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Since December 1st till March 31 you can apply to our programs:
Master in Economic Research and PhD in Economics

Entry requirements are:
- BA or MA degree or equivalent
- Proficiency in spoken and written English
- Solid background in mathematics
- Previous education in economics is recommended

Your online application must content following documents:
- Curriculum vitae
- Statement of motivation
- Copies of your diplomas and transcripts
- Proof of English proficiency level
- Contact details for two (or max. three) referees

For more information please see sections: How to apply to MAER or How to apply to PhD
In case of any question, please do not hesitate to contact us at This email address is being protected from spambots. You need JavaScript enabled to view it. or This email address is being protected from spambots. You need JavaScript enabled to view it.
or see the FAQ sections for MAER or Phd

Prof. Dirk Engelmann

Humboldt-University Berlin, Germany

Join online: https://call.lifesizecloud.com/17229581  (password 6987)

Authors: Dirk Engelmann, Jana Friedrichsen, Roel van Veldhuizen, Pauline Vorjohann, Joachim Winter

Abstract: Trust is an important driver of economic growth and other economic outcomes. Previous studies suggest that the decision to trust is driven by a combination of risk attitudes, distributional preferences, betrayal aversion, and beliefs about the probability of being reciprocated. We compare the results of a binary trust game to the results of a series of control treatments that by design remove the effect of one or more of these components of trust. This allows us to decompose variation in trust behavior into its underlying factors. Our results imply that beliefs are the main driver of trust, and that the additional components only play a role when beliefs about reciprocity are sufficiently optimistic. Our decomposition approach can be applied to other settings where multiple factors that are not mutually independent affect behavior. We discuss its advantages over the more traditional approach of controlling for measures of relevant factors derived from separate tasks in regressions, in particular with respect to measurement error and omitted variable bias.