Bibliographic record
Abstract
My dissertation focuses on the role of incentives in the workplace. In Chapter 1, I study peer effects in pay-for-individual-performance jobs. Specifically, I explore whether, how, and why coworker performance matters when rewards are based on individual performance. When teamed with high-performing peers, I find that workers are more productive overall. I also find that workers who resign are unaffected by coworker performance in the period after they hand in their resignation notice. The findings suggest peer effects in pay-for-individual-performance jobs reflect reputational concerns about relative performance rather than competitive preferences.\n\nIn Chapter 2, I present field evidence that sheds new light on incentive provision in multitask jobs. Specifically, I design and conduct a field experiment at a large-scale restaurant, where the pre-existing wage contract encourages workers to carry out their tasks in a way that is not perfectly aligned with the firm's preferences. The experimental treatment pays bonuses to waiters for the number of customers they serve, in addition to their tips for customer service and hourly wages. I compare worker performance under the treatment to that under the pre-existing contract, where workers are rewarded for overemphasizing customer service, to evaluate the effect of a wage contract that encourages undesirable behavior. I find that the average worker earns more, is more productive, and generates higher short-run profits for the firm when paid bonuses for customer volume. Overall, the findings suggest that sharpening wage contracts to deal with incentive problems in multitask jobs has benefits for workers as well as the firm.\n\nIn Chapter 3, I present joint work (with Arvind N. Magesan at the University fo Calgary) on the beauty premium's role in the workplace. Specifically, we investigate whether, how, and why the beauty premium can be explained by the behaviour of workers after they are hired. We find that attractive workers earn more because they transfer effort from tasks that reward looks to tasks that reward effort. We also provide evidence against favorable treatment by customers and the employer as sources for the beauty premium. We conclude that the premium is largely driven by the worker's on-the-job behavior.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".