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Record W2972670644 · doi:10.1093/icc/dtab058

The birth and development of the Italian automotive industry (1894–2015) and the Turin car cluster

2021· preprint· en· W2972670644 on OpenAlexaff
Aldo Enrietti, Aldo Geuna, Consuelo Rubina Nava, Pier Paolo Patrucco

Bibliographic record

VenueIndustrial and Corporate Change · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicItaly: Economic History and Contemporary Issues
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsAutomotive industryMetropolitan areaEconomies of agglomerationEconomic geographyExternalityCluster (spacecraft)Industrial organizationRelation (database)BusinessEconomyRegional scienceEconomicsEngineeringEconomic growthGeographyComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

Abstract This paper describes the early genesis and later evolution of the Italian automotive industry employing the traditional agglomeration approach combined with Klepper’s spinoff theory. It highlights the key role played by the Turin car cluster from the late 19th century. We provide the first comprehensive database of Italian automobile companies from 1894 until 2015, based on original archival research. We use historical analysis and econometric models to identify the factors contributing to the creation and success of the automotive industry in Turin. More specifically, we investigate agglomeration economies and the part played by spinoffs and institutional factors with a special emphasis on the role of local education. Our model confirms the existence of a spinoff effect and, especially, the positive effect of inherited technical skills embedded in pilots. We find support for positive agglomeration effects at the regional level, technological complementarities with aeronautics, a metropolitan cluster effect, and importance of local education. Basic and technical education seem to be particularly important initial institutional preconditions for further technical learning and scientific advancement and would make an interesting research topic.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.186
GPT teacher head0.227
Teacher spread0.041 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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