Analysis of the determinants of financial inclusion in Central and West Africa

Abstract

Using data from the Global Financial Inclusion database (Global Findex) of the World Bank, this study attempts to identify and analyse the determinants of financial inclusion in Central and West Africa, two of the least financial inclusive regions of the Africa continent. The findings indicate that access to formal finance in the two regions is mainly driven by individual characteristics such as gender, education, age, income, residence area, employment status, marital status, household size and degree of trust in financial institutions. However, Central Africa and West Africa differ with the entire Africa region on a number of important determinants of access to finance. Specifically, educated, working-age, urban resident and full-time employed are significant individual characteristics of access to formal account in both regions and in Africa. However, being male and/or married are positive determinants of financial inclusion for Central Africa and Africa, whereas income is significant in West Africa and Africa. In addition, household size has a negative impact on account ownership in West African and not in Central Africa. When we use the other financial inclusion indicators (saving, borrowing or frequency of use), the above determinants remain all significant for Africa, but not necessarily for Central Africa or West Africa, where they have different degree of significance. As policy recommendations, governments and their partners in these regions should adopt or strengthen regulatory laws to better protect financial services consumers, enlarge population access to education, ease access to finance for the vulnerable groups (women, youth, poor, etc.), and continue their effort to increase the number of permanent and stable jobs created with special focus on gender and marital status in Central Africa and income and household size in West Africa.

Keywords:

• Access to finance
• financial inclusion
• Global Findex
• Central Africa
• West Africa

Notes

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Notes

1 CEMAC is the French acronym of Central African Economic and Monetary Community (in French: Communauté Économique et Monétaire de l’Afrique Centrale). UEMOA is the French acronym of West African Economic and Monetary Union (in French: Union Économique et Monétaire Ouest Africaine).

2 The sample for ECOWAS is composed of 10 countries: 6 (out of 8) UEMOA countries (Benin, Burkina Faso, Mali, Niger, Senegal, Togo) and 4 (out of 6) WAMZ countries (Ghana, Guinea, Nigeria, Sierra Leone). Note that we do not have data for 2 UEMOA countries (Côte d’Ivoire, Bissau Guinea) and 2 WAMZ countries (Gambia, Liberia). The sample for ECCAS is composed of 8 countries (out of 10): 5 (out of 6) from CEMAC – Cameroon, Congo, Gabon, Central African Republic and Chad; and 3 outside CEMAC – Angola, Burundi and Democratic Republic of Congo. We do not have data for the following countries: Equatorial Guinea (CEMAC member) and Sao Tome & Principe.

3 This concerns only withdrawal operations, savings and borrowing have already being captured by the other variables above.

4 See Demirguc-Kunt and Klapper (2012a) for a detailed description of the Global Findex database or visit the following website for more recent works on financial inclusion using this database: http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTPROGRAMS/EXTFINRES/EXTGLOBALFIN/0,,contentMDK:23147627∼pagePK:64168176∼piPK:64168140∼theSitePK:8519639,00.html.

5 Our database does not contain data for two countries in UEMOA (Côte d’Ivoire, Bissau Guinea) and two countries in WAMZ (Gambia, Liberia).

6 We do not have data for the following ECCAS countries: Equatorial Guinea (CEMAC member) and Sao Tome & Principe.

7 For simplicity, we did not report the results with the age split, these results are available from the authors upon request.

8 From our analyses, there is no perfect correlation between income quintiles and education level. Hence, we assume the two variables to be exogenous to each other in our analysis.

9 These are adults with an account at a formal financial institution, including postal offices and microfinance institutions.