Department of Economics

The paper begins with the problem of a firm subject to random productivity shocks drawn from a particular distribution. We are concerned with the case whereby the distribution of the shocks changes without the knowledge of the firm. Over time the firm learns about the nature and extent of the change in the distribution of the shock and adjusts, incurring adjustment costs in the process. The long run loss in profits (Ž) due to the shift in the distribution we term the adaptation costs. The transitory profit loss, incurred while the firm is learning about the distribution shift, is termed the adjustment cost. The theory is developed and then applied to the problem of measuring adaptation and adjustment costs in the face of unanticipated and imperfectly observed climate change in agriculture. The empirical part of the paper involves estimating a supply function for corn that depends on actual weather realizations and expected weather, using county level data for the US. We then simulate the effect of an unobserved climate shock, where learning about the climate shock is by observing the weather and updating prior knowledge using Bayes Rule.