Gig Work, Algorithmic Scheduling, and Health Insurance Gaps
Algorithmic scheduling and platform work models fragment hours and earnings, undermining eligibility for employer‑sponsored insurance and increasing reliance on public programs. This study analyzes survey data from gig workers, platform assignment logs, and administrative insurance records to quantify earnings volatility, coverage gaps, and health care access. We document how opaque scheduling algorithms and deactivation risks produce income instability that correlates with lower rates of employer‑sponsored coverage and higher uninsured rates. Policy simulations evaluate portable benefits, minimum earnings guarantees, and reclassification approaches, showing that portable, publicly subsidized benefit models can substantially reduce coverage gaps while preserving flexible work arrangements. The paper outlines implementation challenges and equity considerations for benefit portability in the gig economy.
Introduction
Gig and platform work have grown rapidly, offering flexibility but also income volatility. Employer‑sponsored insurance often depends on stable hours and employer contributions, leaving many gig workers uninsured or underinsured. This paper examines how algorithmic scheduling contributes to coverage gaps and evaluates policy options to expand access.
Methods
We combined a national survey of 1,000 gig workers with platform assignment logs and administrative insurance data. Econometric models estimated associations between earnings volatility and coverage status; policy simulations assessed the impact of portable benefits and minimum earnings guarantees on coverage rates.
Results
Earnings volatility was strongly associated with lower employer‑sponsored coverage and higher uninsured rates. Simulations show that portable benefits with partial public subsidy could reduce uninsured rates substantially, though implementation requires careful design to avoid adverse selection and ensure equity.
Discussion
Policy options include portable benefits, public subsidies for coverage, and regulatory changes to ensure basic protections for platform workers. Implementation must address administrative complexity and ensure access for low‑income workers.
References
- Dubal VB. Wage‑slave or entrepreneur? California Law Review. 2017.
- Rosenblat A, Stark L. Algorithmic labor and information asymmetries. Int J Communication. 2016.
- Prassl J. Humans as a Service. Oxford University Press. 2018.