Misattribution prevents learning

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...

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Detalles Bibliográficos
Autores principales: Hoel, J.B., Michelson, H., Norton, B., Manyong, V.
Formato: Journal Article
Lenguaje:Inglés
Publicado: Wiley 2024
Materias:
Acceso en línea:https://hdl.handle.net/10568/140521
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author Hoel, J.B.
Michelson, H.
Norton, B.
Manyong, V.
author_browse Hoel, J.B.
Manyong, V.
Michelson, H.
Norton, B.
author_facet Hoel, J.B.
Michelson, H.
Norton, B.
Manyong, V.
author_sort Hoel, J.B.
collection Repository of Agricultural Research Outputs (CGSpace)
description 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.
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spelling CGSpace1405212025-11-11T10:05:21Z Misattribution prevents learning Hoel, J.B. Michelson, H. Norton, B. Manyong, V. east africa fertilizers quality learning 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. 2024-10 2024-03-20T11:11:51Z 2024-03-20T11:11:51Z Journal Article https://hdl.handle.net/10568/140521 en Open Access application/pdf Wiley Hoel, J.B., Michelson, H., Norton, B. & Manyong, V. (2024). Misattribution prevents learning. American Journal of Agricultural Economics, 1-24.
spellingShingle east africa
fertilizers
quality
learning
Hoel, J.B.
Michelson, H.
Norton, B.
Manyong, V.
Misattribution prevents learning
title Misattribution prevents learning
title_full Misattribution prevents learning
title_fullStr Misattribution prevents learning
title_full_unstemmed Misattribution prevents learning
title_short Misattribution prevents learning
title_sort misattribution prevents learning
topic east africa
fertilizers
quality
learning
url https://hdl.handle.net/10568/140521
work_keys_str_mv AT hoeljb misattributionpreventslearning
AT michelsonh misattributionpreventslearning
AT nortonb misattributionpreventslearning
AT manyongv misattributionpreventslearning