Diplômes et croissance économique : quelques chaînons manquants
Bibliographic record
Abstract
Depuis les années 1970 la connexion entre capital humain et croissance économique a été supposée évidente, et on a donc prôné l’augmentation du capital humain – plus spécifiquement les diplômés universitaires (OCDE, 2012) – comme moteur de croissance national et régional. Or, les études empiriques ne sont pas aussi robustes qu’elles paraissent : bien qu’il n’y ait aucun doute que dans chaque cohorte les diplômés tendent à gagner plus que les non diplômés, il y a de plus en plus de résultats qui montrent que le lien entre diplômé et croissance économique est désormais scindé. La connexion, si elle existe, dépend du contexte. Dans cet article je résume les arguments selon lesquels le capital humain – et plus spécifiquement les diplômés – entraîne la croissance, et je distingue les deux. Alors que le capital humain au sens large (connaissances et savoir-faire qui permettent d’obtenir un revenu) est évidemment lié à l’activité économique et aux processus de croissance, ce concept est difficile à cerner et à mesurer. Les politiques publiques qui découlent de cette théorie sont inefficaces car elles se focalisent sur un indicateur, le nombre de diplômés, dont il y a une suroffre. Les diplômes servent autant de marqueurs sociaux (ils indiquent le capital social et culturel du détenteur) et de droit d’entrée au marché du travail (le diplôme est nécessaire pour que la demande d’emploi soit considérée) que d’indicateurs du capital humain des détenteurs. Un exemple empirique tiré du Canada montre toute la difficulté à établir un lien clair entre croissance locale et la présence de diplômés locaux.=====Abstract \nIt is has been accepted as almost self-evident, at least since the 1970s, that human capital is related to economic growth, and that therefore increases in human capital – and specifically of degree holders (OECD, 2012) – is a prerequisite for regional and national growth. However, the empirical evidence for this is not as robust as it seems: whilst there is no doubt that in any cohort \ndegree holders tend to earn more than non-degree holders, there is evidence that higher numbers of degree holders are decreasingly associated with growth. Any connection, if it exists, is context dependent. In this paper I go over the arguments that relate human capital – and more specifically university degree holders – with growth, and I distinguish the two. Whilst human capital, in a broad sense (knowledge and know-how from which the holder derives income), is self-evidently associated with economic activity and growth processes, the concept is elusive and very difficult to measure. The policies derived from the theoretical insight linking human capital to growth are misguided because they are focussing upon an indicator (number of degree holders - of which there is increasingly an over-supply). Degrees serve as much as social markers (they demonstrate the social and cultural capital of the holder) and credentials (they are necessary just to be considered for a job) as they do for the human capital that they impart. An empirical example using Canadian data highlights the difficulty in establishing any clear connection between local human capital levels and growth.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".