Will automation and robotics replace most workers across the world? Which industries are at the greatest risk? What will societies do with and for people who lose their jobs? What can individuals, families, churches, and communities do to help?
By Mark D. Harris
By the 1920s, the automated production line, new tools, and the principles of “scientific management” had dramatically increased worker productivity in the US. In 1930, John Maynard Keynes predicted that productivity would increase so much that in 100 years, his grandchildren would need to work only 15 hours per week (Bessen, 2020). This has not happened, of course, because of the vaster array of goods and services now produced, the much larger number of people those products are produced for, and the skyrocketing expectations of consumers throughout the world.
More recently, voices in business, labor, and the general population, have decried automation and robotics as job killing. CNBC reported in 2019 that 25% of US jobs, especially the “boring and repetitive ones,” were at risk for vanishing due to automation (Nova, 2019). Such predictions frighten workers and introduce a list of questions and policy problems. Whose jobs are likely to go? How can we retrain these people into jobs through which they and their families can thrive? What degree of safety net do we need to have for these people in the meantime? Will robots and other types of automation decrease the human need to work so much that in the future, Keynes will be right? Will we all be working 15 hours per week, or less?
Unemployment has a litany of negative health impacts. In addition to the obvious loss of money, unemployment drains meaning, social contacts, and a sense of identity from the unemployed. Unemployed people have poorer psychological health, worse quality of life, and more high-risk behaviors such as alcohol, tobacco, and drug use (Vancea & Utzet, 2016). Those who were unemployed as young adults are more likely to be unemployed or on disability pensions later in life (Vancea & Utzet, 2016).
What will automation do?
Bessen (2020) looked at whether or not automation and subsequent increased worker productivity was associated with high levels of job loss. He discovered that while technology does impact job loss, demand elasticity, defined as a change in demand occasioned by a change in price or income, plays a major role. After a technological advance, productivity begins to rise and then accelerates. As prices decrease, customer demand becomes elastic, as experience in textiles, steel, and automobiles has shown. Employment remains high. Later in the cycle, if the market is sated, demand loses elasticity and jobs evaporate. At that point, the need for retraining and having an adequate social safety net becomes acute. Shackleton (2020) usefully summarized two centuries of predictions and controversies about automation related job losses and focused on two books, The Technology Trap: Capital, Labor and Power in the Age of Automation by Carl Benedikt Frey, and The AI Economy: Work, Wealth, and Welfare in the Robot Age by Roger Bootle.
McKinsey Inc. (2017) estimates that 60% of occupations have at least 30% of their constituent activities which could be replaced by automation. Up to 33% of work activities could be replaced worldwide by 2030 (McKinsey Inc., 2017). By their models, 3% to 14% of the global workforce will need to switch occupational categories by 2030 (McKinsey Inc., 2017). The number of physical labor and office support positions should decrease, while professionals, care providers, builders, managers, technology professionals, and creative artists should increase (McKinsey Inc., 2017).
Using primary and secondary data, Patel and colleagues examined the relationship between concern about job-loss related to automation and self-reported general health (Patel et al., 2018). They discovered that worry about job-loss worsens general health and increases physical and mental distress. Poorer general health imposes absenteeism and presenteeism costs on workplaces as well. As a reminder, absenteeism is productivity lost when a worker is not at work, and presenteeism is productivity lost when a worker is at work, the later occurring because sick or injured employees do not work as well as healthy ones. If they have an infectious disease, they can make others ill by exposing them at work. COVID-19 was an example.
Another question involves the relationship between job retraining and the generosity of the welfare state. If people can live comfortably without working, will they refuse to rejoin the job market entirely? Ioannidou & Parma (2021) discovered that the likelihood of unemployed adults to attend and complete job related adult education and training (AET) varied depending upon the type and generosity of the welfare system present in a European country. One might reason that more generous welfare, such as that present in Scandinavia, would disincentivize working while a stingier system, such as those present in southern and eastern Europe, would incentivize employment. Neither seemed to be the case. Instead, people who were the greatest risk for losing their jobs from automation, regardless of the welfare system, were also the least likely to attend AET.
Borland & Coelli (2017) penned an encouraging historical review on the question of how automation would impact jobs in the near and distant future. Unlike the pessimistic assessments of Frey, Patel, and Ioannidou, Borland expected robots to augment but not replace workers in the overall economy. Like Bessen, Borland looked to demand elasticity to minimize job loss in automated industries and the creation of new jobs, and new industries, to absorb the few workers whose positions are lost to automation. This cheery assessment, however, depends on the availability and acceptance of job retraining and placement. Workers who can’t or won’t transition into new work will neither benefit from nor add benefit to the new economy.
Churches and communities can help the unemployed. Many have food pantries and clothing closets to help with immediate physical needs. One church in northern Virginia hosted a job fair at least once per year. The church brought in experts in resume writing, interviewing, dressing for success, and other skills. Prospective employers attended. In most communities, the congregation itself is a place where hirers and job seekers get together.
Conclusion
Bessen and Borland expected automation to increase worker productivity and therefore lower the price of goods and services. Lower prices and widespread availability should improve demand for most goods and services, which should mitigate the number of workers lost in any given industry.
The global workforce today is more stressed, less hopeful, and less engaged than ever (Gallup Inc, 2022). The impending shrinkage of the global population and the current decline in workforce size in much of the developed world will make jobs better paying and more available, but demand will also decrease. Futurists argue that robots can make up for a lack of people. This may be true of supply, but it is not true of demand. How many hamburgers do robots eat?
Societies must discover how best to retrain those who lose their jobs, how to minimize situational, dispositional, and institutional obstacles to AET, and how to motivate workers to get retrained in the first place. National safety nets must provide life-sustaining resources to those who cannot live without them, but no more than is absolutely required, lest potentially industrious people drown in a sea of unaffordable and unsustainable government programs.
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References
Bessen, J. (2020). Automation and Jobs: When Technology Boosts Employment. Economic Policy. https://doi.org/10.1093/epolic/eiaa001
Borland, J., & Coelli, M. (2017). Are Robots Taking Our Jobs? Australian Economic Review, 50(4), 377–397. https://doi.org/10.1111/1467-8462.12245
Gallup Inc. (2022). State of the Global Workplace Report. Gallup.com. https://www.gallup.com/workplace/349484/state-of-the-global-workplace-2022-report.aspx#ite-393254.
Ioannidou, A., & Parma, A. (2021). Risk of Job Automation and Participation in Adult Education and Training: Do Welfare Regimes Matter? Adult Education Quarterly, 074171362110266. https://doi.org/10.1177/07417136211026635
McKinsey Inc. (2017). Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. McKinsey Inc. https://www.mckinsey.com/~/media/McKinsey/Industries/Public%20and%20Social%20Sector/Our%20Insights/What%20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages/MGI-Jobs-Lost-Jobs-Gained-Executive-summary-December-6-2017.pdf.
Nova, A. (2019, January 25). Automation threatening 25% of jobs in the US, especially the “boring and repetitive” ones: Brookings study. CNBC; CNBC. https://www.cnbc.com/2019/01/25/these-workers-face-the-highest-risk-of-losing-their-jobs-to-automation.html.
Patel, P. C., Devaraj, S., Hicks, M. J., & Wornell, E. J. (2018). County-level job automation risk and health: Evidence from the United States. Social Science & Medicine, 202, 54–60. https://doi.org/10.1016/j.socscimed.2018.02.025
Shackleton, J. R. (2020). Worrying about automation and jobs. Economic Affairs, 40(1), 108–118. https://doi.org/10.1111/ecaf.12392
Vancea, M., & Utzet, M. (2016). How unemployment and precarious employment affect the health of young people: A scoping study on social determinants. Scandinavian Journal of Public Health, 45(1), 73–84. https://doi.org/10.1177/1403494816679555
