New York City Cab Drivers' Labor Supply Revisited: Reference-Dependent Preferences with Rational- Expectations Targets for Hours and Income †
read more
Citations
Desert and Inequity Aversion in Teams
Your Loss Is My Gain: A Recruitment Experiment with Framed Incentives
An Experiment on Reference Points and Expectations
A theory of the islamic revival
Desert and inequity aversion in teams
References
Prospect theory: an analysis of decision under risk
Prospect theory: analysis of decision under risk
Loss Aversion in Riskless Choice: A Reference-Dependent Model
A Model of Reference-Dependent Preferences
Labor Supply of New York City Cabdrivers: One Day at a Time
Related Papers (5)
Frequently Asked Questions (6)
Q2. What is the key function of gain-loss utility?
The key function η(λ − 1) of the parameters of gain-loss utility is plausibly and precisely estimated, robust to the specification of proxies for drivers’ expectations, and comfortably within the range that indicates reference-dependent preferences.
Q3. How many hours is the average within-driver standard deviation of the income target proxies?
The average within-driver standard deviation of the income target proxies is $34, and that of the hours target proxies is 1.62 hours.
Q4. What is the effect of income on stopping probability?
if the second target reached on a given day normally has the stronger influence, then on good days, when the income target is reached before the hours target, hours has a stronger influence on stopping probability, as in the *** coefficient in the first row of the right-hand panel of Table 2 in the column headed “first hour’s earnings > expected.”
Q5. What is the effect of the second-reached target on the stopping probability?
Even so, the targets have a very strong influence on the stopping probabilities, and the second-reached target has a stronger effect than the first-reached target.
Q6. What is the effect of the targets on stopping probabilities?
Despite the influence of the targets on stopping probabilities, the heterogeneity of realized earnings yields a smooth aggregate relationship between stopping probability and realized income, so the model can reconcile Farber’s (2005) finding that aggregate stopping probabilities are significantly related to hours but not income with a negative aggregate wage elasticity of hours as found by Camerer et al. (1997).