The Effects of Institutional and Organizational Characteristics on Work Force Flexibility: Evidence from Call Centers in Three Liberal Market Economies
Bibliographic record
Abstract
This comparative study examines survey data from 464 call centers in the United States, 167 in the United Kingdom, and 387 in Canada to explore two questions: whether institutional differences shape employers' choices of ways to improve work force flexibility, both numerical and functional; and whether strategies for numerical flexibility and functional flexibility are related. The results suggest that institutional differences across these liberal market economies—specifically, in dismissal regulations and union strength—did affect how employers chose to achieve work force flexibility. For example, the use of part-time workers was more common in countries with more stringent rules regulating dismissals. Organizational characteristics also mattered, with outsourced firms being more likely than in-house firms to use part-time workers. Evidence also suggests that managers used numerical flexibility and functional flexibility strategies as substitutes: higher employee job discretion was associated with both lower dismissal rates and a lower likelihood of temporary use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".