Work-Ready Graduates: The Role of Co-op Programs in Labour Market Success
Bibliographic record
Abstract
Adapting to the labour market after post-secondary education and finding a job that matches graduates’ skills, while providing a good standard of living, can be a daunting challenge for new graduates.This Commentary investigates whether work-integrated learning (specifically co-op programs) results in higher incomes or other benefits after graduation.It provides an analysis of National Graduate Survey (2013) data to determine (i) the returns to participation in co-op for different fields of study at both the college and university levels, (ii) differential outcomes based on individual characteristics, and (iii) the effects associated with non-monetary success in the labor market. Estimates suggest that co-op programs have significant benefits for participants in the form of eased transition to the labor market and higher incomes after graduation and that they may play a role in overcoming wage gaps associated with bias toward individual characteristics (race, gender, immigration status). Overall, participating in co-op generally appears to be beneficial for graduates’ incomes – three years after graduation co-op participants have incomes about $2, 000 to $4, 000 higher than non-participants. At the college level, participating in co-op does not necessarily lead to higher incomes after graduation across all fields of study.There are, however, significant benefits to participating in co-op at the college level in science and engineering programs. Aggregate results, however, do not capture underlying and important differences in the effects associated with participating in co-op programs that depend on individuals’ characteristics and chosen fields of study. The estimated effect of participating in co-op programs differs for women, visible minorities and immigrants, relative to Canadian men. For visible minority and immigrant university graduates, participation in co-op programs is associated with similar incomes to white-male co-op participants. Female co-op program participants that graduated from university received wages similar to male peers that did not participate. Immigrants, women and visible minority individuals that participated in co-op were more likely to be employed full time than non-participants with similar characteristics.Women, unfortunately, tend to receive lower benefits than men from participating in co-op programs in terms of income, getting a first job related to their field of study, or securing a permanent position. Together, these results highlight that co-op programs and work-integrated learning more generally might have a role in reducing wage and employment gaps traditionally associated with bias toward individual characteristics. Government policymakers and educational institutions should continue their support for expanding the programs so they are accessible to more students. At present, co-op programs in arts, education and social science do not appear to be as beneficial as the programs in STEM subjects. While co-ops are generally beneficial, the differences between fields of study suggests a need for caution in assuming that expanding co-op programs to more individuals or new areas would have the same benefits for new graduates as do current co-op programs. This highlights a need to carefully monitor the results of participating in co-op for students both during school and after graduation to continuously improve and adapt the programs to maximize benefits for individual fields of study.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".