Gold Smuggling in India and Its Effect on the Bullion Industry
Bibliographic record
Abstract
This study strives to examine when and where most of the gold smuggling takes place in India. It further analyses the causal relationship between smuggled gold and other macroeconomic variables. Finally, it analyses how the smuggled gold affects the Indian bullion industry. The data related to gold smuggling has been sourced from the website of the Directorate Revenue Intelligence and analysed using graphs and the Granger causality test. The variables used in the study are the quantity of smuggled gold, exchange rates, the major stock indices in the world, the number of auspicious days in a month, domestic and international gold prices, India’s jewellery export, the GDP, customs duty, and the domestic gold supply. The results revealed that most of the gold smuggling takes place on Fridays and mostly occurs in the months of October, November, and December. The states of West Bengal, Delhi, Maharashtra, and Tamil Nadu account for most of the gold smuggling in India. A positive correlation is observed between the smuggled gold, India’s gold demand, the number of auspicious days in the month, India’s jewellery export, India’s GDP, India’s domestic gold supply, and stock indices such as SENSEX, FTSE100, DFMGI. Gold smuggling in India is caused by India’s gold demand, the level of jewellery export, the GDP, domestic and international gold prices, and India’s customs duty.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".