Vocational Rehabilitation Funding Formula
Options for Improving Equity in State Grants and Considerations for Performance Incentives Gao ID: GAO-09-798 September 30, 2009State vocational rehabilitation (VR) agencies play a crucial role in helping individuals with disabilities obtain employment. In fiscal year 2008, the Department of Education (Education) distributed over $2.8 billion in grants to state agencies, using a funding formula that was last revised in 1978. Questions have been raised about whether this formula is outdated, allocates funds equitably, and adequately accounts for state agencies' performance. GAO was asked to: (1) examine the extent to which the current formula meets generally accepted equity standards, (2) present options for revising the formula, and (3) identify issues to consider with incorporating performance incentives into the formula. To address these objectives, GAO relied upon two equity standards commonly used to design and evaluate funding formulas: beneficiary equity, which stipulates that funds should be distributed so that each state can provide the same level of services to each person in need; and taxpayer equity, which stipulates that states should contribute about the same proportion of their resources to a given program. GAO analyzed data from Education, Department of the Treasury, Census Bureau, and other agencies; surveyed state VR agencies; interviewed agency officials and disability experts; and reviewed literature on performance incentives.
The VR funding formula falls short of meeting equity standards because it uses imprecise measures of state needs and resources. The formula does not account for differences among states in the proportion of people with a disability or the costs of providing services. As a result, the amount of services that states can purchase per person with a disability varies, from $83 to $277 (see figure). In addition, the formula uses only per capita income to measure a state's ability to contribute to the program, excluding other taxable resources. GAO presents three options for revising the formula to illustrate a range of possibilities: the first distributes funds based on states' disability populations, the second also accounts for costs of providing services, and the third further accounts for state resources beyond per capita income. Because any formula change would redistribute funds among states, potentially disrupting services to individuals, GAO also presents options for establishing a transition period. Including performance incentives in the funding formula has potential for improving performance but can also pose challenges. These include: effectively balancing the VR program's multiple goals, rewarding agencies for meeting individuals' specific needs, and basing awards on an agency's performance rather than influences outside its control. GAO identified ways to mitigate these risks, such as using multiple performance measures to address different goals, and adjusting the performance level required for an agency to receive an incentive award. However, these approaches would still require careful consideration of several issues, such as how to account for clients' varying disability levels and needs and provide appropriate incentives for achieving desired outcomes.
GAO-09-798, Vocational Rehabilitation Funding Formula: Options for Improving Equity in State Grants and Considerations for Performance Incentives
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Report to Congressional Requesters:
United States Government Accountability Office:
GAO:
September 2009:
Vocational Rehabilitation Funding Formula:
Options for Improving Equity in State Grants and Considerations for
Performance Incentives:
GAO-09-798:
GAO Highlights:
Highlights of GAO-09-798, a report to congressional requesters.
Why GAO Did This Study:
State vocational rehabilitation (VR) agencies play a crucial role in
helping individuals with disabilities obtain employment. In fiscal year
2008, the Department of Education (Education) distributed over $2.8
billion in grants to state agencies, using a funding formula that was
last revised in 1978. Questions have been raised about whether this
formula is outdated, allocates funds equitably, and adequately accounts
for state agencies‘ performance.
GAO was asked to: (1) examine the extent to which the current formula
meets generally accepted equity standards, (2) present options for
revising the formula, and (3) identify issues to consider with
incorporating performance incentives into the formula.
To address these objectives, GAO relied upon two equity standards
commonly used to design and evaluate funding formulas: beneficiary
equity, which stipulates that funds should be distributed so that each
state can provide the same level of services to each person in need;
and taxpayer equity, which stipulates that states should contribute
about the same proportion of their resources to a given program. GAO
analyzed data from Education, Department of the Treasury, Census
Bureau, and other agencies; surveyed state VR agencies; interviewed
agency officials and disability experts; and reviewed literature on
performance incentives.
GAO makes no recommendations in this report.
What GAO Found:
The VR funding formula falls short of meeting equity standards because
it uses imprecise measures of state needs and resources. The formula
does not account for differences among states in the proportion of
people with a disability or the costs of providing services. As a
result, the amount of services that states can purchase per person with
a disability varies, from $83 to $277 (see figure). In addition, the
formula uses only per capita income to measure a state‘s ability to
contribute to the program, excluding other taxable resources.
GAO presents three options for revising the formula to illustrate a
range of possibilities: the first distributes funds based on states‘
disability populations, the second also accounts for costs of providing
services, and the third further accounts for state resources beyond per
capita income. Because any formula change would redistribute funds
among states, potentially disrupting services to individuals, GAO also
presents options for establishing a transition period.
Including performance incentives in the funding formula has potential
for improving performance but can also pose challenges. These include:
effectively balancing the VR program‘s multiple goals, rewarding
agencies for meeting individuals‘ specific needs, and basing awards on
an agency‘s performance rather than influences outside its control. GAO
identified ways to mitigate these risks, such as using multiple
performance measures to address different goals, and adjusting the
performance level required for an agency to receive an incentive award.
However, these approaches would still require careful consideration of
several issues, such as how to account for clients‘ varying disability
levels and needs and provide appropriate incentives for achieving
desired outcomes.
Figure: Estimated State VR Allotments per Working-Aged Person with a
Disability, Cost-Adjusted, Based on Fiscal Year 2008 Funding:
[Refer to PDF for image: U.S. map]
Less than $115:
Alabama;
California;
Colorado;
Connecticut;
Kentucky;
Maryland;
Massachusetts;
Nevada;
New Hampshire;
New Jersey;
New York;
Oregon;
Tennessee;
Washington.
$115 - 128:
Arizona;
Florida;
Georgia;
Maine;
Michigan;
Missouri;
North Carolina;
Oklahoma;
Pennsylvania;
Rhode Island;
Virginia;
West Virginia;
$129 - 150:
Arkansas;
Delaware;
Hawaii;
Illinois;
Indiana;
Minnesota;
Mississippi;
New Mexico;
Ohio;
South Carolina;
Texas;
Wisconsin.
More than $150:
Alaska;
District of Columbia;
Idaho;
Iowa;
Kansas;
Louisiana;
Montana;
Nebraska;
North Dakota;
South Dakota;
Utah;
Vermont;
Wyoming.
Source: GAO analysis of data from Education, Census Bureau, BLS, HUD,
and responses to GAO survey; Map, Map Resources (presentation).
[End of figure]
View GAO-09-798 or key components. For more information, contact Daniel
Bertoni, (202) 512-7215, bertonid@gao.gov.
[End of section]
Contents:
Letter:
Background:
The Current Formula Falls Short of Meeting Equity Standards for
Beneficiaries and Taxpayers:
Greater Equity in VR Funding Could Be Accomplished in Several Ways:
Incorporating Financial Performance Incentives in the VR Formula Poses
Challenges:
Concluding Observations:
Agency Comments and Our Evaluation:
Appendix I: Objectives, Scope, & Methodology:
Appendix II: Description of the Current Vocational Rehabilitation (VR)
Funding Formula:
Appendix III: Percentage of Population that Is Civilian Working Age
with a Disability, 2006 and 2007:
Appendix IV: Estimates of Differences among States in the Cost of
Providing Vocational Rehabilitation (VR) Services:
Appendix V: Vocational Rehabilitation (VR) Grant Allotments by State
and Cost-Adjusted Allotments per Working-Aged Person with a Disability,
Fiscal Year 2008:
Appendix VI: Comparison of Per Capita Income and Total Taxable
Resources (TTR), by State, 2004 to 2006:
Appendix VII: Description of Formula Options:
Appendix VIII: Funding Allocations under Three Vocational
Rehabilitation Formula Options:
Appendix IX: Responses to Selected Questions from GAO Survey of State
Vocational Rehabilitation (VR) Agencies:
Appendix X: GAO Contacts and Staff Acknowledgments:
Tables:
Table 1: Redistribution of Funds under Three Formula Options:
Table 2: Disability Questions from the American Community Survey, 2006
and 2007:
Table 3: Breakdown of the Total U.S. Population into Components of our
Measure of the Need Population for the VR program, 2007:
Table 4: Summary Statistics for Wage Indices from Data Series Reviewed:
Table 5: Correlations between Wage Indices:
Table 6: Combining Education's Expenditure Data and Survey Data to
Estimate Weights for Inputs:
Table 7: Number of States Considered to Be Under Order of Selection by
Type of VR Agency:
Table 8: Percentage of Population that Is Civilian Working Age with a
Disability, 2006 and 2007:
Table 9: Estimates of Differences among States in the Cost of Providing
VR Services:
Table 10: VR Grant Allotments and Cost-Adjusted Allotments per Working-
Aged Person with a Disability:
Table 11: Comparison of Per Capita Income and TTR Per Capita for 2004
to 2006, by State:
Table 12: Funding Allocations under Three VR Formula Options:
Figures:
Figure 1: Percentage of Each State's Total Population that Is Working-
Aged and Has a Disability, 2007:
Figure 2: Estimated State VR Allotments per Working-Aged Person with a
Disability, Cost-Adjusted, Based on Fiscal Year 2008 Funding:
Figure 3: Ratio of Per Capita Income to Total Taxable Resources Per
Capita:
Figure 4: A 5-Year Phase-In Transition to a New Funding Formula:
Figure 5: Distribution of Average State Wages in the Education,
Healthcare, and Social Assistance Industry Sector, 2005 to 2007:
Figure 6: Distribution of Average State Rents, Fiscal Years 2007 to
2009:
Figure 7: Distribution of the Cost of Services Index:
Figure 8: State Agency Officials' Opinions about the Appropriateness or
Inappropriateness of Factors Currently Included in the Funding Formula:
Figure 9: State VR Agency Officials' Opinions about Options for
Modifying the Formula:
Figure 10: State VR Agency Officials' Opinions about Whether the
Matching Requirement Should Vary to Account for States' Total Financial
Resources:
Figure 11: State VR Agency Officials' Opinions about Incorporating
Performance Awards in the VR Program:
Figure 12: State VR Agency Officials' Opinions about Potential Results
of Including Performance Awards in the VR Program:
Figure 13: State VR Agency Officials' Opinions about Challenges of
Incorporating Performance Awards into the VR Program:
Figure 14: VR Agencies' Responses on Whether They Receive Funding from
Their State based on Performance:
Figure 15: Current Use of Performance Awards by State VR Agencies:
Abbreviations:
ACS: American Community Survey:
BLS: Bureau of Labor Statistics:
CMS: Centers for Medicare and Medicaid Services:
CPI-U: Consumer Price Index for All Urban Consumers:
CSAVR: Council of State Administrators of Vocational Rehabilitation:
CSE: Child Support Enforcement:
Education: Department of Education:
ERIC: Education Resources Information Center:
FMR: Fair Market Rents:
HUD: Department of Housing and Urban Development:
JTPA: Job Training Partnership Act:
NAICS: North American Industry Classification System:
OES: Occupational Employment Statistics:
QCEW: Quarterly Census of Employment and Wages:
SOC: Standard Occupational Classification:
SSA: Social Security Administration:
SSDI: Social Security Disability Insurance:
SSI: Supplemental Security Income:
Treasury: Department of the Treasury:
TTR: Total Taxable Resources:
VR: Vocational Rehabilitation:
WIA: Workforce Investment Act:
[End of section]
United States Government Accountability Office:
Washington, DC 20548:
September 30, 2009:
The Honorable George Miller:
Chairman:
The Honorable John P. Kline:
Ranking Member:
Committee on Education and Labor:
House of Representatives:
The Honorable Howard P. "Buck" McKeon:
House of Representatives:
State vocational rehabilitation (VR) agencies, under the auspices of
the Department of Education (Education), play a crucial role in helping
individuals with disabilities prepare for and obtain employment.
[Footnote 1] In fiscal year 2008, Education distributed over $2.8
billion in grants to state VR agencies, using a funding formula that
was last updated in 1978.[Footnote 2] Questions have been raised about
whether this funding formula is outdated and allocates funds equitably.
In addition, growing interest in program accountability has prompted
some to ask whether the formula should incorporate performance measures
to reward high performance. As currently constructed, the VR formula
distributes funds according to the size of a state's population, its
per capita income, and the amount of VR funds the state received in
1978. States must match their federal grant allocations by contributing
about $1 to their VR programs for each $4 they receive.
There are two standards concerning the concept of equity that have
commonly been used to design and evaluate funding formulas.[Footnote 3]
The first--known as beneficiary equity--stipulates that funds should be
distributed to states according to the needs of their respective
populations so that each state, with their federal allocation, can
provide the same level of services to each person in need. The second
standard--known as taxpayer equity--applies to programs, such as VR, in
which states contribute funds. This standard also seeks to provide
individuals in need with the same level of services, but in addition,
considers a state's ability to finance a program from its own
resources. It stipulates that states should be able to provide
comparable services to individuals, with each state contributing about
the same proportion of their resources to the program. To achieve this
standard, taxpayer equity formulas may allocate more funds to states
with fewer taxable resources, set the contribution they are required to
make at a lower level, or do both, so that poorer states do not
contribute a larger share of their resources than wealthier states. To
meet the taxpayer equity standard, a formula requires a reliable
measure of a state's ability to finance a program from its own
resources. To meet both equity standards, a formula should use reliable
and appropriate measures of the need population in each state and the
cost of providing services in each state.
In this context, you asked us to (1) examine the extent to which the
current formula meets generally accepted equity standards, (2) present
options for revising the formula to better meet these standards, and
(3) identify issues to consider with incorporating performance
incentives into the formula.
To determine the extent to which the VR funding formula meets equity
standards and to develop options for revising the formula, we examined
data from a number of sources, namely Education's data on state VR
grants, Census Bureau data on state disability populations, Department
of the Treasury (Treasury) data on state taxable resources, Bureau of
Labor Statistics (BLS) data on wages, and Department of Housing and
Urban Development (HUD) data on rents. In addition, we interviewed
experts in the disability field and had key portions of our analyses
formally reviewed by three disability experts. We also surveyed all 80
VR agencies in the states, territories, and District of Columbia to
obtain their perspectives on the current formula, options for changing
it, and views on the use of performance incentives in the VR
program.[Footnote 4] We received responses from 74 (93 percent) of the
agencies. We also conducted in-depth interviews with state VR officials
at 11 agencies in 9 states, as well as with members of advisory
councils to the VR agencies in 5 of these states. To further consider
potential benefits and challenges of using performance incentives in
the VR funding formula, we reviewed academic literature on the subject
and spoke with officials in three federal programs that currently
provide incentive awards. We conducted this performance audit from
September 2008 to September 2009, 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.
See appendix I for a detailed discussion of our scope and methodology.
Background:
The Rehabilitation Act, as amended, sets out a formula for distributing
VR grants to states and territories.[Footnote 5] Through this formula,
a portion of the funds appropriated for the VR program are distributed
to states based upon the grant allotment they received for fiscal year
1978. States' 1978 allotments served to ensure that no state
experienced a funding decrease when the formula was revised through a
1978 amendment to the Rehabilitation Act.[Footnote 6] Of the remainder
of the funds, one-half is distributed based upon states' general
population and a factor that compares their per capita income to the
national per capita income, and the other one-half, according to their
population and the square of the per capita income factor. The larger a
state's population, the more funds it will receive. Conversely, the
higher a state's per capita income compared to the national level, the
lower its allotment will be. The squaring of per capita income
increases its influence on a state's allotment. However, the formula
mitigates the effect of per capita income for states with very high or
very low per capita income levels by setting upper and lower limits.
[Footnote 7] Ultimately, the final allotment for a state cannot be less
than 1/3 of 1 percent of the total amount appropriated, or $3 million
dollars, whichever is greater.[Footnote 8] In fiscal year 2008, the
minimum allotment was $9.5 million, and 6 states were allotted this
amount.[Footnote 9] See appendix II for further information on the
funding formula.
The Act requires states to share in funding the costs of the VR
program. Specifically, the Act sets the federal share for the funding
of a state's VR program at 78.7 percent. As a result, in order to
receive their full federal allotment, each state must contribute at
least 21.3 percent of the funds for their VR programs.[Footnote 10] In
cases where states do not meet this matching requirement, the unmatched
federal funds are redistributed to other states near the end of the
fiscal year.[Footnote 11] The Act also calls for annual funding
increases to the VR program, overall, to be minimally pegged to the
increase in the Consumer Price Index for All Urban Consumers (CPI-U).
However, funding changes for an individual state may differ from the
change to the CPI-U in any given year because state allocations are,
ultimately, determined by the funding formula. In redistributing the
funds, Education currently gives priority to those states that did not
receive an inflation-adjusted increase over their prior year's
allotment.
Under the VR program, state VR agencies are to provide vocational
rehabilitation services for individuals with disabilities--consistent
with the individual's strengths, resources, priorities, abilities,
interests, and informed choice--so that they may prepare for and engage
in gainful employment. To do so, state agencies provide a variety of
services to individuals such as job placement assistance, medical
treatment, postsecondary education, occupational training, and
assistive technologies. Individuals may be eligible for VR services if
they have a physical or mental impairment that constitutes or results
in a substantial impediment to employment, and if they need VR services
to prepare for, secure, retain, or regain employment. According to the
Rehabilitation Act, if state VR agencies determine that they will not
have enough funding to serve all eligible individuals who apply for
services, they may state the order in which they will select
individuals for services. Agencies using such an "order of selection"
process must develop criteria for ensuring that individuals with the
most significant disabilities will be selected first for services.
The current VR funding formula does not include factors for rewarding
agency performance; however, pursuant to the Rehabilitation Act,
Education evaluates state VR agencies' performance using a set of
performance indicators.[Footnote 12] These indicators are designed to
assess how well the agencies are helping individuals obtain, maintain,
or regain high-quality employment and, also, how well they are ensuring
that individuals from minority backgrounds have equal access to VR
services. The Rehabilitation Act gives Education the authority to
reduce or suspend payments to a state agency whose performance falls
below a certain level and fails to enter into a program improvement
plan or to substantially comply with the terms and conditions of such a
plan.
The Current Formula Falls Short of Meeting Equity Standards for
Beneficiaries and Taxpayers:
The Formula Does Not Account for Varying Disability Rates and Service
Costs:
The VR funding formula does not achieve equity for beneficiaries--the
individuals likely to be served by the VR program--for two reasons.
First, it does not recognize differences among states in the size of
their populations potentially needing VR services. Second, it does not
account for state differences in the costs of providing those services.
By targeting funds based on a state's general population, the formula
assumes that the proportion of people needing services is largely the
same from state to state. In fact, as shown in figure 1, the proportion
of the general population that is working-aged and has a disability
varies across states, from 5.6 percent (in New Jersey) to 12.8 percent
(in West Virginia) in 2007.[Footnote 13] See appendix III for
information on disability rates in each state. In effect, the formula
treats alike any two states with similar population sizes, irrespective
of the size of their working-aged disability population. For example,
New Mexico has a slightly greater population than West Virginia (2.0
million and 1.8 million, respectively), and, therefore, would receive
more funding under the current formula (all other things being equal)
than West Virginia. However, working-aged people with disabilities
comprise nearly 13 percent of West Virginia's population, compared to
8.7 percent in New Mexico. By not factoring in state disability
populations, the formula does not account for West Virginia having over
60,000 more working-aged people with disabilities than New Mexico.
Figure 1: Percentage of Each State's Total Population that Is Working-
Aged and Has a Disability, 2007:
[Refer to PDF for image: U.S. map]
6.5 percent or less:
California;
Connecticut;
Hawaii;
Illinois;
Minnesota;
Nebraska;
Nevada;
New Jersey;
North Dakota;
Utah;
6.6 to 7.6:
Arizona;
Arkansas;
Colorado;
Delaware;
District of Columbia;
Florida;
Georgia;
Idaho;
Iowa;
Kansas;
Maryland;
Massachusetts;
New Hampshire;
New York;
South Dakota;
Texas;
Virginia;
Wisconsin.
7.7 to 8.7:
Indiana;
Michigan;
Montana;
New Mexico;
North Carolina;
Ohio;
Oregon;
Pennsylvania;
Rhode Island;
South Carolina;
Vermont;
Washington;
Wyoming.
8.8 percent or more:
Alabama;
Alaska;
Kentucky;
Louisiana;
Maine;
Mississippi;
Missouri;
Oklahoma;
Tennessee;
West Virginia.
Source: GAO analysis of Census Bureau‘s ACS data; Map, Map Resources
(presentation).
[End of figure]
Education officials and one expert we spoke with speculated that the
formula's use of per capita income might serve to target funds to
states with higher rates of disability since people with disabilities
have, on average, lower incomes. We found some correlation between
states' disability rates and their per capita income.[Footnote 14] As
such, per capita income is, at best, an imprecise measure of states'
disability rates.
The funding formula also fails to account for differences among states
in the cost of providing VR services. Focusing on average wages and
rents in each state, we estimated that the cost is 13 percent below the
national average in Idaho, for example, while it is 13 percent above
average in Massachusetts.[Footnote 15] This means that Massachusetts
would need to pay $1.13 for the same set of services that Idaho could
purchase for $0.87. By not taking into account cost differences, VR
allocations purchase fewer services in states that have higher costs.
See appendix IV for a table of our estimates of state cost differences.
Also, see appendix I for information on how we estimated state service
costs.
Not accounting for state differences in both disability populations and
cost of services results in a substantial variation in the amount of
services that states are able to purchase per person with a disability,
from a low of $83 in Connecticut, to a high of over three times as
much--$277 in North Dakota. Figure 2 shows estimated state VR
allotments, per working-aged person with a disability, based on fiscal
year 2008 funding, adjusted for differences in costs of wages and rents
between states.[Footnote 16] See appendix V for a state-by-state
listing of VR grant allocations and cost-adjusted allotments per person
with a disability.
Figure 2: Estimated State VR Allotments per Working-Aged Person with a
Disability, Cost-Adjusted, Based on Fiscal Year 2008 Funding:
[Refer to PDF for image: U.S. map]
Less than $115:
Alabama;
California;
Colorado;
Connecticut;
Kentucky;
Maryland;
Massachusetts;
Nevada;
New Hampshire;
New Jersey;
New York;
Oregon;
Tennessee;
Washington.
$115 - 128:
Arizona;
Florida;
Georgia;
Maine;
Michigan;
Missouri;
North Carolina;
Oklahoma;
Pennsylvania;
Rhode Island;
Virginia;
West Virginia;
$129 - 150:
Arkansas;
Delaware;
Hawaii;
Illinois;
Indiana;
Minnesota;
Mississippi;
New Mexico;
Ohio;
South Carolina;
Texas;
Wisconsin.
More than $150:
Alaska;
District of Columbia;
Idaho;
Iowa;
Kansas;
Louisiana;
Montana;
Nebraska;
North Dakota;
South Dakota;
Utah;
Vermont;
Wyoming.
Source: GAO analysis of data from Education, Census Bureau, BLS, HUD,
and responses to GAO survey; Map, Map Resources (presentation).
[End of figure]
The Formula Does Not Capture Certain Taxable Resources:
While proper measures of need and cost are important for both
beneficiary and taxpayer equity, the VR funding formula lacks equity
for state taxpayers, in particular, because its measure of a state's
ability to contribute to the VR program is limited to per capita income
and does not include all potentially taxable resources. Per capita
income is based on the personal income in a state, including income
received by state residents in the form of wages, rents, and interest
income. However, using only this measure excludes certain categories of
corporate income that are not received as income by state residents.
For example, the formula does not factor in corporate income that is
retained by corporations for investment purposes, which could
theoretically be subject to state taxation through corporate income
taxes. The formula also excludes business income received by out-of-
state residents, such as dividends, that are potentially taxable by the
state. Although states may differ in their decisions about whether to
tax these resources, the measure used in a funding formula to compare
states' ability to finance a program should capture all possible
revenue resources and should not be affected by an individual state's
fiscal decisions. Treasury's Total Taxable Resources provides more
comprehensive data on the amount of resources that are potentially
taxable in each state. Comparing states' per capita income with their
total taxable resources shows that, for most states, the two measures
are similar. However, the formula's use of per capita income
particularly understates the taxable resources in certain states and
overstates it in others (see figure 3). For example, the ratio of per
capita income to total taxable resources per capita is 0.80 in Alaska,
which suggests that the use of per capita income in the formula
understates Alaska's taxable resources by 20 percent.[Footnote 17] The
formula's use of per capita income especially understates the taxable
resources in energy-exporting states, such as Alaska and Wyoming, and
in states with numerous corporate headquarters, such as Delaware. The
lack of precision in using per capita income is accentuated by the
squaring of the per capita income factor in the formula.[Footnote 18]
See appendix VI for a comparison of per capita income and total taxable
resources in each state. Also, see appendix I for a more detailed
explanation of our analyses of per capita income and total taxable
resources data and appendix II for a detailed explanation of the
current formula.
Figure 3: Ratio of Per Capita Income to Total Taxable Resources Per
Capita:
[Refer to PDF for image: U.S. map]
Less than 0.95:
Alaska;
Delaware;
District of Columbia;
Louisiana;
Nevada;
South Dakota;
Wyoming.
0.95 to 0.99:
Connecticut;
Georgia;
Hawaii;
Iowa;
Nebraska;
New Mexico;
New York;
North Carolina;
North Dakota;
Oregon;
Rhode Island;
Texas;
Utah;
Virginia.
1 to 1.05:
Arizona;
Arkansas;
California;
Colorado;
Florida;
Idaho;
Illinois;
Indiana;
Kansas;
Kentucky;
Maryland;
Massachusetts;
Minnesota;
Missouri;
New Hampshire;
New Jersey;
Ohio;
South Carolina;
Tennessee;
Vermont;
Washington;
Wisconsin.
Greater than 1.05:
Alabama;
Maine;
Michigan;
Mississippi;
Montana;
Oklahoma;
Pennsylvania;
West Virginia.
Source: GAO analysis of Department of Commerce data on per capita
income and Treasury data on total taxable resources, 2004 to
2006; Map, Map Resources (presentation).
Note: These are ratios of a per capita income index to an index of
total taxable resources per capita. The indexes were created by
dividing the per capita income and total taxable resource per capita
levels by the U.S. averages for each.
[End of figure]
Over One-Quarter of Funds are Distributed Based on States' 1978
Allotments:
In fiscal year 2008, 27 percent of federal VR funds were distributed to
states based upon the amount of funds they received for fiscal year
1978. This provision of the formula served a purpose when the formula
was last revised, in 1978, to ensure that no state experienced a
funding decrease. However, most disability experts we spoke with
considered this provision outdated and no longer an appropriate factor
for distributing VR funds. The Congressional Research Service also
reported that due to the 1978 allotment, VR funding allotments do not
fully reflect population changes since the mid-1970s.[Footnote 19]
Most State Agencies Indicated Satisfaction with the Formula, though
Some Believe It Results in Inadequate Funding for their States:
Most state agencies that responded to our survey indicated satisfaction
with the current formula. Of respondents, 62 percent (46 of 74)
expressed the view that the current formula is appropriate, while 31
percent (23 of 74) viewed it as inappropriate. When asked about
specific parts of the formula, opinions varied. For example, 86 percent
(64 of 74) considered the use of general population to be appropriate,
while only 27 percent (20 of 74) considered the 1978 allotment
provision to be appropriate. See appendix IX for responses to our
survey questions. However, in their comments to the survey and in
interviews, some state agency officials asserted that the formula does
not provide them with adequate funds. For instance, VR officials we
spoke with in Massachusetts and Maryland said that due to current
funding allotments, their agencies are on an "order of selection," in
which they give priority to individuals with significant disabilities
and place other individuals on waiting lists.
When we compared allotments per person with a disability against order
of selection status, we found that states that receive less funding per
person with a disability were somewhat more likely to report being
under an order of selection than those states that receive relatively
more funding.[Footnote 20] Specifically, we found in fiscal year 2008
that among states with lower than median allotments per person with a
disability (adjusting for costs), 72 percent reported being under an
order of selection, compared to 52 percent of states above the median.
However, the data do not explain whether or the extent to which the VR
funding formula is causing states to be under an order of selection.
For example, many states above the median allotment are also under
orders of selection. Further, in interviews state VR officials
indicated that factors other than allotment levels could also influence
a state's decision to be under an order of selection, such as the level
of state resources provided to the VR program, the effectiveness of the
agency's management of program costs, and the agency's decisions about
how to use existing funding.
Greater Equity in VR Funding Could Be Accomplished in Several Ways:
Options for Introducing Greater Equity for Taxpayers or Beneficiaries:
There are a number of ways to redesign the VR funding formula to
achieve greater equity for beneficiaries, taxpayers, or to balance
equity for both. We present three options, or prototypes, to illustrate
the range of options. See appendix VII for a more detailed description
of each formula option. For each of these options, we have retained the
minimum allotment that the current formula provides to ensure that each
state would receive at least a certain level of funds for its VR
program.
* A partial beneficiary equity formula: This option bases allocations
solely on the size of a state's population potentially needing VR
services. To measure the need population, this option would use data on
the states' civilian working-aged disability populations from the
Census Bureau's ACS.
* A full beneficiary equity formula: This option also allocates funds
based on states' working-aged disability populations using Census data,
but also includes estimates of the cost of providing VR services in
each state. These cost estimates reflect differences among states with
respect to two basic costs (i.e., wages and rents), which underlay the
provision of many VR services. We developed estimates of state costs
using data on wages from BLS and on rents from HUD. This option does
not reflect differences in other types of basic costs for which
reliable data may not be readily available. See appendix I for further
information on the development of our cost estimates.
* A taxpayer equity formula: This option also distributes funds based
on states' working-aged disability populations and the cost of
providing VR services, but it adds a third factor to the formula--a
measure of each state's ability to contribute to the VR program. More
funds would be allocated to states with fewer taxable resources. To
measure a state's ability to finance the VR program, we utilized data
from Treasury on a state's total taxable resources, which includes per
capita income as well as other sources of potentially taxable state
income, such as corporate income produced within the state but not
received by state residents.
For the taxpayer equity option, an issue to consider is whether the
matching requirement would be the same across all states, as is the
case with the current formula, or would vary based upon a state's
ability to finance the VR program. To fully achieve taxpayer equity,
the matching requirement would need to vary according to each state's
financing ability. If the matching requirement were the same for all
states, those with fewer resources would receive more federal funds but
would also need to provide more state funds for the match. This could
result in poorer states having to contribute a greater share of their
resources to the VR program than wealthier states. See appendix VII for
an explanation of how a variable matching requirement could be
incorporated into the taxpayer equity option.
Table 1 shows the amount of funds redistributed among states, as well
as the number of states gaining and losing funds, for each of the three
formula options. For example, each of our three prototypes would
redistribute approximately 4 to 6 percent of the VR funds, with about
20 states receiving more funds and at least 20 states receiving less in
funds than they do under the current formula. Between 5 and 11 states
would experience a change in funding levels of 20 percent or more. See
appendix VIII for a state-by-state table of allocations under our three
formula options.
Table 1: Redistribution of Funds under Three Formula Options[A]:
Amount of funds that would be redistributed, in millions;
Partial beneficiary equity: $103.9;
Full beneficiary equity: $153.4;
Taxpayer equity: $138.3.
Percentage of total allocation;
Partial beneficiary equity: 3.8;
Full beneficiary equity: 5.6;
Taxpayer equity: 5.0.
Number of states receiving more funds;
Partial beneficiary equity: 23;
Full beneficiary equity: 19;
Taxpayer equity: 23.
Number of states receiving less funds;
Partial beneficiary equity: 22;
Full beneficiary equity: 26;
Taxpayer equity: 22.
Number of states with no change[B];
Partial beneficiary equity: 6;
Full beneficiary equity: 6;
Taxpayer equity: 6.
Number of states experiencing an increase in funds of 20 percent or
more;
Partial beneficiary equity: 2;
Full beneficiary equity: 4;
Taxpayer equity: 4.
Number of states experiencing a decrease in funds of 20 percent or
more;
Partial beneficiary equity: 3;
Full beneficiary equity: 7;
Taxpayer equity: 7.
Source: GAO analysis of data from Education on VR grants in fiscal year
2008, Census Bureau's 2006 and 2007 ACS, Treasury's data on total
taxable resources, BLS' Quarterly Census of Employment and Wages, HUD's
data on fair market rents, and responses to GAO survey.
[A] The options presented in Table 1 are cumulative, i.e., the second
option presented (full beneficiary equity) includes the first option
(partial beneficiary equity). The third option (taxpayer equity)
includes both of the other options.
[B] The same six states--Alaska, Delaware, North Dakota, South Dakota,
Vermont, and Wyoming--experience no change in their allotment due to
the fact that they receive the minimum allotment under the current
formula and would continue to do so under each of our options.
[End of table]
In our survey of state VR agencies, many respondents expressed
reservations about options for revising the current funding formula.
Our survey presented state agencies with three general approaches to
revising the formula, roughly based on our three formula options
[Footnote 21]. Most respondents expressed reservations about options
that were generally based on partial beneficiary and taxpayer equity,
and they were divided on the option generally based on full beneficiary
equity. Specifically, 45 percent of respondents expressed support for
an approach that would distribute funds so that all states would
receive funding to be able to provide the same level of services to
each individual potentially eligible for VR services, taking into
account certain differences in the cost of providing services, while 47
percent expressed disapproval of this approach, and the remainder
expressed no opinion or preference.
Options for Phasing In Changes to Minimize Disruption to State
Programs:
When a new federal formula is implemented, Congress often provides a
transition period so that grant recipients have time to adjust,
especially those recipients whose grants will be reduced. An abrupt
reduction in funding level could disrupt a state agency's ability to
provide VR services. Transition periods allow for greater
predictability and stability in state funding levels, which in turn,
help avoid major disruptions to existing state services and allow
states to develop long-range plans and program commitments.
One way to ease the change to a new formula is to phase it in gradually
over a number of years. During the phase-in period, the state
allocations would be a combination of the old and new formulas, with a
gradual increase in the portion of funding distributed through the new
formula, until the phase-in period is complete. By way of example,
figure 4 depicts a 5-year transition period, under which the amount of
money allocated under the old formula would be reduced by 20 percent
each year, and the amount allocated under the new formula would be
increased by 20 percent each year, until all of the allocations are
made using the new formula. To further minimize the disruptive effects
of a new formula, the phase-in period could be longer, although this
would, of course, postpone full use of the new formula.
Figure 4: A 5-Year Phase-In Transition to a New Funding Formula:
[Refer to PDF for image: vertical bar graph]
Year 0;
New Formula, Increased 20% each year: 0%;
Old Formula, Reduced 20% each year: 100%;
Year 1;
New Formula, Increased 20% each year: 20%;
Old Formula, Reduced 20% each year: 80%;
Year 2;
New Formula, Increased 20% each year: 40%;
Old Formula, Reduced 20% each year: 60%;
Year 3;
New Formula, Increased 20% each year: 60%;
Old Formula, Reduced 20% each year: 40%;
Year 4;
New Formula, Increased 20% each year: 80%;
Old Formula, Reduced 20% each year: 20%;
Year 5;
New Formula, Increased 20% each year: 100%;
Old Formula, Reduced 20% each year: 0%.
Source: GAO analysis.
[End of figure]
Another approach to minimize disruption to state VR programs is to
establish a hold harmless provision that limits the amount of funding
that states could lose under a new formula. One example of this
approach would be to hold states entirely harmless in the first year
that the new formula is implemented, but would allow minimal decreases
during the second and successive years, such as by 1 or 2 percent.
Because state agencies could also have difficulties adjusting to large
and sudden funding increases, limits could also be set on the increases
that states would receive from one year to the next. This graduated
approach would allow agencies to better plan for the additional funds
and manage growth in their VR programs. It should be noted that use of
a hold harmless provision would effectively reduce the amount of funds
available for distribution through the new formula in the early years
of a change because most of the funds would be allocated through the
hold harmless provision. However, over time, as the total amount of
funds appropriated for the VR program increases, more of the funds
would be allocated through the new formula.[Footnote 22]
Incorporating Financial Performance Incentives in the VR Formula Poses
Challenges:
Some research and experiences suggest that providing financial
incentive awards based on program performance has the potential to
improve government programs, and a slight majority of VR agencies
surveyed are open to using them in the VR program. Some federal
programs currently provide incentive awards and officials we spoke with
from three of these programs noted some benefits, such as motivating
state and local agencies to improve performance. Of the state VR agency
officials who responded to our survey, 59 percent were open to
including some form of incentive awards in the VR program. Some state
officials noted that doing so could reward high performing agencies,
improve VR client success, or motivate agencies to focus on continuous
improvement.
Nevertheless, there are challenges to incorporating incentive awards
into the VR program, whether through its funding formula or outside it
due, in part, to the multiple and potentially competing facets of the
VR program's mission. According to the Rehabilitation Act, state VR
programs should help clients achieve employment by providing
individualized services, while also prioritizing service to those with
the most significant disabilities when agencies cannot provide services
to all eligible applicants. VR stakeholders we spoke with, including
state agency officials, state advisory council officials,
representatives from private sector VR companies, and disability
researchers, identified three main challenges to incorporating
incentive awards into the VR program. To some extent, these challenges
are already present in the VR program's current performance measurement
system and could be accentuated by linking program performance to
incentive awards. These challenges are:
* Challenge of balancing potentially competing program goals: Unless
carefully designed, a financial performance incentive system could run
the risk of encouraging state VR agencies to focus on achieving certain
program goals, at the expense of others. For example, 89 percent of
state VR officials who responded to our survey thought it likely that
providing state agencies with additional funds based on performance
would result in agencies focusing more heavily on clients who were
expected to positively impact agency performance. In interviews, many
VR stakeholders expressed particular concern that if incentive awards
were focused on achieving employment outcomes for clients, they could
induce state agencies to concentrate on serving those most likely to
obtain employment, at the expense of those with greater barriers, such
as those with the most significant disabilities. This would run counter
to the VR program requirement that state agencies serve individuals
with the most significant disabilities first when operating under an
order of selection. Similar concerns have arisen in other employment
programs that have used incentive awards. For example, we previously
reported that local agency officials in the Department of Labor's
Workforce Investment Act (WIA) Title IB programs may be reluctant to
provide services to job seekers less likely to find and maintain a job.
[Footnote 23]
* Challenge of rewarding agencies for providing appropriate
individualized services to clients: The VR program provides both long-
term services, such as supporting youth with disabilities as they
transition out of high school and pursue higher education, and short-
term services, such as identifying job opportunities for people who
already have skills and qualifications. Several VR stakeholders
expressed concern that if incentive awards did not take into account
some clients' specialized needs for higher-cost or longer-term
services, they could cause agencies to focus on providing short-term
services. Officials from one state advisory council cautioned that
incentive awards might encourage VR counselors to focus on providing
short-term services, even if it resulted in low-paying jobs, instead of
placing VR clients in higher education programs that could ultimately
yield long-term, higher paying career positions.
* Challenge of basing incentive awards on an agency's performance,
without accounting for factors outside its control: A variety of
factors outside an agency's control may influence performance outcomes,
such as the state's economy and the characteristics and needs of the
individuals who seek rehabilitation services. If an agency's
performance cannot be distinguished from these factors, the provision
or withholding of incentive awards would not necessarily reflect agency
actions.[Footnote 24] Of state VR agency officials who responded to our
survey, 77 percent stated that isolating an agency's performance from
factors outside its control would be a great or very great challenge to
appropriately distributing incentive awards. For example, officials in
one VR agency said that some parts of their state have 20 percent
unemployment, which decreases their ability to place clients in jobs.
[Footnote 25] Another state agency official noted that his agency has
had very high employment outcomes, in part because the state had one of
the lowest unemployment rates in the country. He added that agencies in
other states whose economies are weak have had poorer employment
outcomes through no fault of their own. In addition, some state
officials suggested that agencies operating under an order of selection
would be at a competitive disadvantage compared to those that are not,
because the caseload of the former would include a greater proportion
of clients with the most significant disabilities and barriers to
employment.
Our research into the types of incentive awards used by other federal
programs, as well as the views of VR stakeholders, revealed several
ways to mitigate such challenges, but none are without potential
pitfalls. Specifically, research on designing incentive award systems
suggests the following options:
* Using multiple measures of success: Using a range of performance
measures to determine incentive awards could help motivate state
agencies to focus attention on all aspects of the VR program's mission.
[Footnote 26] For example, the performance of state VR agencies might
be measured in terms of both the proportion of people with the most
significant disabilities who find employment, and the proportion of all
clients who achieve this outcome. Another option that could encourage
agencies to provide long-term services, when appropriate, is to
establish intermediate measures of client achievement or the services
provided that have increased a client's prospect for employment. Such
an intermediate measure could be the number of VR clients who
successfully complete training programs or college degrees.
Nevertheless, there are challenges to developing appropriate measures.
For example, although Education already uses a measure for the VR
program focused on people with significant disabilities, it may be
difficult to develop a measure specifically on people with the most
significant disabilities because the Rehabilitation Act allows states
to individually define the term, and our past work found that state
agencies' definitions vary.[Footnote 27] Although Education uses
multiple measures to evaluate state VR agencies' performance, an issue
to consider is whether or not the current measures would be appropriate
to use for distributing incentive awards. Our prior work on the VR
program found that the current measures do not consider agencies'
success in assisting individuals who have not yet exited the program
and do not specifically track outcomes for youth transitioning out of
high school. As a result, we recommended that Education reevaluate its
performance measures to determine whether they reflect the agency's
goals and values.[Footnote 28]
* Adjusting performance standards to account for differences in local
economies and program clients: The level of performance required of
each state VR agency to receive an incentive award could be adjusted to
account for the challenges they face.[Footnote 29] For instance, the
performance standard required to receive an incentive award may be set
lower for agencies in states with poor economies than for states with
better economies.[Footnote 30] These adjustments could be made using a
mathematical model, negotiations between federal and state agencies, or
a combination of the two approaches. For example, the Job Training
Partnership Act program (JTPA) used mathematical models to quantify the
relative effect of participants and economies on agency outcomes. Some
researchers of the JTPA program found that this approach was perceived
to "level the playing field" for agencies and had lessened the perverse
incentives to focus more heavily on the most promising clients.
[Footnote 31] However, the research also suggests that it is difficult
to identify and measure all the external factors that can undermine or
lessen agency performance. If key factors are missing from mathematical
models, the adjusted performance standards may lead to inaccurate
estimates.[Footnote 32] Taking a different approach, WIA Title IB
employment and training programs set performance standards by
negotiating with state agencies. Although advocates of this approach
say that it increases agency involvement and may better capture
qualitative factors that affect agency performance, we and others have
criticized the WIA negotiation approach as unsystematic and
inconsistent across states.[Footnote 33] Specifically, we have
suggested using an approach that combines negotiations with
mathematical models. In a prior report, we recommended that Labor
develop an adjustment model or other systematic method to account for
different populations and local economies when negotiating performance
levels.[Footnote 34] Labor officials generally agreed with our
recommendation and told us recently that they are starting to provide
states with adjustment models to inform the negotiation process.
Finally, some researchers and public officials identified concerns
about adjusting performance standards, regardless of the method. For
example, some researchers have expressed concern that adjusting
performance standards may be unfair to clients because it allows
agencies to settle for less desirable outcomes for harder-to-serve
populations.
Beyond these risks, there are a number of considerations involved in
deciding to incorporate incentive awards directly into the VR funding
formula itself. First, it would be important to consider whether or not
states should be required to match the additional funds allocated for
performance, and if not, whether those funds could be treated as part
of the state's matching contribution. Another consideration is that
rewarding high performance through the funding formula would, in
effect, penalize other states insofar as it reduces the total funding
available for distribution to all agencies based on other formula
factors. Some state VR agency officials we spoke with suggested that
incentive awards should not result in any decrease to base funding
allocations. HUD's Public Housing Capital Fund program, which provides
incentive awards through its funding formula, includes provisions that
minimize the impact of the awards on states' funding.[Footnote 35]
However, HUD officials we spoke with still expressed concern that this
system penalizes housing authorities that may need additional funds if
they are to improve their performance.
Alternatively, incentive awards could be distributed independently of
the VR funding formula to avoid these inherent penalties. Options for
distributing funds outside the main VR funding formula include
providing incentive awards as grants, as occurs in the WIA Title IB
programs, or through a separate incentive award formula, as occurs in
the Child Support Enforcement program.[Footnote 36] Of the state VR
officials who responded to our survey, 51 percent supported providing
incentive awards distributed independently of formula-determined funds,
while only 22 percent supported providing the incentive awards through
the formula.[Footnote 37]
Finally, regardless of whether incentive awards are provided through
the VR funding formula or independent of it, there are still other
considerations involved in designing and carrying out an incentive
award system. For instance, it is important that incentive awards are
based on reliable data about agency performance. It is also important
to consider the extent to which an incentive award system will allow
for future modifications should there be changes in program priorities
or available data, or if perverse and unanticipated results ensue. Our
earlier work discusses these and other, related considerations.
[Footnote 38]
Concluding Observations:
Although the measures currently used to allocate VR funds may have been
the best available when the formula was last revised in 1978, better
data are now available for factoring in both the potential need for and
ability to support a program. Improved information offers policymakers
the opportunity to update the formula to more closely align funding
with the need for services, as well as with each state's ability to
contribute to the program. In deciding whether a revision to the
formula is warranted, policymakers will likely want to consider how to
strike a balance among all important factors--need, the cost of
providing services, and the extent to which state resources are
available. Certainly, revising the funding formula poses challenges
because any formula change will result in funding decreases for some
states, along with increases for others. However, there are ways to
ease the transition to a new formula, so as to minimize disruption to
VR programs and the people they serve. On the other hand, incorporating
performance incentives into the formula might introduce more complexity
and risk. While there are mechanisms to mitigate these challenges, the
potential benefits for the VR program would need to be carefully
weighed against the potential risks.
Agency Comments and Our Evaluation:
We provided a draft of this report to Education for review and comment.
Education provided technical comments and we modified the report, as
appropriate, to address these comments.
We are sending copies of this report to the Secretary of Education,
relevant congressional committees, and other interested parties. The
report will also be available at no charge on GAO's Web site at
[hyperlink, http://www.gao.gov].
If you or your staffs have any questions about our report, please
contact me at (202) 512-7215 or bertonid@gao.gov. Contact points for
our Offices of Congressional Relations and Public Affairs may be found
on the last page of the report. Key contributors to this report are
listed in appendix X.
Signed by:
Daniel Bertoni:
Director, Education, Workforce, and Income Security Issues:
[End of section]
Appendix I: Objectives, Scope, & Methodology:
Our objectives were to: (1) assess the extent to which the funding
formula meets generally accepted equity standards, (2) develop options
for revising the formula to better meet these standards, and (3)
identify issues to consider with incorporating performance incentives
into the formula. We used two generally accepted formula design
standards intended to achieve equity for beneficiaries and
taxpayers.[Footnote 39] To meet both equity standards, a formula should
use reliable and appropriate measures of the need population in each
state and the cost of providing services in each state. A taxpayer
equity formula additionally requires a reliable measure of a state's
ability to finance a program from its own resources. In the following
sections, we describe how we measured the need population, cost of
providing services, and financing capacity in each state, and how we
analyzed the extent to which the current formula meets equity standards
and developed various formula options.[Footnote 40] To address all
three objectives, we also surveyed the 80 vocational rehabilitation
(VR) agencies in the states, territories, and District of Columbia and
conducted in-depth interviews with 11 VR agencies in 9 states. Finally,
for the third objective, we reviewed literature on performance
incentives and obtained the opinions of officials at 3 federal
government programs that use incentive awards, which we describe in
more detail in the last section.
We conducted this performance audit from September 2008 to September
2009, 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.
Measuring State Need Populations:
Data that directly measure the number of people in a state who
potentially need VR services do not exist. Although state VR agencies
have data on the size of their own caseloads, these data are not
appropriate for use in a funding formula for two reasons. First,
caseloads may be influenced by state funding levels. For example, an
agency's caseload may be relatively small because of limited funds, not
because of limited demand for services. Second, data that can be
controlled by state agency officials should not be used in a funding
formula because they could introduce some "undesirable incentives" into
the program. For instance, if a state's allotment were determined by
the size of its caseload, a state agency might be rewarded for taking
inappropriate actions, such as enrolling individuals into the VR
program who do not require VR services or, in the case of an agency
under an order of selection, enrolling individuals who do not meet its
criteria for receiving priority for services.
There are, however, several national surveys that provide estimates of
the number of people with disabilities by state. These surveys are
conducted by statistical agencies such as the Census Bureau and Bureau
of Labor Statistics (BLS). We reviewed several of these surveys: (1)
the Census Bureau's American Community Survey (ACS), (2) the Decennial
Census,[Footnote 41] (3) the Current Population Survey's Basic Monthly
Survey, (4) the Current Population Survey's Annual Social and Economic
Supplement, and (5) the Center for Disease Control's Behavioral Risk
Factor Surveillance System. We sought to identify data on the
populations with all types of disabilities in each state.[Footnote 42]
We ultimately selected the Census Bureau's ACS to use as a measure of
state populations potentially in need of VR services for several
reasons. First, the ACS provides data on states' disability populations
on an annual basis. Second, the ACS has a large sample size (with about
3 million housing units surveyed across all 50 states, the District of
Columbia, and Puerto Rico), which would allow for more accurate
estimates of the need population in each state. Third, the ACS surveys
people in more types of group quarters than any of the other surveys,
such as those living in college dormitories, group homes, prisons, and
nursing care facilities. This is significant, since about 10 percent of
VR clients exiting the program in 2006 and 2007 lived in group quarters
such as group homes or rehabilitation facilities when they applied to
receive VR services, according to Education's data. Fourth, the ACS
asks six questions that are designed to capture a wide variety of
disabilities (see table 2), and these questions are asked consistently
across all states. One limitation of the ACS for purposes of allocating
VR funds, however, is that the data are not available for U.S.
territories, with the exception of Puerto Rico.
Table 2: Disability Questions from the American Community Survey, 2006
and 2007:
Disability question:
1. Does this person have any of the following long-lasting conditions:
Blindness, deafness, or a severe vision or hearing impairment?
Disability question:
2. Does this person have any of the following long-lasting conditions:
A condition that substantially limits one or more basic physical
activities such as walking, climbing stairs, reaching, lifting, or
carrying?
Disability question:
3. Because of a physical, mental, or emotional condition lasting 6
months or more, does this person have any difficulty in doing any of
the following activities: Learning, remembering, or concentrating?
Disability question:
4. Because of a physical, mental, or emotional condition lasting 6
months or more, does this person have any difficulty in doing any of
the following activities: Dressing, bathing, or getting around inside
the home?
Disability question:
5. Because of a physical, mental, or emotional condition lasting 6
months or more, does this person have any difficulty in doing any of
the following activities: Going outside the home alone to shop or visit
a doctor's office?
Disability question:
6. Because of a physical, mental, or emotional condition lasting 6
months or more, does this person have any difficulty in doing any of
the following activities: Working at a job or business?
Source: Census Bureau.
[End of table]
We analyzed the data produced by only 5 of the 6 disability questions
from the 2006 and 2007 ACS. We did not use data from the sixth question
regarding the difficulty of working (question 6 in table 2) because the
Census Bureau had removed this question in 2008 at the recommendation
of an inter-agency task force. The 2008 ACS data is expected to be
released in the fall of 2009 and was not available to use for this
study. Since this question will not be included on future surveys, we
sought to produce an analysis that would more closely reflect future
available data. However, because changes were also made to each of the
five other ACS questions for the 2008 survey, we cannot say whether our
analysis of 2006 and 2007 data will be predictive of conditions in 2008
or thereafter.[Footnote 43]
We measured each state's disability population by counting the number
of civilians of working age (16 to 64) who responded "yes" to any of
the five disability questions. By excluding the difficulty working
question, our measure excluded 12.5 percent of the population who
responded "yes" to the difficulty working question. The remaining 87.5
percent of those who responded "yes" to this question were people who
also responded "yes" to one or more of the five other disability
questions. As a result, they were included in our measure.
Table 3 provides a breakdown of the total U.S. population into the
different components of our measure of the need population for the VR
program. As shown in the table, our measure--the civilian working-aged
population with a disability--comprises 7.6 percent of the total U.S.
population.
Table 3: Breakdown of the Total U.S. Population into Components of our
Measure of the Need Population for the VR program, 2007:
Total U.S.;
Population: 301,621,159;
Percentage of total population: 100.
Civilian working-aged (16 to 64);
Population: 197,630,139;
Percentage of total population: 65.5.
Civilian working-aged with a disability[A] (16 to 64);
Population: 22,886,919;
Percentage of total population: 7.6.
Source: GAO analysis of data from the Census Bureau's ACS.
[A] For purposes of this study, the population of people with
disabilities is based upon data from five disability questions asked in
the 2007 ACS. We did not include data from a sixth question on
difficulty working.
[End of table]
We assessed the reliability and validity of ACS data by interviewing
Census Bureau officials and disability experts, reviewing documentation
and literature, and conducting comparisons with other disability data.
Specifically, we compared the ACS data with data from the Social
Security Administration (SSA) on recipients of two types of disability
benefits, Social Security Disability Insurance (SSDI) and Supplemental
Security Income (SSI) benefits. For individuals to receive SSDI or SSI
benefits, SSA or a state agency must first determine that they have a
disability that prevents them from working. We compared the proportion
of state populations with disabilities, according to ACS data, with the
proportion of their populations receiving SSA disability benefits and
found a high correlation--0.872 for SSDI and 0.788 for SSI. This
indicates that ACS data and the SSA data showed similar trends; states
with higher rates of disability according to ACS data also tended to
have higher proportions of their population receiving SSA disability
benefits. We had our work on identifying a measure of need population
reviewed by three disability experts, and they concurred that ACS data
provide a reasonable measure of the size of a state's population
potentially needing VR services.
Measuring State Cost Differences:
It is difficult to estimate differences among states in the costs of
providing VR services. Although one approach for estimating cost
differences is to estimate the costs for the same basket of goods in
different states, attempting to do this for VR services would be
extremely costly and labor-intensive because of the wide array of
services that VR agencies provide, including assessment, counseling,
higher education, occupational training, medical diagnosis and
treatment, and transportation. Another challenge is finding a reliable
data source on cost. State agencies' data on expenditures, in part,
reflect cost of services, but using these data in a formula runs the
risk of allowing undesirable incentives to be introduced into the
program. For example, a state agency that efficiently manages its
program will be able to provide the same quality of services at a lower
cost than an agency with less efficient management. In this case, if
the formula provided higher allocations to states with higher reported
costs, it could reward agencies that are more inefficient. In addition,
expenditures data are of limited use in measuring state cost
differences because they reflect many other factors besides costs. For
example, the amount of funds that agencies spend per client reflects
the level of funds they receive from state and federal sources, as well
as the types of clients they serve and the types of services they
provide. For example, some agencies may choose to provide more
intensive, higher-cost services to a smaller number of clients, while
other agencies may choose to serve a larger number of clients with
lower-cost services.
To address these challenges, we developed and used measures for the
costs of resources--or inputs--that go into providing services, which
are beyond the direct control of state agencies. Specifically, we
focused on two basic inputs--labor and office space--that are needed to
provide the different types of VR services. Where wages or rents are
higher because the general cost of living is high, state agencies must
pay more for workers or office space to provide services. In the
following subsections, we describe in detail how we developed a cost
index to reflect differences in costs for these two types of inputs,
labor and office space. There are many other resources used to provide
VR services, such as equipment and supplies, but data are not readily
available on them. Obtaining such data would be time-intensive and
costly, requiring detailed surveys of the specific services that each
VR agency provides and the particular resources that go into each
service. As a result, our cost index may not capture differences in the
cost of some key inputs. For example, due to lack of data, our index
would not capture transportation costs, which could be higher in states
that have geographically dispersed populations.
While our index has some limitations, we believe it is a reasonable
proxy that can reflect general differences across states in the cost of
providing VR services. Because any cost index will only be an
approximation of true cost differences, the index we developed is based
on what we believe are reasonable assumptions that avoid overstating or
exaggerating cost differences among states. We believe our measure
allows us to at least partially recognize real cost differences among
the states, while avoiding inappropriate incentives. A similar cost of
services index is used in the funding formulas for the Community Mental
Health Services and the Substance Abuse Prevention and Treatment block
grants.
In the following subsections, we describe: (1) our work to identify a
data source for estimating wages in each state, (2) the data source we
used to estimate rental costs in each state, (3) our methodology for
estimating how much to weight wages and rents in the cost index, and
(4) how we combined our weights with the data on wages and rents to
develop a cost of services index for each state. Since the purpose of
our cost index is to help distribute federal VR funds among states, we
did not examine cost differences between agencies for the blind and
general VR agencies. In states with two VR agencies, the state
determines how to divide the federal grant allocation among the
agencies.
Identifying a Proxy for Wages in the VR Program:
A measure of state labor costs should reflect the wages of all types of
workers potentially involved in the VR program, including those
directly employed by state VR agencies, as well as those employed by
public or private-sector organizations that VR agencies have contracted
with to provide VR services. To obtain an understanding of the types of
workers who may be involved in state VR programs, we first reviewed
data from Education's Annual Vocational Rehabilitation Program/Cost
Report (RSA-2), which provides information on state agencies'
expenditures on various types of services, both those provided by state
agency employees and those provided through contracts or purchases from
other organizations.[Footnote 44] The data indicate the proportion of
expenditures state agencies spent on the following types of services in
fiscal year 2007:
* 7 percent on postsecondary education:
* 14 percent on occupational and vocational, job readiness, and all
other training:
* 13 percent on assessment, counseling, guidance, and placement:
* 7 percent on diagnosis and treatment of physical and mental
impairments:
* 4 percent on rehabilitation technology:
* 1 to 2 percent each on other types of services, such as income
support, transportation, and personal assistance services:
Once we obtained information on the types of services that the VR
program provides, we reviewed sources of data from BLS on average
annual wages in each state for various industries and occupations, as
well as wage data from the Centers for Medicare and Medicaid Services
(CMS) that are used to adjust payments to healthcare providers.
Specifically, we examined BLS data from the Quarterly Census of
Employment and Wages (QCEW) and Occupational Employment Statistics
(OES). The QCEW data, which come from employer filings for unemployment
insurance, cover nearly all civilian employment. It classifies wages
and employment levels by industry, using the North American Industry
Classification System (NAICS). We examined private sector wages, but
also included public sector wages for state government employees. In
addition, we also reviewed data on specific occupations related to the
VR industry from the OES. The OES classifies occupations according to
the Standard Occupational Classification (SOC) system. Finally, we
examined wage indices used to allocate funds in the Medicare program
for skilled nursing and inpatient rehabilitation facilities. The
specific data series we reviewed are listed below:
* BLS, Quarterly Census of Employment and Wages:
- Vocational rehabilitation services industry, private sector (NAICS
6243):
- Social assistance industries, private sector (NAICS 624):
- Healthcare and social assistance industries, private sector (NAICS
62):
- Education, healthcare, and social assistance industries, private
sector (QCEW industry code 1025):
- Service-providing industries, private sector (QCEW industry code
102):
- State government sector (QCEW industry code 10, state government):
- BLS, Occupational Employment Statistics:
- Rehabilitation counselors (SOC 21-1015):
- Substance abuse and behavioral disorder counselors (SOC 21-1011):
- Educational, vocational, and school counselors (SOC 21-1012):
- Community and social services occupations (SOC 21):
* Wage indices for Medicare's Prospective Payment System:
- Inpatient Rehabilitation Facilities:
- Skilled Nursing Facilities:
We determined that the data for the most narrowly-defined industries
and occupations--the QCEW data for the vocational rehabilitation
services industry, and the OES data on rehabilitation counselors--were
less suitable for use in a cost index after examining the data and
speaking with officials from BLS. The QCEW data on the vocational
rehabilitation industry showed some peculiar values. For example, the
wages in Vermont were the highest in the nation in the vocational
rehabilitation services industry, but they were not among the highest
in other data series we examined. We contacted BLS officials to better
understand these data. They informed us that Vermont's data for the
vocational rehabilitation services industry come from a small number of
employers, and as a result, could be affected by two factors: possible
differences in the types of work performed and in the number of hours
worked per week. The average annual wage in the QCEW is calculated by
including both employees who worked full-time and part-time. If the
proportion of VR employees working part-time varies substantially
across states, this could cause state annual wages to vary. At a
broader industry level, this is less likely to be a problem because the
data cover more employers and employees. With regard to the OES data on
rehabilitation counselors, the data were incomplete. There were no
published wages for Alaska and Utah.
The remaining data series could serve as reasonable proxies for state
wages in the VR program, but we selected the QCEW data for the
education, healthcare, and social assistance industry sector for our
proxy because it covers the wide array of services that the VR program
provides. These include training, healthcare-related services, and
social services. In addition, this industry had the smallest range for
wage differences across states--the state with the lowest wage in this
industry was 17 percent below the average, and the state with the
highest wage was 25 percent above average. As a result, compared to the
other industries, the education, healthcare, and social assistance
industry would produce the more conservative results. Table 4 shows the
median, minimum, and maximum values for each of the data series we
examined for which data were available for all 50 states and the
District of Columbia. To compare wages across states and across data
series, we used wage indices. A value of 1 is equal to the national
average. Values greater than 1 are above the national average, and
values less than 1 are below the national average.
Table 4: Summary Statistics for Wage Indices from Data Series Reviewed:
Industry/occupation: Vocational rehabilitation services industry (NAICS
6243);
Minimum: 0.66;
Median: 0.94;
Maximum: 1.47.
Data source: QCEW;
Industry/occupation: Social assistance industries (NAICS 624);
Minimum: 0.70;
Median: 0.92;
Maximum: 1.84.
Data source: QCEW;
Industry/occupation: Healthcare and social assistance industries (NAICS
62);
Minimum: 0.83;
Median: 0.95;
Maximum: 1.31.
Data source: QCEW;
Industry/occupation: Education, healthcare, and social assistance
industries (QCEW 1025);
Minimum: 0.83;
Median: 0.95;
Maximum: 1.25.
Data source: QCEW;
Industry/occupation: Service-providing industries (QCEW 102);
Minimum: 0.67;
Median: 0.87;
Maximum: 1.63.
Data source: QCEW;
Industry/occupation: State government (QCEW 10, state government);
Minimum: 0.73;
Median: 0.95;
Maximum: 1.32.
Data source: OES;
Industry/occupation: Community and social services occupations (SOC
21);
Minimum: 0.72;
Median: 0.95;
Maximum: 1.19.
Data source: Medicare;
Industry/occupation: Inpatient rehabilitation facilities;
Minimum: 0.74;
Median: 0.93;
Maximum: 1.25.
Data source: Medicare;
Industry/occupation: Skilled nursing facilities;
Minimum: 0.74;
Median: 0.93;
Maximum: 1.27.
Source: GAO analysis of BLS and CMS data.
[End of table]
Table 5 presents correlations of the various wage indices. It shows
that the various wage data we reviewed are correlated with each other,
which suggests that the different data series would generally produce
similar results in funding allocations. The vocational rehabilitation
services industry (NAICS 6243), in the first row, has the lowest level
of correlation with the other indices. Its highest correlation
coefficient is 0.63 with the social assistance industry (NAICS 624),
while no two other wage indices have a correlation coefficient less
than 0.65.
Table 5: Correlations between Wage Indices:
QCEW: Vocational rehabilitation services;
QCEW, Social assistance: 0.63;
QCEW, Healthcare and social assistance: 0.51;
QCEW, Education, healthcare, and social assistance: 0.52;
QCEW, Service-providing: 0.61;
QCEW, State government: 0.49;
OES, Community and social services occupations: 0.50;
Medicare, Inpatient rehabilitation facilities: 0.49;
Medicare, Skilled nursing facilities: 0.48.
QCEW: Social assistance:
QCEW, Social assistance: [Empty];
QCEW, Healthcare and social assistance: 0.80;
QCEW, Education, healthcare, and social assistance: 0.78;
QCEW, Service-providing: 0.84;
QCEW, State government: 0.69;
OES, Community and social services occupations: 0.69;
Medicare, Inpatient rehabilitation facilities: 0.66;
Medicare, Skilled nursing facilities: 0.65.
QCEW: Healthcare and social assistance:
QCEW, Social assistance: [Empty];
QCEW, Healthcare and social assistance: [Empty];
QCEW, Education, healthcare, and social assistance: 0.99;
QCEW, Service-providing: 0.81;
QCEW, State government: 0.66;
OES, Community and social services occupations: 0.79;
Medicare, Inpatient rehabilitation facilities: 0.72;
Medicare, Skilled nursing facilities: 0.72.
QCEW: Education, healthcare and social assistance:
QCEW, Social assistance: [Empty];
QCEW, Healthcare and social assistance: [Empty];
QCEW, Education, healthcare, and social assistance: [Empty];
QCEW, Service-providing: 0.83;
QCEW, State government: 0.67;
OES, Community and social services occupations: 0.79;
Medicare, Inpatient rehabilitation facilities: 0.72;
Medicare, Skilled nursing facilities: 0.72.
QCEW: Service-providing:
QCEW, Social assistance: [Empty];
QCEW, Healthcare and social assistance: [Empty];
QCEW, Education, healthcare, and social assistance: [Empty];
QCEW, Service-providing: [Empty]; QCEW,
State government: 0.72;
OES, Community and social services occupations: 0.74;
Medicare, Inpatient rehabilitation facilities: 0.68;
Medicare, Skilled nursing facilities: 0.66.
QCEW: State government:
QCEW, Social assistance: [Empty];
QCEW, Healthcare and social assistance: [Empty];
QCEW, Education, healthcare, and social assistance: [Empty];
QCEW, Service-providing: [Empty];
QCEW, State government: [Empty];
OES, Community and social services occupations: 0.75;
Medicare, Inpatient rehabilitation facilities: 0.72;
Medicare, Skilled nursing facilities: 0.71.
OES: Community and social services;
QCEW, Social assistance: [Empty];
QCEW, Healthcare and social assistance: [Empty];
QCEW, Education, healthcare, and social assistance: [Empty];
QCEW, Service-providing: [Empty];
QCEW, State government: [Empty];
OES, Community and social services occupations: [Empty];
Medicare, Inpatient rehabilitation facilities: 0.82;
Medicare, Skilled nursing facilities: 0.82.
Medicare: Inpatient rehabilitation facilities;
QCEW, Social assistance: [Empty];
QCEW, Healthcare and social assistance: [Empty];
QCEW, Education, healthcare, and social assistance: [Empty];
QCEW, Service-providing: [Empty];
QCEW, State government: [Empty];
OES, Community and social services occupations: [Empty];
Medicare, Inpatient rehabilitation facilities: [Empty];
Medicare, Skilled nursing facilities: 1.00.
Source: GAO analysis of BLS and CMS data.
[End of table]
The distribution of values for the education, healthcare, and social
assistance wage index is shown in figure 5. Values for more than half
of the states lie between 0.9 and 1.1, which suggests that most states
have similar wages in this industry sector.
Figure 5: Distribution of Average State Wages in the Education,
Healthcare, and Social Assistance Industry Sector, 2005 to 2007:
[Refer to PDF for image: vertical bar graph]
State wage index: 0.75;
Number of states: 0.
State wage index: 0.85;
Number of states: 7.
State wage index: 0.90;
Number of states: 9.
State wage index: 1.00;
Number of states: 18.
State wage index: 1.10;
Number of states: 10.
State wage index: 1.15;
Number of states: 5.
State wage index: 1.25;
Number of states: 2.
State wage index: 1.65;
Number of states: 0.
Source: GAO analysis of data from BLS‘ Quarterly Census of Employment
and Wages.
[End of figure]
Identifying a Proxy for Commercial Rents:
State-by-state data on the cost of office space are not available. As a
result, we used as a proxy residential rental rates. The Department of
Housing and Urban Development (HUD) annually collects the rental cost
of housing for 530 metropolitan areas and 2,045 non-metropolitan
counties across the nation. These Fair Market Rents (FMR) data are used
by several programs to set housing subsidies. The FMR data are also
used in Medicare's physician fee schedule as a measure of office rents.
Since the FMR provides data on a local level, we aggregated the data to
statewide averages and used an index to compare rental costs across
states. The distribution of rents is shown in figure 6. Similar to
figure 5, a value of 1 is equal to the national average. Values greater
than 1 are above the national average, and values less than 1 are below
the national average. Unlike the wage index, the rental cost index
shows that over half of the states have a rental index of 0.85 or less,
while 11 states have a rental index of 1.15 or higher. This suggests
that rental costs vary substantially among states.
Figure 6: Distribution of Average State Rents, Fiscal Years 2007 to
2009:
[Refer to PDF for image: vertical bar graph]
State rent index: 0.75;
Number of states: 16.
State rent index: 0.85;
Number of states: 11.
State rent index: 0.90;
Number of states: 4.
State rent index: 1.00;
Number of states: 5.
State rent index: 1.10;
Number of states: 4.
State rent index: 1.15;
Number of states: 1.
State rent index: 1.25;
Number of states: 2.
State rent index: 1.65;
Number of states: 8.
Source: GAO analysis of HUD data.
[End of figure]
Weighting of Inputs:
To determine how much to weight the wage and rental costs, we first
surveyed state VR agencies to learn what proportion of their fiscal
year 2007 and 2008 expenditures was spent on wages and rents. Because
many agencies contract out or purchase services, our survey asked them
first to report how much of their expenditures were for contractors or
purchased services, and how much was spent in-house. Then we asked them
to report how much of their in-house expenditures went to wages and
rents. We used these responses to compute average proportions, which we
then applied to categories of expenditures using RSA-2 data. In doing
so, we assumed that the proportions of expenditures on wages and rents
that state agencies reported for in-house expenditures was the same as
the proportions for contracts and purchased services.[Footnote 45] With
regard to the RSA-2 data, we examined state VR agencies' spending in
three broad categories: administration, individual services, and group
services.
Table 6 shows the results of our analyses of the survey responses and
the RSA-2 data, as well as how we combined these results to develop the
weights for wages, rents, and other inputs in the cost index. In table
6, the first column shows the expenditure categories from the RSA-2
data. The second column shows the average proportion of expenditures
that went to administration, individual, and group services, according
to the RSA-2 data. The third through fifth columns show our survey
results on the average proportion of expenditures that went to labor
costs, rents, and other inputs. We assumed that for administration and
individual services, expenditures went to labor, rents, and other
inputs in the precise proportions that the survey results suggested.
For example, we assumed that 69 percent of expenditures for
administration and individual services were spent on wages, 5.6 percent
on rents, and the remainder for "other," such as materials and
supplies. However, group services can include activities such as
construction of a community rehabilitation program. It is not clear
what comprises these services. As a result, its input costs were
assigned entirely to the "other" category. Finally, the last three
columns multiply the prior columns, and the sum of the columns yields
some preliminary weights.
Table 6: Combining Education's Expenditure Data and Survey Data to
Estimate Weights for Inputs:
VR expenditure categories: Administrative;
VR expenditure breakdown (from RSA-2 data): 0.106;
Input cost proportions (from survey): Labor: 0.690;
Input cost proportions (from survey): Rents: 0.056;
Input cost proportions (from survey): Other: 0.254;
Input cost weights: Labor: 0.073;
Input cost weights: Rents: 0.006;
Input cost weights: Other: 0.027.
VR expenditure categories: Individual services;
VR expenditure breakdown (from RSA-2 data): 0.862;
Input cost proportions (from survey): Labor: 0.690;
Input cost proportions (from survey): Rents: 0.056;
Input cost proportions (from survey): Other: 0.254;
Input cost weights: Labor: 0.595;
Input cost weights: Rents: 0.048;
Input cost weights: Other: 0.219.
VR expenditure categories: Group services;
VR expenditure breakdown (from RSA-2 data): 0.032;
Input cost proportions (from survey): Labor: 0.000;
Input cost proportions (from survey): Rents: 0.000;
Input cost proportions (from survey): Other: 1.000;
Input cost weights: Labor: 0.000;
Input cost weights: Rents: 0.000;
Input cost weights: Other: 0.032.
VR expenditure categories: Total;
VR expenditure breakdown (from RSA-2 data): 1.000;
Input cost weights: Labor: 0.668;
Input cost weights: Rents: 0.054;
Input cost weights: Other: 0.278.
Source: GAO analysis of Education's RSA-2 data and GAO survey.
[End of table]
Once we obtained the results shown in table 6, we rounded the weights
to the nearest 5 percent. Ultimately, we estimated the weights to be
0.65 for wages, 0.05 for rents, and 0.30 for all other inputs.
Constructing the Cost Index:
We then constructed the index, using the following formula:
Cost index = 0.65 Wages + 0.05 Rent + 0.30 Other:
With this formula, we calculated a cost index for each state, using
each state's average wage from 2005 to 2007 in the education,
healthcare, and social assistance sector, according to QCEW data, and
its average rental cost from fiscal years 2007 to 2009, according to
the FMR data. The cost for "other" inputs besides wages and rents was
assigned a weight of 0.30 and assumed to be constant for all states. We
made this assumption to simplify the construction of the cost index
because we were unable to readily or reliably capture differences in
the costs of these inputs among states. As noted earlier, estimating
these costs would be difficult because identifying the various
materials and supplies used to provide the wide variety of VR services
would be costly and labor-intensive. In addition, it is unlikely that
there would be nationally available data on the costs of any materials
or supplies we could identify. The assumption that the cost of "other"
inputs is the same across states may be reasonable because some
materials and supplies are likely to be purchased by state VR agencies
from a national market and, therefore, the geographical variation in
these costs would be limited.
Figure 7 shows the distribution of the cost index. If a state's cost
index is 1, its costs are estimated to be the same as the national
average. If a state's index is greater than 1, its costs are estimated
to be above the national average. Finally, if a state's index is less
than 1, its costs are estimated to be below the national average.
Values for 36 of the states lie between 0.9 and 1.1. See appendix IV
for a listing of the cost index for each state.
Figure 7: Distribution of the Cost of Services Index:
[Refer to PDF for image: vertical bar graph]
Cost of services index: 0.75;
Number of states: 0.
Cost of services index: 0.85;
Number of states: 0.
Cost of services index: 0.90;
Number of states: 12.
Cost of services index: 1.00;
Number of states: 22.
Cost of services index: 1.10;
Number of states: 14.
Cost of services index: 1.15;
Number of states: 2.
Cost of services index: 1.25;
Number of states: 1.
Cost of services index: 1.65;
Number of states: 0.
Source: GAO analysis of data from Education, BLS, HUD, and responses to
GAO survey.
[End of figure]
We had our work on the cost of services index reviewed by three
external experts in the disability field. They generally concurred with
our methodology for developing the cost index using existing data.
However, each noted that our index does not capture certain key inputs
or costs underlying the provision of VR services, such as higher
education, transportation, contracted services, and tax rates. We
generally agree that it would be preferable to reflect all key cost
differences that affect the provision of VR services; however, to do so
requires reliable data from VR agencies (to determine average costs and
develop weights for each input), and from independent sources (to
estimate cost differences for those inputs among states). Either one or
the other, or both, were not readily available. For example, with
respect to transportation costs, data were not readily available from
VR agencies that would allow us to develop average costs and weights,
and we are not aware of independent data on transportation cost
differences among states.[Footnote 46] Similarly, data were not readily
available from VR agencies to identify the specific services that
agencies contracted for and to determine the inputs that are used to
provide those services. Finally, one of the experts noted that high tax
rates in certain states may result in higher wages and rents, and
suggested we incorporate states' tax rates into our cost index. We
agree that tax rates may influence wages and rents; however, research
suggests that many factors affect state differences in wages and rents,
and determining the relative significance of state tax rates would be a
highly complex analytical undertaking. In summary, although data were
not readily available to reliably reflect cost differences for
additional inputs suggested by our experts, these experts generally
agreed that we appropriately accounted for cost differences in two
basic inputs, i.e., wages and rents.
Measuring State Financing Capacity:
A taxpayer equity standard stipulates that funds are distributed so
that states can provide individuals comparable services using both
state and federal funds, while each state contributes about the same
proportion of their resources to a given federal program. This equity
standard requires a formula to include an indicator of each state's
ability to finance a given program from its own sources. In a funding
formula, a good indicator of a state's financing ability would measure
all types of taxable resources and would not be affected by an
individual state's actual fiscal decisions.
We used Total Taxable Resources (TTR), as reported by Treasury, to
measure state resources. The Treasury, as required by federal law,
provides annual estimates of TTR in order to estimate states' financing
ability. The estimates are used in formulas to allocate federal funds
among states for the Community Mental Health Services and the Substance
Abuse Prevention and Treatment block grants. TTR is a more
comprehensive measure of financing ability than per capita income
because it includes personal income received by state residents, types
of corporate income and capital gains that per capita excludes, as well
as income produced within a state that is received by individuals who
reside out-of-state.
Analysis of the Extent to which the Current Formula Meets Equity
Standards:
For our first objective, we reported state-level information on the
proportion of the population of working age with a disability,
allotments per person with a disability (adjusting for costs), and
ratio of per capita income to total taxable resources. In addition, we
analyzed the extent to which state per capita income is correlated with
state disability rates in order to determine whether the current use of
per capita income in the funding formula adequately accounts for a
state's need population. Finally, we examined whether agencies in
states with below median levels of funding per person with a disability
were more often on an order of selection, compared to states above the
median. For each of the above analyses, we examined only the 50 states
and the District of Columbia. We did not analyze the U.S. territories
because complete data were not available on the territories from the
various data series we used. This section describes each of the
analyses we conducted for our first objective.
Proportion of states' population that is civilian, of working age, and
with a disability: To determine the proportion of each state's general
population that is civilian, of working age, and has a disability, we
analyzed 2007 ACS data to obtain the number of civilians in each state
from age 16 to 64 who responded "yes" to at least one of five
disability questions. As discussed above, we did not include
individuals who responded "yes" to the question on difficulty working
because this question was eliminated from the ACS, starting in 2008. We
then divided the working-aged disability population numbers by the
total population (all ages) for each state, which we also obtained from
the 2007 ACS. These proportions are presented in appendix III.
Correlation between states' disability populations and their per capita
income: To test whether the formula's use of per capita income is a
reasonable proxy for states' disability rates, we analyzed the
correlation between their disability rates and their "allotment
percentage," which is the part of the formula that includes per capita
income. As discussed above, we determined a state's disability rate as
the proportion of a state's total population that is civilian, of
working age, and has a disability. For the per capita income factor, we
calculated each state's "allotment percentage," which according to the
formula, is one minus one-half of a state's per capita income level
divided by the national per capita income level (see appendix II for a
further explanation of the allotment percentage). We then obtained the
correlation coefficient between states' disability rates and their
allotment percentages.
Allotments per working-aged person with a disability (cost-adjusted):
To determine for each state the allotment per working-aged person with
a disability, as adjusted for the costs of providing services, we used
Education data on the VR grant allotments that states received in
fiscal year 2008, ACS data on state disability populations in 2007
(using the five disability questions, as described earlier), and the
cost index, as described earlier. Specifically, we used the allotments
that Education initially calculated for each state for fiscal year
2008.[Footnote 47] To calculate the allotments per working-aged person
with a disability while adjusting for costs, we divided each state's
grant allotment by the product of the cost index and the state's 2007
working-aged disability populations. See appendix V for a state-by-
state listing of allotments per working-aged person with a disability,
as adjusted for costs of services.
We tested the reliability of Education's data on VR grant allotments by
replicating Education's formula calculations and interviewing Education
officials knowledgeable about the data. Our replications of the formula
calculations produced results that were virtually identical to
Education's. As a result, we determined that the data are sufficiently
reliable for our purposes.
Order of Selection Status: We examined whether agencies in those states
with below median allotments per working-aged person with a disability
(adjusting for costs) more often reported being under an order of
selection than those states whose allotments were above the
median.[Footnote 48] To obtain information on states' order of
selection status, we used Education's RSA-113 data, which are quarterly
data that states submit on their caseloads. For states with two VR
agencies, we considered a state to be under an order of selection if
either of its agencies reported being under an order of selection.
Table 7 shows the number of states that we considered under an order of
selection, by type of VR agency.
Table 7: Number of States Considered to Be Under Order of Selection by
Type of VR Agency:
States with one VR agency (combined) under order of selection: 16.
States with two VR agencies, both general and blind agencies under
order of selection: 4.
States with two VR agencies, only general agency under order of
selection: 10.
States with two VR agencies, only blind agency under order of
selection: 1.
Total states with an agency under order of selection: 31.
Source: GAO analysis of Education data.
[End of table]
We assessed the reliability of RSA-113 data by interviewing Education
officials knowledgeable about the data and conducting edit checks.
Education officials informed us that when a state agency reports being
under an order of selection, the Department verifies that the state
agency has documented in its state plan its intention to provide
services on an "order of selection" basis. However, Education officials
also informed us that the RSA-113 data on a state's order of selection
status do not necessarily indicate whether state agencies are currently
operating on this basis by actively limiting services to individuals.
For example, they noted, and we subsequently confirmed through our own
review of the data, that some states reported being under an order of
selection, but reported having no individuals on a waiting list. As a
result, we determined that the RSA-113 data on order of selection was
sufficiently reliable to provide information on the number of states
reporting they were on an order of selection, but we cannot say whether
these states were actually operating under their order. Our analysis
also did not allow us to conclude whether there is any causal link
between states' funding levels and their order of selection status.
Comparison of per capita income and total taxable resources: We
analyzed how per capita income compares with TTR in each state. To do
this, we obtained data on per capita income from the Department of
Commerce and TTR data from Treasury from 2004 to 2006, the latest years
for which data from both sources were available. We took 3-year
averages (2004, 2005, and 2006) of the per capita income and total
taxable resources levels for each state in order to limit the effects
of any year-to-year fluctuations. We then calculated the total taxable
resources per capita for each state by dividing the average total
taxable resource amount by the state's average population from 2004 to
2006. Next, we created indices by dividing each state's per capita
income and total taxable resources per capita by the U.S. averages of
per capita income and total taxable resources per capita, respectively.
To compare states' per capita income with their total taxable
resources, we divided each state's per capita income index by its index
of total taxable resources per person to obtain ratios. See appendix VI
for a state-by-state listing of indices of per capita income and total
taxable resources per capita, as well as their ratios.
Analysis of Formula Options:
For our second objective, we developed three formula options based upon
equity standards commonly used to design and evaluate funding formulas.
See appendix VII for detailed descriptions of the formula options. We
also estimated the grant allotments that each state would receive under
each formula option, using data on states' disability populations from
the ACS, our cost index, and TTR data from Treasury. See appendix VIII
for a table of our estimates of the grant allotments. Specifically, we
used for states' need populations, the average of their 2006 and 2007
populations of people of working age with a disability in order to
limit the effects of any year-to-year fluctuations. As described above,
we used the five disability questions from the ACS. As a measure of
cost of services, we used the cost index that we developed, also
described above. As a measure of state resources, we used the average
of states' total taxable resources from 2004 to 2006.
Survey of State VR Agencies:
We conducted a Web-based survey of VR agencies in states, territories,
and the District of Columbia to gather information on agency officials'
opinions regarding the current formula, potential modifications to the
formula, and the incorporation of performance incentives into the
formula. In addition, we used the survey to obtain data on agency
expenditures that we needed to develop our cost index. The Web-based
survey was conducted using a self-administered electronic questionnaire
posted on the Web. This Web-based survey was compatible with computer
software that makes Web sites accessible to people with visual
impairments. We received completed surveys from 74 of 80 VR agencies,
for a response rate of 93 percent.
We took steps in the development of the questionnaire, the data
collection, and the data analysis to minimize nonsampling errors. The
practical difficulties of conducting any survey may introduce errors,
commonly referred to as nonsampling errors. For example, difficulties
in how a particular question is interpreted, in the sources of
information that are available to respondents, or in how the data are
entered into a database or were analyzed can all introduce unwanted
variability into survey results. To minimize such nonsampling errors, a
social science survey specialist designed the initial questionnaire, in
collaboration with GAO staff who had subject matter expertise. The
draft questionnaire was pretested with officials from 5 state VR
agencies to ensure that the questions were relevant, clearly stated,
and easy to comprehend. When the data were analyzed, an independent
analyst checked all answers using a statistical program. Since the
survey was a Web-based survey, respondents entered their answers
directly into the electronic questionnaire, thereby eliminating the
need to have the data keyed into a database and avoiding data entry
errors. See appendix IX for selected responses to our survey.
Interviews with Experts and Agency Officials:
To learn more about state agency officials' opinions on all three of
our research objectives, we spoke with VR agency officials from 3
states when we designed our methodology and we followed our survey work
by interviewing officials from 8 agencies in 6 states. In selecting
these 8 agencies, we identified states with a diversity of
characteristics in terms of their: (1) disability population rates; (2)
per capita income levels; (3) geographic dispersion; and (4) order of
selection status. In addition, we sought to interview both state
agencies that serve individuals with a wide variety of disabilities and
agencies that primarily serve blind individuals. In 5 states, we also
spoke with representatives from the state rehabilitation councils,
which are advisory councils for state VR agencies.
We also spoke with officials from: (1) Education's Rehabilitation
Services Administration to obtain relevant programmatic data and
perspectives on the VR program; (2) SSA regarding data on the
population receiving Social Security disability benefits; (3) the
Census Bureau regarding ACS disability data; (4) BLS regarding data on
wages; and, (5) the Department of Transportation regarding the use of
Census data on disability that was used in a formula to distribute
funds for the Department's New Freedom program.
In addition, we conducted about a dozen interviews with researchers
having expertise in disability data and the VR program and with
advocacy groups. We also attended the fall 2008 conference of the
Council of State Administrators of Vocational Rehabilitation (CSAVR) to
learn more about matters of interest to VR stakeholders and to obtain
the views of the council's executive committee members. Also, we spoke
with representatives from four private sector companies that provide
vocational rehabilitation services, in order to learn about their
experiences with performance incentives in the private sector. We chose
these four companies based on the recommendations of a trade
organization official familiar with the fields of disability insurance
and private vocational rehabilitation.
Review of Literature on Performance Incentives:
To identify issues to consider when incorporating performance
incentives into the VR formula, we reviewed literature produced by
academic experts, think tanks, and government agencies such as the
Congressional Research Service. We also reviewed prior GAO studies
dealing with performance accountability in government programs.
[Footnote 49] We identified relevant literature by reviewing research
databases, such as EconLit and the Education Resources Information
Center (ERIC). We were also referred to literature through citations in
other literature and by the recommendations of GAO staff and the
external experts we interviewed. In conducting our search and review,
we endeavored to collect a diverse body of literature that offered
different views about the use of incentive awards.
Aside from conducting a general review of literature on performance
incentives, we also identified and reviewed literature specific to four
federal programs in order to understand their experiences and identify
issues related to providing incentive awards. These programs are the
Workforce Investment Act (WIA), Child Support Enforcement (CSE), Public
Housing Capital Fund, and the Job Training Partnership Act (JTPA)
programs. The latter was discontinued and replaced by WIA Title IB
programs, authorized in 1998. We also obtained the views of federal
officials in the Departments of Labor, Health and Human Services, and
HUD, which are responsible, respectively, for the WIA, CSE, and Capital
Fund programs.
[End of section]
Appendix II: Description of the Current Vocational Rehabilitation (VR)
Funding Formula:
The current VR funding formula allocates federal funds to states
annually, based on three factors: (1) the amount of federal funds they
received for their VR program for fiscal year 1978, (2) their
population size, and (3) their per capita income level, as compared
with the national per capita income.
States' fiscal year 1978 allotments became part of the formula when it
was revised through a 1978 amendment to the Rehabilitation Act.
[Footnote 50] This provision ensured that no state experienced a
funding decrease with the formula change. As currently constructed, the
formula first provides states with the amount of federal funds that
they were allotted for their VR program in fiscal year 1978.
The remaining funds appropriated for the VR program are then
distributed based on a state's population and its "allotment
percentage." The allotment percentage is calculated using the ratio of
a state's per capita income to the national per capita income,
according to the following formula:
Allotment percentage = 1 - 0.50 x
State per capita income/U.S. per capita income.
The allotment percentage is designed to be higher for poorer states.
For example, a state that has a per capita income level equal to the
national level will have an allotment percentage of 0.50. If a state's
per capita income is lower than the national level, its allotment
percentage will be above 0.50. If a state's per capita income is higher
than the national level, its allotment percentage will be lower than
0.50. However, to mitigate the influence of per capita income for
states with very high or very low per capita income levels, the
Rehabilitation Act sets both a floor and a ceiling on the allotment
percentage--it cannot be less than 33 1/3 percent or greater than 75
percent. Further, the allotment percentage is set at 75 percent for
U.S. territories and the District of Columbia. Federal law requires the
Department of Education (Education) to calculate the allotment
percentages in even-numbered years, using the average of the three most
recent years of available data on per capita income. Education obtains
the data from the Department of Commerce's Bureau of Economic Analysis.
The portion of the formula used to distribute funds after each state is
allocated its 1978 allotment allocates half of the remaining funds
based upon a state's population and allotment percentage, and the other
half based upon its population and the square of its allotment
percentage. The squaring of the allotment percentage magnifies its
effect. Education is required to determine state populations annually,
using the most recently available data from the Department of Commerce.
The following is the formula for calculating states' funding
allotments:
Allotment = FY1978 Allotment,
+ Population x Allotment percentage2)/Sum (Population x Allotment
percentage2);
x Excess amount/2;
+
(Population x Allotment percentage)/Sum (Population x Allotment
percentage)
x Excess amount/2;
Excess amount = FY Appropriation - Sum FY1978 Allotments.
Finally, the Rehabilitation Act also stipulates a minimum level of
funding for each state. Specifically, no state may receive less than $3
million or 1/3 of 1 percent of the total federal funds appropriated to
the VR program that fiscal year, whichever is greater.[Footnote 51] If
a state's allotment is calculated to fall below this amount, its
allotment is increased to that level, and the allotments of other
states are decreased proportionately.
[End of section]
Appendix III: Percentage of Population that Is Civilian Working Age
with a Disability, 2006 and 2007:
Table 8 below shows the proportion of each state's total population
that is civilian, of working age (16 to 64), and with a disability. See
appendix I for further information on our data source and methodology
for determining these proportions.
Table 8: Percentage of Population that Is Civilian Working Age with a
Disability, 2006 and 2007:
State: Alabama;
2006: 10.8;
2007: 11.1.
State: Alaska;
2006: 9.2;
2007: 9.3.
State: Arizona;
2006: 6.7;
2007: 6.8.
State: Arkansas;
2006: 11.3;
2007: 11.1.
State: California;
2006: 6.4;
2007: 6.5.
State: Colorado;
2006: 6.9;
2007: 6.7.
State: Connecticut;
2006: 6.3;
2007: 6.3.
State: Delaware;
2006: 7.5;
2007: 7.2.
State: District of Columbia;
2006: 7.1;
2007: 7.5.
State: Florida;
2006: 7.5;
2007: 6.9.
State: Georgia;
2006: 7.7;
2007: 7.5.
State: Hawaii;
2006: 5.8;
2007: 6.1.
State: Idaho;
2006: 8.0;
2007: 7.4.
State: Illinois;
2006: 6.2;
2007: 6.1.
State: Indiana;
2006: 7.8;
2007: 8.1.
State: Iowa;
2006: 7.0;
2007: 7.3.
State: Kansas;
2006: 7.4;
2007: 7.1.
State: Kentucky;
2006: 12.0;
2007: 11.4.
State: Louisiana;
2006: 9.9;
2007: 9.6.
State: Maine;
2006: 10.5;
2007: 10.6.
State: Maryland;
2006: 6.4;
2007: 6.6.
State: Massachusetts;
2006: 6.8;
2007: 6.9.
State: Michigan;
2006: 8.5;
2007: 8.5.
State: Minnesota;
2006: 6.1;
2007: 6.3.
State: Mississippi;
2006: 11.1;
2007: 10.9.
State: Missouri;
2006: 8.8;
2007: 8.9.
State: Montana;
2006: 9.3;
2007: 8.0.
State: Nebraska;
2006: 6.8;
2007: 6.3.
State: Nevada;
2006: 6.3;
2007: 6.4.
State: New Hampshire;
2006: 7.4;
2007: 6.9.
State: New Jersey;
2006: 5.8;
2007: 5.6.
State: New Mexico;
2006: 8.8;
2007: 8.7.
State: New York;
2006: 6.7;
2007: 7.0.
State: North Carolina;
2006: 8.7;
2007: 8.7.
State: North Dakota;
2006: 6.8;
2007: 6.0.
State: Ohio;
2006: 8.3;
2007: 8.4.
State: Oklahoma;
2006: 10.4;
2007: 10.1.
State: Oregon;
2006: 8.7;
2007: 8.5.
State: Pennsylvania;
2006: 8.0;
2007: 8.1.
State: Rhode Island;
2006: 7.9;
2007: 8.1.
State: South Carolina;
2006: 9.1;
2007: 8.7.
State: South Dakota;
2006: 6.4;
2007: 7.5.
State: Tennessee;
2006: 10.2;
2007: 10.0.
State: Texas;
2006: 7.7;
2007: 7.4.
State: Utah;
2006: 6.3;
2007: 6.1.
State: Vermont;
2006: 9.5;
2007: 8.2.
State: Virginia;
2006: 7.2;
2007: 6.8.
State: Washington;
2006: 8.6;
2007: 8.5.
State: West Virginia;
2006: 12.7;
2007: 12.8.
State: Wisconsin;
2006: 6.7;
2007: 6.9.
State: Wyoming;
2006: 8.3;
2007: 8.1.
State: National;
2006: 7.7;
2007: 7.6.
Source: GAO analysis of data from the Census Bureau's ACS.
Note: Our measure of state disability populations is derived from five
disability questions asked in the 2006 and 2007 ACS. See table 2 in
appendix I for the disability questions on the ACS. We did not include
data from a sixth disability question--about whether a person has
difficulty working at a job or business because of a physical, mental,
or emotional condition lasting six months or more--because this
question was no longer included in the ACS, starting in 2008.
[End of table]
[End of section]
Appendix IV: Estimates of Differences among States in the Cost of
Providing Vocational Rehabilitation (VR) Services:
Table 9 provides a cost index for each state, designed to estimate the
differences among states in the cost of providing VR services. The
index is a weighted average of the costs of two primary resources
needed to provide VR services, labor and office space. See appendix I
for further information on the development of our cost index. The
average cost nationally is represented by an index of 1.00. A state
with an index above 1.00 is estimated to have costs greater than
average, while a state with an index below 1.00 is estimated to have
costs less than average. For example, with a cost index of 0.95,
Alabama is estimated to have costs 5 percent below the national
average.
Table 9: Estimates of Differences among States in the Cost of Providing
VR Services:
State: Alabama;
Cost index: 0.95.
State: Alaska;
Cost index: 0.98.
State: Arizona;
Cost index: 1.04.
State: Arkansas;
Cost index: 0.88.
State: California;
Cost index: 1.12.
State: Colorado;
Cost index: 1.01.
State: Connecticut;
Cost index: 1.09.
State: Delaware;
Cost index: 1.07.
State: District of Columbia;
Cost index: 1.19.
State: Florida;
Cost index: 1.02.
State: Georgia;
Cost index: 1.01.
State: Hawaii;
Cost index: 1.03.
State: Idaho;
Cost index: 0.87.
State: Illinois;
Cost index: 1.01.
State: Indiana;
Cost index: 0.94.
State: Iowa;
Cost index: 0.88.
State: Kansas;
Cost index: 0.89.
State: Kentucky;
Cost index: 0.93.
State: Louisiana;
Cost index: 0.89.
State: Maine;
Cost index: 0.93.
State: Maryland;
Cost index: 1.06.
State: Massachusetts;
Cost index: 1.13.
State: Michigan;
Cost index: 0.98.
State: Minnesota;
Cost index: 0.99.
State: Mississippi;
Cost index: 0.90.
State: Missouri;
Cost index: 0.95.
State: Montana;
Cost index: 0.87.
State: Nebraska;
Cost index: 0.93.
State: Nevada;
Cost index: 1.10.
State: New Hampshire;
Cost index: 1.04.
State: New Jersey;
Cost index: 1.09.
State: New Mexico;
Cost index: 0.89.
State: New York;
Cost index: 1.04.
State: North Carolina;
Cost index: 0.93.
State: North Dakota;
Cost index: 0.89.
State: Ohio;
Cost index: 0.94.
State: Oklahoma;
Cost index: 0.88.
State: Oregon;
Cost index: 0.98.
State: Pennsylvania;
Cost index: 1.00.
State: Rhode Island;
Cost index: 0.99.
State: South Carolina;
Cost index: 0.93.
State: South Dakota;
Cost index: 0.92.
State: Tennessee;
Cost index: 1.01.
State: Texas;
Cost index: 0.96.
State: Utah;
Cost index: 0.91.
State: Vermont;
Cost index: 0.92.
State: Virginia;
Cost index: 1.00.
State: Washington;
Cost index: 0.97.
State: West Virginia;
Cost index: 0.88.
State: Wisconsin;
Cost index: 0.97.
State: Wyoming;
Cost index: 0.89.
State: National average;
Cost index: 1.00.
Source: GAO analysis of BLS' Quarterly Census of Employment and Wages,
HUD data on fair market rents, Education data on VR expenditures, and
responses to GAO survey.
[End of table]
[End of section]
Appendix V: Vocational Rehabilitation (VR) Grant Allotments by State
and Cost-Adjusted Allotments per Working-Aged Person with a Disability,
Fiscal Year 2008:
Table 10 shows the amount of federal funds each state received for
fiscal year 2008, and the amount of services each state would be able
to purchase per working-aged person with a disability with those funds.
The per person allotments were adjusted to take into account
differences among states in the cost of wages and rents, using the cost
index shown in appendix IV. See appendix I for more information about
our data sources and methodologies.
Table 10: VR Grant Allotments and Cost-Adjusted Allotments per Working-
Aged Person with a Disability:
State: Alabama;
FY 2008 grant allotment[A]: $55,816,789;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 114.
State: Alaska;
FY 2008 grant allotment[A]: $9,463,837;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 153.
State: Arizona;
FY 2008 grant allotment[A]: $57,950,200;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 128.
State: Arkansas;
FY 2008 grant allotment[A]: $35,809,204;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 130.
State: California;
FY 2008 grant allotment[A]: $275,593,209;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 104.
State: Colorado;
FY 2008 grant allotment[A]: $36,013,729;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 109.
State: Connecticut;
FY 2008 grant allotment[A]: $19,947,115;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 83.
State: Delaware;
FY 2008 grant allotment[A]: $9,463,837;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 141.
State: District of Columbia;
FY 2008 grant allotment[A]: $12,618,252;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 240.
State: Florida;
FY 2008 grant allotment[A]: $152,844,034;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 119.
State: Georgia;
FY 2008 grant allotment[A]: $92,258,790;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 127.
State: Hawaii;
FY 2008 grant allotment[A]: $11,052,823;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 138.
State: Idaho;
FY 2008 grant allotment[A]: $15,867,655;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 165.
State: Illinois;
FY 2008 grant allotment[A]: $105,254,070;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 133.
State: Indiana;
FY 2008 grant allotment[A]: $66,660,094;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 139.
State: Iowa;
FY 2008 grant allotment[A]: $31,155,664;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 162.
State: Kansas;
FY 2008 grant allotment[A]: $26,929,144;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 154.
State: Kentucky;
FY 2008 grant allotment[A]: $51,743,094;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 114.
State: Louisiana;
FY 2008 grant allotment[A]: $56,383,213;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 154.
State: Maine;
FY 2008 grant allotment[A]: $15,030,202;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 116.
State: Maryland;
FY 2008 grant allotment[A]: $38,114,000;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 97.
State: Massachusetts;
FY 2008 grant allotment[A]: $45,530,340;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 90.
State: Michigan;
FY 2008 grant allotment[A]: $97,347,491;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 115.
State: Minnesota;
FY 2008 grant allotment[A]: $43,124,084;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 133.
State: Mississippi;
FY 2008 grant allotment[A]: $41,288,450;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 144.
State: Missouri;
FY 2008 grant allotment[A]: $62,037,506;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 125.
State: Montana;
FY 2008 grant allotment[A]: $10,762,027;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 161.
State: Nebraska;
FY 2008 grant allotment[A]: $17,356,124;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 167.
State: Nevada;
FY 2008 grant allotment[A]: $17,931,565;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 99.
State: New Hampshire;
FY 2008 grant allotment[A]: $10,736,013;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 114.
State: New Jersey;
FY 2008 grant allotment[A]: $55,184,632;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 104.
State: New Mexico;
FY 2008 grant allotment[A]: $22,684,862;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 149.
State: New York;
FY 2008 grant allotment[A]: $147,351,564;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 105.
State: North Carolina;
FY 2008 grant allotment[A]: $92,812,979;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 126.
State: North Dakota;
FY 2008 grant allotment[A]: $9,463,837;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 277.
State: Ohio;
FY 2008 grant allotment[A]: $120,400,886;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 133.
State: Oklahoma;
FY 2008 grant allotment[A]: $40,628,883;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 126.
State: Oregon;
FY 2008 grant allotment[A]: $35,175,174;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 113.
State: Pennsylvania;
FY 2008 grant allotment[A]: $121,101,676;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 122.
State: Rhode Island;
FY 2008 grant allotment[A]: $10,051,281;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 118.
State: South Carolina;
FY 2008 grant allotment[A]: $50,734,708;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 143.
State: South Dakota;
FY 2008 grant allotment[A]: $9,463,837;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 174.
State: Tennessee;
FY 2008 grant allotment[A]: $65,575,720;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 106.
State: Texas;
FY 2008 grant allotment[A]: $217,749,584;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 129.
State: Utah;
FY 2008 grant allotment[A]: $28,030,439;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 191.
State: Vermont;
FY 2008 grant allotment[A]: $9,463,837;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 202.
State: Virginia;
FY 2008 grant allotment[A]: $62,084,119;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 118.
State: Washington;
FY 2008 grant allotment[A]: $51,125,448;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 97.
State: West Virginia;
FY 2008 grant allotment[A]: $25,312,666;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 125.
State: Wisconsin;
FY 2008 grant allotment[A]: $55,246,877;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 148.
State: Wyoming;
FY 2008 grant allotment[A]: $9,463,837;
Allotments per working-aged person with a disability (cost
adjusted)[B]: 250.
Source: GAO analysis of data from Education on VR grants in fiscal year
2008, Census Bureau's 2007 ACS, BLS' Quarterly Census of Employment and
Wages, HUD's Fair Market Rents, and responses to GAO survey.
[A] Grant allotments listed are the initial allotments distributed to
states using the funding formula. The allotments do not include any
adjustments that occur due to states' inability to match federal funds
or the application of maintenance of effort penalties.
[B] The denominator in these calculations is the state's civilian
working-aged population with a disability, according to data from the
2007 ACS.
[End of table]
[End of section]
Appendix VI: Comparison of Per Capita Income and Total Taxable
Resources (TTR), by State, 2004 to 2006:
Table 11 illustrates the difference between each state's financial
resources as measured by per capita income and TTR. The second column
shows, for each state, its per capita income indexed to national per
capita income, and the third column shows each state's TTR per capita
indexed to national TTR per capita. These indices are based on averages
of three years of data from 2004 to 2006. They were created by dividing
each state's three-year average by the national average. States with
income or TTR per capita levels that are higher than the national
averages have indices that are greater than 1, and states with levels
that are below the national averages have indices that are less than 1.
For example, the TTR per capita index for Alabama is 0.79, meaning that
the state's TTR per capita is 21 percent below the national average.
The final column in the table shows the ratio of each state's per
capita income index to its TTR per capita index. States in which the
formula's use of per capita income understates its potentially taxable
resources have ratios that are less than 1. States in which the use of
per capita income overstates its potentially taxable resources have
ratios above 1. For example, the ratio is 0.80 for Alaska, meaning that
the formula's use of per capita income understates Alaska's taxable
resources by 20 percent. See appendix I for further information
regarding our analysis of per capita income and TTR.
Table 11: Comparison of Per Capita Income and TTR Per Capita for 2004
to 2006, by State:
State: Alabama;
Index of per capita income: 0.84;
Index of TTR per capita: 0.79;
Ratio of per capita income index to TTR per capita index[A]: 1.07.
State: Alaska;
Index of per capita income: 1.04;
Index of TTR per capita: 1.30;
Ratio of per capita income index to TTR per capita index[A]: 0.80.
State: Arizona;
Index of per capita income: 0.88;
Index of TTR per capita: 0.87;
Ratio of per capita income index to TTR per capita index[A]: 1.00.
State: Arkansas;
Index of per capita income: 0.78;
Index of TTR per capita: 0.74;
Ratio of per capita income index to TTR per capita index[A]: 1.04.
State: California;
Index of per capita income: 1.08;
Index of TTR per capita: 1.07;
Ratio of per capita income index to TTR per capita index[A]: 1.01.
State: Colorado;
Index of per capita income: 1.08;
Index of TTR per capita: 1.08;
Ratio of per capita income index to TTR per capita index[A]: 1.00.
State: Connecticut;
Index of per capita income: 1.39;
Index of TTR per capita: 1.42;
Ratio of per capita income index to TTR per capita index[A]: 0.98.
State: Delaware;
Index of per capita income: 1.06;
Index of TTR per capita: 1.59;
Ratio of per capita income index to TTR per capita index[A]: 0.67.
State: District of Columbia;
Index of per capita income: 1.57;
Index of TTR per capita: 1.71;
Ratio of per capita income index to TTR per capita index[A]: 0.92.
State: Florida;
Index of per capita income: 1.00;
Index of TTR per capita: 1.00;
Ratio of per capita income index to TTR per capita index[A]: 1.00.
State: Georgia;
Index of per capita income: 0.89;
Index of TTR per capita: 0.91;
Ratio of per capita income index to TTR per capita index[A]: 0.98.
State: Hawaii;
Index of per capita income: 1.00;
Index of TTR per capita: 1.01;
Ratio of per capita income index to TTR per capita index[A]: 0.99.
State: Idaho;
Index of per capita income: 0.83;
Index of TTR per capita: 0.80;
Ratio of per capita income index to TTR per capita index[A]: 1.03.
State: Illinois;
Index of per capita income: 1.05;
Index of TTR per capita: 1.05;
Ratio of per capita income index to TTR per capita index[A]: 1.01.
State: Indiana;
Index of per capita income: 0.88;
Index of TTR per capita: 0.89;
Ratio of per capita income index to TTR per capita index[A]: 1.00.
State: Iowa;
Index of per capita income: 0.91;
Index of TTR per capita: 0.93;
Ratio of per capita income index to TTR per capita index[A]: 0.97.
State: Kansas;
Index of per capita income: 0.93;
Index of TTR per capita: 0.93;
Ratio of per capita income index to TTR per capita index[A]: 1.00.
State: Kentucky;
Index of per capita income: 0.81;
Index of TTR per capita: 0.79;
Ratio of per capita income index to TTR per capita index[A]: 1.03.
State: Louisiana;
Index of per capita income: 0.81;
Index of TTR per capita: 0.94;
Ratio of per capita income index to TTR per capita index[A]: 0.86.
State: Maine;
Index of per capita income: 0.89;
Index of TTR per capita: 0.83;
Ratio of per capita income index to TTR per capita index[A]: 1.08.
State: Maryland;
Index of per capita income: 1.20;
Index of TTR per capita: 1.17;
Ratio of per capita income index to TTR per capita index[A]: 1.03.
State: Massachusetts;
Index of per capita income: 1.25;
Index of TTR per capita: 1.20;
Ratio of per capita income index to TTR per capita index[A]: 1.05.
State: Michigan;
Index of per capita income: 0.93;
Index of TTR per capita: 0.85;
Ratio of per capita income index to TTR per capita index[A]: 1.09.
State: Minnesota;
Index of per capita income: 1.07;
Index of TTR per capita: 1.07;
Ratio of per capita income index to TTR per capita index[A]: 1.01.
State: Mississippi;
Index of per capita income: 0.73;
Index of TTR per capita: 0.68;
Ratio of per capita income index to TTR per capita index[A]: 1.08.
State: Missouri;
Index of per capita income: 0.90;
Index of TTR per capita: 0.88;
Ratio of per capita income index to TTR per capita index[A]: 1.02.
State: Montana;
Index of per capita income: 0.84;
Index of TTR per capita: 0.79;
Ratio of per capita income index to TTR per capita index[A]: 1.06.
State: Nebraska;
Index of per capita income: 0.94;
Index of TTR per capita: 0.96;
Ratio of per capita income index to TTR per capita index[A]: 0.98.
State: Nevada;
Index of per capita income: 1.06;
Index of TTR per capita: 1.18;
Ratio of per capita income index to TTR per capita index[A]: 0.90.
State: New Hampshire;
Index of per capita income: 1.09;
Index of TTR per capita: 1.08;
Ratio of per capita income index to TTR per capita index[A]: 1.01.
State: New Jersey;
Index of per capita income: 1.27;
Index of TTR per capita: 1.27;
Ratio of per capita income index to TTR per capita index[A]: 1.00.
State: New Mexico;
Index of per capita income: 0.80;
Index of TTR per capita: 0.83;
Ratio of per capita income index to TTR per capita index[A]: 0.96.
State: New York;
Index of per capita income: 1.17;
Index of TTR per capita: 1.19;
Ratio of per capita income index to TTR per capita index[A]: 0.99.
State: North Carolina;
Index of per capita income: 0.89;
Index of TTR per capita: 0.94;
Ratio of per capita income index to TTR per capita index[A]: 0.95.
State: North Dakota;
Index of per capita income: 0.89;
Index of TTR per capita: 0.90;
Ratio of per capita income index to TTR per capita index[A]: 0.98.
State: Ohio;
Index of per capita income: 0.91;
Index of TTR per capita: 0.90;
Ratio of per capita income index to TTR per capita index[A]: 1.02.
State: Oklahoma;
Index of per capita income: 0.87;
Index of TTR per capita: 0.82;
Ratio of per capita income index to TTR per capita index[A]: 1.06.
State: Oregon;
Index of per capita income: 0.92;
Index of TTR per capita: 0.93;
Ratio of per capita income index to TTR per capita index[A]: 0.98.
State: Pennsylvania;
Index of per capita income: 1.00;
Index of TTR per capita: 0.94;
Ratio of per capita income index to TTR per capita index[A]: 1.06.
State: Rhode Island;
Index of per capita income: 1.03;
Index of TTR per capita: 1.05;
Ratio of per capita income index to TTR per capita index[A]: 0.98.
State: South Carolina;
Index of per capita income: 0.82;
Index of TTR per capita: 0.79;
Ratio of per capita income index to TTR per capita index[A]: 1.04.
State: South Dakota;
Index of per capita income: 0.91;
Index of TTR per capita: 0.97;
Ratio of per capita income index to TTR per capita index[A]: 0.94.
State: Tennessee;
Index of per capita income: 0.88;
Index of TTR per capita: 0.87;
Ratio of per capita income index to TTR per capita index[A]: 1.02.
State: Texas;
Index of per capita income: 0.95;
Index of TTR per capita: 0.98;
Ratio of per capita income index to TTR per capita index[A]: 0.97.
State: Utah;
Index of per capita income: 0.79;
Index of TTR per capita: 0.83;
Ratio of per capita income index to TTR per capita index[A]: 0.96.
State: Vermont;
Index of per capita income: 0.95;
Index of TTR per capita: 0.91;
Ratio of per capita income index to TTR per capita index[A]: 1.05.
State: Virginia;
Index of per capita income: 1.09;
Index of TTR per capita: 1.14;
Ratio of per capita income index to TTR per capita index[A]: 0.96.
State: Washington;
Index of per capita income: 1.05;
Index of TTR per capita: 1.05;
Ratio of per capita income index to TTR per capita index[A]: 1.00.
State: West Virginia;
Index of per capita income: 0.76;
Index of TTR per capita: 0.72;
Ratio of per capita income index to TTR per capita index[A]: 1.06.
State: Wisconsin;
Index of per capita income: 0.94;
Index of TTR per capita: 0.92;
Ratio of per capita income index to TTR per capita index[A]: 1.02.
State: Wyoming;
Index of per capita income: 1.12;
Index of TTR per capita: 1.34;
Ratio of per capita income index to TTR per capita index[A]: 0.84.
Source: GAO analysis based on data from the Department of Commerce and
Treasury.
[A] Dividing a state's index in the second column by its index in the
third column may not result in the ratio provided in the fourth column
due to rounding.
[End of table]
[End of section]
Appendix VII: Description of Formula Options:
This appendix provides detailed information on three formula options or
prototypes for revising the VR funding formula: (1) a partial
beneficiary equity formula that distributes funds based only on the
size of a state's population potentially needing services, (2) a full
beneficiary equity formula with the addition of a cost of services
factor, and (3) a taxpayer equity formula with the addition of a
measure of state resources.
The following is the Partial Beneficiary Equity Formula Option:
State allocation = Appropriation x
Need population/Sum Need population;
where:
Sum Need population = sum of the need population across states, or the
total need population nationally.
This formula would allocate funds based on each state's share of the
total need population nationally. It would only partially achieve
beneficiary equity because it does not account for differences among
states in the cost of providing services.
The following is the Full Beneficiary Equity Formula Option:
State allocation = Appropriation x
Cost adjusted need population/Sum Cost adjusted need population;
where:
Cost adjusted need population = Need population x Cost index;
Sum Cost adjusted need population = Sum of the cost adjusted need
population across states.
This formula would achieve full beneficiary equity because it accounts
for both states' need populations and costs of providing services. The
cost index in the formula estimates each state's cost of providing
services.
The final option is the Taxpayer Equity Formula Option:
State allocation = Appropriation x
Cost adjusted need population x allotment percentage/Sum Cost adjusted
need population x allotment percentage;
where:
Cost adjusted need population = Need population x Cost index;
Allotment percentage = 1 - 0.20 (TRR/Cost adjusted need population/Sum
TRR/Sum need population).
This formula would achieve taxpayer equity by basing allotments on a
state's need population, adjusted for the cost of providing services,
and its ability to fund program services. In this option, the formula
includes an "allotment percentage" to account for a state's ability to
contribute funding to the VR program. A state with fewer taxable
resources compared to other states would have a larger allotment
percentage and, therefore, a larger final allotment (all else being
equal). "TTR" is used to indicate a measure of state's financing
ability, since we regard Treasury's Total Taxable Resources (TTR) data
to be a comprehensive measure of a state's taxable resources. The 0.20
in the allotment percentage equation indicates that, nationally,
states' required contribution to the VR program is approximately 20
percent. If the matching requirement were to vary for each state, then
an individual state's matching rate would simply be determined by its
allotment percentage.
[End of section]
Appendix VIII: Funding Allocations under Three Vocational
Rehabilitation Formula Options:
Table 12 shows the allocations for each state and the percentage change
from their fiscal year 2008 allocations under the three formula options
(which are described in detail in appendix VII). For each of these
options, we retained the minimum allotment that the current formula
provides, 1/3 of 1 percent of the total federal funds appropriated to
the VR program, or $9,463,837 for fiscal year 2008.
Table 12: Funding Allocations under Three VR Formula Options:
State: Alabama;
Current allocation[A] (2008): 55,816,789;
Funding allocations under each formula option: Partial beneficiary
equity: 60,474,140;
Funding allocations under each formula option: Full beneficiary equity:
57,792,826;
Funding allocations under each formula option: Taxpayer equity:
63,890,152;
Percentage change from current formula allocations: Partial beneficiary
equity: 8.3;
Percentage change from current formula allocations: Full beneficiary
equity: 3.5;
Percentage change from current formula allocations: Taxpayer equity:
14.5.
State: Alaska;
Current allocation[A] (2008): 9,463,837;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.0;
Percentage change from current formula allocations: Full beneficiary
equity: 0.0;
Percentage change from current formula allocations: Taxpayer equity:
0.0.
State: Arizona;
Current allocation[A] (2008): 57,950,200;
Funding allocations under each formula option: Partial beneficiary
equity: 50,637,184;
Funding allocations under each formula option: Full beneficiary equity:
53,231,144;
Funding allocations under each formula option: Taxpayer equity:
54,419,676;
Percentage change from current formula allocations: Partial beneficiary
equity: -12.6;
Percentage change from current formula allocations: Full beneficiary
equity: -8.1;
Percentage change from current formula allocations: Taxpayer equity:
-6.1.
State: Arkansas;
Current allocation[A] (2008): 35,809,204;
Funding allocations under each formula option: Partial beneficiary
equity: 37,745,777;
Funding allocations under each formula option: Full beneficiary equity:
33,384,156;
Funding allocations under each formula option: Taxpayer equity:
36,944,930;
Percentage change from current formula allocations: Partial beneficiary
equity: 5.4;
Percentage change from current formula allocations: Full beneficiary
equity: -6.8;
Percentage change from current formula allocations: Taxpayer equity:
3.2.
State: California;
Current allocation[A] (2008): 275,593,209;
Funding allocations under each formula option: Partial beneficiary
equity: 281,848,929;
Funding allocations under each formula option: Full beneficiary equity:
316,454,689;
Funding allocations under each formula option: Taxpayer equity:
306,280,311;
Percentage change from current formula allocations: Partial beneficiary
equity: 2.3;
Percentage change from current formula allocations: Full beneficiary
equity: 14.8;
Percentage change from current formula allocations: Taxpayer equity:
11.1.
State: Colorado;
Current allocation[A] (2008): 36,013,729;
Funding allocations under each formula option: Partial beneficiary
equity: 39,237,548;
Funding allocations under each formula option: Full beneficiary equity:
39,789,523;
Funding allocations under each formula option: Taxpayer equity:
38,041,490;
Percentage change from current formula allocations: Partial beneficiary
equity: 9.0;
Percentage change from current formula allocations: Full beneficiary
equity: 10.5;
Percentage change from current formula allocations: Taxpayer equity:
5.6.
State: Connecticut;
Current allocation[A] (2008): 19,947,115;
Funding allocations under each formula option: Partial beneficiary
equity: 26,435,688;
Funding allocations under each formula option: Full beneficiary equity:
29,061,990;
Funding allocations under each formula option: Taxpayer equity:
24,834,700;
Percentage change from current formula allocations: Partial beneficiary
equity: 32.5;
Percentage change from current formula allocations: Full beneficiary
equity: 45.7;
Percentage change from current formula allocations: Taxpayer equity:
24.5.
State: Delaware;
Current allocation[A] (2008): 9,463,837;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.0;
Percentage change from current formula allocations: Full beneficiary
equity: 0.0;
Percentage change from current formula allocations: Taxpayer equity:
0.0.
State: District of Columbia;
Current allocation[A] (2008): 12,618,252;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: -25.0;
Percentage change from current formula allocations: Full beneficiary
equity: -25.0;
Percentage change from current formula allocations: Taxpayer equity:
-25.0.
State: Florida;
Current allocation[A] (2008): 152,844,034;
Funding allocations under each formula option: Partial beneficiary
equity: 155,945,283;
Funding allocations under each formula option: Full beneficiary equity:
159,610,063;
Funding allocations under each formula option: Taxpayer equity:
158,513,829;
Percentage change from current formula allocations: Partial beneficiary
equity: 2.0;
Percentage change from current formula allocations: Full beneficiary
equity: 4.4;
Percentage change from current formula allocations: Taxpayer equity:
3.7.
State: Georgia;
Current allocation[A] (2008): 92,258,790;
Funding allocations under each formula option: Partial beneficiary
equity: 85,813,700;
Funding allocations under each formula option: Full beneficiary equity:
87,120,651;
Funding allocations under each formula option: Taxpayer equity:
89,613,060;
Percentage change from current formula allocations: Partial beneficiary
equity: -7.0;
Percentage change from current formula allocations: Full beneficiary
equity: -5.6;
Percentage change from current formula allocations: Taxpayer equity:
-2.9.
State: Hawaii;
Current allocation[A] (2008): 11,052,823;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: -14.4;
Percentage change from current formula allocations: Full beneficiary
equity: -14.4;
Percentage change from current formula allocations: Taxpayer equity:
-14.4.
State: Idaho;
Current allocation[A] (2008): 15,867,655;
Funding allocations under each formula option: Partial beneficiary
equity: 13,590,279;
Funding allocations under each formula option: Full beneficiary equity:
11,904,099;
Funding allocations under each formula option: Taxpayer equity:
12,214,799;
Percentage change from current formula allocations: Partial beneficiary
equity: -14.4;
Percentage change from current formula allocations: Full beneficiary
equity: -25.0;
Percentage change from current formula allocations: Taxpayer equity:
-23.0.
State: Illinois;
Current allocation[A] (2008): 105,254,070;
Funding allocations under each formula option: Partial beneficiary
equity: 94,868,688;
Funding allocations under each formula option: Full beneficiary equity:
96,486,772;
Funding allocations under each formula option: Taxpayer equity:
89,701,873;
Percentage change from current formula allocations: Partial beneficiary
equity: -9.9;
Percentage change from current formula allocations: Full beneficiary
equity: -8.3;
Percentage change from current formula allocations: Taxpayer equity:
-14.8.
State: Indiana;
Current allocation[A] (2008): 66,660,094;
Funding allocations under each formula option: Partial beneficiary
equity: 59,852,987;
Funding allocations under each formula option: Full beneficiary equity:
56,580,945;
Funding allocations under each formula option: Taxpayer equity:
57,833,670;
Percentage change from current formula allocations: Partial beneficiary
equity: -10.2;
Percentage change from current formula allocations: Full beneficiary
equity: -15.1;
Percentage change from current formula allocations: Taxpayer equity:
-13.2.
State: Iowa;
Current allocation[A] (2008): 31,155,664;
Funding allocations under each formula option: Partial beneficiary
equity: 25,509,633;
Funding allocations under each formula option: Full beneficiary equity:
22,527,433;
Funding allocations under each formula option: Taxpayer equity:
21,748,645;
Percentage change from current formula allocations: Partial beneficiary
equity: -18.1;
Percentage change from current formula allocations: Full beneficiary
equity: -27.7;
Percentage change from current formula allocations: Taxpayer equity:
-30.2.
State: Kansas; Current allocation[A] (2008): 26,929,144;
Funding allocations under each formula option: Partial beneficiary
equity: 23,901,732;
Funding allocations under each formula option: Full beneficiary equity:
21,497,454;
Funding allocations under each formula option: Taxpayer equity:
20,923,115;
Percentage change from current formula allocations: Partial beneficiary
equity: -11.2;
Percentage change from current formula allocations: Full beneficiary
equity: -20.2;
Percentage change from current formula allocations: Taxpayer equity:
-22.3.
State: Kentucky;
Current allocation[A] (2008): 51,743,094;
Funding allocations under each formula option: Partial beneficiary
equity: 59,192,108;
Funding allocations under each formula option: Full beneficiary equity:
55,614,304;
Funding allocations under each formula option: Taxpayer equity:
61,905,089;
Percentage change from current formula allocations: Partial beneficiary
equity: 14.4;
Percentage change from current formula allocations: Full beneficiary
equity: 7.5;
Percentage change from current formula allocations: Taxpayer equity:
19.6.
State: Louisiana; Current allocation[A] (2008): 56,383,213;
Funding allocations under each formula option: Partial beneficiary
equity: 49,964,835;
Funding allocations under each formula option: Full beneficiary equity:
44,807,381;
Funding allocations under each formula option: Taxpayer equity:
46,381,939;
Percentage change from current formula allocations: Partial beneficiary
equity: -11.4;
Percentage change from current formula allocations: Full beneficiary
equity: -20.5;
Percentage change from current formula allocations: Taxpayer equity:
-17.7.
State: Maine; Current allocation[A] (2008): 15,030,202;
Funding allocations under each formula option: Partial beneficiary
equity: 16,621,731;
Funding allocations under each formula option: Full beneficiary equity:
15,516,400;
Funding allocations under each formula option: Taxpayer equity:
16,878,542;
Percentage change from current formula allocations: Partial beneficiary
equity: 10.6;
Percentage change from current formula allocations: Full beneficiary
equity: 3.2;
Percentage change from current formula allocations: Taxpayer equity:
12.3.
State: Maryland;
Current allocation[A] (2008): 38,114,000;
Funding allocations under each formula option: Partial beneficiary
equity: 43,538,487;
Funding allocations under each formula option: Full beneficiary equity:
46,623,678;
Funding allocations under each formula option: Taxpayer equity:
43,151,763;
Percentage change from current formula allocations: Partial beneficiary
equity: 14.2;
Percentage change from current formula allocations: Full beneficiary
equity: 22.3;
Percentage change from current formula allocations: Taxpayer equity:
13.2.
State: Massachusetts;
Current allocation[A] (2008): 45,530,340;
Funding allocations under each formula option: Partial beneficiary
equity: 52,850,766;
Funding allocations under each formula option: Full beneficiary equity:
60,263,074;
Funding allocations under each formula option: Taxpayer equity:
57,419,144;
Percentage change from current formula allocations: Partial beneficiary
equity: 16.1;
Percentage change from current formula allocations: Full beneficiary
equity: 32.4;
Percentage change from current formula allocations: Taxpayer equity:
26.1.
State: Michigan;
Current allocation[A] (2008): 97,347,491;
Funding allocations under each formula option: Partial beneficiary
equity: 102,640,869;
Funding allocations under each formula option: Full beneficiary equity:
101,655,273;
Funding allocations under each formula option: Taxpayer equity:
107,105,165;
Percentage change from current formula allocations: Partial beneficiary
equity: 5.4;
Percentage change from current formula allocations: Full beneficiary
equity: 4.4;
Percentage change from current formula allocations: Taxpayer equity:
10.0.
State: Minnesota;
Current allocation[A] (2008): 43,124,084;
Funding allocations under each formula option: Partial beneficiary
equity: 38,337,539;
Funding allocations under each formula option: Full beneficiary equity:
38,187,943;
Funding allocations under each formula option: Taxpayer equity:
35,046,524;
Percentage change from current formula allocations: Partial beneficiary
equity: -11.1;
Percentage change from current formula allocations: Full beneficiary
equity: -11.5;
Percentage change from current formula allocations: Taxpayer equity:
-18.7.
State: Mississippi;
Current allocation[A] (2008): 41,288,450;
Funding allocations under each formula option: Partial beneficiary
equity: 38,318,781;
Funding allocations under each formula option: Full beneficiary equity:
34,709,706;
Funding allocations under each formula option: Taxpayer equity:
38,861,081;
Percentage change from current formula allocations: Partial beneficiary
equity: -7.2;
Percentage change from current formula allocations: Full beneficiary
equity: -15.9;
Percentage change from current formula allocations: Taxpayer equity:
-5.9.
State: Missouri;
Current allocation[A] (2008): 62,037,506;
Funding allocations under each formula option: Partial beneficiary
equity: 62,009,758;
Funding allocations under each formula option: Full beneficiary equity:
59,133,917;
Funding allocations under each formula option: Taxpayer equity:
62,083,180;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.0;
Percentage change from current formula allocations: Full beneficiary
equity: -4.7;
Percentage change from current formula allocations: Taxpayer equity:
0.1.
State: Montana;
Current allocation[A] (2008): 10,762,027;
Funding allocations under each formula option: Partial beneficiary
equity: 9,819,094;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: -8.8;
Percentage change from current formula allocations: Full beneficiary
equity: -12.1;
Percentage change from current formula allocations: Taxpayer equity:
-12.1.
State: Nebraska;
Current allocation[A] (2008): 17,356,124;
Funding allocations under each formula option: Partial beneficiary
equity: 13,861,787;
Funding allocations under each formula option: Full beneficiary equity:
12,978,945;
Funding allocations under each formula option: Taxpayer equity:
12,302,501;
Percentage change from current formula allocations: Partial beneficiary
equity: -20.1;
Percentage change from current formula allocations: Full beneficiary
equity: -25.2;
Percentage change from current formula allocations: Taxpayer equity:
-29.1.
State: Nevada;
Current allocation[A] (2008): 17,931,565;
Funding allocations under each formula option: Partial beneficiary
equity: 19,284,243;
Funding allocations under each formula option: Full beneficiary equity:
21,323,149;
Funding allocations under each formula option: Taxpayer equity:
20,057,472;
Percentage change from current formula allocations: Partial beneficiary
equity: 7.5;
Percentage change from current formula allocations: Full beneficiary
equity: 18.9;
Percentage change from current formula allocations: Taxpayer equity:
11.9.
State: New Hampshire;
Current allocation[A] (2008): 10,736,013;
Funding allocations under each formula option: Partial beneficiary
equity: 11,253,039;
Funding allocations under each formula option: Full beneficiary equity:
11,765,675;
Funding allocations under each formula option: Taxpayer equity:
11,439,120;
Percentage change from current formula allocations: Partial beneficiary
equity: 4.8;
Percentage change from current formula allocations: Full beneficiary
equity: 9.6;
Percentage change from current formula allocations: Taxpayer equity:
6.6.
State: New Jersey;
Current allocation[A] (2008): 55,184,632;
Funding allocations under each formula option: Partial beneficiary
equity: 59,366,303;
Funding allocations under each formula option: Full beneficiary equity:
65,132,995;
Funding allocations under each formula option: Taxpayer equity:
55,977,949;
Percentage change from current formula allocations: Partial beneficiary
equity: 7.6;
Percentage change from current formula allocations: Full beneficiary
equity: 18.0;
Percentage change from current formula allocations: Taxpayer equity:
1.4.
State: New Mexico;
Current allocation[A] (2008): 22,684,862;
Funding allocations under each formula option: Partial beneficiary
equity: 20,433,059;
Funding allocations under each formula option: Full beneficiary equity:
18,374,225;
Funding allocations under each formula option: Taxpayer equity:
19,274,803;
Percentage change from current formula allocations: Partial beneficiary
equity: -9.9;
Percentage change from current formula allocations: Full beneficiary
equity: -19.0;
Percentage change from current formula allocations: Taxpayer equity:
-15.0.
State: New York;
Current allocation[A] (2008): 147,351,564;
Funding allocations under each formula option: Partial beneficiary
equity: 158,395,726;
Funding allocations under each formula option: Full beneficiary equity:
165,414,325;
Funding allocations under each formula option: Taxpayer equity:
153,342,264;
Percentage change from current formula allocations: Partial beneficiary
equity: 7.5;
Percentage change from current formula allocations: Full beneficiary
equity: 12.3;
Percentage change from current formula allocations: Taxpayer equity:
4.1.
State: North Carolina;
Current allocation[A] (2008): 92,812,979;
Funding allocations under each formula option: Partial beneficiary
equity: 93,532,116;
Funding allocations under each formula option: Full beneficiary equity:
87,623,917;
Funding allocations under each formula option: Taxpayer equity:
90,647,005;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.8;
Percentage change from current formula allocations: Full beneficiary
equity: -5.6;
Percentage change from current formula allocations: Taxpayer equity:
-2.3.
State: North Dakota;
Current allocation[A] (2008): 9,463,837;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.0;
Percentage change from current formula allocations: Full beneficiary
equity: 0.0;
Percentage change from current formula allocations: Taxpayer equity:
0.0.
State: Ohio;
Current allocation[A] (2008): 120,400,886;
Funding allocations under each formula option: Partial beneficiary
equity: 114,324,378;
Funding allocations under each formula option: Full beneficiary equity:
107,741,843;
Funding allocations under each formula option: Taxpayer equity:
110,803,992;
Percentage change from current formula allocations: Partial beneficiary
equity: -5.1;
Percentage change from current formula allocations: Full beneficiary
equity: -10.5;
Percentage change from current formula allocations: Taxpayer equity:
-8.0.
State: Oklahoma;
Current allocation[A] (2008): 40,628,883;
Funding allocations under each formula option: Partial beneficiary
equity: 44,144,467;
Funding allocations under each formula option: Full beneficiary equity:
39,257,904;
Funding allocations under each formula option: Taxpayer equity:
42,324,327;
Percentage change from current formula allocations: Partial beneficiary
equity: 8.7;
Percentage change from current formula allocations: Full beneficiary
equity: -3.4;
Percentage change from current formula allocations: Taxpayer equity:
4.2.
State: Oregon;
Current allocation[A] (2008): 35,175,174;
Funding allocations under each formula option: Partial beneficiary
equity: 38,181,743;
Funding allocations under each formula option: Full beneficiary equity:
37,519,882;
Funding allocations under each formula option: Taxpayer equity:
39,024,885;
Percentage change from current formula allocations: Partial beneficiary
equity: 8.6;
Percentage change from current formula allocations: Full beneficiary
equity: 6.7;
Percentage change from current formula allocations: Taxpayer equity:
10.9.
State: Pennsylvania;
Current allocation[A] (2008): 121,101,676;
Funding allocations under each formula option: Partial beneficiary
equity: 118,988,284;
Funding allocations under each formula option: Full beneficiary equity:
119,170,356;
Funding allocations under each formula option: Taxpayer equity:
121,867,201;
Percentage change from current formula allocations: Partial beneficiary
equity: -1.8;
Percentage change from current formula allocations: Full beneficiary
equity: -1.6;
Percentage change from current formula allocations: Taxpayer equity:
0.6.
State: Rhode Island;
Current allocation[A] (2008): 10,051,281;
Funding allocations under each formula option: Partial beneficiary
equity: 10,186,243;
Funding allocations under each formula option: Full beneficiary equity:
10,174,087;
Funding allocations under each formula option: Taxpayer equity:
10,128,252;
Percentage change from current formula allocations: Partial beneficiary
equity: 1.3;
Percentage change from current formula allocations: Full beneficiary
equity: 1.2;
Percentage change from current formula allocations: Taxpayer equity:
0.8.
State: South Carolina;
Current allocation[A] (2008): 50,734,708;
Funding allocations under each formula option: Partial beneficiary
equity: 46,262,110;
Funding allocations under each formula option: Full beneficiary equity:
43,300,526;
Funding allocations under each formula option: Taxpayer equity:
46,345,994;
Percentage change from current formula allocations: Partial beneficiary
equity: -8.8;
Percentage change from current formula allocations: Full beneficiary
equity: -14.7;
Percentage change from current formula allocations: Taxpayer equity:
-8.7.
State: South Dakota;
Current allocation[A] (2008): 9,463,837;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.0;
Percentage change from current formula allocations: Full beneficiary
equity: 0.0;
Percentage change from current formula allocations: Taxpayer equity:
0.0.
State: Tennessee;
Current allocation[A] (2008): 65,575,720;
Funding allocations under each formula option: Partial beneficiary
equity: 73,518,535;
Funding allocations under each formula option: Full beneficiary equity:
74,726,613;
Funding allocations under each formula option: Taxpayer equity:
81,366,285;
Percentage change from current formula allocations: Partial beneficiary
equity: 12.1;
Percentage change from current formula allocations: Full beneficiary
equity: 14.0;
Percentage change from current formula allocations: Taxpayer equity:
24.1.
State: Texas;
Current allocation[A] (2008): 217,749,584;
Funding allocations under each formula option: Partial beneficiary
equity: 212,890,595;
Funding allocations under each formula option: Full beneficiary equity:
205,105,733;
Funding allocations under each formula option: Taxpayer equity:
204,243,635;
Percentage change from current formula allocations: Partial beneficiary
equity: -2.2;
Percentage change from current formula allocations: Full beneficiary
equity: -5.8;
Percentage change from current formula allocations: Taxpayer equity:
-6.2.
State: Utah;
Current allocation[A] (2008): 28,030,439;
Funding allocations under each formula option: Partial beneficiary
equity: 19,126,893;
Funding allocations under each formula option: Full beneficiary equity:
17,574,143;
Funding allocations under each formula option: Taxpayer equity:
17,143,931;
Percentage change from current formula allocations: Partial beneficiary
equity: -31.8;
Percentage change from current formula allocations: Full beneficiary
equity: -37.3;
Percentage change from current formula allocations: Taxpayer equity:
-38.8.
State: Vermont;
Current allocation[A] (2008): 9,463,837;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.0;
Percentage change from current formula allocations: Full beneficiary
equity: 0.0;
Percentage change from current formula allocations: Taxpayer equity:
0.0.
State: Virginia;
Current allocation[A] (2008): 62,084,119;
Funding allocations under each formula option: Partial beneficiary
equity: 64,168,621;
Funding allocations under each formula option: Full beneficiary equity:
64,512,140;
Funding allocations under each formula option: Taxpayer equity:
60,687,435;
Percentage change from current formula allocations: Partial beneficiary
equity: 3.4;
Percentage change from current formula allocations: Full beneficiary
equity: 3.9;
Percentage change from current formula allocations: Taxpayer equity:
-2.2.
State: Washington;
Current allocation[A] (2008): 51,125,448;
Funding allocations under each formula option: Partial beneficiary
equity: 65,447,069;
Funding allocations under each formula option: Full beneficiary equity:
63,657,218;
Funding allocations under each formula option: Taxpayer equity:
64,260,364;
Percentage change from current formula allocations: Partial beneficiary
equity: 28.0;
Percentage change from current formula allocations: Full beneficiary
equity: 24.5;
Percentage change from current formula allocations: Taxpayer equity:
25.7.
State: West Virginia;
Current allocation[A] (2008): 25,312,666;
Funding allocations under each formula option: Partial beneficiary
equity: 27,640,419;
Funding allocations under each formula option: Full beneficiary equity:
24,406,947;
Funding allocations under each formula option: Taxpayer equity:
27,506,549;
Percentage change from current formula allocations: Partial beneficiary
equity: 9.2;
Percentage change from current formula allocations: Full beneficiary
equity: -3.6;
Percentage change from current formula allocations: Taxpayer equity:
8.7.
State: Wisconsin;
Current allocation[A] (2008): 55,246,877;
Funding allocations under each formula option: Partial beneficiary
equity: 45,317,537;
Funding allocations under each formula option: Full beneficiary equity:
44,300,827;
Funding allocations under each formula option: Taxpayer equity:
43,478,226;
Percentage change from current formula allocations: Partial beneficiary
equity: -18.0;
Percentage change from current formula allocations: Full beneficiary
equity: -19.8;
Percentage change from current formula allocations: Taxpayer equity:
-21.3.
State: Wyoming;
Current allocation[A] (2008): 9,463,837;
Funding allocations under each formula option: Partial beneficiary
equity: 9,463,837;
Funding allocations under each formula option: Full beneficiary equity:
9,463,837;
Funding allocations under each formula option: Taxpayer equity:
9,463,837;
Percentage change from current formula allocations: Partial beneficiary
equity: 0.0;
Percentage change from current formula allocations: Full beneficiary
equity: 0.0;
Percentage change from current formula allocations: Taxpayer equity:
0.0.
State: Total;
Current allocation[A] (2008): 2,761,189,401;
Funding allocations under each formula option: Partial beneficiary
equity: 2,761,189,401;
Funding allocations under each formula option: Full beneficiary equity:
2,761,186,401;
Funding allocations under each formula option: Taxpayer equity:
2,761,189,401.
Source: GAO analysis of Education data on VR grants in fiscal year
2008, Census Bureau's 2006 and 2007 ACS, BLS' Quarterly Census of
Employment and Wages, HUD data on fair market rents, Treasury data on
total taxable resources, and responses to GAO survey.
[A] These funding allocations are based on the initial allocations
distributed to states using the funding formula. The allocations do not
include any adjustments that occur due to states' inability to match
federal funds or the application of maintenance of effort penalties.
[End of table]
[End of section]
Appendix IX: Responses to Selected Questions from GAO Survey of State
Vocational Rehabilitation (VR) Agencies:
As part of our study, we distributed a Web-based survey to all 80 VR
agencies in the states, territories, and District of Columbia to obtain
agency officials' views regarding the current formula, potential
modifications to the formula, and the possibility of incorporating
performance incentives into the formula. In addition, we used the
survey to obtain data on agency expenditures that were needed to
develop our cost index. We received completed surveys from 74 of 80 VR
agencies, for a response rate of 93 percent. The following figures show
responses to all closed-ended questions, except for those questions
concerning agency expenditures, which are discussed in appendix I. For
more information about our methodology for designing and distributing
the survey, see appendix I.
Figure 8: State Agency Officials' Opinions about the Appropriateness or
Inappropriateness of Factors Currently Included in the Funding Formula:
[Refer to PDF for image: stacked horizontal bar graph]
Percentage based on response from 74 VR agencies:
The funding formula as a whole:
Very appropriate: 19%;
Somewhat appropriate: 43%;
Neither appropriate nor inappropriate: 4%;
Somewhat inappropriate: 26%;
Very inappropriate: 5%;
No opinion or no basis to judge: 3%.
The state‘s general population:
Very appropriate: 58%;
Somewhat appropriate: 28%;
Neither appropriate nor inappropriate: 0%;
Somewhat inappropriate: 7%;
Very inappropriate: 4%;
No opinion or no basis to judge: 3%.
The state‘s per capita income relative to national per capita income:
Very appropriate: 26%;
Somewhat appropriate: 31%;
Neither appropriate nor inappropriate: 12%;
Somewhat inappropriate: 15%;
Very inappropriate: 14%;
No opinion or no basis to judge: 3%.
Squaring of state per capita income relative to national per capita
income:
Very appropriate: 20%;
Somewhat appropriate: 11%;
Neither appropriate nor inappropriate: 16%;
Somewhat inappropriate: 15%;
Very inappropriate: 20%;
No opinion or no basis to judge: 18%.
The state‘s 1978 VR funding allotment:
Very appropriate: 12%;
Somewhat appropriate: 15%;
Neither appropriate nor inappropriate: 19%;
Somewhat inappropriate: 19%;
Very inappropriate: 16%;
No opinion or no basis to judge: 19%.
The state funding match of 21.3%:
Very appropriate: 39%;
Somewhat appropriate: 34%;
Neither appropriate nor inappropriate: 8%;
Somewhat inappropriate: 12%;
Very inappropriate: 4%;
No opinion or no basis to judge: 1%.
The minimum funding level for small states:
Very appropriate: 45%;
Somewhat appropriate: 26%;
Neither appropriate nor inappropriate: 8%;
Somewhat inappropriate: 7%;
Very inappropriate: 5%;
No opinion or no basis to judge: 9%.
Source: GAO analysis of survey results, questions 2 and 3.
Note: One agency did not respond to the question about the
appropriateness or inappropriateness of the 21.3% state matching rate.
For other questions, responses may not total 100% due to rounding.
[End of figure]
Figure 9: State VR Agency Officials' Opinions about Options for
Modifying the Formula:
[Refer to PDF for image: stacked horizontal bar graph]
Percentage based on responses from 74 VR agencies:
Funds should be distributed so that all states would receive the
same amount of funds per individual potentially eligible for
VR services:
Definitely yes: 9%;
Probably yes: 12%;
No preference: 5%;
Probably no: 31%;
Definitely no: 36%;
No opinion or no basis to judge: 5%.
Funds should be distributed so that all states would receive
funding to provide the same level of services per individual
potentially eligible for VR services by the formula taking into
account certain differences in the cost of services among states:
Definitely yes: 15%;
Probably yes: 30%;
No preference: 4%;
Probably no: 23%;
Definitely no: 24%;
No opinion or no basis to judge: 4%.
Funds should be distributed so that all states would contribute
about the same proportion of their total state financial resources
(e.g., states‘ tax base plus other revenues) to provide VR services:
Definitely yes: 4%;
Probably yes: 11%;
No preference: 9%;
Probably no: 22%;
Definitely no: 46%;
No opinion or no basis to judge: 8%.
Source: GAO analysis of survey results, question 5.
Note: Responses may not total 100 percent due to rounding.
[End of figure]
Figure 10: State VR Agency Officials' Opinions about Whether the
Matching Requirement Should Vary to Account for States' Total Financial
Resources:
[Refer to PDF for image: pie-chart]
Percentage based on responses from 74 VR agencies:
No opinion or no basis to judge: 3%;
Definitely yes: 7%;
Probably yes: 8%;
No preference: 7%;
Probably no: 26%;
Definitely no: 50%.
Source: GAO analysis of survey results, question 6.
Note: Responses total to greater than 100 percent due to rounding.
[End of figure]
Figure 11: State VR Agency Officials' Opinions about Incorporating
Performance Awards in the VR Program:
[Refer to PDF for image: stacked horizontal bar graph]
Percentages based on responses from 74 VR agencies:
Is it possible to develop measures of performance across agencies
that are both fact-based and fair that could be used to distribute
funds to VR agencies?
Definitely yes: 9;
Probably yes: 30;
No preference: 4;
Probably no: 38;
Definitely no: 15;
No opinion or no basis to judge: 4.
Do you think that VR agencies should receive additional funding
based on agency performance through the funding formula?
Definitely yes: 7;
Probably yes: 15;
No preference: 8;
Probably no: 42;
Definitely no: 22;
No opinion or no basis to judge: 7.
Do you think that VR agencies should receive additional funding
based on agency performance through a mechanism outside
the funding formula?
Definitely yes: 14;
Probably yes: 38;
No preference: 5;
Probably no: 22;
Definitely no: 9;
No opinion or no basis to judge: 12.
Source: GAO analysis of survey results, questions 9, 11, and
12.
Note: Responses may not total 100 percent due to rounding.
[End of figure]
Figure 12: State VR Agency Officials' Opinions about Potential Results
of Including Performance Awards in the VR Program:
[Refer to PDF for image: stacked horizontal bar graph]
Percentages based on responses from 74 VR agencies:
VR agencies would be appropriately rewarded:
Very likely: 9;
Somewhat likely: 36;
Would have no effect: 3;
Somewhat unlikely: 22;
Very unlikely: 18;
No opinion or no basis to judge: 12.
Outcomes for VR clients would improve:
Very likely: 11;
Somewhat likely: 38;
Would have no effect: 12;
Somewhat unlikely: 20;
Very unlikely: 12;
No opinion or no basis to judge: 5.
VR agencies would focus on clients who are likely to positively
impact performance measures:
Very likely: 55;
Somewhat likely: 34;
Would have no effect: 3;
Somewhat unlikely: 3;
Very unlikely: 0;
No opinion or no basis to judge: 4.
VR agencies would be less likely to serve individuals who are
unlikely to positively impact performance measures:
Very likely: 42;
Somewhat likely: 39;
Would have no effect: 7;
Somewhat unlikely: 4;
Very unlikely: 3;
No opinion or no basis to judge: 5.
VR agencies would be less likely to serve individuals with
significant disabilities:
Very likely: 30;
Somewhat likely: 39;
Would have no effect: 11;
Somewhat unlikely: 8;
Very unlikely: 7;
No opinion or no basis to judge: 5.
VR agencies would strengthen management practices:
Very likely: 12;
Somewhat likely: 30;
Would have no effect: 31;
Somewhat unlikely: 8;
Very unlikely: 7;
No opinion or no basis to judge: 12.
VR agencies most in need of additional resources would have
difficulty obtaining the performance-based funds:
Very likely: 28;
Somewhat likely: 45;
Would have no effect: 4;
Somewhat unlikely: 7;
Very unlikely: 3;
No opinion or no basis to judge: 14.
VR agencies operating under order of selection may have difficulty
obtaining the performance-based funds:
Very likely: 50;
Somewhat likely: 28;
Would have no effect: 4;
Somewhat unlikely: 4;
Very unlikely: 3;
No opinion or no basis to judge: 11.
VR agencies would have difficulty estimating future budgets:
Very likely: 35;
Somewhat likely: 43;
Would have no effect: 14;
Somewhat unlikely: 7;
Very unlikely: 0;
No opinion or no basis to judge: 1.
Competition between VR agencies would increase, potentially resulting
in less sharing of best practices among agencies:
Very likely: 22;
Somewhat likely: 35;
Would have no effect: 8;
Somewhat unlikely: 15;
Very unlikely: 11;
No opinion or no basis to judge: 8.
Source: GAO analysis of survey results, question 14.
Note: For three questions, 73 agencies provided responses. These
questions asked about the following results: (1) "Outcomes for VR
clients would improve;" (2) "VR agencies would focus on clients who are
likely to positively impact performance measures;" and (3) "Competition
between VR agencies would increase, potentially resulting in less
sharing of best practices among agencies." For other questions,
responses may not total 100 percent due to rounding.
[End of figure]
Figure 13: State VR Agency Officials' Opinions about Challenges of
Incorporating Performance Awards into the VR Program:
[Refer to PDF for image: stacked horizontal bar graph]
Percentages based on responses from 74 VR agencies:
Time gaps between when performance occurred and when the
performance is rewarded with funding, due to timing of data
collection:
Very great challenge: 30;
Great challenge: 23;
Moderate challenge: 32;
Slight challenge: 5;
Not a challenge at all: 7;
No opinion or no basis to judge: 3.
Ensuring the reliability of data used to determine funds for
performance:
Very great challenge: 36;
Great challenge: 23;
Moderate challenge: 23;
Slight challenge: 7;
Not a challenge at all: 5;
No opinion or no basis to judge: 4.
Designing measures that appropriately capture performance:
Very great challenge: 42;
Great challenge: 34;
Moderate challenge: 16;
Slight challenge: 4;
Not a challenge at all: 1;
No opinion or no basis to judge: 3.
Isolating agency performance from factors outside the control of the VR
agency (such as the economic conditions in the state):
Very great challenge: 45;
Great challenge: 32;
Moderate challenge: 18;
Slight challenge: 4;
Not a challenge at all: 0;
No opinion or no basis to judge: 1.
Accounting for differences in the populations served by state VR
agencies:
Very great challenge: 34;
Great challenge: 26;
Moderate challenge: 30;
Slight challenge: 8;
Not a challenge at all: 1;
No opinion or no basis to judge: 1.
Source: GAO analysis of survey results, question 16.
Note: One agency did not provide a response regarding the extent to
which data reliability would be a challenge. For other questions,
responses may not total 100 percent due to rounding.
[End of figure]
Figure 14: VR Agencies' Responses on Whether They Receive Funding from
Their State based on Performance:
[Refer to PDF for image: pie-chart]
Percentages based on responses from 74 VR agencies:
Yes: 5%;
No: 93%;
Not sure: 1%.
Source: GAO analysis of survey results, questions 2 and 3.
Note: Responses total to less than 100 percent due to rounding.
[End of figure]
Figure 15: Current Use of Performance Awards by State VR Agencies:
[Refer to PDF for image: stacked horizontal bar graph]
Percentages based on responses from 74 VR agencies:
VR counselors:
Awards provided to all: 11;
Awards proved to some: 19;
Awards not provided: 70.
VR managers:
Awards provided to all: 8;
Awards proved to some: 15;
Awards not provided: 76.
External entities such as contractors, partners, and community
rehabilitation organizations:
Awards provided to all: 3;
Awards proved to some: 27;
Awards not provided: 69.
VR district offices, field offices, or local service delivery centers:
Awards provided to all: 3;
Awards proved to some: 9;
Awards not provided: 88.
Source: GAO analysis of survey results, question 20.
Note: For two questions, 73 agencies provided responses. These
questions asked whether agencies provided awards for (1) VR managers,
and (2) external entities such as contractors, partners, and community
rehabilitation organizations. For other questions, responses may not
total 100 percent due to rounding.
[End of figure]
[End of section]
Appendix X: GAO Contacts and Staff Acknowledgments:
GAO Contact:
Daniel Bertoni, Director, (202) 512-7215 or bertonid@gao.gov.
Acknowledgments:
Michele Grgich, Assistant Director; Yunsian Tai, Analyst-in-Charge;
Gregory Dybalski, Thanh Lu; Karine McClosky; and Barbara Steel-Lowney
made significant contributions to all aspects of this report. Susan
Bernstein provided writing assistance. Other advisers included Joanna
Chan, Robert Dinkelmeyer, Ronald Fecso, DuEwa Kamara, Stuart Kaufman,
Jacqueline Nowicki, Patricia Owens, Max Sawicky, and Roger Thomas.
[End of section]
Footnotes:
[1] In this report, unless otherwise noted, the term states or state VR
agencies refers to VR agencies in the 50 states, the District of
Columbia, and the territories of American Samoa, Guam, Northern Mariana
Islands, Puerto Rico, and the U.S. Virgin Islands.
[2] An additional $35 million in VR funds was set aside for American
Indian Vocational Rehabilitation services in fiscal year 2008. This
report does not discuss the American Indian set-aside funds, since they
are not distributed through the funding formula.
[3] These equity standards are not statutory requirements for the VR
program, but are commonly used as standards in social science research
to evaluate and design funding formulas. See, for example, GAO,
Maternal and Child Health: Block Grant Funds Should Be Distributed More
Equitably, [hyperlink, http://www.gao.gov/products/GAO/HRD-92-5]
(Washington, D.C.: Apr. 2, 1992); RAND Corporation, Review and
Evaluation of the Substance Abuse and Mental Health Services Block
Grant Allotment Formula, MR-533-HHS/DPRC, 1997; National Research
Council, Statistical Issues in Allocating Funds by Formula, Panel on
Formula Allocations, Thomas A. Louis, Thomas B. Jabine, and Marisa A.
Gerstein, ed., Committee on National Statistics, (Washington, D.C.,
2003).
[4] Some states have two VR agencies - one that serves individuals who
are blind, and another that serves individuals with other types of
disabilities. Other states have one VR agency that serves people with
all types of disabilities.
[5] The Rehabilitation Act was most recently reauthorized as part of
the Workforce Investment Act (Pub. L. No. 105-220). The funding formula
we describe is used to distribute federal funds to states. In states
with two VR agencies, the division of a state's allotment between the
blind and general VR agency is determined by the state.
[6] Prior to 1978, funds were allotted based upon states' populations
and the square of the allotment percentage. See footnote 7 and appendix
II for an explanation of the allotment percentage.
[7] The allotment percentage, which is determined by comparing a
state's per capita income to the national per capita income, cannot be
less than 33 1/3 percent or greater than 75 percent. Default levels for
the allotment percentage are set at 75 percent for the District of
Columbia and U.S. territories. In fiscal year 2008, the limits on the
allotment percentage affected only one state, Connecticut, whose
allotment percentage was increased to 33 1/3 percent. See appendix II
for a further explanation.
[8] If a state's allotment falls below this level, it is increased to
this amount and the final allotments for all of the other states are
decreased in proportion to their share of the total appropriation. The
minimum allotment provision does not apply to U.S. territories, but it
does apply to the District of Columbia.
[9] The six states were Alaska, Delaware, North Dakota, South Dakota,
Vermont, and Wyoming.
[10] The Rehabilitation Act also includes a maintenance-of-effort
requirement to encourage states to maintain their level of contribution
to the VR program over time. If a state's expenditures from nonfederal
sources in the prior fiscal year is less than its expenditures from
nonfederal sources for the fiscal year two years prior to the previous
fiscal year, Education can reduce the state's current year allotment by
the difference between the two years' expenditures. For example, if a
state's nonfederal expenditures in fiscal year 2007 were less than its
nonfederal expenditures in fiscal year 2005, Education could reduce its
fiscal year 2008 allotment by the difference between the 2005 and 2007
nonfederal expenditures. Funds incurred from maintenance-of-effort
penalties are redistributed to all other states. In fiscal year 2008, 1
state and 1 territory incurred maintenance-of-effort penalties totaling
$364,421, or 0.01 percent of total federal VR funds. However, the Act
specifies that under exceptional or uncontrollable circumstances
affecting the state, the maintenance-of-effort requirement may be
waived. Education officials informed us that such circumstances may
include a serious economic downturn or natural disaster.
[11] The redistribution process takes place through a memorandum that
Education sends to each VR agency near the end of each fiscal year,
asking them whether they will relinquish any funds or request
additional ones. Education then redistributes the funds that states
relinquished to other states who requested additional funds. In fiscal
year 2008, 4 states and 1 territory relinquished $17.8 million in
funding, or 0.6 percent of total federal VR funds. The required 21.3
percent state contribution also applies to any additional funds that
states receive through the redistribution process.
[12] The performance indicators are established in 34 C.F.R. § 361.84.
For additional background on Education's oversight of state VR agency
performance, see GAO, Vocational Rehabilitation: Better Measures and
Monitoring Could Improve the Performance of the VR Program, GAO-05-865
(Washington, D.C.: Sept. 23, 2005).
[13] These disability rates are calculated as the civilian working-aged
population of people with disabilities (ages 16-64) divided by the
total state population. Our measure of the disability population is
derived from five disability questions asked in the 2007 American
Community Survey (ACS), conducted by the Census Bureau. We did not
include data from a sixth disability question about whether a person
has difficulty working at a job or business because of a physical,
mental, or emotional condition lasting six months or more because this
question was no longer included in the ACS, starting in 2008. We were
not able to calculate the disability rates of U.S. territories. Data
are available from the ACS on the population of people with
disabilities in Puerto Rico, but are not available for other
territories. For further information on our analysis of states'
disability populations, see appendix I.
[14] We calculated the correlation to be 0.53 between states'
disability rates and their per capita income, where 1 would be a
perfect correlation between two variables and 0 would indicate a lack
of correlation.
[15] We estimated the relative cost of providing services by estimating
the costs of two inputs to providing VR services, labor and office
space. Specifically, we used data on state average wages in the
education, healthcare, and social assistance sector from BLS' Quarterly
Census of Employment and Wages, as well as data on fair market rents
from HUD. To determine how much to weight wages and rents in our cost
estimates, we used data obtained from our survey and Education's data
on expenditures. Although our cost index provides a reasonable
approximation of basic cost differences among states, we were unable to
develop a more precise estimate reflecting all possible inputs due to
the lack of readily available data. For further information about our
data sources and methodology, see appendix I.
[16] Grant allotments used in this analysis are the initial allotments
distributed to states using the funding formula. The allotments do not
include any adjustments that occur due to states' inability to match
federal funds or the application of maintenance-of-effort penalties.
[17] Specifically, we present ratios of a per capita income index to an
index of total taxable resources per capita. The indexes were created
by dividing a state's per capita income and total taxable resource per
capita by the U.S. averages for each.
[18] Squaring of the per capita income factor (i.e., multiplying the
per capita income factor by itself) increases the influence of per
capita income in the formula.
[19] The Congressional Research Service found that states with the
largest population growth since 1976 would have received larger funding
allotments if the 1978 provision were not part of the VR funding
formula, while states with lower rates of population growth since 1976
benefit from this provision. Congressional Research Service, Vocational
Rehabilitation Grants to States and Territories: Overview and Analysis
of the Allotment Formula, RL34017 (Washington, D.C., January 2008).
[20] In states with two VR agencies, we considered a state to be under
an order of selection if either of its agencies reported being under an
order of selection. In one state--Delaware--the state VR agency for the
blind was under an order of selection, but not the general agency. In
all other states that we considered to be under an order of selection,
either only the general agency reported being under an order of
selection, or both the general and blind agencies did so. We did not
include U.S. territories in this analysis due to lack of data that
would allow us to determine the size of their allotments per working-
aged person with a disability.
[21] State agencies were not provided with specific details of the
options presented in this report because these were not finalized at
the time of our survey. In asking state agencies to provide their views
on three options, generally based upon the equity standards, we sought
to minimize the effect of any perceptions that they might gain or lose
funding as a result of a formula change; however, we do not know
whether such perceptions ultimately influenced their responses.
[22] The amount of time it would take for the new formula to fully be
in place would depend on the level of the hold harmless provision. For
example, if the hold harmless provision did not allow any state to
experience a decrease from the prior year's allocation, we estimate
that it would take 10 to 13 years before all states' allocations were
determined by the new formula. However, if the hold harmless provision
allowed states to experience a decrease of up to 10 percent from the
prior year's allocation, we estimate that it would take 7 to 10 years
for all states' allocations to be determined by the new formula. These
estimates assume that the federal funds available to be distributed for
the VR program would increase by 4 percent per year, and that states'
relative levels of need population, costs, and taxable resources would
remain the same over time.
[23] GAO, Workforce Investment Act: Improvements Needed in Performance
Measures to Provide a More Accurate Picture of WIA's Effectiveness,
[hyperlink, http://www.gao.gov/products/GAO-02-275] (Washington D.C.:
Feb. 1, 2002).
[24] Prior GAO reports have noted that Education's performance measures
for the VR program do not account for factors such as the economic
health and demographics of a state. GAO, Vocational Rehabilitation:
Improved Information and Practices May Enhance State Agency Earnings
Outcomes for SSA Beneficiaries, [hyperlink,
http://www.gao.gov/products/GAO-07-521] (Washington D.C.: May 23, 2007)
and Vocational Rehabilitation: Better Measures and Monitoring Could
Improve the Performance of the VR Program, [hyperlink,
http://www.gao.gov/products/GAO-05-865] (Washington D.C.: Sept. 23,
2005).
[25] A 2004 report commissioned by the Department of Education found
that poor labor market conditions, particularly high unemployment
rates, were reported as being among the most influential hindrances to
a VR agency's performance. See RTI International, Study of Variables
Related to State Vocational Rehabilitation Agency Performance (Revised
Draft Final Report), (Research Triangle Park, N.C., October 2004).
[26] According to our past work, one key attribute of a successful
performance measure system is its coverage of all activities that an
entity is expected to perform to support the intent of the program.
GAO, Tax Administration: IRS Needs to Further Refine Its Tax Filing
Season Performance Measures, [hyperlink,
http://www.gao.gov/products/GAO-03-143] (Washington D.C.: Nov. 22,
2002). Also, researchers of performance incentives have noted that the
exclusion of measures for some key program goals can lead agencies to
focus on goals that are measured, to the detriment of those that are
not.
[27] Under the Rehabilitation Act, a significant disability is defined
as one that seriously limits one or more functional capacities and can
be expected to require multiple VR services over an extended period of
time. In the VR program, "significant disability" is a separate and
broader category than "most significant disability." For more
information on our prior work related to the definition of most
significant disability, see GAO-05-865.
[28] [hyperlink, http://www.gao.gov/products/GAO-05-865]. For example,
GAO recommended that Education consider additional measures (e.g., to
better reflect special needs populations, such as transitioning
students) and accounting for factors outside of its control. Education
generally agreed with our findings. Since then, Education has developed
a draft strategic plan that proposes long-term performance goals,
objectives, and measures for use in its performance monitoring
activities. This draft plan includes measures of agency success in
serving youth transitioning out of high school, individuals with
significant disabilities, and individuals with significant disabilities
who receive public financial support at the time of application to the
VR program.
[29] For more detail about potential approaches to adjusting
performance standards, see Pascal Courty, Carolyn Heinrich and Gerald
Marschke, "Setting the Standard in Performance Measurement Systems,"
International Public Management Journal 8:3 (2005); Joe Siedlecki and
Christopher T. King, "Approaches to Adjusting Workforce Development
Performance Measures," Ray Marshall Center for the Study of Human
Resources, Lyndon B. Johnson School of Public Affairs, The University
of Texas at Austin Occasional Brief Series 1:2 (2005); and Carolyn J.
Heinrich and Burt S. Barnow, "One Standard Fits All? The Pros and Cons
of Performance Standard Adjustments," La Follette School Working Paper,
2008-023. These articles discuss adjusting performance standards based
on mathematical models; negotiation among local, state and federal
agencies; or approaches that use both.
[30] Incentive award decisions could also be based on the extent to
which an agency improved over its past performance. While this approach
allows agencies to be measured against their own progress rather than
general standards applied to all states regardless of their
circumstances, it can also pose challenges because the circumstances
within a state may change from year to year. For example, if a state
enters a recession, improving performance from previous years may not
be feasible. In addition, setting performance goals in this way may
create perverse incentives for officials to limit performance gains in
a given year in order to decrease the performance level required in
future years.
[31] The JTPA was the predecessor to the Department of Labor's WIA
Title IB employment and training programs for adults, youth and
dislocated workers which were authorized in 1998. For more information,
see Heinrich and Barnow, 2008 and Pascal Courty, Do Han Kim, and Gerald
Marschke, "Curbing Cream-Skimming: Evidence on Enrollment Incentives,"
Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of
Labor, Discussion Paper No. 3909, (December 2008). Courty, Kim, and
Marschke found that the JTPA agencies responded to the adjustment model
by increasing the proportion of clients in the program from demographic
groups estimated to have more barriers to employment. However, they
also found that JTPA program staff continued to select the individuals
who were most likely to find employment from within these demographic
groups. While this shows continued evidence of a perverse reaction to
the performance award system, the authors described it as a much
smaller problem than if these adjustments had not occurred.
[32] For more information, see Arthur C. Brooks, "The Use and Misuse of
Adjusted Performance Measures," Journal of Policy Analysis and
Management, 19:2 (Spring 2000) and Heinrich and Barnow, 2008.
[33] GAO, Workforce Investment Act: States and Local Areas Have
Developed Strategies to Assess Performance, but Labor Could Do More to
Help, [hyperlink, http://www.gao.gov/products/GAO-04-657] (Washington,
D.C: June 1, 2004) and [hyperlink,
http://www.gao.gov/products/GAO-02-275].
[34] [hyperlink, http://www.gao.gov/products/GAO-04-657].
[35] High performing local public housing authorities receive a 5
percent increase in their base formula allocation, with the stipulation
that no public housing authority will lose more than 5 percent of its
formula allocation due to the redistribution of funds for incentive
awards.
[36] For more information on the process for distributing WIA
performance incentive awards as grants, see Employment and Training
Administration, Training and Employment Guidance Letter No. 9-07,
Revised Incentive and Sanction Policy for Workforce Investment Act
Title IB Programs (Washington D.C., Oct. 10, 2007). For more
information on the process for distributing Child Support Enforcement
Program performance incentive awards through a specific formula, see
Congressional Research Service, Child Support Enforcement Program
Incentive Payments: Background and Policy Issues, RL 34203 (Washington
D.C., October 2007).
[37] Specifically, officials from 14 percent of the agencies that
responded to our survey supported incentive awards, regardless of
whether they were provided inside or outside the formula; 38 percent
only supported incentive awards outside the formula; and 8 percent only
supported incentive awards within the formula. Also, 41 percent did not
support, or had no opinion regarding, using incentive awards either
within or outside the funding formula. These numbers total to greater
than 100 percent due to rounding.
[38] GAO, Grants Management: Enhancing Performance Accountability
Provisions Could Lead to Better Results, [hyperlink,
http://www.gao.gov/products/GAO-06-1046] (Washington D.C.: Sept. 29,
2006).
[39] As noted earlier, beneficiary equity stipulates that funds should
be distributed to states according to the needs of their respective
populations, so that each state, with its federal allocation, can
provide the same level of services to each person in need. Taxpayer
equity stipulates that funds are distributed so that states can provide
individuals comparable services using both state and federal funds,
while each state contributes about the same proportion of its resources
to a given federal program.
[40] The following experts reviewed our analysis of need population and
cost of providing services: Andrew Houtenville, Associate Professor of
Economics and Research Director of the Institute on Disability,
University of New Hampshire; Mitchell LaPlante, Associate Adjunct
Professor of Social and Behavioral Sciences and Director of the
Disability Statistics Center, University of California San Francisco;
and David Stapleton, Senior Fellow and Director, Center for Studying
Disability Policy, Mathematica Policy Research, Inc.
[410] The Department of Transportation's New Freedom program, which
provides funds to support public transportation services and
alternatives beyond those required by the Americans with Disabilities
Act, uses a formula that allocates funds based on a locality's
population of people with disabilities. The Department of
Transportation measures the disability populations of large urbanized
areas, small urbanized areas, and rural and small urban areas with
populations less than 50,000 using data from the 2000 Decennial Census.
[42] We did not separately identify data on blind populations in each
state. In a state with two VR agencies, the state determines how to
divide federal VR funds among the agency for the blind and the general
VR agency.
[43] Each of the other five disability questions in the ACS were
changed, starting in 2008. For example, question 1 on blindness,
deafness, or severe vision or hearing impairment was made into two
separate questions--one specifically on deafness or serious difficulty
hearing, and another one on blindness or serious difficulty seeing even
when wearing glasses. Question 2 about difficulty with physical
activities such as walking, climbing stairs, reaching, lifting, or
carrying was changed to ask whether the person has a serious difficulty
walking or climbing stairs. Question 3 about difficulty learning,
remembering, or concentrating was changed to ask about difficulty
concentrating, remembering, or making decisions. Question 4 about
difficulty dressing, bathing, or getting around inside the home was
changed to ask whether the person has difficulty dressing or bathing.
Finally, Question 5 about difficulty going outside the home alone to
shop or visit a doctor's office was changed to ask about difficulty
doing errands alone, such as visiting a doctor's office or shopping.
[44] The RSA-2 data showed that 46 percent of state agencies'
expenditures were spent on services that were contracted out or
purchased. The data did not, however, allow us to determine the extent
to which each specific type of service was provided by state agency
employees or was contracted out or purchased from other organizations.
[45] We were not able to identify data that could be used to estimate
the proportion of contract expenditures that went to wages and rents.
Further, state agency officials who tested our survey informed us that
they would not be able to estimate the amount of contract expenditures
that went to wages and rents.
[46] Some of the experts also suggested that we include states'
postsecondary education costs in our cost index. Although VR agencies
track education expenditures that would allow us to develop weights,
and data are available on states' postsecondary education costs (such
as average tuition and fees), we did not include the data in our cost
index for two main reasons. First, due to the availability of financial
aid, we could not readily determine the extent to which these data
would reflect actual costs incurred by state VR agencies. Second, costs
of public higher education institutions are, in part, determined by
states' funding decisions, and as a result, including these costs in
our index may reward or penalize states based upon their fiscal
decisions. Although we did not directly account for cost differences in
higher education, the wage data we used for the education, healthcare,
and social assistance industry may partly capture differences in
education costs because it includes workers in education industries.
[47] The allotments data we used did not include any adjustments made
to states' allotments at the end of the fiscal year, such as funds that
state agencies returned because they could not fully match them or
additional funds that agencies received through the reallotment
process.
[48] The state with the median allotment per working-aged person with a
disability, adjusted for costs (Arizona) was excluded from the analysis
so that our analysis was specifically of the 25 states (including the
District of Columbia) above the median and the 25 states below it.
[49] GAO, Grants Management: Enhancing Performance Accountability
Provisions Could Lead to Better Results, [hyperlink,
http://www.gao.gov/products/GAO-06-1046] (Washington D.C.: Sept. 26,
2006).
[50] Prior to 1978, funds were allotted based upon states' populations
and the square of its allotment percentage.
[51] This minimum allotment does not apply to the U.S. territories, but
it does apply to the District of Columbia.
[End of section]
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