Employment status and the on-demand economy: a natural experiment on reclassification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article uses data from a natural experiment to address one of the most contentious issues in the on-demand platform economy—whether gig work is compatible with standard employment. We analyze a US-based package delivery platform that shifted a subset of its workers from independent contractors to employees, thereby creating a natural experiment that allowed us to exploit variation over time and across locations. We examine the impact of employment status on work scheduling practices, hours of work and the firm’s ability to match workers’ scheduled hours with the amount of time they were actively engaged in parcel delivery. We find that after the transition to employment, flexibility with respect to how work schedules were determined was maintained, and drivers’ total hours of work increased. We also find that the switch to employee status increased the firm’s ability to match scheduled and actual working time, indicating greater operational efficiency. We conclude, contrary to claims commonly made by platform firms, that employment status can coexist with the platform model.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
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 it