Independent Contracting & Gig Work

Uber and Lyft, along with a group of delivery network companies, have filed a ballot proposition in Massachusetts to create a separate set of labor standards for their drivers. After considering multiple loopholes, we find that the majority of Massachusetts drivers could earn as little as the equivalent of a $4.82 wage, while the minority of drivers who qualify for a health care stipend could earn the equivalent of just $6.75 per hour.

A new UC Berkeley analysis finds that a Massachusetts measure proposed by Uber, Lyft, and several delivery network companies would create a subminimum wage of as little as $4.82 an hour.

Thornberg over-estimates driver gross earnings (before expenses) based on data that is not representative of drivers in California. He also underestimates driver costs. In doing so, he significantly overstates what drivers earn on net now, and would earn under Proposition 22.

In this data brief, we estimate how much Uber and Lyft would have contributed to the state’s Unemployment Insurance Fund between 2014 and 2019, had the companies classified the drivers as employees. Our finding: If Uber and Lyft had treated workers as employees, the two TNCs would have paid $413 million into the state’s Unemployment Insurance Fund between 2014 and 2019.

New UC Berkeley Research Finds Uber & Lyft Could Owe California More than $400 Million Dollars in Unpaid Unemployment Insurance Funds.

The companies have a choice over how they adjust to comply with California’s laws protecting workers—or if they choose to fight those laws with a ballot initiative that would take pay standards back seventy years.

This report analyzes a major barrier to successful implementation of new clean truck standards: the common trucking industry practice of classifying (and often misclassifying) truck drivers as independent contractors rather than employees.

New UC Berkeley Report: Stop Truck Driver Misclassification to Meet California’s Climate Goals. Misclassified drivers can’t afford clean trucks; Current Legislation—AB 5 would help make California’s transition to low- and zero-emissions trucks a reality.

In this fact sheet we look at three industries in California where misclassification is known to be disproportionately high and could potentially be reduced by an ABC test – trucking, construction, and janitorial services. For each industry, we describe the demographics and wages of workers and misclassification practices by employers.

Third in the Guest Blogger Series: Voices of Labor and Allies from Labor in the Climate Transition Conference. “Freight and delivery companies are finding ways to pass on the responsibility of cleaning up fleets to individual drivers, while minimizing their own investments. This is what corporations do when we don’t have policies that protect workers or incentivize companies to do the right thing.”

Without action from policymakers, driverless trucks are projected to eliminate some of America’s best trucking jobs while also creating low-wage gig jobs, according to the first in-depth study of how autonomous trucks could be adopted by specific segments of the industry and affect wages and working conditions.

If you’ve consumed any media on the gig economy during the last several years, you were probably seriously confused last Thursday. That’s when the Bureau of Labor Statistics (BLS) released its new survey data on contingent workers, which was much anticipated because the last time the survey was conducted was in 2005. Given the endless coverage of Uber and freelancing as paradigmatic of the 21st century labor market, expectations were that the new data would show significant increases in contingent work.

In this paper we (1) provide a clear framework to help define gig work and understand how it relates to other forms of work being discussed and (2) draw on current research to identify what we know (and don’t know) about the prevalence of gig work, the demographics of the workforce, and job quality outcomes, using data on California where possible.

This paper identifies data gaps and research questions that need to be answered in order to better understand trends in workplace restructuring during the era of growing wage inequality.