Matt Fiedler

Addendum: Ack. This tweet had crucial typo. The 2nd sentence should be "Enrollees who shift from TM to MA when MA grows are likely *cheaper* than the average TM enrollee but *costlier* than the average MA enrollee, so *both* groups likely get more costly as MA grows."

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Ack. This tweet had a crucial typo. The second sentence should read "Enrollees who shift from TM to MA when MA grows are likely *cheaper* than the average TM enrollee but *costlier* than the average MA enrollee, so *both* groups likely get more costly as MA grows."

To be clear, the MA payment system’s *existing* accuracy problems may (and, in my view, do) offer a strong rationale for reform. But the dynamics we consider here seem unlikely to do much, if anything, to bolster that case. /end

A second is that rising MA penetration is unlikely to change selection patterns in ways that seriously reduce the accuracy of the MA payment system and necessitate reforms that would break the link between MA payments and TM costs.

If that’s right, it has a couple of implications. One is that TM is likely at little risk of entering a “death spiral” in which higher MA penetration leads to greater favorable selection that induces still further increases in MA penetration, and so on.

Our approach has limitations, including that it cannot address potential confounding from county differences that vary over time. But these results suggest that further growth in MA will have little effect on the degree of favorable selection into the program.

For example, the results imply that if TM’s market share fell by 50%, then the effect on the TM-MA difference in risk-adjusted costs would lie somewhere between a negligible change and a decline of around 0.6 percentage points.

Details are in the paper, but this figure shows the main results: across a wide range of assumptions about who “switchers” are (reflected in the different values of theta), changes in MA penetration have little effect on the degree of favorable selection into MA.

The panel data allow us to control for persistent cross-county differences, while the model structure allows us to explicitly account for the fact that stayer-switcher cost differences may not coincide with differences in average costs between TM and MA enrollees.

To address these issues, we use county-year panel data on MA penetration and stayer-switcher differences to estimate an empirical version of the theoretical model sketched above.

Under this assumption, the model sketched above suggests that stayer-switcher cost differences may shrink as MA grows even if the difference in average costs between TM and MA enrollees is stable or growing. See, in particular, panels A and B below.

This matters because MA penetration may not affect the two differences in the same way. To see why, suppose we make the (arguably fairly plausible) assumption that TM-to-MA “switchers” correspond to the enrollees on the margin between TM and MA.

The second issue is more subtle. TM-to-MA “switchers” are likely not representative of MA enrollees as a whole, so the cost difference between stayers and switchers may not measure what we’re actually interested in: the difference in the *average* cost of TM and MA enrollees.

Indeed, it’s notable that *changes* in MA penetration are associated with modest declines in stayer-switcher differences, consistent with the concern that cross-sectional relationships are confounded to some degree.

The first is the potential for confounding. Counties with higher MA penetration may differ in other ways that affect stayer-switcher differences, masking the true causal relationship between MA penetration and stayer-switcher differences.

But for a couple of reasons, this may not be a good guide to the causal effect of MA penetration on the degree of favorable selection into MA. There are two main issues.

Prior work has examined the cross-sectional relationship between MA penetration and stayer-switcher differences in risk-adjusted costs, finding little relationship. We replicate that finding:

If “switchers” have lower prior year spending than “stayers” (after risk adjustment), that’s indicative of favorable selection into MA. Larger stayer-switcher differences suggest more intense selection.

Thus, we tackle this question empirically. To do so, we first construct a measure of favorable selection at the county-year level. Following prior work, our measure is the difference in the prior-year spending between TM-to-MA “switchers” and TM “stayers.”

As illustrated in the figure below, the *gap* between the average cost of MA and TM enrollees can either grow or shrink as MA gets larger, depending on the exact shape of the relationship between beneficiary cost and propensity to enroll in MA vs. TM.

As a theoretical matter, it’s ambiguous how MA’s growth will affect selection. Enrollees who shift from TM to MA when MA grows are likely costlier than the average TM enrollee but cheaper than the average MA enrollee, so *both* groups likely get more costly as MA grows.

Many observers have asked how selection might change as MA grows—and whether this might necessitate policy changes, especially steps to break the link between MA payments and TM costs. See e.g.,
www.healthaffairs.org/content/fore...
jamanetwork.com/journals/jam...
www.medpac.gov/wp-content/u...

It’s well-documented that Medicare beneficiaries who choose MA cost less to cover than those who choose TM. Because payments to MA plans are based on local TM costs, this “favorable selection” causes MA plans to be paid more than intended.

Will Medicare Advantage's rise strengthen or weaken favorable selection? | Brookings Samantha Crow and Matthew Fiedler examine the effect of Medicare Advantage penetration on favorable selection.

Will Medicare Advantage’s growth change the degree of favorable selection into the program? My new paper w/ Samantha Crow offers an answer: probably not much.

www.brookings.edu/articles/wil...

Full piece on the House bill is here: healthaffairs.org/content/fore.... I’ll release an updated analysis once final numbers on the Senate bill are available.

What will the Senate bill mean for health coverage? We don’t yet have final CBO estimates, but it’s clear they’ll be pretty similar to the House bill. That puts the U.S. on track for an unprecedented increase in the uninsured rate that will wipe out ~3/4 of post-2013 declines.

The Senate reconciliation bill, at least in its current form, appears likely to reduce coverage about as much as the House bill. If it becomes law, that would mean reversing most of recent years' insurance coverage gains:

bsky.app/profile/matt...

How Medicaid Work Requirements Betray Work and Waste Money I Oversaw Work Requirements in Michigan - Here is What I Told Congress

New-to-me nugget on work requirements implementation in here:

Pinging Equifax's The Work Number — which states are likely to rely on to get recent-enough employment data — "sometimes costs over $20 per person per query"

Full piece is here: www.healthaffairs.org/content/fore...

Clever new piece from Kennah Watts and Jack Hoadley takes a first look at how decisions under the No Surprises Act arbitration process vary across arbitrators. Lots of interesting findings here, including that provider win rates vary a lot by arbitrator.