Commercialization on “Sharing Platforms”: The Case of Airbnb Hosting

Abstract

An underdeveloped theme in scholars’ understanding of the personal services sector of the platform economy—also known as the “sharing economy”—is change. Most research on ride-hail, food delivery, accommodations, and other personal services has offered largely essentialist accounts. In this paper, we focus on how platforms have become increasingly “commercialized.” In labor-intensive services, commercialization occurs as a growing fraction of the work is done by a core of full-time, dedicated workers. However, platforms that rely primarily on capital may display similar dynamics, in which a small number of participants account for the majority of activity and capture the largest share of value. In this paper, we present the first comprehensive account of commercialization of a major platform. We analyze how Airbnb markets in the 10 largest short-term rental markets in the United States changed between 2015 and 2019. We find considerable evidence of commercialization, as a rising majority of properties are rented on a very frequent basis, and casual listings, while still present, are a small and falling percentage. Relying on an original database of regulations, we show that enactment of even the strictest regulations has not durably reduced the number of listings and has had limited success in altering the mix of commercialized and casual listings over this period. We also consider the impact of COVID-19 on this platform and the sharing sector. We conclude that the short-term rental market on Airbnb has become a fairly conventional one, with little of the peer-to-peer character of its earlier days.

Keywords

Airbnb commercialization platform economy COVID-19

Introduction

An underdeveloped theme in scholars’ understanding of the personal services sector of the platform economy—also known as the “sharing economy”—is change. Most research on ride-hail, food delivery, accommodations, and other personal services has offered largely essentialist accounts of how to understand these platforms. Economists emphasize the efficiencies afforded by the digital technologies (Horton & Zeckhauser, 2016; Sundararajan, 2016). Proponents point to their peer-to-peer nature, and the opportunities afforded to build trust and social connection (Botsman & Rogers, 2011; Gansky, 2010). Critical sociologists focus on the exploitation of workers and legal scholars identify misclassification of employees as independent contractors (Dubal, 2017; Rogers, 2016). Technology researchers worry about algorithmic bias and control (Pasquale, 2015; Rosenblat & Stark, 2016). Some are focused on conceptualizing the platforms as organizational forms, and whether they represent a new stage in capitalist development—Uberization (Davis, 2016), platform capitalism (Srnicek, 2016), or a platform economy in which these corporations have unprecedented power (Kenney & Zysman, 2016). All of these characterizations identify important insights into the nature of sharing platforms. However, over the first decade of their existence, companies such as Uber, Lyft, Postmates, and Airbnb have changed in important ways. Documenting and analyzing that change is essential to a complete understanding of platform activity and work. Does this sector represent something novel, as many have claimed (Sundararajan, 2016)? Is it old wine in new bottles—in this case, piece-work dressed up with a fancy technology (Dubal, 2020b)? Is it following a trajectory of convergence with conventional firms and work patterns? In this paper, we focus on changes in the platform sector, in particular, a process of transformation in which earners and the market itself become less part-time, casual, and multiply motivated and more full time, and growth and profit oriented.

In the early days of the sector, most platforms embraced an “idealist discourse” which emphasized collaboration, person-to-person relations, and “sharing” (Schor, Attwood-Charles, Cansoy, Carfagna, et al., 2020; Schor & Vallas, 2021; Slee, 2015). They positioned themselves as different from conventional firms who were only interested in profitability, and asserted a hybrid identity and their ability to contribute to the common good (Acquier et al., 2020; Martin, 2016). This discourse was rooted in the idea that for participants in the “sharing economy,” motives were pluralistic, and earners were largely casual participants (Hall & Krueger, 2018; Ravenelle, 2019). For a ride-hail driver, Uber was a “side-hustle.” For an Airbnb host, renting out a spare room was often a cultural or a social act, or perhaps to help combat income stagnation. In either case, the assumption was that hosts used their Airbnb earnings to supplement other sources, help pay a mortgage, or save for luxury spending. Our early research on participants revealed that many saw the sharing economy as an alternative to an impersonal market made up of powerful and greedy global corporations. Our informants felt that the sharing sector was creating a human-scaled, kind economy, made up of individuals helping and serving other individuals (Fitzmaurice et al., 2020). For these participants sharing drew on a “domestic imaginary” in which the market replicated the close and caring relations of the family.

However, there was always another side to the sharing economy. While many participants saw their actions as an alternative to corporate-dominated markets, some scholars considered the idealist discourse a fig leaf and pointed to the capitalist nature of platforms. Early on, Kenney and Zysman (2016) argued that platforms were in the process of amassing enormous power, comparing them to factory owners in the Industrial Revolution. Their more recent work has emphasized the growing size, reach, and market dominance of platforms (Kenney et al., 2021). Scholz (2016); Srnicek (2016), and others argued they are rapacious and exploitative firms building a new, technologically enabled stage of capitalism, in which platform power is becoming ever more expansive.

One approach has been to emphasize the polysemic nature of the sector (Acquier et al., 2020; Fraanje & Spaargaren, 2019; Frenken et al., 2020; Schor & Vallas, 2021), arguing that it is a mix of motives, practices, and discourses. The platforms are motivated by multiple considerations, as the idealist discourse suggests. Earners want to make money but also care about others, feel like they are contributing to the common good, and act in complex ways (Cansoy, Eddy, et al., 2021). Consumers also want to contribute to an alternative market (Schor, Attwood-Charles, Cansoy, Carfagna, et al., 2020). Associated with this position, albeit rarely stated explicitly, is that this is a relatively stable configuration we can expect to continue. The alternative is to see the early days as a temporary phase on the way to growth- and profit-oriented platform capitalism.

The topic which has gotten the most attention in this area is the treatment of labor, in particular by ride-hail and food-delivery platforms. Findings of researchers in the early days of the sector (Hall & Krueger, 2018) are being overturned by newer empirical work showing that labor conditions are deteriorating (Dubal, 2020a; Griesbach et al., 2019; Schor, Attwood-Charles, Cansoy, Carfagna, et al., 2020). A key dimension of this narrative is the shift from a part-time, casual labor supply to long-hour, full-time workers who are dependent on the apps to support themselves and families (Benner, 2020; Parrott & Reich, 2020). A shift to a majority of work being done by a dedicated labor force is taken as evidence of the convergence of platforms to more conventional firms. This shift is at the heart of the struggle now taking place over the classification of gig workers as independent contractors or employees.

The shift to employment-like conditions is one example of a transformation that is occurring across a number of platforms. We term this trend “commercialization.” While there has been considerable attention to the growing prevalence of full-time workers in sectors such as ride-hail and food delivery, there is another major sector of the platform economy in which commercialization is also a concern—accommodations.

In the labor-intensive services, commercialization occurs as a growing fraction of the work is being done by a core of full-time, dedicated workers. In ride-hail, researchers have found two trends that suggest commercialization—more workers who are full time, and a growing fraction of trips by those workers. In accommodations, the added element is that the service relies more heavily on capital—the lodging itself. Therefore, commercialization involves both the situation of the primary earners, in this case the hosts, and others who aid them (e.g., cleaners, managers), and the property. Does the host spend an increasing amount of time arranging and providing lodging to guests? Does the host spend an increasing amount of money hiring others to do this work? Is the property available and rented for more days a year? These are the hallmarks of commercialization.

The growth of Airbnb and similar apps (e.g., VRBO, booking.com), particularly in larger cities, has given rise to considerable conflict. Housing advocates have argued that hosts are operating illegal hotels, taking rental units off the market, and contributing to increases in rents (Cox & Slee, 2016; Wachsmuth & Weisler, 2018; Wachsmuth et al., 2018). Residents complain about the transformation of their neighborhoods as listings rise and tourists and other guests proliferate. Social connections and quality of life are eroded. The influx of Airbnb properties also contributes to gentrification, and racialized gentrification in particular (Cox, 2017). As a result, after initially adjusting regulations to permit these short-term rentals, many cities across the country began to try and reduce Airbnb activity by passing regulations. An early effort occurred in 2015 in San Francisco, when the city legalized short-term rentals, but with conditions designed to restrict commercialization. These efforts picked up momentum in 2018 and mainly consisted of attempts to eliminate commercial actors, that is, hosts with multiple units which were available for rental on a consistent basis. In New York, a 2018 law required bank account information from hosts to verify that they were not evading the regulations. Casual hosts and those who rented out spare rooms in units they lived in were generally not subject to prohibitions. These regulatory efforts suggest the need to study commercialization in this sector, as they are designed to stem or reverse it.

In this paper, we look at these issues in detail, and present the first comprehensive account of commercialization of a major platform. We expand the focus to multiple cities, using a database of Airbnb listings in 10 major US markets. We analyze changes over time in the extent of commercialization and assess the impact of regulation. To do this, we develop a measure of commercialization which relies on the availability and bookings of rental units. We track it from the beginning of 2015 to the end of 2019. Then, relying on a new database of regulations that we have compiled, we look at whether regulations have affected the process of commercialization. To summarize our findings briefly, we find considerable evidence of commercialization, as a rising majority of properties are rented on a very frequent basis, and casual use, while still present, is a small and falling percentage of total listings. We also find that in general, the enactment of even the strictest regulations has not durably reduced the number of listings, and has had limited success in altering the mix of commercialized and casual listings over this period. We conclude that this market has become a fairly conventional one, with little of the alternative, hybrid character of its earlier days. We end the paper with a discussion of how the pandemic has affected the sharing sector.

Understanding Platforms and their Workers

The literature on platforms is often reliant on static formulations and has not been organized around their transformation. However, there is a body of work that is relevant to the question of change, and specifically commercialization. It focuses on the hybrid nature of platforms, arguing that they are polysemic, contested, and to a certain extent incoherent. By emphasizing the contested and contradictory nature of platforms, this view identifies contradiction as an engine for change and helps to explain the transformation from hybrid origins to more conventional commercialized entities. A second group of studies looks at one commercialized feature in particular—the shift to a full-time gig labor workforce. We address these literatures in turn.

The characterization of the sharing, or platform economy as a polysemic entity is common to formulations across numerous disciplines, including sociology, organization and innovation studies, and geography. To a large extent, the contested nature of the sector comes from its utopian origins. In the early days, almost all platforms (with the notable exclusion of Uber) presented themselves as alternatives to conventional capitalist firms, and claimed a mantle of democracy, sustainability, access, and community (Cockayne, 2016; Martin, 2016; Ravenelle, 2019; Slee, 2015). They emphasized their dual nature as contributors to the common good (Schor, 2014) while they were also commercial entities. In this way, they were examples of a larger business discourse which affirmed the possibility of “doing well while doing good”—the idea that making money and improving social welfare are consistent. This “idealist discourse” (Schor, Attwood-Charles, Cansoy, Carfagna, et al., 2020) was appealing to users on both sides of the market. It allowed earners to feel that they were contributing to a trend which would undermine impersonal, excessively powerful global corporations and re-make markets (Fitzmaurice et al., 2020). Consumers believed their patronage of person-to-person platforms was helping worthy individuals, rather than greedy companies. There was a feel-good rhetoric in which everyone was gaining, people were being treated well, and social change was in the offing. Laamanen et al. described it as a “paradoxical combination of community optimism and commercial extractivism” (Laamanen, Wahlen, & Lorek, 2018, p. 1220).

However, there has always been a certain inherent incoherence and contradiction to this discourse. Frenken et al. (2020) argue that the incoherence is a function of multiple misaligned institutional logics of market, state, and corporations. Conventional profit-making is juxtaposed with helping behaviors, and a focus on community and sustainability. Fraanje and Spaargaren (2019) see platform actions as a “set of social practices that move along the edge of ‘market’ and ‘civil society’ by merging understandings, teleo-affective structures and rules from both spheres.” Organization scholars (Martin, 2016) and Acquier et al. (2020) have argued that this field incoherence has been a persistent feature of the field (see also Kirchner & Schüßler, 2020). This ambivalence is also present at the level of the individual platform (de Peuter et al., 2017, p. 689). It has been found in interviews with platform employees as well. Cockayne’s (2016) early study found that they were aware of the contradictions in their employers’ discourse, and attempted to downplay the ways in which they were not living up to the rhetoric. However, Cockayne argues that the discourse should be seen as legitimating and justificatory, rather than authentic. Contributions which chart potential pathways for the sector also typically identify a commercialized path in which the platforms shed their polysemy and become more like conventional firms (Frenken, 2017; Pasquale, 2016).

Over time, the mix between aspirational and economically oriented actions and discourse may have shifted. While we lack direct measures, some research suggests this trajectory. Martin et al. (2015) using discourse analysis, see a cooptation process at work in which even non-profit platforms experience coercive and isomorphic pressures to become more commercially oriented. Murillo et al. (2017) also argue that pressures to commercialize dominate. Slee (2015) discusses the shift from the early more idealistic days, to a more predatory set of practices. Schor, Attwood-Charles, Cansoy, Carfagna, et al. (2020), studying a variety of platforms over a period of 8 years, describe a trajectory in which earners are treated less well. Acquier et al.’s (2020) discourse- and interview-based analyses suggest that more commercially oriented players may leave the “sharing economy” sector as they converge to more conventional entities.

Studies of individual platforms also contain a commercialization narrative. Fraanje and Spaargaren (2019) describe the path of Peerby, a gifting/loaning platform that commercialized by adding a for-profit rental site. Schor, Attwood-Charles, Cansoy, Carfagna, et al. (2020) describe disillusionment by TaskRabbit earners as the platform became more market-oriented and less friendly to them. Cansoy, Eddy, et al., 2021 describe how three platforms (Airbnb, TaskRabbit, and Stocksy United) have altered policies to encourage higher levels of activity and more profit-seeking behavior from earners.

Other studies, while not explicitly studying changes over time, offer accounts of largely commercialized sites. Fraanje and Spaargaren (2019) also studied a car-sharing site and found that although it is a not-for-profit site, its success is due to adopting practices that prioritized standard market values such as efficiency and professionalism. Arcidiacono and Podda’s (2017) research on a for-profit time bank found that values such as reciprocity and community, which are at the core of the time-bank concept, but were largely absent on this platform. Even without monetary transactions, this time bank replicated normative market relations. However, there are also scattered accounts of platforms which move in a different direction. Setiffi and Lazzer’s (2018) study of BlaBla-Car suggests that over time the company was able to create trust and an idiosyncratic culture that emphasized the formation of new relationships. Thus, while it retained some commercial aspects, it has not been “commercializing.”

There is another strand of literature on labor conditions which is relevant to the question of commercialization, focused largely on ride-hail and food delivery. From the beginning, platforms have emphasized the part-time and casual nature of their employees’ work patterns (Ravenelle, 2019). They assert that gig workers consider their platform efforts as a “side-hustle,” that is, a way to make extra income. This formulation aligns with their claims that this arrangement gives the earner freedom and autonomy, and supports the platforms’ opposition to classifying their workers as employees. Early studies did reveal that a large majority of earners are part-time (Hall & Krueger, 2018; Smith, 2016) lending superficial plausibility to the companies’ discourse. However, there is a growing body of literature questioning the companies’ narrative, which shows that the majority of work is done by full-time workers, and that many of the casual workers do very little work (Benner, 2020; Parrott & Reich, 2018, 2020; UCLA Institute for Research on Labor and Employment, 2018; Wells et al., 2019). For example in New York and San Francisco, after an early phase in which casual earners were the largest group, full-timers have come to predominate (Benner, 2020; Parrott & Reich, 2018). Even in cities where part-time workers remain the majority, the proportion of rides that they give is far less than what full-time workers do. A Seattle study found that drivers who worked fewer than 20 hours a week provided only 19% of trips (Parrott & Reich, 2020).

For full-time, “commercialized” workers, platform work is not a “gig,” but an all-consuming job with long hours which entails substantial dependence on one (or two) platforms for livelihood (Schor, Attwood-Charles, Cansoy, Ladegaard, et al., 2020). Furthermore, the proportion of workers who are dependent appears to have grown over time, given recent studies of ride-hail and food delivery. This trajectory represents a kind of commercialization. We turn now to accommodations, and the case of Airbnb specifically, to see whether a similar transformation has taken place.

A number of earlier studies have addressed whether Airbnb is a largely commercialized market. In 2014, Tom Slee published analyses on his blog, looking at the prevalence of commercialized hosts, in New York, and later a global study of 18 cities (Slee, 2014). O’Neill and Ouyang (2016) looked at trends in multi-unit hosting and revenue from 2014 to 2016 and found that a growing number of units were offered by hosts with multiple listings, and that “mega-operators” (3+ units) were growing most rapidly. Dogru et al. (2020) did similar analyses for the period 2017–2018 and found higher levels of multi-unit listings, and concentration of these professionalized hosts in 12 states. Analyses of individual cities’ markets have also highlighted the concentration of bookings among a small number of hosts and the prevalence of illegal, often “ghost hotels” in major markets (Cox & Slee, 2016; Ferré-Sadurní, 2019; Wachsmuth et al., 2018). Our paper builds on these previous analyses in a number of ways. It includes the 10 largest U.S. markets and covers a longer period of time, thus allowing a more comprehensive view of trends. Second, we focus on listings, rather than hosts, and categorize them based on frequency of bookings, following Wachsmuth and Weisler (2018). Finally, in contrast to previous studies, we consider how the advent of regulation has affected the commercialization trend. We turn now to our analysis.

The Case and the Data

Airbnb occupies a central position in the sector, both due to the amount of economic activity it has been able to attract and retain on its platform and its financial and organizational success. Yet, debates about platforms, and especially how they have changed over time, have generally not focused on Airbnb, potentially because it is seen primarily as a capital, rather than a labor platform. However, all platforms in the personal services sector offer a mix of labor and capital. Furthermore, the labor that goes into being a successful Airbnb host is not negligible. In addition, we believe that Airbnb is critical to understanding how the sector has changed over time and where the other platforms might be headed given similar economic and institutional pressures.

In this paper, we use data about all active listings on the Airbnb platform in Airbnb’s 10 largest urban U.S. markets from January 1, 2015 to December 31, 2019. The urban markets are defined using the metropolitan statistical areas designated by the Office of Management and Budget. The markets and the total number of listings that were active in them any time during our study period are listed in Table 1.

Table 1.

Airbnb Markets and Active Listings.

Metropolitan statistical area	Active listings (2015–2019)
New York–Newark–Jersey City, NY-NJ-PA Metro Area	254,159
Los Angeles–Long Beach–Anaheim, CA Metro Area	137,103
Chicago–Naperville–Elgin, IL-IN-WI Metro Area	43,255
Washington–Arlington–Alexandria, DC-VA-MD-WV Metro Area	40,048
Miami–Fort Lauderdale–West Palm Beach, FL Metro Area	69,916
San Francisco–Oakland–Hayward, CA Metro Area	66,734
San Diego–Carlsbad, CA Metro Area	43,827
Austin–Round Rock, TX Metro Area	40,449
Seattle–Tacoma–Bellevue, WA Metro Area	26,046
Boston–Cambridge–Newton, MA-NH Metro Area	20,279

The data were obtained from a commercial entity (AirDNA) which does daily web scraping of listings to create its database. There are legitimate concerns about the validity of scraped data, especially in the sharing economy due to the restrictive practices of the platforms (Gelman, 2016). This is the case with our data as well, since we have no way of checking whether some Airbnb listings are excluded from our dataset. However, a comparison of our data to other publicly available data on Airbnb listings suggests that we have relatively comprehensive coverage. Similarly, scraped data from the platform have been used before in studies of discrimination on the platform (Edelman & Luca, 2014; Edelman et al., 2017) and the dynamics of bookings (Lee, 2016; O’Neill & Ouyang, 2016; Wachsmuth & Weisler, 2018).

One important advantage of having access to daily data on Airbnb listings is the ability to study whether they are booked and to calculate the revenue they generate. The variable that measures whether a listing is booked, or simply unavailable on Airbnb is produced with a proprietary prediction model developed by AirDNA (2017), trained on data from a period during which the dates that were booked by a guest were publicly available on the Airbnb platform. Currently, the platform only indicates when a certain date can or cannot be booked by a prospective guest. The prediction model takes this information and parses out which dates cannot be booked because the host has declined to make them available and which dates cannot be booked because someone else has already booked them.

Findings

Commercialization

Over the period covered by our analysis, Airbnb has experienced very rapid growth, both in terms of the number of listings available on the platform and the revenue they have generated. Figure 1 shows the monthly aggregate numbers for both categories across the 10 markets we are studying. There was dramatic growth in the number of listings for roughly the first half of the study period (until mid-2017), but listings plateaued in the second half. However, this stagnation in the total number of listings was coupled with continued growth in revenue, albeit with significant seasonal swings and at a somewhat slower rate toward the end of the study period.

Figure 1.

Active listings and estimated revenue.

To better understand how these patterns defined the platform, we have broken listings into four categories based on the level of market activity from a full year of participating on Airbnb. Units that were listed for fewer than 30 days or booked for fewer than 12 days were classified as Very Casual; those that were listed between 30 and 119 days or booked between 30 and 59 days were classified as Casual; those listed between 120 and 239 days and booked between 60 and 119 days were classified as Frequently Rented; and those listed 240 days or more and booked 120 days or more were classified as Very Frequently Rented. The last two categories were originally developed by Wachsmuth and Weisler (2018) in their study of rental patterns in NYC. Figure 2 shows how the numbers of listings in these categories changed over time across all 10 cities. The data start in December 2015, because that is the month for which we have a full year of data with which to classify rentals into the four categories. As the figure makes clear, the stagnation in the overall numbers of active listings has been primarily driven by trends in Casual and Frequently Rented properties, and in the final year, reduced growth in Very Casual ones. The number of Very Frequently Rented properties grew consistently over the whole period.

Figure 2.

Active listings by annual market roles.

If anything, Figure 2 understates the dominant role of Very Frequently Rented properties on the Airbnb platform. When we look at the revenue generated by categories of listings (Figure 3), we see that Very Frequently Rented properties account for the largest amount of economic activity on the platform, by a wide margin. This is partly due to how the categories are constructed; however, changes in the relative revenue of the four categories show the increased commercialization of Airbnb hosting. The average Airbnb listing, over the course of the 5 years of our study, has become more frequently available—the median number of days a listing was available to be booked went from 92 in 2015 to 188 in 2019. In addition, the most frequently booked and available listings have increasingly come to dominate revenue generation on the platform. They generated 62.5% of all the revenue in 2016, and 69.4% of all revenue in 2019.

Figure 3.

Estimated revenue earned by annual market roles.

Impacts of Regulation

The undesirable impacts of commercialized Airbnb listings such as rising rents, housing scarcity, and declining neighborhood quality have led cities to attempt to restrict commercialization of listings. To date, there is little analysis of whether these regulations have been effective. (A notable exception is Cox (2021) which we discuss below.) To assess the impact of regulatory activities on Airbnb, we have focused on three cities: Austin, San Francisco, and Seattle. We chose them because they have had very different approaches to regulation. Table 2 shows the number of active short-term rentals in these cities during our study period.

Table 2.

Active Airbnb Listings in Three Cities.

City	Active listings (2015–2019)
Austin, TX	40,644
San Francisco, CA	42,273
Seattle, WA	26,236

To assess the effectiveness of regulation, we have compiled a database of regulations and proposed regulations in the 10 metropolitan areas. We include regulations at the state, county, and municipal levels. We restricted our efforts to the urban core of these areas by including only the municipal units that were closer to the city center of the principal city than the median distance between the city center and short-term rentals during our study period. We gathered information on regulatory attempts in 122 governmental bodies based on these criteria. We began by searching news sources using LexisNexis and digital archives of the governmental bodies using a large range of keywords to identify any attempts at regulating short-term rentals or at overturning pre-existing regulations. We then conducted more targeted searches about these attempts, collecting texts of proposals, laws, and ordinances. We then coded them for the types of restrictions (or lack thereof) placed on short-term rentals. Because space restrictions prohibit in-depth discussion of all 10 cities, we have chosen to highlight three—Austin, San Francisco, and Seattle—which offer a range of regulations. The information we discuss is based on the database of regulations which we have compiled and our data on listings. In all three cases, we are interested in whether regulation has been effective in curbing or reversing commercialization.

Austin

Austin has a large short-term rental market relative to its size, marked by significant seasonal expansion of available properties during the annual popular culture festival SXSW. It also has a long history of contentious regulation attempts of the short-term rental industry. In 2012, the city council first passed an ordinance requiring short-term rental properties to pay a 9% hotel occupancy tax and register with the city and pay a relatively high registration fee. This ordinance also limited the number of entire-unit rentals (rentals without the host present in the same unit) to only 3% of dwellings in any census tract in the city. However, the ordinance failed to meet the demands of people arguing for more restrictions, as the city’s ability to enforce registrations and collect taxes was limited. It was amended in 2013 to address those concerns and again in 2016. The 2016 restrictions put in place stronger enforcement procedures, restrictions on noise, and occupancy and density limits. It also set a goal of eliminating entire-unit rentals of single-family housing by 2022. However, this regulatory action was met with significant resistance both from state-level politicians and city residents.

At least six bills restricting the ability of municipal governments to regulate short-term rentals or outright pre-empting municipal regulations have been introduced in the Texas legislature since 2017. This tactic of pre-emption of municipal regulations has been successfully used by platform companies across the United States, particularly in ride-hail (Borkholder et al., 2018). In contrast to the ride-hail case (Racabi, 2018), the Texas legislature failed to enact any of these pre-emption bills for accommodations. However, resistance to regulations from residents and state courts has proved to be effective in undermining regulation. A group of hosts initially filed a lawsuit against the 2016 ordinance in 2017, and in 2019 an appeals court overturned critical parts of the regulation. The clauses that would have banned entire-unit rentals in single-family housing beyond 2022, and occupancy restrictions were thrown out. While the city has been considering appealing this ruling to the Texas Supreme Court, its regulatory attempts have effectively stalled.

The impacts of this contested and currently neutralized regulatory drive against the commercialization of short-term rentals in the city are clear from our data. Figure 4 shows the share of active listings and estimated revenue earned by different listing categories. Over the study period, the share of active listings that were Very Frequently Rented has increased consistently, while those of the other three categories have declined or stayed relatively flat. More importantly, the share of revenue going to the VFRs has increased dramatically, from slightly less than 43% in December 2015 to more than 67% in December 2019. These trends show that in the absence of sustained regulatory action, the commercialization of the Airbnb platform has been rapid and extensive.

Figure 4.

Share of active listings and estimated revenue by annual market role, Austin.

San Francisco

As the birthplace of Airbnb, San Francisco has witnessed both the highs and lows of the short-term rental practices enabled by the platform. The city has a large number of short-term rentals, which have become very controversial as the affordable housing crisis in the city has become increasingly dire. Before 2015, short-term rentals were technically illegal in the city due to older regulations banning rentals of fewer than 30 days. However, these regulations were rarely enforced and listings grew rapidly. In late 2014, the San Francisco Board of Supervisors approved a new ordinance that legalized the practice of short-term renting, but also put in place regulations limiting rentals without the host present to 90 days a year. The ordinance also introduced registration and density requirements and charged hotel taxes. At the same time, there was an active movement for a ballot measure, Proposition F, that would constrain short-term rentals even further by restricting them only to buildings that were zoned commercial and reducing the maximum number of days a unit could be rented without the host present to 75.

Proposition F failed in November 2015, following extensive lobbying and campaigning by Airbnb. Nevertheless, the city’s regulations did become stricter. In 2016, the city reaffirmed the necessity for registrations and required Airbnb to display registration numbers on all listings it advertises. Airbnb sued over this requirement, but settled in 2017. In the settlement, the company agreed to display registration numbers and take listings off the platform if they were not registered with the city or if the city determined them to be violating the ordinance.

Figure 5 shows the evolution of the market shares of different categories of listings in terms of active listings and estimated revenue. Compared to Austin, the early regulatory attempts of San Francisco have been relatively successful in preventing further commercialization. The relative shares of all four categories of listings were essentially the same throughout our study period. However, this equilibrium describes an already heavily commercialized market, with Very Frequently Rented listings making up around 40% of all active listings in any given month. They also earned about 60% of all estimated revenue on the platform, levels seen in Austin only at the end of our study period.

Figure 5.

Share of active listings and estimated revenue by annual market role, San Francisco.

Seattle

Compared to Austin and San Francisco, Seattle was a latecomer to enacting short-term rental regulations, perhaps because Airbnb had a relatively small presence in the city in the early years. As late as January of 2015, there were only 2,218 active short-term rental listings within city limits. However, over the period we studied these numbers grew, reaching 6,108 active listings within the city limits in December of 2019. As in Seattle and San Francisco, the rapid growth of listings led the city to take action. In December 2017, the city passed a new ordinance requiring hosts to register with the city, and to pay fees and taxes. Most importantly, the city also required short-term rental platforms to obtain licenses to operate, demanding that the companies enforce registration rules, collect taxes, and most importantly share data on their activities with the city. These new regulations went into effect on January 1, 2019. A few months later, the state of Washington also took regulatory action, mandating consumer safety rules, insurance coverage, tax collection, and registration with state authorities.

The regulatory regime in Seattle thus became one of the most comprehensive included in our study. However, as Figure 6 shows, this did not have a considerable impact on the commercialization of short-term rentals in the city. (Its effects on overall growth of short-term rentals were relatively small as well.) Very Frequently Rented properties made up 40% of active listings in December of 2015, and increased to 53% by December of 2019. Their share of the revenue also grew by more than 7%, from 60% to 67%; however, this figure has actually come down from a high of 81% in March 2018. Thus, there is some evidence that the second, more restrictive regulation has had some limited success in reversing commercialization of short-term rentals.

Figure 6.

Share of active listings and estimated revenue by annual market role, Seattle.

The Impact of the Pandemic

Our analysis ends in 2019, before the onset of COVID-19. The pandemic has prompted numerous discussions and speculations about how the virus will affect sharing services such as ride-hail, food delivery, and short-term rentals. The fact that so many of these services are face-to-face, intimate, and conducted among strangers suggests that they would be particularly vulnerable to fears of contagion. In addition, the largest platforms are in transport and travel, two industries that were drastically curtailed by government restrictions. So it was not surprising that with the onset of the lockdown in March 2020, demand disappeared almost immediately. By early May, the New York Times reported that the sector had been “gutted,” and that consumer demand has “all but vanished” (Conger & Griffith, 2020). Company executives from Uber and Lyft reported declines of 80% in the immediate aftermath of the lockdown. Airbnb, which had been planning to go public in 2020, instead saw its bookings disappear. It raised emergency cash and laid off a quarter of its staff (Griffith, 2020). Of course, sharing services which did not require consumers to interact with workers, such as shopping and food delivery, were not only not vulnerable, but their demand soared. Shoppers, such as those who worked on Instacart, became perhaps the most in-demand workers in the nation. By the third week in March, the company announced plans to hire an additional 300,000 workers (O’Brien, 2020). Prepared food delivery also grew rapidly during this time.

While there is still little scholarly literature on how the pandemic will affect the sharing economy, some researchers appear to believe that its long run impact will be negative. Hossain (2021), on the basis of a content analysis of press accounts, highlighted the anxiety felt by consumers and earners, as well as widespread sentiment from earners that the companies did not protect them or treat them fairly. As part of a National Science Foundation funded project on “The Algorithmic Workplace,” we have also been conducting interviews with gig laborers in shopping, delivery, and ride-hail, and we have some similar findings. Organized protests by workers in shopping, driving, and delivery also emerged during this period, with frequent strikes and calls for boycotting the apps on specific days (Covert, 2020). de Medeiros et al. (2021) surveyed consumers about their emotions in relation to sharing services and found that their subjects went from expressing more positive to negative emotions, and that fear, anxiety, and stress about sharing services have become more prominent. These feelings serve to inhibit demand. Another group of scholars (Brydges et al., 2021) looked at fashion apps such as Rent The Runway and concluded that the “seemingly overnight” disappearance of demand may turn into a longer trend as working from home has led to a shift from fashionable professional attire to lounge and athleticwear, and concerns about hygiene and cleaning processes may make users less inclined to wear shared apparel.

In their 2021 review of the “sharing economy,” Schor and Vallas (2021) speculated that the large platforms would be able to adjust due to their highly flexible structure. This appears to be happening. Uber pivoted quickly to emphasize food delivery, a service it already offered through UberEats, allowing drivers to make a seamless transition. They also began aggressive acquisitions in services that were experiencing increases in demand: attempting to purchase Grubhub (food delivery) in May 2020, and succeeding in buying Postmates (food delivery) in July 2020 and Drizly (alcohol delivery) in February 2021. (Uber continues to lose large sums of money, but this was the case long before the pandemic.) In addition, pessimism about platform companies going public turned out to be unwarranted. Airbnb and Doordash both had wildly successful IPOs in 2021. Airbnb’s shares were priced at $68 and ended their first day at $144, despite the collapse of the travel market (Sorkin et al., 2020). On the other hand, Schor and Vallas (2021) surmised that smaller, less profit-oriented, community-based sharing platforms might suffer on account of its high degree of face-to-face activity and the strong motive of personal connection. There is less reporting or analysis of these smaller platforms. However, a study of Olio, a for-profit, but free food donation app (Makov et al., 2023), has found that after a sharp initial decline in activity, the pre-pandemic trajectory of growth resumed. There is little evidence that the pandemic has dampened enthusiasm for either giving away or receiving free unwanted food. This is particularly notable given the strong symbolic function of food, and its historically particular association with the ability to contaminate.

There are also some contributions about Airbnb that bear on our findings in this paper. Chen et al. (2021) analyzed the impact of the pandemic on hosts’ earnings in Sydney and found a 90% decline. A similar perspective has been taken by Dolnicar and Zare (2020), who argue that COVID is “Disrupting the Disrupters,” referencing the companies’ own narratives about their positive “disruptive” impacts. Differentiating between “professional” or “capitalist” (what we have called commercial) hosts and non-professionals, they argued that the pandemic will reduce the prevalence of the former on the platform. Their reasoning is that the collapse in demand will lead capitalist hosts, who have mortgage expenses, to redirect their properties to longer-term rentals to ensure demand. Thus, they predict that the platform will return to the original “sharing” ethos, which was being eroded by commercialization, and they claim this will obviate the need for regulation. However, recent events have not borne out these predictions. It seems that the market for Airbnb and other rental platforms has rebounded. Hosts have been able to shift to offering longer-term stays, for which there is a robust market. Total listings recovered and exceed pre-pandemic levels (Lane, 2021). The Points Guy, a travel advice site, reports that Airbnb prices between 2019 and 2020 rose 7% while hotels rates are being deeply discounted (Fan, 2021).

In sum, it seems that reports of the death of the sharing economy have been premature. Although some suffered large initial declines in business, the large platforms have been able to adjust due to their flexible structure. Others have seen soaring demand which they have been able to take advantage of by offering customers safe, contactless delivery. While some of the market for shopping and delivery will undoubtedly disappear if COVID risks decline and life gets back to “normal,” it is likely that there will be some “sticky” impacts of the pandemic on these services (McKinsey Global Institute, 2021).

Limitations

The analysis presented above has a number of limitations imposed by the data. First, it is not possible to assess whether the scraped data have systematically failed to capture some subset of listings, or whether the scraping effort has been consistent across the 5-year period we are studying. However, compared to other publicly available datasets on Airbnb, our data appear to have more comprehensive coverage. Without access to data held by the platforms themselves, large-scale web scraped data provide the best alternative to study these key sites of economic activity.

A second limitation is that the number of days a short-term rental was booked by renters is predicted using a proprietary model and not directly observed. While direct observation of this variable would have been preferable, given the obfuscation of this variable on the Airbnb platform, the AirDNA predictive model offers us the best chance to study the platform dynamics beyond what listings were on it.

The final limitation is based on our decision to classify short-term rentals based on their participation patterns over 12 months. This is a useful metric because it allows us to consider long-term patterns rather than short-term fluctuations. It also means that our analysis excludes units that are active for fewer than 12 months. This is not an insignificant number of listings on a platform that has as much turnover as Airbnb does. However, when we repeated our analysis using monthly measures to categorize listings into different market roles, the aggregate trends we discuss in the paper were not meaningfully different.

Conclusion

Things happen quickly in the platform economy. How might trends in commercialization and the impact of regulations have changed in the period after our data ends? A recent report by Murray Cox of Inside Airbnb (2021) sheds some light on the issues raised in this paper. Cox is interested in how the markets in New York, Los Angeles, and Toronto have responded to recent regulations, including data sharing. Like us, he is also interested in commercial hosts. He finds three main trends. First, as registration and data sharing came into force, many inactive listings were purged from the platform. This is expected, especially because in some cases, such as New York, in order to remain on the platform hosts had to give affirmative agreement for data sharing. Second, many illegal rentals remain. For these, the question is how active authorities will be in delisting them. Finally, Cox also finds that across these cities many listings have been converted to long-term (i.e., greater than 30 days) rentals. This trend to longer-term stays may be partly due to COVID-19, as noted above, but is also likely a way to keep listings compliant with ordinances.

Our interpretation of Cox’s findings is that these regulations have failed to de-commercialize the platform, especially if rents for longer-term stays are above what long-term residents are paying. Therefore, we conclude that commercialization is a trend that is likely durable, as platform-based services shift from their early days as “sharing economies” into conventional markets. We are seeing this trend in low-wage services such as ride-hail and food delivery. It is also present in the winner-take-all markets of resale or craft platforms such as Etsy or ebay. And as we have argued in this paper, commercialization is central to the dynamics of Airbnb.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Fairfield University College of Arts and Sciences and a Research Incentive Grant Boston College.

ORCID iD

Juliet B. Schor

Author Biographies

Mehmet Cansoy Dr. Cansoy started teaching at Fairfield University in 2018, after receiving his PhD from Boston College. He was hired as an Assistant Professor at Fairfield in 2019. His research primarily focuses on technological change and inequality, with recent work appearing in Theory and Society, Socio-Economic Review, and Sociologica. He has served as the co-director of the College of Arts and Sciences Fredrickson Family Innovation Lab since 2020.

Juliet B. Schor is an economist and sociologist at Boston College. Schor’s research focuses on work, consumption, and climate change. A graduate of Wesleyan University, Schor received her PhD in economics at the University of Massachusetts. Before joining Boston College, she taught at Harvard University for 17 years, in the Department of Economics and the Committee on Degrees in Women's Studies. Since 2011, Schor has been studying the “sharing” and “gig” economies.

References

Acquier

Carbone

Vasseur

(2020). The sharing economy as an emerging and contested field–how classic and institutional entrepreneurs cope with plural theoretical frames. In Maurer

Mair

Oberg

(Eds.), Research in the sociology of organizations (pp. 107–129). Emerald Publishing Limited.

AirDNA. (2017). AirDNA. Retrieved January 1, 2017, from https://www.airdna.co/

Arcidiacono

Podda

(2017). Sharing time: New forms of reciprocity in the digital economy. Work Organisation, Labour & Globalisation, 11(2), 39. https://doi.org/10.13169/workorgalaboglob.11.2.0039

Benner

(2020). On-demand and on-the-edge: Ride-hailing and delivery workers in San Francisco. Institute for Social Transformation UC Santa Cruz.

Borkholder

Montgomery

Chen

M. S.

Smith

(2018). Uber state interference: How transportation network companies buy, bully, and bamboozle their way to deregulation. National Employment Law Project.

Botsman

Rogers

(2011). What’s mine is yours: How collaborative consumption is changing the way we live. Collins.

Brydges

Heinze

Retamal

Henninger

C. E.

(2021). Platforms and the pandemic: A case study of fashion rental platforms during COVID-19. The Geographical Journal, 187(1), 57–63. https://doi.org/10.1111/geoj.12366

Cansoy

Eddy

Ladegaard

Schor

J. B.

(2021). Homines diversi: Heterogeneous earner behaviors in the platform economy. Sociologica, 14(3), 143–165. https://doi.org/10.6092/ISSN.1971-8853/11508

Chen

Cheng

Edwards

(2021). COVID-19 pandemic exposes the vulnerability of the sharing economy: A novel accounting framework. Journal of Sustainable Tourism, 30(5), 1141–1158. https://doi.org/10.1080/09669582.2020.1868484

10.

Cockayne

D. G.

(2016). Sharing and neoliberal discourse: The economic function of sharing in the digital on-demand economy. Geoforum, 77, 73–82. https://doi.org/10.1016/j.geoforum.2016.10.005

11.

Conger

Griffith

(2020, May 7). The results are in for the sharing economy. They are ugly. New York Times.

12.

Covert

(2020, March 30). Like uber, but for gig worker organizing. The American Prospect.

13.

Cox

(2017). Airbnb as a racial gentrification tool. Inside Airbnb.

14.

Cox

(2021). Inside Airbnb, NYC: Short-term rental market decimated in advance of data sharing law. Inside Airbnb.

15.

Cox

Slee

(2016). How Airbnb hid the facts in New York City. Inside Airbnb.

16.

Davis

G. F.

(2016). What might replace the modern corporation? Uberization and the web page enterprise. Seattle University Law Review, 39(2), 501–515.

17.

de Medeiros

J. F.

Marcon

Ribeiro

J. L. D.

Quist

D’Agostin

. (2021). Consumer emotions and collaborative consumption: The effect of COVID-19 on the adoption of use-oriented product-service systems. Sustainable Production and Consumption, 27, 1569–1588. https://doi.org/10.1016/j.spc.2021.03.010

18.

de Peuter

Cohen

N. S.

Saraco

. (2017). The ambivalence of coworking: On the politics of an emerging work practice. European Journal of Cultural Studies, 20(6), 687–706. https://doi.org/10.1177/1367549417732997

19.

Dogru

Mody

Suess

Line

Bonn

(2020). Airbnb 2.0: Is it a sharing economy platform or a lodging corporation? Tourism Management, 78,104049. https://doi.org/10.1016/j.tourman.2019.104049

20.

Dolnicar

Zare

(2020). COVID19 and Airbnb–disrupting the disruptor. Annals of Tourism Research, 83, 102961. https://doi.org/10.1016/j.annals.2020.102961

21.

Dubal

(2020a). An uber ambivalence: Employee status, worker perspectives, and regulation in the gig economy. In Das Acevedo

(Ed.), Beyond the algorithm: Qualitative insights for gig work regulation (pp. 33–56). Cambridge University Press.

22.

Dubal

(2020b). Digital piecework. Dissent Magazine.

23.

Dubal

V. B.

(2017). The drive to precarity: A political history of work, regulation & labor advocacy in San Francisco’s taxi & uber economies. Berkeley Journal of Employment and Labor Law, 38(1), 73–135. https://doi.org/10.15779/z387p8td1d

24.

Edelman

Luca

(2014). Digital discrimination: The case of Airbnb.com (Working Paper Harvard Business School 14-054).

25.

Edelman

Luca

Svirsky

(2017). Racial discrimination in the sharing economy: Evidence from a field experiment. American Economic Journal: Applied Economics, 9(2), 1–22. https://doi.org/10.1257/app.20160213

26.

Fan

(2021). This, not that: Why to book hotels instead of Airbnbs during the pandemic. https://thepointsguy.com/news/covid-book-hotels-airbnbs/

27.

Ferré-Sadurní

(2019, January 14). New York empire of illegal Airbnb rentals booked 75,000 guests, suit says. New York Times.

28.

Fitzmaurice

C. J.

Ladegaard

Attwood-Charles

Cansoy

Carfagna

L. B.

Schor

J. B.

Wengronowitz

(2020). Domesticating the market: Moral exchange and the sharing economy. Socio-Economic Review, 18(1), 81–102. https://doi.org/10.1093/ser/mwy003

29.

Fraanje

Spaargaren

(2019). What future for collaborative consumption? A practice theoretical account. Journal of Cleaner Production, 208, 499–508. https://doi.org/10.1016/j.jclepro.2018.09.197

30.

Frenken

(2017). Political economies and environmental futures for the sharing economy. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375(2095), 20160367. https://doi.org/10.1098/rsta.2016.0367

31.

Frenken

Vaskelainen

Fünfschilling

Piscicelli

(2020). An institutional logics perspective on the gig economy. In Maurer

Mair

Oberg

(Eds.), Research in the sociology of organizations (pp. 83–105). Emerald Publishing Limited.

32.

Gansky

(2010). The mesh: Why the future of business is sharing. Portfolio Penguin.

33.

Gelman

(2016). How do we research the ‘sharing’ economy — When the data can’t be validated? Washington Post. Retrieved October 5, 2016, from https://www.washingtonpost.com/news/monkey-cage/wp/2016/06/06/how-do-we-research-the-sharing-economy-when-the-data-cant-be-validated/?utm_term=.5d59b5561b3c.

34.

Griesbach

Reich

Elliott-Negri

Milkman

(2019). Algorithmic control in platform food delivery work. Socius: Sociological Research for a Dynamic World, 5, 1–15. https://doi.org/10.1177/2378023119870041

35.

Griffith

(2020, May 6). Airbnb raises $1 billion to stockpile cash in pandemic. New York Times.

36.

Hall

J. V.

Krueger

A. B.

(2018). An analysis of the labor market for uber’s driver-partners in the United States. ILR Review, 71(3), 705–732. https://doi.org/10.1177/0019793917717222

37.

Horton

J. J.

Zeckhauser

R. J.

(2016). Owning, using and renting: Some simple economics of the “Sharing Economy” (NBER Working Paper 22029).

38.

Hossain

(2021). The effect of the COVID-19 on sharing economy activities. Journal of Cleaner Production, 280, 124782. https://doi.org/10.1016/j.jclepro.2020.124782

39.

Kenney

Bearson

Zysman

(2021). The platform economy matures: Measuring pervasiveness and exploring power. Socio-Economic Review, 19(4), 1451–1483.

40.

Kenney

Zysman

(2016). The rise of the platform economy. Issues in Science and Technology, 32(3), 61–69.

41.

Kirchner

Schüßler

(2020). Regulating the sharing economy: A field perspective. Research in the Sociology of Organizations, 66, 215–236.

42.

Laamanen

Wahlen

Lorek

(2018). A moral householding perspective on the sharing economy. Journal of Cleaner Production, 202, 1220–1227. https://doi.org/10.1016/j.jclepro.2018.08.224

43.

Lane

(2021). Lost supply? How has the COVID-19 pandemic impacted the supply of short-term rentals on Airbnb? AirDNA. https://www.airdna.co/blog/covid-19-pandemic-impacted-the-supply-of-strs

44.

Lee

(2016). How Airbnb short-term rentals exacerbate Los Angeles’s affordable housing crisis: Analysis and policy recommendations. Harvard Law and Policy Review, 10, 27.

45.

Makov

Tamar

Mehmet

Alon

Juliet

B. S.

(2023). Digital food sharing and food insecurity in the COVID-19 era. Resources, Conservation and Recycling, 189, 106735.

46.

Martin

C. J.

(2016). The sharing economy: A pathway to sustainability or a nightmarish form of neoliberal capitalism? Ecological Economics, 121, 149–159. https://doi.org/10.1016/j.ecolecon.2015.11.027

47.

Martin

C. J.

Upham

Budd

(2015). Commercial orientation in grassroots social innovation: Insights from the sharing economy. Ecological Economics, 118, 240–251. https://doi.org/10.1016/j.ecolecon.2015.08.001

48.

McKinsey Global Institute. (2021). The consumer demand recovery and listing effects of COVID-19. McKinsey.

49.

Murillo

Buckland

Val

(2017). When the sharing economy becomes neoliberalism on steroids: Unravelling the controversies. Technological Forecasting and Social Change 125, 66–76. https://doi.org/10.1016/j.techfore.2017.05.024

50.

O’Brien

S. A.

(2020, March 23). Instacart plans to hire 300,000 more workers as demand surges for grocery deliveries. Cnn.Com .

51.

O’Neill

Ouyang

(2016). From air mattresses to unregulated business: An analysis of the other side of Airbnb. Penn State State of Hospitality Management.

52.

Parrott

J. A.

Reich

(2018). An earnings standard for New York City’s app-based drivers. Center for New York City Affairs.

53.

Parrott

J. A.

Reich

(2020). A minimum compensation standard for Seattle TNC drivers. Center on Wage and Employment Dynamics.

54.

Pasquale

(2015). The black box society: The secret algorithms that control money and information. Harvard University Press.

55.

Pasquale

(2016). Two narratives of platform capitalism. Yale Law and Policy Review, 35(1), 309–319.

56.

Smith, A. (2016). Gig work, online selling and home sharing. PEW Research Center. Retrieved June 19, 2019, from https://www.pewresearch.org/internet/2016/11/17/gig-work-online-selling-and-home-sharing/

57.

Racabi

(2018). State TNC and MC legislation: Preemption and employment status of drivers. On Labor. Retrieved June 19, 2019, from https://onlabor.org/state-tnc-and-mc-legislation-preemption-and-employment-status-of-drivers/

58.

Ravenelle

A. J.

(2019). Hustle and gig: Struggling and surviving in the sharing economy. University of California Press.

59.

Rogers

(2016). Employment rights in the platform economy: Getting back to basics. Harvard Law and Policy Review, 10, 479–520.

60.

Rosenblat

Stark

(2016). Algorithmic labor and information asymmetries: A case study of uber’s drivers. International Journal of Communication, 10, 3758–3784.

61.

Scholz

(2016). Uberworked and underpaid: How workers are disrupting the digital economy. Polity Press.

62.

Schor

J. B.

(2014). Debating the sharing economy. Great Transition Initiative.

63.

Schor

J. B.

Attwood-Charles

Cansoy

Carfagna

L. B.

Eddy

Fitzmaurice

C. J.

Ladegaard

Wengronowitz

(2020). After the gig: How the sharing economy got hijacked and how to win it back. University of California Press.

64.

Schor

J. B.

Attwood-Charles

Cansoy

Ladegaard

Wengronowitz

(2020). Dependence and precarity in the platform economy. Theory and Society, 49(5), 833–861. https://doi.org/10.1007/s11186-020-09408-y

65.

Schor

J. B.

Vallas

(2021). The sharing economy: Rhetoric and reality. Annual Review of Sociology, 47, 369–389.

66.

Setiffi

Lazzer

G. P.

(2018). Riding free-riders? A study of the phenomenon of BlaBlaCar in Italy. In Cruz

Ganga

Wahlen

(Eds.), Contemporary collaborative consumption (pp. 77–96). Springer Fachmedien Wiesbaden.

67.

Slee

(2014). The shape of Airbnb’s business. http://tomslee.net/2014/05/the-shape-of-airbnbs-business.html#comment-45499

68.

Slee

(2015). What’s yours is mine: Against the sharing economy. OR Books.

69.

Sorkin

A. R.

Karaian

de la Merced

M. J.

Hirsch

Livni

(2020, December 11). Airbnb’s stunning I.P.O. New York Times.

70.

Srnicek

(2016). Platform capitalism. Polity.

71.

Sundararajan

(2016). The sharing economy: The end of employment and the rise of crowd-based capitalism. MIT Press.

72.

UCLA Institute for Research on Labor and Employment. (2018). More than a gig: A survey of ridehailing drivers in Los Angeles. UCLA.

73.

Wachsmuth

Chaney

Kerrigan

Shillolo

Basalaev-Binder

(2018). The high cost of short-term rentals in New York City. Urban Politics and Governance research group School of Urban Planning McGill University.

74.

Wachsmuth

Weisler

(2018). Airbnb and the rent gap: Gentrification through the sharing economy. Environment and Planning A: Economy and Space, 50(6), 1147–1170. https://doi.org/10.1177/0308518X18778038

75.

Wells

Attoh

Cullen

(2019). The uber workplace in D.C. Kalmanowitz Initiative for Labor and the Working Poor, Georgetown University.