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on Information and Communication Technologies |
| By: | BEN CHEIKH, Nidhaleddine (ESSCA School of Management); Rault, Christophe (University of Orléans) |
| Abstract: | Using a sample of 67 countries, this paper examines how financial inclusion shapes the transition to inclusive and sustainable growth. First, we analyze the heterogeneous and asymmetric effects of key determinants using panel quantile regression. The results show that financial inclusion, institutional quality, and ICT diffusion significantly affect inclusiveness only in the lower tail of the distribution. While financial inclusion and ICT diffusion appear detrimental, institutional quality promotes shared prosperity. Second, we explore a mediating effect using a non-linear panel threshold model. The findings highlight the role of financial inclusion in enhancing inclusive growth. Although ICT infrastructure negatively affects inclusiveness at low levels of financial inclusion, this relationship becomes positive beyond a certain threshold. These results suggest that policymakers should combine financial inclusion, governance quality, and ICT development to foster inclusive growth. |
| Keywords: | inclusive growth, financial inclusion, non-linear panel data modelling |
| JEL: | C23 O11 O16 O43 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18582 |
| By: | Gmyrek, Pawel,; Viollaz, Mariana,; Winkler, Hernan, |
| Abstract: | This paper examines how Generative Artificial Intelligence (GenAI) may affect labour markets across 135 countries, covering around two-thirds of global employment. It focuses on how exposure differs between advanced and developing economies, and how digital infrastructure and task composition shape the balance between automation risks and productivity gains. |
| Keywords: | artificial intelligence, labour market analysis, access to information technology, labour productivity |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ilo:ilowps:995694369002676 |
| By: | Jin, Yan,; Charpe, Matthieu,; Mei, Yang,; Li, Zeshuo, |
| Abstract: | This study presents the first high-resolution (0.005°) gridded labor market data, generated by downscaling district-level census data for Ghana using random forest algorithms and remote sensing. It addresses the lack of spatially disaggregated labor market data by mapping 17 employment categories—including age, gender, skills, status, sectors, unemployment, and NEET. Auxiliary data (64 variables) such as land cover, nighttime lights, infrastructure, and points of interest are integrated to capture demographic, economic, and participation factors. The model achieves high accuracy (R2 > 90% for most categories) and reveals significant spatial heterogeneity, with employment rates ranging from 10% to 98% across pixels. Results highlight urban-rural and North-South divides, as well as sectoral concentrations. Variable importance analysis underscores the role of built-up areas, nighttime light, road density, and vegetation health in predicting employment patterns, with specificity across different employment categories. The methodology advances beyond traditional GDP or population gridding by incorporating labor market complexity. Findings demonstrate the potential of machine learning and geospatial data to enhance socio-economic mapping in data-scarce contexts. |
| Keywords: | labour market analysis, mapping, human geography, information technology. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ilo:ilowps:995694369302676 |
| By: | Cheon, EunJeong,; Lee, Hee Rin, |
| Abstract: | This paper examines the gap between what nurses need and what actually gets built, through a three-month ethnographic study at Mirae Hospital (MH) in Seoul, South Korea, combining interviews, ward observations, and participatory design workshops with hospital staff and union nurses. Our findings reveal three persistent patterns: technologies that succeeded eliminated acknowledged problems through collaborative design; technologies that failed attempted to model volitional human behavior or assumed laboratory conditions that clinical environments cannot provide; and high-impact automations that nurses explicitly requested were never developed, displaced by technically sophisticated investments aligned with institutional prestige rather than frontline need. We further show that AI adoption differs systematically between unionized and non- unionized hospitals, with union representation playing a meaningful role in ensuring AI serves workers rather than institutions. Together, these findings point to a structural problem: bedside nurses – those most directly affected by clinical AI – remain least likely to shape what gets built. Addressing this requires not only better design methods, but institutional reform in how technology priorities are set and whose needs are treated as authoritative. |
| Keywords: | nurse, information technology, artificial intelligence, hospital |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ilo:ilowps:995697769302676 |
| By: | Maksym Nechepurenko |
| Abstract: | April 2026 saw notable methodological convergence in the academic study of informed trading on decentralized prediction markets. Three approaches surfaced almost simultaneously: Mitts and Ofir (2026) apply a composite screen to over 210, 000 wallet-market pairs; Gomez-Cram et al. (2026) apply an event-level sign-randomization test to Polymarket's complete transaction history, classifying 3.14% of accounts as "skilled winners" and separately flagging 1, 950 accounts as "insiders" via a lifecycle heuristic; Nechepurenko (2026) develops the Information Leakage Score (ILS) framework, which quantifies per-market information front-loading at an article-derived public-event timestamp. This paper provides a methodological comparison. The central claim is that these are three distinct layers of detection, not competing methods on a single layer. Sign-randomization is best understood as an account-level test of persistent directional skill conditional on opportunity selection -- not a direct test of insider trading, and not a per-market measure. The heuristic insider flag is separate from the skill classifier, applies to a population the classifier excludes by design, and has unknown precision. The Polymarket sample pools politics, sports, crypto, and other categories with different information technologies, so a platform-wide "skilled winner" classification is mechanism-ambiguous. The January 2026 U.S.-Venezuela operation cluster, where the DOJ indictment of Master Sergeant Gannon Van Dyke provides a rare external enforcement benchmark, illustrates how the layers stack: lifecycle heuristics identify suspicious accounts; legal investigation addresses non-public-information possession; per-market scoring would quantify how much information was leaked into each contract. A combined pipeline gains in precision because each layer filters a different dimension. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.02287 |