How do Young People Choose College Majors?

Previous studies on the determinants of the choice of college major have assumed a constant probability of success across majors or a constant earnings stream across majors. Our model disregards these two restrictive assumptions in computing an expected earnings variable to explain the probability that a student will choose a specific major among four choices of concentrations. The construction of an expected earnings variable requires information on the student s perceived probability of success, the predicted earnings of graduates in all majors and the student s expected earnings if he (she) fails to complete a college program. Using data from the National Longitudinal Survey of Youth, we evaluate the chances of success in all majors for all the individuals in the sample. Second, the individuals' predicted earnings of graduates in all majors are obtained using Rumberger and Thomas's (1993) regression estimates from a 1987 Survey of Recent College Graduates. Third, we obtain idiosyncratic estimates of earnings alternative of not attending college or by dropping out with a condition derived from our college major decision-making model applied to our sample of college students. Finally, with a mixed multinominal logit model, we explain the individuals' choice of a major. The results of the paper show that the expected earnings variable is essential in the choice of a college major. There are, however, significant differences in the impact of expected earnings by gender and race.

The Efficiency of Collective Bargaining in Public Schools.

This paper develops a bargaining model of wage and employment determination for the public sector. the solution to the model generates structural wage and employment equations that are estimated using data from New York State teacher-school district collective bargaining agreements.

Manipulation via Capacities Revisited

This paper revisits manipulation via capacities in centralized two-sided matching markets. Sönmez (1997) showed that no stable mechanism is nonmanipulable via capacities. We show that non-manipulability via capacities can be equivalently described by two types of non-manipulation via capacities: non-Type-I-manipulability meaning that no college with vacant positions can manipulate by dropping some of its empty positions; and non-Type-II-manipulability meaning that no college with no vacant positions can manipulate by dropping some of its filled positions. Our main result shows that the student-optimal stable mechanism is the unique stable mechanism which is non-Type-I-manipulable via capacities and independent of truncations. Our characterization supports the use of the student-optimal stable mechanism in these matching markets because of its limited manipulability via capacities by colleges.

School Choice with Control

Controlled choice over public schools is a common policy of school boards in the United States. It attempts giving choice to parents while maintaining racial and ethnic balance at schools. This paper provides a foundation for controlled school choice programs. We develop a natural notion of fairness and show that assignments, which are fair for same type students and constrained non-wasteful, always exist in controlled choice problems; a "controlled" version of the student proposing deferred acceptance algorithm (CDAA) always finds such an assignment which is also weakly Pareto-optimal. CDAA provides a practical solution for controlled school choice programs.

«School choice with controlled choice constraints: hard bounds versus soft bounds»

Controlled choice over public schools attempts giving options to parents while maintaining diversity, often enforced by setting feasibility constraints with hard upper and lower bounds for each student type. We demonstrate that there might not exist assignments that satisfy standard fairness and non-wastefulness properties; whereas constrained non-wasteful assignments which are fair for same type students always exist. We introduce a "controlled" version of the deferred acceptance algorithm with an improvement stage (CDAAI) that finds a Pareto optimal assignment among such assignments. To achieve fair (across all types) and non-wasteful assignments, we propose the control constraints to be interpreted as soft bounds-flexible limits that regulate school priorities. In this setting, a modified version of the deferred acceptance algorithm (DAASB) finds an assignment that is Pareto optimal among fair assignments while eliciting true preferences. CDAAI and DAASB provide two alternative practical solutions depending on the interpretation of the control constraints. JEL C78, D61, D78, I20.