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Using the American Community Survey: Benefits and Challenges
uses that are similar to those of local government agencies (see Section 3-C below). Because of these similarities, the only major use by states that we explore in detail here is allocation of state funds to localities in Section 3-B.1. Strategies for using ACS data instate fund allocations are considered in Section 3-B.2.
It is worth noting that many state uses are to respond to requirements of the federal government. For example, HUD requires states and localities to have a Comprehensive Housing Affordability Strategy. This plan includes an assessment of the housing needs of families residing in a jurisdiction that is developed, in part, from long-form-sample data on demographic and housing unit characteristics for individual census tracts in the area. Such applications in the ACS context will require use of the 5-year period estimates for census tracts, which will likely need to be aggregated into larger areas to obtain sufficient precision.
3-B.1 Allocating State Funds to Localities
Under many federal fund allocation programs, states are responsible for distributing most or all of their funds to localities by using long-form-sample data. Many states also allocate their own funds by means of formulas to local jurisdictions, such as counties and school districts (see examples in National Research Council, 2000b:Table 2-1). The most used sources of data for state funding formulas are estimates from the previous long-form sample and state administrative records, such as school lunch data and income tax records.
The problems with long-form-sample estimates, as noted throughout this report, include that they are not timely, that they become increasingly out of date over a decade, and that they suffer from high levels of item nonresponse because long-form data collection takes a back seat to completing the basic census count. The long-form-sample estimates also have large sampling errors for small areas.
Administrative records have problems as well. They may not correspond that closely to the target population for a program—for example, school lunch data, which are often used in state formulas to target funds to school districts with poor children, may not closely track the poverty population because children in families with incomes as high as 185 percent of the poverty threshold are eligible for reduced-price lunches. In addition, program participation may be affected by such factors as outreach activities that operate more strongly in some areas than others. To the extent that this is true, the use of administrative data on school lunch or food stamp participants as a proxy for the poverty population may not give consistent estimates across areas (see National Research Council, 2000a:App. D).