My new piece @Health_Affairs Forefront finds that if the House bill becomes law and enhanced premium tax credits expire on schedule, the US will see an unprecedented increase in the uninsured rate, wiping out about ~3/4 of the post-2013 decline.

Viewpoint: Court decisions regarding the Trump administration’s most-favored-nation drug pricing proposals will determine the legal limits of health agencies’ ability to enact large-scale reforms. https://t.co/E7AXpWnAOk

Addendum: This tweet had typo. The 2nd sentence shld 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." https://t.co/Nd2YrQKpXY

Ack. Typo above. 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.”