Estimating public values for marine 

protected areas in the northeast 

United States: a latent class modeling 

approach 

K. Wallmo and S. Edwards 

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

NOAA Technical Memorandum NMFS-F/SPO-84 

October 2007
Estimating public values for marine 

protected areas in the northeast 

United States: a latent class modeling 

approach 

K. Wallmo and S. Edwards 

NOAA Fisheries Office of Science and Technology 

NOAA Fisheries Northeast Fisheries Science Center 

NOAA Technical Memorandum NMFS-F/SPO-84 

October 2007

U.S. Department of Commerce 

Carlos M. Gutiérrez, Secretary 

National Oceanic and Atmospheric Administration 

Vice Admiral Conrad C. Lautenbacher, Jr., USN (Ret.) 

Under Secretary for Oceans and Atmosphere 

National Marine Fisheries Service 

William T. Hogarth, Assistant Administrator for Fisheries
Suggested citation: 

Wallmo, K., and S. Edwards. 2007. Estimating public values for marine protected areas 

in the northeast United States: a latent class modeling approach. U.S. Dep. Of 

Commerce, NOAA Tech. Memo. NMFS-F/SPO-84, 75 p. 

A copy of this report may be obtained from:

Office of Science and Technology 

NMFS, NOAA 

1315 East-West Highway, F/ST5 

Silver Spring, MD 20910 
Summary 

Although popular with the environmental community for quite a while, the designation of 

the 362 thousand km2

 Northwest Hawaiian Islands Marine National Monument in 2007 

by President George W. Bush symbolizes the political ascension of Marine Protected 

Areas (MPAs) in the United States. MPAs are not panaceas for resource allocation, 

though (Degnbol et al. in press). The benefits of fishery reserves, in particular, are 

arguable. There is general agreement, however, that no-take ecological reserves are the 

most effective way to enhance and preserve the ecological diversity of marine species 

and their habitats on the sea floor. 

Scientific research indicates that 10%-40% of an ecosystem is required to preserve all 

species and their habitats. To insure against catastrophic events would require 

considerably more. But policy-makers must ask whether complete protection is too costly 

when compared to the opportunity costs of displaced activities. The zoning plans of 

MPAs generally exclude or substantially restrict many activities that are important to the 

economy and consumers, including commercial and recreational fishing, oil and natural 

gas production, sand and gravel mining, and clean renewable energy from windmills that 

require being attached to the sea floor. 

Debate over non-fungible objectives such as environmental protection and opportunity 

costs can be informed by adding the public’s valuation of ecological diversity. A person 

might value ecological reserves as a bequest to younger generations, or they might simply 

get personal satisfaction from knowing that a part of the environment exists in a natural 

state. 

Bequest value and existence value are a class of economic benefits not revealed by 

markets. Instead economists use surveys that describe hypothetical market-like situations 

to elicit valuations of an environmental good, service, or asset. The contingent choice 

class of non-market methods is ideally suited to collecting data on the non-use value of 

multi-attribute resources such as marine ecological reserves. 

This research presents estimates of non-market values of marine protected areas in the 

Northeast Region of the US. A random sample of over 1300 households in the Northeast 

Region was presented with sets of hypothetical alternatives which differed in terms of 

reserve size (5%-40%), compatible uses (No-Take, Science and Education, passive forms 

of Leisure and Tourism, pelagic Fishing), and personal costs ($10-$150) and asked to 

choose the bundle that they preferred. Answers from the 77% of responders were 

analyzed with a latent class specification of the random-utility-model (RUM) to 

objectively test for heterogeneous preferences. 

Three distinct latent classes were identified in the sample. Roughly half of the responders 

(48%) saw reserve size as a normal economic good with positive, but diminishing 

marginal utility. Compensating variation was maximized at $133 per-household per-year 

for this group when total reserve size was 27% of the EEZ and the areas could only be 

used for scientific and educational purposes. Another class was characterized by 

1 
negative utility from MPAs (28%), and the final class (24%) had an incongruous positive 

response to personal cost, i.e. paying more for reserve size the more costly it became, 

possibly due to hypothetical bias in the questionnaire or non-conforming preferences. 

The model was applied to estimate the publics’ valuation of the Habitat Areas of 

Particular Concern (HAPC) being considered by the New England Fishery Management 

Council for an amendment to its Essential Fish Habitat Omnibus Plan. Together, the 

seamounts, canyons, and diverse areas of the shelf comprise 5.2% of the EEZ in the 

region. Allowing scientific and educational uses doubled estimates of compensating 

variation by Class 1 (positive utility) responders from more than $50 to almost $110 

depending on the status quo. In contrast, compensating variation for Class 3 (disutility) 

averaged -$40 for the No-Take alternative and increased to -$9 as more uses were 

allowed. The positive parameter on cost for Class 2 is intractable in this model. 

In addition to presenting the methodology and results in detail, the report addresses the 

need to control for heterogeneous preferences in contingent choice research, a benefitcost analysis framework that accounts for non-use value, and the effect of operating and 

opportunity costs on scientists’ estimates of optimum reserve size. 

2 
Introduction 

In the first assessment of the state of the oceans in 35 years, the US Commission on 

Ocean Policy (2004) reported continued threats to resource sustainability, degradation of 

marine ecosystems, and unproductive competition for ocean space by traditional and new 

stakeholders. These persistent problems can be traced to defective governance and 

property rights arrangements that fail to allocate resources effectively. However, even a 

“perfect” institutional arrangement (whatever that might be) would be compromised by 

the dearth of information on the value of things not exchanged in markets. The NRC 

Panel on Integrated Environmental and Economic Accounting (NRC 1999) warned that 

ignorance of the size of the “non-market” economy biases measures of aggregate welfare 

in favor of markets, such as National Income and Product Accounts. Perhaps least known 

(and understood) are the “non-use” values – i.e., the personal satisfaction that people get 

from protecting the environment for the benefit of others, particularly subsequent 

generations or wildlife itself. 

Marine Protected Areas (MPAs) – especially ecological reserves -- are the leading 

vehicle for promoting “non-use” values in the ocean. In 2000 when President Clinton 

signed Executive Order 13158 Marine Protected Areas to “help protect the significant 

natural and cultural resources within the marine environment for the benefit of present 

and future generations” (EO 13158, 2000), less than 1% of U.S. territorial waters was 

part of an MPA (Kelleher 1999). However, pressure from environmental organizations 

worldwide has begun to take effect. For example, in the United States in 2007, President 

Bush created the largest MPA in the world – the 362 thousand km2 Northwest Hawaiian 

Islands Marine National Monument which takes up a third of the Insular-Pacific 

Hawaiian Large Marine Ecosystem, and is bigger than the combination of all the states in 

the Northeast Region. 

MPAs are used for three general purposes – fisheries management, protection of cultural 

resources, and preservation of species and habitat diversity. There is scientific evidence 

that closing an area to extractive uses can rebuild fish stocks inside the area (Palumbi 

2002; Halpern 2003; Roberts 2005), but scientists (Hilborn et al. 2004) and 

conservationists (Agardy 2005) are less sanguine about the overall benefit of fishery 

reserves. Similarly, economists point out that even fishery reserves that are successful 

from a biological standpoint could still fail on economic grounds if there is sufficient 

excess capacity to dissipate resource rents (Hannesson 1999). In large measure, the 

economic value of fishery reserves depends primarily on whether any net gains in the 

fishery from the migration of fish from inside the protected area is greater than the 

opportunity costs of closing the area (Sanchirico 2004). 

While the utility of fishery reserves is in doubt, there appears to be a consensus among 

scientists that MPAs are the only viable way to protect habitat and conserve biodiversity 

(Hilborn et al. 2004, Lubchenco et al. 2003; NRC 2001; Lauck et al. 1998; Scientific 

Consensus Statement on Marine Reserves and Marine Protected Areas 2001). The main 

objective of the MPA Federal Advisory Committee (Anonymous 2005; p. 4) is 

“conserving, enhancing, and/or restoring marine biodiversity ... [and] ... representative 

3 
examples of the nation’s marine habitats, as well as unique biophysical and geological 

features”. However, the committee also highlights the objective of “[p]roviding both 

appropriate access to and use of marine resources within MPAs consistent with the goals 

and objectives of the MPA” (p. 4). 

Scientists estimate that 10%-40% of a marine ecosystem (depending on its characteristics) 

would have to be set aside to preserve all ecological diversity (NRC 2001). The 

percentage can increase substantially to insure against the risk of catastrophe from oil 

spills, fishing gear, storms, and introduced species (Allison et al. 2003; Halpern 2003). 

From a policy standpoint, however, the question is not so much “how much is enough?” 

(Lubchenco et al. 2003), but “how much is too much?”. There are costs associated with 

displaced activities, such as the value of forgone energy and seafood. 

MPAs could not possibly be designed without scientific data, but E.O. 13158 is a public 

policy imbued with subjectivity about humans place in marine ecosystems, not an 

experiment. It is therefore legitimate to inquire about the public’s valuation of ecological 

reserves and the appropriate mix of protected areas and exploited areas. Even if someone 

does not expect to ever experience the sea floor first-hand, (s)he might value ecological 

reserves as a bequest to younger generations (bequest value) or for the knowledge that a 

part of the environment exists in a natural state (existence value). Together, these values 

are called non-use values. 

The contingent choice class of non-market methods is ideally suited to researching the 

non-use value of ecological reserves. Non-use values are, by definition, neither directly 

nor indirectly revealed in market data or by household production behavior (e.g., travel 

costs used as the price of a fishing trip); therefore, data can only be collected from a 

survey. In addition, the contingent choice method is designed to collect data on the 

attributes of a multi-characteristic commodity, such as ecological reserves. 

Data were collected from a survey of households in the Northeast Region of the US (for 

this research the Northeast Region refers to the states of Maine, New Hampshire, 

Massachusetts, Rhode Island, Connecticut, New York, New Jersey, Delaware, Maryland, 

and Virginia) to test the hypothesis that individuals have preferences not only for the size 

of an ecological reserve, but also for different types of uses of the reserve. Model results 

and estimates of non-use value are presented and discussed in the main text. Appendices 

present the questionnaire, summarize the survey data, and report test results for sampling 

and selection biases. 

This research project was funded jointly by the Economics and Social Sciences Division 

and the Habitat Division of NOAA Fisheries’ headquarters. We are grateful for the 

support of these divisions, and the patience of their chiefs – Dr. Rita Curtis (Economics 

and Social Sciences) and Mr. Thomas Bigford (Habitat) – and Kathi Rodrigues (Habitat). 

4 
Related Research 

The economics literature on the non-market benefits of MPAs is meager, says virtually 

nothing about non-use values, and cannot address questions about reserve size or 

allowable uses. Most of the literature reports estimates of the use-value of marine 

recreation, such as diving and snorkeling. In a contingent valuation study, Wielgus et al. 

(2003) found that divers were willing to pay US$2.60 per dive for a marginal 

improvement in coral and fish diversity at Eilat coral reefs in Israel. Using a similar 

methodology, divers at the Turks and Caicos Islands reportedly valued large increases in 

the size and abundance of Nassau grouper at US$50 (Rudd, Gore, and Tupper 2000). 

Other studies found that international tourists in the Seychelles were willing to pay about 

US$12 on average to prevent coral reef degradation in Seychelles’ marine parks (Mathieu 

et al. 2003). Arin and Kramer (2002) found that protecting coral reefs in the Philippines 

for local and international divers could generate revenues ranging from several thousand 

dollars a year up to one million, depending on the location of the coral reef being 

protected. Leeworthy (1991) reported estimates of divers’ willingness-to-pay ranging 

between $356 and $533 for trips to coral reefs in the Florida Keys National Marine 

Sanctuary. For comparison, Bhat (2003) estimates that, under the current quality 

conditions, trip values for diving, snorkeling, and glass-bottom boating also in the 

Florida Keys are about $463. The same study suggests that with significant 

improvements in fish abundance, visibility, and coral quality, the per-trip value would 

increase by 69%. 

Estimates of non-use values for MPAs are rare, but what exist are not applicable to US 

policy either because they are site-specific and carried out overseas, responders were 

visitors and local residents instead of the general public, or they are outdated (Davis 

2003). Of the studies that do exist, Bennett (1984) reported that in 1979 local visitors to 

the coastal Nadgee Nature Reserve in New South Wales, Australia, were willing to pay 

an average of US$3 a year in perpetuity to preserve the park’s existence. Spash et al. 

(2000) reported that locals and tourists surveyed during 1998 were willing to pay 

US$1.17 and US$4.26 annually for five years for the uncertain proposition that money in 

a trust fund would support ways to improve marine biodiversity by 25% on coral reefs in 

Montego Bay, Jamaica. 

There are also a limited number of studies in the field of environmental and natural 

resource economics that have used the latent class estimator to test for heterogeneous 

preferences. The contingent valuation study of endangered species by Aldrich et al. (2007) 

is noteworthy because our results are qualitatively similar. In addition to improving 

model fit, they identified three classes of responders with very strong preferences for 

preservation, moderate preferences, or disutility. Conventional practice would combine 

these groups with structurally different preferences, which could lead to incorrect 

inferences, or address the differences through a priori specifications of the demand 

function or through the use of random parameters models (Morey 1993; Layton 1996). 

Our study extends the literature in several ways. First, it is one of the first studies of the 

non-use value of preserving species and habitat diversities in a large marine ecosystem. 

5 
Second, we are unaware of any other study that has estimated the economic value of an 

MPA as a function of the policy-relevant attributes, size and allowable uses. Finally, as 

just mentioned, our research contributes to the limited though growing use of latent class 

models for the valuation of environmental goods and services, and underscores the 

benefits, particularly for policy questions, of more flexible models (see Boxall and 

Adamowicz 2002). 

Survey Design 

Questionnaire development: The questionnaire was developed during January to 

September 2005 when three focus groups, three sets of cognitive interviews, and two 

pilot tests were conducted. Three challenges cropped up during these meetings. First, it 

became clear from the first focus group that managing the information effects in the 

survey would be a critical issue, as most participants had heard of the term marine 

protected area but had quite different views and understandings of what they are and why 

they are established. Further, many participants in focus groups associated coral reefs or 

other warm water habitats with MPAs. Thus one of the first challenges in survey design 

was to clearly communicate to responders what the primary purpose of the MPAs 

discussed in the survey would be. We stressed at several different points in the survey 

that the benefits of the MPAs would include (1) the protection of habitat and marine life 

diversity on the sea floor in the Northeast Region, and (2) the prevention of industrial 

development, such as drilling for oil or gas, within the MPA boundaries. Any other 

benefits would be incidental at best (e.g., protection of migratory species). 

A second challenge was to convey in clear and concise text the findings from the 

scientific literature about reserve size. This was difficult, as many of the findings do not 

enjoy consensus among all scientists, and even when they do they are often case or sitespecific. Ultimately we relied on wordsmithing the NRC (2001) report which 

summarized the views of thirteen marine scientists on the relationship between reserve 

size and preservation of ecological diversity. 

A third challenge was ensuring that we presented balanced information on both the 

potential benefits and costs of MPAs. This was particularly important since MPAs are a 

relatively contentious in the northeastern US, due in part to strong ties to fishing and 

other marine related industries. Not surprisingly, all three focus groups contained 

participants who, prior to the focus group, either strongly supported or opposed MPAs, 

and thus it was important that the information in the survey was presented neutrally. We 

felt that the benefits were aptly described by communicating the purpose of the MPAs, 

e.g. protecting habitat and diversity on the sea floor, and the relationship between reserve 

size and diversity summarized by the NRC. To balance that information we developed a 

section of the survey that described the costs associated with MPAs such as establishment 

and monitoring costs as well as opportunity costs of displaced production, the potential 

loss of jobs, and increased regulation of activities within MPA boundaries. 

The qualitative research was also used to refine the list of potential attributes for the 

choice experiment and determine the range of attribute levels. At the onset of the 

6 
research we had several loosely defined attributes that were of policy interest, including 

the size of an MPA, the types of use that would be allowed, different types of marine 

habitat that may be included within the MPA boundaries, the proportions of each habitat 

type, and an individuals willingness to pay for an MPA. During the qualitative research 

period we learned that some of these attributes were either not meaningful to responders 

or the set of attributes was too complex for making the types of trade-offs required in a 

choice experiment survey. Ultimately the attribute set was refined to include three 

attributes: size, use, and cost. Size was defined as the percent of water within the 

northeastern EEZ that would be part of a network of integrated protected areas. Use 

refers to the types of activities that would be allowed within the boundaries of the 

network which would be compatible with the objective to promote ecological diversity. 

Cost was the cost to the responder of choosing a particular scenario. 

Two pretests were conducted prior to the final survey implementation. As the final 

survey was implemented as a web-based survey, both pretests were also implemented 

online, using subsets of a web-enabled panel. The first pretest was administered to a 

random sample of 200 households, and a total of 117 responders completed the survey. 

The pretest assessed responders’ comprehension of the survey instrument, obtained an 

estimate of survey time (about 20 minutes), and examined the validity of the 

experimental design, discussed below. The second pretest investigated a slightly 

different experimental design with smaller levels for the size attribute. A total of 68 out 

of 100 panelists completed this pretest. After completing both pretests slight 

modifications were made to the instrument and a final experimental design was 

developed. 

Final questionnaire: The final questionnaire, entitled Marine Protected Areas in the 

Northeast United States, consisted of 19 pages and 48 questions divided among eight 

sections (Appendix A). Sections 1-6 and 8 described below contained 2 – 6 attitudinal or 

informational questions that supported the section topic, including a total of 21 questions 

requiring responses on the Likert scale. Section 7 was the choice experiment. 

• Section 1 showed the federal waters in the Northeast Region on a map and 

informed households that MPAs are in a discussion stage. 

• Section 2 provided background information on the state of the ocean and the use 

of MPAs as a tool for marine management, drawn largely from the U.S. 

Commission on Ocean Policy (2004). 

• Section 3 described the potential benefits and costs of MPAs, specific to the types 

of MPAs discussed in the survey. 

• Section 4 addressed the relationship between MPA size and ecological diversity, 

drawn largely from the NRC (2001) report. 

• Section 5 described the current status of MPAs in Northeast region (only 

Stellwagen Bank National Marine Sanctuary, i.e. the “Current Situation” in the 

choice task), and the presence of other, non-permanent closed areas used for 

fisheries management. 

• Section 6 described four possible use levels of the MPA network, including NoTake, Scientific Research and Education, passive Recreation and Tourism, and 

compatible Limited Fishing. The last level allows for fishing in the water column 

7 
with gear that does not contact the sea floor (e.g., herring purse seine and 

swordfish harpoons). The levels were essentially ordinal, ranging from least to 

most intrusive. 

• Section 7 was the choice experiment. Each household in the sample faced five 

choice tasks, with each task containing two alternatives plus the Stellwagen Bank 

status quo (SQ) option. A sample choice task is shown in Figure 1. 

• Section 8 consisted of Likert-scale questions concerning a more general 

environmental ethic, and gave responders the opportunity to comment on the 

survey. 

If desired, responders could connect to previous information or additional information 

about a topic using hyperlinks throughout the survey. 

Experimental Design Plan 

An experimental design plan was used to create the alternative MPA scenarios that varied 

in size, use, and cost. The attributes size and cost each took one of five levels, and use 

took one of four levels that were cumulative (e.g., Limited Fishing includes all other 

uses; Table 1). The design plan was computed using the SAS experimental design and 

choice modeling macros (Kuhfeld 2005). The final design plan allowed for variable 

interactions, second order effects, and restrictions that eliminated unrealistic designs. For 

example, a design that produced a scenario where one large, more restrictive MPA costs 

less than a smaller, less restrictive MPA would be considered unrealistic given that the 

cost attribute was used to offset losses to industry. The final design plan consisted of 200 

alternative scenarios which were then paired and blocked into groups of five using the 

SAS choice efficiency and blocking macros, ultimately resulting in 20 survey versions. 

The versions were randomly distributed among 1342 sample households from the webenabled panel. Each version was allocated approximately 67 times. 

The payment vehicle was specified as an annual contribution to an environmental 

organization. Responders were told that contributions would be used in negotiations with 

the federal government to lease, monitor, and enforce the MPA network, and to offset 

costs to industries and other parties who are impacted by the closures. Despite the 

potential for “free-riding”, this vehicle was clearly preferred by focus groups over 

donations via federal tax returns because there is a direction connection between one’s 

choice and the outcome, and because of latent distrust of government actions. 

Furthermore, this construction is similar to a real-life mechanism used recently by The 

Nature Conservancy and Environment Defense to preserve marine life and habitats in 

large areas of the Pacific Ocean (Marsh, Beck, and Reisewiitz, 2002). 

Implementation and Weighting Corrections 

The survey was administered to a random sample of households from a web-enabled 

panel maintained by Knowledge Networks, Inc. On October 5, 2005, the questionnaire 

was sent to a random sample of 1342 households on the panel who lived in the Northeast 

region (Maine, Vermont, New Hampshire, Massachusetts, Rode Island, Connecticut, 

New York, New Jersey, Delaware, Maryland, Washington D.C., Pennsylvania, Virginia, 

8 
West Virginia, and North Carolina). Up to two reminders were made if necessary. The 

first reminder was e-mailed a few days after the initial electronic mailing, and the second 

reminder was a telephone call on week after the initial mailing. The survey was taken 

offline on October 19, 2005 after achieving a 77% response from 1037 households. Only 

four responders were removed due to high item non-response (> 33%). 

Data weighting was necessary to correct for known deviations from the equal-probability 

design which are an inherent part of the sampling process. These deviations result from 

several sources, including partial sub-sampling of telephone numbers without matched 

addresses, RDD sampling rates being proportional to the number of phone lines in a 

house, double-sampling in the four largest states, under-sampling households not serviced 

by Microsoft TV, over-sampling of minority households (Black and Hispanic), oversampling of households with personal computer and internet access, and selection of one 

adult per household. Post-stratification of survey weights reduces sampling error for 

characteristics that are highly correlated with reliable demographic and geographic totals. 

For this study, the most recent Census data on gender, age, race, education, state, 

household internet access, and residence in a metro or non-metro area were used to 

weight a household records individually. Weights averaged 1.0 but ranged from 0.0994 to 

3.7308. 

Self-selection and non-response bias may also exist in survey data, because cooperation 

from some people will be determined by their opportunity costs of time as well as 

intensity of interest. Because Knowledge Networks maintains a data profile of common 

demographic, social, and economic characteristics of each panelist, we were able to 

examine self-selection and sample bias by comparing responders and non-responders to 

each other and to the Census data using the following characteristics: (a) distribution of 

total population by state in the region; (b) distribution of the population of persons 18 

years-old or older by state in the region; (c) distribution of the population between coastal 

counties and inland areas across the region; (d) total number of households in the region 

by state; (e) distribution of households in coastal counties and inland areas by state; (f) 

average household size in the region; (g) race and ethnicity in the region; and (h) mean 

household income in the region. 

Discrete ratio data (i.e., counts) were tested for goodness of fit to a theoretical 

distribution using the chi-square statistic: 

∑=

=

k

1i

F

2 )F - (f

i

2 ii χ

where fi is the sample count in class i, Fi is the expectation in class i (i.e., percent of 

population in class i times the sample total), and k is the number of classes. When k=2

and there is only one degree of freedom (i.e., ν=k-1), the Yates Correction for Continuity 

is required: 

9 
∑ . =

−− =

2

1i

F

2 0.5)|Fifi(|

c i

2

χ

In contrast, continuous data on mean income and household size ( X )were simply 

compared to the Census values (μ) using the t-test: 

Xs

X t −μ)( =

where X s is the standard error of the mean. 

Self-selection bias in the discrete characteristics was tested by comparing responders and 

non-responders with the log-likelihood ratio for contingency tables. Twice the value of 

the log-likelihood ratio (G) approximates the Chi-square distribution: 

G 2[ ] nnCCRRff )ln()ln()ln()ln(

j

i jj

i

ijij i

ji

= −∑∑∑ −∑ +

where Ri and Cj are the individual i row and j column totals, and n is the sum of all values. 

Finally, self-selection bias in the two continuous variables for income and household size 

was tested for the difference between the two means and the t-test: 

21

21

XX s

XX

t

−

− =

where XX 21s − is the pooled standard error. 

Test results can be found in Appendix B. Tests for sample bias (Table B-1) suggest that 

the weighted data improved all fits, but not always by enough to accept the null 

hypothesis that sample data were selected from the population described by the Census. 

Total 18+-year population, total households, average household size, and race/ethnicity 

were not significantly different from the Census. However, the null hypotheses 

concerning total population, the distribution of people and households between coastal 

counties and inland areas, and average income population were rejected with high levels 

of confidence. Specifically, the sample had (a) too many people from Delaware, Maine, 

and Pennsylvania and too few people from Maryland and Rhode Island; (b) too few 

people and households in coastal counties; and (c) relatively low income. While these 

results are not a clear rejection of sample bias, the survey’s interest in households (vs. 

population) lessens concerns about sample bias. 

Self-selection bias (Table B-2) was examined by comparing responders and nonresponders. The tests suggest a significant difference in the population data, but the vast 

majority of the variance is actually due to difference between states instead of between 

the responder and non-responder factors. Likewise, the household measures were not 

10
significantly different, and mean incomes were also similar. The only potential source of 

self-selection bias would be due to relative differences in race and ethnicity. 

Econometric Model 

Random utility theory provides the modeling framework for this research. The theory 

specifies that utility (U ) for a good consists of a systematic, known component (V ) and 

a random component (ε ). In this case, the good in question is an MPA network, and the 

utility that individual i receives from MPA alternative a can be expressed as 

VU iaiaia ( )1 += ε

where is the unobservable utility that i associates with a, is the quantifiable, 

known portion of utility, and 

Uia Via

ia ε is the random, unobservable effects associated with a

for individual i. Alternative a can be decomposed into its specific attributes of size, use, 

and cost, and the systematic component of utility is then Via

ia = βXV ia ( )2 

where is a vector of attributes and the associated levels for MPA alternative a and β

are the attribute coefficients. Substituting the expression for , the utility function can 

be expressed as 

Xia

Via

ia XU iaia )3( β += ε

Under the assumption that individuals are utility maximizers, the probability that an 

individual i will choose MPA alternative a from a set of C alternatives is equal to the 

probability that the utility derived from a is greater than the utility derived from any other 

alternative in the choice set C, expressed as 

)4( Pr( ) .

Pr( )

choosesPr( Pr()from )

X X Cj

VV Cj

UUCai Cj

iaia ijij

ijijiaia

ijia

+>+= ∈∀

+>+= ∈∀

>= ∀ ∈

εβεβ

εε

Assuming a type I extreme value distribution for the error component (a common 

assumption for discrete choice models; Louviere, Hensher, and Swait 2000), (4) is 

operationalized as 

)5( )exp(/)exp()choosesPr(

1

∑=

=

J

j

βXai ia βXij 

If choice observations are ordered so that the first n1 individuals chose alternative a, the 

next n2 individuals chose alternative b, and so on for all j elements of the choice set C, the 

likelihood function for (5) can be written as 

11
∏ ∏ ∏ + + = −=

+

=

=

I

nIi

Ji

n

i

nn

ni

i

j

PiPPL

1

1 2 1

1 1

....... 1

1 2

which simplifies to 

)6( .ln

1 1

∏∏= =

=

I

i

J

j

f

ij

ij L P

 

Defining a dummy variable fij, where fij = 1 when alternative j is chosen and fij = 0 

otherwise, the function can be can be written as 

)7( * .ln

11

ij

J

j

ij

I

i

L ∑∑ Pf === 

By replacing the term Pij with (5), the only unknown parameters are the elements of β , 

which are estimated through maximum likelihood techniques. 

The multinomial logit model above is a popular choice for modeling discrete choice data, 

and when data are rich and disaggregate the model is often robust (in terms of prediction 

success) to the implicit behavioral assumptions arising from the chosen error distribution 

(Louviere, Hensher, and Swait 2000), notably, the assumption of independence from 

irrelevant alternatives (IIA). This assumption, however, has motivated much of the 

research on extensions to the basic model, including the nested logit, mixed logit, and 

latent class specifications (Greene and Hensher 2002). We apply the latent class 

extension as a way to accommodate taste parameter heterogeneity, a situation that was 

clearly evidenced during qualitative research. 

The underlying theory of the latent class model is that choice depends on attributes that 

are observable, e.g. the attributes in the choice scenarios, and on latent heterogeneity that 

varies with factors that are not observable by the researcher (Greene and Hensher 2002). 

In the latent class model individuals are sorted into k classes, and given class assignment, 

parameters are the same for all individuals in that class but may vary between classes. 

The latent class model is the same as equation (5) except that an individual’s choice is 

now conditional on belonging to class k 

)8( ).exp(/)exp()|choosesPr(

1

∑=

=

J

j

X jk X jk kji β β

Following Greene and Hensher (2002), the probability of individual i belonging to class k

is denoted Hik and itself determined by the conditional logit model 

)9( )exp(/)exp(

1

∑=

=

K

k

ik ik ik H δ s δ s

12
where is a set of individual characteristics that enter the model for class membership. i s

Error distributions for (9) are assumed to be type I, as in the multinomial logit. The 

choice likelihood of an individual is then expressed as the joint probability of (8) and (9), 

ki

K

k

i ik PHP |

1

)10( ∑== 

Again using the dummy variable fij, where fij = 1 when alternative j is chosen and fij = 0 

otherwise, the log-likelihood function can be can be written as 

)11( lnln ln . 1 1

|

1 11 1 1

∑ ∑∑ ∏ | ∑∑ ∑ = == = == =

⎥

⎦

⎤ ⎢

⎣

⎡ =

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ==

I

i

K

k

kij

J

j

ijik

I

i

I

i

K

k

J

j

PL i ik PH kij PfH

Replacing and with (8) and (9), respectively, the unknown parameters P |kij Hik β k and 

δ k can be estimated using maximum likelihood techniques. 

Latent Class Construction 

We relied on qualitative and quantitative criteria to determine the number and 

specification of the latent classes. During the focus groups we noted that three types of 

preferences usually emerged – a “pro-MPA” stance from individuals who strongly 

supported MPAs of most sizes, a “moderate-MPA” stance from individuals who 

supported MPAs but seemed to consider things like size and use when expressing their 

opinions, and individuals who were against MPAs in principle, often because of maritime 

industry interests or anti-state sentiments. Although anecdotal, this assessment was fairly 

intuitive and represented what seemed to be a logical continuum of preferences for MPAs. 

Based on this we considered a latent class models with two, three, and four classes. 

To specify the latent class itself we conducted a factor analysis on the attitudinal and 

socio-economic survey variables. A varimax rotated solution extracted three factors with 

eigenvalues greater than 1.00 (3.87, 3.21, and 1.77). Because we preferred a 

parsimonious specification for (9), we chose to retain only one of the three factor scores 

to include in . The retained factor (eigenvalue = 3.21) had strong factor loadings (> 

0.65) for variables concerning employment ties to the ocean (e.g. someone in family 

fishes commercially, operates a charter fishing boat, etc...) and variables asking for 

general attitudes toward environmental conservation and economic growth. 

i s

Information criterion measures were used to test models with two, three, and four classes 

(Roeder, Lynch, and Nagin 1999; Lee et al. 2003). We calculated Bayesian Information 

Criterion (BIC) and Akaike Information Criterion (AIC) following Lee et al. (2003) as 

BIC LL β }{ ρ ⋅+⋅−= N)log()(2

13
AIC {LL β)(2 −⋅−= ρ}

where ρ is the number of model parameters, and N is the number of observations in the 

sample. Results (Table 2) show that the three class model minimizes both the AIC and 

BIC measure, and since earlier qualitative work with focus groups also supported a three 

class model it was selected as the “best” model for welfare analysis. 

 

Results 

Responses to attitudinal questions that focused on general views of MPAs and the marine 

environment are summarized in Appendix C. Public opinion supports the creation of a 

network of MPAs in the Northeast Region, although not unequivocally. Nearly threequarters of responders believed that MPAs could be used to balance environmental 

protection and extractive uses of the ocean, such as fishing and oil and gas production, 

and there was about 80% agreement with the statement “I like knowing that part of the 

ocean in the Northeast Region is protected even if I never see or use it.” Further, about 

two-thirds of the responders favored a reduction in their material standard of living if it 

meant that the environment could be protected for its own sake or for the benefit of their 

children and future generations. 

These results certainly bolster the relevance of marine reserves in public policy. 

However, public support of MPAs was not blind to practical considerations, including the 

requirement that “their rules and boundaries can be enforced” and whether MPAs needed 

to be “... large enough to protect every type of plant and animal regardless of costs.” 

Support for MPAs dropped to 47% when responders were asked if they “would be 

willing to pay higher prices for products such as seafood and energy to preserve areas of 

the ocean.” This finding appears to be in conflict with the support just mentioned, but 

attitudinal questions are imprecise and probably induce conservative responses on 

personal costs. That is, households need to be asked about specific amounts and payment 

mechanisms. 

Consistent with results from the qualitative research, the majority of responders were able 

to choose among the three alternatives with a moderate degree of confidence. Nonresponse to the total number of choice scenarios (1033 x 5=5165) was only 0.7%. One of 

the two MPA alternatives was selected in about 76% of choice tasks. There is also 

evidence that many responders compared alternatives and did not choose arbitrarily. For 

example, 85% of responders varied their choices among the 5 choice tasks, and 20% 

switched between the SQ and one of the MPA alternatives. Only 3.8% of responders 

consistently chose either MPA Alternative 1 or Alternative 2. In contrast, the SQ was 

always chosen by 11% of responders. Choice-certainty was requested after each choice 

task with the following question: “Inexperience with a commodity such as Marine 

Protected Areas can sometimes cause uncertainty. How certain do you feel about your 

choice?” Over 60% of the responders were either very certain or somewhat certain about 

their choices each time, and 75% of them never chose somewhat uncertain or not certain 

at all after the choice task. 

14
Choice model results are presented in Table 3. We focus primarily on the results of the 

latent class specification; however, the base multinomial logit model is presented for 

comparative purposes, underscoring the need for more flexible models. Independent 

variables in the model included the choice experiment attributes size, use, and cost. Sizesquared was specified because focus group information suggested that there is 

diminishing marginal utility for increasing the size. Size and cost attributes were treated 

as continuous variables. Effects-coding (Fx) was used for the use level attribute because 

it is ordinal but not linear (Table 4). Effects-coding creates dummy variables for all N-1 

use levels, leaving one level - in this case the SQ - as a base case (coded as -1). 

Results of the multinomial logit model confirmed diminishing marginal utility for size. 

Use had a negative influence on value in the strict no-take case, but became positive once 

access for scientific research and education were allowed. The other uses diminished the 

value of MPAs, but it was still positive. The cost parameter in the multinomial logit 

model has the expected negative sign and is significant. Though the pseudo R2

 value is 

small, this model seems generally intuitive and not out of line with any opinions 

expressed during the focus groups and cognitive interviews. 

The multinomial logit model conceals important differences among responders that can 

lead to results that are not always consistent with economic theory. The latent class 

estimator identified three classes of responders with distinctly different preferences that 

are homogenized by the multinomial logit model. Segregating the data into three groups 

significantly improved the fit (the pseudo R2 tripled). 

The multinomial and Class1 models are qualitatively similar, although parameter 

estimates are quite different due the mixing of different preferences. We describe Class 1 

(59% likelihood being in the class) as having relatively moderate preferences because 

they get positive utility from reserves but this is augmented by allowing compatible uses. 

There was a 24% likelihood that responders received disutility from MPAs judging from 

the sign on the size attribute and fell into Class 3. If there are reserves, Class 3 prefers 

that they be used to the maximum extent. 

Class 2 (17% likelihood that responder fell into this class) appears to have the strongest 

preferences for ecological reserves in the region (compare parameters on size; Table 3), 

and it was the only group of responders who valued fully-protected areas without any 

type of use positively and fishing negatively. What is peculiar though is the positive 

parameter on the cost variable. This result precludes including responders in Class 2 in 

any welfare analyses, as discussed below. 

Analysis of the Latent Classes 

To help understand the differences among the three classes, we sorted responders by class 

based on their individual-specific class probabilities, and then examined class responses 

to other questions in survey. For all variables in the survey that were ordinal, such as the 

15
Likert-scale questions, education, and income categories, we used a Mann-Whitney U 

test to determine differences between a class pair. For continuous variables such as the 

distance of a household from the coast (Table 5) and age we used a t-test to examine 

differences between class pairs. Results of these analyses (Table 6) suggest that there are 

significant differences between classes for the socio-economic variables education level 

and distance from coast (based on respondent zip code information), and for several of 

the Likert scale questions, including: 

• We should allow exploration and drilling for oil and natural gas in the 

Northeast region to help create a stable supply and lower prices 

• MPAs can be an effective way to balance environmental protection and 

activities such as fishing and oil exploration 

• People can live with less economic growth in order to preserve and protect 

the environment 

• MPAs are not needed in Northeast Region because marine life and ocean 

habitat in the region are in good health 

Tests for significant differences between these variables are presented in Table 6 and 

Figures 2a-e. In general, Class 2 is the most highly educated and lives farthest from the 

coast, while Class 3 has the least education and lives closest to the coast. Class 1 falls in 

the middle on both variables, and was not significantly different in most comparisons of 

distance and education. 

Class 1 appears to consist of “middle of the road” individuals, whose preferences and 

attitudes about MPAs and the environment in general lie somewhere between those of the 

other two classes. Individuals in Class 2 are those who most strongy support MPA 

networks, even when the use level is completely restricted, i.e. a no-take network. They 

also disagree with the other two classes on drilling and oil exploration, as well as their 

opinions about the ecological health of the Northeast Region. Finally, while the model 

results suggested that individuals who fall into Class 3 receive disutility from protecting 

any additional area of the northeast waters in an MPA, a closer examination of this class 

suggests that these individuals are not necessarily anti-environmentalists, but may in fact 

simply not favor the use of MPAs as a means of protection. Results of the Mann-Whitney 

U test suggest that Class 3 responses only differed from Class 1 responses on one Likert 

scale question about whether MPAs are an effective way to balance environmental 

protection with other activities such as fishing and oil exploration. In addition, Class 3 

only differed from Class 2 on three Likert scale questions. These results represent the 

largest number of differences between any class pair. In short, the two classes who were 

the most different in terms of their model output only differed on three of the 21 Likert 

scale questions examining attitudes toward MPAs and the environment. Thus, an 

appropriate depiction of Class 3 may be “anti-MPA” rather than “anti-environment.” 

It is worth noting the counterintuitive insignificant difference between Classes 2 and 3 on 

the variable ‘MPAs can be an effective way to balance environmental protection and 

other activities’ in Table 6. As the difference between Class 1 and three was significant 

for this variable, we would expect the difference between Class 2 and three to also be 

significant. We suggest that, for this variable, Class 2 has an unusually high proportion of 

16
“I am unsure” responses (relative to their unsure responses on other questions) because 

the wording of the question implies that other activites such as fishing, oil exploration 

and drilling, etc... would be coexisting with MPAs, when in fact individuals in Class 2 

may not want these other activities to exist at all. If this were true, the best answer option 

for them would be “I am unsure.” When the unsure responses are removed from the data 

the difference between Class 2 and Class 3 becomes significant for this variable. 

While we might not know why Class 2 individuals had a positive cost response, the latent 

class specification allows us to determine who they are in the dataset, and to calculate 

their individual choice probabilities. In the empirical application below, we are then able 

to exclude these choice probabilities when calculating the compensating variation of 

different policy scenarios. 

Benefit Estimation

Hanemann (1982) derived the expression for compensating variation associated with a 

logit-type model under the assumption of no income effect: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ −⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = ∑ ∑ ∈ ∈Cj

jk

Cj

jk

k

CVi X X 0 1 expln expln 1 )12( β β β

where X0 represents the SQ and X1 the policy change. Boxall and Adamowicz (2002) 

modified equation (12) to accommodate classes of tastes in the sample with the weights, 

Hik: 

expln expln . 1 )13( 0 1

1 ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ −⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = ∑∑ ∑ = ∈ ∈Cj

jk

Cj

jk

k

K

k

i HCV ik β X β X

β

That is, the total average compensating variation is the sum of the K individual class 

values. As noted earlier, however, we do not allow CV for Class 2 to enter the equation, 

essentially excluding this segment from the analysis. 

Welfare estimates for the range of the size and use attributes are reported in Table 7 and 

graphed on Figures 3a-d. For comparative purposes, we also present calculations from the 

multinomial logit. Because the use attribute was effects-coded, we calculate a SQ 

coefficient by summing the four use level coefficients and multiplying by -1 (Louviere et 

al. 2000). We are then able to calculate welfare changes from SQ scenarios. 

The compensating variations for Class 1 are positive for various combinations of size and 

use (Figure 3a). Science and education was most highly valued by Class 1 at $134, 

followed closely by leisure and tourism and then pelagic fishing with maxima at about 

$123 and $130, respectively. Whether the latter three sets of value are significantly 

different is unknown. The no-take valuation is clearly lower than the other scenarios. 

Class 3 had negative valuations of each size-use combination, which is consistent with 

the disutility these responders receive from ecological reserves, particularly the no-take 

17
type (Figure 3b). In other words, Class 3 responders would require compensation to by 

indifferent to a network of ecological reserves. 

The combination of Class 1 and Class 3 results are simply a weighted average of the 

separate results. The mixture of distinctly different preferences results in an 

unconventional total value surface (Figure 3d). This is one example of how mixing 

people with structurally different preferences can yield results contrary to theory. The 

multinomial logit model is an amalgam of all responders, including Class 2. Estimates are 

qualitatively similar to those from Class 1 of the latent class model (Figure 3c). 

The marginal value curves for Class 1, Class 3, and multinomial logit are plotted in 

Figure 4. Curves for the four use levels of the same model are identical because the 

slopes of the total value curves are equal. Compensating variation for Class 1 is 

maximized at a 27% size regardless of the use level. The multinomial logit welfare is 

maximized at 26% size. In contrast, Class 3 is worse off when size is about 33%. 

Empirical Application – Valuation of Habitat Areas of Particular Concern (HAPC) 

In 2004 the New England Fishery Management Council requested formal proposals for 

HAPCs from the general public. Some of the qualifying criteria concerned fisheries 

management, but several were related to environmental protection: (1) the importance of 

historical or current ecological functions of the area; (2) the sensitivity of the area to 

anthropogenic stress; (3) the extent of current or future development stress faced by the 

area; and (4) rare habitat within the area. 

Nearly all proposals were submitted by environmental organizations interested in setting 

aside exclusive areas dedicated to protecting species and their habitats Three seamounts 

in the U.S. chain, 16 deep sea canyons throughout the shelf edge in New England and the 

Mid-Atlantic, several areas in the Gulf of Maine and Georges Bank described as having 

unique characteristics in the region, and an extensive area around the Great South 

Channel were nominated for HAPC protections. 

At its June, 2007, meeting the New England Council accepted all of the canyon areas and 

two of the seamounts. However, it tabled the Great South Channel area for further 

development and replaced nominations on the shelf with several existing EFH Habitat 

Closure Areas. The current areas proposed by the council for HAPCs are listed in Table 8 

and mapped on Figure 5. 

Selecting a SQ for estimation of compensating variation has become problematical since 

the survey was completed because the proposed and many of the current HAPCs and 

EFH Habitat Closure Areas share some of the characteristics of ecological reserves. As 

mentioned above, the Stellwagen Bank National Marine Sanctuary (which occupies about 

0.56% of federal waters; Figure 6) is the only official MPA in federal waters in the 

Northeast Region, but it does not regulate fisheries, including dredges and bottom trawls. 

Alternatively, some of the fishery management areas also provide incidental diversity 

benefits, particularly the juvenile cod HAPC on Georges Bank, the monkfish closures in 

18
two canyons, and the groundfish and sea scallop EFH Habitat Closure Areas created by 

Amendments 13 and 10 (Figure 6). However, these managed areas were not designed as a 

network to promote general ecological diversity, and they are not indefinite because they 

could be erased by fishery managers at any time if they are not promoting stock recovery. 

Thus, neither candidate for SQ was ideal, so both were used in the analysis to test the 

sensitivity of compensating variation estimates for Classes 1 and 3 (Table 9). The 

proposed HAPCs (Table 8) were grouped into two bundles: seamounts and canyons 

(2.2% of the EEZ), and all proposed HAPCs (5.1%). Double-counting was avoided by 

using ArcGIS 9.1 to erase areas of the HAPCs that overlapped each other or the SQ (see 

Figure 6). 

Valuation of the two HAPC combinations by Class 1 responders ranged from $52-$59 

per-household annually for the no-take use-level, depending on the SQ (Table 9). 

Estimates of average compensating variation doubled to $107-$114 when scientific 

research and education were allowed. The other use-levels were $3-$10 less than for 

Science and Education. Although more clearly seen at larger sizes, estimates of 

compensating variation were smaller for the larger SQ due to an endowment effect and 

diminishing marginal utility. That is, the value of a particular combination of HAPCs 

decreases when your initial holdings increase. 

Class 3 estimates of the average household valuation of the proposed HAPCs ranged 

from -$40 to -$28 for No-take status and increased with use-level and became positive in 

some cases (Table 9). 

Discussion 

Importance of latent class estimator: It was not surprising to learn that the general public 

has heterogeneous preferences for ecological reserves. However, the latent class results 

demonstrate the importance of using an estimator that can differentiate structural 

preferences in a sample. Responders with normal, positive preferences for ecological 

reserves were combined with responders who would experience dis-utility. Mixing 

preference this way can result in peculiar economic relationships, such as the convex total 

value curve graphed in Figure 3d. 

Perhaps more troubling with the usual practice of combining data from all responders in 

one equation is inclusion of incongruous responses to costs. Taken at face value, the 

Class 2 model suggests that nearly a fifth of the responders would ignore their budget 

constraint and pay for more reserve size the more costly it became. This result might be 

an artifact of the survey design (see below), but the modeling results otherwise made 

sense. Class 2 responders valued size more than any other group and they were the only 

ones who had positive value for fully-protected no-take areas and who gave fishing a 

negative valuation. 

There are several possible explanations for the positive estimates on the Class 2 cost 

parameter: (1) high income response; (2) careless answers to choice questions; (3) 

19
hypothetical bias; (4) an experimental design that induced a high degree of collinearity 

between size and cost attributes; and (5) non-conforming preference structures. The idea 

that these households had much higher incomes than the other classes and annual costs of 

up $150 (the highest value for cost used in our study) would be negligible was rejected 

because the classes’ income distributions were indistinguishable. 

Hypothetical bias is a strong possibility. It exists when the hypothetical nature of the 

survey causes the responder to ignore the price constraint and, in our case, choose 

alternatives on the basis of size and use attributes alone. A growing literature examines 

this issue, and while many studies find evidence of discrepancies between stated and 

actual behavior (List and Gallett 2001; Johannesson 1997) some research suggests that 

hypothetical bias may not be universal (Johnston 2006) or “may not be as significant a 

problem in stated preference analyses as is often thought” (Murphy et al. 2005). If a 

responder who was vulnerable to hypothetical bias favored larger, more restrictive MPAs, 

a positive cost parameter is likely. It is also conceivable that some responders inferred or 

associated something about the scenarios with higher costs that was not described in the 

survey. For example, responders may have inferred that programs with higher prices 

would be more successful, or would stand a better chance of being instituted, and thus 

they may have strategically chosen higher-cost programs. 

A confounding factor to hypothetical bias is the experimental design that was used to 

generate the choice task questions. In order to exclude what responders would likely 

consider unrealistic scenarios (i.e., a larger MPA costing less than a smaller MPA that 

had the same allowable uses), constraints were placed on the experimental design. In 

effect, the constraint introduced a degree of collinearity between MPA size and cost, and 

ultimately may have forced responders who preferred larger MPAs to ignore the price 

constraint. Although this collinearity is undesirable, our focus groups and previous 

experience suggested that cognitively unacceptable choice scenarios would likely be a 

larger problem for the majority of responders, thus we chose to include the experimental 

design constraints. 

Notwithstanding the possibility that Class 2 responders ignored the cost constraint, the 

above discussion of the consistency of the Class 2 model results suggests that these 

responders answered thoughtfully. This raises the possibility that Class 2 exhibits a 

preference structure that is incommensurable with the conventional neoclassical 

assumption of indifference (Clark et all. 2000; Edwards 1986; Rekola 2003, Stevens et al. 

1993). The environmental economics literature on incommensurable preferences is sparse, 

but reports of up to nearly 80% lexicographic responses to contingent valuation questions 

about wildlife have been published (Stevens et al. 1991). 

Rekola (2003) examines the question rigorously beginning with a theoretical model. He 

finds evidence of L*-ordering in his and others’ empirical work, but the frequency in a 

population depends on a number of factors, including endowments and how narrowly the 

environmental good or service is defined. The incidence of lexicographic preferences 

should be considerably lower when the environmental good is narrowly-defined as a 

particular resource and paired with income in specific circumstances (e.g., preservation of 

20
species and habitat diversities in federal waters of the Northeast Region versus 

preservation of global biodiversity in the world’s oceans). After reviewing several 

empirical studies, Rekola (2003) reported that the percent of responders demonstrating 

non-compensatory behavior and attitudes was not as high as reported, but ranged from 

0% to 48% with a 15% mean. 

Although interesting, the question about why Class 2 responded positively to costs can 

not be easily resolved with data from our survey. To explore the topic more fully ex-post 

interviews would be required, which are beyond the scope of this research. 

Reserve size: Our results are germane to the scientific and public policy debate about the 

size of ecological reserves. Here, too, it is important to separate the analysis by latent 

class due to qualitative differences in preferences. The preference of the disutility class of 

households for no reserves is antithetical to the objectives of the environmental 

community in the northeast (and elsewhere) which is seeking at least 20% protection of 

the ecosystem, including the New England seamounts, submerged canyons, cold water 

corals, and rare habitats (CLF 2006). Class 1’s optimal size of 27% comports with the 

regional environmental community’s goal (CLF 2006) and falls well inside the range of 

10%-40% that scientists say is needed to preserve ecological diversity at its maximum 

level (NRC 2001). Class 2 might prefer an amount greater than 27%, but the optimum for 

the disutility Class 3 is 0% 

We do not know the size requirements for complete protection of the species and habitat 

diversities of the Northeast Continental Shelf Large Marine Ecosystem and the deeper 

waters of the EEZ. However, responders who had an opinion were evenly split (a third 

each) on the statement “MPAs should be large enough to protect every type of plant and 

animal, regardless of cost” (question 6_1, Appendix). Unlike scientific research which 

does not consider opportunity costs, households look at a wide range of needs and wants 

competing for their dollars. Optimum size for Class 1 will be less than 27% once costs 

(and comparisons at the margin) are factored in (discussed next). 

Comparing reserve benefits and costs: A benefit-cost analysis of ecological reserves is 

beyond the scope of this project. Although the New England Fishery Management 

Council has selected HAPCs for evaluation, alternative bundles of areas and regulations 

of fishing and other activities have not been developed. Nevertheless, a few comments 

about where non-use value fits in would be useful. To simplify matters, we ignored any 

potential sampling and selection biases when extrapolating valuations to the region. We 

also restricted the inquiry to the Science and Education use and the Stellwagen SQ. In 

these circumstances, estimates of compensating variation for Class 1 was $108 for the 

seamounts and canyons HAPCs (2.83% of the EEZ,) to $114 for all HAPCs (7.83%) 

(Table 9). For Class 3 the respective values were -$22 and -$33. 

A simple extrapolation of Class 1 (48% of responders) to the region’s 22 million 

households in the region equals about 11 million households. Therefore the total annual 

value of the two bundles of HAPCs is approximately $1.2 billion. However, the 28% of 

responders in Class 3, which extrapolates to more than 6 million households, had 

21
negative valuations amounting to -$0.14 billion and -$0.20 billion for the two HAPC 

scenarios. On net, the regional annual valuation of proposed HAPCs is about $1 billion. 

This huge number needs context before anyone concludes that ecological reserves are the 

highest valued use of the ocean. First, a calculation of the total value of any popular good, 

service, or asset would yield eye-popping results. For instance, consider the total gross 

value of product landed by only one fishery. The sea scallop fishery landed 59 million 

pounds of sea scallop meats in 2006 with an estimated total gross value of about $0.5 

billion (assuming a linear demand between the $6.54 dockside price and a $12 choke 

price). 

A second point to make is that use has at least as much influence on value as does size 

(Table 7; Figure 3). Further, most value associated with size is gained in the first 5% - 

e.g., 85% for Class 1 and the Science and Education use (Figure 3a). Therefore, the 

marginal value of an increase in reserve size beyond 5% appears to be rather small. 

Finally, costs will have a strong effect on efficient size. There are three categories. One 

category is the transaction costs of developing a viable network over several years of 

research, debate, lobbying, consulting, contracting, and planning to get a consensus on 

reserve location, size, allowable uses, and rules and regulations (Helvey 2004). These 

activities expend a lot of productive resources and, in general, probably are the most 

costly part of the process of MPA formation. 

A second category of costs which is incurred each year after reserves are established is 

operating costs, including research, management, maintenance, monitoring, and 

enforcement (Balmford et al. 2004). Operating costs are the most tangible costs, but they 

probably are the least expensive of the three categories. 

The third category of costs is also incurred annually. The opportunity costs of forgone 

production by activities that have been excluded or restricted from a reserve area (net of 

gains elsewhere, if at all, and reduced by spillover costs) could be quite high in some 

places. Commercial and recreational fishing (Figure 7), oil and gas exploration and 

production, sand and gravel mining, and, potentially, aquaculture and renewable energy 

from windmills (etc.) contribute valuable products for the economy and consumers. 

The final remark about context concerns how costs affect the efficient size. One way to 

envision this is through a benefit-cost analysis framework which combines the value and 

costs of a network of ecological reserves: 

t

nt

t

O

t

nt

t

P

t

nt

t

I

t

t

kt

R

t

r

LSCLSCLSVLSC

NPV )1(

),(),(),(),(

0 0 0

0

+

⎥

⎦

⎤ ⎢

⎣

⎡

− + − −

=

∑∑∑∑

=

=

=

=

=

=

=

−=

where NPV is net present value, S is reserve size, L is use-level, CR

 are transaction costs 

of reserve development, VI

 is reserve value for Class 1, CP is reserve operating costs, CO

22
is reserve opportunity costs, t is time period, and r is the discount rate. CR

 are incurred 

before a network is implemented (hence the negative value on t). This simple formula 

belies many difficulties in data collection, extrapolation of value data to the population, 

and estimation of the opportunity costs of excluded current and sometimes future 

activities (commercial, recreational, and industrial). 

Heterogeneous preferences for reserves would also have to be factored in. Class 3’s disutility is incorporated by augmenting the value term to be [VI

 (S,L) + VIII (S,L)]. The 

positive preference for reserves expressed by Class 2 does not fit into this equation, but it 

needs to be counted by decision-makers. 

The theoretical point of efficient size is illustrated in Figure 8 for Class 1 and the Science 

and Education use. Household demand, or marginal value, was derived by estimating a 

quadratic regression of compensating variation on reserve size and differentiating with 

respect to size. Being a public good with non-use characteristics, many people can enjoy 

knowing about ecological diversity at the same time without causing any negative 

spillovers either to the resource or on each other. As a result, the marginal valuations for 

individuals in Class 1 are concatenated vertically (instead of horizontally as for 

consumers’ demand for sea scallops). Household demand was expanded by 11 million 

households (i.e., 48% of responders in Class 1 times 22 million households in the region). 

The two marginal cost curves on the graph represent hypothetical low cost and high cost 

situations. The most efficient allocation is found where marginal benefit is equal to 

(intersects) marginal cost. In an unrealistic no-cost situation - which is tacit in the 

requests of the environmental community – efficient resource allocation is where 

marginal benefits equal zero (27% for Class 1). Adding costs shifts the efficient choice to 

lower levels of protection, but how far depends primarily on the opportunity costs of 

excluded production and other activities and on operating costs. (At this point in the 

MPA process, transaction costs would be considered sunk costs.) Adding the negative 

valuation of Class 3 responders would lower aggregate demand and further shift the 

efficient solution to a smaller size. 

Conclusions 

Coastal states worldwide have begun to designate MPAs that occupy 25% or more of a 

Large Marine Ecosystem. This scale comports with scientific estimates of needs for 

ecosystem protection. However, boundaries are set without information about the general 

public’s valuation of MPAs, or the costs to industries and the economy of displaced 

activities. Our research is one of the first empirical analyses of a general public’s 

valuation of the non-market benefits of important attributes in the design of ecological 

reserves - total size and different levels of low impact uses by others. No study found in 

the literature provides comparables to validate our estimates because of differences in the 

type of benefits studied (e.g., personal use of an MPA for sport diving versus non-use) 

and our choice of the latent class model. 

23
Although our data support a public policy that protects ecological diversity in the ocean, 

strict no-take, no-use reserves were the least valuable type of design for 76% of the 

responders (Classes 1 and 2). For these responders ecological reserves that allowed 

access for scientific research and education were the most highly valued. Beyond that, 

leisure and tourism and limited fishing diminished value. In contrast, valuations by the 

28% of responders in Class 3 who are identifiable by their disutility for reserves became 

less negative as access became more liberal. 

Over 80% of the responders said that they supported having ecological reserves in federal 

waters of the Northeast Region. This claim is supported by the choice experiments where 

about three-quarters of the responders selected alternatives with costs ranging from $10 

to $150 per year indefinitely. It also matches the findings of a telephone survey 

sponsored by environmental organizations which asked households in New England and 

the Canadian Maritimes about their attitudes towards fully-protected marine areas (Edge 

Research 2002). Our results do not, however, endorse unconstrained sizes, and in fact 

suggest that smaller reserves with liberal uses may provide considerably more value than 

larger no-take reserves. 

 

24
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29
Table 1 

Attributes and Levels of Experimental Design 

Attribute Level 

MPA Network Size as a 

percent of the total federal 

waters of the northeast region 

5% (5 million acres) 

10% (11 million acres) 

20% (22 million acres) 

30% (32 million acres) 

40% (43 million acres) 

Allowable Uses within the 

MPA Network 

Level 1 – No-take 

Level 2 – Science and Education 

Level 3 – Recreation and Tourism 

Level 4 – Limited Fishing 

Cost 

$10 per year 

$25 per year 

$50 per year 

$100 per year 

$150 per year 

30
Table 2 

Information Criterion Measures 

Number of Latent 

Classes 

AIC BIC 

Two 9330 9346 

Three 9250 9274 

Four 9254 9286 

31
Table 3 

Parameter Estimates for Choice Models 

Latent Class 

Attribute Base MNL 

Latent Class 1 Latent Class 2 Latent Class 3 

Size 0.0207** 0.0537** 0.0604** -0.1239** 

Size2 -0.0004* -0.0010** -0.0006* 0.0019** 

No-take -0.3811** -0.4736** 0.3158** -0.5144** 

Science and Education 0.3391** 0.7952** 0.5224** -0.2853** 

Recreation and Tourism 0.2936** 0.5524** 0.2908** 0.3611** 

Limited Fishing 0.2753** 0.7154** -0.2901** 0.4048** 

Cost -0.0063** -0.0127** 0.0026** -0.0071** 

Latent class probs. na 0.59 0.17 0.24 

Pseudo R-squared 0.0638 0.1877 

** significant at p < .01 

 * significant at p < .05 

32
Table 4 

Effects Coding of Use Attribute 

Use Level Fx1 Fx2 Fx3 Fx4 

No-take 1 0 0 0 

Science and Education 0 1 0 0 

Recreation and Tourism 0 0 1 0 

Limited Fishing 0 0 0 1 

Status Quo -1 -1 -1 -1 

33
Table 5 

Average Distance from Shore (miles) 

Class Distance 

1 7.11 

2 5.67 

3 7.16 

34
Table 6 

Significant Differences Between Classes 

Distance Education Allow gas 

exploration 

Balance 

protection 

and other 

activities 

Live with less 

growth to 

protect 

environment 

Region in 

good 

health, 

MPAs not 

needed 

Mann-Whitney U 

(p-value) Class 0.06 0.07 ns ns ns 

1 & 2 t-test (p-value) 0.00 

Mann-Whitney U 

(p-value) 

Class 0.09 ns 0.08 ns ns 

1 & 3 t-test (p-value) ns 

Mann-Whitney U 

(p-value) 0.01 0.06 ns 0.09 0.09 Class 

2 & 3 t-test (p-value) 0.03 

35
Table 7

Estimates of Compensating Variation Corresponding to Survey Attribute Levels 

Other than Costs. Values are annual changes in the total welfare of households in 

the Northeast Region due an increase in the size of MPAs in federal waters from 

current levels of the Stellwagen Bank National Marine Sanctuary (0.6%). Class 2 

can not be analyzed because of the theoretically incongruous estimate of the positive 

cost parameter. Estimates from the multinomial logit model are presented for 

comparison. 

Size 

Use Level Model 

5% 10% 15% 20% 25% 30% 35% 40% 

Class 1 58 66 72 76 78 78 75 71 

Class 3 -38 -54 -67 -76 -83 -86 -86 -83 

Classes 1 & 3 22 15 8 3 -2 -6 -8 -10 

No-Take 

Multinomial logit 20 26 31 34 35 35 32 28 

Class 1 113 121 127 131 133 133 131 126 

Class 3 -30 -46 -59 -69 -75 -78 -78 -75 

Classes 1 & 3 85 78 71 65 61 57 55 53 

Science and 

Education 

Multinomial logit 83 89 94 97 98 98 95 91 

Class 1 102 111 117 121 123 123 120 115 

Class 3 -8 -24 -37 -47 -53 -56 -57 -53 

Classes 1 & 3 96 89 82 77 72 68 66 64 

Leisure 

(passive) 

Multinomial logit 79 85 90 93 94 94 91 87 

Class 1 109 118 124 128 130 130 127 123 

Class 3 -7 -23 -36 -45 -52 -55 -55 -52 

Classes 1 & 3 105 97 91 85 81 77 74 73 

Fishing 

(pelagic) 

Multinomial logit 77 84 88 91 93 92 90 86 

36
Table 8 

Areas Proposed for Habitat Areas of Particular Concern 

Size Category Area 

Size 

(% of EEZ waters)

Bear & Retriever seamounts 0.310 

Heezen canyon 0.032 

North assemblage (Lydonia, 

Gilbert, Oceanographer) 0.744 

Hydrographer canyon 0.068 

Veatch canyon 0.062 

Alvin and Atlantis canyon 0.312 

Hudson canyon 0.211 

South assemblage (Toms, 

Hendrickson, & inter-areas) 0.296 

Wilmington canyon 0.093 

Baltimore canyon 0.062 

Washington canyon 0.036 

Seamounts and 

Canyons (1.8%) 

Norfolk canyon 0.042 

Cashes Ledge Habitat Closure 

Area 0.114 

Western Gulf of Maine Habitat 

Closure Area Shelf areas (2.6%) 0.583 

Gulf of Maine inshore cod (20 

m line) 1.970 

Total HAPC 4.6 

37
Table 9

Estimates of Compensating Variation Associated with HAPCs Proposed for EFH 

Omnibus Amendment #2.Two status quo (SQ) conditions are used (a) Stellwagen 

Bank National Marine Sanctuary (SBNMS) and the combination of SBNMS and 

pre-existing HAPCs (Georges Bank cod, closures in monkfish plan) and EFH 

habitat closures in the groundfish and sea scallop plans. HAPCs are grouped in two 

bundles: (a) seamounts and canyons and (b) all proposed HAPCs. Use-level is 

Science and Education. Values are average annual valuation per household in 

Classes 1 and 3. HAPC percentages include the SQ. 

SQ 1: SBNMS (0.56%) 

SQ 2: SBNMS, PRE-EXISTING 

HAPCs, AND ALL EFH HABITAT 

CLOSURES (5.56%)

Class Use 

Level

Seamounts and 

Canyons 

(2.83% with SQ) 

All HAPCs 

(5.71% with SQ) 

Seamounts and 

Canyons 

(7.83% with SQ) 

All HAPCs 

(10.71% with SQ) 

 No-take $53 $59 $52 $57 

 Science $108 $114 $107 $112 

 Leisure $98 $104 $97 $101 

1 

 Fishing $105 $111 $104 $109 

 No-take -$30 -$40 -$28 -$37 

 Science -$22 -$33 -$21 -$29 

 Leisure $0 -$11 $1 -$8 

3 

 Fishing $1 -$9 $3 -$6 

38
Figure 1 

Sample Choice Task 

Please compare the Alternatives below and choose the one Alternative you would most 

prefer for the Northeast Region. 

Alternative A Alternative B Current Situation 

What is the combined Size

of all of the MPA areas in 

the network? 

2% 

(2 million acres) 

2% 

(2 million acres) 

What are the Allowable 

Uses within the MPA 

network? 

Level 4 

Limited Fishing

Level 2 

Science and 

Education

What is the Cost to you 

each year if you voluntarily 

support an MPA network? 

$25 $150 

Click here to see 

the current 

situation again 

<hyperlinked to 

Section 5>

Which do you most prefer 

for the Federal Waters of 

the Northeast Region? 

 

I prefer 

Alternative A 

 

I prefer 

Alternative B 

 

I prefer the 

Current Situation 

39
Figure 2 

Demographic and responses to Likert-scale attitudinal questions that were 

significantly different between at least two latent classes. 

Education

0

5

10

15

20

25

30

35

40

Refused

Less than HS

Some HS

HS Grad

Associates Degree

Bachelors Degree

Masters Degree

Professional Degree

Doctorate

class 1

class 2

class 3

2a. 

Allow oil/gas exploration and drilling

0

5

10

15

20

25

30

Strongly

Agree

Agree Neither

Agree

nor

Disagree

Disagree Strongly

Disagree

I am

unsure

class 1

class 2

class 3

2b. 

40
MPAs effective for balancing competing activities

0

5

10

15

20

25

30

35

40

45

50

Strongly

Agree

Agree Neither

Agree nor

Disagree

Disagree Strongly

Disagree

I am

unsure

class 1

class 2

class 3

2c. 

Live with less growth to protect environment

0

5

10

15

20

25

30

35

40

45

50

Strongly

Agree

Agree

Neither

Agree nor

Disagree

Disagree

Strongly

Disagree

I am

unsure

class 1

class 2

class 3

2d. 

41
MPAs not needed b/c region is in good health

0

5

10

15

20

25

30

35

40

45

50

Strongly

Agree

Agree

Neither

Agree nor

Disagree

Disagree

Strongly

Disagree

I am

unsure

class 1

class 2

class 3

2e. 

42
Figure 3 

Household average annual valuations (compensating variation) of the Size and Use 

Level attributes, by latent class. No-take is black diamond; Science and Education is 

white circle; Leisure and Tourism is black circle; Fishing is white diamond. 

a. Class 1

$0

$30

$60

$90

$120

$150

0 10 20 30 40 5

Size (% EEZ)

0

b. Class 3

-$150

-$120

-$90

-$60

-$30

$0

0 10 20 30 40 50

Size (% EEZ)

43
c. Basic Logit Model

$0

$30

$60

$90

$120

$150

0 10 20 30 40 5

SIZE (%)

0

d. Classes 1 and 3

-$30

$0

$30

$60

$90

$120

0 10 20 30 40 50

SIZE (%)

44
Figure 4 

Marginal Value of Size and Optimal Allocation when Costs are Zero 

-$2

-$1

$0

$1

$2

0 10 20 30 40 5

Size (% of EEZ)

0

Class 1 (positive utility)

Class 3 (disutility)

Basic logit

27%

26%

45
Figure 5 

HAPCs Proposed by the New England Fishery Management Council for EFH 

Omnibus Amendment # 2. 

46
Figure 6 

Overlap of Areas in Status Quo Scenarios with Proposed HAPCs 

47
Figure 7 

2005 Commercial and For-Hire (recreational) Fishing Trips that Fell Within the 

Boundary Lines of the Nominated HAPCs 

48
Figure 8 

Regional Demand for Marine Ecological Diversity by Class 1 (approximately 13 

million households). Individual marginal valuations were concatenated vertically 

because ecological diversity is a public good. Marginal costs are hypothetical. Size is 

allocated efficiently where marginal cost crosses demand. Values are $million 

annually. 

$0

$5

$10

$15

$20

$25

0 5 10 15 20 25 30

Size (% of EEZ)

Demand

Low cost

High cost

Optimum size equals 

 27% when MC=0

Optimum size 

 < 27% when MC 

 is relatively low

 Optimum size is

 much < 27% when MC is 

relatively high

49
Appendices to: 

“Estimating Non-market Values for Marine Protected Areas: 

A Latent Class Modeling Approach”

Appendix A: The web-based questionnaire 

Appendix B: Sampling and self-selection biases 

Appendix C: Responses to attitudinal and opinion questions 

50
Appendix A 

Web-based Questionnaire 

[display] Marine Protected Areas 

NOAA Fisheries is sponsoring a survey to understand opinions and support for Marine 

Protected Areas in the Northeast Region of the United States. The survey is being 

given to a select group of households from coastal states between North Carolina and 

Maine. 

YOUR OPINIONS ARE IMPORTANT! The only way for managers to understand the variety 

of viewpoints people have about Marine Protected Areas is for everyone who is 

contacted to fill out the questionnaire completely, even if you have not heard about 

Marine Protected Area before. 

The survey should take about 20 minutes to complete. All answers are confidential and 

anonymous. You will receive 7,500 bonus points for completing this survey.

In the survey, you can click on any <in yellow>yellow text</in yellow> to see a map or more 

information on the highlighted subject. 

Thank you very much for your participation. 

51
[display (show section numbers and titles in programmed survey)] 

Section 1. Marine Protected Areas in the Northeast Region 

This survey asks for your views on having Marine Protected Areas in the Northeast Region 

of the United States, to protect a variety of marine life and sea floor habitats. 

Marine Protected Areas could be located in the federal waters of the Northeast Region. 

Federal waters lie between state waters and the 200-mile limit of the U.S. government’s 

jurisdiction. This survey is not about Marine Protected Areas in state waters along the 

coast. 

Currently there are no proposals to create Marine Protected Areas in the Northeast Region 

that are designed primarily to preserve all kinds of marine life on the seafloor, but the 

possibility is being discussed. In theory, this type of Marine Protected Areas could be 

placed anywhere in federal waters that are acceptable on economic, social, and scientific 

grounds. 

52
[radio]

Q1. Before receiving this survey, had you heard or read about MPAs (Marine Protected Areas)? 

  Yes 

  No 

Q2. Have you ever visited a MPA anywhere in the United States? 

  Yes 

  No 

[DISPLAY]

Section 2. Background Information About the Ocean and Marine Protected Areas 

In 2000, the U.S. Congress created an independent Commission on Ocean Policy to 

review the condition of the oceans and to recommend ways to improve resource 

management. The 16 members of the Commission came from local, state, and federal 

governments, private industry, and academic and research institutions. 

[DISPLAY]

The Commission found that: 

 fishing, shipping, tourism, and other commercial, industrial, and recreational uses 

of the ocean are vital to the economies of coastal areas and for leisure 

 new industries, such as offshore aquaculture and renewable energy from winds 

and tides, can also be beneficial 

 the ocean environment and marine life are being degraded 

[prompt if any row skipped] 

Q2b – Q2f. Please answer each question by checking the appropriate box. 

Yes No 

Do you go saltwater fishing for fun or food in the Northeast Region?  

Is your job directly tied to the ocean (fishing, defense, shipping, marina, 

etc.)  

Do you or does anyone in your family fish commercially in the Northeast 

Region? 

 

Do you or does anyone in your family work for a charter boat or party boat 

fishing company?  

Are you currently a member of any type of organization involved with 

marine-based activities?  

[TEXT BOX] 

[IF Q2F=YES] 

Q2a. What is the name of the organization you belong to? 

________________ 

53
[DISPLAY]

Balancing economic growth with environmental protection is sometimes complicated. For 

example, ocean-based industries provide important jobs, income, and goods and 

services for the economy in the Northeast Region. At the same time, these industries 

can sometimes pollute the water, damage large areas of habitat, or harvest too many 

fish.. 

As one strategy to resolve the conflicts, the Commission recommended setting aside Marine 

Protected Areas, or MPAs, where most types of industrial and commercial activities are 

prohibited or strictly regulated. 

Q3a. Please check the one box that most closely represents your opinion. There are not any 

right or wrong answers – your opinion is what’s important. 

Strongly 

Agree Agree 

Neither 

Agree nor 

Disagree 

Disagree Strongly 

Disagree 

I am 

unsure 

MPAs can be an effective way to 

balance environmental protection 

and activities such as fishing and 

oil extraction 

     

[DISPLAY] 

Section 3. Benefits and Costs of Marine Protected Areas 

BENEFITS

Marine Protected Areas can be designed for many purposes. This survey is about a Marine 

Protected Area network that is designed to preserve ecological diversity on the sea floor. 

 A network means that instead of one large reserve there may be several smaller 

reserves placed throughout the Northeast Region. 

 Within the boundaries of the MPA network, human uses would be limited and 

regulated. 

54
[display] 

The primary benefits of the Marine Protected Area network are: 

 Preservation of the variety of marine life in the Northeast Region (fish, shellfish, 

plants) in their natural habitats on the bottom of the ocean - regardless of their 

commercial or recreational importance. 

 Preventing future industrial uses within the MPA network boundaries. 

There might also be incidental benefits associated with the MPA network, but these are 

uncertain. Incidental benefits might include increased catches by fisheries outside of the 

boundaries of the MPA network, a temporary sanctuary for animals that migrate into and 

out of the region (such as herring and tuna), and possible medical or pharmaceutical 

discoveries from species that were preserved. 

[GRID] 

[SHOW HEADER ON TOP AND BOTTOM OF SCREEN]

Q3. For each statement below, please check the one box that most closely represents your 

opinion. There are not any right or wrong answers – your opinion is what’s important. 

Strongly 

Agree Agree 

Neither 

Agree nor 

Disagree 

Disagree Strongly 

Disagree 

I am 

unsure 

MPAs are not needed in the 

Northeast Region because marine 

life and ocean habitats are in good 

shape 

     

I like knowing that part of the 

ocean in the Northeast Region is 

protected even if I never see or 

use it 

     

MPAs should not be established 

unless their rules and boundaries 

can be completely enforced 

     

55
[DISPLAY] 

COSTS

There are also costs associated with Marine Protected Areas. The primary costs are: 

 Higher production costs for industries that are affected by MPAs. 

 Higher costs to producers could mean higher prices for consumers. 

 Increased regulation of recreationists, scientists, and businesses who are allowed to 

use the MPAs. 

 Financial expenses associated with MPA management, administration, and negotiation 

with stakeholders, possibly leasing sites from the government, monitoring, and 

enforcement. 

[GRID] 

[SHOW HEADER ON TOP AND BOTTOM OF SCREEN]

Q4. For each statement below, please check the one box that most closely represents your 

opinion.. 

Strongly 

Agree Agree 

Neither 

Agree nor 

Disagree 

Disagree Strongly 

Disagree 

I am 

unsure 

I would be willing to pay higher 

prices for products such as 

seafood and energy to preserve 

areas of the ocean in the 

Northeast Region 

     

Businesses and employees should 

be compensated for any lost 

income as a result of MPAs 

     

The costs of establishing MPAs in 

the Northeast Region most likely 

outweigh the benefits 

     

56
[DISPLAY] 

Section 4. The Size of Marine Protected Areas 

The appropriate size of a network of Marine Protected Areas is a difficult and 

controversial question for policy makers. Economic, social, and political considerations 

become as important as scientific relationship when final decisions are made. 

We are concerned here with the scientific relationships between the size of Marine 

Protected Areas and the variety of fish, shellfish, and plants that can be preserved in their 

natural habitat on the sea floor. In other words, how much ecological diversity can be 

protected in Marine Protected Area networks of different sizes? 

[DISPLAY] 

In 2001, the National Academy of Sciences assembled a group of 13 marine scientists from 

universities around the world to summarize what was known about the design of Marine 

Protected Areas in Florida, California, and other countries. The group reported that no two 

places are alike, but in general: 

 Between 10% and 40% of a regional ocean ecosystem might be needed to preserve 

all of the species of marine life in their natural habitats 

o Even so, Marine Protected Areas that cover relatively small areas still 

protect at least part of the ecological diversity. 

This brief summary of the scientific research on Marine Protected Areas serves as a rough 

guideline for the Northeast Region, although no one knows the exact size that would be 

needed to protect all types of marine life and habitats on the sea floor in the Northeast 

Region. 

[GRID] 

[SHOW HEADER ON TOP AND BOTTOM OF SCREEN] 

Q6. For each statement below, please check the one box that most closely represents your 

opinion. 

Strongly 

Agree Agree 

Neither 

Agree nor 

Disagree 

Disagree Strongly 

Disagree 

I am 

unsure 

MPAs should be large enough 

to protect every type of plant 

and animal regardless of costs 

     

The federal government has a 

duty to protect marine life and 

habitats in a natural state 

     

57
[DISPLAY] 

Section 5. What’s the Current Situation in the Northeast Region? 

The MPA network discussed so far in this survey would be designed to provide lasting 

protection for ecological diversity on the sea floor. Currently, there are no Marine 

Protected Areas in the Northeast Region that are designed specifically for this purpose. 

Currently there is one official MPA in the Northeast Region - the Stellwagen Bank National 

Marine Sanctuary near Massachusetts, which covers less than one percent of the federal 

waters. In addition, other marine managed areas have been set up by federal fishery 

managers to help rebuild fish stocks and to protect marine mammals from fishing gear. 

These areas: 

• Allow types of fishing that can damage seafloor habitats and catch all types of fish 

on the sea floor, including fish that will be discarded 

• Are designed to protect only a select few number of species of marine life or their 

habitat 

• Can be canceled at any time once the fishery objectives are met (protections is not 

necessarily lasting) 

• Prohibit most industrial activities, such as oil exploration, waste disposal, and sand 

and gravel mining 

Fishery managers are currently deciding which of these marine managed areas qualify as an 

MPA. In addition, managers are discussing policies to protect habitat of commercially and 

recreationally important fish. 

[GRID] 

[SHOW HEADER ON TOP AND BOTTOM OF SCREEN] 

58
Q5. For each statement below, please check the one box that most closely represents your 

opinion. 

Strongly 

Agree Agree 

Neither 

Agree nor 

Disagree 

Disagree Strongly 

Disagree 

I am 

unsure 

Fishing gear that sits on the 

seafloor and does not damage 

habitat should be allowed in the 

MPAs even if it catches fish and 

shellfish living on the sea floor 

     

The areas now used by fishery 

managers probably provide 

enough protection for marine 

life in the Northeast Region 

     

MPAs in federal waters should 

be placed as far away from the 

coast as possible 

     

[DISPLAY] 

Section 6. Uses of Marine Protected Areas

Some types of human uses may be compatible with preservation of marine life and habitat 

on the sea floor. On the next screen, we will show you four use levels. The four use levels 

below describe activities that would not have long-term impacts or interfere with the 

preservation goals of the MPA network for the Northeast Region. Assume that: 

 The MPA network is located to avoid problems with shipping, transportation, and 

national defense. 

 All other industrial uses not described, such as oil and gas production, sand and 

gravel mining, waste disposal, and dumping, would not be allowed in the network, 

because these activities damage the sea floor. 

[DISPLAY] 

59
USE LEVEL 

Level 1: NO-TAKE 

All uses are prohibited inside the network of Marine Protected Areas. The areas are 

only monitored to determine their effectiveness at preservation 

Level 2: SCIENTIFIC RESEARCH AND EDUCATION

Uses involving scientific research or education are allowed, and a limited amount of 

resources can be extracted for these purposes. These activities are regulated. 

Level 3: TOURISM AND LEISURE

Tourism and leisure activities that do not remove anything, such as SCUBA, boating, or 

sightseeing, are allowed, plus all Level 2 uses. These activities are regulated. 

Level 4: LIMITED FISHING 

Commercial and recreational fishing in designated areas with fishing gear that does not 

touch the sea floor is allowed, plus all Level 2 and 3 uses. These activities are 

regulated. 

Most 

Use

Least 

Use

Q6a. Please check the one box that most closely represents your opinion. 

Strongly 

Agree Agree 

Neither 

Agree nor 

Disagree 

Disagree Strongly 

Disagree 

I am 

unsure 

The marine environment is a 

public resource and none of it 

should be restricted 

     

We should allow exploration 

and drilling for oil and natural 

gas in the Northeast Region to 

help create a stable supply and 

lower prices 

     

Fishing with gear that sits on 

the seafloor and does not 

damage habitat should be 

allowed in the MPAs 

     

60
[DISPLAY] 

Section 7. Public Input into the Network Design 

The next several questions ask you to assume that you are part of a Citizens Advisory Panel 

(CAP). CAP’s are often used when policy-makers need public input on specific topics. CAP’s 

are intended to be representative of a community, and consist of citizens from a wide 

variety of occupations. Most CAP members do not have in-depth scientific or technical 

knowledge of the specific topic. 

You, as a member of the CAP, are being asked about your opinions of alternative Marine 

Protected Area networks for the Northeast Region. The alternatives are different in their 

size, the uses they allow, and their cost. 

[radio] 

Q6b. Have you ever been a participant on a Citizens Advisory Panel before? 

Yes 

No 

[text box] 

[Q6b=yes] 

Q6c. What was the topic of the Panel? 

[DISPLAY] 

ON THE NEXT SCREEN, YOU WILL FIND A BRIEF DESCRIPTION OF WHAT WE MEAN BY SIZE, ALLOWABLE 

USES, AND COST. 

[DISPLAY] 

Size

The percentage of the sea floor in the federal waters of the Northeast Region 

that will be protected by the MPA network. Remember that coastal waters are not 

part of this study. 

Allowable 

Uses

The types of uses that will be allowed within the MPA network which are 

compatible with preserving sea floor habitats and marine life, ranging from Level 1 

to Level 4 

Cost

One viable mechanism to pay for the economic and financial costs of Marine 

Protected Areas is for households to make a voluntary contribution each year to an 

environmental organization: 

 the environmental organization works with the federal government to 

negotiate, lease, monitor, and enforce the Marine Protected Areas in the 

network 

 monies received by the government could be used to offset costs to 

industries and other parties restricted or excluded from the areas 

61
[display]

On the next several screens, you will see several pairs of Alternative A and Alternative B. 

For each pair, please review the information carefully and choose the alternative you would 

prefer. You may also select the current situation as your preference. 

[RADIO] 

[REPEAT THIS QUESTION FIVE TIMES] 

[ 

USE THE DESIGN PROVIDED IN THE EXCEL SHEET] 

Q8-Q12. Please compare the Alternatives below and choose the one Alternative you would 

most prefer for the Northeast Region. Remember you can click on Size, Allowable Use, and 

Cost links to be reminded of important information. 

Alternative A Alternative B Current Situation 

What is the combined Size

of all of the MPA areas in 

the network? 

What are the Allowable 

Uses within the MPA 

network? 

What is the Cost to you 

each year if you voluntarily 

support an MPA network? 

Click here to see 

the current 

situation again 

<hyperlinked to 

Section 5> 

Which do you most prefer 

for the Federal Waters of 

the Northeast Region? 

 

I prefer 

Alternative A 

 

I prefer 

Alternative B 

 

I prefer the 

Current Situation 

[RADIO] 

Q8A-Q12AInexperience with a commodity such as Marine Protected Areas can cause 

uncertainty. How certain do you feel about your choice? (Please check only one box) 

Very 

certain 

Somewhat 

certain 

Neither certain nor 

uncertain 

Somewhat 

uncertain 

Very 

uncertain 

    

[check box] 

62
[IF Q8=Q12: CURRENT SITUATION] 

Q8b-Q12b. Why did you choose the Current Situation? (Please check all responses that 

apply) 

 The annual costs of Alternatives A and B are too expensive 

 The costs of Marine Protected Areas for the economy are too high 

 Alternatives A and B restrict too many uses 

 No part of the ocean should be closed to the public 

 I don’t trust the government to be part of the program 

 I distrust environmental organizations being part of the program 

 Too much of the federal waters in the region is already restricted 

 I need more information to make a choice 

 I am too unsure about how I feel about Marine Protected Areas 

 Other(please explain) ___________________________________

[GRID] 

[SHOW HEADER ON TOP AND BOTTOM OF SCREEN]

Section 8. General Views about the Environment 

In the last section we would like to ask you a few questions about some general environment 

views. 

 [prompt if any row skipped] 

Q13. For each statement below, please check the one box that most closely represents 

your opinion. 

Strongly 

Agree Agree

Neither 

Agree 

nor 

Disagree 

Disagree Strongly 

Disagree 

I am 

unsure 

We should protect the environment 

for our children and future 

generations, even if it means 

reducing our own standard of living 

     

Plants and animals exist primarily to 

be used by humans      

I enjoy being outdoors to 

experience the beauty of nature      

People can live with less economic 

growth in order to preserve and 

protect the environment 

     

We can rely on new technology to 

fix environmental problems      

Government regulations and planning 

are always too costly and ineffective 

to benefit the environment 

     

63
64

[RADIO] 

Q14. How familiar were you with Marine Protected Areas before taking this survey? 

 Very familiar  Somewhat familiar  Unfamiliar 

[RADIO] 

Q15. How familiar are you now with Marine Protected Areas? 

 Very familiar  Somewhat familiar  Unfamiliar 

[TEXT BOX] 

Thank you for taking the time to complete this survey. Please provide any additional 

comments you have about the survey in the box below. 
Appendix B 

Sampling and Self-selection Biases 

Sampling Bias Table B-1. Test results for sample: comparison of full sample (responders and non-responders) to Census. 

Results Characteristic Un-weighted data We

General Specific Test Statistic df P (Ho: true) Statistic 

Total by state 115.427 14 <0.001 30.919 18-years or older by state 86.150 14 <0.001 19.610 

Populationa Total by coastal counties 

and inland arease

Chisquare 101.170 1 <0.001 19.878 

Total by state 16.399 14 <0.50 11.454 Total by coastal counties and inland arease

ChiHouseholds square 26.076 1 <0.001 6.085 

b

Average size in region t-testf 1.720 1341 <0.10 0.049 1

Race/ethnicity in regionc Chi- square 29.557 3 <0.001 5.829 Mean household income in regiond t-testf -15.216 1341 <0.001 -9.913 1aSurvey population was estimated as the responder plus the value of the PPHHSIZE variable provided by KN. bEach record was interpreted as a single household. cWe assumed that everyone in the household had the same race/ethnicity as the responder. dKN information on total household income was provided as intervals (e.g., $40,000 to $50,000). We used the m

intervals and the upper value of $200,000 . 

eWhen there is only one degree of freedom, the Yates Correction for Continuity is required. ft-tests are two-sided. 

65
66

Self-selection bias Table B-2. Self-selection bias: comparison of responders and non-responders. 

Results Characteristic Un-weighted data We

General Specific Test Statistic df P (Ho: true) Statistic 

Total by state 43.754 14 <0.001 34.162 18-years or older by state 34.931 14 <0.005 31.832 

Populationa Total by coastal counties 

and inland arease

Chisquared 0.763 1 <0.25 6.579 

Total by state 12.610 14 <0.75 16.750 Total by coastal counties and inland arease

Chisquared Households 0.138 1 <0.75 1.047 

b

Average size in region t-testf -4.566 1340 <0.001 -2.401 1

Race/ethnicity in regionc Chi- square 51.992 3 <0.001 97.925 Mean household income in regiond t-testf 0.619 1340 >0.50 0.774 1aSurvey population was estimated as the responder plus the value of the PPHHSIZE variable provided by KnowlbEach record was interpreted as a single household. cWe assumed that everyone in the household had the same race/ethnicity as the responder. dTwice the value of the log-likelihood ratio for contingency tables has a Chi-square distribution. 
Appendix C 

Summary of Responses to Attitudinal and Opinion Questions 

Q1. Before receiving this survey, had you heard or read about MPAs (Marine Protected 

Areas)? 

 REFUSED 1 

 Yes 203 

 No 833 

Q2. Have you ever visited a MPA anywhere in the United States? 

 REFUSED 3 

 Yes 106 

 No 928 

Q2B_1. Do you go saltwater fishing for fun or food in the Northeast Region? 

 Yes 173 

 No 864 

Q2B_2. Is your job directly tied to the ocean (fishing, defense, shipping, marina, etc.) 

 Yes 9 

 No 1028 

Q2B_3. Do you or does anyone in your family fish commercially in the Northeast 

Region? 

 Yes 37 

 No 1000 

Q2B_4. Do you or does anyone in your family work for a charter boat or party boat 

fishing company? 

 Yes 11 

 No 1026 

Q2B_5. Are you currently a member of any type of organization involved with marinebased activities? 

 Yes 29 

 No 1008 

Q3A_1. MPAs can be an effective way to balance environmental protection and activities 

such as fishing and oil ext 

 REFUSED 2 

 Strongly Agree 259 

 Agree 485 

 Neither Agree nor Disagree 207 

 Disagree 20 

 Strongly Disagree 9 

 I am unsure 55 

67
3_1. MPAs are not needed in the Northeast Region because marine life and ocean habitat 

 REFUSED 4 

 Strongly Agree 14 

 Agree 38 

 Neither Agree nor Disagree 248 

 Disagree 437 

 Strongly Disagree 221 

 I am unsure 75 

 Q3_2. I like knowing that part of the ocean in the Northeast Region is protected even i 

 REFUSED 6 

 Strongly Agree 315 

 Agree 521 

 Neither Agree nor Disagree 120 

 Disagree 28 

 Strongly Disagree 16 

 I am unsure 31 

Q3_3. MPAs should not be established unless their rules and boundaries can be complete 

 REFUSED 6 

 Strongly Agree 98 

 Agree 424 

 Neither Agree nor Disagree 272 

 Disagree 145 

 Strongly Disagree 45 

 I am unsure 47 

Q4_1. I would be willing to pay higher prices for products such as seafood and energy to 

preserve areas of the ocean in the Northeast Region 

 REFUSED 4 

 Strongly Agree 110 

 Agree 374 

 Neither Agree nor Disagree 260 

 Disagree 176 

 Strongly Disagree 58 

 I am unsure 55 

68
Q4_2. Businesses and employees should be compensated for any lost income as a result 

of MPAs 

 REFUSED 5 

 Strongly Agree 41 

 Agree 252 

 Neither Agree nor Disagree 383 

 Disagree 241 

 Strongly Disagree 53 

 I am unsure 62 

Q4_3. The costs of establishing MPAs in the Northeast Region most likely outweigh the 

benefits 

 REFUSED 4 

 Strongly Agree 43 

 Agree 197 

 Neither Agree nor Disagree 359 

 Disagree 234 

 Strongly Disagree 115 

 I am unsure 85 

Q6_1. MPAs should be large enough to protect every type of plant and animal regardless 

of costs] 

 REFUSED 5 

 Strongly Agree 86 

 Agree 253 

 Neither Agree nor Disagree 286 

 Disagree 295 

 Strongly Disagree 56 

 I am unsure 56 

Q6_2. The federal government has a duty to protect marine life and habitats in a natural 

state] 

 REFUSED 7 

 Strongly Agree 199 

 Agree 511 

 Neither Agree nor Disagree 214 

 Disagree 57 

 Strongly Disagree 18 

 I am unsure 31 

69
Q5_1. Fishing gear that sits on the seafloor and does not damage habitat should be 

allowed in the MPAs even if it catches fish and shellfish living on the sea floor 

 REFUSED 6 

 Strongly Agree 29 

 Agree 293 

 Neither Agree nor Disagree 287 

 Disagree 257 

 Strongly Disagree 80 

 I am unsure 85 

Q5_2. The areas now used by fishery managers probably provide enough protection for 

marine life in the Northeast Region 

 REFUSED 8 

 Strongly Agree 14 

 Agree 132 

 Neither Agree nor Disagree 342 

 Disagree 316 

 Strongly Disagree 99 

 I am unsure 126 

Q5_3. MPAs in federal waters should be placed as far away from the coast as possible 

 REFUSED 10 

 Strongly Agree 39 

 Agree 235 

 Neither Agree nor Disagree 411 

 Disagree 191 

 Strongly Disagree 45 

 I am unsure 106 

Q6A_1. The marine environment is a public resource and none of it should be restricted] 

 REFUSED 7 

 Strongly Agree 29 

 Agree 92 

 Neither Agree nor Disagree 200 

 Disagree 495 

 Strongly Disagree 168 

 I am unsure 46 

70
Q6A_2. We should allow exploration and drilling for oil and natural gas in the Northeast 

Region to help create a stable supply and lower prices 

 REFUSED 4 

 Strongly Agree 75 

 Agree 242 

 Neither Agree nor Disagree 245 

 Disagree 228 

 Strongly Disagree 169 

 I am unsure 74 

Q6B. Have you ever been a participant on a Citizens Advisory Panel before? 

 Yes 21 

 No 1016 

Q13_1. We should protect the environment for our children and future generations, even 

if it means reducing our own standard of living 

 REFUSED 2 

 Strongly Agree 247 

 Agree 464 

 Neither Agree nor Disagree 219 

 Disagree 71 

 Strongly Disagree 8 

 I am unsure 26 

Q13_2. Plants and animals exist primarily to be used by humans 

 REFUSED 3 

 Strongly Agree 54 

 Agree 182 

 Neither Agree nor Disagree 281 

 Disagree 327 

 Strongly Disagree 161 

 I am unsure 29 

Q13_3. I enjoy being outdoors to experience the beauty of nature 

 REFUSED 2 

 Strongly Agree 439 

 Agree 454 

 Neither Agree nor Disagree 105 

 Disagree 13 

 Strongly Disagree 3 

 I am unsure 21 

71
Q13_4. People can live with less economic growth in order to preserve and protect the 

environment 

 REFUSED 2 

 Strongly Agree 230 

 Agree 440 

 Neither Agree nor Disagree 254 

 Disagree 68 

 Strongly Disagree 15 

 I am unsure 28 

Q13_5. We can rely on new technology to fix environmental problems 

 REFUSED 2 

 Strongly Agree 56 

 Agree 208 

 Neither Agree nor Disagree 342 

 Disagree 268 

 Strongly Disagree 114 

 I am unsure 47 

Q13_6. Government regulations and planning are always too costly and ineffective to 

benefit the environment 

 REFUSED 2 

 Strongly Agree 96 

 Agree 234 

 Neither Agree nor Disagree 390 

 Disagree 194 

 Strongly Disagree 74 

 I am unsure 47 

Q14. How familiar were you with Marine Protected Areas before taking this survey? 

 REFUSED 5 

 Very familiar 7 

 Somewhat familiar 180 

 Unfamiliar 845 

Q15. How familiar are you now with Marine Protected Areas? 

 REFUSED 4 

 Very familiar 133 

 Somewhat familiar 793 

 Unfamiliar 107 

72