The widespread use of the Internet, especially in the Western countries but also increasingly in many other parts of the world, has resulted in what has come to be known by various phrases: “sharing economy", “gig economy", “collaborative economy", “on-demand economy", etc. Broadly, this consists of intermediaries that leverage the Internet and act as a platform connecting buyers and sellers of various goods and services.

The range of offerings (and some of the platforms that facilitate these) include taxi services (Uber, Ola), short-term accommodation (Airbnb, Oyo), labour services ranging from plumbing (Handy) to programming (Upwork), food delivery (Grubhub), peer-to-peer lending (Prosper) and many more. Some of these platforms are among the most highly valued companies in the world, and in a few cases, have grown to match the biggest traditional players in the markets they serve. Airbnb, for instance, is now reported to have a higher valuation and room listings than some of the world’s largest hotel chains.

The success of the sharing economy lies in facilitating market exchange between complete strangers by connecting them through the Internet. While crucial, this feature in itself is not what distinguishes it from others, since this practice has long been followed by online retailers such as Amazon and eBay. What then describes this part of the economy?

While there is no universally acceptable definition, the one recently proposed by the US commerce department—which considers “sharing/collaborative economy" to be a misnomer and prefers to call companies in the sector “digital matching firms"—captures several aspects peculiar to this setting: first, firms in this economy use information technology-enabled platforms, such as mobile apps on Internet-enabled devices, to facilitate peer-to-peer transactions; second, these platforms rely on user-based rating for quality control; third, they offer workers providing services via the platform flexibility in deciding their work hours; and fourth, the firms rely on workers to use their own tools to provide a service.

How large is this economy? Since many of these platforms are privately owned, there is no publicly available data on their revenues and the overall size of the sharing economy. However, there are some estimates based on surveys conducted by private organizations.

For instance, a PricewaterhouseCoopers study estimates that the service providers in the “sharing economy" in the US comprise about 7% of the total US population and are made up of various income and age groups. Further, the study estimates that the global revenues of five key sectors in the sharing economy—travel, car sharing, finance, staffing and music and video streaming—will grow from about $15 billion in 2014 to $335 billion by 2025. This indicates that while its current size is not very large, the sector is projected to grow rapidly in the near future.

The basis for such healthy projections is the increasing popularity of the sharing economy, in turn derived from the various benefits it confers on its users: lower prices for consumers than those offered by traditional players; flexible employment and additional sources of income for the workers; opportunities to exchange excess capacity for a fee on the market; and so on.

As a result, many services that hitherto could not be commoditized, such as renting out an unused part of your house to a guest for an overnight stay, are now in the realm of the market.

At the heart of the success of this model is the trust that buyers and sellers place in each other: buyers should trust that sellers will deliver on the quality of goods and services they promised and sellers should trust that buyers will pay.

In a recent working paper, Michael Luca at the Harvard Business School reviews evidence on the various strategies that digital platforms have adopted to facilitate trust among buyers and sellers and highlights the challenges and trade-offs they face.

As the commerce department definition suggests, user-based reviews or ratings have been a crucial feature of online platform markets. In the absence of any prior information about the individuals that one is going to deal with, reviews act as an online reputation system such that those with favourable reviews can be trusted to fulfil their obligations to the transaction.

If not designed carefully, however, such systems may fail to fulfil their intended purpose.

For instance, on platforms with reciprocal reviewing (i.e., where buyers and sellers both review each other, such as in Airbnb), it was found that users can have strategic incentives to manipulate reviews: if sellers can see buyers’ reviews before they give their own, sellers can retaliate by reciprocating an adverse feedback. This can bias both buyers and sellers towards providing favourable reviews, distorting the accurate picture.

This problem can be reduced by specifying that reviews of both the sides to the transaction be revealed simultaneously. However, buyers might still fear providing negative feedback as this might discourage other sellers from transacting with them.

Luca suggests that a potential solution to this problem could be to allow users to leave anonymous reviews or make users share the information with the platforms, but not publicly. The platforms could then act upon this feedback by appropriately modifying its sorting algorithm or by providing only aggregate feedback to potential users.

The other problem with reviews could be that they suffer from selection bias, where only those who have extreme experiences (good or bad) leave reviews. This can be addressed by sending repeated email reminders to users about providing reviews and by paying those who leave reviews.

Sometimes, reviews can be distorted either by promotional content where businesses leave favourable reviews for themselves or when their competitors leave negative feedback. Such “manufacturing of reviews" reduces the trust people place on the review system, research suggests.

To overcome such issues, verification of the transaction could be put as a precondition for placing a review. Broadly, Luca suggests that platforms could authenticate information about sellers and buyers by engaging in screening mechanisms such as conducting background checks and provide insurance in case something goes wrong.

While providing more information to buyers and sellers to reduce anonymity is desirable, it might sometimes lead to unintended consequences such as discrimination. For instance, a study on Airbnb found that non-black hosts were able to charge approximately 12% more than African-American hosts after keeping location, rental characteristics and quality constant.

Similarly, it was found that employers of Indian origin are substantially more likely to hire workers from India on Upwork (previously known as oDesk).

Critics of the sharing economy also point out that the temporary nature of transactions undermines the need to build long-term relationships between buyers and sellers.

In an interesting exchange with economist and writer Diane Coyle, the former lead economist of the World Bank, Branko Milanovic lamented that “fast turnover of labour breaks the relations of confidence and trust that are often established between people (buyers and sellers)" and that “it does not pay to be nice (high fixed cost) if you keep on having only one-off relationships".

Whether one agrees entirely with Milanovic or not, it is well recognized that perhaps the greatest impact the sharing economy has had is in disrupting the traditional nature of work.

The benefit of accessing work through a platform implies that workers need not be “attached" to any employer. Similarly, platforms just need to engage the worker with a customer for performing a task or a “gig" (such as driving people around, delivering a meal, etc.) while avoiding any other obligations that a traditional employer might have towards her workers, including paying wages when there is no work and contributing towards their social security.

This has led to criticism that the gig economy weakens workers’ bargaining power considerably. Recognizing this, policymakers in the US are beginning to think about how to provide social security to these “independent workers", a new category defined by Seth Harris of Cornell University and Alan Krueger of Princeton University encompassing those who work in the “gig economy" as well as also those who work through offline intermediaries (such as traditional taxi drivers and direct sales workers).

How the intermediaries and workers will react to the proposal is to be seen.

What does all of this mean for those of us in developing countries such as India? A few of the platforms (such as taxi aggregators) that originated in the West have penetrated the Indian markets and have also found their Indian-origin counterparts. But in some other respects, our familiarity with the gig economy predates any of these technological innovations.

Take the case of casual day-labour markets. The sight of labourers assembling at street corners every morning in search of work is all too familiar. These street corners act as a platform connecting workers with employers who hire them for construction or other casual work, sometimes for durations as short as a few hours.

Further, given the size of these markets and the fact that work is “on demand", it is highly likely that a worker and his employer for the day may not meet again.

Our study of such markets in Navi Mumbai illustrates Milanovic’s concerns about the gig economy: in the absence of appropriate reputation mechanisms to discipline opportunistic behaviour, we found that employers do not just stop being nice to the workers but also go to the extent of reneging on their wage payments.

Our findings suggest that strengthening reputation mechanism, through a review/rating system perhaps, could improve outcomes for the workers. Given the experience we now have about reputation systems in the context of online platforms, designing the same for a casual labour market should not be difficult.

While apps such as UrbanClap provide platforms for lifestyle services, what we lack is a mass-scale Uber prototype for casual labour. Any start-up enthusiasts?

Karthikeya Naraparaju is an assistant professor of economics at the Indian Institute of Management Indore.

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