Ekaterina Oparina, Sorawoot Srisuma
arXiv 15 May 2019 · Econometrics · publishedJournal of Business and Economic Statistics (2020) · 1 citations (OpenAlex)
arXiv:1905.06037 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bond, T. and Lang, K (2018) The Sad Truth About Happiness Scales | 1.000 | 7 | 4 | 100% |
| 2 | Hu, Y (2008) Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution | 1.000 | 7 | 4 | 100% |
| 3 | Chen, L. Y., Oparina, E., Powdthavee, N., and Srisuma, S (2019) Have Econometric Analyses of Happiness Data Been Futile? A Simple Truth About Happiness Scales self | 1.000 | 6 | 5 | 100% |
| 4 | Wilhelm, D (2018) Testing for the Presence of Measurement Error | 1.000 | 6 | 3 | 100% |
| 5 | Bertrand, M. and Mullainathan, S (2001) Do People Mean What They Say? Implications for Subjective Survey Data | 0.737 | 3 | 2 | 100% |
| 6 | Dolan, P., Peasgood, T., and White, M (2008) Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being | 0.737 | 3 | 2 | 100% |
| 7 | Hu, Y (2017) The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor econo… | 0.737 | 3 | 2 | 100% |
| 8 | Chen, S. and Khan, S (2003) Rates of convergence for estimating regression coefficients in heteroskedastic discrete response models | 0.644 | 2 | 2 | 100% |
| 9 | Ferrer-i-Carbonell, A. and Frijters, P (2004) How Important is Methodology for the estimates of the determinants of Happiness? | 0.644 | 2 | 2 | 100% |
| 10 | Helliwell, J., Layard, R., and Sachs, J (2012) World happiness report | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Robust Ranking of Happiness Outcomes: A Median Regression Perspective | 0.644 | 2 | 2 |
| 2 | Identifying causal effects with subjective ordinal outcomes | 0.000 | 1 | 1 |