Recreational Fishing and the Benefits of Oyster Reef Restoration in the Chesapeake Bay. Robert L. Hicks College of William and Mary

Transcription

1 Recreational Fishing and the Benefits of Oyster Reef Restoration in the Chesapeake Bay Robert L. Hicks College of William and Mary College of William and Mary Department of Economics Working Paper Number 1 May 2004

2 COLLEGE OF WILLIAM AND MARY DEPARTMENT OF ECONOMICS WORKING PAPER # 1 May 2004 Recreational Fishing and the Benefits of Oyster Reef Restoration in the Chesapeake Bay Abstract In this paper, I use a travel cost model of recreation demand to analyze the economic benefits to the Bay s recreational fishermen from proposed oyster reef restoration programs. The model explicitly links historical oyster bottom conditions to recreational fishing catch to capture ecosystem and habitat benefits. I find that observed catch is indeed higher in areas associated with higher quality reef areas. This relationship enables the estimation of recreational fishing values for improved Bay habitat since fishermen value higher catches and restoration of oyster bottom will lead to higher quality reef areas hence higher catch. It should be noted that our model provides a reduced form relationship between catch, the underlying fish population, and habitat quality; however, recent work show that oyster bottom provide good foraging habitat for a number of species and may therefore act as attractors and perhaps may lead to larger numbers of striped bass in the future. We find that benefits from oyster reef restoration are measurable and can account for a substantial portion of the costs of restoration. JEL Classification: Q260, Q280, Q220, Q510 Keywords: recreational demand, random utility modeling, ecosystem restoration Robert L. Hicks Department of Economics College of William and Mary P.O. Box 8795 Williamsburg, VA Rob.Hicks@wm.edu

3 I. Introduction Population growth and rates of development in coastal areas this century have placed considerable strain, and in many cases caused significant declines, in coastal resources such as wetlands, aquatic habitat, land habitat, water quality, and species diversity. One approach for addressing these problems is to actively restore impaired resources in an effort to recover ecosystem services to some more desirable level. For an economist, the question of the value of restoration and the larger question of how much restoration should occur are central to the understanding of whether society should spend money on restoration in lieu of some other important issue. In many cases, restoration efforts are aimed at resources that may not be directly valued by people. For example, oyster reefs in the Chesapeake Bay provide some direct monetary value to people as a source of seafood protein and in the past for infrastructure development (e.g. shell for road building), yet many of their services (e.g. water filtering and enhanced water quality, habitat for other organisms) provide services to people in ways that may not be expressed in markets. Further, these services may be an additional level of abstraction away from what is directly valued by people. For example, consider ecosystem services associated with oyster reefs in the Chesapeake Bay and how recreational anglers may value those reefs. That is, anglers are more likely to choose sites with higher catch and are more likely to take more trips when they expect to

4 catch more fish 1. How might a restoration effort that enhances fish habitat be valued by recreational fishermen? If recreational fishermen expect that restoration efforts will impact the quality of their fishing experience (via improved catch or water quality) then they may value the effort. Consequently for an analysis of how fishermen may value restoration activities, linking improved bottom conditions to improved trip quality by way of expected catch per trip is a vital step in the analysis of measuring economic benefits. Figure 1. Relating Oyster Bottom Conditions to Tradeoffs Stock Conditions Angler Skill Time Spent Fishing i Bottom Conditions other factors i Historic Fishing Outcomes i Expected Catch i Travel Cost i Expected Catch k Travel Cost k Choose Fishing Site 1 Many papers have examined how people s preferences are related to recreational amenities (Bockstael et al., McConnell et al., Jones and Lupi, Caulkins et al., Kaoru et al., Morey et al.).

5 Figure 1 demonstrates the data requirements for such a model. First, it is important to have spatial data on bottom conditions, the stock, and angler specific data that vary spatially across the Bay for each fishing site (denoted by i and k). This information allows a model to be estimated that relates observed catch on a given fishing trip to all of the factors listed above. The angler must still decide where to go fishing, so he compares expected catch and costs of traveling to other fishing sites and decides where to go fishing. Therefore, data are required for trips taken throughout the Bay, and this allows the relationship between bottom conditions and angler choices to get at the question of what a recreational angler would be willing to give up if oyster restoration in the Bay happens. Most applications of the recreation demand framework have not explored the linkage between what people value (e.g. improved trip quality) with the types of policy interventions commonly employed in resource management (e.g. oyster reef restoration, runoff prevention, or effluent limits). While many methodological issues have been explored within this framework, there has been little research into how the natural system reacts to policy changes and, in turn, delivers benefits to some user group like recreational fishermen 2. In this Chapter, we examine how to make such a linkage explicit and how to the model to value oyster restoration in the Chesapeake Bay. This model can provide guidance into how the restoration process should proceed. 2 For exceptions, see Lipton and Hicks and Kaoru et al.

6 II. Oyster Reefs and the Data As described above, values attributable to oyster reefs in the Bay stem from 1) enhanced fishing experiences and fish habitat and 2) nutrient filtering and improved water quality. The MRFSS intercept survey of recreational anglers, conducted by the National Marine Fisheries Service, offers a unique vehicle for getting data (both biological catch-effort and economic data) in a costeffective manner. Use value (excluding commercial fishermen and final demand consumers) for Bay fishing accrues directly to recreational fishermen. We also used the intercept data collection to obtain information about whether the trip took place on oyster bottom, although we were unable to pinpoint an exact fishing location 3. The intercept portion of the MRFSS survey collects data necessary for the estimation of revealed preference discrete site-choice models. These models directly relate actual site choices made by recreational fishermen to environmental quality factors that might affect the enjoyment of recreational trips. In the case of Bay fishing those environmental factors directly affecting the quality of a fishing trip might include the biological stocks, water quality affecting either those biological stocks or the experience of being on the water, or aquatic habitat that might in turn affect biological stocks or water quality. Given data on costs incurred to go fishing (cost of accessing the fishing site- including travel cost, wear and tear on vehicle, etc.) and on environmental factors, a 3 The MRFSS intercept survey contacts anglers in the field and collects angler-specific information (e.g. home state, county, and zipcode, target species, and mode of fishing. The survey also collects biological catch-effort data on angler catch per trip.

7 model can be estimated that allows for the quantification of economic value attributable to the environmental resource (e.g. Bay oyster reefs). These models measure the trade-offs recreational anglers are observed making when choosing to travel longer distances to access better quality fishing sites. We focus our study on a complex of species sought by recreational anglers over oyster reefs: seatrout/weakfish, striped bass, bluefish, croaker, and spot. We treat these species as interchangeable and do not make a distinction in our modeling across species. Appendix A contains the questionnaires for the intercepted recreational anglers. The MRFSS survey contacted over 7000 anglers in the field for the states of Maryland and Virginia during the calendar year 2001 (sampling is not done during January and February). Of these, roughly 60% agreed to participate in the intercept follow-up. The survey allowed us to collect a random sample of trips for Chesapeake Bay anglers. These data make estimation of the site choice model presented below possible.

8 Figure 2. Oyster Bottom Conditions in Virginia Figures 2 and 3 show the oyster bottom data for the states of Maryland and Virginia. Notice that there is a definite difference across states in data quality. The Virginia data, dating from the middle 1980 s is not as spatially refined as the Maryland data. The Maryland data also has more information about the type of reef. For both states, we include any type of hard bottom, which may include rock, cultch, or three dimensional reefs as suitable oyster bottom likely to influence recreational catch.

9 Figure 3. Oyster Bottom Conditions in Maryland A challenge associated with implementing our model is combining the spatial database of recreational fishing with the spatial oyster reef data obtained from the states of Maryland and Virginia. The MRFSS data on fishing tells us where someone launched from (if boating) but we don t know exactly where on the water that person fished. What oyster bottom conditions are operational for these fishing sites? In the bay it is common for vessels to travel some miles on the water to reach the day s fishing site. At the same time, the ability of boaters to access all fishing sites is limited by time and costs associated with steaming. For this reason, we assign the effective bottom conditions using a distance-based criterion. Consider a fishing site in the Bay. First, we overlay the point data for sites over the oyster bottom data. We then find all oyster bottom polygons within a

10 radius of eight miles. Areas (acres) of oyster bottom are then summed and assigned to each site. All computations were performed using ArcView GIS. The coverage we used to delineate oyster bottom is less than ideal for our purposes, since it does not distinguish between hard and three dimensional bottoms. Consequently, the empirical results are limited to valuing general hard bottom creation and not the more specific value of three dimensional reef creation. III. The Model Consider a recreational angler deciding where to fish in the Chesapeake Bay. In Figure 1, it was argued that the angler would compare the expected catch and travel costs at each site and then decide where to fish. The random utility model (RUM) has been widely used to examine observed choices like thiswhere decisionmakers must choose one of several discrete alternatives. The RUM helps illuminate how anglers make trade-offs between travel costs of going fishing and fishing quality at competing fishing sites. It is well known that the RUM offers a convenient way to measure welfare changes from environmental quality changes (Bockstael, Hanemann, and Strand). An individual i is assumed to maximize utility by selecting one site from S i possible alternatives, where the set S i can be different for each individual. Let the individual s indirect utility function for alternative j be represented by (1) U ( q, y p, ε ) = V ( q, y p ) + ε. j j j j j j j j

11 wherev ( q, y p ) represents the observable portion of the individual s indirect j j j utility function (with vector of quality characteristics q j, income y, and price of access to the i th site p j ) and an error, ε j, which is assumed to be distributed as a Type-1 extreme value distribution and arises from unobservable factors (to the researcher). For the results presented in this document, the specification of V(.) for each site is given by (2) Vj,m = α1 * TCOSTj + α2m ECR j,m + α3lnm j where TCOST is the travel cost to site j 4. The other variables in Equation (2) are the square root of the expected catch rate, which is mode of fishing (m) and site specific (j), and the log of the number of sites in each zone (LNM) 5. Further, we allow the parameter estimated on the expected catch rate to differ by mode. For a given recreation fishing choice occasion, the individual will choose j if (2) V (q, y p ) + ε V (q, y p ) + ε, j S, k S i. j j j j k k k From the researcher s perspective, the probability that individual i chooses alternative j can be written: k i (3) k S i V j (q j,y p j ) e P i (j) =. Vk (qk,y pk ) e 4 TCOST is defined as (roundtrip distance x $.32/per mile) + (roundtrip time spent traveling) x 1/3 wage rate, where the wage rate is computed from the mean wage rate from our sample. Roundtrip distance is calculated using the program PC Miler and is calculated based on zipcode centroids. 5 Historic catch rates were computed for each MRFSS intercept site in the Bay region with exceptions. For sites in some tributaries sites were grouped. Following Ben-Akiva and Lerman, we control for the bias that this type of aggregation might introduce on our estimates by controlling for aggregation size.

12 The above model can be used to measure changes in an individual s economic welfare from a policy change. Define WTP as the amount of money an individual is willing to pay so that he is as well off after a policy change as before. Assuming that the specification of the observable portion of the indirect function is linear in income, Hanemann (1982) shows that the WTP of an environmental improvement from q 0 to q 1 can be written as (4) ln WTP = k S i e Vk 1 ( ) 0 q,pk Vk ( qk,pk ) k / ln e k S i α 1 where α 1 is the marginal utility of income. For the problem of recreational fishing, the environmental quality at site j, q j, is related to the quality of the fishing experience the angler is likely to encounter at site j. Results from Breitburg as well as analysis of charter and party trips in the Chesapeake Bay reveal that oyster reefs attract a large number of bait fish which in turn attract significant numbers of highly sought species (i.e., striped bass, blue fish, weakfish, spot, and croaker). Similarly, recreational charter and private boats can be seen congregating over hard oyster bottom or oyster reefs in great numbers in an effort to land the above mentioned fish. The challenge is to relate the quality component of the trip, expected catch, with the underlying environmental conditions at the various sites in the Bay (in this case, oyster bottom).

13 Like Lipton and Hicks, we link a policy-relevant environmental factor to expected catch. We extend the, McConnell et al. (1995) model of expected catch and link the objective measures of the quality of a recreational experience (physical bottom conditions) with subjective expectations of the angler (expected catch). Freeman (1995) argues that in recreational fishing,...the links between policy and the attributes of the activity that people value (catch rate) have not been established. By making these links explicit, we address Freeman s concern and provide the pathway necessary to compare the benefits of oyster restoration with the costs of undertaking the project. Following the McConnell et al. work, we model the catch that fisherman i is likely to receive at site j by relating observed catch (Q ij ) to fisherman specific characteristics (e.g. experience (YRSF i ), log of hours fished (HRSF i )), factors associated with their chosen fishing site (the historic catch per trip at that site (CR j ) the area (measured in 1000 acres) of hard oyster bottom at site j (BOTTOM j ), and whether the angler acknowledged fishing over a reef on that day (OYSTER i )): (5) Q ij = f(yrsf i, HRSF i, CRj, BOTTOM j, OYSTER i ) By estimating this function, f(.), we can quantify how angler skill and physical factors associated with the site both contribute to the quality of the fishing trip. Since the physical factors vary from site to site, we can then investigate how choices and more importantly, preferences relate indirectly to the resource of interest, oyster bottom. Notice, that this specification allows us to investigate

14 how the spatial distribution of oyster bottom influences observable recreational catches in the Bay. In this paper, we model equation (5) using a Poisson regression for each mode 6. Consequently, we can use the estimated parameters (denoted by the hat symbol) to calculate expected catch for mode m and site j for each angler as (6) ECR i j,m = exp(βˆ + βˆ + βˆ 3m 4m 0m + βˆ BOTTOM YRSF + βˆ i 1m CR j 5m j,m + βˆ * OYSTER * d HRSF i 2m * d BOTTOM m= PR m= SH,PR ) j The term d m=pr is equal to one if the observation was observed in the private rental mode, and d m=sh,pr is equal to one if the observation was observed in either the shore or private rental mode. In equation (6), we expect that more experienced anglers will catch more fish, and that higher historic catch patterns for a zone will lead to higher actual catches for a given angler. Additionally, we expect that the bottom conditions will lead to higher catch. Further, if anglers report fishing over oyster bottom, we expect the linkage of bottom conditions to actual catch to be even stronger. IV. Results Recall the aim of this research is to analyze some of the benefits and costs of oyster restoration and preserve creation. In this chapter we have restricted our attention to benefits accruing to recreational anglers from oyster restoration. 6 We found that the negative binomial model, which relaxes the Poisson restriction that the mean and variance of the error process are equal, to be the preferred model. However, a closer inspection of the predictive accuracy of the two models reveals the Poisson to be a better predictor of expected catch. Parameter signs and significance did not differ appreciably across the models.

15 The travel cost model of recreational demand relates the anglers tradeoffs relating to travel cost and site quality. The angler might be willing to spend more on a trip by traveling further distances if the quality of the fishing site is a sufficient level of quality. Linking the bottom condition (hard oyster bottom) to site quality (fishing success) is an important step in attempting to value restoration projects that might impact bottom conditions. Table 2 presents the regression results for the Poisson expected catch model. As expected, anglers with more experience (YRSF) and who spend more time on the water (HRSF) tend to catch more striped bass, bluefish, croaker, spot, and weakfish. The exception was for Party/Charter fishing. For this mode, we found that time spent on the water was not a significant positive predictor of catch. Also, sites with high historic catch rates per trip (CR) tend to lead to higher catches of fish. Those sites associated with relatively high concentrations of oyster bottom (BOTTOM) tend to lead to higher catches of fish even beyond what would be expected based upon historical averages. Further, for Private/Rental boat fishing, we found that anglers who knew they were fishing over oyster bottom received even more of a boost for the oyster bottom conditions. This indicates that the use of historic catch, while capturing much of the site-specific variation in actual catch, does not capture all of it.

16 Table 2. Poisson Expected Catch Model. Parameter Estimates (95% CI in Parenthesis) Parameter Party Private Shore Charter Rental Intercept (1.2450,1.4979) (.1255,.2846) ( , ) CR.0339 (.0286,.0393).1160 (.1092,.1228).1679 (.1481,.1877) CR*BOTTOM*OYSTER N/A.0018 N/A (.0015,.0021) BOTTOM.0105 (.0074,.0135).0075 (.0061,.0089).0249 (.0058,.0183) HRSF N/A.4859 (.4441,.5276) (.9710,1.2760) YRSF.0085 (.0055,.0114).0078 (.0066,.0089).0085 (.0041,.0129) N=298 χ 2 ~12.46 N=2137 χ 2 ~12.49 N=456 χ 2 ~10.88 Environmental factors are also an important determinant of catch. It is difficult to know the relative importance of the variables in the expected catch model given our formulation of the relationship between knowledge of reefs, historic catch, and acres of oyster bottom. In Table 3, we show the sensitivity of expected catch to changes in each of the variables in the model. The numbers in the Table can be interpreted as elasticities, or the percentage change in expected catch with a percentage change in the explanatory variable. For example, historic catch across all of the modes has an elasticity of over 1%. Therefore, we would expect a greater than 1% increase in an angler s expected catch per trip with a 1% change in historic catch per trip. Comparing the magnitudes of the percentages in the Table, shows that expected catch is most responsive to historic catch, but is

17 also responsive to oyster acreage and in particular, knowledge about that acreage for the Private/Rental modes. More fishing experience and time spent fishing also increase expected catch, but at a percentage less than one with the exception of shore fishing. This means that a 1% increase in time spent fishing, on average leads to less than a 1% increase in expected catch. It is useful to note the relative magnitudes of oyster bottom conditions and historic catch. Table 3. Expected Catch Elasticity Estimates (95% Confidence Intervals in Parenthesis). CR BOTTOM YRSF HRSF OYSTER 3.38% (2.86,3.92) Party/Charter Mode 1.05%.85% (.76,1.38) (.54,1.13) N/A N/A 12.16% (11.51,12.77).90% (.76,1.02) Private/Rental Boat Mode.78% (.66,.89).49% (.45,.53) 10.20% (8.61,11.86) 16.78% (14.78,18.76) 2.49% (1.85,3.17) Shore Mode.85% (.41,1.28) 1.12% (.97,1.28) N/A The random utility model of site choice results are presented in Table 4. Anglers, regardless of mode, are less likely to choose sites with higher travel costs holding all other factors constant. Depending on the observed mode of fishing (Shore, Party/Charter, or Private Boat), we estimate separate coefficients on the expected catch per trip term. For all modes, higher expected catches lead to an increased probability of a site being chosen. The most responsive

18 coefficient was associated with Party/Charter fishing followed by Private/Rental, and then Shore fishing. Table 4. The Site Choice Model Parameter Estimate Parameter (95% Confidence Interval) TCOST (-.021,-.023) ECR PC (Party Charter) 4.62 (4.23,4.99) ECR PR (Private Boat) 1.38 (1.27,1.49) ECR SH (Shore) 1.13 (.86,1.40) LNM 1.00 (.95,1.04) N=2891 Likelihood Ratio~χ Having established a relationship between oyster bottom and angler s preferences for fishing, we can examine their willingness to pay for improvements in habitat improvements through oyster restoration projects. We are valuing a particular restoration project- if actual restoration acreages or specific locations are different, it is not appropriate to scale our results to obtain an estimate of WTP. The project we are envisioning is as follows: 73 reef sites are created in and around the mouths of medium to large tributaries in the Bay for a total of 1890 restored acres. The total cost of this project is $14,800 per acre or $27.97 million dollars.

19 In Table 5, we present estimates for anglers WTP for oyster restoration under two scenarios. Using the estimated relationships implied by Equations (4) and (6) we calculate the WTP for the policies. We first consider changes in the historical catch levels to see if the model is yielding plausible results. We calculate WTP for a +1% and +25% change in catch rates at all sites. We note that these values are within reasonable ranges compared with other studies (e.g. McConnell and Strand; McConnell et al.). Table 5. Willingness to Pay per Trip for Oyster Restoration Program (95% Confidence Intervals in Parenthesis). Party/Charter Private/Rental Shore Oyster Restoration: No Increased Catch per Trip (Scenario 1) $0.31 (.21,.43) $0.13 (.11,.15) $0.08 (.05,.13) Oyster Restoration: Increased Historic Catch per Trip (Scenario 2) $0.79 (.57,.1.03) $1.32 (1.12,1.55) $0.20 (.13,.28) +1% Change in Historic Catch per Trip $0.47 $1.19 $0.11 (.31,.67) (1.00,1.41) (.07,.16) +25% Change in Historic Catch per Trip $14.16 (9.42,19.82) $35.98 (30.16,42.18) $3.22 (2.06,4.72) In Table 5, we also present estimates for angler WTP for the restoration project. In Scenario 1, we assume that the reef restoration will enhance catch by increasing bottom conditions but will have no underlying impact on the stock dynamics of the system (that is, historic catch per trip does not change) 7. In this 7 That is historic catch per trip is assumed roughly proportional to the stock of fish at a site up to some constant which is the catchability coefficient.

20 scenario, we see that anglers have a small but significantly different WTP for all modes. Party/Charter anglers are willing to pay the most ($.31 per trip) followed by Private Boat ($.13) and shore anglers ($.08). It should be noted that in our model, anglers only value oyster bottom as it increases their catch per trip. Consequently, restoration with no impact on the stock yields small WTP, since the size of the restored reefs is small relative to the quantity of hard bottom in the Bay. Even these low values, however, add up in aggregate (Table 6). We assume that anglers would not change the number of trips relative to 2003 levels (Number of trips were obtained from the NOAA Fisheries, Marine Recreational Fisheries Statistics Survey). We believe this is a conservative assumption, since it is likely that improved Bay conditions would lead to more trips by recreational anglers. In total, we estimate that anglers would be willing to pay nearly $640,000 per year for the restoration project we analyzed. Assuming that the services and benefits produced by the restored reef would continue for the next thirty years, we estimate that benefits to the recreational anglers alone account for roughly 50% of the total cost of the restoration project within thirty years 8,9. 8 In this chapter we ignore the uncertainties associated with restoration efforts. 9 Assuming a discount rate of 3%.

21 Table 6. Total Yearly Fishing Trips and WTP for the Oyster Restoration Project in the Chesapeake Bay (2003). Scenario 1 Scenario 2 Mode Trips WTP per Trip Total WTP WTP per Trip Total WTP Shore 1,801,742 $0.08 $131,386 $0.20 $328,465 Private Rental 3,688,236 $0.13 $458,132 $1.32 $4,651,803 Party Charter 171,360 $0.31 $48,741 $0.79 $124,211 Totals $638,259 $5,104,478 Unfortunately there is a paucity of evidence regarding the long-term benefits of oyster reef creation. Many believe it could provide spillover benefits directly to stocks of fish in addition to providing a good place to fish. In scenario 2, we demonstrate anglers WTP for the project if stocks increase, thereby increasing historic catch per trip. We stress that the estimates given for Scenario 2 are highly speculative and not based on any biological evidence linking increases in stock size with oyster restoration. In Scenario 2, the benefits from restoration are significantly higher, because the reefs are assumed to positively impact fish stocks. Here, private boat fishermen have the highest WTP per Trip ($1.32) followed by party charter and shore fishermen. Under this scenario, fishermen WTP could cover the full cost of the restoration project in less than five years time.

22 V. Conclusion In this Chapter, we have demonstrated a clear link between the quantities of fish caught per trip in the Bay by recreational anglers to oyster bottom conditions. We used these findings to predict how oyster bottom improvements might lead to more fish caught per trip. We then estimate whether fishermen are willing to trade off fishing costs for fishing quality- what the fisherman expects to catch per trip. Our model allows us to estimate what Bay anglers are willing to pay for a restoration program. In our model, Bay anglers only value oyster bottom if it increases their expected catch. We do not include any other ways that bottom conditions might benefit the fishing trip such as an improvement in water clarity. In the results, we do demonstrate that angler willingness to pay could cover a sizable portion of the cost of the program ($27 million). Given that we provide a conservative estimate of WTP, our results show that the recreational angler group is likely to be a strong supporter of the restoration program.

23 References Ben-Akiva, M. and S. Lerman. Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Bockstael, N. E., K. E. McConnell, and I. E. Strand Measuring the benefits of improvements in water quality: The Chesapeake Bay. Marine Resource Economics 6(1):1-18. Breitburg, Denise. Are three dimensional structure and healthy oyster populations the keys to an interesting and important fish community?, in Oyster Reef Habitat Restoration: A Synopsis and Synthesis of Approaches, edited by Mark Luckenbach, Roger Mann, and Jim Wesson. Proceedings for Symposium, Caulkins, P., R. Bishop and N. Bouwes The Travel Cost Model for Lake Recreation: A Comparison of Two Methods for Incorporating Site Quality and Substitution Effects. American Journal Of Agricultural Economics 68 (2): Freeman, R The benefits of water quality improvements for marine recreation: A review of the empirical evidence. Marine Resource Economics 10(4): Haab, Timothy and Kenneth McConnell. Valuing Environmental and Natural Resources Edward Elgar Publishing, Hanemann, W. M Applied welfare analysis with qualitative response models. California Agricultural Experiment Station Working Paper No University of California, Berkeley, California. Hargis, W. and D. Haven. Chesapeake Bay Oyster Reefs, Their Importance, Destruction, and Guidelines for Restoring Them, in Oyster Reef Habitat Restoration: A Synopsis and Synthesis of Approaches, edited by Mark Luckenbach, Roger Mann, and Jim Wesson. Proceedings for Symposium, Henderson, Jim and Jean O Neal. Economic Values Associated with Construction of Oyster Reefs by the Corps of Engineers. U.S. Army

24 Corps of Engineers, technical note, ERDC TN-EMRRP-ER-01, September Jones, C., and F. Lupi The Effect of Modeling Substitute Activities on Recreational Benefit Estimates: Is More Better? Working Paper, NOAA/Damage Assessment Center. Kaoru Y., V. K. Smith, and J. L. Liu Using random utility models to estimate the recreational value of estuarine resources. American Journal of Agricultural Economics, 77(1): McConnell, K. E., I. E. Strand, and L. Blake-Hedges Random utility models of recreational fishing: Catching fish using a Poisson process. Marine Resource Economics 10(3): Morey, E. R., R. D. Rowe, and M. Watson A repeated nested-logit model of Atlantic salmon fishing. American Journal of Agricultural Economics 75(3): Virginia Oyster Reef Heritage Program Fact Sheet available on-line at