Modeling within-household associations in household panel studies

Fiona Steele, Paul Clarke and Jouni Kuha

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Household panel data provide valuable information about the extent of similarity in coresidents' attitudes and behaviours. However, existing analysis approaches do not allow for the complex association structures that arise due to changes in household composition over time. We propose a flexible marginal modeling approach where the changing correlation structure between individuals is modeled directly and the parameters estimated using second-order generalized estimating equations (GEE2). A key component of our correlation model specification is the 'superhousehold', a form of social network in which pairs of observations from different individuals are connected (directly or indirectly) by coresidence. These superhouseholds partition observations into clusters with nonstandard and highly variable correlation structures. We thus conduct a simulation study to evaluate the accuracy and stability of GEE2 for these models. Our approach is then applied in an analysis of individuals' attitudes towards gender roles using British Household Panel Survey data. We find strong evidence of between-individual correlation before, during and after coresidence, with large differences among spouses, parent-child, other family, and unrelated pairs. Our results suggest that these dependencies are due to a combination of non-random sorting and causal effects of coresidence.

Keywords: household effects; household correlation; longitudinal house-holds; homophily; multiple membership multilevel model; marginal model; generalised estimating equations; Internal OA fund (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2019-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

Published in Annals of Applied Statistics, March, 2019, 13(1), pp. 367-392. ISSN: 1932-6157

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:88162

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