RCEP and sectoral development in ASEAN less developed countries
Bibliographic record
Abstract
The Regional Comprehensive Economic Partnership (RCEP) is expected to reshape trade, investment, and sectoral dynamics across Asia, yet its implications for less-developed ASEAN members remain underexplored. Existing studies often rely on econometric modeling, but the recent entry into force of RCEP in 2022 limits their ability to capture emerging structural trajectories. This paper provides an early structural mapping of five economies—Brunei, Cambodia, Laos, Myanmar, and Vietnam—selected as ASEAN's less-developed members. Although only Cambodia, Laos, and Myanmar are formally classified by the United Nations as Least Developed Countries (LDCs), Brunei and Vietnam are included due to structural vulnerabilities that constrain diversification and resilience. Using 2009–2023 data, the paper traces long-term dynamics through a comparative exploratory approach. The findings reveal pronounced divergence: Vietnam and Brunei exhibit industrial and services-led growth supported by high-tech and ICT-oriented FDI and export diversification, while Cambodia and Laos remain largely agriculture-based with limited diversification; Myanmar shows moderate but uneven diversification. Early incorporation into RCEP offers these countries potential benefits through expanded market access, regional integration, and investment inflows. For Cambodia, Laos, and Myanmar, targeted policies in infrastructure, workforce development, and export diversification will be critical to fully leverage these opportunities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| 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) | 0.003 | 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".