The Effect of Financial Development and International Trade on Deregulation
Bibliographic record
Abstract
This paper provides robust evidence of financial development and international trade liberalization on deregulation in some developing economies. Specifically, it investigates the effect of financial development and international trade liberalization on deregulation in 45 African countries in a panel set between January 1, 1980, to December 31, 2017. It employed the system Generalized Method of Moments (GMM) panel data estimation to address potential endogeneity concerns. Demir and Dahi (2011) showed that system GMM can effectively deal with any endogeneity issue originating from unobserved country-specific effects, and bias. The study found a robust positive effect of financial development and international trade liberalization on deregulation. The key finding was that technological impact is observed when private credit is regressed on market capitalization on Gross Domestic Product (GDP). It found that both GDP and gross per capita negatively impact financial development, conceivably causing the selected African countries’ markets to be insulated.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".