Three Essays in Environmental, Labor, and Education Economics

Abstract

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 owners of new wells extract more water than owners of old wells. 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 regarding the speed of learning about using technology. 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. In chapter two, we discuss the effects of precipitation shocks on the rural labor market and migration. The welfare of both agrarian and non-agrarian workers in rural areas is highly affected by agricultural output volatility, caused in part by weather shocks. This paper examines the impact of precipitation shocks on labor supply and out-migration in rural Iran. We use individual-level panel data combined with station-based precipitation data at the rural-agglomeration level to study the intensive and extensive margins of employment. Our results indicate different types of responses to positive and negative shocks. Using a fixed-effects panel data model, we find that workers in agriculture and industry sectors increase their hours of work in response to positive shocks. At the extensive margin, we find that negative shocks reduce the labor market participation of women. We observed heterogeneity in responses based on the sector of employment, gender, and the roles of individuals in the household. We also show that positive shocks affect the division of labor at the household level. Our estimates for the probability of migration indicate that negative shocks raise the probability of migration for young men. We show that the labor-migration of the same group is also affected by negative shocks, but the impact could be explained by the local unemployment rate, implying the labor market is a channel through which precipitation shocks affect migratory decisions. In the final chapter, we investigate parents' investment in the quality of their children. While school enrollment at the primary level has been rising in developing countries to almost complete national coverage, international measures of education quality, especially in basic knowledge of reading and math, do not exhibit a parallel improvement. Since parents' expenditure is an important determinant of children's school performance, we investigate parents' investments in the quality of their children's education, measured by their spending on books and other school materials. We develop an overlapping generations model, in which we consider families' expenditure as an input to their children's human capital. Moreover, in our model, parents use the current status of their children's human capital as a screening measure for adjusting their investment, instead of the standard tradition of simply balancing the trade-off between future income and the current stream of direct and indirect school costs. From our theoretical analysis, our main hypothesis is that families consider better school performance to be a reliable predictor of future return, and this incentivizes them to invest more in children who are academically promising, considering other determinants of children's schooling output, such as school quality. Empirically, we use an instrumental variables approach to test our main hypothesis, and it is accepted using data for Ghanaian primary school students in rural areas.