The ongoing debate over the definition of the gig economy and its legal implications has overshadowed research and investigation into fundamental changes in work behavior in Latin America and the world.
To continue the debate on the gig economy, we must first define the term.
The conversation oscillates from simple definitions to complicated and evolving labor law frameworks. One Scientific work 2020 provides a basic definition of the gig economy, describing it as “paid tasks performed by independent contractors that are brokered online”. Platforms“.
BBVA, Mexico’s largest financial institution, takes the definition further and includes all types of tasks that are performed for a limited and specific period of time and that do not have an exclusive relationship with an employer. Meanwhile, the World Economic Forum and the British Government define it as “the exchange of labor for money between individuals or companies via digital platforms that actively facilitate the matching between providers and customers, on a short-term ad payment per task”. In short, and in the context of the Mexican and Latin American economic landsc -e, R -pi, Above, Cornershop, Didi, 99minutos and Zubale are all part of the gig economy.
But more importantly, each and every partner on these platforms (i.e. drivers, couriers, deliverers, etc.) is part of the gig economy. Today, One in five workers in Mexico is part of the gig economy. And that number should increase. This represents one of the biggest changes in the work landsc -e in the last decade – if not the biggest.
The World Economic Forum and the UK government define the gig economy as “the exchange of labor for money between individuals or businesses via digital platforms that actively enable vendors and customers to coordinate on a short-term ad pay per task basis”.
These unexplored social changes influence most important aspects of consumer behavior, including the availability of cash flows and spending, data consumption and technology literacy, and information and knowledge transfer. We want to concentrate on the latter. We would argue that the digital gig economy and non-digital freelance platforms inadvertently laid the foundation for the formation of dynamic learning communities that provide solid training and retraining opportunities for partners who are otherwise inaccessible. In other words, gig economy partners learn a lot of practical knowledge through their professional experience. It is extremely important to start by recognizing and consistently measuring the improvement in the professional skills of these workers.
To shed light on this subject, we speak of two categories of skills: industry-specific technical skills and cross-sectoral (or transferable) skills.
Industry or job-specific skills are set as those skills that enable an -plicant to excel in a particular job. For example, all of the skills a teacher needs to do a great teaching job, such as lesson planning. However, it has historically been implied that you will need formal training and long-term employment to develop these skills. For gig economy partners, and due to the nature of short-term gigs, this process is less linear and less tangible.
Uber drivers are a great example of this. Instead of just describing an everyday driver, Uber created a narrative that professionalises driving. On his website, Uber goes so far as to call this gig an “alternative to traditional driving” Jobs.” And while it doesn’t define specific skills for drivers, it does guide drivers through online courses, in- -p gamification, incentives, and most importantly, user-based assessments, to a multi-level training process.
But we are more interested in the end result: a consistent and “good” driver can expect to understand the driving rules of the country better and to respond to them better; Understand, design and implement hygiene protocols; Technology and smartphone management; M -ping skills; Cash and deposit management; Empathy and customer service; good communication skills; and of course driving skills.
The sheer increase in these skills completely transforms the worker without ever going through a “formal training process”. We spoke to drivers who really had no experience with smartphones and can now use 10 different -ps at the same time.
Additionally, the constant acquisition of skills like city m -ping, customer service and consistency in ride delivery is pushing drivers into other services like Uber Eats, Uber Delivery / Flash, Uber Black and other more exclusive ride hailing models and generating other revenue streams. These professionalization skills are fundamental to either generating a flow of income that can make up for the lack of a formal job opportunity or to push drivers into a formal job opportunity where drivers can use their newly acquired empirical experience and knowledge to keep going growing.
Gig Economy Skillsets
However, the reality of gig economy partners is much more complex. We cannot say what the labor supply is like in the same industry (i.e. how many formal driver positions are available versus the supply of highly skilled Uber drivers), nor the intrinsic motivations of drivers.
In fact, most drivers do not become official drivers. Hence, 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 R -pi, couriers develop a significant amount of transferable skills that are fundamental to their professional growth, such as: B. Customer service, -pointment management and technology skills.
We would argue that the digital gig economy and non-digital freelance platforms inadvertently laid the foundation for the formation of dynamic learning communities that provide solid training and retraining opportunities for partners who are otherwise inaccessible
Other skills are also learned, such as debt and cash flow management. It is really worth underlining this ability. Deuda (debt) is a negative balance on R -pitendero’s account (R -pi’s name for his couriers). It comes from various places such as cash payments, canceled orders after product pickup, and pseudo-microcredit to give their change back to customers. R -pitenderos need to be extremely agile in managing and comparing their cash with the electronic payment balance. Failure to do so could seriously affect the availability of cash at the time of receiving income.
But this is just one layer for it, the concept of deuda itself depends on a combination of the courier’s historical debt management, a track record as a courier, the speed of depositing cash purchases to R -pi and increasing the skills we believe to be that they are user-rating factors. In other words, couriers can add to your potential Deuda balance for more complex and cash operations if and only if they build a good debt balance.
This is really just a superficial overview of the complexities of debt management, and debt management is just one of the effects these platforms and algorithms have on their partners. Each of these implications drives the partners to engage in learning communities that involve an organic transfer of knowledge from their peers, the platform itself, the customers and their reviews and, more importantly, from the experience itself – the constant iteration of these processes through to development a range of die-hard skills. One undertone of this reality, however, is that the validation of the skill itself is tied to the platform’s assessment and algorithm.
We have never heard of people -plying for new jobs using their R -pi rating
Few people know these skills, and given new professional development opportunities outside of any single gig economy community or -p, they have low transaction value. We have never heard of people -plying for new jobs using their R -pi rating. People already struggle enough to convey their transferable skills, much less when they are non-transferable.
The heart of the gig economy is to offer its users and partners both income and value. However, most of the added value, especially the acquisition of skills and knowledge, does not -ply to professional growth. It is imperative that gig economy platforms not only provide this knowledge, be it empirically or through courses, but also provide means for consistent validation over time. Only then will the gig economy come closer to its role as the new cornerstone for the income and value production of the labor force.