The Census Bureau counts the U.S. population once every decade through its decennial census. For the years in between, the Bureau estimates states’ populations from annual data on changes in births, deaths, and net migration (including net movements of military personnel). These annual population estimates are called postcensal population estimates because they are based on the prior census (see table 1 for definitions of different population counts used in this report). This process of making annual postcensal population estimates continues until the next census. Once the new census is taken, the Bureau compares the population estimates to the census population counts for the same date. The difference between the population estimate and the census count is called the error of closure. Subsequently, annual population estimates are revised for the prior decade using the counts from the new census. For example, after the 2000 census, the annual population estimates from the 1990s were revised to be consistent with both the 1990 and 2000 censuses. These revised population estimates are called the intercensal population estimates because they rely on the preceding and the succeeding censuses. Of the four programs we analyzed, Medicaid is the largest, comprising 43 percent of all federal formula-based programs and 94 percent of the total funding for the four programs analyzed for this report (see table 2). The SSBG formula allocates an amount of funding, set by annual appropriation, directly to the states. A state’s allocation is proportional to its share of the total U.S. population. State allocations for fiscal year 2002 used the April 2000 census, and allocations for prior years used postcensal population estimates that were based on the 1990 census. In contrast with the SSBG’s fixed appropriation, the Medicaid, Foster Care, and Adoption Assistance programs are open-ended entitlement programs—the states determine the level of program expenditures, and the federal government reimburses a share of their expenditures according to matching rates, called the Federal Medical Assistance Percentages (FMAP), set by statutory formula. All three programs use the same formula, which is based on a 3-year average of state per capita income— the ratio of aggregate personal income to state population. As a state’s per capita income increases, its matching rate decreases, and vice versa. In addition, unless a state experiences changes in aggregate personal income, its federal payment generally declines if the state’s population growth is less than the national average. Matching rates range from a minimum of 50 percent to a maximum of 83 percent of a state’s Medicaid expenditures. The minimum 50 percent rate affects only the high per capita income states. For fiscal year 2002, for example, a high-income state such as Connecticut would receive a 15 percent federal matching rate if the 50 percent minimum was not in place. For fiscal year 2002, the federal matching rates for Medicaid, Foster Care, and Adoption Assistance were based on a 3-year average of per capita income from 1997 through 1999. Rates for fiscal year 2003 are based on a 3-year average from 1998 through 2000. Although the formulas use overlapping years, the state population numbers used to compute per capita income differ depending on which fiscal year the grant is for. For these three programs, the fiscal year 2002 formula calculations used postcensal population estimates derived from the 1990 census for 1997 through 1999 to calculate per capita income. Fiscal year 2003 formula calculations used population estimates for 1998 through 2000 derived from the 2000 census. Thus, the 2000 census affects matching rates for these programs beginning in fiscal year 2003 (see table 3). The difference between the 2000 census count and the 1999 postcensal population estimate was 3.2 percent, which is large compared with the 1 percent average annual growth rate estimated over the preceding decade. Most of the difference was due to the correction of the error that had occurred during the 1990s. According to the Census Bureau, the size of the error was the result of an underestimate in the measurement of net international migration during the 1990s and the improved coverage of the 2000 census compared with the 1990 census. Consequently, the postcensal population estimate for 2000 was smaller than the 2000 census count. Every state’s population growth was underestimated and needed correction, but the correction amounts varied widely. Among the four Census regions, only the Midwest showed a consistent pattern: all 12 states were close to or below the national average correction. California, Florida, and New York accounted for a high percentage of the correction in population estimates in their respective regions. The 2000 census count of 281.4 million people as reported by the Census Bureau exceeded the 1999 postcensal population estimate by 8.7 million people, or 3.2 percent. Slightly more than three-quarters of this difference (2.5 percent) was the result of correcting errors in the population estimates that occurred over the decade, called the error of closure (see app. I for detailed data for all states). The error of closure was 6.8 million people, substantially larger than the 1.5 million error of closure associated with the 1990 census. The error of closure for the 2000 census was four times the corresponding percentage error for the 1990 census (2.5 percent compared with 0.6 percent). The large error of closure in 2000 was due to underestimating the annual growth in population during the 1990s and to the improved coverage of the 2000 census over the 1990 census. The postcensal population estimates for the decade grew an average 1.0 percent annually. However, the 2000 census showed that the average annual growth rate in population was 0.2 percent higher than the estimated rate, or 1.2 percent. The Census Bureau revised its annual population estimates upward when it released its intercensal population estimates in the spring of 2002. The Census Bureau cited two reasons for the size of the error in its postcensal estimated population growth through the 1990s. First, the net international migration was underestimated during the decade, especially for the Hispanic population. The Hispanic population was underestimated by approximately 10 percent, four times higher than the national average population underestimate, 2.5 percent. Second, the 2000 census was more accurate than the 1990 census. The population undercount from the 2000 census was much smaller compared with the 1990 census (1.18 percent, compared with 1.62 percent, making the 2000 census more accurate); the 2000 census counted people who were probably missed in the 1990 census. The error of closure shows a wide variation across states. For example, West Virginia and Michigan had the smallest percentage corrections, 0.27 and 0.34 percent, respectively. The District of Columbia and Nevada had the largest percentage corrections in their population estimates, 10.2 percent and 7.5 percent, respectively. Twenty-eight states had a lower- than-average percentage difference, and 23 states had a greater-than- average percentage difference (see fig. 1 for the correction percentages for all states). Among the four Census regions, the Midwest had the smallest correction in population, 1.5 percent; all 12 Midwest states had corrections close to or below the national average. In the other three regions, a single state accounted for a large share of the population change for the region. For example, in the South, Florida’s correction in population of 4.7 percent constituted about 25 percent of the correction for the entire region. Similarly, New York’s correction was 44 percent of the northeastern states’ correction, and California’s correction was 26 percent of the correction for the western states. The correction to the population estimates generally redistributes federal funding for the four programs we analyzed from the states with the smallest corrections to those having the largest. Federal funding for the 28 states that had below-average corrections decreases by an estimated $380.3 million. In contrast, federal funding in the 23 states with above- average corrections to their population estimates increases by an estimated $388.8 million. Most of the change in funding is concentrated in states with larger populations. Michigan and Ohio, for example, account for 57 percent of the total decrease in funding for states with below- average population corrections. A number of high-income states, including California and New York, are largely unaffected by the correction in their populations because their matching rates for the Medicaid, Foster Care, and Adoption Assistance programs cannot decrease below the minimum 50 percent matching rate. Without this minimum, more funding would be shifted among the states. While the redistribution of funding in the four programs began to occur in fiscal year 2002, almost all of it occurs in fiscal year 2003, when the 2000 census data are used to determine federal matching rates in the three open-ended entitlement programs. The correction in state populations resulting from the 2000 census causes significant changes in the funding levels among the states for the four programs we examined. We estimate that the funding for the 28 states that had below-average corrections in their populations decreases by a total of $380.3 million. Conversely, funding for the 23 states that had above- average corrections in their populations increases by an estimated $388.8 million (see table 4). These results are dominated by a few highly populated states whose corrections were among the largest—meaning they are estimated to receive the most additional money or to lose the most. For example, Michigan, the eighth most populous state, has an estimated $119 million decline in funding because of its 0.34 percent correction in population. Michigan’s federal funding decrease accounts for about one-third of the decreases for the 28 states with a below-average correction in population. Moreover, when Michigan’s decrease is combined with that of Ohio, the seventh most populous state, the two states account for 57 percent of the estimated total decline in funding from the corrections of the population estimates. Conversely, Florida, the fourth most populous state, has the largest estimated increase in funding (about $126 million) because of the 4.7 percent correction in its population estimate. This is almost double the national average correction and accounts for about one-third of the estimated increase for the 23 states with an above-average correction in population.