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背景很重要。

Context matters.

作者信息

Zhou Wenting, Hey John

机构信息

Department of Economics, University of York, Heslington, York, YO10 4GA UK.

出版信息

Exp Econ. 2018;21(4):723-756. doi: 10.1007/s10683-017-9546-z. Epub 2017 Oct 25.

DOI:10.1007/s10683-017-9546-z
PMID:30459523
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6223722/
Abstract

Eliciting the level of risk aversion of experimental subjects is of crucial concern to experimenters. In the literature there are a variety of methods used for such elicitation; the concern of the experiment reported in this paper is to compare them. The methods we investigate are the following: Holt-Laury price lists; pairwise choices, the Becker-DeGroot-Marschak method; allocation questions. Clearly their relative efficiency in measuring risk aversion depends upon the numbers of questions asked; but the method itself may well influence the estimated risk-aversion. While it is impossible to determine a 'best' method (as the truth is unknown) we can look at the differences between the different methods. We carried out an experiment in four parts, corresponding to the four different methods, with 96 subjects. In analysing the data our methodology involves fitting preference functionals; we use four, Expected Utility and Rank-Dependent Expected Utility, each combined with either a CRRA or a CARA utility function. Our results show that the inferred level of risk aversion is more sensitive to the elicitation method than to the assumed-true preference functional. Experimenters should worry most about context.

摘要

引出实验对象的风险厌恶程度是实验者至关重要的关注点。在文献中有多种用于此类引出的方法;本文所报告实验的关注点是对它们进行比较。我们所研究的方法如下:霍尔特 - 劳里价格列表;成对选择、贝克尔 - 德格鲁特 - 马尔沙克方法;分配问题。显然,它们在测量风险厌恶方面的相对效率取决于所问问题的数量;但方法本身很可能会影响对风险厌恶的估计。虽然不可能确定一种“最佳”方法(因为真实情况未知),但我们可以看看不同方法之间的差异。我们进行了一个分为四个部分的实验,对应四种不同方法,有96名受试者。在分析数据时,我们的方法涉及拟合偏好泛函;我们使用四种,即期望效用和秩依赖期望效用,每种都与常相对风险厌恶(CRRA)或常绝对风险厌恶(CARA)效用函数相结合。我们的结果表明,推断出的风险厌恶程度对引出方法比对假定的真实偏好泛函更敏感。实验者最应该担心的是背景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/1bbd7bb49370/10683_2017_9546_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/99f9786e0a29/10683_2017_9546_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/07e57c6f046a/10683_2017_9546_Fig2a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/15581716d431/10683_2017_9546_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/b589d1e137bb/10683_2017_9546_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/79c57cc9793b/10683_2017_9546_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/7cf1a9534887/10683_2017_9546_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/5a1e5597ca47/10683_2017_9546_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/6749155e79a9/10683_2017_9546_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/1bbd7bb49370/10683_2017_9546_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/99f9786e0a29/10683_2017_9546_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/07e57c6f046a/10683_2017_9546_Fig2a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/15581716d431/10683_2017_9546_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/b589d1e137bb/10683_2017_9546_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/79c57cc9793b/10683_2017_9546_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/7cf1a9534887/10683_2017_9546_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/5a1e5597ca47/10683_2017_9546_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/6749155e79a9/10683_2017_9546_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/799b/6223722/1bbd7bb49370/10683_2017_9546_Fig9_HTML.jpg

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