The ongoing debate about the definition of the gig economy and its legal implications has overshadowed the exploration and study of fundamental transformations in labor behavior in Latin America and the world.
It is vital that in order to continue the debate about the gig economy, we first define the term.
The conversation oscillates from simple definitions to complicated and evolving labor law frameworks. One 2020 academic paper offers a baseline definition for the gig economy, describing it as “paid tasks carried out by independent contractors mediated by online platforms”.
BBVA, Mexico’s largest financial institution, pushes the definition further to include any type of tasks performed for a limited and concrete amount of time and no exclusive relationship with an employer. Meanwhile, the World Economic Forum and UK government define it as “the exchange of labor for money between individuals or companies via digital platforms that actively facilitate matching between providers and customers, on a short-term ad payment-by-task basis”. In short, and within the context of the Mexican and Latin American business landscape, Rappi, Uber, Cornershop, Didi, 99minutos, and Zubale are all part of the gig economy.
But more significantly, every single one of these platforms’ partners (i.e. drivers, couriers, delivery men and women, etc.) are part of the gig economy. Today, one in every five workers in Mexico is part of the gig economy. And that number is expected to grow. This represents one of the largest transformations in the labor landscape in the last decade—if not the largest.
The World Economic Forum and UK government define the gig economy as “the exchange of labor for money between individuals or companies via digital platforms that actively facilitate matching between providers and customers, on a short-term ad payment-by-task basis”
These uninvestigated social changes influence most major aspects of consumer behavior, including cash flow availability and cash expenditure, data consumption and technology literacy, and the transfer of information and knowledge. We want to focus on the latter. We would argue that unintendedly, the digital gig economy and non-digital freelance platforms laid the groundwork for the formation of dynamic communities of learning, offering robust upskilling and reskilling opportunities for partners, something otherwise unattainable. In other words, gig economy partners are learning a substantial amount of empirical knowledge through professional experience. It is extremely significant to begin acknowledging and consequently measuring the improvement in professional proficiency of these workers.
To shed light on this topic, we speak of two categories of skill: industry-specific technical skills and cross-sectoral (or transferable) skills.
Industry or job-specific skills are defined as those abilities that allow a candidate for employment to excel in a particular job. For instance, all the skills a teacher needs to do a great teaching job, such as lesson planning. However, it has been historically implied that you need a formal education and a long-term job to develop these skills. For gig economy partners, and because of the nature of the short time gigs, this process is less linear and less tangible.
Uber drivers are an excellent example of this. Rather than just describing an everyday driver, Uber has come up with a narrative that professionalizes driving. On its website, Uber goes to the extent of describing this gig as “an alternative to traditional driving jobs.” And while it does not define a specific skill set for drivers, it does push drivers to a multi-level upskilling process through online courses, in-app gamification, incentives, and, most importantly, user-based ratings.
But we are more interested in the final output: a consistent and “good” driver can expect to recognize and engage better with the country’s driving rules; comprehend, design, and implement sanitary protocols; technology and smartphone management; mapping skills; cash and deposit administration; empathy and customer service; good communication skills; and of course, driving skills.
The sheer increase of these skills transforms the worker completely, without ever undergoing a “formal schooling process”. We’ve spoken with drivers who really hadn’t had any previous experience with smartphones and now can use 10 different apps at the same time.
Furthermore, the constant acquisition of skills, such as city mapping, customer service, and consistency in rider delivery pushes drivers into other services, such as Uber Eats, Uber Delivery/Flash, Uber Black and other more exclusive ride-hailing models and generating other sources of revenue. These professionalization skills are fundamental for either generating an income flow that can compensate for the lack of a formal employment opportunity, or for pushing the drivers into a formal employment opportunity, in which the drivers can leverage their newly acquired empirical experience and knowledge to continue growing.
Gig Economy Skillsets
The reality of gig economy partners, however, is a lot more complex. We can neither tell how the labor supply looks like in the same industry (i.e. how many formal driver positions are available versus the supply of highly-skilled Uber drivers) nor the drivers’ intrinsic motivations.
As a matter of fact, most drivers won’t become formal drivers. Therefore, we must try to understand that most of this newly acquired knowledge will be transferable. Transferable skills are defined as, “skills or talents that can be used in different jobs, career paths, and industries.”
At Rappi, couriers develop a significant amount of transferable skills that will become fundamental to their professional growth, such as customer service, schedule management, and technology proficiency.
We would argue that unintendedly, the digital gig economy and non-digital freelance platforms laid the groundwork for the formation of dynamic communities of learning, offering robust upskilling and reskilling opportunities for partners, something otherwise unattainable
Other skills are learned too, such as debt and cash flow management, among others. It really is worth it to underscore this skill. Deuda (debt) is a negative balance in the Rappitendero (Rappi’s name for its couriers) account. It comes from different places, such as payments in cash, canceled orders after picking up products, and pseudo micro loans to give back their change to clients. Rappitenderos have to be extremely agile in managing and matching their cash against electronic payment balance. Failure to do so can seriously hinder their cash availability at the time of receiving their earnings.
But this is only a layer for it, the concept of deuda itself depends on a combination of the courier’s historical debt management, track record as a courier, speed in depositing cash purchases back to Rappi, and skill increase, which we imagine are factors in user rating. In other words, couriers can increase your potential deuda balance and thus receive more complex and cash operations if and only if they build a good debt track record.
This really is just a shallow overview of the complexity behind debt administration and debt administration is just one of the implications these platforms and algorithms entail for their partners. Each of these implications propel partners to engage in learning communities, involving organic transfers of knowledge from their peers, the platform itself, clients and their ratings, and more significantly, from the experience itself—the constant iteration of these processes until developing a series of hard-shelled skills. However, an undertone of this reality is that the validation of the skill itself is attached to the rating and the algorithm of the platform.
We’ve not heard of people applying to new jobs using their Rappi rating
Few people know about these skills and they have little transaction value in the face of new professional growth opportunities outside each individual gig economy community—or app. We’ve not heard of people applying to new jobs using their Rappi rating; people already struggle enough to communicate their employable skills, let alone when they are not transferable.
The heart of the gig economy is in providing both income and value-added to their users and partners too. However, most value-added, especially skill and learning acquisition, isn’t valid for professional growth. It is indispensable that gig economy platforms not only provide this knowledge, whether it is empiric or taught through courses, but also to provide means for consistent validation over time. Only then, the gig economy will be closer to fulfilling its role as a new cornerstone for income and value production for the labor force.