Carrying out the travel cost analysis requires data on participation rates, cost of travel to sites, and site quality. Data on participation rates is collected in the recreational survey. Data on travel costs to the target and substitute sites, including the opportunity cost of travel time, are collected from the survey and augmented, as necessary, with distance information from road maps. Data on water quality comes from two sources. First, we use field measurements of various water quality parameters from the SCDHS PES water sampling program, including nitrogen, coliform counts, water clarity ( Secchi disk measurements) and Brown Tide cell counts. These are objective measures of quality. Second, we use data from the recreation survey which asked respondents their subjective evaluation of water quality ( excellent, good, fair or poor). These evaluations of course are subjective.

For swimming, our analysis combines the subjective assessments by individuals and the objective field measurements of water quality. Ultimately, we are interested in whether we can provide sequential linkages between:

( 1) management actions that effect water quality parameters, ( 2) the resultant perceptions of water quality, and ( 3) participation in specific recreation activities.

If we can establish such a linkage we can evaluate water quality policy alternatives. 10 To attempt to do so, we relate participation to subjective ratings of water quality, as indicated in the recreational survey, and in turn, we relate these subjective water quality ratings to field measurements of water quality. These are: water clarity, nitrogen concentration, coliform counts, and Brown Tide cell counts for each location and time period. This allows us to estimate the recreational benefits associated with water quality improvements resulting from management options.

The annual benefits of a policy improving water quality has two parts. One is that individuals will enjoy the swimming experience more; the other is that they will make more trips. This is the basis of our approach for estimating the economic benefits of water quality improvements in the PES for swimming.

For example, consider an assessment of proposed improvements in the Riverhead sewage treatment plant. Upgrading the sewage treatment plant will result in improvements in water clarity, reductions in coliform counts, etc., with the largest effect in Flanders Bay and progressively small changes in bays to the east. Our model allows us to predict how these changes in water quality 59 parameters effect the subjective assessment of water quality by recreational users, and the resultant changes in participation rates and in recreational values at various sites. Thus, we can identify benefits to recreational swimming that result from management options that improve water quality.

In order to implement this methodology, we will coordinate our assessments with ongoing water quality modeling efforts in the PES. Thus, we anticipate that water quality modeling will be used to project changes in quality parameters from prospective management policies. Our recreational modeling will then be used to estimate recreational benefits resulting from these changes in quality.

Similarly for recreational fishing, quality of the experience depends upon catch rates, which in turn depend upon fish populations. Our recreational fishing model includes catch rates as a predictor of participation rates and recreational values. Thus, if predictions can be made concerning the effect of management actions on fish populations, we could use our recreational model to estimate resultant benefits to recreational anglers. However, it may not be feasible for natural scientists to predict changes if salt water fish populations that would likely result from management actions. Nevertheless, the opportunity exists for estimating recreational fishing benefits from management actions, if resultant changes in populations can be estimated. Even in cases where precise predictions are not possible, it may be possible to provide possible ranges of population changes, which will enable us to provide order of magnitude estimates of possible benefits.

The economic benefits from improving ( or maintaining) water quality could be long run benefits, if the policy is sustained. To recognize the long run benefits, we sum the annual benefits over time, discounting them as appropriate to get an equivalent value today the asset value of the resource in providing services supporting that activity.

IV. L. ESTIMATES OF ECONOMIC VALUE: RESULTS IV. L. 1. Value ( Consumer Surplus) Per Trip The methodology described above yields a consumer surplus estimate per person, per trip ranging from $ 8.59 for swimming to $ 49.83 for non residential bird watching and wildlife viewing ( Table IV. 17). Fishing and boating values per trip fall within that range at $ 40.25 and $ 19.23, respectively. These estimates of consumer surplus are the average value individuals receive per trip over and above the cost of their recreational trip. Looked at another way, it is the unpaid for benefit that individuals receive, on average, from a recreational trip. The relative sizes of these values are consistent with the literature ( e. g. Walsh et al., 1988; Freeman, 1995).

IV. L. 2. Total Annual Economic Value for All Trips Total annual benefits from each of the four recreational activities studied are given in Table IV. 17. The total benefits are estimated by multiplying the average consumer surplus for an activity by the estimated total number of trips to engage in that activity during the year ( 1995). Viewing of Birds and Wildlife is the most valued of the activities studied ($ 27.3 million). Of the water based 60 activities, recreational fishing is the most highly valued ($ 23.7 million).

We emphasize that total values rarely are useful for policy analyses since most policies involve small changes in an activity or its quality and are not " all or nothing" choices. Nevertheless, we recognize that information on total values, such as that given in Table IV. 17 helps put the scale of these activities in some perspective.

Table IV. 17 Total Annual Value of Recreational Activities at the PES ( Baseline Water Quality)

Swimming Boating* Recreational Fishing Bird Watching& Wildlife Viewing Total Trips/ Year 1,409,970 937,387 588,493 547,317 Consumer Surplus Per Trip $ 8.59 $ 19.23 $ 40.25 $ 49.83 Total Annual Consumer Surplus $ 12,113,216 $ 18,025,952 $ 23,685,985 $ 27,272,806 * Excludes boating trips taken primarily for fishing IV. L. 3. Asset Value of PES for Key Recreation Activities Using the results given above, the asset value of the PES for providing each of the recreational activities (" services") can be estimated. We use a discount rate of 7 percent and time horizon of 25 years and assume that the estimated values remain the same over the 25 year period. Using these assumptions, and our annual estimates from the above table, the asset values range from $ 318 million for Bird Watching and Wildlife Viewing to$ 141 million for Swimming. The PES has an asset value of $ 276 million for Recreational Fishing and $ 210 million for Boating.

IV. L. 4. Change in Swimming Trips and Benefits Due to Hypothetical Water Quality Changes To illustrate how the economic benefits estimated in this report can be used to contribute to estuary management policy, consider a hypothetical policy or set of policies that improves all water quality measures by 10 percent in all five PES Bays. That is, the policy reduces field measurements of Nitrogen, Total Coli, Brown Tide cell counts by 10 percent and increases Secchi depth by 10 percent throughout the PES. Note that since water quality measures differ among individual bays ( and indeed within Bays), the absolute water quality changes due to a 10 percent 11Subsequent research by EAI will address benefit cost analysis of proposed water quality actions. 12Recall, the recreational model for swimming used objective data from SCDH water quality sampling. No data are available outside the range of these field measurements. Obviously, a major deterioration of water quality would have enormous adverse impacts on recreation and could even drive activity at some locations to zero if serious enough ( e. g., beach or fishing closures).

61 improvement will also differ among and within PES Bays. Only benefits are considered; the costs of achieving the water quality improvements are not taken into account in this report11.

Note that EAI's research on recreational activity, we found that only swimming was affected by the five water quality estimates available, within the range of data available12 for these variables. We also found that recreational fishing was affected by the catch rate and by Brown Tide cell counts. The hypothetical water quality policy illustrated here is restricted to the effects on swimming in the PES; a hypothetical policy affecting recreational fishing is described below.

The hypothetical uniform 10 % improvement for all quality measures for all Bays increases the number of yearly swimming trips in the PES as a whole by about 151 thousand and has annual benefits of $ 1.3 million dollars ( Table IV. 18). This is an increase of about 11 percent in use and in benefits. If maintained for 25 years, this improvement has a present value of $ 15.1 million, using a discount rate of 7 percent. This number represents the change in the asset value of the PES for swimming due to the quality improvement, all else remaining the same. Thus, a policy ( or set of policies) that created and maintained such an improvement water quality would be a good investment of scarce resources if the present value of these costs was less than $ 15.1 million.

Most of the incremental benefit to swimmers ($ 752 thousand of the $ 1.3 million annual benefit) is due to increased water clarity ( Secchi depth). The lowest benefit is due to the 10 percent improvement in Coli counts ($ 81 thousand per year). Among the five PES Bays, the 10 percent uniform water quality improvement has the highest incremental benefit for those who swim in Shelter Island Sound ( an annual increase in swimming benefits of $ 312 thousand). 62 Table IV. 18 Benefits to Swimmers of 10% Improvements in Each Water Quality Indicator at Each PES Water Body PES Water Body Total Kjeldahl Nitrogen Total Colifrm Brown Tide Cell Counts Secchi Disk Depth Water Body Total Flanders Bay $ 65,278 7,598 $ 71,310 8,300 $ 14,424 1,679 $ 125,753 14,637 $ 276,766 32,215 Great Peconic Bay $ 24,801 2,887 $ 3,522 410 $ 48,095 5,598 $ 126,362 14,708 $ 202,779 23,603 Little Peconic Bay $ 20,584 2,396 $ 1,140 133 $ 70,207 8,172 $ 139,929 16,288 $ 231,860 26,988 Shelter Is. Sd. $ 22,598 2,630 $ 4,553 530 $ 109,790 12,779 $ 175,093 20,381 $ 312,033 36,320 Gardiners Bay $ 14,138 1,646 $ 129 15 $ 76,863 8,947 $ 185,286 21,567 $ 276,416 32,174 PES Total $ 147,399 17,156 $ 80,653 9,387 $ 319,378 37,175 $ 752,423 87,581 $ 1,299,854 151,299 Italics indicate number of trips A hypothetical uniform 20 % improvement in water quality increases the number of trips by 169 thousand and annual swimming benefits by $ 2.6 million ( Table IV. 19 Again most of the increase in benefits ($ 1.46 million) is attributable to greater water clarity. However, benefits due to reduced Brown Tide cell count ( about $ 652 thousand) also are important.

In policy analyses, it is important to examine the incremental benefits of policies in order to know what the public gets from stricter policies and the higher costs that they entail. The annual, incremental swimming benefits of going from an initial 10 percent improvement in water quality ($ 1.299 million) are somewhat larger than the added benefits ($ 1.264 million) of the second 10 percent water quality improvement ( i. e. going from 10 percent to 20 improvement). The issue of incremental benefits from alternative management actions that affect different PES bays differently will be addressed more comprehensively in Phase III economic studies. 63 Table IV. 19. Benefits of 20% Improvements in Each Water Quality Indicator at Each PES Water Body PES Water Body Total Kjeldahl Nitrogen Total Colifrm Brown Tide Cell Counts Secchi Disk Depth Water Body Total Flanders Bay $ 130,030 15,135 $ 141,908 16,518 $ 28,888 3,362 $ 253,807 29,543 $ 554,633 64,558 Great Peconic Bay $ 48,869 5,688 $ 6,200 722 $ 95,937 11,1678 $ 249,204 29,152 $ 400,201 46,729 Little Peconic Bay $ 41,319 4,809 $ 2,510 292 $ 139,892 16,283 $ 279,957 32,262 $ 463,678 53,647 Shelter Is. Sd. $ 44,207 5,145 $ 7,907 920 $ 235,633 27,427 $ 315,939 36,775 $ 603,685 70,268 Gardiners Bay $ 28,555 3,324 $ 5567 648 $ 151,556 17,641 $ 356,181 41,459 $ 541,859 63,071 PES Total $ 292,979 34,102 $ 164,653 19,100 $ 651,905 75,881 $ 1,455,088 169,191 $ 2,564,065 298,273 Italics indicate number of trips IV. L. 5. Change in Fishing Trips and Benefits Due to Hypothetical 10 Percent Change in Catch Rate We also simulate the effects of an assumed policy that leads to a 10 percent increase in the recreational fishing catch rate. The 10 percent increase in catch rates raises the benefit ( Consumer Surplus) per trip by $ 0.80, increases the number of recreation trips by 11,249, and boosts total annual benefits by $ 472,359. The present value of this increase in catch rates the increase in the asset value of the PES in providing this service is $ 5.5 million, using the 7 percent discount rate and time horizon of 25 years used for all cases. 64 Table IV. 20. Benefits of Improving Expected Recreational Catch Rate by 10 Percent Baseline 10 Percent Increase in Expected Catch Rate Incremental Benefits Number of Fishing Trips 588,493 599,742 11,249 Consumer Surplus/ Trip $ 40.25 $ 41.05 $ 0.80 Total Consumer Surplus $ 23,685,985 $ 24,158,344 $ 472,359 We re emphasize that the results presented here are for benefits only; the cost of water quality improvements, or of improvements that might increase recreational fishing catch rates, have not been considered. Thus, we do not know the net benefits of potential management actions. Later Benefit Cost analyses to be done by EAI of proposed program actions will address these issues.

IV. M. REFERENCES Freeman. A. Myrick III, 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Washington, D. C.: Resources for the Future.

Grigalunas, Thomas A. and Jerry Diamintides, 1996. " The Peconic Estuary System: Perspective on Uses, Sectors and Economic Impacts". Peacedale, RI: Economic Analysis, Inc.

McConnell, Kenneth and Ivar Strand, 1996. " Marine Recreational Sportfishing". College Park: University of Maryland.

Walsh, Richard G., Donn M. Johnson, and John R. McKean, 1988. " Review of Outdoor Recreation Economic Demand Studies with Nonmarket Benefit Estimates, 1968 1988". Fort Collins, CO: Water Resources Center, Colorado State University. 65 Supplementary Tables and Appendices Supplementary Tables Table S. 1 East End Resident Respondent Distribution by Town Town Number of Respondents Percentage of All Respondents Percentage of Yearround Population a East Hampton 50 14% 15%

Riverhead 42 12% 22%

Shelter Island 57 16% 2%

Southampton 107 30% 43%

Southold 102 28% 19%

Totals 358 100% 100%* * adjusted to account for rounding a Source: Long Island Regional Planning Board Table S. 2 Second Home Owner Primary Residence Area Number of Respondents Percentage of All SHO Respondents New York City 72 45%

Nassau County 30 19%

Suffolk County 22 14%

Other Areas 1 35 22%

Totals 159 100% 1Includes Westchester and Rockland Counties in New York and parts of Northeastern New Jersey. 66 Table S. 3 Second Home Owner Distribution by Town Number of Respondents Percentage of all Respondents Percentage of Seasonal Home Population a East Hampton 29 22% 29%

Riverhead 6 5% 5%

Shelter Island 17 13% 3%

Southampton 39 29% 49%

Southold 42 32% 14%

Totals 133 100%* 100%* * adjusted for rounding a Source: Long Island Regional Planning Board Table S. 4 Primary Residency of Visitors to the East End Area Number of Respondents Percentage of All Respondents New York City 146 19%

Nassau County 118 16%

Suffolk County 300 40%

Other New York State and New Jersey 63 8%

Connecticut and Massachusetts 39 5%

Other 84 11%

Totals 750 100%* * adjusted for rounding 67 Table S. 5 Accommodations for Overnight Visitors Accommodation Number of Respondents Percentage of All Overnight Visitor Respondents Hotel or Motel 95 22%

Rental 101 24%

Friends or Relatives 168 39%

Other 63 15%

Totals 427 100%* * Adjusted for rounding Table S. 6 Overall Participationa in Key Recreation Activities  All Respondents ( n= 1354)

Number of Participants Percentage of all Respondents Any Activity* 944 70%

Multiple Activities 648 48%

Water Based Activities 820 61%

Land Based Activities 644 48% * Any of the eight activities listed in the survey questionnaire. Water Based Activities: Swimming, Boating, Fishing, Shell fishing; Land Based Activities: Beach Use, Bird Watching, Wildlife Viewing, Hunting. a Includes water based recreation at PES, BIS, LIS, and Atlantic Ocean. 68 Table S. 7 Participation in PES Water Based Recreation  All Respondents ( n= 1354)

Activity Number of Participants Percentage of Total Participation Number of Experiences Average Experiences Per Participant Fishing 224 35% 2732 12 Boating 338 52% 5470 16 Swimming 470 73% 6538 14 Shell fishing 92 14% 766 8 Totals 644 100% 15506 24 * * Includes more than one activity.

Table S. 8 Participation in Land Based Recreation  All Respondents ( n= 1354)

Activity Number of Participants Percentage of Total Participation Number of Experiences Average Experiences Per Participant Beach Use 736 90% 12248 17 Bird Watching 243 30% 5371 22 Wildlife Viewing 275 34% 5628 20 Hunting 23 3% 255 11 Totals 820 100% 23502 29 * * Includes more than one activity. 69 S. 9  Estimated Trips for Key Recreation Activities for Sample*

Residents SHO Overnight Totals Fishing 21,218 9,520 14,850 45,588 Boating 31,402 14,647 20,791 66,840 Swimming 44,133 18,309 35,641 98,083 Shell fishing 11,882 4,394 2,970 19,246 Beach Use 67,048 23,801 54,452 145,301 Bird Watching 8,555 3,032 5,346 16,933 Wildlife Viewing 17,313 6,225 10,098 33,636 Hunting 2,546 732 990 4,268 * Includes activities at the five East End towns and PES water bodies only Appendix B  Technical Appendix: Swimming Trips and Value Estimation Overview A " count data" model is used to estimate the demand for recreational activities in the PES. Count data models are appropriate for estimating recreational demand because the number of recreational trips taken by an individual must take on an integer value. The Poisson distribution is used to estimate the number of trips to each site in the PES and to estimate the economic value ( consumer surplus) of those trips.

For swimming, the number of trips to each site is modeled as a function of the travel cost to the site, costs of travel to substitute sites, the number of boating occasions at the same site, and a subjective measure of water quality at the site. The measure of water quality is the probability that water quality at the site will be ranked either good or excellent by survey respondents who recreate at that site.

This model can be used to estimate the effect of management actions to improve water quality on the number of recreational trips to each site, and to the value of a recreational trip to a site. In order to do so, we need to link management actions to changes in water quality, and to link objective field measures of water quality to subjective perceptions of water quality by recreational users.

Predictions of changes in water quality due to management actions is done as part of ongoing water quality modeling work, carried out as part of the Peconic Estuary Program ( Tetra Tech, 1998). The relationship between objective water quality measures and subjective perceived water quality is 70 estimated by linking data collected in the recreational survey with field measure of water quality. Specifically, for those respondents who indicate that they went swimming on their most recent recreation day, the survey asked where they went swimming and how highly they rated water quality ( excellent, good, fair or poor) at that site. We also obtained data on measures of key water quality variables, including brown tide cell counts, total coliform bacteria, total Kjeldahl nitrogen, and Secchi disk depth readings from sampling locations distributed across the PES. The data was grouped into ten sites  north fork and south fork sites for Flanders, Great Peconic, Little Peconic, Shelter Island Sound and Gardners bay.

An ordered LOGIT model was used to estimate the probability that an individual would rate water quality high ( good or excellent), fair or poor as a function of the objective field measures of water quality, discussed above. We then include these predicted probabilities into the travel cost estimation, so that the number of trips to a site is related to the cost of traveling to the site, the cost of traveling to key substitute sites, and the predicted probabilities of the subjective rankings of water quality. These steps are described in detail below.

Water Quality Model The water quality model links individuals' perception of water quality  expressed in a ranking of poor, fair, good, and excellent  to physical measures of water quality collected at sampling stations by the SCDHS. An ordered logit model is used to estimate the probabilities for each rank based on physical measures of water quality and on the effects of Brown Tide on the individual's swimming that year. The purpose of the water quality model is to provide input into the travel cost model i. e., the probability that the water quality of a site would be ranked good or excellent.

Data for the water quality model consists of site readings during August 1995 for the following water quality indicators:

1. Brown Tide, Aureococcus anophagefferens, in cells per milliliter. 2. Total Kjeldahl nitrogen in milligrams per liter. 3. Total coliform bacteria in MPN per 100 milliliters. 4. Secchi disk depth measured in feet.

These water quality indicators were selected for their relevance to potential management actions and for their modeling compatibility. Also included in the water quality model data set are responses to the Recreational Use Survey Questionnaire administered during August 1995. Three questions from the questionnaire are included. One question asks individuals if their swimming was affected by Brown Tide. Another question asks respondents to rank the water quality  poor, fair, good, excellent  at their last swimming outing in the PES. The third question asks respondents to identify the swimming location of their last swimming outing in the PES.

The data is grouped into ten sites: Flanders bay north and south, Great Peconic bay north and south, Little Peconic bay north and south, Shelter Island sound north and south, and Gardiners bay north and south. Swimming locations were allocated to the ten sites according to locations indicated by 71 Pr ( ) . '

ob Y e e x x = = + 1 1    ' the survey respondent. Only observations that indicate an identifiable swimming location for trips taken during August 1995 were included in the data set. Water quality sampling stations were allocated to the ten sites according to SCDHS sampling station maps, as described in Table A1. The number of samples recorded from each sampling station varies. An average of the data recorded at each station during August 1995 ( the period during which our survey was administered) was taken. Sampling stations with a central position, as opposed to north or south, were included in the averages of both north and south sites.

Data for Brown Tide, total Kjeldahl nitrogen, and total coliform bacteria were used to construct indices of water quality relative to threshold values for each parameter to make the measures more comparable.

The average Brown Tide cell count for each of the ten sites was divided by 250,000cells/ ml  the visibility threshold for Brown Tide. The resulting value was then multiplied by 1 if the respondent indicated that their swimming was affected by Brown Tide, or by 0 if not. In this way, the Brown Tide cell count is modeled to affect the water quality ranking of only those respondents who indicated they had knowledge of and were affected by Brown Tide.

The average total Kjeldahl nitrogen reading for each of the ten sites was divided by .5mg/ l, the nitrogen guideline established by the SCDHS. The average total coliform bacteria reading for each of the ten sites was divided by 400MPN/ 100 ml, a threshold for bathing water quality. The total Kjeldahl nitrogen index and the total coliform bacteria index were added together to form a joint nitrogen bacteria index. This joint index was used in the ordered logit model instead of the individual indices because high colinearity in field measurements of nitrogen and coliform bacteria precluded separate estimation of the effects of these two water quality variables. All indices were multiplied by 100 to preclude the use of fractions. Average input values for each site are presented in table A2.

The modeling procedure uses an ordered logit model that predicts water quality rank based on the Brown Tide Index, Nitrogen Bacteria Index, and Secchi Disk depth readings. The ordered logit model is based on the logistical distribution,

where   represents parameters to be estimated and x represents water quality field measures ( e. g., water clarity) that contribute to the subjective water quality rankings. Given that the rankings are discrete and finite ( excellent, good, fair and poor), it is reasonable to talk about the probability that any ranking would be chosen based on the levels of the water quality indicators encountered by an individual. Since the individual is faced with a predetermined ranking order  poor, fair, good, excellent  the choice made reflects the rank order that is most similar to the individual's own perception. In other words, the individual may feel that water quality at their swimming location that day was somewhere between good and excellent, but because of the ranking order presented 72 by the questionnaire, the individual will choose either good or excellent as the best choice. This selection process is modeled as a multinomial logit model where y* =  ' x +  .

Y* is a continuous measure of site quality, not constrained by the discrete ranking,   is the vector of coefficients estimated by the ordered logit model, x is the vector of field measures of water quality, and   is a vector of unknown factors that might affect the individual's rank order choice ( e. g., the individual's mood or the prevailing weather conditions). The unknown factors are assumed to be logistically distributed across individuals with a mean of zero and a variance of one.

Although y* is unobserved, the rank order selections indicated in the survey questionnaire are observed. These selections can be modeled as y = 0 ( poor) if y* 0, y = 1 ( fair) if 0 < y* µ 1, y = 2 ( good) if µ 1 < y* µ 2, y = 3 ( excellent) if µ 2 y*.

where the µ i are threshold values that determine when water quality ranking would change for a " representative" individual. The ordered logit model estimates the µ threshold parameters along with the vector of   coefficients.

The results of the ordered logit model are presented in Table A3. The estimated coefficients indicate that a reduction in Brown Tide cell counts or a reduction in the nitrogen bacteria index would result in higher water quality rankings. Similarly, an increase in Secchi disk depth ( increased water clarity) would raise the subjective water quality ranking by survey respondents.

The probabilities that water quality would be ranked poor, fair, good, or excellent are calculated from the ordered logit model results. These probabilities are:

Prob ( y = 0; poor) =   (  ' x), Prob ( y = 1; fair) =   ( µ 1   ' x)    (  ' x), Prob ( y = 2; good) =   ( µ 2   ' x)    ( µ 1   ' x), Prob ( y= 3; excellent) = 1    ( µ 2   ' x),

where   indicates the logistic cumulative distribution function.

The rank order probabilities are given for nine sites only, as no survey respondents reported swimming at Flanders Bay south. These probabilities are generated by substituting the site specific water quality indicator values for the mean values of the entire sample. An average of each probability weighted by the number of observations for each site is calculated for the five PES water bodies. For example, the probability that water quality at Gardiners bay will be ranked good 73 is based on the weighted average of the probability for good at Gardiners bay north and Gardiners bay south. The probabilities for the nine sites are presented in table A4. The probabilities for the five PES water bodies are presented in table A5.

The effects of management actions on the rank order probability at any or all of the nine sites can be simulated by changing the water quality measures for the sample station readings. Changes in these values will in turn change the value of the water quality indices and/ or Secchi Disk readings used as input into the ordered logit model. These simulated values for objective water quality field measures are used to provide at a new set of probabilities for subjective ratings of water quality for the sites, which in turn affect predicted participation rates for swimming in each of the PES water bodies.

Tables A6 A9 contain estimated water quality ranking probabilities i. e., the probability that water quality will be ranked poor, fair, good, or excellent, for each of nine PES sites at a 10% improvement in an objective water quality parameter. The objective water quality parameters used in the 10% improvement model are Brown Tide cell counts, Total Kjeldahl Nitrogen readings, Total Coliform readings, and Secchi Disk depth.

For example, under baseline water quality levels in 1995, Great Peconic Bay ( Table A5) is predicted to obtain a " poor" rating for water quality by 8% of sample respondents, a " fair" rating by 37% of sample respondents, a " good" rating by 45% of sample respondents and an " excellent" rating by 10% of sample respondents. These probabilities are the weighted averages of the ranking probabilities for Great Peconic Bay North and Great Peconic Bay South ( Table A4). If, continuing our example, Brown Tide Cell counts were to improve ( decrease) by 10% at sampling stations in Great Peconic Bay North and Great Peconic Bay South, the model predicts that Great Peconic Bay overall ( Table A10) would obtain a " poor" rating by 7% of sample respondents, a " fair" rating by 36% of sample respondents, a " good" rating by 46% of sample respondents and an " excellent" rating by 10% of sample respondents.

Travel Cost Model The travel cost model predicts the annual number of swimming trips to each of the five water bodies in the PES based on the cost of travel to the swimming location, the cost of travel to substitute swimming locations, the number of times the individual went to the same location that year, and the probability that the water quality at that location would be ranked good or excellent. The predicted number of trips and the estimated coefficient on the travel cost variable are used to calculate the value of swimming trips to each PES water body.

The travel cost model is also used to estimate the increased number of trips and increased value per trip that results due to management actions that increase water quality. This is done in three steps. First, the changes in water quality resulting from specific management actions ( e. g. upgrading the Riverhead sewage treatment plant) are to be simulated using the water quality modeling results ( Tetra Tech, 1998). Next, changes in subjective probabilities are predicted using the methods discussed above. Finally, predicted perceptions of water quality by users are substituted into the 74 Travel cost model, to calculated the estimated number of trips and value per trip under the new level of water quality.

Data for the travel cost model comes from the Recreational Use Survey and from the results of the water quality model. The survey questionnaire asks respondents to indicate the number of times they went swimming this year at a variety of water bodies on the East End. These water bodies include Flanders Bay, Great Peconic Bay, Little Peconic Bay, Shelter Island Sound, Gardiners Bay, Long Island Sound, Block Island Sound, and the Atlantic Ocean.

The price, or marginal cost, of swimming in the PES is not identified in any existing market. Since there is no readily available market data on the price of swimming in the PES, a proxy for price is used. In the travel cost model, the cost of getting to and from a swimming location is considered the implicit price of swimming at that location. Travel costs to swimming locations are based on distance traveled, travel time, and the household income according to the formula:

Travel Cost = Round Trip Distance * $ 0.32 + { Round Trip Distance/ 40mph * 40% hourly wage}.

The round trip distance traveled is double the one way highway distance between the respondent's point of origin and each swimming location. This distance was determined on a case by case basis using a road atlas and assessing the most direct travel route. Verification of distance traveled was made possible when respondents answered the question concerning distance traveled on their most recent swimming occasion. Travel time was estimated by dividing the round trip distance by 40 miles per hour. This speed was used as a reasonable compromise between highway speeds of 55 mph and more and local speeds between 25 and 40 mph. The opportunity cost of travel time was estimated at 40% of the wage rate, a common practice in benefit estimation. For respondents who did not indicate household income, income was estimated using a simple OLS regression that modeled income as a function of a constant value and education.

In the case of substitute swimming locations, the same travel cost formulation used for the PES locations was applied to the substitute swimming locations. The average of travel costs to Long Island Sound, Block Island Sound, and the Atlantic Ocean, for each respondent, was used as the cost of travel to substitute sites and input into the travel cost model. Data on the number of trips to the PES swimming location comes directly from the survey questionnaire. The water quality variable used in the travel cost model is the sum of the probabilities that the PES swimming location would be ranked good and excellent. These probabilities are generated by the water quality model.

Overall, 199 respondents provided sufficient information to be included in the travel cost model. Given that there are five PES swimming locations, 995 observations were used in the travel cost model estimation.

The travel cost modeling procedure is based on a Poisson regression model. The Poisson regression model is often used when the dependent variable is discrete and includes zero as a viable choice. In this case, many individuals took no trips to one or more of the PES swimming locations. This 75 New CS = { T + T P}        ( ) C type of data is referred to as count data, meaning that the data indicates the number of times some phenomenon occurs, such as swimming trips to Flanders Bay.

The Poisson regression model is based on the assumption that each observation is drawn from a Poisson distribution. The probability that the actual number of trips taken is equivalent to the estimated number of trips is formulated as:

Prob ( Yi = yi) = e i  i yi / yi !

Where yi = 0,1,2,3,....; ln  i =  ' xi;   is the vector of estimated coefficients; and x is the vector of independent variables.

The results of the Poisson estimation are consistent with prior expectations, as presented in Table A14. The estimated coefficients indicate that the number of trips to a site decreases as the cost of travel to the site increases; increases when the cost of travel to a substitute site increases; increases the more times the individual participates in swimming at the same location; and increases as water quality at the site increases.

The value of swimming trips to PES locations is estimated from the results of the travel cost model according to the formula:

Consumer Surplus = ( Predicted number of trips)/( coefficient on travel costs).

Consumer surplus is the net value of swimming at a PES location above and beyond the costs of participating. Consumer surplus is affected by the same variables that influence the number of trips to a swimming location. Therefore, the value of a swimming trip varies among the five PES swimming locations due to differences in water quality.

The value of a swimming trip to a PES location will be affected by management actions to improve water quality. The results of the travel cost model can be used to estimate the changes in consumer surplus due to water quality improvements. The recreational swimming benefits of improving water quality are estimated by applying new probabilities of water quality ratings to the results of the travel cost model. This can be calculated using the following formula:

.

Where T represents the number of trips taken prior to the quality change,   T represents the change in the number of trips that occurs due to a 1% change in the probability of obtaining a " good" or " excellent" water quality rating,   P represents the change in the probability of obtaining a " good" or " excellent" rating that results from the management action, and C represents the coefficient on the travel cost variable. 76 Increases in the number of swimming days to PES locations and increases in consumer surplus due to a hypothetical 10% improvement in water quality measures are presented in Table A15.

Aggregate benefits are based on the estimated baseline number of swimming trips to the PES. This number of trips  1,409,970  was estimated by extrapolating the number of trips respondents identified in the Recreational Use Survey to the population of residents, second home owners, and overnight visitors. The population of overnight visitors was estimated based on the proportion of overnight visitors in the survey sample ( see section on Participation). The aggregate value of swimming in the PES under baseline conditions is presented in table A16.

Table A1. PES Sampling Station Locations Location Sampling Station Flanders Bay North 170 ( Central), 220, 240 Flanders Bay South 170 ( Central)

Great Peconic Bay North 101, 130 ( Central)

Great Peconic Bay South 130 ( Central)

Little Peconic Bay North 102, 103, 105, 113 ( Central)

Little Peconic Bay South 113 ( Central)

Shelter Island Sound North 106,107,108,109,111,112,114,

115,119,122 Shelter Island Sound South 118,121,126,127,131 Gardiners Bay North 116 Gardiners Bay South 132,133,134 77 Table A2. Average Input Value for the Water Quality Model ( Ordered Logit)

Site Brown Tide Index Nitrogen  Bacteria Index Secchi Disk Depth ( Feet)

Flanders Bay North 5.57 818.09 3.2 Great Peconic Bay North 18.33 169.85 3.13 Great Peconic Bay South 14.81 117.75 3.5 Little Peconic Bay North 28.04 126.39 3.5 Little Peconic Bay South 16.21 126.76 3.5 Shelter Is. Sd. North 39.51 136.44 4.35 Shelter Is. Sd. South 47.5 256.26 4.37 Gardiners Bay North 32.41 94.21 5.75 Gardiners Bay South 33.37 124.7 5.08 78 Table A3. Results of the Water Quality Model ( Ordered Logit)

Observations 199 Chi Squared 27.50 Log Likelihood  225.26 D. Frdm. 3 Res. L. Likelihood  239.00 Sig. Level .000005 Variable Coefficient Std. Err. T Stat. Sig. Level Mean of X Constant 2.0679 1.1136 1.857 0.06332 Brown Tide Index  0.02297 0.00499  4.605 0.00000 33.83 Nitro.  Bact. Index  0.00147 0.00089  1.652 0.09860 173.4 Secchi Disk 0.34515 0.23232 1.486 0.13737 4.397 µ 1 2.2812 0.25915

µ 2 4.6968 0.3522   79 Table A4. Baseline Probabilities for Nine Sites Site Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay North 0.14 0.47 0.34 0.05 Great Peconic Bay North 0.08 0.37 0.45 0.10 Great Peconic Bay South 0.06 0.32 0.49 0.13 Little Peconic Bay North 0.08 0.38 0.45 0.10 Little Peconic Bay South 0.06 0.33 0.49 0.12 Shelter Is. Sd. North 0.08 0.38 0.45 0.10 Shelter Is. Sd. South 0.11 0.43 0.39 0.07 Gardiners Bay North 0.04 0.25 0.53 0.18 Gardiners Bay South 0.05 0.30 0.50 0.14 * Probabilities may not sum to one due to rounding Table A5. Baseline Probabilities for PES Water Bodies PES Water Body Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay 0.14 0.47 0.34 0.05 Great Peconic Bay 0.08 0.37 0.45 0.10 Little Peconic Bay 0.08 0.37 0.45 0.10 Shelter Is. Sd. 0.08 0.39 0.44 0.09 Gardiners Bay 0.05 0.28 0.52 0.16 80 Table A6. Probabilities at Nine Sites for 10% Improvements in Brown Tide Cell Count Site Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay North 0.14 0.47 0.34 0.05 Great Peconic Bay North 0.07 0.37 0.46 0.10 Great Peconic Bay South 0.06 0.32 0.50 0.13 Little Peconic Bay North 0.08 0.37 0.46 0.10 Little Peconic Bay South 0.06 0.32 0.49 0.13 Shelter Is. Sd. North 0.07 0.36 0.46 0.10 Shelter Is. Sd. South 0.10 0.42 0.41 0.08 Gardiners Bay North 0.04 0.24 0.53 0.19 Gardiners Bay South 0.05 0.29 0.51 0.15 Table A7. Probabilities at Nine Sites for 10% Improvements in Total Kjeldahl Nitrogen Readings Site Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay North 0.13 0.46 0.35 0.06 Great Peconic Bay North 0.08 0.37 0.45 0.10 Great Peconic Bay South 0.06 0.32 0.49 0.13 Little Peconic Bay North 0.08 0.38 0.45 0.10 Little Peconic Bay South 0.06 0.33 0.49 0.12 Shelter Is. Sd. North 0.08 0.37 0.45 0.10 Shelter Is. Sd. South 0.11 0.43 0.39 0.07 Gardiners Bay North 0.04 0.25 0.53 0.18 Gardiners Bay South 0.05 0.30 0.51 0.14 81 Table A8. Probabilities at Nine Sites for 10% Improvements in Total Coliform Readings Site Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay North 0.13 0.46 0.35 0.06 Great Peconic Bay North 0.08 0.37 0.45 0.10 Great Peconic Bay South 0.06 0.32 0.49 0.13 Little Peconic Bay North 0.08 0.38 0.45 0.10 Little Peconic Bay South 0.06 0.33 0.49 0.12 Shelter Is. Sd. North 0.08 0.38 0.45 0.10 Shelter Is. Sd. South 0.11 0.43 0.39 0.07 Gardiners Bay North 0.04 0.25 0.53 0.18 Gardiners Bay South 0.05 0.30 0.50 0.14 Table A9. Probabilities at Nine Sites for 10% Improvements in Secchi Disk Depth Site Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay North 0.12 0.46 0.36 0.06 Great Peconic Bay North 0.07 0.35 0.47 0.11 Great Peconic Bay South 0.05 0.30 0.51 0.14 Little Peconic Bay North 0.07 0.36 0.46 0.11 Little Peconic Bay South 0.06 0.31 0.50 0.13 Shelter Is. Sd. North 0.07 0.35 0.47 0.11 Shelter Is. Sd. South 0.09 0.41 0.41 0.08 Gardiners Bay North 0.03 0.22 0.54 0.21 Gardiners Bay South 0.05 0.27 0.52 0.16 82 Table A10. Probabilities at PES Water Bodies for a 10% Improvement in Brown Tide Cell Counts PES Water Body Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay 0.14 0.47 0.34 0.05 Great Peconic Bay 0.07 0.36 0.46 0.10 Little Peconic Bay 0.07 0.36 0.46 0.10 Shelter Is. Sd. 0.08 0.37 0.45 0.10 Gardiners Bay 0.04 0.26 0.52 0.17 Table A11. Probabilities at PES Water Bodies for a 10% Improvement in Total Kjeldahl Nitrogen Readings PES Water Body Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay 0.13 0.46 0.35 0.06 Great Peconic Bay 0.07 0.37 0.46 0.10 Little Peconic Bay 0.08 0.37 0.45 0.10 Shelter Is. Sd. 0.08 0.38 0.44 0.09 Gardiners Bay 0.05 0.27 0.52 0.16 83 Table A12. Probabilities at PES Water Bodies for a 10% Improvement in Total Coliform Readings PES Water Body Prob. Poor Prob. Fair Prob. Good Prob. Excellent Flanders Bay 0.13 0.46 0.35 0.06 Great Peconic Bay 0.08 0.37 0.46 0.10 Little Peconic Bay 0.08 0.37 0.45 0.10 Shelter Is. Sd. 0.08 0.39 0.44 0.09 Gardiners Bay 0.05 0.28 0.52 0.16 Table A13. Probabilities at PES Water Bodies for a 10% Improvement in Secchi Disk Depth PES Water Body P( Poor) P( Fair) P( Good) P( Excellent)

Flanders Bay 0.12 0.46 0.36 0.06 Great Peconic Bay 0.07 0.35 0.47 0.11 Little Peconic Bay 0.07 0.35 0.47 0.11 Shelter Is. Sd. 0.07 0.36 0.46 0.11 Gardiners Bay 0.04 0.25 0.53 0.19 84 Table A14. Results of the Travel Cost Model ( Poisson Regression)

Observations 995 Chi Squared 1846.693 Log Likelihood  3707.225 D. Frdm. 4 Res. L. Likelihood  4630.572 Sig. Level .000000 Variable Coefficient Std. Err. T Stat. Sig. Level Mean of X Constant  0.76984 0.16607  4.636 0.00000 Travel Cost  0.028321 0.00210  13.494 0.00000 23.29 Boating 0.0781 0.00157 49.757 0.00000 0.9246 Travel Cost to Substitute Locations 0.00978 0.00119 5.282 0.00000 31.74 Water Quality Rank 2.688 0.28209 9.529 0.00000 0.5404 Scale Factor 1.5002 Marginal Effect for Water Quality Rank 4.0325 Appendix C. Comparison of Recreation Activity Estimates: Phase I and Phase II Reports Comparison with Phase I Total Outdoor Recreational Trip Estimates: Beach Use. Beach use, in this report, refers to any beach in the five East End towns, including Long Island Sound and Atlantic Ocean beaches, as well use of PES beaches. The Phase I report did not estimate beach use on Long Island Sound or the Atlantic Ocean and therefore is not presented for comparison.

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Non Consumptive Wildlife Use. Bird watching and wildlife viewing estimates in this Phase II report are combined to create a " non consumptive wildlife use" estimate of 547 thousand trips ( Table V. 11). This estimate greatly exceeds the PES Phase I estimate of non consumptive wildlife use ( 92 thousand trips).

The major reason for the large differences in estimates between the Phase I and II reports is explained as follows. Our survey based estimate finds a high participation rate ( 19% for bird watching and 21% for wildlife viewing) for respondents and a large number of trips per participant ( 37.55 bird watching trips and 34.36 wildlife viewing trips for residents). The Phase I survey, in 85 contrast, was based on NFWS statewide estimates for New York. These statewide participation rates ( 9.4%) and number of trips per participant ( 8.3) are very much lower than those found in the PES sample. This major difference is not surprising since residents and seasonal visitors to the PES would be expected to be more interested in outdoor recreation than the general population of the state.

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Recreational Fishing. Not surprisingly the recreational use survey data yields a much larger estimate ( 588 thousand trips) of recreational fishing trips in the PES than the Phase I study ( 114 thousand). The Phase I estimate is based on the NFWS survey covering all of New York State. In the NFWS study, the average estimates of participation rate ( 9%) and trips per participant ( 8 for residents, 4.3 for visitors) is much lower than found in the PES survey. This estimated participation rate is 17% and trips per participant range from 15.27 for overnight visitors to 9.78 for second homeowners. Again, we would expect PES residents and visitors to be more avid recreational fishers than the New York population at large.

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Swimming. The Phase I swimming estimate ( 715 thousand) is simply the sum of all available beach attendance data for the PES. The Phase II survey based estimate ( 1.4 million) includes participation not only at official beaches but also at the many unadministrated beaches throughout the PES, and in addition includes the Atlantic Ocean and Long Island Sound, as well as the PES.

Comparison with Phase I Total Outdoor Recreational Trip Estimates: Hunting. The discrepancy between the Phase I ( 77 thousand trips) and Phase II ( 52 thousand trips) hunting estimates also is based on differences in participation rate and number of hunting days per participant, as estimated by the two surveys. The NFWS estimates that 11% of the New York State population over 16 hunts, and that participants average 18.6 hunting trips per year. On the other hand, the PES survey indicates that only 2% of residents and visitors to the PES hunt in the PES. These participants also hunt less than the state average, ranging from 16.33 trips per second homeowner participant to 5.5 trips per participating day tripper. The Phase I hunting estimate was based only on residents and did not attempt to account for participation by seasonal residents and visitors since such data are unavailable. 13Two major changes are noted. French and Schuttenberg valued PES wetlands over a 100 year period and used a 3 % discount rate. We use a 7 % discount rate and a 25 year time period for consistency with results given elsewhere in this report. The higher discount rate and shorter tim horizon means that our results for estimated economic values will be lower than in French and Schuttenberg.

86 V. WETLAND PRODUCTIVITY VALUES IN THE PES V. A. INTRODUCTION Eelgrass, saltmarsh, and intertidal mud bottom (" wetlands") provide many services to the public. These services include contributing to the production of commercial and recreational harvests of fin fish and shell fish, and of birds and other wildlife used for viewing and for hunting. Other services include protection of shoreline property from storm damage and erosion.

Wetland services may occur on site or off site and may or may not be valued in markets. For example, some wetland services, such as scallop harvests, occur on site, while others are realized offsite, for example, when fin fish or birds " produced" by an eelgrass bed of intertidal salt marsh are harvested or viewed many miles away. Some wetland services are valued in the market place ( e. g., commercially harvested fish or shellfish), whileothers are not ( e. g., bird species used for viewing and waterfowl that are hunted).

Understanding the economic value of the various natural services provided by ecosystems can provide useful information for policy analyses about preservation and restoration decisions. For this purpose, the most useful information is the value of a small change in wetlands, that is, the marginal value of wetlands. Marginal values rather than the total value of all wetlands are important because most policies address relatively small changes in wetlands not whether or not to preserve all wetlands.

This chapter adapts13 estimates by French and Schuttenberg ( 1998) of the marginal value of PES eel grass beds, salt marshes, and intertidal mud flats. Two types of wetland productivity ( biological) gains are considered:

( 1) The increase in food produced by the habitat which is utilized by higher trophic levels ( such as fish and shellfish) in the PES, and ( 2) The increase in production of higher trophic levels brought about by the increased availability of habitat.

The biological gains from restoring or protecting increments of each wetland type ( eelgrass, saltmarsh, and intertidal mud flats) are assigned an economic value. This value is based on the ( 1) commercial value of the fin fish and shell fish, ( 2) the viewing value of birds, and ( 3) the hunting value of waterfowl ultimately " produced" by wetlands. Other direct and indirect services and values wetlands may provide are not considered. 14A summary of the productivity approach and examples for coastal areas is given in Grigalunas and Congar ( 1995).

87 Two assumptions are critical to the analysis that follows. One is that food and habitat are biologically limiting factors for the species considered; that is, fish, shell fish and birds depend upon the availability of wetlands, so that small changes in wetlands will causes changes in the populations of these species. If wetlands are not limiting for these species, then an increment of wetlands would have no productivity value for the species concerned, although it may have other values ( e. g., shoreline erosion protection, esthetics, or existence value)

The second critical assumption concerns effort and its cost. Fishing, viewing or hunting require the use of labor, capital and other inputs used for harvesting, viewing or hunting; the net gain from these activities is the benefit ( e. g., value of fish landings) minus the costs of the effort required. However, very small changes in the abundance of fish, shellfish or birds due to a small change in wetland areas will lead to only a very slight increase in harvests per unit of effort. Slight increases in ctch per unit effort will have a negligible effect on the level of effort itself. The changes in the availability of each wetland category due to preservation or restoration actions are presumed to be small enough so that fishing, hunting, or viewing effort remains the same. This assumption implies that we do not need to net out the cost of any change in effort due to small changes in abundance of fish, or birds arising from marginal changes in wetlands. Under these assumptions, the value of additional harvests, viewing or hunting is the gross value.

V. B. METHODOLOGY AND DATA V. B. 1. Introduction Several studies estimate the economic values provided by natural ecosystems14. For example, Lynne et al. ( 1981) estimated the relationship between mangrove area, fishing effort, and landings of a single species, blue crab in Florida. They estimated a marginal value for blue crab of several dollars. Bell ( 1989) also used a time series of data on mangrove area, effort, and catch to evaluate the marginal productivity of Florida mangroves in the production of principal Florida commercial and recreational fish. He estimated a marginal value of several thousand dollars per acre of mangrove over all commercial and recreational species. Kahn and Kemp ( 1985) estimated the incremental value of subtidal vegetation in contributing to striped bass populations in the Chesapeake Bay and their subsequent harvest by recreational fishermen. Costanza and Farber ( 1986) assessed the per acre services of Lousiana wetlands for recreation and as a buffer for storm protection, in addition to contributing to the production of commercial fisheries. Finally, Pornpinatepong ( 1997) used the results in the Natural Resource Damage Assessment Model ( French, et al, 1996) to estimate the asset value of coastal wetland services in the Northeast.

The approach used in this chapter derives estimates of wetland productivity using data specific to the PES. The virtue of this approach is that it captures the underlying biological structure and 88 productivity of PES wetlands, and it uses biological information and economic values specific to the PES. The method in effect a " simulation" side steps some of the data and other problems faced in statistical studies, such as that by Lynne et al., by Bell, and by Costanza and Farber. On the other hand, the method used does not allow for statistical tests of significance and hence heavily relies upon professional judgement.

The methodology used is described next. First, we explain how the food web estimates were made. Then, the methodology and data used for habitat values is presented. A detailed statement of the methodology, data and assumptions is given in French and Scuttenberg ( 1998).

V. B. 2. Food Web Estimates Saltmarsh and eelgrass beds benefit the entire food web by primary ( plant) production. Similarly, the net gain in lower trophic level, animal production is passed up the food web. Ultimately this production via the food web results in the production of species of economic value to people.

To estimate the economic value of these food web effects, several pieces of information are needed. First, it is necessary to quantify the amount of food produced by a habitat. To do this, primary ( plant) and bottom ( amphipods, worms, etc., in and on the sediments) production rates were estimated for PES wetland categories, using results in the literature. Then, the fraction of the additional production passed up the food web was estimated. Next, this additional production is translated into commercial fin fish and shell fish production and landings using average relationships estimated across many estuaries by Nixon ( 1982). Finally, the estimated fish and shell fish landings are valued using species specific fishery values for PES landings.

Thus, lower trophic levels are translated into equivalent upper trophic level fish and shellfish production harvested and valued by people. Specifically, fishery production is estimated to be 0.16 percent of primary production and 4 % percent of bottom ( benthic macrofaunal) production. Details of these calculations are given in French and Schuttenberg ( 1998).

V. B. 3. Habitat Estimates Habitat values are estimated for species ( bay scallops, blue crab, softshell clams, and birds) having human use values. Habitat values are based on ( 1) the expected yield of fish or shellfish dependent upon the habitat, and ( 2) the abundances of wildlife ( birds) that utilize the habitat. Fish and shellfish values are commercial values; wildlife values are for hunting ( waterfowl) and viewing ( waders).

Bay scallops depend upon eelgrass as nursery habitat for juveniles. The grass provides a refuge from predators for juvenile scallops. It is assumed that eelgrass is a limiting factor for scallops, so that the entire PES scallop fishery depends upon eelgrass beds. Blue crab use saltmarsh and eelgrass habitats preferentially. Again, it is assumed that the entire blue crab fishery depends upon the saltmarsh and eelgrass beds of the estuary. Softshell clams prefer intertidal mud flats and sand 15Use of lower discount rate and longer time horizon would lead to a larger value. Use of the 7 % discount rate reflects the opportunity cost of resources used in restoration/ preservation. That is, resources used in these activities to some extent will be drawn away from private and public investments.

89 flats. Softshell clams are assumed to all have been produced in intertidal mudflats and shoals in the PES.

Abundance of birds depends upon habitat type. An average abundance per unit area of habitat is assumed, based on the results for the coastal area including the PES, as given in the Natural Resource Damage Assessment Model ( Version 2.4, April, 1996) developed by the authors for the US Department of the Interior ( French, et al, 1996a, b, c). Birds that are specifically benefitted by saltmarsh, eelgrass or mud flats were selected and included in the present analysis. These species are waders ( herons, egrets, and ibis), shorebirds, brant and black ducks. Waders use all three of the habitat types, while shorebirds use marsh and mud flats. Brants specifically feed on eelgrass. Black ducks are know to require structured habitat, marsh and eelgrass.

The value of bird species usage of the habitat is based on the benefits human receive from viewing or hunting ( waterfowl) birds. The values are marginal values, that is, the additional value people obtain by seeing additional birds per day. The values per animal per year are proportional to the number of viewing trips and the rareness of the species in the local area ( see French et al. ( 1996a, b, c) for a full description of the development of these values.

V. C. RESULTS V. C. 1. Introduction Results are provided for the ( 1) Marginal value of existing wetlands, and ( 2) the marginal value for restored wetlands. Restored wetlands have a lower value than existing wetlands since it may take years for an existing wetland to become fully functional.

V. C. 2. Results Table V. 1 shows the marginal value for existing and restored wetlands. The value for existing wetlands would be used for policy issues dealing with preservation decisions; results for restoration are critical for assessing policies to restore wetlands.

Two values are calculated: An annual value and an asset value. The annual value is the sum of the food web values and the habitat values. Asset values were calculated by discounting the annual value over a 25 period using a discount rate of 7 percent, the same time frame and discount rate used elsewhere in this report15. Eelgrass has a 20 % higher marginal value per acre than that for saltmarsh. Intertidal mud flats have the lowest marginal value.

Estimated marginal values for restoring wetlands are much lower than the value of existing wetlands of the same category. This is because it takes several years before a restored wetland 90 becomes fully functional. Thus, in the table it is noted that eelgrass, saltmarsh, and intertidal mud flats take 15, 10, and 3 years, respectively, to become fully functional.

The marginal asset values of PES wetlands appear to be substantial, especially in light of the fact that other services wetlands may provide, such as protection from erosion and storms, aesthetics and existence value, are not considered.

Table V. 1. Marginal Values of PES Wetlands Existing Habitats Created Habitats Wetland Type Annual Value per Acre a Asset Value per Acre b Years to Become Fully Functinal a Asset Value per Acre b Estimated Number of Acres in PES ( millions)

Eelgrass $ 1,065 $ 12,412 10 $ 9,996 6.04 Saltmarsh $ 338 $ 4,291 15 $ 3454 13.51 Intertidal Mud Flat $ 67 $ 786 3 $ 626 14.05 a French and Schuttenberg ( 1998) estimate habitat values using a discount rate of 3 % and time horizon of 25 years. Our use of 7 % and 25 years reduces the estimated wetland values by almost one half. b Using a discount rate of 7 percent and a time horizon of 25 years. Assumes linear recovery to full ( 99%) restoration over the period estimated by French and Schuttenberg ( 1998). French and Schuttenberg used a sigmoid function to approximate the time path or recovery.

V. D. SUMMARY AND CONCLUSIONS This chapter presents estimates of the marginal economic value of the productivity services provided eelgrass, salt marsh, and intertidal mud flats in the PES. The productivity services covered are food web and habitat services. Economic values estimated are for commercial fishing value for crab, scallop, and clams; viewing values for birds; and hunting values for waterfowl. Other possible values provided by PES wetland services, such as erosion and storm protection, aesthetics, and existence value are not considered. 91 V. E. REFERENCES Costanza, Robert and Steven Farber, 1986 " The Economic Value of Coastal Louisiana Wetlands". Baton Rouge: Lousiana State University.

Bell, Frederick W., 1989. Application of Wetland valuation Theory to Commercial and Recreational Fisheries in Florida. Sea Grant Program. Tallahassee: Florida State Univeristy ( June).

French, et al., 1996. Natural Resource Damage Assessment Model for Coastal and marine Environments. Version 2.

French, Deborah and Heidi Shuttenberg, 1998. Estimated Food Web and Habitat Values for Habitats in the Peconic Estuary System. Submitted to Economic Analysis Inc. ( January 23).

Grigalunas, Thomas and Richard Congar, 1995. Environmental Economics for Integrated Coastal Area Management. Nairobi: United Nations Environmental Programme.

Kahn, James and W. M. Kemp, 1985. " Economic Losses Associated With the Degredation of An Ecosystem: The Case of Submerged Aquatic Vegetation in Chesapeake Bay", Journal of Environmental Economics and Management Lynne, G., P. Conroy, and F. Prohaska, 1981. " Economic Valuation of Marsh Areas for Marine Production Processes", Journal of Environmental Economics and Management 8( 2): 175 186.

Nixon, Scott W., 1982. " Nutrient Dynamics, Primary production and Fisheries Yields of Lagoons". Narragansett: Graduate School of Oceanography, University of Rhode Island.

Pornpinatapong, K., 1997. " Valuing Coastal Wetlands: Insigths from the Type A Natural Resource Damage Assesment model". Unpublished M. S. Major Paper. Kingston: Department of Environmental and Natural Resource Economics, University of Rhode Island. 16 Other important goals were to create a survey that would minimize some of the problems often associated with valuation surveys; would be easily understandable to members of the public; could be answered in a reasonable amount of time; and could be administered in a variety of public places.

92 VI. NATURAL RESOURCE PRIORITIES AND VALUES VI. G. INTRODUCTION This section describes a study of natural resource values for the Peconic Estuary System ( PES). In August 1995, we surveyed 968 year round and seasonal residents of the area surrounding the Peconic Estuary. Respondents to the survey were asked about their priorities and values for protecting and restoring important natural resources of the PES. The results include:

( 1) an analysis of values and priorities for a set of important natural resources of the area   farmland, undeveloped land, wetlands, safe shellfishing areas, and eelgrass; ( 2) estimates of economic benefits that would result from protecting or restoring these resources; and ( 3) public opinions related to the Estuary.

The survey was designed to complement scientific and technical studies, using data available at the time, to provide information that will be useful in the final policy analysis. This information will be used in a cost benefit analysis of policy alternatives ( Phase III of the Comprehensive Economic Valuation Study), that will help policy makers prioritize alternative management actions.

The remainder of this section is organized as follows: Part B describes the survey development and implementation, and the questionnaire design; Part C gives descriptive results of the survey; Part D presents the estimates of economic values; and Part E is a summary and conclusions.

VI. B. THE NATURAL RESOURCE SURVEY VI. B. 1. Survey Development and Implementation We developed the survey over a six month period, from February to August 1995, in an extensive process that included individual interviews, focus groups, and pretests. The primary goal of the survey was to learn about the public's preferences, priorities and values for natural resources of the Peconic Estuary that might be affected by preservation and restoration actions. 16 93 VI. B. 1. a. Initial Meetings, Interviews, and Information Gathering We began the survey development process in February, 1995 by meeting with members of the Management Committee. Shortly afterwards, we met with representatives of different interests, including the head of the Local Government Committee; the Chairman of the Citizen's Advisory Group, who is also president of a local environmental group; a representative of the Nature Conservancy; a biologist from the NY State Department of Environmental Conservation; a marina owner who was head of the local Marine Trades Association; a commercial fisherman who represented the Long Island Inshore Trawlermen's Association; and a bank president. These meetings, and a meeting with the Citizen's Advisory Group, helped us to learn about the area and about the concerns of various interest groups.

VI. B. 1. b. Informal Interviews With the Public Next, we conducted a set of informal interviews with members of the public, where we asked about their uses of and concerns about the Estuary. A total of sixteen randomly selected people were interviewed in Montauk, Springs, Sag Harbor, Shelter Island and Greenport. They included business owners, store clerks, a police officer, visitors, and residents.

These interviews provided information on some of the most important natural resources and related concerns, and how people think and talk about the bays. Most people interviewed were very concerned about water quality, declines in fish populations over the years, and impacts on business if water quality continues to decline. We also learned that many people were not familiar with the word " estuary;" and most people in Montauk did not consider themselves to be located on the Peconic. Consequently, in the final survey, the Peconic Estuary System was referred to as the Peconic Bays System, and was defined by a map at the beginning of the survey.

VI. B. 1. c. Focus Groups and Preliminary Survey The next step was a series of focus groups, and a short preliminary survey. Table VI. 1 lists the dates of the focus groups and pretests, their locations, and the number of participants. In the first three focus groups, we asked the participants general questions about how they define the study area; their familiarity with the Peconic Estuary Program; the characteristics of the area that are most and least important to them; their uses of the local waters; the attributes they look for in choosing recreational sites; their perceptions of water quality; and their concerns about the natural environment and natural resources of the Estuary.

A short preliminary survey was carried out in Montauk on March 19, before the St. Patrick's Day parade, an event that draws a large number of visitors and residents. This survey included both closed and open ended questions, including questions about:

( 1) participation in recreational activities around the water; ( 2) concerns related to the Estuary; and ( 3) positive and negative aspects of the East End in general. 94 Table VI. 1  Focus Groups and Pretests Date Location Number of Participants March 18 Montauk Focus Group 1 6 March 19 Montauk Preliminary Survey 35 March 22 Jamesport Focus Group 2 10 March 31 Riverhead Focus Group 3 7 April 19 Shelter Island Focus Group 4 3 April 20 Southampton Focus Group 5 9 April 29 East Hampton Focus Group 6 6 May 2 Springs Focus Group 7 7 May 11 Rhode Island Focus Group 8 5 May 18 Springs Pretest 1 10 June 21 Rhode Island Pretest 2 5 June 27 Rhode Island Pretest 3 5 July 9 Jamesport Pretest 4 13 July 9 Mattituck Pretest 5 17 The thirty five usable responses gave some preliminary information about important attributes of the area and its recreational sites; and about people's greatest concerns for the Estuary. Respondents were asked to rank a list of concerns on a scale of 1 to 10, where 1 indicates the highest level of concern. Table V. 2 lists these concerns, in order of importance to respondents. The most important were declining stocks of shellfish and finfish, water quality, trash on beaches, and areas closed to shellfishing. The least important were crowding in boating harbors, public access to the water and availability of wildlife for viewing.

In the next several focus groups, we asked participants general questions about their concerns, their perceptions of water quality, and actions they would like to see. We also tested preliminary survey questions and question formats, which were revised between groups. Several of these questions were primarily intended to stimulate discussion. For example, one question asked about participation in recreational activities, and was used to generate a discussion of these activities and related concerns. Other questions asked participants which of a list of human impacts on the Bays were most important to them; which of a list of environmental problems they believed were most serious; and which potential actions should be given highest priority.

The questions asking participants to prioritize impacts, concerns, and actions were difficult for people to answer, for two important reasons. First, many people in the focus groups believed that almost everything was important, and therefore resisted ranking their priorities. Instead, they wanted 17 Several additional questions were also tested in these focus groups. One question asked respondents to indicate their three most important reasons for living on the East End, from a list of fifteen reasons given by participants in earlier focus groups. This question was later dropped because it did not provide enough information to justify the space and time it took in the survey. A set of environmental attitude questions was also tested, with the intention of correlating respondents' attitudes with their values for natural resources. The attitude questions generated quite a bit of controversy, as they were designed to elicit strong opinions, and were dropped from the final survey for several reasons. First, they tended to draw respondents' focus from the more important questions in the survey; second, they took too long to answer; and finally, it was feared that the controversial nature of the questions might result in a negative reaction to the entire survey.

95 to rate everything as most important. Second, many participants stated that they did not know enough to rank the causes of environmental problems and the effects of potential actions, but believed that priorities should be decided by experts. Nonetheless, these questions provided a good focus for the discussion of people's concerns.

Table VI. 2  Concerns of Preliminary Survey Respondents, Montauk, March 19, 1995 Concern Mean Ranka Declining stocks of shellfish 3.09 Declining stocks of finfish 3.34 Water quality 3.43 Trash on beaches 3.59 Areas closed to shellfishing 3.66 Overcrowding of recreational sites 4.23 Declining open space 4.57 Crowding in boating harbors 4.74 Too little public access to the water 4.88 Need to improve public access points 4.90 Less wildlife available for viewing 5.21 a  Ranked from 1 to 10, with 1 denoting the most important.

Other questions in these focus groups asked about priorities related to natural resources, and tested possible question formats for the survey, including standard contingent valuation questions and contingent choice questions. Contingent valuation asks people directly to state their willingness to pay to preserve or improve a natural resource. Contingent choice asks people to make tradeoffs between alternative actions with results specified in terms of the natural resources that would be protected or restored and the cost. 17 Contingent choice questions are similar to marketing surveys often done by businesses to allow them to understand tradeoffs that customers are willing to make among product characteristics under diffrent programs and costs. 18 Several of these pretests were conducted in Rhode Island, in order to save time and money. Because the objective at this point was to refine the format, layout and wording, rather than the content, it was not necessary to conduct all tests on the East End. 19 Note that the example shown in the Appendix is only one of 12 different survey booklets, each of which contains five different contingent choice questions.

96 As discussed in more detail below, the contingent choice method appeared to be the most promising format, and thus we developed this method further in the next focus groups. As the contingent choice questions were refined through the focus group process, the survey became less complicated and less wordy, with fewer attributes included in each comparison; and graphics were added to the questions.

VI. B. 1. d. Survey Pretests and Implementation In May and June, we conducted several pretests of preliminary survey instruments. 18 This series of pretests helped to refine the question wording and layout of the survey, resulting in large improvements in respondents' ability to easily make the comparisons. In July, a fifth draft of the survey was pretested on Long Island, with a group of people at the First Parish Church in Jamesport, and with randomly selected people on the beach in Mattituck. In these pretests, people were able to easily comprehend and answer the survey questions. In early August, after meeting with members of the Management Committee, we made final modifications to the survey, and conducted final pretesting. An example of the final survey is included in Appendix A19.

We implemented the survey during the week of Aug. 22 29, 1995 in a variety of pre selected public locations around the East End, using convenience intercept sampling. We selected this method over a mail survey with probability sampling primarily because budget limitations made it necessary to administer both the natural resource survey and the recreation survey together, and because names and addresses of visitors were not available. Thus, we designed the surveys to be administered in public places where visitors to the area could be intercepted. We selected a wide variety of locations, in order to intercept a representative sample in terms of demographic characteristics and location of residence. The survey locations, and the number of surveys collected at each, are listed in Table 3. A total of 968 resource surveys were collected from year round and seasonal residents of the East End.

In implementing the survey, interviewers were instructed to approach people and say:

Hi, my name is , and I'm working for Suffolk County. We're doing a survey of the public to help develop a plan to protect and manage the bays. Would you be willing to fill out a survey, which will take about 5 10 minutes?

The interviewer then asked those who agreed to fill out the survey if they were visitors, year round residents, or seasonal residents of the East End. Only year round and seasonal residents were asked to fill out the resource survey. All visitors, and every third resident, were given the recreational use 97 survey. When the survey was handed to the person, they were told that there are no right or wrong answers, and that all answers would be confidential. Interviewers were instructed to give brief and neutral answers to any questions.

VI. B. 2. Questionnaire Design VI. B. 2. a. Selection of Survey Format As mentioned above, we considered two methods for asking the public about their values for protecting and restoring natural resources   contingent valuation and contingent choice. We selected the contingent choice format for several reasons. At the time the survey was developed and implemented, the Management Committee had not yet determined specific actions that might be taken to protect and restore natural resources of the Estuary, and their results. Therefore, the survey needed to ask the public about their preferences for important natural resources that would most likely be affected by restoration and preservation programs. The contingent choice format allows for comparisons and valuation of many different combinations of improvements in resources that might be obtained by management actions. This will allow for assessment of programs that would affect any combination of the natural resources evaluated in the survey, either by ranking alternative programs or valuing benefits of specific programs.

Contingent choice may also minimize some of the problems associated with contingent valuation. For example, people often have trouble putting dollar values on specific natural resources. Contingent choice does not require that values be expressed in monetary terms, but elicits choices among alternative outcomes of actions. Therefore, responses are focused on tradeoffs among resources, and respondents are not asked to directly express monetary values, although these can be inferred from the analysis. Focus group participants found this type of question to be easier to answer.

An additional issue, which may be a factor in both contingent valuation and contingent choice surveys is the expression of " symbolic" values. This might be related to expressions of approval or disapproval of the actions to be taken to protect natural resources, or expressions of the importance of improving or protecting the environment in general, versus values for specific levels of natural resources. For example, a survey might ask respondents to state how much they would be willing to pay to protect a particular species of birds from oil spills. A person answering the survey may not care about those specific birds, but may state a positive willingness to pay because they think that oil spills should be prevented. Thus, their response may not reveal the value of the birds, per se. 98 Table V1.3  Survey Locations and Number Collected Location Number Collected Beaches: Orient State Beach, Southold 3 Southold Town Beach 10 Cedar Point County Beach, Southold 3 New Suffolk Beach, Southold 4 East Creek Marina Beach, Southold 2 Alberts Landing Town Beach, East Hampton 16 Fresh Pond Town Beach, East Hampton 9 Maidstone Beach, East Hampton 7 Indian Wells Town Beach, East Hampton 16 Hither Hills Beach, East Hampton 4 Montauk Point State Park, East Hampton 4 Wades and Cascade Beaches, Shelter Island 4 Shopping Greenport Corner 70 Areas: Greenport IGA 44 Genovese Shopping Center, Southold 71 King Kullen Shopping Center, Mattituck, Southold 11 K Mart Shopping Center, Riverhead 72 Downtown Southampton 33 IGA Southampton 16 King Kullen and Caldor Shopping Centers, Bridgehampton 107 IGA Shopping Center, Shelter Island 27 Libraries and Southold Library 4 Post Offices: Cutchogue Library, Southold 10 Riverhead Free Library 16 East Hampton Post Office 87 Sag Harbor Post Office 37 Shelter Island Heights Post Office 34 Shelter Island Center Post Office 60 Miscellaneous: New London Orient Point Ferry 19 Goose Creek, Southold 3 County Center Cafeteria, Riverhead 23 Okeanos Aquarium, Riverhead 71 Department of Motor Vehicles, Riverhead 39 Pindar Winery, Southold 19 John Drew Theatre, East Hampton 8 Location unidentified 5 Because the survey used in this study presents only outcomes of actions, respondents are not given the opportunity to focus on the means of achieving results. Thus, responses are less likely to be 99 expressions of approval or disapproval for actions, and more likely to focus on the specific natural resources. However, people may still express symbolic values for protecting the environment, beyond their values for the natural resource improvements. This is more easily detected and corrected for in contingent choice than contingent valuation, as discussed below.

Finally, it has been pointed out that " survey methods are better at estimating relative demand than absolute demand," even for market goods, and " absolute willingness to pay is hard to pin down" ( National Oceanic and Atmospheric Administration 1993, p. 4609). Thus, the relative values or priorities for natural resources elicited by a contingent choice survey may be valid, even if the absolute dollar values of specific resource changes are not. For a policy analysis that focuses on prioritizing actions, information on relative values and priorities for resources is more important than the estimation of total dollar values. Hence, contingent choice, rather than contingent valuation, may be more effective in obtaining the information needed to compare alternative policies.

Based on the results of focus groups and past research ( Mazzotta and Opaluch 1995), each comparison was designed to include only three attributes: Two of the five natural resources included in the survey, and the cost. This simplifies the choice, so that it is more likely that choices will be based on considering and balancing all of the attributes.

In the final survey, respondents were asked to select from a set of three hypothetical choices: no new action or one of two restoration/ protection programs. Each option was described by different levels of resulting natural resources, and the annual cost to each household. In early focus groups, only the two programs were presented, but focus groups participants indicated that a " no new action" option should be added, for two reasons. First, it allowed people to express a preference for no action if they did not support either program. Second, it provided a baseline from which to judge the benefits of each of the programs.

VI. B. 2. b. Selection of Natural Resources and Levels VI. B. 2. i. Natural Resources Included in the Survey Based on concerns expressed by participants in focus groups and natural resources identified as important by the Technical Advisory Committee, we selected five natural resources to be included in the survey:

  farmland   undeveloped land   wetlands   safe shellfishing areas   eelgrass We included farmland and undeveloped land because most people in preliminary interviews and focus groups expressed concern about the rate of development in the area and resulting loss of farmland and open space. Both farmland and open space are important components of the quality 20 Brown Tide is an algal bloom of the species Aureococcus anophagefferens, which first occurred in 1985 1988, and has subsequently recurred for shorter periods. The major brown tide episodes severely affected scallops, eelgrass, and other shellfish in the estuary ( see Suffolk County Department of Health Services 1992). 21 We compiled this information from the recent brown tide study ( Suffolk County Department of Health Services 1992); a 1981 land use report ( Long Island Regional Planning Board 1981); and information provided by the New York State Department of Environmental Conservation and other members of the Technical Advisory Committee. The acres of farmland and undeveloped land in the Long Island Regional Planning Board's land use report were presented for the entire East End. Therefore, to calculate the number of acres within the Peconic Estuary Program study area, we assumed that the fraction of farmland and undeveloped land within the study area is proportional to the amount of total land on the East End that is within the study area.

100 of life, or " sense of place," ( Sagoff 1992; Kellert 1995) for many residents of the East End, who enjoy the rural quality of the area and shopping at numerous local farm stands. The amount of development also affects environmental quality of the Estuary. Thus, it is important to consider uses of the surrounding land as well as resources more directly associated with the Estuary.

Many people were aware of the importance of wetlands to water quality and as wildlife habitat, and expressed concern for declines in the quantity of wetlands in the area. People also expressed concern for reopening closed shellfishing areas. This is due to the historical significance of shellfishing to the local economy, and its importance as a recreational activity, combined with the declines in shellfish caused by brown tide20 and the recent large increases in areas closed to shellfishing.

Finally, we included eelgrass for two reasons. First, much of the Estuary's eelgrass was destroyed by the brown tide, and one proposed action is restoring eelgrass areas. Second, eelgrass serves as a proxy for fish and shellfish populations. Many participants in the preliminary interviews and focus groups expressed great concern over declines in fish over the years. However, the technical consultants could not easily determine potential changes in these populations resulting from proposed actions. Therefore, we hoped that by including eelgrass, which serves as fish and shellfish habitat, the survey would capture some of these concerns.

VI. B. 2. ii. Levels of Natural Resources in the Survey The objective of the survey was to determine respondents' values for improvements in natural resources above a baseline level. We defined the baseline as the level that would exist in the year 2020, if no action is taken to preserve or restore the resource. We determined the baseline in consultation with the Technical Advisory Committee, based on historical declines and the judgment of experts, for each resource.

First, we determined the level of each resource in 1981 and the current level. 21 Next, we projected the levels in 2020 based on information and judgments provided by members of the Technical Advisory Committee regarding the anticipated change in the resource if no new actions were to be taken. In cases where no technical projections were available, we based the projection on extrapolation of past trends. 101 In the survey, respondents were presented with two pages of background information, which described the level of each resource in 1981, the current level, and projections of levels in 2020 if no new actions are taken. These levels are shown in Table 4, and the survey pages are shown in Appendix A. Respondents were told that " trends indicate approximate conditions in 2020," in order to make it clear that these are not precise, scientifically based projections.

When the survey was created, the Technical Advisory Committee had not completed their scientific studids of how potential actions might affect specific natural resources. To get around this lack of information, we chose the levels of each natural resource to be included in the survey in order to bracket a reasonable range of changes under various policy actions, including the " no new action" option. The Technical Advisory Committee assisted us in determining the largest realistic and feasible change for each resource. Although, in some cases, anticipated results of restoration and preservation programs are likely to be smaller than those presented in the survey, this approach is conservative because it brackets the full range of feasible resource changes. This allows for interpolation when valuing resource changes, rather than extrapolating results beyond the range included in the survey.

In the contingent choice questions, each resource was included at three different levels: The projected level for 2020 ( the " no new action," or baseline, scenario), and two levels associated with hypothetical programs that would preserve or restore the resource. In order to make the hypothetical context clear, survey respondents were told:

" The following programs are hypothetical. We are trying to learn which resources are most important to you and how much you would pay to protect them."

The levels for each resource and for cost are shown in Table IV. 5.

If no action is taken, farmland is projected to decrease by twenty five percent. The results of different hypothetical preservation programs were projected to reduce the loss of farmland to fifteen percent or maintain it at the current level. Undeveloped land was also projected to decrease by twenty five percent if no new action is taken. Because it did not seem realistic to preserve undeveloped land at the current level, hypothetical preservation programs were projected to reduce the loss of undeveloped land to ten percent or five percent. 102 Table VI. 4  Past, Present and Projected Natural Resource Levels Natural Resource 1981 Level 1995 Level Projected 2020 Level Farmlanda 13,500 acres 12,000 acres 9,000 acres Undeveloped Landa 74,000 acres 66,000 acres 50,000 acres Wetlandsb 18,000 acres 16,000 acres 12,000 acres Safe Shellfishing Areasb 28,000 acres 26,000 acres 25,000 acres Eelgrassb 10,000 acres 9,000 acres 8,000 acres a  Calculated based on Long Island Regional Planning Board 1981, and Suffolk County Department of Health Services 1992. b  Calculated based on Suffolk County Department of Health Services 1992 and information provided by NY State DEC.

Wetlands were projected to degrade by twenty five percent if no new action is taken; or they might be preserved at the current level or restored to ten percent above the current level. The baseline for safe shellfishing areas is a twenty five percent decrease, and hypothetical programs might maintain the current level or increase safe areas by ten percent. The baseline for eelgrass is a ten percent decrease, and it might be preserved at the current level or increased by twenty five percent.

The costs of the hypothetical programs were designed to learn how much people would pay for the resource changes presented above, and to bracket a range of costs that seemed feasible to members of the Management Committee. However, they are not intended to reflect actual costs of some particular set of programs. The no new action option was always presented at no additional cost to each household. The other levels of annual household cost included were $ 50, $ 100, $ 200, $ 300, and $ 500. 22 The combinations of attributes and levels were selected using a method based on Addelman's fractional factorial design, which produces orthogonal arrays of attributes ( Addelman 1962a, 1962b; Addelman and Kempthorne, 1961). This allows for statistical independence among the attributes so that the model estimation will be statistically efficient.

103 Table VI. 5  Levels of Natural Resources and Cost in the Survey No Action, Baseline Level (% Change) Level with Moderate Preservation or Restoration (% Change) Level with High Preservation or Restoration (% Change) Farmland 9,000 acres ( 25%) 10,000 acres ( 15%) 12,000 acres ( current) Undeveloped Land 50,000 acres ( 25%) 59,000 acres ( 10%) 63,000 acres ( 5%) Wetlands 12,000 acres ( 25%) 16,000 acres ( current) 17,500 acres (+ 10%) Safe Shellfishing Areas 25,000 acres ( 5%) 26,000 acres ( current) 29,000 acres (+ 10%) Eelgrass 8,000 acres ( 10%) 9,000 acres ( current) 11,000 acres (+ 25%) Cost Levels $ 0 $ 50 $ 100 $ 200 $ 300 $ 500 In order to statistically estimate values for each resource, we chose a statistical design that required sixty different comparisons. 22 However, we wanted to make sure that the survey could be answered in a reasonable amount of time ( 10 15 minutes). Thus, we included five comparisons in each of twelve different survey booklets.

VI. B. 2. iii. Description of The Survey Questionnaire The first page of the full survey booklet described the goal of the survey and showed a map of the relevant area. The first three questions asked about participation in recreational activities in the area; support for different possible actions; and opinions about brown tide.

Next the background information for the contingent choice questions were presented, followed by a page of instructions and the five comparison questions. The survey ended with a set of demographic questions about the respondent, in order to obtain information that could be used to adjust the results to reflect the values of a representative member of the public.

VI. C. DESCRIPTIVE RESULTS 23 Note that census data are only available for year round residents, so these results are for year round residents in the sample.

104 VI. C. 1. Who Responded to the Survey?

Table VI. 6 reports demographic information for all respondents, and Table VI. 7 compares the sample demographics to actual population demographics for gender, age, education, and income. Table VI. 8 gives the number of respondents by town.

The major characteristics of our sample can be summed up as follows:

  Most are year round residents ( 73%).   Most are homeowners ( 85%).   A majority live closer to the Peconic Estuary than to Long Island Sound or the Atlantic Ocean ( 58%).   15% live on the waterfront and a majority live ½ mile or less from the water ( 51%).   A majority have lived in the area for more than 10 years ( 63%).   The average household size is just under 3 members, and 46% have no children under age 18 in their household.   Most are employed ( 67%), and 21% are retired.   Compared to 1990 population figures, the sample contains a slightly higher percent of women and people in the middle age groups, and the sample population is better educated and more wealthy than the general population. 23 105 Table VI. 6  Respondent Demographics, Full Sample Question Category Number Percent of Sample 9: Primary Home 673 73.1% Type of Second Home 248 26.9% residence Did Not Answer 47 4.86% 10: Own 773 84.7% Own or Rent 140 15.3% rent Did Not Answer 55 5.7% 12: Peconic Bays 564 58.3% Closest Long Island Sound 216 22.3% water body Atlantic Ocean 219 22.6% 12: Waterfront 143 14.8% Distance # 1/ 2 Mile From Water 347 35.8% from any > 1/ 2 Mile From Water 382 39.5% water body Did Not Answer 96 9.9% 12: Waterfront 116 12.0% Dist. from # 1/ 2 Mile From Water 234 24.2% Peconic Bays > 1/ 2 Mile From Water 618 63.8% 13: 0 10 Years 297 30.7% Length of 11 20 Years 245 25.3% residence 21 30 Years 176 18.2% > 30 Years 185 19.1% Did Not Answer 65 6.7% 14: Female 540 57.8% Gender Male 394 42.2% Did Not Answer 34 3.5% 15: 1 2 Household Members 481 49.7% Size of 3 6 Household Members 430 44.4% household > 6 Household Members 24 2.5% Did Not Answer 33 3.4% Average hh size 2.94 16: No Children 440 45.5% Number 1 2 Children 254 26.2% of children > 2 Children 66 6.8% Did Not Answer 208 21.5%

Table VI. 6 ( Continued) 106 Question Category Number Percent of Sample 17: up to 20 23 2.5% Age 21 24 36 3.9% 25 34 121 13.1% 35 44 207 22.5% 45 54 194 21% 55 64 162 17.6% 65 74 144 15.6% 75 84 33 3.6% 85 and older 2 .2% Did Not Answer 46 4.8% 18: Less Than High School 5 .5% Education Some High School 16 1.7% High School Graduate 126 13.6% Some College 167 18.1% Associate's Degree 90 9.7% Bachelor's Degree 236 25.5% Advanced Degree 284 30.7% Did Not Answer 44 4.6% 19: Employed Full Time 497 53.9% Employment Employed Part Time 123 13.3% Full Time Homemaker 66 7.2% Full Time Student 28 3% Retired 197 21.4% Unemployed 11 1.2% Did Not Answer 46 4.8% 20: <$ 15,000 43 5.1% Income $ 15,000$ 24,999 77 9.1% $ 25,000$ 34,999 102 12.1% $ 35,000$ 49,999 111 13.2% $ 50,000$ 74,999 201 23.8% $ 75,000$ 99,999 113 13.4% $ 100,000$ 149,999 86 10.2% $ 150,000 or more 111 13.2% Did Not Answer 124 12.8% 107 Table V. 7  Population Demographics vs. Survey Respondent Demographics 1990 Populationa Year Round Resident Sampleb Seasonal Resident Sampleb Gender Female 51.83% 60.85% 51.36% Male 48.17% 39.15% 48.64% Age up to 20 23.89% 3.17% 0.45% 21 24 4.53% 4.76% 3.62% 25 44 29.07% 40.39% 26.24% 45 54 11.01% 19.05% 24.89% 55 64 11.04% 14.99% 22.62% 65 74 11.17% 14.11% 19.00% 75 84 7.25% 3.17% 3.17% 85 up 2.04% 0.35% 0.00% Education < High School 7.44% 0.00% 0.00% ( over Some H. S. 11.55% 1.53% 0.94% age 24) H. S. Graduate 31.77% 15.33% 2.83% Ed. categories 1 3 50.76% 16.86% 3.77% Some College ( Ed. cat. 4) 18.35% 21.26% 8.49% Assoc. Degree 6.66% 11.30% 7.08% Bachelor's Deg. 13.51% 23.18% 31.60% Advanced Deg. 10.72% 27.39% 49.06% Ed. categories 5 7 30.90% 61.88% 87.74% Income < $ 15,000 19.17% 5.45% 1.81% $ 15,000$ 24,999 14.60% 11.60% 1.81% Inc. categories 1& 2 33.77% 17.05% 3.62% $ 25,000$ 34,999 14.40% 14.94% 4.98% $ 35,000$ 49,999 17.52% 15.99% 5.88% Inc. categories 3& 4 31.93% 30.93% 10.86% $ 50,000$ 74,999 18.90% 27.94% 14.48% $ 75,000$ 99,999 7.04% 11.07% 18.55% $ 100,000$ 149,999 4.93% 7.38% 18.10% Inc. categories 5 7 30.86% 46.40% 51.13% $ 150,000 and up ( Inc. cat. 8) 3.41% 5.62% 34.39% a  The 1990 population figures are for year round residents only; sources of data are Long Island Lighting Company 1995 and Suffolk County Department of Planning 1991, based on 1990 census data and projections. No demographic data are available for seasonal residents. b  Sample percents refer to percentages of those who answered the question. 108 Table VI. 8  Numbers of Respondents by Town Town Number of Respondents Percent of Sample Brookhaven 19 1.9% Riverhead 110 11.4% Southold 247 25.5% Shelter Island 136 14.1% East Hampton 182 18.8% Sag Harbor 66 6.8% Southampton 139 14.4% Other 25 2.6% Did not Answer 44 4.5%

VI. C. 2. Participation in Recreational Activities The responses to Question 1, which asked about participation in recreational activities, are shown in Table VI. 9. Almost all of the respondents ( 97%) participate in at least one of the listed activities, and 81% participate in at least one activity in the Peconic Estuary. Swimming is the most popular activity, with 86 percent participating. Swimming is followed by walking and hiking ( 71%), boating ( 54%), fishing ( 53%), other beach use ( 34%), shellfishing ( 32%), artwork ( 26%) and other activities ( 11%).

More people participate in activities on or around the Peconic Estuary than any other water body. This result is consistent with results of a telephone survey of residents conducted in the fall of 1993 by the Center for Community Research of Suffolk Community College ( the SCC survey), which also found that the Peconic Estuary System was used more frequently than any other water body ( Suffolk Community College 1994).

VI. C. 3. Support for Management Actions Table VI. 10 summarizes the responses to Question 2, which asked about support for specific management actions. We included this question in order to allow respondents to express their opinions about proposed actions, and to give them an idea of the types of actions that might be taken to obtain the natural resource changes described in the contingent choice questions. 109 Table VI. 9  Participation in Recreational Activities Activity Peconic Bays Atlantic Ocean L. I. Sound Other All Locations Fishing 391 ( 40.4%) 167 ( 17.3%) 216 ( 22.3%) 74 ( 7.6%) 516 ( 53.3%) Shellfishing 261 ( 27.0%) 18 ( 1.9%) 55 ( 5.7%) 39 ( 4.0%) 311 ( 32.1%) Walking/ Hiking 518 ( 53.5%) 339 ( 35.0%) 306 ( 31.6%) 69 ( 7.1%) 684 ( 70.7%) Swimming 610 ( 63.0%) 468 ( 48.3%) 362 ( 37.4%) 79 ( 8.2%) 833 ( 86.1%) Other Beach Use 201 ( 20.8%) 169 ( 17.5%) 131 ( 13.5%) 44 ( 4.5%) 324 ( 33.5%) Boating 432 ( 44.6%) 144 ( 14.9%) 224 ( 23.1%) 67 ( 6.9%) 520 ( 53.7%) Artwork 204 ( 21.1%) 120 ( 12.4%) 129 ( 13.3%) 40 ( 4.1%) 252 ( 26.0%) Other 68 ( 7.0%) 46 ( 4.8%) 51 ( 5.3%) 24 ( 2.5%) 103 ( 10.6%) All Activities 779 ( 80.5%) 586 ( 60.5%) 534 ( 55.2%) 205 ( 21.2%) 936 ( 96.7%)

As expected, all of the actions are supported by a majority of respondents. The actions with the greatest level of support are building more pumpout stations so that boat discharge can be prohibited; improving sewage treatment plants; public education; and more research on water quality issues. The actions with the least support are required pumpouts of septic systems, and restrictions on the use of lawn chemicals.

The Suffolk Community College survey also asked about support for actions. Although their questions were more general and not perfectly comparable to those presented here, their results are similar. For example, 83 percent of their sample support stronger building regulations, which is comparable to the 76.3 percent of our sample who support zoning regulations, the 78.7 percent who support waterfront restrictions, and the 76.7 percent who support restrictions on vegetation. The SCC survey found that 71 percent of respondents were willing to change fertilizers, which is consistent with the 68.2 percent of this sample who support restrictions on fertilizers and lawn 110 Table VI. 10  Support for Actions Strongly Strongly No Average Action Support Support Neutral Oppose Oppose Opinion Rank Prohibit sewage discharge from boats. ( requires building more pumpout stations) 79.0% 10.5% 1.0% 1.2% 1.4% 6.7% 1 Improving sewage treatment plants. 78.0% 11.8% 1.5% 0.3% 0.6% 7.7% 2 Public education to teach people how to reduce their impacts on the environment. 70.0% 17.4% 4.6% 0.7% 1.0% 6.2% 3 More research on water quality issues. 64.9% 19.4% 6.9% 0.7% 1.1% 6.9% 4 Better enforcement of existing regulations. 66.3% 17.0% 3.2% 0.4% 0.9% 12.1% 5 Restrictions on waterfront property, including requiring buffer zones ( areas left untouched) between development and the water, drywells for roof runoff, etc. 61.9% 16.8% 8.5% 2.5% 2.5% 7.9% 6 Zoning to limit future development. 62.3% 14.0% 11.1% 2.4% 2.2% 8.1% 7 Limiting removal of vegetation on newly developed land. 58.5% 18.2% 9.9% 2.8% 2.6% 8.1% 8 Requiring repair or upgrade of septic systems when property is sold or improved. 51.4% 21.8% 12.4% 2.5% 2.6% 9.3% 9 Controlling stormwater runoff with diversion, catch basins, etc. 47.6% 26.1% 12.9% 1.7% 0.9% 10.7% 10 Restricting use of fertilizer and lawn chemicals for residential property. 43.4% 24.8% 16.7% 5.4% 2.2% 7.5% 11 Requiring pumpouts of existing septic systems every 4 years. 32.7% 21.3% 20.9% 6.7% 4.2% 14.2% 12 chemicals. While 75 percent of the SCC sample stated that they were willing to improve their own septic system, 73.2 percent of our sample support required upgrades when property is sold or improved. However, only 54 percent support required pumpouts every four years. 24 A total score for all actions was calculated by summing the level of support for all actions, where 1 indicates strong support and 5 indicates strong opposition. The mean score for members of environmental groups was 19.51, and that of non members was 25.1, with a lower score indicating greater strength of support.

111 It was hypothesized that certain groups of people would be more likely to support or oppose specific actions. For example, boaters, who would be most inconvenienced by discharge prohibitions, were expected to show less support for this action. However, 90.4 percent of boaters, versus 88.2 percent of non boaters support no discharge regulations ( although, based on a test of equality of the means, the difference in the mean strength of support for discharge prohibitions between boaters and non boaters is not statistically significant). Thus, boaters appear to be willing to undergo some inconvenience in exchange for cleaner water. This is consistent with the SCC survey finding that 63 percent of boaters are willing to change their boating habits to improve water quality.

Based on similar reasoning, waterfront residents were expected to be less likely to support restrictions on waterfront property. There is some support for this hypothesis, with 76.2 percent of waterfront residents, 79.8 percent of residents who live up to ½ mile from the water, and 82.5 percent of people who live more than ½ mile from the water supporting restrictions on waterfront property. However, there was no statistically significant difference in the mean strength of support across these groups.

Finally, the strength of support for all actions was compared for members of environmental groups versus those who are not members. 24 Although the average level of support was slightly greater for members of environmental groups, the means were not statistically significantly different.

From these results, it can be concluded that the majority of respondents are in favor of environmental actions, with slightly lower support for some actions that are personally inconvenient. However, as expected, the results appear to be primarily symbolic of a general desire for action. That is, people seem to support most actions. Thus, these results do not provide adequate information with which to make policy decisions.

VI. C. 4. Opinions About Brown Tide Question 3 asked respondents whether they have heard of brown tide, and if they have, about their level of concern, whether their activities have been affected, and whether they support funding for further study. The results are reported in Table V. 1.

Most people ( 90%) had heard of brown tide, and 75 percent said that at least one activity was affected by the brown tide. The most commonly affected activity was swimming, with 59 percent of those who had heard of brown tide affected, followed by shellfishing ( 40%) and fishing ( 37%). A small number of people said that boating or other activities were affected. The most commonly mentioned " other" activity affected was eating seafood. The majority ( 97%) of those who had heard 112 of brown tide are concerned ( 21%) or very concerned ( 76%) about it. Most of those who had heard of brown tide ( 86%) also support more funding to study brown tide and possible remedies for it.

Table V. 11  Responses to Brown Tide Questions Heard of Brown Tide Heard of and Affected by Brown Tide 872 ( 90.1%) 725 ( 74.9%) Q. 3B  How concerned are you about brown tide? No Opinion 22 ( 2.5%) 8 ( 1.1%) Not Concerned 5 ( 0.6%) 1 ( 0.1%) A Little Concerned 183 ( 21.0%) 120 ( 16.6%) Very Concerned 662 ( 75.9%) 596 ( 82.2%) Q. 3D  Do you support or oppose more funding to study the causes of brown tide and how to end it? No Opinion 26 ( 3.0%) 14 ( 1.9%) Opposed 17 ( 1.9%) 13 ( 1.8%) Neutral 79 ( 9.1%) 61 ( 8.4%) Support 750 ( 86.0%) 637 ( 87.9%) Q. 3C  Activities Affected by Brown Tide Swimming 512 ( 58.7%) Boating 121 ( 13.9%) Fishing 319 ( 36.6%) Shellfishing 349 ( 40.0%) Other 89 ( 10.2%)

VI. C. 5. Membership in Organizations Question 21 asked respondents whether they are members of different types of organizations. The choices listed are sports, community service, environmental, political, business related, farm or agricultural, religious, and civic organizations. The purpose of this question was to supplement the standard demographic questions by providing information about respondents' interests and level of commitment to different causes. Table VI. 12 shows the statistics for membership in organizations. Sixty five percent of respondents belong to at least one organization, and 31 percent belong to an environmental organization. This is comparable to the results of the SCC survey, which found that 57 percent of respondents had donated to environmental organizations, and 21 percent had been active in an environmental group. 113 Table VI. 12  Membership in Organizations Organization Number (%) Sports 171 ( 17.7%) Community Service 182 ( 18.8%) Environmental 300 ( 31.0%) Political 146 ( 15.1%) Business 94 ( 9.7%) Agricultural 24 ( 2.5%) Religious 275 ( 28.4%) Civic 152 ( 15.7%) Other 91 ( 9.4%) Any Organization 628 ( 64.9%)

VI. D. ESTIMATES OF ECONOMIC VALUE VI. D. 1. Responses to Contingent Choice Questions As discussed above, there were 60 different choice questions, with five included in each booklet, to create twelve different booklets. There were between 73 and 85 responses to each of the twelve booklets. The demographic groups   year round vs. seasonal residence, gender, age, education, employment, and income   were fairly evenly distributed across booklets.

Table VI. 13 shows how many of the contingent choice questions were answered by respondents. Of the 968 people who completed the survey, 897 ( 92.7%) answered at least one of the five contingent choice questions in their survey booklet, and 790 ( 81.6%) answered all of the choice questions. Of the 4,840 total choice questions, 4,307 ( 89%) were answered. Seventy one respondents ( 7.3% of the sample) did not answer any of the choice questions. 25 Of respondents who answered more than one question, 434 ( 48%) chose Program A for more than half of the questions they answered, and 76 ( 8.5%) chose Program A for all of the questions they answered. This compares to 287 ( 32%), who chose Program B for more than half of the questions they answered, and 15 ( 2%) who chose Program B for all of the questions they answered. 26 These calculations are described in detail the Technical Appendix.

114 Table VI. 13  Number of Choice Questions Answered Number Answered Number of Respondents Percent of Total Sample 0 71 7.3% 1 8 .8% 2 8 .8% 3 31 3.2% 4 60 6.2% 5 790 81.6%

Older respondents, and those with lower education and income levels, were slightly less likely to answer all of the choice questions. Of those who answered at least one choice question, almost all ( 91.6%) chose an action, rather than no action, for more than half of their answers. Only 27 people ( 3.0%) chose no action for all of the choice questions answered. These results demonstrate the strong environmental concern expressed by survey respondents, but also may suggest some symbolic bias, if respondents voted to " take action," irrespective of the associated levels of resource protection and cost. Below we discuss some methods that we employed to identify and control for this possible bias.

Of those who chose either Program A or Program B, more people chose Program A than Program B. 25 This may indicate a tendency to select the option placed in the center, or may simply indicate that the options presented as Program A were preferred more often. This is discussed further in the Technical Appendix.

VI. D. 2. Results and Discussion The statistical models used to estimate the results are described in detail in the Technical Appendix. These methods calculate the relative weights, or values, for an additional acre of each natural resource, and for an additional dollar of cost to each household. These weights are measured by the estimated coefficients for each resource and cost. From these coefficients, relative values for the different resources, and dollar values for protecting an additional acre of each resource, can be calculated. 26 27 Model 1 is the standard conditional logit model, and Model 2 is the nested logit model.

115 The results for two different models are reported in Table VI. 14.27 The model results indicate that the order of priorities for protection or restoration of resources is as follows: farmland, eelgrass, wetlands, shellfish, and undeveloped land.

Table VI. 14  Estimation Results Coefficient Value / Acre/ hh/ year 95% Confidence Intervala Avg. Value / Acre/ Yearb Model 1: c Farmland 0.000511 $ 0.136 $ 0.122 $ 0.150 $ 9,979 Undeveloped Land 0.000107 $ 0.028 $ 0.025 $ 0.032 $ 2,080 Wetlands 0.000336 $ 0.089 $ 0.079 $ 0.100 $ 6,560 Shellfish Areas 0.000233 $ 0.062 $ 0.053 $ 0.071 $ 4,555 Eelgrass 0.000419 $ 0.111 $ 0.098 $ 0.125 $ 8,186 Cost  0.003765 Model 2: d Program B . 1586 Farmland .000300 $ 0.087 $ 0.073 $ 0.101 $ 6,398 Undeveloped Land .000056 $ 0.016 $ 0.013 $ 0.019 $ 1,203 Wetlands .000228 $ 0.066 $ 0.056 $ 0.077 $ 4,863 Shellfish Areas .000128 $ 0.037 $ 0.031 $ 0.044 $ 2,724 Eelgrass .000281 $ 0.082 $ 0.069 $ 0.094 $ 6,003 Cost . 003441 a  The 95% confidence interval indicates the range within which the " true" value is likely to fall, with a 95% probability. b  Calculated based on 73,423 households. c  Conditional Logit model. d  Nested Logit model. 28 This is the rate required by the government for Federal water and related land resources planning, and is based on the average yield during the preceding fiscal year on interest bearing marketable securities of the United States with 15 years or more remaining to maturity ( U. S. Department of the Interior 1995).

116 As discussed above, symbolic values are often an issue for both contingent valuation and contingent choice methods. The estimation results show that this is an issue in this survey, and that respondents to the survey are more likely to choose to take action, independent of the specific results of the action. Thus, these respondents may be expressing a symbolic willingness to pay to take action, as opposed to revealing values for the specific natural resources of concern. The higher probability of choosing to take action is not surprising, given the level of concern among residents of the area for the environment of the Estuary.

However, Model 2 provides a correction for such values, by separating the probability of taking action vs. no action, from the probability of selecting either Program A or Program B. Thus, the estimated dollar values for Model 2 are approximately half to two thirds as large as those estimated from Model 1. The estimated dollar values range from around $ 2.1 thousand per acre per year for undeveloped land, to around $ 10 thousand for farmland for Model 1; and around $ 1.2 thousand to $ 6.4 thousand for Model 2. The values from Model 2 might be interpreted as the portion of respondents' willingness to pay to take action which can be attributed to the described changes in natural resource levels. This is smaller than the estimated value in Model 1, which includes a " symbolic" effect.

Although the estimated dollar values differ, both models result in the same ordering of priorities and relative values for the natural resources. These results indicate that priorities and relative values are robust with respect to different model specifications, and are independent of symbolic effects, but that the estimated dollar values vary somewhat, although they are close in magnitude. Therefore, it may be concluded that the model is relatively robust to different specifications, and that the proportion of value that is " symbolic" is not great.

3. Discounted Present Values Discounting is a means of aggregating dollar values over time, and is based on the idea that natural resources are assets that provide a flow of services over time. Thus, "[ t] he economic value of a resource environment system as an asset can be defined as the sum of the discounted present values of the flows of all of the services. The benefit of any public policy that increases the flow of one type of service is the increase in the present value of that service" ( Freeman 1993, p. 5).

The discounted present values for two discount rates are presented in Table IV. 15. The 7 percent rate is used to approximate a " typical" interest rate for public planning. For example, 7.625 percent was the official water resources planning discount rate for fiscal year 1996 ( U. S. Department of the Interior 1995). 28 The 3 percent rate is included for comparison, because it is often argued that the social discount rate should be lower. The 25 year time horizon was chosen to match the time period 117 presented in the survey. The 100 year time horizon was used to identify values associated with a longer time horizon.

Table IV. 15  Comparison of Discounted Present Values PV/ Acre r=. 03, t= 100* PV/ Acre r=. 03, t= 25* PV/ Acre r=. 07, t= 100* PV/ Acre r=. 07, t= 25* Model 1: Farmland $ 315,314 $ 173,759 $ 142,387 $ 116,286 Undeveloped Land $ 65,728 $ 36,221 $ 29,681 $ 24,240 Wetlands $ 207,286 $ 114,228 $ 93,605 $ 76,446 Shellfish Areas $ 143,945 $ 79,323 $ 65,001 $ 53,086 Eelgrass $ 258,675 $ 142,547 $ 116,811 $ 95,398 Model 2: Farmland $ 202,175 $ 111,412 $ 91,297 $ 74,562 Undeveloped Land $ 38,028 $ 20,956 $ 17,172 $ 14,024 Wetlands $ 153,659 $ 84,676 $ 69,388 $ 56,669 Shellfish Areas $ 86,070 $ 47,430 $ 38,867 $ 31,742 Eelgrass $ 189,703 $ 104,539 $ 85,665 $ 69,962 * r is the discount rate; t is the time horizon in years.

VII. E. CONCLUSIONS AND SUMMARY This section described the development, implementation, and results of a survey of public values for important natural resources of the Peconic Estuary System. We developed the survey through an extensive six month process of interviews, focus groups, and pretests. We selected the contingent choice method to elicit values for five important resources: farmland, undeveloped land, wetlands, safe shellfishing areas, and eelgrass. The survey was implemented in August, 1995, to 968 yearround and seasonal residents of the East End of Long Island.

In summary, residents of the East End are very concerned about protecting of the area's natural resources, and are willing to pay a significant amount to do so. Our statistical results indicate that respondents are so concerned about protecting the East End's environment that they would be willing to pay to take action, independent of specific results of the action. We corrected for this " symbolic" value in our estimation models, and present the results of both the standard and " corrected" model. 118 Overall priorities for the five natural resources included in this survey are for farmland, followed by wetlands, eelgrass, shellfish areas, and undeveloped land. Discounted present values for the most conservative model range from around $ 14 thousand to preserve an acre of undeveloped land, to around $ 75 thousand to preserve an acre of farmland. The results of this survey will be combined with those of several other studies to evaluate benefits of a set of proposed management actions for the Estuary.

REFERENCES Addelman, Sidney. 1962a. " Orthogonal Main Effect Plans for Asymmetrical Factorial Experiments," Technometrics 4 No. 1 ( February): 21 46. Addelman, Sidney. 1962b. " Symmetrical and Asymmetrical Fractional Factorial Plans," Technometrics 4 No. 1 ( February): 47 57. Addelman, Sidney, and O. Kempthorne. 1961. Orthogonal Main Effect Plans. United States Airforce, Office of Aerospace Research, Aeronautical Research Laboratory, ARL Technical Report 79. Freeman, A. Myrick III. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Washington, D. C.: Resources for the Future. Greene, William H. 1993. Econometric Analysis. New York: Macmillan. Greene, William H. 1995. LIMDEP Version 7.0 User's Manual. Bellport, NY: Econometric Software, Inc. Hanemann, W. Michael. 1982. " Applied Welfare Analysis with Qualitative Response Models," California Agricultural Experiment Station, October. Hanemann, W. Michael. 1984. " Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses," American Journal of Agricultural Economics 66 ( August): 332 341. Kellert, Stephen. 1995. " Environmental Values, Coastal Context, and a Sense of Place," in L. Anathea Brooks and Stacy VanDeveer, eds., Saving the Seas: Science, Values, and International Governance. New Haven: Yale University Press. Kling, Catherine L., and Joseph A. Herriges. 1995. " An Empirical Investigation of the Consistency of Nested Logit Models with Utility Maximization," American Journal of Agricultural Economics 77 No. 4 ( November): 875 884. Kling, Catherine L., and Cynthia J. Thomson. 1996. " The Implications of Model Specification for Welfare Estimation in Nested Logit Models," American Journal of Agricultural Economics 78 No. 1 ( February): 103 114. Long Island Lighting Company. 1995. Population Survey 1995: Current Population Estimates for Nassau and Suffolk Counties. Hicksville, NY. Long Island Regional Planning Board. 1981. Land Use: Quantification and Analysis of Land Use for Nassau and Suffolk County. Maddala, G. S. 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press. Mazzotta, Marisa J. and James J. Opaluch. 1995. " Decision Making When Choices are Complex: A Test of Heiner's Hypothesis," Land Economics 71, No. 4 ( November): 500 515. 119 McFadden, Daniel. 1981. " Econometric Models of Probabilistic Choice," in C. F. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications. Cambridge: MIT Press. National Oceanic and Atmospheric Administration. 1993. Natural Resource Damage Assessments Under the Oil Pollution Act of 1990. 58 Federal Register 4601 ( January 15). Neave, H. R., and P. L. Worthington. 1988. Distribution Free Tests. London: Unwin Hyman. Sagoff, Mark. 1992. " Settling America or The Concept of Place in Environmental Ethics," Journal of Energy, Natural Resources and Environmental Law 12 No. 2: 349 418. Suffolk Community College. 1994. Peconic Estuary Public Opinion Survey. Center for Community Research, Riverhead, NY. Suffolk County Department of Health Services. 1992. Brown Tide Comprehensive Assessment and Management Program. Suffolk County Department of Health Services. 1994. Peconic Estuary Program Action Plan. Suffolk County Department of Planning. 1991. Estimated Peak Seasonal Population  1990. Unpublished spreadsheet. Suffolk County Department of Planning. 1991. Estimated Peak Seasonal Population  1990. Unpublished spreadsheet. U. S. Department of the Interior. 1995. Change in Discount Rate for Water Resources Planning. 60 Federal Register 66323 ( December 21) 120 Appendix A  Example Survey *

* Readers who would like to obtain a copy of the survey should contact the authors.

Appendix B  Technical Appendix Based on the random utility model, relative values and priorities for the natural resources were estimated using the standard conditional logit method ( Greene 1993; Maddala 1983), where ( 1) P ij j j i j j i k = +

+     exp( ) exp( ) . '

'         z zw z zw Pij is the probability that individual i will select option j; zj is a vector of attributes of the choice ( e. g., the levels of natural resources and the cost), which may also vary across individuals; wi is a vector of characteristics of the individual; and   and   are vectors of parameters of the model, estimated using maximum likelihood techniques. Box Cox tests were carried out to select an appropriate functional form and, based on these tests, the linear form was chosen for all subsequent estimation.

The second alternative specification is the nested logit model, which allows for correlations between the two action alternatives. The nested logit model was structured so that respondents are assumed to first choose whether to take action or not, and then to choose a specific program conditional on taking action. In the nested logit model, the probability that an individual chooses alternative k is ( 2) P Pk jP j kj = ( | ) ( ).

The choice probability for each of the three lowest level alternatives is conditional on the choice to take action or not. In this case, if the person chooses not to take action, then the probability of selecting the " No New Action" alternative is 1, since it is the sole alternative on that branch of the nested model. If they choose to take action, the probability of selecting " Program A" or " Program B" is ( 3) P k j V V k j n j n j k j n j n j k j j ( | ) exp( ) exp( ) exp( ) exp( ) exp( ) exp( ) , |

| | |

| | | = =

  =

          x x x J 14 Note that, alternatively, this could also suggest use of the incorrect functional form, although the Box Cox test shows that the linear form is the most appropriate of the commonly used functional forms.

121 where k is one of the two alternatives, Program A or Program B; j is the choice to take action; n is the number of alternatives in choice j, which would be 2; and Jj is the inclusive value for choice j, which represents the expected maximum utility from the choice of an alternative that sub branch.

This is defined as ( 4) J j nj n j =     log( exp( )). | |   x The probability of choosing to take action or not is ( 5) P j Y J Y J j j j m m m m ( ) exp( ) exp( ) , =   +

  +           where m is the number of branches, which in this case is two.

The parameter J is the inclusive value coefficient, which is related to the correlation between alternatives within a branch. A value of   between 0 and 1 indicates that there is greater substitutability within, rather than across, groups of alternatives. In terms of this study, this would indicate that Program A and Program B are closer substitutes than either program and the " No New Action" alternative. If   is equal to 1, then all alternatives are equally substitutable, and the model becomes identical to the standard conditional logit model ( McFadden 1981; Kling and Herriges 1995; Kling and Thomson 1996).

The results of the conditional logit model were compared to two alternative specifications. These results are presented in Table B 1. The first is the conditional logit model with two alternativespecific constants, one for the choice of an action versus no action, and one for Program B. Thus, the coefficient on the first constant term reflects factors other than the levels of attributes that affect the choice of an action versus the choice of no action. 14 For example, respondents may be expressing a symbolic willingness to pay to take action, as opposed to revealing values for the specific natural resources of concern. The coefficient on the second constant term reflects any difference in preference for Program B versus Program A that is unrelated to the levels of attributes of A and B, such as an order effect.