07 Aug Surging Suspicions in the Gig Economy
In a nutshell (TL;DR)
A 2017 working paper claimed Uber drivers coordinated mass “switch‑offs” via an online forum to trigger surge pricing. The evidence—1,012 forum posts and a handful of interviews—was anecdotal, non‑peer‑reviewed, and never cross‑checked with Uber’s own data. Media outlets nonetheless amplified the story, sparking public outrage at drivers rather than examining Uber’s opaque algorithmic management. True large‑scale collusion is implausible given driver atomization, deactivation risks, and surge‑pricing safeguards. The episode ultimately exposes deeper gig‑economy tensions: workers chafing under algorithmic control, weak research fueling sensational headlines, and a pressing need for transparency, fair labor standards, and sober scrutiny of platform power.
|
Surging Suspicions in the Gig Economy By Daniel G. Rego August 7, 2017 | Washington, DC |
The Claim: Drivers “Gaming” Surge Pricing
Are Uber drivers forming digital cartels to make riders pay more? In August 2017, headlines suggested exactly that. A University of Warwick and NYU study claimed that some Uber drivers conspired via an online forum to simultaneously go offline, artificially triggering Uber’s surge pricing and then reaping the higher fares. The Independent breathlessly reported that drivers “work together to create price surge and charge customers more”. According to the researchers, drivers in London and New York coordinated mass “switch-offs” – logging out en masse so that the ride supply plummeted, and Uber’s algorithm would jack up prices by 50% or more. Once surge pricing was in effect, those same drivers would quickly log back in to earn a premium on the next rides.
This narrative of cunning gig-workers gaming the system proved irresistible to media outlets. Stories appeared about Uber drivers “ganging up on passengers” with a techno-trick to force surge fares. It was a dramatic role reversal: the algorithm that usually squeezes drivers could apparently be used by drivers to squeeze riders. The timing was apt, as Uber had been mired in months of bad press – from executive scandals to driver protests – feeding public appetite for any twist in the tale. A new study of driver collusion made perfect fodder for social media outrage: were gig workers turning into price-gouging cabals?
Uber, for its part, swiftly denied that such collusion was common. The company insisted this behavior was “neither widespread nor permissible on the Uber platform” and pointed to technical safeguards against manipulation. Uber’s community guidelines explicitly forbid “fraudulent or illegitimate behavior,” including any form of rider-driver collusion to game fares. In short, Uber publicly treated the surge hack as an isolated, rogue tactic – and one that could get drivers deactivated (banned) if detected. The notion of drivers uniting to boost prices cuts against Uber’s narrative that surge pricing simply reflects natural supply and demand. But did the study prove that a significant number of drivers were pulling off this scheme? A closer look suggests the evidence was shaky at best.
Flimsy Evidence and Academic Shortcomings
The Warwick/NYU study was not a peer-reviewed journal publication but a working paper and press release, yet its bold claims went viral before undergoing rigorous scrutiny. On inspection, the findings hinge on anecdotes and insinuation more than hard data. The researchers had interviewed an unspecified number of drivers and scraped 1,012 posts from the independent UberPeople.net internet forum. From this trove, they highlighted a single conversation between forum users musing about a coordinated log-off: one user urges “Guys stay logged off until surge,” another warns of Uber finding out, and the first nonchalantly replies that “it happens every week”. This snippet of online chatter is presented as smoking-gun evidence of regular, organized surge manipulation. Yet it is essentially hearsay from an internet forum – what one PBS journalist wryly termed “more anecdata from the website… entered into evidence.” The study offered no quantitative measures of how often these sync’d logoffs occurred or how much they inflated fares. In fact, the authors admitted “it’s unclear how much the collusion affects fare prices”.
Reliance on self-reported tricks and driver folklore is a shaky foundation for sweeping conclusions. Forum posts can be bravado or brainstorming rather than proof of action. Not all drivers read UberPeople.net, and those who do are a vocal subset that may overstate their exploits. Indeed, drivers on the forum itself have expressed skepticism of such risky tactics. When a video circulated in 2015 purporting to show a method to goose the surge (by repeatedly accepting and canceling rides), other UberPeople users immediately criticized it. “This is a terrible strategy. You’ll get deactivated within a week,” one driver commented. Uber’s own spokesperson at the time warned that drivers caught manipulating the app would be terminated. The Washington Post, examining that incident, concluded that an individual or even a small group of drivers would be hard-pressed to significantly move prices: In a city with hundreds of drivers, surge pricing is “unlikely to be affected by the actions of a single driver — or even a handful of drivers”. In other words, without massive participation – far beyond what any forum thread documented – the scheme would have limited effect before Uber’s algorithms or enforcers stepped in.
These practical constraints went largely ignored as the 2017 study’s narrative spread. The researchers did not supply statistics on how many drivers coordinated or estimates of how big an area went into surge due to such logoffs. No Uber data was analyzed to correlate forum chatter with actual surge events. It’s telling that Uber flatly said the behavior was “not common”. A reasonable interpretation is that only small pockets of drivers occasionally attempted to game the system, with success likely fleeting. Yet press reports implied a broad pattern. The study’s authors were arguably over-interpreting qualitative evidence – a few interviews and posts – to make a headline-friendly claim. By academic standards, the methodology had clear weaknesses: a self-selected sample, anecdotal evidence, and no peer review vetting the robustness of conclusions.
None of this is to say drivers never try to bend the rules. Some surely do experiment with ways to improve their earnings under Uber’s opaque and shifting system. But extraordinary claims (a coordinated conspiracy to spike prices weekly) require solid proof, and here the proof was thin. The researchers themselves seemed aware of the limits, couching some findings in careful language. Unfortunately, nuance was lost in translation to the public. In the rush for a spicy scoop about Uber’s algorithm being thwarted by its own workers, media outlets skipped the fine print. A working paper with tentative findings was elevated to the status of established fact overnight. This reflects a broader issue in today’s news cycle: weakly substantiated research can be amplified and distorted before it’s properly validated. When that happens, perception can outrun reality, especially on fast-moving social platforms.
Algorithmic Management Breeds Contention
Ironically, whether drivers really orchestrated widespread surge manipulation, the popularity of this story underscored a very real phenomenon: growing driver discontent under Uber’s algorithmic management. The study’s most credible insights were about the power asymmetry between the platform and its drivers. Unlike traditional employees, Uber drivers have no human supervisor to negotiate with or complain to; they are managed by code. Uber’s app algorithm allocates rides, sets prices, monitors performance, and even nudges driver behavior with videogame-like prompts – all without human intervention. As Mareike Möhlmann, one of the study’s authors, observed, “Uber uses software algorithms for oversight, governance and to control drivers, who are tracked and their performance constantly evaluated”. This high-tech command-and-control system is what scholars’ term “algorithmic management.” Drivers often feel they are serving an impersonal system rather than a company with accountable human managers. Little wonder, as another researcher noted, that many drivers have “the feeling of working for a system rather than a company” and virtually “no interaction with an actual Uber employee.”
This algorithmic boss can be both inscrutable and unyielding. Drivers are subject to automated decisions – sudden fare cuts, mysterious changes in how bonuses are calculated, automatic warnings for declining rides – with scant explanation. They live under the constant threat of deactivation by algorithm if their ratings fall or if they’re flagged for “irregular” activity. In the study, drivers complained that Uber’s strategy is “not at all transparent” and that they’re left in the dark about how jobs are assigned or why their earnings fluctuate. The system dictates terms unilaterally: for example, UberPool (carpooling service) is imposed as a condition of using the platform, even if it’s “not economically beneficial” for drivers who earn lower commissions on those rides. Many drivers resent being forced to accept pooled rides; the study found some found crafty ways to avoid them (ignoring second-passenger requests or quickly logging off after accepting one rider). These acts, too, are a form of on-the-ground resistance – minor rebellions against a system that drivers feel overworks and underpays them.
Uber’s algorithmic micromanagement, while efficient in theory, may backfire in practice. The more Uber tried to optimize every moment of a driver’s time through code, the more drivers looked for cracks in the system to reclaim a sliver of autonomy. The alleged surge collusion can be seen in this light: not as sheer greed or malice, but as a reaction to an exploitative setup. As one commentary put it bluntly, “It’s complete manipulation, possibly stemming from the relatively low amounts of money many Uber drivers make” – perhaps a tit-for-tat response to how Uber “psychologically manipulates drivers” with its app incentives. In other words, drivers have felt “gamed” by Uber, and some are inclined to game it right back. The study’s authors themselves framed drivers’ tactics as attempts to “regain their autonomy… when faced with the power asymmetry imposed by algorithmic management.” Under constant surveillance and pressure to accept unprofitable trips, a subset of drivers will undoubtedly seek creative workarounds – whether it’s running multiple ride-hail apps simultaneously to cherry-pick better fares or coordinating (even just informally) with fellow drivers to avoid being exploited by a ruthless pricing algorithm.
Coordination in the Gig Economy: A High-Tech Labor Struggle
If a group of Uber drivers indeed conspired to trigger surge pricing, it resembles a digital-age labor action – a sudden, if short-lived, strike to demand higher pay. Such collective action among gig workers is noteworthy precisely because it is so difficult to achieve. Uber drivers are classified as independent contractors, not employees, which legally limits their ability to unionize or bargain collectively. They lack a physical workplace where solidarity can spark over lunch breaks or on the shop floor. Instead, they sit alone in cars, connected only via an app that explicitly does not connect them to each other. “The Uber app that connects drivers to their fares doesn’t connect drivers to one another,” as Vox observed in a report on an attempted driver protest. There is no built-in chat for drivers to rally colleagues for a cause. Unofficial online forums like UberPeople.net or Facebook groups fill this void, but not every driver knows about them. And even if they do, getting thousands of dispersed freelancers to act in unison is like herding cats – especially when any individual who breaks rank can immediately benefit by scooping up rides while others log off.
This is the classic collective action problem: drivers might all profit from higher fares if everyone holds the line, but each one has a strong incentive to defect and take advantage of a surge others helped create. Uber’s surge-pricing system, paradoxically, encourages free-riding on any attempted strike. In past cases, some drivers who tried to organize boycotts of Uber ended up complaining that many peers kept driving and merely capitalized on the higher surge rates caused by the would-be strike. Organizing gig workers is so fraught that a 2015 nationwide Uber driver protest largely fizzled, with many drivers never even hearing about it in time. Fear of retaliation also looms large. Traditional employees who strike have some legal protections; independent contractors do not. As one labor expert noted, if contractors collectively stop working, “they could lose their positions, whereas employees are legally protected” when striking. Uber has not hesitated to deactivate drivers who band together in ways it deems fraud or interference. This threat naturally undermines trust: even on a forum, drivers warning “Uber will find out” are effectively reminding everyone of the risk of punishment.
Seen in this context, the idea of large-scale driver collusion on pricing starts to feel more symbolic than practical. It is significant that drivers are angry enough to discuss collective tricks but pulling them off consistently would require coordination and trust that Uber’s atomized gig model actively discourages. The gig economy, by design or coincidence, Balkanizes its workforce. As scholar Trebor Scholz quipped, the “sharing economy” is Reaganism by other means – invoking the 1980s era of union-busting and labor casualization, now repackaged in tech-friendly form. The very structure of platforms like Uber, which treat workers as interchangeable “driver-partners” rather than employees, makes sustained solidarity elusive. (It is no accident that Reagan and Thatcher’s policies weakened unions and promoted individualism; the platform economy extends that trend.) Drivers today face the precarity of being on their own, stuck between the algorithm’s demands and the absence of collective bargaining power.
And yet, the chatter about beating Uber at its own game hints at nascent forms of digital worker resistance. In the late-night online discussions and fleeting log-off experiments, one can see gig workers groping toward what little leverage they have. When Uber cut rates or introduced a new unpopular policy, drivers have occasionally attempted “log-off strikes” or coordinated refusals to drive at certain times. These efforts have had mixed results – often they simply fizzle as drivers trickle back to work, unable to afford prolonged idling. But even the attempt is notable. It shows that despite structural hurdles, gig workers are not the passive, isolated actors the platforms might imagine. They share information on forums, compare notes on company tactics, and sometimes test the waters of collective action, however informally. Juliet Schor, a sociologist studying the sharing economy, observed that these platforms often turn educated people toward low-paid work, displacing others in the process. The result is a growing class of gig workers (from ex-bankers to career cabbies) who feel the system is rigged against them. Some will choose exit (switching to rival apps, or leaving the gig altogether), but others voice loyal opposition – pushing back against the algorithm in small ways that can resemble a modern kind of union agitation.
Hype, Headlines, and the Need for Nuance
The Uber driver “collusion” saga also offers a cautionary tale in how tech news is digested. A loosely substantiated study, on a topic ripe for outrage, was amplified by eager news outlets into a global story. From there it was a short jump to furious tweets and Facebook rants – those greedy Uber drivers! – and equally misguided counter-reactions. In today’s media ecosystem, provocative headlines can outrun the underlying facts. As soon as the idea of drivers conspiring to gouge customers hit the press, it fit a popular narrative and spread unchecked. It’s the kind of story that plays into everyone’s biases: critics of Uber gleefully shared it as evidence of the platform’s dysfunction, while riders shared it to vent about being fleeced. What got lost was the nuance: that this “price conspiracy” was more hypothesis than proven epidemic, and that if it occurred at all, it was as much a symptom of Uber’s power imbalance as a scheme by drivers to get rich quick.
Evgeny Morozov, a noted technology critic, has argued that the so-called sharing economy often “amplifies the worst excesses of the dominant economic model: it is neoliberalism on steroids.” Platforms push risk and uncertainty onto workers and then invite techno-celebration of their efficiency. In such an environment, sensational stories can overshadow the structural critique. The image of crafty drivers milking surge pricing made for great clickbait, but it might distract from the more uncomfortable reality that many Uber drivers struggle to make ends meet under a system that sets pay dynamically and opaquely. By focusing on a few drivers supposedly gaming the algorithm, the media conversation risked painting drivers collectively as schemers, rather than victims of a scheme far larger – one designed by Uber’s engineers and executives. As The Economist and other thoughtful outlets have noted in broader analyses, the gig economy’s promise of flexibility often masks a new form of exploitation: workers with “all the responsibility of running a business, but none of the security or leverage” that traditional employment might offer.
The uproar over surge collusion thus contained a dose of irony. The public was incensed at drivers manipulating prices, even as many drivers were angry at Uber for manipulating them through constant monitoring and algorithmic nudges. Each side of the platform eyed the other with mistrust, while the real power – the platform itself – churned on. Going forward, it’s crucial to apply healthy skepticism to splashy research claims, especially in fast-evolving domains like the gig economy. A forum anecdote is not the same as a verified phenomenon. Yes, Uber drivers will continue to seek ways to improve their lot, sometimes collectively. But before accusing them of behaving as a cartel, one must ask: Who really holds the cards in this system? The evidence suggests it is still Uber’s algorithm and corporate policies that set the tone, and driver “collusion” (to the extent it exists) is a reaction forced by desperation and opacity.
In the end, the 2017 surge-pricing scare turned out to be more smoke than fire. Uber’s pricing machinery remains intact – if anything, the company has only tightened its control and monitoring of drivers since. But the episode was enlightening. It peeled back the curtain on the tensions underlying the gig economy, illustrating how digital workers, lacking formal power, explore informal means to assert themselves. It also highlighted the media’s role in framing those tensions: either as curious anecdotes or alarmist tales. The challenge is to move beyond the clickbait cycle and address the core issues at hand. Uber’s drivers shouldn’t have to choose between exploitation by algorithm and risky rebellion. The real solution lies in creating fairer standards for gig work – greater transparency, better pay guarantees, perhaps even new forms of worker representation in algorithm-driven platforms. Until then, we can expect flare-ups of conflict (and news coverage of them) as workers navigate an economic model that often feels stacked against them.
In the gig economy’s tug-of-war between algorithmic control and worker agency, the story of “surge pricing collusion” is a cautionary vignette. It reminds us that sensational claims need careful verification, and that beneath the hype, there’s an ongoing struggle for dignity in an era of apps and algorithms. The next time we hear about drivers gaming the system, we would do well to remember who designed that system – and ask whether the game itself is fair. – Dan Rego
Keywords
Uber surge‑pricing controversy, Uber driver collusion, price‑spike manipulation, algorithmic management, gig economy, digital labor markets, ride‑hailing platforms, log‑off strike, driver protest, algorithm gaming, platform governance, independent contractors, precarious work, dynamic pricing, worker exploitation, collective action dilemma, media sensationalism, viral news cycle, clickbait headlines, online outrage, algorithm transparency, labor standards reform, platform accountability, sharing‑economy critique, digital worker resistance, power asymmetry, worker surveillance, gamification of labor, post‑truth politics, research sensationalism, anecdotal evidence, forum data scraping, questionable data quality, lack of peer review, UberPeople forum, passenger fare inflation, surge‑pricing safeguards
References
Chris Keall, “Uber drivers colluding to trigger surge pricing: study.” National Business Review, 3 Aug 2017. Link
Ben Chapman, “Uber drivers work together to create price surge and charge customers more, researchers find.” The Independent, 2 Aug 2017. Link
Lisa Vaas, “Uber drivers game the system – force up fares.” Sophos News (Naked Security), 4 Aug 2017. Link
Paul Solman, “How Uber drivers game the app and force surge pricing.” PBS Newshour, 4 Aug 2017. Link
Erik Sherman, “Uber Costs Too Much Because Drivers Trick the System. Here’s How to Fight Back.” Inc.com, 2 Aug 2017. Link
Abby Ohlheiser, “The flop of Uber drivers’ national protest shows how hard it is for them to organize.” Vox/Recode, 18 Oct 2015 . Link
Abby Phillip, “No, Uber drivers can’t game the ‘surge pricing’ system the way one driver claims.” The Washington Post, 19 May 2015. Link
Mareike Möhlmann et al., “Uber drivers are gaming the system and even going offline en masse to force ‘surge’ pricing.” University of Warwick Press Release, 2 Aug 2017. Link
Trebor Scholz, “Platform Cooperativism: Challenging the Corporate Sharing Economy.” Rosa Luxemburg Stiftung NYC, 2016. Link
David Murillo, Heloïse Buckland & Esther Van den Berg, “When the sharing economy becomes neoliberalism on steroids: Unravelling the controversies.” Technological Forecasting & Social Change 125 (2017): 66–76. Link