This dissertation studies topics related to labor economics and education economics. The first chapter studies how employer reputation affects employers' posting behavior and job seekers' search strategy in the labor market. We investigate this question using a novel dataset combining reviews from Glassdoor.com and job applications data from Dice.com and focus on the effects of online employer reputation on both the demand and supply side of labor market outcomes, including the number of job postings and applications received. Labor market institutions such as Glassdoor.com crowd-sources information about employers to alleviate information problems faced by workers when choosing an employer. Raw crowd-sourced employer ratings are rounded when displayed to job seekers. By exploiting the rounding threshold, we identify the causal impact of Glassdoor ratings on employers' posting behavior and job seekers' search strategies using a regression discontinuity framework. We document the effects of such ratings on both the demand and supply sides of the labor market. We find that displayed employer reputation affects an employer’s ability to attract workers, especially when the displayed rating is "sticky''. Employers respond to having a rating above the rounding threshold by posting more new positions and re-activating more job postings. The effects are the strongest for private, smaller, and less established firms, suggesting that online reputation is a substitute for other types of reputation.

The second chapter examines how supplemental active learning sessions affect students' learning outcomes at the university level. Taking advantage of administrative data from a public university, we investigate the effects of supplemental active learning sessions and mentoring sessions provided by an academic support program on students' learning outcomes, including course grades and graduation rates in STEM majors. We find that attending these sessions significantly increases the course GPA by 0.5 points, which is large enough to change student letter grades by a plus or minus. In addition, STEM-intended students who participate in such sessions graduate at higher rates within STEM fields compared to non-participants, with the 6-year and 4-year graduation rates with a STEM major increasing by around 25 percentage points and 9 percentage points, respectively. Our findings also indicate additional positive effects for students from underrepresented groups, especially females, Hispanic/Latino students, and low-income students.

The third chapter leverages variations around the timing of the announcements and high-frequency data from a large online job board that tracks postings and applications second by second, we examine how salient local unemployment announcements affect employers' recruitment strategy and job seekers' search behavior in the labor market. Our findings suggest both employers and job seekers have strong responses to salient unemployment rate announcements. After the 6-month highest announced rate, employers reduce 0.7% job postings, and the average applications received per posting drop by 2.1% compared with those who do not experience a salient announcement. In contrast, a 6-month lowest announced rate increases the number of job postings by 0.8% and applications by 3.6%. In addition, firms with more employees and firms that are active in more MSAs tend to have stronger responses. The effects of salient announcements are exacerbated when there have also been historical highest or lowest in prior months.

My dissertation studies labor market structure and evaluates effects of policies relating to transportation improvements and immigration enforcement in different countries including Germany, the Untied States, and China. Specifically, I examine thenature and extent of labor market frictions, explore the causes and consequences, as well as study the role in policy evaluations.

In the first chapter, I investigate one potential source of labor market frictions from limited labor mobility by examining how integrating labor markets through improved transportation infrastructure affects both wages and the allocation of workersacross establishments. I take advantage of the expansion of the High-Speed Rail in Germany, which connected medium-sized districts located on existing rail lines, providing a natural experiment to study the effects of labor market integration. Using administrative panel data on establishments and workers linked to their employers, I estimate difference-in-differences and event-study models that compare newly connected districts to matched controls that were never connected. In theory, policies that improve labor mobility might raise wages both by facilitating more productive matches of workers to firms and by reducing the monopsony power that employers have on workers. I find evidence of increased labor mobility for workers in treated districts, especially those that are more likely to commute. Worker wages increase significantly and I find that both reduced monopsony power and better match quality are possible mechanisms. I also test for establishment adjustments including entry, exit, size, and wage that are predicted by each mechanism. This study shows evidence of reduced labor market frictions from improved transportation. It also sheds light on the importance of policies that enhance workers’ ability to switch employers.

In the second chapter, I explore the effects of LAWA on labor markets based on evidence that LAWA has significantly reduced the population of Hispanic noncitizens in Arizona. Specifically, I focus on the composition of the labor force and industry heterogeneity. I first show that a synthetic control has similar concurrent economic trends with Arizona, and verify that changes in employee composition are due to the replacement of Hispanic noncitizen workers by other subgroups and not by a change in overall employment. In response to LAWA, firms tend to reduce both the new hire rate and the separation rate. Several robustness checks are conducted to test the accuracy of the estimates and several mechanisms are considered that may drive the results.

In the third chapter, we study the structure of labor markets and the effects on wages in China. A growing literature has emphasized the existence of monopsony power stemming from employer concentration within local labor markets, which deviates from the conventional view of labor markets as perfectly competitive. We use firm-level data from the Chinese Annual Survey of Industrial Firms to analyze how employer concentration affects wage behavior. We first verify that local employment concentration measured by the Herfindahl-Hirschman Index of firm employment decreases between 1998 and 2013, in contrast to the increasing trend found in developed countries. Then with OLS and IV models, we show a negative relationship between labor market concentration and wages both the at the market level and at the firm level, indicating theexistence of imperfect labor market competition in China.

This dissertation uses reduced-form techniques to causally answer questions of direct importance in the fields of public policy broadly, but more specifically in the areas of health economics, labor economics, and education. The first chapter examines the effectiveness of Ohio's "Vax-a-Million" vaccination campaign -- a state-funded program that offered entry to a cash lottery for getting (or having already received) the vaccine for the COVID-19 coronavirus. We used an improvement upon the synthetic control method, which allowed us to generate a ``Synthetic Ohio'' which we could use as an untreated counterfactual. Using public health data, we find that the lottery was effective not only in boosting vaccination rates, but in also reducing COVID cases and ICU utilization. Finally, using an estimate of the high costs of ICU occupancy, we perform a back of the envelope cost-benefit analysis and find that the lottery had a benefit cost ratio of at least 10:1, saving the state of Ohio over 60 million dollars.

The second and third chapters explore teacher labor supply responses to the recent, rapid adoption of a radical change in the traditional school schedule -- going from five days a week to four -- in K-12 education observed throughout the country. While this policy has been adopted by 26 U.S. states, I focus my efforts on Oklahoma -- a state that has seen more than 20 percent of its districts make the scheduling change since 2010 -- and use publicly-available employment records from the Oklahoma State Department of Education to examine the effect that this schedule change had on teacher retention and quality.

In the second chapter, I investigate the impact that this schedule change had on schools' ability to recruit quality teachers and to retain current (and new) faculty. Since the policy had a staggered rollout, I use an event study design to align schools in event time. I find that adoption of the policy is associated with higher retention of new teachers with zero prior experience, increased recruitment of teachers with prior experience, and a reduction in the need for emergency certifications for teachers, which serves as a proxy for improved teaching quality.

In the third chapter, I describe the labor market for Oklahoma public schoolteachers and analyze the career trajectory of teachers who are early in their careers and new at their school, some of whom are exposed to, or have selected into, the four-day schedule change. I model their tenure using a duration (or hazard/survival) model and find confirmatory evidence that the four-day week substantially improves retention.

The COVID-19 pandemic has caused widespread economic destruction throughout the state of California. In this paper, I use employment and small business data from the Opportunity Insights Economic tracker combined with income and industry sector from the national census and BLS to explore the relationship between median county income, industry distribution, and changes in small business revenue and employment levels over the course of the pandemic. While my findings regarding industry distribution were inconclusive, I found a negative relationship between county median income and changes in employment, as well as a negative relationship between small business revenue and county median income. I conclude my paper by putting forth some potential causal hypotheses.

Using personnel data from a large U.S. retail firm, we examine whether the race of the hiring manager affects the racial composition of new hires. We exploit manager changes at hundreds of stores to estimate models with store fixed effects. We find significant effects of manager race and ethnicity. First, all non-black managers—i.e., whites, Hispanics, and Asians—hire more whites and fewer blacks than do black managers. The differences between non-black and black managers are especially large in the South. Second, in locations with large Hispanic populations, Hispanic managers hire more Hispanics and fewer whites than white managers.

Think tanks are constructed on the premise of research-based advocacy of a subject. Yet, it has been observed that the published opinions of think tanks are often devoid of unbiased objectivity. In my Masters’ thesis, I intend to establish that there exists an underlying bias in the articles published by a think tank, which is inferred from the choice of vocabulary used. For this, I used articles published between 2005 and 2019 by six prominent US-based think tanks to create a pool of about 100,000 articles. The think tanks are chosen randomly while ensuring that this selection represents the diversity of American political orientation. Since, the intrinsic nature of textual data poses the challenge of high-dimensional feature space, I chose to pre-emptively cluster the articles into prominent issue areas using word embeddings. This procedure of mapping articles to the maximum probable issue area is in part analogous to a probabilistic multi-label classification. Next, for each of the resulting subsets, I performed supervised multi-class classification and compared the output of various classifiers employed. The estimators deduced from these models help to construct a set of phrases that is characteristic of each think tank in each issue area. From this, I observed that for a classification rate in the range of 75 – 83%, these algorithms render similar feature sets for each think tank in a given issue area. These feature sets are further used to analyze the degree of overlap in the vocabulary used in the articles published by different think tanks. I observe that the data reflects a minimum overlap of approximately 20% between ideologically different think tanks, which increases to a maximum of 55% for certain ideologically aligned think tanks, thereby indicating that there exists an inherent bias in the analyses performed by think tanks, which can be primarily attributed to their founding ideology.

Using data from a large U.S. retail firm, we examine how differences in race, age, and gender between a manager and a subordinate affect the subordinate’s rate of quits, dismissals, and promotions. We find that these demographic differences can have statistically significant and sometimes large effects on employment outcomes. This is especially true of differences in race and ethnicity, which consistently produce significant effects and which produce the largest effects. In general, demographic differences tend to produce adverse effects on employment outcomes (i.e., higher quit and dismissal rates, and lower promotion rates). But in three striking cases, where traditionally lower-status managers are supervising traditionally higher-status employees, differences produce favorable effects for employees.

Using data from a large U.S. retail firm, we examine how racial matches between managers and their employees affect rates of employee quits, dismissals and promotions. We exploit changes in management at hundreds of stores to estimate hazard models with store fixed effects that control for all unobserved differences across store locations. We find a general pattern of own-race bias across all outcomes in that employees usually have better outcomes when they are the same race as their manager. But we do find anomalies in this pattern, particularly when the manager-employee match violates traditional racial hierarchies (e.g. nonwhites managing whites).

Using data from a large U.S. retail employer, we examine how demographic differences between manager and subordinate affect the subordinate’s rate of quits, dismissals, and promotions. We distinguish between two effects that demographic differences can produce: (1) the effects of dissimilarity per se, and (2) the effects of role breaking where the differences violate traditional social roles and status norms (e.g., non-whites managing whites). Our results suggest that both dissimilarity and role breaking can have statistically significant effects. Race: Dismissals and Promotions: Blacks and Hispanics with dissimilar managers are much more likely to be fired, and less likely to be promoted. We interpret these as dissimilarity effects. By contrast, white employees with non-white managers are less likely to be dismissed than whites with white managers, and more likely to be promoted. This suggests role breaking leads non-white managers to defer to white employees. Quits: While dissimilarity surprisingly has no effect on black quit rates, it does cause a moderate increase in Hispanic quits. Racial differences also cause a moderate rise in white quit rates. We expected the effect for whites to be larger because it is the sum of dissimilarity and role-breaking effects. Further analysis suggests the effect for whites is suppressed by pre-hire sorting; whites who dislike having non-white managers tend to avoid working for non-whites in the first place. Age: Age dissimilarity per se does not have effects. However, role breaking does; employees who are at least 20 percent older than their managers are much less likely to be dismissed, and more likely to be promoted. Gender: Gender differences have modest, adverse effects on all three employment outcomes.