Automation expected to disproportionately affect the less educated Hispanics, African-Americans, and the young particularly at risk

Jess Chen Analysis, Employment, Income & Wealth, Reports 0 Comments

Automation expected to disproportionately affect the less educated

© Institute for Spatial Economic Analysis. This Press Release may be shared freely and reprinted in full or partially as long as proper attribution and link back to the source website (www.iseapublish.com) is provided in the reprint or repost.

Whom will job automation hurt the most? Previous research suggests that lack of education increases job automation risk, but how much education is enough? New data specifies automation risk at each level of education and identifies the demographic groups most susceptible to job automation. These groups include Hispanics, African Americans, and the young.

Recent research by the Institute for Spatial Economic Analysis (ISEA) at the University of Redlands breaks down the risks of job automation within the next 20 years by educational attainment, race/ethnicity, gender, and age group. Job automation risk falls with education: workers without a high school degree face an almost six times higher risk than those with a doctorate. Hispanics are 25 percent more likely than Whites to lose their jobs to automation. For African Americans, this number is 13 percent, while Asians are 11 percent less likely as compared to Whites. Workers ages 16-19 have a 66 percent higher chance of job automation than workers in the 35-44 age range. Since more women than men work in professions that are highly automatable (above a 95 percent chance of automation in the next 20 years), twice as many women than men are likely to lose a job that is highly automatable.

ISEA economists combined research by Oxford professors on the probability of automation for various occupations with employment data published by the Bureau of Labor Statistics. The Oxford study suggests advances in machine learning and robotics will render many occupations obsolete.

“While new jobs are likely to be created as existing jobs disappear, there is no guarantee that enough well-paying jobs will be created or that all demographics will share equally in the gains and losses,” explains Dr. Jess Chen, lead researcher on the project. “We set out to understand which demographic groups are most vulnerable to having their jobs automated.”

Comparing the jobs likely to disappear with those likely to stay, it became clear that the complexity of tasks and the education required to master them were the driving forces. “Educational achievement and career orientation varies by demographics. Specifically, certain racial/ethnic groups have lower educational profiles on average than others, and this is going to hurt them in the future – possibly big time,” adds Professor Johannes Moenius, Director of ISEA.

Demographic Breakdown

The chart below shows the demographic breakdown of all jobs in 2016 and the share of jobs in each group expected to be technically automatable within the next 20 years. While Hispanics comprise only 16.7 percent of the working population, at 61.4 percent they face the highest probability of job automation, followed by African-Americans, Whites, and Asians. Gender differences are small on average. The following chart displays the share of each group in total employment (red diamonds) and the probability for each group to face job automation (grey column).

The report also specifies risks across groups for jobs with the highest risk of automation. The chart below shows the demographic breakdown of jobs that have a 95 percent chance or higher of being automated. Most prominently, women (13.4 percent) are more than twice as likely as men (5.8 percent) to lose a job that is highly automatable. The chart again displays the share of each group in total employment (red diamonds). The grey columns now indicate the share of high risk jobs expected to be automated by demographic group.

The Age Factor

Young workers face a particularly high risk of job automation. The share of jobs automatable drops and levels off after age 25 around the 45-49 percent range. The chart below breaks down all jobs by age and displays the share of each age group with automatable jobs.

The Main Driver: Education

The study also quantifies automation risks by educational attainment. The data show a clear pattern: higher educational attainment is associated with a lower chance of job automation. Interestingly, the benefits increase with the degree level: a high school degree reduces automation risk by roughly 6 percent, some college by 10 percent relative to a high school degree, an associate’s degree by 13 percent relative to some college, a bachelor’s by 23 percent, a master’s by 40 percent and a doctorate by almost 50 percent relative to a master’s degree.

Differences in educational attainment likely explain the differences between demographic groups. Young people, workers of Hispanic ethnicity and African-Americans all tend to have lower educational attainment and therefore tend to work in jobs at a higher risk of automation. The chart below shows educational attainment by race and ethnicity. Lower shares of Hispanics and African-Americans have a bachelor’s degree or higher than do Whites and Asians.

The researchers emphasize that technical feasibility does not necessarily translate into job displacement or unemployment, as technology has historically been a job creator. However, automation requires workers to transition from jobs no longer in demand to newly created jobs. This process renders certain skills obsolete and requires acquisition of new skills through education and training.

“Decisions to get education or embark on certain careers are diverse and influenced by many factors,” says Professor Moenius. “But we do think it’s important to see how different groups may be affected. Every worker and every middle school and high school kid needs to know: if you are not getting a broad and solid education, if you don’t embrace life-long learning, there is no guaranteed pay-check around the corner in the future.”

Contact

For more information about this research, please contact:

Jess Chen, PhD
Lead Researcher
Faculty Fellow, Institute for Spatial Economic Analysis, University of Redlands
jess_chen@redlands.edu
(909) 312-7865

Johannes Moenius, PhD
Director, Institute for Spatial Economic Analysis, University of Redlands
johannes_moenius@redlands.edu
(909) 557-8161

About the University of Redlands Institute for Spatial Economic Analysis (ISEA)

The Institute for Spatial Economic Analysis (ISEA) serves regional, national and global business and government leaders in their needs to better understand how socio-economic phenomena affect their communities. A division of the University of Redlands School of Business, ISEA publishes ongoing, timely reports covering retail, employment, housing, logistics and other special topics. The Institute’s ability to illustrate economic trends and patterns through the use of geo-spatial mapping techniques allows easy visual access to its analysis. In addition, ISEA’s ability to provide Zip code level analysis for many of its reports provides valuable detail. Current ISEA economic data and interactive maps may be found at http://www.iseapublish.com/map

About ISEA Publish

ISEA Publish is the independent publishing arm of the Institute for Spatial Economic Analysis at the University of Redlands, School of Business. ISEA Publish offers consulting services to help regions understand their local economy and prepare for potential threats like job automation. Areas of expertise include:

  • Analyzing local economic structure
  • Identifying “peer” regions with similar economic structure
  • Understanding the threat of automation by region

These services are big-data driven, affordable and customizable, and are based on ISEA’s zip code economic data and mapping platform.

For more information about ISEA Publish and its data and consulting offers, please contact:

Christian Staack, CEO, ISEA Publish, info@iseapublish.com, 909-312-3750

www.iseapublish.com

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