Preventing Land Loss in
Coastal Louisiana:
Estimates of WTP and WTA
Dan Petrolia & Ross Moore
Mississippi State University
Tae-goun Kim
Korea Maritime University
2010 CNREP Conference, New Orleans, May 26-28
Abstract
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A dichotomous-choice contingent-valuation survey was conducted
on a sample of Louisiana households to estimate compensating
surplus (CS) and equivalent surplus (ES) welfare measures for the
prevention of future coastal wetland losses in Louisiana
Valuations were elicited using both WTP (tax) and WTA
compensation (tax refund) payment vehicles
PV of welfare estimates were very sensitive to discount rates, but
were estimated in the neighborhood of $9,000 per LA household
for CS (WTP) and $21,000 for ES (WTA)
The results of a probit model using a Box-Cox specification on
income indicate that the major factors influencing support for
land-loss prevention were:
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perceived hurricane protection benefits (positive)
environmental and recreation protection (positive)
distrust of government (negative)
income
age (positive)
race (positive for whites)
Motivation
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Louisiana’s 3 million acres of wetlands represent about 40% of coastal
wetlands in U.S.
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Coastal LA lost 1,900 miles2 between 1932 and 2000 (USGS 2003)
Katrina and Rita eliminated an additional 217 miles2 in 2005 (Barras 2006)
Additional 700 miles2 expected to be lost over next 50 years (USGS 2003)
Several reports have been published over the last decade to heighten
awareness of these losses and to identify solutions
Ongoing restoration projects: CWPPRA & state
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but account for about 80% of losses (USGS 1995).
As of 2006, an estimated 32,345 acres of coastal land had been re-established
under CWPPRA, at cost of $624.5 million
Most projects are small and independent; do not appear to be part of a
comprehensive coast-wide restoration strategy called for in reports.
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Cost estimates for a comprehensive strategy:
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from as low as $1.9 billion over 10 years for a scaled-down version of the Coast 2050
plan (National Research Council 2006),
to $14 billion over 30 years for the full-blown Coast 2050 plan (LCWCR Task Force 1998),
to as much as $100 billion (Winkler-Schmit 2009).
Prior work on Wetland Valuation
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Brander, Florax, and Vermaat (ERE 2006)
Woodward and Wui (Ecol. Econ. 2001)
Kazmierczak (LSU Staff Paper 2001)
Research specific to Louisiana is now 14-23 years
old
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Farber (Cont. Econ. Pol. 1996)
Bergstrom et al. (Ecol. Econ. 1990)
Costanza, Farber, and Maxwell (Ecol. Econ. 1989)
Farber (JEEM 1987)
Farber and Costanza (JEM 1987)
Our Approach
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We address wetland valuation from the
perspective of future land-loss prevention
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Similar to how one approaches damage assessment
Our referendum asks respondents to evaluate a
policy that will prevent expected future losses
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rather than a policy that will restore land already lost.
The former is more readily-feasible than the latter
The scenario on which our results are based are coastwide.
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our estimates are intended to reflect preferences for a
unified large-scale approach to land-loss prevention that
spans the entire Louisiana coast
The CV Scenario
The questionnaire opened with this introduction:
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Coastal Louisiana has lost an average of 34 square miles of land,
primarily marsh, per year for the last 50 years. From 1932 to
2000, coastal Louisiana lost 1,900 square miles of land, roughly
an area the size of the state of Delaware.
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PLEASE SEE MAP INCLUDED WITH YOUR SURVEY.
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Additionally, Hurricanes Katrina and Rita eroded an additional 217
square miles in 2005 alone. (Not shown on map.)
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If no action is taken, Louisiana could potentially lose an additional
700 square miles of land, about equal to the size of the greater
Washington D.C. – Baltimore area, by the year 2050.
Other scenario details
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Program timing: To make the scenario appear
realistic, we specified that it would take 5 years
for the program to be fully implemented.
To make the scenario consistent with the map
included with the questionnaire, we specified that
the program, if implemented, would maintain
land area at current levels through the year 2050.
In other words, the prevention program would, at
minimum, shift projected losses as shown 35
years into the future.
WTP vs. WTA
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One can conceive of at least two sets of
respondents in the LA restoration case:
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WTA: perceives coastal land to be their birthright,
where WTA would be the appropriate welfare measure
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WTP: perceives coastal land loss as a natural
phenomenon
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This set may, perhaps, perceive any losses as the result of
human activity such as oil and gas exploration or the
building of levees
likely perceives future losses as a given, with any losses
prevented being something gained, rather than something
saved, relative to no action
Unable to identify each respondent ex ante
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we split the sample in two, half receiving a WTP-style
referendum question, and the other a WTA-style question.
WTP Question: A tax
Suppose the State of Louisiana proposed a coast-wide project that
would prevent the projected future losses (as shown in yellow
on the map) from occurring.
It is expected that it would take approximately 5 years for the
project to be fully implemented, and projected land area would be
maintained at current levels until the year 2050.
If the project received a majority vote of support, it would be
implemented, and each tax-paying household in Louisiana would
be obligated to pay an additional tax of $X per year for 10
years. The tax payments would be collected on annual state
income tax returns.
How would you vote?
1- I would vote FOR the project: In other words, PREVENT
future land losses and PAY AN ADDITIONAL ANNUAL TAX OF
$X FOR 10 YEARS.
2- I would vote AGAINST the project: in other words, DO NOT
PREVENT future land losses and pay NO ADDITIONAL TAX.
WTA Question: A tax refund
If the project did not receive a majority vote of support, it
would not be implemented, and the State would
redistribute the funds such that each tax-paying household
in Louisiana would receive an additional tax refund of $X
per year for 10 years. The refunds would be distributed
on annual state income tax returns.
How would you vote?
1- I would vote FOR the project: In other words,
PREVENT future land losses and receive NO ADDITIONAL
TAX REFUND.
2- I would vote AGAINST the project: in other words, DO
NOT PREVENT future land losses and RECEIVE AN
ADDITIONAL TAX REFUND OF $X FOR 10 YEARS.
Data Collection
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Questionnaire mailed to a stratified (by parish)
random sample of 3,000 Louisiana households,
obtained from Survey Sampling International, Inc.
1st mailing sent during 3rd week of May 2009
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included pre-paid $1 cash incentive, shown to increase
response rates relative to either no incentive or postpaid incentives (Dillman 2007, Petrolia and
Bhattacharjee 2009).
Replacement questionnaires sent during 3rd week
of June 2009.
A total of 680 questionnaires were returned
(22.7% response rate).
Referendum Responses by Bid ($/year)
Bid
50
71
101
144
204
291
413
588
836
1189
Total
No
10
10
12
15
5
14
9
13
15
22
125
Yes
24
27
18
27
19
20
23
11
18
7
194
WTP
Total
34
37
30
42
24
34
32
24
33
29
319
% Yes
0.71
0.73
0.60
0.64
0.79
0.59
0.72
0.46
0.55
0.24
0.61
No
7
3
5
5
2
5
2
5
4
9
47
Yes
33
27
28
25
20
35
28
22
29
36
283
WTA
Total
40
30
33
30
22
40
30
27
33
45
330
% Yes
0.83
0.90
0.85
0.83
0.91
0.88
0.93
0.81
0.88
0.80
0.86
Empirical Model
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Standard RUM approach
 Utility of individual i is defined as Ui = U(yi , zi ; q )
 where y is household income; z is a vector of individualspecific characteristics, and q is coastal land quantity
 In WTP case, Individual votes Yes if Ui (yi – ti , zi ; q0) > Ui (yi,zi
; q1)
 In WTA case, Individual votes Yes if Ui (yi + ti , zi ; q1) > Ui (yi,zi
; q0)
 where t is the bid
 q1 is the state of nature where the program is implemented
 q0 is where it is not (q0 > q1)
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Weighted likelihood function to mitigate sample bias (pseudolikelihood)
 Ratio of population income category proportion over sample
income category proportion
Estimated (weighted) pseudo-likelihood probit model using Stata 11
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Modeling Income and Bid
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Because bids were relatively large, ranging from $50 to
$1,189, we did not wish to impose the commonly-made
assumption of constant marginal utility of income.
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We adopted the Box-Cox Transformation, which specifies
a composite bid-income term of ( y  t )  y 
K
i
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i
K
K
i
where λ is Box-Cox Transformation parameter with K possible
values
When λ = 0, the Box-Cox transformation converges to the loglinear specification
Following Greene (2000), estimated model for λ = {-2, 2} in
increments of 0.1.
The survey gathered household income by income categories.
To construct our composite variable, income was interpolated
as the mid-point in the category.
Our search resulted in the adoption of λ = 0.7.
Variable Descriptions
Variable Name
Type
Vote (dependent var.)
binary
continuous
Box-Cox Income-Bid Term (λ = 0.7)
Age
continuous
Age x WTA
continuous
Education ord. categor.
Male
binary
White
binary
Latitude
continuous
No Confidence in Govt
binary
Consequential ord. categor.
Climate Change
binary
Storm-Protection Priority
binary
Other Benefits Priority
binary
WTA Dummy
binary
Question Sequence
binary
Mean
0.755
-15.508
54.341
27.584
2.709
0.604
0.820
30.679
0.457
-0.278
0.418
0.558
0.306
0.508
0.506
Std. Weighted
dev.
Mean
0.018
0.727
0.670
-17.300
0.644
54.517
1.254
27.962
0.034
2.564
0.021
0.548
0.017
0.772
0.041
30.716
0.021
0.431
0.033
-0.224
0.021
0.444
0.021
0.546
0.020
0.305
0.021
0.515
0.021
0.514
Regression Results
Std.
Coef.
Err.
Box-Cox Income-Bid Term (λ = 0.7)
0.015 **
0.004
Age
0.004
0.006
Age x WTA
0.019
0.010
Education
0.086
0.094
Male
0.075
0.161
White
0.590 **
0.190
Latitude
-0.079
0.079
No Confidence in Govt
-0.427 *
0.167
Consequential
0.134
0.115
Climate Change
0.115
0.157
Storm-Protection Priority
1.514 **
0.211
Other Benefits Priority
1.190 **
0.233
WTA Dummy
-0.243
0.555
Question Sequence
-0.249
0.154
Constant
1.233
2.506
**,* Parameter estimate significant at p = 0.01, 0.05 levels, resp.
2
Marginal effect for the equivalent of a $1,000 increase in income.
# of obs = 543
Log Pseudolikelihood = -219.64
pvalue
0.000
0.496
0.056
0.359
0.640
0.002
0.314
0.010
0.245
0.463
0.000
0.000
0.661
0.105
0.623
Marginal
Effects 1
0.0012 2
0.003
0.015
0.067
0.022
0.192
-0.062
-0.128
0.105
0.034
0.446
0.282
-0.071
-0.073
Pseudo R-squared = 0.29
Nominal (Annual) Welfare Estimates
Turnbull Lower
Bound (Mean)
Box-Cox
(Median)
Mean/Median
$490
$1,116
95% CI
$484 ~ $496
$755 ~ 2,029 *
Mean/Median
$982
$2,496
WTA
95% CI
$977 ~ $986
$1,727 ~ $4,892 *
*Box-Cox confidence intervals calculated using the KrinskyRobb method.
WTP
PV(WTP)
$20,000
$18,000
$16,000
$14,000
Box-Cox WTP w/ KR Conf. Int.
NPV of WTP
$12,000
$10,000
$8,000
$6,000
$4,000
$2,000
Turnbull WTP w/ Conf. Int.
$0
0.02
0.04
0.06
0.08
0.10
0.12
Discount Rate
0.14
0.16
0.18
0.20
PV(WTA)
$45,000
$40,000
$35,000
Box-Cox WTA w/ KR Conf. Int.
NPV of WTP
$30,000
$25,000
$20,000
$15,000
$10,000
$5,000
Turnbull WTP w/ Conf. Int.
$0
0.02
0.04
0.06
0.08
0.10
0.12
Discount Rate
0.14
0.16
0.18
0.20
Summary of Results
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The WTA dummy variable, included to capture any residual treatment
differences was not found to be significantly different from zero
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The Box-Cox income-bid term was significant and positive
Age was significant among WTA respondents only (at the p = 0.1 level),
increasing the probability of a Yes vote by 15% for a 10-year increase in
age
Whites were 19% more likely to vote Yes
Respondents with no confidence in government to enact restoration efforts
in a timely manner were 13% less likely to vote Yes
Those citing storm protection were 45% more likely to vote Yes relative to
all others
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Although it does affect welfare estimates
over half of respondents indicated storm protection as their leading concern
while voting
respondents citing some other concern (including environmental
protection, protection of recreational opportunities, and protection against
sea-level rise) were 28% more likely to vote Yes
Depending on the discount rate, our results indicate:
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$1,000 < WTP < $18,000
$5,000 < WTA < $45,000
Questions and Comments
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Contact: Dan Petrolia
petrolia@agecon.msstate.edu
Sponsoring Agency (NOAA):