Governance cost and financial service efficiency in Nigeria


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This study explored the influence of the governance cost on financial service efficiency in Nigeria. The recurrent collapse of reputable companies and banks due to agency problems have motivated this investigation. The study empirically sampled 40 financial service firms from the 50 firms registered on the stock market. The study adopted an ex-post-facto research design. Data was collected using secondary sources from the firms’ annual reports to determine the influence the governance cost has on Nigeria’s financial service efficiency for nine years (2012–2020). Also, the study utilized the Panel Tobit regression to test the hypothesis. The Principal Component Analysis (PCA) was used to ascertain the aggregate governance cost, and the proxies were directors’ fees, auditors’ fees, CEO compensation, and chairman fee. At the same time, financial service analysis was derived using the Input-oriented Data Envelopment Analysis (DEA) technique under the constant return to scale (CRS) assumption. Consequently, findings from the study show a significant and positive influence of governance costs on the efficiency of financial services. The study, therefore, concludes that the current governance cost of the sampled firms drives efficiency within the sampled firms, and increasing the governance cost, based on the reviews on corporate governance structures, will not harm the efficiency of financial services. However, the consistent increase over time will harm efficiency. Thus, the study recommends an internal balance on the pay structure within the financial services.

The authors acknowledge Covenant University for solely providing the platform for this research and for fully sponsoring the publication of this research work.

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    • Table 1. Measurement for governance costs and performance efficiency
    • Table 2. Descriptive statistics for banks
    • Table 3. Descriptive statistics for Insurance firms
    • Table 4. Descriptive statistics for investment companies
    • Table 5. Descriptive statistics for the technical efficiency scores
    • Table 6. Number of efficient companies per year
    • Table 7. Tobit results for aggregate datasets
    • Table 8. FGLS results for aggregate datasets
    • Conceptualization
      Emmanuel Ozordi, Damilola Eluyela
    • Data curation
      Emmanuel Ozordi, Olubunkola Uwuigbe, Stephen Ojeka
    • Methodology
      Emmanuel Ozordi, Olubunkola Uwuigbe
    • Project administration
      Emmanuel Ozordi
    • Validation
      Emmanuel Ozordi
    • Writing – original draft
      Emmanuel Ozordi, Olubunkola Uwuigbe, Uwalomwa Uwuigbe, Damilola Eluyela
    • Supervision
      Olubunkola Uwuigbe, Stephen Ojeka
    • Writing – review & editing
      Olubunkola Uwuigbe, Uwalomwa Uwuigbe, Stephen Ojeka, Damilola Eluyela
    • Formal Analysis
      Uwalomwa Uwuigbe
    • Investigation
      Uwalomwa Uwuigbe, Stephen Ojeka
    • Resources
      Uwalomwa Uwuigbe, Stephen Ojeka, Damilola Eluyela