Document ID: EPA-HQ-OW-2002-0049-0056
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2003-03-19T05:00Z

EAI
Economic
Analysis,
Inc.

PO
Box
3462
Peace
Dale,
Rhode
Island
02883
(
401)
789­
0050
FINAL
REPORT
Recreational
and
Resource
Economic
Values
for
the
Peconic
Estuary
System
Final
Report
on
Phase
II
of
the
Estuarine
Use
and
Economic
Value
Assessment
of
the
Peconic
Estuary
System
Prepared
by:

James
J.
Opaluch,
Thomas
A.
Grigalunas,
Jerry
Diamantides,
Marisa
Mazzotta
and
Robert
Johnston
ECONOMIC
ANALYSIS,
INC.
P.
O.
Box
3462
Peace
Dale,
Rhode
Island
02883
With
a
contribution
by
Applied
Science
Associates
Narragansett,
RI
Prepared
for:

Peconic
Estuary
Program
Suffolk
County
Department
of
Health
Services
County
Center
Riverhead,
NY
11901
Estuarine
Use
and
Economic
Value
of
the
Peconic
Estuary
System
Phase
II:
A
Perspective
on
Nonmarket
Values.

This
is
the
final
report
of
the
Phase
II
entitled
"
Estuarine
Use
and
Economic
Value
of
the
Peconic
Estuary
System".
The
goal
of
the
study
is
to
provide
economic
information
to
support
management
decisions
for
the
Peconic
estuary
under
the
National
Estuaries
Program.

Phase
I
of
the
study
used
available
data
to
summarize
the
level
of
key
estuarine­
related
uses
and
to
estimate
market
values
associated
with
the
Peconic
Estuary
system.
Major
estuarine
uses
are:
(
1)
commercial
fishing,
(
2)
sportfishing,
(
4)
beach
use,
(
5)
non­
consumptive
wildlife­
associated
use,
(
6)
hunting
and
(
7)
other
uses.
Market
values,
or
"
economic
impacts",
are
measures
of
economic
activity
supported
by
the
estuary,
and
include
number
of
establishments,
employment,
wages
and
total
revenues.
Phase
I
also
included
a
study
of
the
potential
for,
and
constraints
on,
aquaculture
in
the
Peconic
estuary.

The
goal
of
Phase
II
was
to
collect
primary
data
to
quantify
non­
market
uses
and
non­
market
values
of
the
Peconic
estuary.
Phase
II
was
comprised
of
(
1)
a
recreational
survey,
(
2)
a
property
value
study,
(
3)
a
wetlands
productivity
study,
and
(
4)
an
amenity
valuation
survey.
The
recreation
study
collected
data
on
the
level
participation
in
various
recreational
activities.
The
recreational
study
also
estimated
the
economic
value
the
public
has
for
key
activities
and
determined
how
the
value
of
recreation
activities
changes
with
key
indicators
of
the
quality
of
the
experience
(
e.
g.,
water
quality
for
swimming,
or
changes
catch
rates
for
fishing).
The
property
value
study
identifies
how
amenities
like
farmland
and
open
space
affect
the
value
of
nearby
properties,
providing
a
measure
of
amenity
(
or
disamenity)
services
to
nearby
residents.
The
wetlands
productivity
analysis
estimates
biological
"
production"
of
important
fish
and
bird
species
by
wetlands
based
on
habitat
and
nursery
functions.
The
amenity
valuation
survey
identifies
priorities
and
values
for
key
amenities
contained
in
the
Peconic
estuary
study
area,
including
open
space,
eel
grass,
unpolluted
shellfish
areas,
farmland
and
wetlands.

The
goal
of
Phase
III
is
to
draw
together
these
various
studies
in
order
to
prioritize
specific
management
actions
for
the
Peconic
estuary.
Phase
III,
scheduled
for
completion
in
Winter
1999,
will
link
together
science
oriented
studies,
such
as
water
quality
modeling,
with
the
economic
studies
described
above
to
carry
out
cost­
benefit
analysis
of
specific
policy
actions.
1
Recreational
and
Resource
Economic
Values
for
the
Peconic
Estuary
System
EXECUTIVE
SUMMARY
I.
A.
PURPOSE
AND
SCOPE
The
environmental
and
natural
resources
("
natural
assets")
of
the
Peconic
Estuary
System­­
the
bay
waters,
beaches,
wetlands,
ecosystems,
habitats,
and
parks
and
watershed
lands­­
provide
many
services
to
the
public.
Outdoor
recreation,
scenic
views,
and
the
productivity
of
wetlands
that
contribute
to
stocks
of
fish,
birds,
and
other
species
used
for
commercial
and
recreational
purposes
are
but
a
few
examples
of
these
services.

While
the
quality
of
PES
coastal
amenities
is
widely
recognized,
little
information
exists
about
the
uses
and
users
of
PES
natural
resources.
Even
less
is
known
about
the
value
that
the
public
holds
for
the
natural
asset
services
of
the
PES­­
that
is,
what
they
are
"
worth"
to
the
public.
This
is
because
recreation,
scenic
views,
and
ecosystem
productivity
are
not
directly
valued
in
markets.
Lacking
information
on
recreational
and
resources
values,
comparisons
cannot
be
made
of
the
benefits
and
costs
of
prospective
actions
to
preserve
or
restore
PES
natural
assets.
This
report
is
designed
to
help
fill
this
major
gap.

In
this
report,
we
present
the
results
of
four
non­
market
valuation
studies
carried
out
by
Economic
Analysis
Inc.
("
EAI")
to
estimate
the
uses
and
economic
value
that
the
public
holds
for
the
natural
assets
of
the
PES.
We
provide
estimates
of
(
1)
outdoor
recreational
uses
and
of
the
non­
market
economic
values
of
key
recreational
activities,
and
(
2)
other
resource
values
provided
by
the
natural
assets
of
the
PES.
The
economic
valuation
studies
described
herein
were
done
to
contribute
to
benefit­
cost
analyses
of
proposed
management
actions.

This
document
is
Phase
II
of
a
series
of
economic
studies
being
done
by
EAI
for
the
Peconic
Estuary
Program.
A
Phase
I
report
(
Grigalunas
and
Diamantides,
1996)
(
1)
provided
an
assessment
of
the
"
economic
impacts"
of
the
PES,
in
terms
of
business
revenues,
employment,
and
wages
of
estuarine­
related
sectors,
and
(
2)
summarized
available
information
on
recreational
uses.
Subsequent
work
by
EAI
will
include
benefit­
cost
analyses
of
resource
management
actions
and
sustainable
financing
options
for
the
selected,
preferred
actions.
2
I.
B.
ESTUARINE­
RELATED
USES,
RESOURCES
AND
ECONOMIC
VALUES
I.
B.
1.
Introduction
and
Overview
No
single
method
can
capture
the
value
of
the
variety
of
services
provided
by
the
natural
assets
of
the
PES.
Recognizing
the
many
uses
of
PES
natural
resources,
we
designed
and
implemented
a
suite
of
four
non­
market
valuation
studies
in
order
to
provide
estimates
of
the
value
of
particular
services:

(
1)
A
Property
Value
study
examines
the
contribution
of
environmental
amenities
to
the
market
price
of
property.
Using
the
Town
of
Southold
as
a
case
study,
the
Property
Value
study
was
designed
to
measure
values
of
amenities
to
residents
living
in
the
immediate
vicinity.

(
2)
A
Travel
Cost
study
uses
original
survey
results
to
estimate
outdoor
recreational
uses
in
the
PES
and
the
economic
value
that
users
have
for
four,
key
PES
outdoor
recreation
activities:
swimming,
boating,
fishing,
and
bird
and
wildlife
viewing.
This
study
also
examines
the
impact
that
(
A)
water
quality
has
on
the
number
of
trips
and
the
value
of
swimming
and
(
B)
the
effect
of
the
catch
rate
on
recreational
fishing,
important
recreational
uses
of
the
estuary
and
activities
much
affected
by
water
quality
and
resource
abundance.

(
3)
A
Wetlands
Productivity
Value
study,
carried
out
in
collaboration
with
Applied
Science
Associates,
gives
estimates
of
the
economic
value
of
eelgrass,
intertidal
salt
marsh,
and
sand/
mud
bottoms,
based
on
the
value
of
the
fish,
shellfish
and
bird
species
that
these
ecosystems
help
"
produce".
The
primary
focus
is
on
the
nursery
and
habitat
services
of
the
wetland
ecosystems
in
the
production
of
commercial
fisheries.

(
4)
The
Resource
Value
study
uses
original
survey
results
to
estimate
the
relative
preferences
that
residents
and
second
homeowners
have
for
preserving
and
restoring
key
PES
natural
and
environmental
resources:
Open
space,
farmland,
unpolluted
shellfish
grounds,
eelgrass
beds,
and
intertidal
salt
marsh.
This
study
also
provides
a
perspective
on
the
economic
value
the
public
has
for
these
resources,
as
indicated
in
their
stated
willingness
to
pay
for
programs
to
preserve
and
restore
them.

Key
results
for
each
of
these
studies
are
outlined
below.
A
detailed
discussion
of
each
method,
its
purpose,
the
data
used,
assumptions
and
limitations,
and
results
is
given
in
the
chapters
that
follow.

A
note
on
the
style
adopted
for
this
document.
We
believe
that
the
results
of
the
economic
analyses
presented
in
this
report
provide
a
wealth
of
data
to
decision
makers
and
to
the
public.
To
make
the
report
accessible
to
a
wide
audience,
we
deliberately
adopt
a
non­
technical
style.
For
those
interested,
Appendixes
present
the
technical
details
of
the
methodologies
used.
3
I.
C.
SUMMARY
OF
SELECTED
RESULTS
I.
C.
1.
Property
Value
Study
(
Chapter
III).

Data
for
property
sales
in
the
Town
of
Southold
were
used
to
estimate
the
contribution
of
specific
environmental
attributes
to
the
market
value
of
nearby
property.
Using
a
property
value
model,
we
found
that
proximity
to
open
space
and
other
environmental
attributes
had
a
significant,
positive
impact
on
nearby
property
values.

For
example,
a
parcel
of
land
located
next
to
open
space
has,
on
average,
a
12.83%
higher
per­
acre
value
than
a
similar
parcel
located
elsewhere.
To
illustrate
the
how
this
result
might
be
used,
we
estimate
that
a
hypothetical
contribution
of
a
parcel
of
approximately
10
acres
of
open
space
would
increase
adjoining
property
values
by
$
410,907.

The
model
results
also
show
that
density
of
development
and
other
attributes
affect
property
values.
For
example,
2
or
4
acre
zoning
(
i.
e.,
R­
80
or
R­
120)
has,
on
average,
a
16.65%
higher
per­
acre
value
than
a
similar
parcel
located
elsewhere
(
i.
e.,
in
a
1/
2­
acre
zone).
Conversely,
property
located
on
a
main
road
(
highway
25
or
48),
or
property
adjoining
a
farm,
has
a
lower
value,
after
taking
into
account
other
property
attributes.

I.
C.
2.
Recreational
Uses
and
Economic
Value
(
Chapter
IV).

A
recreational
survey
adminstered
to
residents,
second
homeowners,
and
visitors
allowed
us
to
estimate
(
1)
participation
in
outdoor
recreation,
and
(
2)
the
economic
value
the
public
has
for
four
key
PES
recreational
activites.
We
estimate
that
in
1995:

127,762
people
took
some
3.3
million
swimming,
boating,
fishing,
or
shell
fishing,
outings
156,184
people
engaged
in
about
5.2
million
beach
use,
bird
watching,
wildlife
viewing,
or
hunting
trips
Swimming
and
beach
use
were
the
most
popular
activities,
followed
by
bird
and
wildlife
viewing,
boating,
and
fishing.
Shell
fishing
and
hunting
had
the
fewest
estimated
number
of
trips.
Measurement
problems
prevented
us
from
including
other
common
activities,
such
as
hiking/
walking
and
bicycling.

Among
the
PES
Bays,
Great
Peconic
Bay
is
the
most
popular,
accounting
for
28%
of
all
recreational
trips
to
the
PES.
Flanders
Bay
is
the
least
frequently
used,
with
8%
of
PES
trips.
Great
Peconic
Bay
is
the
most
used
for
swimming
(
30%),
fishing
(
29%),
and
boating
(
25%).
For
shell
fishing,
Gardiners
Bay
is
the
most
popular
PES
location,
with
33%
of
all
PES
shell
fishing
trips.

Outdoor
recreation
in
the
PES
is
enormously
valuable
to
users.
Using
a
Travel
Cost
model,
we
estimate
the
economic
value
per
person,
per
trip,
for
four
key
recreation
activities:
Swimming,
1Boating
for
the
primary
purpose
of
fishing
is
valued
under
recreational
fishing.

4
boating1,
fishing,
and
for
viewing
of
birds
and
wildlife.
The
estimated
values
per
trip
range
from
$
49.83
for
viewing
birds
and
wildlife
to
$
8.59
for
swimming
(
in
1995
dollars).
These
are
estimates
of
what
participants
would
be
willing
to
pay,
per
trip,
above
and
beyond
the
amount
that
they
actually
pay
to
participate.

Adding
across
all
trips,
in
1995
Viewing
of
Birds
and
Wildlife
($
49.3
million)
has
the
highest
total
annual
value,
followed
by
Recreational
Fishing
($
22.4
million),
Boating
($
18.1
million),
and
Swimming
($
12.1
million).
The
corresponding
asset
values
of
the
PES
(
over
25
years
at
7
percent)
for
these
key
recreational
activities
range
from
$
318
million
for
Bird
Watching
and
Wildlife
Viewing
to
$
141
million
for
Swimming.
The
PES
has
an
asset
value
of
$
276
million
for
Recreational
Fishing
and
$
210
million
for
Boating.

Quality
is
important
to
PES
recreationists.
Swimming
was
found
to
depend
upon
perceptions
of
water
quality,
and
swimmers
perceptions,
in
turn,
were
related
to
objective
(
i.
e.,
SCDH
field
sampling)
measures
of
water
quality
for
nitrogen,
water
clarity,
Brown
Tide,
and
coliform
bacteria.

To
illustrate
how
the
results
might
be
used
in
a
Benefit­
Cost
analysis,
we
simulated
hypothetical,
uniform
improvements
in
water
quality.
For
example,
a
10
percent
uniform
improvement
in
water
quality
in
each
Bay
increases
the
estimated
number
of
annual
swimming
trips
by
151
thousand
and
adds
yearly
benefits
of
$
1.3
million.
Most
of
the
benefits
($
754
thousand)
are
due
to
improvements
in
water
clarity
(
as
measured
by
Secchi
depth).
Swimming
benefits
increase
further
with
a
hypothetical,
20
percent
uniform
water
quality
improvement,
although
the
added
benefits
from
the
second
10
percent
improvement
is
slightly
less
than
that
due
to
the
first
10
percent
improvement.

The
present
value
of
the
$
1.3
million
increase
in
benefits
due
to
the
10
percent
hypothetical
water
quality
improvement
is
$
15.1
million,
using
a
7
percent
discount
rate
and
a
25­
year
time
horizon.
Thus,
if
the
present
value
of
the
costs
of
the
policy
(
or
set
of
policies)
to
improve
water
quality
did
not
exceed
$
15.1
million
over
the
same
period,
it
would
be
a
good
investment
of
scarce
resources.

Recreational
fishing
also
was
found
to
depend
upon
the
quality
of
the
experience­­
in
particular,
the
catch
rate.
A
hypothetical
10
percent
increase
in
catch
rates
raises
the
economic
value
per
trip
by
$
0.80,
the
number
of
trips
by
11,249,
and
total
annual
fishing
benefits
by
$
472,359.
The
present
value
of
this
increase
in
catch
rates­­
the
increase
in
the
asset
value
of
the
PES
in
providing
this
service­­
is
$
5.5
million,
using
the
7
percent
rate
and
time
horizon
of
25
years
used
for
all
cases.

On
another
issue,
to
address
a
data
gap
identified
in
EAI's
Phase
I
study,
Phase
II
survey
respondents
were
asked
how
much
they
spent
at
road
side
farmstands
and
at
vineyards
and
for
rental
accomodations.
In
total,
in
1995,
the
public
spent
some
$
19.4
million
at
East
End
roadside
farmstands
and
$
5.4
million
at
vineyards.
Using
the
same
assumption
as
in
the
Phase
I
study
that
45
percent
of
these
activities
are
PES­
related
(
Grigalunas
and
Diamantides,
1996),
the
annual
PESrelated
expenditures
for
these
two
activities
are
$
8.7
and
$
2.4
million,
respectively.
5
I.
C.
3.
Wetlands
Productivity
Values
(
Chapter
V)

Eelgrass,
saltmarsh
and
intertidal
mud
bottoms
provide
food
web
and
habitat
services
for
many
species
of
fish,
shell
fish,
waterfowl
and
birds,
and
thus
directly
or
indirectly
benefit
people
who
consume,
hunt,
or
view
these
species.
A
simple
food
web
model
was
developed
that
used
available
biological
data,
commercial
fish
and
shell
fish
prices,
and
viewing
values
for
birds
to
provide
a
perspective
on
the
economic
value
of
the
productivity
of
these
habitat
types.

The
results
suggest
an
asset
value
per
acre
for
existing
wetlands
of
approximately
$
12.4
thousand
for
eelgrass,
$
4.3
thousand
for
salt
marsh,
and
$
786
for
mud
flats,
using
a
discount
rate
of
7
percent
and
25
year
period
for
the
services
valued.
Restored
wetlands
are
worth
somewhat
less
since
it
may
take
several
years
to
restore
fully
their
natural
functions
as
a
food
source
or
habitat.

I.
C.
4.
Resource
Preferences
and
Economic
Values
(
Chapter
VI)

Early
discussions
revealed
that
the
public
has
a
strong
attachment
to
environmental
and
amenity
resources
of
the
PES,
even
if
they
do
not
use
these
resources
directly.
As
outlined
above,
the
Property
Value
study
captures
the
value
of
amenities
(
or
disamenities)
to
nearby
properties,
the
Recreation
study
estimates
use
value
for
key
outdoor
recreational
activities,
and
the
Wetlands
Productivity
study
yields
estimates
of
the
value
of
wetland
ecosystems
in
the
"
production"
of
fish,
shell
fish,
and
birds.
However,
none
of
these
studies
reflects
the
value
residents
and
second
homeowners
hold
for
the
general
ambience
of
the
PES­­
the
"
sense
of
place"
it
provides,
a
phrase
used
often
by
participants
in
EAI's
focus
groups.

To
try
to
capture
these
elusive
but
important
non­
market
resource
values,
a
survey
developed
and
administered
to
968
residents
and
second
homeowners
was
used
to
learn
which
amenities
are
most
important
to
people
in
the
study
area,
and
the
amount
that
they
would
pay
to
preserve
or
restore
them.
Our
purpose
was
(
1)
to
account
for
the
preferences
many
residents
have
for
resources,
even
if
they
do
not
use
these
resources
directly,
and
(
2)
to
measure
these
preferences
in
dollars,
if
possible,
to
compare
with
other
study
results.

Most
respondents
to
the
resource
survey
(
97
percent)
supported
at
least
one
hypothetical
action
to
protect
resources,
and
indicated
they
would
financially
support
such
actions.
The
relative
priorities
of
respondents,
in
order,
were
for
farmland,
eelgrass,
wetlands,
shellfish,
and
undeveloped
land.

We
also
estimated
the
monetary
value
that
the
survey
responses
imply
for
the
resources
concerned.
The
estimated
per
acre
dollar
values
were
about
$
13
thousand
for
undeveloped
land,
$
30
thousand
for
unpolluted
shellfish
grounds,
$
54
thousand
for
saltmarsh,
$
66
thousand
for
eelgrass
and
$
70
thousand
for
farmland,
using
a
25­
year
time
horizon
and
7
percent
discount
rate.
However,
we
believe
that
the
resource
priorities
or
relative
values
of
resources
are
more
reliable
than
are
the
dollar
estimates
of
values
and
recommend
that
relative
values,
rather
than
dollar
values,
be
used
in
the
process
of
selecting
management
actions.
.
6
I.
D.
REFERENCES
Freeman,
A.
M.
III,
1993.
The
Measurement
of
Environment
and
Resource
Values:
Theory
and
Methods.
Washington,
D.
C.:
Resources
for
the
Future.

Grigalunas,
T.
A.
and
J.
Diamantides,
1996.
The
Peconic
Estuary
System:
Perspective
on
Uses,
Sectors,
and
Economic
Impacts.
Peace
Dale,
RI:
Economic
Analysis,
Inc.

Kopp,
R.
and
V.
K.
Smith,
1993.
Valuing
Natural
Assets:
The
Economics
of
Natural
Resource
Damage
Assessment.
Washington,
D.
C.:
Resource
for
the
Future.

Tietenberg,
Tom,
1995.
Environmental
and
Natural
Resource
Economics
(
4th
ed.)
New
York:
John
Wiley
&
Sons
7
II.
BACKGROUND
AND
INTRODUCTION
II.
A.
BACKGROUND
Located
within
Suffolk
County
at
the
East
End
of
Long
Island,
the
PES
is
comprised
of
the
Peconic­
Flanders
Bays
system,
Gardiners
Bay
and
part
of
Block
Island
Sound,
and
the
adjoining
watershed
lands
(
Figure
II.
1).
Included
within
the
PES
are
five
towns:
East
Hampton,
Southampton,
Riverhead,
Southold,
and
Shelter
Island,
as
well
as
a
small
part
of
a
sixth
town,
Brookhaven.
In
total,
the
five
towns
comprise
about
38
percent
of
the
land
area
and
8
percent
of
the
year­
round
population
of
Suffolk
County
(
SCDHS,
1992;
Long
Island
Business
News,
1994).

Important
characteristics
of
the
PES
include:
(
1)
the
generally
high
quality
of
its
coastal
estuarine
environment,
(
2)
its
economy,
which
is
strongly
influenced
by
seasonal,
estuarine­
related
activities,
particularly
tourism
and
recreation,
and
(
3)
the
population,
which
is
highly
seasonal
and
for
yearround
residents
is
comprised
of
smaller,
more
elderly,
and
lower­
income
households
than
for
the
County
or
Long
Island
as
a
whole
(
Long
Island
Business
News,
1994;
Grigalunas
and
Diamantides,
1996).

Water
quality
in
the
PES
generally
is
very
good,
and
the
study
area
contains
many
beaches,
parks,
open
space,
and
habitat
for
birds
and
other
wildlife,
including
some
endangered
species.
These
and
other
environmental
and
natural
resources
of
the
PES
can
be
viewed
as
natural
assets.
A
significant
feature
of
assets­­
natural
or
otherwise­­
is
that
they
can
provide
a
stream
of
valuable
services
("
interest")
over
time,
if
maintained.

Some
of
the
natural
assets
of
the
PES,
however,
are
not
being
maintained,
and
others
are
at
risk.
Localized
water
quality
problems
exist
due
to
runoff,
failing
septic
systems,
and
inadequate
sewage
treatment.
Substantial
areas
of
open
space
and
farms
have
been
lost
due
to
development;
and
pollution
has
closed
thousands
of
acres
of
shellfish
grounds
used
for
commercial
and
recreational
purposes.
Further,
large
areas
of
ecologically
productive
eelgrass
and
saltmarsh
ecosystems
have
been
lost
due
to
development,
Brown
Tide,
and
water
pollution.
Fin
fish
and
shell
fish
landings
have
declined
substantially
over
time,
in
part
due
to
these
problems.
The
combination
of
all
of
these
adverse
developments
threaten
the
quality
of
life
of
residents
of,
and
visitors
to,
the
PES.

Management
programs
being
developed
by
the
Peconic
Estuary
Program
would
preserve
or
restore
key
PES
environmental
or
natural
resources.
None
of
these
programs
is
free,
however,
and
some
could
be
very
costly.
Deciding
whether,
where,
how
much,
and
when
to
invest
in
preserving
or
restoring
PES
natural
resources
requires
consideration
of
many
factors.
One
of
these
is
the
economic
benefits
and
cost
of
such
actions.
8
II.
B.
NATURE
AND
IMPORTANCE
OF
NON­
MARKET
RESOURCE
VALUES
II.
B.
1.
Overview
of
Studies
and
Basic
Concepts
Economic
benefits
provided
by
natural
asset
services
show
up
in
at
least
two
ways:

(
1)
As
market
benefits
to
the
owners,
operators
and
employees
of
over
1,000
businesses
that
engage
in
or
support
recreation
and
tourism,
commercial
fishing
and
seafood
activities,
and
other
estuarine­
related
economic
operations
(
Grigalunas
and
Diamantides,
1996).

(
2)
As
non­
market
benefits
to
the
thousands
of
recreationists,
visitors,
property
owners,
renters,
and
others
who
use
or
otherwise
enjoy
the
quality
of
the
PES's
natural
amenities.

EAI's
Phase
I
study
summarized
available
information
for
the
first
of
these
two
categories
by
quantifying
"
economic
impacts"
of
estuarine­
dependent
economic
sectors
(
Figure
II.
2).
Estimates
were
made
of
employment,
wages,
sales,
and
number
of
establishments
for
29
economic
sectors
identified
as
estuarine­
related,
in
whole
or
in
part.
These
sectors
include
commercial
fishing,
marinas,
eating
and
drinking
establishments,
hotels/
motels,
and
other
estuarine­
related
activities.

The
Phase
I
results
show
that
estuarine­
related
economic
activity
is
a
major
component
of
the
PES
economy
and
an
important
part
of
the
livelihood
of
over
10,000
residents
who
own,
operate,
or
are
employed
in
more
than
1,000
marine
and
tourism­
related
businesses
(
Grigalunas
and
Diamantides,
1996).

Market
data
alone,
however,
give
only
a
very
limited
view
of
the
overall
benefit
or
value
of
the
services
provided
by
PES
estuary­
related
resources.
This
is
because
much
of
what
people
enjoy
in
the
PES­­
for
example,
natural
amenities,
like
open
space,
attractive
views,
good
beaches
and
high
levels
of
water
quality­­
are
not
bought
and
sold
in
markets.
Benefits
cannot
be
counted
in
economic
impact
studies
if
they
are
not
bought
and
sold
on
markets;
and
many
benefits
that
do
show
up
on
markets
are
indirect
and
hence
not
linked
to
the
natural
asset
that
provides
the
service
(
e.
g.,
fish
or
shell
fish
"
produced"
by
PES
wetlands
and
later
harvested
by
fishermen).

This
Phase
II
report
focuses
on
non­
market
benefits
provided
by
amenities
in
the
Peconic
Estuary
System
("
PES").
We
adopt
the
view
that
the
natural
resources
and
environmental
amenities
of
the
PES
can
be
looked
at
as
natural
assets
(
Freeman,
1993;
Kopp
and
Smith,
1993;
Tietenberg,
1995)
that
provide
a
flow
of
benefits
over
time,
if
maintained.

Several
standard
economic
concepts
are
used
throughout
this
report.
Two
of
these
are
Consumer
Surplus
and
Present
Value.
To
avoid
later
confusion,
these
concepts
should
be
explained
at
the
outset.
2Of
course
if
the
price
is
more
than
we
are
willing
to
pay
we
will
not
buy
the
item
because
it
is
"
too
expensive"
or
"
not
worth
it".

9
Consumer
Surplus.
People
buy
goods
or
engage
in
activities
like
recreation
when
the
benefit
they
receive
is
at
least
as
great
as
the
cost
to
them.
For
example,
if
I
can
buy
a
discount
airplane
ticket
for
$
200,
I
would
do
so
only
if
it
was
worth
at
least
that
amount
to
me.
If
I
would
pay
up
to
$
500
for
the
flight,
then
I
will
buy
the
ticket
and
receive
a
net
benefit­­
an
unpaid­
for
benefit­­
of
$
300.
Similarly,
on
a
summer
day
I
might
be
willing
to
pay
as
much
as
$
15
to
use
a
beach,
but
if
using
it
costs
me
only
$
5,
then
I
receive
an
unpaid­
for
benefit
of
$
10
for
that
trip
to
the
beach2.

In
the
above
examples,
the
most
that
I
am
willing
to
pay
is
my
assessment
of
the
economic
value
of
the
airplane
ticket
or
the
day
at
the
beach­­
what
each
is
worth
to
me.
The
net
benefit
to
me
in
each
case
is
the
difference
between
the
most
I
would
pay,
less
what
I
actually
pay.
This
"
unpaid­
for
benefit"
realized
in
each
example
captures
the
notion
of
"
Consumer's
Surplus".
Consumer
Surplus­­
the
benefit
people
receive
from
a
good
or
service
above
and
beyond
the
cost
to
them­­
provides
the
basis
for
the
measurement
of
economic
benefits
used
in
this
document.

Of
course,
the
"
trick"
is
to
use
sound
methods
to
discover
the
Consumer
Surplus
the
public
receives
for
the
services
provided
by
the
natural
assets
of
the
PES.
Fortunately,
standard
methods
are
available
to
do
just
that.
These
methods,
and
how
they
were
used
to
value
the
services
of
PES
natural
assets,
are
summarized
in
the
next
section
and
explained
in
detail
in
succeeding
chapters.

Discounting
and
Present
Value.
A
second
important
concept
involves
discounting.
Many
benefits
or
costs
occur
over
time,
and
discounting
is
the
process
used
to
convert
future
benefits
or
costs
to
an
equivalent,
lump
sum
value
today.
This
discounting
process
is
used,
for
example,
to
convert
a
retirement
or
lottery
annuity
(
an
equal
cash
amount
each
year)
to
a
lump
sum
equivalent
today.
The
equivalent,
lump
sum
value
today
arrived
at
by
discounting
is
called
the
"
present
value".

To
estimate
the
present
value
of
a
stream
of
dollar
flows
over
years
1
through
"
T",
we
discount
each
annual
flow
using
the
formula:

Present
Value
=
V1/(
1+
r)
1
+
....+
VT/(
1+
r)
T
where
V
is
a
dollar
value
and
r
is
the
discount
rate­­
the
interest
rate
used
to
convert
future
flows
into
a
value
today.
For
example,
if
the
total
Consumer
Surplus
from
swimming
is
$
1,000
in
year
1
and
$
1,000
in
year
2,
and
the
discount
rate
is
10
percent,
then
the
present
value
of
these
annual
amounts
is
$
1,735
(=
$
909
+$
826)
today.

The
present
value
calculated
using
the
above
formula
depends
upon
three
factors:
(
1)
the
size
of
the
annual
monetary
values,
Vt,
(
2)
when
they
occur,
and
(
3)
the
discount
rate,
r,
used.
We
estimate
the
annual
monetary
values,
Vt
for
non­
market
services
in
the
chapters
that
follow,
employ
a
time
37
%
is
the
representative
value
used
in
this
report.
In
later
efforts,
the
effects
of
different
rates
will
be
examined.

10
horizon,
T,
of
25
years,
and
use
a
discount
rate
of
7
percent,
a
standard
rate
recommended
for
use
in
many
resource
management
projects3.

II.
C.
PURPOSE
AND
SCOPE
This
report
describes
the
four
non­
market
valuation
studies
comprising
Phase
II
of
a
series
of
studies
being
carried
out
by
EAI
for
the
Peconic
Estuary
Program.
The
Phase
II
studies
provide
estimates
of
recreational
uses
and
the
economic
values
that
the
public
holds
recreation
and
for
a
variety
of
other
services
provided
by
key
PES
environmental
and
natural
resources.

The
Phase
II
studies
reported
on
here
are
a
major
component
of
a
larger
program
to
assess
the
benefits
and
costs
of
proposed
management
actions
as
part
of
the
Peconic
Estuary
Program
(
Figure
II.
3).
Phase
I
was
designed
to
estimate
market
effects
provided
by
the
PES.
As
discussed
above,
Phase
I
measures
"
economic
impacts"
of
the
PES,
including
employment,
sales,
wages,
and
the
number
of
establishments
associated
with
29
economic
sectors
identified
as
estuarine­
related.
Phase
II
provides
estimates
of
non­
market
benefits.
Our
studies
were
carried
out
for
the
purpose
of
eventually
contributing
to
benefit­
cost
analyses
of
proposed
management
actions
by
the
PES
Management
Committee
in
Phase
III
of
the
project.

Phase
III
analyses
will
(
1)
provide
cost
analyses
for
specific
management
actions
designed
to
protect
or
restore
PES
amenities,
(
2)
combine
this
cost
information
and
the
benefit
information
from
Phases
I
and
II
to
provide
benefit­
cost
analyses
of
potential
management
actions,
and
(
3)
compare
various
financing
options
for
the
proposed
management
actions.

We
designed
a
suite
of
non­
market
valuation
studies
in
order
to
be
able
to
estimate
the
value
of
key
services
provided
by
amenities
in
the
PES.
Multiple
non­
market
valuation
studies
are
needed
because
of
the
many
different
types
of
services
provided
by
PES
environmental
and
natural
resources.
For
example,
recreational
participants
directly
use
Bay
waters
and
beaches;
hence,
for
these
activities
we
want
to
measure
participants'
recreational
use
values.
In
other
cases,
PES
resources
may
provide
amenity
or
aesthetic
benefits
to
residents,
which
will
be
captured
in
property
values,
another
type
of
use
value.
Additionally,
some
may
also
enjoy
just
knowing
that
PES
environmental
amenities,
such
as
open
space
and
salt
marsh,
are
being
preserved
or
restored,
whether
or
not
they
directly
use
these
resources.
For
these
resources,
other
values,
not
involving
direct
use,
may
also
be
important
and
should
be
included.
In
sum,
our
use
of
a
variety
of
methods
was
designed
to
allow
us
to
gain
a
better
understanding
of
the
non­
market
values
provided
by
different
resource
services,
information
that
will
prove
useful
for
later
benefit­
cost
studies.

The
key
Phase
II
studies
are
the
property
value
study,
the
recreation
survey,
the
wetlands
productivity
analysis,
and
the
resource
survey.
The
property
value
study
was
designed
to
estimate
benefits
that
PES
amenities
provide
to
individuals
who
own
adjacent
properties.
The
recreational
survey
allows
us
to
estimate
recreational
use
and
use
values
for
a
variety
of
outdoor
activities.
This
11
survey
was
designed
fill
data
gaps
identified
in
Phase
I
report,
and
to
estimate
recreational
uses
and
use
values
to
contribute
to
assessment
of
management
actions
in
Phase
III.
The
wetlands
productivity
analysis
uses
available
data
to
estimate
the
productivity
of
specific
habitat
types,
and
to
place
a
dollar
measure
on
the
ecosystem
services
provided.
The
productivity
study
includes
only
food
web
and
habitat
services
and
focuses
on
the
"
production"
of
commercial
species,
and
to
a
lesser
extent,
on
non­
commercial
values
(
viewing
and
hunting
values).
Finally,
the
resource
survey
is
used
to
identify
public
priorities
and
values
for
specific
environmental
amenities.
The
resource
survey
used
a
contingent
choice
model
to
learn
which
amenities
are
most
important
to
the
public
and
the
amount
that
respondents
indicate
that
they
would
pay
to
preserve
or
restore
them.
Summary
information
for
each
study
is
give
in
Table
II.
1;
each
study
is
described
in
detail
in
the
chapters
and
appendices
that
follow.

II.
D.
REFERENCES
Grigalunas,
T.
A.
and
J.
Diamantides,
1996.
The
Peconic
Estuary
System:
Perspective
on
Uses,
Sectors,
and
Economic
Impacts.
Peace
Dale,
RI:
Economic
Analysis,
Inc.

Long
Island
Business
News,
1994.
Long
Island
Almanac
1994.
Ronkonkoma,
NY:
Long
Island
Business
News.

Suffolk
County
Department
of
Health
Services,
1992.
Brown
Tide
Comprehensive
Assessment
and
Management
Program.
3
volumes.
Suffolk
County
Dept.
of
Health
Services
(
November).
12
Table
II.
1.
Summary
of
EAI's
Phase
II
Non­
Market
Valuation
Studies
for
PES
Study
Purpose
Data
and
Method(
s)

Recreational
Uses
and
Use
Values
1.
Estimate
Resident,
Second
Home
Owners
&
Visitor
Recreational
Uses:
!
Activities
i
8
Key
Activities
i
8
Locations
5
PES
Bays
Atlantic
Ocean
LI
&
BI
Sounds
i
5
Towns
2.
Estimate
Use
Value
for:
!
Swimming
!
Boating
!
Fishing
!
Bird
&
Wildlife
Viewing
Convenience
sampling/
Intercept
Survey
Self
administered
1,354
completed
surveys
Travel
Cost
Method
Demand
for
swimming
and
recreational
fishing
depends
on
quality
Resource
Values
Estimate
Residents'
and
Second
Homeowners'
Preferences
and
Economic
Value
for
preserving
and
restoring
PES:
!
Open
space
!
Farmland
preservation
!
Unpolluted
shellfish
beds
!
Wetlands
!
Eelgrass
beds
Convenience
sampling/
Intercept
Survey
Self
administered
968
completed
surveys
Contingent
Choice
Method
Wetlands
Productivity
Values
Estimate
the
Economic
Value
for
Nursery
and
Habitat
Productivity
functions
for:
!
Saltmarsh
!
Eelgrass
!
Intertidal
mud
bottom
Based
on
Productivity
approach
and
results
in
available
literature.

Property
Value
Estimate
the
effect
on
Property
Value
in
Town
of
Southhold
of
attributes,
including:
!
Site
i
Lot
&
home
size
i
Garage
i
Special
features
!
Neighborhood
i
Main
road
!
Environmental
i
Wetlands
i
Open
Space
i
Farmlands
i
Zoning
Size
All
property
sales
in
Town
for
1996
374
sales
transactions
Geographical
Information
System
Property
Value
("
Hedonic")
Model
III.
ENVIRONMENTAL
AMENITIES
AND
PROPERTY
VALUES:
13
A
CASE
STUDY
OF
THE
TOWN
OF
SOUTHOLD
III.
A.
INTRODUCTION
Property
values
in
coastal
communities
depend
upon
many
factors.
These
include
not
only
the
size
of
the
property
and
the
characteristics
of
the
home
on
the
property,
but
also
on
environmental
amenities,
such
as
open
space
and
proximity
to
the
shoreline.
For
example,
we
would
expect
a
home
located
near
the
PES
waterfront
to
be
more
valuable
than
another
home,
identical
in
all
respects,
but
located
a
greater
distance
from
the
shore.
If
we
could
find
two
such
homes,
we
could
simply
compare
the
prices
at
which
they
sell
and,
by
that,
calculate
the
value
of
being
near
the
shore.

Of
course,
rarely
are
two
homes
identical
in
all
respects
except
for
one
attribute.
Instead,
as
anyone
who
has
bought
a
home
knows,
the
price
that
a
property
commands
on
real
estate
markets
reflects
a
great
many
factors.
These
include:
lot
size,
size
of
the
home
and
its
location,
the
characteristics
of
the
surrounding
neighborhood,
and
a
wide
range
of
other
environmental
factors.
Nevertheless,
if
all
of
these
important
factors
can
be
taken
into
account,
it
is
possible
to
isolate
the
value
of
individual
factors,
much
as
a
real
estate
broker
or
tax
assessor
does
when
appraising
a
property.

This
Chapter
exploits
these
simple
insights.
We
analyze
data
from
many
residential
housing
transactions
using
a
standard
property
value
model,
as
we
explain
in
detail
below.
The
model
allows
us
to
estimate
the
value­­
as
captured
in
market
prices­­
that
people
attach
to
various
environmental
attributes,
such
as
proximity
to
open
space
and
farmland.
If
efforts
to
estimate
the
value
of
environmental
attributes
are
successful,
the
results
can
be
used
to
assess
the
potential
benefits
of
possible
management
actions
that
would
affect
any
of
the
factors
studied.
That
is,
we
could
estimate
the
potential
benefits
from
a
proposed
management
action,
as
measured
through
changes
in
market
prices
for
property.

We
use
the
Town
of
Southold,
located
on
the
North
Fork
of
the
PES,
as
a
case
study
(
Figure
III.
1).
Southold
is
an
interesting
example,
for
several
reasons.
First,
it
has
a
wide
range
of
environmental
amenity
levels
and
neighborhood
conditions.
For
example,
Southold
has
large
areas
of
farmland
and
open
space,
although
its
population
density
(
0.67
persons
per
acre)
is
highest
among
all
five
PES
towns.
In
addition,
Southold
has
a
long
and
varied
coastline,
both
on
the
Peconic
Estuary
and
on
Long
Island
Sound,
and
is
characterized
by
a
variety
of
development
densities
and
types.
About
a
quarter
(
26%)
of
the
town
is
currently
in
agricultural
use,
compared
with
30%
in
residential
use,
12%
is
preserved
as
open
space,
and
18%
is
classified
as
vacant.
Less
than
3%
is
classified
as
commercial
or
industrial
(
Suffolk
County
Department
of
Planning
1997a).

Southold's
population
increased
8.9%
between
1980
and
1990,
yet
the
number
of
housing
units
increased
at
nearly
twice
this
level
(
16.6%).
Thus,
the
growth
of
housing
and
the
associated
loss
of
undeveloped
lands
has
far
out
paced
population
growth
(
Suffolk
County
Department
of
Planning
1997b).
Given
the
recent
rapid
pace
of
development,
the
protection
of
open
space,
undeveloped
land,
and
other
environmental
amenities
will
play
an
important
role
in
determining
future
quality
of
life
in
the
Town
and,
perhaps,
in
the
PES
area.
14
The
impacts
of
environmental
amenities
on
Southold
property
values,
and
the
impacts
resulting
from
possible
PES
management
actions,
will
depend
upon
the
unique
characteristics
of
the
town
(
and
perhaps
the
quality
of
surrounding
waters
and
land
areas).
To
assess
these
impacts,
EAI
conducted
a
property
value
analysis
specific
to
the
town
of
Southold.
We
apply
economic
methods
using
the
property
value
(
or
"
hedonic"
method)
to
a
database
comprised
of
all
Southold
real
estate
transactions
in
1996
and
GIS
parcel
coverage
data
for
the
town.
Briefly,
the
analysis
estimates
correlations
between
property
values
and
levels
of
valued
environmental
attributes,
including
open
space.

The
policy
relevance
of
the
results
lies
in
the
assumption
that
established
relationships
between
environmental
amenities
and
property
values
estimated
using
the
property
value
model
will
continue
to
hold
in
the
future,
as
future
events
(
including
policies)
lead
to
changes
in
the
level
of
these
amenities.
Through
the
detailed
study
of
existing
property
values,
we
seek
the
best
possible
statistical
estimate
of
the
environmental
impacts
on
local
property
values,
given
the
available
data.

III.
B.
RELATIONSHIP
BETWEEN
ENVIRONMENTAL
AMENITIES
AND
PROPERTY
VALUES
Environmental
amenities
provide
valued
services
to
residents
of
local
communities.
For
example,
open
space
contributes,
in
a
physical
sense,
to
the
character
of
local
communities,
while
providing
a
wide
variety
of
services,
including
scenic
views,
outdoor
recreation,
insulation
from
noise
and
other
aspects
of
the
urban
landscape,
and
protection
from
erosion,
flooding,
and
other
physical
hazards
(
Johnston
1997a).
These
amenities
are
valued
by
local
homeow
ners,
making
communities
with
a
high
level
of
valued
environmental
amenities
(
i.
e.,
environmental
quality)
more
attractive
than
similar
communities
without
such
amenities.
As
a
result,
home
buyers
are
willing
to
pay
more
for
land
or
housing
with
higher
levels
of
environmental
quality.
The
property
value
method
(
also
called
the
hedonic
model)
can
be
used
to
estimate
the
impact
of
environmental
amenities
on
the
values
of
local
property,
thereby
estimating
the
value
of
these
amenities
to
local
residents,
as
evidenced
by
their
actual
willingness
to
pay
higher
prices
for
properties
with
higher
levels
of
desired
environmental
characteristics.

A
well­
documented
example
of
the
relationship
among
environmental
amenities
and
property
values
involves
open
space.
Many
studies
show
that
nearby
open
space
increases
property
values,
reflecting
home
buyers'
values
for
the
services
and
character
offered
by
open
space
(
e.
g.,
Freeman,
1993).
In
a
recent,
coordinated
property
value/
geographic
information
system
study
conducted
in
Middletown,
Rhode
Island,
Johnston
(
1997)
shows
that
positive
property
values
impacts
of
open
space
can
range
from
less
than
1%
to
greater
than
13%,
depending
on
the
size
of
nearby
contiguous
open
space
parcels,
the
total
amount
of
open
space
in
a
region,
and
the
distance
of
property
from
open
space
parcels.
Larger
property
value
impacts
are
associated
with
larger
contiguous
parcels,
larger
acreage
of
open
space,
and
a
closer
proximity
between
valued
parcels
and
open
space
(
Johnston
1997a).
Similar
results
hold
elsewhere
in
the
New
England
region.
A
study
in
Worchester,
MA,
for
example,
found
that
houses
located
less
than
20
feet
from
a
community
park
sell
for
over
$
2,000
more
than
similar
houses
located
more
than
2000
feet
from
the
park
(
More,
et
15
al.,
1982).
In
an
analysis
of
cluster
zoning
in
Amherst,
Massachusetts,
Lacy
(
1990)
estimates
that
cluster­
zoned
housing
with
permanently
protected
open
space
appreciates
at
a
faster
rate
that
similar
housing
in
conventional
subdivisions.
As
of
1989,
this
difference
had
created
an
average
$
17,100
difference
between
the
average
selling
price
in
the
two
types
of
developments.

Comparable
results
concerning
the
value
of
open
space
also
have
been
found
in
other
coastal
areas.
For
example,
a
study
of
land
values
adjacent
to
Chesapeake
Bay
shows
a
positive
relationship
between
the
percentage
of
preserved
open
space
in
a
250
acre
area
surrounding
a
parcel
of
land,
and
the
per­
acre
selling
price
of
that
land.
This
study
also
shows
that
agricultural
pasture
land
and
forest
land
increases
the
per
acre
value
of
nearby
land,
other
things
being
the
same
(
Bockstael
1996).
Similar
results
are
shown
by
Geoghegan
et
al.
(
1995).
Parsons
(
1992)
estimates
that
land
use
restrictions
in
the
Chesapeake
Bay
watershed
cause
a
14­
27%
increase
in
housing
prices
within
1,000
feet
of
the
bay,
and
a
4­
11%
increase
up
to
a
3
mile
distance.
Garrod
and
Willis
(
1992)
conclude
that
housing
prices
are
positively
related
to
the
existence
of
nearby
broadleaf
forests.
Correll
et
al.
(
1978)
find
that,
on
average,
residential
housing
prices
in
Boulder,
Colorado
decrease
by
$
4.20
for
each
additional
foot
of
distance
from
a
central
green
way,
up
to
a
distance
of
3200
feet.

Of
course,
the
impacts
of
environmental
amenities
on
Southold
property
values
depend
on
the
unique
characteristics
of
the
town,
and
may
be
greater
or
less
than
those
cited
above.
To
assess
these
impacts,
we
conducted
a
property
value
analysis
specific
to
the
town
of
Southold.
We
apply
economic
methods
(
hedonic
methods)
to
a
database
comprised
of
GIS
parcel
coverage
data
and
real
estate
sales
data.
In
simple
terms,
the
property
value
model
estimates
correlations
between
property
values
and
levels
of
valued
environmental
attributes,
including
open
space.
The
steps
involved
and
data
used
are
described
next.

III.
C.
APPLICATION
OF
PROPERTY
VALUE
MODEL
TO
SOUTHOLD
This
study
applies
the
property
value
model
to
real
estate
parcels
sold
in
Southold
during
1996.
The
analysis
estimates
how
various
site,
neighborhood
and
environmental
characteristics
affect
the
value
of
individual
properties.
Property
value
analysis
is
a
common
means
of
estimating
the
effect
of
environmental
changes
on
land
values,
as
described
in
detail
by
Freeman
(
1993)
and
Garrod
and
Willis
(
1992),
among
many
others.

III.
C.
1.
Explanation
of
The
Property
Value
Method
People
buying
homes
in
effect
purchase
many
site,
neighborhood,
and
environmental
characteristics.
For
example,
the
characteristics
of
a
residential
property
include:
the
size
of
the
house,
the
number
of
baths,
the
square
footage
of
the
structure,
the
zoning
classification
of
the
land,
whether
the
land
is
served
by
public
sewers
and
water,
the
existence
of
wetlands
on
the
property,
and
the
proximity
of
the
property
to
amenities
such
as
open
space,
coastlines,
and
farms.
These
and
other
characteristics
define
the
property,
and
make
it
more
or
less
attractive
to
potential
buyers.
Other
things
being
the
same,
home
buyers
are
willing
to
pay
more
for
properties
that
they
find
have
4
The
accuracy
of
hedonic
analysis
depends
on
a
well­
functioning
real
estate
market,
in
which
consumers
have
accurate
information
regarding
all
home
characteristics.
The
technique
also
assumes
that
a
large
number
of
different
housing
types
are
available
for
purchase,
so
that
consumers
can
choose
the
"
package"
of
housing
characteristics
that
is
most
to
their
liking,
and
can
"
mix
and
match"
different
types
of
characteristics.
Finally,
the
technique
assumes
the
availability
of
appropriate
data,
concerning
all
characteristics
that
influence
property
values
Bias
can
result
from
the
application
of
hedonic
techniques
to
ill­
functioning
real
estate
markets,
in
which
few
different
types
of
housing
options
are
available,
and/
or
in
which
consumers
do
not
have
accurate
information
regarding
housing
characteristics
(
Johnston
1997a).

16
desirable
characteristics,
and
less
for
properties
with
undesirable
characteristics.
These
likes
and
dislikes
across
many
prospective
buyers
and
sellers
are
reflected
in
market
prices.
The
Property
Value
Method
attempts
to
estimate
the
portion
of
a
property's
value
related
to
each
relevant
characteristic,
by
that
providing
an
estimate
of
the
(
implicit)
value
of
each
characteristic.
The
model
works
by
statistically
comparing
values
of
a
large
number
of
properties
with
differing
levels
of
the
identified
characteristics
(
e.
g.
lot
sizes,
number
of
rooms,
proximity
to
roads,
farms,
open
space,
etc.)
(
Freeman,
1993;
Johnston,
1997a).

The
Property
Value
technique
is
based
on
the
assumption
that
a
relationship
exists
between
the
market
value
of
a
property,
and
the
characteristics
of
the
property.
The
Property
Value
method
uses
a
statistical
technique
called
"
multiple
regression"
to
assess
the
impact
of
each
characteristic
on
the
market
value
of
the
property.
The
technique
simultaneously
compares
a
large
number
of
properties
with
different
prices
and
different
levels
of
each
characteristic.
The
method
establishes
which
characteristics
are
associated
with
higher
values,
which
are
associated
with
lower
values,
and
which
have
no
significant
impact
on
values.
The
model
also
estimates
the
dollar
magnitude
of
these
impacts­­
that
is,
it
estimates
how
large
an
impact
is
likely
to
be
caused
by
a
specific
level
of
a
specific
characteristic.
Using
this
technique,
the
impact
of
different
environmental
amenities
on
nearby
property
values
can
be
estimated.
4
The
technical
details
of
the
property
value
model
(
or
hedonic
technique)
are
presented
in
Appendix
A.

Actual
1996
sales
prices
are
used
in
the
analysis
of
Southold
property
values.
The
data
for
the
analysis
is
drawn
from
two
sources:

(
1)
Town
of
Southold
property
record
cards
for
all
properties
sold
during
1996;
and
(
2)
The
computerized
geographic
information
system
(
GIS)
maintained
by
the
Suffolk
County
Planning
Department.

Although
sales
data
were
available
for
401
parcels,
complete
GIS
coverage
data
exists
for
only
374
of
these
parcels.
Thus,
the
Southold
data
set
used
in
the
analysis
has
full
information
on
374
parcels.

III.
D.
STATISTICAL
ANALYSIS
OF
SOUTHOLD
LAND
VALUES
5
Wichelns
and
Kline
(
1996),
Chicoine
(
1981),
Shonkwiler
and
Reynolds
(
1986),
and
Garrod
and
Willis
(
1992).

17
Model
variables
are
the
characteristics
that
influence
buyers'
willingness
to
purchase
a
land
parcel,
or
pay
higher
prices
for
that
parcel.
These
include
proximity
to
amenities
such
as
open
space
and
coastlines;
the
size
of
the
parcel;
the
size
and
other
characteristics
of
the
structures
on
the
parcel;
applicable
use
restrictions
such
as
zoning
codes;
and
the
existence
of
features
such
as
wetlands
on
the
parcel.
These
variables
may
be
grouped
into
three
general
categories:
parcel
characteristics,
neighborhood
characteristics,
and
environmental
characteristics.
To
distinguish
the
effects
of
parcel
size
on
land
values,
we
consider
the
effect
of
the
characteristics
on
per­
acre
value.
This
is
calculated
by
dividing
selling
price
by
the
number
of
acres
in
the
parcel.
Note
that
these
per­
acre
land
values
include
the
value
of
all
structures
built
on
the
land.
For
a
full
description
of
the
23
variables
included
in
the
final
analysis,
see
Appendix
A.

The
statistical
analysis
uses
the
ordinary
least
squares
(
OLS)
multiple
regression
approach.
This
approach
estimates
the
direction,
magnitude,
and
statistical
significance
of
correlations
between
each
of
a
set
of
independent
variables
(
the
characteristics
of
the
property),
and
a
single
dependent
variable
(
per­
acre
property
value).

To
establish
the
nature
of
the
specific
functional
relationship
between
these
variables
and
assessed
land
values,
the
study
relies
on
the
findings
of
prior
research.
In
particular,
the
regression
analysis
applies
a
"
transcendental"
or
"
translog
functional
form".
5
The
transcendental
form
is
chosen
for
its
ability
to
capture
realistic
relationships
between
parcel
characteristics
and
land
values
(
Chicoine
1981).
Such
functional
forms
are
also
preferred
for
their
statistical
properties
in
cases
where
data
concerning
certain
relevant
variables
may
not
exist
in
the
data
set
(
Garrod
and
Willis
1992).
For
a
technical
description
of
the
statistical
model
and
its
properties,
see
Appendix
B.

III.
E.
ANALYSIS
OF
SOUTHOLD
LAND
VALUES:
RESULTS
The
hedonic
analysis
provides
information
on
the
effect
of
20
characteristics
on
per­
acre
land
values
in
Southold.
Final
model
results
are
illustrated
in
Appendix
C.
Overall
measures
of
model
fit
indicate
high
levels
of
correlation
between
land
characteristics
and
property
values.
An
R2
value
of
0.8352
indicates
that
over
83%
of
the
variation
in
per­
acre
value
is
"
explained"
by
the
model,
or
is
correlated
with
model
variables.
An
F­
statistic
of
75.945
indicates
only
a
.01%
chance
that
the
set
of
correlations
reported
by
the
model
could
be
observed
through
random
chance
alone.
The
signs
associated
with
model
variables
are
consistent
with
prior
expectations
and
with
the
findings
of
earlier
hedonic
studies
(
e.
g.,
Bockstael
1996;
Johnston
1997a;
Des
Rosiers
and
Theriault
1992;
Garrod
andWillis
1992).
Of
those
variables
with
demonstrated
impacts
on
property
values,
seven
have
potential
implications
for
environmental
policy.
These
include
the
following:

°
openspace:
This
variable
identified
parcels
adjacent
to
open
space
land,
as
defined
by
the
GIS
database.
A
parcel
of
land
located
next
to
open
space
has,
on
average,
12.83%
higher
per­
acre
value
than
a
similar
parcel
located
18
elsewhere.
Parcels
adjacent
to
open
space
are
defined
as
those
with
a
border
within
25
ft.
(
7.62
meters)
of
an
open
space
parcel.
As
defined
by
the
GIS
for
Suffolk
County,
open
space
includes:
parks;
nature
and
wildlife
preserves;
recreational
fields;
unbuildable
swamps
or
wetlands;
large
tracts
of
land
associated
with
schools,
cemeteries,
or
other
institutions;
and
selected
large
parcels
of
preserved
farmland.

°
farmdistance:
This
variable
represents
the
distance
between
a
specific
parcel
and
the
nearest
farmland
(
farmland
on
which
agricultural
crops
or
nursery
products
are
grown),
in
meters.
For
every
meter
of
additional
distance
between
a
parcel
and
the
nearest
farmland,
average
per­
acre
property
value
increases
by
0.0017%
(
i.
e.,
the
magnitude
of
the
impact
is
quite
small).

°
onfarm:
This
variable
identifies
parcels
contiguous
to
farmland.
A
parcel
of
land
located
next
to
farmland
has,
on
average,
a
13.32%
lower
per­
acre
value
than
a
similar
parcel
located
elsewhere.

°
onroad:
This
variable
identifies
parcels
within
20
meters
of
a
major
road
(
defined
as
Rts.
25
and
48).
A
parcel
of
land
located
next
to
a
major
road
has,
on
average,
a
16.16%
lower
per­
acre
value
than
a
similar
parcel
located
elsewhere.

°
largezone:
This
variable
identifies
parcels
located
in
districts
zoned
R­
80
or
R­
120
(
two­
or
three­
acre
zoning).
A
parcel
of
land
located
within
a
district
zoned
R­
80
or
R­
120
has,
on
average,
16.65%
higher
per­
acre
value
than
a
similar
parcel
located
elsewhere.

°
wetland:
This
variable
indicates
the
percentage
of
a
parcel
classified
as
a
freshwater
wetland.
For
every
one
percentage
point
increase
in
the
percent
of
a
parcel
classified
as
wetlands,
average
per­
acre
property
value
increases
by
0.27%.
(
However,
this
variable
is
only
significant
at
the
14%
level.)

III.
F.
SIMPLE
ILLUSTRATION
OF
PROPERTY
VALUE
IMPACTS:
VALUE
OF
OPEN
SPACE
Given
the
definition
of
the
open
space
variable,
the
impact
of
open
space
on
neighboring
property
values
will
differ
depending
on
the
characteristics
of
the
surrounding
property.
However,
to
illustrate
the
basic
implications
of
the
model
results
for
policy,
the
following
section
simulates
the
predicted
property
value
impact
of
the
hypothetical
loss
of
10
acres
of
open
space,
in
a
highly
simplified
scenario.
Given
specific
locations
for
the
open
space
land
under
consideration,
and
the
characteristics
of
the
surrounding
parcels,
similar
results
could
be
generated
for
actual
areas
in
Southold.
6In
this
regard,
we
note
that
recent
support
for
an
open
space
initiative
in
Suffolk
County
suggests
that
the
electorate
has
a
strong
preference
for
open
space
as
a
public
good.

19
According
to
the
model
results,
properties
adjacent
to
open
space
land
(
or
within
25
ft.)
are
an
average
of
12.83%
more
valuable
than
those
not
adjacent
to
open
space.
Within
the
Southold
data
sample,
the
average
parcel
sales
price
was
$
213,514,
with
an
average
parcel
size
of
2941.95
square
meters
(
0.72
acre).
Given
this
average
value,
a
loss
of
12.83%
(
related
to
the
loss
of
our
hypothetical
10
acres
of
adjacent
open
space)
implies
that
average
property
value
for
the
adjacent
parcels
would
decrease
by
$
27,394,
all
else
held
constant.

To
estimate
a
hypothetical
total
impact
in
a
simple
case,
assume
that
the
lost
open
space
was
ten
acres
in
a
square
shape,
surrounded
by
parcels
of
average
0.72
acre
size
(
the
average
in
our
sample),
also
square
in
shape.
Assuming
a
square
shape
for
the
open
space
parcel,
it
would
have
a
perimeter
of
805.2
meters
that
would
be
bordered
by
other
parcels.
Given
an
average
per­
parcel
area
of
2941.95
square
meters
(
also
assumed
square),
the
length
per­
side
of
these
bordering
parcels
would
be
approximately
54
meters.
Given
these
measurements,
approximately
15
parcels
would
fit
adjacent
to
the
10
acre
open
space
parcel,
assuming
each
had
a
full
54
meter
side
bordering
the
open
space
(
805.2
÷
54
=
14.9).
Accordingly,
for
this
simple
example,
we
assume
that
15
average
parcels
would
be
affected
by
the
loss,
or
development,
of
our
square
ten
acres
of
open
space.

As
stated
previously,
development
of
the
open
space
parcel
would
result
in
a
loss
of
12.83%
of
average
value
per
lot
or
$
213,514,
according
to
the
calculations
made
above.
Thus,
the
average
parcel
would
lose
$
27,394
in
total
value.
Multiplied
by
the
15
parcels
adjacent
to
the
newly
developed
parcel,
the
total
property
value
loss
is
equal
to
$
27,394
×
15=$
410,907.
This
equates
to
an
average
loss
of
$
41,090
in
total
property
value
per­
acre
of
open
space
lost.
Looked
at
another
way,
the
estimated
benefits
of
preserving
the
ten
acres
of
open
space
thus
would
be
$
410,907,
as
this
is
the
increase
in
nearby
property
value
associated
with
this
open
space
parcel.

For
this
illustration
we
do
not
compare
costs
with
benefits
since
we
do
not
have
information
on
the
costs
of
acquiring
specific
undeveloped
property
for
open
space.
However,
in
the
illustration,
if
the
10
acres
of
undeveloped
property
for
open
space
could
be
acquired
for
less
than
$
410,907
or
$
41,097
per
acre,
then
the
benefits
would
be
greater
than
costs.
Note
that
even
if
the
property
to
be
acquired
for
open
space
costs
more
than
$
410,907,
benefits
still
may
exceed
costs.
This
is
because
not
all
of
the
public
benefits
of
open
space
are
captured
in
our
hedonic
analysis.
For
example,
our
results
in
this
section
do
not
capture
general
amenity
benefits
enjoyed
by
all
local
residents,
regardless
of
the
location
of
their
homes
(
see
Chapter
VI
for
discussion
of
this
issue).
6
We
note
that
the
impacts
illustrated
above
should
not
be
regarded
as
exact
results
expected
in
any
single
instance,
and
are
included
for
illustration
purposes
only.
A
full
benefit­
cost
analysis
would
require
much
more
detailed
analysis
of
the
proposed
project
and
its
impacts,
including
impacts
on
surrounding
property
values.
However,
the
illustrated
results
do
indicate
the
general
types
of
benefits
and
costs
that
may
be
expected,
on
average,
over
the
entire
community.
Although
the
results
require
various
assumptions
as
noted
above,
they
are
robust
with
respect
to
the
quantity
of
open
space
preserved.
20
III.
G.
LIMITATIONS
IN
PROPERTY
VALUE
ANALYSIS
As
noted,
the
model
provides
a
good
fit
for
sales
data
for
the
town,
and
the
results
of
the
Southold
property
value
analysis
correspond
to
prior
expectations
and
with
the
results
of
similar
analyses.
Nevertheless
several
potential
limitations
should
be
noted.
The
most
important
of
these
limitations
is
that
the
results
of
the
statistical
analysis
are
sensitive
to
characteristics
of
the
data
set.
The
combined
property
card­
GIS
database
may
exclude
information
on
certain
variables
that
may
affect
property
values.
Although
numerous
environmental
variables
may
influence
property
purchase
decisions,
data
limitations
allowed
us
to
include
only
a
sub­
set
of
these
variables
in
the
model.
For
example,
the
data
set
includes
water
quality
monitoring
data
for
the
Peconic
Estuary,
conducted
at
numerous
sites.
However,
no
parcels
in
the
1996
sales
database
are
located
within
100
meters
of
the
Peconic
Estuary.
Thus,
for
those
parcels
in
the
current
database,
water
quality
is
not
expected
to
have
an
impact.
However,
were
data
to
exist
for
parcels
close
to
the
estuary,
one
would
expect
that
water
quality
might
have
an
impact
on
property
values.
Other
variables
for
which
data
was
unavailable
included
fresh
water
(
including
groundwater)
quality;
beach
erosion
(
likely
only
important
for
beachfront
properties);
and
proximity
to
creeks,
all
of
which
would
be
expected
to
affect
property
values.
If
in
fact
important
variables
are
excluded
from
the
model,
statistical
results
may
show
upward
or
downward
bias.
Omission
of
important
variables
can
either
increase
or
decrease
the
estimated
effects
of
environmental
amenities
on
property
values,
based
on
the
extent
to
which
these
variables
are
correlated
with
environmental
variables.
(
If
these
omitted
variables
are
uncorrelated
with
the
environmental
policy
variables
of
interest,
then
their
absence
should
not
influence
reported
results.)

For
all
of
the
above
reasons,
the
estimated
Property
Value
model
results
should
not
be
interpreted
as
"
exact".
Rather,
they
provide
the
best
possible
estimate,
given
available
data,
of
the
impacts
of
existing
environmental
attributes
on
current
per­
acre
land
values.

A
second
limitation
is
that
increased
property
values,
related
to
improved
environmental
quality,
may
not
be
favored
by
all
residents.
For
example,
residents
may
resist
open
space
preservation
on
the
grounds
that
it
tends
to
increase
property
values,
thereby
increasing
the
costs
of
housing
for
young
home
buyers.
Others
may
reject
increases
in
property
taxes
that
accompany
higher
property
values.
Still
others
may
object
that
higher
property
values
tend
to
"
squeeze
out"
low
income
residents.
Real
estate
agents
may
oppose
additional
open
space
on
the
basis
that
fewer
housing
units
are
available,
despite
the
act
that
average
property
values
in
the
area
affected
will
be
higher
with
the
additional
open
space.
Such
arguments
should
be
acknowledged
when
considering
environmental
policy
changes
using
the
results
of
property
value
methods.

Finally,
we
note
that
many
people
beyond
the
immediate
area
may
derive
benefits
from
open
space
but
their
benefits
are
not
included
in
numbers
given
in
the
above
illustration.
For
example,
residents
of
the
town
who
live
outside
of
the
grid
used
in
the
illustration
may
value
open
space
elsewhere
in
town
(
see
Chapter
V).

III.
H.
SUMMARY
21
Environmental
amenities
increase
property
values
of
nearby
parcels,
reflecting
the
valuable
services
offered
by
these
amenities.
An
analysis
of
Southold
property
values
supports
this
contention,
and
corresponds
with
the
results
of
prior
studies.
In
Southold,
existing
open
space
increases
the
values
of
adjacent
properties
by
an
estimated
12.83%.
In
addition,
average
per­
acre
property
values
are
higher
in
parcels
located
further
from
farms
and
major
roads.
Finally,
higher
property
values
appear
to
be
associated
with
the
existence
of
wetlands
on
the
property,
and
with
large
lot
(
2­
and
3­
acre)
zoning.
These
results
indicate
that
environmental
policies
can
have
significant
impacts
on
property
values,
reflecting
the
influence
of
local
environmental
amenities
on
local
quality
of
life.

III.
I
REFERENCES
Bockstael,
N.
E.
1996.
"
Modeling
Economics
and
Ecology:
The
Importance
of
a
Spatial
Perspective,"
American
Journal
of
Agricultural
Economics
78:
1168­
1180.

Chicoine,
D.
L.,
1981.
Farmland
Values
at
the
Urban
Fringe:
An
Analysis
of
Sales
Prices."
Land
Economics
57(
3):
353­
362.

Correll,
M.,
J.
Lillydahl
and
L.
Singell,
1978.
"
The
Effects
of
Greenbelts
on
Property
Values:
Some
Findings
on
the
Political
Economy
of
Open
Space."
Land
Economics
54:
207­
217.

Des
Rosiers,
F.
and
M.
Theriault.
1992.
"
Integrating
Geographic
Information
Systems
to
Hedonic
Price
Modeling:
An
Application
to
the
Quebec
Region.
Property
Tax
Journal
11(
1):
29­
57.

Edwards,
Steven
and
Glenn
Anderson,
1984.
"
Land
Use
Conflicts
in
the
Coastal
Zone:
An
Approach
for
the
Analysis
of
the
Opportunity
Costs
of
Protecting
Coastal
Resources."
Journal
of
the
Northeastern
Agricultural
and
Resource
Economics
Association
(
April).

Freeman,
M.
A.,
1979.
The
Benefits
of
Environmental
Improvement:
Theory
and
Practice.
Baltimore:
Johns
Hopkins
University
Press.

Freeman,
M.
A.
1993.
The
Measurement
of
Environmental
and
Natural
Resource
Values.
Washington,
DC:
Resources
for
the
Future.

Garrod,
G.
and
K.
Willis,
1992.
"
The
Environmental
Economic
Impact
of
a
Woodland:
A
Two­
Stage
Hedonic
Price
Model
of
the
Amenity
Value
of
Forestry
in
Britain."
Applied
Economics
24:
715­
728.

Geoghegan,
J.,
N.
Bockstael,
and
D.
Lipton.
1996.
The
Economics
of
Land
Use
Change
in
the
Patuxent
Watershed.
prepared
for
the
Land
Use
and
Management
in
Maryland
Seminar,
1996.
College
Park,
MD:
Center
for
Agricultural
and
Natural
Resource
Policy.
22
Geoghegan,
J.,
N.
Bockstael,
and
L.
Wainger.
1995.
"
Spatial
Landscape
Indices
in
a
Hedonic
Framework:
An
Ecological
Economics
Analysis
Using
GIS."
presented
at
the
annual
meetings
of
the
Association
of
Environmental
and
Resource
Economics,
Indianapolis,
IN.

Hammer,
T.
R.,
R.
E.
Coughlin
and
E.
T.
Horn
IV.
1974.
Research
Report:
The
Effect
of
a
Large
Park
on
Real
Estate
Values.
Journal
of
the
American
Institute
of
Planners,
July.

Johnston,
R.
J.
1997a
Aquidneck
Island
and
Open
Space:
An
Economic
Perspective:
Technical
Manual
Narragansett,
RI:
The
Aquidneck
Island
Partnership
and
the
Coastal
Resources
Center,
University
of
Rhode
Island.

Johnston,
R.
J.
1997b.
Aquidneck
Island
and
Open
Space:
An
Economic
Perspective.
Narragansett,
RI:
The
Aquidneck
Island
Partnership
and
the
Coastal
Resources
Center,
University
of
Rhode
Island.

Kask,
S.
B.
and
S.
A.
Maani,
1992.
"
Uncertainty,
Information,
and
Hedonic
Pricing."
Land
Economics
68(
2):
170­
184.

Kimmel,
M.
M.,
1985.
Parks
and
Property
Values:
An
Empirical
Study
in
Dayton
and
Columbus,
Ohio.
Thesis.
Oxford,
OH:
Miami
Univ.,
Inst.
of
Environmental
Sciences.

Lacy,
J.
1990.
An
Examination
of
Market
Appreciation
for
Clustered
Housing
with
Permanently
Protected
Open
Space.
Center
for
Rural
Massachusetts
Monograph
Series.
Amherst,
MA:
University
of
Massachusetts.

Lardaro,
L.,
1993.
Applied
Econometrics.
New
York:
Harper
Collins
College
Publishers.

More,
T.
A.,
Thomas
Stevens
and
P.
Geoffrey
Allen,
1992.
"
The
Economics
of
Urban
Parks."
Parks
and
Recreation.

National
Park
Service,
1995.
The
Economic
Impacts
of
Protection
Rivers,
Trails,
and
Greenway
Corridors.
Washington,
D.
C.:
Rivers,
Trails,
and
Conservation
Assistance
Program.

Nelson,
A.
C.,
1985.
"
A
Unifying
View
of
Greenbelt
Influences
on
Regional
Land
Values
and
Implications
for
Regional
Planning
Policy."
Growth
and
Change
16(
2):
43­
48.

Parsons,
G.
R.,
1992.
"
The
Effect
of
Coastal
Land
Use
Restrictions
on
Housing
Prices:
A
Repeat
Sale
Analysis."
Jour.
of
Environmental
Economics
and
Management
22:
25­
37.

Rosen,
S.,
1974.
Hedonic
Prices
and
Implicit
Markets:
Product
Differentiation
in
Pure
Competition."
Journal
of
Political
Economy
82(
1):
34­
55.

SAS
Institute
Inc.
1996.
SAS
User's
Guide:
Statistics.
Cary:
NC:
SAS
Institute
Inc.
23
Shonkwiler,
J.
S.
and
J.
E
Reynolds,
1986.
"
A
Note
on
the
Use
of
Hedonic
Models
in
the
Analysis
of
Land
Prices
at
the
Urban
Fringe."
Land
Economics
62(
1).

Wichelns,
D.
and
J.
Kline,
1993.
"
The
Impact
of
Parcel
Characteristics
on
the
Cost
of
Development
Rights
to
Farmland".
Agric.
and
Resource
Economics
Review:
150­
158.
24
APPENDIX
A.
Variables
in
the
Hedonic
Analysis
All
model
variables
were
generated
from
one
of
two
sources:
1]
Official
Southold
Property
Record
Cards
for
all
parcels
sold
during
1996;
2]
GIS
coverages
maintained
Suffolk
County
Planning
Department,
updated
1995.

Dependent
Variable:

LVALACRE:
The
natural
log
of
per­
acre
sales
price,
in
dollars.
Based
on
Southold
property
record
cards
for
properties
sold
during
1996.

Independent
Variables:

LNACRES:
the
natural
log
of
acres
in
each
parcel.
Estimated
using
GIS
coverages.

WATFRONT:
dummy
variable:
value
of
1
assigned
if
property
is
located
on
the
waterfront
(
Long
Island
Sound
or
Peconic);
value
of
value
of
0
assigned
if
property
is
not
located
on
a
waterfront.
Value
assigned
based
on
waterfront
classification
of
property
record
cards.

BULKHEAD:
dummy
variable:
value
of
1
assigned
if
property
has
a
bulkhead
on
the
waterfront,
value
of
0
assigned
if
no
bulkhead.
Value
assigned
based
on
property
record
cards.

LARGEZONE:
dummy
variable:
value
of
1
assigned
for
parcels
located
in
districts
zoned
R­
80
or
R­
120;
value
of
zero
assigned
to
all
other
zoning
classifications.

YEAR:
year
the
house
was
built,
based
on
property
records,
minus
1950.

YEARSQ:
YEAR
*
YEAR
(
year
squared).

GARSQFT:
the
number
of
square
feet
in
the
garage,
based
on
property
records.
If
no
garage
exists,
value
of
0
is
assigned.

PATIO:
dummy
variable:
value
of
1
assigned
if
the
house
has
a
patio
or
deck;
value
of
0
assigned
if
there
is
no
patio
or
deck,
based
on
property
records.

BATHS:
the
number
of
bathrooms,
based
on
property
records.

NOHEAT:
dummy
variable:
value
of
1
assigned
if
the
house
has
no
heat,
value
of
0
assigned
if
house
has
heat,
based
on
property
records.
25
FIRE:
dummy
variable:
value
of
1
assigned
if
the
house
has
a
fireplace
or
woodburning
stove;
value
of
0
assigned
if
there
is
no
fireplace
or
woodburning
stove,
based
on
property
records.

SQFT:
square
footage
of
the
house,
based
on
property
records.

SPECIAL:
dummy
variable:
value
of
1
assigned
if
the
house
has
a
"
special"
feature
(
tennis
court,
swimming
pool,
etc.);
value
of
0
assigned
if
no
special
features
are
present,
based
on
property
records.

DSOUNDT:
The
linear
distance
to
Long
Island
Sound,
truncated
at
800
meters
(
i.
e.,
the
maximum
possible
value
is
800).
Based
on
GIS
coverages.

PECON100:
dummy
variable:
value
of
1
assigned
if
the
parcel
is
within
100
meters
of
the
Peconic
Estuary;
value
of
zero
assigned
if
the
parcel
is
further
than
100
linear
meters
from
the
estuary.
Based
on
GIS
coverages.

SOUND100:
dummy
variable:
value
of
1
assigned
if
the
parcel
is
within
100
meters
of
Long
Island
Sound;
value
of
zero
assigned
if
the
parcel
is
further
than
100
linear
meters
from
the
Sound.
Based
on
GIS
coverages.

ONROAD:
dummy
variable:
value
of
1
assigned
if
the
parcel
is
within
20
meters
of
a
main
road
(
Rts.
25
and
48);
value
of
0
assigned
if
the
parcel
is
further
than
20
meters
from
a
main
road.
Based
on
GIS
coverages.

FARMDISTANCE:
the
linear
distance
between
the
parcel
and
the
nearest
farmland
(
farmland),
in
meters.
Based
on
GIS
coverages.

ONFARM:
dummy
variable:
value
of
1
assigned
if
the
parcel
is
contiguous
(
distance=
0)
to
farmland;
value
of
0
assigned
if
the
parcel
is
further
than
0
meters
from
farmland.
Based
on
GIS
coverages.

WETLAND:
indicates
the
percentage
of
the
property
covered
by
freshwater
wetlands.

OPENSPACE:
dummy
variable:
value
of
1
assigned
if
the
parcel
is
adjacent
to
open
space
(
where
adjacent
is
defined
as
a
parcel
whose
border
is
within
25
feet
of
an
open
space
parcel);
value
of
0
assigned
if
the
parcel
is
further
than
25
feet
from
open
space.
Based
on
GIS
coverages.

The
Southold
GIS
coverage
classifies
the
following
land
uses
as
open
space:
fish,
game,
and
wildlife
preserves;
public
golf
courses;
private
golf
courses
and
country
clubs;
improved
beaches;
camps
and
camping
facilities;
parks;
nature
trails
and
bike
paths;
cemeteries;
private
hunting
and
fishing
clubs;
state
owned
forest
land;
reforested
land
and
other
conservation
land;
public
26
parks;
other
wild
or
conservation
lands;
and
taxable
state
owned
conservation
easements.

Farms
and
undeveloped
private
property,
in
general,
are
not
considered
open
space.

Mean
values
of
variables
included
in
the
analysis,
for
the
374
observations
in
the
database,
are
illustrated
below.

Table
B.
1
Mean
Values
of
Model
Variables
Variable
Mean
Value
LVALACRE
12.8362
LNACRE
­
0.7521
WATFRONT
0.2340
BULKHEAD
0.0718
PORCH
0.5266
YEAR
16.7500
GARSQFT
265.1861
PATIO
0.4734
BATHS
1.6303
NOHEAT
0.1197
FIRE
0.5372
SQFT
1270.3200
SPECIAL
0.1064
PECON100
0.0053
SOUND100
0.0848
ONROAD
0.0585
ONFARM
0.0452
DFARM
2395.1400
OPENSPACE
0.050398
WETLAND
2.4548
LARGEZONE
0.1011
27
#

#

#

#

#
pond
Long
Island
Sound
100­
meter
radius
ROUTE
25
Agricultural
Land
Distance
>
100­
m
Agricultural
Land
Distance
<
100­
m
GIS
Maps
Used
to
Define
Model
Variables:
Examples
Many
of
the
variables
in
the
analysis
were
derived
from
GIS
parcel
coverages
of
Southold.
The
following
GIS
map
illustrates
the
type
of
analysis
applied.
In
this
case,
the
map
illustrates
the
process
used
to
determine
the
existence
of
agricultural
land
within
100
meters
of
each
parcel
in
the
sales
database.
28
Wetlands
pond
Long
Island
Sound
RO
UTE
25
The
following
GIS
map
covers
the
same
area
of
Southold,
yet
illustrates
the
distribution
of
wetlands,
used
to
assess
the
percentage
of
each
sales
parcel
covered
by
freshwater
wetlands.
29
APPENDIX
B.
The
Statistical
Model
To
establish
the
functional
(
mathematical)
relationship
between
model
variables
and
assessed
land
values,
the
analysis
on
the
findings
of
prior
research.
In
particular,
the
analysis
follows
Wichelns
and
Kline
(
1996),
Chicoine
(
1981),
Shonkwiler
and
Reynolds
(
1986),
and
Garrod
and
Willis
(
1992)
applying
a
transcendental
or
translog
functional
form
to
the
property
price
 
characteristics
equation
Vi
=
 
0Xi1
 1exp[
  
jXij]
j=
2...
n
where
Vi
is
the
1996
selling
price
of
the
ith
parcel,
in
dollars
per­
acre,
Xi1
is
parcel
size
in
acres,
and
Xij
are
measures
of
the
j
=
2 
n
other
characteristics
that
affect
land
value.
The
 '
s
are
parameters
to
be
estimated,
and
represent
the
effects
of
each
of
the
characteristics
on
per­
acre
land
price.
Estimated
parameters
are
used
to
calculate
the
effects
of
different
open
space
characteristics
on
the
value
of
land
in
Southold.
The
transcendental
form
is
chosen
for
its
ability
to
capture
realistic
relationships
between
parcel
characteristics
and
land
values
(
Chicoine
1981).
For
example,
the
transcendental
form
permits
a
positive
or
negative
marginal
relationship
between
value
and
parcel
size
(
acres),
requires
value
to
be
zero
when
parcel
size
is
zero,
can
detect
proportional
value­
size
relationships,
and
allows
for
increasing
or
decreasing
returns
to
scale
for
land
parcel
characteristics
(
Chicoine
1981;
Wichelns
and
Kline
1993).
Moreover,
functional
forms
such
as
the
semi­
log
or
transcendental
are
preferred
(
to
the
more
flexible
functional
forms)
when
there
is
potential
omitted
variable
bias
(
Garrod
and
Willis
1992).

The
empirical
version
of
the
translog
model
is
as
follows:

where
the
variables
are
as
defined
above,
and
ln(.)
represents
the
natural
log.
This
is
the
form
used
for
statistical
analysis
in
the
Southold
property
value
study.

The
estimated
trans­
log
model
appears
to
fit
the
data
well.
Overall
model
statistics
and
individual
variable
significance
levels
indicate
good
model
fit,
and
trials
with
alternate
functional
forms
(
e.
g.,
linear,
semi­
log)
indicate
that
these
alternate
forms
result
in
lower
significance
levels
and
explanatory
power.
A
White
test
for
heteroskedasticity
(
White
1980)
fails
to
reject
the
null
hypothesis
of
homoskedasticity
at
the
5%
level.
Although
multicollinearity
is
ubiquitous
in
hedonic
models,
tests
for
multicollinearity
indicate
that
it
does
not
have
severe
impacts
on
model
results
in
the
present
application.
As
the
data
is
cross­
sectional
and
data
order
has
been
randomized,
tests
for
autocorrelation
are
unnecessary.
30
APPENDIX
C.
OLS
Model
Results
Dependent
Variable:
LVALACRE
Sum
of
Mean
Source
DF
Squares
Square
F
Value
Prob>
F
Model
20
152.03723
7.60186
89.930
Error
355
30.00860
0.08453
C
Total
375
182.04583
Root
MSE
0.29074
R­
square
0.8352
Dep
Mean
12.83621
Adj
R­
sq
0.8259
C.
V.
2.26502
Parameter
Estimates
Parameter
Standard
T
for
H0:
Variable
Estimate
Error
Parameter=
0
Prob
>
|
T|

INTERCEP
11.409748
0.08174613
139.575
0.0001
LNACRE
­
0.905956
0.02606823
­
34.753
0.0001
WATFRONT
0.333735
0.04497479
7.420
0.0001
BULKHEAD
0.200245
0.06780928
2.953
0.0034
YEARSQ
0.000302
0.00011945
2.528
0.0119
YEAR
­
0.012017
0.00484814
­
2.479
0.0136
GARSQFT
0.000165
0.00006797
2.428
0.0157
PATIO
0.103269
0.03166099
3.262
0.0012
BATHS
0.109587
0.02678655
4.091
0.0001
NOHEAT
­
0.099710
0.04189498
­
2.380
0.0178
FIRE
0.117428
0.03516537
3.339
0.0009
SQFT
0.000264
0.00004262
6.193
0.0001
SPECIAL
0.123980
0.05349404
2.318
0.0210
SOUND100
0.294474
0.07076478
4.161
0.0001
PECON100
0.671739
0.30810728
2.180
0.0299
ONROAD
­
0.176308
0.06582347
­
2.678
0.0077
FARMDISTANCE
0.000017320
0.00000374
4.625
0.0001
ONFARM
­
0.142932
0.07661228
­
1.866
0.0629
WETLAND
0.002729
0.00181653
1.502
0.1339
LARGEZONE
0.154045
0.06636055
2.321
0.0208
OPENSPACE
0.120691
0.07154488
1.687
0.0925
31
IV.
OUTDOOR
RECREATIONAL
USES
AND
USE
VALUES
IN
THE
PES
IV.
A.
INTRODUCTION
This
chapter
presents
results
of
EAI's
study
of
outdoor
recreational
uses
and
use
values
for
the
Peconic
Estuary
System
("
PES").
The
information
presented
includes:
(
1)
a
review
of
participation
and
location
choice
for
key
recreational
uses,
and
(
2)
estimates
of
the
economic
value
of
those
uses.
Also
presented
is
a
summary
of
expenditures
at
PES
farm
stands,
wineries,
and
rental
housing,
data
that
was
identified
as
a
gap
in
EAI's
Phase
I
study
and
is
of
interest
to
decision
makers.

The
main
tool
for
providing
information
for
this
analysis
is
a
recreational
use
survey
carried
out
in
August,
1995.
The
recreational
survey
was
designed
to:

identify
key
outdoor
recreational
activities
and
locations
in
the
PES
estimate
the
level
of
participation
in
key
recreational
activities
identify
key
characteristics
and
preferences
of
users
estimate
the
value
of
recreational
uses
of
the
PES
fill
data
gaps
identified
in
the
Phase
I
study.

Key
recreational
uses
and
locations.
More
than
15
recreational
uses
of
the
PES
were
identified
through
focus
groups
and
discussions
with
local
citizens
and
officials.
One
key
component
of
the
recreational
use
survey
is
to
assess
where
these
recreational
uses
take
place
throughout
the
PES.

Participation
in
key
recreational
uses.
EAI's
Phase
I
study
(
Diamantides
and
Grigalunas,
1996)
estimated
participation
in
only
a
few
key
recreational
uses,
using
"
off­
the­
shelf"
information.
Phase
I
also
identified
many
data
gaps
concerning
participation
rates
and
values
for
outdoor
recreation
in
the
PES.
The
Phase
II
recreational
survey
is
designed
to
fill
these
data
gaps.

Characteristics
and
preferences
of
users.
Prior
to
the
recreational
use
survey,
only
anecdotal
information
existed
concerning
recreational
users
of
the
PES.
Information,
such
as
residency,
accommodations,
recreational
preferences,
etc.,
are
provided
by
EAI's
Phase
II
recreational
survey.

Recreational
use
values.
Data
from
the
Phase
II
recreational
survey
is
used
to
estimate
the
economic
value
of
key
recreational
activities
in
the
PES
and,
when
possible,
to
estimate
the
change
in
recreational
value
resulting
from
changes
in
water
quality.

Phase
I
economic
impact
data
gaps.
Key
data
gaps
identified
in
the
Phase
I
study
include
the
economic
impact
of
specialized
sectors
of
the
local
economy.
These
sectors
include:
summer
rentals,
winery
tours
and
sales,
and
farm
stand
sales.
Each
of
these
sectors
was
identified
by
local
citizens
and
officials
as
playing
an
important,
but
unquantified
role,
in
the
local
economy.
Economic
data
for
these
sectors­­
sales­­
is
presented
in
this
report.
7Hunting
was
not
included
in
the
occasion­
specific
questioning,
due
to
the
time
of
year
and
anticipated
low
participation.

32
IV.
B.
IDENTIFICATION
OF
RECREATIONAL
USES
AND
LOCATIONS
Outdoor
recreation
is
a
major
activity
in
the
PES.
Based
on
recreational
uses
identified
during
the
survey
development
process
(
see
Sec.
C,
Survey
Development),
the
survey
questionnaire
included
16
major
natural
resource­
based
recreational
uses.
Of
these,
eight
key
recreational
activities
were
identified:

Fishing
Boating
Swimming
Shell
fishing
Beach
Use
Bird
Watching
Wildlife
Viewing
Hunting
Data
were
collected
for
these
eight
key
recreational
uses,
including
annual
participation
during
the
past
year.
Detailed
information
also
was
obtained
on
these
activities
for
the
respondent's
most
recent
recreational
outing
to
the
East
End.

Annual
participation
data
on
key
recreational
activities
include
the
number
of
times
the
respondent
had
done
each
activity
at
specific
East
End
locations
during
that
year
(
1995).
The
survey
instrument
identified
eight
water
bodies
as
potential
locations
for
fishing,
boating,
swimming,
and
Shell
fishing.
The
eight
locations
identified
by
water
body
are:

Flanders
Bay
Great
Peconic
Bay
Little
Peconic
Bay
Shelter
Island
Sound
Gardiners
Bay
Block
Island
Sound
Long
Island
Sound
Atlantic
Ocean.

Locations
for
the
remaining
key
recreational
activities
(
beach
use,
bird
watching,
wildlife
viewing,
and
hunting)
were
categorized
by
the
town
in
which
the
activity
took
place.
Five
East
End
towns
were
identified
by
the
survey
instrument.
The
five
recreation
locations
identified
by
town
are:

East
Hampton
Riverhead
Shelter
Island
Southampton.
Southold.

Single­
day
data
on
key
recreational
activities
include
information
on
activities
the
respondent
participated
in
during
their
most
recent
recreation
day
at
the
East
End.
This
question
was
followed
by
occasion­
specific,
write­
in
questions
concerning
the:
(
1)
location
of
the
activity,
(
2)
perceived
water
quality,
(
3)
number
of
people
in
the
recreation
party,
(
4)
travel
time,
and
(
5)
time
on
site.
Other
activity­
specific
data
collected
included
fishing
catch,
shellfish
harvest,
choice
of
marina
or
ramp,
perception
of
beach
facility
quality,
and
number
of
birds
or
type
of
wildlife
sighted7.
33
IV.
C.
SURVEY
DEVELOPMENT
AND
IMPLEMENTATION
The
development
and
implementation
of
the
recreational
use
survey
parallels
the
development
and
implementation
of
the
resource
survey.
Meetings,
interviews,
and
focus
groups
were
instrumental
in
the
development
of
both
surveys.
Similarly,
implementation
of
the
two
surveys
occured
at
the
same
time
and
places
using
the
same
survey
staff.
Only
significant
aspects
of
the
development
of
the
recreational
survey
that
differ
from
the
resource
survey
will
be
discussed
here.
For
a
full
discussion
of
the
resource
survey
development
and
implementation
including
similarities
with
the
recreational
survey
(
see
Chapter
VI,
Resource
Value
Survey).

A
major
challenge
in
the
development
of
the
recreational
use
survey
was
the
creation
of
a
question
format.
A
single
instrument
was
selected
to
cover
all
the
major
recreational
uses
and
locations
of
the
PES.
The
single­
instrument
format
allowed
collection
of
data
for
multiple
current
and
past
uses
and
was
a
much
easier,
and
less
costly,
method
for
acquiring
data,
an
important
issue
given
realistic
limits
on
budgets
for
this
project.

Questionnaire
development
began
with
meetings
with
the
Management
Committee,
the
Citizen's
advisory
group,
and
"
expert
informants"
from
major
stakeholder
groups,
such
as
marina
operators
and
Baymen.
Based
on
issues
raised
by
the
three
groups,
questionnaire
development
then
proceeded
with
informal
interviews
with
the
public,
focus
groups,
and
pretests
in
order
to
determine
the
most
relevant
questions,
wording
of
questions,
and
survey
presentation.

Informal
interviews
with
the
public
were
a
critical
component
of
questionnaire
development.
From
these
interviews
we
gained
insight
into
who
the
users
are
and
their
perspective
on
recreation
in
the
PES.
For
example,
during
these
interviews,
it
became
apparent
that
many
recreational
users
did
not
think
in
terms
of
the
PES
as
an
estuary
system.
Instead
individuals
more
easily
identified
the
East
End
or
individual
bays
within
the
PES
as
geographic
entities.
The
questionnaire,
therefore,
asks
about
recreational
uses
at
locations
at
the
East
End
with
which
individuals
could
easily
identify.

Focus
groups
and
pretests
were
used
to
hone
the
questionnaire
down
to
an
efficient
presentation
that
would
allow
us
to
collect
all
the
required
data.
For
example,
one
issue
we
used
focus
groups
to
refine
was
the
questionnaire's
approach
to
eliciting
water
quality
perceptions.
Originally
we
had
hoped
to
use
the
questionnaire
to
ask
very
specific
water
quality
questions,
such
as
clarity,
smell,
debris,
etc.,
so
that
we
might
be
able
to
estimate
the
contribution
of
these
different
attributes
to
an
individual's
overall
perception
of
water
quality.
However,
during
focus
groups
we
found
that
recreational
users
can
have
very
complex
and
inconsistent
ways
of
assessing
water
quality,
which
include
numerous
additional
attributes
such
as
beach
condition,
signs,
and
plant
abundance.
Eliciting
information
on
all
of
these
attributes
became
too
cumbersome,
and
a
more
simplified
approach
was
eventually
used.
The
simplified
approach
asks
the
respondent
to
rank
water
quality
at
the
site
on
a
four
point
scale
from
"
excellent"
to
"
poor".
As
is
described
below,
this
approach
worked
satisfactorily
in
that
we
found
a
statistical
association
between
respondents'
subjective
estimates
of
water
quality
and
objective
measures
of
water
quality
based
on
field
sampling
undertaken
by
the
County.
34
A
key
objective
in
the
survey
development
phase
was
to
keep
the
questioning
simple
and
consistent
across
uses.
Many
versions
of
the
recreational
use
survey
were
pretested,
to
assess
the
ease
with
which
respondents
could
understand
and
answer
the
questions.
The
survey
pretests
were
instrumental
in
reducing
the
size
of
the
questionnaire.
One
of
our
concerns
was
that
if
the
questionnaire
was
too
long,
only
the
first
few
questions
would
be
answered,
or
respondents
would
only
answer
selected
questions.
Another
important
concern
was
that
the
survey
be
clear
and
relatively
brief
in
order
to
allow
us
to
use
an
intercept­
survey,
self­
administration
approach,
to
economize
on
scarce
research
funds.
The
final
version
of
the
recreational
use
survey
questionnaire
is
available
from
the
authors.

The
recreational
use
survey
and
the
resource
use
survey
were
implemented
simultaneously
during
the
week
of
August
22­
29,
1995,
in
pre­
selected,
public
places
in
each
of
the
five
East
End
towns.
Interviewers
solicited
potential
respondents
by
simply
approaching
them
and
asking
if
they
would
be
willing
to
spend
10­
15
minutes
filling
out
a
survey
to
be
used
to
help
develop
a
plan
to
protect
and
manage
the
bays.
Survey
locations
included
beaches,
shopping
areas,
libraries,
Post
Offices,
and
miscellaneous
public
places
throughout
the
study
area.

Other
natural
resource­
based
recreational
activities
were
identified
in
the
survey
development
process.
These
activities
include:
walking/
hiking,
biking,
sightseeing,
jet
skiing,
jogging,
windsurfing,
art
work,
and
photography.
Questions
on
these
activities
were
retained
in
the
questionnaire
for
comprehensiveness.
However,
these
activities
are
considered
less
significant
than
the
key
activities
identified
above,
and
data
collected
on
these
"
non­
key"
activities
are
limited
to
a
single
question
on
participation.

IV.
D.
DESCRIPTION
OF
THE
RECREATIONAL
USE
SURVEY
QUESTIONNAIRE
The
survey
booklet
cover
provides
a
map
of
the
East
End
to
orient
respondents.
The
first
set
of
questions
asks
whether
the
respondent
is
an
East
End
resident,
owns
a
second
home
in
the
East
End,
or
is
a
visitor.
Visitors
are
also
asked
the
mode
of
transportation
they
used
to
travel
to
the
East
End
and
the
type
of
accommodations
used,
if
any.

Subsequent
questions
address
the
issue
of
Brown
Tide
("
BT").
BT
refers
to
the
discoloration
of
the
waters
in
and
around
the
PES
due
to
an
algae
that
sporadically
grows
in
enormous
numbers.
Recent
incidents
of
BT
may
have
contributed
to
a
significant
decline
in
ecologically
productive
eelgrass
beds
and
caused
major
disruptions
in
the
formerly
valuable
scallop
industry.
BT
also
was
a
significant
concern
to
recreationists,
as
described
in
some
detail
later.

Throughout
the
survey
development
process,
the
issue
of
BT
repeatedly
surfaced.
Questions
concerning
the
BT
were
placed
in
the
beginning
of
the
questionnaire
in
part
to
diffuse
this
issue
early
on,
allowing
respondents
to
focus
on
the
questions
that
follow.
We
asked
respondents
whether
they
were
aware
of
BT
and
which,
if
any,
of
their
recreational
activities
were
affected
by
BT.

The
next
set
of
questions
asked
respondents
to
indicate
their
annual
participation
in
key
recreational
35
activities
by
filling
out
two
brief
charts.
One
chart
identified
recreation
locations
by
water
body.
The
other
chart
identified
recreation
locations
by
town.
Respondents
indicated
the
number
of
times
that
they
did
each
activity
in
each
location
during
the
past
year
(
1995).

The
annual
participation
questions
were
followed
by
single­
day
recreation
activity
questions.
Respondents
were
asked
to
identify
their
most
recent
recreation
day
at
the
east
End.
They
then
were
asked
to
identify
all
the
recreational
activities
participated
during
that
day.
Shell
fishing,
swimming
and
beach
use,
boat
use,
fishing,
bird
watching,
and
wildlife
viewing
were
selected
as
key
activities.
For
each
of
these
recreational
pursuits,
a
separate
page
of
questions
asked
for
activity­
specific
information.
Activity­
specific
questions
included:
location,
travel
distance,
time
on
site,
number
of
people
in
the
party,
catch
or
harvest,
and
water
quality
and
site
quality
ratings.

After
the
participation
questions,
respondents
were
asked
about
selected
expenditures
made
at
the
East
End
during
the
past
twelve
months.
As
noted,
these
questions
were
designed
to
fill
data
gaps
in
the
Phase
I
study
for
farm
stands,
wineries,
and
housing
rentals.
Residents
and
second
home
owners
were
also
asked
a
referendum­
type
question
about
their
willingness
to
support
selected
water
quality
initiatives
(
see
section
on
water
quality
initiative
referendum).
Finally,
a.
set
of
questions
was
asked
in
order
to
obtain
socio­
demographic
data,
such
as
residency,
household
status,
income,
age,
education,
and
employment
status.

IV.
E.
DESCRIPTIVE
RESULTS:
DEMOGRAPHICS
A
total
of
1,354
respondents
provided
usable
survey
questionnaires,
although
not
every
respondent
answered
all
questions.
Selected,
major
demographic
results
of
the
survey
are
summarized
as
follows:

Residents
and
second
homeowners
from
all
East
End
towns
were
represented
in
the
sample
A
majority
(
59
%)
of
respondents
were
visitors
to
the
East
End
Most
visitors
(
76%)
came
from
either
Long
Island
or
New
York
City
Most
visitors
(
60
%)
stayed
at
the
East
End
overnight
The
most
common
overnight
accommodation
was
with
friends
or
relatives
(
39%)
Compared
to
1990
Census
data,
the
sample
has
a
slightly
higher
share
of
women
and
people
in
the
middle
age
groups;
is
better
educated;
and
is
wealthier
than
the
resident
population.

Residency
Status.
Of
the
1,267
respondents
who
provided
this
information,
over
half
of
those
responding
(
59%)
were
visitors
from
outside
the
East
End
(
Table
IV.
1).
Of
the
remainder,
28%
indicated
that
their
primary
residence
was
at
one
of
the
five
East
End
towns,
and
13%
owned
a
second
home
at
the
East
End,
but
had
a
primary
residence
somewhere
else.
Some
Brookhaven
residents
considered
themselves
East
End
residents
but
were
not
counted
as
such
in
this
study.

Residents
and
Second
Home
Owners
­
Place
of
Residency.
Residents
and
second
home
owners
from
each
of
the
five
East
End
towns
were
represented
in
the
survey
sample.
Southampton
(
30%)
and
Southold
(
28%)
had
the
largest
percentages
of
resident
respondents.
The
remainder
was
36
closely
distributed
among
Shelter
Island
(
16%),
East
Hampton
(
14%),
and
Riverhead
(
12%).
The
relatively
high
proportion
of
Shelter
Island
resident
respondents
is
not
expected
to
have
a
significant
effect
on
survey
results.

Table
IV.
1
­
Residency
Status
of
Survey
Respondents
Residency
Number
of
Respondents
Percentage
of
all
Respondents
East
End
Residents
358
28%

Second
Home
Owners
159
13%

Visitors
750
59%

Total
1267
100%

For
second
home
owners,
New
York
City
was
the
dominant
primary
place
of
residence
(
45
%
of
second
home
owners
surveyed),
followed
by
Nassau
(
19
%)
and
Suffolk
counties
(
14
%).
Other
locations,
largely
distributed
across
Westchester
and
Rockland
counties
in
New
York
and
northeastern
New
Jersey,
were
the
primary
residences
of
the
remaining
22%.

Visitors
­
Place
of
Residency.
Visitors
to
the
East
End
predominately
came
from
Long
Island
and
New
York
City.
40%
of
visitors
came
from
Suffolk
county,
20%
came
from
New
York
City,
and
16%
from
Nassau
county.
Only
8%
came
from
other
locations
in
New
York
State
and
New
Jersey,
and
just
5%
came
from
Connecticut
and
Massachusetts.
Other
locations,
such
as
Florida,
California,
and
several
foreign
countries
accounted
for
the
primary
residences
of
the
remaining
11%.

Visitor
Classification
and
Accommodations.
Most
visitors
in
our
sample
(
60%)
were
overnight
visitors.
Day
trippers
(
those
who
returned
to
a
home
outside
the
East
End
at
the
end
of
the
day)
accounted
for
39%
of
visitors
surveyed.

Of
the
700
overnight
visitors
who
provided
accommodation
information,
the
most
common
(
39%)
type
of
accommodation
was
with
friends
or
relatives.
Rental
(
24%)
and
hotel
or
motel
(
22%)
accommodations
were
closely
matched.
Surprisingly,
15%
of
overnight
visitors
to
the
East
End
reported
that
they
stayed
at
accommodations
other
than
friends
or
relatives,
hotels
or
motels,
or
rental
properties.
Presumably
these
other
accommodations
are
predominately
boats
and
camp
sites.

Sample
characteristics:
comparison
with
resident
census
data.
Gender,
age,
education,
and
income
characteristics
of
survey
respondents
can
be
compared
to
the
characteristics
of
the
resident
population
as
identified
by
the
most
recent
(
1990)
census.
Only
comparisons
to
the
resident
population
are
possible,
since
characteristics
of
the
second
homeowner
and
visitor
populations
are
unknown.
37
For
all
residency
and
visitor
categories,
the
share
of
female
respondents
was
only
slightly
more
than
in
the
resident
population.
The
age
distribution
of
the
sample
reflects
the
survey
implementation
practice
of
not
including
individuals
less
than
18
years
of
age.

Our
survey
sample
is
much
more
educated
than
the
resident
population.
For
example,
census
data
indicates
that
49%
of
the
resident
population
had
some
level
of
post­
secondary
education.
The
same
level
of
education
was
achieved
by
80%
of
the
resident
sample,
95%
of
second
homeowners,
91%
of
overnight
visitors,
and
82%
of
day
trippers.

The
survey
sample
also
has
higher
household
incomes
than
the
resident
population.
According
to
the
census,
34%
of
the
resident
population
has
a
household
income
of
$
50,000
or
more.
However,
nearly
half
(
48%)
the
residents
in
the
survey
sample
have
household
incomes
of
$
50,000
or
more,
as
do
80%
of
second
homeowners,
64%
of
overnight
visitors,
and
59%
of
day
trippers.

Sample
characteristics:
comparison
among
resident
and
visitor
categories.
The
distribution
among
female
and
male
respondents
slightly
favored
females
in
all
categories.
Day
trippers
have
the
most
respondents
under
24
years
of
age
(
10%),
and
overnight
visitors
have
the
most
respondents
in
the
25­
44
age
group
(
60%).
Second
homeowners
have
the
largest
percentage
of
45­
74
year
olds
(
62%).
Second
homeowners
are
also
the
most
highly
educated
(
95%
post­
secondary
schooling)
and
have
the
highest
incomes
(
80%
above
$
50,000
household
income).

Sample
characteristics:
East
End
Town
of
Residence.
Residents
and
second
home
owners
from
Shelter
Island
and
Southold
were­
over
represented
in
the
survey
sample,
based
on
comparisons
to
year­
round
and
seasonal
population
statistics.
Residents
were
under
represented
in
Riverhead
and
Southampton.
Second
home
owners
were
under
represented
slightly
in
East
Hampton
and
also
in
Southampton.
This
disproportionate
distribution
of
the
sample
most
likely
has
little,
if
any,
effect
on
survey
results
since
the
type
and
frequency
of
recreational
activity
is
not
strongly
related
to
the
East
End
town
the
respondent
lives
in.
38
Table
IV.
2
­
Population
Demographics
vs.
Survey
Respondent
Demographics
1990
Population
Resident
Sample
Second
Homewner
Sample
Overnight
Visitor
Sample
Day
tripper
Sample
Gender
Female
51.83%
53.57%
54.67%
53.63%
55.34%

Male
48.17%
46.43%
45.33%
46.37%
44.66%

Age
up
to
20
23.89%
3.83%
3.36%
1.49%
2.76%

21­
24
4.53%
1.91%
1.34%
4.95%
7.09%

25­
34
16.23%
14.48%
8.72%
25.50%
22.83%

35­
44
12.84%
21.86%
19.46%
34.65%
24.02%

45­
54
11.01%
18.85%
30.87%
21.29%
20.87%

55­
64
11.04%
13.39%
18.79%
8.42%
12.20%

65­
74
11.17%
21.31%
12.08%
2.97%
7.87%

75­
84
7.25%
3.83%
4.70%
0.50%
1.97%

85
and
up
2.04%
0.55%
0.67%
0.25%
0.39%
39
Table
IV.
2
­
Population
Demographics
vs.
Survey
Respondent
Demographics,
cont.

1990
Population
Resident
Sample
Second
Homewner
Sample
Overnight
Visitor
Sample
Day
tripper
Sample
Education
<
High
School
7.44%
1.27%
0.00%
0.28%
0.46%

Some
HS
11.55%
2.55%
0.77%
0.84%
2.78%

HS
Grad.
31.77%
16.56%
4.62%
7.58%
14.35%

Some
Coll.
18.35%
21.66%
13.85%
17.42%
26.39%

Assoc.
Deg.
6.66%
12.74%
4.62%
7.30%
12.04%

Bachelor's
Deg.
13.51%
21.97%
23.85%
29.78%
19.44%

Advanced
Deg.
10.72%
23.25%
52.31%
36.80%
24.54%

Income
<$
15,000
19.17%
9.42%
1.57%
5.16%
3.70%

$
15,000
­
$
24,999
14.60%
12.01%
3.15%
4.01%
8.80%

$
25,000
­
$
34,999
14.40%
13.31%
5.51%
10.32%
8.33%

$
35,000
­
$
49,999
17.52%
17.53%
10.24%
16.62%
20.37%

$
50,000
­
$
74,999
18.90%
23.38%
13.39%
19.48%
26.85%

$
75,000
­
$
99,999
7.04%
11.36%
14.96%
11.75%
17.59%

$
100,000
­
$
149,999
4.93%
6.82%
18.11%
17.19%
9.26%

$
150,000
and
up
3.41%
6.17%
33.07%
15.47%
5.09%
40
IV.
F.
DESCRIPTIVE
RESULTS:
KEY
RECREATION
ACTIVITY
PROFILE
Respondents
were
asked
the
number
of
times
that
they
performed
each
of
the
following
outdoor
recreational
activities
at
various
East
End
locations
(
Bays
and
towns)
this
year
(
1995):

Activities:
Fishing,
Boating,
Swimming,
Shell
fishing
Locations:
Flanders
Bay,
Great
Peconic
Bay,
Little
Peconic
Bay,
Shelter
Island
Sound,
Gardiners
Bay,
Block
Island
Sound,
Long
Island
Sound,
and
Atlantic
Ocean
Activities:
Beach
Use,
Bird
Watching,
Wildlife
Viewing,
and
Hunting
Locations:
East
Hampton,
Riverhead,
Shelter
Island,
Southampton,
and
Southold.

The
water
bodies
of
the
Peconic
Estuary
System
are
identified
as
Flanders
Bay,
Great
Peconic
Bay,
Little
Peconic
Bay,
Shelter
Island
Sound,
and
Gardiners
Bay.
Substitute
water
bodies
included
in
the
survey
were
Long
Island
Sound,
Block
Island
Sound,
and
the
Atlantic
Ocean.

Swimming
and
beach
use
were
treated
as
separate
activities
in
the
questionnaire
in
order
to
identify
swimming
locations
by
body
of
water
and
identify
the
number
of
beach
goers
by
town.
Since
each
of
the
five
towns
has
multiple
water
body
options
for
swimming,
it
was
necessary
to
ask
for
location
by
both
water
body
and
town.

Figures
for
swimming
and
beach
use
should
not
be
summed.
This
is
because
the
activities
are
not
mutually
exclusive,
and
it
is
expected
that
most
respondents
gave
a
positive
answer
to
both
categories
even
though
they
may
be
referring
to
a
single
outing.

Major
characteristics
of
respondents'
annual
recreational
activity
during
the
year
are:

Most
(
83%)
engaged
in
at
least
one
key
recreational
activity,
half
in
more
than
one
activity.
Most
residents
(
95
%)
participated
in
key
recreational
activities
Swimming
was
the
most
popular
activity
The
PES
was
the
most
popular
swimming
location
(
42%
of
all
swimming
trips)
Boating,
fishing,
and
Shell
fishing
were
predominantly
done
in
the
PES.

Sample
Participation
Overall.
Most
respondents
(
83%)
engaged
in
at
least
one
outdoor
recreational
activity
at
the
East
End
and
during
the
past
year
averaged
38
recreational
experiences
per
respondent.
In
total,
respondents
engaged
in
more
than
48,400
outdoor
recreational
experiences
during
the
past
year.
Of
this
number,
more
than
26,000
were
water­
based
outdoor
recreational
experiences,
and
the
majority
of
these
(
15,506)
were
in
the
PES.
Land­
based
outdoor
recreational
experiences
the
five
East
End
towns
amounted
to
22,300.

Sample
Participation
By
Activity.
Among
all
respondents,
swimming
and
beach
use
were
the
most
popular
activities.
Swimming
was
done
on
more
than
15,000
occasions
by
respondents.
The
most
popular
area
for
swimming
was
the
PES,
which
accounted
for
42%
of
all
swimming
trips.
More
than
12,000
beach
visits
at
the
five
East
End
towns
were
reported.
41
Shell
fishing
was
the
least
frequent
water­
based
activity
(
1,304
trips).
More
than
half
(
59%)
of
these
Shell
fishing
trips
took
place
in
the
PES.
Hunting
was
the
least
popular
land­
based
activity
(
255
occasions).
Note
that
bird
watching
and
wildlife
viewing
responses
given
in
Table
IV.
3
were
adjusted
to
include
only
trips
for
viewing,
i.
e.,
activity
less
than
one
mile
from
home
was
not
included.

Table
IV.
3
­
Participation
in
Key
Recreation
Activities
­
By
Residency
Status
Residents
(
n=
358)
SHO
(
n=
159)
Overnight
(
n=
427)
Day
trippers
(
n=
273)
Total
(
n=
1217)

Fishing
88
(
25%)
41
(
26%)
62
(
15%)
13
(
5%)
204
(
17%)

Boating
132
(
37%)
63
(
40%)
88
(
21%)
39
(
14%)
322
(
26%)

Swimming
186
(
52%)
80
(
50%)
153
(
36%)
28
(
10%)
447
(
37%)

Shell
fishing
49
(
14%)
19
(
12%)
14
(
3%)
6
(
2%)
88
(
7%)

Beach
use
282
(
79%)
103
(
65%)
235
(
55%)
74
(
27%)
694
(
57%)

Bird
watching
36
(
10%)
13
(
8%)
23
(
5%)
32
(
12%)
104
(
9%)

Wildlife
viewing
73
(
20%)
39
(
17%)
45
(
11%)
40
(
15%)
185
(
15%)

Hunting
10
(
3%)
3
(
2%)
6
(
1%)
2
(
1%)
21
(
2%)

Any
key
activity*
342
(
96%)
132
(
83%)
287
(
67%)
117
(
43%)
878
(
72%)

Multiple
key
activities
262
(
73%)
103
(
65%)
182
(
43%)
65
(
24%)
612
(
50%)

*
May
include
bird
watching
and
wildlife
viewing
trips
less
than
one
mile
from
home.

Participation
Rate
Overall
By
Residency
Status.
East
End
residents
are
more
likely
to
participate
in
outdoor
recreation
than
any
other
group.
Fully
95%
of
the
residents
surveyed
engaged
in
at
least
one,
key
water­
based
activity
in
the
PES
or
one
key
land­
based
activity
in
one
of
the
East
End
towns.
By
comparison,
83%
of
second
homeowners
and
67%
of
overnight
visitors
also
participated
in
outdoor
recreation.
Day
trippers
have
the
lowest
rate
of
participation
(
43%).

A
direct
comparison
between
the
number
of
people
engaging
in
land­
based
and
water­
based
activities
cannot
be
made.
This
is
due
to
the
inability
to
tell
the
difference
between
beach
use
in
the
PES
and
beach
use
on
the
Atlantic
Ocean
or
Long
Island
Sound
shoreline
at
the
East
End.
42
However,
the
majority
of
residents
(
73%)
and
second
home
owners
(
65%)­­
and
50%
of
all
survey
respondents­­
engaged
in
more
than
one
key
recreational
activity
during
the
past
year.

Participation
Rate
in
Key
Recreational
Activities­
By
Residency
Status.
Proportionately
more
residents
(
70%)
and
second
homeowners
(
65%)
swim,
boat,
fish,
and
shellfish
than
overnight
visitors
(
45%)
and
day
trippers
(
22%).
Among
these
activities,
the
highest
participation
rates
across
all
residency
status
categories
are
for
swimming
(
37%)
followed
by
boating
(
26%)
and
fishing
(
17%).
Shell
fishing
is
more
popular
with
residents
(
14%)
and
second
home
owners
(
12%)
as
compared
with
the
overall
sample
(
only
7
%).

Residents
and
second
homeowners
are
more
likely
to
go
to
the
beach,
watch
birds,
view
wildlife,
and
hunt
in
the
East
End
than
other
groups.
Proportionately
more
residents
(
85%)
and
second
homeowners
(
70%)
engage
in
these
activities
than
overnight
visitors
(
59%)
and
day
trippers
(
37%).
Among
these
activities,
the
highest
participation
rates
across
all
groups
are
for
beach
use
(
57%)
followed
by
wildlife
viewing
(
21%)
and
bird
watching
(
19%).
Only
2%
of
survey
respondents
hunt.

Location
of
Key
Recreational
Activities.
Respondents
used
the
PES
more
times
(
15,506
occasions)
for
all
key
outdoor
recreation
activities­­
swimming,
boating,
fishing,
and
Shell
fishing­­
than
any
other
water
body
at
the
East
End
(
Table
IV.
4).

Table
IV.
4
­
Water
Body
Selection
by
Activity
­
(
n=
1267)

Fishing
Shell
fishing
Boating
Swimming
Totals
Block
Island
Sd.
320
(
6%)
71
(
5%)
467
(
5%)
380
(
2%)
1238
(
5%)

Long
Island
Sd.
1422
(
25%)
266
(
20%)
1760
(
20%)
2801
(
18%)
6249
(
24%)

Atlantic
Ocean
1125
(
20%)
201
(
15%)
1056
(
12%)
5685
(
37%)
7116
(
27%)

PES
2732
(
49%)
766
(
59%)
5470
(
62%)
6538
(
42%)
15506
(
60%)

Totals
5599
(
100%)
1304
(
100%)
8753
(
100%)
15404
(
100%)
26060
(
100%)
*
Percentages
may
not
sum
precisely
due
to
rounding
No
single
town
is
the
dominant
site
for
all
categories
of
recreation
by
respondents.
For
example,
beach
use
occurs
most
often
at
Southampton
(
3334
occasions)
and
East
Hampton
(
3139).
Shelter
Island
was
the
most
used
for
bird
watching
(
567)
and
hunting
(
92),
and
East
Hampton
had
the
most
43
(
1123)
wildlife
viewing
experiences.
On
the
other
hand,
Riverhead
was
the
least
used
town
for
beach
use
(
1075),
bird
watching
(
68),
and
wildlife
viewing
(
220).
Southampton
had
the
least
number
of
hunting
occasions
(
26).

Table
IV.
5
­
Town
Selection
by
Activity
­
(
n=
1267)

Beach
Use
Bird
Watching
Wildlife
Viewing
Hunting
Total
Trips
East
Hampton
3139
(
26%)
300
(
20%)
1123
(
29%)
50
(
20%)
4612
(
25%)

Riverhead
1075
(
9%)
68
(
5%)
220
(
6%)
56
(
22%)
1419
(
7%)

Shelter
Island
2461
(
20%)
567
(
37%)
1030
(
27%)
92
(
36%)
4150
(
25%)

Southampton
3334
(
27%)
386
(
25%)
690
(
18%)
26
(
10%)
4436
(
24%)

Southold
2239
(
18%)
215
(
14%)
764
(
20%)
31
(
12%)
3249
(
18%)

Totals
12248
(
100%)
1536
(
100%)
3827
(
100%)
255
(
100%)
17866
(
100)

*
Percentages
may
not
sum
precisely
due
to
rounding
Location
of
PES
Water­
Based
Activities.
Great
Peconic
Bay
is
the
most
popular
water
body
in
the
PES
for
recreational
activity,
with
28%
of
recreational
trips
in
the
PES,
while
Flanders
Bay
is
the
least
frequently
used,
with
8%.
Great
Peconic
Bay
is
also
the
most
popular
location
in
the
PES
for
swimming
(
30%),
fishing
(
29%),
and
boating
(
25%).
Gardiners
Bay
is
the
most
popular
PES
location
for
Shell
fishing,
accounting
for
33%
of
all
PES
Shell
fishing
trips.
44
Table
IV.
6
­
PES
Key
Recreational
Activity
Outings
­
(
n=
1267)

Fishing
Shell
fishing
Boating
Swimming
Totals
Flanders
Bay
288
(
11%)
45
(
6%)
562
(
10%)
366
(
6%)
1261
(
8%)

Gt.
Peconic
Bay
793
(
29%)
199
(
26%)
1362
(
25%)
1991
(
30%)
4345
(
28%)

Ltl.
Peconic
Bay
486
(
18%)
109
(
14%)
1021
(
19%)
1298
(
20%)
2914
(
19%)

Shelter
Isl.
Sound
511
(
19%)
158
(
21%)
1321
(
24%)
1410
(
22%)
3400
(
22%)

Gardiners
Bay
654
(
24%)
255
(
33%)
1204
(
22%)
1473
(
23%)
3586
(
23%)

Totals
2732
(
100%)
766
(
100%)
5470
(
100%)
6538
(
100%)
15506
(
100%)
*
Percentages
may
not
sum
precisely
due
to
rounding
PES
Key
Recreational
Activities
by
Residency
Status.
Swimming
is
the
most
popular
waterbased
activity
in
the
PES
for
all
residency
groups
and
overall
accounts
for
42%
of
all
water­
based
recreation
trips
(
Table
IV.
7).
Boating
is
nearly
as
popular
(
36%
of
all
PES
trips).

Overnight
visitors
fish
more
(
22%
of
their
outdoor
recreation
activity)
than
any
other
group.
Day
trippers
allocate
a
larger
percentage
of
their
PES
trips
to
Shell
fishing
(
16%)
than
to
fishing
(
9%),
although
Shell
fishing
overall
is
a
modest
activity
(
5%
of
PES
all
trips).

Table
IV.
7
­
PES
Key
Recreational
Activity
Outings
by
Residency
Status
­
(
n=
1217)

Residents
SHO
Overnight
Day
tripper
Total
Fishing
1114
(
17%)
401
(
11%)
947
(
22%)
30
(
9%)
2492
(
17%)

Boating
2354
(
37%)
1275
(
36%)
1450
(
34%)
110
(
34%)
5189
(
36%)

Swimming
2520
(
39%)
1676
(
48%)
1839
(
43%)
132
(
41%)
6167
(
42%)

Shell
fishing
396
(
6%)
154
(
4%)
92
(
2%)
52
(
16%)
694
(
5%)

Totals
6384
(
100%)
3506
(
100%)
4328
(
100%)
324
(
100%)
14542
(
100%)
*
Percentages
may
not
sum
precisely
due
to
rounding
45
Locations
of
PES
Activities
­
By
Residency
Status.
For
residents,
Shelter
Island
Sound
(
27%)
is
the
most
popular
location
in
the
PES,
followed
closely
by
Great
Peconic
Bay
(
25%)
(
Table
V.
8).
Gardiners
Bay
(
31%)
is
the
most
frequented
destination
for
second
home
owners,
with
Great
Peconic
Bay
(
30%)
again
nearly
as
popular.
Over
night
visitors
and
day
trippers
prefer
Great
Peconic
Bay
(
30%
and
34%,
respectively)
over
all
other
locations.

Flanders
Bay
is
by
far
the
least
often
visited
location
for
residents
(
7%),
second
home
owners
(
3%),
and
overnight
visitors
(
10%).
The
least
visited
location
by
day
trippers
is
Gardiners
Bay
(
13%),
presumably
because
it
is
the
furthest
of
the
bays
from
their
homes.
Flanders
Bay
is
more
popular
with
day
trippers
(
17%)
than
with
any
other
group.

Table
IV.
8
­
PES
Key
Recreational
Activity
Locations
by
Residency
Status
(
n=
1217)

Residents
SHO
Overnight
Day
tripper
Total
Flanders
Bay
451
(
7%)
114
(
3%)
446
(
10%)
54
(
17%)
1065
(
7%)

Gt.
Peconic
Bay
1589
(
25%)
1048
(
30%)
1304
(
30%)
110
(
34%)
4051
(
28%)

Ltl.
Peconic
Bay
1260
(
20%)
740
(
21%)
736
(
17%)
60
(
19%)
2796
(
19%)

Shelter
Isl.
Sound
1752
(
27%)
525
(
15%)
1031
(
24%)
58
(
18%)
3366
(
23%)

Gardiners
Bay
1332
(
21%)
1079
(
31%)
811
(
19%)
42
(
13%)
3264
(
22%)

Total
6384
(
100%)
3506
(
100%)
4328
(
100%)
324
(
100%)
14542
(
100%)
*
Percentages
may
not
sum
precisely
due
to
rounding
IV.
G.
DESCRIPTIVE
RESULTS:
PARTICIPATION
AND
TRIP
ESTIMATES
Introduction
The
total
number
of
trips
(
or
"
outings")
for
outdoor
recreation
is
the
product
of
three
factors:
(
1)
The
potential
number
of
participants,
(
2)
the
proportion
who
actually
participate,
and
(
3)
the
number
of
times
those
who
participate
engage
in
an
recreation
activity.
Thus,
we
have:

Trips
=
No.
of
Potential
Participants
x
Percent
Participating
x
No.
of
Times
Participate
8Day
tripper
are
excluded
because
we
have
no
reliable
way
of
estimating
the
total
number
in
this
group.
This
omission
slightly
understates
total
participation
in
PES
recreation
(
see
text).

46
For
example,
assume
that
there
are
2
million
potential
participants
(
i.
e.,
residents,
second
home
owners,
over
night
visitors,
and
day
trippers)
for
swimming,
that
20
%
of
these
actually
swim,
and
that
of
these
participants,
each
swims
an
average
of
10
times
per
year.
In
this
case,
the
number
of
swimming
trips
or
outings
is
4
million:

=
2
million
x
20
%
x
10
=
400,000
x
10
=
4
million
trips
The
data
and
assumptions
used
to
estimate
trips
for
each
PES
recreation
activity
is
explained
below.

Estimation
of
Total
Number
of
Trips.
The
population
of
potential
participants
include
all
East
End
residents,
second
homeowners,
overnight
visitors,
and
day
trippers.
To
estimate
this
population,
we
used
1990
census
data
from
the
Long
Island
Regional
Planning
Board
("
LIRPB").
The
LIRPB
analysis
provides
estimates
of
the
year­
round
resident
population;
the
seasonal
population,
including
second
homeowners
and
guests;
year­
round
resident
guests;
and
hotel,
motel,
and
campsite
capacities.
The
number
of
participants
in
each
recreational
activity
was
estimated
separately
for
each
of
these
residency
category.
The
resident
population
over
age
15
is
estimated
by
the
LIRPB
to
be
84,871.

Data
are
not
available
for
the
age
breakdown
of
the
seasonal
population.
Lacking
authoritative
data,
we
assume
that
the
seasonal
population
has
the
same
percentage
over
the
age
of
15
(
80%)
as
the
resident
population.
This
implies
an
estimated
seasonal
population
over
age
15
of
135,620.

The
seasonal
population
is
split
into
two
groups,
based
on
EAI's
recreational
use
survey
results:
Second
Home
Owners
(
27%)
and
Overnight
Visitors
(
73%).
Our
survey
results
indicate
that
the
two
components
of
the
seasonal
population
have
different
participation
rates
and
therefore
should
be
considered
separately,
when
possible.
Note
that
many
overnight
visitors
stay
with
second
homeowners
and
residents
as
renters
or
guests.

Population
data
is
not
available
for
day
trippers;
therefore
this
group
is
excluded
from
our
participation
estimates.
The
absence
of
this
information
will
slightly
understate
outdoor
recreation
participation.

Information
concerning
the
percent
of
potential
participants
that
participates
in
one
or
more
recreational
activity
was
presented
earlier.
The
trips
per
participant
are
given
in
Table
IV.
9.

The
major
results
of
estimated
annual
participation
in
PES
outdoor
recreation
(
excluding
day
trippers8)
for
1995
can
be
summarized
as
follows:
47
127,762
people
participate
in
either
swimming,
boating,
fishing,
or
Shell
fishing
in
the
PES,
taking
3.3
million
outings.

156,184
people
participated
in
either
beach
use,
bird
watching,
wildlife
viewing,
or
hunting
in
the
East
End
towns,
engaging
in
5.2
million
trips.

Table
IV.
9
­
Average
Annual
Outings
per
Participant
by
Activity
and
Residence*

Residents
SHO
Overnight
Day
trippers
Fishing
12.66
9.78
15.27
2.31
Boating
17.83
20.24
16.48
2.82
Swimming
13.55
20.95
12.02
4.71
Shell
fishing
8.08
8.11
6.57
8.67
Beach
Use
22.50
25.85
9.20
6.11
Bird
Watching
37.55
23.49
8.83
3.28
Wildlife
Viewing
34.36
25.39
9.99
3.80
Hunting
13.20
16.33
7.00
5.50
*
Includes
activities
at
the
five
East
End
towns
and
PES
water
bodies
only
The
total
number
of
trips
by
each
group,
for
each
activity,
is
estimated
by
multiplying
the
estimated
number
of
trips
per
participant
(
Table
IV.
9)
for
each
group
(
i.
e.,
residents,
second
homeowners,
etc.)
by
the
estimated
total
number
of
participants
by
group.
The
results
are
shown
in
Table
IV.
10.

Overall,
there
are
5.15
million
beach
use,
bird
watching,
wildlife
viewing,
and
hunting
outings
taken
in
East
End
towns
and
3.35
million
swimming
fishing,
boating,
and
Shell
fishing
outings
taken
in
the
PES
in
1995
(
Table
IV.
10).
Most
outdoor
recreational
activity
is
accounted
for
by
residents,
who
engaged
in
3.3
million
land­
based
and
1.5
million
water­
based
occasions
locally.
Second
homeowners
took
1.1
million
land­
based
trips
and
more
than
800
thousand
water­
based
trips.
Overnight
visitors
engaged
in
more
than
780
thousand
land­
based,
and
1
million
water­
based,
trips.

Total
trip
estimates
slightly
understate
actual
recreation
trips
because
day
tripper
trip
estimates
are
not
included
due
to
the
lack
of
population
data
for
this
group,
as
noted
earlier.
According
to
EAI's
recreational
use
survey,
day
trippers
account
for
3%
of
land­
based
and
2%
of
water­
based
recreational
trips
taken
by
the
sample.
48
Comparison
with
Phase
I
Total
Outdoor
Recreational
Trip
Estimates:
Introduction.
The
Phase
I
study
(
Grigalunas
and
Diamantides,
1996)
gave
estimates
of
total
recreation
trips
to
the
area
for
selected
activities
based
on
available
"
off
the
shelf"
data
and
were
comprised
of
a
variety
of
sources.
No
original
data
was
collected
during
Phase
I.
In
Phase
II
original
data
was
assembled
and
it
is
important
to
note
and
to
explain
the
major
differences
between
the
two
sets
of
results
(
Table
IV.
11).

Table
IV.
10
­
Estimated
Annual
Key
Recreation
Activity
Outings*

Residents
SHO
Overnight
Totals
Fishing
268,617
93,110
226,766
588,493
Boating
559,902
296,451
342,630
1,198,983
Swimming
598,001
383,563
428,406
1,409,970
Shell
fishing
96,006
35,636
19,513
151,155
Beach
Use
1,508,582
615,257
500,955
2,624,794
Bird
Watching
219,008
32,139
41,699
292,846
Wildlife
Viewing
174,861
39,218
40,392
254,471
Hunting
33,609
11,959
6,930
52,498
Totals
3,458,586
1,507,333
1,607,291
6,573,210
*
Includes
activities
at
the
five
East
End
towns
and
PES
water
bodies
only
In
general,
we
found
major
that
the
Phase
I
estimates
of
recreational
activity
were
understated­­
in
some
cases,
greatly
understated­­
as
compared
to
the
Phase
II
results.
The
major
reason
for
the
large
differences
in
estimates
between
the
Phase
I
and
II
reports
can
be
explained
as
follows.
Our
survey­
based
estimates
find
much
high
participation
rates
and
trips
per
participant
than
the
Phase
I
report,
which
used
a
state­
wide
average
for
New
York.
For
example,
in
Phase
II
for
bird
watching
and
wildlife
viewing
we
found
a
high
participation
rate
(
19%
for
bird
watching
and
21%
for
wildlife
viewing)
for
respondents
and
a
large
number
of
trips
per
participant
(
37.55
bird
watching
trips
and
34.36
wildlife
viewing
trips
for
residents).
The
Phase
I
survey,
in
contrast,
was
based
on
NFWS
statewide
estimates
for
New
York.
These
statewide
participation
rates
(
9.4%)
and
number
of
trips
per
participant
(
8.3)
are
very
much
lower
than
those
found
in
the
PES
survey
sample.

This
major
difference
between
the
Phase
I
and
Phase
II
results
is
not
surprising
(
at
least
in
retrospect).
Residents
and
seasonal
visitors
to
the
PES
are
attracted
to
the
area
precisely
because
they
are
interested
in
estuary­
related
outdoor
recreation.
We
thus
would
expect
this
group
to
be
49
much
more
interested
in
outdoor
recreation
than
the
general
population
of
the
state.
Similar
large
differences
between
the
Phase
I
and
II
results
were
found
for
other
activities
(
see
Table
IV.
11
and
also
see
Appendix
for
details)
and
the
same
explanation
seems
relevant.

Table
IV.
11
­
Comparison
of
Participation
Estimates
Phase
I
Versus
Phase
II
PES
­
Phase
II
PES
­
Phase
I*

Beach
Use
2,624,794
NA**

Non­
Consumptive
547,317
91,713
Hunting
52,498
77,300
Fishing
588,493
113,589
Boating
1,198,983
NA
Swimming
1,409,970
714,600
*
Grigalunas
and
Diamantides
(
1996).
**
NA
=
Not
available
IV.
H.
DESCRIPTIVE
RESULTS:
SELECTED
EXPENDITURE
ESTIMATES
Introduction.
During
the
early
survey
development
phase
of
our
work,
members
of
the
Citizens
Advisory
Committee
and
officials
at
the
Suffolk
County
Dept.
of
Health
Services
expressed
interest
in
our
collecting
information
on
sectors
of
the
PES
economy
for
which
data
are
difficult
to
find
using
standard
economic
sources.
Specific
sectors
of
the
economy
targeted
for
special
consideration
were
Farm
Stands,
Wineries,
and
Rental
Lodging.

The
survey
questionnaire
asked
respondents
to
estimate
the
amount
that
their
household
spent
at
Farm
stands
and
Wineries
at
the
East
End
over
the
past
year.
Visitors
also
were
asked
whether
they
rented
a
house,
condo,
apartment,
etc.,
at
the
East
End
over
the
past
twelve
months.
Respondents
who
answered
"
yes"
to
this
question
were
asked
the
length
of
the
rental
agreement
and
the
amount
they
spent.
Responses
to
these
questions
were
given
in
1995
dollars,
summarized
in
Table
IV.
12
and
explained
briefly
below.

Population
of
Potential
Participants.
Expenditure
data
on
Farm
stands,
Wineries,
and
Rental
Lodging
collected
in
this
survey
are
household
(
not
per
person)
expenditures.
The
number
of
East
End
resident
households
(
44,241)
and
seasonal
households
(
29,183)
are
based
on
1995
Long
Island
Lighting
Company
estimates.
50
The
number
of
overnight
visitor
households
cannot
be
adequately
estimated
with
available
data.
LIRPB
seasonal
population
estimates
account
for
occupancy
of
some
rental
lodgings,
such
as
hotels,
motels,
and
campsites.
However,
this
component
of
the
population
estimate
does
not
account
for
turnover,
i.
e.,
the
same
rental
lodgings
will
be
used
by
an
unknown
number
of
overnight
visitors
over
the
course
of
a
season.
Estimating
the
number
of
overnight
visitor
households
is
further
complicated
by
the
large
percentage
of
overnight
visitors
(
39%)
who
indicated
that
they
stayed
with
friends
or
relatives,
which
implies
that
some
amount
of
their
expenditures
may
be
included
in
resident
and
seasonal
household
estimates.
For
these
reasons,
expenditures
of
overnight
visitors
are
not
estimated
in
this
analysis.
Still,
the
average
annual
expenditure
for
rental
accommodations
per
household
from
our
survey
­­$
5,400­­
make
it
evident
that
seasonal
rentals
are
a
major
source
of
income
for
PES
rental
property
owners
and
owners
of
hotels,
motels,
and
inns.

Estimated
Total
Participation
and
Expenditures.
Table
IV.
13:
Estimated
Farm
Stand
and
Winery
Expenditures
presents
estimates
(
in
1995
dollars)
of
participation
and
expenditures
in
these
sectors
for
resident
and
seasonal
households.
Table
IV.
13
also
presents
estimates
of
PES­
related
expenditures
in
these
sectors.
PES­
related
expenditures
were
estimated
using
the
same
seasonal
adjustment
(
45
%)
used
in
the
Phase
I
Report
to
estimate
the
PES­
related
impacts
of
tourism
and
recreation
sectors.
Total
rental
lodging
expenditures
could
not
be
estimated
due
to
incomplete
information
on
the
number
of
participants.
51
Table
IV.
12
­
Survey
Farm
Stand,
Winery,
and
Rental
Lodging
Expenditures
(
1995
dollars)*

Farm
stand
Winery
Rentals
Residents
Participation
316
(
88%)
114
(
32%)
­­­

Total
Expenditures
$
86,400
$
20,871
­­­

Average
Expenditures
$
273
$
183
­­­

Second
Home
Owners
Participation
131
(
82%)
67
(
42%)
­­­

Total
Expenditures
$
48,122
$
15,350
­­­

Average
Expenditures
$
367
$
229
­­­

Overnight
Visitors
Participation
225
(
53%)
114
(
27%)
103
(
26%)

Total
Expenditures
$
41,228
$
18,360
$
556,000
Average
Expenditures
$
183
$
161
$
5,398
Day
Trippers
Participation
153
(
56%)
94
(
34%)
­­­

Total
Expenditures
$
12,755
$
9,121
­­­

Average
Expenditures
$
83
$
97
­­­

Totals
Participation
825
(
68%)
389
(
32%)
103
(
9%)

Total
Expenditures
$
188,505
$
63,702
$
556,000
Average
Expenditures
$
228
$
164
$
5,398
*
Figures
in
parentheses
indicate
the
percent
who
made
such
expenditures
52
Table
IV.
13
­
Estimated
PES
Farm
Stand
and
Winery
Expenditures
Farm
stand
Winery
Residents
Estimated
Participating
Households
38,932
14,157
Estimated
Total
Expenditures
$
10,628,436
$
2,590,731
Estimated
PES­
Related
Expenditures
$
4,782,796
$
1,165,829
Second
Home
Owners
Estimated
Participating
Households
23,929
12,256
Estimated
Total
Expenditures
$
8,781,943
$
2,806,624
Estimated
PES­
Related
Expenditures
$
3,951,874
$
1,262,981
Totals
Estimated
Participating
Households
62,861
26,413
Estimated
Total
Expenditures
$
19,410,379
$
5,397,355
Estimated
PES­
Related
Expenditures
$
8,734,671
$
2,428810
An
interesting
finding
is
that
PES­
related
Farm
stand
expenditures
are
greater
than
earlier
estimates
for
several
other
food­
related
sectors
presented
in
the
Phase
I
Report.
For
example,
PESrelated
farm
stand
expenditures
are
greater
than
expenditures
(
in
1995
dollars)
in
Meat
and
Fish
Markets
($
6.5
million),
Fruit
and
Vegetable
Markets
($
3.8
million),
Retail
Bakeries
($
2.9
million),
and
Drinking
Establishments
($
6.0
million)
(
Grigalunas
and
Diamantides,
1996).
53
IV.
I.
DESCRIPTIVE
RESULTS:
REFERENDA
AND
BROWN
TIDE
RESULTS
IV.
I.
1.
Hypothetical
Referenda
Introduction.
One
section
of
the
recreational
use
survey
posed
referendum­
type
policy
questions
to
East
End
residents
and
second
home
owners.
These
questions
were
added
to
"
test
the
waters"
on
selected
policy
issues
and
were
not
designed
as
a
basis
for
resource
valuation,
which
would
have
required
considerably
more
effort
and
data
than
was
possible
for
this
project.

Referenda
Questions.
The
referenda
questions
focus
on
four
policy
actions:

(
1)
reseeding
scallops
following
Brown
Tide,

(
2)
planting
hard
clams,

(
3)
upgrading
the
Riverhead
sewage
treatment
plant,
and
(
4)
reducing
road
runoff.

Respondents
were
presented
with
one
of
these
programs
and
were
asked
whether
they
support
the
program,
if
they
had
to
pay
a
given
annual
cost.
Annual
costs
were
varied
for
each
of
the
proposed
policy
actions
as
described
in
Table
IV.
14.

Referenda
Results.
Respondents
overwhelmingly
supported
the
policy
actions
and
indicated
a
willingness
to
pay
the
annual
costs.
Support
ranged
from
a
high
of
100%
for
reseeding
scallops
following
Brown
Tide
at
an
annual
cost
of
$
10,
to
a
low
of
75%
for
upgrading
the
Riverhead
sewage
treatment
plant
at
an
annual
cost
of
$
100.
This
high
level
of
support
for
PES
management
programs
was
consistent
across
residents
and
second
home
owners.
Overall,
87%
of
respondents
indicated
they
support
the
policy
actions
at
the
given
costs
(
Table
IV.
14).

IV.
I.
2.
Descriptive
Results:
Effects
of
Brown
Tide
Respondents
were
asked
whether
they
had
heard
of
Brown
Tide
("
BT").
Those
who
had
heard
of
BT
were
asked
about
their
level
of
concern
and
about
any
activities
that
may
have
been
affected
by
BT.
Overall,
most
respondents
(
83%)
had
heard
of
BT,
and
of
those,
most
(
71%)
were
also
very
concerned
(
Table
IV.
15).

BT
had
the
biggest
effect
on
swimming.
Fifty­
eight
percent
of
those
who
had
heard
of
BT
indicated
that
it
had
affected
their
swimming.
Recreational
fishing
(
34
percent)
and
shell
fishing
(
33
percent)
also
were
affected
by
BT.

Residents
and
second
home
owners
had
the
most
knowledge
of
and
the
highest
concern
for
BT.
Overnight
visitors
had
the
least
knowledge
and
indicated
that
their
activities
were
affected
the
least.
54
Table
IV.
14.
Management
Action
Referenda
Results
Proposal;
Annual
Cost
Resident
s
Y/
N
SHO
Y/
N
Total
s
Y/
N
Total
%
Yes
Reseed
Scallops
Following
Brown
Tide;
$
5
21/
2
6/
0
27/
2
93%

Reseed
Scallops
Following
Brown
Tide;
$
10
14/
0
10/
0
24/
0
100%

Reseed
Scallops
Following
Brown
Tide;
$
25
17/
2
5/
1
22/
3
88%

Plant
Hard
Clams;
$
5
21/
4
12/
0
33/
4
89%

Plant
Hard
Clams;
$
10
18/
3
5/
0
23/
3
88%

Plant
Hard
Clams;
$
25
14/
2
2/
2
16/
4
80%

Upgrade
Riverhead
Sewage
Treatment
Plant;$
5
16/
2
6/
0
22/
2
92%

Upgrade
Riverhead
Sewage
Treatment
Plant;$
25
18/
3
9/
1
27/
4
87%

Upgrade
Riverhead
Sewage
Treatment
Plant;$
50
15/
4
6/
0
21/
4
84%

Upgrade
Riverhead
Sewage
Treatment
Plant;$
100
73/
28
30/
6
103/
34
75%

Reduce
Road
Run
Off;
$
10
19/
1
11/
0
30/
1
97%

Reduce
Road
Run
Off;
$
25
22/
1
15/
1
37/
2
95%

Reduce
Road
Run
Off;
$
50
22/
1
6/
0
28/
1
97%

Totals
290/
53
123/
11
413/
64
87%
55
Table
IV.
15
Awareness
of
Brown
Tide
Second
Overnight
Day
Residents
Home
Owners
Visitors
Trippers
Total
Heard
of
Brown
370
159
279
202
1010
97.11%
91.91%
67.72%
78.29%
82.52%

Have
NOT
Heard
of
11
14
133
56
214
Brown
Tide
2.89%
8.09%
32.28%
21.71%
17.48%

Very
Concerned
295
113
174
131
713
about
Brown
Tide*
80.16%
71.52%
62.37%
65.17%
70.87%

A
Little
Concerned
70
44
100
64
278
about
Brown
Tide*
19.02%
27.85%
35.84%
31.84%
27.63%

NOT
Concerned
3
1
5
6
15
about
Brown
Tide*
0.82%
0.63%
1.79%
2.99%
1.49%
*
Includes
only
respondents
who
have
heard
of
Brown
Tide
56
Table
IV.
16.
Activities
Affected
By
Brown
Tide
Second
Resident
s
Home
Owners
Overnght
Visitors
Day
Trippers
Total
Swimming
224
98
151
118
591
60.54%
61.64%
54.12%
58.42%
58.51%
Fishing
148
49
82
69
348
40.00%
30.82%
29.39%
34.16%
34.46%
Shell
fishing
168
52
62
58
340
45.41%
32.70%
22.22%
28.71%
33.66%
Boating
72
30
43
37
182
19.46%
18.87%
15.41%
18.32%
18.02%
Other
32
13
20
10
75
8.65%
8.18%
7.17%
4.95%
7.43%
*
Includes
only
respondents
who
have
heard
of
Brown
Tide
IV.
J.
ESTIMATES
OF
THE
ECONOMIC
VALUE
OF
RECREATION
IV.
J.
1.
Introduction
Our
intention
was
to
use
data
from
the
recreational
survey
to
assess
the
use
value
for
many
waterbased
and
shore­
based
recreational
activities.
The
survey
collected
activity­
specific
data
on
fishing,
Shell
fishing,
boating,
and
swimming
in
the
PES
and
surrounding
waters.
Information
also
was
collected
on
beach
use,
birdwatching,
wildlife
viewing,
and
hunting
in
the
five
PES
towns.

Unfortunately,
the
total
number
of
surveys
was
limited
to
1,354
completed
responses
due
to
budgetary
reasons.
The
number
of
completed
responses
for
each
activity
was
the
limiting
factor
in
assessing
which
activities
could
be
valued.
Values
were
estimated
for
primary
activities
of
interest:
swimming,
fishing,
boating,
and
for
birdwatching
and
wildlife
viewing
(
the
last
two
of
which
were
combined
into
one
activity).
However,
the
number
of
responses
was
insufficient
to
estimate
the
value
of
Shell
fishing
and
hunting.
Beach
use
was
not
valued
separately
from
swimming.

Swimming
is
the
most
popular
activity,
and
swimming
and
recreational
fishing
are
the
activities
most
directly
affected
by
water
quality
and
hence
by
management
actions
aimed
at
enhancing
water
9This
contrasts
with
policy
actions
to
replant
shellfish
or
to
stock
fish,
the
latter
of
which
is
often
done
in
fresh
water
to
enhance
recreational
fishing.
In
these
cases,
it
may
be
easier
to
link
a
policy
to
a
change
in
catch
rate
and
hence
to
benefits.

57
quality
(
e.
g.,
Table
IV.
15)
For
this
reason,
swimming
is
a
focus
of
our
efforts
to
estimates
the
benefits
of
management
actions
in
terms
of
changes
in
swimming
values.
We
recognize
that
participation
in,
and
the
economic
value
of,
all
activities
depends
upon
the
quality
of
the
experience.
For
example,
substantial
research
on
recreational
fishing,
including
the
results
of
this
study,
shows
the
importance
of
the
quality
of
the
experience­­
the
catch
rate­­
on
the
number
of
trips
and
their
economic
value
(
e.
g.,
Freeman,
1995;
McConnell
and
Strand,
1994).
However,
establishing
the
necessary
cause­
and­
effect
scientific
links
between
policies
to
preserve
or
restore
habitat,
for
example,
with
changes
in
salt
water
recreational
catch
rates
appears
to
be
beyond
the
state
of
the
art9.
Nevertheless,
we
can
simulate
how
benefits
to
recreation
fishing
would
change
if
the
catch
rate
increased
by,
say,
10
percent.

Individual
recreation
trip
value
estimates
and
annual
aggregate
value
estimates
are
given
for
three
cases.
First,
we
present
the
existing
("
baseline")
conditions
for
swimming,
fishing,
boating,
and
for
birdwatching
and
wildlife
viewing.
For
the
baseline,
we
show
consumer
surplus
per
trip,
total
estimated
number
of
trips
in
1995,
and
total
benefits
for
the
year
for
the
four
key
recreation
activities
studied.

Second,
we
show
how
economic
value
for
swimming
changes
for
illustrative,
hypothetical
policies
affecting
water
quality.
One
such
policy
is
assumed
to
increase
water
quality
by
10
percent
in
each
of
the
five
PES
bays.
We
also
simulate
a
20
percent
uniform
improvement
in
water
quality
for
comparative
purposes.
Finally,
a
10
percent
improvement
in
the
recreational
fishing
catch
rate
is
simulated.

IV.
J.
2.
Methodology
Value
estimates
for
each
activity
are
derived
using
the
Travel
Cost
method
of
valuation
(
see,
for
example,
Freeman,
1993).
The
rationale
behind
this
methodology
is
that,
although
there
is
no
market
price
that
reveals
the
value
of
a
recreational
activity,
an
implicit
price
can
be
observed
through
the
cost
of
traveling
to
the
activity's
location.
Included
in
this
implicit
price
is
the
cost
of
transportation,
such
as
gas,
wear
and
tear
on
the
automobile,
etc.,
and
the
value
of
the
individual's
travel
time.
In
theory,
an
individual's
opportunity
cost
is
valued
as
his
or
her
next
best
use
of
their
time.
However,
a
fraction
of
the
individual's
wage
is
generally
used,
in
practice,
to
represent
income
foregone
in
favor
of
recreation.

In
the
travel
cost
model,
the
number
of
trips
a
person
takes
for
recreation
at
a
specific
site
depends
on
the
costs
of
getting
to
that
site,
the
comparative
costs
of
getting
to
substitute
sites,
and
the
quality
of
the
recreational
experience
at
the
sites.
In
other
words,
the
cheaper
it
is
to
get
to
a
site,
the
cheaper
travel
to
that
site
is
relative
to
other
sites,
and
the
better
the
quality
of
the
recreational
experience
at
that
site,
the
more
times
the
individual
will
visit
that
site,
all
else
equal.
Thus,
observed
recreation
choices
reveal
tradeoffs
between
cost
of
participation,
as
measured
by
travel
10We
note
that
a
water
quality
model
developed
by
Tetra
Tech
of
Fairfax,
VA
for
the
PES
Program
will
estimate
water
quality
changes
in
each
of
the
bays
due
to
proposed
program
policies.

58
cost,
and
participation
rates.
In
addition,
participants
might
reveal
values
for
site
quality
by
participating
more
at
sites
of
higher
quality,
and
less
at
sites
of
lower
quality,
all
else
being
the
same.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Riverhead
42
12%
22%

Shelter
Island
57
16%
2%

Southampton
107
30%
43%

Southold
102
28%
19%

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

Nassau
County
30
19%

Suffolk
County
22
14%

Other
Areas
1
35
22%

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

Riverhead
6
5%
5%

Shelter
Island
17
13%
3%

Southampton
39
29%
49%

Southold
42
32%
14%

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

Nassau
County
118
16%

Suffolk
County
300
40%

Other
New
York
State
and
New
Jersey
63
8%

Connecticut
and
Massachusetts
39
5%

Other
84
11%

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

Rental
101
24%

Friends
or
Relatives
168
39%

Other
63
15%

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

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

Multiple
Activities
648
48%

Water
Based
Activities
820
61%

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

where
 
indicates
the
logistic
cumulative
distribution
function.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

.

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

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

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

Great
Peconic
Bay
North
101,
130
(
Central)

Great
Peconic
Bay
South
130
(
Central)

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

Little
Peconic
Bay
South
113
(
Central)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

+
 
 
exp(
)
exp(
)
.
'

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

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

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

|
|
|

|
|
|
=
=
 

 
=
 

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

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

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

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

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

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

The
results
of
both
of
these
models
indicate
that
there
may
be
effects
on
choices
unrelated
to
the
described
attributes.
In
the
alternative­
specific
constant
model,
both
constant
terms
are
statistically
significant,
indicating
that
there
is
an
effect
on
choices
unrelated
to
the
quantities
of
the
individual
attributes,
but
is
instead
related
to
the
choices
themselves.
The
positive
and
significant
coefficient
for
"
Action"
indicates
that
people
are
more
likely
to
choose
an
action
rather
than
"
No
New
Action,"
independent
of
the
action's
specific
effects
on
natural
resources.
Similarly,
the
negative
and
122
significant
coefficient
for
"
Program
B"
indicates
that,
even
if
Program
A
and
Program
B
produced
the
same
results
in
terms
of
preservation
of
natural
resources,
respondents
are
more
likely
to
choose
Program
A.
The
coefficient
on
Action
indicates
that
there
is
an
87
percent
probability
that
the
average
respondent
would
select
action
over
no
action,
if
that
action
cost
nothing
and
provided
zero
resource
protection.
Similarly,
conditional
on
taking
action,
there
is
a
55
percent
probability
that
the
representative
respondent
would
choose
Program
A
over
Program
B
if
their
costs
and
levels
of
resource
protection
were
identical.

Table
B­
1
­
Comparison
of
Model
Results
Coefficient
Value
/
Acre
95%
Confidence
Interval
Avg.
Value
/
Acre/
Year*

Conditional
Logit
Model:
Farmland
0.000511
$
0.136
$
0.122
$
0.150
$
9,979
Undeveloped
Land
0.000107
$
0.028
$
0.025
$
0.032
$
2,080
Wetlands
0.000336
$
0.089
$
0.079
$
0.100
$
6,560
Shellfish
Areas
0.000233
$
0.062
$
0.053
$
0.071
$
4,555
Eelgrass
0.000419
$
0.111
$
0.098
$
0.125
$
8,186
Cost
­
0.003765
 2=
1­(
L
(
 )/
L
(
0))
.138
Model
with
Alternative­
Specific
Constants:
Action
1.2866
Program
B
­
0.1799
Farmland
.000300
$
0.094
$
0.078
$
0.109
$
6,872
Undeveloped
Land
.000057
$
0.018
$
0.014
$
0.022
$
1,304
Wetlands
.000179
$
0.056
$
0.045
$
0.066
$
4,090
Shellfish
Areas
.000108
$
0.034
$
0.023
$
0.044
$
2,467
Eelgrass
.000214
$
0.067
$
0.052
$
0.081
$
4,909
Cost
­.
003207
 2
.171
Nested
Logit
Model:
Program
B
­.
1586
Farmland
.000300
$
0.087
$
0.073
$
0.101
$
6,398
Undeveloped
Land
.000056
$
0.016
$
0.013
$
0.019
$
1,203
Wetlands
.000228
$
0.066
$
0.056
$
0.077
$
4,863
Shellfish
Areas
.000128
$
0.037
$
0.031
$
0.044
$
2,724
Eelgrass
.000281
$
0.082
$
0.069
$
0.094
$
6,003
Cost
­.
003441
 
.3397
 2
.309
*
­
Calculated
based
on
73,423
households.
123
These
constant
terms
for
taking
action
and
for
differences
in
probability
of
selecting
A
and
B
may
be
interpreted
as
representing
a
qualitative
or
symbolic
dimension
of
respondents'
preferences,
while
the
coefficients
on
the
natural
resources
represent
the
quantitative
dimension
that
can
be
attributed
to
the
stated
levels
of
resource
protection.
Thus,
if
respondents
exhibit
a
tendency
to
a
resource
protection
action,
rather
than
"
No
New
Action,"
beyond
that
which
can
be
associated
with
the
stated
levels
of
resource
protection
and
the
cost,
they
may
be
expressing
a
symbolic
willingness
to
pay
to
take
action
to
protect
the
environment
of
the
East
End.
Similarly,
the
coefficient
on
the
constant
term
for
Program
B
indicates
that
there
is
some
qualitative
reason
that
people
choose
Program
A,
and
measures
the
degree
of
preference
for
A
over
B
which
cannot
be
explained
by
the
described
attributes
of
the
two
programs.
Note
that
this
constant
term
is
statistically
significant,
but
quantitatively
small.

The
higher
probability
of
choosing
to
take
action
is
not
surprising,
given
the
level
of
concern
among
residents
of
the
area
for
the
environment
of
the
Estuary.
However,
the
preference
of
one
program
over
another
beyond
the
described
effects
is
not
expected,
and
could
occur
for
a
variety
of
reasons.
For
example,
the
effect
could
be
related
to
the
ordering
of
the
two
programs,
their
placement
on
the
page,
or
could
possibly
indicate
that
respondents
infer
some
preference
from
the
labels
(
e.
g.,
an
A
is
better
than
a
B).

The
constant
for
the
choice
of
action
versus
no
action
accounts
for
qualitative
aspects
of
the
decision
to
choose
an
action
rather
than
no
new
action.
However,
it
is
estimated
as
a
constant
term
and
thus
assumes
a
fixed
effect,
where
the
constant
represents
a
mean
"
bias"
towards
action
versus
no
action,
beyond
that
which
can
be
explained
by
the
described
levels
of
resource
protection
and
cost.
An
alternative
approach
to
modeling
is
to
use
a
random
effects
model,
where
the
random
components
of
preferences
for
the
two
action
programs
are
correlated.
This
implies
that
an
action/
no
action
bias
might
exist,
but
that
the
bias
is
randomly
distributed
across
choices.
For
example,
some
individuals
might
exhibit
a
bias
towards
taking
action,
while
others
might
exhibit
a
bias
against
taking
action.

The
random
effects
model
can
be
implemented
using
the
nested
logit
approach,
which
captures
the
correlation
of
the
random
components
of
utility
associated
with
the
two
action
alternatives.
Tests
of
the
inclusive
value
parameter
in
the
nested
logit
model
indicate
that
there
is
greater
substitutability
between
Program
A
and
Program
B
than
between
taking
action
or
not,
and
that
there
is
significant
correlation
between
the
Program
options.
The
constant
term
for
Program
B
is
similar
in
magnitude
to
that
estimated
in
the
previous
model.

Economic
values
for
the
conditional
logit
model
were
estimated
based
on
Hanemann
(
1984),
and
are
measured
by
the
cost,
C,
that
would
make
a
person
indifferent
between
the
choice
selected
and
the
baseline,
no
action,
which
has
zero
cost.
Thus,
for
the
conditional
logit
model,
124
(
6)
U
R
M
U
R
M
C
ij
i
ik
i
k
(
,
)
(
,
)
=
 
 
for
all
k
j,

where
j
represents
the
"
No
New
Action"
alternative,
or
the
baseline
levels
of
the
resources,
so
that
Cj=
0;
k
is
the
option
selected;
and
Ck
is
the
maximum
willingness
to
pay
for
option
k.
For
the
linear
approximation
of
the
utility
function
presented
above,
this
can
be
solved
for
Ck
as
follows:

(
7)
 
 
 
 
 
 
R
M
R
M
C
C
R
R
ij
i
ik
i
k
k
ik
ij
+
=
+
 
= 
 
(
)
(
)
(
).
and
Thus,
for
the
conditional
logit
model,
the
dollar
value
to
the
average
respondent
for
a
unit
change
in
each
of
the
natural
resources
is
calculated
as
the
ratio
of
the
coefficient
on
the
resource,
 ,
to
the
coefficient
on
cost,
 .

The
calculation
of
dollar
values
for
the
nested
logit
model
must
account
for
the
nested
structure
and
the
inclusive
value
parameter,
 .
The
formula
for
the
compensating
variation
associated
with
a
change
in
one
of
the
attributes
of
the
choice
is
(
Kling
and
Thomson
1996;
Hanemann
1982):

(
8)
CV
V
V
jk
j
k
K
j
J
jk
j
k
K
j
J
j
j
j
j
=
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
=
=
=
=
 
 
 
 
1
2
1
1
1
1
1
 
 
 
 
 
ln
exp(
/
)
ln
exp(
/
)

where
V
is
the
utility
function,
the
superscripts
on
V
indicate
whether
the
attributes
are
set
at
the
new
level
or
the
old
level,
and
 
is
the
coefficient
on
cost.

The
estimated
dollar
values
and
relative
values,
calculated
as
ratios
between
the
coefficients
on
each
pair
of
resources,
were
compared
for
each
model
using
Friedman's
test
for
more
than
two
related
samples
(
Neave
and
Worthington,
1988).
Based
on
this
test,
the
hypothesis
of
equality
of
the
estimated
dollar
values
for
the
three
models
is
rejected.
However,
a
comparison
of
the
estimated
dollar
values
for
the
nested
logit
model
and
the
alternative­
specific
constants
model
using
the
Wilcoxon
signed
rank
test
does
not
reject
the
hypothesis
of
equality
of
values
for
these
two
models.

The
Friedman
test
does
not
reject
the
hypothesis
that
the
relative
values
for
natural
resources
are
equal
for
all
three
models.
Additionally,
the
ordinal
priorities
for
all
three
models
are
the
same,
with
farmland
most
important,
followed
by
eelgrass,
wetlands,
shellfishing
areas
and
undeveloped
land.
These
results
indicate
that
priorities
and
relative
values
are
robust
with
respect
to
different
model
specifications,
and
are
independent
of
symbolic
effects,
but
that
the
estimated
dollar
values
vary
somewhat
between
the
base
model
and
the
two
alternative
specifications.
However,
the
estimated
dollar
values
for
the
three
models
are
close
in
magnitude.
Therefore,
it
may
be
concluded
that
the
model
is
relatively
robust
to
different
specifications,
and
that
the
proportion
of
value
that
is
"
symbolic"
is
not
great.
125
These
results
indicate,
however,
that
there
may
be
a
statistically
significant
symbolic
component
to
choices,
which
is
comprised
of
an
effect
that
is
unrelated
to
the
described
levels
of
resource
protection
provided
by
the
hypothetical
programs
and
the
associated
cost.
The
similarity
of
results
from
the
nested
logit
and
conditional
logit
with
constants
models,
and
the
fact
that
the
results
are
not
statistically
different,
indicate
that
these
biases
are
likely
overwhelmingly
in
one
direction
 
towards
taking
action
rather
than
no
action.
Thus,
both
of
these
models
appear
to
account
for
a
"
symbolic"
aspect
of
values,
and
to
separate
that
from
estimated
values
for
specific
natural
resource
improvements.
Note,
however,
the
Nested
Logit
model
provides
considerable
improvement
in
fit,
as
measured
by
the
 2
statistic.
126