The Non-Convexity Issues in a Limited-Commitment Economy
Bibliographic record
Abstract
After reviewing some basic self-enforcing labour contracts models, we expose how self-enforcing labour market theory can help explain some important dynamic properties of key macroeconomic variables. Calmès (1999, 2003) detail how self-enforcing labour contracts improve the way macroeconomic models account for the response of the economy to external shocks. The introduction of a state-dependent outside opportunity for the manager is the first step in generalizing the theory (Calmès 2007, Thomas and Worrall 2007). In this paper, we discuss the next step, the endogenization of capital. Although desirable, this task is not straightforward as the contract set might no longer be compact in this case. Relatedly, we also discuss the introduction of a third agent (the financial intermediary) in the model. We also analyse the link between stationarity and set convexity when incorporating growth in the model. A stochastic trend may be considered but then the non-convexity issue arises again. The aggregation of heterogeneous individual contracts can also lead to the same problem.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".