this cross-state variation should be reflected in aid amounts. But the true cost of insuring each eligible child will not be known and must be estimated.

Many aid formulas include ad hoc adjustments for variation in need. For example, to compensate for geographic differences in prevailing salaries and thereby in the cost of education, the Title I, Part A, grants to local education agencies formula includes state per-pupil expenditure (PPE) as one factor in determining the allocation (Brown, 2002). However, to our knowledge no existing school aid formula includes cost adjustments that are closely linked to evidence on the costs of providing particular services. There is general agreement that aid formulas should account for differences in costs, but generally they do not because developing cost estimates is extremely difficult and somewhat contentious. For example, although differences in PPE reflect relative costs, they also reflect variations in local wealth and commitment to education.

The SCHIP and Title I education examples highlight the important issue of whether aid should compensate primarily for costs that are beyond the control of decision makers in the recipient jurisdictions. If compensation is also influenced by controllable costs, the aid formula could generate perverse or undesirable incentives. For example, in SCHIP the method used to estimate the number of eligible children must be designed to avoid penalizing states for the success of the program (Czajka and Jabine, 2002). Also, if administrative data are used to estimate the number of eligible children or the cost per eligible child, these data must be chosen so that they are immune to manipulation.

A critical task for researchers is to compare the results of the alternative methodologies with the goal of developing consensus estimates of need. Duncombe and Lukemeyer (2002) provide a superb model of the style of research that must be produced.


Measures of fiscal capacity are the second shared component of aid formulas. While per capita personal income is the most commonly used measure of fiscal capacity, other measures are more consistent with the goals of individual aid programs (Tannenwald, 1999; Downes and Pogue, 2002). For example, Downes and Pogue (1994) and Ladd (1994) agree that, if the goal of the aid program is to close the gap between need and effort and thereby reduce fiscal disparities, capacity measures based on the representa-

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