Money Matters: The Role of Yields and Profits in Agricultural Technology Adoption

Abstract

Despite the growing attention to technology adoption in the economics literature, knowledge gaps remain regarding why some valuable technologies are rapidly adopted, while others are not. This paper contributes to our understanding of agricultural technology adoption by showing that a focus on yield gains may, in some contexts, be misguided. We study a technology in Ethiopia that has no impact on yields, but that has nonetheless been widely adopted. Using three waves of panel data, we estimate a correlated random coefficient model and calculate the returns to improved chickpea in terms of yields, costs, and profits. We find that farmers’ comparative advantage does not play a significant role in their adoption decisions and hypothesize that this is due to the overall high economic returns to adoption, despite the limited yield impacts of the technology. Our results suggest economic measures of returns may be more relevant than increases in yields in explaining technology adoption decisions.

Estimating returns to fertilizer adoption with unobserved heterogeneity: Evidence from Ethiopia

“…After establishing the average return from the use of chemical fertilizer, we then examine the distribution of such returns by explicitly accounting for comparative advantage in the use of fertilizer using the correlated random coefficient (CRC) model (Michler, Tjernstro, Verkaart, & Mausch, ; Suri, ). Considering such heterogeneity is important as there might be significant heterogeneity in returns across fertilizer adopters .…”

“…In the above specification, β measures average return from fertilizer adoption; θ i captures the key unobservables that determine selection into fertilizer adoption and measures the relative productivity of a farmer with and without fertilizer. , denotes the sorting of farmers in fertilizer adoption ( θ i ) and measures the importance of comparative advantage (Michler et al., ; Suri, ). For example, if a farmer with high θ i 's has lower gains from adoption, then (Michler et al., ; Suri, ).…”

“…, denotes the sorting of farmers in fertilizer adoption ( θ i ) and measures the importance of comparative advantage (Michler et al., ; Suri, ). For example, if a farmer with high θ i 's has lower gains from adoption, then (Michler et al., ; Suri, ). Similarly, if then the self‐selection process leads to greater inequality in returns as farmers with high θ i 's has higher gains from adoption.…”

“…In the above specification, the parameter (θ i ), is correlated with the adoption decision of farmers ( f it ). Therefore, the correlation between θ i and f it is eliminated by projecting θ i onto the full adoption history of farmers (Michler et al., ; Suri, ). In our case, since we have two rounds of panel data, the projection coefficient is given by: …”

“…Once θ i and are determined, returns are estimated as ). These values are the predicted counterfactual returns for non‐adopters using weighted averages of all possible returns (Michler et al., ; Suri, ).…”