Posted: August 14th, 2022

Automation and Job Disparities

Automation and Job Disparities: How Emerging Technologies Impact Employment Across Industries and Groups
Automation and Job Disparities: Analyze how automation and AI affect employment disparities, considering affected sectors and demographics.
Rapid advances in automation technologies such as robotics and artificial intelligence (AI) are transforming workplaces around the world. While these innovations hold great promise for boosting productivity and economic growth, they also threaten certain types of jobs and may exacerbate existing inequalities in the labor market if not properly addressed. This article analyzes how the ongoing automation of tasks is affecting employment disparities across different industries and worker demographics based on the latest research.
Impact on Sectors
Existing studies have found that the risk of computerization and automation varies significantly depending on the specific sector. Occupations intensive in predictable physical activities (38% of U.S. jobs) and collection of data (12% of jobs) are most at risk (McKinsey Global Institute, 2017). According to a recent Brookings report, transportation and logistics occupations have a high average automation potential of 56%, with jobs like food preparation and serving related work having a 47% potential (Muro et al., 2019). Sectors like manufacturing, retail trade, and food services are projected to undergo major transformations.
However, other sectors like healthcare, education, and professional and business services are shifting more towards a mix of human and technical tasks. For instance, AI technologies are augmenting radiologists’ work by automatically detecting anomalies, but human judgment is still required for diagnosis (Topol, 2019). Occupations intensive in managing and developing people such as senior executives, managers, teachers and professors have less than 25% of activities that could be automated (McKinsey Global Institute, 2017). Therefore, while certain industries face severe disruptions, others are complemented by emerging technologies to varying degrees.
Impact on Demographics
The effects of automation also differ significantly across demographic groups. According to a Pew Research study, younger workers are more likely to work in jobs at high risk of automation compared to older generations (Frey & Osborne, 2013). Men have a higher average automation potential of around 46% compared to women at around 44% (Muro et al., 2019). Minority groups also tend to be overrepresented in more vulnerable occupations. For example, 16% of Hispanic workers hold transportation and logistics jobs that are highly exposed to automation (Muro et al., 2019).
Workers without a college degree, who make up the majority, face greater risks as well. Jobs requiring lower educational qualifications like food service, retail, and office administrative support have higher automation potentials (Frey & Osborne, 2013). However, occupations intensive in managing, developing people, and applying expertise like senior executives and professors have less than 25% of activities that could be automated (McKinsey Global Institute, 2017). Therefore, those with higher educational attainment have better long-term job security prospects.
Policy Recommendations
To address the disparate impacts of automation across sectors and demographics, policymakers must adopt a multi-pronged strategy. First, workforce retraining programs need to be expanded and improved to help displaced workers transition into new roles. Second, public-private partnerships can help create jobs in growing fields like healthcare, education, and green energy. Third, universal basic income or wage subsidy programs may provide a safety net for the most vulnerable groups. Fourth, K-12 education must emphasize skills like critical thinking, creativity and social-emotional learning to better prepare youth. Finally, more inclusive growth policies can help disadvantaged communities and minority-owned businesses benefit from new technologies (Muro et al., 2019). With proactive planning and investments in people, the ongoing automation revolution does not need to exacerbate existing inequalities in the labor market.
Conclusion
In summary, while automation brings opportunities for productivity gains, certain industries, occupations and demographics face disproportionate risks based on an analysis of the latest research. Proactive policies are needed to ensure the benefits of emerging technologies are widely shared and no one is left behind during this period of immense change. With a comprehensive strategy focused on retraining, job creation, social protection and inclusive growth, governments can help workers and communities successfully navigate ongoing automation.
References:
Frey, C. B., & Osborne, M. A. (2013). The future of employment: How susceptible are jobs to computerisation. Oxford Martin School, 1-72.
McKinsey Global Institute. (2017). A future that works: Automation, employment, and productivity. McKinsey & Company.
Muro, M., Maxim, R., Whiton, J., & Hathaway, I. (2019). Automation and artificial intelligence: How machines are affecting people and places. Metropolitan Policy Program at Brookings.
Topol, E. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

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