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Robust ranking of happiness outcomes: a median regression perspective

Chen, Le Yu, Oparina, Ekaterina ORCID: 0000-0002-1544-8751, Powdthavee, Nattavudh and Srisuma, Sorawoot (2022) Robust ranking of happiness outcomes: a median regression perspective. Journal of Economic Behavior and Organization, 200. 672 - 686. ISSN 0167-2681

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Identification Number: 10.1016/j.jebo.2022.06.010


Ordered probit and logit models have been frequently used to estimate the mean ranking of happiness outcomes (and other ordinal data) across groups. However, it has been recently highlighted that such ranking may not be identified in most happiness applications. We suggest researchers focus on median comparison instead of the mean. This is because the median rank can be identified even if the mean rank is not. Furthermore, median ranks in probit and logit models can be readily estimated using standard statistical softwares. The median ranking, as well as ranking for other quantiles, can also be estimated semiparametrically and we provide a new constrained mixed integer optimization procedure for implementation. We apply it to estimate a happiness equation using General Social Survey data of the US.

Item Type: Article
Official URL:
Additional Information: © 2022 Elsevier B.V.
Divisions: Centre for Economic Performance
Subjects: H Social Sciences > H Social Sciences (General)
B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences > HM Sociology
JEL classification: C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C25 - Discrete Regression and Qualitative Choice Models
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C61 - Optimization Techniques; Programming Models; Dynamic Analysis
I - Health, Education, and Welfare > I3 - Welfare and Poverty > I31 - General Welfare; Basic Needs; Living Standards; Quality of Life; Happiness
Date Deposited: 14 Jul 2022 10:45
Last Modified: 14 Jun 2024 19:18

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