Non-performing loans and capital adequacy in Jordan’s banking sector: Evidence from 2010–H1 2024

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Type of the article: Research Article

Abstract
This study examines aggregate asset quality and capitalization in Jordan’s banking sector from 2010 to H1 2024. A source audit showed that the international database values used previously repeated the same non-performing loan (NPL) ratio and capital adequacy ratio for 2010–2014 and were not demonstrably comparable with later supervisory observations. The revised dataset therefore uses Central Bank of Jordan publications consistently for banking indicators, World Development Indicators for macroeconomic variables, and treats H1 2024 as descriptive only. Annual regressions use 2010–2023 year-end data. Pearson correlations and two parsimonious OLS specifications are estimated with HC3 standard errors and t-based finite-sample inference. The NPL ratio averaged 5.743% and the capital adequacy ratio 18.393% over the 14 annual observations. In the contemporaneous model, capital adequacy is positively associated with the NPL ratio (coefficient 1.243, p = 0.005), but this association is not interpreted causally because regulation, provisioning, and common trends can generate reverse or simultaneous responses. In the limited dynamic model, the lagged NPL coefficient is 0.769 (p = 0.001), whereas lagged capital adequacy is imprecise (0.079, p = 0.866). These results indicate short-run persistence in aggregate asset quality, not a comprehensive measure of banking resilience. The small annual sample, influential observations, and incomplete public methodological detail require cautious interpretation.

Acknowledgment
The author thanks the reviewer for comments that led to a complete source audit and a more transparent empirical design. Public data were provided by the Central Bank of Jordan and the World Bank.

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    • Figure 1. Jordan banking-sector NPL and capital adequacy ratios, 2010–H1 2024
    • Table 1. Data sources, treatment, and comparability decisions
    • Table 2. Descriptive statistics for the year-end model sample, 2010–2023
    • Table 3. Validated annual data and period treatment
    • Table 4. Pearson correlation matrix with exact two-sided p-values and N
    • Table 5. Contemporaneous OLS model with primary HC3/t inference
    • Table 6. Limited dynamic OLS model with primary HC3/t inference
    • Table 7. HC1/t sensitivity estimates
    • Table 8. Residual and influence diagnostics
    • Conceptualization
      Khaled Qasem Bataineh
    • Data curation
      Khaled Qasem Bataineh
    • Formal Analysis
      Khaled Qasem Bataineh
    • Funding acquisition
      Khaled Qasem Bataineh
    • Investigation
      Khaled Qasem Bataineh
    • Methodology
      Khaled Qasem Bataineh
    • Project administration
      Khaled Qasem Bataineh
    • Resources
      Khaled Qasem Bataineh
    • Software
      Khaled Qasem Bataineh
    • Supervision
      Khaled Qasem Bataineh
    • Validation
      Khaled Qasem Bataineh
    • Visualization
      Khaled Qasem Bataineh
    • Writing – original draft
      Khaled Qasem Bataineh
    • Writing – review & editing
      Khaled Qasem Bataineh