Valuing river characteristics using combined site choice and participation travel cost models

These models were then linked to estimate the welfare associated with marginal changes in river quality using the participation levels as estimated in the trip prediction model.. Methodo

Valuing river characteristics using combined site choice and participation

travel cost models

C Johnstonea,*, A Markandyaa,b

a Department of Economics and International Development, University of Bath, Bath, UK

b Fondazione Eni Enrico Mattei (FEEM), Milan, Italy Received 1 September 2004; received in revised form 19 July 2005; accepted 29 August 2005

Available online 27 December 2005

Abstract

This paper presents new welfare measures for marginal changes in river quality in selected English rivers The river quality indicators used include chemical, biological and habitat-level attributes Economic values for recreational use of three types of river—upland, lowland and chalk—are presented A survey of anglers was carried out and using these data, two travel cost models were estimated, one to predict the numbers

of trips and the other to predict angling site choice These models were then linked to estimate the welfare associated with marginal changes in river quality using the participation levels as estimated in the trip prediction model The model results showed that higher flow rates, biological quality and nutrient pollution levels affect site choice and influence the likelihood of a fishing trip Consumer surplus values per trip for a 10% change in river attributes range from £0.04 to £3.93 (£2001) depending on the attribute

q2005 Elsevier Ltd All rights reserved

Keywords: Valuation; River quality; Angling; RUM

1 Introduction

The aim of this study is to provide new welfare estimates of use

value for changes in river quality in the UK Recent cost-benefit

analyses of large-scale environmental improvement projects such

as the 4th Periodic Review of the Water Industry (PR04)

Environment Programme have highlighted the need for more

specific and up-to-date values for marginal changes in river

quality The extensive benefits transfer carried out in previous

cost-benefit analyses showed that, currently, values for angling

are only available for broad-scale changes in quality of fishery,

e.g ‘coarse-poor’ to ‘coarse good’, or ‘coarse-good’ to

‘game-moderate’ However, where environmental improvements will

result in specific outcomes such as reductions in phosphorous

concentrations or increases in biodiversity, what is needed are

values for marginal changes in these specific river attributes In

addition, such specific marginal values will be useful in meeting

the new demands in the field of policy and environmental management in implementing the water framework directive (WFD)

The main gaps in the literature that this study seeks to fill can therefore be summarised as follows:

† lack of UK use values for marginal changes in a range of river quality indicators, e.g flow, species richness, nutrient pollution levels;

† lack of UK use values for habitat-level physical river characteristics, such as extent of river modification;

† lack of values for different types of rivers, e.g lowland, upland etc

The aim of the study is therefore to generate new economic use values that would meet the needs of policy and project appraisal in valuing specific and marginal changes in environmental quality, for different river types

The rest of this paper is structured as follows Section 2 briefly outlines the background to the econometric models used Section 3 describes the study area and ecological data used to measure river quality In Section 4 the angling data gathering and some descriptive data for the study sample are provided In Section 5, the models and results are set out, and the welfare measures shown The results are discussed in Section 6, and in Section 7 some conclusions are offered

www.elsevier.com/locate/jenvman

0301-4797/$ - see front matter q 2005 Elsevier Ltd All rights reserved.

doi:10.1016/j.jenvman.2005.08.027

* Corresponding author Address: Environment Agency, Economics, Rio

House, Aztec West, Bristol BS32 4UD, UK Tel.: C44 1454 205580; fax: C44

1454 205566.

E-mail address: claire.johnstone@environment-agency.gov.uk (C.

Johnstone).

2 Methodology

The study combined two types of revealed preference travel

cost models commonly used in calculating welfare measures

for changes in river quality These were a random utility site

choice model (RUM) and a trip prediction or participation

model Because RUM site choice models cannot predict total

recreational trips taken in a season, researchers have proposed

various methods for linking participation and site choice

decisions in a single model.Parsons et al (1999)compare four

models for doing this The first, developed by Morey et al

(1993)is a repeated nested logit model, where the participation

decision is the first level, and site choice a second level The

second approach uses the inclusive value index from the site

choice model as an explanatory variable in the trip prediction

model The last two are variations on this in that they split the

inclusive value term into two separate price and quality terms,

but differ from each other in the specification of the quality

term1

In this study, the trip and site choice models are linked by

substituting the actual number of trips in the site choice model

with the predicted number of trips from a change in river

quality as estimated in the participation model This expands

the site choice model by embedding the participation decision

inside it, and as such allows the researcher to estimate welfare

gains from both site characteristics and trip behaviour A

similar approach was originally proposed by Bockstael et al

(1987), later modified byHausman et al (1995) In Bockstael’s

approach, the per trip welfare measure from the site choice

model is multiplied by the total number of trips per season

estimated in the participation model

This study is the first application of such an approach to

recreational use of rivers in the UK, and as such is expected to

generate useful empirical results, which will help inform policy

decisions for future environmental legislation such as the water

framework directive

3 Study area and ecological data

3.1 Study area

The study area comprised a range of ecologically varied

regions around England, and the spatial unit of analysis for the

study was the river reach, as defined for water quality

monitoring purposes by the Environment Agency In order to

get a broad range of river types, the rivers for the study were

selected from the natural areas/countryside character initiative

characterisation system devised by English Nature and the

location of the study areas (natural area shadings randomly

assigned)

The study area was split into upland and lowland areas2 These broad categories were created to test whether significant differences in angling participation and choice existed for different types of river and in different parts of the country, and

to produce more specific and policy relevant welfare estimates Table 1below shows the principal rivers and total number of river stretches for each study area

3.2 Ecological and environmental data The river quality variables included in the study encompass physical/structural data; chemical water quality data; indicators

of the river’s biological quality, and also indicators of angling quality, in terms of fish population data The river quality indicators thus cover a wide range of river attributes, so welfare estimates for a range of environmental outcomes could be produced Fig 2 below shows the environmental/ecological variables used to describe river quality

3.2.1 Chemical data The most well-established means of measuring freshwater quality is through reporting on the chemical composition of river water Three determinants are commonly used— biological oxygen demand (BOD), ammonia and dissolved oxygen (DO) Organic wastes are generally considered to be the most widespread pressure on river systems

Nutrient data, namely orthophosphates and total oxidised nitrogen (nitrates) levels were also included in the dataset, as a recent report estimating the costs of eutrophication in fresh-waters (Pretty et al., 2002) suggests that nutrient pollution is a pervasive and significant pressure on river quality

3.2.2 Habitat data The physical structure or habitat, along with hydrological factors, determine the biodiversity and wildlife potential of

Table 1 Study area dataset of rivers and stretches

Berkshire and Marlbor-ough downs

Southern magnesian limestone

Lark

56

1 Both use a vector of quality indices, but one includes the estimated

coefficients of the quality indices, and the other does not.

2

A third sub-sample ‘Chalk’ was also defined for the welfare estimation Chalk rivers are one of the priority habitats identified under the UK Biodiversity Action Plan Consequently estimates of the recreational value of changes in the environmental quality of chalk streams would be expected to provide useful input into policy making.

a river system Habitat quality was measured with an indicator

that describes the extent of physical modification of the river

channel, the habitat modification score (HMS)3

3.2.3 Biological data

A biological assessment of rivers and aquatic life gives a more

complete picture of the ecological health of a river system, as the

chemical and physical measures cannot account for other types of

environmental stresses, for example, heavy metals and pesticides

This biological assessment of river quality is based on the

diversity and pollution tolerance of families of

macroinverte-brates—animals such as snails, shrimps, mayflies/dragonflies etc

Two variables were used—NTaxa which is a measure of species

diversity; and ASPT, which is a measure of organic pollution

in-stream

3.2.4 Fish population data

As this researchJohnstone (2004)focuses on angling, it was felt to be important to have at least one variable (fish populations), which could measure both recreational, i.e angling, and ecological quality The data used were: the number of fish species present in a river, which is a measure of species richness; an estimate of the number of fish per 100 m2, which is a measure of density, and thirdly status, a dummy variable that takes the value

of one when game (salmon and trout) fish species are present, and

0 when only coarse fish are to be found

4 Survey design and sample 4.1 Survey design

The questionnaire was designed to be as concise and simple to answer as possible and fits onto one sheet of A4 paper The first part elicited the travel cost information, and the second part the Fig 1 Geographical location of the study areas.

3 Raven et al (1998)

questions on motivations for choice of river site (not reported in

this paper)

The first five questions ask the angler to state their age,

gender, occupation and home postcode, and the names of any

angling clubs they belong to This information is used to

calculate the respondent’s travel cost, in terms of the distance

travelled to the fishing site, and their wage rate, in order to

estimate the value of their leisure time4 Respondents were not

asked directly for their income level, as it was thought that this

might be perceived as intrusive and reduce the response rate

In question six the respondents were asked to give three

pieces of information for the five main rivers fished in the last

year5: the name of the river fished, the site on that river, and the

approximate number of visits made to that site per year

4.2 Data gathering and sample

The data were gathered in four ways Initially around 1300–

1500 questionnaires were sent out to angling clubs and as a

regional insert into Angling Times magazine, which resulted in

approximately 300 responses, about two-thirds of which were

returned from angling clubs, giving a 20–23% response rate An

online version of the questionnaire was also created and linked to

national angling websites6; this generated about 100 responses

As originally a large number of questionnaires had been

produced, the remainder were sent to fishing tackle shops7 In

total, 421 responses to the questionnaire were received

The response rate from the primary data collection method is

similar to that achieved byDavies and O’Neill (1992)—22%—in

their survey of anglers The final total number of responses obtained in the survey is within the accepted sample size range for

a study of this scale, i.e between 300 and 500 useable records (Ward and Beal, 2000) Although such a sample frame is not ideal and may be slightly biased towards anglers who buy Angling Times or look at angling websites, half of the responses received were from angling clubs Also, the data were scrutinised to ensure duplicate entries were removed

Whilst it is possible that the relatively low response rate means that the sample may be biased, comparing the sample to some recently collected statistics shows it is broadly representative of the angling population as a whole On their website the Environment Agency (EA) give some recent statistics on the angling population from a survey of the general public carried out

in 20018, which can be roughly compared to the study sample: 1%

of the study sample is female compared to their estimates of between 5 and 20%; 72% are 40 years old or over compared to 70% over 35 years old The EA survey found that anglers were most frequently in social class C2; approximately 45% of the study sample were in social class C2 Analysis of the study data showed that 37% of the sample were retired, where weekly income was estimated to be £172 (ONS, 2000), 30% had weekly incomes of between £240 and £440, 32% between £440 and £640 and 1% earned more than £700 p/w Thus in terms of age and income the study sample is fairly representative of the angling population as a whole, but is slightly biased in terms of gender balance, with male anglers over represented Table 2 below shows the descriptive statistics for the explanatory variables Fig 2 Environmental and ecological variables used to measure river quality.

Table 2 Descriptive statistics of independent variables

a

The variable age is a categorical variable reflecting the age group of the respondent, where 1Zteenager, 2Ztwenties, 3Zthirties, etc; there are seven categories.

b

Income is the mean weekly wage for the respondent’s occupation and age, derived from the Office for National Statistics (2000).

c

Travel cost is calculated as the marginal cost of motoring (10 pence per mile) plus the travel time cost at 40% of the respondent wage rate (A sensitivity analysis was conducted for two different percentages of the wage rate—20– 60%) Travel cost data were derived from travel distances and times for each fishing trip, obtained from Multimap.com.

4

It is worth noting again here however that whilst the majority of valuation

studies have applied, and continue to apply, this standard procedure, it has recently

been argued (e.g Feather and Shaw, 1999) that the process relies on assumptions

regarding the labour market that are unlikely to hold in a number of cases.

5

It is acknowledged however that ‘past year’ may not precisely equal the last 12

months, and so the sample-period may not be exactly the same for each angler.

6

Each electronic questionnaire submitted via the angling websites generated

a file containing the data that was sent to the University of Bath internet server.

This permitted us to keep track of responses gathered in this way.

7 However this was found to be too indirect a method of data collection and

did not generate any significant number of responses.

8 Survey of Rod Licence Holders, Simpson, D and Mawle, G, Environment Agency 2001.

5 Models and results

5.1 The trip prediction model

The first type of travel cost model estimated was a count

data model, which provided estimates of proportional changes

in trips following marginal changes in the river quality

attributes The model is specified as

TijZ bCijC

X

k

gkXkjC

X

i

Where TijZnumber of trips made by individual i to site j; Cijis

the travel cost for individual i to go to site j, Xk are site

characteristics (e.g quality of fishing), and Ii are individual

characteristics (e.g occupation) The coefficients b, g and h

determine the impact of the explanatory variables on the

number of trips and 3 is an error term9

Count data such as numbers of trips often follow a Poisson

distribution, so a Poisson regression may be an appropriate

analysis for the ecological-economic travel cost model The

Poisson distribution describes the occurrence of sparse events,

for example in this case on how often a river stretch j will be

fished or not (including the possibility that it will not be fished

at all), and can be written:

Pr TijjCij; Xj; Ii

Z

eK lijðlijÞTij

Where Tijis the number of trips made by individual i to site j

and the Poisson parameter, lij, which is the expected (mean)

value of Tij, is constrained such that

lnðlijÞ Z bCijC

X

k

gkXkjC

X

i

hiIi

whereas the Poisson distribution has only one parameter (the

mean), the Negative Binomial distribution has two separate

parameters, the mean and the variance which gives it more

flexibility The Negative Binomial distribution is given by

PrðTijZ xijÞ Z ðxðrijCrijK1Þ!

ijK1Þ!xij!

qrij

ijð1KqijÞxij

xijZ 0; 1; 2;

(3)

with the properties

Mean Zrij

qij

Variance Zrijð1KqijÞ

q2

ij

Here xijis the number of trips made by individual i to site j In this case the mean number of trips rij/qijis constrained so that

rij

qijZ bCijCP

kgjXkjCP

ihiIi Both the Poisson and negative binomial models were estimated, plus the zero-inflated versions of each—‘ZIP’ and

‘ZINB’ The zero-inflated versions are designed for datasets with excess numbers of zeros created by two distinct stages, firstly where a binomial probability distribution (logit or probit) is used

in a ‘transition’ or ‘hurdle’ stage, where the observation either moves from 0 to 1 or not—in this case whether a person decides

to visit a river stretch or not (the participation decision) The second or ‘event’ stage is then modelled with a symmetrical Poisson or negative binomial distribution in which the ‘event’ (trip) could have a zero or a positive value.Table 3below shows the results

The highly significant (at a probability level of !0.001%) likelihood ratio (LR) test of alpha10in the bottom row of the table shows that the negative binomial distribution provides a better fit

of the data than the Poisson distribution and the positive significant (O1.96) Vuong statistics in the second-to-last row show that the zero-inflated models are a better fit than the standard normal versions Thus overall, these measures of fit suggest that the zero-inflated negative binomial (ZINB) model is preferred This model gives generally the same results as the others, in terms of which variables are significant and have the expected sign, except for the fish species richness variable number of fish species, which has changed to be a significant negative predictor of trips The relatively low pseudo R2values suggest that the models do not explain much (less than 10% in the preferred model) of the variance in trips: the explanatory power of the trip prediction models is relatively low

The preferred model (ZINB) finds the river quality variables that significantly decrease the likelihood of a fishing trip to be higher levels of orthophosphates and nitrates and a higher number

of fish species instream11 The significance of status meant that the likelihood of a fishing trip is increased in rivers supporting coarse fish species Significant positive predictive variables were NTaxa, DO and Flow, suggesting that anglers make more trips to rivers with greater macroinvertebrate species diversity, higher levels of dissolved oxygen and higher flow rates

5.1.1 Welfare measures from the trip prediction model The model coefficients can also be used to predict the total numbers of trips with the current levels of river quality and

9

The error term captures specific individual departures from the population

model that are not captured in the set of explanatory variables C, X and Y; in our

case these could include family traditions of fishing, family status, health of the

individual etc The error terms could be correlated across individuals (e.g.

individuals belonging to a club may all go to one site a fixed number of times),

and across sites (two sites may be visited alternately by the same group of

individuals).

10

The negative binomial model command in Stata includes an ancillary parameter alpha a which is an estimate of the degree of overdispersion—when

a is zero, negative binomial has the same distribution as Poisson The larger a

is the greater the amount of overdispersion in the data, and the worse fit a Poisson distribution.

11

This rather counter-intuitive result may be reflecting anglers preferences for upland rivers The counter-intuitive signs on the variables in the count-data models may also be due to multicollinearity among the variables—this was explored by dropping variables known to be collinear and seeing if this changed the results Whilst no variables changed sign, dropping certain variables made others less, or non-significant; this analysis showed that the variables that were consistently significant and correctly signed were travel cost, orthophosphates, dissolved oxygen and flow rate.

with respect to changes in the river quality levels The expected

numbers of trips with current levels of river quality based on

the zero-inflated negative binomial count data model as

described above is written as

EfNTripsijjXj; Yi; Cijg

Z exp

X

k

gkXkjC

X

i

hiIiKbCij

where Xj, Iiand Cijare the explanatory variables as defined in

Eq (1); g, h and b are corresponding coefficients and
Tijis the

predicted number of trips Using this formula, the expected

number of trips as predicted by the count model is 5937, and

the actual number of trips in the dataset is 4853, thus actual

number of trips is 82% of predicted trips This is similar to the

results achieved byHanley et al (2003), who estimated that

actual trips were approximately 70% of their predicted trips

The expected number of trips with an increase in river

quality can be written

EfNTripsijjXj; Yi; Cijg

k

gkXkj
C

X

i

hiIiKbCij

where X* is the river attribute vector with the quality changes

The consumer surplus is calculated by dividing the expected

number of trips by the coefficient on the travel cost variable, b

CSijZexp

P

kgkjXkjCP

ihiIiKbCij

Tij

Thus the consumer surplus per trip can be calculated by dividing the total consumer surplus by the total number of trips, which gives a value of £25 per trip12 This is based on a linear form of the demand function Although this particular model could not be tested for sensitivity to the assumption of linearity, similar calculations of consumer surplus for alternative forms

of the demand function did not generate significant differences when applied to the kinds of changes in quality being considered here

The consumer surplus from a change in river quality is therefore:

DCSijZexp

P

kgjX
kjCP

ihiIiKbCij

b K

kgkXkjCP

jhiIiKbCij

T
ijK
Tij b

(7)

In other words, the change in consumer surplus resulting from a change in river quality is the estimated number of trips in the changed condition minus the estimated number of trips in the original condition divided by the coefficient on travel cost This procedure was used to estimate the percentage reduction in trips and the associated per trip consumer surplus values for a 10% increase in each significant river attribute, which are shown inTable 4

Table 3

Results of the trip prediction models

Model

a

Significant at the 001% level or lower.

b

Significant at the 05% level.

c

Significant at the 01% level.

12 This value is for all rivers as there were insufficient observations to estimate values for the three river types with the trip prediction model.

5.2 The site choice model

The second type of travel cost model estimated is a site

choice random utility model (RUM) The RUM theoretical

framework is commonly used in both stated and revealed

preference studies where the focus is on valuing changes in

specific attributes The theory is based on a framework for

modelling individual choice developed by McFadden in the

1970s, and states that an individual’s choice is informed by an

evaluation of measurable alternatives, plus a random

com-ponent, which the researcher cannot measure The basic RUM

model is specified as:

UijZ bCijC

X

k

gkXkjC

X

i

Where UijZthe utility of individual i at site j; Cijis the travel

cost of travelling to site j; Xkis a vector of the characteristics of

site j, I is the vector of individual characteristics and 3ijis the

random error term An individual will choose the site that

maximises her utility U, thus an individual will choose site 1 if

U1OUjfor all jZlocally available substitute sites

The regression model estimates the parameters: b,g and h so

as to maximise the likelihood of the observed pattern of fishing

trips A regression model that is commonly used in RUM site

choice models is the multinomial logit or conditional logit (CL)

model13 The probability of an individual choosing site j* out

of the set of n alternatives is formally written as:

P

gkXkj
CP

hiIi

Pn

jZ1ðexpðbCjCPg

kXkjCPh

This is the exponential of the utility of site j divided by the sum

of all of the exponentiated utilities The probability thus

depends on the attributes of all the river sites in the individual’s

choice set, as well as the chosen site, in other words, the model

takes account of the locally available substitute sites

In this study, the dataset contains 319 respondent

observations—an observation is created for each respondent

visiting a fishing site, and is composed of at least one trip to a site in a set of n number of locally available fishing sites This n

is the set of river stretches in the study Area where the respondent fished (seeTable 1), and therefore will vary across individuals The log-likelihood of observing the pattern of fishing trips using the conditional logit (CL) model is therefore

lnðLðbÞÞ ZX319

iZ1

Xn jZ1

P

gkXkjCP

hiIi

Pn jZ1exp bCijCP

gkXkjCP

hiIi

(10) where dijtakes the value one if individual I visits site j and the value zero if she does not The results are given inTable 5

On first inspection, the upland and lowland site choice models give a rather mixed message As would be expected, travel cost is always negative and highly significant, most of the river quality variables are highly significant and the models explain a relatively high proportion of the variance in site choice However, many of the river quality variables have unexpected signs The only river quality variable that is consistently signed as expected and significant is Flow14, which is actually a measure of quantity, although it is often used as a proxy indicator of river quality As shown inTable 5 above, the sub-sample models upland lowland and chalk produce markedly different results, and with the exception of flow and status, the river quality variables vary in terms of their signs and significance

The chalk model has the fewest significant variables— status, ASPT, DO, HMS and flow—but the variables that are

Table 4

Predicted changes in trips from a 10% increase in the significant river attributes

with the expected signs from the ZINB count model

trips

Change in consumer surplus per trip (£2001)

a

A 1 category increase, e.g from 4–5; a category increase is a doubling of

flow rate.

Table 5 Results of the conditional logit model for three sub-samples of the dataset

River type Explanatory

variables

a

Significant at the 001% level or lower.

b

Significant at the 05% level.

c Significant at the 01% level.

13

These two terms are used interchangeably in the literature, although there

are subtle differences, in that the multinomial logit can incorporate individual

specific variables such as age and income, i.e variables that are constant within

groups, whereas the conditional logit cannot.

14 Status is consistently negatively signed, suggesting anglers predominantly chose rivers supporting mixed and coarse fish species.

significant have the intuitively expected signs, in that they

confirm what would be expected: that rivers with lower levels

of pollutants/higher biological and chemical quality would be

chosen over those with higher levels of pollutants This model

also has the highest Pseudo R2measure, explaining over

two-thirds of the variance in site choice

A possible reason for the conflicting signs on the river

quality variables in the upland and lowland models is that the

quality variables are highly collinear Analysis showed that

some of the river quality variables were fairly highly

correlated, particularly the biological quality indicators ASPT

and NTaxa In order to explore whether the unexpected signs

are the result of multicolinearity, some of the variables that are

known to be collinear were dropped from the upland and

lowland models and the models re-run This analysis showed

that variables that were significant and had the expected signs

were status, NTaxa and flow in the upland model, and number

of species, orthophosphates, ASPT and flow in the lowland

model

Another possible reason for the unintuitive results for some

of the river quality variables is temporal mismatch between

river quality and angling data, where the river quality data does

not reflect the current river conditions More up-to-date and

temporally commensurate data, in particular fish population

data would undoubtedly improve the models and resulting

welfare estimates

One of the shortcomings of the conditional logit model is

the independence of irrelevant alternatives (IIA) assumption

This states for example that the probability of choosing

between two fishing sites is not affected by the presence of

other sites in the choice set It is reasonably likely that the

alternative sites within each anglers choice set will in fact

affect the probability of choosing site x over site y, for example

if they supported similar fish species It is possible therefore the

unexpected signs on the variables could also be a result of the

restrictiveness of the IIA assumption

One way to deal with this is to use a slightly different

modeling approach, a nested or mixed logit, for example as

used in Parsons and Massey (2003) Nested or mixed logit

models partitions the choice set of sites into n number of

groups, and thereby allows for correlation among sites by

specifying separate price and attribute aspects of the error term

This is a potentially useful way the research dataset could be

extended in the future

5.2.1 Welfare measures from the RUM conditional logit site

choice model

The ‘log sum’ approach is used to calculate the consumer

surplus associated with the changes in the river quality

variables from the RUM site choice model In this process, the

total welfare each individual gains from each site under a

hypothetical improved condition is compared to the total

welfare from the original, unimproved condition Dividing the

difference by the marginal utility of money gives an estimate

written as:

CSimprZfWimprKWorigg

the improved river quality, Worig is the welfare level in the original river condition, and l is the individual’s marginal utility of income, which is the travel cost coefficient from the site choice model, and translates the utility into monetary terms

As the conditional logit was used, this is expressed for each individual i and each site j as:

WimprZiZ319X

iZ1

XjKn jZ1

dijln exp bCijC

X

gxXk
C

X

hiIi

(12)

WorigZiK319X

iK1

XjZn jZ1

dijln exp bCijC

X

gkXkChiIi

where, as before, Cijis the travel cost of individual I to site j and Xkare the river quality variables In this example, one or more of these change from the original to the improved valuations and the new values are marked with a star, while the original values are marked with an ‘0’ dijtakes the value one for each site if the individual visits that site and zero if

he does not These welfare estimate calculations were carried out for each of the sub-sample models, upland, lowland and chalk Table 6 presents the results; the values are in grey where the characteristic has the expected sign but is not significant

To calculate the welfare gain from the total trips the welfare

in the original unimproved condition is divided by the coefficient on travel cost: Worig/l The welfare gain per trip is then simply {Worig/l}/Stij, i.e the total welfare gain divided by the total number of trips Applying these values results in an estimated welfare gain per trip of £47.31 for lowland sites,

£19.27 for upland sites and £5.78 for chalk sites

Table 6 Consumer surplus per trip for a 10% increase in significant river attributes with the expected sign (£2001)

Nitrates

A one category change for the variable flow.

5.3 Linking the count and site choice models

The count and site choice models can be linked by using the

predicted number of trips from a 10% change in the river

quality variables as estimated in the trip prediction model as

shown above in place of the actual numbers of trips in Eq (12)

above In other words dijis replaced with the predicted number

of trips under the original and improved conditions Worigand

Wimpr, are then as shown below:

i

X

j

TNij ln exp bCijC

X

gxX
kC

X

hiIi

(13)

i

X

j

T0ijln exp bCijC

X

gkXk0C

X

hiIi

The predicted number of trips under the original and improved

conditions are represented by
T0ijand
TNij respectively.Table 7

below shows the welfare estimates with respect to a 10%

increase in the river quality variables using the number of trips

estimated by the trip prediction model In general using the

estimated number of trips in the improved condition results in

slightly larger welfare estimates, except for ASPT and

ammonia, which reduce the welfare per trip slightly; this is

due to the opposite signs for these coefficients in the trip

prediction model

6 Discussion

6.1 Empirical results

The importance of river quality in angling participation and

site choice confirms the results of previous studies and the

associated consumer surplus values estimated are broadly of a

similar magnitude to previous studies For example in £2002,

ECOTEC Research and Consulting (1993)estimated £26–£40

per trip,Radford et al (1991) estimated £22 per trip, and an

earlier travel cost study by Radford et al (1984) estimated

£33–£50 per trip

Overall, most of the empirical results were reasonable in

that more explanatory variables had the right signs and the

travel cost variable was always negative, which confirms economic expectations The importance of river flow rates on recreational use supports a number of previous studies, for

different method of elicitation—the CVM) that anglers preferred, and were willing to pay for, more natural flow levels in rivers The statistically significant and positive relationship between dissolved oxygen (DO) and angling site choice in the upland and chalk models supports an early study

by Smith et al (1986), who also found this river quality variable was a significant predictor of the number of recreational trips to rivers

The site choice models have also shown that, as would

be expected, different river characteristics are important in predicting fishing site choice in the three sub-samples In upland areas, the variables shown to be significantly (p! 0.01) related to site choice were NTaxa, (reflecting macroinvertebrate species richness and thus indirectly, habitat quality) and flow (representing rivers with higher

were also less significant predictors The importance of aquatic invertebrate species richness (NTaxa) in choice of fishing site in upland areas confirms anglers’ preferences towards rivers with higher numbers of species in regions that are not predisposed geologically or physically to high species richness

In lowland areas, significant variables were ASPT, which is

a measure of organic/nutrient pollution levels; number of fish species, a measure fish species diversity; orthophosphates, which are a particular type of nutrient pollution and flow That nutrient enrichment should be more of an influential factor in recreational use of rivers in lowland areas, where agriculture and human inputs of nitrates and orthophosphates are greater,

is consistent with ecological theory Upland aquatic areas are less prone to nutrient enrichment problems such as eutrophica-tion as they have pre-existing lower levels of nutrients before anthropogenic inputs are taken into account (Petts and Foster,

1985)

6.2 Methodological results The RUM site choice model used in this study has the advantage that it allows the researcher to include the effects of the substitute sites available to each respondent within the study areas in which they were observed to have made fishing trips The models were able to evaluate the influence of the cost and quality attributes of the substitute sites on site choice, and the significance and coefficients of these variables reflect this Overall, the model design can be considered to have been a success: the travel cost variable was always negative and significant and there were more consistently statistically significant river quality variables with the expected sign than unexpectedly signed variables

It could be argued that one should compare the standard conditional logit (CL) site choice model with a mixed version that allowed for collinearity between sites, to see how this affected the results However, in their paper, Parsons et al

Table 7

Consumer surplus per trip for a 10% increase in significant river attributes with

the expected sign using the predicted number of trips from the count model

(£2001)

Nitrates

(1999)found that the models that split the inclusive value term

into price and quality terms resulted in significantly smaller

welfare measures, which were inconsistent with the site choice

model per trip estimates

The results of the count data ‘trip prediction’ model

generally corresponded with the results of the site choice

models, in that biological quality, as measured by number of

fish 100 m2(fish population density) and NTaxa, was important

in both participation and site choice This was also the case for

nutrient pollution (although demonstrated through the variable

nitrates as opposed to orthophosphates), and the most

significant variable was flow

Interestingly, the welfare estimates from the trip prediction

model were higher for number of fish per 100 m2, NTaxa,

dissolved oxygen and flow by a factor of 10 or more than

those from the RUM site choice model (see Table 8) Only

for orthophosphates, were the estimates at similar levels in

the three methods One explanation for the higher welfare

estimates in the trip prediction model is that count data

models do not explicitly incorporate substitution effects,

while RUM models do allow for them This can be seen in

the markedly larger influence of travel cost in the RUM

models

Also the welfare value per trip was very similar for the

Count and RUM models Note, however, that the relatively low

explanatory power of the count model meant that the trip

predictions and associated consumer surplus should be used

with some caution Linking the trip prediction and site choice

models was also successful in that the welfare estimates for

improvements in river quality with the estimated number of

trips from the trip model are slightly larger, as would be

expected

As such the travel cost models used in this study provide

some initial estimates of the likely impact of river quality on

recreational use of rivers, and have usefully identified a number

of ways this analysis could be extended with respect to rivers

and angling in the UK

7 Conclusions

This study has provided consumer surplus values for

marginal changes in a number of river quality indicators for

three different types of river, ‘Upland’, ‘Lowland’ and ‘Chalk’

The study also produced per trip angling welfare values for

these three river types As noted above, it is anticipated that

these per trip and quality change values will be useful as inputs

into various environmental and resource management policy

decisions, for example where it is necessary to gauge the value

of increases in river species richness, diffuse pollution or

abstraction Methodologically, the study has successfully

participation and site choice, which allows the researcher to

estimates produced by the trip prediction and RUM travel

cost models

Given the fairly low response rate in the angling survey, these welfare estimates should be used with caution to give broad estimates of welfare impacts There are a number of ways future studies could develop this area of research For example, whilst there were insufficient observations in this study, both the trip prediction values and the linked welfare estimates could be improved by splitting the trip prediction model into upland lowland and chalk, as was done in the site choice models Using GIS to select the set of locally available substitute sites, or to select the relevant river quality data would also improve the data gathering process

Acknowledgements This paper is part of Dr Johnstone’s PhD thesis carried out at the University of Bath, which was joint funded by ESRC and NERC The authors would like to thank Nick Hanley for his input and advice, Pam Mason, David Howard and Mike Hornung for their help on the wider research project, and Ron Thomas at the Environment Agency for assistance with GIS software and providing much of the environmental data They are also indebted to two anonymous referees who made

a number of important comments that have improved the paper None of these people are responsible for any errors that remain

References Bockstael, N., et al., 1987 Estimating the value of water quality improvements in a recreational oemand framework Water Resources Research 23 (5), 951–960 Davies, J., O’Neill, C., 1992 Discrete-choice valuation of recreational angling Journal of Agricultural Economics 43 (3), 452–457.

ECOTEC Research and Consulting, 1993 A Cost Benefit Analysis of Reduced Acid Deposition: UK Natural and Semi-Natural Aquatic Ecosystems: A

Table 8 Per trip consumer surplus values for a 10% increase in the river quality variables that were significant in both trip prediction and RUM travel cost models

A one category (e.g 4–5) increase for flow, which is a doubling of flow rate (£2001).

a

The combined model calculates the change in consumer surplus by substituting the predicted number of trips from the count data model in place of the actual number of trips in the RUM model Wimpr and Worig are then calculated as shown in Equation (14), and the consumer surplus change is calculated as shown in Eq (11).

b Upland rivers only.

c Lowland rivers only.

d Average of chalk and upland rivers values.

e Average of all three river type values.