Learning Management Through Matching: a Field Experiment Using Mechanism Design

Girum Abebe (World Bank)
Marcel Fafchamps (Stanford University)
Michael Koelle (OECD)
Simon Quinn (University of Oxford)

Abstract : We place young professionals into 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. We match individuals to firms using a deferred-acceptance algorithm, and show how this allows us to identify heterogeneous treatment effects by firm and intern characteristics. We find striking heterogeneity in self-employment effects, and show that some assignment mechanisms can substantially outperform random matching in generating employment and income effects. These results demonstrate the potential for matching algorithms to improve the design of field experiments.

Accounting for Cross-country Income Differences: New Evidence from Multinational Firms

Vanessa Alviarez (University of British Columbia)
Javier Cravino (University of Michigan )
Natalia Ramondo (University of California San Diego )

Abstract : We develop a new accounting framework to decompose cross-country differences in output-per worker into differences in ‘country-embedded factors’ and differences in ‘aggregate firm know-how’. By country-embedded factors we refer to the components of productivity that are internationally immobile and affect all firms in a country, such as institutions, natural amenities, and workers’ quality. In contrast, firm know-how encompasses those components that generate differences across firms within a country, and that can be transferred internationally, such as blue-prints, management practices and intangible capital. Our approach relies on data on the cross-border operations of multinational enterprises (MNEs). It builds on the notion that MNEs can use their know-how around the world, but they must use the factors from the countries where they produce. We find a strong positive correlation between our measure of aggregate firm know-how and external measures of TFP and output per worker across countries. In our sample, differences in aggregate firm know-how account for about 30 percent of the observed cross-country differences in TFP, and for more than 20 percent of the differences in output per-worker.

Mandatory Apprenticeship Training in Firms

Santiago Caicedo (Universidad de los Andes/ University of Chicago)
Miguel Espinosa (Universitat Pompeu Fabra)
Arthur Seibold (University of Mannheim)

Abstract : We quantify the effect of training apprentices on firms and aggregate welfare, exploiting a unique reform to apprenticeship regulation in Colombia. The reform mandates training in firms by setting minimum and maximum apprentice quotas that vary discontinuously in the number of full-time workers. We document strongly heterogeneous firm responses across sectors, revealing differences in the net cost of training. In sectors with high skill requirements, firms decrease their size and bunch just below the regulation thresholds to avoid training apprentices. In contrast, firms in low-skill sectors increase their size to qualify for more apprentices. Guided by these reduced-form findings, we develop a structural model featuring firms with heterogeneous training costs. We find small static effects on aggregate output despite the sizeable labor input responses. Yet, our results indicate potentially large benets to both firms and apprentices when training increases the future supply of productive workers. Finally, we show that counterfactual policies that consider heterogeneity across sectors can deliver similar benefits from training while inducing fewer distortions in the firm size distribution and in the allocation of resources across sectors.