Everyone Has a Side Hustle Now. Not Everyone Has the Same Reason.

Job security may be improving across the U.S. workforce, but financial confidence tells a more divided story. “Wage to Wallet Index: Side Work Patterns in the Labor Economy” explores how roughly 60 million hourly workers earning $50,000 or less annually are navigating rising costs, uneven income timing and growing economic pressure. Higher-income, salaried professionals report improving financial sentiment. But many Labor Economy workers continue to feel stuck, even as perceptions of job stability rise. The disconnect suggests that employment alone is no longer the primary driver of financial security. Cash flow is.

This latest installment of PYMNTS Intelligence’s Wage to Wallet Index is a collaboration with Ingo Payments and WorkWhile. The index examines how side work has evolved from supplemental income into a structural feature of household financial management. Nearly one in five Labor Economy workers now performs regular side work, often stacking multiple income streams. Yet these income sources are frequently fragmented, task-based and vulnerable to interruption. Sudden income stops, equipment loss, seasonal slowdowns and life events create ongoing volatility for hourly households. This volatility reinforces the importance of payment speed, income certainty and access to funds. Side work is growing across income levels, but motivations differ sharply. For many salaried workers, it builds savings. For many hourly workers, it covers essentials.

As side income becomes more central to household budgets, the findings point to broader implications for banks, platforms, employers and payment providers seeking to serve a workforce increasingly defined by fragmented earnings and time-sensitive cash needs.

In this report, learn how:

Financial sentiment is diverging despite similar gains in job outlook. Employment confidence is rising across segments, yet only higher earners feel materially better off. The data shows why improved job security does not automatically translate into financial progress for hourly households.

The structure of side work differs dramatically by income level. Labor Economy workers rely more heavily on fast, local, task-based gigs, while salaried workers monetize specialized skills. This structural gap affects income scalability and resilience.

Income interruptions are reshaping payment expectations. With many hourly workers experiencing sudden income loss, faster disbursements and flexible financial tools are now a competitive differentiator.

Download “Wage to Wallet Index: Side Work Patterns in the Labor Economy” to learn more.

About “Wage to Wallet Index: Side Work Patterns in the Labor Economy”

The “Wage to Wallet Index” series is a new monthly data initiative designed to bring visibility to the financial and economic importance of the Labor Economy, the 60 million workers who form the connective tissue of America’s production, distribution and service sectors.

This collaboration provides a quantitative and behavioral lens into how wage growth, employment dynamics and income access among these essential workers influence household well-being and macroeconomic performance. The index combines WorkWhile’s real-time labor utilization and wage data, Ingo Payments’ wage access and disbursement data and PYMNTS Intelligence’s proprietary research on consumer sentiment, savings, spending and financial lifestyle.

The report compares the assessments and outlooks of Labor Economy workers (paid hourly and typically earning less than $50,000 per year) with those of non-Labor Economy workers (typically salaried and earning more than $50,000 per year). It includes February 2026 sentiment and job outlook results, and data on side work participation, activity types, motivations, financial stress and income interruptions. PYMNTS Intelligence uses a proprietary model to estimate the spending power and economic footprint of the U.S. Labor Economy workforce. The model combines official government data on spending, income and the labor force with demographic and job mapping, then estimates trends over time using broad economic reference points.

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