Gender and the dynamics of technology adoption: Empirical evidence from a household-level panel data

Very few empirical studies account for the dynamic nature of the agricultural technology adoption decision and none of these explores if this dynamic nature depends on the gender of the decision maker. Using four waves of a household-level Ugandan panel data, this is the first empirical analysis to account for self-learning (one's own adoption experience) in explaining current adoption decision in a developing country context, and the first to study the interaction between self-learning and gender. Technology adoption is defined as adoption of hybrid seed, inorganic fertilizer, or pesticides. Our results indicate that the dynamic panel data Probit model is superior to its static counterpart in the sense that self-learning, captured by lagged technology adoption indicators, is by far the most important determinant of technology adoption. We also find a weaker impact of self-learning for female-headed households than male-headed households. Female-headed households face fewer learning opportunities, which produce a lower self-learning impact in later periods, further exacerbating the gap in technology adoption among male- and female-headed households.