Video Transcript
[Moises Ramirez]:
All right. Good morning, everyone. It is 9 a.m., so we're going to go ahead and get started this morning. Thank you
all for joining us this morning for the ninth installment of our Train. Local. Grow. Global. webinar series, where
we share information on the more than 50 business services the Dallas College has to offer.
[Moises Ramirez]:
Now, as some of you might know. Today we're actually celebrating World Statistics Day and in honor of that, we have
today's presentation entitled Make More Strategic Moves with data Driven decision. Now, the celebration of World
Statistics Day is a global collaborative endeavor organized under the guidance of the United Nations Statistical
Commission, proposed back in February of 2010. The United Nations Statistical Commission acknowledged that the
production of reliable, timely statistics and indicators is indispensable for informed policy decisions.
[Moises Ramirez]:
In addition to that, there are many benefits to being a highly data driven organization, including reducing risk,
increasing efficiency and revenue, and being able to demonstrate progress towards organizational goals. Our Labor
Market Intelligence Center provides data on the labor market and social economic barriers to enhance data informed
decision making. Stakeholders apply the research to inform designed equity initiatives, strategic planning, economic
development efforts and business retention to and expansion.
[Moises Ramirez]:
The LMC has conducted study partnerships with organizations like the Child Poverty Action Lab and the City of Dallas
and is expanding these efforts. In this session, the team will cover the kinds of data they obtained, recent project
successes, and how to ask great research questions that can ensure you get the data that you need to calculate your
next steps.
[Moises Ramirez]:
Now, my name is Moses Ramirez, director of business engagement here in Dallas College, and today you have two Dallas
college presenters. Unfortunately, our third presenter will not be joining us today, although we'll still be sharing
her contact information at the end of this presentation. Now, starting with Rodgers Oliveira, Rodgers is his
director of Future of Work in the labor market Intelligence Center here at Dallas College.
[Moises Ramirez]:
He joined the team in the summer of 2021 after six years of holding various positions in the college's institutional
Research and strategic analytics department. He brings the skills of a researcher, the curiosity of a journalist,
the creativity of a marketer, and the passion of a futurist to his unique role in the Economic Opportunity, Division
of Workforce and Advancement.
[Moises Ramirez]:
Rodgers earned his associates of Arts degree from Spokane Falls Community College in Washington, igniting his passion
for learning and education. He later completed his bachelor's degree in business administration at Eastern
Washington University. After spending some time working in both real estate and investment banking. He joined Dallas
College in 2015 and plans to continue his work in the field of education for many years.
[Moises Ramirez]:
Next, we have Camille Gilchriest. Camille is a director of GIS and data visualization for Dallas and Dallas College,
the Labor Market Intelligence Center. She produces analysis to reveal patterns in spatial distribution of labor
income and wealth in the Dallas Fort Worth region. She believes that an geospatial data analysis can be a powerful,
powerful tool in the hands of students, workers and communities as they mobilize to build a more just and equitable
society.
[Moises Ramirez]:
She holds a bachelor's degree in geography from the University of Chicago and has worked with the LMI since 2019. And
lastly, now it's only largely because she's not here today is hematoma. Lomas Hamlin is the director of Schools of
Instruction at the Labor Market Intelligence Center at Dallas College. Hammer joined the team in May of this year
and has served in Dallas College in multiple capacities for the last 17 years.
[Moises Ramirez]:
Now, with all that said, a couple of housekeeping items. Please post any questions you might have during the
presentation on the Q&A section or the chat sound that you can find across the top. Additionally, if we don't
have time to address all questions that are being posted after this, today's presentation, I will end up sharing an
email with a recording of today's presentation.
[Moises Ramirez]:
Along with the presentation. All contact information for today's presenters and answers to questions that we weren't
able to get you now. With that said, let's get this started. ROGERS Fine. Go ahead and take it away, sir.
[Rogers Oliveira]:
Thank you so much. I very much appreciate that that introduction. I'm looking forward to speaking with everyone today
and as as my mentioned, we don't have Hammer with us today, so I'll be acting as a poor substitute, but I'll do my
best to represent her area for us. So let's start with Labor Market Intelligence Center, who we are.
[Rogers Oliveira]:
I know it sounds like maybe we're an espionage organization, but we aspire to be the leading source of regional
workforce information, both for ourselves as Dallas College, but also some of our partners. We can provide a lot of
quality data to help overcome the socioeconomic barriers that we know a lot of people in our community face. And
also, do you know, kind of the housekeeping of the work of an institution of higher education, We want to be able to
identify those opportunities and trends as they emerge, looking for those high growth possibilities in critical
industries.
[Rogers Oliveira]:
We need to be able to always estimate the gap between what is currently happening in the labor market and be able to
provide the training to close those gaps. And of course, as I mentioned, you know, pinpoint what those socioeconomic
barriers are and how we can overcome them. So. So why LMI for for you person in the audience.
[Rogers Oliveira]:
So perhaps you are looking for ways to remain competitive with the various other people out there doing what you're
doing. Perhaps you are looking at ways to make positions attractive if you're opening up a new position, are you
making sure to offer enough to to bring the top talent, you know, identify any hiring issues? There are a lot of
different ways that you can utilize the data that exists out there.
[Rogers Oliveira]:
And what we try to do is to help contextualize that and make it relevant for what you're trying to do for those
strategic objectives that you want to accomplish. The way we go about doing this, we and I do apologize that we
don't have our senior director with us today, but she has put together this really great framework that I'm just
really proud to be a part of.
[Rogers Oliveira]:
We have three different lenses in which we conduct this work. So the first will be speaking about is are our schools
of instruction analysis. The department that I'm in charge of the future of work. And then, of course, our goals and
data visualization that that can be a little touch on as well. So our team members up there, I won't go through and
read every single name just for the sake of getting on with our presentation here.
[Rogers Oliveira]:
But we have an absolutely amazing team of folks here. So proud to be on this team again, a little, little just kind
of history background about us. The Labor Market Intelligence Center came into fruition in the year 2015, and we're
very focused on that program alignment aspect. So, making sure that we were funneling our students into the right
industries and occupations within our region.
[Rogers Oliveira]:
Sometimes circa 2018, focus shifts a little bit to include that, but also just more general alignment and also those
socioeconomic barriers that our students are facing. And as we are going through 2022 or 2023, taking all that and
continuing out and making sure that we're doing the type of relationship building and outreach that we're doing with
you today.
[Rogers Oliveira]:
So this is a great opportunity for us to continue our mission and to build those relationships and partnerships that
are so crucial to really making sure that students have the best outcome available to them. This slide, I really
hope everyone's eyes cross a little bit with just how much is on the slide, because yes, it is a lot.
[Rogers Oliveira]:
It's a lot for us. And just imagine for a student navigating through this system, these are all the different parts
that someone could navigate through to get from being a high school student out to finishing up their their higher
education and getting into the workforce. So the the point of this slide is really just to demonstrate, you know,
all the different things that someone has to go through to have this career connected learner network that that we,
a Dallas college, are very proud to help people navigate.
[Rogers Oliveira]:
So, again, you know, I'm not going to sit here and read every single one for you, but if you do download this
presentation afterwards, you'll you'll see just how complex the system is and how much effort really goes into to
navigating through it.
[Rogers Oliveira]:
I love this visual. So we will sometimes refer to ourselves as an anchor institution. Here is the exact reason why is
look at all the different students that we have flying to us from various different places or on the left. This is
where they originate. So up near the top, you'll notice Dallas City, we take these students and we are kind of a
central location before we send them out into either the workforce with a credential or of course, we always love to
hear that they're going on to a four year to continue their education.
[Rogers Oliveira]:
That's so important. But this is why we, you know, refer to ourselves that way. And other people have begun to
acknowledge that as well as just how important we are as community college to be able to funnel all this through.
[Rogers Oliveira]:
We are not the only department within Dallas College that is doing this type of work. So I'll kind of start on the
student success side in that that blue box. Consider that to be, you know, Dallas College itself. We have a
strategic analytics department. You may possibly refer to it as institutional research, but that's the the internal
data about students, the type of state reporting, any kind of like publication where we're sharing data, that's
that's where it's coming from.
[Rogers Oliveira]:
And we rely on them very heavily to get that information. That's really good. Starting point when you're thinking
about higher education is you have to look at the student's data that you have. Just below them, you'll see the
research institute. They are looking at more of a long form of, you know, the students journey from where they're
coming from and then where they might be, you know, ten, 15 years down the line.
[Rogers Oliveira]:
And we do share some of that program alignment exploration with them also. Both of those kind of funnel to us. So we
have half one one foot in the blue box and one one foot out. So we are one of the mechanisms that we can reach out
to other external research partners or partners in the community to discuss those socioeconomic barriers and ways to
improve the workforce.
[Rogers Oliveira]:
And again, we do share some of that responsibility with the Research Institute, and we all work very closely together
to make sure that we're focused on the same mission together. All right. So how do we do this? We have a couple of
tools that I think are the most worthy of highlighting. The first being as and I won't dive into that too much
because I think we all will be able to cover that much better than I ever could.
[Rogers Oliveira]:
But that allows us to do geo spatial exploration and like last formerly known as EMS slash burning glass, you might
be familiar with their old name, but their tool allows us to take a lot of that data that kind of sits out there in
the world and be able to interact with it in a central location. So that analyst tool that you see at the top that
acts as an analyst for us is it puts together a lot of this information quickly, efficiently and in a way that's
very legible and easy to share out with friends such as yourself.
[Rogers Oliveira]:
We also have some other ongoing projects that we're working with on like cast skill, ABI skills, matching careers
coach or excuse me, career coach. These are tools that allow us to take our syllabi. Syllabi see the joke there,
being able to translate that into very specific skills so that we can then match them to a career. Perfect, right.
[Rogers Oliveira]:
We also can partner with them for some economic impact studies so that we know the overall impact that we are having
on our community and also try to quantify the value of a degree and to what everyone wants to know about is the
dollars and cents, right? How much how much more valuable is your career going to be if you walk away with that
degree?
[Rogers Oliveira]:
All right. So again, I'm going to be a substitute for how much today. We're going to start about our first lens is,
as we like to refer to it as the school analysis. Here's a little peek behind the curtain when we're interacting
with that light cast tool. This is what kind of comes out of the box and what is very easy to share with our friends
such as yourself.
[Rogers Oliveira]:
We're able to take a very quick look at different occupations. Here we have an example of the automotive service
technician, and at a glance you can see we've got, you know, the amount of jobs that we have, the average
compensation that one might expect. And my personal favorite is being able to look at the job posting demand. So
whereas, you know, traditionally we would have to rely on things that came from the BLS to understand how many jobs
are in any one area at a time, we can go ahead and actually scrape that data and be able to look in close to real
time.
[Rogers Oliveira]:
What kind of jobs are being posted, what their job descriptions are, being able to pass through those descriptions,
looking at the pay when available, and be able to gain some additional insight from that. So that's two very
important ways, is we have our traditional labor market data, but we also have our job posting data as well, which
is invaluable.
[Rogers Oliveira]:
So these are our seven schools and, you know, I think that we cover pretty much all the big bases that we would have
in our area. Hopefully one of these, you know, as you're thinking about your activities, probably one of them
closely aligns and this is how we are able to organize ourselves both for the student, but also probably how we're
going to be serving you.
[Rogers Oliveira]:
Is that in just a moment, I'll show you some examples of how we focus on these individual areas, not just as
instruction, but also how we interact with the industry. So, our school reports this example. Here we have the
School of Engineering, Technology, Mathematics and Science. So, if you've ever been in a meeting with someone from
Dallas College, they might walk in with a, a folio full of papers to help inform.
[Rogers Oliveira]:
We, we want to see one of these. And in there as as alumni see as we want them to be armed with that general
information about what's going on in that industry as categorized by our school of instruction. So it makes that
conversation easier. We can point pinpoint to exactly where this pipeline is, is coming from and where it's leading
as well.
[Rogers Oliveira]:
So, you can see we've got some examples of the different employers in the area as well have set that nice table, kind
of explaining the basics information about a particular occupation. Again, a lot of this we're able to source from
our partner light cast, but this is where we kind of contextualize it for what we are doing as an institution.
[Rogers Oliveira]:
And in addition, we do the same thing looking at that 3000 foot view where we're looking at the general state of
workforce in the DFW area. Again, I'm not going to bore you guys by going point by point here, but we do like to arm
our folks as they're going out there so that they're not just kind of making things up off the top of their head.
[Rogers Oliveira]:
They'll know exactly how many job postings are in the area where they're happening and what those industries are.
[Rogers Oliveira]:
Okay. So now we get to my area, director of Future of Work. I very much respect the fact that the word future is in
my title. Very cool title. I'll admit I never would have imagined I would have such a cool title, but I
unfortunately do not have a crystal ball. Instead, what we're really trying to do is to, instead of necessarily
always trying to predict the future, as we do want to help shape the future and the ways that we go about doing
that.
[Rogers Oliveira]:
Of course, relying on our data and statistics that we have available, I forgot to wish everyone a happy statistics
day at the top of this, but I am going to start by sharing a parable. You may be familiar with the work of Isaiah
Berlin. This is a concept that actually started out as a Greek parable and he expanded on this.
[Rogers Oliveira]:
It boils down to one line. I would say, as a fox knows many things, but a hedgehog knows one big things. He took this
as initially just sort of almost a game. I think it was quote there, Yeah, an enjoyable intellectual game that ended
up being taken seriously. Every classification throws light on something. So two different types of thinkers that
might be out there.
[Rogers Oliveira]:
The Fox, you know, with its cunning nature, has a wide variety of knowledge and dabbles in various pursuits adapting
to different situations. Compare that to the hedgehog who has one defining idea, sees the world through one single
lens, is always willing to quickly defend itself, and it picks one thing to be really, really great at. Whereas the
fox might move on quickly to from one thing to the next, and you might be able to examine your own self and think,
Oh, am I more of a a hedgehog or a fox?
[Rogers Oliveira]:
At the future of work, we try to think of ourselves as firstly, the fox in the competitive landscape out there. Right
now. Things are changing so rapidly that I think you almost have to at least have some of that to be able to keep
up. But on the other hand, we certainly don't want to dismiss those folks that are dedicating themselves to to that
one big idea.
[Rogers Oliveira]:
So, I you know, if I were to put myself into this classification system, I'd say, where were the fox that's out there
looking for hedgehogs as we want to be able to talk to hedgehogs and learn more about their good ideas and being
able to integrate that into these kind of projections and forecasts that we're asked to perform.
[Rogers Oliveira]:
So, we kind of break this down into two, four different types of projects that we do. And I'll briefly touch on each
one of these quickly. The first and kind of our bread and butter is what I call the data with an opinion. So not
only were we just going to be giving you that that data, we're also going to let you know what we think it means.
[Rogers Oliveira]:
What's the story behind it? So here are a couple of very quick examples of the type of thing we do is just being able
to look at the landscape, what programs are out there and you know, who you might be competing with if you were to
be trying to fill a particular job. Another kind of example here is, you know, someone thinks, okay, is is my
workforce getting older?
[Rogers Oliveira]:
I work in construction and I'm noticing that people will seem a little bit more long in the tooth. Is that really the
case? So we're able to kind of help not only just confirm or deny some of that with the data, but then we say, okay,
well, now that we understand that, yeah, maybe the workforce is a little bit older here, What do we do about that?
[Rogers Oliveira]:
You know, our recommendation in that particular case was, of course, really focusing on apprenticeships and building
up those relationships between the older workforce that you have and trying to get those younger people up to speed
more quickly to reduce that pain. That's going to happen if you have a workforce that ages out suddenly we also
perform what I would call a data environment.
[Rogers Oliveira]:
This is an example of an internal tool, but it does allow anyone within Dallas College to be able to click on one of
our programs and then it will help them understand what the occupations that are being led to from that and being
able to see the performance of that particular program. So we want to be able to arm every single person at Dallas
College with that information about the labor market.
[Rogers Oliveira]:
We have, I believe at last count, 501. Programs. It is not possible for any one person to be able to keep all of that
at the top of their head. Okay. So these kind of tools are almost like a second brain for someone that that needs to
be able to quickly navigate that that information, what we might call a case study or a future cast.
[Rogers Oliveira]:
If you want to be a part time futurist like me, these are those questions that don't necessarily have an answer
already in the data, but we can try to ascertain at least some ideas of what it might look like. So an example would
be a career progression alternate. If for someone who I believe this example was someone who wanted to be a program
director in health care but didn't necessarily want to be a nurse in the interim, so what possibilities are there
for them to kind of circumvent that?
[Rogers Oliveira]:
Another one, the differences between Dallas and Raleigh, North Carolina. If we were going to be competing in the
biotech space, which we very, very much are, what are the occupations that they have that we don't that we might
need to expect to fill if we continue this this rapid growth?
[Rogers Oliveira]:
I hope my and my slides don't get stuck here. There we go. And last but certainly not least, Brett casting this. This
is our newest offering. This is actually a conceptual framework that I have borrowed from a futurist at Arizona
State University, Brian David Johnson. It's a way to systematically plan against threats that are specifically ten
years in the future, and it uses a really fun kind of narrative approach where you convene some of your thinkers
around some sort of a problem or a threat, and you use everyone's collective knowledge to take steps to mitigate any
of those problems.
[Rogers Oliveira]:
And if it sounds complicated, it actually isn't. If you've ever done a pros and cons list, guess what? You are a
threat caster. You you know how to do this. This is just a way for us to do this with large groups of people and
being able to capture all that information and knowledge and projections of the future.
[Rogers Oliveira]:
I was thinking as a kid. Big jar of jellybeans sitting on the teacher's desk and she asks everyone to make a guess as
to how many jellybeans are in that jar. And everyone's there's going to be wild answers all the way across the
board. But you take the average and it gets remarkably close to how many jellybeans are actually in there.
[Rogers Oliveira]:
Very similar concept. I don't think I'm even allowed to talk about future of work without at least mentioning
artificial intelligence. The big question, of course, is always, you know, is I going to take our jobs? I play with
chat GPT quite a bit. What we're looking at there on the left side of the screen is some of the measurement
differences of performance between three and four of that model.
[Rogers Oliveira]:
One thing, and I think that maybe the love affair or that the hype cycle is starting to die down a little bit with
A.I. and we're thinking a little bit more practically about it and not having that kind of alarmist reaction where
we're starting to realize that it doesn't really understand the nuance or context of what's happening. The The
Independent thought it can do things that seem remarkable and like it's thinking on its own.
[Rogers Oliveira]:
But I think it's really just more a, you know, the language model is more realizing that that that language maybe as
humans, were not quite as special as we thought we were, because it's able to do it and to to speak with us that
way. But one of the dangers of it is that it's reflecting any kind of bias that it's getting from its training data
so it can lead to some insensitive responses.
[Rogers Oliveira]:
And if you've paid attention to the news, you know, sometimes they have to go and very quickly make changes in what
they're presenting out to the world because it's just regurgitate doing things that it had found from its training
data.
[Rogers Oliveira]:
This is kind of a fun example. So if it takes 3 hours for one car to drive from Dallas to Austin, how quickly could
you do it with two cars and changing its response? Of course, if you have two cars, you can potentially reduce the
travel time from Dallas to Austin. So both cars maintain the same speed. It's possible to cut that time in half.
[Rogers Oliveira]:
Wrong. Right. We understand that. We know that we have that context. A language model doesn't. Okay. So it's is
thinking like any other word problem where if we were, you know, you double the input and you double the output.
Right. So that's one kind of danger of if we were to, you know, just give the AI our jobs, it's really just not
going to understand things like that in all cases.
[Rogers Oliveira]:
And I do think they patch these type of tricks and riddles as they go. But there will always be someone that's
thinking of that next riddle and we can trick it.
[Rogers Oliveira]:
I think there's actually been another case since I made the slide about people lawyers specifically getting in
trouble for using chat and their closing arguments or even just to prepare their cases. It's very easy to get A.I.
to do what they call a hallucination mode, where it's so eager to give you the answer that it will actually make up
citations.
[Rogers Oliveira]:
I've been able to have it do. That exact thing is make up a citation. So you can get in big trouble if you were to
over rely on this. We still need that that human element for any role to be replaced by AI. Your boss stakeholder
will have to clearly communicate what they want. Your job is secure tongue in cheek.
[Rogers Oliveira]:
I know, but the reality is yes, it just doesn't have that that human element to it. And if you can't explain what it
is that you want, then I simply will not be able to reproduce it for you. So we still have a role, even if we become
more efficient by using these type of tools, we still need someone in the proverbial driver's seat.
[Rogers Oliveira]:
I'm not saying that this is going to be like this forever. I think of all the things that I've seen in technology
happen over the past few decades, this one is probably going to have the most impact on us overall. But I say all
this just because I want us to be, you know, kind of walking into a reality where we're not panicking about
something that is not an immediate problem yet.
[Rogers Oliveira]:
So I'm much more concerned about how we as a society are reacting to it and what we do in response to these things as
opposed to saying, oh my gosh, the sky is falling and 90% of jobs are going to be eliminated. I think that's an
important thing is just to maintain some realism. There. So how can we use AI?
[Rogers Oliveira]:
It's really great for certain administrative tasks. I've used it to summarize meetings. I think that's a really great
use. You can give it a nice simple title table of data and it'll kind of turn that into human language for you. So
if you're, I don't know, making a slide deck for a presentation and you want to turn a table into something that
would be easy to share with other folks, great, great thing.
[Rogers Oliveira]:
Customer support being able to, you know, not have to wait in line when you're calling somewhere for support,
training, onboarding folks so that they can ask questions that normally you might need an h.r. Person just sitting
there to answer a chat bot could handle. And it's really great for brainstorming as well as i love to just kind of
ask it a question and just sort of let it give me some ideas and not necessarily taking every single idea, but just
using it in the brainstorming process.
[Rogers Oliveira]:
So there are some ways that that you can use it in the interim. It's not going to necessarily be taking sitting at
your desk here in the next year or. All right. I'm going to hand the baton over to my colleague Camille.
[Camille Gilchriest]:
And thank you, Roger. That was a lot of information. It's like I work with you a lot and I still learn things every
time you give a presentation. I'll let you do that. Thank you for introduction. And you are going to have to excuse
me. My allergies are horrible right now. I'm sure I'm not the only one who's dealing with this, but I've been about
to sneeze for like 10 minutes.
[Camille Gilchriest]:
So if I pause suddenly, that's probably what's going on. Yeah. My name is Camille Gilchrist. I'm the director of GIS
and data visualization. I've been with the Labor Market Intelligence Center for about four years, and I have an
assistant director on my team. I'm Amar Nanjiani. It's not on the call today, but he and I were really closely to
kind of develop some of our data projects.
[Camille Gilchriest]:
So I kind of like to get started on introducing what spatial data is and why I think we should use it. So the data on
where things are relative to one another is very, very useful for some of you. All that may be intuitive, some of
you may be wondering, but how does that even work? Right? We already know where everything is.
[Rogers Oliveira]:
We've, you know, we.
[Camille Gilchriest]:
Have Google Maps that has basically everything you need on there. What else do we really need spatial data for? And
the best example I can think of is, is spatial data is not necessarily, you know, where is the state of Virginia or
where is it business. But it's the answer to the question, like, why is Virginia right?
[Camille Gilchriest]:
Why is something where it is and how is that information useful to me? Or how can that really help me kind of tell a
story? I also think maps are just a very powerful visual. You can show someone a map and they'll have an extremely
negative reaction, but if you just look in a bar chart, usually don't get the same visceral reaction, right, if they
disagree with their content or alternatively, if they do agree with it.
[Camille Gilchriest]:
So I'm going to cover two types of projects that I think will be kind of useful and maybe even familiar to this
audience. The first is going to be a commute pattern analysis and the second is going to be a local industry
analysis. And a lot of these types of projects use data from the two areas that Roger is kind of covered.
[Camille Gilchriest]:
Our schools, big area in our future of work area and our three different teams. We really work together in the Labor
market Intelligence Center. So if you submit a request looking for something, we'll figure out who cut. It needs to
be consulted within our team to really get you the information you need. So kind of the background for this type of
commute analysis and one of the things that happens a lot is that we have assumptions about a neighborhood or we
have assumptions about the area around a particular place.
[Camille Gilchriest]:
An example might be, you know, maybe we're placing a facility somewhere and we have this assumption that, well, we
want to know about the workforce within three miles of the facility. Right. And this is something we run into at
Dallas College kind of all the time. Right. We're constantly wondering, you know, what is the community around our
campuses look like and how is this relevant for our recruiting efforts?
[Camille Gilchriest]:
How is this relevant for our employer outreach? But sometimes when you look at the data, you might find, well, you
know, most of the workforce or most of the students commuting to a particular campus, they're not coming from three
miles around the campus. Maybe there's a huge cluster of students commuting from, you know, 20 miles from the
campus.
[Camille Gilchriest]:
That's really important information for us, for us to know. And that's kind of the background of this this commute
pattern analysis. And we actually had this very specific question for our South Dallas Center. So that's located in
the Frazier neighborhood just east of Fair Park. And we're really trying to figure out what sort of programing we
should offer at that site and which nearby employers we should engage.
[Camille Gilchriest]:
And kind of had this working assumption that if we engage the employers near that particular location, we would be
able to identify local workers who might benefit from training or local employers who might benefit from training.
But when we actually did the analysis, which is reflected in this map, we realized that that assumption wasn't
totally true. So if you look at this inset in kind of the top, right, that's the location of our South Dallas
Training Center.
[Camille Gilchriest]:
And we looked at a 1.5 mile buffer. So right around the neighborhood of that training center. And then we looked at
for all of the people who live within that area, where do they work? Right. So, you know, get in your car, you drive
to work, you take the bus to work. Where is that end destination? What we found is that only about 112 workers are
working within that kind of immediate hour, 1.5 mile buffer.
[Camille Gilchriest]:
But we had 164 workers who are commuting over to the Cedar's neighborhood, and there were 250 workers commuting to
downtown and 205 in the medical district. And there were more workers commuting out to Garland to work at some of
the industrial and warehousing operations in Garland than in the immediate neighborhood around, you know, the
campus. So what is the lesson from this and how is this kind of relevant for a business who's kind of looking to
have a similar analysis?
[Camille Gilchriest]:
Right. Sometimes the location that's nearest to a point is not necessarily the location that's most connected to a
point. So in this case, we're trying to do outreach with employers who are employing residents from this
neighborhood. We might actually have to go out to Garland or go out some farmers branch or into the medical
district, which we would not have done otherwise without this analysis, because we had the assumption that if we
just looked at those nearby employers, we would find we would find where our potential students are employed.
[Camille Gilchriest]:
So kind of building off this, another type of analysis we do is a local industry analysis. Again, this is something
that's probably familiar with a lot of folks on this call. You know, a company wants to locate to Dallas Fort Worth
or maybe set up a new operation somewhere in Dallas, Fort Worth. But they don't really know what a competitive
wages.
[Camille Gilchriest]:
They don't know how many workers are already in the area versus how many they'll have to train. And they don't know
who their competitors are or other businesses they can kind of work with in like a synergistic sort of way with the
other nearby firms in their industry or a similar related industry. One of the things that we can do at Dallas
College and kind of using our spatial analysis is identifying those existing local wages, identifying the existing
employment levels and kind of identifying broader demographic trends that might affect that industry.
[Camille Gilchriest]:
And the best example I can think of for this is some work that we've done around the semiconductor industry. And
there's been a lot of you know, federal programs set up to really support a domestic semiconductor manufacturing
industry. Of course, we have Texas Instruments and a few other large companies in the area already in Dallas
colleges, because we're such a large institution.
[Camille Gilchriest]:
We've been kind of brought into some conversations and around potentially taking advantage of those new agreements
and setting up some training programs and pipelines for students who are interested in working in the semiconductor
industry. So kind of one of the first things we did when we were, you know, in these conversations to really get a
sense of where the industry is located is we looked at the different facilities that are categorized and the
different employers that are categorized in the semiconductor industry.
[Camille Gilchriest]:
So what this map shows, each dot represents a firm that's registered in the in our business data with an industry
categorization that's related to the semiconductor industry. So just from looking at this, I'm sure it reflects
people's kind of general understanding, right? You've got the massive of Texas Instruments and a lot of related
companies in the telecom corridor.
[Camille Gilchriest]:
And then if you look at the very north, you can also see some of the facilities that have been set up outside of
Sherman, which is another kind of hot spot for semiconductor manufacturing. Why is this kind of relevant when, you
know, we want be able to reflect what industry already knows and kind of provide them a useful tool to really tell
the story about where the industry has been and where it might be going.
[Camille Gilchriest]:
So in this case, Texas adjustments, you know, they're looking to set up new facilities in Sherman. It's helpful for
them to see, you know, kind of the pull of those firms up north. It's a Collin County and then into southern Grayson
County. Kind of adding on to this. One thing we could look at is the where the existing workforce lives in the
semiconductor industry.
[Camille Gilchriest]:
So what this map shows are two occupations, electromechanical assemblers and semiconductor processing technicians.
And it shows the count of workers who are already employed in that industry. So a darker color means there are more
workers who are already working. And in the semiconductor industry and this is where they live. So if you're looking
to site a new facility, you might want to know how many of these workers are going to be nearby facility, Right.
[Camille Gilchriest]:
So if you were going to put a facility and let's just say Wise County, right, there's not a lot of workforce nearby.
And then what this chart kind of summarizes is three locations. We've got Richardson, Sherman and then each bar
shows the percentage of the existing workforce that's captured by the city center from each of these three cities
within a particular range.
[Camille Gilchriest]:
So the way to kind of read this, Richardson within ten miles captures 13% of the existing workforce. Dallas captures
8%, and Sherman captures one person. If you were looking at this, you'd say, okay, Richardson is the obvious choice,
right? But as you kind of expand that distance, you start to see that the differences between the cities kind of
shrink.
[Camille Gilchriest]:
So by the time you're looking at a 75 mile range and Richardson, Sherman and Dallas all have roughly the same
percentage of the existing workforce living within that buffer from their city. So again, I'm hoping this kind of
makes sense why this could be really useful information for a business seeking to locate and have a conversation
about, you know, what is the spatial makeup of this semiconductor industry.
[Camille Gilchriest]:
Another question we often get are what are the wages in the industry? How does Dallas compare to the rest of the
country? There's kind of two perspectives on this. And if you're kind of talking to students, right, you want to be
able to say, Dallas, wages are very competitive and we pay higher than other locations. There's an advantage to
staying here, whereas businesses, they might have a different perspective and say, well, we don't want to have to
pay a wage premium just to locate in Dallas.
[Camille Gilchriest]:
We want to be, you know, closer to the state or the national average. So this information is obviously subject to
interpretation, but it's still valuable information to have. What are those wages in different regions? So this is,
again, just an example of what semiconductor processing technician occupation. You can see that in Dallas Fort
Worth, the median wage is about 36,000 per year.
[Camille Gilchriest]:
In Texas, it's 34,000. And in the United States, it's about 39 or $40,000 a year. You can also see kind of a
different distribution in the wage. So in Dallas and Texas and the kind of top 10% of wage earners in this
particular occupation, it caps out pretty low at about $42,000 a year. If you look at the United States as a whole,
that top 10% caps out at around $72,000 a year.
[Camille Gilchriest]:
And then these vertical bars kind of going across the chart are just examples of different wage levels to kind of get
a sense of, you know, how much actually is this from the perspective of an economic developer, a worker or a city
business? Right. So we've got our mid-tier living wage is about $36,000 a year, whereas if you look at the different
tax breaks or tax incentives, agreements that that cities are signing on for, you know, the wage agreement for those
is about $50,000.
[Camille Gilchriest]:
So already you're seeing the advantage and they're kind of promised higher wages through those economic development
incentives. So, again, there's not necessarily one outcome to take away from this chart. It's just to kind of show
the different data that's available to really get in depth at a particular occupation. And so I'm going to close
with just some general points on the local labor market and local workforce.
[Camille Gilchriest]:
And we kind of wanted to end with this just to kind of summarize some of that information. So there's kind of one of
the things that we're always trying to make the case for is the advantage of seeking education, higher educational
attainment for our students and the way we kind of do that is showing you how many jobs exist at different wage
levels for workers in Dallas County.
[Camille Gilchriest]:
So if we look at the kind of entire workforce in 2024 and we look at the percentage of workers that will be hired
above a particular wage level, in this case, that's the minimum wage. If you earn a high school diploma or below,
only one in three jobs are going to be available to you paid above that living wage.
[Camille Gilchriest]:
And if you have an associate's or a post-secondary credential, you've twice as many jobs available to you above a
living wage. So that's going to be 65% of jobs if you have a bachelor's degree or above almost every job that has a
bachelor's degree is at the entry level, is going to give you access to that commodity living wage in the labor
market, intelligence matter.
[Camille Gilchriest]:
We've also studied a kind of different wage level, which we call the community prosperity wage. And I don't know how
familiar anyone is on this call. I was trying to rent an apartment or even purchase a home. It's really hard to do
it on $18.24 an hour. I would say it's actually impossible. And so one of the things we've tried to kind of capture
and understand is like, what is that wage level that gets you access to that, like middle class quality of life,
Right.
[Camille Gilchriest]:
You know, able to afford an apartment in kind of a median income neighborhood in Dallas County. And that wage level
we've we kind of calculated out to be $33.76, so quite a bit higher. And you can see the same numbers for high
school diploma associates or second post-secondary credential. And degree. You know, as you have more educational
attainment, more of the jobs on the job market, the higher for your educational level offer, you got that higher
wage.
[Camille Gilchriest]:
And I'm going to close with two more slides just on the kind of wage growth. So what's really interesting in the last
few years in the economy, we've seen actually that if you have less than a high school diploma or just a high school
graduate degree, the wage level, the median earnings has actually grown faster than for wages, with the graduate
degree or a bachelor's degree.
[Camille Gilchriest]:
So there's, you know, going back to 2009, the median earnings for less than high school graduates has risen by about
70%, which is quite a lot. It's double the rate of the graduate or professional degrees. And for high school
graduates, it's risen about 47%, which is higher than that 40% or 38% or 35% of folks with college degrees.
[Camille Gilchriest]:
So you're seeing kind of the gap close a little bit between, you know, high school diploma and the wages you could
earn and then a bachelor's degree or an associate's degree, which is great news. Right? We wouldn't want folks to
not be able to earn a good wage with those. We think that's a great outcome. It's the more money you can make, the
easier it is.
[Camille Gilchriest]:
You can earn those degrees, you know, for having to work why you want to go to school. But the kind of problem with
this or the situation that we also want to point out is that wages remain really, really low for folks without
higher education. So even though there's been a higher percentage increase that the wages for folks without some
college or without a bachelor's degree, without a post-grad degree, remains pretty much below that living wage
level.
[Camille Gilchriest]:
So that's going to be 35,000 for folks, which is the high school degree. And then you see a bump to about $43,000 for
folks with an associate's degree and then 70,000 for a bachelor's degree and about 88,000 for someone with a
graduate degree. So we think this makes the case for, you know, Dallas College and the value of a degree.
[Camille Gilchriest]:
But also just wanted to point out some of the general kind of demographic data that we can pull the labor market
intelligence ladder to kind of help make a case. Right. It may not be the case that we've presented here, but we
have access to all this data and can really help you kind of sort through it.
[Camille Gilchriest]:
So with that, that's our final slide of our presentation thank you all for your patience for listening. I know it's a
lot of information. Again, I'm surprised that all we do every time I'm kind of in a presentation about this, I think
we have a Q&A. So I'm going to go to the last slide. And I don't know if you want to take back over Moses that.
[Moises Ramirez]:
Yeah, sorry. That was a lot of information. You're right. You know, But it's really important information, really
useful. And it's tends to be a lot of things that people don't think about asking sometimes, right now. And then
going back to the Q&A, we have a lot of questions posted in the Q&A. But I also want to invite people to
speak up.
[Moises Ramirez]:
If you have a question I've enabled I went ahead and I enabled mikes and cameras for everybody. If you'd like to jump
in, hang on one second. Sorry about that. Now, I've enabled them, but I'll go ahead and I'll start with a couple of
questions. One moment. So how will the concept of a career change in the next few decades for students and
professionals?
[Rogers Oliveira]:
I'd love to take that one.
[Moises Ramirez]:
Yeah.
[Rogers Oliveira]:
I think for a long time we've been really focused on saying that specialization is kind of the key to a good career.
So if you and I know this is a very oversimplified example, but if you were able to put the widget onto the car in
the appropriate place, then you will always have that job to be able to do it because that's what you do and that's
your specialty.
[Rogers Oliveira]:
I think that is going to start changing as we go forward. It's really going to be more about the person who can look
back at the entire system and being able to understand how each widget fits onto the car, to be able to create the
whole product because it's not, you know, out of the realm of possibility that a lot of that is going to be
automated.
[Rogers Oliveira]:
I know that's a very silly example, but you can apply it to a lot of different things. Is that specialization going
to be the one thing that that that narrow AI or that automation, the robotics can take over. So the idea of a
career, I think we're going to be seeing the concept of a lifelong learner become absolutely much more important for
everyone across blue collar, white collar or green collar.
[Rogers Oliveira]:
It's going to be really important that you can adapt to those changing circumstances because it will be changing
faster than even our own lifetimes. I can tell you firsthand that a lot of the things that I learned umpteen years
ago in college is basically obsolete now. And that's just going to go faster and faster.
[Moises Ramirez]:
I agree. I definitely agree. And so for this next question, you kind of touched on this a little bit early on in your
presentation, Roger's but as automation and I continue to evolve, which jobs sectors do you predict will be most
impacted and how? I mean, this is fascinating.
[Rogers Oliveira]:
Yeah, short term, I think the people that are probably feeling it the soonest are if you are working in any kind of
like developing marketing copy there you can there's a million different places out there that you can go and it's
going to generate a lot of that for you. But the one that I'm most interested and kind of exploring is the software
developer is that entry level software developer is going to be competing a little bit more with the air that can do
a lot of that quickly.
[Rogers Oliveira]:
So I can get on to chat right now and ask you to write a Python script for me. So I'm most interested to see that one
also, because every survey that I've seen put out of people who are actually using it in the workplace, it's often
people in tech close to software. They're usually kind of millennials or younger.
[Rogers Oliveira]:
And my favorite kind of thing of interest is that they don't like to tell their boss that they're doing it. So that
can only last for so long. And I think that at some point we're going to see these big tech companies and maybe they
are already kind of acknowledging that and trying to understand what they can do to cut costs.
[Rogers Oliveira]:
At the end of the day, though, there's a million different ideas out there of software that can be developed. So is
it really going to decimate the workforce? I don't think so. I think we're just going to be able to have a lot more
output in these areas that do get affected by AI.
[Moises Ramirez]:
Yeah, it's really interesting. So like I mentioned earlier, there's a lot of questions that are posted. I think some
of these may have been answered as your presentation went on. So I might jump back and forth. But there's this
there's one which I invite both of you guys to jump in on if you'd like. Is there unique data or analysis request
that stands out in your mind?
[Moises Ramirez]:
Like, did it provide unexpected results?
[Rogers Oliveira]:
If you had one you wanted to share? I'll let you go first.
[Camille Gilchriest]:
So I got to think on that.
[Rogers Oliveira]:
You know, sometimes.
[Moises Ramirez]:
These.
[Rogers Oliveira]:
The questions that we get, people walk in knowing the answer already and a good percentage of the time we're just
kind of confirming what people intuitively know with data. One kind of interesting one, though, is the work that
we've done, the biotech space and being able to look at some other areas that that were ahead of the game.
[Rogers Oliveira]:
So I really enjoyed seeing how the research triangle in North Carolina kind of came into fruition and seeing
different avenues that Dallas-Fort Worth might be able to learn from that. I was very surprised to learn just how
specialized biotech can be. So when you're comparing two different areas, you really have to look at what type of
science that they are doing in order to understand how that's going to impact the employment around the area.
[Rogers Oliveira]:
So you can have something that's very, very health focused and you're going to have a lot of different people that
you're going to need to attract with that. On the other hand, you know, an extreme example is brewing. If you're
brewing beer, that's kind of a biotech life science thing, right? Is you have live yeast that is producing these
results of these taste profiles.
[Rogers Oliveira]:
So you if you are a biotech hub that has that type of more food focused objectives, then you're going to attract a
very different type of person or you're going to need to attract a different type of person.
[Moises Ramirez]:
So, yeah, throw out a question. I think this one aligns more with Camille. Is there is the inflation rate included
your wage growth analysis?
[Camille Gilchriest]:
And let me double check real quick. I'm going to look back on my data and see, okay.
[Moises Ramirez]:
Well, while you do that, go ahead and ask this one. What new jobs or professions do you anticipate emerging in the
next 10 to 20 years?
[Rogers Oliveira]:
I'll take that one. Let me let me start with one I don't think is going to emerge. I think a lot of people have put a
lot of pressure on this idea of a prompt engineer being one of the first new jobs to come. I have to wonder if that
entire concept is dead on arrival, because the whole point of having this ability to interact using common languages
is that it's going to get easier and I've already seen other companies out there that specifically are using AI to
generate the prompts, right?
[Rogers Oliveira]:
You can even ask Chad GPT to do that. So putting on my, my fox hat for a moment, you know, I hesitate to even try to
guess as to what they are. I think it's more important to think about how we respond to when these things are
happening. When a job gets displaced, what do we do?
[Rogers Oliveira]:
So I'm also kind of looking at things like the universal basic income and what type of impact that would have if a
job just does get suddenly eliminated. If you were to put me on the spot and start, you know, saying, like what? No,
I want to know a specific job, I think we'll see a lot more user experience designers in that.
[Rogers Oliveira]:
That's something that there's that human element that an AI could produce a website, a thousand websites very, very
quickly. It's really going to be more about the discernment of which one is a good one that can apply to music as
well as you could have every single melody produced from a key and then up to a complex melody.
[Rogers Oliveira]:
But which one is actually good? Which one is going to resonate with your audience? So, you know, I think that the
roles that we have will start to be more about being able to discern the output that we get from these tools as to
what is actually important or not. On your muted.
[Moises Ramirez]:
Thank you. And I was saying, Camille, thank you for answering on the chat. Here's another one. Do you think that the
Telecom quarter in North Dallas and the new telecom center developing in Sherman will connect fill in all along 75
in the future?
[Camille Gilchriest]:
I would say no, if only because I think that Sherman is really leading the way in terms of those economic development
subsidies, which are really important for that industry because of their high capital costs. So it's so expensive to
build those plants without the like the property tax exemptions are really, really valuable there. But I do think
that it's possible that like all of those cities along that corridor, could copy the success of Sherman in that
respect.
[Camille Gilchriest]:
And I would imagine there's going to be a lot of housing development, kind of ancillary companies that spring up
along the corridor, but, you know, could go the other way. You never know with these sort of things. But that would
be my interpretation.
[Moises Ramirez]:
Awesome. All right. We have another question. So higher education also equals specialized training. Is that taking
into consideration with wage and education correlation?
[Rogers Oliveira]:
We passed that for. I want to make sure I'm understanding the question. So is that taken into consideration with wage
slash education training?
[Camille Gilchriest]:
So is I can answer the one. So I think I think a great example of this is like apprenticeship type programs, right?
An apprenticeship program. You get a really type of specialized training for a particular occupation, and then there
are higher wages associated with that. And so that would be my, my interpretation of that question, I think does the
data reflect that?
[Camille Gilchriest]:
And it really depends on the data. So a lot of times that those sort of apprenticeship programs are not captured
super well in the American community survey data because unfortunately they're relatively uncommon relative to kind
of traditional educational pathways. But in the data that we do have access to, especially for particular
occupations, you can really see the value out of like certificates or like non-degree post-secondary awards.
[Camille Gilchriest]:
Those maybe I don't even want to say unconventional, but just less common pathways to the occupation. So the question
is like in the data that that I just presented and we typically present and it's kind of buried in there in that
associate's degree or in that certificate level data. But when you do pass it out, it does show the value of those
apprentice programs and particular occupations and particular pathways.
[Rogers Oliveira]:
Is going to be on that. That just reminded me that in the job postings data, as we can see, when one of those skills
is actually impacting to to what extent the wage of that that occupations, that's something that we can kind of look
out and see which particular skills carry a salary premium, if you will, a picture.
[Moises Ramirez]:
All right. Well, with that, any questions that we haven't been able to address? I'll make sure to include them in our
follow up email that that's going to have, again, a copy of this presentation contact information and also a copy of
the recording. So any questions that we didn't get to is going to be answers will be included in that email.
[Moises Ramirez]:
And with that, thank you all for joining us today and thank you to our presenters. You guys did an awesome job. I'll
give you a little reaction and to everybody, please have a great morning.
[Rogers Oliveira]:
Thank you.
[Moises Ramirez]:
Thank you all. Have a good one.