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.

Nonseparable multinomial choice models in cross-section and panel data

| Working Paper

Multinomial choice models are fundamental for empirical modeling of economic choices among discrete alternatives. We analyze identifcation of binary and multinomial choice models when the choice utilities are nonseparable in observed attributes and multidimen- sional unobserved heterogeneity with cross-section and panel data.

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Information redundancy neglect versus overconfidence: a social learning experiment

| Working Paper

We study social learning in a continuous action space experiment.

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Binarization for panel models with fixed effects

| Working Paper

In nonlinear panel models with fixed effects and fixed-T, the incidental parameter problem poses identification difficulties for structural parameters and partial effects. Existing solutions are model-specific, likelihood-based, impose time homogeneity, or restrict the distribution of unobserved heterogeneity. We provide new identification results for the large class of Fixed Effects Linear Transformation (FELT) models with unknown, time-varying, weakly monotone transformation functions.

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