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davidtrump

The future of work in black America

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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.

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. 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.

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. 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.

mckinsey.com

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