The Impact of Social Networks on Labour Market Outcomes: New Evidence from Cape Breton
Bibliographic record
Abstract
Debates centered on the role of social networks as a determinant of labour market outcomes have a long history in economics and sociology; however, determining causality remains a challenge. In this study we use information on random assignment to a unique intervention to identify the impact of changes in the size of alternative social network measures on subsequent employment at both the individual and community level. Our results indicate that being assigned to the treatment protocol significantly increased the size of social networks, particularly weak ties. Nevertheless, these increases did not translate into improved employment outcomes 18 months following study completion. We do not find any evidence of treatment effect heterogeneity based on the initial size of one's social network; but those whose strong ties increased at a higher rate during the experiment were significantly less likely to hold a job following the experiment. We find that many of these results also hold at the community level among those who did not directly participate in the intervention. In summary, our results suggest that policies can successfully influence the size of an individual's social network, but these increases have limited impacts on long run labour market outcomes with the notable exception of changes in the composition of individuals who hold jobs.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".