Learning Management Through Matching: A Field Experiment Using Mechanism Design

45 Pages Posted: 9 Jul 2019 Last revised: 22 Aug 2021

See all articles by Girum Tefera Abebe

Girum Tefera Abebe

Ethiopian Development Research Institute (EDRI)

Marcel Fafchamps

Stanford University

Michael Koelle

University of Oxford

Simon Quinn

University of Oxford

Date Written: July 2019

Abstract

What is the effect of exposing motivated youth to firm management in practice? To answer this question, we place young professionals for one month in established firms to shadow middle managers. Using random assignment into program participation, we find positive average effects on wage employment, but no average effect on the likelihood of self-employment. Within the treatment group, we match individuals and firms in batches using a deferred-acceptance algorithm. We show how this allows us to identify heterogeneous treatment effects by firm and intern. We 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. We 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.

Suggested Citation

Abebe, Girum Tefera and Fafchamps, Marcel and Koelle, Michael and Quinn, Simon R., Learning Management Through Matching: A Field Experiment Using Mechanism Design (July 2019). NBER Working Paper No. w26035, Available at SSRN: https://ssrn.com/abstract=3416338

Girum Tefera Abebe (Contact Author)

Ethiopian Development Research Institute (EDRI) ( email )

P.O. Box 2479
Addis Ababa
Ethiopia

Marcel Fafchamps

Stanford University ( email )

Stanford, CA 94305
United States

Michael Koelle

University of Oxford ( email )

Mansfield Road
Oxford, OX1 4AU
United Kingdom

Simon R. Quinn

University of Oxford ( email )

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