would permit BIG CITY to make a much more informed assessment of the extent of displacement of current residents that was already occurring and would likely occur with the rezoning. The results of the analysis would inform policy makers, lawmakers, and advocacy groups about neighborhood change, ultimately affecting which policies would be supported and where limited resources would be spent.

3-C.2
Small Jurisdiction Applications of the ACS

Generally, smaller counties, cities, and other governmental and statistical areas will not benefit as much from the ACS as larger areas, if only because larger areas will have more sets of estimates published for them (1-year, 3-year, and 5-year period estimates for areas with at least 65,000 people, and 3-year and 5-year period estimates for areas with at least 20,000 people). In some states, sizeable proportions of the population live in small counties, cities, towns, and school districts that will have only 5-year period estimates from the ACS. In 2000, for example, the percentages of people living in counties with fewer than 25,000 residents exceeded 20 percent in 7 states: Alaska (22 percent), Arkansas (27 percent), Idaho (25 percent), Montana (34 percent), North Dakota (47 percent), South Dakota (57 percent), and Wyoming (31 percent) (from the 2002 Census of Governments, U.S. Census Bureau, 2002a:Table 6).

Five-year period estimates for areas this small will be subject to large levels of sampling error (refer back to Tables 2-7a, 2-7b, and 2-7c), although the oversampling of housing units in very small areas will help their precision somewhat. Consider the 15 percent of people in North Dakota and 24 percent of people in South Dakota who live in cities with fewer than 1,000 residents. Over a 5-year period, these areas will be sampled initially at rates of 1 in 3 housing units (if they have between 500 and 1,000 residents) or 1 in 2 housing units (if they have fewer than 500 residents), compared with the average ACS initial sampling rate of 1 in 9 housing units (refer back to Table 2-3, Part A). This oversampling will reduce the sampling error of estimates for these areas by about 40-50 percent compared with the sampling error of estimates for areas with a 1 in 9 sampling rate (assuming that the areas have the same combined mail and computer-assisted telephone interviewing [CATI] response rates and therefore the same computer-assisted personal interviewing [CAPI] subsampling rates).

Oversampling also benefits many larger areas that contain very small cities, townships, or school districts. Selecting just one of many such examples, in 2000, Iowa County, Wisconsin, had 22,780 residents living in 11 cities and 14 towns (U.S. Census Bureau, 2002a:Table 16). Careful examination of the population size of each subcounty jurisdiction would be required to determine the effect of oversampling, but it seems likely that the



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