Financial Precarity in the Platform Economy: An Empirical Analysis of Algorithmic Strain, Earnings Volatility and Financial Well-Being among Gig Workers

Abstract

The rapid expansion of platform-mediated labor has reconfigured income generation for millions of workers, yet the structural determinants of their financial well-being remain insufficiently quantified, particularly in emerging-market contexts. This study empirically examines how algorithmic work strain and earnings volatility shape the financial well-being of platform gig workers engaged in ride-hailing and delivery services. Using a quantitative, cross-sectional survey design, primary data were collected from 100 active gig workers through a structured, digitally administered questionnaire. Data were analyzed in SPSS using descriptive statistics, Principal Component Analysis, linear regression, and Pearson correlation at α = 0.05. Five latent constructs, were extracted which were productivity and daily output, financial well-being and satisfaction, operational and workplace challenges, work-related financial attitudes, and platform support and engagement. Regression analysis was also conducted to confirm the work-related financial attitudes and predict their daily productivity, indicating that financial pressure drives compensatory labor supply. Operational and workplace challenges were studied on the basis of mean score among the five constructs, closely followed by financial well-being and satisfaction, while platform sector type (ride-hailing versus delivery) relationship with financial satisfaction suggested that precarity is systemic rather than sector-specific. These findings extend the algorithmic-management literature by empirically linking financial strain to labor-supply behavior and carry direct implications for platform governance, minimum-earnings policy, and portable social-security design.