Arvanitis, Y. (2014); “Providing Banking Services in a Fragile Environment. Structure, Performance and Perspectives of the Banking Sector in Guinea-Bissau,” West Africa Policy Note, African Development Bank Group.
Barajas, A., T. Beck, E. Dabla-Norris, and S. R. Yousefi, (2013); “Too Cold, Too Hot, or Just Right? Assessing Financial Sector Development across the Globe,” IMF Working Paper WP/13/81
E. Al-Hussainy, A. Coppola, E. Feyen, A. Ize, K. Kibbuka, and H. Ren, (2011); “A Ready-to-Use Tool to Benchmark Financial Sectors Across Countries and Over Time,” FinStats 2011 (Washington, D.C., World Bank).
Musuku, T. B., M. C. Malaguti, A. McEwan Mason, and C. Pereira, (2011); “Lowering the Cost of Payments and Money Transfers in UEMOA”; Africa trade policy notes (23), Washington, D. C., World Bank.
Groupe Speciale Mobile Association, 2014; “State of the Industry 2013: Mobile Financial Services for the Unbanked,” GSM Association.
IMF, 2012, “Enhancing Financial Sector Surveillance in Low Income Countries (LICs)—Case Studies,” Supplement to IMF Policy Paper, Washington, D. C.
IMF, 2015b, “West African Economic and Monetary Union. Staff Report on Common Policies of Member Countries—Selected Issues Paper.”
World Bank, 2014b, “Utilisation des Services de Téléphonie Mobile pour la Mise en Oeuvre du Projet « Cash for Work » de la Banque Mondiale en Guinée-Bissau,” Presentation, December, 2014.
Prepared by Monique Newiak.
For consistency of the comparison between Guinea-Bissau and the WAEMU, the source of all financial soundness indicators (FSI) used in this note is BCEAO headquarter; these FSI may differ from the ones reported in the accompanying staff report.
It regresses the ratio of private sector credit-to-GDP on: (i) the log of GDP per capita and its square, (ii) the log of the population to proxy for market size, (iii) the log of population density to proxy for the ease of service provision, (iv) the log of the age dependency ratio to account for demographic trends and the related savings behavior, (v) an oil exporters dummy, and time dummies to control for global factors.
For most indicators, WAEMU averages or ranges are provided. More ambitious benchmarks include Ghana, Kenya, Lesotho, Rwanda, Tanzania, Uganda, and Zambia for Africa, and Bangladesh, Cambodia, India, Laos, Nepal, and Vietnam for Asia.