Zero-Inflated Modelling for Characterizing Coverage Errors of Extracts from the US Census Bureau'sMaster Address File

Abstract

SummaryTo meet the strategic goals and objectives for the 2020 census, the US Census Bureau must make fundamental changes to the design, implementation and management of the decennial census. The changes must build on the successes and address the challenges of the previous censuses. Of particular interest is to gauge the on-going quality of the census frames. We address this topic by discussing a set of statistical models for the Master Address File that will produce estimates of coverage error at levels of geography down to the block level. The distributions of added and deleted housing units in a block are used to characterize the undercoverage and overcoverage respectively. The data used are from the 2010 address canvassing operation. As will be shown, these distributions are highly right skewed with a very large proportion of 0 counts. Hence, we utilize zero-inflated regression modelling to determine the predicted distribution of additions and deletions. In addition to standard statistical measures, we gauge the performance of this model by simulating a 2010 address canvassing operation using a specified coverage level. We also discuss future maintenance and updating of this model.