Has Public Health Insurance for Older Children Reduced Disparities in Access to Care and Health Outcomes? Janet Currie, Sandra Decker, and Wanchuan Lin AcademyHealth Washington D.C. June 10, 2008

Disclaimer The findings and conclusions in this presentation are those of the authors and do not necessarily represent the views of the National Center for Health Statistics or the Centers for Disease Control and Prevention.

Background Children of lower income parents are in worse health compared to other children. Children of lower income parents are in worse health compared to other children. This fact is even more true for older children compared to younger (Case, Lubotsky, and Paxson (CLP), American Economic Review, 2002) This fact is even more true for older children compared to younger (Case, Lubotsky, and Paxson (CLP), American Economic Review, 2002) Since nearly one in five U.S. children live in poor families, it is important to understand how low income results in poor health and why this relationship is even stronger for older children than younger. Since nearly one in five U.S. children live in poor families, it is important to understand how low income results in poor health and why this relationship is even stronger for older children than younger.

Question #1: Has the relationship between income and child health changed over time? Has it changed differently for children of different ages?

Data National Health Interview Survey (NHIS),  After dropping children missing information on key variables such as race or health status and children whose age and recorded birth month and year don’t match, our final sample size consists of 475,517 children under age 18 from NHIS  We use NCHS income imputations for , and perform our own income imputations for using a method similar to the method used by NCHS for  Following CLP, we assign precise incomes to income brackets (by education of reference person or spouse) using the March Current Population Survey (CPS)

Ages 0-3Ages 4-8Ages 9-12Ages Log Family Income ($1986) [0.011]*** [0.010]***[0.011]*** * Time Period [0.013] [0.014][0.015] * Time Period [0.014][0.013] [0.014]*** * Time Period [0.013] [0.013]*** Observations101,386136,677109,033128,420 The Effect of Income on Child Health The table reports coefficients and standard errors (in brackets) from ordered probit models where the dependent variable is child self-reported health. Although not reported, controls include year, state and age effects, as well as (log of) family size, whether mother and father present, race (white, black), mother's age, father's age, mother's education (less than 12 or 12 years), father's education (less than 12 or 12 years), and whether the mother or father is unemployed. * significant at 10%, ** significant at 5%, ***significant at 1%. Ordered Probit (1=Excellent, 2=Very Good, 3=Good, 4=Fair, 5=Poor)

Ages 0-3Ages 4-8Ages 9-12Ages Log Family Income ($1986) [0.004]*** * Time Period [0.005] [0.006] * Time Period [0.006][0.006]**[0.006][0.006]** * Time Period [0.006][0.005][0.006]*[0.006]*** Observations101,386136,677109,033128,420 The Effect of Income on Child Health The table reports coefficients and standard errors (in brackets) from linear probability models estimating the probability that a child is in less than excellent health. Although not reported, controls include year, state and age effects as well as (log of) family size, whether mother and father present, race (white, black), mother's age, father's age, mother's education (less than 12 or 12 years), father's education (less than 12 or 12 years), and whether the mother or father is unemployed. * significant at 10%, ** significant at 5%, ***significant at 1%. Linear Probability (Dependent Variable: Less Than Excellent Health)

Question # 2: What explains the decline in the relationship between income and health for older children?

Medicaid/SCHIP Eligibility 1. Historically, a child’s eligibility for Medicaid was tied to the receipt of cash welfare payments under the Aid for Families with Dependent Children (AFDC) program. 2. Beginning in 1984, Congress expanded Medicaid coverage to pregnant women, infants and younger children not on welfare. By April 1, 1990, states were required to offer coverage to all children up to age six in families with income up to 133% of the Federal poverty level. 3. OBRA 1990 required states to increase eligibility of older children under the poverty level by one more year of age per year for children born after September By 2001, all poor children were required to be made eligible. 4. SCHIP initiated in 1996 provided an additional source of public health insurance for low income children.

Empirical Specification (2)y = b 0 + b 1 PUBINS + b 2 INC + b 3 INC*AGE9 to 17 + b 4 INC*T_ b 5 AGE9 to 17*T_ b 6 INC*AGE9 to 17*T_ b 7 X + b 8 STATE + b 9 YEAR + b 10 STATE*AGE9 to 17 + e, wherePUBINS indicates that the child is eligible for public health insurance,AGE9 to 17 indicates that they are aged 9 to 17 and T_ indicates that it is the second half of our time period.

Endogeneity of PUBINS  Persons who are eligible for public health insurance may be different from others in unobserved ways  For example, a sick child may cause lower parental income, leading to a spurious correlation between public health insurance eligibility and poor health.  We therefore use a two-staged instrumental variables approach to estimate the effect of Medicaid/SCHIP eligibility (PUBLINS) on child health

Simulated Medicaid/SCHIP Eligibility  We generate Medicaid/SCHIP simulated eligibility by sampling 500 children by single year of age and calendar year from the CPS, and then calculating the fraction of this fixed group of children who would be eligible for Medicaid/SCHIP in each state and year.  Instrument for individual eligibility using the simulated eligibility.  Goal: Abstract from characteristics of the child and or family that may be correlated with both eligibility and the dependent variables to achieve identification using only legislative variation in Medicaid/SCHIP policy.

Dependent Variable: OLS TSLS Medicaid/SCHIP Eligible [0.004]***[0.020] Log Family Income ($1986) [0.002]***[0.003]***[0.006]*** Log Family Income ($1986) * Ages [0.003]*** Log Family Income ($1986) * [0.003] Ages 9-17 * [0.040][0.040]*[0.042] Log Family Income ($1986) * Ages 9-17 * [0.004]**[0.004]***[0.004]** First Stage F-statistic4,125 P-value for first stage F-statistic0.000 The Effect of Medicaid/SCHIP Eligibility on Child Health The table reports coefficients and standard errors (in brackets) from linear probability models estimated using ordinary least squares (OLS) and two staged least squares (TSLS). Other than those indicated in the table, control variables include age effects, year effects, state effects, state effects interacted with age group, and (log of) family size, whether mother and father present, race (white, black), mother's age, father's age, mother's education (less than 12 or 12 years), father's education (less than 12 or 12 years), and whether the mother or father is unemployed. The sample size is 475,516 for less than excellent health and 378,233 for no doctor visit in the past year. * significant at 10%, ** significant at 5%, ***significant at 1%. Less Than Excellent Health Linear Probability Models

17 Question #3: Does Medicaid/SCHIP eligibility in early childhood have positive future effects on child health?

Lagged Medicaid/SCHIP Eligbility  Since health is a stock, it is affected by past investments as well as current ones.  It may not be very surprising that contemporaneous Medicaid/SCHIP eligibility has little effect on overall health status.  Therefore, we investigate whether Medicaid/SCHIP eligibility in early childhood puts children on a better health trajectory, resulting in better health at older ages.

Empirical Specification …reduced form models… Because we are interested in the effect of lagged health insurance eligibility on the health status of older children, we estimate these models using only the children aged These models are of the form: (3) y = b 0 + b 1 PUBINS + b 2 INC + b 3 INC*T_ b 4 X + b 5 STATE + b 6 YEAR + e, where PUBINS is now a measure of the fraction eligible for public health insurance when the child was age 0, age 1, age 2, etc. Eligibility at each age is included in a separate regression since there is a good deal of multicollinearity between eligibility at various ages.

Reduced Form Models Controlling for Simulated Eligibility at: Age 0Age 1Age 2Age 3Age 4Age 5Age 6Age 7Age 8 Dependent Variable: Less Than Excellent Health Lagged Simulated Eligible [0.024] [0.022]*[0.024]**[0.023]*[0.022][0.020] Log Family Income ($1986) [0.003]*** Log Family Income ($1986) * [0.003]*** The Effect of Medicaid/SCHIP Eligibility and Lagged Medicaid/SCHIP Eligibility on Health of Children Aged 9-17 The table reports coefficients and standard errors (in brackets) from linear probability models. Other than those indicated in the table, control variables include state, year and age effects, and (log of) family size, whether mother and father present, race (white, black), mother's age, father's age, mother's education (lesss than 12 or 12 years), father's education (less than 12 or 12 years), and whether the mother or father is unemployed. The sample size is 237,453. * significant at 10%, ** significant at 5%, ***significant at 1%. Linear Probability Models

Reduced Form Models Controlling for Simulated Eligibility at: Age 0Age 1Age 2Age 3Age 4Age 5Age 6Age 7Age 8 Dependent Variable: No Doctor Visit in the Past Year Lagged Simulated Eligible [0.020]***[0.023]***[0.022]*** [0.022]**[0.020]** [0.019]*** Log Family Income ($1986) [0.003]*** Log Family Income ($1986) * [0.003]** The Effect of Medicaid/SCHIP Eligibility and Lagged Medicaid/SCHIP Eligibility on Access to Health Care of Children Aged 9-17 The table reports coefficients and standard errors (in brackets) from linear probability models. Other than those indicated in the table, control variables include state, year and age effects, and (log of) family size, whether mother and father present, race (white, black), mother's age, father's age, mother's education (lesss than 12 or 12 years), father's education (less than 12 or 12 years), and whether the mother or father is unemployed. The sample size is 188,299. * significant at 10%, ** significant at 5%, ***significant at 1%. Linear Probability Models

Conclusions  In , the relationship between income and health continues to be stronger for older children compared to younger.  However, the gradient increase with age is less in than it was in  Increases in Medicaid/SCHIP eligibility increase the probability that a child has seen a doctor in the past year.  We see no evidence that this improvement in the use of health services can explain the decline in the strength of the relationship between income and health for older children.  We find some evidence that Medicaid/SCHIP eligibility in early childhood puts children on a better health trajectory, resulting in better health at older ages.