Debi Pezzuto: Thank you, Mandlee. Good morning, everyone. Good afternoon to Meghan, who's in Georgia. And thank you for joining us today. We're excited to be here for the Data-Driven Advocacy for Noncredit CTE-- Making the Case for Labor Market and Demographic Data webinar.

Today's webinar will show the untapped potential and regional relevance of noncredit CTE or Career Technical Education. We'll demonstrate how to access and interpret local labor market and demographic data, equipping you with tools and talking points to build support for noncredit CTE programs.

We'd be happy to meet with you one on one outside of this webinar if you have any additional questions or you'd like to learn more about how we can help your individual area. So if you're interested in a more in-depth, customized data analysis or strategic planning, we'll have our information up in just a moment.

So before we get started, we'd like to also thank the Chancellor's Office, CAEP team, Gary Adams, Mayra Diaz, and Cora Rainey and the folks from CDE-- Carolyn Zachry, Diana Batista, and Neil Kelly, who support this type of work to ensure quality adult education across the state. We'd also like to thank SCOE TAP for the support they provide to us and the field. Thank you, guys.

And before we dive in, I'd like to introduce our team of presenters. We are from WestEd, where we provide professional development and technical assistance to California adult education programs. I'm Debi Pezzuto, the senior program associate.

And I'm joined today by Meghan McBride, the senior-- well, actually, you're not the senior program manager anymore. You're a director of adult education, yes. And Greg Hill jr. the senior research associate. Greg is going to jump in to help us. He is most recently responsible for updating the CAEP fact sheets with the '23, '24 data.

And throughout today's presentation, I'll be monitoring the chat. So if you have any questions, please add it to the chat. If it's not the right moment for me to interrupt, I'll take a note. And we'll either respond to you outside of it, or we'll respond at the end. Feel free to drop any questions that you have there or comments. I'll be watching it. Meghan will be pausing periodically to address them and keep the discussion pretty interactive.

I'd like to introduce the lovely Meghan McBride. Give us a wave, Meghan. She will be facilitating our webinar. Meghan joined WestEd in 2024 as the senior program manager for adult education. And in this role, she leads efforts to support adult education consortia in using data-driven strategies to strengthen educational services.

Meghan's area of expertise include adult literacy, English as a second language, high school equivalency preparation, integrated education and training, and WIOA Title II. Previously, Meghan served as a vice president at a local community college in Metro Atlanta, managing a large adult literacy program. And now I'd like to turn it over to Meghan to get us started.

Meghan McBride: Thank you, Debi. Hey, everybody. It's so good to see so many people here and so many familiar names. Can't see all of your faces, although I see you, Molly.

All right, so as Debi mentioned, today's session is about using data to make the case for noncredit CTE. As many of you know, noncredit CTE programs are one of the most accessible and equity centered entry points into our community college system.

They serve adults who may not be ready or able to enroll in credit programs, but who are eager to skill up, get to work, and begin or restart an educational journey that may have been interrupted. But despite their power, noncredit programs are often overlooked, misunderstood, or underleveraged. So that's where today's webinar comes in.

Today we're going to explore how to use regional labor market data and demographic data to tell a stronger story. So we're going to walk through some practical data tools and sources like DataVista, the CAEP Fact Sheets, census data, and Centers of Excellence report.

And we'll show you how to find your community's most urgent needs and match them to the programs that you're offering or it could be offering. And we'll also give you some tools in an advocacy toolkit, which includes a couple of one pagers, some examples of how you could put together a story using data, and some talking points for engaging stakeholders. And then we'll finish up with some Q&A. So in short, our goal today is to help you become champions for noncredit CTE using data, your voice, and your community story.

So let's start with what noncredit CTE is. And we'll do that by comparing it to credit CTE. So I've got a comparison between the two. And credit CTE students pay enrollment fees. And noncredit CTE, they do or they don't, depending on whether it's adult ed and K-12 or in the community colleges. So that's why I have no or low enrollment fees. That low cost or no cost certainly removes financial barriers for students to enroll.

Credit CTE program length varies. Could be a semester or longer. Noncredit CTE is often shorter and designed for rapid skill acquisition and workforce entry. That's not even. Credit CTE has limited repeatability. There are restrictions after a student passes a course. Noncredit CTE is generally repeatable, and students can retake courses as needed.

Credit CTE is usually transferable. Noncredit CTE generally nontransferable and designed for immediate workforce entry. Greg, anything to add there?

Greg Hill Jr: The only thing I would say is it's worth noting for some folks who are less familiar with the community college system, that there is a difference between noncredit and not for credit. Not for credit is often called-- it falls under-- there are lots of different names for it. And invariably, however I define it now, someone's going to say, no, that's not it because that's not how it's implemented at our college or something.

But basically, the differentiator is noncredit is gets apportionment dollars. So enrollments generate revenue from the state. Not for credit is often community education where, say, businesses will pay colleges to create programs. In some cases other partners might do that as well.

And so those are two very different things. And as has been articulated a bunch of times, noncredit falls under CAEP. Not for credit does not. That's it.

Meghan McBride: All right. Thank you, Greg. That gave me a chance to have a sip of water. All right. Now let's look at some common misconceptions about noncredit CTE. And also, if you have any of your own, please put these out here. These are things I just thought of as I've been roaming around California, talking to y'all, and also from my own experience in trying to start noncredit programs in my own job.

So the first misconception is noncredit programs don't lead anywhere. And if you hear that, you can respond with, well, yes, they do lead directly to in-demand jobs, industry certifications, or credit-bearing certificate and degree pathways. And it can be the first step on a career ladder.

Misconception number 2 is that employers only value credit bearing or degree programs. However, increasingly and certainly since the pandemic, employers increasingly prioritize skills and certifications over formal degrees, especially in fast-growing industries like health care, logistics, and IT. And noncredit programs can deliver to the job ready competencies that many employers are looking for.

Anything to add on those two, Greg, before I move to the next few? Nope? OK. Misconception number 3, noncredit students don't count in our metrics. Under the Strong Workforce Program, and CAEP, noncredit students do count toward outcomes like employment completion and transition. And they generate enhanced FTE funding under CAEP.

Misconception number 4, noncredit programs are only for basic skills or ESL. While that is true, it does include basic skills and ESL, it can also include robust career pathways that are workforce aligned, stackable, and outcomes focused.

And finally, number 5, noncredit is just less than credit. And the response to that would be it's not remedial. It is responsive and that it meets learners where they are, often removing financial scheduling and academic barriers that credit programs can't. And it can bridge directly to credit if you are intentional about it, acting as a feeder system for the college and supporting that enrollment. Anything to add there, Gregory?

All right. So why does noncredit CTE matter? As I mentioned, it removes barriers to entry with the low cost, open enrollment in and out. Sorry, my dogs are knocking on the door and it distracted me. It's fast, focused, and flexible. They can quickly upskill job seekers. It serves equity, priority population.

Designed for employees-- I'm sorry, for adults who are unemployed, underemployed, returning to the workforce, or learning English. They're aligned with regional workforce needs in that they're targeted training for in-demand jobs. They create on ramps to credit pathways with stackable credentials and seamless transitions into credit-bearing programs.

And they are responsive and employer informed. So curriculum can be updated more nimbly than credit programs to meet labor market shifts. Anything to add there, Greg?

Greg Hill Jr: No, I think that's actually really good. Yeah, no, nothing right now. Yes.

Meghan McBride: All right. Well, that set the table for us. Now we're going to jump into our data tools. And we're going to review four sources that you can use. And as Debi mentioned, we're happy to do this individually with you all to look at your regional data. We'll be using an example of Los Angeles, but we can do this in any area one on one if you'd like.

Our first data tool is my personal favorite, DataVista. She's new to California, formerly known as Launchboard. And if you've been to my regional trainings, you've went through DataVista with me. We won't go that in depth with it as we did in our regional trainings, but we will look at some pertinent information from DataVista.

But DataVista essentially brings together data from TOPSpro Enterprise and the COMIS data systems to create these visualizations that show us what is happening in your enrollment, transition, and success for the adult ed consortia. And it can drill down to the institution level, but we typically use it at the consortium level. And it gives you insights into what your programs are doing.

Anything to add on DataVista, Greg? Yeah, so I'll talk more about that when I open it up. I'm not going to open it yet.

We'll also review the CAEP Fact Sheets. These, like DataVista, are curated by WestEd in concert with the Chancellor's Office. The CAEP Fact Sheets are useful for looking at regional data within the boundaries of the consortia. So we're not looking at counties. We're looking at your actual consortia demographics.

We can also compare what's happening in your regions to what's happening in your programs to look for equity gaps. And so we'll look at some of that today with the CAEP Fact Sheets for the express purpose of looking for data that can help us write compelling narratives to support noncredit CTE. Anything to add on CAEP Fact Sheets, Greg? Greg is our CAEP Fact Sheets master.

Greg Hill Jr: Thanks, Debi. Not I think right now. I know that folks often have questions about where to find this data and how it was calculated and so forth. And I'm happy to answer those questions offline or if they're important as they come up in chat.

There is a question that popped up here that I can help to address if you want.

Meghan McBride: Please do. Read it out loud first. Greg.

Greg Hill Jr: So Nikki Moultrie asks, "Some of our faculty are interested in mirroring their credit versions of their programs. Is there a standard definition of short-term?" So the answer is a yes and no. So typically, short-term CTE within the context of CAEP is understood to mean one year or less. That is not a hard and fast rule by any means.

Ultimately, the heuristic to use for definition is, are you expediting folks into jobs? And if it's taking the same amount of time as it takes to get a degree, that's probably adjacent to but not inherently in alignment with the CAEP funding. It's not-- I mean, you can use the funding for it if it's on credit, but I hope that answers.

Shannon also asked, just to confirm again, fact sheets are consortia, not county. One qualification, sometimes they're county. Sometimes they're not. So the 71 regions that make up CAEP are based on community college district boundaries. In some cases, those boundaries conform one to one with county boundaries, but not in all cases.

And so this is, in fact, why we created these fact sheets-- because it is difficult in some instances to be able to get good data for consortium areas that cut across multiple counties, so.

Debi Pezzuto: And real quick, Greg, before we move on. Just to clarify that, in an earlier slide, there was a mention of fees for noncredit CTE program. And so I just want to clarify that the fees are for materials and not for the registration fees since the noncredit programs are free and they don't charge for registration. So we just want to make sure that's clear. Thanks, Mayra. She's like the little bird in my ear. [laughs]

Meghan McBride: It takes a village. Thank you, Greg, also. All right our next source will be from the Centers of Excellence. And I have to say, this is one of the most valuable resources that I've found in addition to what WestEd provides in California. We don't have anything like this in Georgia.

But it's the Centers of Excellence for Labor Market Research or the COEs. And of course, these are part of the community college-- or California Community Colleges. And they're charged with providing actionable, region-specific labor market data to help colleges make informed decisions about what programs and pathways to offer.

So you're very lucky to have this so that you're not just winging it, like many other places do in the United States. Each COE serves a specific region. And I've got all nine regions there that they serve, and their reports are designed for practitioners like us, people who are working on the ground in CTE and workforce development and in adult education. And they publish these subregional profiles, sector briefs and employer demand reports and many other useful tools for making these decisions for those of us on the ground.

So whether you're making the case for launching a new program or perhaps advocating to expand an existing one, the COE data is a go-to source that can strengthen your story and align it with regional workforce needs. So we'll look at one specific COE report as we build a data story for LA.

And finally, we will finish off with the census or American Community Survey data. And so this will help us understand who we're not serving. So in order to build our data story, we need to look beyond just our program enrollment numbers, which we would find in DataVista, and look at the community itself.

And one of the most powerful tools to do that is census data, especially the American Community Survey. So today, we'll be looking specifically at tables that show educational attainment for adults 25 and older. That is broken down by census tract.

And we use census tracts because it gives us neighborhood-level insight. And this is how we see what specific areas have the highest concentration of adults without a high school diploma, for example. And this tells us something vital, where there are communities that need low-barrier, skill-building pathways like noncredit CTE programs to help people improve their economic mobility.

And so we'll be using this data to help identify educational deserts and build equity-driven arguments to inform where we could focus our outreach partnerships and program development. Greg, anything about either of those last two sources?

Greg Hill Jr: Yep, just one quick qualification. Oftentimes, census tracts do conform to neighborhoods. They are drawn intentionally, but not necessarily. What census areas are contiguous? So what that means is that they all abut each other.

And so the criteria has to do with numbers of individuals. So in urban areas, they're much smaller and are more reflective of neighborhoods. In larger areas, they're not necessarily. And so case in point would be to consider like Kern or counties that are on the east side of the state. You'll see census tracts that are utterly enormous. And no one could reasonably say it reflects a neighborhood. But in other areas, it will.

And there are ways, by the way, to drill into when you do encounter huge census tracts or you're playing with census data and you're like, hey, how am I supposed to serve this area? This is just telling me all the numbers in one area. There are techniques that you can use to identify where the locus is within those areas. And again, if you want to reach out, we can help with that.

Meghan McBride: All right. Thank you, Greg. David has a question in the chat. But if it's OK, I'd like to let Greg elaborate on that when we get to the CAEP Fact Sheets. So I know you've answered it, Greg, but you might want to expand upon that when we get to the fact sheets, which will be in just a moment.

So what we're going to do now is actually look into those data sources, using these data tools to craft a case for creating a noncredit program for a high-demand, low-barrier occupation in a specific region. And then we're going to match that occupation to a potential noncredit pathway.

So as I was preparing for this webinar, I was just poking around on the different Centers of Excellence websites. And I came to the LA region. And I was reading through their report for the LA-- for LA County and the various subregions within LA. And I found one that inspired me. And so that is where the example comes from today.

So we're going to be looking at the Los Angeles area, specifically central LA, to build our data story. And again, we can do this with you one on one. Or maybe you'll be able to do it on your own after you've watched us walk through it.

So I'm going to start with DataVista because I love her. So again, we're not going to go very far into DataVista. We're going to just live in one place. And there's multiple ways to get where we want to go. And maybe someone can drop DataVista in the chat in case you want to follow along. I'm going to go this way.

So I'm going to click the dropdown on Quicklinks. Are you all seeing my DataVista? Thank you. Yes. All right. Thank you, Debi, for dropping the link. I'm going to go to the Score Card. And when we land on this score-- we click on the Score Card. We land on the statewide metrics for 23-24. This was recently updated about a month ago or so. When I'm in the DataVista, any page in DataVista, I like to collapse the toolkit just to have a little bigger screen there.

So what you're seeing here are all of the metrics that are reported to the legislature for the entire state of California. But as I mentioned, we want to look at just one particular area. So to do that, I'm going to click on the dropdown area here under Locale.

For any of you who are in my regional trainings, you should be experts at this by now. So you should be able to do this on your own. But we're going to click Consortia. Again, I like to look at consortium-level data. You are able to see smaller units, assuming they have large enough populations that don't get suppressed.

And there are multiple ways to find your consortium. I am a typer. Debi is a scroller, but I'm just going to type in Angel. And there's LARAEC. So we're going to look at LARAEC today. Now for those of you in more rural populations, you won't see numbers like this. LARAEC is gigantic.

As we can see, LARAEC has 116,000 reportable individuals. These are folks with at least one hour of enrollment. But frankly, that number really doesn't matter as much as this one. This is the number that they have converted into participants who have had at least 12 hours of enrollment.

And what's important about this number in terms of our goal here, to make a case for noncredit CTE programs, is that with nearly 93,000 participants, these are students who have reached 12 hours of enrollment. It shows that there is a demand for noncredit programs in their area. Stakeholders love a pull effect. So high enrollment demonstrates a clear demand from your community.

Now I'm going to look at the time trend. We can see that from one year to the next, from 22-23 to 23-24, that LARAEC had a 17% increase in enrollment, which is very, very strong. But that only shows us one year. So I'm going to click on this metric to see what happened over the last several years.

And what I see immediately is a very common pandemic curve, where we bottomed out in '20 and '21, but they've had a very strong recovery from the pandemic there in LA. And what's notable here that would be useful for our argument is that LARAEC has now surpassed their prepandemic high of 92,000 students by about 600 students.

So again, showing a very strong demand. If we hover over any data point, we see a percentage, 80%. This is their retention rate, the rate at which they are converting actually students-- either one. Converting students from reportable to participants.

Again, once we hit 12 hours, then whatever the student does in terms of their outcomes or transitions shows up in our metrics. Whatever they do before 12 hours doesn't count. So it's important to get a lot of students across that line. And LARAEC's doing great at 80% So this shows that they're retaining their students, which also could be very compelling for their data story.

Now I'm going to isolate something here. I'm going to do that under AE program I just want to see their ESL enrollment. What we know about this area we'll see soon is that there are a lot of multilingual residents or people who speak English less than well.

So what you want to see is a large ESL enrollment here, students who are learning English as a second, third, fourth, whatever language. And as we can see, LARAEC has very strong ESL enrollment. So to me, this confirms that they're reaching underserved populations. And we can use this to reinforce the equity case for noncredit programs.

These are students who would likely benefit from noncredit. And they're already in-house. They're already engaged in school and perhaps a captive audience. And again, we see that they have surpassed their prepandemic enrollment, which is great to see. Greg, anything to add? Any thoughts here?

Greg Hill Jr: Doing great.

Meghan McBride: All right. I'm going to get out of DataVista now. Are there any questions out there about DataVista? Are we good here? Looks like we're good.

Debi Pezzuto: No, we're good.

Meghan McBride: All righty. Moving into the CAEP Fact Sheets. So again, with the CAEP Fact Sheets-- and somebody, if you please, drop the link there in the chat. So if people want to follow along, they can. Greg, I'm going to let you walk through this one. We'll look at LARAEC at demographic data and then their labor market data. So if you don't mind doing the talk, and I'll do the clicking.

Greg Hill Jr: You got it. I was actually right about to put the link in the chat, but.

Meghan McBride: Oh, thank you. Debi got it.

Greg Hill Jr: Oh, there we go. Of course, I knew Debi would get there faster. I don't even know why I try. But anyway. So did you say you want to do the clicking?

Meghan McBride: I'll do the clicking, yeah, if that's OK.

Greg Hill Jr: All right, there we go. OK, so it sounds like already, just based on some of the chat, some of you are already familiar with the fact sheets. So we won't-- we'll spend a few minutes on this. But ultimately, where we want to get to is those conversations about noncredit, the importance of noncredit. And so we'll try to highlight those aspects of the fact sheets that might help build the value proposition for it a bit.

But first, some basic information. So for those of you who are unfamiliar with fact sheets, these fact sheets were created in order to help consortia with their annual and three-year plans. The metrics shown here are ones that are generally agreed upon as being valuable indicators of potential need for traditional adult education programming.

And the way you navigate it is along the top, where it says at a glance, any of those icons there, you can click on. And if you do, it will filter all of the chart objects below. This can be particularly valuable for looking at intersectional information.

So case in point, we're looking statewide right now. You mentioned LARAEC. So if we find LARAEC, that'll happen. And so just considering educational attainment and English language, if you click on High School Diploma up top, where it says 871,000 people lack a high school diploma, we also can see by looking at the English language ability pie chart that a third speak English less than well, which typically aligns to your, I want to say, the lower level ESL. Actually, no, I mean, it could be high level too. But that's quite a significant proportion.

And conversely, you could go the other direction and say, OK, what is the-- what we're calling limited English, but in truth, it is the less than-- just less than well, which could also be inclusive of well. But if we click on that guy, we can see that roughly 63% have some schooling but never got past a high school diploma. And so this can provide a lot of really valuable information about how you might wish to design programs to meet the needs of your unique constituents.

Likewise, we have languages spoken at home. So I mean, not surprisingly, it's Los Angeles. The vast majority of those who don't speak English well speak Spanish at home. And employment status is, actually, surprisingly low, if I'm honest. But anyway.

So these are just some of the ways you can use the fact sheets. And we want to scroll up just a second. And so I encourage all of you to experiment a little bit.

Debi Pezzuto: We have a question in the chat real quick.

Greg Hill Jr: "Are fact sheets available by college instead of consortium?" So in a sense, they are by college because the boundaries correspond to community college district boundaries. But individual colleges have service areas as well. But they're not necessarily defined geographically.

Oftentimes the service area for an individual college-- and I'm thinking here in instances where it's a multicollege district-- would be based on anecdotal evidence or, frankly, more often than not, distribution of enrolled students within a particular radius.

I love that idea. And I've often really thought about trying to-- I do this when I support strategic planning. But right now, yeah, we don't have that data to be able to drill into what I'll call the actual service areas versus the administrative service areas, although that would be amazing.

So I think that's the only one-- I see actually a question from Scott. I'll text you directly. Yeah, all right. Well, actually, Scott is asking about time's date span. And what he was asking about was, well, OK, when you look at total number 18 plus, et cetera, what years does that span?

And it spans the current-- strictly speaking, it is 2023. But that shouldn't-- so I often-- folks, I often have to explain this. But everybody wants the most recent census data. It's like we need the most recent, the most accurate. But the most recent means something different than I think what most people think.

So census data is collected constantly. But what the US Census does is all of the estimates for any given year are predicated on the full decennial census that happens every 10 years. And then everything that follows is an estimate based on modeling done from surveys that are sent out throughout the year.

And so you're not going to see enormous variations from individual year to year unless there is some major event. In the case of the data set-- the data source we're using here, it's public use microdata sample. And what that is a unique data set that the US Census provides that allows researchers to slice and dice in different ways than what's available in the published tables. And those are released in five-year and one-year increments.

Five year, meaning that they're using five years' worth of data to create the estimates for the latest year. One year, meaning they're using the singular year. And there are very few instances where you'd want to use the singular year.

Scott asked, "What if there's a mismatch between CAEP scorecard data and the fact sheet?" That is absolutely correct. Thank you, Scott. Yes. What we're looking at here on this population demographics, this is not-- this is census community data.

So this is not enrollment. The only thing that this shows for your consortium is who is there. In terms of enrollment and comparing that with community information, that happens on the population exactly, population demographics with adult ed pipeline.

Are we still looking at LARAEC? I feel like that's pretty-- oh, I guess we are. OK. That cannot be right. Is that right? Yeah, I guess--

Meghan McBride: Just head up in DataVista.

Greg Hill Jr: Yeah, you're right. You're totally right. Sorry. I don't know. It just seemed really low to me. So this is where, Scott, you would make those comparisons. And these numbers should match DataVista.

The point of this tab is to highlight where there might be potential gaps. So in a perfect world, if your agency is serving the various communities within your larger community equitably, then you should see proportions that are roughly equivalent. So depending on what the level of need is.

So just put really simply, if your community say is-- and this will depend largely on size and, again, need. If your community is 50% Hispanic, then it would be typically understood that like, OK, you should expect that roughly 50% who are enrolled in programs are Hispanic. Again, not necessarily in every case. And this is very sensitive to small ends. So I wouldn't treat it as a 1 to 1. But it's called a proportionality index.

And so what this does is it helps to highlight where those potential gaps might be. So if we're looking at just race and ethnicity here, case in point, I mentioned 46%. Over 18 are Hispanic. Enrollment is about 74%. This means overrepresentation. Is that a problem?

No, not necessarily, especially given what we know about the regional needs. Those who don't speak English-- who speak English less than well, 80% speak Spanish. And so it would follow that you'd have a much higher proportion.

Where it starts to become an issue is when there's the underrepresentation. But it's not necessarily bad inherently because some populations have different levels of-- sorry, different levels of educational attainment and different needs. And the example I like to point to are Asian populations.

So if we look at within LA, roughly 14% are Asian. And that is really a huge swath of ethnicities. And actually, I've something I can say about that in a minute. But enrollment in the consortium is only at about 5.7%. This may or may not be a big deal.

But the way to zero in on this, I think, is, well, what is the level-- what is the proportion of individuals say-- if you're trying to serve Asian populations, consider what is the English language ability of the population. And so that's why we added the ability to drill down under the population by demographic group English language skills.

So yeah, exactly. And then we can do the same thing over there, limited English. And so this is interesting, actually. What we're seeing here is a potential disparity. So roughly 16% are Asian and have limited English skills, but only 7.2% are enrolled.

Now let's look at high school diploma quickly, though, and see how that differs. Yeah, it's secondary. All right, so there, that's interesting. There is underrepresentation. That is highly unusual. So oftentimes what you'll see when you do these comparisons is a disparity that ostensibly exists at the highest level.

But when you drill into, say, the actual program areas and compare program area to where-- apples to apples, you will see things even out a bit. And the reason why I asked to take a look at high school diploma is because statistically, Asian populations have higher levels of educational attainment. And so they are less likely to have not completed high school diplomas. But here, this clearly is not the case.

And the last thing I want to point out is gender. So there are major disparities there. And this is not surprising, all of you who run programs. And it's not necessarily a problem, but it is something to think about.

I think that's-- any questions about that as we're-- you're absolutely right, Jason. Age does matter here. And what I like to think of-- yeah, totally. Yeah, I mean, in earlier versions, we used to be able to select and filter by all any chart object, which was great. But unfortunately, the margins of error go haywire. And also just the computational intensity is just a lot. It made it really slow. But you're 100% right, Jason.

And so what I like to do is consider, OK, what is the proportion between under 24, your traditional population, traditional college going-- traditional postsecondary folks compared to, say, you're over 25? Also, even just the 45 to 59 can be really insightful. Just the 45 to 54 is huge, frankly, because that is a population-- I mean, this is where folks really need upskilling.

Yeah, you got it, Jason. And so what Jason is mentioning is if you ask for it, I'll give you this data. And you can slice and dice it however you like. So feel free to reach out for that too. But thank you for that, Jason. All right.

Meghan McBride: Greg, I'm going to go to the labor market information, if you could walk us through that. We'll have to do Los Angeles County. Not LARAEC. And we'll do postsecondary nondegree award on the entry-level education.

Greg Hill Jr: Marvelous. So there is one major caveat to what we said earlier, what I said earlier about these. The data and the fact sheets reflect consortium boundaries. Labor market data does not. Labor market data is reflective of counties or macro or micro regions.

And the reason why is fairly intuitive in that where people live isn't necessarily where they work. And since counties tend to be-- well, anyway, just setting that aside. So it is just simply a more accurate representation of what the needs are within the larger communities that you might be serving to show by county.

If we cut it by consortium boundary, if those boundaries didn't conform to counties, it would be fundamentally artificial and not methodologically defensible. And so some of you may already know, like where you-- the boundaries cut across counties-- I'll be glad to tell you-- and in what proportion.

But ultimately, this is where it's important to contextualize data that we provide with local data that you have either collected anecdotally from your partners or that you've obtained in conjunction with, say, your workforce development boards who are really going to know your communities much better and a much more granular level.

So what this does, at least to give you a high-level view of things-- and so you can drill into macro or micro region If you don't know what those are, ignore it. Counties, all of you will recognize. You can also drill into typical level, entry level education; work experience, typically required; and on-the-job training.

So what this will tell you right is, OK, if you're wanting to understand, well, we're just getting going on CTE and we need to make an immediate impact. Most of our students, they're not coming in with advanced degrees. If by clicking on, say, any one of those items there that are beneath a degree, this tells you the jobs and industries that would be either fastest growing or largest within your community.

And so based on the filters that Meghan has selected, we see that there's a lot of growth projected for like manicurists, skincare specialists. You see a lot in health care and social assistance work. And you see that reflected in the table below in the indirect way with like nursing assistants and things like that most likely.

But you'll notice that not in every case do. In fact, really very few of these-- not very few, actually. Many of these jobs don't necessarily reflect a living wage. And so skincare specialists, manicurists, the median is 16. Cost of living adjusted median is 10.

And as we can see up top, the living wage for that for Los Angeles is 26.63. And that's for one adult, zero children. By the way, this is from the MIT Living Wage Calculator. If you're curious about it, you can just email or even just Google it. They have it by county and state.

Now one quick caveat. Just because an occupation isn't living wage, that doesn't necessarily mean it's not worth pursuing. There are occupations where there is extraordinarily high demand. Health care and social assistance being one of them, where the living wage is slightly lower than living wage, but-- or the median wage is slightly lower than the living wage. But it often is an entree for recent immigrants, non-native speakers of English, and, actually, frankly, women to have second-- for folks who-- sorry, I'm not being clear.

So it's often an entree into other occupations, but also it can be a really valuable second occupations for a two-parent or a two-adult household. And so I wouldn't, just, as an FYI, throw the baby out with the bathwater just because it's like $1 or 2 below living wage.

The key is, can you develop opportunities for folks to be able to leverage the skills they garner in those, say, nonliving wage jobs, but that ladder up to other living wage jobs and careers? So anything else you, folks, have, questions about, or? Meghan.

There is a note from Scott as you look at other programs, i.e., emeritus, older adult. I'm not familiar by emeritus programs. What is emeritus program? I'm unfamiliar with how that's used.

Audience: I can comment if it helps.

Greg Hill Jr: Please.

Audience: The older adult programs, it's one of the 10 refundable noncredit areas. And there's maybe 30 of them statewide across 116 colleges.

Greg Hill Jr: Oh, OK. So you're talking about older adults. So CAEP defines-- so you're right. So--

Audience: It's tangential. It's not directly CAEP related. Right.

Greg Hill Jr: Yeah, thank you. I was going to say, it's like, historically-- for those of you who are new to this, by the way. So adult ed-- CUA Adult Ed actually had 10 program areas. And the community college noncredit programs were actually designed to align to those allowable 10 programs.

But over the last-- ooh-- several years, I guess, starting with CAEP, the allowable program areas for traditional adult education funding were cut back. And so an older adult is a part of CAEP funding, but it's connected with a workforce component.

And the reason why I differentiated is because back in the day, there'd be programs for older adults. And it'd be like basket weaving. And there's nothing wrong with that. It's great, awesome, if there's a demand for it and folks are enrolling in it and getting benefit from it, but it wouldn't necessarily be an allowable area for CAEP unless it were connected to entrepreneurship in basket weaving or something, which would be really silly.

But that's an interesting point. I mean, it is, you're right, in the community college area, but it's less so in CAEP. And actually, even more questions that we can think about what that would entail to later on. Thank you, Scott, for that and for the clarification.

Meghan McBride: All right. Thank you, Greg. Great job, as always, with the CAEP Fact Sheets. So as Greg mentioned when he was looking at the labor market information, that was for LA County as a whole. And as you all know, in your gigantic areas like the Bay Area and LA, there are subregions that can be very different.

And so the Centers of Excellence really put out excellent information regarding the different subregional profiles. I really love what they've done in the Bay Area. They have a great visualization that can help you look at gaps in supply and demand.

But today we're going to look at Central Los Angeles. And we're going to see how all of this plays out because it's very different than other areas of Los Angeles. So looking at subregional data really helps to add nuance and build stronger, localized arguments and help you make maybe programmatic decisions for where you might lift your programs.

So I'm going to start with page 3. And again, this is just as I was digging around where I got really inspired to look at this area. So on page 3 here, we see the different subregions of Los Angeles. And I chose to focus on Central because it has the lowest median household income of any of the subregions and the lowest per capita income.

So that just interested me to see what was going on in South Central LA-- or, I'm sorry, in Central LA and how we might be able to build programs that are responsive to the needs of that community. So as you can see, this region has about 1.55 million residents and, again, the lowest income of any of the subregions.

Now we're going to scroll a little further. Anything on that, Greg?

Greg Hill Jr: No, other than that is low. I live in LA, and that is low.

Meghan McBride: Indeed, it is. It'd be low for--

[interposing voices]

Greg Hill Jr: But ooh, yeah.

Meghan McBride: All right. So I'm going to scroll down to page 8. And I encourage you to dig into these. There's a lot of great information. For your edification, this is the area where we're looking at here.

On page 8, we see some more interesting data here. Again, the subregion has about 1 and 1/2 million residents. And if we look at the population under 35. It is solidly 50%. Central LA is here in blue compared to yellow is Los Angeles County as a whole.

So in other words, many of the residents in this subregion are young, working age adults. And as we scroll down to page 9, we see that Central Los Angeles has a high percentage of folks who either have a high school diploma or less. And the percentage is higher than in Los Angeles County as a whole.

And this group here is exactly who our noncredit CTE programs are built to serve. So these folks have not gone to college and potentially have no training beyond high school if they even made it to high school. So I feel like Central Los Angeles is prime real estate for noncredit CTE programs.

As we look at-- and, Greg, this-- as you were talking through the fact sheets, it made me think of what we're seeing here with regard to the race and ethnicity. And we'll see the race and ethnicity by educational level on the next page. Here we go.

So when you look at across ethnicities in this particular subregion, we noticed a very interesting trend. The Hispanic population is far more likely to not have a high school diploma than any other ethnic group in the region. And only 10% of the Hispanic population have a bachelor's degree or higher.

As we saw in LARAEC's demographics on DataVista, they appear to be serving the Hispanic population quite well. You'd have to really dig in a little further to see if there are any potential equity gaps there. But certainly, this is a population to target for noncredit CTE programs that can help them acquire their high school diploma, learn English, and enter into living wage occupations.

Any thoughts here, Greg?

Greg Hill Jr: Yeah, just one quick addition. So what I like to encourage folks to look at in addition to what, Meghan, you were just highlighting is that last category of two or more races. So oftentimes, if you're looking at census data, the way it's collected isn't necessarily a single select, but it is aggregated as though it's a single select.

And if we're thinking about equity gaps, simply put, if someone identifies as biracial, the extent to which they may or may not have face in their day-today lives various kinds of social and barriers, it's going to vary somewhat, which is just another way of saying this, that there could be, as Meghan is talking about, well, the proportion who are identifying as Hispanic, those who two or more races might be Hispanic and Black. They might be Hispanic and Asian. And in that way likely are subject to similar kinds of barriers.

And so this is where it's really valuable to drill in and understand what the subcommunities look like. Or even looking at when you-- Meghan's going to walk you through census data. Because you can go to the US Census and get that information. There are ways of-- there are reports, published reports that show disaggregation, say, of African-American plus anything else.

And so that can give you a much better view of, OK, what is the overall population who would be typically understood as being, say, African-American or Hispanic within the community? And are they facing-- what barriers are they facing? And what kinds of services do we need in order to mitigate them?