The second essay investigates the effect of extended unemployment insurance (UI) coverage in the United States in recent years on job search. The U.S. government extended UI benefits in several phases in 2008--2009, increasing the duration of the benefits to a maximum of 99 weeks, up from the regular 26 weeks. Using the American Time Use Survey (ATUS) data, I find that women are more sensitive to the extended UI benefits than men. Difference-in-differences estimation shows that the average effect of the UI extensions for women is over a 10 percentage points decline in the probability of job search. However, I do not find any statistically significant effect on men.

My dissertation covers areas in applied economics: labor economics, public economics, and the economics of education. I use a variety of econometric tools and other economic analysis to study welfare program rules and regulations as well as assess the efficacy of a high school science curriculum. My first chapter uses data from the Survey of Income and Program Participation (SIPP), spanning 2003-2013, to estimate whether the ABAWD-specific 20 hour per week minimum work requirement influences their labor supply outcomes and SNAP participation. I employ binary response models to estimate average partial effects (APE) and find the work requirement has statistically significant effects: ABAWDs are 1 percentage point (pp) less likely to participate in SNAP and are 2.6 pp more likely to meet the 20-hourwork requirement. This negative effect of the work requirement on SNAP participation is larger among non-white (1.37 pp), specifically blacks (2.09 pp), suggesting that the impacts of a work requirement must be considered in areas with higher percentages of minorities. This paper contributes to the study of ABAWDs, a relatively understudied population in the context of SNAP.Chapter 2 uses the 2014 Survey of Income and Program Participation (SIPP), spanning from January 2013 to December 2016, to study the effect of SNAP and Medicaid expansion on labor market outcomes (income, hours worked and employment status) and SNAP participation. Using a suite of empirical methods, I find no evidence that the interaction of the SNAP and Medicaid expansions has an effect on labor outcomes of the head and second adult in a household. However, I do find that the Medicaid expansion increases SNAP participation in states with the least generous state-level SNAP policy options. These findings demonstrate the importance of analyzing the effect of both expansions jointly, as both SNAP and Medicaid serve low income households that may simultaneously choose their labor supply and program participation.Chapter 3 uses data from the NSF funded project Crafting Engaging Science Environments(CESE), a cluster-randomized controlled trial to study the effect of project-based learning on the scientific achievement of high school chemistry and physics students in Michigan and California during the 2018-2019 school year. I extend the analysis conducted in Schneideret al. (2021) and use pooled OLS with school level fixed effects to estimate the treatment effect. I find sound evidence to support the findings in Schneider et al., 2021 that the CESE intervention had a positive and significant effect on students' scientific learning, even in the presence of multiple levels of attrition. The point estimates range from 0.24 to 0.34 standard deviations. Additionally, I compute the Lee Bounds for the estimates and find the bounds do not contain zero, suggesting that differential attrition alone likely does not drive the entire treatment effect.

ABSTRACT Essay 1: In this study, we used agglomerative hierarchical cluster analysis to group 2778 farming-defined counties into six clusters, revealing farm patterns across the contiguous 48 states of the United States. Economists have endeavored to identify patterns in US farming to understand the differences in economic performance and improve farm households' well-being. The US is a leading global producer and exporter of many agricultural and food products. Our primary objective is constructing a policy-relevant farm clustering to characterize agricultural homogeneity in US farms' production potential. We identify six relatively homogeneous clusters in five dimensions: farm size, farm assets, farm labor, farm output, degree of mechanization, and government programs. Minimizing diversity within a cluster allows for analysis of public policy changes on specific clusters and comparison of differential effects of the change across clusters. Essay 2: In this study, we developed the most comprehensive county-level datasets covering the 48 contiguous states of the United States to measure the impact of climate change on the US livestock industry. In the first part of our study, we utilized ordinary least squares (OLS) and Fixed effect (FE) models to perform both cross-sectional and panel analysis on five types of livestock: beef cows, milk cows, layer chickens, broiler chickens, and hogs. Unlike the general Ricardian approach in the literature, we attempted a novel approach using livestock inventory share as our models' dependent variable instead of land value. We found that climate change may or may not affect livestock inventory levels depending on the types of livestock and geographical locations. Increased temperature and precipitation may benefit a particular livestock industry depending on geographical location and production settings. However, we did not predict any adverse effect of climate change on any of the five types of livestock we analyzed. In the second part, we fitted our regression estimates to a climate model and projected the US livestock industry in 2070. Comprehending livestock and region-specific impacts of climate change will allow policymakers to craft better strategies and policies to combat and mitigate the adverse externalities of climate change. Essay 3: This study establishes a statistically significant negative association between public transit funding and private vehicle usage. We used the propensity score matching, genetic matching, and diff-in-diff frameworks to conduct county-level and individual household-level analyses to conclude that increasing public transit funding can successfully decrease private vehicle usage. Our results provide empirical backing for encouraging public transit funding as an intervention strategy to reduce private vehicle usage in communities. More specifically, the counties or households that received public transportation funding have lower average vehicle miles traveled (2.35 miles or roughly 6 percent on the county level and 1306.5 miles or approximately 6 percent on the household level) compared to the counties that did not receive any funding. We also conducted a longitudinal study to understand the causal impact of changes in public transit funding on county and household annual private vehicle mileage. This study uses four datasets. The 2019 National Transit Database Annual Data Products (NTD) provides public transit data. Transportation data are collected from the 2017 and 2009 National Household Travel Survey (NHTS). Data from the 2017 national census provide this study's necessary demographic and geographic data (United States Census Bureau). We matched observations from these four datasets at the county and household levels to create the panel datasets with 3138 counties and 4588 households from 50 states of the United States.

"This thesis consists of three independent essays in applied economics. Chapter 1 evaluates the impact of high-tech clusters on inequality by focusing on a Chinese placed-based industrial policy called "Made in China 2025". We conduct an event-study analysis to investigate the effects of high-tech clusters on labor demand, wages, and living costs across occupations and regions. We find that the pilot cities experience a significant increase in online job vacancies and offered wages relative to non-pilot cities. At the same time, the wage gap between non-routine and routine occupations widens. On the contrary, this policy lowers labor demand and wages in neighboring areas of the pilot cities. Combining the labor market effects with increasing living costs in the pilot cities, we demonstrate that the welfare of nonroutine job workers and workers in the neighboring areas disproportionately declines with the construction of high-tech clusters. Our results suggest that policymakers should be cautious about occupational and regional inequalities when constructing high-tech clusters. Chapter 2 investigates whether and how top executives impact their firm's hiring behavior in terms of employment, job postings and job posting requirements. I construct a unique dataset that provides information on top executives, firm characteristics, and job postings for S&P1500 companies from 2010 to 2019. This dataset enables me to track top executives in different firms over the sample period. Executive fixed effects explain significant variation in employment, number of job postings, experience and skills required by each job posting. Moreover, CEOs with a business degree, are correlated with more job postings but fewer years of education or experience required. CEOs with an MBA are correlated with fewer job postings but more years of education. Finally, Chapter 3 studies more than one-third of individuals who experience long-COVID. The objective is to identify risk factors associated with long-COVID. This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). Multi-variable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. We find that, middle-age categories, female sex, hospitalization associated with COVID-19, long or extended hospital stay, receipt of mechanical ventilation, and several comorbidities including depression, chronic lung disease, and obesity were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic."--Pages ix-x.

With the rapid development of computing devices and the improved Internet access, our daily lives and business world have been dramatically reshaped. While technology allows companies to attain unprecedented efficiency gains, it also brings about fundamental changes to the competition environment. The goal of this dissertation is to improve our understanding of three major technological developments through the lens of applied economics, econometrics, and machine learning. The first chapter is on the mobile application (app) market. The mobile app market is a fast growing platform which offers consumers millions of applications for smartphones and tablets, but the astronomical number of apps creates a search and discovery problem. I propose an optimal matching for a new advertising strategy, “Cross Promotion (CP),” which allows app developers to locate valuable users while helps app users discover new apps. Distinct from the extant platform literature, which consists mostly of theoretical formulations of platforms, I develop a two-stage model using data from a random matching experiment to examine how users preferences are influenced by apps characteristics. I propose a matching scheme which significantly improves the effectiveness of the CP using the deferred acceptance algorithm. The second chapter is on the issue of cybersecurity. I propose a potential information disclosure policy to alleviate cybersecurity threats. With a large-scale randomized field experiment consisting of 7,919 organizations in the U.S., I show through a random forest-assisted heterogeneous treatment effect analysis that the combination of information notification and publicity significantly reduces large spammers outbound spam volume, which is an indicator of an organizations underlying security condition. The last chapter investigates social media platforms, which is on companies social media strategies under challenging situations. Through a theoretical model, the use of support vector machine, and an empirical analysis using a large Twitter data set, I show the existence of both a negative spillover and a customer encroachment effect after the airline industrys major product-harm crises. In addition, I show that non-focal companies appear to harness two distinct functions of social media–offensive and defensive marketing strategies–to alleviate the negative influence and exploit the unexpected benefit as a result of such incidents