Efficiency, Justified Envy, and Incentives in Priority-Based Matching

Top trading cycles (TTC) is Pareto efficient and strategy-proof in priority-based matching, but so are other mechanisms including serial dictatorship. We show that TTC minimizes justified envy among all Pareto-efficient and strategy-proof mechanisms in one-to-one matching. In many-to-one matching, TTC admits less justified envy than serial dictatorship in an average sense. Empirical evidence from New Orleans OneApp and Boston Public Schools shows that TTC has significantly less justified envy than serial dictatorship. (JEL C78, D61)

Optimizing for Distributional Goals in School Choice Problems

I investigate three goals of school choice: welfare, encouraging neighborhood schools, and diversity. I use optimization problems to find the best stable and incentive compatible match for any combination of these objectives. These problems assume there is a continuum of students and school seats, which allows me to describe the incentive compatibility conditions in a tractable form. I prove that the set of stable matchings is generically continuous in the distribution of students and the school capacities, which implies that the characterization of the possible stable matches in the continuum model approximates the set of stable matches in a matching market with a large, but finite, number of students. I then apply my framework to data from Boston Public Schools. If the mechanism conditions on demographics, the improvement (relative to the status quo) in student welfare is equivalent to moving 291 students (out of 3,479) to schools one rank higher in their preference lists. In contrast, if the mechanism does not condition on demographics, the welfare improvement is equivalent to moving only 25.1 students to schools one rank higher. Improvements in the distributional goals can be made (e.g., increasing enrollment in neighborhood schools by 50%) without reducing welfare or diversity. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

The Long-Run Impacts of Specialized Programming for High-Achieving Students

I evaluate long-run academic impacts of specialized programming for high-achieving students by analyzing Advanced Work Class (AWC), an accelerated curriculum delivered in dedicated classrooms for fourth through sixth graders in Boston Public Schools. Fuzzy regression discontinuity estimates show that AWC has positive yet imprecise impacts on test scores and improves longer-term outcomes, increasing high school graduation and college enrollment. These gains are driven by black and Latino students. An analysis of mechanisms highlights the importance of staying “on track” throughout high school, with little evidence that AWC gains result from peer effects. (JEL H75, I21, I28, J15)

The Equal Earth Projection: Distortion Characteristics, Adoption, and (Social) Media Impact

<p><strong>Abstract.</strong> The Equal Earth map projection (Figure 1) is a new pseudocylindrical projection for world maps. It is similar to the popular Robinson projection, but unlike the Robinson, it is an equal-area projection. The projection shows continental outlines in a visually pleasing and balanced way. Its equations are simple to implement and fast to evaluate. The creation of this projection was motivated by a wave of news stories about Boston Public Schools switching to maps using the Peters projection. Since its online publication in August 2018, the projection has already been adopted in various software and projection libraries and it has been featured by media outlets around the world.</p><p>This presentation will show the distortion characteristics of Equal Earth and compare them to the Robinson projection as well as a few other well-known equal-area projections. We will cover some of the published world maps that use Equal Earth and list the software that has adopted the projection. Finally, media reporting and the impact of rapid popularity via social media circulation will be discussed.</p>

If You Offer It, Will They Come? Patterns of Application and Enrollment Behavior in a Universal Prekindergarten Context

Universal public prekindergarten programs have been expanding in recent years, but not all eligible families apply to these programs, for reasons that are not well understood. Using two cohorts of students ( N = 8,391) enrolled in Boston Public Schools, we use geographic information systems to combine administrative records with census data to compare the student-, neighborhood-, and school-level characteristics of kindergarteners who did and did not apply to the Boston Public Schools prekindergarten program. We find that nonappliers are more likely to be non-White ( SD = 0.27), low income ( SD = 0.11), and dual language ( SD = 0.58), particularly those who did not attend any other prekindergarten program. We find similar differences at the neighborhood and school levels. Our study provides some of the first descriptive information on the sociodemographic characteristics and spatial distribution of families who opt out of applying to universal prekindergarten programs. Findings may inform recruitment strategies to promote equitable and universal prekindergarten enrollment.