Regulatory Issues in the Sharing Economy: TaskRabbit and the issue of Employment Regulations IntroductionDigital platforms are playing an increasingly important role in today’s economy. They form the basis for the fast growing sharing economy, and their value is estimated to increase from €28 billion in 2016 to €570 billion in 2025 (Parke, 2016). Digital platforms e.g. allow market players to offer goods, services or capital to users of the platform (Strowel & Vergote, 2016). TaskRabbit, a US based digital platform, matches freelance labour with local demand (Sundararajan, 2016). It contributes to the sharing economy by creating new labour opportunities and increases the number of working hours by allowing people who have extra time to increase their salary by using TaskRabbit. Although the growing number of digital platforms offers several new business opportunities, e.g. individuals using TaskRabbit to increase their income, they also pose various challenges to the existing structure of the economy. While TaskRabbit grants access to a workforce, it also raises certain concerns. (Strowel & Vergote, 2016). TaskRabbit as a digital platform poses service quality concerns, liability concerns and questions concerning labour laws when freely used without any type of regulation. However, this paper will only deal with the challenges related to labour rights. Applying existing regulation to digital platforms such as TaskRabbit could stifle their growth. According to Codagnone & Martens (2016) it is doubtful that these new platforms would be able to survive if governments start to heavily regulate them. The central question is thus: how does TaskRabbit pose challenges to existing labour rights and how can governments deal with these issues without stifling its innovation? The research method applied to answer the central question is based on a literature review. The paper is structured by firstly examining the characteristics of the sharing economy and specifically TaskRabbit as a digital platform. Secondly, the challenges and regulatory issues raised will be discussed. Thirdly, research on the topic with regard to regulatory alternatives will be analysed and a conclusion will be drawn based on the discussion throughout the paper. 2. Theoretical FrameworkIn the descriptive part of the theoretical framework, a definition of the sharing economy will be suggested and the role of digital platforms in the sharing economy briefly discussed. A description of TaskRabbit, how it functions as a digital platform and the main business model behind the platform will be explained. Secondly, the regulatory challenges that stem from the nature of TaskRabbit will be identified. In section 2.2, three types of possible regulatory interventions will be considered and analysed with respect to their relevance to regulating TaskRabbit without stifling its innovation. 2.1. DescriptiveWithin the sharing economy, there are different models such as Business- to- Peer (B2P) and peer- to- peer (P2P). This paper will be centred around P2P businesses that earn money on commissions on the exchanges that take place on their platform (Schor, 2014). There is no explicit accepted definition of the sharing economy as of today. According to Hamari et al., (2015) the sharing economy can be defined as peer- to- peer activities involving attaining, giving or sharing the access of goods and services coordinated through an online digital platform. The main idea behind TaskRabbit is to allow people with little time to outsource small jobs to people with extra time. TaskRabbit is an example of a digital platform based on a two-sided business model that gives access to a workforce, by connecting “task posters” (people wanting a job done) with “taskers”. Taskers are pre- approved individuals who are background checked by the platform (Prassl & Risak, 2016). The platform functions by matching jobs posted by task posters with three suggested taskers. The taskers can either accept or cancel the job according to their availability. Upon completion of the job, the payment is processed through the application and the taskers receive their salary (Codagnone & Martens, 2016). The platform gets its revenue from a commission taken on each completed task on the application. When the said task has been completed the task poster and Tasker are rated. TaskRabbits original business model of bidding consisted of task posters placing a task on the platform and taskers bidding on for how much they were willing to complete the task. However, auctions are often time consuming and require a lot of effort. TaskRabbit therefore changed its model to offering only standardized tasks at fixed prices set by the taskers (Codagnone & Martens, 2016). This change has led to an overall increase in the users of TaskRabbit with approximately 60,000 taskers signed up as of 2017 in 24 locations (Swischer & Schleifer, 2017). In 2017 TaskRabbit was bought by IKEA and it remains to be seen whether IKEA will further develop/ enhance their business model. For TaskRabbit to be successful, it’s essential to build a large and active “crowd” so there’s always taskers available, and for competition to function on the platform (Prassl & Risak, 2016). The more taskers that are registered on the platform, the more choices for task posters and the higher network value of the platform (Birke, 2009). This means that the more Taskers are available, the more service suppliers thereby creating a continuous feedback loop. (Codagnone & Martens, 2016). The technological development of the Internet and particularly high- speed networks is the basis for digital platforms and the emergence of the gig- economy. The use of Internet technologies has made TaskRabbit profitable by reducing transaction costs. TaskRabbit can e.g. facilitate micro transactions, which otherwise would never have been profitable (Drahokoupil, 2015 as cited by Schmid-Drüner, 2016,p. 2). TaskRabbit is using advantaged algorithms developed by the platform to match the task poster and the tasker. The increased availability of cheap mobile devices (for example smartphones, tablets) which provide immediate access to the internet at all times are also an important driver for real on- demand services (Degryse, 2016 as cited by Schmid-Drüner, 2016, p. 2). TaskRabbit relies on a digital reputation mechanism on the platform in order to guide and advise the selection of taskers. After each completed task, the task poster, and the tasker, are rated. This is an important mechanism to increase trust with the platform. However, as the ratings are usually not transferable between different platforms, the tasker can become tied to the platform and risk losing their bargaining power when looking for alternative jobs (Prassl & Risak, 2016). The crowd work created by the gig- economy creates particular challenges with respect to the legal status of the crowd worker. There is no employment contract established between the crowd worker and the platform. The crowd worker simply registers herself on the platform with her data. The platform on its side checks the validity of the profile data and performs a background check e.g. no criminal background. Following an interview, the crowd worker is accepted into the database of TaskRabbit and signs an agreement for the use of the platform. In this way, the platform TaskRabbit acts as an intermediary between the independent tasker and the task poster. When TaskRabbit considers itself an intermediary, it creates a legal dilemma, as the Tasker does not have status as an employee and does not enjoy the rights and legal protection that a traditional worker has. These rights usually include: minimum wage requirements, working hours limitations, overtime pay, rights related to unfair dismissal, and safety and health protection, in addition to the more universal labour rights (abolition of child labour, no discrimination, no forced labour and right to collective bargaining) (ILO Declaration on Fundamental Principles and Rights at Work, 2011). In most cases, crowd workers are considered independent contractors, however, this is also not a suitable categorization because independent contractors normally have specific skills and involved in a predefined and limited piece of work, whereas crowd workers, on various digital platforms, typically are unskilled and normally continuously work for the same platform (JRC, 2016 as cited by Schmid- Drüner, 2016, p.6). This issue is further explained by Krueger and Harris (2015), who state that the existing U.S. law wrongly implies that there are only two forms of employment categories: independent contractors and traditional employees. However, the new digital economy has pushed the boundaries for traditional categories of employees and has created a grey area between these two categories, namely the “independent worker” (Harris & Krueger, 2015, p. 2). To avoid creating a class of invisible workers without any rights, a regulatory discussion is needed while at the same time observing the characteristics of new businesses on digital platforms. This is even more important as these new businesses are expected to grow significantly in the future.2.2. Discussion The lack of suitable policy frameworks creates uncertainty in the market for new digital platforms and may prevent growth because entrepreneurs are uncertain as to whether their company will withstand future regulations. To provide more certainty and predictability in the market, some kind of intervention or regulation is therefore warranted whether by governments or otherwise. In regards to crowd workers, an objective for the intervention should be to establish a set of minimum rights for example minimum wage, health insurance and right to port the private ratings. If policy makers simply apply the conventional industry standards and regulation to digital platforms it could threaten their innovation and future survival (Dervojeda, Verzijl, Nagtegaal, Lengton, & Rouwmaat, 2013). On the other hand, leaving the activities on digital platforms in limbo, and not subject to any regulation is not sustainable either. A possibility is to establish a new legal category of workers that would gain certain rights adapted to working on digital platforms and fill the grey area created by the emergence of TaskRabbit and other similar digital platforms. However, because of the short innovation cycle, the rapid growth of the sharing economy and the usually lengthy political decision- making process, policy decisions risk falling behind and become irrelevant. In this respect, it may be helpful to consider various forms of self-regulation also called soft regulation, such as a digital reputation feedback mechanism, self- regulation collective and information- based regulation (Sundararajan, 2016). Self- regulation is a way of allocating the regulatory responsibility to parties other than the government. Generally, the aim of self-regulation is to regulate an industry by framing systems of collective rulemaking in which different platforms come together to develop, monitor and enforce standards to oversee the behavior of platforms (Sundararajan, 2016). It should be underlined that self- regulation is not the same as deregulation, an organisation policing itself or no regulation. Instead, self- regulatory systems are based on either industry representatives or a third- party organization that is not the government defining a set of rules. Self- regulatory systems vary a lot depending on the levels of voluntariness, accountability, enforcement and government intervention (Cohen & Sundararajan, 2015).In recent literature reputational feedback has been proposed as a self- regulatory method. This method is the result of market competition that is supposed to encourage the development of reliable services. This form of regulation is also called “bottom- up governance” and uses information provided by users (Allen & Berg, 2014). Libertarian thinkers argue that self- regulation by user generated reputational ratings is more effective in ensuring consumer welfare than traditional consumer protection measures warranted by the government (Codagnone, Biagi & Abadie, 2016). Reputation feedback seems well suited for the fast developing sharing economy with constant evolving digital platforms. An advantage of this method is that “no single benchmark needs to apply to every transaction” meaning that each digital platform can establish its own type of rating system (Schmid- Drüner, 2016, p. 23). It is also relatively easy and less costly to implement for a digital platform. A main limitation to this form of regulation is that when both suppliers and consumers rely on positive reviews, there is a risk for retaliation that could lead users to soften negative reviews and lead to less reliable ratings. Einav et al., (2015) recognises that ratings can be biased and inflated and rendered useless unless overseen by a third- party. Other researchers challenge the reputational feedback mechanism and argue that this type of regulation may not be effective when it comes to regulating the behavior of platforms with regards to consumer protection and labor rights for crowd workers e.g. minimum wages, safety measures and working hours; some of the challenges identified in relation to TaskRabbit (Codagnone, Biagi & Abadie, 2016). This also holds true as crowd workers can normally not transfer their private good ratings between different platforms thus increasing their dependencies on single platforms when seeking alternative jobs. The reputational feedback is already, integrated into TaskRabbit as taskerposter and tasker are rated after each job completed. While it provides the customers and crowd workers with valuable information about the quality of the service it does not serve crowd workers when it comes to their terms and conditions. The implementation of self- regulatory organisations (SRO) may be preferred to the feedback mechanism because it can be used for setting standards and rules for both customers and crowd workers and for the behaviour of the platform itself. According to Allen & Berg (2014) regulators should encourage “bottom- up” self- regulating institutions as self- regulation is less time consuming and could easily be adapted to the new services in the digital space. The rules can be formulated by either the industry itself or a third- party organisation. Implementation costs are also most likely lower than if “top- down” government regulation was adopted (Allen & Berg, 2014). The effectiveness of an SRO, however, depends a lot on the level of voluntariness e.g. the extent of commitment by the industry of platforms and whether the industry players are held accountable by the authorities or a third party organization. Accountability is essential because there is otherwise no promise that digital platforms will consider the broader interests of society (Cohen & Sundararajan, 2015). An abundance of labour rights would, for example, be beneficial for the crowd workers but not for the platform. Besides demonstrating strong enforcement capabilities, it is essential for an SRO to establish credibility and “take advantage of participants’ reputational concerns” (Sundararajan, 2016, p. 176). If these objectives can be fulfilled an SRO could be beneficial in dealing with the labour rights challenges. First of all, an SRO can set standards throughout the industry thereby protecting both customers and workers. Secondly, an SRO would be more effective in setting the standards compared to a government body as the participants of the SRO would be are market players who know best how to regulate without incurring high costs upon themselves. For example, if independent contractors (workers) are turned into employees in an attempt to improve increase labour rights it could will make it difficult for startups to compete for labour because they have less capital than well- established players. Although pure self- regulation has its advantages and policy makers should be looking towards integrating SRO’s into the economy, a clear drawback is the lack of legislative certainty in terms of enforcement, especially with respect to the labour uncertainty presented above. As a result of this shortcoming, it might be helpful to look towards co- regulation. Co- regulation is a method of self- regulation with government involvement and thus legislative backing, often done by an organisation with the participation of industry players (Sundararajan, 2016). The government will not adopt detailed legislation but rather leave it up to the co-regulatory organisation to establish a set of standards for one or more specific challenges. With legislative backing, the government has the possibility to sanction the market players in case of no compliance and in that respect co-regulation method is superior to pure self-regulation. The creation of a co- regulatory organisational collective could be a way of integrating the digital platform into the regulatory procedures while considering their specific interests and avoid inhibiting their innovation. In summary, the effectiveness of self- regulation is highly dependent on the commitment of the industry in cases where there no is legal backing behind the SRO. One single digital platform can hijack the reputation of the entire industry, in terms of workers rights, if it does not comply with the standards. Information based regulation seeks to delegate responsibility to the platform by mandating it to provide an audited summary of its compliance with the law, or rules eventually defined via self- regulation, through data (Sundararajan, 2016). Information based regulation is mainly a method of enforcement to ensure compliance. This method of self- regulation is considered to be effective as data will stay within the platform and not raise privacy concerns or costly interfaces when transferring data from the platform. Information based regulation could e.g. address social issues such as gender and race discrimination on digital platforms as data could verify the variety of workers on the platform (Codagnone, Biagi & Abadie, 2016). This model can also be useful for complying with tax limits, which could be relevant to regulating TaskRabbit as each tasker needs to pay tax on their income through the platform (Sundararajan, 2016). Finally, information based regulation could also be effective to enforce potential minimum earning requirements set in the industry as the platform can control all their data. However, this method of self- regulation relies heavily on the integrity of the platform to follow the standards that have been set, for example, in the terms and conditions. Based on this limitation, an alternative could be to replace audits with a binding application programming interface available to the government only for audit purposes. Such a programme would not provide access to raw data, solving the privacy issue, and would ensure that the government can verify compliance with certain standards. The downside is that it most likely would have to be standardised and could be costly for small start- up platforms hindering their initial growth. 2.3 Hypothesis: Currently, there is a lack of well-suited policy frameworks for today’s sharing economy. Based on the analysis above, there are several options for regulatory solutions that could be applied to solve the labour rights issues raised by TaskRabbit. “Top- down” government intervention will most likely harm the growth of new and existing digital platforms. Instead a new approach to regulation is needed. This has led me to the following hypothesis: If co- regulatory collectives (SRO’s) are able to combine and enforce reputational feedback regulation, self- regulation and information based regulation to the shortcomings of labour rights posed by TaskRabbit then these challenges could be dealt with without stifling the innovation of sharing economy platforms. There are limitations to this hypothesis. Current literature and empirical evidence on how (level and type of intervention) to regulate digital platforms is still too new to draw definite conclusions. Digital platforms do not share their data, so it is difficult to estimate the actual working conditions of independent contractors (workers) affiliated with TaskRabbit. Further research is needed to determine what regulatory measures will have positive or negative impacts on the growth of sharing economy digital platforms. Conclusion: As the amount of independent workers (freelance workers) are expected to increase considerably in the future, it becomes the more important to consider their social rights. Any future regulation that is focusing on protection of workers should avoid a sudden increase in costs for the platform. It must, however, be kept in mind that the sharing economy is an economy of experimentation. To benefit from the sharing economy, A new type of regulation that is responsive and flexible enough to adapt to the new and emerging companies of the sharing economy will need to be created. It is clear that the existing regulation of labour rights is not well suited for the fast developing sharing economy and the current state is one of regulatory uncertainty. Three alternative regulatory methods have been considered, however, none of them, on their own, can provide the full solution to worker rights challenges. Although the reputational feed model may not be appropriate to deal with labour rights issues, it should not be disregarded as a form of regulation. The Self- regulation model and information based regulation model could be more useful for dealing with workers rights as found in the analysis above. In conclusion, future regulation should focus on a mix of governmental and non- governmental regulation taking advantage of the various self-regulatory methods discussed.