Absenteeism, Productivity, and Relational Contracts Inside the Firm
Bibliographic record
Abstract
Abstract We study relational contracts among managers using unique data that tracks transfers of workers across teams in Indian ready-made garment factories. We focus on how relational contracts help managers cope with worker absenteeism shocks, which are frequent, often large, weakly correlated across teams, and which substantially reduce team productivity. Together these facts imply gains from sharing workers. We show that managers respond to shocks by lending and borrowing workers in a manner consistent with relational contracting, but many potentially beneficial transfers are unrealized. This is because managers’ primary relationships are with a very small subset of potential partners. A borrowing event studies around main trading partners’ separations from the firm reinforces the importance of relationships. We show robustness to excluding worker moves least likely to reflect relational borrowing responses to idiosyncratic absenteeism shocks. Counterfactual simulations reveal large gains to reducing costs associated with forming and maintaining additional relationships among managers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".