Job Applications and Labour Market Flows
Bibliographic record
Abstract
"Job search technologies have improved greatly since the 1980s, allowing workers to submit more applications over time. Despite this increase in applications, job-finding rates (the probability of moving from unemployment to employment) in the United States have remained relatively unchanged, while job-separation rates (the probability of moving from employment to unemployment) have significantly declined. We argue that the main benefit of sending more applications is not to increase the probability of finding a job but rather the probability of finding a good match that lasts longer. This benefit is shown in the decline in job-separation rates. We develop an equilibrium search model of the labour market with two key features. First, workers can apply to many vacancies, and vacancies can receive many applications. Second, information is costly: firms can identify job applicants’ qualities only if they pay for that information. In our model, firms are more willing to acquire information when they have more applicants because the probability of having at least one high-quality applicant is higher. Thus, a rise in applications leads to a larger share of informed firms and more high-quality matches that are less susceptible to job destruction. While a higher number of applications raises the number of firms a worker contacts, it also affects the probability a worker receives an offer and, conditional on receiving an offer, the probability she or he accepts. More applications increase competition among workers and reduce the probability of receiving an offer, while the probability that a worker accepts any offer declines with more options. These opposing forces offset the effect that a higher number of applications has on the job-finding rate. Our model can generate the changes observed in application outcomes such as offers, acceptance rates, tenures and the minimum wage an individual would accept. Crucially, our model’s ability to match these empirical outcomes allows it to reproduce the trends in unemployment flows over time."
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".