The Algorithm’s Blind Eye: Measuring Real Exposure Behind Hour Caps
Keywords:
gig work hour caps alogirithmic management social protection fatigue nudgesAbstract
Platforms and regulators increasingly promote hour caps (≤12 hours) and in-app fatigue nudges for ride-hailing and delivery workers. This study asks whether these rules reduce crashes and injury claims, or merely displace risk as drivers multi-app to stabilize income. A twenty-four-week, multi-site quasi-experiment in Jakarta, Surabaya, and Yogyakarta evaluates whether caps lower injury risk or reshape labor supply without reducing total exposure. A difference-in-differences and event-study framework exploits staggered introductions and enforcement; outcomes include crash and near-miss rates per 100 online hours, injury claims, seamless hours across apps, and net hourly earnings. A mechanism study links accumulated hours to alertness using weekly Karolinska Sleepiness Scale surveys and two-minute smartphone psychomotor-vigilance tests at shift start, midpoint, and end. Evidence comes from administrative injury microdata, an IRB-approved Android telemetry tool reconstructing continuous seamless hours, and a 1,200-driver rotating panel with shift diaries. Models control for weather, fuel prices, holidays, surge, locality, day of week, seasonality, macro shocks, and driver fixed effects. The study anticipates threshold effects and identifies conditions for risk reductions: cross-platform hour reconciliation paired with income floors or contribution top-ups.
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