Higher Education

Article excerpt

The NBER's Working Group on Higher Education met in Cambridge on November 9. Working Group Director Charles T. Clotfelter of Duke University organized the meeting. These papers were discussed:

Susan M. Dynarski, Harvard University and NBER, and Judith E. Scott-Clayton, Harvard University, "The Cost of Complexity in Federal Student Aid: Lessons from Optimal Tax Theory and Behavioral Economics" (NBER Working Paper No. 12227)

Discussant: Eric Bettinger, Case Western Reserve University

Marko Tervio, University of California, Berkeley, "Network Analysis of Three Academic Labor Markets"

Discussant: Richard Jensen, University of Notre Dame

Brian C. Cadena and Benjamin J. Keys, University of Michigan, "Self-Control Induced Debt Aversion: Evidence from Interest-Free Student Loans"

Discussant: Ofer Malamud, University of Chicago

Megan MacGarvie, Boston University and NBER, "Foreign Students and the Diffusion of Scientific and Technological Knowledge to and from American Universities"

Discussant: William Kerr, Harvard University

Zeynep Hansen, Washington University and NBER; Hideo Owan, Aoyama Gakuin University; and Jie Pan, Washington University, "The Impact of Group Diversity on Performance and Knowledge Spillover: An Experiment in a College Classroom" (NBER Working Paper No. 12251)

Discussant: Jacob Vigdor, Duke University and NBER

The federal system for distributing student financial aid rivals the tax code in its complexity. Both have been a source of frustration and a focus of reform efforts for decades, yet the complexity of the student aid system has received comparatively little attention from economists. Dynarski and Scott-Clayton describe the complexity of the aid system, and apply lessons from optimal tax theory and behavioral economics to show that complexity is a serious obstacle to both efficiency and equity in the distribution of student aid. They show that complexity disproportionately burdens those with the least ability to pay and undermines redistributive goals. They use detailed data from federal student aid applications to show that a radically simplified aid process can reproduce the current distribution of aid using a fraction of the information now collected.

Tervio analyzes the academic labor market as a citation network, where departments gain citations by placing their Ph.D. graduates into the faculty of other departments. The aim is to measure the distribution of influence and the possible division into clusters between academic departments in three disciplines (economics, mathematics, and comparative literature). Departmental influence is measured by a similar method to that used by Google to rank web pages. In all disciplines, the distribution of influence is significantly more skewed than the distribution of academic placements because of a strong hierarchy of schools in which movements are seldom upwards. This hierarchy is strongest in economics. Tervio also finds that, in economics, there are clusters of departments that are significantly more connected within than with each other. These clusters are consistent with anecdotal evidence about Freshwater and Saltwater schools of thought. …