Issue 2 | Article 6

Abstract
Academics are continually updating their courses to reflect changes in the content (structure and behaviour) of their discipline, the understanding by the academic community of this content, and the content needs of the students who are enrolled in the course. In this article, two authors who teach an undergraduate course in Economics at a higher education institution in Sydney discuss the increasing importance of technological unemployment, and how they have changed the content of their course to accommodate this development.

Introduction
The content of an academic course can change because the subject matter of the discipline has changed, the academic community’s understanding of that subject matter has changed, or the requirements of those taking the course have changed. Examples include the effects of the COVID-19 virus on the discipline of medicine, our better understanding of the relationship between the burning of fossil fuels and average global temperature on the discipline of climate science, and the switching of automobile demand from petrol-driven to electricity-driven cars on the discipline of automotive engineering. In Economics, they include the growing impact of new technologies on unemployment, our greater appreciation of the role of budget deficits on economic management (Modern Monetary Theory), and the ageing of populations on welfare analysis. In this article, the authors discuss some of the changes they have made to an undergraduate course in Economics in response to the growing impact of, and concern about, technological unemployment.       

A brief history of technological unemployment
The concept of technological unemployment is not new. More than two millennia ago, Aristotle (384-322 BCE) observed that:

“If every instrument could accomplish its own work, obeying or anticipating the will of others, … if, in like manner, the shuttle would weave and the plectrum touch the lyre without a hand to guide them, chief workmen would not want servants, nor masters’ slaves.” (Medved, 2015)

Nor is fear of technological unemployment a new phenomenon. In 1589 an Englishman named William Lee made the first stocking-frame-knitting machine. (He made it, not because he was a dyed-in-the wool entrepreneur, but because a young girl he was courting was showing more interest in knitting than in him.) Twice William asked Queen Elizabeth I to grant him a patent for his invention. Twice the Queen denied his request, the second time stating:

"Thou aimest high, Master Lee. Consider thou what the invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment, thus making them beggars." (Laskow, 2017))

Concern about the impact of technology on employment reached a peak in the northern counties of England in the early 19^th century. In 1811, a group of six workers broke into a hosiery factory in Nottingham. They waited until midnight and then, using large wooden hammers and timing their blows so the sound was drowned out by the striking of the town clock, smashed as many machines as they could in the short time available to them. Their action sparked a wave of similar attacks throughout the Midlands. When the police arrived at the factories and asked who had destroyed the machines, the workers would reply “It must have been Ned Ludd!”, referring to the intellectually challenged son of a textile worker in Nottingham who, in 1799, had smashed two textile frames. Their answer to the police brought possibly unwanted fame to Ned, with the anti-technology movement now often referred to, disparagingly, as the Luddite Movement and its adherents described as Luddites.

The term, technological unemployment, is generally attributed to the famous English economist John Maynard Keynes who, during the Great Depression, when more than 30% of the European and North American workforces were unemployed, wrote:

“We are being afflicted by a new disease … technological unemployment. This means unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.” (Keynes, 1930)

However, during the Industrial Era (1800-2000), the fears of the Luddites – and of Keynes -seemed to be unfounded. Over this 200-year period, cyclically adjusted unemployment in Europe and North America was reasonably stable at about 5% of the work force. The share of wages in GDP was also roughly unchanged at around 60% (the share in NDP – Net Domestic Product – which subtracts depreciation from GDP, was higher, at about 75%). The prevailing view among labour market economists was that technological change affected the composition of the demand for labour, but not its level (Autor, 2015). In an analysis of data from 21 developed countries for 1985–2009, Feldmann (2013) concluded that technological innovations did give rise to a short-term rise in unemployment, but overall unemployment returned to its original level after about three years. Hence, Economics courses in the late 20^th and early 21^st centuries tended either to ignore technological unemployment or to use it as an example of the failure of non-economists to understand the nature and strength of the adjustment mechanisms that operate in flexible labour markets. Those who expressed deep concern about technological unemployment were accused of being guilty of the Luddite Fallacy.

However, during the middle part of the Industrial Era there was a harbinger of what many think now might be the fate of the human workforce in the current era - the Digital Era. At the turn of the 20^th century, England had two great work forces - horses and humans - each with about 3 million members. The invention and popularity of automobiles and electricity then caused the demand for work horses to fall dramatically. Initially, this impacted on the real wage of the horses – the quantity and quality of their feed. (Horses were notably skinnier in 1920 than in 1900). Then, the rise of the new technologies affected the number of horses, which fell to a few hundred thousand by 2030. Now England has only about 50,000 horses, and they are used mainly for racing or kept as pets.

Technological unemployment and the Digital Era
Signs that technology might be having a larger effect on employment – specifically, automation on manual jobs and artificial intelligence (AI) on cognitive jobs - began to emerge toward the end of the Industrial Era. In the United States, the share of workers who had not received a wage increase in the preceding decade rose from about 10% in 1980 to 30% in 2000. By 2016, when Donald Trump was courting unskilled and semi-skilled workers in the “rust-belt” states, the ratio was close to 50%.

Many recent studies support the view that job destruction due to technological change will increase and that the number of jobs destroyed will be high. The most prominent and oft-cited of these is the seminal study by Carl Benedikt Frey and Michael Osborne (2013), which estimated that 47% of the US workforce was under high risk (70% probability or more) of losing their jobs to computerization (automation by computer-controlled equipment) in the succeeding decades. Following widespread dissemination of this study, other researchers applied a similar methodology in other countries, with equally concerning results: UK (35%), Canada (42%), Germany (42%), Switzerland (48%), Uzbekistan (55%), Brazil (60%), and Ethiopia (85%) (Lima et al, 2021).

On the other hand, technological change creates new jobs, and one major study suggests that, in the short run at least, it could produce a net increase in jobs. The Future of Jobs Report (WEF, 2018). which was commissioned by the World Economic Forum, surveyed a group of companies employing about 15 million workers and found that while these companies expected technological change to destroy about one million of their jobs in the five-year period ending 2022, they also predicted that it would create about 1.75 million additional jobs during the same period.

The longer-run outlook is more pessimistic. Some economists and most futurists, applying Moore’s Law or plausible variants of it to technological change, believe it is inevitable that, at some future date, machines and algorithms will be able to perform all jobs – physical, mental, and emotional – much more quickly and at lower cost than can natural (i.e., unenhanced) biological humans. Calum Chase points that, if machines continue to double in capability every 18 month (Moore’s law), they will be 100 times more powerful in 10 years, 8 thousand times more powerful in 20 years, and more than a million times more powerful in 30 years. Eventually, he argues, technology will lead to natural, biological humans becoming unemployable.   

Changes in course content
The discipline of economics can be divided into six areas: the two microeconomic areas of resource allocation and income distribution; and the four macroeconomic areas of unemployment, inflation, the balance of payments, and economic growth. The unemployment component of the course taught by the two authors focuses on the types, causes, and effects of unemployment, and on policies that can be implemented to address unemployment. Earlier versions of the course either ignored technological unemployment or used it as one example of structural employment. The current version of the course treats it as a self-contained and major type of unemployment. Below, the authors provide some examples of policies they discuss with students that address the issue of rising technological unemployment. The policies are divided into ameliorating and coping policies.  

Ameliorating policies are designed to reduce technological unemployment. On the labour supply side, policy makers might try to speed up the secular decline in the number of hours people work. While many of us claim to be working harder and longer than ever, we may be fooling ourselves. In 1800, in the countries that now form the Organisation for Economic Cooperation and Development (OECD), a typical adult worked about 4,000 hours a year. By 2000, the number had fallen to 1,832 hours, a reduction of about 11 hours a year over the 200-year period. During the first two decades of this century, the average working year in the OECD area declined by a further seven hours a year, to 1,687 hours in 2020 (see table below). Accelerating the substitution of leisure for work would reduce the magnitude of the challenge faced by policies aimed at reducing technological unemployment by increasing the demand for labour.

Hours worked per worker per year in the OECD area, 2000-20

On the demand side, the most promising policy might be to encourage innovation and entrepreneurship. In Australia, the secular supply of labour is currently increasing by about 150,000 workers a year. At the same time, existing firms are shedding a net 50,000 workers a year. But unemployment is not rising by 200,000 every year. Just as they have done since the start of the Industrial Era, new firms are filling the gap. Switching government support from subsidising existing firms to support old jobs (e.g., Holden) to providing a more favourable environment for innovation and entrepreneurship, especially in digitally based products, could be expected to accelerate the overall demand for labour.

But, as Calum Chase notes, eventually the power of Moore’s Law or some variant of it may dominate our best efforts to contain technological unemployment. A very large proportion of the population will probably become unemployable. As with COVID-19, eventually we will have to live with the problem. We will need coping policies. The authors address this worst-case scenario (or best-case scenario, depending on one’s standpoint on the intrinsic value of work) and discuss with students how an income can be provided to the population when there is limited or no market-based work for biological humans. This leads to a discussion of the basic income scheme, which uses the output generated by the machines to provide a minimum weekly or monthly income based on only one criterion – residency or citizenship.  

The authors complete this segment of the course by considering one more coping measure, often referred to as the Merging of Man and Machine (MMM), or the “Join them!” measure. Students are invited to discuss scenarios in which biological and synthetic enhancements to originally natural humans might narrow and then eliminate the work-place advantages of the machines, describe timelines for different outcomes, assign probabilities to the outcomes and timelines, and consider the short- and longer-term implications for their own careers.

References
Autor, D.H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives. Vol. 29, No. 3, Summer. Pp. 3-30.

Chase, C. (2018). The Economic Singularity. https://www.youtube.com/watch?v=FZh_SzVDQVI

Ford, M. (2015). Rise of the robots: Technology and the threat of a jobless future. New York: Basic Books.

Keynes, John Maynard. (1930) Economic Possibilities for our Grandchildren.  https://medium.com/8vc-news/the-future-of-labor-pt-i-keynes-f3ae0f2808b6.

Kurt, R. (2019).  Industry 4.0 in Terms of Industrial Relations and Its Impacts on Labour Life. Procedia Computer Science. Volume 158, pp 590-601

https://doi.org/10.1016/j.procs.2019.09.093

Laskow, S. (2017). A machine that made stockings helped kick off the industrial revolution. https://www.atlasobscura.com/articles/machine-silk-stockings-industrial-revolution-queen-elizabeth

Lima, Y.; Barbosa, C.E.; dos Santos, H.S.; de Souza, J.M. (2021). Understanding Technological Unemployment: A Review of Causes, Consequences, and Solutions. Societies. 11, 50. https://doi.org/10.3390/soc11020050

Biographies

Angus Hooke is Professor, Senior Scholarship Fellow, and Co-Director of the Centre for Scholarship and Research (CSR) at UBSS. His earlier positions include Division Chief in the IMF, Chief Economist at BAE (now ABARE), Chief Economist at the NSW Treasury, Professor of Economics at Johns Hopkins University, and Head of the Business School (3,300 students) at the University of Nottingham, Ningbo, China. Angus has published 11 books and numerous refereed articles in prestigious academic journals.

Harpreet Kaur has a Master of Philosophy and a PhD in Economics. She teaches Economics, Statistics, Governance and Business Ethics at Charles Sturt University, Central Queensland University, and the Australian Institute of Higher education. Her research interests commenced with development dynamics, socio-economic conditions, and the role of women in India and now include economic relationships between Asia, Oceania, and the rest of the world.