Hello. My name is Thor Osborn.
I work at Sandia National Laboratories as a systems research analyst.
That's basically a combination of operations,
research, and investigative reporting.
I'm going to present an analysis of personnel movements
pre- and post-COVID for a large organization.
In this case, the large organization
is the State employees of the State of Florida,
the State of Florida government employees.
I'll say that that's fortuitous
because that's part of their transparency policy.
So we can look at that data, anyone can, and I'll give you the link for that.
The data that I'm going to be showing analysis of
is from August of 2017 through July of this year.
Why do this?
C OVID-19 pandemic and the mitigations that have been instituted to address that
have been cited as catalysts for substantive changes in the workplace.
For example, 5 or 10 years ago,
work from home was considered an unusual opportunity
or a temporary thing to address
some kind of temporary issue like a [inaudible 00:01:20] .
It wasn't considered something that most companies would do a lot of
on an ongoing basis.
Now, it's considered a thing that job seekers may rate companies on,
if you take a look at the web, for example.
Also, a nursing profession was especially impacted
by C OVID-19 response, extreme hours, burnt out.
But that has, in a lot of cases, led to exodus from the profession.
F or many who' ve stayed in the profession,
departure from typical long- term employment like working in a hospital
in favor of traveling nurse or concierge contracts
where they get paid a lot more
and they have more flexible hours and aren't committed to,
say, 12 hours since one after the other in a hospital.
I'll say that the hospital,
our end turnover went up to 8.4% from 2020 to 2021 to about 27% per year,
according to the NSI National Health Care Retention Report.
RN vacancies, meaning slots that hospitals want to fill,
have gone up from about 8% in 2018% to 17% this year.
So basically, one in every six nurses who's supposed to be there
to help give care isn't.
Now, motivation for doing it the way I'm doing it.
Periodic tabulation of movements or rates
is a typical business approach to business reporting,
and almost every company does this.
But it may obscure underlying behavior patterns
because tallies don't tell you the micro behavior,
and time- to- event analysis will enable a deeper look.
I like to use parametric time- to- event analysis for this
because the parameters can be informative.
But to do that, you have to have a lot of events,
and to get a lot of events unless events are extremely frequent
and need an even larger population.
This is fortuitous that the State of Florida
makes weekly employee data available for about 100,000 people.
Quick synopsis of the show for those with no attention span
or just to help me and you,
the COVID-19 pandemic was implicated as catalyst for many changes.
I went over that.
A longitudinal examination of behavior based on the evidence
from a large organization seems timely.
We need to look at these things.
This is a natural experiment of magnificent or awful proportions.
The data available on a weekly basis, as I said,
straddle the beginning of the COVID-19 pandemic.
This is a fortuitous collection.
I started it on Intuition back in 2017, but then things happened.
The State of Florida's decision to build its broadband structure
around a categorization system
that mirrors and links up with the federal SOC,
or Standard Occupation Classification code structure,
also provides a well established and readily available frame of reference,
meaning you can get it and it's free,
and it's reasonably well worked out, documented.
You can look at employee populations, you can look at hiring, separations,
all longitudinally within that framework at varying levels of specificity,
and that's pre- framed by the SOC structure.
The fact that they've melded with that structure
provides an easy window into that level of analysis.
I finish off with an analysis of the nursing profession
as represented by registered nurses.
That demonstrates what I'm calling a substantive difference.
It's definitely visible in personnel flows
between the pre- and post-COVID timeframes.
This is an example.
I haven't been able to go into the level of depth
that I would like to with this analysis and this data set,
but I wanted to show at least an example
of what I was talking about [inaudible 00:05:36] .
Again, just to really beat this one down, an unusual opportunity.
Typical practice in HR is to frame salary structures in context
with other similar organizations.
Salary information is generally compiled by a consulting firm in HR
from a collection of organizations
that chose to participate in a defined pool
for survey and referencing purposes.
They don't do that for free.
It costs a substantial amount of money.
Now, the BLS also compiles salary surveys of its own on a national and state basis
with jobs categorized by a standard structure,
which they call the SOC.
That data can be downloaded for free,
and the State of Florida has referenced its structure to that.
It's fortuitous in a way for them,
because they don't have to pay for seller surveys if they don't want to,
because it's all referenced against the federally established free data set.
Now, I'm going to show if I can.
Here's just a table.
Apologies if it's a little small.
A table showing broadband code right here,
10, that means Executive, 1011-03, Chief Executives.
The point is, everything in the Florida set,
this is about 3,000 codes.
Except for a few recent ones, everything in the Florida set
is referenced against this.
The first six digits are the six digits in the SOC code.
The first two are the major code.
It's the job family, like 10 is Executive s, 11 is Management,
13 is Business jobs,
and then a four- digit code for more specificity.
In the case of Florida, they have an extra two digits
which denotes a job level within their salary structure.
But this framing allows you to link things back to the SOC.
What did they give you?
They give you an agency name of which there are 33 state agencies;
budget entity, an office within the agency;
a position number, that's a position within the agency;
employee names.
I'm not showing you that because I feel uncomfortable
even though when you download it, you can obviously see who's who,
whether the person is salaried or exempt hourly,
or other personal services they call them,
full- or part- time.
A class code which is a code
that indicates both the profession and the level.
A class title which is essentially the same thing in words.
State hire date, which is the first date that the individuals hired by the state.
They could have had many terms of employment, come and left ,
but the state hire date is a fixed point in time for each person
salary or hourly rate if the person is doing an hourly job.
Again, this is freely available at the link noted on the screen.
Just for a bit more framing,
long- term view of wages in the State of Florida,
looking at BLS, Bureau of Labor Standards data for SOC,
is 00-000, just a weighted all occupations number.
It covers everything.
These are a lot of people, so I don't have error bars,
130 million people nationally and 7 million employees in Florida.
What you see is that Florida's salaries , the blue line,
are typically less than national, but they've been tracking pretty closely.
There's not been much relative change in a long time except for this past year.
Sometimes there are revisions.
I'm not going to say this is necessarily meaningful.
If it is a real difference then obviously be interesting to know about that.
I haven't seen anything reported about that though,
so I can't give you any further insight on that.
If you look at Florida State employees versus typical Floridians,
I don't have enough data in the set to really say very much,
except for it looks like being a state employee is fairly attractive,
at least if the jobs are typically comparable.
There's no overriding incentive for people who work for the state to leave
to go into the private sector there based on this.
These are for median salaries, annual salaries.
Looking at the Florida State employee population totals in the green line here
starting at around 100,000 for exempt staff,
doesn't include the hourly folks in either case here or here.
Looking at separation rates and hiring rates
as nine- week moving averages to be about two months
as a centralized moving average,
with JMP's usual capability for handling the endpoints.
What you see is that
for a fairly long time, except for this spike,
which again, I haven't found anything to explain in the literature,
nor in HR reports published by Florida.
Pretty constant.
After the pandemic hit, there was a long time period
where the hiring rate was below the separation rate.
So people were slowly leaving Florida.
You can see that here in a downward slope on the green line.
And then just this year, that stopped and began to reverse.
Now, to be clear, the population is only salaried workers,
only those holding one salaried state position at all times.
Anybody with two salaried positions was removed
because it could be a flawed data or it could be a very ambitious person.
But I can't handle that with the time- to- event data
because it's hard to understand exactly what a separation means
when you still have a job at the same place.
But it's only less than half a percent of the total people,
so it shouldn't be a huge perturbation.
Now, when I show this is a bit of a demo as well.
Florida State personnel flows by SOC major code.
But you can see on the right table…
Here's the population by SOC major code,
every individual grouping over time.
This is code 43. That's Office Administrative Assistants.
What I've done is I've used the hide and exclude capability
to remove everything except for six codes, which are the largest codes.
You see, the Administrative Assistants is 43.
And also down here, 19 for Life and Physical Sciences is included,
Business is included, Manager is included.
What I'm trying to say here is simply
that this is only including six out of something like 20 or so
major SOC codes.
But these are the largest.
Using graph builder, it only shows those.
That's really all it amounts to.
Business and Finance,
Community and Social Services, that we code 21, code 19, code 11.
Now, one thing you'll see with Manager
is that the hiring rate is always quite a bit less than the separation rate,
and yet the net number of managers is roughly the same,
and that's because only about half of the managers
come from external sources,
a lot of them come from internal promotions.
You see this population over time,
despite the vast difference in separations versus hiring,
that's simply because about half of them come from internal.
Now, you can also, again, as I was saying earlier,
you can do detailed codes and the same principle applies.
All I've done here is I've only included three SOC detail codes.
The 29 major code, which is Medical Professionals,
the 31 which is Support Folks in Medical Work,
and then back to 29 again for Registered Nurses,
but this is the Nurses and Nursing Assistants taken together.
This code is no longer used and hasn't been for a while.
But Florida set up its code system about two decades ago
and so it's been kept in
and they use it even though it isn't part of the standard SOC now.
But the bottom line you'll see from here
is that Florida is not attracting enough nurses to compensate for attrition.
If you look at the State of Florida HR R eports,
what you'll see there is that
they think most separations are voluntary, about 92%.
The number of authorized positions in the health agency
has only been reduced by about 5% in the last several years,
and yet the number of RNs has dropped by about a fourth.
You can see that the number of nurses is falling rapidly
compared to the allocation of nursing spots.
If you go to the State of Florida website and look for a job in nursing,
you'll see that there's plenty of opportunity.
They've been trying to hire.
Now, I am going to show some time- to-e vent analysis.
I'm not going to show the script work that generated the data set for this,
because although I find it fascinating,
I know that a lot of folks don't do scripting.
It's essentially an inference between who's there and who wasn't.
If you go from one week to the next and people disappear
and you've allowed for the fact that people do name changes sometimes,
which requires coming up with a different way of IDing people
to straddle the difference.
Once you've accounted for that, then they must have left.
Having left, that's a separation.
They can also get promoted,
and you can see that because one week they have a job,
and then the next week they have a job that pays better,
often the same general line, but with a different title.
Capturing those movements
is a bit of work but it's pretty straightforward, really.
What you see here, I tried to capture four different kinds of events: demotions,
a lateral to another SOC,
could be moving out of the nursing profession,
but nevertheless haven't changed their salary much,
promotion, or separations.
Separations is obviously the dominant factor here
in terms of total counts.
I'm using the Weibull typically because I find it more informative
and it's not a bad fit.
Post-COVID, you see a very similar curve,
more promotions, relatively speaking.
That's interesting.
Now, here is the detail in tabular form
so that you can see all the different pre- and post- cases
for the major movements, lateral movement, promotion, and separation.
What I'm talking about though, let's just go back to pre-COVID.
Here's a subset of Exit Events,
essentially exit from the status as an RN to whatever they moved to.
Just to make it clear what was done here.
If you relaunch, what you see is that I have a Censor column,
just ones and zeros.
The Exit Event is however they exit,
or if they didn't exit, then it's just an active person in the field
and they are not marked with a censor code.
The Employment Segment Span,
which is how long have they been employed in that particular segment of employment.
Now,
see that the number of laterals is really small compared to everything else.
Promotions is definitely visible.
Another thing you can see if you go down and look at separations
is that the Weibull beta,
which you can think of as the acceleration factor,
even at the high end of the 95% limits, it's still below unity,
and below unity means that
people are less likely to go through that transition as time goes on,
less likely to separate the longer they've done there.
That's straightforward.
You'll see it here.
In fact, that's also through post-COVID,
same basic beta factor or parameter, rather.
Now, I'm going to show the post-COVID.
Again, this is the same basic analysis.
This is what happens when you do live demos.
Something goofy with this one.
Now it's giving me grief.
Here, you see it's basically the same thing.
Lateral is distorting because the lateral, there's only two counts.
If you just get rid of that , you can see a much more clear picture.
What you see is that the Promotions piece is moving up faster,
50 versus 744, whereas pre over a longer time spent,
it was about 50 for about 1,200 separations.
There's a predominance is shifting there.
Going back to the more convenient layout here.
Pre-COVID promotions were in this range where beta was a little over unity.
But the 95% limits basically tell you that that's ambiguous.
It could be really anywhere between a bit below and a bit above unity.
Post-COVID, it's about 1.29,
and within these 95% limits, always above unity.
In other words, it's accelerating with time.
The longer you go, the more likely you are to go through a promotion
if you stay in that job.
Here with the lateral movement,
there really was never enough counts to do much of anything with that.
The limits are very broad.
I wouldn't put too much tal k in that, regardless.
Now, if you put these on a common scale
just to make sure that this isn't too confusing, I hope.
You see very similar.
I've shifted the color for the post-COVID case a little bit.
On a similar scale, if you didn't superimpose these,
promotions are clearly accelerated, p ost -COVID.
Clearly a bigger impact, they're more opportunity.
We do know from many news reports that people
who are closest to retirement,
often within the COVID complications and changes,
simply moved forward with retirement more quickly
because they wanted to get out other than deal with things.
There is a shift here with the separations,
and it does look real, but it's also small enough
compared to the overall magnitude
that it isn't quite as obviously different.
In either case, the separation rate is similar
and not changed overly much.
This is a factor of two.
This is a factor of a few percent.
To conclude,
wages in Florida have run lower than national values typically
over the last decade,
but haven't proportionally changed much.
There certainly doesn't seem to be any obvious change
in Florida salaries that would cause people to suddenly leave.
The State of Florida's registered nurses have enjoyed greater
and earlier promotion opportunities post-COVID.
But I think it's also worth noting here
that they work in a health organization for the State.
This is not a State hospital.
This is a health management, health support,
health education activity.
It's not 24/7 in a hospital.
That moderates expectations.
But you might expect the separation behavior
among their RNs would change
because opportunities have changed in the private sector.
There's a lot of demand.
On the other hand, state employees, they might be thought to be comfortable.
I was expecting my hypothesis was that they would be more likely to separate,
but that didn't happen.
There really is no apparent difference.
Now, this is not a complete bibliography of everything that I read
in the last five years, before and after COVID
that may have influenced things.
This is just a handful of things.
I thought they were fairly telling.
The National Healthcare Retention & RN Staffing Report
is a fairly thorough assessment
of what people expect in hospital administration
and what's actually been happening in terms of the employment
and the separations, the turnover behavior of nurses.
Three State of Florida annual reports.
They do an annual report on a fiscal year that straddles two calendar years.
The last one available is 2020.
But essentially, they're simply reiterating that,
yes, they have a number of open slots, they don't have them all full.
Employment and nursing is dropping.
They don't have any explanations for these things.
I also don't have any explanation
for the spikes and activity earlier, pre-COVID.
I have a question into someone in the Governor's office there,
but I haven't heard back yet.
That's basically all I have for this presentation.
I would be happy to entertain questions.
The slides show at the beginning, there's my email.
You can contact me if you want.
Thanks.
