The Role of Learning in Adaptation to Technology : The Case of Groundwater Extraction

Learning plays an important role in adopting new technology. While the role of learning in the decision to adopt is widely investigated in the literature, its role in knowing how to best use technology and the speed of learning is not. For instance, when farmers adopt groundwater extraction technology, they need to learn their private marginal cost and marginal benefit of extracting water. Comparing the extraction behavior of the owners of new wells with old wells, we explore the role of experience in shaping farmers' decisions. We use three identification strategies to test the hypothesis that the owners with less experience (owners of new wells) with groundwater technology, extract more water than the experienced owners. Employing panel data at the district level in a fixed-effects model, we find that groundwater extraction rises as the growth rate in new wells increases. Our second strategy uses the exogenous variation in precipitation shocks in a double-difference approach. Employing census data at the well level, we show that more water is extracted from new wells than older wells and that the difference in extraction increases in areas that experience negative precipitation shocks. The third strategy is the nearest-neighbor matching method which confirms the above findings and indicates that old wells are more efficient in maintaining their inter-temporal extraction. We also provide evidence that it takes at least two years for farmers to fill the knowledge gap with their experienced counterparts. Our findings have important implications for discussions of common pool regulation. Firms are often considered entities with complete private information about their true abatement costs. Our findings imply that quantity instruments for regulating groundwater extraction fail to guarantee productive efficiency when farmers face uncertainty about their marginal abatement cost. This paper also provides new insights for optimizing climate change scenarios, in light of the importance of the learning lag in using new technologies