Professors and Hamburgers: An International Comparison of Real Academic Salaries
Bibliographic record
Abstract
In recent years, academic staff unions and associations have argued for higher salaries for academics on the grounds that existing salaries have not kept pace with inflation, are well-below commercial salaries and, most glaringly, are much lower than the salaries of their overseas counterparts. However, most international comparisons are made based on exchange rate conversions, which is inappropriate since purchasing power differentials are only reflected in exchange rates in the long term. Furthermore, the volatility of exchange rates makes such conversions highly inaccurate. In this chapter, we provide a comparison of real academic salaries by converting the nominal salaries in each country to their purchasing power equivalents, using the Big Mac Index. Our results show that real academic salaries are highest in Hong Kong and Singapore, relative to the developed countries, while Hong Kong tax and social security deductions are lowest. Furthermore, real salary levels, combined with intrinsic considerations such as the quality of life, indicate that Canada and New Zealand are unattractive places for visiting/migrating academics, while Australia and the US are relatively attractive. We suggest that our findings could be of use to policy-makers and academic unions in salary negotiations, as well as academics making relocation decisions. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.001 |
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".