My dissertation combines the notion of residential sorting from Tiebout (1956) with Grossman’s (1972) concept of a health production function to develop a new empirical framework for investigating what individuals’ residential location choices reveal about their valuation of amenities, the welfare effects of climate change, the forces underlying environmental justice, and the value of a statistical life. Location

choices are affected by age, health, and financial constraints, and by exposure to local amenities that affect people’s health and longevity. Chapter 1 previews how I formalize this idea and investigate its empirical implications in three interrelated essays. Chapter 2 investigates interactions between health, the environment, and income. Seniors tend to move at higher rates after being diagnosed with new chronic medical conditions. While seniors generally tend to move to locations with less polluted air, those who have been diagnosed with respiratory conditions move to relatively more polluted locations. This counterintuitive pattern is reconciled by documenting that new diagnoses bring about increases in medical expenditures, thereby limiting disposable income that can be spent on housing. Relatively cheaper places tend to be more polluted, and higher exposure to pollution leaves seniors more vulnerable to future health shocks. In Chapter 3, I combine information about housing prices with estimates of location-specific effects on mortality to estimate the Value of a Statistical Life (VSL) for seniors - one of the most important statistics used to evaluate policies affecting mortality. Since local amenities correlate with causal mortality effects, but also provide utility independently, the difficulty in controlling for local amenities implies that my VSL estimates are best interpreted as bounds. Chapter 4 builds a new structural framework for evaluating spatially heterogeneous changes to local amenities. I estimate a dynamic model of location choice with a sample of 5.5 million seniors from 2001-2013. Their average annual willingness-to-pay to avoid future climate change in the United States under a “business as usual” scenario ranges from $962 for older, sicker groups who are more vulnerable to climate change’s negative effects on health to -$1,894 for younger, healthier groups, who value warmer winters and are relatively resilient.

Prior research on consumer behavior in health insurance markets has primarily focused on individual decision making while relying on strong parametric assumptions about preferences. The aim of this dissertation is to improve the traditional approach in both dimensions. First, I consider the importance of joint decision-making in individual insurance markets by studying how married couples coordinate their choices in these markets. Second, I investigate the robustness of prior studies by developing a non-parametric method to assess decision-making in health insurance markets. To study how married couples make choices in individual insurance markets I estimate a stochastic choice model of household demand that takes into account spouses' risk aversion, spouses' expenditure risk, risk sharing, and switching costs. I use the model estimates to study how coordination within couples and interaction between couples and singles affects the way that markets adjust to policies designed to nudge consumers toward choosing higher value plans, particularly with respect to adverse selection.

Finally, to assess consumer decision-making beyond standard parametric assumptions about preferences, I use second--order stochastic dominance rankings. Moreover, I show how to extend this method to construct bounds on the welfare implications of choosing dominated plans.

Environmental regulations such as carbon taxation and air quality standards can lead to notable improvements in health outcomes and ambient air quality. However, these types of policies may have significant impacts on the labor market, in particular for workers in energy-intensive industries, especially if these workers have acquired specific human capital in those industries. This dissertation focuses on the general equilibrium consequences of environmental regulation on the labor market. Specifically, I examine costly reallocation of workers between sectors, the welfare effects of involuntary unemployment, and the heterogeneous effects of this policy on different types of workers. To this end, I develop a two-sector search model with sectoral human capital accumulation to explore the effects on the labor market of implementing a per unit of energy use carbon tax in the US. I separate the economy into a high-intensive sector (’dirty’) and a low-intensive sector (’clean’). I calibrate the model using 2014 U.S. data. I find that a carbon tax increases total unemployment by 0.06 percentage points, decreases the dirty employment rate by 2.1 percent, and increases the clean employment rate by 1.04 percent. Firms in the dirty sector adjust by decreasing the demand for high-skilled workers and increasing the number of vacancies in the low-skilled market

We examine the bias resulting from temporal and spatial aggregation of weather variables in environmental economics. In order to include temporally and/or spatially continuous environmental variables (such as temperature and precipitation), many studies discritize them. The finer the scale of discrization chosen, the more difficult it can be to obtain a complete and reliable data set. Studies performed at very fine scales often find tighter and more dramatic relationships between variables such as temperature and income per capita. We examine this question by repeating the same empirical study at various temporal and spatial scales and comparing the resulting parameter estimates.