Introduction
The recent surge in the development of digital labor platforms in India has heightened worries about the issue of income instability and financial insecurity among gig workers. The allocation of tasks, pricing, incentives, and performance assessments are controlled by computerized management systems that tend to bring uncertainty in earnings. At the same time, financial vulnerability is made worse by the lack of access to formal social protection mechanisms. Although earlier studies have addressed the subject of algorithmic control and labor precarity in isolation, there is very limited empirical information that addresses worker perceptions of fairness, income stability, and social protection as a combined factor in income volatility in emerging economies.

Methods
This study used a validated 20-item Likert-scale measure to collect primary data on 220 gig workers on ride-hailing, food delivery, logistics, and home-service platforms in India. Cronbach's alpha (α >.79) was used to assure reliability. A five-factor model with 67.27 percent of overall variance was supported by Exploratory Factor Analysis. Multiple regression analysis was done to discover predictors of income volatility.

Results
The regression model was statistically significant (R² = .419, p < .001). Perceived Income Stability (β = .278, p < .001), Algorithmic Fairness (β = .231, p = .001), and Perceived Social Protection Adequacy (β = .188, p = .005) significantly predicted income volatility. Social Security Awareness was not statistically significant (p = .073).

Conclusions
The conditions of structural platforms and subjective perceptions of fairness, stability, and institutional support play a role in the volatility of incomes in the Indian gig economy, in addition to other factors. To decrease economic precarity within digital labor markets, policy reforms must focus on earnings predictability, algorithmic transparency and sufficient social protection systems.