Policies for Cooperative Ownership in the Digital Economy

Trebor Scholz

The past decade gave rise to the so-called ‘gig economy’—a cluster of service sector jobs contingent workers fulfill through digital platforms. Firms like Uber, TaskRabbit, and GrubHub established themselves as two-way intermediaries between workers and customers with the promise of revolutionizing work itself. While the gig economy has provided some convenience and savings to customers and flexibility to workers, the rise of the gig economy has also been disastrous. Using legal
loopholes, well-funded lobbying efforts, and publicity campaigns, platform companies have eroded labor protections, worsened environmental conditions, and undermined public services. In contrast to the early, high-minded dreams of a ‘sharing economy,’ the gig economy is in effect defined by precarity and exploitation.

On the one hand, these problems have been exacerbated by the Covid-19 crisis. Gig workers were on the frontline of the emergency, delivering groceries, cleaning supplies, and preparing food. They were, however, also the workers who were most exposed to the economic dislocation of the pandemic.

On the other hand, effective government response has caused a tightening labor market that leaves some platforms without a sufficient supply of cheap labor. The promise of tech companies was that they would become hegemonic service providers, and thus their losses would be justified with long-term profits. Many of these already unprofitable firms face a real danger of failure just as their aggressive expansion has weakened public infrastructure, leaving vital gaps in essential services.

Our report provides a path forward at this critical juncture: the active promotion of platform cooperatives. Platform cooperatives are democratically-governed organizations owned by workers, customers, and other stakeholders. These entities match workers and customers and return a greater share of income to workers, increase worker protections, and build communities. Though still early in their development, platform cooperatives build on the proven business models of cooperatives to establish alternatives to the gig economy and its supporting digital infrastructure.

Platform cooperatives are critical to creating a fairer economy and building back better from the pandemic. However, they require active government intervention to be able to compete with well-funded and established private platforms.

This report suggests that governments on every level, from national to municipal, can take measures to empower platform cooperatives through actions including but not limited to:

• Procurement policies to provide preferential treatment of platform cooperatives over privately-owned platforms.
• Public solidarity lending to finance early-stage platform cooperatives as part of national, regional, and municipal development strategies.
• Public participation in multi-stakeholder cooperatives via direct state ownership of co-op shares that provide a public voice in cooperative management.
• Conduct legal research and review to ensure that laws governing cooperative enterprises reflect the changing realities brought by digital technology.
• Create a system of public benefits available to the workers of platform cooperatives such as healthcare, childcare, and worker training.
• Establish a network of public spaces that can be used explicitly by platform cooperatives to serve as hubs.

The ultimate goal of these policy prescriptions is to create a more level playing field for platform cooperatives by reducing the risks their members bear through the provision of collective goods.

Such basic services allow alternative economic institutions to compete with often unprofitable platform companies flush with venture capital funds.

The policy suggestions found throughout this report are the results of rigorous case studies on government policies toward platform cooperatives and their effects in the following localities:

• California, United States of America
• Kerala, India
• Barcelona, Spain
• Bologna, Italy
• Berlin, Germany
• Paris, France
• Preston, United Kingdom

We selected these localities because of the presence of platform cooperatives in their economies and to offer diverse geographical, legal, political, and economic perspectives. Each case study examines the status of platform cooperatives and corresponding government policies towards cooperatives and suggests specific improvements and additional actions that local and national authorities can pursue to foster a cooperative ecosystem.

Fullscreen Mode

composed by Arswain
machine learning consultation by Anna Tskhovrebov
commissioned by the Berggruen Institute
premiered at the Bradbury Building
downtown Los Angeles
april 22, 2022

Human perception of what sounds “beautiful” is necessarily biased and exclusive. If we are to truly expand our hearing apparatus, and thus our notion of beauty, we must not only shed preconceived sonic associations but also invite creative participation from beings non-human and non-living. We must also begin to cede creative control away from ourselves and toward such beings by encouraging them to exercise their own standards of beauty and collaborate with each other.

Movement I: Alarm Call
‘Alarm Call’ is a long-form composition and sound collage that juxtaposes, combines, and manipulates alarm calls from various human, non-human, and non-living beings. Evolutionary biologists understand the alarm call to be an altruistic behavior between species, who, by warning others of danger, place themselves by instinct in a broader system of belonging. The piece poses the question: how might we hear better to broaden and enhance our sense of belonging in the universe? Might we behave more altruistically if we better heed the calls of – and call out to – non-human beings?

Using granular synthesis, biofeedback, and algorithmic modulation, I fold the human alarm call – the siren – into non-human alarm calls, generating novel “inter-being” sonic collaborations with increasing sophistication and complexity. 

Movement II: A.I.-Truism
A synthesizer piece co-written with an AI in the style of Vangelis’s Blade Runner score, to pay homage to the space of the Bradbury Building.

Movement III: Alarmism
A machine learning model “learns” A.I.Truism and recreates Alarm Call, generating an original fusion of the two.

Movement IV: A.I. Call
A machine learning model “learns” Alarm Call and recreates A.I.Truism, generating an original fusion of the two.


RAVE (IRCAM 2021) https://github.com/acids-ircam/RAVE