Essay on Unemployment and the Lack of Economic Opportunity Due to Automation and Disruption

Published: 2022/01/11
Number of words: 2433

Are you scared of losing your job? 22 million Americans lost their jobs due to the impacts of the COVID-19 pandemic and related lockdowns in 2020, and many still remain at risk of long-term unemployment and a lack of economic opportunity. Even before the pandemic, systemic issues linked to globalization, such as the offshoring and automation of low-wage jobs had already displaced much of the workforce in vital economic sectors such as agriculture and manufacturing. There are serious ethical implications to this topic, ranging from equity of income distribution by ethnicity and race, to the rise of poverty, educational inequality, poor living standards and mental health issues. A long-term gap in employment opportunities could lead to greater income inequality and lower economic development, health and standard of living outcomes across the board, and more research is therefore crucial to understand how this issue may be addressed effectively. Government policy should play a significant role in alleviating this issue, because it can create new jobs and skilled workers through training and strategic investments, when faced with the impact of the disruptive trends listed above.

Background Information on Problem

Globalization has placed a broad swathe of jobs at risk from offshoring, automation and the global pandemic, and this is only set to accelerate in years to come. Frey & Osborne (2017) showed that up to 47% of the global population’s careers and professions could be automated over the next 20 years. These include both low-wage, low-skilled professions such as manufacturers, farmers and machinists, who have their roles taken over by automated tractors and assembly robots, as well as high-wage, high-skilled professions such as consultants and accounting advisors, who could be displaced by data mining insights software (Frey & Osborne, 2017). The authors also show empirical evidence that it is more likely for an individual with lower wage and skills to have their roles automated than one with high wage and skills (Frey & Osborne, 2017). For example, 60% of food preparation and cleaning jobs may be automated in the coming years, but only 35-45% of healthcare and information technology jobs may be automated in a similar way over the same period (Grossman et al, 2018).

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The brunt of the impact from such a loss in employment and economic opportunity is disproportionately borne by gender and racial minorities, as well as lower-income groups. This poses significant ethical implications. For example, Latinos and African Americans have a much higher risk of job automation, at 61.2% for Hispanics and 55.1% for blacks, versus a lower risk level for Asians (43.2%) and Whites (48.9%) (Grossman et al, 2019). Chessell (2018) also notes that labor market outcomes are set to decline in a disproportionately unfair manner for women and ethnic minorities, and that targeted interventions are necessary for these segments. This trend is driven not only by lower educational qualifications and access to adequate tools for hiring and employment within these communities, but also by implicit discrimination against these minority groups within the hiring process.

Solutions

Foremost, from a policy perspective, governments should intervene to provide unemployment benefits and a universal basic income, which would help to alleviate the short-term impacts of unemployment and a lack of economic opportunity. These financial subsidies would help to ensure that the basic material, health and education needs of citizens who have been left displaced by automation, offshoring and pandemics are met, and help them to build a stable buffer of income to reinvest in training initiatives and entrepreneurship, which could help provide greater employment and economic opportunities. Furthermore, such universal basic income and subsidy policies would help to address the ethical implications of a lack of access to income for marginalized groups, and would help to break the poverty cycle for those groups (Brunn & Duka, 2018). This would allow them to invest more in their children’s healthcare, education and training, thereby ensuring stronger employment and economic opportunity outcomes for succeeding generations. Furthermore, the government can provide subsidies for workers to pursue their own entrepreneurial activities, such as home baking, repair and maintenance businesses. This is particularly helpful in empowering workers to seek their own employment opportunities in a meaningful way.

Secondly, from an education perspective, a key area of intervention for governments to alleviate the issue of unemployment and a lack of economic opportunity is in the area of school curricula reform and retraining programs (Brunn & Duka, 2018). These programs should target disproportionately marginalized and affected students and workers across their employment lifecycle. For example, students should be trained in digital literacy and high-touch employment roles such as nursing, counselling and design, which are at lower risk of displacement by offshoring and automation, and would provide a resilient form of employment opportunity for students. For example, a study of unemployed individuals who received subsidized training for elderly care professions in Germany showed that retraining courses over short and long term timeframes have a significant impact in improving the long-term employment prospects of workers, with 5% of employed nurses being trained unemployed workers (Dauth & Lang, 2019). Given that nursing and healthcare is a vital sector that is set to grow and is hard to offshore or automate, this is a viable approach for intervention by governments to solve the issue of unemployment and a lack of economic opportunity.

Finally, from a professional development perspective, the government can implement programs to place working adults who have been displaced from vulnerable sectors such as manufacturing and agriculture in retraining courses such as professional conversion programs and skills upgrading programs to ensure that they have the requisite skills to proceed to transition to a new sector or industry. This is particularly effective if the industry of transition is of close relevance to their present skills, and is in a sector which is in lower risk of disruption and displacement. For example, workers in travel and aviation can pivot to new industries such as nursing or financial advisory, which require similar levels of empathy and customer service excellence (Chessell, 2018).

Evaluation of the Evidence

The evidence utilized in this paper is credible, peer-reviewed and free of bias, given the use of scholarly articles backed by valid panel data and existing literature. Furthermore, secondary sources, where used, were from reputable printing presses by renowned economists.

Frey & Osborne (2017) shed light on the issue of unemployment by calculating the probability of automation for 700 job roles, and predicted that there was a high risk of automation for up to 47% of American-based professions by 2033, as depicted in Figure 1 (Frey & Osborne, 2017). These included both high-wage careers such as accountants and lawyers, whose roles could be taken over by text mining, analytics, and semantic software, alongside manufacturers and machinists whose roles could be automated by assembly line robots. The authors also demonstrate empirical evidence that the probability a job is automated is negatively correlated with wages and educational qualifications. In other words, low-wage, low-skill and low-education jobs would be more likely to be automated. In terms of the article’s strengths, the authors provide strong empirical evidence using a machine learning methodology and a Gaussian classification process to demonstrate the external validity of their findings. As shown by Figure 1, the article also clearly shows how the issue of automation is linked to the loss of employment and economic opportunity, which supports the need for government policy to alleviate the forthcoming loss of jobs. However, the article is limited because it does not contain qualitative or anecdotal evidence to support its claims, and does not segment the impacts of automation by gender and race, which would provide an indication of which groups may need more government support to address their unemployment issues.

Figure 1: Probability of automation of sectoral jobs by industry

Reference: Frey & Osborne (2017)

Grossman et al (2018) discusses the role of automation in reducing economic opportunity by country, gender, job role, age and geography, and shows that there are structural inequalities in the way automation impacts employment and economic opportunity for certain groups. The authors of this study use empirical methods and existing research to demonstrate how automation would lead to a decline in economic opportunity in terms of labor’s proportion of national income and displace key jobs in areas such as manufacturing, psychiatrists, statisticians, telemarketers, cooks and technicians. The study also shows that Hispanics and blacks are disproportionately more likely to have their jobs automated, at 61.2% for Hispanics and 55.1% for blacks, compared to Asians (43.2%) and Whites (48.9%) (Grossman et al, 2019). Notably, the source provides a counterpoint to Frey & Osborne (2017), by demonstrating how automation’s impact on employment cuts across almost all job industry roles, although lower-income, lower-skill roles are more adversely affected. For example, food preparation, construction and cleaning have a 60% automation risk, but business administration, IT and healthcare jobs do not fare much better at 35-45% risk. Hence, targeted government policy is necessary to retrain and reskill workers accordingly.

Angelucci et al (2020) analyses how the COVID-19 pandemic has affected employment and health outcomes, comparing between remote and non-remote workers in the US. Remote workers are defined as those that have the capability to work from home, while non-remote workers are defined as those who have to be on-premise to work. The authors leverage a large representative panel dataset over four months of 2020 for the study. The authors demonstrate that non-remote workers had three times the rate of unemployment as remote workers. From an ethics and equity perspective, this gap in unemployment and job losses widened for women and ethnic minorities such as Hispanics and African Americans, alongside less-educated workers. Furthermore, non-remote workers who retained their jobs had a decline in respiratory health, likely because they could not protect themselves properly from COVID-19 in their frontline worker roles. The income and health losses were thus borne disproportionately by non-remote workers from minority, female-led, low-income households, which worsened existing inequality. Through the use of sufficient panel data, the source credibly demonstrates the unequal impact of the pandemic on employment. However, the source does not compare the impacts of this period to previous disruptive periods, and does not include ethnographic research to substantiate its claims.

Ethical Outcomes of Solutions

Two ethical outcomes emerge from interventions to protect workers from the impacts of automation, offshoring and global pandemics through policies such as universal basic income, unemployment benefits, education and training. These outcomes are market efficiency, and equity of employment outcomes and income distribution.

Foremost, free-market economists such as the Austrian school economists and the neoliberals would contend that automation, offshoring and pandemics are natural disruptions that would impact the labor market in the course of time, and that intervention to cushion their impact would be wasteful at best and distortionary at worst. For example, funding education and training could be a waste of taxpayer funds, given that the jobs that workers would take up could themselves be displaced in the near future, while universal basic income and unemployment benefits may reduce the workers’ incentives to take up a job, thereby creating a perverse incentive to avoid work. These may cause market distortions that would lead more individuals to be voluntarily unemployed or inadequately trained, and may result in an overall fall in market efficiency and economic output. Furthermore, there is a possibility that the disruption caused by these phenomena may result in a net positive increase in jobs due to an increase in job opportunities in other sectors, which would render government intervention unnecessary and wasteful. In other words, government intervention would have been harmful to the economy, thus posing a negative ethical outcome.

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However, it is worth considering that intervention to cushion the disruption from automation, offshoring and global pandemics also provides a positive ethical impact in terms of the equity of employment outcomes and income distribution. By retraining and funding workers through policies such as universal basic income and education subsidies, governments are throwing individuals, particularly those from marginalized backgrounds, a lifeline to continue supporting themselves. In the process, the government is also preventing the development of further income inequality, and the deterioration of access to healthcare, education and a living wage that may result in further societal problems. Hence, from a utilitarian perspective, it is ethical for the government to actively intervene to preserve these policies and benefits for the public good, and to ensure that employment and income is distributed in as fair and equitable a manner possible to the general population.

Conclusion

The issue of unemployment and a lack of opportunity continues to be a major economic and ethical issue linked to the automation, offshoring and global pandemics wrought by globalization. In a globalized world, workers of all industries and sectors are at risk of losing their jobs and being structurally employed, although empirical evidence demonstrates that women, lower-income workers and ethnic minorities bear the majority of this risk. Therefore, it is crucial for governments to intervene through policies such as universal basic incomes, wage subsidies, retraining programs and curricular reform to ensure that workers can continue to have a viable and sustainable form of employment and economic opportunity in the years to come.

References

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30. http://doi.org 10.1257/jep.33.2.3

Angelucci, M., Angrisani, M., Bennett, D. M., Kapteyn, A., & Schaner, S. G. (2020). Remote work and the heterogeneous impact of covid-19 on employment and healthNational Bureau of Economic Research, 277(49), 120-129. http://doi.org/10.3386/w27749

Bruun, E. P., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies13(2), 112-119. https://doi.org/10.1515/bis-2018- 0018

Chessell, D. (2018). The Jobless Economy in a Post-Work Society: How Automation Will Transform the Labor Market. Psychosociological Issues in Human Resource Management, 6(2), 74–79. https://doi.org/ 10.22381/PIHRM6220187

Dauth, C., & Lang, J. (2019). Can the unemployed be trained to care for the elderly? The effects of subsidized training in elderly care. Health economics, 28(4), 543-555. https://doi.org/10.1002/hec.3863

Frey, C. B. & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114 (1), 254-280. https://doi.org/10.1016/j.techfore.2016.08.019

Grossman, T., Sorells, B., Chessell, D., McQuay, L., & Connolly-Barker, M. (2018). Artificial Intelligence, Workplace Automation, and Collective Joblessness. Annals of Spiru Haret University Journalism Studies, 19(2), 64–86. Retrieved from https://www.ceeol.com/search/article-detail?id=729608

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