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BAYESIAN HERDERS: ASYMMETRIC UPDATING OF RAINFALL BELIEFS IN RESPONSE TO EXTERNAL FORECASTS

Travis Lybbert (), Christopher Barrett (), John G. McPeak and Winnie K. Luseno

No 14762, Working Papers from Cornell University, Department of Applied Economics and Management

Abstract: Temporal climate risk weighs heavily on many of the world's poor. Recent advances in model-based climate forecasting have expanded the range, timeliness and accuracy of forecasts available to decision-makers whose welfare depends on stochastic climate outcomes. There has consequently been considerable recent investment in improved climate forecasting for the developing world. Yet, in cultures that have long used indigenous climate forecasting methods, forecasts generated and disseminated by outsiders using unfamiliar methods may not readily gain the acceptance necessary to induce behavioral change. The value of model-based climate forecasts depends critically on the premise that forecast recipients actually use external forecast information to update their rainfall expectations. We test this premise using unique survey data from pastoralists and agropastoralists in southern Ethiopia and northern Kenya, specifying and estimating a model of herders updating seasonal rainfall beliefs. We find that those who receive and believe model-based seasonal climate forecasts indeed update their priors in the direction of the forecast received, assimilating optimistic forecasts more readily than pessimistic forecasts.

Keywords: Agribusiness (search for similar items in EconPapers)
Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:ags:cudawp:14762

DOI: 10.22004/ag.econ.14762

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