Karen Macours, Paris School of Economics

"The Complexity of Multidimensional Learning in Agriculture"


The adoption of new technologies often requires farmers to re-optimize production decisions over various inputs. Yet few studies examine how exposure to information signals on different inputs translate into dynamic learning and adoption decisions. We randomly invited farmers in Kenya to participate in agronomic research trials, giving them an opportunity to conduct side-by-side comparisons of different combinations of inputs during three consecutive seasons. Drawing from detailed data collection during six seasons, we study farmers’ learning process. Farmers react to the exogenous signal by increasing experimentation and their farming know-how increases rapidly. High skill farmers experiment the most as a response to the treatment and learn faster, but also make new mistakes and endure a reduction in short term profit. We present a theoretical model with a multidimensionality of input and practice decisions where complementarities and substituabilities make searching for a better equilibrium costly, and then test the model’s implications.

(Joint with Rachid Laajaj)

Contact person: Neda Trifkovic