The interest in promoting food and water security through development projects has led to the need for tools that can evaluate the impact of these projects and ensure that they reach the most vulnerable and enhance the quality of life for the members of the community (Gertler et al., 2011). This study brings together the stochastic frontier model with impact evaluation methodology to measure the impact on farmers’ technical efficiency (TE) within a modern irrigation technology transition framework. Deciding to adopt or not a new agricultural technology is not necessarily a random determination. Unobserved heterogeneity across the farms in the sample can have an impact on the farmer’s decision to adopt the new technology.
Therefore, selection bias can be present, and when the model is not correcting for it this can lead to biased measures of economic performance. In this study, we apply both Heckman (1979) and Greene (2010) specifications to correct for self-selectivity biases, and then we measure and compare technical efficiency scores (TE) resulting from these models. The empirical application uses data covering 56 small-scale greenhouse farms, cultivating vegetables, from Crete (Greece) for the cropping years from 2009 to 2013 transitioning from drip to overhead sprinkler irrigation technology. Results from the stochastic frontier model corrected for self-selection reveal that there is significant selection bias in the sub-sample of adopters and, if the model does not control for this bias, an overestimation of the technical efficiency scores can be observed.
Also, adopters' technical efficiency scores are lower than those of the non-adopters when the corrected for self-selection stochastic frontier model is applied. This implies that in the short-term the adoption of the new irrigation technology has a cost in terms of technical efficiency. However, this outcome can be explained by the fact that after the adoption of new technologies, farmers may need more time to incorporate the know-how of the newly acquired technology in the production process.