The Fourth Industrial Revolution: Focusing on task-centric skills

Regarding jobs and skills, World Economic Forum says, "The accelerating pace of technological, demographic and socio-economic disruption is transforming industries and business models, changing the skills that employers need, and shortening the shelf-life of employees' existing skill sets in the process."

Such observation is ringing alarm bells: what skills should we focus on? To answer it, WEF (World Economic Forum) has also come up with a prescription of the top 10 skills. They are skills of : (1) complex problem solving, (2) critical thinking, (3) creativity, (4) people management, (5) coordinating with others, (6) emotional intelligence, (7) judgment and decision making, (8) service orientation, (9) negotiation, and (10) cognitive flexibility. Well, how are those skills related to countervailing measures to address destructive, productive, and creating effects of the Fourth Industrial Revolution (4IR) should be looked up. Are they equally crucial for all categories of tasks, which are subject to experience rapid transformation? Should every country, irrespective of development state, pay similar attention to the WEF prescribed skill set?

To find answer to these and other pertinent questions, we should focus on the role of technology in both destroying and creating tasks. And we should also focus on the distribution of those tasks across firms, industries, and countries. Two recent examples are worth investigating to draw some lessons from reality. The first one is about Adidas's attempt to develop robotic shoe making factories and closing them down after a couple of years. The second is about the job polarisation effect, being reported by numerous studies. In a recent report, Brookings has reported change in employment figures at three significant layers: top, middle, and bottom. Despite the widespread claim that technology creates jobs, the report states negative employment share in the middle segment in the US labour market. It has been reported that employment share in the middle layer diminished by 4.5 per cent from 1982 to 1992, followed by 6.7 per cent during 1992-2002, and the 2002-2017 period witnessed the most significant drop 13.5 per cent. On the other hand, both the top and bottom layers experienced gains. The job loss at the middle layer is very much in parallel with the growth of software and information technology-based innovations for taking over low-level managerial tasks requiring codified skills such as accounting or making financial transaction.

In developing countries, the bulk of formal jobs is in the industrial sectors, whether for import substitution or export. For example, as high as 40 per cent of industrial employment in Bangladesh is in the export-oriented readymade garments industry. A similar situation has been in other developing countries like Vietnam, Indonesia, or Thailand. The nature of productive tasks being performed by the industrial labour force in these countries is highly relevant to figure out the risk of automation and designing countervailing measures like skilling.   

Based on comparative advantage, the production factor -- whether labour or machine (capital) -- is assigned to a particular task. The labour requirement in a firm, industry, or country depends on product set, the volume of production, and the comparative advantage of labour and capital. A task execution complexity demands the capacity of production factors. Execution complexity depends on the need for the capacity in the form of (i) innate abilities, (ii) knowledge and skill earned through training, and (iii) tacit capability gained through experience. Human workers attain the eligibility of performing a task due to both innate ability and earned capacity through training as well as experience (tacit capacity).

On the other hand, machines are built with inanimate materials, which are devoid of task execution capacity to begin with. 

The value addition of developing countries in the global industrial value chain has been at the bottom layer. It requires the execution of tasks primarily through innate capability. To address competitiveness, global companies have been investing in developing machine capability as a better substitute for the labour of developing countries. But the threat of automation of taking over low-end manufacturing tasks is real. As a countervailing measure, what should be a re-skilling intervention for developing countries? Should they keep give training to factory workers in acquiring the WEF recommended skills? There appears to be a very weak correlation between WEF recommended skill sets and the capability need of the factory workers. Instead, the focus should be on sharpening innate capabilities like control precision, manual dexterity, fuzzy rate control, and multi-limb coordination. Honing these skills will lead to the improvement of productivity on one hand and on the other, it will increase the barrier for technology to attain comparative advantage.

Not all tasks in an occupation are equally vulnerable to technology. As a result, automation will start taking over one after another task. Therefore, the task-level analysis should be taken into consideration to figure out the skill transformation and to design compensatory measures. The gradual progression of technology in taking over tasks will demand labour to collaborate with machines in performing jobs. Such collaboration is not only limited to workers but also extends to the user level. For example, a bank employee requires to interact with computing machinery to perform her job. On the other hand, a bank client needs to operate an automated teller machine to consume financial services such as drawing cash, depositing cheque, or checking the status of the account. In performing respective tasks whether as an employee of a bank or as an account holder, they need to focus on attaining the skill of how to interact with intelligent machines, as opposed to being skilled in WEF recommended skills like creativity or cognitive flexibility. On the other hand, to design next-generation artificially intelligent machines, WEF recommended skill set becomes vital. Despite the possibility, developing countries need to build substantial capacity to benefit from this creative prospect of the Fourth Industrial Revolution. 

It appears that for the foreseeable future, developing countries will face two significant challenges in the age of intelligent machines. The first one is about to increase comparative advantage of their industrial labour force. And the second one is to increase the capability of the consumers to interact with smart machines to consume services. To address them, WEF recommended skill set does not appear to be helpful for developing countries. They should better focus on analysing the innate capabilities needed for performing those tasks and  determine the necessity for collaboration with machines in getting jobs done, and developing necessary skills accordingly. 

M Rokonuzzaman, PhD is an academic and researcher on technology, innovation and policy. [email protected]