Abstract
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.
Highlights
Our model development is most similar in structure to that of [19], we note that we use intrinsic conditional autoregressive (ICAR) prior distributions for the spatial random effects and estimate the model automatically via Stan, rather than relying on custom sampling techniques
As in the Bernoulli case, we develop a variant of this model that uses an additional spatial random effect with an ICAR prior distribution
To compare the model- and design-based estimates, we considered the empirical mean square error (MSE) and squared bias of our estimates
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. RANDS focuses on healthcare, such as telemedicine and access, as well as loss of work due to illness [3] These surveys can better inform the American public as well as lawmakers, regarding the efficacy of the U.S pandemic response and the effects of stimulus measures that were enacted in order to sustain the economy. Our model development is most similar in structure to that of [19], we note that we use ICAR prior distributions for the spatial random effects and estimate the model automatically via Stan, rather than relying on custom sampling techniques. As a motivating application, we construct state level estimates using the Household Pulse Survey, in order to better understand the societal effects of COVID-19 in the United States
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