Measuring the Shadow Economy in the ASEAN Nations: The MIMIC Approach
Bibliographic record
Abstract
The purpose of this empirical study is to estimate the size of the shadow economy and its trend for countries in the Association of the South East Asian Nations (ASEAN), except Singapore and Brunei, for the period from 1995 to 2014. While other approaches, which can be used for the same purpose, such as a monetary demand approach or an electricity consumption approach only focus on one indicator/factor, an extensive economic literature review indicates that the shadow economy is affected by various factors such as production, labour and monetary market. In this study, a MIMIC approach is adopted to estimate the size of the shadow economy and its trend for the ASEAN nations including Vietnam. The findings from this study indicate that the shadow economy of Viet Nam lies between 25 per cent and 30 per cent of the official economy for the period from 1995 to 2014, given the base year estimate of 15.8 per cent in 1999 being adopted. A deep concern is that this size of the shadow economy in Vietnam has been on a rise at a more significant level over the last 20 years, from 1995 to 2014 in comparison with other countries in the sample. Findings from this study also present evidence that tax rate, labour freedom, and business freedom have provided significant effect to the shadow economy of the ASEAN countries. Implications for macroeconomic policies in Vietnam, in particular, and for other ASEAN nations, in general, are that reducing the shadow economy of the ASEAN nations requires a larger degree of labour and business freedom. In addition, the government may also need to consider lowering the tax rate in the 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".