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Analyzing Subjective Well-Being Data with Misclassification

Ekaterina Oparina () and Sorawoot Srisuma

Papers from arXiv.org

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.

Date: 2019-05
New Economics Papers: this item is included in nep-bec, nep-eur and nep-hap
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http://arxiv.org/pdf/1905.06037 Latest version (application/pdf)

Related works:
Journal Article: Analyzing Subjective Well-Being Data with Misclassification (2022) Downloads
Working Paper: Analyzing subjective well-being data with misclassification (2020) Downloads
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