Towards Algorithm-Assisted Career Management - a Challenge for New Immigration Countries. Predicting Migrants' Work Trajectory Using Ensemble Learning

Jolanta Maj, Bogdan Ruszczak and Sabina Kubiciel-Lodzińska

Central European Business Review, 2024, vol. 2024, issue 5, 1-22

Abstract: Migration processes have emerged as crucial social, political and economic concerns, affecting societies, industries and organisations. The challenge lies in effectively utilizing immigrants' resources. This research aims to determine how AI tools can support matching migrants' skills with labour markets in host countries. We propose the application of an ensemble learning methodology. To validate this approach, we collect data to assess the career trajectories of 248 tertiary-educated Ukrainian immigrants in Poland, a new immigration destination. Various machine learning models are evaluated using the decision tree algorithm on these feature sets. To ensure credible results, a 10-fold cross-validation procedure is employed for each training process of every submodel. This research introduces an original ensemble machine learning classifier that combines pre-selected models with the highest performance, thereby reducing the number of parameters to be investigated. Its application in determining the career paths of highly skilled migrants, specifically Ukrainians, is novel. The study offers significant implications for Central Europe, notably Poland, where migration patterns and the integration of highly skilled migrants, mainly from Ukraine, are increasingly important. Implications for Central European audience: The ensemble machine learning classifier developed in this study could aid in optimising the career paths of these migrants, combating brain waste and facilitating their successful integration into the labour market. Integrating tools like these into decision-making processes may enhance career management and contribute to Central Europe's social and economic growth.

Keywords: career management; migration; immigrants; machine learning; ensemble learning; decision trees; labour market (search for similar items in EconPapers)
JEL-codes: F22 J61 O15 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.18267/j.cebr.365

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