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The fed. gov¿t. has annually distributed over $300 billion in fed. assistance through grant programs using formulas driven in part by census population data. The U.S. Census 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 fed. 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: how census data are used in the allocation of fed. formula grant funds; and how the structure of the formulas and other factors can affect those allocations. Illus.
Formula Grants: Census Data Are among Several Factors That Can Affect Funding Allocations
Many federal assistance programs are funded by formula grants that have historically relied at least in part on population data from the decennial census and related data to allocate funds. In June 2009, the Census Bureau reported that in FY 2007 the fed. gov¿t. (FG) obligated over $446 billion through funding formulas that rely at least in part on census and related data. This report determined: (1) how much the FG obligates to the largest fed. assistance programs based on the decennial census and related data, and how the Recovery Act changed that amount; and (2) what factors could affect the role of population in grant funding formulas. The report identified the 10 largest fed. assistance programs in FY 2008 and 2009. Charts and tables.
How do federal statistics strengthen our nation's science as well as its policy? In this latest volume of The ANNALS, leading academics, along with key federal officials, including the president's science advisor, the chief statistician of the U.S., the director of the Office of Management and Budget (OMB), the presidents of the National Academies, and the director of the Census Bureau address the argument that the statistics that the federal statistical system produces should be understood as constituting a scientific infrastructure for the empirical social sciences. Further, they see the current federal statistical system as "the best hope for bringing strong science to bear on new data sources" and "the best place to navigate unforeseen challenges in preserving the independence of statistical information from political interference." If federal statistics are the knowledge base from which policy problems and solutions emerge, it is imperative that we pay attention to the lessons they offer. Never before has this topic received this level of attention from such an array of contributors. A must read for all social scientists and policy-makers.
In 2000, the federal government distributed over $260 billion of funding to state and local governments via 180 formula programs. These programs promote a wide spectrum of economic and social objectives, such as improving educational outcomes and increasing accessibility to medical care, and many are designed to compensate for differences in fiscal capacity that affect governments' abilities to address identified needs. Large amounts of state revenues are also distributed through formula allocation programs to counties, cities, and other jurisdictions. Statistical Issues in Allocating Funds by Formula identifies key issues concerning the design and use of these formulas and advances recommendations for improving the process. In addition to the more narrow issues relating to formula design and input data, the book discusses broader issues created by the interaction of the political process and the use of formulas to allocate funds. Statistical Issues in Allocating Funds by Formula is only up-to-date guide for policymakers who design fund allocation programs. Congress members who are crafting legislation for these programs and federal employees who are in charge of distributing the funds will find this book indispensable.
Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.
As the United States continues to be a nation of immigrants and their children, the nation's school systems face increased enrollments of students whose primary language is not English. With the 2001 reauthorization of the Elementary and Secondary Education Act (ESEA) in the No Child Left Behind Act (NCLB), the allocation of federal funds for programs to assist these students to be proficient in English became formula-based: 80 percent on the basis of the population of children with limited English proficiency1 and 20 percent on the basis of the population of recently immigrated children and youth. Title III of NCLB directs the U.S. Department of Education to allocate funds on the basis of the more accurate of two allowable data sources: the number of students reported to the federal government by each state education agency or data from the American Community Survey (ACS). The department determined that the ACS estimates are more accurate, and since 2005, those data have been basis for the federal distribution of Title III funds. Subsequently, analyses of the two data sources have raised concerns about that decision, especially because the two allowable data sources would allocate quite different amounts to the states. In addition, while shortcomings were noted in the data provided by the states, the ACS estimates were shown to fluctuate between years, causing concern among the states about the unpredictability and unevenness of program funding. In this context, the U.S. Department of Education commissioned the National Research Council to address the accuracy of the estimates from the two data sources and the factors that influence the estimates. The resulting book also considers means of increasing the accuracy of the data sources or alternative data sources that could be used for allocation purposes.