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A New Method of Estimating Risk Aversion
Abstract
I show existing evidence on labor supply behavior places an upper bound on risk aversion in the expected utility model. I derive a formula for the coefficient of relative risk aversion (γ) in terms of the ratio of the income elasticity of labor supply to wage elasticity and degree of complementarity between consumption and labor. I bound the degree of complementarity using data on consumption choices when labor supply varies across states. Using labor supply elasticity estimates, I find a mean estimate of [Formula: see text], then show generating γ > 2 requires that wage increases cause sharper labor supply reductions.
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Barsky, R. B., Juster, F. T., Kimball, M. S., & Shapiro, M. D. (1997) · The Quarterly Journal of Economics · doi:10.1162/003355397555280 · Cited by 2,400
Imbens, G. W., Rubin, D. B., & Sacerdote, B. I. (2001) · American Economic Review · doi:10.1257/aer.91.4.778 · Cited by 512
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Cohen, A., & Einav, L. (2007) · American Economic Review · doi:10.1257/aer.97.3.745 · Cited by 708
Sydnor, J. (2010) · American Economic Journal: Applied Economics · doi:10.1257/app.2.4.177 · Cited by 389
The Autocratic Welfare State: Old-Age Pensions, Credible Commitments, and Regime Survival
Knutsen, C. H., & Rasmussen, M. (2018) · Comparative Political Studies · doi:10.1177/0010414017710265 · Cited by 146
Lakdawalla, D. N., & Phelps, C. E. (2021) · Value in Health · doi:10.1016/j.jval.2020.10.003 · Cited by 120
Slicing the Pie: Quantifying the Aggregate and Distributional Effects of Trade
Galle, S., Rodríguez-Clare, A., & Yi, M. (2017) · doi:10.3386/w23737 · Cited by 111
Metadata
- Authors
- Chetty, R.
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2006-11-01
- Volume
- 96
- Issue
- 5
- Pages
- 1821-1834
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W3124404638
- Cited by
- 699
- References
- 2
- FWCI
- 10.68
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
