The Effect of Public Health Insurance Expansion on Private Insurance Premiums: Evidence from SCHIP

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1 The Effect of Public Health Insurance Expansion on Private Insurance Premiums: Evidence from SCHIP Liu Tian Department of Economics and Center for Policy Research Syracuse University Oct

2 Abstract This paper investigates the consequences of crowd-out on the private insurance market induced by public insurance expansions. Specifically, it analyzes the extent to which the employer-sponsored insurance (ESI) premiums are affected by the State Children s Health Insurance Program (SCHIP). The ESI premiums may change because the health composition of the ESI pool may change even though the majority in the pool are not eligible for SCHIP. For instance, individuals who drop ESI coverage once their children can be covered by SCHIP or their children may have worse than average health. It would lead to a decline of the average health spending of those who remain in the ESI insurance pool. Thus, overall premiums would decline. Changes in private health insurance premiums in turn affect program-ineligible individuals with private coverage. My estimates, relying on variations in time of implementation and size of expansions across states, imply that private insurance premiums in the group market would have increased by two to three percent, had SCHIP not been expanded. The children who switch from ESI to SCHIP have relatively poor health. JEL Classifications: H4, I1, I3 Keywords: SCHIP, Medicaid, crowd-out, ESI, health insurance premium I m very grateful to my primary advisor, Len Burman, for his supervision and guidance. I m also thankful to Jeffery Kubik, Sarah Hamersma and Thomas Dennison for their comments and suggestions. Thanks also to Carole Roan Gresenz for his generously provision of the income disregards data. This paper benefits from the conversation with Karen Morrissey and participants in the dissertation workshop and department seminars at Syracuse University. All remain errors are my own. 2

3 1. Introduction Interactions between the private insurance market and expansions in the public insurance market have long been studied in the literature. Most studies have focused on estimating the effect of Medicaid expansions on crowd-out. Crowd-out is a phenomenon describing the extent to which private insurance coverage decreases, as public insurance is made available to a larger population 1. Estimates of crowd-out range from almost nothing to as high as 70 percent, depending on specific Medicaid expansions targeting various populations that have been studied and on the data sets being used (Ham and Shore-Sheppard, 2005). The State Children's Health Insurance Program (SCHIP), also has been found to crowd-out from private coverage: 25 to 50 percent of new enrollees in public insurance come from private insurance (CBO 2002). Although we have learned much about the existence and magnitude of crowd-out, little is known about its consequences. The goal of this paper is to begin to fill that gap. This paper examines the effect of public insurance expansions in the SCHIP-era on private insurance premiums in the group market. SCHIP expansions may affect premiums of employer-sponsored insurance (ESI) by changing the health composition of the ESI insurance pool. Program-ineligible people are affected; for instance, if individuals crowded out are more likely to have inferior health, overall health conditions of the private insurance pool would be improved after those individuals leave for SCHIP simply because the health composition of the private insurance pool changed. Better health conditions reduce costs for insurance companies, which in a competitive market give them an incentive to reduce overall premiums (or not increase them as much). Insurance companies are not allowed to distinguish potential customers by costs they may incur or to charge them different premiums. As a result, reduced premiums 1Cutler and Gruber, 1996; Gruber and Simon, 2008; Shore-Sheppard et al., 2000; Shore-Sheppard, 2008; Blumberg et al., 2000; Hamersma and Kim, 2012; Ham and Shore-Sheppard, 2005; Card and Shore-Sheppard,

4 apply to everyone under the same plan, including those in families who have no dependents qualifying for SCHIP. However, policy-untargeted groups receive much less attention in the literature except for a few studies that extended their research to examine the effects on other family members of SCHIP eligible individuals 2. Nearly all the studies on crowd-out thus far are restricted to evaluating effects on either low-income children, parents or pregnant women who are the targets of Medicaid expansions, despite that individuals under private coverage in SCHIP-ineligible families, seemingly irrelevant, are affected by the public program because the premiums they pay could be affected. Understanding the effect of SCHIP expansion on private insurance premiums is essential for several reasons. First, changes in private insurance affect a large population because private insurance, in particular ESI, is the main form of health insurance coverage in the United States. Currently, around 60 percent of Americans are covered by ESI (Census 2011). The percentage is even higher when concentrating on working individuals. Second, fluctuations in premiums reflect sorting behavior of individuals into different markets. Decreases in private insurance premiums suggest more adverse selection in public programs because individuals with poor health conditions move to public programs. Third, evaluation of SCHIP cannot be complete without fully investigating the consequences of crowd-out and its effect on ESI enrollees. The heterogeneity in time and size of expansions across states allows for identifying the effect of SCHIP on ESI premiums. Public health insurance for the low-income, non-elderly experienced multiple expansions since its inception. The most recent and the largest one, in 2 Blumberg,2000; Cutler and Gruber, 1996; Gruber and Simon, 2008 and Buchmueller et al

5 terms of federal government funding, was the introduction of SCHIP in SCHIP expanded public health insurance to children from families with income too high to qualify for Medicaid but not high enough to afford private insurance. Timing of the SCHIP expansions varied greatly across states. Meanwhile, states had considerable flexibility in deciding how much to increase the income eligibility thresholds subject to the minimum requirements set by the federal government. As of 2000, all 50 states and the District of Columbia had expanded their Medicaid to some extent. The average thresholds for children went up from 121 percent of the federal poverty level before SCHIP to 205 percent in 2000, as shown in Table 1. The empirical estimation of the effects on premiums is based on aggregate data at the firm size by state level, which is drawn from the Medical Expenditure Panel Survey--Insurance Component (MEPS-IC). The MEPS-IC is then matched with state characteristics collected from the Bureau of Labor Statistics (BLS) and with income thresholds for each state from the Kaiser Family Foundation (KFF) and the Urban Institute over the period of 1996 to Using these data, I perform regressions controlling for a series of fixed effects, including year, firm-size and state fixed effects. Separate regressions are carried out for family plans and single plans and by firm size. Distinct from the routine in crowd-out literature, in which simulated individual eligibility is used as a measure of Medicaid/SCHIP expansions, I follow Hamersma and Kim (2012) by using the income thresholds. The SCHIP threshold is a parameter under direct control, and more salient to policy makers. For robustness checks, I also experiment with two other measures of income thresholds. One is a threshold in terms of gross income, which takes into account income disregards and the other is an income threshold averaged across age groups. The estimates are 3 There have been 40 billion dollars allocated to states from federal government fund throughout 2007, which makes it the largest expansion in Medicaid history to date. 5

6 not sensitive to the choice of income threshold measurements. In this study, three main results are obtained. It first confirms that SCHIP produces significant crowd-out 4 using my empirical model. My estimates, using panels 1996, 2001 and 2004 of the Survey of Income and Program Participation (SIPP), suggest Medicaid/SCHIP coverage for children under the age of 18 would increase by 3.67 percentage points if the income threshold increases by 100 percent of the poverty level. At the same time, the ESI coverage of the same group would go down by 1.5 percentage points. In net, I estimate that about 40 percent of the children who are newly insured with Medicaid/SCHIP would otherwise have been covered by ESI previously, which is in line with previous research. Second, I find a negative relationship between ESI premiums and the extent of the SCHIP expansion. The negative relationship suggests that, on average, the premiums of ESI for both single and family plans would have increased by another 2 to 3 percent, had SCHIP not existed. These results are supported by the evidence that children who switched from private insurance to SCHIP/Medicaid are more likely to have poor health conditions (Shaefer et al., 2011), which implies improved overall health conditions in the ESI insurance pool when those children exit. As a result, ESI premiums decrease. Third, small firms are more responsive to SCHIP. Premiums for single plans only fall significantly in small firms. This appears to be because small firms are more likely subject to medical underwriting, a procedure used by insurance companies to determine overall plan premiums based on the health status of employees. Consequently, health shocks to an employee would be more likely to be captured by insurance companies in small firms. Or it might be because public insurance is more attractive to ESI enrollees in small firms than to in large firms 4 Estimates of crowd-out are based on SIPP. 6

7 because small firms typically offer less comprehensive plans (Doty et al., 2009). For family plans, an increase in the income threshold by 100 percent of the poverty level would decrease premiums by 3.25 to 3.55 percent in small firms compared with 1.34 to 1.83 percent in large firms. The next section describes the history of the Medicaid program, expansion of SCHIP and the relevant literature. Section 3 presents the conceptual framework. Data and empirical models are discussed in Sections 4 and 5, followed by main results in Section 6. Discussions are presented in Section 7, and the last section concludes. 2. Background and Previous Literature 2.1. Medicaid and SCHIP Medicaid provides public health insurance to low-income families. When first introduced in the 1960s, Medicaid was tied to Aid to Families with Dependent Children (AFDC), a state-run cash welfare program which required beneficiaries: 1) to have low income, well below the federal poverty level; and 2) to be a single-parent family with children under the age of 18. As a result, only families that met both income and family structure criteria were eligible for Medicaid. The income eligibility thresholds varied across states, but on average, families with income below 60 percent of the poverty level 5 were eligible for Medicaid in 1987 (Cutler and Gruber, 1996). Multiple expansions in the 1980s made Medicaid available to more families. First, pregnant women who met the AFDC income standard became eligible regardless of presence of children 6. Further, states were allowed to increase the income thresholds in the late 1980s 7. In 5 The federal poverty level for a family of three is $19,530 in Deficit Reduction Act 1984 requiring states to cover children in families that meet the income standard for AFDC. 7 States were given the option to increase the income threshold above the AFDC limit for infants and very young children according to OBRA 1986,

8 1992, all children born after September 30, 1983 were made eligible for Medicaid up to 100 percent of the poverty level 8 ; younger children under age 6 and pregnant women were covered up to 133 percent of the poverty level 9. In 1996, AFDC was replaced by Temporary Assistance for Needy Families (TANF) as part of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), commonly referred to as welfare reform. Although TANF beneficiaries were not categorically eligible for Medicaid, they were automatically covered as long as income was below the AFDC thresholds in effect when TANF was enacted. SCHIP, signed into law as part of the Balanced Budget Act in 1997, was the largest expansion in terms of the federal funding in Medicaid history 10. SCHIP was designed to cover uninsured children in families with incomes too high to qualify for Medicaid but not high enough to afford private health insurance 11. Like Medicaid, it is jointly funded by the federal and state government and administrated by the states. States follow guidelines established by the federal government yet have more leeway in program design, eligibility and generosity. The expansion can take one of the three forms: establishing a separate SCHIP program, expanding the existing Medicaid program, or combining the two 12. States varied a great deal in both the time when SCHIP was implemented and the size of the expansion. Variation in program parameters across states allows for the identification of the 8 OBRA 1990 mandates states to cover children born after Sep 30, 1983 with a family income below 100 percent of the poverty level. 9 OBRA 1989 require states to cover pregnant women and children up to 6 years old, with income at or below 133 percent of the poverty level. 10 See footnote 3 11 Families with incomes above the SCHIP income thresholds were made eligible for Medicaid if they had very high medical expenses that were a large percentage of their income. 12 States opting to expand existing Medicaid simply increase the income threshold to higher levels. In this case, coverage is guaranteed even if SCHIP funding runs out. States that chose separate programs could offer different coverage packages; applicants for SCHIP are put on a waiting list when federal funds are used up. 8

9 effects of SCHIP on private health insurance premiums. Eight states enacted SCHIP when the law was passed in 1997; 34 states established their programs in 1998; and the remaining nine states created programs in 1999 and Thus, by the end of 2000, all 50 states plus the District of Columbia expanded their Medicaid programs and there was considerable variation in income thresholds, as shown in Table After this wave of expansion, the average income threshold for children increased from 121 percent to 206 percent of the poverty level (Ullman et al., 1999). As of 2000, the lowest income threshold is 133 percent of the FPL in Wyoming, compared with 400 percent of the FPL in Massachusetts and Tennessee. States also vary in their treatment of incomes that are counted towards the SCHIP threshold. For example, in 2008, Massachusetts, Oregon, New Jersey and many other states use gross income to determine eligibility, but other states allowed income disregards. Those states exclude part of monthly earnings or childcare expenditures from gross income when determining eligibility Crowd-out An issue with public health insurance expansions, especially for households with somewhat higher incomes who are more likely to have private insurance, is that it creates incentive for the eligible individuals to opt out of private coverage and pick up public insurance, a phenomenon termed crowd-out. This behavioral response raises the cost of the public program and may also affect private insurance premiums, which is the focus of this study. An extensive body of literature has tried to gauge the crowd-out effect of past Medicaid expansions, from expansions to pregnant women and parents with low income to expansions to 13 Under Medicaid before 1997, eligibility thresholds differ for different age groups within states. Income thresholds for younger children are higher compared to older age groups. The income threshold for those between the ages of 1 through 5 is shown in the table in For states that chose separate SCHIP programs, income thresholds may differ from that of Medicaid. I chose the maximum of the two to reflect the generosity of the expansion. 14 Income disregard rules for Medicaid and SCHIP may differ. The common types of disregard are AFDC standard. It deducts $90/month/worker, $175 per child of childcare expenses, and up to $50 in child support received. 9

10 children from higher-income families. The crowd-out estimates range from zero (Aizer and Grogger, 2003; Ham and Shore-Sheppard, 2005; Shore-Sheppard, 2008; Hamersma and Kim, 2012) to as high as 70 percent 15 (Cutler and Gruber, 1996; Blumberg et al., 2000; Sasso and Buchmueller, 2004; Gruber and Simon, 2008). Cutler and Gruber (1996) were among the first to examine the crowd-out effect. They used data from the Current Population Survey (CPS) to study changes in Medicaid coverage, ESI coverage and the uninsurance rate when pregnant women and more children were made eligible for Medicaid in the late 1980s. Cutler and Gruber realized Medicaid eligibility might be endogenous because there could be some immeasurable factors associated with both having low income and access to ESI. To address the endogeneity issue, eligibility is instrumented by a simulated eligibility, which reflects exogenous change stemming from differences in Medicaid rules among different age groups in states across years. This identification strategy became popular in the crowd-out literature. Cutler and Gruber concluded that approximately 50 percent of new Medicaid enrollees would otherwise have been covered by private insurance. Cutler and Gruber also noted that the CPS suffers from reporting errors because the interviews are based on a 15-month recall period while Medicaid eligibility is determined on a monthly basis. Ham and Shore-Sheppard (2005) analyzed Cutler and Gruber s (1996) methodology to a different dataset, the SIPP, but with the same specifications. They found smaller effects on Medicaid take-up and little evidence of crowd-out, which suggested the wide variation of crowd-out effects could partially be explained by the data set used. Cutler and 15 There are two ways to measure crowd-out effect. One is percentage of Medicaid enrollees who come from private insurance; the other is one minus percentage of Medicaid that comes from the uninsured. These two measures are identical if Medicaid and private insurance are exclusive. 10

11 Gruber (1996) also may have been affected by specification errors. After replicating Cutler and Gruber (1996), Shore-Sheppard (2008) showed that the estimated effects of Medicaid expansion on ESI coverage almost vanished after controlling for age-specific time trends. Gruber and Simon (2005) avoided the data issue by using SIPP, which is inclined to find small effects as demonstrated by Ham and Shore-Sheppard (2005), and addressed critics on the specification by adding an age-specific time trend. They concluded that crowd-out is 60 percent, which is in line with other studies focusing on SCHIP. Similarly, LoSasso and Buchmueller (2004) estimated a 50 percent crowd-out effect with an increase in the SCHIP take-up rate being 9 percent. Bansak and Raphael (2006) found a similar effect on take-up of SCHIP and 25 to 33 percent of crowd-out. Dubay and Kenney (1996) compared changes in private coverage for children and men in different income groups because men are not affected directly by the expansions. Nevertheless, men do not serve as a good control group because they may drop their own private insurance when their wives or children become eligible for Medicaid. Blumberg et al. (2000) focused on the Medicaid expansion in 1990 when children under the age of 6 from families with income below 133 percent of the poverty level were mandated to be covered by Medicaid. By applying a difference-in-differences methodology and comparing the insurance coverage of children who are eligible for Medicaid pre- and post- the expansion and controlling for secular trends 16, she estimated that 23 percent of new Medicaid enrollees would otherwise have had private insurance. In general, crowd-out estimates under expansions for adults are small. Aizer and Grogger 16 Secular trends of insurance coverage come from that of older children from low-income families who were not subject to expansions. 11

12 (2003) compared changes of coverage between individuals who became newly eligible for Medicaid with those who were consistently covered by Medicaid. Of newly eligible mothers, Medicaid take up is 2.7 percent with little effect on private coverage. Busch and Duchovny (2005) found a somewhat larger take-up rate (14.8%) and moderate crowd-out (24%). Hamersma and Kim (2012) applied a different identification strategy using income eligibility threshold rather than a dichotomous eligibility indicator. Applying income thresholds can take into account a possible differential effect of various initial income thresholds and is suggestive to policy makers. Hamersma and Kim found Medicaid take-up increases with no effect on private insurance coverage. It is not surprising the crowd-out effect varied widely given that the targeted populations for expansion were so much different. Studies focused on earlier Medicaid expansions, which relaxed the family structure requirements and made it available to low-income pregnant women and young children typically found zero crowd-out. Studies focused on SCHIP tend to find larger crowd-out effects; it is believed the crowd-out among children is between 25 and 50 percent (CBO 2007). Besides varying targeted populations, other reasons can also contribute to the large variation of crowd-out estimates, e.g., time of the expansions, data sets and identification strategies being used. Comparing to earlier Medicaid expansions, SCHIP covers children from relatively high-income families who are more likely to have access to private health insurance through employers, as illustrated in Table 2. Table 2 summarizes of children s ESI coverage across different income levels. ESI coverage rises with income. 75 to 90 percent of the children in families with income above 300 percent of the poverty level are covered by the ESI, compared with only 20 percent of poor families and 50 percent of families with incomes between one and two times the poverty 12

13 threshold Spillover Effects Public insurance expansions are not limited to affecting the targeted population, who are typically children and pregnant women from low-income families. Other family members could be affected indirectly by the public program expansions because they might be induced to make different decisions about insurance coverage for themselves and other family members. However, almost all the studies targeted the same population as the program expansions did. The spillover effects of public insurance expansion are generally under-studied in the literature. Covering more children could influence insurance coverage of their parents and siblings. Some parents might choose to enroll in private insurance just for the sake of their children. Once their children become eligible for Medicaid, those parents may decide to drop private coverage. Although Blumberg (2000) did not find spillover effects siblings, both Cutler and Gruber (1996) and Gruber and Simon (2008) found that parents with Medicaid eligible children are more likely to drop their own private coverage. Similarly, Buchmueller et al. (2005) found a negative effect between the SCHIP expansion and ESI take up among employees. On the other hand, expansions for adults may affect children s insurance coverage. This is not as intuitive since the income threshold for children is higher than that for adults. Nevertheless, the benefit of applying for Medicaid increases as more members in a family become eligible, which motivates applications for both children and parents. This is supported by Aizer and Grogger (2003) and Dubay and Kenney (2003) in which expansions to parents led to an increase in coverage rates for children. In sum, although these studies expand our understanding of spillover effects, they all 13

14 concentrated on Medicaid eligible families and focused on take-up decisions. In fact, the SCHIP expansion can spill over to Medicaid ineligible families by affecting private insurance premiums. Limiting to crowd-out effect and leaving out program ineligible families indicates the literature fails to fully explore the effects of SCHIP expansion. 3. The Impact of SCHIP on Private Health Insurance Premiums Private insurance premiums can be affected by SCHIP because crowd-out, induced by the SCHIP expansion, can alter the health composition of the ESI insurance pool. This study attempts to uncover the effect of SCHIP expansions on private insurance premiums Private Insurance premium Health insurance premiums are determined by two key elements: 1) expected average health care costs, which accounts for 80 to 90 percent of the premiums (Wickizer and Feldstein, 1995); 2) loading charges, including profit, administrative costs, and marketing costs. Expected health care cost for insurance companies depends significantly on enrollees health. Insurance companies treat small firms and large firms differently in estimating medical cost 17. Medical underwriting, a procedure used by insurers to determine premiums, is prevalent among small firms in most states 18. Under medical underwriting, insurers request medical histories from employees and covered family members. In contrast, experience-rating (basing premiums on average costs of past claims), is popular among large firms. Therefore, a change in the health composition of a firm would affect premiums. An ESI plan could offer multiple tiers; for instance, employee only, employee plus 17 Typically, holding everything else constant, premium for small firms are higher because there are fewer enrollees to share the fixed administrative costs. 18 Small firm is defined as firms with no more than 50 employees. 38 states allow medical underwriting. An alternative to medical underwriting is community rating, which requires insurance companies to charge the same premium for people living in the same area, regardless of health condition and employer. 14

15 spouse, employee plus children, and employee plus spouse and children. Each option is assigned a value, usually fixed over time, representing relative cost to a single option. In general, the premium for each option is determined as the following: once the entire cost of a plan is determined, insurance companies calculate a base premium based on the number of enrollees in different options and the relative values of each option. The base premium is essentially the same as the single option premium. In the end, the premium for each option can be determined by the relative value compared with the single option premium. It is worth emphasizing that, regardless of firm size, insurance companies have to charge the same overall premium to all eligible employees within a firm 19 for a same plan being offered 20. Consequently, any adjustments in premiums will apply to all employees. It is this feature of the group insurance market that leads to the effects on premiums spillover to untargeted families Mechanisms of Crowd-out Evaluating the consequence of crowd-out on premiums requires a better understanding of the mechanisms of crowd-out. Three reasons could contribute to crowding out: 1) employers stop providing health insurance; 2) employers ask employees to pay a higher share of the overall premium to induce them to take advantage of the public insurance; and 3) employees may refuse taking the ESI when offered. These paths of crowd-out point to various effects on the premiums. If employers stop providing ESI in response to the expansions, we would expect to see a decrease in private 19 Under medical underwriting, some states require insurance companies to charge the same premiums in a community. 20 Economic incidence of premiums is ignored in the paper. If fact, employees may end up paying different premiums if employers reduce wages more for some employees than for others. 15

16 coverage overall, while expecting little or no change in the private insurance premiums. However, premiums might change if some but not all employees drop out of the ESI, especially when those dropping out have better or worse than average health conditions. This can happen if employers increase employees share of the premiums, which causes price sensitive enrollees to drop out, or if some employees simply prefer Medicaid/SCHIP to ESI because of the cost savings, even if the premium share does not change at all. Detailed discussions are presented in Section 7. Among a limited number of studies discussing mechanisms of crowd-out, Cutler and Gruber (1996) shed some light examining employers likelihood of providing health insurance and employers cost-share plans with employees. The study found employers are as likely to provide health insurance after public insurance expanded, although these results are only suggestive because of data limitations 21. Shore-Sheppard et al., 2000, and Buchmueller et al., 2005, used firm level data and confirmed Cutler and Gruber s result. In addition, both studies concluded that crowd-out largely comes from employees turning down the ESI. The effect on premium cost-sharing between employers and employees is unclear. Cutler and Gruber (1996) and Buchmueller et al. (2005) found that employers are inclined to increase employees share of the premiums, while Shore-Sheppard et al. (2000) did not find such an effect. Even if employers do ask employees to pay more of the premiums, the literature on price sensitivity of insurance plan choices suggests employees are not responsive to the employee share of premiums in taking up decisions (Gruber and Washington, 2005). 21 Although Cutler and Gruber (1996) focused on workers as a whole rather than workers taking up ESI to avoid selection bias, the increase in women s labor supply because of Medicaid expansion 11 (Yelowitz QJE1995) (high income eligible threshold) introduce another fold of selection bias. It s reasonable to believe those who enter the labor market because of Medicaid expansion are less likely to take ESI if offered because they place a low value on private insurance (because their dependents are eligible for Medicaid). This selection problem will bias the result toward finding a big effect. 16

17 Crowd-out that stems from denying ESI because of the SCHIP expansion could directly affect the health composition of the insurance pool if the health status of those children who switched from private insurance to public insurance were different from the rest of ESI enrollees. On one hand, premiums would decrease if the private insurance pool were left with healthier enrollees. On the other hand, ESI premiums would increase if private insurance enrollees on the margin were low cost customers. Theoretically, the effect of the SCHIP expansion on private insurance premiums is ambiguous, leaving this question to be answered empirically. 4. Data 4.1. SIPP My analysis draws upon multiple datasets. Estimation of crowd-out is from 1996, 2001 and 2004 panels of SIPP, which covers the years from 1996 to SIPP is a panel survey that interviews households once every four months for three to four years. It gathers extensive information on personal characteristics, income and government program participation each month for the four months preceding the interview 23. By design, observations are at the individual-month level. However, SIPP suffers from seam bias because interviewees tend to report status change, e.g., insurance coverage, at the transition of two interviews (Young, 1989; Marquis and Morre, 1990). Thus, to mitigate seam bias, we follow standard practice and only use observations in the fourth reference month 24. The sample used in this study is at the person-interview level. 22 Individual level MEPS-IC data is not suitable for this analysis because it does not release state identifiers, which is necessary to this paper in which state by year variation is used to identify effects of SCHIP. 23 Relative to CPS with 15 months of recall period, SIPP is less likely to suffer from measurement error of reporting. 24 Ham and Shore-Sheppard, 2005; Gruber and Simon, 2008; Hamersma and Kim,

18 The sample is restricted to 0-18 year-old children with parents aged between 22 and 64 with income below 300 percent of the poverty level 25. The age restriction excludes parents who may be eligible for Medicare. States that cannot be separately identified are dropped, including North Dakota, South Dakota, Maine, Wyoming and Vermont. The SIPP sample is then augmented with the SCHIP income threshold data introduced in Section 4.3. The sample consists of 308,781 observations. Among them, 41 percent are covered by ESI and 36 percent are enrolled in Medicaid/SCHIP 26 (see Table 4). Private insurance coverage is lower, and the uninsurance rate is higher, than the overall population because the focus is on lower-income children Premium Data The premium data is from MEPS-IC, an annual, national representative survey collecting information on employer-sponsored insurance of establishments from both private and public sectors. MEPS-IC is uniquely suitable for carrying out the analysis for two reasons. First, the survey started in 1996 one year before SCHIP was signed into law; therefore, it covers pre- and post- periods of the SCHIP expansions. Second, it collects employers decisions to offer health insurance, number and type of health insurance plans being provided and more importantly, total premiums of plans, employees share of the premiums, and other employers characteristics. Although the firm-level data is not available for public release, data aggregated at the firm size level is reported annually for each state 27. Issues associated with aggregate data will be discussed 25 My cutoff of low-income is higher compared with past works because SCHIP expansions thresholds for some states reach 400 percent of the poverty level. Results are similar using lower cut offs percent of children report coverage from both Medicaid/SCHIP and ESI, which is counted as Medicaid/SCHIP coverage in this study because the transition to public insurance can be relatively prompt compared to ESI coverage usually being allowed in an open enrollment period, which is typically once a year. The results are similar when I count children under dual coverage as private coverage

19 in detail in Section 7. In the aggregate data, firms are grouped into five categories according to their sizes 28 ; thus there are five observations associated with each state every year 29. The sample spans from 1996 to The descriptive statistics of premiums by firm size and type of plans are reported in Table 3. Both overall premiums and the employees share of premiums are higher for family plans. The average overall premium for a family plan is $9,216 per year, which is more than twice the single plan premium. Employees pay almost 30 percent of the overall premiums toward family plans but less than 20 percent toward single plans 31. Average premiums in small firms are actually lower than in large firms despite the higher underwriting and overhead costs because small firms usually offer less comprehensive insurance (Doty et al., 2009) Medicaid and SCHIP Income Eligibility Threshold The SCHIP income thresholds in each state are collected from three separate sources: the KFF s Commission on Medicaid and the Uninsured, which reports thresholds in each year started from ; Mathematica Policy Research Inc. for thresholds in 1997 and 1998; and the Urban Institute for 1996 and The income thresholds are expressed in terms of percent of the federal poverty level, which may reflect gross income or net income, depending on states treatment of income disregards. SCHIP is more generous than it appears in states that set 28 The five categories are: 1-9, 10-24, 25-99, and over 1,000 employees. 29 States in years when those states cannot be uniquely identified because of budget issues of MEPS are dropped. Shortage of government funding did not allow collecting a large enough sample size in less-populated states. 11 states are dropped from 1996 to 2000, including: Delaware, Idaho, Montana, Nebraska, New Hampshire, New Mexico, North Dakota, South Dakota, Vermont, West Virginia and Wyoming. Eight states are dropped from 2001 to 2002, including: Alaska, Arkansas, District of Columbia, Idaho, North Dakota, Rhode Island, South Dakota and Vermont. 30 No MEPS-IC data are available for 2007 because of the transition from retrospective to current data collection. 31 Of course, employees indirectly pay the entire premium at least on average through reduced wages. However, employees decisions about take-up and plan choice depend only on the portion of the premium that they pay directly. 32 Those reports are compiled at 19

20 thresholds based on net income 33. The income thresholds usually vary by children s age under Medicaid, with higher thresholds applying to younger children than to older children. For infants, the average threshold for Medicaid eligibility was 168 percent of the poverty level. The income threshold was 152 percent of the poverty level for 1-5 year-old children and even lower for older children 34. Under SCHIP, however, income thresholds are the same for all age groups. The average net income threshold in terms of net income for the 1-5 year-old group is 205 percent of the poverty level 35. Because older children face lower thresholds, the overall average net income threshold or all ages is lower than for the 1-5 year-old group. The data is augmented with other state characteristics from the BLS, including median income, median age, unemployment rate, percentage of children under the age of 18 in poverty, percentage white, and percentage female. 5. Methodology 5.1. Effect on Private Insurance Premiums I exploit variations in time and magnitude of the SCHIP expansions to estimate the effect on premiums. This is done separately for the single and family plans by the following model: ln(prem) f, st, = β0+ β1 SCHIP st, 1 + X st, β + λ f + γ t + η s + φ f, t + θ f, s + ε f, st, (1) The dependent variable is either log of the average overall premium of family plans or single plans in firms of size f, in state s, and year t. Premiums are averaged over all enrolled employees working in the same firm size. This measure of premiums combines plan premiums 33 I thank Carole Roan Gresenz for generously providing me the data of income disregards of each state. By using this data, income thresholds can be lined up in gross income across states. 34 Income threshold may represent gross income or net income. 35 It is 237 percent of the poverty level, 32 percent of the poverty level higher, when taking into account income disregards. 20

21 and choices among plans when multiple options are available. The focus is primarily on single plan premiums because changes in family plan premiums may confound changes in premiums with other factors. For instance, a family plan holder may switch from a plan that covers both parents and children to a cheaper plan that covers parents only once children are covered by Medicaid/SCHIP. On the other hand, however, it is not intuitive that single premiums can be affected, because children are only under converge of family plans. The reason why single premiums are affected is because insurance companies do not separate single options and families options of a plan when determining premiums. 36 SCHIPs, t-1 stands for the income thresholds of SCHIP/Medicaid in state s at year t-1. The income threshold is lagged for one year to allow for time to adjust. Instead of using Medicaid eligibility status as a measurement of expansions, which is popular in the literature, I follow Hamersma and Kim (2012) in adopting income thresholds. Income thresholds are parameters over which policy makers have direct control in policy design and reforms. The coefficient on SCHIPs, t-1 can be interpreted as semi-elasticity of premiums with respect to income thresholds 37. As noted earlier, income thresholds are a function of children s age under Medicaid. I chose income threshold for the 1-5 year-old group as SCHIPs, t-1. is a vector of state s s specific characteristics in year t, including the unemployment rate, log of median income, median age, percentage white, percentage female, and percentage of children in poverty. is a set of dummies, which accounts for inherent differences among firms of various sizes. Year fixed effect,, controls for the national secular trend of premiums and overall macroeconomic factors. State fixed effect,, controls for time-invariant 36 This is benefit from the conversation with a health insurance expert at Syracuse University. 37 For example, if the SCHIP income threshold increases by one percent of the poverty level, the premium would change by 100 β1 percent. 21

22 characteristics of each state such as overall health conditions, residents attitudes toward insurance and means-tested transfer programs, and time-invariant disparities in the premiums across states. In addition, I also introduce two sets of controls: (1) is an interaction between firm-size and state that picks up heterogeneity of firms with the same size across states. (2) ττ ff,tt allows for specific time effects at firm size level; for example, small firms may be more vulnerable than large firms to an economic downturn. All premiums and average income variables are in 2008 dollars. Standard errors are clustered at the state level to account for possible correlation among firms within the same state Crowd-out A linear probability model is used to measure the crowd-out effect of SCHIP using SIPP data: (2) Two regressions are estimated separately for Medicaid/SCHIP coverage and ESI coverage. The dependent variables are dichotomous indicating Medicaid/SCHIP coverage and ESI coverage, respectively. As in regression (1), SCHIPs, t-1 is the SCHIP income threshold. The same set of fixed effects is also controlled. Other control variables including personal characteristics such as the maximum education level of children's parents, children's gender, race, type of families (female headed family, male headed family, headed by husband/wife), number of parents working in large firms (over 100 employees), number of working parents and a set of dummies indicating maximum age of parents are included in the regressions. In a linear probability model, β1 measures percentage point changes in insurance coverage. 6. Results 22

23 6.1. Crowd-out The estimation for crowd-out, displayed in Table 5, shows that if the income threshold increased by 100 percent of the poverty level, Medicaid/SCHIP coverage would increase by 3.7 percentage points with a parallel 1.5 percentage point reduction in ESI coverage. The increase in Medicaid/SCHIP coverage is significant at the 5 percent level, while the reduction of ESI enrollment is only significant at the 10 percent level. Therefore, we cannot rule out that there is no crowd-out effect in the aggregate. The point estimates suggest that about 40 percent of new Medicaid/SCHIP enrollees are offset by a drop in ESI enrollment, 38 which is in line with conclusions in the SCHIP crowd-out literature, in which 25 to 50 percent of the new Medicaid/SCHIP enrollees would have ESI coverage without SCHIP (CBO 2002). Thus, there is at least suggestive evidence that the SCHIP expansion affected the ESI pool The Effects on Premiums of the SCHIP Expansion Estimating equation (1) reveals the effect of SCHIP expansions on private insurance premiums, as shown in Table 6. The left panel displays the effect on single plan premiums and the right panel is for family plan premiums 39. In both panels, the first columns come from models that control only for year and state fixed effects. The second columns show the results with firm size by year and firm size by state fixed effects included. Both specifications produce negative and statistically significant effects on premiums, meaning increasing the SCHIP income thresholds decreases the total premiums for family plans as well as for single plans. Increasing the SCHIP thresholds by 100 percent of the poverty level results in a 2.3 percent drop in single-plan 38 It is possible that some or all of the drop in enrollment is due to other factors that are correlated with the SCHIP expansion 39 A certain health insurance plan often offers a couple of choices: single option, which covers employee only; employee plus one option, which covers employee and one dependent; and family option, which covers employees with more than one dependent. Plans are categorized into single and family plans by whether or not dependents are covered in MEPS-IC data. 23

24 premiums and 2.2 percent for family plans. Given that the income thresholds increased by 67 percent of poverty after SCHIP, these estimates imply a $59/year drop in single premiums and $137/year for family plans 40. Small at the first glance, these numbers do not necessarily imply trivial effects. In fact, the SCHIP expansion slowed down the upward trend of overall premiums by about one-third to one-half. The family-plan premium increased 5.6 percent per year and the single-plan premium increased 4.4 percent per year on average from 1996 to 2008 (MEPS-IC). Without SCHIP, the private premiums would have increased even faster. The negative effect on premiums supports the hypothesis that average covered health care costs declined as SCHIP expanded. In general, the health status of Medicaid/SCHIP enrollees is inferior to that of ESI enrollees. Table 7 compares health conditions of low-income children by type of insurance coverage. 5.2 percent of children with Medicaid/SCHIP coverage report fair or poor health compared to only 1.6 percent under ESI coverage 41. The differences are statistically significant at the 1 percent level 42. It is also consistent with studies that try to identify characteristics of children who transition from ESI to Medicaid/SCHIP. Shaefer, et al. (2011) found that these children are in relatively poor health condition compared with peers covered by private insurance. This evidence strongly supports the inference that the ESI insurance pool consists of healthier enrollees after SCHIP. All control variables have expected signs. The premium increases with firm size, because large firms typically offer more comprehensive health insurance plans (Doty et al., 2009). The premiums are positively correlated with median income. Medical services in high-income states are more costly to compensate for higher living costs 43. The premiums in elder states 40 The average overall premium is $3,829/year for single plans and $9,216/year for family plans in 2008 dollars percent of parents under Medicaid relative to 5.25 percent under ESI are in fair and poor health. 42 T-statistic is for children and for parents. 43 Per capita health care expenditures by states: 24

25 (population is composed of more aged people) are higher, because the elderly consume more health services. Per capita health spending was $7,787 for the year-old group, which was three times higher than for the 0-18 year-old age group in 2004 (Centers for Medicare and Medicaid Services (CMS)) Differential Effects on Premiums of SCHIP To obtain a more comprehensive understanding of the effects of SCHIP on premiums, interaction terms between the SCHIP thresholds and firm size are added to equation (1), which allows for differential effects on premiums by firm size. (2) The estimates of model (2) are presented in Table 8. Not surprisingly, the effect is more pronounced in small firms 45. The family-plan premiums drop by more than 3 percent in small firms for an increase in threshold by 100 percent of the poverty level, which is nearly twice as much for large firms. Reduction in single plan premiums comes entirely from small firms. Small firms are expected to be more sensitive to the SCHIP expansions for several reasons. First, per enrollee cost hinges greatly on each employee s medical expenditure because of medical underwriting, which is prevalent in small firms. Furthermore, small firms lack the ability to absorb shocks. Shocks of health status cannot be spread among a large number of employees as they would be in large establishments. Meanwhile, it is plausible that public insurance is more attractive to ESI enrollees in small firms than in large firms, because small 44 The report is available at: Downloads/2004-age-tables.pdf 45 Small firm is defined in this paper as firms with no more than 25 employees. Results are similar when cut off of small firms is set at 100 employees. 25

26 firms typically provide less comprehensive health plans (Doty et al., 2009). Therefore, small firms are more sensitive to changes in health composition triggered by crowd-out Discussions 7.1. Policy Endogeneity Validity of the estimates using variations in the SCHIP parameters across states is threatened by the possibility of policy endogeneity. The estimates would be biased if states decided to what extent to expand their public health insurance programs based on state-specific trends in premiums. The endogeneity issue can be addressed by controlling for state-year fixed effects. However, state-year dummies cannot be added to the regressions because the state by year variations in policy parameters are the source of the identification. I attempted to control for unobservable time variant geographic heterogeneity by including census division * year dummies in the regressions. Policy endogeneity is no longer a concern if omitted factors determining premium tendency that are correlated with states decisions in how much to increase the income threshold are constant at the census division level. However, bias may still exist if the endogeneity is at any level lower than census division. Results are robust to inclusion of the census * year dummies, as shown in columns 3 of Table Average Insurance Premiums The ideal dependent variable is the premium of each health plan, which reflects per capita spending for insurers of each plan. However, premiums of each insurance plan across states are not available. As an alternative, average premium at the firm-size level is used in the study. One 46 Changes in loading factor in premiums for small firms may also contribute to the change in premiums because fewer employees are covered by ESI after crowd-out. With a smaller pool, administrative costs per covered worker would increase, and thus also premiums. 26

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