Perhaps the single biggest story about the implementation of the Affordable Care Act has been the battle in states deciding whether to accept the Medicaid Expansion. The expansion is perhaps the single most important tool in the ACA’s coverage expansion tool kit. It takes 50 statebased single payer systems and drastically expands eligibility for them, which is the single largest progressive victory in politics since the Great Society. Other important Medicaid reforms drastically streamline the application procedures and eliminated asset tests to draw out formerly eligible people who might have gotten tangled up in the system or not bothered applying because the state made you apply in person on Tuesday between the hours of 2:30 p.m. and 3:04 p.m.
However, the expansion is not some magical talisman that instantly enrolls
all eligible individuals. Some people will remain ignorant of the program
despite the best outreach efforts, while other will not enroll for any variety
of reasons. And more to the point, 24 states hadn’t fully taken advantage of
the Medicaid expansion by the beginning of 2015. Pennsylvania, Indiana, Montana
and Alaska have all signed on this year, leaving 20 holdouts (half of whom were
in the former Confederacy – but I digress).
The cool thing that we have some real data of how ACA has
actually performed on the ground over the last two years, we make some
interesting dynamic projections of what would
have happened had some states accepted the Medicaid, instead of simply
discussing the number of people who would be eligible for help under the
expansion.
I mean, heck, this Charles Gaba fellow has been counting the people who actually signed up for coverage for two years, I might as well take one shot at figuring out who would have signed up if they could have.
I mean, heck, this Charles Gaba fellow has been counting the people who actually signed up for coverage for two years, I might as well take one shot at figuring out who would have signed up if they could have.
To accomplish that, follow me below the fold, where I build
a simple interactive regression model to project the reduction in the uninsured
population in states that haven’t expanded Medicaid. Don’t worry; I’ll label
the scary part where I work through the model so you can skip the simple
summary where I discuss the results in plain English (but you really should read
the model section, it’s rather of important and it makes fun of Bobby Jindal).
Detailed Explanation
of the Model
This model differs from my last simple twovariable regression
model using Gallup data because I’m using a different dependent variable: the reduction of the
uninsured rate in percentage points, which I simply think is a bit more
intuitive to understand for the question at hand. (e.g. Massachusetts had 4.9
percent of its population uninsured in 2013 before the ACA kicked in and had
3.0 percent uninsured in the first half of 2015, for a reduction of 1.9
percentage points, which would be the MA case’s value in the dependent variable
column. The big issue with that, methodologically speaking, is because some
states start with much higher percentages of uninsured population they have the
potential for much greater reductions.
That’s something we have to control for, or we might get skewed results
that may even suggest that expanding Medicaid may very well increase the
uninsured rate.
But how do we do that? We build an interactive model. We
start with the same two independent variables in the other model: whether or
not the state expanded Medicaid by 2015 (0 if it didn’t expand and 1 if it
did); and what type of exchange the state had (0 for Federal, 1 for a
partnership and 2 for a full statebased exchange). From there, we add two more variables. The
first one is the initial level of the state’s uninsured rate, which is
straightforward. The second variable is the product of the Medicaid expansion
variable and the initial insured rate variable. This fourth variable is what
makes the model interactive: the total effect that accepting the Medicaid
expansion will have is dependent on how large the initial uninsured rate is.
We get a model that looks like this:
Reduction in Uninsured Rate= (m1)(Expansion) +(m2)(State
Exchange) +(m3)(Original Uninsured Rate) + (m4)(Expansion)(Original Uninsured
Rate) + Constant
It’s still pretty similar to that basic y=mx+b model you saw
in your firstyear algebra course, except 1. It have more variables and 2. Two
of those variables get multiplied together (it also should have an error term,
but don’t worry about that for now).
Note that the interactivity also makes it a bit more
complicated to calculate the effect size of a given variable. To calculate the
predicted effect of the Medicaid expansion in a given state, we can’t simply
multiply the coefficient on the “Expansion” variable by the value of the
Expansion variable, we have add that product to the value of the Original
Uninsured Rate variable and its coefficient AND the interactive term. Mathematically:
Reduction in Uninsured Rate due to Medicaid Expansion =
m1(Expansion) +(m3)(Original Uninsured Rate) + (m4)(Expansion)(Original
Uninsured Rate).
OK, let’s estimate the regression with our ever handy Stata software:
Variable
Name

Coefficient

Medicaid Expansion:

.447

Statebased Exchange

.354

Initial 2013 Uninsured
Rate

.298

Interaction
(Expansion x Initial Uninsured Rate)

.198

Constant

.543

N= 50


Adjusted Rsquared =.4422


Before we get into what the coefficients mean, note that this
simple model predicts about 44 percent of the total variation in our data.
Also, again I don’t report standard errors because this is the universe of
states, not a subsample. These effects are the “real” effects (baring omitted
variable bias etc.)
At a glance it looks like accepting the Medicaid expansion
actually increases the uninsured rate, I mean look at that negative
coefficient! (which would equate to a negative decrease (increase) in the
percent uninsured). But it doesn’t –the
other variables will interact to end up giving it a positive effect. Let’s take an example and go through the math
(no, not that math).
Louisiana, governed by Bobby Jindal  possibly the dumbest
man ever to win a Rhodes Scholarship  has refused to take the Medicaid
expansion. Between 2013 and 2015, the state has seen its uninsured rate decline
from 21.7 percent to 16.3 percent (a 5.4 point decline), despite its best
efforts to fight the socialist menace of Obamacare. But if Zombie Karl Marx had
staged a coup and forced the state to accept Communis er, I mean the Medicaid
expansion, what would have happened? Well, we can estimate it in our model:
Reduction in Uninsured Rate= .447(Expansion) +(.354)(State
Exchange) +(.298)(Original Uninsured Rate) + (.198)(Expansion)(Original
Uninsured Rate) + .543
Where:
Mediciad Expansion =1 (we’re accepting it)
State Exchange =0 (Louisiana defaulted to the Federal
Exchange)
Original Uninsured Rate= 21.7
OK campers, Plug and chug!
= .447(1) + .354(0) + .298(21.7) + .198(1)( 21.7) + .543
=.447+ 0 +6.47+4.3 + 0.543
= 9.6 (more or less after rounding)
So instead of declining by 5.4 points, to 16.3 percent it
would have declined by 9.6 points to 12.1 percent. That works out to about 194,000 more people
with health insurance.
Clearly a certain Cajun state governor didn’t do his maths
homework at Oxford.
One final thought: Notice how much easier computationally the
equation is to estimate when Medicaid isn’t expanded: two terms become zero,
leaving us with a reduction in the percentage of uninsured of about 30 percent
less of the original uninsured rate less one half a percentage point (The estimated
reduction for Louisiana without Medicaid Expansion was 5.8 percentage points,
very close to the actual rate of 5.4, which indicates our model is working
fairly well.)
Again, there are several sources of error here. One
potential problem is leaving out variables that are predicting systemic bias in
the uninsured rate. One possibility that jumps to my head is the percentage of
the state’s population that are undocumented immigrants, who are generally not
eligible for subsidies or Medicaid. I feel a bit more comfortable with this as
states with (Arizona, New Mexico, California) as well as without (Texas,
Florida) have large populations of undocumented residents. However, if the
average between the two sets of states is different, these measures will be
skewed. Also, remember the Gallup data is based on samples from each of the
states, and states with small samples – especially in 2015, which only takes up
the first six months of the year, have a larger margin of error, which could
skew these results a bit as well.
Plain English Summary
of Results
Based on the simple interactive model, using the state’s
decision to expand Medicaid, decision to implement a statebased exchange and
the state initial uninsured rate as variables, I was able to calculate the
following potential additional reductions in the uninsured rate for each state
in Table 2 below. The first column shows the state’s uninsured rate at the end
of 2013, the second shows where it was in July 2015. The third column is the
projected uninsured rate in July 2015 if the state had accepted Medicaid. The
fourth column shows the improvement between columns two and three. And the
fifth give our estimate of the number of additional people who would have been
insured had the state not been governed by vandals.
State

Uninsured % 12/2013

Uninsured %
7/2015

Projected Uninsured 7/2015 % with Medicaid
Expansion

Difference

Total increase in projected number insured

WY

16.6

18.2

9.4

8.8

51,274

TX

27.0

20.8

14.6

6.2

1,639,788

OK

21.4

17.7

11.8

5.9

227,184

ID

19.9

16.2

11.7

4.5

72,546

VA

13.3

12.5

7.7

4.8

396,499

LA

21.7

16.3

12.1

4.2

194,270

UT

15.6

13.2

8.9

4.3

124,737

KS

12.5

11.3

7.3

4.0

115,758

GA

21.4

15.3

11.8

3.5

349,726

NC

20.4

14.7

11.3

3.4

334,834

TN

16.8

12.9

9.5

3.4

220,863

FL

22.1

15.2

12.1

3.1

606,139

MT

20.7

14.4

11.4

3.0

30,455

MO

15.2

11.4

8.7

2.7

163,193

IN

15.3

11.1

8.7

2.4

157,702

SC

18.7

12.6

10.4

2.2

105,046

AL

17.7

12

9.9

2.1

101,508

MS

22.4

14.2

12.3

1.9

56,833

NE

14.5

10

8.3

1.7

31,765

PA

11.0

7.7

6.5

1.2

153,286

ME

16.1

9.4

9.1

0.3

3,985

AK

18.9

10.3

10.5

0.2

1,470

SD

14.0

7.2

8.1

0.9

7,604

Total:





5,128,316

Don’t worry too much about those numbers for Alaska and
South Dakota – the Medicaid Expansion would decrease the rate of uninsured
there, but they have already over performed according to the model, so
the estimates look negative.
Bottom line is the estimate of the number of people who
would get insurance through Medicaid in the right hand column: 5.13 million. 30
percent of those would be in Texas, which had the distinction of being governed
during 2013 and 2014 by a man who literally could not count to three. Molly Ivins save us all.
Medicaid Expansion; it's still worth fighting for.
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