This dissertation investigates the impact of noise pollution on residential properties in the Phoenix metropolitan area. It leverages quasi-random changes in flight paths to and from Phoenix Sky Harbor Airport, focusing on the adoption of the NextGen policy and subsequent flight path reversal upon court intervention. NextGen aimed to reduce flight costs by using computer-algorithm-generated optimized flight paths. In addition to estimating the value of quiet, this dissertation analyzes the distributional welfare impacts of policies that spatially alter noise pollution exposure, residential sorting, and environmental justice. In Chapter 1, the capitalization effect of noise pollution on the housing market is examined. Findings reveal that a mere 1 decibel increase results in a 1% reduction in property values. Furthermore, the chapter examines the distributional impacts of flight path changes across various demographic and socioeconomic characteristics of residents. I find that initially, computer-generated flight paths benefited neighborhoods with a higher proportion of Black residents. However, a court-ordered reversal ultimately left them worse off than before the initial change. Chapter 2 introduces a residential sorting model designed to assess welfare effects and simulate market responses to alternative policies. The estimated average marginal willingness to pay (MWTP) to avoid noise pollution stands at $3,038 per decibel, with variations based on household demographics. Additionally, a Pigouvian tax on airplane passengers to compensate for noise pollution amounts to approximately $16 per one-way flight, notably comparable to the carbon tax for a similar journey. Various counterfactual scenarios regarding noise pollution exposure are explored to inform policy decisions. Chapter 3 addresses two challenges in using property transaction data to estimate the MWTP to residential exposure to noise pollution. First, ignoring information frictions among home buyers may bias estimates of the MWTP for an amenity. Second, changes in the amenity of interest may affect other endogenous amenities, further complicating the identification of MWTP. I address both challenges using a novel instrumental variable based on spatial variation in the salience of noise pollution caused by hourly variation in flight paths due to wind direction. I find the mean MWTP for a one-decibel reduction in noise pollution be $4,742.

This dissertation consists of two chapters. First, I explore the role health dynamics play in determining the value and cost-effectiveness of public health insurance. Current healthcare consumption may yield future health benefits that lower longer-run expenditures. I leverage exogenous variation in the expansion of Medicaid under the Affordable Care Act to assess how comprehensive insurance affects immediate healthcare spending for the near-elderly (aged 60-64) and future healthcare spending once they receive Medicare at age 65. I provide evidence that Medicaid coverage increases immediate expenditures by roughly 112 percent; but, after receiving Medicare, the same individuals are healthier and consume 77 percent less care measured by total expenditures. I then develop and estimate a life-cycle model of near-elderly individuals that incorporates the Medicaid expansions as well as dynamic health investment and endogenous mortality. I find that the Medicaid expansions were valued by the near-elderly population at slightly above net government costs, and that modest reforms to increase Medicare's generosity could generate considerable welfare improvements relative to program costs. Second, I document that flexible employment and educational arrangements can reduce the barriers that some students face in seeking a degree by mitigating the contemporaneous earnings losses associated with enrolling in college. Using detailed data, I analyze how labor supply and study efforts respond to changes in labor market conditions and college activities/tasks. My findings indicate a low ``short run" opportunity cost of studying when working. I also show that study time is not particularly sensitive to changes in labor market conditions. Consistent with these results, I find that workers take advantage of their flexible schedules by changing their usual working hours when their college courses are more demanding. Encouragingly, I do not find adverse effects of work hours on academic performance or of study hours on workplace performance. Finally, students value flexible working and educational formats highly and view online education as an important vehicle for increasing expected future income. Overall, this study underscores that combining flexible working and learning formats constitutes a suitable path for many students who work to pay for college or workers who aim to improve their skills.

The welfare consequences of price versus quantity-based regulation are known to differ when information about marginal benefits or costs of abatement is imperfect. Does uncertainty about demand for the polluting good also matter for welfare of these two approaches to regulation? In chapter 1, I use plant-level survey data and high frequency variation in power consumption to assess the dynamic implications of uncertainty about future demand for the relative welfare consequences of carbon taxes and cap-and-trade regulation. I address this question in the context of the electricity sector where demand risk is particularly salient. I show that the choice between policy instruments depends on how firms and consumers balance unpredictable output volatility (higher with carbon taxes) vs. price volatility (higher with cap-and-trade regulation). Over a wide range of policy-relevant abatement targets, I find carbon taxes outperform cap-and-trade in terms of welfare. Financial incentives like the Production Tax Credit are central initiatives behind wind power as the leading renewable energy source in the U.S. But do institutional design features of energy markets matter for cost-effectiveness of subsidies to wind investments? In chapter 2, I answer this question by investigating how the design of procurement contracts that are typically used by wind developers affects their investment incentives. Using unit-level data from wind farm production and installed capacity, I find that structuring subsidies based on key features of the type of procurement contracts associated to wind projects leads to major reductions in public expenditures in terms of subsidy payments to wind developers without undermining their investment incentives. The U.S. federal government is known to have a history of heavily subsidizing the wind power industry. Subsidies either to output (Production Tax Credit) or investment goods (Investment Tax Credit) have been critical to replace emissions-intensive technologies with wind power. Which type of subsidy is best to incentivize wind investments at the least cost? In chapter 3, I use plant-level data of wind facilities from the Texas electricity market to develop and estimate a model of investment decisions that accounts for productivity shocks at the wind farm level and prudent behavior of developers. I find that subsidizing production can increase average yearly investment rates in wind capacity up to 2.5 percentage points over mean investment rates under alternative subsidies to capital. This is driven by precautionary savings that developers accumulate to smooth out potential future shocks to investment income when adverse weather conditions lead to low subsidy payments.

Since their inception in 1997, Arizona policymakers have debated the merits of Arizona’s income tax credits for contributions to certified school tuition organizations (STOs), though the programs have grown year over year. This study aims to answer lingering questions about the beneficiaries of STO scholarships. First, using publicly available reports from the Arizona Department of Revenue, the Arizona Board of Education, and American Community Survey 5-year estimates, multiple regression analysis indicates a weakly negative relationship between scholarship dollars and family income but no statistically significant effect of public-school quality on scholarship dollars. Second, using data from a survey of parents whose students’ attend Arizona private schools, logistic regression suggests a weakly negative relationship between scholarship utilization and family income, with public school quality having no statistically significant effect on STO scholarship utilization. Moreover, multiple regression analysis again shows a weakly negative relationship between scholarship dollars and family income but no statistically significant relationship between public school quality and scholarship dollars. This paper concludes by offering policy suggestions to improve the accountability of these programs.

This paper seeks to highlight the strong correlation and potential causation between the presence of physical community bank branches in rural communities and local economic outcomes like payroll, employment, and establishments in a given region. To do this, I conduct a two-part analysis involving a fixed effects model with data from across the US and a regression discontinuity model of a subset of the data in parts of Delaware and Maryland. Overall, my results show a significant strong correlation between the number of bank branches in a region and the expected percent changes in economic outcomes, but I lack the results to claim causality between the opening or closure of a bank branch and changes in the local economy. This has relevance in understanding the need for physical bank branches as changes in the financial industry since the 2008 Financial Crisis, like online banking, have continued to accelerate.

In this dissertation, I study two different sides of inequality. In the first part of this dissertation, I show what the role of unwanted childbirth is on women’s wages and education. I document that on average, mothers whose first childbirth was unwanted have lower levels of education, lower wages, and have their first childbirth at younger ages compared to the rest of the mothers. In the second part of this dissertation, I show how the introduction of a carbon tax affects individuals with different educational attainment. In particular, I show how the carbon tax affects their consumption, but also how the tax reduces air pollution and consequently affects individual mortality. I find that introducing this mortality channel reduces the aggregate welfare cost of a carbon tax by about half. In terms of the distributional effect of the policy, the mortality channel deepens the regressivity of the tax, since the benefits in terms of mortality reductions are similar for all individuals, the valuation of this benefit is higher for more educated individuals.

This thesis explores the relationship between the performance of beauty and Potential New Member (PNM) success across various formats of formal sorority recruitment at ASU. It builds off of existing scholarship in economics of beauty premiums in labor markets, as well as sociological research on the intersection of beauty and human interaction. Through interviews of women who went through formal recruitment across three different modalities (in-person, virtual, and hybrid), themes emerged that suggest the current policies in place by ASU Panhellenic make it so that the performance of beauty hinders the facilitation of a recruitment process that is truly values-based.

This dissertation studies how forecasting performance can be improved in big data. The first chapter with Seung C. Ahn considers Partial Least Squares (PLS) estimation of a time-series forecasting model with data containing a large number of time series observations of many predictors. In the model, a subset or a whole set of the latent common factors in predictors determine a target variable. First, the optimal number of the PLS factors for forecasting could be smaller than the number of the common factors relevant for the target variable. Second, as more than the optimal number of PLS factors is used, the out-of-sample explanatory power of the factors could decrease while their in-sample power may increase. Monte Carlo simulation results also confirm these asymptotic results. In addition, simulation results indicate that the out-of-sample forecasting power of the PLS factors is often higher when a smaller than the asymptotically optimal number of factors are used. Finally, the out-of-sample forecasting power of the PLS factors often decreases as the second, third, and more factors are added, even if the asymptotically optimal number of the factors is greater than one. The second chapter studies the predictive performance of various factor estimations comprehensively. Big data that consist of major U.S. macroeconomic and finance variables, are constructed. 148 target variables are forecasted, using 7 factor estimation methods with 11 information criteria. First, the number of factors used in forecasting is important and Incorporating more factors does not always provide better forecasting performance. Second, using consistently estimated number of factors does not necessarily improve predictive performance. The first PLS factor, which is not theoretically consistent, very often shows strong forecasting performance. Third, there is a large difference in the forecasting performance across different information criteria, even when the same factor estimation method is used. Therefore, the choice of factor estimation method, as well as the information criterion, is crucial in forecasting practice. Finally, the first PLS factor yields forecasting performance very close to the best result from the total combinations of the 7 factor estimation methods and 11 information criteria.

There is a growing consensus that environmental hazards and changing weather patterns disproportionately affect the poor, vulnerable, minority communities. My dissertation studies the nature of risk faced by vulnerable groups of individuals, how these risks affect their labor choice, income, consumption, and migration patterns. In Chapter 1, I study how seniors of different racial and income groups respond to information about hazardous waste sites in their neighborhood and their cleanup process. I find white seniors tend to move out at a higher rate when informed about the presence of a waste site as well as when the site is cleaned up compared to non-white seniors. This suggests that neighborhood gentrification exhibits inertia in the manifestation after the cleanup of Superfund sites. I find an assortative matching of seniors to neighborhoods based on their race and income, reinforcing findings in the environmental justice literature. Chapter 2 documents the effect of drought on labor choices, income, and consumption of rural households in India. I find that household consumption, as well as agricultural jobs, declines in response to drought. Further, I find that these effects are mediated by job skills and land ownership. Specifically, I find that households with working members who have completed primary education account for most of the workers who exit the agricultural sector. In contrast, I find that households with farmland increase their agricultural labor share post-drought. Cultural norms, relative prices, and land market transaction costs provide potential explanations for this behavior. Chapter 3 builds a simple model of household labor allocation based on reduced-form evidence I find in chapter 2. Simulation of the calibrated model implies that projected increases in the frequency of droughts over the next 30 years will have a net effect of a 1\% to 2\% reduction in agricultural labor. While small in percentage terms, this implies that 2.5 to 5 million individuals would leave agriculture. An increase in drought will also increase the size of the manufacturing wage subsidy needed to meet the goals of `Make in India’ policy by 20\%. This is driven by the need to incentivize landowners to reduce farm labor.