Estimating the Financial Incentive for Caribbean Teachers to Migrate: An Analysis of Salary Differentials using Purchasing Power Parity (PPP)
Bibliographic record
Abstract
The teaching stock within the Caribbean region has been eroded by migration to developed countries. Higher potential earnings are one of the motivating factors to move abroad, but little is known about the extent of the income disparity between countries in the Caribbean and popular destination countries. Teacher salary comparisons are undertaken between selected countries in the Caribbean; Suriname, Trinidad and Tobago, St. Lucia, and Jamaica and popular destination countries, namely; United Kingdom, United States, and Canada using a purchasing power parity (PPP) exchange rate. Results show that newly qualified teachers can earn substantially more abroad, with Canada paying over twice the PPP adjusted salary compared to that offered in Jamaica (133.1%) and Suriname (110.6%). The United States offers the highest earning increases for mid- and late career teachers at over three times that offered in Jamaica (214.5%) and Suriname (223.4%). Canada is a close second across all Caribbean countries, whilst the United Kingdom offers the smallest salary differentials at 153.6% for Jamaica and 64.8% for St. Lucia. The study further reveals that there are salary disparities within the Caribbean, which may be a motivating factor for intra-regional migration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".