Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?

Fifer, Simon; Rose, John M.; Greaves, Stephen

doi:10.1016/j.tra.2013.12.010

Cited by 118 publications

(73 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This results are in line with the National Oceanic and Atmospheric Administration's blue ribbon panel (NOOA's BRP) recommendation, which suggested that hypothetical bids be deflected using a "divide by 2" [23,24] approach when using the contingent valuation method (another technique affiliated to the stated preference family). In addition to that, researchers have found [22,25,26] that HB tends to be smaller when: (1) goods assessed are private, as opposed to public; (2) people are asked to state their willingness to pay (WTP) instead of their willingness to accept (WTA); (3) experiments are realistic (i.e., payment vehicles are familiar and people rely on the feasibility to provide the attributes of the assessed goods) (In addition to that, researchers have designed ex ante and ex post techniques to reduce HB. The first includes "cheap talks" [27] and reminders of individuals' budgets, while the second includes special statistical techniques, such as stochastic frontier regressions [28]); and (4) individuals are carefully selected so as to ensure convenient experience and appropriate demographics.…”

Section: Methodology and Case Studymentioning

confidence: 88%

Does the Energy Label (EL) Matter in the Residential Market? A Stated Preference Analysis in Barcelona

Duarte

Bravi

2017

Buildings

View full text Add to dashboard Cite

Energy performance in buildings has become a milestone in EU environmental policy in the last 15 years. Nonetheless, in some countries such as Spain the full acceptance of the Energy Performance of Buildings Directive is still relatively rare and novel, with little evidence as to how relevant it is in the real estate market. There are different reasons for this phenomenon: on the one hand, the real estate crisis has paralyzed the completion of most of the works in new/refurbished efficient buildings (which renders it impossible to use revealed preference methods); on the other hand, information is not complete and the consumer's perception of the relevant cost/benefits could be incorrect. For this reason, assessing the trade-off between the energy label and other specific attributes of the housing market has become important. To that end, a choice experiment following an RPL (Random Parameter Logit) model was employed. This form is suitable to accommodate various empirical situations where the assumptions of the Conditional Logit model have to be relaxed. Results suggest that the Energy Label (EL) does matter in the real estate market in relation to other residential attributes. Marginal willingness to pay for an "A" label instead of an "E" is significantly higher than the stated savings in energy costs. The respondents' utility function seems to be also influenced by their educational level, as well as by their preference to own a house as opposed to renting it.

show abstract

Section: Methodology and Case Studymentioning

confidence: 88%

Does the Energy Label (EL) Matter in the Residential Market? A Stated Preference Analysis in Barcelona

Duarte

Bravi

2017

Buildings

View full text Add to dashboard Cite

show abstract

“…Their cross‐price elasticity estimate for fish of 0.61–0.83 is higher than our estimate of 0.48–0.53 (Table ), while their bushmeat own price estimate of −0.69 is very close to our estimate of −0.66 to −0.69. We note that our empirical findings are based on stated preferences rather than actual market transactions, and so may be subject to hypothetical market bias (Fifer, Rose & Greaves, ).…”

Section: Discussionmentioning

confidence: 99%

A stated preference investigation of household demand for illegally hunted bushmeat in the Serengeti, Tanzania

Moro

Fischer

Milner‐Gulland

et al. 2014

Animal Conservation

View full text Add to dashboard Cite

Illegal hunting for bushmeat is regarded as an important cause of biodiversity decline in Africa. We use a stated preferences method to obtain information on determinants of demand for bushmeat and two other protein sources, fish and chicken, in villages around the Serengeti National Park, Tanzania. Our study focuses particularly on the impact of price changes, as anticipating and understanding the impact of price changes (whether caused by conservation interventions or market changes) on demand for bushmeat enables effective responses to be planned. We estimate the effects of changes in the price of bushmeat and in the prices of two substitute protein sources -fish and chicken -on household demand for bushmeat. Results suggest that the availability of lower-priced protein substitutes would reduce demand for bushmeat, and therefore potentially pressure on wildlife populations. However, raising the price of bushmeat (e.g. as a result of reducing illegal hunting) would reduce household demand to a greater degree than equivalent decreases in the price of alternative protein sources. In both cases, elasticities are reported which summarise the relative response to households to these alternative interventions: a 10% rise in bushmeat prices would reduce demand by around 6-7%, whilst a 10% fall in chicken or fish prices would reduce bushmeat demand by around 3-4%. The response to price changes varied between ethnic groups, and also according to household size (with the direction of the effect depending on whether the substitute was chicken or fish), but was not significantly affected by wealth or income. (248 words)

show abstract

“…Thus, for example, qualitative methods, such as focus groups or in-depth interviews, in combination with quantitative methods, like stated choice experiments, could be used for exploring questions about the impacts of vehicle automation on travel comfort, utilization of travel time while on the move, value of time, travel, and location choices. Yet, people may have difficulties in envisioning automated vehicles, so stated choice experiments could suffer from hypothetical bias (see Fifer, Rose, & Greaves, 2014). More creative techniques such as virtual reality or serious gaming would be useful in behavioral experiments about the impacts of automated vehicles.…”

Section: Methodological Challenges In Exploring the Implications Of Amentioning

confidence: 99%

Policy and society related implications of automated driving: A review of literature and directions for future research

Milakis

Arem

Wee

2017

Journal of Intelligent Transportation Systems

706

383

View full text Add to dashboard Cite

KEYWORDSautomated driving; first, second, and third order impacts; policy and societal implications; ripple effect ABSTRACTIn this paper, the potential effects of automated driving that are relevant to policy and society are explored, findings discussed in literature about those effects are reviewed and areas for future research are identified. The structure of our review is based on the ripple effect concept, which represents the implications of automated vehicles at three different stages: first-order (traffic, travel cost, and travel choices), second-order (vehicle ownership and sharing, location choices and land use, and transport infrastructure), and third-order (energy consumption, air pollution, safety, social equity, economy, and public health). Our review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial. The magnitude of these benefits will likely increase with the level of automation and cooperation and with the penetration rate of these systems. The synergistic effects between vehicle automation, sharing, and electrification can multiply these benefits. However, studies confirm that automated vehicles can induce additional travel demand because of more and longer vehicle trips. Potential land use changes have not been included in these estimations about excessive travel demand. Other third-order benefits on safety, economy, public health and social equity still remain unclear. Therefore, the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.

show abstract

Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?

Cited by 118 publications

References 34 publications

Does the Energy Label (EL) Matter in the Residential Market? A Stated Preference Analysis in Barcelona

Does the Energy Label (EL) Matter in the Residential Market? A Stated Preference Analysis in Barcelona

A stated preference investigation of household demand for illegally hunted bushmeat in the Serengeti, Tanzania

Policy and society related implications of automated driving: A review of literature and directions for future research

Contact Info

Product

Resources

About