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Imputing for Extraordinary Sample Events: A Story of Targeted Donor Pools and Administrative Data

Bradley D. Rhein


The Current Employment Statistics (CES) program produces monthly employment estimates by subnational geography and industry through a survey of about 666,000 establishments.  Occasionally, a large-scale event, such as a hurricane, occurs near the survey reference period, significantly affecting data collection for that month, and challenging an implicit missing-at-random assumption in the estimator.  Several methods have been activated to catch the ensuing employment drop, though none of these methods account for the nonresponse directly.  This paper proposes the potential use of a random cold deck imputation approach, constructing a targeted donor pool from a similar circumstance occurring in past administrative data from the Quarterly Census of Employment and Wages (QCEW) program.  The author explores using a donor pool from an earlier hurricane to impute for missing reporters due to hurricanes in 2017 and 2018.