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Thomas G. Koch is an economist with the Federal Trade Commission. Scholars who wish to attain the key variables used here may apply to CFACT Data Center. The author is willing to offer guidance. The appendix mentioned in this paper can be found at jhr.uwpress.org. This research was supported by the Non- Senate Faculty Fund at UC- SB. The research in this paper was conducted at the CFACT Data Center, and the support of AHRQ is acknowledged. The results and conclusions of this paper are those of the author and do not indicate concurrence by AHRQ or the Department of Health and Human Services. The views expressed in this article are those of the author and do not necessarily refl ect those of the Federal Trade Commission. The data used in this article can be obtained from the author beginning March 2016 through April 2019 from 600 Pennsylvania Avenue NW, M- 8059, Washington D.C. 20580. Telephone: 512- 809- 8014. Email: tkoch.at.ftc.gov. [Submitted June 2013; accepted May 2014] ISSN 0022- 166X E- ISSN 1548- 8004 © 2015 by the Board of Regents of the University of Wisconsin System T H E J O U R N A L O F H U M A N R E S O U R C E S • 50 • 4 All Internal in the Family? Measuring Spillovers from Public Health Insurance Thomas G. Koch Koch ABSTRACT Measurements of the impact of public health insurance have typically focused on the health and insurance outcomes of the newly eligible child. In this paper, I investigate the consequences of public health insurance for the other members of the household. Using a regression discontinuity design, I fi nd that a child’s public health insurance eligibility crowds out the private health insurance of parents by 11 percentage points when it is not accompanied by parental eligibility. This loss of insurance corresponds to changes in self- reported health and preventive care for women.

I. Introduction

Measurements of the impact of public health insurance have focused on the outcomes of the newly eligible child. Starting with Cutler and Gruber 1996 and Currie and Gruber 1996a, the consequences of Medicaid on insurance and uti- lization were child- centric—measuring the crowdout of children’s private insurance or whether the number of children’s lives saved by public health insurance satisfi ed cost- benefi t analysis. Pregnant women were also considered, but, with few exceptions, economists have measured the impact of own eligibility on own outcomes. This view informed some of the expansions of public health insurance in the late 1990s and early 2000s, which saw some states expand public health insurance for children with more limited expansions for their parents. This was done even though medical insurance in the employer- provided market is explicitly oriented around fam- ily structure. Public insurance was being given to individuals children who were expressly part of a larger unit the family that shares a budget constraint. Using detailed family- level data and state- year- age public insurance eligibility guidelines, I estimate the causal impact of children’s public health insurance eligibil- ity on the insurance received by the adults within the child’s household, as well as on the quantity and quality of the parents’ healthcare. These effects are estimated using a regression discontinuity design based upon the income thresholds used to determine child eligibility. Making a child eligible for public health insurance without making the adult also eligible decreases the incidence of health insurance among the adults in the same household by 11 percentage points. This is due to a large and statistically signifi cant decrease in the incidence of private health insurance among those adults. This decrease in private health insurance is associated with decreases in the use of some preventive care and with lower self- reported health. Previous investigations on the causal effects within a child’s household have been limited to searching for potential instruments, as in Gruber and Simon 2008. Yet little, if anything, is known about the external impact on the family of a newly eligible child. Cutler and Gruber 1996 estimated the impact of Medicaid expansions for chil- dren and pregnant women for the private health insurance on adults. Their focus on adult men who were not the subject of those earlier expansions points to spillovers, though their focus is limited to insurance outcomes. Monheit and Vistnes 2010 used the variation in the parent versus child insurance patterns private and private versus private and public, for example but cannot rule out that the insurance patterns are endogenous. My fi ndings also add texture to the “job lock” literature that, starting with Madrian 1994, found that workers were hesitant to switch jobs due to health insurance. More to the point of this paper, Garthwaite, Gross, and Notowidigdo 2014 found that an expansion of public health insurance in Tennessee decreased labor supply on the extensive margin likely because it relieved such pressures. My fi ndings are also distinct from the measurements of parental eligibility on pa- rental outcomes. Busch and Duchovny 2005, Hamersma and Kim 2009, 2013, and Aizer and Grogger 2003 perform the classical crowdout analysis in this fashion. Here, I am able to isolate the effects of child eligibility on parental outcomes, which refl ects a spillover rather than a direct effect. The estimates presented here complement the fi ndings of Koch 2013, which used the same identifi cation strategy to measure the causal impact of eligibility on the newly eligible child. My analysis expands our understanding of how these policies impact the broader population. Like Leininger, Levy, and Schanzenbach 2010, which measured the effect of eligibility on household consumption, focusing on the household provides a broader picture of how public insurance impacts families. The impact on spending measures the consumption- based welfare effects. By measuring the external aside from the child, within the house- hold consequences of eligibility, my estimates capture a more complete picture of the impact on healthcare utilization of Medicaid and State Children’s Health Insurance Program SCHIP. My fi ndings are also related to Finkelstein et al. 2012, which uses a lottery for Medicaid in Oregon to estimate the treatment effects for Medicaid for adults. The es- timates under study here are superfi cially similar: The measured outcomes for health- 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.

II. A Theory of Crowdout and Group Insurance