Shadow banking and micro-, small and medium scale enterprises: A municipal assessment in Nigeria


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Shadow banking is usually considered as offering financial and financial-related support outside of the mainstream conventional financial system. The biggest issue facing micro-, small, and medium-sized businesses (MSMEs) in Nigeria is the inconveniences and challenges associated with obtaining funds or credit from conventional banks, which encourages remote business operations and small-scale expansion. Thus, shadow banking activity is still widespread among MSMEs in Nigeria. This study used MSMEs operating in the Marian and Watt markets to analyze the impact of shadow bank interest income, savings products, and loans on the performance of MSMEs. A systematic Likert scale questionnaire was given to a group of 160 people, with 157 questionnaires duly returned. The survey research design was adopted, while the SPSS software was used to analyze the data acquired. As such, shadow banking interest income has a non-significant positive impact (0.022%) on the performance of MSMEs in Calabar metropolis; shadow banking savings products have a negative but significant impact (–0.160%) on MSME performance in Calabar metropolis, while shadow banking loans have a positive and significant effect (0.194%) on micro-, small, and medium-scale firm performance in Calabar metropolis. The study concluded that shadow bank operators should ensure that their service costs are standardized and supplied at affordable rates to attract MSMEs to patronize them for more successful business operations.

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    • Table 1. Cronbach’s alpha reliability test of the instrument
    • Table 2. Respondents by work experience and gender cross-tabulation
    • Table 3. Respondents by qualification-gender cross-tabulation
    • Table 4. Respondents by work experience and age cross-tabulation
    • Table 5. Respondents by qualification-age cross-tabulation
    • Table 6. Respondents by work experience-marital status cross-tabulation
    • Table 7. Descriptive statistics analysis
    • Table 8. Regression analysis: MSMEP is the dependent variable
    • Conceptualization
      Anthony Ogar
    • Funding acquisition
      Anthony Ogar, Joseph Anyadighibe, Jeremiah Abanbeshie, Aniebiet Etuk, Basil Eja
    • Methodology
      Anthony Ogar, Aniebiet Etuk
    • Project administration
      Anthony Ogar, Jeremiah Abanbeshie
    • Software
      Anthony Ogar, Jeremiah Abanbeshie, Aniebiet Etuk, Basil Eja
    • Supervision
      Anthony Ogar, Joseph Anyadighibe, Aniebiet Etuk
    • Writing – review & editing
      Anthony Ogar, Joseph Anyadighibe, Jeremiah Abanbeshie, Aniebiet Etuk, Basil Eja
    • Data curation
      Joseph Anyadighibe
    • Formal Analysis
      Joseph Anyadighibe, Aniebiet Etuk
    • Resources
      Joseph Anyadighibe, Jeremiah Abanbeshie, Aniebiet Etuk, Basil Eja
    • Validation
      Joseph Anyadighibe, Aniebiet Etuk, Basil Eja
    • Investigation
      Jeremiah Abanbeshie
    • Writing – original draft
      Jeremiah Abanbeshie
    • Visualization
      Basil Eja