This dissertation consists of three papers on the economics of natural resources. Two of these examine the impact of price on consumption, and the third illustrates the benefits to applying big data methods to the estimation of fishery production functions.
Chapter 1 estimates the price elasticity of demand for residential water across the distribution of users and over the course of time after the change. Using monthly billing data provided by a private utility in Phoenix, Arizona, the distribution of price responses is estimated via quantile regression. The results indicate that most households reduced their usage about 200 gallons per month, while the top 20% reduced usage by 500 to 800 gallons per month. Converting this into a percentage change to compute the elasticity inverts the results such that the lowest 20% of households have the largest percentage reduction in use. The reductions in usage take four months to emerge, after which it is apparent that summer reductions are greater than winter reductions.
Chapter 2 explores the strategic behaviors exhibited by households in response to high-low pricing patterns for purchases of canned tuna, and how these behaviors differ for purchases of eco-labeled brands. Promotional price elasticities are estimated using a demand system for six retailers over five years. The promotional price elasticity for the eco-labeled brand (-2.8) is similar to Bumble Bee (-2.2) and StarKist (-2.9), implying that purchases of eco-labeled canned tuna respond similarly to sales prices. A survival model is estimated using household scanner panel data to analyze the inter-purchase timing decision, with the results showing that stockpiling behaviors are similar between eco-labeled and conventional tuna but price is a more significant factor in accelerating purchases for the eco-labeled brand.
Chapter 3 illustrates the efficacy of LASSO variable and instrument selection methods for estimating production in fisheries. Production functions are estimated using traditional model selection and LASSO model selection for both ordinary least squares and instrumental variables methods. The results show that LASSO performs marginally better at ordinary least squares and significantly better for instrumental variables, which tests for endogeneity indicate should be the preferred method.
