EconPapers    
Economics at your fingertips  
 

Analyzing Subjective Well-Being Data with Misclassification

Ekaterina Oparina () and Sorawoot Srisuma

Journal of Business & Economic Statistics, 2022, vol. 40, issue 2, 730-743

Abstract: We use novel nonparametric techniques to test for the presence of nonclassical 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 in objective outcomes such as income and employment.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2020.1865169 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Analyzing subjective well-being data with misclassification (2020) Downloads
Working Paper: Analyzing Subjective Well-Being Data with Misclassification (2019) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:40:y:2022:i:2:p:730-743

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2020.1865169

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2022-12-20
Handle: RePEc:taf:jnlbes:v:40:y:2022:i:2:p:730-743