ABSTRACT 'Make-or-Buy' of Peripheral Services in Manufacturing: Evidence from Spanish Plant-Level Data *
Bibliographic record
Abstract
In this paper we empirically explore the ‘make-or-buy’ decisions of peripheral services in manufacturing plants using detailed information on a data set from a new plant-level survey from 926 plants distributed in all manufacturing industries in Spain. In particular, survey respondents are asked how their contracting practices of peripheral services had changed in the last three years. The answer to this question is informative of the changes in the importance of backward integration for each of the plants interviewed. Using other information provided in the survey, we relate reported changes in backward integration to changes in other relevant plant characteristics. We show that increases in outsourcing of services are positively correlated with increases in the plant’s market share as well as increases in product market competition and product prices. These findings are robust to controlling for whether plants belong to single-plant or multi-plant firms. This result is consistent with the view that market size limits the degree of specialization at the plant level in the Spanish manufacturing industry.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".