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). 

