Self-Selection into Health Professions

Alessandro Fedele (), Mirco Tonin and Daniel Wiesen ()
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Alessandro Fedele: Free University of Bozen/Bolzano
Daniel Wiesen: University of Cologne

No 17941, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: The health sector requires skilled, altruistic, and motivated individuals to perform complex tasks for which ex-post incentives may prove ineffective. Understanding the determinants of self-selection into health professions is therefore critical. We investigate this issue relying on data from surveys and incentivized dictator games. We compare applicants to medical and healthcare schools in Italy and Austria with non-applicants from the same regions and age cohorts. Drawing on a wide range of individual characteristics, we employ machine learning techniques for variable selection. Our findings show that higher cognitive ability, greater altruism, and the personality trait of conscientiousness are positively associated with the likelihood of applying to medical or nursing school, while neuroticism is negatively associated. Additionally, individuals with a strong identification with societal goals and those with parents working as doctors are more likely to pursue medical education. These results provide evidence of capable, altruistic, and motivated individuals self-selecting into the health sector, a necessary condition for building a high-quality healthcare workforce.

Keywords: personality traits; cognitive ability; altruism; health professions; self-selection; machine learning (search for similar items in EconPapers)
JEL-codes: I1 J24 J4 (search for similar items in EconPapers)
Date: 2025-06
New Economics Papers: this item is included in nep-big, nep-cbe, nep-hea and nep-lma
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Related works:
Working Paper: Self-selection into Health Professions (2025)
Working Paper: Self-Selection Into Health Professions (2025)
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