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

Zhiwei Shen, Martin Odening and Ostap Okhrin

No 2013-030, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: Although 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.

Keywords: expert knowledge; data scarcity; crop insurance pricing; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C14 Q19 (search for similar items in EconPapers)
Date: 2013
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Related works:
Journal Article: Can expert knowledge compensate for data scarcity in crop insurance pricing? (2016)
Working Paper: Can expert knowledge compensate for data scarcity in crop insurance pricing? (2013)
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