Banking system stability: A prerequisite for financing the Sustainable Development Goals in Nigeria

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The banking system, which has been the fulcrum of funding for Nigeria’s economy, is plagued by instability in the face of a growing amount of non-performing loans. This is examined in the current milieu of the need for funding the Sustainable Development Goals (SDGs). Using a number of proxies for SDGs 8 and 9, annual time series data covering 1992 to 2019 were used with variables such as GDP per capita, commercial banks’ loans to small-scale enterprises, banking system stability indicators and liquid assets to total assets of banks. The study utilized the Autoregressive Distributed Lag. Findings showed that banking system stability has a significant positive effect on funding the SDGs 8 and 9 beyond the five per cent level of significance within the study period. Non-performing loans remained negative throughout the study. The result suggests that banking stability would enhance funding of the SDGs, and banks would be stable if they finance the SDGs. The policy implication explains the importance of banks actively pursuing opportunities to build sustainable enterprises and developing strategies that will enable their core banking business to be more venture-driven rather than consumer-oriented. In conclusion, there is a need to completely eliminate or reduce the quantum of non-performing loans from the system and establish a regulatory framework that will facilitate its expected role of intermediation in the economy profitably and successfully.

Acknowledgment
The authors would like to appreciate Covenant University for financial support to publish this paper.

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    • Figure 1. A stable banking system and SDG funding framework
    • Figure 2. Trend of banking stability and SDGs
    • Figure 3. CUSUM and CUSUM of Squares Stability Tests – Model I and II
    • Table 1. Description of variables, proxies, justification and a priori
    • Table 2. List of variables and descriptive statistics
    • Table 3. Correlation matrix
    • Table 4. ADF unit root tests for the variables at levels and first differences
    • Table 5. ARDL bounds test result for co-integration relationship
    • Table 6. Long-run tests: Model I and II
    • Table 7. Short-run tests: Model I and II
    • Table 8. Diagnostic and stability tests for models I and II
    • Conceptualization
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Data curation
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Formal Analysis
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Funding acquisition
      Agatha Amadi
    • Investigation
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Methodology
      Agatha Amadi, Abiola Babajide, Idimmachi Amadi
    • Project administration
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide
    • Resources
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Supervision
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Validation
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Writing – original draft
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Writing – review & editing
      Agatha Amadi, Kehinde A. Adetiloye, Abiola Babajide, Idimmachi Amadi
    • Software
      Kehinde A. Adetiloye, Idimmachi Amadi
    • Visualization
      Idimmachi Amadi