Labour Market Engineers: Reconceptualising Labour Market Intermediaries with the Rise of the Gig Economy in the United States

Abstract

Gig work – accessing job opportunities through an app – has brought renewed attention to precarious non-standard labour arrangements. Scholars have begun to consider the intermediary role that platforms such as Uber, Lyft and Doordash play in exploiting and controlling workers. Yet, literature on labour market intermediaries has muddied conceptions of their role, impact and outcomes for workers by lumping a variety of institutions under the same umbrella term. Drawing from previous theoretical and empirical works throughout the temporary help and gig industries, this article proposes a reconceptualisation of labour market intermediaries as labour market engineers highlighting four mutually reinforcing features. This sociological reconceptualisation updates the understanding of for-profit labour market intermediaries by demonstrating the market making behaviours of firms of on-demand labour in the US context. Likewise, this reconceptualisation notes how gig firms have adapted and expanded these features in ways that increase precarity for workers.

Keywords

gig economy labour market intermediaries labour markets labour regulation on-demand labour temporary labour

Introduction

The rise of on-demand ‘gig’ work in the 2010s – accessing job opportunities through an online or mobile application (hereafter ‘app’) such as driving for Uber or completing tasks for TaskRabbit – has brought renewed attention to precarious work arrangements (Gandini, 2019; Hall and Krueger, 2015; Rosenblat, 2018; Schor, 2020; Thelen, 2019; Veen et al., 2019). Indicative of the breakdown in the standard employment relationship (Weil, 2017), gig work represents a recent iteration of casualised or non-standard labour arrangements similar to those observed in the temporary help industry – where employment is gained on a temporary basis through the use of an agency – throughout the United States (Barratt et al., 2020; Leonardi and Pirina, 2020; Thelen, 2019; van Doorn, 2017). The experiences of gig and temporary help workers have been well documented across the literature noting pay insecurity, schedule uncertainty, and various risks assumed by workers (Gottfried, 1992; Gregory, 2021; Purser, 2012; Rosenblat, 2018; Schor, 2020; Veen et al., 2019).

What is less clear from the literature covering non-standard and on-demand labour is the role of third-party firms that are situated between the worker and their work. Temporary help workers are mediated by a temporary help agency (e.g. Manpower or Adecco) and gig workers are mediated by a platform or app (e.g. Uber or Doordash). These firms are most often understood using the umbrella term of labour market intermediary, which describes any third-party institution that mediates the relationship between employee and employer (Benner, 2002; Benner et al., 2007; Taras, 2002). Labour market intermediaries can be institutions as varied as unions, head-hunters, temporary help agencies and more recently gig platforms (Benner et al., 2007; De Stefano, 2016; Freeman and Gonos, 2009; Stewart and Stanford, 2017). Yet, intermediaries such as unions function in significantly different ways compared with a gig platform or temporary help agency as unions work to protect workers rather than exploit them. Likewise, the role of gig platforms and temporary help agencies are not as passive as the term intermediary suggests. As just one example, Gonos (1997) demonstrates how temporary help firms, such as Manpower, lobbied state governments in the US to categorise temporary workers as employees of the temporary help agency rather than their work placement location absolving firms of the costs associated with standard employment. Insights like these suggest that firms across these precarious non-standard labour arrangements insert themselves between the employee and employer to extract value, exert control and transfer employment risks and costs onto workers. Moreover, documentation of gig firm behaviour suggests that mediating actions transcend the employee-employment relationship. In the US, gig firms have leveraged their economic and political capital to sway legislators and the public in order to avoid regulations that the firms argue will prohibit their operations, thus shaping labour markets in the process (Pollman and Barry, 2017; Thelen, 2018). Instead of passive players within non-standard labour arrangements, firms of on-demand labour from the temporary help and gig industries play a primary role in shaping markets. Although there has been much written about labour market intermediaries with a particular focus on the temporary help industry, a clear sociological explication of the features indicative of these particular types of for-profit firms has not been developed.

This article pulls together insights from the literature on labour market intermediation in the temporary help and gig industries to demonstrate the market making behaviours of firms that use their intermediary role as their primary revenue stream. Reconceptualising the term labour market intermediary to labour market engineer (LME), this article makes a key contribution to the literature arguing that for-profit firms that place on-demand and in-person labour with employment have historically and contemporarily crafted non-standard labour markets and workers. The term labour market engineer is conceptualised as institutions that intervene from both above and below – shaping the worker, the employment opportunities they encounter and labour arrangements. This term is intended to be distinct from labour market intermediaries such as community based organisations, career services or other institutions that passively mediate the employment relationship. At present, not all firms of on-demand labour are LMEs. However, all LMEs are firms of on-demand labour in the US context.

Drawing from the theoretical and empirical literature on labour market intermediation in the temporary help and gig industries, this article makes a second contribution, identifying and developing four mutually reinforcing features that are indicative of this active manipulation of workers and labour markets used by firms. The four features are as follows: 1. Use a triangular employment relationship to extract value; 2. Manipulate regulations; 3. Saturate the market with workers; 4. Manipulate cultural understandings of on-demand labour. Each of these features will be described in detail in subsequent sections. The literature on labour market intermediaries in the past has been muddied with descriptions that draw commonalities and traits across all institutions that play an intermediary role in the labour market. Moreover, scholars that do isolate for-profit firms, have not specified clearly how their intermediary role shapes markets and workers in distinguishable ways. This reconceptualisation and explication of features identified from reviewing existing literature is intended to provide a better understanding of the terms over which precarious labour arrangements are formed. This article also contributes new insights by demonstrating how contemporary firms of on-demand labour, such as gig platforms, have adapted and expanded how this engineering takes place. This article focuses on the US because of the specific political nature of some features, such as the strategic use of hierarchical decision making city and state decision makers pit against one another to advance regulatory agendas. Despite these US specificities, examples and parallels to other countries are drawn throughout.

This article begins with a review of the literature on labour market intermediation. As a result of the significance of the intermediary in the temporary help industry, much of the literature is drawn from studies on these firms. Only recently have scholars begun to analyse the intermediary role of the platform (see Barratt et al., 2020; Stewart and Stanford, 2017). This article then defines the four features of labour market engineers followed by a discussion section that compares the temporary help and gig industries demonstrating how gig firms have adapted and expanded each of the features. After which, this article briefly discusses the applicability of this concept for firms of on-demand labour outside of the US context. The article concludes with some suggestions for future research.

Labour market intermediation

Generally considered, labour market intermediaries (LMIs) are any third-party institution that mediates the relationship between employee and employer (Benner, 2002; Benner et al., 2007; Hilton and Lambert, 2015; Taras, 2002) . Common examples include temporary help agencies, day labour organisations and unions (Benner et al., 2007; Freeman and Gonos, 2009). This section describes the rise of labour market intermediaries noting the tendency in the literature to consider any third-party institution under the same umbrella term and the failure to clearly articulate defining features. In doing so this article highlights the need for a reconceptualisation of for-profit LMIs that use on-demand labour models by pointing to specific features that indicate their role in engineering markets.

Particular attention to LMIs was propelled by the rise of non-standard labour arrangements and in particular the temporary help industry throughout the 1990s and early 2000s. Freeman and Gonos (2009) explain that within this for-profit arrangement, client firms contact the temporary help agency in search of short-term workers. The LMI then sends out one of the many workers waiting for job placement. This can happen on a day-by-day basis or for a specified amount of time, however the arrangement is always temporary. Some agencies are large multi-national corporations such as Manpower or Adecco while others may be smaller localised firms operating one or just a few offices (Enright, 2013; Gonos, 1997; Peck and Theodore, 2002). Either way, the temporary help agency extracts profit from the workers’ wages as their sole source of revenue (Freeman and Gonos, 2009).

Major temporary help firms such as Manpower and Kelly Services grew rapidly throughout the second half of the 21st century coinciding with changes in production and macro-economic fluctuations (Kalleberg, 2000; Peck and Theodore, 2007). Standard employment relationships decreased while non-standard employment relationships increased, often referred to as the ‘fissuring’ of the workplace (Weil, 2017). Reflective of this change, from the 1970s to the end of the 1990s, the temporary help industry grew steadily by about 11% each year (Kalleberg, 2000). Bolstered by periods of economic downturn, temporary help work increased as short-term on-demand work often absorbs employment problems associated with a volatile economy (Peck and Theodore, 2007). Scholars quickly noted that this arrangement created increased precarity for workers and represented a degradation of the traditional labour market (Gonos, 1997; Gottfried, 1992; Hatton, 2011; Smith and Neuwirth, 2008).

At the same time, throughout the literature, LMIs became understood as any institution that brokered the relationship between employee and employer (Benner, 2003; Hilton and Lambert, 2015). While there was some effort to delineate differences across groups of LMIs such as for-profit firms from those working in the interest of employees (Freeman and Gonos, 2009), these institutions were overwhelmingly considered in similar ways (Autor, 2008; Benner, 2003; Benner et al., 2007; Chapple, 2006; Hilton and Lambert, 2015; Taras, 2002; Wial, 1991). Most often, analysis has focused on questions of why employers choose to use a LMI and what purpose the LMI serves for the employee (Benner et al., 2007; Chapple, 2006; Hilton and Lambert, 2015; Houseman, 2001). For example, Benner et al. (2007), studying the negative or positive impact that different types of LMIs have for employees find that temporary help agencies have negative impacts on long-term employment opportunities and that ‘other LMIs’ have little to no impacts. Houseman (2001), studying the use of flexible employment relations in the US finds that companies primarily use LMIs to address fluctuations in permanent staffing. Likewise, Hilton and Lambert (2015) find that LMIs in the hotel service industry are more likely to use a variety of firms to quickly fill low-level jobs compared with a more targeted approach used when filling high-level positions.

Despite much attention to the uses, outcomes and impact of LMIs, previous work describing these institutions broadly has concealed key features that distinguish firms of on-demand labour from many of the aforementioned forms. For example, Hatton (2011) demonstrates how Manpower sold the idea of on-demand labour to both the firm and the worker by marketing the ‘flexible’ nature of the labour arrangements using advertisements targeting ‘housewives’ in the 1970s. Smith and Neuwirth (2008) point to how temporary help firms specifically crafted ‘good’ temporary workers using particular rhetoric in trade magazines and other publications.

As on-demand labour through the use of gig platforms such as Uber, Lyft and DoorDash emerged in the 2010s, scholars began to note the intermediary role gig firms play in the employment relationship (Barratt et al., 2020; De Stefano, 2016; van Doorn, 2017; Woodcock and Graham, 2020). Positioning on-demand firms as ‘platform intermediaries’, van Doorn (2017) explains how immunity, control and superfluity/fungibility ‘turn labour into a captive revenue stream that secures shareholder value while rendering workers largely invisible to customers, to each other, and even to themselves’ (p. 904). Similarly, Barratt et al. (2020), studying food delivery workers in Australia, demonstrate how platforms actively intermediate through a quadrangular relationship between worker, end-user, the restaurant, and the firm. In doing so, they exhibit how firms make dual markets by drawing delivery drivers into particular geographies and through offering delivery services. Referring to platforms as ‘digital intermediaries’, Stewart and Stanford (2017) point towards a triangulation of gig work that mirrors the labour arrangements in the temporary help industry focusing on the lobbying efforts of companies such as Uber to gain favourable regulations around local operations.

Similar to the murky definitions of the LMI in the temporary help industry, scholars studying intermediation within the gig industry have yet to agree on how to understand the role of these firms. These insights from studies on labour market intermediation highlight key characteristics of firms of on-demand labour that have yet to be synthesised throughout the literature as indicative of particular types of firms. The importance of explicating these features for a clear sociological conception of the function of for-profit LMIs increases as on-demand labour appears in new forms, such as gig work.

Labour market engineers

Developing a sociological conception of labour market intermediation that centralises the characteristics that lead to labour market rearrangements takes on new importance with the growth of on-demand labour in the gig industry. As such, this article proposes a new term to describe the interventionist nature of firms of on-demand labour centralising them as market shaping agents. Labour market engineers (LMEs) are firms that extract revenue from the job placement of in-person and on-demand workers, actively intervening in regulatory processes to maintain and expand casualised labour markets. Pointing to key characteristics of on-demand labour firms, this definition can be used to understand firms such as temporary help agencies (e.g. Manpower and Adecco) and gig companies (e.g. Uber, Lyft and Doordash).

Building from the work of scholars who have noted the interventionist or market making behaviour of on-demand labour firms (De Stefano, 2016; Freeman and Gonos, 2009; Hatton, 2011; Peck and Theodore, 2002; Richardson, 2020; Smith and Neuwirth, 2008; Stewart and Stanford, 2017), this article pinpoints four key features, which are defined and discussed below, exhibiting how intermediaries within on-demand labour arrangements engineer markets. LMEs have a profit model based on a triangular employment relationship (Gonos, 1997), they manipulate regulations (Freeman and Gonos, 2009; Peck and Theodore, 2002), saturate labour markets to ensure demand is met, and craft cultural conceptions around their services (Hatton, 2011; Smith and Neuwirth, 2008). Each feature will be described in turn. These four mutually reinforcing features mark LMEs as distinct and separate from other forms of labour market intermediaries such as unions, head hunters or career counsellors. Furthermore, labour market engineering results in shifts within labour markets and increased precarity for workers. Importantly, the adaptability and influence of these institutions in finding new ways to maintain the same forms of exploitation should be examined closely and seriously as scholars of on-demand labour have long cautioned (Osterman, 1999; Peck and Theodore, 2002; Standing, 2011).

Triangular employment relationship

Scholars studying labour market intermediation point out that the triangular employment relationship (Figure 1) is a defining aspect of temporary help work (Gonos, 1997; Gottfried, 1992; Kalleberg et al., 2000; Theodore and Peck, 2002). Temporary workers, the temporary help agency and the client are organised in a triangle where the client contacts the temporary agency seeking workers, after which the temporary agency sends an available worker to the client’s firm (Gonos, 1997).

Figure 1.

Temporary help industry – Triangular employment relationship.

Likewise, gig firms rely on a triangulation between the worker, the customer seeking services and the ‘algorithm’, which places the aforementioned in contact with one another (Figure 2). Similar to the temporary help industry, the arrangement of labour is facilitated through a triangulation between the gig worker, gig company and the customer (Barratt et al., 2020; De Stefano, 2016; van Doorn, 2017). While the triangular employment relationship is descriptive of the location of the institution as situated between employee and employer and can therefore be applied to any labour market intermediary – LMEs use the location as the primary component of their business model and sole form of profit therefore becoming a distinguishing feature of the LME. The triangular employment relationship for LMEs enables value extraction and is used as a mechanism of worker control.

Figure 2.

Gig economy – Triangular employment relationship.

As shown in Figures 1 and 2, the triangular employment relationship is the way in which temporary and gig firms extract revenue. The temporary help firm takes a cut of a worker’s wages with each job placement and the gig firm takes a cut of each service performed after matching the worker with an end-user. In both cases, the triangular employment relationship enables firms and their clients to attend to fluctuations within the economy or avoid costs associated with standard employment (Peck and Theodore, 2007; Rosenblat, 2018; Schor, 2020; Theodore and Peck, 2002; Vallas and Schor, 2020; Woodcock and Graham, 2020). Similarly, the triangulation of employment within the gig industry enables costs to be transferred almost wholly onto the worker otherwise known as the ‘demutualization of risk’ (p. 396) (Stewart and Stanford, 2017). For example, Uber drivers are responsible for vehicle maintenance as well as any accidents or issues they incur during service provision (Gregory, 2021; Schor, 2020; Stewart and Stanford, 2017; Vallas and Schor, 2020).

Beyond value extraction, avoiding employment related costs and devolving risk, the triangular employment relationship places the firm in a unique position to control workers. As Gottfried (1992) explains, temporary help workers are subjected to control at both a bureaucratic and decentralised level experiencing surveillance and policies from the agency and their work placement location. Likewise, gig workers are controlled by the ‘app’ or through the algorithmic management process and by their ‘customer’ as they provide services (Barratt et al., 2020). Despite the narratives presented of flexible labour arrangements or claims that you can ‘be your own boss’, the LME uses their position to craft a particular type of worker through training, rules and policies in the instance of the temporary help agency (Gottfried, 1992; Purser, 2012) and through customer ratings, algorithmic assignment of jobs and app based surveillance mechanisms in the case of gig firms (Barratt et al., 2020; Purcell and Brook, 2022; Rosenblat, 2018).

Regulatory manipulation

Firm strategies to shape or avoid regulations have been documented across industries in the US including the food and alcohol industry (Miller and Harkins, 2010), the cellular phone industry (Duso, 2005) and the healthcare industry (Quadagno, 2004), as just a few examples. These strategies often involve lobbying, forming networks with elites, coalition building and marketing (Anastasiadis, 2014; Duso, 2005; Lamberg et al., 2004; Lord, 2000; Miller and Harkins, 2010; Quadagno, 2004; Reese and Rosenfeld, 2002; Walker, 2009; Walker and Rea, 2014). In line with this long history, firms of on-demand labour seek to limit or change rules that they deem as an impediment to their operations. Regulatory manipulation is a key feature of market engineering as these firms purposely and actively work to shape the rules that govern their operations. Unlike the triangular employment relationship, this feature is not observed as a behaviour indicative of most labour market intermediaries. Firms of on-demand labour engineer regulations by building political alliances, disrupting existing markets and shifting the terms over which regulations are formed across scales of decision making (Berg and Johnston, 2019; Gonos, 1997; Peck and Theodore, 2001; Zwick, 2018).

Employee misclassification has been the prime example across both the temporary help and gig industries where firms have built alliances with elite decision makers, lobbied and used aggressive marketing to obtain favourable regulations (Baber, 2022; Borkholder et al., 2018; Gonos, 1997; Kalleberg et al., 2000; Kessler, 2018; Rosenblat, 2018). Regulatory manipulation is sometimes in direct relation to labour law and in other instances tangential. Both reveal an effort to protect their business model of undercutting their more heavily regulated counterpart and will be covered in greater detail in the discussion.

Saturation of markets

Both the temporary help and gig industries rely on saturation of the market place to ensure quick provision of services. On-demand labour firms take on little additional costs to have large pools of available workers and as a result saturate their own market. For example, scholars studying temporary work in the manufacturing and warehouse industries have noted that workers have to be prepared to line up in the early morning hours to increase their chances of employment for the day. New comers often lose out on job opportunities due to an unfamiliarity with the hidden rules for work acquisition (Purser, 2012). By relying on a core contingent of workers seeking work to show up day after day, temporary help agencies are able to easily provide their clients with workers and can pick and choose their ‘best’ workers for particular jobs (Ofstead, 1999). While gig companies do not pick particular workers for particular jobs, they similarly rely on an overabundance of service providers to guarantee rapid placement with customers. Companies such as Uber and Lyft have used gamification strategies, incentive programmes and heat mapping to lure more drivers than necessary onto the road (Rosenblat, 2018; Veen et al., 2019; Woodcock and Johnson, 2018), ultimately driving down wages for workers.

Saturation of labour markets with workers facilitates an increasingly competitive environment. Workers are forced to learn hidden rules or try to game the system to guarantee wages for the day (Veen et al., 2019). Saturation of markets with workers to meet manufactured demands (more on this below) is a distinguishing feature of labour market engineers. Contrary to the traditional labour market where employment is limited to ensure efficiency, the temporary and gig industries over ‘employ’ to maintain quick service provision. Furthermore, workers are put in direct competition with one another as a mechanism of control and engineering of labour markets. Workers are forced to go above and beyond in often costly ways to maintain high ratings (gig workers) or to ensure a re-request of services (temporary workers). Beyond the saturation of markets, the need and desire for these types of on-demand jobs is curated through marketing campaigns targeted at both workers and end-users.

Cultural manipulations

Labour market engineers craft cultural meanings of flexible labour for workers and the labour market as a whole. This is done primarily through aggressive advertising and trade publications that tout the benefits of flexibility for both the worker and the client or end-user (Hatton, 2011; Kessler, 2018; Smith and Neuwirth, 2008). Critical to market making is selling the cultural perception of need and efficiency. As an example, the temporary help industry targeted ‘housewives’ with marketing that suggested they could earn a little extra cash for vacations. At the same time, Kelly Services (a temporary help firm) targeted businesses crafting the image of the ‘never never girl’ who never gets sick or takes a vacation. In other words, from both angles the temporary help firm aggressively marketed their new product – temporary labour – shifting cultural perceptions around the nature of work (Hatton, 2011). Similar marketing strategies are observed in the gig industry. For example, Uber, Lyft and TaskRabbit have branded themselves as a ‘side hustle’ also suggesting working on their platforms to earn money for vacations (Kessler, 2018). The guise of flexibility is marketed in specific ways while obscuring the necessary economic need to supplement low wages or lack of full employment in the primary labour market. As such, LMEs engage in a marketing project that encourages workers and clients alike to use their services. This project has been identified in the temporary help industry but has yet to be noted in the gig industry.

Discussion

LMEs have distinct features that have been adapted over decades of market making behaviour from the rise of the temporary help industry in the late 20th century to the gig economy of 2010s. These four features of LMEs are mutually reinforcing resulting in manipulation and intervention of labour markets from both above and below. From below, LMEs control workers and create the need and desire for flexible on-demand employment and services. Drawing from the failure of the traditional labour market to provide full-time well paid employment, LMEs encourage workers to search for autonomy and supplemental income. Labour market engineers then aggressively market their services to clients and end-users. In order to meet demand (real or fictionalised), markets are saturated with on-demand workers. From above, LMEs actively target amenable regulatory bodies to gain favourable regulations that work to retain revenue and undermine otherwise heavily regulated industries. The position of labour market engineers as located between the end-user or client and worker enables value extraction in obscured ways. LMEs from both angles carefully and purposely manipulate markets.

This section compares the features of LMEs in the temporary help and gig industries to demonstrate their market making tactics and further articulate their differences from labour market intermediaries. Importantly, this comparison across industries demonstrates how these institutional behaviours have been updated to attend to new economic and political contexts. Using a variety of strategies, firms navigate different pathways to make markets. While some of these features may be indicative of other industries, intermediaries or firms, the interconnected use of these four key features appears to be unique to firms of on-demand labour.

Triangular employment relationship compared

As noted above, the triangular employment relationship is a key feature of LMEs. It is important to note that labour market intermediaries are also located triangularly between the employee and the employer. Unions and worker centres, as examples, mediate the relationship between employers and employees by negotiating contracts, attending to employee/employer disputes, and managing worker action and organising among other roles (Fine, 2011; Milkman, 2013; Yates, 2009). The key difference between an LMI and an LME is the use of the triangular employment relationship to extract value, control workers and shift risk and employment related costs onto workers.

The value extraction process has been adapted from the triangulation used by the temporary help industry where the gig firm has in some ways taken on the role of the client firm or the agency (see Figures 1 and 2). In temporary help work, the agency takes a cut of wages for job placement and the de-facto employer extracts surplus value from the point of production or service. Conversely, gig firms extract value from the gig worker but also receive the payment of services from the end-user. Gig firms tack on extra fees to the cost of service as an added way to increase revenue. Gig workers are forced to enter labour arrangements without a clear understanding of what their compensation will be since rates, tips and fees are often concealed until after the service has been provided. For example, there have been numerous reports of gig companies taking portions of tips that were administered through the platform (Roose, 2019). This purposeful obscuration of compensation opens the door for the gig firm to double dip on value extraction. Across platforms it is difficult to pinpoint the exact percentage of service costs that workers retain as rates vary by platform and municipality and are commonly updated with new terms of agreement (van Doorn, 2017). This concealment of fees functions as an acute way for industries to continually increase revenue by extracting additional value from workers’ wages (Burawoy, 1979; Gonos, 2001). Gig firms have adapted the value extraction enabled by the triangular employment relationship endemic of the temporary help industry to cut out the client firm and draw additional revenue from hidden fees.

Regulatory manipulation compared

Historically, the temporary help industry has incited legal battles to protect their economic interests, retain a lack of state intervention and limit employer-provided social provisions in US markets (Gonos, 1997; Smith and Neuwirth, 2008). Likewise, gig firms have employed strategies targeting amenable levels of governance to maintain a lack of regulation on their daily operations and overall business model including fighting for favourable employee categorisation as independent contractors (Collier et al., 2018; Dubal, 2017; McCormick, 2016; Pollman and Barry, 2017). For example, in a comparison of Uber in the United States, Germany and Sweden, Thelen (2018) highlights how Uber mobilised interest groups, politicians and particular policy approaches to shift points of contestation and ultimately regulations. Enabled by large pots of venture capital, gig companies target decision makers across scales of government (Hanks, 2017; Johnston, 2016; McCormick, 2016) pointing to the political influence that labour market engineers can have over markets. Acting as ‘regulatory entrepreneurs’ firms of on-demand labour move into local markets with the intent of creating new rules for operation (Pollman and Barry, 2017). These rules may relate to their procedures such as background checks or licensing, but are also more directly related to employment.

For both gig and temporary help firms, employee misclassification has been a key regulatory issue. The temporary help industry in the US strategically lobbied for temporary workers to be designated as de-jure employees of the agency and de-facto employees of the client firm, absolving clients of employee related costs (Gonos, 1997; Kalleberg et al., 2000; Peck and Theodore, 2007). Decades later, gig firms followed suit pushing to retain independent contractor status for gig workers at the city and state levels (Borkholder et al., 2018; Kessler, 2018; Rosenblat, 2018). Independent contractor status is often used to transfer risk and costs on to service providers (Esbenshade et al., 2019). This means that gig companies have no legal requirement to pay employee related protections or provisions such as minimum wage, workers’ compensation or any other employee benefits. Gig companies extract value in part by avoiding costs related with traditional employment and can undercut their more heavily regulated competitor. By employing a variety of strategies to shape regulations, firms of on-demand labour have crafted operating rules in their favour making markets in the process.

Regulatory manipulation for gig firms goes beyond employee misclassification. Gig firms shape regulations about operating rules through lobbying, building political alliances and drumming up public support through marketing campaigns. For example, as New York City worked to implement baseline wages for drivers, in app advertisements targeted users of the platform to call their representatives and voice their concerns over increased costs of services (Baber, 2022). Dudley et al. (2017) explains that Uber expanded into urban areas marketing themselves as the ‘disruptive innovator’ that brought with them ‘friendly’ technology to build alliances with public officials. Other scholars have noted that Uber and Lyft interfere with policy making by lobbying regulators, and threatening to abandon markets when regulations are unfavourable (Borkholder et al., 2018). These examples highlight the economic and political power plays that stand as a key marker of LMEs.

The flip side of regulatory manipulation is the attempt by both the gig and temporary industries to operate as the deregulated alternative to or within an already existing industry. As the temporary help industry steadily grew into the 2000s their success was propelled through strategic efforts around employee misclassification (Freeman and Gonos, 2009), emerging into new markets (Ofstead, 1999) and offering a solution to rapidly changing production or service needs during economic downturns (Houseman, 2001). Firms seeking to reduce labour costs turned to the temporary help agency as the deregulated other in an otherwise regulated sphere. Peck et al. (2005) find that as companies such as Manpower and Adecco worked to gain more market share globally in the early 2000s, they were most successful within countries that had undergone a shift toward stricter regulation such as Spain, Germany, Italy and Japan. In this way, temporary help work operates as both a complement to and siphon from the traditional labour market facilitated by contextually specific regulatory frameworks.

This process is mirrored by gig firms. A particularly resonant example is ride-hailing platforms that operate as deregulated taxis. The taxi industry has a long history of conflict with municipalities over licensing, training, vehicle maintenance and operating procedures (Bagchi, 2018). While each municipality governs ground transport in slightly different ways, across most cities there are regulatory frameworks in place that at minimum collect revenue from permit fees. Uber strategically rolled out their fleet of drivers without oversight by municipalities often times operating illegally (Schor, 2020; Thelen, 2018; Tzur, 2017). By the time municipalities had caught on to their operation the public had already been subjected to the very persuasive marketing campaigns making them more difficult to regulate retroactively (van Doorn, 2017). The very existence of Uber is premised on operating as the deregulated counterpart to the taxi industry. Unlike labour market intermediaries across groups such as non-profits, unions and professional associations, LMEs rely on a more heavily regulated counterpart as a mechanism to provide a less regulated option outside of a variety of rules including labour, licensing and safety, which they have actively avoided or shaped.

Market saturation

In an effort to craft labour markets and create ‘good’ workers, LMEs saturate the market place. The temporary help industry relies on the overabundance of unemployed or underemployed workers to fill their clients’ needs. Likewise, gig firms have taken similar cues as studies suggest that many workers perform this type of work as a ‘side hustle’ or supplement to full-time or other part-time work (Kessler, 2018; Schor, 2020). Worker control is facilitated in similar ways. According to Smith and Neuwirth (2008), the temporary help industry used ‘recruitment specialists’ to weed out workers who did not appear to have the traits of ‘good’ employees. Gig firms have changed the terms, relying instead on algorithms and end-users to determine who constitutes a ‘good’ worker. Both are able to effectively deploy these strategies because they are situated between employee and end-user or client firm and have an overabundance of workers to choose from.

Related to the regulatory manipulation described above, efforts fighting to be the deregulated other ensure that services and subsequently workers cost less for end-users or clients. Likewise, relying on reserve armies of labour, including people who have been excluded from the standard labour market, ensures that LMEs will have large pools of workers. Firms have even been shown to place their brick and mortar locations in geographies that are most likely to have high populations of the chronically unemployed and underemployed (Peck and Theodore, 2001). Tellingly, in New York City, as regulations were proposed to improve the working conditions and wages for drivers in the black car industry (which includes Uber and Lyft), gig firms directed the most attention (in terms of dollars and campaigning) to vehicle caps rather than wage increases (Baber, 2022). Gig firms in the transportation industry rely on saturating the market with drivers so that services can be provided in just minutes. As a result, workers have to spend more time on the road and compete with their fellow gig workers in other ways.

What was previously part of the recruitment or selection process observed in the temporary help industry (Gottfried, 1992; Purser, 2012) has since been embedded in the ‘app’. Workers have to navigate an unknown set of standards in an effort to appease the algorithm and the end-user. In a study of Deliveroo workers in Australia, Veen et al. (2019) explain that drivers were encouraged to go into different areas of their cities by colour coded indicators on the map portion of their app. They report that some workers try to anticipate where the higher request areas would be based on previous experience. Other workers explained that they would try to hack the systems coding to capitalise on surcharges in high demand areas. Workers also compete with each other by attempting to go above and beyond during service provision to ensure high ratings and favourable feedback (Gandini, 2019). This may include providing extras such as bottled water during a Lyft ride or by doing more than specified for a job on the TaskRabbit platform (Schor, 2020). By design, workers are pitted against one another through saturation of the market, which creates the illusion of surplus to customers but demand for workers. In this sense, co-workers are made to compete with one another for scarce jobs and little pay, and are coerced into particular types of behaviour noted as ‘good’ as a result of this core feature of LMEs.

Cultural manipulations compared

While temporary work became firmly situated as part of the labour market from the 1950s to today, it did not evolve as a necessary condition (Hatton, 2011; Smith and Neuwirth, 2008). As Hatton (2011) describes, there were widespread marketing campaigns by temporary help firms during the mid-20th century to encourage businesses to exploit the cost saving benefits of on-demand work. This push included advertising as well as the development of specialised sectors of temporary help workers (Hatton, 2011). Importantly, this cultural co-option of the labour market coincides with the regulatory features described above. While on the one hand, these industries are attempting to maintain their profit model by intervening in labour market regulations, industries are simultaneously attempting to craft the image of a necessary secondary labour market, making this a feature of labour market engineers.

The allure of flexibility for both the worker and temporary help firm have historically been used by the temporary help industry and it specifically targeted women as they made up a disproportionate amount of the workforce during the mid-20th century (Hatton, 2011; Smith and Neuwirth, 2008). Recent advertisements and cultural co-options across the gig and temporary help industry are remarkably similar using buzz words and language that suggests flexible schedules, autonomy and great opportunities. Yet, studies show that flexible jobs are not always available, often require extensive amounts of time waiting for work (Purser, 2012; Schor, 2020), and rarely translate into full-time employment (Berg and Johnston, 2019). Studies also indicate that the time of on-demand workers is actually quite structured by the demands of flexible markets and the institutions overseeing the deployment of workers (Berg and Johnston, 2019; Purser, 2012; Schor, 2020).

A key adaptation to crafting cultural perceptions of on-demand work has been through the use of the ‘side-hustle’ narrative and independent contractor classification, which enables workers to ‘be their own boss’. The guise of autonomy is sold to on-demand workers through strategic marketing campaigns, which purport that while working for their platform, workers are entrepreneurs (Kessler, 2018). Gig workers have been shown in a variety of contexts including in Brazil (Vaclavik and Pithan, 2018), Australia (Barratt et al., 2020) and the US (Schor, 2020) to align their views with hegemonic discourses that suggest entrepreneurship to be a preferred alternative to standard employment (Purcell and Brook, 2022). Moreover, it was recently revealed that as ride-hailing emerged in cities across the world, Uber executives paid academic researchers to produce reports that would translate into favourable press for the firms. In particular, economists reported high earnings for ride-hail drivers that were only later found to have excluded the operating costs taken on by drivers (Lawrence, 2022). The convincing narrative of profitable entrepreneurship enables gig firms to rally up gig workers in support of employee misclassification bills as was the case in California in 2018 (Hawkins, 2020). The gig worker group, Independent Drivers Guild, originally funded by Uber, has continued to grow across the US with independent contractor status as their core issue. While the group has since divested from Uber, they remain tied to self-employment status.

Considering LMEs outside of the US context

This article develops the concept of LMEs using the US context because of particular political and economic circumstances that enable some of these features. Specifically, the regulatory manipulation described above is in part facilitated by the hierarchical nature of regulatory decisions at the city, state and federal levels. In the case of ride-hailing, regulation of ground transportation is commonly the responsibility of municipalities. Therefore, many cities in the early days of Uber and Lyft attempted to implement regulations that would put these services in line with existing rules for taxis. Instead of abiding by local rules, in several cases, Uber and Lyft lobbied state governments to pre-empt city level legislation (Baber, 2022; Wolf, 2022). As a result of the tiered design of decision making in the US, gig firms were able to jockey between scales of governance to shape operating rules in their favour. Outside of the US, there are sometimes fewer, or different, pathways for firms to manoeuvre around and through to dictate regulatory frameworks. Similarly, weak social safety nets and minimal labour protections in the US context facilitate control of workers. As a result of low minimum wages across the US, where minimums can range at the state and city level from $7.25/hr to $16.10/hr (Economic Policy Institute, 2022), workers often have to string together multiple part-time jobs to make ends meet. Considering the cultural manipulations articulated earlier, workers become particularly susceptible to the guise of flexibility and ‘side-hustle’ narratives in order to supplement low wages in the US. Lastly, the US is protective of the ‘independent contractor status’ employment designation and sides with firms that this classification should absolve companies of employee related costs including minimum wage pay and social provisions. In nation states with more robust welfare systems, stronger unions and labour laws, this designation may be less critical and harmful for workers’ economic wellbeing.

Despite these particularities of the US context, studies from outside of the US demonstrate that aspects of the four key features of labour market engineers can be observed in other countries (Barratt et al., 2020; Dudley et al., 2017; Gregory, 2021; Mitlacher, 2007; Pollio, 2019; Richardson, 2020; Thelen, 2018). Evidence of regulatory manipulation is widespread, even when gig firms do not have the specific benefit of a federalist system. For example, in a study of on-demand food couriers in Edinburgh, Scotland, Gregory (2021) describes how worker classification has been contested by gig firms in similar ways to the US context, often through the use of the legal system. Likewise, Dudley et al. (2017) explain that in the context of London, Uber leveraged their political power in 2016, convincing the Competition and Markets Authority to intervene in proposed regulations by the Transport of London (transport regulator). While not quite the same strategy leveraged by gig firms in the US, they still navigated through the different state institutions to find actors more amenable to their business model.

Beyond regulatory manipulation, examples of worker control facilitated through a triangulation can be found across contexts. Research from Scotland shows that gig firms take no responsibility for instances of bodily and physical harm that are experienced during service provision as a result of their intermediary role (Gregory, 2021). In a study of the food delivery platform, Deliveroo, Richardson (2020) shows how marketisation is used to build contingency by placing demands on geographic locations and timing highlighting the third-party coordination and control of service providers in the UK context. Moreover, in a study of the gig platform Airtasker in New South Wales, Australia, Minter (2017) explains that independent contractor status, which relies on a triangular model, has historically been used to undermine labour protections. However, she finds that opposition to Airtasker’s use of independent contractors was led by the Unions New South Wales organisation, which resulted in negotiations and ultimately minimum wages, safety rules and insurance protections for workers (Minter, 2017). This case demonstrates some of the differences between the US and other countries. While gig firms in the US and Australian context similarly use independent contractor status to undermine labour laws, Australian unions, in this case, have more political power and control to limit gig firm influence. Finally, the case of Cape Town, South Africa reveals that Uber tapped into discourses around entrepreneurial empowerment to secure their market share (Pollio, 2019) demonstrating evidence of cultural manipulations by firms of on-demand labour.

The four features of labour market engineers derived from the US context should serve as a spring board for scholars to consider how firms of on-demand labour shape markets across geographies. While particular aspects may be unique to the US, it is worth exploring whether the underlying political and economic manoeuvres remain consistent. Moreover, these features may vary in degree depending on context. For example, countries in need of employment opportunities and economic drivers may be more vulnerable to the regulatory and marketing manipulations of gig firms. Importantly, leading firms of on-demand labour across the temporary help and gig industries have shown to be particularly adept at learning the context specific vulnerabilities of their target locations and adapting their methods for their desired ends.

Conclusion

This article argues that ’labour market intermediaries’ has become an insufficient term to describe the role of for-profit firms of on-demand labour. Temporary and gig firms operate in strategic and manipulative ways from both ends of the labour market suggesting a new description be used to describe the role of these intervening institutions. Labour market engineers, as explained above, have four mutually reinforcing features that more fully describe the active interventions and manipulations that are characteristic across firms of on-demand and in-person labour. Through the triangular employment relationship, LMEs are positioned to extract value and exert control. LMEs use specific political strategies to avoid or shape regulations often enabling the saturation of markets while carefully crafting narratives around flexibility and entrepreneurship. Importantly, economic downturns, failures of the social safety net in the US and a lack of labour protections for workers in general create the opening for these LMEs to step in and manipulate workers and markets. By allowing the space for a secondary somewhat unregulated labour market to undermine the standard labour market, governments participate and support the growth of labour market precarity.

Precarious and on-demand work is rising (Lehdonvirta, 2018). By presenting a new description of third-party intervention by for-profit institutions, clearer analysis of market manipulation and adaptation can be more fully explored. Scholars who study both the gig and temporary help industries should consider expanding on the features provided above. Future research should also pay attention to the adaptability of on-demand labour as these industries find new pathways to skirt regulatory processes, which in turn hurt already vulnerable workers. Furthermore, as the details and embeddedness of these features are explicated, policy makers, organisers and workers can be better prepared to respond to market engineering that benefits firms at the expense of workers.

Footnotes

Acknowledgements

The author thanks the reviewers for their helpful comments. The author would also like to thank the valuable comments and constructive feedback received from Peter Rosenblatt, Stephanie Farmer, Rhys H Williams, George Gonos and Steven Tuttle.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ashley Baber

Ashley Baber is a part-time instructor in the Sociology department at Loyola University Chicago. Her research interests include the gig economy, labour market regulation, urban governance and the political economy of cities. Her current project compares the regulatory process and outcomes of transportation network companies in Austin, Texas, Chicago, Illinois and New York, New York. She has recently published in the journals Planning, Theory and Practice and Journal of Urban Affairs.

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