Weather Information and the Potential for Intertemporal Adverse Selection in Crop Insurance

Abstract

This study investigates the potential usefulness of early-season weather information in forecasting corn yields for the Midwest. To the extent that farmers are able to forecast yields prior to sales closing dates for crop insurance, farmers may use such information in deciding which years to purchase crop insurance and which years not to purchase insurance. Such intertemporal adverse selection would result in increased losses for the crop insurance program. Three yield forecasting models were developed using early-season weather information: (1) simple weather forecasts; (2) yield-weather regression models; and (3) yield-weather discriminant functions. This study presents procedures that would allow farmers to predict low corn yields before the purchasing deadline of corn insurance on 95 percent of planted acreage in the Midwest.