This paper brings together evidence from various data sources and the most recent studies to describe what we know so far about the impacts of the COVID‐19 crisis on inequalities across several key domains of life, including employment and ability to earn, family life and health. We show how these new fissures interact with existing inequalities along various key dimensions, including socio‐economic status, education, age, gender, ethnicity and geography. We find that the deep underlying inequalities and policy challenges that we already had are crucial in understanding the complex impacts of the pandemic itself and our response to it, and that the crisis does in itself have the potential to exacerbate some of these pre‐existing inequalities fairly directly. Moreover, it seems likely that the current crisis will leave legacies that will impact inequalities in the long term. These possibilities are not all disequalising, but many are.
Structural mean models (SMMs) were originally formulated to estimate causal effects among those selecting treatment in randomised controlled trials a¤ected by non-ignorable non-compliance. It has already been established that SMM estimators identify these causal e¤ects in randomised placebo-controlled trials where no-one assigned to the control group can receive the treatment. However, SMMs are starting to be used for randomised controlled trials without placebo-controls, and for instrumental variable analysis of observational studies; for example, Mendelian randomisation studies, and studies where physicians select patients'treatments. In such scenarios, identi…cation depends on the assumption of no e¤ect modi…cation, namely, the causal e¤ect is equal for the subgroups de…ned by the instrument. We consider the nature of this assumption by showing how it depends crucially on the underlying causal model generating the data, which in applications is almost always unknown. If its no e¤ect modi…cation assumption does not hold then an SMM estimator does not estimate its associated causal e¤ect. However, if treatment selection is monotonic we highlight that additive and multiplicative SMMs do identify local (or complier) causal e¤ects, but that the double-logistic SMM estimator does not without further assumptions. We clarify the proper interpretation of inferences from SMM estimators using a data example and simulation study.
This paper presents a review of non‐experimental methods for the evaluation of social programmes. We consider matching and selection methods and analyse each for cross‐section, repeated cross‐section and longitudinal data. The methods are assessed drawing on evidence from labour market programmes in the UK and in the US.
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