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on Information and Communication Technologies |
| By: | Anish Sugathan; Arpit Shah; Deepak Malghan |
| Abstract: | This study quantifies the dynamic impacts of floods on industrial capital and labor in India using a novel dataset combining geocoded flood events with firm facility-level data from 2000 to 2021. Employing a stacked difference-in-differences approach with carefully matched controls, we uncover persistent negative effects of floods on firms’ assets and employment, with striking heterogeneity across sectors and regions. In the post-flood period, we estimate declines from mean values in total assets of 46.1% (16.68 billion INR ? 225 million USD), employment of 49.0% (8.20 thousand workers), and the wage bill of 74.5% (5.52 billion INR ? 74 million USD). The sectoral impacts are highly varied: the information technology and communication, manufacturing, and utilities sectors experience significant declines in assets, while the financial services sector exhibits growth. Mapping the spatial distribution of flood events and industrial facilities reveals pronounced regional heterogeneity in flood exposure and economic impacts. Adding nuance to the empirical investigation of the “creative destruction” hypothesis, we find limited evidence of systematic capital reallocation toward better-performing sectors, suggesting instead that floods generate sector-specific impacts with varying recovery patterns. These findings challenge assumptions of rapid post-disaster equilibration and have important implications for policymakers and firm managers in developing sector-specific strategies to mitigate the adverse impacts of floods in an increasingly climate-uncertain world. |
| Date: | 2024–12–03 |
| URL: | https://d.repec.org/n?u=RePEc:iim:iimawp:14718 |
| By: | Hajer Aloulou (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, Université de Sfax - University of Sfax); Claudio Vitari (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon); Zouhour Smaoui Hachicha (IMSIU - Imam Mohammad Ibn Saud Islamic University) |
| Abstract: | There is a rapid and extensive progress in digital technologies and organizations need resources to build a successful path. Frugal solutions help companies to make economical digital investment which could be called frugal digitalization. This study examines the scope of frugal digitalization by synthesizing the existing articles that combine digital and minimal resource consumption. Therefore, a scoping review is conducted, and 59 articles are finally included. The result revealed that frugality, bricolage, jugaad, and moderation are the close concepts used to represent the minimal resource consumption. The study's results indicate that digitalization, digital transformation, digital orientation, and information communication technologies (ICT) adoption are the close concepts used to represent the digital phenomenon. These concepts can intertwine and establish connections between them. This study contributes to the literature by identifying the most often used concepts that reflect the digital era and the economical approach. Furthermore, this study offers practical implications: it validates the existing intersection in the business world and motivates professionals to go further in such a way. We conclude by outlining the next steps for the matrix, which will show how minimal resource consumption connects with the three stages of digital transformation-digitization, digitalization, and digital transformation. |
| Keywords: | digitalization, frugality, frugal digitalization, scoping review |
| Date: | 2024–10–02 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04875479 |
| By: | Payal Malik (Indian Council for Research on International Economic Relations (ICRIER)); Nikita Jain; Shiva Kanwar; Bhargavee Das; Saloni Dhadwal |
| Abstract: | Artificial Intelligence (AI) technologies are becoming integral to businesses and public markets alike, enabling innovation and efficiency and creating avenues for economic growth. The emphasis in public discourse has been on the technological advances enabled by AI and the risks and benefits associated with them. It is equally important that discussions on market implications of firm behavior active in AI are also understood. This report explores the evolving market dynamics in India and the critical challenges faced by policymakers and regulators in creating a competitive and innovative AI ecosystem. The report also examines the AI technology stack, highlighting its distinct layers and their implications for industrial organization and market competition. Key themes include the role of major cloud providers in shaping the AI ecosystem, the complexities of open-source models, the expanding network of partnerships between global technology companies, AI startups, and domestic IT incumbents, and the creation of new dependencies. Drawing on global best practices, the report emphasizes the need for a nuanced mix of competition and industrial policies, including a Digital Public Infrastructure paradigm, to foster a competitive, inclusive, and innovative AI ecosystem in India. It also highlights India's push for technological sovereignty through initiatives like the IndiaAI Mission and investments in indigenous AI models and supercomputing capabilities. The recommendations proposed in the report include promoting interoperability, enhancing access to computing resources, strengthening data-governance frameworks while facilitating access to high-quality open datasets, and leveraging public-private partnerships to support emerging AI startups. |
| Keywords: | Artificial Intelligence, Competition Policy, Generative AI, Digital Public Infrastructure, Data Governance, AI Regulation, Prosus, icrier |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:bdc:report:25-r-01 |
| By: | Christoph Grimpe; Fuad Hasanov; Wolfgang Sofka; Geoffrey Borchardt; Philip Schulz |
| Abstract: | A well-functioning market for technology, or ideas, is an important determinant for the type, scope, and distribution of innovation activities. We use a panel of 20 industries across 24 European countries to study the key determinants driving the market for technology. We explore whether the expenditures on external knowledge depends on the sectoral pattern of innovation and an industry’s distance to the global technological frontier. Disseminating knowledge and technology within the industry, bringing it closer to the global technological frontier, tends to reduce the expenditures for external knowledge except in supplier-dominated industries. We also find important complementarity effects in the market for external knowledge. Industries with high R&D spending, with increasingly large firms, and with large investments in machinery and software foster growth of the market for technology. Our findings suggest tailoring innovation policies to help expand both the size of the market for technology and the use of these markets in specific industries. |
| Keywords: | Markets for technology; patterns of innovation; industry studies; R&D; external knowledge; industrial policy |
| Date: | 2025–01–17 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/020 |
| By: | Bryan T. Kelly; Boris Kuznetsov; Semyon Malamud; Teng Andrea Xu |
| Abstract: | The core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and nonlinearity. We also develop a linear transformer that serves as a simplified surrogate from which we derive an intuitive decomposition of the transformer's asset pricing mechanisms. We find large reductions in pricing errors from our artificial intelligence pricing model (AIPM) relative to previous machine learning models and dissect the sources of these gains. |
| JEL: | C45 G10 G11 G14 G17 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33351 |