Preventing Land Loss in Coastal Louisiana:

Estimates of WTP and WTA

Dan Petrolia & Ross MooreMississippi State University

Tae-goun KimKorea Maritime University

2010 CNREP Conference, New Orleans, May 26-28

Abstract 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:

perceived hurricane protection benefits (positive) environmental and recreation protection (positive) distrust of government (negative) income age (positive) race (positive for whites)

Motivation Louisiana’s 3 million acres of wetlands represent about 40% of coastal

wetlands in U.S. but account for about 80% of losses (USGS 1995).

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

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.

Cost estimates for a comprehensive strategy: 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 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 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 We address wetland valuation from the

perspective of future land-loss prevention Similar to how one approaches damage assessment

Our referendum asks respondents to evaluate a policy that will prevent expected future losses 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 coast-

wide. 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 ScenarioThe questionnaire opened with this introduction:

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.

PLEASE SEE MAP INCLUDED WITH YOUR SURVEY.

Additionally, Hurricanes Katrina and Rita eroded an additional 217 square miles in 2005 alone. (Not shown on map.)

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 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 One can conceive of at least two sets of

respondents in the LA restoration case: WTA: perceives coastal land to be their birthright,

where WTA would be the appropriate welfare measure This set may, perhaps, perceive any losses as the result of

human activity such as oil and gas exploration or the building of levees

WTP: perceives coastal land loss as a natural phenomenon

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 we split the sample in two, half receiving a WTP-style

referendum question, and the other a WTA-style question.

WTP Question: A taxSuppose 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 refundIf 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 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 included pre-paid $1 cash incentive, shown to increase

response rates relative to either no incentive or post-paid 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)

WTP WTA Bid No Yes Total % Yes No Yes Total % Yes

50 10 24 34 0.71 7 33 40 0.83 71 10 27 37 0.73 3 27 30 0.90

101 12 18 30 0.60 5 28 33 0.85 144 15 27 42 0.64 5 25 30 0.83 204 5 19 24 0.79 2 20 22 0.91 291 14 20 34 0.59 5 35 40 0.88 413 9 23 32 0.72 2 28 30 0.93 588 13 11 24 0.46 5 22 27 0.81 836 15 18 33 0.55 4 29 33 0.88

1189 22 7 29 0.24 9 36 45 0.80 Total 125 194 319 0.61 47 283 330 0.86

Empirical Model 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 individual-

specific 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)

Weighted likelihood function to mitigate sample bias (pseudo-likelihood) Ratio of population income category proportion over sample

income category proportion Estimated (weighted) pseudo-likelihood probit model using Stata 11

Modeling Income and Bid 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.

We adopted the Box-Cox Transformation, which specifies a composite bid-income term of

K

iiiKK yty

)(

where λ is Box-Cox Transformation parameter with K possible values

When λ = 0, the Box-Cox transformation converges to the log-linear 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 Mean Std. dev.

Weighted Mean

Vote (dependent var.) binary 0.755 0.018 0.727 Box-Cox Income-Bid Term (λ = 0.7) continuous -15.508 0.670 -17.300

Age continuous 54.341 0.644 54.517 Age x WTA continuous 27.584 1.254 27.962

Education ord. categor. 2.709 0.034 2.564 Male binary 0.604 0.021 0.548

White binary 0.820 0.017 0.772 Latitude continuous 30.679 0.041 30.716

No Confidence in Govt binary 0.457 0.021 0.431 Consequential ord. categor. -0.278 0.033 -0.224

Climate Change binary 0.418 0.021 0.444 Storm-Protection Priority binary 0.558 0.021 0.546

Other Benefits Priority binary 0.306 0.020 0.305 WTA Dummy binary 0.508 0.021 0.515

Question Sequence binary 0.506 0.021 0.514

Regression Results Coef.

Std. Err.

p-value

Marginal Effects 1

Box-Cox Income-Bid Term (λ = 0.7) 0.015 ** 0.004 0.000 0.0012 2 Age 0.004 0.006 0.496 0.003

Age x WTA 0.019 0.010 0.056 0.015 Education 0.086 0.094 0.359 0.067

Male 0.075 0.161 0.640 0.022 White 0.590 ** 0.190 0.002 0.192

Latitude -0.079 0.079 0.314 -0.062 No Confidence in Govt -0.427 * 0.167 0.010 -0.128

Consequential 0.134 0.115 0.245 0.105 Climate Change 0.115 0.157 0.463 0.034

Storm-Protection Priority 1.514 ** 0.211 0.000 0.446 Other Benefits Priority 1.190 ** 0.233 0.000 0.282

WTA Dummy -0.243 0.555 0.661 -0.071 Question Sequence -0.249 0.154 0.105 -0.073

Constant 1.233 2.506 0.623 **,* 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 Pseudo R-squared = 0.29

Nominal (Annual) Welfare Estimates

Turnbull Lower Bound (Mean)

Box-Cox (Median)

Mean/Median $490 $1,116 WTP 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 Krinsky-Robb method.

PV(WTP)

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

$18,000

$20,000

0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

Discount Rate

NP

V o

f W

TP

Box-Cox WTP w/ KR Conf. Int.

Turnbull WTP w/ Conf. Int.

PV(WTA)

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

$45,000

0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

Discount Rate

NP

V o

f W

TP

Box-Cox WTA w/ KR Conf. Int.

Turnbull WTP w/ Conf. Int.

Summary of Results The WTA dummy variable, included to capture any residual treatment

differences was not found to be significantly different from zero Although it does affect welfare estimates

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 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: $1,000 < WTP < $18,000 $5,000 < WTA < $45,000

Questions and Comments Contact: Dan Petrolia petrolia@agecon.msstate.edu

Sponsoring Agency (NOAA):