Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Economic Behavior & Organization. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Behavior & Organization, 202, 2022 DOI: 10.1016/j.jebo.2022.08.013
Accepted author manuscript, 267 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Risk preferences, gender effects and Bayesian econometrics
AU - Alam, Jess
AU - Georgalos, Konstantinos
AU - Rolls, Harry
N1 - This is the author’s version of a work that was accepted for publication in Journal of Economic Behavior & Organization. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Economic Behavior & Organization, 202, 2022 DOI: 10.1016/j.jebo.2022.08.013
PY - 2022/10/31
Y1 - 2022/10/31
N2 - Gender differences in decision making is a topic that has attracted much attention in the literature and the debate seems to be inconclusive. A method that is often used in the economics literature to account for gender effects is by estimating econometric structural models and testing the significance of the estimated parameters. In this paper we focus on estimations of preference models and we show how omitting to account for behavioural heterogeneity can lead to failures in identifying potential differences. Using data from risky choice experiments, we compare the traditional representative agent Maximum Likelihood Estimation approach against two more flexible inference methods that allow for heterogeneity at the individual level, the Maximum Simulated Likelihood Estimation, and the Hierarchical Bayesian modelling. We show how ignoring heterogeneity may lead to failures capturing gender differences and we suggest the use of Bayesian modelling to effectively estimate the underlying parameters.
AB - Gender differences in decision making is a topic that has attracted much attention in the literature and the debate seems to be inconclusive. A method that is often used in the economics literature to account for gender effects is by estimating econometric structural models and testing the significance of the estimated parameters. In this paper we focus on estimations of preference models and we show how omitting to account for behavioural heterogeneity can lead to failures in identifying potential differences. Using data from risky choice experiments, we compare the traditional representative agent Maximum Likelihood Estimation approach against two more flexible inference methods that allow for heterogeneity at the individual level, the Maximum Simulated Likelihood Estimation, and the Hierarchical Bayesian modelling. We show how ignoring heterogeneity may lead to failures capturing gender differences and we suggest the use of Bayesian modelling to effectively estimate the underlying parameters.
KW - Gender differences
KW - Risk preferences
KW - Loss aversion
KW - Rank-dependent utility
KW - Prospect theory
KW - Maximum likelihood
KW - Hierarchical Bayesian modelling
U2 - 10.1016/j.jebo.2022.08.013
DO - 10.1016/j.jebo.2022.08.013
M3 - Journal article
VL - 202
SP - 168
EP - 183
JO - Journal of Economic Behavior and Organization
JF - Journal of Economic Behavior and Organization
SN - 0167-2681
ER -