Competition and efficiency in an oligopolistic audit market: Evidence from the Nigerian banking industry

  • 267 Views
  • 77 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Economic theory posits that competition drives efficiency; the extent to which this is true in an oligopolistic audit market poses an empirical challenge. Furthermore, studies have postulated that both traditional and modern industrial organization theories are relevant for analyzing market competition. Therefore, this study investigated the effects of static and dynamic audit market competition on audit efficiency in the Nigerian banking industry. Secondary data were obtained from the audited annual financial statements of 12 banks from 2006 to 2020. The study adopted a 2-stage regression model; in the first stage, the audit efficiency scores were derived from an output-based, variable-return-to-scale version of data envelopment analysis (DEA) comprising audit report lag and audit fees as audit input variables and audit quality as the audit output variable. The efficiency scores were regressed on audit market competition and some control variables in the second stage via the bootstrapped truncated regression technique to analyze the effect of competition on efficiency in the audit market. The results showed a positive association between static competition and audit efficiency (50.57, p = 0.014). Because high concentration implied low competition, this finding implied that efficiency was impaired because of a lack of significant competition. The results also showed a positive and significant association between dynamic competition and efficiency, which implied that dynamic competition enhanced efficiency (0.21, p = 0.000) in the audit market. The study concluded that static competition impairs efficiency, while dynamic competition ensures efficiency in the Nigerian banking industry.

view full abstract hide full abstract
    • Table 1. Sample selection
    • Table 2. Descriptive statistics
    • Table 3. Correlation analysis
    • Table 4. Regression results
    • Conceptualization
      Tajudeen John Ayoola
    • Data curation
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Lawrence Ogechukwu Obokoh, Peace Ebunoluwa Kolawole
    • Formal Analysis
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Lawrence Ogechukwu Obokoh
    • Investigation
      Tajudeen John Ayoola, Peace Ebunoluwa Kolawole, Ebunoluwa Tokunbo Adeoye
    • Methodology
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Lawrence Ogechukwu Obokoh, Peace Ebunoluwa Kolawole, Ebunoluwa Tokunbo Adeoye
    • Project administration
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Lawrence Ogechukwu Obokoh
    • Software
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Peace Ebunoluwa Kolawole, Ebunoluwa Tokunbo Adeoye
    • Supervision
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Lawrence Ogechukwu Obokoh
    • Validation
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Peace Ebunoluwa Kolawole, Ebunoluwa Tokunbo Adeoye
    • Visualization
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Peace Ebunoluwa Kolawole, Ebunoluwa Tokunbo Adeoye
    • Writing – original draft
      Tajudeen John Ayoola, Eghosa Godwin Inneh
    • Writing – review & editing
      Tajudeen John Ayoola, Eghosa Godwin Inneh, Lawrence Ogechukwu Obokoh, Peace Ebunoluwa Kolawole