| My thesis consists of three essays in economics. In the first essay, I study the effect of market size on investment in R&D and innovation. To control for supply side effects I focus on the demand change driven by the increase of federal procurement contracts after 2001. I use 9/11, and the wars in Afghanistan and Iraq as an exogenous shocks to firms' market size. I find that the defense and the defense-related industries experience a significant increase in the demand for their products and proportionally increase their R&D expenditures. My second essay estimates the speed of employer learning and the signaling value of schooling using a structural model. I find that the speed of employer learning for the black and Hispanic workers is not statistically different from the speed of learning for the white workers. The result is robust when controlling for actual experience, employer-provided training, and industry-specific skills. This evidence does not support the view that employers have limited information about the skills of minority workers. In a joint work with John Tyler, we examine the extent to which the Math and Science Partnership (MSP) Program of the National Science Foundation might impact the quantity and quality of math and science teachers. We first develop a model that supports a premise that shifts in underlying supply can be inferred from shifts in the percentage of certified math teachers employed when (a) salaries are constrained to be below market clearing salaries and (b) uncertified or out-of-field certified teachers can compete as substitutes for certified math teachers. The study then tests the plausibility of the model using data from Texas and in so doing provides preliminary estimates of the extent to which a school or school districts MSP participation affected the supply of certified math teaches available to that school or district. The results, although inconclusive on the question of the labor supply effects of MSP participation by a school or school district, do suggest the reasonableness of the model for future work when more appropriate data will be available. |
