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Record W1977297400 · doi:10.1177/0973801013483501

Market Power in the Log and Lumber Import Market in Japan

2013· article· en· W1977297400 on OpenAlexaboutno aff
Tsaiyu Chang, Masafumi Inoue

Bibliographic record

VenueMargin The Journal of Applied Economic Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMarket powerCompetition (biology)Market share analysisOrder (exchange)Imperfect competitionMarket competitionBusinessMarket analysisMarket structureEconomicsFactor marketAgricultural economicsInternational tradeMarket economyMarket microstructureIndustrial organizationMicroeconomicsMonopoly

Abstract

fetched live from OpenAlex

Japan imports rather than exports forestry products even though it has ample forestry resources. Moreover, the leading exporters of forest products often have strong market power in the trade market of forestry products. The possibility of incomplete competition in the Japanese wood import market can also be suspected from the fact that the structure of imports of logs and lumber has remained unchanged over the past 10 years. However, there has been limited empirical economic analysis of the timber trade market to support this assertion. In this study, we examine the market power of the primary exporting countries in the Japanese log and lumber market based on the residual demand model. We analysed the import data of forestry products from 1988 to 2010 with respect to every main item exported to Japan. The analysis shows that Canada has market power over several items of logs in the Japanese import market, while the United States and Canada have market power over several items of lumber. The study also clarifies that imperfect competition exists in the Japanese timber import market and that the timber price in Japan is partially determined by the exporting countries. JEL Classification: Q17, Q23, Q27

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.001

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 head0.019
GPT teacher head0.281
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2013
Admission routes1
Has abstractyes

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