Behavioural Responses to Imperfect Information: Evidence from Post-Secondary Education and Early Career Choices
Bibliographic record
Abstract
This thesis investigates how young adults make post-secondary and early career decisions. Through a combination of experimental and traditional applied microeconomic methods, this research aims to better understand behavioural responses to information and institutional factors that contribute to inequality. The first chapter examines how measurement error in grades influences students’ major choices. Using administrative data from the University of Toronto and leveraging natural variation in exam scheduling as an instrument, I find that minor negative grade shocks caused by tightly scheduled exams significantly reduce the likelihood a student majors in that subject area. These findings demonstrate that students are highly sensitive to imprecise signals of academic performance, suggesting that policies relying on grades may unintentionally amplify inequality in educational outcomes. The second chapter, joint with Alexandra Ballyk, investigates how beliefs about job competitiveness shape application decisions. In a controlled laboratory experiment, we find most undergraduate job seekers consider both the quality and quantity of other applicants when making the decision to apply. We find that men and women hold, suggestively, different beliefs about the competitiveness of different jobs. These gender differences in beliefs align with observed gender differences in jobs applied to and are driven by those who hold beliefs about the quality and quantity of other applicants. Quantifying the role of these beliefs on application decisions can help explain gender gaps in job application patterns and, ultimately, early career outcomes. The third chapter evaluates how financial information influences university enrolment. In collaboration with the University of Toronto’s Registrar’s Office, I conducted a randomized controlled trial providing early financial aid estimates to admitted students. While all students received personalized estimates, only the treated group received guaranteed aid packages. We find that guaranteed aid only significantly affected the enrolment decisions of students whose guaranteed amount of aid perfectly matched their estimated amount of aid. Together, this thesis highlights how incomplete or imprecise information—whether about grades, finances, or labour market competition—shapes educational and economic decisions. These insights are relevant for both economic theory and policy, suggesting the need for interventions that account for behavioural responses to un- certainty and institutional design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".