Estimating the causal effects of income on health: how researchers' definitions of "income" matter

Abstract

Background: There is a well-established cross-sectional association between income and health, but estimates of the causal effects of income vary substantially. Different definitions of income may lead to substantially different empirical results, yet research is often framed as investigating "the effect of income" as if it were a single, easily definable construct.

Methods/results: The aim of this paper is to introduce a taxonomy for definitional and conceptual issues in studying individual- or household-level income for health research. We focus on (1) the definition of the income measure (earned and unearned; net, gross, and disposable; real and nominal; individual and household; relative and absolute income) and (2) the definition of the causal contrast (amount, functional form assumptions/transformations, direction, duration of change, and timing of exposure and follow-up). We illustrate the application of the taxonomy to four examples from the published literature.

Conclusions: Quantified estimates of causal effects of income on health and wellbeing have crucial relevance for policymakers to anticipate the consequences of policies targeting the social determinants of health. However, much prior evidence has been limited by lack of clarity in distinguishing between different causal questions. The present framework can help researchers explicitly and precisely articulate income-related exposures and causal questions.

Keywords: Causal inference; Health; Income; Income change; Social epidemiology.

Fig. 1

Visual overview of key definitional and conceptual issues in studying individual- or household-level income as an epidemiological exposure

Fig. 2

Illustration of how income measures can be constructed by combining or subtracting other measures

Fig. 3

Illustration of how the effect of income on health can be confounded due to causes of income change that also affect health directly