Outsourcing when Investments are Specific and Complementary
Bibliographic record
Abstract
Using the universe of large Canadian manufacturing firms in 1988 and 1996, we investigate to what extent outsourcing decision can be explained by a simple property rights model.The unique availability of disaggregate information on outputs as well as inputs permits the construction of a very detailed measure of vertical integration.We also construct five different measures of technological intensity to proxy for investments that are likely to be specific to a buyer-seller relationship.A theoretical model that allows for varying degrees of investment specificity and for complementarities---an externality between buyer and supplier investments---guides the analysis.Our main findings are that (i) greater specificity makes outsourcing less likely; (ii) complementarities between the investments of the buyer and the seller are also associated with less outsourcing; (iii) property rights predictions on the link between investment intensities and optimal ownership are only supported for transactions with low complementarities.High specificity and a low risk of appropriation strengthen the predictions in the model and in the data.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".