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 exploits area-based piloting and age-related eligibility rules to identify treatment effects of a labor market program-the New Deal for Young People in the U.K. A central focus is on substitution/displacement effects and on equilibrium wage effects. The program includes extensive job assistance and wage subsidies to employers. We find that the impact of the program significantly raised transitions to employment by about 5 percentage points. The impact is robust to a wide variety of nonexperimental estimators. However, we present some evidence that this effect may not be as large in the longer run. (JEL: J18, J23, J38)

We estimate a dynamic model of employment, human capital accumulationincluding education, and savings for women in the United Kingdom, exploiting tax and benefit reforms, and use it to analyze the effects of welfare policy. We find substantial elasticities for labor supply and particularly for lone mothers. Returns to experience, which are important in determining the longer-term effects of policy, increase with education, but experience mainly accumulates when in full-time employment. Tax credits are welfare improving in the U.K. and increase lone-mother labor supply, but the employment effects do not extend beyond the period of eligibility. Marginal increases in tax credits improve welfare more than equally costly increases in income support or tax cuts.

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We review the effects of the COVID-19 pandemic on inequalities in education, the labor market, household living standards, mental health, and wealth in the United Kingdom. The pandemic has pushed up inequalities on several dimensions. School closures, in particular, disrupted the learning of poorer children, leading to lower attainment. Mental health worsened for those groups (women and younger adults) who had poorer mental health pre-pandemic. Lockdowns and social distancing particularly reduced the ability of younger, lower-earning, and less educated people to work. However, job-support programs combined with the expanded welfare system meant that, if anything, disposable income inequality fell. Rising house prices have benefited people around the middle of the wealth distribution. In the longer term, lower work experience for the less educated and missed schooling could push up some inequalities. Increased rates of working from home seem likely to persist, which may increase some inequalities and decrease others. Expected final online publication date for the Annual Review of Economics, Volume 14 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.