Using Elicited Choice Probabilities to Estimate Random Utility Models: Preferences for Electricity Reliability*

Blass, Asher; Lach, Saul; Manski, Charles F.

doi:10.1111/j.1468-2354.2010.00586.x

Cited by 108 publications

(129 citation statements)

References 27 publications

(29 reference statements)

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“…While such campaigns have been conducted in developing countries (Jensen, 2010;Nguyen, 2010), our results make a case for such interventions in developed countries as well. 16 A possible alternative to our quasi-experimental approach is the methodology used in Blass et al (2010), who estimate preferences for electricity reliability by asking survey participants to value various bundles of electricity generation bills and outage probabilities. The shortcoming of their counterfactual scenarios approach is that it may be di¢ cult to operationalize meaningful counterfactual scenarios for some applications of interest-it is not clear how one would pose simple counterfactual situations in complicated occupational choice contexts, such as college major choices.…”

Section: Resultsmentioning

confidence: 99%

“…The intuition for our identi…cation strategy is clearly seen in (11). The numerator of this expression measures the extent of the relative probability revision from the pre-treatment to post-treatment period.…”

Section: Examplementioning

confidence: 99%

“…We asked respondents about the percent chance that their own earnings at age 30 would exceed $35,000 and $85,000. 11 We …t each student's response to these questions as well as the reported average earnings for each …eld to a log-normal distribution, and obtain individual …eld speci…c parameters of the earnings distribution. The third column of Table 2 shows the average and standard deviation of the individual standard deviations of the earnings distributions for each …eld before the information treatment was provided.…”

Section: Revisions Of Self Beliefsmentioning

confidence: 99%

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Determinants of College Major Choice: Identification Using an Information Experiment

2011

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper studies the determinants of college major choice using a unique "information" experiment embedded in a survey. We first ask respondents their self-beliefs-beliefs about their own expected earnings and other major-specific outcomes conditional on various majors, their population beliefs-beliefs about the population distribution of these characteristics, as well as their subjective beliefs that they will graduate with each major. After eliciting these baseline beliefs, we provide students with information on the true population distribution of these characteristics, and observe how this new information causes respondents to update their beliefs. Our experimental design creates unique panel data. We first show that respondents make substantial errors in population beliefs, and logically revise their self-beliefs in response to the information. Subjective beliefs about future major choice are positively and strongly associated with beliefs about self-earnings, ability, and spouse's earnings. However, cross-sectional estimates are severely biased upward because of the positive correlation of tastes with earnings and ability. The experimental variation in beliefs allows us to identify a rich model of college major choice, with which we estimate the relative importance of earnings and earnings uncertainty on the choice of college major versus other factors, such as ability to complete coursework, spouse's characteristics, and tastes for majors. While earnings are a significant determinant of major choice, tastes are the dominant factor in the choice of field of study. We also investigate why males and females choose different college majors.

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Section: Resultsmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Revisions Of Self Beliefsmentioning

confidence: 99%

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Determinants of College Major Choice: Identification Using an Information Experiment

2011

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“…In an experimental design, in which consumers state their probability over different sets of attributes, Blass et al (2010) are able to derive preferences for electricity reliability in Israel using elicited choice probabilities. Contrary to stated choices, choice probabilities allow consumers to express uncertainty about their actual behaviour and provide more information to researchers.…”

Section: Methodology and Empirical Analysismentioning

confidence: 99%

Price or performance? A probabilistic choice analysis of the intention to buy electric vehicles in European countries

2018

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Price or performance? A probabilistic choice analysis of the intention to buy electric vehicles in European countries AbstractTraditional choice models perform poorly in understanding the determinants of the adoption of new products. First, data on pioneers and early adopters are biased towards specific performance characteristics of the product and the socio-demographic characteristics of the consumers. Second, surveys on the intention to buy underperform in detecting movements of those who do not intend to buy, who are the majority in the case of new products. Probabilistic choice models try to overcome this issue. By using survey data on electric vehicles, we theoretically contribute to this stream of literature and empirically estimate the impact of specific performance improvements and price reduction on the probability of consumers switching from non-intention to buy to intention to buy. Results show that price reduction is the most important triggering factor for the diffusion of electric vehicles, as it determines more than other factors the transition of consumers from the nonintention to the intention to buy an electric vehicle. The improvement in the driving range constitutes the second most important factor for low initial values of the stated intention to buy, while the possibility of recharging at home matters significantly more for consumers with high initial values of the stated intention to buy.

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“…This study joins a growing literature on the valuation of electricity attributes based on stated preferences methods (Blass et al 2010;Hensher et al 2014;Carlsson et al 2011;Abdullah and Mariel 2010;Morrison and Nalder 2009;Carlsson andMartinson 2007, 2008; Yu et al 2009). This literature has considered fewer attributes, and it has mainly been concentrated on developed countries.…”

Section: Introductionmentioning

confidence: 99%

Are Blackout Days Free of Charge?: Valuation of Individual Preferences for Improved Electricity Services

Mori¹,

Alberto²

2017

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work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed.Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license.Following a peer review process, and with previous written consent by the Inter-American Development Bank (IDB), a revised version of this work may also be reproduced in any academic journal, including those indexed by the American Economic Association's EconLit, provided that the IDB is credited and that the author(s) receive no income from the publication. Therefore, the restriction to receive income from such publication shall only extend to the publication's author(s). With regard to such restriction, in case of any inconsistency between the Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives license and these statements, the latter shall prevail.Note that link provided above includes additional terms and conditions of the license.The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent.

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Using Elicited Choice Probabilities to Estimate Random Utility Models: Preferences for Electricity Reliability*

Cited by 108 publications

References 27 publications

Determinants of College Major Choice: Identification Using an Information Experiment

Determinants of College Major Choice: Identification Using an Information Experiment

Price or performance? A probabilistic choice analysis of the intention to buy electric vehicles in European countries

Are Blackout Days Free of Charge?: Valuation of Individual Preferences for Improved Electricity Services

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