Bibliographic record
Abstract
Using data from 38,916 businesses in war-torn Colombia and from 5,138 attacks by the two rebel groups, FARC and ELN, we study how firms manage inventory during civil war. We obtain exogenous variation in the conflict intensity via a difference-in-differences model, which hinges on the peace process between Colombia’s government and FARC. Relying on this identification strategy, we hypothesize and show that war causes two effects on firm-level inventories. First, it leads firms to replace physical assets (inventory) with fungible assets (cash), causing them to operate with an oversecured financial buffer, but a fragile operational buffer. Second, this inventory reduction occurs mostly in unprocessed inventories (finished-goods inventories are insensitive to violence), meaning that, although war-torn businesses are equipped to fulfill planned orders, they become inflexible at handling uncertain future demand. We then show that the magnitude of these effects is highly contingent on the firm’s position in the supply chain, its proximity to distribution markets, and the type of attacks it is subject to. We then propose policies to address war-related risk in supply chains. This paper was accepted by Vishal Gaur, operations management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".