Learning Management Through Matching: A Field Experiment Using Mechanism Design

Working Paper
Published on 1 June 2019


What is the effect of exposing motivated youth to firm management in practice? To answer this question, Abebe, Fafchamps, Koelle and Quinn (2019) place young professionals for one month in established firms to shadow middle managers. Using random assignment into program participation, they find positive average effects on wage employment, but no average effect on the likelihood of self-employment. Within the treatment group, the authors match individuals and firms in batches using a deferred-acceptance algorithm. They show how this allows them to identify heterogeneous treatment effects by firm and intern. The authors find striking heterogeneity in self-employment effects, but almost no heterogeneity in wage employment. Estimates of marginal treatment effects (MTE) are then used to simulate counterfactual mechanism design. The researchers find that some assignment mechanisms substantially outperform random matching in generating employment and income effects. These results demonstrate the importance of treatment heterogeneity for the design of field experiments and the role of matching algorithms in intervention design.


Girum Abebe

World Bank

Marcel Fafchamps

University of Oxford

Michael Koelle

Organisation for Economic Co-operation and Development

Simon Quinn

University of Oxford