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Record W7015670518

Tariff effects on MNC decisions to engage in intra-firm and arm's-length trade

2009· article· en· W7015670518 on OpenAlexaboutno aff

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsTariffMultinational corporationMargin (machine learning)Sample (material)Commercial policyTrade barrierRules of originFree trade
DOInot available

Abstract

fetched live from OpenAlex

Using confidential firm-level data from the Bureau of Economic Analysis (BEA) on activities of U.S. multinational corporations (MNCs) and their Canadian affiliates, we study the dramatic growth of intra-firm and arm's-length U.S.-Canada trade over the 1984-95 period. We find that decisions to engage in intra-firm and arm's-length trade are essentially unrelated to tariff and transport cost reductions over this sample period. Thus, we find that the increase in trade occurred almost entirely on the intensive rather than the extensive margin. This is consistent with case study evidence in Keane and Feinberg (2006), where MNC executives consistently indicate that the modest tariff reductions of the 1984-95 period were not sufficient to justify fixed costs of overhauling international supply chains. Our results have important implications for recent influential models of international trade that rely on sensitivity of intra-firm trade to tariffs at the extensive margin to explain how small tariff declines could have led to the explosion of intra-firm trade since the 1980s. We also find that initial conditions (i.e., 1983 tariffs) are uncorrelated with whether firms engaged in intra-firm or arm's-length trade activity at the start of the sample period. This result is surprising as it implies that firms/ industries with a greater propensity to engage in trade were not, in general, successful at lobbying for more favourable tariff treatment. © 2009 Canadian Economics 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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.188
Teacher spread0.165 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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