Here is a brief excerpt from a special report — in this instance by Anu Madgavkar, James Manyika, Mekala Krishnan, Kweilin Ellingrud, Lareina Yee, Jonathan Woetzel, Michael Chui, Vivian Hunt, Sruti Balakrishnan — for the McKinsey Global Institute that was featured in the McKinsey Quarterly, published by McKinsey & Company. To read the complete report, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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This new research explores potential patterns in “jobs lost” (jobs displaced by automation), “jobs gained” (job creation driven by economic growth, investment, demographic changes, and technological innovation), and “jobs changed” (jobs whose activities and skill requirements change from partial automation) for women by exploring several scenarios of how automation adoption and job creation trends could play out by 2030 for men and women given current gender patterns in the global workforce.

These scenarios are not meant to predict the future; rather, they serve as a tool to understand a range of possible outcomes and identify interventions needed. We use the term jobs as shorthand for full-time-equivalent workers.

The research examines six mature economies (Canada, France, Germany, Japan, the United Kingdom, and the United States) and four emerging economies (China, India, Mexico, and South Africa), which together account for around half of the world’s population and about 60 percent of global GDP.

Men and women tend to cluster in different occupations in both mature and emerging economies, and this shapes the jobs lost and gained due to automation for each. In the mature economies studied, women account for 15 percent on average of machine operators, but over 70 percent on average of clerical support workers. In the emerging economies in our sample, women make up less than 25 percent of machine operators on average, but over 40 percent of clerical support workers. Over 70 percent of workers in healthcare and social assistance in nine of the ten countries (the exception is India) are women. However, less than 15 percent of construction workers, and only around 30 percent of manufacturing workers, are female in many countries.

If a scenario of automation unfolds on the scale of past technological disruptions, women and men could face job losses and gains of a broadly similar magnitude. In this research, we explore various scenarios to 2030 developed using MGI’s past future of work research, and its analysis of jobs lost and gained. Our current research breaks new ground by adding a gender lens to that work, and by looking at a broad range of effects on women’s jobs including potential job displacement, opportunities for job creation, the changing nature of jobs, and a quantitative assessment of the transitions that women will need to make to capture these new opportunities, including implications for wages and average education levels. Our main scenario to 2030 is based on a “midpoint” scenario of automation adoption, which models automation at a similar scale to that of other major technological disruptions in the past.

In the case of jobs lost, women may be only slightly less at risk than men of their job being displaced by automation. In the ten countries, an average of 20 percent of women working today, or 107 million women, could find their jobs displaced by automation, compared with men at 21 percent (163 million) in the period to 2030 (See Exhibit 1).

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Here is a direct link to the complete article.

Anu Madgavkar is a partner of the McKinsey Global Institute, where James Manyika is chairman and a director, Mekala Krishnan is a senior fellow, Jonathan Woetzel is a director, and Michael Chui is a partner. Kweilin Ellingrud is a senior partner in McKinsey’s Minneapolis office. Lareina Yee is a senior partner in the San Francisco office and chief diversity and inclusion officer for McKinsey. Vivian Hunt is a senior partner and managing partner for McKinsey in the United Kingdom and Ireland. Sruti Balakrishnan is a consultant based in Chicago.