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Tiffani Williams
Hello, everyone. It’s great to have you here. I’m Tiffani Williams from the Federal Reserve Bank of Atlanta, and I’m honored to welcome you to day one of the Uneven Outcomes in Labor Market Conference. This conference was organized for you by the community development staff of the Federal Reserve Board and the Federal Reserve Banks of Atlanta, Boston, Cleveland, Philadelphia, San Francisco, and St. Louis.
We’ve convened researchers, policymakers and practitioners that examine disparities in labor market outcomes and explore policy solutions to address these inequities. And over the next four days, we hope to deepen your understanding of disparities in employment, labor force participation, income and wealth. And we hope to learn more about the implications for economic growth, the health of communities and individual wellbeing.
So, we welcome your questions throughout the session. Please enter any questions that you have in the Q&A function that you’ll see on your screen. The chat function has been disabled for this session. But with that, I’m very excited that we will begin the conference with remarks from Raphael Bostic, President of the Federal Reserve Bank of Atlanta.
Raphael Bostic
Thank you for joining us for this important conference. I’m absolutely thrilled that the Atlanta Fed is one of the six regional reserve banks that have joined with the Board of Governors to bring you this exploration of persistent disparities in labor market outcomes along with the latest thinking on how we can remedy those disparities.
At the Atlanta Fed, our tagline is an economy that works for everyone. Such an economy will feature a labor market in which everyone can maximize their human capital and potential and find work commensurate with that full potential. The appeal of having this kind of economy is that it will be more resilient, more innovative, and more prosperous.
In this context, few topics are more important than uneven labor market outcomes because the path to full participation that is an inclusive economy starts with everyone having a good job.
Over the next four days, we will see presentations on issues critical to practitioners and policymakers, including how subsidized childcare could affect women’s labor force participation, why labor force participation among men remains below pre-pandemic levels and generally continues a 60-year decline, the effective labor market outcomes on firm structure and access to financing, and the indispensable role of public policy in seeking to understand and improve outcomes.
I’m especially pleased there will be presentations about workforce development and education. These topics have long been a special focus of the Atlanta Fed. We have a center for workforce and economic opportunity, for example, because we believe workforce development and education are essential for spreading opportunity and advancing the concept of maximum employment.
As you may know, maximum employment is one of two parts of the Fed’s monetary policy mandate. A core goal of this conference is to expand the body of knowledge that informs the definition and measurement of maximum employment by examining and explaining disparities in labor market outcomes within and among demographic segments.
Refining our understanding of maximum employment matters in part because gauging progress toward the two sides of the Fed’s mandate differs in an important way. The price stability objective is clear. We pursue 2% inflation over time. However, maximum employment is a more ambiguous notion that evolves with changing economic circumstances.
For instance, when I started this job in 2017, the accepted wisdom was that full employment meant an unemployment rate of roughly 4.5%. Well, that’s changed. Outside of the brief but severe pandemic recession, unemployment has been below 4.5% for nearly seven years and in many months by a significant amount. My colleagues have acknowledged this shift. The median long run expectation among federal open market committee participants for unemployment is now about 4.1%.
So, as we formulate policy to try to foster sustained maximum employment, it is critical to ground policy in the best current research and dispense with assumptions and conventional wisdom that do not characterize today’s labor markets.
The challenges we face in closing labor market gaps are as acute as ever. Disparities persist among many dimensions, race, gender, rural metropolitan geography, and even among ZIP codes within cities. And the pandemic exacerbated many of those challenges.
Now, my staff and I have identified nearly a dozen potentially structural changes to labor markets associated with the pandemic. Now don’t worry, I’m not going to review all of them here. Instead, I’ll discuss a few changes that I think are significant.
First, we see signs suggesting that the work from home phenomenon is here to stay. Now that is good news for higher wage workers in metropolitan areas, but less meaningful to people in lower wage service jobs. Research, including work by Atlanta Fed staff, suggested in coming years, some 40% of workdays will be performed remotely in densely populated areas, and about half that much in less populated places.
And the occupational breakdown of those likely to work from home probably won’t change much as lower wage workers and frontline public facing jobs will be much less likely to work remotely than higher paid professionals. Thus, it appears that the amenity benefits of the shift to work from home could be unevenly distributed across industries, occupations, locations, and demographic groups. And if historical patterns hold, workers from certain demographic groups will be less likely to enjoy the amenity benefits of working from home.
Now, presentations by practitioners and other participants at the Federal Reserves’ Racism in the Economy webinar series suggests that Black and Latino workers may fall into this category as they are disproportionately in occupations linked to a location such as food preparation, cleaning and health service support.
Now on a more hopeful note, remote work opportunities may erase barriers to work for some others. For example, the chance to work at home could improve job opportunities for those with disabilities and remote work might also help workers who face transportation challenges or those whose family responsibilities require flexibility in their schedules.
Next, the pandemic was quite unusual in terms of wages. When the virus’ penetration was most intense and a departure from usual economic downturns, lower wage workers generally enjoyed the biggest proportional pay increases. Available labor was particularly scarce, so employers had to pay up to attract employees.
Our bank’s wage growth tracker shows that growth in the bottom half of the wage distribution was significantly higher than in the upper half during the thick of the pandemic. Coupled with significant pandemic policy support, this has meant that lower wage workers are in a stronger economic position than they would ordinarily be. This truth may have contributed to the increase in entrepreneurship that we witnessed during the past several years.
Now, however, that gap in pay hikes has largely closed as a strong labor market has attracted more people off the sidelines boosting labor force participation to a surprising degree. That means that supply and demand for labor are more aligned and wage growth across the pay spectrum is settling back into more normal patterns.
The pandemic triggered other meaningful shifts in the labor market. For one, it appears to have sped up a retirement boom that was already entrained because of an aging population. The faster than usual exodus of older workers early in the pandemic contributed greatly to the extreme tightness of the labor market in 2021 and 2022. Now though baby boomers will continue retiring a pace, it remains an open question as to whether we will see another sudden spike in retirements.
We’ve also witnessed interesting labor force participation effects during the pandemic era. We saw an initial drop in participation among women, especially women with young children as the virus exploited weaknesses in the childcare sector. Opioid abuse also spiked during this time, resulting in a drop in participation of younger men. These dynamics point to lost economic potential, a barrier to sustainable maximum employment.
More recently, the surprises in labor force participation have been to the upside. For example, women have returned to the labor market in force and have enjoyed some of their highest participation rates in US history. And this has contributed to supply increasing more than many expected, feeding a more rapid decline in inflation than many had forecast.
We have also recently seen some interesting sectoral labor market developments. As you know, unemployment across the economy remains historically low, yet employment growth is slowing. And in recent months, job creation has been heavily concentrated in one comparatively small sector, healthcare and social assistance.
Healthcare and social assistance account for only 14% of private sector employment but have accounted for 60% of private employment growth over the past several months. Healthcare makes up the bulk of employment in the sector and there are good reasons to believe healthcare employment will continue to punch above its weight in employment growth.
For one, healthcare employment plummeted early in the COVID wave and still hasn’t caught up to its pre-pandemic trend line. Longer term, an aging population should continue to fuel job growth in healthcare and social assistance. An older population uses more healthcare/ And the US Bureau of Labor Statistics recently projected that the 65 and older population will increase by 14.4 million people over the next 10 years.
Now, it’s dangerous to proclaim with certainty that any pandemic era phenomenon will shape future labor markets, but these are all potentially significant developments that could be particularly important drivers of labor market dynamics in the future. They and other forces will thus shape our understanding of maximum employment and will be important considerations for us as we pursue a monetary policy path seeking to achieve both of our mandates.
So, let me stop there. This conference is an excellent example of the role the Federal Reserve can play in bringing together research, policy and practice on these issues and for connecting people who might otherwise not meet. These convenings can be incredibly powerful in moving us forward towards better understanding and addressing uneven outcomes in labor markets. Thank you again for joining us and please participate, engage, and enjoy the next four days.
Tiffani Williams
Thank you, President Bostic. So, we titled today’s panel, What’s It Worth to You? Returns to Education and Training. Education and training are broadly seen as the foundation of career advancement and economic mobility. And today, our speakers will discuss the economic returns of educational investment by types of institutions and demographic characteristics.
Just a reminder, full bios for all of the speakers are available on the conference website. There should be a link available in the Q&A box on the screen. But just so that you know, today we will hear from the following speakers.
Earl Buford, President of CAEL, will provide a framing for today’s topic. Then Peter Bahr, Associate Professor at the University of Michigan, will present his research on economic returns to skill building. Next, Justin Heck, Director of Research at Opportunity@Work, will present his research on the limits of educational attainment in mitigating occupational segregation between Black and White workers. Then the Fed’s own, Laura Ullrich, Senior Regional Economist at the Federal Reserve Bank of Richmond is presenting her research on the Federal Reserve Bank of Richmond’s Survey of Community College Outcomes.
To close today’s session, Nicole Smith, Research Professor and Chief Economist at Georgetown University Center on Education and the Workforce, will discuss all of the research presented and then moderate the questions and answers portion of the session. I’m very much looking forward to having these speakers share their perspectives. We’re so grateful for their expertise.
Please note that during today’s session, the views expressed are those of the presenters and not necessarily those of the Federal Reserve System or the Board of Governors of the Federal Reserve System. Okay. So, now, I will turn it over to Earl Buford. Welcome, Earl.
Earl Buford
Good afternoon, Tiffani. Thank you and so happy to be here, and thank you for the honor. As Tiffani said, I’m Earl Buford, President of CAEL. CAEL is a 50-year-old organization, membership and systems alignment organization with a focus on adult learners and workers and all the systems that intertwine with that. I am beyond excited for the opportunity to frame for this panel. So, the definition of framing, it’s known as an act of intentionally setting the stage for the conversation you want to have. And I can say that this is definitely a conversation I want to have and I’d like to hear from others in the field continue to have this conversation as we move forward.
I have an interesting background. I’ve had the pleasure of running training programs, running training initiatives. I’ve led two workforce boards throughout my career and now as a leader of CAEL, as I said, a membership and systems alignment group, acting as an honest broker intermediary between higher education workforce systems and the needs of employers in the industry.
Going on the four years now after the onset of the pandemic, employers and communities across the United States are still reeling from unparallel labor and talent shortages as President Bostic mentioned earlier. Right now, with an estimated nine plus million opening jobs across the United States with approximately six million actively unemployed workers, still there are simply not enough people to fill the open jobs. Skills alignment aside. While this talent shortage is not new in this country, we are facing several factors that are shifting this talent landscape. I want to hit on a few of those.
As President Bostic mentioned earlier, the declining labor force participation rates while have improved are still below the pandemic levels. The labor shortage stems from a variety of factors including generation retirement, which was mentioned earlier, which is now slated to lead what insiders are calling a forever labor shortage. Interesting. Geographical shifts and changing employee priorities. Workers want more than just better pay. While they want that, they’re also looking for intangible benefits like flexible scheduling and growth opportunities.
There’s also some massive layoffs through the economy and also stagnant office occupancy, which is eroding the confidence in traditional work and indicating that hybrid remote work is here to stay. Again, as mentioned earlier. While also the sentiments of employer-employees about return to office continue to be a major conflict. And then there’s also social and political unrest coupled with lower consumer sentiment as a result of inflation and economic uncertainty. So, a variety of factors, effectiveness, there are others, but those are just some that I wanted to mention.
These are just some of the factors that are contributing to a new era of workforce challenges and opportunities. Where there’s challenges, there are always opportunities. Communities across the country are under pressure to identify the trends, the tools and the tactics that will help retain existing workers while attracting new talent to help ease employer hiring challenges and demonstrating capacity to attract new investment. The idea here today is to better understand the why behind it and identify emerging trends.
There are lots of publications out there that I tend to refer to when I have these conversations or when I want to catch up on what’s going in the field and we’re just so fortunate to have these great panelists today to talk about parts of what they’re seeing and researching and feeling in the field.
So, you think about closing uneven outcomes in labor market with the particular interest of mine on education and workforce, it’s often related to disparities and opportunities, representations or treatment requires a comprehensive and strategic approach. When you think about uneven outcomes in developing talent, especially in the context of professional growth and career advancement and how it evolves recognizing and addressing those barriers that certain individuals or certain groups have to face, you cannot move forward without addressing these things.
I just want to touch on and frame a few trends and solutions to promote this type of equity and talent development that I often think about, and I think most of you in the field are thinking about. Systems alignment, strategy and work. When I think about this, it’s really about the needs of employers and industry matching and coupling and partnering with education, K12, colleges to the universities and how does that all align with needs of in local municipalities and local regions, the workforce systems and the practitioners are doing in key communities, how to make this path or their roadmap easier to attain for people, for workers, for learners.
Next, I think about skills-based hiring development, needs to be industry led, needs to have partnerships, needs to be articulated, needs to have agreements, and needs to agree on the matching of skills and classifications. How does work-based learning? How does apprenticeship fit into this world?
We also should consider blended learning approaches. And of course, there’s the world of public policy. How does that fit into this equation, this alignment, reauthorization, Pell, et cetera. How does affordability? We start to use tools like credit for prior learning. Where does DEI integration fit in? And then how do you start to really couple tools and tactics like employee research groups, ERGs and the two forms of LERs learning employment records, leadership development groups? How you think about tactics like coaching, quality coaching, and again, credit for prior learning? You need to have clear outcomes and data and probably more than anything else, you need to have a desire to fix the problem.
I’m often asked my preference in this long-winded debate over certificates versus credentials versus degrees. My answer is always a strong yes. I want them all to have value because a true roadmap or true career pathway will entail the variety of those things at some point, which makes it easier for people and talent to articulate what this will mean along their journey and how to attain each of them.
So, I’m excited, as I said earlier, to hear from panelists as I’ve read over their work and they touch on many of these solutions or tactics and also the data that comes with it. So, I’m excited to turn it over to Peter Bahr. Just know that this panel will hit on a variety of great subjects, everything from the limits of educational attainment between Black and White workers, how the Federal Reserve Bank of Richmond is surveying community college outcomes? And of course, Peter would talk about short-term reskilling courses, the sequencing and how does that lead to family sustainable wages.
So, with that, I’d like to thank you all for giving me the time today to speak about what I’m seeing in the field and framing this conversation today. And with that, I’ll turn over to Peter Bahr.
Peter Riley Bahr
Thank you, Dr. Buford. I appreciate your time and introduction. Hello, everyone. Thanks for joining us today. I am very excited to share some of the recent work of myself and my team at the University of Michigan. We’re in the Center for the Study of Higher and Postsecondary Education and working intensely on questions about community colleges and technical colleges, open enrollment institutions and opportunities in the workforce.
So, this work is literally happening at this time. I’ll share a working paper with you in just a moment. Slide please. There’s a QR code here for the working paper. It is very much a working paper in the sense that it is evolving right as we’re speaking. So, do expect change as you go along. Let me introduce my team, Yiran Chen, Ying Sun, both at the University of Michigan, Dr. Jennifer May-Trifiletti, also at the University of Michigan, and my colleague, Kathy Booth at WestEd. Slide please.
I want to thank ECMC Foundation for funding for this work and recognize that the views expressed are ours alone and do not represent either ECMC Foundation or our partners at the Colorado Community College system. Slide.
So, let me give you a brief outline. I think I have about eight minutes. So, let me tell you what we’re going to do in eight minutes. I’m going to talk just very briefly about the significant role of community colleges in upskilling and reskilling. I’ll talk about a method for identifying community college students who are there to upskill or reskill. It turns out self-reported goal is not a very effective method of doing that. We’ll talk about the big picture view of skills builders in the four states at which we’ve looked at them closely.
We’ll talk about the characteristics of skills builders and their upskilling and reskilling course combinations. And then where we’ve been going with this work, where we’re headed is into the earnings gains. So, we’re going to look at the earnings gains that follow from completion of upskilling and reskilling course combinations, the factors affecting those earning gains and what upskilling and reskilling course combinations can and can’t do. What can we expect of them really. Slide please.
So, this is pretty well established that community college enrollment surge during times of economic downturn. And you can see here in the blue circles, these surges and enrollment, community colleges really are the go-to destination for individuals, especially adult age individuals seeking to reskill or upskill during economic downturns. Slide.
So, we know some things from prior research about these upskilling and reskilling students. We know that they tend to enroll in community college for a very short time. We know that they tend to attempt relatively few credits. There’s quite a bit of variability, but on average relatively few credits, usually just a couple of courses. They take most or all of their courses in CTE fields and they complete their courses successfully at an exceptionally high rate, well in excess of 95% success rate.
As I mentioned, students’ goals are not, as it turns out, not a very effective method of spotting the upskilling and reskilling students. So, my team and I pioneered a study, pioneered a method of identifying upskilling and reskilling students, whether at the college level or at a state system level. And you can pull that paper up there if you’re interested. It was published a few years back. Slide please.
So, what we’ve found is that in the four states we’ve examined, skills builders are roughly one in eight, give or take, community new entrance to community college. By new entrants, I mean not just first-time students, but students who are newly enrolled in the community college system. We know they rarely complete post-secondary credentials. Although Colorado, which we’re going to look at more closely, has a very high success rate in getting upskilling and reskilling students across the finish line to complete a credential, which makes it an especially valuable state to partner with and model to look at. They tend to be above average age and they’re also disproportionately male. Slide.
Now here’s the thing that’ll really catch your eye and it’s what sent us down the rabbit hole that we’re digging in now. You can see here the average earnings among employed, this is conditional earnings, average earnings among the employed skills building students in four states. And you can see pretty much as you would expect this decline in earnings leading up to the point of community college enrollment. And then a very quick reversal in individuals’ wages.
And this creates an interesting conundrum. State systems that are interested in measuring skills builders, if they go in and look and see what were the earnings two years before and two years after community college, they’ll often find no difference there. But in reality, if you take account of this V-shaped decline, we can see that community colleges or skills builders in the absence of community college may have suffered significant earnings losses and stabilized earning losses. So, it seems that there’s something worth looking at here. Slide please.
So, we’re going to focus in on Colorado’s Skills Builders. I’ll save you the burden of walking through all the methodology. You’re welcome to consult our manuscript. It’s pretty detailed. But the gist of what we’re going to look at today and then the remainder of the time that I have are about 10,000 skills builders in Colorado who took 317 different course combinations. These are the well-populated course combinations and they had earnings records, at least two quarters of earnings records before and after college. Slide please.
We investigated a number of methods of measuring. Looking at these skills builders, we can see, and this is something we find pretty consistently across states, about a third had suffered an economic shock, about a third had some prior college education. The range of credits completed was quite wide. Everything from just half a credit on up to 16 or more credits. And as I mentioned in Colorado specifically, there’s a very high certificate completion rate. Here we see about 37% within six years. Slide.
Now we can see when we look at the fields of study in which the skills builders are enrolled, we find again wide variation in credits and a similarly wide variation in certificate completion. And the two do not always operate in lockstep. For example, average credits completed in skills builders in health tend to be quite low around seven, but nearly half of these students earn certificates within two years in the CCCS, Colorado Community College System. Slide.
So, we tried a number of methods of looking at change in earnings from pre-enrollment to post-enrollment. I’m going to talk about the preferred model after quite a bit of analysis. And this is a weighted average of pre-enrollment earnings and a weighted average of post-enrollment earnings. We’re favoring in the pre-enrollment earnings closer to college entry and in the post-enrollment earnings further from college entry. And this seemed to offer quite a bit of stability in measuring earnings change. Slide.
So, here this is a violin plot and essentially what you’re seeing here is the distribution of skills builders, changes in earnings. Everything on the positive side in that blue block is our gains and everything on the left-hand side in red are losses. So, what we can see here is that there’s quite a lot of variation in change in earnings pre to post college. The majority of skills builders are realizing moderate gains in earnings typically under $2,000 per quarter, but still a sizable minority do experience no gains or losses. Slide.
We looked at different fields of study. It’s a very similar pattern, although some fields of study clearly yield better earnings. You can see there, for example, protective services has a much larger share of the distribution on the right-hand side of the purple line, which are earnings gains, whereas other fields that we’ll see sometimes had distributions going in the other direction. Slide please. And this is the other four fields of study. Let’s go ahead and move on to the next slide please.
All right. So, taking a little bit closer look. What we would hope to see in answer to the question about whether upskilling and reskilling can yield a living wage, we would hope to see here that earnings gains among those who enter college at the lowest wage levels would be quite high.
And so, what you’re seeing here is a plot of pre-enrollment, pre-training earnings and post-training earnings or post-enrollment earnings. And what you can see overall is that yes, the lion’s share of students do achieve earnings gains of some level, but earnings before and after entering college are quite closely correlated with one another such that those who enter with low earnings tend to come out of college still with low earnings, although higher than they began with. So, it’s encouraging, but on the face of it, we see that it doesn’t look like for the most part this upskilling and reskilling is producing a life-changing amount of earnings gains. Slide.
So, we had to ask what’s working? What types of course combinations are working? What types of students tend to realize the strongest earnings gains? And can upskilling and reskilling course combinations really lift impoverished students up to a living wage. Slide.
It’s almost commensurate. It has to happen with a presentation of a research paper. But here’s our lovely regression table. We won’t wade through all these numbers. Here’s the gist of what we found. First of all, as you would’ve guessed from the plot you just saw a moment ago, the magnitude of earnings gains from upskilling and reskilling are inversely related to pre-enrollment earnings.
Students at the bottom end tend to get better gains but not life-changing amount of gains overall. Students with prior college education, especially degree holders, net out the largest earnings gains from upskilling and reskilling. That is those who are already starting ahead in terms of college seem to get the best advantage or the best leverage from upskilling and reskilling.
Students who experienced a prior economic shock, pre-college economic shock tend to realize smaller gains. Overall earnings gains tend to decline with age, although they’re fairly flat in the 30s to 40s range. Women experience on average lower gains than men. Equivalent in magnitude to an economic shock. And there are overall no significant differences between White and African American White and Hispanic, White and Asian and Pacific Islander and White and American Indian groups in terms of the earnings gains that they achieve. Slide.
So, here we took a look at, we did some predictions from this model to try to figure out what share of all the course combinations that we examined could actually lift a student recognizing that these course combinations are netting earnings gains, what share of them can lift students up out of poverty from something below 130% of the federal poverty line up to 185% or above? And we justify the reasons for those thresholds in the manuscript.
We can see here that although most course combinations are yielding earnings gains for most students and most students are walking out of upskilling and reskilling with an economic advantage, by and large, very few, just over 10% of all the course combinations we examine could move at least 50% of impoverished students up to a living wage based on our predictions. Slide.
So, what are our takeaways here? First of all, it is possible to identify skills building students on a state or system-wide scale or even in individual colleges. We have a methodology now to do this based on students’ actual course taking behavior rather than what they’re reporting when they come into college.
Two, skills builders are about one in eight new community college entrants. A surprisingly large number of students. Three, skills builders are highly varied group and they enroll in an equally varied set of course combinations. Fourth, a majority of upskilling and reskilling course combinations do yield earnings gains and the majority of skills builders are realizing earnings gains.
Fifth, earnings gains vary substantially by the characteristics of the course combinations, particularly field of study. And the characteristics of the students themselves seem to be related to earnings gains with the greatest gains going to students who have prior education who are men who did not experience an economic shock and so on. And then most upskilling and reskilling course combinations based on predictions will not lift impoverished students up to a living wage. Slide.
So, we’re really digging into the policy considerations around this. One of them is that we probably should not put our competence in upskilling and reskilling as a life-changing as the opportunity to move individuals from poverty up to a living wage. But because of the gains that we can see, it looks like there are opportunities here to leverage high value upskilling and reskilling sequences and build those into stackable credential sequences that could move students into a truly life-changing economic situation.
Thank you for your time. I appreciate it. I know that was moving very briskly. I’ll be happy to answer any questions later and here are access to some of our papers. Thank you. On to my colleague, Justin. Thank you.
Justin Heck
Hi, everyone. Thank you, Dr. Bahr. So thankful to be here today to share a bit of our work on The Limits of Educational Attainment in Mitigating Occupational Segregation between Black and White Workers. This research is co-authored with Dr. Ashley Jardina at the University of Virginia, Dr. Peter Blair at the Harvard Graduate School of Education, and Dr. Papia Debroy at Opportunity@Work. Next slide please.
I want to ground our conversation in an understanding of who is in the labor market. Among the 145 million workers who are 25 years and older and active and the US labor force, over half of them are skilled through alternative routes instead of a bachelor’s degree. These 75 million workers have a high school diploma. They may have some college, community college and associate’s degree. They maybe have built their skills through military service, through software boot camps, and most commonly through on-the-job experience. Next slide please.
I want to highlight this group because in many of the conversations between employers, policymakers, practitioners and employers and academics, we often describe this group of workers as unskilled or low-skilled using the bachelor’s degree as the bivariate measure of whether or not a worker is bringing skills to the labor force. And yet our past research has demonstrated that millions of STARs have the skills to do higher wage work.
Consider the following transition, a worker might be developing and deploying skills in a role as a retail salesperson. On average, this worker is making 18.75 an hour and is building skills in persuasion, an active listening and speaking and negotiation. These skills are in fact the same skills that a worker might deploy as a sales representative in wholesale, a role that plays significantly higher wages.
Now, the reason why I want to start here is on the next slide. If we advance the next slide, STARs are reflecting the full diversity of the US workforce, and in particular for our conversation on occupational segregation between Black and White workers, a disproportionate share of Black workers are STARs. And if we want to think about reducing racial inequality and access to a set of occupations for the set of workers, we need to be thinking about STARs and the ways in which they might experience mobility in the labor force. Next slide please.
So, to zoom in on the paper that we’re going to chat about today, there’s been significant research thinking about racial inequality in the labor market and why it might exist. But lest has focused on one specific dimension of racial inequality, and that’s occupational segregation, which can be defined as the degree to which members of different racial groups are distributed unequally across different types of jobs.
Now, in many conversations, folks have come to the conclusion that this segregation exists because differences in workers. What’s made implicit or explicit is that employers are assuming White workers have higher levels of education. They’re more likely to have college attainment and as a result, have access to a different set of jobs that Black workers don’t have access to. And yet in the last two decades in particular, we’ve seen significant effort successfully increase Black college attainment. And yet as the paper will show in the following slides, we haven’t seen corresponding declines in segregation.
And so, this paper thinks about two core questions by thinking both about race and education. First, we directly ask the question, is occupational segregation lower among similarly educated workers? That is, if we control for education and compare Black STARs to White STARs or Black workers with a bachelor’s degree to White workers with a bachelor’s degree, do we see less segregation? Do we see those workers have more overlap in the roles that they have access to?
And similarly, we ask the following question, is occupational segregation lower among workers who have completed a bachelor’s degree? College is such a successful way of improving mobility and solving certain types of inequality. And our question is, how does it impact occupational segregation? Are folks who’ve gone to college actually gaining access to the same set of roles?
If you can advance the slide, I’m going to spend some time with this table. So, let me talk about what you’re seeing. In our study, we look at segregation from 1980 to 2019 over four decades using an index of dissimilarity. Now, this measure is bounded between zero and one, where one represents a world in which two groups of workers, two types of workers, are in completely different occupations. So, for example, Black and White workers have no overlap in the jobs that they do. A value of zero represents a world in which those two groups have equal access to the same set of occupations.
And so, let’s go ahead and look at the second row here. For Black and White workers in 2019, we see that the index of dissimilarity has a value of 0.276. And the way this can be interpreted is that 27.6% of Black workers would need to change occupations to have access to the same set of roles as White workers. Now, notably, we can look back in time and see there was a bit of a decline. Segregation went down from 1980 to 1990. But in the last 29 years, last three decades, that value has held constant despite the efforts to reduce racial inequality, despite the efforts to improve Black access and attainment of college.
And so, in the rows below, we zoom in on educational groups specifically, first comparing Black and White STARs. Again, similarly educated workers who presumably should have access to the same set of roles. And yet we see that the level of segregation between Black and White STARs is just as high as between Black and White workers overall.
If we look at workers with a bachelor’s degree, we see a bit of a decline, about four percentage points lower, and yet 24% of Black workers with a bachelor’s degree would need to change occupations to be equally distributed to their similarly educated White peers.
Now, a natural question that you might have and we had as researchers was, well, how bad is this? This is certainly lower than occupational segregation by gender, and yet it’s certainly not zero. And so, we ended up in our study doing a Monte Carlo simulation where we controlled for education, geography, and gender, understanding the way that those factors impact the choices and options that workers have when it comes to occupational choice.
And we found that the segregation that we see today is three to five times higher than it would be in a race neutral labor market. That is after controlling for gender, geography, and education, segregation is three to five times higher than we would see if race wasn’t a factor.
If you advance to the next slide, I’d love for us to start thinking about the ways that workers are experiencing this segregation. Perhaps there’s a world where this segregation leads folks into separate jobs that are relatively equal in pay, but our research shows that the labor market is separate and very unequal. In these figures, we show the top 10 jobs in which a group of workers are overconcentrated.
So, for example, in the figure on the left, these are the roles in which there’s the highest concentration of Black STARs. And while Black STARs make up 7.6% of the US labor force, in these 10 jobs, they make up more than 20% of the workers in roles like nursing and home health aides, postal service clerks, barbers, LPNs.
These roles, as you can tell by the color coding, are relatively low wage, particularly when we compare these roles to those in which White STARs are most overconcentrated. White STARs make up 33% of the US labor force, and yet in these roles they make up more than 50% of the workers in roles like tool and dye makers, millwright surveying and map technicians. And again, by the color, these are roles that are all higher paying than the roles that similarly educated Black workers have access to.
Now, before we advance to the next slide, I want you to stare at the color pattern on the right, lot of green, little bitty yellow. If you can advance to the next slide, we see the same color pattern for Black workers with a bachelor’s degree. If we look at the top roles in which Black workers with the bachelor’s degree are concentrated in, the pay in these roles is actually very similar to the roles in which White STARs are concentrated in. And then when we look to the right at the roles that White workers with a bachelor’s degree are in, we then see this pay jump.
The purpose of this analysis is really to demonstrate that the segregation that exists in our labor market, even after controlling for education leads to wide disparities between Black workers.
If you can advance to the next slide. I’d love to leave you all with three takeaways. First is that occupational segregation has not decreased even as college attainment among Black workers has increased. College is a solution to so many things. It doesn’t seem to be the solution to this form of racial inequality.
Secondly, Black workers are in jobs that are qualitatively different. Or let’s be frank, qualitatively worse than their White counterparts. And last, the impact of college on occupational segregation is minimal.
Let me end with the thought that college is inherently a future-oriented solution. For the 10 million Black STARs, for the 70 million STARs in the labor force, college is often an impractical solution and it also overlooks the skills that they’ve already developed often through decades in the labor market. And so, yes, college and we also want to open up pathways for a broader set of workers. Thank you so much for your time, and I’m happy to pass it off to Dr. Laura Ullrich.
Laura Ullrich
Thanks so much, Justin. I’m Laura Ullrich with the Federal Reserve Bank of Richmond. And I’ll start out by reminding you that the views I’m going to express are mine alone and don’t represent an official position of the Federal Reserve System or the Bank of Richmond. I do want to point out from the beginning that this work is co-authored by my colleagues, Jason Kosakow and Jacob Walker at the Richmond Fed.
So, today, I’m going to be talking about the work we’ve done on our survey of community college outcomes. And if you’ll move to the next slide, I can go over this very briefly because President Bostic already mentioned this, but the Federal Reserve does have a dual mandate. And so, the work we’ve been doing on community college outcomes fits very squarely in the maximum employment side of the mandate. And why do we care about educational attainment? Well, it’s pretty clear when you look at the data, if you’ll go to the next slide. We’re interested in how people go along both the education pipeline and the workforce pipeline.
And so, people have choices when it comes to education. They can drop out of high school if they so choose. They can get a high school degree, go on to higher-ed and make a lot of choices within that pipeline. And all of these in theory can lead to work. All of these exits off the education pipeline can result in a job. However, the problem is this is a leaky pipeline and it is leakier on the left-hand side of the education spectrum. So, those who have less than an associate’s degree, for example, have much leakier workforce outcomes than those with bachelor’s degree or higher.
And so, we’re interested in figuring out how this pipeline works, how to get people further along the education pipeline, but also interested in these leaks, how those might be sealed. And like I said, if you look at the data, it’s clear why we would be interested in that. And you can go to the next slide.
There’s a clear relationship between income and educational attainment that’s long been established. And next slide please. The same is true for labor force participation rates. And this is actually pretty interesting. Those that have less than a high school diploma have historically been between about 45 and 50% of those individuals are in the labor force. And those with higher educational attainment are much more likely to be in the labor force, although all of those numbers have been declining over time.
So, one of the things we talk about sometimes is that this pipeline, while it’s always been leaky, those leaks might be becoming more important to pay attention to now with labor force participation declines, but also with an aging demographic. Next slide please.
So, if we want to look at higher education outcomes, if you ask someone how might you measure higher education outcomes, a common answer you would give would be graduation rates, which seems pretty, pretty logical. We want to know what percentage of people that start off in higher education graduate. And these are what are known as the IPEDS traditional graduation rate. So, this is data from the federal government. If you’re not familiar with IPEDS, it’s a database that all higher education institutions, public and private, must submit their data to annually.
And this breaks down a private four-year institution, public four-year institution and public two-year or community colleges, their graduation rates in the Fifth District. So, the Fifth District of the Federal Reserve, the Richmond Fed is the Carolinas, Virginia, Maryland, West Virginia and DC. And what you’ll notice right away is that community college outcomes, community college graduation rates are much lower than those four-year institutions. Next slide.
And that can become even more stark to look at when you consider this darker blue line, which is really the percentage of people that don’t complete based on this IPEDS data. So, several years ago, we were looking at these data and wondering, what’s the story here? Why are community college students so much less likely to graduate? Next slide please.
So, we started reaching out and one of the beauties of the work that we do on our team at the Fed is not only are we doing work with the data, but we’re also on the ground interacting with institutions, organizations. So, we started asking community college leaders about this data and they immediately told us, “Do not use the data.” It doesn’t do a great job explaining what we do.
IPEDS doesn’t work great for community colleges. And IPEDS is an incredible higher education database. It serves a very important role in informing all different higher education stakeholders about higher education institutions across the country. But the traditional IPEDS graduation rate, it is problematic for community colleges in several ways. And I’m going to talk about both the numerator and the denominator of this metric.
But we heard this very, very commonly when we spoke to community college leaders. But as an economist, I’m interested in how people make decisions, how individuals, how firms make decisions. And if I were to Google blank community college outcomes, blank community college graduation rate, that IPEDS graduation rate is the one comes up first. As a matter of fact, it’s typically bolded in large print. And so, even though this metric might not be the best suited for community colleges, it is the one that is used most commonly by stakeholders, especially if you’re measuring across states. Next slide.
So, the data I’m going to show you today, and I’ll go through this very quickly, but I wanted you to have these slides because they will be available to attendees afterwards. This is from our extended pilot. We did a pilot of 10 schools in our district in 2022. We expanded to 63 schools this year. And so, I’m going to go through what we do in our survey and talk about why we think the Richmond Fed can play an important role in this research. Next slide.
So, our goals in creating the survey really was to make sure we were focused on the community colleges themselves. So, one of the issues with evaluating different types of higher education institutions is they are quite different. We wanted to make sure we were independent, that it was universally available. Our goal is to have all of the institutions in our district as a part of this survey. We didn’t want to be overly burdensome and we want this to have longevity. This is something we want to do on an annual basis. Next slide.
So, what do we bring to the table? I think as a team, we would all say that the most important thing we have found that we bring to the table is that we are independent and nonpartisan. We don’t offer grants to community colleges. We’re not evaluating them and we’re not accrediting them, things like that. And we have existing survey and data analysis expertise that we’re able to lend to this project.
In addition, our mandate really makes us workforce focused. So, when we’re developing our measured success, we’re really focusing on success as defined by those who meet their workforce goals at the community college. And our extended survey, we had four of our five states participate nearly universally. And that was Maryland, South Carolina, Virginia and West Virginia. So, the data you’re going to see are from those states. Next slide.
So, here are the states that participate. You can see we had full participation for Maryland, South Carolina, Virginia, and all but one school in West Virginia. And the data I am going to present, these are all still should be viewed as experimental and subject to change because we still are iterating with the community college systems and internally with our team to make sure we’re asking questions in the right way and getting the exact data we want to attain. Next slide.
Okay. So, let’s talk about the numerator and the denominator of our success metric. There are a lot of things in our survey. I have QR codes at the end for you to see more about what we’re doing, but I’m going to focus today on one part of it, which is the success rate. The denominator of the graduation rate is the total number of students in the cohort.
One of the reasons why community colleges say that IPEDS graduation rate doesn’t work great for them is the calculation of this cohort. The IPEDS cohort uses a full-time, first-time cohort, which means students have to be attending a college for the first time and they also have to be enrolled for at least 12 credit hours a semester.
This works pretty well for many four-year institutions. Just to give you a quick example, at Washington and Virginia last year, 97% of their students were in the cohort. At Virginia Tech, it was 85%. But at Tidewater Community College in Virginia, it was only 35% of their students. So, it represents a much smaller percentage of community college students that end up in the cohort.
For the Richmond Fed cohort, we expanded to also include part-time students and non-first-time students. Through a workforce lens, we really were not concerned if whether a student was enrolling for the first time or at the fifth different institution or if they were taking nine hours a semester or 15, we wanted to include everyone.
So, I have Maryland as an example here. And you can see that the Richmond Fed cohort is larger than the IPEDS cohort in all cases. And in some cases, it is much larger. This tends to be at very large urban institutions where they have many more part-time students. Next slide.
So, how do we define success? Once again, using that workforce lens, we define success not only students who graduated, but also students who attained an industry recognized credential or licensure. We also include students if they transferred prior to attaining the associate’s degree, if they transfer to a four-year institution, we can count them as a success. And then we also count students who persisted for at least four years in good standing as a success. And our timeframe of measurement is four years from the time they entered the institution. Next slide.
So, if we look at the 63 schools in our extended pilot, we can see that the IPEDS graduation rate is 28.2% for those schools. And our Richmond Fed success rate is 51.8%, much larger. Next slide.
And you’ll see that in Maryland, once again showing you at Maryland just to give you an idea of what this looks like in each state, we do have the schools anonymized. You can see our success rate is higher than IPEDS, but the difference depends on the school. And generally, this depends on two things. There are some schools that have a lot of students that are transferring before they get to the associate’s degree. Those schools tend to look much better in our success rate. And there are also some schools that are highly focused on getting their students’ credentials and those schools also showed up as being a greater success. Next slide please.
Here’s the range for each state. So, in Maryland you can see the graduation rate, IPEDS rate ranges from 11 to 41%, but in our success rate, 41 to 62%. You can see in Virginia there was even a school that had 86% success rate. That school is one of those schools. It’s a small rural institution in Virginia where they are highly focused on getting their students industry recognized credentials for the workplace. Next slide.
Just to show you a couple of slides by demographics. This is very different whether you’re at a rural institution or an urban institution. Probably not surprisingly. There are more students who are transferring at an urban institution than rural. But I will point out to you that our percentage of persisters was relatively the same in urban and rural places, which was a bit of a surprise to us.
So, these are generally students who are taking one or two classes at a time but are remaining in good standing and it’s just taking them longer to finish because they’re in part-time status. Next slide.
Pell recipients had success rates below non-Pell recipients in all but South Carolina. But I will say within the states there’s variation here. So, there are some schools where Pell students are performing higher than non-Pell students. This is something we’ll be digging into as a team and writing about in the coming year. Next slide.
We see the same thing amongst Black students and White students. This gap was the largest of all the demographics that we look at. And I’ll have a QR code where you can see all of this on our website. You can see it by school as well. But we did see a stark difference in success in Black versus White students. But again, some schools were serving their Black students in a way where their success rates actually exceed White students. Next slide.
So, to summarize, we know community colleges play an important role in workforce training across the Fifth District and beyond. We believe that including shorter term credentials and persistence and transfer is a better way to define community college success.
And the main reason for that is that community colleges are serving a wide variety of students who have a wide variety of goals. We also do see that state, local and institutional policy play important roles in the success rates and in the way, schools are choosing to serve students we think policy matters. We’re really excited to move forward with the survey and we have plans hopefully to expand beyond our district at some point. So, if you’re located outside of the Fifth District, we’d still be eager to collaborate with you guys. Next slide please.
Here are those QR codes, I promise. So, the one on the left takes you to our survey of community college outcomes website where you can see the results I showed you, but much more. We also have data on non-credit programs. We have data on dual enrollment students. And then on the right, that will take you to a link of everything we’re writing about community colleges, which is quite a lot these days. We welcome you for you to check all of that out and please connect. We’re eager to collaborate. Thanks so much. Now, I’ll pass it off to Nicole Smith, our discussant today.
Nicole Smith
So, good afternoon, everyone. Are you hearing me well? It’s a delight to be here. As you know, I’m at Georgetown University and we’ve spent a lot of time looking at the field of labor economics and the role of education in ensuring that we are very much connected to the labor market.
I’d have to say that college has long been perceived as a pathway to success offering individuals the opportunity to gain knowledge, skills, credentials that can lead them to well-paying jobs and a fulfilling career.
But as the cost of college continues to rise and the job market becomes increasingly competitive, the question of whether college is worth it has emerged as a topic of heated debate. And this debate has gained momentum in recent years due to several factors, including rising tuition costs, concerns about student loan debt, and an ever evolving and complex job market.
So, I was very, very happy to see that we have three papers that individually represented important additions to our understanding of the role of human capital in labor market outcomes. And even if we think of the BA as being the center and the traditional route for human capital development and maybe everything else as the periphery, then some of these peripheral discussions are very important routes to skill development and presented in this report.
So, the first of these was presented by Peter Bahr. And I think this particular report raises an important question that is relevant to the rising and high cost of education and the opportunity cost of upskilling in a rapidly changing technological environment.
So, here we have this report that is looking at several states with Colorado being the most important of them in the conversation. It’s estimating the prevalence and measure of success of skill building in four states. I think earnings gains as he’s demonstrated in their words as intriguing, and they show some success where success is defined as average above the trended mean.
My question here is are these earnings enough for a living wage? Which is something that they’ve discussed later on. And are we able to disaggregate some of these earnings by demographics? Have we controlled for the tight job market? Because we are seeing some really positive earnings if we’re looking within the last few years because we do still have a really tight job market.
I think one concern or question that I had was the average age of entry. It seems as if they gave 37 as the average age of entry, which seemed a little bit curious to me just from my understanding of who we’re looking at, since we’re looking as some alternative to college. So, maybe we find that some people are entering for skills building at a little bit of an older age in defining their career ladders and career lattices.
I think the discussion spoke positively about the role of community college in allowing students to enroll during slumps in the economy. And you can do so in a shorter period of time with fewer credits using CTE. All of these are very, very positive.
I have a question again to Peter about the use of the phrase “student reskilling” and whether or not we’re talking about student workers or learning earners. And there’s a little subtle difference here as to whether students are going to school at the time and continuing to work so that they can pay their way through college, or are we seeing people who are already entrenched in the labor market going back to school to upskill?
So, I just wanted to make sure that I had a good understanding of who the one in eight was. And I think the thing that catches your eye is the declining in earnings of community colleges and decrease in wages right after community college. So, I wanted to just ask about whether or not we had any demographic information regarding that.
Peter Riley Bahr
Dr. Smith, thank you. Those are outstanding questions. Thank you for your read of this work, and I know this was a ponderous to me that you were handed, so thanks for your time in that.
I think you’re absolutely spot on. Our next line of work in this area is that we’re beginning to develop now and talking to states about is investigating the interchange, how students are moving in and out of the labor force during this time of upskilling and reskilling? It’s one thing to see earnings gains and to try to figure out who’s getting a gain and who isn’t. It’s another thing to figure out how they’re being squeezed out of the labor market?
Some of these students are part-time or are completely out of work or are juggling multiple jobs or changing industries. And I think we can model a lot of that and be able to answer many of the questions that you asked, I think, and which I think are really the most important questions around this topic.
We won’t know precisely what to do with upskilling and reskilling, how to leverage it for maximum benefit and how to make upskilling and reskilling work for students and workers if we don’t understand how students are actually utilizing that pattern or that pathway now. So, you’re spot on. Thank you.
Nicole Smith
Thank you. So, I’m going to go in order a presentation. So, second, we’ll talk about Justin and STARs and Opportunity@Work. And again, I love Opportunity@Work. You guys have been doing an amazing job at pointing out how difficult it is for some people who don’t necessarily have access to a bachelor’s degree to really connect with the labor market and also highlighting the extent to which those who have been working in jobs and doing those jobs successfully over time are still not earning as much as someone who is doing the very same thing with some other type of paper credential.
So, I think it was really interesting for us to add now a racial component to this, but even before we get there, I think whenever I think of the STARs, Skill Through Alternative Routes, I always ask if the one cog that’s missing from this conversation is demonstrating the skill because we talk about, okay, they’ve been doing the skill, they ought to be getting what someone with a higher credential is getting, they’ve been doing the job all along. Maybe if we can even connect those STARs to what Bahr has been talking about, those upskilling and reskilling, that’s the final nail in the coffin that can help some of those STARs demonstrate this skill in my opinion.
I think where Justin Heck spoke about the index of dissimilarity is interesting to me because I’ve spoken to many educators, especially those at HBCUs, talking to them about not only differences in occupational segregation by race, but also differences in occupational, sorry, segregation by majors that people pursue.
And I tend to get a lot of pushback from HBCU sometimes because they say, “Look, I understand that there are differences in pay, I understand that there are differences in outcome, but our community needs these types of roles such as the intellectual and caring professions. We need to have more social workers. We need to have more people in home health aid professions. And I was just wondering how you deal with that in your conversations about these differences in occupation?”
I think I’m also interested in the driving forces that lead to these outcomes. And though it might be beyond the scope of this research, how can we mitigate against? What are some of those policy prescriptions to make sure that these Black students who pursue bachelor’s degrees in a race neutral society or race neutral market who should be earning more, how do we make sure that we point them in the right direction? So, it’s more of a policy prescription.
And I wonder as well, if you’ve done any analysis by the type of organization, maybe the size of organization. Because I’m thinking, here, we’re having a discussion about occupational differences, differences in pay by race ethnicity in a world that still exists by the Fair Labor Standards laws and EEOC and equal pay. So, is it a function of the size of the organization that’s also allowing some of these differences?
And I really think if I looked at the study did well to recognize differences in labor force characteristics between Blacks and Whites, rates of incarceration and what that might entail. So, due to the time limitations, I’m just going to go straight into Ullrich’s discussion and then I’ll let Peter, sorry, I’ll let Justin respond to any of those questions that I just raised, but thank you.
And so, to this third paper that is proposing adjustments to the way we define success in community college. Again, kudos, kudos. We define community college success and why am I so happy about this? You might think I’m happy about all these papers, for all of the reasons that were raised. I mean the mere fact that this survey aims at better estimating student outcomes and successes already a step in the right direction.
We’ve spent so much time talking about the shortcomings of IPEDS. I mean, I haven’t been to a meeting that doesn’t say IPEDS doesn’t do justice to community colleges because we’re not defining completion correctly. I mean, so many students come into community college with the objective of transferring and yet the way it’s defined, transfers aren’t necessarily seen as a success.
So, I am very, very happy about this new definition of success, which includes receiving an award and transferring and continuing to persist. I have a question for Laura in terms of the scalability of the method. I’m asking this because I read about cognitive interviews that seemed a little bit time-consuming though they were success in the type of information that was obtained.
So, I was just wondering if in terms of scalability, if we see any real opportunity for us to take this nationally to expand it to some of the other community colleges because we really are doing so many of them a disservice by … We keep with the narrative that graduation rates are so much lower.
Laura Ullrich
Nicole, I really appreciate your comments very, very much and your question as well. We have gotten a lot of support from the broader community college community and we have been taking this on the road to more of a national audience than are investigating right now, how to move this nationally. Our goal for 2024 is to survey the entire Fifth District and then our plans in 2025 are hopefully to go beyond our district in some way. So, we’re investigating all of that right now.
But I will say if anyone is interested in talking to us from outside of the Fifth District, please do engage. We’ve got a great team of folks working on this that’s growing every day and we’re excited to collaborate.
Nicole Smith
And just so we wrap up this, Laura, I know you spoke about the urban-rural dimension, but I just wonder if you’ve had any opportunity to increase an age dynamic or race ethnicity dynamic? Because I am always interested to see how those would impact some of our outcomes.
Laura Ullrich
Yes. So, we do have the data by race. Age we do not. But actually, right after this, we have a meeting about how to change it for this year, and that’s going to be one of the recommendations. So, we have the ability to look at success and there are some very interesting patterns. And in 2024, we’re hoping to have this data available by school on our website. We anonymize the results this year because it was a pilot, extended pilot, but we hope to have the results by school this year. So, we’re really excited to show all the data we have. We have a lot of data.
Nicole Smith
Wonderful. And Justin, I know I cut you off because I was so focused at the time, but please, are you able to answer those questions I raised about STARs?
Justin Heck
Yeah. Thank you so much for your comments and questions, Nicole, and for your careful read of our work. To your first comment about how workers go about demonstrating their skill, at the end of the day when we think about skills matching between workers and employers, so much of it is about bad information and signaling. How do I know that this worker has the skills that I want? How do I know the skills that I am looking for in a role?
And unfortunately, the way our labor market operates today, we often rely on poor indicators to estimate whether someone has this skill. Oh, they have a college degree. Great. They probably are good or can figure it out. Oh, they’re from my alma mater, even better because I know that I love my alma mater. And that’s not a great way to measure skill.
And then racial considerations and biases pop up, too. That person is like me and I’m making assumptions about their skills. And so, for STARs and for Black STARs in particular, a shift to more intentional skills-based hiring, thinking more carefully about the skills that we’re looking for in roles, and essentially offering STARs the same opportunities that we’re offering workers with bachelor’s degrees today.
I’m going to give an example that’s pointing to myself a little bit. Today, I don’t know all that much about generative AI, but I do know that given my credentials, I could go learn a little bit and people would start believing me that I know something about it. And other folks don’t have that same opportunity to demonstrate they know stuff. And I think we essentially want to start broadening the aperture about how we think about the skills that folks have and how we measure the skills that we’re looking for.
When it comes to the importance of intellectual and caring professions and particularly, the disproportionate role that Black workers have been playing in terms of filling that need, absolutely. We need to recognize that there is a lot of work in the United States that is incredibly valuable even if we don’t reward it in the ways that we ought to.
And so, there’s two things to be doing. One is how do we improve the jobs that workers have and improve job quality? And that’s important work that unfortunately is outside of my purview. Because the focus of Opportunity@Work is to think about mobility from the worker’s perspective. Yes, do that job and get recognized for the skills you develop there to experience mobility across your career.
The last comment that I’ll make before giving space for other folks to keep talking is what are the policy solutions? How do we overcome occupational segregation? Again, really big question. I’m not the person with all of the answers, but the one thing I want to suggest from our research that we’ve done to date is that perhaps it has far less to do with fixing workers than fixing employers and fixing the way that we think about workers.
So much good valuable work is about improving the skills that workers have, reskilling, giving them credentials, improving who they are. In fact, I think a lot of workers have the skills we need today if we started measuring it correctly, if we started thinking about the ways that we’re hiring. So, thank you again for your questions, Nicole.
Nicole Smith
So, I am going to look at some of the questions in the chat to see make sure we have any additional things that aren’t answered. I think there was one that was raised about STARs, defining STARs, that’s already answered there. If you guys see anything because having a hard time reading these questions that are not yet answered that you are able to, please feel free to jump in or else I’m going to just ask my further questions that I have. So, there was a question for Peter. Someone was asking what are those sequences? I’m not sure-
Peter Riley Bahr
Sorry. Go ahead. I breezed through that pretty quick, but we first identified all of the students who the skills building students, these upskilling, reskilling students based on their global enrollment patterns. And then we looked, we drilled down into the course combinations that these students enrolled in.
So, each student had one or more courses that they took. They completed them at exceptionally high rates. That was a prerequisite for falling into our group. And from there, we were able to see that something like, I’m struggling for the percentage, but I want to say something like 60, 70% of all the course combinations were taken by just a single student.
So, looking more closely at those course combinations that were actually taken by a meaningful number of students with a threshold of 10 yielded with some other constraints on the sample around wage and so on, 317 course combinations in the fields of study that were described there. I don’t have obviously the full list of all of them here in front of me, but happy to provide that.
Nicole Smith
Another question for you, Peter, from Dan Rumer, which is saying, “Did you take into account the cost of attending college?”
Peter Riley Bahr
We did not take that into account. It is a very good question though. You might be thinking of that in terms of ROI like return on investment. We haven’t looked at that specifically, but it’s a great question. Something definitely worth examining. Here, we are trying to tackle that broader discourse about what the upskilling and reskilling can do. The discourse says goes two ways, sort of dichotomous. It says on one hand, “Well, if you’re not getting a credential, you didn’t succeed and you’re not going to get anything out of it.” That’s speaking grossly about it. So, it’s credential or nothing. It’s credential or a failure for the college or for the student or what have you.
On the other side, you hear the argument, “Oh, upskilling and reskilling can be a substitute for a credential.” That can produce a life-changing amount of money for somebody. But it looks like it’s very much the middle space there, where most of these upskilling and reskilling opportunities really are yielding meaningful gains for students and particularly for some groups of students. And even actually more so for students at the lower income range than the upper income range, but typically not enough to move students out of poverty into self-sustaining income.
Nicole Smith
I have a question from an anonymous attendee, which I think either of the three of you is able to answer. So, I’m just going to throw it out there and see who wants to take a jab at it. WIOA reauthorization in its current forum commits 50% of WIOA money for training, which tends to focus on this short-term training. Given these outcomes don’t look that great, what does it look like for the workforce system of WIOA to help people move into living wage jobs?
So, what do you think? So, now we’re talking about here we’re going to be providing funding for these short-term programs. We spend some time saying not all of them are going to be living wage. I know there was a conversation that, “Well, it’s not living wage, but it’s better than what you were doing before.” What are your thoughts about committing this money?
Peter Riley Bahr
I think that there is a very promising path here if we’re thinking about it from the upskilling/reskilling standpoint. I mean, this is giving us the reality of what’s happening. We can’t discount the important role that community colleges are playing in upskilling, reskilling, and clearly, it is paying off to some degree for a majority of students and in a majority of the course combinations or course sequences that they’re taking.
I think the bigger question is, as Nicole mentioned, how is this related to movement in and out of the workforce? That’s question A. We’re not really going to understand what’s happening around earnings gains if we don’t understand how students are using these sequences in relationship to participation in the labor market. That’s question A.
Question B is how can we connect these high yield course sequences or even the moderate yielding course sequences or course combinations to credentialing paths? So, a number of states are working very hard. One of our partner states, Ohio, is just at the leading edge of career and technical education, work on their career in technical education on the transferability of credits and awarding credits for industry credentials and making it possible to move from primarily non-credit institutions to four credit institutions and so on. Colorado is another really innovative state doing amazing work there and you can see that in their high credentialing rate.
So, the question is how can we connect the high value sequences to even more high value credentials? And something my team and I have done, and we’re working with administrative data as we’ve looked holistically at millions of records and been able to find ways, sort of data mining-esque ways to identify the shortest course taking path from a given high yield course sequence or course collection to the nearest high value credential.
And so, if we can put that kind of information in the hands of colleges or in system offices, chancellor’s level or a president level at the college, the colleges can begin building out with intentionality, can build credentialing paths from the high value core sequence, I have to use hand motions to gesture to this stuff, to the high value credentials.
So, I think there’s a lot of hope and promise in that and it’s very, very exciting for individuals who are having to balance work and school. I’m going to pause my jabbering here for a minute, but I was one of those people. I was a community college student myself. I worked at night at a wastewater treatment plant. I went to school during the day. I took classes alongside my colleagues. I often mention I still have my wastewater license.
So, I took classes with skills builders, with these skills building students, my colleagues. It happens that I went on and continued in college for quite a long time. But this can work for students and can be high value if we can be very intentional about connecting to credentialing paths.
Laura Ullrich
Nicole, can I add just one really quick thing there, too? I think one thing we have learned in the project we’ve been doing is that there is extreme variation in the data that states have on their non-credit students and students that are in these short-term credential programs. And so, some states are doing a pretty good job measuring how those students are doing and others are not really collecting much data at all on those students.
So, I think we can’t really say yet if the outcomes are great or not in a lot of cases. And I’m hopeful that better data collection will lead to better answers that will help guide decision-making going forward.
Peter Riley Bahr
If I can inject on that, I know we’re trying to wrap up, but that’s a great point. And I think one thing we didn’t bring in here is that non-credit component. Many of these community colleges have both a credit path, a credentialing, so the four-credit side of the house and then often of equal size, the non-credit side of the house that frequently is offering the occupational training and so on.
And as Dr. Ullrich is doing, my team and I are also looking at non-credit number of states and with colleagues at Rutgers, Michelle Van Noy and Mark D’Amico at UNC Charlotte and in California, the UCs. We are looking at how states can build out better data systems and begin to really intentionally collect information about occupational non-credit in particular, but non-credit across the board.
And so, that’s a place where I think we need to be. That’s another bridge point. Can we bridge from four-credit sequencing to credentials? Can we bridge from non-credit occupational training over to the four-credit side? Can we have parallel credit systems and so on? Or can we at least be sure that non-credit is paying off? So, in some states, we’ve been able to see that and it’s very exciting to see the developments in that space.
Nicole Smith
Wow. Thank you guys so very much. I have so many more questions I want to ask of you, but I’ve been told we need to wrap up. So, I am going to pass it along and let’s see who’s by name … Back to Tiffani. And thank you all so very much.
Tiffani Williams
Thanks so much, Nicole. Great discussion. So, we are out of time. So, on behalf of our organizing committee, I just want to acknowledge all of our speakers for their time and the insights that they shared during today’s discussion.
If you’re looking for more great content on Uneven Outcomes in the Labor Market, please join us tomorrow for day two at the same time to continue the conversation. Tomorrow, we’ll focus on outcomes for men and women in the workforce. So, it should be a great discussion.
We will open with remarks from Susan Collins, the president of the Federal Reserve Bank of Boston, followed by Richard Reeves, President of the American Institute for Boys and Men, followed by a discussion of new research.
Again, research links, speaker bios, and a full agenda for each day are available on the conference website. But we so look forward to seeing you tomorrow and for the rest of the conference. Thank you again so much for joining us. Goodbye.