Dollarization, Openness and Tendency towards Stagnation in El Salvador
Bibliographic record
Abstract
This paper presents a series of aspects related to the tendency towards economic stagnation that the Salvadoran economy has shown for three decades, emphasizing the role of dollarization that took place in 2001. The first section analyzes the determination of the money supply in the context of dollarization. It shows that the main determinant of the money supply is the payment for imports, which cancels out the contributions of remittances, official foreign aid and bank credit. The negative effects on the money supply from foreign investment, public consumption, the current account deficit and the trade account of the balance of payments are also identified. The savings and economic growth rates of El Salvador are compared with those of Panama and Ecuador, other countries also dollarized in the region, and it is deduced that the poor performance of the Salvadoran economy in relation to these two countries can be explained by its low savings rate, by the greater incidence of remittances in its economy, and by its low ratio of female to male employment. Another topic analyzed is the impact of the economic crisis on the Salvadoran economy, finding that this leads to a permanent contraction in the rate of economic growth, but that it can be countered by means of investments in human capital, and by increasing female labor force participation. The paper concludes with a series of conclusions and recommendations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".