Transcript Benefit Transfer of Non

Valuing rivers and wetlands: A meta
analysis of CM values
Roy Brouwer and John Rolfe
Outline of this talk
• Benefits transfer & meta-analysis
• Database
• Statistical results
Benefit transfer
• The transfer of values from one case study to
another policy situation
• Attractive because of cost and time
advantages over the separate conduct of
non-market valuation experiments
• Can be complex because source and target
sites may not be identical
– Benefit transfer may involve some adjustment of
values
– BT may be associated with increased uncertainty
about values
Three main approaches to BT
• ‘The Prospector’ – searches for suitable
previous studies and transfers results
across to target site
• ‘The Systematic’ – designs a database
of values suitable for benefit transfer
• ‘The Bayesian’ – combines both a
review of previous studies with potential
data gathering
How a benefit transfer function works
Survey site:
Values = αs + βs1Xs1 + βs2Xs2
Policy site:
Valuep = αs + βs1Xp1 + βs2Xp2
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X1 : site and good characteristics
X2 : population characteristics
Stages in BT process
#
1
2
3
Stage
Assess target situation
Identify source studies available and select
benefit transfer type
Assess site differences
4
Assess population differences
5
Assess scale of change in both cases
6
Assess framing issues (scope, scale, instrument,
payment vehicle, payment length, willingnessto-pay or willingness-to-accept format used, use
versus non-use)
7
Assess statistical modelling issues
8
Perform benefit transfer process
Notes
Transfer type largely dependent on source
studies available
(a) identify if BT possible
(b) identify basis for BT adjustment
(a) identify if BT possible
(b) identify basis for BT adjustment
(a) identify if BT possible
(b) identify basis for BT adjustment
(a) test if source study is appropriate
for BT
(b) Identify any basis for BT
adjustment
(a) identify appropriateness of model
in source study
(b) Identify any basis for BT
adjustment
Key mechanisms for benefit transfer
• Point – total value
– Total value from a previous study
• Point – marginal value
– Value per unit transferred
• Benefit function transfer
– Function allows adjustments for site and
population differences
• Integrations across multiple studies
– Meta analysis
– Bayesian methods
Meta analysis
• Meta-analysis for use in benefit transfer involves
the summarizing of results for several existing
source studies in a regression function,
• This function is then used to predict value
estimates for a target site
• Often difficult to do in practice because of
methodological and framing differences between
studies
Meta-analysis
Survey sites:
Values = αs + βs1Xs1 + βs2Xs2+ βs3Xs3
Policy sites:
Valuep = αs + βs1Xp1 + βs2Xp2+ βs3Xp3
X1 : site and good characteristics
X2 : population characteristics
X3 : study characteristics
9
Meta-analysis
• Statistical analysis of the summary findings of empirical studies
• Helpful tool to summarize and explain differences in outcomes
• Advantages:
- transparant structure to understand underlying patterns of assumptions, relations and causalities
- avoids selective inclusion of studies and weighting of findings
10
Main objective of this
study
• Meta-analysis of Australian water valuation
studies
• Different studies, different values
• Policy need for more structured overview of
existing values and their usefulness in
policy analysis
• Comparability of results and insight in
transfer errors
11
When to apply Benefits Transfer?
• = When to apply monetary economic valuation?
• Never as good as original valuation study!
• Consider a priori what is acceptable transfer error
• Use meta-analysis if possible
• Build databases (EVRI)
• Strict reporting requirements (wider applicability of results) more emphasis on meaning,
interpretability and potential use of results in different policy contexts
• Often BT remains matter of expert judgement
12
Overview (1)
•
8 discrete choice studies related to rivers in Australia
1.
Blamey, R., Gordon, J., Chapman, R. (1999). Choice modelling: assessing the
environmental values of water supply options. AJARE, 43(3): 337-357.
Rolfe, J., Loch, A., Bennett, J. (2002). Tests of benefits transfer across sites and
population in the Fitzroy basin. Valuing floodplain development in the Fitzroy basin
Research Report no.4.
Windle, J. and Rolfe, J. (2004). Assessing values for estuary protection with choice
modelling using different payment mechanisms. Valuing floodplain development in the
Fitzroy basin Research Report no.10.
Van Bueren, M. and Bennett, J. (2004). Towards the development of a transferable set of
value estimates for environmental attributes. AJARE, 48(1): 1-32.
Morrison, M. and Benett, J. (2004). Valuing New South Wales rivers for use in benefits
transfer. AJARE, 48(4): 591-611.
Rolfe, J. and Windle, J. (2005). Valuing options for reserve water in the Fitzroy basin.
AJARE, 49: 91-114.
Windle, J. and Rolfe, J. (2006). Non market values for improved NRM outcomes in
Queensland. Research report 2 in the non-market valuation component of AGSIP project
# 13.
Kragt, M., Bennett, J., Lloyd, C., Dumsday. R. (2007). Comparing choice models of
river health improvement for the Goulburn River. Paper presented at 51st AARES
conference.
2.
3.
4.
5.
6.
7.
8.
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Overview (2)
•
•
•
•
•
•
•
•
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4 journal papers (AJARE)
3 research reports
1 conference paper
WTP for improvement in river flows, waterway
restoration, healthy rivers, water dependent
wildlife, water quality (recreational use)
93 observations in total (implicit prices)
12 observations per study on average
Range of observations per study: 1-36
Author bias: Bennett & Rolfe both in 4 studies
Time coverage : 1997-2006
Spatial coverage: see Fig
15
Overview (3)
16
Overview (4)
17
Overview (5)
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Response variable
130
Study
Bueren-Bennett
120
Morrison-Bennett
Blamey et al.
110
Rolfe et al.
WindleRolfe(2004)
100
RolfeWindle(2005)
WindleRolfe(2006)
Implicit price (WTP)
90
Kragt et al.
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
Observation
19
70
80
90
100
Response variable
Blamey et al. (1999)
Rolfe et al. (2002)
Windle & Rolfe (2004)
Morrison & Bennett (2004)
Van Bueren & Bennett (2004)
Rolfe and Windle (2005)
Windle & Rolfe (2006)
Kragt et al. (2007)
0
20
10
20
30
40
50
60
Influences on the precision of
implicit prices
• Variation coefficients calculated from confidence
intervals and cross tabulated with study
characteristics
• Mann-Whitney tests used to calculate differences
– No difference between annual and regular payments
– Sample size correlated with precision
• Sample of less than 200 generate low levels of precision
– Mail more precise than drop-off&collect
– Nested logit more precise than conditional logit
Study characteristic
Mean
95% CI
n
MW-Z
p<
Annual payments
43.4
24.5-62.2
51
One-time-off payments
29.9
24.9-34.9
61
-0.772
0.440
 200
48.7
29.6-67.8
49
201-300
29.6
21.4-37.8
27
-2.709
0.0071
301-500
28.3
14.2-42.3
15
-0.714
0.4752
>500
18.7
12.2-25.1
16
-1.791
0.0733
Mail
26.7
21.7-31.8
64
Drop off - pickup
49.2
28.8-69.6
46
-4.014
0.001
Water rate
28.0
21.4-34.5
46
-0.229
0.819a
Local tax
25.6
19.9-31.4
29
-1.645
0.100b
Environmental levy
29.9
23.8-36.1
9
-1.223
0.221c
Trust fund
37.3
32.4-42.2
16
-1.868
0.062d
Conditional logit
45.2
28.7-61.7
57
Nested logit
28.2
22.0-34.4
50
-3.317
0.001
Periodicity
Sample size
Survey method
Payment vehicle
Statistical model
Multivariate analysis
• Responses combined in a random effects
Tobit regression model
– Random effects captures heteroscedasticity
• Implicit prices regressed against a number
of potential explanatory factors
Fixed effects
Random effects
Tobit model
Tobit model
Estimate
Standard error
Estimate
Standard error
0.939*
0.513
1.177**
0.514
Healthy wetlands
-1.943***
0.523
-2.102***
0.469
Native vegetation
-1.455***
0.517
-1.211**
0.506
Native fish species
-1.222**
0.513
-0.878*
0.495
Water birds
-1.401***
0.459
-1.074**
0.435
Option value
1.517***
0.334
1.307***
0.328
Fitzroy catchment
-0.771***
0.294
-0.955***
0.289
Study carried out before 2000
2.765***
0.600
-
-
Study carried out after 2000
-0.439**
0.200
-
-
Sample size
-0.002***
0.0004
-0.003***
0.0004
Mail survey
2.624***
0.512
2.318***
0.481
Number of cards shown = 6
1.909***
0.322
2.042***
0.326
Number of cards shown > 6
0.562*
0.328
1.080***
0.242
Payment vehicle = local tax
0.629**
0.293
0.440
0.287
Accuracy (standard error)
0.019***
0.006
0.020***
0.006
Covariate
Good and site characteristics
Water quality
Study characteristics
Significant design effects
•
•
•
•
•
•
Year of study
Sample size
Mail survey
Number of choice sets
Payment vehicle
Accuracy
Challenges
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• Low number of observations
• Wide variety of attributes
• Different measurement units (some
imprecise)
• Small number of people doing the research
>> researcher bias (advantage: easy to
contact)
• Meaningfulness of attributes to policy & lay
public?