Benefit transfer is the use of pre-existing empirical estimates from one or more settings where research has been conducted previously to predict measures of economic value or related information for other settings. These transfers offer a feasible means to provide information on economic values when time, funding and other constraints impede the use of original valuation studies. The methods used for applied benefit transfers vary widely, however, and it is not always clear why certain procedures were applied or whether alternatives might have led to more credible estimates. Motivated by the importance of benefit transfers for decision-making and the lack of consensus guidance for applied practice, this article provides recommendations for the conduct of valid and reliable transfers, based on the insight from the combined body of benefit transfer research. The primary objectives are to: (a) advance and inform benefit-transfer applications that inform decision making, (b) encourage consensus over key dimensions of best practice for these applications, and (c) focus future research on areas requiring further advances. In doing so, we acknowledge the healthy tension that can exist between best practice as led by the academic literature and practical constraints of real-world applications.
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https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NRMRL&dirEntryId=340067, accessed April 8, 2020. Also see discussion by Newbold et al. (2018a).
For example, recent benefit transfers applied by the U.S. EPA to support regulatory impact analyses have used procedures ranging from scaled unit-value transfers (e.g., U.S. EPA 2011) to benefit-function transfers from meta-analysis (e.g., U.S. EPA 2010, 2012, 2015), with “surprisingly little discussion of the academic literature on benefit transfer” (Wheeler 2015, p. 111). Review of benefit transfer applications by other governmental and intergovernmental organizations reveals similar heterogeneity and opacity on why certain methods were applied.
The term “benefit(s) transfer” did not become common until the 1990s.
See Water Resources Research, Vol. 28, No. 3, 1992.
For additional discussion of the divergences between academic research and practitioner needs, see McComb et al. (2006), Loomis and Rosenberger (2006), Boyle et al. (2010), Johnston and Rosenberger (2010), Johnston et al. (2015c), Richardson et al. (2015), Rosenberger and Loomis (2017), and Johnston et al. (2018).
This terminology is used as shorthand only; the target for a benefit transfer need not be a new, unstudied (geospatial) site; it may be a new policy question at a site where a study has already been conducted.
Within the benefit transfer literature, a transfer is typically considered valid if it provides a statistically unbiased estimate of the true value at the policy site. Reliable transfers, in contrast, are associated with lower transfer errors or variances (Bishop and Boyle 2019; Rosenberger 2015). Both are elements of the accuracy of transfer estimates.
Updated guidance in US EPA (2014) includes similar acknowledgement of benefit transfer.
See https://ec.europa.eu/environment/water/water-framework/index_en.html, accessed April 12, 2020.
For example, Hines (1999) notes that “… to quantify the economic costs of (taxes, regulations, externalities, monopolistic practices, etc.) … it is standard practice … to use a small number of assumptions and selected elasticities to estimate areas of the relevant ‘Harberger triangles’” (p. 167). Other economic data transfers include replacement cost (De Groot, Wilson and Boumans, 2002) and resource (habitat) equivalency (Flores and Thacher, 2002). Multiple types of economic information transfers are used within national accounting efforts such as the UK Office for National Statistics extended GDP Household Satellite Account, which accounts for the contribution of unpaid household production activity (https://www.ons.gov.uk/economy/nationalaccounts/satelliteaccounts, accessed March 31, 2021). Beyond economics, engineers use “steam tables” to predict pressure and flow in power facilities, “weight-load” tables to predict weight holding capacity of floors and roofs, and the U.S. government publishes an actuarial table of the “probability of death.” Other examples include dose–response functions used by physical scientists to identify the health effects of changes in environmental quality (see Spash and Vatn 2006).
For example, a real estate appraisal is a type of economic transfer that has two important differences from the transfer of a typical neoclassical welfare estimate (such as consumer surplus). First, the appraisal is grounded in actual observed sales, not estimated sales prices. Second, the appraisal estimate can be validated by a subsequent sale of the subject property.
Another alternative is expert elicitation such as Delphi approaches. While these approaches have not been extensively investigated and are not directly based on affected peoples’ preferences, some recent studies suggest potential for benefit transfers applied to global public goods (Strand et al. 2017, Navrud and Strand 2018, Dugstad and Navrud 2019, Siikamaki et al. 2019).
Even if the direction of the bias is suspected the magnitude of bias can rarely be determined.
For example, the study-site population may be from a high-income majority group and the policy-site population is a low-income minority group, and the analyst may not be able to control for these differences in the transfer process. Alternatively, a meta-function might be specified as a linear function while the true underlying value relationship is nonlinear. A preference calibration transfer might assume a utility function that does not match policy-site population preferences. As a final example, values may differ between study and policy sites due to factors that are unobservable to the analyst, and hence remain uncorrected within the transfer.
As another example, some study sites used to estimate a meta-equation might provide estimates with upward biases while others provide estimates with downward biases. In such cases, the meta-equation coefficients could lead to a policy-site value estimate that is an overestimate or underestimate, with a bias that is likely not as large as the over- and under-estimation that occurs in some study-site estimates.
Although we frame the subsequent discussion primarily around the content validity of benefit-transfer procedures, it is also important to consider the content validity of the study-site value estimates that may be used to support the transfer. This consideration takes place as part of study-site information selection as discussed below.
Consider the example of convergent validity tests. When these tests are applied to benefit transfers, the study-site and policy-site studies are often designed simultaneously by the researchers and have identical features. This is not the case when an actual transfer is conducted, because no study is available at the policy site. This type of convergent-validity test therefore abstracts from reality. It is also possible to confirm convergent validity for two estimates that are equally biased. As a final example, the outcome of these tests can vary depending on whether a classical null hypothesis of equality is assumed or whether one begins instead with a presumption that prior value estimates are different (Muthke and Holm-Mueller 2004; Kristofersson and Navrud 2005). Thus, convergent-validity investigations are informative but additional evidence is needed to draw strong conclusions on validity.
As an illustration, discoveries made during the literature search may guide the selection of a transfer method—for example uncovering a large study-site literature that facilitates the estimation of a meta-equation to support computation of transfer estimates. Subsequent screening and coding may then lead the analyst to revisit and update the original methods used to search the literature or may encourage a rethinking of intended transfer method. For example, upon closer inspection and screening of the data, the analyst may determine that the previously discovered literature is too heterogeneous or poorly documented to support a meta-regression analysis. Such a determination can then prompt a return to earlier steps in the benefit transfer process.
Multiple spatial dimensions of the policy site and policy-site values can be important. For example, where will changes in the studied good or service occur relative to the individuals who might value those changes? How large is the geospatial area affected by environmental quality improvements? What is the relevant extent of the market for the analysis? What are the geospatial dimensions of relevant substitute and complements? Works such as Hanley et al. (2003), Bateman et al. (2006, 2011a, 2011b, 2013), Johnston and Duke (2009), Martin-Ortega et al. (2012), Perino et al. (2014), Schaafsma (2015), Kuminoff (2018), Artell et al. (2019), and Johnston et al. (2017a, 2019) discuss these and other issues related to spatial dimensions of benefit transfer.
See: https://www.epa.gov/sites/production/files/2017-09/documents/ee-0568-07.pdf, p. 7–46, accessed April 13, 2020.
For example, in some settings it might be defensible to use otherwise identical Hicksian and Marshallian estimates interchangeably within a benefit transfer (as they are for practical purposes in many applied studies), whereas in other cases the anticipated differences between these estimates might be large enough that such practices could jeopardize validity. See discussions in Londoño and Johnston (2012) and Johnston and Moeltner (2014).
Some original valuation studies estimate WTP when property rights suggest that a WTA measure is more applicable, due to the complexities involved in estimating WTA (e.g., Bishop et al. 2017).
Consider, for example, an otherwise identical increase in bowhead whale populations used for subsistence harvest by Native communities in the Arctic, compared to the same biophysical population change used for recreational whale watching. Even though the biophysical change might be identical (e.g., an increase in X whales within a population stock), these changes influence welfare through fundamentally different channels, and would therefore be unlikely to have sufficient consistency to support valid benefit transfer.
Relevant contextual considerations will vary across settings and the types of values considered. As an example, the value of otherwise similar environmental quality improvements may vary depending on whether public access is permitted to improved areas, so that legal access provisions can represent an important socioeconomic dimension of similarity in some cases (Olander et al. 2018). Another illustrative example is that the value of a riparian buffer for treatment of water pollution depends on the extent to which that buffer is located downstream of pollution sources—a purely biophysical characteristic that is central to the values provided by a given buffer area. Biophysical features such as these can also represent relevant contextual dimensions of site similarity for benefit transfers (Simpson 2016).
Section 4.9 in Johnston et al. (2018) reviews past findings on international benefit transfers.
Some papers conclude that “international benefit transfer is as valid as intra-country transfer” (Ready and Navrud 2006, p. 434), although there does not appear to be consensus on this issue.
For example, study sites may be excluded because of insufficient correspondence to the policy site across one or more potentially important dimensions, because study documentation lacks information on key variables involved in the transfer (Moeltner et al. 2007), because the valuation approach is inconsistent with established practices (Newbold et al. 2018a), or due to other considerations that may render a study unsuitable for a benefit transfer application.
For example, this work seems to suggest that transfers of contingent valuation estimates are more accurate than transfers of other types of value information.
For example, accurate characterization and modeling of the biophysical dimensions of the environmental good(s) or service(s) to be valued are important dimensions of validity for many types of valuation studies, beyond economic methods that are applied (Mendelsohn and Olmstead 2009; Bateman et al. 2011b; Johnston et al. 2012; Schultz et al. 2012; Boyd et al. 2016; Simpson 2016).
https://sweep.ac.uk/portfolios/natural-environment-valuation-online-tool-nevo/, accessed April 1, 2021.
https://naturalcapitalproject.stanford.edu/software/invest, accessed April 1, 2021.
For example, InVEST provides online documentation which may be used to evaluate the extent to which economic components rely on best practices applicable to specific benefit-transfer applications. See https://naturalcapitalproject.stanford.edu/software/invest, accessed April 1, 2021. Similar documentation for NEVO is available at https://www.leep.exeter.ac.uk/nevo/documentation/, accessed May 20, 2021.
The benefit transfer literature frequently overlooks the distinction between estimating a new meta-regression analysis to support a benefit transfer and using an existing meta-analysis. There are important differences between these two types of benefit transfer. For example, the latter does not require the compilation of new metadata but does require that the properties of the already-existing meta-analysis be evaluated with respect to policy-site conditions and benefit-transfer needs. In addition, the quality of meta-regression models and suitability for value prediction vary (Boyle and Wooldridge 2018).
For example, when estimating a single benefit function using primary data from multiple study sites, one assumes implicitly that the function can provide a valid representation of conditions and behavior across all sites. Similar assumptions are implied when estimating and using meta-functions.
For example, an initial data search may suggest the availability of data suitable for development of a meta-regression analysis. Once an initial decision is made to pursue this type of transfer, additional data collection may be required to complete the metadata, especially in terms of regressors specified in the meta-equation, necessitating a return to data search and review of coding procedures. If sufficient study-site meta-data are not ultimately available to estimate a meta-function, then the analyst must revisit the transfer-method decision—for example, considering a value- or single-site function transfer instead.
For example, water quality valuation studies have used many different types of water quality measures when estimating values, including different types of water quality indices or ladders (Johnston et al. 2005, 2017a, 2019; Van Houtven et al. 2007; Walsh and Wheeler 2013). As described by Johnston and Bauer (2020), various steps are typically required to reconcile water-quality measurements available for the policy site with the measures used to qualify water quality within valuation studies or meta-analyses. Similar steps are required when comparing values across studies, for example as part of a meta-regression analysis.
For example, some studies might report value estimates per household whereas others might provide estimates per person for a common unit of change but for different years.
For example, transformations between values per household and values per person require information on the number of persons per household, along with assumptions on how to consider adults versus children when making this transformation.
For example, structural benefit transfer methods can include mechanisms to calibrate between otherwise identical Marshallian and Hicksian welfare measures. Meta-regression analyses often include moderator variables that identify inconsistencies across included value measures (e.g., values measured as recurring annual payments versus lump-sum payments), allowing the model to estimate systematic differences associated with different measurement units. Model-based procedures of this type can at least partially reduce the need for some types of data adjustments.
For example, a study-site benefit function might incorporate water quality change measured via a standardized water quality index (Walsh and Wheeler 2013), whereas quality data at the policy site might be available in other units (e.g., changes in Secchi depth).
See, e.g., https://www3.epa.gov/airnow/aqi_brochure_02_14.pdf, accessed April 19, 2020.
The literature provides insight into the relevance of concepts such as these for benefit transfer (e.g., Bateman et al. 2006, 2011a; Brouwer 2006; Zandersen et al. 2007; Rolfe and Windle 2008; Rosenberger and Johnston 2009; Boyle et al. 2010; Johnston and Rosenberger 2010; Bliem and Getzner 2012; Fetene et al. 2014; Schaafsma et al. 2014; Schaafsma 2015; Lew and Wallmo 2017; Loureiro and Loomis 2017; Johnston et al. 2017a, 2018; He and Poe 2020).
As a case in point, choice experiments often produce linear utility functions based on an implied assumption that considered changes are sufficiently close to the margin. Analysts should consider this issue when determining whether a choice-experiment benefit function is suitable for policy-site transfers, but this type of linearity should not be used as a prima facie litmus test to automatically exclude such functions (Carson et al. 2015).
As described by Boyle and Wooldridge (2018), econometric requirements for meta-analyses used to predict values can differ from similar models used to synthesize and characterize a literature. For example, the former implies greater attention to consistent measurement of the dependent variable, the importance of specifications that allow for potential nonlinearities, and can imply that different regression techniques should be used (e.g., OLS is generally preferred to WLS when the goal is value prediction).
Older reviews are provided by Brouwer and Spaninks (1999) and Brouwer (2000), among others. Also see Boyle et al. (2010). Recent evidence suggests that median transfer errors are in the general vicinity of 35–45%, with Kaul et al. (2013) reporting a median error of 39% and Rosenberger (2015) finding median errors of 36% for function transfers and 45% for value transfers. These errors vary widely over different applications.
For example, within recreation demand modeling, the use of on-site versus general population sampling, together with the type of model that was estimated, influence sample representativeness and the ability to aggregate estimates in various ways (Lupi et al. 2020).
For example, within a hedonic property value study, do homes that sold during the studied period represent all homes within the studied area? Within a stated preference study, do survey respondents represent the studied population?
We use the term “quantities” in a general sense to reflect the scope or magnitude of change in environmental conditions, realizing that these changes can represent environmental qualities (e.g., water quality) or quantities (e.g., hectares of forest).
For example, some types of value transfers may not permit one to calculate expected error bounds, if insufficient study-site reporting is available.
The issue is also discussed by Newbold et al. (2018a).
These issues can be addressed by evaluating the value of information (VOI) provided by a benefit transfer. Existing VOI studies suggest that the use of original valuation studies to inform environmental decisions is often warranted, but that benefit transfers nonetheless enhance the efficiency of environmental decisions (Barton 2007; Allen and Loomis 2008; Newbold and Johnston 2020).
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We express our thanks to Ian Bateman and two anonymous reviewers for helpful suggestions and to Lu Ann Pacenka for technical editing assistance. Ståle Navrud acknowledges support by the Research Council of Norway; projects no. 280393 (Valuechange), 255777 (Coast-benefit), and 267834 (MarES). Any remaining errors or omissions are the sole responsibility of the authors.
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Johnston, R.J., Boyle, K.J., Loureiro, M.L. et al. Guidance to Enhance the Validity and Credibility of Environmental Benefit Transfers. Environ Resource Econ 79, 575–624 (2021). https://doi.org/10.1007/s10640-021-00574-w