Subsidy Policies with Learning from Stochastic Experiences
Jing Cai (),
Alain de Janvry () and
Elisabeth Sadoulet ()
Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley
Many new products presumed to be privately beneficial to the poor have a high price elasticity of demand and ultimately zero take-up rate at market prices. This has led gov- ernments and donors to provide subsidies to increase the take-up, with the hope of reducing the subsidies once the value of the product is better known. In this study, we use data from a two-year field experiment in rural China to define the optimum subsidy scheme that can insure a given take-up for a new weather insurance product for rice producers. We estimate both reduced form causal channels and a structural model of learning from stochastic expe- rience which we use to conduct policy simulations. Results show that the optimum current subsidy necessary to achieve a desired level of take-up rate depends on both past subsidy levels and past payout rates, implying that subsidy levels should vary locally year-to-year.
Keywords: Social and Behavioral Sciences; Subsidy; Insurance; Take-up; Stochastic Learning (search for similar items in EconPapers)
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