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.