small jurisdictions would require a significant expansion of the ACS. Many such groups are of interest to users, including not only poor school-age children, as discussed in this report, but also ethnic and language minorities, veterans, and people with disabilities. Increasing the final 5-year ACS sample size (after subsampling for CAPI follow-up) to equal the originally proposed size (which was double the current size—see Section 1-B.3) would certainly help. However, acceptable precision for small groups could still often require aggregating estimates over 8 to 10 years.

Of course, ACS estimates for larger population groups will be more precise than those for small groups, and the 5-year period estimates for some large groups in small jurisdictions may reach acceptable precision, particularly if the jurisdiction’s housing units are oversampled. For example, a 5-year period estimate of 15 percent total poor people in an oversampled jurisdiction of 1,500 people will have a 90 percent confidence interval of 11.4 to 18.6 percent, which is much narrower than the interval of 7.0 to 23.0 percent for poor school-age children.

Small jurisdictions may be able to use the levels and trends in the more precise 5-year period estimates for similar but larger jurisdictions to improve understanding of what is occurring for their jurisdiction. Moreover, small jurisdictions, just as large jurisdictions, will benefit from the fact that ACS multiyear period estimates never become as outdated as the long-form-sample estimates do before they are replaced by estimates from the next census.

Some jurisdictions in the United States have large, seasonal fluctuations in population. Examples include many college towns, the west and east coasts of Florida, parts of Arizona, the northern parts of Wisconsin, Minnesota, and Michigan, and the Atlantic beaches. Because of the continuous sampling and data collection for the ACS and its use of a 2-month residence rule instead of the “usual residence” rule of the decennial census, the ACS estimates for an area with seasonal fluctuations in population will likely differ from the long-form-sample estimates for the same area.

Table 3-6 works through a simplified example for a hypothetical county in Florida. This county is assumed to have a year-round population of 100,000, of whom 20,000 (20 percent) are poor, and a winter (December-March) population of 300,000, of whom 35,000 are poor (11.7 percent, averaging the 20 percent year-round poverty rate with a rate of 7.5 percent for the richer, part-time residents). Over the entire year, on average, there were 166,667 people in the county, of whom 25,000 were poor (15 percent poverty rate, averaging the year-round poverty population for 8 months and the winter poverty population for 4 months).