Measuring Farm and Market Level Economic Impacts of Improved Maize Production Technologies in Ethiopia: Evidence from Panel Data

Abstract

While it is often recognised that agricultural technology adoption decisions are intertwined and best characterised by multivariate models, typical approaches to examining adoption and impacts of agricultural technology have focused on single technology adoption choice and ignored interdependence among technologies. We examine farm-and market-level impacts of multiple technology adoption choices using comprehensive household survey data collected in 2010/11 and 2012/13 in Ethiopia. Economic surplus analysis combined with panel data switching endogenous regression models are used to compute the supply shift parameter (K-shift parameter), while at the same time controlling for the endogeneity inherent in agricultural technology adoption among farmers. We find that our improved technology set choices have significant impacts on farm-level maize yield and maize production costs, where the greatest effect appears to be generated when various Journal of Agricultural Economics, Vol. 69, No. 1, 2018, 76-95 doi: 10.1111/1477 Ó 2017 The Agricultural Economics Society technologies are combined. The change in maize yield and production costs results in an average 26.4% cost reduction per kilogram of maize output (the K-shift parameter). This increases the producer and consumer surpluses by US$ 140 and US$ 105 million per annum, respectively. These changes in economic surplus help to reduce the number of poor people by an estimated 788 thousand per year. We conclude that deliberate extension efforts and other policies that encourage integration of technologies are important for maize technologies to yield their full potential at both farm and market levels.

Poverty Reduction Effects of Agricultural Technology Adoption: The Case of Improved Cassava Varieties in Nigeria

“…The ESM captures adoption induced supply responses (per unit cost reductions) through a simple shift in the supply function of producers (Alston et al, 1995). Estimating the aggregate poverty reduction effect of adoption in ESM requires an estimate of the counterfactual price and quantity (P ct and Q ct ), which are not observable but can be calculated algebraically based on observed price (P obs ), observed production (Q obs ), extent of supply shift (cost reduction per unit of output, commonly referred as the k-shift parameter), the size of supply elasticity (ɛ) and demand elasticity (g) (Zeng et al, 2015;Kassie et al, 2017). The k-shift parameter measures adoption induced cost reduction per unit of output (the extent of outward shift in the supply curve).…”

“…Capturing wage adjustments is also beyond the scope of this paper. However, as a robustness check, we followed the approach of Kassie et al (2017) and calculated effects using empirically estimated poverty elasticities.…”

“…Technological change is critical for agricultural productivity growth and poverty reduction in developing countries especially in sub-Saharan Africa (SSA) (Gollin et al, 2002;Alene et al, 2009;Suri, 2011;Zeng et al, 2015). It is widely recognised that the development and dissemination of improved crop varieties and complementary agronomic practices are major drivers of smallholder agricultural productivity growth (Alene et al, 2009;Suri, 2011;Zeng et al, 2015;Kassie et al, 2017;Abdoulaye et al, 2018). However, empirical evidence on the relationship between agricultural research and poverty reduction suggests that improved agricultural technologies may not necessarily lead to poverty reduction as the poor are often constrained by structural barriers that make improved technologies inaccessible and less profitable for them (Suri, 2011;Zeng et al, 2015;Kassie et al, 2017).…”

“…It is widely recognised that the development and dissemination of improved crop varieties and complementary agronomic practices are major drivers of smallholder agricultural productivity growth (Alene et al, 2009;Suri, 2011;Zeng et al, 2015;Kassie et al, 2017;Abdoulaye et al, 2018). However, empirical evidence on the relationship between agricultural research and poverty reduction suggests that improved agricultural technologies may not necessarily lead to poverty reduction as the poor are often constrained by structural barriers that make improved technologies inaccessible and less profitable for them (Suri, 2011;Zeng et al, 2015;Kassie et al, 2017). Therefore, understanding how and why farm households adopt improved varieties and their subsequent effects on productivity and poverty outcomes is important to the design of an effective pro-poor technology dissemination strategy.…”

“…Consequently, such measurement errors in farm size may affect productivity estimates and hence the estimated poverty reduction effect of adoption. Third, following Zeng et al (2015) and Kassie et al (2017), we link farm-level treatment effects to a market level model to measure the aggregate poverty reduction effect of adoption. The rest of the paper is structured as follows.…”