Data and Code for: Daily Labor Supply and Adaptive Reference Points
Principal Investigator(s): View help for Principal Investigator(s) Neil Thakral, Brown University; Linh Tô, Boston University
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
|---|---|---|---|
| constrain_transform | 03/09/2021 03:43:PM | ||
| gslab_misc | 03/09/2021 03:43:PM | ||
| gslab_mle | 03/09/2021 03:44:PM | ||
| gslab_model | 03/09/2021 03:45:PM | ||
| numerical_derivatives | 03/09/2021 03:45:PM |
Project Description
Summary:
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Data and code accompanying the article "Daily Labor Supply and Adaptive Reference Points":
This paper provides field evidence on how reference points adjust, a degree of freedom in reference-dependence models. Examining this in the context of cabdrivers' daily labor-supply behavior, we ask how the within-day timing of earnings affects decisions. Drivers work less in response to higher accumulated income, with a strong effect for recent earnings that gradually diminishes for earlier earnings. We estimate a structural model in which drivers work towards a reference point that adjusts to deviations from expected earnings with a lag. This dynamic view of reference dependence reconciles conflicting “neoclassical” and “behavioral” interpretations of evidence on daily labor-supply decisions.
This paper provides field evidence on how reference points adjust, a degree of freedom in reference-dependence models. Examining this in the context of cabdrivers' daily labor-supply behavior, we ask how the within-day timing of earnings affects decisions. Drivers work less in response to higher accumulated income, with a strong effect for recent earnings that gradually diminishes for earlier earnings. We estimate a structural model in which drivers work towards a reference point that adjusts to deviations from expected earnings with a lag. This dynamic view of reference dependence reconciles conflicting “neoclassical” and “behavioral” interpretations of evidence on daily labor-supply decisions.
Scope of Project
Subject Terms:
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Labor supply;
Structural estimation;
Behavioral economics;
Taxi data
JEL Classification:
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C52 Model Evaluation, Validation, and Selection
D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
J22 Time Allocation and Labor Supply
C52 Model Evaluation, Validation, and Selection
D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
J22 Time Allocation and Labor Supply
Geographic Coverage:
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New York City, U.S.
Universe:
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All cab fares in NYC
Data Type(s):
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administrative records data
Methodology
Data Source:
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New York City Taxi and Limousine Commission
Unit(s) of Observation:
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Trip
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