nep-ict New Economics Papers
on Information and Communication Technologies
Issue of 2023‒10‒16
five papers chosen by
Marek Giebel, Universität Dortmund

  1. Nonparametric estimation of k-modal taste heterogeneity for group level agent-based mixed logit By Xiyuan Ren; Joseph Y. J. Chow
  2. Remote working across the European Union before and in Covid-19 pandemic By Davide Dazzi; Daniela Freddi
  3. A Comparative Analysis of Information Communication Technologies Development: A Study of Azerbaijan and Balkan Countries By Niftiyev, Ibrahim
  4. Artificial Intelligence and Its Impact on Information Technology (IT) Service Sector in Bangladesh By Fahmida Khatun; Nadia Nawrin
  5. Analyzing Economic Growth Within the Framework of the Knowledge Economy Ecosystem model By Dilaka Lathapipat

  1. By: Xiyuan Ren; Joseph Y. J. Chow
    Abstract: Estimating agent-specific taste heterogeneity with a large information and communication technology (ICT) dataset requires both model flexibility and computational efficiency. We propose a group-level agent-based mixed (GLAM) logit approach that is estimated with inverse optimization (IO) and group-level market share. The model is theoretically consistent with the RUM model framework, while the estimation method is a nonparametric approach that fits to market-level datasets, which overcomes the limitations of existing approaches. A case study of New York statewide travel mode choice is conducted with a synthetic population dataset provided by Replica Inc., which contains mode choices of 19.53 million residents on two typical weekdays, one in Fall 2019 and another in Fall 2021. Individual mode choices are grouped into market-level market shares per census block-group OD pair and four population segments, resulting in 120, 740 group-level agents. We calibrate the GLAM logit model with the 2019 dataset and compare to several benchmark models: mixed logit (MXL), conditional mixed logit (CMXL), and individual parameter logit (IPL). The results show that empirical taste distribution estimated by GLAM logit can be either unimodal or multimodal, which is infeasible for MXL/CMXL and hard to fulfill in IPL. The GLAM logit model outperforms benchmark models on the 2021 dataset, improving the overall accuracy from 82.35% to 89.04% and improving the pseudo R-square from 0.4165 to 0.5788. Moreover, the value-of-time (VOT) and mode preferences retrieved from GLAM logit aligns with our empirical knowledge (e.g., VOT of NotLowIncome population in NYC is $28.05/hour; public transit and walking is preferred in NYC). The agent-specific taste parameters are essential for the policymaking of statewide transportation projects.
    Date: 2023–09
  2. By: Davide Dazzi; Daniela Freddi
    Abstract: Policymakers, social parts, businesses, employees, media and citizens became familiar with a broad use of words such as remote working, teleworking, working from home, mobile worker, ICT-based worker. In this view, it is of crucial importance to define a general conceptual framework related to the terms referred to when a person works from a distance. The present paper delves into a taxonomy of the regulations and approaches to remote work within the EU. The results highlight that several characteristics of teleworking, positive and negative, were already known before the pandemic and they have substantially been confirmed by the massive shift occurred after the pandemic outbreak. As we saw in the report, no specific EU Directives were dedicated to remote working before Covid-19 even if many directives and EU regulations had indirect implications on it.
    Keywords: Remote work; post-pandemic recovery; regulation
    JEL: J51 J83 K31
    Date: 2023–09–17
  3. By: Niftiyev, Ibrahim
    Abstract: evelopment of information and communication technologies (ICTs) plays a pivotal role in promoting overall technological progress in a nation and enabling transformative changes in various sectors. By providing a solid foundation for digital infrastructure, ICTs facilitate innovation, increase productivity, and spur economic growth, placing a nation at the forefront of the global technological landscape. The main objective of this study is to compare Azerbaijan's ICTs development with that of Balkan countries. The growing cooperation between Azerbaijan and the Balkan countries is primarily focused on the energy sector, but there is limited understanding of the technological similarities and differences. To further enhance this cooperation, a comprehensive study of the technological infrastructure and the identification of areas of convergence and divergence are essential. This study will facilitate informed decision-making, pave the way for expanded cooperation in various sectors beyond the energy sector, and promote mutually beneficial relations between Azerbaijan and the Balkan countries. The results of this study, based on hierarchical cluster analysis (HCA), show that Azerbaijan is similar to Balkan countries such as Albania, North Macedonia, and Bosnia and Herzegovina when calculating the average values (between the years 2010 and 2020) for various ICTs variables (e.g., 4G coverage, Internet users). The HCA of recent years, like 2020, shows the same picture. This means that there are some similar patternsof ICTs usage and investment in these countries. At the same time, an oil-rich country like Azerbaijan could be comparable to the leading Balkan countries like Greece, Romania, Slovenia, etc. While this gives the Azerbaijani government food for thought, the findings also highlight the potential for cooperation and knowledge sharing between Azerbaijan and the Balkan countries in the field of ICTs, as they can learn from each other's experiences and work together to further improve their respective ICTs sectors.
    Keywords: Azerbaijani economy, Balkan countries, hierarchical cluster analysis, information communication technologies, technological change
    Date: 2023
  4. By: Fahmida Khatun; Nadia Nawrin
    Abstract: This paper has attempted to examine the 4IR’s penetration and impacts on the workforce in the IT services sector in Bangladesh. This study also discusses some of the challenges that Bangladesh IT sector faces at present. Finally, contemplating Bangladesh’s preparedness for the digital age of 4IR in terms of access to technology and policy framework, the paper makes a number of recommendations which can enable the country to reap the full benefits of 4IR.
    Keywords: Artificial Intelligence, Fourth industrial revolution, 4IR, CPD-FES Publication
    Date: 2021–11
  5. By: Dilaka Lathapipat
    Abstract: This paper builds on Chen and Dahlman (2006)’s Knowledge Economy concept by introducing the Knowledge Economy Ecosystem model consisting of five pillars: ICT infrastructure, innovation infrastructure, financial infrastructure, quality of institutions, and educated and skilled workers. The subindices for the first four pillars contribute to the Knowledge Economy Infrastructure (KEI) Index, while the human capital pillar is represented by the learning-adjusted years of schooling (LAYS), a measure introduced by the World Bank in 2018. The utilization of LAYS in our model is important, because it recognizes that mean years of schooling is a poor measure of human capital simply because the quality of education can differ greatly across countries. Employing a dynamic panel data framework, we empirically examine the influence of the KEI Index and LAYS on total factor productivity (TFP) and GDP per capita growth. Our findings affirm the substantial positive impact of both LAYS and the KEI Index on TFP and economic growth. This empirical evidence underscores the essential role of sustained investments in these five pillars for fostering long-term economic growth, offering vital insights for policymakers. Drawing on Thailand as a case study, the analysis illuminates the nation's specific challenges within the Knowledge Economy Ecosystem framework, especially in the realms of human capital development, innovation, and institutional quality. The study underscores the considerable obstacles Thailand encounters in these domains, impeding its transition toward a knowledge-based economy.
    Keywords: Human capital; Education; Knowledge economy; Productivity
    JEL: I25 O40
    Date: 2023–09

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