Author ORCID Identifier

Date of Award

Spring 5-6-2024

Degree Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

Carlianne Patrick

Second Advisor

Tim Sass

Third Advisor

Dan Kreisman

Fourth Advisor

Cathy Liu


In recent years, increasing attention has been given to the role of space in not only generating inequality but also alleviating it through place-based policies. This research focuses on mechanisms for inequality generation through local labor markets and potential routes for mitigation. In “Skills, Matching, and Skill Specificity Across Space”, we test whether urban agglomeration is skill-biased by using text data on skills from a near universe of job postings and resumes. Creating a new measure of skill specificity by modeling the network of relationships between skill, we find evidence that an increase in urban population increases match quality on average and the premium is greater for specific skills. Premiums appear to be driven by both labor market thickness and sorting between cities.

Early childhood education is often regarded as an ideal economic development investment. Numerous studies on high-quality, model programs in the 1960s and 1970s demonstrated a strong link between participation in pre-K programs and both short-term student achievement and positive later-life outcomes. However, evidence on state-funded, ‘universal’, pre-K programs is inconclusive. In “Assessing the Benefits of Education in Early Childhood: Evidence from a Pre-K Lottery in Georgia”, we use enrollment lotteries for over-subscribed school-based sites in Georgia’s Pre-K Program to analyze the impact of participation on elementary school outcomes. Lottery winners enter kindergarten more prepared in both math and reading, but gains fade by the end of kindergarten. Further, some negative achievement effects emerge by grade 4. Our evidence suggests greater benefits and lesser attenuation of gains for economically disadvantaged students.

Monopsony power in the labor market drives a substantial portion of between-city wage inequality by allowing firms in smaller areas to set wages below the competitive wage. In “Monopsony in the Market for Remote Work”, I use a double machine-learning estimator and data on job postings in the United States between 2012 and 2022 to estimate the elasticity of labor supply for fully-remote jobs. The small estimated elasticity indicates the presence of monopsony power in the market for fully-remote work. Furthermore, differences in fully-remote work’s monopsony power across city size persists despite the work’s geographically divorced nature.