A Split-Sample Revealed and Stated Preference Demand Model to Examine Homogenous Subgroup Consumer Behavior Responses to Information and Food Safety Technology Treatments

The combination and joint estimation of revealed and stated preference (RP/SP) data approach to examining consumer preferences to relevant policy-based measures typically fail to account for heterogeneity in the data by considering behavior of the average individual. However, in policy-based analyses, where the research is often driven by understanding how different individuals react to different or similar scenarios, a preferred approach would be to analyze preferences of homogenous population subgroups. We accomplish this by developing a split-sample RP/SP analysis that examines whether homogenous subgroups of the population, based on individual health and behavioral characteristics, respond differently to health-risk information and new food safety technology. The ongoing efforts by the US Food and Drug Administration (FDA) to reduce illness and death associated with consuming raw Gulf of Mexico oysters provide an ideal platform for the analysis as the health risks only relate to a very specific consumer subgroup. Results from split-sample demand models indicate that educational information treatments cause vulnerable at-risk consumers to reduce their oyster demand, implying that a more structured approach to disseminating the brochures to the at-risk population could have the desired result of reducing annual illness levels. Also, findings across all subgroups provide strong empirical evidence that the new FDA policy requiring processing technology to be used in oyster production will have a detrimental effect on the oyster industry.     

Combining Revealed Preference Data with Stated Preference Data: A Latent Class Approach

A substantial literature exists combining data from revealed preference (RP) and stated preference (SP) sources, aimed either at testing for the convergent validity of the two approaches used in nonmarket valuation or as a means of drawing on their relative strengths to improve the ultimate estimates of value. In doing so, it is assumed that convergence of the two elicitation approaches is an “all or nothing” proposition; i.e., the RP and SP data are either consistent with each other or they are not. The purpose of this paper is to propose an alternative framework that allows for possible divergence among individuals in terms the consistency between their RP and SP responses. In particular, we suggest the use of a latent class approach to segment the population into two groups. The first group has RP and SP responses that are internally consistent, while the remaining group exhibits some form of inconsistent preferences. An EM algorithm is employed in an empirical application that draws on the Alberta and Saskatchewan moose hunting data sets used in earlier combined RP and SP exercises. The empirical results suggest that somewhere between one-third and one-half the sample exhibits consistent preferences. We also examine differences in welfare estimates drawn from the two classes.     

Willingness to Pay and Sensitivity to Time Framing: A Theoretical Analysis and an Application on Car Safety

Stated preference (SP) surveys attempt to obtain monetary values for non-market goods that reflect individuals’ “true” preferences. Numerous empirical studies suggest that monetary values from SP studies are sensitive to survey design and so may not reflect respondents’ true preferences. This study examines the effect of time framing on respondents’ willingness to pay (WTP) for car safety. We explore how WTP per unit risk reduction depends on the time period over which respondents pay and face reduced risk in a theoretical model and by using data from a Swedish contingent valuation survey. Our theoretical model predicts the effect to be nontrivial in many scenarios used in empirical applications. In our empirical analysis we examine the sensitivity of WTP to an annual and a monthly scenario. Our theoretical model predicts the effect from the time framing to be negligible, but the empirical estimates from the annual scenario are about 70 % higher than estimates from the monthly scenario.     

Heterogeneous Preferences for Community Recycling Programs

This paper examines household preferences for community recycling programs, which have both public and private good dimensions. The data come from a survey conducted in Seattle (WA) which elicited stated preference-contingent ratings for different recycling programs relative to status quo, with experimental variation in the overall community recycling rate achieved and expected household cost. The recycling rate is interpreted as capturing the public benefits of recycling programs and constitutes the most common measure used by policy agencies for setting waste management objectives and evaluating policy initiatives. The analysis begins with fixed parameter models that explore different ways of handling rating data and mixed logit estimations that capture household preference heterogeneity both within and across different recycling programs. This analysis yields unique estimates of willingness to pay for an increase in the community recycling rate. Predicted individual-specific utility parameters are then regressed on household background information to explicitly examine the nature of preference heterogeneity. Overall, this paper generates interpretable policy-relevant insights into the public and private good dimensions of community recycling programs.     

Monopolists and viscous demand

We characterize the optimal dynamic price policy of a monopolist who faces “viscous” demand for its services. Demand is viscous if it adjusts relatively slowly to price changes. We show that with the optimal policy the monopolist stops short of achieving 100% market penetration, even when all of the consumers have the same long-run willingness to pay for the service. Furthermore, for certain parameter values in the model, the price policy requires rapid oscillations of the price path.     

Are Internet surveys an alternative to face-to-face interviews in contingent valuation?

Internet is an increasingly popular data collection mode for stated preference research in environmental economics. However, little is known about how this survey mode may influence data quality and welfare estimates. As part of a national contingent valuation (CV) survey estimating willingness to pay (WTP) for biodiversity protection plans, we assign two groups of respondents either to an Internet or face-to-face (in-home) interview mode. Our design aims to better isolate measurement effects from sample composition effects by drawing both samples from the same sample frame. We find little evidence of social desirability bias in the interview setting or satisficing (shortcutting the response process) in the Internet survey. The share of “don't knows”, zeros and protest responses to the WTP question with a payment card is very similar between modes and equality of mean WTP cannot be rejected. Results are fairly encouraging for the use of Internet in CV as stated preferences do not seem to be significantly different or biased compared to face-to-face interviews.     

Stochastic dominance and behavior towards risk: The market for Internet stocks

Internet stocks registered large gains in the late 1990s, followed by large losses from early 2000. Using stochastic dominance theory, we infer how investor risk preferences have changed over this cycle, and relate our findings to utility theory and behavioral finance. Our major findings are as follows. First, risk averters and risk seekers show a distinct difference in preference for Internet versus “old economy” stocks. This difference is most evident during the bull market period (1998–2000) where Internet stocks stochastically dominate old economy stocks for risk seekers but not risk averters. In the bear market, risk averters show an increased preference for old economy stocks, while risk seekers show a reduced preference for Internet stocks. These results are inconsistent with prospect theory and indicate that investors exhibit reverse S-shaped utility functions.     

Globally evolutionarily stable portfolio rules

The paper examines a dynamic model of a financial market with endogenous asset prices determined by short-run equilibrium of supply and demand. Assets pay dividends that are partially consumed and partially reinvested. The traders use fixed-mix investment strategies (portfolio rules), distributing their wealth between assets in fixed proportions. Our main goal is to identify globally evolutionarily stable strategies, allowing an investor to “survive,” i.e., to accumulate in the long run a positive share of market wealth, regardless of the initial state of the market. It is shown that there is a unique portfolio rule with this property—an analogue of the famous Kelly rule of “betting your beliefs.” A game theoretic interpretation of this result is given.     

Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment

Different approaches to modelling the distribution of WTP are compared using stated preference data on Tanzanian Clinical Officers’ job choices and mixed logit models. The standard approach of specifying the distributions of the coefficients and deriving WTP as the ratio of two coefficients (estimation in preference space) is compared to specifying the distributions for WTP directly at the estimation stage (estimation in WTP space). The models in preference space fit the data better than the corresponding models in WTP space although the difference between the best fitting models in the two estimation regimes is minimal. Moreover, the willingness to pay estimates derived from the preference space models turn out to be very high for many of the job attributes. The results suggest that sensitivity testing using a variety of model specifications, including estimation in WTP space, is recommended when using mixed logit models to estimate willingness to pay distributions.     

Temporal stability of preferences and willingness to pay for natural areas in choice experiments: A test–retest

The main objective of this paper is to test the temporal stability of stated preferences and willingness to pay (WTP) values from a Choice Experiment (CE) in a test–retest. The same group of participants was asked the same choice tasks in an internet-based CE, conducted twice with a time interval of one year without interviewer interference. We examine choice consistency at individual choice task level and transferability of the underlying indirect utility function and associated WTP values. The results show that choices are consistent in 57 percent of the choice occasions. Comparison of the choice models over time shows that the estimated preference and scale parameters are significantly different, suggesting that choice behaviour changed between the two surveys. Differences between marginal WTP estimates for individual choice attributes are statistically significant only at the 10 percent level. However, we show that this can result in significantly different WTP values for policy scenarios. The instability of estimated mean WTP values for different policy scenarios asks for caution when including WTP values in cost-benefit analysis.     

Position-Dependent order effects on the prediction of consumer preferences in repeated choice experiments

Using stated choice data collected by experimental design with repeated choice tasks, this study developed an approach to quantify the position-dependent order effects on the prediction of preferences and marginal willingness to pay for product attributes. Results showed that repeated choice tasks allow learning to occur. Models with order effect adjustments showed significant improvements in goodness of fit. Attribute-specific polynomial trends showed the best fit among all models, which could possibly be explained by respondents’ familiarity and sensitivity to different product attributes. Repeated-choice experiments have a good potential to capture consumer preferences more accurately than the single-choice design. But order effects need to be taken into account for preferences and market prediction.     

Embracing Model Uncertainty: Strategies for Response Pooling and Model Averaging

Econometricians modeling Stated Preference (SP) data, and most other types of data, are confronted with the uncomfortable reality that our knowledge of the “true” model is limited, with only certain variables suggested by the application at hand and general classes of functional forms and error structures suggested by the literature. Accepting our limited knowledge, we pursue strategies for analyzing SP data that are more robust to uncertainties in our knowledge of the true model. These include non-parametric and parametric likelihood-based tests of pooling responses from different elicitation formats, and a frequentist-based model averaging approach for estimating willingness to pay functions. We argue that these strategies lead to increased econometric integrity and empower the ultimate users of models, such as policy decision-makers and even juries, to better assess the robustness of the results. We apply these approaches to an SP survey of saltwater angling in Alaska which utilized split-sample rankings and ratings elicitation methods. While an important goal of our paper is to develop practicable modeling strategies that will ultimately lead to more robust conclusions and more confidence by the users of SP results, an equally important goal is to engender a critical discussion of how we can make the analysis of SP data more robust.     

Distributional Preferences and the Incidence of Costs and Benefits in Climate Change Policy

We explore the relationship between willingness to pay (WTP) for climate change mitigation and distributional preferences, by which we mean individuals’ opinions about who should be responsible for climate change prevention and whether the share of climate change impacts borne by the poor is a cause for concern. We use 1,770 responses to an online stated preference survey. The domestic costs in our survey’s policy choice scenarios are expressed as a set of randomized shares across four different payment vehicles, and the international cost shares are randomized across four groups of countries. We also elicit respondents’ perceptions of the likely regressivity of climate change impacts under a policy of business-as-usual. WTP is higher when larger cost shares are borne by parties deemed to bear a greater responsibility for mitigation, and when respondents believe (and care) that the impacts of climate change may be borne disproportionately by the world’s poor. That WTP for an environmental policy depends on the distributional consequences of the policy is an unsettling result: efficiency assessments are typically assumed to be separate from equity considerations in most benefit-cost analyses.     

Design of Stated Preference Surveys: Is There More to Learn from Behavioral Economics?

We discuss the design of stated preference (SP) surveys in light of findings in behavioral economics such as context dependence of preferences, learning, and differences between revealed and normative preferences. More specifically, we discuss four different areas: (1) revealed and normative preferences, (2) learning and constructed preferences, (3) context dependence, and (4) hypothetical bias. We argue that SP methods would benefit from adapting to some of the findings in behavioral economics, but also that behavioral economics may gain insights from studying SP methods.     

Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range

Range anxiety – consumers’ concerns about limited driving range – is generally considered an important barrier to the adoption of electric vehicles. If consumers cannot overcome these fears it is unlikely that they will consider purchasing an electric car. Hence, a successful introduction of low emission vehicles in the market requires a full understanding of consumer valuation of driving range. By analyzing experimental data on vehicle purchase decisions in California, I derive and study the statistical behavior of Bayes estimates that summarize consumer concerns toward limited driving range. These estimates are superior to marginal utilities as parameters of interest in a discrete demand model of vehicle choice. One of the empirical results is the posterior distribution of the willingness to pay for electric vehicles with improved batteries offering better driving range. Credible intervals for this willingness to pay, as well as both parametric and nonparametric heterogeneity distributions, are also analyzed.     

Beyond mobile number portability: measuring consumer preferences for service portability in Japan's mobile phone market

Japan's mobile phone market has been oligopolized by three incumbents who are seeking vertically integrated business models, which may prevent competitors from using platform layers to provide original services. We conduct two types of conjoint analysis to measure consumer stated preferences and draw two main conclusions from the analyses. First, the average consumer is willing to pay more than JPY 2000 (US $20) to increase mobile service portability. Second, the average consumer's willingness to pay corresponds to JPY 100–200 (US $1–2) per song for securing music download platforms. A dilemma exists in consumer preferences for service portability in Japan's mobile phone market, namely the choice between free mobile service portability and convenient music download platforms.     

Flexible customer willingness to pay for bundled smart home energy products and services

Energy markets are rapidly changing with smarter, connected, more reliable infrastructure and cleaner generation on the supply side, and more choice, greater control and enhanced flexibility for customers. This paper examines willingness to pay for bundled smart home energy products and information services, using data from a set of two discrete choice experiments that were part of a survey by the regional energy provider of upstate New York. To let the data reveal how preferences are distributed in the population, a logit-mixed logit model in willingness-to-pay space and a combination of observed and unobserved preference heterogeneity was specified and fitted. Results show that residents of Tompkins County are willing to pay more than in other counties for residential storage, and that for home energy management there is an important generational divide with millennials being much more likely to perceive the economic value in the smart energy technologies. The flexible logit-mixed logit estimates provide evidence of important heterogeneity in preferences: whereas most of the population has a positive—albeit rather low—valuation of smart energy products and services, there is a considerable percentage of customers with negative perceptions.     

Convergent Validity of Revealed and Stated Recreation Behavior with Quality Change: A Comparison of Multiple and Single Site Demands

We consider the convergent validity of several demand models using beach recreation data. Two models employ multiple site data: a count data demand system model and the Kuhn–Tucker demand system model. We explore the role of existing variation in beach width in explaining trip choices, and analyze a hypothetical 100 foot increase in beach width. We compare these models to a single equation model where we jointly estimate revealed and stated preference trip data, and focus on a hypothetical scenario considering a 100 foot increase in beach width. In each case we develop estimates of the change in beach visits and the welfare impacts from the increase in width. The trip change estimates from two of the three models are similar and convergent valid, though the willingness to pay estimates differ in magnitude.     

A proactive approach for assessing alternative management programs for an invasive alien pollinator species

Most evaluations of the economic impacts of invasive species are done post facto and concentrate on direct production loss caused. However, the effects of invasive species on non-market services such as biodiversity and landscapes can be considerable. A proactive approach of assessing the expected economic impact of invasive species prior to their occurrence may contribute to greater efficiency of policy makers. Here we used a stated preference method for a priori evaluating the willingness of the population to pay for different control programs of a new invasive bee species in Israel, the dwarf honey bee, Apis florea. We evaluated possible economic impacts of A. florea using two model plant species expected to be adversely affected by its invasion due to decreased pollination. The plants have no market value but they add aesthetic value to the open landscape. Using a mixed logit model we found that the mean willingness to pay (WTP) differed between the model plants, and increased with the extent of plant loss. Respondents differentiated between levels of damage to the plants and between control methods in their preferences for a specific program. Our results provide means for informed proactive decision making in preventing the continued invasion of the bee.     

What to Value and How? Ecological Indicator Choices in Stated Preference Valuation

The ecological literature accepts that many policy outcomes cannot be observed directly and must be characterized using indicators. Multiple indicators can often be used to communicate similar ecological outcomes. Previous studies using alternative indicators in stated preference surveys suggest that welfare estimates may be indicator-dependent, casting doubt on whether welfare estimates are sufficiently reliable for cost benefit analysis. We suggest that the reason for such indicator dependence may be that indicators used in these prior studies represented different outcomes valued by respondents. This possibility underscores the need for greater attention to selection of indicators and their properties within stated preference survey design. This paper develops a model introducing the concept of outcome equivalent indicators, defined as indicators that provide alternative representations of identical underlying outcomes. To assess empirically whether welfare estimates are indeed robust to indicator choice when alternative indicators are expected to be outcome equivalent, we analyze data from a choice experiment estimating willingness to pay for migratory fish restoration in Rhode Island, USA. Results demonstrate that welfare estimates are robust to the use of alternative ecological indicators within stated preference scenarios.