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The Household Pulse Survey (HPS), recently released by the U.S. CensusBureau, gathers timely information about the societal and economic impacts ofcoronavirus. The first phase of the survey was quickly launched one month afterthe beginning of the coronavirus pandemic and ran for 12 weeks. To track theimmediate impact of the pandemic, individual respondents during this phase werere-sampled for up to three consecutive weeks. Motivated by expected job lossduring the pandemic, using public-use microdata, this work proposes unit-level,model-based estimators that incorporate longitudinal dependence at both theresponse and domain level. In particular, using a pseudo-likelihood, weconsider a Bayesian hierarchical unit-level, model-based approach for bothGaussian and binary response data under informative sampling. To facilitateconstruction of these model-based estimates, we develop an efficient Gibbssampler. An empirical simulation study is conducted to compare the proposedapproach to models that do not account for unit-level longitudinal correlation.Finally, using public-use HPS micro-data, we provide an analysis of "expectedjob loss" that compares both design-based and model-based estimators anddemonstrates superior performance for the proposed model-based approaches.

Bayesian Unit-level Models for Longitudinal Survey Data under  Informative Sampling: An Analysis of Expected Job Loss Using the Household  Pulse Survey