How CBO handles imputing benefits #71
Comments
|
I agree that the simple approach is right for us at this time. If someone
wants to study distribution across age groups (as Larry Kotlicoff does)
they would want benefits to vary by age. But many applications do not
require that sort of detail, and our resources our limited.
dan
…On Fri, 4 May 2018, Martin Holmer wrote:
There has been recent discussion of how best to impute to filing units a
Medicare benefit amount and a Medicaid benefit amount. Most of this
discussion has taken place in C-TAM issue #68 and taxdata pull request 185.
One issue in those discussions has been whether it makes sense to impute
different actuarial values (not "insurance values") for Medicare (Medicaid)
depending on the Medicare (Medicaid) enrollee's income.
Thinking about how to do the imputations is essentially a benefit/cost
analysis of two approaches. There is a simple approach that assigns each
enrollee the same actuarial value. Others want to assign different values
that vary by income (see the use of 16 income subgroups in taxdata pull
request 185). But the focus on income subgroups seems too narrow. For
example, the government's cost of providing Medicare seems likely to vary
more by age subgroup than by income subgroup. So, a sensible more
complicated approach would include more subgroups than just income. Of
course, that complicates the imputation process and thus increases the cost
of the complicated approach.
A potential cost of the simple approach could be the fact that CBO used
something like the complicated approach. I've said in the earlier discussion
that as far as I understood, CBO follows the simple approach to assigning a
benefit amount to each enrollee. But I didn't supply any documentation to
support that view. A couple of minutes of Google searching produced this
September 2017 CBO slide presentation by Kevin Perese and Bilal Habib
entitled Methodological Improvements for CBO’s Analysis of the Distribution
of Household Income.
In that CBO presentation, the authors describe how CPS under-reporting of
the receipt of benefits is corrected using a regression approach (see the
Medicaid participation discussion on slides 43-48). But then slide 55 says
each Medicaid participant is assigned the same benefit amount:
For Medicaid, CBO derives the average cost to the government per
recipient from administrative data (by eligibility category).
Those averages are then assigned to all recipients (CPS
“reported” values are overwritten).
The phrase "by eligibility category" likely refers to subprogram
distinctions made in both the CPS and in aggregate administrative data (for
example, the actuarial cost of providing CHIP benefits is likely to differ
from the actuarial cost of providing Medicaid benefits to those who qualify
for Medicare by virtual of being an SSI beneficiary). But it is clear that
within each Medicaid subprogram all the enrollees are assigned the same
actuarial value as their benefit amount.
And then on slide 66 there is this description of assigning Medicare benefit
amounts (emphasis added):
To impute Medicare benefits, CBO makes no change to [CPS]
reported recipients. CBO assigns the average cost to the
government per participant to all recipients. Benefits from the
Low-Income Subsidy for Medicare Prescription Drug Coverage are
allocated separately.
Notice again the Medicare subprograms can have different actuarial values,
but there is no income or age variation in the imputed Medicare benefit.
In conclusion, it seems to me that the simple approach has a much higher net
benefit than does the complicated approach. This is mainly because the
complicated approach takes a lot of extra work that CBO is not doing. What
do the rest of you think?
@Amy-Xu @feenberg @andersonfrailey @MattHJensen @MaxGhenis
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the
thread.[AHvQVe7Hl28AYnfiZehvWc8n-poZkTLWks5tvMCrgaJpZM4TzQum.gif]
|
|
@martinholmer Thanks very much for this detailed outline. I agree that we should assign the same actuarial value to everyone for the moment. |
|
An issue that @Amy-Xu pointed out with using just a simple average benefit amount, for Medicare at least, is that Medicare provides a lot of benefits for the institutionalized population that we don't have in our datasets. Should we worry at all about maybe trying to back out spending on that subset of the population? @Amy-Xu do you think it's a large enough issue to warrant the extra work? |
|
I imagine institutionalized population might have a different actuarial value, and this part of population has not been included in the CPS tax unit dataset yet. It would be the best using one universal value for everyone if this is a negligible point. But I want to hear more thoughts before we proceed with the universal value. |
|
I'm confused by this discussion because I was under the impression that the CPS excludes the institutionalized population. Isn't this what this says?
|
|
@martinholmer You're absolutely right. But is it a problem when CPS doesn't while Medicare, and potentially Medicaid, do? |
|
@Amy-Xu said:
I thought what you said yesterday was that the "scaling factors" applied to the MEPS Medicare and Medicaid amounts were constructed to exclude program costs attributable to the institutionalized population. |
|
I interpreted your citation of CBO report
as we need to calculate the actuarial value by dividing the total government cost by the total number of enrollee. Is that what you interpreted from the CBO report? It seems in the current context that "total non-institutional benefit expenditure over total non-instituional enrollee' would be more accurate. What do you think? Lastly, because the total benefit target is coming from the Medicare Trustee's Report, I assume this assignment of actuarial value has nothing to do with the MEPS imputation (for individual level benefit) anymore. Thus my understanding is that this issue is a separate one from C-TAM PR #70. Do you agree? |
|
On Fri, 11 May 2018, andersonfrailey wrote:
An issue that @Amy-Xu pointed out with using just a simple average benefit amount, for
Medicare at least, is that Medicare provides a lot of benefits for the
institutionalized population that we don't have in our datasets. Should we worry at all
about maybe trying to back out spending on that subset of the population? @Amy-Xu do
you think it's a large enough issue to warrant the extra work?
The omission of the institutionalized is not really a problem. Although
they represent a lot of spending, the insurance value of Medicare and
Medicaid to the currently non-institutionalized should include that
spending because the present value of their expected benefit includes that
spending. There is a slight problem that the dataset is not representative
of the population, but that would correctly be handled by adding some
elderly or low income records, not by subtracting their benefits from the
existing records. In any case, it isn't a large number of records so it
won't have the distribution tabs very much.
dan
…
cc @martinholmer @feenberg
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the
thread.[AHvQVS3n_MhZGacLVOP4jNPYQNEvGAFhks5txapJgaJpZM4TzQum.gif]
|
|
On Fri, 11 May 2018, Amy Xu wrote:
@martinholmer
I interpreted your citation of CBO report
CBO assigns the average cost to the government per participant to all
recipients.
as we need to calculate the actuarial value by dividing the total government cost by
the total number of enrollee. Is that what you interpreted from the CBO report?
It seems in the current context that "total non-institutional benefit expenditure over
total non-instituional enrollee' would be more accurate. What do you think?
I explained in my last message why I do not agree. Suppose 1% of the
population is missed, and 50% of the expenditure is on that 1%. Then
dividing the total expenditure by number of enrollees give the correct
insurance value to each enrollee. Including only half the expenditure
would underestimate the value of the insurance.
dan
|
I see your argument, but somehow I feel your argument supports my conclusion rather than yours. Before I explain my logic, I think we need a clarification on the terms we use in this issue.
I assume you're speaking about actuarial value. (Correct me if I'm wrong) Then my logic is that non-institutional population shouldn't get assigned the actual whole population average because a huge amount of the expenditure is not on them. |
|
On Fri, 11 May 2018, Amy Xu wrote:
@feenberg
Suppose 1% of the population is missed, and 50% of the expenditure is on that 1%. Then
dividing the total expenditure by number of enrollees give the correct
insurance value to each enrollee.
I see your argument, but somehow I feel your argument supports my conclusion rather than yours. Before I
explain my logic, I think we need a clarification on the terms we use in this issue.
*
Insurance value: It seems Martin, in his initial post of this issue, interprets as how much it worth
to a enrollee. cc @martinholmer (Am I right?)
*
Actuarial value: 'book value', aka how much is paid.
I assume you're speaking about actuarial value. (Correct me if I'm wrong)
Then my logic is that non-institutional population shouldn't get assigned the actual whole population
average because most of the expenditure is not on them.
But that is always true of insurance. Most of the ex post benefit goes to
a few. The ex ante benefit is spread evenly. We have chosen to model the
ex ante benefit for good reason.
dan
…
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the
thread.[AHvQVYEtGaVeXDbWcUfXT_s-b3MiEVUbks5txcvjgaJpZM4TzQum.gif]
|
|
@feenberg said
I see. That makes sense to me. Then it seems we can just proceed with the average calculated as the total benefit expenditure divided by the total number of enrollee. Is that sensible to you? @martinholmer |
|
Sorry for letting this slip through the cracks. Has this discussion been settled? And if so are we just going with the changes made in TaxData PR #185? |
|
Since the corresponding TaxData Issue has been merged, I'm closing this issue. Thanks to everyone. |
There has been recent discussion of how best to impute to filing units a Medicare benefit amount and a Medicaid benefit amount. Most of this discussion has taken place in C-TAM issue #68, C-TAM pull request #70, and taxdata pull request 185. One issue in those discussions has been whether it makes sense to impute different actuarial values (not "insurance values") for Medicare (Medicaid) depending on the Medicare (Medicaid) enrollee's income.
Thinking about how to do the imputations is essentially a benefit/cost analysis of two approaches. There is a simple approach that assigns each enrollee the same actuarial value. Others want to assign different values that vary by income (see the use of 16 income subgroups in taxdata pull request 185). But the focus on income subgroups seems too narrow. For example, the government's cost of providing Medicare seems likely to vary more by age subgroup than by income subgroup. So, a sensible more complicated approach would include more subgroups than just income. Of course, that complicates the imputation process and thus increases the cost of the complicated approach.
A potential cost of the simple approach could be the fact that CBO used something like the complicated approach. I've said in the earlier discussion that as far as I understood, CBO follows the simple approach to assigning a benefit amount to each enrollee. But I didn't supply any documentation to support that view. A couple of minutes of Google searching produced this September 2017 CBO slide presentation by Kevin Perese and Bilal Habib entitled Methodological Improvements for CBO’s Analysis of the Distribution of Household Income.
In that CBO presentation, the authors describe how CPS under-reporting of the receipt of benefits is corrected using a regression approach (see the Medicaid participation discussion on slides 43-48). But then slide 55 says each Medicaid participant is assigned the same benefit amount:
The phrase "by eligibility category" likely refers to subprogram distinctions made in both the CPS and in aggregate administrative data (for example, the actuarial cost of providing CHIP benefits is likely to differ from the actuarial cost of providing Medicaid benefits to those who qualify for Medicare by virtual of being an SSI beneficiary). But it is clear that within each Medicaid subprogram all the enrollees are assigned the same actuarial value as their benefit amount.
And then on slide 66 there is this description of assigning Medicare benefit amounts (emphasis added):
Notice again the Medicare subprograms can have different actuarial values, but there is no income or age variation in the imputed Medicare benefit.
In conclusion, it seems to me that the simple approach has a much higher net benefit than does the complicated approach. This is mainly because the complicated approach takes a lot of extra work that CBO is not doing. What do the rest of you think?
@Amy-Xu @feenberg @andersonfrailey @MattHJensen @MaxGhenis
The text was updated successfully, but these errors were encountered: