Essays on Interviews and Matching
Bibliographic record
Abstract
This thesis contains three essays on the topic of quantifying the impact of interviews in a matching market. The first two essays are empirical and use novel preference and matching data from the Canadian Residency Matching Service (CaRMS), and the third essay presents a formal identification result. In the first essay, I measure the impact of interviews on employers' preferences, and in the second essay, I measure the impact of reducing interviews on match outcomes. Both essays require me to quantify employers' pre-interview information about their post-interview preferences, but employers observe information unobservable to the econometrician. To address this econometric challenge, I estimate a joint structural model of interview offers and post-interview ranks in which unobservables may be correlated across the two periods, and thereby I use the information contained in post-interview preferences to correct for employers' additional pre-interview information. The third essay presents a non-parametric identification result that formalizes the possibility of using selection (e.g., interview-offer) data and binary outcome (e.g., job-offer) data jointly to correct for the role of unobservable factors in selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".