Endogenous, State-Dependent Matching with Implications for the Cyclical Behavior of Unemployment
Bibliographic record
Abstract
Recent work by Shimer (2005) suggests that the standard search and matching model of the labor market cannot account for the observed volatility in cyclical unemployment under reasonable parametric assumptions. While many extensions and alternative calibration techniques have been employed to better fit the model to data, one assumption that has been maintained is that the process by which workers and firms are matched is exogenous and state-independent. In this paper, I solve a model of directed search in which firms make decisions along both the extensive and intensive margins; they decide whether or not to enter an industry, and how many vacancies to post conditional on entry. In equilibrium, a matching function arises endogenously. Unlike the typical “black box”matching function, the matching function derived here depends on the state of the economy, as well as the number of vacancies and unemployed workers. More specifically, the matching function becomes more (less) efficient in good (bad) states of the world. As a result, the endogenously generated, state-dependent matching function characterized here will predict greater volatility in unemployment in response to shocks of any magnitude.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".