CHAPTER 3: METHODOLOGY
3.1 Introduction
This chapter is devoted to the methodologies adopted to achieve the objectives of the thesis. It
presents the research methods and techniques that have been applied in estimating the
relationship between financial inclusion and rural development/wellbeing of rural communities
in SSA countries. It describes the estimation techniques employed, the type and sources of the
data, the time period covered, the sample methods and size, as well as the model specification
and description of variables that was applied towards estimating the effect of financial inclusion
on the development/wellbeing of rural communities in SSA.
3.2 Research Design
This study employed a deductive quantitative research method to analyse the interlinkages
between financial inclusion and rural development of rural communities in sub sahara africa.
Deductive Quantitative Analysis of the research question will be performed for this study since this
thesis entails analyzing financial inclusion numerical data and its contribution towards the rural
development of SSA rural communities measured via relevant rural development indicators which is thus
expected to produce quantitative output in form of changes in the wellbeing or socio-economic
development of rural communities. As outlined by Sreejesh, Mohapatra, and Anusree (2014), the
research approach for this thesis requires the collection, measurement and analysis of data to
answer the research questions using selected statistical, computational or mathematical
techniques.
The research design which is the overall strategy used to effectively address the research
questions. Based on this strategy, data collection method, and the data analysis technique are
chosen. The type of research design is determined by the goal of the research study. (KirshenblattGimblett, 2006). As explained in the introduction, this research is conducted along the lines of two main
objectives: 1) to gain greater insight into whether and how financial inclusion contributes to rural
development communities in Sub Sahara Africa and 2) to examine to what extent increasing financial
inclusion and its effects (in rural areas of SSA) contributes to sustainable development as described by
the SDGs.
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3.2.1 Sampling
According to the World Bank, Sub Sahara Africa is made up of 46 of the 54 African countries
located south of the Sahara Desert. The region accounts for 14% of the global population at
approximately 1.14billion inhabitants out of which 667million live in rural communities.
Table 3.1: List of Selected Sub Sahara Africa countries
Central
West
East
Angola
Benin
Djibouti
Cameroon
Burkina Faso
Kenya
Central African Rep.
Cabo Verde
Rwanda
Chad
Côte d'Ivoire
South Sudan
Congo
Gambia
Uganda
Congo DR
Ghana
Equatorial Guinea
Guinea
Sao Tome and Principe Guinea-Bissau
Mali
Mauritania
Niger
Nigeria
Senegal
Togo
South
Botswana
Comoros
Eswatini
Lesotho
Madagascar
Malawi
Mauritius
Mozambique
Namibia
Seychelles
South Africa
Zambia
Zimbabwe
Source: Result from research data
This thesis obtained secondary data from 40 SSA countries with readily available data and
information analysed as follows:
Table 3.2: Geographical spread of selected countries
REGION
NUMBER OF COUNTRIES
PERCENTAGE
Southern Africa
13
33%
East Africa
5
13%
West Africa
14
35%
Central Africa
8
20%
Source: Result from research data
As shown in table 3.1, SSA countries is well represented in the study inspite of the general lack
of data availability from the regions. A comprehensive list of countries across the four regions
was examined and multiple selection methods were used in creating the sample for this thesis.
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As a starting point, the United Nation membership list for Sub Sahara Africa was used as a
filtering mechanism together with online search engines for identifying the countries with
reliable and available continuous data and information during the period covered by the study.
The sampling method used to select the representative sample was the purposive sampling, in
order to obtain a sample that was large enough and as representative as possible. According to
Teddlie and Yu (2007) purposive sampling is advantageous where the goals of sampling are to
achieve a sample that is as representative as possible of the population and to achieve
comparability across a dimension of interest.
Following the initial filtering, the specific data for the selected countries was sifted and
downloaded from the World Development Indicator database of the World Bank, the Financial
Access Survey database of the IMF and the UNDP human development database. Only SSA
countries with sufficient data covered in the sample period were included.
In summary, the sample selection process yielded a total of forty (40) airlines that cut across the
four regions of the sub-continent with specific characteristics or proxy indicators that provide
relevant information on the financial inclusion and rural development profile of each SSA
country. The sample is representative of the population studied in order to address the research
problem stated above.
3.2.2 Data Type, Sources and Period
The data employed in this study is an unbalanced dynamic panel data of 400 observations for 40
Sub Saharan Africa economies between 2011 and 2020. The choice of period is informed by the
availability of data on the Databases and countries with issues on data integrity were excluded.
Table 3.1 provide a list of the countries and table 3.2 shows the spread across the sub-continent.
These countries and the period are selected mainly due to data availability on all the variables
evaluated in the study. The data considered in this study are human development index (a
measure for welfare and wellbeing. Proxy for rural development), financial inclusion index,
percentage of rural population with access to electricity (an indicator of the level of rural
development in terms of infrastructure). The control variables are life expectancy, education,
income, and gross national income per capita. The regression model is also adjusted for
inequality and the following variables are used; inequality adjusted human development index,
inequality adjusted life expectancy, inequality adjusted education index and inequality adjusted
income index. Data on human development index (HDI and IHDI) and education index (EDU
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and IEDU) are obtained from the United Nations Development Programme database, those on
rural electricity penetration, life expectancy, income index and gross national income per capita
are obtained from the World Banks’ World Development Indicators. The data used for the
calculation of financial inclusion index such as number of depositors per 1000 adults, automated
teller machines per 100,000 adults and outstanding loans from commercial banks as a
percentage of gross domestic product as shown in table 3.3 below is obtained from the Financial
Access Survey (FAS) of the International Monetary Fund database.
Table 3.3: Summary of dimension parameters
Dimension
Significance
Accessibility
(Penetration)
Number of bank
accounts in the
study area
Availability
Number of access
points in the study
area
Usage
Accounts hold by
the respondents
Parameters
• Depositors per 1,000 adults
• Branches of commercial
banks per 1,000 km2
• Branches of commercial
banks per 100,000 adults
• ATMs per 1000 km2
• ATMs per 100,000 adults
• Deposits with commercial
banks (%GDP)
• Domestic credit to private
sector (% of GDP)
Description
Stepping stone for
financial inclusion.
Opening a bank
account to operate
financial transactions.
Opening operative
bank account.
Supply side
indicators.
Information gathered
from financial
institutions in person
or website, based on
the availability.
Demand side
indicators.
Information gathered
from the respondents.
Source: Result from research data
The data required for the regression analysis was extracted from the World Development
Indicator database of the World Bank, the Financial Access Survey of the IMF (2019) and the
UNDP human development databases which provide data for 189 countries across the globe
thus enabling better cross-country comparisons. The data/information obtained directly from the
World bank, IMF and UNDP databases is expected to be reliable, valid and a sound common
basis for comparing the effect of financial inclusion and the level of development in rural
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communities despite the attendant differences in inequality, geography and demography.
Furthermore, this study used secondary data since – by the time of writing – globally many
countries face both an economic and health crisis as a result of the COVID-19 pandemic.
Gathering primary data from sources in emerging and developing economies was expected to be
very difficult due to this crisis. Using secondary data is time-efficient, although a disadvantage
is that the researcher depends on the data availability and secondary data might not exactly
match what is ideally required (Van der Kolk, 2013c). In practice, online accessible
international databases turned out to be suitable sources on many indicators matching the
identified variables. This data is available only in the form of quantitative data. A benefit of
using the international databases is that data is available for roughly every country in the world
per similar set of indicators. It thus allows analysis and comparison of all selected SSA
countries.
This dissertation selected a panel data structure as the preferred data model because according to
Baltagi (1995), it discloses dynamics in cross sectional data and also resolves the problem of
heterogeneity bias. Panel data recently became popular and widely acceptable amongst
researchers because of its increased availability and greater capacity of statistical software for
modelling complex research problems beyond a single cross-section or time-series (Hsiao, 2007,
p. 2). The compelling case for panel data modelling according to Hsiao (2007) is premised on
the fact that it presents adequate inference of model parameters due to higher degrees of
freedom in the statistical models and also allows for improved capacity to accurately capture
complex human behaviour in data format. and lastly, the panel data model allows for the control
of omitted variables. It is often argued in the literature “that the real reason for the presence or
lack of certain effects is due to ignoring the effects of certain variables which are correlated with
the explanatory variables. Panel data contains information on both the inter-temporal dynamics
and the individuality of the entities allowing one to control the effects of missing or unobserved
variables” (Hsiao, 2007, p. 5). These advantages are particularly relevant to this study in view
of the peculiar characteristics of the rural communities of each of the selected SSA country as
well as the inconsistency in data availability.
3.2.3 The Empirical Model Specification
The present study aims to estimate the effect of financial inclusion on rural development of rural
communities in Sub Sahara Africa. The model specification for this thesis was derived from the
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prior work of Amendola et al. (2016), Zhang (2017) and Ofori-Abebrese, Baidoo & Essiam
(2020). This dissertation attempts to expand on their research findings by establishing similar
interlinkages for Sub Saharan Africa rural communities and using the result to make a
qualitative deduction on the attainment of sustainable development goals. generic panel data
regression model for this dissertation is described as follows:
RURALDEVELOPMENT i,t = α + β1 FINANCIALINCLUSION i,t +β2 ELECTRICITYACCESS i,t +∑βiControlVariablei,t + εi,t
……………………………………………………………………………………………………………….…… (1)
Where,
RURALDEVELOPMENTi,t
t=time.
is the dependent variable, the subscript i represents the ith country and
FINANCIALINCLUSIONi,t
and
ELECTRICITYACCESSi,t
are
the
independent
variables.
∑ControlVariablei,t represent a set control variables εi,t represents time invariant fixed effects.
The estimable form of Equation (1) is specified in two least square regression models as
follows:
HDIi,t = α0 + α1FIIi,t + α2ELECT_%_RURALi,t + α3LIFE_EXP_INDEXi,t + α4EDU_INDEXi,t +
α5INCOME_INDEXi,t + α6LN_GNI_PCi,t + Ԑi,t ……………………………….…..… (2)
INEQ_ADJT_HDIi,t
=
α0
α3INEQ_ADJT_LIFE_EXP_INDEXi,t
α1FIIi,t
+
+
α2ELECT_%_RURALi,t
+
α4INEQ_ADJT_EDU_INDEXi,t
+
+
α5 INEQ_ADJT_INCOME_INDEXi,t + α6LN_GNI_PCi,t + Ԑi,t ……………….…..… (3)
HDIi,t = α0 + α1FIIi,t + α2ELECT_%_RURALi,t + α3DUMMYi,t + α4LN_GNI_PCi,t
+ Ԑi,t
…………………………………………………………..…………………………..… (4)
The proxy measurements for the dependent and explanatory variables are analysed as follows:
Table 3.4: Summary of regression variables
VARIABLE
PROXY MEASUREMENTS
SYMBOL
Rural Development
Human Development Index
HDI
Inequality Adjusted
Development Index
Human INEQ_ADJT_HDI
Financial Inclusion
Financial Inclusion Index
Electricity Access
Access to electricity, rural (% of ELECT_%_RURAL
rural population)
Control Variables
Life Expectancy Index
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FII
LIFE_EXP_INDEX
VARIABLE
PROXY MEASUREMENTS
SYMBOL
Inequality Adjusted Life
Expectancy Index
INEQ_ADJT_LIFE_EXP_INDEX
Education Index
EDU_INDEX
Inequality Adjusted Education INEQ_ADJT_EDU_INDEX
Index
Income Index
Inequality
Index
INCOME_INDEX
Adjusted
Gross National
Capita
Income INEQ_ADJT_INCOME_INDEX
Income
Dummy Variable
Per LN_GNI_PC
Alliance for Financial Inclusion DUMMY
membership status
Source: Researcher's own compilation from research
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