The sharing economy: Sharing and inequality
Phuah Eng Chye (9 December 2017)
Initially, sharing was welcomed as a demonstration of how technology could be used to promote self-reliance, monetise assets, empower work and reward entrepreneurs. The ideological premise was that sharing platforms would unleash market forces and community collaboration, be used to roll back government overreach and loosen the suffocating grip of costly regulation and welfare entitlements.
But sharing is not working as advertised. Juliet Schor suggests “the debut of the sharing economy was marked by plenty of language about doing good, building social connections, saving the environment, and providing economic benefits to ordinary people. It was a feel-good story in which technological and economic innovation ushered in a better economic model.” But questions are being raised as to “whether the popular claim that the sharing economy is fairer, lower-carbon, and more transparent, participatory, and socially-connected is anything more than rhetoric for the large, monied players.”
In this regard, sharing creates flexibilities in resource use but these flexibilities have been exploited and has aggravated inequalities.
- Uncertain work and income. Sharing was supposed to provide individuals the opportunity to earn extra income from monetising their assets and spare time. Instead sharing has come to be associated with uncertain work at piecemeal rates. Robert Reich argues “this on-demand economy means a work life that is unpredictable, doesn’t pay very well and is terribly insecure…where everyone is doing piecework at all odd hours, and no one knows when the next job will come, and how much it will pay? What kind of private life can we possibly have, what kind of relationships, what kind of families?”[1] Other features reinforcing inequality include the widening salary differentials between core employees and the income of independent contractors and contract employees.
- Unequal relationships. The sharing platforms has a dominant position relative to its contractors. For example, drivers initially enticed by attractive rates to join ridesharing companies are vulnerable when the platforms eventually reduce rates. Sarah O’Connor documents contractors were closely monitored[2] and were arbitrarily subject to penalties such as deactivation of accounts or termination of service agreements for not responding or accepting jobs in a timely manner. Sharing platforms have been accused of imposing strict employee-type controls on its contractors while refusing to extend the pay and benefits associated with employment. The advent of sharing is seen as undermining the institutional protection of labour rights. Steven Hill argues “its practically a medieval system in all of its modern glory, yet all wrapped around the New Age mantle of sharing – without the employers having to worry about labour protections, minimum wage, safety nets, healthcare, retirement, enforceable contracts or ongoing relationships of noblesse oblige towards those they hire.”
- Shifting risks to workers. Sharing platforms have shifted costs, risks and obligations (unutilised time, insurance, medical expenses, asset maintenance) to individual workers. For example, sharing platforms minimise “unproductive labour time” by only paying for the time when production occurs. Sarah O’Connor observes contractors “gain flexibility but shoulder the financial risk – borne previously by the company – that they will wait around unpaid when demand is slow.” Steven Hill concludes “whatever the merits of the origins, the sharing economy has become a highly capitalised, Silicon Valley-hatched scheme to shift risks from companies to workers, and ensure that investors can reap huge profits and lower fixed costs by stripping away worker protections and middle-class wages, ignoring government regulations and avoiding taxes.”
- Reinforcing discrimination. Juliet Schor notes “sharing economy sites can also reproduce class, gender, and racial biases and hierarchies…cultural capital, a type of class privilege, limited the trades members were willing to make. Only participants with the right offerings, packaging, appearance, or taste received offers or, in some cases, even felt comfortable returning…some people screen potential trading partners by grammar and education, and that many highly educated people were unwilling to offer their most valuable skills (like programming or web design), preferring instead to act as amateur electricians or manual workers. A recent study also reported evidence of racial discrimination among Airbnb users, finding that non-black hosts were able to charge 12% more than blacks for comparable properties.” The use of demand-driven pricing can also reinforce discrimination by increasing the risks of unequal access to services. Demand-driven prices means the rich can outbid for better services while lower-income demand may be underserved.
It may not be fair to put all the blame on sharing. Sharing is just the messenger of the information society. As the information effects such as speed, size and transparency intensify, there will a tendency towards concentration. Pay scales between the top talent and other individuals will widen due to the Winner-Take-All effects. The transparency of information will congest opportunities around those perceived favourably to the disadvantage of the masses in the fragmented long-tail. Hence, the behaviour of ridesharing companies is just a consequence of information effects but it has the effect of causing income distributions to become highly skewed.
References
Juliet Schor (October 2014) “Debating the sharing economy”. Great Transition Initiative. http://www.greattransition.org/publication/debating-the-sharing-economy.
Phuah Eng Chye (2015) Policy paradigms for the anorexic and financialised economy: Managing the transition to an information society. http://www.amazon.com/dp/B01AWRAKJG
Phuah Eng Chye (15 July 2017) “The significance of information effects.” Economicsofinformationsociety.com. http://economicsofinformationsociety.com/the-significance-of-information-effects/
Sarah O’Connor (8 September 2016) “When your boss is an algorithm”. FT Magazine. http://www.ft.com/cms/s/2/88fdc58e-754f-11e6-b60a-de4532d5ea35.html?siteedition=intl
Steven Hill (2015) Raw deal: How the Uber economy and runaway capitalism are screwing American workers. St Martin’s Press.
[1] Quote sourced from Steven Hill.
[2] Indicators such as personalised monthly service level assessments on their average time to accept orders, travel time to restaurant, travel time to customer, time at customer, late orders and unassigned orders.