The Impact of the Digital Economy on Labor-Market Structure: Evidence and Governance from the Platform Economy
Bibliographic record
Abstract
In the last ten years, digitally mediated platforms have changed the way that work is matched, tracked, and paid for in ride-hailing, last-mile deliveries, online freelancing, and creative economies. This study integrates international scholarship, comparative regulation, and illustrative instances (Meituan, Didi, Uber, and Douyin/TikTok) to evaluate the impact of platformization on labor market structure. I contend that platforms expedite a transition from conventional employment to varied non-standard arrangements, facilitated by algorithmic management and two-sided market principles that redistribute risk from employers to employees. The effects are mixed: platforms make it easier to get started and create more flexible income opportunities, but they also make it harder for employers to take responsibility, make income less stable, and put workers under opaque, data-driven control. A review of policies in the EU, the U.S., China, Singapore, Spain, and the U.K. shows that there is more agreement on five regulatory levers: (1) presumptions of employment or intermediate dependent contractor statuses; (2) portability of social protection with shared financing; (3) transparency and human oversight for algorithmic systems; (4) data access to enable enforcement and collective bargaining; and (5) targeted inclusion of youth, women, and migrants. The paper ends with a macro-structural framework that connects platform governance to labor market segmentation. It also suggests a policy mix for China (quasi-employment pilots, co-financed social insurance, algorithmic audits, and sectoral dialog) to improve job quality without hurting the growth benefits of the digital economy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".