Three essays in labour economics and the economics of education
Bibliographic record
Abstract
This thesis consists of three empirical essays.The first chapter is focused on the economics of gender, and the other two chapters are focused on the economics of education.A common theme in all these three chapters is studying the outcomes of disadvantaged groups in society, with an eye to policy interventions that could improve these outcomes.The first chapter examines whether women face a glass ceiling in the labour market, which would imply that they are under-represented in high wage regions of the wage distribution.I also measure the extent to which the glass ceiling comes about because women are segregated into lower-paying firms (glass doors), or because they are segregated into lower-paying jobs within firms (within-firm glass ceilings).I find clear evidence that women experience a glass ceiling that is driven mainly by their disproportionate sorting across firm types rather than sorting across jobs within firms.I find no evidence that gender differences in sorting across firms can be accounted for by compensating differentials.However, my results are consistent with predictions of an efficiency wage model where high-paying firms discriminate against females.The second chapter estimates the effect of publicly-disseminated information about school achievement on school choice decisions.We find that students are more likely to leave their school when public information reveals poor school-level performance.Some parents' respond to information soon after it becomes available.Others, including non-English-speaking parents, alter their school choice decisions only in response to information that has been disseminated widely and discussed in the media.Parents in low-income neighbourhoods are most likely to alter their school choice decisions in response to new information.The third chapter measures the extent to which cross-sectional differences in schools' average achievement on standardized tests are due to transitory factors.Testbased measures of school performance are increasingly used to shape education policy, and recent evidence shows that they also affect families' school choice decisions.There are, however, concerns about the precision of these measures.My results suggest that v sampling variation and one-time mean reverting shocks are a significant source of crosssectional variation in schools' mean test scores.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".