Dekker, T orcid.org/0000-0003-2313-8419 and Chorus, CG (2018) Consumer surplus for random regret minimisation models. Journal of Environmental Economics and Policy, 7 (3). pp. 269-286. ISSN 2160-6544
Abstract
This paper is the first to develop a measure of consumer surplus for the Random Regret Minimisation (RRM) model. Following a not so well-known approach proposed two decades ago, we measure (changes in) consumer surplus by studying (changes in) observed behaviour, i.e. the choice probability, in response to price (changes). We interpret the choice probability as a well-behaved approximation of the probabilistic demand curve and accordingly measure the consumer surplus as the area underneath this demand curve. The developed welfare measure enables researchers to assign a measure of consumer surplus to specific alternatives in the context of a given choice set. Moreover, we are able to value changes in the non-price attributes of a specific alternative. We illustrate how differences in consumer surplus between random regret and random utility models follow directly from the differences in their behavioural premises.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | (c) 2018 Journal of Environmental Economics and Policy Ltd. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Environmental Economics and Policy on 17 Jan 2018, available online: https://doi.org/10.1080/21606544.2018.1424039 |
Keywords: | Random regret minimisation; consumer surplus; welfare; probabilistic demand function; context dependency |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Jan 2018 12:29 |
Last Modified: | 17 Jan 2019 01:38 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/21606544.2018.1424039 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125762 |