From experience, Kenyan lecturers — a good number of whom have their families in the capital, Nairobi, had been used to driving themselves out of Nairobi to lecture and return to their families for the weekend reunion. Using a familiar example, of Taita Taveta University, located some 350 km from Nairobi, the associated direct costs would include expenditure on fuel and car maintenance due to the incurred mileage. The common expenses associated with a standard saloon car have been used in this example. Compared to connecting using Uber taxi then traveling by train from Nairobi at the current rates of 7 USD (economy class) or 20 USD (first class), the direct annual savings would range from 1,800–3,000 USD. If the lecturer decides not to own a car, given reliable Uber taxi and train services, an additional saving on car insurance of about 500 USD would easily arise.
The indirect costs are much more amazing since an accomplished lecturer would also be a consultant, a knowledge leader. The same person has to forgo productive hours by self-driving the 350 km, complete with stressing instances of traffic jam. An average of six hours, one way, or 48 hours for four weeks in a month, would be the result. The opportunity cost may be estimated using a very modest hourly compensation rate of only 100 USD for a research consultancy team headed by such a highly qualified consultant. Based on these assumptions, the total amount that would be accruing to the consultant (lecturer) and the lean team he leads or mentors is a whopping 54,800–56,500 USD in a year.
The Promises of Automation and Gig Economy
The opportunities in the gig economy and such a sharing economy model would, therefore, boost the productivity of knowledge workers and their teams by shunning the huge indirect costs associated with time lost to daily physical activities such as driving to work stations. The margins can only go higher if remote working makes up for most of the face-to-face sessions that made traveling a routine necessity in the pre-COVID era. Finally, key challenges and opportunities still accompany the future of work. As a result, the demand for adaptive and transferrable skillsets is on the rise.
In summary, the following are my key observations premised on the stated stylized facts.
Stylized Fact 1: Automation and thinking machines are progressively replacing human labour.
65% of primary school entrants today will handle completely new jobs, hitherto unknown (The Future of Jobs and Skills, World Economic Forum).
Achieving an optimal mix of manned and unmanned/automated workflows will be a key focus area.
Lifestyle ramifications of the future of work such as isolation, increase in sedentary lifestyle devoid of the “water cooler effect” and the implications for cumulative social capital will grow in importance and urgency.
Stylized Fact 2: Automation is creating more new jobs than the ones it is decimating.
The World Economic Forum has reported that for every two jobs lost to automation, three new jobs are created. This reinforces the need for reskilling, upskilling, and deep-skilling in the face of the rapidly reducing half-life of skills.
New scholarship options and jobs are emerging, e.g. optimal workflow programmers to match humans with machines.
The digital transformation is a boon for knowledge and distant workers in the diaspora (hence the academia) in the new gig economy model. As efficient public mass transit solutions such as fast trains and the sharing economy model leveraged by digital platform services such as Uber and Airbnb gain a footing in Africa, labour productivity, resource savings, and economic vibrancy are expected to rise.
Once machines learn from training data, they don’t forget as human beings do. Accuracy, precision and transparency gains are expected thus.
Automation is helping to free up the human brain from routine tasks to focus more on the tasks that require creativity and complex problem-solving. Automated journalism by the Associated Press and The New York Times, equity research reports in the banking sector initiated by banks such as Commerzbank, are sterling examples
Nashon, a geospatial expert, lecturer and trained policy analyst applies dynamic models to complex adaptive systems. He is a youth mentor on career development and the founder of Impact Borderless Digital.