Gender bias in customer perceptions: The case of agro-input dealers in Uganda
CONTEXT
Faced with incomplete and imperfect information, economic actors rely predominantly on perceptions and often base decisions on heuristics prone to bias. Gender bias in perceptions favoring men has been found in a variety of settings and may be an important reason why some sectors remain dominated by men and gender gaps in terms of benefits persist. In modernizing food supply chains in a patriarchal context such as the maize sub-sector in Uganda, this may result in women facing significant barriers to entry.
OBJECTIVE
Using a unique dataset of ratings of agro-input dealers provided by smallholder farmers in their vicinity, we test if farmers perceive male-managed agro-input shops differently than agro-input shops managed by women.
METHODS
We use a dyadic dataset of farmer-dealer links to explicitly control for quality differences between male- and female-managed agro-input shops and use the fact that a farmer has generally rated more than one agro-input to account for farmer-level heterogeneity using fixed-effects regression.
RESULTS AND CONCLUSIONS
We find that farmers rate male-managed agro-input outlets higher on a range of attributes related to the dealership in general, as well as on the quality of inputs sold by the dealer. After controlling for both dealer and farmer level confounders, we conclude that gender bias in customer perceptions persists.
SIGNIFICANCE
Our results suggest a comparative disadvantage and an important entry barrier for female agro-input dealers. The gender bias may also affect social outcomes like women’s capabilities, aspirations, and empowerment in seed systems but also impairs development at more aggregate levels: as a considerable share of agro-input shops is managed by women, this finding may impose challenges for varietal turnover, hindering agricultural productivity, food security, and rural transformation. Policies and interventions designed to challenge gender norms and customs are needed to correct this bias.