The comparative advantages in the wooden furniture industry: does the export price matter?
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate the global competitiveness of the top ten wooden furniture exporting countries with several approaches and to test the effect of export prices (EXPRs) on the global competition. Design/methodology/approach Countries' competitiveness levels were measured with revealed comparative advantage (RCA), normalised RCA (NRCA), revealed symmetric comparative advantage (RSCA) and trade balance index. Furthermore, panel regression analysis techniques were used to test the effects of EXPR on RCA, NRCA and RSCA in the wooden furniture industry (WFI). Findings Although the comparative advantage approaches give different results, the global competitiveness of Poland and Vietnam is at a high level in all approaches. Canada has been the country with the weakest global competitiveness in all approaches. According to the results of the analysis, EXPRs positively affect all the competitive advantage indexes. As a result, the competitiveness of the WFI is affected by the non-price factors instead of the EXPR. Research limitations/implications The framework allows us to measure and illustrate the export competitiveness of the WFI and permits a global comparison. Similar analyses can be made for different labour-intensive sectors. In addition, analysis can be made to identify non-price factors for the WFI sector. Thus, more specific inferences can be made. Practical implications This study is useful for policymakers, government officials, the industry associations and the company executives to assess their export competitiveness in the WFI. Thus, they can determine whether to shift scarce resources to this industry or other industries. In addition, this study may affect the price competition policy of the sector representatives in the global market. Originality/value This study deals with the competitiveness of the WFI with different approaches. And this study determines the importance of price for global competition in this sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".