[1] a of country I and on

1
“Europe and Central Asia (“ECA”) includes Albania, Armenia, Azerbaijan,
Belarus, Bosnia-Herzegovina, Bulgaria, Croatia, Georgia, Kazakhstan, Kosovo,
Kyrgyz Republic, Macedonia, FYR; Moldova, Montenegro, Poland, Romania, Russian
Federation, Serbia, Tajikistan, Turkey, Turkmenistan, Ukraine, Uzbekistan
according to World Bank Group classification.” As per World Bank’s definition.

 

After the completion of the index
formation, the relationship between the explanatory variables and financial inclusion
was explored. For usage indices including household index and firm-level index:
cross sectional analysis; for access index panel data analysis was conducted
while looking at the effect of financial inclusion on growth and on equality. The
available access to finance data for a longer time period made panel data
analysis feasible with robust results.

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3.2 Regression Analysis

 

In the existing literature, several methods were used
to estimate the weights comprising of the  principal component analysis, factor analysis,
as well as equal weights assigned within a subcomponent of the index. Looking
at the previous and on-going studies, it was decided that the equal weights
method is more robust for the aggregation. Norris and Deng used the index with
equal weights for the simplicity of exposition and the weights. (EKLE)

 

                                                  Ia,it  
= 1- ait- min (ait)/ max (ait)-
min (ait)

 

Where  Indexa,it   is the normalized variable of a of country I
and on year t, min (ait) is the minimum value of variable ait over
all it; and max (ait) is the maximum value of ait. For
those indicators which display a lack of financial inclusion, (for example the
percentage of firms identifying access or cost of finance as major constraint),
the reserve formulation was utilized:

 

                                                 
Indexa,it   = ait-
min (ait)/ max (ait)- min (ait)

The subcategories are chosen based
on the previous studies including X,Y,Z which were explained in detail in the literature review
section.  All variables were normalized
as shown below, while formulating the composite index:

 

Indices

Subcomponents

Variables

Sources

Use of Financial Services

 
Households (%, age 15+)
 

•       
Account at a formal financial institutions
•       
Debit card
•       
Credit card
•       
Loan from a financial institution in the past
year
•       
Saved at a financial institution in the past
year

Global Findex

Firms/SMEs (Enterprise Survey

x

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