Wage Transparency and Social Comparison in Sales Force Compensation
Bibliographic record
Abstract
When wages are transparent, sales agents may compare their pay with that of their peers and experience positive or negative feelings if those peers are paid (respectively) less or more. We investigate the implications of such social comparisons on sales agents’ effort decisions and their incentives to help or collaborate with each other. We then characterize the firm’s optimal sales force compensation scheme and the conditions under which wage transparency benefits the firm. Our results show that the work environment—which includes such aspects as demand uncertainty, correlation across sales territories, and the possibility of help/collaboration—plays a significant role in the firm’s compensation and wage transparency decisions. In particular, wage transparency is more likely to benefit the firm when demand uncertainty is low, sales outcomes are positively correlated across different sales territories, and sales agents can collaborate at low cost. We find that, contrary to conventional wisdom, social comparisons need not reduce collaboration among agents. Our study also highlights the importance of providing the right mix of individual and group incentives to elicit the benefits of wage transparency. This paper was accepted by Juanjuan Zhang, marketing.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".