oral presentation ii
TRANSCRIPT
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Presenter: Jenny Chen 陳瑩珍
Instructor: Dr. Pi-Ying Teresa Hsu
November 12, 2009 1
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Saz-Salazar, S. D., & Rausell-Koster, P. (2008). A
double-hurdle model of urban green areas valuation:
Dealing with zero responses. Landscape and Urban
Planning, 84(3-4), 241-251.
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Contents
I. Introduction
II. Methodology
III. Results
IV. Conclusion
V. Personal Reflection
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Introduction
In Spain, 77% of the population lives in urban areas.
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Introduction
Public parks and open spaces:
1. improve physical and psychological health
2. provide substantial environmental benefits
3. have an important aesthetic value in an environment
designed around the automobile
4. are important for wildlife
5. are valuable contributors to wider urban objectives
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Introduction
hedonic pricing method travel cost method averting behaviour
contingent valuation method (CVM)
Revealed Preferences Techniques
Stated Preferences Techniques
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Purpose
to estimate the social benefits arising from the use of an urban park in the city of Valencia (Spain)
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Study Site
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Study Site
El Jardín del Túria
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Methodology
Time spring 2005
Participants 1455
Sampling stratified
Payment Vehicle annual increase in local taxes
Elicitation Approach open-ended
Survey face-to face interview
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Methodology
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Methodology
First Section attitudinal questions behavior
Third Section demographic and economic
questions
Second Section
valuation question “Now I want to ask you how much you would be willing to pay each year in extra property taxes considering the whole array of benefits that you receive every time you visit the park.”
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Methodology
indirect utility function
V = U [x(p, y, z), z] = V (p, y, z) (1)
x : vector of private goods
p : prices
y : income
z : provision or quality of environmental commodities
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Methodology
change in utility
Δ V = V (p, y, z1) – V (p, y, z0) (2)
V (p, y – CV, z1) = V (p, y, z0) (3)
p : prices
y : income
z : provision or quality of environmental commodities
CV: WTP for an improvement14
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Methodology
Zero WTP
preference unfamiliarity
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Methodology
Ordinary Least-Square Regressions
biased and inconsistent estimates
of the parameters
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Methodology
only allows for one type of zero observations
(corner solution)
treats the decision to participate in the market and the stated degree of support together
Tobit Model
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Methodology
two stages of estimation
considers the possibility of zero outcomes in
the second hurdle
distinguishes between factors affecting the
decision to participate and the stated amount
of WTP
Double-Hurdle Model
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Methodology
Participation decision
yi = yi * , if yi * > 0 and Di > 0
yi = 0 , otherwise
Di = Zi θ + ui (4)
Di : latent variable
Zi : a vector of explanatory variables
θ : a vector of parameters
Double-Hurdle Model
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Methodology
WTP decision
yi = yi * , if yi * > 0
yi = 0 , otherwise
yi * = Xi β + ei (5)
yi : revealed WTP for individual i
yi * : corresponding latent value of individual i’s actual WTP
Xi : vector of the individual’s characteristics
β : vector of parameters20
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Methodology
Probit Model
to evaluate the
censoring rule (Zi θ)
Truncated Regression Model
to obtain the bid function
(Xi β) for the subsample
of censored observations
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Results
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Results
Average expenditure:
total cost
number of households
= 4.62 €
× relative surface (26.76%)
< 7.60 €
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Results
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Results
H0: Tobit model is the correct specification
H1: Tobit model is not the appropriate specification
λ = - 2(LT- LP – LTR) (6)
LT : log-likelihood for the Tobit model
LP : log-likelihood for the Probit model
LTR : log-likelihood for the truncated model
α = 0.01Reject H0
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Results
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Results
Aggregation
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Conclusion
WTP increases with income and education as
expected.
WTP varies depending on which section of the park
the interview was conducted in.
Distinct sets of variables influence the decision to
participate and the decision about how much the
individual is willing to pay.
Demand for various green areas is culturally
dependent.
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Personal Reflection
Sampling procedures were careful.
WTP question was not detailed and clear
enough.
The WTP question should be revised to induce
both use and preservation values.
Strategic bias was very likely to occur in this
study because of the payment vehicle.
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