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<p>This study models the dynamics of digital skills in Hungary and the European Union using discrete-state Markov chains applied to Digital Economy and Society Index (DESI) indicators from 2017 to 2022. Empirical transition matrices are estimated for four DESI-relevant indicators: internet use and basic digital skills, ICT specialist employment, ICT graduate production, and enterprises providing ICT training. Limiting distributions are computed to identify long-run equilibria and divergence from the EU average. The findings reveal distinct patterns in enterprise training and specialist development, highlighting areas where policy interventions may yield the greatest long-term benefits. By explicitly linking dynamic DESI indicators to the United Nations Sustainable Development Goals (SDGs), this research demonstrates how Markov-based transition and steady-state measures can inform progress toward SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 10 (Reduced Inequalities). Policy recommendations leverage transition probabilities to target enterprise incentives, lifelong learning, and regional upskilling, thereby accelerating equitable digital outcomes. Bootstrap confidence intervals indicate that most steady-state estimates carry substantial uncertainty given the short estimation window (six annual observations per indicator–country dyad); results should therefore be interpreted as indicative of directional tendencies rather than as definitive long-run predictions.</p>

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Markov chain analysis of digital skills dynamics across Hungary and the European Union

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Khalil M. Bourgou,
  • Mohammad Jaber,
  • Anna Dunay

摘要

This study models the dynamics of digital skills in Hungary and the European Union using discrete-state Markov chains applied to Digital Economy and Society Index (DESI) indicators from 2017 to 2022. Empirical transition matrices are estimated for four DESI-relevant indicators: internet use and basic digital skills, ICT specialist employment, ICT graduate production, and enterprises providing ICT training. Limiting distributions are computed to identify long-run equilibria and divergence from the EU average. The findings reveal distinct patterns in enterprise training and specialist development, highlighting areas where policy interventions may yield the greatest long-term benefits. By explicitly linking dynamic DESI indicators to the United Nations Sustainable Development Goals (SDGs), this research demonstrates how Markov-based transition and steady-state measures can inform progress toward SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 10 (Reduced Inequalities). Policy recommendations leverage transition probabilities to target enterprise incentives, lifelong learning, and regional upskilling, thereby accelerating equitable digital outcomes. Bootstrap confidence intervals indicate that most steady-state estimates carry substantial uncertainty given the short estimation window (six annual observations per indicator–country dyad); results should therefore be interpreted as indicative of directional tendencies rather than as definitive long-run predictions.