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Analyzing Subjective Well-Being Data with Misclassification

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  • Ekaterina Oparina
  • Sorawoot Srisuma

Abstract

We use novel nonparametric techniques to test for the presence of non-classical measurement error in reported life satisfaction (LS) and study the potential effects from ignoring it. Our dataset comes from Wave 3 of the UK Understanding Society that is surveyed from 35,000 British households. Our test finds evidence of measurement error in reported LS for the entire dataset as well as for 26 out of 32 socioeconomic subgroups in the sample. We estimate the joint distribution of reported and latent LS nonparametrically in order to understand the mis-reporting behavior. We show this distribution can then be used to estimate parametric models of latent LS. We find measurement error bias is not severe enough to distort the main drivers of LS. But there is an important difference that is policy relevant. We find women tend to over-report their latent LS relative to men. This may help explain the gender puzzle that questions why women are reportedly happier than men despite being worse off on objective outcomes such as income and employment.

Suggested Citation

  • Ekaterina Oparina & Sorawoot Srisuma, 2019. "Analyzing Subjective Well-Being Data with Misclassification," Papers 1905.06037, arXiv.org.
    • Handle: RePEc:arx:papers:1905.06037
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    1. is not listed on IDEAS
    2. Goerke, Laszlo & Huang, Yue, 2022. "Job satisfaction and trade union membership in Germany," Labour Economics, Elsevier, vol. 78(C).
    3. Foliano, Francesca & Tonei, Valentina & Sevilla, Almudena, 2024. "Social restrictions, leisure and well-being," Labour Economics, Elsevier, vol. 87(C).
    4. Niccolò Gentile & Michela Bia & Andrew E. Clark & Conchita D'Ambrosio & Alexandre Tkatchenko, 2025. "What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning Algorithms," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 71(2), May.
    5. Chen, Le-Yu & Oparina, Ekaterina & Powdthavee, Nattavudh & Srisuma, Sorawoot, 2022. "Robust Ranking of Happiness Outcomes: A Median Regression Perspective," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 672-686.
    6. Oparina, Ekaterina & Clark, Andrew E. & Layard, Richard, 2024. "The Easterlin paradox at 50," LSE Research Online Documents on Economics 126798, London School of Economics and Political Science, LSE Library.
    7. Christopher P Barrington-Leigh, 2025. "Are international happiness rankings reliable?," Papers 2509.06867, arXiv.org.
    8. Kaiser, Caspar, 2022. "Using memories to assess the intrapersonal comparability of wellbeing reports," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 410-442.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

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