Quality of Work Experience and Economic Development - Estimates using Canadian Immigrant Data
Bibliographic record
Abstract
There is increasing evidence in the economic development literature that the quality of schooling considerably varies across countries that are at different stages in their economic development. However, an issue that has been overlooked is the role of the quality of work experience in explaining differences in economic development. This paper uses Canadian census data on immigrant earnings to show that per capita GDP in the country of origin can be used as a quality indicator for both education and work experience. Coefficients estimated from immigrant earnings regressions are then used to estimate the effects of difference in human capital quality on development gaps between rich and poor countries. The analysis shows that while differences in the quality of schooling account for substantial differences in living standards across countries, differences in the quality of work experience can account for even more. Policywise, our results suggest that the immediate effects of improving the quality and the quantity of schooling in less-developed countries might be rather limited if labour-market institutions and ways of doing things are not changed at the same time to improve the quality of work experience. / La littérature sur le développement économique reconnait de plus en plus que la qualité de l’éducation varie considérablement d’un pays à l’autre selon le stade de développement. Cependant, une question qui a été ignorée jusqu’à maintenant est la contribution de la qualité de l’expérience de travail à l’explication des écarts de niveaux de vie entre les pays. L’analyse présentée dans ce document utilise des données canadiennes de recensement sur les salaires des immigrants pour montrer que le PIB par habitant du pays d’origine peut être utilisé comme indicateur de qualité pour l’éducation et l’expérience de travail acquises dans ce pays. Les coefficients estimés à partir d’équations de régression des salaires des immigrants sont ainsi utilisés pour mesurer les effets de différences de qualité de capital humain sur les écarts de niveaux de vie entre les pays riches et les pays pauvres. Les résultats montrent que quoique la qualité de l’éducation soit un déterminant majeur des écarts de niveaux de vie, la qualité de l’expérience de travail est encore plus importante du point de vue quantitatif. En ce qui concerne les politiques de développement économique, les résultats de notre analyse donnent à penser que les bénéfices à court terme d’augmenter la qualité et la quantité de scolarité dans les pays pauvres sont plutôt faibles si les institutions et les façons de faire sur les marchés du travail ne sont pas en même temps changées pour améliorer la qualité de l’expérience de travail.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".