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Record W1844950269 · doi:10.1111/caje.12413

Deep trade agreements and vertical FDI: The devil is in the details

2019· article· en· W1844950269 on OpenAlexvenueno aff
Alberto Osnago, Nadia Rocha, Michèle Ruta

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentMultinational corporationInternationalizationIntellectual propertyBusinessOutsourcingInternational economicsInternational tradeInvestment (military)Production (economics)Free tradeLiberalizationEconomicsMarket economyMicroeconomicsFinance

Abstract

fetched live from OpenAlex

Abstract Although pre‐1990s preferential trade agreements focused mostly on tariff liberalization, recent agreements increasingly contain deep provisions in diverse areas, such as intellectual property rights, investment and standards. At the same time, there has been a remarkable increase in the internationalization of production through foreign direct investment and outsourcing. This paper studies how deep trade agreements affect the international organization of production. Using new measures of the depth and content of preferential trade agreements and of vertical foreign direct investment, the analysis finds evidence that the depth of trade agreements is correlated with vertical foreign direct investment. Furthermore, this relationship is driven by the provisions that improve the contractibility of inputs provided by suppliers, such as standards, while provisions that increase the contractibility of headquarter services, such as intellectual property rights and investment protection, are generally negatively correlated with foreign investment. This finding is consistent with the so‐called “property rights” theory of the multinational firm according to which improving the contractibility of an input reduces the importance of giving incentives through ownership.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.174
Teacher spread0.013 · 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.

Study designTheoretical or conceptual
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

Citations73
Published2019
Admission routes1
Has abstractyes

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