Assessing Economic Connectedness Degree of the Malaysian Economy: Input-Output Model Approach
Bibliographic record
Abstract
Economic connectedness can be defined as the degree of internal connectedness of interdependence between the sectors of an economy. In input-output models intersectoral connectedness is a crucial feature of analysis, and there are many different methods of measuring it. These measures are believed to be important structural indicators, helpful in model estimation. Also, such measures could be analytical useful, along with the input-output models themselves, as descriptions of the nature of the modeled economies, as aids in model estimation, and perhaps as indication of the level of economic development. However, they allow for a summary description and comparative analysis of various linear flow systems. Most of the measures, however, have important drawbacks to be used as a good indicator of economic connectedness, because they were not explicitly made with this purpose in mind. In this paper, I present, discuss, compare and interpretation empirically different indexes of economic connectedness as sectoral connectedness, using a set of four empirical models for the Malaysian economy. The results suggest that mean intermediate coefficient total per sector, % intermediate transaction and % nonzero coefficients are the most generally useful interconnectedness measures for Malaysian Economy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".