App drivers' monthly income near average, but hourly pay lags, TST study finds
A study by Brazil's Superior Labor Court (TST) reveals that while app drivers can achieve a monthly income close to the national average, their hourly earnings are lower due to longer working hours, significant operational costs, and a model that places most risks on the driver. The research indicates that app drivers earn an average of R$ 2,996 per month, exceeding the federal minimum wage of R$ 1,621 but falling short of the overall Brazilian average income of R$ 3,367 in 2025. Drivers typically work 44.8 hours per week, compared to the general workforce's 39.3 hours, resulting in an hourly rate that is 8.3% less than that of non-platform workers.
The TST's comprehensive diagnosis, which combined official data with empirical studies, challenges the notion of freedom and flexibility often promoted by app companies. The study found that drivers have minimal influence over crucial aspects of their work, such as ride pricing and call distribution, and can be blocked from platforms. Instead of direct supervision, drivers are managed through evaluation systems, ratings, and rankings, which directly impact their opportunities and income. Furthermore, drivers bear nearly all expenses, including fuel, maintenance, insurance, taxes, food, and internet, which can exceed R$ 5,500 monthly, significantly reducing their net earnings. This financial pressure contributes to high levels of debt, with 92% of platform workers reporting being in debt, sometimes exacerbated by company-offered credit lines.
Additionally, the study highlights a lack of labor protections, with most drivers lacking access to benefits like paid leave, 13th salary, unemployment insurance, or guaranteed retirement. Over 60% do not contribute to social security due to income instability. The TST emphasizes that the current app-based work model shifts costs, risks, and responsibilities onto workers and calls for discussions that go beyond employment status to ensure minimum working conditions, fair pay, and adequate protection for these professionals, who also face daily risks of accidents and violence without platform-provided safety nets.
This TST study critically examines the economic realities and systemic risks inherent in the app-based work model, particularly for ride-sharing drivers in Brazil. It deconstructs the perceived flexibility by demonstrating how algorithmic management and cost-shifting mechanisms create a precarious employment structure. The analysis reveals a tension between platform narratives of autonomy and the operational constraints and financial burdens placed on drivers, suggesting that the "gig economy" may externalize significant costs onto individual workers. Looking ahead, this model's sustainability will likely be tested by evolving labor regulations, increasing driver organization, and the potential for AI-driven efficiency gains to further concentrate value away from the labor force. The findings prompt consideration of policy interventions that balance innovation with worker welfare and ensure a more equitable distribution of economic gains and risks within digital labor platforms.
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