Bibliographic record
Abstract
This dissertation explores key mechanisms underlying earnings inequality through three self-contained essays in labor economics. Each chapter addresses a distinct source of heterogeneity in wage outcomes—firm-specific wage-setting, employer learning and signaling, and occupational sorting—using large-scale administrative and survey data from Canada and the United States.The first essay examines how changes in firm-specific wage premiums contribute to gender earnings inequality. Using Canadian linked employer-employee data from 2001 to 2019, I estimate a gender-specific, time-varying firm fixed effects model to assess the role of wage-setting practices in shaping the gender pay gap. I find that while firms with rising wage premiums expand employment for both men and women, women capture only two-thirds of the wage gains men receive from reallocating to these firms, offsetting nearly one-quarter of the progress in narrowing the gender wage gap. This disparity is driven primarily by two factors: women’s lower likelihood of sorting into firms with rising premiums and their reduced ability to capture those gains relative to equally qualified men within the same firm. These effects are closely linked to gender differences in how wages and employment respond to firm-level productivity shocks.The second essay investigates the role of job market signaling in shaping the returns to education, drawing on canonical models of employer learning. The empirical literature on employer learning posits that employers learn about unobserved ability differences across workers as they spend time in the labor market. This chapter outlines the testable implications of this hypothesis and describes how they have been employed to estimate the relative contributions of job market signaling and human capital to observed returns to education. Although the empirical evidence remains limited, we conclude that signaling accounts for, at most, one-quarter of the measured returns to education.The third essay examines the flattening of the ability–experience profile in the U.S. since the early 2000s, identifying changes in occupational sorting as the primary driver. Using data from the 1979 and 1997 waves of the National Longitudinal Survey of Youth (NLSY), I show that life-cycle returns to cognitive ability have declined among less-educated workers due to increased concentration in lower-return occupations and reduced upward mobility. In contrast, more-educated workers in recent cohorts begin with substantially lower initial returns to cognitive ability but experience steeper growth in ability premiums, ultimately converging with the levels attained by earlier cohorts. This convergence is driven by relative improvements in occupational sorting by ability over the life cycle. These patterns are closely linked to broader structural changes—particularly the post-2000 reversal in the demand for cognitive tasks—which have reshaped occupational allocation and weakened life-cycle returns to ability
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".