Bibliographic record
Abstract
This study examines the effect of capital control measures initiated during the last two decades in terms of all-in-cost ceilings and enhanced limits on ECB in India over the sample period 2004Q1 to 2020Q2. Using global liquidity, the exchange rate between INR/USD, imports and interest rate differentials as control variables and changes in capital control measures from 2008 to 2011 in the all-in-cost ceiling, and changes in the enhanced limits on ECBs from USD 500 million to USD 750 million under the automatic route in 2012, regression analysis of three ECB series show interesting results. Using Robust Least Squares method, we document that (1) the successive increment in all-in-cost ceilings on ECB from 2008 to 2011 is inducing ECBs to flow, indicating that Indian firms benefit more than they pay due to increase the cost for ECBs having maturities 3<5 years. However, such capital control measures are not effective on ECBs having maturities >5 years. (2) The effect of the enhanced limits on ECBs from USD 500 million to USD 750 million under the automatic route in 2012 has a pronounced impact on ECB, averaging 1602.1 USD million per quarter. We observed that CCAs in India are initiated in response to the volatility of the exchange rate and global liquidity, imports, and interest rate differentials are significant variables in India's required capital control actions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".