Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Author:

Dalhaus Tobias1,Finger Robert1

Affiliation:

1. Agricultural Economics and Policy Group, D-MTEC, ETH Zürich, Zurich, Switzerland

Abstract

Abstract Adverse weather events occurring at sensitive stages of plant growth can cause substantial yield losses in crop production. Agricultural insurance schemes can help farmers to protect their income against downside risks. While traditional indemnity-based insurance schemes need governmental support to overcome market failure caused by asymmetric information problems, weather index–based insurance (WII) products represent a promising alternative. In WII the payout depends on a weather index serving as a proxy for yield losses. However, the nonperfect correlation of yield losses and the underlying index, the so-called basis risk, constitutes a key challenge for these products. This study aims to contribute to the reduction of basis risk and thus to the addition of risk-reducing properties of WII. More specifically, the study tests whether grid data for precipitation (vs weather station data) and phenological observations (vs fixed time windows for index determination) that are provided by public institutions can reduce spatial and temporal basis risk and thus improve the performance of WII. An empirical example of wheat production in Germany is used. No differences were found between using gridded and weather station precipitation, whereas the use of phenological observations significantly increases expected utility. However, even if grid data do not yet reduce basis risk, they enable overcoming several disadvantages of using station data and are thus useful for WII applications. Based on the study’s findings and the availability of these data in other countries, a massive potential for improving WII can be concluded.

Publisher

American Meteorological Society

Subject

Atmospheric Science,Social Sciences (miscellaneous),Global and Planetary Change

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