Inferring Risk Perceptions and Preferences Using Choice from Insurance Menus: Theory and Evidence.
Demand for insurance can be driven by high risk aversion or high-risk. We show how to separately identify risk preferences and risk types using only choices from menus of insurance plans. Our revealed preference approach does not rely on rational expectations, nor does it require access to claims da...
| Publicado en: | Economic Journal Vol. 131; no. 634; pp. 713 - 745 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Feb2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=149178744&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 149178744 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00130133 EJN jtl: Economic Journal issn: 00130133 maglogo: N pubinfo: dt: Feb2021 vid: 131 iid: 634 pid: 622 pub: Oxford University Press / USA artinfo: ui: 149178744 10.1093/ej/ueaa069 ppf: 713 ppct: 32 formats: tig: atl: Inferring Risk Perceptions and Preferences Using Choice from Insurance Menus: Theory and Evidence. aug: au: Ericson, Keith Marzilli Kircher, Philipp Spinnewijn, Johannes Starc, Amanda affil: Boston University Questrom School of Business , USA University of Edinburgh , Belgium London School of Economics , UK Kellogg School of Management , USA su: Massachusetts Insurance Risk perception Health insurance exchanges Menus sug: subj: Insurance Massachusetts All Other Insurance Related Activities Third Party Administration of Insurance and Pension Funds Other Insurance Funds Risk perception Health insurance exchanges Menus ab: Demand for insurance can be driven by high risk aversion or high-risk. We show how to separately identify risk preferences and risk types using only choices from menus of insurance plans. Our revealed preference approach does not rely on rational expectations, nor does it require access to claims data. We show what can be learned non-parametrically about the type distributions from variation in insurance plans, offered separately to random cross-sections or offered as part of the same menu to one cross-section. We prove that our approach allows for full identification in the textbook model with binary risks, and extend our results to continuous risks. We illustrate our approach using the Massachusetts Health Insurance Exchange, where choices provide informative bounds on the type distributions, especially for risks, but do not allow us to reject homogeneity in preferences. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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