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. 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. 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. 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. 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. 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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Summary: 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. 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.