Policy impact estimation

IFS is involved in assessing the effectiveness of a number of labour market programmes, tax and transfer programmes and social programmes in a variety of fields, from education and training, to labour supply, childcare, health and welfare. Determining whether such policy interventions work and whether their cost is justified is of crucial importance in the presence of limited public resources, and allows policy decisions to be guided by evidence on programme effectiveness.

Estimating the causal impact of a programme is difficult because one can never observe the outcome that programme participants would have experienced had they not participated. Constructing this unobserved counterfactual for programme participants is the central issue that evaluation methods need to overcome. In addition to the evaluation of specific government interventions, our research contributes to the development of econometric and statistical methods to address the evaluation problem.

Uncertain identification

| Working Paper

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which, in general, leads to a mix of point- and set-identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set-identified models and combines them with a single posterior for models that are either point-identified or that impose non-dogmatic assumptions. The output is a set of posteriors (post-averaging ambiguous belief) that are mixtures of the single posterior and any element of the class of multiple posteriors, with weights equal to the posterior model probabilities. We suggest reporting the range of posterior means and the associated credible region in practice, and provide a simple algorithm to compute them. We establish that the prior model probabilities are updated when the models are "distinguishable" and/or they specify different priors for reduced-form parameters, and characterize the asymptotic behavior of the posterior model probabilities. The method provides a formal framework for conducting sensitivity analysis of empirical findings to the choice of identifying assumptions. In a standard monetary model, for example, we show that, in order to support a negative response of output to a contractionary monetary policy shock, one would need to attach a prior probability greater than 0.32 to the validity of the assumption that prices do not react contemporaneously to such a shock. The method is general and allows for dogmatic and non-dogmatic identifying assumptions, multiple point-identified models, multiple set-identified models, and nested or non-nested models.

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A nonlinear principal component decomposition

| Working Paper

A nonlinear principal component decomposition

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Nonparametric instrumental variable estimation under monotonicity

| Working Paper

Nonparametric instrumental variable estimation under monotonicity

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