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
Studies
Urban pollution valuation
Smith (1989), Smith and Huang (1993), Smith and Huang (1995),
Schwartz (1994), van den Bergh et al., (1997)
Recreation benefits
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)
Recreational fishing
Sturtevant et al. (1995)
Groundwater quality
Boyle et al., (1994); Poe et al., (2001)
Wetland functions
Brouwer et al., (1999), 1999); Woodward and Wui (2001)
Valuation of life estimates
Van den Bergh et al., (1997), Mrozek and Taylor (forthcoming);
Noise nuisance
Nelson (1980), Button (1995), van den Bergh et al., (1997)
Visibility Improvement
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
Value Transfers
Function Transfers
Loomis (1992)
4-39%
1-18%
Parsons and Kealy (1994)
4-34%
1-75%
Bergland et al (1995)
25-45%
18-41%
Kirchoff et al (1997)
35-69%
2-210%
Brouwer and Spanks
(1999)
27-36%
22-40%
Vanderberg et al (2001)
 Individual sites
 Pooled sites


 0-298%
 1-56%
Rosenberger and Phipps
(2001)
4-490%
1-239%
0-105%
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