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Income Diversification and Banks’ Profitability from an African Market Perspective: A Relief for SMEs?

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African Entrepreneurship

Part of the book series: Palgrave Studies of Entrepreneurship in Africa ((PSEA))

Abstract

The financial sectors in many African countries have witnessed some regulatory changes and financial reforms in the early 1990s. These reforms have shifted the focus to the generation of non-traditional income. Notwithstanding, the empirical evidence on the impact of income diversification on banks’ profitability in Africa banking markets appears inadequate. The study employs a panel data of 584 banks and well-functioning internationally active banks with various specializations headquartered in 50 African countries spanning from 2001 to 2013. Empirical results document a negative relationship between income diversification and banks’ profitability. This implies that diversified banks in Africa are less profitable. Further, although the study invalidates a non-linear relationship between income diversification and banks’ profitability, the findings indicate that income diversification has a diminishing marginal impact on banks’ profitability. Thus, the nexus between income diversification and bank diversification is linear in African banking environment.

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Notes

  1. 1.

    Williams, Mark (April 12, 2010). Uncontrolled Risk. McGraw-Hill Education. ISBN 978-0-07-163829-6.

  2. 2.

    OECD SME and Entrepreneurship Outlook 2005 Edition.

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Author information

Authors and Affiliations

  1. School of Management, Open University, Heerlen, The Netherlands

    Isaac Boadi

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  1. Isaac Boadi
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Editor information

Editors and Affiliations

  1. GSCM Montpellier Business School, Montpellier, France

    Leo-Paul Dana

  2. Department of Management and Marketing, La Trobe University, Melbourne, Victoria, Australia

    Vanessa Ratten

  3. Department of Management Sciences, Ho Technical University, Ho, Ghana

    Ben Q. Honyenuga

Appendices

Appendix 1: Variance Inflation Factor (VIF) for the Explanatory Variables

Variable

VIF

1/VIF

DIV_Ratio

1.36

0.73705

Prtxpr

2.33

0.42919

Impairm

1.73

0.57804

GrL

1.43

0.69723

Coin

1.22

0.82151

Eq_TAs

1.14

0.87783

Lo_TAs

1.09

0.9202

INF

1.06

0.94586

Spec_t

1.05

0.95459

EX

1.03

0.97015

RINT

1.02

0.97611

Mean VIF

1.31

 
  1. Note: The mean VIF was 1.31, which is much lower than the threshold of 10. The VIF for individual variables was also below 10. This indicates that the explanatory variables included in the model were not substantially correlated with each other, indicating an absence of multicollinearity between the variables

Appendix 2: Fisher Unit Root Test of Variables Based on Augmented Dickey-Fuller Test (ADF)

Variables

Inverse chi sq.

Inverse normal

Inverse logit

Modified inv. chi sq.

Statistic

Prob

Statistic

Prob

Statistic

Prob

Statistic

Prob

ROA

3357.9526

0.0000

−32.7407

0.0000

−45.0581

0.0000

65.1358

0.0000

DIV_Ratio

4419.1853

0.0000

−38.9748

0.0000

−57.6823

0.0000

87.7836

0.0000

Prtxpr

2519.3053

0.0000

−24.8063

0.0000

−31.9186

0.0000

43.5054

0.0000

Impairm

2923.6621

0.0000

−28.8038

0.0000

−37.1205

0.0000

51.9462

0.0000

GrL

2816.4375

0.0000

−25.6317

0.0000

−34.0667

0.0000

48.6475

0.0000

Coin

3849.0864

0.0000

−38.389

0.0000

−50.6791

0.0000

73.8247

0.0000

Eq_TAs

3568.5977

0.0000

−36.736

0.0000

−46.6997

0.0000

67.2162

0.0000

Lo_TAs

4484.1608

0.0000

−42.9688

0.0000

−59.4364

0.0000

89.3746

0.0000

INF

3984.4261

0.0000

−37.8152

0.0000

−52.1047

0.0000

77.1385

0.0000

Spec_t

2870.6609

0.0000

−35.7258

0.0000

−45.7547

0.0000

49.8679

0.0000

EX

4487.5738

0.0000

−33.0863

0.0000

−55.6801

0.0000

89.4581

0.0000

RINT

3931.6951

0.0000

−35.6641

0.0000

−49.2331

0.0000

75.8474

0.0000

  1. Source: Author’s estimate (2017) using STATA 14
  2. Ho All panels contain unit roots, Ha At least one panel is stationary

Appendix 3: Hausman Specification Tests Between FE and RE Estimates

Variables

(b) fixed

(B) random

(b−B) difference

sqrt(diag(V_b−V_B)) S.E.

DIV_Ratio

−0.7874

−0.7680

−0.0194

0.0264

Prtxpr

0.4421

0.4381

0.0040

0.0045

Impairm

−0.2312

−0.2293

−0.0019

0.0035

GrL

−0.0228

−0.023

0.0159

0.0954

Coin

−0.2482

−0.2316

−0.0166

0.0140

Eq_TAs

0.5122

0.4986

0.0136

0.0108

Lo_TAs

0.0289

0.0225

0.0065

0.0085

INF

0.1310

0.1412

−0.0102

0.0064

Spec_t

−0.0286

−0.0269

−0.0017

0.0061

EX

−0.3100

−0.3072

−0.0028

0.0193

RINT

0.0067

0.0078

−0.0010

0.0005

  1. Source: Author’s estimate (2017) using STATA 14
  2. Note: Thus, the Hausman specification test is carried out to inform whether RE estimation gives more consistent results, given the data used for this study. When Prob > Χ2 = α, the null hypothesis is rejected. This reinforces the consistency of the RE in estimating the chosen model

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Boadi, I. (2018). Income Diversification and Banks’ Profitability from an African Market Perspective: A Relief for SMEs?. In: Dana, LP., Ratten, V., Honyenuga, B. (eds) African Entrepreneurship. Palgrave Studies of Entrepreneurship in Africa. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-73700-3_8

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