Higher Education, automation, retraining Australians and micro-credentialing.

The higher education sector in Australia has received government attention in response to the health, social and financial crisis that is unfolding in response to the current pandemic. A recent announcement indicated that the government was focused on funding universities to offer short courses (micro-credentialing) to domestic students who have experienced job loss (Duffy 2020). Federal Education Minister, Mr Tehan, was reported to have said that these reforms were intended to promote the closer alignment of universities with domestic industry and student demands through “innovative micro-credentials delivered flexibly online”.

In reading this, I had a moment of déjà vu. In preparation for being a panel member for an industry focused discussion of mobility, I researched current knowledge focused on the social implications of driverless vehicles and AI. To direct the discussion for the panel, the organisers provided a series of question prompts, one of which related to the question of job loss for drivers with the implementation of driverless vehicles into commercial transportation and delivery services. For this, I reviewed the research on the types of job loss automation will provoke and looked at the case study of the UK coalminers in relation to retraining. I believe, that this research has utility to our understanding how we can retrain those people who have lost their jobs during the pandemic. Particularly when it is not automation that has created job loss, but the immediate need for people to stay at home and do what they can online. Australia is in Stage 3 restrictions currently and the impact of these on the economy and the job market has been intense and drastic.

I will also include my response to another question that related to how the higher education sector would service this retraining need. My research took me to micro-credentials and I think the threads of insight I found are also relevant to the focus of the government reforms to the education sector. In the following discussion, I will keep the original focus of the essay on the social impacts of automation, however I will indicate where the parallels of this semi-hypothetical discussion with the issues that we face right now of the movement of work online and wide scale job loss, particularly across the entertainment and cultural sectors.

How can job loss arising from automation be tackled?

This section will look at the figures and types of work that will be affected by automation. The parallel here with our current situation, where these trends are also at play, is the displacement of work practices into the online environment, as in online teaching in the higher education sector, and job loss across industry sectors that involve tasks reliant on in person practices and co-located services.

MGI research on the automation potential of the global economy included 46 countries representing about 80 percent of the global workforce. The report examined more than 2,000 work activities and quantified the technical feasibility of automating each of them. Manyika (2017) highlights from this research that the proportion of occupations that can be fully automated using currently demonstrated technology is actually small—less than 5 percent.

An additional important finding is that even if whole occupations are not automated, partial automation (where only some activities that make up an occupation are automated) will affect almost all occupations to a greater or lesser degree. The impact will be felt not just by factory workers and clerks but also by landscape gardeners and dental lab technicians, fashion designers, insurance sales representatives, and even CEOs. They find that about 60 percent of all occupations have at least 30 percent of activities that are technically automatable, based on currently demonstrated technologies. This means that most occupations will change, and more people will have to work with technology.

Highly skilled workers working with technology will benefit. While low-skilled workers working with technology will be able to achieve more in terms of output and productivity, these workers may experience wage pressure, given the potentially larger supply of similarly low-skilled workers, unless demand for the occupation grows more than the expansion in labour supply.

Jin et al. (2018) forecast two major trends in relation to ridesharing technologies that may significantly change urban transportation configuration:

• the further integration of ridesourcing and public transit and the adoption of automated vehicles by TNCs.
• Autonomous driving technology is a key competition area for TNCs as “robots can work tirelessly, do not demand a salary, and don’t care for employment status or benefits” (Dudash, 2017).

They argue that having fleets of driverless cars on the street will not only affect congestion and transportation accessibility and safety, but also have a strong impact on the livelihoods of ridesourcing drivers, and even drivers working for the traditional transportation and logistics industry.

Similarly Spencer (2017) argues that digital technologies threaten to eliminate many of the jobs currently held by workers. Advances in robotics mean that machines can replace jobs that have thus far survived automation. He highlights that manual, routine jobs remain most vulnerable to automation. The task of driving a car, for example, has proved difficult for machines to master. With the advent of driverless cars, however, the human tasks of taxi driver and trucker may be under threat.

West (2018) observes that truck driving has long been a well-paying job for high school graduates. In the US, where the analysis is drawn from, this occupation does not require a college degree and is an attractive entry level position for those not seeking higher education. Drawing on the work of Alice Rivlin, an economist, in 2016 the rough estimate would be that driverless deliveries would put at least 2.5 million US drivers out of work.

How could this be tackled? Can we draw on case studies of other industries that have gone through seismic employment shifts to learn ways through?

Case Study: UK coalmine closure

Thursfield and Henderson (2004) explore the impacts of the Selby Coalfield closure where 2071 employees were estimated to lose their jobs. Many of those to be evacuated from the industry had little or no experience of work outside coal mining and their skills were highly industry-specific. The average age of these men is 40, and many have no formal educational or vocational qualifications.

Issues they identified as emerging from the retraining programme, that was implemented to ease the transition from mining to alternative forms of employment, were:

• Historically (white) working class men are the most difficult to attract to education and training and result in non-participation (McGiveny 1999)
• In the context of the Selby closure the survey evidence indicated a preference for retraining by the men, however little attempt was made to offer information that would facilitate meaningful choice.
• The distinction between lifelong learning and retraining is crucial. Selby represents a missed opportunity to promote the benefits of lifelong learning to a section of the UK workforce traditionally excluded from education. Rather, the emphasis was on retraining in narrow job-related competencies.
• Not only must there be a commitment to retraining as part of any retrenchment programme, but that the retraining has to be appropriate to both the trainee and the potential employer.
• The desire for a short course leading to some form of employment is not surprising given the need to sustain a certain income level once the mine closed.

Frey and Osborne (2015) explore trends in automation and points to sluggish job creation caused partly by increasing automation. They argue that secular stagnation in the digital age can only be avoided by a shift towards inclusive growth. The authors highlight the need for long term thinking to mitigate the negative effects of an ever more automated and digital economy.

From this series of case studies, we can see that there are several points that must be considered in retraining people into other occupations. Firstly, if the occupation has not previous required more than secondary education, then there needs to be the development of educational capital and learning skills required to complete a micro-credential course in a new area. Secondly, those who are undergoing retraining, need to have a financial safety net in places that supports them during this time where they are not earning an income.

Is the education system in Australia ready to handle this retraining program?

In their discussion of retrenchment and retraining in the Coalmining sector Thursfield and Henderson (2004) foreground a set of principles that consider the conditions of human emancipation, and prioritise the transformation of society to a more just and egalitarian one through human agency, which they argue is the proper aim of lifelong learning.

At one level, lifelong learning is narrowly defined in terms of employment and the economy (Edwards et al, 1998). Thursfield and Henderson (2004) note that the emphasis of UK policy documents of the time was on the assumed link between economic success and learning. Alternative and more expansive conceptualisations focus on the personal development of the learner. From this perspective, Fryer (1999) states that learning can take many forms, both formal and informal. It can include developing a variety of skills, abilities, competences, and problem-solving capacities. It includes acquiring new information and knowledge, as well as the pursuit of credits and qualifications through programmes of study more conventionally recognised as ‘learning’.

Frey and Osborne (2015) argue that in the context of rising automation, while the concern over technological unemployment has so far proven to be exaggerated, the reason why human labour has prevailed relates to its ability to acquire new skills. At a time when technological change is happening even faster, they predict that a main hurdle for workers to adapt is the surging costs of education (p. 89).

Within the Higher education sector, I would suggest that the focus of the university and academics is on the following:

• Building Industry partnerships, specifically with those industries that may offer job growth in areas where skill-translation is possible.
• Preparation of our graduates for new industry trends within the course of study.
• The provision of online and accessible courses.
• Affordable education for those experiencing technological unemployment and easily accessible and quick turnaround credentialing to support employment pathways.
• Effective placement of our graduates and interns with companies that are effectively integrating automation with work practices.
• Engagement with public dialogue and policy on the topic of technological unemployment.
• The unlocking of academic knowledge outside of paywalls so that it is accessible to a broader public.

The following case study explores the possible application of micro-credentialing and the use of blockchain technology.

Case study: Blockchain based micro-credentialing

Credentials are a type of institutional technology that are produced by the education sector, professional and trade associations, and by government. These certifications benefit consumers by facilitating trust in professional and trade services, and employers by facilitating trusted information about skills and capabilities. The credentials market is expanding with the rise of micro-credentials (mini qualifications that demonstrate skills, knowledge and experience in a given subject area or capability). Industry, employers and students are demanding short courses to quickly address skills gaps, including for rapidly developing technologies such as blockchain.

The Australian government has provided a National Blockchain Roadmap. One of the areas of focus is micro-credentialing within the education sector. This report identifies that Universities have responded to calls to ensure student data is portable in an increasingly digital, globalised environment by establishing a centralised higher education repository. My eQuals, launched in 2017, provides secure access to certified official transcripts and degree documents for 47 universities across Australia and New Zealand and are expanding to non-university higher education providers and TAFEs.

The report frames Blockchain technology as a technological infrastructure on which credentials can be managed and shared. It argues that the ability to record or reference credentials on a blockchain provides benefits to students, education providers, employers and other service providers (including recruitment agencies) in the employment value chain.

Since late 2018, RMIT has offered blockchain-enabled credentials to students and RMIT Online learners through a pilot program with their credentials platform partner, Credly. Students who complete the nominated micro-credentials and online short course from the RMIT Creds and Future Skills portfolio, are given the opportunity to ‘publish’ their earned digital credential to the blockchain, providing meaningful data about their earned skills and capabilities. The digital credentials selected for this program—Collaborating Online; Global Leader Experience; Application Package; and; Developing Blockchain Strategy—had enrolments from individuals from diverse backgrounds and experiences. Through this pilot they found that many participants did not fully understand the potential benefits of having a blockchain record—of having a streamlined, immediate and verifiable record of one’s skills, competencies or academic credential. They concluded that all agents and players within the ecosystem—students, staff, education providers, employers, government—must be convinced of the benefits, including verification, increased efficiencies and speed of transactions.

The report lists several opportunities for the stakeholders involved: advantages for the student include real time credentialing; advantages for learning providers include efficiency gains in issuing of certificates, transcripts and other credentialing resources; and advantages for employers include trusted verification of soft skills and micro-credentials in jobseekers.

Challenges or areas for further consideration and planning identified for blockchain verified micro-credentialing by the report include:

• Maintaining the security and privacy of user data
• Editing or altering an existing blockchain.
• Credentialing outside of an official learning institution will remain a difficult endeavour, unless the blockchain technology can be adequately taken up by workplaces themselves.

The report concludes that Blockchain technology may play a role in the future of the credentials sector by offering a more effective, scalable and secure alternative platform for the production and use of credentials. In doing so, it has the potential to improve the functioning of Australia’s labour markets, increasing the quality of job matching and lowering the cost of HR functions.

When considering the mining case study and other sources, it appears that a clear pathway between the course of study, micro-credentialing and employment opportunities within industry needs to be made for people from lower socio-economic backgrounds to provide them with a pragmatic motivation to re-train. Another successful element of the strategy appears to be that these students are financially supported during the re-certification and skills development process so that they can actually afford to do it.

Whilst the education sector can respond to the opportunity for providing targeted and industry-connected lifelong learning opportunities and perhaps greater affordability and relevance through micro-credentialing, they are only a part of the solution. Peters (2017) argues that in this general environment it seems increasingly unlikely that education by itself will be sufficient to solve problems of technological unemployment. He suggests that technological unemployment, associated with automation, will create greater inequalities and an increasing gap between the returns to labour and the returns to capital.

This is a concern that we also face currently in which unemployment is exacerbated by the degree of an individual’s access to technologies of co-presence for online work. For those who’s profession does not translate into paid online services or who do not have the technology and internet connectivity to continue their work from home, retraining alone will not solve everything.

Currently, the Australian government is focusing on retraining within sectors that either present a frontline response to the pandemic, such as nursing and other health services, technical and medical responses through IT and science, and support the retraining initiative, teaching. Whilst I fall into the teaching response category, I am also a social scientist and researcher. I believe this skill set should be considered as aligned with national priorities. I am able to provide insights that provide context and social intelligence that must accompany the implementation of these initiatives to ensure that they are successful.

References

Frey, CB & Osborne, M 2015, Technology at work: The future of innovation and employment, Citi GPS.

Jin, ST, Kong, H, Wu, R & Sui, DZ 2018, ‘Ridesourcing, the sharing economy, and the future of cities’, Cities, vol. 76, pp. 96-104.

Manyika, J 2017, Technology, jobs and the future of work, McKinsey Global Institute.

Peters, MA 2017, ‘Technological unemployment: Educating for the fourth industrial revolution’, Journal of Self-Governance and Management Economics, vol. 5, no. 1, 2017/01//, p. 25+.

Spencer, D 2017, ‘Work in and beyond the Second Machine Age: the politics of production and digital technologies’, Work, Employment and Society, vol. 31, no. 1, pp. 142-52.

Thursfield, D & Henderson, R 2004, ‘Participation in lifelong learning: Reality or myth? Issues arising from a United Kingdom coalfield closure’, Journal of Vocational Education and Training, vol. 56, no. 1, pp. 117-36.

West, DM 2018, The future of work: robots, AI, and automation, Brookings Institution Press.