Valuation 11: Benefit Transfer and Meta-

Analysis• Why benefit transfer?• Different types of benefit transfer

– Value transfer– Function transfer

• Validity and sources of error• Examples

Last weeks we looked at

• Various methods to estimate the value of environmental goods and services not traded on markets

• Also, some empirical examples were shown• It turned out that it is actually very hard to

reliably estimate prices – one needs many assumptions, good data, and smart statistics, and even then all sorts of things may go wrong

What is benefit transfer?

• Benefit transfer uses economic information captured at one place and time to make inferences about the economic value of environmental goods and services at another place and time

• Benefit transfer involves economic values that may be either positive (benefits) or negative (costs)

Study Site

Policy Site$

Why benefit transfer?• Valuation is hard• As a result, applied valuation studies are

expensive• A small hedonic pricing study, for instance, costs

about a year of a PhD student – that is, after the data have been collected and digitised

• Ditto for travel costs• A contingent valuation study is more expensive• Monetary values are also hard needed• Wouldn't it, therefore, be nice if we could take the

estimated values of case 1 and apply them to case 2?

History• Environmental benefit transfer came into being

only once the non-market literature itself grew large enough to allow comprehensive synthesis and cross-study comparison

• In 1973, the U.S. Water Resources Council began publishing unit day estimates for recreation activities relates to water projects

• In 1980, the U.S. Forest Service began publishing Resources Planning Act values for recreation (per person per day)

• First synthesis study mid to late 1980s• Today, applied to issues involving values for

recreation, water quality, fishing, air quality, wetlands, biodiversity

Value Transfer

Single point transfer

Measure of central tendency

Single point transfer adm. approved

Benefit/ Demand Function

Meta-Analysis Function

Use estimate at policy site

Adapt function to policy site

Use tailored estimate at policy site

Function Transfer

Types of BT

The basis for any benefit transfer analysis

• The basis is a formal literature review • The original research studies are screened for

– relevance– how well they correspond to the policy context– the quality of research– what kind of information is provided

• Advantages include a much larger sample of data, different analytical techniques, and different analysts

• The main disadvantage is that one typically only has access to published results, which are always incomplete

Value transfer: Point estimates

• Point estimate transfer typically uses a single measure

• Example: The U.S. Army Corps of Engineers was considering the removal of four dams on the Lower Snake River from its confluence with the Columbia River

• One of the benefits would be the restoration of spawning habitat for native salmon populations (by about 47,500 fish)

• The agency was interested in the passive use value based on per household annual WTP

• The literature search found four studies that provided values for salmon population

Study Study Site Context

I ncrease in

salmon

population

Sample

f rame

WTP per

HH (1998$)

Marginal

passive use

value per fi sh

Olsen et al.

(1991)

Doubling of Columbia

river basin salmon and

steelhead population

2,500,000 I D, MT,

OR, WA

$32.52 $163

Hanemann et al.

(1991)

I ncreasing salmon

population on the San

J oaquin river

14,900 CA $221.96 $186,829

Loomis (1996) I ncreasing salmon

population on the Elwha

River due to dam

removal

300,000 WA $76.48 $3,197

Layton et al.

(1999)

I ncreasing anadromous

fi sh population on the

Columbia River

1,000,000 WA $119.04 $1,492

Layton et al.

(1999)

I ncreasing anadromous

fi sh population on the

Columbia River

250,000 WA $227.64 $11,420

Other value transfer

• The measure of central tendency entails using a mean, median or other measures of central tendency based on all or a subset of original research outcomes– Same example as above; great disparity between

average marginal passive use value ($40,620) and median value ($3,197)

• Administratively approved estimate transfer is the simplest approach– Estimates are derived from empirical evidence in the

literature, expert judgment, and political screening– Problems: Criteria for political screening are

unknown, selection might be biased and only updated every so often

Function transfer• Value transfer requires a strong similarity

between the study site and the policy site is required

• Function transfers are generally considered to perform better than value transfers

• Unlike value transfers, they may be tailored to fit some of the characteristics of the policy site

• Function transfers entails the application of a statistical function that relates the summary statistics of original research to the specifics of the study site

• There are two types of function transfers– Demand/Benefit function transfer– Meta-regression analysis

Demand/benefit function transfer

• For benefit function transfer additional information is required

• We need a function that models the statistical relationship between the summary measures of interest and characteristics of the original site

• This function is then adjusted to specific characteristics of the policy site

( , , )environmentpopulation goodWTP f X Y Z

Example• Case study by VandenBerg, Poe and Powell

(2001) estimates the benefits of improving groundwater quality used for drinking to a very safe level in 12 towns in New York, Massachusetts, and Pennsylvania

• The authors use a contingent valuation survey with a payment card question format

• To perform the benefit function transfer all of the survey data except for one town are pooled and a WTP equation is estimated

Variable

Regression

coeffi cient

Policy site

measure Partial WTP

Constant -29.68 1 -29.68

Perception of contamination (0, 1 = no experience) -23.48 1 -23.48

Perception of contamination (0, 1 = don't know) -26.96 0.24 -6.47

Likelihood of f uture cont. (0, 1 = likely/ very likely) 17.51 0.5 8.76

Likelihood of f uture cont. (0, 1 = not sure) 9.41 0.5 4.70

I nterest in community water issues (0, 1 = mild/ no

interest) -20.66 0.5 -10.33

I nterest in community water issues (0, 1 = interested) -11.15 0.5 -5.58

Perceived water quality (0, 1 = unsaf e/ somewhat saf e) 29.92 0.5 14.96

Perceived water quality (0, 1 = saf e) 21.07 0.5 10.54

College Degree (0, 1 = has degree) -17.51 0.24 -4.20

Some College (0, 1 = some college, but no degree) -15.72 0.24 -3.77

Average risk perception (1=saf e, 5=unsaf e) 9.91 4 39.64

Number of perceived potential contamination sources 2.56 3.17 8.12

Trust in government/ organisations (1=do not trust,

3=trust) 15.67 1.91 29.93

Household income ($/ year) 0.0008 45500 36.40

Total Mean WTP $69.54

OLS regression, dependent variable is mean WTP/HH/year, n=667

Validity of benefit transfer• A number of studies performed the following

test: Estimate the value of something at two sites, and predict the value of the one site from the observations of the other

• Resources or activities include– Sport fishing (distance, harvest, quality)– Recreation (costs, size, substitutes, population, age) – Rafting (flow, costs, intensity, reason for visit, home,

income, sex, age, education)– Water quality (costs, size, depth, accessibility,

quality, use, income)– Water quality (bid, use, education, age, user)

Study ref erence I ssue

Range of error f or

value transf er

Range of error f or

f unction transf er

Loomis (1992) Fishing 4 - 39% 1 - 18%

Parson and Kealy (1994) Water quality 4 - 34% 1 - 75%

Loomis et al. (1995) Recreation 1 - 475%

Berland et al. (1995) Water quality 25 - 45% 18 - 41%

Downing and Ozuna (1996) Fishing 0 - 577%

Kirchhoff et al. (1997) Raf ting 35 - 69% 2 - 210%

Kirchhoff (1998) Recreation 1 - 7028%

Brouwer and Spaninks (1999) Agricultural land 27 - 36% 22 - 40%

Morrison and Bennett (2000) Wetlands 4 - 191%

Rosenberger and Loomis (2000) Recreation 0 - 319%

VendenBerg et al. (2001) Water quality 0 - 105% 1 - 56%

Rosenberger and Phipps (2001) Recreation 4 - 490% 2 - 62%

Shrestha and Loomis (2001) I nternational recreation 1 - 81%

Sources of error• Generalisation error

– When estimates from study sites are adapted to represent different policy sites

– The error is inversely related to the degree of correspondence between the sites

• Measurement error– Occurs when researcher’s decision affect the accuracy of

the transferability of values– Methodological choices, non-reporting of study

characteristics

• Publication selection error– There is a preference for publishing statistically significant

results that conform theoretical expectations

• Authorship effect

Meta-regression analysis• A drawback of the demand function transfer is the

assumption that the statistical relationship between the dependent and independent variables are the same for both study as well as policy site

• Also, demand function transfer relies mostly on a single study

• Meta-regression analysis summarises and syntheses outcomes from several studies

• Meta-analysis not only provides a rigorous synthesis of the literature, it is also able to explain the variation found across empirical studies and to identify outlier studies, knowledge gaps, and priors for further analysis

• Meta-analysis is a technique that originates in medical science

Meta-regression analysis (2)• Some of the variation in value estimates may be due to

identifiable characteristics among the different studies themselves– valuation method, survey mode, geographic location,

time etc.• These characteristics are not explanatory variables in

the original studies as they are constant• Advantage of the method: it does not prejudge research

findings on the basis of the original study‘s quality (input) , while it avoids a differential subjective weighting of studies in the interpretation of findings (output side)

• However, the publication selection error still applies

Example: Meta-regression analysis

• Brouwer, Langford, Bateman and Turner (1999) is one of the early applications of meta-regression analysis

• Analysis covers 30 studies of the WTP per person for wetland preservation in temperate climate zones in developed economies in North America and Europe

• In total, 103 data points as some studies reported more than one observation (split sample)

• Wetlands were made comparable by looking at their types and functions: flood control, water supply, water purification, and nature/recreation

• Each observation was associated with one or more functions

Wetlands/Brouwer• Additional explanatory variables included

– payment vehicle (tax or other)– elicitation format (open-ended or other)– value type (use value, non-use value or both)– country (USA or Canada, Europe)

• Quality was measured by response rate, but as an explanatory variable

• All studies are CVM studies• In the regression analysis wetland size and

income were excluded due to data availability

Regression results

Parameter Estimate Standard error

Payment vehicle (tax) 1.576*** 0.362

Elicitation (open-ended) -0.376* 0.183

Country (North America) 1.629*** 0.363

Response rate 30-50% -1.722*** 0.451

Response rate >50% -1.461** 0.450

Flood control 1.134* 0.456

Water supply 0.441 0.479

Purification 0.659* 0.327

*** significant at 0.001; ** significant at 0.01; * significant at 0.05; R2 0.38

GLS model specification

Wetlands/Brouwer (2)• On average, people are willing to pay

$93/person/year for wetland preservation• Note that the median is only $51• Taxes attract higher contributions• Open ended questions lead to smaller

answers• North Americans are willing to pay more• Higher response rates imply lower values• Less differences between functionality of

wetlands

Conclusion• Benefit transfer can perform no better than the

quality of original studies• The underlying questions of accuracy and

appropriateness of non-market methods are not solved in benefit transfer

• However, some type of benefit estimates are subject to less controversy

• Benefit transfers are defensible as long as they are based on organised research agenda and seek to expand knowledge

• There‘s a great deal of pragmatism in policy-decision making – not all decisions require the same level of accuracy