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2017 | OriginalPaper | Chapter

10. Experimental Methods in Valuation

Author : Craig E. Landry

Published in: A Primer on Nonmarket Valuation

Publisher: Springer Netherlands

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Abstract

This chapter discusses the role of behavioral experiments in evaluation of individual economic values. Principles of experimental design play a role in application and assessment of non-market valuation methods. Experiments can be employed to assess the formation of preferences and the role of personal characteristics, social factors, and economic constraints on economic values. Experiments can be used to test the efficacy of nonmarket valuation methods and to study the effect of the valuation task, information, and context on valuation responses. We discuss these issues in turn, incorporating pertinent literature, to provide a review and synthesis of experimental methods in valuation.

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previous chapter Substitution Methods

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Chamberlain taught economics at Harvard from the 1930s to the 1960s, where he conducted what might have been the first economic experiments, initially as classroom exercises, to demonstrate the formation of prices. See Roth (1995) for more about the history of experimental economics.

Examples include the use of field experiments to value water filters in rural Ghana (Berry et al. 2015) and bednets for protection from insects in Uganda (Hoffman 2009; Hoffman et al. 2009).

See Davis and Holt (1993, Chap. 1), for more details.

A robust experimental design that employs within-subject comparisons will also provide for between-subject comparisons with a changing of the ordering of stimuli. For example, consider an experiment employing two sets of stimuli, A and B, presented in treatments that switch the order of the stimuli sets. Thus we have the following treatments: 1.A-B stimuli ordering and 2.B-A stimuli ordering, which allow for: (i) within comparisons—1.A-B and 2.B-A; (ii) testing of order effects—1.A versus 2.A, and 1.B versus 2.B; and (iii) between comparisons—1.A versus 2.B and 2.A versus 1.B. Under standard microeconomic arguments, the final comparison (iii) is generally presumed valid in the presence of ordering effects (in which 1.A (1.B) is judged to be different from 2.A (2.B)).

Some experiments that use group interactions or collective decision-making can entail some loss of control in evolution of interpersonal group dynamics, which can introduce an additional dimension of heterogeneity into treatment effects (Duflo et al. 2007). If group, firm, or village interactions are an integral part of the study, clustered experimental designs can be used (Bloom 1995; Spybrook et al. 2006).

For example, if α = 0.05 and κ = 0.8, then z _α = Φ⁻¹ (0.05) = −1.645 and z _1−κ = Φ⁻¹ (0.20) = −0.842, so that |z _α + z _1−κ| ≅ 2.487.

This discussion closely follows Duflo et al. (2007).

If control and treatments are applied to grouped data (e.g., villages in a developing country), power analysis must take account of intragroup correlation. See Duflo et al. (2007, pp. 31-33) for details.

Equations (10.3)-(10.5) can be adjusted for unequal variances by employing the weighted-average variance estimate in Eq. (10.7).

“Money illusion” usually refers to a tendency to treat money in nominal rather than real terms, but here it refers more generally to any behavior that does not treat money as actual currency.

For example, a two-stage procedure can be employed to induce risk neutrality by (i) allowing subjects to earn points (n out of a total possible N) in round 1, and (ii) mapping the points into a binomial utility function in round 2, in which the probability of winning a large sum of money (or prize) is n/N and the probability of winning less (or no money, or a small prize) is (1 − n)/N. (For more details, see Davis and Holt, 1993).

Such flexibility is typically absent from revealed-preference studies. For example, resource quality might not have adequate variability in a revealed-preference hedonic price or recreation demand dataset, or it might be correlated with one or more existing factors in the study. Of course, the potential downside of stated preference is that the scenarios are usually hypothetical and could lack realism or plausibility.

Revealed-preference studies can also observe the same sets of behaviors under different conditions, but the conditions are typically not controlled by the researcher, or the level of control is considerably lower.

The collection of planned stated preference behavior under current conditions is a method proposed by Whitehead et al. (2000). The stated reference baseline trips may suffer from hypothetical bias (optimistic assessment of future trips). Since recreation conditions are held constant in the stated preference baseline treatment, the researcher can use the stated preference data under current conditions to control for hypothetical bias or any other expected change in household conditions (expected increase in income, reduced opportunity cost of time). Measuring stated preference demand under baseline conditions before proceeding to changing conditions represents careful experimental design that avoids changing multiple factors (data type and conditions) within a single treatment (Whitehead et al.).

Landry and Liu (2011) provided an overview of econometric models for analyzing these types of data.

The coherent-but-arbitrary perspective is similar to constructed preferences (Slovic 1995; Kahneman 1996), which models preferences as not prior to, but rather constructed during the decision-making process. These models have not been embraced by economists because they are antithetical to axiomatic preference modeling that is the cornerstone of microeconomic theory and welfare analysis (Braga and Starmer 2005).

Corrigan et al. (2012) noted some of the controversies surrounding repeated auctions with price feedback. While this design allows for learning about preferences and auction institutions, it can engender value interdependence, anchoring on posted prices, detachment from the auction, and competition among participants.

To minimize attrition, Shogren et al. (2000) provided an additional incentive payment to subjects who participated in all sessions.

Consistent with the idea of value learning, Holmes and Boyle (2005) and Kingsley and Brown (2010) found that the variability of the error component of the random utility model decreased with the number of choices a subject made; subjects appeared to better discriminate among multiattribute choice sets as they gained experience with evaluating and selecting their preferred option (DeShazo and Fermo 2002). Among an extensive series of pairwise choices, Kingsley and Brown also found that the probability of an inconsistent choice decreased with choice experience.

A number of researchers have produced experimental findings in stated preference analysis that are indicative of anchoring effects (e.g., Boyle et al. 1985; Holmes and Kramer 1995; Green et al. 1998).

Hypothetical bias can be distinguished from strategic bias, which stems from perceived or actual perverse incentives in stated preference protocol (a fundamental reversal of incentive compatibility). Such perverse incentives for subject response can result from poor protocol design (in which subjects’ optimal response is an untruthful statement), subject misperceptions that lead them to respond strategically to an otherwise well-designed protocol, or other artifacts of stated preference design that could lead to inadvertent strategic response.

In addition, two provision prices—high and low—were evaluated, with sequencing of the prices varied randomly among treatments. The rationale for two prices was threefold: to provide more information on preferences, to increase the domain for inference of willingness to pay, and to test for preferences consistent with the theory of demand. For treatments that involved actual provision, a coin flip determined which price level would be executed.

The language of the cheap talk script followed the original text in Cummings and Taylor (1999) with necessary changes for the nature of the good and provision mechanism.

Potential consequences stemming from survey responses have been shown to produce referendum voting patterns that accord with actual voting patterns in both the lab (Cummings and Taylor 1998; Vossler and Evans 2009) and field (List et al. 2004).

The ticket stubs, dated October 12, 1997, corresponded with the game in which Barry Sanders passed Jim Brown for the No. 2 spot in NFL all-time rushing yardage. One of the study’s authors collected the ticket stubs after the game.

While this is a clever way to simulate a public good, there could be issues creating divergence between these quasi-public goods and more typically public goods in the field. In particular, all potential beneficiaries were clearly present in the framed field experiment and the scope of the public good (in terms of both payment and provision) is clearly defined. The authors are unaware of any research that has explored the relevance of these dimensions in the context of mitigation hypothetical bias.

Field surveys that involve choice experiments often implore respondents to treat choice sets as if they are independent. In practice, however, it is often unclear whether these exhortations are effective.

The six treatments allowed for between tests of price and quantity effects (more commodity at the same price, and same commodity at different prices), as well as within tests of consistency in individual choices; these tests are based on neoclassical theory.

Day and Prades (2010) employed stated preference methods. Thus, they were unable to compare their results with behavior in which actual payment or provision occurs. Moreover, their commodity is a private good, unlike the commodity valued by Vossler et al. (2012). Obviously, the role of independence across choice sets, provision rules, and nature of the commodity deserves further exploration.

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Title: Experimental Methods in Valuation
Author: Craig E. Landry
Publisher: Springer Netherlands
Book: A Primer on Nonmarket Valuation
Print ISBN: 978-94-007-7103-1

Electronic ISBN: 978-94-007-7104-8

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-94-007-7104-8_10

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