The execution of work has changed tremendously in recent years. This is as a result of increased technological advancements, followed by their unprecedented adoption on a global scale. One notable development in this regard is the ‘gig’ economy. Also referred to as the collaborative economy, sharing economy, platform economy or crowd work, this economy entails transactions between peers, in which platforms take the role of brokers, with the aim of achieving efficiency in resource utilization ( Farrell & Greig , 2017; De Stefano, 2015 ). Existing businesses may also use online platforms in a bid to outsource for particular tasks - which normally would be executed by a single employee – from a larger pool of ‘virtual’ workers. Two forms of work are likely in the gig economy ( Farrell & Greig , 2017 ). On the one hand, the online platform can be used to mediate such physical services as childcare, plumbing or driving of passengers in which case the services are undertaken locally. This form of work can be termed as ‘work-on-demand’ and is executed though the internet or mobile applications (apps). On the other hand, the platform can offer a virtual service whose transmission takes place via the internet implying that its execution can be done from any part of the world. This form of work is referred to as ‘crowdwork’ and could include such services as translation or accounting. The two forms of work in the gig economy are made possible by the internet, consequent to which demand and supply are met efficiently thus reducing both labour market frictions as well as transaction costs. The different labour platforms in the gig economy have resulted in declined labour protections and employment relations. This paper will highlight the challenges of regulating labour in the gig economy with a focus on Uber which is the most prominent ride-hailing on-demand labour platform.
In recent years, the gig economy has enjoyed increased popular, academic as well as regulatory attention which is a reflection of its unprecedented growth ( Farrell & Greig , 2016; Farrell & Greig , 2017) . Often, this growth is viewed from the perspective of the capitalization of the platform companies. However, despite this, the increase in the number of individuals earning money on such platforms has also grown tremendously. It is estimated that between October 2012 and September 2015, the monthly participation in the gig economy increased ten-fold ( Farrell & Greig , 2016 ). Likewise, the cumulative participation rate in the United States (U.S) reached 4.2% within the same period. Farrell & Greig (2016) reckon that the annual growth rate of online labour platforms in 2013 and 2014 ranged between 300% and 400% respectively. Despite the slower rate of growth in 2016, the growth of the platform remained robust.
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The rapid growth of the gig economy has resulted in numerous regulatory issues that pertain to the environment within which the platforms operate. On the one hand, the labour platforms are viewed as being disruptive. However, scholars argue that they still characterized by a continuation of previous trends ( Collier et al., 2017 ). These include capital restructuring to attain new employment relations such as new forms of intermediated employment relations. Another notable factor was a switch to contingent or alternative forms of employment which were unprotected by both labour and employment laws. In line with these trends, the labour platforms characteristic of the gig economy not only fast track this trend but also compensate for it. This is achieved by the provision of supplemental work to jobs that result in irregular hours or inadequate income. Likewise, the labour platforms also relate to older as well as new kinds of dynamism in the economy for example by compensating for insufficient retirement savings and pensions and giving income to start-up entrepreneurs.
Type and rationale of platforms in the gig economy
Labour platforms are aimed at facilitating the sale of labour to perform a service or a task for monetary compensation. Labour platforms are different from the platforms in which individuals sell goods, rent assets or engage in consignment-based work. They are also distinct from platforms that take the form of classified ads or electronic bulletin boards ( Kenney & Zysman , 2016). Labour platforms specialize in offering contract labour on a temporal basis and coordinating communication between a requester of work and the prospective worker. Further, a difference exists between crowdwork and on-demand work (Aloisi, 2016; De Stefano, 2015). The arrangement for as well as the fulfilment of crowdwork is carried out online and remotely. Conversely, on-demand work is fulfilled both physically and in person. Therefore, crowdwork platforms construct a potential global labour market integrating both low-wage and high-wage economies. On-demand work platforms, on the other hand, build a local market. However, on-demand platforms can expand to serve many localities, in the process organizing separate but local markets. The other distinction between labour platforms pertains to the skills required for different forms of work.
An example of low-skilled crowdwork is that offered by Amazon Mechanical Turk (AMT). On AMT workers are expected to carry out micro-tasks of parcelled nature which are both monotonous and menial. The work executed by designers, coders and other professional personnel, on the other hand, requires high-skilled workers ( Kenney & Zysman, 2016 b). On-demand work also features different skill level. Delivery tasks, as well as such services as dog walking and housecleaning, are characteristic of the low-skill end. The high-skill end entails such professionals as doctors, electricians, caretakers and lawyers. A notable case of on-demand work is exemplified by Uber ( Collier et al., 2017) . The ride-hailing company is an example of on-demand, low-skilled and in-person platform. It is also a hybrid platform since it also entails monetization of an asset. Specifically, Uber entails the monetization of car ownership's ‘unused' time. Unlike a car-renting or sharing platform, Uber acts as a labour platform that facilitates the work of the drivers. A notable extension of this is the fact that Uber is also facilitating and encouraging car leases to enable the drivers to work on the Uber platform.
The efficiency of labour platform is enabled by technology ( Farrell & Greig, 2017 ). However, this takes place distinctively from the traditional applications of technology. As opposed to the conventional application of technology to boost productivity, its use is of a different purpose in labour platforms. The use of technology in labour platforms does not make a worker more productive in the process of production. Instead, technology makes the entire market efficient through the reduction of transaction costs ( Collier et al., 2017) . Gig work thus takes advantage of the technology-driven efficiency. This efficiency is achieved through the ability of technology to shift the balance between the search and the gig. Specifically, the work of an independent contractor or a freelancer is two-fold. Firstly, it entails the unpaid efforts of looking for a ‘gig’ and establishing a contract and secondly, the paid work of honouring the contract. In this case, the payment obtained from the paid component ought to cover the unpaid component of searching for work. While it makes searching efficient, technology does not influence the productivity of the worker as he or she fulfils the contract. Therefore, assuming that a uniform remuneration rate is adopted for all the paid contract/gig work, an individual’s wages are determined by the number of gigs executed within a specified period. Maintenance of a particular wage rate is of particular concern to crowdwork platforms which are likely to exert pressure on wage rates by integrating low-wage and high-wage labour markets globally ( Collier et al., 2017) .
In the gig economy, total remuneration can be kept even when the price per gig is low by shifting the balance between the paid gig and the unpaid search. This is because the unpaid search time can be compensated by the presence of more gigs. Nevertheless, the degree to which a requester enjoys increased efficiency via lowered prices, or a worker enjoys higher compensation owing to the availability of more gigs within a given period is dependent on the particular platform, type of work, and the existing regulatory framework ( Collier et al., 2017) . The lowering of prices per gig is a vital component of some labour platforms business models. For example, such on-demand platforms as Uber emphasize lowering of the paid component, in the process putting the weight of maintaining or increasing income of drivers on the technology-driven speed-up efficiency of the search which is unpaid. Uber employs the logic of provision of efficient services through its software in support of the argument that intensification of the rate of paid work or the number of gigs available per unit time enables the maintenance of incomes to remain even at low wage-rates per gig. Despite this argument, drivers disagree with the claim that incomes are maintained.
The growth of a labour platform takes place in two ways. These are diversification of the services offered and expansion of the market for a particular service. In this regard, most crowdwork platforms are unspecialized and cover a wide variety of skills, tasks as well as prices. The degree of specialization in in-person and on-demand labour platforms varies, with many starting out as single-service platforms. Examples include those offering dog walking, meal preparation, rides, restaurant delivery and laundry tasks. Uber is an example of an on-demand platform that has successfully embraced diversification. This is with regard to the types of cars availed for ride-hailing, the available riding services (pooled and individual), the delivery of other products and the ability to deliver a broad set of logistics services. Initially, most on-demand platforms attempt to foster growth via expansion of the number of requesters and workers available for a particular service ( Kenney & Zysman, 2016 a). Crowdsourcing platforms tend to be worker-to-business (W2B) coordinators, in which case they match taskers or workers with extensive expertise and skills, to taskmasters or requesters who are producers or in business. Thus, the tasks are inputs that the requesters need in their production process. In contrast, on-demand platforms are worker-to-worker (W2W) coordinators whose aim is to match taskers or workers to the final consumers. In this regard, on-demand platforms avail personal services to consumers.
Low-skill services can be viewed as those that majority of the people are used to carrying out themselves. In this regard, on-demand platforms commodify these domestic tasks. This is achieved through the purchasing of services offered by such persons as washers, gardeners, drivers, cooks, babysitters as well as those that undertake such tasks as picking and delivering packages, dog walking, making purchases or dry cleaning (Collier et al., 2017). Likewise, provision of these chores or tasks exemplifies the ‘massification’ of servant tasks via their decomposition. For instance, as opposed to engaging one servant, an individual ends up hiring the personal consumption or household services by the task or chore. Due to this decomposition, individual duties are more affordable to those are not in a position to hire a servant. Likewise, due to the stratification hierarchy, the household help market ultimately expands. The market expansion for these chores is dependent on their provision at lower costs.
Relations and worker autonomy in the gig economy
The online gig economy is characterized by a tripartite relationship between the worker, the requester of service or work and the platform. A key feature in this relationship is whether some degree of control is exercised by the platform over the exchange between the requester and the worker. The platform is capable of exercising control over numerous conditions of work. However, the most crucial form of control is the process of wage or price setting ( Collier et al., 2017) . Some platforms allow workers to fix their rate and offer labour at a particular price. The requester subsequently selects among the workers based on their offers combined with such information as the worker qualifications, experience as well as ratings given to him or her on the platform by past requesters. Other platforms only allow the requester to list a project or task at a particular set price, and the workers have to decide whether to apply or not. In the latter case, the wage rates are not controlled by the platform although it might affect them indirectly by constructing a larger market for requesters and workers. Some platforms have delineated a ‘fair' price range that has to be met. In others, particularly such low-skilled on-demand platforms like Uber, Postmates, Swifto dog walking or Handy housecleaning, the price of work is set by the platform ( Collier et al., 2017) .
On-Demand platforms are likely to exert greater control compared to crowdwork platforms. The W2C platforms coordinate similar and relatively unskilled tasks repeatedly while on W2B platforms tend to have more ‘customized' work. Matching a requester and a worker is thus logistically easy for on-demand platforms. Control over the matching also enables these platforms to control the pricing and subsequently the workers' wages. Platform control is vital since it is a key issue on numerous platforms. In Uber, this has led to worker dissatisfaction. It is also the most noticeable legal criterion that gives such platforms the ‘employee' status. Despite this, the majority of the platforms reckon that the workers are not considered as employees. Instead, they are viewed as independent contractors who exhibit autonomy in their work, particularly with regard to the number of hours worked as well as the decision to undertake a particular gig. Therefore, the manner in which a platform controls work conditions is a prominent issue for labour regulation in the growing gig economy ( Collier et al., 2017) .
Labour regulation in the gig economy: The case of Uber
Uber is the most prominent on-demand labour platform, particularly with regard to the regulation of worker issues. This is because it has the most extensive regulation record and due to the unusual control that it exercises over ride-hailing on its platform. Uber is also an excellent exemplar of employees' right regulation since it offers more conducive conditions for the collective action of its workers. Launched in 2010, Uber is an app that connects licenced drivers to passengers. Its competitors, Sidecar and Lyft were launched in 2012. The three apps subsequently changed the existing regulatory framework as well as the transport industry ( Collier et al., 2017; Rosenblat & Stark, 2016) . Two attributes characterized the new apps. Firstly, they featured non-professional drivers who use their cars. Secondly, they had a non-mandatory price for each ride. The launch of UberX changed these conditions slightly. For instance, as opposed to the non-mandatory price that was characteristic of Lyft, the fare was set. This was opposed to the case of taxis whose fares were set by the municipals governments. Lyft, Uber and Sidecar's argument was that since they were software companies, they were exempted from the regulations imposed on traditional transportation companies.
Uber’s model challenged the existing and highly regulated sector. This is because the company not only entered into an already existing market but also because it ended up expanding and improving it. Notable improvements included faster matching of drivers and customers, higher availability and affordability. It also featured such consumer protection and safety measures as route monitoring and price estimation. Entry of Uber has led to the reduction of taxicabs in such cities as New York. However, Uber raised numerous issues pertaining to regulations on public safety, licencing and consumer protection which were already available for limos and taxis Collier et al. (2017) . In response, Uber adopted such self-regulatory measures as incorporating car requirements and driver checks to meet consumer safety and protection regulation issues. However, these were deemed insufficient by the regulators. These regulatory issues subsequently became contested, pitting Uber against taxi interests and warranting the entry of the public officials responsible for regulating ride-hailing.
Both states and cities have responded to these issues with the widespread regulation of Uber as well as similar companies. Traditionally, regulation of taxis was undertaken by municipal governments. However, they have proceeded to regulate Uber. Likewise, state legislatures which have traditionally played a partial role in the regulation of the taxi sector have also come on-board to regulate Uber and its counterparts. Collier et al. (2017) argue that a total of 34 states in the U.S had passed such legislation as of August 2017. Likewise, the discourse on the employment status and work conditions of Uber drivers has gained prominence. Central to this debate is the amount of control that Uber has exerted over the work performed by its drivers. For instance, Uber controls such aspects as the pace of work and the rating system's operations. The latter, in some cases, can result in the suspension or even suspension of a worker (Rosenblat & Stark, 2016; Lee et al., 2015). Other notable issues of concern are control of hours and price setting. Due to the amount of control exercised by Uber on its drivers, their employment status has been contested. The company has opposed such regulations as those giving drivers benefits and rights that are conventionally part of employment. It has also opposed the regulations constraining the supply of drivers. Since Uber drivers are not considered its employees, they don't have the right to establish a labour union which would help them negotiate the conditions of their employment. They would also be supported by the pre-existing laws in which case such issues as social security contributions, minimum wages, employment safety net and overtime compensation would be addressed. To prevent the increased cost of operations associated with these aspects, Uber has consistently fought attempts to reclassify it and laws that extend its worker's employee rights (Collier et al., 2017).
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