Oparina, Ekaterina ORCID: 0000-0002-1544-8751 and Srisuma, Sorawoot (2022) Analyzing subjective well-being data with misclassification. Journal of Business and Economic Statistics, 40 (2). 730 - 743. ISSN 0735-0015
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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 misreporting 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.
Item Type: | Article |
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Official URL: | https://www.tandfonline.com/toc/ubes20/current |
Additional Information: | © 2021 The Authors |
Divisions: | Centre for Economic Performance |
Subjects: | B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HM Sociology H Social Sciences > HC Economic History and Conditions |
JEL classification: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation I - Health, Education, and Welfare > I3 - Welfare and Poverty > I31 - General Welfare; Basic Needs; Living Standards; Quality of Life; Happiness |
Date Deposited: | 25 Jan 2021 12:18 |
Last Modified: | 16 Nov 2024 02:30 |
URI: | http://eprints.lse.ac.uk/id/eprint/108543 |
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