Bibliographic record
Abstract
Abstract This paper incorporates credit constraints into a model of global sourcing and heterogeneous firms. Following Antràs and Helpman, heterogeneous firms decide whether to outsource or integrate input suppliers. Financing of fixed organizational costs requires borrowing with credit constraints and collateral based on tangible assets. The party that controls intermediate inputs is responsible for these financing costs. Sectors differ in their reliance on external finance and countries vary in their financial development. The model predicts that increased financial development decreases the share of integration relative to outsourcing in a country. The effect is more pronounced in sectors with a high reliance on external finance. However, this effect is mitigated by higher productivity (TFP) and headquarter intensity. Empirical examination confirms the predictions of the model. An improvement in financial development from the 25th to the 75th percentile in industries at the 75th percentile in finance dependence relative to those at the 25th percentile is associated with a 16.8% decrease in the median share of US intra‐firm imports. An increase in TFP from the 25th to the 75th percentile in the TFP triple interactions increase the share of US intra‐firm imports at the median by 3.2%. An increase in headquarter intensity from the 25th to the 75th percentile in the headquarter intensity triple interactions increase the share of US intra‐firm imports at the median by 21%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".