Medicaid eligibility is determined by several factors, including an individual’s or a family’s income in relation to the FPL, age, and eligibility for certain other federal program benefits. For example, federal law requires state programs to cover pregnant women and children under age 6 if their family income is at or below 133 percent of the FPL, children under age 19 in families with incomes at or below the FPL, and individuals who receive Supplemental Security Income because they have disabling conditions. For most covered populations, state Medicaid programs are required to offer certain benefits, such as physician services, inpatient and outpatient hospital services, and nursing facility and home health services. State Medicaid programs must provide Early and Periodic Screening, Diagnostic, and Treatment (EPSDT) services for most children, intended as comprehensive, periodic evaluations of children’s health and developmental history, that include vision, hearing, and dental screening. States’ Medicaid programs can differ dramatically because states may expand their programs beyond the minimum requirements to cover, for example, individuals whose incomes exceed federally mandated eligibility thresholds and optional services, such as prosthetic devices and prescription drugs. For example, a state may extend Medicaid eligibility to certain population groups, such as pregnant women who have family incomes above 133 percent of the FPL, or make optional services such as prescription drugs available to its entire covered population. Since the Medicaid program began, total program costs have been apportioned between states and the federal government using a formula that provides more generous federal matching aid to states with lower PCI. The use of PCI in federal grant formulas dates to 1946, when it was State U.S.  chosen as a proxy for a state’s ability to fund public services. Consistent with the purpose described in the formula’s legislative history, PCI is used as a proxy for both state resources and the low-income population. As a state’s PCI increases, relative to the national average, the formula provides for a decreasing federal matching rate, meaning the federal government shares a smaller portion of a state’s costs. By statute, the federal matching rate may range from 50 percent to 83 percent. The formula’s multiplier, currently 0.45, represents the state’s share of its total Medicaid costs for a state with PCI equal to the national average, and the federal government thus pays a 55 percent share of total costs. The Medicaid formula reduces by 20 percent the differences among states in their ability to fund program services, compared with the national average funding ability. While the formula narrows differences for 30 states, making the average difference in funding ability smaller, it moves 21 states farther away from the national average, making the average difference wider. These 21 states include 3 that are among those with the largest populations in poverty—California, Florida, and New York. Because of the formula’s current structure, in many instances, two states devoting the same proportion of their own resources toward funding Medicaid services are unable, after receiving federal matching aid, to spend the same amounts per person in poverty, adjusted for cost differences related to age and geographic location. Because state resources, numbers of people in poverty, and the cost of serving this population vary widely across the states, there also are wide differences in states’ ability to fund health care services. Considering these indicators of state funding ability, Alaska has the highest funding ability— exceeding the national average by 119 percent—and Mississippi has the lowest funding ability—46 percent below the national average, as measured using states’ TTR and the number of people in poverty, adjusting the poverty count for age and geographic cost differences (see fig. 1). Nationwide, the average difference between a state’s funding ability and that of the average state is 22.7 percent. Nineteen states have funding ability 25 percent or more above the national average, and 10 states have funding ability 25 percent or more below the national average. After the value of federal matching aid is added to states’ own resources, the average difference in states’ funding ability drops from 22.7 percent to 18.1 percent. This represents a 20 percent reduction of aggregate differences in states’ funding ability. After the receipt of federal matching aid, differences in states’ funding abilities ranged from 26 percent below the national average for California and New York to 179 percent above for Alaska. The aggregate 20 percent reduction of differences in states’ funding ability under the formula masks the effect of the formula on individual states. For example, as shown in figure 1, consistent with the formula’s goals, the one- quarter of states with the lowest funding ability before the match move closer to the average state’s funding ability after the value of the federal match is added. In total, 30 states move closer to the national average after adding the federal match. However, as the right panel of figure 1 shows, adding the value of federal matching aid often has inconsistent effects. For example, including the value of federal matching aid moves Alaska’s and Utah’s funding ability farther above, rather than closer to, the national average funding ability. This happens because PCI does not adequately reflect that these two states have fewer people in poverty than the national average. In addition, Utah has lower-than-average costs to provide health care services. The current formula actually moves 21 states farther above or below the average: Four of the 21 states—California, Florida, Hawaii, and New York—have below-average funding ability before federal matching aid is added and move farther below the average after federal matching aid is added. These 4 states have approximately 31 percent of the nation’s people in poverty. For example, California’s funding ability drops from 15 percent below the average to 26 percent below the average and New York’s funding ability drops from 12 percent below the average to 26 percent below the average. These two states thus rank last in terms of state funding ability after the value of federal matching aid is added. Thirteen states that have above-average funding ability before adding the value of federal matching aid move farther above the average after it is added. For example, Utah’s funding ability is 73 percent above the national average before the federal match is added but increases to 155 percent above the national average after the match. Of the 4 remaining states, 3—Idaho, Maine, and North Dakota—have below-average funding ability before the match is added and above- average funding ability after the match is added. For the fourth state— Rhode Island—the reverse is true: Rhode Island has above-average funding ability before the match and below-average funding ability after the match is added. States commit widely varying proportions of their own financial resources to fund Medicaid benefits. For example, in fiscal year 2000, New York devoted $18.16 per $1,000 of its TTR toward its Medicaid program, roughly 5 times the proportion of resources that Utah devoted ($3.74 per $1,000) (see left panel of fig. 2). States’ Medicaid cost-adjusted spending per person in poverty varies as well. For example, Alaska’s combined federal and state spending was over $10,000 per person in poverty, while Nevada’s spending was approximately $2,500 per person in poverty (see right panel of fig. 2). Because the federal matching formula does not fully eliminate differences in states’ funding ability, states devoting similar proportions of their own resources to Medicaid cannot spend the same amounts per person in poverty, cost adjusted, with federal matching aid factored in. In addition, because the formula further increases the already high funding ability of some states and decreases the low funding ability of others, these spending differences can be quite large. For example, in fiscal year 2000, both California and Wisconsin devoted roughly the same proportion of their own resources to fund program benefits—about $8 per $1,000 of taxable resources—which was close to the national average ($8.37) proportion of resources states devoted to Medicaid that year. However, the current formula moved California’s below-average funding ability farther below the national average and increased Wisconsin’s above- average funding ability farther above. This occurred because Wisconsin receives a high federal match despite its relatively high funding ability, whereas California receives a low federal match despite its relatively low funding ability. Once federal matching aid was factored in, with their nearly identical funding effort, Wisconsin is enabled to spend more than twice what California could spend per person in poverty—$7,532 compared with $3,731. Similarly, Florida and Iowa each devoted $6.48 per $1,000 in state resources toward their Medicaid programs. After adding the federal match, Iowa could spend $6,729 per person in poverty, cost adjusted, while Florida could spend just $3,160 per person. (See fig. 3.) Two factors prevent the Medicaid formula from further reducing differences in states’ funding ability. First, PCI—the single measure used to establish federal matching rates—is not a comprehensive measure of state resources and is a poor proxy for the size of and cost to serve a state’s population in poverty. Second, special statutory provisions, including the minimum 50 percent federal matching rate, give several states with already high funding ability a higher federal matching rate than they would receive without these provisions. PCI is an inadequate measure of states’ funding ability because it is an incomplete measure of states’ resources, it is a poor proxy for the size of a state’s population in poverty, and it does not take into account differences in the cost of providing health care services to people in poverty. As an indicator of state resources, PCI measures income received by state residents, such as wages, rents, and interest income, but it does not include other sources of income potentially subject to state taxation, such as corporate income produced within the state but not received by state residents. For example, PCI especially understates the taxable resources in energy-exporting states, such as Alaska and Wyoming, and in states that house numerous corporate headquarters, such as Delaware. By comparison, because TTR comprises the income included in PCI as well as income from other sources, such as corporate income and capital gains, states’ TTR exceeds PCI by about 32 percent nationwide. As shown in figure 4, which compares states’ TTR with PCI, states whose resources are particularly poorly represented by PCI include the District of Columbia, Delaware, Alaska, and Wyoming. Using PCI to measure the size of a state’s low-income population assumes that the lower a state’s PCI, the greater its population in poverty. However, two states with similar PCIs may differ widely in their percentages of people in poverty. In addition, PCI is not a good proxy for the differences in the cost of providing health care services that are related to the ages of the population served and the geographic area in which services are provided. Persons who are elderly typically use health care services at higher rates than adults and children and therefore cost more to serve. Two states with low PCIs may have very different proportions of elderly persons potentially eligible for Medicaid. In addition, costs to provide health care services vary widely depending on geographic location because wages and other costs of office space vary regionally. For example, the District of Columbia and Connecticut have similar PCIs, but the share of the District’s population in poverty is more than twice Connecticut’s. Health care costs also are 10 percent higher in the District than in Connecticut. (Fig. 5 compares state rankings by PCI and by people in poverty, adjusted for cost differences related to age and geographic location.) Because of the 50 percent floor, 11 states received higher federal matching rates in fiscal year 2002 than they would have if their rates had been based only on their PCI. Two others—Alaska and the District of Columbia— received special federal matching rates set in statutes that gave them higher matching rates than they would have received solely on the basis of PCI. (See table 1.) Eleven of these 13 states (all except the District of Columbia and New York) had above-average funding ability in fiscal year 2002. Their receipt of a higher federal matching rate than they would have received without statutory minimums increases the overall differences in funding ability among the states. Connecticut and New Jersey benefit the most from the statutory minimums, receiving—as a result of the 50 percent floor— matching rates that are 35 and 20 percentage points higher, respectively, than the rates they would have received based solely on their PCI. Receiving a higher matching rate than what the formula provides on the basis of PCI enables these states to spend more on program benefits per person in poverty than states with less funding ability that devote a higher percentage of their resources to funding program benefits. The statutory minimums benefit the District of Columbia and New York by providing them a higher matching rate than they would otherwise have. Because these two states have below-average funding ability, the minimum matching provisions have the effect of moving them closer to the funding ability of the average state and thus help to reduce overall differences in funding ability among the states. For example, New York’s funding ability without the value of federal matching aid added is 12 percent below the average funding ability; with the value of federal matching aid added, its funding ability is farther from the average funding ability—26 percent below the average. Without the floor, New York’s matching rate would be 37 percent, rather than 50 percent. Therefore, the 50 percent minimum brings New York’s funding ability closer to the average funding ability than it would be with the matching rate it would receive without the minimum. We received comments on our draft report from two external reviewers who have Medicaid formula expertise. The reviewers generally agreed with our analysis and provided technical comments, which we incorporated as appropriate. As agreed with your office, unless you publicly announce its contents earlier, we plan no further distribution of this report until 30 days after its issue date. At that time, we will send copies of this report to appropriate congressional committees and will make copies available to others on request. In addition, the report will be available at no charge on the GAO Web site at http://www.gao.gov. If you or your staff have any questions about this report, please call me at (202) 512-7118 or Jerry Fastrup at (202) 512-7211. Major contributors to this report include Richard Horte, Robert Dinkelmeyer, Michael Williams, Elizabeth T. Morrison, and Michael Rose. This appendix summarizes the legislative history that led to the use of per capita income (PCI) in the Medicaid matching formula and describes how matching rates are calculated. Federal grants-in-aid for public assistance are intended to help in aiding the aged and blind persons and dependent children in all parts of the country and to some extent to equalize the financial burden throughout the Nation. . . . The present 50 percent basis of Federal participation does not recognize differences in the ability of States to finance public assistance, nor does it recognize the greater incidence of poverty in States with low economic resources. To assist their needy people, the low income States must make greater tax effort than States with larger resources where relatively fewer persons are in need. The Social Security Amendments of 1958 established a PCI-based variable rate matching formula, with certain maximums, for public assistance and reimbursement of medical providers. Under this formula, federal matching rates ranged from a minimum of 50 percent for high-income states to a maximum of 65 percent for low-income states. The Social Security Amendments of 1960 increased the maximum matching rate from 65 percent to 80 percent. State U.S. The current matching formula is calibrated with a 0.45 “multiplier.” The value of the multiplier determines the percentage of a state’s Medicaid spending for which the state is responsible. For example, using the 0.45 multiplier, a state with a PCI equal to the U.S. average would receive a federal matching rate of 55 percent (1 - 0.45 = 0.55). A smaller multiplier of 0.40 would raise the federal matching rate for all states and would raise the matching rate for a state with the national average PCI from 55 percent to 60 percent, whereas a higher multiplier of 50 percent would reduce the federal matching rate for a state with average PCI from 55 percent to 50 percent. Relative PCI is intended to represent states’ funding ability, which is a combination of states’ resources and states’ people in poverty. Consistent with this intent, squaring PCI has the effect of making PCI appear in the formula twice, thus reflecting both state resources and people in poverty. Squaring PCI magnifies the difference between the state’s and the national average PCI. For example, if a state’s PCI is 90 percent of the national average, the squared value of its relative PCI would be 81 percent (0.9 x 0.9 = 0.81), resulting in a federal matching rate of 64 percent (that is, 1.00 - 0.45 x 0.81 = 0.64), rather than the 60 percent rate the state would receive if relative income was not squared (that is, 1.00 - 0.45 x 0.9 = 0.60). If PCI were a good proxy for people in poverty, squaring would be appropriate since squaring would reflect the effect on states’ funding ability of both resources and people in poverty. However, to the extent that PCI does not accurately reflect state resources and people in poverty, squaring magnifies this inaccuracy. The Department of Health and Human Services (HHS) is responsible for calculating matching rates under the formula. HHS is required to calculate matching rates 1 year before the fiscal year in which they are effective, using a 3-year average of the most recently available PCI data reported by the Department of Commerce. Thus, fiscal year 2003 matching rates were calculated at the beginning of fiscal year 2002 using a 3-year average of PCI for 1998 through 2000. Publicly announcing matching rates a year in advance of their use allows states time to make program changes in response to changes in the rate at which the federal government will reimburse eligible program costs. However, the combination of a 1-year lag between the computation of state matching rates and their implementation, coupled with the fact that a 3-year average of PCI is used, also means that the distribution of states’ matching rates reflects economic conditions that existed several years earlier. Federal matching rates for fiscal years 2002 through 2004 are shown in table 2. This appendix describes our methodology for measuring the extent to which the current Medicaid matching formula reduces differences in states’ funding abilities and the data, and their sources, we used to measure the elements of states’ funding ability. While we considered alternative indicators of state resources, people in poverty, and the cost of health care, and we chose those indicators we believed were most appropriate, we did not perform an exhaustive comparative analysis of other potential indicators, nor did we attempt to develop new indicators. We defined a state’s ability to fund Medicaid services as the economic resources a state is potentially able to tax to fund its Medicaid program relative to the number of persons with incomes below the federal poverty level (FPL), adjusted for the cost of providing health care to them. Specifically, we took into account differences in the utilization of health care services by children, adults, and the elderly, and we developed an index for the differences in the cost of health care personnel and the cost of medical facilities and supplies used to provide the services. We calculated state funding ability according to the following formula:           Y = State resources potentially subject to state taxation P = People with incomes below the FPL, adjusted for differences in service utilization by children, adults, and the elderly c = Index of the cost of factors in the provision of health care services (e.g., health care personnel, medical facilities, and supplies). We explain later in this appendix how we adjusted the counts of people in poverty for differences in service utilization and in the cost of personnel, facilities, and supplies. Federal matching aid, in effect, adds to a state’s ability to fund program costs from its own resources. For example, when federal matching aid pays for half the cost of a state’s program, it effectively doubles that state’s ability to fund program services. The higher the federal matching rate, the more federal matching aid contributes to a state’s ability to fund Medicaid services. In general, a state’s funding ability after the value of its federal matching aid is added can be determined using the following formula: FMAP = State’s federal matching rate Y = State resources potentially subject to state taxation P = People with incomes below the FPL, adjusted for differences in service utilization by children, adults, and the elderly c = Index of the cost of factors in the provision of health care services (e.g., health care personnel, medical facilities, and supplies). The first term after the equals sign represents the multiple by which a state’s matching rate increases the state’s funding ability. For example, if a state receives a federal match of 75 percent, its funding ability is increased by a factor of 4 [(1/(1 - 0.75) = 4]. To measure the effect of the current formula in reducing differences in states’ funding ability, we compared differences between each state’s funding ability before and after the value of federal matching aid is added and calculated the percentage reduction in these differences. In performing these calculations, we measured each state’s funding ability relative to the average funding ability of all states. The resulting indexes of states’ funding abilities provide a means of comparing relative differences in states’ ability to fund their Medicaid programs. We used the weighted absolute mean deviation as a quantitative measure of differences in states’ funding ability. This statistic is a measure of average differences in states’ funding ability. It is calculated by taking the absolute value of each state’s index of relative funding ability and computing the arithmetic average of these differences, using the following formula: X = A state’s funding ability index XAVG = Weighted average of all states’ funding ability indexes w = A state’s weighting factor (people in poverty). In calculating the mean absolute deviation, we took into account differences in the potential size of state programs by using the number of people living in poverty in each state. We chose the mean absolute deviation rather than the more commonly used weighted standard deviation because the latter, by squaring differences between each state’s funding ability and the national average funding ability, gives much greater weight to states at the extreme ends of the distribution of states’ funding abilities, resulting in a measure that is more sensitive to extreme values and thus less likely to reflect the norm. We calculated the mean absolute deviation in states’ funding ability both without and with the value of federal matching aid added. Calculating the percentage change in the two mean absolute deviations measures the extent to which the current formula reduces differences in states’ funding ability. For example, if the current formula completely eliminated differences in states’ funding ability, total funding ability of all states would equal the average of all states, and the mean absolute deviation would be zero, representing a 100 percent reduction in differences in states’ funding ability (the maximum possible). Alternatively, if the formula had no effect in reducing differences in states’ funding ability, the mean absolute deviation in states’ funding ability with the value of federal matching aid taken into account would be the same as the mean absolute deviation in states’ funding ability from their own resources. In this case, there would be no change in the mean absolute deviation, meaning that the matching formula had no effect in reducing relative differences in states’ funding ability. Table 3 shows each state’s index of Medicaid funding ability without and with the value of its federal matching aid. The mean absolute deviation of states’ funding ability before taking into account the value of federal matching aid (column 1 of table 3) yielded an average difference in states’ relative funding ability of 22.7 percent. The mean absolute deviation in states’ funding ability after taking into account the value of federal matching aid (column 2 of table 3) yielded an average difference of 18.1 percent. This difference represents a 20 percent overall reduction in differences in states’ funding ability as a result of adding federal matching aid. As the indicator of state resources in the formula, PCI includes income received by state residents (“personal income”), such as wages, rents, and interest income, but excludes other important taxable income. For example, PCI excludes corporate income not received as income by state residents, such as undistributed corporate profits and dividends received by people who reside out-of-state. An ideal resources measure would count all income that states are able to tax. Even certain types of income that states exempt from taxation or tax at preferential rates should be counted as potentially taxable income because these enhance taxpayers’ ability to pay all taxes levied in the state. We used Total Taxable Resources (TTR), as reported by the Department of the Treasury, to measure state resources because it comprises the income included in PCI as well as income from other sources, such as corporate income and capital gains, and thus it is a more comprehensive indicator of income than PCI alone. TTR includes personal income received by state residents as well as income produced within a state but received by individuals who reside out-of-state (which is considered a portion of the Gross State Product (GSP)). As indicated in table 4, nationwide, the TTR measure of income is 32 percent larger than PCI. While TTR is a more comprehensive measure of state resources than PCI, recent definitional changes to GSP and state personal income (SPI) data made by the Bureau of Economic Analysis (BEA) may have implications for the methodology used by the Department of the Treasury to calculate TTR. For example, BEA has changed its treatment of the value of services provided by government-owned fixed assets that are now included in GSP and benefit payments of government employee pension plans, which are now excluded from SPI. Since the Treasury initially developed the TTR methodology, it has not reported why definitional changes made by BEA should or should not be reflected in TTR. In the case of the changes to government pension plans, the Treasury has reported it is currently studying whether they necessitate any modifications to the TTR methodology. To measure people in poverty, we adjusted the Bureau of the Census’s estimates of people in households with incomes at or below the FPL for (1) differences in the cost of providing health care services to children, adults, and the elderly (to account for the higher health care costs for the elderly) and (2) geographic differences in the cost of providing health care services (such as wages and salaries of health care professionals and the rental cost of medical facilities). We obtained estimated counts of people living in poverty from the Bureau of the Census’s Current Population Survey (CPS). Because the CPS sample sizes for individual states are especially small when disaggregated by age cohorts, they are subject to greater statistical error than a sample representing all age groups. To improve the accuracy of these estimates, we averaged poverty counts over the 5-year period 1995 through 1999. We used the FPL as a basis for making cross-state comparisons of the number of people in poverty. (See table 5.) Official poverty counts are not a good proxy for the low-income population because they do not take into account the higher cost of serving elderly individuals. For example, elderly individuals represented 27 percent of Medicaid beneficiaries in fiscal year 2000, the latest year for which data are available. However, because they are more intensive users of the health care system and utilize more expensive long-term care services, elderly persons accounted for 66 percent of all Medicaid spending that year. To account for differences in costs to serve each group, we weighted the numbers of children, adults, and the elderly. We calculated Medicaid spending per beneficiary for each age group nationwide, then compared spending per beneficiary for each age group with average spending per beneficiary for all age groups. We used a 5-year average of Medicaid spending per beneficiary derived from data reported by the Centers for Medicare & Medicaid Services (CMS) for fiscal years 1995 through 1999. The results suggest that, nationwide, elderly beneficiaries utilize health care services at about two-and-one-half times the rate of the average Medicaid beneficiary, and children utilize services at less than half the rate of the average beneficiary. (See the cost weight index column in table 6.) Number Poverty 64 to 21 Aged                     In table 7, the columns representing official poverty rates report the percentage of people in poverty based on the official government poverty statistics reported by the Bureau of the Census. The age-weighted columns are the percentages of people in poverty after weighting children, adults, and the elderly. Comparing the percentages in the official poverty rate columns with the percentages after age-weighting illustrates the effect of differences in utilization rates by age cohort. For example, Florida’s official poverty rate is revised upward from 14.0 percent to 15.3 percent when weighted for age differences. Similarly, the District of Columbia’s poverty rate increases from about 21.1 percent to about 22.7 percent after weighting. The cost of providing health care services is affected by three factors: (1) the cost of the personnel who provide the services (wages, for example), (2) the rental cost of facilities in which the services are provided, and (3) the cost of medical equipment and supplies. We used the average wage per worker in the health industry (Standard Industrial Classification (SIC) code 8000), produced by the Bureau of Labor Statistics (BLS), to measure the cost of personnel for 1996 through 1998. The BLS cost data cover personnel in a wide variety of settings, including offices, clinics, hospitals, and medical and dental laboratories, as well as health care providers who work for home health agencies. To measure the cost of facilities through which services are delivered, we used apartment rents as reported by the Department of Housing and Urban Development (HUD) because data on commercial office space rental rates in the health sector of the economy were not available. Apartment rental rates were an appropriate alternative because the same factors that affect the cost of office space (for example, population density and income) affect housing rental rates, and apartment rental rates are likely to more closely mimic office space costs than would owner- occupied housing units. In addition, data are available for apartment rentals by the size of the unit, which allowed us to take size differences into account. Data on the geographic differences in the cost of medical equipment and supplies were not readily available. Because medical equipment and supplies generally are purchased in national markets, we assumed that the costs of these items do not vary across states. We calculated an index of health industry wage rates and apartment rents (our proxy for the rental cost of medical facilities). For medical supplies, we used a cost index of 1.0 for all states to reflect the assumption that these costs do not vary across states. We then combined the three factors into an overall index of the cost of health care services by state, weighting each factor on the basis of its respective proportion of the total cost of health care services. Personnel costs represent the greatest share of health care costs, as much as 75 percent of total costs, according to one study. We constructed our cost index conservatively by reducing the personnel cost weight to 60 percent. We applied a cost weight of 30 percent for medical equipment and supplies and other miscellaneous costs that are assumed to be the same across states. The remaining 10 percent is the cost weight for rent. Using these cost weights is likely to understate cross-state cost differences. Nineteen states had health care costs estimated to be at least 10 percent above or below the national average. The states with costs 10 percent or more above the national average were California, Connecticut, the District of Columbia, Hawaii, Nevada, and New Jersey. States with lower costs tended to be southern or midwestern states. (See table 8.) We compared states’ ability to fund Medicaid services without and with the value of federal matching aid added. Column 1 of table 9 shows states’ funding ability: states’ TTR per person in poverty adjusted for differences in the cost of providing them health care services. Column 2 shows states’ effective fiscal year 2000 federal matching rates used in the analysis and column 3 shows the resulting “multipliers” (i.e., 1/(1 - FMAP)) that reflect the effect of federal matching on states’ funding ability. Funding ability with federal aid is shown in column 4. The data used to show the relationship between a state’s effort to fund Medicaid benefits from its own financial resources and its total Medicaid spending per person in poverty, shown in figure 2, are displayed in table 10.

Summary: A primary goal in establishing Medicaid's statutory formula, whereby states with lower per capita incomes (PCI) receive higher rates of federal reimbursement for program costs, was to narrow differences among states in their ability to fund Medicaid services. States' ability to fund services depends on their financial resources in relation to their number of and costs to serve people in poverty. GAO and others have testified before Congress that the current formula does not address wide differences among states in their ability to fund their Medicaid programs and that the formula's reliance on PCI is the primary cause. GAO was asked to determine the extent to which the formula narrows these differences and to identify factors that impede further narrowing of differences. To evaluate the extent to which the formula narrows differences in states' funding ability, GAO used an alternative to PCI that more directly measures states' resources, number of people in poverty, and cost of providing services to this population. Using this measure, GAO determined the effect of the current formula by comparing states' funding ability before and after receiving their federal matching aid. If differences in funding ability were eliminated, the formula would have reduced differences by 100 percent. The Medicaid formula narrows the average difference in states' funding ability by 20 percent but often widens the gap between individual states and the national average. Although the receipt of federal matching aid moves 30 states closer to the national average, making the average difference in funding ability smaller, it also moves 21 states farther away from the average, widening the average difference. These 21 states include 3 that are among the states with the largest populations in poverty--California, Florida, and New York. After federal matching aid is added, states' funding ability ranges from 26 percent below the national average for two states to 179 percent above for another. Because of the formula's current structure, in many instances, two states devoting similar proportions of their own resources to Medicaid can spend very different amounts per person in poverty. For example, in fiscal year 2000, California and Wisconsin each devoted about $8 for every $1,000 of their own state resources toward Medicaid. However, under the current formula, Wisconsin receives a relatively high federal matching rate despite its relatively high ability to fund program services, whereas California receives a low federal matching rate despite its relatively low ability to fund program services. With the addition of federal matching aid, Wisconsin is enabled to spend more than twice what California is able to spend per person in poverty ($7,532 versus $3,731). Two factors constrain the formula from further decreasing differences in states' funding ability. First, PCI is not a comprehensive indicator of a state's total available resources and is a poor measure of the size of and cost to serve a state's people in poverty. Second, the statutory provision that guarantees no state will receive less than a 50 percent matching rate benefits many states that already have above-average resources to fund health care for their populations in poverty. For example, 2 of the 11 states that benefit the most from the 50 percent "floor" receive matching rates that are 35 and 20 percentage points higher, respectively, than the rates they would receive based solely on their PCI. GAO received comments on a draft of this report from two external reviewers who have Medicaid formula expertise. They generally agreed with the analysis and provided technical comments, which were incorporated as appropriate.