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.