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A Deep Learning Approach to Digitalization and Economic Growth

In: Digital Economy and the Green Revolution

Author

Listed:
  • Irina Georgescu

    (Bucharest University of Economics)

  • Ane-Mari Androniceanu

    (Bucharest University of Economics)

  • Irina Virginia Drăgulănescu

    (University of Bucharest)

Abstract

From an economic point of view, shortly digitalization will lead not only to progressive growth, but also to important transformations of jobs and to the reorganization of the way of carrying out the activity of retail, transport, and banking services. Our research aimed to identify and analyze the correlations between digitization and economic growth of EU countries in the period 2019–2021. In this paper, we use deep learning and principal component analysis as an efficient technique to improve the accuracy of classification for the set of EU countries classified according to The Digital Economy and Society Index. The used databases were Eurostat and World Bank. We selected 15 indicators on which we first trained a 2-layer neural network and we obtained a classifier with 92.52% accuracy. Then, we applied principal component analysis and reduced the original dataset to 3 principal components which retain together 78.21% of the initial variability. We train a 2-layer neural network on the score matrix given by the three retained principal components. The results revealed that the classification improved from 92.52 to 100%.

Suggested Citation

  • Irina Georgescu & Ane-Mari Androniceanu & Irina Virginia Drăgulănescu, 2023. "A Deep Learning Approach to Digitalization and Economic Growth," Springer Proceedings in Business and Economics, in: Mihail Busu (ed.), Digital Economy and the Green Revolution, pages 79-92, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-19886-1_6
    DOI: 10.1007/978-3-031-19886-1_6
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    Cited by:

    1. Liu, Shihua & Padhan, Hemachandra & P., Jithin & Jose, Annmary & Rahut, Dil, 2024. "Do green trade and technology-oriented trade affect economic cycles? Evidence from the Chinese provinces," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

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