Medicaid Formula: Differences in Funding Ability among States Often Are Widened
Published: 2003-07-10T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: 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.
section_title: What GAO Found
paragraphs: 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.

section_title: Letter
paragraphs: Created in 1965, Medicaid is the largest federal program assisting states in  financing medical and health-related services for certain categories of the  country’s low-income population. In fiscal year 2000, Medicaid served  about 43 million beneficiaries and had expenditures totaling about $196  billion, $111 billion of which was financed by the federal government and  the rest financed by the states. The federal share of total Medicaid  program costs is determined using a statutory formula that calculates the  portion of each state’s Medicaid expenditures that the federal government  will pay, known as the Federal Medical Assistance Percentage (FMAP),  referred to in this report as the federal matching rate. The formula  calculates the federal matching rate for each state on the basis of its per  capita income (PCI) in relation to national PCI. States with a low PCI  receive a higher federal matching rate, and states with a high PCI receive a  lower rate. The Medicaid statute also provides for a 50 percent minimum  federal matching rate (“50 percent floor”) that reflects a federal  commitment to fund at least half the cost of each state’s program.
One of the goals of the formula has been to narrow differences among  states in their ability to fund Medicaid services, which is determined by a  state’s financial resources in relation to its low-income population. By  providing higher matching rates to states with low PCI, it was expected  that these states would be in a better position to provide health care  services to low-income populations. (App. I contains a legislative history  of the formula.)
In 1995, we and other witnesses testified before the Senate Committee on  Finance that the current Medicaid formula did not adequately address  wide differences among states in their ability to fund program services and  that the formula’s reliance on PCI is the primary cause. Witnesses  generally testified that PCI is an unreliable indicator of states’ ability to  fund Medicaid programs.
Because the formula has not been changed since the program’s inception  and concerns persist regarding its performance with respect to narrowing  differences in states’ ability to fund program services, you asked us to  address the following questions: (1) To what extent does the Medicaid  formula reduce differences in states’ ability to fund program services? (2)  What factors prevent the formula from further narrowing differences in  states’ funding ability?
To evaluate the extent to which the formula narrows differences in states’  ability to fund program services, we defined a state’s ability to fund its  Medicaid programs as the financial resources potentially subject to state  taxation relative to its number of low-income residents, adjusted for the  cost of providing health care to them. For state resources, we used Total  Taxable Resources (TTR), a measure of all income potentially subject to  taxation that is either produced within a state or received by state  residents from out-of-state sources. TTR is reported annually by the  Department of the Treasury. To determine the number of low-income  people in each state (“people in poverty”), we obtained the Bureau of the  Census’s counts of people with incomes at or below the federal poverty  level (FPL). We adjusted the counts of people in poverty to reflect (1) the  higher cost of serving the elderly, who utilize health care services at higher  rates than other age groups, and (2) geographic differences in the cost of  medical personnel, facilities, and supplies used to deliver health care  services. To adjust for age differences in people in poverty, we used data  on Medicaid spending by age group from the Department of Health and  Human Services’ (HHS) Centers for Medicare & Medicaid Services (CMS).  We used 5-year averages of people in poverty for each age group for 1995  through 1999 to increase the reliability of the state-level population counts  because they are subject to statistical error, especially in smaller states. To  measure geographic differences in the cost of medical personnel, facilities,  and supplies, we used data from the Department of Labor’s Bureau of  Labor Statistics (BLS) and from the Department of Housing and Urban  Development (HUD).
We compared states’ funding ability from their own resources with their  funding ability after their resources have been augmented to include the  value of the federal Medicaid matching aid they receive. Throughout this  report, we refer to augmenting a state’s taxable resources this way as state  funding ability with the “value” of federal matching aid included. If  differences in funding ability were completely eliminated by adding the  value of federal matching aid, the formula would have reduced differences  in states’ funding ability by 100 percent. We did our work between June  2001 and June 2003 in accordance with generally accepted government  auditing standards. (App. II provides a more detailed discussion of our  methodology.)
subsections: section_title: Background
paragraphs: 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.
section_title: Medicaid Formula Narrows Differences in Some States’ Funding Ability and Widens Differences in Others
paragraphs: 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.
subsections: section_title: Formula Reduces Overall Differences in States’ Funding Ability by 20 Percent
paragraphs: 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.
section_title: Funding Ability of 21 States Moves Farther from Average State’s Funding Ability after Federal Match Is Added
paragraphs: 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.
section_title: Many States Devoting the Same Proportion of Their Own Resources to Medicaid Cannot Spend Comparable Amounts per Person
paragraphs: 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.)
section_title: Use of PCI and 50 Percent Floor Inhibits Formula’s Ability to Further Narrow Differences in States’ Funding Ability
paragraphs: 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.
subsections: section_title: PCI Is Not a Comprehensive Measure of States’ Resources and Is a Poor Proxy for the Size of and Cost to Provide Services to Their People in Poverty
paragraphs: 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.)
section_title: Minimum Federal Match Generally Helps States That Already Have High Funding Ability
paragraphs: 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.
section_title: Comments from External Reviewers
paragraphs: 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.
section_title: Appendix I: Legislative History and Description of the Matching Formula
paragraphs: 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.
subsections: section_title: Legislative History of the Medicaid Formula
paragraphs: 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.
section_title: Current Medicaid Matching Formula
paragraphs: 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.
section_title: Appendix II: Methodology
paragraphs: 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.
subsections: section_title: Measuring States’ Funding Ability
subsections: section_title: Funding Ability from State Resources
paragraphs: 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.
section_title: State Funding Ability with the Value of Federal Matching Aid Added
paragraphs: 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].
section_title: Calculating the Reduction of Differences in States’ Funding Ability
paragraphs: 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.
section_title: Measuring State Resources
paragraphs: 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.
section_title: Measuring People in Poverty and the Costs to Provide Them Program Services
paragraphs: 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).
subsections: section_title: Measuring the Number of People in Poverty
paragraphs: 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.)
section_title: Adjusting Poverty Counts for Differences in Costs to Serve Children, Adults, and the Elderly
paragraphs: 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.
section_title: Adjusting Poverty Counts for Differences in the Cost of Providing Health Care Services
paragraphs: 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.)
section_title: Calculating States’ Ability to Fund Medicaid Services without and with Value of Federal Matching Aid Added
paragraphs: 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.
section_title: Comparing Proportion of States’ Resources Devoted to Medicaid with Their Total Spending per Person in Poverty
paragraphs: 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.