Robust Ranking of Happiness Outcomes: A Median Regression Perspective

Chen, Le‐Yu; Oparina, Ekaterina; Powdthavee, Nattavudh; Srisuma, Sorawoot

doi:10.48550/arxiv.1902.07696

Cited by 2 publications

(2 citation statements)

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“…Regarding measurement error, we estimate ordered probit models and show that our main conclusions continue to hold with this specification, as the estimated coefficients are comparable in both sign and magnitude to those from OLS. Regarding heteroskedacity, we follow Chen et al (2019) who suggest that the results of ordered models can be re-interpreted by looking at the effects at the median rather than at the mean. The intuition is that in these types of ordered models the mean and the median of the underlying latent variable coincide due to the symmetric nature of logistic and normal distributions.…”

Section: Life Satisfactionmentioning

confidence: 97%

The Full Returns to the Choice of Occupation and Education

2022

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Section: Life Satisfactionmentioning

confidence: 97%

The Full Returns to the Choice of Occupation and Education

2022

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“…3 There has been controversy about whether such data can support inferences about underlying wellbeing (Bond and Lang (2019), Chen et al (2019), Kaiser and Vendrik (2020), Schröder and Yitzhaki (2017)). We here remain The SOEP is representative of the German adult population, with interviews conducted in person.…”

Section: Datamentioning

confidence: 99%

Human Wellbeing and Machine Learning

Oparina¹,

Kaiser²,

Gentile³

et al. 2022

Preprint

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There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches do perform better than traditional models. Although the size of the improvement is small in absolute terms, it turns out to be substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms -i.e. material conditions, health, and meaningful social relations -are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.

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Robust Ranking of Happiness Outcomes: A Median Regression Perspective

Cited by 2 publications

References 0 publications

The Full Returns to the Choice of Occupation and Education

The Full Returns to the Choice of Occupation and Education

Human Wellbeing and Machine Learning

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