Stated Preference Data Research Articles

Economic evaluation of a prospective Farm Animal Welfare program in Turkey

PurposeThe purpose of this paper is to examine producer preferences for changing Farm Animal Welfare (FAW) levels in regards to sheep and goat husbandry in Bursa-Turkey.Design/methodology/approachThe paper tests “panel estimators” in a stated preference data by using the payment card question format. Probit panels are employed to measure individual effects on FAW levels by considering producers’ willingness to accept. Three different FAW levels were identified for valuation as “base” level, “better” level, and the “best” level. The current study suggests a protocol with WTA(P) nomenclature to resolve complexity issues in FAW studies by investigating producers rather than consumers because the scenarios regarding FAW levels include quite technical and difficult topics which are vague to consumers.FindingsIf half of the total number of the sheep and goats in Turkey are assumed to be in bad animal welfare conditions, which are worse than base level, the non-use benefits of bringing them to at least the base level would be about US$130.3m. Figures would be 166.2m US$/year and 175m US$/year for “better” and “best” FAW conditions, respectively.Originality/valueThis paper provides a contribution to the existing literature by examining the producers’ responses to new FAW schemes. Also it helps policy makers to understand producers’ environmental behavior as well as their sensitivity to FAW schemes.

A Review of Service Reliability Measures for Public Transportation Systems

The aim of this paper is to review various public transport reliability measures including the factors responsible for causing variability in the travel time. A four quadrant approach is used to summarize various reliability measures. These indicators use both Intelligent Transportation System (ITS) data and the stated preference data to measure reliability. Further various supply and demand side factors causing uncertainties in travel time are listed and discussed. In the end a brief case study on two routes of Bus Rapid Transit System(BRTS) of Ahmedabad is reported to apply and test the reviewed travel time measures on the ITS data.

Structurally-consistent estimation of use and nonuse values for landscape-wide environmental change

We address the problem of estimating the use and nonuse value derived from a landscape-wide programme of environmental change. Working in the random utility framework, we develop a structural model that describes both demand for recreational trips to the landscape's quality-differentiated natural areas and preferences over different landscape-wide patterns of environmental quality elicited in a choice experiment. The structural coherence of the model ensures that the parameters of the preference function can be simultaneously estimated from the combination of revealed and stated preference data. We explore the properties of the model in a Monte Carlo experiment and then apply it to a study of preferences for changes in the ecological quality of rivers in northern England. This implementation reveals plausible estimates of the use and nonuse parameters of the model and provides insights into the distance decay in those two different forms of value.

Evaluating producer preferences and information processing strategies for drought risk management tools in Bangladesh

We use stated preference data to study farmers’ preferences for two alternative strategies to manage drought risks in Bangladesh. A choice experiment on drought tolerant rice varieties and weather index insurance was administered to 2306 farmers. Two overarching preference classes or meso-groups were identified using an equality constrained latent class model, each containing various patterns of attribute non-attendance. Our analysis finds that farmers rely on information processing strategies to simplify risk management tool decisions to mitigate drought risk. Differences in the use of farm inputs, and access to savings mechanisms, both formal and informal, are shown to affect decision making.

Choosing the Future: Economic Preferences for Higher Education Using Discrete Choice Experiment Method

This study illustrates how respondents’ stated choices (the discrete choice experiment method) combined with the random utility framework can be used to model preferences for higher education. The flexibility offered by stated preference data circumvents limitations of other approaches, and allows quantifying young people’ preferences for selected attributes of higher education programs that are typically highly correlated in revealed preference data. The empirical study presented here is based on a survey of 20,000 Polish respondents aged 18–30, who stated their preferences for higher education programs in carefully prepared hypothetical choice situations. The attributes considered include tuition fee, expected salary after graduation, quality of institution, interest in the field of study, distance from home, and mode of study. Using random parameters and latent class mixed multinomial logit models, young peoples’ preferences are formally described, and the financial trade-offs they are willing to make are identified (willingness to pay for specific attribute levels in terms of increased tuition fees or expected salary after graduation). Accounting for respondents’ observed and unobserved preference heterogeneity addresses a few research questions related to, for example, distinct preferences of students with parents who never attained tertiary education, students from lower socio-economic groups, or students of a particular gender. Overall, the usefulness of stated preference methods as a tool for exploring economic preferences is demonstrated, allowing for better understanding the determinants of choices, forecasting, and designing the services offered by higher education institutions in an optimal way.

Understanding joint retirement

Evidence from different sources shows that spouses’ retirement decisions are correlated. Retirement policies affecting individuals in couples are therefore also likely to affect behavior of their spouses. It is therefore important to account for joint features in modeling retirement. This paper studies a structural collective model of labor supply and retirement of both partners in a couple with interdependent preferences, imperfect knowledge of preferences of the spouse, and subjective expectations about the future. We propose a novel method to estimate preferences and the intra-household bargaining process, which relies on stated preferences data collected in the Health and Retirement Study. Respondents were asked to choose between hypothetical retirement trajectories describing the retirement ages and replacement rates of both spouses from three perspectives: considering their own preferences only, the preferences of their spouse only, or the most likely decision for the household. With these data, all model parameters are identified and potential sources of joint retirement can be disentangled. Our results suggest that males misperceive their wives’ preferences, overestimating their disutility of work. Our estimates correct for this. We find strong positive correlations between preferences for joint leisure (leisure complementarity) of the two partners. Counterfactual simulations with stylized retirement paths suggest that the leisure complementarities explain a substantial part of joint retirement, much more than correlation in unobserved heterogeneity or potential wage rates.

Modeling the cordon pricing policy for a multi-modal transportation system

Cordon pricing is a promising policy through which the travel demand is managed in order to alleviate the negative impacts of traffic congestion. The models proposed in the literature are usually based on optimization procedures, which make them particularly complex for practitioners. To facilitate the modeling of this policy, this paper presents a simple method to model the cordon pricing policy in a multi-modal urban network. To this end, the origin-destination matrices subjected to cordon pricing conditions are obtained by the current origin-destination matrices as well as a car utility function. These current origin-destination matrices are built upon revealed preference data while the car utility function is based on stated preference data. Then, matrices subjected to cordon pricing condition are assigned to the transportation network of Isfahan, Iran. The simultaneous use of revealed preference and stated preference data in the presented modeling improves accuracy and allows prediction of people’s reactions to the cordon pricing policy. Consistent with previous studies, the results show a 49% fall in car users and a 70% rise in public transit users within the pricing area, which leads to 2.5% decrease in both the travel time and air pollution costs in the entire network.

Simulating the impact of tax incentives using a type choice model for lease cars

Tax incentives for hybrid and electric cars are one of the possible policy instruments to reduce the CO2 emissions of cars. This paper looks at the impacts of this policy in the context of The Netherlands, where up to 2016 substantial tax incentives were provided to fuel efficient lease cars, especially for hybrid and electric lease cars. For this analysis this paper uses a new model of vehicle type choice. Existing models for the choice among car type alternatives either exclude electric and hybrid cars or are based on stated preference data. In both cases, the models usually focus on privately-owned cars. The car type choice model for lease cars, that is used in this paper, includes electric and hybrid alternatives and is estimated on revealed preference data from lease car users in The Netherlands. Simulations with this model show that the tax incentives provided over the period 2011–2016 have led to a reduction in CO2 emissions of lease cars, but also to an increase in the share of diesel cars and local air pollution.

Something Is Better Than Nothing: The Value of Active Intervention in Stated Preferences for Treatments to Delay Onset of Alzheimer's Disease Symptoms

BackgroundThe objective of the study was to understand respondents' willingness to accept hypothetical treatment-related risks in return for the benefit of additional time with normal memory from potential Alzheimer's disease interception therapies. MethodsA US web-based discrete-choice survey was administered to respondents ages 60 to 85 years with no Alzheimer's disease diagnosis and no cognitive symptoms. Choice questions required respondents to indicate whether they preferred a constant, no-treatment condition described as 4 years of normal memory followed by 3 years of cognitive impairment and 5 years of dementia or an interception treatment with chosen risks of disabling stroke and death, but with increased duration of normal memory. The study design included internal validity tests to verify data quality. ResultsOn average, respondents were willing to accept a 5% to 13% risk of stroke or death in the first year for treatments that could provide 1 or more additional years with normal memory. Nevertheless, 30% of respondents failed a simple internal-validity test question where the treatment alternative offered no improvement in disease progression but had significant side effects. These respondents also were more likely to choose active treatment in the subsequent series of choice questions. This unexpected finding is consistent with hopeful attitudes of patients with debilitating and potentially fatal conditions. ConclusionPro-treatment attitudes are clinically relevant and can affect the analysis and interpretation of stated-preference data. Internal-validity tests generally are underutilized in preference research. This study demonstrated how analysis of apparent validity failures can yield important insights about patient preferences.

No longer in the driver’s seat: How do affective motivations impact consumer interest in automated vehicles?

Emerging advancements in automated vehicles may dramatically change how transportation system users engage cars as symbols and tools to exert control over their lives, time use, and activity spaces. Transportation technologies do not only serve users. They can also elicit affective reactions and make users experience feelings, including anxiety, freedom, health, and the sense of being in control of different facets of their lives. By changing the role of humans in actively moving through urban environments, fully-automated vehicles could impact users’ perceived control over their environments and their use of cities. To better understand the use and implications of automated vehicles, this study explores whether affective motivations for automobile ownership are likely to change in a potential future of fully-automated vehicles. This study focuses on AV ownership conditions using a consumer survey of 3201 residents of the Greater Toronto and Hamilton Area to estimate consumer interest in regularly using and purchasing privately-owned AVs using a combination of revealed preference (vehicle ownership) and stated preference (AV interest) data. Structural equation models are estimated to explore whether the affective motivations for vehicle ownership are likely to be different than the affective motivations for using or adopting an automated vehicle. Findings indicate that the role of affective motivations are likely to be different for vehicle ownership than for interest in AV use and adoption. While a car freedom and control affective motivation is a strong predictor of current household vehicle ownership, it is negatively associated with AV interest and adoption. The affective motivation related to being a daring achiever is a strong predictor of AV interest and adoption, but it is unrelated with current vehicle ownership levels. Finally, individual control over activities and travel is negatively associated with current vehicle ownership levels, but it is unrelated with AV interest and adoption. Findings suggest significant differences between individuals’ motivations for vehicle ownership and AV adoption and use. Nevertheless, these findings should be interpreted cautiously, as data related to interest in AV technology remains in the domain of stated preference data, rather than revealed preferences, which are subject to broader household decision-making processes. As such, if transportation planning and forecasting concentrate only on instrumental factors and ignore changes in affective motivations, forecasts may miss key components of behavioral change and may miss the balance of who benefits (or loses) from this new technology.

Is an Incentive Policy for Energy Efficient Products Effective for Air Purifiers? The Case of South Korea

Recent increases in fine and ultrafine dust in South Korea have led to sharp increases in the sale of air purifiers, and that trend is expected to continue. As the sale of air purifiers increases, the energy that is consumed by air purifiers also increases. Therefore, improving the energy efficiency of air purifiers is an important part of improving the overall energy efficiency of society. We studied how different incentive policies affect consumer behavior because encouraging people to buy energy efficient air purifiers is important. We first investigated consumer preferences regarding air purifiers. Stated preference data were gathered from a choice experiment and a mixed logit model was used for the analysis. The results show that the most preferred attribute was price, followed by an eco-label. Based on that result, we conducted a scenario analysis to examine the economic and environmental effects of an incentive policy and eco-labeling. The monetary incentive policy increased the market share for air purifiers with a first-grade energy efficiency rating to 2.2%. The annual electricity use reduction was 5.9 GWh, with a CO2 emission reduction of 2520 tons and a policy monetary benefit of KRW 441,340,922 when we converted the effect of that market share change into economic and environmental terms. Eco-labeling also brought considerable change in the market share. These results provide a reference for implementing policies to encourage consumers to purchase energy efficient air purifiers.

Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data

In health, stated preference data from discrete choice experiments (DCEs) are commonly used to estimate discrete choice models that are then used for forecasting behavioral change, often with the goal of informing policy decisions. Data from DCEs are potentially subject to hypothetical bias. In turn, forecasts may be biased, yielding substandard evidence for policymakers. Bias can enter both through the elasticities as well as through the model constants. Simple correction approaches exist (using revealed preference data) but are seemingly not widely used in health economics. We use DCE data from an experiment on smokers in the US. Real-world data are used to calibrate the scale of utility (in two ways) and the alternative-specific constants (ASCs); several innovations for calibration are proposed. We find that embedding revealed preference data in the model makes a substantial difference to the forecasts; and that how models are calibrated also makes a substantial difference.

What Do Potential Residents Really Want When Hosting a Nuclear Power Plant? An Empirical Study of Economic Incentives in South Korea

To increase acceptance of new nuclear power plants (NPPs) by local communities, some countries offer those communities economic incentives. This study analyzes potential residents’ preferences for economic incentives provided during the construction and operation of NPPs in South Korea. This study uses stated preference data through a discrete choice experiment and the mixed logit model to reflect the heterogeneity of respondents’ preferences. The analysis results confirm heterogeneity by various incentive types, and show that respondents consider the distance between the NPP and the residential area as well as reduced electricity bills as crucial attribute of new NPPs. In addition, the result for the marginal willingness to be near to an NPP shows that reduced electricity bills, construction of new public facilities, and increased residents’ participation are relatively more effective incentives than job creation or solar panel installation. In particular, increased residents’ participation could greatly improve acceptance although it is not a direct form of financial support. The simulation analysis results indicate that acceptance of NPPs rapidly reduces as distance to the plant becomes shorter, although acceptance can change within a 0–30% range depending on the level of incentives. Several policy implications are suggested for policymakers based on the results.

Valuing the water supply: ecosystem-based potable water supply management for the Legedadie-Dire catchments, Central Ethiopia

BackgroundThere is a substantial interest in the values that consumers place on drinking water quality and supply. Financial resources are crucial to improving the urban potable water supply in developing countries that are characterized by low-cost recovery rates and a high and rapidly growing demand for more reliable services. This study examined households’ willingness to pay (WTP) for the improvement of water services by identifying their water choice decisions and the mode of water supply that they prefer the water supply authority to use among several alternative water supply options. Stated-preference data were collected from 322 randomly selected households in Addis Ababa, who were presented with three sets of choices (three alternative bundle choices, including the reference scenario). The data were analyzed using the mixed logit WTP space model. Three approaches to modeling the distribution of WTP (fixed, uncorrelated, and correlated) using mixed logit WTP space models were compared.ResultsThree-quarters of the households agreed to contribute money toward ecosystem-based water supply management (EBWSM) intervention programs on a monthly basis. The average contribution that the respondents were willing to pay was 150.5 Ethiopian Birr (ETB) as a one-off lump sum to kick off the EBWSM activities. Most of the respondents chose a bundle of water supply options that provides risk-free and high-quality water with no months of shortages than moderate water quality that is safe to drink and palatable with 1 month shortages annually. This implies that households would need to be supplied with risk-free, high-quality water without interruption at an appropriate flow pressure. The model with correlations fitted the data well with the highest simulated log-likelihoods at convergence and gave the best estimate of the households’ WTP for water improvement. Nearly 46% of the sampled households were willing to pay more than 33 ETB per month, and 49% of the households were willing to pay between 21 ETB and 33 ETB per month for the monthly water bill. Overall, approximately 95% of the sampled households were willing to pay more than 21 ETB.ConclusionCustomers are willing to pay to avoid most types of water supply restrictions. Moreover, WTP is sensitive to the scope of service improvement, income, affixed price, and elicitation method. In summary, mixed logit WTP-space models can help accurately predict household-level WTP, which can be used to select improvements in drinking water access and services in the Legedadie-Dire catchments.

Efficacy of a Preference-Based Decision Tool on Treatment Decisions for a First-Time Anterior Shoulder Dislocation: A Randomized Controlled Trial of At-Risk Patients.

First-time anterior shoulder dislocations (FTASD) provide an opportunity to examine the value of integrating stated-preference data with decision modeling to differentiate between patients whose preferred management strategy involves operative or nonoperative treatment. The objective of this study was to evaluate the efficacy of a FTASD decision tool intervention with individual preference measurement compared with a text-based control in a randomized controlled trial. Two hundred respondents between 18 and 35 years of age at risk for experiencing an FTASD were enrolled from the orthopedic clinics and randomized to receive either an interactive decision tool intervention capable of eliciting patient preferences for treatment of an FTASD or a text-based control on shoulder dislocations and treatments. The primary outcome was preference for operative or nonoperative treatment choice. Secondary outcomes included the decisional conflict scale (DCS), stage of decision making, patient activation and engagement, awareness of preference sensitive decisions, knowledge retention, and instrument acceptability. One hundred respondents were randomized to the intervention and 100 to the control. A total of 154 men and 46 women with an average age of 23.6 years completed the survey. Participants in the intervention group made treatment decisions that aligned more closely with evidence-based recommendations than those in the control group ( P = 0.016). Secondary outcomes showed no difference between intervention and control, excluding several DCS subscales. An interactive, preference-based decision tool for treatment of FTASD affects patient decision making by guiding respondents toward treatment decisions that align more closely with evidence-based recommendations in the absence of a consultation with an orthopedic provider compared with a standard-of-care control tool. Additional study is needed to evaluate the long-term effects of this tool on treatment outcomes, patient adherence, and satisfaction. 2.

Estimating recreation benefits through joint estimation of revealed and stated preference discrete choice data

We develop econometric models to estimate jointly revealed preference (RP) and stated preference (SP) models of recreational fishing behavior and preferences using survey data from the 2007 Alaska Saltwater Sportfishing Economic Survey. The RP data are from site choice survey questions, and the SP data are from a discrete choice experiment. Random utility models using only the RP data may be more likely to estimate the effect of cost on site selection well, but catch per day estimates may not reflect the benefits of the trip as perceived by anglers. The SP models may be more likely to estimate the effects of trip characteristics well, but less attention may be paid to the cost variable due to the hypothetical nature of the SP questions. The combination and joint estimation of RP and SP data seeks to exploit the contrasting strengths of both. We find that there are significant gains in econometric efficiency and differences between RP and SP willingness-to-pay estimates are mitigated by joint estimation. We compare a number of models that have appeared in the environmental economics literature with the generalized multinomial logit model. Naive (1) scaled, (2) mixed logit, and (3) generalized multinomial logit models produced similar results to a generalized multinomial logit model that accounts for scale differences in RP and SP data. Willingness-to-pay estimates do not differ across these models but are greater than those in the mixed logit error components model that accounts for scale differences.

The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population

The present study investigates how social influence and social interactions can affect the adoption of new technologies, using stated preference (SP) survey data combined with an “accelerated reality” experience of social interaction among the respondents. Specifically, the intention to use a pro-environmental transport mode (the bike sharing) during a public transport strike within a cohort of students has been analysed. Previous studies have modelled social influence effects using SP data by providing a hypothetical scenario with simulated interactions or information about social conformity processes (i.e. social adoption) during the survey. In our paper, in addition to the impact of assumed social norms, the effect of live/real social interactions is included in the survey. SP survey is developed to investigate the effect of Level-of-Service attributes on the hypothetical choices in the scenario of a public transport strike. Besides the pre-defined attributes characterising the alternatives in the SP design, the survey includes techniques to acquire information on conformity and social interactions. Specifically, the interviewees undertake a before and after stated preference experiment (SP1 and SP2), with a period of group discussion in between the two parts. This SP experiment involves different cognitive and interpersonal mechanisms, such as the functional information exchange on benefits and drawbacks of cycling and bike sharing. The aim is to establish whether hypothetical scenarios of social conformity are different from real/live social interactions and whether these social influence processes actually affect the individuals' mode choice. A joint SP1/SP2 mixed logit (ML) model has been estimated to explore the choice behaviour of individuals and allows us to incorporate the inertia/propensity to change behaviour between SP1 and SP2. Moreover, considering the “Reflexive Layers of Influence” (RLI) framework, the processes generated by social interactions (diffusion, translation and reflexivity) are measured and incorporated in the model. We finally show the effect of these social influence variables on the goodness-of-fit of the models and choice simulation for prediction. We also draw conclusions about the value of such enhanced choice models in understanding and predicting the impacts of social interactions on choice behaviour in the context of new transport technologies.

The impacts of office relocation on commuting mode shift behaviour in the context of Transportation Demand Management (TDM)

Interior Health Authority (IHA) will be co-locating ten programs to a new office location in downtown Kelowna, BC by October 2016. As a result, approximately 900+ employees will be commuting to the downtown core every day. This number of attracted daily trips will increase traffic congestion and parking pressure at peak periods at the new site, which is already suffering from parking constraints. Moreover, the projected number of parking stalls available for employees is insufficient to meet the anticipated parking demand. The economic, social, and environmental impacts associated with increasing parking capacity makes finding alternative strategies more desirable. This research adopts a threefold approach to study the travel patterns and mode shift behaviour of IHA employees before designing a custom-made Transportation Demand Management (TDM) plan for IHA. First, a reveled/stated preference survey is designed and conducted to collect information on the travel behaviour of IHA employees. Second, analytical tools are developed to identify the determinants of mode shift of IHA employees. Third, an implementation strategy is recommended to maximize transportation mode shift and reduce the number of parking stalls required. The study shows how revealed preference information complements and confirms stated preference data as a way of dealing with the uncertainty associated with people’s stated choices. Implementation strategies include carpooling programs, incentivizing the use of public transit and non-motorized modes, and educating IHA employees on the carbon footprint associated with their choices.

Potential demand for voluntary community-based health insurance improvement in rural Lao People's Democratic Republic: A randomized conjoint experiment.

IntroductionIn Lao People’s Democratic Republic (PDR), community-based health insurance (CBHI) is the only voluntary insurance scheme; it typically targets self-employed people, most of whom reside in rural areas and are dependent on agricultural activities for subsistence. However, until very recently, the enrollment rate has fallen short and failed to reach a large percentage of the target group. To promote the CBHI scheme and increase demand, some supporting components should be considered for inclusion together with the health infrastructure component.ObjectivesThis paper provides empirical evidence that the benefit package components of hypothetical CBHI schemes have causal effects on enrollment probabilities. Furthermore, we examine the distribution of willingness to pay (WTP) in response to policy changes based on a sample of 5,800 observations.MethodsA randomized conjoint experiment is conducted in rural villages in Savannakhet Province, Lao PDR, to elicit stated preference data. Each respondent ranks three options—two hypothetical alternatives and the CBHI status quo scheme. The levels of seven attributes—insurance coverage for medical consultations, hospitalizations, traffic accidents, pharmaceuticals and transportation; premiums; and prepaid discounts—are randomly and simultaneously assigned to the two alternatives.ResultsThe findings suggest that the average WTP is at least as large as 10.9% of the per capita income of those who live in rural areas, which is higher than the WTP for health insurance averaged across low- and middle-income countries (LMICs) in the literature. The component of round-trip transportation insurance coverage has a significant effect on WTP distribution, particularly increasing the share of the highest bin.ConclusionTherefore, the low CBHI scheme enrollment rate in Lao PDR does not necessarily imply low demand among the targeted population, as the finding from the WTP analysis illustrates potential demand for the CBHI scheme. Specifically, if transportation is addressed, enrollment is likely to significantly increase.

Segmentation of Theatre Audiences: Latent Class Approach for Combined Data

Theatrical productions are perishable goods, since the tickets for a particular play cannot be inventoried and sold after the time of the play. In the revenue management of a perishable good, price discrimination is widely used. Since the theatre audience is heterogeneous in terms of visit purpose, ability to perceive quality, willingness-to-pay, the strategy of price discrimination should be developed in the context of theatre segments. In this paper, we segment consumers of the Perm Opera and Ballet Theatre and propose marketing instruments to increase theatre revenue. Since the development of a detailed price discrimination strategy requires data on consumer purchase history, behavioral and socio-demographic characteristics, we combine two data sources: data on ticket purchases and survey data. Using a modication of a latent class logit model for joint revealed and stated preference data we identify four segments of the theater's audience. The study reveals theatregoer segments with dierent willingness-to-pay for performance and seat location characteristics, which allows the development of detailed recommendations on the pricing strategy for various theatre audiences