Misattribution prevents learning

Abstract

In many markets, consumers believe things about products that are not true. We study how incorrect beliefs about product quality can persist even after a consumer has used a product many times. We explore the example of fertilizer in East Africa. Farmers believe much local fertilizer is counterfeit or adulterated; however, multiple studies have established that nearly all fertilizer in the area is good quality. We develop a learning model to explain how these incorrect beliefs persist. We show that when the distributions of outcomes using good and bad quality products overlap, agents can misattribute bad luck or bad management to bad quality. Our learning model and its simulations show that the presence of misattribution inhibits learning about quality and that goods like fertilizer with unobservable quality that are inputs into production processes characterized by stochasticity should be thought of as credence goods, not experience goods. Our results suggest that policy makers should pursue quality assurance programs for products that are vulnerable to misattribution.