In this episode of the McKinsey Global Institute’s Forward Thinking podcast, Michael Chui talks with Byron Auguste and Beth Cobert, whose professional lives are dedicated to fostering a more skills-based labor market. Their focus is on the United States, but their diagnostic can just as easily apply to other countries and regions. Both worked at McKinsey & Company for many years on labor-market issues before going on to work in the Obama administration. Today, Cobert is the chief operating officer of the Markle Foundation and helps lead the Rework America Alliance. Auguste is co-founder and CEO of Opportunity@Work. They answer questions including the following:
- Who is actually working in the US labor market?
- How does the language we use—“low skill,” “high skill”—cause problems?
- What could a potential future of a more skills-based workforce look like?
- If employers and people want a skills-based workforce, why aren’t we there already?
- Do we all have to wait until there’s some global skills taxonomy we all use? How does such a thing evolve so that it becomes practical?
Michael Chui, co-host: Janet, let me ask you a question. Of the people with whom you come into contact at work, what percentage would you estimate have graduated from university?
Janet Bush, co-host: Well, I was a journalist specializing in economics for a long time, many years, and I would say a high proportion of my colleagues had a degree when they were journalists. But of course in the broadcast media and the newspaper business, there are a lot of people with different roles, so they wouldn’t necessarily be graduates. But yes, overall I would say high.
Michael Chui: Yes, I would view it as high too, particularly here at MGI. But one of the great things about doing MGI research, and, quite frankly, doing research in general, is learning more about the real world. And it turns out that in the US, where I am based, only about one-third of the workforce holds a four-year college degree.
Janet Bush: Yes, and I actually looked up the figure for the UK, and it’s a little over 40 percent, so the majority of workers don’t have a degree. But nevertheless if you look at job postings, they often say you need a university degree. But lots of people I know who are really good at their jobs aren’t doing anything related to what they actually studied at university.
Michael Chui: And if that’s the case, you could wonder about what a university degree really signals, especially in a place like the United States where students often have to pay a considerable amount of money to attend college.
Janet Bush: Yes, and you could also ask what would it take to create a labor market that matches the real skills a worker has to the real skills required for a job, instead of just going to the default, “Did you go to university?”
Michael Chui: That’s why I was so looking forward to today’s conversation. Our guests are not only old friends. They’re also what some people call “trisector athletes”: they have spent time in the private sector, served in the public sector, and are now both leading social sector organizations focused on creating a more skills-based labor market. And of course the future of work and skills is one of the topics at MGI that we have continued to do research on.
Janet Bush: Well, I am incredibly interested to hear what the conversation holds.
Michael Chui: Beth Cobert is the chief operating officer of the Markle Foundation and helps lead the Rework America Alliance. She previously served as the acting director of the Office of Personnel Management in the US government. Byron Auguste is the CEO and a co-founder of Opportunity@Work. He previously served as the deputy director of the National Economic Council, also in the US government.
Michael Chui: Beth and Byron, welcome to the podcast.
Byron Auguste: Thank you, Michael.
Beth Cobert: Great to be here, Michael.
Michael Chui: If you don’t mind, I’d love to just get a sense from each of you about your own personal story to where you got to today. Byron, maybe I’ll start with you.
Byron Auguste: I grew up 50/50 in Detroit, Michigan, and in Phoenix, Arizona. But my story doesn’t start there. The way I got to Detroit and the way we got to Phoenix had to do with my parents’ story. When I was growing up, my mom graduated from college. She was the first woman to graduate from her architectural engineering program. And my dad did not [graduate]. He basically spent a year at college, and once he married my mom, he dropped out.
He hadn’t really found what he was passionate about. And when I was little, in Detroit, he was working as a shipping clerk in a factory. He just sort of wrote down what came in on the dock and what went out. And he saw an ad in the newspaper saying, “Learn COBOL and punch your own ticket.” COBOL, he didn’t know really what that was, but punching his own ticket sounded good. And so he researched it and found out it was the IBM mainframe computer language.
And in fact, there was an enormous demand for people who could write COBOL. It was a new language. It wasn’t taught in college or anything. And there was this program that said, “You can learn COBOL. And we can teach COBOL.”
My mom had a job. She was sort of an architect’s assistant at that point. There were a lot of barriers to women doing everything that they were qualified to do. She had a steady job, and so it was possible for my dad to take that chance. And he did. And it turns out he learned COBOL. Now, he had never worked in an office before, far less worked on technology. But he turned out to be a good COBOL programmer. But first what happened is that my mom talked to someone in the Detroit Edison IT department—then called MIS department, Management Information Systems—to give my dad a job shadow.
And he did a job shadow. He seemed to know some COBOL. So they hired him into an entry-level programming job. And really, that change was our family’s trajectory shift into the American middle class. And to have higher earnings.
That’s why we could move to Arizona, because my dad literally could write his own ticket. You could get a job anywhere as a COBOL programmer at that time. That story has been very meaningful to our family. And it’s been very meaningful to me. And a lot of the work I’m doing right now I think relates to that.
Michael Chui: Terrific. Beth?
Beth Cobert: Well, I’m a Jersey girl. Not quite born there, but raised there. Went to public school in the suburbs of New York City, in Montclair. In our family, I was the younger sibling, the girl. My brother was the boy. But in a typical role reversal of those areas, I was the math and science person, and he was the guy with words.
My dad was the general manager of a textile company. Both my parents were born and raised in the Depression, with all the things that go with it—which says that it is hard work and to focus on things that will get you ahead.
I spent lots of summers with my dad in his textile company. Our summer jobs were working in the un-air-conditioned factory in New Jersey in the summer, where I learned a lot. And one of the things I learned was actually through watching my dad and how he was so attuned to every person there and really saw the opportunity to learn from each person and what they were doing and how that could make things better.
And I saw the respect he gave to every single person. And I saw how much he got out of those interactions, so much so that actually two of the frontline folks—immigrants who barely spoke English—ended up under his mentorship, both growing in those roles, and ultimately buying the company out from its owners. A real proof to me in real time that if you look for talent and you listen to people, you can learn a lot. And that was a lesson that I have taken, frankly, throughout the rest of my career, which was more traditional.
I spent decades at McKinsey with Byron. In fact, Byron and I left within about a month of each other to join the Obama administration. And coming out of that, I combined my passion for—how do I help people realize their potential? How do you harness the potential of government? How do you think about how you can work with the philanthropic sector, and bringing those things together in what I do at Markle these days, which is really going back to the roots of what I learned from my dad, which is finding the talent that there is in the world and helping people realize their potential, and knowing it will benefit those individuals, their communities, and the companies they go to work for.
Michael Chui: Thank you both for sharing those stories, which quite frankly are stories of economic mobility. And that’s amazing. Again, in full disclosure to our listeners, I’ve known Beth and Byron for years because of the overlap at McKinsey.
I sometimes reflect on the difference between my lived experience and what happens if you look at the totality of the labor force. First of all, I live in San Francisco, which is its own weird bubble. But there’s barely anyone that I interact with on a professional basis who didn’t graduate from a four-year college, or in many cases have a graduate degree. And I would love to—maybe Beth, start with you—just look at the American labor market. Maybe describe it in terms of who’s actually working out there.
Beth Cobert: The people who are working, the people who are making the country run, are largely people who do not have a college degree. Great talent, but not that full degree. Seventy percent of the population virtually does not have a college degree. And that percentage is even higher if you look at Black populations, Latinx populations. So when you have this model that it’s just a college degree is the marker of whether someone can succeed and has skills and makes contributions, you are missing enormous amounts of talent.
If you look at job postings for roles where they’re putting college degrees on them, the majority of people in executive assistant roles don’t have college degrees, yet well over two-thirds of those postings do. Why is that?
There are so many places where, if you really think about what are the talents people have learned that they can bring to the party. And, if you learn these things in college—it's not necessarily that relevant. If you studied computer science, the odds are, unless you graduated in the last five years, you were not required to take a single class in cybersecurity. It is hard to be an IT professional, a computer science person, today without knowing something about cybersecurity.
There are many ways to gain experience. And there's so much experience out there. But that's not reflected in the way people think. The labor market is full of talented people. And the challenge is getting them recognized. And that's what we're working on with Byron and others—to try and address that issue.
Michael Chui: Byron, I have heard you speak eloquently about some of the terms that we use to describe workers: “low skill,” “high skill.” I’d love for you to reflect on some of the ways in which even the language that we use causes problems.
Byron Auguste: I appreciate Beth underscoring that 70 percent of the population. And 60 percent of the current workforce doesn’t have a bachelor’s degree. But, of course, almost everyone in the current workforce has real skills.
Even if you were to take people who did graduate from high school, which is supposed to be where foundational skills and a formal education are taught, and those who are attached to the labor market, they’re gaining skills. And by the way, they’ve also done some training. On-the-job training. They may have been in the military. Lots of different sources. You’re really talking about 71 million Americans who are skilled through alternative routes.
We talk about “STARs”—skilled through alternative routes. And we say that because they don’t have bachelor’s degrees, but they do have skills. And when I speak to audiences, I would often say, “Raise your hands. I’m going to have two groups: those of you for whom the value you are adding on your job today comes mainly from what you learned in formal education, and then those of you for whom the value you’re adding in the job today mainly comes from what you’ve learned on the job in the course of your career.”
And I’ve never had less than 90 percent of the room raise their hands for what we’ve learned in the course of our career, in doing the work. And I’ve had as high as, like, 98 percent, 99 percent [laughs].
If you really think about that, where does this so-called skills gap that we talk about come from, if most of the skills we have that are relevant are coming from what we learn on the job and at work? Particularly since the financial crisis and the slow jobs recovery, you say, “Look, there’s tens of millions of people that we won’t let into these gateway jobs, these career path jobs, these jobs where they’re going to be learning.”
And then you roll that train forward a few years, well, of course you’re going to get a skills gap. But a skills gap is not the cause of the problems we have. A lot of it is about the way we hire. I mean, when you say, “Nobody without a bachelor’s degree need apply for this job. And we will not consider your skills.”
In other words, you say you’ve got a skills gap, but you’re screening out three-fifths of your job applicants without knowing anything about their skills. This is the skill base that you are absolutely overlooking when you exclude people who are skilled through alternative routes, when you exclude STARs.
There is no job that does not require skills. The question is which skills, to which degree, in which formations? When you actually break that down and take the best evidence available through the Bureau of Labor Statistics, the O*NET database and other sources, what you find is that many, many low-wage jobs are not at all low-skill jobs.
There are 30 million STARs that, by dint of the work they are doing today, have the skills for jobs that pay at least 50 percent more than the jobs they’re in. Does this mean they don’t need to learn new acronyms? No, they do. Might they need to learn a new software package, new protocols? Yes. But the core skills in the jobs they’re in could fill these middle-wage roles. And so we don’t ever say “low skill.”
The number of people who will call a job “low skill” who couldn’t do that job for 15 minutes is ridiculous. I mean, honestly. So these are not low-skill jobs. They are low paid for a variety of reasons, including structural reasons in the labor market. We talk about STARs—skilled through alternative routes—because it calls attention to what businesses are looking for, which is skills, as opposed to an absence of degrees.
You know, there’s a lot of things I don’t have. But I’m not defined by the absence of those things. Focus on what I do have, what I do bring. And everybody should be in that situation, because there are so many varieties of skills, of talents, of permutations—everybody can be good at something. Nobody can be good at everything. And so when we say, “Oh, could these people learn how to code?,” well, some can, some can’t. That’s true of coal miners, that’s true of accountants. We are conflating our class markers and our class biases, and to some extent our racial markers, with actual skill.
And we need to stop it. And I think there’s a much better way.
Michael Chui: Let’s click into this idea a little more, about what that potential future of more of a skills-based workforce might look like. Beth, you could opine.
Beth Cobert: I think there are a lot of ways it would be different. And I think it starts by making this transformation both around how employers think about what they need and how they invest in their workforce, and how individuals come to understand the real value they bring to the party.
Just one end or the other end is not going to solve this problem and how training fits in the middle. So you’ve got to look across this. If we start with employers—because people don’t get economic opportunities unless they have jobs—it really starts with employers understanding what is it they really need in the jobs.
We built a job-posting generator that’s based on skills. With one company we worked with in our early days in Colorado, they kept looking for mechanical engineers to help build these heating and cooling systems for fancy RVs. And you know what? No one was showing up. They had a skills gap. But when you looked at what they really needed, they needed someone who understood hydraulics and how to make hydraulics work. Turns out that a diesel mechanic is actually really good at that, and, in many ways, much better than a mechanical engineer. And so it opened up this opportunity for a whole range of folks. Or a lens manufacturer, high-end precision lenses, who ended up hiring a mix of sushi chefs and manicurists because those are people who understood that attention to detail.
It’s employers thinking differently about what people can bring, what’s required, what’s more foundational, what they can train for, and how they think about that, enhancements, and sourcing and looking for things differently.
Individuals need to feel like they are valued for what they bring and, as Byron said, not defined by what they don’t have. If you have 115 out of 130 credits, you’ve got a lot of things you built and skills and capabilities that you’ve accomplished. And you’re not defined by the 15 that you’re missing.
Then there’s a piece in the middle because you do sometimes need some training. You may need to learn some technical or other sets of skills. How do you get the educational institutions of all types aligned to give people what they need, when they need it, in a way that they can access it that’s accessible and affordable and relevant to get them to the next place? That’s what a skills-based labor market looks like. It starts with skills. And that’s the language we use to define things. That’s where we start.
Michael Chui: That’s an amazing vision for a skill-based workforce. Byron, you mentioned that there are some structural barriers to getting there, or structural barriers for workers. Why aren’t we there yet already [laughs]? If it’s so well recognized, if employers want it and people want it.
Byron Auguste: One barrier there is not (that I think people spend a lot of time talking about) is that “people don’t want to learn new things” or “they’re set in their ways” or whatever. That is ridiculous and it’s absolutely not true.
People know it. And we have a lot of these conversations that imply in a way that the people we’re talking about—people who are skilled through alternative routes, people who don’t have bachelor’s degrees, people who are STARs—that they are a problem for us to solve. But they’re not. They’re problem solvers. And the barriers to transition to this skills-based labor market have everything to do with the kind of barriers they face. So rather than take your question as to “What are all the barriers?,” let me talk a little bit, flip it a little bit to say, “How do we get there?”
I think this is the most important problem that I know for sure that we can solve. Like, for sure. Part of it is because of the way STARs are working and focused. But part of it is the passion people have for it.
For example, knowing what you can do with your skills. It’s honestly very hard for an individual to do that on their own. And if you think about your career, Michael, or yours, Beth, or mine, we didn’t know exactly what we could do with our skills. We learned it from other people or other systems and from mentors and coaches. And I don’t see why anyone thinks it should be any different for STARs.
We did our early experiments on how to match individuals to companies based on their skills. We used a mechanism for soft-skills assessment because we didn’t have a systematic way of doing it. We had volunteer recruiters to do a soft-skills assessment interview over—what was then a novel thing, to do it face-to-face over the computer [laughs]. This was before. Everyone wasn’t sick of it. Everyone thought it was kind of cool then. And they would do 20 minutes of the rubric-based, soft-skills assessment.
These are people who have the hard skills, and it was testing for soft skills. But they gave them a little feedback. And it turned out that that ten minutes of feedback was unbelievably magical. It was so useful. Things that the recruiter might think [were] very basic would be real revelations to people and would be so helpful to them in the actual interviews they had.
Just giving you one example, there was a gentleman who’s a headwaiter at a pretty high-volume restaurant in Providence, Rhode Island. And he was learning software development. And he wanted a job at this particular company I won’t name. And he said, “You’ll never get me a job there because I applied four times and they rejected me.” But, of course, they hadn’t rejected him. He had just been screened out.
Michael Chui: What’s the difference between rejected and screened out?
Byron Auguste: This is really important because honestly, the way the job market works for STARs right now is, like, extreme gaslighting. Meaning he had seen a job posting. He’s, like, “I think I could maybe do that.” He applied for the job and he got an email saying, “We’re not moving forward with your application.” So his interpretation of that is that this company who kind of knows their business, they had evaluated him and said, “Oh no, you can’t do this.”
The search for the keyword that said he had a bachelor’s degree didn’t turn up. He didn’t have a bachelor’s degree. And so he got an automatic turndown without any evaluation of his skills whatsoever. So in other words, he hadn’t actually learned anything. And the reason markets work is because you learn things, right?
He was asked by the recruiter, the volunteer recruiter, “What is your experience working in teams? Tell me about your experience working in teams.” And he absolutely froze up because he was, like, “Well, I don’t work in teams. You know, I just code alone in my basement.”
But this guy’s running crews in a restaurant, right? He has way more team experience than almost anyone that would be applying for this job [laughs]. He literally didn’t know that his experience working in crews in a restaurant was relevant. And it absolutely is. Just to give an illustration of what Beth was talking about earlier, you see this all the time. And there’s a huge unlock there.
Beth, you can jump in if you want. But I just wanted to say, on the other elements of transition, one of the things that’s a real challenge is that you do have to have a skill signal. Just like having a bachelor’s degree doesn’t mean you’re ready to do the job, and there’s no company I know of that just says, “If you have a bachelor’s degree, you could do this job.” They all use some additional way of evaluating it.
You have to have these indicators of skill. And I think part of our mission is to help build the data infrastructure, the connective tissue that allows more and more effective skill signals to live there, because it’s not just going to be one size fits all.
And we have some research that’s been done with the National Bureau of Economic Research showing that those signals don’t apply in the same way to people without bachelor’s degrees right now as they do to people who have bachelor’s degrees. So there’s that work there.
And then we’ve been experimenting on an increasingly large scale in a multisided marketplace, Stellarworx, that allows companies that want to hire STARs based on skills [to] just drop in the regular job description, and we can parse it for skills.
I’m actually very interested in the work that Beth and her organization are doing on skills-based résumés, because that’s even better. And then we do the same kind of parsing on a training provider side and, let’s say, a competency-based curriculum.
And then on the individual side, from the résumé. And we have a product integration with Workday skills cloud to synonymize those things and to do the matching. But that allows companies, instead of having to negotiate and sort of figure out, “Is this particular training provider the right one?” in advance before they know, they can plug in and can try many.
We have, I think, about 50 or 60 training providers on right now, and more and more every day. And we have, I don’t know, about 150 companies and more and more every day. And that matching is very powerful because then it also allows the feedback loop, because, as Beth knows, a training program could be 70 percent what you need, but how are they going to find out about the rest of the 30 percent?
It’s very expensive and time-consuming. So I think that’s a lot of what we need. Beth and I have both worked in the public sector, and we both worked in the private sector. And I don’t know what you would say, Beth, but I think the main reason the private sector is more efficient than government is because government has to serve everybody and a whole range of needs. And in the private sector, individual companies will target a very narrow segment and a very narrow piece of it. And they can benefit and they can do that because they have supply chains and distributors and subassemblies.
When I say “rewire the US labor market,” which is the tagline of Opportunity@Work, what I mean is making it possible for training someone well in a low-income community of color to be enough to grow and gain market share, and to be able to plug in. You don’t need to have your whole sales force for big companies. That can be intermediated. And this is what we’re trying to do.
Michael Chui: Beth, go ahead.
Beth Cobert: Byron and I spend lots of time together, so I agree with what he said. And I think one of the barriers, Michael, is people sometimes say, "I have to change everything." I've been a firm believer in my private-sector, and even more so in my public-sector experience, that the way you get change to happen is by starting with changing some things and letting those be successful—because we know that the talent is there.
We know that these individuals can be great contributors. So how do you get the flywheel going? And you often start with the coalition of the willing, or at least willing to try, and leave some of the skeptics to the end. And so, you need to build new sets of habits for people. Institutions have developed ways of describing themselves and what they're doing, that they repeat without thinking. And we're trying to change that. It's the way you change a habit. You do it by repetition, repetition, repetition. Where can you get started? If you're a big company or a midsize company, spend a lot of time with small companies; what are the roles where you know that, in fact, it is highly likely that these individuals bring real talent?
Where can you get started? If you’re a big company or a midsize company, spend a lot of time with small companies, what are the roles where you know that, in fact, it is highly likely that these individuals bring real talent?
McKinsey did for us this fabulous piece of analysis during the pandemic about the folks who were displaced, who were thrown out of their jobs for nothing that they had done. What were job progressions that they had been able to make to what we called gateway jobs and target jobs that were actually proven ones?
They had happened for real people at some level of scale. And that identifies, OK, those are jobs where you can find talent if you’re looking in different places. So, let’s start by focusing on those gateway jobs, because you know in fact that that front-of-house restaurant manager has a lot of what they need to take on a customer service role or an IT help desk role or some of these other roles.
How do you create the opportunities to make that happen? And then you can grow from those experiences because that’s where you build the case for success. That’s what we’re doing with our partners. And by the way, to do this, you need lots of digital tools, but you also need real people with real connections.
The person who convinced me that I was able to make the next transition in my job, that was someone I trusted. And how do I get those connections? And how do you get employers to try? So whether it’s a different way of writing a job description, whether it’s tools and digital training that we now can give to those employers to help them shift the way they do those practices, how do you get it going in some specific things?
And then people will find that, in fact, that person they hired really is that good. In fact, they’re better than they thought they’d be. And you get the flywheel moving by starting in a few places where you’ve got the coalition of the willing.
In today’s market, the person who’s winning is the person who actually finds more talent and is more open to that because otherwise, they’re going to have a job that’s not being filled, and they’re missing that opportunity. So it’s about getting it started on the ground. And I think that’s how you get there, because if you try and design the whole system perfectly from the start, we’ll all get stuck.
Byron Auguste: I agree with a lot of what Beth just said. And I think it’s really important to recognize there is a lot of capacity. Goodwill is a great example. There’s goodwill within ten miles of something like 84 percent of the US population. There’s a tremendous amount of energy and capability out there in the field. But it’s not connected. We have to do this together. And we have to do it in a way that brings out the strength of every part of the sector. Employers, for sure, but not just employers.
Michael Chui: Let me pull on a thread that Byron mentioned earlier, which is being able to provide a skill signal. You both know I’m a bit of a data geek. At MGI, we did a lot of work on the Future of Work. And one of the most challenging things is just trying to understand—we have competencies and skills taxonomies. Credential Engine, that organization says there’s at least a million credentials in the United States right now.
Do we all have to wait until there’s some global skills taxonomy we all use? How does this thing evolve so that it actually becomes practical?
Beth Cobert: I think if we have to wait for all those taxonomies to get aligned, it will never happen. So the answer is no. But I do think there’s lots of ways, and this is where data can help you. That [is] how you can use big data.
There’s many words that describe communication skills. And it’s very different. The communication skills you need in interacting in a frontline customer service environment are different than the communication skills you need if you’re a budget analyst and are trying to explain statistics to people.
This is where actually the data can help us. What are the ways people describe this? What are the ones that most come aligned with each other? You don’t need to define them all. You need sort of the equivalent of the Rosetta Stone. We don’t have to do the one-to-one matching. We can let the machine solve that problem for us. And that’s what we’ve started to do with some of the tools we’ve built. We’ve all been trying to do the same thing. Look, you first have to start with the presumption that says, “We are going to look for these things.” And then you can look for the ones that are most aligned and have most in common with each other.
Big data and AI are going to solve this one, Michael. This is a place where it actually could be really helpful.
Byron Auguste: I couldn’t agree more with Beth that it’s not going to be waiting for the taxonomy. We believe there’s a need for heterogeneity in approaches as we converge. The reason we invested in this multisided marketplace architecture of Stellarworx is so that as new companies come in, they can learn, because every company is saying, “Well, what have other companies done?” Well, this is exactly what the multisided marketplace does [laughs].
You can start to use both the similarities among companies and what they need as something that helps them discover new sources, new alternative routes. And to make alternative routes recruiting as standard as campus recruiting, or as standard as executive recruiting. This is important. And this is a subtlety around why we and many partners in the field believe that this idea, this group of STARs—people skilled through alternative routes—matters, is because it’s 70 million people. It’s too big to ignore. In other words, STARs need to be part of your talent strategy.
And thin slicing enough so it becomes part of corporate social responsibility or community relations for a number of companies. And by the way, it’s a fact. The companies that actually turn out to do this well are going to beat the companies that don’t. And that’s where so much of the innovation comes in this country now. It’s not from people in lab coats. It’s actually at the front lines. It’s those people that Beth was talking about who are coming from the nail salons with their attention to detail and noticing things in that optics company. They are pushing the envelope of making companies better.
That’s what we need. But we need there to be not just awareness but action. And that action has to include intention. We need companies to have STARs recruiting strategies, alternative routes recruiting strategies. And then we need a wide variety of ways, a wide variety of signals. Right now in this market for inclusive hiring, things that work don’t particularly gain market share, to be honest. And so that’s how you know the market is broken. And that’s why we need the signals. And ultimately, we need resources to flow behind that.
Michael Chui: I would love to go through a quick lightning round of quick questions and quick answers with you. All right, here we go. What’s your favorite source of information about the labor market? Byron?
Byron Auguste: Uber drivers.
Michael Chui: Beth?
Beth Cobert: That was going to be my answer. Uber drivers. Or Lyft dri—
Byron Auguste: Lyft drivers, too [laughs].
Beth Cobert: Actually, but literally, it is asking people, “How’d you get to where you’re going? What are you doing there?”
Michael Chui: Beth, what was your first job?
Beth Cobert: My first job was in my father’s factory folding fabric. Taking two-yard pieces and folding them so they could fit into a six-inch-wide envelope.
Michael Chui: Byron, what was your first job?
Byron Auguste: My first part-time job was as a Teen Gazette reporter, paid by the column inch to inform the people of the Valley of the Sun what was going on in northeast Phoenix high schools. And then my first full-time job was as a law library assistant, in which I did a lot of photocopying. And became very adept at speed photocopying, because they had all these fancy features. So I guess I’ve been sort of automated out of a job in that sense [laughs]. A lot of shelving books, which is great because I love books.
Michael Chui: Byron, if you weren’t doing what you’re doing now at Opportunity@Work, what would you be doing?
Byron Auguste: I might be catching up on a long list of great books that I’ve been missing for the last 30 years.
Michael Chui: Beth?
Beth Cobert: If you told me I wasn’t going to be working, I would actually be learning more about big data and coding, because I sort of miss that. And I want to know more about it.
Michael Chui: Beth, what one policy would you recommend to improve the functioning of the labor force? Public policy.
Beth Cobert: Public policy? There are so many. But one I’ll pick is recognizing that, for people to have the opportunity to get to training, they need not just training, but they need support around training.
Michael Chui: Byron?
Byron Auguste: If you could wave a wand, which you can’t, you would want to have a little bit more of a take-or-pay system for employers in training. In other words, employers who do a lot of training should spend that money. And employers who don’t do much training and basically free-ride by poaching other people’s trained employees maybe would have to pay into the pot for a much more robust common pool for training dollars, which is just really anemic. We spend 10 percent of what we did in real terms before, and much less than other countries that are doing it well.
And then the only other thing I would say is people should have income support when they’re learning.
Not just basic income to stop you from falling through [the] bottom. The biggest barrier to adult learning is not necessarily the tuition, it’s the living expenses. How do you pay your rent? How do you feed your kids while you do it? And when you’re ready to make your move and there’s a real opportunity, you should have that support.
Michael Chui: Byron, what’s the one thing corporations can do to improve the functioning of the labor force?
Byron Auguste: The one thing if you had to do one thing today is to remove bachelor’s degree requirements, because it just bars so many different people. But that’s the beginning, not the end. Companies could do a lot and would have a high ROI to invest in the talent supply chain through their physical supply chain, through their suppliers, because suppliers don’t have the money to invest as fully in training as they do. But their suppliers are actually where they’re getting their talent because they’re poaching and people are self-poaching from them.
Michael Chui: Beth?
Beth Cobert: I’m assuming that Byron has waved his magic wand and we’ve removed bachelor’s requirements. I think the next piece is really actually having companies understand in a much more granular way the real benefits to them of investing in their workforce, finding different sources of talent that they don’t do very well.
What’s the return on their loyalty, because when you invest in them, people will often invest back in you. I think greater visibility to that would make companies realize that it is in their interest to look at this talent and realize that the talent’s there. And I don’t think they have good mechanisms for doing that now.
Michael Chui: And what’s one piece of advice you have for listeners of this podcast? Beth?
Beth Cobert: I’d say for each of them, the next time you are thinking about a role to fill in your organizations, think about what are the skills you really need and the many, many places you can go to find [them].
Michael Chui: Byron?
Byron Auguste: I absolutely agree with that. And it’s not just externally, it’s internally, to make sure that when you think about pathways within your company, that you’re thinking about frontline workers and forget about this whole exempt-versus-nonexempt distinction people make a big deal about.
Michael Chui: Byron Auguste, Beth Cobert, thank you very much.
Beth Cobert: Thanks, Michael.
Byron Auguste: Thank you.