Reciprocity in Labor Relations: Evidence from a Field Experiment with Long-Term Relationships
Bibliographic record
Abstract
We followed field workers administering a household survey over a 12-week period and examined how their reciprocal behavior towards the employer responded to a sequence of exogenous wage increases and wage cuts. To disentangle the effects of reciprocal behavior from other explicit incentives that occur naturally in long-term employment relationships, we devised a novel measure of effort that not only captures the notion of work morale but that field workers perceived as unmonitored. While wage increases had no significant effect, wage cuts led to a strong and significant decline in unmonitored effort. This finding provides clear evidence of a highly asymmetric reciprocity response to wage changes. Our estimates further imply that field workers quickly adapted to higher wages and revised their reference point accordingly when deciding on reciprocity. Finally, we consider a second measure of effort that was explicitly monitored and found no significant effect to any of the wage changes. This lack of impact illustrates that explicit incentives can easily outweigh the effects of reciprocity and highlights the importance of having a measure of effort that workers perceive as unmonitored when testing for reciprocity in long-term relationships.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".