Applications and Interviews: Firms’ Recruiting Decisions in a Frictional Labour Market
Bibliographic record
Abstract
I develop a directed search model to study the recruitment decisions of firms competing for workers who ex post differ in two dimensions: (1) their match productivity and (2) their probability of accepting a job offer, endogenously determined by their choice of application portfolio. To attract these workers, firms post a recruiting intensity and a hiring standard, in addition to terms of trade. A higher recruiting intensity is costly, but allows the firm to select more applicants for an interview, which reveals their productivity. The hiring standard solves the tradeoff between immediate hiring and waiting for a potentially better match in the future. I characterize equilibrium and find that various outcomes, including uniqueness of equilibrium and the cyclicality of recruiting intensity, crucially depend on firms’ recruiting cost and workers’ search cost. Calibration of the model to the U.S. labour market indicates a continuum of equilibria. Given selection of a particular equilibrium, hiring standards are countercyclical while recruiting intensity is procyclical. The calibrated model creates more amplification than a standard model without intensive margins and gives rise to procyclical match efficiency when viewed through the lens of a Cobb–Douglas matching function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".