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The Household Pulse Survey, recently released by the U.S. Census Bureau, gathers information about the respondents’ experiences regarding employment status, food security, housing, physical and mental health, access to health care, and education disruption. Design-based estimates are produced for all 50 states and the District of Columbia (DC), as well as 15 Metropolitan Statistical Areas (MSAs). Using public-use microdata, this paper explores the effectiveness of using unit-level model-based estimators that incorporate spatial dependence for the Household Pulse Survey. In particular, we consider Bayesian hierarchical model-based spatial estimates for both a binomial and a multinomial response under informative sampling. Importantly, we demonstrate that these models can be easily estimated using Hamiltonian Monte Carlo through the Stan software package. In doing so, these models can readily be implemented in a production environment. For both the binomial and multinomial responses, an empirical simulation study is conducted, which compares spatial and non-spatial models. Finally, using public-use Household Pulse Survey micro-data, we provide an analysis that compares both design-based and model-based estimators and demonstrates a reduction in standard errors for the model-based approaches.
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This content will become publicly available on May 12, 2026
Bayesian unit-level models for longitudinal survey data under informative sampling: an analysis of expected job loss using the Household Pulse Survey
Abstract The Household Pulse Survey (HPS), released by the US Census Bureau at the start of the coronavirus pandemic, gathers timely information about the societal and economic impacts of coronavirus. The first phase of the survey was launched in April 2020 and ran for 12 weeks. To track the immediate impact of the pandemic, individual respondents during this phase were re-sampled for up to three consecutive weeks. Motivated by expected job loss during the pandemic, using public-use microdata, this work proposes unit-level, model-based estimators that incorporate longitudinal dependence at both the response and domain level. In particular, using a pseudo-likelihood, we consider a Bayesian hierarchical unit-level, model-based approach for both Gaussian and binary response data under informative sampling. To facilitate construction of these model-based estimates, we develop an efficient Gibbs sampler. An empirical simulation study is conducted to compare the proposed approach to models that do not account for unit-level longitudinal correlation. Finally, using public-use HPS micro-data, we provide an analysis of ‘expected job loss’ that compares both design- and model-based estimators and demonstrates superior performance for the proposed model-based approaches.
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- Award ID(s):
- 2215169
- PAR ID:
- 10627729
- Publisher / Repository:
- Oxford
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series A: Statistics in Society
- ISSN:
- 0964-1998
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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