Counterproductive Worker Behavior After a Pay Cut
Bibliographic record
Abstract
Abstract We examine how workers reacted to a pay cut in a sales call center setting in the United States. The pay cut was implemented by raising two pre-existing sales targets, that is, by “moving the goalposts”. Using a difference-in-difference approach, we show that among the workers who experienced the pay cut, some chose to leave the firm (exit); others generated abnormally high customer refunds, in a way that hurt both them and the firm. (We define this work practice as counterproductive.) The firm believed, and we present evidence, that these workers intentionally sold the wrong items, as opposed to simply optimally shirking on effort in response to the pay cut. We show that the most loyal workers (those with longer tenure) expressed themselves only through counterproductive work practices and not through exit. Less loyal workers reacted more strongly than loyal workers, and did so through a balanced mix of exit and counterproductive behavior. To our knowledge, this is the first study to document individual-level patterns of exit and (counter-) productivity following a pay cut and, how these differ for high- versus low-loyalty workers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".