A quantitative assessment of the trade openness – economic growth nexus in India
Bibliographic record
Abstract
Abstract Purpose The purpose of this paper is motivated by research-based assertions that: the causes of economic growth in countries like India are not well understood; they are not elucidated by using simple bivariate relationships between economic growth and other variables, taken one at a time; and dynamic linkages between growth, trade openness and financial sector depth are required for any comprehensive treatment of this inquiry. Design/methodology/approach This paper investigates the pivotal role of financial depth (defined as the relative importance in the economy of the banking sector or the stock market) and whether it bears any evidential relationship to trade openness and economic growth during the era of Indian post-globalization since 1990. Two key objectives are to uncover whether there is a long-run relationship between the variables and whether they can be said to cause one another. Autoregressive distributive lag (ARDL) bounds testing procedures and vector autoregressive error correction model (VECM) approaches were used to derive the results. Findings This paper affirms that the variables are indeed formally cointegrated. It was also found that trade openness, economic growth and financial sector depth Granger-cause each other. Practical implications This paper demonstrates that greater trade openness can predictably accelerate India's economic growth. If policymakers wish to maintain sustainable economic growth in India, they can do so by encouraging both freer trade and financial market development in the long run. Originality/value No investigation of this type and sophistication has hitherto been performed for India. The methods developed for this study can also be applied to any of the vast range of countries for which dynamic growth-openness-financial depth interactions have not already been investigated.
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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 | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".