Estimations of the price transmission and market power in canola export market: implication to canola import of Japan
Bibliographic record
Abstract
In this paper, I analyze the price transmission of canola, or low erucic acid rapeseed, in the international market and the market power of canola exporting countries over one of the major importing countries, Japan. Canola oil production in Japan is almost completely dependent on the canola imported from Canada and Australia. The export from Canada generally accounts for more than 80% in recent years. Does Canada exercise market power over Japan? Does Canada have more market power than Australia in the Japanese canola market? What strategies should Japan have on canola import? To answer these questions, firstly I conducted an empirical analysis of asymmetric price transmission (APT), in which the speed of adjustments of the output price after the input price increases or decreases is different; the existence of APT possibly implies the existence of market power. Widely used threshold autoregressive (TAR) model was applied to test the existence of APT from futures price in Winnipeg to the Canadian FOB price of canola for each country. Significant APT was found in the Canadian canola export to Japan in such a way that Canada enjoyed long-lasting excess profits over Japan. Moreover, such tendency became more evident in recent years when Canada expanded and diversified export counterparts of canola. To answer the second question and establish the relationship between APT and market power, I estimated the market power of Canada and Australia over Japan when exporting canola. Considering the existence of adjustment process in canola exporting countries, linear-quadratic dynamic duopoly model was employed, in which the parameterized conjectural variations of both exporting countries were estimated. I found that Canada had intermediate level of market power. Meanwhile, I found that Australia had no market power, which means that the Australian canola prices are set at competitive level or price-taking level. Based on these empirical analyses, I drew some implications. The major implication is that Japan should diversify the origins of canola to avoid the use of market power of an exporting country. This paper also contributes in that it constructs the relationship between APT and market power empirically.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".