Centre for Development Economics
Department of Economics, Delhi School of Economics


Multidimensional and Selective Learning: A case study of Bt cotton farmers in India 


Srijita Ghosh  

Ashoka University

Thursday, 24 October 2019 at 3:05 P.M.

Venue: AMEX Room (Second Floor)

Department of Economics, Delhi School of Economics

All are cordially invited

Most production technologies require using an optimal combination of multiple inputs. Farmers need to choose the best combination of seeds, fertilizers, pesticides, etc. to maximize yield.  In this paper, I build a model where the farmers can learn about the production function by observing the conditional productivity of combinations of inputs (cell) or by the marginal productivity of each input across cells (average), where both types of learning are costly. I characterize the optimal learning strategy in terms of uncertainty in the belief distribution: observing an average is optimal for higher uncertainty and observing a cell is optimal for lower uncertainty. In a sequential learning problem with optimal stopping time, under mild sufficiency condition, the learning strategy is to start with observing averages and then switch permanently to observing cells. Depending on the uncertainty of averages, optimal learning strategy can be observing only averages, at the cost of a higher probability of error (“selective learning”). I further show that selective learning describes the behavior of Indian cotton farmers when they switched to pest-resistant Bt seeds, as they did not reduce their pesticide use optimally. This informs about optimal extension policies (what type of information) for various types of production functions.

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