How will a changing economy redistribute jobs and economic growth in the United States?
McKinsey just published a new report, “The future of work in America: People and places, today and tomorrow” (summary; longer document), looking ahead to 2030 after new technologies have had some impact. Its thesis is that we’ll probably see urban areas that are economically growing now continue to do well, while a large chunk of America will stagnate or fall behind. The report isn’t happy with this, and asks us to make policy changes now.
I’m fascinated by this, and think the report offers a lot to chew on. Let me identify what I see as the key bits for the future of higher education and technology.
To dive in, some recent historical context:
Twenty-five megacities and high-growth hubs, plus their peripheries, have generated the majority of job growth since the Great Recession. By contrast, 54 trailing cities and roughly 2,000 rural counties that are home to one-quarter of the US population have older and shrinking workforces, higher unemployment, and lower educational attainment. Automation technologies may widen these disparities at a time when workforce mobility is at historic lows.
Differences may be stark geographically. Consider this map, projecting job growth and loss ahead 11 years:
Those black squares are McKinsey’s “megacities,” urban areas with the right mix of population and finance to take off. The white and grey squares are the opposite, and aren’t there a lot of them? We’ll return to that down below.
What about encouraging folks to move from white to black? Not a good idea, it seems:
geographic mobility in the United States has eroded to historically low levels. While 6.1 percent of Americans moved between counties or states in 1990, only 3.6 percent did so in 2017. Furthermore, when people in rural segments and less vibrant cities do move, it is usually to places with a similar profile rather than to megacities or high-growth hubs…
Even as this may occur: “The 25 megacities and high-growth hubs, plus their peripheries, may account for about 60 percent of net job growth by 2030, although they have just 44 percent of the population. ”
Differences can also accelerate by education, race, and age:
The labor market could become even more polarized. Workers with a high school degree or less are four times as likely as those with a bachelor’s degree to be displaced by automation. Reflecting more limited access to education, Hispanic workers are most at risk of displacement, followed by African Americans. Jobs held by nearly 15 million workers ages 18–34 may be automated, so young people will need new career paths to gain an initial foothold in the working world. Roughly 11.5 million workers over age 50 could also be displaced and face the challenge of making late-career moves.
Let’s drill down into those age issues for a moment. Surely young people will be in good shape because digital natives etc? Not so fast:
Tens of millions of Americans can think back to their first jobs in retail or food service— roles that gave them valuable soft skills and experience that propelled them on their way. But these are the very roles that automation could phase out. Roughly 14.7 million workers under age 34 could be displaced by automation; almost half of them are in roles with high separation rates, so employers may not see a clear business case for retraining and redeploying them.
As for folks over 50:
On the opposite side of the generational divide, some 11.5 million US workers over the age of 50 could be displaced by automation. While some of these workers are close to retirement, others have years to go. One study looking at labor market recovery after recessions found that displaced workers ages 55 to 64 were 16 percentage points less likely to be re-employed at the time of follow-up surveys than workers ages 35 to 44.16 While some displaced older workers who have spent much of their career doing one thing may not be willing or able to make a drastic change, millions more might embrace the opportunity to train for different lines of work.
Plus employers don’t want to pay more for older folks when they can pay less for younger ones and robots.
Speaking of paying less, the gender breakdown over time is less clear, and varies by field, not to mention sexism:
Overall, women represent 47 percent of the displaced workers in our midpoint automation scenario, while men are 53 percent. Based on the current gender share of occupations, our modeling suggests that women could capture 58 percent of net job growth through 2030, although the gender balance in occupations can and does change over time. Much of this is due to women’s heavy representation in health professions and personal care work—and some of these roles are low-paying. Improving the representation of women in the tech sector is a priority; today they hold only 26 percent of computing jobs in the United States.
The longer report adds that gender diversity efforts seem to have stalled.
Differences also break out by profession:
What does all of this mean for education?
We can start with enrollment and fields of study. Consider the job growth/decline chart above again. My readers will be unsurprised to see STEM and business continuing to boom, which suggests even more students will enter those programs. The drop in production work, machine operations, mechanical work, and office support suggests vocational tech schools and programs will not be so healthy.
We can also consider which colleges will be hit by their physical location. Goldie Blumenstyk reflects:
I find myself imagining an overlay that shows the locations of colleges across the country. Many of the gray areas on the McKinsey map would be covered by dots, representing many of the same small, private and regional public institutions now facing enrollment and financial challenges. You get my drift here, right? The regions of the country likely to face the biggest economic challenges in the next decade because of automation are also the places filled with established educational organizations that may need a new agenda. Talk about an opportunity.
Here’s one such map, from NCES:
How many of those dots are in and around McKinsey’s megacities? How many are located in those grey and white zones, and recruit students locally? Think about how this vision will play into state legislatures as they consider merging or closing campuses, or how funders plan their investments, or how presidents consider their campus and its strategies.
Moreover, the digital divide – that perennially unpopular problem – can widen as a result of these changes. As Tom Var put it on Twitter,
We have kids who dont know how to use technology because their communities/schools/parents cant afford it. In the fast-pace digital age they wont be able to compete and they are just going to be left behind.
Now imagine that problem but exponentially worse when we add AI into it https://t.co/bbhd39FqDX
— Tom Var (@Tom_Var) July 18, 2019
As I’ve said before, this is vital for higher ed to consider. Do we invest scarce resources into helping our local communities improve their connections? Do we shape our digital services to connect with students on the digital divide’s wrong side, reducing multimedia content?
At the same time McKinsey sees education correlated with economic growth. The report mashes up educational attainment and spatial location like so:
On the one hand, this is great news for higher ed, as it argues for getting more people more college and university degrees. On the other, it’s awful news for low education areas, especially given our low geographical mobility (noted above).
One more detail: McKinsey is pro-apprenticeship. “It will be important to create a wider variety of pathways from high school to work, perhaps through apprenticeship.” Let’s see if trustees and legislators follow this idea.
And let’s see what you think. The comments box stands open.
PS: Let me also say that some of the report’s conclusions could well be wrong. Americans could rediscover the old desire to up stakes and move cross-country. Rural counties could become high tech hubs in a national drive to build up a post-carbon, modernized electrical power grid. Some megacities could decline thanks to crime, disease, natural disaster, or terror attack. As always, we think about the future in the plural, in terms of possibilities.