The Benefits and Costs of Fish 

Consumption Advisories for Mercury 

Paul Jakus, Meghan McGuinness, and Alan 

Krupnick 

October 2002 • Discussion Paper 02–55 

Resources for the Future 

1616 P Street, NW 

Washington, D.C. 20036 

Telephone: 202–328–5000 

Fax: 202–939–3460 

Internet: http://www.rff.org 
The Benefits and Costs of Fish Consumption Advisories for Mercury 

Paul Jakus, Meghan McGuinness, and Alan Krupnick 

Abstract 

Mercury contamination of the Chesapeake Bay is a concern to health authorities in the 

region. We evaluate the economic and health effects of postulated recreational and commercial 

fishing advisories for striped bass on the Maryland portion of the bay. Awareness of and 

response to the advisory is estimated using a meta-analysis of the literature. Three values are 

estimated: welfare losses to recreational anglers, welfare losses in the commercial striped bass 

fishery, and health benefits. An estimate of percentage of consumer surplus loss is applied to the 

value of all fishing days in the bay to estimate recreational welfare loss. Welfare losses to the 

commercial fishery are estimated based on a model of supply and demand. Health benefits are 

estimated using estimated exposure and epidemiological relationships, and while potentially 

large, are highly uncertain. Results also suggest most individuals are below advisory standards 

ex ante, such that advisories should target high-frequency consumers. 

Key Words: fisheries, mercury, advisories, recreation, health benefits 

JEL Classification Numbers: I18, Q22, Q25, Q26 
Executive Summary 

Mercury contamination of the Chesapeake Bay is a concern to health authorities in the 

region. Authorities are considering issuing fish consumption advisories (FCAs) to warn people 

about the health dangers of consuming contaminated fish. Prior to December 2001, Maryland 

had issued only four fish consumption advisories. On December 12, 2001, the state issued 

several advisories concerning contamination by polychlorinated biphenyls (PCBs) and mercury. 

Currently, there is no advisory on consumption of fish from the Chesapeake Bay. We evaluate 

the economic and health effects of potential recreational and commercial fishing advisories in the 

Maryland portion of the Chesapeake Bay, counting effects experienced by or through all users of 

this portion of the bay. Because the Maryland Department of Natural Resources indicates that 

striped bass (Morone saxatilis) is the species of greatest concern for mercury contamination in 

the Chesapeake Bay, we assume that advisories are limited to this species. Based on 1992–1994 

weighted average tissue concentration levels of 0.205 mg/kg in Chesapeake Bay striped bass 

from Gilmour (1999), and the current oral reference dose for methylmercury from the 

Environmental Protection Agency (EPA), we postulate a recreational FCA suggesting restricted 

consumption for the general population (< four meals per month) and restricted consumption for 

sensitive subpopulations (< two meals per month for children and women of childbearing age). 

Furthermore, we postulate that the state may issue “Commercial Health Advice” consistent in 

severity with the projected FCAs for the recreational fishery, since the fisheries occupy 

essentially the same area. 

A review of the literature assesses the degree to which anglers are aware of advisories 

and engage in consumption-related averting behaviors, based on estimates from ex post analysis 

of fisheries with characteristics similar to the bay. The mean percentage of estuarine anglers who 

are aware of advisories is estimated to be 48% (95% confidence interval, CI: 46%–50%). 

Anglers who are aware of advisories are 26.1% less likely to consume listed species than anglers 

who are not (95% CI: 22.1%–30%). 
We estimate three endpoints: welfare losses to recreational anglers, welfare losses to 

consumers and producers of commercial striped bass, and health benefits to recreational anglers 

due to reduced consumption of contaminated striped bass. Under a recreational FCA, aware 

anglers will undertake some combination of behavioral adjustments that may range from 

ignoring the advisory to altogether ceasing trips to the affected water body. Such behavioral 

modification results in economic losses to anglers. Applying an estimate of the percentage of 

consumer surplus lost due to an advisory from the literature to consumer surplus estimates for a 

fishing day in the Chesapeake Bay, we estimate an annual consumer surplus loss over all 

Maryland saltwater fishing days of $8.83 million ($2000). For the commercial striped bass 

fishery, we estimate a very simple model of supply and demand that predicts equilibrium price 

and quantity with reasonable accuracy. Using parameter estimates from this model, we estimate 

annual consumer and producer surplus losses of $215,800 and $304,500, respectively, under 

commercial consumption advice, for a total annual surplus loss of $520,300. 

In our analysis of health effects, we estimate changes in methylmercury uptake for 

recreational anglers and their families as a result of the advisory, and quantify changes in three 

primary endpoints: paresthesia (prickling, tickling, or itching sensation, and an initial symptom 

of methylmercury disease), abnormal scores on tests of childhood neurological development, and 

cardiovascular health and mortality effects. Additionally, we estimate the number of individuals 

exceeding both the Chesapeake Bay advisory and EPA’s reference dose (RfD). Although there is 

no evidence of either paresthesia or childhood neurological development delays at current 

exposure levels, we do predict reductions in acute myocardial infarction (AMI) and all-cause 

mortality under an advisory because of a lower mercury exposure threshold for these effects. 

However, these estimates are surrounded by much uncertainty, and because the study used to 

estimate this relationship has not been replicated, our confidence in them is even further 

attenuated. 

We find that most anglers are in compliance with the advisory standards, even before the 

advisory is announced. About 3% of anglers exceed advisory standards before it is implemented, 

and only 2% do so afterward. Furthermore, we find that approximately 9% of exposed women 

of childbearing age exceed EPA’s RfD, and 7% do so once the advisory has been implemented. 

The finding that most individuals are already in compliance with advisory guidelines suggests 

that advisories are likely to be relevant to only a small percentage of angler families at the high 

end of the consumption distribution, and that compliance might be further increased if 

educational efforts are directed at this segment of the population. 
Finally, based on our mortality estimate, we estimate annual health benefits from an 

advisory to be approximately $14 million. The value of further information for this mercurymortality relationship is quite high, as it suggests that significant health benefits may accrue at 

lower mercury levels than has been suggested by the research focusing on neurological 

development effects from fetal exposure, the health endpoint that has been the focus of policy 

discussion to date. 
Contents 

1. Introduction......................................................................................................................... 1

1.1. Plan of the Report ...................................................................................................4 

2. Background ......................................................................................................................... 5

2.1. Current Fish Consumption Advisories ...................................................................5 

2.2. Calculating Fish Consumption Limits ..................................................................12 

2.3. Design of Advisories for Chesapeake Bay ...........................................................16 

2.4. Relating MeHg Concentration to Fish Size ..........................................................17 

2.5. Estimating Mean MeHg Concentration Levels for “Kept” Fish...........................18 

2.6. Projecting Probable Fish Consumption Advisories ..............................................19 

2.7. Summary...............................................................................................................21 

3. Recreational Fishing Losses from an FCA ..................................................................... 22

3.1. Awareness, Compliance, and Averting Behaviors Associated with FCAs ..........22 

3.2. Economic Analysis of Fish Consumption Advisories and Chesapeake Bay 

Recreational Fishing ..............................................................................................42 

3.3. The Recreational Angler Response Module .........................................................56 

4. Commercial Fishing Losses from FCAs ......................................................................... 57

4.1. Commercial Striped Bass Fishing Models............................................................58 

4.2. Modeling the Commercial Striped Bass of Chesapeake Bay ...............................62 

4.3. Welfare Analysis for the Commercial Striped Bass Fishery ................................67 

4.4. Summary...............................................................................................................70 

4.5. Commercial Fishing Response Module................................................................72 

5. Health Benefits of Mercury Exposure Reductions ........................................................ 72

5.1. Health Effects of Methylmercury .........................................................................72 

5.2. Methodology.........................................................................................................81 

5.3. Modeling Health Effects.......................................................................................88 

5.4. Results...................................................................................................................96 

5.5. Summary.............................................................................................................106 

6. Sensitivity Analysis ......................................................................................................... 107

6.1. Perfect Awareness and Perfect Compliance .......................................................108 

6.2. Averting Anglers Are Exposed to Mercury from Substitute Sources of Fish ....109 

6.3. Removal of the Threshold for IQ Effects ...........................................................112 
6.4. Application of the Mercury-AMI and Mercury-Mortality Relationships to Women

..............................................................................................................................112 

6.5. Potential Implications of Alternative Assumptions for Recreational Consumer 

Surplus Loss.........................................................................................................114 

6.6. Additional Opportunities for Sensitivity Analysis..............................................115 

References............................................................................................................................ 117

Appendix: The Maryland Model....................................................................................... 124

A.1. The Recreational Angler Response Module ......................................................125 

A.2. Commercial Fisheries Response Module...........................................................127 

A.3. Mercury Health Effects Module ........................................................................128 

A.4. Health Benefits Valuation..................................................................................133 
1

The Benefits and Costs of Fish Consumption Advisories for Mercury 

Paul Jakus, Meghan McGuinness, and Alan Krupnick∗

1. Introduction 

Mercury (periodic table element Hg) is found in the environment in a wide variety of 

forms. It generally appears in its elemental form (Hg0

) or as divalent mercury (Hg2+) and can be 

found in the atmosphere, in rocks and soils, and in water (U.S. EPA 2000). Surface waters are 

contaminated by mercury from both naturally occurring releases and industrial emissions. 

Sources of mercury include emissions from power plants, paper and pulp mills, and wastewater 

treatment plants; depositions from the atmosphere; and soil runoff. Some 85% of mercury 

emissions in the United States are believed to come from power plants as a result of fossil fuel 

combustion (U.S. EPA 2001a). Fish encounter mercury in the aquatic environment. Biological 

processes of animal species convert elemental and divalent mercury into an organic form called 

methylmercury (MeHg). Nearly all (>90%) mercury found in fish tissues is MeHg. 

Consumption of mercury-contaminated fish can cause serious health problems in 

humans. MeHg is rapidly absorbed by the gastrointestinal tract and binds itself to all tissues. In 

 

∗ Dr. Paul Jakus is an Associate Professor of Economics at Utah State University, and Meghan McGuinness and 

Alan Krupnick are at Resources for the Future. The authors wish to thank Diane Brown, Manager, Energy Planning 

Projects, Power Plant Research Program (PPRP), Maryland Department of Natural Resources for support of this 

research; John Sherwell and Paul Miller of PPRP for their generosity in providing guidance and expertise; and Matt 

Kahal, Exeter Associates, Inc., for his project management. We would also like to thank Gina Hunt, Vicki Johnson, 

Connie Lewis, and Harry Hornick, all of Maryland Department of Natural Resources, for providing fisheries data. 

Finally, we would like to thank David Austin, formerly of RFF and now at the Congressional Budget Office, for his 

initial design of the project and research and modeling efforts.
Resources for the Future Jakus, McGuinness, and Krupnick 

2

humans the greatest concentrations of MeHg are found in the kidneys, although MeHg easily 

crosses the blood/brain and placental barriers. The estimated lethal dose of MeHg is 10–60 

mg/kg. Methylmercury does vacate the body, with an estimated half-life of 44–80 days (U.S. 

EPA 2000). 

Two major MeHg contamination episodes have been associated with eating fish, both 

occurring in regions of Japan where average per capita consumption of fish, a food staple, was 

very high, about 300 g/day (U.S. EPA 2000; U.S. EPA 2001a).1 The symptoms of mercury 

contamination are called Minamata disease, after the region of Japan where it was first 

recognized. Those with the disease suffer from impaired peripheral vision, paresthesia (prickling, 

tickling, or itching sensation), and some loss of motor control.2 In addition to these effects, 

recent studies have highlighted abnormal scores on tests of childhood neurological development 

as a result of fetal exposure, and cardiovascular health and mortality effects. 

To manage the risks associated with eating contaminated fish, federal and state 

authorities have issued fish consumption advisories (FCAs) to reduce the probability of health 

effects, and in the case of commercial FCAs, to alter the behavior of the fishing industry 

affected. Assuming such FCAs are perfectly effective, in theory, there would be no mercuryrelated health effects, but there would be economic losses associated with the FCAs themselves 

and perhaps on ancillary fishing markets. In reality, many people ignore advisories, which 

lessens the costs associated with the FCA but also reduces health benefits. 

 

1 In the United States, about 3% to 5% of the population regularly consumes in excess of 100 g/day. 

2 A major contamination episode in Iraq was associated with mercury-contaminated seed grain. This population 

suffered similar symptoms to the Japanese populations. 
Resources for the Future Jakus, McGuinness, and Krupnick 

3

In this study, we evaluate the economic costs and health benefits associated with potential 

recreational and commercial fish consumption advisories in the Chesapeake Bay.3 Health 

benefits are calculated for two endpoints: changes in children’s IQ score due to prenatal mercury 

exposure, and mercury-related all-cause mortality in middle-aged men. Our model is applied to a 

specific case study area (the Maryland portion of the Chesapeake Bay) and a specific species 

(striped bass, Morone saxatilis), which the Maryland Department of Natural Resources considers 

the species of greatest concern for mercury contamination in the Chesapeake Bay. 

This project is designed to provide information useful to analyses of the benefits of 

reducing mercury emissions. A primary benefit of reducing mercury emissions is the reduced 

likelihood of fish consumption advisories and the resulting welfare losses from changes in 

anglers’ behavior associated with advisory compliance. In addition, of course, reduced mercury 

emissions will lead to a reduction in mercury-related health effects, assuming that baseline 

mercury levels in fish (and other exposure pathways) are above those found to cause health 

effects. However, health improvements may be mitigated if, with the lifting of a fish advisory, 

consumption of fish containing methylmercury increases. Because we do not have information 

linking mercury emissions to concentrations in the environment, our report focuses not on 

mercury emissions reductions, but rather on the costs and benefits associated with fish 

consumption advisories themselves. 

 

3 Prior to December 2001, Maryland had issued only four fish consumption advisories. On December 12, 2001, the 

state issued several consumption advisories for freshwater anglers concerning contamination by PCBs and mercury. 

As of July 2002, there was no fish consumption advisory for the Chesapeake Bay. 
Resources for the Future Jakus, McGuinness, and Krupnick 

4

We develop three modules within the Maryland Externalities Screening and Valuation 

Model, or “Maryland Model,” developed for Maryland Department of Natural Resources by 

Resources for the Future. These are the Recreational Angler and Commercial Fishery Response 

Modules, and the Mercury Health Effects Module. The basis for the Maryland Model is the 

Tracking and Analysis Framework (TAF) model, a peer-reviewed probabilistic model that was 

used by the National Acid Precipitation Assessment Program to estimate costs and benefits 

associated with reductions in acid precipitation in the United States. For descriptions of TAF and 

the Maryland Model, see Bloyd et al. (1996) and Austin et al. (1999). 

1.1. Plan of the Report 

Section 2 of the report provides background on FCAs, briefly reviewing the mechanisms 

by which mercury concentrates in fish tissues. The current state of consumption advisories in the 

United States and in Maryland is then outlined, followed by a description of the Environmental 

Protection Agency (EPA) methodology for calculating safe levels of fish consumption and 

appropriate advisory levels. Mercury concentration levels of Chesapeake Bay striped bass are 

then evaluated within the context of EPA’s FCA methodology to establish the likely FCA 

outcome for the Chesapeake Bay. 

Section 3 of the report presents the model for estimating consumer surplus losses from an 

FCA for recreational fishing of striped bass in the Chesapeake. This section has two parts: first, a 

literature review and evaluation of behavioral choices made by anglers in response to an 

advisory, and second, an economic analysis and model for estimating welfare losses associated 

with these behaviors. Section 4 presents the model and results for an economic analysis of an 

advisory on commercial striped bass fishing in the bay. Section 5 presents a literature review and 
Resources for the Future Jakus, McGuinness, and Krupnick 

5

analysis of the human health effects of methylmercury and describes the model used to quantify 

and value such effects under a recreational advisory. Finally, Section 6 presents results of 

sensitivity analyses. 

2. Background 

2.1. Current Fish Consumption Advisories 

Fish consumption advisories are a standard risk management tool in the United States. 

The goal of advisories is to warn the public about contamination of wildlife species, the adverse 

health affects associated with consumption of these species, and the methods to avoid or 

minimize potential contamination. The populations most at risk for MeHg exposure are those 

who tend to have high fish consumption rates relative to the general public, so much of the effort 

at publicizing advisories has been aimed at those who consume sport-harvested fish—that is, 

anglers and their families. Fish consumption advisories generally come in one of five forms, four 

of which recommend consumption levels for specific segments of the population; the fifth is 

associated with commercial species (Table 2.1; U.S. EPA 2001b). If “restricted” consumption of 

a particular species is recommended, consumption levels are communicated in the form of 

“meals per month” for either the general population or a subpopulation. These consumption 

levels are based on a standard portion, or “meal size,” and the level of contaminant concentration 

found in the species at that site. 
Resources for the Future Jakus, McGuinness, and Krupnick 

6

Table 2.1. Types of Consumption Advisories 

Type of Advisory (Abbreviation) Definition 

No Consumption: General Population 

(NCGP) 

Issued when levels of chemical contamination 

in fish or wildlife pose a health risk to the 

general public. The general population is 

advised to avoid eating certain types of locally 

caught fish or wildlife. 

No Consumption: Sensitive 

Subpopulation (NCSP) 

Issued when levels of contamination in fish or 

wildlife pose a health risk to sensitive 

subpopulations, such as pregnant women or 

and children. The sensitive subpopulation is 

advised to avoid eating certain types of locally 

caught fish or wildlife. 

Restricted Consumption: General 

Population (RGP) 

Issued when levels of contamination in fish or 

wildlife pose a health risk if too much fish or 

wildlife is consumed. The general population 

is advised to limit eating of certain types of 

locally caught fish or wildlife. 

Restricted Consumption: Sensitive 

Subpopulation (RSP) 

Issued when levels of contamination in fish or 

wildlife pose a health risk if too much fish or 

wildlife is consumed. The sensitive 

subpopulation is advised to limit eating of 

certain types of locally caught fish or wildlife. 

Commercial Fishing Ban (CFB) Issued when high levels of contamination are 

found in fish caught for commercial purposes. 

These bans prohibit the commercial harvest 

and sale of fish, shellfish, and/or wildlife from 

a designated water body. 
Resources for the Future Jakus, McGuinness, and Krupnick 

7

In addition to the advisory types listed above, ten states also issue “Commercial Health 

Advice” (U.S. EPA 2001c).4 The advice falls well short of a commercial fishing ban and is 

closely aligned with the information included in many consumption advisories. “Advice” is 

generally targeted at sensitive subpopulations (children, pregnant women, women who may soon 

be pregnant) and recommends restricted consumption of specified commercial species. 

In 2000 almost 6,500 advisories were issued, a 124% increase over 1993 (Figure 2.1; 

U.S. EPA 2001b). This increase was due to more intensive monitoring by federal and state 

agencies rather than an increase in the general contaminant level (U.S. EPA 2001b, 2). A simple 

count of advisories, however, obscures the extent to which they vary. A single advisory issued 

by, say, a state agency may cover a single species at a single site for a single pollutant. 

Alternatively, an advisory may cover multiple species and pollutants at multiple sites. 

 

4 These are Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, Rhode Island, 

Vermont, and Washington. 
Resources for the Future Jakus, McGuinness, and Krupnick 

8

0

1000

2000

3000

4000

5000

6000

7000

1993 1994 1995 1996 1997 1998 1999 2000

# of Advisories

Figure 2.1. Number of Advisories, by Year. Source: U.S. EPA 2001b 

By early 2001, approximately 9% of the nation’s river miles and 23% of its lake acreage 

were under some form of consumption advisory. As of July 2002, the Chesapeake Bay was not 

under any type of advisory, but many of its tributaries (including the Potomac, James, Back, and 

Anacostia Rivers) have had fish consumption advisories issued. Although there are different 

ways of counting advisories, EPA reports that by 2001, some 2,259 advisories had been issued 

by 41 states for mercury contamination of a wide variety of fish species (U.S. EPA 2001c). 

Twenty-six of these advisories have been issued by 12 states for mercury contamination of 

coastal or estuarine regions. Six of the 12 states are located along the Atlantic coast (Delaware, 

Florida, Georgia, Maine, North Carolina, and South Carolina). In addition, 3 states in the 

immediate vicinity of Maryland—Delaware, Pennsylvania, and Virginia—have issued fish 

consumption advisories for mercury (Figure 2.2). 
Resources for the Future Jakus, McGuinness, and Krupnick 

9

All states that share a border with Maryland, including the District of Columbia, have 

issued FCAs. Delaware has issued 20 advisories covering a wide variety of fish species and 

contaminants, including dioxins, polychlorinated biphenyls (PCBs), Dieldrin, arsenic, and 

pesticides. One advisory was issued for the Chesapeake and Delaware Canal, which links the 

Chesapeake Bay and the Delaware River; the FCA recommends no consumption by the general 

population for any fish harvested from this water body because of PCB contamination. Five 

Delaware FCAs address mercury contamination: Beck’s Pond (RGP, all species), the Delaware 

River (NCGP, all species), Silver Lake Dover (RGP, all species), lower Delaware River and Bay 

(RGP, striped bass), and the St. Jones River (RGP, all species). The last 2 FCAs cover estuarine 

waters. 
Resources for the Future Jakus, McGuinness, and Krupnick 

10

 

Figure 2.2. Mercury Advisories Issued by States Bordering Maryland. 

Source: U.S. EPA 2001c

Pennsylvania has issued 36 advisories, 2 for mercury contamination. Other advisories 

have been issued for PCBs, chlordane, and mirex. The first mercury advisory, issued in 1993, 

covers Lake Wallenpaupack and recommends NCGP for walleye greater than 19 inches. The 

second mercury advisory was issued in 2001 and covers all freshwater rivers and lakes in the 

state. This broad advisory recommends RGP for all species caught in any of Pennsylvania’s 

freshwaters. 
Resources for the Future Jakus, McGuinness, and Krupnick 

11

West Virginia has issued 11 advisories for dioxin, PCBs, and chlordane contamination. 

There have been no FCAs for mercury. Two advisories have been issued for tributaries of the 

Chesapeake Bay, the Potomac River, and the North Branch of the Potomac. Both advisories are 

for dioxin contamination of nonsportfish species, with a recommendation of NCGP. 

Virginia has issued 10 advisories because of contamination by three chemicals: mercury, 

PCBs, and kepone. Mercury advisories have been issued for the North Fork of the Holston River 

(NCGP, all species), the South Fork of the Shenandoah River (NCGP, all species), and the South 

River (NCGP, all species). Tributaries of the Chesapeake are also under an advisory. The James 

River has a kepone-related advisory recommending restricted consumption by the general 

population of all species. The Potomac River has an FCA for PCB contamination of channel 

catfish greater than 18 inches (RGP). 

The District of Columbia has issued a single advisory covering all its lakes and rivers, 

including the Potomac and Anacostia Rivers, both tributaries of the Chesapeake Bay. This FCA 

recommends no consumption by the general population of carp, catfish, or American eel, and it 

also extends an RGP advisory on all other fish species. 

Prior to December 2001, Maryland had issued only four fish consumption advisories. 

Three FCAs involved chlordane contamination; the fourth was due to PCB contamination. Two 

of the chlordane advisories were issued for tributaries of the Chesapeake. Restricted 

consumption by the general population has been recommended for channel catfish and American 

eel on the Back River and for Baltimore Harbor. On December 12, 2001, however, the Maryland 

Department of the Environment issued new FCAs covering a wide variety of water bodies and 

fish species because of PCB, pesticide, and mercury contamination (Huslin 2001; Maryland 
Resources for the Future Jakus, McGuinness, and Krupnick 

12

Department of the Environment 2001). The species under FCAs for PCB and pesticide 

contamination now include channel catfish, white perch, striped bass, blue crab, American eel, 

white catfish, brown bullhead, black crappie, spot, common carp, and yellow perch. Yellow 

perch is also under an FCA because of mercury contamination, as are smallmouth bass, 

largemouth bass, pickerel, northern pike walleye, and bluegill. The advisories for all species but 

yellow perch are statewide advisories for all publicly accessible lakes and impoundments. The 

bass advisory extends to all rivers and streams in Maryland. The yellow perch advisory covers 

Piney Dam, Deep Creek Lake, and the main stem of the Susquehanna River. All advisories are 

RGPs and RSPs, recommending limited meals for the general population and sensitive 

subpopulations. 

Finally, personal communications with Maryland Department of Natural Resources 

personnel indicate that the primary Chesapeake Bay species for which there is concern regarding 

MeHg contamination is striped bass, Morone saxatilis (Sherwell and Miller 2001). Some 43 

advisories have been issued nationwide for striped bass, the majority due to PCB or dioxin 

contamination. Fifteen of these advisories have been issued for estuarine waters, of which 11 

cover the Newark–New York City region, including Newark Bay, New York Harbor, and the 

Hudson River. Three of the advisories were issued for mercury contamination. Maine included 

striped bass in its statewide mercury advisory. The other 2 mercury-related striped bass 

advisories covered the San Francisco Bay and the Lower Delaware River and Delaware Bay. 

2.2. Calculating Fish Consumption Limits 

EPA has established a methodology for determining whether advisories for fish 

consumption should be issued (U.S. EPA 2000), based on assessed risks of contamination. The 
Resources for the Future Jakus, McGuinness, and Krupnick 

13

variable needed is simply the contaminant concentration in the local fish population. 

Calculations are sufficiently straightforward that a local agency with more information (e.g., 

details such as meal size and average body weight of consumers) can adjust the EPA 

recommendations to fit local conditions. 

To understand this procedure, a few preliminaries are in order. The first concerns how 

mercury gets into fish tissue. Fish draw oxygen from water via thin membranes of the gill 

tissues. As water is pumped across the gills, mercury and other contaminants cross the gill 

membrane and enter directly into the blood (Reinert et al. 1996). Contaminants thus achieve a 

concentration within a single fish that is greater than the surrounding aquatic environment. Fish 

may also bioaccumulate contaminants from their food. Generally, the concentration of mercury 

in tissues increases with the age and size of the fish. Finally, fish that are higher in the aquatic 

food chain accumulate higher concentrations than fish or other organisms lower in the food 

chain, a process known as biomagnification. Fish are relatively more susceptible to contaminant 

concentration than terrestrial species because they accumulate contaminants not only through the 

food they consume but also through their constant contact with water. In some cases, fish species 

have MeHg concentrations 1,000 to 10,000 times greater than the surrounding aquatic 

environment (U.S. EPA 2001a). In contrast, terrestrial species accumulate little mercury 

contamination from airborne (vaporized) forms of mercury or from food sources. 
Resources for the Future Jakus, McGuinness, and Krupnick 

14

Mercury taken up by fish is deposited into muscle tissues (fillets) as well as skin and fatty 

tissues, so it is difficult to remove the contaminant prior to consumption by humans.5 Further, 

there are no cooking practices that reduce potential contaminant consumption; in fact, as cooking 

reduces the moisture content of the fillet, mercury actually becomes more concentrated per unit 

of fish. 

The other crucial concept is the oral reference dose (RfD), an estimate of the lifetime 

daily exposure to a contaminant above which harmful health effects will occur (U.S. EPA 

2001d). The calculation of an RfD also includes an “uncertainty factor” that adjusts the dose 

downward to reflect uncertainties about the accuracy of the calculated exposure level. In 2001 

EPA established the RfD for mercury at 1×10-4 mg/kg/day. The RfD is based on the “critical 

effect” of developmental neuropsychological impairment, which is measured using several 

evaluative endpoints. The EPA RfD does not take into account potential carcinogenic impacts of 

MeHg contamination because of inadequate data for humans and limited evidence from animal 

studies (U.S. EPA 2001d). 

The calculation consists of two parts. The first part calculates the maximum daily 

allowable consumption of contaminated fish for humans, expressed in kilograms of fish per day, 

CRlim = (RfD × BW) ÷ Cm (2.1)

 

5 Other contaminants, such as PCBs, bind primarily to fatty tissues, making contaminant reduction prior to 

consumption much easier. 
Resources for the Future Jakus, McGuinness, and Krupnick 

15

where CRlim is the allowable daily consumption limit, RfD is the established reference dose 

(mg/kg/day) of the contaminant, BW is body weight (kg), and Cm is the estimated contaminant 

concentration (mg/kg) in the fish species. 

The reference dose is based on chronic exposure studies and assumes that a threshold 

exists for toxic effects (U.S. EPA 2001d). It is an estimate of the daily exposure level below 

which the risk of toxic effects in humans is “acceptable.” The unit of measurement expresses the 

level of uptake per kilogram of weight in the subject; thus RfD measures the milligrams of 

contaminant that can be consumed per kilogram of weight of the person. The body weight (BW) 

variable in equation (2.1) adjusts for consumption by persons of different size. Thus, the 

allowable daily consumption limit for a heavier adult is greater than that for a small child, all else 

equal. The final portion of the equation adjusts for the contaminant concentration in the species 

of interest (Cm). All else equal, as the contaminant concentration in the species increases, the 

allowable daily consumption limit falls.6 

CRlim represents a maximum average daily consumption level that is “safe.” That is, 

average daily consumption at this level over a lifetime would not result in adverse, 

noncarcinogenic health effects. Exceeding this dose during a single day would not necessarily 

cause either chronic or acute health effects, but exceeding the limit over a long period of time 

would likely produce symptoms of contamination. 

 

6 Equation (2.1) is the standard EPA calculation. One can modify the equation such that the contaminant 

concentration is on the left side of the equation, as in Cm = (RfD × BW) ÷ Clim where Clim measures average daily 

consumption of fish in a given time period. This form of the equation allows one to calculate the maximum 

allowable contaminant concentration in a species at a fixed consumption rate. 
Resources for the Future Jakus, McGuinness, and Krupnick 

16

The daily limit can be converted to meals per unit of time, a measure that is more easily 

communicated to a target audience. The recommended consumption depends upon an assumed 

meal size and is calculated as 

CRmm = (CRlim × Tap) ÷ MS (2.2) 

where CRmm is recommended consumption (meals per month), CRlim is the maximum allowable 

daily consumption limit, Tap is a time averaging period (1 month = 30.44 days), and MS is meal 

size. EPA recommendations are based on a standard meal size of 227 g of fish (about 8 ounces). 

Those who consume more than this amount are at greater risk of developing symptoms, whereas 

those who consume less than this amount are at less risk. In the case of extremely low allowable 

daily consumption limits, the calculation may yield a value for CRmm equal to zero—that is, no 

consumption. 

2.3. Design of Advisories for Chesapeake Bay 

To date, the Maryland Department of Environment (MDE) has not issued any advisories 

for the Chesapeake Bay, pending analysis of fish samples from the bay (Huslin 2001). For the 

purposes of this project, it is necessary to project the likely advisories for the bay. As noted 

above, state Department of Natural Resource officials anticipate mercury advisories to be issued 

for striped bass. Mercury contamination concentrations vary by the size of fish, so projecting the 

potential set of advisories will first require an estimate of the relationship between MeHg 

concentrations and fish size. Second, the predicted concentration levels must be related to the 

size of striped bass kept by Chesapeake Bay anglers. This will allow us to estimate the mean 

concentration levels of MeHg in striped bass that are consumed. Finally, the predicted mean 
Resources for the Future Jakus, McGuinness, and Krupnick 

17

concentration can be used in the EPA recommended consumption equations (equations 2.1 and 

2.2) to predict the recommended consumption advisories to be issued by MDE. 

2.4. Relating MeHg Concentration to Fish Size 

The only available data on which a preliminary analysis of potential Chesapeake estuary 

FCAs can be conducted are provided by Gilmour (1999). The Gilmour data consist of 18 

samples of striped bass from the upper bay, 10 samples from the midbay, near Annapolis, and 14 

samples from the Potomac River, a tributary of the bay, all collected from 1992 to 1994. The 

upper bay and midbay striped bass samples (n=28) showed a mean concentration of 0.182 

mg/kg, with a median concentration of 0.155 mg/kg and a maximum concentration of 0.521 

mg/kg.7 When the Potomac sample is included, the mean, median, and maximum concentrations 

are 0.201 mg/kg, 0.164 mg/kg, and 0.607 mg/kg, respectively. Regression analysis shows a 

positive relationship between the fish weight and Hg concentrations (Table 2.2). The models 

indicate that for every 1% increase in weight, Hg concentrations increase by 0.6% to 0.9%. 

New data will be available soon. The state collected striped bass from several regions of 

the bay during fall 2001 and spring 2002 to ascertain a temporally relevant, comprehensive, fishlength-based assessment of mercury levels before issuing an FCA, should one be deemed 

necessary. 
Resources for the Future Jakus, McGuinness, and Krupnick 

18

Table 2.2. Regression Analysis of the Gilmour Dataa

Variable Potomac Riverb Upper Bayb Midbayb

Intercept -2.275 

(-9.303) 

-2.180 

(-10.508) 

-3.198 

(-4.774) 

ln(Weight) 0.876 

(3.154) 

0.636 

(2.529) 

0.890 

(1.758) 

Adjusted R2 0.408 0.241 0.l88 

N 14 18 10 

Dependent Variable: ln(Hg concentration). a

 t-ratios in parentheses . 

b Specification from Gilmour (1999, 16).

2.5. Estimating Mean MeHg Concentration Levels for “Kept” Fish 

The mean length of Chesapeake Bay fish in the Gilmour data was 64.4 cm, or a little over 

25 inches, with a maximum length of 84 cm (33 inches); the mean weight of the sampled fish 

was 2.76 kg. The Gilmour sample may not be representative of the size and number of fish kept 

by Chesapeake anglers, however. Over the 1997–2000 period, anglers participating in the state’s 

Cooperative Striped Bass Survey reported keeping approximately 5,300 striped bass. Using the 

distribution of kept fish to calculate mean fish lengths and fish weights, the average “kept” 

striped bass was 64 cm long and weighed approximately 2.75 kg, very close to the means from 

the Gilmour sample.8 The data also showed, however, that 12% of kept fish were larger than the 

largest fish in the Gilmour sample. Because the Gilmour data did not include relatively large 

fish, the mean concentration level using the Gilmour data alone may be underestimated. 

 7 Samples measured total mercury, not just MeHg. This is fairly common, given the cost of MeHg measurement 

relative to total Hg measurement costs and the fact that nearly all mercury in fish is MeHg. 

8 The survey data reported only the fish length. Predicted weights for the cooperative survey data were predicted 

using a model based on the Gilmour data. The weight-length relationship was modeled using a simple linear model, 

Weight = -4.706 + 0.116 Length, where the intercept and the length variables were highly significant. The model 

explained nearly 94% of the variation in weight.
Resources for the Future Jakus, McGuinness, and Krupnick 

19

The distribution for “kept fish” using the 1997–2000 Cooperative Striped Bass Survey 

data is given in Table 2.3, along with predicted values for fish weight and Hg concentration 

levels. The weight-length relationship given in footnote 8 and in the Gilmour mercury 

concentration models was used to predict Hg concentrations found in larger fish. For example, 

consider a 36-inch (91.4 cm) fish caught in the upper bay. The weight-length model predicts that 

such a fish would weigh 5.90 kg. Using Gilmour’s upper bay mercury concentration model 

(column 3 of Table 2.2), a fish of this weight is predicted to have a mercury concentration of 

0.35 mg/kg. 

2.6. Projecting Probable Fish Consumption Advisories 

Given the distribution estimated from the 1992–1994 Gilmour data and the 1997–2000 

striped bass survey, the probability-weighted estimate of mercury concentration of fish 

consumed from the upper Chesapeake Bay is 0.205 mg/kg. This concentration value can be used 

in EPA equations (2.1) and (2.2) to estimate the recommended number of meals per month for 

consumers of striped bass. EPA standards assume an average adult body weight of 60 kg and an 

RfD of 1 × 10-4 mg/kg/day. Given a contaminant concentration level (Cm) of 0.205 mg/kg, 

equation (2.1) yields a maximum allowable daily concentration limit of 0.034 kg per day. 

Assuming an 8-ounce meal size (0.227 kg), equation (2.2) indicates that an adult should consume 

no more than four meals per month, .and a child of 35 kg, no more than two meals per month. 

Given the advisory actions of other states, it is likely that the recommendation for women of 

childbearing age will match that for children. 
Resources for the Future Jakus, McGuinness, and Krupnick 

20

Table 2.3. Distribution of Chesapeake Bay Striped Bass Kept by Anglers (1997–2000), 

Predicted Weight, and Mercury Concentration Levels 

Length 

(in.) 

Fish 

(n) 

Percentage of 

Fish 

Cumulative 

Distribution 

Weight 

(kg)a

Upper Bay Hg 

(mg/kg)b

Midbay 

Hg 

(mg/kg)c

18 252 4.8% 4.8% 0.60 0.0815 0.0258 

19 610 11.5% 16.3% 0.89 0.1051 0.0369 

20 550 10.4% 26.7% 1.19 0.1261 0.0476 

21 466 8.8% 35.5% 1.48 0.1452 0.0579 

22 483 9.1% 44.6% 1.78 0.1629 0.0681 

23 299 5.7% 50.3% 2.07 0.1796 0.0781 

24 315 6.0% 56.2% 2.37 0.1955 0.0879 

25 187 3.5% 59.8% 2.66 0.2106 0.0976 

26 203 3.8% 63.6% 2.95 0.2252 0.1071 

27 115 2.2% 65.8% 3.25 0.2392 0.1166 

28 208 3.9% 69.7% 3.54 0.2528 0.1260 

29 226 4.3% 74.0% 3.84 0.2660 0.1352 

30 212 4.0% 78.0% 4.13 0.2788 0.1444 

31 207 3.9% 81.9% 4.43 0.2913 0.1536 

32 202 3.8% 85.7% 4.72 0.3035 0.1626 

33 125 2.4% 88.1% 5.02 0.3154 0.1716 

34 161 3.0% 91.1% 5.31 0.3270 0.1806 

35 112 2.1% 93.2% 5.61 0.3384 0.1895 

36 104 2.0% 95.2% 5.90 0.3497 0.1983 

37 60 1.1% 96.3% 6.20 0.3607 0.2071 

38 50 0.9% 97.3% 6.49 0.3715 0.2158 

39 29 0.5% 97.8% 6.78 0.3821 0.2245 

40 42 0.8% 98.6% 7.08 0.3926 0.2332 

41 22 0.4% 99.0% 7.37 0.4029 0.2418 

42 18 0.3% 99.4% 7.67 0.4131 0.2504 

43 11 0.2% 99.6% 7.96 0.4231 0.2589 

44 11 0.2% 99.8% 8.26 0.4330 0.2675 

45 6 0.1% 99.9% 8.55 0.4427 0.2759 

46 3 0.1% 100.0% 8.85 0.4524 0.2844 

48 2 0.0% 100.0% 9.44 0.4713 0.3012 

Source: Chesapeake Bay Cooperative Striped Bass Survey, conducted by the Maryland Department of Natural 

Resources. a

Calculated using the weight-length model in footnote 8. 

b

Calculated using Gilmour’s (1999) upper bay mercury concentration model (Table 2.2). 

c

Calculated using Gilmour’s (1999) midbay mercury concentration model (Table 2.2). 
Resources for the Future Jakus, McGuinness, and Krupnick 

21

2.7. Summary 

Given the scope of the most recent advisories issued by the state of Maryland and the Hg 

concentration levels found in Chesapeake Bay striped bass tissues, the most likely consumption 

advisories can be hypothesized (Table 2.4). At mean concentration levels, the advisory for 

recreational anglers would most likely suggest that the general population restrict consumption 

of striped bass to no more than four meals per month; the likely advisory for children and women 

of childbearing age would be no more than two meals per month. Given the concentration levels 

found in Chesapeake Bay striped bass, a commercial fishing ban is very unlikely. Instead, MDE 

would likely follow other coastal states and issue “Commercial Health Advice” consistent with 

the recommendations given to recreational anglers. 

Table 2.4. Most Likely FCA Scenario for Chesapeake Bay Striped Bass 

Population Recommendation 

General population Restricted consumption, 

four meals per month 

Children and women of childbearing age Restricted consumption, 

two meals per month 

Commercial fisheries Issue “Commercial Health Advice” 

 

The FCA scenario summarized in Table 2.4 is based upon assumptions about how MDE 

might implement EPA’s FCA methodology. Should MDE choose a different set of default 

parameters, such as meal size or body weight, recommended consumption restrictions may 

change. A smaller meal size will increase the recommended consumption rates; a larger meal 
Resources for the Future Jakus, McGuinness, and Krupnick 

22

size will decrease the recommended consumption rate, all else equal. Similarly, larger body 

weights would relax consumption restrictions; smaller weights would tighten them.9 It is 

unlikely, however, that different parameter values for these variables will change the 

recommendation by more than one meal per month in either direction. Finally, the recommended 

restrictions are based on a contaminant concentration of 0.205 mg/kg, the mean of the 

concentration levels in upper Chesapeake Bay fish based on the “kept fish” distribution in Table 

2.3. The Chesapeake midbay fish have lower concentration levels. If the state agency bases its 

recommendation on fish caught from all portions of the Chesapeake, the mean contaminant 

concentration level would be lower, and those lower estimates would loosen recommended 

consumption restrictions. 

3. Recreational Fishing Losses from an FCA 

3.1. Awareness, Compliance, and Averting Behaviors Associated with FCAs 

When a fish consumption advisory is issued, anglers have a number of potential responses: 

1. ignore the advisory and continue current fishing practices; 

2. follow advisory consumption limits or change target species (or both); 

3. cease consumption of listed species and change target species; 

4. cease consumption of all species from the affected water body; and 

5. take fewer fishing trips, or none, to the affected water body. 

In the absence of an original study designed to elicit the potential responses of 

Chesapeake anglers, it is necessary to review the literature on advisories and make an educated 

guess regarding their awareness and their potential responses. This section and Section 3.2 

 

10 In some cases one study may have reported a number of estimates, each of which is based on an independent 

sample of a different population. Where possible, each independent estimate is reported. The source for each 
Resources for the Future Jakus, McGuinness, and Krupnick 

23

attempt to distill and synthesize the literature related to FCAs. As such, these sections should be 

viewed as a meta-analysis of the literature. 

The same parameter of interest—say, the proportion of anglers who are aware of fish 

consumption advisories—may have many estimates, each of which comes from a different study 

or study region. Each estimate of the parameter of interest is initially treated with equal weight, 

in that all estimates from all studies are reported.10 This allows the reader to make an informed 

judgment with respect to the subsequent treatment of the data. The information gathered from the 

literature is then subjected to two filters. First, all estimates are evaluated with respect to 

applicability to the Chesapeake Bay region. Where a sufficient number of estimates are identified 

for regions that share many attributes in common with the Chesapeake Bay region (e.g., 

estuarine waters located near major urban populations), only this subset of estimates is retained 

for analysis. For other parameters of interest, the literature yields so few estimates that this is not 

possible; subsequent analysis is based on all estimates gleaned from the literature. The rationale 

for each approach is explained in detail. 

A second filter is used to incorporate the statistical properties of each estimate. A 

Bayesian weighting procedure, described below, is applied to the data. Essentially, this method 

gives greater weight to parameters based on larger sample sizes (i.e., more precise estimates) 

than those estimates based on smaller sample sizes. Admittedly, this is only one of many ways in 

which a meta-analysis may be conducted. 

 

estimate is always identified. 
Resources for the Future Jakus, McGuinness, and Krupnick 

24

Although the fish consumption advisory literature is not very extensive, it is adequate to 

make reasonable assumptions regarding the potential behavioral impacts of any FCA issued for 

the Chesapeake. In all, some 20 advisory-related studies were reviewed to assess the following 

recreational anglers’ behaviors: 

1. awareness of fish consumption advisories; 

2. probability of consuming species caught from waters under advisory, given an angler’s 

awareness of the advisory; 

3. the degree to which other averting behaviors are adopted; and 

4. probability of exceeding advisory limits. 

Analysis of advisory awareness and changes in consumption behavior are used to assess 

the degree to which mercury uptake by Chesapeake Bay anglers will change under an advisory. 

This is done by relating the results of the literature review to data provided by the Chesapeake 

Bay Cooperative Striped Bass Survey. 

3.1.1. Percentage of Recreational Anglers Aware of Advisories

The probability of an angler’s awareness of advisories is the most frequently encountered 

statistic in the advisory literature. Table 3.1 summarizes the studies used to estimate the 

probability an angler will be aware of an advisory. Many studies report on more than one region; 

here, these are treated as separate samples where appropriate and where the data permit. Fourteen 

studies provide 22 estimates of the probability that an angler is aware of advisories. The vast 
Resources for the Future Jakus, McGuinness, and Krupnick 

25

majority of the studies reported in Table 3.1 are intercept surveys conducted at contaminated 

sites.11 

The estimates of angler awareness range widely, from 19% to 96%. Regression analysis 

does not reveal a statistically significant relationship with the length of time an advisory is in 

place. Thus, angler awareness is more likely a function of advisory severity, angler 

characteristics, and state efforts to make anglers aware of advisories. Treating all studies equally, 

the mean of all the estimates suggests that 71% of anglers were aware of advisories, indicating 

that very high levels of angler awareness are possible. In particular, the ten estimates from the 

Great Lakes states and states that border the Ohio River show exceptionally high levels of 

advisory awareness, with a mean awareness of 87%. 

Three of the studies concern freshwater anglers in southern states (the Tennessee studies 

are not intercept surveys and include awareness of anglers who do not fish a contaminated site). 

Two reports examine freshwater anglers from the New England states, and another examines 

subsistence anglers in Puerto Rico. This leaves six estimates (from four studies) of interest: those 

for estuarine and coastal waters. These are highlighted in bold italicized text in Table 3.1. 

 

11 This type of study surveys people at the site where they recreate. As such, it is not a random sample even of 

recreators, since the most frequent recreators are more likely to be “intercepted.” To the extent more frequent 

recreators behave differently than less frequent recreators, the results of such studies may be biased with respect to 

all recreators. 
Resources for the Future Jakus, McGuinness, and Krupnick 

26

Table 3.1. Percentage of Anglers Aware of an Advisory 

Authors Location 

Survey 

Date 

Sample 

Size 

Percentage

Aware 

Belton et al. (1986) New York Harbor 1983–85 1900 50% 

Burger and Gochfeld 

(1991) Puerto Rico 1988 25 96% 

Burger et al. (1993) Jamaica Bay, NY 1990 154 19% 

Krieger and Hoehn (1998) Michigan waters 1991 951 86% 

Connelly and Knuth (1995) 

Lake Champlain, 

NY and VT 1992 744 84% 

Connelly et al. (1996) Lake Ontario 1992 366 95% 

Knuth et al. (1993) Ohio River 1992 839 87% 

Knuth et al. (1993) Ohio River, PA 1992 123 79% 

Knuth et al. (1993) Ohio River, WV 1992 233 80% 

Knuth et al. (1993) Ohio River, OH 1992 250 86% 

Knuth et al. (1993) Ohio River, IN 1992 265 90% 

Knuth et al. (1993) Ohio River, KY 1992 278 92% 

Knuth et al. (1993) Ohio River, IL 1992 119 87% 

MacDonald and Boyle 

(1997) Maine open waters 1994–95 999 63% 

May and Burger (1996) Arthur Kill, NJ 1994 168 60% 

May and Burger (1996) Raritan Bay, NJ 1994 60 28% 

May and Burger (1996) New Jersey Shore 1994 44 30% 

Phlugh et al. (1999) Newark Bay, NJ 1995 300 60% 

Burger (1998) Savannah River, SC 1997 258 62% 

Jakus et al. (1998) Tennessee lakes 1997 222 65% 

Jakus and Shaw (2002) Tennessee lakes 1997–99 457 70% 

Breffle et al. (1999) Green Bay, WI 1998 647 85% 

Bold italic type indicates estimate for estuarine or coastal region.

 All of the estuarine and coastal estimates are derived from intercept surveys of anglers at 

contaminated waters in the New York–New Jersey region. As with the Chesapeake Bay region, 

the waters are near a major metropolitan area. Further, it seems likely that many of the 

characteristics of New York–New Jersey’s recreational anglers are shared by Chesapeake 

anglers: they may be diverse in ethnicity, first language, and angling experience. A big 

difference between the two regions is that the New York–New Jersey estuarine region has a “no 
Resources for the Future Jakus, McGuinness, and Krupnick 

27

consumption” advisory, a recommendation more severe than that anticipated for the Chesapeake 

Bay. 

The four estuarine and coastal studies were conducted over a 13-year period (1983–1995) 

and revealed a wide range of estimates for angler awareness, from 19% to 60%. Table 3.2 shows 

the calculated standard error for each study, along with a 95% confidence interval (CI) for the 

estimate. Treating all the estimates equally, the mean probability that an angler was aware of 

advisories was 41%. 

Table 3.2. Standard Errors and Confidence Intervals for Advisory Awareness Estimates, 

Estuarine and Coastal Regions 

Authors Location 

Percentage 

Aware 

Standard 

Error 95% CI 

Belton et al. (1986) New York Harbor 50% 1.1% 47.7%–52.2% 

Burger et al. (1993) Jamaica Bay, NY 19% 3.2% 12.8%–25.2% 

May and Burger (1996) Arthur Kill, NJ 60% 3.8% 52.6%–67.4% 

May and Burger (1996) Raritan Bay, NJ 28% 5.8% 16.6%–39.4% 

May and Burger (1996) New Jersey Shore 30% 6.9% 16.5%–43.5% 

Phlugh et al. (1999) Newark Bay, NJ 60% 2.8% 54.5%–65.5% 

 

Some of the estimates (e.g., Raritan Bay and New Jersey Shore) are based on relatively 

small sample sizes, so it is advisable to take into account the error associated with each study 

estimate. Similarly, one might not wish to assign the studies equal weight, instead giving greater 

weight to studies with larger sample sizes. One method to accomplish both goals is a Bayesian 

weighting methodology, in which the weights are based upon the inverse of the variance of each 

estimate. In this case the estimate with the smallest variance (Belton et al.) receives the most 

weight, and the estimate with the largest variance (May and Burger, New Jersey Shore) receives 
Resources for the Future Jakus, McGuinness, and Krupnick 

28

the least. Desvousges et al. (1998, 34) provide the formulas to calculate the updated mean and 

variance, 

(1 / ) (1 / )

(( / ) ( / )) [ | ]

b

b b

E b

σ σ

β σ σ β

β

β

+

+ =

(1 / ) (1 / )

1 [ | ]

b

Var b

σ σ

β

β + =

where β and σβ are the prior estimate of the mean and variance, respectively, and b and σb are 

“new” estimates of the mean and variance. The equations from Desvousges et al. provide 

estimates for the mean and variance of the posterior distribution. Ordering the estimates 

chronologically, the Belton et al. mean-variance estimates become the basis for the subsequent 

Bayesian calculations. Based upon this analysis, the mean percentage of anglers aware of 

advisories is predicted to be 48%, with a 95% CI of 46%–50%. 

3.1.2. Percentage of Recreational Anglers Consuming Sport-Caught Fish 

Ten studies provided 12 estimates of the percentage of anglers consuming some or all of 

their sport-caught fish. Table 3.3 presents these data.12 The estimates range from 39% in 

Tennessee to 100% among subsistence anglers of Puerto Rico. The mean proportion of anglers 

keeping some or all of their catch is 0.69, or 69%. If the estimate for subsistence anglers is 

dropped, the mean percentage falls to 66%. If the data are restricted to the New York–New 

Jersey harbor region, the estimated proportion of anglers consuming sport-caught fish is 71%. 

 

12 The sample size for the Knuth et al. (1993) study changed from Table 3.2 to Table 3.3. This study comprises 

several independent samples, but the consumption estimate is reported only for the combined dataset. 
Resources for the Future Jakus, McGuinness, and Krupnick 

29

Table 3.3. Percentage of Anglers Consuming All or Some of Their Catch 

Authors Location 

Survey 

Date 

Sample 

Size 

Percentage 

Consuming 

Belton et al. (1986) New York Harbor 1983 1900 58% 

Burger and Gochfeld 

(1991) Puerto Rico 1988 25 100% 

Diana et al. (1993) Lake Ontario 1988 256 70% 

Burger et al. (1993) Jamaica Bay, NY 1990 154 85% 

Connelly and Knuth (1995) Lake Champlain, 

NY and VT 1992 744 66% 

Knuth et al. (1993) Ohio River 1992 2110 43% 

MacDonald and Boyle 

(1997) Maine open waters 1994–95 999 41% 

May and Burger (1996) Arthur Kill, NJ 1994 168 70% 

May and Burger (1996) Raritan Bay, NJ 1994 60 88% 

May and Burger (1996) New Jersey Shore 1994 44 82% 

Burger (1998) Savannah River, SC 1997 258 82% 

Jakus et al. (1998) Tennessee lakes 1997 222 39% 

The consumption estimates presented in Table 3.3 do not account for the influence of 

advisory knowledge. How awareness of an advisory affects the probability of consumption is of 

interest because changing propensity to consume is a key averting behavior in response to an 

advisory. Some percentage of anglers will choose not to consume listed species (or perhaps any 

species) from the water body under advisory. What we need is a statistic that gauges the response 

of consumption to the presence of an advisory. To do this, it is necessary to completely 

characterize the sample of consumption anglers by knowledge of fish consumption advisories. 

The following probability relationship must hold: 

P(Consume) = P(Consume ∩ Aware) + P(Consume ∩ Not Aware) 
Resources for the Future Jakus, McGuinness, and Krupnick 

30

Conditional estimates of the probability of consumption can be recovered according to 

P(Consume│Aware) = P(Consume ∩ Aware) ÷ P(Aware) 

P(Consume│Not Aware) = P(Consume ∩ Not Aware) ÷ P(Not Aware)

The two conditional probability estimates then can be used to gauge the degree to which 

anglers will cease consumption of sport-caught fish when an advisory is in place. This will, in 

turn, affect the overall probability (percentage) of consumption by anglers. 

Unfortunately, only three studies (offering five estimates) provide the necessary 

information. Table 3.4 shows the data from each of the studies and the estimates of the 

conditional probabilities. 

Table 3.4. Estimating Conditional Consumption Probabilitiesa

Study P(A) P(NA) P(C) P(C ∩ A) 

P(C ∩

NA) P(C|A) P(C|NA) 

Belton et al. (1986) 0.580 0.420 0.500 0.205 0.295 0.353 0.702 

May and Burger 

(1996), Arthur Kill 0.600 0.400 0.700 0.396 0.304 0.660 0.760 

May and Burger 

(1996), Raritan Bay 0.280 0.720 0.880 0.280 0.600 1.000 0.833 

May and Burger 

(1996), New Jersey 

Shore 

0.300 0.700 0.820 0.210 0.610 0.700 0.871 

MacDonald and 

Boyle,(1997), All 0.630 0.370 0.413 0.236 0.177 0.375 0.478 

a

Abbreviations for Probabilities: 

A = Aware of Advisories 

NA = Not Aware of Advisories 

C = Consume Fish from Contaminated Waters 

Numbers in bold italics are provided in study documents. 
Resources for the Future Jakus, McGuinness, and Krupnick 

31

The two estimates of interest are shown in the last two columns of Table 3.4, the 

consumption probabilities conditional on knowledge of advisories. The probability that an angler 

would consume fish from a contaminated water body given knowledge of advisories ranges from 

0.35 to 1.00. Denote this probability as P(Consume | Aware). If the 100% probability is not 

included (it was based on a very small sample), the estimates of P(Consume | Aware) range from 

0.35 to 0.70. The conditional estimates of the probability an angler would consume fish from a 

contaminated water body given no awareness of advisories, denoted P(Consume | Not Aware), 

range from 0.48 to 0.87. In all cases except the 100% conditional probability estimate, 

P(Consume | Aware) is less than P(Consume | Not Aware). This suggests that anglers who are 

aware of advisories are less likely to consume fish than anglers who are not aware of advisories. 

Applying the Bayesian equations cited by Desvousges et al., the mean and variance for the 

conditional consumption probabilities can be estimated. The mean P(Consume|Aware) is 0.493, 

with a 95% CI of 0.473–0.51.3. The mean P(Consume|Not Aware) is 0.667, with a 95% CI of 

0.642–0.692.13 

These estimates can now be evaluated to gauge the degree to which anglers will cease 

consumption of listed species. For example, the Belton et al. (1986) study shows that anglers 

who were aware of advisories were only half as likely to consume fish from the contaminated 

water as anglers who were unaware of advisories. The degree to which aware anglers are less 

likely to consume from contaminated waters is given by 

 

13 The standard error for the conditional consumption probabilities is likely underestimated because they themselves 

are a product of random variables. Not all the variance was accounted for, such that the 95% CIs are likely too 

narrow. 
Resources for the Future Jakus, McGuinness, and Krupnick 

32

100

P(Consume | Not Aware)

P(Consume | Aware) P(Consume | Not Aware) %Reduction × − =

Evaluated at the means for P(Consume|Aware) and P(Consume|Not Aware), the 

percentage reduction in the probability of consumption is 26.1%. An empirical distribution for 

this value was formed by taking 1,000 random draws from the distributions of 

P(Consume|Aware) and P(Consume|Not Aware) and calculating the percentage reduction for 

each draw. The empirical distribution yields a 95% CI for the percentage reduction of 22.1%–

30.0%. 

3.1.3. Other Averting Behaviors 

Anglers can respond to advisories in several ways. The change in consumption 

probability calculated in Section 3.1.2 may occur because they target a different species for 

consumption or, more simply, because they avoid all species from a water body with under an 

FCA for any species. Anglers may also respond to an advisory by eating fewer meals of the 

listed species or reducing the number of trips to contaminated water bodies. With many chemical 

contaminants, consumption risks may be reduced by changing fish preparation or cooking 

methods, thus making listed species safer to eat. But cooking and preparation methods do little to 

reduce the risk of eating fish species contaminated by mercury because unlike other 

contaminants, mercury does not concentrate in specific bodily tissues.14 This section focuses on 

averting actions that are relevant to mercury contamination. 

 

14 For example, PCBs concentrate in the fatty tissues of fish. Removal of these tissues greatly reduces PCB 

contamination of those who consume these fish. Unlike PCBs, mercury is more evenly distributed throughout a fish, 

binding to the proteins in muscle tissues. 
Resources for the Future Jakus, McGuinness, and Krupnick 

33

Table 3.5 presents consumption-related averting behaviors as reported in the literature. 

Behaviors are presented slightly differently from study to study, with different conditions 

governing each response. Sometimes the response is based on behavior given the advisory, other 

times it is based on contingent behavior if the advisory were not in place. 

The roughly 26% decrease in consumption of listed species calculated using conditional 

probability rules described in Section 3.1.2 is supported by the consumption reductions noted by 

the five studies reported in Table 3.5. Between 13% and 25% of those surveyed reported not 

eating any fish from contaminated waters; 23% to 26% reported changing the species targeted 

for consumption; and 15% to 54% reported adjusting overall fish consumption. 

Table 3.5. Consumption-Related Behaviors Due to Fish Consumption Advisories 

Question/Behavior Location Authors 

If advisory were not in place... 

 54% would eat more fish Lake 

Champlain 

Connelly and Knuth 

(1995) 

 15% would consume more fish Maine MacDonald and Boyle 

(1997) 

In response to present advisory... 

 42% reduced fish consumption Ohio River Knuth et al. (1993) 

 13% stopped eating all fish Ohio River Knuth et al. (1993) 

 26% changed target species Ohio River Knuth et al. (1993) 

23% changed target species Green Bay Breffle et al. (1999) 

45% changed species for consumption Green Bay Breffle et al. (1999) 

If favorite site had an FCA... 

 25% would not eat any fish Michigan Kreiger and Hoehn (1998) 

 14% would eat fewer fish Michigan Kreiger and Hoehn (1998) 

In addition to changing consumption behaviors in response to advisories, anglers may 

also change trip-related behavior. For example, an angler may choose not to visit a contaminated 
Resources for the Future Jakus, McGuinness, and Krupnick 

34

site. This, in turn, may affect the total number of fishing trips taken during a season. Table 3.6 

reports trip-related averting actions. 

Table 3.6. Trip-Related Behaviors Due to Fish Consumption Advisories 

Question/Behavior Location Authors 

If advisory were not in place... 

 10% would fish more days Maine MacDonald and Boyle 

(1997) 

 5% would fish more waters Maine MacDonald and Boyle 

(1997) 

 5% would fish different waters Maine MacDonald and Boyle 

(1997) 

In response to present advisory... 

 7% do not fish contaminated water 

body 

Lake 

Champlain Connelly and Knuth (1995)

 7% do not fish contaminated water 

body Green Bay Breffle et al. (1999) 

 37% take fewer trips Ohio River Knuth et al. (1993) 

30% fish fewer days Green Bay Breffle et al. (1999) 

 26% change fishing site Ohio River Knuth et al. (1993) 

31% change fishing site Green Bay Breffle et al. (1999) 

If favorite site had an FCA... 

36% change fishing site Michigan Kreiger and Hoehn (1998) 

 

In addition to the studies cited in Table 3.6, a number of articles in the economics 

literature examine the impact of advisories on the probability a site is visited and on the number 

of trips taken over the course of the season. These studies support the general findings reported 

in Table 3.6—that FCAs cause anglers to choose other locations to fish and take fewer overall 

fishing trips during any given time period. Jakus et al. (1997) examined the site location 

preferences of freshwater Tennessee anglers, finding that the removal of an advisory at any one 

site would increase the probability that site would be visited on any given occasion by 0.1% to 

2.55%. Parsons et al. (1999) examined fishing in middle Tennessee lakes (2 of 14 sites were 
Resources for the Future Jakus, McGuinness, and Krupnick 

35

contaminated) and predicted that total seasonal trips would increase by more than 0.3 trips per 

person if advisories were removed from both lakes (approximately a 2% increase). Employing a 

different dataset for Tennessee anglers, Jakus et al. (1998) use a site-choice-only model to find 

that anglers are less likely to visits lakes with advisories than lakes without advisories, all else 

equal. Other studies confirm a similar effect (e.g., Montgomery and Needelman 1997; Chen and 

Cosslett 1998; Parsons and Hauber 1998; and Shaw and Shonkwiler 2000). Unfortunately, none 

of these last five studies report the estimated change in seasonal trips due to advisories. The 

welfare effects of changing trip-related behavior are examined in Section 3.2 of this report. 

3.1.4. Percentage of Anglers Exceeding Recommended Consumption Limits 

Thus far, this report has examined the degree to which anglers will eliminate or reduce 

consumption of species under an FCA. Despite FCAs, some anglers will continue to consume 

listed species at current consumption rates, possibly in excess of recommended consumption 

limits. Several authors measure the degree to which anglers exceed limits. Table 3.7 summarizes 

this information.15

 

15 The Knuth et al. (1993) study consisted of independent samples conducted either at different times (the first 

estimate in Table 3.7) or in different locations (the next six estimates). Data were reported in such a way that each 

estimate can be calculated independent of the others. 
Resources for the Future Jakus, McGuinness, and Krupnick 

36

Table 3.7. Percentage of Anglers Exceeding Recommended Consumption Limits 

Authors Location 

Survey 

Year 

Sample 

Size 

Percentage 

Who Exceed 

Limit 

Diana et al. (1993) Lake Ontario 1988 256 57.0% 

Knuth et al. (1993) Ohio River 1992 839 11.1% 

Knuth et al. (1993) Ohio River, PA 1992 123 1.9% 

Knuth et al. (1993) 

Ohio River, 

WV 

1992 233 6.6% 

Knuth et al. (1993) Ohio River, OH 1992 250 1.7% 

Knuth et al. (1993) Ohio River, IN 1992 265 4.8% 

Knuth et al. (1993) Ohio River, KY 1992 278 36.1% 

Knuth et al. (1993) Ohio River, IL 1992 119 0.0% 

Connelly and Knuth (1995) 

Lake 

Champlain 

1992 744 8.0% 

May and Burger (1996) Arthur Kill, NJ 1994 168 30.0% 

The Illinois estimate is 0% only because the state had not issued an Ohio River FCA at 

the time of the survey. Even so, the estimated percentage of anglers exceeding advisory limits 

ranges widely, from 1.7% to 57%. The Desvousges et al. Bayesian calculations can be applied to 

estimate the degree to which Chesapeake Bay anglers may exceed consumption limits. 

Eliminating the Illinois estimate, these calculations yield a mean percentage of anglers exceeding 

the limit of 9.6%, with a 95% CI of 7.7% to 11.5%. 
Resources for the Future Jakus, McGuinness, and Krupnick 

37

3.1.5. Assessing Changes in Consumption-Related Behavior by Chesapeake Bay Anglers 

The analysis of this section can be combined with recreational trip and consumption 

information to assess consumption-related behavior by anglers and see how this behavior might 

change under an advisory. In particular, we need to know how both the propensity to consume 

and the number of trips for striped bass change under an advisory, so that we can estimate preand post-advisory per capita mercury uptake. Although the per capita change in exposure under 

an advisory depends upon the behavioral changes estimated in this section, this value is the 

primary input to the Health Effects Module, and these calculations are discussed in detail in 

Section 5. The change in consumption can be calculated using the results of Sections 3.1.1 and 

3.1.2, if one assumes that the current number of trips per angler is independent of anglers’ 

current propensity to consume fish. With this assumption, the current probability that any given 

angler will consume striped bass is 0.674. 

Table 3.8 displays our pre-advisory estimates of participation and consumption behaviors 

for anglers from any state who use the Maryland portion of the Chesapeake Bay. Note, then, that 

these estimates ignore benefits and costs associated with use by anglers of the Virginia portion of 

the Chesapeake Bay. In particular, the costs of an FCA and the health benefits associated with an 

FCA would both be larger if use of the Virginia portion were counted. 
Resources for the Future Jakus, McGuinness, and Krupnick 

38

Table 3.8. Current Participation and Consumption by Chesapeake Bay 

Recreational Anglers 

Row Measure Value Source 

(1) Total Maryland saltwater fishing 

trips (2000) 3,722,018 MRFSS 

(2) 

Inland (Chesapeake Bay) fishing 

trips 

(92% of row 1) 

3,406,647 MRFSS 

(3) Percentage of trips for striped 

bass fishing 24.6% 1996 FHWAR-MD 

(4) Trips for striped bass fishing 

(row 2 × row 3) 836,672 

(5) 

Percentage of trips on which 

striped bass are kept for 

consumption 

67.4% 1997–2000 MD 

CSBS 

(6) 

Trips on which striped bass are 

kept for consumption 

(row 4 × row 5) 

563,917 

MRFSS = Marine Recreational Fishing Statistics Survey. 

FHWAR-MD = National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, Maryland. 

MD CSBS = Maryland Cooperative Striped Bass Survey. 

When FCAs have been issued in other areas, studies have shown that anglers who are 

aware of the advisory are less likely to consume listed species than those who are not aware of 

the advisory. The conditional consumption probability estimates of Table 3.4 depict this effect. 

Under the assumption that anglers who are unaware of the advisory will not change their 

consumption propensity, then P(Consume | Not Aware of Advisories) is equal to the current 

consumption probability. An estimate of P(Consume | Aware of Advisories) is given by the 

following relationship, 
Resources for the Future Jakus, McGuinness, and Krupnick 

39

(Consume | Not Aware)

(Consume | Aware) (Consume | Not Aware) Reduction

P

P − P =

The left-hand side of this equation is given by the mean percentage reduction in 

probability of consumption from Table 3.4, or 0.261. Given P(Consume|Not Aware) = 0.674, 

P(Consume | Aware) is estimated to be 0.498. The overall consumption probability following an 

advisory can be estimated using the following probability relationship, 

P(Consume) = P(Aware of Advisories) × P(Consume | Aware) + 

P(Not Aware of Advisories) × P(Consume|Not Aware) 

where P(Aware of Advisories) = 0.48 and P(Not Aware of Advisories) = 0.52. These values were 

estimated in section 3.1.1. Thus the overall consumption probability after advisories is estimated 

to be P(Consume) = (0.48 × 0.498) + (0.52 × 0.674) = 0.590. 

The second parameter of interest is the reduction in the total number of trips under an 

advisory. In the presence of an advisory, anglers may choose not to visit a contaminated site, or 

reduce their number of trips to the site. We apply the Parsons et al. (1999) estimate of a 2% 

reduction in trips to all saltwater trips in Maryland, and assume that this reduction occurs 

uniformly throughout the population. This estimate, along with the estimates of changes in 

consumption behavior discussed above, are used to estimate the change in per capita mercury 

uptake under an advisory in Section 5. Table 3.9 displays our postadvisory estimates of 

participation and consumption behaviors. 
Resources for the Future Jakus, McGuinness, and Krupnick 

40

Table 3.9. Participation and Consumption by Chesapeake Bay Recreational Anglers 

under a Striped Bass Fish Consumption Advisory 

Row Measure Value Source 

(1) Total Maryland saltwater fishing trips 

(2000) 3,722,018 MRFSS 

(2) Percentage reduction in total trips due to 

FCA 2% Parsons et al. (1999) 

(3) 

Total Maryland saltwater fishing trips with 

FCA 

(row 1 × (1 – row 2)) 

3,647,578 

(4) Inland (Chesapeake Bay) fishing trips 

(row 3 × 0.92) 3,359,771 MRFSS 

(5) Percentage of trips for striped bass fishing 24.6% 1996 FHWAR-MD 

(6) Trips for striped bass fishing 

(row 4 × row 5) 825,520 

(7) Percentage of trips on which striped bass 

are kept for consumption 59.0% This study 

(8) 

Trips on which striped bass are kept for 

consumption 

(row 6 × row 7) 

486,661 

MRFSS = Marine Recreational Fishing Statistics Survey. 

FHWAR-MD = National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, Maryland. 

MD CSBS = Maryland Cooperative Striped Bass Survey. 

3.1.6. Summary 

The advisory literature has been reviewed with respect to the consumption-related 

averting behaviors. Conditional consumption probability estimates show that the propensity to 

consume sport-caught fish is related to advisory awareness. These estimates will be used in 

Section 5 to estimate the change in total Hg uptake by anglers. Table 3.10 summarizes the 

parameters estimated for the Chesapeake Bay. 
Resources for the Future Jakus, McGuinness, and Krupnick 

41

Table 3.10. Summary of Parameters Estimated for Chesapeake Bay 

Parameter Mean 95% CI 

P(Aware of Advisories) 0.48 0.46–0.48 

P(Consume | Aware) 0.498 0.478–0.518 

P(Consume | Not Aware) 0.674 0.649–0.699 

Percentage reduction in probability of 

consumption (Aware vs. Not Aware) 

26.1% 22.1%–30.0% 

P(Exceed Consumption Limits) 0.096 0.077–0.115 

A number of assumptions were required in the estimation of angler behavioral changes 

under an advisory. Table 3.11 outlines the critical assumptions and the probable direction of bias 

in the estimate of the change in angler striped bass consumption. The various assumptions 

suggest that in all likelihood, we are underestimating reductions in consumption and mercury 

uptake resulting from anglers’ behavioral response to an advisory. 
Resources for the Future Jakus, McGuinness, and Krupnick 

42

Table 3.11. Assumptions and Limitations of Angler Behavior Estimates 

Parameter, Limitations Probable Bias in Estimate of 

∆Consumption 

P(Aware of Advisories) 

 • No control for severity of advisories, angler 

 characteristics, or state efforts to publicize advisories 

 • Advisory awareness based upon awareness in 

Northeast estuaries, which is low compared with other 

 regions; advisory awareness maybe greater in the 

 Chesapeake, especially if state educational efforts are 

 extensive 

Bias ∆consumption 

downward 

P(Consume), P(Consume|Aware) and 

P(Consume|Not Aware) 

 • Based on only three studies 

 • Standard errors do not fully reflect all sources of 

 random error 

 • For “not aware” anglers, P(Consume) before FCA 

 equals P(Consume) after FCA 

Unknown 

Average Consumption (“Kept” Fish) per Trip 

 • Assumed constant after advisory; literature 

 suggests that those who are aware of advisories yet 

 still consume fish tend to reduce consumption 

Bias ∆consumption 

downward 

Reduction in Trips 

 • Quantitative estimate based on only one study Unknown 

 3.2. Economic Analysis of Fish Consumption Advisories and Chesapeake Bay 

Recreational Fishing 

Economic losses are expected to result from the behavioral adjustments undertaken by 

anglers who respond to a recreational advisory. The size of the total consumer surplus loss from 

an advisory is proportional to the magnitude of the average angler behavioral response at the 

margin. This marginal response is from a baseline angler behavior that reflects any preexisting 

advisories. For example, if there were already a consumption advisory in place for Chesapeake 

Bay striped bass for a contaminant other than mercury, anglers would have already undertaken 

some level of behavioral adjustment before the announcement of a mercury advisory. It is 
Resources for the Future Jakus, McGuinness, and Krupnick 

43

possible, then, that the behavioral adjustment under a mercury advisory might be negligible, such 

that the consumer surplus loss from that specific advisory would be close to zero. In reality, 

however, some incremental behavioral adjustment is likely as a result of such factors as the 

increased severity implied by the existence of multiple advisories, increased awareness due to the 

additional advisory, or possibly greater aversion to mercury contamination than to other 

contaminants for some individuals. The size of this marginal response is also a function of 

several other factors, such as advisory severity, agency outreach and information efforts, and the 

availability of noncontaminated substitute species or sites, among others.16 

Economists have only recently begun to publish reports on the economic value of 

behavioral changes induced by fish consumption advisories. Most frequently, researchers have 

applied standard versions of recreational site-choice models to the problem, treating the presence 

or absence of an FCA as a site attribute. In general, these models have found that, all else equal, 

anglers are less likely to visit fishing sites under an advisory than sites not under an advisory. 

The models can then be used to estimate the dollar value of lost consumer surplus associated 

with FCAs. 

Although the major portion of the applicable literature uses the approach noted in the 

previous paragraph (and reported in detail below), economists have also used other techniques 

and value measures to estimate the effects of an FCA. Four studies, in particular, stand out. 

Kreiger and Hoehn (1998, 1999) have published a pair of papers that examine the value of 

information provided by an advisory. They note that an angler may use advisory information ex 

 

16 Because there are currently no FCAs for fish caught in the Chesapeake Bay, we assume that anglers in this 
Resources for the Future Jakus, McGuinness, and Krupnick 

44

ante, prior to making site-choice decisions, and thus such information may have value to that 

individual; they therefore estimate the value of “partial” versus “full” disclosure programs. 

Partial disclosure programs report only those sites that have been tested and found contaminated, 

whereas a full disclosure program reveals the outcome—whether contaminated or safe—for all 

sites that have been tested. By implication, the full disclosure program also reveals which sites 

have not been tested. The payment vehicle in the contingent valuation scenario is increased 

license fees; anglers are willing to pay an additional $5.63 annually for full disclosure on all sites 

that had been tested to that date, with an additional $0.005 for full disclosure on each additional 

site tested (Kreiger and Hoehn 1998); in 2000 dollars ($2000), the values are $6.97 and $0.006. 

The ex ante value of the information estimate is not directly applicable to this study because we 

are interested in potential losses of consumer surplus by Chesapeake anglers. 

MacDonald and Boyle (1997) use contingent valuation to measure the impact of a 

statewide mercury advisory for all open-water fishing in Maine. One of the authors’ goals is 

estimating the economic losses to anglers using a contingent valuation question. Respondents 

were presented with a dichotomous choice question asking whether they would have been 

willing to pay X dollars more for fishing during the season. Questions were designed for the 

“with” and “without” FCA scenarios. Empirical models showed that the presence of the advisory 

was statistically insignificant; that is, the data suggest that the advisory does not significantly 

change the net economic value of fishing in Maine. 

 

analysis have not undertaken any prior behavioral adjustment. 
Resources for the Future Jakus, McGuinness, and Krupnick 

45

Johnson and Desvousges (1997) use conjoint analysis to measure the value of advisories 

(among a variety of other environmental goods). Advisories are directly linked to the reduction 

of pollutants emitted by power plants, although it is not clear whether the survey instrument 

explicitly references mercury contamination of fish. The payment vehicle is an increase in the 

price of electrical power reflected in the respondent’s utility bill. The valuation scenario asks 

willingness to pay (WTP) to decrease the number of fish consumption bans (the base number of 

lakes was 200, although it is not clear how many were under an advisory). The valuation model 

is linear in the number of lakes from which advisories would be lifted. The model indicates that 

respondents are willing to pay approximately 1.1% higher utility bills to reduce emissions 

enough to cancel an advisory on a single lake. The study does not name the location of the 

survey, nor is the percentage of the electrical bill converted to a dollar value. 

Finally, in a study of the Lavaca Bay region of the Gulf Coast of Texas, MacNair et al. 

(1998) use a combined revealed preference–stated preference site-choice model to estimate the 

impact of a coastal consumption advisory. The authors find a statistically significant effect on 

site choice, with anglers less likely to visit the contaminated site relative to other sites, all else 

equal. The model is unconventional in the sense that it does not include a travel cost variable; 

instead, the authors use only the distance traveled to the site as an explanatory variable. Thus, the 

report does not state a monetized estimate of economic losses, instead stating that anglers 

suffered a 3.2% loss in the expected utility index. 

3.2.1. Economic Losses Due to Fish Consumption Advisories 

A number of economists have examined the angler’s economic losses due to the presence 

of contaminants in sufficient quantity that an FCA must be issued. Ten studies are summarized in 
Resources for the Future Jakus, McGuinness, and Krupnick 

46

Table 3.12, which presents the estimates for Great Lakes sites first, followed by the Northeast 

and concluding with the southern United States. As noted in the table, the studies often differ by 

modeling approach. Most use some form of a linked site-choice–trips model, generally a 

multinomial logit site-choice model linked to a trips model using either the Morey, Rowe, and 

Watson (MRW) or the Hausman, Leonard, and McFadden (HLM) version of the utility index. 

Shaw and Shonkwiler’s (2000) seasonal model differs considerably from the MRW and HLM 

indices, relying on an index related to the total distance traveled during the fishing season as 

opposed to the usual formulation based on the number of trips taken during a season. Other 

authors used FCA-related data to examine different model formulations. For example, Jakus et 

al. (1997) estimated a simple site-choice model but linked this model to an equation explaining 

other aspects of angler behavior (e.g., anglers’ catch rates), whereas Chen and Cosslett (1998) 

evaluated different forms of the site-choice model, comparing multinomial logit models to 

multinomial probit results. 

The “seasonal” value models range from the March–August season of the Tennessee 

models and the April–October season of the New York State model to the full-year model of the 

Green Bay study. Because the seasonal estimates are not strictly comparable, the focus of this 

section is on per trip estimates of lost economic surplus. The range of estimates for these losses 

from the site-choice models is relatively narrow, from $2.04 per trip to $5.51 per trip ($2000).17

The lowest values in the range are given by the middle Tennessee studies, which are just over $2 

 

17 In a paper not reported in Table 3.12, Jakus and Shaw (2002) use a dummy variable to indicate the presence of an 

advisory at a given site. This model, which predicts relatively large per trip losses, represents a significant departure 

from previous studies in that the site-choice model uses a site-specific “perceived hazard” index that is related to the 

probability an angler will keep fish at a given site. 
Resources for the Future Jakus, McGuinness, and Krupnick 

47

per trip. The choice set for these models includes 14 sites, 2 of which are contaminated. Jakus et 

al. (1997) report losses in middle Tennessee representing about 8% of total per trip consumer 

surplus. The lowest estimate of lost per trip consumer surplus in middle Tennessee ($2.04) is 

matched by the estimate for New York State (Montgomery and Needelman 1997). The choice set 

for this study also includes very few contaminated sites relative to the number of uncontaminated 

sites (only 23 of nearly 2,600 sites had toxic species).
Resources for the Future Jakus, McGuinness, and Krupnick 

48

Table 3.12. Estimates of Lost Consumer Surplus Due to Fish Consumption Advisories 

Authors Model Location 

Lost 

Economic 

Value per 

Trip 

Lost 

Economic 

Value per 

Season 

Value 

per 

Trip 

($2000) 

Value per 

Season 

($2000 ) 

Chen and Cosslett 

(1998)

MNL 41 Great Lakes 

sites $3.06 $4.93c

Chen and Cosslett 

(1998)

MNP 41 Great Lakes 

sites $3.42 $5.51c

Breffle et al. 

(1999) MNL Green Bay, WI $4.17 $55.00 $4.40c

$58.04 

(12 

months) 

Montgomery and 

Needelman (1997)

MNL 

MRW 

2,586 New York 

ponds, lakes $1.51 $63.25 $2.04 $85.29 (7 months) 

Parsons and 

Hauber (1998) MNL 2,029 Maine lakes, rivers $1.67 $2.25 

Jakus et al. (1997) MNL 

MRW 

14 middle 

Tennessee lakesa $1.85b

 $21.96 $2.13c $25.28 

(6 months) 

Jakus et al. (1997) MNL 

MRW 

14 east Tennessee 

Lakes $2.86 $47.40 $3.29c $54.56 (6 months) 

Jakus et al. (1998) 

MNL

with 

catch 

rate 

model 

12 east Tennessee 

lakes $2.33 $2.49 

Parsons et al. 

(1999) HLM 14 middle 

Tennessee lakesa $1.77 $21.55 $2.04c $24.80 (6 months) 

Parsons et al. 

(1999) MRW 14 middle 

Tennessee lakesa $1.84 b $23.62 $2.12 $27.19 

(6 months) 

Shaw and 

Shonkwiler (2000) SS 14 middle 

Tennessee lakesa $10.67 $12.28 (6 months) 

MNL = Multinomial logit model. 

MNP = Multinomial probit model. 

HLM = Hausman, Leonard, and McFadden index. 

MRW = Morey, Rowe, and Watson index. 

SS = Shaw and Shonkwiler “distance” index. 

a

All 14 middle Tennessee lakes studies used the same dataset. 

b

Jakus et al. (1997) and Parsons et al. (1999) welfare estimates differ slightly due to the bootstrap process used in 

calculations. 

c

Estimates in boldface are used in subsequent analysis. 

In general, larger welfare estimates are obtained for regions with a large proportion of 

contaminated sites. For example, Chen and Cosslett (1998), with 14 of 41 sites contaminated, 
Resources for the Future Jakus, McGuinness, and Krupnick 

49

obtain loss estimates of $4.93 (MNL) and $5.51 (MNP) per trip.18 The two Jakus et al. east 

Tennessee lakes models have choice sets of 14 (1997) and 12 (1998) sites each, of which 6 sites 

have contaminated species. These models, using different datasets, yield economic surplus loss 

estimates of $3.29 (1997) and $2.49 (1998) per trip ($2000). The first estimate represents 

approximately 6% of per trip consumer surplus. The second estimate is restricted to consumption 

anglers who were aware of the advisory. 

Only a few of the estimates reported in Table 3.12 have an accompanying confidence 

interval or standard deviation. Adjusted to $2000, the lost economic surplus estimates for which 

a variance is reported or could be estimated are shown in boldface in Table 3.12. The studies 

selected for further analysis are quite comparable to one another, relying upon revealed 

preference data to estimate a site-choice model. The only exception to this rule is the Breffle et 

al. dataset, which augments revealed preference data with stated preference data. Following the 

Desvousges et al. Bayesian equations presented in Section 3.1, the mean and a 95% confidence 

interval for per trip losses can be estimated. 

The Bayesian approach assumes that a “true” fixed value for per trip loss of consumer 

surplus exists and is invariant to the other factors that affect recreational fishing. This may not be 

the case, especially if the attributes and conditions under which the losses are estimated (say, for 

freshwater fishing in the relatively small lakes of Tennessee) differ substantially from those 

found in the Chesapeake Bay. One could hypothesize that the attributes and conditions of 

 

18 Chen and Cosslett also estimate a varying-parameters version of the MNP model. The per trip welfare loss 

estimated by this model is $0.73 per trip. Given that this estimate is derived from a model that has no benchmark 
Resources for the Future Jakus, McGuinness, and Krupnick 

50

recreational fishing of the Great Lakes are more closely akin to the attributes and conditions 

found in the Chesapeake. Thus, the Bayesian estimates of the mean and variance are first 

calculated using just the three Great Lakes estimates (Chen and Cosslett 1998; Breffle et al. 

1999). For recreational anglers on the Great Lakes, the estimated per trip loss of consumer 

surplus is $5.24 per trip, with a 95% CI of $4.87–$5.62. In contrast, using all six welfare 

estimates for which the variance is provided, the mean per trip loss is $2.55, with a 95% CI of 

$2.20–$2.90. 

3.2.2. Economic Studies of Recreational Fishing in the Chesapeake Bay 

Two studies, in particular, examine recreational angling in the Chesapeake Bay. 

Bockstael et al. (1989) measure the aggregate value of water quality improvements in the bay. 

Their study includes not only recreational angling but beach use and swimming as well. The 

angling portion of the research focuses on striped bass anglers fishing in Maryland. Data were 

gathered from the 1980 National Fishing, Hunting, and Wildlife Associated Recreation 

(FHWAR) survey. Rather than measure the demand for striped bass fishing in terms of trips, the 

FHWAR data provide information only on the number of days of fishing by individual anglers at 

three aggregate sites in Maryland. The statistical analysis of demand for fishing finds that trips 

are positively related to the striped bass catch rate. With respect to welfare measures, the authors 

do not report per day measures of consumer surplus. Rather, aggregate welfare measures are 

provided for a water quality improvement scenario based on a 20% increase in the striped bass 

catch rate. The aggregate annual increase in consumer surplus is estimated at $1.37 million with 

 

against which to gauge its validity, it was decided to use the authors’ estimates from their fixed-parameter MNL and 
Resources for the Future Jakus, McGuinness, and Krupnick 

51

a range of $0.66 million to $2.07 million, or $2.73 million with a range of $1.31 million to $4.12 

million ($2000). 

McConnell and Strand (1994) use 1988–1989 National Marine Fisheries Service data to 

evaluate recreational fishing in the mid- and south- Atlantic sport fisheries. The authors use both 

stated preference and revealed preference models to estimate the value of access to these 

fisheries. In the first value-of-access model, a dichotomous choice question asked anglers their 

willingness to sell the right to fish in the state in which they were interviewed. For example, 

people interviewed in Maryland were asked about their willingness to relinquish the right to fish 

in Maryland, leaving the respondents open to fish in other states. The statistical model shows that 

willingness to sell access is negatively related to the small-game catch rate (the small-game 

species category includes striped bass and 11 other fish). For those anglers interviewed in the 

Chesapeake region (Maryland and Virginia), mean willingness to sell the right to fish for a year 

is $573 with a 95% CI of $555–$591, or a mean of $769 with a 95% CI of $725–$813 ($2000). 

This value can be considered the total annual consumer surplus associated with fishing in 

saltwater regions of the Chesapeake. 

A second value-of-access model is based on a willingness-to-pay framework, implying a 

different property rights perspective. This question is limited to those anglers who had taken a 

multiple-day trip on which they had spent at least one day fishing. Anglers were asked how 

much the cost of fishing would have had to increase to make them give up one day of fishing on 

that multiple-day trip. The statistical model indicates that WTP for the day of fishing is 

 

MNP models. 
Resources for the Future Jakus, McGuinness, and Krupnick 

52

positively related to the small-game catch rate. The mean value for a day of Maryland fishing is 

$20, with a median of $32, or $28 and $44 ($2000), respectively. Results for Virginia show a 

mean of $31 and a median of $53, or $43 and $73 ($2000), respectively. No confidence intervals 

or standard errors are provided for these daily fishing values. 

Finally, McConnell and Strand (1994) estimate a nested multinomial logit model for midand south-Atlantic sport fishing. The model includes a nest for mode and target species choice 

and another for site choice, conditional on mode and species choice.19 The revealed preference 

data indicate that, across all modes, some 32.1% of angler trips were taken with the goal of 

targeting striped bass.20 The models estimate that the value for a day of saltwater fishing in 

Maryland is roughly $27, or $37 ($2000), whereas a day of fishing in Virginia saltwater has a 

value of approximately $42, or $58 ($2000). Again, no confidence intervals or standard errors 

are provided. Still, the revealed preference values arising from the nested multinomial logit 

model, which are based on single-day trips, are remarkably similar to the WTP estimates for a 

day of fishing arising from the stated preference models. 

The McConnell and Strand (1994) estimates of access value (total per day consumer 

surplus) can be linked to the consumption advisory literature via the Jakus et al. (1997) study. 

This is the only study that compared welfare losses associated with FCAs to total consumer 

surplus. In the two Tennessee study regions on which Jakus et al. report, the losses associated 

with FCAs represent 6%–8% of total per trip consumer surplus. Given that the data on which the 

 

19 The different modes include party/charter boat, private/rental boats, or shore fishing. 

20 This figure is reasonably close to the estimate from the 1996 FHWAR, which indicated that 24.6% of all saltwater 

fishing days were for striped bass (Table 3.8). 
Resources for the Future Jakus, McGuinness, and Krupnick 

53

MNL models are based consist of single-day trips, this percentage can be directly applied to the 

McConnell and Strand estimates. Assuming the midpoint of the Jakus et al. loss range (i.e., 7%), 

Table 3.13 presents potential losses associated with an FCA. The average per day surplus loss, 

using just the estimates of the mean, is $2.42.

Table 3.13. Potential Consumer Surplus Losses for Chesapeake Anglers 

McConnell and Strand (1994) Estimates with Jakus et al. (1997) Percentage FCA Loss 

Location 

Type of 

Data Measure 

Consumer 

Surplus 

($2000 ) 7% Loss 

Maryland SP Per day WTP (mean) $28 $1.96 

Maryland SP Per day WTP (median) $44 $3.08 

Maryland RP Per day WTP (mean) $37 $2.59 

Virginia SP Per day WTP (mean) $31 $2.17 

Virginia SP Per day WTP (median) $53 $3.71 

Virginia RP Per day WTP (mean) $42 $2.94 

Chesapeake SP Annual WTP (mean) $769 $53.83 

SP = stated preference. 

RP = revealed preference. 

WTP = willingness to pay. 

Boldfaced values were used to calculate average per day loss. 

The per day loss estimates are quite similar to the loss estimates presented in Table 3.12. 

The per day range for the Chesapeake Bay region lies almost wholly within the range reported in 

Table 3.1, $2.04–$5.51. The annual estimate of loss due to FCAs is also quite close to the Breffle 

et al. (1999) measure ($58.04). 
Resources for the Future Jakus, McGuinness, and Krupnick 

54

3.2.3. Estimating Consumer Surplus Losses to Chesapeake Bay Anglers

The congruity between the estimated losses in Table 3.12 and Table 3.13 is remarkable. 

The application of the Jakus et al. (1997) percentage loss estimate to the McConnell and Strand 

(1994) per day consumer surplus estimates results in an average loss to Chesapeake Bay anglers 

of $2.42, which lies well within the 95% confidence interval implied by the boldfaced estimates 

in Table 3.12 for which some measure of dispersion is reported. Thus, the $2.42 per day loss for 

Chesapeake Bay anglers seems a reasonable place to begin the welfare calculation. 

The per unit losses reported in Table 3.12 are not restricted to those anglers who targeted 

listed species. Rather, the estimates represent ex post losses in consumer surplus—that is, losses 

accruing to all anglers after they respond to FCAs. Conceptually, then, the surplus estimates 

represent the monetized impact on utility of such averting behaviors as changing fishing sites, 

changing target species, and reducing consumption of listed species. As such, the loss should be 

applied to all fishing days for all potential Chesapeake Bay anglers. This figure is not available, 

but a conservative estimate would be to apply the $2.42 loss to all Maryland saltwater fishing 

days. If one applies the per day loss after anglers have adjusted total seasonal trips to saltwater 

areas, the total annual surplus loss is $2.42 per day × 3,647,578 days (Table 3.9, row 3), or $8.83 

million.21

 

21 This loss estimate (as well as our health benefit estimates) would be greater if trips in the Virginia portion of the 

bay were included in the analysis. 
Resources for the Future Jakus, McGuinness, and Krupnick 

55

3.2.4. Summary 

This section of the report summarized the recreational fishing literature. A 

comprehensive review of the literature associated with consumption advisories yielded two 

estimates of the per trip loss in consumer surplus (Table 3.14). A major study of Atlantic coast 

fishing by McConnell and Strand (1994) yielded per day and seasonal estimates of the value of 

saltwater fishing in the Chesapeake region. The percentage loss in per day consumer surplus 

estimated by Jakus et al. (1997) applied to the McConnell/ and Strand (1994) estimates results in 

estimates of daily losses in the Chesapeake. The per day loss is estimated to be $2.42 (range 

$1.96–$2.94), with an annual loss of approximately $8.83 million. 

Table 3.14. Summary of Consumer Surplus Losses Due to Chesapeake FCAs 

Measure Mean 95% CI 

Per trip loss (Great Lakes only) $5.24 $4.87–$5.62 

Per trip loss (all studies) $2.55 $2.20–$2.90 

Per trip loss: Apply 7% consumer 

surplus loss to McConnell and Strand 

daily consumer surplus estimate 

$2.42 $1.96–$2.94 

Aggregate annual loss $8.83 million 

The results of this section must be interpreted within the context of the assumptions and 

limitations of the analysis (Table 3.15). First, the per trip consumer surplus loss estimates are 

averaged over all anglers. “Complete averters,” those who do not eat contaminated fish, are not 

at risk yet may engage in unnecessary defensive actions, such as ceasing consumption of all fish 

or never fishing a site under an advisory. These unnecessary actions are included in the consumer 

surplus loss estimates. Second, FCAs may have effects that in turn affect different anglers in 
Resources for the Future Jakus, McGuinness, and Krupnick 

56

different ways. For example, Jakus et al. (1998) hypothesized that recreational welfare losses 

may differ across those anglers who consume (or had planned on consuming) fish relative to 

those who fish primarily for catch-and-release. Reductions in harvest rates by consumption 

anglers in response to FCAs may increase the overall biomass in the estuary. Increased biomass 

may mean that catch-and-release anglers benefit from catching more, and larger, fish. Such 

effects may cause the consumer surplus losses, which are estimated over all anglers, to be 

smaller in the long run. Finally, the recreational losses do not include any health-related benefits 

or losses associated with (a) reduced Hg uptake by those anglers heeding the advisory or (b) the 

health effects of continued Hg uptake by those anglers who do not know about the advisory or 

choose to ignore it. 

Table 3.15. Assumptions and Limitations of Consumer Surplus Estimates 

Assumption, Limitation Effect on Consumer Surplus 

Estimates 

“Complete averters” may engage in unnecessary 

actions to avoid Hg contamination. These actions are 

included in loss estimate. 

Bias consumer surplus loss 

upward. 

Does not include possible impacts of reduced harvest 

on fish stocks, which may be viewed positively by 

some catch-and-release anglers. 

Bias consumer surplus loss 

upward. 

3.3. The Recreational Angler Response Module 

Within the Maryland Model, the Recreational Angler Response Module uses the 

parameters described in this section to estimate two major endpoints: the consumer surplus loss 

due to an advisory, and the per capita average change in methylmercury uptake under an 

advisory, compared with a baseline of no advisory. When possible, uncertain parameters from 
Resources for the Future Jakus, McGuinness, and Krupnick 

57

either data or the literature are specified as probability distributions, and this uncertainty is 

propagated throughout the model. The model allows the user to vary angler awareness as well as 

consumption preferences (explained in more detail in Sections 5 and 6). Additionally, the user 

can choose between two options to estimate consumer surplus loss. Consumer surplus loss can 

be estimated using the Jakus et al. (1997) and Chesapeake Bay value of a fishing day estimates, 

as reported in Table 3.14, or the combined Great Lakes estimates of per trip welfare loss under 

an advisory, described earlier in this section. 

4. Commercial Fishing Losses from FCAs 

As noted in previous sections of this report, mercury contamination and consumption 

advisories are a concern for striped bass. Not only is striped bass caught by recreational anglers, 

it is also a major commercial species. Two studies from the literature are of interest. The first 

(Kahn and Kemp 1985) estimates a supply-and-demand system for commercial striped bass 

fishing in the Chesapeake Bay, and the second (Buerger and Kahn 1989) estimates a supply-anddemand system for Hudson River, New York, striped bass. The model specifications used in 

each of these reports (“the Kahn studies”) will be reviewed below. These specifications then will 

be used to help specify an “original” model of the Chesapeake Bay striped bass fishery using a 

more up-to-date dataset, which is used to estimate losses under both a commercial fishing ban 

and a fish consumption advisory.22

 

22 Ancillary costs to a fishing ban or advisory, such as employment losses, are beyond the scope of this analysis. 
Resources for the Future Jakus, McGuinness, and Krupnick 

58

4.1. Commercial Striped Bass Fishing Models 

The Kahn and Kemp commercial striped bass model is only a portion of a larger study 

aimed at estimating losses associated with submerged aquatic vegetation in the Chesapeake 

Bay.23 The supply equation is specified as a function of the ratio of the striped bass price to the 

price of two substitute species (oysters and clams), the price of fishing effort, the adult 

population of striped bass, and a time trend. The price of fishing effort is described as “an index 

of labor opportunity costs and energy costs.” Details regarding the construction of the index are 

not provided. The demand equation is specified as a function of the price of striped bass, the 

price of substitute goods (given by the consumer price index, CPI, for meat, poultry, and fish), 

the regional population, regional per capita income, and a time trend. 

The log-linear supply-and-demand model is estimated using two-stage least squares on a 

dataset covering the 1965–1979 period. Exogenous variables include all the variables listed 

above (except, of course, the endogenous price and quantity of striped bass), the lagged price of 

striped bass, and an index of submerged aquatic vegetation in the bay. Table 4.1 shows the sign 

and statistical significance of the variables in each equation. 

 

23 In addition to the commercial fishing model, the authors also estimated a sport fishing model and linked 

submerged aquatic vegetation to an equilibrium catch equation. 
Resources for the Future Jakus, McGuinness, and Krupnick 

59

Table 4.1. Kahn and Kemp Chesapeake Bay Striped Bass Commercial Fishing Modela

Variable Supply Demand 

Intercept + – 

ln(PStriped Bass/POyster) + 

ln(PStriped Bass/PClams) +* 

ln(PEffort) –* 

Ln(Adult Striped Bass Population) +* 

Ln(Time Trend) +* – 

 

ln(PStriped Bass) –* 

ln(CPISubstitutes) + 

ln(Population) + 

ln(Income) + 

Dependent variable: ln(Striped Bass Catch). a

Statistical significance at α=0.10. 

All of the key coefficients have the correct signs as predicted by economic theory. The 

price of striped bass, the price of substitutes, and the “technology” variable of the supply 

equation, as measured by the population of striped bass, all have the proper signs. The demand 

equation, however, has only one significant variable (the price of striped bass). The price 

coefficient can be interpreted as an elasticity, with a value of –1.28. 

With respect to welfare analysis, the authors do not provide aggregate measures of 

producer or consumer surplus. Instead, welfare estimates are restricted to the reduction in 

submerged aquatic vegetation and include welfare losses to sport anglers as well as commercial 

fisheries. An aspect of the study worth noting is that the stocks of striped bass in the Chesapeake 
Resources for the Future Jakus, McGuinness, and Krupnick 

60

were declining over much of the period covered by the data. Following the Kahn and Kemp 

study, Chesapeake Bay stocks declined to the point where the fishery was closed to commercial 

fishing in the late 1980s. In response, many commercial anglers switched species or were 

engaged by the state of Maryland in other fishing activities. After striped bass stocks had 

recovered, the commercial fishery was reopened. Given the unsettled nature of the striped bass 

fishery during the period subsequent to the Kahn and Kemp study, using the Kahn and Kemp 

equations for a direct function transfer to estimate commercial losses due to a striped bass FCA 

may not be desirable. 

The second investigation of commercial striped bass fishing was conducted for the 

Hudson River in New York State (Buerger and Kahn 1989). As in the Kahn and Kemp 

Chesapeake Bay study, these authors link the supply-and-demand analysis to an equilibrium 

catch equation. The striped bass fishery in the Hudson depends not only on the population of 

striped bass in the river but also on the population of striped bass migrating from the Chesapeake 

Bay.24 In this model, the supply equation is specified as a function of the price of striped bass, 

indices of Hudson Bay (adult) and Chesapeake Bay (juvenile) striped bass populations, and the 

price of flukes, porgies, yellowtail flounder, bluefish, and lobsters. The demand equation is 

specified as a function of the price of striped bass, income, New York State population, a time 

trend, and the CPI for meat, poultry, and fish. 

 

24 In this study of the Chesapeake Bay striped bass fishery, we do not consider the implications of migration 

between the Chesapeake and the Hudson in the economic analysis. It is possible that FCAs imposed on the 

Chesapeake affect fishing on the Hudson, and vice versa. 
Resources for the Future Jakus, McGuinness, and Krupnick 

61

The supply-and-demand model is estimated using two-stage least squares for the 1964–

1985 period. Table 4.2 shows the sign and statistical significance of each variable in the model. 

Similar to the Chesapeake Bay model of Table 4.1, nearly all the important economic variables 

have a coefficient with a sign corresponding to economic theory. Evaluated at mean values for 

the independent variables, the demand elasticity for Hudson River striped bass is estimated to be 

–1.32. 

Table 4.2. Buerger and Kahn Hudson River Striped Bass Commercial Fishing Model 

Variable Supply Demand 

Intercept –* + 

PStriped Bass +* –* 

Hudson Bay Striped Bass Population Index + 

Chesapeake Bay Striped Bass Population Index +* 

PFluke +* 

PPorgies +* 

PYellowtail flounder +* 

PBluefish +* 

PLobster –* 

CPISubstitutes +* 

Income +* 

New York State Population –* 

Time Trend –* 

Dependent variable: Striped Bass Catch. a

Statistical significance at α=0.10.
Resources for the Future Jakus, McGuinness, and Krupnick 

62

4.2. Modeling the Commercial Striped Bass of Chesapeake Bay 

In their econometric review of commercial fishery demand elasticities, Roy et al. (1991) 

note the extremely wide range of demand elasticities in the literature. For example, a variety of 

fish products have demands that range from the extremely inelastic (–0.05) to the extremely 

elastic (–22.73)., a range that the authors cannot attribute fully to poor data or poor statistical 

analysis. Monte Carlo analysis is used to evaluate the modeling decisions of an analyst and the 

subsequent impact on elasticity estimates. The authors find that a two-stage least squares 

approach leads to an accurate estimate of the demand elasticity relative to an ordinary least 

squares approach. In the presence of a highly overidentified model or model misspecification 

(e.g., excluded exogenous variables), a quantity-dependent demand model is preferred to an 

inverse demand model. 

Given the results from Roy et al. and the Kahn studies cited in the previous section, a 

quantity-dependent, two-stage least squares approach is used to model supply and demand of the 

commercial Chesapeake Bay striped bass fishery. Further, the Kahn studies can help specify the 

supply-and-demand models. Data on Chesapeake Bay commercial landings in Maryland and 

value of landings were obtained from the Maryland Department of Natural Resources Web page. 

These data provide quantity and value of landings for a variety of commercial species. Agency 

personnel supplied further information useful in the modeling process, including the number of 

commercial licenses used in any given year and the number of striped bass by age group. 

Unfortunately, these data are not available for the full time period. License data are 

available only for 1980–2000, with 1991 license data missing. Striped bass population data are 

available only for 1982–2000. In addition, the striped bass fishery was closed for five years, from 
Resources for the Future Jakus, McGuinness, and Krupnick 

63

1985 through 1989. During this time the quantity of landings was zero, such that price could not 

be defined as an equilibrium outcome of supply and demand. Thus, we have complete data for 

only 13 years (1982–1984, 1990 , and 1992–2000). Given these data shortcomings, the supplyand-demand model must be estimated with parsimony to conserve degrees of freedom. 

In light of the Kahn commercial striped bass models cited above, the supply equation is 

specified using the price of striped bass, the number of commercial licenses, and the total 

population of three-, four-, and five-year-old striped bass in the bay.25 In addition, Maryland 

personnel indicate that oysters and catfish were the primary alternative species sought by 

commercial operations during the striped bass fishery closure of the late 1980s; thus, the 

equilibrium prices for oysters and catfish are also included in the supply equation. On the 

demand side, the specification includes the price of striped bass, household income for midAtlantic states, a price index for substitute goods (the CPI for meat, poultry, and fish), and the 

regional population (Maryland, Virginia, and the District of Columbia). The results are shown in 

Table 4.3.26

The three statistically significant variables in the supply equation all have the expected 

sign. The price of striped bass is positive, the price of oysters is negative, and the sign of the 

technology variable—the striped bass population—is also positive. Neither of the remaining 

variables (the price of catfish and the number of commercial licenses) is statistically significant. 

 

25 This is akin to the juvenile recruitment index of Buerger and Kahn. 

26 Durbin-Watson statistics are reported. The data consist of fewer than 15 observations, however, making it 

difficult to draw definitive conclusions regarding potential autocorrelation problems. 
Resources for the Future Jakus, McGuinness, and Krupnick 

64

Evaluated at mean values for price and quantity, the own price supply elasticity is 1.13, or 

slightly elastic.
Resources for the Future Jakus, McGuinness, and Krupnick 

65

Table 4.3 Two-Stage Least Squares Model of Supply and Demand for the Commercial 

Chesapeake Bay Striped Bass Fisherya

Variable Supply Demand 

Intercept –250.51 

(–0.22) 

–18,709.92 

(–1.82) 

PStriped bass

643.49 

(2.38) 

–911.73 

(–1.50) 

POysters

–507.3 

(–2.32) 

PCatfish

–749.82 

(–1.29) 

# Commercial licenses –0.06 

(–0.09) 

Striped Bass Population 0.18 

(4.69) 

 

Household Income 0.17 

(1.15) 

CPISubstitutes

–229.1 

(–1.61) 

Regional Population 0.003 

(1.63) 

 

R2 0.77 0.34 

Durbin-Watson 2.32 1.34 

Observations 13 13 

Dependent variable: Striped Bass Landings (1,000 lbs.). a

t-ratios in parentheses. 

The demand equation has only one variable (the intercept) statistically significant at 

conventional levels. Many of the remaining variables are reasonably close to significance at the 

0.10 level, however. The most important variable for this analysis is the own price effect. The Pvalue for this variable is 0.14, suggesting that price is an important influence on the demand for 

striped bass even if it is not significant at conventional test levels. The price of substitute goods 

is also close to significance (P=0.11) but has the wrong sign. Evaluated at mean price and 
Resources for the Future Jakus, McGuinness, and Krupnick 

66

quantity, the own price elasticity of demand is elastic, at a value of –1.60, very close to the 

estimates obtained by Buerger and Kahn (–1.32) and Kahn and Kemp (–1.28). 

In 2000, landings of Chesapeake Bay striped bass totaled 2.26 million pounds with an 

equilibrium price of $1.53 per pound. Evaluated at year 2000 values of the explanatory variables, 

the predicted quantity of landings using the supply equation overestimates actual landings by 

0.3%. Although the statistical significance of the estimated demand parameters is not ideal, the 

model predicts quantity reasonably well, overestimating landings by 11.3%.27 The equilibrium 

price and quantity given by the statistical models at year 2000 values for the explanatory 

variables are $1.69 per pound (10.4% error) and 2.37 million pounds (4.9% error), respectively. 

Thus, the models appear to do a reasonable job of prediction. Further, an important economic 

property of the model—the demand elasticity—appears to be in line with demand elasticity 

estimates appearing in the recent literature (Table 4.4). Note that the demand elasticities for 

narrowly defined, single-species commodities tend to be greater in absolute value than the 

elasticity estimates for more broadly defined commodity groups, a result predicted by economic 

theory. 

 

27 In addition to the linear specifications reported in Table 4.3, log-linear and semilog forms were also estimated. 

These models had inferior predictive capabilities relative to the linear specification. 
Resources for the Future Jakus, McGuinness, and Krupnick 

67

Table 4.4. Recent Estimates of Demand Elasticity for Fish 

Study Location 

Demand 

Elasticity 

Species or Commodity 

This study Chesapeake Bay –1.60 Striped bass 

Kahn and Kemp 

(1985) Chesapeake Bay –1.28 Striped bass 

Burger and Kahn 

(1989) Hudson Bay –1.32 Striped bass 

Wessells et al. 

(1995) Montreal –1.98 Mussels 

Eales et al. (1997) Japan –1.18 Medium-value fresh fish 

Salvanes and 

DeVoretz (1997) Canada –0.98 Fresh fish 

Eales and Wessells 

(1999) Japan –0.72 to –1.00 Medium-value fresh fish 

Angrist et al. 

(2000) Boston –0.85 to –1.24 Whiting 

4.3. Welfare Analysis for the Commercial Striped Bass Fishery 

Evaluated at the equilibrium price and quantity predicted by the statistical models ($1.69 

per pound and 2.37 million pounds, respectively), annual consumer surplus is estimated to be 

$3.08 million, whereas annual producer surplus is estimated to be $3.09 million, for a total 

surplus value of $6.17 million. When estimated at the actual 2000 equilibrium price ($1.53 per 

pound) and quantity (2.26 million pounds), annual consumer surplus is $2.80 million, annual 

producer surplus is $2.71 million, and total surplus is $5.51 million.28 These estimates can be 

interpreted as the loss that would be incurred by market participants under a commercial fishing 

ban. 

 

28 This approach essentially adjusts the intercept of each linear equation to force supply and demand curves through 

the observed equilibrium price and quantity, but maintains the estimated price slopes. 
Resources for the Future Jakus, McGuinness, and Krupnick 

68

The statistical insignificance of many demand equation parameters, however, suggests 

that the estimate of consumer surplus may be associated with a substantial amount of error. 

Using the delta method to calculate the variance of the consumer surplus estimate (Greene 2000), 

the 95% confidence interval includes the value $0 (95% CI for consumer surplus is –$1.44 

million to $7.60 million). A similar calculation for the variance of producer surplus in the striped 

bass fishery yielded a much narrower 95% confidence interval, $2.41 million to $3.78 million. 

The welfare analysis for the commercial fishery should be evaluated in light of the wide variance 

of the consumer surplus estimate. 

Given the Hg concentration levels in striped bass as estimated based on Gilmour (1999), 

a complete ban on commercial striped bass fishing is unlikely. Instead, we assume the state is 

likely to issue “Commercial Health Advice” recommending restricted consumption by both the 

general population and subpopulations (children and women of childbearing age). Theoretically, 

this can be modeled as a shift “to the left” of the demand curve as sensitive subpopulations 

restrict their consumption of striped bass. Unfortunately, the literature does not provide any 

guidance for evaluating the magnitude of the shift in commercial demand. We can, however, 

crudely model the impact of commercial advice by assuming that consumer surplus loss in the 

commercial fishery is of equal proportion to the losses incurred by recreational anglers. The only 

estimates of percentage losses in consumer surplus are those given by Jakus et al. (1997) and 

used in Section 3.2: approximately 6% to 8%. 

If commercial advice is issued, it is assumed that consumer surplus losses will amount to 

7% (SD = 0.5) of initial consumer surplus, a reduction in annual consumer surplus from $3.08 

million to $2.87 million ($215,800). Figure 4.1 shows the leftward shift in demand such that the 
Resources for the Future Jakus, McGuinness, and Krupnick 

69

loss in consumer surplus is equal to $215,800. It is seen that the equilibrium price falls to $1.56, 

a 7.7% drop from the initial equilibrium value of $1.69 per pound. Equilibrium quantity falls 

from 2.37 million to 2.29 million pounds, a 3.4% reduction. The demand shift and subsequent 

changes in equilibrium price and quantity result in an annual net loss of producer surplus of 

$304,500, or 9.9% of initial producer surplus. Aggregate annual surplus losses in the commercial 

striped bass market are estimated to be the sum of the changes in consumer and producer 

surpluses, or $520,300.29

 

29 As in the recreational analysis, these estimates are specific to the Maryland portion of the bay. Extending this 

analysis to include Virginia anglers would increase estimated losses (as well as health benefits). 
Resources for the Future Jakus, McGuinness, and Krupnick 

70

4.4. Summary 

This section has reviewed models of the supply and demand for commercial striped bass. 

Two articles were identified in the literature. Although both models have drawbacks that prevent 

direct use for a function transfer exercise, they do provide benchmarks against which to compare 

the original modeling effort for the Chesapeake Bay striped bass fishery. The empirical 

properties of the supply-and-demand models for the Chesapeake are not ideal (particularly for 

the demand equation), but the predictive capability of each model appears acceptable. Given 

conditions in 2000, the total annual economic surplus in the commercial striped bass fishery is 

estimated to be about $6.2 million. The section closed with an estimate that under a notice of 

Figure 4.1: Shift in Demand Following Commercial Advice 

Price

Supply

Original Demand

2371 Quantity (1000 lbs.) 

$4.29

$1.69 

2287 

$1.56 

Demand After Advisory

$4.07 
Resources for the Future Jakus, McGuinness, and Krupnick 

71

Commercial Health Advice, total annual losses in the market for commercially caught 

Chesapeake Bay striped bass would be just over $520,000. 

The limitations of this portion of the analysis stem from two sources. First, because of the 

lack of any estimate of the change in commercial demand due to FCAs, it was necessary to make 

an assumption based on changes in demand found in the literature on recreational fishing. 

Unfortunately, only one estimate of the percentage consumer surplus loss could be found, so it is 

difficult to assess transferability of this estimate to the Chesapeake Bay commercial striped bass 

fishery. Second, whereas some properties of the demand equation suggest that the commercial 

demand model is acceptable (e.g., its predictive ability and the point estimate of the demand 

elasticity), the price parameter was not estimated with a great degree of precision. That lack of 

precision leads to an estimated consumer surplus loss with a very wide confidence interval. 

When the 7% loss in consumer surplus is applied to the endpoints of the confidence interval, 

annual losses could range from $0 to $530,000. In contrast, the producer surplus estimate is 

relatively precise. Applying the 9.9% producer surplus loss to the endpoints of the producer 

surplus CI suggests that annual losses range from $239,000 to $374,000.30 Given uncertainty 

regarding the 7% consumer surplus loss assumption, the actual confidence intervals are likely to 

be even larger. 

 

30 The 2SLS model generates two variance-covariance matrices, one for each equation. CS and PS depend on the 

equilibrium outcome and thus depend on the parameters on both the supply equation and the demand equation. This 

dependence is not accounted for in the reported CS and PS confidence intervals, which are based only on the 

covariance matrix for the appropriate equation. Further, the percentage losses in CS and PS are assumed constant 

when they are, in fact, random variables. In light of these simplifications, the range of CS and PS losses is likely to 

be narrower relative to an than if the random nature of all parameters were fully incorporated. 
Resources for the Future Jakus, McGuinness, and Krupnick 

72

4.5. Commercial Fishing Response Module 

In the Maryland Model, the Commercial Fishing Response Module replicates the 

calculations described in this section. The model first replicates the consumer and producer 

surplus calculations based on the parameters discussed in this section. Both estimated and actual 

equilibrium price and quantity for the commercial striped bass fishery are used as inputs to the 

module. The module simultaneously applies consumer and producer surplus reductions for both 

a ban and the more likely scenario, a commercial consumption “advice.” 

5. Health Benefits of Mercury Exposure Reductions 

This section reviews the epidemiological literature on the relationship between 

methylmercury exposure and three broadly defined health endpoints: adult central nervous 

system effects, childhood neuropsychological development, and cardiovascular health and 

mortality. The quantification of these endpoints in the Maryland Model for a recreational fish 

consumption advisory is also described, and results and benefits estimates are reported.31 

5.1. Health Effects of Methylmercury 

Popular awareness of the health effects of mercury poisoning was first raised by a highdose exposure from consumption of contaminated fish near Minamata Bay in Japan during the 

1950s and resulting in the coining of the term “Minamata disease.” In particular, the danger of 

prenatal exposure was made apparent by the prevalence of congenital Minamata disease in 

 

31 Health benefits would also likely accrue under the issuance of commercial consumption advice. However, given 

an absence of commercial consumption data for the region as well as the reduced probability of such an advisory 

relative to a recreational advisory, we do not attempt to estimate any potential health effects or benefits. 
Resources for the Future Jakus, McGuinness, and Krupnick 

73

children born to exposed mothers, which manifested itself in the form of mental retardation as 

well as several other signs and symptoms. Akagi et al. (1998) estimate that the mean maternal 

hair Hg concentration of patients with congenital Minamata disease was 441 mg/kg (range: 3.8, 

133 mg/kg). A second mass poisoning occurred during the 1970s in Iraq, when seed grain treated 

with a fungicide containing MeHg was ground into flour and consumed by the public. It is 

believed that this poisoning episode involved higher and more acute exposures than did the 

Minamata episode (NRC 2000). Data from these two studies provided the basis for the first 

human dose-response studies for MeHg uptake. 

A large body of literature describes the relationship between MeHg exposure and a 

number of health endpoints, such as cancer and immunological, reproductive, renal, 

cardiovascular, and neurological effects. Within this study, we focus specifically on 

cardiovascular and neurological effects due to adult and prenatal exposure, which currently 

appear to be the most robust health endpoints for chronic low-dose MeHg exposure given the 

existing literature. This section reviews the epidemiological literature pertaining to those broad 

endpoints. (For a more comprehensive review of the literature, see NRC 2000.) 

5.1.1. Central Nervous System Effects in Adults 

Minamata disease encompasses the combination of central nervous system effects that 

adults may experience in the event of mercury poisoning. Although there is no specific test to 

confirm a diagnosis of Minamata disease, it has historically been identified based on a 

characteristic combination of symptoms. One initial symptom, and a commonly relied upon 

indicator of methylmercury disease, is paresthesia, or an itching, prickling, or tickling sensation 

in the extremities. Based on evaluation of data from the Minamata and Iraqi poisonings, the 
Resources for the Future Jakus, McGuinness, and Krupnick 

74

World Health Organization (WHO, IPCS 1990) suggests that 5% of adults with a blood Hg 

concentration at or above 200 ppb will exhibit paresthesia. 

However, further research has suggested that deleterious effects may occur at exposure 

levels below this threshold. Kosatsky and Foran (1996), in a review of 13 studies of long-term 

fish consumers, conclude that at a blood concentration level of 200 ppb, neurological effects may 

be present in as few as 11% and as many as 31% of the exposed population. As a result, they 

suggest a need to better define the portion of the dose-response curve below that threshold (NRC 

2000). Additional studies have suggested neurological and sensory impairments for adults with 

chronic low-dose exposures, though at this point there appears to be no strong evidence of 

ubiquitous, well-defined effects.32

5.1.2 Central Nervous System Effects in Children 

Since the Minamata and Iraqi poisoning episodes, it has become widely accepted that the 

fetus is at a particularly high risk for mercury poisoning. Although much of the attention initially 

focused on mental and psychomotor retardation, recent studies of chronic low-dose prenatal 

consumption have provided evidence for more subtle neuropsychological endpoints. The most 

valid and promising endpoint for analysis appears to be childhood neuropsychological 

development. Three large epidemiological studies attempt to evaluate the relationship between 

childhood neuropsychological development and prenatal methylmercury exposure. These studies 

evaluate cohorts in the Faroe Islands (Grandjean et al. 1997), New Zealand (Kjellström et al. 

1989), and the Seychelles Islands (Davidson et al. 1998). Moreover, Crump et al. (1998) and 

 

32 See, for example, Lebel et al. (1996, 1998) and Beuter and Edwards (1998). 
Resources for the Future Jakus, McGuinness, and Krupnick 

75

Budtz-Jørgensen et al. (1999) have performed benchmark analyses of the New Zealand and 

Faroe Islands studies, respectively. 

Of the three studies, the Faroe Islands study is considered the most robust: it has the 

largest cohort and was subjected to significant peer review and reanalysis. The study 

administered a battery of neuropsychological tests at age 7 to 917 surviving members of a 1986–

1987 birth cohort of 1,022 children. These tests focused on language, attention, memory, mood, 

and visuospatial and motor functions. Mercury exposure, which occurred largely through 

maternal consumption of whale meat and was quantified in both maternal hair and cord blood 

concentrations, was found to be significantly associated with increased dysfunction in language, 

attention, memory, and to a lesser extent, visuospatial and motor functions. The associations 

remained when children with maternal hair mercury concentrations exceeding 10 mg/kg were 

excluded. Overall, the authors estimated that a tenfold increase in cord blood mercury 

concentration was associated with delays of approximately four to seven months in these 

developmental indicators. 

In the New Zealand study, Kjellström et al. (1989) evaluated a cohort of 237 at 6 years of 

age, administering a battery of 26 tests for psychological and scholastic development, 5 of which 

were analyzed further in multiple regressions. In the study each child considered “high Hg” was 

matched with three controls of varying maternal hair mercury concentration and fish 

consumption based on a number of potential confounding factors. In weighted regressions, Hg 

concentrations in maternal hair were associated with reduced scores on full-scale IQ, language 
Resources for the Future Jakus, McGuinness, and Krupnick 

76

development, perceptual performance, and motor skills.33 Unweighted regressions produced 

similar results, though generally at reduced statistical significance. Although the New Zealand 

cohort has the strength of population heterogeneity, it suffers from a small cohort and less 

extensive reanalysis compared with the Faroe Islands study. 

Table 5.1 describes the tests subjected to further analysis from both the Faroe Islands and 

the New Zealand studies. 

 

33 Observations were given a weight of 0 to 1, depending on the extent to which an observation was an outlier. 
Resources for the Future Jakus, McGuinness, and Krupnick 

77

Table 5.1. Description of Administered Tests from the Faroe Islands and 

New Zealand Studies 

Study Test Domain 

Faroe Islands Neurobehavioral Evaluation System 

(NES): finger tapping 

Manual motor ability 

 NES Continuous Performance Test 

(CPT): reaction time 

Vigilance, attention 

 Bender Copying Test Visuospatial ability 

 Boston Naming Test Naming, association 

 California Verbal Learning Test 

(CVLT): children 

Short-term memory 

New Zealand Test of Language Development, 

spoken language quotient (TOLD-SL) 

Language development 

 Weschler Intelligence Scale for 

Children, Revised: performance IQ 

(WISC-RP) 

Intelligence 

 Weschler Intelligence Scale for 

Children, Revised: full-scale IQ 

(WISC-RF) 

Intelligence 

 McCarthy Scales: perceptual 

performance (MCC-PP) 

Intelligence 

 McCarthy Scales: motoric (MCCMOT) Fine and gross motor 

coordination 

Sources: Crump et al. (1998); Grandjean et al. (1997). 

In the Seychelles child development study, 711 children were evaluated at about 5 to 6 

years of age for general cognitive ability, language skills, reading, arithmetic, visual-spatial 

ability, and social and adaptive behavior, using maternal hair mercury concentration as the 

exposure metric. The majority of tests administered in the Seychelles study were global tests of 
Resources for the Future Jakus, McGuinness, and Krupnick 

78

neuropsychological development rather than domain-specific tests, as in the Faroe Islands study. 

Unlike the Faroe Islands and New Zealand studies, the Seychelles study did not find evidence of 

an adverse effect of either prenatal or postnatal MeHg exposure. 

Several plausible explanations surface for the difference in findings between the Faroe 

Islands study and the Seychelles study. Among these are differences in exposure metric, types of 

tests administered, age of subjects at testing, and sources of exposure (whale meat versus fish). 

However, considering these two studies in conjunction with the New Zealand results further 

complicates their comparison because although the New Zealand and Seychelles studies are 

similar in design, the New Zealand results are in agreement with those from the Faroe Islands. It 

is possible that the divergent results are attributable to between-sample variability in the 

expression of neurotoxicity at low doses, as studies with a large cohort may fail to adequately 

capture an adverse response if it is limited to the upper ranges of the exposure distributions 

(NRC 2000). Ultimately, of the three studies, the National Research Council (NRC 2000) 

suggests that the Faroe Islands results warrant the most confidence because of the large cohort 

size and the robustness of results when subjected to reanalysis. 

Two recent studies (Crump et al. 1998; Budtz-Jørgensen et al. 1999) have performed 

benchmark analyses for the New Zealand and Faroe Islands studies, respectively, in the hopes of 

eliciting safe levels of exposure. The benchmark dose (BMD) is the dose of a substance that 

results in an increased probability of an abnormal test performance by a predetermined 

benchmark response (BMR). In other words, the probability of an abnormal test score increases 

from P0 for an unexposed child to P0 + BMR for a child at or in excess of the BMD. The default 

probability of an abnormal test score is typically assumed to be 5% in an unexposed population. 

However, this default is chosen strictly for statistical purposes and may not reflect the true 
Resources for the Future Jakus, McGuinness, and Krupnick 

79

frequency of abnormal scores in an unexposed population.34 The lower 95% limit on the BMD, 

or BMDL, is also reported and is intended to be an alternative to the “no observed adverse 

effects level” (NOAEL).35 The BMDL, then, necessarily depends on the number of observations. 

All else equal, a higher number of observations will tighten the confidence interval and thus 

result in a higher BMDL. In addition to the benchmark analyses of Crump (1998) and BudtzJørgensen et al. (1999), NRC (2000) conducts an integrative analysis of the major endpoints of 

all three studies as a basis for comparison. Table 5.2 summarizes the benchmark estimates from 

the three benchmark studies. 

Table 5.2. Summary of Estimates from Benchmark Analyses 

Approach BMD mg/kg hair BMDL 

Most sensitive endpoint, New Zealand (McCarthy Scales: 

perceptual performance) 

8 4 

Median endpoint, New Zealand 12 6 

Most sensitive endpoint, Faroe Islands (Neurobehavioral 

Evaluation System Continuous Performance Test) 

15 10 

Median endpoint, Faroe Islands 20 12 

NRC integrative analysis 21 8 

Source: NRC (2000). 

 

34 A number of functional forms are explored in these analyses, with preference being given to the K-power model, 

in the form μ(d)=β x dk . k is restricted to be greater than or equal to 1, to prevent supralinear models, which are 

though to be less biologically plausible (NRC 2000). 

35 The NOAEL has been defined as the highest experimental dose that does not produce a statistically or 

biologically significant increase in adverse effects relative to control groups. Several statistical drawbacks, such as 

the fact that the NOAEL must be an observed experimental dose and thus can vary considerably across studies, have 

made its use somewhat controversial (NRC 2000). 
Resources for the Future Jakus, McGuinness, and Krupnick 

80

5.1.3. Cardiovascular Effects 

A significant body of research has found suggestions of a positive relationship between 

fish consumption and cardiovascular health. Fish consumption is thought to reduce 

cardiovascular risk because of the implicit intake of omega-3 fatty acids and selenium. 

Furthermore, a diet high in fish consumption may indicate eating habits that are associated with 

low risk of cardiovascular disease, such as infrequent consumption of red meat. 

However, the presence of mercury in fish tissue confounds this apparent relationship. 

MeHg has been associated with adverse cardiovascular effects, such as increased blood pressure 

and abnormal cardiac function. Two recent studies focus specifically on the relationship between 

low-level dietary exposure to MeHg and cardiovascular health, one of which finds evidence of a 

link between mercury uptake and all-cause mortality. Such findings suggest a potential risk-risk 

trade-off under a fish consumption advisory because averting anglers who reduce fish 

consumption to avoid mercury contamination will be sacrificing, to some extent, the potential 

protective effects of fish consumption. 

Salonen et al. (1995) compared the association between fish consumption and mercury 

concentrations in hair and urine, and then examined the relationship between these 

concentrations and the occurrence of acute myocardial infarction (AMI) and chronic mortality 

from coronary heart disease (CHD), cardiovascular disease (CVD), or any cause over a five-year 

period. These relationships were evaluated in a cohort of 1,833 Finnish men between the ages of 

42 and 60, all of whom were free of heart disease, stroke, claudication (muscle pain due to 

insufficient blood flow), and cancer at the study’s inception. Mean hair concentration for the 

sample was 1.92 mg/kg, with a standard deviation of 1.98 mg/kg. In Cox proportional hazards 

models, with a number of cardiovascular risk factors as covariates, dietary intakes of fish and 
Resources for the Future Jakus, McGuinness, and Krupnick 

81

mercury were associated with a significantly increased risk of AMI and death from CVD or any 

cause. The study found that men in the highest third (tertile) of the sample for hair mercury 

content (>2.0 μg/g) had a 2.0-fold increased risk of AMI relative to the other two tertiles when 

controlling for age and coronary heart disease. For the same tertile, the relative risk of death from 

CVD was 2.9, and from any cause, 3.3. 

In addition, cardiovascular health in adulthood can be linked to the development of risk 

factors in childhood that ultimately may result from prenatal exposure to methylmercury. Blood 

pressure in childhood is an important determinant of hypertension risk later in life, and prenatal 

methylmercury exposure has been linked to increased blood pressure in children. Sørensen et al. 

(1999), in a study of 1,000 children from the Faroe Islands, found an association between 

prenatal methylmercury exposure and cardiovascular function at 7 years of age. An increase in 

maternal cord blood concentration from 1 to 10 μg/L was significantly associated with a 14.6 and 

13.9 mmHg increase in systolic and diastolic blood pressures, respectively. Furthermore, in boys, 

heart rate variability, an indicator of cardiac autonomic control, decreased by 47% as cord blood 

concentrations increased over this same range. 

5.2. Methodology 

This section describes the calculation of pre- and postadvisory methylmercury uptake 

based on catch and consumption data and behavioral parameters estimated in Section 2, and the 

modeling and quantification of health effects from methylmercury exposure. 

5.2.1. Estimating Striped Bass Consumption and Mercury Uptake 

The first step in quantifying the human health effects of a mercury fish consumption 

advisory is the estimation of mercury uptake, and more importantly the change in mercury 
Resources for the Future Jakus, McGuinness, and Krupnick 

82

uptake once an advisory has been implemented. Human mercury uptake occurs primarily via 

three pathways: inhalation, dietary intake, and the leaching of mercury from dental amalgams. 

Human exposure from inhalation is predominantly elemental mercury, though inhalation is also a 

source of human exposure to small quantities of inorganic and methylmercury. Dietary intake is 

primarily methylmercury.36 All uptake from dental amalgams is in the form of elemental 

mercury. 

It is methylmercury uptake that has been most explicitly linked to human health, and to 

which humans are primarily exposed to through fish consumption.37 Almost all methylmercury 

from dietary uptake is through fish consumption, and for the consuming anglers in this study, the 

most significant source of methylmercury exposure is striped bass from the Chesapeake Bay. 

Using data on striped bass fishing trips and fish tissue methylmercury concentration in the 

Chesapeake Bay, we calculate average per capita striped bass consumption and methylmercury 

uptake for anglers and their families. Additionally, we calculate the per capita reduction in these 

parameters based on the behavioral responses to a fish consumption advisory quantified in 

Section 3. 

We derive estimates of striped bass consumption from data from the Chesapeake Bay 

Cooperative Striped Bass Survey for the years 1997–2000. This voluntary survey, taken online 

 

36 Average methylmercury uptake rates from food consumption are estimated at 2.00 μg/day and 1.52 μg/day for 

males and females aged 25 to 30, respectively, the majority of which is from fresh and canned seafood (Rowe et al. 

1995). 

37 Analysis of the health effects of MeHg is somewhat complicated by the fact that MeHg transforms into mercuric 

mercury (Hg++) in the brain, which has a longer half-life and probable—though not well understood—health risks 

(NRC 2000). NRC suggests that future risk assessment for MeHg consider exposure to all species of Hg. This issue 

is important because the literature shows that the health effects of MeHg are subject to a threshold of exposure. If 

other forms of mercury in the body are not counted, this threshold is less likely to be exceeded. 
Resources for the Future Jakus, McGuinness, and Krupnick 

83

or by mail, reports the total number of anglers, the number of fish caught, and for each individual 

fish, its length and whether it was kept or released. We calculate the weight of each kept fish 

using the formula from Gilmour (1999), discussed in Section 2, and calculate a per trip total 

weight of kept fish.38 Incorporating the average meal size (0.25 kg, as assumed in EPA analyses) 

and the edible percentage of caught striped bass, we are able to calculate the number of meals 

caught per trip.39 From the behavioral model we know the average number of anglers keeping 

striped bass for consumption (186,800 pre-advisory; 165,100 postadvisory) as well as the 

average number of trips on which striped bass are being kept for consumption (563,917 preadvisory; 486,661 postadvisory). We assume that an angler’s catch is shared and distributed 

evenly among the average number of anglers per trip (2.89, SD = 1.5, estimated from survey 

data) and the average Maryland household size of 2.61 (U.S. Census 2000a).40 This information 

allows for calculation of a distribution of per capita meals per month. 

We estimate pre-advisory average per capita meals of striped bass per month to be 1.31 

(SD = 0.77). Comparison with other estimates suggests that this estimate is reasonable. A survey 

of recreational anglers at Lake Roosevelt in Washington (Mariёn and Patrick 2001) finds that 

anglers consume an average of 1.67 (SD = 1.17) meals of bass per month. Moreover, although 

 

38 One shortcoming of our data from the Maryland Cooperative Striped Bass Survey is that although an angler 

reports the total catch for the trip, actual data for only 20 of these fish are recorded. To compensate, we assume that 

the average weight of fish and the percentage of kept fish were the same for the remainder of caught fish. 

39 We use conversion factors from NMFS (1981) to determine what percentage of the weight of a given fish can 

actually be consumed. For striped bass, a fillet is 35% of the total fish weight; although no value is reported for 

steaks, we assume 50%, such that 85% of a given fish is consumed. 

40 Although we assume that fish are distributed equally among the household, we do not have sufficient information 

to fully quantify the composition of the consuming population in terms of age and gender. For the quantification of 

health effects, then, we estimate the size of the population at risk based on information we know or can estimate 

with reason. 
Resources for the Future Jakus, McGuinness, and Krupnick 

84

Rowe et al. (1995) report data from the Angler Cohort Study of Lake Ontario fishermen 

suggesting that 20% of anglers consume 1 meal or more per month, and about 3% of anglers 

consume more than 10 meals per month, these numbers appear to be for multiple species of 

freshwater fish. 

From Gilmour (1999) we apply a probability-weighted average mercury concentration in 

fish (mg/kg) in the upper Chesapeake Bay, in order to calculate an average per person daily 

mercury exposure from striped bass using the estimated distribution of per capita meals per 

month. The meals per month and exposure distributions most closely resemble a lognormal 

distribution, and the exposure distribution is shown in Figure 5.1. 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.270.811.351.892.442.983.52

4.064.60

5.145.686.22

6.777.31

7.858.39

8.939.47

10.28

11.10

12.72

MeHg Uptake (ug/day)

Probability

Figure 5.1. Pre-advisory Distribution of Estimated per Capita Mercury Uptake from 

Striped Bass by Chesapeake Bay Angler Families 

Because we assume that, on average, all individuals consuming at the same level before the 

advisory reduce consumption by the same amount, the shape of the distribution remains the same 
Resources for the Future Jakus, McGuinness, and Krupnick 

85

after an advisory,41 and the entire distribution is shifted to the left.42 Reductions are calculated 

based on two parameters from the behavioral analysis in Section 2: a 2% reduction in total trips 

under an advisory, and a mean reduction in the probability of consumption of 26.1%. Across the 

population, we estimate an average reduction in daily per capita MeHg uptake from striped bass 

of 11% to 14%.43 

To estimate the implications of striped bass consumption for mercury-related health 

effects, daily methylmercury uptake from striped bass must be converted into blood and hair 

concentrations. To convert daily mercury uptake to blood concentration, we use the following 

equation (described in U.S. EPA 2001e): 

b v

d a f C

×

× × = (5.1) 

where C is the concentration in blood, measured in μg/L; d is the daily dietary intake of 

methylmercury, measured in μg/day; a is the absorption factor (0.95, unitless), f is the fraction of 

daily intake taken up by blood (0.05, unitless), b is the elimination constant (0.014 days-1), and v

 

41 This is a simplifying assumption made because we lack data regarding the distribution of the behavioral response 

across consumption levels. It is possible that an advisory could instead change the shape of the consumption 

distribution. For example, people at the low end of the distribution may value fish consumption less than people at 

the high end, and reduce consumption disproportionately under an advisory. Or, people at the high end might be 

more likely to be aware of and heed the advisory, giving them a higher propensity to reduce consumption. 

42 Not all individuals actually reduce mercury uptake, however, as was described in Section 2. Thus we calculate an 

average reduction by “consumption group,” which is represented by an individual bar in Figure 5.1. This implicitly 

assumes that an averting angler maintains the pre-advisory level of fish consumption by catching and consuming 

fish from noncontaminated substitute sites, or by catching and consuming Chesapeake Bay fish with insignificant 

mercury concentrations. 

43 A variation to the assumptions behind this estimate is presented in Section 6. 
Resources for the Future Jakus, McGuinness, and Krupnick 

86

is the assumed volume of blood in the body (5L).44 To convert blood concentration to hair 

concentration (mg/kg), we assume a factor of 4 (U.S. EPA 2001e). 

However, additional sources of mercury exposure must be considered. Chesapeake Bay 

anglers are unlikely to be consuming striped bass exclusively, making it necessary to account for 

other potential sources of methylmercury exposure, such as other fish products and inhalation. 

Furthermore, it is also important to account for the fact that although the health effects we 

examine in this study are associated with methylmercury, and thus, fish consumption, 

epidemiological studies typically use blood or hair total Hg concentrations as a proxy for 

methylmercury exposure. These total Hg measurements may reflect other sources of inorganic 

mercury or elemental mercury leached from dental amalgams that may confound the relationship 

between methylmercury from fish consumption and certain health endpoints. To account for 

other sources of exposure, we add a background blood concentration of 1.2 μg/L (CDC 2001) to 

the exposure from striped bass consumption calculated above.45 Finally, because MeHg vacates 

the body rather quickly (a half-life of 40 to 80 days) (U.S. EPA 2000), and individuals are 

assumed to consume fish (and thus expose themselves to MeHg) at a constant rate, we assume 

that an individual’s exposure level (and thus blood and hair mercury concentrations) are 

 

44 We assume that maternal blood and umbilical cord blood concentrations are the same. This is consistent with the 

findings of Kuntz et al. (1982) and Sikorski et al. (1989), although a handful of other studies (Dennis and Fehr 1975; 

Pitkin et al. 1976; and Kuhnert et al. 1981) have found cord blood concentrations to be about 20% to 30% higher 

than maternal blood concentrations (NRC 2000). 

45 This background rate is based on preliminary analysis of the 1999 National Health and Nutrition Examination 

Survey data for U.S. women of childbearing age. The study is touted as the “first nationally representative tissue 

measures of the U.S. population’s exposure to Hg,” and thus should be representative of average uptake levels from 

fish and other sources of exposure. We assume that consuming anglers eat fish more frequently than the average 

population and thus add mercury uptake from Chesapeake Bay striped bass to this background rate. Furthermore, 

because comparable data are not available for males, we apply this background rate to the entire population. 
Resources for the Future Jakus, McGuinness, and Krupnick 

87

constant, barring any behavioral change. Table 5.3 reports summary statistics for estimated 

exposure variables. 

Table 5.3. Summary Statistics for Estimated Mercury Exposure Variables 

Variable Mean SD Minimum Maximum 

Number of exposed anglers 186,700 19,000 136,000 260,500 

Number of exposed women of 

childbearing age 

79,710 8112 58,070 111,200 

Number of potentially at-risk 

births 

2617 266.3 1906 3651 

Average per capita daily 

methylmercury intake from 

striped bass, no advisory (μg)1

1.78 2.15 0.05 13.82 

Average per capita daily 

methylmercury intake from 

striped bass, advisory (μg) 

1.53 1.84 0.04 13.1 

Average per capita blood 

concentration, no advisory 

(μg/L) 

2.63 1.46 0.91 13.15 

Average per capita blood 

concentration, advisory (μg/L) 

2.44 1.24 0.89 11.02 

Average per capita hair 

concentration, no advisory 

(mg/kg) 

0.66 0.36 0.23 3.29 

Average per capita hair 

concentration, advisory 

(mg/kg) 

0.61 0.31 0.22 2.76 

1

All mercury-related variables are reported as geometric means and standard deviations because of the lognormal 

nature of the distribution. 

5.2.2. Estimating Female Exposure 

The estimation of health effects requires estimates of the size of specific subpopulations 

to which the endpoints from the epidemiological literature apply. In particular, we need to 

estimate the number of exposed females of childbearing age and the number of potential births to 
Resources for the Future Jakus, McGuinness, and Krupnick 

88

these women. Unfortunately, Maryland Department of Natural Resources does not record gender 

information for licensed anglers. However, we obtained an estimate of female participation from 

the 1996 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation data for 

Maryland, which reports that 26% (approximately 48,500) of Maryland recreational anglers are 

female. We assume that the remaining exposed females are spouses of anglers. Thus, using the 

marriage rate for males over 15 years of age in Maryland (55%), we estimate approximately 

76,000 additional exposed females, for a total of about 124,500. Given that all recorded anglers 

are over 15, we assume the same for their spouses; based on current population data, about 64% 

of these women are between 15 and 49, what we consider childbearing age. We thus estimate the 

total number of exposed females of childbearing age to be 79,710. Using 2000 birthrates for 

Maryland, for both married and unmarried women, we calculate 2,617 potentially affected 

births.46 Estimating female exposure based on assumptions from these data surely adds error to 

our estimates of health effects. However, the model allows these assumptions to be modified 

with better information. 

5.3. Modeling Health Effects 

Within the Maryland Model, the Mercury Health Effects Module uses MeHg uptake and 

demographic data to quantify three general health endpoints: adult central nervous system 

effects, effects on childhood neuropsychological development, and cardiovascular health and 

 

46 We use a combination of two birthrates to estimate this number. We assume that female anglers marry at the same 

rate as the general population, and that all other exposed women are married and exposed via their husbands. For 

Maryland, the birthrates are 23.06 births per 1,000 unmarried women, and 46.37 births per 1,000 married women 

(U.S. Census 2000b). 
Resources for the Future Jakus, McGuinness, and Krupnick 

89

mortality effects. Generally, the model quantifies the change in the number of cases of an effect 

under an advisory, using the common dose-response equation: 

( ) 0 t ∆C = DR× RATE × POP× I − I (5.2) 

where C is the number of cases; DR is the dose-response coefficient, which represents a 

percentage change in the baseline rate of occurrence for a given level of MeHg exposure; RATE

is the baseline rate of occurrence of the effect in the population; POP is the exposed population; 

and I is some measure of MeHg exposure, which could be μg/day of consumption, mg/kg of hair, 

or μg/L of blood, with I0 being baseline exposure and It being exposure at time t. For effects for 

which an exposure threshold, T, exists, if I0 < T then the total number of cases is zero, unless It

exceeds both I0 and T. Some epidemiological studies report a dose-response coefficient or 

percentage change in risk; others report the risk increase in percentage points associated with a 

given exposure or increase in exposure, circumventing the dose-response coefficient entirely. 

When the latter is the case, the equation appears as follows: 

∆C = (RATE0 + ∆RATE)× POP (5.3) 

where ∆RATE is the percentage point increase to the baseline rate specified for a given dose or 

dose increase, and is equal to zero if this dose or dose increase does not occur. 

Finally, a few additional health endpoints are quantified as an average change in a given 

variable (e.g., blood pressure) across the population. In these cases the model utilizes the 

following linear relationship: 

( ) 0 t ∆V = β I − I (5.4) 
Resources for the Future Jakus, McGuinness, and Krupnick 

90

where ∆V is the average change in the given health variable, I is a measure of MeHg exposure as 

discussed above, and β is an estimated coefficient representing the change in V per unit change in 

I. 

In addition to the endpoints discussed above, the model estimates the number of 

individuals who exceed the assumed advisory guidelines for Maryland, as well as the number of 

individuals who exceed EPA’s reference dose (RfD). The remainder of this section provides a 

brief description of the design of the Mercury Health Effects Module, as well as an explanation 

of the parameters used in the estimation of health effects. 

5.3.1. Central Nervous System Effects in Adults 

One of the earliest signs of mercury poisoning in adults is paresthesia, or a prickling, 

tickling, or itching sensation in the extremities. Although the WHO (IPCS 1990) characterization 

of the dose-response relationship for paresthesia has been criticized, at this point there has been 

no research to further define the lower portion of the dose-response curve, and thus we include 

the WHO relationship in our model. This relationship assumes a 5 percentage point increase in 

the occurrence of paresthesia above a threshold of about 200 μg/day. We limit the population at 

risk for paresthesia in our study to exposed male and female anglers and male anglers’ wives 

aged 15 and over, which corresponds to the age range of the licensed angler population. 

5.3.2. Childhood Neuropsychological Development 

Our primary method of estimating the effects of reduced mercury consumption on 

childhood neuropsychological development is the use of the benchmark analyses described in 
Resources for the Future Jakus, McGuinness, and Krupnick 

91

Section 5.1.47 Following the rationale of NRC (2000), we choose the Faroe Islands study as our 

preferred analysis of the effects of prenatal methylmercury consumption. Of the Faroe Islands 

endpoints that are benchmarked, the Continuous Performance Test is the most sensitive. 

However, because this test is administered to only about half of the cohort, the Boston Naming 

Test, the second most sensitive endpoint, is chosen by NRC as the point of departure for 

calculating the RfD for methylmercury (NRC 2000). Because of this, we choose the Boston 

Naming Test from the Faroe Islands as our preferred indicator of adverse neuropsychological 

effects in children. 

Within the model, however, all endpoints for which benchmark doses were derived in 

Crump et al. (1998) and Budtz-Jørgensen et al. (1999) can be estimated. Furthermore, we also 

allow childhood neuropsychological effects to be estimated using the benchmark derived by 

NRC (2000) in its integrative analysis. For each benchmarked test, one can calculate the number 

of exposed women of childbearing age who exceed the BMD, as well as the reduction in 

abnormal births (births of children who would be expected to score in the abnormal range at age 

6 or 7, when the test is administered) due to a mercury advisory. We assume that the BMD is 

normally distributed and apply the estimated BMDL as the lower 95% limit. We also allow for 

the calculation of abnormal births using a BMD for the Boston Naming Test from BudtzJørgensen et al. (1999) under the assumption of a 16% baseline risk, instead of 5%. Table 5.4 

displays the complete set of benchmark doses included in the model. To facilitate comparison, 

 

47 We chose to quantify these effects using benchmark analyses rather than the original studies based on our notion 

that reporting the number of children facing an increased risk of abnormal scores is more meaningful from a policy 

standpoint than the average change in individual scores at a given average exposure. 
Resources for the Future Jakus, McGuinness, and Krupnick 

92

benchmark doses for the Faroe Islands are reported in mg/kg of maternal hair mercury, though 

they are in terms of blood concentration in the model. 

Table 5.4. Tests and Benchmark Doses Included in the Maryland Model (mgHg/kg of Hair) 

Study End Point BMD BMDL 

Faroe Islands Neurobehavioral Evaluation System: finger 

tapping 

20 11 

Neurobehavioral Evaluation System 

Continuous Performance Test: reaction time 

18 9 

Bender Copying Errors 29 14 

Boston Naming Test 15 10 

California Verbal Learning Test: delayed recall 27 13 

Boston Naming Test (p0=0.16) 8 5 

New Zealand Test of Language Development 12 6 

Weschler Intelligence Scale for Children, 

Revised: performance IQ 

12 6 

Weschler Intelligence Scale for Children, 

Revised: full-scale IQ 

13 6 

McCarthy Scales: perceptual performance 8 4 

McCarthy Scales: motoric 13 6 

NRC integrative 

analysis 

Multiple, across 3 main studies 21 8 

Sources: Budtz-Jørgensen et al. (1999); NRC (2000). Benchmark doses represent a 5 percentage point increase in 

the baseline risk. 

Finally, the model includes one endpoint for childhood neuropsychological development 

with the potential for valuation. From the New Zealand study, we model the average reduction in 

Weschler Intelligence Scale for Children, Revised full-scale IQ score. Kjellström et al. (1989) 
Resources for the Future Jakus, McGuinness, and Krupnick 

93

estimated a 4.41 reduction in IQ score for an increase in maternal hair mercury level of 1 mg/kg, 

above a threshold of 6 mg/kg. Below this threshold, however, no significant relationship was 

observed. Using the average change in maternal hair mercury concentration data for the 

population, the model estimates an average change in IQ score for individuals above the 

threshold. Furthermore, this endpoint is an input to the Health Benefits Module, which assigns a 

dollar value to health effects when possible. 

5.3.3. Cardiovascular Effects

The model currently estimates three cardiovascular-related endpoints: acute myocardial 

infarction (AMI), all-cause mortality, and average change in systolic and diastolic blood pressure 

in children 7 years of age. The parameter estimates for AMI and all-cause mortality are from 

Salonen et al. (1995). We use their risk factor–adjusted estimates in the model. For AMI, the 

study estimates an increase in risk of 69% at a threshold of 2 μg/g methylmercury in hair when 

adjusting for risk factors, with an additional increase in risk of 6.8% for each additional 1 μg/g 

thereafter. The increase in risk for all-cause mortality at the same threshold is 93%, with a further 

increase of 9.0% for each additional 1 μg/g. As was noted earlier, the cohort in Salonen et al. 

(1995) is 1,833 Finnish males aged 42 to 60. Thus, a prudent estimate on our part would limit the 

application of these coefficients to males within the same age range. Because our population data 

do not correspond exactly to those age groups, we apply these coefficients to males aged 40 to 

59.48 Our estimate of the change in mercury-related mortality for males is used to obtain an 

 

48 We extend this relationship to women in Section 6. The mortality rate in the Maryland Model is 803/100,000 

deaths per person per year, and the baseline occurrences of AMI in 1999 for men and women are 0.0049 and 0.0031, 
Resources for the Future Jakus, McGuinness, and Krupnick 

94

estimate of mortality benefits in the Benefits Valuation Module. Benefits estimates are reported 

in Section 5.4. 

The last cardiovascular endpoint we estimate is an average change in systolic and 

diastolic blood pressure at age 7 due to prenatal exposure to methylmercury from maternal 

consumption. High blood pressure in childhood is believed to be a risk factor for the 

development of hypertension in adulthood. Sørensen et al. (1999) estimate an increase of 14.6 

mmHg (95% CI = 8.3, 20.8) and 13.9 mmHg (95% CI = 7.4, 20.4), respectively, for systolic and 

diastolic blood pressures for an increase in cord blood mercury concentration from 1 to 10 μg/L. 

Above this level, no increase in blood pressure is observed. Thus, in our model, the application 

of these estimates is limited to that portion of the dose-response curve. 

5.3.4. Number of Individuals Exceeding Advisory and RfD 

Finally, using the estimates of consumption and mercury uptake from the model, the 

Health Module calculates the number of individuals who exceed the assumed striped bass 

consumption advisory for the Chesapeake Bay—four meals per month for the general population 

and two meals per month for sensitive subpopulations—as well as EPA’s RfD of 0.1 μg/kg-day. 

For the general Maryland advisory, the model estimates the number of male anglers, female 

anglers, and anglers’ wives not of childbearing age who are consuming in excess of the advisory. 

For the advisory for sensitive subpopulations, the model estimates only the number of women of 

childbearing age who exceed the recommended guidelines, because we lack data on other 

 respectively. Furthermore, it should be noted that because AMIs are often fatal (almost 40% of the time for men and 

60% of the time for women), there is significant overlap between these two endpoints (NRMI 2001). 
Resources for the Future Jakus, McGuinness, and Krupnick 

95

sensitive subpopulations. Lastly, the model calculates the number of male and female anglers 

and anglers’ wives exposed to methylmercury in excess of EPA’s RfD. 

5.3.5. Health Effects Valuation 

The Health Valuation Submodule of the Benefits Valuation Module relies primarily on 

estimates of willingness-to-pay (WTP) from revealed and stated preference studies. When WTP 

estimates are not available, proxies, such as estimated medical or treatment costs, are used. The 

module is explained in greater detail in Bloyd et al. (1996) and Austin et al. (1999). 

The Mercury Benefits Valuation Module is set up to value two endpoints from the 

Mercury Health Effects Module: the average reduction in IQ score due to prenatal exposure, and 

mortality for men aged 42 to 60. Currently, the Benefits Valuation Module assigns a value of 

$10,420 ($2000) per IQ point lost to a child at age 7, as is reported by Rowe et al. (1995) in their 

valuation of health effects from lead.49 Total benefits, then, would equal that amount multiplied 

by both the average reduction in IQ score and the number of births to women of childbearing age 

who exceed the hair concentration threshold for IQ effects. 

We evaluate the model for mortality benefits using three equally weighted estimates of 

the value of a statistical life (VSL). For a low estimate, we use a value of $700,000 ($2000, SD = 

$48,000) from Krupnick et al. (2002). Our central estimate comes from Mrozek and Taylor 

(2002), who in their meta-analysis of 33 wage-risk studies suggest that VSL estimates from most 

revealed preference studies have tended to overestimate willingness to pay for risk reduction by 

not accounting for interindustry wage differentials. They estimate a VSL of $2.32 million (SD = 
Resources for the Future Jakus, McGuinness, and Krupnick 

96

$212,376). Finally, our high VSL assumption is from U.S. EPA (1999), which pooled 26 valueof-statistical-life studies to derive a Weibull distribution with a mean of $6.37 million (SD = 

$4.31 million). EPA uses this value in its Section 812 Retrospective and Prospective Studies 

(U.S. EPA 1997, 1999). Each of these values is given an equal probability weight. These weights 

result in a weighted mean of approximately $3.11 million (SD = $3.37 million). 

5.4. Results 

This section quantifies the changes in the health endpoints described above, as well as the 

number of individuals who exceed both the assumed Chesapeake Bay recreational advisory and 

EPA’s RfD. It also reports estimated health benefits resulting from avoided methylmercuryrelated mortality. Variations to the assumptions made in the quantification of health effects are 

explored in Section 6. 

5.4.1. Paresthesia 

The WHO study (IPCS 1990) reports a methylmercury uptake threshold for paresthesia in 

the range of 190 to 210 μg/day. However, the maximum methylmercury uptake observed in our 

study before a consumption advisory is approximately 14 μg/day, far short of this level. 

Therefore, we do not predict any cases of paraesthesia. At current fish consumption levels, 

paraesthesia from methylmercury uptake remains unlikely, unless fish tissue concentrations 

increase considerably. However, as was mentioned earlier, criticism of the WHO analysis 

suggests that more work is needed on the lower portion of the dose response curve (Kosatsky and 

 49 This value represents a 1.98% reduction in potential lifetime earnings per IQ lost. Its derivation is explained in 

more detail in Rowe et al. (1995). 
Resources for the Future Jakus, McGuinness, and Krupnick 

97

Foran 1996; NRC 2000). Furthermore, it is a general belief that children are a population at 

greater risk of developing mercury disease (Rowe et al. 1995). However, to date, 

epidemiological research on children and methylmercury exposure has focused almost entirely 

on neuropsychological effects. There are currently no dose-response analyses specific to children 

for the manifestation of physical effects of methylmercury disease. Such analysis is difficult 

because consumption data for children—particularly children in angler families—are essentially 

nonexistent. 

5.4.2. Childhood Neuropsychological Development 

Our analysis suggests no evidence of childhood neuropsychological developmental 

effects at current fish consumption and mercury fish-tissue concentration levels. Our preferred 

method is to rely upon the Boston Naming Test benchmark estimates to indicate the presence of 

abnormal effects. However, our maximum estimated blood concentration of approximately 13 

μg/L falls far short of the benchmark dose of 85 μg/L suggested by Budtz-Jørgensen et al. (1999) 

for a 5 percentage point increase in the baseline risk. In fact, the maximum concentration level 

falls far short of even the lowest BMD for the Faroe Islands study, which is 71.75 μg/L for the 

Continuous Performance Test (BMDL = 48.37 μg/L). 

For the sake of comparison, we evaluate neuropsychological effects with both the New 

Zealand and the National Research Council integrative benchmark doses. However, the selection 

of benchmark doses is of little importance to our results. No abnormal test scores are predicted 

either under the NRC integrative BMD assumption or when any of the BMDs from the New 

Zealand benchmark study are assumed. Benchmark doses for both the NRC integrative and the 

New Zealand studies are denominated in mg/kg Hg in maternal hair. The lowest of the BMDs is 
Resources for the Future Jakus, McGuinness, and Krupnick 

98

8 mg/kg (BMDL = 4 mg/kg) for the McCarthy Perceptual Performance scale from the New 

Zealand study, which still exceeds the maximum estimated hair concentration for the 

Chesapeake Bay population of 3.29 mg/kg. For further sensitivity analysis, we apply the BMD 

for the Boston Naming Test modeled under the assumption of a 16% baseline risk (BudtzJørgensen et al. 1999). The estimated BMD (in blood concentration) for a 5% increase in this 

case is 43.98 μg/L (BMDL = 29.74), still far in excess of our maximum estimated concentration 

for the exposed Chesapeake Bay population.50

Finally, we evaluate the average reduction in child IQ score at age 7, based on the 

relationship specified for the Weschler Intelligence Scale for Children, Revised full-scale IQ and 

maternal hair mercury concentration in the original New Zealand study (Kjellström et al. 1989). 

However, once again the concentration levels estimated for the exposed Chesapeake Bay 

population fall short of the threshold above which this relationship is found to be significant. 

Thus, we observe no reduction in child IQ scores for statistical births to exposed females of 

childbearing age in our study. 

5.4.3. Cardiovascular Effects 

The threshold for cardiovascular effects reported by Salonen et al. (1995) (2 mg/kg hair 

mercury) is lower than those for childhood neuropsychological development and falls within our 

estimated distribution of hair mercury concentrations. As a result, we predict cases of AMI and 

all-cause mortality, and subsequent reductions under an advisory, as reported in Table 5.5. The 

 

50 Budtz-Jørgensen et al. (1999) found that logarithmic benchmark models fit the Faroese data well, and because the 

logarithmic model allows the dose-response curve to assume a supralinear form, it produced benchmark doses low 

enough to result in neuropsychological damages in our model. However, because current epidemiological literature 

suggests that a supralinear curve is unrealistic, we do not include these estimates in our model. 
Resources for the Future Jakus, McGuinness, and Krupnick 

99

Salonen et al. estimates are for men aged 42 to 60, and we limit the application of this 

relationship accordingly. However, in Section 6 we extend the relationship to women and 

estimate AMI and mortality cases for both genders. 

Table 5.5. Reduction in Mercury-Related AMI and Mortality to Middle-Aged Males Due to a 

Recreational Advisory 

Endpoint Result Mercury-Related Cases 

(SD) 

Acute myocardial 

infarction 

Pre-advisory 6.85 (17.30) 

 Reduction due to advisory 2.03 (4.59) 

All-cause mortality Pre-advisory 14.53 (34.94) 

 Reduction due to advisory 4.37 (9.48) 

The 90% CIs are (0, 11.95) and (0, 22.69) for AMI and mortality, respectively. Over the full 

distribution, the possibility of a negative value occurs because the original coefficients from the 

study have confidence intervals allowing them to assume slightly negative values. Although 

these results suggest, on average, an approximately 30% reduction in the occurrence of mercuryrelated AMI and all-cause mortality as the result of a mercury fish consumption advisory, the 

large standard deviations imply an inability to say anything conclusive regarding the strength of 

this effect. Our confidence in these results is even further attenuated by the fact that the Salonen 

et al. results have yet to be replicated for other samples. Thus, although the link between 

methylmercury uptake and cardiovascular health is generally accepted, there is not sufficient 

research to confirm the specific relationships evaluated here. 
Resources for the Future Jakus, McGuinness, and Krupnick 

100

We also estimate average systolic and diastolic blood pressure changes at age 7 due to an 

advisory, for children born in the current year to exposed females. These results are reported in 

Table 5.6. 

Table 5.6. Blood Pressure Change at Age 7 Due to a Reduction in Fetal Exposure 

Endpoint Mean Reduction Due to Advisory 

(mmHg) (SD) 

Systolic blood pressure 0.36 (0.42) 

Diastolic blood pressure 0.34 (0.39) 

Under an advisory, children born to women exposed at current levels will experience an 

average reduction in systolic blood pressure of 0.36 mmHg and an average reduction in diastolic 

blood pressure of 0.34 mmHg. As was the case with AMI and mortality, the large confidence 

intervals surrounding these estimates prevent us from saying anything conclusive about this 

effect. Furthermore, given that average systolic and diastolic blood pressures at this age are 

approximately 101 mmHg and 64 mmHg, respectively, our results suggest a percentage change 

in blood pressure of along the lines of 0.5%.51 We are uncertain of the implications of a change 

of this magnitude on the future cardiovascular health of children exposed prenatally. 

5.4.4. Uncertainty Pertaining to Cardiovascular and Mortality Results 

Insight into the uncertainty surrounding the results of cardiovascular and mortality effects 

can be achieved using the importance analysis feature in Analytica. For all four endpoints, this 

analysis suggests the same three predominant sources of uncertainty. These are, in order of 
Resources for the Future Jakus, McGuinness, and Krupnick 

101

importance, the number of anglers per trip, the number of saltwater fishing trips in Maryland, 

and the number of anglers aware of the advisory. The contributions of these variables to the 

uncertainty in these endpoints are not surprising. The three variables have implications for the 

extent to which catch is shared, total catch and consumption, and the size of the consuming 

population, respectively. In the model all of these variables are particularly uncertain because the 

number of anglers per trip varies considerably, and estimates of the number of trips and 

awareness are derived from a survey of relatively few anglers. The uncertainty surrounding these 

estimates leads to significant uncertainty for both average exposure levels and the size of the 

population being exposed. Although surveys are the only way to gain a true sense of total 

participation and awareness, survey data of consumption habits, rather than the derivation of 

consumption levels through the distribution of total catch among anglers, would likely reduce 

uncertainty. 

An additional point to consider is the finding that chronic all-cause mortality is the most 

prevalent quantified health effect from methylmercury. Mortality, being the most severe 

endpoint, would be expected to be the least prevalent; other health endpoints that are 

insufficiently severe to cause mortality would be more frequently observed and associated with 

lower doses. Along the neurotoxic pathway, for example, subtle neurological effects, such as 

reduced intelligence or motor skills, are observed at thresholds lower than those that cause severe 

mercury poisoning and death. A similar result would be expected along the cardiovascular 

pathway investigated by Salonen et al. AMI and other less severe health effects that potentially 

 51 Brotons et al. (1989) suggest this average based on a review of previous studies. 
Resources for the Future Jakus, McGuinness, and Krupnick 

102

precede mortality should be observed more frequently. These results suggest, then, that 

additional endpoints along this cardiovascular pathway remain to be identified and quantified. 

5.4.5. Number of Individuals Exceeding the Advisory and RfD 

In addition to the health effects estimated above, the Health Module also calculates the 

number of individuals who exceed the consumption advisory and EPA’s RfD for methylmercury 

based on striped bass consumption. These results are reported in Table 5.7. 

Table 5.7. Number of Individuals Exceeding Chesapeake Bay Advisory and EPA’s RfD 

Guideline Number Exceeding Guideline1 (SD) Minimum Maximum 

Chesapeake Bay Advisory: 

General Population 

6,352 (10,530) 0 52,860 

Chesapeake Bay Advisory: 

Sensitive Subpopulations 

11,690 (12,440) 0 45,220 

EPA RfD 18,090 (31,070) 0 170,200 

1

 The estimate for the general advisory includes male anglers and exposed females not of childbearing age; the 

estimate for the sensitive advisory includes only exposed females of childbearing age; and the estimate for the EPA 

RfD includes male anglers and all exposed females. 

We estimate that slightly more than 6,300 individuals will exceed consumption 

guidelines of the general FCA for the Chesapeake Bay, about 5,400 of whom are anglers. This 

implies that only approximately 4% of consuming anglers will exceed the advisory 

recommendation, or about 2% of total anglers—a high rate of compliance relative to most other 

studies.52 As was discussed in Section 3.1, the mean percentage of anglers who exceed advisory 

 

52 Because we estimate that only about 26% of consuming anglers avert, this result suggests that the majority of 

those who do not avert are consuming at levels within advisory guidelines. 
Resources for the Future Jakus, McGuinness, and Krupnick 

103

consumption recommendations is 9.6%, with a 95% CI of 7.7%–11.5%. However, we have no 

information about the severity of the advisories in these studies, or the number of species 

involved. Presumably, anglers are more likely to be in compliance with an advisory that is 

limited to one species, as is assumed for the advisory in this study. Furthermore, according to our 

model, only about 5% of consuming anglers, or roughly 3% of all anglers, are consuming in 

excess of these guidelines before they are announced, suggesting that advisory guidelines are 

likely to be relevant to only a small percentage of the population at the high end of the 

consumption distribution. This finding may have implications for advisory-related educational 

efforts by the state, as outreach efforts targeting these high-consumption anglers might do more 

to further compliance than more generalized efforts. 

Our results suggest that women of childbearing age are less likely to be in compliance 

with their relatively more restrictive advisory (no more than two meals per month). We estimate 

that about 15% of mercury-exposed females of childbearing age will consume in excess of 

advisory guidelines. However, this result is likely more indicative of a weakness in our data than 

of an actual behavioral pattern. Because we have no separate data for consumption patterns of 

males and females, we are restricted to the assumption that males and females consume the same 

percentage of fish from a given trip, which implies that on a high-catch trip, women of 

childbearing age will be eating exactly the same amount of fish as their husbands. A more 

realistic assumption might be the existence of some threshold at which a woman of childbearing 

age will limit her fish intake. In the absence of consumption data for recreationally caught fish 

for women in this age range, however, it is difficult to guess where this threshold might be set. 

Finally, we estimate that about 18,000 individuals will be in excess of EPA’s reference 

dose of 0.1 μg/kg/day based on consumption of striped bass alone. Of these individuals, about 
Resources for the Future Jakus, McGuinness, and Krupnick 

104

5,490 are women of childbearing age. This represents about 7% of exposed females of 

childbearing age and slightly more than 4% of total exposed females in Maryland. Before the 

advisory, these numbers are approximately 9% and 6%, respectively. These estimates, however, 

are likely biased by two countervailing forces. The first is that estimates of RfD compliance 

consider only daily intake from striped bass, thus underestimating daily exposure. However, our 

estimates for females of childbearing age are also likely subject to the upward bias discussed in 

the preceding paragraph, namely that they assume women of childbearing age are consuming 

fish at the same rate as male anglers. 

 Estimates of female compliance with the RfD vary. EPA estimates that about 7% of 

women nationwide exceed the RfD (NRC 2000). Stern et al. (1996), using fish consumption data 

from a survey in New Jersey, estimate that 21% of women of childbearing age exceed the RfD. 

In comparing their estimates with those from this study, one must consider (in addition to the 

potential sources of error discussed above) that the percentages we report are only for females 

known to be exposed through their own or their husbands’ participation in recreational angling in 

the Chesapeake Bay. Although freshwater female anglers and anglers’ wives in Maryland might 

be expected to exceed these guidelines at rates similar to or higher than those from the 

Chesapeake Bay, incorporating compliance by females of childbearing age in the population at 

large into the calculation of this percentage will almost certainly drive our estimate of 

noncompliance downward.

5.4.6. Health Benefits Estimates 

Given that we estimate no change in IQ score from reduced prenatal methylmercury 

exposure, we are able to value only mortality benefits from the estimated reduction in statistical 
Resources for the Future Jakus, McGuinness, and Krupnick 

105

deaths under an advisory. We quantify mortality benefits only for mortality among men aged 40 

to 59, though women of the same age range are included in benefits calculations in the sensitivity 

analysis in Section 6. Assuming that this mercury-mortality relationship exists, it would likely be 

similar for both genders and among other age ranges, such that the benefits estimate reported 

here is almost certainly conservative. For middle-aged males, we estimate mean benefits from 

mortality reduction to be $14.36 million (95% CI: 0, $74.66 million). 

The information content of these benefits estimates is limited as a result of the 

tremendous uncertainty surrounding them. However, the estimates do suggest that mortality 

benefits may figure prominently in an analysis of a mercury fish consumption advisory, 

particularly because Salonen et al. (1995) suggest that these benefits are likely to come at lower 

concentration levels than some of the other health benefits estimated in this study, most notably 

childhood neuropsychological development. Should further research corroborate the existence of 

the relationship between mercury uptake and chronic mortality, the health benefits from a 

mercury consumption advisory may outweigh the recreational and consumer surplus losses, 

given sufficiently large populations and fish tissue concentration levels. For example, in our 

study, we estimate a recreational surplus loss of $8.83 million ($2000). In the absence of a 

commercial advisory, mortality benefits exceed this recreational surplus loss by almost 70%. 

Thus, while our results suggest that consumption advisories at the concentration and 

consumption levels for the Chesapeake Bay may not be economically justified based on potential 

neurotoxicity to the fetus, they may be warranted for the potential cardiovascular and mortality 

effects. However, because the study on which we base these results has not been replicated, this 
Resources for the Future Jakus, McGuinness, and Krupnick 

106

insight is fraught with uncertainty. Given the magnitude of potential benefits, further 

epidemiological research on this relationship will be of substantial value. 

 5.5. Summary 

This section reviewed the epidemiological literature summarizing the relationship 

between methylmercury exposure and human health, discussed the quantification of three 

broadly defined health endpoints in the Maryland Model, and estimated changes in these 

endpoints and health benefits from a recreational mercury fish consumption advisory. Although 

we estimate no adult central nervous system effects (as manifested in paresthesia incidences) or 

childhood neuropsychological development effects, we do estimate small reductions in the 

occurrence of acute myocardial infarction and all-cause mortality as the result of an advisory. 

We also predict, on average, a small decrease in systolic and diastolic blood pressures at age 7. 

However, all of these estimates are surrounded by large confidence intervals, largely because of 

uncertainty in exposure levels and the size of the exposed population. Furthermore, our finding 

of chronic mortality as the most prevalent endpoint suggests that there are other, less severe 

health effects associated with methylmercury exposure that remain to be identified and 

quantified. 

In general, angler compliance with the advisory is quite high, with only 2% of total 

anglers consuming in excess of advisory recommendations. Although females of childbearing 

age are more likely to exceed consumption guidelines in our model, we attribute this finding 

more to the assumptions made and the limitations of our data rather than to an actual behavioral 

pattern. In addition, our estimate of females of childbearing age who exceed the EPA RfD for 
Resources for the Future Jakus, McGuinness, and Krupnick 

107

methylmercury suggests that the percentage of women in Maryland exceeding the RfD is lower 

than in other parts of the country. 

Finally, our mean estimate of mortality benefits from a mercury advisory for males aged 

40 to 59 is $14.36 million (95% CI: 0, $74.66 million). Although estimated health benefits are 

uncertain, surplus losses are more certain and likely to be sizable. Policymakers should attempt 

to minimize these costs by targeting high-quantity consumers and communicating a precise 

message to these anglers. Additionally, because there is so much uncertainty surrounding our 

estimate of mortality benefits, the potential for such benefits from a mercury fish consumption 

advisory warrants further epidemiological research on this relationship. 

6. Sensitivity Analysis 

A number of sources of potential error arise in our analysis of changes in welfare as a 

result of some of the simplifying assumptions made in the model. This section presents results 

from selected sensitivity analyses that were conducted to determine the magnitude of the effect 

of various assumptions or restrictions on estimates of changes in welfare. In particular, this 

section addresses assumptions that could potentially have a large impact on estimated health 

benefits. Additionally, this section discusses the potential for incorporating other policy and 

behavioral scenarios into the Maryland Model. 

In this section, we alter five assumptions made in our earlier analysis that are expected to 

have significant implications for health benefits estimates, and compare benefits estimates for 

these alternative scenarios with our original estimates. We examine the following alternative 

scenarios: 
Resources for the Future Jakus, McGuinness, and Krupnick 

108

1. Perfect information, such that all consuming anglers are aware of the current 

advisory. 

2. Perfect compliance, such that all mercury-related health effects are eliminated. 

3. Averting anglers continue to be exposed to mercury through other sources of fish 

consumption. 

4. The absence of a maternal hair mercury concentration threshold for IQ effects. 

5. The mercury-mortality relationship is applied to both males and females. 

6.1. Perfect Awareness and Perfect Compliance 

As discussed in Section 3, we derive an estimate of angler awareness for Chesapeake Bay 

anglers using a Bayesian-weighted mean estimate of anglers’ FCA awareness of based on 

estimates from the literature. The applicability of parameter estimates from other sites to this 

analysis of the Chesapeake Bay is somewhat uncertain because angler characteristics and 

outreach and education efforts by the state may vary by location, and the characteristics at the 

locations examined may differ from those of the Chesapeake Bay. However, we can estimate the 

magnitude of the effect of angler awareness on health benefits by estimating health benefits 

under a scenario of perfect awareness. An assumption of perfect awareness implies that state 

education and outreach efforts are sufficient to ensure that every consuming angler is aware of 

the advisory. The probability that an aware angler will continue to consume striped bass, 

however, remains the same as in our original analysis (0.498). An assumption of perfect 

awareness more than doubles the number of aware anglers in our analysis. Because more anglers 

will avert under this scenario, average methylmercury intake and concentration estimates under 

an advisory are reduced relative to our original analysis, by approximately 14%. We also 

estimate maximum total potential mortality benefits under original assumptions, the equivalent 

of perfect advisory compliance. 
Resources for the Future Jakus, McGuinness, and Krupnick 

109

Under perfect awareness, greater average reductions in mercury intake across the angler 

population result in an increased reduction of excess mortality to men aged 40 to 59, such that 

mortality benefits under our central assumptions increase by approximately 87%. Table 6.1 

reports mortality benefits estimates for this scenario and compares these results with our original 

estimate and estimated benefits for a scenario of perfect compliance. Mortality-related benefits 

under a scenario of perfect awareness are approximately 55% of total potential mercury-related 

mortality benefits, given our original assumptions regarding consumption levels and angler 

propensity to consume. 

Table 6.1. Comparison of Estimated Mortality Benefits under an Assumption of Perfect 

Awareness, with Original Estimates (million $2000) 

Scenario Reduction in 

Number of Cases 

Benefits from Estimated Mortality 

Reduction (SD) 

Original 4.37 (9.48) $14.36 ($45.12) 

Perfect awareness 7.98 (16.10) $27.87 ($88.35)

Perfect compliance 14.53 (34.94) $45.30 ($157.20) 

6.2. Averting Anglers Are Exposed to Mercury from Substitute Sources of Fish 

We assume in the main analysis that consuming anglers who avert under an advisory 

maintain their original level of consumption of recreationally caught fish, but do so by catching 

their fish from noncontaminated substitute sites or by consuming Chesapeake Bay species with 

negligible mercury concentrations. By allowing for a full elimination of mercury exposure from 

the Chesapeake Bay for averting anglers, this assumption also allows for the possibility that 
Resources for the Future Jakus, McGuinness, and Krupnick 

110

many anglers will avert by ceasing consumption of recreationally caught fish altogether, rather 

than seeking such fish from a substitute site or source.53 A variation of this original assumption is 

that all averting anglers switch to alternative sources of fish but expose themselves to new 

sources of mercury in the process. Thus, methylmercury exposure is reduced only to the extent 

that the mercury tissue concentrations of the substitute fish are lower than those of striped bass. 

Absent data on Chesapeake anglers’ behavior under an advisory, it is difficult to estimate 

the extent to which anglers might be limiting their exposure reduction by consuming other 

contaminated fish. However, one means of addressing this potential effect is to assume that 

averting anglers, in the presence of an advisory, replace their methylmercury uptake from 

Chesapeake Bay striped bass with the average per capita daily mercury uptake from canned and 

other commercial seafood products. This assumption will likely overestimate mercury exposure 

to anglers under an advisory, however, because in theory background blood concentrations 

should already be accounting for average fish consumption levels. 

Once an advisory has been announced, using our original assumptions for awareness and 

compliance, we estimate that approximately 15,990 of 165,000 consuming anglers avert, or cease 

consumption of recreationally caught striped bass from the bay. These anglers and their spouses 

are assigned the background FDA uptake rate from commercial seafood products, which 

averaged over the population is 1.76 μg/day (Rowe et al.1995). Consuming anglers who do not 

 

53 Although this extension to the assumption can be made in terms of mercury exposure, it should be noted that we 

are actually assuming that these individuals are holding their total fish consumption levels constant. This implies 

that these averting individuals are still retaining the protective health benefits of fish consumption and thus are not 

really making a trade-off in terms of health risks. 
Resources for the Future Jakus, McGuinness, and Krupnick 

111

avert in the presence of an advisory continue to consume striped bass from the Chesapeake Bay 

at pre-advisory levels. 

Under this scenario, baseline all-cause mortality rates are the same as in our original 

analysis, but the reduction in methylmercury-related mortality under an advisory is smaller 

because of a smaller average reduction in methylmercury uptake across the exposed population. 

Table 6.2 compares benefits estimates from this scenario with our original benefits and potential 

benefits estimates. When averting anglers are assumed to substitute canned and other 

commercial seafood products rather than switching to recreationally caught fish from 

noncontaminated substitute sites, mortality benefits are about 30% as large as our original 

estimates. 

Table 6.2. Comparison of Estimated Mortality Benefits If Averting Anglers Are Exposed to 

Mercury through Substitute Fish, with Original Estimates (million $2000) 

Scenario Reduction in 

Cases 

Benefits from Estimated Mortality 

Reduction (SD) 

Original 4.37 (9.48) 14.36 (45.12) 

This scenario 1.53 (3.90) 4.30 (17.55) 

Perfect compliance 14.53 (34.94) 36.90 (105.00) 

Furthermore, although our estimates of compliance with a striped bass advisory remain 

the same as in our original analysis under this assumption, a smaller per capita reduction in 

mercury exposure implies that more individuals will be exceeding EPA’s RfD. In this scenario, 

we estimate that 21,200 (SD = 32,600) individuals, 6,603 (SD = 9,891) of whom are women of 

childbearing age, will be exposed to mercury through fish consumption in excess of EPA 
Resources for the Future Jakus, McGuinness, and Krupnick 

112

guidelines, compared with 18,000 and 5,490, respectively, in our original analysis. These new 

estimates represent about 8% of exposed women of childbearing age and slightly more than 5% 

of total exposed females in Maryland, about a 1 percentage point increase in both values from 

our original analysis. 

6.3. Removal of the Threshold for IQ Effects 

In this scenario, we eliminate the threshold for IQ effects from the New Zealand study, 

which in the main analysis was set at a maternal hair mercury concentration of 6 mg/kg. 

Although Kjellström et al. (1987) did not find a significant relationship between prenatal 

methylmercury exposure and IQ score below this level, we apply the relationship for the entire 

dose-response curve for sensitivity purposes. 

Elimination of this threshold, with all other original assumptions intact, results in a per 

capita average increase in IQ score of 0.52 points (SD = 0.50) under an advisory, or about a 0.5% 

increase in an average score of 100 points. This average is over total births to exposed females of 

childbearing age (2,617), and an IQ point is valued at $10,420 ($2000); thus we estimate total 

benefits from avoided intelligence loss to be $10.57 million (SD = $9.91 million). Although 

research currently does not support the changing of this threshold, if it were reduced or 

eliminated, sizable gains from avoided reductions in intelligence due to prenatal methylmercury 

exposure would be expected under a fish consumption advisory. 

6.4. Application of the Mercury-AMI and Mercury-Mortality Relationships to 

Women 

The cohort in the Salonen et al. (1995) study is limited to males aged 42 to 60. As a 

result, in the main analysis, we limit the application of their estimated relationship accordingly. 
Resources for the Future Jakus, McGuinness, and Krupnick 

113

However, it is plausible that a similar relationship exists for females of the same age range. In 

this scenario we calculate increased AMI and mortality cases for both males and females and 

sum benefits over both genders. Table 6.3 reports mortality and AMI for both males and females, 

given all other original assumptions. 

Table 6.3. Reduction in Mercury-Related AMI and Mortality for Both Genders Due to 

Advisory 

Endpoint Result Mercury-Related Cases (SD) 

 Males Females Total 

Acute myocardial 

infarction 

Pre-advisory 6.85 (17.30) 3.38 (8.53) 10.23 (25.83)

 Reduction due 

to advisory 

2.03 (4.59) 1.00 (2.26) 3.03 (6.85) 

All-cause 

mortality 

Pre-advisory 14.53 (34.94) 11.40 (27.42) 25.93 (62.36)

 Reduction due 

to advisory 

4.37 (9.48) 3.43 (7.44) 7.80 (16.92) 

Occurrences of AMI and all-cause mortality are lower for females than for males because 

of the relatively smaller size of the exposed female population and the lower baseline risk of 

AMI for females. However, the inclusion of females in an analysis of cardiovascular and 

mortality risk increases our central estimate of mortality benefits by approximately 78%, as is 

seen in Table 6.4. Because of the magnitude of potential benefits with the addition of females, 

research should explore the existence of these relationships for women. 
Resources for the Future Jakus, McGuinness, and Krupnick 

114

Table 6.4. Comparison of Estimated Mortality Benefits When Females Are Included in the 

Analysis, with Original Estimates ($2000) 

Scenario Benefits from Estimated Mortality 

Reduction (SD) 

Original (males only) 14.36 (45.12) 

Males and females 25.63 (80.53) 

Perfect compliance (males and females) 80.85 (280.6) 

6.5. Potential Implications of Alternative Assumptions for Recreational Consumer 

Surplus Loss 

Estimates of consumer surplus loss due to a recreational fish consumption advisory in the 

Chesapeake Bay are also likely to be sensitive to the assumptions made in our model. For 

example, altering such parameters as angler awareness via outreach and education efforts or 

changing anglers’ propensity to heed an advisory will change the magnitude of their behavioral 

response, and thus should affect consumer surplus accordingly: a greater behavioral change 

should imply greater economic losses. The magnitude of this relationship will depend primarily 

on the availability and proximity of noncontaminated substitute sites. However, given that there 

is no literature linking per trip consumer surplus loss to advisory severity or educational efforts, 

we have no obvious means of adjusting per trip consumer surplus losses for a given change in 

awareness or compliance. 

 Incorporating dynamic stock effects into the analysis may also have implications for 

consumer surplus loss. As discussed in Section 2, an averting response by consuming anglers 

may, over time, reduce the per trip consumer surplus losses borne by catch-and-release anglers: 

reductions in harvest by consuming anglers may increase biomass in the estuary, improving 

catch rates—and consequently per trip value—for catch-and-release anglers. This effect will to 
Resources for the Future Jakus, McGuinness, and Krupnick 

115

some extent attenuate the present value of overall consumer surplus loss from an advisory, 

though the magnitude of this effect is quite uncertain. Any change in average per trip consumer 

surplus loss will affect total welfare losses under an advisory proportionally. 

6.6. Additional Opportunities for Sensitivity Analysis 

Currently, the Maryland Model allows the user to alter several types of input parameters, 

all of which have been discussed either in this chapter or in the main analysis. Table 6.5 lists the 

parameters that can be altered. 

Table 6.5. Options for Sensitivity Analysis Provided in the Maryland Model 

Option Choices 

Choose angler awareness Bayesian weighted mean from literature (default) 

Perfect awareness 

Choose mercury exposure scenario Averting anglers eliminate mercury uptake from striped 

bass (default) 

Averting anglers are exposed to mercury from other fish 

sources 

Choose method of estimating 

recreational surplus loss 

Percentage loss applied to Maryland fishing day estimates 

(default) 

Consumer surplus estimates from Great Lakes 

Choose a study for assessing 

childhood neuropsychological 

development effects 

Faroe Islands (default) 

New Zealand 

NRC integrative analysis 

Choose an assumption for IQ 

effects 

Threshold (default) 

No threshold 
Resources for the Future Jakus, McGuinness, and Krupnick 

116

The user can also directly change other input parameters or data in the model to facilitate 

the analysis of different populations or fisheries, simulate a variety of policy or behavioral 

scenarios, or incorporate new parameter estimates from the literature. To do so, however, the 

user must open the edit function in Analytica and change the input values. An advisory of 

increased severity, for example, might be modeled by increasing the percentage reduction in trips 

taken as well as the percentage of anglers complying with consumption guidelines. Various 

levels of education and outreach efforts by the state could be modeled by altering the percentage 

of anglers aware of the advisory. Although the literature may not provide specific estimates for 

adjusting these variables, one could postulate reasonable adjustments to our original 

assumptions. Additionally, modification of relevant demographic variables could allow for the 

examination of effects on selected or altogether different populations. Further variations to the 

original assumptions could certainly be explored. 
Resources for the Future Jakus, McGuinness, and Krupnick 

117

References 

Akagi, H., et al. 1998. Methylmercury Dose Estimation from Umbilical Cord Concentrations in 

Patients with Minamata Disease. Environmental Research 77: 98–103. 

Angrist, J.D., K. Graddy, and G.W. Imbens. 2000. The Interpretation of Instrumental Variables 

Estimators in Simultaneous Equations Models with an Application to the Demand for 

Fish. Review of Economic Studies 67: 499–527. 

Austin, D., et al. 1999. The Benefits of Air Pollutant Emissions Reductions in Maryland: Results 

from the Maryland Externalities Screening and Valuation Model. RFF Discussion Paper 

99–05. 

Belton, T., R. Roundy, and N. Weinstein. 1986. Urban Fishermen: Managing the Risks of Toxic 

Exposure. Environment 28: 19–37. 

Beuter, A., and R. Edwards. 1998. Tremor in Cree Subjects Exposed to Methylmercury: A 

Preliminary Study. Neurotoxicology and Teratology 20: 581–89 

Bloyd, C., et al. 1996. Tracking and Analysis Framework (TAF) Model Documentation and 

Users Guide. Argonne National Laboratory, ANL/DIS/TM-36, December.

Bockstael, N.E., K.E. McConnell, and I.E. Strand. 1989. Measuring the Benefits of 

Improvements in Water Quality: The Chesapeake Bay. Marine Resource Economics 6: 

1–18. 

Breffle, W.S., E.R. Morey, R.D. Rowe, D.M. Waldman, and S.M. Wytinck. 1999. Recreational 

Fishing Damages from Fish Consumption Advisories in the Waters of Green Bay. 

Boulder, CO: Stratus Consulting, Inc. 

Brotons, C., et al. 1989. Blood Pressure by Age in Childhood and Adolescence: A Review of 129 

Surveys Worldwide. International Journal of Epidemiology 18: 824–29. 

Budtz-Jørgensen, E., et al. 1999. Benchmark Modeling of the Faroese Methylmercury Data. 

Research Report 99/5. Prepared at the University of Copenhagen for the U.S. 

Environmental Protection Agency. 

Buerger, R., and J.R. Kahn. 1989. New York Value of Chesapeake Striped Bass. Marine 

Resource Economics 6: 19–25. 
Resources for the Future Jakus, McGuinness, and Krupnick 

118

Burger, J. 1998. Fishing and Risk Along the Savannah River: Possible Intervention. Journal of 

Toxicology and Environmental Health, Part A 55: 409–19. 

Burger, J., and M. Gochfeld. 1991. Fishing a Superfund Site: Dissonance and Risk Perception of 

Environmental Hazards by Fishermen in Puerto Rico. Risk Analysis 11: 269–77. 

Burger, J., K. Staine, and M. Gochfeld. 1993. Fishing in Contaminated Waters: Knowledge and 

Risk Perception of Hazards by Fisherman in New York City. Journal of Toxicology and 

Environmental Health 39: 95–105. 

Centers for Disease Control (CDC). 2001. Blood and Hair Mercury Levels in Young Children 

and Women of Childbearing Age, United States, 1999. Morbidity and Mortality Weekly 

Report 50:140–43. 

Chen, H.Z., and S.R. Cosslett. 1998. Environmental Quality Preference and Benefit Estimation 

in Multinomial Probit Models: A Simulation Approach. American Journal of 

Agricultural Economics 80: 512–20. 

Connelly, N.A., and B.A. Knuth. 1995. Patterns of Harvest and Consumption of Lake Champlain 

Fish and Angler Awareness of Health Advisories. Ithaca, NY: Cornell University Human 

Dimensions Research Unit. 

Connelly, N.A., B.A. Knuth, and T.L. Brown. 1996. Sportfish Consumption Patterns of Lake 

Ontario Anglers and the Relationship to Health Advisories. North American Journal of 

Fisheries Management 16: 90–101. 

Crump, K., et al. 1998. Influence of Prenatal Mercury Exposure upon Scholastic and 

Psychological Test Performance: Benchmark Analysis of a New Zealand Cohort. Risk 

Analysis 18: 701–13. 

Davidson, P.W., et al. 1998. Effects of Prenatal and Postnatal Methylmercury Exposure from 

Fish Consumption on Neurodevelopment: Outcomes at 66 Months of Age in the 

Seychelles Child Development Study. Journal of the American Medical Association 280: 

701–707. 

Dennis, C.A., and F. Fehr. 1975. Mercury Levels in Whole Blood of Saskatchewan Residents. 

The Science of the Total Environment 3: 267–274. 

Desvousges, W.H., F.R. Johnson, and H.S. Banzhaf. 1998. Environmental Policy Analysis with 

Limited Information. 1st ed., New Horizons in Environmental Economics. Northampton, 

MA: Edward Elgar. 
Resources for the Future Jakus, McGuinness, and Krupnick 

119

Diana, S.C., C.A. Bisogni, and K.L. Gall. 1993. Understanding Anglers' Practices Related to 

Health Advisories for Sport-Caught Fish. Journal of Nutrition Education 25: 320–28. 

Diana, S.C., C.A. Bisogni, and K.L. Gall. 1993. 320–28. 

Eales, J., and C.R. Wessells. 1999. Testing Separability of Japanese Demand for Meat and Fish 

within Differential Demand Systems. Journal of Agricultural and Resource Economics

24: 114–26. 

Eales, J., C. Durham, and C.R. Wessells. 1997. Generalized Models of the Japanese Demand for 

Fish. American Journal of Agricultural Economics 79: 1153–63. 

Gilmour, C.C. 1999. A Preliminary Survey of Size-Specific Mercury Concentrations in Game 

Fish from Maryland Fresh and Estuarine Waters. 48. St. Leonard, MD: Academy of 

Natural Sciences Estuarine Research Center. 

Grandjean, P., et al. 1997. Cognitive Deficit in 7-Year-Old Children with Prenatal Exposure to 

Methylmercury. Neurotoxicology and Teratology 19: 417–28. 

Huslin, A. 2001. Md. Issues Advisories on Eating Fish. Washington Post, December 13. 

International Programme on Chemical Safety (IPCS). 1990. Environmental Health Criteria 

Document 101, Methylmercury. Geneva: World Health Organization. 

Jakus, P.M., and W.D. Shaw. 2002. Perceived Hazard and Product Choice: An Application to 

Recreation Demand. Journal of Risk and Uncertainty (forthcoming). 

Jakus, P.M., D. Dadakas, and J.M. Fly. 1998. Fish Consumption Advisories: Incorporating 

Angler-Specific Knowledge, Habits, and Catch Rates in a Site Choice Model. American 

Journal of Agricultural Economics 80: 1019–24. 

Jakus, P.M., M. Downing, M. Bevelhimer, and J.M. Fly. 1997. Do Sportfish Consumption 

Advisories Affect Reservoir Anglers' Site Choice? Agricultural and Resource Economics 

Review 26: 196–204. 

Johnson, F.R., and W.H. Desvousges. 1997. Estimating Stated Preferences with Rated-Pair Data: 

Environmental, Health and Employment Effects of Energy Programs. Journal of 

Environmental Economics and Management 34: 79–99. 

Kahn, J.R., and W.M. Kemp. 1985. Economic Losses Associated with the Degradation of an 

Ecosystem: The Case of Submerged Aquatic Vegetation in Chesapeake Bay. Journal of 

Environmental Economics and Management 12: 246–63. 
Resources for the Future Jakus, McGuinness, and Krupnick 

120

Kjellström, T., et al. 1989. Physical and Mental Development of Children with Prenatal 

Exposure to Mercury from Fish. National Swedish Environmental Protection Board 

Report No. 3642. 

Knuth, B.A., N.A. Connelly, and M.A. Shapiro. 1993. Angler Attitudes and Behavior Associated 

with Ohio River Health Advisories. Series No. 93-6. Ithaca, NY: Cornell University 

Human Dimensions Research Unit. 

Kosatsky, T., and P. Foran. 1996. Do Historic Studies of Fish Consumers Support the Widely 

Accepted LOEL for Methylmercury in Adults? Neurotoxicology 17: 177–86. 

Krieger, D.J., and J.P. Hoehn. 1998. Improving Health Risk Information for Sport Anglers: The 

Value of Information about Chemical Residues. North American Journal of Fisheries 

Management 18: 411–21. 

Krupnick, Alan J., et al. 2002. Age, Health, and the Willingness to Pay for Mortality Risk 

Reductions: A Contingent Valuation Survey of Ontario Residents. Journal of Risk and 

Uncertainty 24: 161–80. 

Kuhnert, P.M., et al. 1981. Comparison of Mercury Levels in Maternal Blood, Fetal Cord Blood, 

and Placental Tissues. American Journal of Obstetrics and Gynecology 139: 209–213. 

Kuntz, W.D., et al. 1982. Maternal and Cord Blood Background Mercury Levels: A Longitudinal 

Surveillance. American Journal of Obstetrics and Gynecology 143: 440–43. 

Lebel, J., et al. 1996. Evidence of Early Nervous System Dysfunction in Amazonian Populations 

Exposed to Low-Levels of Methylmercury. Neurotoxicology 17: 157–67. 

Lebel, J., et al. 1998. Neurotoxic Effects of Low-Level Methylmercury Contamination in the 

Amazonian Basin. Environmental Research 79: 20–32. 

MacDonald, H.F., and K.J. Boyle. 1997. Effect of a Statewide Sport Fish Consumption Advisory 

on Open-Water Fishing in Maine. North American Journal of Fisheries Management 17: 

687–95. 

MacNair, D., et al. 1998. Technical Memorandum on Lavaca Bay Recreational Fishing 

Assessment. Draft for Peer Review. Durham, NC: Triangle Economic Research, October. 

Mariёn, K., and G.M. Patrick. 2001. Exposure Analysis of Five Fish-Consuming Populations for 

Overexposure to Methylmercury. Journal of Exposure Analysis and Environmental 

Epidemiology 11: 193–206. 
Resources for the Future Jakus, McGuinness, and Krupnick 

121

Maryland Department of the Environment. 2001. Fish Consumption Advisories. Maryland 

Department of the Environment. http://www.mde.state.md.us. Accessed December 13. 

May, H., and J. Burger. 1996. Fishing in a Polluted Estuary: Fishing Behavior, Fish 

Consumption, and Potential Risk. Risk Analysis 16: 459–71. 

McConnell, K.E., and I.E. Strand. 1994. The Economic Value of Mid and South Atlantic 

Sportfishing, Vol. 2: 134. College Park, MD: University of Maryland. 

Montgomery, M., and M. Needelman. 1997. The Welfare Effects of Toxic Contamination in 

Freshwater Fish. Land Economics 73: 211–23. 

Mrozek, J.R., and L.O. Taylor. 2002. What Determines the Value of Life? A Meta-Analysis. 

Journal of Policy Analysis and Management (forthcoming). 

National Marine Fisheries Service (NMFS). 1981. Conversion Factors for Fishery Products,

unpublished memo dated July 15. 

http://www.mdsg.umd.edu/Extension/finfish/FF5.html>. 

National Registry of Myocardial Infarction (NRMI). 2001. About AMI. 

http://www.nrmi.org/nrmi_about.htmlhttp://www.nrmi.org/nrmi_about.html. 

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Washington, 

DC: National Academy Press. 

Parsons, G.R., and A.B. Hauber. 1998. Spatial Boundaries and Choice Set Definition in a 

Random Utility Model of Recreation Demand. Land Economics 74: 32–48. 

Parsons, G.R., P.M. Jakus, and T. Tomasi. 1999. A Comparison of Welfare Estimates from Four 

Models for Linking Seasonal Recreational Trips to Multinomial Logit Models of Site 

Choice. Journal of Environmental Economics and Management 38: 143–57. 

Phlugh, K.K., L. Lurig, L.A. von Hagen, S. von Hagen, and J. Burger. 1999. Urban Anglers' 

Perception of Risk from Contaminated Fish. The Science of the Total Environment 228: 

203–18. 

Pitkin, R.M., et al. 1976. Mercury in Human Maternal and Cord Blood, Placenta and Milk. 

Proceedings of the Society for Experimental Biology and Medicine. 151: 65–67. 

Reinert, R.E., B.A. Knuth, M.A. Kamrin, and Q.J. Stober. 1996. A Review of the Basic 

Principles and Assumptions Used to Issue Fish Consumption Advisories. In American 

Fisheries Society Symposium, 98–106. Chattanooga, TN: American Fisheries Society. 
Resources for the Future Jakus, McGuinness, and Krupnick 

122

Rowe, Robert D., et al. 1995. The New York State Environmental Externalities Cost Study. 

Volume I: Introduction and Methods. Prepared by Hagler Bailly Consulting, Inc., 

Boulder, CO, for the Empire State Electric Energy Research Corp., December. 

Roy, N., E. Tsoa, and W.E. Schrank. 1991. What Do Statistical Demand Curves Show? A Monte 

Carlo Study of the Effects of Single Equation Estimation of Groundfish Demand 

Functions. In W.E. Schrank and N. Roy, eds., Econometric Modeling of the World Trade 

in Groundfish. The Netherlands: Kluwer Academic Publishing, 572. 

Salonen, J.T., et al. 1995. Intake of Mercury from Fish, Lipid Peroxidation, and the Risk of 

Myocardial Infarction and Coronary, Cardiovascular, and Any Death in Eastern Finnish 

Men. Circulation 91: 645–55. 

Salvanes, K.G., and D.J. DeVoretz. 1997. Household Demand for Fish and Meat Products: 

Separability and Demographic Effects. Marine Resource Economics 12: 37–55. 

Shaw, W.D., and J.S. Shonkwiler. 2000. Brand Choice and Purchase Frequency Revisited: An 

Application to Recreation Behavior. American Journal of Agricultural Economics 82: 

515–26. 

Sherwell, J., and P. Miller. 2001. Personal communication with the authors, October 9. 

Sikorski, R., et al. 1989. The Interpartum Content of Toxic Metals in Maternal Blood and 

Umbilical Cord Blood. Ginekol. Pol. 60: 151–55. 

Sørensen, N., et al. 1999. Prenatal Methylmercury Exposure as a Cardiovascular Risk Factor at 

Seven Years of Age. Epidemiology 10: 370–75. 

Stern, A.H., et al. 1996. Estimation of Fish Consumption and Methylmercury Intake in the New 

Jersey Pregnant Population. Journal of Exposure Analysis and Environmental 

Epidemiology 6: 503–25. 

U.S. Census. 2000a. Table DP-1. Profile of General Demographic Characteristics for Maryland: 

2000. http://www.census.gov/Press-Release/www/2001/tables/dp_md_2000.PDF. 

______. 2000b. Table QT-02. Profile of Selected Social Characteristics: 2000, Maryland. 

http://factfinder.census.gov. 

U.S. Environmental Protection Agency (U.S. EPA). 1997. The Benefits and Costs of the Clean 

Air Act, 1970 to 1990. Washington, DC: Office of Air and Radiation, Office of Policy. 

______. 1999. The Benefits and Costs of the Clean Air Act, 1990 to 2010. Washington, DC: 

Office of Air and Radiation, Office of Policy. 
Resources for the Future Jakus, McGuinness, and Krupnick 

123

______. 2000. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories, 

Volume 2. Washington, DC: EPA-823-B-00-008. 

______. 2001a. Mercury Update: Impact on Fish Advisories. Washington, DC: EPA-823-F-01-

011. 

______. 2001b. Update: National Listing of Fish and Wildlife Advisories. 6. Washington, DC: 

EPA-823-F-01-010. 

______. 2001c. National Listing of Fish and Wildlife Advisories. http://fish.rti.org. Accessed 

December 12, 2001. 

______. 2001d. Integrated Risk Information System (Iris).

http://www.epa.gov/iris/subst/0073.htm. Accessed December 12, 2001. 

______. 2001e. Water Quality Criterion for the Protection of Human Health: Methylmercury. 

EPA-823-R-01-001. January. 

Wessells, C.R., C.J. Miller, and P.M. Brooks. 1995. Toxic Algae Contamination and Demand for 

Shellfish: A Case Study of Demand for Mussels in Montreal. Marine Resource 

Economics 10: 143–59.
Resources for the Future Jakus, McGuinness, and Krupnick 

124

Appendix: The Maryland Model 

This section highlights some of the major features of the Mercury Fish Advisories 

version of the Maryland Model, showing major endpoints and options provided to the user and 

discussed in this study. Figure A.1 depicts the top-level screen of the model. The Atmospheric 

Transport Module is not included in our analysis, so this appendix focuses on features of the 

Anglers’ Response, Health Effects, and Benefits Modules.54

Figure A.1. The Mercury Fish Consumption Advisory Model 

 

54 A number of the modules shown contain seemingly superfluous nodes, which are generally indexes, such as 

“Policy” or “Awareness,” that are used to characterize the data and results throughout the model. 
Resources for the Future Jakus, McGuinness, and Krupnick 

125

A.1. The Recreational Angler Response Module 

The Recreational Angler Response Module is depicted in Figure A.2. It allows the user to 

select an assumption for anglers’ awareness, either an estimate representative of the literature or 

perfect awareness.55 Two important parameters are calculated in submodules within this module: 

recreational consumer surplus loss under an advisory, and methylmercury uptake from 

Chesapeake Bay striped bass both before and after an advisory. 

Figure A.2. The Recreational Angler Response Module 

 

55 Such choices are often represented by both a rectangular choice node and a pop-up menu. The choice node 

defines the available options and allows the user to make a selection. The pop-up menu is added for convenience, so 

that a user familiar with the choices can make a decision upon entering the module without having to go into the 

choice node. 
Resources for the Future Jakus, McGuinness, and Krupnick 

126

The submodule for calculating consumer surplus losses under an advisory is shown in 

Figure A.3. The user is provided with two options for estimating consumer surplus losses, which 

are described in Section 2.

Figure A.3. Consumer Surplus Loss Calculation Submodule 

Within the Recreational Angler Response Module, the Mercury Uptake Submodule 

quantifies per capita daily methylmercury exposure both before and after an advisory using 

striped bass catch and consumption data, striped bass fish tissue mercury concentration 

estimates, and the estimated behavioral parameters. The module converts raw catch into meals, 

distributes this catch among anglers and their families, and using fish tissue concentration data, 

calculates a per person daily mercury uptake and the change in uptake under an advisory. This 

estimate serves as the primary input to the Health Effects Module. The Mercury Uptake 

Calculation Submodule is depicted in Figure A.4. 
Resources for the Future Jakus, McGuinness, and Krupnick 

127

Figure A.4. Mercury Uptake Calculation Submodule 

A.2. Commercial Fisheries Response Module 

The Commercial Fisheries Response Module calculates consumer and producer surplus 

losses under both a ban on commercial fishing and the issuance of commercial fish consumption 

advice by the state. The basis for these calculations is parameter estimates from an original 

supply-and-demand model of the Chesapeake Bay commercial striped bass fishery, which are 

used as inputs to the module. Currently, options are not included in the model for altering the 
Resources for the Future Jakus, McGuinness, and Krupnick 

128

assumptions made in estimating these parameters. However, the size of reductions in consumer 

and producer surplus can be modified in the model with better information. Figure A.5 depicts 

the top level of the Commercial Fisheries Response Module. 

Figure A.5. Commercial Fisheries Response Module 

A.3. Mercury Health Effects Module 

Figure A.6 shows the uppermost level of the Mercury Health Effects Module. The user 

chooses the assumption for determining anglers’ exposure—that is, whether averting anglers are 

exposed to mercury from new sources of fish consumption. Total human exposure and all five 

health endpoints are calculated in submodules within this module. 
Resources for the Future Jakus, McGuinness, and Krupnick 

129

Figure A.6. Mercury Health Effects Module 

The Human Uptake Submodule, shown in Figure A.7, combines mercury uptake from 

fish with other sources of MeHg exposure, namely inhalation, to determine total exposure. 

However, the submodule inventories all forms of mercury to which humans are exposed. 

Depending on the assumptions chosen by the user, average background MeHg uptake from 

commercially caught fish may or may not be included in the calculation. Mercury from dental 

amalgams is never included because mercury exposure from dental amalgams is in the form of 

elemental mercury. The node labeled Total Human Uptake (ALT) reports human uptake for the 

alternative exposure scenario discussed in Section 6. In the Intake Conversions Submodule, 
Resources for the Future Jakus, McGuinness, and Krupnick 

130

human uptake data is combined with baseline blood mercury concentration levels, and final 

blood and hair concentrations are calculated. 

Figure A.7. Human Uptake Submodule 

Figures A.8 and A.9 display the two layers of complexity within the Childhood 

Neuropsychological Development Submodule. The user selects a study to quantify the reduction 

in abnormal test scores under an advisory. Furthermore, the user can decide to remove the 

threshold in calculating average change in IQ score under an advisory. Within the submodule for 

each study, the user can quantify any particular neuropsychological development test, as is 

depicted for the Faroe Islands study in Figure A.9. The submodule quantifies the number of 

women who exceed any benchmark dose, as well as the number of abnormal test scores. 
Resources for the Future Jakus, McGuinness, and Krupnick 

131

Figure A.8. Childhood Neuropsychological Development Submodule 
Resources for the Future Jakus, McGuinness, and Krupnick 

132

Figure A.9. Faroe Islands Submodule 

The Cardiovascular Effects Submodule of the Mercury Health Effects Module is 

presented in Figure A.10. This submodule estimates cases of mercury-related AMI and mortality 

and the reductions in these two endpoints under a fish consumption advisory. It also allows for 

the calculation of fatal AMIs so that the extent of overlap between these two endpoints can be 

examined. The submodule calculates these endpoints for both men and women. The nodes 

labeled Importance perform importance analysis on the results to determine the primary sources 

of uncertainty. 
Resources for the Future Jakus, McGuinness, and Krupnick 

133

Figure A.10. AMI and Chronic Mortality Effects Submodule 

A.4. Health Benefits Valuation 

The Mercury Health Valuation Module is capable of reporting the value of reductions in 

cases of paresthesia (though cases of paresthesia are not predicted in the analysis), chronic 

mortality, and the value of the average improvement in children’s IQ score under an advisory. 

The VSL can be varied for mortality benefits calculation, within the Health Values Library in the 

larger Health Benefits Module. 
Resources for the Future Jakus, McGuinness, and Krupnick 

134

Figure A.11. Mercury Health Valuation Module