Research shows that automation trends may be widening the racial wealth gap. This article reveals possible interventions that may help African American workers prepare for the future.
There is a well-documented, persistent, and growing racial wealth gap between African American families and white families in the United States. Studies indicate the median white family in the United States holds more than ten times the wealth of the median African American family.1
Apart from its obvious negative impact on African American individuals, families, and communities, the racial wealth gap constrains the entire US economy. In a previous report, we projected that closing the racial wealth gap could net the US economy between $1.1 trillion and $1.5 trillion by 2028.
Despite this, the racial wealth gap threatens to grow as norms, standards, and opportunities in the current US workplace change and exacerbate existing income disparities. One critical disrupter will be the adoption of automation and other digital technologies by companies worldwide. According to estimates from the McKinsey Global Institute, companies have already invested between $20 billion and $30 billion in artificial intelligence technologies and applications. End users, businesses, and economies are hoping to significantly increase their productivity and capacity for innovation through using such technologies.
As determined in our previous report on the racial wealth gap, African Americans start from a deprived position in the workforce, with an unemployment rate twice that of white workers, a pattern that persists even when controlling for education, duration of unemployment, and the cause of unemployment.2 Our prior research also shows that African Americans could experience the disruptive forces of automation from a distinctly disadvantaged position, partially because they are often overrepresented in the “support roles” that are most likely to be affected by automation, such as truck drivers, food service workers, and office clerks.
This article builds on these findings using a new and proprietary data set compiled by MGI to construct a 2030 scenario that projects the impact of automation in the national workplace and specific US counties. We reviewed this demographic and employment data in 13 distinct community archetypes across the country to test our previous findings and discover if African Americans are overrepresented in both at-risk roles and within US regions that are more likely to see job declines because of automation.3
This approach allowed us to examine the “economic intersectionality” of race, gender, age, education, and geography as it relates to the future of work for African Americans.4 Economic intersectionality can refer to the compounded effects of any combination of characteristics associated with economic disadvantage. In this article, we focus on differing levels of automation-based challenges for African American men and women of various ages and education levels in rural and urban America.
We project that African Americans in the 13 community archetypes we analyzed may have a higher rate of job displacement than workers in other segments of the US population due to rising automation and gaining a smaller share of the net projected job growth between 2017 and 2030. By 2030, the employment outlook for African Americans—particularly men, younger workers (ages 18–35), and those without a college degree—may worsen dramatically. Additionally, we find that African Americans are geographically removed from future job growth centers and more likely to be concentrated in areas of job decline. These trends, if not addressed, could have a significant negative effect on the income generation, wealth, and stability of African American families.
The challenges are daunting, but our research reveals opportunities for improvement within the African American workforce through strengthening local economies, shifting education profiles to align with growing sectors, engaging companies and public policy makers in developing reskilling programs, and redirecting resources to ease the transition as automation changes the landscape for African American workers. In this article, we share our findings and note some potential interventions—some of which have already begun.
Understanding the 2030 risk for African Americans
Given that African American workers face a significant amount of risk from the rise of automated technologies in the workplace—and in an effort to identify the most targeted and effective interventions—we analyzed a range of relevant factors including occupations that are most at risk from automation, job growth, and decline in various regions of the United States, and the disproportionate impacts of automation on African American subpopulations. Taken together, these factors reveal the macrolevel and local-level impact of job loss on African Americans.
As shown in prior research, African Americans are overrepresented in occupations likely to be most affected by automation, and this remains accurate for our 2030 projection. In addition, African Americans are underrepresented in the occupational categories that are most resistant to automation-based displacement. African Americans are overrepresented in office support, food services, and production work industries (Exhibit 1). These industries are most vulnerable to a net loss in jobs. Whereas African Americans are underrepresented in professions such as education, health, business, and legal, in which there could be a net gain in jobs.
Our research also shows that African Americans tend to hold occupations at the lower end of the pay scale. Only half of the top ten occupations that African Americans typically hold pay above the federal poverty guidelines for a family of four ($25,750),5 and all ten of those occupations fall below the median salary for a US worker ($52,000) (Exhibit 2).6 Many of these occupations are among the top 15 occupations most at risk of automation-based displacement and are also projected to affect young African American workers without a college degree.
We measured job displacement as a percentage of jobs potentially lost due to automation by 2030 and found that because of their concentration in occupations at risk of automation, African Americans have one of the highest rates of potential job displacement when compared with other groups. While the Asian population has a displacement rate of 21.7 percent and the white population has a displacement rate of 22.4 percent, the African American population has a potential displacement rate of 23.1 percent, which is outpaced only by the Hispanic/Latino population displacement rate of 25.5 percent. While these differences may seem minimal, they translate to a potential loss of approximately 132,000 African American jobs due to automation by 2030.
Our 2030 scenario also indicates that African Americans could capture a smaller share of new job growth in the economy compared with white and Asian populations based on the current job-growth outlook for these groups. There is also a possibility that higher-growth occupations that currently have a high representation of African Americans may become more attractive to workers of other races, further reducing the already small share of new jobs available to African Americans by 2030.
Occupational distribution within the African American community and geographic concentration both affect the potential for job displacement or growth. Building on MGI’s prior identification of 13 discrete community archetypes, we were able to analyze the employment prospects for African Americans in different areas of the United States in the projected wake of automation.
The largest amount of projected African American job disruption from automation could be in areas with the largest African American populations—particularly in megacities, such as the counties that include Chicago and Washington, DC, and in stable cities, such as the counties that include Detroit and Baltimore. However, these geographic archetypes also show the disconnect between areas where African Americans are currently concentrated and areas most likely to see job growth. African Americans are underrepresented in five out of the six projected fastest-growing geographical archetypes and are overrepresented in two of the six slower-growing archetypes, including the one archetype that has shown negative growth—distressed americana (Exhibit 3). Distressed americana showed negative net job growth from 2007 to 2017 and is projected to show negative job growth through 2030. African Americans in these distressed areas may disproportionately feel the negative effects of impending economic and technological changes, see fewer new opportunities, and face additional challenges in transitioning to the economy of the future.
Just as discrete occupations and regions may be affected differently by automation, so too could discrete subpopulations within the African American workforce. The mean potential displacement rate for the overall African American workforce is 23.1 percent according to our research. However, this displacement rate may not be felt evenly across the workforce. African American men have a potential displacement rate of 24.8 percent, and African American women have a significantly lower displacement rate of 21.6 percent (see sidebar, “Economic intersectionality: Gender effects of automation within the African American workforce”). Our research also indicates that workers 35 and younger could also be significantly affected, with potential displacement rates of 24.3 percent. Additionally, African Americans without college degrees have a potential displacement rate of 24.6 percent.