The regression results showed that beyond ensuring
macroeconomic stability, formal factors and financial sector characteristics could
affect financial development. Better financial sector institutions, better
quality of credit information and improved governance could contribute to
promote financial sector development.


Following the literature on the finance-growth nexus
(see, e.g., Levine (2005)), the authors included several macroeconomic
variables such as income per capita, inflation, debt-to-GDP ratio and natural
resources GDP. The natural resource GDP represented the ratio of non-renewable
resources GDP to total GDP. They further included the so called institutional
variables:  government effectiveness,
rule of law and political stability. They expected better institutions to be
positively related to private credit-to-GDP, and negatively associated with the
financial development gap. Other independent variables were related to banking
infrastructure and banking systems’ characteristics. Higher deposits, and lower
interest rate spreads were expected to positively influence the developments in
private credit-to-GDP ratio. Similarly a better institutional framework (including
registry coverage, depth of credit information) was expected to reduce
information asymmetries between lenders and borrowers and, thus, boost
financial development. Higher cost-to-income ratios and operational costs of
banks were expected to be negatively associated with financial development as
these increased the cost of lending.  Once
they accounted for the size of the country, population density and the share of
urban population were expected to positively influence access to financial
services and financial development through allowing economies of scale. High
poverty headcount and infant mortality were usually associated with lower income
and lower financial inclusion.  They
further added two variables measuring the development of new technologies,
namely mobile phone subscriptions and internet utilization rates, to assess
their association with financial sector development. They identified a dummy of
CEMAC by their belonging to the CFA franc zone. A panel data analysis was

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The dependent variables included the ratio of private
credit to GDP and a measure of the financial development gap which was defined
as the ratio between the benchmark and actual private credit to GDP level.
Although access and depth measures had usually different concentrations and were
not theoretically perfect substitutes, these variables were significantly
correlated in the covered countries and factors associated with higher credit
to GDP could be related to larger measures of access to financial sectors. 


In their analysis, yearly observations covering 1997 –
2012 were used. The country dimension covered 42 SSA countries. All variables
were averaged over four-year non-overlapping intervals (i.e., 1997-2000;
2001-2004; 2005-2008; and 2009-2012) following Levine (2005).


Financial development in Sub-Saharan Africa (SSA) has been bumpy and
on average less advanced compared to other low-income regions, notwithstanding
recent progress and reforms. Within SSA, (CEMAC) region lagged further behind.
As the previous studies have comprehensively illustrated, financial development
affects economic growth and could play a substantial role in the reduction of
poverty and inequality. 


The financial benchmarking literature was used by Alter
and Yontcheva (2015) in their analysis. Using cross-country financial data,
Beck, et al. (2008) suggested a methodology to benchmark the policy component
of financial development. ?ihák, et al. (2012) introduced the Global Financial
Development Database (GFDD)
which contained the financial sector characteristics over 200 economies. In
this paper, Cihak, et al. used the benchmarking approach both to evaluate
financial development in the Central African Economic and Monetary Community(CEMAC)
and to make a measure of financial development “gap” for countries covered
under their research.R 


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