Data and Code for: Exit, Tweets and Loyalty
Principal Investigator(s): View help for Principal Investigator(s) Mara Lederman, Rotman School of Management, University of Toronto
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
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| codes | 04/04/2020 04:47:PM | ||
| data | 06/04/2020 06:29:PM | ||
| data-agreements | 06/04/2020 07:52:PM | ||
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application/pdf | 374.6 KB | 04/04/2020 11:42:AM |
Project Citation:
Lederman, Mara. Data and Code for: Exit, Tweets and Loyalty. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-04-12. https://doi.org/10.3886/E118701V1
Project Description
Summary:
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Hirschman’s Exit, Voice, and Loyalty highlights the
role of “voice” when individuals confront an unexpected deterioration in
quality. Yet, voice has received little attention. To motivate our empirical
analysis, we develop a simple model of voice as the equilibrium of a relational
contract between customers and firms. We use data on 4 million tweets to or
about U.S. airlines to study the relationship between quality, voice, and
market structure. Voice increases when quality deteriorates. This relationship
is greater for airlines which operate a large share of flights in a market.
Supplemental analyses support a relational contracting role for voice.
Funding Sources:
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SSHRC (493140)
Scope of Project
Subject Terms:
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market structure;
social media;
complaints;
airlines
JEL Classification:
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D40 Market Structure, Pricing, and Design: General
L10 Market Structure, Firm Strategy, and Market Performance: General
L86 Information and Internet Services; Computer Software
D40 Market Structure, Pricing, and Design: General
L10 Market Structure, Firm Strategy, and Market Performance: General
L86 Information and Internet Services; Computer Software
Geographic Coverage:
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United States
Time Period(s):
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8/1/2012 – 8/1/2014
Universe:
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Tweets made by individuals to or about major US airlines.
Data Type(s):
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event/transaction data;
observational data
Methodology
Data Source:
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4 Data Sources:
1. Data on tweets purchased from GNIP
2. Data on the on-time performance of US airlines (available from the US Bureau of Transportation Statistics)
3. Data on US airlines' flight schedules purchased from the Official Airlines Guide (OAG)
4. Data for weather measured at weather stations located at US airports (available from National Oceanic and Atmospheric Administration).
1. Data on tweets purchased from GNIP
2. Data on the on-time performance of US airlines (available from the US Bureau of Transportation Statistics)
3. Data on US airlines' flight schedules purchased from the Official Airlines Guide (OAG)
4. Data for weather measured at weather stations located at US airports (available from National Oceanic and Atmospheric Administration).
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