UC Santa Cruz

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.