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Robust ranking of happiness outcomes: a median regression perspective

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  • Chen, Le Yu
  • Oparina, Ekaterina
  • Powdthavee, Nattavudh
  • Srisuma, Sorawoot

Abstract

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.

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  • Chen, Le Yu & Oparina, Ekaterina & Powdthavee, Nattavudh & Srisuma, Sorawoot, 2022. "Robust ranking of happiness outcomes: a median regression perspective," LSE Research Online Documents on Economics 115556, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:115556
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    More about this item

    Keywords

    median regression; mixed integer optimization; ordered-response model; quantile regression; subjective well-being;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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