ECONOMIC ANALYSIS OF ENVIRONMENTAL REGULATIONS:

APPLICATION OF THE RANDOM UTILITY MODEL TO RECREATIONAL BENEFIT ASSESSMENT FOR THE MP& M EFFLUENT GUIDELINE Final Report Lynne G. Tudor, Ph. D.

Engineering and Analysis Division Office of Water US EPA 401 M Street, SW Washington, DC 20460 Telephone: ( 202) 566 1043 Tudor. Lynne@ EPA. gov Elena Besedin, Ph. D .

Abt Associates, Inc.

55 Wheeler Street Cambridge, MA 02138 Telephone: ( 617) 349 2770 Elena_ Besedin@ abtasssoc. com Stuart Smith Abt Associates, Inc.

4800 M ontgomery Lane Bethesda, MD 20814 5341 Telephone: ( 301) 347 5619 Stuart_ Smith@ abtassoc. com December 2002 Ohio Case Study 2 TABLE OF CONTENTS Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Modeling the Site Choice Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Modeling Trip Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 Calculating Welfare Changes from Water Quality Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Extrapolating Results to the State Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1 The Ohio Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Estimating the Price of Visits to Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Site Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Site Choice Model Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1 Fishing M odel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Boating Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.3 Swimming Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.4 Viewing ( Near water Activity) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4 Trip Participation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5 Estimating Benefits from Reduced MP& M Discharges in Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.1 B enefiting Reaches in Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.2 E stimating Recreational Benefits in Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 6 Limitations and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6.1 One State Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6.2 Including One Day Trips Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 6.3 Nonuse Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 6.4 Potential Sources of Survey Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Ohio Case Study 1 See Chapter 15 of   Economic, Environmental, and Benefits Analysis of the Final Metal Products & Machinery Rule  ( hereafter EEBA) for detail.

3 INTRODUCTION The recreational benefits analysis outlined in this report focuses on Ohio as a case study of the MP& M regulation's expected benefits. EPA combined water quality modeling and a random utility model of consumer behavior ( RUM) to assess how changes in water quality from the MP& M regulation will affect consumer valuation of water resources for recreational uses.

The RUM analysis provides a framework for estimating the effect of ambient water quality and other site characteristics on the total number of trips taken for different water based recreation activities and the allocation of these trips among particular sites.

The Agency used this case study to address limitations inherent in the benefits transfer method used in the analysis of recreational benefits at the national level. 1 The RUM model assesses water quality characteristics directly affected by the MP& M regulation, such as presence of ambient water quality criteria ( AWQC) exceedances and nonconventional nutrient Total Kjeldahl Nitrogen ( TKN) concentrations and their effect on recreation behavior. The direct link between the water quality measures included in the RUM model and the water quality measures affected by the regulation, as well as the site specific nature of the analysis reduce uncertainty in benefit estimates. In general, RUM models are well regarded in the economic literature and when these models are appropriately applied, the results are thought to be quite reliable.

Benefits transfer results are subject to uncertainty because water quality changes evaluated in available recreation demand studies are only roughly comparable with water quality measures considered in regulatory development. This case study analysis improves upon previous recreation demand studies that focused mainly on directly observable water quality effects,

e. g., designated use support ( i. e., whether a water body supports fishing), the presence of fish advisories, an oil sheen, or eutrophication. The O hio case study includes unobservable water quality effects as well. The MP& M regulation affects a broad range of pollutants, many of which are toxic to human and aquatic life but are not directly observable ( i. e., priority and nonconventional pollutants. These unobservable toxic pollutants degrade aquatic habitats, decrease the size and abundance of fish and other aquatic species, increase fish deformities, and change watershed species composition. Water quality changes ( i. e., changes in toxic pollutant concentrations) affect consumers  water resource valuation for recreation,

even if consumers are unaware of changes in ambient pollutant concentrations.

This study allows for a more complete estimate of recreational benefits from reduced discharges of MP& M pollutants. In addition to estimates of recreational benefits from reduced frequency of AWQC exceedances, the Ohio case study evaluated changes in the water resource values from reduced discharges of TKN. The analysis also values additional recreational uses not addressed in the national analysis, such as swimming.

The study used data from the National Demand Survey for Water Based Recreation ( NDS), conducted by U. S. EPA and the National Forest Service, to examine the effects o f in stream pollutant concentrations on consumer decisions to visit a particular water body ( U. S. EPA, 1994).

1 METHODOLOGY 1.1 Overview The Ohio study combines direct simulation and inferential analyses to assess how changes in water quality will affect consumers  valuation of water resources.

The direct simulation analysis component estimates baseline and post compliance water quality at recre ation sites actually visited by the surveyed consumers and all other sites within the consumers' choice set, visited or not.

The inferential analysis component, a RUM analysis of consumer behavior, estimates the effect of ambient water quality and other site characteristics on the total number of trips taken for different water based recreation activities and the allocation of these trips among particular recreational sites. The RUM analysis is a travel cost model ( TCM), in which the cost to travel to a particular recreational site represents the   price  of a visit. Ohio Case Study 2 The Agency also attempted a model structure that allows for interaction among the choice of recreational activities. In this model, a person first chooses a recreational activity and then chooses a site. This model did not perform very well because less than ten percent of recreational users included in the dataset participate in all four activities.

4 The main advantage of the RUM model is inclusion of the effect of substitute sites on site values. For any particular site,

assuming that it is not totally unique in nature, the availability of substitutes makes the value for that site lower than it would be without available substitutes.

EPA modeled two consumer decisions:

 how many water based recreational trips to take during the recreational season ( the trip participation model); and  conditional on the first decision, which recreation site to choose ( the site choice model).

The econometric estimation proceeded in two steps, each correspo nding to the above decisions. The Agency estimated these decisions in reverse order ( i. e., EPA modeled the second decision, site choice, first).

 Modeling the Site Choice Decision . Assuming that a consumer decides to take a water based recreation trip, EPA estimated the likelihood that the consumer will choose a particular site as a function of site characteristics, the price paid per site visit, and household income. A consumer weighs the attributes for various " choice set" sites against the travel costs to each site. These travel costs include both the cost of operating a vehicle and the opportunity costs of time spent traveling. The consumer then weighs the value given to the site's attributes against the co st of getting to the site when making a site selection. The site choice model estimates how recreational users value access to sp ecific sites, and estimates per trip economic values for changes in water quality at recreational sites in the study area.

EPA estimated the site choice model using a two level nested multinomial logit ( NMNL) model, which groups sites with similar characteristics. The nested logit model assumes that individuals first choose the group of sites and then a site within that group. This study assumes that individuals first choose a water body type ( Lake Erie, rivers, or small lakes) and then a sp ecific site. EPA used the estimated site choice model co efficients to estimate the value to the consumer of being able to choose among Ohio recreation sites on a given day. This measure is referred to as the   inclusive value.

 Modeling Trip Frequency. The site choice models estimated in the previous step treat the total number of recreational trips taken each season as exogenous to the site selection. The Agency estimated the expected number of trips taken during the recreation season using a Negative Binomial Poisson model ( Hausman et al.,

1995; Feather et al., 1995; and Creel and Loomis, 1992), which treats trip frequency as a pre season decision regarding total participation in a given recreation activity.

EPA estimated the total number of trips during the recreation season as a function of the expected maximum utility ( inclusive value) from recreational activity participation on a trip, and socioeconomic characteristics affecting demand for recreation trips ( e. g., number of children in the household). The coefficient of the individual s expected maximum utility of taking a trip) provided a means of estimating the seasonal welfare effect of water quality improvements, because changes in water quality change the value of available recreation sites.

Estimating the site choice and total trip p articipation models jointly is theo retically possible, but computational requirements make an integrated utility theoretic model infeasible. EPA estimated separate site choice and trip frequency models for the four recreational activities: boating, swimming, fishing, and near water recreation ( e. g., viewing wildlife). 2 The Agency used estimated coefficients of the indirect utility function with estimated changes in water quality to calculate per trip changes in consumer welfare from improved water quality at recreation sites within each consumer choice set. Trip frequency per season increases if site water quality changes are substantial. A sample consumer s expected seasonal welfare gain is therefore a function of both welfare gain per trip and the estimated change in number of trips per season.

Combining the trip frequency model s prediction of trips under the baseline and post compliance and the site choice model s corresponding per trip welfare measure yields the total seasonal welfare measure. Ohio Case Study 5 EPA calculated each individual s seasonal welfare gain for each recreatio n activity from post compliance water quality changes, and then used Census population data to aggregate the estimated welfare change to the state level. The sum of estimated welfare changes over the four recreation activities yielded estimates of total welfare gain.

To analyze water quality improvement benefits in the RUM framework, EPA used available discharge, ambient concentration,

and other relevant data to measure baseline and post compliance water quality at the impact sites. Appendix H of the EEBA report provides detail on water quality modeling used in this analysis.

1.2 Modeling the Site Choice Decision EPA used the RUM framework to estimate the probability of a consumer visiting a recreation site. This framework is based on the assumption that a consumer derives utility from the recreational activity at each recreation site. Each visit decision involves choosing one site and excluding others.

The consumer s decision involves comparing each site and choosing the site that produces the maximum utility. An observer cannot measure all potential determinants of consumer utility, so the indirect utility function will have a non random element ( V) and a random error term (  ) , such that the actual determ inants of consumer utility V  = V +  . The probability (  jn) that site j will be visited by an individual n is defined as:

( 1)

where:

V jn +  jn = utility of visiting site j, and V sn +  sn = utility of visiting a substitute site.

Estimating the model requires specifying the functional form of the indirect utility function, V, in which site choice is modeled as a function of site characteristics and the   price  to visit particular sites. For example, a set of conditional utility functions ( one for each site alternative j in the choice set) can be determined as follows:

( 2)

where:

V jn = the utility realized from a conventional budget constrained, utility maximization model conditional on choice of site j by consumer n;

 M = marginal utility of income;

M jn = the income of individual n availab le to visit site j;

P jn = a composite measure of travel and time costs for consumer n on site alternative j;

 = a vector of coefficients representing the marginal utility of a specified site characteristic to be estimated along with  M ( e. g., size of the water body, presence of boating ramps); and X jn = a vector of site characteristics for site alternative j as perceived by consumer n. These characteristics include the actual monitored and/ or modeled water quality parameters that are hypothesized to be determinants of consumer valuation of water based recreation resources, and that may also be affected by the MP& M regulation.

The magnitude of the coefficients in Equation 2 reflects the relative importance of site characteristics when consumers decide which site to visit. The coefficients (  ) of water quality characteristics of recreation sites are expected to be po sitive; that is,

all else being equal, consumers of water based recreation would prefer " cleaner" recreation sites. The coefficient on travel cost is expected to be negative, i. e., consumers prefer lower travel costs.

To estimate the site choice probabilities, EPA specified and estimated a nested multinomial logit model ( NMNL) for fishing,

boating, and swimming activities. The nested structure explicitly groups similar alternatives, which allows for a richer pattern of substitution among alternative sites. The NMNL is based on the assumption that an individual chooses first between groups of alternatives and then, within the chosen group, between individ ual alternatives. Fo r this analysis, EPA grouped all recreational sites in Ohio by water body type based on site similarities. EPA tested various alternative site groupings, but the Ohio Case Study 3 Three of the four models ( fishing, boating, and swimming) passed specification tests for appropriateness of a nested structure ( see Section 3 for detail). Test results showed that only two site groups are appropriate for the boating and swimming models inland sites ( rivers, small lakes, and reservoirs) and Lake Erie sites in Ohio. The fourth activity, wildlife viewing, did not pass specification tests for a nested structure and was estimated as a flat multinomial logit ( MNL) model.

6 models presented here were most successful at explaining the probability of selecting a site. The best model used the following activity specific site groupings: 3  Fishing model:

 Group 1: Lake Erie sites;

 Group 2: river sites;

 Group 3: small lakes and reservoirs;

 Boating model:

 Group 1: Lake Erie sites;

 Group 2: inland sites, including rivers, small lakes, and reservoirs;

 Swimming model:

 Group 1: Lake Erie sites;

 Group 2: inland sites, including rivers, small lakes, and reservoirs;

 Viewing model: EPA used a non nested model in which an individual compares all sites and chooses the one offering the highest utility level for each trip occasion.

First, the Agency attempted to estimate a nested model based on the three water body types lakes, rivers, and Lake Erie for all four recreational activities included in the analysis. This structure, however, performed well only for fishing. A two nested model that included inland and Lake Erie sites seemed to perform better for the bo ating and swimming models.

None of the nested structures performed well for participants in near water/ wildlife viewing activities.

This finding is no t surprising because sites are grouped based on their similarities within a given nest. It is reasonable to assume that inland lakes, rivers, and Lake Erie sites are dissimilar from an angler's point of view, because each of the three water body types is likely to support different fish species. Lake sites may therefore not be close substitutes for rivers sites.

For other activities, differences in fishery resources across water body types are unlikely to be important. Water body size and the presence of recreational amenities are likely to play a more important role than differences in fish species and the type of aquatic habitat. Lake and river sites may therefore be regarded as substitutes for each other by b oaters and swimmers.

Lake Erie, on the other hand, is a unique water resource that differs from inland water bodies because of its physical characteristics ( e. g., size and water temperature); river and lake sites are therefore not likely to be considered substitutes for Lake Erie sites. Finally, participants in near water rec reation use water resources indirectly and are therefore more likely to regard recreational sites located on different water body types as close substitutes to each other. For this reason, the viewing model is a simple logit model without a nested structure.

The models assume that an individual first decides to visit a specific water body grouping ( hereafter, region), then decides which site within that group to visit. An individual probability of visiting site j, given the choice of region R, is a simple multinomial logit. If the random terms  nj for individual n at site j are independently and identically distributed and have an extreme value Weilbull distribution, then  jn takes the form ( McFadden, 1981):

( 3)

where:

  jn| r = probability of selecting site j in region r;

= the consumer s utility from visiting site j;

r = regions    Lake Erie,    rivers,  etc. as specified above for a given activity; and Ohio Case Study 7 = the sum of the consumer s utility at each site j for all sites in the opportunity set for region r.

Estimated parameters of the indirect utility function are then used to estimate the inclusive value. For consumer n, the inclusive value measures the overall quality of recreational opportunities for each water based activity and represents the expected maximum utility of taking a trip. Note that, although EPA used a random draw from the opportunity set for the purpose of estimating the model parameters, the Agency calculated the inclusive value ( i. e., the expected maximum utility)

using all recreation sites in the consumer's opportunity set in a given region.

The inclusive value is calculated as the log of the denominator in Equation 2 ( McFadden, 1981).

( 4)

where:

I r = inclusive value for sites associated with region R;

= individual n s utility from visiting site j; and W = a vector of baseline water quality characteristics.

The probability of choosing a particular region is:

( 5)

where:

  r = probability of selecting region r;

I r = the inclusive values for a given region;

  r = the coefficient on the inclusive value for a given region; and r = activity specific regions ( e. g.,   Lake Erie,    rivers,  and   small lakes  for fishing).

To estimate the model described by Equations 2 and 5, EPA used a standard statistical software package, LIMDEP.

1.3 Modeling Trip Participation After modeling the site choice decision, the next step modeled the determinants of the number of water based recreation trips a consumer takes during a season. To link the quality of available recreation sites with consumer demand for recreation trips,

EPA modeled the number of recreation trips taken during the recreation season as a function of the inclusive value estimated in the previous step and socioeconomic characteristics affecting demand for recreation activities. The dependent variable, the number of recreatio n trips taken by an individual during the recreation season, is an integer value greater than o r equal to zero. To account for the non negative property of the dependent variable, EPA used count data models based on probability densities that have the non negative integers as their domain.

One of the simplest count data models is a Poisson estimation process, which is commonly used with count data, such as number of recreation trips taken during the recreation season. Inherent in the model specification is the assumption that each observation of a number of trips is drawn from a Poisson distribution. Such a distribution favors a large number of observations with small values ( e. g., two trips, four trips) or zeros, resulting in its being skewed toward the lower end. Due to the nature of the observed number of trips, it is quite reasonable to assume that the underlying distribution can be characterized as a Poisson distribution. Figure 1 shows the number of recreation trips taken per year and the number of respondents who reported taking that number of trips. Ohio Case Study 8 Figure 21.1: Number of Trips Per Year By Activity Type Source: U. S. EPA analysis of NDS data ( U. S. EPA, 1994) Ohio Case Study 9 Estimating the Poisson model is similar to estimating a nonlinear regression. The single parameter of the Poisson distribution is  , which is both the mean and variance of y n. The probability that the actual number of trips taken is equal to the estimated number of trips is estimated as follows ( Green, 1993):

( 6)

where:

Y n = the actual number of trips taken by an individual in the sample;

y n = the estimated number of trips taken by an individual in the sample;

n = 1, 2,..., N, the number of individuals in the sample; and  n =   X, expected number of trips for an individual in the sample, where X is a vector of variables affecting the demand for recreational trips ( e. g., inclusive values and socioeconomic characteristics) and   is the vector of estimated coefficients.

From Equation 6, the expected number of water based recreatio n trips per recreation activity season taken by an individ ual is given by:

( 7)

where:

E[ y n| x n] = the expected number of trips, y n, given x n; Var[ y n| x n] = the variance of the number trips, y n, given x n;  = a vector of coefficients on x; and x = a matrix of socioeconomic variables and inclusive values.

An empirical drawback of the Poisson model is that the variance of the number of trips taken must be equal to the mean number of trips, and this equality is not always supported by actual data. In particular, the NDS survey data exhibit overdispersion, a condition where variance exceeds the mean. The estimated variance to mean ratios of the number of trips in the NDS data sample are 31, 27.9, 3 5.6, and 10.5 for fishing, swimming, viewing, and boating trips, resp ectively.

Overdispersion is therefore present in the data set.

To address the problem of overdispersion, EPA used the negative binomial regression model, an extension of the Poisson regression model, which allows the variance of the number of trips to differ from the mean. In the negative binomial model,  is respecified so that ( Green, 1993):

( 8)

where the error term (  ) has a gamma distribution, E[ exp(  i )] is equal to 1.0, and the variance of  is  .

The resulting probability distribution is:

( 9)

where:

y n = 0,1,2... number of trips taken by individual n in the sample;

n = 1,2,..., N number of individuals in the sample; and  n = expected number of trips for an individual in the sample.

Integrating  from Equation 9 produces the unconditional distribution of y n. The negative binomial model has an additional parameter,  , which is the overdispersion parameter, such that: Ohio Case Study 10 ( 10)

The overdispersion rate is then given by the following equation:

( 11)

EPA used the negative binomial model to predict the seasonal number of recreation trips for each recreation activity based on the inclusive value, individ ual socioeconomic characteristics, and the overdispersion parameter,  . If the inclusive value has the anticipated positive sign, then increases in the inclusive value stemming from improved ambient water quality at recreation sites will lead to an increase in the number of trips. The combined MNL model site choice and count data trip participation models allowed the Agency to account for changes in per trip welfare values, and for increased trip participation in response to improved ambient water quality at recreation sites.

1.4 Calculating Welfare Changes from Water Quality Improvements EPA estimated the welfare change associated with water quality improvements from the baseline to post compliance conditions as a compensating variation ( CV), which equates the expected value of realized utility under the baseline and post compliance conditions. The expected seasonal change in welfare attributed to the quality improvements for an individual n in the sample consists of two components:

 per trip welfare gain, and  increased number of trips under the post compliance water quality condition.

The Agency first calculated the welfare gain from water quality improvement for each consumer on a given day by using a CV measure for consumer n ( Kling and Thompson, 1996):

( 12)

where:

CV n = the compensating variation for individual n at site j on a given day;

r =   Lake Erie,    inland,  etc.

j = 1,... J r represents a set of alternative sites for a given recreational activity in region r;

= the inclusive value index ( I);

W 0 = a vector of information describing baseline water quality;

W 1 = a vector of information describing post compliance water quality; and  M = the implicit coefficient on income that influences recreation behavior.

In deriving Equation 12 , EPA assumed that the marginal utility of incom e,  M , is constant across alternatives ( as well as across quality changes). If this assumption does not ap ply, the derivation of Eq. 12 is more complicated ( Hausman et al.,

1995). Ohio Case Study 4 EPA selected this approach for calculating seasonal welfare gain per individual based on Dr. Parsons  recommendation ( G. R. Parsons, 1999).

5 Section 2.1 provides a detailed description of the data sample used in the analysis.

11 EPA then estimated the low and high values of the seasonal welfare gain for individual n in the sam ple as follows: 4 ( 13)

( 14)

where:

W low, n = lower bound estimate of the seaso nal welfare gain for individual n;

W high, n = upper bound estimate of the seasonal welfare gain for individual n;

I 1 = the post policy inclusive value;

Y 1 = the estimated number of trips after water quality improvement;

I 0 = the baseline inclusive value;

Y 0 = the estimated number of trips in the baseline; and  µ = the implicit coefficient on income that influences recreation behavior.

These estimates are per individual in the population for those individuals meeting qualifications for inclusion in the NDS response set ( i. e., respondents whose home state is Ohio and respondents from the neighboring states whose last trip was to Ohio s sites). 5 EPA extrapolated the estimates of value per individual to the Ohio state level based on Census data ( U. S.

Bureau of the Census, 2000). The following section details the extrapolation method used in the analysis.

1.5 Extrapolating Results to the State Level EPA used a simplified extrapolation technique to estimate the state level benefits. EPA first estimated the number of participants in fishing, swimming, boating, and wildlife viewing in Ohio, based on the estimated percentage of the NDS survey respondents residing in Ohio who participate in a given activity and the state adult po pulation. The 2000 Census data provide information on the number of Ohio residents aged 16 and older. EPA then multiplied the estimated average seasonal welfare gain per participant in a given recreational activity by the corresponding number of recreational users. The total welfare gain to the users of water based recreation in Ohio is the sum of fishing, swimming, boating, and wildlife viewing benefits.

2 DATA This section describes the data and supporting analyses required to implement the RUM analysis. The following general categories of data and supporting analyses are required:

 information on the consumers of water based recreation responding to the NDS in Ohio;

 recreation sites identified for the water quality and RUM analyses, including the sites visited by consumers of waterbased recreational activity and supplemental sites in their choice sets;

 estimated price of visiting the sites. The   visit price  is estimated as a function of travel distance ( and travel time)

between each consumer s hometown and each site in the choice set; and  information on site characteristics likely to be important determinants of consumer behavior. Of particular importance to this analysis are the water quality and related characteristics of sites in the choice set, and how those characteristics may be expected to change as a result of regulation. Ohio Case Study 6 These additional observations total 11 across the four activities and thus represent only a small fraction of total observations.

Including only Ohio respondents in the trip participation models underestimates the benefits associated with water quality improvements,

because the welfare gains to recreators from neighboring states are ignored.

12 The following sections discuss each category of data and/ or supporting analysis below.

2.1 The Ohio Data EPA obtained information on survey respondent socioeconomic characteristics and recreation behavior, including last trip profile and the annual number of trips associated with each water based activity, from the NDS ( U. S. EPA, 1994). The 1994 survey collected data on demographic characteristics and water based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. Respondents reported on water based recreation trips taken within the past 12 months, including the primary purpose of their trips ( e. g., fishing, boating, swimming, and viewing), total number of trips, trip length, distance to the recreatio n site( s), and number of participants. W here fishing was the primary purpose of a trip, respondents were also asked to state the number of fish caught. Table 1 shows the number of trips taken per year by primary recreation activity, as reported in the NDS.

EPA selected case study observations for Ohio residents who took trips within or outside of the sta te. Trips to O hio recreation sites by residents of neighboring states were also included in the site choice models, but not in the trip participation models. 6 All four activity models included single day trips only. EPA included only activity participants with valid hometown ZIP codes, whose destination site was uniquely identified. The Agency used data on both Ohio participants and Ohio non participants to estimate total seasonal trips, but included only Ohio participants and several residents of nearby states in the site choice models. Although they could not be used in the site choice model, participant observations from Ohio with missing location information were used to analyze the number of trips. Tables 1 and 2 list valid observations by activity,

residence, and model type. Figure 2 illustrates the distribution of the sample observations in relation to the location of MP& M facilities affected by the rule in Ohio.

Table 1: Classification of Sample Observations for Estimation of the Site Choice Models Total Ohio Residents Ohio Residents with Last Trip In State Valid Ohio Residents with Last Trip In

State Valid Ohio Residents with Last Trip Outside State Valid Nonresidents with Last Trip in Ohio Valid for Site Choice Model Participants ( Total) 609 408 237 35 11 297 Fishing 122 103 66 9 0 84 Swimming 147 100 58 14 2 76 Viewing 231 126 64 2 7 73 Boating 109 79 49 10 2 64 Source: U. S. EPA analysis. Ohio Case Study 13 Table 2: Classification of Sample Observations for Estimation of the Trip Participation Models Ohio Residents Total Residents with Last Trip In State Residents with Last Trip Outside State Valid for Trip Participation Model Non Participants 300 291 Participants ( Total) 609 408 34 322 Fishing 122 103 4 84 Swimming 147 100 9 78 Viewing 231 126 7 75 Boating 109 79 14 85 Total Observations 909 408 34 613 Source: U. S. EPA analysis. Ohio Case Study 14 Figure 2: Location of MP& M Facilities in Relation to the Visited Sites Source: U. S. EPA analysis. Ohio Case Study 7 The program was created by Daniel Hellerstein and is available through the USDA at http:// usda. maunlib. cornell. edu/ datasets/ general/ 93014.

8 Note that all expenditures are in 1993 dollars because the NDS trip choices and the associated expenditure occurred in 1993.

9 The estimate of motor vehicle cost per mile was based on estimates compiled by the Insurance Information Institute.

10 The average travel time to the visited site was fairly uniform across the activities. Average one way time to the visited site was 51.38 minutes, 71.64 minutes, 43.76 minutes, and 58.57 minutes for fishing, boating, swimming, and viewing, respectively.

15 2.2 Estimating the Price of Visits to Sites EPA estimated trip   price  for each consumer of water based recreation as the sum of travel costs plus the opportunity cost of time, following the procedure described in Haab et al. ( 2000). Based on Parsons and Kealy ( 1992), this study assumed that time spent   on site  is constant across sites and can be ignored in the p rice calculation.

To estimate consumers  travel costs, EPA first used ZipFip software to calculate the one way distance to each site for each participant. 7 The average estimated one way distance to the site visited is 37.56 miles. EPA then multiplied round trip distance by average motor vehicle cost per mile ($ 0.29, 1993 do llars). 8,9 The model adds the opportunity cost of travel time,

measured in terms of wages lost, to the travel cost for those who would have lost income by taking the recreation trip. For these consumers the dummy variable LOSEINC equals one. Travel times equal the round trip distance divided by a travel speed of 40 mph and multiplied by the individual s hourly wage as calculated below.

The travel cost variable in the mo del was calculated as follows:

( 15)

Individuals not losing income ( e. g., individuals taking vacation or a weekend trip or individuals whose work schedule is not flexible) do not face lost wages as a result of the trip and inclusion of the opportunity cost of time would be inappropriate.

These consumers still have an opportunity cost for their travel time, which could otherwise be spent doing something else,

like fishing. In other words, a shorter distance traveled allows for a longer time spent at the recreation site. For these consumers, the analysis included an ad ditional round trip travel time variable calculated as:

( 16)

The average one way estimated travel time to the visited site is 56.34 minutes. 10 2.3 Site Characteristics EPA identified 1,954 recreation sites on 1,631 reaches in the universal opportunity set. Of these, 580 observations are known recreational sites ( e. g. parks); 1,366 observations are Reach File 1 ( RF1) reaches without a known recreational site; and eight observations are neither located in RF1 nor identified as known recreation sites but were visited by an NDS respondent. Ohio Case Study 11 McFadden ( 1981) has shown that estimating a model using random draws can give unbiased estimates of the model with the full set of alternatives.

12 Travel distance from respondent s hometown to the Lake Erie sites did not exceed 250 miles.

16 Each consumer choice set theoretically includes hundreds of substitutable recreation sites in Ohio and in the neighboring states. To prevent the recreation site analysis from becoming unmanageable, EPA analyzed a sample of recreation sites for each consumer observation. The Agency then created a randomly drawn reduced choice set for each recreational activity as follows: 11  Fishing. The reduced choice set consists of 20 Lake Erie sites, 20 river sites, and 20 small lakes/ reservoirs. Thus, a total individual choice set consists of 60 alternatives ( including the chosen site);

 Boating. The reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites ( including rivers and lakes/ reservoirs). A total individual choice set consists of 40 alternative sites ( including the chosen site);

 Swimming. Similar to boating, the reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites ( including rivers and lakes/ reservoirs). A total individual choice set consists of 40 alternative sites ( including the chosen site);

 Wildlife View ing. The reduced choice set consists of 40 sites, including Lake Erie, river, and small lake/ reservoir sites.

Each participant choice set, by definition, includes the site actually visited by the respondent. For each consumer, EPA drew the additional sites from a geographic area defined by a distance constraint ( and the water body types listed above). The Agency used a 120 mile distance limit for inland recreation sites ( Ohio rivers, small lakes, or reservoirs). All Lake Erie sites are eligible for inclusion in the choice sets for all models. EPA assumed that consumers of water based recreation would be willing to travel farther to visit Lake Erie sites, because this water resource presents unique recreational opportunities. 12 EPA used the resulting aggregate choice set of sites for all individuals participating in a given recreation activity to model consumer decisions regarding trip allocation across recreation sites.

The Agency used two classes of characteristics to estimate site choice:

 those unaffected by the MP& M regulation, but likely to determine valuation of water based recreational resources;

and  those affected by the regulation and hypothesized to be significant in explaining recreation behavior and resource valuation.

Regulation independent site characteristics include water body type and size, location characteristics, and the presence of site amenities ( e. g., boat ramps, swimming beaches, picnic areas). Regulation dependent site characteristics include regulationaffected water quality variables.

a. Regulation independent site characteristics Site characteristics that are likely to be important determinants of consumer valuation of water based recreational resources but that are independent of the MP& M regulation include general site descriptors. These descriptors include the type and size of the water body and location characteristics, and the presence of site amenities. EPA obtained data on regulationindependent site characteristics from two main sources, RF1 and the Ohio Department of Natural Resources ( ODNR).

RF1 provided water body type ( i. e., lake, river, or reservoir) and physical dimension ( i. e., length, width, and depth). The dummy variables, LAKE ERIE, RIVER, and LAKE characterize water body types. If a site is located on Lake Erie, LAKE ERIE takes the value of 1; 0 otherwise. If a site is located on river, RIVER takes the value o f 1; 0 otherwise. Finally, if a site is located on a small lake or reservoir, LAKE takes the value 1; 0 otherwise. Water body size was determined by the length of the reach segment in miles for rivers and Lake Erie sites. For small lakes and reservoirs, the appropriate water body size is the water body area in acres. The site choice models use the logarithm of water body size as a measure of site importance, Ohio Case Study 13 EPA uses the logarithm of water body size because it expects the effect of water body size on utility to diminish as that size increases.

17 because people are more likely to be aware of large water bodies. 13 Water body size data for sites not located in RF1 came from the ODNR.

ODNR, supplemented by the Ohio Atlas and Gazetteer, provided data on recreational amenities and site setting ( e. g.,

presence/ absence of boat ramps, swimming beaches, or picnic areas; public accessibility; and size of land available for recreation). EPA used land available for recreation, LN( LAND), ( e. g., acreage of state park, fishing, hunting, and other recreation areas) to approximate site setting and attractiveness. Dummy variables represent the presence of three recreational amenities: BEACH is a swimming beach; RAM P is a boating ramp; and PARK indicates a park.

b. Regulation dependent site characteristics Selecting regulation dependent site characteristic variables that are both policy relevant and significant in explaining recreation behavior proved challenging. MP& M facilities discharge many pollutants, m ost of them unlikely to have visible indicators of degraded water quality ( e. g., odor, reduced turbidity, etc). EPA hypothesized that pollutant loadings can,

nonetheless, reduce the likelihood of selecting a recreation site. Reduced pollutant discharges improve water quality and aquatic habitat, thereby increasing fish populations and enhancing the recreational fishing experience. In addition, in stream nutrient concentrations are good predictors o f eutrop hication, which causes aesthetic losses and may thus affect the utility of a water resource for all four recreational uses.

The connection between the policy variables ( i. e., the change in concentrations of MP& M pollutants) and the effects perceived by consumers ( e. g., increased catch rate, increased size of fish, greater diversity of sp ecies, or improved aesthetic qualities of the water body) are not modeled directly, but are captured implicitly in the differential valuation of water resources as reflected in the RUM analyses.

EPA considered two types of pollutant effects in defining water quality variables for model inclusion:

 visible or otherwise directly perceivable effects ( e. g., water turbidity); and  unobservable toxic effects likely to imp act aquatic habitat and species adversely.

The Agency accounted for directly observable effects using the ambient concentrations of nutrients ( e. g., TKN) as an explanatory variable.

Rather than include the concentrations of all toxic pollutants separately, EPA constructed a variable to reflect the adverse impact potential of toxic pollutants on aquatic habitat. EPA identified recreation sites at which estimated concentrations of one or more MP& M pollutants exceeds AWQC limits for aquatic life protection, to assess the likely adverse impacts on aquatic organisms. A dummy variable, AWQC_ EX, takes the value of 1 if in stream concentrations of at least one MP& M pollutant exceed AWQC limits for aquatic life protection, 0 otherwise. This approach accounts for the fact that adverse effects on aquatic hab itat are no t likely to occur below a certain thresho ld level.

c. Biological factors Numerous biological parameters ( e. g., abundance of sport fish) that are a function of the availability and quality of suitab le habitat for breeding and feeding are also likely to affect recreation behavior. To account for biological parameters affecting the demand for water based recreation, EPA used relative fish abundance ( Biomass) obtained from the Ohio Water Resource Inventory ( OWRI) database ( OH EPA, 1996). Relative fish abundance is measured as the total fish weight ( in kg) per 300 meters. Because this variable reflects presence of both tolerant and intolerant fish species, it is less correlated with the two regulation dependent water quality variables ( i. e., TKN and AWQC) included in the analysis compared to the index of well being ( IWB2) used in the proposed rule analysis.

Chemical properties of the waters ( e. g., pollutant concentrations) are likely to affect the diversity and abundance of the fishery resources. Biological parameters may also be affected by numerous anthropogenic stressors unrelated to water quality, such as over fishing, physical alteration of habitat, invasion of exotic species, etc. Although EPA used the baseline values of relative fish abundance to estimate the site choice models, the Agency did not estimate changes in biological parameters caused by the regulation analysis due to data limitations and the challenges posed by modeling population impacts of a broad spectrum of pollutants at hundreds of recreation sites. Ohio Case Study 18 d. Presence of fish advisories Another important factor that may affect a recreational consumer s decision to visit a pa rticular site is presence of fish consumption or contact advisories ( FCAs). EPA obtained information on fish consumption advisories and contact advisories at reaches in Ohio from the ODNR ( Ohio DNR, 1999). Fish consumption advisories and contact advisories were listed by the name of the stream or river with the consumption advisory. An advisory that applied to only part of the river included the names of cities, towns, or highways to identify the stretch of the reach for which the advisory was relevant. The name of the river and the other geographic identifiers were used to assign reach numbers from RF1 to the consumption advisories. EPA created a dummy variable for each type of advisory ( i. e., fish advisories and contact advisories). The variable takes the value of 1 if the relevant advisories are present; 0 otherwise.

3 SITE CHOICE MODEL ESTIMATES EPA estimated four separate models of recreational demand: fishing, boating, swimming, and viewing. The Agency classified trips by the primary activity listed by the respondent. All four activity mo dels co ver single day trips. EPA estimated the site choice model using the site actually visited and randomly drawn sites from the choice set for each recreation activity as described in Section 2 3 above.

EPA estimated activity models for five alternative choice sets ( i. e., five random draws from the universal choice set),

producing five sets of estimated coefficients. Mean estimates from the five alternative draws represent EPA s best estimate of actual coefficient values. Table 3 lists the variables used as arguments in the utility function and presents the mean estimation results for the four models. In estimating site choice models for fishing, boating, swimming, and viewing, the Agency restricted the coefficient on travel cost to be equal across all four models to ensure a constant marginal utility of income across all four activities.

The following sections provide a short description of the results of the site choice model corresponding to each recreation activity. Ohio Case Study 19 Table 3: Site Choice Model Estimation Results ( Mean parameter estimates from five random draws) a Variable Activity Fishing Boating Swimming Viewing TRCOST b  0.044 ( 22.704)  0.044 ( 22.704)  0.044 ( 22.704)  0.044 ( 22.704)

TIME c  1.474 ( 7.482)  0.362 ( 4.27)  0.436 ( 7.007)  0.719 ( 12.647)

RAMP d 0.878 ( 7.509) N/ A N/ A N/ A LN( LAND) e N/ A N/ A 0.058 ( 2.431) 0.162 ( 7.471)

PARK f N/ A N/ A 0.753 ( 3.79) 0.787 ( 4.638)

BEACH g N/ A N/ A 0.491 ( 2.96) N/ A LN( SIZE) h All N/ A 0.502 ( 5.777)  0.273 ( 6.083) N/ A Lake Erie 0.908 ( 6.639) N/ A N/ A 0.665 ( 10.474)

River 0.171( 1.993) N/ A N/ A  0.261 ( 4.937)

Lake 0.050 ( 0.348) N/ A N/ A  0.429 ( 4.329)

Biomass i Lake Erie N/ A  0.130 ( 1.777) N/ A N/ A River 0.068 ( 2.328) 0.017 ( 0.4432) N/ A N/ A TKN j  0.584 ( 3.763)  1.187 ( 6.863)  0.660 ( 4.631)  0.711 ( 4.401)

AWQC k  0.573 ( 3.698)  0.172 ( 1.179) N/ A N/ A Inclusive Values ERIE 0.811( 9.895) 0.296 ( 6.098) 0.730 ( 7.466) N/ A Inland N/ A 0.088 ( 2.525) 0.275 ( 6.302) N/ A RIVER 0.591 ( 6.945) N/ A N/ A N/ A LAKE 0.429 ( 2.629) N/ A N/ A N/ A Adj. R 2 0.467 0.280 0.408 a EPA performed this analysis based on five alternative draws to assess sensitivity of the estimated coefficients with respect to random draws.

b Travel Cost is calculated as 0.29 * round trip distance.

c Travel Time is ( round trip distance / 40)* Wage.

d 1 if a boating ramp is present, and 0 otherwise.

e Log of the number of land acres.

f 1 if the site is a park, and 0 otherwise.

g 1 if a swimming beach is present, and 0 otherwise.

h Log of the size of the water body. For rivers and Lake Erie, this is the log of the reach segment length or Lake Erie shore segment in miles. For lakes, this is log of the lake circumference.

i Biomass is measured as the total fish weight ( in kg) per 300 meters.

j In stream concentrations of TKN ( mg/ l).

k 1 for any reach if in stream concentrations of at least one MP& M pollutant exceeds the AWQC limits for protection of aquatic life, and 0 otherwise.

Note: T statistic for test that the estimated coefficient equals 0 is given in parentheses beside the coefficient estimates. N/ A indicates that the variable was not included in the estimation for this activity.

Source: U. S. EPA analysis.

3.1 Fishing Model The estimated fishing model includes travel cost ( TRCOST), time ( TIME) spent traveling, and site characteristics. The Agency included the following site characteristics in the fishing model: boat ramp ( RAMP), water body size ( LN( SIZE)), Ohio Case Study 14 Inclusive values equal to 1 cause the model to collapse to a flat multinomial logit.

20 relative fish abundance ( Biomass), TKN concentrations, and presence of AWQC exceedances. Table 3 shows that most coefficients have the expected sign and are significantly different from zero at the 95 th percentile. Travel cost and travel time have a negative effect on the probability of selecting a site, indicating that anglers prefer to visit sites closer to their homes ( other things being equal).

Anglers who fish from a boat are likely to view the presence of a boat ramp as an important factor that may affect their site choice. However, the presence of a boat ramp is unlikely to be important for anglers who fish from shore. Thus, the Agency used an interaction variable ( RAMP x USE_ BOAT) such that the ramp variable was turned on only if the angler reported using a b oat on his last fishing trip. A positive sign on the boat ramp indicates that anglers owning a boat are more likely to choose sites with a bo at ramp.

The water body size has a different effect on the probability of selecting a site in the Lake Erie, river, and small lake/ reservoir groups. The larger the river or the Lake Erie shore segment, the more likely that anglers visited the reach. The size of inland lakes and reservoirs does not have a significant effect on the probability of visiting the site.

The Agency used the square roo t of the fish weight per 300 meters as a measure of fish abundance ( Biomass). The probability of a river site visit increases as the relative fish abundance at the site increases. However, inclusion of this variable in the Lake Erie nest was not significant, which indicates that relative fish abundance does not have a significant effect on choosing a Lake Erie site. This finding is counterintuitive and is likely to be due to the lack of variation in the relative fish abundance variable for the Lake Erie sites. This variable was excluded from the Lake Erie nest in the final model presented here. Data on relative fish abundance were not availab le for lakes.

Finally, higher ambient concentrations of TKN, which indicate potential eutrophication problems, and presence of AWQC exceedances negatively affect the probability of site selection. In other words, anglers prefer cleaner sites, all else being equal.

Estimated inclusive values on Lake Erie sites, rivers, and small lakes lie within a unit interval [ 0,1] and are significantly different from 0, indicating that the nested choice structure is appropriate. 14 EPA found other variables, tested as explanatory variables, to be insignificant, including the presence of FCAs. It might be expected, a priori, that the presence of an FCA decreases a site s likelihood as a fishing choice. In fact, the existence of FCAs did not significantly affect a site s probability of being chosen; 59 percent of the sites actually chosen by NDS respondents had an FCA in place. Creel surveys provided by ODNR indicated that, on average, anglers released 70 percent of their catch ( ODNR, 1997). This finding suggests that recreational anglers are aware of FCAs, and catch but do not consume fish in the affected areas.

3.2 Boating Model The estimated boating model includes travel cost ( TRCOST), time ( TIME) spent traveling, and site characteristics. The Agency included the following site characteristics in the boating model: water body size ( LN( SIZE)), relative fish abundance ( Biomass), TKN concentrations, and presence of AWQC exceedances. Table 3 shows that most coefficients have the expected sign and are significantly different from zero at the 95 th percentile.

Travel co st and travel time have a negative effect on the probability of selecting a site, indicating that boaters prefer to visit sites closer to their homes ( other things being equal). However, the magnitude of the travel time coefficient indicates that boaters are willing to travel farther than participants in other recreational activities. This is not surprising, since motorboating and sailing are restricted to the sites where these activities are allowed. The positive coefficient on the water body size variable ( LN( SIZE)) ind icates that the larger the water body the more likely the boaters visited it.

The coefficients on water quality variables ( TKN and AW QC) are negative, indicating that boaters prefer to visit cleaner sites. The Biomass coefficient is positive, but insignificant for inland sites, and negative for Lake Erie sites. The negative coefficient on this variable is likely to be due to the fact that 88 percent of the sample trips used in this model were motorboating trips. Motorboating itself is likely to be a significant environmental stresso r for biological communities due to noise and turbidity associated with this activity. Thus, lower fish abundance at popular boating sites may indicate that intensive motorboating may adversely affect species abundance. As was the case with the fishing model, the estimated Ohio Case Study 21 inclusive value is significantly different from zero and lies within a unit interval [ 0.1], supporting the nested model framework.

3.3 Swimming Model EPA included the travel cost and time variables ( TRCOST, TIME), physical characteristics of the site, and ambient TKN concentrations in the swimming model. This model also includes the presence of recreational amenities that are likely to be important to swimmers: presence of a beach, the designation of the site as a p ark, and the natural log of the land acres. All estimated coefficients have the expected sign and are significantly different from zero at the 95 th percentile.

Price, travel time, the presence of a park with a beach, and the size of the land area around the site all increase the probability of a particular site being chosen for swimming. Swimmers are less likely to visit large sites ( referring to the size of the water body) or sites with visible water quality effects as indicated by higher in stream concentrations of TKN. As for the fishing and boating models, the estimated inclusive value is significantly different from zero and lies within a unit interval [ 0,1]

supporting the nested model framework.

Again, some variables expected to be significant, such the presence of contact ad visories, are no t. This variable s insignificance probably stems from its scarcity. Of 1,954 sites included in the universal opportunity set, contact advisories are in place for only 13. ( None of the sites actually visited had contact advisories in place.) The probability that a chosen site has contact advisories in place is very small, because ind ividual choice sets are randomly selected.

The fish Biomass variable representing biological characteristics of a water body also did not have a significant influence on consumer decisions to visit a particular site and was dropped from the model. This outcome is not surprising, since abundant aquatic life may, in fact, interfere with swimming activities.

3.4 Viewing ( Near water Activity) Model EPA included the travel cost and time variables ( TRCOST, TIME), physical site characteristics, and ambient TKN concentrations in the viewing model. In addition, the Agency included the natural log of the land acres and the designation of the site as a park. All estimated coefficients have the expected sign and are significantly different from zero at the 95 th percentile.

The probability of choosing a site for near water activities is most significantly related to visit price, travel time, land size,

and in stream concentrations of TKN. Similarly to the fishing model, the water body size has a different effect on the probability of selecting a site in the Lake Erie, river, and small lake/ reservoir groups. The larger the Lake Erie shore segment,

the more likely that viewers visited the site. The negative coefficients on river and inland lake size indicate that consumers prefer smaller inland water bodies for near water and wildlife viewing activities.

4 TRIP PARTICIPATION MODEL EPA estimated the determinants of individual choice concerning how many trips to take during a recreation season with a separate model for each of the four activities. These participation models rely on socioeconomic data, and on estimates of individual utility ( the inclusive value) derived from the site choice models. V ariables of imp ortance include age, ethnicity,

gender, education, and the presence of young or older children in the household. W hether or not the individual owns a boat is particularly important in boating participation, and is included in the model for that activity only. Variable definitions for the trip participation model are:

 IVBASE: inclusive value, estimated using the coefficients obtained from the site choice models;

 # TRIPS: number of trips taken by the individual;

 AGE: individual s age. If the individual did not report age, their age is set to the sample mean;

 MALE: equals 1 if the individual is a male, 0 otherwise;

 NOHS: equals 1 if the individual did not complete high school, 0 otherwise; Ohio Case Study 22  COLLEGE: equals 1 if the individual completed college, 0 otherwise;

 AFAM: equals 1 if the individual is African American, 0 otherwise;

 YNGKIDS: equals 1 if there are kids 6 years or younger, 0 otherwise;

 OLDKIDS: equals 1 if there are kids 7 years or older, 0 otherwise;

 OWNBT: equals 1 if individual owns a boat, 0 otherwise;

 Constant: a constant term representing each individual s utility associated with not taking a trip; and   ( alpha): overdispersion parameter estimated by the Negative Binomial Model.

Table 4 presents explanatory variables and a mean value for each.

Table 4: Mean Values for Explanatory Variables Used in the Participation Models Variables ( Mean) Non Participant ( N= 291) Boating ( N= 85) Fishing ( N= 84) Swimming ( N= 78) Viewing ( N= 75)

# TRIPS 0.00 7.71 10.07 9.46 9.59 AGE 43.99 39.06 38.53 34.76 36.91 MALE 0.33 0.49 0.65 0.47 0.47 NOHS 0.17 0.09 0.14 0.13 0.13 COLLEGE 0.15 0.32 0.20 0.32 0.35 AFAM 0.11 0.02 0.05 0.03 0.12 YNGKIDS 0.18 0.26 0.24 0.24 0.27 OLDKIDS 0.38 0.48 0.58 0.56 0.48 OWNBT N/ A 0.53 N/ A N/ A N/ A Source: U. S. EPA analysis. Ohio Case Study 23 Table 5 presents the results for the participation models of the four recreation activities.

Table 5: Trip Participation Negative Binomial Model Estimates Variables/ Statistics Boating Fishing Swimming Viewing IVBASE 0.12 0.82 0.72 0.47 ( 0.71) ( 2.86 ) ( 4.57) ( 3.66)

AGE  0.07  0.04  0.06  0.05 ( 4.73) (  2.06) (  2.24) (  2.77)

MALE 1.23 2.22 1.15 0.91 ( 2.75) ( 3.25) ( 1.52) ( 2.00)

NOHS 1.29  1.09  0.92 0.1 ( 2.37) ( 1.56 ) (  0.96) ( 0.17)

COLLEGE  0.19  0.40 0.53 1.22 ( 0.29) ( 0.721 ) ( 0.71) ( 2.05)

AFAM  3.74  1.44  4.07  1.16 ( 1.81) ( 1.53 ) (  2.68) (  1.34)

YNGKIDS 1.51  0.95 0.35  0.17 ( 2.96) (  1.26) ( 0.42 ) (  0.38)

OLDKIDS  1.67 1.11 0.4 0.8 ( 3.58) ( 2.78) ( 0.65) ( 1.81)

OWNBT 3.82 N/ A N/ A N/ A ( 5.26)

Constant 0.20  5.74  0.1  1.98 ( 0.11) (  3.01) (  0.06) (  1.6)

Alpha  5.77 9.03 8.92 8.17 ( 5.85) ( 7.16) ( 6.78) ( 6.03)

Note: T statistic for test that coefficient equals 0 is given in parentheses below the coefficient estimates. N/ A indicates that the variable was not included in the estimation for this activity.

Source: U. S. EPA analysis.

Parameter estimates of the inclusive value index ( IVBASE) in the swimming, fishing, and viewing models are positive and differ significantly from zero at the 95 th percentile, indicating that water quality improvements have a positive effect on the number of trips taken during a recreation season.

The estimated coefficient on IVBASE in the boating model, while positive, was not statistically significant. Taking a boating trip often requires more preparation ( e. g., taking a boat to the water body) than taking other trips. Therefore, although water quality improvements increase the value of a boating day, factors other than water quality are likely to have a stronger impact on the number of boating trips per season.

The AGE variable is negative and significant for all four recreation activities: younger people are likely to take more recreation trips. Ethnicity and gender ( the AFAM and MALE variables) also have a significant impact on whether an individual participates in water based recreation. African Americans living in Ohio are less likely to participate in any of the four recreation activities than representatives of other ethnic groups. Males are more likely than females to participate in any of the recreation activities.

Education also influences trip frequency significantly. People who did not complete high school ( NOHS= 1) tend to take fewer fishing or swimming trips. Those with a college degree ( COLLEGE= 1) are more likely to participate in swimming and Ohio Case Study 24 viewing. Respondents who attended college are less likely, however, to participate in fishing and boating than those who completed only a high school education. For the boating model, the COLLEGE variable is not significantly different from zero.

The presence of older children ( OLDKIDS) in the household is associated with greater participation in swimming, viewing ( near water recreation), and fishing activities, but is no t a significant determ inant in decisions to participate in boating.

Younger children in the household ( YNGKIDS) tends to lead to greater participation in boating and swimming, but leads to fewer fishing or viewing trips.

5 ESTIMATING BENEFITS FROM REDUCED MP& M DISCHARGES IN OHIO 5.1 Benefiting Reaches in Ohio EPA identified reaches where it expects the MP& M rule to eliminate or reduce the number of existing AWQC exceedances ( hereafter, benefiting reaches). The Agency first identified the reaches in which baseline discharges from industrial sources,

including both MP& M and non MP& M facilities, caused one or more pollutant concentrations to exceed AWQC limits for aquatic species. A reach is considered to benefit from the MP& M rule if at least one AWQC exceedance is eliminated due to reduced MP& M discharges. Although the method for identifying benefiting reaches is similar to the method used in the national analysis ( see Chapter 15 o f the EEBA report for detail), there are three notable differences:

 Unlike the national analysis, the Ohio case study incorporates information on all industrial and municipal point source discharges and non point sources to assess in stream concentrations of toxic and nonconventional pollutants in the baseline and post compliance. Appendix H of the EEBA report provides information on the data sources and methods used to assess ambient water quality conditions in Ohio.

 The water quality model used in this analysis estimates ambient pollutant concentrations in the reaches receiving discharges from MP& M facilities and reaches below the initial discharge reach. Appendix H of the EEBA report provides detail on the water quality model used in this analysis.

 The analysis of recreational benefits accounts for changes in TKN concentrations.

EPA's analysis indicates that po llutant concentrations at the baseline discharge levels fro m all industrial sources ( including all MP& M facilities) exceed acute exposure criteria for aquatic life on 15 reaches, and exceed chronic exposure criteria for protection of aquatic species on 21 reaches. EPA estimates that reducing pollutant discharges from oily waste facilities directly discharging to the receiving streams would not eliminate all concentrations in excess of the acute aq uatic life exposure criteria or the chronic exposure criteria on any reach under the final rule; it would reduce the number of acute and chronic exceedances on one reach.

In addition, baseline pollutant concentrations exceed human health based AWQC for consumption of water and organisms on three reaches and exceed AQWC for consumption of organisms only on two reaches. EPA estimates that reducing pollutant discharges from oily waste facilities directly discharging to the receiving streams would red uce the number of pollutants exceeding the human health based AWQC on one reach under the final rule; it would not eliminate all human health based AQWC exceedances on any reach in Ohio. Table 6 summarizes these results. In addition, the final regulation is estim ated to reduce in stream concentrations of TKN in the affected reaches. The estimated average reductions are 0.54 percent in lakes and 0.45 percent in rivers and streams. Ohio Case Study 15 The choice set of recreational sites available to boaters was restricted to the sites where motorboating and sailing is permitted because the majority of Ohio boaters included in this analysis used either motor or sail boats. Water quality improvements at the sites where boating is not allowed does not result in welfare gain to boaters.

25 Table 6.: Estimated MP& M Discharge Reaches with MP& M Pollutant Concentrations in Excess of AWQC Limits for the Oily Wastes Subcategory for Protection of Aquatic Species or Human Health Regulatory Status Number of Reaches with Concentrations Exceeding AWQC Limits for Human Health Number of Reaches with Concentrations Exceeding AWQC Limits for Aquatic Species Number of Benefiting Reaches All AWQC Exceedances Eliminated Reaches with Some AWQC Exceedances Eliminated H20 and Organisms Org.

Only Acute Chronic Baseline 3 2 15 21

Final Regulation 3 2 15 21 0 1 Source: U. S. EPA analysis.

5.2 Estimating Recreational Benefits in Ohio To estimate peoples  willingness to pay for water quality improvements, the Agency first calculated per person seasonal welfare gain corresponding to the final regulation. Table 7 presents, for each recreation activity, the compensating variation per trip ( the median value over all individuals in the sam ple) associated with the reduced MP& M discharges. Because the trip choices and the associated expenditures occurred in 199 3, the welfare gain was calculated in 1993 dollars and then adjusted to 2001 dollars based on the Consumer Price Index ( CPI).

The model indicates that the reductions in MP& M discharges from the final regulatio n result in a modest increase in per trip values for three of the four recreation activities ( fishing, viewing, and swimming). There is no welfare gain to boaters from improved water quality under the post compliance scenario. 15 Table 7 provides the mean estimates of welfare gain per recreational user in Ohio.

Table 7: Welfare Gain per Recreational User in Ohio ( 2001$)

Per Trip Welfare Gain Average Number of Trips per Person per Year Mean Seasonal Welfare Gain Fishing $ 0.02 13.6 $ 0.17 Boating $ 0.00 6.22 $ 0.00 Viewing $ 0.01 9.26 $ 0.11 Swimming $ 0.01 8.72 $ 0.01 Source: U. S. EPA analysis.

Table 7 also reports seasonal compensating variation per individual. The reported seasonal welfare gain includes both the increase in the utility from better water quality at the available recreation sites receiving MP& M discharges and the increase in utility from greater recreational trip participatio n.

As noted above, the Ohio case study evaluated changes in the water resource values from both reduced discharges of TKN and reduced frequency of AWQC exceedances. Changes in TKN concentration in the Ohio water bodies resulting from reduced MP& M discharges from the Oily Wastes subcategory account for approximately 96 percent of the monetary value of benefits resulting from the final rule. Ohio Case Study 16 Note that EPA used a few observation on visitors from neighboring states to estimate site choice models. The analysis does not include these observations in calculating state level benefits from water quality improvements.

26 Both the per trip and seasonal welfare estimates are much lower than values reported in the existing studies of water based recreation. T his is not surprising, since the water quality changes expected fro m the final rule are very modest.

To calculate state leve l recrea tional benefits from the final rule, EP A first calculated seasonal welfare gain from water quality improvements per individual in the sample. The Agency then multiplied the average welfare gain per individual by the corresponding number of participants in a given activity ( see Section 1.5 above for detail). The resulting product is the annual benefit from the final MP& M rule to consumers of a given water based recreation activity in Ohio. Table 8 summarizes state level results.

Table 8: Estimated Recreational and Nonuse Benefits from Reduced MP& M Discharges from the Oily Wastes Subcategory in Ohio Activity Percentage of Ohio Residents Participating in Single Day Trips ( from NDS) Number of Participants Aged 16 and older a Total Annual Welfare Gain to Recreational Users in Ohio Fishing 10.2% 892,283 $ 153,102 Boating 7.7% 676,026 $ 0 Viewing 9.1% 798,220 $ 88,047 Swimming 9.1% 798,220 $ 9,783 Total Recreational Use Benefit $ 250,933 Nonuse Benefits $ 125,466 Total Recreational Benefits ( Use + Nonuse) $ 375,859 a EPA estimated the number of participants in each recreation activity by multiplying the percent of NDS survey respondents from Ohio participating in a single day trip for each activity by the total adult population aged 16 an older ( 8,790,969). This analysis uses the 2000 Census data to estimate current population in Ohio.

Source: U. S. EPA analysis.

Under the final regulation, the extrapolation from the sample to the adult pop ulation in Ohio yields mean annual benefits estimates of $ 153,102, $ 9,783, $ 88,047, and $ 0 ( 2001$) for fishing, swimming, viewing, and boating, respectively. The total mean recreational use benefit is $ 250,932 ( 2001$). The Agency used the same approach as in the national analysis to estimate nonuse benefits ( see Section 15 .2.3, Nonuse Benefits, for detail). EPA estimated nonuse benefits as one half of recreational use benefits for low, mid, and high estimates, respectively. The estimated mean nonuse benefit is $ 125,466 ( 2001$).

6 LIMITATIONS AND UNCERTAINTY 6.1 One State Approach Some benefits are likely to be m issed by a state leve l case stud y. For example, residents from neighboring states undoubtedly recreate in Ohio waters, and residents of Ohio undoubtedly recreate in neighboring states. A state by state approach that restricts its analysis to recreation activities within the state misses these categories of benefits. 16 This omission is likely to be more significant for unique locations of high quality ( e. g., Lake Erie), where participants travel significant distances, and for sites very close to state boundaries. Ohio Case Study 17 Westat ( 1989) uses ten or more activity days per year as an indicator of an " avid" user.

27 6.2 Including One Day Trips Only Use of day trips only tends to understate recreational benefits for swimming, fishing, boating, and viewing, since recreation as part of multi day trip s is exclud ed. Inclusion of multi day trips, however, can be problematic. M ulti day trips are freq uently multi activity trips. An individual might travel a substantial distance, participate in several recreation activities and go shopping and sightseeing, all as part of one trip. Recreational benefits from improved recreational opportunities for the primary activity are overstated if all travel costs are treated as though they are associated with the one recreational activity of interest. The total benefits per trip from water quality improvements are not overstated, however, if individuals participated only in several water based activities.

6.3 Nonuse Benefits Estimating nonuse benefits using the 50 percent rule is less precise than using a more so phisticated benefits transfer approach.

However, limiting the b enefits of water quality improvements only to recreational benefits would significantly underestimate the benefits of the rule. The effects of using the simpler approach, e. g. either overestimation or und erestimation of benefits, is unknown. Other benefits include aesthetic benefits for residents living near water bodies, habitat values for a variety of species ( in addition to recreational fish), and nonuse values. To correct for this limitation of using only a travel cost model,

EPA quantified nonuse values in proportion to recreation values. This approach provides only a rough approximation of the value of water resources to nonusers. For example, some natural resources have high use values but small or negligible nonuse values ( e. g., cows), while other species have very high nonuse values but small or negligible use values ( e. g., blue whales).

6.4 Potential Sources of Survey Bias The survey results could suffer from bias, such as recall bias ( e. g., W estat, 1989), nonresponse bias, and sam pling effects.

a. Recall bias Recall bias can occur when respondents are asked the number of days in which they recreated over the previous season, such as in the NDS survey. Some researchers believe that recall bias tends to lead to an overstatement of the number of recreation days, particularly for more avid participants. Avid participants tend to overstate the number of recreation days, since they count days in a " typical" week and then multiply them by the number of weeks in the recreation season. 17 They often neglect to consider days missed due to bad weather, illness, travel, or when fulfilling " atypical" obligations. Some studies also found that the more salient the activity, the more " optimistic" the respondent tends to be in estimating number of recreation days.

Individuals also have a tendency to overstate the number of days they participate in activities that they enjoy and value.

Taken together, these sources of recall bias may result in an overstatement of the actual number of recreation days.

b. Nonresponse bias A problem with sampling bias may arise when extrapolating sample means to population means. This could happen, for example, when avid recreation participants are more likely to respond to a survey than those who are not interested in the forms of recreation, are unable to pa rticipate, assume that the survey is not meant for them, or consider the survey not worth their time.

c. Sampling effects Recreational demand studies frequently face two types of observations that do not fit general recreation patterns:

non participants and avid participants:

Non participants are those individuals who would not particip ate in the recreation activity under any conditions. This analysis assumes that an individual is a non participant in a particular activity if he or she did no t particip ate in that activity at any site.

This assumption tends to understate benefits, since some individuals may not have participated during the sampling period simply by chance, or because price/ quality conditions were unfavorable during the sampling period.

Avid particip ants can also be problematic because they claim to participate in an activity an inordinate number of times. This Ohio Case Study 28 reported level of activity is sometimes correct, but often overstated, perhaps due to recall bias ( see Westat, 1989). Even where the reports are correct, these observations tend to be overly influential. EPA dropped observations of participants who reported more than 100 trips per year when estimating trip participation mo dels, to correct for potential bias caused by these observations. Ohio Case Study 29 GLOSSARY ambient water quality criteria ( AWQC): levels of water quality expected to rend er a body of water suitable for its designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,

swimming, farming, fish production, or industrial processes. ( http:// www. epa. gov/ OCEPAterms/ aterms. html)

compensating variation ( CV): the amount of money a person would need to pay or receive in order to leave that person as well off as they were before a change.

consumer choice set: the set of alternatives ( e. g., alternative recreation sites) from which a consumer may choose.

exogenous: external to the inner workings of a system or model; variables are exogenous to the extent that they are " given"

and not the result of the operation o f the system or anything going on in the mo del itself.

expected maximum utility: see   inclusive value.

fish biomass ( Biomass): measure of biological factors in the water body represented by the total fish weight in kilograms per 300 meters.

fish consumption advisories ( FCAs): an official notification to the public about specific areas where fish tissue samples have been found to be contaminated by toxic chemicals which exceed FDA action limits or other accepted guidelines.

Advisories may be species specific or community wide.

inclusive value: the value to the consumer of being able to choose among X alternatives ( e. g., among a number of recreational sites) on a given trip occasion.

indirect utility function: gives the maximum value of utility for any given prices and money income. The indirect utility function is obtained when the quantity of goods that maximizes consumer utility subject to a budget constraint are substituted into a utility function.

inferential analyses: based on interpretation.

multinomial logit ( MNL): a utility maximization model. In this model, an individual is assumed to have preferences defined over a set of alternatives ( e. g., recreation sites). The choice model takes the form of comparing utilities from different alternatives and choosing the one that produces the maximum utility. In this framework, observed data consist of attributes of the choices ( e. g., available recreational amenities at different sites) and the choice actually made. Usually no characteristics of the individuals are observed beyond their actual choice.

National Demand Survey for Water Based Recreation ( NDS): a U. S. EPA survey of recreational behavior. The 1994 survey collected data on socioeconomic characteristics and water based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. ( http:// www. epa. gov/ opei)

negative binomial regression model: an extension of the Poisson regression model that allows the variance of the process to differ from the mean ( see also Poisson distribution and Poisson estimation process).

Negative Binomial Poisson model: ( see negative binomial regression model).

nested multinomial logit model ( NMNL): an extension of MNL ( see above). In this model, an individual is assumed to choose among different groups of alternatives first ( i. e., Great Lakes or inland recreatio n sites) and then to choose specific alternatives ( e. g., a particular river reach, lake, or Great Lakes site) in the choice set for each group.

nonconventional pollutants: a catch all catego ry that includ es all po llutants that are not classified as priority pollutants or conventional pollutants.

Ohio Water Resource Inventory ( OWRI): a biennial report to U. S. EPA and Congress required by Section 305( b) of the Clean Water Act. The report is composed of four major sections: ( 1) inland rivers and streams, wetlands, Lake Erie, and water program description; ( 2) fish tissue contaminants; ( 3) inland lakes, ponds, and reservoirs; and ( 4) groundwater. Ohio Case Study 30 overdispersion: condition for a distribution where the variance exceeds the mean. It usually signifies a nonrandom dispersion, for example the case where a small minority of the population is responsible for the majority of recreational trips taken.

Poisson distribution: a random variable X is defined to have a Poisson distribution if the probability density of X is given by f x( X)= f x ( X;  ) = e   x / x! for x = 0,1,2 .., and 0 otherwise. In this model,  is both the mean and variance of X.

Poisson estimation process: used to model discrete random variables. Typically, a Poisson random variable is a count of the number of events that occur in a certain time interval or spatial area, for example, the number of recreational trips taken during a recreational season.

priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,

sediment, groundwater, or soil samples.

random utility model ( RUM): a model of consumer behavior. The model contains observable determinants of consumer behavior and a random element.

Reach File 1 ( RF1): a database of approximately 700,000 miles of streams and open waters in the conterminous United States. The database contains information on stream flow, time travel velocity, reach length, width, depth, and other stream attributes.

site choice model: used to determine which recreational site is chosen by the consumer. EPA estimated the likelihood that the consumer will cho ose a particular site as a function of site characteristics, the price paid per site visit, and household income.

Total Kjeldahl Nitrogen ( TKN): the total of organic and ammonia nitrogen. TKN is determined in the same manner as organic nitrogen, except that the ammonia is not driven off before the digestion step .

travel cost model ( TCM): method to determine the value of an event by evaluating expenditures by participants. Travel costs are used as a proxy for price in deriving demand curves for recreation sites.

( http:// www. damagevaluation. com/ glossary. htm)

total seasonal welfare: see   welfare effect.

trip participation model: used to estimate the number of water based recreational trips taken during the recreation season.

EPA estimated the total number of trips during the recreation season as a function of the expected maximum utility ( inclusive value) from recreational activity participation on a trip and socioeconomic characteristics affecting demand for recreation trips ( e. g., number of children in the household).

utility theoretic: consistent with the behavioral postulate that individuals act to maximize their welfare ( utility) that underlines the structure of models of consumer behavior.

welfare effect: gain or loss of welfare to the group of individuals ( e. g., fishermen) as a whole. Ohio Case Study 31 ACRONYMS AWQC: ambient water quality criteria CV: compensating variation FCAs: fish consumption advisories IWB2: index of well being LIMDEP: Limited Dependent Variable MNL: multinomial logit NDS: National Demand Survey for Water Based Recreation NMNL: nested multinomial logit model ODNR: Ohio Department of Natural Resources OWRI: Ohio W ater Resource Inventory RUM: random utility model RF1: Reach File 1 TKN: Total Kjeldahl Nitrogen TCM: travel cost model Ohio Case Study 32 REFERENCES Bockstael, N. E., I. E. Strand, and W. M. Hanemann. 1987. T ime and the Recreation Demand Model. American Journal of Agricultural Economics 69: 213 32.

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