Small-Area Income and Poverty Estimates Priorities for 2000 and Beyond (2000) / Chapter Skim
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6 Using Estimates in Allocation Formulas
6 Using Estimates in Allocation Formulas
Pages 150-160
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From page 150... ...
As discussed in Chapter 2, many federal programs include small-area income and poverty estimates as factors in formulas to allocate funds to states or other areas, such as school districts and service delivery areas. Many state programs also allocate funds to substate areas by formulas that use measures related to poverty or income.
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From page 151... ...
Formulas are complex because legislators and other policy makers often seek to satisfy multiple, sometimes conflicting, objectives. For example, they may wish to both target funds to poorer jurisdictions and to
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From page 152... ...
There may also be a desire to provide incentives to localities to contribute more funding of their own. Budget constraints overlay all of these considerations, further complicating matters.2 In considering how to structure fund allocation formulas to satisfy various objectives, it is important to consider the properties of the estimates that will be used for the formula factors and how features of those estimates may interact with formula provisions (see Federal Committee on Statistical Methodology, 1978; National Research Council, 2000b)
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From page 153... ...
Users may determine that, over time, one type of bias is less serious than another (e.g., that it is preferable to use more up-to-date estimates of poor school-age children as a proxy for poor children aged 15-19 instead of using decennial census estimates for the intended age group)
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From page 154... ...
This outcome probably occurred for Title I concentration grants in instances when states used school lunch counts to suballocate county amounts to school districts, given that students approved for free or reduced-price school lunches include near-poor as well as poor children.3 It is likely that biases will be greater for some types of areas than others. For example, if poverty is consistently overestimated for urban areas and consistently underestimated for rural areas, then urban areas will likely receive a greater proportion of total funding than intended by a formula.
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From page 155... ...
Census estimates have the advantage of comparatively small sampling error for many areas, although even census estimates have high sampling error for very small areas, such as many school districts. Census estimates are subject to other kinds of variability and to bias from several sources, including bias for annual allocations because the census measures poverty and income only at 10-year intervals although income and poverty can change markedly over shorter periods.
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From page 156... ...
There may also be other problems with using averages of estimates over time. For example, changes in appropriation levels could mean that the average funding shares received by an area over a long period, calculated by using a weighted moving average of estimates with fixed weights for each year combined with a linear allocation formula (i.e., a formula with no thresholds or other nonlinear provisions)
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From page 157... ...
These simulations ignore the fact that the allocation for a single area typically depends-at least to some extent-on the allocations for other areas because the total funding amount for a program is usually fixed and not open-ended. The results showed that the higher the sampling error, the greater is the expected value of the funding that an ineligible area (i.e., with a low true poverty rate)
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From page 158... ...
looked at the effects of different levels of sampling error on allocations over a 4-year period for a single area when the formula includes an 80 percent hold-harmless provision and there is no change in the poverty rate for the area. In this scenario an area receives funds in direct proportion to its estimated poverty rate without a threshold constraint.
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From page 159... ...
While further analysis of the effects of error is needed, the panel's work strongly suggests that policy makers need to take account of expected levels of bias and variability in the estimates that are considered for formulas. Policy makers need to ask analysts to evaluate both alternative formulas and alternative estimates to determine those formula provisions and kinds of estimates that
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From page 160... ...
These and other options deserve a full-scale research effort that can inform policy makers about the likely advantages and disadvantages of alternative funding formulas and sources and kinds of estimates to use in them. The Committee on National Statistics is planning to conduct more work in this area.
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