Can Greater Openness and Deeper Financial Development Drag ASEAN-5 into Another Series of Economic Crises?
Bibliographic record
Abstract
In the event of economic crises, it is observed that economic volatility becomes more severe. Therefore, the aim of this study is to examine the impact of greater openness and deeper financial sector development in influencing the level of economic volatility which could trigger economic crises in both long-and short-run periods in the case of ASEAN-5 countries, namely Indonesia, Malaysia, Philippines, Singapore and Thailand. Given that more attention is needed to address the issue, the Pooled Mean Group (PMG) estimations developed by Pesaran et al. (1999) and data ranging from 1980 to 2014 were employed to address the issue. With the ability to estimate short-run coefficients at each country level and its speed of adjustment, this study further fills the knowledge gap. Based on the analysis, it is found that greater trade and financial openness may further relax economic volatility in the long-run, suggesting greater international risk sharing which soothes consumption shocks. In terms of the effect of financial development towards economic volatility, it is found that only deeper banking sector development may reduce economic volatility in the long-run, but the same may not apply in the case of greater stock market development. Particularly, it is suggested that it is in the nature of developments in the banking sector to likely provide longer financing options and greater banker capabilities in detecting riskier investments, thus giving a favourable impact on economic volatility. It is a contrast with the stock market sector where it is more likely to be more susceptible towards large and sudden capital outflows. Its tendency to provide capital towards riskier investments may worsen volatility in the longer term. Nevertheless, in the short-run, the effects of greater openness and financial development is not as obvious as in the long-run; financial openness and greater stock market sector development may significantly relax economic volatility. As suggested by the speed of adjustment, the equilibrium from short-run to long-run is corrected by 1.71% and 2.33% in a year. Cumulatively, it can be said that there is no evidence that greater openness and deeper financial development may drag ASEAN-5 into another series of crises except in the case of stock market sector development. Therefore, the source of instability in the region is likely to be driven from greater stock market sector development rather than greater openness and banking sector development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".