How Mechanistic Explanations Reshape Learning and Behavior: Evidence from a Fertilizer Choice Experiment in Eastern Uganda

Mechanistic explanations—descriptions of a system through the causal interactions of its parts—play a key role in human cognition and scientific progress. Despite their importance, we lack systematic evidence on whether and how mechanistic explanations help lay decision-makers interpret information in complex economic environments. We evaluate the causal impact of including mechanistic explanations in an information intervention: public demonstrations of fertilizer use for smallholder tomato farmers in Eastern Uganda. In all demonstrations, extension officers showcased the impact of a recommended fertilizer recipe. In the treatment group, officers also explained the mechanisms underlying the recipe’s effects—introducing the language of macronutrients and the causal processes linking nutrients, soil features, and plant growth. We collected detailed data on beliefs and behaviors from 797 farmers in a lab-in-the-field experiment conducted at the demonstration site and followed up with them over two growing seasons. In the lab-in-the-field, treated farmers generalized more effectively—making better substitution and arbitrage decisions among fertilizers and achieving 9% higher simulated profits in an incentivized fertilizer-application task. At endline, treated farmers’ real fertilizer choices reflected improved nutrient timing and balance, and their yields were 14% higher.