processes responsible for these errors. These methods are limited for that purpose for at least two reasons.

First, demographic analysis and dual-systems estimation measure net coverage error, which obscures many offsetting census omissions and erroneous enumerations. Second, these coverage measurement programs only disaggregate coverage error by a limited set of variables: demographics (age, sex, race, ethnicity), some modest geographic detail (census region), and other variables that measure urban/rural, mail return rate (high/low), and owner/nonowner status. This is true for demographic analysis since it is limited to the information in the record systems utilized. The level of detail in dual-systems estimation has been limited by the restricted number of poststrata used and therefore to variables included in the poststratification. While many of these variables are associated with reasons for census coverage errors, relatively modest differences in net undercoverage rates between many poststrata in 1990 and in 2000 suggest that many of these associations are themselves modest. Furthermore, none of these factors has been chosen on the basis of potential links to potentially deficient census component processes.

Since the past two censuses conducted coverage measurement primarily to support adjustment, it is commendable that the Census Bureau has also devoted substantial resources to the study of factors associated with census coverage error. Studies of reasons for census omissions include several participant observation studies, first in the 1970 census (Valentine and Valentine, 1971), then during the 1986 Test of Adjustment Related Operations (e.g., Garcia-Parra, 1987), the 1988 dress rehearsal (Martin, Brownrigg, and Fay, 1990), and the 1990 census (Ellis, 1995). In addition, the Census Bureau supported ethnographic studies during the 2000 census (de la Puenta, 2004), as well as the 1993 Living Situation Survey, which assessed response to a variety of residence and household composition cues (see, e.g., Martin, 1999). These studies identified person- and household-level characteristics associated with the misinterpretation of the census residence rules or with noncooperation with the census, which might be due to mistrust of government or fear of exposure of illegal behavior (e.g., Brownrigg and de la Puenta, 1993; Bates and Gerber, 1998; Martin, 1999).

More quantitative studies include Fein (1990), who used logistic regression to identify factors associated with census undercoverage, and studies (e.g., Dillman, Treat, and Clark, 1994) of effects of mail presentation on census mail response (and hence potential undercoverage). Analyses by Ericksen et al. (1991) suggest that census undercoverage was greater in areas with low mail response rates, high crime rates and rampant drug use, or high rates of irregular housing, for individuals with low levels of English literacy or unfamiliarity with surveys (the poor and the less well educated), in housing units that share a common address or are likely to be omitted from the census Master Address File for other reasons, and households that include distant relatives and nonrelatives. Ericksen et al. (1991) also pointed out that coverage improvement programs, in particular those more distant from Census Day, were associated with a high rate of census coverage error, especially erroneous enumerations.

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