MEDICAID

Alternative Measures 

Could Be Used to 

Allocate Funding 

More Equitably

Report to Congressional Requesters

May 2013

GAO-13-434

United States Government Accountability Office
United States Government Accountability Office

Highlights of GAO-13-434, a report to 

congressional requesters

May 2013

MEDICAID

Alternative Measures Could Be Used to Allocate 

Funding More Equitably

Why GAO Did This Study

Medicaid is the largest federal program 

assisting states in financing medical 

and health-related services for certain 

low-income individuals. States and the 

federal government share in the 

financing of the Medicaid program, with 

the federal government matching most 

state expenditures for Medicaid 

services on the basis of a statutory 

formula known as the FMAP, which is 

based solely on state PCI in relation to 

national PCI. Prior GAO work has 

raised concerns about the FMAP, 

noting that PCI does not accurately 

represent states’ populations in need 

of Medicaid services or states’ ability to 

finance services, and does not account 

for geographic cost differences among 

states. GAO was asked to examine 

ways to improve the allocation of 

Medicaid funding. 

GAO considered whether available 

data sources could be used to develop 

measures to more equitably allocate 

Medicaid funding. To do so, GAO 

reviewed its prior reports and other 

studies, examined data sources 

produced by federal agencies, and 

illustrated how selected data could be 

used to develop measures to allocate 

Medicaid funding. GAO based its 

analysis on commonly used equity 

standards and focused its efforts on 

readily available data sources, which 

are not inclusive of all possibilities.

The Department of Health and Human 

Services provided technical comments, 

which GAO incorporated as 

appropriate.

What GAO Found

GAO identified multiple data sources that could be used to develop measures to 

allocate Medicaid funding to states more equitably than the current funding 

formula—known as the Federal Medical Assistance Percentage (FMAP)—which 

is based solely on per capita income (PCI). To be equitable from the perspective 

of beneficiaries and allow states to provide a comparable level of services to 

each person in need, a funding allocation mechanism should take into account 

the demand for services in each state and geographic cost differences among 

states. To be equitable from the perspective of taxpayers, an allocation 

mechanism should ensure that taxpayers in poorer states are not more heavily 

burdened than those in wealthier ones, by taking into account state resources.

To illustrate, GAO identified at least one federal data source that could be used 

to develop measures of each of these aspects, in order to allocate Medicaid 

funding more equitably. 

• Demand for services. A measure of the demand for Medicaid services 

should account for both the size of the target population in need of services 

and the health services needs of that population. Nationally representative 

federal surveys, such as the U.S. Census Bureau’s American Community 

Survey (ACS) and Current Population Survey (CPS), are available data 

sources that can be used to directly estimate the number of persons residing 

in each state with incomes low enough to qualify them as potentially in need 

of Medicaid services. These estimates can then be adjusted to reflect 

variation in health services needs within the identified population, using 

additional information collected in the surveys or from data sources external 

to the surveys, such as Medicaid data on enrollment or spending. 

• Geographic cost differences. A measure of geographic cost differences 

should account for all components of health care costs, including the cost of 

the personnel who provide services, which represents the greatest share of 

costs. National data that can be used to estimate average wages for health 

care personnel by state include the Occupational Employment Statistics 

survey conducted by the Bureau of Labor Statistics. 

• State resources. A measure of state resources should account for all 

income—regardless of whether the state taxes the income or not. While PCI 

includes the personal income of state residents, it excludes other taxable 

income, such as undistributed corporate profits. In contrast, the Total 

Taxable Resources (TTR) measure, as generated by the Department of the 

Treasury from multiple data sources, comprises not only the income included 

in PCI but also other significant sources of taxable income. As a result, 

nationwide, the TTR measure of income was 42 percent larger on a per 

capita basis than PCI in 2010, and provided a more comprehensive measure 

of state resources. 

GAO’s analysis shows that measures of the demand for services, geographic 

cost differences, and state resources can be combined in various ways to 

provide a basis for allocating Medicaid funds more equitably among states. View GAO-13-434. For more information, 

contact Carolyn L. Yocom at (202) 512-7114 

or yocomc@gao.gov.
Page i GAO-13-434 Medicaid Funding Allocation

Letter 1 

Background 3

Available Data Sources Could Be Used to Develop Measures to 

Allocate Medicaid Funding More Equitably 5

Agency Comments 8

Appendix I Examples of Alternative Measures That Could Be Used 

to More Equitably Allocate Federal Medicaid Funding 10

Appendix II GAO Contact and Staff Acknowledgments 30

Related GAO Products 31

Tables

Table 1: Estimates of Population in Poverty, by State 12

Table 2: Estimates of Medicaid-Service-Need Weights by Age and 

Disability Category 16

Table 3: Estimates of Demand for Services, Including Adjustment 

for Service Need Based on the Composition of the 

Medicaid Population, by State 18

Table 4: Estimates of Demand for Services, Including Adjustment 

for Service Need Based on the Composition of the State 

Poverty Population, by State 22

Table 5: Estimates of Cost Indexes for Health Care Services, by 

State 26

Table 6: Average Total Taxable Resources (TTR) and Per Capita 

TTR, by State, 2008 through 2010 28

Contents 
Page ii GAO-13-434 Medicaid Funding Allocation

Abbreviations

ACS American Community Survey

CMS Centers for Medicare & Medicaid Services

CPS Current Population Survey

FMAP Federal Medical Assistance Percentage

FPL federal poverty level

HHS Department of Health and Human Services

IPPS Inpatient Prospective Payment System

MACPAC Medicaid and CHIP Payment and Access Commission

MedPAC Medicare Program Advisory Commission 

MSIS Medicaid Statistical Information System

OES Occupational Employment Statistics

PCI per capita income

PPACA Patient Protection and Affordable Care Act

TTR Total Taxable Resources

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Page 1 GAO-13-434 Medicaid Funding Allocation

441 G St. N.W.

Washington, DC 20548

May 10, 2013

The Honorable Orrin G. Hatch

Ranking Member

Committee on Finance

United States Senate

The Honorable Fred Upton

Chairman

Committee on Energy and Commerce

House of Representatives

Created in 1965, Medicaid is the largest federal program assisting states 

in financing medical and health-related services for certain low-income 

individuals, such as children and individuals who are disabled or elderly. 

Within broad federal requirements, states have some flexibility in deciding 

the range of medical services to provide and which individuals to cover, 

and state Medicaid programs accordingly vary in scope. In fiscal year 

2011, Medicaid served an estimated 70 million enrollees and had 

expenditures totaling an estimated $432.4 billion, $275.1 billion of which 

was financed by the federal government, with the rest financed by the 

states. Medicaid enrollment and expenditures have increased significantly 

over the most recent 10-year period for which data are available—

enrollment growth averaged 4.2 percent per year between 2001 and 

2011, and expenditures grew, on average, by 6.5 percent per year.

States and the federal government share in the financing of the Medicaid 

program, with the federal government matching most state expenditures 

for Medicaid services on the basis of a statutory formula known as the 

Federal Medical Assistance Percentage (FMAP). The FMAP calculates 

the federal matching rate for each state on the basis of the state’s per 

capita income (PCI) in relation to the national PCI.1

 

1

The FMAP is calculated using the following formula: FMAP = 1.00 – 0.45 (State PCI / 

U.S. PCI)2

. PCI is calculated by the U.S. Bureau of Economic Analysis. Federal law 

specifies that the FMAP will be no lower than 50 percent and no higher than 83 percent. 

The Department of Health and Human Services is required to publish FMAPs for states 

between October 1 and November 30 of each fiscal year. See 42 U.S.C. §§ 1396b(a)(1), 

1396d(b). For fiscal year 2013, states’ FMAPs range from 50.00 percent to 73.43 percent.

Under the FMAP, the 

federal government pays a larger portion of Medicaid expenditures in 

 
 

Page 2 GAO-13-434 Medicaid Funding Allocation

states with low PCI relative to the national average, and a smaller portion 

for states with higher PCIs. PCI is used in the formula as a proxy for both 

state resources and the low-income population in need of Medicaid 

services in each state.2

Our prior work has noted multiple concerns regarding how the FMAP 

formula allocates funds among states. Specifically, we have noted that 

PCI, the sole measure included in the FMAP formula, is an incomplete 

measure of states’ resources, is a poor proxy for the size or 

characteristics of the states’ population in need of Medicaid services, and 

does not take into account geographic differences in the cost of providing 

health care services.3 In addition, we have expressed concerns that the 

FMAP does not take into account states’ current economic circumstances 

due to lags in the calculation of PCI and implementation of the FMAP.4

Given these concerns regarding the Medicaid formula and the overall 

growth in Medicaid enrollment and expenditures, you asked us to 

examine ways to improve the allocation of Medicaid funding. In this 

report, we considered whether available data sources could be used to 

develop measures to more equitably allocate Medicaid funding. To do 

this, we applied two equity standards that we and others have commonly 

used to evaluate federal funding mechanisms: beneficiary equity, which 

stipulates that funds should be distributed so that each state can provide 

a comparable level of services to each person in need; and taxpayer 

equity, which stipulates that each state should receive enough funds to be 

able to provide a comparable level of services as others while contributing 

about the same proportion of its own resources. With these standards in 

mind, we reviewed prior GAO reports and other relevant studies, 

examined potential data sources produced by federal agencies, and 

developed illustrative examples of how selected data sources—largely 

 

2

Consistent with this intent, squaring PCI has the effect of making PCI appear in the 

formula twice as an attempt to reflect both state resources and the population in need of 

Medicaid services.

3

See, for example, GAO, Medicaid Formula: Differences in Funding Ability among States 

Often Are Widened, GAO-03-620 (Washington, D.C.: July 10, 2003).

4

This is of particular concern for states during economic downturns. See, for example, 

GAO, State and Local Governments: Knowledge of Past Recessions Can Inform Future 

Federal Fiscal Assistance, GAO-11-401 (Washington, D.C.: Mar. 31, 2011); and Medicaid: 

Strategies to Help States Address Increased Expenditures during Economic Downturns, 

GAO-07-97 (Washington, D.C.: Oct. 18, 2006). 
 

Page 3 GAO-13-434 Medicaid Funding Allocation

federal statistical data, such as data from surveys conducted by the U.S. 

Census Bureau—could be used to develop measures to more equitably 

allocate Medicaid funding. We reviewed relevant documentation for each 

data source that we used and determined that the data were sufficiently 

reliable for our purposes. We focused our efforts on readily available data 

sources, most of which had been used in our prior reports. Accordingly, 

the data sources we discuss are not inclusive of all possibilities. 

Appendix I in this report provides additional details of our analysis.

We conducted this performance audit from September 2012 to May 2013 

in accordance with generally accepted government auditing standards. 

Those standards require that we plan and perform the audit to obtain 

sufficient, appropriate evidence to provide a reasonable basis for our 

findings and conclusions based on our audit objectives. We believe that 

the evidence obtained provides a reasonable basis for our findings and 

conclusions based on our audit objectives.

In prior work spanning more than three decades, we have emphasized 

that, in federal-state programs such as Medicaid, funds should be 

allocated to states in a manner that is equitable from the perspectives of 

both beneficiaries and taxpayers.5

• demand for services, which varies with the size and characteristics of 

the target population; and

To be equitable from the perspective of 

beneficiaries, a funding allocation mechanism should take into account

• geographic cost differences, which affect the level of funding needed 

by each state to provide a comparable level of services to each 

person in need.

In general, the demand for services depends not only on the size of the 

target population, but also on the amount or level of services required, 

which may vary with the characteristics of the population. An allocation 

mechanism that adjusts for cost differences would reflect the variation in 

 

5

See, for example, GAO, Older Americans Act: Options to Better Target Need and 

Improve Equity, GAO-13-74 (Washington, D.C.: Nov. 30, 2012); and the list of related 

GAO products at the end of this report.

Background 
 

Page 4 GAO-13-434 Medicaid Funding Allocation

the costs of key inputs used to provide services, such as the cost of the 

personnel involved.6

To be equitable from the perspective of taxpayers, a funding formula 

should ensure that taxpayers in poorer states are not more heavily 

burdened than those in wealthier ones. To do so, it must take into 

account each state’s ability to finance its share of the costs of a given 

program from its own sources, which we refer to as state resources.7

Our prior work has found that the FMAP does not adequately address the 

demand for services, geographic cost differences, and state resources. 

As we previously reported, PCI is a poor proxy for the size of a state’s 

population in need of Medicaid services, as two states with similar PCI 

can have substantially different numbers of low-income residents.

A 

good measure of a state’s resources would include all types of taxable 

resources and would not be affected by an individual state’s taxing 

authorities or policies, in that it would measure all income flows and not 

just those that a state currently taxes.

8

Moreover, PCI does not take into account differences among states in the 

health care services needs of this population. As we have reported, 

persons who are elderly typically use health care services at higher rates 

than younger adults and children.9

 

6

See, for example, 

Similarly, disabled individuals typically 

have greater health care needs than nondisabled individuals. Accordingly, 

a state with the same number of Medicaid-eligible individuals, but a 

higher number of Medicaid-eligible elderly or disabled individuals than 

another state, may face a higher demand for Medicaid services. For 

example, in fiscal year 2010, Medicaid spent approximately $15,000 for 

each beneficiary aged 65 or older and $17,000 for each disabled 

beneficiary, compared with approximately $3,000 per child and $4,000 

per adult under age 65.

GAO-03-620. 

7

In this report, we use the term “state resources” to refer to funding available to states for 

financing the nonfederal share of their Medicaid expenditures, which may include funding 

provided by local government sources.

8

For example, as we reported in 2003, New York and Maryland had similar PCIs, but the 

percentage of New York’s population that was in poverty was almost twice Maryland’s. 

See GAO-03-620.

9

See GAO-03-620. 
 

Page 5 GAO-13-434 Medicaid Funding Allocation

Additionally, the FMAP does not include any measure of geographic 

differences in the costs of providing health care services, which can vary 

widely. For example, in our work on Medicare payment policy, we have 

noted that when such differences were taken into account, physicians 

practicing in the District of Columbia received a higher overall payment for 

the same service—20 percent higher for a mid-level office visit in 2007—

compared with physicians practicing in South Carolina.10

Finally, PCI does not adequately account for state resources. Although 

PCI measures income received by state residents, such as wages, rents, 

and interest income, it does not include other components of state 

resources, such as corporate income produced within the state but not 

received by state residents. We previously reported that PCI especially 

understated the taxable resources in energy-exporting states, such as 

Alaska and Wyoming, and in states that house numerous corporate 

headquarters, such as Delaware.

PCI does not 

account for these cost differences, as they are largely driven by the cost 

of the personnel who provide the services (wages, for example).

11

We identified multiple data sources that could be used to develop 

measures of the demand for Medicaid services, geographic cost 

differences, and state resources. These measures could be combined in 

various ways to provide a basis for allocating Medicaid funds more 

equitably among states.

Demand for services. A measure of demand for Medicaid services 

should account for both the size of the target population in need of 

services and the health services needs of that population. Nationally 

representative federal surveys, such as the U.S. Census Bureau’s 

American Community Survey (ACS) and Current Population Survey 

(CPS), are available data sources that can be used to directly estimate 

the number of persons residing in each state with incomes low enough to 

 

10GAO, Medicare: Geographic Areas Used to Adjust Physician Payments for Variation in 

Practice Costs Should Be Revised, GAO-07-466 (Washington, D.C.: June 29, 2007).

11See GAO-03-620. 

Available Data 

Sources Could Be 

Used to Develop 

Measures to Allocate 

Medicaid Funding 

More Equitably
 

Page 6 GAO-13-434 Medicaid Funding Allocation

qualify them as potentially in need of Medicaid services.12 The specific 

income level chosen for this measure, such as the federal poverty level 

(FPL), could vary depending on Medicaid eligibility criteria for the target 

population.13

Geographic cost differences. A measure of geographic differences in 

the cost of providing health care services should account for the cost of 

the personnel who provide the services, the cost of medical equipment 

and supplies, and the rental cost of facilities in which the services are 

provided. Of these three components, personnel costs represent the 

greatest share of total costs. For example, in the 2012 Medicare 

physician fee schedule, the Centers for Medicare & Medicaid Services 

(CMS) attributed an average of 72 percent of the total cost of physician 

services to personnel costs,

These estimates could then be adjusted to reflect variation 

in health services needs within the identified population. These 

adjustments could be based on additional information collected in the 

surveys or on sources external to the surveys, such as Medicaid data on 

enrollment or spending. In appendix I, we illustrate how ACS data could 

be used, alone or in combination with Medicaid data, to measure states’ 

relative demand for Medicaid services.

14

 

12Unless funds were to be allocated on a per-enrollee grant basis, Medicaid program data 

on the number of actual Medicaid enrollees would generally not be an ideal data source 

for this measure because Medicaid eligibility criteria vary by state and because not all 

eligible individuals enroll in Medicaid. 

which vary substantially across geographic 

areas. Data that can be used to estimate average wages for health care 

personnel by state are available through several national data-collection 

efforts, including the Occupational Employment Statistics (OES) survey 

conducted by the Bureau of Labor Statistics and the cost reports that 

hospitals must submit to CMS in order to be paid by Medicare under its 

13For example, states are currently required to cover children up to age 6 in families with 

income at or below 133 percent of the federal poverty level (FPL) and children from ages 

6 to 19 in families with income below 100 percent of the FPL. Under the Patient Protection 

and Affordable Care Act (PPACA), beginning in January 2014, states must cover all 

children ages 6 to 19 up to 133 percent of the FPL and states may extend coverage to 

nonpregnant, nonelderly adults up to 133 percent of the FPL. 

14In the November 16, 2012, update to the Medicare physician fee schedule, CMS 

attributed 72 percent of the cost of physician services to personnel costs, 10 percent to 

office rent, and 17 percent to all other costs, including the cost of medical equipment and 

supplies. (Percentages do not sum to 100 due to rounding.) For other services, the 

percentages CMS attributes to personnel costs vary.
 

Page 7 GAO-13-434 Medicaid Funding Allocation

acute care hospital Inpatient Prospective Payment System (IPPS).15

State Resources. A measure of state resources should account for all 

income, regardless of whether the state taxes the income or not, as 

states’ taxing authorities or policies may vary. While PCI—the measure of 

state resources used in the current Medicaid funding formula—includes 

the personal income of state residents, it excludes other taxable income, 

such as undistributed corporate profits. In contrast, Total Taxable 

Resources (TTR) comprises not only the income included in PCI but also 

other significant sources of taxable income. For example, 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. As a result, nationwide, 

the TTR measure of income was 42 percent larger on a per capita basis 

than PCI in 2010. In appendix I, we illustrate how estimates of state TTR, 

generated by the Department of the Treasury from data provided primarily 

by the Bureau of Economic Analysis, could be used to measure state 

resources.

In 

appendix I, we illustrate how OES wage data could be used to measure 

geographic cost differences.

Combining Measures. Measures of the demand for services, geographic 

cost differences, and state resources could be combined in various ways 

to provide a basis for allocating Medicaid funds equitably among states.16

 

15Under the IPPS, hospitals are paid a standardized amount for an entire inpatient 

episode. CMS calculates IPPS payments through a series of adjustments applied to 

national base payment rates covering operating and capital expenses. CMS adjusts 

hospital payments under IPPS using an area wage index to account for geographic 

differences in personnel costs across the country. The wage index reflects how average 

hospital wages in each geographic area compare to average hospital wages nationally, 

set at 1.0.

For example, when determining states’ ability to fund Medicaid services, 

rather than simply considering total state resources or state resources per 

16In technical comments provided on a draft of this report, the Department of Health and 

Human Services noted that an alternative allocation methodology could result in 

significant changes in federal funding for individual states and suggested that we consider 

detailing the projected funding effect of such an alternative. We do not provide such 

details because the measures we discuss could be combined in various ways in different 

allocation mechanisms; additionally, it was not within the scope of this engagement to 

develop or test new allocation mechanisms, only to consider possible sources of data. As 

previously noted, the data we used to illustrate these measures are not the only data 

sources that could be used.
 

Page 8 GAO-13-434 Medicaid Funding Allocation

capita, a funding formula could reflect state resources in relation to the 

population in need of Medicaid services—that is, in relation to demand for 

services. This would result in a more equitable allocation of funding, 

because two states with similar resources and populations may have very 

different numbers of residents in need of Medicaid services.

For example, one way to combine measures would be to calculate each 

state’s TTR per person in poverty, using a measure of the poverty 

population that is adjusted to account for both the service needs of the 

population and geographic differences in the cost of services.17 We have 

previously found that such a combination of measures could be the basis 

of an alternative to the existing FMAP formula, whereby this calculation of 

TTR per person in poverty would replace the use of PCI in the formula.18

These measures could also be combined in other ways and could be 

used to take into account specific circumstances, such as economic 

downturns.19

We provided the Secretary of Health and Human Services with a draft of 

this report. The Department of Health and Human Services provided 

technical comments, which we incorporated as appropriate.

We are sending copies of this report to the Administrator of CMS and 

other interested parties. In addition, the report will be available at no 

charge on the GAO website at http://www.gao.gov. 

 

17The results of this calculation would differ somewhat depending on which measures of 

demand for services or geographic cost differences were selected. 

18For example, see GAO, Medicaid: Matching Formula’s Performance and Potential 

Modifications, GAO/T-HEHS-95-226 (Washington, D.C.: July 27, 1995).

19For example, because states’ economic conditions affect both demand for services and 

state resources, we have suggested that both of these should be considered when 

targeting increased assistance to states during economic downturns. Such consideration 

is important because rising unemployment during economic downturns can lead to 

increases in the number of individuals who are eligible for Medicaid coverage at the same 

time that states’ tax revenues decline. See, for example, GAO, Medicaid: Prototype 

Formula Would Provide Automatic, Targeted Assistance to States during Economic 

Downturns, GAO-12-38 (Washington, D.C.: Nov. 10, 2011).

Agency Comments
 

Page 9 GAO-13-434 Medicaid Funding Allocation

If you or your staff have any questions about this report, please contact 

me at (202) 512-7114 or yocomc@gao.gov. Contact points for our Offices 

of Congressional Relations and Public Affairs may be found on the last 

page of this report. GAO staff who made key contributions to this report 

are listed in appendix II.

Carolyn L. Yocom

Director, Health Care
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 10 GAO-13-434 Medicaid Funding Allocation

This appendix illustrates how selected data sources could be used to 

measure (1) the demand for services, (2) geographic differences in the 

cost of health services, and (3) state resources. We considered and 

evaluated multiple data sources, focusing our efforts on federally 

available data sources, most of which had been used in our prior reports.1

To illustrate possible measures of the demand for Medicaid services, we 

used data from the American Community Survey (ACS) to estimate the 

size of the target population in need of services and then used ACS data 

and Medicaid program data from the Centers for Medicare & Medicaid 

Services (CMS) to adjust for differences in the health services needs of 

the target population in each state.

To illustrate one possible measure of the size of a potential target 

population by state, we used data and population weights from the ACS 

3-year file for 2009-2011 to estimate the number of people living in each 

state with family incomes at or below 100 percent of the federal poverty 

level (FPL),2

 

1

The data sources we used have a significant time lag and may be appropriate when state 

economic conditions are stable or improving. However, funding adjustments to states 

during economic downturns may require more current data sources, such as 

unemployment rates, to reflect increasing demand for services. For example, see GAO, 

Medicaid: Prototype Formula Would Provide Automatic, Targeted Assistance to States 

during Economic Downturns, 

hereafter referred to as our unadjusted estimates of the 

population in poverty. We chose the ACS as our data source because of 

its large sample size and because its sample includes people living in 

institutions, such as nursing homes. Residents of nursing homes are an 

important component of the potential Medicaid-eligible population but are 

not included in other key surveys, such as the Current Population Survey. 

GAO-12-38 (Washington, D.C.: Nov. 10, 2011); Medicaid: 

Improving Responsiveness of Federal Assistance to States during Economic Downturns, 

GAO-11-395 (Washington, D.C.: Mar. 31, 2011); Medicaid: Strategies to Help States 

Address Increased Expenditures during Economic Downturns, GAO-07-97 (Washington, 

D.C.: Oct. 18, 2006).

2

The Census Bureau produces 1-year, 3-year, and 5-year ACS data files. The 2009-2011 

3-year file is a multiyear combination of 1-year files, with appropriate adjustments to 

weights and other data elements. We chose the 3-year file over the 1- and 5-year files in 

order to include only the more recent years of available data while also increasing the 

sample size and thus the precision of our estimates. However, both the 1-year and 5-year 

files would also be appropriate options depending on relative preferences in terms of how 

current the data are versus the precision of estimates that can be generated from them.

Appendix I: Examples of Alternative 

Measures That Could Be Used to More 

Equitably Allocate Federal Medicaid Funding

Demand for Services

Measuring the Size of the 

Target Population
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 11 GAO-13-434 Medicaid Funding Allocation

While the ACS does not provide family income data for respondents living 

in institutions, we included respondents living in institutions in the 

population in poverty if they reported receipt of Medicaid.

In addition to using the ACS data to generate unadjusted estimates of the 

population in poverty, we also used the data to generate an adjusted 

estimate of the population in poverty that takes into account cost-of-living 

differences among states, such as differences in the costs of renting an 

apartment. To do so, we used state-specific regional price parity 

estimates published by the Bureau of Economic Analysis to adjust family 

income relative to the FPL prior to estimating the population in poverty.3

For states with higher than average cost of living, this adjustment has the 

effect of increasing the amount of income that a family can have while still 

being considered to be in poverty, while the reverse is true for states with 

lower than average cost of living. For example, table 1 shows that in 

Alaska the number of people counted as being in poverty is 8.1 percent 

higher after the cost-of-living adjustment, because Alaska has a higher 

than average cost of living. Conversely, the number of people counted as 

being in poverty in Wyoming is 8.7 percent lower after the cost of living 

adjustment, because Wyoming has a lower than average cost of living. 

Additional possible options for measuring the size of the target population 

include using higher or lower family income thresholds (for example, 

133 percent of FPL) for specific population subgroups or for the entire 

population, or calculating family income in a manner that more closely 

corresponds to Medicaid eligibility rules than does the ACS family income 

variable.4

 

3

Regional price parities are indexes that measure price-level differences across regions 

during a given period by comparing the average price level of an area to the national 

average price level for all areas. See Bettina H. Aten, Eric B. Figueroa, and Troy M. 

Martin, “Regional Price Parities for States and Metropolitan Areas, 2006-2010,” Survey of 

Current Business (Bureau of Economic Analysis, August 2012). Alternative price indexes 

are available; for example, see Trudi Renwick, “Geographic Adjustments of Supplemental 

Poverty Measure Thresholds: Using the American Community Survey Five-Year Data on 

Housing Costs,” U.S. Census Bureau, Social, Economic, and Housing Statistics Division, 

Working Paper 2011–21 (July 2011).

4

The ACS family income variable is based on all family members aged 15 years and older 

residing within a household. The Patient Protection and Affordable Care Act (PPACA) 

specified that, beginning in 2014, for purposes of determining Medicaid eligibility for most 

individuals, income will be defined based on the income of every individual in a household, 

including the income of individuals that a household’s primary taxpayer expects to claim 

as tax dependents, unless the dependents are not required to file tax returns.
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 12 GAO-13-434 Medicaid Funding Allocation

Table 1: Estimates of Population in Poverty, by State

State 

Population in 

poverty

Cost-of-living-adjusted 

population in poverty

Percentage 

difference 

Alabama 893,216 789,200 -11.6%

Alaska 72,387 78,215 8.1

Arizona 1,136,376 1,123,273 -1.2

Arkansas 548,677 473,915 -13.6

California 5,929,053 6,717,844 13.3

Colorado 687,964 680,010 -1.2

Connecticut 385,454 426,196 10.6

Delaware 108,723 113,331 4.2

District of Columbia 113,653 125,540 10.5

Florida 3,079,970 3,079,970 0.0

Georgia 1,727,122 1,607,924 -6.9

Hawaii 153,295 179,899 17.4

Idaho 240,044 220,514 -8.1

Illinois 1,846,804 1,846,804 0.0

Indiana 995,798 916,037 -8.0

Iowa 392,032 341,986 -12.8

Kansas 389,181 340,643 -12.5

Kentucky 829,055 730,839 -11.8

Louisiana 855,925 796,600 -6.9

Maine 183,849 177,418 -3.5

Maryland 582,585 646,042 10.9

Massachusetts 770,818 838,820 8.8

Michigan 1,648,959 1,561,684 -5.3

Minnesota 628,483 606,052 -3.6

Mississippi 664,170 576,936 -13.1

Missouri 938,980 812,178 -13.5

Montana 144,955 134,372 -7.3

Nebraska 232,418 203,135 -12.6

Nevada 401,723 397,253 -1.1

New Hampshire 117,629 123,594 5.1

New Jersey 925,547 1,047,850 13.2

New Mexico 415,761 386,481 -7.0

New York 3,001,976 3,464,867 15.4

North Carolina 1,656,411 1,517,681 -8.4
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 13 GAO-13-434 Medicaid Funding Allocation

State 

Population in 

poverty

Cost-of-living-adjusted 

population in poverty

Percentage 

difference 

North Dakota 78,796 68,993 -12.4

Ohio 1,882,856 1,698,819 -9.8

Oklahoma 649,564 570,564 -12.2

Oregon 614,086 593,279 -3.4

Pennsylvania 1,715,637 1,680,290 -2.1

Rhode Island 144,196 142,555 -1.1

South Carolina 847,614 774,451 -8.6

South Dakota 118,358 102,631 -13.3

Tennessee 1,133,239 1,021,628 -9.8

Texas 4,553,162 4,397,261 -3.4

Utah 363,480 341,294 -6.1

Vermont 70,410 70,040 -0.5

Virginia 937,069 964,561 2.9

Washington 918,816 937,728 2.1

West Virginia 338,273 291,751 -13.8

Wisconsin 767,774 699,022 -9.0

Wyoming 64,480 58,865 -8.7

United States 47,896,803 47,496,835 -0.8%

Source: GAO analysis of U.S. Census Bureau data.

Note: Data are 2009-2011 data from the American Community Survey (ACS). To account for cost-ofliving differences, we used state-specific regional price parity estimates published by the Bureau of 

Economic Analysis to adjust family income relative to the federal poverty level (FPL).

An ideal estimate of the demand for Medicaid services would take into 

account the health needs of the target population—defined in this 

example as the population in poverty—based on individuals’ specific 

health conditions and the costs of services to treat those conditions, an 

approach known as risk adjustment. A simplified approach is to identify 

categories—such as age or disability categories that describe the 

composition of the population—that are known to be correlated with 

different levels of health service needs and expenditures as a proxy for 

the health service needs of individuals within the categories. For example, 

the Medicaid and CHIP Payment and Access Commission (MACPAC) 

estimated that disabled individuals represented about 15 percent of 

Medicaid enrollees in fiscal year 2009, yet accounted for about 43 percent 

Adjusting for Differences 

in Health Services Needs
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 14 GAO-13-434 Medicaid Funding Allocation

of Medicaid benefit spending that year—in line with their greater need for 

health services.5

• children ages 0 through18,

We used age and disability categories as proxy 

measures of the health services needs of this population. We 

distinguished the following categories, which we used to weight the 

estimated population in poverty obtained from the ACS:

• adults ages 19 through 64,

• individuals with disabilities ages 0 through 64,

• elderly individuals ages 65 through 84, and

• elderly individuals ages 85 and older.

Other possible options for accounting for health service needs include 

using more detailed age categories; separating out certain Medicaid 

eligibility categories,

6 such as foster children; or using age categories 

without consideration of disability status, as we did in a prior report.7

We used CMS Medicaid program data to estimate per-enrollee Medicaid 

spending in order to create Medicaid-service-need weights specific to 

each of the five categories. CMS has two key data sets that report state 

Medicaid expenditures—the Medicaid Statistical Information System 

(MSIS) and CMS-64. The MSIS data set was established as a national 

eligibility and claims data set and can provide a summary of expenditures 

linked to specific enrollees on the basis of their medical claims for care, 

while the CMS-64 data set aggregates states’ expenditures and is used to 

reimburse the states for their Medicaid expenditures. We have previously 

reported that Medicaid expenditures as captured in the MSIS were less 

than the CMS-64 amounts and that MSIS data were considered less 

 

5

See MACPAC, Report to the Congress on Medicaid and CHIP (Washington, D.C.: June 

2012). 

6

Eligibility for Medicaid is based on a variety of categorical and financial requirements. For 

example, Medicaid eligibility focuses on certain categories of individuals, such as lowincome children, individuals who are aged, and individuals with disabilities.

7

See GAO, Medicaid Formula: Differences in Funding Ability among States Often Are 

Widened, GAO-03-620 (Washington, D.C.: July 10, 2003), in which we used age 

categories of 0 through 20, 21 through 64, and 65 and older.
Appendix I: Examples of Alternative Measures 

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Federal Medicaid Funding

Page 15 GAO-13-434 Medicaid Funding Allocation

reliable when compared with the CMS-64.8 However, MSIS data are 

linked to specific enrollees, unlike the CMS-64 data, and therefore can be 

used on their own to measure per enrollee spending. For the purpose of 

providing an illustrative example for this report, we therefore relied on 

MSIS data to develop our estimates of Medicaid spending per enrollee 

and Medicaid-service-need weights. It is also possible to combine 

information from MSIS and the CMS-64 in order to develop more 

comprehensive estimates of per-enrollee Medicaid spending. Therefore, 

our per-enrollee estimates—based on MSIS data alone—differ from other 

published estimates that are based on a combination of CMS-64 and 

MSIS data, such as certain estimates published by the CMS Office of the 

Actuary and MACPAC.9

Specifically, we used fiscal year 2009 MSIS data, the latest year for which 

complete data were available for every state, to estimate Medicaid 

spending per enrollee for each category nationwide, measuring enrollees 

in terms of full-year equivalents rather than the total number of persons 

who were enrolled for any length of time during the year. We then 

compared Medicaid spending per enrollee for each category with average 

Medicaid spending per enrollee for all categories. For example, in table 2 

the service-need-weight for the disabled category indicates that, on 

average, Medicaid spending per enrollee among disabled enrollees ages 

0 through 64 is 2.6 times higher than the average for all enrollees.

We compared our calculated service-need 

weights with those that would be obtained if we had used per-enrollee 

spending estimates published by MACPAC and the CMS Office of the 

Actuary and found them to be similar for comparable categories. We used 

our own calculations because of our interest in dividing enrollees who 

were older than age 64 into two age groups.

 

8

See GAO, Medicaid: Data Sets Provide Inconsistent Picture of Expenditures, GAO-13-47

(Washington, D.C.: Oct. 29, 2012).

9

See Department of Health and Human Services, Centers for Medicare & Medicaid 

Services, Office of the Actuary, 2011 Actuarial Report on the Financial Outlook for 

Medicaid (Washington, D.C.: Mar. 16, 2012) and MACPAC, Report to the Congress on 

Medicaid and CHIP (Washington, D.C.: June 15, 2012). 
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That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 16 GAO-13-434 Medicaid Funding Allocation

Table 2: Estimates of Medicaid-Service-Need Weights by Age and Disability 

Category

Category

Average spending

per enrollee 

(dollars)

Medicaid-serviceneed weight

Children ages 0 through 18

a

$2,526 0.4

Adults ages 19 through 64 3,692 0.6

Individuals with disabilities ages 0 through 64 16,027 2.6

Elderly individuals ages 65 through 84 10,472 1.7

Elderly individuals ages 85 and older 24,918 4.0

All categories $6,267 1.0

Source: GAO analysis of Centers for Medicare & Medicaid Services (CMS) data.

Notes: Categories are mutually exclusive. Data are fiscal year 2009 data from the Medicaid Statistical 

Information System (MSIS).

a

We next examined two possible methods for calculating the demand for 

services measure, by adjusting the state populations in poverty on the 

basis of the Medicaid-service-need weights shown in table 2. For the first 

method, we adjusted the state population in poverty by applying the 

service-need weights according to the composition of the state Medicaid 

population—specifically, according to the extent to which each age and 

disability category is represented within a state’s Medicaid population. To 

do so, we used fiscal year 2009 MSIS data to calculate the percentage of 

Medicaid enrollees in each category by state. We then multiplied the state 

poverty population by the percentage of Medicaid enrollees in each 

category in that state and by the national service-need weight for that 

category, and summed the results.

Calculated as average spending per enrollee by category divided by average spending per enrollee 

for all categories.

10

 

10Under this first method, the final demand for services measure is calculated as follows:

State population in poverty * percentage of state Medicaid enrollees who were ages 0 

through 18 * 0.4 

+ state population in poverty * percentage of state Medicaid enrollees who were ages 19 

through 64 * 0.6 

+ state population in poverty * percentage of state Medicaid enrollees who were disabled 

(under age 65) * 2.6 

+ state population in poverty * percentage of state Medicaid enrollees who were ages 65 

through 84 * 1.7 

+ state population in poverty * percentage of state Medicaid enrollees who were ages 85 

and older * 4.0.
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 17 GAO-13-434 Medicaid Funding Allocation

This first method accounts for potential differences among categories in 

the participation rates among eligible individuals within those categories, 

as well as potential differences in the proportions of children, adults, 

disabled individuals, and elderly individuals within each state’s population 

in poverty. It also reflects state-specific coverage decisions about 

eligibility criteria for each category, in that—all else being equal—states 

with stricter eligibility criteria for a given age or disability category relative 

to other states would have a smaller proportion of enrollees in that 

category, while the reverse would be true for states with more generous 

eligibility criteria relative to other states.

Table 3 shows the results of calculating demand for services measures 

by this first method, which weights estimates of the state population in 

poverty for service needs on the basis of the composition of the Medicaid 

population. We applied the same service-need weights to (1) the 

unadjusted population in poverty and (2) the cost-of-living adjusted 

population in poverty, and then calculated the percentage difference 

compared with the unweighted population in poverty estimates shown in 

table 1. Table 3 illustrates, for example, that in West Virginia—a state with 

a higher than average proportion of disabled Medicaid enrollees—the 

demand for services measure increases by about 27 percent after using 

this method to account for the service needs of the population in poverty. 

The percentage differences shown in columns three and five of table 3, 

between weighted and unweighted measures, are the same for the 

population in poverty and the cost-of-living-adjusted population in poverty 

because the same weights—reflecting the distribution of each state’s 

Medicaid population among age and disability categories and the relative 

cost of serving each category—are applied to each population measure.
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 18 GAO-13-434 Medicaid Funding Allocation

Table 3: Estimates of Demand for Services, Including Adjustment for Service Need Based on the Composition of the Medicaid 

Population, by State

Population in poverty Cost-of-living-adjusted population in poverty

State 

Weighted by service 

need

Percentage difference 

compared with 

unweighted 

population in poverty a b Weighted by service need

Percentage difference 

compared with 

unweighted, cost-ofliving-adjusted 

population in poverty c

Alabama

d

1,047,183 17.2% 925,237 17.2

Alaska 65,533 -9.5 70,809 -9.5

Arizona 914,545 -19.5 904,000 -19.5

Arkansas 596,947 8.8 515,607 8.8

California 5,271,251 -11.1 5,972,529 -11.1

Colorado 650,065 -5.5 642,549 -5.5

Connecticut 369,451 -4.2 408,502 -4.2

Delaware 97,983 -9.9 102,136 -9.9

District of Columbia 122,937 8.2 135,795 8.2

Florida 3,384,924 9.9 3,384,924 9.9

Georgia 1,743,384 0.9 1,623,064 0.9

Hawaii 140,330 -8.5 164,684 -8.5

Idaho 246,076 2.5 226,055 2.5

Illinois 1,570,507 -15.0 1,570,507 -15.0

Indiana 924,821 -7.1 850,745 -7.1

Iowa 393,682 0.4 343,426 0.4

Kansas 436,818 12.2 382,338 12.2

Kentucky 1,035,117 24.9 912,489 24.9

Louisiana 889,593 3.9 827,934 3.9

Maine 219,147 19.2 211,481 19.2

Maryland 577,868 -0.8 640,811 -0.8

Massachusetts 834,562 8.3 908,187 8.3

Michigan 1,574,151 -4.5 1,490,835 -4.5

Minnesota 641,583 2.1 618,685 2.1

Mississippi 777,059 17.0 674,997 17.0

Missouri 982,026 4.6 849,411 4.6

Montana 152,432 5.2 141,303 5.2

Nebraska 234,093 0.7 204,599 0.7

Nevada 383,647 -4.5 379,378 -4.5

New Hampshire 120,245 2.2 126,343 2.2

New Jersey 1,037,859 12.1 1,175,003 12.1
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 19 GAO-13-434 Medicaid Funding Allocation

Population in poverty Cost-of-living-adjusted population in poverty

State 

Weighted by service 

need

Percentage difference 

compared with 

unweighted 

population in poverty a b Weighted by service need

Percentage difference 

compared with 

unweighted, cost-ofliving-adjusted 

population in poverty c

New Mexico

d

344,122 -17.2 319,887 -17.2

New York 2,994,488 -0.2 3,456,224 -0.2

North Carolina 1,767,180 6.7 1,619,173 6.7

North Dakota 85,362 8.3 74,742 8.3

Ohio 1,903,295 1.1 1,717,261 1.1

Oklahoma 625,371 -3.7 549,313 -3.7

Oregon 645,999 5.2 624,110 5.2

Pennsylvania 2,082,818 21.4 2,039,906 21.4

Rhode Island 170,425 18.2 168,485 18.2

South Carolina 869,468 2.6 794,419 2.6

South Dakota 118,482 0.1 102,739 0.1

Tennessee 1,237,668 9.2 1,115,772 9.2

Texas 4,412,370 -3.1 4,261,290 -3.1

Utah 317,608 -12.6 298,222 -12.6

Vermont 70,630 0.3 70,259 0.3

Virginia 1,009,078 7.7 1,038,683 7.7

Washington 857,040 -6.7 874,680 -6.7

West Virginia 428,467 26.7 369,541 26.7

Wisconsin 824,210 7.4 750,405 7.4

Wyoming 57,543 -10.8 52,532 -10.8

United States 47,896,803 0.0% 47,496,835 0.0%

Source: GAO analysis of U.S. Census Bureau and Centers for Medicare & Medicaid Services data.

Notes: The data are 2009-2011 data from the American Community Survey (ACS) and 2009 data 

from the Medicaid Statistical Information System (MSIS).

The percentage differences shown in columns three and five, between weighted and unweighted 

measures, are the same for the population in poverty and the cost-of-living-adjusted population in 

poverty because the same weights—reflecting the distribution of each state’s Medicaid population 

among age and disability categories and the relative cost of serving each category—are applied to 

each population measure.

a

Calculated by multiplying the state population in poverty by the proportion each age and disability 

category represents among all state Medicaid enrollees and by the category service-need weight, 

then summing the results for each category.

b

Calculated in comparison to the unweighted, unadjusted population in poverty.

c

Calculated by multiplying the cost-of-living adjusted state population in poverty by the proportion 

each age and disability category represents among all state Medicaid enrollees and by the category 

service-need weight, then summing the results for each category.

d

Calculated in comparison to the unweighted, cost-of-living-adjusted population in poverty.
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 20 GAO-13-434 Medicaid Funding Allocation

For the second method, we adjusted the state population in poverty by 

applying the service-need weights on the basis of the composition of the 

state poverty population rather than on the basis of the composition of the 

Medicaid population. Specifically, we generated poverty population 

estimates from ACS data separately for each age and disability category, 

and then multiplied these estimates by the Medicaid-service-need 

weights, summing the results.11

This second method more directly measures differences in the 

representation of each category within each state’s population in poverty 

and is not affected by individual state coverage decisions. However, it 

also does not account for differences in Medicaid participation rates 

among categories, nor does it track federal eligibility criteria for the 

categories. (For example, the adult poverty population includes adults 

who generally have not been eligible for Medicaid under federal law, and 

may or may not become eligible in future years depending on state 

decisions about whether to expand Medicaid as provided for under 

PPACA.)12

Table 4 shows the results of calculating a demand for services measure 

by this second method, which—in contrast to the previous method—

weights the state population in poverty for service need based on the 

composition of the population in poverty itself instead of the composition 

of the Medicaid enrollee population. Again, we applied the same service-

 

11Under this second method, the final demand for services measure is calculated as 

follows:

State population in poverty * percentage of that population who were children ages 0 

through 18 * 0.4

+ state population in poverty * percentage of that population who were adults ages 19 

through 64 * 0.6

+ state population in poverty * percentage of that population who were disabled individuals 

under age 65 * 2.6 

+ state population in poverty * percentage of that population who were elderly individuals 

ages 65 through 84 * 1.7

+ state population in poverty * percentage of that population who were elderly individuals 

ages 85 and older * 4.0.

12These considerations could, however, be addressed by additional modifications to the 

measure. For example, the category-specific estimates of the population in poverty could 

be further weighted to account for each category’s national Medicaid participation rate 

(that is, the national number of Medicaid enrollees in the category divided by the number 

of persons in the national poverty population who are in the category). For an example of 

this modification, see GAO-03-620. 
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 21 GAO-13-434 Medicaid Funding Allocation

need weights to (1) the unadjusted population in poverty and (2) the costof-living adjusted population in poverty, and then calculated the 

percentage difference compared with the unweighted population in 

poverty estimates shown in table 1. Due to the different weighting method 

employed, the results of the demand for services calculation shown in 

table 4 generally differ from those shown in table 3.13 For example, 

table 4 shows that the demand for services measure for West Virginia 

increases by about 13 percent when the service-need adjustment is made 

based on the composition of the poverty population, as opposed to by 

about 27 percent when the adjustment is made based on the composition 

of the Medicaid enrollee population as shown in table 3.

 

13The percentage differences shown in columns three and five of table 4, between 

weighted and unweighted measures, differ because the application of the cost-of-living 

adjustment affects not only the size of the population in poverty, but the distribution of that 

population across the age and disability categories used in weighting. In contrast, the 

cost-of-living adjustment does not affect the weighting used in table 3, which is based on 

distribution of states’ Medicaid populations across categories.
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 22 GAO-13-434 Medicaid Funding Allocation

Table 4: Estimates of Demand for Services, Including Adjustment for Service Need Based on the Composition of the State 

Poverty Population, by State

Population in poverty Cost-of-living-adjusted population in poverty

State 

Weighted by 

service need

Percentage difference 

compared with unweighted 

population in poverty a b Weighted by 

service need

Percentage difference compared 

with unweighted, cost-of-livingadjusted population in poverty c

Alabama

d

916,994 2.7% 809,176 2.5%

Alaska 65,074 -10.1 69,654 -10.9

Arizona 941,599 -17.1 929,741 -17.2

Arkansas 559,659 2.0 480,214 1.3

California 5,013,297 -15.4 5,686,186 -15.4

Colorado 610,160 -11.3 604,119 -11.2

Connecticut 408,524 6.0 450,223 5.6

Delaware 103,156 -5.1 107,240 -5.4

District of Columbia 114,624 0.9 126,924 1.1

Florida 2,909,093 -5.5 2,909,093 -5.5

Georgia 1,540,854 -10.8 1,435,464 -10.7

Hawaii 146,227 -4.6 174,422 -3.0

Idaho 218,125 -9.1 199,745 -9.4

Illinois 1,669,156 -9.6 1,669,156 -9.6

Indiana 956,705 -3.9 877,350 -4.2

Iowa 397,264 1.3 345,596 1.1

Kansas 382,114 -1.8 333,048 -2.2

Kentucky 895,273 8.0 779,190 6.6

Louisiana 834,311 -2.5 770,853 -3.2

Maine 212,808 15.8 205,964 16.1

Maryland 581,446 -0.2 642,543 -0.5

Massachusetts 856,630 11.1 932,803 11.2

Michigan 1,611,096 -2.3 1,522,285 -2.5

Minnesota 627,103 -0.2 603,857 -0.4

Mississippi 650,244 -2.1 562,051 -2.6

Missouri 964,236 2.7 834,033 2.7

Montana 139,985 -3.4 129,316 -3.8

Nebraska 225,390 -3.0 198,046 -2.5

Nevada 340,050 -15.4 336,691 -15.2

New Hampshire 126,953 7.9 134,087 8.5

New Jersey 901,993 -2.5 1,018,230 -2.8

New Mexico 365,621 -12.1 338,296 -12.5
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 23 GAO-13-434 Medicaid Funding Allocation

Population in poverty Cost-of-living-adjusted population in poverty

State 

Weighted by 

service need

Percentage difference 

compared with unweighted 

population in poverty a b Weighted by 

service need

Percentage difference compared 

with unweighted, cost-of-livingadjusted population in poverty c

New York

d

2,928,975 -2.4 3,384,800 -2.3

North Carolina 1,574,434 -4.9 1,432,154 -5.6

North Dakota 82,997 5.3 71,554 3.7

Ohio 1,914,640 1.7 1,720,652 1.3

Oklahoma 652,381 0.4 568,368 -0.4

Oregon 584,968 -4.7 566,970 -4.4

Pennsylvania 1,841,165 7.3 1,803,930 7.4

Rhode Island 156,997 8.9 154,398 8.3

South Carolina 807,345 -4.8 734,110 -5.2

South Dakota 118,266 -0.1 101,276 -1.3

Tennessee 1,148,919 1.4 1,025,634 0.4

Texas 3,915,084 -14.0 3,780,997 -14.0

Utah 289,619 -20.3 271,339 -20.5

Vermont 80,753 14.7 80,165 14.5

Virginia 918,232 -2.0 949,145 -1.6

Washington 881,651 -4.0 900,148 -4.0

West Virginia 383,995 13.5 329,769 13.0

Wisconsin 756,778 -1.4 683,516 -2.2

Wyoming 62,976 -2.3 57,919 -1.6

United States 45,385,937 -5.2% 44,832,438 -5.6%

Source: GAO analysis of U.S. Census Bureau and Centers for Medicare & Medicaid Services data.

Notes: The data are 2009-2011 data from the American Community Survey (ACS) and 2009 data 

from the Medicaid Statistical Information System (MSIS).

The percentage differences shown in columns three and five, between weighted and unweighted

measures, differ for the population in poverty and the cost-of-living-adjusted population in poverty 

because the application of the cost-of-living adjustment affects not only the size of the population in 

poverty, but the distribution of that population across the age and disability categories used in 

weighting.

a

Calculated by multiplying the age and disability-category-specific state population in poverty by the 

service-need weight for the category, then summing the results for each category.

b

Calculated in comparison to the unweighted, unadjusted population in poverty.

c

Calculated by multiplying the age and disability-category-specific, cost-of-living adjusted state 

population in poverty by the service-need weight for the category, then summing the results for each 

category.

d

Calculated in comparison to the unweighted, cost-of-living-adjusted population in poverty.
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 24 GAO-13-434 Medicaid Funding Allocation

To illustrate possible measures of geographic differences in the cost of 

providing health care services, we used data from the Bureau of Labor 

Statistics’ Occupational Employment Statistics (OES) survey to estimate 

personnel costs, which represent the greatest proportion of total health 

care costs. We then constructed two indexes of geographic cost 

differences, each of which gives a different weight to cost of personnel 

relative to the cost of other inputs, which include health care facilities, 

equipment, and supplies.

To measure geographic differences in the cost of the personnel who 

provide health care services, we used data from the 2009-2011 OES to 

estimate average wages, by state, for health care workers. We used OES 

data instead of CMS’s Inpatient Prospective Payment System (IPSS) 

hospital cost report data in part because of concerns about the latter. 

Specifically, the Medicare Program Advisory Commission (MedPAC) has 

noted that the IPPS hospital cost report data reflect at least to some 

extent hospitals’ choices regarding wages and staff mix and proposed an 

alternative wage index based on OES data to adjust Medicare payments 

to hospitals.14

To construct a wage index, we used OES wage data for 22 broadly 

defined occupations within the health care and social assistance industry 

sector.

We also chose to use the OES data because it covers 

personnel in a wide variety of health care settings in addition to hospitals, 

including offices, clinics, nursing homes, and medical and dental 

laboratories, as well as health care providers who work for home health 

agencies.

15

 

14See MedPAC, Report to the Congress: Promoting Greater Efficiency in Medicare 

(Washington, D.C.: June 15, 2007).

For each state, we calculated a weighted average wage for the 

sector, on the basis of the number employed in each occupation 

nationwide, and then constructed an index by dividing each state’s 

weighted average wage by the national weighted average wage for the 

sector.

15Because nursing home care represents a sizeable percentage of total Medicaid 

expenditures—more than 11 percent in 2011, not including hospital-based nursing home 

care—we also constructed an alternative wage index using data for occupations within the 

nursing and residential care facilities industry sector. This index did not differ substantially 

from the one we constructed from wage data on occupations in the health care and social 

assistance industry sector, so we present only the latter index here.

Geographic Cost 

Differences 
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 25 GAO-13-434 Medicaid Funding Allocation

Data are not readily available on geographic differences in the cost of 

other inputs to health care services—medical equipment and supplies 

and the facilities through which health care services are delivered. 

However, because medical equipment and supplies are generally 

purchased in national markets, we assumed that the costs of these items 

did not vary across states.16

Accordingly, to calculate illustrative indexes of geographic differences in 

the cost of health care services by state, we weighted our OES wage 

index on the basis of the proportion of total cost of health care services 

attributed to personnel. Because estimates of the proportion of total 

health care costs that is attributable to personnel vary depending on the 

type of service, we calculated two separate indexes of geographic 

differences in health care costs—one in which we assumed personnel 

costs accounted for 50 percent of total costs and another in which we 

assumed personnel costs accounted for 77 percent of the total. These 

percentages are the minimum and maximum proportions of health care 

costs attributed to personnel costs in Medicare’s prospective payment 

systems for a variety of services.

Similarly, we assumed that facility costs did 

not vary across states, as CMS does in calculating Medicare prospective 

payment rates for many services other than acute inpatient hospital or 

physician services.

17

 

16Under Medicare’s prospective payment systems, providers are paid a predetermined, 

fixed amount, which is derived on the basis of the classification system for the type of 

service provided (for example, diagnosis-related groups for inpatient hospital services). 

CMS uses separate prospective payment systems for different types of services, including 

hospital outpatient and home health services. For certain services, the payment 

calculation includes a geographic cost adjustment factor for differences in personnel 

costs, but no adjustment for differences in the other costs of providing health care 

services, including facility costs. However, for other services, adjustments are made for 

geographic differences in nonpersonnel costs. For example, for physician services, the 

Medicare payment system includes a Geographic Practice Cost Index based on 

apartment rents as captured in the ACS to account for geographic differences in health 

care facility costs.

All other costs were assumed to 

account for 50 and 23 percent of the total, respectively. So, for example, 

when we assumed that personnel costs accounted for 77 percent of total 

costs, if a state had personnel costs 10 percent higher than the national 

average, and thus an index of 1.10, the state’s index for geographic cost 

17These services include outpatient hospital services and home health care services, as 

well as services provided in ambulatory surgical centers, skilled nursing facilities, inpatient 

rehabilitative facilities, and psychiatric hospitals, among others.
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 26 GAO-13-434 Medicaid Funding Allocation

of services was calculated as follows: (0.77) * (1.10) + (0.23) * (1.0) = 

1.033. See table 5.

Table 5: Estimates of Cost Indexes for Health Care Services, by State

State 

Cost index with 50 percent 

weight for personnel costs

Cost index with 77 percent 

weight for personnel costs a

Alabama

b

0.95 0.92

Alaska 1.09 1.14

Arizona 1.01 1.02

Arkansas 0.93 0.89

California 1.10 1.15

Colorado 1.03 1.05

Connecticut 1.08 1.12

Delaware 1.05 1.08

District of Columbia 1.10 1.16

Florida 0.99 0.98

Georgia 0.98 0.97

Hawaii 1.07 1.10

Idaho 0.96 0.94

Illinois 1.02 1.02

Indiana 0.97 0.95

Iowa 0.95 0.92

Kansas 0.96 0.94

Kentucky 0.95 0.93

Louisiana 0.94 0.91

Maine 0.99 0.99

Maryland 1.07 1.11

Massachusetts 1.09 1.13

Michigan 1.00 1.00

Minnesota 1.03 1.05

Mississippi 0.92 0.87

Missouri 0.95 0.93

Montana 0.94 0.91

Nebraska 0.96 0.93

Nevada 1.06 1.09

New Hampshire 1.02 1.03

New Jersey 1.08 1.13

New Mexico 0.98 0.98
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 27 GAO-13-434 Medicaid Funding Allocation

State 

Cost index with 50 percent 

weight for personnel costs

Cost index with 77 percent 

weight for personnel costs a

New York

b

1.07 1.11

North Carolina 0.98 0.97

North Dakota 0.94 0.91

Ohio 0.99 0.98

Oklahoma 0.93 0.89

Oregon 1.05 1.07

Pennsylvania 1.00 0.99

Rhode Island 1.05 1.07

South Carolina 0.96 0.94

South Dakota 0.93 0.89

Tennessee 0.95 0.92

Texas 0.99 0.98

Utah 0.98 0.96

Vermont 1.01 1.01

Virginia 1.02 1.02

Washington 1.06 1.09

West Virginia 0.92 0.87

Wisconsin 1.00 1.00

Wyoming 0.99 0.98

United States 1.00 1.00

Source: GAO analysis of U.S. Bureau of Labor Statistics data.

Notes: Data are 2009-2011 data from the Occupational Employment Statistics (OES).

a

Calculated as 0.5 + 0.5 * (weighted average wage for health care industry workers in the state / 

weighted average wage for health care industry workers in the nation).

b

Calculated as 0.23 + 0.77 * (weighted average wage for health care industry workers in the state / 

weighted average wage for health care industry workers in the nation).

To illustrate two possible measures of state resources, we used data on 

states’ Total Taxable Resources (TTR), as reported by the Department of 

the Treasury. TTR is 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. As such, TTR comprises the income 

included in per capita income (PCI), as well as income from other 

sources, such as corporate income and capital gains. For each state, 

TTR is reported by the Department of the Treasury both as a total dollar 

amount and in dollars per capita. The total dollar amount reflects state 

resources, and the per capita amount reflects the state resources relative 

to the size of the state population. We calculated average TTR and 

State Resources
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 28 GAO-13-434 Medicaid Funding Allocation

average per capita TTR for 2008 through 2010, the most recent years for 

which data are available. See table 6.

Table 6: Average Total Taxable Resources (TTR) and Per Capita TTR, by State, 2008 

through 2010

State 

Average TTR 

(dollars in billions) 

Average per capita TTR 

(dollars)

Alabama $188 $39,627

Alaska 49 70,074

Arizona 277 43,689

Arkansas 114 39,322

California 2,018 54,608

Colorado 273 55,007

Connecticut 260 72,903

Delaware 67 74,980

District of Columbia 58 97,608

Florida 882 47,246

Georgia 431 44,875

Hawaii 70 52,197

Idaho 62 39,938

Illinois 701 54,750

Indiana 287 44,494

Iowa 151 49,725

Kansas 143 50,377

Kentucky 172 39,765

Louisiana 230 51,262

Maine 56 42,355

Maryland 351 61,263

Massachusetts 406 62,377

Michigan 397 40,067

Minnesota 286 54,222

Mississippi 107 36,233

Missouri 270 45,270

Montana 41 41,571

Nebraska 96 52,886

Nevada 143 53,231

New Hampshire 73 55,339

New Jersey 573 65,453
Appendix I: Examples of Alternative Measures 

That Could Be Used to More Equitably Allocate 

Federal Medicaid Funding

Page 29 GAO-13-434 Medicaid Funding Allocation

State 

Average TTR 

(dollars in billions) 

Average per capita TTR 

(dollars)

New Mexico 83 40,807

New York 1,215 62,936

North Carolina 442 46,841

North Dakota 36 54,299

Ohio 505 43,782

Oklahoma 165 44,300

Oregon 195 51,124

Pennsylvania 617 48,685

Rhode Island 56 53,162

South Carolina 178 38,806

South Dakota 43 53,033

Tennessee 271 43,070

Texas 1,275 51,455

Utah 123 45,403

Vermont 28 45,564

Virginia 465 58,651

Washington 370 55,547

West Virginia 69 37,437

Wisconsin 266 46,857

Wyoming 41 72,917

United States $15,675 $51,108

Source: GAO analysis of Department of the Treasury data.

One possible alternative to TTR as a measure of state resources would 

be a measure based on the concept of the representative tax system, 

which refers to a hypothetical tax system that is representative of the 

taxes actually levied by all states in the aggregate. Such a measure 

would estimate the resources that would be available to each state if their 

tax policies were in line with those of most other states.18

 

18For example, see Advisory Commission on Intergovernmental Relations, 1988 State 

Fiscal Capacity and Effort (Washington, D.C.: August 1990).
Appendix II: GAO Contact and Staff 

Acknowledgments

Page 30 GAO-13-434 Medicaid Funding Allocation

Carolyn L. Yocom, (202) 512-7114 or yocomc@gao.gov

In addition to the contact named above, Robert Copeland, Assistant 

Director; Emily Beller; Kye Briesath; Greg Dybalski; Nancy Fasciano; 

Kaycee M. Glavich; Drew S. Long; and Hemi Tewarson made key 

contributions to this report.

Appendix II: GAO Contact and Staff 

Acknowledgments 

GAO Contact

Staff 

Acknowledgments 
Related GAO Products

Page 31 GAO-13-434 Medicaid Funding Allocation

Older Americans Act: Options to Better Target Need and Improve Equity. 

GAO-13-74. Washington, D.C.: November 30, 2012.

Medicaid: Data Sets Provide Inconsistent Picture of Expenditures. 

GAO-13-47. Washington D.C.: October 29, 2012.

Medicaid: Prototype Formula Would Provide Automatic, Targeted 

Assistance to States during Economic Downturns. GAO-12-38. 

Washington, D.C.: November 10, 2011.

State and Local Governments: Knowledge of Past Recessions Can 

Inform Future Federal Fiscal Assistance. GAO-11-401. Washington, D.C.: 

March 31, 2011.

Medicaid: Improving Responsiveness of Federal Assistance to States 

during Economic Downturns. GAO-11-395. Washington, D.C.: March 31, 

2011.

Formula Grants: Funding for the Largest Federal Assistance Programs Is 

Based on Census-Related Data and Other Factors. GAO-10-263. 

Washington, D.C.: December 15, 2009.

Medicaid: Strategies to Help States Address Increased Expenditures 

during Economic Downturns. GAO-07-97. Washington, D.C.: October 18, 

2006.

Federal Assistance: Illustrative Simulations of Using Statistical Population 

Estimates for Reallocating Certain Federal Funding. GAO-06-567. 

Washington, D.C.: June 22, 2006.

Medicaid Formula: Differences in Funding Ability among States Often Are 

Widened. GAO-03-620. Washington, D.C.: July 10, 2003.

Medicaid Formula: Effects of Proposed Formula on Federal Shares of 

State Spending. GAO/HEHS-99-29R. Washington, D.C.: February 19, 

1999.

Federal Grants: Design Improvements Could Help Federal Resources Go 

Further. GAO/AIMD-97-7. Washington, D.C.: December 18, 1996.

Block Grants: Issues in Designing Accountability Provisions. 

GAO/AIMD-95-226. Washington, D.C.: September 1, 1995.

Related GAO Products
Related GAO Products

Page 32 GAO-13-434 Medicaid Funding Allocation

Medicaid: Matching Formula’s Performance and Potential Modifications. 

GAO/T-HEHS-95-226. Washington, D.C.: July 27, 1995.

Block Grants: Characteristics, Experience, and Lessons Learned. 

GAO/HEHS-95-74. Washington, D.C.: February 9, 1995.

Block Grants: Lessons Learned. GAO/T-HEHS-95-80. Washington, D.C.: 

February 9, 1995.

Maternal and Child Health: Block Grant Funds Should Be Distributed 

More Equitably. GAO/HRD-92-5. Washington, D.C.: April 2, 1992.

Changing Medicaid Formula Can Improve Distribution of Funds to States. 

GAO/GGD-83-27. Washington, D.C.: March 9, 1983.

(291058)
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