Assessing the Impact of Great Recession on India’s Trade in Gravity Model Framework
Bibliographic record
Abstract
This study examines the efficacy of trade channel in the transmission of recent Great Recession impulses to the Indian economy. To investigate the impact of Great Recession on India’s trade, gravity model of trade was estimated by regressing trade flows on size of economies, level of economic development, geographical distance and dummies for common border, landlocked country, islands, colonial history, common language, etc. For the same, quarterly data with respect to 11 advanced nations (namely, Austria, Australia, Canada, Denmark, Japan, Korea, New Zealand, Sweden, Switzerland, the United Kingdom and the USA) and nine emerging market economies (EMEs), including the BRICS nations (namely, Brazil, Russia, Indian, China, South Africa; Indonesia, Mexico, Saudi Arabia and Turkey) for the period from 2001q1 to 2013q4 were considered. Estimations suggest that Great Recession had an adverse impact on India’s bilateral import volume and total trade volume after a lag of three quarters. Findings validate that trade channel acted as a conduit for transmission of Great Recession impulses to the Indian economy. This suggests that as the Indian economy becomes progressively more integrated with the global economy, containment of potential adverse shocks emanating from trade sector would call for more pro-active policies. Lessons from the Indian economy could be useful for other similar EMEs. JEL Classification: F14, G01
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".