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Record W2159419602 · doi:10.1111/jeea.12023

OUTSOURCING WHEN INVESTMENTS ARE SPECIFIC AND INTERRELATED

2013· article· en· W2159419602 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of the European Economic Association · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsOutsourcingExternalityAppropriationProperty rightsProxy (statistics)Investment (military)Industrial organizationMicroeconomicsBusinessEconomicsTransaction costMarketingComputer sciencePolitics

Abstract

fetched live from OpenAlex

Using the universe of large Canadian manufacturing firms in 1988 and 1996, we investigate to what extent outsourcing patterns concord with the predictions of a simple property rights model. The unique availability of disaggregate information on outputs as well as inputs permits the construction of a detailed measure of vertical integration. We rely on five 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 interrelatedness-externalities between buyer and supplier investments-guides the analysis. Property rights predictions on the link between investment intensities and optimal ownership are strongly supported, but only for transactions with low interrelatedness. High specificity and low risk of appropriation strengthen the predictions in the model and in the data. © 2013 by the European Economic Association.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.017
GPT teacher head0.174
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it