Bibliographic record
Abstract
This thesis contains three essays on wages, worker mobility and the macroeconomy. Chapter 1 develops an equilibrium model of the labor market with on-the-job search, wage-tenure contracts, and heterogeneity in match productivity. The model predicts an inverse relationship between match productivity and post-unemployment wages where workers in high productivity matches earn a low wage initially, but experience significant wage growth over tenure. The model is considered in the context of labor market entry for young adults. Unemployment risk in the transition process reduces worker mobility and limits the ability of workers to recover from a low quality initial match. Quantitatively, workers that initially form low quality matches expect to produce 8.0% less and consume 5.2% less in their first three years in the labor market relative to workers that initially form high quality matches. Chapter 2 studies efficiency in the model developed in Chapter 1. The social planner's match formation strategy perfectly insures employed workers against unemployment risk and results in an 11.3% increase in permanent consumption relative to the competitive equilibrium. Two self-financed policies in the forms of an extension of unemployment insurance and a hiring subsidy are considered as alternative strategies to increase worker welfare. The optimal policies increase permanent consumption by 5.1% and 1.3% respectively relative to the competitive equilibrium, however both policies are associated with a decrease in average output per worker. Chapter 3, joint with Shouyong Shi, develops an equilibrium model of the labor market where workers have incomplete information about their ability. Search outcomes yield information for updating the belief about the ability, which affects optimal search decisions in the future. Firms respond to updated beliefs by altering vacancy creation and optimal wage contracts. To study equilibrium interactions between learning and search, this paper integrates learning into a search equilibrium with on-the-job search and wage-tenure contracts. The model is calibrated to quantify the extent to which learning and on-the-job search can explain empirical facts related to wage decreases in job-to-job transitions, duration dependence in unemployment, and frictional wage dispersion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".