Virginia, Vermont, New Hampshire, Massachusetts, Connecticut, Rhode Island, Maine, North Carolina, South Carolina, Georgia, or Florida. These were chosen b ased on their immediate proximity to the Bay (Maryland, Virginia, District of Columbia) and/or lakes, streams and rivers in its Watershed (Delaware, New York, Pennsylvania, West Virginia). Households in these areas are more likely to hold “use” values for improvements to the Chesapeake Bay and its Watershed than those farther away. The remaining states (i.e., Vermont, New Hampshire, New Jersey, Massachusetts, Connecticut, Rhode Island, Maine, North Carolina, South Carolina, Georgia, and Florida) lie within 100 miles of the Atlantic Ocean. Residents of these states are more likely to be familiar with estuarine issues. At the same time, the greater distance between these states and the Chesapeake Bay will improve the survey’s ability to capture and isolate n on

- use values.39

2(b) Sampling Design ( i ) Sampling Frame

The sampling frame for this survey is the Unite d Sta t es Postal Service Computerized Delivery Sequence File (DSF), the standard frame for address

- based sampling (Iannacchione, 2011; Link et al, 2008)

. The DSF is a non

- duplicative list of residential addresses where U.S. postal workers deliver mail; it includes city

- style addresses and P.O. boxes, and covers sin gle

- unit, multi

- unit, and other types of housing structures with known business excluded. In total the DSF is estimated to cover 97% of residences in the U.S., with coverage gradually increasing over the last few years as rural addresses are being convert ed to city

- style, 911

- compatible addresses 3 . The universe of sample units is defined as this set of residential addresses, and hence is capable of reaching all individuals who are 18 years of age or older living at a residential address in the 17 target st ates and the District of Columbia. Samples from DSF are taken indirectly, as USPS cannot sell mailing addresses or otherwise provide access to DSF. Instead, a number of sample vendors maintain their own copies of the DSF, and through verifying them
with U SPS, update the list quarterly. The sample vendors can also augment the mailing addresses with additional information (household demographics, landline phone numbers, etc.) from external sources. For discussion of techniques that EPA will use to minimize n on

- response and other non

sampling errors in the survey sample, refer to Section 2(b)(II), below.

( ii ) Sample Sizes

The target responding sample size for the survey is 2,592 completed household surveys. This sample size was chosen to provide statistica lly robust regression modeling while minimizing the cost and burden of the survey. Given this sample size, the level of precision (see section 2(c)) achieved by the analysis will be more than adequate to meet the analytic needs of the benefits analysis fo r the Chesapeake Bay TMDLs. For further discussion of the level of precision required by this analysis, see Section 2(c)( i ) below. The sample design features three geographic strata based upon proximity to the Chesapeake Bay and its Watershed: Bay State s, Watershed States, and East Coast States. Relative to the geographic distribution of households across the East Coast, EPA plans to over

- sample households in states adjacent

3 For example, in rural areas, Rural Route box addresses have been converted to physical street addresses.40

to Chesapeake Bay and within the Chesapeake Bay Watershed. EPA believes this a pproach is appropriate because households in these areas will incur the costs of Chesapeake Bay water quality improvements, and therefore this group is most likely to receive use

- value benefits from the improvements. Within each survey region, the househol d sample will be allocated in proportion to the geographic distribution of households within states in the three regions. The target number of responding households in each region and state is given below (Table B1). For discussion of the required sampl e size by state, please refer to Error! Reference source not found.

.

Table B1. Total Households and Expected Number of Completed Surveys for Each Study Regi on. Sampling Stratum and State Total Number of Households Expected Number of

Completed Surveys Bay States Stratum, total 5,479,176 864 District of Columbia 266,707 42 Maryland 2,156,411 340 Virginia 3,056,058 482 Watershed States Stratum, total 13,442,787 864 Delaware 342,297 22 New York 7,317,755 470 Pennsylvania 5,018,904 323 West Virginia 763,831 49 Other East Coast States Stratum, total 25,431,478 864 Connecticut 1,371,087 47 Florida 7,420,802 252 Georgia 3,585,584 122 Maine 557,219 19 Massachusetts 2,547,075 87 New Hampshire 518,973 17 New Jersey 3 , 214 , 360 109 North Carolina 3,745,155 127 Rhode Island 413,600 14 South Carolina 1,801,181 61 Vermont 256,442 9 Total 44,353,441 2,592 Source: U.S. Census Bureau (2012). 2010 Census Summary File 1. Retrieved May 31, 2012 from http://factfinder2.census.gov/

.41

( iii ) Stratification Variables

The population of households in the eastern United States is stratified by the geographic boundaries of three study regions in Table B1: states adjacent to Chesapeake Bay (“ Bay States ”), states which contain the Chesapeake Bay Watershed (“Watershed States”), and additional East Coast states ( “East Coast St ates ”). Bay States include MD, VA, and DC; Watershed States include DE, NY, PA, and
WV ; and East Coast States include VT, NH, NJ, MA, CT, RI, ME, NC, SC, GA, and FL. The sample will be allocated in equal proportions of 33% for each stratum, thus leadin g to the highest sampling rate in the Bay State stratum, and the lowest sampling rate in the Other East Coast States stratum. The expected number of completed interviews in each stratum is presented in Table B2. This allocation is designed to minimize the variance of the main effects under an experimental design
model as introduced in Section 4(a) of Part A . As a result of stratification, the analysis will produce estimates of the geographic distribution of values for the Chesapeake Bay water quality improvements with greater precision.

( iv ) Sampling Method

Using the stratification design discussed above, sample households will be randomly selected
from the U.S. Postal Service DSF database. Assuming 92% of the sampled addresses are eligible and
30% of eligible households will return a completed mail survey, 9,3 91 households will be sampled from
the DSF

. 4 For obtaining population

- based estimates of various parameters, each responding household will be assigned a sampling weight . The weights will be used to produce estimates that: are generalizable to the population from which the sample was selected; account for differential probabilities of selection across the sampling strata; match the population distributions of selected demographic variab les within strata; and allow for adjustments to reduce potential nonresponse bias.
These weights combine:

4 A 92% eligibility rate is based on Link, et al. (2008). A 30% response rate is based on Helm (2012,), Mansfield, et al. (2012), and Johnston, et al. (2012).42

a base sampling weight which is the inverse of the probability of selection of the household;
a within

- stratum adjustment for differential non

- res ponse across strata; and a nonresponse weight.

P ost

- stratification adjustments may be made to match the sample to known population values ( e.g., from Census data). There are various models that can be used for nonresponse weighting. For example, nonresponse weights can be constructed based on estimated response propensities or on weighting class adjustments. Response propensities are designed to treat nonresponse as a stochastic process in which there are shared causes of the likelihood of nonresponse and the value of the survey variable. The weighting class approach assumes that within a weighting class (typically demographically

- defined), non

- respondents and respond ents have the same or very similar distributions on the survey variables. If this model assumption holds, then applying weights to the respondents reduces bias in the estimator that is due to

nonresponse. S everal factors, including the difference between the sample and population distributions of demographic characteristics, and the plan for how to use weights in the regression models will determine which approach is most efficient for both estimating population parameters and for the stated

preference mod eling.
To estimate total value for the quantified environmental benefits of the Chesapeake Bay TMDLs, data will be analyzed statistically using a standard random utility model framework.

( v ) Multi

- Stage Sampling

Multi

- stage sampling will not be necessary for this survey.

2(c) Precision Requirements (i ) Precision Targets

Table B2 presents expected sample sizes for each geographic stratum. The maximum acceptable sampling error for predicting response probabilities (i.e., the likelihood of choos ing a given alternative) in the present case is ±10%, assuming a true response probability of 50% associated with a utility indifference point. Given the survey population size, this level of precision requires a minimum sample size of approximately 96 ob servations. The number of observations (i.e., completed surveys) required to43

obtain large sample properties for the choice experiment design provide more than sufficient observations to obtain this required precision for population parameters. Across all regions, a sample of 2,592 households (completed surveys) will provide estimates of population percentages with a level of precision ranging from 1.1% at the 50% incidence level to 0.7% at the 10% incidence level (Table B2).

Table B2 . Sample size and accuracy projections

. Geographic
division Population size Expected sample size (completed surveys) Expected weights Standard

error, 50%
incidence Standard

error, 10%
incidence Bay States 5,479,176 864 6,341 0.017 0.010
Watershed 13,442,787 864 15,559 0.017 0.010
Other East Coast 25,431,478 864 29,434 0.017 0.010
Overall 44,353,441 2,592 17,081 0.011 0.007
Source for household population size : U.S. Census Bureau (2012). 2010 Census Summary File 1. Retrieved May 31, 2012 from http://factfinder2.census.gov/

. The margin of error is 1.96 times the standard error.

(ii ) Power analysis Power analysis in this section is performed for a one

- sample t

- test of proportions for study as a whole. The accuracy of the WTP estimates, and hence the power to detect differences in WTP, depends on the true values of the parameters of the logistic model used in WTP estimation, and hence can only be conducted post

- hoc after the parameter estimates are obtained. Given the stratified nature of the survey, the variance of a z
- test statistic comparing the null incidence p 0 with the alternative incidence p 1 is

where CV is the coefficient of variation of post

- stratified weights within a stratum is the design effect due to variable weights , or ‘DEFF’ ), n =864 is the target number of completed surveys for a given stratum, and W

h is the proportion of the popula tion in stratum h =1 (Bay States) , 2 (Watershed)
, 3 (Other East Coast) :44

Thus
, . For a given power level (e.g.. 80%), the effec t size that can be determined by solving

for p 1 . This is an extension of the standard power analysis for stratified samples.
Table B3 lists effect sizes using the most typical values for significance level ( α =5% ) and power ( β =80% ) , and for various scenarios concerning variability of weights within strata (which will be caused by differential non

- response). Across these scenarios, the margin of error on an estimate of population proportions 5 ranges from 1.72 to 2.84 percentage points at the 95% confidence level.

Table B3

. Power analysis. Effect size detectable with power 80% by a test of size 5% p 0 = 50% p 0 = 10% CV = 0.32, within

- strata DEFF due to weights = 1.1 2.61% 1.58% CV = 0.55, within

- strata DEFF due to weights = 1.3 2.84% 1.72%

( iii ) Non

- Sampling Errors

Several non

- sampling errors may be encountered in stated preference surveys. First, protest responses can occur when individuals reject the survey format or question design, even though they may value the resources being considered (Mitchell and Carson 1989). To help identify protest responses EPA has included several survey debriefing questions, e.g., whether the respondent would oppose any government program that imposes more regulation and spending (see section 4(b) (i) in Part A of this ICR for details). The use of such methods to identify protest responses is well

- established in the literature (Bateman et al. 2002). Moreover, researchers (e.g., Bateman et al. 2002) s uggest that a choice experiment format, such as that proposed here, may ameliorate such responses (as opposed to say, a contingent valuation format).
Non

- response bias is another type of non

- sampling error that can potentially occur in stated

5 i.e., Whether it is true that the target population contains 50% women (p 0 =50%), or 10% women (p 0 =10%).45

preference surveys. Non

- response bias can occur when households choose not to participate in a survey (i.e., not return the mail survey, in this case) or do not answer all relevant questions on the survey instrument. EPA has designed the survey instrument to maximiz e the response rate. EPA will also follow Dillman’s (2008) mail survey approach (see subsection 4(b) for details). If necessary, EPA will use appropriate weighting or other statistical adjustments to correct for any bias due to non

- response. To determine w hether there is any evidence of significant non

- response bias in the completed sample, EPA will conduct a non

- response follow

- up study. This will enable EPA to identify potential differences between respondents to the mail survey and those who received a q uestionnaire but did not
return it.

Non

- response Follow

- up Survey In order to ascertain if and how respondents and non

- respondents differ EPA will administer a
short non

- response follow

- up survey to a random sample of households that receive the main sur vey but do not complete and return it. The short questionnaire will ask a few awareness, attitudinal and demographic questions that can be used to statistically examine differences, if any, between respondents
and non

- respondents. It will take responde nts about 5 minutes to complete the non

- response follow

- up survey. T he short questionnaire will be implemented using priority mailing contact. The samples for the

non

- response follow up will be allocated proportionately to the number of the original maili ngs in the geographic division (state). The mailing will include $2 in cash as an unconditional incentive for completion of the short questionnaire to encourage response

. EPA expects a 20% response rate. Based on the response rate observed in the nonres ponse survey pretest study, EPA will send (via priority mail) the nonresponse survey to enough households in order to obtain the expected target sample of 720 completed nonresponse questionnaires. This sample size will enable EPA to reject the hypothesis of no difference in population percentages between respondents and non

- respondents with 80% power when there is a difference of 10.1% according to a

two

- sided statistical test at the base incidence of 50%, or a difference of 6.9% at the base incidence of
1 0%. Table B4 illustrates the target sample size of the nonresponse survey across survey regions.46 Table B4: Expected N umber of Completed Non

- response Follow

- up (NRFU) Surveys

Region Number of Households Expected to

Return Priority Mail NRFU Survey
Bay 240

Watershed 240

Other East Coast 240

Total 720

2(d) Questionnaire Design

The information requested by the survey is discussed in Section 4(b)(I) of Part A of the supporting statement. The full texts of the three draft questionnaires are provided in Error! Reference
source not found. , 2, and 3. Several categories of questions are included in the survey. The reasons for including each of these categories are discussed below: Familiarity with the Chesapeake Bay Watershed and Watershed Issues (pgs. 1 to 2)

. Responses to these questions provide information on whether respondents visited or viewed Chesapeake Bay and lakes in the Watershed. These questions will allow EPA to identify respondents who are users of the resource or non

- users. Identifying non

- users is important for estimating non

- user WTP. Additionally, respondents who have increased contact with the aquatic resources are expected to have higher values for improvements in environmental conditions (Johnston et al. 2005). The questions in this section will identify whether respondents’ recreational experiences have included the Chesapeake Bay or lakes in the Watershed or both. This section also provides respondents with some background information on nutrient and sediment pollution issues in th e Chesapeake Bay Watershed. Responses to question 4 will be used to assess respondents’ a priori knowledge about these issues, and potentially test for knowledge effects on respondent answers to the program choice questions.47

Respondents who are more famil iar with these pollutants may have different values, although the
direction of such effects is unknown a priori

. Current and Future Conditions in the Chesapeake Bay and Watershed Lakes (pgs. 3 to 4)

. In this section respondents are presented with the envi ronmental attributes that are applied in the later choice questions: bay water clarity; striped bass, blue crab, and oyster populations; and the
number of W atershed lakes with relatively low algae levels. Respondents are given the current
levels of these attributes, along with the levels predicted for 2025 under one of three baseline assumptions: declining, constant, or improving. Responses to question 5 gauge respondents’ expectations regarding future environmental condit ions in the Chesapeake Bay and W a tershed lakes (absent additional pollution control actions). Additional Pollution Programs for the Chesapeake Bay Watershed (pg 5 to 6)

. This section introduces the concept that additional pollution reduction programs are being considered. Question 6 inqu ires about respondents’ knowledge of whether they currently pay any environmentally related taxes or fees. Responses to this question can be used to examine whether respondents already paying for environmental improvements are less willing to pay additiona l amounts for further environmental improvements. This section also provides text introducing the payment vehicle for the later choice questions. Additional text is included to further emphasize the consequentiality of one’s choices. In order to minimize consideration of omitted variables, t his section also provides text encouraging respondents to only consider the environmental outcomes presented in the choice questions, thus reducing the potential for
omitted variable bias. Deciding Future Actions ( pgs 7 to 8)

. This section provides instructions and an example of how one responds to the choice questions. This section also includes “cheap talk” text emphasizing that even though this exercise is hypothetical, respondents should consider their choices as if they were real. Voting for Programs to Improve the Condition of Chesapeake Bay and l akes in the
Watershed (pgs. 9 to 11)

. The questions in this section are the key component of the survey. Respondents’ choices among alternatives with specific environment al quality improvements and household cost increases are the main data that allow estimation of willingness to pay. The questions are presented in a choice experiment, where respondents choose their preferred option: option A (status quo), option B, and op tion C. This elicitation format has been successfully used48

by a number of previous valuation studies (e.g., Adamowicz et al. 1998; Bateman et al. 2002; Bennett and Blamey 2001; Louviere et al. 2000; Johnston et al. 2002a, 2005, 2012; Opaluch et
al. 1993). Debriefing Questions (pg. 12)

. These questions ask respondents about their motivations as to why they chose certain choice options over others, and whether they accepted the hypothetical scenario when making their choices. These questions will help to ide ntify respondents who incorrectly interpreted the choice questions or did not believe the choice scenarios to be credible. In other words, the responses to these questions will be used to identify potentially invalid responses, such as: protest response (e .g., protest any government program), scenario rejection, omitted variable considerations (e.g., availability and/or price of seafood, economy and employment, r ecreation in lakes outside the W atershed, etc.), and symbolic (warm glow) responses. Some questi ons in this section are also included in the non

- response follow

- up survey, which allows for an examination of whether there are any statistical differences between respondents and non

- respondents. The responses to some of the questions will also be used to identify use versus nonuse motivations behind respondents’ choices, including: altruism, option value, bequest, etc. Recreational Experience and Time Preferences (pg 13). Responses to these questions elicit recreational experience data to test if certa in respondent characteristics influence responses to the choice questions. These questions will also allow EPA to identify resource non

- users for purposes of estimating nonuser WTP, and therefore to gauge the relative importance of non

- use values to overal l benefits. The questions will also identify frequency of use and whether the respondent’s recreational experiences have included lakes in the W atershed, the Chesapeake Bay itself, or both. Question 15 is included to elicit information on respondents’ int ernal discount

rate. Demographics (pg. 14)

. Responses to these questions will be used to estimate the influence of demographic variables on respondents’ voting choices, and ultimately, their values to improve environmental quality in the C hesapeake Bay and lakes in the W atershed. By including this page in both the survey and non

- response follow

- up survey, statistical comparisons of household characteristics can be across the samples of responding and nonresponding households. These data can also be compare d to household characteristics from the sample frame population, which
are available from the 2010 Census.49

3. Pretests

EPA conducted extensive testing of the survey instrument during a set of 10 focus groups and 72 cognitive interviews ( OMB Control Numb er 2090

- 0028 ) . Individuals in these focus groups participated in discussions about the Chesapeake Bay and its Watershed. They also completed draft survey questionnaires and provided comments and feedback about the survey format and content, their interpre tations of the questions, and other issues relevant to stated preference estimation. I ndividual cognitive interviews with survey respondents were conducted using think

- aloud or verbal protocol analyses ( see Schkade and Payne 1994 for a discussion)

. These discussions were used to develop a survey that provides respondents with the necessary information to complete the questionnaire, develop choice scenarios that are incentive compatible, and minimize the burden placed on respondents while collecting the ne cessary information. Particular emphasis in these survey discussions was on testing for the presence of potential biases associated with poorly

- designed stated preference surveys, including hypothetical bias, strategic bias, symbolic (warm glow) bias, fr aming effects, embedding biases, methodological misspecification, and protest responses (Mitchell and Carson 1989). Based on focus group and cognitive interview responses, EPA made various improvements to the questionnaire including making changes to ameli orate and minimize these biases in the final survey instrument. EPA intends to implement this survey in two stages: a p retest and a main study. First, EPA will administer the p retest to a sample of 5 44 households using a mail survey and the Dillman Total Design
Method (Dillman 2008)

. Assuming 92% of the sampled addresses are eligible and 30% of eligible
households return the survey (see Helm 2012; Mansfield, et al. 2012; Johnston, et al. 2012), EPA estimates that this will result in 150 returned and comple ted pretest surveys. Households in the pretest will be selected from each of the three geographic strata. Each selected household will be sent one version of the survey to complete (improving, constant, or declining baseline) and will be assigned that

vers ion on a random basis. Responses and preliminary findings to this pilot study will be used to inform EPA regarding the response rates and the quality of survey data. EPA will evaluate pilot responses and determine whether any changes to the survey instrum ents or implementation approach are needed before proceeding with the administration of the main survey. EPA will use results from the pretest to validate the survey design. Specifically, the pretest
results will be used to:50 Compare the actual and expect ed response rates. Based on typical mail survey response

rates for surveys of this type, the expected response rate is approximately 30% (Helm 2012; Mansfield, et al. 2012; Johnston, et al. 2012). Assess whether demographic characteristics of the responde nts are significantly different from the average demographic characteristics in the study region. Examine the proportion of respondents choosing the status quo. If no one is choosing the status quo, it often indicates that the cost levels are too low. Pure random selection would result in 33% of survey respondents choosing status quo. If less than 15

- 20% of

responses choose the status quo in the pilot study then EPA would consider increasing the
cost levels. Identify unusual patterns, such as the vast m ajority of respondents always choosing Option B. (E.g., if 2/3 of respondents (66%) choos e O ption B it might indicate that there is a systematic bias )

. Determine whether responses suggest that appropriate tradeoffs are being made, and that protest and other invalid responses are minimal. Examine response rates for individual survey questions and evaluate whether adjustments to survey questions are required to promote a higher response rate. If required, EPA will make the appropriate adjustments to the questionnaire, sampling frame or attribute levels (e. g., increase or reduce the number of surveys mailed to households, or increase costs to households in the choice questions).

4. Collection Methods and Follow

- up 4(a) Collection Methods

The survey wil l be administered as a mail survey. Respondents will be asked to mail the completed survey back to EPA. Several considerations justify the use of a mail survey over alternative modes (i.e., telephone and internet surveys; in

- person interviews). Foremost, E PA believes respondents will face fewer obstacles in completing the mail survey compared to other collection methods. In particular, the format of key survey questions (i.e., the multi

- attribute choice experiments) necessitates a visually

- oriented survey, and possible substitutes to a mail survey (i.e., in

- person interviews) are cost

prohibitive given the target responding sample size. Additionally, best practices for mail survey51

administration methods are well

- established and widely accepted (Dillman 2008 ); EPA has designed its collection methods using these best practices and believes the mail survey will be relatively easily accessible to respondents compared to other less well

- tested methods (i.e., internet surveys).

4(b) Survey Response and Follow

- up

The estimated response rate for the mail survey is 30% (Helm 2012; Mansfield, et al. 2012; Johnston, et al. 2012). That is, 30% of the eligible households that are sent the mail survey are expected to return a completed survey. To obtain the highest resp onse rate possible, EPA will follow Dillman’s (2008) mail survey approach. A preview letter will be sent prior to a household receiving the survey ( Attachment 5). The survey will then be sent, accompanied by a cover letter ( Attachment 6), explaining the pu rpose of the survey, emphasizing the importance of their responses, and reminding the respondent whether they live inside or out side of the Chesapeake Bay Watershed. To improve the response rate, all of these households will receive a reminder postcard app roximately one week after the initial questionnaire mailing ( Attachment 7). Then, approximately three weeks after the reminder postcard, all those who have not responded will receive a second copy of the questionnaire with a revised cover letter ( Attachmen t 8). The following week, a letter reminding them to complete the survey will be sent ( Attachment 9).

5. Analyzing and Reporting Survey Results 5(a) Data Preparation

Since the survey will be administered as a mail survey, survey responses will be entered into an electronic database after they are returned. EPA will also clean the data to ensure that the data are entered in a consistent manner and any inconsistencies a re addressed. Specifically, we will use the Double Entry data entry method for closed

- ended responses. The Double Entry method consists of data being keyed twice and compared. Discrepancies are reconciled upon completion of the second entry

. After all res ponses have been entered, the database contents will be converted into a format suitable for use with a statistical analysis software package.52

5(b) Analysis

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

Analysis of Stated Preference Data The model for analysis of stated preference data is grounded in the standard random utility
model of Hanemann (1984) and McConnell (1990). This model is applied extensively within stated preference research, and allows well

- defined welfare measures (i.e., willingness to pay) to be derived from choice experiment models (Bennett and Blame y 2001; Louviere et al. 2000). Within the standard random utility model applied to choice experiments, hypothetical program alternatives are described in terms of attributes that focus groups reveal as relevant to respondents’ utility, or well

- being (John ston et al. 1995; Adamowicz et al. 1998; Opaluch et al. 1993). One of these attributes would include a monetary cost to the respondent’s household. Applying this standard model to choices among programs to improve environmental quality in the Chesapeake Bay, a standard utility function U i ( . ) includes environmental attributes of pollution reduction programs and the net cost of the program to the respondent. Following standard random utility theory, utility is assumed known to the respondent, but stochasti c from the perspective of the researcher,
such that:

(1) U i ( . ) = U( X i , D , Y

- F i ) = v( X i , D , Y

- F i ) + ε i

where:
X i = a vector of variables describing attributes of pollution reduction program i and the
baseline conditions if no further action is taken ; D = a vector characterizing demographic and other attributes of the respondent.
Y = disposable income of the respondent.
F i = mandatory additional cost faced by the household under program i ; v( . ) = a function representing the empirically estimable compone nt of utility;53

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

Econometrically, a model of such a preference function is obtained by methods designed for limited dependent variables, because researchers only observe the respondent’s choice among alternative programs, rather than observing values of U i (.) directly ( Mad dala 1983; Hanemann 1984 ). Standard random utility models are based on the probability that a respondent’s utility from program i , U i (.) , exceed the utility from alternative programs j , U j (.) , for all potential programs j ≠ i considered by the respondent. In this case, the respondent’s choice set of potential programs also includes maintaining the status quo. The random utility model presumes that the respondent assesses the utility that would result from each pollution redu ction program i (including the “No Further Action” or status quo option ) , and chooses the program that provides the highest utility.
When faced with k distinct programs defined by their attributes, the respondent will choose

program i if the anticipated ut ility from program i exceeds that of all other k

- 1 programs. Drawing from (1), the respondent will choose program i if:

(2) (v( X i , D ,Y

- F i ) + ε i ) ≥ (v( X j , D , Y

- F

j ) + ε j ) j≠i.

If the ε i are assumed independently and identically drawn from a type I e xtreme value ( Gumbel) distribution, the model may be estimated as a conditional logit model, as detailed by Maddala (1983), Greene (2003)

. This model is most commonly used when the respondent considers more than two options in each choice set (e.g., Progr am A, Program B, No Further Action), and results in an econometric (empirical) estimate of the systematic component of utility v( . ) , based on observed choices among different programs. Based on this estimate, one may calculate welfare measures (willingness to pay) following the well

- known methods of Hanemann (1984) , as described by Freeman (2003)

. Following standard choice experiment methods (Adamowicz et al. 1998; Bennett and Blamey 2001)
, each respondent will consider questions including thre e potential choice options (i.e., Program A, Program B, No Further Action)

— choosing the program that provides the highest utility a s noted above. Following clear guidance from the literature, a “no further action” or status quo option is always
included in the visible choice set, to ensure that WTP measures are well

- defined (Louviere et al. 2000)

. Three choice questions are included within the same survey to increase information obtained from each respondent. This is standard practice within choice experim ent and dichotomous choice54

contingent valuation surveys (Poe et al. 1997; Layton 2000). While respondents will be instructed to consider each choice question as independent of other choice questions, it is nonetheless standard practice within the literatu re to allow for the potential of correlation among questions answered within a single survey by a single respondent. That is, responses provided by individual respondents may be correlated even though responses across different respondents are considered independent and identically distributed ( Poe et al. 1997; Layton 2000; Train 1998). There are a variety of approaches to such potential correlation. Models to be assessed include random effects and random parameters (mixed) discrete choice models, common in the stated preference literature (Greene 2003; McFadden and Train 2000; Poe et al. 1997; Layton 2000 ). Within such models, selected elements of the coefficient vector are assumed normally distributed across respondents, often
with free correlation allow ed among parameters ( Greene 2002 ). If only the model intercept is assumed to include a random component, then a random effects model is estimated

. If both slope and intercept parameters may vary across respondents, then a random parameters model is estim ated. S uch models will be estimated using standard maximum likelihood for mixed conditional logit techniques, as described by Train (1998), Greene (2002) and others. Mixed logit model performance of alternative specifications will be asses sed using stand ard statistical measures of model fit and convergence, as detailed by Greene (2002, 2003) and Train (1998).

Advantages of Choice Experiments Choice experiments (also called choice modeling) following the random utility model outlined above are favored by many researchers over other variants of stated preference methodology (Adamowicz et al. 1998; Bennett and Blamey 2001), and may be viewed as a “natural generalization of a binary discrete choice CV [contingent valuation]” (Bateman et al. 2002, p. 271). A dvantages of choice experiments include a capacity to address choices over a wide array of potential policies, grounded in
well

- developed random utility theory, and the similarity of the discrete choice context to familiar referendum or voting formats (Ben nett and Blamey 2001). Compared to other types of stated preference valuation, choice experiments are better able to measure the marginal value of changes in the characteristics or attributes of environmental goods, and avoid response difficulties and bia ses (Bateman et al. 2002). For example, choice experiments may reduce the potential for ‘yea

- saying’ and symbolic biases (Blamey et al. 1999; Mitchell and Carson 1989), as many pairs of multi

- attribute policy choices (e.g., Program A, Program B, No Furthe r Action) will offer no clearly superior choice for a respondent55

wishing to express solely symbolic environmental motivations. For similar reasons, choice experiments may ameliorate protest responses (Bateman et al. 2002). Choice experiments are well

- est ablished in the stated preference literature (Adamowicz et al. 1998; Bennett and Blamey 2001; Louviere et al. 2000) andand are commonly applied to assess values for ecological resource improvements of a type quite similar to those at issue in Chesapeake Bay TMDLs. Examples of the application of choice experiments to estimate values associated with changes in aquatic environmental quality and habitat include Hoehn et al. (2004), Johnston et al. (2002b), and Opaluch et al. (1999), among others. EPA has dr awn upon these and other examples of successful choice experiment design to provide a basis for survey design in the present case. Additionally, choice experiments permit a straightforward assessment of the impact of resource scope and scale on respondents ’ choices. This will enable EPA to easily conduct scope tests and other assessments of the validity of survey responses (Bateman et al. 2002, p. 296

- 342). A final key advantage of choice experiments in the present application is the ability to estimate

re spondents’ values for a wide range of different potential outcomes of Chesapeake Bay pollution reduction programs, differentiated by their attributes. The proposed choice experiment survey versions will allow respondents to choose among a wide variety of h ypothetical program options, some with larger and others with smaller changes in the presented attributes (including household cost). That is, because the survey is to be implemented as a choice experiment survey, levels of attributes in choice
scenarios w ill vary across respondents (Louviere et al. 2000). The ability to estimate values for a wide range of different policy outcomes is a fundamental property of the choice experiment method (Bateman et al. 2002; Louviere et al. 2000; Adamowicz et al. 1998). The experimental design (see below) will allow for survey versions showing a range of different baseline and resource improvement levels, where these levels are chosen to (almost certainly) bound the actual levels expected under pollution reduction progra ms. Given that there will almost certainly be some uncertainty regarding the specifics of the actual baselines and improvements, the resulting valuation estimates will allow flexibility in estimating values for a wide range of circumstances.

Comment on Su rvey Preparation Following standard practice in the stated preference literature (Johnston et al. 1995; Desvousges and Smith 1988; Desvousges et al. 1984; Mitchell and Carson 1989), all survey elements and methods were subjected to extensive development a nd pretesting in focus groups to ameliorate the potential for56

survey biases (Mitchell and Carson 1989), and to ensure that respondents have a clear understanding of the policies and goods under consideration, such that informed choices may be made that ref lect respondents’ underlying preferences. Following the guidance of Arrow et al. (1993), Johnston et al. (1995), and Mitchell and Carson (1989), focus groups were used to ensure that respondents are aware of their budget constraints and the scope of the en vironmental quality improvements under consideration. As noted above, survey development included individual cognitive interviews conducted using
think

- aloud or verbal protocol analyses (Schkade and Payne 1994). Individuals in these interviews completed d raft survey questionnaires and provided comments and feedback about the survey format and content, their interpretations of the questions, and other issues relevant to stated preference estimation. Based on their responses, EPA made various improvements to the survey questionnaire including how the attributes are described and labeled, including an example choice question before asking respondents to complete theirs, and the appearance of the choice questions. Results from focus groups and cognitive intervi ews provided evidence that respondents answer the stated preference survey in ways appropriate for stated preference WTP estimation, and that respondents were evaluating trade

offs between program attributes and the household cost. The number of focus grou ps and cognitive interviews used in survey design, 10 focus groups and 72 cognitive interviews, exceed the number s used in typical applications of stated preference valuation. Moreover, EPA incorporated cognitive interviews as detailed by Kaplowicz et al. (2004). EPA will pretest the mail survey. Making the same assumptions about eligibility and response rates as for the main survey (i.e., 92% and 30%, respectively), EPA will mail out 544 pre

- test surveys ( 108 or 109 t o each of 5 cells of the main survey, with the target number of 30 completes per cell). The main goal of the pretest is to assess whether the questionnaire is likely to produce quality data necessary estimate willingness to pay for water quality improvements in the Chesapeake Bay and lakes in the Watershed. More specifically, EPA will use pretest results to (1) assess respondents’ ability to understand background information and respond to the choice questions; (2) evaluate potential for protest responses, warm glow effects, and hypothetica l bias; (3) verify the expected response rate; and (4) evaluate the range and levels chosen for the cost attribute. To test the feasibility of the non

- response follow up study in the main survey, as proposed in Section 3.2(c)(iii), a small scale non

- respon se follow up (NRFU) study will be conducted after the pre

test. EPA anticipates to mail a copy of the pilot NRFU survey to 250 households. These households will be randomly selected from the set of non

- responding households from the pre

- test, defined as th ose57

which did not return a completed survey response, and for which EPA did not receive a USPS delivery
failure notification. EPA will send to each of the selected households a non

- response follow up Priority Mail package (including the incentive). Assumin g a 20% response rate, this implies that approximately fifty households will be included in the pilot NRFU study sample. The results of the pre

- test NRFU will
be used to refine the non

- response questionnaire and to assess the expected response rate among non

respondents.

Econometric Specification Based on prior focus groups, expert review, and attributes of the policies u nder consideration, EPA anticipates that five attributes will be incorporated in the vector of variables describing attributes of the pollution reduction programs (vector X i ), in addition to the attribute characterizing unavoidable
household cost F i . Thes e attributes will characterize improvements in water clarity ( x 1 ), blue crab abundance (

x 2 ), striped bass abundance ( x 3 ), oyster abundance ( x 4 ), and lake condition ( x 5 ). These variables will allow respondents’ choices to reveal the potential impact of Ches apeake Bay environmental quality improvements on utility. Although the literature offers no firm guidance regarding the choice of specific functional forms
for v( . ) within choice experiment estimation, in practice, linear forms are often used (Johnston et al. 2003), with some researchers applying more flexible (e.g., quadratic) forms (Cummings et al. 1994). Standard linear forms are anticipated as the simplest form t o be estimated by EPA, from which more flexible functional forms (e.g., quadratic) can be derived and compared. EPA anticipates estimating all models within the mixed logit framework outlined above. Model fit will be assessed following standard practice in the literature (e.g., Greene 2003; Maddala 1983). Since they are common in practice and theory the functional forms considered for this analysis are presented and discussed in many existing sources (e.g., Hoehn 1991, Cummings et al. 1994, Johnston et al. 1999, and Johnston et al. 2003). For example, for each choice occasion, the respondent may choose Option A (status quo), Option B, or Option C. Assuming that the model is estimated using a standard approximation for the observable component of utility, an econometric specification of the desired model (within the overall multinomial logit model) might appear as:58

2 ( ) = 0 + 1 (change in water clarity) + 2 (change in blue crab abundance) + 3 (change
in striped bass abundance) + 4 (change in oyster abundan ce) + 5 (change in lake condition) + 6 (Cost)

This sample specification allows one to estimate the relative “main effects” of program attributes on utility. Specifications such as this allow WTP to be estimated for a wide

- range of potential program

outcomes.

Experimental Design Experimental design for the choice experiment surveys will follow established practices. Fractional factorial design will be used to construct choice questions with an orthogonal array of attribute levels, with questions randomly divided among distinct survey versions (Louvie re et al. 2000). Based on standard choice experiment experimental design procedures (Louviere et al. 2000), the number of questions and survey versions will be determined by, among other factors: a) the number of attributes in the final experimental design and complexity of questions, b) pretests revealing the number of choice experiment questions that respondents are willing/able to answer in a single survey session, and c) the number of attributes that may be varied within each question while maintaining respondents’ ability to make appropriate neoclassical tradeoffs. Based on the models proposed above and recommendations in the literature, EPA anticipates an experimental design that allows for an ability to estimate main effects of program attributes (L ouviere et al. 2000). Choice sets (Bennett and Blamey 2001), including variable level selection, were designed by EPA based on the goal of illustrating realistic policy scenarios that “span the range over which we expect respondents to have preferences, an d/or are practically achievable” (Bateman et al. 2002, p. 259), following guidance in the literature. This includes guidance with regard to the statistical implications of choice set design (Hanemann and Kanninen 1999) and the role of focus groups in devel oping appropriate choice sets (Bennett and Blamey 2001). Based on these guiding principles, the following experimental design framework is proposed by EPA. A description of the statistical design is presented in Error! Reference source not found.

. The experimental design will allow for estimation of main effects based on a choice experiment framework. Each treatment (survey question) includes Option A (status quo) and two ch oice options (Option B and Option C), each characterized by five attributes and a cost variable. Hence, there is a total of eighteen59

attributes for each treatment (including six implicit attributes for “No Additional Action”). Based on focus groups and pre tests, and guided by realistic ranges of attribute outcomes, EPA allows for three different potential levels for environmental attributes and six different levels of annual household cost for Programs A and B. It also allows for three different potential l evels for environmental attributes under “No Additional Action,” the first set reflecting a declining baseline, the second reflecting a constant baseline, and the third reflecting an improving baseline. The “No Additional Action” option included for each q uestion will be characterized by a household cost of $0.

The attribute combinations can be summarized as follows: Water Clarity N ( 3 levels) Water Clarity A , Water Clarity B (3 levels) Blue Crab Abundance N ( 3 levels) Blue Crab Abundance A , Blue Crab Abundance B (3 levels) Striped Bass Abundance N ( 3 levels) Striped Bass Abundance A , Striped Bass Abundance B (3 levels) Oyster Abundance N ( 3 levels) Oyster Abundance A , Oyster Abundance B (3 levels) Lake Condition N ( 3 levels) Lake Condition A , Lake Condition B (3 levels) Cost A, Cost B (6 levels)
For a more detailed discussion of attribute levels assigned across survey versions, refer to Error!
Reference source not found. 2

. Following standard practice, EPA will constrain the design to remove dominant/dominated pairs, where one option dominates the other in all attributes. Respondents have been found to react n egatively and often protest when offered such choices. Given that such choices provide negligible statistical information compared to choices involving non

- dominant/dominated pairs, they are typically avoided in choice experiment statistical designs. For e xample, Hensher and Barnard (1990) recommend eliminating profiles including dominating or dominated profiles, because such profiles generally provide no useful information. Following this guidance, EPA will constrain the design to eliminate such
dominant/d ominating pairs.60

5(c) Reporting Results

The results of the survey will be made public as part of the benefits analysis for the Chesapeake Bay TMDLs. Provided information will include summary statistics for the survey data, extensive
documentation for t he statistical analysis, and a detailed description of the final results. The survey data will be released only after it has been thoroughly vetted to ensure that all potentially identifying
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