-133349628650 Myth or Magic

-133349628650
Myth or Magic: The impact of financial technology on financial inclusion in Africa
A Dissertation
Presented to
The Development Finance Centre (DEFIC),
Graduate School of Business
University of Cape Town
In partial fulfilment
Of the requirements for the Degree of
Master of Commerce in Development Finance
By
SIWE YENGENI
(YNGSAN003)
October 2018
Supervisor: Abdul Latif Alhassan, Ph.D.

Table of Contents
TOC h u z Background of study3Statement of Research Problem4Research questions, objectives and/or hypotheses5Literature Review5Data and Methodology6Work Plan9Significance of study10Scope and limitations of the study10Organization of the study10Bibliography11
Abstract:
The following paper endeavours to compute financial inclusion indices (FII) for 36 African countries over 3 periods. The IFI model developed by Cámara & Tuesta (2014), using a two stage Principal Component Analysis is employed. Using these indices, this paper seeks to further understand the relationship between financial technology (fintech) and financial inclusion by running a regression analysis of fintech variables to a logit financial inclusion index. An analysis of cross-sectional data of the countries collected by the World Bank Global Findex and the IMF FAS in 2011, 2014 and 2017 is used in the paper.

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CHAPTER 1
INTRODUCTION
1.1 Background of study
African countries, like most developing economies, have long struggled with a large population that has little or no access to traditional banking services. Lack of access to banking services has had dire effects on people’s abilities to send or receive money in an affordable manner, as well as the ability to access other financial services such as insurance and savings. This phenomenon, termed financial exclusion, not only affects the individuals but their economies as well. To prove this, countries where the citizens have access to savings products, the money saved can be ploughed back into the economy through investments which accelerate economic development and promote economic growth. With Africa’s young and growing population, the need to promote economic growth has never been more urgent. This is why developments within digital finance services (often referenced as fintech) have been well-received. Innovations such as M-Pesa, Zoona etc. have been instrumental in promoting improvements in the ability of the financially excluded to access financial services. While this has been hailed as improving financial inclusion, not much has been done to measure the extent of this improvement.
The use of a financial inclusion index to measure the extent of financial inclusion in countries over time is a globally topical issue. The main advantage of an index is that it allows for relative comparisons and assessment of the claim of improvement over time. Furthermore, while it may be true that financial inclusion has increased in Africa over time, the extent to which that increase in financial inclusion can be attributed to fintech needs to be understood. Understanding the extent to which fintech explains the improvement in financial inclusion in African countries also allows researchers, policy makers and the private sector to either create an environment that promotes the development of fintech innovations within the sector and redirect resources to solutions that may actually be leading the increase in financial inclusion.

To meet this gap in research, this study seeks to use the FII model) to compute financial inclusion indices for 36 African countries over 3 periods (2011, 2014 & 2017). Furthermore, the study will then examine the relationship between fintech and the financial inclusion indices using a regression analysis.

1.2 Statement of Research Problem
Studies have shown that access to financial services can drastically improve the financial standing of people in an economy, and help them avoid poverty traps (Demirgüç-Kunt & Klapper, 2012b); (Adato, Carter, & May, 2006); (Arun & Kamath, 2015); (Beck et al., 2009). Access to financial services means that those previously excluded are now able to participate in the economy, and to smooth out any risk that they might encounter through the use of savings and insurance products. The inverse of this financial inclusion, one widely prevalent in Africa, has meant that a large portion of the African population is trapped in poverty and is unable to actively participate in economic activities. This is why the advent of M-Pesa in Kenya, then on the rest of East Africa, has created a huge expectation on financial technology (fintech) to bank the unbanked. Chief among these, is the expectation of fintech to leapfrog a large population in the continent from having little or no interactions with the formal financial services sector, to leading the use of technological innovations in accessing financial services.
The World Bank defines financial inclusion in three levels: as (i) the ability to access financial services, (ii) the appropriateness of those services, measured through the level of usage and finally, (iii) the quality of the financial services which is seen in the improvement in people’s economic standing over time (Bruhn ; Love, 2014). In 2014, the Global Findex estimated that only 34% of the banking age population in Africa (15+) had access to financial services, 12% of which was attributable to mobile money financial services (Demirguc-Kunt, Klapper, Singer, ; Van Oudheusden, 2015). This finding exacerbated the sentiment of fintech being the solution to all financial inclusion challenges in the continent. The optimism over the ability of fintech to bank the unbanked can be seen in the investment capital that its innovations are attracting. WeeTracker Research asserts that in 2017 alone, $167 million was invested in the African fintech market, a 28% year-on-year increase from 2016. While the impact of fintech cannot be denied, the extent of it is rarely quantified. This can partly be attributed to the difficulty of quantifying financial inclusion itself. However, recent attempts to construct a financial inclusion index (FII) by several researchers (Sarma, 2008); (Chakravarty ; Pal, 2010); (Gupte, Venkataramani, ; Gupta, 2012); (Cámara ; Tuesta, 2014); (Park ; Mercado, Jr, 2018) are indicative of the need identified by researchers to quantify financial inclusion. While these measurements using indices are being attempted in other economies, little has been done in African economies. Given the growing sentiments about fintech and the abilities attributed to it in Africa, the need to quantify both financial inclusion and the impact of fintech has never been more urgent.
1.3 Research questions, objectives and hypotheses
This paper aims to exam the questions: What are the levels of financial inclusion in the identified African countries and how have they improved over time? To what extent has this improvement been driven by fintech innovations? To the best of the author’s knowledge, this is the first comparative study to compute the financial inclusion index on the 2014 and 2017 Findex data, as well as study the relationship between financial technology and the financial inclusion index in Africa.

The objectives of this paper are to:
Compute financial inclusion indices for 36 African countries over 3 periods, using the Financial Inclusion Index (FII) model
Examine the relationship between financial technology and financial inclusion through a regression analysis of the following hypothesis:
H0: Fintech has no impact on financial inclusion levels
H1: Fintech has an impact on financial inclusion levels
1.4 Significance of study
This study is important for a number of reasons. The ability to use an index when measuring financial inclusion, allows for year-on-year comparisons by countries on their financial inclusion levels. The computation of financial inclusion indices for 36 African countries, over 3 periods allows for the assessment of improvements in these countries over time. Furthermore, it allows for a cross-country comparison on the financial inclusion measure, as is done with other indices such as the HDI. This information is useful for policy makers to establish the factors that will help improve their financial inclusion levels. It is important for the private sector so that financial resources are directed more towards innovations that will be useful to the economies, while being profitable. The study is also important for researchers as it opens the door for further research on the use of indices in measuring financial inclusion in Africa as well as establishing a clearer definition of what constitutes financial technology.
1.5 Scope and limitations of the study
This paper does not include all African countries due to data challenges but will include 36 countries. Furthermore, due to data limitations, the study only starts in 2011 and looks at the 3 periods when World Bank data is collected (2011, 2014, 2017). Finally, this study only uses digital finance as a descriptor for fintech.
1.6 Organization of the study
The rest of the paper is organised as follows: Chapter 2 gives an overview of current quantitative and qualitative literature on financial inclusion and fintech. Chapter 3 explains the dataset used and the methodology employed in the study. Chapter 4 will then outline the results of the study and explain the implications of the study. The closing chapter concludes the paper, gives the limitations to take into account and recommendations for future studies on the subject matter.

CHAPTER 2
Literature Review
2.1 Introduction
Studies have shown that when people participate in the financial system, they are better able to save, start and expand businesses and invest in education, manage risk, as well as absorb financial shocks (Demirguc-Kunt et al., 2015); (Mas & Radcliffe, 2009). This ability to manage risk, access financial services and to participate in the financial system is broadly known as financial inclusion. “Financial inclusion, at its most basic level, has been said to start with having a bank account, however, it does not stop there. Only with regular use do people fully benefit from having an account” (Demirguc-Kunt et al., 2015). Furthermore, access to other quality financial services such as insurance and savings products also constitutes getting people financially included.
In the study of the barriers to financial inclusion (defined as access to banking services), Demirgüç-Kunt & Klapper (2012a) found that the most prevalent barriers were: high costs; physical distance; and lack of proper documentation, though there were significant differences across regions and individual characteristics. A similar study by Allen et al. (2014) asserts that population density is considerably more important for financial development in Africa than elsewhere. Therefore, to improve financial inclusion, these barriers need to be combatted.
The advent of fintech services has been viewed as the solution to the barriers above. Fintech encompasses innovative financial services or products delivered using technology (Chuen & Teo, 2015). It is often described as the use of technology to support or enable the delivery of banking and other financial services. The term has come to collectively represent technologies that are disrupting traditional financial services, including mobile payments, money transfers, loans, fundraising, and asset management (Dermish, Kneiding, Leishman, & Mas, 2012). Agrawal (2008) and Mbiti & Weil (2011) studied the indicators that explain financial exclusion and concluded that these will potentially be eradicated by fintech. For starters, the distance of financial institutions becomes less important when financial services and products are digitized. Secondly, identity verification technologies allow the screening of people to be easier, a challenge most financial institutions currently face. In contrast to current financial products, the increase in service providers makes the markets more competitive. Competition drives down prices, making financial products and services more affordable. Furthermore, the collection and mining of data allows for better tailoring of services to the context of the community that the financial products are being designed for. Finally, the ability of fintech services to use the technologies available to tailor the financial products to the people being serviced means that wider networks of people are likely to use the products. Given the amount of innovation happening in the continent, it is imperative to study whether the impact is being actually felt.

Overview of fintech and financial inclusion in Africa
Historically, the supply of financial services in Africa has lagged behind the demand.

As a case in point, in 2014, the banking penetration (number of banking accounts to population) was said to be at 17% on average compared with 50% in other emerging markets. However, innovation within the financial services sector in Africa is dramatically accelerating as a result of a combination of factors, namely: the entry of disruptive innovations, collaboration and convergence between banks and mobile network operators, and virtualisation of financial services. For instance, the aggressive push by MTN & Orange in Cote d’Ivoire is said to have resulted in an increase in mobile money penetration from 10% to 64% between 2012 and 2016. Similarly in Kenya, the partnership between CBA and Safaricom resulted in 25 million nano-loans issued in 2015 for a population of 44 million inhabitants. In Africa, dematerialization mainly takes the form of the distribution of financial services through agent networks and mobile phones. With a mobile phone penetration rate of 77%, mobile money penetration is expected to increase even more.
2.2 Fintech and FI: Theoretical Framework
In academic literature, initial definitions of financial inclusion were focused on the social impact. Leyshon ; Thrift (1995) defined financial exclusion in terms of the social impact of the exclusion of some groups and individuals from access to formal financial systems. This paper focuses on the economic definition of financial inclusion. Sinclair (2001) attempted to refine this definition by focusing on the inability of individuals to access necessary financial services in an appropriate form.
Only recently has the definition started focusing on the economic aspects. Sarma (2008) defined financial inclusion as the enablement of ease of access, availability, and usage of formal financial systems for a society. Amidži?, Massara and Mialou (2014) define financial inclusion as a situation where individuals and firms have access to basic financial services. Camara and Tuesta (2014) on the other hand, define an inclusive financial system as one that maximises usage and access while minimising involuntary barriers to financial inclusion. They distinguish between voluntary exclusion (a situation where the individual is able to have access but chooses not to due to personal, religious or cultural reasons) and involuntary exclusion in a society.
While a few of the researchers who define financial inclusion in economic terms have built models to measure its levels, there is no clearly accepted measure of financial inclusion. Consequently, measures of financial inclusion often vary across studies. Honohan (2007, 2008) constructed a financial access indicator that captures the fraction of the adult population in each economy with access to formal financial intermediaries, for instance. This model endeavours to explain only one variable in the financial inclusion indices. Amidži?, Massara and Mialou (2014) constructed a composite financial inclusion indicator for multiple variables: outreach (geographic and demographic penetration); usage (deposit and lending); and quality (disclosure requirement, dispute resolution, and cost of usage). The model that most recent ones attempt to build on to is the index for financial inclusion (IFI) created by Sarma (2008). The index has been used widely in assessing the extent of financial inclusion in Indian states as well as in countries like Turkey (Sarma and Pais, 2008); (Yorulmaz, 2013); (Yorulmaz, 2012). The model works as follows: it first constructs a subindex for each dimension of financial inclusion (access, availability, and usage) and then aggregated each index as the normalized inverse of Euclidean distance. The advantage of this approach is that it is easy to compute and does not impose varying weights for each dimension. In Sarma (2015), dimensional weights are set at arbitrary values due to the lack of available data to fully characterize availability and usage dimensions.
Improvements on the allocation of the weightings criteria employed by the IFI then saw further creations of financial inclusion indices (Chakravarty ; Pal, 2010); (Arora, 2010); (Gupte et al., 2012). In an attempt to consolidate the different indices and to create a more robust statistical measure and weight-allocation method, Cámara ; Tuesta (2014) created the financial inclusion index FII. Cámara ; Tuesta (2014)) use a two-stage Principal Component Analysis Pearson (1901), wherein, in the first stage, they estimate three subindices—usage, access, and barriers—which define their financial inclusion measure. In the second stage, they estimate the dimension weights and the overall financial inclusion index by using the dimension subindices in the first stage as explanatory variables. The financial inclusion measure is therefore, a weighted average of three dimensions, where the weights are derived from principal component analysis.
This paper follows the model in Cámara ; Tuesta (2014), using the definition of financial inclusion of Sarma (2008), who views it as a process that enables ease of access, usage and availability of financial services for all members of society. The advantage in this definition is that it builds the concept of financial inclusion based on several dimensions: accessibility, usage and availability, which can be assessed separately. Park and Mercado Jr (2018) assert that this is inappropriate to include barriers because it confuses the conceptual clarity of financial inclusion, by combining the reasons for having and not having financial access in a financial inclusion measure. Cámara ; Tuesta (2014) advocate for the inclusion of barriers to financial access as a dimension of financial inclusion because they reflect demand-side measures of financial services. However, demand-side indicators could also be included in a multidimensional approach of Sarma (2008). In other words, the lack of demand-side measures in existing financial inclusion measures does not fully justify the inclusion of barriers dimension in the aggregate financial inclusion measure (Park ; Mercado Jr, 2018).

2.3 Empirical literature on Fintech and FI
Financial inclusion in the Sub-Saharan Africa region is said to be influenced by both demand side factors: level of income, location and literacy, and supply side factors: interest rate and bank innovation, proxied by ATM usage (Oyelami,2017.Honohan (2008) argues that a set of country-specific variables matter for financial access. Rojas-Suarez (2010) used the same indicator to test the significance of various macroeconomic and country characteristics for financial access among a group of emerging economies. The results show that economic volatility, weak rule of law, higher income inequality, and social underdevelopment and regulatory constraints significantly lower financial inclusion. ). Therefore, the historical shortages of supply in financial services in African is correlated to the increasing financial exclusion and poverty. As a case in point, Burgess and Pande (2005), report that state-led expansion of rural bank branches in India has helped reduce poverty. Specifically, the authors find robust evidence that opening bank branches in rural unbanked locations in India is associated with lower poverty in those areas. Allen et al. (2013) illustrate that by tapping underprivileged households, commercial banks can help improve the financial access of the poor in Kenya. While, Brune et al. (2011) show that increased financial access through commitment savings accounts improves the well-being of poor households in rural Malawi. Park and Mercado (2016, 2018) later confirmed these earlier findings, showing that per capita income, rule of law, and demographic characteristics are significantly positively correlated with financial inclusion for both global and Asian samples. They also find that financial inclusion is significantly correlated with lower poverty. It is therefore imperative to support all initiatives and endeavours that promote/ increase financial inclusion.

Following the success of M-Pesa, East Africa has since been recognised as the leading innovator in payments and transfers. M-Pesa not only spread across the region, it also created an environment for similar innovations to thrive across the continent. The need for access has propelled similar but context-specific innovations in countries such as Cameroon and Nigeria. This ability for context-specific innovations to thrive is seen as the power of fintech to bridge the gap and facilitate access to financial products and services for financially excluded people (Mbiti ; Weil, 2011). For instance, the popularity of money transfer services in previously unbanked areas is a testament to this. Mbiti ; Weil (2011); Morawczynski ; Pickens (2009) found that these technologies not only support and facilitate money transfers between current account-holders, thereby allowing households to better manage shocks, but also increase the access to financial services for unbanked households and SME’s at low cost and risk. Lacking in literature is the computation of a quantitative measure of the increase on financial inclusion. This paper fills the gap in research of quantifying financial inclusion and its improvements over time, as well as its relationship with fintech.
CHAPTER 3
Data and Methodology
3.1 Introduction
This chapter presents the research methodology followed in testing the hypotheses presented. It further presents data used for purposes of this study as well as the sources thereof, the research design employed, model specification and model estimation. The chapter is organised into six distinct yet coherent and unified sections. The second section describes the data used in the study and the method followed in choosing a sample size as well as the periods under study. The third section explains the analytical framework while the fourth section details the regression equation and the fifth and sixth sections show the variables in the regression model as well as the estimation techniques.

3.2 Sample size and data period
A sample of 36 countries in Africa is selected from the data collected by the Global Findex , over 3 periods (2011,2014,2017) to assess financial inclusion over time. The Global Financial Inclusion database (Global Findex) provides in-depth demand-side data showing how people save, borrow, make payments, and manage risk. The Global Findex data collection was carried out in partnership with the Gallup World Poll and with funding by the Bill & Melinda Gates Foundation. The indicators are based on interviews with about 150,000 nationally representative and randomly selected adults age 15 and above. Furthermore, data collected by the IMF FAS, is used. The FAS is the sole source of global supply-side data on access to and use of financial services by households and firms. The FAS database contains 152 time series resulting in 47 basic indicators, which are grouped by the geographic outreach and use of financial services.

Due to economic and practical constraints, the feasibility of collecting information pertaining to the entire population was not possible. Taking these constraints into account, this paper follows the convenience sampling method in selecting the data for this study from both data sources.
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To explore a more robust dataset, the missing data values were imputed. The single imputation method ?unconditional mean imputation has been used to determine the missing values in terms of the World Bank classification and relevant years for the dataset.

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3.3 Analytical framework
The issue of how financial inclusion can be measured is one that is of concern to researchers and policy makers alike. This paper uses the Financial Inclusion Index (FII) as a method of analysis, with the definitions set out by Sarma (2008) in the Index for Financial Inclusion (IFI) she developed. The index assigns weights to the variables (access, usage and availability) using a two-stage Principal Component Analysis (PCA). The first PCA is used to estimate the three sub-indices representing financial inclusion. The second PCA is used to estimate the overall financial inclusion index, using the previously constructed sub-indices as causal variables.

Generally, there are two parametric analyses commonly used for indexing: PCA and Common Factor Analysis (CFA). Empirically, PCA is preferred over CFA as an indexing strategy because it is not necessary to make assumptions on the raw data, while the CFA requires a selection of the underlying number of common factors. “A good composite index should comprise important information from all the indicators, but not be strongly biased towards one or more of these indicators. We apply two-stage principal components methodology to estimate the degree of financial inclusion as an indexing strategy. The purpose of dividing the overall set of indicators into three sub-indices is twofold. On the one hand, the three sub-indices have a meaning so, we get additional disaggregated information that is also useful for policy making. On the other hand, for methodological purposes, since the sub-indices contain highly inter-correlated indicators, we estimate the sub-indices first, rather than estimating the overall index directly by picking all the indicators at the same time. This is a preferred strategy because empirical evidence supports that PCA is biased towards the weights of indicators which are highly correlated with each other”(Cámara & Tuesta, 2014).

To assess the extent to which these indices are explained by fintech, a regression analysis on the log of the indices is performed.

3.4 Regression equation
The financial inclusion equation is linearly determined as follows:

?? = ?1?? a + ?2?? u + ?3?? ?v + ?? (1)
where subscript ? denotes the country, and w1Yi a, w2Yi u, w3Yi av; capture the access, usage, and availability dimension respectively. As usual, the total variation in financial inclusion is represented by two parts: variation due to causal variables and variation due to error term (??). If the model is well specified, including an adequate number of explanatory variables, ?(?) = 0 and the variance of the error term should be relatively small compared to the variance of the latent variable, financial inclusion. Given the above, we can reasonably assume that the total variation in financial inclusion can be largely explained by the variation in the causal variables.

The first stage aims to estimate the dimensions, the three unobserved endogenous variables and the parameters in the following system of equations:
Yiaccess= ?1accounti+ui (2)
Yiusage= ?1savingsi+?loani+?i (3)
Yiavailability= ?1ATMpopi+ ?2branchpopi+?3ATMkm2i+?3branchkm2i+vi (4)
The unknown endogenous variables are estimated with the ?, ? and ? parameters.

The dimension estimators are formulated as follows:
Yia=j,k=1p?jaPkiaj=1p?ja (5)
Yiu=j,k=1p?juPkiuj=1p?ju (6)
Yiav=j,k=1p?javPkiavj=1p?jav (7)
Subscript j refers to the number of principal components that also coincides with the number of sub-indices, p. where ?? = ????? represents the variance of the ?-t? principal component (weights) and ? is the indicators matrix. The weights given to each component are decreasing, so that the larger proportion of the variation in each dimension is explained by the first principal component and so on. Following this order, the ???? principal component is a linear combination of the indicators that accounts for the smallest variance
The second stage of the PCA computes the overall financial inclusion index by replacingYiaccess, Yiusage and Yiavailability in the first equation. This results in the following index estimator:
FIi=j=1p?jaPkij=1p?j (8)
The highest weight, ?1, is attached to the first principal component because it accounts for the largest proportion of the total variation in all causal variables. Similarly, the second highest weight, ?2, is attached to the second principal component etc. Using algebra, we can express the equation (8) as a linear combination of the three sub-indices (? = 3) and the eigenvectors of the respective correlation matrices.

?1? = ?1?? a + ?1?? u + ?1?? ?v (9)
?2? = ?2?? a + ?2?? u + ?2?? ?v (10)
?3? = ?3?? a + ?3?? u + ?3?? ?v (11)
The index is therefore expressed as:
FIi= w1Yi access+ w2Yiusage+w3Yiavailability+?i , where the relative weights of
each dimension are computed as:
wk=j=13??j?jkj=13??j, k=1,2,3 (12)
After the weights are assigned, the final FII is computed.
Fintech Regression
Following the allocation of weights and computation of financial inclusion indices over the
periods, we attempt to identify the impact of fintech on financial inclusion by performing a
regression analysis on the financial inclusion index with selected variables. While the
factors which affect financial inclusion are likely to be vast and complexly interactive with
each other, this paper will follow the method used by Sarma & Pais (2008) in performing a
similar study. The general form of the regression equation is:
Y=a0+a1X1+a2X2+….anXn+?i (13)
Where X1, X2..Xn are regressor variables and a1,a2….an are the parameters to be estimated and ?is the error term for the classical OLS assumptions. The dependent variable, Y, is the logit transformed from the FII described above. The independent variables, X, are the components that make up financial technology. Fintech is indicated by the percentage if 15+ population using their mobile phone to i) send money, ii) receive money, iii) pay bills from the World bank data collected in each period measured. To account for the correlation between the variables mentioned above, a PCA on the components will be performed. The second indicator in determining the use of fintech looks at the population with an ID4D, a technology identification, whose data is also collected by the World Bank. Finally, the variable of people who use the internet to manage their financials by means of saving and borrowing money, data found from the World Bank as well is included in the measure. Using these three variables, we are able to regress fintech against financial inclusion to determine its impact.

3.5 Description/Definition/Measurement of variables in regression model
276225106680ACCESS
USAGE
AVAILABILITY
FINANCIAL INCLUSION
Account
Loan
Savings
ATMs
Bank branch
Retail agents
00ACCESS
USAGE
AVAILABILITY
FINANCIAL INCLUSION
Account
Loan
Savings
ATMs
Bank branch
Retail agents

323850137795Source: Camara and Tuesta (2014); own computation
00Source: Camara and Tuesta (2014); own computation

Financial inclusion is assessed on the following variables: access; usage and availability.
Usage is measured by the following variables: keeping savings and having a loan at a formal institution. The savings and loan indicators represent the percentage of adult population that saves and has a loan in a formal financial institution respectively. Access is represented by having an account at a financial institution. An account at a financial institution includes respondents who report having an account at a bank or at another type of financial institution, as well as a mobile money account but do not have a bank account, credit or debit card but do not have an account and individuals who do not have a bank account because one of their family members has one. They are contemplated as indirect users of formal financial services. Availability is measured using the number of ATMs and commercial banks per 100 000 adults, as well as commercial banks and ATMs per 1000 km2. These account for the physical point of services offered by commercial banks per the IMF’s FAS. The weight assignment to the indicators or sub-indices is critical to maximize the information from a data set included in an index. For all the demand-side indicators, data is aggregated at country level by computing the proportion of individuals in each category and then applying the weighting scheme corresponding to the sample in each.

3.5 Estimation technique
Financial inclusion is an unobservable concept which cannot be measured quantitatively in a straightforward way. However it can be determined by the interaction of a number of causal variables. We assume that behind a set of correlated variables we can find an underlying structure that can be identified with a latent variable as is the case of financial inclusion. In doing this, the key things to consider are: the selection of relevant causal variables and the estimation of parameters (weights). Regarding the first issue, it is not possible to apply standard reduction of information criterion approaches for the selection of variables. For the second, since financial inclusion is unobserved, standard regression techniques are also unfeasible to estimate the parameters. The weight assignment to the indicators or sub-indices is critical to maximize the information from a data set included in an index. A good composite index should comprise important information from all the indicators, but not be strongly biased towards one or more of these indicators. Thus, we seek to determine the best weighted combination of indicators that define our underlying structure by applying two-stage principal components methodology to estimate the degree of financial inclusion as an indexing strategy. The purpose of dividing the overall set of indicators into three sub-indices is twofold. On the one hand, the three sub-indices give disaggregated information that is also useful for policy making. On the other hand, for methodological purposes, since the sub-indices contain highly correlated indicators within dimension, we estimate the subindices first, rather than estimating the overall index directly by picking all the indicators at the same time. This is a preferred strategy because it avoids weight’s biases towards indicators which exhibit the highest correlation (Mishra, 2007). We minimize this problem by applying a two-stage Principal Component Analysis (Nagar and Basu, 2004). In the first stage, we estimate the three sub-indices: usage, availability and access, which define financial inclusion. In the second stage, we estimate the weights for each dimension and the overall financial inclusion index by using the dimensions as explanatory variables. Regarding the number of variables included in our index, the PCA is robust to redundant information. Missing variables are estimated using the mean/median of the available data in the analysis.

CHAPTER 4
RESULTS & INTERPRETATION
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