March 27, 2019
There are numerous reports that highlight potential effects that new technology will have on the U.S. labor market, and many of them are not exactly what you would expect. For example, with the advent of the internet and ubiquity of spreadsheets in the 1980s, analyst employment soared.1 The new technology unlocked latent demand for more analysis that had been simply too expensive before the new communication and productivity technologies became common. The need for more analysis led to more analysts…even though there were new technologies that made the work more efficient or productive.
In fact, the world economy has gone through major technological changes many times over. Those changes have not led to fewer people working; instead, more people work in new and different types of jobs. However, these changes are not felt equally by workers and the transition to the new normal can be quite painful for some individuals. Consider the different experiences that an agricultural worker and an urban manufacturing worker would have had during the Industrial Revolution. The agricultural worker may have had to change his or her life completely and move to a city or enter a completely new field of work. The manufacturing worker would have seen business grow and new machines change the work, likely making the worker more productive and better off. This means that places are affected by technological change differently, depending on where work is performed. Rural areas felt the changes in agricultural production much more acutely than urban areas during the Industrial Revolution.
Where you live or work could play a significant role in how you experience—or do not experience—technological change and disruption of the labor market. The recent report America at Work: A National Mosaic and Roadmap for Tomorrow explores just this topic. The report, commissioned by Walmart Inc., uses McKinsey Global Institute methodologies and data. It develops a typology of counties across the country and explores the automation potential across the labor market in each of the archetypical counties. One of the goals is to classify and create a better understanding of communities that are similar—for example, from understanding that rural communities that serve as hubs of services and shopping are different from rural communities experiencing disinvestment and economic transition. Similarly, the study looks at differences in economies between strong urban centers and the urban periphery. In all, the report’s authors present eight different archetypes of county economies across the country, and each archetype has a distinct economic base. These different county economies make up a mosaic of activities and economic conditions that create the national economy.
Many counties in these archetypes deal with very similar risks of automation; on the low end, most counties could see about a third of the work tasks disrupted or changed significantly in the near future by automation.2 There is significant variation in the proportion of tasks that could be disrupted on the higher end of potential automation, with the greatest potential disruption coming in resource rich regions (communities that are often based on natural resource extraction industries), where between 34 percent and 62 percent of jobs could be automated.
The report suggests that urban centers are best prepared to deal with automation: their higher-end potential for disruption is only 45 percent. Also, these big, dense, often diversified economies frequently have industries that tap into the same labor pools and skills, so if the area experiences an economic transition, it can be easier for workers to move to different types of work utilizing the same skills. Almost 190 million people—about 60 percent of the U.S. population—live outside of these dense urban centers, though, often in rural and suburban communities that need support in developing community responses to technological change and economic transitions and the related changes in jobs.3
Not only does location matter in how one experiences technological change, but who one is matters as well. Intuitively, this makes sense—when certain industries change, the workers in those industries have to adapt more than people outside those industries. Returning to the earlier example of analysts, they had to incorporate new software and communications strategies into their day-to-day work. Workers in the coal extraction industry have had to deal with more significant transitions, sometimes to different fields of work.
Additionally, minorities and women face greater risks from increased automation because of the types of jobs they hold. At a recent conference held by the Atlanta Fed’s Center for Workforce and Economic Opportunity, the W.E. Upjohn Institute for Employment Research, and Economic Development Quarterly, Emily Wornell, Srikant Devaraj, and Michael Hicks presented research that shows significantly higher exposure to automation for women and black men, in particular.4 One reason is that women and minorities disproportionately fill jobs that have higher risks of job loss and disruption due to automation.5 For example, health care has a high potential for changes due to automation and a high proportion of female workers. Although there may be significant disruption in health care, increasing demand for services in the industry will likely mean very different types of work rather than net job loss.6
Ultimately, these disruptions could have disproportionate effects on the labor market outcomes of groups of workers like women and minorities—and they could be positive or negative. If workers are prepared and trained for changes in work driven by technology, they will be more productive and likely enjoy higher incomes, potentially closing income and employment gaps. If the disruptions lead to negative outcomes—job loss or transitions to lower-paying work—changes in technology could further exacerbate income and employment gaps between gender, race, and ethnic groups.
Whether focused on place or population, workforce development and educational institutions play an important role in preparing workers for technology-driven changes to work. In Investing in America’s Workforce: Improving Outlooks for Workers and Employers, John Hagel, Jeff Schwartz, and Josh Bersin highlight the need to align businesses, workers, and institutions in order to navigate the future of work and the attendant disruptions that could come. In the same book, Michael King, Richard Cave, Mike Foden, and Matthew Stent describe how educational institutions are embracing technology in their own provisioning of services to enhance opportunities for workers and learners. Failing to tailor responses to changes in the labor market and the economy—whether that customization is for a person or a place—could deepen challenges that the future of work may bring. Enacting policies and programs that support transitions and new skill development could play a big role in promoting economic opportunity for disadvantaged individuals, and economic development in disadvantaged places.
Stuart Andreason is the director of the Center for Workforce and Economic Opportunity. The views expressed here are the author's and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System.
1 McKinsey Global Institute (December 2017). "Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation."
2 These estimates are based on the McKinsey Global Institute’s analysis detailed in America at Work: A National Mosaic and Roadmap for Tomorrow.
3 America at Work, p. 5.
4 Wornell, Emily, Srikant Devaraj, and Michael Hicks (2018). "Gender, Race, and Ethnic Differences in Exposure to Automation and Trade-Related Job-Loss Risk" presentation to Expanding Opportunities through Economic and Workforce Development Initiatives conference on May 21, 2018, in Atlanta, Georgia.
5 The presentation does not directly look into mechanisms of why minorities and women end up in certain occupations, but it remains an increasingly important question given the potential exposure to technology-driven disruption in a person’s career. If certain population groups experience disruption and job loss more frequently, it could ultimately affect long-term labor market outcomes and equality of opportunities.