The Effect of Institutional Quality on the Balance of Payments in African Countries. A Comparative Study
Bibliographic record
Abstract
This study tries to examine the effect of the quality of the institutional framework on the accounts of the balance of payments in a sample of African countries (28 countries) and a sample of countries occupying advanced positions in international economics (15 countries) to determine different indicators of the institutional framework that affect the balances of the current and financial accounts of the balance of payments in the two sample countries through the period 2002-2019. The study applied the panel autoregressive distributed lag (ARDL) model, Akaike info criterion (AIC), to determine the short- and long-run relationships. The empirical findings illustrate that the institutional indicators that support the current accounts of the balance of payments, in the long run, are not the same that support the financial accounts of the balance of payments of African countries. In addition, the effect of institutional indicators on international transactions is related to the level of economic development, where the effect of institutional indicators on countries with relatively low levels of economic development is more powerful than their effects on countries with advanced levels of development. Thus, the low quality of the institutional framework is considered an important impediment to the development of international transactions in African countries.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".