Data and Code for: Happy Times - Measuring Happiness Using Response Times

Principal Investigator(s): View help for Principal Investigator(s) Shuo Liu, Guanghua School of Management, Peking University; Nick Netzer, Department of Economics, University of Zurich

Version: View help for Version V1

Name	File Type	Size	Last Modified
LN2_happy.pdf	application/pdf	56.6 KB	09/21/2023 04:58:AM
LN2_patience.pdf	application/pdf	54.3 KB	09/20/2023 10:39:PM
LN2_political.pdf	application/pdf	57.8 KB	09/20/2023 10:39:PM
LN2_risk.pdf	application/pdf	57.2 KB	09/20/2023 10:39:PM
LN2_social.pdf	application/pdf	56.4 KB	09/20/2023 10:39:PM
LN2_trust.pdf	application/pdf	57.7 KB	09/20/2023 10:39:PM
LN2_work.pdf	application/pdf	56.5 KB	09/20/2023 10:39:PM
LN3_happy.pdf	application/pdf	56.5 KB	09/20/2023 10:39:PM
LN3_patience.pdf	application/pdf	55.9 KB	09/20/2023 10:39:PM
LN3_political.pdf	application/pdf	57.7 KB	09/20/2023 10:39:PM

Project Description

Summary:

Surveys measuring happiness or preferences generate discrete ordinal data. Ordered response models, which are used to analyze such data, suffer from an identification problem. Their conclusions depend on distributional assumptions about a latent variable. We propose using response times to solve that problem. Response times contain information about the distribution of the latent variable through a chronometric effect. Using an online survey experiment, we verify the chronometric effect. We then provide theoretical conditions for testing conventional distributional assumptions. These assumptions are rejected in some cases, but overall our evidence is consistent with the qualitative validity of the conventional models.

Scope of Project

Subject Terms: surveys; ordinal data; response times; non-parametric identification

JEL Classification:
      C14 Semiparametric and Nonparametric Methods: General
      D60 Welfare Economics: General
      D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
      I31 General Welfare; Well-Being

Geographic Coverage: US

Universe: MTurk workers from the US with an MTurk approval rate of at least 95%.

Data Type(s): survey data

Methodology

Response Rate: We recruited 8,007 subjects on MTurk.

Sampling: We initially restricted access to workers with an MTurk approval rate of at least 98%. Recruitment became slow after the first 5'976 subjects, at which point the approval requirement was reduced to 95%.

Data Source: Self-collected survey data from MTurk

Collection Mode(s): web-based survey

Scales:

There were 6 standard socio-demographic questions (gender, age, education, marital status, co-residence with children, family income).

There were 7 substantive survey questions (job satisfaction, social life satisfaction, overall happiness, trust attitude, political attitude, time preference, risk preference) which were recorded on a Likert scale with 2 or 3 response catergories.

The exact phrasing of all questions and answer possibilities is described in Word files that are part of this replication package.

Unit(s) of Observation: Individuals

Geographic Unit: US

Related Publications

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Published Versions

V1 [2023-11-22]

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