Benefit Transfer: Past, Present and Future

Professor Mark Morrison Charles Sturt University

What is benefit transfer

Benefit transfer is defined as the transfer of existing estimates of nonmarket values to a new study which different from the study for which the values were originally estimated. In essence, this is simply the application of secondary data to a new policy issue.

Boyle and Bergstrom (1992)

In the beginning…

• Earliest transfers were of valuations derived using expert opinions

 eg “unit day values” for recreation were developed in 1962 for evaluating water resource developments

Early transfers involving RP studies

• Series of studies in the US in early 1970s in which benefit estimates from travel cost models were extrapolated  Burt and Brewer (1971), Brown and Hansen (1974) Cicchetti et al. (1976) and Dwyer et al. (1977) • Benefit transfer of hedonic pricing estimates increased with the publication of Nelson’s (1980, 1982) summary studies on the value of road and aircraft noise • Reflected acceptance of RP approaches

Transfer of CVM estimates in the 1990s

• Benefit transfer of stated preference estimates began in the 1990s  Desvousges, Naughton and Parsons (1992) and Luken, Johnson and Kibler (1992) used existing CVM studies to infer the value of tightening water quality regulations for the pulp and paper industry  Dumsday, Jakobsson and Ransome (1991) used the results from a number of SP studies to infer the value of protecting river segments in Victoria

1992 Workhop on the Use of Benefit Transfer

• 1992 Workshop on the Use of Benefit Transfer, special issue in Water Resources Research.

Outcome 1: Protocols

Protocols were suggested for selecting studies for use in benefit transfer (Freeman 1984, Boyle and Bergstrom 1992, Desvouges, Naughton and Parsons 1992, Smith 1992): 1. Study is methodologically sound 2. The change in environmental quality at the study and policy sites are similar 3. Study contains regression results that are a function of sociodemographic characteristics 4. The study and policy sites are similar 5. The markets for the sites are similar (substitute sites, geographic extent of the market)

Outcome 2: Use more refined transfers

2.

In the special issue, more sophisticated approaches for benefit transfer demonstrated  Meta-analysis: a large number of value estimates are collected and an “econometric review” is conducted to identify what influences the value across these studies. WTP = f(characteristics of the site/population, methodology)  Meta-equation can be used to generate value estimates for benefit transfer

Outcome 2: Use more refined transfers

• Spawned a huge growth in the use of meta-analysis for benefit transfer (Bateman and Jones 2001)

Subject area

Urban pollution valuation Recreation benefits Recreational fishing Groundwater quality Wetland functions

Studies

Smith (1989), Smith and Huang (1993), Smith and Huang (1995), Schwartz (1994), van den Bergh

et al.,

(1997) Markowski,

et al

., (2001); Rosenberger and Loomis (2000); Shrestha and Loomis (2001);Bateman

et al.,

(1999), Smith and Kaoru (1990a), Walsh

et al

. (1989, 1992) Sturtevant

et al

. (1995) Boyle

et al

., (1994); Poe

et al

., (2001) Brouwer

et al.,

(1999), 1999); Woodward and Wui (2001) Valuation of life estimates Noise nuisance Visibility Improvement Van den Bergh

et al.,

(1997), Mrozek and Taylor (forthcoming); Nelson (1980), Button (1995), van den Bergh

et al.,

(1997) Smith and Osborne (1996), Desvousges

et al

., (1998);

Outcome 2: Use more refined transfers

• Benefit function transfer – the whole demand function (regression equation) is transferred rather than a mean value (eg Loomis 1992) • Benefit estimates are often a complex function of the site and user characteristics -- benefit function transfer can directly account for (one or both of) these by using the relationship between user/site characteristics and the benefit estimate.

• WTP = f(sociodemographics, site characteristics)

Outcome 2: Use more refined transfers

Loomis (1992, p.701) These researchers proposed that a zonal travel cost method demand function be estimated for the existing sites that were similar to the new proposed site.

Then, the values of the independent variables in the existing demand equation for own price, substitute prices, income, etc., would be replaced by values for the new proposed site.

Multiplying the existing site coefficients by the new site’s values of the independent variables would give a reasonable estimate of both the use and benefits at the new site.

Range of errors across mean value and function transfers

Study

Loomis (1992) Parsons and Kealy (1994) Bergland et al (1995) Kirchoff et al (1997)

Value Transfers

4-39% 4-34% 25-45% 35-69%

Function Transfers

1-18% 1-75% 18-41% 2-210% Brouwer and Spanks (1999) 27-36% 22-40% Vanderberg et al (2001)  Individual sites  Pooled sites   1-239% 0-105%   0-298% 1-56% Rosenberger and Phipps (2001) 4-490% 2-62%

Outcome 3: Need for validity testing

We could…compare the benefit transfer values for the policy site…with the value estimates for the policy site from primary data… If benefit transfer estimates are not statistically different from the primary data value estimates developed at the policy site, convergent validity is established. When benefit transfer estimates are biased, these concurrent evaluations can examine the size of the bias, direction of the bias and adjustments that might be made in study site estimates to mitigate the bias. Validity investigations ultimately will identify conditions where benefit transfer works and procedures necessary to make benefit transfer operational (Boyle and Bergstrom 1992, p.661).

The Rise of the Databases

• 1995 Launch of the ENVALUE database. Followed a couple of years later by the EVRI database and others • Provided researchers, government officers and consultants with much greater access to primary studies

Contingent valuation and benefit transfer

• While the evidence regarding RP transfers was generally more encouraging (eg Loomis 1992), in the 1990s evidence emerged that transfers involving CVM estimates were not statistically valid, even when benefit functions were transferred (Bergland, Magnussen and Navrud 1995, Downing and Ozuna 1996, Kirchhoff, Colby, and LaFrance 1997) • Bergland et al (1995) suggested that this was in part because CVM estimates did not allow for differences in the change in environmental quality across sites

The rise of multi-attribute stated preference techniques based on random utility

• A significant development (1974) earning Daniel McFadden a Nobel Prize • Based on Lancastrian demand theory – a good is decomposed into attributes  Utility of an alternative depends on its attributes • Implications:  easy to value marginal changes in a good  These marginal values are likely to be more transferable because you are comparing apples with apples

The rise of multi-attribute stated preference techniques

• Couple of early but “hidden” BT applications using RUMs and RP data  Atherton and Ben-Akiva (1976) estimated MNL models in the context of travel choice and tested the transferability of their results  Atkinson, Crocker and Shogren (1992) tested benefit transfer using RUM models and travel cost data with Bayesian updating

Choice Modelling, Non-Use Values and Benefit Transfer

• Morrison and Bennett recognised the potential for using choice modelling for benefit transfer when estimating non-use values • Stimulated by the presence of Jordan Louviere in Sydney, an international expert in choice modelling who introduced choice modelling to the environmental valuation literature in 1993 in the context of use values • Louviere and Richard Carson ran a workshop in 1994 at Sydney University on SP techniques, including choice modelling

Choice Modelling, Non-Use Values and Benefit Transfer

• Morrison and Bennett received funding from Land and Water Australia, NSW EPA and NSW NPWS to test the validity of using choice modelling for estimating non-use values – focus of Morrison’s PhD (1996-1998) • Focus on two wetlands (Macquarie Marshes and Gwydir Wetlands) • Sampling in two locations (Sydney and Moree)

Choice Modelling, Non-Use Values and Benefit Transfer

• Two main tests conducted:   Validity across sites given the same population Validity across populations given the same site • Results were encouraging. Only one out of four implicit prices statistically different in both tests. Results published in American Journal of Agricultural Economics.

• At last count, 12 subsequent studies have been conducted that have tested a range of different transfer types (Morrison and Bergland 2006, Ecological Economics)

Types of Benefit Transfer Tests

Type 1: Across Population Transfers

• Eight studies • Four types: 1. regional centres vs state capitals 2. populations with similar relationships to the study site eg different regional or urban centres 3. state capital vs all other areas in state 4. within study area vs outside of study area

• Testing supportive of 1-3 but not 4

Type 2: Transfers Across Sites

• Two studies by Morrison et al (2002) and Rolfe et al (2006) • Both studies found that 3 out 4 implicit prices were equivalent • Surplus estimates were found to be equivalent in 25% of cases tested by Morrison et al and 100% of cases tested by Rolfe et al (2006)

Type 3: Transfers Across Sites and Equivalent Populations

• Seven studies with mixed results • One study found strong evidence of convergence (Colombo et al 2006), three studies found mixed evidence (Yiang et al 2005, Hanley et al 2006a, Van Bueren and Bennett 2004) and three studies found little evidence of convergence (Christie et al 2004, Morrison and Bennett 2004, Hanley et al 2006b) • Overall (1) less evidence of convergence and (2) the results suggest that as the populations and sites sample become more different, value estimates are less likely to be equivalent

Summary of the choice modelling BT Literature

• Choice modelling based benefit transfer an improvement on the use of contingent valuation • Doesn’t adjust for everything  eg differences in the base level of environmental quality, population differences not picked up by sociodemographics • The results indicate that as sites and populations become more different, value estimates are less likely to be equivalent • Question: how can we further modify value estimates so that they pick up these other site and population differences?

Option 1: Meta-analysis

• Is widely used for travel cost, hedonic pricing and contingent valuation estimates • Problem: difficult to use for multi-attribute techniques because most studies use different attributes and different levels

Option 2: Pooled BT Models

• One application by Morrison and Bennett (2004) • Collect a large amount of data across multiple sites and multiple populations, estimate a pooled model that includes dummy variables for site and population specific characteristics ie  WTP = f(attributes, sociodemographics, site characteristics, population characteristics) • Problem: data intensive and requires sub-samples that vary across the population and site characteristics that you wish to adjust for in future benefit transfers

Option 3: Bayesian Benefit Transfer

• Proposed by Atkinson et al (1992) but has only generated considerable interest over the past few years • Bayesian inference involves first developing prior information about a set of model parameters (and hence value estimates) based on previous studies. • This prior information is combined with new information from a small survey which is used to form a posterior distribution which is then used for value estimation • Studies by Leon et al (2002) and Lehr (2005) demonstrated that combining a prior with a small sample resulted in very precise benefit transfer

Option 3: Bayesian Benefit Transfer

• Researchers are currently working on operationalising BBT for choice modelling • Potential to greatly reduce sampling costs for larger projects where multiple valuation estimates are needed • Fund one large study (or use an existing one), then just collect small samples in areas of interest and use the Bayesian approach to develop value estimates for these areas

Concluding comments

• The equivalence found in a number of across population and across site tests has important implications for research design • Lack of equivalence in some tests is an encouraging finding!

• Little meta-analysis completed in Australia – potential opportunity • Bayesian Benefit Transfer has huge potential – but technically demanding • Lack of systematic thinking and planning about what studies are needed in Australia – case for a more carefully developed research agenda