State Estimates of Uninsured Children, January 1998

I. Overview

A. INTRODUCTION

State estimates of not only the number of uninsured children but their characteristics have attracted growing interest with the passage of legislation establishing the State Children's Health Insurance Program (SCHIP). The Current Population Survey (CPS), the most widely cited source of data on the uninsured, has been criticized because its state samples are inadequate to provide state estimates of uninsured children with sufficient statistical precision to serve policy needs. To produce detailed breakdowns of uninsured children goes well beyond what most of the state samples can support. But while it may be inappropriate to rely on "direct sample" estimation to produce the tabulations that policymakers require, there exist innovative but well-grounded techniques for developing state and substate estimates by the application of statistical procedures that "borrow strength" from other data sources. This report employs statistical procedures that allow us to (1) make use of the entire CPS sample in developing estimates for each state and (2) incorporate data from a wide variety of other sources. We apply these procedures to the March 1998 CPS to develop state estimates of the number of uninsured children in January 1998 by poverty level and age. The report also includes illustrative estimates of the number of children simulated to be eligible for Medicaid and the number who would be eligible for coverage under the SCHIP if the September 1999 rules had been in effect in 1997.

Part I of this report is organized as follows. Section B discusses the purpose of the tables in this report, and Section C provides a brief description of the methodology. Section D presents estimates of uninsured rates among children for the 50 states and the District of Columbia (DC), and Section E presents illustrative estimates of the number of children who are eligible for Medicaid and SCHIP, by state. These estimates are based on a simulation model that incorporates state program detail. Finally, Section F discusses some caveats regarding these estimates.

Part II of the report consists primarily of the state tables--10 per state. Three of the tables are based entirely on the March 1998 CPS data without any of the enhancements that we have introduced. They are included to demonstrate, first, how limiting they are for most states and, second, to show how closely (or not) our enhanced estimates match in broad terms certain state characteristics that can be estimated directly from the CPS sample data. A Technical Appendix follows the state tables and outlines the procedures that we employed to develop the estimates.

B. PURPOSE OF THE TABLES IN THIS REPORT

While much attention has focused on the inadequacy of the state estimates of uninsured children that the CPS provides, state and federal needs go well beyond a simple count of the number of uninsured children (or adults) in each state. How many of the uninsured children fall into different poverty levels? What are their ages? The answers to these questions are important because they have a direct bearing on how many children are eligible for existing public health insurance programs or may be eligible for new programs. To states that are trying to incrementally expand their coverage of children, it is very important to know how many children may become eligible for coverage with a given incremental change in the eligibility criteria. States need to know what it will cost, potentially, to extend coverage beyond current levels. Uncertainty about these numbers and therefore the cost implications of expanding coverage usually translates into caution in the design of new programs or modifications to existing programs. With better information, many states may find that they can afford to be more generous in the coverage that they extend to the uninsured.

The tables in this report are intended to provide baseline information that states could find useful in planning future expansions of their SCHIP initiatives and in evaluating their progress in reducing the number of children who are without health insurance.⁽¹⁾ Moreover, the database that was used to create these tables can be used to generate customized tables that better suit the needs of individual states or federal policymakers.

C. METHODOLOGY

The estimates presented in this report were derived from the March 1998 CPS public use file after making a number of enhancements discussed below and in the Technical Appendix. The CPS is a monthly sample survey of 50,000 households in the United States. It serves as the source of the official monthly unemployment estimates for the nation and the 50 states and the District of Columbia. Interviews are conducted with all civilian non institutionalized persons age 15 and older in the sample households. Each March, the Census Bureau includes a supplemental questionnaire in the CPS interview. The data collected in this questionnaire are the source of the official estimates of the incidence of poverty in the United States. In addition, the March supplement collects information on the health insurance coverage of all household members (including children). These data have become the source of the most widely-cited estimates of the number of uninsured children in the United States each year.

While the CPS sample was designed to support state-level estimates of unemployment rates and other labor force statistics, the sample sizes for most states are inadequate for satisfactorily precise monthly estimates that use only the survey data for that month.⁽²⁾ Thus, even if we are producing just a single estimate for each state--such as the number of uninsured children--that estimate will generally be imprecise due to the high sampling error associated with small samples. This means that we will be very uncertain about the true number of uninsured children and able to say for a typical state only that the number falls within a wide range, a range that is too wide to provide useful guidance for developing policy or administering a program.

This problem is even more severe when we must produce many estimates for each state, such as a table showing the distribution of uninsured children across poverty and age categories. Then, the already small sample for a state must be spread across the many cells of the table. It is not unusual for the available sample to have no observations in some cells even though the state population obviously has uninsured children with the characteristics defined by those cells.

When large samples are not available, a standard approach to improving the precision of sample estimates is to borrow strength.⁽³⁾ This entails the development of statistical models that allow us to derive, say, a 1998 estimate of uninsured children in Virginia using not only the 1998 data for Virginia from the main sample survey database but also data from other states, earlier years, and auxiliary sources, such as administrative records. Knowing something about the numbers of uninsured children in Virginia in 1996 and 1997 and the numbers of uninsured children in other states with economic and demographic conditions similar to Virginia's, we would generally be able to reduce substantially our uncertainty about how many uninsured children lived in Virginia in 1998.

To derive most of the estimates presented in this report, we have used a method for borrowing strength that is described in detail by Schirm and Zaslavsky (1997). With this method, we have reweighted the March 1998 CPS database to produce 51 sets of weights--one set for each state and the District of Columbia. We use the Virginia weights to derive estimates for Virginia. Each of the roughly 50,000 households in the CPS database--regardless of state of residence--gets a Virginia weight, greatly increasing the size of the sample from which to obtain estimates for Virginia. How much Virginia weight a household gets and, therefore, its relative contribution to estimates for Virginia depends on the household's characteristics. If a household from, say, Montana has characteristics that would make it unusual were it in Virginia, as opposed to other states, it receives a relatively small--probably negligible--Virginia weight. If, instead, a household of that type would be more common in Virginia than in other states, it receives a relatively large Virginia weight.

The Virginia weight assigned to a household with a particular set of characteristics depends on the aggregate characteristics of Virginia. Specifically, Virginia weights are controlled so that totals derived for Virginia using Virginia weights equal specified values. For example, the weights might be controlled so as to reproduce specified totals of children, children below 100 percent of poverty, and uninsured children. By controlling the weights, we ensure that the entire database--when weighted according to the Virginia weights--looks like Virginia in terms of totals that are relevant to ascertaining the patterns of insurance coverage among children.⁽⁴⁾

In reweighting the March 1998 CPS database, we used controls reflecting the age and racial/ethnic structure of each state's child population, the distribution of children across poverty categories, and the numbers of uninsured children (by poverty category). The full list of totals to which we controlled weights appears in the Technical Appendix.

Reweighting a database as we have described can substantially improve the precision of estimates because the samples used to derive the estimates are much larger than when we use only the observations from a single state. Using observations from all the states allows us to borrow strength. Although this alone improves precision, we have further improved precision by using administrative estimates or empirical Bayes shrinkage estimates--rather than direct sample estimates --for many of the control totals used in the reweighting.⁽⁵⁾ The administrative totals, which are population estimates derived from mainly vital records (and decennial census) data, have essentially no sampling error, and the shrinkage totals, which are derived by borrowing strength, are more precise than direct sample estimates. The specific sources of control totals are described in greater detail in the Technical Appendix.⁽⁶⁾

D. ESTIMATES OF THE UNINSURED

Table I.1 reports the March 1998 CPS sample sizes for all children and uninsured children by state and compares the model-based estimates of the uninsured rate with the direct sample estimates. The sample sizes underscore why it is necessary to employ procedures of the kind used here to construct state-level tabulations of uninsured children. Most state samples include fewer than 100 uninsured children, and 15 states have fewer than 50. Spreading such small numbers of observations over a table with 24 cells (the number of cells in each of the state tables in Part II) cannot yield much information of value.

Differences between the model-based and direct sample estimates vary from 0.1 to 5.2 percentage points (plus or minus). The two estimates are within one percentage point for 25 states but differ by three percentage points or more for 8 states. Sample size is clearly relevant but not the sole factor affecting the size of the difference. For example, all nine states with CPS sample sizes of more than 1,000 children have differences of 1 percentage point or less, but the two largest differences occur in states with above average sample sizes (Arizona and Arkansas). Perhaps more importantly, the model-based estimates show the impact of shrinkage toward the mean. Generally the most extreme rates--which are probably too extreme--are pulled toward the center. We see this in the states of Arizona, Arkansas, Louisiana, and Texas, where the model-based estimates are lower than the (high) direct sample estimates, and in Hawaii, South Dakota, Washington, and Wisconsin, where the model-based estimates are greater than the (low) direct sample estimates. This result is not universal, however. There are states with low direct sample uninsured rates (for example, Vermont and Minnesota) that get assigned even lower rates by the model-based procedure. As low as the direct sample estimates were in these states, the regression model predicted even lower rates. Thus the model did not indiscriminantly eliminate high and low rates.

Table I.2 reports uninsured rates by poverty level for the model-based estimates. It is quite clear from an examination of these rates that the model-based procedure does not generate homogenous uninsured rates across the states. Rather, there are distinctly different patterns in the rates for groups of states.

For example, there is a general tendency for children who are between 100 percent and 150 percent of poverty to have the highest uninsured rates. Children below 100 percent of poverty often have access to Medicaid while children above 150 percent of poverty are more likely to have employer-sponsored or other private insurance. But despite these tendencies we do see a number of states in which the uninsured rates are highest among children under 50 percent of poverty and then decline with each succeeding higher income level. Arizona, Florida, Idaho, Louisiana, Nevada, Oklahoma, Oregon, Utah, and Wyoming are among the states that fit this pattern. It is likely that these states have low participation in Medicaid since most children under 50 percent of poverty will be covered by Medicaid. In general, the states that fit this pattern have large Hispanic populations or western locations. The high uninsured rates of Hispanic children are well-documented. For the western states, the high uninsured rates at low income levels may reflect low participation in safety net programs generally. Whatever the reason, there is a clear pattern that the model-based estimates are able to identify.

Four states--Hawaii, Minnesota, Vermont, and Wisconsin--have single-digit uninsured rates in the two lowest poverty classes. The first three of these are noted for their broad Medicaid coverage expansions, and we would guess that Medicaid participation is very high among the eligible populations. That the model-based estimates can differentiate between these states and the rest provides additional face validity.

E. ILLUSTRATIVE ESTIMATES OF UNINSURED CHILDREN ELIGIBLE FOR MEDICAID AND SCHIP

Development of the model-based estimation procedures employed here was motivated by an interest in applying the methodology of microsimulation to individual states. Microsimulation is particularly useful for estimating the incremental impact of small changes in program eligibility on caseloads and costs, but it requires a very large sample.⁽⁷⁾ To illustrate the application of microsimulation to the reweighted March 1998 database, we have prepared a simulation of eligibility under both Medicaid (1997 rules) and SCHIP (September 1999 rules) and applied this simulation model to the reweighted data. The simulation program captures most of the major elements of state differentiation in income eligibility limits by age, the use of gross versus net income, and, to some degree, the application of asset tests.⁽⁸⁾ We base eligibility on annual family income rather than trying to construct monthly income streams that would allow a more literal replication of the Medicaid eligibility determination. This is a widely-used practice--in large part because the CPS and other major surveys collect only annual income data. Furthermore, given that the CPS provides only annual rather than monthly estimates of insurance coverage, basing eligibility on simulated monthly rather than reported annual income would not solve the problem of relating eligibility to insurance coverage.

The first four columns of Table I.3 present state estimates of the number of uninsured children, the number of all children who were simulated to be eligible for Medicaid (without regard to insurance coverage), and both the number and percentage of these Medicaid-eligible children who were reported as uninsured. The final three columns present estimates of children who were simulated to be eligible for either Medicaid or SCHIP (again without regard to insurance coverage) and the number and percentage of these Medicaid/SCHIP-eligible children who were uninsured. Medicaid eligibility is based on program rules that were in effect in 1997 while SCHIP eligibility is based on state program provisions that were in effect in September 1999. Thus SCHIP eligibility is prospective or hypothetical rather than actual eligibility in 1997. It should be noted as well that even in the absence of SCHIP, Medicaid eligibility would have grown between 1997 and 1999. Earlier reforms extended eligibility to low income children who were born after September 30, 1983. As these children age, a larger and larger share of all children are made eligible by these provisions.

Across all of the states, our estimates of uninsured children who were eligible for Medicaid total 4.2 million out of the 11.5 million uninsured children under 19. Our simulation of SCHIP eligibility suggests that SCHIP would have extended eligibility for public insurance coverage to about 2.4 million additional children.

The estimated percentage of simulated Medicaid-eligible children who were uninsured in each state helps us to understand the patterns of uninsurance among low income children that we saw in Table I.2. Nationally, 22.0 percent of our simulated Medicaid-eligible children were uninsured. Among the states, this rate varies from a low of 7 percent (Minnesota and Wisconsin) to a high of 33 percent (Arizona). Generally, states with higher uninsured rates among children under 100 percent of poverty than among children between 100 and 150 percent of poverty have high rates of Medicaid-eligible uninsured in Table I.3. For example, Arizona, Florida, and Idaho have higher uninsured rates in the two lowest poverty classes than in the 100 to 150 percent class, and all three have Medicaid-eligible uninsured rates in excess of 30 percent. At the other end of the distribution, we singled out Hawaii, Minnesota, Vermont, and Wisconsin for their relatively low uninsured rates among children below 100 percent of poverty, and all four of these have Medicaid-eligible uninsured rates below 10 percent.

Some important implications of the impact of SCHIP eligibility at the state level can be seen in Table I.4, which shows by state the number of uninsured children who were not simulated to be Medicaid-eligible and both the number and percentage of these who would be made eligible for coverage under SCHIP. Nationally, SCHIP would extend eligibility to about one-third of the uninsured children who were not otherwise eligible for Medicaid in 1997. This varies substantially by state--in part because some states were already covering a large part of the population that other states would now cover under SCHIP. Hawaii and Minnesota, which provide broad coverage under Medicaid, would extend coverage to fewer than 5 percent of the uninsured who were not eligible for Medicaid, whereas Alabama, with comparatively low Medicaid coverage, would extend coverage through SCHIP to nearly 60 percent of its remaining uninsured children. At the same time, Texas with comparatively low Medicaid coverage would extend coverage through SCHIP to only 12 percent of its remaining uninsured children while DC would extend coverage to 57 percent.

Table I.5 summarizes the coverage of children under 200 percent of poverty by state, breaking down the low income population into those with insurance, those who were uninsured but eligible for Medicaid in 1997, those who were uninsured and not Medicaid-eligible in 1997 but would be eligible for Medicaid or SCHIP by the rules that were in effect in September 1999, and those who would remain uninsured.⁽⁹⁾ In the final column we see that three states would leave more than 10 percent of their low income children uninsured and ineligible for Medicaid or SCHIP: Arkansas (16 percent), Mississippi (11 percent), and Texas (13 percent).⁽¹⁰⁾ Most states, however, would leave fewer than 2 percent of their low income children uninsured and ineligible for Medicaid or SCHIP.

F. CAVEATS ABOUT THESE ESTIMATES

We highlight three caveats that apply to the tables presented in Part II and one additional caveat that applies to further use of the reweighted CPS database. The first three are the CPS's undercount of the Medicaid population, the overstatement of uninsurance among infants, and the limitations of the Medicaid simulation. The fourth involves limitations on the kinds of data that can be tabulated with the reweighted database.

1. Medicaid Undercount

It is widely known that the Medicaid enrollment in the CPS understates estimates compiled from the states' program administrative statistics. It is much less widely known that this Medicaid undercount has been growing.⁽¹¹⁾ It is very likely that at least some of the children reported as uninsured were actually covered by Medicaid.⁽¹²⁾ We have not attempted to adjust our estimates in any way for this Medicaid undercount, so a portion (and perhaps a large portion) of those children that we report as eligible for Medicaid but uninsured may have actually been covered by Medicaid.⁽¹³⁾

2. Uninsured Rates for Infants

Despite their greater access to Medicaid, infants are reported to have higher uninsured rates than children 1 to 5. This is peculiar to the CPS, however, and it is very likely due to a combination of two factors: (1) the uninsured being identified as those who report no insurance (as opposed to reporting that they were uninsured) and (2) insurance coverage being measured for the previous year (Czajka and Lewis 1999). Children born between the end of the reference year and the March survey date cannot in truth be described as having had coverage of any kind the previous year, and parents who answer the questions literally will end up with their newborn infants classified as uninsured. It is not possible to identify infants born after the end of the reference year, and so it is not possible to screen out those who may have been misclassified. Users of the data need to be aware that the rate of uninsurance among infants is overstated.

3. Medicaid Eligibility

The rules governing Medicaid eligibility, which vary by state, are extraordinarily complex. A complete simulation of all the ways that a child can become eligible for Medicaid is impossible--both because of the limitations of survey data and because the full details of eligibility, state by state, are not documented in any accessible form. For this reason, any Medicaid eligibility simulation is going to involve simplifications. It is quite rare, for example, that anyone simulates eligibility under the medically needy provisions, other than indirectly, and we have not done so here. Nor have we incorporated state-specific differences in the calculation and application of disregards. The information required to do so for all of the states is not readily available, and that limits not only our own simulation but those that could be constructed by others--even with substantially more resources.

4. Tabulating the Reweighted Database

The reweighting of the CPS database for state estimation was accomplished by applying a number of controls that, depending on which of the 51 sets of weights is chosen, make the database "look like" a specific state. The controls were chosen because of their relevance to estimating the number of uninsured children by age and poverty level. While it is possible to tabulate any field on the CPS file with the state weights that we have constructed, the state-specificity of the resulting tabulation deteriorates with the declining relevance of children's age, race and Hispanic origin, poverty level, and insurance coverage (insured or not) to the fields being tabulated. In addition, fields that depend on rules that differ across states, such as AFDC participation or Medicaid participation, may be inconsistent with the rules in most other states and, for this reason, should not be tabulated or used to infer eligibility in a simulation algorithm.⁽¹⁴⁾

II. State Estimates

The sections that follow present ten tabulations for each of the 50 states and the District of Columbia. The tables are organized by state. Each table consists of a cross-tabulation of a defined population of children by poverty level and age. The tables utilize six poverty levels:

• Less than 50 percent of the federal poverty level (FPL)
• 50 percent to less than 100 percent
• 100 percent to less than 150 percent
• 150 percent to less than 200 percent
• 200 percent to less than 350 percent
• 350 percent or greater

The four age categories are:

• Less than 1 year of age (infant)
• 1 to 5
• 6 to 12
• 13 to 18

These age categories match those that the Health Care Financing Administration is using in reports of SCHIP enrollment.

Each table describes a different population or sub population within the state. The first seven tables were produced from the reweighted CPS database. That is, they are model-based estimates. They are dated January 1998 rather than March 1998 because the population estimates that were used as external controls are effectively January 1998 numbers.

• Table II.A: Model-based Estimates of the Number of Children by Age and Poverty Level, January 1998
• Table II.B: Model-based Estimates of the Number of Children in January 1998 Who Were Uninsured in the Previous Year
• Table II.C: Model-based Estimates of the Percent of Children in January 1998 Who Were Uninsured in the Previous Year
• Table II.D: Model-based Estimates of Simulated Medicaid-Eligible Children, January 1998
• Table II.E: Model-based Estimates of Simulated Medicaid-Eligible Children in January 1998 Who Were Uninsured in the Previous Year
• Table II.F: Model-based Estimates of Simulated SCHIP- or Medicaid-eligible Children, January 1998
• Table II.G: Model-based Estimates of Simulated SCHIP- or Medicaid-eligible Children in January 1998 Who Were Uninsured in the Previous Year

The final three tables are direct sample estimates from the March 1998 survey. They use no other data. They provide counterparts to the first three of the tables listed above and were included so that we could demonstrate on a state-by-state basis how much our borrowing strength methodology has improved our ability to produce usable state-level information. These final three tables are:

• Table II.H: Direct Sample Estimates of the Number of Children by Age and Poverty Level, March 1998
• Table II.I: Direct Sample Estimates of the Number of Children in March 1998 Who Were Uninsured in the Previous Year
• Table II.J: Direct Sample Estimates of the Percent of Children in March 1998 Who Were Uninsured in the Previous Year

These tables are dated March 1998 because the March CPS sample is weighted to population estimates for the month of March.⁽¹⁵⁾

We walk through these tables for Alabama to illustrate their interpretation. Table II.A.1 indicates that there were about 1.2 million children in Alabama in January 1998 and provides a breakdown by poverty level and age.⁽¹⁶⁾ These estimates indicate, for example, that there were 122,000 children in families with incomes below 50 percent of poverty and that about 33,000 were 13 to 18 years old. Table II.B.1 shows the number of uninsured children, which totaled 195,000. Children in families between 100 and 150 percent of poverty accounted for the largest number of uninsured (47,100) in any poverty class and nearly three times as many as children in families above 350 percent of poverty. Table II.C.1 gives the percentage of children who were uninsured in the previous year. The estimates in this table were prepared by dividing each cell entry in Table II.B.1 by the corresponding cell entry in Table II.A.1. Table II.C.1 indicates, for example, that 19 percent of all Alabama children 13 to 18 years old were uninsured while 38 percent of the 13 to 18 year-olds in families under 50 percent of poverty were uninsured.

Table II.D.1 presents estimates of the number of Alabama children who were simulated to be eligible for Medicaid, without regard to their existing insurance coverage. Very small numbers with family incomes above 200 percent of poverty appear to be eligible, indicating the impact of income disregards that extend eligibility beyond the nominal limits. Table II.E.1 shows how many of these simulated Medicaid-eligible children were uninsured the previous year. Note that none of the children who were simulated to be eligible with family incomes above 200 percent of poverty were uninsured. Table II.F.1 provides estimates of the number of children who were simulated to be eligible for either Medicaid or SCHIP.⁽¹⁷⁾ Table II.F.1 shows that about 533,000 Alabama children were simulated to be eligible for either program when existing insurance coverage was ignored while Table II.G.1 shows that only 141,000 of these eligible children were actually uninsured the previous year.

Tables II.H.1, II.I.1, and II.J.1 are direct sample estimates from the CPS, and they correspond in content to Tables II.A.1, II.B.1, and II.C.1. Table II.H.1 shows all children, and it is about 90,000 lower than the total represented in Table II.A.1. The difference is due to our use of population controls that are external to the CPS and that include detail for children under 15.⁽¹⁸⁾ While the totals are nearly the same, however, there are striking differences between Tables II.A.1 and II.H.1 in the estimates for combinations of poverty level and age. For example, Table II.A.1 shows almost 7,000 infants below 50 percent of poverty while table II.H.1 shows double that number (and half as many as the number 1 to 5). The differences between Tables II.B.1 and II.I.1, which show the number of uninsured children, are even more dramatic. Table II.I.1 contains several cells for which there were no sample observations whereas Table II.B.1 has no empty cells. The totals are different because our model-based estimate of the uninsured rate in Alabama was higher than the CPS direct sample estimate (see Table I.1). The relative numbers of children in adjacent age categories in Table II.I.1 fluctuate dramatically across poverty levels. For example, there are more than twice as many 13 to 18 year-olds as 6 to 12 year-olds between 100 and 150 percent of poverty but 10 times as many 6 to 12 year-olds as 13 to 18-year olds between 200 and 350 percent of poverty. In Table II.B.1 the relative numbers of children in the two age groups vary little by poverty level until the top category. Finally, the uninsured rates reported in Table II.J.1 show sharp fluctuations across the table and even between adjoining cells whereas those reported in Table II.C.1 are much smoother.

Footnotes:

1. An earlier application of the methodology employed in this report was used to assist the State of New Jersey in its SCHIP design and planning efforts (see Czajka, Rosenbach, and Schirm 1999).

2. Recognizing the limitations of the CPS, Congress has appropriated funds for enhancing the CPS to support more precise state estimates of the numbers of uninsured children. But, such enhancements have not yet been designed, let alone implemented, and it is unlikely that the expanded sample will support detailed breakdowns of uninsured children.

3. Estimators that borrow strength have been used successfully in administering important public programs. For example, the Bureau of Labor Statistics (BLS) uses data from administrative sources to help construct the monthly estimates of state unemployment rates. Another estimator has been used for several years to derive state estimates for allocating federal funds under the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) (Schirm and Long 1995). Similar estimators have also been used to obtain state and county estimates of poor school-aged children for allocating federal Title I funds for compensatory education in elementary and secondary schools (National Research Council 1998). Thus the methodology that we employ in producing the tables presented in this report follows a long line of successful applications of statistical enhancements to the CPS for the purpose of preparing estimates at the state level.

4. In contrast, with the original CPS sample weights, the database looks like the whole United States.

5. Empirical Bayes shrinkage methods average direct sample estimates with predictions from regression models that derive their predictions based on state characteristics measured by decennial census and administrative records data (e.g., the poverty rate according to the census or the ratio of children enrolled in Medicaid, according to Medicaid administrative data, to the total population of children, which is derived from a combination of census and administrative data).

6. Prior to reweighting the CPS database to borrow strength, we controlled the weights of households within each state to a set of population totals characterizing the age and race/ethnic structure of the state's child population (the same set of totals that is later used in the reweighting). Such control totals are not used by the Census Bureau in weighting the CPS because of the emphasis placed on using the survey for producing employment estimates. Indeed, the only state-level control on weights used by the Census Bureau is the total population aged 16 and over, which is regarded as the population of working age. Because we are developing estimates pertaining to the child population, our within-state adjustment of weights to child population totals should improve the precision of the estimates. Then, borrowing strength by reweighting should lead to further improvements in precision.

7. If the underlying database is too small, the estimates of incremental changes to programs tend to be either zero or excessively large.

8. For Medicaid, the most important limitation of our simulation is the exclusion of eligibility under state medically needy provisions. The CPS lacks the medical expenditure data required to simulate these provisions. For SCHIP the most important limitation is our inability to take into account the waiting periods that a number of states have introduced to discourage parents from dropping employer-sponsored coverage for their children and enrolling them in SCHIP. The CPS provides no information on duration of uninsurance and, furthermore, the details on state waiting periods that we would require in order to simulate them are not readily available. These waiting periods can reduce potential eligibility by a significant amount.

9. Poverty in this table is measured relative to the Census Bureau's poverty thresholds while program eligibility is based on the poverty guidelines released by the Department of Health and Human Services (DHHS). While generally similar, the two series differ in a number of respects, and this may account for the fact that small percentages of children below 200 percent of poverty are simulated to be ineligible for Medicaid or SCHIP in states with SCHIP eligibility limits of 200 percent. The Census Bureau poverty threshold may define 200 percent of poverty as a slightly higher dollar figure than the corresponding DHHS guideline used to determine eligibility, such that a small number of children fall between the two figures. In addition, the poverty guidelines recognize the markedly higher living costs in Hawaii and Alaska than the rest of the nation and are set at higher levels while the Census Bureau thresholds are undifferentiated across the states.

10. Access to coverage among low income children in Arkansas, currently, is actually better than this suggests. In September 1997, Arkansas implemented the ARKids First program under a section 1115 Medicaid waiver. Because of its late introduction, this program which provides coverage for children up to 200 percent of poverty is not included in our simulation of Medicaid eligibility in 1997 for the state of Arkansas. Nor is it included in the combined Medicaid and SCHIP eligibility simulation, which mixes Medicaid eligibility rules in 1997 with SCHIP eligibility rules as of September 1999. A simulation based on a later point in time would very likely include this extended coverage under SCHIP; state officials have indicated that there are plans to transform ARKids First into a state SCHIP (Irvin and Czajka 2000).

11. Comparison of CPS estimates with Medicaid administrative statistics indicates that the CPS undercount of Medicaid children under 15 grew by about 3 million children between the March 1994 and March 1998 surveys. In March 1994 the undercount was estimated to be about 17 percent or 3.2 million children. See Czajka and Lewis (1999) for a discussion.

12. Underreporting of coverage probably accounts for most of the Medicaid undercount. Persons who failed to report their Medicaid coverage could have reported another form of coverage during the year--either as an incorrect description of their Medicaid coverage or as other coverage that they actually had during the year. Part of the undercount could also be due to CPS underrepresentation of low income households. This has quite different implications, however. If low income households are underrepresented, then not only Medicaid enrollees but uninsured people will be undercounted as well. We are not aware of any evidence of an under-representation of low income households in the CPS, but the possibility is one that must be recognized. Under-representation of low income households might arise for many of the same reasons that the decennial census undercounts the population.

13. Both our ASPE project officers and the majority of a technical advisory committee recommended against any adjustment for the Medicaid undercount. The concern is not about the reality of a sizable undercount but the uncertainty as to what it implies about the estimate of the uninsured.

14. Researchers who apply microsimulation to estimate changes in eligibility under hypothetical program reforms are familiar with the limitations of reported participation under such scenarios. Schirm and Zaslavsky (1998) propose a solution to this problem.

15. As we explain in the Technical Appendix, one of the ways in which our methodology reduces the sampling error in state estimates of uninsured children is by incorporating more state-specific detail for children than the Census Bureau uses in weighting the March CPS. The added detail includes race and Hispanic origin and multiple age categories. In order to include this additional detail, however, we had to use population estimates that the Census Bureau prepares for July 1 of each year. We averaged the July 1 estimates for 1997 and 1998, yielding estimates that we then characterize as January 1, 1998.

16. The numeral 1 is appended to each of the 10 tables for Alabama, the numeral 2 to each of the 10 tables for Alaska, and so on.

17. We combine the two programs because the SCHIP eligibility criteria that we were asked to use refer to September 1999 while the Medicaid eligibility criteria apply to 1997. Medicaid eligibility is continuing to grow because of the phase-in of poverty-related eligiblity provisions, so subtracting the 1997 Medicaid eligibles from the 1999 SCHIP eligibles would attribute too much of the growth in eligibility to SCHIP.

18. As we noted in an earlier footnote, the Census Bureau applies no state-specific controls to the population under 16 when it constructs weights for the CPS.

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