 Corruption drives brain drain: Cross-country evidence from machine learningQiang Li, Lian An and Ren Zhang Economic Modelling, 2023, vol. 126, issue C Abstract: This paper examines the impact of corruption and other governance qualities on migration using a novel approach. By employing machine learning techniques and analyzing data from 130 countries, we isolate the effects of corruption and find that it is the primary driver of brain drain, challenging existing beliefs. Our innovative machine-learning technique allows us to select optimal instrumental variables, enhancing the accuracy of our findings. We identify weakened property rights protection, unmet basic needs, and declining well-being as key channels through which corruption influences migration. Our results are robust to various estimations, falsification tests, and alternative corruption measures. By emphasizing the dominant role of corruption in driving brain drain, our study underscores the urgent need for effective anti-corruption measures and improved governance practices to retain talent. Keywords: Corruption; Brain drain; Good governance; IV-Lasso; Machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:126:y:2023:i:c:s0264999323001918 DOI: 10.1016/j.econmod.2023.106379 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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