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on Information and Communication Technologies |
| By: | Mirko Draca; Max Nathan; Viet Nguyen-Tien; Juliana Oliveira-Cunha; Anna Rosso; Anna Valero |
| Abstract: | Which types of human capital influence the adoption of advanced technologies? We study the skill-biased adoption of information and communication technologies (ICT) across two waves in the UK. Specifically, we compare the 'new wave' of cloud and machine learning / AI technologies during the 2010s-pre-LLM-with the previous wave of personal computer adoption in the 1990s and early 2000s. At the area-level we see the emergence of a distinct STEM-biased adoption effect for the second wave of cloud and machine learning / AI technologies (ML/AI), alongside a general skill-biased effect. A one-standard deviation increase in the baseline share of STEM workers in areas is associated with around 0.3 of a standard deviation higher adoption of cloud and ML/AI. We find similar effects at the firm level where we are able to test for the influence of a wide range of skills. In turn, this STEM-biased adoption pattern has encouraged the concentration of these technologies, leading to more acute differences between high-tech and low-tech areas and firms. In contrast with classical technology diffusion, recent cloud and ML/AI adoption in the UK seems more likely to widen inequalities than reduce them. |
| Keywords: | Technology Diffusion, ICT, Human Capital, STEM |
| JEL: | D22 J24 O33 R11 |
| Date: | 2024–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:csl:devewp:495 |
| By: | Roth, Felix; Mitra, Alessio |
| Abstract: | The European Union (EU) faces challenges such as an ageing population, migratory pressures, geopolitical vulnerabilities, and climate change, highlighting the need to enhance its ability to do more with less. This paper examines the drivers of EU labour productivity before and after the 2007 financial crisis, across goods and services sectors, tangible and intangible assets, and Information and Communication Technologies (ICT) and non-ICT tangibles. Using the EUKLEMS 2022 dataset for 14 EU countries and the UK from 1995-2019 and growth regression analysis, we find that Research & Innovation (R&I) is crucial for productivity growth. Labour productivity in the goods sector benefits most from non-ICT tangible assets, while in the service sector, it benefits more from the non-R&D intangibles software, training, and organisational capital. On the other hand, training and ICT tangibles became more important drivers of labor productivity growth after the economic crisis. We argue that the productivity gap between the EU and the United States is largely due to insufficient investment in non-R&D intangibles like software, training, and organizational capital. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:uhhhdp:19 |
| By: | Flavio Calvino; Chiara Criscuolo; Luca Fontanelli; Lionel Nesta; Elena Verdolini |
| Abstract: | We leverage a uniquely comprehensive combination of data sources to explore the enabling role of human capital in fostering the adoption of predictive AI systems in French firms. Using a causal estimation approach, we show that ICT engineers play a key role for AI adoption by firms. Our estimates indicate that raising the current average share of ICT engineers in firms not using AI (1.66%) to the level of AI users (6.7%) would increase their probability to adopt AI by 0.81 percentage points - equivalent to an 8.43 percent growth. However, this would imply substantial investments to fill the existing gap in ICT human capital, amounting to around 450.000 additional ICT engineers. We also explore potential mechanisms, showing that the relevance of ICT engineers for predictive AI is driven by the innovative nature of its use, make-vs-buy choices, large availability of data, ICT and R&D intensity. |
| Keywords: | artificial intelligence, human capital, technological diffusion |
| Date: | 2024–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2055 |
| By: | Rasmus Bøgh Holmen; Timo Kuosmanen; Jaan Masso; Per Botolf Maurseth; Kenneth Løvold Rødseth |
| Abstract: | This paper ties broadband development to regional economic growth and focuses on the optimal timing of investments. A Directional Distance Function framework is proposed for characterising the relationship between broadband investment and economic development, and a two-stage estimation procedure combining Convex Nonparametric Least Squares with Linear Programming is developed for estimating optimal investment paths. The model framework is applied to a novel dataset comprising 21 regions in the Baltic countries. The results indicate that Gross Regional Domestic Product could be increased by up to 10 per cent by adopting optimal regional investment paths. We find intercountry differences, where Latvian regions exhibit more inefficient investment strategies compared to regions subordinate to their neighbouring countries. There are also signs of over-investment in broadband in some regions. |
| Keywords: | Regional economic growth; Broadband; Directional Distance Function; Convex Nonparametric Least Squares; Baltic countries; productivity |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:mtk:febawb:149 |
| By: | A. Caria; A. Di Liberto; S. Pau |
| Abstract: | We used data collected during the COVID-19 pandemic, from March to June 2021, to examine how Italian upper secondary schools reorganized their activities for remote learning (RL). We conducted a three-level survey, administering questionnaires to students (11th and 13th graders), teachers, and school principals at each institution. The final sample includes 11, 154 students, 3, 905 teachers, and 105 school principals. The data allow us to describe - a) how schools adjusted to the pandemic to ensure learning effectiveness during RL, b) how teachers and school principals managed the transition from traditional to online teaching, c) the perceptions of students, teachers, and school principals regarding the effectiveness of RL. This analysis highlights Italian schools' challenges in changing teaching styles during RL and identifies inequality patterns at individual and school levels. It also underscores a significant gap between teachers' perceptions of their digital skills and the actual use of ICT in class during RL activities. Our results identify a positive and robust relationship between the use of innovative teaching methodologies in class, the adoption of appropriate organizational innovations at the school level, and specific teachers' training with the student's perceptions of learning and other outcomes related to student success. |
| Keywords: | remote learning;COVID-19;socio-economic disparities |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202425 |
| By: | Martina Nannelli (University of Florence); Niccolò Innocenti (University of Florence); Luciana Lazzeretti (University of Florence) |
| Abstract: | This paper examines the role of cultural heritage in the development of smart cities, focusing on the role of digital technology in enhancing the preservation, management, accessibility and sustainability of heritage sites. Utilizing a twofold methodological approach that combines a bibliometric analysis and a critical literature review, the study analyzes existing academic papers to evaluate the opportunities and challenges associated with smart technologies—such as Artificial Intelligence (AI), Internet of Things (IoT), and big data—in the preservation, accessibility, and promotion of cultural heritage. Through this dual analysis, key themes emerge regarding the enhancement of citizen engagement, innovative heritage management practices, and the cultural preservation in smart city contexts. This paper contributes to the discourse on sustainable smart city development by emphasizing the importance of a balanced, heritage-sensitive approach to urban digitalization, advocating for policies that support both technological advancement and cultural integrity aimed at supporting sustainable and inclusive urban development. |
| Keywords: | cultural heritage, smart cities, digitalization. |
| JEL: | O18 O21 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:frz:wpmmos:wp2024_03.rdf |
| By: | Shuochen Bi; Tingting Deng; Jue Xiao |
| Abstract: | The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting, the challenges and implications. The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things, also highlight the continued improvement in data processing capabilities. Explore how AI is reshaping financial services with precisely tAIlored services that can more precisely meet the diverse needs of individual investors. The integration of AI challenges regulatory and ethical issues in the financial sector, as well as the implications for data privacy protection. Analyze the limitations of current AI technology in financial forecasting and its potential impact on the future financial industry landscape, including changes in the job market, the emergence of new financial institutions, and user interface innovations. Emphasizing the importance of increasing investor understanding and awareness of AI and looking ahead to future trends in AI tools for user experience to drive wider adoption of AI in financial decision making. The huge potential, challenges, and future directions of AI in the financial sector highlight the critical role of AI technology in driving transformation and innovation in the financial sector |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13562 |