Essays on environmental and natural resource economics




Stafford, Teresa Michelle

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In the first essay, I assess the effect of indoor air quality (IAQ) in school buildings on student test performance and attendance rates. Results indicate that performance on standardized tests significantly improves while attendance rates are unresponsive to improvements in IAQ. The improvement in math scores ranges from 0.102 - 0.189 standard deviations per $500,000 spent on IAQ-related renovations and is 35% - 50% greater than the improvement in reading scores. For the same budget, results suggest that the improvement in math scores following IAQ-related renovations is several times larger than the improvement associated with class size reductions.

In the second essay, I examine the responsiveness of the daily labor supply of fishermen to transitory variations in the daily wage using data from the Florida spiny lobster fishery. The applicability of this research is both narrow and general. Understanding this relationship is key to determining the effectiveness of landing fees as a means to regulate fisheries. Tracing out the labor supply curve is also fundamental to labor economics and policy. I find that the wage elasticity of labor supply (participation) is positive and statistically different from zero, with a point estimate of 0.967. This suggests an upward slopping labor supply curve and refutes the notion of reference dependent preferences.

In the third essay, I examine the bias associated with ignoring the multi-species aspect of labor supply decisions in spatially explicit bioeconomic fishery models. Using a complete 15-year panel of all fishing trips made by fishermen possessing a Florida spiny lobster license, including non-lobster trips, I show that the simplifying assumption of a dichotomous choice structure at the first node (i.e. participate in the target fishery or not) is not innocuous and that predicted participation rates can change substantially with the addition of another species as an outside alternative in the first decision node.




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