Background: Financing emergency healthcare remains a major challenge for households worldwide considering the issue of uncertain income levels and increasing healthcare costs. Financial limitations may result in delayed use of healthcare services. Saudi Vision 2030 emphasizes the need for enhancing financial inclusion alongside healthcare delivery. However, little research done on financial inclusion impacts on household access to emergency healthcare financing. Methodology: This study uses a panel dataset comprising 13 regions within Saudi Arabia spanning from 2015 to 2023 (117 observations). We use the fixed effects approach, system GMM model, and the panel ARDL model. Indicators of financial inclusion comprise formal finance, digital financial services, and savings indicators while controls are income stability and healthcare infrastructure. Findings: Financial inclusion increases household access to emergency healthcare financing both in short run and long run. Digital finance and savings are strong predictors of emergency healthcare finance capacity (FI=0.287, p<0.001). Income stability and healthcare infrastructure reinforce the effect of financial inclusion. The error correction measure indicates that convergence to equilibrium happens rapidly (−0.412, p<0.001). Conclusion: Improvement of financial inclusion via adoption of digital financial services and savings can enable households to deal with health emergencies effectively. Access to finance in combination with income stability and health infrastructure development is essential in ensuring that health financing is sustainable in Saudi Arabia. The above findings have significant policy recommendations concerning financial-sector reform and health system resilience in line with Vision 2030. Jel Classification: G21; I13; I18; O16; O53.

Can we conclude that rapid digital transformation leads to geographically equitable development? This research paper investigates the simultaneous and spatial dynamics of digital development and digital inclusion within the administrative regions of Saudi Arabia from 2012 to 2024. The research employs β-convergence, σ-convergence, Theil inequality decomposition techniques, and Spatial Durbin Models (SDM) to determine whether successful digital advancement results in better inclusion. The findings present a significant discrepancy between technological improvement and its results in terms of equity. The Digital Progress Index (DPI) reveals that convergence occurs at 4.71% annually, while the Digital Inclusion Score (DIS) reaches only 1.41% per year. While there is a significant reduction in the spread of digital progress, the outcome for digital inclusion inequality is only 15%, which has been stagnant after 2020. The Theil decomposition indicates that the inequality component among the regions has dropped from 69.7% to 39.8%, and the inequality gap among regions has increased. The SD estimates demonstrate that digital infrastructure has a positive influence across the regions, indicating that digital advancement also produces wider benefits. However, they are insufficient to solve the inclusion issues. The results refute the belief that rapid digitalization brings about geographically inclusive development, as the report argues that the processes of digital advancement and digital inclusion occur along separate convergence trajectories. As a result, the question raised by this research is how to migrate from infrastructure-based digitalization to place-based policies that will enable investments in digital capabilities, affordability, accessibility, and economic impediments, affecting the implementation of Vision 2030 in Saudi Arabia, among other rapidly digitalized economies.

The study of regime-dependent predictability of cryptocurrency market distress is examined in this paper using the dynamics of cross-exchange spread signals as an innovative early-warning tool. The research offers a three-stage econometric framework comprising: a Markov regime-switching VAR to infer endogenous bull, transition, and bear regimes in the BTC, ETH, BNB, and SOL markets; a Panel Smooth Transition Regression (PSTR) model for the regime-dependent spread-distress predictive relationship; and quantile regression providing the complete profile of the distress distribution. The full sample spans January 2020–December 2025 (2191 daily observations); the January 2020–December 2022 sub-period (1095 observations) is used for baseline in-sample estimation, while January 2023–December 2025 is reserved for out-of-sample validation. The results show that cross-exchange spread dynamics contribute to the occurrence of distress regardless of the market state and confirm that bear markets predict distress better than bull markets (with AUROC values changing from 0.848 to 0.963, respectively). In addition, the results of the PSTR function suggest that the predictive role of the spread increases in stressful periods (from 0.152 to 0.588), as supported by LM tests yielding results with p-values less than 0.001. The suggested model outperforms seven competing models, including MS-VAR, GARCH-M, linear logit, LSTM, and XGBoost, on all evaluation measures (AUROC, Brier score, Diebold–Mariano) across every regime. The results remain stable across different exchange-pair configurations and COVID-19-related sub-samples, and when varying the threshold definition.