Children's Health Insurance Influence on Family Structure. SaraJane Parsons. Michigan State University. Abstract

Transcription

1 Children's Health Insurance Influence on Family Structure SaraJane Parsons Michigan State University Abstract Since the late 1980s, Medicaid has been separating its eligibility from the Aid to Families with Dependent Children (AFDC) by eliminating eligibility restrictions to single-parent households. In 1997, the State Children's Health Insurance Program, SCHIP, an expansion of Medicaid, was established to provide health insurance to low-income children whose family income was just above the Medicaid income requirement. Using March CPS data, this paper describes effect of the implementation SCHIP on family structure decisions and children's living arrangements.

2 I. Introduction Medicaid is the largest means-tested program in the United Statesthat provides generous benefits to families and children. Beginning in the 1980s and 1990s, these generous benefits stopped having categorical requirement of single parent households. They have the potential to influence family structure decisions by providing low income single mothers a source of independence to remain single or incentive for marrying to continue receiving Medicaid benefits. The expansions in the last three decades, which include the State Children's Health Insurance Program (SCHIP) and the Affordable Care Act (ACA), granted health care eligibility to an entirely new group of working poor who are eligible to receive the direct benefit of health care coverage, but also may have incentive to change family structure, which has indirect implications for well-being. This paper looks at whether family structure changes when public health insurance benefits are expanded. Medicaid is a large in-kind transfer received by low-income individuals to provide health insurance for themselves and for their children. Whether health insurance is available for the child or the parent may affect a parent's decision to find a spouse since health insurance can decrease the cost of medical spending. (Finkelsten and McKnight, 2008, Sommers and Oellerich, 2013, and Selden, et al., 2015) If a single person does not have employer-provided health insurance, their options in the United States are limited to public healthcare, such as Medicaid or Medicare, or expensive private individual health insurance. The cost of health insurance, without Medicaid, could be shared with another individual if they marry. With Medicaid, or some other form of public insurance, health insurance is provided for the child, removing the incentive for a single parent to marry for health insurance coverage through a spouse. Medicaid's eligibility with respect to family structure has changed since the program's

3 inception in To be eligible for Medicaid, from its inception until the 1980s and 1990s, an individual also had to be eligible for other forms of cash assistance, particularly AFDC, a program that categorically required children to be deprived of parental support, mainly singleparent households. During the 1980s and 1990s, Medicaid separated its eligibility requirements from AFDC, eliminating the categorical requirement that the child must live in a single-parent household to qualify. While family structure was dropped from eligibility criteria, Medicaid experienced many expansions in financial eligibility, and as a result, many more individuals who might be eligible for it as a resource could also have this resource affecting their family structure decisions. After the expansions of the 1980s and 1990s, Medicaid raised the income eligibility level to a cut off higher than that of AFDC. Starting in July 1991, states were required to cover all children under age 19, born after September 1983, with incomes below 100 percent of the federal poverty line. Many states expanded their Medicaid programs even further, whether by covering older children or raising their income eligibility cut-offs higher than the federal mandate. These expansions caused the fraction of children eligible for Medicaid to be nearly a third by 1992, doubling the fraction that was eligible in 1984 (Currie and Gruber, 1996). In 1997, the State Children's Health Insurance Program (SCHIP) was established specifically to increase health coverage for lowincome children, specifically among children to those who were low income, but not eligible for Medicaid. While this public coverage will directly affect the child's well-being, the decision of their parents to marry, stay married, or separate will also affect the child's well-being. In general, children living with a single parent are associated with greater behavioral problems and lower educational attainment. If a child lives in a household with cohabiting parents (one biological

4 and one nonbiological), this may negatively affect the child's well-being relative to a child living with both biological parents. (McLanahan and Sandefur, 1994 and Case, Lin, and McLanahan,2000). This paper describes the effect of SCHIP's expansion of Medicaid on family structure at the state level for the population of interest. Using March CPS data, the sample is restricted to women with children and household incomes below 400 percent of the Federal Poverty Line (FPL) and then constructed into rates by state and year to measure the possible flows into and out of marriage. The paper proceeds as follows: the next section describes SCHIP, followed by a section on conceptual framework with emphasis on how family structure can be affected. The fourth section gives a review of the literature. The fifth and sixth sections describe the data and the changes in eligibility, and marriage associated with SCHIP. II. Institutional Details of SCHIP In 1996, the Personal Responsibility and Work Opportunity Act (PRWORA) made major changes to welfare, changing requirements to focus on keeping children in homes of families and relatives and encouraging the formation of two-parent families. In the following year, the Balanced Budget Act of 1997 established the State Children's Health Insurance Program (SCHIP) to provide health insurance coverage to children in low-income families whose total family income was too high to qualify for Medicaid. The goal of this legislation was to increase the insurance coverage of children, targeting children in families with incomes too high for Medicaid but too little to afford private health insurance. Federal funding is allocated among states on the basis of the number of children in low-income families in the state, the number of those children that are uninsured and the wages in the health services sector in the state relative

5 to the national average (Congressional Budget Office, 2007). 1 With this freedom, states vary in the amount and how they use the funds. States are allowed to provide SCHIP coverage three different ways. They can provide a program separate from Medicaid, expand Medicaid to meet the new requirements, or they can provide some combination of the two. (Rosenbach, et al., 2001) Table 1 shows which version of the expansion each state used, as well as when they implemented their expansion. Each state is paid a matching rate for its SCHIP expenditures up to the total of their allotted amount. If states provide SCHIP funding through an expansion of their Medicaid system and they spend more than their allotted amount, they will continue to receive their federal funds to support their Medicaid program, but these funds will be provided at the Medicaid match rate instead of the higher SCHIP match rate. States that offer a separate program for SCHIP receive no additional federal funds once they have exhausted their allotment and must use state funds. However, states that choose to offer a SCHIP program separate from Medicaid can restrict their state spending on this program through capping enrollment or increasing insurance premiums, which is not allowed for the states that offer SCHIP as a Medicaid expansion. (Congressional Budget Office, 2007) Table 2 shows how states used SCHIP funds to increase income eligibility cutoffs and how they used these funds, whether as an expansion of Medicaid, establishing a separate SCHIP program or a combination of both of these. To be eligible for SCHIP, the federal government requires states to cover children in families with income up to 200 percent of the federal poverty level or 50 percentage points above the state's Medicaid threshold. States can disregard certain types of income and expenses when determining eligibility(buchmueller et al., 2015). In 2006, the lowest eligibility threshold was 1 From when the funds are allotted by the federal government, states have three years to spend their funds, or unspent funds may be redistributed to other states who request more funding.

6 140 percent of the federal poverty level (North Dakota), while the highest was 350 percent of the federal poverty level (New Jersey). Some states have applied and received waivers to expand SCHIP coverage to adults such as parents, related caretakers, legal guardians and pregnant women (13 states). In general, if a child is enrolled in SCHIP, they receive the same benefits as a child in Medicaid. This includes coverage for: inpatient services, emergency hospital services, outpatient hospital services, physician services, X-ray and laboratory services, immunizations, well-baby visits, well-child visits, inpatient and outpatient mental health treatment, vision screening, and prescription drug benefits. (Rosenbach et al., 2003) States with a separate program for SCHIP, rather than an expansion for Medicaid, may include more services such as dental and corrective lenses coverage. States were also encouraged to implement requirements to anti-crowd out measures, such as waiting periods, since private insurance coverage increases with income. Recent literature on SCHIP find large estimates of crowd-out, around 50% (Schuttringer, 2015, LoSasso and Buchmueller, 2004 and Gruber and Simon, 2008), implying that some of the take-up of SCHIP comes from parents who already had private insurance. III. Conceptual Framework Since SCHIP further increases Medicaid benefits to families just above the income cutoffs for Medicaid and does not have a family structure requirement, giving a child health insurance would increase the size of the budget set for both a single parent and a married parent. This expansion reduces the cost of the child, as well as the cost of forming a two-parent family, since benefits are extended to both family types. Increasing the Medicaid benefits to families who were previously unable to receive these benefits, may open family structure options (marriage or divorce) that were previously unavailable to these families.

7 By expanding the budget constraints, this may encourage a single mother to marry, since they would still have access to health insurance through SCHIP and would be able to increase their budget constraint with their spouse's income as well. Figures 1 and 2 show where this expansion increases the budget set for either single mothers or married mothers, respectively. In Figure 1, if the single mother does not work, she can collect welfare, food stamp benefits and Medicaid. As she starts to work, these benefits decrease, with benefits from other forms of welfare ending at H1. As a result of the expansions of the late 1980s and 1990s, the single mother's eligibility for Medicaid ends at a higher level of income H2. SCHIP expands this cut off even further, to H3, Single mothers who were not eligible previous to the expansion, for example at point A, now have access to health insurance for their child. The shaded green area, ABCD, represents this expanded budget sets for single mothers. Similarly for married mothers, shown in Figure 2, the expansions of the 1980s and 1990s resulted in an initial expansion of her budget set, causing mothers at point T to become eligible, now married mothers at point Q will also be eligible to receive health insurance for their child. Marriage could also cause a child to become ineligible for SCHIP, if the marriage is to someone whose income could increase the child's total family income to over the eligibility cutoff. Similar to the Medicaid expansions in the 1980s and 1990s, SCHIP can also allow a married mother to become divorced, an example of the independence effect, because of the increases to the budget set for a single mother. To help illustrate this change in eligibility, Table 3 takes a hypothetical family of four and see if they would be eligible for Medicaid or SCHIP. For sake of brevity, I assume that if the mother or single male is working they make 200 percent of the Federal Poverty Line. This compares both whether a mother of two would be eligible if she was married or single and whether she or the prospective spouse are working. In this table, I use six

8 different states: Colorado, Georgia, Idaho, Illinois, Missouri, and New Jersey. These states provide a variation in their income eligibility limits before and after SCHIP was implemented and how they implemented SCHIP, whether it was an expansion of their Medicaid program, a separate SCHIP program or a combination of the two. Idaho and Missouri used the federal funds from SCHIP to expand their Medicaid programs. Idaho had a low income cut off before and after they implemented SCHIP, only increasing from 133 percent FPL to 150 percent FPL. Missouri expanded their Medicaid program from an income cut off of 133 percent FPL for children to 300 percent FPL, resulting in both single mothers being eligible for Medicaid in the state as well as more Married families with incomes above the previous Medicaid cut off. Colorado and Georgia created separate SCHIP programs in their states, at varying levels of income cut-offs. Colorado met federal requirements by increasing their eligibility cut off for SCHIP to be 50 percentage points FPL higher than Medicaid, an increase from 133 to 185 percent FPL. Georgia used the SCHIP funds for a major expansion in eligibility, increasing their income cut off around 100 percentage points FPL, from 133 to 235 percent FPL. States also had the opportunity to create a combination of expansions, both to their Medicaid program and implement a separate SCHIP program. New Jersey increased their Medicaid cut offs from 133 percent FPL to 185 percent FPL and created a separate SCHIP programs in their state, increasing income cut offs even further to 350 percent FPL. Illinois increased the cut off for their separate SCHIP program to 185 percent FPL, meeting the new federal requirement, but left the cut off for Medicaid itself at 133 percent FPL. Throughout this table, there are four main transitions that could occur with respect to family structure. First, single mothers are now still eligible for health insurance for their child if

9 they marry, and may choose to marry. Second, some married women whose children may qualify for SCHIP if they divorce and become single. Third, there are single mothers who will choose to stay single, as if they were to marry, they would still loose health insurance for their child. And finally, some married women will choose to remain married, as they do not qualify for public health insurance for their child, whether they are single or married (represented by high income women not listed on the table). As I currently have these structured, the hypothetical families affected are those with a total family income between 200 and 400 percent FPL, however, more variation would be found with finer cut offs for families anywhere between 133 percent FPL and 350 percent FPL, the range that the income cut offs for Medicaid and SCHIP. When considering how increases in generosity can affect marital decisions, those who live in more generous states will have more options with respect to their eligibility. Much of the variation in eligibility by family type comes from whether the mother or her spouse are working and how much income they are bringing to the household. If either the mother or the spouse are not working, this expansion allows the couple to marry, since now their spouse's income will not make them ineligible for Medicaid. However, if it is the mother who is working, this expansion also gives her the option to remain single and still receive Medicaid. For those families with two working parents, there are cases where the mother would be eligible for Medicaid if she were not married to their spouse. With new bundles available for both budget sets, for both married and single mothers, the overall effect of the expansions on family structure decisions is theoretically ambiguous. However, there may be a differential effect based on the generosity of the increase in the income cut offs. While some states only met the the federal requirements for increases, others almost

10 doubled their income cut-offs, an effect that would drastically change the budget set of both a single parent and a married parent. Few states that began with higher Medicaid income cut-offs did not have large increases in their cut-offs under SCHIP. For example, Minnesota used SCHIP funds to expand Medicaid program, but they already met the income requirement prior to the implementation of SCHIP. Using this variation within the state, I hope to measure the effect of these changes in generosity on marriage rates within the state. IV. Literature Review This paper draws from three main literatures and their effects on family structure. A large literature examines the effects of welfare and family structure with many papers using Aid to Families with Dependent Children (AFDC), now Temporary Aid to Needy Families (TANF), to estimate the effects of the incentives toward remaining unmarried to receive these benefits. A smaller literature focuses specifically on the effect of Medicaid and family structure and other outcomes such as labor supply decisions and crowding out effects on private insurance are also discussed. The decision to marry is also affected by the presence of health insurance, whether public or employer-provided. What follows is a brief discussion on these literatures. A. Welfare and Family Structure The eligibility requirements in the United States welfare program have evolved over time, including their family structure requirements. What started as a group of programs that were aimed at low-income single parents, most of these programs have expanded to include married or cohabiting couples, especially when a child is involved. These eligibility changes have been studied with respect to AFDC (now TANF), by many, but I will be focusing on Bitler, et al., (2004) and Moffitt, et al., (2015). Bitler, et al. (2004) use state-level data to measure the flows into and out of marriage

11 during the time period around the creation of TANF. Prior to the creation of TANF, many states were granted waivers to expand AFDC leading into the creation of TANF. The authors find that states who used these expansion waivers before the creation of TANF saw a reduction in transitioning into marriage and from marriage to divorce. Moffitt, et al. (2015) re-examine the effects of welfare reform around this time period on changes in family structure, focusing on the correct classification of family structure, whether the biological father is in the household, not whether the parents are married, single, or cohabiting. Using data from the 1996 and 2004 panels of the Survey of Income and Program Participation (SIPP), they estimate the effect of TANF (through variation in specific policies used) to find a reduction in single parenthood and an increase in mothers partnering with the biological fathers, primarily through marriage. B. Medicaid Decker (2000) uses the variation in state implementation of Medicaid between 1966 and 1970 to estimate the effect of the Medicaid program on childbearing and marital status. She finds that the introduction of Medicaid lead to a significant increase in the likelihood of single mothers. She estimates that Medicaid led to a 10% decrease in the probability of a woman being married, since it was still aimed at single parents, implying that the decision to marry is influenced by the ability to acquire health insurance, as well as other benefits for the mother or child. In the 1980s and early 1990s, Medicaid eliminated the requirement that a child live in a single-parent household and increased the income eligibility limit to above that of AFDC. Yelowitz (1998) estimates that this policy change in addition to extending Medicaid benefits to all children in a household increases the probability of marriage by 1.7 percentage points.

12 However, he finds that this result can differ between states, based on the prior generosity of their welfare programs. States that have high AFDC benefits had a stronger effect (more women were married after Medicaid permitted married couples) and states with lower AFDC benefits had a smaller effect, implying that some of these women were getting divorced since the Medicaid expansions increased the benefits, and thus the benefits of being single. He finds a small independence effect that appears to be part of the effect of Medicaid on marriage. Since Medicaid is the largest means-tested program in the United States, see Buchmueller, et al. (2015) for a recent summary of the broader literature, much of the research on this program has focused on the effectiveness of the program, eligibility impacts on coverage, crowd-out, and labor supply. Some of the existing literature provides useful insight into sources of identification for the effect of SCHIP on family structure. Currie and Gruber (1996) study the effect of Medicaid for children on their health outcomes thought the expansions in Medicaid of the 1980s and 1990s. Using a simulated instrument to capture the variation in each state's generosity of their Medicaid program, the authors find that making children eligible for Medicaid has significant positive effects on the utilization of medical care (lower probability of the child not visiting a doctor in the last year). C. Health Insurance and Family Structure Health insurance can lead to what the literature calls marriage lock, those who marry and stay married to receive health insurance. Becker (2015) uses a sample of cohabiting individuals to determine a relationship between health insurance coverage and the timing of marriage. Her results suggest that having private health insurance is associated with a higher probability of marriage, however, this can be explained by selection of those with socioeconomic status to marry, as they are more likely to have private health insurance coverage. At the other

13 end of the transition, Sohn (2015) finds that people who are insured through their spouse are at a lower risk of divorce, especially for those who did not have an alternative source of healthcare (outside of job or marriage). Using data from the 2004 panel of the SIPP, the author uses a hazard model to estimate divorce hazards for individuals who were married at some time during the panel. Those who were dependent on their spouse for health insurance were almost 70% less likely to divorce than those who had insurance in their own name. Abramowitz (2015) examines the relationship between the ACA young adult provision, which expanded parental coverage for young adults through age 25, and marriage using pooled 2008 to 2013 American Community Survey data. Using a difference-in-differences framework, she finds that this provision of the ACA is associated with a decrease in the likelihood of young adults marrying, roughly a 9 percent decrease. She also finds an increase in the probability of divorce and a decrease in cohabitation and spousal health insurance coverage in the affected age group. IV. Data I use data from the Current Population Survey's Annual Social and Economic Supplement (CPS ASEC) for survey years This data set includes demographics on family structure at the time of the survey, as well as income from the previous year to help determine eligibility. The CPS also includes summary variables on health insurance coverage, including private, public, and any other Medicaid program; data specific to SCHIP begins in The CPS ASEC also began using a verification process in 2000 to confirm if the individual had coverage. The sample of women used includes all married and single women between 18 and 55 with at least one child younger than 17 present and a total household income less than 400

14 percent FPL. Table 4 provides descriptive statistics for the women in 1997 and 2002 before and after SCHIP was enacted. Using this population, I aggregate at the state-level by year for the descriptive evidence. The summary statistics of this aggregation are presented in Table 5. V. Descriptive Evidence With the increase in income cutoffs provided by SCHIP, we would expect to find an increase in the number of people eligible for SCHIP. Table 6 shows that the percent of the sample eligible for SCHIP (and Medicaid) has stayed at about 50 percent for this sample, peaking in 1998, the year before most SCHIP programs were enacted. There does appear to be an initial increase with the implementation of SCHIP, then a return to the average before SCHIP by Table 7 presents the marriage rates by eligibility level before and after the implementation of SCHIP. Overall we see a much higher, and a slight increase in the marriage rate for those who are not eligible for Medicaid or SCHIP over time, which is what we expect. Those who are not eligible for either of these programs have total family incomes higher than the eligibility cut offs, and we expect to see higher marriage rates among those with higher incomes. Among those who are eligible for Medicaid, and then SCHIP, Marriage rates slightly decreased. This could arise from the opportunity for single mothers to remain single longer, or become a single parent if the mother was previously married. But without controlling for other covariates, this is just strictly speculation. Table 8 shows the results of running basic OLS regression using the amount of the change in the income cut off as the variable of interest. Column 1 only includes an indicator variable for the year being post SCHIP implementation, the amount of the change in the income cutoff in that year, and the interaction of the two. The coefficient on the amount of the change in

15 the income cut off implies that for every 10 percentage point increase of the income cut off (in terms of FPL) would result in a decrease in the marriage rate of.5 percentage points. Since this is only estimated in the years of any change, this effect quickly becomes insignificant as we control for other factors in the state, such as the unemployment rate and the share of black population in the state, shown in column 2. Column 3 further controls using state and year dummies. As these are strictly preliminary regressions, I would not trust the results reported here. I would like to try different identifying variables as well as adding in more demographics to control for differences between states, such as labor market conditions or generosity of other benefits. Other state factors that may affect marriage rates in that state such as share fraction of the population that is Hispanic, lives in metropolitan areas, or the distribution of age for females in the state.

16 Figure 1. Single Mother's Budget Set. The green shaded area represents new bundles available after SCHIP

17 Figure 2. Married Mother's Budget Set. The green shaded area represents new bundles available after SCHIP

18 Table 1: Summary of SCHIP expansions, by state for the years 1996 and 2002 State Expansion Type Date Implemented % FPL eligibility % FPL eligibility cutoff, 1 5 year olds cutoff, 15 year olds AK M March AL C February AR M October AZ S October CA C March CO S April CT C July DC M October DE S October FL C April GA S September HI M January IA C September ID M October IL C January IN C October KS S July KY C July LA M November MA C October MD C July ME C August MI C May MN M September MO M October MS C March MT S January NC S October ND C October NE M May NH C May NJ C February NM M March NV S October NY C April OH M January OK M December OR S September PA S June RI M October SC M August SD C July TN M October TX C July UT S August VA S October VT S October WA S January WI M April WV S July WY S April

19 Table 2: Variation in States across Eligibility increases and SCHIP Expansion Type Medicaid Expansion No Increase in Eligibility (>50% FPL) ID, MN, RI, SC, TN, WI Increase in Eligibility Met Federal Requirements (covers 200% FPL or 50% higher than state Medicaid) AK, AR, DC, HI, LA, NE, NM, OH, OK Separate SCHIP MT, OR, WY AZ, CO, DE, KS, NC, NV, PA, UT, VA, WA, WV Combination ND AL, FL, IA, IL, IN, KY, MA, ME, MI, MS, SD, TX Major Eligibility Expansion (>100% FPL) MO GA, VT CA, CT, MD, NH, NY, NJ

20 Table 3: Eligibility Differences for a Hypothetical Family in Six States Medicaid Expansion Missouri Idaho Income Major Eligibility Expansion No change in Eligibility Families (as a percent of FPL) Medicaid SCHIP Medicaid SCHIP Mother of Two 0 Yes Yes Yes Yes Single Male 200 No No No No Marriage, Family of four 200 No Yes No No Mother of Two 200 No Yes No No Single Male 0 No No No No Marriage, Family of four 200 No Yes No No Mother of Two 200 No Yes No No Single Male 200 No No No No Marriage, Family of four 400 No No No No Separate SCHIP Georgia Colorado Income Major Eligibility Expansion Met Federal Requirements Families (as a percent of FPL) Medicaid SCHIP Medicaid SCHIP Mother of Two 0 Yes Yes- MedicaidYes Yes- Medicaid Single Male 200 No No No No Marriage, Family of four 200 No Yes- SCHIP No No Mother of Two 200 No Yes- SCHIP No No Single Male 0 No No No No Marriage, Family of four 200 No Yes- SCHIP No No Mother of Two 200 No Yes- SCHIP No No Single Male 200 No No No No Marriage, Family of four 400 No No No No Combination SCHIP New Jersey Illinois Income Major Eligibility Expansion Met Federal Requirements Families (as a percent of FPL) Medicaid SCHIP Medicaid SCHIP Mother of Two 0 Yes Yes- MedicaidYes Yes- Medicaid Single Male 200 No No No No Marriage, Family of four 200 No Yes- SCHIP No No Mother of Two 200 No Yes- SCHIP No No Single Male 0 No No No No Marriage, Family of four 200 No Yes- SCHIP No No Mother of Two 200 No Yes- SCHIP No No Single Male 200 No No No No Marriage, Family of four 400 No No No No

21 Table 4: Summary Statistics for Women mean mean VARIABLES (sd) (sd) Age (9.644) (9.225) Married (0.499) (0.486) Total Household Income 26,468 37,002 (19,952) (23,795) Total Household Income (as Percent FPL) (119.8) (116.7) Family Size (1.838) (1.681) Number of Own Children in the Household (1.084) (1.147) Number of Children less than age (0.697) (0.736) Eldest Child's age (7.930) (7.522) Youngest Child's age (7.165) (6.926) Black (0.371) (0.319) Unemployed (0.248) (0.255) Labor Force Participation (0.476) (0.484) Any Health Insurance (0.464) (0.453) Health Insurance Through Employer (0.494) (0.500) Health Insurance- Purchased (0.206) (0.235) Health Insurance- Public (0.439) (0.408) Health Insurance- Other Public (0.420) (0.377) SCHIP (0.116) Observations 1,078 3,075

22 Table 5: State Summary Statistics VARIABLES mean S.E. Marriage Rate Median Household Income (as percent FPL) Eligible Child in the Household Maximum Income Cut Off Health Insurance Coverage Change in Income Cut Off Labor Force Participation Unemployment Rate Employment Rate Black Observations 505

23 Table 6: Eligibility by Year Note: Counts in parentheses. Includes Eligibility for Medicaid as well as SCHIP Eligibility Not Eligible Eligible Total (472) (482) (954) (408) (532) (940) (441) (505) (946) (458) (575) (1033) (1178) (1131) (2309) (1199) (1160) (2359) (1382) (1289) (2671) (1268) (1251) (2519) (1287) (1240) (2527) (1235) (1221) (2456) (1266) (1245) (2511) Total (10594) (10631) (21225)

24 Table 7: Marriage Rates of Families by year and eligibility Note: Counts presented in Parentheses Eligibility Marriage Rate Marriage Rate Not Eligible (264) (1056) Eligible (174) (617) Observations

25 Table 8: OLS Regressions Using Change in Cutoff amounts Marriage Rate (1) (2) (3) Post Implementation * (0.0485) (0.0403) (0.0359) Change in Cutoff ** ( ) ( ) ( ) Post Implementation*Change in Cutoff ( ) ( ) ( ) Black *** *** (0.0350) (0.107) Unemployment Rate * ** (0.160) (0.129) Constant 0.509*** 0.547*** 0.582*** (0.0502) (0.0436) (0.0655) Observations R-squared Year Controls NO NO YES State Controls NO NO YES Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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