### 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 Co nsumption Advisories for Mercury Paul Jakus, Meghan McGui nness, 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 e ffects of postulated recreational and commercial fishing advisories for striped bass on the Ma ryland portion of the bay. Awareness of and response to the advisory is estimated using a meta -analysis of the literatu re. Three values are estimated: welfare losses to recreational anglers, welfare losses in the commercial striped bass fishery, and health benefits. An estimate of percen tage of consumer surplu s 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 epidem iological relationships , and while potentially large, are highly uncertain. Results also suggest most individuals are be low advisory standards ex ante, such that advisories shou ld target high-frequency consumers.

Key Words: fisheries, mercury, advisori es, recreation, health benefits JEL Classification Numbers: I18, Q22, Q25, Q26Executive 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 advi sories. On December 12, 2001, the state issued several advisories concerning contamination by polychlorinated bipheny ls (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 fishi ng advisories in the Maryland portion of the Chesapeake Bay, counting e ffects experienced by or through all users of this portion of the bay. Because the Maryland Depa rtment of Natural Resources indicates that striped bass ( Morone saxatilis ) is the species of greatest conc ern for mercury contamination in the Chesapeake Bay, we assume that advisories are limited to this species. Based on 1992–1994 weighted average tissue concen tration 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 postu late a recreational FCA suggesting restricted consumption for the general population (< four m eals 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 recr eational fishery, since the fisheries occupy essentially the same area.

A review of the literature a ssesses the degree to which angl ers are aware of advisories and engage in consumption-related aver ting behaviors, based on estimates from ex post analysis of fisheries with characteristics similar to the ba y. 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 advisori es are 26.1% less likely to cons ume listed species than anglers who are not (95% CI: 22.1%–30%).We estimate three endpoints: we lfare losses to recreational a nglers, welfare losses to consumers and producers of commerci al striped bass, and health be nefits to recreational anglers due to reduced consumption of contaminated striped bass. Under a recreational FCA, aware anglers will undertake some comb ination of behavioral adjustments that may range from ignoring the advisory to altogether ceasing trip s to the affected water body. Such behavioral modification results in economic losses to angler s. 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 estimat e an annual consumer surplus loss over all Maryland saltwater fishing days of $8.83 millio n ($2000). For the commercial striped bass fishery, we estimate a very simple model of s upply and demand that predicts equilibrium price and quantity with reasonable accuracy. Using para meter 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 surp lus 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 (p rickling, tickling, or itching se nsation, and an initial symptom of methylmercury disease), abnormal scores on te sts of childhood neurological development, and cardiovascular health and mortality effects. Add itionally, 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 acu te myocardial infarcti on (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 replicat ed, our confidence in them is even further attenuated. We find that most anglers are in compliance w ith the advisory standards, even before the advisory is announced. About 3% of anglers exceed advisory sta ndards 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 va lue 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 suggest ed by the research fo cusing on neurological development effects from fetal exposure, the hea lth 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 Consump tion 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 Co mmercial 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 Merc ury from Substitute Sources of Fish ....109 6.3. Removal of the Threshold for IQ Effects ...........................................................1126.4. Application of the Mercury-AMI and Me rcury-Mortality Relationships to Women ..............................................................................................................................1 12 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..................................................................................1331 

## The Benefits and Costs of Fish Co nsumption Advisories for Mercury Paul Jakus, Meghan McGui nness, 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 (Hg 0 ) or as divalent mercury (Hg 2+ ) and can be found in the atmosphere, in rocks and soils, a nd 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 atmos phere; and soil runoff. Some 85% of mercury emissions in the United States ar e believed to come from power pl ants as a result of fossil fuel combustion (U.S. EPA 2001a). Fish encounter mercury in the a quatic environment. Biological processes of animal species convert elemental an d 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 gastroin testinal tract and binds itself to all tissues. In

∗ Dr. Paul Jakus is an Associate Professor of Econom ics at Utah State Univers ity, and Meghan McGuinness and Alan Krupnick are at Resource s 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 concentra tions 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-lif e 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 impa ired 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 cardiova scular health and mortality effects. To manage the risks associated with eating contaminated fish, federal and state authorities have issued fish consumption adviso ries (FCAs) to reduce th e 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 igno re advisories, which lessens the costs associated with the FC A 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-contam inated 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 a nd health benefits associated with potential recreational and commercial fish consump tion advisories in the Chesapeake Bay. 3 Health benefits are calculated for two e ndpoints: 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 th e resulting welfare losses from changes in anglers’ behavior associated w ith advisory compliance. In add ition, of course, reduced mercury emissions will lead to a reduction in mercury-re lated health effects, assuming that baseline mercury levels in fish (and other exposure pa thways) are above those found to cause health effects. However, health improvements may be mitig ated if, with the lifting of a fish advisory, consumption of fish containing methylmercury in creases. 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 Mary land Externalities Screening and Valuation Model, or “Maryland Model,” developed for Maryland Department of Natural Resources by Resources for the Future. These are the Recrea tional Angler and Commer cial Fishery Response Modules, and the Mercury Health Effects Modul e. 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 Assessm ent 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 pr ovides 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 calculati ng safe levels of fish consumption and appropriate advisory levels. Me rcury concentration levels of Chesapeake Bay striped bass are then evaluated within the c ontext of EPA’s FCA methodology to establish the likely FCA outcome for the Chesapeake Bay. Section 3 of the report presen ts the model for estimating consumer surplus losses from an FCA for recreational fishing of stri ped 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 m odel and results for an ec onomic analysis of an advisory on commercial striped bass fishing in the bay. Section 5 presents a literature review andResources for the Future Jakus, McGuinness, and Krupnick

5 analysis of the human health e ffects of methylmercury and descri bes the model used to quantify and value such effects under a recreational advi sory. 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 a bout contamination of wild life 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 relativ e to the general public, so much of the effort at publicizing advisories has been aimed at t hose 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 specif ic 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, consumpti on levels are communicated in the form of “meals per month” for either the general popu lation 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 eati ng 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 wh en 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 t ypes listed above, ten states al so 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 include d in many consumption advisories. “Advice” is generally targeted at sensitive subpopulations (children, pregnant women, women who may soon be pregnant) and recommends restricted cons umption of specified commercial species. In 2000 almost 6,500 advisories were issu ed, a 124% increase over 1993 (Figure 2.1; U.S. EPA 2001b). This increase was due to more intensive m onitoring by federal and state agencies rather than an increase in the gene ral contaminant level (U.S. EPA 2001b, 2). A simple count of advisories, however, obscu res the extent to which they vary. A single advisory issued by, say, a state agency may cover a single speci es 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 advisor y. As of July 2002, the Chesapeake Bay was not under any type of advisory, but many of its tribut aries (including the Potomac, James, Back, and Anacostia Rivers) have had fish consumption a dvisories issued. Althoug h there are different ways of counting advisories, EPA reports that by 2001, some 2,259 advisories had been issued by 41 states for mercury contamin ation of a wide variety of fi sh species (U.S. EPA 2001c). Twenty-six of these advisories have been i ssued by 12 states for mercury contamination of coastal or estuarine regions. Six of the 12 states are located along the Atla ntic coast (Delaware, Florida, Georgia, Maine, North Carolina, and South Carolina). In addi tion, 3 states in the immediate vicinity of Maryland—Delaware, Pe nnsylvania, 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 th e District of Columbia, have issued FCAs. Delaware has issued 20 advisories covering a wide variety of fish species and contaminants, including dioxins, polychlorina ted biphenyls (PCBs), Di eldrin, arsenic, and pesticides. One advisory was issued for the Ch esapeake 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 (R GP, 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 a dvisories, 2 for mercury cont amination. Other advisories have been issued for PCBs, chlordane, and mi rex. The first mercury a dvisory, issued in 1993, covers Lake Wallenpaupack and recommends NCGP for walleye greater than 19 inches. The second mercury advisory was issued in 2001 and c overs all freshwater rivers and lakes in the state. This broad advisory recommends RGP fo r all species caught in any of Pennsylvania’s freshwaters.Resources for the Future Jakus, McGuinness, and Krupnick

11 West Virginia has issued 11 advisories fo r dioxin, PCBs, and ch lordane contamination. There have been no FCAs for mercury. Two advisori es 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 sp ecies, with a recommendation of NCGP. Virginia has issued 10 advisories because of contamination by three chemicals: mercury, PCBs, and kepone. Mercury advisories have been i ssued for the North Fork of the Holston River (NCGP, all species), the South Fork of the Shen andoah 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 recomme nding restricted consumption by the general population of all species. The Potomac River ha s an FCA for PCB contamination of channel catfish greater than 18 inches (RGP).

The District of Columbia has issued a single advisory coveri ng 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 fo r tributaries of the Chesapeake. Restricted consumption by the general population has been r ecommended for channel catfish and American eel on the Back River and for Baltimore Har bor. On December 12, 2001, however, the Maryland Department of the Environment issued new FCAs covering a wide variet y of water bodies and fish species because of PCB, pesticide, a nd mercury contamination (Huslin 2001; MarylandResources 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, bl ack crappie, spot, common ca rp, 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 ri vers and streams in Maryland. Th e 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 meal s for the general population and sensitive subpopulations. Finally, personal communications with Mary land 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 st riped 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, incl uding Newark Bay, New York Harbor, and the Hudson River. Three of the advisories were is sued for mercury contamination. Maine included striped bass in its statewide mercury advisor y. 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 dete rmining whether advisories for fish consumption should be issued (U.S. EPA 2000), based on assessed risks of contamination. TheResources for the Future Jakus, McGuinness, and Krupnick

13 variable needed is simply the contaminan t concentration in th e local fish population. Calculations are sufficiently st raightforward that a local agen cy with more information (e.g., details such as meal size and average body we ight of consumers) can adjust the EPA recommendations to fit local conditions. To understand this procedure, a few preliminar ies 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, me rcury and other contaminants cross the gill membrane and enter directly into the blood (Reine rt et al. 1996). Contaminants thus achieve a concentration within a single fish that is great er than the surrounding aquatic environment. Fish may also bioaccumulate contaminants from thei r 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 ar e relatively more suscep tible to contaminant concentration than terrestrial sp ecies because they accumulate contaminants not only through the food they consume but also through their constant c ontact 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, te rrestrial species accumulate little mercury contamination from airborne (vaporized) fo rms 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 c onsumption; in fact, as cooking reduces the moisture content of the fillet, merc ury actually becomes more concentrated per unit of fish. The other crucial concept is the oral refere nce dose (RfD), an estimate of the lifetime daily exposure to a contaminant above which harmful health effect s will occur (U.S. EPA 2001d). The calculation of an RfD al so includes an “uncertainty factor” that adjusts the dose downward to reflect uncertaintie s about the accuracy of the ca lculated 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 im pairment, which is measured using several evaluative endpoints. The EPA Rf D 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 tw o parts. The first part ca lculates the maximum daily allowable consumption of contaminated fish for hu mans, expressed in kilograms of fish per day,

CR lim = (RfD × BW) ÷ C m (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 CR lim is the allowable daily consumption limit, RfD is the established reference dose (mg/kg/day) of the contaminant, BW is body weight (kg), and C m is the estimated contaminant concentration (mg/kg) in the fish species. The reference dose is based on chronic expos ure 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 kilogr am of weight of the person. The body weight ( BW ) variable in equation ( 2.1) adjusts for consumption by pers ons 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 fo r the contaminant concentration in the species of interest ( C m ). All else equal, as the contaminant c oncentration in the species increases, the allowable daily consumption limit falls. 6

CR lim represents a maximum average daily cons umption level that is “safe.” That is, average daily consumption at this level ove r 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 th e 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 su ch that the contaminant concentration is on the left side of the equation, as in C m = (RfD × BW) ÷ C lim where C lim 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 r ecommended consumption depends upon an assumed meal size and is calculated as

CR mm = ( CR lim × T ap

) ÷ MS (2.2)

where CR mm is recommended consumption (meals per month), CR lim is the maximum allowable daily consumption limit, T ap 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 devel oping symptoms, whereas those who consume less than this amount are at less risk. In the case of extremely low allowable daily consumption limits, the ca lculation may yield a value for CR mm equal to zero—that is, no consumption.

2.3. Design of Adviso ries for Chesapeake Bay To date, the Maryland Department of Envir onment (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 pr oject the likely advisories for the bay. As noted above, state Department of Natura l Resource officials anticipate me rcury advisories to be issued for striped bass. Mercury contamination concentrat ions 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 predicte d concentration levels mu st be related to the size of striped bass kept by Chesapeake Bay angler s. This will allow us to estimate the mean concentration levels of MeHg in striped bass that are consumed. Fina lly, the predicted meanResources for the Future Jakus, McGuinness, and Krupnick

17 concentration can be used in the EPA recomm ended consumption equations (equations 2.1 and 2.2) to predict the recommended consumption advisories to be issued by MDE.

2.4. Relating MeHg Concen tration to Fish Size The only available data on which a preliminar y analysis of potential Chesapeake estuary FCAs can be conducted are provided by Gilm our (1999). The Gilmour data consist of 18 samples of striped bass from the upper bay, 10 sa mples 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, respec tively. Regression analysis shows a positive relationship between the fish weight an d Hg concentrations (Table 2.2). The models indicate that for every 1% in crease in weight, Hg concentr ations increase by 0.6% to 0.9%.

New data will be available soon. The state coll ected striped bass from several regions of the bay during fall 2001 and spring 2 002 to ascertain a temporally relevant, comprehensive, fishlength-based assessment of mercury levels befo re 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 Data a
Variable Potomac River b Upper Bay b Midbay b 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 R 2 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 Concen tration Levels for “Kept” Fish The mean length of Chesapeake Bay fish in th e Gilmour data was 64.4 cm, or a little over 25 inches, with a maximum length of 84 cm (33 in ches); the mean weight of the sampled fish was 2.76 kg. The Gilmour sample may not be represen tative of the size and number of fish kept by Chesapeake anglers, however. Over the 1997–2 000 period, anglers particip ating in the state’s Cooperative Striped Bass Survey reported keep ing approximately 5,300 striped bass. Using the distribution of kept fish to cal culate mean fish lengths and fish weights, the average “kept” striped bass was 64 cm long and weighed approx imately 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 th e 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 coopera tive survey data were predicted using a model based on the Gilmour data. The weight-lengt h 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 rela tionship given in footnote 8 and in the Gilmour mercury concentration models was used to predict Hg c oncentrations found in larg er fish. For example, consider a 36-inch (91.4 cm) fish caught in the upper bay. The wei ght-length model predicts that such a fish would weigh 5.90 kg. Using Gilmou r’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 fr om the 1992–1994 Gilmour data and the 1997–2000 striped bass survey, the probability-weighted es timate 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 th e 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 ( C m ) 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 chil d of 35 kg, no more than two meals per month. Given the advisory actions of ot her 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 stri ped bass tissues, the most likely consumption advisories can be hypothesized (T able 2.4). At mean concentra tion levels, the advisory for recreational anglers would most likely suggest th at 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 th e concentration levels found in Chesapeake Bay striped bass, a commerci al fishing ban is very unlikely. Instead, MDE would likely follow other coastal states and issu e “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 childbearin g 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. Shoul d MDE choose a different set of default parameters, such as meal size or body wei ght, recommended consumption restrictions may change. A smaller meal size will increase the re commended consumption ra tes; a larger mealResources 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 restricti ons; 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 mont h in either direction. Finally, the recommended restrictions are based on a contaminant c oncentration 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 concentr ation levels. If the st ate agency bases its recommendation on fish caught from all portions of the Chesapeake, the mean contaminant concentration level would be lower, and thos e lower estimates would loosen recommended consumption restrictions.

3. Recreational Fishing Losses from an FCA
3.1. Awareness, Compliance, and Averti ng Behaviors Associated with FCAs When a fish consumption advisory is issued, an glers have a number of potential responses: 1. ignore the advisory and conti nue current fishing practices; 2. follow advisory consumption limits or change target species (or both); 3. cease consumption of listed specie s 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 desi gned to elicit the pot ential responses of Chesapeake anglers, it is necessaryto review the literature on a dvisories 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, eac h independent estimate is re ported. The source for eachResources for the Future Jakus, McGuinness, and Krupnick

23 attempt to distill and synthesize th e 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 pr oportion 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 tr eated 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. Firs t, all estimates are eval uated 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 ma jor urban populations), only this s ubset 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 larg er 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 advi sory literature is not very extensive, it is adequate to make reasonable assumptions regarding the potentia l behavioral impacts of any FCA issued for the Chesapeake. In all, some 20 advisory-related studies were reviewedto 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 aver ting 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 result s 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 a dvisories is the most frequently encountered statistic in the advisory lite rature. Table 3.1 summarizes the studies used to estimate the probability an angler will be aware of an adviso ry. 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 vastResources for the Future Jakus, McGuinness, and Krupnick

25 majority of the studies reporte d in Table 3.1 are intercept surv eys conducted at contaminated sites. 11 The estimates of angler awareness range wi dely, from 19% to 96%. Regression analysis does not reveal a statistically significant relations hip with the length of time an advisory is in place. Thus, angler awareness is more likel y a function of advisory severity, angler characteristics, and state efforts to make anglers aware of adviso ries. Treating all studies equally, the mean of all the estim ates suggests that 71% of anglers we re aware of advisories, indicating that very high levels of angler awareness are po ssible. In particular, the ten estimates from the Great Lakes states and states that border th e Ohio River show exceptionally high levels of advisory awareness, with a mean awareness of 87%.

Three of the studies concern fr eshwater 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 si x estimates (from four st udies) of inte rest: those for estuarine and coastal waters. These are high lighted in bold italicized text in Table 3.1.

11 This type of study surveys people at the site where th ey recreate. As such, it is not a random sample even of recreators, since the most frequent recr eators 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) Oh io River, OH 1992 250 86% Knuth et al. (1993) Ohio River, IN 1992 265 90% Knuth et al. (1993) Oh io 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 Jers ey 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 re creational anglers are shared by Chesapeake anglers: they may be divers e in ethnicity, first language, and angling experience. A big difference between the two regions is that the Ne w York–New Jersey estuarine region has a “noResources for the Future Jakus, McGuinness, and Krupnick

27 consumption” advisory, a recommendation more se vere than that anticipated for the Chesapeake Bay.

The four estuarine and coas tal studies were conducted over a 13-year period (1983–1995) and revealed a wide range of estimates for angl er awareness, from 19% to 60%. Table 3.2 shows the calculated standard error fo r each study, along with a 95% confidence in terval (CI) for the estimate. Treating all the estimates equally, the m ean 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 Yo rk Harbor 50% 1.1% 47.7%–52.2% Burger et al. (1993) Jama ica 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 Je rsey 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 a nd New Jersey Shore) are based on relatively small sample sizes, so it is a dvisable 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 th e weights are based upon the inverse of the variance of each estimate. In this case the estimate with the sma llest variance (Belton et al.) receives the most weight, and the estimate with the largest variance (May and Burger, New Jersey Shore) receivesResources 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 b E σ σ σ σ β β β β 

### + + =

) / 1 ( ) / 1 ( 1 ] | [ b 

### b Var σ σ β β 

### + =

where β and σ β are the prior estimate of the mean and variance, respectively, and b and σ b are “new” estimates of the mean and variance . The equations from Desvousges et al. provide estimates for the mean and variance of the pos terior 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 Angl ers Consuming Sport-Caught Fish Ten studies provided 12 estimates of the percen tage of anglers consuming some or all of their sport-caught fish. Tabl e 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 ca tch is 0.69, or 69%. If the estima te for subsistence anglers is dropped, the mean percentage falls to 66%. If th e data are restricted to the New York–New Jersey harbor region, the estimated proportion of anglers consuming spor t-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) Ar thur 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 Ta ble 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 choos e 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 c onsumption 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 wh en an advisory is in place. This will, in turn, affect the overall probability (per centage) of consumption by anglers. Unfortunately, only three st udies (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 Probabilities a
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 wate r body given knowledge of a dvisories 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 probab ility 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 likel y 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 probabilitie s 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 ga uge 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 we re only half as likely to consum e fish from the contaminated water as anglers who were unawa re 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 likel y 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

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

Evaluated at the means for P(Consume|Aware) and P(Consume|Not Aware) , the percentage reduction in the probability of consum ption is 26.1%. An empirical distribution for this value was formed by taking 1,000 ra ndom 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 se veral 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 num ber of trips to contaminated water bodies. With many chemical contaminants, consumption risks may be redu ced 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 c ontaminated by mercury because unlike other contaminants, mercury does not conc entrate in specific bodily tissues. 14 This section focuses on averting actions that are releva nt 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 averti ng behaviors as reporte d in the literature. Behaviors are presented slightly differently from study to study, with different conditions governing each response. Sometimes the response is based on beha vior 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 calculat ed 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 Oh io River Knuth et al. (1993) 26% changed target species Ohio River Knuth et al. (1993) 23% changed target species Gr een Bay Breffle et al. (1999) 45% changed species for consumpti on Green Bay Breffle et al. (1999) If favorite site had an FCA... 25% would not eat any fish Mi chigan Kreiger and Hoehn (1998) 14% would eat fewer fish Mi chigan Kreiger and Hoehn (1998)

In addition to changing consumption behavior s in response to advisories, anglers may also change trip-related behavior . For example, an angler may c hoose not to visit a contaminatedResources for the Future Jakus, McGuinness, and Krupnick

34 site. This, in turn, may affect the total number of fishing tr ips 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 Oh io 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 Mich igan 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 th e probability a site is visited and on the number of trips taken over the course of the season. These studies suppor t the general findings reported in Table 3.6—that FCAs cause anglers to choose ot her 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 Tennesse e anglers, finding that the rem oval of an advisory at any one site would increase the probability that site w ould 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 wereResources 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 la kes (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 wit hout 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 Sh aw and Shonkwiler 2000). Unfortunately, none of these last five studies repor t the estimated change in season al 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 degr ee 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, po ssibly in excess of recommended consumption limits. Several authors measure the degree to wh ich 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 repo rted 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) Oh io River, OH 1992 250 1.7% Knuth et al. (1993) Ohio River, IN 1992 265 4.8% Knuth et al. (1993) Oh io 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) Ar thur Kill, NJ 1994 168 30.0%

The Illinois estimate is 0% only because the st ate 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 calculation s 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-Relate d Behavior by Chesapeake Bay Anglers The analysis of this section can be combin ed with recreational trip and consumption information to assess consumption-related behavi or by anglers and see how this behavior might change under an advisory. In particular, we n eed to know how both the propensity to consume and the number of trips for striped bass change un der an advisory, so that we can estimate preand post-advisory per capita mercury uptake. Although the per capita ch ange in exposure under an advisory depends upon the beha vioral 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 ca lculated using the resu lts 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 prob ability 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 thos e who are not aware of the advisory. The conditional cons umption probability estimates of Table 3.4 depict this effect. Under the assumption that anglers who are unawa re 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 ) Aware Not | Consume ( ) Aware Not | Consume ( ) Aware | Consume ( 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 th e following probability relationship, P(Consume) = P(Aware of Adviso ries) × 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 consum ption 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 reduc tion in the total numb er 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 Ma ryland, 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 postadvi sory 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 stri ped 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 probab ility estimates show th at the propensity to consume sport-caught fish is rela ted to advisory awareness. Th ese 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 lik elihood, we are underestimating reducti ons in consumption and mercury uptake resulting from anglers’ behavi oral 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 Probab le 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 awarene ss maybe greater in the Chesapeake, especially if st ate 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 fu lly 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 Cons umption Advisories and Chesapeake Bay Recreational Fishing Economic losses are expected to result from the behavioral adjustments undertaken by anglers who respond to a recreati onal advisory. The size of the total consumer surplus loss from an advisory is proportional to th e magnitude of the average angl er behavioral response at the margin. This marginal response is from a baselin e 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 me rcury, anglers would have already undertaken some level of behavioral adjustment before the announcement of a merc ury advisory. It isResources for the Future Jakus, McGuinness, and Krupnick

43 possible, then, that the behavior al adjustment under a mercury advi sory might be negligible, such that the consumer surplus loss from that specific advisory would be clos e 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 possibl y 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 severit y, agency outreach and information efforts, and the availability of noncontaminated substitu te 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 freque ntly, researchers have applied standard versions of r ecreational site-choice models to the problem, treating the presence or absence of an FCA as a site attribute. In gene ral, these models have fo und that, all else equal, anglers are less likely to visit fi shing sites under an advisory th an sites not under an advisory. The models can then be used to estimate the do llar 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 ha ve 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 th at an angler may use advisory information ex

16 Because there are currently no FCAs for fish caught in the Chesapeake Bay, we assu me that anglers in thisResources 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 site s that have been tested and found contaminated, whereas a full disclosure program reveals the out come—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 th e 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 a dditional $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 di rectly applicable to this study because we are interested in potential losses of c onsumer surplus by Chesapeake anglers. MacDonald and Boyle (1997) use contingent valuation to measure the impact of a statewide mercury advisory for all open-water fi shing in Maine. One of the authors’ goals is estimating the economic losses to anglers usi ng a contingent valuat ion question. Respondents were presented with a dichotomous choice que stion asking whether they would have been willing to pay X dollars more for fishing during the s eason. 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). A dvisories 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 fi sh. 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 a dvisory). The valuation model is linear in the number of lakes from which advi sories 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 el ectrical bill converted to a dollar value. Finally, in a study of the Lav aca 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 consumpti on advisory. The authors find a st atistically significant effect on site choice, with anglers less likely to visit the c ontaminated site relative to other sites, all else equal. The model is unconventional in the sense that it does not in clude a travel cost variable; instead, the authors use only the distance traveled to the site as an explan atory variable. Thus, the report does not state a monetized estimate of economic losses, instead stating that anglers suffered a 3.2% loss in the e xpected utility index.
3.2.1. Economic Losses Due to Fish Consumption Advisories A number of economists have examined the angler’s economic losse s due to the presence of contaminants in sufficient quantity that an FCA must be issued. Ten studies are summarized inResources for the Future Jakus, McGuinness, and Krupnick

46 Table 3.12, which presents the esti mates for Great Lakes sites first, followed by the Northeast and concluding with the southern United States. As noted in the ta ble, 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 Mc Fadden (HLM) version of the utility index. Shaw and Shonkwiler’s (2000) seasonal model di ffers considerably from the MRW and HLM indices, relying on an index rela ted to the total distance travel ed during the fishing season as opposed to the usual formulation based on the nu mber 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 m odel, 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 ar e 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 na rrow, from $2.04 per trip to $5.51 per trip ($2000). 17 The lowest values in the range are given by th e 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 relativ ely 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 repres enting 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 (M ontgomery and Needelman 1997). The choice set for this study also includes very few contaminat ed 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 $4.93 c Chen and Cosslett (1998) MNP 41 Great Lakes sites $3.42 $5.51 c
Breffle et al. (1999) MNL Green Bay, WI $4.17 $55.00 $4.40 c $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 lakes a $1.85 b $21.96 $2.13 c $25.28 (6 months) Jakus et al. (1997) MNL MRW 14 east Tennessee Lakes $2.86 $47.40 $3.29 c $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 lakes a $1.77 $21.55 $2.04 c $24.80 (6 months) Parsons et al. (1999) MRW 14 middle Tennessee lakes a $1.84 b $23.62 $2.12 $27.19 (6 months) Shaw and Shonkwiler (2000) SS 14 middle Tennessee lakes a $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 obt ained for regions with a large proportion of contaminated sites. For example, Chen and Co sslett (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 Ta ble 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 comp arable to one another, relying upon revealed preference data to estimate a site-choice model. Th e only exception to this rule is the Breffle et al. dataset, which augments revealed preference data with stated pref erence data. Following the Desvousges et al. Bayesian equa tions presented in Section 3.1, the mean and a 95% confidence interval for per trip lo sses 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 re creational fishing. This may not be the case, especially if the attr ibutes and conditions under which th e losses are estimated (say, for freshwater fishing in the relatively small lake s of Tennessee) differ substantially from those found in the Chesapeake Bay. One could hypothesi ze 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 benchmarkResources 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 es timates of the mean and variance are first calculated using just the three Great Lakes es timates (Chen and Cosslet t 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 cont rast, 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 recr eational 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 anglin g but beach use and swimming as well. The angling portion of the research focuses on stri ped bass anglers fishing in Maryland. Data were gathered from the 1980 National Fishing, Hun ting, and Wildlife A ssociated Recreation (FHWAR) survey. Rather than measure the demand fo r striped bass fishing in terms of trips, the FHWAR data provide information onl y on the number of days of fi shing by individual anglers at three aggregate sites in Maryland. The statistical analysis of dema nd for fishing finds that trips are positively related to the stri ped bass catch rate. With respect to welfare measures, the authors do not report per day measures of consumer surp lus. 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 cons umer surplus is estimat ed at $1.37 million with

against which to gauge its validity, it was decided to use the authors’ estimates from their fixed-parameter MNL andResources for the Future Jakus, McGuinness, and Krupnick

51 a range of $0.66 million to $2.07 million, or $2.7 3 million with a range of $1.31 million to $4.12 million ($2000). McConnell and Strand (1994) use 1988–1989 Nationa l Marine Fisheries Service data to evaluate recreational fishing in th e mid- and south- Atlantic spor t fisheries. The authors use both stated preference and revealed preference mode ls to estimate the value of access to these fisheries. In the first value- of-access model, a dichotomous c hoice question asked anglers their willingness to sell the right to fish in the stat e in which they were interviewed. For example, people interviewed in Maryland were asked about th eir willingness to relinquish the right to fish in Maryland, leaving the respondent s 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 surplu s 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 perspec tive. 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 incr ease 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 m ode and target species choice and another for site choice, condi tional 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 ($20 00), 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 reveal ed preference values arising fr om the nested multinomial logit model, which are based on single- day trips, are remarkably sim ilar 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 advi sory literature via the Jakus et al. (1997) study. This is the only study that comp ared welfare losses associated with FCAs to total consumer surplus. In the two Tennessee study regions on whic h Jakus et al. report, the losses associated with FCAs represent 6%–8% of total per trip cons umer surplus. Given that the data on which the

19 The different modes include party/charter bo at, private/rental boats, or shore fishing. 20 This figure is reasonably close to the estimate from th e 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 th e loss estimates presented in Table 3.12. The per day range for the Chesapeake Bay region lie s 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) perc entage 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% confid ence interval implied by the boldfaced estimates in Table 3.12 for which some measure of disp ersion 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. Conceptuall y, 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 appl y 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 surplu s 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 l iterature associated with cons umption advisories yielded two estimates of the per trip loss in consumer surp lus (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. Th e 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 Mc Connell 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. “Com plete 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 in cluded in the consumer surplus loss estimates. Second, FCAs may have effect s that in turn affect different anglers inResources for the Future Jakus, McGuinness, and Krupnick

56 different ways. For example, Ja kus et al. (1998) hypothesized th at 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 ov erall biomass in the estuary. Increased biomass may mean that catch-and-release anglers benefi t 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 a bout 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 estimat e 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 adviso ry. When possible, uncertain parameters fromResources for the Future Jakus, McGuinness, and Krupnick

57 either data or the literature are specified as pr obability distributions, and this uncertainty is propagated throughout the model. The model allows th e user to vary angler awareness as well as consumption preferences (explained in more deta il in Sections 5 and 6) . Additionally, the user can choose between two options to estimate cons umer surplus loss. Cons umer 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 Grea t Lakes estimates of pe r trip welfare loss under an advisory, described ea rlier in this section.

4. Commercial Fishing Losses from FCAs As noted in previous sections of this re port, mercury contamination and consumption advisories are a concern for striped bass. Not onl y 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, stri ped 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 stri ped bass fishery using a more up-to-date dataset, which is used to es timate 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 Stripe d Bass Fishing Models The Kahn and Kemp commercial striped bass m odel is only a portion of a larger study aimed at estimating losses asso ciated with submerged aquatic vegetation in the Chesapeake Bay. 23 The supply equation is specified as a function of the ratio of the st riped bass price to the price of two substitute species (oysters and clams), the pri ce of fishing effort, the adult population of striped bass, and a time trend. The pri ce of fishing effort is described as “an index of labor opportunity costs and energy costs.” Deta ils 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 me at, poultry, and fish), the regional population, regional per capita income, and a time trend. The log-linear supply-and-dema nd model is estimated using tw o-stage least squares on a dataset covering the 1965–1979 peri od. 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 aquati c vegetation in the bay. Table 4.1 shows the sign and statistical signif icance 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 Model a Variable Supply Demand Intercept + – ln(P Striped Bass /P Oyster ) + ln(P Striped Bass /P Clams ) +* ln(P Effort ) –* Ln(Adult Striped Bass Population) +* Ln(Time Trend) +* –

ln(P Striped Bass ) –* ln(CPI Substitutes ) + 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 stripe d bass, all have the proper signs. The demand equation, however, has only one significant vari able (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 aut hors 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 stri ped bass in the ChesapeakeResources for the Future Jakus, McGuinness, and Krupnick

60 were declining over much of the period cove red by the data. Following the Kahn and Kemp study, Chesapeake Bay stocks declined to the poin t where the fishery was closed to commercial fishing in the late 1980s. In response, many co mmercial anglers switched species or were engaged by the state of Maryland in other fish ing activities. After striped bass stocks had recovered, the commercial fishery was reopened. Given the unsettled natu re of the striped bass fishery during the period subsequent to th e Kahn and Kemp study, using the Kahn and Kemp equations for a direct function transfer to estim ate 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 supp ly-and-demand analysis to an equilibrium catch equation. The striped bass fishery in th e 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 equa tion is specified as a function of the price of striped bass, indices of Hudson Bay (adult) and Chesapeake Ba y (juvenile) striped bass populations, and the price of flukes, porgies, yellowtail flounder, bl uefish, and lobsters. The demand equation is specified as a function of the price of stripe d bass, income, New York State population, a time trend, and the CPI for m eat, 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 fo r 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 –* + P

Striped Bass +* –* Hudson Bay Striped Bass Population Index + Chesapeake Bay Striped Bass Population Index +* P

Fluke +* P

Porgies +* P Yellowtail flounder +* P

Bluefish +* P

Lobster –* CPI Substitutes +* 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 fi shery demand elasticities, Roy et al. (1991) note the extremely wide range of demand elasticities in the literatur e. For example, a variety of fish products have demands that range from th e 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 th e demand elasticity relative to an ordinary least squares approach. In the presence of a highl y overidentified model or model misspecification (e.g., excluded exogenous variable s), a quantity-dependent demand model is preferred to an inverse demand model. Given the results from Roy et al. and the Ka hn studies cited in th e previous section, a quantity-dependent, two-stage leas t squares approach is used to model supply and demand of the commercial Chesapeake Bay striped bass fishery. Fu rther, the Kahn studies can help specify the supply-and-demand models. Data on Chesapeak e Bay commercial landings in Maryland and value of landings were obtained from the Mary land Department of Natural Resources Web page. These data provide quantity and value of landing s for a variety of comm ercial species. Agency personnel supplied further information useful in the modeling process, in cluding the number of commercial licenses used in any given year and the number of striped bass by age group. Unfortunately, these data are not availabl e 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 ba ss fishery was closed for five years, fromResources 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 st riped bass, the number of comme rcial 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 we re the primary altern ative species sought by commercial operations during the striped bass fi shery closure of the late 1980s; thus, the equilibrium prices for oysters and catfish ar e also included in the supply equation. On the demand side, the specification in cludes 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, Virgin ia, and the District of Columb ia). The results are shown in Table 4.3. 26 The three statistically signifi cant variables in the supply e quation all have the expected sign. The price of striped bass is positive, the pr ice of oysters is negativ e, and the sign of the technology variable—the striped bass population—is also positive. Neither of the remaining variables (the price of catfish a nd the number of commercial license s) is statistically significant.

25 This is akin to the juvenile recruitment index of Buerger and Kahn. 26 Durbin-Watson statistics are reported. The data consis t 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 quant ity, 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 Fishery a Variable Supply Demand Intercept –250.51 (–0.22) –18,709.92 (–1.82) P

Striped bass 643.49 (2.38) –911.73 (–1.50) P Oysters –507.3 (–2.32) P Catfish –749.82 (–1.29) # Commercial licenses –0.06 (–0.09) Striped Bass Population 0.18 (4.69)

Household Income 0.17 (1.15) CPI Substitutes –229.1 (–1.61) Regional Population 0.003 (1.63)
R 2 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 reasonabl y close to sign ificance at the 0.10 level, however. The most impor tant variable for this analysis is the own price effect. The Pvalue for this variable is 0.14, s uggesting that price is an important influe nce on the demand for striped bass even if it is not signi ficant at conventional test levels. The price of substitute goods is also close to significance (P=0.11) but has the wrong sign. Ev aluated at mean price andResources 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 s upply equation overestimates actual landings by 0.3%. Although the statistical signi ficance 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 th at 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 resu lt predicted by economic theory.

27 In addition to the linear specifications reported in Table 4.3, log-linear a nd 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 equilib rium price and quantity predicted by the statistical models ($1.69 per pound and 2.37 million pounds, respectively), annua l 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 mill ion. When estimated at the actual 2000 equilibrium price ($1.53 per pound) and quantity (2.26 million pounds), annua l 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 w ould be incurred by market partic ipants 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 de mand 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 th e variance of producer surplus in the striped bass fishery yielded a much narrower 95% c onfidence interval, $2.41 million to $3.78 million. The welfare analysis for the commercial fishery s hould be evaluated in light of the wide variance of the consumer surplus estimate. Given the Hg concentration levels in stripe d 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” r ecommending restricted consumption by both the general population and subpopulatio ns (children and women of ch ildbearing age). Theoretically, this can be modeled as a shif t “to the left” of the demand cu rve 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 surp lus are those given by Jakus et al. (1997) and used in Section 3.2: approximately 6% to 8%.

If commercial advice is issue d, 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 theResources for the Future Jakus, McGuinness, and Krupnick

69 loss in consumer surplus is e qual to $215,800. It is seen that th e equilibrium price falls to $1.56, a 7.7% drop from the initial equilibrium valu e of $1.69 per pound. Equilibrium quantity falls from 2.37 million to 2.29 million pounds, a 3.4% re duction. The demand shift and subsequent changes in equilibrium price a nd quantity result in an annual ne t loss of producer surplus of $304,500, or 9.9% of initial producer surplus. Aggreg ate annual surplus loss es 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 speci fic to the Maryland portion of the bay. Extending this analysis to include Virginia anglers would increas e 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 s upply 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 benc hmarks against which to compare the original modeling effort for the Chesap eake Bay striped bass fishery. The empirical properties of the supply-and-demand models for th e Chesapeake are not id eal (particularly for the demand equation), but the predictive capabi lity of each model appears acceptable. Given conditions in 2000, the total annual economic surplu s in the commercial st riped bass fishery is estimated to be about $6.2 million. The section cl osed with an estimate that under a notice of

Figure 4.1: Shift in Demand Following Commercial Advice Price

Su pp l y

Ori g inal Demand

Quantity (1000 lbs.) 2371 $ 4

. 2 9

$1.69

2287 $1.56

Demand After Advisor y $4.07Resources for the Future Jakus, McGuinness, and Krupnick

71 Commercial Health Advice, total annual loss es in the market for commercially caught Chesapeake Bay striped bass w ould 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 commercia l demand due to FCAs, it was necessary to make an assumption based on changes in demand foun d 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 sugges t that the commercial demand model is acceptable (e.g., its predictive ab ility 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 applie d 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 su rplus loss to the endpoi nts 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 an d PS confidence intervals, wh ich are based only on the covariance matrix for the appropriate eq uation. Further, the percentage losses in CS and PS are assumed constant when they are, in fact, random variable s. In light of these simplifications, the range of CS and PS losses is likely to be narrower relative to an than if the random na ture of all parameters were fully incorporated.Resources for the Future Jakus, McGuinness, and Krupnick

72 4.5. Commercial Fi shing Response Module In the Maryland Model, the Commercial Fishing Response Module replicates the calculations described in this section. The model first replic ates the consumer and producer surplus calculations based on the parameters discu ssed 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 cons umer and producer surplus reductions for both a ban and the more likely scenario, a commercial consumption “advice.”

5. Health Benefits of Me rcury Exposure Reductions This section reviews the epidemiological literature on the relationship between methylmercury exposure and three broadly defi ned health endpoints: adult central nervous system effects, childhood neuropsychological de velopment, and cardiovascular health and mortality. The quantification of these endpoints in the Maryland Model fo r a recreational fish consumption advisory is also described, a nd results and benefits estimates are reported. 31

5.1. Health Effects of Methylmercury Popular awareness of the health effects of mercury poisoning was fi rst 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 issu ance 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 congenita l 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 hi gher 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. W ithin 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 endpoint s for chronic low-dose MeHg exposure given the existing literature. This section reviews the epid emiological literature pe rtaining to those broad endpoints. (For a more comprehensive re view of the litera ture, see NRC 2000.)
5.1.1. Central Nervous System Effects in Adults Minamata disease encompasses the combinati on of central nervous system effects that adults may experience in the ev ent of mercury poisoni ng. 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, theResources for the Future Jakus, McGuinness, and Krupnick

74 World Health Organization (WHO, IPCS 1990) sugge sts that 5% of adu lts with a blood Hg concentration at or above 200 ppb will exhibit paresthesia. However, further research ha s suggested that deleterious effects may occur at exposure levels below this threshold. Kosatsky and Fora n (1996), in a review of 13 studies of long-term fish consumers, conclude that at a blood concen tration level of 200 ppb, ne urological 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 sugge sted neurological a nd 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 episode s, 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 child hood neuropsychological development. Three large epidemiological studies attempt to evaluate the relationship between childhood neuropsychological development and prenat al methylmercury exposure. These studies evaluate cohorts in the Faroe Islands (Grandjea n et al. 1997), New Zeal and (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 Faro e Islands study is considered the most robust: it has the largest cohort and was subjected to signifi cant peer review and reanalysis. The study administered a battery of neuropsychological te sts 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. Mercur y exposure, which o ccurred largely through maternal consumption of whale meat and was qu antified in both maternal hair and cord blood concentrations, was found to be significantly a ssociated 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 author s 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 cohor t of 237 at 6 years of age, administering a battery of 26 tests for psyc hological 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 mate rnal hair mercury concentration and fish consumption based on a number of potential confounding factors. In weighted regressions, Hg concentrations in maternal hair were associat ed with reduced scores on full-scale IQ, languageResources for the Future Jakus, McGuinness, and Krupnick

76 development, perceptual performance, and motor skills. 33 Unweighted regressions produced similar results, though generally at reduced st atistical significance. Although the New Zealand cohort has the strength of popul ation heterogeneity, it suffers from a small cohort and less extensive reanalysis compared with the Faroe Islands study.

Table 5.1 describes the tests su bjected to further analysis fr om 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 cognitiv e ability, language skills, read ing, 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 ofResources for the Future Jakus, McGuinness, and Krupnick

78 neuropsychological development rath er 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 prenat al or postnatal MeHg exposure. Several plausible explanations surface for th e 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 expos ure (whale meat versus fish). However, considering these two studies in conjunction with th e 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 re sults are attributable to betw een-sample variability in the expression of neurotoxicity at lo w doses, as studies with a larg e 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; B udtz-Jørgensen et al. 1999) have performed benchmark analyses for the New Zealand and Faro e Islands studies, respec tively, in the hopes of eliciting safe levels of exposure. The benchm ark 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 pr obability of an abnorma l test score increases from P 0 for an unexposed child to P 0 + BMR for a child at or in excess of the BMD. The default probability of an abnormal test sc ore is typically assumed to be 5% in an unexposed population. However, this default is chosen strictly for statistical purposes and ma y not reflect the trueResources 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 depe nds on the number of observations. All else equal, a higher number of observati ons 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 in tegrative analysis of the major endpoints of all three studies as a basis for comparison. Ta ble 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, Faro e 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 expl ored in these analyses, with preferen ce being given to the K-power model, in the form μ(d)= β

x d k

. 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 experiment al dose that does not pr oduce 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 sugge stions of a positive relationship between fish consumption and cardiovascular health . Fish consumption is thought to reduce cardiovascular risk because of the implicit in take 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 cardiovasc ular effects, such as increased blood pressure and abnormal cardiac function. Two recent studie s focus specifically on the relationship between low-level dietary exposure to Me Hg and cardiovascular health, one of which finds evidence of a link between mercury uptake and all-cause mortalit y. 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 fi sh consumption and mercury concentrations in hair and urine, and th en examined the relationship between these concentrations and the occurrence of acute myo cardial infarction (AMI) and chronic mortality from coronary heart disease (CHD), cardiovascula r 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 dise ase, stroke, claudicati on (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 devia tion of 1.98 mg/kg. In Cox proportional hazards models, with a number of cardiovasc ular risk factors as covariates , dietary intakes of fish andResources for the Future Jakus, McGuinness, and Krupnick

81 mercury were associated with a significantly in creased risk of AMI and death from CVD or any cause. The study found that men in the highest thir d (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 hear t disease. For the same tertile, the relative risk of death from CVD was 2.9, and from any cause, 3.3. In addition, cardiovascular hea lth in adulthood can be linked to the development of risk factors in childhood that ultimately may result fr om 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 Fa roe 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 a nd diastolic blood pressures, resp ectively. Furthermore, in boys, heart rate variability, an indica tor of cardiac autonomic control, decreased by 47% as cord blood concentrations increased over this same range.

5.2. Methodology This section describes the cal culation of pre- and posta dvisory 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 heal th effects of a mercury fish consumption advisory is the estimation of mercury uptake, and more importantly th e change in mercuryResources 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 i norganic and methylmercur y. 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 mo st 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 exposur e is striped bass from the Chesapeake Bay. Using data on striped bass fishing trips and fish tissue methylmercur y 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 year s 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 eff ects 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 Me Hg consider exposure to all species of Hg. This issue is important because the literature shows that the health e ffects 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 rele ased. We calculate the weight of each kept fish using the formula from Gilmour (1999), discussed in Section 2, and calcu late 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-ad visory; 165,100 postadviso ry) 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 tr ip (2.89, SD = 1.5, estimated from survey data) and the average Ma ryland household size of 2.61 (U.S. Census 2000a). 40 This information allows for calculation of a distribu tion 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 suggest s that this estimate is reasonable. A survey of recreational anglers at Lake Roosevelt in Washington (Mari ё n 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 th e 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 si ze 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 A ngler Cohort Study of La ke Ontario fishermen suggesting that 20% of anglers consume 1 meal or more per m onth, 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-w eighted average mercury concentration in fish (mg/kg) in the upper Chesapeake Bay, in or der to calculate an average per person daily mercury exposure from striped bass using the es timated distribution of per capita meals per month. The meals per month and exposure dist ributions most closely resemble a lognormal distribution, and the exposure dist ribution is shown in Figure 5.1.

0 0.02 0.04 0.06 0.08

0.1 0.12 0.14

0 .

2 7 0 . 8 1 1 . 3 5 1 . 8 9 2 . 4 4 2 . 9 8 3 . 5 2 4 . 0 6 4 . 6 0 5 . 1 4 5 . 6 8 6 . 2 2 6 . 7 7 7 . 3 1 7 . 8 5 8 . 3 9 8 . 9 3 9 . 4 7 1 0.28 1 1.10 1 2.72 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 indivi duals consuming at the same level before the advisory reduce consumption by the same amount, the shape of the distribution remains the sameResources 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 analys is 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 averag e reduction in daily per capita MeHg uptake from striped bass of 11% to 14%. 43 To estimate the implications of striped ba ss consumption for mercury-related health effects, daily methylmercury uptake from stripe d bass must be converted into blood and hair concentrations. To convert daily mercury uptak e to blood concentration, we use the following equation (described in U.S. EPA 2001e):

v b f

a d 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 catch ing 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 consum ing striped bass exclusively, ma king it necessary to account for other potential sources of methylmercury exposure, such as other fish products and inhalation. Furthermore, it is also importa nt to account for the fact that although the health effects we examine in this study are associated with me thylmercury, and thus, fish consumption, epidemiological studies typically use blood or hair total Hg concentrations as a proxy for methylmercury exposure. These total Hg measurem ents 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 themselv es 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. Th is 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 analys is of the 1999 National Health and Nutrition Examination Survey data for U.S. women of childb earing age. The study is touted as th e “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 availa ble for males, we apply this backgrou nd rate to the en tire 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 es timates of the size of specific subpopulations to which the endpoints from the epidemiological lit erature apply. In particular, we need to estimate the number of exposed females of childb earing age and the number of potential births toResources for the Future Jakus, McGuinness, and Krupnick

88 these women. Unfortunately, Maryland Departmentof Natural Resources does not record gender information for licensed anglers. However, we obt ained an estimate of female participation from the 1996 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation data for Maryland, which reports that 26% (approximate ly 48,500) of Maryland re creational anglers are female. We assume that the remaining exposed fe males 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 s pouses; based on current popu lation data, about 64% of these women are between 15 and 49, what we c onsider childbearing age. We thus estimate the total number of exposed females of childbeari ng age to be 79,710. Using 2000 birthrates for Maryland, for both married and unmarried wome n, 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 He alth Effects Module uses MeHg uptake and demographic data to quantify th ree general health endpoints: adult central nervous system effects, effects on childhood ne uropsychological development, a nd 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 expo sed women are married and expo sed 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 th e change in the number of cases of an effect under an advisory, using the common dose-response equation: ) ( 0 t I I POP RATE DR C

− × × × = ∆ (5.2) where C is the number of cases; DR is the dose-response coeffi cient, 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 coul d be μg/day of consumption, mg/kg of hair, or μg/L of blood, with I 0 being baseline exposure and I t being exposure at time t . For effects for which an exposure threshold, T , exists, if I 0 < T then the total number of cases is zero, unless I t exceeds both I 0 and T . Some epidemiological studies repo rt 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, circum venting the dose-response coefficient entirely. When the latter is the case, the equation appears as follows:

POP RATE RATE C × ∆ + = ∆ ) ( 0 (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 populat ion. In these cases the model utilizes the following linear relationship: ) ( 0 t I I V

− = ∆ β (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 a bove, the model estimates the number of individuals who exceed the assumed advisory guidel ines for Maryland, as well as the number of individuals who exceed EPA’s refe rence dose (RfD). The remainde r of this section provides a brief description of the design of the Mercury He alth 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 me rcury poisoning in adults is pa resthesia, or a prickling, tickling, or itching sensation in the extrem ities. Although the WHO (IPCS 1990) characterization of the dose-response relationship fo r 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 relation ship assumes a 5 percentage point increase in the occurrence of paresthesia above a threshol d of about 200 μg/day. We limit the population at risk for paresthesia in our study to exposed male and female a nglers and male anglers’ wives aged 15 and over, which corresponds to the ag e range of the license d angler population.
5.3.2. Childhood Neuropsychological Development Our primary method of estimating the eff ects of reduced mercury consumption on childhood neuropsychological development is the us e of the benchmark analyses described inResources 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 effect s 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 c hosen 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 pref erred indicator of adve rse neuropsychological effects in children. Within the model, however, all endpoints fo r which benchmark doses were derived in Crump et al. (1998) and Budtz-Jø rgensen et al. (1999) can be es timated. Furthermore, we also allow childhood neuropsychological effects to be estimated using the benchmark derived by NRC (2000) in its integrative an alysis. For each benchmarked test, one can calculate the number of exposed women of childbearing age who exc eed 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 th e 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 in cluded in the model. To facilitate comparison,

47 We chose to quantify these effects using benchmark analys es rather than the original studies based on our notion that reporting the number of children facing an increased ri sk of abnormal scores is more meaningful from a policy standpoint than the average change in indivi dual scores at a given average exposure.Resources for the Future Jakus, McGuinness, and Krupnick

92 benchmark doses for the Faroe Islands are reporte d in mg/kg of maternal hair mercury, though they are in terms of blood c oncentration 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 (p 0 =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). Benchmar k 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, Revise d full-scale IQ score. Kj ellström et al. (1989)Resources for the Future Jakus, McGuinness, and Krupnick

93 estimated a 4.41 reduction in IQ score for an increas e in maternal hair merc ury 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 matern al hair mercury concentration data for the population, the model estimates an average chan ge 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 esti mates for AMI and all-cause mortality are from Salonen et al. (1995). We use thei r risk factor–adjusted estimate s in the model. For AMI, the study estimates an increase in risk of 69% at a th reshold of 2 μg/g methylmercury in hair when adjusting for risk factors, with an additional incr ease in risk of 6.8% for each additional 1 μg/g thereafter. The increase in risk for all-cause mortal ity 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 a pply these coefficients to males aged 40 to
59. 48 Our estimate of the change in mercury-rela ted 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 ag e 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 co rd 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 consumpti on and mercury uptake from the model, the Health Module calculates the number of indi viduals who exceed th e assumed striped bass consumption advisory for the Chesapeake Bay—f our meals per month for the general population and two meals per month for sensitive subpopulati ons—as well as EPA’s RfD of 0.1 μg/kg-day. For the general Maryland advisory, the model es timates the number of male anglers, female anglers, and anglers’ wi ves not of childbearing age who are co nsuming in excess of the advisory. For the advisory for sensitive subpopulations, th e model estimates only the number of women of childbearing age who exceed the recommended gui delines, because we lack data on other

respectively. Furthermore, it should be noted that becaus e AMIs are often fatal (almos t 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, th e model calculates the number of male and female anglers and anglers’ wives exposed to methyl mercury in excess of EPA’s RfD.

5.3.5. Health Effects Valuation The Health Valuation Submodule of the Bene fits Valuation Module relies primarily on estimates of willingness-to-pay (WTP) from rev ealed 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 reduc tion in IQ score due to prenatal exposure, and mortality for men aged 42 to 60. Currently, the Benefits Valuation Modu le assigns a value of $10,420 ($2000) per IQ point lost to a child at age 7, as is reported by Rowe et al. (1 995) 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 concentrati on 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 ove restimate 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 fr om U.S. EPA (1999), which pooled 26 valueof-statistical-life studies to derive a Weibu ll distribution with a mean of $6.37 million (SD = $4.31 million). EPA uses this value in its Sec tion 812 Retrospective and Prospective Studies (U.S. EPA 1997, 1999). Each of these values is gi ven an equal probability weight. These weights result in a weighted mean of approxi mately $3.11 million (SD = $3.37 million).

5.4. Results This section quantifies the cha nges in the health endpoints desc ribed above, as well as the number of individuals who ex ceed 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 methylmerc ury 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 approx imately 14 μg/day, far short of this level. Therefore, we do not predict a ny cases of paraesthesia. At cu rrent fish consumption levels, paraesthesia from methylmercur y uptake remains unlikely, unless fish tissue concentrations increase considerably. However, as was men tioned earlier, criticism of the WHO analysis suggests that more work is needed on the lowe r portion of the dose respon se 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 genera l belief that children are a population at greater risk of developing mercury disease (Rowe et al. 1995). However, to date, epidemiological research on child ren and methylmercury exposure has focused almost entirely on neuropsychological effects. Ther e are currently no dose-response an alyses specific to children for the manifestation of physical effects of met hylmercury disease. Such analysis is difficult because consumption data for children—particula rly 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 consump tion and mercury fish-tissue con centration levels. Our preferred method is to rely upon the Boston Naming Test be nchmark estimates to indicate the presence of abnormal effects. However, our maximum estim ated 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 integr ative benchmark doses. However, the selection of benchmark doses is of little importance to ou r results. No abnormal test scores are predicted either under the NRC integrative BMD assumptio n or when any of the BMDs from the New Zealand benchmark study are assumed. Benchmar k 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 isResources for the Future Jakus, McGuinness, and Krupnick

98 8 mg/kg (BMDL = 4 mg/kg) for the McCarthy Pe rceptual Performance scale from the New Zealand study, which still exceeds the maximu m estimated hair concentration for the Chesapeake Bay population of 3.29 mg/kg. For furt her sensitivity analys is, 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 bl ood 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 Intelligen ce Scale for Children, Revised full-scale IQ and maternal hair mercury concentration in the or iginal 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 th is 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 reporte d by Salonen et al. (1995) (2 mg/kg hair mercury) is lower than those for childhood neurop sychological development and falls within our estimated distribution of hair mercury concentrat ions. As a result, we predict cases of AMI and all-cause mortality, and subsequent reductions unde r 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 mode l. However, because curren t 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 Secti on 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 AM I and mortality, respectively. Over the full distribution, the possibilit y of a negative value occurs because the original coefficients from the study have confidence intervals allowing them to assume sli ghtly negative values. Although these results suggest, on average, an approximately 30% reducti on 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 re garding the strength of this effect. Our confidence in thes e results is even further attenua ted 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 specifi c 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 bor n 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 mo rtality, the large confidence intervals surrounding these estimates prevent us from saying anything conclusive about this effect. Furthermore, given that average systolic and diastolic blood pre ssures 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 ch ildren exposed prenatally.
5.4.4. Uncertainty Pertaining to Cardiovascular and Mortality Results Insight into the uncertainty su rrounding the results of cardiov ascular and mortality effects can be achieved using the importan ce analysis feature in Analytica. For all four endpoints, this analysis suggests the same three predominant so urces of uncertainty. Th ese are, in order ofResources 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 a nd consumption, and the size of the consuming population, respectively. In the m odel all of these variables are pa rticularly uncertain because the number of anglers per trip varies considerab ly, 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 bot h 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, surv ey 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 met hylmercury. Mortality, being the most severe endpoint, would be expected to be the least prevalent; other hea lth endpoints that are insufficiently severe to cause mo rtality would be more frequently observed and associated with lower doses. Along the neurotoxic pathway, for ex ample, 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 averag e based on a review of previous studies.Resources for the Future Jakus, McGuinness, and Krupnick

102 precede mortality should be observed more fr equently. These results suggest, then, that additional endpoints along this cardiovascular path way 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 consump tion advisory and EPA’s RfD for methylmercury based on striped bass consumption. Thes e results are reported in Table 5.7.

Table 5.7. Number of Individuals Exceeding Chesapeake Bay Advisory and EPA’s RfD Guideline Number Exceeding Guideline 1 (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 c onsuming 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 angler s who exceed advisory

52 Because we estimate that on ly about 26% of consuming an glers avert, this result sugg ests 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 adviso ry in this study. Furthermore, according to our model, only about 5% of consum ing anglers, or roughly 3% of all anglers, are consuming in excess of these guidelines before they are annou nced, suggesting that advisory guidelines are likely to be relevant to only a small percen tage 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 targe ting these high-consumption anglers might do more to further compliance than more generalized efforts. Our results suggest that wome n 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 da ta for consumption patterns of males and females, we are restricted to the a ssumption that males and females consume the same percentage of fish from a given trip, whic h 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 so me 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 indivi duals 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, aboutResources for the Future Jakus, McGuinness, and Krupnick

104 5,490 are women of childbearing age. This re presents 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 counter vailing forces. The first is th at 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 childbear ing age exceed the RfD. In comparing their estimates with those from th is study, one must consid er (in addition to the potential sources of error disc ussed above) that the percentage s we report are only for females known to be exposed through their own or their hus bands’ 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 rate s similar to or higher than those from the Chesapeake Bay, incorporating compliance by fema les 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 sc ore from reduced prenatal methylmercury exposure, we are able to value only mortality benefits from the estimated reduction in statisticalResources for the Future Jakus, McGuinness, and Krupnick

105 deaths under an advisory. We quantify mortalit y benefits only for mortality among men aged 40 to 59, though women of the same age range are incl uded in benefits calculat ions 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 conser vative. For middle-aged males, we estimate mean benefits from mortality reduction to be $14.36 m illion (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 analys is of a mercury fish consumption advisory, particularly because Salonen et al . (1995) suggest that these benef its are likely to come at lower concentration levels than some of the other health bene fits estimated in this study, most notably childhood neuropsychological development. Should fu rther research corroborate the existence of the relationship between mercury uptake and chro nic mortality, the health benefits from a mercury consumption advisory may outweigh the recreational and consumer surplus losses, given sufficiently large populations and fish ti ssue 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 warrant ed for the potential cardiovascular and mortality effects. However, because the study on which we ba se these results has not been replicated, thisResources for the Future Jakus, McGuinness, and Krupnick

106 insight is fraught with uncertainty. Given the magnitude of potential benefits, further epidemiological research on this relati onship will be of s ubstantial value.

5.5. Summary This section reviewed the epidemiological literature summarizing the relationship between methylmercury exposure and human hea lth, discussed the quant ification of three broadly defined health endpoints in the Maryla nd Model, and estimated changes in these endpoints and health benefits from a recreati onal mercury fish consumption advisory. Although we estimate no adult central nervous system effect s (as manifested in paresthesia incidences) or childhood neuropsychological development effects, we do estimate small reductions in the occurrence of acute myocardial in farction 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 endpo int suggests that there are other, less severe health effects associated with methylmercur y exposure that remain to be identified and quantified. In general, angler compliance with the advi sory 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 guidelin es 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, ou r estimate of females of childbeari ng age who exceed the EPA RfD forResources 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 millio n). Although estimated health benefits are uncertain, surplus losses are more certain and likel y 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 epidemiologi cal 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 c onducted to determine the magnitude of the effect of various assumptions or restrictions on estimates of changes in welfare. In particular, this section addresses assumptions th at could potentially have a large impact on estimated health benefits. Additionally, this section discusses th e potential for incorporating other policy and behavioral scenarios in to 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 co nsuming anglers are aware of the current advisory. 2. Perfect compliance, such that all mercur y-related health eff ects are eliminated. 3. Averting anglers continue to be expose d to mercury through other sources of fish consumption. 4. The absence of a maternal hair mercur y concentration thresh old 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 es timate of angler awareness for Chesapeake Bay anglers using a Bayesian-weighted mean estima te 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 va ry by location, and the characteristics at the locations examined may differ from those of th e 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 awar eness. An assumption of perfec t awareness implies that state education and outreach efforts are sufficient to en sure that every consumi ng angler is aware of the advisory. The probability that an aware a ngler 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 awar e anglers in our analysis . Because more anglers will avert under this scenario, average methylmercury intake and concentration estimates under an advisory are reduced relativ e to our original analysis, by approximately 14%. We also estimate maximum total potential mortality benef its 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 re duction of excess mortality to men aged 40 to 59, such that mortality benefits under our central assumpti ons increase by approximately 87%. Table 6.1 reports mortality benefits estimates for this scenar io 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 awar eness are approximately 55% of total potential mercury-related mortality benefits, given our original assumpti ons 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 Me rcury from Substitute Sources of Fish We assume in the main analysis that c onsuming 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 site s or by consuming Chesapeake Bay species with negligible mercury concentrati ons. By allowing for a full elimination of mercury exposure from the Chesapeake Bay for averting anglers, this assumption also allows for the possibility thatResources 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, methyl mercury exposure is reduc ed only to the extent that the mercury tissue concentrations of the substitute fish are lower than those of striped bass. Absent data on Chesapeake angler s’ behavior under an advisory , it is difficult to estimate the extent to which anglers might be lim iting their exposure reduction by consuming other contaminated fish. However, one means of addre ssing this potential effect is to assume that averting anglers, in the presence of an a dvisory, replace their methylmercury uptake from Chesapeake Bay striped bass with the average pe r capita daily mercury uptake from canned and other commercial seafood products. This assumpti on 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 a nglers avert, or cease consumption of recreationally caug ht 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 individu als are holding their total fish consumption levels constant. This implies that these averting individuals are still re taining 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 morta lity 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 met hylmercury uptake across the exposed population. Table 6.2 compares benefits estimates from this s cenario with our original benefits and potential benefits estimates. When averting anglers are assumed to substitute canned and other commercial seafood products rather than swit ching to recreationally caught fish from noncontaminated substitute sites, mortality bene fits 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 complia nce 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 w ill 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 mercur y through fish consumption in excess of EPAResources for the Future Jakus, McGuinness, and Krupnick

112 guidelines, compared with 18,000 and 5,490, respec tively, 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 Mary land, about a 1 percentage point increase in both values from our original analysis.

6.3. Removal of the Thr eshold for IQ Effects In this scenario, we eliminate the threshol d for IQ effects from the New Zealand study, which in the main analysis was set at a mate rnal hair mercury concentration of 6 mg/kg. Although Kjellström et al. (1987) did not find a significant re lationship between prenatal methylmercury exposure and IQ score below this le vel, we apply the rela tionship 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 scor e 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 to tal births to exposed females of childbearing age (2,617), and an IQ point is va lued at $10,420 ($2000); th us we estimate total benefits from avoided intelligence loss to be $10.57 million (SD = $9.91 million). Although research currently does not suppor t the changing of this thres hold, 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) st udy is limited to males aged 42 to 60. As a result, in the main analysis, we limit the applica tion 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 ar e lower for females than for males because of the relatively smaller size of the exposed female population a nd the lower baseline risk of AMI for females. However, the inclusion of fe males 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 Alternat ive Assumptions for Recreational Consumer Surplus Loss Estimates of consumer surplus loss due to a r ecreational 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 aw areness via outreach a nd education efforts or changing anglers’ propensity to heed an advisory will change the magnitude of their behavioral response, and thus should affect consumer surp lus 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 si tes. 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 c onsumer 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 Secti on 2, an averting response by consuming anglers may, over time, reduce the per trip consumer surp lus losses borne by catch-and-release anglers: reductions in harvest by consuming anglers ma y increase biomass in the estuary, improving catch rates—and consequently per trip value—for catch-and-release a nglers. This effect will toResources 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 uncer tain. Any change in aver age per trip consumer surplus loss will affect to tal welfare losses under an advisory proportionally.

6.6. Additional Opportuniti es for Sensitivity Analysis Currently, the Maryland Model allows the user to alter several types of input parameters, all of which have been discussed e ither 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 wei ghted mean from literature (default) Perfect awareness Choose mercury exposure scenario Averting angl ers 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 Ma ryland 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 thresholdResources for the Future Jakus, McGuinness, and Krupnick

116 The user can also directly change other input pa rameters or data in the model to facilitate the analysis of different populati ons or fisheries, si mulate a variety of policy or behavioral scenarios, or incorporate new parameter estimat es from the literature. To do so, however, the user must open the edit function in Analytica a nd change the input valu es. An advisory of increased severity, for example, might be modele d by increasing the percen tage reduction in trips taken as well as the percenta ge 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 adviso ry. Although the literature may not provide specific estimates for adjusting these variables, one could postula te reasonable adjustments to our original assumptions. Additionally, modifi cation of relevant demographic variables could allow for the examination of effects on selected or altogether different populations. Fu rther 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 Estim ation from Umbilical Co rd 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 Pollu tant 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 A dvisories 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 th e Savannah River: Possible Intervention. Journal of Toxicology and Environmental Health, Part A 55: 409–19. Burger, J., and M. Gochfeld. 1991. Fishing a Supe rfund Site: Dissonance and Risk Perception of Environmental Hazards by Fi shermen in Puerto Rico. Risk Analysis 11: 269–77. Burger, J., K. Staine, and M. Gochfeld. 1993. Fi shing 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. Environmen tal 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 Adviso ries. Ithaca, NY: Cornell University Human Dimensions Research Unit. Connelly, N.A., B.A. Knuth, and T.L. Brown. 1996. Sportfish Consumpti on 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 Pr enatal Mercury Exposure upon Scholastic and Psychological Test Performance: Benchmar k Analysis of a New Zealand Cohort. Risk Analysis 18: 701–13. Davidson, P.W., et al. 1998. Effects of Prenat al and Postnatal Methylmercury Exposure from Fish Consumption on Neurodevelopment: Outcomes at 66 Months of Age in the Seychelles Child Development Study. Journal of the Americ an 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. Johns on, 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 fo r 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 Separa bility 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 Si ze-Specific Mercury Con centrations 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. Gene va: World Health Organization. Jakus, P.M., and W.D. Shaw. 2002. Perceived H azard 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 Reservoi r Anglers' Site Choice? Agricultural and Resource Economics Review 26: 196–204. Johnson, F.R., and W.H. Desvousges. 1997. Estimati ng Stated Preferences with Rated-Pair Data: Environmental, Health and Employm ent 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. P hysical and Mental Developmen t 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 H ealth 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, a nd 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 Bl ood, Fetal Cord Blood, and Placental Tissues. American Journal of Obstetrics and Gynecology 139: 209–213. Kuntz, W.D., et al. 1982. Mate rnal and Cord Blood Background Me rcury 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. Neurotoxi c Effects of Low-Level Methyl mercury 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 La vaca Bay Recreational Fishing Assessment. Draft for Peer Review. Durham , NC: Triangle Economic Research, October. Mari ё n, K., and G.M. Patrick. 2001. Exposure Analys is 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: Fish ing 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 We lfare Effects of Toxic Contamination in Freshwater Fish. Land Economics 73: 211–23. Mrozek, J.R., and L.O. Taylor. 2002. What Dete rmines the Value of Life? A Meta-Analysis. Journal of Policy Analysis and Management (forthcoming). National Marine Fisherie s 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. Spatia l 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 I ssue Fish Consumption Advisories. In American Fisheries Society Symposium , 98–106. Chattanooga, TN: Amer ican 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 Elect ric 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 fr om Fish, Lipid Peroxidation, and the Risk of Myocardial Infarction and Coronary, Cardiova scular, and Any Death in Eastern Finnish Men. Circulation 91: 645–55. Salvanes, K.G., and D.J. DeVoretz. 1997. Hous ehold 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 C hoice and Purchase Frequency Revisited: An Application to R ecreation Behavior. American Journal of Agricultural Economics 82: 515–26. Sherwell, J., and P. Miller. 2001. Personal co mmunication with the au thors, October 9. Sikorski, R., et al. 1989. The Interpartum Cont ent 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 Cardiovasc ular Risk Factor at Seven Years of Age. Epidemiology 10: 370–75. Stern, A.H., et al. 1996. Estimation of Fish C onsumption 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 Gene ral Demographic Characte ristics for Maryland: 2000. http://www.census.gov/Press-Rel ease/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 a nd 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 Cont aminant Data for Use in Fish Advisories, Volume 2. Washington, DC: EPA-823-B-00-008. ______. 2001a. Mercury Update: Impact on Fish Advisories. Washingt on, DC: EPA-823-F-01- 011. ______. 2001b. Update: National Listing of Fish a nd 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.ht m. Accessed December 12, 2001. ______. 2001e. Water Quality Criterion for the Protec tion 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 provi ded to the user and discussed in this study. Figure A. 1 depicts the top-leve l screen of the model. The Atmospheric Transport Module is not included in our analysis, so this appe ndix focuses on features of the Anglers’ Response, Health E ffects, 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 charact erize 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 depict ed 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 calculate d in submodules within this module: recreational consumer surplu s loss under an advisory, and methylmercury uptake from Chesapeake Bay striped bass both be fore and after an advisory.

Figure A.2. The Recreational Angler Response Module

55 Such choices are often represente d 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 surp lus 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 Respons e Module, the Mercury Uptake Submodule quantifies per capita daily methylmercury exposur e both before and after an advisory using striped bass catch and consumption data, stri ped bass fish tissue mercury concentration estimates, and the estimated behavioral paramete rs. The module converts raw catch into meals, distributes this catch among anglers and their fam ilies, and using fish tissue concentration data, calculates a per person daily mercury uptake and th e 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 Re sponse Module calculates consumer and producer surplus losses under both a ban on commercial fishing and th e issuance of commercial fish consumption advice by the state. The basis for these calculatio ns is parameter estimates from an original supply-and-demand model of the Chesapeake Bay commercial striped ba ss fishery, which are used as inputs to the module. Currently, options are not included in the model for altering theResources 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 m odel with better informa tion. 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 Effect s Module. The user chooses the assumption for determining anglers’ exposure—that is, whethe r 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 Fi gure A.7, combines mercury uptake from fish with other sources of MeHg exposure, na mely inhalation, to determine total exposure. However, the submodule inventories all form s 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 incl uded in the calculation. Mercury from dental amalgams is never included because mercury expos ure from dental amalgams is in the form of elemental mercury. The node labeled Total Huma n Uptake (ALT) reports human uptake for the alternative exposure scenario discussed in Sec tion 6. In the Intake Conversions Submodule,Resources for the Future Jakus, McGuinness, and Krupnick

130 human uptake data is combined with baseline blood mercury concentrat ion levels, and final blood and hair concentra tions are calculated.

Figure A.7. Human Uptake Submodule Figures A.8 and A.9 display the two laye rs of complexity within the Childhood Neuropsychological Development Submodule. The user selects a study to quantify the reduction in abnormal test scores under an advisory. Furt hermore, the user can decide to remove the threshold in calculating average change in IQ score under an a dvisory. 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 SubmoduleResources 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 estimat es 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 exte nt 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 paresthesi a 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 calcul ation, 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