Economic and Sectoral Convergence in Latin America and the Caribbean: An Analysis of Beta, Sigma, and Gamma Convergence
Bibliographic record
Abstract
The purpose of this study is to examine the economic and sectoral convergence of 32 countries in Latin America and the Caribbean (LAC) region from 1980 to 2022. The economic convergence hypothesis suggests that two economies with similar structural characteristics but different per capita income levels can tend to equalize in terms of income level in the long run. Confirming economic convergence has led to the development of various methodologies, among which dynamic and static disparity measures stand out. To achieve the objective of this research, both types of measures were calculated, determining beta and sigma convergence for dynamic disparity and gamma convergence for static disparity. This was accomplished by adopting the methodological approaches proposed by Sala-I-Martin and Marchante, Ortega, and Sánchez. The results show a gradual but steady evolution towards economic and sectoral convergence in LAC region during the 1980–2022 period. However, inequalities and divergences persist, requiring less developed countries to strengthen their institutions, implement sound macroeconomic policies, and diversify their economies. These measures are essential to driving economic growth and fostering more balanced and sustainable development across the region.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".