Digital financial inclusion and socioeconomic sustainability in Saudi Arabia examining drivers disparities and policy pathways
Bibliographic record
Abstract
This study explores the evolution, determinants, and disparities of digital financial inclusion (DFI) in Saudi Arabia from 2011 to 2021, including the initial post-COVID-19 phase. Although our data are cross-sectional, we infer changes over time by comparing results across four waves of the World Bank’s Global Findex surveys (2011, 2014, 2017, and 2021). Using multiple Probit regressions, we examine the drivers of DFI across demographic, socioeconomic, and infrastructural dimensions. While Saudi Arabia has made notable progress in digital finance, gaps persist among women, individuals with lower education, low-income groups, and the unemployed. Access to mobile phones and internet connectivity significantly enhances DFI, highlighting the importance of digital infrastructure. To ensure the reliability of our findings, we conduct two sets of robustness checks. First, we use seemingly unrelated estimation (SUEST) to jointly test the equality of coefficients across probit models. Second, we construct a latent DFI index via Multiple Correspondence Analysis (MCA) and re-estimate the model using both OLS and probit frameworks. These robustness checks confirm the consistency and direction of the main effects, particularly the gender gap and the role of income, education, and mobile access. As one of the first systematic analyses of DFI in Saudi Arabia using Global Findex data, this study offers timely insights into the country’s inclusive digital transformation. It emphasizes how expanding equitable access to digital financial services can support broader goals of socioeconomic sustainability, reduce structural inequalities, and contribute to the Vision 2030 agenda. The findings offer practical guidance for policymakers seeking to build inclusive and sustainable digital financial ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".