Teacher salary differentials using Purchasing Power Parity (PPP): a South African perspective as both a ‘source’ and ‘destination’ country
Bibliographic record
Abstract
Teacher migration is a problem for developing countries as it impacts on delivery of quality education. The potential to earn higher incomes remains the most common factor driving teacher migration. This study seeks to investigate how the South African teacher salary structure compares with the equivalent salary structure in six prominent migrating countries whilst highlighting the economic appeal of South Africa from a Zimbabwean teacher perspective. Using a representative basket of commonly bought goods (including food, entertainment, fuel and utilities), a purchasing power parity (PPP) ratio is used to equalise the international price of buying that basket. Our study makes comparisons, using a PPP index, and allows the identification of real differences in salaries for our selected countries (South Africa, United States, United Kingdom, Canada, Australia, New Zealand, Japan and Zimbabwe) for selected teaching categories. Even when controlling for differences in the cost of living, the incentive for a South African teacher to seek work overseas remains strong and increases with career experience. A worrying conclusion for South Africa concerned with keeping its experienced teachers is that as more human capital is gained by experience, the greater the incentive to emigrate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".