Census Bureau should prefer confidentiality protection procedures that preserve the ability to aggregate smaller geographic areas into larger, user-defined areas.
Recommendation 4-10: The Census Bureau should monitor the extent of collapsing of cells that is performed in different tables to meet minimum precision standards of 1-year and 3-year period tabulations from the ACS and assess the implications for comparisons among geographic areas and over time. After sufficient information has been gleaned about the extent of data collapsing, and its impact on users, the Census Bureau, in consultation with data users, should assess whether its collapsing rules are sound or should be modified for one or more subject areas.
Recommendation 4-11: The Census Bureau should provide users with a full explanation of its inflation adjustment procedures and their effects on multiyear ACS estimates of income, housing costs, and housing value. It should consult with users about other kinds of income and housing amount adjustments they may need and conduct research on appropriate estimation methods (for example, methods to produce latest-year amounts from multiyear averages). It should consider publishing selected multiyear averages in nominal dollars as well as inflation-adjusted dollars.
Recommendation 4-12: If some or all group quarters residents continue to be included in the ACS, the Census Bureau should consult with users regarding the most useful population universe for tabulations, which, depending on the table, could be the entire population, the household and group quarters populations separately, or the noninstitutional and institutional populations separately.
Recommendation 4-13: The Census Bureau should consider expanding the geographic areas for ACS tabulations in order to afford users greater flexibility for aggregating small areas into larger user-defined areas. Two possibilities to investigate are to lower the population threshold for 1-year period estimates to, say, 50,000, and to produce 3-year (and possibly 1-year) period estimates for user-defined statistical