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Adjusting for Selection Bias in Longitudinal Analyses Using Simultaneous Equations Modeling: The Relationship Between Employment Transitions and Mental Health

Steele, Fionaa; French, Roberta; Bartley, Melb

doi: 10.1097/EDE.0b013e31829d2479

Background: Effects of labor force participation on mental health can be difficult to discern due to the possibility of selection bias. Previous research typically adjusts for direct selection (reverse causality) but ignores indirect selection (unmeasured confounders).

Methods: We investigate the relationship between men’s employment transitions and mental health using a dynamic simultaneous equations model applied to data from the British Household Panel Survey (1991–2009). Outcome is self-reported distress and anxiety as summed on a 12-point scale. We allow for direct selection by allowing prior mental health to affect both subsequent mental health and employment transitions in the joint model. We adjust for indirect selection by allowing for residual correlation between mental health and employment.

Results: Moving from unemployment to employment was strongly associated with an improvement in mental health, whereas becoming unemployed was detrimental. However, these associations were attenuated by unmeasured confounders. After adjustment for indirect selection, the increased distress and anxiety associated with becoming unemployed decreased from 2.5 (95% confidence interval = 2.2 to 2.7) to 2.2 (2.0 to 2.5). (A change of 2.5 equates to half a standard deviation on the 12-point scale.) The improvement with moving from unemployment to employment was also weakened slightly (from −2.1 [−2.4 to −1.7] to −1.8 [−2.1 to −1.5]).

Conclusions: There was strong evidence of indirect selection, but less support for direct selection. Nevertheless, the effects on psychological health of transitions between employment and unemployment, and between employment and economic inactivity, remained substantial after adjusting for selection.

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From the aUniversity of Bristol, Bristol, United Kingdom; and bUniversity College London, London, United Kingdom.

The research was funded by grants from the UK Economic and Social Research Council (grant numbers RES-576-25-0032 and RES-596-28-0001) and the UK Medical Research Council (G1000726).

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article ( This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.

Correspondence: Fiona Steele, Centre for Multilevel Modelling, University of Bristol, 2 Priory Road, Bristol BS8 1TX, UK. E-mail: fiona.steele@bristol.a.cuk.

Received November 29, 2012

Accepted March 26, 2013

© 2013 by Lippincott Williams & Wilkins, Inc