Abstract: Intermediation is ubiquitous in international labor migration. I examine the market structure of the recruitment supply chain for migrant workers, and how exposure to labor demand shocks affect intermediaries' conduct and the quality and quantity of migration. Using a simple model and a sudden increase in the minimum wage for overseas Filipino workers, I document that (i) exits of intermediaries that serve multiple destination and origin markets induce spillovers and reduce market access to and from untreated markets, and that (ii) the policy disproportionately drives out illegal intermediaries, who tend to have small market shares and place workers in low-wage jobs. Migrant protection policies increase intermediaries' costs of operating legally, increasing both exits and incentives for illegal practices.

Abstract: Institutional curriculum design in higher education is often discussed in terms of breadth and depth. While studies often rely on coarse disciplinary classifications or the number of majors, more granularity may be desired if students select courses from a broad range of fields. We develop a method to quantify curricular breadth by applying deep learning and graph theory to text data found in course catalogs. Using 17 years of data from a liberal arts college, we show that mapping the curriculum as a high-dimensional network based on semantic similarity recovers intuitive relationships between courses cutting across disciplinary boundaries. We apply Weitzman (1992)'s diversity index to measure breadth in a fully distance-based and discipline-agnostic way, and show three facts about the college's curriculum: i) differences in content within disciplines and similarities across disciplines are substantial, ii) our breadth metric has good theoretical properties and can be interpreted as a combination of more "primitive" measures, and iii) our breadth metric predicts student enrollment even conditional on the number of courses offered.

Abstract: Cross-country data is scarce in the international labor market for low-skilled migrant workers. I build a daily web-scraping pipeline to collect centralized job postings from six of the largest migrant-sending countries across greater Asia (India, Indonesia, Nepal, Pakistan, Philippines, and Sri Lanka), where licensed recruitment agencies submit job orders they receive from destination-country employers before they start recruiting. I validate this data against ex-post migration statistics and find that it recovers cross-sectional variation in migration across destination countries and occupations, but not temporal variation across months. Consistent with this, discussions with recruitment agencies reveal that initial job orders do not map easily to the realized timing or quantity of migration. I then use this data to estimate the own-country and cross-country elasticity of migrant labor demand with respect to the 2025 increase in the minimum wage for Filipino workers, and find null effects.