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on Information and Communication Technologies |
| By: | Patrick Healy (Department of Economics, Monash Business School, Monash University; SoDa Laboratories, Monash Business School, Monash University); Simon D. Angus (Department of Economics, Monash Business School, Monash University; SoDa Laboratories, Monash Business School, Monash University); Paul Raschky (Department of Econometrics and Business Statistics, Monash Business School, Monash University; SoDa Laboratories, Monash Business School, Monash University); Klaus Ackermann (Department of Economics, Monash Business School, Monash University; SoDa Laboratories, Monash Business School, Monash University); Nathan Lane (SoDa Laboratories, Monash Business School, Monash University; Department of International Development, London School of Economics and Political Science); Weijia Li (Department of Econometrics and Business Statistics, Monash Business School, Monash University; SoDa Laboratories, Monash Business School, Monash University); Cynthia Huang (SoDa Laboratories, Monash Business School, Monash University; Social Data Science and AI Lab, LMU Munich) |
| Abstract: | Digital State Capacity is the ability of governments to deploy ICT infrastructure and information systems to implement policy. This paper introduces a new measure of government ICT capacity based on an observable stock of deployable public-sector network infrastructure: public IPv4 address space held by government organisations. These address holdings are key inputs into digital administration because they support internet-facing systems, networked information exchange, and coordination across agencies and functions. The core panel covers approximately 150, 000 country-entity records classified as government across more than 150 countries from 2019 to 2024 and can be disaggregated by administrative level and government function. In the 2019 to 2024 Admin-1 panel, government IP holdings are observed in 1, 681 subnational regions across all years. We validate the measure at the crosscountry and subnational levels and apply it to government tasks related to corruption control and vaccination rollout. In illustrative country-year analysis, higher Digital State Capacity is associated with higher-quality governance and publicservice outcomes in the expected directions, including lower measured corruption and higher vaccination coverage. These associations are descriptive; they demonstrate the empirical relevance of the measure and are not causal estimates. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.03221 |
| By: | Lauro Carnicelli; Tuomo Suhonen |
| Abstract: | This report examines the evolution of the economic status and role of PhD holders in Finland’s labor market and firms using Statistics Finland’s register and survey data. Special attention is paid to PhD graduates in the business, ICT, and engineering fields. The findings indicate a very tight labor market for PhD holders until the late 2000s, followed by rising unemployment and overeducation among them, as well as a stagnating wage premium for completing a PhD, in the 2010s. While PhDs have earned more than master’s and bachelor’s graduates on average, the PhD wage premium is found to be much higher for women than for men and to vary across fields of study. The firm-level analyses show no significant changes in productivity or profitability around the event of hiring the first PhD in a firm, whereas a higher share of PhD workers is found to be associated with increased wages and productivity. The results also provide suggestive evidence of PhDs, particularly those in the business, ICT, and engineering fields, playing a role in business-university collaboration. |
| Keywords: | Higher education, Doctorate, Human capital, Labor markets, Wages, Firms, Research and development, Productivity, Business-university collaboration |
| JEL: | I23 I26 J24 J31 |
| Date: | 2025–09–03 |
| URL: | https://d.repec.org/n?u=RePEc:pst:studie:117 |
| By: | Yu Chang Yeh; Ming Chieh Shih; Daniel De Backer; Leo Anthony Celi; Kay Choong See; Tomoko Fujii; Lowell Ling; Wasineenart Mongkolpun; Hsiang Wei Hu; Hsuan Yu Chen; Wei Cheng Chen; Bernard P. Cholley; Kean Khang Fong; Ho Geol Ryu; Sungwon Na; Moritoki Egi; Wing Sum Chan; Kuan Fu Chen; Rishikesan Kamaleswaran; Yu Chen Chuang; Chi Ju Yang; Wei Ling Hsiao; Sheng Ru Lai; David Ku; Ahsina Jahan; Greg Martin |
| Abstract: | Background Generative artificial intelligence (GenAI) is increasingly used for clinical decision support in critical care, yet standardized methods for evaluating GenAI content in intensive care settings are lacking. Existing metrics assess textual similarity but fail to capture clinical accuracy, reasoning quality, or urgency. Methods We developed and validated the IMPACT framework through a five-phase multinational panel consensus process. Reporting adhered to the ACCORD guideline. A steering committee of eight persons provided clinical and methodological oversight. Panelists were recruited through purposive sampling to ensure geographic and multidisciplinary representation. Content validity was assessed using the Content Validity Ratio (CVR) and Item-level Content Validity Index (I-CVI), with retention thresholds set at 70% agreement and I-CVI ≥0.80. Results A total of 58 panelists from 12 countries and regions participated, with 42 completing formal consensus voting. Participants included intensivists, physicians with AI research expertise, information technology specialists, and other critical care professionals. All six IMPACT domains exceeded validity thresholds (mean agreement 89.3%, CVR = 0.79, I-CVI = 0.92). Of 24 candidate subitems, 21 met retention criteria (mean agreement 85.7%, CVR = 0.71, I-CVI = 0.90). Three subitems were removed due to insufficient consensus and conceptual overlap. The validated framework comprises six domains with 21 subitems. Conclusions The IMPACT framework provides a consensus-validated approach for evaluating GenAI clinical decision support in intensive care, addressing gaps in current evaluation methods. |
| Keywords: | Clinical decision support; Consensus; Content validity; Critical care; Generative artificial intelligence |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/413053 |
| By: | Tomohiro Hirano; Keiichi Kishi; Alexis Akira Toda |
| Abstract: | We develop a macro-finance model linking stock price bubbles to a general-purpose technology (GPT), such as information technology and artificial intelligence. Knowledge spillovers differ across production factors, generating unbalanced growth and causing stock prices to outgrow dividends. Under our conditions, the unique equilibrium contains a bubble on dividend-paying stocks even though agents share common beliefs and rationally anticipate its collapse. The probability that spillovers persist affects the bubbles duration but not its existence. When spillovers equalize as the technology matures, the economy reaches balanced growth and the bubble collapses. Through IPO proceeds, the bubble can increase R&D employment, while accumulated knowledge remains productive afterward. More broadly, balanced growth is a knife-edge property: the restrictions used to obtain it make stock prices and dividends grow at the same rate, thereby ruling out rational bubbles on dividend-paying assets by construction. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:cnn:wpaper:26-013e |
| By: | Huiying Ye; Richard S. J. Tol; Fangzhi Wang |
| Abstract: | Artificial intelligence (AI) interacts with climate in various ways, while a unified analytical framework of this intricate interplay is lacking. To align AI investment with climate policy, we propose such a framework integrating AI's impact on emissions, output, and climate damages into the DICE model. We distinguish between ICT-like and Industrial Revolution (IR)-like AI prospects. Calibrated to the best available evidence, we find that AI development is net polluting. Under current low abatement, ICT-like AI adds 0.1 degree C to 2100 warming, while IR-like AI adds 0.8 degree C. The associated climate costs offset roughly one-fifth and one-quarter of AI's economic gains, respectively. Meeting the 2 degree C target saves the optimal ICT(IR)-like AI investment rate by 2100 from 3.3% (5.1%) under the low-abatement scenario to 3.7% (12.7%), indicating that mitigation is complementary to AI development. We further show that the investment trade-off between AI and abatement is driven primarily by AI's economic prospects, not by its emissions footprint. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24670 |