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Nanak Chand, Charles H. Alexander
Component ID: #ti1732534179


The American Community Survey (ACS) component of the Continuous Measurement program is designed to provide reliable direct estimates of the various population characteristics for substate areas. For small areas, such as census tracts, it is desirable to improve the ACS estimates by borrowing strength from other areas and other sources of data. In this project, we will develop procedures to derive indirect estimates of characteristics of interest by integrating ACS data with administrative records and the previous census data.

Synthetic estimators which borrow strength from similar areas may be sensitive to the similarity assumption. Regression synthetic estimators based on auxiliary data taken from other sources for the same and similar areas will be less sensitive to this assumption. The composite estimation (Singh, Gambino and Mantel (1994)) combines direct and synthetic estimators, and thus balances the potential bias of synthetic estimators against the instability of the direct estimators. In addition, the procedure may provide estimators with between area variation much smaller than the prior known variance (Spjotvoll and Thomsen (1987)).

However, composite estimators under fixed effect models provide best linear unbiased estimators which reduce to synthetic estimators for areas with small sampling fractions, irrespective of the size of between area variance relative to the within area variance. This limitation is avoided by using models which take into account random area effects (Chand and Alexander (1995), Cressie (1989, 1990, 1992), Datta et al (1992), Ericksen and Kadane (1985, 1987, 1992), Fay (1987), Fay and Herriot (1979), Ghosh and Rao (1994), and Prasad and Rao (1990)).

The paper adapts the small area methods for application to the ACS variables of interest such as proportion of population below poverty level. The applications pertain to developing estimates and their mean squared errors of such proportions for census tracts.

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