Older Americans Act: Funding Formula Could Better Reflect State Needs
(Letter Report, 05/12/94, GAO/HEHS-94-41).
In response to congressional concerns that current title III allocations
do not fully reflect indicators of states' needs, GAO examined the
interstate funding formula of the current Older Americans Act of 1965.
This formula allocated more than $770 million in federal title III
dollars in fiscal year 1993 among the 50 states and the District of
Columbia. GAO concludes that Congress should modify the formula for
distributing title III money to better target those elderly persons with
the greatest social and economic needs. In this report, GAO (1)
develops equity standards appropriate to evaluating the allocation of
title III assistance to the states, (2) uses these standards to create
alternative formulas under which funds might be distributed more
equitably, (3) shows how each of the alternatives would redistribute
funding among the states, and (4) explores ways of phasing in a new
formula to moderate the degrees of funding changes in a single year.
--------------------------- Indexing Terms -----------------------------
REPORTNUM: HEHS-94-41
TITLE: Older Americans Act: Funding Formula Could Better Reflect
State Needs
DATE: 05/12/94
SUBJECT: Aid for the elderly
State-administered programs
Formula grants
Population statistics
Elderly persons
Appropriated funds
Grant administration
Grants to states
Allotment
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Cover
================================================================ COVER
Report to the Chairman, Special Committee on Aging, U.S. Senate
April 1994
OLDER AMERICANS ACT - FUNDING
FORMULA COULD BETTER REFLECT STATE
NEEDS
GAO/HEHS-94-41
Older Americans Act
Abbreviations
=============================================================== ABBREV
AoA - Administration on Aging
ADL - Activities of Daily Living
BLS - Bureau of Labor Statistics
GSP - Gross State Product
HHS - U.S. Department of Health and Human Services
IADL - Instrumental Activities of Daily Living
NCHS - National Center for Health Statistics
OAA - Older Americans Act
OAFP - Older Americans Federal Percentage
RTS - Representative Tax System
TTR - Total Taxable Resources
USDA - U.S. Department of Agriculture
Letter
=============================================================== LETTER
B-249687
May 12, 1994
The Honorable David Pryor
Chairman, Special Committee on Aging
United States Senate
Dear Mr. Chairman:
Because of your concern that current title III allocations do not
fully reflect available indicators of states' needs, you asked us to
examine the interstate funding formula of the current Older Americans
Act of 1965 (OAA), as amended (P.L. 102-375). This formula
allocated over $770 million in federal title III dollars in fiscal
year 1993 among the 50 states and the District of Columbia (hereafter
referred to as "the states"). Briefly, we have concluded that the
Congress should modify the formula for distributing title III funds
to better target federal funds to those portions of the elderly
population who need it most due to the greatest social and economic
need, as defined in the act.
During our review, we undertook to (1) develop equity standards that
are appropriate to evaluating the allocation of title III assistance
among states, (2) use these standards to create alternative formulas
under which title III funds might be distributed more equitably among
the states, (3) show how implementing each of the alternatives would
redistribute funding among the states, and (4) explore ways of
phasing in a new formula to moderate the degree of funding changes in
a single year. (See app. I for further discussion of equity-based
formulas.) More detailed discussions of our method for measuring
social and economic needs are contained in appendix II; the cost of
services in appendix III; and the capacity of states to fund services
from their own resources in appendix IV.
BACKGROUND
------------------------------------------------------------ Letter :1
The Older Americans Act was enacted in 1965 and is administered by
the Administration on Aging (AoA) in the Department of Health and
Human Services (HHS). The act is intended to assist elderly
Americans to live independently in their own communities by removing
barriers to independent living and providing a continuum of care for
vulnerable older individuals. OAA's title III provides grants for
state and community-based programs to foster the development and
implementation of comprehensive and coordinated systems to serve
older individuals in their communities. Specifically, OAA's title
III helps fund numerous community-based programs such as congregate
and in-home meals, transportation, information and referral, and
housekeeping services. In fiscal year 1993, federal funding was over
$770 million. Data on states' spending from their own revenues are
very limited, but one recent study estimates that federal funds
support approximately 35 percent of such services, with states,
localities, and private sources funding the remaining 65 percent.\1
Title III funds are allocated to the states through a statutory
funding formula. The interstate formula is based on each state's
proportion of the U.S. population over 60 years of age, but it also
guarantees that each state will receive at least as much funding as
it received in fiscal year 1987--the "hold harmless" provision--and
that each state will receive at least one-half percent of the total
funds available for distribution in that year--the "minimum funding"
provision.\2
This report focuses on the question of how the formula that
distributes title III funds could be changed to better reflect the
goal of serving the elderly with greater economic and social needs.
Economic and social needs are important because, while title III
distributes funds to states based on the proportion of older
Americans in each state, the statute requires the states, when
distributing these funds, to provide preferences to older individuals
with greatest economic and social need, with particular attention to
low-income minority individuals.\3 Thus, plans developed by the state
agencies and approved by AoA, and plans developed by local areas and
approved by states, must ensure that title III funds are distributed
to those in greatest economic and social need.
In a January 1994 report on a related title III funding matter, we
concluded that AoA does not implement the title III formula in
accordance with the statute.\4 In our view, funding inequities are
occurring because AoA incorrectly calculates title III state grants.
Grant funds will be distributed differently if AoA revises its
formula allocation calculations to comply with OAA provisions.
--------------------
\1 State expenditure estimates are based on the National Association
of Area Agencies on Aging, Staff Compensation Survey (Washington,
D.C.: Sept. 1992).
\2 In fiscal year 1993, seven states and the District of
Columbia--Alaska, Delaware, District of Columbia, Montana, North
Dakota, South Dakota, Vermont, and Wyoming--received an allocation
based on the one-half of 1 percent minimum funding provision.
\3 The statute defines "greatest economic need" as a need resulting
from an income level at or below the poverty line. "Greatest social
need" is defined as need caused by physical and mental disabilities;
language barriers; and cultural, social, or geographical isolation
that restricts an individual's ability to perform normal daily tasks
or that threatens an individual's capacity to live independently.
\4 See Older Americans Act: Title III Funds Not Distributed
According to Statute (GAO/HEHS-94-37, Jan. 18, 1994).
RESULTS IN BRIEF
------------------------------------------------------------ Letter :2
The current OAA grant distribution formula fails to achieve
"beneficiary equity," meaning that the state allocations are either
too much or too little for purchasing comparable services for the
at-risk elderly population. There are two reasons for this
situation. First, the funding allocation formula, because it
distributes money according to the number of people over 60 years of
age in a state, does not take into account the greater incidence in
some states of social and economic dependence among certain at-risk
segments of the elderly population--namely, the very old, the poor,
minorities, and females. States may have roughly the same-sized
populations over 60, but have significantly different-sized at-risk
elderly populations.
A second reason that the current formula does not allow states to
purchase comparable services for the elderly is that the formula does
not recognize differences among states in the costs of purchasing
services. Cost differences are caused by differences in the cost of
personnel, office space, and materials used to deliver title III
services. At this time, states with roughly equal-sized populations
over 60 get about the same allocations, even though some of the
states may face significantly higher costs of providing services.
The current OAA formula also does not achieve taxpayer equity.
States with roughly the same-sized populations, but with different
financial resources, get about the same allocations. Thus, poorer
states would have to impose higher tax burdens to raise sufficient
"own source" funds to provide, when combined with the OAA grant
monies, comparable financing of state services for the elderly.
It is possible to develop a formula for distributing title III funds
that would reflect the equity standards we considered. However, a
formula cannot fully achieve both beneficiary equity and taxpayer
equity standards at the same time. This is because the states that
would receive the most funding under the beneficiary equity standard
are not the same states that would receive the most funding under the
taxpayer equity standard. Consequently, we cannot recommend a single
formula because the choice of a particular formula depends on
congressional policymakers' judgments about whether beneficiary
equity or taxpayer equity should be emphasized.
To assist in congressional deliberations, we present six options for
distributing funds that we believe reflect the full range of possible
formulas based on the beneficiary and taxpayer equity standards. All
options target more funding to states with high concentrations of the
elderly population, especially the at-risk segments of the elderly
population. Additionally, all options continue to reflect the act's
"hold harmless" and one-half percent "minimum funding" levels. The
range of alternatives should enable the Congress to select an option
that best reflects the equity standard it believes should be
emphasized.
Changing the method of distributing title III funding to improve
equity could potentially disrupt the administration of state programs
because funding changes could be substantial for some states.
Therefore, we suggest that a new formula be phased in over a
multiyear period in order to allow states to gradually adjust to new
funding levels. Under this method, the proportion of title III funds
distributed would be gradually transferred from the existing
allocation formula to a new formula.
CURRENT OAA FUNDING ALLOCATIONS
DO NOT ACHIEVE BENEFICIARY
EQUITY
------------------------------------------------------------ Letter :3
The current distribution of federal aid is based on the number of
elderly residents in each state.\5 However, this method fails to
achieve beneficiary equity because some states have a higher
percentage of their elderly populations who experience impairments to
independent living and for whom the cost of providing services is
greater. Since the title III formula does not compensate for these
variations in states' needs, federal aid currently purchases services
per person in need that are well above the national average in some
states and substantially below average in others. For example, under
the current formula, Alaska is able to purchase an average service
level per person-in-need with its federal aid that is over five times
above the national average. In contrast, Florida's grant is only
able to purchase services that are 11 percent below average.
Overall, 16 states differ from the national average by more than +10
percent.
Data showing funding inequities for the states, based on the
beneficiary equity criterion, are listed in table V.1 in appendix V.
--------------------
\5 Except for states subject to the one-half percent minimum of the
total appropriation, which receive more.
STATES DIFFER IN THE
CONCENTRATION OF HIGH-RISK
INDIVIDUALS
---------------------------------------------------------- Letter :3.1
The current method of distributing title III funding does not take
into account those portions of the elderly population most at risk of
experiencing social and economic barriers to independent living.
This means that states with low concentrations of the elderly most at
risk are overfunded, and states with high concentrations are
underfunded.
The current formula implicitly assumes that the incidence of
impediments to an independent lifestyle are the same in every state.
Yet, states differ in the concentration of high-risk individuals. We
estimate that, nationwide, approximately 25 percent of the
noninstitutionalized population over age 65 experiences mobility and
self-care limitations. However, this percentage ranges from a low of
about 21 percent in Nevada to a high of over 29 percent in the
District of Columbia.
Our review of the research literature on elderly dependence reveals a
higher incidence of mobility and self-care limitations among
population subgroups: minorities, the very old (i.e., individuals
over 80 years of age and especially over 85 years), the poor, and
females. Our analysis shows that members of minority groups and
individuals in the oldest age groups are the most important
predictors of a state's incidence of mobility and self-care
limitations. The number of elderly in poverty and the number of
females also help predict a state's incidence rate.
Appendix II explains how we identified age, sex, minority status, and
poverty as high-risk population groups. How each of these factors
should be weighted to reflect social and economic barriers to
independent living is reported in table II.4.
STATES FACE DIFFERING COSTS
IN PROVIDING TITLE III
SERVICES
---------------------------------------------------------- Letter :3.2
The current interstate funding formula also does not take into
account the sometimes substantial differences in service costs from
state to state. Consequently, federal grants purchase fewer services
for elderly populations in states that face higher costs of providing
services. Although cost differences (personnel, office space, and
supplies used in the process of providing services to the elderly)
are difficult to measure, we estimate that the costs of providing
title III-related services range from approximately 31 percent above
the national average in Alaska to approximately 11 percent below the
national average in North Dakota.
See appendix III for a more detailed discussion of how cost
differences are measured.
CURRENT OAA FUNDING ALLOCATIONS
DO NOT ACHIEVE TAXPAYER EQUITY
------------------------------------------------------------ Letter :4
Because the current title III formula does not take into
consideration states' varying financial capacity to fund services
from their own resources, the allocation method also fails to achieve
taxpayer equity. The key to understanding this concept is knowing
that states also spend their own dollars on the elderly, with OAA
grant monies supplementing state funds. When the two sources of
funds are considered, it is seen that poorer states would have to
impose a higher tax burden on state residents to produce enough
additional state revenues (when combined with the federal OAA funds)
to finance an average level of services.
States' abilities to finance their share of elderly services (broadly
measured by residents' income) vary widely--from 340 percent above
the national average in Alaska, to 32 percent below average in West
Virginia. When states' tax capacity differences are considered in
conjunction with differences in states' at-risk populations and the
cost of delivering services, we find that state tax burdens would
have to vary greatly in order to fund comparable services. For
example, Alaska's and Wyoming's title III funding is currently high
enough that they are able to finance a national average basket of OAA
services without having to contribute any state resources. In
Arkansas and Mississippi, however, state taxpayers would have to
expend a tax effort that is as much as 60 percent above the national
average in order to finance a national average basket of services.
Overall, the tax burden of 46 states would differ from the national
average by more than +10 percent, while only 5 states are within +10
percent of the national average.
Appendix IV provides a more detailed explanation of the taxpayer
equity concept. Differences in state taxpayer burdens for all states
are shown in appendix V in table V.2.
SEVERAL APPROACHES EXIST THAT
WOULD IMPROVE EQUITY IN FUND
DISTRIBUTION
------------------------------------------------------------ Letter :5
An appropriately redesigned title III formula could improve equity
from the standpoint of either providing funds sufficient to purchase
comparable services in all states (beneficiary equity), or by
providing funds sufficient to enable all states to finance comparable
services with comparable burdens on state taxpayers (taxpayer
equity). We designed formulas that would achieve each standard
separately in order to demonstrate the range of possible equity
approaches. We also developed several options designed to reflect
the trade-off between each standard ("balanced equity" options). In
total, six different formula options were developed. We believe they
reflect a wide range of possibilities that would improve equity.
Table 1 summarizes the effects that our six formula alternatives
would have on states' funding amounts.\6 The number of states that
would receive increased funding ranges from as few as 12 states under
the beneficiary equity option, to as many as 25 states under option
#5. The alternatives differ dramatically in terms of the percentage
of title III dollars they would redistribute, ranging from 2.8
percent under the beneficiary equity option, to 11.3 percent under
the taxpayer equity option.
Table 1
GAO Proposed Alternative Formula
Allocations Under the Older Americans
Act
Formula # #1 #2 #3 #4 #5 #6
-------------------------------- ------ ------ ------ ------ ------ ------
Funds redistributed
--------------------------------------------------------------------------------
Amount $21.1 $85.9 $59.7 $83.8 $66.4 $50.8
(in millions)
Percent 2.8% 11.3% 7.9% 11.0% 8.8% 6.7%
States affected
--------------------------------------------------------------------------------
Number increasing 12 23 22 24 25 24
Number decreasing 31 20 21 19 18 19
Number no change 8 8 8 8 8 8
--------------------------------------------------------------------------------
In general, the formula options based on the beneficiary and taxpayer
equity standards redistribute funding from larger to medium-sized
states and from higher- to lower- income states. Small states tend
not to be affected because under all formula options they receive the
guaranteed 0.5 percent of the total appropriations. Also, the
formula options we developed do not attempt to calculate grants for
the U.S. insular areas. The data necessary to reflect the equity
standards we used are not available for these jurisdictions. For our
analysis, we assumed the insular areas will continue to receive the
same percentage share of the total appropriations that they receive
under current law.
--------------------
\6 The effect on individual state funding amounts is shown in table
VI.4.
SOME STATES ARE CONSISTENTLY
UNDERFUNDED RELATIVE TO THE
EQUITY STANDARDS CONSIDERED
---------------------------------------------------------- Letter :5.1
In reviewing the options, we identified 18 states that are
consistently disadvantaged under the current formula. These are
states that would receive more funding under at least five of the six
options we considered. Similarly, there are 16 states that
consistently receive more funding than what would be indicated by our
indicators of need. Another eight states would be unaffected by any
formula change because they are subject to the minimum funding
guarantee embodied in current law. The funding impact on the
remaining states varies across the six options. The geographic
pattern of how states are affected is reflected in figure 1.
Figure 1: Changes in States'
Title III Funding Under Six
Equity-Based Formulas
(See figure in printed
edition.)
PROVIDING A TRANSITION
------------------------------------------------------------ Letter :6
If a new formula were to be adopted, it could produce significant
changes in funding for some states. As a means of reducing the
disruption in administration of the program in these states, a new
formula could be phased in over a period of years. We illustrate in
table VII.1, on a state-by-state basis, one method of phasing in a
new formula. This method would shift funding from the current
formula to a new formula over a 5-year period.
RECOMMENDATION TO THE CONGRESS
------------------------------------------------------------ Letter :7
To better ensure that the distribution of title III funds is based on
economic and social indicators of need, we recommend that the
Congress improve the Older Americans Act's interstate funding formula
to better reflect the goal of helping the elderly maintain an
independent lifestyle. This goal could be achieved by adopting a
formula, to be implemented over a multiyear period, for distributing
title III funds that reflects state needs and that specifically takes
into account the issues of beneficiary and taxpayer equity.
In its deliberations to improve the fairness in the distribution of
title III funds, the Congress may wish to consider the six allocation
formulas we developed. Each formula option would improve the current
title III funding process by permitting all states to finance
comparable services for their respective elderly populations
experiencing barriers to independent living.
AGENCY COMMENTS AND OUR
EVALUATION
------------------------------------------------------------ Letter :8
In its December 22, 1993, review of a draft of this report, HHS did
not offer comments on the specific formula options we put forward for
congressional consideration because it reviewed them as policy issues
addressed to the Congress and not to AoA or HHS officials. HHS did,
however, comment on the data sources we used to reflect state
differences in (1) potential caseloads, (2) the cost of providing
services, and (3) state funding capabilities (see app. VIII for
comments from HHS).
HHS believes that funding formulas should be based on data that are
reliable, from independent (preferably federal) sources, and
regularly updated. In HHS's view, some of the data elements we used
in our formula options do not meet these criteria. We agree with
HHS's criteria but disagree with its conclusion. In fact, the data
we used in our formula options are reliable statistical measures
collected by federal sources--the Bureau of the Census, the Labor
Department's Bureau of Labor Statistics (BLS), the National Center
for Health Statistics, the Department of Housing and Urban
Development, and the Department of the Treasury--and they can be
periodically updated.
In regard to measuring potential caseloads, HHS notes that our
measure is derived from studies that examine the relationship between
Activities of Daily Living (ADL) and Instrumental Activities of Daily
Living (IADL)\7 and demographic factors such as age, sex, race, and
poverty. HHS raised the issue that because these studies rely on
surveys conducted in the mid-1980s, subsequent demographic trends
"may" have rendered our caseload indicator invalid.
We believe HHS's concern on this issue is overly cautious. Our
analysis identifies the very old, females, minorities, and the poor
as experiencing greater disabilities in terms of being able to
perform activities necessary to maintain independent lifestyles.
These are the same population groups the Older Americans Act itself
identifies as having high social and economic needs and instructs the
states to use in allocating federal funds among substate service
areas. Thus, our analysis serves to validate what is already
embodied in the current program. Consequently, we believe our
analysis sufficiently identifies the high-need groups within the
over-60 population with the greatest social and economic needs.
Although we believe our population measure reflects the intended
populations in the act, we would endorse any measure adopted by AoA
that further improves the accuracy and reliability of the formula's
potential caseloads measure.
HHS also notes, as we did in our draft report, that the prevalence of
ADL and IADL disabilities among various demographic groups may change
over time. Each of the demographic factors (population by age group,
minorities, the poor, and females) we used are obtainable from the
Bureau of the Census and can be updated on a regular basis.
Consequently, to the extent that a state's needy population changes
because of the changing composition of these demographic groups, the
formulas we have proposed for congressional consideration will
reflect changing demographic trends, contrary to HHS's opinion.
Although HHS does not say so explicitly, it may be raising a concern
about the weights we have placed on each of the demographic groups so
that they reflect the geographic pattern of ADLs and IADLs. We
recognized this concern in our report where we stated the view that
the weights given the various demographic groups should be
periodically reevaluated. Even if this reevaluation were done,
however, we do not believe new data would contradict our findings of
higher disability prevalence rates among the very old, poor,
minorities, and females. For example, we believe it highly unlikely
that a more current study would find that the poor began to
experience a lower prevalence of ADL and IADL disabilities than the
nonpoor, thus invalidating the use of poverty as an indicator of
potential caseload. At most, such an analysis would much more likely
call for some marginal changes in the relative weights given each.
Finally, we would like to point out that the current interstate
funding formula (using the general population aged 60 and over) does
not reflect the high-need demographic groups identified in the act.
Our review of the literature shows that there is a higher prevalence
of ADL and IADL disabilities among individuals with the greatest
social and economic needs. Therefore, HHS's concern regarding our
need indicators is more appropriately a criticism of the current
formula. In this regard, the current formula does not reflect
changes in high-need populations both across states at a given point
in time and over a period of years.
HHS also voiced its concern over the limitations of our method of
measuring interstate service cost differences. However, HHS did not
recognize that the current formula, by excluding a cost factor,
implicitly assumes that there are no differences in the cost of
providing OAA services across all states and that service cost
differences do indeed exist. For example, the cost of food (which is
over two-thirds of title III expenditures) is higher in Alaska and
Hawaii than it is for the rest of the country. These service cost
differences are reflected in other federal programs such as food
stamp allocations. Additionally, BLS data presented in our report
reveal that the labor costs for food preparation also differ across
states.
Because we were unable to identify direct cost data or studies
specifically on OAA services across all states, we used a methodology
that we believe is reasonable and conservative. Assumptions were
made to guard against overstating interstate cost differences. Our
report fully discusses the assumptions we made in developing the cost
index and its methodological limitations. In addition, we present
formula options both with and without the cost index in order to
present a full range of alternatives, should the Congress not want to
adopt the cost index we developed. A similar cost measure is
currently included in the formula distributing the Alcohol, Drug
Abuse, and Mental Health Services block grant.
HHS also commented that our indicator of a state's capacity to fund
program services from state sources (the Treasury Department's Total
Taxable Resources (TTR)) may reflect a state's expenditures and
efforts in providing title III services. Unfortunately, HHS does not
state the basis for its belief. In response, we can only point out
that TTR in no way reflects a state's program choices or practices.
This measure neither rewards nor penalizes a state's expenditures and
program commitments. TTR reflects income received by state residents
as well as nonresident income produced within the state and,
therefore, potentially subject to state taxation. Fiscal capacity is
included in our formula options so that the Congress can consider the
equalization of tax burdens as an additional goal for the program.
Fiscal capacity measures are already used in major federal funding
programs such as Medicaid, Foster Care, and Vocational Education.
HHS also makes the observation that the issues addressed in this
report regarding the federal formula are equally applicable to the
formulas states must develop for allocating federal assistance among
substate service areas. We agree and would point out that HHS is
required by law to approve state formulas. Therefore, we believe
that the equity criteria developed in this report can provide HHS
with stronger criteria that would assist it in analyzing and
approving state formulas for allocating title III funds among
substate service areas.
Finally, HHS noted its disagreement with a recommendation in our
recent report, Older Americans Act: Title III Funds Not Distributed
According to Statute. In that report, we concluded that AoA does not
correctly calculate state grants under the existing statute. In this
report, we took the same position because it affected the way we
implemented the equity criteria. We continue to believe AoA's
allocation method is inconsistent with the act's basic requirement
that the distribution of funds among the states be proportional to
their elderly populations, except that no state is to get less than
the minimum established by law. The distorting effects of AoA's
existing allocation method are that states not affected by the
statutory minimums receive unequal allocations per elderly person,
and states with more rapidly growing populations are underfunded.
We did our work between January 1992 and November 1993 in accordance
with generally accepted government auditing standards.
--------------------
\7 See app. II for a further description of Activities of Daily
Living and Instrumental Activities of Daily Living.
---------------------------------------------------------- Letter :8.1
We will send copies of this report to appropriate congressional
committees and subcommittees, the Secretary of HHS, and the
Commissioner of AoA. Copies will also be made available to others on
request.
If you or your staff have any questions about this report, please
call me on (202) 512-7215, or contact Jerry Fastrup, Assistant
Director, on (202) 512-7211. Other major contributors to this report
are listed in appendix IX.
Sincerely yours,
Joseph F. Delfico
Director, Income Security Issues
DESCRIPTION OF EQUITY-BASED
FORMULAS
=========================================================== Appendix I
To develop equity standards, we drew from economic and social science
literature and previous GAO reports on federal formula grant programs
(see Related GAO Products). Based on this review, we arrived at two
useful standards. We call the first standard "beneficiary equity."
It would distribute federal funds so that all states could purchase a
comparable level of title III services under the Older Americans
Act\1 for elderly persons at risk. This criterion means that dollars
would be distributed according to two indicators: (1) the potential
number of elderly persons in need, especially those with economic and
social needs; and (2) the cost of providing title III services.
We call the second standard "taxpayer equity." It recognizes that
states finance a significant percentage of benefits from state
resources. This criterion therefore evaluates the distribution of
federal funds from the vantage point of state taxpayers.
Specifically, it considers the degree to which states are able to
finance a comparable level of services with comparable burdens on
state taxpayers. This second standard is broader than the first one,
including the two indicators used in the first standard (the number
of potential beneficiaries and the cost of services) plus a measure
of each state's capacity to fund title III services from its own
resources.
Implementing the first of these equity standards--beneficiary
equity--requires that funds be distributed based on two possible
factors: (1) potential caseloads, which reflect the size of the
at-risk population, (those elderly most likely to need title III-type
services) and (2) the cost of providing title III services (the cost
of personnel, building space, and other materials necessary to
deliver services to those in need). Implementing the second equity
standard--taxpayer equity--builds upon the first standard's
components of potential caseloads and service costs by adding a third
component, namely, states' abilities to fund services from state
financial resources.
The indicators used to represent potential caseloads are discussed in
appendix II, the proxy for the cost of providing title III services
is discussed in appendix III, and the indicators used to reflect
states' abilities to fund title III-type services from state
resources are discussed in appendix IV. Appendix V evaluates the
current distribution of title III funding against these criteria,
appendix VI presents several options for implementing these criteria,
and appendix VII shows the funding effects of implementing a new
formula over a 5-year transition period.
In this appendix we describe how each of our two equity standards
incorporates two of the need factors (potential caseloads and cost)
and how the taxpayer equity standard adds the third factor (financing
capacity). However, as noted earlier, both standards cannot be
achieved at the same time. For example, if equal funding for elderly
beneficiaries is provided, it means taxpayers in poorer states would
have to bear higher tax burdens to finance the average level of
benefits. Conversely, if state taxpayer burdens were equalized,
wealthier states would receive less funding per beneficiary than
poorer states. Because both equity standards cannot be fully
achieved at the same time, we also describe formulas that trade off
the two standards.
--------------------
\1 Older Americans Act of 1965, as amended, P.L. 102-375, section
301.
DESCRIPTION OF THE BENEFICIARY
EQUITY FORMULA
--------------------------------------------------------- Appendix I:1
The basic structure of a formula designed to achieve beneficiary
equity is relatively simple:
Figure I.1: Beneficiary Equity
Formula
(See figure in printed
edition.)
Beneficiary equity only requires that state grants be proportional to
the potential caseload the state must serve, adjusted to compensate
for state differences in the cost of providing services. The term
"" represents a constant of proportionality and depends on
the amount of funds to be distributed among the states and the size
of potential caseloads.
DESCRIPTION OF THE TAXPAYER
EQUITY FORMULA
--------------------------------------------------------- Appendix I:2
The basic structure of a taxpayer equity formula is also simple. It
only requires that an indicator of states' abilities to fund program
services from state resources be added to the beneficiary equity
formula previously described. The state resources indicator is
similar to the federal medical assistance percentage used to
determine state reimbursement rates under the Medicaid program. The
difference is that the state resources indicator is based on need
indicators applicable to title III needs rather than the needs
relevant to the Medicaid program. We therefore refer to this factor
as the Older Americans Federal Percentage (OAFP).\2 A taxpayer equity
formula would take the following form:
Figure I.2: Taxpayer Equity
Formula
(See figure in printed
edition.)
In a taxpayer equity formula, the constant of proportionality, "
," can be interpreted as the national average level of
services measured in real dollars per caseload unit.
--------------------
\2 We describe later how this factor works in more detail.
DETERMINATION OF STATE OAFPS
------------------------------------------------------- Appendix I:2.1
OAFP represents the share of a state's expenditure needs (i.e., the
dollars needed to fund an average basket of title III services) that
is to be funded by both the federal grant and state dollars. To
equalize state taxpayer burdens under title III, this percentage must
be higher in poor states and lower in richer states according to the
following formula:
Figure I.3: Older Americans
Federal Percentage
(See figure in printed
edition.)
The proxy we used to measure state resources will be discussed in
appendix IV. For our purposes here, it is only important to
understand that the state resources index is an index number that is
equal to 1.0 for the state whose taxable resources are equal to the
national average; exceeds 1.0 for states with above average
resources; and is less than 1.0 for states with below average
resources.
The 0.65 weight attached to the state resource index is a parameter
that determines what percentage of a state's expenditure need (the
potential caseloads and cost factors that appear in fig. I.2) will
be counted for formula purposes. For example, a state with average
resources (i.e., a state resource index of 1.0) would have a federal
percentage of 0.35. That is, 35 percent of the state's expenditure
needs would be counted for formula purposes.\3
To offset differences in state tax burdens, the weight on the state
resources index (0.65 in fig. II.3) must be the same as the share of
total program benefits financed from nonfederal resources.\4
Based on the limited data we were able to obtain, we estimate that
approximately 65 percent of program services provided for the elderly
are financed from nonfederal sources.\5 Consequently, we have used a
value of 0.65 for the coefficient on state financing resources.
--------------------
\3 In the Medicaid program, the state resource index is given a
weight of 0.45, which results in federal Medicaid equal to
approximately 55 percent of total program benefits.
\4 See Maternal and Child Health: Block Grant Funds Should be
Distributed More Equitably (GAO/HRD-92-5, Apr. 2, 1992), pp. 55-62,
for a more complete discussion that demonstrates this point.
\5 Staff Compensation Survey, National Association of Area Agencies
on Aging (Washington, D.C.: Sept. 1992).
DESCRIPTION OF THE BALANCED
EQUITY FORMULA
--------------------------------------------------------- Appendix I:3
A beneficiary equity formula would provide equal federal funding per
beneficiary, but result in unequal taxpayer burdens across states.
In contrast, the taxpayer equity formula would equalize state
taxpayer burdens but result in unequal federal funding per
beneficiary, with larger federal grants for states with fewer
resources for funding program benefits. Another equity goal may be a
middle ground, whereby differences in state taxpayer burdens are
reduced but not totally eliminated and the unequal funding required
to completely equalize state taxpayer burdens would be moderated. We
refer to this equity goal as "balanced equity."
An allocation formula that will produce this result can be developed
by introducing an additional parameter into the OAFP, defined in
figure I.3. Introducing a fractional exponent (0<<1) will
move each state's OAFP closer to the national average value of 0.65.
This step would have the effect of moderating the degree to which
federal aid would be targeted to the poorer states, and conversely
provide more funding in wealthier states than is necessary to
equalize state taxpayer burdens.
The exponent "" can be interpreted as a policy parameter.
It controls the degree to which either the beneficiary equity or the
taxpayer equity standard is achieved. If =1, grants will be
targeted to achieve full taxpayer equity. That is, all states will
be able to finance the national average basket of title III services
with comparable burdens on state taxpayers. If the exponent is set
equal to zero, the OAFP reduces to a constant of 0.35 for all states,
and the formula becomes identical to the beneficiary equity
standard.\6 Consequently, choosing values for between zero
and 1 represents a balancing of full taxpayer equity and beneficiary
equity. A formula with a value close to zero will produce
a distribution of grants very close to the beneficiary equity
formula, and will reduce tax burden disparities to a limited degree.
Alternatively, a value of closer to 1 will largely, but not
completely, eliminate tax burden disparities.\7
--------------------
\6 Any number raised to the zero power is by definition equal to 1.0.
Therefore, the expression in brackets reduces to 1 minus 0.65, or
0.35, which can be incorporated into the constant of proportionality
'.
\7 A more complete discussion of partial taxpayer equity appears in
appendix V of GAO's report on the formula used to distribute federal
funding under the Maternal and Child Health program, GAO/HRD-92-5,
April 2, 1992.
A GENERAL GRANT ALLOTMENT
FORMULA
------------------------------------------------------- Appendix I:3.1
Based on this discussion, a general formula that encompasses both
beneficiary and taxpayer equity, as well as various trade-offs
between them, would take the following form:
Figure I.4: Grant Allotment
Formula
(See figure in printed
edition.)
Beneficiary equity would be represented by a formula with
=0, taxpayer equity by a formula with =1, and
partial equity by a formula with 0<<1.
INDICATORS USED TO MEASURE
POTENTIAL TITLE III CASELOADS
========================================================== Appendix II
This appendix describes our method for estimating potential caseloads
for title III services, the first factor in our general formula for
calculating state grant amounts (see fig. II.1).
Figure II.1: Equity-Based
Formula for Calculating State
Grants--Potential Caseloads
(See figure in printed
edition.)
Potential caseload represents the number of people who are
potentially eligible to receive title III services. Our method of
measurement is based on congressional intent as described in OAA and
in previous congressional hearings focusing on improving title III
targeting, as well as work in the fields of public finance and
gerontology. We consulted the gerontology literature that was
germane to the subject. We then described the chosen indicators and
briefly compared them to others that were rejected.
PURPOSES OF TITLE III REFLECT
POPULATION'S NEEDS
-------------------------------------------------------- Appendix II:1
The purpose of the act specifies that title III grants are intended
to
1. secure and maintain maximum independence and dignity,
2. remove individual and social barriers to economic personal
independence for older individuals,
3. provide a continuum of care for vulnerable older individuals, and
4. secure the opportunity for older individuals to receive managed
in-home and community-based long-term care services.
As a means of implementing these goals, targeting title III funds to
high-need groups has been specified in the act since it was amended
in 1978. States are required to consider states' populations of
elderly in the "greatest economic and social need" when allocating
funds to local service providers. The act defines "economic need" as
"income level at or below the poverty threshold established by the
Office of Management and Budget"; and "social need" as being " . .
. caused by non-economic factors which include physical and mental
disabilities, language barriers, cultural, social, or geographical
isolation including that caused by racial or ethnic status which
restricts an individual's ability to perform normal daily tasks or
which threatens such individual's capacity to live independently."\1
--------------------
\1 U.S.C. 42 sec. 3021(1) and 3022(20), (21).
POTENTIAL CASELOADS ARE BASED
ON IMPEDIMENTS TO INDEPENDENT
LIVING
-------------------------------------------------------- Appendix II:2
In order to statistically represent the act's goals, we used two
health-based measures of impediments to elderly
independence--Activities of Daily Living and Instrumental Activities
of Daily Living. They reflect physical and cognitive skills and
independent living limitations and are consistent with the act's
definition of needs. We believe that many impediments to independent
daily living are ultimately connected with health status.
Administration on Aging officials and a financial gerontology expert
expressed concerns that this measure will not reflect those needs
that are not health based, such as cultural isolation. However, they
were unable to identify other statistical data that would reliably
measure non-health-based causes of social isolation. We believe that
this measure of elderly dependence represents the majority of the
act's economic and noneconomic needs.
ADL measures a person's ability to perform "basic" daily activities,
such as eating, bathing, dressing, and toileting. IADL includes
activities such as handling personal finances, meal preparation,
shopping, traveling, housework, using the telephone, and taking
medication. IADL disabilities represent less severe dysfunctions.
Taken together, ADLs and IADLs reflect a full range of activities
necessary for independent living.
TWO SOURCES OF INFORMATION
CONSIDERED
-------------------------------------------------------- Appendix II:3
There are two basic sources of information for estimating the number
of people with impediments to maintaining an independent living
style: national surveys conducted by the National Center for Health
Statistics (NCHS), and the 1990 census. We decided to base our
estimates of need on the national surveys conducted by NCHS. The
reasons we did not use indicators from the 1990 census are discussed
in the following section.
NCHS SURVEY IS BASED ON
SOUND STATISTICAL PROCEDURES
------------------------------------------------------ Appendix II:3.1
The National Health Interview Survey's Supplement on Aging, developed
and maintained by NCHS, is a comprehensive assessment of ADLs and
IADLs.\2 The NCHS survey is an in-person, household survey of 16,148
persons age 55 and older. About 11,500 interviews were obtained for
persons over 65. The NCHS survey includes a series of questions
measuring a person's ability to perform various tasks. It also
contains information on various health-related topics such as family
structure, disability, and health service use. Each respondent is
asked to classify his or her ADL limitations by the level of
difficulty in performing them (e.g., "some," "a lot," "unable").
NCHS maintains and regularly updates this database.
The National Health Interview Survey's Supplement on Aging, however,
does not provide data on the number of people with impediments to
maintaining an independent living style across all states. In order
to calculate the relative sizes of states' potential caseloads, we
had to identify a study that used a reliable estimation technique to
extrapolate NCHS data.
--------------------
\2 The following article reviews the various surveys made on ADLs.
Joshua M. Wiener, Raymond J. Hanley, Robert Clark, and Joan F. Van
Nostrand, "Measuring the Activities of Daily Living: Comparisons
Across National Surveys," Journal of Gerontology: Social Sciences,
Vol. 45, No. 6 (1990), pp. 229-37.
STATE ESTIMATES OF ADL/IADL
POPULATIONS ARE AVAILABLE
------------------------------------------------------ Appendix II:3.2
The Interagency Forum on Aging-Related Statistics\3 and a study by
Elston, Koch, and Weissert\4 estimate the population reporting
difficulty in performing ADLs and IADLs across states. Both studies
are based on National Health Interview Survey data. The Forum on
Aging-Related Statistics uses two variables (age and sex) to predict
the prevalence of ADL/IADL limitations among elderly individuals. It
then applies this relationship (based on the national sample) on a
state-by-state basis. The Elston, Koch, and Weissert study applies
the same general method, but includes minority status and poverty,
besides age and sex, to estimate both ADL and IADL populations.\5
Using data from the 1990 census for age, sex, minority status, and
poverty, we followed the method employed by the Elston, Koch, and
Weissert study to develop current state-by-state estimates of the
prevalence of ADL/IADL impediments. These estimates are shown in
table II.1.\6 The first column reports the estimated number of
elderly individuals with ADL/IADL impediments, column 2 reports the
prevalence rate, and column 3 reports the prevalence rate expressed
as a percentage of the national average rate.
Table II.1
State Populations, Prevalence Rates, and
Indexes for ADL/IADL Dependency, 1990
Number of
individual Prevalence
States s rate Index
---------------------------- ---------- ---------- ------
Alabama 135,040 0.258 105.2
Alaska 4,894 0.219 89.1
Arizona 110,083 0.230 93.7
Arkansas 88,931 0.254 103.5
California 759,933 0.242 98.7
Colorado 78,658 0.239 97.3
Connecticut 107,883 0.242 98.6
Delaware 19,165 0.237 96.7
District of Columbia 22,840 0.293 119.5
Florida 560,909 0.237 96.4
Georgia 166,005 0.254 103.4
Hawaii 33,262 0.266 108.4
Idaho 28,439 0.235 95.5
Illinois 356,025 0.248 101.0
Indiana 169,462 0.243 99.2
Iowa 107,712 0.253 103.0
Kansas 86,600 0.253 103.0
Kentucky 115,194 0.247 100.5
Louisiana 121,268 0.259 105.3
Maine 40,036 0.245 99.8
Maryland 125,898 0.243 99.1
Massachusetts 202,621 0.247 100.8
Michigan 268,545 0.242 98.7
Minnesota 137,100 0.251 102.1
Mississippi 86,770 0.270 110.0
Missouri 181,558 0.253 103.1
Montana 25,348 0.238 97.0
Nebraska 56,813 0.255 103.8
Nevada 27,132 0.213 86.6
New Hampshire 30,115 0.241 98.1
New Jersey 247,865 0.240 97.8
New Mexico 39,015 0.239 97.5
New York 592,751 0.251 102.2
North Carolina 200,296 0.249 101.5
North Dakota 22,873 0.251 102.3
Ohio 340,646 0.242 98.6
Oklahoma 107,462 0.253 103.2
Oregon 92,860 0.237 96.7
Pennsylvania 440,570 0.241 98.1
Rhode Island 36,846 0.245 99.7
South Carolina 98,572 0.248 101.2
South Dakota 25,923 0.253 103.2
Tennessee 155,056 0.251 102.1
Texas 427,381 0.249 101.4
Utah 34,869 0.233 94.7
Vermont 16,185 0.245 99.7
Virginia 163,370 0.246 100.2
Washington 136,167 0.237 96.4
West Virginia 64,885 0.241 98.3
Wisconsin 159,677 0.245 99.9
Wyoming 11,065 0.234 95.5
============================================================
U.S. 7,668,575 0.245 1.0
------------------------------------------------------------
Our estimate of need, based on the prevalence rate of ADL/IADL
impediments, shows that these rates vary across states by relatively
small amounts. State prevalence rates range from a low of .213 in
Nevada--13 percent below the national average--to as much as .293 in
the District of Columbia--19.5 percent above the national rate.
Forty-three states are within +5 percent of the national average
rate. The national rate of ADL/IADL dependence for the
noninstitutionalized population over age 65 is estimated to be a rate
of .245 of the over-65 population, shown in the last row of the
table.
The method used by Elston, Koch, and Weissert is superior to previous
studies for two basic reasons. First, the minority status and
poverty variables included in their analysis are specifically
referenced in the act itself. Second, and more importantly, these
variables were found to be important predictors of the prevalence of
ADL/IADL disabilities. Additionally, AoA statistics on program
participation show that minorities and low-income individuals
participate at a higher proportionate rate than would be expected
from their share of the general population.\7
--------------------
\3 "Synthetic State Estimates of the Health of Older Persons:
Synthetic Estimation of State Health Characteristics for the
Population 65 Years of Age and Over," Interagency Forum on
Aging-Related Statistics (Chicago: University of Illinois, Jan.
1992).
\4 Jennifer M. Elston, Gary G. Koch, and William G. Weissert,
"Regression-Adjusted Small Area Estimates of Functional Dependency in
the Non-institutionalized American Population Age 65 and Over,"
American Journal of Public Health, Vol. 81, No. 3 (Mar. 1991), pp.
335-43.
\5 The Elston, Koch, and Weissert study examined an extensive array
of possible predictors of ADL/IADL dependency. It investigated such
variables as (1) age, (2) gender, (3) race, (4) income, (5) poverty,
(6) the number of nursing home and hospital beds, (7) the prevalence
of physicians, (8) the percent of the poverty population covered by
Medicaid, (9) mortality, (10) climate conditions, (11) rural/urban
population, and (12) population density. It found that ADL and IADL
dependency is strongly associated with four variables: minority
status (white and nonwhite), five age groups (65 to 69, 70 to 74, 75
to 79, 80 to 84, and 85 and over), poverty, and gender (females and
males). The other variables (hospital beds, mortality, etc.) did not
provide any additional explanation of ADL/IADL dependency once the
four major variables were taken into account. In summary, the four
demographic variables (age, sex, minority status, and poverty) are
strong predictors of ADL/IADL limitations to self-care and
independence.
\6 Updating the Elston, Koch, and Weissert model assumes the
relationship between ADL/IADL dependency and the demographic
variables associated with ADL/IADLs remains stable over time. If the
relationship does change, for example, the prevalence of ADL/IADL
dependency of one subgroup diminishes or increases relative to
another, the revised estimates will under- or overpredict ADL/IADLs
across states.
\7 "National Summary of State Program Performance Reports for
Programs for the Elderly Authorized Under Title III of the Older
Americans Act: Federal Fiscal Year 1990," Administration on Aging
(Washington, D.C.).
CENSUS DATA REJECTED
------------------------------------------------------ Appendix II:3.3
We prefer the ADL/IADL measure based on the NCHS survey and the
Elston, Koch, and Weissert method over the census' mobility and
self-care measures for several reasons. First, the NCHS survey only
applies to the noninstitutionalized population, whereas the census
estimates are for the entire population,\8 institutionalized and
noninstitutionalized. Second, the ADL/IADL measure is a more
comprehensively defined measure for elderly dependency than the
census measure. Third, NCHS collects the data using an interviewer,
which improves the reliability that the respondent understands each
question and, thus, improves the quality of his or her responses. In
contrast, the Census Bureau collects its data through a self-reported
questionnaire. Finally, the census' mobility and self-care data were
collected for the first time in the 1990 census and may not be
collected in the next census. As a consequence, at best, current
mobility and self-care data may only be available once every 10
years, and, at worst, be unavailable for future years.
The ADL/IADL estimates obtained using the Elston, Koch, and Weissert
method also have a major drawback, which is that these states'
estimates are based on the relationship between the 1984 ADL/IADL
populations and their socioeconomic characteristics. Our estimates
for 1990 depend on the constancy of this relationship over time.
However, we believe the relationship between socioeconomic
characteristics and ADL/IADLs is reasonably stable and not subject to
large change over time.
We also analyzed the Census data and found that the data do not
appear to be consistent with previous research results. We analyzed
the Census data to determine if the data are (1) similar to ADL/IADL
estimates based on NCHS surveys and (2) consistent with previous
research regarding the relationship between demographic
characteristics and ADL/IADL impediments. Our analysis of census
data is described in further detail at the end of this appendix.
--------------------
\8 The census population excludes institutionalized inmates in
prisons.
DETERMINATION OF WEIGHTS FOR
DEMOGRAPHIC FACTORS USED TO
MEASURE NEEDS
-------------------------------------------------------- Appendix II:4
The Elston, Koch, and Weissert method of estimating state ADL/IADL
populations based on age, sex, minority status, and poverty cannot be
readily incorporated into an allocation formula because of its
complexity.\9 We therefore employed a simplified method that very
nearly replicates the Elston, Koch, and Weissert state estimates.
The result of our simplification is that estimates of each state's
share of the ADL/IADL population can be expressed as a weighted sum
of each state's respective shares of (1) five age groups, (2) female
populations, (3) minority populations, and (4) poverty populations.
Estimates of each state's share of need would be expressed in the
form of the following formula:
Figure II.2: Formula for State
Shares of ADL/IADL Populations
(See figure in printed
edition.)
To determine the weight each factor should receive (i.e., wi), we fit
a regression model using estimates of ADL/IADLs based on the Elston,
Koch, and Weissert methodology as the dependent variable and age,
sex, poverty, and minority status as independent variables.
Before estimating the model, we first divided the equation in figure
II.2 by each state's share of the over-60 population. Expressing
each variable relative to its share of the over-60 population avoids
the problem of multicolinearity among the regressors. State shares
of each of the independent variables are likely to be highly
correlated with one another since they all reflect the size of the
state (e.g., California will always have a large percentage of each
variable and Rhode Island a small percentage because of the
difference in their sizes). Making this adjustment produces the
following regression equation:
Figure II.3: Regression
Equation for State's ADL/IADL
Population
(See figure in printed
edition.)
The intercept, b, can be interpreted as the proportion of the index
attributable to the population of white, nonpoor males aged 65 to 69.
This fact can be seen by noting that the intercept is the value of
the dependent variable when all independent variables in the model
are equal to zero. That is, if there were no residents aged 70 and
over, poor, nonwhites, or females, the state's elderly population
would be composed of only nonpoor, white males aged 65 to 69.
Because the intercept has this interpretation, the population 65 to
69 is not explicitly included in the model to avoid double counting.
On the other hand, the regression coefficients for the variables
represent the increase in weight for each of the subgroupings. So,
for example, the coefficient for the 70- to 74-year age group,
b70-74, is the increase in weight over and above the weight for the
60 to 69 age group, represented by the intercept.
Data for each of the explanatory variables are shown in table II.2.
States differ significantly with respect to some dependent elderly
demographic groups, and very little with respect to others. For
example, females and the percent of the population between 70 and 79
are more or less uniformly distributed across states, while minority
populations are much more concentrated in some states than others.
This fact can be seen by noting that females and the 70 to 79 age
group have the smallest standard deviations (see top row of table
II.2), while minority status has the largest.
Table II.2
Indexes of State Population, by Age,
Poverty Status, Race, and Gender
States 65-69 70-74 75-79 80-84 85+ Poverty Nonwhite Female
---------- ------ ------ ------ ------ ------ -------- -------- --------
Standard 7.9 3.0 3.8 8.6 15.5 39.1 129.4 3.6
deviation
Alabama 99.4 99.3 103.0 102.8 94.1 187.5 195.3 101.9
Alaska 133.0 103.5 83.0 68.6 56.7 59.4 229.3 88.4
Arizona 103.4 106.0 101.0 93.3 79.9 84.4 61.1 95.0
Arkansas 94.1 99.7 104.3 107.4 102.0 178.9 115.3 99.0
California 104.0 100.0 98.0 95.6 96.8 59.4 142.8 98.1
Colorado 104.3 98.8 95.8 96.7 101.5 85.9 51.5 98.4
Connecticu 97.3 101.6 100.0 98.5 106.9 56.3 44.3 101.1
t
Delaware 109.0 102.2 93.6 90.3 89.7 78.9 107.6 99.3
District 98.6 99.0 102.7 99.8 102.2 134.4 635.5 105.9
of
Columbia
Florida 96.7 103.6 104.6 102.0 89.9 84.4 58.4 96.0
Georgia 103.0 101.5 100.3 97.7 88.7 159.4 193.9 103.2
Hawaii 112.7 103.4 92.7 84.3 84.4 62.5 668.2 87.8
Idaho 96.8 102.4 103.2 102.1 95.3 89.8 15.5 94.5
Illinois 98.0 99.6 101.0 101.0 104.2 83.6 103.3 101.7
Indiana 100.3 98.8 98.8 100.0 104.5 84.4 54.5 101.4
Iowa 88.8 95.8 101.2 110.7 131.5 87.5 9.9 101.0
Kansas 91.5 95.0 100.7 111.4 125.1 93.8 40.6 100.5
Kentucky 99.9 98.1 100.8 102.2 100.7 160.9 55.1 100.9
Louisiana 102.7 99.3 99.5 99.8 94.4 188.3 226.4 101.1
Maine 96.1 97.5 99.0 106.2 113.2 109.4 3.7 100.5
Maryland 107.6 101.4 95.0 92.3 91.1 82.0 156.8 100.9
Massachuse 95.1 99.0 99.9 103.6 114.2 73.4 33.5 103.5
tts
Michigan 102.9 101.1 97.8 95.5 97.8 84.4 101.1 99.8
Minnesota 90.4 96.1 101.2 109.1 127.7 94.5 14.1 99.5
Mississipp 96.0 97.8 103.9 107.3 102.1 229.7 264.0 101.7
i
Missouri 94.3 95.4 101.8 109.6 114.8 115.6 69.2 101.6
Montana 94.3 104.2 102.6 100.9 101.7 97.7 23.0 94.9
Nebraska 88.7 93.9 101.2 113.9 132.8 95.3 21.1 100.3
Nevada 121.3 109.2 90.2 73.5 59.3 75.0 59.1 90.5
New 98.2 99.1 98.0 103.3 107.8 79.7 4.7 100.5
Hampshire
New Jersey 101.9 102.2 99.6 96.1 93.9 66.4 87.5 101.0
New Mexico 105.1 101.0 99.3 95.0 88.5 128.9 114.8 94.9
New York 98.7 98.0 99.7 102.6 106.5 93.0 120.0 102.3
North 105.5 101.3 98.6 94.6 88.2 152.3 166.8 102.0
Carolina
North 84.7 97.0 106.4 115.9 125.2 114.1 12.0 96.1
Dakota
Ohio 102.4 100.5 97.5 97.1 99.5 83.6 74.7 101.2
Oklahoma 95.8 95.4 102.1 109.3 109.6 139.8 93.8 100.1
Oregon 97.0 101.4 102.4 100.5 100.6 78.9 23.2 96.8
Pennsylvan 99.8 102.4 100.8 98.1 95.3 82.8 62.2 101.6
ia
Rhode 96.9 99.7 100.6 101.5 107.9 90.6 24.9 103.2
Island
South 109.3 104.2 96.3 90.0 78.6 160.2 220.7 101.6
Carolina
South 89.0 95.5 101.3 110.3 132.3 121.1 23.7 97.2
Dakota
Tennessee 100.3 98.8 101.1 102.8 96.4 163.3 112.3 101.7
Texas 102.8 97.2 99.3 100.7 98.4 143.8 128.6 99.5
Utah 100.3 103.0 99.6 100.1 92.1 68.8 25.1 95.6
Vermont 96.3 97.2 98.9 104.9 115.3 96.9 3.3 100.1
Virginia 106.4 101.1 96.2 94.2 91.1 110.2 159.7 101.0
Washington 100.3 101.5 99.8 97.3 99.3 71.1 43.6 96.8
West 100.2 99.6 101.9 100.4 96.0 130.5 32.0 100.6
Virginia
Wisconsin 92.7 97.9 102.9 106.4 115.7 71.1 21.4 99.2
Wyoming 102.9 99.7 98.5 97.2 97.8 83.6 25.7 95.6
--------------------------------------------------------------------------------
--------------------
\9 Its method of estimating ADL/IADLs requires (1) the solving of two
nonlinear equations to estimate the 20 ADL/IADL prevalence rates and
(2) the breaking down of the elderly population for the states into
20 subgroupings for each of the 50 states and the District of
Columbia.
REGRESSION RESULTS
------------------------------------------------------ Appendix II:4.1
The results of estimating the model are shown in table II.3. The R\2
for the regression model is 0.99, which indicates that the linear
model very closely approximates the more complex model by Elston,
Koch, and Weissert.\10 The regression coefficients have the expected
positive signs for each of the variables.\11
Table II.3
Regression Results for ADL/IADL State
Population Estimates on State
Demographic Variables
Regression Beta
Independent variables coefficients coefficients
-------------------------------- ------------ ------------
Intercept 0.30
Population70-74 0.03 0.02
Population75-79 0.08 0.07
Population80-84 0.09 0.17
Population85+ 0.15 0.49
Nonwhite 0.04 0.84
Poverty 0.03 0.20
Female 0.27 0.21
------------------------------------------------------------
As stated earlier, the intercept is interpreted as that portion of
the index attributed to the 65- to 69-year-old population. As the
intercept term, this value is also the base upon which the values for
the other subgroupings are calculated. That is, the coefficient for
the 70- to 74-year-old population, 0.03, is added to the intercept
(or base value) and can be interpreted as the "incremental" weight
for nonpoor, white males aged 70 to 74. The regression coefficients
for the remaining variables have similar interpretations, that is,
they are incremental weights.
Using this model, the older age groups are given progressively
greater weight in our estimate of potential caseloads. This result
accords with the greater prevalence of ADL/IADL dependency in older
age groups, as identified by Elston, Koch, and Weissert. Similarly,
the weights for females, minorities, and the poor are arrived at in
the same manner. The incremental weight given each indicator also
accords with the results reported by Elston, Koch, and Weissert and
is consistent with the act's guidance for states to target services
to the poor and minorities because they are believed to experience a
greater need for services.
By virtue of the relatively large coefficient on females in the
model, one might conclude that this factor is the most important
determinant of the potential caseload. However, this conclusion
would be unwarranted. The reason is that the states differ very
little in terms of the proportion of females in their total
populations. So, even though the female coefficient is quite large
compared to the other variables, the end result is that the female
variable has little effect on state estimations of ADL/IADL
dependency rates.
To determine the relative importance of each variable, we report the
beta coefficient associated with each variable. This statistic takes
the variance of each variable into account.\12 That is, the
regression coefficient is adjusted for the amount of variation in the
variable itself. By comparing beta coefficients, one can determine
which variables have greater influence in estimating each state's
dependency rate. The beta coefficients reported in table II.3
indicate that minority status (with a coefficient of 0.84) is the
single most important variable in determining state dependency rates.
The next most important variable is the population over 85, followed
by females and poverty rates. The least important variables are the
70 to 74 and the 75 to 79 age groups.
The importance of taking the variability of each variable into
account is best illustrated by comparing the coefficients of poverty
and females. The regression coefficient for poverty is only 0.03
compared to 0.27 for females. However, since states differ very
little in terms of the share of the females but significantly with
respect to their poverty rates, both variables have about the same
impact in determining state dependency rates.
--------------------
\10 Higher values for the R\2 statistic indicate greater accuracy.
The maximum value for the R\2 statistic is 1, which indicates perfect
prediction.
\11 We do not report t-statistics for this model because this
procedure is only identifying a simpler functional form to
approximate the Elston, Koch, and Weissert model. Because these
variables are statistically significant in their model, they, by
definition, are significant variables in our simplified model.
\12 The beta coefficients are computed by multiplying the regression
coefficients by the ratio of the standard deviation of the
independent variable to the standard deviation of the dependent
variable, the ADL/IADL index. Robert S. Pindyck and Daniel L.
Rubinfeld, Econometric Models and Economic Forecasts (New York:
McGraw-Hill Book Company, 1976), pp. 71-2.
FORMULA FOR CALCULATING
NEEDS FROM DEMOGRAPHIC DATA
------------------------------------------------------ Appendix II:4.2
The estimated regression coefficients reported in table II.4
represent the weights needed to calculate each state's share of need
as defined in figure II.3. These weights yield the following formula
for calculating need:
Table II.4
Potential Caseloads Factor: Weights Used
in Estimating State Prevalence Rates of
ADL/IADL
Need factor: state share of Weight
-------------------------------------------------- --------
Pop. over 60 0.30
Pop. 70-74 0.03
Pop. 75-79 0.08
Pop. 80-84 0.09
Pop. 85+ 0.15
Females 0.27
Nonwhite 0.04
Poverty 0.03
------------------------------------------------------------
SENSITIVITY ANALYSIS
------------------------------------------------------ Appendix II:4.3
Next, we examine whether estimates of state ADL/IADLs can be further
simplified by eliminating one or more of the demographic variables
from the model. Doing so would simplify the ultimate formula without
sacrificing the accuracy of estimating needs. To do this, we
reestimated the model deleting selected demographic variables and
examined the extent to which the resulting model reflects ADL/IADL
estimates.
We found that all the demographic variables included in the full
model are important predictors of state ADL/IADL dependency rates.
However, either poverty or females could be excluded with little loss
in accuracy, but eliminating both would have a significant impact.
Minority population and the older age groups, especially those over
age 85, are the most important factors needed to predict state
dependency rates.
1990 CENSUS DATA NOT A GOOD
PREDICTOR OF MOBILITY
LIMITATIONS
-------------------------------------------------------- Appendix II:5
The following describes in greater detail our analyses of the 1990
census population data on mobility and self-care limitations. The
first analysis investigates if the census data are consistent with
estimates based on the NCHS surveys. To do this, we examined the
correlation between state estimates of ADL/IADL using the Elston,
Koch, and Weissert method and two census measures: mobility
limitations and self-care limitations.\13 Specifically, we calculated
correlation coefficients between the two census measures with ADLs
and IADLs separately, and together. The estimates are shown in table
II.5.
The only statistically significant correlations are between the
census self-care variable and ADL/IADL and the total (ADL plus IADL)
measures. However, even in the case of the highest correlation
(census' self-care measure and our estimate for IADLs), only 30
percent of the interstate variation in one measure is captured in the
other.\14 Overall, the correlation is surprisingly low for data that,
on the surface, appear to measure similar things. For example, ADL
dysfunctions include the questions on mobility and self-care.
Table II.5. Correlation Between Census
Data for Mobility, Self-Care, and ADLs
and IADLs
Census estimates ADLs IADLs Total
------------------------------ -------- -------- --------
Mobility 0.17 0.23 0.21
Self-care 0.33\a 0.55\a 0.48\a
============================================================
Total 0.21 0.31 0.28
------------------------------------------------------------
\a Significant at a 5-percent level of confidence.
We have also analyzed the census data with respect to the demographic
and poverty variables that were used in the Elston, Koch, and
Weissert study. Specifically, we separately regressed the census
estimate for mobility limitations and self-care limitations against
each age group, poverty, nonwhite, and female populations.\15 The
focus of this analysis was to determine if the data are consistent
with prevailing research on aging, that is, do particular
subgroupings of the elderly have more limitations than others?
The regression results, reported in table II.6, show that we did not
obtain results similar to prior research findings. That is, the
census mobility and self-care measures at the state level do not
display the associations with demographic characteristics that
previous research has shown with respect to ADLs and IADLs. The four
major demographic variables (age, sex, minority status, and poverty)
do predict the census' measures of mobility and self-care reasonably
well; the R-squared for the regression is 0.86. However, the
regression coefficients for many of these variables have the opposite
sign of what would be expected based on prior research. For example,
the regression coefficients for the age groups 70 to 74 and 85 and
over, and the nonwhite population have negative coefficients. This
fact implies that the nonwhite and very old individuals have fewer
mobility and self-care problems than the younger age groups or the
white race.\16 This result contradicts existing research, which
concludes that older age groups and nonwhites have greater ADL/IADL
limitations than younger age groups and the white population.
Table II.6
Regression Analysis of Census Mobility
and Self-Care Data
Regression
coefficien t-
Independent variables t statistic
------------------------------------ ---------- ----------
Intercept 2.01 2.50
Population70-74 -1.38 -1.72
Population75-79 0.13 0.17
Population80-84 0.36 0.57
Population85+ -0.75 -3.54
Nonwhite -1.07 -1.59
Poverty 0.26 7.42
Female 0.40 1.47
------------------------------------------------------------
In conclusion, we decided not to use Census data for our indicator of
need. The ADL/IADL measure better matches the potential caseload for
title III services and also appears more reflective of the
socioeconomic characteristics of title III program participants.
--------------------
\13 1990 Census of Population and Housing, U.S. Department of
Commerce, Economics and Statistics Administration, Bureau of the
Census (Washington, D.C.: Mar. 1991).
\14 Squaring the correlation coefficient measures the amount of
variation in one data series that is present in the other.
\15 We also regressed the mobility and self-care limitations against
the same set of variables, and achieved similar results. The data
are expressed as index numbers for each state relative to the state's
65-and-over population.
\16 Only the regression coefficient for the eldest population, 85 and
above, is statistically significant at the 5-percent level. The
coefficients for the other two variables are not statistically
significant.
MEASURING STATE COST DIFFERENCES
========================================================= Appendix III
This appendix describes our method for measuring the cost index
component of the equity-based formula (see fig. III.1).
Figure III.1: Equity-Based
Formula for Calculating State
Grants: Cost Index
(See figure in printed
edition.)
An equity-based allocation formula would distribute federal grant
dollars such that states would be able to purchase a comparable level
of services. Ideally, such a distribution would compensate states
that have higher costs of services that are beyond their direct
control. For example, states where wage rates are higher because the
general cost of living is high must pay more for workers providing
title III services.
The cost index is constructed using available information on the
services provided by AoA and from the pertinent research literature.
Because scant data exist on the cost of providing title III services,
we have had to use some judgment in order to construct the index.
The index is broad-based and is not related to actual costs from
title III programs. We believe the index is a reasonable proxy that
reflects state differences in the cost of providing title III
services.
BACKGROUND
------------------------------------------------------- Appendix III:1
There are several reasons for using a broad-based index of title III
services rather than an index of actual state costs. A cost index
based on actual state performance could have the perverse effect of
rewarding states that inefficiently administer the program. For
example, an inefficiently managed program in a state could result in
a higher per unit cost of delivering services, and consequently
result in a larger grant. If states can directly control the cost
factor that affects their grant size, states could increase their
federal funding by operating at inefficiently higher cost levels.
Such a cost factor would weaken the incentive for grantees to operate
their programs in a cost-effective manner. Thus, the issue becomes
one of finding an appropriate cost proxy that reflects "real"
differences among states in terms of the cost of resources necessary
to provide title III services but not directly influenced by the
grantees' own actions. On the practical side, choosing a suitable
proxy is far from clear, and even then the choices made will only
approximate "true" cost differences among the states.
Because any cost index will only be an approximation of true cost
differences, the index we used is based on what we believe are
reasonable assumptions that avoid overstating or exaggerating
interstate cost differences. Although our reasoning is conservative,
we believe our measure allows us at least partially to recognize real
cost differences among the states and, at the same time, avoid
introducing undesirable incentives into the grant formula.
OVERVIEW OF APPROACH TO COST
MEASUREMENT
----------------------------------------------------- Appendix III:1.1
To identify suitable proxies for our cost index, we analyzed AoA
program expenditures for 2 recent years, fiscal years 1989 and 1990.
Specifically, we reviewed title III program expenditures and
classified them into three broad categories: meals, transportation,
and miscellaneous. We then identified the major inputs involved in
the provision of these services. Each input factor was weighted and
combined into an overall cost index for the states. Finally, the
overall cost index was adjusted for use of volunteer labor in the
provision of services to the elderly.
OAA SERVICES CAN BE GROUPED
INTO THREE MAJOR CATEGORIES
------------------------------------------------------- Appendix III:2
AoA identifies about 30 types of services provided under OAA. In
table III.1 we list the various types of services provided, the
amount of federal expenditures for fiscal years 1989 and 1990, and
the percent distribution of expenditures by function.\1
Further breakdown of expenditures by input factors, such as
personnel, equipment, office space, etc., is unavailable.
The information in table III.1 reveals that the single most important
use of the federal grant is for the preparation of meals for the
elderly; almost 60 percent of the federal grants in fiscal years 1989
and 1990 were spent on meals: congregate and in-home.
Transportation is the second most important type of service provided
under OAA.\2 The remaining services are quite varied and comprise
slightly less than 30 percent of federal expenditures; none of them
constitutes more than 4 percent of federal expenditures. The
expenditures appear to be mainly for personal services.
Table III.1
Title III Spending by Service Category
for Fiscal Years 1989 and 1990
(Dollars in thousands)
Averag
Service category FY 89 FY 90 e Value Cumulative
------------------------------------ ------ ------ ------ ------ ----------
Meals, congregate $233,6 $246,4 $240,0 41.41 41.41
72 59 62
Meals, in-home 101,47 106,86 104,16 17.97 59.38
5 0 8
Transportation 67,746 68,383 68,064 11.74 71.12
Miscellaneous
--------------------------------------------------------------------------------
Information 20,720 22,878 21,799 3.76 74.88
Housekeeping 19,378 20,458 19,918 3.44 78.31
Personal care 17,462 17,317 17,389 3.00 81.31
Legal service 16,429 17,797 17,113 2.95 84.27
Outreach 15,549 13,339 14,444 2.49 86.76
Chore 11,402 9,757 10,579 1.82 88.58
Recreation 9,845 9,544 9,694 1.67 90.25
Assessment 7,659 11,465 9,562 1.65 91.90
Advocacy 8,769 8,640 8,704 1.50 93.41
Education 6,993 6,497 6,745 1.16 94.57
Follow-up 4,559 4,685 4,622 0.80 95.37
Counseling 3,484 3,913 3,698 0.64 96.00
Visiting 3,477 3,279 3,378 0.58 96.59
Telephoning 3,250 3,182 3,216 0.55 97.14
Repair/maintenance 2,838 2,891 2,864 0.49 97.64
Material aid 3,063 2,474 2,768 0.48 98.11
Treatment 2,575 2,487 2,531 0.44 98.55
Escort 2,027 2,264 2,146 0.37 98.92
Diagnosis 2,018 1,976 1,997 0.34 99.26
Placement 1,126 1,312 1,219 0.21 99.47
Supervision 727 851 789 0.14 99.61
Shopping 687 823 755 0.13 99.74
Guardianship 622 560 591 0.10 99.84
Discount 465 403 434 0.07 99.92
Interpreting 299 380 340 0.06 99.98
Letter-writing 117 150 134 0.02 100.00
================================================================================
Total $568,4 $591,0 $579,7 100.00 100.00
33 24 25
--------------------------------------------------------------------------------
Note: Totals may not add because of rounding.
--------------------
\1 The expenditures reported in table III.1 are federal expenditures
and do not include expenditures made by state and local governments
for the elderly. Expenditures by state and local governments by
function are not available.
\2 Included under transportation services is the cost of transporting
the elderly to congregate meals.
INPUTS USED TO PROVIDE MEALS
----------------------------------------------------- Appendix III:2.1
Expenditures for meals are divided into two input components: food
and labor. To estimate the cost for food, we use information from
the Department of Agriculture (USDA) to quantify cost differences for
food among the states.\3 In table III.2, we report USDA's food cost
index. According to USDA, the states in the continental United
States have comparable food costs, while Alaska and Hawaii's food
costs are, respectively, 68 and 39 percent higher than those of the
continental United States.\4 At the bottom of the table we present
the standard deviation to show the amount of interstate variability
in the data.
The second input factor we considered for meal preparation is labor.
For this factor we used the Bureau of Labor Statistics' wage rate for
food preparation services.\5 The highest wage rate for food
preparation is 147 for Alaska, shown in table III.2. The lowest wage
states are Iowa and North Dakota, at 29 percent below the national
average.\6
Table III.2
Interstate Cost Indexes for Food, Labor,
and Building Space
(U.S average = 100.0)
Miscellaneou
s Capita
State Food Labor Constant labor l
-------------------------------- ------ ------ -------- ------------ ------
Alabama 97.8 90.8 100.0 87.8 73.5
Alaska 167.6 146.9 100.0 137.8 137.9
Arizona 97.8 90.4 100.0 99.6 110.4
Arkansas 97.8 79.1 100.0 89.4 71.8
California 97.8 112.6 100.0 99.6 147.6
Colorado 97.8 92.5 100.0 100.3 103.6
Connecticut 97.8 128.4 100.0 136.6 135.1
Delaware 97.8 100.6 100.0 98.8 113.6
District of Columbia 97.8 146.6 100.0 107.0 145.3
Florida 97.8 107.1 100.0 95.8 99.8
Georgia 97.8 99.4 100.0 89.6 88.6
Hawaii 138.7 133.0 100.0 116.1 134.1
Idaho 97.8 74.0 100.0 77.4 93.3
Illinois 97.8 101.2 100.0 98.4 113.4
Indiana 97.8 84.2 100.0 85.7 84.4
Iowa 97.8 70.9 100.0 82.8 85.2
Kansas 97.8 84.3 100.0 84.1 79.9
Kentucky 97.8 88.8 100.0 88.8 74.8
Louisiana 97.8 96.1 100.0 76.3 86.7
Maine 97.8 94.4 100.0 90.3 99.0
Maryland 97.8 116.3 100.0 106.0 105.7
Massachusetts 97.8 121.0 100.0 119.7 146.4
Michigan 97.8 89.1 100.0 93.8 99.5
Minnesota 97.8 86.5 100.0 90.7 99.3
Mississippi 97.8 80.6 100.0 76.6 72.0
Missouri 97.8 86.9 100.0 81.7 86.3
Montana 97.8 79.5 100.0 92.9 89.6
Nebraska 97.8 75.0 100.0 110.3 80.5
Nevada 97.8 106.8 100.0 98.7 133.7
New Hampshire 97.8 103.1 100.0 110.3 123.7
New Jersey 97.8 124.5 100.0 119.2 140.0
New Mexico 97.8 84.9 100.0 90.8 91.7
New York 97.8 124.1 100.0 128.3 139.4
North Carolina 97.8 92.1 100.0 81.7 80.7
North Dakota 97.8 71.2 100.0 75.6 83.1
Ohio 97.8 88.0 100.0 96.4 85.3
Oklahoma 97.8 87.1 100.0 84.8 84.6
Oregon 97.8 92.6 100.0 78.1 105.9
Pennsylvania 97.8 92.4 100.0 104.1 98.6
Rhode Island 97.8 101.8 100.0 115.0 111.9
South Carolina 97.8 92.9 100.0 79.7 75.5
South Dakota 97.8 73.0 100.0 98.8 75.6
Tennessee 97.8 97.8 100.0 84.2 81.6
Texas 97.8 101.7 100.0 99.3 85.4
Utah 97.8 73.5 100.0 77.7 97.0
Vermont 97.8 101.5 100.0 87.1 102.0
Virginia 97.8 98.7 100.0 88.1 88.3
Washington 97.8 97.4 100.0 93.7 101.5
West Virginia 97.8 81.5 100.0 88.6 82.0
Wisconsin 97.8 76.4 100.0 86.5 88.2
Wyoming 97.8 76.7 100.0 93.2 87.1
================================================================================
Standard deviation 11.1 18.2 0.0 14.8 21.9
--------------------------------------------------------------------------------
--------------------
\3 We spoke to an official from USDA's food stamp program, who
claimed that the state variation in food costs is minimal except for
Alaska and Hawaii.
\4 Food Stamp Program--Monthly Allotments and Deductions, USDA
(Washington, D.C.: Oct. 1991-Sept. 1992).
\5 The Standard Industrial Classification code for eating and
drinking places is SIC 5800. Employment and Wages, Annual Averages,
1990, U.S. Department of Labor, Bureau of Labor Statistics, Bulletin
2393 (Washington, D.C.: Nov. 1991).
\6 We converted the BLS wage rates into an index by dividing each
state's wages by the average U.S. wages. This conversion
facilitates the comparison of wage rates among the states and also
the comparison among other factors.
INPUTS USED TO PROVIDE
TRANSPORTATION SERVICES
----------------------------------------------------- Appendix III:2.2
The cost of transportation depends on wages paid for drivers and the
cost of cars and vans, etc. Little data is available that identifies
what percentage of transportation costs depends on personnel, cars
and vans, and other factors used to provide transportation services.
Therefore, we have not identified separate inputs for the
transportation function.
Available research on elderly transportation programs suggests that
the costs of transportation services are equal across states.\7 The
transportation cost per mile is higher in urban areas than rural
areas, owing to the higher cost for labor, insurance, and overhead.
However, in contrast, the distances travelled per trip in rural areas
are longer than in urban areas. As a consequence, the higher urban
cost per mile is offset by the longer trips in the rural areas.
Thus, the resulting difference in costs between rural and urban
programs may be negligible. As a result, we assume that the cost of
providing transportation services does not differ across states.
This assumption is reflected in a uniform cost index, equal to one,
for transportation services for all states.
--------------------
\7 Evaluation of Differences in Needs and Service Programs Between
the Rural and Urban Elderly: Results of Secondary Data Analysis,
Ecosometrics, prepared for HHS, Office of Human Development Services,
Administration on Aging (Washington, D.C.: Apr. 30, 1982); and The
Cost of Services to the Elderly: A Resource-Based Approach to Cost
Analysis, Institute for Economic and Social Measurements, Inc.,
prepared for HHS, Office of Human Development Services,
Administration on Aging, and The Institute for Social Research,
University of Michigan, Ann Arbor, Michigan, under Grant No.
90-1A-1279 (Sept. 14, 1984).
INPUTS USED TO PROVIDE
MISCELLANEOUS SERVICES
----------------------------------------------------- Appendix III:2.3
For the miscellaneous expenditure category, we assume costs are
mainly for labor. This miscellaneous category consists of a great
number of services, none of which dominates the category, and all
appear to be for personal care. To reflect the variety of services,
we are using BLS' wage rates for social services, residential care.\8
This index appears to be a reasonable approximation for many of the
services and is shown in table III.2. Again, Alaska has the highest
wage cost, 38 percent above the U.S. average; in contrast, North
Dakota has the lowest, 24 percent below the average (see table
III.2).
--------------------
\8 Employment and Wages, Annual Averages, 1990. The Standard
Industrial Classification code for social services, residential care,
is 8360.
INPUTS USED TO PROVIDE ALL
SERVICES
----------------------------------------------------- Appendix III:2.4
Missing from the above input cost factors are the costs for capital
equipment, such as building and office space, used in providing meals
and miscellaneous services. We were unable to obtain interstate data
on the cost of office space. To account for this factor, we are
including a proxy based on residential rental rates to estimate the
cost of commercial building space.\9
This proxy is currently used in the Alcohol, Drug Abuse, and Mental
Health Services Block Grant. We are assuming that capital (building
space) enters into the expenditure categories for meals,
transportation,\10 and miscellaneous services.
Like the previous cost measures, Alaska has the highest cost for
building space, almost 38 percent higher than the U.S. average,
while Arkansas and Mississippi have the lowest, around 18 percent
below the average.
--------------------
\9 Gregory C. Pope, Adjusting the Alcohol, Drug Abuse, and Mental
Health Services Block Grant Allocations for Poverty Population and
Cost-of-Service, Health Economics Research, Inc. (Needham, MA: Mar.
30, 1990).
\10 We are not separating out the capital costs for transportation
expenditures. See prior discussion,
p. 43.
AN AGGREGATE COST INDEX FOR OAA
SERVICES
------------------------------------------------------- Appendix III:3
To incorporate the cost indexes into a grant formula, we have
weighted each index and combined them into a single composite index.
This section describes how we weighted each input factor in arriving
at an overall cost index.
So far as we are aware, comprehensive information on what proportion
of program costs is associated with each of the input factors
identified in table III.2 is not available. Several studies have
examined the input costs for specific AoA services. In addition, we
have reviewed studies that examine costs for other government grant
programs. We are utilizing their results to determine the weights on
each input factor in order to construct an overall cost index.
Table III.3 shows the three major expenditure categories and the
input cost categories. The proportions shown in column two (program
expenditure weights) are the program category percentages from table
III.1 expressed as proportions. The columns to the right (labeled
Capital, Labor, Materials, and Constant) indicate the relative
importance of the input factor within each expenditure category. The
first three factors indicate the costs that vary across states. The
fourth factor--the constant--is not an actual cost factor but rather
reflects the transportation function, whose costs do not vary across
states.
Table III.3 Cost Index Weights Broken
Down by Program Expenditure Category
Program Program
expenditure expenditur
categories e weights Capital Labor Materials Constant Total
---------------- ---------- -------- -------- ---------- -------- --------
Meals 0.59 0.15 0.240 0.37 0.240 1.00
Transportation 0.12 \a \a \a 1.000 1.00
Miscellaneous 0.29 0.15 0.375 \a 0.475 1.00
================================================================================
Subtotal 1.00
Weighted total 0.13 0.25 0.22 0.40 1.00
--------------------------------------------------------------------------------
\a Not applicable.
Capital. Although funds for capital, e.g., building space, are not
listed in the categories of title III expenditures, we believe that
building space and capital represent a cost of providing title III
services. However, we cannot quantify the approximate proportion of
total costs this item represents. In order to incorporate this input
factor into our cost index, we assume that office and building space
represents about 15 percent of total cost.\11
Labor. For meals, we estimate that the proportion of total meal
costs attributed to labor is approximately 0.240. The 0.240 is
obtained by a downward adjustment of labor's weight, 0.57,\12 in the
preparation of meals. The first adjustment is the inclusion of
capital and lowers the 0.57 proportion by 15 percent, to 0.48. The
second adjustment is intended to give recognition to the fact that
some labor used in providing title III services is provided on a
voluntary basis. This assumption decreases the 0.48 weight by half
to 0.240, which is shown under the labor column for meals. Volunteer
labor equalizes labor costs across the country (i.e., to the extent
that much of the labor is free, then effectively the labor cost would
be more uniform across the states). The one-half volunteer labor
adjustment is not based on any data, as no information is available
on the extent of volunteer labor, and is judgmental. The remaining
nonattributable labor proportion, 0.24, is placed under the constant
cost column.
For the miscellaneous category, we assume that the labor costs make
up 0.375 of total miscellaneous costs. This proportion is obtained
by halving its initial proportion of 0.75.\13 Again, the one-half
adjustment is an allowance to reflect the use of volunteer labor.
The remaining nonattributable labor proportion, 0.375, is placed
under the constant cost column.
Materials. For meals, we estimate that materials (food) make up
approximately 0.37 of total expenditures for meals. The 0.37 is
obtained by adjusting the proportion that food constitutes of total
meal expenditures (0.43).\14 For the inclusion of capital
expenditures, see our discussion on page 47.
For the miscellaneous category, we assume that material costs make up
0.10 of total miscellaneous costs.\15 This proportion, 0.10, is also
used in the Alcohol, Drug Abuse, and Mental Health Block Grant. We
assume that these materials are purchased in a national market and,
accordingly, the costs are constant across states. Therefore, their
weight is added into the constant cost category.
To calculate the final weights to be applied to each factor, the
weights for the input cost factors are multiplied by the weights in
the program expenditure column. So, for example, the total weight
for capital cost for meals is approximately 0.09, which is obtained
by summing (1) the product of the program expenditure weight for
meals (0.59) and capital's weight for meals (0.15) and (2) the
product of the program expenditure weight for miscellaneous services
(0.29) and capital's weight for miscellaneous services (0.15). The
other weights for the three other factors are obtained in similar
manner. The final weights by input factor are shown in the bottom
row of table III.3. The formula for the cost index is
Cost Index = 0.13 Capital
+0.14 Service Wage Index
+0.11 Miscellaneous Services Wage Index
+0.22 Food Cost Index
+0.40 Constant
The cost index for each of the states is shown in table III.4. We
refer to this cost index as a conservative cost index, as it may
underestimate some of the cost differences among the states. Forty
percent of the index is constant, and another 22 percent (for food)
shows little variation.\16 Alaska and Hawaii have the highest overall
cost, 30 and 19 percent above the national average, respectively,
while Mississippi and North Dakota have the lowest, almost 10 percent
below average. Overall, 29 states differ from the national average
by more than 5 percent.
Table III.4
Interstate Cost Index
(U.S. Average = 100.0)
State Cost index
------------------------------------------------ ----------
Alabama 93.4
Alaska 130.5
Arizona 99.5
Arkansas 91.8
California 107.4
Colorado 99.0
Connecticut 112.1
Delaware 101.2
District of Columbia 112.7
Florida 100.0
Georgia 96.8
Hawaii 119.3
Idaho 92.5
Illinois 101.3
Indiana 93.7
Iowa 91.6
Kansas 93.0
Kentucky 93.4
Louisiana 94.6
Maine 97.5
Maryland 103.2
Massachusetts 110.7
Michigan 97.2
Minnesota 96.5
Mississippi 90.6
Missouri 93.9
Montana 94.5
Nebraska 94.6
Nevada 104.7
New Hampshire 104.2
New Jersey 110.3
New Mexico 95.3
New York 111.1
North Carolina 93.9
North Dakota 90.6
Ohio 95.5
Oklahoma 94.1
Oregon 96.8
Pennsylvania 98.7
Rhode Island 103.0
South Carolina 93.1
South Dakota 92.4
Tennessee 95.1
Texas 97.8
Utah 93.0
Vermont 98.6
Virginia 96.5
Washington 98.7
West Virginia 93.3
Wisconsin 93.2
Wyoming 93.8
============================================================
Standard deviation 7.9
------------------------------------------------------------
--------------------
\11 The 0.15 proportion is used in the Alcohol, Drug Abuse, and
Mental Health Block Grant. See Pope, Adjusting the Alcohol, Drug
Abuse, and Mental Health Services Block Grant Allocation. To
accommodate the capital cost category, we have proportionately
decreased the other input cost categories by 0.15.
\12 Patricia Welch and Lorna Bush, "Food and Labor Costs, Menu
Quality and Client Participation in Fourteen Illinois Title III
Nutrition Programs," Journal of Nutrition for the Elderly, Vol. 6(2)
(Winter 1986). They estimated, on average, that food comprised 42.98
percent and labor 57.02 percent of meal costs. These results are
based on a sample taken of 13 counties in southern Illinois.
\13 See Pope, Adjusting the Alcohol, Drug Abuse, and Mental Health
Services Block Grant Allocation.
\14 See Welch and Bush, Food and Labor Costs.
\15 See Pope, Adjusting the Alcohol, Drug Abuse, And Mental Health
Services Block Grant Allocation.
\16 The standard deviation of the index is 0.07, which is less than
the standard deviation for food.
SUMMARY OF THE INTERSTATE COST
INDEX
------------------------------------------------------- Appendix III:4
We identified the weights attached to the input factors and estimated
an overall cost index for interstate differences in the cost of
providing title III services. Though we believe the cost indexes are
based on reasonable assumptions, they are not without fault. The
main weaknesses are the following:
(1) The breakdown of program outlays, table III.1, is for the federal
dollars and does not include expenditures from the states' own
sources. If state expenditures from their own sources are of similar
magnitude, and if state expenditures do not follow a similar
distribution, the weights presented may deviate from the values
shown.
(2) The breakdown of program outlays into input cost factors is based
on scant information. For example, the breakdown of meals into food
and labor is based on information from a single state and assumes
that this cost breakdown carries over into other states. Moreover,
we have no information on the use of volunteer labor.
(3) The breakdown of program outlays for capital expenditures is not
available. We are estimating this cost by assuming it is similar to
other grant programs that offer services different from the services
under AoA.
(4) The breakdown of labor into volunteer and paid is based on
judgment. No information is available on the extent to which
volunteer labor is used to provide services.
Notwithstanding these reservations, we believe program costs do vary,
and probably vary considerably in many instances. As a consequence,
we decided it was better to use a rough proxy for cost differences
rather than ignore them, which is to assume all states have the same
cost of providing services. Because the cost index is only a proxy
for cost differentials, we have developed some formula options that
include the cost index and others that do not. These options are
described in appendix VI.
INDICATORS USED TO MEASURE STATE
FINANCING CAPACITY
========================================================== Appendix IV
This appendix describes our method of reflecting differences in
states' abilities to fund title III services from their own
resources, represented by the "State Resource Index" part of the
formula (see fig. IV.1).
Figure IV.1: Equity-Based
Formula For Calculating State
Grants-- Fiscal Capacity
(See figure in printed
edition.)
The taxpayer equity principle would distribute federal funds so all
states are able to finance an average level of title III services
with an average burden on state taxpayers. In appendix I, we
explained that this equity standard requires an indicator of each
state's ability to finance title III services from its own sources.
In this appendix, we define the concept of states' ability to finance
title III services and describe how it is used to achieve taxpayer
equity.\1
--------------------
\1 Throughout this report, we use the terms "state resources" and
"fiscal capacity" interchangeably to refer to states' abilities to
fund program services from their own sources.
MEASURING STATE RESOURCES FOR
FUNDING TITLE III SERVICES
-------------------------------------------------------- Appendix IV:1
A good indicator of state fiscal capacity would measure the relative
ability of state taxpayers to finance public services from their own
resources. A measure of fiscal capacity should have these qualities:
Comprehensiveness. A fiscal capacity indicator should measure the
total ability of a state to finance public services. This
statement implies that the indicator should measure all types of
potential resources.
Reflect Tax Exporting. In order to be comprehensive, a fiscal
capacity measure should take into account the phenomenon of tax
exporting. Tax exporting arises when nonresidents pay taxes to
a state.
Measure Available, Not Actual, Use of Fiscal Resources. A fiscal
capacity measure should reflect a state's inherent ability to
finance public services. It should not be affected by an
individual state's actual fiscal decisions.
INCOME-BASED AND
REVENUE-BASED APPROACHES
------------------------------------------------------ Appendix IV:1.1
In recent years, public finance specialists have developed two
approaches for measuring fiscal capacity. One estimates the ability
of a state to raise revenue by gauging its taxing capacity against an
average or typical revenue system.\2 A second estimates the ability
of taxpayers to pay taxes according to estimates of economic income,
broadly defined.\3 Revenue-based approaches would be used to equalize
government capacities to raise revenues, while income-based
approaches would be used to equalize taxpayer burdens.
Between these notions of equalization, the income-based approach was
well suited to our reporting objective of assessing the extent to
which the current allocation of title III funding accords equity to
state taxpayers. Since the revenue-based approach focuses on the
capacity of governments to raise revenue, rather than on taxpayers'
ability to pay taxes, we eliminated this approach from consideration.
--------------------
\2 The well-known version of this revenue-based approach to measuring
fiscal capacity is the Representative Tax System (RTS). RTS measures
fiscal capacity by estimating the tax yields that would result if a
standard set of tax base definitions and tax rates were applied in
every state. The 27 taxes included in the Advisory Commission on
Intergovernmental Relations' system represent all state and local
taxes commonly used in the United States. RTS does not seek to
establish an "ideal" tax structure. Instead, it relies on revenue
sources that are currently taxed. From these, national average rates
are applied to calculate the tax revenues that hypothetically could
be raised from existing bases. By applying national averages, RTS
does not reflect a state's actual tax policy when estimating its
fiscal capacity. However, by tying a state's measured fiscal
capacity to its tax base, RTS estimates do reflect differences in
public and private consumption within states.
\3 Income-based measures of fiscal capacity draw on economic theory
to provide a comprehensive definition of income (total consumption
plus the change in net worth) to reflect the total purchasing power
of state residents. Because total purchasing power is measured by
income, determinations of fiscal capacity based on this approach are
made without regard to actual state or local tax policies or
practices. A comprehensive fiscal capacity measure also should
include the capacity to collect taxes from nonresidents. Within an
income-based framework, this goal is achieved by including the income
of nonresidents whom states have the ability to tax (corporate
income, for example).
TOTAL TAXABLE RESOURCES A
BETTER MEASURE OF FINANCING
CAPACITY
------------------------------------------------------ Appendix IV:1.2
Total Taxable Resources measures a state's fiscal capacity by
measuring all income potentially subject to a state's taxing
authority. TTR is an average of personal income and per capita Gross
State Product (GSP). Personal income is compiled by the Department
of Commerce and used to measure the income received by state
residents, including wages and salaries, rents, dividends, interest
earnings, and income from nonresident corporate business. It also
includes an adjustment for the rental value of owner-occupied housing
on the grounds that such ownership is similar to the interest income
earned from alternative financial investments. GSP measures all
income produced within a state, whether received by residents,
nonresidents, or retained by business corporations. Consequently, it
reflects the income received by out-of-state commuters, landlords,
and business owners operating in a state as well as income produced
in-state and received by state residents. GSP also includes indirect
business taxes, such as retail sales and excise taxes, that are
excluded from measures such as personal income. TTR includes GSP
taxes without regard to whether they are paid out of income received
by residents or nonresidents.
By averaging GSP with personal income, the TTR measure covers more
types of income than personal income alone, including income received
by nonresidents. Finally, TTR reflects states' economic resources
rather than states' revenue-raising choices, like some other fiscal
capacity measures such as RTS. A state-by-state comparison of fiscal
capacity using the TTR measure is shown in table IV.1. and is
compared to an index of personal income.
Thus, TTR is a better overall measure of fiscal capacity because it
is a more comprehensive indicator of economic income and addresses
tax exporting. TTR has the added feature of technical and political
feasibility, as it is currently in use within the Alcohol, Drug
Abuse, and Mental Health Block Grant formula.
Table IV.1
Indexes of Fiscal Capacity
Person
al
States TTR income
-------------------------------------------- ------ ------
Alabama 80 80
Alaska 142 115
Arizona 87 86
Arkansas 76 76
California 112 110
Colorado 99 101
Connecticut 133 138
Delaware 111 107
District of Columbia 219 128
Florida 92 99
Georgia 94 91
Hawaii 111 105
Idaho 79 80
Illinois 108 109
Indiana 91 91
Iowa 91 92
Kansas 95 97
Kentucky 83 80
Louisiana 84 77
Maine 92 92
Maryland 108 117
Massachusetts 118 123
Michigan 96 99
Minnesota 102 100
Mississippi 70 69
Missouri 94 94
Montana 81 82
Nebraska 93 93
Nevada 109 99
New Hampshire 110 114
New Jersey 131 134
New Mexico 78 76
New York 118 118
North Carolina 91 87
North Dakota 82 80
Ohio 94 94
Oklahoma 81 83
Oregon 90 91
Pennsylvania 96 100
Rhode Island 96 102
South Carolina 82 80
South Dakota 80 82
Tennessee 88 85
Texas 93 89
Utah 77 74
Vermont 96 94
Virginia 106 106
Washington 98 99
West Virginia 74 74
Wisconsin 93 94
Wyoming 103 87
U.S. average 100 100
Standard deviation 22.8 15.8
------------------------------------------------------------
Although TTR and personal income appear to be similar, they differ in
important respects. Most significantly, personal income understates
the ability to export taxes for states like Alaska, Texas, and
Louisiana. For example, personal income understates Alaska's fiscal
capacity by 27 percent. A comparison of the indexes in table IV.1
indicates greater differences in revenue- raising ability based on
the more comprehensive measure of TTR.
DEVELOPING AN INDEX OF STATE
FINANCING CAPACITY
-------------------------------------------------------- Appendix IV:2
To create an index of state financing capacity, TTR must be adjusted
in two ways. First, TTR does not take into account state differences
in the cost of providing title III services. If a dollar of income
purchases different quantities of services, then TTR will overstate
the financing capacity of high-cost states and understate it in
states with lower costs. We therefore have adjusted each state's TTR
by the cost index described in appendix III (see table III.4). In
addition, to create an index, TTR needs to be expressed on a
per-person basis. To achieve taxpayer equity, TTR needs to be
measured relative to the number of potential recipients (i.e.,
measured relative to the size of each state's potential caseload).
For comparison purposes, we have also calculated TTR indexes based on
total population and the population over 60 years of age, with and
without the cost adjustment. The results are shown in table IV.2.
Table IV.2
Total Taxable Resources Relative to
State Populations
(U.S. average = 100)
Potentia
l
Total No cost Cost caseload
populati adjustme adjust , cost
States on nt ed adjusted
---------------------- -------- -------- ------ --------
Alabama 80 78 83 79
Alaska 142 372 285 340
Arizona 87 84 85 89
Arkansas 76 65 71 68
California 112 132 123 124
Colorado 99 122 123 127
Connecticut 133 124 111 112
Delaware 111 112 111 116
District of Columbia 219 216 192 161
Florida 92 66 66 67
Georgia 94 115 119 116
Hawaii 111 119 100 93
Idaho 79 84 91 94
Illinois 108 108 107 106
Indiana 91 90 96 97
Iowa 91 77 84 80
Kansas 95 88 95 91
Kentucky 83 82 88 87
Louisiana 84 93 98 94
Maine 92 87 89 89
Maryland 108 122 119 121
Massachusetts 118 111 100 99
Michigan 96 100 103 105
Minnesota 102 104 108 105
Mississippi 70 71 78 71
Missouri 94 85 91 88
Montana 80 76 80 82
Nebraska 93 85 90 86
Nevada 109 122 116 137
New Hampshire 110 122 117 120
New Jersey 131 122 110 113
New Mexico 78 90 94 97
New York 118 112 101 99
North Carolina 91 93 99 98
North Dakota 82 75 83 79
Ohio 94 90 94 96
Oklahoma 81 76 81 78
Oregon 90 84 87 89
Pennsylvania 96 79 80 81
Rhode Island 96 82 79 79
South Carolina 82 89 95 95
South Dakota 80 70 76 73
Tennessee 88 87 92 90
Texas 93 113 116 115
Utah 77 111 119 126
Vermont 96 103 105 105
Virginia 106 121 126 127
Washington 98 105 107 110
West Virginia 74 62 67 68
Wisconsin 93 89 96 95
Wyoming 103 121 129 137
============================================================
Standard deviation 23 45 33 39
------------------------------------------------------------
The first column shows each state's TTR index when measured on a
total population basis. Alaska had the highest value with taxable
resources, 42 percent above the national average, and Mississippi the
lowest, 30 percent below average. The effect of expressing TTR
relative to the elderly population is shown in the second column.
Because there are relatively few elderly people living in Alaska, its
taxable resources per elderly individual is over 3.7 times the
national average, rather than 42 percent above average when measured
relative to total population. Because Mississippi's share of elderly
individuals is about the same as its share of total population, its
TTR index changes by only 1 percentage point, from 70 to 71.
The situation is quite different in Florida and Georgia. Florida has
a relatively high concentration of elderly individuals.
Consequently, when its financing capacity is expressed relative to
its elderly population, its TTR index is 34 percent below average
instead of 8 percent below. The opposite is true of Georgia.
Because Georgia has a lower percentage of elderly individuals, its
taxable resources per elderly individual are 15 percent above
average. Thus, while both states have nearly equal resources when
expressed on a per capita basis, they differ significantly when
measured relative to their elderly populations.
The impact of adjusting each state's TTR for differences in the cost
of services is shown in the third column. As would be expected,
states that face higher costs have lower taxable resources after
adjusting for cost differences. Alaska's TTR index, for example, is
adjusted downward from 372 to 285, and Connecticut's index is
adjusted down from 24 percent above the average to 11 percent above.
In contrast, low-cost states are adjusted upward. Mississippi's TTR
index increases from 29 percent below the average to 22 percent
below, and Georgia's index rises from 15 percent above the national
average to 19 percent above average.
The effect of adjusting TTR relative to potential caseloads is shown
in the last column. Because Alaska has comparatively fewer caseloads
(i.e., fewer people in the oldest age groups, of minority status,
poor, or female), its taxable resources per potential caseload rise
to almost 3-1/2 times the national average. In contrast, Florida and
West Virginia are each about one-third below the national average
when their taxable resources are expressed relative to their
populations in need.
DETERMINATION OF THE FEDERAL
PERCENTAGE FOR TITLE III
SERVICES
-------------------------------------------------------- Appendix IV:3
As explained in appendix I, the taxpayer equity standard would
distribute federal assistance in accordance with the described
formula. The last term highlighted in the formula represents what we
have called the OAFP and represents the percentage of each state's
need (as reflected by potential caseloads and the cost of services)
that is subject to federal assistance. States with high needs and a
low financing capacity would be subject to a higher federal
percentage, and states with low needs and a higher financing capacity
would be subject to a lower federal percentage.\4
This factor, by providing more generous federal funding in poorer
states, serves to offset the higher tax burden low-income states
would otherwise have to pay to provide a national average basket of
title III services.
--------------------
\4 This OAFP is analogous to the federal medical assistance
percentage used to calculate federal reimbursement rates under the
Medicaid programs, whereby lower income states receive more generous
federal reimbursements.
BALANCING BENEFICIARY AND
TAXPAYER EQUITY
------------------------------------------------------ Appendix IV:3.1
The exponent in the formula controls the degree to which
either the beneficiary equity or the taxpayer equity standard is
achieved. As we noted in appendix I, if =1.0, grants will
be targeted to achieve full taxpayer equity. That is, all states
will be able to finance the national average basket of title III
services with comparable burdens on state taxpayers. If the exponent
=0, each state's OAFP is identically equal to 0.35 for every
state.\5 Since this number is a constant that can be incorporated
into the constant of proportionality, ', the formula becomes
identical to the beneficiary equity formula in that it allocates
funding only on the basis of potential caseloads and costs.
Consequently, if the exponent is between zero and 1,
federal funds will be targeted to reduce taxpayer burdens, but they
will not be eliminated. We therefore refer to formulas where
0<<1 as "balanced equity" formulas since the title III
percentage will offset, but not completely eliminate, differences in
state taxpayer burdens.\6
The OAFP for each state is shown in table IV.3 using our measure for
potential caseloads. The first column shows what each state's
federal percentage would have to be to achieve full taxpayer equity.
If strictly applied, the negative percentage for Alaska implies that
the state would have to contribute to the federal government to help
finance other state programs rather than receive a grant from the
federal government.\7 To avoid this outcome, we have arbitrarily
placed a minimum value on each state's OAFP of zero. We refer to
this circumstance as "full taxpayer equity" with a "floor" on the
federal percentage. This outcome is shown in column two. All states
with a positive federal percentage remain unchanged, and Alaska's
percentage is raised to zero.
The case of balanced equity is illustrated using values of 0.7 and
0.5 for the exponent . As can be seen in table IV.3, the
lower the value of this parameter the closer each state's OAFP moves
to the national average value of 0.35. This has the effect of making
states with above average TTR scores appear less wealthy for formula
purposes, and poorer states appear richer. The effect will be to
lower state tax burden disparities but not to eliminate them.
Table IV.3
Older Americans Federal Percentage by
State Under Full and Partial Taxpayer
Equity
Beta = Beta =
States No floor Floor 0.7 0.5
-------------------- -------- -------- -------- --------
Alabama 48.0% 48.0% 44.4% 44.0%
Alaska -122.8 0.0 0.0 0.0
Arizona 41.4 41.4 39.5 38.6
Arkansas 55.4 55.4 50.0 48.5
California 18.5 18.5 23.9 24.8
Colorado 16.6 16.6 22.6 27.0
Connecticut 26.5 26.5 29.1 27.0
Delaware 24.2 24.2 27.6 29.6
District of Columbia 38.3 38.3 37.3 33.0
Florida 56.2 56.2 50.7 46.8
Georgia 24.1 24.1 27.6 31.1
Hawaii 39.3 39.3 38.0 31.6
Idaho 38.2 38.2 37.2 39.2
Illinois 30.6 30.6 32.0 32.6
Indiana 36.2 36.2 35.8 37.8
Iowa 47.5 47.5 44.1 44.3
Kansas 40.2 40.2 38.7 40.1
Kentucky 42.7 42.7 40.5 41.2
Louisiana 38.5 38.5 37.5 38.7
Maine 41.5 41.5 39.6 39.3
Maryland 20.8 20.8 25.3 27.3
Massachusetts 35.2 35.2 35.1 31.9
Michigan 31.5 31.5 32.6 34.4
Minnesota 31.3 31.3 32.5 34.6
Mississippi 53.4 53.4 48.5 47.8
Missouri 42.6 42.6 40.4 41.0
Montana 46.0 46.0 42.9 42.6
Nebraska 43.7 43.7 41.2 41.3
Nevada 10.5 10.5 18.7 22.2
New Hampshire 21.7 21.7 25.9 27.4
New Jersey 25.9 25.9 28.8 27.3
New Mexico 36.6 36.6 36.1 37.5
New York 35.4 35.4 35.2 31.9
North Carolina 35.6 35.6 35.4 37.5
North Dakota 47.9 47.9 44.4 44.8
Ohio 37.4 37.4 36.7 37.8
Oklahoma 49.0 49.0 45.1 44.3
Oregon 41.8 41.8 39.8 39.7
Pennsylvania 46.7 46.7 43.4 41.7
Rhode Island 48.4 48.4 44.7 41.4
South Carolina 37.9 37.9 37.1 38.9
South Dakota 52.5 52.5 47.8 46.7
Tennessee 41.2 41.2 39.4 39.9
Texas 24.7 24.7 28.0 31.0
Utah 17.6 17.6 23.3 29.6
Vermont 31.2 31.2 32.4 33.8
Virginia 16.9 16.9 22.8 28.0
Washington 28.0 28.0 30.2 32.2
West Virginia 55.8 55.8 50.3 48.3
Wisconsin 37.6 37.6 36.9 38.7
Wyoming 10.4 10.4 18.6 26.3
============================================================
United States 35.1% 35.1% 35.1% 35.1%
------------------------------------------------------------
--------------------
\5 From elementary algebra, any number raised to an exponent of zero
is identically equal to 1.0. In this case, the formula for the OAA
percentage reduces to 1.0 - 0.65 = 0.35.
\6 This conclusion will be demonstrated for several formula options
described in appendix VI.
\7 This situation occurs because Alaska's taxable resources are so
far above the national average that the state could provide the
national average level title III benefits without assistance from the
federal government and be able to do so with a below-average tax
burden on state taxpayers. To raise its tax burden to the national
average, Alaska would have to contribute to financing other state
programs.
CURRENT OAA DISTRIBUTION IS NOT
ALLOCATED EQUITABLY
=========================================================== Appendix V
The current method of distributing federal assistance under title III
does not achieve either beneficiary or taxpayer equity. Because the
title III formula uses only the population over 60 years old, the
distribution of federal assistance does not take into account the
potential caseloads and cost factors needed to achieve beneficiary
equity, nor does it consider the additional factor, fiscal capacity,
needed to achieve taxpayer equity. In this appendix, we provide
state-by-state detail on the relatively wide variation in funding per
person in need and in state taxpayer burdens.
CURRENT FUNDING DOES NOT
ACHIEVE BENEFICIARY EQUITY
--------------------------------------------------------- Appendix V:1
If federal funding were distributed so that the aid provided
purchased comparable services per person in need, all states would
receive identical grants when adjusted for cost differences and
expressed on a per-person-in-need basis. The result of making these
adjustments is shown in table V.1. The 50 states and the District of
Columbia have been sorted into two groups: (1) states whose funding
is below the national average, and (2) states whose funding is above
the national average.
If the beneficiary equity standard were achieved, every state would
receive the same funding per person in need. This situation would be
represented by every state's having an index of 100. Therefore, the
degree to which these index numbers differ from one another provides
a measure of the degree to which the current distribution of federal
funding falls short of the beneficiary equity standard.
There are 17 states that are underfunded under the beneficiary equity
standard. For example, Florida's funding per person in need is 11
percent below the national average. At the other extreme, there are
34 states that are consistently funded above the national average.
The most extreme cases are Alaska and Wyoming. Alaska's funding per
person in need is over 5 times the national average, and Wyoming's
funding is more than 3.7 times the national average.
Table V.1
Title III Funding Per Person in Need
(U.S. average = 100
Standard deviation = 77)
Averag Averag
State e State e
--------------------- ------ --------------------- ------
Alaska 554 Illinois 100
Wyoming 373 Alabama 100
Vermont 246 South Carolina 98
Delaware 199 Hawaii 98
North Dakota 192 Colorado 97
South Dakota 166 Virginia 97
Montana 165 Washington 97
District of Columbia 154 North Carolina 97
Idaho 152 Texas 96
Nevada 136 Georgia 95
New Hampshire 127 Maryland 93
Utah 125 New Jersey 92
West Virginia 113 Massachusetts 92
Iowa 109 New York 91
New Mexico 109 Connecticut 91
Rhode Island 108 Arizona 89
Nebraska 108 Florida 89
Arkansas 108 California 88
Kansas 107
Kentucky 107
Wisconsin 107
Maine 107
Indiana 106
Oklahoma 105
Ohio 105
Missouri 105
Mississippi 104
Pennsylvania 103
Michigan 102
Minnesota 102
Louisiana 101
Oregon 101
Tennessee 101
------------------------------------------------------------
CURRENT FUNDING DOES NOT
ACHIEVE TAXPAYER EQUITY
--------------------------------------------------------- Appendix V:2
The current distribution of title III funding also falls short on our
taxpayer equity standard. Because the current distribution of
federal assistance does not reflect differences in the capacity of
state taxpayers to finance program services, substantial differences
in state taxpayer burdens exist.
The taxpayer equity standard would be achieved if federal funds were
distributed so that all states could finance a national average
basket of services with comparable burdens on state taxpayers. To
measure state differences in state tax burdens, we calculated the tax
burden each state would have to bear if it were to provide the
national average basket of services, given the level of federal
funding actually received for fiscal year 1993.\1 The results are
shown in table V.2. To facilitate state-by-state comparisons, we
have expressed each state's tax burden relative to the national
average. Again, states were placed in one of two groups: (1) states
whose burdens are below average and (2) states whose burdens are
above average. If federal grants were distributed to offset tax
burden disparities, each state's tax burden would be equal to the
national average--all the numbers reported in table V.2 would be
equal to 100. Therefore, deviations from 100 represent tax burden
disparities.
The results reported in table V.2 indicate a wide range of tax
burdens. There are 25 states whose tax burdens are above the
national average. For example, Florida would incur a tax burden 58
percent above the national average if it were to provide an average
basket of title III services. Arkansas' burden would be over 61
percent above the national average. At the other extreme, 26 states
would have tax burdens that are below the national average. For
example, federal funding for Alaska and Wyoming is sufficiently high
that they are able to fund an average level of title III services
without having to commit any state resources. Hence, their tax
burdens are zero. Vermont and Delaware are able to provide an
average service level with tax burdens 77 and 61 percent below the
national average, respectively.
Table V.2
Tax Burdens Required to Finance Average
Title III Services
(U.S. average = 100
Standard deviation = 35)
Averag Averag
State e State e
--------------------- ------ --------------------- ------
Arkansas 161 Minnesota 99
Mississippi 160 Michigan 98
Florida 158 Washington 94
West Virginia 153 Georgia 93
Alabama 139 Illinois 92
Oklahoma 137 Texas 92
Iowa 136 Massachusetts 90
Pennsylvania 123 New York 90
Missouri 122 Montana 90
Kentucky 122 Idaho 89
Nebraska 121 Virginia 84
Tennessee 119 North Dakota 83
Arizona 119 Hawaii 82
South Carolina 118 Maryland 82
Kansas 118 Colorado 81
Oregon 117 New Jersey 79
Rhode Island 115 Connecticut 78
Louisiana 115 Utah 78
North Carolina 113 California 77
Wisconsin 112 New Hampshire 66
Maine 112 Nevada 54
Indiana 110 Delaware 39
Ohio 109 District of Columbia 33
South Dakota 106 Vermont 23
New Mexico 106 Alaska 0
Wyoming 0
------------------------------------------------------------
--------------------
\1 In making these calculations, we used the national average
spending per person in need as our proxy for the national average
basket of services. We then calculated how much funding would have
to come from state sources to finance that service level, given the
amount of federal assistance states received. This amount was
expressed as a percentage of their TTR to measure the tax burden
associated with financing the average service level.
SUMMARY
--------------------------------------------------------- Appendix V:3
The current title III funding formula ignores differences among the
states in terms of their potential caseloads, the cost of providing
services, and state taxpayers' capacity to fund program services from
their own resources. As a consequence, there are substantial
differences among states in the services their federal grant will
purchase and in the tax burdens state taxpayers would face if they
were to provide an average basket of title III services for their
needy population.
DESCRIPTION OF GAO'S EQUITY-BASED
FORMULA OPTIONS
========================================================== Appendix VI
We used two equity standards (beneficiary and taxpayer equity) to
evaluate the formula now used to distribute funding for title III
programs among the states. In this appendix, we describe six formula
options designed to achieve these equity standards to varying
degrees. We first describe the grant distribution formulas that
would achieve beneficiary and taxpayer equity. This description is
followed by a more detailed description of how each factor was
measured and incorporated into a formula. The remainder of the
appendix provides state grant amounts under each option and an
assessment of how well each option satisfies our beneficiary and
taxpayer equity standards.
DESCRIPTION OF EQUITY-BASED
GRANT FORMULAS
-------------------------------------------------------- Appendix VI:1
The grant distribution formulas that would achieve beneficiary and
taxpayer equity were described in appendix I and are shown again here
for convenience:
Figure VI.1: Beneficiary
Equity Formula
(See figure in printed
edition.)
Figure VI.2: Taxpayer Equity
Formula
(See figure in printed
edition.)
To achieve beneficiary equity, grants should be distributed in
proportion to each state's potential caseload and adjusted for state
differences in the cost of providing title III services.\1
Taxpayer equity requires that, in addition to these factors, funds
also be distributed in proportion to states' own resources for
funding program services, achieved by the last term in figure VI.2.\2
Both equity standards cannot be achieved simultaneously because each
implies different funding amounts for individual states. The concept
of balanced taxpayer equity was introduced in appendix I and
discussed in more detail in appendix IV. Balanced equity formulas
reduce, but do not eliminate, disparities in state taxpayer burdens.
They therefore move the distribution of grant funding to an
intermediate position between beneficiary and taxpayer equity
allocations. As explained in appendix IV, the trade-off between
beneficiary and taxpayer equity is achieved through the exponent
, used to calculate each state's OAFP. When the exponent is
equal to one, federal grants will be distributed so that differences
in state taxpayer burdens will be eliminated. If 0<<1,
partial taxpayer equity will be achieved in the sense that state
taxpayer burdens will be reduced but not eliminated.
--------------------
\1 The measurement of these factors was discussed in appendixes II
and III.
\2 Measurement of states' financing capacity and OAFP was discussed
in appendix IV.
SIX FORMULA OPTIONS ILLUSTRATE
ALTERNATIVES
-------------------------------------------------------- Appendix VI:2
We developed six formula options to illustrate the range of funding
outcomes possible under the equity standards we considered. The
alternatives reflect beneficiary equity, taxpayer equity, and four
balanced equity versions that reflect various trade-offs between the
two standards.
The balanced equity options were selected to illustrate the impact of
including or excluding a cost factor, using different values for the
exponent , and different ceilings placed on OAFP.\3 The
detailed specifications of each of the six options are summarized in
table VI.1.
Table VI.1
Formula Parameters Used in the Six GAO
Formula Options
Formula
parameters # 1 # 2 # 3 # 4 # 5 # 6
---------------- ---------- ------ ---- ---- ---- ----
Cost Yes Yes Yes No No No
Fiscal capacity No Yes Yes Yes Yes Yes
Beta () \a 1.0 0.7 0.7 0.7 0.5
Ceiling \a \a \a \a 0.4 0.4
------------------------------------------------------------
\a Not applicable.
Options 3 through 6 represent our balanced equity alternatives.
Option 3 is the same as the full taxpayer equity option except the
exponent, , is reduced from 1.0 to 0.7. Option 4
demonstrates the effect of ignoring cost differences among the states
by deleting this factor from the formula. Option 5 reduces the
degree of taxpayer equity further by placing a ceiling on OAFP. This
action has the effect of reducing funding for states with the lowest
financing capacity. Finally, option 6 shows the effect of reducing
the exponent further, from 0.7 to 0.5.
--------------------
\3 Lower values for the exponent produce less targeting to
low- income states, moving the distribution of aid closer to the
beneficiary equity standard. In addition, placing a ceiling on OAFP
limits the amount of funding to low-income states.
GAO FORMULA OPTIONS WOULD
TARGET MORE FUNDING TO SMALLER,
LOW-INCOME STATES
-------------------------------------------------------- Appendix VI:3
The impact of each of the formula options on state funding amounts
varies significantly, both in terms of the number of states whose
funding would increase or decrease and in terms of the percentage of
available funds that would have to be reallocated if appropriation
levels did not increase.\4 The amount redistributed ranges from as
little as 2.8 percent to as much as 11.3 percent of the total amount
to be distributed (see table VI.2). Similarly, the number of states
that would receive more funding ranges from as few as 12 states to as
many as 25. Finally, under the GAO alternatives, there are eight
states whose funding level does not change due to the minimum funding
guarantees under the act.
Table VI.2
Summary Statistics for the Six GAO
Equity Options, Changes in Allocations,
and the Number of States Changing
Allocations
(Dollars in millions)
Funds
redistributed # 1 # 2 # 3 # 4 # 5 # 6
---------------- ---------- ------ ---- ---- ---- ----
Amount $21.1 $85.9 $59. $83. $66. $50.
7 8 4 8
Percent 2.8% 11.3% 7.7% 11.0 8.8% 6.7%
%
No. increasing 12 23 22 24 25 24
No. decreasing 31 20 21 19 18 19
No. no change 8 8 8 8 8 8
------------------------------------------------------------
Table VI.3 further summarizes the redistributive effects with respect
to state population size and fiscal capacity. There is some modest
redistribution between large and medium-sized states under the
beneficiary equity option. Generally, more redistribution occurs
under the other options. Small states are largely unaffected because
most small states are guaranteed at least 0.5 percent of the total
appropriation under all formula options considered.
The beneficiary equity option (option 1) would redistribute about 6.7
percent of federal funding to high-income (as measured by TTR, see
app. IV) states, with corresponding reductions in middle- and
low-income states. All other options would produce a substantial
redistribution in favor of states whose incomes are low, relative to
their potential caseloads and the cost of services.
Table VI.3
GAO-Proposed Alternative Formula
Allocation, by Population and TTR
(Dollars in thousands)
# 1 # 2 # 3 # 4 # 5 #6
---------------- ------------ -------- -------- -------- -------- --------
By population
--------------------------------------------------------------------------------
Largest 13 states
--------------------------------------------------------------------------------
Amount $11,947 - -$6,577 - - -
$15,299 $30,316 $26,284 $19,484
Percent 5.87% -7.52% -3.23% -14.90% -12.92% -9.57%
Middle 15 states
--------------------------------------------------------------------------------
Amount -$11,567 $14,113 $5,953 $30,300 $26,394 $19,708
Percent -3.07% 3.75% 1.58% 8.05% 7.01% 5.24%
Smallest 13 states
--------------------------------------------------------------------------------
Amount -$380 $1,186 $623 $16 -$110 -$224
Percent -0.21% 0.67% 0.35% 0.01% -0.06% -0.13%
By per capita TTR
--------------------------------------------------------------------------------
Highest 13 states
--------------------------------------------------------------------------------
Amount $13,700 - - - - -
$49,599 $30,528 $69,858 $58,568 $44,448
Percent 6.73% -24.37% -15.00% -34.33% -28.78% -21.84%
Middle 15 states
--------------------------------------------------------------------------------
Amount -$8,702 $31,568 $19,164 $47,310 $45,256 $33,860
Percent -2.31% 8.39% 5.09% 12.57% 12.03% 9.00%
Lowest 13 states
--------------------------------------------------------------------------------
Amount -$4,998 $18,031 $11,364 $22,549 $13,312 $10,588
Percent -2.82% 10.16% 6.40% 12.70% 7.50% 5.96%
--------------------------------------------------------------------------------
The balanced equity options achieve less dramatic redistributive
effects. Option 3 decreased the exponent, , from 1.0 to
0.7, effectively limiting the funding redistribution from higher to
lower income states and thus curtailed the increase that would occur
among the middle- and lowest-income states. Comparing options 2 and
3 in table VI.3 shows the reduction for high-income states falls from
-24 percent to -15 percent. The gain among middle- and low-income
states is curtailed accordingly.
Eliminating the cost factor (option 4) from the formula has the
opposite effect. Funding for the highest-income states is nearly the
same as under option 2, and the gains to the middle- and low-income
states are also similar. This conclusion suggests that reducing the
exponent from 1.0 to 0.7 and eliminating the cost factor have roughly
offsetting effects in terms of the extent to which funding is
targeted to low-income states. This effect occurs because low-income
states tend to be low-cost states. Consequently, eliminating the
cost factor roughly offsets the reduced income targeting that results
from lowering the exponent to 0.7.
Option 5 demonstrates that placing a ceiling on OAFP only moderates
the funding increase of the lowest-income states and moderates the
reduction among high-income states, while leaving the middle-income
group unaffected. Again, the cost factor is not used in this option.
The middle-income states are largely unaffected by this change; the
gain to the lowest-income states is reduced from 12.7 percent to 7.5
percent, while the corresponding reduction among high-income states
falls from -34.3 percent to -28.8 percent.
Finally, option 6 demonstrates that further reducing the exponent
further moderates the funding loss among high-income states, falling
from -28.8 percent to -21.8 percent, and reduces the gain among
middle- and low-income states from 12 percent to 9 percent and 7.5
percent to 6 percent, respectively. Again, the cost factor is not
used in this option.
--------------------
\4 Higher appropriation levels would, of course, reduce the number of
states that would receive lower funding amounts and mitigate the
amount lost for states that would otherwise receive less.
STATE FUNDING AMOUNTS UNDER GAO
FORMULA OPTIONS
-------------------------------------------------------- Appendix VI:4
The impact on each state's funding amount varies considerably. In
table VI.4 we compare each state's funding amount for fiscal year
1993 with what they would receive if each formula option distributed
the same $757.4 million funding amount. Each state's funding amount
for fiscal year 1993 is shown, and the percent change in funding
under each of the options is shown in the remaining columns. Actual
funding amounts under each option are shown in table VI.5.
Table VI.4
Title III Formula Allocations and the
Percent Change in Allocations from the
GAO-Proposed Equity Options, Fiscal Year
1993
Current
States allocation # 1 # 2 # 3 # 4 # 5 # 6
-------------------- ---------- ------------ -------- ---- ---- ---- ----
Alabama $12,443,80 -2.8% 33.0% 26.5 38.1 24.4 21.0
8 % % % %
Alaska\a 3,860,888 0 0 0 0 0 0
Arizona 9,617,154 8.7 28.2 19.0 23.0 30.7 22.7
Arkansas 8,535,259 -9.7 42.5 29.7 47.2 17.7 14.5
California 71,593,899 10.1 -41.9 - - - -
32.3 41.5 37.9 26.1
Colorado 7,579,540 -0.3 -49.1 - - - -
35.7 33.4 29.2 21.0
Connecticut 10,788,799 6.9 -19.3 - - - -
9.8 36.8 32.8 25.0
Delaware\a 3,860,888 0 0 0 0 0 0
District of 3,860,888 0 0 0 0 0 0
Columbia\a
Florida 48,285,368 9.3 75.1 57.6 56.5 30.7 27.1
Georgia 15,229,845 1.9 -29.9 - - - -
18.7 12.7 7.2 4.8
Hawaii\b 3,934,808 -0.7 11.1 0.4 - - -
1.9 1.9 1.9
Idaho\b 3,906,539 -1.2 -1.2 - - - -
1.2 1.2 1.2 1.2
Illinois 35,516,551 -2.8 -15.1 - - - -
11.7 14.5 9.1 8.9
Indiana 16,667,921 -8.5 -5.5 - 7.1 13.8 7.5
6.5
Iowa 10,441,164 -11.1 20.5 8.1 29.9 16.0 12.9
Kansas 8,398,805 -9.6 3.7 - 15.0 16.4 13.2
0.7
Kentucky 11,424,796 -9.5 10.2 7.4 18.6 15.9 12.7
Louisiana 11,573,982 -4.1 5.4 3.3 14.4 21.2 14.0
Maine 4,095,877 -5.7 7.7 - 7.5 11.7 6.6
0.9
Maryland 12,105,916 4.6 -38.1 - - - -
21.2 31.6 27.4 19.5
Massachusetts 20,090,885 5.8 6.2 1.0 - - -
17.5 12.3 11.2
Michigan 26,554,303 -4.9 -14.6 - - - -
11.6 6.0 0.1 2.2
Minnesota 13,128,289 -4.6 -14.8 - - 1.6 -
13.9 4.4 0.6
Mississippi 7,973,881 -6.3 42.8 29.0 52.0 23.8 20.4
Missouri 17,394,341 -7.1 12.9 6.8 20.4 18.4 15.2
Montana\a 3,860,888 0 0 0 0 0 0
Nebraska 5,619,061 -9.9 12.3 7.8 17.1 14.0 10.8
Nevada\b 3,952,673 -2.3 -2.3 - - - -
2.3 2.3 2.3 2.3
New Hampshire\b 3,930,385 -1.8 -1.8 - - - -
1.8 1.8 1.8 1.8
New Jersey 25,059,178 5.1 -22.3 - - - -
15.0 35.5 31.4 24.2
New Mexico 4,064,724 -5.0 -5.0 - 1.1 7.4 1.9
5.0
New York 59,528,710 6.6 7.4 6.7 - - -
17.5 12.3 11.1
North Carolina 18,116,462 0.3 1.9 1.5 15.7 22.9 16.5
North Dakota\a 3,860,888 0 0 0 0 0 0
Ohio 33,733,071 -7.1 -1.0 - 6.5 13.2 7.0
0.3
Oklahoma 10,407,873 -7.7 28.9 18.6 31.6 17.4 14.2
Oregon 8,822,016 -3.5 15.0 5.9 16.2 19.2 15.0
Pennsylvania 43,851,246 -5.3 26.1 18.3 19.3 14.8 11.6
Rhode Island 4,004,384 -3.6 23.8 13.7 7.4 4.3 1.4
South Carolina 8,939,853 -0.7 7.3 6.2 21.1 27.5 20.5
South Dakota\a 3,860,888 0 0 0 0 0 0
Tennessee 14,662,584 -3.5 13.3 9.9 19.2 21.5 17.7
Texas 40,017,295 1.3 -28.6 - - - -
14.6 14.3 9.0 6.5
Utah \b 4,012,455 -3.8 -3.8 - - - -
3.8 3.8 3.8 3.8
Vermont\a 3,860,888 0 0 0 0 0 0
Virginia 15,285,026 0.0 -51.8 - - - -
34.1 27.1 22.5 15.6
Washington 12,808,320 0.2 -20.0 - - - -
12.2 11.1 5.5 4.8
West Virginia 6,787,523 -14.3 36.2 24.6 36.7 9.8 6.8
Wisconsin 15,585,323 -9.4 -2.7 - 9.9 16.3 9.5
6.8
Wyoming\a 3,860,888 0 0 0 0 0 0
================================================================================
United States $757,356,9 0 0 0 0 0 0
98
--------------------------------------------------------------------------------
\a AoA's calculation of a state receiving the minimum 0.05 percent
funding.
\b GAO's calculation of a state receiving the minimum 0.05 percent
funding.
\c Total does not add because of rounding.
Table VI.5
Title III Allocations Under the GAO-
Proposed Equity Options
(Dollars in thousands)
States # 1 # 2 # 3 # 4 # 5 # 6
------------------------ ------------ -------- ------ ------ ------ ------
Alabama $12,090 $16,549 $15,23 $17,18 $15,47 $15,05
1 8 8 4
Alaska 3,861 3,861 3,861 3,861 3,861 3,861
Arizona 10,457 12,330 11,723 11,826 12,567 11,800
Arkansas 7,709 12,166 10,941 12,564 10,050 9,774
California 78,802 41,624 53,351 41,852 44,472 52,895
Colorado 7,560 3,861 4,850 5,047 5,363 5,991
Connecticut 11,534 8,707 9,536 6,823 7,251 8,087
Delaware 3,861 3,861 3,861 3,861 3,861 3,861
District of Columbia 3,861 3,861 3,861 3,861 3,861 3,861
Florida 52,779 84,572 75,904 75,567 63,122 61,391
Georgia 15,518 10,671 12,134 13,297 14,129 14,499
Hawaii 3,906 4,373 4,212 3,861 3,861 3,861
Idaho 3,861 3,861 3,861 3,861 3,861 3,861
Illinois 34,536 30,144 31,313 30,376 32,278 32,362
Indiana 15,255 15,746 15,513 17,845 18,962 17,920
Iowa 9,281 12,582 11,601 13,567 12,116 11,784
Kansas 7,597 8,708 8,338 9,656 9,776 9,508
Kentucky 10,344 12,591 11,880 13,545 13,243 12,880
Louisiana 11,096 12,194 11,805 13,236 14,026 13,196
Maine 3,861 4,412 4,191 4,401 4,573 4,367
Maryland 12,657 7,493 9,097 8,275 8,793 9,739
Massachusetts 21,260 21,336 21,194 16,581 17,619 17,845
Michigan 25,255 22,673 23,328 24,955 26,518 25,977
Minnesota 12,524 11,187 11,530 12,555 13,341 13,043
Mississippi 7,475 11,390 10,294 12,117 9,871 9,600
Missouri 16,167 19,634 18,537 20,943 20,598 20,033
Montana 3,861 3,861 3,861 3,861 3,861 3,861
Nebraska 5,065 6,312 5,924 6,583 6,403 6,228
Nevada 3,861 3,861 3,861 3,861 3,861 3,861
New Hampshire 3,861 3,861 3,861 3,861 3,861 3,861
New Jersey 26,342 19,472 21,503 16,166 17,179 19,005
New Mexico 3,861 3,861 3,861 4,110 4,367 4,140
New York 63,446 63,949 63,441 49,123 52,198 52,919
North Carolina 18,173 18,457 18,269 20,960 22,273 21,114
North Dakota 3,861 3,861 3,861 3,861 3,861 3,861
Ohio 31,343 33,402 32,610 35,932 38,182 36,091
Oklahoma 9,608 13,411 12,298 13,697 12,221 11,886
Oregon 8,514 10,148 9,623 10,250 10,518 10,141
Pennsylvania 41,532 55,306 51,176 52,297 50,326 48,946
Rhode Island 3,861 4,959 4,557 4,301 4,176 4,061
South Carolina 8,873 9,592 9,327 10,826 11,400 10,777
South Dakota 3,861 3,861 3,861 3,861 3,861 3,861
Tennessee 14,155 16,609 15,811 17,478 17,809 17,265
Texas 40,540 28,588 32,171 34,289 36,435 37,429
Utah 3,861 3,861 3,861 3,861 3,861 3,861
Vermont 3,861 3,861 3,861 3,861 3,861 3,861
Virginia 15,282 7,369 9,890 11,149 11,847 12,904
Washington 12,829 10,243 10,983 11,387 12,099 12,196
West Virginia 5,816 9,246 8,307 9,276 7,456 7,251
Wisconsin 14,124 15,159 14,770 17,121 18,129 17,066
Wyoming 3,861 3,861 3,861 3,861 3,861 3,861
================================================================================
United States $757,357 $757,357 $757,3 $757,3 $757,3 $757,3
57 57 57 57
--------------------------------------------------------------------------------
Note: Totals do not add because of rounding.
THE GAO OPTIONS IMPROVE EQUITY
RELATIVE TO THE CURRENT FORMULA
-------------------------------------------------------- Appendix VI:5
In general, the GAO formula options offer substantial improvements
over the current formula allocations. Using the beneficiary equity
criteria--potential caseloads and costs--every GAO option improves
upon the current formula. Under taxpayer equity, all options offer
an improvement over the current formula allocation, except option 1.
Table VI.6 reports summary measures of equity improvement\5 for the
six options. Larger values indicate greater distributional
inequities, and smaller values indicate smaller distributional
inequities. The first row in table VI.6 shows the summary statistic
for beneficiary equity for the current formula allocations and the
GAO options. The second row in the table shows the taxpayer equity
statistics.
Under the beneficiary equity criteria, the beneficiary equity option
shows dramatic improvement over the current distribution. The
remaining four GAO options show higher levels of beneficiary
inequity. Under the taxpayer equity criteria, every GAO option
significantly improves upon taxpayer equity. For example, the
beneficiary equity option has the highest taxpayer inequity among the
GAO options, and yet the taxpayer inequity under this option is less
than half the value under the current formula. The taxpayer equity
option has the least taxpayer inequity.
Table VI.6
Equity Statistics for Current AoA
Allocations and the GAO Options Using
Social Need
Curren
t
formul
Equity criteria a # 1 # 2 # 3 # 4 # 5 # 6
-------------------- ------ ------------ -------- -------- ---- ---- ----
Beneficiary 0.088 0 0.355 0.263 0.34 0.27 0.22
0 8 2
Taxpayer 0.590 0.236 0.012 0.072 0.08 0.14 0.15
7 2 0
--------------------------------------------------------------------------------
The GAO beneficiary equity option outperforms the current formula
allocations under our equity standards. The beneficiary equity
option has the best beneficiary equity, and yet still improves upon
the current formula under taxpayer equity. The drawback to the
beneficiary equity option, however, is the large number of states
losing funds under this option: 31 states lose funding, while only
12 gain (see table IV.2).
On the other hand, the balanced equity options offer a blend of the
beneficiary and taxpayer equity options without as large a
redistribution of money and with more states losing funds than
gaining. For example, option 5 shows improvements over the current
allocations and has more states gaining funds than losing.
--------------------
\5 The summary measures are weighted standard deviations. Larger
values indicate greater distributional inequities among the states,
and smaller values indicate smaller distributional inequities. For
beneficiary equity, the values analyzed are the grants per person in
need, as reported in table V.I. For taxpayer equity, the values are
the tax burden state taxpayers would have to pay to finance an
average basket of title III services, as reported in table V.2.
SOME STATES ARE CONSISTENTLY
UNDERFUNDED RELATIVE TO THE
EQUITY STANDARDS CONSIDERED
-------------------------------------------------------- Appendix VI:6
Overall, through our calculations, the six options presented show
that three states-- Arizona, Florida, and North Carolina--
systematically receive lower funding under the current formula than
under any of the six options (see table VI.7). Another 15 states
receive less funding than under five of the six options presented.
Because these options were designed to show the full range of
possible outcomes under the two equity standards, we conclude that
these 18 states are underfunded based on criteria that reflect
potential caseloads, the cost of providing services, and financing
capacity.
Conversely, eight states--Colorado, Idaho, Illinois, Michigan,
Nevada, New Hampshire, Utah, and Virginia--receive higher funding
under the current formula than under any of the six equity-based
formula options. An additional eight states receive higher funding
under the current formula than under five of the six options we
considered. Consequently, we conclude that these 16 states receive
more funding under the current formula than would be justified on the
basis of our three need indicators of potential caseloads, cost, and
financing capacity. Overfunded states are generally scattered across
the country but outside the Southeast.
Table VI.7
States Systematically Losing or Gaining
Funds
States receiving less funding States receiving more funding
under current formula under current formula
----------------------------- -----------------------------
Alabama California
Arizona Colorado
Arkansas Connecticut
Florida Georgia
Iowa Idaho
Kentucky Illinois
Louisiana Maryland
Mississippi Michigan
Missouri Minnesota
Nebraska Nevada
North Carolina New Hampshire
Oklahoma New Jersey
Oregon Texas
Pennsylvania Utah
Rhode Island Virginia
South Carolina Washington
Tennessee
West Virginia
------------------------------------------------------------
Note: States in boldface represent those states that receive
more/less funding under all GAO formula options.
PROVIDING A TRANSITION TO A NEW
OAA FORMULA
========================================================= Appendix VII
The adoption of a more equitable formula for distributing OAA grant
funds could cause some states to receive fewer funds so that others
with greater needs could receive more. When a new federal aid
formula is implemented, it often provides a transition period so that
grant recipients have time to adjust, especially those recipients
whose grants will be reduced. The rationale for the transition to a
new allocation formula is that a phase-in period helps to avoid
dramatic changes in state funding, especially for states facing
significant reductions. A new formula should foster predictability
and stability so as to allow states to develop long-range planning
and program commitments, as well as to avoid major disruptions to
existing state services.
A redesigned interstate funding formula would mean changes for the
states, both in the standards for receiving title III funding and in
the amounts received. The Congress would need to determine the rate
at which and the way in which those changes would be implemented.
Central to this issue would be a choice between holding title III
allocations at the current level or raising them so that no state
experiences a reduction in its present level of funding.
PROVIDING A TRANSITION
------------------------------------------------------- Appendix VII:1
Under the following transition alternative, the overall title III
appropriation is assumed to remain at its current level of $757
million. We illustrate one formula transition that would gradually
shift grant funding from the existing formula to a new formula over a
5-year period (see table VII.1). The allocations are divided between
two formulas: the current allocation formula and formula option 5,
described in appendix VI. During the transition period, the amount
of money allocated under the current formula is reduced by 20 percent
each year; the amount of money allocated under the new formula is
increased by 20 percent each year. Table VII.1 shows the
transitional allocations starting with the current allocation in
fiscal year 1993 and ending in fiscal year 1998 with the new formula
allocation.
Alternative transition periods can be formulated to either shorten
the time to a new formula or lengthen the time. For example, to
minimize the disruptive effect of a new formula, the transition
period could be extended to 10 years, whereby the changes in
allocations would become smaller.
Table VII.1
Transition from Current Formula
Allocations to the Balanced Equity
Formula #5, 5-Year Transition
(Dollars in thousands)
FY
1993
curren FY FY FY FY
t 1994 1995 1996 1997 FY 1998
formul 80-20 60-40 40-60 20-80 GAO
State a split split split split formula
----------------------------- ------ ------ ------ ------ ------ ---------
Alabama $12,44 $13,05 $13,65 $14,26 $14,87 $15,478
4 1 8 4 1
Alaska 3,861 3,861 3,861 3,861 3,861 3,861
Arizona 9,617 10,207 10,797 11,387 11,977 12,567
Arkansas 8,535 8,838 9,141 9,444 9,747 10,050
California 71,594 66,170 60,745 55,321 49,897 44,472
Colorado 7,580 7,136 6,693 6,250 5,807 5,363
Connecticut 10,789 10,081 9,373 8,666 7,958 7,251
Delaware 3,861 3,861 3,861 3,861 3,861 3,861
District of Columbia 3,861 3,861 3,861 3,861 3,861 3,861
Florida 48,285 51,253 54,220 57,187 60,155 63,122
Georgia 15,230 15,010 14,790 14,569 14,349 14,129
Hawaii 3,935 3,920 3,905 3,890 3,876 3,861
Idaho 3,907 3,897 3,888 3,879 3,870 3,861
Illinois 35,517 34,869 34,221 33,573 32,926 32,278
Indiana 16,668 17,127 17,586 18,045 18,503 18,962
Iowa 10,441 10,776 11,111 11,446 11,781 12,116
Kansas 8,399 8,674 8,950 9,225 9,501 9,776
Kentucky 11,425 11,788 12,152 12,516 12,879 13,243
Louisiana 11,574 12,064 12,555 13,045 13,535 14,026
Maine 4,096 4,191 4,287 4,382 4,478 4,573
Maryland 12,106 11,443 10,781 10,118 9,456 8,793
Massachusetts 20,091 19,596 19,102 18,608 18,113 17,619
Michigan 26,554 26,547 26,540 26,532 26,525 26,518
Minnesota 13,128 13,171 13,213 13,256 13,299 13,341
Mississippi 7,974 8,353 8,733 9,112 9,492 9,871
Missouri 17,394 18,035 18,676 19,317 19,957 20,598
Montana 3,861 3,861 3,861 3,861 3,861 3,861
Nebraska 5,619 5,776 5,933 6,090 6,247 6,403
Nevada 3,953 3,934 3,916 3,898 3,879 3,861
New Hampshire 3,930 3,916 3,903 3,889 3,875 3,861
New Jersey 25,059 23,483 21,907 20,331 18,755 17,179
New Mexico 4,065 4,125 4,186 4,246 4,307 4,367
New York 59,529 58,063 56,596 55,130 53,664 52,198
North Carolina 18,116 18,948 19,779 20,610 21,441 22,273
North Dakota 3,861 3,861 3,861 3,861 3,861 3,861
Ohio 33,733 34,623 35,513 36,402 37,292 38,182
Oklahoma 10,408 10,771 11,133 11,496 11,859 12,221
Oregon 8,822 9,161 9,500 9,839 10,179 10,518
Pennsylvania 43,851 45,146 46,441 47,736 49,031 50,326
Rhode Island 4,004 4,039 4,073 4,107 4,141 4,176
South Carolina 8,940 9,432 9,924 10,416 10,908 11,400
South Dakota 3,861 3,861 3,861 3,861 3,861 3,861
Tennessee 14,663 15,292 15,921 16,551 17,180 17,809
Texas 40,017 39,301 38,584 37,868 37,152 36,435
Utah 4,012 3,982 3,952 3,922 3,891 3,861
Vermont 3,861 3,861 3,861 3,861 3,861 3,861
Virginia 15,285 14,597 13,910 13,222 12,535 11,847
Washington 12,808 12,667 12,525 12,383 12,241 12,099
West Virginia 6,788 6,921 7,055 7,188 7,322 7,456
Wisconsin 15,585 16,094 16,603 17,111 17,620 18,129
Wyoming 3,861 3,861 3,861 3,861 3,861 3,861
================================================================================
United States $757,3 $757,3 $757,3 $757,3 $757,3 $757,357
57 57 57 57 57
--------------------------------------------------------------------------------
Note: Totals do not add because of rounding.
(See figure in printed edition.)Appendix VIII
COMMENTS FROM THE DEPARTMENT OF
HEALTH AND HUMAN SERVICES
========================================================= Appendix VII
(See figure in printed edition.)
(See figure in printed edition.)
MAJOR CONTRIBUTORS TO THIS REPORT
========================================================== Appendix IX
Jerry Fastrup, Assistant Director, (202) 512-7211
John Vocino, Evaluator-in-Charge
Greg Dybalski, Senior Economist
RELATED GAO PRODUCTS
============================================================ Chapter 0
Maternal and Child Health: Block Grant Funds Should Be Distributed
More Equitably (GAO/HRD-92-5, Apr. 2, 1992).
Substance Abuse Funding: High Urban Weight Not Justified by
Urban-Rural Differences in Need (GAO/T-HRD-91-38, June 25, 1991).
Mental Health Grants: Funding Not Distributed in Accordance With
State Needs (GAO/T-HRD-91-32, May 16, 1991).
Adequacy of the Administration on Aging's Provision of Technical
Assistance for Targeting Services Under the Older Americans Act
(GAO/T-PEMD-91-3, Apr. 25, 1991).
Drug Treatment: Targeting Aid to States Using Urban Population as
Indicator of Drug Use (GAO/HRD-91-17, Nov. 27, 1990).
Federal Formula Programs: Outdated Population Data Used to Allocate
Most Funds (GAO/HRD-90-145, Sept. 27, 1990).
Older Americans Act: Administration on Aging Does Not Approve
Interstate Funding Formulas (GAO/HRD-90-85, June 8, 1990).