Medicaid
Strategies to Help States Address Increased Expenditures during Economic Downturns Gao ID: GAO-07-97 October 18, 2006During economic downturns, states may struggle to finance Medicaid, a federal-state health financing program for certain low-income individuals. States receive federal matching funds for their Medicaid programs according to a statutory formula based on each state's per capita income (PCI) in relation to national PCI. The number of individuals eligible for Medicaid can increase during downturns as a result of rising unemployment. GAO previously reported that any federal assistance to respond to downturns should be well-timed and account for each state's fiscal circumstances. GAO was asked to consider strategies to help states offset increased Medicaid expenditures in the event of future economic downturns. GAO analyzed policy proposals and federal and state strategies to cope with downturns to identify and develop three potential strategies. GAO explored (1) targeting assistance to states most affected by a downturn, (2) using 2 instead of 3 years of PCI data to compute federal matching rates to more accurately reflect states' economic circumstances, and (3) giving states the option to obtain assistance based on their own determination of need. GAO discussed the strategies with experts, identified design considerations, and analyzed each strategy's potential effects. The Department of Health and Human Services received a draft of this report and did not comment.
No single strategy or combination of strategies can meet the varied economic needs of all states at all times, but one or more of the following strategies GAO analyzed may be useful for Congress as it deliberates how to help states cope with Medicaid expenditure increases during economic downturns. Any potential strategy would need to be considered within the context of broader health care and fiscal challenges, including continually rising health care costs, a growing elderly population, and Medicaid's increasing share of the federal budget. Supplemental federal assistance provided to states based on changes in states' unemployment rates would target funds to states most affected by downturns. GAO used unemployment as the key variable because it reflects the potential for increases in Medicaid enrollment resulting from an economic downturn. GAO created a simulation model to illustrate this strategy, which also adjusts the amount of funding relative to each state's per person spending on Medicaid services. The model captured about 90 percent of states' increases in unemployment during 2001, and all states would have received some federal assistance. A few states with relatively earlier or later increases in unemployment would not have received a commensurate amount of funding because a portion of their downturns was outside the period of the simulation. Using 2 years of PCI data to compute federal matching rates instead of the 3 years required under current law did not result in matching rates that consistently reflected current economic circumstances, as measured by PCI or changes in states' unemployment. Under certain conditions, reducing the number of years of data also skewed rates farther from current economic conditions. This strategy would also result in greater annual fluctuations in matching rates for most states. For these reasons, eliminating 1 year of PCI data is not a feasible alternative to help states address increased Medicaid expenditures. States could be given the option to decide whether and to what extent they need federal assistance, through a loan, either from the federal government or from the private capital market (subsidized and possibly guaranteed by the federal government), or a Medicaid-specific national "rainy day" fund. This strategy's viability would depend on states' willingness to pay into a national fund or assume additional Medicaid-specific debt and on states' accepting the terms of the loan or rainy day fund. Federal funding required for this strategy would vary depending on design factors such as whether federal loan subsidies or Medicaid rainy day matching funds are included.
GAO-07-97, Medicaid: Strategies to Help States Address Increased Expenditures during Economic Downturns
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United States Government Accountability Office:
GAO:
Report to Congressional Requesters:
October 2006:
Medicaid:
Strategies to Help States Address Increased Expenditures during
Economic Downturns:
GAO-07-97:
GAO Highlights:
Highlights of GAO-07-97, a report to congressional requesters:
Why GAO Did This Study:
During economic downturns, states may struggle to finance Medicaid, a
federal-state health financing program for certain low-income
individuals. States receive federal matching funds for their Medicaid
programs according to a statutory formula based on each state's per
capita income (PCI) in relation to national PCI. The number of
individuals eligible for Medicaid can increase during downturns as a
result of rising unemployment. GAO previously reported that any federal
assistance to respond to downturns should be well-timed and account for
each state‘s fiscal circumstances. GAO was asked to consider strategies
to help states offset increased Medicaid expenditures in the event of
future economic downturns.
GAO analyzed policy proposals and federal and state strategies to cope
with downturns to identify and develop three potential strategies. GAO
explored (1) targeting assistance to states most affected by a
downturn, (2) using 2 instead of 3 years of PCI data to compute federal
matching rates to more accurately reflect states‘ economic
circumstances, and (3) giving states the option to obtain assistance
based on their own determination of need. GAO discussed the strategies
with experts, identified design considerations, and analyzed each
strategy‘s potential effects.
The Department of Health and Human Services received a draft of this
report and did not comment.
What GAO Found:
No single strategy or combination of strategies can meet the varied
economic needs of all states at all times, but one or more of the
following strategies GAO analyzed may be useful for Congress as it
deliberates how to help states cope with Medicaid expenditure increases
during economic downturns. Any potential strategy would need to be
considered within the context of broader health care and fiscal
challenges, including continually rising health care costs, a growing
elderly population, and Medicaid‘s increasing share of the federal
budget.
Supplemental federal assistance provided to states based on changes in
states‘ unemployment rates would target funds to states most affected
by downturns. GAO used unemployment as the key variable because it
reflects the potential for increases in Medicaid enrollment resulting
from an economic downturn. GAO created a simulation model to illustrate
this strategy, which also adjusts the amount of funding relative to
each state‘s per person spending on Medicaid services. The model
captured about 90 percent of states‘ increases in unemployment during
2001, and all states would have received some federal assistance. A few
states with relatively earlier or later increases in unemployment would
not have received a commensurate amount of funding because a portion of
their downturns was outside the period of the simulation.
Using 2 years of PCI data to compute federal matching rates instead of
the 3 years required under current law did not result in matching rates
that consistently reflected current economic circumstances, as measured
by PCI or changes in states‘ unemployment. Under certain conditions,
reducing the number of years of data also skewed rates farther from
current economic conditions. This strategy would also result in greater
annual fluctuations in matching rates for most states. For these
reasons, eliminating 1 year of PCI data is not a feasible alternative
to help states address increased Medicaid expenditures.
States could be given the option to decide whether and to what extent
they need federal assistance, through a loan, either from the federal
government or from the private capital market (subsidized and possibly
guaranteed by the federal government), or a Medicaid-specific national
’rainy day“ fund. This strategy‘s viability would depend on states‘
willingness to pay into a national fund or assume additional Medicaid-
specific debt and on states‘ accepting the terms of the loan or rainy
day fund. Federal funding required for this strategy would vary
depending on design factors such as whether federal loan subsidies or
Medicaid rainy day matching funds are included.
[hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-07-97].
To view the full product, including the scope and methodology, click on
the link above. For more information, contact Kathryn G. Allen at (202)
512-7118 or Stanley J. Czerwinski at (202) 512-6806.
Contents:
Letter:
Results in Brief:
Background:
Targeting Supplemental Federal Assistance to States Requires Careful
Consideration to Address Differences in States' Downturns:
Using Fewer Years of Data to Compute Matching Rates Would Not
Consistently Result in Assistance that Better Reflects States' Current
Economic Conditions:
States Could Determine Their Own Needs for Assistance with Medicaid-
Specific Loans or a National Rainy Day Fund:
Concluding Observations:
Agency Comments:
Appendix I: Objectives, Scope, and Methodology:
Appendix II: Designing a Strategy of Targeted Supplemental Medicaid
Assistance:
Appendix III: Designing a Strategy to Better Reflect States' Current
Economic Conditions:
Appendix IV: Information on Selected Intergovernmental Loan Programs
and State Rainy Day Funds:
Appendix V: GAO Contacts and Staff Acknowledgments:
Tables:
Table 1: Average Annual State Medicaid Expenditures per Beneficiary, by
Population Group, 2003:
Table 2: Key Design Decisions, Parameters of GAO's Model, and
Alternative Parameters that Could Be Applied for Targeting Supplemental
Medicaid Assistance to States:
Table 3: Characteristics of Economic Downturns and Their Effect on
States' Receipt of Supplemental Assistance:
Table 4: Analysis of Three Strategies to Help States Respond to
Increased Medicaid Costs during Economic Downturns:
Table 5: Simulated Supplemental Assistance for Economic Conditions of
the 2001 Downturn:
Table 6: Matching Rates Used to Analyze Strategy:
Table 7: Comparison of States' Year-to-Year Differences in 2-Year and 3-
Year Matching Rates, 1990-2004:
Figures:
Figure 1: Medicaid Beneficiaries and Expenditures by Population Group,
Fiscal Year 2003:
Figure 2: States' Percentage Point Change in Unemployment, March 2001
to March 2002:
Figure 3: Number of States Experiencing a 10 Percent or More Increase
in Their Unemployment Rate, 2000 to 2004:
Figure 4: Timing of Data Used to Calculate States' Federal Matching
Rates for Fiscal Year 2006:
Figure 5: Number of States with a 10 Percent or More Increase in Their
Unemployment Rate Compared to the Same Quarter 1 Year Earlier, 1979-
2004:
Figure 6: Total of States' Quarterly Increase in Unemployment Covered
by Simulation Model's Supplemental Assistance:
Figure 7: Effects of Alternative Threshold Parameters on the Start and
Number of Quarters of Supplemental Assistance, 2000 through 2005:
Figure 8: Simulated Supplemental Assistance for a State with an Early,
Long, and Deep Economic Downturn:
Figure 9: Simulated Supplemental Assistance for a State with a
Relatively Early, Long-Lasting, and Shallow Downturn:
Figure 10: Simulated Supplemental Assistance for a State with a Late,
Short, and Shallow Downturn:
Figure 11: Simulated Supplemental Assistance for a State with a Short,
Deep Downturn:
Figure 12: Correlations of the Changes in the 3-Year and 2-Year
Matching Rates with Changes in PCI:
Figure 13: Correlations of the Changes in the 3-Year and 2-Year
Matching Rates with Changes in the Unemployment Rates:
Figure 14: Correlations of the Changes in the Simulated Matching Rate
with the Changes in PCI, 1990 to 2004:
Figure 15: Correlations of the Changes in 3-Year and 2-Year Matching
Rates with the Changes in the Simulated Matching Rate:
Abbreviations:
BCCA: Breast and Cervical Cancer Act:
BEA: Bureau of Economic Analysis:
BLS: Bureau of Labor Statistics:
CMS: Centers for Medicare & Medicaid Services:
CWSRF: Clean Water State Revolving Fund:
CDL: Community Disaster Loan:
EPA: Environmental Protection Agency:
FEMA: Federal Emergency Management Agency:
FMAP: Federal Medical Assistance Percentage:
FUA: Federal Unemployment Account:
GDP: gross domestic product:
JGTRRA: Jobs and Growth Tax Relief Reconciliation Act of 2003:
NASBO: National Association of State Budget Officers:
NBER: National Bureau of Economic Research:
NCSL: National Conference of State Legislatures:
PCI: per capita income:
TANF: Temporary Assistance for Needy Families:
UI: Unemployment Insurance:
[End of section]
United States Government Accountability Office: Washington, DC 20548:
October 18, 2006:
The Honorable Susan M. Collins:
Chairman:
Committee on Homeland Security
and Governmental Affairs:
United States Senate:
The Honorable Gordon H. Smith:
Chairman:
Special Committee on Aging:
United States Senate:
The Honorable Jeff Bingaman:
The Honorable Ben Nelson:
The Honorable John D. Rockefeller, IV: United States Senate:
During economic downturns, states can experience difficulties financing
programs such as Medicaid, a joint federal-state health financing
program that covers medical costs for certain categories of low-income
individuals. Economic downturns result in rising unemployment, which
can lead to increases in the number of individuals who are eligible for
Medicaid coverage, and in declining tax revenues, which can lead to
less available revenue with which to fund coverage of additional
enrollees. For example, during a period of economic downturn, Medicaid
enrollment rose 8.6 percent between 2001 and 2002, which was largely
attributed to states' increases in unemployment. During this same time
period, state tax revenues fell 7.5 percent. Further complicating the
challenge of responding to increased Medicaid expenditures during
economic downturns is the fact that Medicaid funding consumed a growing
share of state general fund or operating budgets, increasing from 15
percent in 1994 to 18 percent in 2004.:
Both the federal government and the states have responded to the
demands of Medicaid expenditure increases related to economic
downturns. Following the 2001 recession, Congress passed the Jobs and
Growth Tax Relief Reconciliation Act of 2003 (JGTRRA), which provided
$10 billion in fiscal relief through a temporary increase in federal
Medicaid funding for all states, as well as $10 billion in general
assistance divided among the states to be used for essential government
services. States have responded to downturns in various ways, such as
by using cost-cutting program modifications; budget stabilization, or
"rainy day," funds; and borrowing.
Problems that states face in financing Medicaid cost increases during
an economic downturn can be exacerbated because, by design, the formula
used to calculate the amount of federal assistance that states receive
for Medicaid includes data that are as much as 5 years old. The federal
government matches state Medicaid spending according to this formula,
which is based on each state's per capita income (PCI) in relation to
national PCI. The amount of federal assistance states receive for
Medicaid is determined by a statutory formula known as the Federal
Medical Assistance Percentage (FMAP), or federal matching rate. The
statute specifies that matching rates be calculated 1 year before the
fiscal year in which they are effective, using a 3-year average of the
most recently available PCI data reported by the Department of
Commerce. For example, fiscal year 2007 matching rates were calculated
at the beginning of fiscal year 2006 using a 3-year average of PCI for
2002 through 2004. Consequently, federal matching rates reflect
economic conditions that existed several years earlier.
Recognizing the complex combination of factors affecting states during
economic downturns--increased unemployment, declining state revenues,
and increased downturn-related Medicaid costs--policymakers and others
have considered the possibility of establishing a legislative response
that would help states better cope with Medicaid cost increases. Any
potential legislative response would need to be considered within the
context of broader health care and fiscal challenges--including
continually rising health care costs, a growing elderly population, and
Medicare and Medicaid's increasing share of the federal budget. Absent
fundamental Medicaid reform, legislative actions and proposals have
generally focused on targeting assistance to states, improving the
timing of the assistance provided, or helping states build financial
reserves for Medicaid.:
In 2004, we reported on the assistance provided by the federal
government to the states through JGTRRA, noting that federal assistance
is most effective when it takes into account each state's fiscal
circumstances as well as when and how severely states are affected by
an economic downturn. On the basis of these findings, you asked us to
consider strategies to help states address the increased costs of
Medicaid in any future economic downturn. An underlying assumption was
that, in the event of any future nationwide economic downturn, Congress
would act to appropriate additional funds, as it did following the 2001
recession. Your interest was in exploring strategies whereby any
additional funds could be accurately timed and targeted to respond to a
downturn but could also be established in advance so that Congress
would not have to wait to act until a nationwide economic downturn is
clearly identified. Accordingly, we reviewed prior GAO reports, policy
proposals, and federal and state strategies to cope with downturns to
identify and develop three potential strategies. In this report, we
explore the design considerations and possible effects of three
potential strategies aimed at helping states with their share of
Medicaid expenditures during an economic downturn by (1) targeting
supplemental funds to specific states on the basis of the relative
depth and duration of their economic downturns (as measured by changes
in their unemployment rates) as well as the extent to which their
Medicaid costs are likely to increase during a downturn, (2) using 2
instead of 3 years of PCI data to compute federal matching rates in an
attempt to better reflect states' current economic conditions, and (3)
providing states with options for obtaining assistance through a
Medicaid-specific rainy day fund or loan based on their own
determination of need.
To do our work, we analyzed research, including prior GAO reports that
examined the effects of economic downturns on Medicaid enrollment and
expenditures, the responsiveness of federal matching rates to economic
cycles, and policy proposals to help states respond to increased
program costs during downturns. We discussed the three potential
strategies with technical experts and representatives of key research
groups and state associations to gain insights on the extent to which
strategies could help states cope with the Medicaid-related fiscal
consequences of economic downturns. These discussions provided an
opportunity to evaluate our selection of strategies and discuss their
potential effects. Our analysis of the strategies differed depending on
the strategy. For the first strategy, we identified factors to consider
in developing the targeting strategy and devised a model to illustrate
the extent to which different methods of targeting supplemental federal
funds would help states with their Medicaid programs during economic
downturns. The assumptions built into our model were based on our
analysis of data indicators from the past three recessions. We chose
unemployment as the key variable because it reflects the potential for
increases in Medicaid enrollments as a result of an economic downturn.
For the second strategy, which focused on using 2 years of PCI data--
instead of the 3 years currently required by statute--to compute
federal matching rates in an attempt to better reflect states' economic
conditions, we analyzed how closely the federal matching rates
approximated states' economic conditions and constructed statistical
simulations to compare the federal assistance states would receive
under the strategy with the assistance they would receive under current
policy. To determine the potential of each of the first two strategies
to help states address increased Medicaid spending, we simulated how
the implementation of the strategy could differ depending on the
timing, depth, and duration of a state's economic downturn. For the
third strategy, which focused on providing states with options for
obtaining assistance through a Medicaid-specific national rainy day
fund or loan, we identified key factors that could be considered, such
as the structure and use of existing intergovernmental loan programs
and state rainy day funds. We determined that the unemployment, PCI,
and Medicaid expenditure data used in this report are sufficiently
reliable for describing the three strategies and illustrating their
potential effects. (Appendixes I through IV provide additional detail
about our methodology for assessing the three strategies.) We did our
work from April 2005 through September 2006 in accordance with
generally accepted government auditing standards.
Results in Brief:
No single strategy or combination of strategies can meet the varied
economic needs of all states at all times. However, the following
strategies may be useful starting points for Congress as it deliberates
how to help states cope with increased Medicaid expenditures during any
future economic downturn. Having an automatic mechanism in place could
provide a targeted and predictable response. The three strategies we
explored are:
* target supplemental Medicaid assistance to states most affected by a
downturn,
* use 2 years of PCI data to compute federal matching rates in an
effort to better reflect states' current economic circumstances, and:
* give states the option to obtain assistance through a Medicaid-
specific national rainy day fund or loan.
First, a strategy that provides supplemental assistance to states based
on changes in their unemployment rates would target funds to states
most affected by a downturn, but the design of such a strategy would
need to address the different characteristics of states' downturns. To
illustrate this strategy, we constructed a simulation model that
adjusts the amount of funding a state would receive on the basis of
each state's percentage increases in unemployment and per person
spending on Medicaid services. Our simulation model captured about 90
percent of states' increases in unemployment during the most recent
(2001) recession. While all states received some amount of assistance
under the model, states that experienced the largest percentage
increases in unemployment within the same period in which the national
downturn occurred received the largest proportion of supplemental
assistance. A smaller number of states received less assistance than
others in our simulation model because their increased unemployment
occurred either earlier or later than the national downturn.
Adjustments to the strategy design, such as extending the period of
assistance, could be applied to ensure that states with earlier or
later increases in unemployment also receive a commensurate amount of
funding, but such adjustments would add to the overall cost of the
strategy. Targeted supplemental federal assistance to states most
affected by a downturn could assist states relative to the depth and
duration of a downturn as well as increased Medicaid expenditures while
also reflecting congressional policy choices.
Second, using 2 years of PCI data to compute federal matching rates
instead of the 3 years required under current law did not result in
matching rates that consistently reflected states' recent economic
circumstances as measured by PCI or changes in states' unemployment. To
illustrate this strategy, we analyzed matching rates that varied in the
number of years of data used and compared them with changes in PCI and
unemployment data. Our analysis of this strategy, however, did not
result in federal matching rates that consistently increased during
economic downturns. In some cases, reducing the number of years of data
also skewed rates farther away from current economic conditions. In
addition, this strategy would result in larger year-to-year changes in
matching rates for most states compared to the fluctuations experienced
under current law. For these reasons, eliminating a year of data from
the current matching formula does not present a feasible alternative to
help states address increased Medicaid expenditures during economic
downturns.
Third, giving states the opportunity to decide whether and to what
extent they need federal assistance could take the form of a loan,
either from the federal government or from the private capital market
(subsidized and possibly guaranteed by the federal government), or a
Medicaid-specific national rainy day fund. A federal Medicaid loan or
rainy day fund could give states greater autonomy in determining their
need for assistance, but utilization of either approach would depend on
states' own economic and political constraints as well as the program's
design. For example, limitations on the use of a loan may exist because
of a state's statutory or constitutional debt restrictions as well as
federal restrictions on the obligation of federal funds. While a
national rainy day fund could allow states to pool their risk and
thereby spend less than they would if they chose to establish
individual Medicaid rainy day funds at the state level, representatives
of some public policy and research organizations we contacted believed
that some states might be reluctant to contribute to a national fund
that other states or the federal government could draw from. Federal
funding required for this strategy would vary depending on design
factors such as the inclusion of federal subsidies or matching funds. A
loan or national rainy day fund strategy could help address states'
Medicaid funding challenges during downturns, but the feasibility and
utility of this strategy would depend on the design of the loan or
fund, among other possible constraints.
Background:
Economic downturns are characterized by reductions in output and income
as well as increased unemployment--and an accompanying increase in
Medicaid enrollment. Generally, as unemployment rises, the number of
households with incomes low enough to qualify for Medicaid coverage
also rises. Across the four broad populations eligible for Medicaid--
children; nondisabled, nonelderly adults; the elderly; and individuals
with disabilities--increases in eligibility for Medicaid during an
economic downturn are most concentrated among children and nondisabled,
nonelderly adults. One analysis of the relationship between
unemployment and Medicaid enrollment found that a 1 percentage point
increase in the unemployment rate would result in a nationwide increase
in Medicaid enrollment of more than 857,000 individuals--about 470,000
children and 387,000 nondisabled, nonelderly adults. While these two
populations make up the largest share of Medicaid beneficiaries, they
represent a small share of total Medicaid expenditures (see fig. 1).
Nondisabled, nonelderly adults and children make up 76 percent of
beneficiaries but account for just 30 percent of expenditures.
[Footnote 11]
Figure 1: Medicaid Beneficiaries and Expenditures by Population Group,
Fiscal Year 2003:
This figure depicts two pie charts showing beneficiaries and
expenditures.
Beneficiaries (48.2 million):
Children and nondisabled, nonelderly adults: 76%; Aged: 8%; Blind and
disabled: 16%.
Expenditures ($223.8 billion):
Children and nondisabled, nonelderly adults: 30%; Aged: 25%; Blind and
disabled: 45%.
[See PDF for image]
Source: GAO analysis of CMS data.
Note: Percentages are based on Centers for Medicare & Medicaid Services
(CMS) beneficiary and expenditure data for fiscal year 2003, the most
recent year for which data are available by type of beneficiary. Total
fiscal year 2003 expenditures for Medicaid were $276 billion.
Expenditures in figure 1 do not include administrative expenses,
disproportionate share hospital payments, and other expenses that could
not be attributed to specific beneficiary populations. Beneficiaries do
not include women covered under the Breast and Cervical Cancer Act
(BCCA) or individuals whose eligibility status is unknown.
[End of figure]
Additionally, increases in Medicaid enrollment and expenditures that
occur during nationwide downturns are not distributed evenly among
states because of differences in states' economic conditions, Medicaid
program design, and health care costs. Among states, downturns vary
widely in their onset, depth, and duration. For example, in March 2001,
the United States entered a recession, as indicated by a significant
decline in overall business activity, including an increase in
unemployment, over several months. During the next year, the national
unemployment rate increased by 1.4 percentage points, from 4.3 percent
to 5.7 percent. During this same period, the unemployment rate
increased by more than 2 percentage points in some states but actually
decreased in others (see fig. 2).
Figure 2: States' Percentage Point Change in Unemployment, March 2001
to March 2002:
This is a map of the United States with states shaded according to
percentage point change in five categories:
-0.5 to 0.1;
0.2 to 0.8;
0.9 to 1.5;
1.6 to 2.2;
2.3 to 2.9.
[See PDF for image]
Source: GAO.
Note: Percentage point unemployment changes from GAO, Health Insurance:
States' Protections and Programs Benefit Some Unemployed Individuals,
GAO-03-191 (Washington, D.C.: Oct. 25, 2002).
[End of figure]
The Medicaid enrollment and expenditure increases associated with a
given increase in unemployment also vary across states because of
differences in the scope of states' coverage for groups most affected
by the downturn. For example, in 2003, average annual state
expenditures for children and nondisabled, nonelderly adults ranged
from $1,258 per beneficiary to $4,377, with a national average of
$1,823. Table 1 shows the range in states' Medicaid expenditures per
beneficiary by population group in 2003.
Table 1: Average Annual State Medicaid Expenditures per Beneficiary, by
Population Group, 2003:
State Medicaid expenditures: Average;
Children and nondisabled, nonelderly adults: $1,823;
Elderly: $14,540; Blind/disabled: $14,079.
State Medicaid expenditures: Minimum;
Children and nondisabled, nonelderly adults: 1,258;
Elderly: 6,781; Blind/disabled: 6,792.
State Medicaid expenditures: Maximum;
Children and nondisabled, nonelderly adults: 4,377;
Elderly: 26,384; Blind/disabled: 25,553.
Source: GAO analysis of CMS data.
Note: Data represent annual state expenditures per beneficiary.
[End of table]
The federal matching formula for Medicaid adjusts for differences in
state fiscal capacity and reduces program benefit disparities across
states by providing more federal funds to states with weaker tax bases
[Footnote 13]. The statutory matching formula calculates the federal
matching rate for each state on the basis of its PCI in relation to
national PCI as follows.
Federal matching rate = 1.00 - 0.45*[(State PCI) / (U.S. PCI)]2:
Relative PCI is included as a representation of states' funding
ability, as a combination of states' resources and people in poverty.
Squaring PCI has the effect of making PCI appear in the formula twice,
to reflect both states' resources and people in poverty. The formula
uses a 3-year average of PCI, the effect of which is to smooth out
fluctuations in state PCI so that it reflects longer-term trends rather
than short-term fluctuations of the business cycle. This smoothing
effect helps minimize year-to-year changes in federal matching funds,
which could be disruptive to states' budget planning.
The use of PCI as a measure of states' funding ability, however, is
problematic. Our prior work concluded that PCI is not a comprehensive
indicator of states' total available resources and thus does not
accurately represent states' funding ability. PCI also does not account
for the size and cost of serving states' poverty populations, which
vary considerably; for example, two states with low PCIs may have very
different proportions of elderly persons potentially eligible for
Medicaid and thus very different amounts of Medicaid spending.
Moreover, concerns have been raised regarding the age of the data used
to calculate the matching rate. In particular, the use of a 3-year PCI
average to compute matching rates, combined with a 1-year lag between
computation and implementation, means that the rates reflect economic
conditions that existed several years earlier.[Footnote 14]
To cope with the difficulties of financing Medicaid and other programs
during an economic downturn, states have, among other actions, borrowed
from intergovernmental loan programs and drawn down state budget
stabilization funds, which are also referred to as rainy day funds.
Intergovernmental loan programs can generally be categorized as direct
loans or loan guarantees. Both require federal involvement and can
include a federal subsidy, but loan guarantees are administered by
nonfederal lending institutions. Federal credit programs can vary in
their design and purpose. While federal guidelines offer broad
standards and principles for administering credit programs, specific
loan terms are set in statute or by administering agencies based on the
program's policy goals[Footnote 15]. According to the National
Association of State Budget Officers (NASBO), budget stabilization
funds exist in almost all states and allow states to set aside surplus
revenue during periods of economic growth for use during downturns.
States have different legislative requirements regarding the amount of
funds that can be accumulated, the process for releasing funds, and the
purposes for which funds can be used.
Targeting Supplemental Federal Assistance to States Requires Careful
Consideration to Address Differences in States' Downturns:
Providing supplemental federal assistance to states that is based on
changes in their unemployment rates would target additional Medicaid
funds to states most affected by a downturn, but the design of such a
strategy would need to address the different characteristics of states'
downturns. A strategy to target funds to states based on the duration
and depth of states' downturns assumes that, if authorized by Congress,
supplemental assistance could begin when predetermined thresholds are
reached. This approach is in contrast with the 2003 fiscal relief
package, JGTRRA, which provided assistance to states after the
recession had ended. This supplemental assistance strategy would leave
the existing Medicaid formula unchanged and add a new, separate
assistance formula that would operate only during times of economic
downturn and use variables and a distribution mechanism that differ
from those used for calculating matching rates. We identified key
design considerations for a strategy that would target funds based on
states' downturns and devised a model to illustrate the extent to which
it could help target supplemental federal Medicaid funds to states
experiencing economic downturns of different depths and durations. The
design we simulated in our model would deliver the most assistance to
the group of states that experience increases in unemployment within
the same relative period of time. However, a smaller number of states
with relatively earlier or later increases in unemployment would
receive less assistance. Further adjustments to the strategy design,
such as methods to extend the period of assistance, could be applied to
ensure that states with earlier or later increases in unemployment
would receive more quarters of supplemental assistance payments. Such
extensions, however, would add to the overall cost of the strategy.
Design Considerations:
Development of a strategy to target funds based on differences in
states' economic downturns involves three key considerations: (1)
deciding the starting and ending points of assistance, (2) determining
the amount of additional federal Medicaid assistance that will be
available, and (3) determining how this additional assistance will be
distributed to the states. Using data from the past three recessions,
we developed a model to simulate targeted supplemental assistance to
states experiencing increased unemployment. The model focused on
mechanisms to distribute supplemental federal funds depending on the
extent of a state's downturn and its relative Medicaid expenditures.
To determine the amount of federal assistance that would be provided
based on this strategy, our model incorporated a retrospective
assessment, which would involve assessing the increase in each state's
unemployment rate for a particular quarter compared to the same quarter
of the previous year. The economic trigger for this strategy would be
when 23 or more states had increased unemployment of 10 percent or more
compared to the unemployment rate that existed for the same quarter 1
year earlier (such as from 5 percent to 5.5 percent unemployment). This
is an increase of 10 percent compared to the unemployment rate of the
same quarter in the previous year and not a 10 percentage point change
in unemployment rates (such as from 5 percent unemployment to 15
percent). We chose these two threshold values--23 or more states and
increased unemployment of 10 percent or more--to work in tandem to
ensure that the national economy had entered a downturn and that the
majority of states were not yet in recovery from the downturn [Footnote
16]. Table 2 summarizes the key design decisions, our model's
parameters, and some alternative parameters. (See app. II for
additional discussion of the key design decisions incorporated into the
GAO model.)
Table 2: Key Design Decisions, Parameters of GAO's Model, and
Alternative Parameters that Could Be Applied for Targeting Supplemental
Medicaid Assistance to States:
Key design decision: Establish starting and ending point;
Parameters of GAO model[A]: Starting point;
* The starting point would be when 23 or more states show a quarterly
state unemployment rate increase of 10 percent or more (the
retrospective assessment).[C];
* Once started, any state with any increase in unemployment would be
eligible to receive assistance; Alternative parameters[B]: Starting
point;
* Varying numbers of states and percentage changes in unemployment
could be applied;
* Indicators other than unemployment--or indicators used in conjunction
with unemployment--could be used to start the program;
* Congressional action could be required to start the program (rather
than establishing an automatic trigger based on threshold values).
Key design decision: Establish starting and ending point;
Parameters of GAO model[A]: Ending point;
* The ending point would be when fewer than 23 states had quarterly
unemployment increases of 10 percent or more;
* The number of quarters that assistance continued would depend on the
severity and duration of the economic downturn; Alternative
parameters[B]: Ending point;
* Varying numbers of states, percentage changes in unemployment, and
quarters of assistance could be applied;
* Other indicators could be used to end the program;
* Congressional action could be required to end the program (rather
than establishing an automatic stopping point based on threshold
values).
Key design decision: Determine amount of federal assistance to be
available;
Parameters of GAO model[A]:
* The amount of federal assistance would be determined on the basis of
the relationship between changes in unemployment and increases in
Medicaid expenditures;
* Based on the depth of the 2001 recession, the amount of federal
assistance would have been $4.2 billion; Alternative parameters[B]:
* The amount of federal assistance could be set by Congress based on
factors other than changes in unemployment and increases in Medicaid
expenditures.
Key design decision: Determine distribution of assistance;
Parameters of GAO model[A]:
* Funds would be distributed quarterly through a targeted supplement to
states' federal matching rates;
* Distribution amount varies based on a state's change in unemployment
and its average cost of providing services to children and nondisabled,
nonelderly adults; Alternative parameters[B]:
* Model could allow for a lump-sum grant distributed on some schedule
other than quarterly payments tied to states' federal matching rates;
* Retroactive rebate payments could be provided to the states based on
their actual increased expenditures;
* Assistance could be determined based on an alternative threshold
(other than a 10 percent or more increase in unemployment).
Source: GAO.
[A] Our model assumed that once enacted, the targeted assistance would
operate without the need for congressional action to initiate
assistance during an economic downturn.
[B] Most alternative parameters were not simulated in our model.
Appendix II provides additional details on alternative parameters that
could be used.
[C] The retrospective assessment is based on a quarterly moving average
of seasonally adjusted unemployment data for the 12 most recent months.
The GAO model included these parameters based on quantitative analysis
of prior recessions combined with subjective judgment. We chose these
threshold values based on evidence which indicated that 23 states
experiencing a 10 percent or more increase in unemployment provided
considerable certainty that an economic slowdown had extended
nationwide and that at least 23 states had not yet entered a recovery.
These parameters could be adjusted up or down to tighten or loosen the
threshold for providing supplemental assistance. The use of
unemployment as an indicator also reflects research establishing a
connection between increased unemployment and Medicaid enrollment.
[End of table]
To determine the amount of supplemental federal assistance needed to
help states address increased Medicaid expenditures during a downturn,
we relied on research that estimated a relationship between changes in
unemployment and changes in Medicaid spending while holding constant
other factors that influence Medicaid spending[Footnote 17] . Using
data from the 2001 and the 1991-1992 recessions and this research, our
model assumes federal assistance of approximately $4.2 billion, which
would be less than 1 percent of Medicaid spending for a 2-year
period[Footnote18]. Depending on the fund distribution method,
budgeting sufficient amounts for the supplemental federal funding would
require estimating the potential economic effects of a downturn because
forecasting states' unemployment increases is difficult. If the
targeting strategy was designed to function as an open-ended grant that
provides states with an incremental increase to their matching rates,
then states' expenditures would be matched as the downturn-induced
growth of enrollments increased their Medicaid spending. However, if
the program was designed to provide a lump-sum amount of assistance or
to function as a closed-ended assistance program, then setting a
funding level would be necessary.
Within the key parameters that frame this strategy are many variations
in design that could be considered to achieve different policy goals.
For example, if it was deemed important to provide states with a longer
period of assistance, the retrospective assessment of the increase in
the unemployment rate could be extended in order to help states with
longer-lasting or late downturns. Additional criteria could be
established to accomplish other policy objectives, such as controlling
federal spending by limiting the number of quarters of payments or
stopping payments after predetermined spending caps are reached.
Effects:
Our simulation model showed that a retrospective assessment resulting
in a 10 percent or more increase in unemployment in 23 or more states
would trigger supplemental assistance for 7 quarters, the period
beginning with the first quarter of 2002 and continuing through the
third quarter of 2003. Overall, about 90 percent of state increases in
unemployment from the second quarter of 2000 through the fourth quarter
of 2004 were captured by our simulation, which began the assistance in
the first quarter of 2002 and continued it through the third quarter of
2003[Footnote 19]. If the simulation model had been in effect during
the 2001 recession, this strategy's starting point would have provided
assistance to states a full year earlier than the enhanced matching
rate implemented by Congress under the previous fiscal assistance
legislation, JGTRRA, which began providing supplemental assistance in
the third quarter of 2003. (See fig. 3.)
Figure 3: Number of States Experiencing a 10 Percent or More Increase
in Their Unemployment Rate, 2000 to 2004:
This is a vertical bar graph with bars representing the number of
states experiencing the increase per quarter. The vertical axis
represents Number of states, from 0 to 50. The horizontal axis
represents yearly quarters from year 2000 through year 2004.
[See PDF for image]
[A] 2002, first quarter: The quarter in which payment begins under this
strategy reflects a two-quarter lag for data to become available.
Therefore, the count of states represents the count from the third
quarter of 2001.
[B] 2003, third quarter: In response to the 2001 recession, our model
would have had the strategy in operation from the first quarter of 2002
to the third quarter of 2003, the period when 23 states had a 10
percent or more increase in unemployment compared to the same quarter
of the previous year.
[C] Quarters: For comparison purposes, the enhanced matching rate under
the 2003 fiscal relief package, JGTRRA, was implemented in the third
quarter of 2003.
[End of figure]
Under this strategy, our model's results show that the timing and depth
of a state's economic downturn can affect the amount of supplemental
assistance a state receives. In general, states with deep downturns
that occur coincident with the period in which supplemental assistance
payments would be made would receive the largest proportion of federal
assistance. States experiencing an earlier or later economic downturn-
-meaning more than 1 year before or 1 year later than the start of the
payments--would not receive payments to cover the full period of their
economic downturn, regardless of the extent of the state's increased
unemployment. With regard to the depth of each state's downturn, the
results of our model simulation showed that all states would receive
some amount of supplemental federal Medicaid assistance, with the
increased matching rate ranging from 0 percent to 2.01 percent[Footnote
20]. (See table 3.) In contrast, the previous fiscal assistance
legislation, JGTRRA, provided the same matching rate increase to all
states.
Table 3: Characteristics of Economic Downturns and Their Effect on
States' Receipt of Supplemental Assistance:
Downturn characteristic: Timing;
Effect on states' receipt of supplemental assistance: States with
unemployment increases that are relatively earlier or later than the
strategy's starting point may not receive the maximum amount of
supplemental federal assistance;
Results of GAO model[A]: 37 states would have had increases in
unemployment commensurate with the start of the supplemental federal
assistance; 12 states would have had increases in unemployment that
began before the start of supplemental federal assistance; 1 state
would have had an increase in unemployment that started after the
supplemental federal assistance ended.
Downturn characteristic: Duration;
Effect on states' receipt of supplemental assistance: States with
economic downturns lasting 7 or fewer quarters would be most likely to
receive the maximum amount of supplemental federal assistance;
Results of GAO model[A]: 4 states had downturns lasting 7 quarters; 28
states had downturns lasting fewer than 7 quarters.[B]; 18 states had
downturns lasting more than 7 quarters.
Downturn characteristic: Depth;
Effect on states' receipt of supplemental assistance: The supplemental
federal assistance a state would receive is determined in part by the
depth of its economic downturn and the amount of its unemployment
increase;
Results of GAO model[A]: 0.80 percent was the median increase in a
state's federal matching rate; 0.00 percent was the lowest increase in
a state's federal matching rate.[C]; 1.77 percent was the highest
increase in a state's federal matching rate.
Source: GAO simulation using data from BLS and CMS.
[A] Based on the first quarter of 2002 through the third quarter of
2003.
[B] One state showed no indication of a downturn based on increases in
unemployment.
[C] One state received a matching rate increase that was less than
0.005 percentage points.
[End of table]
Additionally, assistance provided to individual states would vary
depending on the relative size and composition of their expenditures
for cyclically sensitive Medicaid populations. Because economic
downturns are likely to increase Medicaid enrollment for children and
nondisabled, nonelderly adults--but generally not for the elderly or
individuals with disabilities--we adjusted the amount of supplemental
federal Medicaid assistance based on the characteristics of each
state's Medicaid spending by beneficiary population category in order
to target the amount of supplemental federal assistance. As a result,
two states with similar downturns in terms of percentage change in
unemployment could receive different amounts of supplemental assistance
depending on their average cyclically sensitive Medicaid expenditures
per nonelderly person in poverty. For example, Arizona and Wisconsin
had an average quarterly percentage change in unemployment of 41
percent and 52 percent during the 2001 recession, which would have
resulted in lump sum amounts of assistance of $86 million and $106
million, respectively. However, applying a Medicaid expenditure index
that we developed, which takes into account each state's relative
Medicaid spending per nonelderly person in poverty, Arizona would have
received $93 million in supplemental federal Medicaid payments compared
with $45 million for Wisconsin using the parameters described for this
strategy.[Footnote 21]
Using Fewer Years of Data to Compute Matching Rates Would Not
Consistently Result in Assistance that Better Reflects States' Current
Economic Conditions:
A second strategy uses fewer years of data by eliminating the oldest
data from the computation of federal matching rates in an attempt to
better reflect states' current economic conditions. However, based on
our analysis of a 15-year period (1990 to 2004), we found that using
fewer years of data did not result in federal matching rates that
better reflected states' current economic conditions. In particular,
the inherent time lag necessary to obtain data and calculate the
matching rates limited the ability of this strategy to provide
assistance to states that reflected more recent economic conditions. In
some cases, reducing the number of years of data skewed rates farther
away from current economic conditions. This strategy would result in
larger year-to-year changes in matching rates for most states compared
with the fluctuations experienced under current law[Footnote22]. Based
on this analysis, eliminating a year of data from the current matching
formula would not help states address increased Medicaid expenditures
during economic downturns.
Design Considerations:
This strategy would use fewer years of PCI data to compute federal
matching rates. This strategy relies on the current matching formula,
with the adjustment of using 2 years of PCI data instead of the 3 years
required under current law (see fig. 4). Implementation of this
strategy would require a statutory change to the federal matching
formula and could be made permanent. Unlike the first strategy, which
would require that an established number of states reach a certain
percentage change in unemployment, this strategy would not require
monitoring of economic conditions to trigger implementation. In
addition, this strategy would not distribute the supplemental Medicaid
assistance required for implementation of the first strategy but would
instead adjust the relative proportion of Medicaid funding distributed
to the states.
Figure 4: Timing of Data Used to Calculate States' Federal Matching
Rates for Fiscal Year 2006:
2001[a], 2002, 2003: Average of PCI data from these 3 fiscal years
included in fiscal year 2006 matching rate; October 2004: Department of
Health and Human Services calculates and publishes matching rates for
fiscal year 2006; October 2005: Fiscal year 2006 Medicaid matching
rates become effective in October 2005 and remain in place through
September 2006.
Source: GAO.
[See PDF for image]
[A] Under this strategy, 2001 data would be eliminated from the
matching rate calculation.
[End of figure]
To analyze the effect of using fewer years of data to calculate the
matching rates, we used three matching rates that employed the current
statutory formula but varied in the years of data used. The first
matching rate mirrored the current statutory construction, using 3
years of PCI data that are 3 to 5 years old. The second matching rate
was based on the statutory construction, except that it eliminated the
oldest year of PCI data and only used 2 years of data. The third
matching rate used PCI data for the current year (the year in which the
calculations are made) and for 1 year prior, thus showing no time lag
in the data used [Footnote 23]. We compared the three matching rates
with year-to-year percentage changes in PCI and year-to-year percentage
changes in the unemployment rate and analyzed the extent to which the 3-
year and 2-year matching rates fluctuated from year to year. (Appendix
III provides additional detail regarding our methodology.)
Effects:
Contrary to our expectations that eliminating the oldest year of data
from the computation of matching rates would make them more sensitive
to current economic conditions, our simulation results showed that
using 2 years of PCI data instead of 3 did not consistently improve the
correlation of the rates with state PCI--one measure of state economic
conditions. In addition, rates based on 2 years of PCI data did not
result in rates that more closely correlated with states' PCI trends.
We repeated the same analysis using unemployment data and confirmed
that matching rates also did not correlate with state unemployment
trends. These results remained consistent during the full period of our
analysis, 1990 through 2004.
We found that using 2 years of data would result in larger average
fluctuations in matching rates from year to year than states currently
experience. Our simulation of matching rates from 1990 to 2004 showed
that when rates were computed using 2 years of PCI data instead of 3,
the average percentage point change in rates from year to year
increased to 0.44, from 0.39 under current law. A small number of
states experienced substantially larger fluctuations (more than 0.5
percentage points larger) under this strategy than they currently
experience. The effects of these fluctuations for individual states
would depend on whether they represented a substantial increase or
decrease in federal funds. Depending on the scope of a state's Medicaid
program, a 0.5 percentage point difference in the matching rate would
have meant a difference of $1.7 million to $77.1 million in federal
matching funds for a given state in 2003[Footnote 24]. In 8 of 14
years, fewer than 22 states would have experienced larger fluctuations
in their matching rates than they experienced under current
law,[Footnote 25] and in 9 of 14 years, fewer than 4 states would have
experienced fluctuations that were more than 0.5 percentage points
larger.[Footnote 26]
States Could Determine Their Own Needs for Assistance with Medicaid-
Specific Loans or a National Rainy Day Fund:
Giving states the option to decide whether and to what extent they need
federal assistance could take the form of a loan, either from the
federal government or from the private capital market (subsidized and
possibly guaranteed by the federal government), or a Medicaid-specific
national rainy day fund. We considered the features of existing
intergovernmental loan programs and state rainy day funds to better
understand how these programs are structured and utilized by states.
Implementation of this strategy would require approval of legislation
to authorize a Medicaid-specific loan program or national rainy day
fund as well as appropriation of federal funds to cover any federal
expenditures required for either the loan program or national rainy day
fund. While this strategy would provide states with greater autonomy
over their receipt of additional federal assistance, their ability to
utilize either broad approach would depend on their debt restrictions,
their borrowing costs, the availability of future state revenues to
repay loans, and their willingness to participate in a Medicaid-
specific loan or national rainy day program. State participation also
could depend on the depth and duration of states' downturns (deep or
shallow and short or long) and the availability of state funds to fill
funding gaps. Federal funding required for this strategy could vary
depending on factors such as whether federal subsidies are included in
a loan program or whether a national rainy day fund includes federal
matching funds as well as decisions on the overall federal budget.
Design Considerations:
To identify the factors likely to be involved in designing this
strategy, we considered the features of existing intergovernmental loan
programs and state rainy day funds to better understand how these
programs are structured, how they are utilized by states, and how they
could contribute to a conceptual model of this strategy [Footnote 27].
This strategy draws on the features of existing programs to inform our
understanding of ways to increase the states' role in determining the
timing and targeting of increased federal assistance to the states
during economic downturns. We analyzed approaches to this strategy
based on two broad methods of providing federal credit: (1) a loan,
administered directly from the federal government or indirectly through
the private capital market (subsidized and possibly guaranteed by the
federal government); and (2) a Medicaid-specific national rainy day
fund that could distribute federal fiscal assistance during an economic
downturn. Implementation of one or more approaches to this strategy
would require numerous decisions about the use, structure, financing,
and repayment of a loan or national rainy day fund. Any new federal
loan program would have to comply with the Federal Credit Reform Act of
1990 requirements that budget authority sufficient to cover the
program's cost to the government be provided in advance, before new
direct loan obligations could be incurred or new loan guarantee
commitments could be made.
Direct Intergovernmental Loans:
Congress could authorize a new federal program so that states could
borrow funds directly from the federal government based on a rate-
setting and repayment process specified in law. For example, the law
could specify that rates be determined by the Treasury based partially
on Treasury's borrowing costs. CMS could be designated as the
administering agency. This approach could allow states that might
otherwise face high interest rates in the private capital market access
to federal funds that reflect a lower interest rate subsidy. The
administering agency would have to develop a method to estimate any
subsidy costs (e.g., the estimated long-term cost to the federal
government on a net-present value basis of all cash flows to and from
the government, such as interest rate subsidies and defaults over the
life of the loan) in order to conform with the Federal Credit Reform
Act of 1990 [Footnote 28]. The administering agency would have to
analyze and control the risk and cost of the program, obtain budget
authority and record outlays to cover the subsidy cost of the program,
and could also specify loan repayment terms. States would have to
designate funding sources to repay the loans.
Facilitated Private Lending:
Under this approach, instead of lending money directly to states, the
federal government could facilitate private lending, such as through a
guaranteed loan [Footnote 29]. The federal government could help offset
the risk of lending money to states by covering all or part of the risk
of loan defaults and by providing an interest subsidy to states. This
approach would enable the federal government to minimize direct
involvement with the loan process by placing the burden of loan
administration on third- party nonfederal lenders. However, the
administering agency would still have to analyze and control the risk
and cost of the program and obtain budget authority to cover the
subsidy costs. States would still have to identify repayment sources.
State-managed capital access programs, in which state governments
provide a fixed share of lenders' loan loss reserves, provide another
model for possible consideration and adaptation to facilitate private
lending.
National Rainy Day Fund:
Legislative approval of a Medicaid-specific national rainy day fund
would allow states to pool their resources to help cope with the
increased costs of Medicaid during economic downturns. We previously
found that the adequacy of states' own rainy day funds is unknown and
that choices on competing priorities would have to be made in a fiscal
crisis [Footnote 30]. Furthermore, some states have placed caps and
restrictions on the use of these funds [Footnote 31]. States could
capitalize a national rainy day fund in whole or in part, depending on
whether the program design included matching contributions from the
federal government. Determining the amount of money that each state
should pay into a national rainy day fund would present an additional
design challenge, given that state Medicaid programs vary widely in the
population groups and services covered.
Effects:
States' decisions about whether to access any new federal Medicaid
loans or a national rainy day fund could depend on the nature of the
economic downturn in terms of when and to what extent states experience
increased unemployment, each state's own resources, and the design
features of the program. States generally have resources available to
weather short-term economic downturns but may be more likely to utilize
a loan or national rainy day fund approach when they are affected by a
deeper downturn. States with a 50 percent federal matching rate could
also view federal loans or a national rainy day fund as an additional
tool for increasing funding on a short-term basis during an economic
downturn, filling gaps created by a matching rate that does not
necessarily rise when additional funds are needed. However, some states
also face constraints on their ability to borrow because of statutory
or constitutional debt restrictions and most states have some form of
balanced budget requirements [Footnote 32]. Consequently, states might
not be able to take advantage of a loan program.
The effects of either a loan or national rainy day fund approach would
also depend on the numerous technical decisions required, including,
but not limited to, interest rates, repayment terms, allowable uses of
funds, borrowing limits, and any requirements governing maintenance of
states' efforts in providing their own funds or Medicaid eligibility. A
direct or guaranteed loan could give states greater autonomy in
determining their need for assistance but would also result in a
requirement to repay the loans (an additional financial burden for
states) as they try to recover from an economic downturn. States would
have to consider the availability of future revenues to repay loans and
their borrowing costs, as well as statutory debt restrictions that
could limit their loan access. A national rainy day fund could allow
states to pool risk and thereby spend less than they would if they
chose to establish individual Medicaid rainy day funds or address
economic downturn-related Medicaid cost increases on an as-needed
basis. However, representatives of public policy and research
organizations we contacted cautioned that states may be reluctant to
contribute to a national fund that could be drawn down by other states
or tapped by the federal government. The impact on federal outlays of
this strategy could depend on subsidy costs as well as whether the
federal government provided matching funds for a national rainy day
fund. Unless mandated, state participation in a loan or national rainy
day fund would likely depend on the terms of the program as well as
state economic circumstances.
Concluding Observations:
Economic downturns, typically accompanied by increases in unemployment,
can leave states with increased demand for Medicaid program services
and spending, decreased revenues to help states finance the increased
demand, and few strategies for grappling with difficult fiscal
circumstances that will not place them in worse financial positions in
the future. Current federal and state approaches to help states cope
with the increased cost of Medicaid during economic downturns present
temporary solutions to a recurring combination of circumstances. Having
an automatic mechanism in place to address significant downturns in the
economy could provide for a more predictable and targeted response to
states' situations. The targeted supplemental assistance and loan or
national rainy day fund strategies considered in this report illustrate
potentially more responsive measures that could help states adjust to
economic downturns similar to the last three national recessions.
However, each also presents challenges.
No single strategy or combination of strategies for providing federal
financial assistance could fully meet the varied economic needs of all
states at all times. Any strategy also is inhibited by the lags
inherent in the collection and publication of data, thus limiting its
ability to have a real-time effect. However, the first and third
strategies--targeting supplemental assistance to states most affected
by a downturn and allowing states to determine their own need for
assistance from a national rainy day fund or loan--could potentially
better address some of the difficulties faced by states during
downturns in a more timely and cost-efficient manner than the JGTRRA,
which provided assistance to all states. Additionally, these two
strategies are not mutually exclusive and could be used in combination.
Any strategy to help states cope with increased Medicaid costs during
economic downturns requires trade-offs as Congress seeks to provide
assistance to states that have the greatest financial need and the
least capacity to meet those needs while balancing the federal
government's own long-term fiscal challenges. While none of the
strategies may fully satisfy all dimensions of targeting, timing, and
increasing states' own options, Congress may find one or more of these
strategies useful as starting points in considering whether and how to
provide supplemental Medicaid assistance during the most difficult
economic times faced by states.
Agency Comments:
We provided the Secretary of Health and Human Services (HHS) with a
draft of this report. HHS stated that it did not have comments.
As agreed with your offices, we plan no further distribution of this
report until 28 days from its date, unless you publicly announce its
contents earlier. At that time, we will send copies of this report to
the Secretary of Health and Human Services and the Administrator of the
Centers for Medicare & Medicaid Services. We will also make copies
available to others upon request. In addition, the report will be
available at no charge on the GAO Web site at [hyperlink,
http://www.gao.gov].
If you or your staffs have any questions about this report, please
contact Kathryn G. Allen at (202) 512-7118 or Stanley J. Czerwinski at
(202) 512-6806. Contact points for our Offices of Congressional
Relations and Public Affairs may be found on the last page of this
report. GAO staff who made major contributions to this report are
listed in appendix V.
Signed by:
Kathryn G. Allen:
Director, Health Care:
Signed by:
Stanley J. Czerwinski:
Director, Strategic Issues:
[End of section]
Appendix I: Objectives, Scope, and Methodology:
This appendix describes our objectives and the scope and methodology of
the work we did to address them, including how we illustrated the range
of economic conditions affecting states during economic downturns. We
include a list of the organizations we contacted during the course of
our work.
Objectives and Scope:
We explored the design considerations and potential effects of
strategies aimed at helping states with their share of Medicaid
expenditures during an economic downturn by (1) targeting supplemental
funds to specific states on the basis of the relative depth and
duration of their economic downturns as well as the extent to which
their Medicaid enrollment and expenditures are likely to increase
during a downturn, (2) using 2 years of per capita income (PCI) data
instead of the 3 years of data required by statute to compute federal
matching rates in an attempt to better reflect states‘ current economic
conditions, and (3) providing states with options for obtaining
assistance from a Medicaid-specific national rainy day fund or loan
based on their own determination of need.
Identifying and Evaluating the Strategies:
To address the objectives, we:
* analyzed research, including prior GAO reports and other policy
proposals, that assessed the effects of economic downturns on Medicaid
enrollment and expenditures across states, the responsiveness of the
current Medicaid formula to the effects of economic downturns, and
differences in Medicaid expenditures across states;
* simulated the potential effects of the strategies to use fewer years
of data to compute federal matching rates and target supplemental
federal assistance; and:
* analyzed the features of existing intergovernmental loan programs and
state rainy day funds as potential models for providing states with
discretion in determining the timing and targeting of assistance
through a federal government-sponsored Medicaid-specific loan program
or rainy day fund.
To evaluate the strategies identified, we:
* conducted statistical simulations of the strategies by comparing the
actual matching rates in states during recessionary times with the
matching rates that could exist under the strategies to provide
targeted supplemental Medicaid assistance and have Medicaid matching
rates better reflect states‘ current economic conditions;
* consulted with experts in Medicaid financing issues on our targeting
simulation in terms of its design and suggestions to refine it, and:
* discussed the strategies with key research groups and state
associations to discern the potential utility of the strategies as well
as the feasibility of states‘ implementing different strategies.
Table 4 summarizes the three strategies considered for this report.
Appendixes II, III, and IV provide additional detail regarding the
analysis of these strategies.
Table 4: Analysis of Three Strategies to Help States Respond to
Increased Medicaid Costs during Economic Downturns:
Goal of strategy: Provide targeted supplemental Medicaid assistance;
Approach: Target supplemental funds to states based on projected
Medicaid spending increases and depth and duration of economic
downturn;
Analysis: Identify design considerations involved in defining national
downturns and distributing supplemental federal funds; Estimate amounts
states would receive based on economic conditions present during three
prior recessions.
Goal of strategy: Have Medicaid matching rates better reflect states‘
current economic conditions;
Approach: Use 2 years of PCI data in the statutory formula used to
compute federal matching rates; Analysis: Compare matching rates
computed using 2 years of PCI data to rates based on the 3 years of PCI
data required under current law;
Analyze extent to which existing matching rates and matching rates
based on 2 years of PCI data were consistent with states‘ economic
circumstances.
Goal of strategy: Provide states with options to improve timing and
targeting of increased Medicaid assistance;
Approach: Allow states to determine whether and when they need
increased assistance in response to economic downturns;
Analysis: Identify considerations involved in designing loans or a
national rainy day fund; Identify potential effects based on structure
and use of existing intergovernmental loan programs.
Source: GAO.
[End of table]
Illustrating the Range of Economic Conditions Affecting States during
Economic Downturns:
To illustrate the potential ability of each strategy to help states
address increased expenditures during economic downturns, we analyzed
how implementation of each strategy might differ with respect to the
varied economic effects of downturns, including (1) early onset of a
shallow downturn, (2) early onset of a deeper downturn, (3) later onset
of a shallow downturn, and (4) later onset of a deeper downturn. We
also reviewed examples of states whose matching rates generally
remained at the lowest level allowable by federal statute.
Organizations GAO Contacted:
We contacted representatives of public policy and research
organizations to (1) gain insights into various issues, such as the
extent to which strategies could help states cope with the Medicaid-
related fiscal consequences of economic downturns; (2) obtain referrals
to related research; (3) validate our selection of strategies; and (4)
obtain views regarding the feasibility and utility of the three
strategies, as well as to discuss the potential effects of these
strategies. The organizations we contacted were as follows:
American Enterprise Institute:
Cato Institute:
Center on Budget and Policy Priorities: Heritage Foundation:
National Association of State Budget Officers: National Conference of
State Legislatures: National Governors Association.
In addition, we consulted with technical experts from Federal Funds
Information for States and The Urban Institute regarding our
simulations for the strategies to target supplemental Medicaid
assistance to specific states based on the depth and duration of their
economic downturns as well as their Medicaid expenditures and to use 2
instead of 3 years of PCI data to calculate federal matching rates.
Data and Data Reliability:
We obtained and analyzed data on personal income and state population
from the Bureau of Economic Analysis, data on unemployment from the
Bureau of Labor Statistics, and data on Medicaid expenditures from the
Centers for Medicare & Medicaid Services. We discussed our use of these
data with agency officials and reviewed relevant documentation. On the
basis of these efforts and our use of the data to illustrate potential
policy strategies and their simulated effects, we determined that the
data were sufficiently reliable for this report.
[End of section]
Appendix II: Designing a Strategy of Targeted Supplemental Medicaid
Assistance:
This appendix describes the design decisions and policy considerations
involved in creating a strategy aimed at targeting supplemental funds
to states based on the extent to which their Medicaid expenses increase
during an economic downturn. This supplemental assistance strategy
would leave the existing Medicaid formula unchanged and add a new,
separate assistance formula that would operate only during times of
economic downturn and use variables and a distribution mechanism that
differ from those used for calculating matching rates. The strategy
would require policy decisions on three basic steps: (1) deciding when
to start and stop the supplemental assistance to states, (2)
determining the level of assistance provided (including defining the
formula for distributing funds), and (3) deciding how to distribute the
assistance (principally, deciding whether assistance should be an
incremental increase in federal matching rates or provided as a lump-
sum grant payment). To illustrate these design considerations, we
developed a model to simulate supplemental assistance. The following
sections describe the choices made to simulate and illustrate the
resulting supplemental assistance as well as some possible
alternatives.
Design Considerations for Starting and Stopping Assistance:
This section presents information about how we chose unemployment as
the indicator for an economic downturn and how we selected the rules
for starting and stopping the provision of supplemental assistance. We
reviewed how these rules would have applied to the past three
recessions (2001, 1991-1992, and 1981-1983) using our simulation model.
Choice of Unemployment as an Indicator:
We used unemployment as the key variable because it reflects the
potential for increases in Medicaid enrollment as a result of an
economic downturn. Although other indicators of economic downturn are
widely reported and important in other contexts,[Footnote 33] experts
consider increases in unemployment to be an indicator of the likely
increase in Medicaid enrollments of adults and children.[Footnote 34]
To simulate how supplemental assistance could be provided, we used
Bureau of Labor Statistics (BLS) unemployment data by state. Monthly
BLS unemployment data by state become available with a lag of less than
one quarter.[Footnote 35]
Use of Unemployment as an Economic Indicator:
Ideally, the indicator used should reflect the economic downturn and
exclude other influences such as long-term trends, seasonal influences,
and other shorter fluctuations. In order to minimize the influence of
seasonality and the month-to-month fluctuations on the unemployment
data used in our model simulations, we used a quarterly average of
seasonally adjusted unemployment data for the 12 most recent
months.[Footnote 36] Because the level of unemployment is driven by
trends in the structure of a state‘s economy, we used increases in
unemployment during a period of economic downturn as our measure of the
effects of the economic cycle.[Footnote 37] (The problem of deciding on
a base period from which to calculate those increases in unemployment
was a key issue that is discussed later in this appendix.) This is an
inexact method for isolating the effects of cyclical downturn on
unemployment, especially if the trend should change along with the
economic downturn. For example, if an economic downturn is a
precipitating event that leads to long-lasting declines in a state‘s
manufacturing industries, at some point the state‘s increases in
unemployment are attributable to structural change in its economy. When
the increases in unemployment are long term rather than cyclical, this
may be a policy consideration in deciding when to stop the supplemental
assistance.
Alternative Indicators of Downturns and Increases in Medicaid
Enrollments:
Economists generally prefer indicators other than unemployment to
signal economic downturns. Unemployment sometimes lags behind the
cyclical turns in the economy; it can be both slow to increase when the
downturn begins and slow to return to pre-downturn levels when other
indicators show the economy is recovering. In general, other indicators
show an earlier and briefer downturn than unemployment.
For example, researchers at the Philadelphia Federal Reserve Bank
developed a monthly index of four state economic indicators intended to
coincide with the economic cycle.[Footnote 38,39] Such a broad index of
economic conditions could provide a more reliable and timely indication
of a state‘s cyclical downturn than unemployment. Furthermore, if the
purpose of supplemental assistance was to include the provision of some
countercyclical stimulus”that is, provide incentives to increase
spending to boost macroeconomic activity”rather than to help states
address the impacts of the downturn on increased Medicaid expenditures,
then an alternative to unemployment as a variable for triggering
funding would have better prospects for providing well-timed
assistance.
However, there is some leeway in providing supplemental assistance to
compensate states for the impact of a downturn on their Medicaid
enrollments and spending. According to experts, states have budget
resources and financial management techniques to temporarily sustain
them for a year or two with downturn-driven increases in Medicaid
expenditures. To assist states with the costs of Medicaid enrollment
increases, the relatively brief lags caused by using unemployment rates
to trigger supplemental assistance payments would not present a problem.
Starting and Stopping Supplemental Assistance:
Supplemental federal assistance could be set to begin payments to
states when economic evidence shows a significant number of states are
in an economic downturn. For example, when a certain number of states
have each exceeded a specified increase in their unemployment rate,
supplemental assistance could be authorized to begin for the next
quarter.
A similar criterion could be used to stop payments. Such a rule could
be designed to provide a high degree of certainty that the nation had
entered a downturn and that states were not all in recovery. For our
simulation model, we chose the rule that payments to states would begin
when 23 or more had an increase in their unemployment rate of 10
percent or more from the comparable quarter a year earlier, and
payments would stop when fewer than 23 states had increases of 10
percent or more.[Footnote 40] We chose 23 states and a 10 percent or
more increase in unemployment on the basis of a review of states‘
unemployment rates over past economic cycles and made a judgment that
these levels would provide considerable certainty that an economic
slowdown was nationwide. Other thresholds could be selected to tighten
or loosen the parameters to start and stop supplemental federal
assistance.
Automatic Trigger Design Objectives and Issues:
An automatic trigger would need to specify several key parameters or
rules that together would control when assistance payments would begin,
how long they would last, and when they would stop. Though the trigger
would control all supplemental assistance payments, it should utilize
state-by-state data rather than national aggregates because it involves
assistance to state Medicaid programs. The trigger should distinguish
between small up-and-down movements in unemployment, which could be
associated with an economy that is basically stagnant, from those
movements that clearly show a state whose economy has entered a
downturn. The trigger must clearly identify the duration of the period
of economic downturn because of the previously mentioned difficulty of
separating a state‘s trend in unemployment from its cyclical changes.
Furthermore, the design decision should involve consideration of
potential risks. A trigger that is too sensitive could provide more
payments than are reasonably justified by the economic downturn, while
a trigger with standards that are too rigorous would penalize states
whose downturns are exceptionally long-lasting, early or late. Also, an
automatic trigger for supplemental assistance would need to be designed
with some degree of simplicity and transparency.
An Illustrative Automatic Trigger:
For our simulation model, state payments would be triggered when 23 or
more states had an increase of 10 percent[Footnote 41] or more in the
state‘s unemployment rate compared to the same quarter in the previous
year, and payments would stop when those conditions were no longer
present. This trigger consists of three key elements:
* a threshold number of states (23);
* a threshold percentage increase in the unemployment rate (10 percent
or more), and:
* a ’retrospective assessment“ used to derive the percentage increase
in the unemployment rate compared to the same quarter in the previous
year.
We chose the two threshold values of 23 states and 10 percent or more
to work in tandem to ensure that when the program starts, the national
economy has entered a downturn and that many states (at least 23 and
probably more) are not yet in recovery.[Footnote 42] We chose both
numbers based on a review of states‘ unemployment rates over past
economic cycles and made a judgment that these levels would provide
considerable certainty that the economic slowdown was nationwide.
To illustrate the application of this trigger, figure 5 shows the
number of states with a 10 percent or greater increase in their
unemployment rate from the same quarter a year earlier for the period
from 1979 through the third quarter of 2004.[Footnote 43] This period
covers three recessions and offers supplemental assistance as follows:
* for the 2001 recession, 7 quarters of assistance is provided
beginning in the first quarter of 2002 and ending as of the fourth
quarter of 2003;
* for the 1991-1992 recession, 6 quarters of assistance is provided
beginning with the fourth quarter of 1991 and ending as of the second
quarter of 1993; and:
* for the 1981-1983 recession, 11 quarters of assistance is provided in
two phases, with the first phase beginning in the fourth quarter of
1980 and ending as of the second quarter of 1982, and the second phase
resuming assistance in the fourth quarter of 1982 and ending as of the
first quarter of 1984.
Each recessionary period has different characteristics. For example,
the 1991-1992 recessionary period shows a more gradual increase in
unemployment compared to the other recessions”and fewer states are
affected.[Footnote 44]
Figure 5: Number of States with a 10 Percent or More Increase in Their
Unemployment Rate Compared to the Same Quarter 1 Year Earlier, 1979-
2004:
The is a vertical bar graph. The vertical axis represents number of
states from 0 to 50. The horizontal axis represents quarters per year
fro 1979 through 2004. There is a vertical bar for every quarter, and a
horizontal line indicating the 23-state trigger.
[See PDF for image]
Source: GAO analysis of BLS data.
[End of figure]
Performance of the Automatic Trigger:
A rough method of evaluating the performance of the automatic trigger
is the degree to which the period it identifies encompasses states‘
increases in unemployment in that period.[Footnote 45] The trigger must
delineate a period of payments that coincides well with most states‘
increases in the number of unemployed, in order for the supplemental
federal assistance calculated on the basis of those unemployment
increases to also be well targeted. Overall, about 90 percent of the
unemployment increases in the period from the second quarter of 2000
through the fourth quarter of 2004 are captured by the time period of
the trigger plus the 1-year retrospective assessment used by the
simulation model. When the trigger identifies the start of the first
quarter of the program of supplemental federal assistance, then the
process of computing each state‘s assistance for that first quarter and
each subsequent quarter of assistance takes place. As part of that
process, the simulation model calculates each state‘s increase in
unemployment, which is the increase in unemployment compared to the
base quarter. For each state, the base quarter is whatever quarter had
the lowest unemployment within the preceding 4 quarters. Thus, though
the program begins in the first quarter of 2002, it could use states‘
increases in unemployment that occurred as early as the first quarter
of 2001. Figure 6 shows the sum of states‘ increases in unemployment
over the previous quarter for the first quarter of 2000 through the
fourth quarter of 2005. While the trigger in the first quarter of 2002
appears late relative to when some states actually experienced an
increase in unemployment, the simulation model‘s retrospective
assessment captures much of the preceding unemployment.
Figure 6: Total of States‘ Quarterly Increase in Unemployment Covered
by Simulation Model‘s Supplemental Assistance:
This is a line graph. The vertical axis represents number of unemployed
in thousands, from 0 to 600. The horizontal axis represents yearly
quarters from 2000 through 2005. Also indicated on the graph are the
payment calculation period (first quarter, 2001 through first quarter
2002) and the payment period (first quarter 2002 through third quarter
2003).
[See PDF for image]
Source: GAO analysis of BLS data.
[End of figure]
Use of Alternative Parameters in the Automatic Trigger:
A lower threshold for the increase in the unemployment rate or
requiring a smaller number of states to pass that threshold could
trigger supplemental assistance somewhat sooner and provide more
quarters of payments (especially for states that may enter a downturn
much earlier or later than others). These parameters would also have
potential disadvantages: (1) they could provide less certainty that
there has been a nationwide downturn, and (2) with more quarters of
supplemental assistance, the overall cost could be greater (other
things remaining the same). To show the way in which the threshold
parameters included in our simulation model work together, figure 7
displays the effects of choosing alternative combinations of these
parameters for the period 2000 through 2005. For example, if we use 21
rather than 23 states, supplemental assistance would be triggered with
the same first quarter but last for 8 rather than 7 quarters. Many
adjoining cells of the figure have the same first quarter and number of
quarters because small changes in the threshold parameters may not
change when supplemental assistance is triggered. However, over the
broad ranges shown in the figure, the clear pattern is that lowering
the percentage increase or lowering the number of states generally
moves in the direction of an earlier first quarter and a greater number
of quarters of payments.
Figure 7: Effects of Alternative Threshold Parameters on the Start and
Number of Quarters of Supplemental Assistance, 2000 through 2005:
This chart depicts the quarter and year assistance would begin and the
number of quarters of assistance provided based on the percentage
increase in unemployment rate and the number of states. The vertical
axis of the chart represents the percentage increase in unemployment
rate from 6 to 16. The horizontal axis represents the number of states
from 9 to 37. The combination used in GAO's simulation was 23 states
with a 10 percent increase in unemployment rate: in this simulation
assistance would begin in the first quarter of 2002, and assistance
would be provided for seven quarters.
[See PDF for image]
Source: GAO analysis of BLS data.
Note: Number in bold font are those used in GAO's simulation.
[End of figure]
The trigger for our simulation is based on the increase in the
unemployment rate over the same quarter of the previous year. Depending
on congressional preferences, the period could instead be longer than 1
year, or it could be based on the increase from the pre-downturn
levels. Because unemployment is slower to recover than other economic
indicators, it may be a number of years into the national recovery
before unemployment rates return to the levels immediately preceding
the downturn. Therefore, the effect of a longer retrospective
assessment would be to provide supplemental assistance for more
quarters and also to provide more assistance to the states with longer-
lasting or late downturns. Using a shorter period reflects a policy
judgment that the program should be temporary and, in particular, that
after 1 year the states should then adjust their budgets and programs
to reflect changed economic conditions.[Footnote 46]
Alternative Ways to Start and Stop Supplemental Assistance:
An alternative way to start and stop supplemental assistance is through
legislation. Congress could consider other indicators and criteria to
start or stop assistance with the intention of implementing other
policy objectives. For example, decisions could be made regarding
limiting the number of quarters of payments or stopping payments after
spending caps are reached. Additionally, instead of an automatic
trigger, supplemental assistance could begin when Congress enacted
legislation. However, enacting appropriately funded and timely
legislation under the pressure of worsening national and state
economies presents its own challenges. Studies of the past performance
of discretionary federal fiscal policy actions in response to recession
have shown instances of enactment of belated and inappropriate levels
of fiscal stimulus.[Footnote 47] Also, some of the groups we contacted
for this study believed that an ’automatic trigger“ based on economic
criteria would be the most likely method of implementing assistance in
a consistent and timely manner.[Footnote 48]
Determining the Level of Supplemental Assistance:
There are three important aspects to determining the level of
supplemental assistance. First, a level of funding must be developed.
The level of funding in our model is based on the average costs to
states attributable to increases in unemployment. Second, the estimates
and allocations of quarterly funding must be consistent with the annual
appropriations process. Third, assistance needs to be targeted to
states on the basis of the impact of increases in unemployment on their
Medicaid programs.
Level of Funding:
Several studies in the economics literature have estimated a
relationship between changes in unemployment rates and changes in
Medicaid spending while holding constant other factors that influence
Medicaid spending.[Footnote 49] While these models cannot provide state-
by-state estimates of enrollment increases, they provide national
average estimates from which we can calculate an average amount of
additional federal Medicaid spending per additional unemployed person.
We have chosen to use the estimate of $300 per additional unemployed
person derived from a recent econometric study of the responsiveness of
Medicaid enrollments and spending to changes in unemployment rates and
other factors, such as states‘ spending on certain Medicaid
populations.[Footnote 50] Based on the depth of the 2001 recession, the
amount of federal assistance would have been $4.2 billion.
Funding and the Appropriations Process:
Given the difficulties of forecasting the depth and duration of a
downturn, as well as the pace of the recovery, estimating the cost of
supplemental assistance can be difficult. However, within the context
of the overall Medicaid program, the amount of supplemental assistance
provided in our simulation ($4.2 billion) is relatively small”less than
1 percent of total Medicaid spending for a 2-year period. As an open-
ended matching grant that provides states with an incremental increase
to their matching rates, funding may need to be appropriated.[Footnote
51] Similarly, supplemental assistance designed to provide a lump-sum
grant or to be closed-ended, could also require an appropriation
amount. The funding would need to be apportioned across quarters of the
fiscal year in order to provide proportionately equal treatment between
the states that enter a downturn early and those that enter late,
presuming equal treatment is defined as providing states with equal
funding for equal increases in unemployment and commensurate with state
Medicaid populations (all other factors remaining the same). Past
economic data show that the middle quarters of the supplemental
assistance are certain to have much greater increases in unemployment
than the earlier and later quarters (see fig. 5).[Footnote 52]
Therefore, a policy of spending until funds are gone would seem to
leave the states with late-starting downturns, or prolonged
contractions, at risk of receiving little or no supplemental funding.
Allocation Model:
Our simulation model targets funds to states in proportion to the
product of two factors. The first is the state‘s increase in the number
of unemployed persons in that quarter compared to the number of
unemployed in the base quarter.[Footnote 53] The second factor is a
Medicaid spending index intended to adjust the first factor for the
relative size of the states‘ Medicaid programs for the nonelderly. The
first factor is intended to gauge the impact of the economic downturn
on Medicaid enrollment in the state. The factor is the amount by which
unemployment for the most recent quarter exceeds the number of
unemployed in the base quarter. The base quarter is the quarter with
the lowest number of unemployed in the year immediately preceding the
first quarter in which assistance is triggered. However, if the state‘s
number of unemployed decreased after the first quarter, that lowest
quarter would then become the base quarter unemployment. If a state has
a decrease in the number of unemployed compared to the base quarter, it
would not receive funding because of a lack of discernible impact from
the economic downturn. However, states with even small increases in the
number of unemployed would receive some assistance, in proportion to
the increase in unemployment.[Footnote 54] We excluded increases in the
number of unemployed that predated this retrospective assessment.
Presumably, such increases would be small and possibly unrelated to the
nationwide economic downturn.
The purpose of the second factor is to adjust the number of unemployed
for the relative cost of state Medicaid programs. Two states with an
equal increase in the number of unemployed could have very different
increases in Medicaid expenditures, depending on their rate of Medicaid
spending. The Medicaid index is calculated for each state as its
average Medicaid spending per nonelderly poor person relative to the
national average. Thus, a state whose Medicaid spending per nonelderly
person in poverty was equal to the national average would have an index
value equal to one (1.00). CMS spending data are used to approximate
each state‘s Medicaid spending for the cyclically sensitive population.
Census Bureau data provide an estimate of adults and children in
poverty, who are the potential beneficiaries of such Medicaid spending.
The Medicaid index factor would not be updated quarterly because it is
intended to supply relative positions of the states and not quarterly
impacts of the economic cycle.[Footnote 55] The Medicaid index varies
widely among the states because of differing Medicaid program
characteristics and funding efforts. If Congress did not want
supplemental assistance funding to reflect the full magnitude of
variations in Medicaid spending, constraints could be designed to
moderate this factor, or it could be eliminated from the methodology
for allocating supplemental assistance.
Deciding How to Distribute Supplemental Assistance:
Matching Assistance or Lump-sum Grants:
Assistance could be provided either as an incremental increase to
states‘ federal matching rates or as a lump-sum grant. Representatives
of one organization we contacted preferred matching assistance on the
grounds that it would better ensure maintenance of state contributions
to the Medicaid program, in contrast to lump-sum grant payments that
could more readily allow states to reduce their own Medicaid spending
effort and thus use state funds for other purposes. Supplemental
federal assistance as described in this appendix could be provided as a
targeted incremental increase in each state‘s matching rate or targeted
lump-sum grant to states. Either approach could provide a state with a
comparable amount of funding.
Calculation of Lump-sum and Matching Assistance Amounts:
Supplemental assistance could provide either a lump-sum grant to each
state or a comparable level of funding through an incremental increase
in the state‘s matching rate. The lump-sum formula would provide funds
in proportion to the state‘s increase in the number of unemployed, with
that increase adjusted by the index of relative Medicaid cost. The
increase in the Medicaid matching rate is calculated by dividing the
lump-sum grant amount by a state‘s total Medicaid spending. Thus, if a
state left its Medicaid spending unchanged, it would receive the full
assistance amount.
Simulation Model Results:
This section highlights results from our supplemental targeted
assistance simulation model for the 1998 through 2004 time
span.[Footnote 56] Individual states vary in different recessions in
terms of unemployment levels and supplemental federal assistance that
would result from changes in the number of unemployed. A state with
minimal unemployment increases in one recession can experience much
greater increases in the number of unemployed in another recession. The
widely differing nature of states‘ experiences suggests that simulated
supplemental assistance is unlikely to reflect what a particular state
would receive in a future economic downturn.
Table 5 shows data related to the factors used in the formula and the
resulting supplemental assistance, by state. As shown in table 5, the
average percentage increase in the number of unemployed ranged from 0.1
to about 80 percent. With a few exceptions, every state would begin
receiving assistance during the first quarter of 2002 and would receive
7 quarters of payments. The next column shows the Medicaid index used,
and the final two columns show the average increase in each state‘s
matching rate during the 7 quarters, with and without the Medicaid
expenditure index factor. Because of the importance of the Medicaid
expenditure index in determining assistance (especially to those states
with relatively large or small indexes), we present the assistance
computed with and without the Medicaid factor. In general, the
simulated increases in matching rates show the targeting with respect
to the variations in the increases in unemployment that the formula is
designed to provide. This targeting is especially apparent for the
supplemental matching rates that exclude the Medicaid index. For some
states, the Medicaid index is an important determinant of the
supplemental assistance, but much less important to those states whose
index value is closer to the U.S. average of 1.00. For example, table 5
shows that Alaska‘s average percentage point increase in matching rate
would more than triple by including the Medicaid expenditure index,
increasing from 0.26 to 0.86. In contrast, Oregon‘s average percentage
point increase in matching rate experienced a minimal change by
including the Medicaid expenditure index, increasing from 1.68 to 1.70.
Table 5: Simulated Supplemental Assistance for Economic Conditions of
the 2001 Downturn:
State: Alabama;
Average percentage increase in unemployment: 22.6;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.654;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.54;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.43.
State: Alaska;
Average percentage increase in unemployment: 9.8;
Initial payment quarter: 2002Q1;
Number of quarters: 7
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 3.272;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.26;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.86.
State: Arizona;
Average percentage increase in unemployment: 40.9;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.078;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.00;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.08.
State: Arkansas;
Average percentage increase in unemployment: 19.8; Initial payment
quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.685;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.49;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.33.
State: California;
Average percentage increase in unemployment: 27.4;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.933;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 2.36;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.61.
State: Colorado;
Average percentage increase in unemployment: 80.3;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.682;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 2.36;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.61.
State: Connecticut;
Average percentage increase in unemployment: 68.9;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.576;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.12;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.77.
State: Delaware;
Average percentage increase in unemployment: 16.8;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 2.429;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.34;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.83.
State: District of Columbia;
Average percentage increase in unemployment: 12.7;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.553;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.24;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.37.
State: Florida;
Average percentage increase in unemployment: 40.2;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.705;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.13;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.80.
State: Georgia;
Average percentage increase in unemployment: 29.2;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.059;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.74;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.78.
State: Hawaii;
Average percentage increase in unemployment: 8.8;
Initial payment quarter: 2002Q1;
Number of quarters: 6;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 2.309;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.27;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.63.
State: Idaho;
Average percentage increase in unemployment: 15.1;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.852;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.59;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.50.
State: Illinois;
Average percentage increase in unemployment: 33.8;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.813;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.16;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.95.
State: Indiana;
Average percentage increase in unemployment: 61.0;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.075;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.37;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.48.
State: Iowa;
Average percentage increase in unemployment: 36.4;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.033;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.84;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.87.
State: Kansas;
Average percentage increase in unemployment: 29.3;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.665;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.02;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.68.
State: Kentucky;
Average percentage increase in unemployment: 30.5;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.908;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.74;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.67.
State: Louisiana;
Average percentage increase in unemployment: 18.7;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.581;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.44;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.26.
State: Maine;
Average percentage increase in unemployment: 28.4;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 2.589;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.49;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.28.
State: Maryland;
Average percentage increase in unemployment: 24.0;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.467;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.62;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.91.
State: Massachusetts;
Average percentage increase in unemployment: 67.2;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.225;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.85;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.05.
State: Michigan;
Average percentage increase in unemployment: 57.9;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.712;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.57;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.11.
State: Minnesota;
Average percentage increase in unemployment: 46.5;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.948;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.84;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.63.
State: Mississippi;
Average percentage increase in unemployment: 17.1;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.821;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.43;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.35.
State: Missouri;
Average percentage increase in unemployment: 62.4;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.192;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.11;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.32.
State: Montana;
Average percentage increase in unemployment: 0.1;
Initial payment quarter: 2002Q4;
Number of quarters: 4;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.675; Average percentage point increase in matching
rate, Excluding Medicaid expenditure index: 0.00[B]; Average percentage
point increase in matching rate, Including Medicaid expenditure index:
0.00[B].
State: Nebraska;
Average percentage increase in unemployment: 26.0; Initial payment
quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.973;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.57;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.56.
State: Nevada;
Average percentage increase in unemployment: 33.5;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.703;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.78;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.25.
State: New Hampshire;
Average percentage increase in unemployment: 51.5;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.539;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.01;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.55.
State: New Jersey;
Average percentage increase in unemployment: 40.8;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.735;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.96;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.71.
State: New Mexico;
Average percentage increase in unemployment: 8.9;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.286;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.20;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.26.
State: New York;
Average percentage increase in unemployment: 28.7;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.796;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.34;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.61.
State: North Carolina;
Average percentage increase in unemployment: 77.4;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.915;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.70;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.55.
State: North Dakota;
Average percentage increase in unemployment: 16.3;
Initial payment quarter: 2002Q2;
Number of quarters: 6;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.595;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.39;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.23.
State: Ohio;
Average percentage increase in unemployment: 28.5;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.041;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.69;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.72.
State: Oklahoma;
Average percentage increase in unemployment: 39.7;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.667;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.98;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.66.
State: Oregon;
Average percentage increase in unemployment: 39.1;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.011;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.68;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.70.
State: Pennsylvania;
Average percentage increase in unemployment: 26.3;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.035;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.57;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.59.
State: Rhode Island;
Average percentage increase in unemployment: 18.1;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.106;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.30;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.33.
State: South Carolina;
Average percentage increase in unemployment: 53.1;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.972;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.17;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.14.
State: South Dakota;
Average percentage increase in unemployment: 24.9;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.122;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.57;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.64.
State: Tennessee;
Average percentage increase in unemployment: 27.0;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.431;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.53;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.75.
State: Texas;
Average percentage increase in unemployment: 34.4;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.749;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.16;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.87.
State: Utah;
Average percentage increase in unemployment: 61.0;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 00.752;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 2.20;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.65.
State: Vermont;
Average percentage increase in unemployment: 41.0;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.992;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.55;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.10.
State: Virginia;
Average percentage increase in unemployment: 69.3;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.610;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.77;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.08.
State: Washington;
Average percentage increase in unemployment: 41.6;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.971;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.41;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 1.37.
State: West Virginia;
Average percentage increase in unemployment: 6.3;
Initial payment quarter: 2002Q3;
Number of quarters: 5;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.345;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.16;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.22.
State: Wisconsin;
Average percentage increase in unemployment: 52.3;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 0.422;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 1.38;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.58.
State: Wyoming;
Average percentage increase in unemployment: 8.1;
Initial payment quarter: 2002Q1;
Number of quarters: 7;
Index of Medicaid expenditures per nonelderly person in poverty[a]
(U.S.=1.000): 1.267;
Average percentage point increase in matching rate, Excluding Medicaid
expenditure index: 0.27;
Average percentage point increase in matching rate, Including Medicaid
expenditure index: 0.34.
Source: GAO calculations based on BLS, CMS, and Census data.
[a] Expenditures for categories that would be cyclically sensitive such
as spending for children and nondisabled, nonelderly adults.
[b] Less than 0.005.
[End of table]
Next are figures showing changes in states‘ matching rates resulting
from the supplemental assistance and changes in unemployment for
selected states with widely varying economic downturns in order to
illustrate patterns of simulated supplemental assistance in relation to
changes in unemployment. These states provide a broader picture to
illustrate the different economic circumstances that states can
experience during the same economic downturn. On each of the next four
figures, the trend line shows the percentage increase in unemployment
from the base quarter and is plotted with respect to the percentage
change in unemployment. The bars show supplemental matching rate
increases, and relate to the increased matching rate. Figure 8 depicts
a downturn in a state that had increasing unemployment from the first
quarter of 2000 and shows an increase in unemployment that continues
through the third quarter of 2004. The bars show that the supplemental
assistance would be responsive to the increase in unemployment during
the 7 quarters the state received the assistance.
Figure 8: Simulated Supplemental Assistance for a State with an Early,
Long, and Deep Economic Downturn:
This is a combination line and bar graph. The left vertical axis
represents percentage change in unemployment from -20 to 100. The right
vertical axis represents increase in matching rate from -0.80 to 4.00.
The horizontal axis represents quarters from third quarter, 1998
through fourth quarter, 2004. The line in the graph depicts change in
unemployment from base quarter (second quarter, 2001). Seven bars
depict supplemental assistance from first quarter, 2002 through third
quarter, 2003. Also shown are boxes depicting the payment calculation
period (first quarter, 2001 through first quarter, 2002) and the
payment period (first quarter, 2002 through third quarter, 2003).
[See PDF for image]
Source: GAO analysis of BLS, CMS, and Census data.
[End of figure]
In figure 9, the state experiences a ’double dip“ with increasing, then
decreasing, and again increasing unemployment. The first increase in
unemployment is 3 years before the start of simulated supplemental
assistance in the first quarter of 2002. The second increase begins in
the second quarter of 2002, so the state misses the first quarter of
assistance entirely, and the assistance received in the second quarter
of 2002 would be relatively small. The first increase in unemployment
is relatively small, so it could be considered a transitory economic
event rather than a real economic contraction. By the final quarter in
which supplemental assistance would be provided, unemployment has
leveled off.
Figure 9: Simulated Supplemental Assistance for a State with a
Relatively Early, Long-Lasting, and Shallow Downturn:
This is a combination line and bar graph. The left vertical axis
represents percentage change in unemployment from -20 to 100. The right
vertical axis represents increase in matching rate from -0.80 to 4.00.
The horizontal axis represents quarters from third quarter, 1998
through fourth quarter, 2004. The line in the graph depicts change in
unemployment from base quarter (first quarter, 2001). Seven bars depict
supplemental assistance from first quarter, 2002 through third quarter,
2003. Also shown are boxes depicting the payment calculation period
(first quarter, 2001 through first quarter, 2002) and the payment
period (first quarter, 2002 through third quarter, 2003).
[See PDF for image]
Source: GAO analysis of BLS, CMS, and Census data.
[End of figure]
Figure 10 shows a state with a particularly late and short economic
downturn, in which unemployment was leveling off by the final quarter
of the supplemental assistance provided and declining thereafter.
Nevertheless, the state would have received 5 quarters of supplemental
assistance.
Figure 10: Simulated Supplemental Assistance for a State with a Late,
Short, and Shallow Downturn:
This is a combination line and bar graph. The left vertical axis
represents percentage change in unemployment from -20 to 100. The right
vertical axis represents increase in matching rate from -0.80 to 4.00.
The horizontal axis represents quarters from third quarter, 1998
through fourth quarter, 2004. The line in the graph depicts change in
unemployment from base quarter (second quarter, 2001). Five bars depict
supplemental assistance from third quarter, 2002 through third quarter,
2003. Also shown are boxes depicting the payment calculation period
(first quarter, 2001 through first quarter, 2002) and the payment
period (first quarter, 2002 through third quarter, 2003).
[See PDF for image]
Source: GAO analysis of BLS, CMS, and Census data.
[End of figure]
Figure 11 shows a state with a short and relatively deep recession.
Supplemental assistance would have been provided through 7 quarters of
increased unemployment and would have been phased out about the time
when unemployment peaked.
Figure 11: Simulated Supplemental Assistance for a State with a Short,
Deep Downturn:
This is a combination line and bar graph. The left vertical axis
represents percentage change in unemployment from -20 to 100. The right
vertical axis represents increase in matching rate from -0.80 to 4.00.
The horizontal axis represents quarters from third quarter, 1998
through fourth quarter, 2004. The line in the graph depicts change in
unemployment from base quarter (third quarter, 2001). Seven bars depict
supplemental assistance from first quarter, 2002 through third quarter,
2003. Also shown are boxes depicting the payment calculation period
(first quarter, 2001 through first quarter, 2002) and the payment
period (first quarter, 2002 through third quarter, 2003).
[See PDF for image]
Source: GAO analysis of BLS, CMS, and Census data.
[End of figure]
[End of section]
Appendix III: Designing a Strategy to Better Reflect States‘ Current
Economic Conditions:
This appendix presents additional detail about the development and
analysis of our strategy to use fewer years of per capita income (PCI)
data to compute Medicaid matching rates. As currently constructed, the
PCI data in the Medicaid formula reflect economic conditions that
existed several years earlier.[Footnote 57] The age of the data used to
calculate the matching rate can result in states not receiving a
matching rate consistent with their current economic situation because
state PCI for a particular year becomes available nearly 2 years after
the start of the calendar year for which the data are reported.
[Footnote 58] For example, the United States entered a recession in
2001, but matching rates for 2001 were based on PCI data from 1996 to
1998, when the national economy was expanding. Efforts to use fewer
years of data to calculate the matching rate assume that eliminating
the oldest year of data would more accurately reflect a state‘s current
economic circumstances. We tested this assumption by analyzing the
effects of using fewer years of data to calculate states‘ federal
matching rates. To develop and analyze this strategy, we reviewed a
similar proposal published in a 2004 AARP Public Policy Institute
report [Footnote 59] and our previous work on the Medicaid matching
formula. [Footnote 60]
Overview of Analysis:
To analyze the effect of using fewer years of data to calculate the
matching rates, we used three matching rates that employed the current
statutory formula but varied in the years of data used (see table 6).
The first matching rate (’the 3-year matching rate“) mirrors the
current statutory construction of the Medicaid matching rate
calculation, using 3 years of PCI data that are 3 to 5 years old. The
second matching rate (’the 2-year matching rate“) is based on the
statutory construction, except that it eliminates the oldest year of
data and uses 2 years of PCI data. The third matching rate (’the
simulated matching rate“) only uses PCI data for the current year (the
year in which the calculations are made) and for 1 year prior, thus
showing no time lag in the data used. Although not feasible to
implement because of lags in data publication, we devised the simulated
matching rate in order to evaluate whether changing the years of data
used to calculate the matching rate resulted in a better approximation
of states‘ current economic circumstances.
Table 6: Matching Rates Used to Analyze Strategy:
Matching rate: 3-year;
Description: Uses 3 years of PCI data, as outlined in federal statute;
Years of PCI data used to calculate matching rate for 2001: 1996-1998.
Matching rate: 2-year;
Description: Removes the oldest year of PCI data from the current
statutory matching rate calculation;
Years of PCI data used to calculate matching rate for 2001: 1997-1998.
Matching rate: Simulated;
Description: Uses current year and 1 prior year of PCI data to
calculate matching rate;
Years of PCI data used to calculate matching rate for 2001: 2000-2001.
Source: GAO analysis using Bureau of Economic Analysis (BEA) PCI data.
We calculated these matching rates for the period from 1990 through
2004, which covers the last two national recessions.Footnote 61] We
then compared (1) the annual percentage point changes in the three
matching rates with annual percentage changes in PCI and annual
percentage point changes in the unemployment rate, (2) the simulated
matching rate with changes in PCI, and (3) the 3-year and 2-year
matching rates with the simulated matching rate. Finally, we analyzed
the extent to which the 3-year and 2-year matching rates fluctuated
from year to year.
Comparison of Changes in Matching Rates with Changes in PCI and
Unemployment:
To measure the extent to which the 3-year and 2-year matching rates can
assist states throughout the economic cycle, we did a correlation
analysis that compared the annual changes in matching rates with the
changes in PCI [Footnote 62] and the unemployment rate, two commonly
used indicators of economic performance. A negative correlation
coefficient would mean that when current PCI decreased, matching rates
would increase, and vice versa. A positive correlation coefficient
would mean that when the current unemployment rate increased, matching
rates would increase, and vice versa. For example, it would indicate
that the matching rates would increase assistance provided to the
states during an economic downturn.
Specifically, we examined the correlation between the annual changes in
the 3-year matching rate and the percentage change in PCI and the
annual changes in the 2-year matching rate and the percentage change in
PCI. For the 3-year and 2-year matching rates, to offer states relief
during an economic downturn, the correlation should be negative. In
other words, a decline in PCI would be associated with an increased
matching rate. (Similarly, in an economic upturn the matching rates
would decline.) However, we found that the correlation between the
changes in the 3-year and 2-year matching rates, and the changes in PCI
fluctuated (see fig. 12). Changes in current economic conditions were
essentially uncorrelated with changes in matching rates during this
time period. [Footnote 63] For example, while a moderate positive
correlation existed in 1990 (+0.54 and +0.47 for the respective 3-year
and 2-year matching rates), the correlation became negative for both
the 3-year and 2-year matching rates 4 years later. For most years,
correlations between 3-year and 2-year matching rates followed similar
patterns, but occasionally they diverged. For example, in 1993, the 3-
year matching rate had a negative correlation (-0.25), while the 2-year
matching rate essentially showed no correlation (0.04). Importantly,
during the recession years 1990 through 1991 and 2001, the correlation
coefficients were positive. Therefore, this indicated a declining
current PCI associated with a declining matching rate. Consequently,
the 3-year and 2-year matching rates do not tend to assist the states
during economic downturns.
Figure 12: Correlations of the Changes in the 3-Year and 2-Year
Matching Rates with Changes in PCI:
This is a bar graph with the vertical axis representing Correlation
coefficient from -0.30 to 0.60. The horizontal axis represents years,
from 1990 through 2004. For each year, there are two bars: one depicts
the 3-year matching rate correlation with PCI; the other depicts the 2-
year matching rate correlation with PCI.
[See PDF for image]
Source: GAO.
[End of figure]
We also examined the relationship between the changes in the 3-year and
2-year matching rates and changes in the unemployment rate. If the
matching rates assisted the states during periods of increased
unemployment, the relationship between the change in matching rates and
the change in the unemployment rate would be positive. In other words,
increases in the unemployment rate would be associated with increases
in the matching rates, and vice versa. Similar to the results with PCI,
the relationship is mixed”in some years, the relationship is positive
and in some it is negative (see fig. 13). In the 1990-1991 recession,
the relationship was negative, indicating that increases in the
unemployment rate were associated with decreased matching rates. In the
2001 recession, the relationship was positive: increases in
unemployment were associated with increased matching rates.
Figure 13: Correlations of the Changes in the 3-Year and 2-Year
Matching Rates with Changes in the Unemployment Rates:
This is a bar graph with the vertical axis representing Correlation
coefficient from -0.50 to 0.50. The horizontal axis represents years,
from 1990 through 2004. For each year, there are two bars: one depicts
the 3-year matching rate correlation with PCI; the other depicts the 2-
year matching rate correlation with PCI.
[See PDF for image]
Source: GAO.
Note: A positive correlation coefficient would show that when
unemployment increased, matching rates would also increase.
[End of figure]
Comparison of Changes in PCI with Changes in the Simulated Matching
Rate:
To assess whether the simulated matching rate provided a better
approximation of states‘ current economic conditions as measured by
changes in PCI, we did a correlation analysis of the changes in PCI
with the changes in the simulated matching rate, which is based on the
current and prior year‘s PCI. Comparing changes in PCI with the
simulated matching rates allowed us to assess whether (1) the time lag
in the data affected the correlation between matching rates and changes
in PCI or (2) the construction of the matching rate formula itself
affected the correlation between matching rates and changes in PCI. The
correlation between the changes in PCI and the changes in the simulated
matching rate is uniformly negative during the period from 1990 through
2004 (see fig. 14), suggesting that the matching formula structure is
not the cause of the mixed relationship.
Figure 14: Correlations of the Changes in the Simulated Matching Rate
with the Changes in PCI, 1990 to 2004:
This is a bar graph with the vertical axis representing Correlation
coefficient from -0.80 to 0.00. The horizontal axis represents years,
from 1990 through 2004. For each year a bar depicts the negative change
in the simulated match rate.
[See PDF for image]
Source: GAO.
[End of figure]
Overall, the changes in the simulated matching rate provided a more
consistent link to changes in states‘ PCI than did the changes in the 2-
year and 3-year matching rates. Decreases in PCI were consistently
associated with increases in the simulated matching rates. Conversely,
increases in PCI were associated with decreases in the matching rates.
The correlation coefficients ranged from -0.79 to -0.31, thus
indicating variations in the strength of the relationship during the
time period. The relationship between matching rates and PCI reflects
that the simulated matching rate is constrained by the 50 percent floor
in some states, whereas changes in PCI do not reflect this constraint.
[Footnote 64] This reduces the correlation. For example, although
Connecticut‘s PCI fluctuated more than the majority of the states, its
matching rate remained at the 50 percent floor during the entire 1990
to 2004 time period. The number of states affected by the 50 percent
floor during this time period varied from 10 to 12. In addition, the
simulated matching rate used a 2-year average of PCI, whereas the
changes in PCI reflected year-to-year differences in PCI. The matching
rate formula also squares PCI, thus reducing the correlation between
PCI and the simulated matching rate. (PCI changes are linear. The
squared PCI values in the simulated matching rate resulted in nonlinear
changes.)
The 2-year PCI average in the simulated matching rate reduced the
annual PCI fluctuations. As a result, the annual correlations between
the simulated matching rate and PCI fluctuated depending on the
underlying volatility of PCI across states.
Comparisons of Changes in the 3-Year and 2-Year Matching Rates with
Changes in the Simulated Matching Rate:
We also compared changes in the 3-year and 2-year matching rates with
changes in the simulated matching rate to determine whether a 2-year
matching rate better approximated states‘ current economic conditions.
Figure 15 shows the annual correlation coefficients of the 3-year and 2-
year matching rates compared with the simulated matching rate. A higher
positive correlation coefficient for the 2-year matching rate would
indicate that the 2-year matching rate is more sensitive to changes in
current economic conditions than the 3-year matching rate. The
generally negative correlation indicates that the 3-year and 2-year
matching rates do not track the current economic conditions reflected
in the simulated matching rate. In general, the correlations of the 3-
year and 2-year matching rates with the simulated matching rate were
practically identical during the entire period, 1990 to 2004 (on
average, -0.130 and -0.135, respectively). These correlations
fluctuated during the period of analysis and ranged from -0.58 (1991)
to 0.28 (1993). The correlations were negative during the 1990-1991
recession, indicating that the matching rates would not have assisted
states during this economic downturn. However, in the 2001 recession,
the correlations were essentially zero.
Figure 15: Correlations of the Changes in 3-Year and 2-Year Matching
Rates with the Changes in the Simulated Matching Rate:
This is a bar graph with the vertical axis representing Correlation
coefficient from -0.70 to 0.40. The horizontal axis represents years,
from 1990 through 2004. For each year, there are two bars: one depicts
the 3-year matching rate correlation with PCI; the other depicts the 2-
year matching rate correlation with PCI.
[See PDF for image]
Source: GAO.
Note: A positive correlation coefficient would mean that when PCI
increased, matching rates would decrease.
[End of figure]
The lack of substantial positive correlations during either recession
is of particular concern because it indicates that when the states are
under the most economic stress, the matching rates for states decline
or, at best, remain on average unchanged. These correlation results
occur because when PCI declines, the 3-year and 2-year matching rates
depend upon PCI data that reflect economic conditions of several years
earlier.
Comparisons of 2-Year and 3-Year Matching Rates in Year-to-Year
Fluctuations:
We analyzed the extent to which the 2-year and 3-year matching rates
differed in year-to-year percentage point changes by comparing annual
differences in matching rates to understand whether a reduction in the
number of years of PCI data in the matching rate formula (from 3 years
to 2 years of PCI data) yielded changes that differed from the year-to-
year percentage point changes resulting from the current, statutory
matching rate. We compared year-to-year percentage point changes in
matching rates for the 2-year matching rate and the 3-year matching
rate. As expected, the 3-year PCI average produced a smoother time
trend than a 2-year average. In general, the 2-year matching rates
showed slightly larger year-to-year fluctuations compared with the 3-
year matching rates.
Specifically, from 1990 through 2004, we found that:
* 43 percent of the annual changes in 2-year matching rates exceeded
the changes in the 3-year matching rates;
* 33 percent of the annual changes in the 3-year matching rates
exceeded the changes in the 2-year matching rates; and:
* 24 percent of the annual changes were identical (reflecting those
states at the 50 percent matching rate floor). (See table 7.)
Table 7: Comparison of States‘ Year-to-Year Differences in 2-Year and 3-
Year Matching Rates, 1990-2004:
Differences in the changes in the matching rates (percentage points): 1
or more;
Number of instances 2-year matching rate exceeded 3-year matching rate:
7;
Percentage of instances 2-year matching rate exceeded 3-year matching
rate: 1.0;
Number of instances 3-year matching rate exceeded 2-year matching rate:
0;
Percentage of instances 3-year matching rate exceeded 2-year matching
rate: 0.0.
Differences in the changes in the matching rates (percentage points):
0.5 to less than 1;
Number of instances 2-year matching rate exceeded 3-year matching rate:
31;
Percentage of instances 2-year matching rate exceeded 3-year matching
rate: 4.3;
Number of instances 3-year matching rate exceeded 2-year matching rate:
22;
Percentage of instances 3-year matching rate exceeded 2-year matching
rate: 3.1.
Differences in the changes in the matching rates (percentage points):
0.25 to less than 0.5;
Number of instances 2-year matching rate exceeded 3-year matching rate:
87;
Percentage of instances 2-year matching rate exceeded 3-year matching
rate: 12.2;
Number of instances 3-year matching rate exceeded 2-year matching rate:
51;
Percentage of instances 3-year matching rate exceeded 2-year matching
rate: 7.1.
Differences in the changes in the matching rates (percentage points):
Greater than 0 to less than 0.25;
Number of instances 2-year matching rate exceeded 3-year matching rate:
182;
Percentage of instances 2-year matching rate exceeded 3-year matching
rate: 25.5;
Number of instances 3-year matching rate exceeded 2-year matching rate:
165;
Percentage of instances 3-year matching rate exceeded 2-year matching
rate: 23.1.
Source: GAO analysis of changes for current 3-year matching rate and
proposed 2-year matching rate.
Notes: Differences represent differences in the absolute-value annual
changes in 3-year matching rates with absolute-value annual changes in
2-year matching rates.
The second and fourth columns represent the number of instances any
state experienced a variation within this range during 1990 to 2004.
The third and fifth columns represent the percentage of states
experiencing a variation within this range between 1990 and 2004.
There were 169 instances with no state differences between the 2-year
and 3-year matching rate changes. This lack of variation reflects
states whose matching rates were at the 50 percent matching rate floor
and thus had no annual changes.
[End of table]
In those years in which the 2-year matching rate exceeded the 3-year
matching rate, it occasionally did so by a wide margin. For example,
the 2-year matching rates in a few states”in several years”had an
annual change 1 percentage point greater than the annual change in the
3-year matching rate. [Footnote 65] The changes in the 3-year matching
rates never exceeded the changes in the 2-year matching rates by more
than 1 percentage point.
[End of section]
Appendix IV: Information on Selected Intergovernmental Loan Programs
and State Rainy Day Funds:
This appendix contains information about some of the existing programs
we reviewed to understand the design decisions and policy
considerations involved in a strategy to allow states to determine
whether and when to access increased federal Medicaid assistance in
response to economic downturns. These programs provided a conceptual
framework for reviewing existing design alternatives that could inform
consideration of a potential Medicaid-specific loan or national rainy
day fund. We examined features of existing federal programs that
include intergovernmental loan components. [Footnote 66] In addition,
we examined state rainy day funds as well as prior GAO work to inform
our understanding of some of the issues likely to be involved in
creating a Medicaid-specific national rainy day fund. [Footnote 67]
Selected Intergovernmental Loan Programs:
The Environmental Protection Agency‘s (EPA) Clean Water State Revolving
Fund (CWSRF) program provides an independent, permanent, low-cost
source of financing for a wide range of efforts to protect or improve
water quality. [Footnote 68] Through the CWSRF, EPA provides annual
grants to the states to capitalize state-level CWSRFs. States must
match these EPA grants with a minimum of 20 percent of their own
contributions. States loan their CWSRF dollars to local governments and
other entities for various water quality projects, and loan repayments
are cycled back into the state-level programs to fund additional
projects. In June 2006, we reported that, since 1987, the 50 states as
well as Puerto Rico have used 96 percent (about $50 billion) of their
CWSRF dollars to build, upgrade, or enlarge conventional wastewater
treatment facilities and conveyances. Although the CWSRF is primarily a
low-interest loan program, states can also use it to refinance,
purchase, or guarantee local debt and purchase bond insurance. States
may customize their loan terms, including interest rates (from 0
percent to market rates) and repayment periods (up to 20 years),
depending on the financial and environmental needs of potential
borrowers. All programs are also subject to annual independent
financial audits.
The Federal Emergency Management Agency (FEMA) provides Community
Disaster Loans (CDL) to local governments in designated disaster areas
that have suffered a substantial loss of tax and other revenue. The
state‘s governor requests a presidential declaration of an emergency or
disaster through the FEMA Regional Director. Once the president has
made the declaration, loans can be provided up to a maximum of $5
million. Loans are not to exceed 25 percent of the local government‘s
annual operating budget for the fiscal year in which the major disaster
occurs. The CDL program provides for loan forgiveness (cancellation)
when it is determined that the affected government will not be able to
repay the loan for 3 fiscal years following a disaster. A total of 55
CDLs were made from the initiation of the program in August 1976
through September 30, 2005. Of the 55 loans made, 36 were paid back in
part or in full. [Footnote 69]
The Temporary Assistance for Needy Families (TANF) program offers block
grants under which states receive federal funds to design and operate
their own welfare programs within federal guidelines. TANF also offers
a direct loan program to provide assistance to states. This program is
funded though a permanent appropriation of $1.7 billion. States can
access direct loan funds for any purpose for which TANF grants can be
used, such as welfare assistance, but states must repay any loans
within 3 years. However, in 2001, we reported that the TANF loan
program is likely the wrong mechanism to provide assistance during a
fiscal crisis because states are eligible for better financing terms in
the tax-exempt municipal bond market and because officials in some
states had indicated that borrowing specifically for social welfare
programs in times of fiscal stress would not incur popular support.
[Footnote 70] No state had applied for a TANF loan prior to 2005. In
2005, Congress made a TANF loan available to three states affected by
Hurricane Katrina”Alabama, Louisiana, and Mississippi”and included
language stating that penalties would not be imposed against these
states for failure to repay the loan or interest on the loan.
Unemployment Insurance (UI), administered by the U.S. Department of
Labor in partnership with the states, provides temporary cash benefits
to eligible workers who become involuntarily unemployed. Eligibility
for UI benefits, benefit amounts, and the length of time benefits are
available are determined by state law, within broad federal guidelines.
The UI system is funded through federal and state taxes levied on
employers. States deposit their taxes with the U.S. Treasury, which
maintains one trust fund with a separate account for each state. States
are responsible for ensuring the solvency of their individual trust
funds, which they use to pay benefits to UI claimants in their states.
To ensure solvency, states may choose to build trust fund reserves
during good economic times so that if unemployment rises they will have
reserves sufficient for paying UI claims without raising taxes or
borrowing money from the federal government. If states have
insufficient reserves for paying claims, they may request a loan from
the federal government. [Footnote 71] The federal government maintains
a loan trust fund, which is built up using a portion of the federal UI
tax. The Federal Unemployment Account (FUA) funds loans to state
unemployment compensation programs. If states fail to repay any loans
within the time frame specified in statute, [Footnote 72] the federal
taxes on employers in a state increase each year the debt is not paid.
As of July 2006, the FUA had a balance of about $13 billion, and one
state had an outstanding loan totaling about $238 million. [Footnote
73] States utilize the loan program periodically.
State Rainy Day Funds:
According to the National Association of State Budget Officers (NASBO),
almost all states have established rainy day funds as one way to cope
with fiscal constraints that states experience. These fiscal
constraints can be imposed either by law, such as balanced budget
requirements and borrowing restrictions, or by bond markets, which
encourage states to provide funding in advance for particular budgetary
uncertainties. [Footnote 74] Without adequate reserves available to
mitigate a fiscal crisis, states without short-term borrowing
capabilities would have little choice but to reduce spending, increase
revenue, or make other short-term budget adjustments. Even if a state
is permitted to borrow short-term to fund unanticipated needs, the
practice may be viewed unfavorably by bond-rating agencies that
establish credit ratings for states and therefore play a role in
determining a state‘s borrowing costs.
State rainy day fund requirements vary in a number of ways. [Footnote
75] Some state rainy day funds can be used only in years of economic
downturn (determined through formulas) or in the case of a revenue
shortfall or a deficit. State rainy day funds also may include
requirements specifying whether funds can be used for general purposes,
agency-specific purposes, or in the event of natural disasters or other
emergencies. States may also require a minimum rainy day fund balance.
The National Conference of State Legislatures (NCSL) recommends a
minimum rainy day fund balance of 5 percent.
[End of section]
Appendix V: GAO Contacts and Staff Acknowledgments:
GAO Contacts:
Kathryn G. Allen, (202) 512-7118:
Stanley J. Czerwinski, (202) 512-6806
Acknowledgments: Major contributors included Assistant Directors
Michael Springer and Carolyn L. Yocom; and Meghana Acharya, Robert
Dinkelmeyer, Greg Dybalski, Nancy Fasciano, Jerry Fastrup, Summer
Lingard, Romonda McKinney, Donna Miller, Elizabeth T. Morrison, and
Michelle Sager.
[End of section]
Footnotes:
[1] In fiscal year 2004, total expenditures for the Medicaid program
(federal and state) were about $298 billion.
[2] The $10 billion temporary increase in federal Medicaid funding made
available through JGTRRA provided supplemental Medicaid funding to
states for the last two calendar quarters (April through September) of
fiscal year 2003 and the first three calendar quarters (October through
June) of fiscal year 2004.
[3] The federal matching rate is intended to adjust for differences in
state fiscal capacity and reduce program benefit disparities across
states by providing more federal funds to states with weaker tax bases.
For fiscal year 2006, federal matching rates ranged from 50 to 76
percent of state Medicaid expenditures.
[4] See GAO, Federal Assistance: Temporary State Fiscal Relief, GAO-04-
736R (Washington, D.C.: May 7, 2004); GAO, Medicaid Formula:
Differences in Funding Ability among States Often Are Widened, GAO-03-
620 (Washington, D.C.: July 10, 2003); Miller and Schneider, The
Medicaid Matching Formula: Policy Considerations and Options for
Modification, #2004-09, AARP Public Policy Institute (Washington, D.C.:
September 2004); and GAO, Medicaid: Restructuring Approaches Leave Many
Questions, GAO/HEHS-95-103 (Washington, D.C.: Apr. 4, 1995).
[5] GAO-04-736R.
[6] Throughout this report, the term state refers to the 50 states and
the District of Columbia.
[7] We analyzed the past three recessions”1981 through 1983, 1991
through 1992, and 2001”to understand differences in the timing, depth,
and duration of different economic downturns. However, similar economic
patterns may not repeat themselves in future economic downturns.
[8] Where we conducted simulations for the first and second strategies,
we asked experts in Medicaid financing issues to provide suggestions
regarding their construction.
[9] Stan Dorn, Barbara Markham Smith, and Bowen Garrett, Medicaid
Responsiveness, Health Coverage, and Economic Resilience: A Preliminary
Analysis, Prepared for the Health Policy Institute of the Joint Center
for Political and Economic Studies (Washington, D.C.: Joint Center for
Political and Economic Studies, Sept. 27, 2005).
[10] In contrast, 70 percent of Medicaid spending goes to elderly
individuals and individuals with disabilities, who are least affected
by economic downturns, as reported by Dorn et al.
[11] In some cases, expenditures could not be attributed to specific
beneficiary populations and thus were excluded from these calculations.
[12] The National Bureau of Economic Research (NBER) identifies
recessions on the basis of several indicators, including employment,
sales in the manufacturing and trade sectors, and industrial
production. A recession is a significant decline in economic activity
spread across the economy, lasting more than a few months, normally
visible in real gross domestic product (GDP), real income, employment,
industrial production, and wholesale-retail sales. A recession begins
just after the economy reaches a peak of activity and ends as the
economy reaches its trough. Not all economic downturns are recessions.
Economic downturns would include”but not be limited to”recessions
identified by NBER.
[13] By statute, the federal share of Medicaid spending ranges from 50
to 83 percent. The 50 percent minimum federal share (’50 percent
floor“) reflects a federal commitment to fund at least half the cost of
each state‘s Medicaid program. For 2006, 12 states received federal
matching rates of 50 percent.
[14] See GAO-03-620.
[15] OMB Circular A-129 outlines guidelines on federal government
loans.
[16] We chose both numbers based on a review of states‘ unemployment
rates over the past three recessions and determined that these levels
would have provided considerable certainty that the economic slowdown
was nationwide.
[17] See Dorn et al. (Sept. 27, 2005).
[18] For our model, we used Dorn et al.‘s estimates to derive an
average increase in Medicaid expenditures per additional unemployed
person of $300, which could be adjusted over time by inflation and
changes in demographics of the Medicaid population. See Dorn et al.
(Sept. 27, 2005).
[19] This is an increase of 10 percent or more compared to the
unemployment rate that existed a year earlier and not a 10 percentage
point change in unemployment rates. Unless otherwise specified, all
percentage changes are stated in terms of a percentage increase over a
base quarter.
[20] One state received a matching rate increase that was less than
0.005 percentage points.
[21] Appendix II provides details on the calculation of this index and
how it affects the amount of assistance a state would receive. We use
poverty in lieu of actual enrollments because states vary in terms of
the services provided and eligibility for those services.
[22] Changes in states‘ federal matching rates can have a significant
effect on the amount of federal funds available to a state. For
example, a 0.25 percent increase in states‘ federal matching rates for
2004 would have resulted in a minimum increase in federal funds of more
than $0.9 million in Wyoming and more than $102 million in New York.
[23] Although not feasible to implement because of lags in data
publication, we devised this simulated matching rate in order to
evaluate whether changing the years of data used to calculate the
matching rate resulted in a better approximation of states‘ current
economic circumstances.
[24] These amounts represented 0.08 to 0.29 percent of state own-source
revenues. Also referred to as general revenues from own sources, these
revenues are state and local total receipts, excluding federal grants-
in-aid. We excluded from this analysis the 14 states whose matching
rates in 2003 were at the 50 percent floor or had been established in
legislation. (As we have previously reported, because of the 50 percent
floor, some states receive higher federal matching rates than they
would if their rates were based only on their PCI.)
[25] Across all of the years of our analysis (1990-2004), the number of
states that would have experienced larger fluctuations under this
strategy than under current law ranged from 17 to 27.
[26] Across all years, the number of states that would have experienced
fluctuations more than 0.5 percentage points larger under this strategy
than under current law ranged from 0 to 8.
[27] Appendix IV includes background information on selected federal
programs that include intergovernmental loan components.
[28] The Federal Credit Reform Act of 1990, P.L. 101-508, requires that
credit subsidy costs be financed from new budget authority and be
recorded as budget outlays at the time direct or guaranteed loans are
disbursed. Agencies must have appropriations for the subsidy cost
before they can enter into direct loan obligations or loan guarantee
commitments. Subsidy costs include the estimated long-term cost to the
federal government on a net-present value basis of all cash flows to
and from the government, such as interest rate subsidies and defaults
over the life of the loan.
[29] Specific examples of facilitated lending include The Federal
Family Education Loan Program and the Health Center Loan Guarantees.
[30] GAO, Welfare Reform: Challenges in Saving for a ’Rainy Day“, GAO-
01-674T (Washington, D.C.: Apr. 26, 2001).
[31] GAO, Budgeting for Emergencies: State Practices and Federal
Implications, GAO/AIMD-99-250 (Washington, D.C.: Sept. 30, 1999).
[32] GAO/AIMD-99-250.
[33] For example, in its retrospective determination of the dates of
nationwide economic peaks and troughs, the Business Cycle Dating
Committee of the National Bureau of Economic Research (a private,
nonprofit, nonpartisan research organization) relies primarily on real
gross domestic product (GDP), real income, employment, industrial
production, and wholesale-retail sales. The Committee views real GDP as
the single best available measure. These data are not all available at
the state level.
[34] Centers for Medicare & Medicaid Services (CMS) data on Medicaid
enrollments would not be useful for this purpose because they reflect
both changes in enrollments due to changes in state policies affecting
eligibility as well as increases in enrollment that are attributable to
economic downturn.
[35] More specifically, we used monthly, seasonally adjusted
unemployment data and unemployment rates from BLS Local Area
Unemployment Statistics by state.
[36] Month-to-month fluctuations are dampened by using a quarterly
rolling average of the 12 most recent months, though it also somewhat
dampens the indicator‘s sensitivity to turns in the economy. However,
we retained some degree of sensitivity by recomputing these 12-month
averages for each quarter. For this strategy, when referring to
unemployment or the unemployment rate, we are referring to the average
of the 12 most recent months.
[37] More sophisticated statistical methods could perhaps better
isolate cyclical change from trends and other noncyclical factors
causing changes. We chose this quarterly moving average method because
it offers greater simplicity that helps make the assistance formula
mechanism easier to explain and understand.
[38] Theodore M. Crone, ’What a New Set of Indexes Tells Us About State
and National Business Cycles,“ Federal Reserve Bank of Philadelphia
Business Review (2006, Q1): pp. 11-24.
[39] The National Bureau of Economic Research establishes widely used
dates of the start and end of expansions and contractions of the U.S.
business cycle. These dates are determined retrospectively and would
not be available on a timely basis for use in an automatic trigger.
[40] This 10 percent threshold is used as a criterion for beginning
federal supplemental assistance to states. As explained later in this
appendix, it does not restrict an individual state‘s eligibility. In
other words, a state with a 2 percent increase in unemployment would
receive assistance, but its supplemental increase to its matching rate
would be smaller.
[41] This is an increase of 10 percent compared to the unemployment
rate for the same quarter in the previous year and not a 10 percentage
point change in unemployment rates. Unless otherwise specified, all
percentage changes in unemployment or unemployment rates for this
strategy are expressed in terms of a percentage increase over a base
quarter, and not percentage points. (However, supplemental increases to
states‘ matching rates are reported in percentage points because that
is the common way to present that information.)
[42] This is the percentage increase in a state‘s unemployment rate
compared to the same quarter in the previous year (the retrospective
assessment). We do not use the national unemployment rate as a
reference point because many states usually remain well above or below
the national unemployment rate. The use of state-by-state unemployment
rates is also appropriate because supplemental assistance is intended
for individual states, whose Medicaid programs vary.
[43] Note that in all the data displays in this appendix, a 2-quarter
administrative lag is assumed between the date of the increase in
unemployment data and the date the supplemental assistance could be
provided. Such an administrative lag would reflect time for data to
become available, for allocations to be computed, and for other
administrative purposes. For example, on a table or figure showing
unemployment for the third quarter of 2002, those are actually
unemployment data as of the first quarter of 2002, with the difference
due to the assumed 2-quarter administrative lag.
[44] If the onset of the downturn is very gradual, it is more likely
that fewer states will have the requisite 10 percent increase over the
unemployment rate from the prior year.
[45] Note that this is an increase in the number of persons unemployed
and not the unemployment rate.
[46] The choices are not merely limited to the choice between a longer
and shorter retrospective assessment. For example, the retrospective
assessment could be a weighted average of long and short periods, with
less weight on the long periods.
[47] For example, see John Taylor, ’Reassessing Discretionary Fiscal
Policy,“ Journal of Economic Perspectives, v. 14, n. 3 (Summer 2000):
pp. 21-36.
[48] Congressional action could override any approach in place. For
example, if there were signs of an incipient national economic
downturn, supplemental assistance could be enacted ahead of an
automatic trigger. Alternatively, supplemental assistance could be
blocked if funding of other budget priorities was deemed more
important.
[49] John Holahan and Bowen Garrett, ’Rising Unemployment and
Medicaid,“ Urban Institute Health Policy Online (Oct. 16, 2001). This
description somewhat oversimplifies the econometric methods of these
studies. For instance, these studies rely on several estimating
equations, and they also estimate increases in Medicaid enrollments
from which the impact on Medicaid spending is calculated.
[50] Stan Dorn, Barbara Markham Smith, and Bowen Garrett, Medicaid
Responsiveness, Health Coverage, and Economic Resilience: A Preliminary
Analysis, prepared for the Health Policy Institute of The Joint Center
for Political and Economic Studies (Washington, D.C.: The Joint Center
for Political and Economic Studies, Sept. 27, 2005).
[51] Open-ended matching grants increase the capacity of state and
local governments to provide services, but because of difficulty in
predicting expenditures, they create a degree of fiscal uncertainty at
the federal level.
[52] This variation by quarter is one reason why calculating quarterly
supplemental assistance payments could better target funds than
calculating payments on an annual basis.
[53] We used the number of unemployed persons rather than the
unemployment rate because state size must be taken into account. Two
states with identical unemployment rate increases may have different
increases in their numbers of unemployed persons. The state with a
larger increase in the number of unemployed persons would have greater
resulting Medicaid spending, assuming everything else remained the
same. This increase in the number of unemployed could be adjusted to
take into account the change in the labor force from the base period.
However, we chose not to take this approach to avoid complicating the
simulation model.
[54] While states could cope with the impact of small increases in the
number of unemployed, it could be problematic to specify a level of
increase that is small enough for states to cope without federal aid.
Furthermore, because of our inability to separate trends from the
effects of economic cycles, a fast-growing state that has a small
increase in the number of unemployed could claim to be significantly
affected by the national downturn, considering how large its decrease
in the number of unemployed might have been without the downturn.
[55] CMS does not make these data available frequently enough to permit
their use on a quarterly basis by states. For our simulation model, we
used 2003 expenditure data, which were the most recent data available
at the time we did our work.
[56] Similar targeting was displayed in other recessionary periods.
That is, the targeted assistance was proportional to the increases in
unemployment. In addition, a relatively small number of states (usually
different states in each period) would receive small payments because
their recessions began either earlier or later compared with the
national downturn.
[57] For example, the fiscal year 2006 matching rate includes a 3-year
average of PCI data from 2001 to 2003.
[58] The age of the data used to calculate the matching rate results
from both a data reporting lag and an announcement lag. The reporting
lag occurs because the Bureau of Economic Analysis reports state PCI
amounts about 9 to 12 months after the end of a calendar year. For
instance, state PCI for 2004 was reported toward the end of 2005. The
announcement lag occurs because matching rates are announced 1 year
before the year in which they become effective. This is referred to as
the announcement period, because it gives states time to plan their
budgets based on Medicaid matching rates for the upcoming fiscal year.
[59] Vic Miller and Andy Schneider, The Medicaid Matching Formula:
Policy Considerations and Options for Modification, #2004-09, AARP
Public Policy Institute (Washington, D.C.: September 2004).
[60] GAO, Medicaid Formula: Differences in Funding Ability among States
Often Are Widened, GAO-03-620 (Washington, D.C.: July 10, 2003).
[61] The first recession occurred in 1990-1991. The second recession
occurred in 2001.
[62] State PCIs were deflated using the price index for personal
consumption expenditures from BEA.
[63] However, in fig. 12, positive correlations were more prevalent
than negative correlations.
[64] By statute, the federal share of Medicaid spending ranges from 50
to 83 percent. The 50 percent minimum federal share (’50 percent
floor“) reflects a federal commitment to fund at least half the cost of
each state‘s Medicaid program. For 2006, 12 states received federal
matching rates of 50 percent.
[65] The standard deviations for the annual changes in the 3-year and 2-
year matching rates, respectively, were 0.52 and 0.60 percent.
[66] Other loan programs included in our background research were the
Student Loan Program, the Drinking Water Revolving Loan Program, and
state Capital Access Programs. Any new federal loan program would have
to comply with the Federal Credit Reform Act of 1990 requirements that
agencies have budget authority to cover the program‘s cost to the
government in advance, before new direct loan obligations are incurred
and new loan guarantee commitments are made.
[67] GAO, Medicaid: Restructuring Approaches Leave Many Questions,
GAO/HEHS-95-103 (Washington, D.C.: Apr. 4, 1995).
[68] GAO, Clean Water: How States Allocate Revolving Loan Funds and
Measure Their Benefits, GAO-06-579 (Washington, D.C.: June 5, 2006).
[69] The Community Disaster Loan Act of 2005 (CDLA), provided for up to
$750 million of disaster funds to be used to subsidize ’special“
community disaster loans, up to a total of $1 billion, for local
governments to provide essential services. For purposes of these
special loans, the new law removed the $5 million per loan limit but
prohibited their cancellation. As of May 3, 2006, 59 special CDL
applications had been approved for local governments in Louisiana and
47 for those in Mississippi, for a total of 106 loans.
[70] GAO, Welfare Reform: Challenges in Saving for a ’Rainy Day,“ GAO-
01-674T (Washington, D.C.: Apr. 26, 2001).
[71] They may also choose to increase taxes on employers or raise funds
through other means such as municipal bonds, which potentially offer a
lower interest rate.
[72] If a state has an outstanding balance on January 1 for 2
consecutive years, it has until November 10 of the second year to repay
the loan.
[73] These data were the most recent available balances as of Aug.
2006.
[74] GAO, Budgeting for Emergencies: State Practices and Federal
Implications, GAO/AIMD-99-250 (Washington, D.C.: Sept. 30, 1999).
[75] NASBO, Budget Processes in the States (Washington, D.C.: January
2002).
[End of sction]
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