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Working Paper, Nº 14/26
Madrid, September 2014
Measuring Financial Inclusion:
A Multidimensional Index
Noelia Cámara David Tuesta
14/26 Working Paper
Measuring Financial Inclusion: A Multidimensional Index
Noelia Cámara* and David Tuesta
August 2014
We rely on demand and supply-side information to measure the extent of financial inclusion at country level for eighty-two developed and less-developed countries. We postulate that the degree of financial inclusion is determined by three dimensions: usage, barriers and access to financial inclusion. Weights assigned to the dimensions are determined endogenously by employing a two-stage Principal Component Analysis. Our composite index offers a comprehensive measure of the degree of financial inclusion, easy to understand and compute.
Keywords: Financial inclusion, Principal Component Analysis, inclusion barriers.
: The authors want to thank Mónica Correa, Santiago Fernández de Lis, Pedro Gomes and Sara Riscado for their helpful comments. We are also grateful to the participants in the 77 th International Atlantic Economic Society Conference. This paper's findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of BBVA. No part of our remunerations were, are or will be related to the findings obtained in this paper.
1 /39
Issues relating to financial inclusion are a subject of growing interest and one of the major socioeconomic challenges on the agendas of international institutions, policymakers, central banks, financial institutions and governments. The World Bank's declared objective of achieving universal financial access by 2020 is another example of financial inclusion being recognised as fundamental for economic growth and poverty alleviation. 1 The World Bank's latest estimates state that half the adult population in the world does not have a bank account in a formal financial institution. However, the concept of financial inclusion goes beyond single indicators, such as percentage of bank accounts and loans and number of automated teller machines (ATMs) and branches. The attempts to measure financial inclusion through multidimensional indices are scarce and incomplete. To the best of our knowledge, literature lacks a comprehensive indicator that can bring together information on financial inclusion by using a statistically sound weighting methodology and takes into account both demand- and supply-side information. Our study aims to fill this gap. The major contribution of this paper is the construction of a multidimensional financial inclusion index covering eighty-two countries for the year 2011. The weights of the index are obtained from a two-stage Principal Component Analysis (PCA) for the estimation of a latent variable. First, we apply PCA to estimate a group of three sub-indices represent- ative of financial inclusion. Second, we apply again PCA to estimate the overall financial inclusion index by using the previous sub-indices as causal variables. Our index improves existing financial inclusion indices in several ways. First, we use a parametric method that
1 The Global Financial Development report for 2014, by the World Bank (2013), is the second report that focuses on the relevance of financial inclusion. It offers an overview of financial inclusion status and problems based on new evidence about financial sector policy. The Maya Declaration is another example that evidences the importance of financial inclusion. It consists of a set of measurable commitments by developing countries' governments to enhance financial inclusion. There are more than 90 countries in the agreement and they represent more than 75 per cent of the unbanked population. Finally, the G20 also express its interest in promoting financial inclusion in non-G20 countries through the Global Partnership for Financial Inclusion (GPFI). This platform, officially launched in Seoul in 2010, recognizes financial inclusion as one of the main pillars of the global development agenda endorsed in its Financial Inclusion Action Plan.
avoids the problem of weight assignment. Second, we offer a harmonized measure of finan- cial inclusion for a larger set of countries, 82 developed and less-developed countries, that allows comparisons across countries and over time. Finally, we provide a comprehensive definition of financial inclusion combining information from a large set of indicators from both demand and supply-side data sets, and from two perspectives: banked and unbanked population. It is the first time that a composite index uses a demand-side data set at individual level to measure the level of financial inclusion across countries. We identify two problems in the current financial inclusion indices. First, existing attempts to build financial inclusion indices rely only on supply-side country level data and come up with inaccurate readings of financial inclusion due to the existence of measurement errors in the usage indicators. Supply-side indicators, particularly the number of accounts or loans, can overestimate the inclusiveness of financial systems since one person can have more than one account or loan. It is a very common practice in developed countries. Second, assign- ing exogenous weights to indicators is often criticized for lack of scientific rigour because exogenous information is imposed. The lack of a harmonized measure that collects multidimensional information to define financial inclusion is a pitfall that complicates the understanding of several related prob- lems. The multidimensional measurement of financial inclusion is important in several aspects. First, a measure that aggregates several indicators into a single index aids in summarizing the complex nature of financial inclusion and helps to monitor its evolution. A good index is better at extracting information. Second, a better measure of financial inclusion may allow us to study the relationship between financial inclusion and other macroeconomic variables of interest. Third, information by dimension helps to better un- derstand the problem of financial inclusion. It can be a useful tool for policy making and policy evaluation. There are two commonly used approaches to constructing composite indices: non- parametric and parametric methods. Non-parametric methods assign the importance of
indicators by choosing the weighs exogenously, based on researchers' intuition. There is evidence that indices are sensitive to subjective weight assignment, since a slight change in weights can alter the results dramatically (Lockwood, 2004). 2 Sarma (2008, 2012) and Chakravarty and Pal (2010) are examples of financial inclusion indices that apply this methodology to usage and access indicators from supply-side country level data sets. Parametric methods sustain that there exists a latent structure behind the variation of a set of correlated indicators so that the importance of indicators (weights) in the overall index can be determined endogenously through the covariation between the indicators on each dimension of the structure. In brief, weights are determined by the information of sample indicators. There are two parametric analyses commonly used for indexing: PCA and Common Factor Analysis. Amidˇzi´c et al. (2014) attempt to measure financial inclusion based on a Common Factor Analysis. However, the indicators used to define financial inclusion only include limited supply-side information at country level. What is more, from an empirical point of view, PCA is preferred over Common Factor Analysis as an indexing strategy because it is not necessary to make assumptions on the raw data, such as selecting the underlying number of common factors (Steiger, 1979). The rest of the paper is organized as follows. In section 2, we describe the data and the rationale for our chosen indicators as well as for the use of sub-indices that measure financial inclusion dimensions. Section 3 describes the methodology for constructing our composite index from multi-dimensional data. Section 4 discusses the results of the sub- indices as well as the composite financial inclusion index. Section 5 analyses the robustness of our index. Finally, Section 6 concludes.
2 There is also a problem with weight reassignment when new indicators are included into an existing index.
2 Financial Inclusion Dimensions and Data Sources
How to measure financial inclusion is a topic of concern among researchers, governments and policy makers. To date, financial inclusion measurement has been mainly approached by the usage and access to the formal financial services by using supply-side aggregate data (e.g. Honohan (2007); Sarma (2008, 2012); Chakravarty and Pal (2010) and Amidˇzi´c et al. (2014)). The only work that relies on demand-side data, at individual level, focuses on several usage- and barriers-related indicators individually (Demirg¨u¸c-Kunt and Klapper, 2013). However, monitoring different indicators individually, although useful, does not offer a comprehensive understanding of the level of financial inclusion across countries. On the other hand, as we mentioned, the few attempts to measure financial inclusion through composite indices are incomplete and subject to methodological problems and measurement errors. High usage levels of formal financial services or a broad availability of points of access do not mean necessarily that a system is inclusive per se. The usage of formal financial services can be conditioned by other socio-economic factors such as GDP per capita, human capital, legal framework, cultural habits or development status that make individuals use these kinds of services in a particular manner. We consider the use of formal financial services as an output of financial inclusion rather than a measure of the inclusiveness of a financial system in itself. Likewise, the availability of infrastructure, ATMs and bank branches, captures the extent of accessibility to the formal financial system only partially. Since we do not have information about location or concentration of these points of service, it is not accurate to assert that higher measured levels of these indicators represent a more inclusive financial system. This paper considers that access and usage are both necessary but not sufficient conditions for measuring the inclusiveness of a financial system. Our hypothesis is that focusing only on usage and access leads to limited measurement of financial inclusion. In this context, demand-side individual surveys that gather information on the perceived reasons why people fail to use formal financial services add significant information about
the degree of inclusiveness of a financial system. We define an inclusive financial system as one that maximizes usage and access, while minimizing involuntary financial exclusion. 3 Involuntary financial exclusion is measured by a set of barriers perceived by those individuals who do not participate in the formal financial system. Thus, we postulate that the degree of financial inclusion is determined by three dimensions: usage, barriers and access. These dimensions are, at the same time, determined by several demand-side individual level indicators for the cases of usage and barrier, and supply-side country level indicators for access. Regarding demand-side inform- ation, we approach financial inclusion measurement from a double perspective. On the one hand, we account for the inclusiveness, from the banked side, by measuring the actual use of formal financial services , namely, inclusion output of financial systems. On the other hand, we also include information from the unbanked side to assess the barriers to financial inclusion through the obstacles perceived by people prevented from using formal financial services. To compute the index, we take advantage of the largest demand-side harmonized data set ever collected at individual level, the World Bank's Global Findex (2011). It is the first public database of indicators to offer a homogeneous measure for individuals' use of financial products across economies for 2011. This survey collects information about 150,000 nationally representative and randomly selected adults from 148 countries around the world. Data available at individual, rather than household, level is also an asset that improves accuracy and comparability of the analyses. This database fills an important gap in the financial inclusion data landscape. We also use supply-side aggregate data on access from the International Monetary Fund's Financial Access Survey (2013). This is a source of supply-side data that offers information on an unbalanced panel of 189 countries, covering the period 2004-2012.
3 For the CGAP financial inclusion means that all working age adults have effective access to credit, savings, payments and insurance from formal service providers. Effective access involves convenient and responsible service delivery, at a cost affordable to the customer and sustainable for the provider with the result that financially excluded customers use formal financial services rather than existing informal options.
2.1 Usage
To assess the extent of usage of the formal financial service by individuals, we consider the utility of these services in three different indicators: holding at least one financial product, keeping savings and having a loan in a formal financial institution. Taking advantage of the information in the Global Findex data set, we can measure the usage dimension of formal financial services. We built the indicator to account for people using at least one formal financial service by adding information from several questions in the Global Findex. We consider as formal financial service users: people who have a bank account, people who use mobile banking services but do not have an account, and people who have a credit or debit card but do not have an account. 4 Also, we consider as banked those individuals who reported not having a bank account because someone else in the family already has one. This reason identifies individuals who use financial services indirectly. 5 The savings and loan indicators represent the percentage of adult population that saves and has a loan in a formal financial institution respectively. The upper panel in Table 1 shows descriptive statistics of the indicators that we use to measure usage dimension. Data is aggregated at country level by computing the proportion of individuals in each category and then applying the weight corresponding to the population in each country.
2.2 Barriers
The barriers to financial inclusion, perceived by unbanked individuals, provide information about the obstacles that prevent them from using formal financial services. This informa- tion is useful to assess the extent of financial inclusion since it offers a perception of why
4 Since we want to compute and index including both developed and less-developed countries we cannot take into account the usage of financial services for enterprises due to the lack of harmonized information for developed countries. This information is only available for less-developed countries in the World Bank's Enterprise Survey. 5 We do not consider people with insurance since this information is only available for less-developed countries.
some individuals are excluded from the formal financial system. There are two types of financial exclusion: voluntary or self-exclusion and involuntary. If we treat financial inclu- sion as a behavioural issue, individuals need to decide whether to participate in the formal financial system given their budget constraints. One possibility is that some individuals do not have a demand for formal financial services, leading them to self-exclusion because of cultural reasons, lack of money or just because they are not aware of the benefits of these types of services. This choice can be shaped by imperfect information about the utility of financial services for managing risk, savings for the future and affordability of different investments such as education or buying a house. However, exclusion can also be due to other market imperfections such as the lack of access to financial services or an inappropriate product range that does not satisfy needs. The latter obstacles that hinder financial inclusion may be associated with a sort of involuntary exclusion. This means that people cannot satisfy their demand. In order to measure the degree of inclusiveness of financial systems, from the unbanked perspective, we take into account only the information about barriers that represent in- voluntary exclusion such as distance, lack of the necessary documentation, affordability and lack of trust in the formal financial system. The question about perceived barriers is formulated in the Global Findex questionnaire in such a way that individuals can choose multiple reasons for their not having a bank account. As we mentioned, according to the Global Findex data set, almost 20 per cent of the unbanked population cites distance as one of the reasons that prevents them from having an account. This reason is observed more frequently in developing countries where access points are remote (Demirg¨u¸c-Kunt and Klapper, 2013). Documentation requirements are also cited as a perceived barrier for financial inclusion by almost 20 per cent of the un- banked. Affordability is the second most cited obstacle for financial inclusion, after only lack of money, and prevents 25 per cent of the unbanked from using formal financial ser- vices. Finally, the lack of trust in the financial system is cited by 13 per cent of adults.
All these variables are introduced in our analysis in their negative form so that the fewer people reporting the barrier, the greater the inclusiveness of the financial system.
Access to formal financial services represents the possibility for individuals to use them. However, greater access does not necessarily imply a higher level of financial inclusion. We believe that there is a threshold for access since, when it reaches a certain level, a marginal increase does not necessarily generate a financial inclusion increase by our definition. It may enhance frequency in the use of financial services, by improving intensive margin of usage but does not necessarily increase extensive margin, in terms of higher percentages of accounts held or any other financial service. However, greater access is expected to foster financial inclusion when access levels are below the threshold, via greater availability, if financial services meet the needs of the population. Also, when increasing access is generated from different financial companies, more intense competition may increase the consumption of financial services via prices too, even above the threshold. We construct the access dimension with supply-side data at country level from four ba- sic indicators: automated teller machines (ATMs) (per 100,000 adults), commercial bank branches (per 100,000 adults), ATMs (per 1,000Km2) and commercial bank branches (per 1,000Km2). They account for the physical point of services offered by commercial banks, credit unions, saving and credit cooperatives, deposit-taking microfinance and other de- posit takers (savings and loan associations, building societies, rural banks and agricultural banks, post office giro institutions, post office savings banks, savings banks, and money market funds). This information is collected by financial services providers though the International Monetary Fund's Financial Access Survey (FAS). 6 The traditional indicators used to measure access are currently incomplete. New tech- nology adopted by the financial sector goes beyond the traditional banking access measured
6 Data on adult population and land mass come from the World Development Indicators provided by the World Bank.
by number of branches and ATMs. New mobile banking developments and the use of fin- ancial services on the internet open up new channels for accessing formal financial services that, under certain circumstances, overcome the distance as a barrier for access. Banking correspondents play an important role, too, in enhancing the problem of access. Nev- ertheless, distance is still one of the reasons why people do not participate in the formal financial system. While nearly 20 per cent of the unbanked in the world state that financial access points are too far away, this problem involves mainly less-developed countries. In developed countries, the proportion of the unbanked who perceive distance as a problem is only 10 per cent. Both technology and banking correspondents are greatly broadening access in terms of availability of physical access. However, measuring these aspects is not straightforward. The lack of homogeneous measures for a wide range of countries makes it difficult to assess the impact of these new channels on financial inclusion. 7 Although we cannot get an accurate proxy to take into account the new access channels, we do include information on mobile and internet banking in the usage dimension.
3 Principal Component Analysis as an Indexing Strategy
Financial inclusion is an abstract concept which cannot be measured quantitatively in a straightforward way. However this variable is supposed to 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 latent structure that can be identified with a latent variable as is the case of financial inclusion. Two important issues arise in the estimate of any latent variable: the selection of relevant variables and the estimation of parameters (weights).
7 The bias introduced for omitting this information might be different for developed countries and less- developed countries. We cannot quantify this bias but we have some intuitive information about its dir- ection. Although the lack of data to measure financial service access via internet and smart phone un- derestimates access more for developed countries than for less-developed countries, the effect on financial inclusion may be larger for less-developed countries than for developed countries. The latter have greater access levels and, as such, increases in access may have a larger effect on less-developed countries that start from lower levels. Likewise, less-developed countries benefit more from banking correspondents as well as from basic mobile phones.
Regarding the first issue, it is not possible to rely on 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 paramet-
ers. 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 com- prise 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. Our dataset contains causal variables which summarize the information for financial
inclusion. As explained in the previous section, each causal variable relates to different dimensions that define financial inclusion. The purpose of dividing the overall set of in- dicators 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 (Mishra, 2007). We minimize this problem by applying two-stage PCA (Nagar and Basu, 2004). In the first stage, we es- timate the three sub-indices: usage, barriers and access, which defined financial inclusion. In the second stage, we estimate the dimension weights and the overall financial inclusion index by using the dimensions as explanatory variables. Let us postulate that the latent variable financial inclusion is linearly determined as follows:
FI i = ω 1 Y
ω 2 Y
where subscript i denotes the country, and (Y
ω 3 Y
e i ,
a ) capture the usage, barriers and
access dimension respectively. Thus, the total variation in financial inclusion is represented by two orthogonal parts: variation due to causal variables and variation due to error (e i ). If the model is well specified, including an adequate number of explanatory variables, we can reasonably assume that the total variation in financial inclusion can be largely explained by the variation in the causal variables. 8
3.1 First Stage PCA
The first stage aims to estimate the dimensions, that is, the three unobserved endogenous
variables Y
a and the parameters in the following system of equations:
β 1 account i + β 2 savings i + β 3 loan i + u i
θ 1 distance i +
γ 1 ATM popi +
θ 2 affordability i + θ 3 documents i + θ 4 trust i + i
γ 2 branch popi + γ 3 ATM km 2 i + γ 4 branch km 2 i + v i
where account is a variable that represents the individuals who have at least one of the
financial products described in section 2.1, and savings and loan represent individuals who save and have a loan in the formal financial system. Hence, the three dimensions are also indices that we estimate by principal components as linear functions of the explanatory variables described in Table 1. Note that the endogenous variables are unobserved so we need to estimated them jointly with the unknown parameters: β, θ and γ. Let R p , (pxp) define the correlation matrix of the p standardize indicators for each dimension. We de-
, p) as the j-th eigenvalue, subscript j refers to the number of principal
components that also coincides with the number of indicators or sub-indices, p. φ j (px1) is
the eigenvector of the correlation matrix. We assume that λ 1 > λ 2 >
> λ p and denote
note λ j (j = 1,
8 If the model is well specified, E(e) = 0 and the variance of the error term is relatively small compared to the variance of the latent variable, financial inclusion.
P k (k = 1,
each dimension according to the following weighted averages:
, p) as the k-th principal component. We get the corresponding estimator of
i =
j,k=1 λ j
j=1 λ j
where P k = Xλ j . λ j represents the variance of the kth principal component (weights)
and X 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 pth principal component is a
linear combination of the indicators that accounts for the smallest variance. In brief,
this method represents a p-dimensional dataset of correlated variables by p orthogonal
principal components, with the first principal component explaining the largest amount
of information from the initial data. One issue using principal component analysis is to
decide how many components to retain. Although a common practice is to replace the
whole set of causal variables by only the first few principal components, which account
for a substantial proportion of the total variation in all the sample variables, we consider
as many components as the number of explanatory variables. Our concern is to estimate
accurately financial inclusion rather than reducing the data dimensionality so, in order to
avoid discarding information that could affect our estimates, we account for 100 per cent
of the total variation in our database.
3.2 Second Stage PCA
The second stage of the principal component analysis computes the overall financial inclu-
sion index by replacing Y
a in Eq. (1) and applying a similar procedure to that
described in the first stage (to estimate the vectors of parameters λ). This produces the
following estimator of the financial inclusion index:
and Y
FI i =
j=1 λ j P ki
The highest weight, λ 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, λ 2 , is attached to the second principal component and so on. After some algebra, we can write each component, P ki of (9) as a linear combination of the three sub-indices (p = 3) and the eigenvectors of the respective correlation matrices represented by φ:
P 1i
P 2i
P 3i
= φ 11 Y
= φ 21 Y
= φ 31 Y
φ 12 Y + φ 13 Y
φ 22 Y + φ 23 Y
φ 32 Y + φ 33 Y
so that the financial inclusion index can be expressed as:
j=1 λ j (φ j1 Y
u + φ j2 Y
φ j3 Y
Rearranging terms, we can express the overall financial inclusion index as a weighted
average of the dimensions as in Eq. (1):
FI i
ω 1 Y
+ e i
where the relative weights (importance) of each dimension, ω k , in the final index are com- puted as: 9
ω k =
3 j=1 λ j φ jk
3 j=1 λ j
, k = 1, 2, 3.
In this section we present the estimated financial inclusion indices for 82 developed and less-developed countries by two-stage PCA for the year 2011. 10 The correlation matrix for the causal variables used to measure financial inclusion is reported in Table 2.
4.1 First Stage Empirical Results
In the first stage, we compute the weights for the causal variables for each sub-index and estimate the latent variables: usage, barriers and access that represent the dimensions of financial inclusion. Since we construct the sub-indices as weighted averages of the principal components, it is possible to gather the coefficients for each causal variable. These weights are derived by Eqs. (2-4) and normalized such that their sum is 1. With regard to the weighting scheme, for the usage dimension, the indicator for loans has the highest weight (0.42), followed by having an account and savings, at 0.30 and 0.28 respectively, (see upper panel of Table 3). It is important to notice that although the
9 In general the sum of the weights expressed by the formula above does not necessarily have to equal 1 due to the fact that principal component methodology normalizes the mode of each eigenvector to 1. The weights therefore could be very close to but not always equal to 1. 10 Although the Global Findex reports reliable data for 123 countries, the lack of data to measure access in some of these countries and the tax haven status of others require us to reduce our sample size to 82 countries.
weights are not evenly distributed, none of the indicators is dominant; this is a desirable condition for an index. For the access dimension, the ratios of ATMs and branches per adult population have higher weights than these ratios per square kilometre. The weights for the latter are half of the former (see middle panel of Table 3). This means that the indicators relative to population contain more information than the ones relative to area for exploring the access dimension. Finally, the lower panel of Table 3 shows the weights for the indicators in the barriers dimension. For the first three indicators (distance, affordability and documentation), the weights are very similar, at 0.23, 0.24 and 0.24 respectively. Lack of trust is the most important indicator in defining the barriers dimension, with a weight of 0.29. Since weights are obtained from the information in the principal components and the corresponding eigenvalues, it is worth studying the composition of these components to understand the structure of our estimated indices. Table 4 shows, in a cumulative way and by dimensions, the amount of the total variance explained by the different components. For the usage dimension, we observe that the first component, which contains 75% of the total information in this dimension (see Table 4) has an even contribution of the three indicators:
account, loan and savings. This suggests that these three indicators measure the same latent structure. However, only the indicator referring to loans adds extra information through the second component. It might indicate that having a loan also represents a stage of greater financial inclusion since most people who have a loan already have another financial product, such as a bank account or pay-roll account. 11 As a result, having a loan may be an accurate indicator to identify more consolidated stages of financial inclusion. When defining the access dimension, as shown in the middle panel of Table 3, we again find an even contribution of the four indicators to the first principal component since the coefficients in the eigenvector for this component are similar. However, variables related to population are more powerful in measuring access since they add information in the
11 People who start to use formal financial services by having a loan, although they might exist, are a very small minority.
second and third component as well. Finally, for the barriers dimension, we also find that the four indicators contribute evenly to the first component, which accounts for almost 80 per cent of the total variation in the data. Distance, affordability and documentation have their highest loadings in the first component. Although lack of trust contributes to the first component, it has its highest weighting in the second component, which indicates that this variable also adds extra information in a different structure from the first component. Lack of trust is a structural variable that can be related to not only idiosyncratic financial system issues (efficiency of financial institutions, financial stability, episodes of bank failures, etc.) but also to broader issues beyond the financial markets, such as governance, cultural norms, economic crises or macroeconomic variables such as inflation. Table 5 shows the list of countries ranked by the degree of usage, access and barriers. 12 For a more intuitive interpretation, the sub-indices are normalized to be between 0 and 1, where 1 indicates the highest degree of financial inclusion and 0 the lowest. The computation of the sub-indices to estimate the dimensions can be useful information for policy-makers and governments when designing financial inclusion strategies. The idea is that policies to foster financial inclusion should focus on the dimension in which the country ranks worse by comparison.
4.2 Second Stage Empirical Results
In the second stage, we apply PCA on the three sub-indices (usage, access and barriers ) to compute their weights in the overall index. Table 6 presents the composition of the principal components and the normalized weights for each dimension or sub-index. The last column shows that PCA assigns the highest weight to access (0.42), followed by usage with a weight of 0.29 and barriers at 0.28. Thus, this information reveals that access is the most important dimension for explaining the degree of financial inclusion. Supply of formal financial services contributes more than number of users to explain the latent
12 Using two-stage PCA, we can compute indices by countries as well as aggregated by regions. Due to space limitations, we report the county-based analysis only.
structure behind our pool of indicators, ie. the degree of financial inclusion. Access is key since it represents a necessary but not sufficient condition for using formal financial services. In terms of the principal component structure, we observe that the first and most important component, accounting for 76 per cent of the total variation in the data (see Table 7), has an even contribution of the three dimensions. This indicates that the three dimensions measure the same latent structure which is interpreted as the degree of financial inclusion. 13 Moreover, unlike usage and barriers, access allocates part of its information in the second component, so this dimension not only contributes to the overall index through the first principal component, but also adds extra information through the second component and gains importance in explaining the overall index. Table 8 shows the ranking of countries in the sample according to the value of our fin- ancial inclusion index. As expected, developed countries have the most inclusive financial systems. The first quarter of the ranking corresponds to developed countries with only two exceptions: Mongolia and Thailand. These two low-income Asian countries outperform other low-to-middle income countries, their East Asian neighbours and even some high- income countries. For Mongolia, the high level of financial inclusion may be due in large part to universal cash hand-outs from the government's Human Development Fund as well as pensions, health insurance and student tuition payments. 14 The degree of financial inclusion in this country is higher than developed countries such as Sweden, Ireland or Austria. In the case of Thailand, its high position in the ranking is mainly due to the large number of bank accounts and the insurance schemes, particularly for healthcare, offered by the Government. Thailand's financial inclusion level is higher than that of Greece. The second quarter of the ranking, down to the position 42, is made up mostly of the Eastern
13 Tables 4 and 7 show that, in most of the cases, only the first component explains more than 75 per cent of the causal variables' total variation (except for the access dimension that explains 62 per cent). Thus, the strategy of taking only the first principal component may be a good approximation for estimating the dimensions and the degree of financial inclusion as well. 14 Around 50% of all bank account holders over the age of 15 cite receiving government payments as the most common use for a bank account, according to the Global Findex database.
European middle-income countries. Three Latin American countries (Brazil, Costa Rica and Dominican Rep.) and Malaysia are the exceptions. Brazil exhibits the best perform- ance, in terms of financial inclusion, among Latin American countries. Its success can be seen in the existence of social support programs sponsored by the government through the formal financial system. 15 This way of facilitating money transfers is analogous to the one followed in Mongolia and Thailand. After these two groups, the second half of the ranking is a heterogeneous group that includes countries from Latin America, Asia, a few Eastern European and all the African countries in the sample. The last ten countries, at the bottom of the ranking, are low- income African countries. Most African countries in our sample perform poorly in financial inclusion terms, with the only exceptions being South Africa, which is in 45 th position, Kenya and Mozambique in the 54 th and 58 th position respectively. Given the relevance of the access dimension in the financial inclusion index, the low levels of financial inclusion in some African countries should improve by including data on e-money outlets since this business model is widespread in the region. This also applies to some Latin American countries which use a banking correspondent business model.
4.3 Preliminary Stylized Facts
In this section, we show some correlations between our index and some variables of interest. We compare our index with GDP per capita. Figure 1 shows a high correlation (0.69) between these two variables. This result supports the theoretical literature linking financial inclusion and economic growth. 16 Another variable that may determine the level of financial inclusion is the efficiency of the financial system. More efficient financial systems are more likely to provide services at a more competitive price, so this may minimize the
15 Brazil has a huge banking correspondent network, pioneering in the region. Brazil's ranking position would improve if data on this access channel were considered in our index. 16 Similarly, Allen et al. (2012) find a high correlation when regressing the percentage of adults with a formal account and GDP per capita. These authors show R-square equals 0.73 based on a country-level OLS regression of account penetration on the log of GDP per capita.
barriers perceived by individuals in terms of the affordability of formal financial services. Figure 2 shows a high negative correlation (-0.65) between the net interest margin, a measure of the inefficiency of the banking system, and our financial inclusion index. Our index is also negatively correlated with the instability of the financial system, measured as the aggregate volatility of the credit gap over GDP, in the last 15 years (Figure 3). Financial inclusion is very much related to education, so we also correlate our index with different education-related metrics. It exhibits a positive correlation with the average years of schooling (0.66) and a negative (-0.50) correlation with the illiteracy rate. We find similar positive correlations if we consider different education levels completed (0.53, 0.58 and 0.57 for people who have completed primary, secondary and tertiary education, respectively). If we look at the correlations by dimensions, we find that for the first three variables (GDP per capita, inefficiency and instability), the correlations between different dimen- sions and the total financial inclusion index are quite similar. However, education-related variables exhibit much higher correlation with the usage dimension than with any other dimension. We find high correlations, 0.65 for the average years of schooling and -0.50 for the population with no formal education, when correlating these variables with the usage dimension. Moreover, we find that completed secondary and tertiary education have almost the same correlation (0.57 and 0.55 respectively) with usage, but that it is slightly lower for the case of completed primary education (0.50). 17
5 Conclusions and policy recommendations
Financial inclusion is an essential ingredient of economic development and poverty reduc- tion and it can also be a way of preventing social exclusion. A person's right to use formal
17 Our index also exhibits high correlations when comparing with other financial inclusion indices and indicators in the literature. The percentage of people with an account at a formal financial institution, proposed by Demirg¨u¸c-Kunt and Klapper (2013), as a proxy for financial inclusion, exhibits a correlation of 0.92 with our index. Existing Financial Inclusion composite indices, such as the ones developed by Honohan (2007), Sarma (2008) and Amidˇzi´c et al. (2014), have a correlation coefficient of 0.79, 0.84 and 0.90, respectively, with our index.
financial services, as a way of preventing social exclusion, must be a priority. However, efforts to measure financial inclusion are scarce and incomplete. Financial inclusion is a multidimensional concept that cannot be captured accurately by single indicators on their own, but is determined by a much larger set of indicators than the few considered so far. The nature of the financial systems is complex and heterogeneous. An inclusive financial system needs particularly to encourage usage of its services on the part of society's most vulnerable groups; that is, those most affected by obstacles to financial inclusion. Existing composite indices to measure financial inclusion, taking arbitrary weights, are questionable. This paper proposes a two-stage PCA to measure the extent of financial inclusion for a country or region. This methodology is statistically sound for index con- struction and robust to high dimensional data. We measure financial inclusion through composite indices for 82 countries by using 11 causal variables as financial inclusion de- terminants for 2011. Specifically, our index assumes that the degree of financial inclusion is determined by the maximization of usage and access to formal financial services, on the one hand, as well as by the minimization of obstacles causing involuntary exclusion. Demand-side information to assess the usage and barriers dimensions is key in determining the degree of financial inclusion. The dimension of usage measures financial inclusion from the banked perspective, and barriers do so from the perspective of the unbanked. Informa- tion from excluded people helps to reveal a comprehensive picture of the extent to which a financial system is inclusive. Our major contribution is twofold. First, we use a parametric method to determine the contribution of each indicator in our financial inclusion index. It has the advantage of not employing any exogenous, subjective information. Second, we build a comprehensive index that includes both demand- and supply-side information. As shown by our estimates, access is the most important dimension for measuring the level of financial inclusion. This result suggests that supply of formal financial services is more important than number of users in explaining our index. Access represents a necessary but not sufficient condition for using formal financial services.
We find that the degree of financial inclusion is highly correlated with some macroe- conomic variables such as GDP per capita, education, efficiency of a financial system and financial stability. The creation of such an index is useful to shed some light on the determ- inants of financial inclusion as well as its contribution to economic growth and development. Also, we believe that desegregated information on the different dimensions will be useful for policy recommendations. Efforts in such direction yield relevant improvements on the analysis of financial inclusion's causes and consequences.
Developed countries
l ess-developed countries
Albania Angola Argentina Armenia Azerbaijan Belarus Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burundi Cameroon Chad Colombia Congo, Dem. Rep. Costa Rica Croatia Dominican Rep. El Salvador Gabon Georgia Ghana Honduras
Hungary India Indonesia Kazakhstan Kenya Latvia Lesotho Lithuania Macedonia, FYR Madagascar Malaysia Mexico Moldova Mongolia Mozambique Nepal Nicaragua Pakistan Paraguay Peru Philippines Romania
Czech Rep.
Korea, Rep.
New Zealand
Slovak Rep.
United States
Notes: Countries classified according to World Bank criteria.
Allen, Franklin; Asli Demirguc-Kunt; Leora Klapper; Maria Soledad Martinez Peria, 2012. The Foundations of Financial Inclusion: Understanding Ownership and Use of Formal Accounts. World Bank Policy Research Working Paper no. 6290. Amidzic, Goran, Alexander Massara and Andre Mialou, 2014. Assesing Countries' Finan- cial Inclusion- A New Composite Index. IMF Working Paper, WP/14/36. Beck, Thorsten, Asl Demirg-Kunt, and Maria Soledad Martinez Peria, 2007. Reaching Out:
Access to and Use of Banking Services across Countries. Journal of Financial Economics 85 (2), 23466. Chakravarty, Satya and Rupayan Pal, 2010. Measuring Financial Inclusion: An Axiomatic Approach. Indira Gandhi Institute of Development Research, Working Paper no. WP
Demirg-Kunt, Asli, and Leora Klapper, 2012. Measuring Financial Inclusion: The Global Findex Database. Policy Research Working Paper no. 6025. Washington: World Bank. Demirg-Kunt, Asli, and Leora Klapper, 2013. Measuring Financial inclusion: Explaining Variation in Use of Financial Services across Countries and within Countries. Brookings papers on Economic Activity, Spring Financial Access Survey, 2013. International Monetary Fund Global Financial Inclusion database, 2011. The World Bank Lockwood, B, 2004. How Robust is the Foreign Policy-Kearney Globalization Index?. The World Economy, 27, 507-523 Mishra, S.K., 2007. A Comparative Study of Various Inclusive Indices and the index Constructed by the Principal Components Analysis. MPRA Paper No.3377. Nagar, Anirudh L., and Sudip R. Basu, 2002. Weighting Socioeconomic Indicators of Human Development: A Latent Variable Approach. In Ullah A. et al. (eds) Handbook of Applied Econometrics and Statistical Inference. Marcel Dekker, New York Sarma, Mandira, 2008. Index of Financial Inclusion. ICRIER Working Paper 215.
Sarma, Mandira, 2012. Index of Financial Inclusion
iveness. Berlin Working Papers on Money, Finance, Trade and Development, Working Paper no. 07/2012. Steiger, J.H., 1979. Factor Indeterminacy in the 1930s and the 1970s: Some Interesting Parallels. Psychometrika 44, 157-167.
A measure of financial sector inclus-
Descriptive Statistics
Std. Dev.
ATMs/100,000 pop. Branches/100,000 pop. ATMs/1,000 Km2 Branches/1,000 Km2
Distance Affordability Documentation Lack of trust
TABLE 2 Correlation Matrix
Variables Account Loan Savings ATMs/100,000 pop. Branches/100,000 pop. ATMs/1,000 Km2 Branches/1,000 Km2 Distance High cost Documentaion Lack of trust
Principal Components Estimates
V ariable
norm. weight
ATMs per 100,000 pop. Branches per 100,000 pop. ATMs per 1000 Km 2 Branches per 1000 Km 2
Notes: The weights are normalised add up to 1
Cumulative Variance Explained by Components
Cumulative variance
Ranking of Countries by Dimension
New Zealand Sweden Finland Canada Denmark Australia United States France Mongolia Ireland Korea, Rep. Netherlands Thailand Belgium Austria Slovenia Spain Japan Slovak Republic Croatia Malaysia Czech Republic Estonia Belarus Portugal Bolivia Kenya Vietnam Poland Macedonia, FYR Hungary Greece Dominican Republic Latvia Bosnia and Herzegovina Armenia Costa Rica Uruguay Azerbaijan Kazakhstan South Africa Lithuania Swaziland Angola
Korea, Rep. Spain Portugal Italy Belgium Japan Bulgaria United States France Canada Brazil Russian Federation Slovenia Australia Croatia Netherlands Mongolia Denmark Greece Peru Ireland Poland Romania Austria New Zealand Latvia Slovak Republic Bosnia and Herzegovina Estonia Czech Republic Macedonia, FYR Lithuania Costa Rica Thailand El Salvador Turkey Hungary Albania Chile Armenia Moldova Sweden Georgia Honduras
Australia Finland Netherlands Denmark Austria Belgium New Zealand Sweden Japan France Slovenia Estonia Canada Korea, Rep. Ireland Latvia Portugal Croatia Spain Thailand Mongolia Italy Greece United States Lithuania Slovak Republic Macedonia, FYR Czech Republic Poland Hungary Malaysia Venezuela, RB Dominican Republic Bosnia and Herzegovina Bulgaria Costa Rica Belarus Georgia Albania Vietnam Romania Turkey Mozambique Kazakhstan
Notes: Countries are ranked according to their scores in each dimension
TABLE 5 Cont
Philippines Colombia Italy Paraguay Peru Russian Federation Romania Chile Uganda Brazil Albania Bulgaria Georgia Nepal Ukraine India Mozambique Indonesia Turkey Mexico Botswana Ghana Tanzania Argentina Honduras Gabon Nicaragua Zambia Venezuela, RB Chad Moldova El Salvador Cameroon Lesotho Burundi Pakistan Madagascar Congo, Dem. Rep.
Mexico Malaysia Venezuela, RB Ukraine South Africa Argentina Dominican Republic Finland Uruguay Colombia Kazakhstan India Azerbaijan Philippines Bolivia Botswana Indonesia Swaziland Belarus Paraguay Angola Vietnam Pakistan Nepal Nicaragua Gabon Kenya Ghana Zambia Lesotho Mozambique Uganda Burundi Tanzania Cameroon Madagascar Chad Congo, Dem. Rep.
Uruguay Paraguay Brazil Nepal India Pakistan Russian Federation South Africa Burundi Argentina Kenya Ghana Azerbaijan Armenia Chile Angola Honduras Moldova Colombia Gabon El Salvador Ukraine Nicaragua Swaziland Zambia Cameroon Indonesia Mexico Botswana Bolivia Philippines Madagascar Tanzania Lesotho Peru Congo, Dem. Rep. Uganda Chad
Notes: Countries are listed according to the scores in each dimension
Principal Component Estimates
Financial Inclusion Index
Financial Inclusion Index Country Ranking
South Africa
Venezuela, RB
El Salvador
Russian Federation
Macedonia, FYR
Bosnia and Herzegovina
Costa Rica
Dominican Rep.
Congo, Dem. Rep.
Notes: Countries are listed according to their scores the Financial Inclusion Index
Figure 1: Financial inclusion and income
Figure 2: Financial inclusion and inefficiency of financial system
Figure 3: Financial inclusion and instability
Working Paper
14/26 Noelia Cámara and David Tuesta: Measuring Financial Inclusion: A Multidimensional Index.
14/25 Ángel de la Fuente: La evolución de la financiación de las comunidades autónomas de régimen común, 2002-2012.
14/24 Jesús Fernández-Villaverde, Pablo Guerrón-Quintana, Juan F. Rubio-Ramírez: Estimating Dynamic Equilibrium Models with Stochastic Volatility.
14/23 Ana Rubio, Jaime Zurita, Olga Gouveia, Macarena Ruesta, José Félix Izquierdo, Irene Roibas:
Análisis de la concentración y competencia en el sector bancario.
14/22 Ángel de la Fuente: La financiación de las comunidades autónomas de régimen común en 2012.
14/21 Leonardo Villar, David Forero: Escenarios de vulnerabilidad fiscal para la economía colombiana.
14/20 David Tuesta: La economía informal y las restricciones que impone sobre las cotizaciones al régimen de pensiones en América Latina.
14/19 David Tuesta: The informal economy and the constraints that it imposes on pension contributions in Latin America.
14/18 Santiago Fernández de Lis, María Abascal, Tatiana Alonso, Wojciech Golecki: A banking union for Europe: making virtue of necessity.
14/17 Angel de la Fuente: Las finanzas autonómicas en 2013 y entre 2003 y 2013.
14/15 Ximena Peña, Carmen Hoyo, David Tuesta: Determinants of financial inclusion in Mexico based on the 2012 National Financial Inclusion Survey (ENIF).
14/14 Ximena Peña, Carmen Hoyo, David Tuesta: Determinantes de la inclusión financiera en México a partir de la ENIF 2012.
14/13 Mónica Correa-López, Rafael Doménech: Does anti-competitive service sector regulation harm exporters? Evidence from manufacturing firms in Spain.
14/12 Jaime Zurita: La reforma del sector bancario español hasta la recuperación de los flujos de crédito.
14/11 Alicia García-Herrero, Enestor Dos Santos, Pablo Urbiola, Marcos Dal Bianco, Fernando Soto, Mauricio Hernandez, Arnulfo Rodríguez, Rosario Sánchez, Erikson Castro: Competitiveness in the Latin American manufacturing sector: trends and determinants.
14/10 Alicia García-Herrero, Enestor Dos Santos, Pablo Urbiola, Marcos Dal Bianco, Fernando Soto, Mauricio Hernandez, Arnulfo Rodríguez, Rosario Sánchez, Erikson Castro: Competitividad del sector manufacturero en América Latina: un análisis de las tendencias y determinantes recientes.
14/09 Noelia Cámara, Ximena Peña, David Tuesta: Factors that Matter for Financial Inclusion: Evidence from Peru.
14/08 Javier Alonso, Carmen Hoyo & David Tuesta: A model for the pension system in Mexico: diagnosis and recommendations.
14/07 Javier Alonso, Carmen Hoyo & David Tuesta: Un modelo para el sistema de pensiones en México:
diagnóstico y recomendaciones.
Alicia Garcia-Herrero,
Sumedh Deorukhkar: What explains India's
surge in outward
14/06 Rodolfo Méndez-Marcano & José Pineda: Fiscal Sustainability and Economic Growth in Bolivia.
14/05 Rodolfo Méndez-Marcano: Technology, Employment, and the Oil-Countries' Business Cycle.
14/04 Santiago Fernández de Lis, María Claudia Llanes, Carlos López- Moctezuma, Juan Carlos Rojas
& David Tuesta: Financial inclusion and the role of mobile banking in Colombia: developments and
14/03 Rafael Doménech: Pensiones, bienestar y crecimiento económico.
14/02 Angel de la Fuente & José E. Boscá: Gasto educativo por regiones y niveles en 2010.
14/01 Santiago Fernández de Lis, María Claudia Llanes, Carlos López- Moctezuma, Juan Carlos Rojas
& David Tuesta. Inclusión financiera y el papel de la banca móvil en Colombia: desarrollos y
13/38 Jonas E. Arias, Juan F. Rubio-Ramírez & Daniel F. Waggoner: Inference Based on SVARs Identied with Sign and Zero Restrictions: Theory and Applications
13/37 Carmen Hoyo Martínez, Ximena Peña Hidalgo & David Tuesta: Demand factors that influence financial inclusion in Mexico: analysis of the barriers based on the ENIF survey.
13/36 Carmen Hoyo Martínez, Ximena Peña Hidalgo & David Tuesta. Factores de demanda que influyen en la Inclusión Financiera en México: Análisis de las barreras a partir de la ENIF.
13/35 Carmen Hoyo & David Tuesta. Financing retirement with real estate assets: an analysis of Mexico
13/34 Carmen Hoyo & David Tuesta. Financiando la jubilación con activos inmobiliarios: un análisis de caso para México.
13/33 Santiago Fernández de Lis & Ana Rubio: Tendencias a medio plazo en la banca española.
13/32 Ángel de la Fuente: La evolución de la financiación de las comunidades autónomas de régimen común, 2002-2011.
13/31 Noelia Cámara, Ximena Peña, David Tuesta: Determinantes de la inclusión financiera en Perú.
13/30 Ángel de la Fuente: La financiación de las comunidades autónomas de régimen común en 2011.
13/29 Sara G. Castellanos & Jesús G. Garza-García: Competition and Efficiency in the Mexican Banking Sector.
13/28 Jorge Sicilia, Santiago Fernández de Lis & Ana Rubio: Banking Union: integrating components and complementary measures.
13/27 Ángel de la Fuente & Rafael Doménech: Cross-country data on the quantity of schooling: a selective survey and some quality measures.
13/26 Jorge Sicilia, Santiago Fernández de Lis &Ana Rubio: Unión Bancaria: elementos integrantes y medidas complementarias.
13/25 Javier Alonso, Santiago Fernández de Lis, Carlos López-Moctezuma, Rosario Sánchez & David Tuesta: The potential of mobile banking in Peru as a mechanism for financial inclusion.
13/24 Javier Alonso, Santiago Fernández de Lis, Carlos López-Moctezuma, Rosario Sánchez & David Tuesta: Potencial de la banca móvil en Perú como mecanismo de inclusión financiera.
13/23 Javier Alonso, Tatiana Alonso, Santiago Fernández de Lis, Cristina Rohde &David Tuesta:
Tendencias regulatorias financieras globales y retos para las Pensiones y Seguros.
13/22 María Abascal, Tatiana Alonso & Sergio Mayordomo: Fragmentation in European Financial Markets: Measures, Determinants, and Policy Solutions.
13/21 Javier Alonso, Tatiana Alonso, Santiago Fernández de Lis, Cristina Rohde & David Tuesta:
Global Financial Regulatory Trends and Challenges for Insurance & Pensions.
13/20 Javier Alonso, Santiago Fernández de Lis, Carmen Hoyo, Carlos López-Moctezuma & David Tuesta: Mobile banking in Mexico as a mechanism for financial inclusion: recent developments and a closer look into the potential market.
13/19 Javier Alonso, Santiago Fernández de Lis, Carmen Hoyo, Carlos López-Moctezuma & David Tuesta: La banca móvil en México como mecanismo de inclusión financiera: desarrollos recientes y aproximación al mercado potencial.
13/18 Alicia Garcia-Herrero & Le Xia: China's RMB Bilateral Swap Agreements: What explains the choice of countries?
13/17 Santiago Fernández de Lis, Saifeddine Chaibi, Jose Félix Izquierdo, Félix Lores, Ana Rubio & Jaime Zurita: Some international trends in the regulation of mortgage markets: implications for Spain.
13/16 Ángel de la Fuente: Las finanzas autonómicas en boom y en crisis (2003-12).
13/15 Javier Alonso & David Tuesta, Diego Torres, Begoña Villamide: Projections of dynamic generational tables and longevity risk in Chile.
13/14 Maximo Camacho, Marcos Dal Bianco & Jaime Martínez-Martín: Short-Run Forecasting of Argentine GDP Growth.
13/13 Alicia Garcia Herrero & Fielding Chen: Euro-area banks' cross-border lending in the wake of the sovereign crisis.
13/12 Javier Alonso & David Tuesta, Diego Torres, Begoña Villamide: Proyecciones de tablas generacionales dinámicas y riesgo de longevidad en Chile.
13/11 Javier Alonso, María Lamuedra & David Tuesta: Potentiality of reverse mortgages to supplement pension: the case of Chile.
13/10 Ángel de la Fuente: La evolución de la financiación de las comunidades autónomas de régimen común, 2002-2010.
13/09 Javier Alonso, María Lamuedra & David Tuesta: Potencialidad del desarrollo de hipotecas inversas:
el caso de Chile.
13/08 Santiago Fernández de Lis, Adriana Haring, Gloria Sorensen, David Tuesta, Alfonso Ugarte:
Banking penetration in Uruguay.
13/07 Hugo Perea, David Tuesta & Alfonso Ugarte: Credit and Savings in Peru.
13/06 K.C. Fung, Alicia Garcia-Herrero, Mario Nigrinis: Latin American Commodity Export Concentration:
Is There a China Effect?
13/05 Matt Ferchen, Alicia Garcia-Herrero & Mario Nigrinis: Evaluating Latin America's Commodity Dependence on China.
13/04 Santiago Fernández de Lis, Adriana Haring, Gloria Sorensen, David Tuesta, Alfonso Ugarte:
Lineamientos para impulsar el proceso de profundización bancaria en Uruguay.
13/03 Ángel de la Fuente: El sistema de financiación regional: la liquidación de 2010 y algunas reflexiones sobre la reciente reforma.
13/02 Ángel de la Fuente: A mixed splicing procedure for economic time series.
13/01 Hugo Perea, David Tuesta &Alfonso Ugarte: Lineamientos para impulsar el Crédito y el Ahorro. Perú.
Click here to access the list of Working Papers published between 2009 and 2012
Click here to access the backlist of Working Papers:
Spanish and English
The analysis, opinions, and conclusions included in this document are the property of the author of the report and are not necessarily property of the BBVA Group.
BBVA Research's publications can be viewed on the following website: http://www.bbvaresearch.com
Contact Details:
BBVA Research Paseo Castellana, 81 – 7º floor 28046 Madrid (Spain) Tel.: +34 91 374 60 00 y +34 91 537 70 00 Fax: +34 91 374 30 25 firstname.lastname@example.com www.bbvaresearch.com


text:
There is no 'zero risk' strategy in easing travel restrictions: WHO
WHO tells countries there is no 'zero risk' as travel bans ease
WHO: Coronavirus pandemic is a 'once-in-a-century' health crisis | News | Al Jazeera
Coronavirus Effects Will Be Felt For Decades: World Health Organisation
UN health agency: 'Must learn to live with COVID-19'
Impact of coronavirus will be felt for decades to come, says World Health Organization | Deccan Herald
Young people 'not invincible' in COVID-19 pandemic: WHO chief | CGTN Africa
Global COVID-19 total surges past 17 million | CIDRAP
WHO urges countries to prioritise essential international travel | The Star
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No zero risk strategy for renewed travel during Covid-19 pandemic, warns WHO, Europe News & Top Stories - The Straits Times
Trump: Young People 'Almost Immune' to Coronavirus. WHO Chief: No. | Common Dreams News
Young people 'letting their guard down' are driving Covid resurgence, WHO warns
WHO: Effects of Covid-19 to last decades | New Straits Times
WHO Urges Young People To Avoid Nightclubs After A Recent Surge In COVID-19 Cases
COVID-19: WHO urges countries to prioritise essential international travel - CNA
Coronavirus effects to last decades, WHO says six months on | Inquirer News
COVID-19: WHO urges countries to prioritise essential international travel, blanket ban not the answer - Khmer Times
WHO's emergency panel reviews pandemic | Dhaka Tribune
Coronavirus: 'Young people are not invincible,' warns WHO, says they are partly driving new cases
Effects of 'once in a century' coronavirus will be felt for decades, WHO warns | SBS News
News On AIR - News Services Division, All India Radio News
The effects of the coronavirus pandemic will be 'felt for decades to come,' WHO chief says | TheHill
Coronavirus: Young people are not invincible to COVID-19 effects, warns WHO chief | Euronews
Careless young people driving some COVID-19 spikes, WHO warns | Daily Sabah
WHO warns of COVID-19 spike due to recklessness of youth
WHO: Young people not cautious enough about virus | NHK WORLD-JAPAN News
Coronavirus spikes in some countries due to carelessness of some youths: WHO
Covid-19 wrap: Former US Presidential candidate dies of virus, 'careless' young people driving virus spikes | News24
WHO chief says some COVID-19 spikes due to 'young letting down guard' - The Peninsula Qatar
Trump: Young people 'almost immune' to coronavirus. WHO chief: No. – Raw Story
Virus effects to last decades, WHO says six months on - Manila Bulletin
Impact of coronavirus will be felt for decades to come, WHO says - The Peninsula Qatar
Coronavirius impact will be felt far into the future - WHO
Effects of coronavirus pandemic will be felt for decades: World Health Organization - National | Globalnews.ca
Impact of coronavirus will be felt for decades to come: WHO Chief Tedros Adhanom Ghebreyesus - The Economic Times
WHO warns partying youngsters not to 'let down guard' on Covid-19, World News - AsiaOne
COVID-19: You Are 'Not Invincible,' WHO Warns Young People – Channels Television
Covid-19 virus effects to last decades, WHO says six months on | Life | Malay Mail
Prioritise essential international travel, wear masks on planes: WHO - travel - Hindustan Times

subset: commoncrawl


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We are closed August for Summer Break.
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July 19 – A ladder to God
July 12 – We are freed!
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April 10 – "Friday's here, but Sunday's comin" & Holy Week bible study
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March 29 – "Be still……"
March 22 – "Living in a time between"
Hampton United Church
Rooted in Faith
Growing in Love
At Hampton United Church you will find a community that cherishes the history of this community and the heritage that has been passed on to us. We work to honour the wisdom of our foremothers and fathers, while also responding to the challenges of our ever-changing world. We recognize that people coming to our church are at different places on their spiritual journeys. The ministry of our church is focused on creating a space so that wherever you may be on your journey you can find others to learn and grow with.
Weekly worship services see above. Join us Sundays at 11 a.m for "live" online services, posted above.
"Five Great Reasons to Attend This Sunday"
- You've been looking for a sign from God. Here is it!
- Worship and Prayer reduce stress and brings us closer together with our loved ones. Who doesn't want that?
- You've been saying you want to make new friends and here are a bunch of people who want the same thing.
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During the midst of a battle, Misty throws out one of her Pokemon but it then uses Hypnotize on her, leaving her helpless under his spell! For way too long these Pokemon have wanted to fuck Misty - and now that she's a puppet, it's the perfect time for them to go wild and fuck all of her holes!
See how sharp your puzzle solving skills are in Grand Mazeon 2. You'll be navigating a winding, twisted labyrinth and you'll have to manage to make your way towards the exit, which is easier said than done. You're bound to encounter lots of dead ends and do ample amounts of back tracking, but on a plus side, there's lots of erotic sights and sounds to stimulate your senses along the way!
Just recently you turned 20, found out that you're adopted, and got a picture of your true parents, and address. You go there to find a ruined brothel they once ran, and a girl tells you they got in debt with a gang and this happened. Make your family proud and restore it to it's former glory!
Never underestimate the lengths a reporter will go to in order to keep their ratings high! Tired of everyone else trying to one up her antics, she decides to go big - she's going to fuck one of her coworkers, totally live while it's being broadcast to millions of homes - let's see them top that!
The Boobitch sisters have always been a very competitive pair, and as usual, conflict has arisen, and it's quickly getting worse and worse - the two sisters are foaming at the mouth, ready to tear each other into tiny little shreds as they fight and bicker over who is the biggest slut and who has the biggest tits. Frankly, there's only one way a conflict like this can be settled - through a competition between the two girls, being judged by an unbiased third party - namely, you! The two horny sisters will take turns licking and sucking his cock, getting their amazing curvy bodies felt up by his eager hands, have their tits, pussies, mouths, and assholes fucked by a nice big cock. After the two babes have taken their turns on your pole, it's up to you to decide once and for all who is the winner! Though, to be honest, we're pretty sure no matter the outcome these two hoes will be at each other's throats all over again within a few days.
You're a regular at this bar, but you'd never seen this bartender before, looking all sex with her purple hair and punk vibe. You're hitting it off pretty well with her, doing shots together and chatting - but now she's drunk, and her tits are so big and heavy, hanging out of her top she cant stand
Jenny is young - she just turned 18, and is still a virgin. She's never had sex yet - but tonight that's about to change in a big way! You see, not only is she getting fucked for the first time, but it's getting filmed - and her pussy, it's getting filled up to the brim with a thick load of cum!
A game that offers tons of laughs courtesy of a great sense of humor - you're going to be struggling to stay erect from how hard you'll be laughing as you play! Don't worry though - it's not all just laughs, there's a whole ton of steamy sights in this game that will have you hard as a rock!
Over at the local radio station, they're holding a contest for a hell of a grand prize - sexy starlet Shaundi will fuck the winner! She's delighted to find out that the winner is a handsome ebony hunk - she loves that BBC, and she can't wait to feel it stretching out her eager little fuck slit!


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Tiffany A. Buckley-Norwood is a Principal in the Detroit, Michigan, office of Jackson Lewis P.C. and a core team member of the Healthcare Industry Group. With over 12 years of experience and as counsel to management, she advises employers of varying sizes and in varying industries, particularly those in healthcare and manufacturing.
Ms. Buckley-Norwood's employment law advice and litigation defense practice focuses on three specific equal employment opportunity / affirmative action areas: disability accommodation, diversity and inclusion best practices, and appropriately responding to internal complaints (minimizing retaliation claims). Complementing these focus areas, Ms. Buckley-Norwood counsels clients on the strategic creation and implementation of company-wide policies and OFCCP-compliant affirmative action plans.
Ms. Buckley-Norwood is also frequent author and presenter, with over 30 presentations and over 20 published works. Her speaking engagements include presentations for the American Bar Association, Institute for Continuing Legal Education, American Society of Employers, Association of Corporate Counsel Michigan, State Bar of Michigan Labor & Employment and Health Law Sections, Michigan Defense Trial Counsel, Michigan Industry Liaison Group, and Greater Lansing Society for Human Resource Management.
In her spare time, Ms. Buckley-Norwood volunteers as a pro bono attorney, in addition to other civic activities.
Honors and Recognitions
- Michigan Lawyers Weekly, Up & Coming Lawyers (2015)
- Michigan Super Lawyers®, Top 50 Women (2018)
Tiffany A. Buckley-Norwood
Rated by Super Lawyers
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Pro Bono and Community Involvement
- D. Augustus Straker Bar Association, President (2018-2019)
- Michigan Legal Aid Defenders Association, Pro Bono Attorney (2013-Present)
- Travelers Aid Society of Metropolitan Detroit, Board of Directors (2009-2012)
Professional Associations and Activities
- D. Augustus Straker Bar Association, President
Published Works
- Employment Discrimination Law, 3d cumulative supp., (BNA Books 2010) [Chapter Monitor]
- Employment Discrimination Law, 2d cumulative supp., (BNA Books 2009) [Chapter Monitor]
- Employment Law Manual 9 (Community Legal Resources 2009) [Contributing Author]
- Introducing Evidence At Trial, 3d ed. (ICLE 2007) [Contributing Author]
- 7th Annual Michigan Women's Business Conference & Expo (presenter)
- 36th Annual Labor and Employment Law Institute (Plymouth, MI) (presenter)
- Health Care Law Section – State Bar of Michigan Annual Meeting (presenter)


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**The information contained herein neither constitutes an offer for nor a solicitation of interest in any securities offering; however, if an indication of interest is provided, it may be withdrawn or revoked, without obligation or commitment of any kind prior to being accepted following the qualification or effectiveness of the applicable offering document, and any offer, solicitation or sale of any securities will be made only by means of an offering circular, private placement memorandum, or prospectus. No money or other consideration is hereby being solicited, and will not be accepted without such potential investor having been provided the applicable offering document. Joining the Fundrise Platform neither constitutes an indication of interest in any offering nor involves any obligation or commitment of any kind.
passive income business
Passive income is income that requires little to no effort to earn and maintain. It is called progressive passive income when the earner expends little effort to grow the income. Examples of passive income include rental income and any business activities in which the earner does not materially participate. Some jurisdictions' taxing authorities, such as the Internal Revenue Service in the United States of America, distinguish passive income from other forms of income, such as earnings from regular or contractual employment, and may tax it differently.
What stock pays highest dividend?
The United States Internal Revenue Service categorizes income as active income, passive income, or portfolio income.[1] It defines passive income as only coming from two sources, or "passive activities": rental activity or "trade or business activities in which you do not materially participate."[2][3] Other financial and government institutions also recognize it as an income obtained as a result of capital growth or in relation to negative gearing. Passive income is usually taxable.
jobs with passive income
I don't look at Checkout 51 before I go grocery shopping. I just do it after I grocery shop so that I don't get influenced by their product coupons. A lot of the coupons are for non-produce goods, but for certain things like diapers, it really saves money because I can stack my coupons (e.g. $2.00 print out coupon, and then another $3.00 from Checkout 51 for a total of $5 off the economy box of diapers).
passive income internet
Tangerine Savings Account 3.00%– If you open up an account before Aprl 30, 2019, you get 3.00% on your deposits but this decreases to 1.25% after 6 months. You can use my Orange Key 33530953S1 to get $25 cash bonus into your account if you deposit $100. You can get an additional $50 cash bonus if you set up an Automatic Savings Program. If you refer your friends with your own Orange Key you also get a $50 bonus for each friend you refer.
I have only dabbled in drop-shipping before when I had an eCommerce platform 6 years ago or so. I think it is something that you could do on the side, but you would want to do in depth research on the industry you want to get into before setting up shop. It may be a little less passive up front, but over time you could take your hands off the wheel.
How long does it take for Google to verify your business?
The robo-advisor company charges a 0.50% fee and on top of that there the fee charged for the ETFs (which is anywhere from 0.25 to 0.5% as well). Robo-advisor companies help you rebalance your money automatically so your original asset allocation is preserved. Basically, you can be completely hands off and all you need to do is funnel your money in there and they will invest it for you.
How do I generate multiple streams of income?
Passive income is the Holy Grail for online marketers. It's automatic. Effortless. But, not at first. In the beginning, it's grueling. I liken this to doing the most amount of work for the least initial return. However, over time, as your passive income begins to increase, your reliance on an active income plummets. That's when the real magic starts to happen.
What is considered a passive activity?
If you know anything well, a place, how to fix something, how to make something, how to do something, you can write a guide for it. You can sell your guide as an e-book, offer it as a download for a fee on your site or reach out to bloggers with similar content and ask if they will offer it as a paid download on their website (for a price of course).
How long does it take to rank on Google?
The credit card bonuses and offers are not as good as the credit cards that our neighbours down south have, but they are still pretty good! One of my favourites is the MBNA Rewards World Elite Mastercard and it is owned by TD Bank. It is a straight up 2% cash back on all purchases. I got it for free annually because I was grandfathered from another card (this no longer exists).
passive income how to
But, it took me a while to get to that point. Where I once worked in my financial planning office on most days, I started by testing things out with a few days per month away from the office at first. From there, I began working remotely 2-3 days per week, then 4-5 days per week, and so on. Eventually, I was able to run my business remotely without ever having to show up for work.


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Things You Need To Know When Selecting A Car Dealership Firm
Cars are great assets, and they help people in so many ways. If you have saved enough money, it is pivotal to buy a vehicle. The thing is that, with a car, you will always get into your desired location in good time. Owning a vehicle is also great as it helps you save on cash. The good news is that there are so many affordable vehicles out there, therefore, your dream will come true. Ensure you strive hard to look for a car dealership firm known for having great vehicles and services. The car models sold in different companies vary. Looking for a car dealership agency is a cumbersome task as bearing in mind they are in massive numbers. Explained below are attributes of a good car dealership agency.
It is advisable to make a financial plan before commencing your research. The budget will act as a guide when it comes to looking for a car dealership agency.
You can never regret making good use of many sources when it comes to finding a car dealership company. The best thing about relying on many sources is that you will be in a position to compare the car models sold in different firms. In a case whereby you need the car as soon as possible, you should make good use of the web to carry out the research.
As soon as you identify a prospective car dealership agency, you should call the customer care services. You can always find the contact details on the car dealership agency's website. The response you get is very critical. You can only sign up with the car dealership company if you find that the customer care service is good.
The authorization of the car dealership firm is a vital factor to consider. The worst mistake you can make is entrusting a car dealership firm and later find that it is known for stealing vehicles. Since you would not want to be in such a situation, you should opt for an authorized car dealership firm. In case of any doubt, you should pay a visit to the car dealership company to see the license.
It is pivotal to know whether the car dealership company has served other people in the past. The best way of confirming that is by visiting the online platform of the car dealership company. Also, ensure you go through the comments posted by the clients on the company's website. As soon as you go through the testimonials, you will be able to rate the reputation of the car dealership firm. It is crucial to select a car dealership agency with a commendable image.


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Gov. John Baldacci made a surprise appearance at a press conference this morning in which supporters of same-sex marriage emphasized why they will continue to fight for full marriage rights.
The group of more than 100 cheered and clapped when Baldacci rounded the corner from his office and stood at the foot of the Hall of Flags steps. He said a few brief words before heading out the side door.
"I'm so impressed with all of you and your work," he said. "I know we came up a little bit short in the last election, but I think you really did change a lot of minds and hearts."
Voters in November repealed a same-sex marriage law by a 53-47 percent margin.
At noon, the National Organization for Marriage will hold a rally in Capitol Park to express their opposition to same-sex marriage. The group is launching a 20-city tour today that begins in Maine.
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