Strengthening ASEAN's electrical and electronics sector: Enhancing regional production networks and economic resilience
Bibliographic record
Abstract
This study explores the evolution of Electrical and Electronic (E&E) industry within ASEAN between 2019 and 2023, emphasising its domestic interdependencies and regional production structures. Using input-output model of average propagation length (APL), the analysis categorises ASEAN economies into three production network clusters, namely, peripheral, intermediate, and advanced. The peripheral group, comprising Cambodia and Laos, is characterised by limited industrial capacity and short supply chains, reflecting constraints in skilled labour, capital availability, and infrastructure that hinder deeper industrial integration. The intermediate group, represented by Indonesia, Thailand, and Vietnam, displays longer APLs that indicate expanding but incomplete supply chain integration. These economies demonstrate concentration in upstream assembly activities yet remain weak in downstream distribution and innovation functions. The advanced group, led by Malaysia and Singapore, exhibits high network embeddedness, robust backward and forward linkages, and greater infrastructural and institutional support, functioning as coordination and innovation hubs within the regional E&E production system. The findings underscore the significance of regional collaboration, logistics optimisation, and technological upgrading to strengthen intra-ASEAN linkages. Policy implications include enhancing local supplier capabilities in peripheral economies, promoting industrial diversification in intermediate economies, and consolidating innovation ecosystems in advanced economies. Overall, this study highlights the hierarchical yet interconnected nature of ASEAN’s E&E production network and its pivotal role in achieving a resilient and sustainable regional industrial base.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".