Can expert knowledge compensate for data scarcity in crop insurance pricing?

Vista sencilla de ítem
dc.creatorShen, Zhiwei
dc.creatorOdening, Martin
dc.creatorOkhrin, Ostap
dc.date2017-04-01T14:07:40Z
dc.date.accessioned2026-07-09T07:07:21Z
dc.descriptionAlthough there is an increasing interest in index-based insurances in many developing countries, crop data scarcity hinders its implementation by forcing insurers to charge higher premiums. Expert knowledge has been considered a valuable information source to augment limited data in insurance pricing. This article investigates whether the use of expert knowledge can mitigate model risk which arises from insufficient statistical data. We adopt the Bayesian framework that allows for the combination of scarce data and expert knowledge, to estimate the risk parameter and buffer load. In addition, a benchmark for the evaluation of expert information is created by using a richer dataset generated from resampling. We find that expert knowledge reduces the parameter uncertainty and changes the insurance premium in the correct direction, but that the effect of the correction is sensitive to different strike levels of insurance indemnity.
dc.identifierdoi:10.22004/ag.econ.149431
dc.identifierhttps://ageconsearch.umn.edu/record/149431/files/paper_expert_data_scarcity_aaea_2013_shen.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/149431
dc.identifier.urihttp://hdl.handle.net/123456789/584730
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/149431
dc.titleCan expert knowledge compensate for data scarcity in crop insurance pricing?
dc.typeText

Archivos

Colecciones

University of Minnesota