Bibliographic record
Abstract
The majority of labor transactions throughout much of history and a significant fraction of such transactions in many developing countries today are "coercive", in the sense that force or the threat of force plays a central role in convincing workers to accept employment or its terms. We propose a tractable principal-agent model of coercion, based on the idea that coercive activities by employers, or "guns", affect the participation constraint of workers. We show that coercion and effort are complements, so that coercion increases effort. Nevertheless, coercion is always "inefficient", in the sense of reducing utilitarian social welfare. Better outside options for workers reduce coercion, because of the complementarity between coercion and effort: workers with better outside option exert lower effort in equilibrium and thus are coerced less. Greater demand for labor increases coercion because it increases equilibrium effort. We investigate the interaction between outside options, market prices, and other economic variables by embedding the (coercive) principal-agent relationship in a general equilibrium setup, and study when and how labor scarcity encourages coercion. We show that general (market) equilibrium interactions working through prices lead to a positive relationship between labor scarcity and coercion along the lines of ideas suggested by Domar, while those working through outside options lead to a negative relationship similar to ideas advanced in neo-Malthusian historical analyses of the decline of feudalism. A third effect, which is present when investment in guns must be made before the realization of contracting opportunities, also leads to a negative relationship between labor scarcity and coercion. Our model also predicts that coercion is more viable in industries that do not require relationship-specific investment by 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".