Management Experience and Diversity in an Ageing Organisation: A Microsimulation Analysis
Bibliographic record
Abstract
The ageing of the Canadian population is a well recognized phenomenon and has received considerable policy research attention, particularly in the health and public pension domains. Very little work has been focused on the impacts of ageing at the organisational level. Foot and Venne studied the advancement of the baby boom through traditional organisational hierarchies, noting its impacts on human resource policies that encourage horizontal career development. Saba et al looked more particularly at the management of older professionals in the Quebec public service, finding that employee recognition was an important human resource strategy for motivating this group. We extend these studies further along the ageing ladder -- to the point where retirement and replacement become the major concerns. Looking at the management hierarchy within Statistics Canada, we use a microsimulation model first to estimate the expected level of retirements over the next 10 years. We then detail the adjustments to promotion and hiring rates required to replace outgoing managers. We then examine simulated microdata to estimate the experience effects of increasing turnover. Finally, we use the demographic features of the model to examine whether the increasing turnover is likely to increase the representation of women and visible minorities among Statistics Canada managers. Given the assumptions outlined in the paper, we find that increasing turnover rates in the next 10 years will generally not reduce management experience to below recently observed levels. We also find that given equal promotion rates for men and women, the representation rate of women among Statistics Canada managers is likely to increase rapidly in coming years. On the other hand, visible minority representation among managers will likely stall for several years, even with proactive recruitment and advancement policies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".