Formula Grants: Census Data Are among Several Factors That Can Affect Funding Allocations
Published: 2009-07-09T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: In past years, the federal government has annually distributed over $300 billion in federal assistance through grant programs using formulas driven in part by census population data. Of the more than $580 billion in additional federal spending, the American Recovery and Reinvestment Act of 2009 will obligate an estimated additional $161 billion to federal grant programs for fiscal year 2009. The U.S. Census Bureau (Bureau) puts forth tremendous effort to conduct an accurate count of the nation's population, yet some error in the form of persons missed or counted more than once is inevitable. Because many federal grant programs rely to some degree on population measures, shifts in population, inaccuracies in census counts, and methodological problems with population estimates can all affect the allocation of funds. This testimony discusses (1) how census data are used in the allocation of federal formula grant funds and (2) how the structure of the formulas and other factors can affect those allocations. This is based primarily on GAO's issued work on various formula grant programs and the allocation of federal funds.
section_title: What GAO Found
paragraphs: Federal grants use various sources of population counts in their funding formulas. They include the decennial census, which provides population counts once every10 years, and also serves as the baseline for estimates of the population for the years between censuses--known as postcensal estimates. Other sources of population data include the Bureau's American Community Survey and the Current Population Survey conducted by the Bureau for the Bureau of Labor Statistics, which provides monthly data. The degree of reliance on population in funding formulas varies. For example, the Social Services Block Grant formula allocates funding based solely on a state's population relative to the total U.S. population. Other programs use population plus one or more variables to determine funding levels. Medicaid, for example, uses population counts and income to determine its federal reimbursement rate. On the basis of simulations GAO conducted of federal grant allocations by selected federal grant programs--for illustrative purposes only--we found that changes in population counts can affect, albeit modestly, the allocations of federal funds across the states. For example, in 2006 we found that compared to the $159.7 billion total federal Medicaid funding in 2004, 22 states would have shared an additional $208.5 million in Medicaid funding, 17 states would have lost a total of $368 million, and 11 states and the District of Columbia would have had their funding unchanged. In total 0.2 percent of Medicaid funds would have shifted as a result of the simulation. In addition to population data, various other factors related to the design of federal grant programs may mitigate the effect that population changes can have on the distribution of federal funds. For example, in order to prevent funding losses from a formula change, several programs include hold-harmless provisions guaranteeing that each recipient entity will receive a specified proportion of the prior year's amount or share regardless of population changes.

section_title: Letter
paragraphs: Thank you for the opportunity to be here today to discuss the role that the  nation’s population count plays in the allocation of federal funds to states  and localities. As agreed with the Subcommittee, my remarks today  describe (1) how census data are used in the allocation of federal formula  grant funds, and (2) how the structure of the formulas and other factors  can affect those allocations.
My main point is that although population counts play an important role in  the distribution of federal funds, other factors, such as the design of the  grant formulas, can mitigate the effect that any population changes have  on funding levels. It does not necessarily follow that an increase or  decrease in population size would have a proportional effect on the  amount of federal assistance an entity ultimately receives. Nevertheless,  because population estimates are important for federal funding  allocations, and the decennial census is the foundation for these estimates,  an accurate enumeration in 2010—including a reduction in the historic  undercount of minority and other populations, as previously reported—is  essential.
As you well know, the decennial census is a critical national effort  mandated by the Constitution, and census data are used to apportion  congressional seats and redraw congressional districts. Data from the  decennial census, and annual estimates of the nation’s population that are  derived from the decennial, directly and indirectly affect the allocation of  federal assistance to state and local governments. In past years, the federal  government distributed over $300 billion annually in federal assistance  through federal grant programs to states and localities using formulas  driven in whole or in part by census population data. The enactment of the  American Recovery and Reinvestment Act of 2009 (Recovery Act)— which is intended to help restore the economy, invest in our na tional  infrastructure, and minimize and avoid reduction in state and local  government services—will allocate additional money through grant  programs. Of the $580 billion in additional federal spending associated  with the Recovery Act, the federal government will obligate an estimated  additional $161 billion to federal grant programs for fiscal year 2009,  including some programs that depend on census population data in whole  or in part to determine the amount of federal assistance.
The Census Bureau (Bureau) puts forth tremendous effort to conduct an  accurate count of the nation’s population. However, some degree of error  in the form of persons missed (an undercount), counted more than once  (an overcount), or in the wrong location is inevitable. Such errors are  particularly problematic because of their differential impact on various  subgroups. Minorities, renters, and children, for example, are more likely  to be undercounted by the census, while more affluent groups, such as  people with vacation homes, are more likely to be enumerated more than  once.
Further, the U.S. has an increasingly mobile population, and natural  disasters such as Hurricane Katrina can have a dramatic impact on  population counts of affected communities. For example, in the wake of  Hurricane Katrina, the Red Cross estimated that over a half a million  people were displaced and either temporarily or permanently migrated to  other areas. Because many federal grant programs rely to some degree on  population measures, shifts in population, inaccuracies in census counts,  and methodological problems with population estimates can all affect the  allocation of funds.
My remarks are based primarily on reports we have previously issued on  various formula grant programs and the allocation of federal funds (please  see the final pages of this testimony for a list of related GAO products). To  update information from our prior work, we reviewed funding data for  selected grant programs in the Office of Management and Budget’s  Analytical Perspectives, Budget of the U.S. Government, Fiscal Year  2010 and interviewed Bureau officials. We selected five grant programs  based on prior work we conducted that illustrate how population and  other factors can affect the allocation of federal funds. According to the  General Services Administration’s Catalog of Federal Domestic  Assistance, the federal government administers over 1,800 different grant  programs. Some grant programs use census population data in their  allocation formulas while others do not. The five programs we selected  constituted about $225.7 billion in fiscal year 2008 obligations, and  represented more than 40 percent of federal program grant obligations in  that year. The programs we selected (and the amount of money obligated  in fiscal year 2008) include:  the Medical Assistance Program (Medicaid), which is a joint federal-state  program that finances health care for certain low-income individuals  (about $214.0 billion in fiscal year 2008 obligations); the Community Development Block Grant program (CDBG), which is  intended to develop viable urban communities by providing decent  housing and a suitable living environment and expanding economic  opportunities, principally for persons of low and moderate income (about  $4.9 billion in fiscal year 2008 obligations);  the Vocational Rehabilitation Program (VR),  which administers grants for  the purpose of providing vocational rehabilitation services to persons with  disabilities who are seeking competitive employment ($2.9 billion in fiscal  year 2008 obligations); the Social Services Block Grant (SSBG) program, which is a federal  program that provides funds to assist states in delivering social services to  adults and children ($1.7 billion in fiscal year 2008 obligations); and the Ryan White Comprehensive AIDS Resources Emergency (CARE) Act  of 1990, which was enacted to address the needs of jurisdictions, health  care providers, and people with human immunodeficiency virus/acquired  immunodeficiency syndrome (HIV/AIDS) and their family members (about  $2.2 billion in fiscal year 2008 obligations).
subsections: section_title: Changes in Census Population Counts Can Affect the Allocation of Federal Funds
paragraphs: Federal grants use various sources of population counts in their funding  formulas. First, the Bureau conducts the decennial census, which provides  population counts once every 10 years, and also estimates the population  for the years between censuses—known as postcensal estimates. For  example, the SSBG allocation formula uses the most recent postcensal  population estimates to distribute funds. Second, the Bureau’s American  Community Survey provides detailed annual data on socioeconomic  characteristics for the nation’s communities and is used to allocate federal  funds for such programs as the Section 8 housing voucher program, an  effort aimed at increasing affordable housing choices for very low-income  households. In addition, the Current Population Survey conducted by the  Bureau for the Bureau of Labor Statistics provides monthly data and is  used to allocate funds for programs under the Workforce Investment Act  of 1998, which provides workforce development services to employers  and workers.
Among funding formulas that rely on population data, the degree of  reliance varies. On the one hand, the SSBG formula allocates funding  based on a state’s population relative to the total U.S. population. On the  other hand, some formulas use population plus one or more other  variables to determine funding levels. Medicaid, for example, uses  population counts and income to determine the federal reimbursement  rate. The Medicaid formula is based on the ratio of a state’s aggregate  personal income to the same state’s population relative to aggregate U.S.  per capita personal income. Other grant programs, such as CDBG, are  driven by multiple factors in addition to population such as poverty,  housing overcrowding, and the age of the housing. Population plays a  more limited role in other programs. Federal assistance under one part of  the CARE Act does not use census population counts in its funding  formula. Rather, census population counts are used in this part as criteria  for program eligibility—CARE Act funds under this part are awarded to  urbanized areas, which are determined in part by census population  counts. The actual amount of federal assistance is based on the counts of  people with HIV/AIDS.
subsections: section_title: Accurate Population Counts Are Important for Allocating Federal Assistance
paragraphs: On the basis of simulations we conducted of formula grant allocations, we  found that changes in population counts can affect, albeit modestly, the  allocations of federal funds for the programs analyzed. Note that these  simulations were for illustrative purposes only—to demonstrate the effect  that alternative population estimates could have on selected federal grant  programs.
In two prior reports, we simulated the reallocations that would have  resulted from using alternative population counts for Medicaid  allocations. In our 2003 report, based on population estimates that  differed from the 2000 Census count by about 3.2 percent across the U.S.  and varying state by state, we found that of the $110.9 billion total federal  Medicaid spending in 2002, 18 states would have shared an additional  $377.0 million in Medicaid funding, 21 states would have lost a collective  $363.2 million, and 11 states and the District of Columbia would have had  their funding unchanged.
In our 2006 report, based on population estimates that differed from the  2000 Census count by about 0.5 percent across the U.S. and varying state  by state, we found that of the $159.7 billion total federal Medicaid funding  in 2004, 22 states would have shared an additional $208.5 million in  Medicaid funding, 17 states would have lost a total of $368 million, and 11  states and the District of Columbia would have had their funding  unchanged. In total, 0.2 percent of Medicaid funds would have shifted as  a result of the simulation. In our 2006 report, we also simulated the  reallocations of SSBG funding and found that of the $1.7 billion in SSBG  allocations, 27 states and the District of Columbia would have shared a  gain of $4.2 million and 23 states would have shared a loss of $4.2 million.  In total 0.2 percent of SSBG funds would have shifted as a result of the  simulation.
section_title: The Census Bureau Has Procedures for Addressing Errors in Population Counts
paragraphs: Since the completeness and accuracy of population data can modestly  affect grant funding streams and other applications of census data, the  Bureau has used a variety of programs to address possible errors in  population counts and estimates. Not all of these programs are completed  by December 31 of the decennial year—the date on which population data  are to be sent to the President for purposes of congressional  apportionment. Corrections made after this date may be reflected in the  population counts made available for redistricting or the allocation of  federal funds.
Demographic Full Count Review: For the 2000 Census, analysts were hired  under contract by the Bureau to identify, investigate, and document  suspected data discrepancies in order to clear census data files and  products for subsequent processing or public release. Bureau reviewers  were to determine whether and how to correct the data by weighing  quality improvements against time and budget constraints. Bureau officials  told us that they expect to implement something similar to the 2000  program, but they have not made a final decision for 2010.
Count Question Resolution (CQR): In addition, for the 2000 Census the  Bureau implemented the CQR program to provide a mechanism for state,  local, and tribal governments, as well as Bureau personnel, to correct the  counts of housing units and other types of dwellings and their associated  populations. Governmental entities could use the updated information  when applying for federal assistance that uses census data as part of the  allocation formula. Between the program’s initiation in June 2001 and its  completion in September 2003, the CQR program corrected data affecting  over 1,180 of the nation’s more than 39,000 governmental units. Although  the national- and state-level revisions were relatively small, in some cases  the corrections at the local level were substantial. For example, the  Bureau added almost 1,500 persons to the population count of Cameron,  Missouri, when CQR found that a prison’s population was erroneously  omitted. Bureau officials told us that they expect to implement something  similar to the 2000 program, but they have not made a final decision for  2010.
Census Challenge Program: Further, to permit challenges to population  estimates prepared by the Bureau, the Bureau administers a program  whereby governmental units—including states, counties, and tribal and  local governments—may file informal challenges within a designated  period of time after the estimate is released by the Bureau. In the event  that the challenge cannot be resolved informally, the governmental unit  may proceed with a formal challenge where the state or local government  unit has a right to a hearing. Using such documentation as new  construction permits, and data from water and electrical utilities, localities  can ask the Bureau to review and update their population counts. Between  2001 and 2007, 259 challenges led to adjustments in census population  estimates.
Coverage Measurement: Beginning with the 1980 Census, the Bureau has  had procedures in place to measure the accuracy of the census (or  “coverage”) by relying on additional information obtained from an  independent sample survey of the population. However, due to concerns  over the quality of the data and other factors, the Bureau has never used  the results of its coverage measurement efforts to adjust the census  population count. For the 2010 Census, the Bureau plans to measure  coverage error for various demographic groups and geographic areas, but  does not plan to use the results to adjust the final population counts.
section_title: Factors Other Than Population Can Affect Distribution of Federal Funds
paragraphs: Although accurate population counts and estimates play an important role  in allocating federal assistance, various other factors related to the design  of federal grant programs may mitigate or increase the effect that  population changes can have on the distribution of federal funds. These  factors include floors on matching rates, floors for small states, hold- harmless provisions, complex formula structures, lags in data, and  whether funding for a specific program is from a fixed pool or open ended.  I will describe each in greater detail.
Floors on Matching Rates: Some grant programs employ floors in order to  mitigate the outcome that would result if a particular grant allocation were  determined by the funding formula alone. For example, the Medicaid  statute provides for a 50 percent floor. In our 2003 report on federal  formula grant funding, we found that for certain states the Medicaid  matching provisions mitigated the effect of the Medicaid funding formula,  which has a population component. In 2002, under the statutory formula,  which is based on personal income relative to state’s population,  Connecticut—a state with a high per-capita income—would have received  a 15 percent federal matching rate. Because of the statutory floor,  Connecticut instead received a 50 percent federal match.
Floors for Small States: To ensure at least a minimum level of funding for  all states, program formula allocations with formulas that rely on  population data can include floors for small states. The VR formula  employs a floor allocation that overrides the population-based  allocations. The least-populated states receive a higher allocation than  they would have otherwise received under the formula.
Hold-Harmless Provisions: In order to prevent funding losses from a  formula change, programs can include hold-harmless provisions  guaranteeing a level of funding that is based on a prior year’s funding. For  example, one part of the CARE Act contains hold-harmless provisions  whereby some recipients are guaranteed they will receive at least as much  funding as in the previous year.
Complex Formula Structures: Many formulas include measures other than  population to distribute funds. VR allocations depend upon three factors:  the state’s 1978 allocation, population, and per capita income. As a result,  the effect of increases in population may be mitigated by their 1978  allocations and changes to the state’s per capita income. CDBG allocations  are based on a complex dual formula structure using statistical factors  reflecting several broad dimensions of need. Each metropolitan city and  urban county is entitled to receive an amount equaling the greater of the  amounts calculated under two formulas. The factors involved in the first  formula are population, extent of poverty, and extent of overcrowded  housing, weighted 0.25, 0.50, and 0.25, respectively. The factors involved in  the second formula are population growth lag, poverty, and age of  housing, weighted 0.20, 0.30, and 0.50, respectively. In these formulas, the  inclusion of population moderates the targeting impact of the other  formula factors. We previously reported that complex approaches such as  this can result in widely different payments to communities with similar  needs.
Lags in Data Used to Allocate Funds: Statutes that require formulas to use  specific sources of data can introduce lags in the data being used when  those data are not immediately available. Lags inherent in the collection  and publication of data by statistical agencies that gather and process data  can result in a formula relying on data that are several years old. For  example, the Medicaid statute generally specifies that matching rates for  Medicaid be calculated 1 year before the fiscal year in which they are  effective, using a 3-year average of the most recently available per capita  income data reported by the Department of Commerce. For fiscal year  2007, matching rates were calculated at the beginning of fiscal year 2006  using 3-year average data for 2002 through 2004—the latest then available.  Where recipients have been affected by recent changes to their population,  the recipient may view such allocations as slow to respond to these  changes in population.
Fixed Pool versus Open Ended Funding: Most programs have a finite or  fixed pool of funds to distribute, while others do not. In a fixed pool  program, such as SSBG, when a population change results in an increased  allocation for one state, the increase is offset by decreases in allocations  to one or more other states. In open-ended programs, such as Medicaid,  states can receive more funding when states spend more from their own  source of revenue, without a corresponding decrease to other states.
In conclusion, while population data play an important role in allocating  federal assistance through formula grant programs, other grant-specific  features—several of which I have discussed today—can also play a role in  funding allocations, and in some cases can mitigate or entirely mute the  impact of a change in population. Importantly, not all grants work the  same, and an increase or decrease in population size may not have a  proportional impact on ultimate funding levels. Nevertheless, given the  importance of census data as a baseline for postcensal estimates used for  grant programs, as well as for congressional apportionment and  redistricting, counting the nation’s population once, only once, and in the  right location in 2010 is an essential first step.
Mr. Chairman, this concludes my remarks. I will be glad to answer any  questions that you or other Subcommittee members may have.
section_title: Contacts and Acknowledgments
paragraphs: For further information regarding this statement, please contact Robert  Goldenkoff, Director, Strategic Issues, on (202) 512-2757 or at  goldenkoffr@gao.gov. Contact points for our Offices of Congressional  Relations and Public Affairs may be found on the last page of this  testimony. Individuals making key contributions to this statement included  Ty Mitchell, Assistant Director; Sarah Cornetto; Erin Dexter; Robert  Dinkelmeyer; Gregory Dybalski; Amber G. Edwards; Amanda Harris; and  Tamara F. Stenzel.
section_title: Related GAO Products
paragraphs: Ryan White CARE Act: Estimated Effect of Proposed Stop-Loss Provision  on Urban Areas. GAO-09-472R. Washington, D.C.: March 6, 2009.  2010 Census: Population Measures Are Important for Federal  Allocations. GAO-08-230T. Washington, D.C.: October 27, 2007.
Ryan White CARE Act: Impact of Legislative Funding Proposal on Urban  Areas. GAO-08-137R. Washington, D.C.: October 5, 2007.
Vocational Rehabilitation: Improved Information and Practices May  Enhance State Agency Earnings Outcomes for SSA Beneficiaries.  GAO-07-521. Washington, D.C.: May 23, 2007.
Medicaid: Strategies to Help States Address Increased Expenditures  during Economic Downturns. GAO-07-97. Washington, D.C.: October 18,  2006.
Community Development Block Grants: Program Offers Recipients  Flexibility but Oversight Can Be Improved. GAO-06-732. Washington,  D.C.: July 28, 2006.
Community Development Block Grant Formula: Options for Improving  the Targeting of Funds. GAO-06-904T. Washington, D.C.: June 27, 2006.
Federal Assistance: Illustrative Simulations of Using Statistical  Population Estimates for Reallocating Certain Federal Funding.  GAO-06-567. Washington, D.C.: June 22, 2006.
Data Quality: Improvements to Count Correction Efforts Could Produce  More Accurate Census Data. GAO-05-463. Washington, D.C.: June 20, 2005.
Grants Management: Additional Actions Needed to Streamline and  Simply Processes. GAO-05-335. Washington, D.C.: April 18, 2005.
Medicaid Formula: Differences in Funding Ability among States Often  Are Widened. GAO-03-620. Washington, D.C.: July 10, 2003.
Formula Grants: 2000 Census Redistributes Federal Funding Among  States. GAO-03-178. Washington, D.C.: February 24, 2003.  2000 Census: Coverage Measurement Programs’ Results, Costs, and  Lessons Learned. GAO-03-287. Washington, D.C.: January 29, 2003.
Formula Grants: Effects of Adjusted Population Counts on Federal  Funding to States. GAO/HEHS-99-69. Washington, D.C.: February 26, 1999.
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