of Montreal, Federal Reserve Bank of R...
Bibliographic record
Abstract
Empirical evidence shows that vertically integrated producers are more productive, bigger and are matched to better suppliers (with high productivity and size). I present a dynamic stochastic model of an industry with heterogeneous …rms interacting as buyers and sellers, and market frictions that induce a hold-up problem to the manufacturers to account for these facts. In the model economy, an industrial structure emerges as the result of optimal investment decisions that …rms undertake under uncertainty. Firms choose whether to integrate, link to external sellers or buy inputs in the market. This theoretical environment provides a natural framework to answer several questions: Why do supply relations vary across industries and across …rms within industries? Why aren’t all large …rms vertically integrated? How do changes in the properties of uncertainty at …rm level determine di¤erences in the vertical structure of an industry? We …nd that higher uncertainty is associated with higher likelihood of outsourcing; vertically integrated …rms are larger and more e ¢ cient; otherwise identical downstream …rms may di¤er in their vertical structure, and those that are vertically integrated can end up disintegrated or remain integrated. We also analyze the e¤ects of changes in costs of vertical integration and outsourcing
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.527 | 0.393 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".