nep-ict New Economics Papers
on Information and Communication Technologies
Issue of 2024‒04‒08
three papers chosen by
Marek Giebel, Universität Dortmund

  1. Automation and Employment over the Technology Life Cycle: Evidence from European Regions By Florencia Jaccoud; Fabien Petit; Tommaso Ciarli; Maria Savona
  2. The Contribution of Government to Business (G2B) Online Administration Systems to Investment Attractiveness: A Delphi Method Survey. By Kenza Abbouti; Mohammed Zouhri; Abdelrhani Bouayad
  3. Prediction Of Cryptocurrency Prices Using LSTM, SVM And Polynomial Regression By Novan Fauzi Al Giffary; Feri Sulianta

  1. By: Florencia Jaccoud (UNU-MERIT, United Nations University); Fabien Petit (Centre for Education Policy and Equalising Opportunities, UCL); Tommaso Ciarli (United Nations University, UNU-MERIT and University of Sussex, SPRU); Maria Savona (University of Sussex, SPRU)
    Abstract: This paper examines the labor market implications of investment in automation over the life cycle of ICT and robot technologies from 1995 to 2017 in 163 European regions. We first identify major technological breakthroughs during this period and classify phases of acceleration and deceleration in investment. We then examine how exposure to automation technologies affects employment and wages across these different phases of their life cycle. We find that the negligible long-term impact of automation on employment conceals significant short-term positive and negative effects within phases of the technology life cycle. We also find that the negative impact of ICT investments on employment is driven by the phase of the cycle when investment decelerates (and the technology is more mature). The phases of the technology life cycles are more relevant than differences in regions' structural characteristics, such as productivity and sector specialization in explaining the impact of automation to on regional employment.
    Keywords: Automation; Technology Life Cycle; Employment; Wages; ICT; Robot;
    JEL: J21 O33 J31
    Date: 2024–02
  2. By: Kenza Abbouti (UMI - جامعة مولاي إسماعيل = Université Moulay Ismaïl); Mohammed Zouhri (UMI - جامعة مولاي إسماعيل = Université Moulay Ismaïl); Abdelrhani Bouayad (UMI - جامعة مولاي إسماعيل = Université Moulay Ismaïl)
    Abstract: In the era of digitalization, the imperative necessity of digitally transforming public services has become increasingly evident. This imperative has been accentuated by the escalating proliferation and the massive use of Information and Communication Technologies (ICT). Governments worldwide are adapting to this context by modernizing their institutions and administrations through the dematerialization of services intended for investors and businesses. Consequently, the growing and significant role of online administration in promoting the attractiveness of a country's investments is increasingly acknowledged. In line with global trends, Morocco has initiated and launched a series of digitization reforms, and has also implemented online administration services as a promotional strategy, its goals consist in eliminating obstacles to both national and foreign investments. These obstacles include those related to research and information accessibility, minimizing administrative procedures, and reducing bureaucratic complexities that investors might traditionally face in a host country. This article seeks to understand the extent to which these systems contribute to creating a conducive environment by examining the relationship between investment attractiveness and the use of Government to Business (G2B) online administration systems. In order to address this relationship, this study focuses on the case of the Regional Investment Center (CRI), a central and pivotal actor in investment promotion, focusing specifically on the "CRI-Invest" system in the Fès-Meknès region. The adopted approach is qualitative, relying on the Delphi method, the study aims to provide an analysis of the relational dynamics between investment attractiveness and the use of the G2B online administration system in this specific context. Indeed, the results obtained from the Delphi study confirm that the use of G2B online administration systems by investors has a positive impact on promoting investment attractiveness.
    Abstract: A l'ère du numérique, la transformation digitale des services publics s'affirme comme une nécessité impérative. Cette nécessité s'est prononcée encore plus avec la diffusion croissante des technologies de l'information et de la communication (TIC), les gouvernements s'adaptent à ce contexte en modernisant ses institutions à travers la dématérialisation des services destinés aux investisseurs et aux entreprises. De ce fait, le rôle croissant de l'administration en ligne dans la promotion de l'attractivité des investissements des pays est de plus en plus reconnu. Ainsi, à l'instar d'autres pays le Maroc a entamé plusieurs réformes en matière de digitalisation, et a mis en place des services d'administration en ligne en tant que stratégie promotionnelle visant à éliminer les obstacles liés aux investissements tant nationaux qu'étrangers, notamment ceux relatifs à la recherche et à l'accessibilité de l'information, aux procédures administratives et bureaucratiques auxquels les investisseurs peuvent être confrontés dans un pays d'accueil. Cet article vise à comprendre dans quelle mesure ces systèmes contribuent à créer un environnement favorable, en traitant la relation entre l'attractivité des investissements et l'utilisation des systèmes d'administration en ligne Government to Business (G2B). Afin de traiter cette relation, cette étude prend comme étude de cas le système du centre régional de l'investissement (CRI), un acteur central dans la promotion des investissements, il s'agit du système « CRI-Invest » dans la région Fès-Meknès. L'approche adoptée est qualitative s'appuyant sur la méthode Delphi, visant ainsi à offrir une analyse de la dynamique relationnelle entre l'attractivité des investissements et l'utilisation du système d'administration en ligne G2B dans ce contexte particulier. De fait, les résultats obtenus à partir de l'étude Delphi permettent de corroborer que l'utilisation des systèmes d'administration en ligne G2B par les investisseurs, exerce un impact positif sur la promotion de l'attractivité des investissements.
    Keywords: E-government, Government-to-Business (G2B) online administration system, Investment attractiveness., e-gouvernement, système d'administration en ligne G2B, attractivité des investissements, African Scientific Journal, E-gouvernement, système d’administration en ligne G2B, attractivité des investissements.
    Date: 2024–02
  3. By: Novan Fauzi Al Giffary; Feri Sulianta
    Abstract: The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine
    Date: 2024–03

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