Open Access
Ueda, Takeshi
Graduate Program:
Agricultural Economics
Doctor of Philosophy
Document Type:
Date of Defense:
December 13, 2001
Committee Members:
  • Jill Leslie Findeis, Committee Member
  • Bee Yan Roberts, Committee Member
  • Darren L Frechette, Committee Member
  • Spiro E Stefanou, Committee Chair
  • technological change
  • high-yielding varieties
  • green revolution
  • leanring-from-others
  • technical efficiency
  • learning-by-doing
  • Stochastic frontier analysis
Technological innovation has been recognized as a major source of economic growth with the potential to be an effective force for alleviating economic inequality around the world. Hence, articulating the mechanism of technological adoption and diffusion has been of special interest to development economists. The Green Revolution as a case in point has been the subject of many studies since it necessitated a drastic change in production practices by farmers. The change led by the Green Revolution generated a great deal of “learning” opportunities in the production of High Yielding Varieties (HYV). Learning is a dynamic process spanning the adoption of technologies to the realization of their yield potentials. Capturing such a dynamic process is a crucial issue. Learning is generally regarded as an increase in the stock of knowledge and skills in production processes that are associated with new technologies. In the economics literature, productivity improvement is assumed to reflect learning, and it is often indicated by output or profit increase, or cost reduction. This study employs efficiency measurement in connection with production frontier analysis as a gauge of individual learning in the case of HYV castor production in Aurepalle village, India over the period 1975 to 1984. Specifically, we investigate a) whether learning, as well as other determinants, impact the technical efficiency in production and b) the existence of systematic patterns in learning-from-others. First, estimation of the stochastic frontier production function yields technical inefficiencies of individual farms. Then, inefficiency is regressed on its potential determinants, which include variables representing learning, i.e. experiences, where the coefficients of the learning terms indicate learning impacts on inefficiency reduction. Among the potential determinants, human capital-related variables, such as schooling, experiences, and age, are of special interest to ascertain how much they contribute to the efficiency gain. Three models are specified to reflect the impact of learning-from-others, especially learning within a reference group, which is defined based on similar farm-size, similar household size, and caste rank. One of the major motivations characterizing learning in a framework of the stochastic frontier analysis is to achieve a careful measurement of learning. There exists a body of individual learning literature capturing learning through the effects of experiences with new technologies on outputs (or profits). However, this approach is not capable of distinguishing changes in outputs (or profits) driven by technological changes as opposed to those driven by learning. On the other hand, the stochastic frontier approach enables the separation of productivity gains led by the technological progress from efficiency gains through learning of the technology. This separation is crucial when impacts of technological changes on production processes are substantial. The learning-by-doing effect is robust, but modest. The learning-from-others’ effect varies across the reference group models, indicating the importance of farmers’ learning opportunities. The learning-from-others is statistically significant only when learning from others within the same household size. The result suggests that learning randomly from neighbors may not guarantee efficiency gains since farms cannot simply imitate neighbors’ experiences to enhance efficiency. The results further imply that technological dissemination is better targeted to the reference group level rather than at the village level. This study finds that the potential reference group can be based on household size. Farm size and household size dominate the effects of efficiency enhancement with respect to magnitude and significance, which, in addition to the result of scale elasticities, implies that scale economies play an important role in castor production, especially in HYV production. Age is another efficiency-enhancing factor although its significance is not robust across the models. The dependency ratio, on the contrary, has a consistent and negative impact on efficiency levels. Education is also confirmed to enhance efficiency. This result highlights the importance of investing in children’s schooling and providing quality education.