group and the extent to which accuracy has improved from one census to the next.
The total population count of the United States is probably the most visible output of a census, so one obvious measure of coverage accuracy for the census is the error in the count for the entire United States over all demographic groups. However, essentially all applications of the census—e.g., redistricting and local planning—use population counts at various levels of geographic and demographic detail. Consequently, it is important to assess the rates of net undercoverage by various geographic or demographic domains.
Historically, a key issue has been, and remains, the differential net undercount of blacks, Hispanics, and Native Americans, which has resulted in the repeated underrepresentation of areas in which those groups make up a large fraction of the residents. In particular, the differential net undercount of these groups has led to their receiving less than their share of federal funds and political representation (see, e.g., Ericksen et al., 1991, for more details). Given this, it is as important as ever for the Census Bureau, in evaluating possible alternative designs for the decennial census, to not only assess the likely impacts on the frequency of components of census coverage error, but also to assess the impacts on differential net coverage error for historically undercounted minority groups.
The 1999 Supreme Court decision (Department of Commerce v. United States House of Representatives, 525 U.S. 316) precluded the use of adjustment based on a sample survey for congressional apportionment. In addition, the Census Bureau concluded that time constraints currently preclude the computation and evaluation of adjusted counts (based on a postenumeration survey) by April 1 the year after a census year, therefore preventing the use of adjusted counts for purposes of redistricting (see National Research Council, 2004a:267).
Furthermore, the current approach to adjustment has a number of complications that continue to present a challenge to the production of high-quality estimated counts, including the quality of the data for movers (often missing or collected by proxy), matching errors, the treatment of missing data for nonmovers, the estimation of the number missed by both the census and the postenumeration survey, and the heterogeneity remaining after the use of poststratification of the match rate and the correct enumeration rate (resulting in correlation bias). This last objection will be reduced, but not eliminated, with the likely shift to the use of logistic regression instead of poststratification in 2010 (discussed below).