Contributors

Wena Teng is currently a student at Columbia University studying History and Political Economy. She is a research assistant for economic development and legal history. Most recently, she worked on legal for an international bank, policy for the White House, New York State, and participatory organizing for the Urban Justice Center and New York City Civic Engagement Commission.

Key things to know

Technology, media, rideshare, and other industries are increasingly collecting alarming amounts of personal data. Through algorithmic management including metrics measuring “desperation,” miles traveled for a trip, and more, the data is consequently used for personalized wage discrimination, exploitation, and profit in the digital economy.
Data Cooperatives serve as data intermediary organizations and mediate these challenges by dictating what data should be collected from its members and how to process and monetize the pooled data. Most importantly, they compensate the members for their individual data contributions.
These organizations mainly rely on membership dues (e.g. SAOS) or seek other sources of funding.
By design, members of the data cooperative have control over the quality and quantity of data they share with the cooperative by establishing shared metrics and measurements.
Decision-making about the sharing and use of data with external users is defined by democratic member participation and governance.
Aggregating members’ data increases bargaining power as cooperatives can command a higher price for data buyers.

Case studies

The Driver's Seat

Founded in 2020, The Driver's Seat was created to empower “gig workers [to] get paid more every day, combat gig companies' use of algorithmic management with tech that workers build themselves, bring honest data into workforce and transportation policymaking, and build a cooperatively-owned business.” Although they closed and transitioned their app to the Workers' Algorithm Observatory at Princeton University, they were a major success.

In addition to operating as a cooperatively-owned business, they excelled in advocacy, profits, and partnerships:

“Thousands of ride-hail drivers and delivery workers boosted their pay while taking back control of their work by using the Driver’s Seat app’s pay transparency, time and miles tracking, crowdsourced market information, and AI recommendations tools.”
Partnered “Gig worker organizations like Rideshare Drivers United, Los Deliveristas Unidos, the Colorado Independent Drivers Union, and SEIU by using the Driver’s Seat’s aggregated datasets to champion pro-worker policies and fight back against gig company misinformation.”
Supported “Transportation planners at the City of San Francisco, UC Berkeley, and Fehr and Peers… to analyze the connection between gig worker pay and transportation and environmental outcomes.”

The Driver’s Seat built a technological model for technology that is cooperatively owned and directed by its users and beneficiaries.

The Drivers Cooperative - New York

In 2021, about 2,500 drivers in New York organized The Drivers Cooperative to “build a new ride-hailing platform that gives profit and control to drivers.” The Drivers Cooperative serves to provide centralized billing, worker ownership, and consistent pricing with no surge pricing.

Founded by a former Uber employee, a labor organizer, and a black-car driver, they operate on the 7 Cooperative Principles, adopted by the International Cooperative Alliance, including open and voluntary membership, democratic member control, member economic participation, autonomy and independence, education, training, and information, cooperation among cooperatives, and concern for the community.

The cooperative is owned by the drivers themselves and takes 15% commission from each ride for cooperative costs, as opposed to the 25% to 40% that apps like Uber or Lyft take per ride. In lieu of high commission and the collection of data to determine wages, rates are determined with transparency with attention to additional fees. The Long Pickup Fee, for example, pays the full passenger fare whereas the co-op does not take a commission if the time it takes you to reach the passenger is longer than 8 minutes. The Green Transition Surcharge requires that for each trip, the rider pays a $1 Green Transition Surcharge. These surcharges accrue for the driver in an account with the co-op, which drivers can use to buy an electric vehicle. Additionally, taxes and surcharges that all Black Car bases are required to collect do not come out of driver pay; they are paid by the passenger on top of the fare.

Riders are billed the following rates for Co-op Ride trips, unless on a business account.

Possible Pitfalls

By design, data cooperatives are self-sustained. As a result, some data cooperatives like the Driver’s Seat face significant competition from corporate firms with much more capital.

However, instead of shutting down completely in 2023, the Driver’s Seat Cooperative worked with non-profits, academic researchers, worker organizations, and other pro-gig worker businesses to maintain and continue to develop the Driver’s Seat app. Gig workers, organizers, and policymakers now get the benefits of the Driver’s Seat app and insights via the Workers' Algorithm Observatory (WAO) at Princeton, a crowdsourced auditing collaboration.

The relative newness and the small number of data cooperatives also raise questions about the long-term sustainability and security of data.

Conclusion

Despite the challenges and the novelty of data cooperatives, they are important areas for further research and development as well as local implementation. The decentralized form of data management in data cooperatives promotes collective economic power, participatory governance, and owner-autonomy by returning workers’ ability to collect, share, analyze, and sell access to their data. As the use of algorithms and AI approaches other industries beyond the gig economy and digital economy, other industries may benefit from similar models that advance economic democracy in the workforce at large.

Data Cooperatives

Contributors

Key things to know

Case studies

Possible Pitfalls

Conclusion

Further readings

Worker Centers