Efficacy of Demonetisation in Eliminating Black Money: An Analysis of Indian Demonetisation November 2016
Bibliographic record
Abstract
Indian demonetisation 2016 targeted to eliminate black money and counterfeit currency has been questioned in the economic and political discourse on the ground of hardship faced by millions and return of 98.96% of banned currency to the system. It was a popular expectation that a good proportion of banned currency would not return to the system as the black money holders might destroy them to avoid legal consequences. While the demonetisation strategy is dubbed as a failure based on ‘cash seizure’ parameter, this article reviewed its efficacy using five broader perspectives: • data mining to trace the sources of disproportionate cash holding, • improved tax collection, • balancing currency circulation, • uninterrupted flow of foreign direct investment despite short run economic down turn and• public perception. Economic growth and corruption are found to be negatively correlated except the ‘Asian Paradox’ observed in few research studies. In general, Indian economy is smudged by high level of corruption, tax evasion and accumulated black money which has been reflected in the continuing low rank of India in the Corruption Perception Index. Demonetisation failed in ‘cash seizure’ parameter as the black money held in banned currency was traded with organised money laundering groups at a high discount which then were deposited back to the system. The success of the demonetisation strategy is primarily linked to the success of ‘operation clean money’ launched by the tax authority under which the depositors with unaccounted wealth is traced back through data mining from disproportionate deposits during the transition period.
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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 | 0.001 | 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".