represent the latest year of the period. Say that the state has 1 million total people resident in eight counties: four counties are large, each with 200,000 people, and four counties are small, each with 50,000 people. The four smaller counties make up two PUMAs. (In actuality, two-thirds of counties are smaller than this.)
The state could use 1-year period ACS estimates of poor school-age children for the four large counties directly in the allocations. Also available would be 1-year period estimates for the two PUMAs, individually and combined, which could be used to adjust 5-year period estimates for the four smaller counties to the same 1-year period. The simple updating procedure would apply the ratio for the PUMAs of the 1-year and the 5-year period estimates of poor school-age children to the 5-year period estimates for each county component.
Using separate ratios for the two PUMAs (as in Table 3-2) would better capture differences among the smaller counties than would using a single ratio for the two PUMAs combined, but the combined ratio would be more precise than the two separate ratios. Even using separate ratios, the updated estimates for the counties in PUMA 2 are not as realistic as those for the counties in PUMA 2 because one county in PUMA 2 experienced a decrease in school-age poverty and not an increase as in the other three counties.
The simple procedure works best when it only has to be used—and, therefore, its assumptions only have to be invoked—for a small fraction of the total number of jurisdictions. Because only about half a dozen states have ACS 1-year (or even 3-year) period estimates available for most counties, the procedure may not be widely useful when the goal is to adjust 5-year period estimates for smaller counties to the latest year.
The Census Bureau’s SAIPE program currently uses this type of simple procedure to produce updated estimates of poor school-age children for school districts within counties. In that application, good administrative data are available with which to update the county estimates, but the updating procedure for school districts has to assume that the within-county proportions of poor school-age children for school districts are the same for the estimation year as they are for the previous long-form-sample year. Work is under way that shows promise of improving the currency of SAIPE school district estimates of poor school-age children by using IRS personal income tax data coded to the block level (Maples, 2004). The ACS estimates for school districts may also be helpful in the SAIPE school district-level model.
Local governments—counties, cities, towns, townships, school districts, and areas governed by Alaska Native or American Indian tribes—will likely be major users of the ACS, particularly local governments with sizeable