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.

3.2 Regression Analysis

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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 <100 employees) •        % of SMEs firms a checking or savings account •        % of SME firms with bank loans/ line of credit •        % of SME firms using banks to finance investments •        % of SME firms using banks to finance working capital •        % of SME firms identifying access/ cost of finance as a major constraint Enterprise Survey Access to Financial Infrastructure   •        Number of ATMs per 1,000 sq km •        Number of branches of ODC's per 1,000 sq km •        Number of branches per 100,000 adults •        Number of ATMs per 100,000 adults Financial Access Survey  Table 3.1: Composition of indices   These three indices capture different aspects of financial inclusion, which include access to financial services by both households and firms in addition to effective use of these services. The diagram below shows the indicators included in each of the indices. This study does not calculate a single index from the three separate indices, particularly due to variance in cross-country datasets and their coverage within households and firms. Instead, it will compare ECA to other regions for households across period, and distinctly on each dimension.   Three indices were formulated. First, three different indices were formulated; however there was no composite index given that it would limit the data only for 2011 and 2014 since the household survey (FINDEX) data is only available for those dates. Individual components of the Enterprise Survey were already analyzed (Dider and Schmuckler, 2014). Composite indicator for the Enterprise Survey, Findex and FAS has not been analyzed in detail. The indicators were kept the same with a focus on ECA region. The comprehensive indicator of firms' and household's financial inclusion help understanding the relative position of ECA on various aspects compared to other regions particularly improving access and use of finance are viewed as key policy areas in the region. The other components of the indices are based on the literature XYZ and the indicators that were selected are in line with XYZ. 3.1.1.2. Index methodology   List of FAS Indicators EXPAND The FAS has been conducted annually since 2004. Seventh series was completed on September 2016.  The FAS database contains annual meta-data including 189 countries.    The FAS questionnaire is updated sporadically to reflect the major trends and innovations in provision and usage of the financial services. In 2012, the questionnaire was expanded to include time series for credit unions, financial enterprises, and MFIs; to classify distinctly SMEs, households, both life insurance, and non-life insurance firms. In 2014, the questionnaire was further extended to include data on mobile money indicators.   The IMF's FAS database publicly publishes at no cost key indicators of geographic and demographic reach of financial services, in addition to the primary dataset. The FAS covers the main indicators of financial access and utilization by both households and firms which are globally comparable. It is one of the broadest sources of international supply-side database on financial inclusion.   In June 2010, the IMF began to disseminate the results of its annual Financial Access Survey (FAS) (which can be accessed at http://data.imf.org/fas). http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C FAS Financial Access Survey.   http://www.enterprisesurveys.org/~/media/GIAWB/EnterpriseSurveys/Documents/Misc/Indicator-Descriptions.pdf     The Enterprise Surveys deliver indicators that show how firms finance their operations and firm level characteristics of financial transactions. For instance, Enterprise Surveys deliver indicators that compare the relative use of various sources to finance investment. According to the surveys, relying excessively on internal funds could potentially lead to inefficient financial intermediation. Second indicator set measures individual firms' usage of financial markets. This second set indicator contains the question referring to the amount of working capital which is financed externally.  In this set, a measure of the additional restrictions imposed by loan agreement covenants was measured by collateral levels relative to the loan value. Other indicators concentrate on private firms' financial service usage both on the credit and deposit aspects. On the credit, it looks at the percentage of firms with lines of credit. On the deposit mobilization side, it looks at the percentage of firms either with checking or savings accounts.  Indicator description   On behalf of the World Bank, privately hired contractors finalize the Enterprise Surveys. Due to delicate survey questions referring to relations between businesses and the government; and topics related to bribery, private contractors do not have any association with any government agency or an organization affiliated to the government/government agencies.  The World Bank directly hires the contractors to collect the relevant data.  The interviewees are owners of the businesses or top managers in these businesses. In some cases, the contractors call accountants of the company and managers of human resources to answer questions in the sections of sales and labor of the survey. In general, in large countries, 1200-1800 interviews are conducted, in medium-sized countries, 360 interviews are conducted; and in smaller countries, 150 interviews are conducted.   The economies' business environment dynamics are connected to the firms' performance and productivity by the qualitative and quantitative data collected through the surveys. The Enterprise Survey is beneficial tool both for the policymakers and academics. The surveys are reconducted over time in order to track deviations and benchmark the effect of reforms on surveyed firms' performance.   The Enterprise Surveys brought together a wide range of both qualitative and quantitative information by conducting face to face interviews with managers and owners at the firm level pertaining to the business environment in their economies and the firm productivity. There are various topics covered in Enterprise Surveys including but not limited to business licensing, crime corruption, finance, innovation, informality, infrastructure, labor, perceptions about limitations for doing business, regulations, taxes and trade.     The World Bank Group and its partners globally that cover small, medium, and large companies conducted the Enterprise Surveys. The surveys were directed to a demonstrative sample of firms in the formal private economy excluding agri-firms. The survey respondents were consistent in all economies and included the whole manufacturing, services, transportation and construction sectors. The main sectors were the manufacturing and services. Public utilities, government services, health care, and financial services were not included in the selected group.  Since 2006, majority of Enterprise Surveys have been implemented in a constant universe, implementation methodology. There has been a core questionnaire creating the basis of the Global methodology. See the Appendices X interview questions regarding finance (these include only the questions that were used in the firm level index in this paper)   Enterprise Survey is a vast dataset with an extensive collection of economic data on 131,000 firms in 139 countries. An Enterprise Survey is survey at the firm level representative of a sample of private sector of a country. The surveys contain a variety of business environment topics including but not limited to access to finance, corruption, crime, infrastructure, , competition; and performance measures.   Enterprise Survey http://documents.worldbank.org/curated/en/187761468179367706/pdf/WPS7255.pdf        Appendices YAP Appendix X shows the indicators' summary. Appendix X shows the interview questions.   However, Findex dataset also have some restrictions. The probable econometric methodologies and realistic endogeneity controls were limited due to the lack of a time dimension. Additionally, an examination of development of financial inclusion over time and its impact assessment on macroeconomic outcomes are also very limited. Finally, individuals are not observable between different years (Aslan et all., 2017).   The Findex is complimentary to other datasets given that it focuses on persons, rather than financial institutions. It does not provide aggregate measures of financial depth, as in the case of the IMF's Financial Access Survey data and World Bank Enterprise Surveys.   It should be noted that Findex data were a milestone, which delivered exceptional comprehension to how people saved, borrowed, made payments and managed risks in more than 140 countries.   Data weighting was used to make sure that each country was represented by a nation-wide sample. Closing weights compromised of the base sampling weight, which provided corrections for probability of unequal selection based on size of the household; and the poststratification weight. This then corrected sampling and nonresponse error. Population statistics on gender, age, education and socioeconomic status, where there was reliable data, was used for poststratification weights.   Face to face surveys were carried out in countries where telephone coverage contitutes less than 80 percent of the population. Mostly, the fieldwork was finalized between two to four weeks. In countries where face-to-face surveys were completed, the first sampling stage was the primary sampling unit classification. Survey respondents were randomly selected within the designated households.   The Global Findex dataset for 2014 contains indicators exceeding 100, covering information regarding gender information, age classification, and household income. The indicators were formed by using the survey data from interviews conducted with 150,000 nationwide representative and randomly selected adults (more than age 15) and in 143 countries representing more than 97 percent of the global population (see appendix X for the list of included countries).   The Global Financial Inclusion (Global Findex) dataset was launched in 2011 by the World Bank. It was comprised of comparable indicators showing how people globally tend to save, to borrow, to make payments; and to manage risk. The survey was carried out covering 2011 and the 2014 by Gallup, Inc. under the umbrella of Gallup World Poll. Findex               (iii) access to financial institutions (Financial Access Survey).             (ii) utilization of financial services by SMEs (Enterprise Survey);              (i) utilization of financial services (supply side) by households (Global Findex);   This study will measure the financial inclusion by using a multi-dimensional indices approach. It will use Norris and Deng's approach (Norris et al. 2015) in constructing three multi-dimensional indices capturing different angles of financial inclusion: 3.1.1.1. Composition of the Index 3.1.1. Index formation 3.1 Data   The study aims at measuring financial inclusion with the formation of three indices. (similar indices were formed by using similar approaches from the literature explained in the aforementioned Literature Review). The aim of this study is to quantify financial inclusion and to examine the relationship between financial inclusion and growth and inequality, after controlling for financial structure indicators (both covering financial markets and financial institutions). The intention is to decide whether or not financial inclusion has an impact on growth and on inequality with a particular focus on the ECA region.   The World Bank highlights the concept of financial inclusion ranges from "access and use of services provided responsively and sustainably" to "delivery of financial services at affordable costs to disadvantaged and low-income segments of society". Financial inclusion is defined as "the access to and use of formal financial services by households and firms" in line with Sarma's definition (Sarma 2008). To measure the concept, the definition was narrowed.   This study aims at documenting the current status of financial inclusion in ECA, while shedding light on the impact of financial inclusion on growth and inequality. Lately, Europe and Central Asia ("ECA")1 displayed a noteworthy progress pertaining to financial inclusion. Region's well-advanced microfinance sector and common usage of wage accounts are the success signs; however low savings rates and high levels of suspicion for the formal financial system is challenging for the policy makers in the region. As shown in the contemporary literature, financial inclusion enhances growth and reduces poverty and inequality with greater access to resource necessitated to finance both consumption and investment.   3. Empirical Research

x

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