Introduction – “The Gig Economy”
Over the course of time, work is changing. Some jobs are vanishing off the face of the earth, some are being totally overhauled and some new ones are being created.
The “Gig Economy”, where workers use marketplace platforms over the internet to connect with buyers on demand, looks like it’s here to stay. We can see various manifestations of it interwoven with our daily lives, be it the chauffer driven on demand cab hailing services like Uber, Lyft or the plethora of on-demand services available on fiverr, homejoy etc. This trend dovetails with the perception that the traditional 40-hour work week, 9 to 5 workdays are antiquated and attracts millennial workers who find it hard to resonate with conventional office environments.
Although a few consider the Gig Economy to be a relatively new phenomenon, in a lot of ways it has just been old wine in a new bottle. Self-employment and freelance work, for instance, has been around for decades and was the norm in many areas of the labor market (e.g., freelance photographers, artists, musicians, journalists; hairdressers, who rent chair space). What is new, however, is how swiftly this work has been becoming mainstream; a recent publication stated it “could soon represent as much as 50 percent of the U.S. workforce” (Kaufman, 2013). This is up from the 10-15% noted as recently as 2008 (Hartog, van Praag, & van der Sluis, 2008). As the Gig Economy takes hold, the raging debate is that who benefits more and who is most at risk; the answer isn’t immediately clear and may never be.
It’s the nature of the relationships between these workers and online platforms, that are in a state of constant flux as much as the technology that enables these relationships and raises numerous questions on their implications from an ethical standpoint.
The first amongst a slew of problems with this economy arises from the novelty of the worker/ online platform relationship. This takes a gargantuan size when employees are rather misclassified as independent contractors. At face value, the compelling proposition that workers’ time isn’t controlled by an overbearing boss, rather they can be flexible, picking and choosing their own work and the hours, they wish to undertake. Though this alluring proposition seems to put power back into the hands of the workforce, the self-employed contractors this is infact a double-edged sword for those workers.
The reality, however, is quite stark. Often, the gig economy strips these contractors of the rights and benefits that come along with permanent, full-time work such as paid sick leaves, holidays and, as a result, is tarnishing the reputation of the facilitating companies and attracting scrutiny from all directions, inclusive of regulators, trade unions and the ILO. According to these platform companies, they are facilitators, simply connecting supply with demand. The wider argument that is brewing around the ethics of the gig economy and the responsibilities that a company – whatever their role or purpose – should undertake.
A report published recently by the former work and pensions committee chair, Frank Field, discovered that without the safety veil of benefits and protection of permanent employment, some gig economy workers are being paid as paltry as £2.50 an hour. The report suggests the government to stage an ’emergency intervention’ thus ensuring the fair treatment of workers in the new economy.
New technology firms – such as Uber, Deliveroo, Amazon Mechanical Turk or Crowdflower – whose proposition banks on connecting supply with demand are now forced to walk a tightrope between the financial merit of their innovative business models laden with novelty and the reputational and regulatory backlash that might occur if the public feel that workers are being exploited to a certain extent. This becomes crystal clear when looking at the risk and reputation-focused concerns expressed in numerous online conversations.
Data from Polecat has shown that in the recent months ‘treatment of workers’ has been the most prominent cause for concern in the context of the gig economy with ‘welfare state’ being a recurrent phrase, highlighting the worry that the lack of employment protection for gig economy workers is simply transferring risk away from business and leaving taxpayers to foot the bill.
The disruptive, ‘uberisation’ of industries has been discussed with a lot of anticipation over the recent years, but concerns related to the casualization and informalization of work are now also increasingly coming to the fore, with zero-hour contracts also hogging the spotlight. It all begs the question; how sustainable are these business models, should the regulatory environment change and demand adherence to well established employment law?
The number of people employed in the gig economy are notoriously hard to estimate for obvious reasons – this being a highly distributed and irregular workforce that is often extremely hard to identify. Indeed in certain scenarios, prosecutors and regulators have leveraged social media to engage and identify such a workforce community to inform investigations. According to the McKinsey Global Institute, there are five million workers currently within the UK’s gig economy and that number will continue to rise as each new, disruptive tech startup blooms.
The Abuse of Behavioral Economics
A recent article in the New York Times about how Uber has been using various insights from behavioral economics to push, or nudge, its drivers to pick up more fares, sometimes with very minimal marginal benefit to them has generated quite a bit of criticism of Uber. It’s just one of several stories of late that have cast the company in a poor light.
It reminded me of a question that executives often ponder over when thinking about the benefits of behavioral economics and the use cases of how they could use it in their own organizations: What if, it will be used with ill intent?
I always thought that, like many tools, it can be used in good and bad ways. We should dive deeper into the differences between the two.
According to the traditional view in classical economics, we are rational agents, well informed with stable preferences, self-controlled, self-interested, and optimizing. The behavioral perspective takes issue with this view and suggests that we are characterized by fallible judgment and malleable preferences and behaviors, can make mistakes calculating risks, can be impulsive or myopic, and are driven by social desires (e.g., looking good in the eyes of others). To sum it up, we are simply human.
Behavioral economics starts with this latter assumption as its premise. A multi-faceted discipline that draws and combines insights from various fields like psychology, economics, judgment, decision making, and neuroscience namely to understand, predict, and ultimately influence human behavior in ways that are more powerful than any one of those fields could provide on its own. Over the last few years, organizations in both the private and public sectors alike have applied some of the insights from behavioral economics to address a wide range of problems — from reducing cheating on taxes, work stress, and turnover to encouraging healthy habits, increasing savings for retirement as well as turning up to vote.
Uber has been using similar insights to influence drivers’ behavior all along. As Noam Scheiber writes in the Times article, “Employing hundreds of social scientists and data scientists, Uber has experimented with video game techniques, graphics and noncash rewards of little value that can prod drivers into working longer and harder — and sometimes at ungodly hours and locations that are less lucrative for them.”
One such approach, accordingly reinforces drivers toward collecting more fares based on the insights from behavioral sciences that people are extremely influenced by goals and gamification. According to the article, Uber sends regular alerts to the drivers that they are very close to attaining a certain target when they try logging out. And it also sends drivers their next fare opportunity before their current ride is over.
Now let’s revisit the question of when are nudges considered to be good and when are they considered to be bad. One of my favorites examples is the use of checklists in surgery to reduce patient complications. Checklists elucidate several standard critical processes of care that many operating rooms typically implement from memory. In a paper published in 2009, Alex Haynes and colleagues observed the usage and effectiveness of checklists in eight hospitals in eight cities in the Unites States. They found the mortality rate for patients undergoing surgery fell from 1.6% to 0.8% post the introduction of checklists. Inpatient complications also fell from 11% to 7%.
In a related paper published in 2013, Alexander Arriaga and colleagues had 17 operating-room teams participate in 106 simulated surgical-crisis scenarios. Each team was randomly assigned to work with or without a checklist and instructed to implement the critical processes of care.
The results were striking: Checklists reduced missed steps in the processes of care from 23% to 6%. Every team performed better when checklists were available. Remarkably, 97% of those who participated in the study reported that if one of these crises occurred while they were undergoing an operation, they would want the checklist used.
Another example, concerns the use of fuel- and carbon-efficient flight practices in the airline industry. In a paper published recently, using data from more than 40,000 unique flights, John List and his colleagues found that a significant savings in carbon emissions and monetary costs occured when airline captains received tailored monthly information on fuel efficiency, along with targets and individualized feedback. In the field study, captains were randomly assigned to one of four groups, including one “business as usual” control group and three intervention groups, and were provided with monthly letters from February 2014 through September 2014. The letters included one or more of the following: personalized feedback on the previous month’s fuel-efficiency practices; targets and feedback on fuel efficiency in the upcoming month; and a £10 donation to a charity of the captain’s choosing for each of three behavior targets met.
The result was that all the four groups enhanced their implementation of fuel-efficient behaviors. Thus, informing captains about their involvement in a study significantly changed their actions. (It’s a well-documented social-science finding called the Hawthorne effect.) Tailored information with targets and feedback was the most cost-effective intervention, improving fueling precision, in-flight efficiency measures, and efficient taxiing practices by 9% to 20%. The intervention, it appeared, to have encouraged a new habit, as fuel efficiency measures remained in use after the study ended. The implications were: an estimated cost savings to the tune of $5.37 million in fuel costs for the airline and reduction in emissions of more than 21,500 metric tons of carbon dioxide over the eight-month duration of the study.
In case of captains receiving feedback regarding fuel efficiency or surgeons using checklists, one of the major goals of the intervention was to influence the participants to act in a certain reinforced way. So, in a sense, the researchers were trying to encourage a behavioral change the same way managers at Uber were trying to influence behavioral changes in their drivers’ behavior.
But there is a significant difference across these three examples. Are the nudges used for the benefit of both parties involved in the interaction or do they create benefits for one single side and costs for the other? If the former, then (as Richard Thaler and Cass Sunstein argue in their influential book Nudge) we are “nudging for good.” Thaler and Sunstein identify three guiding principles that should be on top of mind when designing nudges: Nudges should have transparency and never be misleading, easily opted out of, and driven by the strong belief that the behavior being encouraged will improve the welfare of those being nudged.
That’s where the thin line between reinforcing certain beneficial behaviors and manipulating people lies. And that’s also where little difference between applying behavioral economics or any other strategies or frameworks for leadership, talent management, and negotiations can be seen. We always have the choice of using them for either good or bad.
If there exists misalignment between the interests of a company and its employees, then the organization might exploit its own members as Uber appears to have done. But there are plenty of scenarios where the interests can, in fact, be aligned — the company certainly benefits from higher levels of performance and motivation in those cases, and the workers feel more satisfaction with their work.
And that is where there is a great potential in applying behavioral economics in organizations: to create real win-wins for both the parties involved.
Piece work – where an individual gets paid for a piece of work – has been around for a quite a long time. The smartphone revolution has put piece work on steroids and led to the rise of the gig economy. Regulators, courts and trade unions are catching up and there is ongoing scrutiny of the crimes and misdemeanors of certain business models. However, there is nothing inevitable about the future of work and in the same way that the likes of Uber caused disruption to the transport sector, so in turn, they may face disruption from changing societal expectations of what classifies as the acceptable treatment of workers. Reputations are in the balance and the companies that are proactive, responsible and accountable are likely to be the ones that stay around longest.