Is a PhD worth more than a Master’s in the UK labour market? The role of specialisation and managerial position
Bibliographic record
Abstract
This paper examines the potential earnings premium associated with a doctoral degree (PhDs, ISCED9) over postgraduate degrees (PGs, or Masters, ISCED7) in the UK. We assess this premium using a decade-worth of UK Labour Force Survey data (2011–2020). To explore the possible endogenous choice of post-graduate tracks, this paper deploys linear regression, (ordinary least squares, OLS), propensity score matching (PSM), and inverse probability weighting (IPWRA) to estimate the pay premium under varying identifying assumptions. The estimates show a positive return in terms of gross hourly pays in all models, along with a relevant role of managerial positions and degree of specialisation in employment position. Therefore, although a PhD is arguably mostly driven by taste for scientific pursuit, a PhD has on average also an economic pay-off. However, much of it depends on one’s capacity to acquire leadership positions – the most relevant factor disentangling those fulfilling or not their potential in terms of wages. We also provide a cost–benefit analysis over a life course showing that such a premium is overall modest, but subject to positive spikes for those in Science & Technology (STEM disciplines), getting managerial positions, and for women. Our findings suggest investigating further those personal and organisational factors that are conducive of unleashing highly educated potential.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".