Evaluation of public health interventions through econometric methods and microsimulation

This topic explores the use of both ex-post and ex-ante econometric approaches to the evaluation of public health interventions. Traditional approaches to estimating the impact of health policies have relied heavily on ex-post evaluative techniques of existing programmes based on observational evidence. The key to these approaches is identifying a source of variation in treatment that is independent of other factors that influence outcomes. Often natural experiments are used to support estimation approaches such as instrumental variables (IV), regression discontinuity (RD) and difference-in-differences (DD). In recent years there has been a heavy emphasis on keeping parametric assumptions to a minimum and the literature has favoured methods such as matching, nonparametric regression, and control function approaches and on making inferences that are robust to functional form and distributional assumptions.

Ex-ante evaluation techniques offer a useful alternative to the more traditional ex-post methods, and typically involve simulating impacts of hypothetical/ new programmes or forecasting impacts of existing programmes in new contexts and over different time horizons. One such approach to evaluation is microsimulation. Developed in areas outside the health sector, most commonly focusing on the impact of fiscal policies on population income and welfare distributions, this is an established and widely used tool for analysing the impact of policies over the long-term. A related approach to policy evaluation focuses on estimating the parameters of structural models or reduced form behavioural counterparts, based on data from an untreated population, from which simulations of likely outcomes in the presence of a treatment are obtained. Such evaluations are particularly useful to inform policies for extending the target population of an existing programme and offer a useful complement to more commonly encountered ex-post evaluation techniques.

Senior staff undertaking research in this area include: Nigel Rice (HEDG team) and Marc Suhrcke (Global Health team)