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

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Survey site:

Values = αs + βs1Xs1 + βs2Xs2

Policy site:

Valuep = αs + βs1Xp1 + βs2Xp2

How a benefit transfer function works

X1 : site and good characteristicsX2 : population characteristics

Stages in BT process

# Stage Notes 1 Assess target situation 2 Identify source studies available and select

benefit transfer type Transfer type largely dependent on source studies available

3 Assess site differences (a) identify if BT possible (b) identify basis for BT adjustment

4 Assess population differences (a) identify if BT possible (b) identify basis for BT adjustment

5 Assess scale of change in both cases (a) identify if BT possible (b) identify basis for BT adjustment

6 Assess framing issues (scope, scale, instrument, payment vehicle, payment length, willingness-to-pay or willingness-to-accept format used, use versus non-use)

(a) test if source study is appropriate for BT

(b) Identify any basis for BT adjustment

7 Assess statistical modelling issues (a) identify appropriateness of model in source study

(b) Identify any basis for BT adjustment

8 Perform benefit transfer process

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

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Survey sites:

Values = αs + βs1Xs1 + βs2Xs2+ βs3Xs3

Policy sites:

Valuep = αs + βs1Xp1 + βs2Xp2+ βs3Xp3

Meta-analysis

X1 : site and good characteristicsX2 : population characteristicsX3 : study characteristics

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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

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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

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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

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Overview (1)• 8 discrete choice studies related to rivers in Australia1. Blamey, R., Gordon, J., Chapman, R. (1999). Choice modelling: assessing the environmental

values of water supply options. AJARE, 43(3): 337-357.2. 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.4.3. 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.

4. 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.

5. Morrison, M. and Benett, J. (2004). Valuing New South Wales rivers for use in benefits transfer. AJARE, 48(4): 591-611.

6. Rolfe, J. and Windle, J. (2005). Valuing options for reserve water in the Fitzroy basin. AJARE, 49: 91-114.

7. 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.

8. 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.

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Overview (2)• 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

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Time coverage : 1997-2006Spatial coverage: see Fig

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Overview (3)

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Overview (4)

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Overview (5)

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0 10 20 30 40 50 60 70 80 90 100

Observation

0

10

20

30

40

50

60

70

80

90

100

110

120

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Imp

licit

pri

ce (

WT

P)

StudyBueren-Bennett

Morrison-Bennett

Blamey et al.

Rolfe et al.

WindleRolfe(2004)

RolfeWindle(2005)

WindleRolfe(2006)

Kragt et al.

Response variable

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0 10 20 30 40 50 60

Kragt et al. (2007)

Windle & Rolfe (2006)

Rolfe and Windle (2005)

Van Bueren & Bennett (2004)

Morrison & Bennett (2004)

Windle & Rolfe (2004)

Rolfe et al. (2002)

Blamey et al. (1999)

Response variable

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 <

Periodicity

Annual payments 43.4 24.5-62.2 51

One-time-off payments 29.9 24.9-34.9 61 -0.772 0.440

Sample size

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

Survey method

Mail 26.7 21.7-31.8 64

Drop off - pickup 49.2 28.8-69.6 46 -4.014 0.001

Payment vehicle

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

Statistical model

Conditional logit 45.2 28.7-61.7 57

Nested logit 28.2 22.0-34.4 50 -3.317 0.001

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

Tobit model

Random effects

Tobit model

Covariate Estimate Standard error Estimate Standard error

Good and site characteristics

Water quality 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 characteristics

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

Significant design effects

• Year of study

• Sample size

• Mail survey

• Number of choice sets

• Payment vehicle

• Accuracy

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Challenges• 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?