Date of Award
Spring 2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Economics
First Advisor
Moscarini, Giuseppe
Abstract
This dissertation focuses on understanding inequality in US labor markets.The first two chapters focus on technological inequality, and the third on racial and gender disparities. The first chapter, joint with Bledi Taska, asks how software adoption impacts the skill-sets firms require for jobs. We estimate the causal effect of firms adopting a software type—such as sales or accounting software—on their skill requirements and labor demand. Our data are US job postings 2010 –2019 assembled by Lightcast. We use keywords in job posting text to measure jobs’ analytic and social skill requirements, and also to identify software adoptions at the firm-occupation level—for example, a team of accountants switching from Excel to specialized accounting software. We account for potential endogeneity in the timing of software adoption by using a latent variable IV strategy based on Freyaldenhoven, Hansen and Shapiro (2019). This involves controlling for skill requirements in other, non-software-adopting occupations, instrumented for by the lead of the software adoption event. We find that adopting one additional type of software on average increases analytic skill requirements by 0.8pp and social skill requirements by 1.1pp. Software types are heterogenous in the impact on skill requirements. We also find that the firm increases hiring by 30% in the adopting occupation and, to a lesser extent, in other occupations. In the second chapter, also joint with Bledi Taska, we embed the firm-level upskilling effects, found in the first chapter, into an equilibrium model of software adoption and occupation choice. At the core of the model, workers and jobs match on analytic and social skill requirements. Each job produces according to a CES aggregate of labor and optionally software, where software raises skill requirements. Each firm is a generalized CES aggregate of two jobs, one of each of two occupations. Firms face interconnected discrete and continuous input choices across occupations. Workers have preferences over their wage and the type of job, and choose their preferred occupation-software pair subject to their skill bundles. In equilibrium, wages adjust so labor demand equals supply for each occupation-software pair. We estimate the model over the white collar sector using GMM and find the data is consistent with software and workers being complements, particularly in high-wage occupations. Through the lens of our model, a fall in software prices and associated software uptake widens the wage gap between software and non-software jobs within each occupation, while simultaneously increasing the premium of high-wage management and STEM occupations. We show that the effect of software on within-occupation inequality is largely due to upskilling, as higher skill requirements restrict labor supply from moving into software jobs despite their higher labor demand. In the third chapter, joint with Noriko Amano and Julian Aramburu, we focus on a different dimension of labor market inequality—racial and gender disparities. We study Executive Order 11246, an employment-based affirmative action policy targeted at firms holding contracts with the federal government. We find this policy to be ineffective in the 21st century, contrary to positive effects found for the late 1900s (Miller (2017)). Our novel dataset combines data on federal contract acquisition and enforcement with US linked employer-employee Census data 2000–2014. We employ an event study around firms’ acquiring a contract, based on Miller (2017), and find the policy had no effect on employment shares or on hiring, for any minority group. Next, we isolate the impact of the affirmative action plan, which is EO 11246’s preeminent requirement that applies to firms with contracts over $50,000. Leveraging variation from this threshold in event study and regression discontinuity strategies, we find similarly null effects. We show that even randomized audits are not effective, suggesting weak enforcement. Our results highlight the importance of the recent budget increase for the enforcement agency, as well as recent policies enacted to improve compliance.
Recommended Citation
Contractor, Zara, "Essays on Inequality" (2023). Yale Graduate School of Arts and Sciences Dissertations. 1017.
https://elischolar.library.yale.edu/gsas_dissertations/1017
