A Theory of Crowdout and Group Insurance
care and health are in fact the very same. The design of the Oregon lottery measures the treatment effect for the average of the lottery- eligible adults. In contrast, this study
focuses on the local treatment effect for the parents of the marginally eligible child. This study considers these marginal parents in all sampled states, so it may be con-
sidered more general in some ways, as Koch 2013 fi nds a great deal of variation in treatment effects across states.
My work also relates to Anderson, Dobkin, and Gross 2012, which uses regression discontinuity on a different threshold—the age 19 cutoff for public health insurance
plans. Similarly, they estimated a large drop from public to no insurance, though they had limited data on medical spending and its sources. Their fi ndings on emergency and
inpatient care are not a contradiction to my work; they focus on a separate population, using a different source of identifi cation. Moreover, their estimated dropoff in insur-
ance is ostensibly forced as children age out of insurance, while the rise in uninsurance here is at least partly a matter of choice.
A similar RD design was employed in two studies of the causal effects of Medi- care—Card, Dobkin, and Maestas 2009 and Card, Dobkin, and Maestas 2008. Like
Anderson, Dobkin, and Gross 2012, a discontinuity in age, not income, was used to fi nd the causal impact of a separate public insurance program. Those studies focused
on the impact of Medicare as it creates near- universal insurance for the newly elderly population. The predominant transition under study here, the switch from private to
no insurance, is related to studying the universalization of insurance due to Medicare. This is, again, a test of the generalizability of the results for Medicare to the broader
adult population.
Use of age cutoffs raises a more general concern that the strategy used here avoids. When using an RD design in age, the empirical specifi cation compares the just- eligible
to the about- to- be- eligible. Absent extreme discounting, the treatment effect calculated with that comparison may not be valid when individuals gain or lose eligibility with
something less than exact predictability.
Previous estimates, such as those found in Currie and Gruber 1996a, 1996b, found that increasing the number of eligible children would lead to increases in the quality
and quantity of care. While seminal contributions to the literature, their data were limited either to survey questions of healthcare use “Did you go to the doctor in the
previous year?” or focused measures of utilization on special groups the healthcare quantity and outcomes of pregnant women and their newborn children. Here, we can
bridge those two works by utilizing data that is both focused on a variety of healthcare outcomes but also provides a representative sample of those on the margin of public
policy.