When to quit: Narrow bracketing and reference dependence in taxi drivers
Vincent Martin
Journal of Economic Behavior & Organization, 2017, vol. 144, issue C, 166-187
Abstract:
Taxi drivers provide an ideal setting for testing various models of labor supply. Despite this, the literature studying driver behavior has produced mixed evidence of which labor supply model best applies to driver labor supply. Using a novel analysis and large datasets of taxi trips from two different cities, this paper provides a unifying analysis which reconciles previous inconsistencies in evidence for reference dependence in taxi drivers. By testing for a particular non-linear relationship between shift income and drivers’ hazard of stopping, I identify behavior that is consistent with Prospect theoretic (S-shaped) reference dependence as opposed to the more extensively examined loss aversion model of reference dependence. This particular model of reference dependence in this setting allows me to estimate individual driver reference points without an explicit functional form or ex-ante assumptions about the existence of a reference point.
Keywords: Behavioral economics; Reference dependence; Labor supply (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167268117302731
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:144:y:2017:i:c:p:166-187
DOI: 10.1016/j.jebo.2017.09.024
Access Statistics for this article
Journal of Economic Behavior & Organization is currently edited by Houser, D. and Puzzello, D.
More articles in Journal of Economic Behavior & Organization from Elsevier
Bibliographic data for series maintained by Catherine Liu ().