to develop specifications for the subcity areas and the table content. The subcity areas must be large enough in population size and the table content must not be too detailed if 1-year, or even 3-year, period estimates are to be reasonably precise. (See Section 4-D.4 for a recommendation that the Census Bureau consider producing 3-year and even 1-year period estimates for areas smaller than PUMAs in large cities.)
In addition to comparative analyses among subcity areas, users will likely want to analyze trends over time for BIG CITY as a whole and for its subareas. The sampling errors for estimates of differences are always larger than the sampling errors for the individual estimates that are being compared. Consequently, users should anticipate that estimates of year-to-year differences will often be very imprecise and should take care to avoid playing up differences that may appear important in policy terms but are, in fact, within the margin of error. In addition, for analyses of year-to-year differences that must use 3-year or 5-year period estimates and not 1-year estimates, there is the problem of how to interpret the results. Yet an investment in learning how to work with multiple years of ACS estimates, which may require seeking statistical advice, should benefit users who want to exploit the continuous availability of updated information for time trend analysis.
The following text highlights selected aspects of using the ACS to measure change over time. Chapter 6 has a technical discussion of measuring change with ACS period estimates and the implications of alternative approaches for the precision and usefulness of the resulting estimates.
Using 1-Year Period Estimates to Estimate Change for BIG CITY as a Whole Consider two consecutive 1-year period ACS estimates of poor school-age children for BIG CITY (which is assumed to have 50,000 school-age children in a total population of 250,000)—for example, 17 percent poor school-age children in 2010 and 19 percent poor school-age children in 2011. An increase of this magnitude for the nation would be a significant change, both statistically and substantively—over 1 million more children would be poor, and the estimate of change would be very precise. However, the increase for BIG CITY in this example is only 1,000 more poor children, and the estimate of change would not be precise: the 90 percent margin of error for the estimated 2 percentage point increase in school-age poverty would likely be about plus or minus 4.6 percentage points compared with about plus or minus 3.2 percentage points for each year’s