Identifying causal channels of policy reforms with multiple treatments and different types of selection

TL;DR

This paper develops a method to identify and disentangle the effects of policy reforms involving multiple treatments and selection types, using causal inference assumptions, and applies it to a vocational training reform in Germany.

Contribution

It introduces a novel identification strategy for direct and indirect policy effects with multiple treatments and mediators, extending causal analysis in policy evaluation.

Findings

Reform effects are decomposed into policy, selection, and time effects.

Application reveals that considering mediators like course composition alters policy impact conclusions.

The approach clarifies causal channels in complex policy reforms.

Abstract

We study the identification of channels of policy reforms with multiple treatments and different types of selection for each treatment. We disentangle reform effects into policy effects, selection effects, and time effects under the assumption of conditional independence, common trends, and an additional exclusion restriction on the non-treated. Furthermore, we show the identification of direct- and indirect policy effects after imposing additional sequential conditional independence assumptions on mediating variables. We illustrate the approach using the German reform of the allocation system of vocational training for unemployed persons. The reform changed the allocation of training from a mandatory system to a voluntary voucher system. Simultaneously, the selection criteria for participants changed, and the reform altered the composition of course types. We consider the course composition as a mediator of the policy reform. We show that the empirical evidence from previous studies reverses when considering the course composition. This has important implications for policy conclusions.