Robust Ranking of Happiness Outcomes: A Median Regression Perspective

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

doi:10.1016/j.jebo.2022.06.010

Cited by 15 publications

(10 citation statements)

References 73 publications

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“…Our empirical strategy builds upon the parametric setting of Chen et al (2022), by which we specify a parametric distribution for the latent LS, which embeds a parametric form of heteroskedasticity conditional upon the explanatory variables. We estimate the model parameters using the maximum likelihood estimation method and then construct the conditional quantiles of LS based on the postulated distributional specification and the estimated parameters.…”

Section: Methodsmentioning

confidence: 99%

“…Chen et al (2022) consider the notion of median ranking, and their parametric implementation is based on empirical models (1) and (2) where the

σ (∙)

function is exponential (i.e.,

σ (t) = e^{t}

) and the LS disturbance

ε

follows either the standard logistic or normal distribution. Their empirical results are relatively insensitive to the specification of the distribution of

ε

.…”

Section: Methodsmentioning

confidence: 99%

“…This is because the mean ranking is generally not invariant with respect to an arbitrary monotone transformation of the individual LS, yet such invariance is desirable as LS scales are ordinal in nature. Chen et al (2022) propose performing ordinal median regression analysis on LS data, thereby resulting in the notion of median LS comparison, which is unaffected by monotone transformations. To estimate the conditional median of LS, these authors employ both parametric and semiparametric ordered discrete response models where the observed discrete LS scales are driven by a latent continuous happiness index in a threshold-crossing fashion.…”

Section: Ordinal Parametric Quantile Regression Approachmentioning

confidence: 99%

See 2 more Smart Citations

Distributional Effects of Freedom and Income on Life Satisfaction: Evidence from East Asian Chinese Societies

Lim

Shaw

Chen

et al. 2023

Asian Economic Journal

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We estimate the distributional effects of freedom and income on life satisfaction (LS) for East Asian Chinese societies through an ordinal parametric quantile regression approach. The results show that freedom and income exhibit positive and mostly significant effects across societies and LS levels. The freedom effects generally become larger for those at higher LS quantiles. However, the income effects decline as the LS quantile level increases. Thus, one may have a trade‐off between freedom and income without compromising individual LS. The trade‐off of freedom (income) for income (freedom) is more pronounced for people at a lower (higher) LS level.

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Section: Methodsmentioning

confidence: 99%

“…Chen et al (2022) consider the notion of median ranking, and their parametric implementation is based on empirical models (1) and (2) where the

σ (∙)

function is exponential (i.e.,

σ (t) = e^{t}

) and the LS disturbance

ε

follows either the standard logistic or normal distribution. Their empirical results are relatively insensitive to the specification of the distribution of

ε

.…”

Section: Methodsmentioning

confidence: 99%

Section: Ordinal Parametric Quantile Regression Approachmentioning

confidence: 99%

See 1 more Smart Citation

Distributional Effects of Freedom and Income on Life Satisfaction: Evidence from East Asian Chinese Societies

Lim

Shaw

Chen

et al. 2023

Asian Economic Journal

View full text Add to dashboard Cite

show abstract

“…On the issue of heteroscedasticity, we follow the approach developed by Chen et al . (2022), who suggest that the results of ordered models can be re‐interpreted by looking at the effects at the median rather than the mean. The intuition behind this approach 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: The Interpersonal Dispersion Of Overall Rewardsmentioning

confidence: 99%

“…We address these issues using two robustness checks, which are presented in more detail in Appendix E. On the issue of measurement error, we estimate ordered probit models and show that the coefficients are comparable in both size and magnitude, and that our main conclusions remain unchanged using this specification. On the issue of heteroscedasticity, we follow the approach developed by Chen et al (2022), who suggest that the results of ordered models can be re-interpreted by looking at the effects at the median rather than the mean. The intuition behind this approach 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: The Interpersonal Dispersion Of Overall Rewardsmentioning

confidence: 99%

Do wages underestimate the inequality in workers' rewards? The joint distribution of job quality and wages across occupations

Clark,

Cotofan,

Layard

2024

Economica

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Information on both wages and job quality is needed in order to understand the occupational dispersion of wellbeing. We analyse subjective wellbeing in a large UK sample to construct a measure of ‘overall reward’, the sum of wages and the value of job quality, in 90 different occupations. If only wages are included, then labour market inequality is underestimated: the dispersion of overall rewards is one‐third larger than the dispersion of wages. Our findings are similar, and stronger, in data on US workers. We find a positive correlation between job quality and wages in all specifications, both between individuals in the cross‐section and within individuals in panel data. The gender and ethnic gaps in the labour market are larger than those in wages alone, and the overall rewards to education on the labour market are underestimated by earnings differentials alone.

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The female happiness paradox

Blanchflower,

Bryson

2024

J Popul Econ

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Using data across countries and over time, we show that women have worse mental health than men in negative affect equations, irrespective of the measure used — anxiety, depression, fearfulness, sadness, loneliness, anger — and they have more days with bad mental health and more restless sleep. Women are also less satisfied with many aspects of their lives, such as democracy, the economy, the state of education, and health services. They are also less satisfied in the moment in terms of peace and calm, cheerfulness, feeling active, vigorous, fresh, and rested. However, prior evidence on gender differences in happiness and life satisfaction is less clear cut. Differences vary over time, location, and with model specification and the inclusion of controls, especially marital status. We now find strong evidence that males have higher levels of both happiness and life satisfaction in recent years even before the onset of the pandemic. As in the past, women continue to have worse mental health. A detailed analysis of several data files, with various metrics, for the UK confirms that men now are happier than women and the size of the effect is not trivial.

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Robust Ranking of Happiness Outcomes: A Median Regression Perspective

Cited by 15 publications

References 73 publications

Distributional Effects of Freedom and Income on Life Satisfaction: Evidence from East Asian Chinese Societies

Distributional Effects of Freedom and Income on Life Satisfaction: Evidence from East Asian Chinese Societies

Do wages underestimate the inequality in workers' rewards? The joint distribution of job quality and wages across occupations

The female happiness paradox

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